The third edition of my book is out! It covers topics of neuroeducation – how to learn better and how to retain what you have learned.
The topics I cover describe the techniques I used to graduate cum laude in prestigious Masters degree study in Neuroscience Research and how I approach acquiring new skills, including language learning, programming and music.
I have written, edited, illustrated, translated and published the book myself. Along the way I needed to learn many new skills, which are a meta-showcase of what I talk in the book itself.
If you choose to buy it, I sincerely appreciate the support! I hope the book is useful.
THE FOLLOWING TEXT IS A SNEAK PREVIEW OF THE BOOK’S PREFACE.
Preface
During elementary and high school, I had always been a student who got really high grades. I wasn’t necessarily a good student, per se, but I paid attention in class and that made me do well on the exams.
A nagging realization I had the day after graduating from high school was “I don’t remember almost anything from school. At all.” I had spent a good part of my, until then, seventeen years of life in an institution whose purpose was to teach me, but if I had to put on paper “What percentage of things do I remember from elementary, middle and high school”, my answer would be less than one percent.
While I was in elementary school, my “good memory” was obviously a benefit – it meant I didn’t have to struggle to get good grades. Unfortunately, this meant that I never developed good study habits, which made my transition to higher education very turbulent.
After completing high school, I started my Bachelor’s Degree in Biological Sciences at one of the top institutions in Brazil. I immediately went from being one of the top students in the class, like I was in high school, to being someone with mediocre grades.
For a few semesters, I applied the same ‘study technique’ I used to use in high school: go to class, study from the teacher’s slides, review a few days before the exam.
Clearly that wasn’t working: the volume and density of information that I needed to learn in university is significantly greater than in high school. I felt stressed throughout the semester, and when I stopped to sit and study, I didn’t know how to prioritize the material.
For a long time, I just couldn’t understand what I was doing wrong. I blamed teachers for having unrealistic expectations of students; I blamed my schedule, which didn’t leave enough time for me to study; I blamed myself, thinking I wasn’t smart enough to do that bachelor.
What made me not abandon the course, at the time, was a unique opportunity: having the chance to be paid by the Brazilian government to study for a year at the University of Melbourne, one of the best universities in the world.
The scholarship I received would be from the Science without Borders program – which, at the time, was sending Brazilian students to other countries with little expectation of return for the population, which brought much criticism about excessive public spending.
While the program’s lack of academic rigor benefited me – literally anyone with an average academic score could participate – I always had it in my mind that I would prove everyone wrong about the quality of this program. I wanted to prove that there were, yes, excellent students being sent out. I was determined that I could be an example to other students—by putting in the effort, getting excellent grades, doing excellent research in my internship, and applying that knowledge when I got back to Brazil, one year later.
Said and done: I started studying even before classes started. After classes started, I was probably the student who spent the most hours studying for every subject I took.
I studied a lot. I woke up at 6 am, went to classes in the morning, tutoring sessions and workshops in the afternoon and often stayed in the library until 9 pm. On weekends I spent entire days at the library.
My mental and physical health were very compromised: I had serious anxiety and depression problems during this period and I gained about 50 lbs. In my mind, this was all temporary and all my efforts would pay off at the end of the semester.
“What was the result of this semester?” – you must be asking yourself.
Mediocre grades. I almost failed two subjects – 53% and 56% was my final grade in those – and got less than 70% in the other two that I passed more comfortably.
One detail that made this experience more shocking is that, according to the government’s program guidelines, if you failed two subjects, you had to return to Brazil and give all the scholarship money back (approximately 80 thousand reais at the time). That would mean that I would have put me and my family in a risky financial situation, possibly for years.
It was in this moment of existential shock that I finally realized that I was doing something wrong: I saw other students who took more difficult subjects than I did; they worked part-time, they had time for friends and family, and still got near-perfect grades.
What were they doing differently?
In this context of extreme frustration, I found a course called ‘Learning how to Learn’ taught by engineer Barbara Oakley on the Coursera platform.
I don’t like the phrase ‘It changed my life’ because it usually sounds like a cliché or exaggeration, but in this case, I can say that this course did indeed change my life.
The course’s premise is to break learning down into its simplest components and how to maximize the efficiency of your study hours, a subject that has fascinated me ever since.
Since I completed this course in January 2015, I have continued to study the subject of neuroeducation and productivity, trying to apply what I learned to my routine. In the following semester, I returned to Brazil and I took nine courses: I got seven maximum grades (above 90%) and two medium-high grades (80%). Since my return from Australia, I’ve managed to complete my degree taking 36 subjects, getting 27 top grades and 9 mid-upper grades.
But grades are only part of what I applied – I also studied more efficiently. As a result, I felt less stressed during semesters (I knew what I had to do and got my work done a lot faster than before), and I had more time to devote to other projects – I did a series of interviews with professors at my university, I managed to do internships almost every semester in conjunction with my subjects and I kept a blog, which after a few years culminated in the writing of this book.
Since the first edition of this book, I have taught the content described here in lectures and courses at various schools and universities, and the content covered appears to be universally applicable. The same techniques I used to graduate cum laude from a Masters in Neuroscience also can be used by a teenager in high school to get an A+ in a biology exam.
And that’s exactly why I fell in love with this topic of neuroeducation and how to learn better.
Why is this book relevant?
Currently, we live in the so-called knowledge age: more and more, manual processes are automated by machines or algorithms. The job market values people who are able to learn a lot of information quickly.
The ability to learn and master complex subjects, in a fast and efficient manner, is more valued today than ever before in history – and this ability, like any other skill, can be cultivated and developed.
There are efficient and inefficient ways of learning and retaining knowledge – unfortunately this is a topic that is rarely addressed during our school years. In school, the content is ‘given’ by the teachers and almost nothing is discussed about how they could study it in the best way possible.
At the same time, there is a current belief in the zeitgeist that people believe in innate abilities, both positively and negatively: Gary plays the guitar well because he was born with a gift. Maria will never be good at math; she just wasn’t born for that.
If you believe that people have fixed abilities, you probably believe this book is relatively useless.
However, my perspective is that human beings were born knowing anything – apart from some primordial instincts and reflexes inherent to the nervous system. We simply have the ability to develop abilities. There are no innate gifts or abilities, there is effort and practice to master something.
This doesn’t mean that anyone can learn anything, however, every person has the ability to learn magnificent things – even if they struggled to learn them at first.
That’s why I wrote this book, to try to help people who are not easy to learn to discover their potential. If you are not ‘a good student’, if you have difficulty learning new things, or if you have never developed your own study methods, the strategies described in this book will certainly help you.
I hope that the next few pages may be of some value to you.
The following links are amazon affiliate links, which means I receive a cut out of Amazon’s profit with no extra chargers to you. If you decide to buy anything using the following links, your support is very much appreciated.
Despite being a very common term in Neuroscience research, a lot of ambiguity persists in the literature regarding the precise definition of top-down control. In this review, we propose a more rigorous model of ‘top-down control’ as the integration of information contingent upon the maturation of neuronal ensembles. This model is explored in negative and positive valence studies that have investigated the medial prefrontal cortex (mPFC), an important heteromodal association cortex that is related to goal-directed behavior. In face of the new definition, we conclude that the maturation of neuronal ensemble in the mPFC is necessary for goal-directed behavior. We posit that a focus on the mechanisms of ensemble maturation could become a unifying facet of future research around the mPFC, allowing different lines of neuroscientific investigation to contribute to one another.
2 TOP-DOWN CONTROL IN NEUROSCIENCE RESEARCH
The definition of ‘top-down’ and ‘bottom-up’ models has been widely adopted by many scientific fields, with different and often contradictory meanings amongst them. In the field of Neuroscience and Psychology, the term ‘top-down’ is commonly used as jargon in scientific papers but rarely actually defined (Rauss & Pourtois, 2013), which likely stems from a lack of consensus on a rigorous definition for top-down processing. As a result, the term is often used in contradictory ways. For instance, ‘top-down control’ has been used as a defining characteristic of the visual processing in V1 in anesthetized ferrets (Roland et al., 2006), of the stress-regulating influence of the mPFC over the thalamus-BNST-amygdala pathway in rodents (de Kloet, de Kloet, de Kloet, & de Kloet, 2019) and for the role the parietal cortex in attention orienting in primates (Shomstein, 2012) – the same term used for different species, different states of consciousness, different brain regions entirely. It could be argued that such a definition would be rendered useless due to its broadness in scope.
To have a more precise definition of top-down control in the context of Neuroscience, it is necessary to think in terms of hierarchies in information encoding. One of the fundamental functions of the nervous system is to perform information processing, taking complex environmental and interoceptive inputs and allowing the organism to perform actions in accordance with its environment – a process coined as the ‘perception-action cycle’ (Fuster, 2001). As the information flows from the peripheral nervous system to the spine to sensory cortices to association cortices, information is encoded via mechanisms that are intra-neuronal (e.g. changes in gene expression, receptor expression, and spine morphology) and extra-neuronal (e.g. myelin plasticity) (Tozzi, 2015). Changes in neuronal activity promote changes in neuronal connectivity, resulting in the formation of ‘neuronal ensembles’ or ‘memory traces’– biological substrates that encode a particular memory (Thompson, 2005).
We propose a definition of top-down and bottom-up processing as follows: at lower levels of the hierarchy (e.g. peripheral nervous system or PNS) bottom-up processing occurs and the information is processed at a greater level of detail. On the other hand, at higher levels of the hierarchy (e.g. association cortices), top-down processing takes place, meaning that the incoming information is integrated (Figure 1). What differentiates hierarchical levels is their relative sparse connectivity: the information encoded by many ensembles in lower hierarchical levels is condensed into fewer ensembles in higher hierarchical levels. The condensation of information is the characteristic that allows information to be integrated from multiple inputs in higher hierarchical levels. Importantly, this model does not pose that only frontal cortices exert top-down control (e.g. definition adopted by White et al., 2018). For example, sensory cortices may exert top-down control over afferent spinal inputs. Moreover, the same brain region can have top-down and bottom-up processing occurring simultaneously: for instance, a sensory cortex can exert top-down control over the spinal inputs while providing efferent bottom-up signals to an association cortex.
Critically, this definition differs from the general way it is used in literature: some authors have asserted that the activity of neocortical regions implies top-down control over subcortical structures (Chiesa, Serretti, & Jakobsen, 2012). Under our proposed definition, the mere simultaneous firing of a brain region at a higher hierarchical level with another region at a lower hierarchical level does not necessarily imply top-down control. Rather, top-down control occurs at an ensemble level, contingent upon changes in cellular activity and connectivity. Since brain regions at higher hierarchical levels (e.g. heteromodal association cortices) have sparse connections with many other brain regions, we propose that their capacity to integrate information does not occur immediately. Instead, the memory ensemble undergoes a process of maturation, in which the connections in higher hierarchical levels are gradually strengthened over a period of time (see Section 4.3).
3 GOAL-DIRECTED BEHAVIOR AND THE MPFC
As mammals evolved, their actions became more complex – i.e. based less on simple stimulus-response loops and contingent on prior experience (Carlén, 2017). The ability of an organism to appropriately modify its actions to optimize the possible outcomes in a given scenario has been coined as goal-directed behavior (Zwosta et al., 2015). Goal-directed behavior is uniquely different from innate reflexes or habitual actions because there is no predetermined set of actions which could be constructed ex-ante. Instead, the organism needs to promptly adapt its actions based on constantly changing environmental stimuli. (Verschure, Pennartz, & Pezzulo, 2014)
Goal-direction involves brain-wide networks and therefore no single brain region should be considered a ‘goal-direction center’ of the brain. However, the capacity to integrate multimodal forms of input is paramount for animals to behave sensibly to changes in their environment. Therefore, goal-direction is contingent upon the exertion of top-down control from associative cortices over sensory and limbic cortices (see Section 2). While other associative cortices, such as the parietal cortex (Cohen, 2009), have been related to goal-direction, this review will mainly focus on the medial prefrontal cortex (mPFC).
The mPFC can be subdivided into the dorsomedial prefrontal cortex (dmPFC), which constitutes the anterior cingulate (AC) and the most dorsal section of the prelimbic cortex; and the ventromedial prefrontal cortex (vmPFC), which constitutes the infralimbic cortex (IL) and the ventral-most section of the prelimbic cortex (Figure 2) (Uylings & Van Eden, 1991). The vmPFC receives more limbic projections and processes emotional and interoceptive information while the dmPFC has more connections with sensory and motor regions (Heidbreder & Groenewegen, 2003).
The mPFC integrates motor information, exogenous stimuli (incoming mainly from thalamus), and endogenous stimuli (incoming from connections with the limbic system, which includes the amygdala, hippocampus, and nucleus accumbens) (Kamigaki, 2019). Therefore, the mPFC is anatomically positioned to integrate multiple modes of information and to modulate behavior (Gazzaley & Nobre, 2012). This rich anatomical connectivity allows the mPFC to act as an important hub for goal-directed behaviors, allowing the association between certain actions to positive outcomes and others to negative outcomes, thereby increasing the organism’s adaptability over time. (Kamigaki, 2019)
In behavioral neuroscience, two broad types of paradigms can be used: Pavlovian or instrumental. In pavlovian conditioning, the animal learns an association between two stimuli (e.g. a sound and a food reward). In contrast, in instrumental conditioning, the animal associates a self-initiated behavior with a stimulus (e.g. a nose poke with a food reward). Furthermore, paradigms can also be defined by the valence of their stimuli: a stimulus can be appetitive if the outcome is a reward (e.g. a food reward) or aversive if the outcome is a punishment (e.g. a foot shock). The valence of the outcome is important because it primes the attention of the organism towards the context: it is often the case that a neutral outcome does not form a robust memory (Lonsdorf et al., 2017). Therefore, in both Pavlovian and instrumental setups, the valence of a stimulus (whether positive or negative) has an impact on the memory formed: in Pavlovian conditioning, the valence potentiates the association between both stimuli, while in instrumental conditioning, it modifies the likelihood of the animal performing the same behavior in the future.
The following sections are a discussion of two subtypes of study which have investigated the mPFC: Pavlovian-negative (focusing mainly on fear-conditioning paradigms) and Instrumental-positive (focusing on addiction studies and the delay-discounting task). It is important to note that they are extremes in terms of training complexity: fear conditioning might require only one session to establish a memory that will last for the entire lifetime of the animal (Gale et al., 2004), whereas training in the delay-discounting task might take several weeks (Robbins, 2007). Despite this striking difference, we will propose that ensemble maturation is a unifying characteristic of both types of study. We will describe the basic circuitry involved in positive and negative valence studies, followed by a delineation of the relationship between mPFC, top-down control, and goal-direction in each study type.
4 TOP-DOWN CONTROL IN NEGATIVE VALENCE STUDIES
4.1 INTRODUCTION TO FEAR CONDITIONING
The expression of fear offers evolutionary advantages for animals and can be construed as an aspect of goal-direction: the brain must associate environmental cues with negative valence stimuli, which allows the animal to adapt its behavior in a future encounter with the same environment in order to optimize possible outcomes. Moreover, this process of association has an element of uncertainty because no organism can encounter every possible environment. Instead, animals need to have a model of the world which is based on previous experiential evidence (Rusu & Pennartz, 2019). The organism not only learns which environments are safe or unsafe, but it also uses this information to inform the behavioral decisions upon encountering new and unknown environments. Furthermore, animals also need to be flexible and be able to extinct fear memories, because environments which were once threatening in the past may be safe in the future (Moscarello & Maren, 2018).
A paradigm devised to model this natural phenomenon and to explore the mechanisms of associative learning is fear conditioning. Fear conditioning involves the association of a neutral conditioned stimulus (CS) paired with an aversive unconditioned stimulus (US). The animal is placed in an operant box for the first time and it receives a foot shock a few minutes later – the pairing of CS and US is called fear acquisition. In auditory fear conditioning, the CS is a tone and in contextual fear conditioning, the CS is the contextual information. After fear acquisition, the animal is subsequently provided with the same CS, but this time it does not receive a foot shock – this process is named extinction training. Importantly, during extinction, the original fear memory is not erased, but rather a new competing memory is established (An et al., 2017), which explains why extinction training suppresses the fear memory only transiently (Bouton, 2004) and in a context-dependent manner (Bouton & Bolles, 1979). Posterior to extinction training, the animal is confronted with the original CS again to test if the fear memory is reinstated after the extinction phase – which is a process known as ‘renewal’. In all stages of fear conditioning, the animal’s freezing behavior is used as a proxy of the underlying fear memory.
The mPFC seems to be important in two moments of fear conditioning: renewal, which has been mostly associated with the PL, and extinction, which has mostly implicated the IL (Knapska & Maren, 2009; Stern, Gazarini, Vanvossen, Hames, & Bertoglio, 2014). For decades, with increasingly advancing methods, the goal of this field of research has been to unveil the underlying ‘fear circuitry’, which has been found to involve the interaction between the mPFC, amygdala, and hippocampus. (Maren, Phan, & Liberzon, 2013)
4.2 THE INTERPLAY BETWEEN AMYGDALA AND MPFC
The amygdala can be subdivided into two main nuclei: the basolateral amygdala (BLA), which constitutes the lateral, basal and basomedial nuclei; and the central nucleus (CeA), which constitutes lateral and medial subregions (Pitkänen, Savander, & LeDoux, 1997). The BLA receives sensory inputs via thalamus and it receives projections from neocortical structures, such as the hippocampus and the mPFC (Pitkänen, Pikkarainen, Nurminen, & Ylinen, 2006). The BLA has long been thought of as the site in which the pairing of CS and US would occur (Marek, Sun, & Sah, 2019), while the CeA has been considered the main output of conditioned fear responses. Connecting the BLA and CeA is a group of GABAergic neurons called intercalated cells (ITC) (Duvarci & Pare, 2014).
One of the first examples of the interaction between the mPFC and the amygdala in a fear conditioning paradigm was the observation that a lesion in the vmPFC induced impairments in extinction (Morgan, Romanski, & LeDoux, 1993). It has been later shown that in the course of extinction learning, there is a reduction in synaptic efficacy in glutamatergic neurons of the mPFC that project to principal neurons in the BLA; however, the synaptic efficacy of mPFC projections to ITCs remains unchanged, leading to enhanced inhibition of the central nucleus of the amygdala (Cho, Deisseroth, & Bolshakov, 2013). It has also been shown that extinction can be facilitated by the induction of synaptic depression of a monosynaptic projection between BLA and mPFC (Klavir, Prigge, Sarel, Paz, & Yizhar, 2017). Furthermore, optogenetically silencing projections from the IL to the BLA impairs extinction learning while optogenetically stimulation of this pathway enhances extinction learning. (Bukalo et al., 2015)
Although there is a bidirectional anatomical connection between mPFC and amygdala, this does not entail that the mPFC always exerts top-down control over the amygdala. For instance, the behavioral response of freezing itself, immediately after acquisition, is mainly driven by the amygdala and periaqueductal gray function (Herry & Johansen, 2014). The disruption of mPFC activity in the initial fear acquisition stage does not inhibit the freezing response (Gilmartin, Balderston, & Helmstetter, 2014; Heroux, Robinson-Drummer, Sanders, Rosen, & Stanton, 2017; Lee & Choi, 2012; Zelinski, Hong, Tyndall, Halsall, & McDonald, 2010), suggesting that the initial fear expression is not mediated by mPFC function.
However, the function of goal-direction of the mPFC in fear conditioning lays upon the fact that the expression of fear is not appropriate under all circumstances. Upon encountering a possibly threatening environment, the organism needs to recall the context in which it previously had negative experiences and then prime an adaptive response – i.e. to perceive the new context as high-threat and flee/freeze or to perceive it as low-threat and behave in a normal, exploratory way. The choice of behavior from a complex repertoire must be finely controlled and is not only based on current stimuli, but also on the possible imminent threats that could occur the next moment (Giustino & Maren, 2015). Therefore, the mPFC could be considered as a goal-direction hub in fear conditioning, not because of the expression of fear itself, but rather determining when to express fear as the most appropriate behavior. (Moscarello & Maren, 2018)
4.3 THE INTERPLAY BETWEEN HIPPOCAMPUS AND MPFC
The hippocampus is thought to provide contextual information, as evidenced by the fact that hippocampal lesions lead to impaired fear expression when a foot shock is paired with a context, but not when it is paired to an auditory tone (Phillips & LeDoux, 1992). More recent research has corroborated that idea, showing that hippocampal neurons are preferentially active during context presentation, independent of whether the animal is immediately shocked or previously fear conditioned. (Zelikowsky, Hersman, Chawla, Barnes, & Fanselow, 2014). Although the relationship between hippocampus and mPFC has long been thought as one of excitatory feed-forward excitation (Padilla-Coreano et al., 2016), recent evidence has suggested that more important for fear extinction is the feedforward inhibition of the hippocampus to PV-positive interneurons in the IL, but not somatostatin-positive interneurons or principal glutamatergic neurons in the IL (Marek et al., 2018).
Outside the field of fear conditioning studies, the interplay between hippocampus and mPFC has been traditionally been thought as one of the supporters of memory consolidation (Alvarez & Squire, 1994): the hippocampus and its surrounding entorhinal cortex would function as the neural substrates for recent memories while the mPFC and other neocortical regions would serve as substrates for remote memories (McClelland, 2013), although the exact mechanisms of systems consolidation remained elusive for a long time.
In a recent seminal paper, Kitamura et al. used ensemble-specific techniques to demonstrate a possible mechanism to the consolidation of fear memory in the mPFC over time. Rather than being initially encoded in the hippocampus and later transitioning to mPFC, they found that there were mPFC ensembles formed during the initial stage of fear acquisition (Kitamura et al., 2017). These mPFC ensembles were found to receive projections from both the hippocampus and the BLA, indicating that they could be sites of top-down control over the incoming bottom-up signals. These mPFC ensembles, however, were initially immature and were not naturally activated by environmental cues, but they could be artificially activated with optogenetics to induce freezing (Kitamura et al., 2017).
The authors also found an opposing effect of maturation between mPFC and hippocampus: they found that the initial immature mPFC ensemble became mature within 14 days, and the initially active hippocampal ensemble was no longer involved after 14 days. Meanwhile, BLA ensembles were persistent throughout the entire period, which suggests its role in encoding the valence of a fear memory (Kitamura et al., 2017). An interesting hypothesis to explain the transition between a hippocampal-driven memory and a neocortical-driven memory is that the hippocampus is constantly creating new neurons, which could integrate into the hippocampal network and disrupt established memory ensembles. (Kitamura & Inokuchi, 2014)
This study helps to answer an important question discussed previously (see Section 4.2): why is mPFC activity not important for the initial expression of fear, but rather the initial fear expression seems to be driven mainly by subcortical structures? One possible answer that the association of inputs from lower hierarchical levels (hippocampus and amygdala) is not initially strong enough to allow mPFC ensembles to exert top-down control over the primed bottom-up signals. These mPFC ensembles need time to become mature (Figure 3) and in the context of fear conditioning, this maturation takes around 14 days and results on increased complexity in dendritic morphology in these ensemble cells (Kitamura et al., 2017).
4.4 LIMITATIONS OF FEAR CONDITIONING
An important limitation of the fear conditioning paradigm is its dependence on fear and pain circuits (Herry & Johansen, 2014), which results in memory effects that are not generalizable to other forms of learning. This can be exemplified with the phenomenon of incubation, in which the fear response can be potentiated over time without extra training, which is a phenomenon known as incubation (Eysenck, 1968).
Furthermore, stress itself has been shown as a confounding factor in fear conditioning: exposure to chronic stress enhances fear expression (Maroun et al., 2013) and it is correlated with a decrease in dendritic morphological complexity in the mPFC. (Izquierdo, Wellman, & Holmes, 2006). A recent study has demonstrated that mild-fear conditioning promotes the formation of an ensemble in the mPFC, whose activation is sufficient to induce fear expression one month later. However, a strong fear conditioning (using three foot shocks, instead of one) does not induce the formation of mPFC ensembles (Matos et al., 2019).
5 TOP-DOWN CONTROL IN POSITIVE VALENCE STUDIES
In the field of positive valence paradigms, it has been consistently found that mPFC impairments do not necessarily decrease the performance of the animal in the tasks. However, mPFC impairments usually have a negative impact on aspects related to task switching and cognitive flexibility (Floresco, Block, & Tse, 2008). This general phenomenon relates to the importance of the mPFC in the integration of heteromodal information to allow goal-directed behavior.
Similar to negative valence studies, the term top-down control is often used in positive valence studies to describe the role of the mPFC in attention and impulsivity (Miller & D’Esposito, 2005). We posit that this general definition is not correct: instead, similarly to the presented evidence from aversive stimuli studies (see Section 4.3), top-down control should be mediated by mechanisms of ensemble maturation in the mPFC.
5.1 ADDICTION STUDIES
Addiction studies are an all-encompassing definition for paradigms that use addictive substances as positive valence stimuli. One example is the forced-abstinence paradigm, in which the animal goes through a period of training in which it associates an action (e.g. a lever press) with a drug reward. After this initial training period, the drug reward is paired with a negative stimulus (e.g. a foot shock). The goal of this type of study is to assess the control of impulsivity, observing if the animal is capable of refraining from a short-term reward (drug reward) because of its longer-term consequences (after a while, it will receive a foot shock as a punishment).
Using this type of paradigm, it has been shown that long-term cocaine self-administration reduces PL excitability, which can be rescued with optogenetic stimulation. (Chen et al., 2013) This suggests that hypoactivity of mPFC is related to a loss of top-down control and consequent compulsive drug seeking. Furthermore, a recent study has shown that when pairing a lever press to a punishment, there is a notable shift in synaptic plasticity in the vmPFC neurons which project to the shell of the nucleus accumbens (NAc) (Halladay et al., 2020).
While the amygdala has a very clear role in encoding ‘fear memories’ in the context of negative valence paradigms, it has also been implicated in the processing of positive valence stimuli. An increase in neuronal firing of the BLA is necessary for the formation of associative reward memories (Tye, Stuber, De Ridder, Bonci, & Janak, 2008) and BLA neurons respond to both reward and punishment in a pavlovian task with distinct underlying neuronal populations (Beyeler et al., 2016). Although no anatomical distinction seems to exist within the amygdala to separate ensembles that encode positive or negative valence stimuli, there are molecular markers for the encoding of valence. In particular, magnocellular Rspo2+ neurons are mainly activated by aversive stimuli while parvocellular Ppp1r1b+ neurons are mainly activated by appetitive stimuli (Kim, Pignatelli, Xu, Itohara, & Tonegawa, 2016), which suggests a molecular substrate for how the BLA could encode two antagonistic types of memory.
Classically, the vmPFC has been shown as a center for inhibitory control over drug-seeking (Moorman, James, McGlinchey, & Aston-Jones, 2015) and the dmPFC has been thought to drive drug-seeking behavior. This idea of dmPFC as a ‘go’ center and vmPFC as a ‘no-go’ center has an obvious parallel with fear conditioning studies, in which the dmPFC is related to fear expression and vmPFC is related to extinction learning (Giustino & Maren, 2015). However, a more nuanced view is necessary to understand mPFC function. For example, IL activation can either induce increase or decrease in drug-seeking (Koya et al., 2009; Peters, LaLumiere, & Kalivas, 2008), which could potentially be explained by a more complex time-dependent function of the vmPFC in the expression and extinction of cocaine-seeking (Van den Oever et al., 2013).
Cocaine self-administration results in enhancement of excitatory activity in the PL-to-NAc(core) pathway, while extinction results in an increase of excitation in the IL-to-NAc(shell) pathway. Both enhancements of excitation are mediated by synaptic maturation through the upregulation of AMPA receptors and optogenetic inhibition of the synaptic remodeling process results in a decrease of drug-seeking (Ma et al., 2014). While this study did not use ensemble-specific targeting, the evidence related to synaptic modeling with drug craving could fall in line with the proposed idea that the exertion of top-down processing of the mPFC depends on mature memory traces.
To direct assess ensemble function in addiction, a recent study has used the method of targeted recombination of active populations (TRAP) to specifically tag neurons that were naturally active during an alcohol self-administration task. The authors found that a small mPFC ensemble was necessary for cue-induced alcohol-seeking but not necessary for context-induced alcohol-seeking (i.e. when a salient cue was not present in the testing stage) (Visser et al., 2020). Furthermore, chemogenetic inactivation of the small alcohol-associated ensemble in the mPFC (6-7% of total neurons) led to a decrease in cue-induced alcohol-seeking, while inactivation of similarly sized sucrose-related ensemble did not lead to the same effect, which specifically demonstrates the effected of inactivation of the memory ensemble related to drug-seeking (Visser et al., 2020).
An important limitation of studies that use highly addictive drugs as rewards is the demonstration that cocaine-seeking becomes insensitive to devaluation after extensive training, which means that the behavior was not goal-directed, but rather habitual (Zapata, Minney, & Shippenberg, 2010). Therefore, in order to investigate goal-directed behavior, potentially a paradigm be used with two differences: (1) a less potent rewarding stimulus and (2) a task that has variance in the stimulus-response setups, such that an automatized pattern of behavior emerges from the mPFC.
5.2 DELAY-DISCOUNTING TASK (DDT)
Impulsivity can be defined as a premature action without foresight (Dalley, Everitt, & Robbins, 2011). The relationship between different brain regions and impulsivity has been studied for decades with paradigms such as the delay discounting task (DDT). In the DDT, the animal is trained to perform an action to receive a small reward (e.g. one food pellet) or wait for a few seconds and perform the same action to receive a large reward (e.g. five food pellets). One important methodological consideration is that the animal is unable to receive more rewards by performing a series of sequential short-term actions, therefore a time buffer needs to be put in place in between trials (Beckwith, 2017).
DDT is a powerful paradigm to assess a straightforward aspect of goal-direction. This paradigm has a reduction in confounding factors because both choices (short/small or long/large) have the same patterns of behavioral output, i.e. a waiting period followed by the same movement, which is different from the 5-choice serial-reaction time task, for example, where some responses require more movement than others. This is of extreme importance to study the mPFC because the dmPFC is also involved in motor planning (Euston & McNaughton, 2006). Therefore, in studying the waiting periods in the DDT, researchers can assess mPFC activity related to the choice of the animal, i.e. higher-order cognitive function without motor planning as a confounder.
In the DDT, pharmacological inactivation or neurotoxic lesions of the mPFC and disconnection between mPFC-BLA led to increased impulsivity (preference of short-term small reward over long-term big rewards) (Churchwell, Morris, Heurtelou, & Kesner, 2009; Gill, Castaneda, & Janak, 2010), This increase in impulsivity indicates a role in the mPFC of representing the outcome values of certain behavioral responses, which has later demonstrated with electrophysiological studies (Powell & Redish, 2016). In particular, dmPFC neurons are more active during the delay stage of the DDT and pharmacological inactivation of this brain region increases premature responses (Narayanan & Laubach, 2006).
Interestingly, electrophysiological recordings revealed an increase in PL activity during the delay period of a large reward, however, this increase was not observed when the animals were performing forced-choice trials (Sackett, Moschak, & Carelli, 2019). This parallels findings in habitual behavior research, where mPFC function is generally important for the learning stages of a new task, but once the contingencies of the task are learned, the mPFC becomes disengaged and behavior is driven mainly by subcortical structures, such as the dorsal striatum (Everitt & Robbins, 2016).
To date, no studies have investigated the mPFC using ensemble-specific targeting. A potential prolific field of research would investigate the development of ensemble maturation in the mPFC during the initial learning stages of an instrumental task up to when the actions of the animal become habitual, i.e. at which points of learning a complex task does the mPFC exert top-down control and at which points it does not.
6 CONCLUSIONS AND PERSPECTIVES FOR THE FIELD
In this review, we proposed a model of top-down and bottom-up as a hierarchy of sparse connectivity. This model, which places neuronal ensemble maturation as paramount for the function of top-down processing in the nervous system, could be a catalyzer for the integration of multiple lines of research, including negative and positive valence studies. In the mPFC, this entails that top-down control is necessary for goal-directed behavior, as evidenced from positive and negative valence paradigm studies, although the underlying process of maturation depends on the specific parameters of the task. Importantly, the presented evidence does not entail that the mPFC is the only region related to goal-directed behavior or that can exert top-down control. The role of the mPFC is an instantiation of the importance of association cortices in the integration of information from lower hierarchical structures. It is possible that ensemble maturation in other association cortices, such as the parietal cortex (Ivashkina, Gruzdeva, Roshchina, Toropova, & Anokhin, 2019), or even in a cortex-wide dispersed-ensemble fashion (Roy et al., 2019).
As for methodological considerations, the mPFC is a complex heteromodal associative cortex and its function not only depends on the task at hand, but it is also influenced by all previous experiential learnings of the animal throughout its life (Tse et al., 2007). Careful interpretation of results must be done to avoid problems such as the Euston-Cowen-McNaughton hassle, in which dmPFC activity encodes mainly motoric activity rather than cognitive function in a task where the animal is moving. Experimental design to study the mPFC would benefit from either: 1) using paradigms that have the same output response in both task contingencies (like the DDT) and; 2) use analysis of video recordings to try to unveil mPFC activity which is related to movement and mPFC activity related to the task (Mathis et al., 2018).
Furthermore, mPFC function is disrupted through stress, possibly mediated by the upregulation of dopaminergic and noradrenergic receptors, which would enhance calcium-cAMP pathways and ultimately would result in a reduction of mPFC activity and a subsequent reduction in connectivity of the ensembles (Datta & Arnsten, 2019). Therefore, standardization of protocols across labs and advances in technologies that minimize contact between researchers and animals to reduce stress, such as operant chambers connected to the home cage of the animal (Bruinsma et al., 2019), would be useful to reduce confounding effects and possibly contradictory findings in the field.
Another consideration is the fact that, under unchanging environmental conditions, learning becomes habitual, i.e. less dependent upon neocortical activity and more dependent on subcortical activity (Everitt & Robbins, 2016), indicating a loss of top-control of higher hierarchical structures. A useful experiment to elucidate the relationship between habits and ensemble development in the mPFC would be to use fos-Cre-GCaMP to visualize ensemble activity in the mPFC during the learning stages complex instrumental paradigm.
The main reason why ensemble-specific studies are powerful is their reduction of uncertainty in the interpretation of data. This is especially true for association cortices like the mPFC, which is integrated with motor, limbic and sensory cortices. In this brain region, the general manipulation of neurons via optogenetics or chemogenetics might induce downstream changes in neuronal activity which are unrelated to the task at hand. In specific targeting engrams, these confounding effects are reduced and the subsequent change in behavior can be interpreted more precisely.
Recent efforts have tried to unveil the intracellular and extracellular mechanisms of neuronal maturation. In terms of intracellular mechanisms, consolidation of fear memory requires BNDF upregulation in the PL, which leads to the increase in neuroligin 1 (NLGN1) and neuroligin 2 (NLGN2), important markers of synaptic maturation (Ye, Kapeller-Libermann, Travaglia, Inda, & Alberini, 2017). As for extracellular mechanisms, myelin plasticity seems to be important for fear learning: in transgenic animals that cannot produce myelin, there is a deficiency in remote fear memory recall. This phenotype can be partially rescued by the induction of myelin expression in the brain (Pan, Mayoral, Choi, Chan, & Kheirbek, 2020).
To conclude, a focus of future research on unveiling the specifics of ensemble maturation may yield fruitful results and allow the cross-communication between various lines of neuroscience research. Further research is still needed to assess: 1) how the valence and strength of a stimulus are related to ensemble maturation in the learning of different paradigms 2) what is the interplay between changes in vasculature, myelination, synaptic morphology and protein expression in the process of ensemble maturation.
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Over the last decade, developments in calcium imaging have provided helpful tools for the study of brain function. This review describes recent advances in the field, especially regarding the two main techniques used to record neurophysiological activity in freely moving animals: fiber photometry and miniaturized endoscopes (miniscopes). Fiber photometry is used to investigate bulk activity changes in synchronous-firing neuronal populations while miniscopes can be used to visualize neuronal activity at the single-cell level. This review compares the implementation (e.g. technical considerations, surgeries), data acquisition, and data analysis of both techniques, providing insights into the types of research questions suitable for each method.
1 INTRODUCTION – A BRIEF HISTORY OF CALCIUM IMAGING
To study brain function in the context of Behavioral Neuroscience, various manipulations of brain activity such as pharmacology, optogenetics, or chemogenetics have been used. These interventionistic methods of study allow scientists to make claims of function in a counterfactual manner: “activity of cell-type X in brain region Y is necessary and sufficient for behavior Z”. However, if one wants to observe patterns that emerge in the animal’s brain in a more naturalistic way, methods of direct assessment of brain activity are necessary.
One such method is in vivo recording of electrophysiological parameters, which provides unparalleled temporal accuracy and accurate estimation of spike timing of single units (Li et al., 2019). For decades, the field was predominantly attempting to unveil mechanisms of information encoding at the single-cell level, using techniques as patch-clamping in slices of brain tissue. The idea of using multicellular electrophysiology to assess simultaneous brain activity in vivo was met with significant skepticism: the brain was thought to be too complex to be usefully reduced to the encoding properties of only a few dozen single-units (Nicolelis, 2011). This was first proven wrong in the late 90s, in an experiment that demonstrated that the activity of 30-40 neurons accurately encoded the information of the location of a tactile stimulus (Nicolelis et al., 1998). Since then, in vivo electrophysiology has seen significant advances, culminating in inventions such as the neuropixel probe (Jun et al., 2017), which can record thousands of single units simultaneously in multiple brain regions.
In line with the growing interest in the investigation of neuronal population dynamics, calcium imaging technology has evolved concurrently. Initially, this method was performed with small calcium-sensitive dyes (Cobbold & Rink, 1987), and more recently with genetically encoded calcium indicators (GECIs) such as GCaMP (Nakai, Ohkura, & Imoto, 2001). The main advantage of GECIs over calcium-sensitive dyes is that they can be expressed long-term and can potentially bypass invasive loading procedures with the use of transgenic lines.
Calcium imaging utilizes a reporter that transforms calcium availability – which is a second-order effect of cell activity – into a fluorescent signal (Scanziani & Häusser, 2009). Therefore, this method is necessarily indirect, and it consequently has a poorer temporal resolution than electrophysiology due to limitations intrinsic to the dynamics of the calcium indicator. However, calcium imaging has a great advantage over in vivo electrophysiology, which is the ability to target specific neuronal-projections, cell-types, or even subcellular structures, allowing for microcircuit-level studies (Campos, 2019).
In vivo calcium imaging has commonly been performed with head-fixed animals and two-photon microscopy. Two-photon microscopy has several advantages over one-photon/widefield microscopy, including better tissue penetration, less phototoxicity due to the use of longer wavelengths, and the fact that excitation light is focused on a very narrow focal plane, resulting in a better signal-to-noise ratio (Helmchen, 2009). However, the necessity to head-fixate animals and the average price of a two-photon setup being around half-a-million dollars (Girven & Sparta, 2017) has instigated the necessity to search for cheaper 1-photon alternatives that could be used in freely moving animals. Furthermore, a comparison of the same sample under one-photon and two-photon microscopy have shown that both techniques yielded the same neurons in the image stack and with a similar pattern of signal acquisition (Glas et al., 2019), implying that one-photon can yield similar results to two-photon approaches.
This review will discuss two main techniques of one-photon in vivo calcium imaging that allow Behavioral Neuroscience studies in freely moving animals: fiber photometry and miniaturized endoscopes (miniscopes). Each technique will be examined and compared in multiple aspects, including surgeries, impact on behavior, data interpretation, and data analysis. We will describe which technique is more appropriate based upon one’s research question and conclude with perspectives for the field of Behavioral Neuroscience, indicating current limitations and how they could be overcome with future technological advances.
2 GCAMP USAGE IN CALCIUM IMAGING
Neuronal activity is primarily an electric phenomenon which can be visualized directly using voltage indicator probes (Barnett et al., 2012), or indirectly by targeting molecules that increase in concentration as a result of cell activity, such as fluorescent biosensors for neurotransmitters (Marvin et al., 2013) or ion sensors (Arosio & Ratto, 2014). The use of voltage indicator allows for a greater temporal resolution, but their use is currently limited due to poor signal-to-noise ratio (Resendez et al., 2016). The remainder of the review will focus mainly on calcium indicators as they are the most commonly used probe for FP and miniscopes.
One of the numerous downstream consequences of neuronal activity is the increase in intracellular calcium (Ross, 1989). Calcium is important as second-messenger for many biological functions: G-protein coupled receptor activation cascades (Ma et al., 2017), neurochemicals exocytosis (Augustine, Charlton, & Smith, 1985), and synaptic plasticity (Zucker, 1999). Because calcium has a very low intracellular concentration when a neuron is inactive (0.05-0.1 mM) and a significantly higher concentration when a neuron is active (0.7-1 mM), it is a reliable target for optic probing, with a distinguishable signal-to-noise ratio between both states of activity (Oh, Lee, & Kaang, 2019).
The GCaMP protein is comprised of three portions: cpGFP (a fluorescent indicator), calmodulin (a highly sensitive calcium sensor), and M13 (a small peptide that allows a dynamic change between active and inactive states). cpGFP becomes fluorescent when excited with light in the blue color-range (around 470-490 nm). Because some energy is lost to vibration, the emitted photons from the reporter are in the green color range (around 510 nm). The difference between excitation and emission light is called Stokes Shift, and it allows the separation of excitation and emission photons with optical filters (Berezin & Achilefu, 2010).
The molecule of GCaMP has two conformations (Figure 1): unbound to Ca2+, which emits less fluorescence, and bound to Ca2+, which has a different protonation state (Barnett, Hughes, & Drobizhev, 2017) and results in a different protein conformation that emits significantly more fluorescence compared to the unbound conformation – for instance, a variant of GCaMP with fast kinetics (GCaMP6f) fluoresces 27 times more in a calcium-saturated state compared to a calcium-depleted state (Farhana et al., 2019). Because of the pronounced 7- to 20-fold increase in calcium availability during the active neuronal state compared to the inactive state, GCaMP fluorescence can, therefore, serve as a reliable indicator of intracellular calcium concentration, which is, in turn, an indirect measurement of neuronal activity.
GCaMP is an intensiometric indicator, which means that the fluorescent signal observed depends on the concentration of GCaMP in the animal’s brain. It is necessary to be cognizant of GCaMP photobleaching – i.e. the excitation light causes the degradation of GCaMP molecules – throughout a recording session. These two factors complicate the data analysis comparisons (1) between animals since different organisms will inevitably have different intracellular GCaMP levels, and (2) in the same animal because the bleaching will decrease baseline fluorescence at different timepoints of training. This is commonly addressed by using a ratio of fluorescence between active periods and baseline (ΔF/F, see Section 8.1): although the absolute magnitude of GCaMP fluorescence might be different, the relative proportion of activity over baseline should be approximately the same, which allows comparisons of the signal within the same animal at different points in time, and also between different animals.
The potency of calcium indicators has significantly improved since the development of the original GCaMP probe (Nakai et al., 2001). Every iteration has roughly led to a 1.5-2x improvement of signal linearity and sensitivity (Akerboom et al., 2012; Chen et al., 2013; Tian et al., 2009). Recent innovations include the development of jGCaMP7, which has on average has 40% greater ΔF/F compared to its predecessor GCaMP6 (Dana et al., 2019), and XGCaMP, which has a four-color suite of probes which can be used for multi-color imaging (Inoue et al., 2019). Multicolor calcium indicators can be applied with photometry systems, allowing the observation of two GECIs with different excitation spectra simultaneously. This allows, for instance, the combination of a red-shifted calcium indicator combined with a green-fluorescent probe for dopamine (Beyene et al., 2018) or simultaneous photometry measurements with optogenetics intervention in the same brain region (Sych et al., 2019). The advantages of multicolor GCaMP suites are less applicable to miniscopes because GRIN lenses are not appropriate to be used with red/far-red indicators (Ghosh et al., 2011).
There are several challenges when using this probe: GCaMP interferes with the kinetics of L-type calcium channels (Yang et al., 2018) and there is a strong buffering of intracellular Ca2+ which may lead to cytotoxicity (Resendez et al., 2016). These problems can partially be avoided by reducing intracellular GCaMP levels, at the cost of a poorer signal to noise ratio. Therefore, an initial dilution study is often recommended to determine optimal GCaMP expression for FP and miniscope implementation. Furthermore, abnormalities in brain function have been reported in transgenic GCaMP lines (Steinmetz et al., 2017). Transgenic lines can be replaced by cre-dependent lines to bypass this problem, but that requires an additional virus injection in the target brain region.
In the context of Behavioral Neuroscience, GCaMP data is often synced with behavioral data, usually by performing a low-pass filter to infer spiking activity based on the fluorescence data. Because there are GCaMP molecules with different kinetics, the output data depends on the decay time of the probe used (Figure 2A). A confounding effect of the inference of spiking activity is the fact that the concentration of calcium in the neuron remains elevated after activity – such that a 1 ms action potential can potentially increase GCaMP fluorescence for 1-10 s (Sabatini, 2019). This has a consequence of reducing the correlation of fluorescence data and the true spiking, such that slower the kinetics of GCaMP, the poorer this correlation is (Figure 2B). When using a slow GCaMP variant, the utilization of a simple low-pass filter processing results in the obfuscation of fast-consecutive spikes, which could result in false-negative findings (Figure 2C) (Sabatini, 2019). This problem can be minimized by performing deconvoluting processing, i.e. using a more complex algorithm that accounts for GCaMP kinetics and the temporal information of the spiking activity to accurately infer cellular activity. (Figure 2D)
In summary, GCaMP is one of the most commonly used and most rapidly developing GECIs in the field of neuroscience because it is one of the best optic probes in terms of signal-to-noise ratio and the fact that calcium influx is a reliable biological metric of neuronal activity. However, it is necessary to keep the limitations of GCaMP in mind: (1) it may have effects on normal physiology, especially when overexpressed in the cell. This can be minimized by an initial dilution study before the experiment; (2) it is not a real-time probe since the activity spike occurs before the influx of calcium. This, in turn, can be corrected by adding a deconvolution step in the data analysis.
3 FIBER PHOTOMETRY (FP)
FP is a calcium imaging method that uses a single patch cable, connected to an implanted fiber, to guide both excitation of the fluorescent probe and collection of the fluorescence signal (Figure 3). The light emitted by GCaMP in the brain of the animal can be subsequently separated with optical filters before reaching a highly sensitive detector. This analog input is converted into a digital signal: a one-dimensional trace that represents the fluorescence output of all GCaMP-tagged neurons within range of the fiber tip. Compared to traditional techniques such as electrophysiology, FP is more efficient in terms of data collection and ease of use, more stable for long-term analysis, and less expensive (Li et al., 2019).
While it lacks spatial information, FP is useful to study bulk activity of specific neuronal populations, since GCaMP can be expressed in specific cell types. Furthermore, because FP uses either a low-noise amplified photodetector or a photomultiplier tube, it has a sensitivity in the level of single photons, allowing detection of low levels of activity in soma, dendrites, and axons (Dana et al., 2019). The use of a lock-in amplifier and high sensitivity detectors also allow for multiple hour-long recordings over multiple weeks with minimal signal loss due to low excitation light intensity required (Simone et al., 2018). Furthermore, it is possible to implant several fibers to assess the activity of multiple brain regions simultaneously (Kim et al., 2016) due to the relatively small size of the implant (200-400μm).
Traditional FP has a larger cone of detection (200-450 μm) (Kupferschmidt et al., 2017; Pisanello et al., 2018) compared to the relative narrow z-resolution of the miniscope (33.35 μm per plane of focus) (Glas et al., 2019). Recent developments with tapered fiber tips allow for light collection up to 2000 μm depth, while their decreased surface area also reduces the amount of tissue damage (Pisano et al., 2019). Therefore, FP seems to be the most appropriate option to study the dynamics of sporadically tagged neurons, since it is unlikely that miniscopes would capture multiple cells from a sparse neuronal population within a single detection plane.
In summary, FP is used to assess bulk activity of neuronal populations in freely moving animals. The main limitation of the technique is the lack of spatial information, which makes it possible to use more sensitive detectors and have a great volume of acquisition, while simplifying several steps of implementation (surgery, data acquisition, and data analysis), making it a relatively straightforward technique to establish in the lab.
4 MINISCOPES
For a long time, the main limitation of one-photon calcium imaging was that brain tissue presents high levels of light scattering (Bollmann & Engert, 2009; Hamel et al., 2015), which explains why miniscopes were initially developed with two-photon technology. The original system essentially connected excitatory light from a two-photon tabletop system into a fiber that could be implanted in the animals’ head, with the original implant weighing about 25 g (Helmchen et al., 2001). While other lighter two-photon miniscopes have been developed and used successfully since then, the technical challenges of optical limitations, inferior sampling rates, and movement artifacts originating from the use of long wavelengths in femtosecond pulses (Silva, 2017) have instigated the search for one-photon miniscope alternatives.
The problem of one-photon light scattering and consequent inability to reach more deeply than a few millimeters in the brain (Ouzounov et al., 2017) has been partially addressed by the development of GRIN lenses. GRIN lenses have a radially-varying index of refraction, which maximizes the amount of light that reaches the sensor while minimizing optical aberrations (Barretto, Messerschmidt, & Schnitzer, 2009). The miniscope contains a GRIN objective (1.8 to 2.0 mm diameter; Figure 4A), which is sufficient for cortical imaging (Aharoni & Hoogland, 2019). Deeper brain regions require implantation of a second GRIN relay lens (ranging from 500 μm to 1000 μm in diameter; Figure 4B).
The first one-photon miniaturized microscope that allowed for single-cell resolution was developed first by the Schnitzer group at Stanford (Ghosh et al., 2011). Advances in miniaturization technology were used to replace the main components of a traditional widefield microscope with something that the animal could carry on top of its head: they replaced a lamp with a LED, a huge CCD sensor with a tiny CMOS sensor and a big objective by a small GRIN lens (Figure 5A). The development of miniscopes was a substantial advance for Behavioral Neuroscience: miniscope data allows researchers to visually observe the same neuronal population over multiple weeks (Figure 5B) while distinguishing the contributions of single neurons to behavior (Figure 5C).
The use of miniscopes poses technical challenges involving surgery, impact on behavior, and data analysis, which will be described in the following sections (see Sections 5, 6, and 7). Other disadvantages include that there are no waterproof miniscopes, rendering it unfeasible for behavioral paradigms such as the Morris water maze or the forced swim test (Resendez & Stuber, 2015). Furthermore, modern miniscopes systems have limited acquisition frame rates, precluding the use of temporally precise voltage-sensors (Hamel et al., 2015).
In summary, miniscopes can provide significantly more information than fiber photometry: rather than collapsing all information into a single dimension, miniscope data preserves the spatial organization of the neurons in the field-of-view. However, while providing single-cell resolution, the system is more technically challenging to implement and more difficult to analyze.
5 SURGERY
FP and miniscope both require stereotactic surgery to ensure accurate implant placement. Surgical procedures are similar: (1) Virus injection to express GCaMP (when not using a transgenic line); (2) implantation of fiber/GRIN lens; (3) headcap placement; (4) for miniscope, baseplate placement to allow secure connection of the device onto the headcap. When using a viral expression of GCaMP, both techniques benefit from a preliminary dilution study, in which multiple concentrations of the virus are tested. The goal is to have optimal GCaMP expression, which is visually expressed throughout the cytosol, but not the nucleus (Resendez et al., 2016), since overexpression will lead to excessive buffering of calcium ions and eventual cell death (Grienberger & Konnerth, 2012).
The placement of implants requires similar steps for both techniques: making a craniotomy, dura removal, and placement of the fiber or GRIN lens. However, a few complications may arise in the miniscope surgery due to the greater size of the implant. Large GRIN lenses also increase intracranial pressure, potentially leading to shifts in virus diffusion and subsequent mistargeted GCaMP expression. To minimize this issue, one could inject a 15% d-mannitol to reduce intracranial pressure before drilling the holes in the skull (de Groot et al., 2020).
GRIN relay lenses (500 μm –1000 μm) are significantly more damaging to the brain than a fiber implant (200 μm – 400 μm) because a two-fold increase in diameter will result in a four-fold increase in volume (and thus four times more damaged or displaced cells). An important consideration is that the relative impact of the implant diminishes with the size of the animal model. For example, an implant of the same size will induce a proportionally higher volume of damage in a mouse brain, which weighs between 0.4-0.5 g, compared to a rat brain, which weighs around 2 g (Bolon & Butt, 2011).
The amount of tissue damage is also dependent on the brain region of interest: the more ventral in the brain, the larger the GRIN relay lens needs to be to assure proper signal acquisition, and consequently more tissue needs to be removed for the implant. This may preclude one from using the miniscope in ventral brain regions – such as the OFC – since a significant volume of dorsal tissue would need to be removed, which could lead to confounding behavioral effects.
To summarize, even though the surgery steps are similar for FP and miniscopes, the difference in implant size needs to be taken into account in the experimental design, both for which animal model to use and for which brain region one is trying to collect data from.
6 IMPACT ON BEHAVIOR
FP and miniscope systems both require headcaps and the attachment of cables. Important considerations for behavior are: 1) Secondary consequences of individual housing; 2) Induction of stress related to the attachment of the animal to the device and; 3) Limitations of movement as a consequence of the size and weight distribution of the apparatus.
Because the animals have a reasonably fragile implement permanently attached to the top of their heads, most protocols for FP and miniscopes advise that researchers put their animals in individual housing after surgery. Studies have shown that single housing, even in an enriched environment, leads to significant changes in stress levels (Krohn et al., 2006), therefore leading to an unknown source of unsystematic bias in behavioral studies (Manouze et al., 2019).
The connection between headcap and device is different between the two techniques: attaching a cable to the animals headcap for FP is a matter of sliding a cable into a ferrule and can be performed by a single person. On the other hand, the miniscope needs to be fixed onto the baseplate with two screws, which can be more stressful for the animal. Some protocols recommend brief anesthesia every time the animal needs to be attached or detached from the miniscope (Yang et al., 2015), which is problematic because repeated anesthesia has significant side-effects on the animal’s health (Hohlbaum et al., 2017) and a long-lasting effect on brain activity (Wu et al., 2019). An alternative is to perform extensive habituation, which could be aided by work with custom head-fixed setups in which the rodent can run on a treadmill (de Groot et al., 2020) while the scope is being attached to reduce the stress of the animal. The latter setup requires more extensive habituation of the animal to the setup, while also being more expensive and laborious because it requires two researchers – one who holds the ring in place and the other who secures the miniscope with screws.
The miniscope headcap covers a larger skull surface area and volume compared to the fiber photometry headcap. The ring-shaped structure that supports the baseplate for the miniscope usually weighs a few grams (Resendez et al., 2016), which is often unaccounted in the miniscope weight. In terms of direct influence on behavior, it is important to consider the weight of the devices – with the photometry fiber is lighter than the miniscope device – but also how the weight is distributed: although miniscopes have become as light as 1.6 grams (de Groot et al., 2020), they still have a high center of gravity compared to fiber photometry. This creates a stronger torque and potentially interferes more intensely with the animal’s vestibular system, especially for mice compared to rats due to their smaller body size.
To summarize, even though the size of a miniscope has been reduced because of rapid open-source development, it is still a bigger device with a higher center of gravity and a greater impact on behavior compared to FP.
7 DATA ACQUISITION
Both FP and miniscope systems have commercial and open-source hardware and software for data acquisition, each with advantages and disadvantages. The main challenge in data acquisition for calcium data revolves around maintaining the same field-of-view over multiple days of recording.
Regarding hardware cost, there are two big manufacturers of photometry setups which are widely adopted: Doric and Tucker-Davis Technologies. These off-the-shelf photometry systems may cost around 10,000-20,000 dollars, but recent open-source alternatives are currently available for optical components (Simone et al., 2018), the acquisition interface and GUI (Akam & Walton, 2019; Owen & Kreitzer, 2019), resulting in a photometry system which costs about one-tenth of the price of traditional systems (Owen & Kreitzer, 2019).
On the other hand, the miniscope community is intensely driven by open source contributions, which rapidly accelerates the development of new technology and design. Since the original 1P miniscope (Ghosh et al., 2011), several one-photon miniscopes systems were developed and became available to the scientific community: the NiNscope has a built-in optogenetic driver and accelerometer, the FinchScope is optimized for birds as a model species and it has a microphone to correlate vocalization with neuronal activity, the Inscopix nVista V4 has a sophisticated focusing system, such that different z-planes can be interweaved acquired very rapidly (full review available from Aharoni & Hoogland, 2019). Although off-the-shelf proprietary systems such as the Inscopix scope are priced at around 70,000 dollars, open-source alternatives such as the UCLA miniscope allow the construction of a system for about 1,500 dollars.
It is critical to record the same neuronal population over multiple days to be able to accurately interpret the output. Because miniscopes have cellular visualization, one can adjust the focus ring from to maintain the same plane of acquisition. This is impossible for FP because it lacks cellular resolution. Moreover, the cable can occasionally slip from the animal’s head during FP recordings with detachable cables, resulting in the recording of a smaller subset of the tagged population over the session. This can be partially remedied by using a low-loss coupling interconnect (such as the ADAL3 from ThorLabs) between the implanted fiber and cable.
During the recording session, for both FP and miniscopes, it is necessary to always have an experimenter attentive to changes in the fluorescence signal and to take note of any anomalies that might occur (e.g. animals damaging the cable). Failure to do so might lead to improperly annotated data and could lead to incorrect conclusions, e.g. decrease in fluorescence being incorrectly ascribed to changes in behavior. To minimize the chances of cable damage, a rotary joint can be used to minimize torque forces on the cable. Another promising technology to eliminate the issue of cable damage is the development of wireless photometry (Khiarak et al., 2018) or wireless miniscope systems (Barbera et al., 2019).
To summarize, both FP and miniscope require thorough consideration in the steps of data acquisition to ensure that the same population is recorded over multiple days. This problem more easily dealt with in miniscopes, by manual or electronically focusing, but it can also be minimized with hardware changes in FP systems, mainly low-loss connectors.
8 DATA ANALYSIS
FP and miniscope have significantly different analysis pipelines, owing to the greater complexity of miniscope data compared to FP data. In order to properly interpret the results, it is important to understand the core ideas of each analysis pipeline as well as limitations intrinsic to each method and associated behavioral task.
8.1 PHOTOMETRY DATA ANALYSIS
In terms of data complexity, FP data constitutes a simple database that stores incoming fluorescence in a one-dimensional time series (often 100-200 MB/hour). Photometry data analysis consists of two main steps: motion correction and correlation with behavior. Movement artifacts can be resolved in two ways. One option is to use time-correlated single-photon counting, which uses rapid oscillation of the excitation light and uses post-hoc analysis to isolate the fluorescence signal (Gunaydin et al., 2014). Another option is to make use of two excitation lights, a blue light to excite GCaMP and a purple light, which is GCaMP-insensitive and serves as a control channel (Zalocusky et al., 2016, Figure 6). With this system, the ΔF/F is calculated with a straightforward formula: ΔF/F = (Signal(490nm) – Signal(405nm))/ Signal(405nm)
The ΔF/F signal is then usually aligned with behavioral performance, such as lever presses, nose pokes, and food magazine entries. The experimenter often chooses a time window which is representative of the brain function they would like to assess, for instance, during the preparatory attention phase in a choice paradigm to indicate impulse control or before a lever press to assess motor planning. Many parameters can be used to compare ΔF/F traces, ranging from area under the curve and maximum peak amplitude calculations, to inferences of spikes from deviations of baseline activity.
Although the data acquired from the photometry setup is relatively simple, the interpretation can still be challenging. Even though movement artifacts can be corrected with a ΔF/F calculation, there is still a general effect of movement which is difficult to account for, since the execution of movement results in a brain-wide increase of activity (Musall et al., 2019) even in sensory areas (Parker et al., 2020). Because the animal is freely moving, exact behavior and movement will vary on a trial-to-trial basis, which means that even when selecting the same time windows around task events, one might find differences in fluorescence signal – not because there is a change in cognitive function, but because there is a difference in how much the animal is moving at these time points. In the context of mPFC studies, this is known as the ‘Euston-Cowen-McNaughton Hassle’ (Powell & Redish, 2016), the observation that differences in brain activity can be explained by differences in movement at different trial periods. It is worth noting that this effect of movement in brain activity is also present in miniscope data, although the spatial information allows for the general separation of ‘movement-related’ and ‘movement-unrelated’ neurons (da Silva, 2018), which is not possible for FP data.
8.2 MINISCOPE DATA ANALYSIS
Compared to the one-dimensional time series data collected from fiber photometry, miniscope data is multidimensional and present several challenges. First, miniscope data is acquired in a video format and data acquisition can come up to 100 GB/hour (Pnevmatikakis, 2019), which means that miniscope data analysis requires significantly better hardware and IT infrastructure for storage and retrieval of potentially multiple terabytes of data for each experiment. Second, the steps of registration, source separation, and deconvolution need to be high-throughput due to the large data size.
8.2.1 REGISTRATION
Because the brain is a soft organ, it moves and deforms as the animal is moving, and neurons in the field-of-view move in a non-rigid fashion over time, i.e. some neurons might move in different directions while others stay in place. Therefore, a straightforward rigid movement correction, i.e. moving the entire frame by x pixels, is not adequate for miniscope datasets, because they result in neurons being in different locations in the field-of-view over time. A solution is a non-rigid form of registration, which takes into account the brain deformation, for instance, by modeling topological features of elastic bodies and inferring the underlying motion (Ahmad & Khan, 2015) or utilizing probabilistic methods to track the same neurons in different positions across time (Sheintuch et al., 2017). These non-rigid solutions require significantly more computational power compared to rigid registration methods.
The most commonly used method for image registration of miniscope data is the NoRMCorre algorithm (Pnevmatikakis & Giovannucci, 2017), which uses rigid registration to arrive at non-rigid results. To accomplish that, the algorithm subdivides the video input into a grid of overlapping sections (Figure 7). It then applies a rigid motion correction to every single section of the video (e.g. move the entire section upward x pixels). The smaller the sections, the better the approximation to a proper non-linear registration it will be. The entire frame is then reconstructed by stitching the overlapping portions of these segments. Instead of repeating the process de novo for every frame, a template frame is stored, and every subsequent frame is calculated in reference to the template to optimize processing time.
8.2.2 SOURCE SEPARATION
With a stabilized video, the next challenge is to separate every neuron in the frame. This is computationally challenging because of the size of video files. It is also worth noting that one-photon imaging captures a lot of neuronal sources outside the plane of focus, which must be accounted for in the analysis. Furthermore, background noise essentially changes every frame in one-photon data.
The most widely used algorithm for source separation of one-photon miniscope data is CNMF-E (Zhou et al., 2018). This algorithm does not store all the information from every pixel in every frame of the video. Instead, it only captures the information from the fluorescence sources in the field-of-view and an average of the background fluorescence, allowing for a great compression of data size (Figure 8). Once the video information is unveiled into separate components, it is possible to use a memory infrastructure that allows parallel processing, making use of multiple CPU cores to optimize processing time.
A problem that needs to be addressed by the source separation algorithm is the fact that there are overlapping neurons in three-dimensional space that occupy the same pixels in the x-y field of view. This is usually not taken into account when source separation is performed with simpler methods such as manual region-of-interest annotation or PCA/ICA methods (Zhang et al., 2019). CNMF-E can separate neurons with a great overlap in the field of view, distinguishing the different sources by their different periods of activity.
A quality check for the soma shape is required after the putative neurons have been identified and separated. This task can be performed manually or with the assistance of machine learning methods. The use of unbiased machine learning methods is important because even among expert annotators there can be a disagreement level of 20% (Pnevmatikakis, 2019).
8.2.3 DECONVOLUTION
The resulting fluorescence signal depends on the sensitivity and kinetics of the GCaMP isoform used. Therefore, after source separation, the fluorescence signal needs to be deconvolved into spike activity. Importantly, prior to deconvolution, the data needs to be detrended to remove the influences of photobleaching throughout the recording. A common deconvolution method is the OASIS algorithm (Friedrich, Zhou, & Paninski, 2017), which has been benchmarked as superior against nine other deconvolution methods (Berens et al., 2017).
8.2.4 COMPARISON OF OPEN-SOURCE PACKAGES
To facilitate the workflow of the several steps required for miniscope data analysis, several open-source packages compile the required tools for registration, source separation, and deconvolution, including CaImAn (Giovannucci et al., 2019) EZCalcium (Cantu et al., 2020), MiniscopeAnalysis and its subsequent implementation of PIMPN (Etter, Manseau, & Williams, 2020), MIN1PIPE (J. Lu et al., 2018) and CAVE (Tegtmeier et al., 2018) (Table 1).
Table 1. Overview of commonly used miniscope analysis packages.
To summarize, FP and miniscope differ enormously in their data analysis pipelines. FP data is significantly simpler and allows for more straightforward analysis steps, whereas the spatial information of miniscope data poses several technical challenges that need to be tackled with more sophisticated algorithms. Data interpretation needs to be contextualized in terms of the behavioral task the animals are performing and how well the experimental design controls for the effects of movement in brain activity.
9 CHALLENGES IN DATA INTERPRETATION
The miniscope has one great advantage over photometry systems: cellular resolution. However, this considerable advantage comes with several complications: the necessity of a larger implant in the brain to gather sufficient light, a bigger device that interferes more intensely with the vestibular system of the animal, and many technical challenges in data acquisition and data analysis. In this context, there is a crucial question that needs to be addressed: Why is cellular resolution worth these many disadvantages in the first place?
Consider a hypothetical scenario with a total population of three neurons. In the first scenario, each neuron fires once, one after the other (Figure 9A), whereas in the second scenario, the same neuron fires three times (Figure 9B). While this would be easily distinguishable with a miniscope, it would yield the same signal in photometry data – even though the biological meaning of each situation is radically different.
This example illustrates the main limitation of FP: it collapses all spatial information into a single dimension, so there is no way to differentiate the activity of different subsets of a neuronal population at different time points. For instance, miniscope studies have shown that different mPFC ensembles are active during distinct social behavior tests (Liang et al., 2018). It is conceivable that similarly sized neuronal ensembles would yield similar patterns of activity in FP data, possibly leading to erroneous interpretations of the results.
However, photometry data can be informative to characterize synchronous activity of a genetically separable population in a well-defined behavioral paradigm. When these conditions are met, FP has been shown to yield informative links between brain activity and behavior: examples include 1) understanding how the activity of CRH neurons in the paraventricular nucleus influence escape behavior (Daviu et al., 2020); 2) explaining the differences in activity between GABAergic and serotonergic neurons in the dorsal raphe nucleus that promote or inhibit movement in terms of threat potential (Seo et al., 2019); 3) unveiling the dynamics of hypothalamic neuronal subtypes that drive feeding behavior (Chen et al., 2015).
In contrast, miniscope data is multidimensional, allowing for studies where ensemble activity can be observed over time. The spatial information of neurons is important for experimental questions regarding asynchronous populations, when there is no clear genetic marker that separates different populations and when the behavior is more naturalistic and has more degrees of freedom. Examples include 1) unveiling the activity of heterogeneous ensembles of the habenula during escape behavior (Lecca et al., 2020), 2) assessing the complex dynamics hippocampal cell firing in epileptic mice (Shuman et al., 2020) and 3) understanding the relationship of how changes in the maturation of hippocampal ensembles to the consolidation of a fear memory (Kitamura et al., 2017).
To summarize, no technique is necessarily better for any given behavioral task – illustrated by fleeing behavior studies with FP or miniscope (Daviu et al., 2020; Lecca et al., 2020) – or brain region – illustrated by the fact that the dorsal medial striatum has been studied with both techniques (Barbera et al., 2016; Kupferschmidt et al., 2017). In general, photometry is appropriate for genetically separable and synchronous neuronal populations while the miniscope can be used for more nuanced questions, allowing the study of genetically inseparable and asynchronous ensembles.
10 FUTURE OF CALCIUM IMAGING RESEARCH
While it is important to consider the limitations of FP and miniscopes to properly interpret the data from these Behavioral Neuroscience studies, it is worth to scan the horizon for future developments in the field that could overcome some of the current shortcomings.
10.1 BETTER OPTIC PROBES
As previously described (See Section 2), GCaMP is an indirect indicator of neuronal activity which may lead to confounding results when syncing fluorescence data to behavior, especially for molecules with slower kinetics (Sabatini, 2019). An alternative to calcium probes is the use of voltage indicators, which have a better temporal resolution (Resendez et al., 2016), while also avoiding problems with buffering of intracellular calcium. They have seen limited use in neuroscientific research due to a poor signal to noise ratio, but the development of brighter voltage indicators could answer a range of new biological questions (Song, Barnes & Knöpfel., 2017). Currently, FP is more appropriate for indicators with a poor signal-to-noise ratio (L. Li et al., 2017) because of its higher sensitivity of detection compared to the miniscope sensor.
A future alternative to the use of GCaMP could be the utilization of bioluminescent molecules as a calcium indicator (e.g. luciferase bound with calmodulin). Because these molecules do not require excitation light, confounding problems of phototoxicity are avoided, while also reducing the number of parts in a miniscope – without an excitation light, a UCLA miniscope would be 22% lighter and 58% less expensive (Celinskis et al., 2020).
10.2 ENGRAM-SPECIFIC TAGGING
Expressing a calcium indicator in a specific subset of neurons may give insight into whether certain projections or cell-types are active during a behavioral task. However, this tagging strategy also includes neurons unrelated to the behavior being studied (Josselyn & Tonegawa, 2020). This can confound interpretation since no systematic analysis can be done post-hoc to assess which neurons were related to the task. These confounding factors are even more problematic when analyzing associative cortices such as the mPFC, in which any given neuron may have motoric, limbic, or sensory inputs (Heidbreder & Groenewegen, 2003). An interesting technique to reduce this problem is the use of viral-based TRAP (targeted recombination in active populations) to express GCaMP only in the neurons which were naturally active during the task (Matos et al., 2019). Especially for miniscope studies, the utilization of Fos-Cre-GCaMP systems (Ivashkina et al., 2019) to assess long-term changes only in neurons that are related to a task holds a lot of promise for specifically associating shifts in neuronal activity to changes in behavior.
10.3 MULTIPLE-PHOTON MINISCOPES
Despite substantial technical challenges of two-photon miniscopes, recent models have allowed solutions for high-temporal resolution and low motion-artifacts in a light-weight, 2 g apparatus, allowing visualization of soma, dendrites, and axons (Zong et al., 2017). In addition, 3-photon microscopy (which uses wavelengths in the order of 1300 nm) allows the visualization of neurons in the hippocampus 1 mm below the cortical surface (Ouzounov et al., 2017). While the development of multiple-photon microscopy is currently hampered by technical challenges and expensive setups, the technology of using increasingly longer wavelengths holds promise in terms of tissue penetrance and could potentially allow the study of subcortical regions without the necessity of a GRIN lens implant.
10.4 SIMULTANEOUS CALCIUM IMAGING AND VIDEO ANALYSIS
Advances in Behavioral Neuroscience will include the association between neuronal activity and granular annotation of the animal’s behavior from video data analysis. While proprietary software has offered some integration support (e.g. Bonsai and the UCLA miniscope) (Lopes et al., 2015), rapid advances of open-source programs like DeepLabCut (Mathis et al., 2018) will likely be commonplace in a few years. Video analysis software allows researchers to separate of movement-related neuronal activity related to cognitive effects of the task, which allow for a more accurate interpretation of
10.5 REDUCTION OF HUMAN INTERFERENCE
An important consideration for Behavioral Neuroscience is the fact that stress affects brain function (Datta & Arnsten, 2019). Therefore, differences in animal handling between different labs are an important confounding factor and an important part of the current ‘replicability crisis’ (Lonsdorf et al., 2017). One solution is the wide adoption of rigorous and detailed protocols for animal handling, allowing for better comparisons of results and effect sizes across different labs. A technological solution is the removal of human-animal interactions altogether, aided by the development of wireless miniscopes (Barbera et al., 2019) or wireless photometry systems (Lu et al., 2018), especially if these wireless systems could be protected enough such that single-housing was no longer necessary. Another technological advance that will aid in this direction is the development of home cage systems integrated with behavioral paradigms (Bruinsma et al., 2019), notably when these technologies could be combined with an automatic weighing of the animal (Noorshams, Boyd, & Murphy, 2017). This combination of technologies would provide a significant reduction in unsystematic bias between studies, while simultaneously reducing the workload of researchers.
11 CONCLUSION
To conclude, both FP and miniscopes are important techniques for the advance of understanding population dynamics in freely moving animals and future technological advances hold great promise of improvement. The level of analysis at a population level is crucial for advancing the understanding of the brain because complex information is not stored in a single neuron, but rather at a sparse population level in the nervous system (Doetsch, 2000). However, it is important to keep in mind that these methods allow the observation of activity of a few hundred cells, which is only a minuscule percentage of the mouse or rat brain – which have around 70 and 200 million neurons respectively (Herculano-Houzel, Mota & Lent, 2006). The tagged neurons will also invariability contain neurons unrelated to the execution of the behavioral task (Gonzalez et al., 2019) and often contain movement-related increases in brain activity (Musall et al., 2019), leading to confounding effects on the data. Therefore, the interpretation of results acquired with these methods needs to be grounded in a solid understanding of the trade-offs and limitations of each technique.
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Note: This essay was written during my Master’s degree for the course Neurophilosophy and Ethics. I got a 7.5 (B+) and thought I would share it.
Human senses are incredibly limited and fallible. We only experience a small fraction of all the existing sounds and light frequencies (for instance, we see between 400 nm to 700 nm out of an infinite electromagnetic spectrum)1, but even more fundamentally than that, the perception of a ‘true reality’ is inherently impossible because all information that arrives into our consciousness is inexorably multivariate.2
Figure 1. The yellow stickers on the left panel are the exact same shade of grey as the blue stickers on the right panel. Due to the inverse problem, our brain makes a probabilistic decision about what color to see based on previous experiences.
The fundamental ambiguity of our perception of reality, the so-called ‘inverse problem’ was first described in detail by Hermann von Helmhortz3, and it can be exemplified by our sense of vision: what we perceive as ‘color’ comprises three elements: 1) the light characteristics that arrive from a light source; 2) the way that they bounce off of an object and; 3) and the air between the object and our eyes. This means that what we perceive as color has three indistinguishable variables: illumination (which is intrinsic to the light source), reflectance (how the object interacts with light) and transmittance (the influence of the atmosphere between object and eye). As a consequence, our brains need to make judgments about ‘which color to see’, which can result in interesting optical illusions (Figure 1)2. Similar problems of multivariability exist for all of our senses, which means that reality is fundamentally inacessible to our brains.
This seems counterintuitive since humans are arguably the most successful species that have ever existed: we are on the top of our food chain, we lack natural predators and we are capable of inhabiting virtually every ecosystem on earth4 — which must entail that we have been able to adequately navigate our environment. How is it possible that we can appropriately change our behaviors based on our perception of reality if reality itself cannot be assessed by our senses? One possible answer is that we have a Bayesian brain — we make decisions on experiential information and construct our beliefs and decide courses of action based on the probability that they are correct.
Figure 2. A weighing scale can be used as a metaphor for the Bayesian brain.
A metaphor that could explain the principle behind the Bayesian brain is the following: Imagine an old school weighing scale, where objects can be put on each side to verify which one is the heaviest (Figure 2). This can serve as a visual metaphor for the way our brains make decisions based on probabilities: we attribute ‘weight’ to pieces of evidence that we encounter over our lifetime and whichever side of a belief has the most weight, ‘wins’ – becoming the thing that we believe.
One could explain this principle with an extremely simplified binary example: the belief “I like or I do not like bananas” are on the two ends of a scale. Our brain makes the decision about which one to believe (and then act upon) by weighing the evidence for “I like bananas” (maybe I have eaten hundreds of bananas and I always enjoyed them) and the opposing evidence for “I don’t like bananas” (maybe I once ate a banana with a worm in it) against each other. Each ‘pebble of evidence’ has its own weight and whichever side of the scale has the most weight, wins over the other side and becomes the belief we hold. Every new experience with bananas gives another ‘pebble of evidence’ in the scale — which means that we are constantly updating our beliefs based on new evidence, and then changing our behavior based on that updated scale.
Some researchers believe that the principle behind this analogy applies to how the entire brain works!5–8 Every sense that we have (smell, touch, vision) do not represent reality: they represent a set of beliefs that we have obtained through experience in the natural world, which we use as a way to make decisions. A simple example of the role of experience in our perception is the fact that humans tend to interpret nearly-vertical lines as completely vertical and nearly-horizontal lines as completely horizontal – because these are the patterns most commonly observed in nature.9
Figure 3. The Necker cube is an example of bistable perception.
This metaphor has many great features: as with a normal scale, it can be in equilibrium — e.g. for the belief ‘I like or I do not like bananas’, if your experiential evidence is equivalent for both sides, you may come to the conclusion ‘I don’t mind bananas, I do not like them but I do not dislike them either’. As applied to our sense of vision, one could imagine this state of ‘belief equilibrium’ exemplified by the Necker cube, in which we have a bistable perception about which face is closest to us (either the lower-left or the upper-right)10 – which could be analogous to the scale slightly wobbling from left to right depending on which part of the figure we choose to focus on.
Another good feature of the metaphor is that the distinction between the number of pieces of evidence and their ‘value’ makes intuitive sense: if you have 100 pebbles that weigh 1 g each and one pebble that weighs 1000 g, should you place them on opposite sides of the scale, the single heavier pebble will win. Similarly, 100 good experiences may be outweighed by one horrible experience in determining one’s belief about the fruit (for instance, eating a banana with a worm in it, in the decision ‘I like or dislike bananas’).
Because the metaphor is quite simple, it can be also extended to explain many facets of the human experience and how they relate to this probabilistic nature of the Bayesian brain:
1) Humans have intrinsic fears of certain animals, such as snakes and spiders, developed throughout the mammalian evolutionary history.11 For the belief “I like or I do not like spiders”, we could visualize that the scale is naturally tipped to the dislike side — meaning that, even without any experiential evidence, humans have an intrinsic fear of arachnids.
2) Humans have a salience bias, meaning that emotionally evocative information is more persuasive than neutral ones. For instance, when deciding for the belief ‘Planes are safe or unsafe’, one may conclude the latter based on shocking imagery they once saw on TV of a plane crash — even if statistics show that planes are objectively one of the safest modes of transportation.12 In the context of the metaphor, one may visualize that the evidence pebbles have an emotional component that gives them extra weight.
3) Humans tend to make judgments based on more recent information – which is a phenomenon known as the ‘availability heuristic’.13 For example, when deciding ‘Elevators are safe or unsafe’, one’s judgment might be severely skewed to the ‘unsafe’ side if they saw a coworker get stuck in an elevator two days before. In the scale metaphor, one could imagine that the pebbles of evidence wear out and become lighter over time, exerting less influence over the belief scale.
As far as disadvantages, the metaphor is fairly abstract in content, which means that it can be used to explain essentially anything – as exemplified in this article, in which I use it to explain food preferences, visual perception, and cognitive biases. This could be considered a strong weakness because it is often unclear how the individual elements of the metaphor relate to reality. The lack of clarity may induce misunderstandings among laypeople: do humans have millions of scales inside their heads, each one making one individual decision?
The metaphor also attempts to reduce all complexities of learning and decision-making into single inanimate objects. This not only can be viewed as an oversimplification of what occurs in the brain, but it implicates a hard deterministic interpretation of free will14: by stating that beliefs are explained by previous experiences and biological predispositions under the gaze of a purely physical process (placing weights on top of scales), the imagery strongly hints to a fully deterministic universe which does not leave space for the sense of one’s agency in modifying their beliefs.
Another strong weakness of the metaphor is that it does not account for the fact that beliefs are interdependent15: ‘I like or dislike bananas’ is not only dependent on one’s past experience, but also dependents on their current beliefs and feelings, such as ‘I like or dislike fruits in general’, ‘I am really hungry right now’ or ‘I am really tired and I can’t even think about food’. Maybe the metaphor could be extended to include that each ‘belief scale’ is converted into a pebble of evidence into another belief scale, or perhaps you place scales on top of scales to represent this interdependency – but it then becomes convoluted and unclear as an explanatory device.
It is worth noting that the metaphor can be vague in meaning because the hypothesis itself is also fairly abstract: the Bayesian Brain hypothesis has yielded many criticisms, including one of being unscientific16. The statement “The brain is performing probabilistic decisions like a computer” does not provide a mechanism by which the computations are performed and it is therefore unfalsifiable. Under a strict Popperian view of science, this would mean that the hypothesis is unscientific and therefore not worthy of any merit.
Science still does not have a definitive answer about fundamental processes underlying our decision making, and the Bayesian brain is just one hypothesis that tries to explain the disconnect between ‘why we cannot perceive reality’ and ‘how we operate in the environment’. The Bayesian brain has been put in vogue in the last decade, especially by computer scientists who use machine learning techniques that rely heavily on principles of Bayesian statistics17. However, just because computers use probabilistic statistics to make decisions, it does not follow that humans necessarily use the same mechanism – this would be an inductive fallacy of faulty generalization. The proponents of the Bayesian Brain hypothesis still need to demonstrate the existence of this underlying mechanism in humans.
Science communicators must be cautious in the way they explain science in popular media to properly separate what is established science from speculative hypotheses and personal opinions, especially in the field of Neuroscience, in which a lot of mysticism persists in the zeitgeist18. However, it also means that some of the most burning questions one could ask about the world are not scientific: no experiment could ever be devised to experimentally test what the true nature of reality is19. This begs the question: should we only focus on science to understand reality and forget about unfalsifiable hypotheses or should we be open to other forms of explaining how the world works?
To conclude, the use of metaphors is a commonly used device for explanations, because our brains have higher developed visual and language cortices, it is often easier to explain concepts in terms of vivid imagery and stories20,21; however, we need to be careful about the precision of our metaphors and possible misunderstandings they might generate. The scale metaphor provides an easy and generalizable way to understand the process of decision-making in the human brain, but it fails to account for the interdependence of beliefs and it diminishes one’s agency by appealing to purely physical processes.
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Note: This essay was written during my Master’s degree for the course Methods in Behavioral Neuroscience. I got a 7.5 (B+) for it, so I thought I would share.
Abstract
The processes that underlie aversive memory formation have long been elusive, in part because of the complexity of the brain-wide, sparse network of cortical regions that influence fear behavior, which includes the amygdala, hippocampus and mPFC. The mPFC is thought to provide top-down control over behavioral output, but the specifics of the microcircuitry and role of specific neuronal subtypes that are involved in this process remain poorly understood. The goal of this mini-review paper is to obtain a better insight into the role of the mPFC in fear conditioning by providing an overview of papers that have used state-of-the-art technology and provide future perspectives in the field.
What is the PFC?
The prefrontal cortex is the association cortex of the frontal lobe (Fuster, 2001). The PFC is involved in working memory, attention and decision making, cognitive processes collectively referred to as “executive functions” (Domenech & Koechlin, 2015). These higher-level cognitive functions of the PFC derive from its unique anatomy – it has extensive afferent and efferent connections to many brain regions, including the hippocampus, amygdala, basal ganglia, motor and sensory cortices (Gabbott et al., 2005). The PFC is therefore thought to be the ‘goal-directed’ cortex because it exerts top-down control over limbic, sensory and motor information and allows the organism to perform the most optimal behavior to the specific environmental conditions while inhibiting alternative actions primed by these bottom-up systems (Kamigaki, 2019).
Much of our current understanding comes from animal models, particularly mice. It may initially seem counterintuitive that the research on the rodent PFC could be useful to humans, since this brain region comprises 30% of the human cortical area, it is the last brain region to mature in adults and has specific anatomical regions not analogous to any other mammal (Neubert et al., 2014). While it is true that the murine brain is much simpler and has significant anatomical differences to the human PFC, e.g. a lack the granular layer IV in the cortex, it is now widely accepted that the mouse PFC has functional similarities: they can learn tasks that require attention (e.g. 5CSRTT), working memory (e.g. delayed matching-to-position task) and decision making (e.g. delayed reward paradigm).
The rodent PFC is broadly divided into three regions: ventral, lateral and medial. The medial prefrontal cortex (mPFC) has been extensively studied and found to be associated with many processes, including executive functions, recent and long memory formation (Euston, Gruber, & McNaughton, 2012). The mPFC can be broadly subdivided into a dorsomedial portion (dmPFC) and a ventromedial portion (vmPFC) which are differently connected to other brain regions: the vmPFC is more highly connected to limbic structures while the dmPFC is more highly connected to sensory and motor regions. The dmPFC comprises the anterior cingulate (AC) and the dorsal-most part of the prelimbic cortex (PL). The vmPFC comprises the infralimbic cortex (IL) and the ventral-most part of the prelimbic cortex. Because there is no clear division between dmPFC and vmPFC, only a gradient of projections to cortical and limbic structures in the PL (Gabbott et al., 2005), this review will mention the specific mPFC subregion whenever possible.
The goal-orienting nature of the mPFC is evolutionarily important because of the unpredictability of nature – while instincts can be developed for intrinsically stereotypical cues, much of the environment is highly context-dependent, and the organism must learn over its lifetime good or bad associations between environmental stimuli, behavior and outcome results. Fear conditioning has been extensively used to study long-term aversive memory and how sensory input ultimately generate adaptive behavior (Herry & Johansen, 2014). This paradigm has many advantages over other aversive memory paradigms, principally because it allows for a minimal amount of training – one session with a duration of a few minutes will promote permanent memory changes.
The goal of this mini-review paper is to obtain a better insight into the role of the mPFC in the orchestration of behavior in response to aversive stimuli, addressing current controversies in the published literature and indicating potential future research directions that could address the current gaps in knowledge.
The role of the mPFC in fear memory
Fear conditioning involves the association of a neutral stimulus contingently paired with an aversive stimulus. In this paradigm, a conditioned stimulus (e.g. an auditory tone) is paired with an unconditioned stimulus (e.g. a foot shock). The conditioned auditory stimulus and unconditioned shock stimulus enter the amygdala at the level of the basal and lateral nuclei of the amygdala (collectively known as BLA), where they are biologically paired. The BLA is connected to the central nucleus (CeA), which is the major output system of the amygdala, which then promotes the conditioned response. The neuronal connections that underlie a memory are named memory engrams (Josselyn, 2010). The level of fear is measured from the stereotypical behavioral output – i.e. how much time the animal spends on the freezing state (Jacobs, Cushman, & Fanselow, 2010), and manipulations in the memory engram can be performed with techniques such as optogenetics, chemogenetics or activity-dependent labeling.
The mPFC is highly interconnected with the amygdala, mainly with the BLA. The IL is thought to mediate extinction by increasing the activity of GABAergic intercalated cells, which decreases the activity of the CeA. The PL is thought to increase the activity of glutamatergic neurons in the BLA, increasing the output of the CeA and promoting the fear response (Marek, Sun, & Sah, 2019). However, the PL and IL are not as independent as this simplified model might suggest. There are glutamatergic neurons that project from PL to IL, and specific excitation of these enhances fear extinction (Marek et al., 2018). Davis et al. (2017) showed that chemogenetic inhibition of BLA parvalbumin-positive (PV) interneurons lead to an increase in freezing behavior after extinction learning. Using active dependent labeling, the authors found that this disinhibition in the BLA leads to the reactivation of engram neurons in the mPFC – indicating a functional role of BLA PV interneurons in extinction. In the same study, the authors found that the IL had more projections to BLA PV interneurons compared to PL projections and the BLA had more projections to the PL – establishing a reciprocal loop between mPFC and BLA.
The process of extinction, i.e. weakening the association between CS-US upon repeated exposures of the same context without the shock, is mediated by the IL. Extinction does not erase the original fear memory, but rather creates a new memory (An et al., 2017). Extinction learning can be enhanced by optogenetically stimulating the IL and impaired by chemogenetically silencing this brain region (Bloodgood et al., 2018). This leads to an interesting question of how output behavior could be mediated with two contradictory fear memories. Davis et al. (2017) used in vivo electrophysiology to record monosynaptic BLA PV interneurons projecting to the mPFC and found that fear conditioning leads to an increase in the 3-6 Hz range, silencing BLA PV interneurons decreased the 6-12 Hz range and the ratio of these two frequencies in the mPFC was correlated with the amount of freezing after extinction, but not before. This shift in network activity may be the neuronal correlate of the competition between fear memory and extinction memory.
The mPFC is also highly interconnected to the hippocampus, which is thought to provide contextual information about the fear memory. Rajasethupathy et al. (2015)used optogenetics to stimulate monosynaptic projections from the hippocampus to the AC after fear conditioning and found that mice would increase freezing behavior in an unrelated context. This effect was thought to be specific to the fear memory because, after extinction, the activation of this pathway no longer increased fear behavior. After placing the mice in a new context, the fear memory was reinstated, and the optogenetic activation again increased the fear memory. Kitamura et al. (2017) used a combination of activity-dependent cell-labeling, optogenetics and miniscopes to assess the activity of projections from the hippocampus to the PFC related to the fear memory. Inhibition of the engram had no significant effects on early fear memory but disrupted remote fear memory. The engram neurons in the mPFC were active in the original context in which the animals had received the shock 13 days after the original fear conditioning, but were silent 2 days later – i.e. the memory was not retrievable by environmental cues but could be activated with optogenetics. On the other hand, the hippocampal engram was active 2 days after fear conditioning, but silent 13 days later. The same authors hypothesize that the fear memory is initially encoded simultaneously in the mPFC, amygdala and hippocampus, but the engram undergoes maturation at different time points in the mPFC and hippocampus (Figure 1).
Figure 1. Role of mPFC, hippocampus and BLA in recent and remote memory consolidation during fear conditioning. In contrast to the mPFC engram, which is silent during early consolidation, and the hippocampal engram, which is silent during late consolidation, the BLA engram remains active during both stages, possibly because it encodes the valence of the experience. Adapted from Tonegawa, Morrissey, & Kitamura (2018).
Contradictions in current findings
The mPFC has a great degree of complexity and interconnection to other brain regions: it comprises around 90% glutamatergic neurons, 10% GABAergic neurons and receives projections from several neuromodulator centers (serotonin from Ralphe nucleus, dopamine from the VTA, acetylcholine from the basal forebrain and noradrenaline from the locus coeruleus) (Dembrow & Johnston, 2014). Therefore, a unitary function for the mPFC cannot be expected – the role of this brain region will vary depending on specifics of the task at hand, sensory inputs, previous memory associations and internal needs of the animal. For example, the inactivation of PL has been found to have a number of contradictory effects in aversively motivated procedures – increasing fear memory, decreasing fear memory or having no effect at all (Sharpe & Killcross, 2018). The authors attribute those differences to differences in methodology – for instance, the duration of training and intertraining intervals, which may alter the learning association of the context with the aversive stimuli. Inactivation of the mPFC has been found to have no effect on acquisition, consolidation or retention of fear memory using Pavlovian condition, but it was required for acquisition, consolidation and retention when these three aspects of learning were temporally spaced (Heroux et al., 2017).
These contradictory effects could also be explained by the variability in engram targeting. When inactivating a brain region with chemogenetics/optogenetics, it is possible that an engram could or could not be targeted. The advance of more precise techniques for neuronal manipulation allows for the unraveling of the exact role that each cell type plays in the fear engram. For instance, Adhikari et al. (2015) found that optogenetically inhibiting projections from the vmPFC to the amygdala decreased the fear memory, while inhibiting dmPFC-ITC projections showed a decrease in fear behavior only in cued fear-extinction. The authors found that inactivating this specific vmPFC population leads to the suppression of fear memory, but not bulk activation of the entire vmPFC – which suggests a high variance of cell types in this region that may counteract the effects of the vmPFC-amygdala pathway. It is therefore of extreme importance to understand the mechanisms that make the memory engram active or silenced. Some of the possible mechanisms are: epigenetic changes in histone subunit (Zovkic et al., 2014), upregulation of AMPA receptors in engram neurons (Arruda-Carvalho & Clem, 2014), ratio of oscillatory activity between mPFC and BLA in the 3-6 Hz range over the 6-12 Hz range (Davis et al., 2017).
To conclude, the mPFC plays an important role in fear memory – remaining as a silent engram during early consolidation but becoming involved after late consolidation. Engram and cell-specific studies have been shown to be of incredible value, allowing for the modulation of neurons related to the substrate of fear memories and answering important questions raised by contradictory research done in the past. Current important questions remain: how are the neuronal substrates different for different types of memory (e.g. episodic/semantic)? How much of this knowledge applies to paradigms of different valences (either less stressful aversive memories or even positive memory paradigms)? And finally, how much of this knowledge is transferable to humans, based not only on the anatomical differences between humans and rodents but also the ecological validity of these paradigms? Future research could benefit from the use of more complex paradigms that use both aversive and appetitive stimuli depending on different cues and using miniscopes to assess how the engrams are differently formed and how much they overlap. The advance of the experimental toolbox for other mammals would also be of extreme importance since most of the current research on this topic uses mice and some of the findings could be species-specific. Replicating these findings using cell-specific and engram targeting in non-human primates would be of extreme relevance for the translational aspect of memory research.
References
Adhikari, A., Lerner, T. N., Finkelstein, J., Pak, S., Jennings, J. H., Davidson, T. J., … Deisseroth, K. (2015). Basomedial amygdala mediates top-down control of anxiety and fear. Nature, 527(7577), 179–185. https://doi.org/10.1038/nature15698
An, B., Kim, J., Park, K., Lee, S., Song, S., & Choi, S. (2017). Amount of fear extinction changes its underlying mechanisms. ELife, 6. https://doi.org/10.7554/eLife.25224
Arruda-Carvalho, M., & Clem, R. L. (2014). Pathway-selective adjustment of prefrontal-amygdala transmission during fear encoding. Journal of Neuroscience, 34(47), 15601–15609. https://doi.org/10.1523/JNEUROSCI.2664-14.2014
Bloodgood, D. W., Sugam, J. A., Holmes, A., & Kash, T. L. (2018). Fear extinction requires infralimbic cortex projections to the basolateral amygdala. Translational Psychiatry, 8(1), 60. https://doi.org/10.1038/s41398-018-0106-x
Davis, P., Zaki, Y., Maguire, J., & Reijmers, L. G. (2017). Cellular and oscillatory substrates of fear extinction learning. Nature Neuroscience, 20(11), 1624–1633. https://doi.org/10.1038/nn.4651
Dembrow, N., & Johnston, D. (2014). Subcircuit-specific neuromodulation in the prefrontal cortex. Frontiers in Neural Circuits, 8(JUNE), 1–9. https://doi.org/10.3389/fncir.2014.00054
Domenech, P., & Koechlin, E. (2015). Executive control and decision-making in the prefrontal cortex. Current Opinion in Behavioral Sciences, 1, 101–106. https://doi.org/10.1016/j.cobeha.2014.10.007
Euston, D. R., Gruber, A. J., & McNaughton, B. L. (2012). The Role of Medial Prefrontal Cortex in Memory and Decision Making. Neuron, 76(6), 1057–1070. https://doi.org/10.1016/j.neuron.2012.12.002
Fuster, J. M. (2001). The prefrontal cortex – An update: Time is of the essence. Neuron (Vol. 30). https://doi.org/10.1016/S0896-6273(01)00285-9
Gabbott, P. L. A., Warner, T. A., Jays, P. R. L., Salway, P., & Busby, S. J. (2005). Prefrontal cortex in the rat: Projections to subcortical autonomic, motor, and limbic centers. Journal of Comparative Neurology, 492(2), 145–177. https://doi.org/10.1002/cne.20738
Heroux, N. A., Robinson-Drummer, P. A., Sanders, H. R., Rosen, J. B., & Stanton, M. E. (2017). Differential involvement of the medial prefrontal cortex across variants of contextual fear conditioning. Learning and Memory, 24(8), 322–330. https://doi.org/10.1101/lm.045286.117
Herry, C., & Johansen, J. P. (2014). Encoding of fear learning and memory in distributed neuronal circuits. Nature Neuroscience, 17(12), 1644–1654. https://doi.org/10.1038/nn.3869
Jacobs, N. S., Cushman, J. D., & Fanselow, M. S. (2010). The accurate measurement of fear memory in Pavlovian conditioning: Resolving the baseline issue. Journal of Neuroscience Methods, 190(2), 235–239. https://doi.org/10.1016/j.jneumeth.2010.04.029
Josselyn, S. A. (2010). Continuing the search for the engram: Examining the mechanism of fear memories. Journal of Psychiatry and Neuroscience, 35(4), 221–228. https://doi.org/10.1503/jpn.100015
Kamigaki, T. (2019, March 1). Prefrontal circuit organization for executive control. Neuroscience Research. Elsevier Ireland Ltd. https://doi.org/10.1016/j.neures.2018.08.017
Kitamura, T., Ogawa, S. K., Roy, D. S., Okuyama, T., Morrissey, M. D., Smith, L. M., … Tonegawa, S. (2017). Engrams and circuits crucial for systems consolidation of a memory. Science, 356(6333), 73–78. https://doi.org/10.1126/science.aam6808
Marek, R., Sun, Y., & Sah, P. (2019). Neural circuits for a top-down control of fear and extinction. Psychopharmacology, 236(1), 313–320. https://doi.org/10.1007/s00213-018-5033-2
Marek, R., Xu, L., Sullivan, R. K. P., & Sah, P. (2018). Excitatory connections between the prelimbic and infralimbic medial prefrontal cortex show a role for the prelimbic cortex in fear extinction. Nature Neuroscience, 21(5), 654–658. https://doi.org/10.1038/s41593-018-0137-x
Neubert, F. X., Mars, R. B., Thomas, A. G., Sallet, J., & Rushworth, M. F. S. (2014). Comparison of Human Ventral Frontal Cortex Areas for Cognitive Control and Language with Areas in Monkey Frontal Cortex. Neuron, 81(3), 700–713. https://doi.org/10.1016/j.neuron.2013.11.012
Rajasethupathy, P., Sankaran, S., Marshel, J. H., Kim, C. K., Ferenczi, E., Lee, S. Y., … Deisseroth, K. (2015). Projections from neocortex mediate top-down control of memory retrieval. Nature, 526(7575), 653–659. https://doi.org/10.1038/nature15389
Sharpe, M. J., & Killcross, S. (2018). Modulation of attention and action in the medial prefrontal cortex of rats. Psychological Review, 125(5), 822–843. https://doi.org/10.1037/rev0000118
Tonegawa, S., Morrissey, M. D., & Kitamura, T. (2018). The role of engram cells in the systems consolidation of memory. Nature Reviews Neuroscience, 19(8), 485–498. https://doi.org/10.1038/s41583-018-0031-2
Zovkic, I. B., Paulukaitis, B. S., Day, J. J., Etikala, D. M., & Sweatt, J. D. (2014). Histone H2A.Z subunit exchange controls consolidation of recent and remote memory. Nature, 515(7528), 582–586. https://doi.org/10.1038/nature13707
Note: This project proposal was written during my Master’s degree for the course Systems Neuroscience. I got a 8.0 (A) for this essay, so I thought I would share.
Background and Aim
Major Depressive Disorder (MDD) is the one of most common neuropsychiatric disorder, with a lifetime prevalence of 14.6% worldwide and 17.9% in the Netherlands1. MDD is a highly debilitating condition, it has a high economic burden (e.g. around 53 billion dollars in the US alone)2 and yet very little is known about its underlying neurobiology – current hypothesis range from inflammation3, mitochondrial dysfunction4, HPA axis dysregulation5 and gut-brain axis problems6. MDD presents many physical effects in the brain, including a reduction in hippocampal and mPFC volume7 and increase in activity of default mode network8, though the effect size of such human studies is relatively small. Most drugs for MDD have been developed almost 60 years ago, and their efficacy is usually poor for most people – the effect size of pharmacological interventions is 0.35 compared to 0.2 of placebo9, many patients are completely treatment-resistant and for the patients for whom the drug works, around 70% present a recurrence of symptoms within 6 years.10
The lack of effectiveness of traditional pharmacological treatments has led to exploring the development of many alternative treatments, including psychedelic substances. Ayahuasca has been shown to be effective for managing treatment resistant MDD in a randomized clinical trial.11 Although the main ayahuasca active agents, mainly dimethyltryptamine (DMT) and monoamine oxidase inhibitors are well known, the mechanisms through which they act on depression and other affective disorders remain elusive. The effectiveness of ayahuasca and DMT have been also shown in rodents for anxiogenic behavior12 and non-human primates13 for depressive symptoms, suggesting that this drug may be a powerful tool to understand the underlying neurobiology of depression in the mammalian brain.
Marmosets, the model of choice in this study, present many advantages over other non-human primates: they are small, allowing for a simpler housing infrastructure, they have relatively short life cycle (reaching sexual maturity in 18 months and old age around 8 years)14, their entire genome has been sequenced15, they can express GCaMP with viral vector injections16 and, most relevant for this study, they have a well-established model of depression17, which can be ameliorated with the use of ayahuasca13. Marmosets are a great tool for translational neuroscience because of the deep homology in brain circuitry mediating social behavior and reward, extensive use of vision for social signaling and the fact that these primates present depression-like symptoms in nature.17
The goal of this study is to explore the brain mechanisms underlying: 1) the progression of depressive symptoms and 2) what is the mechanism by which ayahuasca counteracts these symptoms. We will use miniscopes implanted in the vmPFC and field electrodes in the hippocampus – both brain regions previously shown to be associated with depression – and analyze brain activity before social isolation, during social isolation and during treatment (both with an SSRI and with ayahuasca).
Project description
The first thing that needs to be considered for this project is the way in which MDD will be modeled in marmosets. To achieve this, the protocol for social-isolation-induced depression in marmosets, described by Galvão-Coelho et al. (2017)17 will be followed. This protocol is relatively simmple and has been shown to accurately reflect multiple behaviors and physiological changes observed in depressed primates in the wild as well as in humans. It consists of separating juvenile marmosets from their original homecage, where thay are housed in a family context, and housing them alone in a new homecage (this alone appears to be sufficient to induce depression).
In order to study how activity in the vmPFC and the hippocampus changes throughout the progression of depression in the selected model, as well as evaluating the effect ayahuasca treatment has on these physiological changes and the associated behavioral profile; animals will be divided into four treatment groups, containing 6 animals each (ideally 3 males and 3 females, in order to account for intersexual variation): 1) baseline group, that won’t be exposed to social isolation at any point, 2) a negative control group, socially isolated but receiving only saline solution as treatment, 3) a possitive control group, receiving 0.2 mL/100 g of nortriptyline (an anti-depressant with clinical use) and 4) an ayahuasca group, receiving 6.32mg/kg of ayahuasca (dose adjusted for mass and body surface from a human effective dose.11The animals will be moved into isolation (except for the baseline group) after 4 weeks of daily baseline behavioral and physiological measuremnts, performed after complete recovery from surgical procedures. They will be kept on isolation for 8 weeks and measurements will be obtained daily during weeks 1, 2, 7 and 8. On weeks 9 and 10 drug and vehicle treatments will be applied. Measurements will be collected daily during these two weeks, along with the following two weeks (possible delayed effects). The behavioral parameters that will be meassured are: scent marking, individual piloerection, self grooming, scratching, somnolence, feeding (quantity and frequency) and sucrose solution ingestion. The physiological data we aim to obtain on the other hand includes fecal cortisol levels and changes in brain activity on the vmPFC and anterior hippocampus, as meassured by changes in calcium dynamics and electical activity, respectively.
For the purpose of meassuring changes in neuron activity in the vmPFC using calcium imaging, AAVs (Thy1S-tTA; TRE3-GCaMP6f) in solution will be injected into Brodmann area 32, a region of the vmPFC associated with depressive behavior in humans and animals, and that has been proven to have depression-relevant connections to the anterior hippocampus in primates18; this should result in sufficient expression of GCaMP6f and allow us to control said expression through doxycyclin treatment. After 3-4 weeks of incubation time, to ensure stable expression of the introduced constructs a new surgical procedure will be performed, this time to introduce a GRIN lens on area 32 (following the protocol by Kondo et al. (2018)19, as well as introducing the electrodes on the anterior hippocampus and mounting a headstage20 that will help support both measuring devices.After 5-7 weeks of recovery the base of the miniscope will be fixated to the skull, allowing us to securely mount the device on the animals’ heads. Baseline data collection will begin after a further 4-6 weeks for recovery and habituation. 1h of behavioral measurements and 15min of imaging/electrophysiologydata will be collected every day at the same time,in order to avoid circadian variation. The time of physiological recording is limited to 15min in order to produce managaeable amounts of data and to avoid the effects of photobleaching. Additional to this fecal cortisol levels will be meassured on samples collected daily, in order to obtain information about HPA function.
Image and data analysis
Miniscope
In the miniscope analysis pipeline, it is important to perform an image registration in order to correct for movement artefacts. The NoRMCorre library for MATLAB would be preferred since it is a processing-optimized non-linear motion correction, which means that it takes into account that the brain stretches and compresses as the animal moves around. To do cell segmentation for miniscope data, the commonly used analysis is constrained nonnegative matrix factorization optimized for epifluorescent data (CNMF-E), which creates a mathematical model for the background noise and updates it every frame – reducing false positive and false negatives compare to normal CNMF or other methods such as manual ROI definition or PCA/ICA. [1]*
Field electrode
Multi-channel data will be processed according to Mohan et al. (2019)21. Briefly, initial spike sorting will be done using Mclust 4.322 and unit clustering will be performed first semi-automatically using klustakwick23 for initial isolation of units, and then manually, taking into consideration standard parameters such as length of refractory period, spike shape and stability of spiking activity throught the recording. Units can then be clasified based on their spike waveform and analized individually.
Combining miniscope data with electrophysiology data
It is possible to compare miniscope data with electrophysiology data by dimension reduction: essentially, for every cell you capture with the miniscope, the calcium data can be deconvolved and thresholded in order to get unidimensional points of activity. In this way, complex video data would be reduced to the same data time that one would get from single-units in electrophysiology data.[2]* (Figure 1)
Figure 1. Miniscope data can be reduced to single points of activity, which can be compared to electrophysiology data.
This dataset could be used to compare similar behaviors (when the animal is not moving) over multiple time points (before social isolation, early social isolation, late social isolation, early treatment and late treatment). One could group the data from individual animals (grouping by gender, grouping by treatment type) and assess if there are any commonalities in the development of symptoms of depression and how treatments acts on those patterns of activity. The statistics used will depend on the parameters being assessed – e.g. Mann-Whitney to compare behavioral data (which is not normally distributed), repeated measures ANOVA to assess changes in activity in vmPFC and hippocampus within the same animal, General Linear Models (GLM) Fisher’s post hoc test to assess correlations (activity and behavior).
Whatever changes are found, it would be important to translate the findings both to lower models to ask more mechanistic questions and to humans to potentially come up with new treatments. For example, if it is found that there is a peak of activity in the vmPFC during depression development but it then becomes less active during late stages of social isolation, one could make use of the advanced genetic toolbox for rodents to devise engram-specific/cell-specific studies (e.g. KO of 5-HT2A receptor and assess effectiveness of treatment, fluorescent serotonin probes); but one could also devise a clinical trial in which that brain region is specifically targeted with transcranial magnetic stimulation in patients with familial depression, potentially preventing the progression of symptoms.
It is important to note that the extent of this research project, given the amount of work it requires as well as the time it will take (around 35 weeks, without counting data analysis) goes beyond what could realistically be achieved by a student on a 6 month internship. If necessary, said student could focus only on the social isolation part of the project and make immense contributions to the study.
Relevance of this project
Although the relevance of rodent models for the study of MDD cannot be denied, it is clear that they suffer from limitations that may be limiting their translatability, both in the context of general neuroscientific research (e.g. phylogenetic distance from humans) and in the specific context of depression, such as most models not reflecting behaviors that naturally occur in wild rodents, a limited social structure24 and considerable divergence in serotonin receptor expression in the brain25. Non-human primate models, such as marmosets, animals with closer phylogenetic relation to humans, relatively complex social structures in the wild24 and display of mood disorder-associated behaviors similar to other larger primates including humans, both in captivity and in their natural environment17,26 make valuable candidates for future development of models, which could result in a higher translation potential. In this context, beginning to understand the mechanisms that drive depressive behavior in the marmoset models at cellular and systemic levels, as well as the effect potential treatments have on them, could improve our knowledge of the neurological phenomena that drive this disease, as well as to some extent bridging the gap between the knowledge that has been developed in rodents and the current deficiencies in the clinic.
The increased similarity marmosets have with humans, which gives them their great potential value for neuroscientific research such as ours, also makes them a justified source of ethical concern. In order to minimize suffering and maintain an appropriate life quality, marmosets will be housed in comfortable, enriched home cages, with appropriate access to water and food and with an appropriate number of family members (this last one with the exception of when necessary for the experiments), all in compliance with local and international standards (Brazilian Institute of Environment and Renewable Natural Resources, Animal Behavior Society and the International Primatological Society). Humane endpoints consistent with international guidelines will be set for every experiment and consistent monitoring of suffering and well-being will be performed, in order to detect and avoid excessive suffering. In order to increase the power of this study without an indiscriminate increase in sample size, this study uses a paired design, in which the neuronal activity in the same animal is measured before, during and after social isolation and during treatment. This intergroup design setup will provide the most amount of data per animal, allowing for an unraveling of more subtle differences than what it would be possible with an intragroup design.
References
1. Kessler, R. C. & Bromet, E. J. The Epidemiology of Depression Across Cultures. Annu. Rev. Public Health34, 119–138 (2013).
2. Li, X. et al. Depression-Like Behavioral Phenotypes by Social and Social Plus Visual Isolation in the Adult Female Macaca fascicularis. PLoS One8, (2013).
3. Felger, J. C. Role of Inflammation in Depression and Treatment Implications. in Handbook of Experimental Pharmacology250, 255–286 (Springer New York LLC, 2019).
4. Bansal, Y. & Kuhad, A. Mitochondrial Dysfunction in Depression. Curr. Neuropharmacol.14, 610–618 (2016).
5. Keller, J. et al. HPA axis in major depression: Cortisol, clinical symptomatology and genetic variation predict cognition. Mol. Psychiatry22, 527–536 (2017).
6. Liang, S., Wu, X., Hu, X., Wang, T. & Jin, F. Recognizing depression from the microbiota–gut–brain axis. International Journal of Molecular Sciences19, (2018).
7. Drevets, W. C., Price, J. L. & Furey, M. L. Brain structural and functional abnormalities in mood disorders: Implications for neurocircuitry models of depression. Brain Struct. Funct.213, 93–118 (2008).
8. Kaiser, R. H., Andrews-Hanna, J. R., Wager, T. D. & Pizzagalli, D. A. Large-scale network dysfunction in major depressive disorder: A meta-analysis of resting-state functional connectivity. JAMA Psychiatry72, 603–611 (2015).
9. Cuijpers, P. et al. The efficacy of psychotherapy and pharmacotherapy in treating depressive and anxiety disorders: A meta-analysis of direct comparisons. World Psychiatry12, 137–148 (2013).
10. Verduijn, J. et al. Reconsidering the prognosis of major depressive disorder across diagnostic boundaries: Full recovery is the exception rather than the rule. BMC Med.15, (2017).
11. Palhano-Fontes, F. et al. Rapid antidepressant effects of the psychedelic ayahuasca in treatment-resistant depression: A randomized placebo-controlled trial. Psychol. Med.49, 655–663 (2019).
12. Cameron, L. P., Benson, C. J., Defelice, B. C., Fiehn, O. & Olson, D. E. Chronic, Intermittent Microdoses of the Psychedelic N, N-Dimethyltryptamine (DMT) Produce Positive Effects on Mood and Anxiety in Rodents. ACS Chem. Neurosci.10, 3261–3270 (2019).
13. Da Silva, F. S. et al. Acute effects of ayahuasca in a juvenile non-human primate model of depression. Brazilian J. Psychiatry41, 280–288 (2019).
14. Preuss, T. M. Critique of pure marmoset. Brain. Behav. Evol.93, 92–107 (2019).
15. Worley, K. C. et al. The common marmoset genome provides insight into primate biology and evolution. Nat. Genet.46, 850–857 (2014).
16. Park, J. E. et al. Generation of transgenic marmosets expressing genetically encoded calcium indicators. Sci. Rep.6, (2016).
17. Galvão-Coelho, N. L., Galvão, A. C. de M., da Silva, F. S. & de Sousa, M. B. C. Common marmosets: A potential translational animal model of juvenile depression. Front. Psychiatry8, 1–17 (2017).
18. Wallis, C. U., Cockcroft, G. J., Cardinal, R. N., Roberts, A. C. & Clarke, H. F. Hippocampal Interaction With Area 25, but not Area 32, Regulates Marmoset Approach–Avoidance Behavior. Cereb. Cortex (2019). doi:10.1093/cercor/bhz015
19. Kondo, T. et al. Calcium Transient Dynamics of Neural Ensembles in the Primary Motor Cortex of Naturally Behaving Monkeys. Cell Rep.24, 2191-2195.e4 (2018).
20. Roy, S. & Wang, X. Wireless multi-channel single unit recording in freely moving and vocalizing primates. J. Neurosci. Methods203, 28–40 (2012).
21. Mohan, H. et al. Functional Architecture and Encoding of Tactile Sensorimotor Behavior in Rat Posterior Parietal Cortex. J. Neurosci.39, 7332–7343 (2019).
22. Fraley, C. & Raftery, A. E. Enhanced Model-Based Clustering, Density Estimation, and Discriminant Analysis Software: MCLUST. J. Classif.20, 263–286 (2003).
23. Harris, K. D., Henze, D. A., Csicsvari, J., Hirase, H. & Buzsáki, G. Accuracy of tetrode spike separation as determined by simultaneous intracellular and extracellular measurements. Journal of Neurophysiology84, (2000).
24. Hendrie, C. A. & Pickles, A. R. Depression as an evolutionary adaptation: Implications for the development of preclinical models. Med. Hypotheses72, 342–347 (2009).
25. Susser, E., Keyes, K. & Mascayano, F. Healthy pregnancy and prevention of psychosis. World Psychiatry17, 357–358 (2018).
26. Barros, M. & Tomaz, C. Non-human primate models for investigating fear and anxiety. Neuroscience and Biobehavioral Reviews26, 187–201 (2002).
[1]* In terms of data size, it would not be possible to do whole day recordings, since the data needs to be stored in an SD card and transferred manually to a computer once a week. The timing of 15 minutes once per day would yield enough data to be able to draw inferences about changes in neuronal activity over time and also reduce the amount of GCaMP photobleaching observed. 30 hours of miniscope data would be acquired per week for the 24 animals – since data would not be acquired during the weekends, it would be optimal to have the more processing-demanding part of the analysis running on a powerful computer over the weekend. Another consideration of the miniscope is that we assume it would be possible to record the same neuronal population over multiple days. One main disadvantage of the wireless UCLA miniscope compared to the NiNscope is a lack of focus control in software, so a pilot study would be necessary to assess if daily changes in the focus ring would be necessary.
[2]* It is important to realize that electrophysiology can provide information about many hundreds of single units, while the miniscope only captures about 100 cells. This sample size difference should be taken into account in the statistical analysis, as well as the delay between neuronal activation and increase in GCaMP fluorescence – which would need to be taken into account in the analysis.
Teeth are comprised of four tissues: Enamel, dentine, cement and pulp. Enamel is the hardest substance present in the human body, consisting mainly (97% w/w*[1]) of calcium hydroapatite. Dentine presents a higher capability of remodelling than enamel, since it contains only about 70% w/w of hydroxyapatite crystals. The pulp contains blood vessels and nerves which supply the teeth and the cementum, with the periodontal ligament, anchors the roots in the sockets of upper and lower jaws.
Because there is no remodelling of enamel after its maturation, which usually occurs after 24 hours after the initial matrix secretion of ameloblasts, its original marks of deposition are retained as cross striations and brown stria of Retzius. Cross striations, also known as short period markers, arise through the circadian rhythm (represented by bands of different levels of mineralization deposited within a single day). The most plausible hypothesis for this pattern of deposition was suggested by Boyde (1964), and further developed more recently by Risnes (1998). They suggested that there would be more carbon dioxide available for incorporation of the enamel during periods of greater metabolic activity, causing enamel to be deposited faster during these moments compared to periods of lesser metabolic activity, which also explains the variation in mineral density found in these markers. The brown stria of Retzius represents a circaseptan cycle, which varies between 7 and 11 days between individuals, but it does not vary throughout the life of a single individual (FitzGerald, 1998). The origin of this cycle is not totally understood. Some authors hypothesise that it is under regulation of melatonin (Hasting and Loudon, 2006), others suggest that it may be a mere stochastic process (FitzGerald, 1995).
Human permanent incisors have about 150 brown stria, canines about 180 and molar contain 120-150 (Bullion, 1987), and the distance between each striae increases towards the incisal or occlusal surface. The wave-like pattern was termed perikymata, subdivided into perikyma grooves and ridges. Counting these markers provides a reliable method of estimating the age of subadults, as tooth eruption follows a strict biological sequence, with a well-known variance between populations. Disruptions of enamel deposition are usually due to genetic enamel defects (collectively referred as amelogenesis imperfecta) or more commonly caused by environmental stress (dietary deficiency, fevers, infections), refered to as hypoplasias.
EVOLUTION IN THE FIELD
Dental morphology plays a key role in multiple aspects of evolution because the dentition is under forceful genetic control. An animal with faulty dentition cannot properly breakdown food in the first stage of digestion, which leads to poorer absorption of energy and ultimately a reduction in the likelihood of its genes being passed onto the next generation.
In their origin, teeth were simple, one-cusped (haplodont), uniform (homodont), conical-shaped structures. Adaptation led to the development of more sophisticated structures, reflecting the versatile dietary aspects of a species. Mammals have heterodont teeth, represented by incisors, canines, premolars and molars: These created occlusal surfaces suitable to a greater variety of food (Phulari, 2013). Herbivorous mammals have molarized premolars, which enhance the surface area and optimize grinding of plant material. Carnivores have dicing/slicing specialized dentition with blade like posterior teeth called carnassials and molars reduced in size or even lost in some species (Hilson, 1986). Omnivores have a more generalized dentition.
Mammals evolved from reptiles in the Mesozoic era, and early mammals presented characteristic triangular-shaped molars (Hillson, 2005). Osborn (1907) hypothesized that the lingual cusp of upper molars and the buccal cusp of lower molars were homologous to the reptilian cone and called these the protocone and the protoconid, respectively. Two other cusps originated as cuspules, and via selective pressure developed into larger structures called the paracone and paraconid at the mesial end of the molar and metacone and metaconid at the distal end in upper and lower molars, respectively. This cusp triad is known as the trigon in upper molars and trigonid in lower molars. In most mammals, the paraconid has been lost in lower molars. An additional ledge of cingulum is present at the distal end of the trigon in primates, known as the talon in upper molars, and the talonid in lower molars. On the upper molars the talon houses a single cusp, the hypocone, but three additional cusps are found on the primate trigonid: hypoconid, entoconid and hypoconulid (Figure 1).
It is now known that the cusp homologous to the reptilian cone in upper molars is the paracone, not the protocone (Butler, 1978), as it was initially suggested by Osborn. Nevertheless, his original nomenclature is still used by most authors, although some prefer to use this terminology and describe the paracone as the primary cusp or cusp 1 (Steele and Bramblett, 1988). Trying to develop a clearer system, Hershkovitz (1978) created an entirely new terminology, replacing the paracone and the protoconid for the eocone and the eoconid. As described by Scott and Turner II (1997), while Hershkovitz has a strong case on paleontological grounds, Osborn’s terminology has such a lengthy history of usage that changing it would create more, rather than less, confusion.
A reduction in the number of teeth and generations of teeth is observed in recent species, possibly as an energetic trade-off for an increase in morphological complexity. Mammals originally presented three incisors in each quadrant of both maxillary and mandibular teeth. The majority of early primates lost one incisor, and by the Eocene there was a reduction in the number of premolars (Swindler, 2004). The modern human dental formula, shared by all catarrhine primates, is 2-1-2-3, while platyrrhines dental formula is either 2-1-3-3 or 2-1-3-2.
How diet influences dental and masticatory morphology is exemplified in early hominins. Paranthropus presents a more robust mandible, larger temporalis and masseter muscles, and larger posterior teeth with extremely thick enamel when compared to early Homo (Figure 2). This suggests that hard material such as nuts and seeds were an important component of the diet of Paranthropus (Grine and Martin, 1988). Some authors try to relate the trend of reduction of molar size, enamel thickness and masticatory muscles throughout the evolution of the genus Homo to factors such as meat eating (Bunn et al, 2012), the control of fire and cooking (Wrangham, 2009), or even tool use (Teaford, 2007).
EVOLUTION AS A FIELD
Fossilization is an extremely complex and ultimately rare phenomenon, which occurs under very specific circumstances. As a result the fossil record presents very few complete specimens of our distant ancestors. Enamel, the ‘inorganic coating’ of teeth, is the main reason teeth preserve so well in most taphonomic contexts and therefore a high percentage of the fossil record is made up of teeth. Even though other osteological elements are more informative in some respects, they are less likely to be preserved, so palaeontologists and paleoarcheologists have to work with teeth and dental traits in most contexts.
The precise description of dental traits is important, but not easy. Without a standard reference showing variability in the occurrence of dental traits, dichotomized nominal measurements, characterised by absence/presence, were used for many years in the field. In 1920, Ales Hrdlička published the famous ‘Shovel-shaped teeth’, which provided a set of ordinal measurements for the shovelling trait, which included the absence of the trait and three degrees of expression. Other workers had discussed morphological gradations in earlier papers (Black, 1902), but as criticised by Hrdlička, they did not focus on specific trait variance.
Although it is arguable that interval measurements from quantitative or metric data provide precise information with a lesser degree of error, the trend of non-metric observations has prevailed in the field. As described by Scott and Turner II (1997), most dental traits are not easily described by quantitative data, because: ‘(1) they exhibit variation in both form and size, not just size; (2) as three dimensional objects, they often lack the kinds of landmarks that are needed to standardize measurements; and (3) they may show such slight levels of expression, there is no way to measure them in millimetres.’
Basically, dental traits are not metric/non-metric per se, but the absence of precise tools and methods makes the description of most dental characteristics more attainable using qualitative rather than quantitative descriptions. Hanihara (1970) presented a creative way of measuring an ‘essentially qualitative’ trait (hypocone) through quantitative measures. He photographed of the occlusal surface of the molar, printed an enlarged version and cut the area delimited by the hypocone, which he weighed using a high-precision scale in order to stablish a relative area of the hypocone in relation to the total crown area, which created incredibly robust results. This technique has been since used by many workers (Yamada and Brown, 1988; Bailey et al, 2004; Benazzi et al, 2011).
Since Hrdlička there have been a lot of improvements in this field. Dahlberg (1956) and Hanihara (1961) were the first to develop a dental trait scoring system with plaster plaque references for permanent and deciduous dentition, respectively. This was an incredibly important step in the field: Since an analysis based on scoring non-metric traits is the most feasible approach for characterizing dental traits, a physical plaster model is not only a good way of establishing a visual reference and therefore reduce intraobserver and interobserver inaccuracies, but it is also a powerful learning tool for students in the field.
More recently, Turner II et al. (1991) developed the Arizona State University Dental Anthropology System (ASUDAS), which has been the main scoring system since its original publication. The traits used in this system were chosen because they are easily observable and persistent, meaning they provide relevant information about a population via trait frequency. Many of the ASUDAS traits are invariable throughout hominoid evolution, suggested by Irish and Guatelli-Steinberg (2003), so they can also be used in characterizing non-human primates and early hominins dental morphology (Irish, 2013).
TEETH WEAR AND DENTAL PATHOLOGIES
As discussed before, enamel is not capable of remodelling after its maturation. With time, the forces applied by masticatory muscles on the occlusal/incisal surfaces of teeth cause the enamel to wear down. Patterns of wear can be used to access dietary behaviours. Hunter-gatherers may use their anterior teeth to process hides, causing the incisal surfaces to wear down faster. Early agriculturalists, on the other hand, wore down their posterior dentition faster, due to abrasive particles introduced by processing grains using grinding stones (Smith, 1984). If the process of tooth wear is slow, secondary dentine is produced by remaining odontoblasts in order to protect the pulp.
Another effect of the forces applied on teeth is the slow decrease in crown height, causing a remodelling of the alveolar process (the portion of the jaw which contains the teeth sockets). This results in the continuous eruption of teeth throughout life, eventually causing tooth loss due to lack of bone support (Hillson, 2008). A different cause of tooth loss is periodontitis: Short episodes of inflammation caused by plaque alternated with periods of recovery leading to bone reabsorption and reduction in height of the alveolar process.
Caries is also an important component in tooth loss, but in a different way. Although the inflammation in pulp and periapical tissues caused by caries does not cause the remodelling of bone tissue associated with the loss of teeth, the pain inflicted by it makes human intervention likely to happen in order to remove the carious tooth. Caries is a demineralization process, caused mainly by the accumulation of a specific type of bacteria (Streptoccocus mutans) of the dental plaque (Loesche, 1998). There is a strong correlation between the presence of caries and the increase of sugar consumption during the twentieth century (Thylstrup and Fejerskov, 1994). There is also noticeable decrease in decay rate of children as a result of sugar rationing in Japan and Norway during World War II. The main cause of caries is commonly regarded as sucrose, but the similarity in the molecular structures of simple carbohydrates reflects a small difference in the cariogenicity of fructose, glucose, lactose and sucrose (Hillson, 2008).
The nature of enamel deposition is used in the context of forensic dentistry in various ways.(1) Teeth found in isolation may be matched comparing the brown striae and cross striations to determine the identity of an individual, as the whole dentition of an individual presents, roughly, the same pattern; (2) Historical stressful events (e.g. volcanic eruptions or long periods without food) are able to be matched biologically, if all individuals s dated to a specific period have similar disruption markers. (3) The first brown stria of Retzius, known as Neonatal line and caused by the physiological stress of birth, can be used to determine if infants died before or after they were born, since prenatal enamel does not usually contain striae (FitzGerald and Rose, 2008). (4) In modern forensics and criminology, in situations where most tissues were destroyed, the extraction of DNA through the pulp and dentin is practical for identification, due to the high chemical and physical resilience of these dental tissues (Datta and Datta, 2012). (5) The mineralized cells present in dental calculus provide an important source of ancient DNA.
With the advance of PCR (polymerase chain reaction), it is feasible to use DNA amounts as miniscule as 1 ng are useful for identification purposes (Sweet, 2001). Dental tartar/calculus retains pathogenic cells, human cheek cells and dietary components such as plant particles, therefore it is useful in many contexts. Because the collection of calculus is a simple and non-destructive procedure, whereas the most common process of collecting DNA from skeletal remains requires powdering of a portion of teeth or bones, the extraction of DNA from calculus presents a major advantage. Plant particles in tartar are useful for recreating specific aspects of a population’s diet. For instance, starch particles can help understand the methods for cooking and processing food (Henry et al, 2009). A well-preserved dietary record may be used to establish environmental changes, changes in subsistence patterns and migration (Dudgeon and Trump, 2012). It is difficult to extract genetic material from pathogenic agents of ancient skeletal remains, since the amount of DNA and RNA present is very small even in highly infected individuals (Stone, 2008). When it is possible to obtain sufficient pathogenic material from PCR, however, it provides strong evidence for migration of populations and the origin and development of disease throughout human history.
CONCLUSION
The dentition and the characteristics of certain aspects of dental morphology are useful in many scientific fields (embryology, genetics, anthropology, palaeontology, forensic science, et cetera). If one wishes to acquire more in-depth information about dental anthropology, see Scott and Turner II (1991) and Hillson (1996); Mammalian teeth are explored thoroughly in Hillson (2005), the same for primate dentition in Swindler (2002).
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