Biology subjects

Musall, S.

Publications and source records attributed to Musall, S..

At least 19 recordsLinked to original sources

bioRxiv2026

Hierarchical dendrite-inspired organic bioelectronic interfaces for neuronal integration

Brain-machine interfaces rely on intimate electrical communication between living neurons and artificial materials, yet conventional electrode architectures remain structurally different to neuronal tissue, limiting stable cell-electrode coupling and long-term recording. Although conducting polymers and microstructured interfaces have individually improved the electrochemical and biological properties of neural electrodes, integrating neuromimetic architecture with electrically active organic materials to direct neuron-electrode interactions has remained challenging. Here we engineer dendrite-inspired organic microelectrodes by programming the self-assembly of conducting polymer fibers directly on microelectrode arrays. By controlling the electrodeposition process, we generate hierarchical architectures that emulate key stages of dendritic development while preserving the relevant electrochemical properties of conducting polymers. These bioinspired interfaces promote nanoscale membrane conformability, reduce the neuron-electrode cleft, and induce localized membrane engulfment of the electrode structures. The enhanced structural integration is accompanied by increased synaptic protein expression and neuronal excitability within mature primary cortical networks. Furthermore, the dendritic electrodes enable localized laser-assisted optoporation, providing transient intracellular electrophysiological access while maintaining extracellular recording functionality.

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bioRxiv2026

A mechanically stable neural probe for percutaneous high-resolution, multichannel recordings in peripheral nerves

The development of neural probes has enabled a deeper understanding and improved treatment for neurological disorders. Microneurography is currently the gold standard for assessing the electrophysiological signature of pain mechanisms in the human peripheral nervous system. However, its clinical utility is limited by the low recording yield and signal-to-noise ratio of single-electrode probes. To overcome these limitations, we developed mechanically robust, multi-electrode probes designed for acute percutaneous insertion and recording in peripheral nerves. The electrical and mechanical stability of these probes was confirmed through repeated insertions in artificial human skin and rat peripheral nerves. In addition, ex vivo and in vivo experiments demonstrated enhanced functional performance, with multi-site recordings enabling the isolation of single-fiber activity. Importantly, our probes can be operated analogously to conventional microneurography needles while substantially increasing the information yield, providing enhanced capabilities for minimally invasive peripheral nerve assessment.

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bioRxiv2025

Cortex-wide circuits for multisensory evidence accumulation and choice formation

Accurate decisions in natural environments require integrating sensory information across modalities. To determine where and when multisensory signals are integrated within the cortical hierarchy, we trained mice in a visuotactile evidence-accumulation task and used widefield and two-photon calcium imaging together with optogenetic perturbations to map cortical circuits. Mice showed enhanced performance in multisensory trials that matched an additive combination of the total unisensory evidence. Cortical imaging also revealed superadditive multisensory responses in parietal and frontal regions, but visual and tactile evidence was accumulated in distinct regions, with the rostrolateral visual area (RL) being more selective for visual and the frontal medial motor cortex (MM) for tactile inputs. This modality-specific segregation persisted beyond stimulus presentation with convergence into a modality-independent choice emerging only late in the trial in the anterolateral motor cortex (ALM). Two-photon imaging confirmed that ALM neurons predominantly encoded modality-independent choices with few neurons responding to specific sensory stimuli. Optogenetic inactivation of RL and frontal cortex during the stimulus or choice period further validated their respective roles in sensory evidence accumulation and choice formation. These findings reveal a hierarchical organization in which modality-specific evidence is maintained until late in the decision process, with frontal cortex turning multisensory signals into unified choices.

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bioRxiv2025

Contralesional activity reflects compensation, while brainstem detour pathways support skilled motor recovery after stroke

Large strokes frequently result in lasting motor deficits and trigger extensive reorganization within the brain and spinal cord. Altered neuronal activity in the contralesional hemisphere has been documented in both humans and rodent models, yet its role in functional recovery versus maladaptation remains unresolved. Here, we used chronic wide-field calcium imaging to monitor bilateral cortical activity in mice performing a skilled reach-to-grasp task before and days to weeks after stroke. Strokes produced persistent fine motor impairments, which were only partly alleviated by intensive rehabilitative training. While cortical activity was in particular suppressed in the ipsilesional cortex after stroke, training promoted sustained increases in contralesional sensorimotor activity. However, ridge regression analysis of neural and behavioral data indicated that this activity largely reflected compensatory use of the intact paw rather than recovery of the impaired forelimb. Axonal tracing nevertheless revealed enhanced projections from contralesional motor cortex to ipsilesional brainstem nuclei - including the midbrain and pontine reticular nucleus - specifically in trained animals. These findings identify a rehabilitation-induced corticofugal pathway supporting motor recovery, highlighting a target for neuromodulation strategies in chronic post-stroke impairment.

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bioRxiv2025

DNMT1 Coordinates PV Interneuron-Glia Coupling to Maintain Cortical Network Stability and Regulate Behavior

Parvalbumin (PV) interneurons are central to cortical network stability and psychiatric vulnerability. Here, we identify DNA methyltransferase 1 (DNMT1) as a key epigenetic regulator linking PV interneuron function to glial and extracellular matrix remodeling. Conditional PV-specific Dnmt1 deletion combined with single-cell RNA-seq, in vivo electrophysiology, histology, and behavioral analyses revealed that loss of DNMT1 increases PV spiking activity but reduces inhibitory efficacy, leading to network desynchronization and depression- and anxiety-like behavior in mice. These physiological alterations were accompanied by broad, non-cell-autonomous transcriptional changes in astrocytes and oligodendroglial populations, prominently affecting pathways involved in perineuronal-net (PNN) organization and neuron-glia communication. Cell-cell interaction analyses revealed disrupted NRXN-NLGN, TNR-integrin, and semaphoring signaling, consistent with weakened perisomatic adhesion and PNN integrity. Together, our findings demonstrate that DNMT1 maintains inhibitory circuit stability through cell-autonomous regulation of PV interneuron function, which secondarily shapes glial transcriptional states and extracellular scaffolds to preserve cortical network synchronization and emotional behavior.

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bioRxiv2025

Spectral envelopes of rhythmic facial movements predict intention and motor cortical representations

1Animals, including humans, use coordinated facial movements to sample the environment, ingest nutrients, and communicate. Rodents, in particular, produce rhythmic facial movements during spontaneous behavior and cognitive tasks. Measuring these movements precisely and linking them to neural activity remains challenging. We introduce face-rhythm, an unsupervised pipeline that combines markerless pointtracking, spectral analysis, and non-negative tensor component analysis to decompose facial video into a small set of interpretable components. Applied to videos of mice during a Pavlovian odor-reward task, a brain-machine interface (BMI) task, and free behavior, face-rhythm recovers human-interpretable behaviors such as whisking, sniffing, licking, and subtler motifs. The resulting components are consistent across animals, are sufficient to decode task variables or internal belief states, and explain cortical activity using a low-rank representation. We also find that the activity of neurons in face-associated primary motor cortex (M1) is predicted well by a phase-invariant spectral transformation of facial movements above [~]0.5 Hz, while slower movements retain a phase-variant representation better predicted by the instantaneous position of the face; individual neurons can show either or both forms of tuning. A systematic comparison against deeplearning point-tracking models, contrastive-learning embeddings, and vision-transformer features places face-rhythm competitively across tasks while also achieving the goal of producing a low-dimensional, interpretable description of rodent facial behavior that is closely linked to cortical activity.

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bioRxiv2025

Transient recurrent dynamics shape representations in mice

Brain networks are high-dimensional and interacting complex systems that exhibit substantial structural heterogeneity as well as temporal variability. Yet, when exposed to a stimulus, their recurrent circuits perform reliable computations. The mechanisms underlying this robustness are, however, still mostly unknown. Here, we combine analyses of Neuropixels recordings of awake, behaving mice with models and theory of recurrent neural networks to identify three core computational characteristics that emerge from the interplay of many network constituents and drive dynamic, reliably classifiable stimulus representations. We find that the level of recurrent inhibition in circuits and the microscopic chaos of dynamics respectively drive mean population responses as well as the within- and across-class stimulus response similarities. These core characteristics in turn non-trivially interact to predict and shape the experimentally observed separability of visual and tactile stimulus representations in mouse superior colliculus. Using these characteristics to assess the information transmitted through the network for multiple stimuli reveals a trade-off in coding space: increasing the number of stimuli conveys more information but also reduces their separability due to their larger overlap in the finitedimensional neuronal space. Our analysis predicts that, only for the experimentally observed small population activity, information keeps increasing with the number of stimuli, revealing another crucial advantage of sparse coding.

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bioRxiv2025

UnitRefine: A Community Toolbox for Automated Spike Sorting Curation

High-density electrophysiology simultaneously captures the activity from hundreds of neurons, but isolating single-unit activity still relies on slow and subjective manual curation. As datasets keep increasing, this poses a major bottleneck in the field. We therefore developed UnitRefine, a classification toolbox that automates curation by training various machine-learning models directly on human expert annotations. Fully integrated in the SpikeInterface ecosystem, UnitRefine combines established and novel quality metrics, cascading classification and comprehensive hyperparameter search to provide optimized models for different applications. UnitRefine achieves human-level performance across diverse datasets, spanning species, probe types, and laboratories, including recordings from mice, rats, mole rats, primates, and human patients. Applied to a large brain-wide dataset, UnitRefine doubled single unit yield and improved behavioral decoding performance. A streamlined graphical interface allows models to be fine-tuned to new datasets and shared via the Hugging Face Hub, enabling broad adoption and community-driven improvement of automated curation workflows.

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bioRxiv2024

Flexible 3D kirigami probes for in vitro and in vivo neural applications

Three-dimensional (3D) microelectrode arrays (MEAs) are gaining popularity as brain-machine interfaces and platforms for studying electrophysiological activity. Interactions with neural tissue depend on the electrochemical, mechanical, and spatial features of the recording platform. While planar or protruding two-dimensional MEAs are limited in their ability to capture neural activity across layers, existing 3D platforms still require advancements in manufacturing scalability, spatial resolution, and tissue integration. In this work, we present a customizable, scalable, and straightforward approach to fabricate flexible 3D kirigami MEAs containing both surface and penetrating electrodes, designed to interact with the 3D space of neural tissue. These novel probes feature up to 512 electrodes distributed across 128 shanks in a single flexible device, with shank heights reaching up to 1 mm. We successfully deployed our 3D kirigami MEAs in several neural applications, both in vitro and in vivo, and identified spatially dependent electrophysiological activity patterns. Flexible 3D kirigami MEAs are therefore a powerful tool for large-scale electrical sampling of complex neural tissues while improving tissue integration and offering enhanced capabilities for analyzing neural disorders and disease models where high spatial resolution is required.

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bioRxiv2024

Region-specific spreading depolarization drives aberrant post-ictal behavior

Confusion, aphasia, and unaware wandering are prominent post-ictal symptoms regularly observed in temporal lobe epilepsy (TLE)1. Despite the potentially life-threatening nature of the immediate post-ictal state2, its neurobiological underpinnings remain understudied3. We provide evidence in mice and humans that seizure-associated focal spreading depolarization (sSD) is a pathoclinical key factor in epilepsy. Using two-photon or widefield imaging (hippocampus, neocortex), field potential and single unit recordings, and behavioral assessment in mice, we first studied seizures during viral encephalitis, and subsequently established an optogenetic approach to dissociate hippocampal seizures and SD. We find region-specific occurrence of sSD that displays distinct spatial trajectories to preceding seizures, and show that seizure-related and isolated hippocampal SD prompt post-ictal wandering. This clinically relevant locomotor phenotype occurred in the absence of hippocampal SD progression to the neocortex. Finally, we confirm sSD existence in human epilepsy, in a patient cohort with refractory focal epilepsy, via Behnke-Fried electrode recordings. In this cohort, sSD displayed a similar temporomesial propensity as in mice. This work uncovers sSD as a previously underrecognized pathoclinical entity underlying postictal behavioral abnormalities in epilepsy. Our results carry wide-reaching ramifications for epilepsy research and neurology, and challenge current EEG-standards.

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bioRxiv2024

Dnmt1-deficiency in PV interneurons alters cortical circuit function and leads to depression-like behavior

Neuropsychiatric disorders, including major depressive disorder (MDD), are highly prevalent in modern society, arising from a complex interplay of genetic and environmental factors. Alterations in the function of cortical inhibitory GABAergic interneurons, along with dysregulations of epigenetic signatures and key regulators such as DNA methyltransferase 1 (DNMT1), have been implicated in these conditions. Through its role in catalyzing DNA methylation, DNMT1 modulates the synaptic activity of parvalbumin-expressing (PV) interneurons, which are essential for cortical inhibition. However, the functional consequences of DNMT1 activity in cortical interneurons at the network level and its impact on behavior remain unknown and must be explored to fully understand the disease implications of dysregulated DNMT1 expression and function. To address this, we utilized a conditional knockout mouse model with Dnmt1 deletion in PV interneurons. Our findings reveal that a Dnmt1 deficiency leads to increased spontaneous firing rates of cortical neurons, reduced cortical gamma oscillations, and altered visually evoked neural responses. Despite intact sensory perception, Dnmt1-deficient mice exhibited reduced physical activity, heightened anxiety-like behavior, and signs of anhedonia and apathy, which represent core features of MDD. These results underscore the critical role of DNMT1 in PV interneuron function and identify it as a potential target for MDD research.

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bioRxiv2024

DNMT1-Mediated Regulation of Inhibitory Interneuron Migration Impacts Cortical Architecture and Function

The fine-tuned establishment of neuronal circuits during the formation of the cerebral cortex is pivotal for its functionality. Developmental abnormalities affecting the composition of cortical circuits, which consist of excitatory neurons and inhibitory cortical interneurons (cINs), are linked to a spectrum of neuropsychiatric disorders. Excitatory neurons originate in cortical proliferative zones, while inhibitory interneurons migrate from discrete domains of the basal telencephalon into the cortex. This migration is intricately governed by intrinsic genetic programs and extrinsic cues. Our current study reveals the role of the DNA methyltransferase 1 (DNMT1) in regulating the expression of key genes implicated in mouse cIN development and in guiding the migration of somatostatin (SST)-expressing interneurons at postmitotic level within the developing cortex. Dnmt1 deletion causes SST+ cINs to exit prematurely from the superficial migratory stream. In addition to the perturbed migration pattern and altered gene expression signatures, Dnmt1-deficient SST+ cINs had a discernible non-cell autonomous effect on cortical progenitors, which culminated in nuanced alterations of layer thicknesses in the adult cortex. Our study uncovers that DNMT1 governs the migration of SST+ cINs and through this, their instructive role in sculpting the intricate cortical layer architecture by signaling to cortical progenitors, with pronounced effects on neuronal network function.

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bioRxiv2024

Ongoing activation of visual cortex and superior colliculus in the rd10 mouse model of retinitis pigmentosa

Efforts in vision restoration have been focused on a condition called Retinitis Pigmentosa, where photoreceptors in the retina degenerate while the rest of the visual pathway remain mostly intact. Retinal implants that replace the phototransduction process by stimulating retinal ganglion cells have shown promising but limited results in patients so far. Apart from technical limitations, cross-modal plasticity of visual areas might contribute to this problem. We therefore investigated if the primary visual cortex (V1) of the rd10 mouse model for retinal degeneration became more sensitive to auditory or tactile sensory inputs, potentially hindering retinal stimulation. After reaching complete blindness confirmed by the lack of optomotor responses, activity in visual cortex and superior colliculus (SC) was recorded using Neuropixels probes. While we could not find any significant differences in tactile or auditory responses compared to wildtype mice, the local field potential revealed distinct oscillatory events (0.5 - 6 Hz) in V1 and SC resembling previously observed aberrant activity in the retina of rd10 mice. Further absence of cross-modal plasticity was confirmed by a lacking increase in zif268 expression in V1 after tactile stimulation. We therefore propose that aberrant retinal activity is transmitted to higher visual areas where it prevents cross-modal changes.

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bioRxiv2023

Engaged decision-makers align spontaneous movements to stereotyped task demands

Existing work demonstrates that animals alternate between engaged and disengaged states during perceptual decision-making. To understand the neural signature of these states, we performed cortex-wide measurements of neural activity in mice making auditory decisions. The trial-averaged magnitude of neural activity was similar in the two states. However, the trial-to-trial variance in neural activity was higher during disengagement. To understand this increased variance, we trained separate linear encoding models on neural data from each state. The models demonstrated that although task variables and task-aligned movements impacted neural activity similarly during the two states, movements that are independent of task events explained more variance during disengagement. Behavioral analyses uncovered that during disengagement, movements become uncoupled to task events. Taken together, these results argue that the neural signature of disengagement, though obscured in trial-averaged neural activity, is evident in trial-to-trial variability driven by changing patterns of spontaneous movements.

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bioRxiv2023

Broadband visual stimuli improve neuronal representation and sensory perception

Natural scenes consist of complex feature distributions that shape neural responses and perception. However, in contrast to single features like stimulus orientations, the impact of broadband feature distributions remains unclear. We, therefore, presented visual stimuli with parametrically-controlled bandwidths of orientations and spatial frequencies to awake mice while recording neural activity in their primary visual cortex (V1). Increasing orientation but not spatial frequency bandwidth strongly increased the number and response amplitude of V1 neurons. This effect was not explained by single-cell orientation tuning but rather a broadband-specific relief from center-surround suppression. Moreover, neurons in deeper V1 and the superior colliculus responded much stronger to broadband stimuli, especially when mixing orientations and spatial frequencies. Lastly, broadband stimuli increased the separability of neural responses and improved the performance of mice in a visual discrimination task. Our results show that surround modulation increases neural responses to complex natural feature distributions to enhance sensory perception.

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bioRxiv2022

Disentangled multi-subject and social behavioral representations through a constrained subspace variational autoencoder (CS-VAE)

Effectively modeling and quantifying behavior is essential for our understanding of the brain. Modeling behavior in naturalistic settings in social and multi-subject tasks remains a significant challenge. Modeling the behavior of different subjects performing the same task requires partitioning the behavioral data into features that are common across subjects, and others that are distinct to each subject. Modeling social interactions between multiple individuals in a freely-moving setting requires disentangling effects due to the individual as compared to social investigations. To achieve flexible disentanglement of behavior into interpretable latent variables with individual and across-subject or social components, we build on a semi-supervised approach to partition the behavioral subspace, and propose a novel regularization based on the Cauchy-Schwarz divergence to the model. Our model, known as the constrained subspace variational autoencoder (CS-VAE), successfully models distinct features of the behavioral videos across subjects, as well as continuously varying differences in social behavior. Our approach vastly facilitates the analysis of the resulting latent variables in downstream tasks such as uncovering disentangled behavioral motifs, the efficient decoding of a novel subjects behavior, and provides an understanding of how similarly different animals perform innate behaviors.

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bioRxiv2021

Cortical glutamatergic projection neuron types contribute to distinct functional subnetworks

The cellular basis of cerebral cortex functional architecture remains not well understood. A major challenge is to monitor and decipher neural network dynamics across broad cortical areas yet with projection neuron (PN)-type resolution in real time during behavior. Combining genetic targeting and wide-field imaging, we monitored activity dynamics of subcortical-projecting (PTFezf2) and intratelencephalic-projecting (ITPlxnD1) types across dorsal cortex of mice during different brain states and behaviors. ITPlxnD1 and PTFezf2 neurons showed distinct activation patterns during wakeful resting, spontaneous movements, and upon sensory stimulation. Distinct ITPlxnD1 and PTFezf2 subnetworks were dynamically tuned to different sensorimotor components of a naturalistic feeding behavior, and optogenetic inhibition of ITsPlxnD1 and PTsFezf2 in subnetwork nodes disrupted distinct components of this behavior. Lastly, ITPlxnD1 and PTFezf2 projection patterns are consistent with their subnetwork activation patterns. Our results show that, in addition to the concept of columnar organization, dynamic areal and PN type-specific subnetworks are a key feature of cortical functional architecture linking microcircuit components with global brain networks.

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bioRxiv2021

Pyramidal cell types drive functionally distinct cortical activity patterns during decision-making

Understanding how cortical circuits generate complex behavior requires investigating the cell types that comprise them. Functional differences across pyramidal neuron (PyN) types have been observed within cortical areas, but it is not known whether these local differences extend throughout the cortex, nor whether additional differences emerge when larger-scale dynamics are considered. We used genetic and retrograde labeling to target pyramidal tract (PT), intratelencephalic (IT) and corticostriatal projection neurons and measured their cortex-wide activity. Each PyN type drove unique neural dynamics, both at the local and cortex-wide scale. Cortical activity and optogenetic inactivation during an auditory decision task also revealed distinct functional roles: all PyNs in parietal cortex were recruited during perception of the auditory stimulus, but, surprisingly, PT neurons had the largest causal role. In frontal cortex, all PyNs were required for accurate choices but showed distinct choice-tuning. Our results reveal that rich, cell-type-specific cortical dynamics shape perceptual decisions.

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