Biology subjects

Sarma, S. V.

Publications and source records attributed to Sarma, S. V..

6 recordsLinked to original sources

bioRxiv2024

Electrical Coordinated Reset Stimulation Induces Network Desynchronization in an in Vivo Model of Status Epilepticus

Epilepsy, a neurological disorder characterized by recurrent seizures, profoundly impacts individuals worldwide. Various electrical stimulation protocols have been investigated to mitigate epileptic seizures, among which Coordinated Reset (CR) stimulation may have potential for inducing long-lasting neural desynchronization. This study explores the acute effects of CR stimulation on synchronization dynamics during Status Epilepticus (SE) in an in vivo animal model. An electrographically sustained seizure-state was induced via 4-aminopyridine (4AP) administration to CA3. Custom-designed electrode probes were implanted to facilitate simultaneous recording and electrical stimulation. Analytical univariate and bivariate features were constructed from the LFP time-series recording. Feature metrics focused on spike synchronization metrics and continuous signal analysis of amplitude, spectral power and phase synchronization across electrode pairs and frequency bands. Significance of modulation was assessed through permutation testing of the observed differences between the CR-stimulated group (N=5) compared to the control (no stimulation) group (N=3) during SE. Results showed overall decrease in amplitude and power univariate features, and a significant modulation of bivariate synchronization and connectivity measures across the spectrum between the CR stimulation and control group. Our findings underscore the potential effectiveness of CR stimulation in attenuating excessive neural synchronization, paving the way for further exploration of CR stimulation as a viable intervention for network desynchronization of epileptiform activity and subsequently treatment of seizures.

neuroscience↗

bioRxiv2023

Optimal reaching subject to computational and physical constraints reveals structure of the sensorimotor control system

Optimal feedback control provides an abstract framework describing the architecture of the sensorimotor system without prescribing implementation details such as what coordinate system to use, how feedback is incorporated, or how to accommodate changing task complexity. We investigate how such details are determined by computational and physical constraints by creating a model of the upper limb sensorimotor system in which all connection weights between neurons, feedback, and muscles are unknown. By optimizing these parameters with respect to an objective function, we find that the model exhibits a preference for an intrinsic (joint angle) coordinate representation of inputs and feedback and learns to calculate a weighted feedforward and feedback error. We further show that complex reaches around obstacles can be achieved by augmenting our model with a path-planner based on via points. The path-planner revealed "avoidance" neurons that encode directions to reach around obstacles, and "placement" neurons that make fine-tuned adjustments to via point placement. Our results demonstrate the surprising capability of computationally constrained systems and highlight new characteristics of the sensorimotor system.

neuroscience↗

bioRxiv2022

Internal states as a source of subject-dependent movement variability and their representation by large-scale networks

A humans ability to adapt and learn relies on reflecting on past performance. Such reflections form latent factors called internal states that induce variability of movement and behavior to improve performance. Internal states are critical for survival, yet their temporal dynamics and neural substrates are less understood. Here, we link internal states with motor performance and neural activity using state-space models and local field potentials captured from depth electrodes in over 100 brain regions. Ten human subjects performed a goal-directed center-out reaching task with perturbations applied to random trials, causing subjects to fail goals and reflect on their performance. Using computational methods, we identified two internal states, indicating that subjects kept track of past errors and perturbations, that predicted variability in reaction times and speed errors. These states granted access to latent information indicative of how subjects strategize learning from trial history, impacting their overall performance. We further found that large-scale brain networks differentially encoded these internal states. The dorsal attention network encoded past errors in frequencies above 100 Hz, suggesting a role in modulating attention based on tracking recent performance in working memory. The default network encoded past perturbations in frequencies below 15 Hz, suggesting a role in achieving robust performance in an uncertain environment. Moreover, these networks more strongly encoded internal states and were more functionally connected in higher performing subjects, whose learning strategy was to respond by countering with behavior that opposed accumulating error. Taken together, our findings suggest large-scale brain networks as a neural basis of strategy. These networks regulate movement variability, through internal states, to improve motor performance. Key pointsO_LIMovement variability is a purposeful process conjured up by the brain to enable adaptation and learning, both of which are necessary for survival. C_LIO_LIThe culmination of recent experiences--collectively referred to as internal states--have been implicated in variability during motor and behavioral tasks. C_LIO_LITo investigate the utility and neural basis of internal states during motor control, we estimated two latent internal states using state-space representation that modeled motor behavior during a goal-directed center-out reaching task in humans with simultaneous whole-brain recordings from intracranial depth electrodes. C_LIO_LIWe show that including these states--based on error and environment uncertainty--improves the predictability of subject-specific variable motor behavior and reveals latent information related to task performance and learning strategies where top performers counter error scaled by trial history while bottom performers maintain error tendencies. C_LIO_LIWe further show that these states are encoded by the large-scale brain networks known as the dorsal attention network and default network in frequencies above 100 Hz and below 15 Hz but found neural differences between subjects where network activity closely modulates with states and exhibits stronger functional connectivity for top performers. C_LIO_LIOur findings suggest the involvement in large-scale brain networks as a neural basis of motor strategy that orchestrates movement variability to improve motor performance. C_LI

neuroscience↗

bioRxiv2021

Source-sink connectivity: a novel resting-state EEG marker of the epileptogenic zone

Over 15 million epilepsy patients worldwide have drug-resistant epilepsy (DRE). Successful surgery is a standard of care treatment for DRE but can only be achieved through complete resection or disconnection of the epileptogenic zone (EZ), the brain region(s) where seizures originate. Surgical success rates vary between 20-80% because no clinically validated biological markers of the EZ exist. Localizing the EZ is a costly and time-consuming process beginning with non-invasive neuroimaging and often followed by days to weeks of intracranial EEG (iEEG) monitoring. Clinicians visually inspect iEEG data to identify abnormal activity (e.g., low-voltage high frequency activity) on individual channels occurring immediately before seizures or spikes that occur on interictal iEEG (i.e., between seizures). In the end, the clinical standard mainly relies on a small proportion of the iEEG data captured to assist in EZ localization (minutes of seizure data versus days of recordings), missing opportunities to leverage these largely ignored interictal data to better diagnose and treat patients. Intracranial EEG offers a unique opportunity to observe epileptic cortical network dynamics but waiting for seizures increases patient risks associated with invasive monitoring. In this study, we aim to leverage interictal iEEG data by developing a new network-based interictal iEEG marker of the EZ. We hypothesize that when a patient is not clinically seizing, it is because the EZ is inhibited by other regions. We developed an algorithm that identifies two groups of nodes from the interictal iEEG network: those that are continuously inhibiting a set of neighboring nodes ("sources") and the inhibited nodes themselves ("sinks"). Specifically, patient-specific dynamical network models (DNMs) were estimated from minutes of iEEG and their connectivity properties revealed top sources and sinks in the network, with each node being quantified by source-sink metrics (SSMs). We validated the SSMs in a retrospective analysis of 65 patients by using the SSMs of the annotated EZ to predict surgical outcomes. The SSMs predicted outcomes with an accuracy of 79% compared to an accuracy of 43% for clinicians predictions (surgical success rate of this dataset). In failed outcomes, we identified regions of the brain with high SSMs that were untreated. When compared to high frequency oscillations, the most commonly proposed interictal iEEG feature for EZ localization, SSMs outperformed in predictive power (by a factor of 1.2) suggesting SSMs may be an interictal iEEG fingerprint of the EZ.

bioengineering↗

bioRxiv2018

Performance Limitations in Sensorimotor Control: Tradeoffs between Neural Computing and Accuracy in Tracking Fast Movements

The ability to move fast and accurately track moving objects is fundamentally constrained by the biophysics of neurons and dynamics of the muscles involved. Yet, the corresponding tradeoffs between these factors and tracking motor commands have not been rigorously quantified. We use feedback control principles to quantify performance limitations of the sensorimotor control system (SCS) to track fast periodic movements. We show that (i) linear models of the SCS fail to predict known undesirable phenomena, including skipped cycles, overshoot and undershoot, produced when tracking signals in the "fast regime", while non-linear pulsatile control models can predict such undesirable phenomena, and (ii) tools from nonlinear control theory allow us to characterize fundamental limitations in this fast regime. Using a validated and tractable nonlinear model of the SCS, we derive an analytical upper bound on frequencies that the SCS model can reliably track before producing such undesirable phenomena as a function of the neurons biophysical constraints and muscle dynamics. The performance limitations derived here have important implications in sensorimotor control. For example, if primary motor cortex is compromised due to disease or damage, the theory suggests ways to manipulate muscle dynamics by adding the necessary compensatory forces using an assistive neuroprosthetic device to restore motor performance, and more importantly fast and agile movements. Just how one should compensate can be informed by our SCS model and the theory developed here.

neuroscience↗

bioRxiv2018

Using Network Analysis to Localize the Epileptogenic Zone from Invasive EEG Recordings in Intractable Focal Epilepsy

Treatment of medically intractable focal epilepsy (MIFE) by surgical resection of the epileptogenic zone (EZ) is often effective provided the EZ can be reliably identified. Even with the use of invasive recordings, the clinical differentiation between the EZ and normal brain areas can be quite challenging, mainly in patients without MRI detectable lesions. Consequently, despite relatively large brain regions being removed, surgical success rates barely reach 60-65%. Such variable and unfavorable outcomes associated with high morbidity rates are often caused by imprecise and/or inaccurate EZ localization. We developed a localization algorithm that uses network-based data analytics to process invasive EEG recordings. This network algorithm analyzes the centrality signatures of every contact electrode within the recording network and characterizes contacts into susceptible EZ based on the centrality trends over time. The algorithm was tested in a retrospective study that included 42 patients from four epilepsy centers. Our algorithm had higher agreement with EZ regions identified by clinicians for patients with successful surgical outcomes and less agreement for patients with failed outcomes. These findings suggest that network analytics and a network systems perspective of epilepsy may be useful in assisting clinicians in more accurately localizing the EZ.\n\nAUTHOR SUMMARYEpilepsy is a disease that results in abnormal firing patterns in parts of the brain that comprise the epileptogenic network, known as the epileptogenic zone (EZ). Current methods to localize the EZ for surgical treatment often requires observations of hundreds of thousands of EEG data points measured from many electrodes implanted in a patients brain. In this paper, we used network science to show that EZ regions may exhibit specific network signatures before, during and after seizure events. Our algorithm computes the likelihood of each electrode being in the EZ and tends to agree more with clinicians during successful resections and less during failed surgeries. These results suggest that a networked analysis approach to EZ localization may be valuable in a clinical setting.

neuroscience↗