Ayşenil Belger

This study identifies small but consistent anatomical correlates of negative symptom dimensions in schizophrenia, linking them to prefrontal cortical thickness and limbic subcortical volumes.

This paper introduces dFNG, a method to estimate dynamic functional network connectivity gradients from ICA, and applies it to schizophrenia data to reveal distinct connectivity patterns between patient and healthy groups.

STRiDE improves dynamic brain state estimation by integrating structural and functional connectivity, revealing disruptions in schizophrenia.

The paper presents a WM intrinsic connectivity network template derived from 100,000 fMRI scans, showing distinct WM and GM networks with task-related activity and differences in schizophrenia.

This paper introduces dynamic fusion, a method to link brain structure and function over time, showing it captures dynamic functional processes and detects nuanced structural components.

This paper introduces sfCICA, a multi-modal ICA model combining structural and functional brain connectivity to estimate ICNs, showing improved modularity and network distinction in schizophrenia.

This study shows that aperiodic neural activity, not oscillatory power, better predicts schizophrenia diagnosis and severity using EEG data during a target detection task.

This paper introduces a multi-scale spatially constrained ICA approach to analyze fMRI data, revealing consistent group differences and predictive patterns in schizophrenia.

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 multi-model order ICA to evaluate brain functional network connectivity across multiple spatial scales and finds that FNC between model orders 25 and 50 best predicts schizophrenia vs. healthy controls.

This paper introduces a method to quantify nonlinear functional connectivity in resting-state fMRI data, showing distinct nonlinear patterns in schizophrenia patients compared to healthy controls.

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 evaluates MMN reliability across multiple EEG sites using different preprocessing methods, finding that mastoid referencing improves reliability.

This paper introduces a method to fuse functional and structural MRI data by integrating temporal information, identifying linked variability and covariation in schizophrenia.

This paper introduces the spatial chronnectome, a method to analyze dynamic spatiotemporal brain network interactions, revealing transient network changes and cognitive relevance in schizophrenia.

The paper examines longitudinal changes in resting-state brain networks in individuals at clinical high risk for psychosis and finds reduced network efficiency and increased diversity in those who later develop psychosis.

This paper introduces a hierarchical approach to analyze spatial and temporal dynamics in brain function, revealing new insights into the chronnectome and identifying changes in schizophrenia patients.

The paper assesses the reliability of functional connectomic measures across different scanners, sessions, and paradigms, finding fair to excellent reliability and demonstrating achievable statistical power in multisite studies.