Dynamically Timed Stimulation of Corticolimbic Circuitry Activates a Stress-Compensatory Pathway

The NERVE-ML (neural engineering reproducibility and validity essentials for machine learning) checklist: ensuring machine learning advances neural engineering. J Neural Eng 2025;22:021002. [PMID: 40073450 PMCID: PMC11948487 DOI: 10.1088/1741-2552/adbfbd] [Citation(s) in RCA: 0] [Impact Index Per Article: 0]

Objective.Machine learning's (MLs) ability to capture intricate patterns makes it vital in neural engineering research. With its increasing use, ensuring the validity and reproducibility of ML methods is critical. Unfortunately, this has not always been the case in practice, as there have been recent retractions across various scientific fields due to the misuse of ML methods and validation procedures. To address these concerns, we propose the first version of the neural engineering reproducibility and validity essentials for ML (NERVE-ML) checklist, a framework designed to promote the transparent, reproducible, and valid application of ML in neural engineering.Approach.We highlight some of the unique challenges of model validation in neural engineering, including the difficulties from limited subject numbers, repeated or non-independent samples, and high subject heterogeneity. Through detailed case studies, we demonstrate how different validation approaches can lead to divergent scientific conclusions, highlighting the importance of selecting appropriate procedures guided by the NERVE-ML checklist. Effectively addressing these challenges and properly scoping scientific conclusions will ensure that ML contributes to, rather than hinders, progress in neural engineering.Main results.Our case studies demonstrate that improper validation approaches can result in flawed studies or overclaimed scientific conclusions, complicating the scientific discourse. The NERVE-ML checklist effectively addresses these concerns by providing guidelines to ensure that ML approaches in neural engineering are reproducible and lead to valid scientific conclusions.Significance.By effectively addressing these challenges and properly scoping scientific conclusions guided by the NERVE-ML checklist, we aim to help pave the way for a future where ML reliably enhances the quality and impact of neural engineering research.

Long-term editing of brain circuits in mice using an engineered electrical synapse

Electrical signaling across distinct populations of brain cells underpins cognitive and emotional function; however, approaches that selectively regulate electrical signaling between two cellular components of a mammalian neural circuit remain sparse. Here, we engineered an electrical synapse composed of two connexin proteins found in Morone americana (white perch fish) - connexin34.7 and connexin35 - to accomplish mammalian circuit modulation. By exploiting protein mutagenesis, devising a new in vitro system for assaying connexin hemichannel docking, and performing computational modeling of hemichannel interactions, we uncovered a structural motif that contributes to electrical synapse formation. Targeting these motifs, we designed connexin34.7 and connexin35 hemichannels that dock with each other to form an electrical synapse, but not with other major connexins expressed in the mammalian central nervous system. We validated this electrical synapse in vivo using C. elegans and mice, demonstrating that it can strengthen communication across neural circuits composed of pairs of distinct cell types and modify behavior accordingly. Thus, we establish 'Long-term integration of Circuits using connexins' (LinCx) for precision circuit-editing in mammals.

Linking Mitochondrial Dysfunction, Neurotransmitter, and Neural Network Abnormalities and Mania: Elucidating Neurobiological Mechanisms of the Therapeutic Effect of the Ketogenic Diet in Bipolar Disorder

There is growing interest in the ketogenic diet as a treatment for bipolar disorder (BD), and there are promising anecdotal and small case study reports of efficacy. However, the neurobiological mechanisms by which diet-induced ketosis might ameliorate BD symptoms remain to be determined, particularly in manic and hypomanic states-defining features of BD. Identifying these mechanisms will provide new markers to guide personalized interventions and provide targets for novel treatment developments for individuals with BD. In this critical review, we describe recent findings highlighting 2 types of neurobiological abnormalities in BD: 1) mitochondrial dysfunction and 2) neurotransmitter and neural network functional abnormalities. We link these abnormalities to mania/hypomania and depression in BD and then describe the biological underpinnings by which the ketogenic diet may have a beneficial effect in individuals with BD. We end the review by describing approaches that can be employed in future studies to elucidate the neurobiology that underlies the therapeutic effect of the ketogenic diet in BD. Doing this may provide marker predictors to identify individuals who will respond well to the ketogenic diet, as well as offer neural targets for novel treatment developments for BD.

Neural modulation by nicotine aerosols and the role of flavor additives: insights from local field potentials in mice

Research on nicotine's neurobiological effects has rarely focused on aerosols, despite their primary role in tobacco product consumption. Here, we utilized in vivo electrophysiology to examine brain activity in mice exposed to nicotine aerosols, both alone and with flavor additives (citric acid and menthol). Local field potential (LFP) recordings from the nucleus accumbens (NAc), basolateral amygdala (BLA), ventral tegmental area (VTA), and ventral posteromedial nucleus (VPM) were analyzed under saline, nicotine, nicotine with citric acid(CA + NIC), and nicotine with menthol(MENT + NIC) conditions. Nicotine exposure significantly reduced power spectral density (PSD) in the NAc-Alpha, NAc-Beta, and BLA-Beta bands, unaffected by flavor additives. Coherence between key brain regions (e.g., VPM-VTA in Beta, VPM-BLA in Alpha) also decreased with nicotine but was restored with citric acid or menthol, suggesting their role in mitigating nicotine's disruptive effects on neural synchronization. Our findings show that LFPs can effectively capture nicotine's neural effects and highlight the modulatory role of flavor additives, offering new insights into nicotine exposure management and tobacco product design.

The neuroscience of mental illness: Building toward the future

Mental illnesses arise from dysfunction in the brain. Although numerous extraneural factors influence these illnesses, ultimately, it is the science of the brain that will lead to novel therapies. Meanwhile, our understanding of this complex organ is incomplete, leading to the oft-repeated trope that neuroscience has yet to make significant contributions to the care of individuals with mental illnesses. This review seeks to counter this narrative, using specific examples of how neuroscientific advances have contributed to progress in mental health care in the past and how current achievements set the stage for further progress in the future.

Unraveling the Pathogenesis of Post-Stroke Depression in a Hemorrhagic Mouse Model through Frontal Lobe Circuitry and JAK-STAT Signaling

Post-stroke depression is a common complication that imposes significant burdens and challenges on patients. The occurrence of depression is often associated with frontal lobe hemorrhage, however, current understanding of the underlying mechanisms remains limited. Here, the pathogenic mechanisms associated with the circuitry connectivity, electrophysiological alterations, and molecular characteristics are investigated related to the frontal lobe in adult male mice following unilateral injection of blood in the medial prefrontal cortex (mPFC). It is demonstrated that depression is a specific neurological complication in the unilateral hematoma model of the mPFC, and the ventral tegmental area (VTA) shows a higher percentage of connectivity disruption compared to the lateral habenula (LHb) and striatum (STR). Additionally, long-range projections originating from the frontal lobe demonstrate higher damage percentages within the connections between each region and the mPFC. mPFC neurons reveal reduced neuronal excitability and altered synaptic communication. Furthermore, transcriptomic analysis identifies the involvement of the Janus Kinase-Signal Transducer and Activator of Transcription (JAK-STAT) signaling pathway, and targeting the JAK-STAT pathway significantly alleviates the severity of depressive symptoms. These findings improve the understanding of post-hemorrhagic depression and may guide the development of efficient treatments.

A widespread electrical brain network encodes anxiety in health and depressive states

In rodents, anxiety is charactered by heightened vigilance during low-threat and uncertain situations. Though activity in the frontal cortex and limbic system are fundamental to supporting this internal state, the underlying network architecture that integrates activity across brain regions to encode anxiety across animals and paradigms remains unclear. Here, we utilize parallel electrical recordings in freely behaving mice, translational paradigms known to induce anxiety, and machine learning to discover a multi-region network that encodes the anxious brain-state. The network is composed of circuits widely implicated in anxiety behavior, it generalizes across many behavioral contexts that induce anxiety, and it fails to encode multiple behavioral contexts that do not. Strikingly, the activity of this network is also principally altered in two mouse models of depression. Thus, we establish a network-level process whereby the brain encodes anxiety in health and disease.

Optogenetic behavioral studies in depression research: A systematic review

Optogenetics has made substantial contributions to our understanding of the mechanistic underpinnings of depression. This systematic review employs quantitative analysis to investigate the impact of optogenetic stimulation in mice and rats on behavioral alterations in social interaction, sucrose consumption, and mobility. The review analyses optogenetic behavioral studies using standardized behavioral tests to detect behavioral changes induced via optogenetic stimulation in stressed or stress-naive mice and rats. Behavioral changes were evaluated as either positive, negative, or not effective. The analysis comprises the outcomes of 248 behavioral tests of 168 studies described in 37 articles, including negative and null results. Test outcomes were compared for each behavior, depending on the animal cohort, applied type of stimulation and the stimulated neuronal circuit and cell type. The presented synthesis contributes toward a comprehensive picture of optogenetic behavioral research in the context of depression.

Large-scale coupling of prefrontal activity patterns as a mechanism for cognitive control in health and disease: evidence from rodent models

Cognitive control of behavior is crucial for well-being, as allows subject to adapt to changing environments in a goal-directed way. Changes in cognitive control of behavior is observed during cognitive decline in elderly and in pathological mental conditions. Therefore, the recovery of cognitive control may provide a reliable preventive and therapeutic strategy. However, its neural basis is not completely understood. Cognitive control is supported by the prefrontal cortex, structure that integrates relevant information for the appropriate organization of behavior. At neurophysiological level, it is suggested that cognitive control is supported by local and large-scale synchronization of oscillatory activity patterns and neural spiking activity between the prefrontal cortex and distributed neural networks. In this review, we focus mainly on rodent models approaching the neuronal origin of these prefrontal patterns, and the cognitive and behavioral relevance of its coordination with distributed brain systems. We also examine the relationship between cognitive control and neural activity patterns in the prefrontal cortex, and its role in normal cognitive decline and pathological mental conditions. Finally, based on these body of evidence, we propose a common mechanism that may underlie the impaired cognitive control of behavior.

Electome network factors: Capturing emotional brain networks related to health and disease

Therapeutic development for mental disorders has been slow despite the high worldwide prevalence of illness. Unfortunately, cellular and circuit insights into disease etiology have largely failed to generalize across individuals that carry the same diagnosis, reflecting an unmet need to identify convergent mechanisms that would facilitate optimal treatment. Here, we discuss how mesoscale networks can encode affect and other cognitive processes. These networks can be discovered through electrical functional connectome (electome) analysis, a method built upon explainable machine learning models for analyzing and interpreting mesoscale brain-wide signals in a behavioral context. We also outline best practices for identifying these generalizable, interpretable, and biologically relevant networks. Looking forward, translational electome analysis can span species and various moods, cognitive processes, or other brain states, supporting translational medicine. Thus, we argue that electome analysis provides potential translational biomarkers for developing next-generation therapeutics that exhibit high efficacy across heterogeneous disorders.

Neuronal tuning to threat exposure remains stable in the mouse prefrontal cortex over multiple days

Intense threat elicits action in the form of active and passive coping. The medial prefrontal cortex (mPFC) executes top-level control over the selection of threat coping strategies, but the dynamics of mPFC activity upon continuing threat encounters remain unexplored. Here, we used 1-photon calcium imaging in mice to probe the activity of prefrontal pyramidal cells during repeated exposure to intense threat in a tail suspension (TS) paradigm. A subset of prefrontal neurons displayed selective activation during TS, which was stably maintained over days. During threat, neurons showed specific tuning to active or passive coping. These responses were unrelated to general motion tuning and persisted over days. Moreover, the neural manifold traversed by low-dimensional population activity remained stable over subsequent days of TS exposure and was preserved across individuals. These data thus reveal a specific, temporally, and interindividually conserved repertoire of prefrontal tuning to behavioral responses under threat.

Unraveling Sex Differences in Affect Processing: Unique Oscillatory Signaling Dynamics in the Infralimbic Cortex and Nucleus Accumbens Shell

Background

Negative affect is prevalent in psychiatric diseases such as depression and addiction. Projections from the infralimbic cortex (IL) to the nucleus accumbens shell (NAcSh) are causally linked to learned negative affect as 20 Hz optogenetic stimulation of this circuit reduces conditioned taste aversion (CTA) in male but not female rats. However, the prior study did not provide insight into how innate versus learned negative affect are processed in these areas across sex.

Methods

To address this issue, local field potential activity was simultaneously recorded in the IL and NAcSh in response to intraoral infusion of rewarding (saccharin) and aversive (quinine) tastants and following induction of a CTA in male and female Sprague Dawley rats.

Results

Local field potential oscillatory activity within each brain region to saccharin varied across sex. In males, CTA increased IL resting-state power, which was correlated with the strength of the learned aversion, and reduced beta power and IL-NAcSh coherence. In females, CTA increased gamma power in the NAcSh. Similar effects were observed in males and females after CTA in theta-low gamma phase-amplitude coupling. Finally, while quinine produced similar effects in oscillatory power across sex, females showed differences in phase-amplitude coupling within the NAcSh that may be linked to aversion resistance.

Conclusions

We revealed sex-specific hedonic processing in the IL and NAcSh and how oscillatory signaling is disrupted in learned negative affect, revealing translationally relevant insight into potential treatment strategies that can help to reduce the deleterious effects of learned negative affect in psychiatric illnesses.

Closing the loop in psychiatric deep brain stimulation: physiology, psychometrics, and plasticity

Deep brain stimulation (DBS) is an invasive approach to precise modulation of psychiatrically relevant circuits. Although it has impressive results in open-label psychiatric trials, DBS has also struggled to scale to and pass through multi-center randomized trials. This contrasts with Parkinson disease, where DBS is an established therapy treating thousands of patients annually. The core difference between these clinical applications is the difficulty of proving target engagement, and of leveraging the wide range of possible settings (parameters) that can be programmed in a given patient's DBS. In Parkinson's, patients' symptoms change rapidly and visibly when the stimulator is tuned to the correct parameters. In psychiatry, those same changes take days to weeks, limiting a clinician's ability to explore parameter space and identify patient-specific optimal settings. I review new approaches to psychiatric target engagement, with an emphasis on major depressive disorder (MDD). Specifically, I argue that better engagement may come by focusing on the root causes of psychiatric illness: dysfunction in specific, measurable cognitive functions and in the connectivity and synchrony of distributed brain circuits. I overview recent progress in both those domains, and how it may relate to other technologies discussed in companion articles in this issue.

Targeting PDK2 rescues stress-induced impaired brain energy metabolism

Depression is a mental illness frequently accompanied by disordered energy metabolism. A dysregulated hypothalamus pituitary adrenal axis response with aberrant glucocorticoids (GCs) release is often observed in patients with depression. However, the associated etiology between GCs and brain energy metabolism remains poorly understood. Here, using metabolomic analysis, we showed that the tricarboxylic acid (TCA) cycle was inhibited in chronic social defeat stress (CSDS)-exposed mice and patients with first-episode depression. Decreased mitochondrial oxidative phosphorylation was concomitant with the impairment of the TCA cycle. In parallel, the activity of pyruvate dehydrogenase (PDH), the gatekeeper of mitochondrial TCA flux, was suppressed, which is associated with the CSDS-induced neuronal pyruvate dehydrogenase kinase 2 (PDK2) expression and consequently enhanced PDH phosphorylation. Considering the well-acknowledged role of GCs in energy metabolism, we further demonstrated that glucocorticoid receptors (GR) stimulated PDK2 expression by directly binding to its promoter region. Meanwhile, silencing PDK2 abrogated glucocorticoid-induced PDH inhibition, restored the neuronal oxidative phosphorylation, and improved the flux of isotope-labeled carbon (U-13C] glucose) into the TCA cycle. Additionally, in vivo, pharmacological inhibition and neuron-specific silencing of GR or PDK2 restored CSDS-induced PDH phosphorylation and exerted antidepressant activities against chronic stress exposure. Taken together, our findings reveal a novel mechanism of depression manifestation, whereby elevated GCs levels regulate PDK2 transcription via GR, thereby impairing brain energy metabolism and contributing to the onset of this condition.

Socioeconomic disparities in sleep duration are associated with cortical thickness in children

INTRODUCTION

Disrupted sleep has been consistently linked with lower academic achievement and worse mental health in children. Less is understood about sleep as a potential factor underlying socioeconomic differences in brain morphometry in children. The goals of this study were to investigate the associations among socioeconomic factors, sleep duration, and brain morphometry in children, and to examine the roles of the sleep environment and family routines in these associations.

METHODS

Participants were 5- to 9-year-old children from socioeconomically diverse families (N = 94; 61% female). Parents reported on children's weekday and weekend sleep durations, sleep environment, and family routines. High-resolution, T1-weighted structural magnetic resonance imaging (MRI) data were acquired. Analyses focused on cortical thickness, cortical surface area, and amygdala and hippocampal volume.

RESULTS

Results indicated that lower family income-to-needs ratio and parental education were significantly associated with shorter weekday sleep duration in children. Shorter weekday sleep duration was significantly associated with reduced thickness in the left middle temporal, right postcentral, and right superior frontal cortices and smaller basolateral but not centromedial amygdala volume. Family routines significantly mediated the associations of family income-to-needs ratio and parental education with weekday sleep duration in children.

CONCLUSION

These results contribute to our understanding of sleep factors as proximal mechanisms through which socioeconomic context may alter neural development during childhood.

Supervising the Decoder of Variational Autoencoders to Improve Scientific Utility

Probabilistic generative models are attractive for scientific modeling because their inferred parameters can be used to generate hypotheses and design experiments. This requires that the learned model provides an accurate representation of the input data and yields a latent space that effectively predicts outcomes relevant to the scientific question. Supervised Variational Autoencoders (SVAEs) have previously been used for this purpose, as a carefully designed decoder can be used as an interpretable generative model of the data, while the supervised objective ensures a predictive latent representation. Unfortunately, the supervised objective forces the encoder to learn a biased approximation to the generative posterior distribution, which renders the generative parameters unreliable when used in scientific models. This issue has remained undetected as reconstruction losses commonly used to evaluate model performance do not detect bias in the encoder. We address this previously-unreported issue by developing a second-order supervision framework (SOS-VAE) that updates the decoder parameters, rather than the encoder, to induce a predictive latent representation. This ensures that the encoder maintains a reliable posterior approximation and the decoder parameters can be effectively interpreted. We extend this technique to allow the user to trade-off the bias in the generative parameters for improved predictive performance, acting as an intermediate option between SVAEs and our new SOS-VAE. We also use this methodology to address missing data issues that often arise when combining recordings from multiple scientific experiments. We demonstrate the effectiveness of these developments using synthetic data and electrophysiological recordings with an emphasis on how our learned representations can be used to design scientific experiments.

Brain-wide electrical dynamics encode individual appetitive social behavior

The architecture whereby activity across many brain regions integrates to encode individual appetitive social behavior remains unknown. Here we measure electrical activity from eight brain regions as mice engage in a social preference assay. We then use machine learning to discover a network that encodes the extent to which individual mice engage another mouse. This network is organized by theta oscillations leading from prelimbic cortex and amygdala that converge on the ventral tegmental area. Network activity is synchronized with cellular firing, and frequency-specific activation of a circuit within this network increases social behavior. Finally, the network generalizes, on a mouse-by-mouse basis, to encode individual differences in social behavior in healthy animals but fails to encode individual behavior in a 'high confidence' genetic model of autism. Thus, our findings reveal the architecture whereby the brain integrates distributed activity across timescales to encode an appetitive brain state underlying individual differences in social behavior.

Advances in optogenetic studies of depressive-like behaviors and underlying neural circuit mechanisms

BACKGROUNDS

The neural circuit mechanisms underlying depression remain unclear. Recently optogenetics has gradually gained recognition as a novel technique to regulate the activity of neurons with light stimulation. Scientists are now transferring their focus to the function of brain regions and neural circuits in the pathogenic progress of depression. Deciphering the circuitry mechanism of depressive-like behaviors may help us better understand the symptomatology of depression. However, few studies have summarized current progress on optogenetic researches into the neural circuit mechanisms of depressive-like behaviors.

AIMS

This review aimed to introduce fundamental characteristics and methodologies of optogenetics, as well as how this technique achieves specific neuronal control with spatial and temporal accuracy. We mainly summarized recent progress in neural circuit discoveries in depressive-like behaviors using optogenetics and exhibited the potential of optogenetics as a tool to investigate the mechanism and possible optimization underlying antidepressant treatment such as ketamine and deep brain stimulation.

METHODS

A systematic review of the literature published in English mainly from 2010 to the present in databases was performed. The selected literature is then categorized and summarized according to their neural circuits and depressive-like behaviors.

CONCLUSIONS

Many important discoveries have been made utilizing optogenetics. These findings support optogenetics as a powerful and potential tool for studying depression. And our comprehension to the etiology of depression and other psychiatric disorders will also be more thorough with this rapidly developing technique in the near future.

Directed Spectral Measures Improve Latent Network Models Of Neural Populations

Systems neuroscience aims to understand how networks of neurons distributed throughout the brain mediate computational tasks. One popular approach to identify those networks is to first calculate measures of neural activity (e.g. power spectra) from multiple brain regions, and then apply a linear factor model to those measures. Critically, despite the established role of directed communication between brain regions in neural computation, measures of directed communication have been rarely utilized in network estimation because they are incompatible with the implicit assumptions of the linear factor model approach. Here, we develop a novel spectral measure of directed communication called the Directed Spectrum (DS). We prove that it is compatible with the implicit assumptions of linear factor models, and we provide a method to estimate the DS. We demonstrate that latent linear factor models of DS measures better capture underlying brain networks in both simulated and real neural recording data compared to available alternatives. Thus, linear factor models of the Directed Spectrum offer neuroscientists a simple and effective way to explicitly model directed communication in networks of neural populations.

All-optical approaches to studying psychiatric disease

Improvements in all-optical means of monitoring and manipulating neural activity have generated new ways of studying psychiatric disease. The combination of calcium imaging techniques with optogenetics to concurrently record and manipulate neural activity has been used to create new disease models that link distinct circuit abnormalities to specific disease dimensions. These approaches represent a new path towards the development of more effective treatments, as they allow researchers to identify circuit manipulations that normalize pathological network activity. In this review we highlight the utility of all-optical approaches to generate new psychiatric disease models where the specific circuit abnormalities associated with disease symptomology can be assessed in vivo and in response to manipulations designed to normalize disease states. We then outline the principles underlying all-optical interrogations of neural circuits and discuss practical considerations for experimental design.

Optogenetic Dissection of Neural Circuits Underlying Stress-Induced Mood Disorders

Objectives: This review aims to (i) summarize the literature on optogenetic applications of different stress-induced mood disorder models of the medial prefrontal cortex (mPFC) and its projection circuits, and (ii) examine methodological variability across the literature and how such variations may influence the underlying circuits of stress-induced mood disorders. Methods: A variety of databases (PubMed, Web of Science, Elsevier, Springer, and Wiley) were systematically searched to identify optogenetic studies that applied to mood disorders in the context of stress. Results: Eleven studies on optogenetic stimulation of the mPFC and the effect of its efferent circuitry on anxiety- and depression-like behaviors in different rodent models were selected. The results showed that the optogenetics (i) can provide insights into the underlying circuits of mood disorders in the context of stress (ii) and also points out new therapeutic strategies for treating mood disorders. Conclusions: These findings indicate a clear role for the mPFC in social avoidance, and highlight the central role of stress reactivity circuitry that may be targeted for the treatment of stress-induced mood disorders.

Cardiac sensory afferents modulate susceptibility to anxio-depressive behaviour in a mouse model of chronic heart failure

AIM

Impairments in cerebral structure and cognitive performance in chronic heart failure (CHF) are critical components of its comorbidity spectrum. Autonomic afferents that arise from cardiac sensory fibres show enhanced activity with CHF. Desensitization of these fibres by local application of resiniferatoxin (RTX) during myocardial infarction (MI) is known to prevent cardiac hypertrophy, sympathetic hyperactivity and CHF. Whether these afferents mediate cerebral allostasis is unknown.

METHODS

CHF was induced by myocardial infarction. To evaluate if cardiac afferents contribute to cerebral allostasis, RTX was acutely applied to the pericardial space in controls (RTX) and in MI treated animals (MI/RTX). Subjects were then evaluated in a series of behavioural tests recapitulating different symptoms of depressive disorders. Proteomics of the frontal cortices (FC) was performed to identify contributing proteins and pathways responsible for behavioural allostasis.

RESULTS

Desensitization of cardiac afferents relieves hallmarks of an anxio/depressive-like state in mice. Unique protein signatures and regulatory pathways in FCs isolated from each treatment reveal the degree of complexity inherent in the FC response to stresses originating in the heart. While cortices from the combined treatment (MI/RTX) did not retain protein signatures from the individual treatment groups, all three groups suffer dysregulation in circadian entrainment.

CONCLUSION

CHF is comorbid with an anxio/depressive-like state and ablation of cardiac afferents relieves the despair phenotype. The strikingly different proteomic profiles observed in FCs suggest that MI and RTX lead to unique brain-signalling patterns and that the combined treatment, potentially through destructive interference mechanisms, most closely resembles controls.

Optogenetics: A revolutionary approach for the study of depression

Depression is a severe and chronic mental disorder that affects millions of individuals worldwide. Symptoms include depressed mood, loss of interest, reduced motivation and suicidal thoughts. Even though findings from genetic, molecular and imaging studies have helped provide some clues regarding the mechanisms underlying depression-like behaviors, there are still many unanswered questions that need to be addressed. Optogenetics, a technique developed in the early 2000s, has proved effective in the study and treatment of depression and depression-like behaviors and has revolutionized already known experimental techniques. This technique employs light and genetic tools to either inhibit or excite specific neurons or pathways within the brain. In this review paper, an up-to-date understanding of the use of optogenetics in the study of depression-like behaviors is provided, along with suggestions for future research directions.

Optogenetic and chemogenetic insights into the neurocircuitry of depression-like behaviour: A systematic review

Major depressive disorder (MDD) and its treatment are challenges for global health. Optogenetics and chemogenetics are driving MDD research forward by unveiling causal relations between cell-type-specific control of neurons and depressive-like behaviour in rodents. Using a systematic search process, in this review, a set of 43 original studies applying optogenetic or chemogenetic techniques in rodent models of depression was identified. Our aim was to provide an examination of all available studies elucidating central neuronal mechanisms leading to depressive-like behaviour in rodents and thereby unveiling the most promising routes for future research. A complex interacting network of relevant structures, in which central circuitries causally related to depressive-like behaviour are implicated, has been identified. As most relevant structures emerge: medial prefrontal cortex, anterior cingulate cortex, amygdala, nucleus accumbens, ventral tegmental area, hippocampus and raphe nuclei. Further evidence, though examined by only few studies, emerges for structures like the lateral habenula, or medial dorsal thalamus. Most of the identified brain areas have previously been associated with MDD neuropathology, but now evidence can be provided for causal pathological mechanisms within a complex cortico-limbic reward circuitry. However, the studies also show conflicting results concerning the mechanisms underlying the causal involvement of specific circuitries. Comparability of studies is partly limited since even small deviations in methodological approaches lead to different outcomes. Factors influencing study outcomes were identified and need to be considered in future studies (e.g. frequency used for stimulation, time and duration of stimulation, limitations of applied animal models of MDD).

Prefrontal cortex circuits in depression and anxiety: contribution of discrete neuronal populations and target regions

Our understanding of depression and its treatment has advanced with the advent of ketamine as a rapid-acting antidepressant and the development and refinement of tools capable of selectively altering the activity of populations of neuronal subtypes. This work has resulted in a paradigm shift away from dysregulation of single neurotransmitter systems in depression towards circuit level abnormalities impacting function across multiple brain regions and neurotransmitter systems. Studies on the features of circuit level abnormalities demonstrate structural changes within the prefrontal cortex (PFC) and functional changes in its communication with distal brain structures. Treatments that impact the activity of brain regions, such as transcranial magnetic stimulation or rapid-acting antidepressants like ketamine, appear to reverse depression associated circuit abnormalities though the mechanisms underlying the reversal, as well as development of these abnormalities remains unclear. Recently developed optogenetic and chemogenetic tools that allow high-fidelity control of neuronal activity in preclinical models have begun to elucidate the contributions of the PFC and its circuitry to depression- and anxiety-like behavior. These tools offer unprecedented access to specific circuits and neuronal subpopulations that promise to offer a refined view of the circuit mechanisms surrounding depression and potential mechanistic targets for development and reversal of depression associated circuit abnormalities.

Where is Cingulate Cortex? A Cross-Species View

To compare findings across species, neuroscience relies on cross-species homologies, particularly in terms of brain areas. For cingulate cortex, a structure implicated in behavioural adaptation and control, a homologous definition across mammals is available - but currently not employed by most rodent researchers. The standard partitioning of rodent cingulate cortex is inconsistent with that in any other model species, including humans. Reviewing the existing literature, we show that the homologous definition better aligns results of rodent studies with those of other species, and reveals a clearer structural and functional organisation within rodent cingulate cortex itself. Based on these insights, we call for widespread adoption of the homologous nomenclature, and reinterpretation of previous studies originally based on the nonhomologous partitioning of rodent cingulate cortex.

Oscillotherapeutics - Time-targeted interventions in epilepsy and beyond

Oscillatory brain activities support many physiological functions from motor control to cognition. Disruptions of the normal oscillatory brain activities are commonly observed in neurological and psychiatric disorders including epilepsy, Parkinson's disease, Alzheimer's disease, schizophrenia, anxiety/trauma-related disorders, major depressive disorders, and drug addiction. Therefore, these disorders can be considered as common oscillation defects despite having distinct behavioral manifestations and genetic causes. Recent technical advances of neuronal activity recording and analysis have allowed us to study the pathological oscillations of each disorder as a possible biomarker of symptoms. Furthermore, recent advances in brain stimulation technologies enable time- and space-targeted interventions of the pathological oscillations of both neurological disorders and psychiatric disorders as possible targets for regulating their symptoms.

A Shared Vision for Machine Learning in Neuroscience

With ever-increasing advancements in technology, neuroscientists are able to collect data in greater volumes and with finer resolution. The bottleneck in understanding how the brain works is consequently shifting away from the amount and type of data we can collect and toward what we actually do with the data. There has been a growing interest in leveraging this vast volume of data across levels of analysis, measurement techniques, and experimental paradigms to gain more insight into brain function. Such efforts are visible at an international scale, with the emergence of big data neuroscience initiatives, such as the BRAIN initiative (Bargmann et al., 2014), the Human Brain Project, the Human Connectome Project, and the National Institute of Mental Health's Research Domain Criteria initiative. With these large-scale projects, much thought has been given to data-sharing across groups (Poldrack and Gorgolewski, 2014; Sejnowski et al., 2014); however, even with such data-sharing initiatives, funding mechanisms, and infrastructure, there still exists the challenge of how to cohesively integrate all the data. At multiple stages and levels of neuroscience investigation, machine learning holds great promise as an addition to the arsenal of analysis tools for discovering how the brain works.