Mohammad S.E. Sendi

The paper links functional brain network connectivity across age and psychiatric disorders to neurobiological microprofiles, showing stage-specific associations.

The paper identifies altered default mode network connectivity dynamics in schizophrenia using a novel iterative feature-removal clustering approach on resting-state fMRI data.

The paper introduces a framework using SHAP to identify FNC biomarkers distinguishing between schizophrenia patients and controls, and young vs. old adults with high accuracy.

This paper develops an active learning framework to personalize brain stimulation therapies, showing it outperforms random sampling in efficiency and accuracy across in silico and in vivo experiments.

SAFE-OPT is a Bayesian optimization algorithm that incorporates safety constraints to find optimal deep brain stimulation parameters without harmful side-effects, validated in rodent studies.

This study identifies a subset of default mode network connectivity features in schizophrenia that reveal disorder-related dynamics not apparent when all features are included.

This study develops novel methods to assess modality importance in multimodal electrophysiology classifiers, finding EEG is globally most important for sleep stages except stage 1, with subject-level differences influenced by sex, medication, and age.

The paper examines the association between static and dynamic functional network connectivity and genetic risk of Alzheimer's disease using rs-fMRI and APOE data from cognitively normal individuals.

This study uses clustering and feature importance analysis on fMRI data to identify cognitive differences between brain network groups in a large population.

The paper introduces iSparse kmeans, a two-step clustering method for large dFNC datasets, which is significantly faster than traditional kmeans while producing comparable results.

This study uses dynamic functional network connectivity to identify overlaps and differences between schizophrenia, bipolar disorder, and schizoaffective disorder, finding distinct patterns in specific states.

This paper introduces an activation maximization method for electrophysiology classifiers, showing it can identify spectral patterns in EEG sleep stages and reveal potential reliance on artifacts for awake classification.

This study introduces a local spectral explainability method for EEG deep learning classifiers, showing that delta, beta, and gamma bands are most important and that age and sex affect classifier patterns.

This study examines dynamic functional connectivity in the cognitive control network across Alzheimer's disease stages, finding altered temporal connectivity patterns associated with symptom progression.

This study uses resting-state fMRI to analyze dynamic functional connectivity in major depressive disorder patients before and after ECT, finding that specific network states predict treatment response.

The paper investigates dynamic functional connectivity (dFC) of the default mode network (DMN) in schizophrenia and links it to symptom severity using resting-state fMRI data from two datasets.

This study examines how dynamic functional network connectivity relates to spatial orientation in healthy young adults using resting-state fMRI and machine learning.

The paper analyzes dynamic functional connectivity in the visual sensory network of schizophrenia patients and healthy controls, finding disrupted connectivity patterns and a correlation between connectivity states and visual learning.

This study shows that chronic electrical stimulation enhances excitability and plasticity in ESC-derived neurons after glutamate-induced inhibition.