bioRxiv Subject Collection: Neuroscience's Journal

Dissociable neurofunctional and molecular characterizations of reward and punishment sensitivity
While the hyper- and hypo- reward or punishment sensitivities (RS, PS) have received considerable attention as prominent transdiagnostic features of psychopathology, the lack of an overarching neurobiological characterization currently limits their early identifications and neuromodulations. Here we combined microarray data from the Allen Human Brain Atlas with a multimodal fMRI approach to uncover the neurobiological signatures of RS and PS in a discovery-replication design (N=655 participants). Both RS and PS were mapped separately in the brain, with the intrinsic functional connectome in the fronto-striatal network encoding reward responsiveness, while the fronto-insular system was particularly engaged in punishment sensitivity. This dissociable functional connectome patterns related to RS and PS were also specific in differentiating decisions driven by social or monetary reward and punishment motivations. Further imaging transcriptomic analyses revealed that functional connectome variations for RS and PS were associated with topography of specific gene sets enriched in ontological pathways, including synaptic transmission, dopaminergic metabolism, immune response and stress adaptation. On the neurotransmitter level, the serotonin neuromodulator was identified as a pivotal hub regulating the intrinsic functional connectome patterns of RS and PS, with this process critically dependent on its interactions with dopaminergic, opioid and GABAergic systems. Overall, these findings indicate dissociable neural connectome mapping of RS and PS and highlight their linkage with transcriptomic profiles, which may offer valuable insights into the treatment evaluation for symptomatology relevant to reward/punishment processing deficits.

Context-Dependent PSIICOS: A Novel Framework for Functional Connectivity Estimation Accounting for Task-Related Power Leakage
Functional connectivity (FC) analysis using non-invasive neuroimaging methods, such as MEG and EEG, is often confounded by artifacts from spatial leakage and task-related power modulations. To address these limitations, we present Context-Dependent PSIICOS (CD-PSIICOS), a novel framework that improves the estimation of FC by incorporating task-specific cortical power distributions into the projection operator applied to the vectorized sensor-space cross-spectrum. Unlike the original PSIICOS approach, which is prone to suppress spatial leakage from all the sources, CD-PSIICOS dynamically adjusts the projection based on the active source distribution, enabling more accurate suppression of spatial leakage while preserving true zero-phase interactions. We validated CD-PSIICOS using realistic simulations and a multi-subject MEG dataset. The results demonstrate that CD-PSIICOS outperforms the original PSIICOS in suppressing artifacts at lower projection ranks, maintaining robust detection of functional networks across theta, alpha, beta, and gamma frequency bands. By requiring lower projection ranks for optimal performance, CD-PSIICOS facilitates the reconstruction of physiologically relevant networks with improved specificity and stability.

Astrocyte-derived extracellular matrix proteins regulate synapse remodeling in stress-induced depression
Major depressive disorder (MDD) is a common mood condition affecting multiple brain regions and cell types. Changes in astrocyte function contribute to depressive-like behaviors. However, while neuronal mechanisms driving MDD have been studied in some detail, molecular mechanisms by which astrocytes promote depression have not been extensively explored. To uncover astrocyte contributions to MDD, we subjected male mice to chronic social defeat stress precipitated by encounters with a dominant male. Animals exposed to this treatment exhibit symptoms indicative of MDD, including reduced social interactions, anxiety, despair, and anhedonia. We then measured astrocyte translating mRNA expression changes in mice that underwent chronic social defeat and control animals using ribosome affinity purification. Bioinformatic analyses reveal significant alterations in the prefrontal cortex (PFC), consistent with previous studies implicating this brain region in MDD. Expression of genes encoding extracellular matrix (ECM) proteins, cell-cell interaction proteins, and proteins controlling glutamatergic synaptic function are significantly altered. These changes correlate with perturbation of glutamatergic transmission, measured by electrophysiology, and increased synaptic cleft size. Among ECM genes, increased expression of mRNAs encoding the synaptic remodeling protein secreted protein acidic and rich in cysteine (Sparc) correlates the most with the depressive phenotype. Furthermore, presence of Sparc and other ECM proteins in synaptosomes is also increased and overexpressing Sparc in PFC partially alleviates stress symptoms. Our results raise the possibility that increased expression of Sparc may be a natural protective mechanism against stress-induced synaptic dysfunction in depression.

How to deal with darkness: Modeling and visualization of zero-inflated personal light exposure data on a logarithmic scale
Personal light exposure, the pattern of ocular light levels across time under free-living conditions measured with wearable devices, has become increasingly important in circadian and myopia research. Very small measurement values in light exposure patterns, especially zero, are regularly recorded in field studies. These zero-lux values are problematic for commonly applied logarithmic transformations, and should neither be dismissed nor be unduly influential in visualizations and statistical models. Common approaches used in zero-inflated data sets fail in at least one of these regards. We compare four ways to visualize such data on a linear, logarithmic, hybrid, or symlog scale and we model the light exposure patterns with a generalized additive model by removing zero-lux values, adding a very small or -1 log10 lux value to the dataset, or using the Tweedie error distribution. We show that a symlog-transformed visualization displays relevant features of light exposure across scales, including zero-lux, while at the same time reducing the emphasis on the small values (<1 lux). Symlog is well-suited to visualize differences in light exposure covering heavy-tailed negative values. The open-source software package LightLogR includes the symlog transformation for easy access. We further show that small but not negligible value additions to the light exposure data of -1 log10 lux for statistical modelling allow for acceptable models on a logarithmic scale, while very small values distort results. We also demonstrate the utility of the Tweedie distribution, which does not require prior transformations, models data on a logarithmic scale, and includes zero-lux values, capturing personal light exposure patterns satisfactorily. Data from field studies of personal light exposure requires appropriate handling of zero-lux values in a logarithmic context. Symlog scales for visualizations and an appropriate addition to input values for modelling, or the Tweedie distribution, provide a solid basis.

The deep winding at the brain surface: replicating a historical report associating the 'bridged' central sulcus with the pli de passage fronto-parietal moyen
In 1876, the anatomist Heschl surveyed 1,087 brains identifying six cases of a unilateral "bridged" central sulcus (CS) at the brain surface. He also measured the height of a minor "deep winding" at the same location within the CS in the remaining 1,081 brains, reporting a distribution skewed towards significantly increased heights. These observations supported his hypothesis that the "bridged" CS represents an extreme form of the "deep winding" within the CS. In this replication we examined structural MRI data from an equally large dataset of 1,112 participants of the Human Connectome Project young adult (HCP-YA) dataset. Through visual inspection, we identified nine cases of a "bridged" CS, confirming its prevalence of less than 1%. The height of the "deep winding", referred to in the HCP-YA dataset as the pli de passage fronto-parietal moyen (PPfpm), was extracted from 1,983 MRI-based hemispheric depth profiles. The resulting PPfpm height distribution, although wider, still mirrored Heschls findings, showing a similar skew towards larger heights. Further analyses of the twin data within the HCP-YA dataset indicated a slightly increased prevalence of the "bridged" CS in monozygotic and dizygotic twins compared to non-twin individuals, though no concordance of "bridged" CS was observed in monozygotic twin pairs. This replication study validates both of Heschls observations, describes additional factors that might influence the prevalence of the "bridged" CS, and refines the characterization of the "deep winding" height distribution. Together, these findings reaffirm and expand historical insights into the intricate anatomical organization of the CS.

When do measured representational distances reflect the neural representational geometry?
The representational geometry of a brain region can be characterized by the distances among neural activity patterns for a set of experimental conditions. Researchers routinely estimate representational distances from brain-activity measurements that either sparsely sample the underlying neural population (e.g. neural recordings) or pool across the activity of many neurons (e.g. fMRI voxels). Here we use theory and simulations to clarify under what circumstances representational distances estimated from brain-activity measurements reflect the representational geometry of the underlying neural population, and what distortions must be expected under other circumstances. We demonstrate that the estimated representational distances are undistorted if single neurons are sampled at random. For voxels that take non-negatively weighted linear combinations, the resulting geometry is linearly distorted, correctly reflecting the population-mean dimension, while downscaling all orthogonal dimensions, for which the averaging cancels a large portion of the signal. Surprisingly, removing the mean from voxel patterns recovers the underlying representational geometry exactly in expectation under idealized conditions. This explains why the correlation distance, the most popular measure of representational dissimilarity in neuroimaging studies, ``works" so well, yielding geometries that can appear similar between fMRI and neural recordings. The Euclidean (or Mahalanobis) distance computed after removing the mean of each pattern (without normalizing its variance) is an attractive alternative to the correlation distance in that it corrects for the inflated relative contribution of the population-mean dimension, while avoiding the drawback of the correlation distance: it can be large for confusable low-norm patterns, failing to reflect decodability. Our results demonstrate that measured representational distances reflect the neural representational geometry when (1) single neurons are sampled at random or (2) the weights with which the measured responses sample the neurons are drawn i.i.d. and (2a) the weights are drawn from a zero-mean distribution or (2b) the population mean is the same for all conditions or (2c) the mean is removed from each estimated pattern. We discuss practical implications for analyses of neural representational geometries.

Weight Transfer in the Reinforcement Learning Model of Songbird Acquisition
Song acquisition behavior observed in the songbird system provides a notable example of learning through trial-and-error which parallels human speech acquisition. Studying songbird vocal learning can offer insights into mechanisms underlying human language. We present a computational model of song learning that integrates reinforcement learning (RL) and Hebbian learning and agrees with known songbird circuitry. The song circuit outputs activity from nucleus RA, which receives two primary inputs: timing information from area HVC and stochastic activity from nucleus LMAN. Additionally, song learning relies on Area X, a basal ganglia area that receives dopaminergic inputs from VTA. In our model, song is first acquired in the HVC-to-Area X connectivity, employing an RL mechanism that involves node perturbation. This information is then consolidated into HVC-to-RA synapses through a Hebbian mechanism. The transfer of weights from Area X to RA takes place via the thalamus, utilizing a specific form of spike-timing-dependent plasticity (STDP). Thus, we present a computational model grounded in songbird circuitry in which the optimal policy is initially guided by RL and subsequently transferred to another circuit through Hebbian plasticity.

Neurodegenerative and neurodevelopmental roles for bulk lipid transporters VPS13A and BLTP2 in movement disorders
BackgroundBridge-like lipid transfer proteins (BLTPs) mediate bulk lipid transport at membrane contact sites. Mutations in BLTPs are linked to both early-onset neurodevelopmental and later-onset neurodegenerative diseases, including movement disorders. The tissue specificity and temporal requirements of BLTPs in disease pathogenesis remain poorly understood.

ObjectivesTo determine the age-of-onset and tissue-specific roles of VPS13A and BLTP2 in movement disorder pathogenesis using Drosophila models.

MethodsWe generated tissue-specific knockdowns of the VPS13A ortholog (Vps13) and the BLTP2 ortholog (hobbit) in neurons and muscles of Drosophila. We analyzed age-dependent locomotor behavior, neurodegeneration, and synapse development and function.

ResultsNeuron-specific loss of the VPS13A ortholog caused neurodegeneration followed by age- onset movement deficits and reduced lifespan, while muscle-specific loss affected only lifespan, revealing neurodegeneration and myopathy as independent comorbidities in VPS13A disease. In contrast, neuronal loss of the BLTP2 ortholog resulted in severe early-onset locomotor defects without neurodegeneration, while muscle loss impaired synaptogenesis and neurotransmission at the neuromuscular junction (NMJ).

ConclusionsVPS13A maintains neuronal survival, while BLTP2 orchestrates synaptic development. VPS13A function in muscle does not play a role in movement defects. The phenotypic specificity of BLTP function provides mechanistic insights into distinct disease trajectories for BLTP-associated movement disorders.

Bipolar and schizophrenia risk gene AKAP11 encodes an autophagy receptor coupling the regulation of PKA kinase network homeostasis to synaptic transmission
Human genomic studies have identified protein-truncating variants in AKAP11 associated with both bipolar disorder and schizophrenia, implicating a shared disease mechanism driven by loss-of-function. AKAP11, a protein kinase A (PKA) adaptor, plays a key role in degrading the PKA-RI complex through selective autophagy. However, the neuronal functions of AKAP11 and the impact of its loss-of-function remains largely uncharacterized. Through multi-omics approaches, cell biology, and electrophysiology analysis in mouse models and human induced neurons, we delineated a central role of AKAP11 in coupling PKA kinase network regulation to synaptic transmission. Loss of AKAP11 disrupted PKA activity and impaired cellular functions that significantly overlap with pathways associated with the psychiatric disease. Moreover, we identified interactions between AKAP11, the PKA-RI adaptor SPHKAP, and the ER-resident autophagy-related proteins VAPA/B, which co-adapt and mediate PKA-RI degradation. Notably, AKAP11 deficiency impaired neurotransmission and decreased presynaptic protein levels in neurons, providing key insights into the mechanism underlying AKAP11-associated psychiatric diseases.

Osmolarity regulates C. elegans egg-laying behavior via parallel chemosensory and biophysical mechanisms
Animals alter their behavior in response to changes in the environment. Upon encountering hyperosmotic conditions, the nematode worm C. elegans initiates avoidance and cessation of egg-laying behavior. While the sensory pathway for osmotic avoidance is well-understood, less is known about how egg laying is inhibited. We analyzed egg-laying behavior after acute and chronic shifts to and from hyperosmotic media. Animals on 400 mM sorbitol stop laying eggs immediately but then resume [~]3 hours later, after accumulating additional eggs in the uterus. Surprisingly, the hyperosmotic cessation of egg laying did not require known osmotic avoidance signaling pathways. Acute hyperosmotic shifts in hyperosmotic-resistant mutants overproducing glycerol also blocked egg laying, but these animals resumed egg laying more quickly than similarly treated wild-type animals. These results suggest that hyperosmotic conditions disrupt a high-inside hydrostatic pressure gradient required for egg laying. Consistent with this hypothesis, animals adapted to hyperosmotic conditions laid more eggs after acute shifts back to normosmic conditions. Optogenetic stimulation of the HSN egg-laying command neurons in hyper-osmotic treated animals led to fewer and slower egg-laying events, an effect not seen following direct optogenetic stimulation of the postsynaptic vulval muscles. Hyperosmotic conditions also affected egg-laying circuit activity with the vulval muscles showing reduced Ca2+ transient amplitudes and frequency even after egg-laying resumes. Together, these results indicate that hyperosmotic conditions regulate egg-laying via two parallel mechanisms: a sensory pathway that acts to reduce HSN excitability and neurotransmitter release, and a biophysical mechanism where a hydrostatic pressure gradient reports egg accumulation in the uterus.

Summary StatementWe find that hyperosmotic conditions inhibit C. elegans egg laying through both a sensory pathway and a separate biophysical pathway affecting a high-inside hydrostatic pressure gradient.

Cortical Changes Associated with Isha Kriya Meditation Revealed by Encephalography in Novice and Experienced Meditators - a Longitudinal Pilot Study
Background: Isha Kriya (IK) is a widely available meditation practice that incorporates breathing regulation that has shown to improve self-reported symptoms of stress, anxiety, and depression. An increasing amount of research has been published on the effects of various meditative practices on scalp electroencephalography (EEG). However, the effects of IK on cortical activity have not been reported previously. Methods: Healthy volunteers aged 18 years or older were invited to participate. Participants were categorized as novice or experienced in meditation. EEG spectral features, computed during the eyes-closed condition before and soon after each IK meditation practice, were evaluated both at the start and after 6 weeks of IK meditation training. Results: This longitudinal study examined the effects of IK meditation on cortical state and trait patterns in a cohort of eight participants who practiced IK meditation over a period of 6 weeks. Across the two sessions, a simultaneous increase in global periodic alpha power was observed in multiple subjects (N=6) but this was not observed in all subjects/sessions. We observed an increase in periodic theta band power, particularly in the frontal regions, which emerged as a common state effect in all participants. Longitudinally, we observed an increased periodic gamma power in the resting state EEG in all the experienced meditators in the parietal occipital region. The changes in novices on the other hand was in the alpha and beta bands. Conclusion: Overall, in this pilot study, we report the changes in the quantitative EEG of the practitioners of Isha Kriya meditation over a 6-week cycle and investigated the difference between the start and end of that period at an individual level. We recommend future studies with a larger sample size and over a longer duration.

Mitochondrial Complex I and ROS control synapse function through opposing pre- and postsynaptic mechanisms
Neurons require high amounts energy, and mitochondria help to fulfill this requirement. Dysfunctional mitochondria trigger problems in various neuronal tasks. Using the Drosophila neuromuscular junction (NMJ) as a model synapse, we previously reported that Mitochondrial Complex I (MCI) subunits were required for maintaining NMJ function and growth. Here we report tissue-specific adaptations at the NMJ when MCI is depleted. In Drosophila motor neurons, MCI depletion causes profound cytological defects and increased mitochondrial reactive oxygen species (ROS). But instead of diminishing synapse function, neuronal ROS triggers a homeostatic signaling process that maintains normal NMJ excitation. We identify molecules mediating this compensatory response. MCI depletion in muscles also enhances local ROS. But high levels of muscle ROS cause destructive responses: synapse degeneration, mitochondrial fragmentation, and impaired neurotransmission. In humans, mutations affecting MCI subunits cause severe neurological and neuromuscular diseases. The tissue-level effects that we describe in the Drosophila system are potentially relevant to forms of mitochondrial pathogenesis.

Personalized cue-reactive delta-theta oscillations guide deep brain stimulation for opioid use disorder
Substance use disorders (SUDs) are a significant public health concern, with over 30% failing available treatment. Severe SUD is characterized by drug-cue reactivity that predicts treatment-failure. We leveraged this pathophysiological feature to personalize deep brain stimulation (DBS) of the nucleus accumbens region (NAc) in an SUD patient. While this DBS lead was externalized for clinical purposes, we administered a personalized drug cue-reactivity task while recording NAc electrophysiology. We identified a cue-evoked signal in the ventral NAc associated with intensification of opioid-related cravings, which attenuated with therapeutic stimulation delivered to this same area. DBS was then programmed to engage this subregion within NAc to account for the biomarker location, heralding the potential for personalized strategies to optimize DBS for SUD.

Sleep patterns predicting stress resilience are dependent on sex
Sleep disturbances and stress have a well-established link with neuropsychiatric illness; however, the nature of this relationship remains unclear. Recently, studies using the mouse social-defeat stress model revealed a causal role for non-rapid eye movement (NREM) sleep in the maladaptive behavioral responses to stress. These results suggest a novel function for NREM sleep; as a response by cortical neurons to mitigate the maladaptive effects of stress. A major limitation in many social defeat studies has been the exclusion of females. Women exhibit a greater prevalence of both affective disorders and sleep disturbances compared to men, thus there is a clear need to understand sleep - stress interactions in females. The present study adapts recently developed female social-defeat stress models to allow social-defeat and EEG in male - female pairs. Our findings duplicated the behavioral responses that occurred in other female, nondiscriminatory, and male models of social-defeat stress. Analysis of electroencephalographic (EEG) recordings, before exposure to stress, revealed that susceptibility to the behavioral effects of stress was associated with increased post-defeat NREM sleep--exclusively in females. In males, increased NREM sleep after social defeat stress occurred only in resilient mice. A potential cause of these sleep differences was also identified prior to stress exposure; we identified sex differences in recovery from NREM-sleep loss, thus, suggesting a sex-difference in the homeostatic process regulating sleep. These contrasting responses reveal sexual dimorphism in both NREM sleep predicting resilience and NREM sleep changes induced by social-defeat stress. When considered in the context of existing human literature, these findings suggest that sex is a major factor influencing the interaction of sleep with maladaptive behavioral responses to stress.

Graph neural networks for integrated information and major complex estimation
This study investigates the potential of graph neural networks (GNNs) for estimating the system-level integrated information and major complex in integrated information theory (IIT) 3.0. Owing to the hierarchical complexity of IIT 3.0, tasks such as calculating integrated information and identifying major complex are computationally prohibitive for large systems, thereby restricting the applicability of IIT 3.0 to small systems. To overcome this difficulty, we propose a GNN model with transformer convolutional layers characterized by multi-head attention mechanisms for estimating the major complex and its integrated information. In our approach, exact solutions for integrated information and major complex are obtained for systems with 5, 6, and 7 nodes, and two evaluations are conducted: (1) a non-extrapolative setting in which the model is trained and tested on a mixture of systems with 5, 6, and 7 nodes, and (2) an extrapolative setting in which systems with 5 and 6 nodes are used for training and systems with 7 nodes are used for testing. The results indicate that the estimation performance in the extrapolative setting remains comparable to that in the non-extrapolative setting, showing no significant degradation. In an additional experiment, the model is trained on systems with 5, 6, and 7 nodes and tested on a larger system of 100 nodes, composed of two subsystems of 50 nodes each, with limited inter-subsystem connectivity resembling a split-brain configuration. When the connectivity between the subsystems is low, "local integration" emerges, meaning that a single subsystem forms a major complex. As the connectivity increases, local integration rapidly disappears, and the integrated information gradually rises toward "global integration," in which a large portion of the entire system forms a major complex. Overall, our findings suggest that GNNs can potentially be used for estimating integrated information, major complex, and other IIT-related quantities.

Discovery of neuronal cell types by pairing whole cell reconstructions with RNA expression profiles
Effective classification of neuronal cell types requires both molecular and morphological descriptors to be collected in situ at single cell resolution. However, current spatial transcriptomics techniques are not compatible with imaging workflows that successfully reconstruct the morphology of complete axonal projections. Here, we introduce a new methodology that combines tissue clearing, submicron whole-brain two photon imaging, and Expansion-Assisted Iterative Fluorescence In Situ Hybridization (EASI-FISH) to assign molecular identities to fully reconstructed neurons in the mouse brain, which we call morphoFISH. We used morphoFISH to molecularly identify a previously unknown population of cingulate neurons projecting ipsilaterally to the dorsal striatum and contralaterally to higher-order thalamus. By pairing whole-brain morphometry, improved techniques for nucleic acid preservation and spatial gene expression, morphoFISH offers a quantitative solution for discovery of multimodal cell types and complements existing techniques for characterization of increasingly fine-grained cellular heterogeneity in brain circuits.

Top-down inputs are controlled by somatostatin-expressing interneurons during associative learning
Associative learning links sensory signals to their behavioral meaning by combining bottom-up inputs with top-down contextual information, enabling decision-making based on expected outcomes. It triggers plastic changes of neuronal responses in both excitatory and inhibitory cell populations. However, the role of inhibition in shaping this plasticity remains debated. Here we used chronic extracellular electrophysiology and optogenetic manipulation of inhibitory neurons and top-down inputs in mice learning an auditory discrimination task. We found that learning enhances stimulus selectivity in a subset of neurons in the primary auditory cortex. Interestingly, somatostatin-expressing (SST) interneurons decrease their response to the rewarded cue and bidirectionally regulate associative learning. More specifically, an increased activity of SST neurons impairs learning by altering bottom-up signaling, whereas the reduction of their activity facilitates learning by gating top-down inputs from the orbitofrontal cortex. These findings demonstrate that inhibition plays a critical role in gating top-down inputs to primary sensory cortices involved in associative learning.

Supervised Spike Sorting Feasibility of Noisy Single-Electrode Extracellular Recordings: Systematic Study of Human C-Nociceptors recorded via Microneurography
Sorting spikes from noisy single-channel in-vivo extracellular recordings is challenging, particularly due to the lack of ground truth data. Microneurography, an electrophysiological technique for studying peripheral sensory systems, employs experimental protocols that time-lock a subset of spikes. Stable propagation speed of nerve signals enables reliable sorting of these spikes. Leveraging this property, we established ground truth labels for data collected in two European laboratories and designed an open-source pipeline to process data across diverse hardware and software systems. Using the labels derived from the time-locked spikes, we employed a supervised approach instead of the unsupervised methods typically used in spike sorting.

We evaluated multiple low-dimensional representations of spikes and found that raw signal features consistently outperformed more complex approaches, which are effective in brain recordings. However, the choice of the optimal features remained dataset-specific, influenced by the similarity of average spike shapes and the number of fibers contributing to the signal.

Based on our findings, we recommend tailoring lightweight algorithms to individual recordings and assessing the "sortability feasibility" based on achieved accuracy and the research question before proceeding with sorting of non-time-locked spikes. Our approach provides the foundation for further development of spike sorting algorithms in noisy extracellular recordings of neural activity.

Author SummaryUsing electrophysiological methods like microneurography, scientists can record nerve activity in humans to understand how peripheral nerves transmit sensations such as pain and itch. These recordings capture electrical signals, known as spikes, which represent nerve impulses. However, since several nerve fibers are often recorded simultaneously, the differentiation of the individual spikes, known as spike sorting, is critical for accurate analysis.

Existing methods for spike sorting in single electrode in-vivo recordings are often insufficient due to low signal-to-noise ratios and the absence of ground truth data needed for validation. In microneurography, low-frequency electrical stimulation (marking method) is used routinely to label part of the recorded spikes. We applied the marking method to create a ground truth data set for developing and validating a supervised approach for spike sorting.

Our transparent and lightweight algorithm showed promising results. Their high variability between the recordings, with a strong reversed link between the morphological similarities of the different fibers spikes and the sorting accuracy indicated a possibility to assess the "sortability" of individual recordings by applying use-case specific thresholds. This work provides a foundation for improving spike sorting in noisy peripheral nerve recordings, helping researchers study better how the nervous system processes sensations like pain and itch.

Predictive routing emerges from self-supervised stochastic neural plasticity
Neurophysiology studies propose that predictive coding is implemented via alpha/beta (8-30 Hz) rhythms that prepare specific pathways to process predicted inputs. This leads to a state of relative inhibition, reducing feedforward gamma (40-90 Hz) rhythms and spiking to predictable inputs. We refer to this model as predictive routing. It is currently unclear which circuit mechanisms implement this push-pull interaction between alpha/beta and gamma rhythms. To explore how predictive routing is implemented, we developed a self-supervised learning algorithm we call generalized Stochastic Delta Rule (gSDR). It was necessary to develop this learning rule because manual tuning of parameters (frequently used in computational modeling) is inefficient to search through a non-linear parameter space that defines how neuronal rhythms emerge and interact. We used gSDR to train biophysical neural circuits and validated the algorithm on simple tasks, e.g., tuning membrane potentials and firing rates. We next applied gSDR to model observed neurophysiology. We asked the model to reproduce a shift from baseline oscillatory dynamics ([~]<20Hz) to stimulus induced gamma ([~]40-90Hz) dynamics recorded in the macaque monkey visual cortex. This gamma-band oscillation during stimulation emerged by self-modulation of synaptic weights via gSDR. We further showed that the gamma-beta push-pull interactions implied by predictive routing could emerge via stochastic modulation of both local inhibitory circuitry as well as top-down modulatory inputs to a network. To summarize, we implemented gSDR to train biophysical neural circuits based on a series of objectives. gSDR succeeded in implementing these objectives. This revealed the inhibitory neuron mechanisms underlying the gamma-beta push-pull dynamics that are observed during predictive processing tasks in systems and cognitive neuroscience.

Significant StatementThis study contributes to the advancement of self-supervised learning for modeling the behavior of complex neural circuits and specifically, biophysical models based on predictive routing framework. Since gSDR is an evolutionary algorithm and does not rely on specific model-based assumptions, it could improve autonomous approaches both in computational neuroscience and neural network research.

Disrupted Maternal Behavior in Morphine-Dependent Pregnant Rats and Anhedonia in their Offspring
It is currently estimated that every 15 minutes an infant is born with opioid use disorder and undergoes intense early life trauma due to opioid withdrawal. Clinical research on the long-term consequences of gestational opioid exposure reports increased rates of social, conduct, and emotional disorders in these children. Here, we investigate the impact of perinatal opioid exposure (POE) on behaviors associated with anhedonia and stress in male and female Sprague Dawley rats. Young adult female rats were administered morphine via programmable, subcutaneous micro-infusion pumps before, during, and through one week post gestation. Maternal behavior was examined for fragmentation and entropy for the first two postnatal weeks; offspring were assessed for sucrose preference, social behavior, and stress responsivity. Overall, dams that received morphine across gestation displayed significantly less pup-directed behavior with increased fragmentation for nursing and higher entropy scores. In adolescence, male and female rat offspring exposed to morphine displayed reduced sucrose preference and, as adults, spent significantly less time socially interacting with familiar conspecifics. Changes in social behaviors were linked to increased activity in nondopaminergic mesolimbic reward brain regions. Although no treatment effects were observed in forced swim test performance, corticosterone levels were significantly increased in POE adult males. Together, these results suggest that perinatal morphine exposure results in anhedonic behavior, possibly due to fragmented and unpredictable maternal behavior in opioid-dependent dams.

Significance StatementClinical and preclinical research has shown that early life stress can produce lifelong depression-like symptoms. Here, we show that maternal opioid use during pregnancy produces depression-like symptoms in the offspring. We also observed disrupted maternal behavior in morphine-exposed dams that is similar to the changes in maternal behavior reported following other forms of early life stress. We propose that the stress of opioid exposure and suboptimal rearing leads to reduced sucrose preference, increased stress reactivity, and social avoidance behavior in the offspring, all of which are characteristics of anhedonia.

Evidence for distinct networks underlying symptom clusters of posttraumatic stress disorder
BackgroundClinical and psychometric evidence has long supported a multidimensional model of PTSD, with symptom subcategories derived from factor analytic methods. Although research on the biological bases of PTSD as a unitary construct is profuse, comparatively few studies have examined the neural mechanisms underlying subcategories of PTSD symptoms. The present study aimed to provide the first evidence of causal relationships between brain structure and PTSD symptom subcategories, using a lesion-behavior mapping approach.

MethodsUsing a group of male combat veterans with focal penetrating traumatic brain injuries (n = 177), we determined the effects of focal damage on the PTSD symptom subcategories of hyperarousal, avoidance, and re-experiencing.

ResultsOur findings revealed two distinct networks that underlie symptom subcategories of PTSD: (1) an amygdala-ventromedial prefrontal cortex network underlying hyperarousal and avoidance symptoms, in which amygdala damage acts as a risk factor for the development of these symptoms, while vmPFC damage acts as a protective factor against the same symptoms; and (2) a hippocampal network underlying re-experiencing and avoidance, in which hippocampal damage acts as a protective factor against these symptoms.

ConclusionsThe present study provides novel insights regarding the causal role of key brain regions in the heterogeneous expression of PTSD symptoms. Results not only contribute to a more complete picture of the neural mechanisms underlying PTSD, but may also aid in the future development of individualized therapeutic strategies that target specific symptom profiles.

Impact of bacterial translocation in stroke outcome. Soluble CD14 as early clinical marker and effect of TLR4
Bacterial infections are among the most common complications in stroke patients. While some factors triggering these infections are well established, bacterial translocation (BT) from the intestine to other organs represents another significant factor to consider. In our study, we observed a high percentage of animals with intestinal barrier dysfunction (GBD) and BT following stroke, with typical intestinal bacteria even detected in the lungs. Moreover, this process not only exacerbates peripheral/central inflammation but also increases lesion size. In this context, our data in stroke patients demonstrate the presence of GBD, associated with elevated levels of soluble CD14 as a marker, and its strong correlation with neurological status, infarct volume, and the development of infections. Finally, our findings highlight the neuroprotective effects of the absence or pharmacological inhibition of TLR4 using ApTOLL, which not only reduces infarct volume and inflammation but also mitigates GBD/BT processes following experimental stroke.