Charles A. Ellis

Diagnosis-Optimized Dynamic Feature Learning Reveals Altered Default Mode Network Connectivity in Schizophrenia

The paper identifies altered default mode network connectivity dynamics in schizophrenia using a novel iterative feature-removal clustering approach on resting-state fMRI data.

Refining Brain Stimulation Therapies: An Active Learning Approach to Personalization

This paper develops an active learning framework to personalize brain stimulation therapies, showing it outperforms random sampling in efficiency and accuracy across in silico and in vivo experiments.

Identifying Reproducibly Important EEG Markers of Schizophrenia with an Explainable Multi-Model Deep Learning Approach

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.

Uncovering Effects of Schizophrenia upon a Maximally Significant, Minimally Complex Subset of Default Mode Network Connectivity Features

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.

Evaluating Augmentation Approaches for Deep Learning-based Major Depressive Disorder Diagnosis with Raw Electroencephalogram Data

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.

Cross-Sampling Rate Transfer Learning for Enhanced Raw EEG Deep Learning Classifier Performance in Major Depressive Disorder Diagnosis

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.

Explainable Fuzzy Clustering Framework Reveals Divergent Default Mode Network Connectivity Dynamics in Schizophrenia

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.

Identifying EEG Biomarkers of Depression with Novel Explainable Deep Learning Architectures

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.

Improving Multichannel Raw Electroencephalography-based Diagnosis of Major Depressive Disorder via Transfer Learning with Single Channel Sleep Stage Data

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.

An Explainable and Robust Deep Learning Approach for Automated Electroencephalography-based Schizophrenia Diagnosis

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.

Pairing Explainable Deep Learning Classification with Clustering to Uncover Effects of Schizophrenia Upon Whole Brain Functional Network Connectivity Dynamics

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.

A Novel Explainable Fuzzy Clustering Approach for fMRI Dynamic Functional Network Connectivity Analysis

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.

Novel Approach Explains Spatio-Spectral Interactions in Raw Electroencephalogram Deep Learning Classifiers

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

Improving Multichannel Raw Electroencephalography-based Diagnosis of Major Depressive Disorder by Pretraining Deep Learning Models with Single Channel Sleep Stage Data

Identifying Neuropsychiatric Disorder Subtypes and Subtype-dependent Variation in Diagnostic Deep Learning Classifier Performance

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

A Framework for Systematically Evaluating the Representations Learned by A Deep Learning Classifier from Raw Multi-Channel Electroencephalogram Data

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.

Novel Methods for Elucidating Modality Importance in Multimodal Electrophysiology Classifiers

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.

A Convolutional Autoencoder-based Explainable Clustering Approach for Resting-State EEG Analysis

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.

Towards Greater Neuroimaging Classification Transparency via the Integration of Explainability Methods and Confidence Estimation Approaches

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.

The link between static and dynamic brain functional network connectivity and genetic risk of Alzheimer's disease

The paper examines the association between static and dynamic functional network connectivity and genetic risk of Alzheimer's disease using rs-fMRI and APOE data from cognitively normal individuals.

Neuropsychiatric Disorder Subtyping Via Clustered Deep Learning Classifier Explanations

This study uses a deep learning classifier to subtype schizophrenia patients based on dynamic functional network connectivity, identifying 5 subtypes with distinct network interactions and symptom severity.

Exploring Relationships between Functional Network Connectivity and Cognition with an Explainable Clustering Approach

This study uses clustering and feature importance analysis on fMRI data to identify cognitive differences between brain network groups in a large population.

Examining Reproducibility of EEG Schizophrenia Biomarkers Across Explainable Machine Learning Models

This study trains and compares random forest and SVM models to identify reproducible EEG biomarkers for schizophrenia using spectral features from resting state data.

Examining Effects of Schizophrenia on EEG with Explainable Deep Learning Models

This study applies explainable deep learning models to raw EEG data to diagnose schizophrenia, identifying key spectral and spatial features that align with prior research.

An Approach for Estimating Explanation Uncertainty in fMRI dFNC Classification

This study introduces a method to estimate uncertainty in gradient-based explanations for fMRI dFNC classification, finding significant differences in explanation relevance between schizophrenia and control groups.

A Model Visualization-based Approach for Insight into Waveforms and Spectra Learned by CNNs

This paper introduces a model visualization approach to analyze CNN filters for EEG waveforms and spectral features, demonstrating its use in sleep stage classification.

A Systematic Approach for Explaining Time and Frequency Features Extracted by CNNs from Raw EEG Data

This study introduces a model visualization approach to explain CNNs' time and frequency features from raw EEG data, showing consistency with clinical sleep stage classification guidelines.

A Novel Activation Maximization-based Approach for Insight into Electrophysiology Classifiers

This paper introduces an activation maximization method for electrophysiology classifiers, showing it can identify spectral patterns in EEG sleep stages and reveal potential reliance on artifacts for awake classification.

Explainable Sleep Stage Classification with Multimodal Electrophysiology Time-series

This study classifies sleep stages using multimodal electrophysiology data and introduces an ablation method to assess modality importance, finding EEG is most important for most stages and EOG for REM and nonREM.

A Novel Local Ablation Approach for Explaining Multimodal Classifiers

This paper introduces a local ablation method to explain multimodal classifiers, showing EEG's importance in sleep staging and identifying demographic effects on classification.