A novel miR-99b-5p-Zbp1 pathway in microglia contributes to the pathogenesis of schizophrenia
Schizophrenia is a psychiatric disorder that is still not readily treatable. Pharmaceutical advances in the treatment of schizophrenia have mainly focused on the protein coding part of the human genome. However, the vast majority of the human transcriptome consists of non-coding RNAs. MicroRNAs are small non-coding RNAs that control the transcriptome at the systems level. In the present study we analyzed the microRNAome in blood and postmortem brains of controls and schizophrenia patients and found that miR 99b-5p was downregulated in both the prefrontal cortex and blood of patients. At the mechanistic level we show that inhibition of miR-99b-5p leads to schizophrenia-like phenotypes in mice and induced inflammatory processes in microglia linked to synaptic pruning. The miR-99b-5p mediated inflammatory response in microglia depended on Z DNA binding protein 1 (Zbp1) which we identified as a novel miR-99b-5p target. Antisense oligos (ASOs) against Zbp1 ameliorated the pathological phenotypes caused by miR-99b-5p inhibition. In conclusion, we report a novel miR-99b-5p-Zbp1 pathway in microglia that contributes to the pathogenesis of schizophrenia. Our data suggest that strategies to increase the levels of miR-99b-5p or inhibit Zbp1 could become a novel therapeutic strategy.

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Contribution of default mode network to game and delayed-response task performance: power and connectivity analyses of theta oscillation in the monkey
Neuroimaging studies have demonstrated the presence of a default mode network (DMN) which shows greater activity during rest, and an executive network (EN) which is activated during cognitive tasks. DMN and EN are thought to have competing functions. However, recent studies reported that the two networks show coactivation during some cognitive tasks. To clarify how DMN works and how DMN interacts with EN for cognitive control, we recorded EEG activities in the medial prefrontal (anterior DMN: aDMN), posterior cingulate/precuneus (posterior DMN: pDMN), and lateral prefrontal (EN) areas in the monkey. As cognitive tasks, we employed a monkey-monkey competitive video game (GAME) and a delayed-response (DR) task. We focused on theta oscillation because of its importance in cognitive control. We also examined theta band connectivity among the three network areas using the Granger causality analysis. DMN and EN were found to work cooperatively in both tasks. In all the three network areas, we found GAME-task-related, but no DR-task-related, increase in theta power from the resting level, maybe because of the higher cognitive demand associated with the GAME task performance. The information flow conveyed by the theta oscillation was directed more to aDMN than from aDMN for both tasks. The GAME-task-related increase in theta power in aDMN is supposed to be supported by more information flow conveyed by the theta oscillation from EN and pDMN.

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Age differences in neural distinctiveness during memory retrieval versus reinstatement
Robust evidence points to mnemonic deficits in older adults related to dedifferentiated, i.e., less distinct, neural responses during memory encoding. However, less is known about retrieval-related dedifferentiation and its role in age-related memory decline. In this study, younger and older adults were scanned both while incidentally learning face and house stimuli and while completing a surprise recognition memory test. Using pattern similarity searchlight analyses, we looked for indicators of neural dedifferentiation during retrieval and asked whether this might explain interindividual differences in memory performance. Our findings revealed age-related reductions in neural distinctiveness during memory retrieval as well as in encoding-retrieval reinstatement in visual processing regions. We further demonstrated that the degree to which patterns elicited during encoding were reinstated during retrieval tracked variability in memory performance better than retrieval-related distinctiveness only. All in all, we contribute to meager existing evidence for age-related neural dedifferentiation during memory retrieval. We propose that the recognition task (as opposed to a cued recall task) may have revealed impairment in perceptual processing in older adults, leading to particularly widespread age differences in neural distinctiveness. We additionally provide support for the idea that well-defined reactivation of encoding patterns plays a major role in successful memory retrieval.

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The value of time in the invigoration of human movements when interacting with a robotic exoskeleton
Time and effort are critical factors that are thought to be subjectively balanced during the planning of goal-directed actions, thereby setting the vigor of volitional movements. Theoretical models predicted that the value of time should then amount to relatively high levels of effort. However, the time-effort tradeoff has so far only been studied for a narrow range of efforts. Therefore, the extent to which humans can invest in a time-saving effort remains largely unknown. To address this issue, we used a robotic exoskeleton which significantly varied the energetic cost associated with a certain vigor during reaching movements. In this situation, minimizing the time-effort tradeoff would lead to high and low human efforts for upward and downward movements respectively. Consistent with this prediction, results showed that all participants expended substantial amounts of energy to pull on the exoskeleton during upward movements and remained essentially inactive by harnessing the work of gravity to push on the exoskeleton during downward movements, while saving time in both cases. These findings show that a common tradeoff between time and effort can determine the vigor of reaching movements for a wide range of efforts, with time cost playing a pivotal role.

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SARS-CoV-2 infection induces dopaminergic neuronal loss in midbrain organoids during short and prolonged cultures
COVID-19 is mainly associated with respiratory symptoms, although several reports showed that SARS-CoV-2 affects the nervous system. We evaluated the effects of infection in prolonged culture of midbrain organoids, showing that the virus induces changes in gene expression, and fragmentation and loss of dopaminergic neurons. Our findings highlight the direct viral-induced damage to midbrain organoids indicating the relevance of assessing the neurological long-term evolution of COVID-19 patients.

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Paying attention to natural scenes in area V1
Natural scene responses in the primary visual cortex are modulated simultaneously by attention and by contextual signals about scene statistics stored across the connectivity of the visual processing hierarchy. We hypothesize that attentional and contextual top-down signals interact in V1, in a manner that primarily benefits the representation of natural visual stimuli, rich in high-order statistical structure. Recording from two macaques engaged in a spatial attention task, we show that attention enhances the decodability of stimulus identity from population responses evoked by natural scenes but, critically, not by synthetic stimuli in which higher-order statistical regularities were eliminated. Attentional enhancement of stimulus decodability from population responses occurs in low dimensional spaces, as revealed by principal component analysis, suggesting an alignment between the attentional and the natural stimulus variance. Moreover, natural scenes produce stimulus-specific oscillatory responses in V1, whose power undergoes a global shift from low to high frequencies with attention. We argue that attention and perception share top-down pathways, which mediate hierarchical interactions optimized for natural vision.

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ZCCHC17 modulates neuronal RNA splicing and supports cognitive resilience in Alzheimer's disease
ZCCHC17 is a putative master regulator of synaptic gene dysfunction in Alzheimer's Disease (AD), and ZCCHC17 protein declines early in AD brain tissue, before significant gliosis or neuronal loss. Here, we investigate the function of ZCCHC17 and its role in AD pathogenesis. Co-immunoprecipitation of ZCCHC17 followed by mass spectrometry analysis in human iPSC-derived neurons reveals that ZCCHC17's binding partners are enriched for RNA splicing proteins. ZCCHC17 knockdown results in widespread RNA splicing changes that significantly overlap with splicing changes found in AD brain tissue, with synaptic genes commonly affected. ZCCHC17 expression correlates with cognitive resilience in AD patients, and we uncover an APOE4 dependent negative correlation of ZCCHC17 expression with tangle burden. Furthermore, a majority of ZCCHC17 interactors also co-IP with known tau interactors, and we find significant overlap between alternatively spliced genes in ZCCHC17 knockdown and tau overexpression neurons. These results demonstrate ZCCHC17's role in neuronal RNA processing and its interaction with pathology and cognitive resilience in AD, and suggest that maintenance of ZCCHC17 function may be a therapeutic strategy for preserving cognitive function in the setting of AD pathology.

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Predicting the Distribution of Serotonergic Axons: A Supercomputing Simulation of Reflected Fractional Brownian Motion in a 3D-Mouse Brain Model
The self-organization of the brain matrix of serotonergic axons (fibers) remains an unsolved problem in neuroscience. The regional densities of this matrix have major implications for neuroplasticity, tissue regeneration, and the understanding of mental disorders, but the trajectories of single fibers are strongly stochastic and require novel conceptual and analytical approaches. In a major extension to our previous studies, we used a supercomputing simulation to model 1000 serotonergic fibers as paths of superdiffusive fractional Brownian motion (FBM), a continuous-time stochastic process. The fibers produced long walks in a complex, three-dimensional shape based on the mouse brain and reflected at the outer (pial) and inner (ventricular) boundaries. The resultant regional densities were compared to the actual fiber densities in the corresponding neuroanatomically-defined regions. The relative densities showed strong qualitative similarities in the forebrain and midbrain, demonstrating the predictive potential of stochastic modeling in this system. The current simulation does not respect tissue heterogeneities, but can be further improved with novel models of multifractional FBM. The study demonstrates that serotonergic fiber densities can be strongly influenced by the geometry of the brain, with implications for brain development, plasticity, and evolution.

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The central renin angiotensin II system: a genetic pathway, functional decoding and selective target engagement characterization in humans
The brain renin angiotensin II system plays a pivotal role in cognition and neuropathology via the central angiotensin II type 1 receptor (AT1R), yet the lack of a biologically informed framework currently impedes translational and therapeutic progress. We combined imaging transcriptomic and meta-analyses with pharmaco-resting state fMRI employing a selective AT1R antagonist in a discovery-replication design (n=132 individuals). The AT1R was densely expressed in subcortical systems engaged in reward, motivation, stress, and memory. Pharmacological target engagement suppressed spontaneous neural activity in subcortical systems with high AT1R expression and enhanced functional network integration in cortico-basal ganglia-thalamo-cortical circuits. AT1R-regulation on functional network integration was further mediated by dopaminergic, opioid and corticotrophin-releasing hormone pathways. Overall, this work provides the first comprehensive characterization of the architecture and function of the brain renin angiotensin II system indicating that the central AT1R mediates human cognition and behavior via regulating specific circuits and interacting with classical transmitter systems.

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Multimodal acoustic-electric trigeminal nerve stimulation modulates conscious perception
Multimodal stimulation can reverse pathological neural activity and improve symptoms in neuropsychiatric diseases. For example, recent research shows that multimodal acoustic-electric trigeminal-nerve stimulation (TNS) (i.e., musical stimulation synchronized to transcutaneous electrical stimulation of the trigeminal nerve) can improve consciousness in patients with disorders of consciousness. However, the reliability and mechanism of this novel approach remain largely unknown. In the present study, we explored the effects of multimodal acoustic-electric TNS in healthy human participants using a double-blinded, randomized, crossover design. We assessed conscious perception before and after stimulation using behavioral and neural measures in tactile and auditory target-detection tasks. To explore the mechanisms underlying the putative effects of acoustic-electric stimulation, we fitted a biologically plausible neural network model to the neural data using dynamic causal modeling. We observed that (1) acoustic-electric stimulation can improve conscious tactile perception in healthy human participants without a concomitant change in auditory perception, (2) this improvement is caused by the interplay of the acoustic and electric stimulation rather than any of the unimodal stimulation alone, and (3) the effect of acoustic-electric stimulation on conscious perception correlates with inter-regional connection changes in a recurrent neural processing model. These results provide evidence that acoustic-electric trigeminal-nerve stimulation can promote conscious perception, and therewith corroborate the usefulness of multimodal stimulation. Alterations in inter-regional cortical connections might be the mechanism by which acoustic-electric TNS achieves its consciousness benefits.

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A spinal synergy of excitatory and inhibitory neurons coordinates ipsilateral body movements
Innate and goal-directed movements require a high-degree of trunk and appendicular muscle coordination to preserve body stability while ensuring the correct execution of the motor action. The spinal neural circuits underlying motor execution and postural stability are finely modulated by propriospinal, sensory and descending feedback, yet how distinct spinal neuron populations cooperate to control body stability and limb coordination remains unclear. Here, we identified a spinal microcircuit composed of V2 lineage-derived excitatory (V2a) and inhibitory (V2b) neurons that together coordinate ipsilateral body movements during locomotion. Inactivation of the entire V2 neuron lineage does not impair intralimb coordination but destabilizes body balance and ipsilateral limb coupling, causing mice to adopt a compensatory festinating gait and be unable to execute skilled locomotor tasks. Taken together our data suggest that during locomotion the excitatory V2a and inhibitory V2b neurons act antagonistically to control intralimb coordination, and synergistically to coordinate forelimb and hindlimb movements. Thus, we suggest a new circuit architecture, by which neurons with distinct neurotransmitter identities employ a dual-mode of operation, exerting either synergistic or opposing functions to control different facets of the same motor behavior.

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Neural Mechanism Underlying NEergic Neurons-Modulated The Arousal Associated With Midazolam Anesthesia
Generation of the unconsciousness associated with arousal during the initial stage of anesthesia by midazolam is critical for general anesthesia, however, the exact mechanism remains unknown. Here, firstly, we found that the destruction of noradrenergic neurons in the locus coeruleus (LCNE) could prolong the emergence time of midazolam-induced anesthesia. Secondly, the same results were found by activation of the noradrenergic pathway between the LC and the ventrolateral preoptic nucleus (VLPO) using optogenetics and chemogenetics approaches, respectively. Thirdly, this effect was mediated by alpha 1 and beta adrenergic receptors rather than alpha2 adrenergic receptors in the VLPO. Moreover, the noradrenergic pathway to modulate the arousal between the LC and VLPO was controlled by GABAA receptors in the LC and VLPO in our models. Our data demonstrate that activation of the NEergic pathway between the LC and VLPO can promote arousal to prevent delayed recovery from midazolam-induced anesthesia.

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A Consequence of Immature Breathing induces Persistent Changes in Hippocampal Synaptic Plasticity and Behavior: A Role of Pro-Oxidant State and NMDA Receptor Imbalance
Underdeveloped breathing results from premature birth and causes intermittent hypoxia during the early neonatal period. Neonatal intermittent hypoxia (nIH) is a condition linked to the increased risk of neurocognitive deficit later in life. However, the underlying mechanistic consequences nIH-induced neurophysiological changes remains poorly resolved. Here, we investigated the impact of nIH on hippocampal synaptic plasticity and NMDA receptor (NMDAr) expression in neonatal mice. Our findings indicate that nIH induces a pro-oxidant state, leading to an imbalance in NMDAr subunit composition that favors GluN2A over GluN2B expression, and subsequently impairs synaptic plasticity. These consequences persist in adulthood and coincide with deficits in spatial memory. Treatment with the antioxidant, manganese(III) tetrakis(1-methyl-4-pyridyl)porphyrin (MnTMPyP), during nIH effectively mitigated both immediate and long-term effects of nIH. However, MnTMPyP treatment post-nIH did not prevent the long-lasting changes in either synaptic plasticity or behavior. Our results underscore the central role of the pro-oxidant state in nIH-mediated neurophysiological and behavioral deficits and importance of stable oxygen homeostasis during early life. These findings suggest that targeting the pro-oxidant state during a discrete window may provide a potential avenue for mitigating long-term neurophysiological and behavioral outcomes when breathing is unstable during early postnatal life.

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Distinct Electrophysiological Signatures of Intentional and Unintentional Mind-Wandering Revealed by Low-Frequency EEG Markers
Mind-wandering is typically characterized by the common experience wherein attention veers off into thoughts unrelated to the task at hand. Recent research highlights the intentionality dimension of mind-wandering as a key predictor of adverse functional outcomes with intentional and unintentional task-unrelated thought (TUT) differentially linked to neural, behavioral, clinical, and functional correlates. We here aimed to elucidate the electrophysiological underpinnings of intentional and unintentional TUT by systematically examining the individual and collective discriminative power of a large set of EEG markers to distinguish between attentional states. Univariate and multivariate analyses were conducted on 54 predefined markers belonging to four conceptual families: ERP, spectral, information theory and connectivity measures, extracted from scalp EEG recordings prior to multidimensional reports of ongoing thought from participants performing a sustained attention task. We report here that on-task, intentional and unintentional TUT exhibit distinct electrophysiological signatures in the low frequency range. More specifically, increased features of the theta frequency range were found to be most discriminative between on-task and off-task states, while features within the alpha band were characteristic of intentional TUT when compared to unintentional TUT. This result is theoretically well aligned with contemporary accounts describing alpha activity as an index of internally oriented attention and a potential mechanism to shield internal processes from sensory input. Our study verifies the validity of the intentionality dimension of mind-wandering and represents a step forward towards real-time detection and mitigation of maladaptive mind-wandering.

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Mammals achieve common neural coverage of visual scenes using distinct sampling behaviors
Most vertebrates use saccadic eye movements to quickly change gaze orientation and sample different portions of the environment. Visual information is integrated across several fixations to construct a more complete perspective. In concert with this sampling strategy, neurons adapt to unchanging input to conserve energy and ensure that only information for novel fixations is processed. We demonstrate how adaptation recovery times and saccade properties interact, and thus shape spatiotemporal tradeoffs observed in the motor and visual systems of different species. These tradeoffs predict that in order to achieve similar visual coverage over time, animals with smaller receptive field sizes require faster saccade rates. We find comparable sampling of the visual environment by neuronal populations across mammals when integrating measurements of saccadic behavior with receptive field sizes and V1 neuronal density. We propose that these mammals share a common statistically driven strategy of maintaining coverage of their visual environment over time calibrated to their respective visual system characteristics.

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Thalamic neurons drive distinct forms of motor asymmetry that are conserved in teleost and dependent on visual evolution
Brain laterality is a prominent feature in Bilateria, where neural functions are favored in a single brain hemisphere. These hemispheric specializations are thought to improve behavioral performance and are commonly observed as sensory or motor asymmetries, such as handedness in humans. Despite its prevalence, our understanding of the neural and molecular substrates instructing functional lateralization is limited. Moreover, how functional lateralization is selected for or modulated throughout evolution is poorly understood. While comparative approaches offer a powerful tool for addressing this question, a major obstacle has been the lack of a conserved asymmetric behavior in genetically tractable organisms. Previously, we described a robust motor asymmetry in larval zebrafish. Following the loss of illumination, individuals show a persistent turning bias that is associated with search pattern behavior with underlying functional lateralization in the thalamus. This behavior permits a simple yet robust assay that can be used to address fundamental principles underlying lateralization in the brain across taxa. Here, we take a comparative approach and show that motor asymmetry is conserved across diverse larval teleost species, which have diverged over the past 200 million years. Using a combination of transgenic tools, ablation, and enucleation, we show that teleosts exhibit two distinct forms of motor asymmetry, vision-dependent and -independent. These asymmetries are directionally uncorrelated, yet dependent on the same subset of thalamic neurons. Lastly, we leverage Astyanax sighted and blind morphs, which show that fish with evolutionarily derived blindness lack both retinal-dependent and -independent motor asymmetries, while their sighted surface conspecifics retained both forms. Our data implicate that overlapping sensory systems and neuronal substrates drive functional lateralization in a vertebrate brain that are likely targets for selective modulation during evolution.

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Mesoscale volumetric light field (MesoLF) imaging of neuroactivity across cortical areas at 18 Hz
Various implementations of mesoscopes provide optical access for calcium imaging across multi-millimeter fields-of-view (FOV) in the mammalian brain. However, capturing the ac-tivity of the neuronal population within such FOVs near-simultaneously and in a volumetric fashion has remained challenging since approaches for imaging scattering brain tissues typically are based on sequential acquisition. Here, we present a modular, mesoscale light field (MesoLF) imaging hardware and software solution that allows recording from thou-sands of neurons within volumes of O 4000 x 200 m, located at up to 400 m depth in the mouse cortex, at 18 volumes per second. Our optical design and computational approach enable up to hour-long recording of ~10,000 neurons across multiple cortical areas in mice using workstation-grade computing resources.

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The unique neural signature of your trip: Functional connectome fingerprints of subjective psilocybin experience
The emerging neuroscientific frontier of brain fingerprinting has recently established that human functional connectomes (FCs) exhibit fingerprint like idiosyncratic features, which map onto heterogeneously distributed behavioural traits. Here we harness brain fingerprinting tools to extract FC features that predict subjective drug experience induced by the psychedelic psilocybin. Specifically, in neuroimaging data of healthy volunteers under the acute influence of psilocybin or a placebo, we show that, post-psilocybin administration, FCs become more idiosyncratic due to greater inter-subject dissimilarity. Moreover, whereas in placebo subjects idiosyncratic features are primarily found in the frontoparietal network, in psilocybin subjects they concentrate in the default-mode network (DMN). Crucially, isolating the latter revealed an FC pattern that predicts subjective psilocybin experience and is characterised by reduced within-DMN and DMN-limbic connectivity, as well as increased connectivity between the DMN and attentional systems. Overall, these results contribute to bridging the gap between psilocybin-mediated effects on brain and behaviour, while demonstrating the value of a brain-fingerprinting approach to pharmacological neuroimaging.

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Neuronal phase shifts differ for excitation vs. inhibition: a computer modeling study
Rhythmic activity is ubiquitous in neural systems, and impedance analysis has been widely used to examine frequency-dependent responses of neuronal membranes to rhythmic inputs. Impedance analysis assumes the neuronal membrane is a linear system, requiring the use of small signals to stay in a near-linear regime. However, postsynaptic potentials are often large and trigger nonlinear mechanisms. We therefore augmented impedance analysis to evaluate membrane responses in this nonlinear domain, analyzing responses to injected current for subthreshold membrane voltage (Vmemb), suprathreshold spike-blocked Vmemb, and spiking in a validated neocortical pyramidal neuron computer model. Responses in these output regimes were asymmetrical, with different phase shifts during hyperpolarizing and depolarizing half-cycles. Suprathreshold chirp stimulation gave equivocal results due to nonstationarity of response, requiring us to use fixed-frequency sinusoids. Sinusoidal inputs produced phase retreat: action potentials occurred progressively later in cycles of the input stimulus, resulting from adaptation. Conversely, sinusoidal current with increasing amplitude over cycles produced a pattern of phase advance: action potentials occurred progressively earlier. Phase retreat was dependent on Ih and Iahp currents; phase advance was modulated by these currents. Our results suggest differential responses of cortical neurons depending on the frequency of oscillatory input in the delta -- beta range, which will play a role in neuronal responses to shifts in network state. We hypothesize that intrinsic cellular properties complement network properties and contribute to in vivo phase-shift phenomena such as phase precession, seen in place and grid cells, and phase roll, observed in non-place cells in hippocampus.

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A Framework for Systematically Evaluating the Representations Learned by A Deep Learning Classifier from Raw Multi-Channel Electroencephalogram Data
The application of deep learning methods to raw electroencephalogram (EEG) data is growing increasingly common. While these methods offer the possibility of improved performance relative to other approaches applied to manually engineered features, they also present the problem of reduced explainability. As such, a number of studies have sought to provide explainability methods uniquely adapted to the domain of deep learning-based raw EEG classification. In this study, we present a taxonomy of those methods, identifying existing approaches that provide insight into spatial, spectral, and temporal features. We then present a novel framework consisting of a series of explainability approaches for insight into classifiers trained on raw EEG data. Our framework provides spatial, spectral, and temporal explanations similar to existing approaches. However, it also, to the best of our knowledge, proposes the first explainability approaches for insight into spatial and spatio-spectral interactions in EEG. This is particularly important given the frequent use and well-characterized importance of EEG connectivity measures for neurological and neuropsychiatric disorder analysis. We demonstrate our proposed framework within the context of automated major depressive disorder (MDD) diagnosis, training a high performing one-dimensional convolutional neural network with a robust cross-validation approach on a publicly available dataset. We identify interactions between central electrodes and other electrodes and identify differences in frontal theta, beta, and gamma low between healthy controls and individuals with MDD. Our study represents a significant step forward for the field of deep learning-based raw EEG classification, providing new capabilities in interaction explainability and providing direction for future innovations through our proposed taxonomy.

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