Seyedeh-Rezvan Farahibozorg

This paper identifies latent subgroups in functional brain organisation using large-scale resting-state fMRI data from UK Biobank participants, revealing associations with cognitive, lifestyle, and health phenotypes.

This paper introduces bi-cross-validation to evaluate and compare dynamic functional connectivity models in fMRI, showing it balances model fit and complexity effectively.

This paper introduces an automated individual cortical parcellation method using a novel weakly supervised classifier, improving performance over previous models and identifying new atypical language-related brain areas.

The paper describes the release of a large fetal fMRI dataset and presents methods for its preprocessing and analysis to study in utero brain connectivity.

This paper introduces mPFMs, a new method for multiscale functional brain connectivity analysis that improves prediction of UK Biobank traits compared to standard techniques.

This paper evaluates individual differences in brain network organization and identifies spatial overlap as indicative of linear additive coupling.

This paper introduces a new analytical framework called matnets to study fetal brain connectivity, revealing that high-level associative areas develop functional connections before birth.

This study compares parcellation and gradient methods for predicting behavior from resting-state functional connectivity in two datasets, finding that individual-specific hard parcellations perform best in HCP and principal gradients perform similarly in ABCD.

This paper introduces TL-MDPC, a novel functional connectivity metric for EEG/MEG data that captures multidimensional pattern transformations, showing improved sensitivity in detecting semantic task-related connectivity.

The paper improves predictions of individual differences in task-evoked fMRI activity using a sparse ensemble learner and shows better performance than previous methods.

The paper investigates the roles of the Anterior Temporal Lobe and Angular Gyrus in semantic processing using EEG data, finding distinct temporal roles as representational and connectivity hubs.

This study examines how semantic task demands modulate spatiotemporal brain dynamics and connectivity in semantic networks using EEG/MEG data, revealing distinct patterns in activation and functional connectivity across different brain regions.

This paper introduces sPROFUMO, a hierarchical model for estimating functional brain networks in individuals and populations from large fMRI datasets, showing improved performance over traditional methods.

This paper introduces an automated pipeline for processing neonatal resting-state fMRI data, improving motion correction and quality assurance in the developing Human Connectome Project.

The paper extends probabilistic functional modes to model subject variability in fMRI data, showing spatial maps capture most cross-subject variability and functional coupling reflects cognitive state.

This paper introduces adaptive cortical parcellation algorithms for EEG/MEG source estimation, improving sensitivity and network reconstruction accuracy by reducing false leakage-induced connections.

This paper introduces a method to detect brain networks from hdEEG data, showing results comparable to fMRI by using realistic head models and source localization.