A Tradeoff in the Neural Code across Regions and Species

Brain region-specific roles of brain-derived neurotrophic factor in social stress-induced depressive-like behavior

Brain-derived neurotrophic factor is a key factor in stress adaptation and avoidance of a social stress behavioral response. Recent studies have shown that brain-derived neurotrophic factor expression in stressed mice is brain region-specific, particularly involving the corticolimbic system, including the ventral tegmental area, nucleus accumbens, prefrontal cortex, amygdala, and hippocampus. Determining how brain-derived neurotrophic factor participates in stress processing in different brain regions will deepen our understanding of social stress psychopathology. In this review, we discuss the expression and regulation of brain-derived neurotrophic factor in stress-sensitive brain regions closely related to the pathophysiology of depression. We focused on associated molecular pathways and neural circuits, with special attention to the brain-derived neurotrophic factor-tropomyosin receptor kinase B signaling pathway and the ventral tegmental area-nucleus accumbens dopamine circuit. We determined that stress-induced alterations in brain-derived neurotrophic factor levels are likely related to the nature, severity, and duration of stress, especially in the above-mentioned brain regions of the corticolimbic system. Therefore, BDNF might be a biological indicator regulating stress-related processes in various brain regions.

A tradeoff between efficiency and robustness in the hippocampal-neocortical memory network during human and rodent sleep

Sleep constitutes a brain state of disengagement from the external world that supports memory consolidation and restores cognitive resources. The precise mechanisms how sleep and its varied stages support information processing remain largely unknown. Synaptic scaling models imply that daytime learning accumulates neural information, which is then consolidated and downregulated during sleep. Currently, there is a lack of in-vivo data from humans and rodents that elucidate if, and how, sleep renormalizes information processing capacities. From an information-theoretical perspective, a consolidation process should entail a reduction in neural pattern variability over the course of a night. Here, in a cross-species intracranial study, we identify a tradeoff in the neural population code during sleep where information coding efficiency is higher in the neocortex than in hippocampal archicortex in humans than in rodents as well as during wakefulness compared to sleep. Critically, non-REM sleep selectively reduces information coding efficiency through pattern repetition in the neocortex in both species, indicating a transition to a more robust information coding regime. Conversely, the coding regime in the hippocampus remained consistent from wakefulness to non-REM sleep. These findings suggest that new information could be imprinted to the long-term mnemonic storage in the neocortex through pattern repetition during sleep. Lastly, our results show that task engagement increased coding efficiency, while medically-induced unconsciousness disrupted the population code. In sum, these findings suggest that neural pattern variability could constitute a fundamental principle underlying cognitive engagement and memory formation, while pattern repetition reflects robust coding, possibly underlying the consolidation process.

Early life stress, literacy and dyslexia: an evolutionary perspective

Stress and learning co-evolved in parallel, with their interdependence critical to the survival of the species. Even today, the regulation of moderate levels of stress by the central autonomic network (CAN), especially during pre- and post-natal periods, facilitates biological adaptability and is an essential precursor for the cognitive requisites of learning to read. Reading is a remarkable evolutionary achievement of the human brain, mysteriously unusual, because it is not pre-wired with a genetic address to facilitate its acquisition. There is no gene for reading. The review suggests that reading co-opts a brain circuit centered in the left hemisphere ventral occipital cortex that evolved as a domain-general visual processor. Its adoption by reading depends on the CAN's coordination of the learning and emotional requirements of learning to read at the metabolic, cellular, synaptic, and network levels. By stabilizing a child's self-control and modulating the attention network's inhibitory controls over the reading circuit, the CAN plays a key role in school readiness and learning to read. In addition, the review revealed two beneficial CAN evolutionary adjustments to early-life stress "overloads" that come with incidental costs of school under-performance and dyslexia. A short-term adaptation involving methylation of the FKBP5 and NR3C1 genes is a liability for academic achievement in primary school. The adaptation leading to dyslexia induces alterations in BDNF trafficking, promoting long-term adaptive fitness by protecting against excessive glucocorticoid toxicity but risks reading difficulties by disruptive signaling from the CAN to the attention networks and the reading circuit.

Dorsal and median raphe neuronal firing dynamics characterized by nonlinear measures

The dorsal (DRN) and median (MRN) raphe are important nuclei involved in similar functions, including mood and sleep, but playing distinct roles. These nuclei have a different composition of neuronal types and set of neuronal connections, which among other factors, determine their neuronal dynamics. Most works characterize the neuronal dynamics using classic measures, such as using the average spiking frequency (FR), the coefficient of variation (CV), and action potential duration (APD). In the current study, to refine the characterization of neuronal firing profiles, we examined the neurons within the raphe nuclei. Through the utilization of nonlinear measures, our objective was to discern the redundancy and complementarity of these measures, particularly in comparison with classic methods. To do this, we analyzed the neuronal basal firing profile in both nuclei of urethane-anesthetized rats using the Shannon entropy (Bins Entropy) of the inter-spike intervals, permutation entropy of ordinal patterns (OP Entropy), and Permutation Lempel-Ziv Complexity (PLZC). Firstly, we found that classic (i.e., FR, CV, and APD) and nonlinear measures fail to distinguish between the dynamics of DRN and MRN neurons, except for the OP Entropy. We also found strong relationships between measures, including the CV with FR, CV with Bins entropy, and FR with PLZC, which imply redundant information. However, APD and OP Entropy have either a weak or no relationship with the rest of the measures tested, suggesting that they provide complementary information to the characterization of the neuronal firing profiles. Secondly, we studied how these measures are affected by the oscillatory properties of the firing patterns, including rhythmicity, bursting patterns, and clock-like behavior. We found that all measures are sensitive to rhythmicity, except for the OP Entropy. Overall, our work highlights OP Entropy as a powerful and useful quantity for the characterization of neuronal discharge patterns.

Hierarchies of description enable understanding of cognitive phenomena in terms of neuron activity

One objective of neuroscience is to understand a wide range of specific cognitive processes in terms of neuron activity. The huge amount of observational data about the brain makes achieving this objective challenging. Different models on different levels of detail provide some insight, but the relationship between models on different levels is not clear. Complex computing systems with trillions of components like transistors are fully understood in the sense that system features can be precisely related to transistor activity. Such understanding could not involve a designer simultaneously thinking about the ongoing activity of all the components active in the course of carrying out some system feature. Brain modeling approaches like dynamical systems are inadequate to support understanding of computing systems, because their use relies on approximations like treating all components as more or less identical. Understanding computing systems needs a much more sophisticated use of approximation, involving creation of hierarchies of description in which the higher levels are more approximate, with effective translation between different levels in the hierarchy made possible by using the same general types of information processes on every level. These types are instruction and data read/write. There are no direct resemblances between computers and brains, but natural selection pressures have resulted in brain resources being organized into modular hierarchies and in the existence of two general types of information processes called condition definition/detection and behavioral recommendation. As a result, it is possible to create hierarchies of description linking cognitive phenomena to neuron activity, analogous with but qualitatively different from the hierarchies of description used to understand computing systems. An intuitively satisfying understanding of cognitive processes in terms of more detailed brain activity is then possible.

Differential functional organization of amygdala-medial prefrontal cortex networks in macaque and human

Over the course of evolution, the amygdala (AMG) and medial frontal cortex (mPFC) network, involved in behavioral adaptation, underwent structural changes in the old-world monkey and human lineages. Yet, whether and how the functional organization of this network differs remains poorly understood. Using resting-state functional magnetic resonance imagery, we show that the functional connectivity (FC) between AMG nuclei and mPFC regions differs between humans and awake macaques. In humans, the AMG-mPFC FC displays U-shaped pattern along the corpus callosum: a positive FC with the ventromedial prefrontal (vmPFC) and anterior cingulate cortex (ACC), a negative FC with the anterior mid-cingulate cortex (MCC), and a positive FC with the posterior MCC. Conversely, in macaques, the negative FC shifted more ventrally at the junction between the vmPFC and the ACC. The functional organization divergence of AMG-mPFC network between humans and macaques might help understanding behavioral adaptation abilities differences in their respective socio-ecological niches.

Local field potentials, spiking activity, and receptive fields in human visual cortex

The concept of receptive field (RF) is central to sensory neuroscience. Neuronal RF properties have been substantially studied in animals, while those in humans remain nearly unexplored. Here, we measured neuronal RFs with intracranial local field potentials (LFPs) and spiking activity in human visual cortex (V1/V2/V3). We recorded LFPs via macro-contacts and discovered that RF sizes estimated from low-frequency activity (LFA, 0.5-30 Hz) were larger than those estimated from low-gamma activity (LGA, 30-60 Hz) and high-gamma activity (HGA, 60-150 Hz). We then took a rare opportunity to record LFPs and spiking activity via microwires in V1 simultaneously. We found that RF sizes and temporal profiles measured from LGA and HGA closely matched those from spiking activity. In sum, this study reveals that spiking activity of neurons in human visual cortex could be well approximated by LGA and HGA in RF estimation and temporal profile measurement, implying the pivotal functions of LGA and HGA in early visual information processing.

Human voltage-gated Na+ and K+ channel properties underlie sustained fast AP signaling

Human cortical pyramidal neurons are large, have extensive dendritic trees, and yet have unexpectedly fast input-output properties: Rapid subthreshold synaptic membrane potential changes are reliably encoded in timing of action potentials (APs). Here, we tested whether biophysical properties of voltage-gated sodium (Na+) and potassium (K+) currents in human pyramidal neurons can explain their fast input-output properties. Human Na+ and K+ currents exhibited more depolarized voltage dependence, slower inactivation, and faster recovery from inactivation compared with their mouse counterparts. Computational modeling showed that despite lower Na+ channel densities in human neurons, the biophysical properties of Na+ channels resulted in higher channel availability and contributed to fast AP kinetics stability. Last, human Na+ channel properties also resulted in a larger dynamic range for encoding of subthreshold membrane potential changes. Thus, biophysical adaptations of voltage-gated Na+ and K+ channels enable fast input-output properties of large human pyramidal neurons.

Multiple routes of communication within the amygdala-mPFC network: A comparative approach in humans and macaques

The network formed by the amygdala (AMG) and the medial Prefrontal Cortex (mPFC), at the interface between our internal and external environment, has been shown to support some important aspects of behavioral adaptation. Whether and how the anatomo-functional organization of this network evolved across primates remains unclear. Here, we compared AMG nuclei morphological characteristics and their functional connectivity with the mPFC in humans and macaques to identify potential homologies and differences between these species. Based on selected studies, we highlight two subsystems within the AMG-mPFC circuits, likely involved in distinct temporal dynamics of integration during behavioral adaptation. We also show that whereas the mPFC displays a large expansion but a preserved intrinsic anatomo-functional organization, the AMG displays a volume reduction and morphological changes related to specific nuclei. We discuss potential commonalities and differences in the dialogue between AMG nuclei and mPFC in humans and macaques based on available data.

Resolving the prefrontal mechanisms of adaptive cognitive behaviors: A cross-species perspective

The prefrontal cortex (PFC) enables a staggering variety of complex behaviors, such as planning actions, solving problems, and adapting to new situations according to external information and internal states. These higher-order abilities, collectively defined as adaptive cognitive behavior, require cellular ensembles that coordinate the tradeoff between the stability and flexibility of neural representations. While the mechanisms underlying the function of cellular ensembles are still unclear, recent experimental and theoretical studies suggest that temporal coordination dynamically binds prefrontal neurons into functional ensembles. A so far largely separate stream of research has investigated the prefrontal efferent and afferent connectivity. These two research streams have recently converged on the hypothesis that prefrontal connectivity patterns influence ensemble formation and the function of neurons within ensembles. Here, we propose a unitary concept that, leveraging a cross-species definition of prefrontal regions, explains how prefrontal ensembles adaptively regulate and efficiently coordinate multiple processes in distinct cognitive behaviors.

Volitional Generation of Reproducible, Efficient Temporal Patterns

One of the extraordinary characteristics of the biological brain is the low energy expense it requires to implement a variety of biological functions and intelligence as compared to the modern artificial intelligence (AI). Spike-based energy-efficient temporal codes have long been suggested as a contributor for the brain to run on low energy expense. Despite this code having been largely reported in the sensory cortex, whether this code can be implemented in other brain areas to serve broader functions and how it evolves throughout learning have remained unaddressed. In this study, we designed a novel brain-machine interface (BMI) paradigm. Two macaques could volitionally generate reproducible energy-efficient temporal patterns in the primary motor cortex (M1) by learning the BMI paradigm. Moreover, most neurons that were not directly assigned to control the BMI did not boost their excitability, and they demonstrated an overall energy-efficient manner in performing the task. Over the course of learning, we found that the firing rates and temporal precision of selected neurons co-evolved to generate the energy-efficient temporal patterns, suggesting that a cohesive rather than dissociable processing underlies the refinement of energy-efficient temporal patterns.

On the evolutionary roots of human social cognition

The aim of this commentary is to highlight the complementarity of the approaches used to investigate the neuronal basis of social cognition. From neuroanatomy, to neurophysiology, to neuroimaging and behavioral studies, the research presented by Braunsdorf, Noritake, Terenzi and colleagues are revealing a complex architecture supporting social cognition as well as the diversity of factors driving our social decisions (Braunsdorf et al., 2021; Noritake et al., 2021; Terenzi et al., 2021). From an evolutionary perspective, results presented indicate strong phylogenic origins to human social cognition, but also point out some issues about the evolution of the social brain that remain to be investigated.

Spatio-Temporally Efficient Coding Assigns Functions to Hierarchical Structures of the Visual System

Hierarchical structures constitute a wide array of brain areas, including the visual system. One of the important questions regarding visual hierarchical structures is to identify computational principles for assigning functions that represent the external world to hierarchical structures of the visual system. Given that visual hierarchical structures contain both bottom-up and top-down pathways, the derived principles should encompass these bidirectional pathways. However, existing principles such as predictive coding do not provide an effective principle for bidirectional pathways. Therefore, we propose a novel computational principle for visual hierarchical structures as spatio-temporally efficient coding underscored by the efficient use of given resources in both neural activity space and processing time. This coding principle optimises bidirectional information transmissions over hierarchical structures by simultaneously minimising temporal differences in neural responses and maximising entropy in neural representations. Simulations demonstrated that the proposed spatio-temporally efficient coding was able to assign the function of appropriate neural representations of natural visual scenes to visual hierarchical structures. Furthermore, spatio-temporally efficient coding was able to predict well-known phenomena, including deviations in neural responses to unlearned inputs and bias in preferred orientations. Our proposed spatio-temporally efficient coding may facilitate deeper mechanistic understanding of the computational processes of hierarchical brain structures.

Correlated Activity in the Degenerate Retina Inhibits Focal Response to Electrical Stimulation

Retinal prostheses have shown some clinical success in patients with retinitis pigmentosa and age-related macular degeneration. However, even after the implantation of a retinal prosthesis, the patient’s visual acuity is at best less than 20/420. Reduced visual acuity may be explained by a decrease in the signal-to-noise ratio due to the spontaneous hyperactivity of retinal ganglion cells (RGCs) found in degenerate retinas. Unfortunately, abnormal retinal rewiring, commonly observed in degenerate retinas, has rarely been considered for the development of retinal prostheses. The purpose of this study was to investigate the aberrant retinal network response to electrical stimulation in terms of the spatial distribution of the electrically evoked RGC population. An 8 × 8 multielectrode array was used to measure the spiking activity of the RGC population. RGC spikes were recorded in wild-type [C57BL/6J; P56 (postnatal day 56)], rd1 (P56), rd10 (P14 and P56) mice, and macaque [wild-type and drug-induced retinal degeneration (RD) model] retinas. First, we performed a spike correlation analysis between RGCs to determine RGC connectivity. No correlation was observed between RGCs in the control group, including wild-type mice, rd10 P14 mice, and wild-type macaque retinas. In contrast, for the RD group, including rd1, rd10 P56, and RD macaque retinas, RGCs, up to approximately 400–600 μm apart, were significantly correlated. Moreover, to investigate the RGC population response to electrical stimulation, the number of electrically evoked RGC spikes was measured as a function of the distance between the stimulation and recording electrodes. With an increase in the interelectrode distance, the number of electrically evoked RGC spikes decreased exponentially in the control group. In contrast, electrically evoked RGC spikes were observed throughout the retina in the RD group, regardless of the inter-electrode distance. Taken together, in the degenerate retina, a more strongly coupled retinal network resulted in the widespread distribution of electrically evoked RGC spikes. This finding could explain the low-resolution vision in prosthesis-implanted patients.

The learnability consequences of Zipfian distributions in language

While the languages of the world differ in many respects, they share certain commonalties, which can provide insight on our shared cognition. Here, we explore the learnability consequences of one of the striking commonalities between languages. Across languages, word frequencies follow a Zipfian distribution, showing a power law relation between a word's frequency and its rank. While their source in language has been studied extensively, less work has explored the learnability consequences of such distributions for language learners. We propose that the greater predictability of words in this distribution (relative to less skewed distributions) can facilitate word segmentation, a crucial aspect of early language acquisition. To explore this, we quantify word predictability using unigram entropy, assess it across languages using naturalistic corpora of child-directed speech and then ask whether similar unigram predictability facilitates word segmentation in the lab. We find similar unigram entropy in child-directed speech across 15 languages. We then use an auditory word segmentation task to show that the unigram predictability levels found in natural language are uniquely facilitative for word segmentation for both children and adults. These findings illustrate the facilitative impact of skewed input distributions on learning and raise questions about the possible role of cognitive pressures in the prevalence of Zipfian distributions in language.

Two sides of the same amygdala: From shared neural mechanisms to comorbidity of social and affective disorders

Recent studies identified a circuitry within the primate amygdala that underlies both social and affective processes. Such shared functions within the same circuit, although beneficial for adaptive behavior and make sense in light of evolution, can also contribute to the growing comorbidity between affective and social disorders.

Automatic Recognition of Macaque Facial Expressions for Detection of Affective States

Internal affective states produce external manifestations such as facial expressions. In humans, the Facial Action Coding System (FACS) is widely used to objectively quantify the elemental facial action units (AUs) that build complex facial expressions. A similar system has been developed for macaque monkeys-the Macaque FACS (MaqFACS); yet, unlike the human counterpart, which is already partially replaced by automatic algorithms, this system still requires labor-intensive coding. Here, we developed and implemented the first prototype for automatic MaqFACS coding. We applied the approach to the analysis of behavioral and neural data recorded from freely interacting macaque monkeys. The method achieved high performance in the recognition of six dominant AUs, generalizing between conspecific individuals (Macaca mulatta) and even between species (Macaca fascicularis). The study lays the foundation for fully automated detection of facial expressions in animals, which is crucial for investigating the neural substrates of social and affective states.

Human brain organogenesis: Toward a cellular understanding of development and disease

The construction of the human nervous system is a distinctly complex although highly regulated process. Human tissue inaccessibility has impeded a molecular understanding of the developmental specializations from which our unique cognitive capacities arise. A confluence of recent technological advances in genomics and stem cell-based tissue modeling is laying the foundation for a new understanding of human neural development and dysfunction in neuropsychiatric disease. Here, we review recent progress on uncovering the cellular and molecular principles of human brain organogenesis in vivo as well as using organoids and assembloids in vitro to model features of human evolution and disease.

Neural optimization: Understanding trade-offs with Pareto theory

Nervous systems, like any organismal structure, have been shaped by evolutionary processes to increase fitness. The resulting neural 'bauplan' has to account for multiple objectives simultaneously, including computational function, as well as additional factors such as robustness to environmental changes and energetic limitations. Oftentimes these objectives compete, and quantification of the relative impact of individual optimization targets is non-trivial. Pareto optimality offers a theoretical framework to decipher objectives and trade-offs between them. We, therefore, highlight Pareto theory as a useful tool for the analysis of neurobiological systems from biophysically detailed cells to large-scale network structures and behavior. The Pareto approach can help to assess optimality, identify relevant objectives and their respective impact, and formulate testable hypotheses.

Toward Predicting Impact of Common Genetic Variants on Schizophrenia Clinical Responses With Antipsychotics: A Quantitative System Pharmacology Study

CNS disorders are lagging behind other indications in implementing genotype-dependent treatment algorithms for personalized medicine. This report uses a biophysically realistic computer model of an associative and dorsal motor cortico-striatal-thalamo-cortical loop and a working memory cortical model to investigate the pharmacodynamic effects of COMTVal158Met rs4680, 5-HTTLPR rs 25531 s/L and D2DRTaq1A1 genotypes on the clinical response of 7 antipsychotics. The effect of the genotypes on dopamine and serotonin dynamics and the level of target exposure for the drugs was calibrated from PET displacement studies. The simulations suggest strong gene-gene pharmacodynamic interactions unique to each antipsychotic. For PANSS Total, the D2DRTaq1 allele has the biggest impact, followed by the 5-HTTLPR rs25531. The A2A2 genotype improved efficacy for all drugs, with a more complex outcome for the 5-HTTLPR rs25531 genotype. Maximal range in PANSS Total for all 27 individual combinations is 3 (aripiprazole) to 5 points (clozapine). The 5-HTTLPR L/L with aripiprazole and risperidone and the D2DRTaq1A2A2 allele with haloperidol, clozapine and quetiapine reduce the motor side-effects with opposite effects for the s/s genotype. The COMT genotype has a limited effect on antipsychotic effect and EPS. For cognition, the COMT MM 5-HTTLPR L/L genotype combination has the best performance for all antipsychotics, except clozapine. Maximal difference is 25% of the total dynamic range in a 2-back working memory task. Aripiprazole is the medication that is best suited for the largest number of genotype combinations (10) followed by Clozapine and risperidone (6), haloperidol and olanzapine (3) and quetiapine and paliperidone for one genotype. In principle, the platform could identify the best antipsychotic treatment balancing efficacy and side-effects for a specific individual genotype. Once the predictions of this platform are validated in a clinical setting the platform has potential to support rational personalized treatment guidance in clinical practice.

Exploring the relation between BOLD fMRI and cognitive performance using a computer-based quantitative systems pharmacology model: Applications to the COMTVAL158MET genotype and ketamine

BOLD fMRI is increasingly used mostly in an observational way to probe the effect of genotypes or therapeutic intervention in normal and diseased subjects. We use a mechanism-based quantitative systems pharmacology computer model of a human cortical microcircuit, previously calibrated for the 2-back working memory paradigm, adding established biophysical principles, of glucose metabolism, oxygen consumption, neurovascular effects and the paramagnetic impact on blood oxygen levels to calculate a readout for the voxel-based BOLD fMRI signal. The objective was to study the effect of the Catechol-O-methyl Transferase Val158Met (COMT) genotype on performance and BOLD fMRI. While the simulation suggests that on average virtual COMTVV genotype subjects perform worse, subjects with lower GABA, lower 5-HT₃ and higher 5-HT_1A activation can improve cognitive performance to the level of COMTMM subjects but at the expense of higher BOLD fMRI signal. In a schizophrenia condition, increased NMDA, GABA tone and noise levels, and lower D₁R activity can improve cognitive outcome with greater BOLD fMRI signal in COMT Val-carriers. We further generate hypotheses about why ketamine in healthy controls increases the BOLD fMRI signal but reduces cognitive performance. These simulations suggest a strong non-linear relationship between BOLD fMRI signal and cognitive performance. When validated, this mechanistic approach can be useful for moving beyond the descriptive nature of BOLD fMRI imaging and supporting the proper interpretation of imaging biomarkers in CNS disorders.

Functional parcellation of human and macaque striatum reveals human-specific connectivity in the dorsal caudate

A wide homology between human and macaque striatum is often assumed as in both the striatum is involved in cognition, emotion and executive functions. However, differences in functional and structural organization between human and macaque striatum may reveal evolutionary divergence and shed light on human vulnerability to neuropsychiatric diseases. For instance, dopaminergic dysfunction of the human striatum is considered to be a pathophysiological underpinning of different disorders, such as Parkinson's disease (PD) and schizophrenia (SCZ). Previous investigations have found a wide similarity in structural connectivity of the striatum between human and macaque, leaving the cross-species comparison of its functional organization unknown. In this study, resting-state functional connectivity (RSFC) derived striatal parcels were compared based on their homologous cortico-striatal connectivity. The goal here was to identify striatal parcels whose connectivity is human-specific compared to macaque parcels. Functional parcellation revealed that the human striatum was split into dorsal, dorsomedial, and rostral caudate and ventral, central, and caudal putamen, while the macaque striatum was divided into dorsal, and rostral caudate and rostral, and caudal putamen. Cross-species comparison indicated dissimilar cortico-striatal RSFC of the topographically similar dorsal caudate. We probed clinical relevance of the striatal clusters by examining differences in their cortico-striatal RSFC and gray matter (GM) volume between patients (with PD and SCZ) and healthy controls. We found abnormal RSFC not only between dorsal caudate, but also between rostral caudate, ventral, central and caudal putamen and widespread cortical regions for both PD and SCZ patients. Also, we observed significant structural atrophy in rostral caudate, ventral and central putamen for both PD and SCZ while atrophy in the dorsal caudate was specific to PD. Taken together, our cross-species comparative results revealed shared and human-specific RSFC of different striatal clusters reinforcing the complex organization and function of the striatum. In addition, we provided a testable hypothesis that abnormalities in a region with human-specific connectivity, i.e., dorsal caudate, might be associated with neuropsychiatric disorders.

Beyond social learning

Cultural evolution requires the social transmission of information. For this reason, scholars have emphasized social learning when explaining how and why culture evolves. Yet cultural evolution results from many mechanisms operating in concert. Here, we argue that the emphasis on social learning has distracted scholars from appreciating both the full range of mechanisms contributing to cultural evolution and how interactions among those mechanisms and other factors affect the output of cultural evolution. We examine understudied mechanisms and other factors and call for a more inclusive programme of investigation that probes multiple levels of the organization, spanning the neural, cognitive-behavioural and populational levels. To guide our discussion, we focus on factors involved in three core topics of cultural evolution: the emergence of culture, the emergence of cumulative cultural evolution and the design of cultural traits. Studying mechanisms across levels can add explanatory power while revealing gaps and misconceptions in our knowledge. This article is part of the theme issue 'Foundations of cultural evolution'.

Common fronto-temporal effective connectivity in humans and monkeys

Human brain pathways supporting language and declarative memory are thought to have differentiated substantially during evolution. However, cross-species comparisons are missing on site-specific effective connectivity between regions important for cognition. We harnessed functional imaging to visualize the effects of direct electrical brain stimulation in macaque monkeys and human neurosurgery patients. We discovered comparable effective connectivity between caudal auditory cortex and both ventro-lateral prefrontal cortex (VLPFC, including area 44) and parahippocampal cortex in both species. Human-specific differences were clearest in the form of stronger hemispheric lateralization effects. In humans, electrical tractography revealed remarkably rapid evoked potentials in VLPFC following auditory cortex stimulation and speech sounds drove VLPFC, consistent with prior evidence in monkeys of direct auditory cortex projections to homologous vocalization-responsive regions. The results identify a common effective connectivity signature in human and nonhuman primates, which from auditory cortex appears equally direct to VLPFC and indirect to the hippocampus. VIDEO ABSTRACT.

Changes in pairwise correlations during running reshape global network state in the main olfactory bulb

Neural codes for sensory inputs have been hypothesized to reside in a broader space defined by ongoing patterns of spontaneous activity. To understand the structure of this spontaneous activity in the olfactory system, we performed high-density recordings of neural populations in the main olfactory bulb of awake mice. We observed changes in pairwise correlations of spontaneous activity between mitral and tufted (M/T) cells when animals were running, which resulted in an increase in the entropy of the population. Surprisingly, pairwise maximum entropy models that described the population activity using only assumptions about the firing rates and correlations of neurons were better at predicting the global structure of activity when animals were stationary as compared to when they were running, implying that higher order (3rd, 4th order) interactions governed population activity during locomotion. Taken together, we found that locomotion alters the functional interactions that shape spontaneous population activity at the earliest stages of olfactory processing, one synapse away from the sensory receptors in the nasal epithelium. These data suggest that the coding space available for sensory representations responds adaptively to the animal's behavioral state.NEW & NOTEWORTHY The organization and structure of spontaneous population activity in the olfactory system places constraints of how odor information is represented. Using high-density electrophysiological recordings of mitral and tufted cells, we found that running increases the dimensionality of spontaneous activity, implicating higher order interactions among neurons during locomotion. Behavior, thus, flexibly alters neuronal activity at the earliest stages of sensory processing.

Redundancy circuits of the commissural pathways in human and rhesus macaque brains

It has been hypothesized that the human brain has less redundancy than animals, but the structural evidence has not been identified to confirm this claim. Here, we report three redundancy circuits of the commissural pathways in primate brains, namely the orbitofrontal, temporal, and occipital redundancy circuits of the anterior commissure and corpus callosum. Each redundancy circuit has two distinctly separated routes connecting a common pair of cortical regions. We mapped their trajectories in human and rhesus macaque brains using individual and population‐averaged tractography. The dissection results confirmed the existence of these redundancy circuits connecting the orbitofrontal lobe, amygdala, and visual cortex. The volume analysis showed a significant reduction in the orbitofrontal and occipital redundancy circuits of the human brain, whereas the temporal redundancy circuit had a substantial organizational difference between the human and rhesus macaque. Our results support the hypothesis that the human brain has less redundancy in the commissural pathways than that of the rhesus macaque brain. Further studies are needed to explore its neuropathological implications.

Growing evidence for separate neural mechanisms for attention and consciousness

Our conscious experience of the world seems to go in lockstep with our attentional focus: We tend to see, hear, taste, and feel what we attend to, and vice versa. This tight coupling between attention and consciousness has given rise to the idea that these two phenomena are indivisible. In the late 1950s, the honoree of this special issue, Charles Eriksen, was among a small group of early pioneers that sought to investigate whether a transient increase in overall level of attention (alertness) in response to a noxious stimulus can be decoupled from conscious perception using experimental techniques. Recent years saw a similar debate regarding whether attention and consciousness are two dissociable processes. Initial evidence that attention and consciousness are two separate processes primarily rested on behavioral data. However, the past couple of years witnessed an explosion of studies aimed at testing this conjecture using neuroscientific techniques. Here we provide an overview of these and related empirical studies on the distinction between the neuronal correlates of attention and consciousness, and detail how advancements in theory and technology can bring about a more detailed understanding of the two. We argue that the most promising approach will combine ever-evolving neurophysiological and interventionist tools with quantitative, empirically testable theories of consciousness that are grounded in a mathematically formalized understanding of phenomenology.

An Evolutionary Perspective of Dyslexia, Stress, and Brain Network Homeostasis

Evolution fuels interindividual variability in neuroplasticity, reflected in brain anatomy and functional connectivity of the expanding neocortical regions subserving reading ability. Such variability is orchestrated by an evolutionarily conserved, competitive balance between epigenetic, stress-induced, and cognitive-growth gene expression programs. An evolutionary developmental model of dyslexia, suggests that prenatal and childhood subclinical stress becomes a risk factor for dyslexia when physiological adaptations to stress promoting adaptive fitness, may attenuate neuroplasticity in the brain regions recruited for reading. Stress has the potential to blunt the cognitive-growth functions of the predominantly right hemisphere Ventral and Dorsal attention networks, which are primed with high entropic levels of synaptic plasticity, and are critical for acquiring beginning reading skills. The attentional networks, in collaboration with the stress-responsive Default Mode network, modulate the entrainment and processing of the low frequency auditory oscillations (1-8 Hz) and visuospatial orienting linked etiologically to dyslexia. Thus, dyslexia may result from positive, but costly adaptations to stress system dysregulation: protective measures that reset the stress/growth balance of processing to favor the Default Mode network, compromising development of the attentional networks. Such a normal-variability conceptualization of dyslexia is at odds with the frequent assumption that dyslexia results from a neurological abnormality. To put the normal-variability model in the broader perspective of the state of the field, a traditional evolutionary account of dyslexia is presented to stimulate discussion of the scientific merits of the two approaches.

Diagnostic Classification for Human Autism and Obsessive-Compulsive Disorder Based on Machine Learning From a Primate Genetic Model

OBJECTIVE

Psychiatric disorders commonly comprise comorbid symptoms, such as autism spectrum disorder (ASD), obsessive-compulsive disorder (OCD), and attention deficit hyperactivity disorder (ADHD), raising controversies over accurate diagnosis and overlap of their neural underpinnings. The authors used noninvasive neuroimaging in humans and nonhuman primates to identify neural markers associated with DSM-5 diagnoses and quantitative measures of symptom severity.

METHODS

Resting-state functional connectivity data obtained from both wild-type and methyl-CpG binding protein 2 (MECP2) transgenic monkeys were used to construct monkey-derived classifiers for diagnostic classification in four human data sets (ASD: Autism Brain Imaging Data Exchange [ABIDE-I], N=1,112; ABIDE-II, N=1,114; ADHD-200 sample: N=776; OCD local institutional database: N=186). Stepwise linear regression models were applied to examine associations between functional connections of monkey-derived classifiers and dimensional symptom severity of psychiatric disorders.

RESULTS

Nine core regions prominently distributed in frontal and temporal cortices were identified in monkeys and used as seeds to construct the monkey-derived classifier that informed diagnostic classification in human autism. This same set of core regions was useful for diagnostic classification in the OCD cohort but not the ADHD cohort. Models based on functional connections of the right ventrolateral prefrontal cortex with the left thalamus and right prefrontal polar cortex predicted communication scores of ASD patients and compulsivity scores of OCD patients, respectively.

CONCLUSIONS

The identified core regions may serve as a basis for building markers for ASD and OCD diagnoses, as well as measures of symptom severity. These findings may inform future development of machine-learning models for psychiatric disorders and may improve the accuracy and speed of clinical assessments.

Lower complexity of motor primitives ensures robust control of high-speed human locomotion

Walking and running are mechanically and energetically different locomotion modes. For selecting one or another, speed is a parameter of paramount importance. Yet, both are likely controlled by similar low-dimensional neuronal networks that reflect in patterned muscle activations called muscle synergies. Here, we challenged human locomotion by having our participants walk and run at a very broad spectrum of submaximal and maximal speeds. The synergistic activations of lower limb locomotor muscles were obtained through decomposition of electromyographic data via non-negative matrix factorization. We analyzed the duration and complexity (via fractal analysis) over time of motor primitives, the temporal components of muscle synergies. We found that the motor control of high-speed locomotion was so challenging that the neuromotor system was forced to produce wider and less complex muscle activation patterns. The motor modules, or time-independent coefficients, were redistributed as locomotion speed changed. These outcomes show that humans cope with the challenges of high-speed locomotion by adapting the neuromotor dynamics through a set of strategies that allow for efficient creation and control of locomotion.

Shared yet dissociable neural codes across eye gaze, valence and expectation

The direction of the eye gaze of others is a prominent social cue in primates and is important for communication^1-11. Although gaze can signal threat and elicit anxiety^6,12,13, it remains unclear whether it shares neural circuitry with stimulus value. Notably, gaze not only has valence, but can also serve as a predictor of the outcome of a social encounter, which can be either negative or positive^2,8,12,13. Here we show that the neural codes for gaze and valence overlap in primates and that they involve two different mechanisms: one for the outcome and another for its expectation. Monkeys participated in the human intruder test^13,14, in which a human participant had either a direct or averted gaze, interleaved with blocks of aversive and appetitive conditioning. We find that single neurons in the amygdala encode gaze¹⁵, whereas neurons in the anterior cingulate cortex encode the social context¹⁶, but not gaze. We identify a shared population in the amygdala for which the neural responses to direct and averted gaze parallel the responses to aversive and appetitive stimulus, respectively. Furthermore, we distinguish between two neural mechanisms-an overall-activity scheme that is used for gaze and the unconditioned stimulus, and a correlated-selectivity scheme that is used for gaze and the conditioned stimulus. These findings provide insights into the origins of the neural mechanisms that underlie the computations of both social interactions and valence, and could help to shed light on mechanisms that underlie social anxiety and the comorbidity between anxiety and impaired social interactions.

Different Multidimensional Representations across the Amygdalo-Prefrontal Network during an Approach-Avoidance Task

The prelimbic (PL) area and basolateral amygdala (lateral [LA] and basolateral [BL] nuclei) have closely related functions and similar extrinsic connectivity. Reasoning that the computational advantage of such redundancy should be reflected in differences in how these structures represent information, we compared the coding properties of PL and amygdala neurons during a task that requires rats to produce different conditioned defensive or appetitive behaviors. Rather than unambiguous regional differences in the identities of the variables encoded, we found gradients in how the same variables are represented. Whereas PL and BL neurons represented many different parameters through minor variations in firing rates, LA cells coded fewer task features with stronger changes in activity. At the population level, whereas valence could be easily distinguished from amygdala activity, PL neurons could distinguish both valence and trial identity as well as or better than amygdala neurons. Thus, PL has greater representational capacity.

Optogenetic activation of corticogeniculate feedback stabilizes response gain and increases information coding in LGN neurons

In spite of their anatomical robustness, it has been difficult to establish the functional role of corticogeniculate circuits connecting primary visual cortex with the lateral geniculate nucleus of the thalamus (LGN) in the feedback direction. Growing evidence suggests that corticogeniculate feedback does not directly shape the spatial receptive field properties of LGN neurons, but rather regulates the timing and precision of LGN responses and the information coding capacity of LGN neurons. We propose that corticogeniculate feedback specifically stabilizes the response gain of LGN neurons, thereby increasing their information coding capacity. Inspired by early work by McClurkin et al. (1994), we manipulated the activity of corticogeniculate neurons to test this hypothesis. We used optogenetic methods to selectively and reversibly enhance the activity of corticogeniculate neurons in anesthetized ferrets while recording responses of LGN neurons to drifting gratings and white noise stimuli. We found that optogenetic activation of corticogeniculate feedback systematically reduced LGN gain variability and increased information coding capacity among LGN neurons. Optogenetic activation of corticogeniculate neurons generated similar increases in information encoded in LGN responses to drifting gratings and white noise stimuli. Together, these findings suggest that the influence of corticogeniculate feedback on LGN response precision and information coding capacity could be mediated through reductions in gain variability.

The association of myelination in the internal capsule with iron deposition in the basal ganglia in macaques: a magnetic resonance imaging study

Background

Iron plays a vital role in myelin synthesis and maintenance. A tight association between iron concentration and myelin content has been demonstrated in local brain regions; however, whether such a relationship exists between distant brain regions that are anatomically connected is largely unknown.

Methods

We conducted an in vivo measurement of iron and myelin content in the brains of 8 young (mean age: 7.7 years) and 8 old (mean age: 24.7 years) macaques by integrating two MRI-based techniques: quantitative susceptibility mapping (QSM) and myelin water fraction (MWF) imaging. We examined the relationship between iron deposition in components of the basal ganglia (BG), and the myelin content of the BG-connecting fiber tract internal capsule (IC) and four more white matter (WM) structures, including the optic tract, and the genu, body, and splenium of the corpus callosum, which are anatomically separate from the BG.

Results

Spearman's correlation analysis revealed a moderate to high (r=0.528-0.808, P<0.05) positive correlation between the magnetic susceptibility of the BG and the MWF of anatomically connected IC structures during myelin production and maintenance, but little significant correlation was found between the susceptibility of the BG and the MWF of WM structures not anatomically connected to the BG.

Conclusions

These results advance the understanding of the relationship between iron and myelin, and suggest that future studies should consider the impact that iron concentration in the BG has on the myelination of WM structures that are anatomically connected to the BG.

A Novel Major Output Target for Pheromone-Sensitive Projection Neurons in Male Moths

Even though insects have comparably small brains, they achieve astoundingly complex behaviors. One example is flying moths tracking minute amounts of pheromones using olfactory circuits. The tracking distance can be up to 1 km, which makes it essential that male moths respond efficiently and reliably to very few pheromone molecules. The male-specific macroglomerular complex (MGC) in the moth antennal lobe contains circuitry dedicated to pheromone processing. Output neurons from this region project along three parallel pathways, the medial, mediolateral, and lateral tracts. The MGC-neurons of the lateral tract are least described and their functional significance is mainly unknown. We used mass staining, calcium imaging, and intracellular recording/staining to characterize the morphological and physiological properties of these neurons in the noctuid moth, Helicoverpa armigera. All lateral-tract MGC neurons targeted the column, a small region within the superior intermediate neuropil. We identified this region as a unique converging site for MGC lateral-tract neurons responsive to pheromones, as well as a dense congregating site for plant odor information since a substantial number of lateral-tract neurons from ordinary glomeruli (OG) also terminates in this region. The lateral-tract MGC-neurons responded with a shorter peak latency than the well-described neurons in the medial tract. Different from the medial-tract MGC neurons encoding odor quality important for species-specific signal identification, those in the lateral tract convey a more robust and rapid signal-potentially important for fast control of hard-wired behavior.

Structure of cortical network activity across natural wake and sleep states in mice

Cortical neurons fire intermittently and synchronously during non-rapid eye movement sleep (NREMS), in which active and silent periods are referred to as ON and OFF periods, respectively. Neuronal firing rates during ON periods (NREMS-ON-activity) are similar to those of wakefulness (W-activity), raising the possibility that NREMS-ON neuronal-activity is fragmented W-activity. To test this, we investigated the patterning and organization of cortical spike trains and of spike ensembles in neuronal networks using extracellular recordings in mice. Firing rates of neurons during NREMS-ON and W were similar, but showed enhanced bursting in NREMS with no apparent preference in occurrence, relative to the beginning or end of the on-state. Additionally, there was an overall increase in the randomness of occurrence of sequences comprised of multi-neuron ensembles in NREMS recorded from tetrodes. In association with increased burst firing, somatic calcium transients were increased in NREMS. The increased calcium transients associated with bursting during NREM may activate calcium-dependent, cell-signaling pathways for sleep related cellular processes.

Dyslexia as an adaptation to cortico-limbic stress system reactivity

A new school of thought in evolutionary developmental biology, combined with research in the neurobiology of stress, suggest that early exposure to stressful circumstances may be a cause of dyslexia. A balance between epigenetic, stress-induced and cognitive-growth genetic programs modulates the brain's cellular, regional, and network homeostasis. This balance is essential for adaptability to the normative range of everyday stress. However, even mild chronic stress exposition may overactivate the hypothalmic-pituitary-adrenal stress axis, upsetting the homeostatic balance between these programs, and exposing the brain to harmful levels of stress hormones. A protective strategy to sustained disequilibrium precociously advances maturation at the cost of neuroplasticity, which blunts stress axis reactivity but also compromises learning potential in the prefrontal cortex and networks associated with dyslexia. Stress exceeding an individual's range of resilience: (1) reduces levels of TFEB and BDNF, gene regulatory factors prolonging maturation and neuroplasticity; (2) interferes with the insular cortex, amygdala and hippocampus in coordinating afferent visceral signals with cognitive performance; (3) over-recruits the brain's Default Mode network; and (4) amplifies release from the Locus coeruleus/norepinephrine system which impairs the entrainment of oscillations in the lower phonological frequencies of speech. Evidence supporting a stress-growth imbalance is preliminary, but holds promise for reconceptualizing the neurobiology of dyslexia and reducing its prevalence.

Reverse engineering human brain evolution using organoid models

Primate brains vary dramatically in size and organization, but the genetic and developmental basis for these differences has been difficult to study due to lack of experimental models. Pluripotent stem cells and brain organoids provide a potential opportunity for comparative and functional studies of evolutionary differences, particularly during the early stages of neurogenesis. However, many challenges remain, including isolating stem cell lines from additional great ape individuals and species to capture the breadth of ape genetic diversity, improving the reproducibility of organoid models to study evolved differences in cell composition and combining multiple brain regions to capture connectivity relationships. Here, we describe strategies for identifying evolved developmental differences between humans and non-human primates and for isolating the underlying cellular and genetic mechanisms using comparative analyses, chimeric organoid culture, and genome engineering. In particular, we focus on how organoid models could ultimately be applied beyond studies of progenitor cell evolution to decode the origin of recent changes in cellular organization, connectivity patterns, myelination, synaptic development, and physiology that have been implicated in human cognition.

Altered dorsal CA1 neuronal population coding in the APP/PS1 mouse model of Alzheimer's disease

While the link between amyloid β (Aβ) accumulation and synaptic degradation in Alzheimer’s disease (AD) is known, the consequences of this pathology on population coding remain unknown. We found that the entropy, a measure of the diversity of network firing patterns, was lower in the dorsal CA1 region in the APP/PS1 mouse model of Aβ pathology, relative to controls, thereby reducing the population’s coding capacity. Our results reveal a network level signature of the deficits Aβ accumulation causes to the computations performed by neural circuits.

Neuromotor Dynamics of Human Locomotion in Challenging Settings

Is the control of movement less stable when we walk or run in challenging settings? Intuitively, one might answer that it is, given that challenging locomotion externally (e.g., rough terrain) or internally (e.g., age-related impairments) makes our movements more unstable. Here, we investigated how young and old humans synergistically activate muscles during locomotion when different perturbation levels are introduced. Of these control signals, called muscle synergies, we analyzed the local stability and the complexity (or irregularity) over time. Surprisingly, we found that perturbations force the central nervous system to produce muscle activation patterns that are less unstable and less complex. These outcomes show that robust locomotion control in challenging settings is achieved by producing less complex control signals that are more stable over time, whereas easier tasks allow for more unstable and irregular control.

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We examined the dynamics of motor control of locomotion in challenging settings

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We extracted muscle synergies (motor modules and primitives) from electromyography

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The dynamics of the time-dependent motor primitives were modified by perturbations

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Primitives were wider, less unstable, and complex in the presence of perturbations

Conserved features of anterior cingulate networks support observational learning across species

The ability to observe, interpret, and learn behaviors and emotions from conspecifics is crucial for survival, as it bypasses direct experience to avoid potential dangers and maximize rewards and benefits. The anterior cingulate cortex (ACC) and its extended neural connections are emerging as important networks for the detection, encoding, and interpretation of social signals during observational learning. Evidence from rodents and primates (including humans) suggests that the social interactions that occur while individuals are exposed to important information in their environment lead to transfer of information across individuals that promotes adaptive behaviors in the form of either social affiliation, alertness, or avoidance. In this review, we first showcase anatomical and functional connections of the ACC in primates and rodents that contribute to the perception of social signals. We then discuss species-specific cognitive and social functions of the ACC and differentiate between neural activity related to 'self' and 'other', extending into the difference between social signals received and processed by the self, versus observing social interactions among others. We next describe behavioral and neural events that contribute to social learning via observation. Finally, we discuss some of the neural mechanisms underlying observational learning within the ACC and its extended network.

Multidimensional Neural Selectivity in the Primate Amygdala

The amygdala contributes to multiple functions including attention allocation, sensory processing, decision-making, and the elaboration of emotional behaviors. The diversity of functions attributed to the amygdala is reflected in the response selectivity of its component neurons. Previous work claimed that subsets of neurons differentiate between broad categories of stimuli (e.g., objects vs faces, rewards vs punishment), while other subsets are narrowly specialized to respond to individual faces or facial features (e.g., eyes). Here we explored the extent to which the same neurons contribute to more than one neural subpopulation in a task that activated multiple functions of the amygdala. The subjects (Macaca mulatta) watched videos depicting conspecifics or inanimate objects, and learned by trial and error to choose the individuals or objects associated with the highest rewards. We found that the same neurons responded selectively to two or more of the following task events or stimulus features: (1) alerting, task-related stimuli (fixation icon, video start, and video end); (2) reward magnitude; (3) stimulus categories (social vs nonsocial); and (4) stimulus-unique features (faces, eyes). A disproportionate number of neurons showed selectivity for all of the examined stimulus features and task events. These results suggest that neurons that appear specialized and uniquely tuned to specific stimuli (e.g., face cells, eye cells) are likely to respond to multiple other types of stimuli or behavioral events, if/when these become behaviorally relevant in the context of a complex task. This multidimensional selectivity supports a flexible, context-dependent evaluation of inputs and subsequent decision making based on the activity of the same neural ensemble.

Selection of Models: Evolution and the Choice of Species for Translational Research

Evolutionary thinking can inform the choice and assessment of model species in neuroscience, particularly when such models are intended to generate knowledge that will translate to humans. Avoiding errors that arise from oversimplified notions of phylogeny or genotype-phenotype mapping is one contribution; evolutionary biology also offers positive guidance. The challenge of finding adequate non-human models for translational research is particularly acute in neuroscience: neurobiological and behavioral phenotypes are complex and plastic, and many traits important in humans are absent, radically different, or difficult to assess in other species. Evolutionary perspectives help to articulate and address these challenges. Darwin's description of "descent with modification" points to two aspects of evolution that can help us assess the matching between a prospective model species and its intended target. One is trees that represent the structure of phylogenetic relationships; the other is phenotypic traits, i.e. the unique characteristics of each species' evolved biology and natural history. Mapping traits onto a phylogeny is the first step toward analyzing the source of similarities between a target and a potential model. Whether similar traits arise from shared ancestry or from adaptive convergence has important implications for what kinds of inferences can be justified, and for the likely translatability of findings. Evolution offers both a rich source of possible models, and guidance for choosing the best ones for a given purpose. Considering model choice from an evolutionary angle not only helps to answer the question "What species might be a good model for studying x?" but also suggests additional questions we should be asking to assess the utility of both potential and current models. Recognizing the diverse ways model organisms can function expands our search image as we seek species to study that can both extend general knowledge, and generate translatable insights relevant to human neurobiology and disease.