Biology subjects

Mathis, M. W.

Publications and source records attributed to Mathis, M. W..

14 recordsLinked to original sources

bioRxiv2026

An explainable AI latent space of brain dynamics reveals a cerebello-prefrontal signature of schizophrenia symptoms

Schizophrenia presents with several partially independent symptom dimensions, including positive symptoms, negative symptoms, and cognitive impairment; yet no neuroimaging framework has provided individual-level markers of symptom severity that remain anatomically interpretable. Here, we present an interpretable AI-based framework that addresses this gap by mapping high-dimensional resting-state rs-fMRI dynamics onto a low-dimensional latent manifold using self-supervised contrastive learning with a new attribution method to localize the highest decodable regions. Applied to two independent schizophrenia-spectrum cohorts, the label-free latent space supports individual-level prediction across clinical features of the disorder, including symptom severity and cognitive function. The attribution maps identify a disease-specific pathological footprint concentrated in prefrontal, posterior cerebellar and temporal areas that diverge from the manifold organization observed in healthy controls, which was dominated by auditory, limbic, and ventral-striatal circuits. These results establish an interpretable latent space framework for characterizing the distributed neural substrates of schizophrenia symptoms at the level of the individual patient, and provide an anatomically grounded route toward precision decoding of symptom severity.

neuroscience↗

bioRxiv2026

Visual uncertainty and task demands shape active sensing strategies in mice

In natural environments, animals actively sample visual information to guide behavior. Sensory feedback is dynamic and often requires active movements, whether saccading across the lines of this page or walking through a park. From high-acuity vision in hawks to low-acuity mice, many animals actively navigate to seek information, which can be called infotaxis. Although mice have relatively low-acuity vision, they still rely on sight for critical behaviors including navigation and prey capture. Yet, how sensitive they are to visual information and performing infotaxis has not been established. Here, we develop a virtual reality object discrimination task to investigate visual decision-making under naturalistic conditions. We show that mice perform infotaxis by actively seeking out informative views to guide their choices. Stimulus manipulations confirm that this strategy is modulated by the amount of available visual information. These results reveal that mice use principled active strategies to resolve visual uncertainty, highlighting a key role for information-seeking in natural vision. HighlightsO_LIMice perform active visual sensing in a free-range VR object discrimination task. C_LIO_LIThey perform infotaxis and select the correct object under variable visibility. C_LIO_LIDecisions decoded from head-body movements and speed reflect occlusion sensitivity. C_LI

neuroscience↗

bioRxiv2026

Closed-loop imitation learning reveals muscle-centric and latent-goal codes in primate sensorimotor cortex

Dexterous grasping requires the seamless integration of proprioceptive feedback with predictive motor commands. Yet, how cortical circuits combine afferent feedback with efference copies to support skilled hand control remains poorly understood. Here we develop a closed-loop, muscle-level model of primate grasping that integrates biomechanics, imitation learning, and neural recordings. A neural network policy trained on a 39-muscle musculoskeletal hand reproduces naturalistic pre-contact shaping and develops internal states that quantitatively explain single-neuron activity in primary motor (M1) and somatosensory (S1) cortices. Three principles emerged. First, muscle-based controllers generate representations that align more closely with cortical dynamics than joint-based controllers, despite lower kinematic accuracy. Second, recurrent architectures with temporal memory, especially LSTMs, provide an inductive bias that enhances neural predictability. Third, model-to-brain alignment peaked at the layer integrating proprioceptive and goal signals. Finally, by decoding the models latent trajectory representation from M1, we demonstrated direct neural control of the policy: with activity from only tens of neurons, the brain-driven controller generated coherent grasp trajectories and showed markedly greater robustness to noise than joint-angle decoding. These findings reveal that S1 and M1 embed integrated, temporally structured, muscle-centric states and establish a stimulus-computable mechanistic framework for modeling sensorimotor control, while opening a novel route for creating brain-body models.

neuroscience↗

bioRxiv2025

Mice navigate scent trails using predictive policies

Many terrestrial animals accurately follow odor trails, but their navigation strategies remain unclear. We presented mice with dynamic, non-repeating odor trails using a paper treadmill, and found that they rapidly learned to track trails effectively. Odor detections by one or both nostrils during inhalation evoked rapid, stereotyped motor responses. Blocking one nostril led to biased tracking, while disconnecting interhemispheric neural communication disrupted tracking almost entirely. We found that trail tracking is more than a reflex, with mice adapting their movements according to a memory of recently encountered trail geometry. A Bayesian model captures these behaviors, suggesting mice combine bilateral olfactory input with short-term memory to guide navigation actively. Functional perturbations and neural recordings from behaving animals implicate an early olfactory cortical region in trail tracking. These results lay the groundwork to uncover the computational architecture of sensorimotor integration in naturalistic tasks.

neuroscience↗

bioRxiv2024

Neuropeptides specify and reprogram division of labor in the leafcutter ant Atta cephalotes

Social insects offer powerful models to investigate the mechanistic foundation of elaborate individual behaviors comprising a cooperative community. Workers of the leafcutter ant genus Atta provide an extreme example of behavioral segregation among many phenotypically distinct worker types. We utilize the complex worker system of Atta cephalotes to test the molecular underpinnings of behavioral programming and, in particular, the extent of plasticity to reprogramming. We identify specific neuropeptides as mediators of worker division of labor in A. cephalotes, finding two neuropeptides associated with characteristic behaviors of leafcutting and of brood care. Manipulation via genetic knockdown or by injection of these neuropeptides led to stark loss or gain of each behavior and to transcriptomic shifts in the predicted direction, that is, towards gene pathways expressed in the natural caste. We also compare specific A. cephalotes worker transcriptomes with those of orthologous workers of the eusocial mammal, the naked mole rat H. gaber, revealing global similarities between caste-biased expression and link to specific roles of our studied neuropeptides in ants. This work underscores the essential function of neuropeptides in establishing complex social behavior and a remarkable plasticity among individual behavioral types.

molecular biology↗

bioRxiv2024

CellSeg3D: self-supervised 3D cell segmentation for microscopy

Understanding the complex three-dimensional structure of cells is crucial across many disciplines in biology and especially in neuroscience. Here, we introduce a set of models including a 3D transformer (SwinUNetR) and a novel 3D self-supervised learning method (WNet3D) designed to address the inherent complexity of generating 3D ground truth data and quantifying nuclei in 3D volumes. We developed a Python package called CellSeg3D that provides access to these models in Jupyter Notebooks and in a napari GUI plugin. Recognizing the scarcity of high-quality 3D ground truth data, we created a fully human-annotated mesoSPIM dataset to advance evaluation and benchmarking in the field. To assess model performance, we benchmarked our approach across four diverse datasets: the newly developed mesoSPIM dataset, a 3D platynereis-ISH-Nuclei confocal dataset, a separate 3D Platynereis-Nuclei light-sheet dataset, and a challenging and densely packed Mouse-Skull-Nuclei confocal dataset. We demonstrate that our self-supervised model, WNet3D - trained without any ground truth labels - achieves performance on par with state-of-the-art supervised methods, paving the way for broader applications in label-scarce biological contexts.

cell biology↗

bioRxiv2024

Molecular and neural mechanisms of behavioural integration in the extended-amygdala

Integration of diverse stimuli is crucial for organisms to adapt and communicate effectively, enabling overall homeostasis and survival. Studies have been performed on identifying specific neuronal encoding of individual behaviours, but how neurons integrate diverse behaviours across contexts remains elusive. Here we use Ca2+ imaging in freely moving mice to identify neural ensembles in the extended amygdala encoding behaviours across six distinct contexts. We found extensive flexibility in these ensemble encodings that may act as reserves for behavioural integration, with those encoding aversive stimuli showing greater specificity. Finally, we identified differential gene expression profiles between ensembles that are enriched in associations with human psychiatric and neurodegenerative disorders. Overall, our results demonstrate the molecular mechanisms behind behavioural integration, and their potential implications in health and disease.

neuroscience↗

bioRxiv2023

Keypoint-MoSeq: parsing behavior by linking point tracking to pose dynamics

Keypoint tracking algorithms have revolutionized the analysis of animal behavior, enabling investigators to flexibly quantify behavioral dynamics from conventional video recordings obtained in a wide variety of settings. However, it remains unclear how to parse continuous keypoint data into the modules out of which behavior is organized. This challenge is particularly acute because keypoint data is susceptible to high frequency jitter that clustering algorithms can mistake for transitions between behavioral modules. Here we present keypoint-MoSeq, a machine learning-based platform for identifying behavioral modules ("syllables") from keypoint data without human supervision. Keypoint-MoSeq uses a generative model to distinguish keypoint noise from behavior, enabling it to effectively identify syllables whose boundaries correspond to natural sub-second discontinuities inherent to mouse behavior. Keypoint-MoSeq outperforms commonly used alternative clustering methods at identifying these transitions, at capturing correlations between neural activity and behavior, and at classifying either solitary or social behaviors in accordance with human annotations. Keypoint-MoSeq therefore renders behavioral syllables and grammar accessible to the many researchers who use standard video to capture animal behavior.

neuroscience↗

bioRxiv2021

Striatal dopamine explains novelty-induced behavioral dynamics and individual variability in threat prediction

Animals exhibit diverse behavioral responses, such as exploration and avoidance, to novel cues in the environment. However, it remains unclear how dopamine neuron-related novelty responses influence behavior. Here, we characterized dynamics of novelty exploration using multi-point tracking (DeepLabCut) and behavioral segmentation (MoSeq). Novelty elicits a characteristic sequence of behavior, starting with investigatory approach and culminating in object engagement or avoidance. Dopamine in the tail of striatum (TS) suppresses engagement, and dopamine responses were predictive of individual variability in behavior. Behavioral dynamics and individual variability were explained by a novel reinforcement learning (RL) model of threat prediction, in which behavior arises from a novelty-induced initial threat prediction (akin to "shaping bonus"), and a threat prediction that is learned through dopamine-mediated threat prediction errors. These results uncover an algorithmic similarity between reward- and threat-related dopamine sub-systems. HighlightsO_LINovelty-induced behaviors are analyzed using modern machine-learning methods C_LIO_LINovelty induces risk assessment which develops into engagement or avoidance C_LIO_LIDopamine in the tail of striatum correlates with individual behavioral variability C_LIO_LIReinforcement learning with shaping bonus and uncertainty explains the data C_LI

neuroscience↗

bioRxiv2021

Multi-animal pose estimation and tracking with DeepLabCut

Estimating the pose of multiple animals is a challenging computer vision problem: frequent interactions cause occlusions and complicate the association of detected keypoints to the correct individuals, as well as having extremely similar looking animals that interact more closely than in typical multi-human scenarios. To take up this challenge, we build on DeepLabCut, a popular open source pose estimation toolbox, and provide high-performance animal assembly and tracking--features required for robust multi-animal scenarios. Furthermore, we integrate the ability to predict an animals identity directly to assist tracking (in case of occlusions). We illustrate the power of this framework with four datasets varying in complexity, which we release to serve as a benchmark for future algorithm development.

animal behavior and cognition↗

bioRxiv2020

Real-time, low-latency closed-loop feedback using markerless posture tracking

The ability to control a behavioral task or stimulate neural activity based on animal behavior in real-time is an important tool for experimental neuroscientists. Ideally, such tools are noninvasive, low-latency, and provide interfaces to trigger external hardware based on posture. Recent advances in pose estimation with deep learning allows researchers to train deep neural networks to accurately quantify a wide variety of animal behaviors. Here we provide a new DeepLabCut-Live! package that achieves low-latency real-time pose estimation (within 15 ms, >100 FPS), with an additional forward-prediction module that achieves zero-latency feedback, and a dynamic-cropping mode that allows for higher inference speeds. We also provide three options for using this tool with ease: (1) a stand-alone GUI (called DLC-Live! GUI), and integration into (2) Bonsai and (3) AutoPilot. Lastly, we benchmarked performance on a wide range of systems so that experimentalists can easily decide what hardware is required for their needs.

neuroscience↗

bioRxiv2020

Dynamic extrinsic pacing of the HOX clock in human axial progenitors controls motor neuron subtype specification

SUMMARYRostro-caudal patterning of vertebrates depends on the temporally progressive activation of HOX genes within axial stem cells that fuel axial embryo elongation. Whether HOX genes sequential activation, the “HOX clock”, is paced by intrinsic chromatin-based timing mechanisms or by temporal changes in extrinsic cues remains unclear. Here, we studied HOX clock pacing in human pluripotent stem cells differentiating into spinal cord motor neuron subtypes which are progenies of axial progenitors. We show that the progressive activation of caudal HOX genes in axial progenitors is controlled by a dynamic increase in FGF signaling. Blocking FGF pathway stalled induction of HOX genes, while precocious increase in FGF alone, or with GDF11 ligand, accelerated the HOX clock. Cells differentiated under accelerated HOX induction generated appropriate posterior motor neuron subtypes found along the human embryonic spinal cord. The HOX clock is thus dynamically paced by exposure parameters to secreted cues. Its manipulation by extrinsic factors alleviates temporal requirements to provide unprecedented synchronized access to human cells of multiple, defined, rostro-caudal identities for basic and translational applications.View Full Text

developmental biology↗

bioRxiv2020

Task-driven hierarchical deep neural networkmodels of the proprioceptive pathway

Biological motor control is versatile and efficient. Muscles are flexible and undergo continuous changes, requiring distributed adaptive control mechanisms. How proprioception solves this problem in the brain is unknown. The canonical role of proprioception is representing the body state, yet we hypothesize that the proprioceptive system can decode high-level, multi-feature actions. To test this theory, we pursue a task-driven modeling approach.We generated a large synthetic dataset of human arm trajectories tracing the alphabet in 3D space and use a musculoskeletal model plus modeled muscle spindle inputs to extract muscle activity. We then contrast two tasks, one character trajectory-decoding and another action recognition task that allows training of hierarchical models to decode position, or classify the character identity from the spindle firing patterns. Artificial neural networks could robustly solve these tasks, and the networks units show tuning properties akin to neurons in the primate somatosensory cortex and the brainstem. Remarkably, only the action-recognition trained, and not the trajectory decoding trained, models possess directional selective units (which are also uniformly distributed), as in the primate brain. Taken together, our model is the first to link tuning properties in the proprioceptive system at multiple levels to the behavioral level. We find that action-recognition, rather than the canonical trajectory-decoding hypothesis, better explains what is known about the proprioceptive system.

neuroscience↗

bioRxiv2018

Using DeepLabCut for 3D markerless pose estimation across species and behaviors

Noninvasive behavioral tracking of animals during experiments is crucial to many scientific pursuits. Extracting the poses of animals without using markers is often essential for measuring behavioral effects in biomechanics, genetics, ethology & neuroscience. Yet, extracting detailed poses without markers in dynamically changing backgrounds has been challenging. We recently introduced an open source toolbox called DeepLabCut that builds on a state-of-the-art human pose estimation algorithm to allow a user to train a deep neural network using limited training data to precisely track user-defined features that matches human labeling accuracy. Here, with this paper we provide an updated toolbox that is self contained within a Python package that includes new features such as graphical user interfaces and active-learning based network refinement. Lastly, we provide a step-by-step guide for using DeepLabCut.

neuroscience↗