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.

Prenatal environmental stressors impair postnatal microglia function and adult behavior in males.

Gestational exposure to environmental toxins and socioeconomic stressors is epidemiologically linked to neurodevelopmental disorders with strong male bias, such as autism. We model these prenatal risk factors in mice by co-exposing pregnant dams to an environmental pollutant and limited-resource stress, which robustly activates the maternal immune system. Only male offspring display long-lasting behavioral abnormalities and alterations in the activity of brain networks encoding social interactions. Cellularly, prenatal stressors diminish microglial function within the anterior cingulate cortex, a central node of the social coding network, in males during early postnatal development. Precise inhibition of microglial phagocytosis within the anterior cingulate cortex (ACC) of wild-type (WT) mice during the same critical period mimics the impact of prenatal stressors on a male-specific behavior, indicating that environmental stressors alter neural circuit formation in males via impairing microglia function during development.

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.

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.

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.

Distribution of FMRFamide-related peptides and co-localization with glutamate in Cupiennius salei, an invertebrate model system.

FMRFamide-related proteins have been described in both vertebrate and invertebrate nervous systems and have been suggested to play important roles in a variety of physiological processes. One proposed function is the modulation of signal transduction in mechanosensory neurons and their associated behavioral pathways in the Central American wandering spider Cupiennius salei; however, little is known about the distribution and abundance of FMRFamide-related proteins (FaRPs) within this invertebrate system. We employ immunohistochemistry, Hoechst nuclear stain and confocal microscopy of serial sections to detect, characterize and quantify FMRFamide-like immunoreactive neurons throughout all ganglia of the spider brain and along leg muscle. Within the different ganglia, between 3.4 and 12.6% of neurons showed immunolabeling. Among the immunoreactive cells, weakly and strongly labeled neurons could be distinguished. Between 71.4 and 81.7% of labeled neurons showed weak labeling, with 18.3 to 28.6% displaying strong labeling intensity. Among the weakly labeled neurons were characteristic motor neurons that have previously been shown to express ɣ-aminobutyric acid or glutamate. Ultrastructural investigations of neuromuscular junctions revealed mixed presynaptic vesicle populations including large electron-dense vesicles characteristic of neuropeptides. Double labeling for glutamate and FaRPs indicated that a subpopulation of neurons may co-express both neuroactive compounds. Our findings suggest that FaRPs are expressed throughout all ganglia and that different neurons have different expression levels. We conclude that FaRPs are likely utilized as neuromodulators in roughly 8% of neurons in the spider nervous system and that the main transmitter in a subpopulation of these neurons is likely glutamate.

Brain-wide Electrical Spatiotemporal Dynamics Encode Depression Vulnerability.

Brain-wide fluctuations in local field potential oscillations reflect emergent network-level signals that mediate behavior. Cracking the code whereby these oscillations coordinate in time and space (spatiotemporal dynamics) to represent complex behaviors would provide fundamental insights into how the brain signals emotional pathology. Using machine learning, we discover a spatiotemporal dynamic network that predicts the emergence of major depressive disorder (MDD)-related behavioral dysfunction in mice subjected to chronic social defeat stress. Activity patterns in this network originate in prefrontal cortex and ventral striatum, relay through amygdala and ventral tegmental area, and converge in ventral hippocampus. This network is increased by acute threat, and it is also enhanced in three independent models of MDD vulnerability. Finally, we demonstrate that this vulnerability network is biologically distinct from the networks that encode dysfunction after stress. Thus, these findings reveal a convergent mechanism through which MDD vulnerability is mediated in the brain.

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

BACKGROUND: The prefrontal cortex plays a critical role in regulating emotional behaviors, and dysfunction of prefrontal cortex-dependent networks has been broadly implicated in mediating stress-induced behavioral disorders including major depressive disorder. METHODS: Here we acquired multicircuit in vivo activity from eight cortical and limbic brain regions as mice were subjected to the tail suspension test (TST) and an open field test. We used a linear decoder to determine whether cellular responses across each of the cortical and limbic areas signal movement during the TST and open field test. We then performed repeat behavioral testing to identify which brain areas show cellular adaptations that signal the increase in immobility induced by repeat TST exposure. RESULTS: The increase in immobility observed during repeat TST exposure is linked to a selective functional upregulation of cellular activity in infralimbic cortex and medial dorsal thalamus, and to an increase in the spatiotemporal dynamic interaction between these structures. Inducing this spatiotemporal dynamic using closed-loop optogenetic stimulation is sufficient to increase movement in the TST in stress-naive mice, while stimulating above the carrier frequency of this circuit suppressed movement. This demonstrates that the adaptations in infralimbic cortex-medial dorsal thalamus circuitry observed after stress reflect a compensatory mechanism whereby the brain drives neural systems to counterbalance stress effects. CONCLUSIONS: Our findings provide evidence that targeting endogenous spatiotemporal dynamics is a potential therapeutic approach for treating stress-induced behavioral disorders, and that dynamics are a critical axis of manipulation for causal optogenetic studies.