Robyn L. Miller
Orcid: 0000-0002-4679-7567Affiliations:
- University of New Mexico, Albuquerque, USA
According to our database1,
Robyn L. Miller
authored at least 82 papers
between 2013 and 2025.
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Bibliography
2025
DSAM: A deep learning framework for analyzing temporal and spatial dynamics in brain networks.
Medical Image Anal., 2025
2024
A Dynamic Entropy Approach Reveals Reduced Functional Network Connectivity Trajectory Complexity in Schizophrenia.
Entropy, July, 2024
Generative forecasting of brain activity enhances Alzheimer's classification and interpretation.
CoRR, 2024
Low-Rank Learning by Design: the Role of Network Architecture and Activation Linearity in Gradient Rank Collapse.
CoRR, 2024
Identifying EEG Biomarkers of Depression with Novel Explainable Deep Learning Architectures.
Proceedings of the Explainable Artificial Intelligence, 2024
Markov Spatial Flows in Bold FMRI: A Novel Lens on the Bold Signal Applied To an Imaging Study of Schizophrenia.
Proceedings of the IEEE Southwest Symposium on Image Analysis and Interpretation, 2024
Distribution of Connectivity Strengths Across Functional Regions has Higher Entropy in Schizophrenia Patients than in Controls.
Proceedings of the IEEE Southwest Symposium on Image Analysis and Interpretation, 2024
Improving Age Prediction: Utilizing LSTM-Based Dynamic Forecasting For Data Augmentation in Multivariate Time Series Analysis.
Proceedings of the IEEE Southwest Symposium on Image Analysis and Interpretation, 2024
Capturing Stretching and Shrinking of Inter-Network Temporal Coupling in FMRI Via WARP Elasticity.
Proceedings of the IEEE International Symposium on Biomedical Imaging, 2024
Cross-Sampling Rate Transfer Learning for Enhanced Raw EEG Deep Learning Classifier Performance in Major Depressive Disorder Diagnosis.
Proceedings of the IEEE International Symposium on Biomedical Imaging, 2024
Uncovering Effects of Schizophrenia upon a Maximally Significant, Minimally Complex Subset of Default Mode Network Connectivity Features.
Proceedings of the 46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2024
Identifying Reproducibly Important EEG Markers of Schizophrenia with an Explainable Multi-Model Deep Learning Approach.
Proceedings of the 46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2024
Exploring Schizophrenia Classification in fMRI Data: A Common Spatial Patterns(CSP) Approach for Enhanced Feature Extraction and Classification.
Proceedings of the 46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2024
Evaluating Augmentation Approaches for Deep Learning-based Major Depressive Disorder Diagnosis with Raw Electroencephalogram Data.
Proceedings of the 46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2024
2023
Disrupted Dynamic Functional Network Connectivity Among Cognitive Control Networks in the Progression of Alzheimer's Disease.
Brain Connect., August, 2023
Interpretable LSTM model reveals transiently-realized patterns of dynamic brain connectivity that predict patient deterioration or recovery from very mild cognitive impairment.
Comput. Biol. Medicine, July, 2023
Novel methods for elucidating modality importance in multimodal electrophysiology classifiers.
Frontiers Neuroinformatics, March, 2023
Proceedings of the 20th IEEE International Symposium on Biomedical Imaging, 2023
Identifying Neuropsychiatric Disorder Subtypes and Subtype-Dependent Variation in Diagnostic Deep Learning Classifier Performance.
Proceedings of the 20th IEEE International Symposium on Biomedical Imaging, 2023
Proceedings of the IEEE International Conference on Acoustics, 2023
Novel Approach Explains Spatio-Spectral Interactions In Raw Electroencephalogram Deep Learning Classifiers.
Proceedings of the IEEE International Conference on Acoustics, 2023
Hyperlocal Spatial Flows in BOLD fMRI Expose Novel Brain-Based Correlates of Schizophrenia.
Proceedings of the 45th Annual International Conference of the IEEE Engineering in Medicine & Biology Society, 2023
Proceedings of the 45th Annual International Conference of the IEEE Engineering in Medicine & Biology Society, 2023
Neuropsychiatric Disorder Subtyping Via Clustered Deep Learning Classifier Explanations.
Proceedings of the 45th Annual International Conference of the IEEE Engineering in Medicine & Biology Society, 2023
A Convolutional Autoencoder-based Explainable Clustering Approach for Resting-State EEG Analysis.
Proceedings of the 45th Annual International Conference of the IEEE Engineering in Medicine & Biology Society, 2023
A Novel Explainable Fuzzy Clustering Approach for fMRI Dynamic Functional Network Connectivity Analysis.
Proceedings of the 45th Annual International Conference of the IEEE Engineering in Medicine & Biology Society, 2023
Improving Multichannel Raw Electroencephalography-based Diagnosis of Major Depressive Disorder via Transfer Learning with Single Channel Sleep Stage Data.
Proceedings of the IEEE International Conference on Bioinformatics and Biomedicine, 2023
Improving Explainability for Single-Channel EEG Deep Learning Classifiers via Interpretable Filters and Activation Analysis.
Proceedings of the IEEE International Conference on Bioinformatics and Biomedicine, 2023
An Explainable and Robust Deep Learning Approach for Automated Electroencephalography-Based Schizophrenia Diagnosis.
Proceedings of the 23rd IEEE International Conference on Bioinformatics and Bioengineering, 2023
2022
A Systematic Approach for Explaining Time and Frequency Features Extracted by Convolutional Neural Networks From Raw Electroencephalography Data.
Frontiers Neuroinformatics, August, 2022
CommsVAE: Learning the brain's macroscale communication dynamics using coupled sequential VAEs.
CoRR, 2022
Spatio-temporally separable non-linear latent factor learning: an application to somatomotor cortex fMRI data.
CoRR, 2022
Comparison of Energy Signals from the 4D DWT of Resting State FMRI Data Obtained from a Study on Schizophrenia.
Proceedings of the 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society, 2022
A two-step clustering-based pipeline for big dynamic functional network connectivity data.
Proceedings of the 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society, 2022
Transient Intervals of Significantly Different Whole Brain Connectivity Predict Recovery vs. Progression from Mild Cognitive Impairment: New Insights from Interpretable LSTM Classifiers.
Proceedings of the 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society, 2022
An Unsupervised Feature Learning Approach for Elucidating Hidden Dynamics in rs-fMRI Functional Network Connectivity.
Proceedings of the 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society, 2022
A Model Visualization-based Approach for Insight into Waveforms and Spectra Learned by CNNs.
Proceedings of the 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society, 2022
Exploring Relationships between Functional Network Connectivity and Cognition with an Explainable Clustering Approach.
Proceedings of the 22nd IEEE International Conference on Bioinformatics and Bioengineering, 2022
Examining Reproducibility of EEG Schizophrenia Biomarkers Across Explainable Machine Learning Models.
Proceedings of the 22nd IEEE International Conference on Bioinformatics and Bioengineering, 2022
Proceedings of the 22nd IEEE International Conference on Bioinformatics and Bioengineering, 2022
Proceedings of the 22nd IEEE International Conference on Bioinformatics and Bioengineering, 2022
2021
Dynamic state with covarying brain activity-connectivity: On the pathophysiology of schizophrenia.
NeuroImage, 2021
Abnormal Dynamic Functional Network Connectivity Estimated from Default Mode Network Predicts Symptom Severity in Major Depressive Disorder.
Brain Connect., 2021
3-way Parallel Fusion of Spatial (sMRI/dMRI) and Spatio-temporal (fMRI) Data with Application to Schizophrenia.
Proceedings of the 18th IEEE International Symposium on Biomedical Imaging, 2021
Proceedings of the 9th IEEE International Conference on Healthcare Informatics, 2021
Proceedings of the 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society, 2021
Multiframe Evolving Dynamic Functional Network Connectivity Motifs (Evodfncs) from Continuity-Preserving Planar Embedding.
Proceedings of the 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society, 2021
Explainable Sleep Stage Classification with Multimodal Electrophysiology Time-series<sup>*</sup>.
Proceedings of the 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society, 2021
A Novel Activation Maximization-based Approach for Insight into Electrophysiology Classifiers.
Proceedings of the IEEE International Conference on Bioinformatics and Biomedicine, 2021
Proceedings of the 21st IEEE International Conference on Bioinformatics and Bioengineering, 2021
Proceedings of the 21st IEEE International Conference on Bioinformatics and Bioengineering, 2021
A Novel Local Explainability Approach for Spectral Insight into Raw EEG-based Deep Learning Classifiers.
Proceedings of the 21st IEEE International Conference on Bioinformatics and Bioengineering, 2021
2020
A Machine Learning Model for Exploring Aberrant Functional Network Connectivity Transition in Schizophrenia.
Proceedings of the IEEE Southwest Symposium on Image Analysis and Interpretation, 2020
Transient Spectral Peak Analysis Reveals Distinct Temporal Activation Profiles for Different Functional Brain Networks.
Proceedings of the IEEE Southwest Symposium on Image Analysis and Interpretation, 2020
Hybrid dictionary learning-ICA approaches built on novel instantaneous dynamic connectivity metric provide new multiscale insights into dynamic brain connectivity.
Proceedings of the Medical Imaging 2020: Image Processing, 2020
Aberrant Functional Network Connectivity Transition Probability in Major Depressive Disorder.
Proceedings of the 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society, 2020
2019
A framework for linking resting-state chronnectome/genome features in schizophrenia: A pilot study.
NeuroImage, 2019
2018
Whole-brain connectivity dynamics reflect both task-specific and individual-specific modulation: A multitask study.
NeuroImage, 2018
Corrigendum to "Lateralization of resting state networks and relationship to age and gender" [NeuroImage 104 (2015) 310-325].
NeuroImage, 2018
Reduced-Order Modeling through Machine Learning Approaches for Brittle Fracture Applications.
CoRR, 2018
Whole-Brain Connectivity in a Large Study of Huntington's Disease Gene Mutation Carriers and Healthy Controls.
Brain Connect., 2018
Dynamic Whole Brain Polarity Regimes Strongly Distinguish Controls from Schizophrenia Patients.
Proceedings of the 2018 International Workshop on Pattern Recognition in Neuroimaging, 2018
2017
Replicability of time-varying connectivity patterns in large resting state fMRI samples.
NeuroImage, 2017
Image Analysis Using Convolutional Neural Networks for Modeling 2D Fracture Propagation.
Proceedings of the 2017 IEEE International Conference on Data Mining Workshops, 2017
2016
A Method for Intertemporal Functional-Domain Connectivity Analysis: Application to Schizophrenia Reveals Distorted Directional Information Flow.
IEEE Trans. Biomed. Eng., 2016
Cross-Frequency rs-fMRI Network Connectivity Patterns Manifest Differently for Schizophrenia Patients and Healthy Controls.
IEEE Signal Process. Lett., 2016
Classification of schizophrenia and bipolar patients using static and dynamic resting-state fMRI brain connectivity.
NeuroImage, 2016
Time-varying frequency modes of resting fMRI brain networks reveal significant gender differences.
Proceedings of the 2016 IEEE International Conference on Acoustics, 2016
2015
Assessing dynamic brain graphs of time-varying connectivity in fMRI data: Application to healthy controls and patients with schizophrenia.
NeuroImage, 2015
Mutually temporally independent connectivity patterns: A new framework to study the dynamics of brain connectivity at rest with application to explain group difference based on gender.
NeuroImage, 2015
Dynamic coherence analysis of resting fMRI data to jointly capture state-based phase, frequency, and time-domain information.
NeuroImage, 2015
NeuroImage, 2015
Classification of schizophrenia and bipolar patients using static and time-varying resting-state FMRI brain connectivity.
Proceedings of the 12th IEEE International Symposium on Biomedical Imaging, 2015
The impact of data preprocessing in traumatic brain injury detection using functional magnetic resonance imaging.
Proceedings of the 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2015
Large scale fusion of brain imaging modalities and features using Markov-style dynamics in a feature meta-space.
Proceedings of the 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2015
Proceedings of the 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2015
2014
Higher dimensional fMRI connectivity dynamics show reduced dynamism in schizophrenia patients.
Proceedings of the International Workshop on Pattern Recognition in Neuroimaging, 2014
A study of spatial variation in fMRI brain networks via independent vector analysis: Application to schizophrenia.
Proceedings of the International Workshop on Pattern Recognition in Neuroimaging, 2014
Higher dimensional analysis shows reduced dynamism of time-varying network connectivity in schizophrenia patients.
Proceedings of the 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2014
2013
Proceedings of the IEEE Global Conference on Signal and Information Processing, 2013