Robyn L. Miller

This study identifies a dynamic directional coordination axis in the cortical hierarchy that is selectively reorganized in schizophrenia, linked to symptoms and cognition.

The paper develops a timescale-aligned framework to measure amplitude balance in brain networks and finds greater imbalance and slower recovery in schizophrenia, linked to symptoms and cognition.

The paper introduces DICE, a framework analyzing dynamic functional connectivity entropy in schizophrenia, revealing distinct pathophysiological signatures linked to symptom dimensions.

This paper introduces TOIBN, a method that uses topological information from group-level brain networks to improve subject-level network estimation while preserving individual variability.

This paper introduces FINE, a frequency-aware deep learning model for Alzheimer's disease classification using resting-state fMRI, achieving ROC-AUC 0.769 on OASIS-3.

This study introduces normalized dynamic time warping to improve sensitivity in detecting functional network connectivity differences in schizophrenia compared to standard DTW and correlation methods.

This paper introduces a novel method to analyze static and dynamic functional connectivity in schizophrenia patients, finding elevated entropy in connectivity patterns compared to healthy controls.

This paper introduces a method to identify linear and explicitly nonlinear dependencies in resting fMRI data and applies it to detect differences in schizophrenia.

This study examines dynamic spatial brain networks in resting-state fMRI data, finding atypical variability and coupling in schizophrenia linked to cognitive impairment.

This study identifies reproducibly important EEG markers of schizophrenia using an explainable multi-model deep learning approach and finds effects on beta, theta frequency bands, left hemisphere, and interhemispheric interactions.

This study identifies a subset of default mode network connectivity features in schizophrenia that reveal disorder-related dynamics not apparent when all features are included.

This study evaluates six EEG data augmentation methods for MDD diagnosis, finding that most do not improve performance beyond using duplicate data, but channel dropout augmentation does.

This study uses dynamic entropy analysis to show that schizophrenia patients have simpler brain connectivity trajectories compared to healthy controls, with reduced complexity linked to cognitive performance.

This study improves EEG-based MDD diagnosis using transfer learning by adapting representations from a sleep dataset to a higher sampling rate, increasing model accuracy and robustness.

This study introduces an explainable fuzzy clustering framework for analyzing dynamic functional network connectivity in schizophrenia, identifying 5 states and linking them to symptom severity.

This paper introduces a new framework called 'flownectome' to analyze BOLD fMRI dynamics by capturing spatial signal propagation and propagative attractor patterns, revealing significant relationships with schizophrenia.

This study introduces two novel convolutional neural networks for MDD diagnosis from EEG data, finding higher beta and delta power and right hemisphere correlation in MDD patients.

This paper introduces a method to analyze spatial propagation in BOLD fMRI using Markov processes, revealing functional attractors that differ between schizophrenia patients and controls.

This study improves major depressive disorder diagnosis using raw multichannel EEG with transfer learning from single-channel sleep stage data, achieving better performance than previous methods.

This study develops a robust and explainable deep learning model for schizophrenia diagnosis using EEG data, showing improved performance and prioritization of specific brain regions and frequency bands.

This study combines explainable deep learning classification with clustering to analyze schizophrenia's effects on brain network dynamics, revealing reduced variability and temporally localized impacts.

This paper introduces an explainable fuzzy clustering method for analyzing dynamic functional network connectivity in fMRI data, showing it better identifies feature importance in schizophrenia compared to existing methods.

This study introduces a novel explainability approach for rs-EEG deep learning classifiers, revealing spatio-spectral interactions in MDD diagnosis.

This study identifies neuropsychiatric disorder subtypes and shows how a deep learning classifier's performance varies across these subtypes using dynamic functional network connectivity.

This paper introduces a framework for evaluating deep learning classifiers on raw EEG data, focusing on spatial, spectral, and temporal explainability, and applies it to MDD diagnosis.

This study develops novel methods to assess modality importance in multimodal electrophysiology classifiers, finding EEG is globally most important for sleep stages except stage 1, with subject-level differences influenced by sex, medication, and age.

The paper uses a novel explainability framework to analyze sex differences in brain network structure using fMRI data, identifying modular and localized differences in temporal brain activity.

This paper introduces a convolutional autoencoder-based clustering method for resting-state EEG data, identifying 8 EEG states in schizophrenia and linking time outside the dominant state to negative symptom severity.

This study integrates confidence estimation and explainability methods for schizophrenia diagnosis using rs-fMRI data, finding that MCBN is better suited for gradient-based explanations than MCD.