Robyn L. Miller
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View article: Generalization vs. Memorization in Autoregressive Deep Learning: Or, Examining Temporal Decay of Gradient Coherence
Generalization vs. Memorization in Autoregressive Deep Learning: Or, Examining Temporal Decay of Gradient Coherence Open
Foundation models trained as autoregressive PDE surrogates hold significant promise for accelerating scientific discovery through their capacity to both extrapolate beyond training regimes and efficiently adapt to downstream tasks despite …
View article: Generative Forecasting of Brain Activity Enhances Alzheimer’s Classification and Interpretation
Generative Forecasting of Brain Activity Enhances Alzheimer’s Classification and Interpretation Open
Understanding the relationship between cognition and intrinsic brain activity through purely data-driven approaches remains a significant challenge in neuroscience. Resting-state functional magnetic resonance imaging (rs-fMRI) offers a non…
View article: Timescale-normalized fMRI reveals disrupted dynamic signal-energy balance in schizophrenia
Timescale-normalized fMRI reveals disrupted dynamic signal-energy balance in schizophrenia Open
Neural systems operate across diverse and nested timescales: fast unimodal fluctuations are gradually integrated within higher-order transmodal networks, complicating direct comparison of regional activity. We propose a systems principle o…
View article: A humanised ACE2, TMPRSS2, and FCGRT mouse model reveals the protective efficacy of anti-receptor binding domain antibodies elicited by SARS-CoV-2 hybrid immunity
A humanised ACE2, TMPRSS2, and FCGRT mouse model reveals the protective efficacy of anti-receptor binding domain antibodies elicited by SARS-CoV-2 hybrid immunity Open
View article: Topologically Optimized Intrinsic Brain Networks
Topologically Optimized Intrinsic Brain Networks Open
The estimation of brain networks is instrumental in quantifying and evaluating brain function. Nevertheless, achieving precise estimations of subject-level networks has proven to be a formidable task. In response to this challenge, researc…
View article: Static and Dynamic Cross‐Network Functional Connectivity Shows Elevated Entropy in Schizophrenia Patients
Static and Dynamic Cross‐Network Functional Connectivity Shows Elevated Entropy in Schizophrenia Patients Open
Schizophrenia (SZ) patients exhibit abnormal static and dynamic functional connectivity across various brain domains. We present a novel approach based on static and dynamic inter‐network connectivity entropy (ICE), which represents the en…
View article: DSAM: A deep learning framework for analyzing temporal and spatial dynamics in brain networks
DSAM: A deep learning framework for analyzing temporal and spatial dynamics in brain networks Open
Resting-state functional magnetic resonance imaging (rs-fMRI) is a noninvasive technique pivotal for understanding human neural mechanisms of intricate cognitive processes. Most rs-fMRI studies compute a single static functional connectivi…
View article: Normalized Dynamic Time Warping Increases Sensitivity in Differentiating Functional Network Connectivity in Schizophrenia
Normalized Dynamic Time Warping Increases Sensitivity in Differentiating Functional Network Connectivity in Schizophrenia Open
Our study advances the application of dynamic time warping (DTW) as a functional connectivity measure by introducing a normalization technique which enhances the detection of schizophrenia effects in comparison to both standard DTW and tra…
View article: Influence of Th1 versus Th2 immune bias on viral, pathological, and immunological dynamics in SARS-CoV-2 variant-infected human ACE2 knock-in mice
Influence of Th1 versus Th2 immune bias on viral, pathological, and immunological dynamics in SARS-CoV-2 variant-infected human ACE2 knock-in mice Open
This work was funded by NIH U19 AI142790-02S1, the GHR Foundation, the Arvin Gottleib Foundation, and the Overton family (to SS and EOS); Prebys Foundation (to SS); NIH R44 AI157900 (to KJ); and by an American Association of Immunologists …
View article: Explicitly Nonlinear Connectivity-Matrix Independent Component Analysis in Resting fMRI Data
Explicitly Nonlinear Connectivity-Matrix Independent Component Analysis in Resting fMRI Data Open
Independent component analysis (ICA) is a widely used data-driven technique for investigating brain structure and function to extract intrinsic networks. However, the ability of ICA, a linear mixing model, to capture nonlinear relationship…
View article: <scp>4D</scp> dynamic spatial brain networks at rest linked to cognition show atypical variability and coupling in schizophrenia
<span>4D</span> dynamic spatial brain networks at rest linked to cognition show atypical variability and coupling in schizophrenia Open
Despite increasing interest in the dynamics of functional brain networks, most studies focus on the changing relationships over time between spatially static networks or regions. Here we propose an approach to study dynamic spatial brain n…
View article: A Dynamic Entropy Approach Reveals Reduced Functional Network Connectivity Trajectory Complexity in Schizophrenia
A Dynamic Entropy Approach Reveals Reduced Functional Network Connectivity Trajectory Complexity in Schizophrenia Open
Over the past decade and a half, dynamic functional imaging has revealed low-dimensional brain connectivity measures, identified potential common human spatial connectivity states, tracked the transition patterns of these states, and demon…
View article: DSAM: A Deep Learning Framework for Analyzing Temporal and Spatial Dynamics in Brain Networks
DSAM: A Deep Learning Framework for Analyzing Temporal and Spatial Dynamics in Brain Networks Open
Resting-state functional magnetic resonance imaging (rs-fMRI) is a noninvasive technique pivotal for understanding human neural mechanisms of intricate cognitive processes. Most rs-fMRI studies compute a single static functional connectivi…
View article: A Dynamic Entropy Approach Reveals Reduced Functional Network Connectivity Trajectory Complexity in Schizophrenia
A Dynamic Entropy Approach Reveals Reduced Functional Network Connectivity Trajectory Complexity in Schizophrenia Open
Over the past decade and a half, dynamic functional imaging has revolutionized the neuroimaging field. Since 2009, it has revealed low dimensional brain connectivity measures, has identified potential common human spatial connectivity stat…
View article: A Dynamic Entropy Approach Reveals Reduced Functional Network Connectivity Trajectory Complexity in Schizophrenia
A Dynamic Entropy Approach Reveals Reduced Functional Network Connectivity Trajectory Complexity in Schizophrenia Open
Over the past decade and a half, dynamic functional imaging has revolutionized the neuroimaging field. Since 2009, it has revealed low dimensional brain connectivity measures, has identified potential common human spatial connectivity stat…
View article: Uncovering Effects of Schizophrenia upon a Maximally Significant, Minimally Complex Subset of Default Mode Network Connectivity Features
Uncovering Effects of Schizophrenia upon a Maximally Significant, Minimally Complex Subset of Default Mode Network Connectivity Features Open
A common analysis approach for resting state functional magnetic resonance imaging (rs-fMRI) dynamic functional network connectivity (dFNC) data involves clustering windowed correlation time-series and assigning time windows to clusters (i…
View article: Identifying EEG Biomarkers of Depression with Novel Explainable Deep Learning Architectures
Identifying EEG Biomarkers of Depression with Novel Explainable Deep Learning Architectures Open
Deep learning methods are increasingly being applied to raw electro-encephalogram (EEG) data. However, if these models are to be used in clinical or research contexts, methods to explain them must be developed, and if these models are to b…
View article: Explainable fuzzy clustering framework reveals divergent default mode network connectivity dynamics in schizophrenia
Explainable fuzzy clustering framework reveals divergent default mode network connectivity dynamics in schizophrenia Open
Introduction Dynamic functional network connectivity (dFNC) analysis of resting state functional magnetic resonance imaging data has yielded insights into many neurological and neuropsychiatric disorders. A common dFNC analysis approach us…
View article: Identifying Reproducibly Important EEG Markers of Schizophrenia with an Explainable Multi-Model Deep Learning Approach
Identifying Reproducibly Important EEG Markers of Schizophrenia with an Explainable Multi-Model Deep Learning Approach Open
The diagnosis of schizophrenia (SZ) can be challenging due to its diverse symptom presentation. As such, many studies have sought to identify diagnostic biomarkers of SZ using explainable machine learning methods. However, the generalizabi…
View article: Low-Rank Learning by Design: the Role of Network Architecture and Activation Linearity in Gradient Rank Collapse
Low-Rank Learning by Design: the Role of Network Architecture and Activation Linearity in Gradient Rank Collapse Open
Our understanding of learning dynamics of deep neural networks (DNNs) remains incomplete. Recent research has begun to uncover the mathematical principles underlying these networks, including the phenomenon of "Neural Collapse", where line…
View article: LOCAL SPATIAL FLOWS AND PROPAGATIVE ATTRACTORS: A NOVEL “FLOWNECTOME” FRAMEWORK FOR ANALYZING BOLD FMRI DYNAMICS
LOCAL SPATIAL FLOWS AND PROPAGATIVE ATTRACTORS: A NOVEL “FLOWNECTOME” FRAMEWORK FOR ANALYZING BOLD FMRI DYNAMICS Open
Although the analysis of temporal signal fluctuations and co-fluctuations has long been a fixture of blood oxygenation-level dependent (BOLD) functional magnetic resonance imaging (fMRI) research, the role of local directional flows in bot…
View article: The dynamics of dynamic time warping in fMRI data: A method to capture inter-network stretching and shrinking via warp elasticity
The dynamics of dynamic time warping in fMRI data: A method to capture inter-network stretching and shrinking via warp elasticity Open
In neuroimaging research, understanding the intricate dynamics of brain networks over time is paramount for unraveling the complexities of brain function. One approach commonly used to explore the dynamic nature of brain networks is functi…
View article: Evaluating Augmentation Approaches for Deep Learning-based Major Depressive Disorder Diagnosis with Raw Electroencephalogram Data<sup>*</sup>
Evaluating Augmentation Approaches for Deep Learning-based Major Depressive Disorder Diagnosis with Raw Electroencephalogram Data<sup>*</sup> Open
While deep learning methods are increasingly applied in research contexts for neuropsychiatric disorder diagnosis, small dataset size limits their potential for clinical translation. Data augmentation (DA) could address this limitation, bu…
View article: Markov Spatial Flows in Bold Fmri: A Novel Lens on the Bold Signal Reveals Attracting Patterns of Signal Intensity
Markov Spatial Flows in Bold Fmri: A Novel Lens on the Bold Signal Reveals Attracting Patterns of Signal Intensity Open
While the analysis of temporal signal fluctuations and cofluctuations has long been a fixture of blood oxygenation-level dependent (BOLD) functional magnetic resonance imaging (fMRI) research, the role and implications of spatial propagati…
View article: Improving age prediction: Utilizing LSTM-based dynamic forecasting for data augmentation in multivariate time series analysis
Improving age prediction: Utilizing LSTM-based dynamic forecasting for data augmentation in multivariate time series analysis Open
The high dimensionality and complexity of neuroimaging data necessitate large datasets to develop robust and high-performing deep learning models. However, the neuroimaging field is notably hampered by the scarcity of such datasets. In thi…
View article: Cross-Sampling Rate Transfer Learning for Enhanced Raw EEG Deep Learning Classifier Performance in Major Depressive Disorder Diagnosis
Cross-Sampling Rate Transfer Learning for Enhanced Raw EEG Deep Learning Classifier Performance in Major Depressive Disorder Diagnosis Open
Transfer learning offers a route for developing robust deep learning models on small raw electroencephalography (EEG) datasets. Nevertheless, the utility of applying representations learned from large datasets with a lower sampling rate to…
View article: Pairing explainable deep learning classification with clustering to uncover effects of schizophrenia upon whole brain functional network connectivity dynamics
Pairing explainable deep learning classification with clustering to uncover effects of schizophrenia upon whole brain functional network connectivity dynamics Open
View article: 4D DYNAMIC SPATIAL BRAIN NETWORKS AT REST LINKED TO COGNITION SHOW ATYPICAL VARIABILITY AND COUPLING IN SCHIZOPHRENIA
4D DYNAMIC SPATIAL BRAIN NETWORKS AT REST LINKED TO COGNITION SHOW ATYPICAL VARIABILITY AND COUPLING IN SCHIZOPHRENIA Open
Despite increasing interest in the dynamics of functional brain networks, most studies focus on the changing relationships over time between spatially static networks or regions. Here we propose an approach to study dynamic spatial brain n…
View article: Resting‐state dynamic functional network connectivity predicts cognition in 37,784 participants of UK Biobank
Resting‐state dynamic functional network connectivity predicts cognition in 37,784 participants of UK Biobank Open
Background Age‐related cognitive decline after 65 is a well‐known phenomenon, but little is known about how brain functional changes are related to cognitive decline. To this end, previous studies explored the link between functional netwo…
View article: An Explainable and Robust Deep Learning Approach for Automated Electroencephalography-based Schizophrenia Diagnosis
An Explainable and Robust Deep Learning Approach for Automated Electroencephalography-based Schizophrenia Diagnosis Open
Schizophrenia (SZ) is a neuropsychiatric disorder that affects millions globally. Current diagnosis of SZ is symptom-based, which poses difficulty due to the variability of symptoms across patients. To this end, many recent studies have de…