Hamid Behjat

The paper shows aging and Alzheimer's disease alter distinct functional connectivity axes in the brain, with AD affecting the sensory-association axis and aging affecting the representation-executive axis.

This paper introduces a framework for constructing EEG-based brain graphs with small-world and scale-free properties, improving motor imagery decoding and brain fingerprinting performance.

The paper shows that individual functional brain connectivity improves the explanation of tau PET patterns in Alzheimer's disease compared to group-level data.

The paper investigates whether hemispheric asymmetry in tau pathology in Alzheimer's disease is linked to reduced inter-hemispheric connectivity or asymmetric amyloid-beta distribution, finding a strong association with amyloid-beta laterality.

This study investigates how brain network dynamics and regional vulnerability influence tau propagation and load in Alzheimer's disease using multi-scale simulations.

This paper introduces a laterality index to quantify hemispheric asymmetry in cortical maps using geometric eigenmodes, validated on fMRI and tau-PET data from Alzheimer's disease patients.

This study evaluates the accuracy of reconstructing cortical functional maps using subject-specific geometric and connectome eigenmodes, finding that spatially smooth eigenmodes best explain functional data.

This study uses graph learning to analyze resting-state MEG data and identifies frequency-specific functional connectome differences in tinnitus patients compared to controls.

This paper develops a multi-task neural network to jointly identify subjects and decode tasks from inferred functional brain graphs, showing improved task-decoding performance using seed-based FC graphs over raw fMRI data.

The paper re-evaluates the comparative explanatory power of geometric and connectome eigenmodes in predicting brain activity, finding that connectome eigenmodes can achieve comparable accuracy when improved connectome mapping is used.

This paper introduces a method to transform EEG data into a spectral representation using learned graphs for motor imagery decoding, showing improved performance over existing methods.

This paper introduces a method to estimate brain graphs using graph signal processing and demonstrates its superior performance over correlation-based functional connectomes in individual characterization.

This paper introduces a method using fMRI spectral signatures on high-resolution cortical graphs to improve individual identification by addressing limitations of conventional connectivity-based approaches.

The paper introduces voxel-wise brain graphs from diffusion MRI, showing that functional MRI data can be approximated by low-frequency graph Laplacian eigenmodes.

This paper introduces spectral graph features to characterize Heschl's Gyrus morphology, showing their superiority in distinguishing HG subtypes compared to conventional measures.

The paper introduces a cortical surface-informed volumetric spatial smoothing method for fMRI data using graph signal processing to adapt smoothing to brain morphology.

This paper introduces an anisotropic spatial filtering method for smoothing fMRI data in white matter, showing improved detection of anisotropic activations compared to isotropic Gaussian filters.

This study uses diffusion-informed white matter harmonics to analyze spatial dynamics of fMRI data, revealing subtle spatial modulations under functional load.