Biology subjects

Turaga, P.

Publications and source records attributed to Turaga, P..

4 recordsLinked to original sources

bioRxiv2026

Dynamics and geometry of emotion and cognition: an interpretable model of individual human behavior

Affective flexibility-the capacity to flexibly process emotional information-has been assessed with affective task switching, in which response times consistently reveal larger behavioral costs when switching to the affective task. These traditional metrics of task behavior, however, discard the sub-trial dynamics that produce them and are vulnerable to trial-sampling noise. Subject-specific generative models were independently fitted (22 adults) with response-time sequences. The learned dynamics were visualized as continuous trajectories in a labeled embedding that was shared across person-specific models, yielding a suite of interpretable geometric metrics that captured how sub-trial behavior is jointly shaped by experimental conditions and between-subject variability. Switch-repeat onset distance tracked individual differences in switch costs; early- and late-epoch tortuosity provided convergence with classical sub-process models; and angular alignment between trajectories supported a task set flexibility account of asymmetric switch costs. Overall, our approach demonstrates the utility of latent geometry for deriving mechanistically informative measures of individual task behavior.

neuroscience↗

bioRxiv2026

Thalamocortical network dynamics in focal epilepsy: SEEG investigation

Thalamic neuromodulation is clinically effective in drug{-}resistant epilepsy, suggesting critical contributions of the thalamus to the epileptogenic process. However, the underlying electrophysiologic mechanisms remain poorly characterized. Converging evidence implicates the thalamus in shaping large{-}scale functional interactions across the cortex. We hypothesized that ictal changes in thalamic activity track cortical network dynamics associated with seizure propagation. We analyzed stereo{-}electroencephalography recordings from 16 patients with focal epilepsy (255 seizures) with simultaneous sampling of the thalamus (anterior nucleus, N=14; pulvinar, N=11) and cortex. Cortical regions of interest included the seizure onset zone (SOZ), surrounding cortices (near{-}SOZ), and control regions from the contralateral hemisphere. We characterized seizure dynamics across spatial scales, from local activity within each region to network{-}level, inter{-}regional interactions. Local activity was decomposed into its periodic (oscillatory) and aperiodic components. Network interactions were characterized by directed functional connectivity computed with a multivariate method. Seizures were associated with increased broadband power (a proxy for neuronal population firing rates) and low{-}frequency rhythmic activity across the thalamocortical network relative to interictal baseline levels. In contrast, consistent changes in aperiodic slope (a putative marker of excitation{-}inhibition balance) were specific to the thalamus, which showed an early and sustained steepening (i.e., more negative slope). While local rhythms were heterogeneous across the canonical frequency bands, inter{-}regional interactions predominantly involved the beta band (13{-}30 Hz). Shortly after onset, both forward outflow from SOZ to near{-}SOZ and feedback inflow in the reverse direction were increased. These bidirectional effects were expressed via both a direct cortico{-}cortical pathway and an indirect transthalamic route, operating in parallel. These dynamics were further stratified based on seizure subtypes, leveraging the fact that there was minimal propagation of ictal activity to the near{-}SOZ in subclinical seizures. The ictal drop in thalamic aperiodic slope was primarily observed in clinical seizures. At the network level, whereas SOZ[->]near{-}SOZ outflow was present across seizure types, reverse feedback was particularly enhanced in clinical seizures. Multivariable regression showed that the degree of thalamic slope steepening uniquely tracked seizure{-}to{-}seizure fluctuations in the strength of near{-} SOZ[->]SOZ feedback, and further predicted seizure durations. Together these findings highlight thalamic aperiodic slope as an index of cortical network dynamics linked to seizure propagation, with potential clinical utility for further development of physiology{-}informed precision neuromodulation.

neuroscience↗

bioRxiv2022

Learning Free Energy Pathways through Reinforcement Learning of Adaptive Steered Molecular Dynamics

In this paper, we develop a formulation to utilize reinforcement learning and sampling-based robotics planning to derive low free energy transition pathways between two known states. Our formulation uses Jarzynskis equality and the stiffspring approximation to obtain point estimates of energy, and construct an informed path search with atomistic resolution. At the core of this framework, is our first ever attempt we use a policy driven adaptive steered molecular dynamics (SMD) to control our molecular dynamics simulations. We show that both the reinforcement learning and robotics planning realization of the RL-guided framework can solve for pathways on toy analytical surfaces and alanine dipeptide.

biophysics↗

bioRxiv2021

Automated movement assessment in stroke rehabilitation

We are developing a system for long term Semi-Automated Rehabilitation At the Home (SARAH) that relies on low-cost and unobtrusive video-based sensing. We present a cyber-human methodology used by the SARAH system for automated assessment of upper extremity stroke rehabilitation at the home. We propose a hierarchical model for automatically segmenting stroke survivors movements and generating training task performance assessment scores during rehabilitation. The hierarchical model fuses expert therapist knowledge-based approaches with data-driven techniques. The expert knowledge is more observable in the higher layers of the hierarchy (task and segment) and therefore more accessible to algorithms incorporating high level constraints relating to activity structure (i.e. type and order of segments per task). We utilize an HMM and a Decision Tree model to connect these high level priors to data driven analysis. The lower layers (RGB images and raw kinematics) need to be addressed primarily through data driven techniques. We use a transformer based architecture operating on low-level action features (tracking of individual body joints and objects) and a Multi-Stage Temporal Convolutional Network(MS-TCN) operating on raw RGB images. We develop a sequence combining these complimentary algorithms effectively, thus encoding the information from different layers of the movement hierarchy. Through this combination, we produce a robust segmentation and task assessment results on noisy, variable and limited data, which is characteristic of low cost video capture of rehabilitation at the home. Our proposed approach achieves 85% accuracy in per-frame labeling, 99% accuracy in segment classification and 93% accuracy in task completion assessment. Although the methodology proposed in this paper applies to upper extremity rehabilitation using the SARAH system, it can potentially be used, with minor alterations, to assist automation in many other movement rehabilitation contexts (i.e. lower extremity training for neurological accidents).

neuroscience↗