Ashkan Faghiri

This paper compares sliding window Pearson correlation and phase synchronization for time-resolved functional connectivity in fMRI data, finding they capture different aspects of brain dynamics and are complementary in their strengths and limitations.

This paper introduces TICA, a method for analyzing fMRI data across multiple spatial scales to detect functional brain networks and differences between healthy and schizophrenia cohorts.

This study introduces SSB+SWPC, a method that improves time-resolved functional connectivity analysis by reducing high-pass filtering effects, showing better performance in both simulated and real fMRI data.

The paper develops a method to estimate time-resolved spatial networks and applies it to study sex-specific schizophrenia disruptions and their genetic links.

The paper develops a reliable network template and methods to estimate individualized functional networks from resting-state fMRI data across 100k+ individuals, showing how data length and spatial resolution affect network estimation.

This study introduces a method to analyze explicitly nonlinear dynamic functional connectivity in resting-state fMRI data and finds significant group differences between schizophrenia patients and healthy controls.

This study shows that intrinsic connectivity networks in fMRI are continuously present and overlapping, not just during discrete events, and that analyzing all time points provides more comprehensive insights into functional patterns and schizophrenia symptoms.

This paper introduces msICA to analyze dynamic functional interactions across spatial scales in schizophrenia, finding sex-specific differences in connectivity patterns and their association with symptom scores.

The paper analyzes resting-state fMRI data across different frequency bins to assess functional network connectivity and finds distinct patterns at higher frequencies.

This paper introduces a new method for estimating connectivity that captures both static and dynamic frequency profiles, showing it can detect group differences in resting fMRI data between schizophrenia patients and controls.