bioRxiv Subject Collection: Neuroscience's Journal

Model-Agnostic Neural Mean Field With The Refractory SoftPlus Transfer Function
Due to the complexity of neuronal networks and the nonlinear dynamics of individual neurons, it is challenging to develop a systems-level model which is accurate enough to be useful yet tractable enough to apply. Mean-field models which extrapolate from single-neuron descriptions to large-scale models can be derived from the neuron's transfer function, which gives its firing rate as a function of its synaptic input. However, analytically derived transfer functions are applicable only to the neurons and noise models from which they were originally derived. In recent work, approximate transfer functions have been empirically derived by fitting a sigmoidal curve, which imposes a maximum firing rate and applies only in the diffusion limit, restricting applications. In this paper, we propose an approximate transfer function called Refractory SoftPlus, which is simple yet applicable to a broad variety of neuron types. Refractory SoftPlus activation functions allow the derivation of simple empirically approximated mean-field models using simulation results, which enables prediction of the response of a network of randomly connected neurons to a time-varying external stimulus with a high degree of accuracy. These models also support an accurate approximate bifurcation analysis as a function of the level of recurrent input. Finally, the model works without assuming large presynaptic rates or small postsynaptic potential size, allowing mean-field models to be developed even for populations with large interaction terms.

Senolytic treatment depletes microglia and decreases severity of experimental autoimmune encephalomyelitis.
The role of senescence in disease contexts is complex, however there is considerable evidence that depletion of senescent cells improves outcomes in a variety of contexts particularly related to aging, cognition, and neurodegeneration. Here, the effect of a bioinformatically-rationalized senolytic was tested in the experimental autoimmune encephalomyelitis (EAE) mouse model of multiple sclerosis (MS). Single-cell analysis from brain tissue isolated from mice subjected to EAE identified microglia with a strong senescence signature including the presence of BCL2-family member transcripts. Cells expressing Bcl2l1 had higher expression of pro-inflammatory and senescence genes than their negative counterparts in EAE, suggesting they may exacerbate inflammation. Notably, in human single-nucleus sequencing from MS, BCL2L1 positive microglia were strongly enriched in lesions with active inflammatory pathology, and likewise demonstrated increased expression of immune related genes suggesting they may contribute to the active lesion pathology and tissue damage in chronic active lesions. Employing a small molecule BCL2 inhibitor, Navitoclax (ABT-263), significantly reduced the presence of microglia in the EAE spinal cord, suggesting that these cells can be targeted by senolytic treatment. ABT-263 treatment had a profound effect on EAE mice, decreasing motor symptom severity, improving visual acuity, promoting neuronal survival, and decreasing white matter inflammation. Together, these results provide evidence to support the idea that senescent glia may exacerbate inflammation resulting in negative outcomes in neuroinflammatory disease and that removing them may ameliorate disease.

Non-cell autonomous OTX2 in the piriform cortex regulates parvalbumin cell maturation states and olfactory-driven behavior
The timing of critical periods of juvenile brain plasticity is driven by the maturation of parvalbumin interneurons in the neocortex, a process regulated in part by non-cell autonomous activity of the OTX2 homeoprotein transcription factor. However, the involvement of critical periods in olfactory paleocortex maturation is unknown. Here, we find that the adult mouse piriform cortex parvalbumin interneurons display particularly low maturation that increases in aged animals. Expression analysis of a large panel of genes reveals that an acute increase in piriform cortex OTX2 levels in young adult mice increases Pvalb expression as well as Adamts9 expression, resulting in increased extracellular perineuronal net levels, while reducing OTX2 transfer decreases Pvalb expression and increases Mmp9 expression, resulting in decreased perineuronal net levels. Reduction in OTX2 also stimulates odor-driven cFos activity in piriform cortex parvalbumin cells and disrupts olfactory-driven behavior. Our findings suggest plasticity in piriform cortex involves OTX2 activity on parvalbumin cells and lacks strictly defined critical periods.

Evaluating Cochlear Implant Stimulation Strategies Through Wide-field Calcium Imaging of the Auditory Cortex
Cochlear Implants (CI) are an effective neuroprosthesis for humans with profound hearing loss, enabling deaf adults to have phone calls without lipreading and babies to have successful language development. However, CIs have significant limitations in complex hearing situations, motivating the need for further research, including studies in animal models. Here, we demonstrate the usefulness of wide field Ca++ imaging in assessing different CI stimulation strategies. One major challenge in electrophysiology in CI animals lies in excluding the CI electric artifacts from the recording, since they are orders of magnitude larger than the amplitude of action potentials. Also, electrophysiology can rarely sample large areas of neuropil at high spatial resolution. To circumvent these problems, we have set up an imaging system allowing us to monitor neural activity in the auditory cortex (AC) of CI supplied rats using the Ca++ sensitive dye OGB. Here we describe an initial experiment with this setup, in which we recorded cortical responses to 4 different stimulation patterns which were delivered across 3 CI channels to the contralateral ear. We then investigated two parameters that have been shown to affect intelligibility in CI users: pulse rate and relative pulse timing across CI channels. While pulse rate had only a very modest effect on the discriminability of the neural responses, the stimulation mode had a major effect, with simultaneous pulse timing, perhaps surprisingly, allowing much better pattern discrimination than interleaved sampling. The result suggests that allowing collisions of pulses on neighboring channels may not always be detrimental, at least if partial overlaps of pulses, in which anodic and cathodic pulse phases might cancel, are avoided.

Predictive processing in biological motion perception: Evidence from human behavior
Biological motion perception plays a crucial role in understanding the actions of other animals, facilitating effective social interactions. While foundation of biological motion perception is rooted in bottom-up processes, as evidenced by point-light display studies, real-world complexities necessitate the involvement of top-down processes, such as attention and expectation. This study investigates the impact of expectations on biological motion perception using a cued individuation task with point-light display stimuli. We conducted three experiments, each providing advance information about distinct aspects of the subsequent biological motion stimuli: specifically information about action, emotion, and gender. Our results revealed a pattern in the action experiment, where participants demonstrated significantly slower response times for incongruent trials than congruent ones, but only under the 75% cue validity condition. This effect was notably absent in the emotion and gender experiments. Our exploration underscores the multi-faceted nature of biological motion perception, highlighting that while the brain adeptly harnesses prior cues to anticipate and interpret stimuli, the nature and reliability of these cues play a pivotal role on their effects. Specifically, action-related information stands out as an important modulator, possibly due to its evolutionary significance and unique neural processing pathway. These findings not only agree with the principles of predictive processing but also pave the way for future research, emphasizing the need to utilize naturalistic, complex stimuli together with neuroimaging methods to create more comprehensive models of biological motion perception.

Visually Induced Involuntary Movements
Looking at a virtual 3D environment with structural features rotating at 60 degrees/s in a head-mounted display soon elicits an illusion of self-rotation and displacement in the opposite direction. We explored in 75 s long trials the effects of visually induced self-rotation on the head, torso, and horizontally extended right arm of standing subjects. The degree of body and limb movement was contingent on whether the arm was extended out freely or pointing at a briefly proprioceptively specified target position, but did not depend on whether the hand held a rod or not. Most subjects in the Free condition showed significant unintentional arm deviations, which averaged approximately 55 degrees in the direction opposite the induced illusory self-motion, and were more than 150 degrees in some cases. In contrast, on average, the deviations in the Pointing condition were a quarter of those in the Free condition. Deviations of head and torso positions also occurred in all conditions. Total arm and head deviations were the sum of deviations of the arm and head with respect to the torso plus deviations of the torso with respect to space. When given a pointing target, subjects were largely able to detect and correct for arm and head deviations with respect to the torso but not for the parts of arm and head deviation that were due to deviations of the torso with respect to space. In all conditions, the arm, head, and torso deviations occurred before subjects began to experience compelling self-rotation and displacement. This is contrasted with the compensations for expected but absent Coriolis forces that are made when stationary subjects make reaching movements to targets during exposure to structured moving visual scenes. These compensations do not occur until subjects experience self-rotation and spatial displacement. These results have implications for vehicle control and maneuvering in environments that induce illusory motion and displacement, and in situations where there is motion in a large area of the visual field. The impact of these effects on joystick control is described and discussed. We also describe the subjective sense of ownership attributed to hand-held objects when experiencing illusory self-motion and displacement.

Cholinergic modulation of interhemispheric inhibition in the mouse motor cortex
Interhemispheric inhibition (IHI) of the homotopic motor cortex is believed to be effective for accurate unilateral motor function. However, the cellular mechanisms underlying IHI during unilateral motor behavior remain unclear. Furthermore, the impact of the neuromodulator acetylcholine (ACh) on IHI and the associated cellular mechanisms are not well understood. To address this knowledge gap, we conducted recordings of neuronal activity from the bilateral motor cortex of mice during the paw-reaching task. Subsequently, we analyzed interhemispheric spike correlation at the cell-pair level, classifying putative cell types to explore the underlying cellular circuitry mechanisms of IHI. We found a cell-type pair-specific enhancement of the interhemispheric spike correlation when the mice were engaged in the reaching task. We also found that the interhemispheric spike correlation was modulated by pharmacological ACh manipulation. The local field responses to contralateral excitation differed along the cortical depths, and muscarinic receptor antagonism enhanced the inhibitory component of the field response in deep layers. The muscarinic subtype M2 receptor is predominantly expressed in deep cortical neurons, including GABAergic interneurons. These results suggest that GABAergic interneurons expressing muscarinic receptors in deep layers mediate the neuromodulation of IHI in the homotopic motor cortex.

Local muscle pressure stimulates the principal receptors for proprioception
Muscle spindles are considered the principal receptors for proprioception, sensing stretch and signaling limb position and velocity. Here, we show that local muscle pressure is an adequate stimulus for human spindles in isometric conditions, and that pressure enhances spindle responses during stretch, as hypothesized due to compression of the spindle capsule. Our findings redefine basic muscle feedback and urge reassessment of proprioception's role in sensorimotor function and various neuromuscular conditions.

Identifying Interpretable Latent Factors with Sparse Component Analysis
In many neural populations, the computationally relevant signals are posited to be a set of "latent factors" - signals shared across many individual neurons. Understanding the relationship between neural activity and behavior requires the identification of factors that reflect distinct computational roles. Methods for identifying such factors typically require supervision, which can be suboptimal if one is unsure how (or whether) factors can be grouped into distinct, meaningful sets. Here, we introduce Sparse Component Analysis (SCA), an unsupervised method that identifies interpretable latent factors. SCA seeks factors that are sparse in time and occupy orthogonal dimensions. With these simple constraints, SCA facilitates surprisingly clear parcellations of neural activity across a range of behaviors. We applied SCA to motor cortex activity from reaching and cycling monkeys, single-trial imaging data from C. elegans, and activity from a multitask artificial network. SCA consistently identified sets of factors that were useful in describing network computations.

Cortico-spinal Mechanisms of Periphery Neuromodulation induced Analgesia
Nociceptive acute and chronic pain significantly impact the quality of life and create tremendous societal burdens. Given the side effects associated with pharmacological analgesia, noninvasive periphery neuromodulation techniques, like Transcutaneous Electrical Nerve Stimulation (TENS), have emerged as promising approaches for pain relief. Current human research, focusing partly on cerebral, brainstem, or peripheral mechanisms of neuromodulation, lacks comprehensive understanding from the perspective of the entire central nervous system. This study utilized a three-way mixed experimental design, combining cutting-edge cortico-spinal fMRI with thermal stimuli, to systematically explore the central analgesic mechanisms of two typical TENS modes: Conventional (high frequency, low intensity) and Acupuncture-Like (low frequency, high intensity). Behavioral and fMRI analysis revealed that, the direct spinal inhibition (PAG-spinal connectivity) partially mediated by PAG-vmPFC connectivity leads to local analgesic effects in Conventional TENS; a top-down diffuse noxious inhibition (PAG-S1 connectivity) fully mediated through PAG-spinal connectivity leads to diffuse analgesic effects in Acupuncture-Like TENS. Employing advanced cortico-spinal fMRI technique, our findings provide systematic neural evidence of the analgesic mechanisms induced by TENS and shed new light on future neuromodulation approaches.

A pharmacological toolkit for human microglia identifies Topoisomerase I inhibitors as immunomodulators for Alzheimer's disease
While efforts to identify microglial subtypes have recently accelerated, the relation of transcriptomically defined states to function has been largely limited to in silico annotations. Here, we characterize a set of pharmacological compounds that have been proposed to polarize human microglia towards two distinct states - one enriched for AD and MS genes and another characterized by increased expression of antigen presentation genes. Using different model systems including HMC3 cells, iPSC-derived microglia and cerebral organoids, we characterize the effect of these compounds in mimicking human microglial subtypes in vitro. We show that the Topoisomerase I inhibitor Camptothecin induces a CD74high/MHChigh microglial subtype which is specialized in amyloid beta phagocytosis. Camptothecin suppressed amyloid toxicity and restored microglia back to their homeostatic state in a zebrafish amyloid model. Our work provides avenues to recapitulate human microglial subtypes in vitro, enabling functional characterization and providing a foundation for modulating human microglia in vivo.

Distinct Effects of Aducanumab and Lecanemab on Intraneuronal Endogenous Aβ42 and Phosphorylated Tau in Alzheimer's Disease Treatment
In the treatment of Alzheimer's Disease (AD), two FDA-approved monoclonal antibodies, Aducanumab (Adu) and Lecanemab (LCN), exhibit significant differences in clinical benefits. By utilizing human-induced basal forebrain cholinergic neurons (BFCNs) derived from AD patient skin fibroblasts, we successfully recapitulated the natural endogenous neuropathologies of A{beta}42 and Tau within just 21 days and revealed distinct intraneuronal effects of Adu and LCN. Both antibodies are internalized into BFCNs and localize with cytosolic A{beta}42. However, LCN, selectively targeting A{beta}42 oligomers and protofibrils, triggers TRIM21 pathway and significantly enhances autolysosome- and proteasome-mediated A{beta}42 clearance, thereby leading to a marked reduction in phosphorylated Tau181 (pTau181) pathology. In contrast, the fibrillized A{beta}42-selective Adu shows considerably weaker effects. This study not only reveals the unique intraneuronal actions of Adu and LCN but also provides a reliable and accessible human neuronal model for evaluating potential AD therapeutics, emphasizing the importance of intraneuronal pathology in the treatment of AD.

Sleep need driven oscillation of glutamatergic synaptic phenotype
The response to sleep loss, induced by experimental sleep deprivation (SD), provides insight into the function of sleep. Earlier observations have shown an overall increase in synaptic strength and number of cortical, glutamate, AMPA receptor (AMPAR) synapses in response to SD that is recovered by sleep. However, other aspects of glutamatergic transmission, including NMDA receptor mediated neurotransmission and related upstream synaptic regulators of the glutamate synapse function, have not been well examined. Following SD, we report increased AMPA/NMDA ratio in whole cell recordings of frontal cortical (FC) pyramidal neurons of layers 2-3. Additionally, the ratio of silent/active synapse is decreased after SD reflecting decreased silent synapses and plastic potential to convert silent NMDA to active AMPA synapses. All aspects recover with sleep and are associated with differentially expressed genes (DEGs) affecting glutamatergic synaptic phenotype. The DEGs are enriched for a functional group of synaptic shaping cellular components (SSCs) controling glutamate synapse phenotype, overlap with autism risk genes and are primarily observed in a subtype of excitatory pyramidal neurons that project intra-telencephalically (ExIT neurons). Upstream, sleep-related control is suggested by significant enrichment of genes controlled by transcription factor, MEF2c and nuclear HDAC4, a repressor of MEF2c transcriptional activation. Taken together, we propose a functional role of sleep/wake in FC controlling gene expression, regulating an oscillation of glutamate-synaptic phenotypes that facilitates motor learning and training, and if dysfunctional, increases risk for autism.

Drosophila HCN mediates gustatory homeostasis by preserving sensillar transepithelial potential in sweet environments
Establishing transepithelial ion disparities is crucial for sensory functions in animals. In insect sensory organs called sensilla, a transepithelial potential, known as the sensillum potential (SP), arises through active ion transport across accessory cells, sensitizing receptor neurons such as mechanoreceptors and chemoreceptors. Because multiple receptor neurons are often co-housed in a sensillum and share SP, niche-prevalent overstimulation of single sensory neurons can compromise neighboring receptors by depleting SP. However, how such potential depletion is prevented to maintain sensory homeostasis remains unknown. Here, we find that the Ih-encoded hyperpolarization-activated cyclic nucleotide gated (HCN) channel bolsters the activity of bitter-sensing gustatory receptor neurons (bGRNs), albeit acting in sweet-sensing GRNs (sGRNs). For this task, HCN maintains SP despite prolonged sGRN stimulation induced by the diet mimicking their sweet feeding niche, such as overripe fruit. We present evidence that Ih-dependent demarcation of sGRN excitability is implemented to throttle SP consumption, which may have facilitated adaptation to a sweetness-dominated environment. Thus, HCN expressed in sGRNs serves as a key component of a simple yet versatile peripheral coding that regulates bitterness for optimal food intake in two contrasting ways: sweet-resilient preservation of bitter aversion and the previously reported sweet-dependent suppression of bitter taste.

The epigenetic landscape of oligodendrocyte progenitors changes with time
Adult oligodendrocyte progenitors (aOPCs) generate myelinating oligodendrocytes, like neonatal progenitors (nOPCs), but they also display unique functional features. Here, using RNA-sequencing, unbiased histone proteomics analysis and ChIP-sequencing, we define the transcripts and histone marks underlying the unique properties of aOPCs. We describe the lower proliferative capacity and higher levels of expression of oligodendrocyte specific genes in aOPCs compared to nOPCs, as well as the greater levels of H4 histone marks. We also report increased occupancy of the H4K8ac mark at chromatin locations corresponding to oligodendrocyte-specific transcription factors and lipid metabolism genes. Pharmacological inhibition of H4K8ac deposition reduces the levels of these transcripts in aOPCs, rendering their transcriptome more similar to that of nOPCs. The repressive H4K20me3 mark is also higher in aOPCs compared to nOPCs and pharmacological inhibition of its deposition results in increased levels of genes related to the mature oligodendrocyte state. Overall, this study identifies two histone marks which are important for the unique transcriptional and functional identity of aOPCs.

A next-generation, histological atlas of the human brain and its application to automated brain MRI segmentation
Magnetic resonance imaging (MRI) is the standard tool to image the human brain in vivo. In this domain, digital brain atlases are essential for subject-specific segmentation of anatomical regions of interest (ROIs) and spatial comparison of neuroanatomy from different subjects in a common coordinate frame. High-resolution, digital atlases derived from histology (e.g., Allen atlas, BigBrain, Julich), are currently the state of the art and provide exquisite 3D cytoarchitectural maps, but lack probabilistic labels throughout the whole brain. Here we present NextBrain, a next-generation probabilistic atlas of human brain anatomy built from serial 3D histology and corresponding highly granular delineations of five whole brain hemispheres. We developed AI techniques to align and reconstruct [~]10,000 histological sections into coherent 3D volumes, as well as to semi-automatically trace the boundaries of 333 distinct anatomical ROIs on all these sections. Comprehensive delineation on multiple cases enabled us to build an atlas with probabilistic labels throughout the whole brain. Further, we created a companion Bayesian tool for automated segmentation of the 333 ROIs in any in vivo or ex vivo brain MRI scan using the NextBrain atlas. We showcase two applications of the atlas: automated segmentation of ultra-high-resolution ex vivo MRI and volumetric analysis of brain ageing based on [~]4,000 publicly available in vivo MRI scans. We publicly release the raw and aligned data (including an online visualisation tool), probabilistic atlas, and segmentation tool. By enabling researchers worldwide to analyse brain MRI scans at a superior level of granularity without manual effort or highly specific neuroanatomical knowledge, NextBrain will accelerate our quest to understand the human brain in health and disease.

Brain and grammar: revealing electrophysiological basic structures with competing statistical models
Acoustic, lexical and syntactic information is simultaneously processed in the brain. Therefore, distinguishing the electrophysiological activity pertaining to these components requires complex and indirect strategies. Capitalizing on previous works which factor out acoustic information, we could concentrate on the lexical and syntactic contribution to language processing by testing competing statistical models. We exploited EEG recordings and compared different surprisal models selectively involving lexical information, part of speech or syntactic structures in various combinations. EEG responses were recorded in 32 participants during listening to affirmative active declarative sentences and compared the activation corresponding to basic syntactic structures, such as noun phrases vs verb phrases. Lexical and syntactic processing activates different frequency bands, different time windows and different networks. Moreover, surprisal models based on part of speech inventory only do not explain well the electrophysiological data, while those including syntactic information do. Finally, we confirm previous measures obtained with intracortical recordings independently supporting the original hypothesis addressed here in a robust way.

Multimodal brain age prediction using machine learning: combining structural MRI and 5-HT2AR PET derived features
To better assess the pathology of neurodegenerative disorders and to evaluate the efficacy of neuroprotective interventions, it is required to develop biomarkers that can accurately capture age-related biological changes in the human brain. Brain serotonin 2A receptors (5-HT2AR) show a particularly profound age-related decline and are also widely reduced in, e.g., Alzheimer's disease. Hence, cerebral 5-HT2AR binding measured in vivo using positron emission tomography (PET) is a potentially useful biomarker for age-related changes in the brain. In this study, we investigate the decline in 5-HT2AR binding to evaluate its usefulness as a biomarker for biological aging. Specifically, we aim to 1) predict brain age using 5-HT2AR binding outcomes, 2) compare 5-HT2AR-based predictions of brain age to predictions based on gray matter (GM) volume, as determined with structural magnetic resonance imaging (MRI), and 3) investigate whether combining 5-HT2AR and GM volume data improves prediction. We used PET and MR images from 209 healthy individuals aged between 18 and 85 years (mean=38, std=18). 5-HT2AR binding and GM volume were calculated for 14 cortical and subcortical regions. Different machine learning algorithms were used to predict age based on 5-HT2AR binding, GM volume, and the combined measures. The mean absolute error (MAE) and a cross-validation approach were used for evaluation and model comparison. We find that both the cerebral 5-HT2AR binding (mean MAE=6.63 years, std=0.78 years) and GM volume (mean MAE=7.76 years, std=0.92 years) predict chronological age accurately. Combining the two measures improves the prediction further (mean MAE=5.93 years, std=0.82). We conclude that when it comes to predicting age, in vivo measurements of the cerebral 5-HT2AR binding are more informative than GM volumes.

Wearable Neural Interfaces: Real-Time Identification of Motor Neuron Discharges in Dynamic Motor Tasks
Objective: Robustness to non-stationary conditions is essential to develop stable and accurate wearable neural interfaces. Approach: We propose a novel adaptive electromyography (EMG) decomposition algorithm that builds on blind source separation methods by leveraging the Kullback-Liebler divergence and kurtosis of the signals as metrics for online learning. The proposed approach provides a theoretical framework to tune the adaptation hyperparameters and compensate for non-stationarities in the mixing matrix, such as due to dynamic contractions, and to identify the underlying motor neuron (MN) discharges. The adaptation is performed in real-time (~22 ms of computational time per 100-ms batches). Main Results: The proposed adaptation algorithm significantly improved all decomposition performance metrics with respect to the absence of adaptation in a wide range of motion of the wrist (80{degrees}). The rate of agreement, sensitivity, and precision were [≥]90% in [≥]80% of the cases in both simulated and experimentally recorded data, according to a two-source validation approach. Significance: The findings demonstrate the feasibility of accurately decoding MN discharges in real-time during dynamic contractions from wearable systems mounted at the wrist and forearm. Moreover, the study proposes an experimental validation method for EMG decomposition in dynamic tasks.

Crebinostat Facilitates Memory Formation
Protein modifications importantly contribute to memory formation. Protein acetylation is a posttranslational modification of proteins that regulates memory formation. Acetylation level is determined by the relative activities of acetylases and deacetylases. Crebinostat is a histone deacetylase inhibitor. Here we show that in object recognition task, Crebinostat facilitates memory formation by a weak training. Further, this compound enhances acetylation of -tubulin, and reduces the level of histone deacetylase 6, an -tubulin deacetylase. The results suggest that enhanced acetylation of -tubulin by Crebinostat contributes to its facilitatory effect on memory formation.

MEA-NAP compares microscale functional connectivity, topology, and network dynamics in organoid or monolayer neuronal cultures
Microelectrode array (MEA) recordings are commonly used to compare firing and burst rates in neuronal cultures. MEA recordings can also reveal microscale functional connectivity, topology and network dynamics--patterns seen in brain networks across spatial scales. Network topology is frequently characterized in neuroimaging with graph theoretical metrics. However, few computational tools exist for analyzing microscale functional brain networks from MEA recordings. Here, we present a MATLAB MEA network analysis pipeline (MEA-NAP) for raw voltage time-series acquired from single- or multi-well MEAs. Applications to 3D human cerebral organoids or 2D human-derived or murine cultures reveal differences in network development, including topology, node cartography, and dimensionality. MEA-NAP incorporates multi-unit template-based spike detection, probabilistic thresholding for determining significant functional connections, and normalization techniques for comparing networks. MEA-NAP can identify network-level effects of pharmacologic perturbation and/or disease-causing mutations and, thus, can provide a translational platform for revealing mechanistic insights and screening new therapeutic approaches.

Deep Normative Tractometry for Identifying Joint White Matter Macro- and Micro-structural Abnormalities in Alzheimer's Disease
This study introduces the Deep Normative Tractometry (DNT) framework, that encodes the joint distribution of both macrostructural and microstructural profiles of the brain white matter tracts through a variational autoencoder (VAE). By training on data from healthy controls, DNT learns the normative distribution of tract data, and can delineate along-tract micro- and macro-structural abnormalities. Leveraging a large sample size via generative pre-training, we assess DNT's generalizability using transfer learning on data from an independent cohort acquired in India. Our findings demonstrate DNT's capacity to detect widespread diffusivity abnormalities along tracts in mild cognitive impairment and Alzheimer's disease, aligning closely with results from the Bundle Analytics (BUAN) tractometry pipeline. By incorporating tract geometry information, DNT may be able to distinguish disease-related abnormalities in anisotropy from tract macrostructure, and shows promise in enhancing fine-scale mapping and detection of white matter alterations in neurodegenerative conditions.

UniFed: A unified deep learning framework for segmentation of partially labelled, distributed neuroimaging data
It is essential to be able to combine datasets across imaging centres to represent the breadth of biological variability present in clinical populations. This, however, leads to two challenges: first, an increase in non-biological variance due to scanner differences, known as the harmonisation problem, and, second, data privacy concerns due to the inherently personal nature of medical images. Federated learning has been proposed to train deep learning models on distributed data; however, the majority of approaches assume fully labelled data at each participating site, which is unlikely to exist due to the time and skill required to produce manual segmentation labels. Further, they assume all of the sites are available when the federated model is trained. Thus, we introduce UniFed, a unified federated harmonisation framework which enables three key processes to be completed: 1) the training of a federated harmonisation network, 2) the selection of the most appropriate pretrained model for a new unseen site, and 3) the incorporation of a new site into the harmonised federation. We show that when working with partially labelled distributed datasets, our methods produce high-quality image segmentations and enable all sites to benefit from the knowledge of the federation. The framework is flexible and widely applicable across segmentation tasks and choices of model architecture.

Human Motor Cortex Encodes Complex Handwriting Through a Sequence of Primitive Neural States
How the human motor cortex (MC) orchestrates sophisticated fine movements such as handwriting remains a puzzle. Here, we investigate this question through Utah array recordings from human MC hand knob, during imagined handwriting of Chinese characters (306 characters tested, 6.3 {+/-} 2 strokes per character). We find MC programs the writing of complicated characters by sequencing a small set of primitive states: The directional tuning of motor neurons remains stable within each primitive state but strongly varies across states. Furthermore, the occurrence of a primitive state is encoded by a separate set of neurons not directly involved in movement control. By automatically identifying the primitive states and corresponding neuronal tuning properties, we can reconstruct a recognizable writing trajectory for each character (84% improvement in reconstruction accuracy compared with baseline). Our findings unveil that skilled, sophisticated movements are decomposed into a sequence of primitive movements that are programmed through state-specific neural configurations, and this hierarchical control mechanism sheds new light on the design of high-performance brain-computer interfaces.

Early altered directionality of resting brain network state transitions in the TgF344-AD rat model of Alzheimer's disease
Alzheimer's disease (AD) is a progressive neurodegenerative disease resulting in memory loss and cognitive decline. Synaptic dysfunction is an early hallmark of the disease whose effects on whole-brain functional architecture can be identified using resting-state functional MRI (rsfMRI). Insights into mechanisms of early, whole-brain network alterations can help our understanding of the functional impact of AD's pathophysiology. Here, we obtained rsfMRI data in the TgF344-AD rat model at the pre- and early-plaque stages. This model recapitulates the major pathological and behavioural hallmarks of AD. We used co-activation pattern (CAP) analysis to investigate if and how the dynamic organization of intrinsic brain functional networks states, undetectable by earlier methods, is altered at these early stages. We identified and characterized six intrinsic brain states as CAPs, their spatial and temporal features, and the transitions between the different states. At the pre-plaque stage, the TgF344-AD rats showed reduced co-activation of hub regions in the CAPs corresponding to the default mode-like and lateral cortical network. Default mode-like network activity segregated into two distinct brain states, with one state characterised by high co-activation of the basal forebrain. This basal forebrain co-activation was reduced in TgF344-AD animals mainly at the pre-plaque stage. Brain state transition probabilities were altered at the pre-plaque stage between states involving the default mode-like network, lateral cortical network, and basal forebrain regions. Additionally, while the directionality preference in the network-state transitions observed in the wild-type animals at the pre-plaque stage had diminished at the early-plaque stage, TgF344-AD animals continued to show directionality preference at both stages. Our study enhances the understanding of intrinsic brain state dynamics and how they are impacted at the early stages of AD, providing a nuanced characterization of the early, functional impact of the disease's neurodegenerative process.

Brain Age Analysis and Dementia Classification using Convolutional Neural Networks trained on Diffusion MRI: Tests in Indian and North American Cohorts
Deep learning models based on convolutional neural networks (CNNs) have been used to classify Alzheimer's disease or infer dementia severity from T1-weighted brain MRI scans. Here, we examine the value of adding diffusion-weighted MRI (dMRI) as an input to these models. Much research in this area focuses on specific datasets such as the Alzheimer's Disease Neuroimaging Initiative (ADNI), which assesses people of North American, largely European ancestry, so we examine how models trained on ADNI, generalize to a new population dataset from India (the NIMHANS cohort). We first benchmark our models by predicting 'brain age' - the task of predicting a person's chronological age from their MRI scan and proceed to AD classification. We also evaluate the benefit of using a 3D CycleGAN approach to harmonize the imaging datasets before training the CNN models. Our experiments show that classification performance improves after harmonization in most cases, as well as better performance for dMRI as input.

A new approach for estimating effective connectivity from activity in neural networks
Inferring and understanding the underlying connectivity structure of a system solely from the observed activity of its constituent components is a challenge in many areas of science. In neuroscience, such link inference techniques for estimating connectivity are paramount when attempting to understand the network structure of neural systems from their recorded activity patterns. To date, no universally accepted method exists for the inference of effective connectivity, which describes how the activity of a neural node mechanistically affects the activity of other nodes. One practical challenge is that, without ground truth structural connectivity data, the inferred underlying structural connections cannot be validated. In this case, information on the nodal dynamics is needed to obtain a more complete causal understanding of the system in the form of its effective connectivity. Here, we describe a systematic comparison of different approaches for estimating effective connectivity. Starting with the Hopf neuron model with known ground truth structural connectivity, we reconstruct the system's structural connectivity matrix using a variety of algorithms. We show that, in sparse networks, combining a lagged-cross-correlation (LCC) approach with a recently published derivative-based correlation analysis method provides the most reliable estimation of the known ground truth connectivity matrix. We then use the estimated structural connectivity matrix as the basis for a forward simulation of the system dynamics, in order to recreate the observed node activity patterns. We show that, under certain conditions, our best method, LCC, works better than derivative-based methods for sparse noise-driven systems. Finally, we apply our LCC method to empirical biological data. Specifically, we reconstruct the structural connectivity of a subset of the nervous system of the nematode C. Elegans. We show that our computationally simple method performs better than another recently published, computationally more expensive reservoir computing-based method. Our results show that a comparatively simple method can be used to reliably estimate directed effective connectivity in sparse neural systems in the presence of noise. We provide a perspective for future work by advocating the combined use of structural network estimation and activity recreation techniques for system identification, thus bridging the gap between structural and effective connectivity.

Proactive selective attention across competition contexts
Selective attention is a cognitive function that helps filter out unwanted information. Theories as the biased competition model (Desimone & Duncan, 1995) explain how attentional templates bias processing towards targets in contexts where multiple stimuli compete for resources. However, it is unclear how the anticipation of different levels of competition influences the nature of attentional templates, in a proactive fashion. In this study, we used EEG to investigate how the anticipated demands of attentional selection (either high or low stimuli competition contexts) modulate target-specific preparatory brain activity and its relationship with task performance. To do so, participants performed a sex judgement task in a cue-target paradigm where, depending on the block, target and distractor stimuli appeared simultaneously (high competition) or sequentially (low competition). Multivariate Pattern Analysis (MVPA) showed that, in both competition contexts, there was a preactivation of the target category to select with a ramping-up profile at the end of the preparatory interval. However, cross-classification showed no generalization across competition conditions, suggesting different preparatory representational formats. Notably, time-frequency analyses showed differences between anticipated competition demands, reflecting larger theta band power for high than low competition, which mediated the impact of subsequent stimuli competition on behavioral performance. Overall, our results show that, whereas preactivation of the internal templates associated with the category to select are engaged in advance in both competition contexts, their underlying neural patterns differ. In addition, these codes could not be associated with theta power, suggesting different preparatory processes. The implications of these findings are crucial to increase our understanding of the nature of top-down processes across different contexts.

Feasibility of decoding covert speech in ECoG with aTransformer trained on overt speech
Several attempts for speech brain-computer interfacing (BCI) have been made to decode phonemes, sub-words, words, or sentences using invasive measurements, such as the electrocorticogram (ECoG), during auditory speech perception, overt speech, or imagined (covert) speech. Decoding sentences from covert speech is a challenging task. Sixteen epilepsy patients with intracranially implanted electrodes participated in this study, and ECoGs were recorded during overt speech, covert speech, and passive listening of eight Japanese sentences, each consisting of three tokens. A Transformer neural network model was applied to decode text sentences from covert speech, which was trained using ECoGs obtained during overt speech. We first examined the proposed Transformer model using the same task for training and testing and then evaluated the model's performance when trained with overt or perception tasks for decoding covert speech. The Transformer model trained on covert speech achieved an average token error rate (TER) of 46.6% for decoding covert speech, whereas the model trained on overt speech achieved a comparable TER of 46.3% (p > 0.05;d = 0.07). Therefore the challenge of collecting training data for covert speech can be addressed using overt speech. The performance of covert speech can improve by using large amounts of overt speech.

Decoding and reconstruction of surface materials from EEG
Human visual system can easily recognize natural surface material categories and can reliably evaluate surface properties such as gloss and transparency. A large number of psychophysical and neurophysiological studies have revealed that these behavioral judgments on surface materials depend on low- and high-level statistical features. In the present study, we investigated how the neural representation of statistical features enables human visual system to recognize material categories, evaluate surface properties, and eventually obtain a complex and rich phenomenal appearance. To achieve this goal, we measured and analyzed VEPs for 191 natural surface images composed of 20 different material categories such as fabric, gravel, and metal. First, we classified the material categories and perceived surface properties from the VEPs using SVM. As a result, the 20 material categories were significantly classified by the VEPs at approximately ~150 ms and the perceived surface lightness, chromaticity, and smoothness were significantly classified by the VEPs at approximately ~175 ms, while the glossiness, hardness, and heaviness were significantly classified by the VEPs at approximately 200 ms or later. Subsequent reverse-correlation analysis revealed that the VEPs at short latencies, which classified material categories and perceived surface properties, were highly correlated with low- and high-level global statistical features (PS texture statistics and style information) included in natural surface images. This dynamic correlation supports the idea that material discrimination and evaluation of surface properties are based on neural representations of global image features. To demonstrate this idea more directly, we trained deep generative models (MVAE) that reconstruct the surface image itself from VEPs via style information (gram matrix of the dCNN output). The model successfully reconstructed realistic visual stimuli and some of them were nearly indistinguishable from the original images. These findings suggest that the neural representation of statistical image features, which were reflected even in low-spatial-resolution EEG and formed at short latencies in the visual cortex, not simply enable human visual system to recognize material categories and evaluate surface properties but provides the essential basis for rich and complex phenomenal impressions (qualia) of natural surfaces.

Brain-controlled augmented hearing for spatially moving conversations in multi-talker environments
Focusing on a specific conversation amidst multiple interfering talkers presents a significant challenge, especially for the hearing-impaired. Brain-controlled assistive hearing devices aim to alleviate this problem by separating complex auditory scenes into distinct speech streams and enhancing the attended speech based on the listener's neural signals using auditory attention decoding (AAD). Departing from conventional AAD studies that relied on oversimplified scenarios with stationary talkers, we present a realistic AAD task that mirrors the dynamic nature of acoustic settings. This task involves focusing on one of two concurrent conversations, with multiple talkers taking turns and moving continuously in space with background noise. Invasive electroencephalography (iEEG) data were collected from three neurosurgical patients as they focused on one of the two moving conversations. We propose an enhanced brain-controlled assistive hearing system that combines AAD and a binaural speaker-independent speech separation model. The separation model unmixes talkers while preserving their spatial location and provides talker trajectories to the neural decoder to improve auditory attention decoding accuracy. Our subjective and objective evaluations show that the proposed system enhances speech intelligibility and facilitates conversation tracking while maintaining spatial cues and voice quality in challenging acoustic environments. This research demonstrates the potential of our approach in real-world scenarios and marks a significant step towards developing assistive hearing technologies that adapt to the intricate dynamics of everyday auditory experiences.

The Effect on Speech-in-Noise Perception of Real Faces and Synthetic Faces Generated with either Deep Neural Networks or the Facial Action Coding System
The prevalence of synthetic talking faces in both commercial and academic environments is increasing as the technology to generate them grows more powerful and available. While it has long been known that seeing the face of the talker improves human perception of speech-in-noise, recent studies have shown that synthetic talking faces generated by deep neural networks (DNNs) are also able to improve human perception of speech-in-noise. However, in previous studies the benefit provided by DNN synthetic faces was only about half that of real human talkers. We sought to determine whether synthetic talking faces generated by an alternative method would provide a greater perceptual benefit. The facial action coding system (FACS) is a comprehensive system for measuring visually discernible facial movements. Because the action units that comprise FACS are linked to specific muscle groups, synthetic talking faces generated by FACS might have greater verisimilitude than DNN synthetic faces which do not reference an explicit model of the facial musculature. We tested the ability of human observers to identity speech-in-noise accompanied by a blank screen; the real face of the talker; and synthetic talking face generated either by DNN or FACS. We replicated previous findings of a large benefit for seeing the face of a real talker for speech-in-noise perception and a smaller benefit for DNN synthetic faces. FACS faces also improved perception, but only to the same degree as DNN faces. Analysis at the phoneme level showed that the performance of DNN and FACS faces was particularly poor for phonemes that involve interactions between the teeth and lips, such as /f/, /v/, and /th/. Inspection of single video frames revealed that the characteristic visual features for these phonemes were weak or absent in synthetic faces. Modeling the real vs. synthetic difference showed that increasing the realism of a few phonemes could substantially increase the overall perceptual benefit of synthetic faces, providing a roadmap for improving communication in this rapidly developing domain.

Neural ensembles that encode affective mechanical and heat pain in mouse spinal cord
Acute pain is an unpleasant experience caused by noxious stimuli. How the spinal neural circuits attribute differences in quality of noxious information remains unknown. By means of genetic capturing, activity manipulation and single cell RNA sequencing, we identified distinct neural ensembles in mouse spinal cord encoding mechanical and heat pain. Re-activation or silencing of these ensembles potentiated or stopped, respectively, affective but not reflex behaviour without altering pain behaviour to cross stimuli modality. Within ensembles, polymodal Gal+ inhibitory neurons with monosynaptic contacts to A-fiber sensory neurons gated affective pain independent of modality. Peripheral nerve injury led to microglia driven inflammation and an ensemble transition with decreased recruitment of Gal+ inhibitory neurons and increased excitatory drive. However, activating Gal+ neurons reversed hypersensitivity associated with neuropathy. Our results reveal the existence of a spinal representation which forms the neural basis of the discriminative and affective qualities of acute pain and that these neurons are under the control of a shared feed-forward inhibition.

Wavelet Phase Coherence of Ictal Scalp EEG-Extracted Muscle Activity (SMA) as a Biomarker for Sudden Unexpected Death in Epilepsy (SUDEP)
Objective: Approximately 50 million people worldwide have epilepsy and 8-17% of the deaths in patients with epilepsy are attributed to sudden unexpected death in epilepsy (SUDEP). The goal of the present work was to establish a biomarker for SUDEP so that preventive treatment can be instituted. Approach: Seizure activity in patients with SUDEP and non-SUDEP was analyzed, specifically, the scalp EEG extracted muscle activity (SMA) and the average wavelet phase coherence (WPC) during seizures was computed for two frequency ranges (1-12 Hz, 13-30 Hz) to identify differences between the two groups. Main results: Ictal SMA in SUDEP patients showed a statistically higher average WPC value when compared to non-SUDEP patients for both frequency ranges. Area under curve for a cross-validated logistic classifier was 81%. Significance: Average WPC of ictal SMA is a candidate biomarker for early detection of SUDEP.

Beyond over- or under-sampling: autistic children's inflexibility in sampling costly information
Background: Efficient information sampling is crucial for human inference and decision-making, even for young children. Information sampling is also closely associated with the core symptoms of autism spectrum disorder (ASD), since both the social interaction difficulties and repetitive behaviors suggest that autistic people may sample information from the environment distinctively. Previous research on information sampling in ASD focused mainly on adolescents and adults, and on whether they over- or under-sample. The specific ways in which autistic children sample information, especially when facing explicit costs and adapting to environmental changes, remain unclear. Methods: We employed an adapted bead task to investigate the sampling behavior of 24 autistic and 41 neurotypical children, matched for age and IQ. In each trial of our experiment, children gathered information about an unknown target isle by drawing samples from it and then guessed the target between two isles based on their samples. In conditions where sampling was costly, children needed to weigh the benefits of information against the costs of acquiring additional samples. Through computational modeling and intricate behavioral measures, we revealed how the two groups of children differed in sampling decisions and underlying cognitive mechanisms. Results: Under conditions involving costs, autistic children showed less efficient sampling than their neurotypical peers. This inefficiency was due to their increased variability in the number of samples taken across trials rather than a systematic bias. Computational models indicated that while both groups shared a similar decision process, autistic children's sampling decisions were less influenced by dynamic changes and more driven by recent evidence, thus leading to their increased sampling variation and reduced efficiency. Limitations: To refine ASD subtyping and correlate symptom severity with behavioral characteristics and computational findings, future research may need larger participant groups and more comprehensive clinical assessments. Conclusions: This study reveals an inefficiency of autistic children in information sampling and tracks down this inefficiency to their increased sampling variability, primarily due to their cognitive preference for more local and static information. These findings are consistent with several influential behavioral theories of ASD and highlight the needs of a multi-level understanding of cognitive flexibility in ASD.

Inferring the Joint Distribution of Structural and Functional Connectivity in the Human Brain using UNIT-DDPM
The structural wiring of the brain is expected to produce a repertoire of functional networks, across time, context, individuals and vice versa. Therefore, a method to infer the joint distribution of structural and functional connectomes would be of immense value. However, existing approaches only provide deterministic snapshots of the structure-function relationship. Here we use an unpaired image translation method, UNIT-DDPM, that infers a joint distribution of structural and functional connectomes. Our approach allows estimates of variability of function for a given structure and vice versa. Furthermore, we found a significant improvement in prediction accuracy among individual brain networks, implicating a tighter coupling of structure and function than previously understood. Also, our approach has the advantage of not relying on paired samples for training. This novel approach provides a means for identifying regions of consistent structure-function coupling.

Explicit and implicit locomotor learning in individuals with chronic hemiparetic stroke
Background: Motor learning involves both explicit and implicit learning processes that are fundamental to post-stroke rehabilitation as they are often utilized in concert. However, stroke may damage the neural substrates underlying explicit or implicit learning, leading to deficits in overall motor performance. Objective: Determine if individuals with chronic stroke have impaired explicit and/or implicit learning, when assessed during a locomotor task that elicits dissociable contributions from both. Methods: We compared explicit and implicit locomotor learning in individuals with chronic stroke to age- and sex-matched neurologically intact controls. We assessed implicit learning using split-belt adaptation (where two treadmill belts move at different speeds). We assessed explicit learning by providing visual feedback during split-belt walking to help individuals explicitly correct for step length errors created by the split-belts. The removal of visual feedback after the first 40 strides of split-belt walking, combined with task instructions, minimized contributions from explicit learning for the remainder of the task. This manipulation, combined with computational modeling, allowed us to determine the individual contributions of explicit and implicit motor learning to overall performance. Results: The behavioral and computational analyses revealed that, compared to controls, individuals with chronic stroke demonstrated deficits in both explicit and implicit contributions to locomotor learning. Conclusions: Post-stroke locomotor rehabilitation involves interventions that rely on explicit and implicit motor learning. Our results demonstrate that both forms of learning are impaired when examined in a single task. Future work should determine how locomotor rehabilitation interventions can be structured to optimize overall motor learning.

Kappa Opioid Receptors Negatively Regulate Real Time Spontaneous Dopamine Signals by Reducing Release and Increasing Uptake
The role of the dynorphin/kappa opioid receptor (KOR) system in dopamine (DA) regulation has been extensively investigated. KOR activation reduces extracellular DA concentrations and increases DA transporter (DAT) activity and trafficking to the membrane. To explore KOR influences on real-time DA fluctuations, we used the photosensor dLight1.2 with fiber photometry in the nucleus accumbens (NAc) core of freely moving male and female C57BL/6 mice. First, we established that the rise and fall of spontaneous DA signals were due to DA release and reuptake, respectively. Then mice were systemically administered the KOR agonist U50,488H (U50), with or without pretreatment with the KOR antagonist aticaprant (ATIC). U50 reduced both the amplitude and width of spontaneous signals in males, but only reduced width in females. Further, the slope of the correlation between amplitude and width was increased in both sexes, suggesting that DA uptake rates were increased. U50 also reduced the frequency of signals in both males and females. All effects of KOR activation were stronger in males. Overall, KORs exerted significant inhibitory control over spontaneous DA signaling, acting through at least three mechanisms - inhibiting DA release, promoting DAT-mediated uptake, and reducing the frequency of signals.

Circadian rhythmicity and photobiological mechanisms of light sensitivity and discomfort glare in humans
Discomfort glare is a common visual sensation, which is generally reported when being exposed to a brighter lit environment. In certain clinical conditions, this sensation is abnormally amplified, and is commonly named photophobia. Despite the frequent appearance of this sensation in everyday life or in pathological conditions, the underlying mechanisms remain poorly understood. We show here, in highly controlled laboratory constant routine conditions, that light-induced discomfort glare is rhythmic over the 24-hour day. We reveal a strong circadian drive, with a sinusoidal rhythmicity, with maximal discomfort glare in the middle of the night and minimal in the afternoon. We also find a modest sleep-related homeostatic drive of visual discomfort, with a linear increase in discomfort glare over 34 hours of prolonged wakefulness. Our study reveals that discomfort glare is primarily driven by the ipRGC pathway, and that mid and/or long wavelengths cones are involved as well. The 6.5-hour phase lag between the rhythms of photoreceptors' sensitivity, assessed through pupillary light reflex, and of glare discomfort, suggests two independent underlying mechanisms. In conclusion, our findings highlight the need to take time-of-day and biological rhythmicity into account in the evaluation of light-induced discomfort glare. Apprehending these mechanisms may help understand photophobia in clinical populations, such as in migraine patients, and should be taken into account to optimize light quality at home and at the workplace, both for day and night work.

A retinotopic reference frame for space throughout human visual cortex
We perceive a stable visual world across eye movements, despite the drastic retinal transients these movements produce. To explain vision spatial stability, it has been suggested that the brain encodes the location of attended visual stimuli in an external, or spatiotopic, reference frame. However, spatiotopy is seemingly at odds with the fundamental retinotopic organization of visual inputs. Here, we probe the spatial reference frame of vision using ultra-high-field (7T) fMRI and single-voxel population receptive field mapping, while independently manipulating both gaze direction and spatial attention. To manipulate spatial attention, participants performed an equally demanding visual task on either a bar stimulus that traversed the visual field, or a small foveated stimulus. To dissociate retinal stimulus position from its real-world position the entire stimulus array was placed at one of three distinct horizontal screen positions in each run. We found that population receptive fields in all cortical visual field maps shift with the gaze, irrespective of how spatial attention is deployed. This pattern of results is consistent with a fully retinotopic reference frame for visual-spatial processing. Reasoning that a spatiotopic reference frame could conceivably be computed at the level of entire visual areas rather than at the level of individual voxels, we also used Bayesian decoding of stimulus location from the BOLD response patterns in visual areas. We found that decoded stimulus locations also adhere to the retinotopic frame of reference, by shifting with gaze position. Again, this result holds for all visual areas and irrespective of the deployment of spatial attention. We conclude that visual locations are encoded in a retinotopic reference frame throughout the visual hierarchy.

Dysregulation of Neuropilin-2 Expression in Inhibitory Neurons Impairs Hippocampal Circuit Development Leading to Autism-Epilepsy Phenotype
Dysregulation of development, migration, and function of interneurons, collectively termed interneuronopathies, have been proposed as a shared mechanism for autism spectrum disorders (ASDs) and childhood epilepsy. Neuropilin-2 (Nrp2), a candidate ASD gene, is a critical regulator of interneuron migration from the median ganglionic eminence (MGE) to the pallium, including the hippocampus. While clinical studies have identified Nrp2 polymorphisms in patients with ASD, whether dysregulation of Nrp2-dependent interneuron migration contributes to pathogenesis of ASD and epilepsy has not been tested. We tested the hypothesis that the lack of Nrp2 in MGE-derived interneuron precursors disrupts the excitation/inhibition balance in hippocampal circuits, thus predisposing the network to seizures and behavioral patterns associated with ASD. Embryonic deletion of Nrp2 during the developmental period for migration of MGE derived interneuron precursors (iCKO) significantly reduced parvalbumin, neuropeptide Y, and somatostatin positive neurons in the hippocampal CA1. Consequently, when compared to controls, the frequency of inhibitory synaptic currents in CA1 pyramidal cells was reduced while frequency of excitatory synaptic currents was increased in iCKO mice. Although passive and active membrane properties of CA1 pyramidal cells were unchanged, iCKO mice showed enhanced susceptibility to chemically evoked seizures. Moreover, iCKO mice exhibited selective behavioral deficits in both preference for social novelty and goal-directed learning, which are consistent with ASD-like phenotype. Together, our findings show that disruption of developmental Nrp2 regulation of interneuron circuit establishment, produces ASD-like behaviors and enhanced risk for epilepsy. These results support the developmental interneuronopathy hypothesis of ASD epilepsy comorbidity.

Perineuronal nets in the rat medial prefrontal cortex alter hippocampal-prefrontal oscillations and reshape cocaine self-administration memories
The medial prefrontal cortex (mPFC) is a major contributor to relapse to cocaine in humans and to reinstatement behavior in rodent models of cocaine use disorder. Output from the mPFC is modulated by parvalbumin (PV)-containing fast-spiking interneurons, the majority of which are surrounded by perineuronal nets (PNNs). Here we tested whether chondroitinase ABC (ABC)- mediated removal of PNNs prevented the acquisition or reconsolidation of a cocaine self-administration memory. ABC injections into the dorsal mPFC prior to training attenuated the acquisition of cocaine self-administration. Also, ABC given 3 days prior to but not 1 hr after memory reactivation blocked cue-induced reinstatement. However, reduced reinstatement was present only in rats given a novel reactivation contingency, suggesting that PNNs are required for the updating of a familiar memory. In naive rats, ABC injections into mPFC did not alter excitatory or inhibitory puncta on PV cells but reduced PV intensity. Whole-cell recordings revealed a greater inter-spike interval 1 hr after ABC, but not 3 days later. In vivo recordings from the mPFC and dorsal hippocampus (dHIP) during novel memory reactivation revealed that ABC in the mPFC prevented reward-associated increases in beta and gamma activity as well as phase-amplitude coupling between the dHIP and mPFC. Together, our findings show that PNN removal attenuates the acquisition of cocaine self-administration memories and disrupts reconsolidation of the original memory when combined with a novel reactivation session. Further, reduced dHIP/mPFC coupling after PNN removal may serve as a key biomarker for how to disrupt reconsolidation of cocaine memories and reduce relapse.

Corticospinal excitability at rest outside of a task does not differ from task intertrial intervals in healthy adults
Human corticospinal excitability modulates during movement, when muscles are active, but also at rest, when muscles are not active. These changes in resting motor system excitability can be transient or longer lasting. Evidence from transcranial magnetic stimulation (TMS) studies suggests even relatively short periods of motor learning on the order of minutes can have lasting effects on resting corticospinal excitability. Whether individuals are able to return corticospinal excitability to out-of-task resting levels during the intertrial intervals of behavioral tasks that do not include an intended motor learning component is an important question. Here, in twenty-six healthy young adults, we used single-pulse TMS and electromyography (EMG) to measure motor evoked potentials (MEPs) during two different resting contexts: 1) intertrial intervals of a choice-reaction time task, and 2) outside the task. In both contexts, five TMS intensities were used to evaluate possible differences in recruitment of corticospinal output. We hypothesized resting state excitability would be greater during intertrial intervals than out-of-task rest, reflected in larger MEP amplitudes. Contrary to our hypothesis, we observed no significant difference in MEP amplitudes between out-of-task rest and in-task intertrial intervals, and instead found evidence of equivalence, indicating that humans are able to return to a stable motor resting state within seconds after a response. These data support the interpretation that rest is a uniform motor state in the healthy nervous system. In the future, our data may be a useful reference for motor disorder populations with an impaired ability to return to rest.

Ramping dynamics in the frontal cortex unfold over multiple timescales during motor planning
Plans are formulated and refined over the period leading to their execution, ensuring that the appropriate behavior is enacted at just the right time. While existing evidence suggests that memory circuits convey the passage of time through diverse neuronal responses, it remains unclear whether the neural circuits involved in planning behavior exhibit analogous temporal dynamics. Using publicly available data, we analyzed how activity in the frontal motor cortex evolves during motor planning. Individual neurons exhibited diverse ramping activity throughout a delay interval that preceded a planned movement. The collective activity of these neurons was useful for making temporal predictions that became increasingly precise as the movement time approached. This temporal diversity gave rise to a spectrum of encoding patterns, ranging from stable to dynamic representations of the upcoming movement. Our results indicate that neural activity unfolds over multiple timescales during motor planning, suggesting a shared mechanism in the brain for processing temporal information related to both past memories and future plans.