Biology subjects

Sheth, K. N.

Publications and source records attributed to Sheth, K. N..

3 recordsLinked to original sources

bioRxiv2026

Human claustrum neurons encode uncertainty and prediction errors during aversive learning

Flexible behavior depends on continuous updating of internal models, yet the neural circuits coordinating this process remain poorly understood [1]. The claustrum -- reciprocally connected to nearly the entire neocortex -- is uniquely positioned to influence cortical processing. Here we report single-neuron recordings from the human claustrum during aversive learning [2], with anterior cingulate cortex and amygdala recordings for comparison. Claustrum and anterior cingulate neurons displayed structured, task-related responses. Distinct subpopulations encoded stimulus onset and action-contingent outcomes, with outcome representations diverging between regions. Critically, both regions encoded model-derived latent variables -- uncertainty and prediction error -- but with different temporal profiles: only the anterior cingulate carried uncertainty signals during the intertrial period, while both regions encoded uncertainty and prediction error during the active-avoidance period. The amygdala, by contrast, showed minimal latent-variable modulation. These findings provide evidence that human claustrum neurons track higher-order cognitive variables not directly observable from sensory input, and reveal dissociable roles for the claustrum and anterior cingulate cortex in tracking latent task states.

neuroscience↗

bioRxiv2026

On the accuracy of image registration in portable low-field 3D brain MRI

Portable low-field MRI offers an affordable and mobile alternative to conventional high-field scanners, enabling imaging in point-of-care and resource-limited settings. However, its lower signal-to-noise ratio, reduced resolution, and acquisition artifacts raise concerns about the accuracy of standard image registration methods. Reliable registration is critical for a wide range of emerging applications, including frequent brain monitoring, assessment of neurodegenerative disease progression, and evaluation of treatment effects such as those of Alzheimers therapeutics. In this work, we systematically evaluated state-of-the-art registration approaches on simulated low-field scans (obtained by downsampling high-field images) and on real low-field brain MRI data. We compared three representative approaches: classical optimization (NiftyReg), learning-based registration (SynthMorph), and synthesis-based registration (SynthSR+NiftyReg). Using downsampled high-field scans, all methods performed well, achieving high Dice scores and smooth deformation fields, indicating that reduced resolution alone does not hinder registration. In contrast, real low-field data exhibited lower accuracy, primarily due to geometric distortion and other acquisition-specific artifacts. Among the tested approaches, the synthesis-based pipeline achieved the most robust performance across subjects and modalities. Overall, existing algorithms can accommodate resolution limitations, however, future methods could further enhance coregistration by explicitly addressing the distortions present in low-field MRI scans.

neuroscience↗

bioRxiv2025

Neuroanatomical Basis of Coma in Acute Ischemic Stroke

BackgroundAcute ischemic stroke (AIS) can lead to profound disturbances in consciousness, including coma, which is associated with poor prognosis and increased mortality. Clarifying the lesion patterns that precipitate loss of consciousness can refine pathophysiological models and guide prognosis. ObjectivesIn this study, we aim to identify the brain regions most commonly affected in comatose AIS and determine whether specific combinations of lesions are necessary and sufficient to produce coma. MethodsWe retrospectively analyzed 476 AIS patients (52 comatose) using diffusion-weighted imaging. Infarcts were automatically segmented, manually verified, and normalized to MNI space. Support vector regression lesion-symptom mapping (SVR-LSM) quantified voxel-wise associations with coma, controlling for lesion volume. To assess the necessity and sufficiency of lesion combinations, we employed permutation-based nested logistic regression models comparing all subsets of four anatomical predictors: brainstem, thalamus, cerebellum, and the rest of brain lesions. ResultsSVR-LSM revealed that coma was strongly associated with lesions involving the brainstem, thalamus, and cerebellum, whereas non-comatose patients exhibited predominantly cortical infarcts. Nested model comparisons showed that concurrent lesions to both the brainstem and thalamus were necessary and sufficient for coma. Additional involvement of the cerebellum or cerebral cortex did not improve predictive performance. ConclusionsComa after AIS results from a dual-node subcortical lesion pattern involving both the brainstem and thalamus. Cerebellar and cortical lesions, even when extensive, did not induce coma in the absence of the dual-brainstem and thalamic lesions. These observations emphasize the predominant role of lesion location over lesion volume in the pathogenesis of coma. They also support mechanistic models that position the brainstem and thalamic hubs as central to the neural circuitry underlying arousal. Furthermore, these findings delineate a specific anatomical substrate that may serve as a strategic target for circuit-based neuroprotective and neuromodulatory therapies.

neuroscience↗