Tülay Adalı

NeuroMark dFNC uses reference-state templates to estimate subject-specific dynamic connectivity states across datasets, enabling cross-cohort comparison while preserving individual patterns.

This paper introduces a chronnectogram-ICA framework to track continuously evolving and overlapping brain states in fMRI data, revealing new insights into schizophrenia's connectivity dynamics.

This paper introduces spatio-dynamical decomposition for fMRI BSS, showing it better recovers sources using both spatial and temporal criteria than methods that use only one.

This paper introduces DyCoM, a framework that unifies dynamic connectivity analysis in neuroimaging by decomposing estimators into fundamental operations, revealing distinct biological interpretations from different computational choices.

This paper introduces MSIVA, a method to capture latent relationships between brain structure and function, showing it identifies phenotype-associated brain regions in neuroimaging data.

PhaseICA extracts reoccurring spatiotemporal brain patterns by incorporating phase information, improving the analysis of nonstationary brain activity.

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 a nonlinear method to estimate brain networks from fMRI data, showing it better distinguishes schizophrenia patients from controls than linear methods.

This paper develops a method to detect linked features across neuroimaging modalities and identifies markers related to age, sex, cognition, and schizophrenia in large datasets.

This paper introduces pmg-jICA, a method that fuses structural and functional brain data to identify structure-function links in Alzheimer's disease, revealing disrupted brain regions and network connectivity.

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.

This paper uses ml-jICA to analyze multimodal MRI data from Alzheimer's patients and controls, finding significant function/structure coupling in brain regions like the default mode network and hippocampus.

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

This paper uses structure-revealing fusion of EEG, fMRI, and sMRI data to identify schizophrenia biomarkers, showing improved clustering and reproducible patterns compared to other methods.

The paper critiques null models for resting-state fMRI dynamics, highlighting issues with their formulation and suggesting alternative metrics for capturing functionally relevant brain dynamics.