Srikantan S. Nagarajan

The paper uses gene gradients to infer directed structural connectivity across species and applies it to human data to reveal source-sink network organization.

The paper develops a computational model to test cerebellar functions in speech feedback control, finding that internal modeling, timing dynamics, and multimodal integration explain speech abnormalities in cerebellar degeneration.

This paper reframes Global Signal Removal (GSR) as graph spatial filtering, introducing variants with different effects on network connectivity.

The paper introduces HONeD-in, a method to deconvolve passive diffusion effects from functional MRI signals, revealing active brain activity patterns.

This study shows that sensory prediction errors, measured as reduced speaking-induced suppression in auditory cortex, drive speech sensorimotor adaptation during altered auditory feedback.

The paper investigates how glioma infiltration affects the aperiodic slope of neural dynamics, finding it correlates with excitation dominance and semantic naming impairments.

The paper introduces a framework to unify integration and segregation in the brain using structural connectivity harmonics and the gap-spectrum, organizing them into three regimes with distinct properties.

This paper uses Bayesian inference to estimate parameters of a state feedback control model for vocal pitch in cerebellar ataxia and healthy controls, finding differences in feedback sensitivity and motor response.

This study compares resting state functional connectivity in children with ASD, SPD, and TDC using MEG, finding distinct beta-band connectivity patterns associated with sensory processing abilities.

This paper introduces a biophysical, connectivity-based generative model for rs-fMRI that integrates spectral and structural information to quantify structure-function relationships.

This study uses event-based sequencing models to analyze neural oscillation trajectories in Alzheimer's disease stages, finding frequency-specific synchrony changes linked to early neurodegeneration and cognitive decline.

This study identifies dynamic functional connectivity features in MEG data that distinguish Alzheimer's disease from healthy controls, showing higher spatial complexity and lower temporal complexity in AD patients.

This paper develops a Bayesian procedure for inferring spectral graph model parameters using simulation-based inference, showing comparable performance to annealing algorithms while enabling uncertainty quantification.

This study investigates whether neural phase locking to speech amplitude envelopes reflects oscillatory entrainment or evoked responses to acoustic edges, finding support for the latter.

This paper introduces a computational framework to reconstruct functional connectivity from structural connectomes using joint subspace eigenmodes, showing competitive performance and better interpretability.

The paper develops a hierarchical Bayesian model for EEG source reconstruction that jointly estimates full-structure noise covariance matrices, improving accuracy in real-world scenarios with non-diagonal noise.

This study uses high-density electrode arrays to analyze neural activity in human brain organoids, revealing functional connectivity patterns and responses to benzodiazepine treatment.

This study examines speech motor control in nfvPPA patients using structure-function imaging, finding reduced alpha-band activity and increased beta-band activity in specific brain regions, unrelated to atrophy patterns.

The paper analyzes the stability and dynamics of a spectral graph model of brain oscillations, showing it can capture alpha band oscillations without mesoscopic oscillations and estimate time-varying parameters from MEG data.

The paper examines associations between neuronal subpopulation parameters and tau/A{beta} in AD patients using MEG and PET data.

The paper develops a deep multi-task multi-modal framework to classify tinnitus and predict its severity using structural MRI data, outperforming existing methods.

The paper finds that high neuronal activity and high functional connectivity are mutually exclusive in both zebrafish and human brains using calcium imaging and fMRI data.

This study identifies abnormal neural oscillations in specific brain regions during different stages of phonation in laryngeal dystonia patients, revealing impaired motor preparation and auditory feedback processing.

This paper introduces an improved spectral graph theory-based model that captures MEG frequency spectra and spatial patterns of alpha and beta bands from resting healthy subjects.

The paper uses multivariate pattern analysis to predict functional outcomes after auditory-based cognitive training in schizophrenia patients based on brain structure.

This paper unifies three sparse Bayesian learning algorithms for EEG/MEG brain source imaging under the majorization-minimization framework and introduces a new method for low SNR settings and noise variance estimation.

This paper shows how functional brain networks can emerge from the structural connectome by using a complex-valued graph Laplacian that outperforms real-valued models.

The paper investigates midfrontal theta activity using MEG and EEG data, finding that it is multidimensional rather than unitary, with multiple uncorrelated sources contributing to the signal.

This paper investigates brain dynamics during irregular word reading in svPPA patients, showing increased reliance on the dorsal route when the ventral route is damaged.

This study uses nrTMS to show that stimulating the medial prefrontal cortex improves reality monitoring and mood in healthy individuals.