bioRxiv Subject Collection: Neuroscience's Journal

Is critical brain dynamics more prevalent than previously thought?
The hypothesis that the brain operates near criticality has far-reaching implications for brain function and is supported by growing experimental evidence. Observations of scale-invariant brain activity agree with this hypothesis, but what about when brain activity is not scale-invariant? Should we reject the criticality hypothesis When power-laws poorly fit the data or when strong oscillations occur (dominated by a specific time scale)? Here we show several ways that criticality can be hidden from traditional data analytic approaches, leading to false negative conclusions. We use a parsimonious high-dimensional model to demonstrate how neural systems may separate different dynamical modes into different subspaces, simultaneously generating non-critical dynamics, critical oscillations, and scale-invariant avalanches. Our results point to a need for new methods capable of revealing hidden criticality and suggest that criticality could be more prevalent than previously thought, hidden in subspaces not readily revealed by standard data analyses.

Altered excitability of dI3 neurons regulates hindlimb motor tone and locomotor recovery after spinal cord injury
Recovery of motor function after spinal cord injury is limited in mammals. Reactivation of locomotor circuits does occur, but primarily through the activation of sensorimotor pathways in the context of locomotor training. Previous investigations have shown that dI3 neurons, a developmentally-defined population of pre-motor, glutamatergic interneurons, are indispensable for this process. However, it remains unclear how dI3 neurons are recruited during locomotor recovery, and whether they could be leveraged to improve locomotor function following spinal cord injury. Herein, we investigated how the excitability of dI3 neurons influences locomotor behaviour and recovery after spinal cord injury. In T9-T10 transected mice, we found that acute chemogenetic silencing of dI3 neurons leads to immediate loss of hindlimb motor tone, and significant reduction in stepping during treadmill locomotion. Conversely, regular chemogenetic stimulation of dI3 neurons led to transient increases in hindlimb motor tone early after injury, but ultimately reduced hindlimb motor tone and locomotor recovery over the long term. These chronic changes resulting from dI3 neuron stimulation were associated with the absence of expression of the constitutive 5-HT2C-R isoform, potentially representing a homeostatic mechanism for the regulation of dI3 excitability following spinal cord injury. Given these findings, we hypothesized that dI3 stimulation effects on motor tone, while insufficient to drive locomotor function alone, may promote stepping improvements when locomotor rhythm-generating circuits are active. The addition of quipazine, a serotonergic agonist known to facilitate locomotor rhythmogenesis, in combination with dI3 stimulation, significantly improved locomotor function, while also mitigating the long-term reduction in treadmill stepping associated with dI3 stimulation alone. In aggregate, our results suggest that hyper-excitable dI3 neurons are involved in the maintenance of motor tone after spinal cord injury, possibly through a 5-HT2C-R-dependent mechanism, and further show that the selective stimulation of dI3 neurons could enhance the recovery of locomotor function following spinal cord injury.

Motor cortex flexibly deploys a high-dimensional repertoire of subskills
Skilled movement often requires flexibly combining multiple subskills, each requiring dedicated control strategies and underlying computations. How the motor system achieves such versatility remains unclear. Using high-density Neuropixels recordings from primary motor cortex (M1) in macaques performing a challenging force-tracking task, we reveal that M1 activity is much higher-dimensional, and far more flexible, than traditionally assumed. Although our task employed only a single external degree of freedom, neural dynamics reflected transitions amongst many dimensions and multiple distinct computations. Different behavioral control strategies were associated with distinct neural locations and dimensions, sometimes used compositionally. Groups of population-level factors became active when a particular form of dynamics was needed, and remained silent otherwise. Neural activity was thus dominated by the engaged subskill, and could be very different even for matched motor output. These findings challenge prevailing views of M1, and reveal an unexpectedly flexible and high-dimensional neural system underlying skilled motor behavior.

Neuronal and Astrocytic Activity Changes Induced by Acute and Chronic Stress
Stress is a major risk factor for depression and exerts complex effects on brain function and behavior. In this study, we examined the neural mechanisms of stress responses in the prefrontal cortex (PFC), with particular focus on neuron/astrocyte interactions. Using dual-color fiber photometry, we simultaneously monitored neuronal and astrocytic activity during acute and chronic stress. Acute stressors, including tail pinch and immobilization, evoked coordinated increases in both neuronal and astrocytic activity. In contrast, prolonged exposure to unpredictable chronic mild stress (UCMS) produced heightened neuronal activity alongside astrocytic dysfunction. Notably, UCMS disrupted the correlation between neuronal and astrocytic signals, pointing to impaired cellular crosstalk. These findings highlight a critical role of disrupted neuron/astrocyte interactions in shaping maladaptive stress responses and suggest a potential mechanism contributing to stress-related neuropsychiatric disorders.

Developmental excitation-inhibition imbalance permanently reprograms autism-relevant social brain circuits
An influential theory proposes that an imbalance between excitation and inhibition (E:I) plays a central role in the etiology of autism and related developmental disorders. However, controversy exists as to whether this imbalance is a direct causal mechanism for autism, or a compensatory response to other primary etiological factors. Using chemogenetic manipulations in neonatal mice, we show that a transient E:I imbalance during development is sufficient to permanently reprogram autism-relevant social brain circuits. Chemogenetically manipulated mice exhibit lifelong impairments in sociability, persistent dysregulation of multiple autism-risk synaptic genes, and sustained cortical hyperexcitability in adulthood. Importantly, these social impairments are robustly rescued by pharmacological inhibition of neuronal excitability. Developmental E:I imbalance also disrupts functional connectivity in social brain regions enriched for transcriptionally dysregulated genes, suggesting a convergence of transcriptional and circuit-level pathology. Finally, multivariate modelling shows that behavioral dysfunction in chemogenetically manipulated animals closely associates with disrupted connectivity between prefrontal and mesolimbic dopaminergic regions. Collectively, our findings reconcile conflicting theories in the field and point to activity-dependent transcriptional remodeling as a foundational mechanism by which transient E:I imbalance during development can cause lasting, autism-relevant circuit dysfunction.

Astrocytes mobilize a broader repertoire of lysosomal repair mechanisms than neurons
Lysosomal damage impairs proteostasis and contributes to neurodegenerative diseases, yet cell-type-specific differences in lysosomal repair remain unclear. Using a neuron-astrocyte coculture system, we compared responses to lysosomal injury induced by a lysosomotropic methyl ester. Both neurons and astrocytes showed lysosomal damage, marked by galectin-3 recruitment to lumenal lysosomal beta-galactosides, elevated lysosomal pH, and engagement of lysophagy receptors TAX1BP1 and p62. However, astrocytes showed a preferential recruitment of ESCRT repair machinery to damaged lysosomes. Additionally, the lysosomal membrane reformation pathway regulated by the RAB7-GAP, TBC1D15, was more robustly activated in astrocytes. By contrast, the PITT pathway, mediating lipid transfer between the ER and damaged lysosomes, was engaged in both cell types. Our data reveal a divergence in how neurons and astrocytes mobilize repair pathways to manage lysosomal damage. These data may reflect differences in lysosomal resilience between astrocytes and neurons and inform therapeutic strategies to correct lysosomal dysfunction in neurodegenerative diseases.

Evidence for hierarchical representations of written and spoken words from an open-science human neuroimaging dataset
Reading and speech recognition rely on multi-level processing that builds from basic visual or sound features to complete word representations, yet details of these processing hierarchies (in particular those for spoken words) are still poorly understood. We re-analyzed the functional magnetic resonance imaging (fMRI) data provided in the Mother Of all Unification Studies (MOUS) open-science dataset by using parametric regressions of word frequency and sublexical unit (bigram or syllable) frequency during reading and speech listening tasks in order to elucidate lexical processing hierarchies in the visual and auditory modalities. We first validated our approach in the written word domain, where the technique identified significant correlations for word frequency in the left mid-fusiform cortex (at the location of the Visual Word Form Area) with a left occipital region tracking bigram frequency, compatible with prior reports. During listening, low-frequency spoken words elicited greater responses in a left mid-superior temporal region consistent with the recently-described Auditory Word Form Area (AWFA), while a more posterior region of the superior temporal gyrus was sensitive to syllable frequency. Activation in the left inferior frontal gyrus correlated with both written and spoken word frequency. These findings demonstrate parallel hierarchical organizations in the anteroventral visual and auditory streams, with modality-specific lexica and upstream sublexical representations that converge in higher-order language areas.

Using time-resolved auditory frequency tagging to capture self-preferential processing of the own (vs other) name
The own name is an important, salient self-related stimulus and ostensive cue. Prioritized processing of ones own name plays a key role in attention, self-awareness, and social and cognitive development. Previous studies using event-related potentials (ERPs) have reported a self-preferential effect for the own name, characterized by enhanced neural responses compared to the names of (close) others. In this study, we aimed to develop and validate an EEG auditory frequency-tagging task to measure self-preferential processing of the own name, leveraging the superior signal-to-noise ratio of this technique, which is highly relevant for future studies on infants and clinical populations. To this end, we ran two separate studies (dichotic: n1 = 31, non-dichotic: n2 = 32). In contrast to previous ERP research, we did not find evidence of a self-preferential effect. We reasoned that collapsing temporal information may have led to the failure to capture the effect, which ERP research has shown to emerge at a late stage of processing, and that this effect may have been overshadowed by early brain responses. To address this issue, we applied a novel approach that we refer to as the time-resolved frequency tagging approach, which incorporates knowledge of the effect in the temporal domain. This did result in a clear self-preferential effect of the own name. Hence, we were able to develop and validate an EEG frequency-tagging task to measure the self-preferential effect of the own name. Our approach can also be used in future EEG frequency-tagging studies investigating other complex cognitive processes.

A multimodal human-computer interaction dataset for neurocognitive user state evaluation
We introduce the Simulated Environment for Neurocognitive State Evaluation (SENSE-42), a multimodal dataset collected during user interactions with desktop computers. It is designed for studying spontaneous fluctuations in the neurocognitive state related to the tonic alertness of computer users, with recordings from 42 participants over 2-hour sessions. Within a simulated desktop environment, participants performed real-world routine tasks, including application switching, file management, typing, and web browsing. High-resolution data were recorded across physiological (electroencephalography, electrocardiography, respiration) and subjective modalities of alertness. At five-minute intervals, alertness state was reported using seven questions, addressing sleepiness (Karolinska Sleepiness Scale), mental and temporal demand, perceived performance, effort and frustration (NASA Task Load Index), as well as attentiveness. Behavioural data included keyboard, mouse and webcam inputs. Demographic information for the experience, habits, and preferences of computer usage was collected. In addition, individual differences in sleep quality were evaluated using the Pittsburgh Sleep Quality Index and the Epworth Sleepiness Scale. The SENSE-42 dataset can contribute to future research in user state monitoring, behavioural analysis and physiological computing.

Immersive NREM dreaming preserves subjective sleep depth against declining sleep pressure
Perceived sleep depth is a key determinant of subjective sleep quality, traditionally thought to reflect unconsciousness and reduced cortical activation. Here, we combined high-density EEG with a serial awakening paradigm during NREM sleep to examine its neural and experiential correlates. As expected, deeper sleep was associated with reduced cortical activation, reflected in a lower high-to-low frequency power ratio. Yet, this relationship weakened in the presence of dreaming, indicating that immersive conscious experiences can counteract the impact of cortical activation on perceived depth. Indeed, perceived sleep depth was lowest during states with a mere sense of presence and highest during immersive dreaming or deep unconsciousness. Across the night, physiological sleep pressure and subjective sleepiness declined, but perceived sleep depth rose alongside increasing dream immersiveness. These results challenge the view that deep sleep stems solely from reduced brain activity and suggest that immersive dreaming sustains perceived sleep depth as homeostatic pressure wanes.

NERV: A Comprehensive Framework for Rapid, Reproducible, and Hardware-Synchronized Neuroscience Experiment Design and Execution
Background: Behavioral neuroscience experiments require precise stimulus control, millisecond timing, hardware integration, and robust data provenance. Increasing use of 3D environments and multimodal recordings adds challenges for development, accessibility, and reproducibility. Fragmented tools often separate presentation, synchronization, and logging, leading to inefficiencies. New Method: The Neuroscience Experimental Runtime by Vanderbilt (NERV) is a Unity-based framework that unifies experiment design, execution, and data logging. It enables rapid, no-code prototyping by automating scene and script generation, event timing, state management, hardware-synchronized data acquisition, and archival of code and experimental configurations. The modular, open-source framework implements a "low floor, high ceiling" design that lowers barriers for non-programmers while remaining extensible for advanced customization. Results: Across 500 trials, Unity-to-TTL delay was 2.10 +/- 1.21 ms, TTL-to-photodiode delay was 28.93 +/- 0.76 ms, and Unity-to-screen delay was 31.04 +/- 1.41 ms. These results confirm stable millisecond precision and frame-locked timing, enabling reliable alignment of neural, behavioral, and visual events. Comparison with existing methods: Existing frameworks involve trade-offs. Some achieve precise timing but require advanced coding, while others improve accessibility but struggle with hardware or 3D graphics. Commercial platforms offer polish yet remain costly, closed-source, and inflexible. NERV combines millisecond precision, modular open-source design, and provenance in a single platform, reducing workflow fragmentation and enabling reproducible, scalable experiments. Conclusion: NERV is an accessible yet extensible framework that unites rapid development, robust data provenance, and millisecond precision. It accelerates development, ensures reproducibility, and establishes a scalable foundation for next-generation neuroscience research.

A Novel Mechanism for Tauopathy in Progressive Multiple Sclerosis: Excitotoxic Misplacement of a Mitochondrial Anchor into Dendrites Driven by Tau-hyperphosphorylation
On April 2nd, 2025, the FDA approved a Fast Track Designation for Biogen to use Antisense Oligonucleotides (ASO) to treat tauopathy in clinical trials for Alzheimer Disease (AD) to meet an unmet medical need [1]. For Multiple Sclerosis (MS), there is a similar unmet medical need regarding tauopathy when MS transitions into the late, or Progressive MS that is currently incurable. AD and MS share commonality: there is comorbidity between AD and MS [2], and the recent awareness that progressive MS may be considered a secondary tauopathy [3]. This study lays the basic science foundation for a future repurposing of ASO tauopathy therapy from AD to MS. The central hypothesis is that in Progressive MS, tauopathy is not a passive bystander but an active contributor to synaptic degeneration through a novel toxic target known as DSI (Dendritic Syntaphilin Intrusion) discovered in our laboratory. In this hypothesis, the excitotoxic N-methyl-D-aspartate receptor (NMDAR) GluN2B activates Tau hyperphosphorylation (p-Tau), leading to the mislocalization or intrusion of a mitochondrial anchor SNPH into neuronal dendrites (DSI). This causes mitochondrial damage and subsequent synapse/dendrite disintegration. In support of this hypothesis that tauopathy is a key driver of DSI, we demonstrated using primary neuronal cultures that inhibitors of p-Tau kinases and Tau-KO both completely abolish DSI. We propose that a therapy for Progressive MS is repurpose the existing FDA-approved ASO Tau knockdown therapy from AD to treat MS.

Distortion rules: audibility creation in the mosquito ear
Hearing begins when ears convert sound into neuronal excitation. The hearing organs of male Anopheles mosquitoes rank amongst the most sensitive ears evolved. Males use them to detect faint female flight tones within noisy mating swarms, but female flight tones largely lie above the male's hearing range. We report a mechanism by which inaudible higher-frequency primary tones become audible through intrinsically generated, lower-frequency distortion tones. Distortion-evoked responses show ~10-times greater sensitivity and 45% higher temporal precision than those of same-frequency primary tones. Moreover, self-sustained oscillations of the male's ear dramatically reshape auditory frequency tuning and neuronal recruitment. By one mechanism, mosquito ears thus create audible signals and isolate these from the most salient frequencies of background noise. In audiology, distortions are used for diagnostics; their biological relevance, however, is unknown. Our results suggest that distortions are not mere by-products of the auditory process, but integral to its signaling logic.

Spatial layout of visual specialization is shaped by competing default mode and sensory networks
Understanding how the brain encodes information started with a map but turned into a maze: paths multiplied; boundaries blurred. Neurons tuned to specific features are not confined to single regions, but distributed across the cortex. Retinotopy, once thought limited to early visual areas, now appears in over 20 cortical regions, from the tip of the occipital cortex to the shore of the lateral frontal cortex. To describe and understand these complex mosaics of functional specialization, we focus on the spatial influences that shape their emergence across the cortical sheet. To this end, we developed Spatial Component Decomposition (SCD), a sparse dictionary learning framework that locates sources of spatial influence without relying on prior assumptions from systems neuroscience. Applied to MRI data capturing retinotopic maps, SCD reveals a dominant linear gradient extending over 60 mm from V1 and covering all the known posterior visual areas. Yet, it also revealed systematic competition from other primary sensory areas and default mode transmodal hubs. These suppressive influences shape the cortical embedding of visual information, even during purely visual tasks. Our results suggest that functional specialization emerges from spatial competition between representational systems, not just from feedforward inputs.

Functional connectivity is linked to symbolic BOLD patterns: replication, extension, and clinical application of the human 'complexome'
Functional connectivity (FC) quantifies the temporal coherence of blood-oxygen-level-dependent (BOLD) signals across brain regions. Recently, the information-theoretic "complexome" framework has linked FC to coinciding 'complexity drops': transient moments in which regional BOLD signals simultaneously become regular. Here, we replicate this relationship in an independent dataset and extend the framework by (i) integrating it with signal cofluctuation analysis through edge-timeseries, (ii) extending the previous binary concept of simultaneous complexity drops to a continuous, threshold-free calculation, (iii) providing evidence of clinical relevance in the model disease of anti-N-methyl-D-aspartate-receptor encephalitis, and (iv) deriving a novel measure of pairwise dissimilarity in local BOLD patterns. This 'index of pattern incongruency' (IPI) explains clinically relevant FC reductions and maps onto novel associations with cognition. These findings show that global FC is closely related to local patterns within underlying BOLD signals, strengthening the link between complexity dynamics and the brain's functional organization as a large-scale network.

Adaptation of Perceived Animacy from Biological Motion: Evidence for a "Life Motion Detector"
Humans can readily perceive animacy from biological motion (BM) - the distinctive movement patterns of living entities. However, how the human brain extracts animacy information from BM remains largely unclear. The current study investigated this issue using visual adaptation, a non-invasive tool for revealing neural mechanisms underlying the selective processing of specific properties. Results showed that prolonged exposure to intact human walkers, compared to non-BM adaptors, biased the perception of subsequent walking stimuli towards less animate. This adaptation aftereffect persisted following adaptation to local foot motions carrying diagnostic kinematic cues, but was absent after exposure to static body forms, indicating the involvement of neuronal populations encoding animacy from BM based on motion signals. Moreover, adapting to pigeon movements also biased animacy perception for human motions, revealing a shared mechanism for encoding animacy from cross-species BM signals. These results support the existence of an inherent "life detection" system in the human brain, attuned to local foot motions and kinematic features prevalent in vertebrate movements, which may lay a foundation for perceiving life from motion across terrestrial species.