Biology subjects

Shirvalkar, P.

Publications and source records attributed to Shirvalkar, P..

4 recordsLinked to original sources

bioRxiv2026

Decoding Chronic Pain States from Distributed Intracranial Recordings

Chronic pain engages distributed cortical and subcortical circuits, and large-scale intracranial recordings in humans offer a valuable opportunity to characterize its neural signatures. Here, we recorded multi-day stereoelectroencephalography (sEEG) from six participants with refractory chronic neuropathic pain, each implanted with sEEG electrodes spanning dozens of cortical and subcortical structures. Using simultaneous chronic pain ratings, we decoded spontaneous high versus low pain states within individuals (median area under the curve = 0.72; five of six participants performed above chance). Pain-predictive signals were broadly distributed and highly participant-specific. However, mapping the spatial distribution of pain-predictive features revealed preferential representation within canonical macroscale networks: beta-band activity in the default mode network and high-gamma activity in the salience network. These results demonstrate that intracranial recordings can capture distributed, network-organized representations of spontaneous chronic pain states.

neuroscience↗

bioRxiv2026

Predicting pain location from resting-state brain fMRI

Low back pain is a prevalent issue with few reliable treatments. Although there is great variation in clinical presentation within the low back pain population, little is known about the neurobiological mechanisms underlying these differences. In this study, we sought to stratify chronic low back pain patients (N = 275) into phenotypes characterized by correlated patterns of resting-state brain activity and sensory abnormalities (pain, numbness, and pins and needles) indicated on hand-drawn body maps. Our cross-decomposition analysis yielded phenotypes that resemble previously documented mechanistic pain types, revealing distinct brain connectivity patterns associated with different clinical presentations. Our model was then used to predict pain body maps from fMRI data in a small novel dataset of chronic pain subjects, suggesting that these relationships may generalize to other chronic pain conditions. Our results support the utility of resting-state fMRI in understanding the heterogeneity of chronic pain, which may be leveraged to develop more targeted pain treatments.

neuroscience↗

bioRxiv2025

Towards individualized deep brain stimulation: A stereoencephalography-based workflow for unbiased neurostimulation target identification

ObjectivesDeep brain stimulation (DBS) is increasingly being used to treat a variety of neuropsychiatric conditions, many of which exhibit idiosyncratic symptom presentations and neural correlates across individuals. Thus, we have utilized inpatient stereoelectroencephalography (sEEG) to identify personalized therapeutic stimulation sites for chronic implantation of DBS. Informed by our experience, we have developed a statistics-driven framework for stimulation testing to identify therapeutic targets. Materials and MethodsFourteen participants (major depressive disorder = 6, chronic pain = 6, obsessive-compulsive disorder = 2) underwent inpatient testing using sEEG and symptom monitoring to identify personalized stimulation targets for subsequent DBS implantation. We present a structured approach to this sEEG testing, integrating a Stimulation Testing Decision Tree with power analysis and effect size considerations to inform adequately powered results to detect therapeutic stimulation sites with statistical rigor. ResultsEffect sizes (Hedges g) of stimulation-induced symptom score changes ranged from -1.5 to +2.39. The standard deviation of sham trial responses was a strong predictor of stimulation response variability, as confirmed by a leave-one-out cross-validated linear regression (R2 = 0.67, permutation p<0.001). Thus, early sham trial data could be used to estimate the variability of stimulation responses for power analysis calculations. We show that approximately 10 sham trials were needed to robustly estimate sham variability. Power analysis (using a paired-t test) showed that for effect sizes [≥] 1.1, roughly 10 trials should be used per stimulation site for sufficiently powered results. ConclusionsThe presented workflow is adaptable to multiple indications and is specifically designed to overcome key challenges experienced during stimulation site testing. Through incorporating sham trials, effect size calculations, and tolerability testing, the described approach can be used to identify personalized and clinically efficacious stimulation sites.

neuroscience↗

bioRxiv2024

Robust Detection of Brain Stimulation Artifacts in iEEG Using Autoencoder-Generated Signals and ResNet Classification

BackgroundIntracranial EEG (iEEG) is crucial for understanding brain function, but stimulation-induced noise complicates data interpretation. Traditional artifact detection methods require manual user input or struggle with noise variability, especially with limited labeled data. ObjectiveWe developed a supervised method to automatically detect stimulation-induced noise in human iEEG recordings using synthetic data generated by Variational Autoencoders (VAEs) to train a ResNet-18 classifier. MethodsMulti-lead iEEG data were collected, preprocessed, and used to train VAEs for generating synthetic clean and noisy signals. The ResNet-18 model was trained on images of spectra generated from these synthetic signals and validated on real iEEG data from five participants. ResultsThe classifier, trained exclusively on synthetic data, demonstrated high accuracy, precision, and recall when applied to real iEEG recordings, with AUC values greater than 0.99 across all participants. ConclusionWe present a novel approach to effectively detect stimulation-induced noise in iEEG, offering a robust solution for improving data interpretation in scenarios with limited labeled data. Additionally, the pre-trained ResNet-18 model is available for the community to use, facilitating further research and application in similar datasets.

neuroscience↗