Marlena Duda

This paper introduces a dynamic fusion framework to integrate structural and functional connectivity data, revealing differences between schizophrenia patients and controls.

The paper develops a normative template for multimodal brain MRI fusion using ICA, showing it guides new dataset analyses and correlates with age.

The paper links functional change patterns to brain structure using multimodal fusion in adolescents, revealing sex-specific differences in neural dynamics.

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 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 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 data-driven method to identify dynamic functional connectivity states in resting state fMRI by leveraging activation changes, showing improved accuracy and efficiency over sliding window approaches.