Brain-wide Electrical Spatiotemporal Dynamics Encode Depression Vulnerability

Towards a "universal translator" for neural dynamics at single-cell, single-spike resolution

Neuroscience research has made immense progress over the last decade, but our understanding of the brain remains fragmented and piecemeal: the dream of probing an arbitrary brain region and automatically reading out the information encoded in its neural activity remains out of reach. In this work, we build towards a first foundation model for neural spiking data that can solve a diverse set of tasks across multiple brain areas. We introduce a novel self-supervised modeling approach for population activity in which the model alternates between masking out and reconstructing neural activity across different time steps, neurons, and brain regions. To evaluate our approach, we design unsupervised and supervised prediction tasks using the International Brain Laboratory repeated site dataset, which is comprised of Neuropixels recordings targeting the same brain locations across 48 animals and experimental sessions. The prediction tasks include single-neuron and region-level activity prediction, forward prediction, and behavior decoding. We demonstrate that our multi-task-masking (MtM) approach significantly improves the performance of current state-of-the-art population models and enables multi-task learning. We also show that by training on multiple animals, we can improve the generalization ability of the model to unseen animals, paving the way for a foundation model of the brain at single-cell, single-spike resolution. Project page and code: https://ibl-mtm.github.io/.

Neural effects of dopaminergic compounds revealed by multi-site electrophysiology and interpretable machine-learning

Background

Neuropsychopharmacological compounds may exert complex brain-wide effects due to an anatomically and genetically broad expression of their molecular targets and indirect effects via interconnected brain circuits. Electrophysiological measurements in multiple brain regions using electroencephalography (EEG) or local field potential (LFP) depth-electrodes may record fingerprints of such pharmacologically-induced changes in local activity and interregional connectivity (pEEG/pLFP). However, in order to reveal such patterns comprehensively and potentially derive mechanisms of therapeutic pharmacological effects, both activity and connectivity have to be estimated for many brain regions. This entails the problem that hundreds of electrophysiological parameters are derived from a typically small number of subjects, making frequentist statistics ill-suited for their analysis.

Methods

We here present an optimized interpretable machine-learning (ML) approach which relies on predictive power in individual recording sequences to extract and quantify the robustness of compound-induced neural changes from multi-site recordings using Shapley additive explanations (SHAP) values. To evaluate this approach, we recorded LFPs in mediodorsal thalamus (MD), prefrontal cortex (PFC), dorsal hippocampus (CA1 and CA3), and ventral hippocampus (vHC) of mice after application of amphetamine or of the dopaminergic antagonists clozapine, raclopride, or SCH23390, for which effects on directed neural communication between those brain structures were so far unknown.

Results

Our approach identified complex patterns of neurophysiological changes induced by each of these compounds, which were reproducible across time intervals, doses (where tested), and ML algorithms. We found, for example, that the action of clozapine in the analysed cortico-thalamo-hippocampal network entails a larger share of D1-as opposed to D2-receptor induced effects, and that the D2-antagonist raclopride reconfigures connectivity in the delta-frequency band. Furthermore, the effects of amphetamine and clozapine were surprisingly similar in terms of decreasing thalamic input to PFC and vHC, and vHC activity, whereas an increase of dorsal-hippocampal communication and of thalamic activity distinguished amphetamine from all tested anti-dopaminergic drugs.

Conclusion

Our study suggests that communication from the dorsal hippocampus scales proportionally with dopamine receptor activation and demonstrates, more generally, the high complexity of neuropharmacological effects on the circuit level. We envision that the presented approach can aid in the standardization and improved data extraction in pEEG/pLFP-studies.

Resting-state dynamic functional connectivity in major depressive disorder: A systematic review

As a novel measure, dynamic functional connectivity (dFC) provides insight into the dynamic nature of brain networks and their interactions in resting-state, surpassing traditional static functional connectivity in pathological conditions such as depression. Since a comprehensive review is still lacking, we then reviewed forty-five eligible papers to explore pathological mechanisms of major depressive disorder (MDD) from perspectives including abnormal brain regions and functional networks, brain state, topological properties, relevant recognition, along with longitudinal studies. Though inconsistencies could be found, common findings are: (1) From different perspectives based on dFC, default-mode network (DMN) with its subregions exhibited a close relation to the pathological mechanism of MDD. (2) With a corrupted integrity within large-scale functional networks and imbalance between them, longer fraction time in a relatively weakly-connected state may be a possible property of MDD concerning its relation with DMN. Abnormal transition frequencies between states were correlated to the severity of MDD. (3) Including dynamic properties in topological network metrics enhanced recognition effect. In all, this review summarized its use for clinical diagnosis and treatment, elucidating the non-stationary of MDD patients' aberrant brain activity in the absence of stimuli and bringing new views into its underlying neuro mechanism.

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.

Neural Decoding and Feature Selection Techniques for Closed-Loop Control of Defensive Behavior

Objective

Many psychiatric disorders involve excessive avoidant or defensive behavior, such as avoidance in anxiety and trauma disorders or defensive rituals in obsessive-compulsive disorders. Developing algorithms to predict these behaviors from local field potentials (LFPs) could serve as foundational technology for closed-loop control of such disorders. A significant challenge is identifying the LFP features that encode these defensive behaviors.

Approach

We analyzed LFP signals from the infralimbic cortex and basolateral amygdala of rats undergoing tone-shock conditioning and extinction, standard for investigating defensive behaviors. We utilized a comprehensive set of neuro-markers across spectral, temporal, and connectivity domains, employing SHapley Additive exPlanations for feature importance evaluation within Light Gradient-Boosting Machine models. Our goal was to decode three commonly studied avoidance/defensive behaviors: freezing, bar-press suppression, and motion (accelerometry), examining the impact of different features on decoding performance.

Main results

Band power and band power ratio between channels emerged as optimal features across sessions. High-gamma (80-150 Hz) power, power ratios, and inter-regional correlations were more informative than other bands that are more classically linked to defensive behaviors. Focusing on highly informative features enhanced performance. Across 4 recording sessions with 16 subjects, we achieved an average coefficient of determination of 0.5357 and 0.3476, and Pearson correlation coefficients of 0.7579 and 0.6092 for accelerometry jerk and bar press rate, respectively. Utilizing only the most informative features revealed differential encoding between accelerometry and bar press rate, with the former primarily through local spectral power and the latter via inter-regional connectivity. Our methodology demonstrated remarkably low time complexity, requiring <110 ms for training and <1 ms for inference.

Significance

Our results demonstrate the feasibility of accurately decoding defensive behaviors with minimal latency, using LFP features from neural circuits strongly linked to these behaviors. This methodology holds promise for real-time decoding to identify physiological targets in closed-loop psychiatric neuromodulation.

Neurobiological basis of stress resilience

A majority of humans faced with severe stress maintain normal physiological and behavioral function, a process referred to as resilience. Such stress resilience has been modeled in laboratory animals and, over the past 15 years, has transformed our understanding of stress responses and how to approach the treatment of human stress disorders such as depression, post-traumatic stress disorder (PTSD), and anxiety disorders. Work in rodents has demonstrated that resilience to chronic stress is an active process that involves much more than simply avoiding the deleterious effects of the stress. Rather, resilience is mediated largely by the induction of adaptations that are associated uniquely with resilience. Such mechanisms of natural resilience in rodents are being characterized at the molecular, cellular, and circuit levels, with an increasing number being validated in human investigations. Such discoveries raise the novel possibility that treatments for human stress disorders, in addition to being geared toward reversing the damaging effects of stress, can also be based on inducing mechanisms of natural resilience in individuals who are inherently more susceptible. This review provides a progress report on this evolving field.

Transcriptomic Evaluation of a Stress Vulnerability Network Using Single-Cell RNA Sequencing in Mouse Prefrontal Cortex

BACKGROUND

Increased vulnerability to stress is a major risk factor for several mood disorders, including major depressive disorder. Although cellular and molecular mechanisms associated with depressive behaviors following stress have been identified, little is known about the mechanisms that confer the vulnerability that predisposes individuals to future damage from chronic stress.

METHODS

We used multisite in vivo neurophysiology in freely behaving male and female C57BL/6 mice (n = 12) to measure electrical brain network activity previously identified as indicating a latent stress vulnerability brain state. We combined this neurophysiological approach with single-cell RNA sequencing of the prefrontal cortex to identify distinct transcriptomic differences between groups of mice with inherent high and low stress vulnerability.

RESULTS

We identified hundreds of differentially expressed genes (padjusted < .05) across 5 major cell types in animals with high and low stress vulnerability brain network activity. This unique analysis revealed that GABAergic (gamma-aminobutyric acidergic) neuron gene expression contributed most to the network activity of the stress vulnerability brain state. Upregulation of mitochondrial and metabolic pathways also distinguished high and low vulnerability brain states, especially in inhibitory neurons. Importantly, genes that were differentially regulated with vulnerability network activity significantly overlapped (above chance) with those identified by genome-wide association studies as having single nucleotide polymorphisms significantly associated with depression as well as genes more highly expressed in postmortem prefrontal cortex of patients with major depressive disorder.

CONCLUSIONS

This is the first study to identify cell types and genes involved in a latent stress vulnerability state in the brain.

Ketamine reverses stress-induced hypersensitivity to sunk costs

How mood interacts with information processing in the brain is thought to mediate the maladaptive behaviors observed in depressed individuals. However, the neural mechanisms underlying impairments in emotion-cognition interactions are poorly understood. This includes influencing the balance between how past-sensitive vs. future-looking one is during decision-making. Recent insights from the field of neuroeconomics offer novel approaches to study changes in such valuation processes in a manner that is biologically tractable and readily translatable across species. We recently discovered that rodents are sensitive to "sunk costs" - a feature of higher cognition previously thought to be unique to humans. The sunk costs bias describes the phenomenon in which an individual overvalues and escalates commitment to continuing an ongoing endeavor, even if suboptimal, as a function of irrecoverable past (sunk) losses - information that, according to classic economic theory, should be ignored. In the present study, mice were exposed to chronic social defeat stress paradigm, a well-established animal model used for the study of depression. Mice were then tested on our longitudinal neuroeconomic foraging task, Restaurant Row. We found mice exposed to this severe stressor displayed an increased sensitivity to sunk costs, without altering overall willingness to wait. Mice were then randomly assigned to receive a single intraperitoneal injection of either saline or ketamine (20 mg/kg). We discovered that stress-induced hypersensitivity to sunk costs was renormalized following a single dose of ketamine. Interestingly, in non-defeated mice, ketamine treatment completely abolished sunk cost sensitivity, causing mice to no longer value irrecoverable losses during re-evaluation decisions who instead based choices solely on the future investment required to obtain a goal. These findings suggest that the antidepressant effects of ketamine may be mediated in part through changes in the processing of past-sensitive information during on-going decision-making, reducing its weight as a potential source of cognitive dissonance that could modulate behavior and instead promoting more future-thinking behavior.

Sustained antidepressant effects of ketamine metabolite involve GABAergic inhibition-mediated molecular dynamics in aPVT glutamatergic neurons

Despite the rapid and sustained antidepressant effects of ketamine and its metabolites, their underlying cellular and molecular mechanisms are not fully understood. Here, we demonstrate that the sustained antidepressant-like behavioral effects of (2S,6S)-hydroxynorketamine (HNK) in repeatedly stressed animal models involve neurobiological changes in the anterior paraventricular nucleus of the thalamus (aPVT). Mechanistically, (2S,6S)-HNK induces mRNA expression of extrasynaptic GABAA receptors and subsequently enhances GABAA-receptor-mediated tonic currents, leading to the nuclear export of histone demethylase KDM6 and its replacement by histone methyltransferase EZH2. This process increases H3K27me3 levels, which in turn suppresses the transcription of genes associated with G-protein-coupled receptor signaling. Thus, our findings shed light on the comprehensive cellular and molecular mechanisms in aPVT underlying the sustained antidepressant behavioral effects of ketamine metabolites. This study may support the development of potentially effective next-generation pharmacotherapies to promote sustained remission of stress-related psychiatric disorders.

Discrete prefrontal neuronal circuits determine repeated stress-induced behavioral phenotypes in male mice

Chronic stress is a major risk factor for psychiatric disorders, including depression. Although depression is a highly heterogeneous syndrome, it remains unclear how chronic stress drives individual differences in behavioral responses. In this study, we developed a subtyping-based approach wherein stressed male mice were divided into four subtypes based on their behavioral patterns of social interaction deficits and anhedonia, the core symptoms of psychiatric disorders. We identified three prefrontal cortical neuronal projections that regulate repeated stress-induced behavioral phenotypes. Among them, the medial prefrontal cortex (mPFC)→anterior paraventricular thalamus (aPVT) pathway determines the specific behavioral subtype that exhibits both social deficits and anhedonia. Additionally, we identified the circuit-level molecular mechanism underlying this subtype: KDM5C-mediated epigenetic repression of Shisa2 transcription in aPVT projectors in the mPFC led to social deficits and anhedonia. Thus, we provide a set of biological aspects at the cellular, molecular, and epigenetic levels that determine distinctive stress-induced behavioral phenotypes.

Pharmacological inhibition of S6K1 rescues synaptic deficits and attenuates seizures and depression in chronic epileptic rats

BACKGROUND

Recent studies have shown that mTOR signaling plays an important role in synaptic plasticity. However, the function of S6K1, the mechanistic target of rapamycin kinase complex 1 (mTORC1) substrate, in epilepsy remains unknown.

AIMS

Our present study aimed to explore the mechanism by which S6K1 is involved in chronic epilepsy.

METHODS

First, immunostaining was used to measure neurite length and complexity in kainic acid (KA)-treated primary cultured neurons treated with PF-4708671, a highly selective S6K1 inhibitor. We obtained evidence for the role of S6K1 in protecting and promoting neuronal growth and development in vitro. Next, to explore the function and mechanism of the S6K1 inhibitor in epilepsy, a pilocarpine-induced chronic epileptic rat model was established. In vivo electrophysiology (including local field potentiation in CA1 and long-term potentiation), depression/anxiety-like behavior tests, and Golgi staining were performed to assess seizure behavior, power spectral density, depression/anxiety-like behavior, and synaptic plasticity. Furthermore, western blotting was applied to explore the potential molecular mechanisms.

RESULTS

We found that inhibition of S6K1 expression significantly decreased seizures and depression-like behavior and restored power at low frequencies (1-80 Hz), especially in the delta, theta, and alpha bands, in chronic epileptic rats. In addition, PF-4708671 reversed the LTP defect in hippocampal CA3-CA1 and corrected spine loss and dendritic pathology.

CONCLUSION

In conclusion, our data suggest that inhibition of S6K1 attenuates seizures and depression in chronic epileptic rats via the rescue of synaptic structural and functional deficits. Given the wide range of physiological functions of mTOR, inhibition of its effective but relatively simple functional downstream molecules is a promising target for the development of drugs for epilepsy.

Sex dependence of opioid-mediated responses to subanesthetic ketamine in rats

Subanesthetic ketamine is increasingly used for the treatment of varied psychiatric conditions, both on- and off-label. While it is commonly classified as an N-methyl D-aspartate receptor (NMDAR) antagonist, our picture of ketamine's mechanistic underpinnings is incomplete. Recent clinical evidence has indicated, controversially, that a component of the efficacy of subanesthetic ketamine may be opioid dependent. Using pharmacological functional ultrasound imaging in rats, we found that blocking opioid receptors suppressed neurophysiologic changes evoked by ketamine, but not by a more selective NMDAR antagonist, in limbic regions implicated in the pathophysiology of depression and in reward processing. Importantly, this opioid-dependent response was strongly sex-dependent, as it was not evident in female subjects and was fully reversed by surgical removal of the male gonads. We observed similar sex-dependent effects of opioid blockade affecting ketamine-evoked postsynaptic density and behavioral sensitization, as well as in opioid blockade-induced changes in opioid receptor density. Together, these results underscore the potential for ketamine to induce its affective responses via opioid signaling, and indicate that this opioid dependence may be strongly influenced by subject sex. These factors should be more directly assessed in future clinical trials.

Identifying and modulating neural signatures of stress susceptibility and resilience enables control of anhedonia

Anhedonia is a core aspect of major depressive disorder. Traditionally viewed as a blunted emotional state in which individuals are unable to experience joy, anhedonia also diminishes the drive to seek rewards and the ability to value and learn about them 1-4.The neural underpinnings of anhedonia and how this emotional state drives related behavioral changes remain unclear. Here, we investigated these questions by taking advantage of the fact that when mice are exposed to traumatic social stress, susceptible animals become socially withdrawn and anhedonic, where they cease to seek high-value rewards, while others remain resilient. By performing high density electrophysiological recordings and comparing neural activity patterns of these groups in the basolateral amygdala (BLA) and ventral CA1 (vCA1) of awake behaving animals, we identified neural signatures of susceptibility and resilience to anhedonia. When animals actively sought rewards, BLA activity in resilient mice showed stronger discrimination between upcoming reward choices. In contrast, susceptible mice displayed a rumination-like signature, where BLA neurons encoded the intention to switch or stay on a previously chosen reward. When animals were at rest, the spontaneous BLA activity of susceptible mice was higher dimensional than in controls, reflecting a greater number of distinct neural population states. Notably, this spontaneous activity allowed us to decode group identity and to infer if a mouse had a history of stress better than behavioral outcomes alone. Finally, targeted manipulation of vCA1 inputs to the BLA in susceptible mice rescued dysfunctional neural dynamics, amplified dynamics associated with resilience, and reversed their anhedonic behavior. This work reveals population-level neural signatures that explain individual differences in responses to traumatic stress, and suggests that modulating vCA1-BLA inputs can enhance resilience by regulating these dynamics.

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.

Chronic, Reusable, Multiday Neuropixels Recordings during Free-Moving Operant Behavior

Electrophysiological recording is a powerful technique to examine neuronal substrates underlying cognition and behavior. Neuropixels probes provide a unique capacity to capture neuronal activity across many brain areas with high spatiotemporal resolution. Neuropixels are also expensive and optimized for acute, head-fixed use, both of which limit the types of behaviors and manipulations that can be studied. Recent advances have addressed the cost issue by showing chronic implant, explant, and reuse of Neuropixels probes, but the methods were not optimized for use in free-moving behavior. There were specific needs for improvement in cabling/connection stability. Here, we extend that work to demonstrate chronic Neuropixels recording, explant, and reuse in a rat model during fully free-moving operant behavior. Similar to prior approaches, we house the probe and headstage within a 3D-printed housing that avoids direct fixation of the probe to the skull, enabling eventual explant. We demonstrate innovations to allow chronic headstage connection with protection against environmental factors and a more stable cabling setup to reduce the tension that can interrupt recording. We demonstrate this approach with rats performing two different behavioral tasks, in each case showing: (1) chronic single- or dual-probe recordings in free-moving rats in operant chambers and (2) reusability of Neuropixels 1.0 probes with continued good single-unit yield after retrieval and reimplant. We thus demonstrate the potential for Neuropixels recordings in a wider range of species and preparations.

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.

Spatial transcriptomic analysis of the mouse brain following chronic social defeat stress

Depression is a highly prevalent and disabling mental disorder, involving numerous genetic changes that are associated with abnormal functions in multiple regions of the brain. However, there is little transcriptomic-wide characterization of chronic social defeat stress (CSDS) to comprehensively compare the transcriptional changes in multiple brain regions. Spatial transcriptomics (ST) was used to reveal the spatial difference of gene expression in the control, resilient (RES) and susceptible (SUS) mouse brains, and annotated eight anatomical brain regions and six cell types. The gene expression profiles uncovered that CSDS leads to gene synchrony changes in different brain regions. Then it was identified that inhibitory neurons and synaptic functions in multiple regions were primarily affected by CSDS. The brain regions Hippocampus (HIP), Isocortex, and Amygdala (AMY) present more pronounced transcriptional changes in genes associated with depressive psychiatric disorders than other regions. Signalling communication between these three brain regions may play a critical role in susceptibility to CSDS. Taken together, this study provides important new insights into CSDS susceptibility at the ST level, which offers a new approach for understanding and treating depression.

Neural signatures of stress susceptibility and resilience in the amygdala-hippocampal network

The neural dynamics that underlie divergent anhedonic responses to stress remain unclear. Here, we identified neuronal dynamics in an amygdala-hippocampal circuit that distinguish stress resilience and susceptibility. In a reward-choice task, basolateral amygdala (BLA) activity in resilient mice showed enhanced discrimination of upcoming reward choices. In contrast, a rumination-like signature emerged in the BLA of susceptible mice; a linear decoder could classify the intention to switch or stay on a previously chosen reward. Spontaneous activity in the BLA of susceptible mice was higher dimensional than controls, reflecting the exploration of a larger number of distinct neural states. Manipulation of vCA1-BLA inputs rescued dysfunctional neural dynamics and anhedonia in susceptible mice, suggesting that targeting this pathway can enhance BLA circuit function and ameliorate of depression-related behaviors.

Cingulate dynamics track depression recovery with deep brain stimulation

Deep brain stimulation (DBS) of the subcallosal cingulate (SCC) can provide long-term symptom relief for treatment-resistant depression (TRD)1. However, achieving stable recovery is unpredictable2, typically requiring trial-and-error stimulation adjustments due to individual recovery trajectories and subjective symptom reporting3. We currently lack objective brain-based biomarkers to guide clinical decisions by distinguishing natural transient mood fluctuations from situations requiring intervention. To address this gap, we used a new device enabling electrophysiology recording to deliver SCC DBS to ten TRD participants (ClinicalTrials.gov identifier NCT01984710). At the study endpoint of 24 weeks, 90% of participants demonstrated robust clinical response, and 70% achieved remission. Using SCC local field potentials available from six participants, we deployed an explainable artificial intelligence approach to identify SCC local field potential changes indicating the patient's current clinical state. This biomarker is distinct from transient stimulation effects, sensitive to therapeutic adjustments and accurate at capturing individual recovery states. Variable recovery trajectories are predicted by the degree of preoperative damage to the structural integrity and functional connectivity within the targeted white matter treatment network, and are matched by objective facial expression changes detected using data-driven video analysis. Our results demonstrate the utility of objective biomarkers in the management of personalized SCC DBS and provide new insight into the relationship between multifaceted (functional, anatomical and behavioural) features of TRD pathology, motivating further research into causes of variability in depression treatment.

Influence of chronic stress on network states governing valence processing: Potential relevance to the risk for psychiatric illnesses

Stress is a major risk factor for psychiatric illnesses and understanding the mechanisms through which stress disrupts behavioral states is imperative to understanding the underlying pathophysiology of mood disorders. Both chronic stress and early life stress alter valence processing, the process of assigning value to sensory inputs and experiences (positive or negative), which determines subsequent behavior and is essential for emotional processing and ultimately survival. Stress disrupts valence processing in both humans and preclinical models, favoring negative valence processing and impairing positive valence processing. Valence assignment involves neural computations performed in emotional processing hubs, including the amygdala, prefrontal cortex, and ventral hippocampus, which can be influenced by neuroendocrine mediators. Oscillations within and between these regions are critical for the neural computations necessary to perform valence processing functions. Major advances in the field have demonstrated a role for oscillatory states in valence processing under physiological conditions and emerging studies are exploring how these network states are altered under pathophysiological conditions and impacted by neuroendocrine factors. The current review highlights what is currently known regarding the impact of stress and the role of neuroendocrine mediators on network states and valence processing. Further, we propose a model in which chronic stress alters information routing through emotional processing hubs, resulting in a facilitation of negative valence processing and a suppression of positive valence processing.

Network models to enhance the translational impact of cross-species studies

Neuroscience studies are often carried out in animal models for the purpose of understanding specific aspects of the human condition. However, the translation of findings across species remains a substantial challenge. Network science approaches can enhance the translational impact of cross-species studies by providing a means of mapping small-scale cellular processes identified in animal model studies to larger-scale inter-regional circuits observed in humans. In this Review, we highlight the contributions of network science approaches to the development of cross-species translational research in neuroscience. We lay the foundation for our discussion by exploring the objectives of cross-species translational models. We then discuss how the development of new tools that enable the acquisition of whole-brain data in animal models with cellular resolution provides unprecedented opportunity for cross-species applications of network science approaches for understanding large-scale brain networks. We describe how these tools may support the translation of findings across species and imaging modalities and highlight future opportunities. Our overarching goal is to illustrate how the application of network science tools across human and animal model studies could deepen insight into the neurobiology that underlies phenomena observed with non-invasive neuroimaging methods and could simultaneously further our ability to translate findings across species.

Single Cell Transcriptome of Stress Vulnerability Network in mouse Prefrontal Cortex

Increased vulnerability to stress is a major risk factor for the manifestation of several mood disorders, including major depressive disorder (MDD). Despite the status of MDD as a significant donor to global disability, the complex integration of genetic and environmental factors that contribute to the behavioral display of such disorders has made a thorough understanding of related etiology elusive. Recent developments suggest that a brain-wide network approach is needed, taking into account the complex interplay of cell types spanning multiple brain regions. Single cell RNA-sequencing technologies can provide transcriptomic profiling at the single-cell level across heterogenous samples. Furthermore, we have previously used local field potential oscillations and machine learning to identify an electrical brain network that is indicative of a predisposed vulnerability state. Thus, this study combined single cell RNA-sequencing (scRNA-Seq) with electrical brain network measures of the stress-vulnerable state, providing a unique opportunity to access the relationship between stress network activity and transcriptomic changes within individual cell types. We found especially high numbers of differentially expressed genes between animals with high and low stress vulnerability brain network activity in astrocytes and glutamatergic neurons but we estimated that vulnerability network activity depends most on GABAergic neurons. High vulnerability network activity included upregulation of microglia and mitochondrial and metabolic pathways, while lower vulnerability involved synaptic regulation. Genes that were differentially regulated with vulnerability network activity significantly overlapped with genes identified as having significant SNPs by human GWAS for depression. Taken together, these data provide the gene expression architecture of a previously uncharacterized stress vulnerability brain state, enabling new understanding and intervention of predisposition to stress susceptibility.

Reinstating olfactory bulb-derived limbic gamma oscillations alleviates depression-like behavioral deficits in rodents

Although the etiology of major depressive disorder remains poorly understood, reduced gamma oscillations is an emerging biomarker. Olfactory bulbectomy, an established model of depression that reduces limbic gamma oscillations, suffers from non-specific effects of structural damage. Here, we show that transient functional suppression of olfactory bulb neurons or their piriform cortex efferents decreased gamma oscillation power in limbic areas and induced depression-like behaviors in rodents. Enhancing transmission of gamma oscillations from olfactory bulb to limbic structures by closed-loop electrical neuromodulation alleviated these behaviors. By contrast, silencing gamma transmission by anti-phase closed-loop stimulation strengthened depression-like behaviors in naive animals. These induced behaviors were neutralized by ketamine treatment that restored limbic gamma power. Taken together, our results reveal a causal link between limbic gamma oscillations and depression-like behaviors in rodents. Interfering with these endogenous rhythms can affect behaviors in rodent models of depression, suggesting that restoring gamma oscillations may alleviate depressive symptoms.

Anxiety and depression: A top-down, bottom-up model of circuit function

A functional interplay of bottom-up and top-down processing allows an individual to appropriately respond to the dynamic environment around them. These processing modalities can be represented as attractor states using a dynamical systems model of the brain. The transition probability to move from one attractor state to another is dependent on the stability, depth, neuromodulatory tone, and tonic changes in plasticity. However, how does the relationship between these states change in disease states, such as anxiety or depression? We describe bottom-up and top-down processing from Marr's computational-algorithmic-implementation perspective to understand depressive and anxious disease states. We illustrate examples of bottom-up processing as basolateral amygdala signaling and projections and top-down processing as medial prefrontal cortex internal signaling and projections. Understanding these internal processing dynamics can help us better model the multifaceted elements of anxiety and depression.

Why is everyone talking about brain state?

The rapid and coordinated propagation of neural activity across the brain provides the foundation for complex behavior and cognition. Technical advances across neuroscience subfields have advanced understanding of these dynamics, but points of convergence are often obscured by semantic differences, creating silos of subfield-specific findings. In this review we describe how a parsimonious conceptualization of brain state as the fundamental building block of whole-brain activity offers a common framework to relate findings across scales and species. We present examples of the diverse techniques commonly used to study brain states associated with physiology and higher-order cognitive processes, and discuss how integration across them will enable a more comprehensive and mechanistic characterization of the neural dynamics that are crucial to survival but are disrupted in disease.

Ketamine rescues anhedonia by cell-type and input specific adaptations in the Nucleus Accumbens

Ketamine's role in providing a rapid and sustained antidepressant response, particularly for patients unresponsive to conventional treatments, is increasingly recognized. A core symptom of depression, anhedonia, or the loss of enjoyment or interest in previously pleasurable activities, is known to be significantly alleviated by ketamine. While several hypotheses have been proposed regarding the mechanisms by which ketamine alleviates anhedonia, the specific circuits and synaptic changes responsible for its sustained therapeutic effects are not yet understood. Here, we show that the nucleus accumbens (NAc), a major hub of the reward circuitry, is essential for ketamine's effect in rescuing anhedonia in mice subjected to chronic stress, a critical risk factor in the genesis of depression in humans. Specifically, a single exposure to ketamine rescues stress-induced decreased strength of excitatory synapses on NAc D1 dopamine receptor-expressing medium spiny neurons (D1-MSNs). By using a novel cell-specific pharmacology method, we demonstrate that this cell-type specific neuroadaptation is necessary for the sustained therapeutic effects of ketamine. To test for causal sufficiency, we artificially mimicked ketamine-induced increase in excitatory strength on D1-MSNs and found that this recapitulates the behavioral amelioration induced by ketamine. Finally, to determine the presynaptic origin of the relevant glutamatergic inputs for ketamine-elicited synaptic and behavioral effects, we used a combination of opto- and chemogenetics. We found that ketamine rescues stress-induced reduction in excitatory strength at medial prefrontal cortex and ventral hippocampus inputs to NAc D1-MSNs. Chemogenetically preventing ketamine-evoked plasticity at those unique inputs to the NAc reveals a ketamine-operated input-specific control of hedonic behavior. These results establish that ketamine rescues stress-induced anhedonia via cell-type-specific adaptations as well as information integration in the NAc via discrete excitatory synapses.

Regulation of social interaction in mice by a frontostriatal circuit modulated by established hierarchical relationships

Social hierarchies exert a powerful influence on behavior, but the neurobiological mechanisms that detect and regulate hierarchical interactions are not well understood, especially at the level of neural circuits. Here, we use fiber photometry and chemogenetic tools to record and manipulate the activity of nucleus accumbens-projecting cells in the ventromedial prefrontal cortex (vmPFC-NAcSh) during tube test social competitions. We show that vmPFC-NAcSh projections signal learned hierarchical relationships, and are selectively recruited by subordinate mice when they initiate effortful social dominance behavior during encounters with a dominant competitor from an established hierarchy. After repeated bouts of social defeat stress, this circuit is preferentially activated during social interactions initiated by stress resilient individuals, and plays a necessary role in supporting social approach behavior in subordinated mice. These results define a necessary role for vmPFC-NAcSh cells in the adaptive regulation of social interaction behavior based on prior hierarchical interactions.

Hippocampal sharp wave ripples underlie stress susceptibility in male mice

The ventral hippocampus (vHC) is a core brain region for emotional memory. Here, we examined how the vHC regulates stress susceptibility from the level of gene expression to neuronal population dynamics in male mice. Transcriptome analysis of samples from stress-naïve mice revealed that intrinsic calbindin (Calb1) expression in the vHC is associated with susceptibility to social defeat stress. Mice with Calb1 gene knockdown in the vHC exhibited increased stress resilience and failed to show the increase in the poststress ventral hippocampal sharp wave ripple (SWR) rate. Poststress vHC SWRs triggered synchronous reactivation of stress memory-encoding neuronal ensembles and facilitated information transfer to the amygdala. Suppression of poststress vHC SWRs by real-time feedback stimulation or walking prevented social behavior deficits. Taken together, our results demonstrate that internal reactivation of memories of negative stressful episodes supported by ventral hippocampal SWRs serves as a crucial neurophysiological substrate for determining stress susceptibility.

The role of astrocytes in behaviors related to emotion and motivation

Astrocytes are present throughout the brain and intimately interact with neurons and blood vessels. Three decades of research have shown that astrocytes reciprocally communicate with neurons and other non-neuronal cells in the brain and dynamically regulate cell function. Astrocytes express numerous receptors for neurotransmitters, neuromodulators, and cytokines and receive information from neurons, other astrocytes, and other non-neuronal cells. Among those receptors, the main focus has been G-protein coupled receptors. Activation of G-protein coupled receptors leads to dramatic changes in intracellular signaling (Ca²⁺ and cAMP), which is considered a form of astrocyte activity. Methodological improvements in measurement and manipulation of astrocytes have advanced our understanding of the role of astrocytes in circuits and have begun to reveal unexpected functions of astrocytes in behavior. Recent studies have suggested that astrocytic activity regulates behavior flexibility, such as coping strategies for stress exposure, and plays an important role in behaviors related to emotion and motivation. Preclinical evidence suggests that impairment of astrocytic function contributes to psychiatric diseases, especially major depression. Here, we review recent progress on the role of astrocytes in behaviors related to emotion and motivation.

Abnormal response to chronic social defeat stress and fear extinction in a mouse model of cholinergic dysregulation

Cholinergic signaling is critical for an individual to react appropriately and adaptably to salient stimuli while navigating a complex environment. The cholinergic neurotransmitter system drives attention to salient stimuli, such as stressors, and aids in orchestrating the proper neural and behavioral response. Fine-tuned regulation of the cholinergic system has been linked to appropriate stress responses and subsequent mood regulation while dysregulation has been implicated in mood disorders. Among the multiple layers of regulation are cholinergic protein modulators. Here, we use validated models of experiential-based affective disorders to investigate differences in responses to stress in a genetic mouse model of cholinergic dysregulation based on the loss of protein modulator. The lynx2 nicotinic receptor modulatory protein provides negative cholinergic regulation within the amygdala, medial prefrontal cortex, and other brain regions. We discovered here that lynx2 knockout (KO) mice demonstrate an inability to update behavior with an inability to extinguish learned fear during a fear extinction test. We also observed, under an increased stress load following exposure to chronic social defeat stress (CSDS) paradigm, there was a unified resilience phenotype in lynx2KO mice, as opposed to the wild-type cohort which was split between resilience and susceptible phenotypes. Furthermore, we provide evidence for the functional role of α7 nicotinic receptor subtypes by phenotypic rescue with MLA or crossing with an α7 null mutant mouse (e.g. lynx2/α7 double KO mice). We demonstrate a direct physical interaction between lynx2 and α7 nAChR by co-immunoprecipitation of complexes from mouse BLA extracts. The genetic predisposition to heightened basal anxiety-like behavior and altered cholinergic signaling impairs individual behavior responses stressors. Together, these data indicate that the effects of social stress can be influenced by baseline genetic factors involved in anxiety regulation.

Ensemble graph neural network model for classification of major depressive disorder using whole-brain functional connectivity

Major depressive disorder (MDD) is characterized by impairments in mood and cognitive functioning, and it is a prominent source of global disability and stress. A functional magnetic resonance imaging (fMRI) can aid clinicians in their assessments of individuals for the identification of MDD. Herein, we employ a deep learning approach to the issue of MDD classification. Resting-state fMRI data from 821 individuals with MDD and 765 healthy controls (HCs) is employed for investigation. An ensemble model based on graph neural network (GNN) has been created with the goal of identifying patients with MDD among HCs as well as differentiation between first-episode and recurrent MDDs. The graph convolutional network (GCN), graph attention network (GAT), and GraphSAGE models serve as a base models for the ensemble model that was developed with individual whole-brain functional networks. The ensemble's performance is evaluated using upsampling and downsampling, along with 10-fold cross-validation. The ensemble model achieved an upsampling accuracy of 71.18% and a downsampling accuracy of 70.24% for MDD and HC classification. While comparing first-episode patients with recurrent patients, the upsampling accuracy is 77.78% and the downsampling accuracy is 71.96%. According to the findings of this study, the proposed GNN-based ensemble model achieves a higher level of accuracy and suggests that our model produces can assist healthcare professionals in identifying MDD.

Neural circuits regulating prosocial behaviors

Positive, prosocial interactions are essential for survival, development, and well-being. These intricate and complex behaviors are mediated by an amalgamation of neural circuit mechanisms working in concert. Impairments in prosocial behaviors, which occur in a large number of neuropsychiatric disorders, result from disruption of the coordinated activity of these neural circuits. In this review, we focus our discussion on recent findings that utilize modern approaches in rodents to map, monitor, and manipulate neural circuits implicated in a variety of prosocial behaviors. We highlight how modulation by oxytocin, serotonin, and dopamine of excitatory and inhibitory synaptic transmission in specific brain regions is critical for regulation of adaptive prosocial interactions. We then describe how recent findings have helped elucidate pathophysiological mechanisms underlying the social deficits that accompany neuropsychiatric disorders. We conclude by discussing approaches for the development of more efficacious and targeted therapeutic interventions to ameliorate aberrant prosocial behaviors.

The hippocampus in stress susceptibility and resilience: Reviewing molecular and functional markers

Understanding the individual variability that comes with the likelihood of developing stress-related psychopathologies is of paramount importance when addressing mechanisms of their neurobiology. This article focuses on the hippocampus as a region that is highly influenced by chronic stress exposure and that has strong ties to the development of related disorders, such as depression and post-traumatic stress disorder. We first outline three commonly used animal models that have been used to separate animals into susceptible and resilient cohorts. Next, we review molecular and functional hippocampal markers of susceptibility and resilience. We propose that the hippocampus plays a crucial role in the differences in the processing and storage of stress-related information in animals with different stress susceptibilities. These hippocampal markers not only help us attain a more comprehensive understanding of the various facets of stress-related pathophysiology, but also could be targeted for the development of new treatments.

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.

Metrics of high cofluctuation and entropy to describe control of cardiac function in the stellate ganglion

Stellate ganglia within the intrathoracic cardiac control system receive and integrate central, peripheral, and cardiopulmonary information to produce postganglionic cardiac sympathetic inputs. Pathological anatomical and structural remodeling occurs within the neurons of the stellate ganglion (SG) in the setting of heart failure (HF). A large proportion of SG neurons function as interneurons whose networking capabilities are largely unknown. Current therapies are limited to targeting sympathetic activity at the cardiac level or surgical interventions such as stellectomy, to treat HF. Future therapies that target the SG will require understanding of their networking capabilities to modify any pathological remodeling. We observe SG networking by examining cofluctuation and specificity of SG networked activity to cardiac cycle phases. We investigate network processing of cardiopulmonary transduction by SG neuronal populations in porcine with chronic pacing-induced HF and control subjects during extended in-vivo extracellular microelectrode recordings. We find that information processing and cardiac control in chronic HF by the SG, relative to controls, exhibits: (i) more frequent, short-lived, high magnitude cofluctuations, (ii) greater variation in neural specificity to cardiac cycles, and (iii) neural network activity and cardiac control linkage that depends on disease state and cofluctuation magnitude.

Basal Forebrain Cholinergic Innervation Induces Depression-Like Behaviors Through Ventral Subiculum Hyperactivation

Malfunction of the ventral subiculum (vSub), the main subregion controlling the output connections from the hippocampus, is associated with major depressive disorder (MDD). Although the vSub receives cholinergic innervation from the medial septum and diagonal band of Broca (MSDB), whether and how the MSDB-to-vSub cholinergic circuit is involved in MDD is elusive. Here, we found that chronic unpredictable mild stress (CUMS) induced depression-like behaviors with hyperactivation of vSub neurons, measured by c-fos staining and whole-cell patch-clamp recording. By retrograde and anterograde tracing, we confirmed the dense MSDB cholinergic innervation of the vSub. In addition, transient restraint stress in CUMS increased the level of ACh in the vSub. Furthermore, chemogenetic stimulation of this MSDB-vSub innervation in ChAT-Cre mice induced hyperactivation of vSub pyramidal neurons along with depression-like behaviors; and local infusion of atropine, a muscarinic receptor antagonist, into the vSub attenuated the depression-like behaviors induced by chemogenetic stimulation of this pathway and CUMS. Together, these findings suggest that activating the MSDB-vSub cholinergic pathway induces hyperactivation of vSub pyramidal neurons and depression-like behaviors, revealing a novel circuit underlying vSub pyramidal neuronal hyperactivation and its associated depression.

Applying Deep Learning on a Few EEG Electrodes during Resting State Reveals Depressive States. A Data Driven Study

The growing number of depressive people and the overload in primary care services make it necessary to identify depressive states with easily accessible biomarkers such as mobile electroencephalography (EEG). Some studies have addressed this issue by collecting and analyzing EEG resting state in a search of appropriate features and classification methods. Traditionally, EEG resting state classification methods for depression were mainly based on linear or a combination of linear and non-linear features. We hypothesize that participants with ongoing depressive states differ from controls in complex patterns of brain dynamics that can be captured in EEG resting state data, using only nonlinear measures on a few electrodes, making it possible to develop cheap and wearable devices that could be even monitored through smartphones. To validate such a perspective, a resting-state EEG study was conducted with 50 participants, half with depressive state (DEP) and half controls (CTL). A data-driven approach was applied to select the most appropriate time window and electrodes for the EEG analyses, as suggested by Giacometti⁠, as well as the most efficient nonlinear features and classifiers, to distinguish between CTL and DEP participants. Nonlinear features showing temporo-spatial and spectral complexity were selected. The results confirmed that computing nonlinear features from a few selected electrodes in a 15 s time window are sufficient to classify DEP and CTL participants accurately. Finally, after training and testing internally the classifier, the trained machine was applied to EEG resting state data (CTL and DEP) from a publicly available database, validating the capacity of generalization of the classifier with data from different equipment, population, and environment obtaining an accuracy near 100%.

Solving brain circuit function and dysfunction with computational modeling and optogenetic fMRI

Can we construct a model of brain function that enables an understanding of whole-brain circuit mechanisms underlying neurological disease and use it to predict the outcome of therapeutic interventions? How are pathologies in neurological disease, some of which are observed to have spatial spreading mechanisms, associated with circuits and brain function? In this review, we discuss approaches that have been used to date and future directions that can be explored to answer these questions. By combining optogenetic functional magnetic resonance imaging (fMRI) with computational modeling, cell type-specific, large-scale brain circuit function and dysfunction are beginning to be quantitatively parameterized. We envision that these developments will pave the path for future therapeutics developments based on a systems engineering approach aimed at directly restoring brain function.

Behavioural and dopaminergic signatures of resilience

Chronic stress can have lasting adverse consequences in some individuals, yet others are resilient to the same stressor1,2. Susceptible and resilient individuals exhibit differences in the intrinsic properties of mesolimbic dopamine (DA) neurons after the stressful experience is over3-8. However, the causal links between DA, behaviour during stress and individual differences in resilience are unknown. Here we recorded behaviour in mice simultaneously with DA neuron activity in projections to the nucleus accumbens (NAc) (which signals reward9-12) and the tail striatum (TS) (which signals threat13-16) during social defeat. Supervised and unsupervised behavioural quantification revealed that during stress, resilient and susceptible mice use different behavioural strategies and have distinct activity patterns in DA terminals in the NAc (but not the TS). Neurally, resilient mice have greater activity near the aggressor, including at the onset of fighting back. Conversely, susceptible mice have greater activity at the offset of attacks and onset of fleeing. We also performed optogenetic stimulation of NAc-projecting DA neurons in open loop (randomly timed) during defeat or timed to specific behaviours using real-time behavioural classification. Both open-loop and fighting-back-timed activation promoted resilience and reorganized behaviour during defeat towards resilience-associated patterns. Together, these data provide a link between DA neural activity, resilience and resilience-associated behaviour during the experience of stress.

Enduring effects of early-life adversity on reward processes: A systematic review and meta-analysis of animal studies

Two-thirds of individuals experience adversity during childhood such as neglect, abuse or highly-stressful events. Early-life adversity (ELA) increases the life-long risk of developing mood and substance use disorders. Reward-related deficits has emerged as a key endophenotype of such psychiatric disorders. Animal models are invaluable for studying how ELA leads to reward deficits. However, the existing literature is heterogenous with difficult to reconcile findings. To create an overview, we conducted a systematic review containing multiple meta-analyses regarding the effects of ELA on reward processes overall and on specific aspects of reward processing in animal models. A comprehensive search identified 120 studies. Most studies omitted key details resulting in unclear risk of bias. Overall meta-analysis showed that ELA significantly reduced reward behaviors (SMD: -0.42 [-0.60; -0.24]). The magnitude of ELA effects significantly increased with longer exposure. When reward domains were analyzed separately, ELA only significantly dampened reward responsiveness (SMD: -0.525[-0.786; -0.264]) and social reward processing (SMD: -0.374 [-0.663; -0.084]), suggesting that ELA might lead to deficits in specific reward domains.

Multiregion neuronal activity: the forest and the trees

The past decade has witnessed remarkable advances in the simultaneous measurement of neuronal activity across many brain regions, enabling fundamentally new explorations of the brain-spanning cellular dynamics that underlie sensation, cognition and action. These recently developed multiregion recording techniques have provided many experimental opportunities, but thoughtful consideration of methodological trade-offs is necessary, especially regarding field of view, temporal acquisition rate and ability to guarantee cellular resolution. When applied in concert with modern optogenetic and computational tools, multiregion recording has already made possible fundamental biological discoveries - in part via the unprecedented ability to perform unbiased neural activity screens for principles of brain function, spanning dozens of brain areas and from local to global scales.

Functional Connectivity of the Nucleus Accumbens and Changes in Appetite in Patients With Depression

Importance

Major depressive disorder (MDD) is characterized by a substantial burden on health, including changes in appetite and body weight. Heterogeneity of depressive symptoms has hampered the identification of biomarkers that robustly generalize to most patients, thus calling for symptom-based mapping.

Objective

To define the functional architecture of the reward circuit subserving increases vs decreases in appetite and body weight in patients with MDD by specifying their contributions and influence on disease biomarkers using resting-state functional connectivity (FC).

Design, Setting, and Participants

In this case-control study, functional magnetic resonance imaging (fMRI) data were taken from the Marburg-Münster FOR 2107 Affective Disorder Cohort Study (MACS), collected between September 2014 and November 2016. Cross-sectional data of patients with MDD (n = 407) and healthy control participants (n = 400) were analyzed from March 2018 to June 2022.

Main Outcomes and Measures

Changes in appetite during the depressive episode and their association with FC were examined using fMRI. By taking the nucleus accumbens (NAcc) as seed of the reward circuit, associations with opposing changes in appetite were mapped, and a sparse symptom-specific elastic-net model was built with 10-fold cross-validation.

Results

Among 407 patients with MDD, 249 (61.2%) were women, and the mean (SD) age was 36.79 (13.4) years. Reduced NAcc-based FC to the ventromedial prefrontal cortex (vmPFC) and the hippocampus was associated with reduced appetite (vmPFC: bootstrap r = 0.13; 95% CI, 0.02-0.23; hippocampus: bootstrap r = 0.15; 95% CI, 0.05-0.26). In contrast, reduced NAcc-based FC to the insular ingestive cortex was associated with increased appetite (bootstrap r = -0.14; 95% CI, -0.24 to -0.04). Critically, the cross-validated elastic-net model reflected changes in appetite based on NAcc FC and explained variance increased with increasing symptom severity (all patients: bootstrap r = 0.24; 95% CI, 0.16-0.31; patients with Beck Depression Inventory score of 28 or greater: bootstrap r = 0.42; 95% CI, 0.25-0.58). In contrast, NAcc FC did not classify diagnosis (MDD vs healthy control).

Conclusions and Relevance

In this study, NAcc-based FC reflected important individual differences in appetite and body weight in patients with depression that can be leveraged for personalized prediction. However, classification of diagnosis using NAcc-based FC did not exceed chance levels. Such symptom-specific associations emphasize the need to map biomarkers onto more confined facets of psychopathology to improve the classification and treatment of MDD.

Non-rapid eye movement sleep determines resilience to social stress

Resilience, the ability to overcome stressful conditions, is found in most mammals and varies significantly among individuals. A lack of resilience can lead to the development of neuropsychiatric and sleep disorders, often within the same individual. Despite extensive research into the brain mechanisms causing maladaptive behavioral-responses to stress, it is not clear why some individuals exhibit resilience. To examine if sleep has a determinative role in maladaptive behavioral-response to social stress, we investigated individual variations in resilience using a social-defeat model for male mice. Our results reveal a direct, causal relationship between sleep amount and resilience-demonstrating that sleep increases after social-defeat stress only occur in resilient mice. Further, we found that within the prefrontal cortex, a regulator of maladaptive responses to stress, pre-existing differences in sleep regulation predict resilience. Overall, these results demonstrate that increased NREM sleep, mediated cortically, is an active response to social-defeat stress that plays a determinative role in promoting resilience. They also show that differences in resilience are strongly correlated with inter-individual variability in sleep regulation.

Early life maternal separation induces sex-specific antidepressant-like responses but has minimal effects on adult stress susceptibility in mice

Early life stress is known to increase the risk of depression and anxiety disorders, which are highly prevalent conditions that disproportionately affect women. However, the results of preclinical studies have been mixed, with some work suggesting that early life stress promotes anxiety-like behavior and/or increases susceptibility to subsequent stressors, and other research suggesting that early life stress reduces anxiety-like behavior and/or confers resilience to subsequent stress exposure. It is likely that factors such as sex and the timing and severity of early life and adult stress exposure dictate whether a particular early life experience promotes adaptive vs. maladaptive behavior later in life. Most work in this area has focused exclusively on males, but several sex differences in the effects of early life stress on subsequent stress susceptibility have been reported. The current study examined the impact of early life maternal separation on susceptibility to behavioral alterations induced by 3 days of variable stress in adulthood in male and female c57BL6 mice. Our results indicate that 3 days of adult stress is sufficient to increase anxiety-like behavior in several paradigms and to increase immobility in the forced swim test. In contrast, a history of maternal separation reduces anxiety-like behavior in several tests, particularly in males. These findings could contribute to our understanding of sex differences in mental illness by demonstrating that males are more likely than females to display adaptive responses to mild early life stressors.

Brain-phenotype models fail for individuals who defy sample stereotypes

Individual differences in brain functional organization track a range of traits, symptoms and behaviours1-12. So far, work modelling linear brain-phenotype relationships has assumed that a single such relationship generalizes across all individuals, but models do not work equally well in all participants13,14. A better understanding of in whom models fail and why is crucial to revealing robust, useful and unbiased brain-phenotype relationships. To this end, here we related brain activity to phenotype using predictive models-trained and tested on independent data to ensure generalizability15-and examined model failure. We applied this data-driven approach to a range of neurocognitive measures in a new, clinically and demographically heterogeneous dataset, with the results replicated in two independent, publicly available datasets16,17. Across all three datasets, we find that models reflect not unitary cognitive constructs, but rather neurocognitive scores intertwined with sociodemographic and clinical covariates; that is, models reflect stereotypical profiles, and fail when applied to individuals who defy them. Model failure is reliable, phenotype specific and generalizable across datasets. Together, these results highlight the pitfalls of a one-size-fits-all modelling approach and the effect of biased phenotypic measures18-20 on the interpretation and utility of resulting brain-phenotype models. We present a framework to address these issues so that such models may reveal the neural circuits that underlie specific phenotypes and ultimately identify individualized neural targets for clinical intervention.

Encore: Behavioural animal models of stress, depression and mood disorders

Animal studies over the past two decades have led to extensive advances in our understanding of pathogenesis of depressive and mood disorders. Among these, rodent behavioural models proved to be of highest informative value. Here, we present a comprehensive overview of the most popular behavioural models with respect to physiological, circuit, and molecular biological correlates. Behavioural stress paradigms and behavioural tests are assessed in terms of outcomes, strengths, weaknesses, and translational value, especially in the domain of pharmacological studies.

Memory-related neurophysiological mechanisms in the hippocampus underlying stress susceptibility

Stress-induced psychiatric symptoms, such as increased anxiety, decreased sociality, and depression, differ considerably across individuals. The cognitive model of depression proposes that biased negative memory is a crucial determinant in the development of mental stress-induced disorders. Accumulating evidence from both clinical and animal studies has demonstrated that such biased memory processing could be triggered by the hippocampus, a region well known to be involved in declarative memories. This review mainly describes how memory-related neurophysiological mechanisms in the hippocampus and their interactions with other related brain regions are involved in the regulation of stress susceptibility and discusses potential interventions to prevent and treat stress-related psychiatric symptoms. Further neurophysiological insights based on memory mechanisms are expected to devise personalized prevention and therapy to confer stress resilience.

Spatiotemporal Dynamics of Stress-Induced Network Reconfigurations Reflect Negative Affectivity

BACKGROUND

Maladaptive stress responses are important risk factors in the etiology of mood and anxiety disorders, but exact pathomechanisms remain to be understood. Mapping individual differences of acute stress-induced neurophysiological changes, especially on the level of neural activation and functional connectivity (FC), could provide important insights in how variation in the individual stress response is linked to disease risk.

METHODS

Using an established psychosocial stress task flanked by two resting states, we measured subjective, physiological, and brain responses to acute stress and recovery in 217 participants with and without mood and anxiety disorders. To estimate blockwise changes in stress-induced activation and FC, we used hierarchical mixed-effects models based on denoised time series within predefined stress-related regions. We predicted inter- and intraindividual differences in stress phases (anticipation vs. stress vs. recovery) and transdiagnostic dimensions of stress reactivity using elastic net and support vector machines.

RESULTS

We identified four subnetworks showing distinct changes in FC over time. FC but not activation trajectories predicted the stress phase (accuracy = 70%, p_perm < .001) and increases in heart rate (R² = 0.075, p_perm < .001). Critically, individual spatiotemporal trajectories of changes across networks also predicted negative affectivity (ΔR² = 0.075, p_perm = .030) but not the presence or absence of a mood and anxiety disorder.

CONCLUSIONS

Spatiotemporal dynamics of brain network reconfiguration induced by stress reflect individual differences in the psychopathology dimension of negative affectivity. These results support the idea that vulnerability for mood and anxiety disorders can be conceptualized best at the level of network dynamics, which may pave the way for improved prediction of individual risk.

Synaptic Mechanisms Regulating Mood State Transitions in Depression

Depression is an episodic form of mental illness characterized by mood state transitions with poorly understood neurobiological mechanisms. Antidepressants reverse the effects of stress and depression on synapse function, enhancing neurotransmission, increasing plasticity, and generating new synapses in stress-sensitive brain regions. These properties are shared to varying degrees by all known antidepressants, suggesting that synaptic remodeling could play a key role in depression pathophysiology and antidepressant function. Still, it is unclear whether and precisely how synaptogenesis contributes to mood state transitions. Here, we review evidence supporting an emerging model in which depression is defined by a distinct brain state distributed across multiple stress-sensitive circuits, with neurons assuming altered functional properties, synapse configurations, and, importantly, a reduced capacity for plasticity and adaptation. Antidepressants act initially by facilitating plasticity and enabling a functional reconfiguration of this brain state. Subsequently, synaptogenesis plays a specific role in sustaining these changes over time.