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2 months ago | 
biorxiv.org | Noah Lewis |Armin Iraji |Robyn Miller |Vince Calhoun
AbstractThe 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, researchers have developed group-inference frameworks that leverage robust group-level estimations as a common reference point to infer corresponding subject-level networks.
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Sep 27, 2024 | 
biorxiv.org | Duc Vo |Vince Calhoun
AbstractIn functional magnetic resonance imaging (fMRI) studies, it is common to evaluate the brain's functional network connectivity (FNC) which captures the temporal coupling between hemodynamic signals. FNC has been linked to various psychological phenomena. However, current FNCs mainly represent linear statistical relationships, which may not capture the fully complexity of the interactions among brain intrinsic connectivity networks (ICNs).
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Jun 26, 2024 | 
mdpi.com | David Blair |Robyn Miller |Vince Calhoun |David Sutherland
All articles published by MDPI are made immediately available worldwide under an open access license. No special permission is required to reuse all or part of the article published by MDPI, including figures and tables. For articles published under an open access Creative Common CC BY license, any part of the article may be reused without permission provided that the original article is clearly cited. For more information, please refer to https://www.mdpi.com/openaccess.
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May 1, 2024 | 
preprints.org | David Blair |Robyn Miller |Vince Calhoun
Preprint Article Version 1 This version is not peer-reviewed Version 1 : Received: 30 April 2024 / Approved: 1 May 2024 / Online: 1 May 2024 (09:18:11 CEST) Blair, D.S.; Miller, R.L.; Calhoun, V.D. A Dynamic Entropy Approach Reveals Reduced Functional Network Connectivity Trajectory Complexity in Schizophrenia. Preprints 2024, 2024050068.
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Apr 28, 2024 | 
biorxiv.org | David Blair |Robyn Miller |Vince Calhoun
AbstractOver 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 states, has tracked the transition patterns of these states, and has demonstrated meaningful alterations in these transition and spatial patterns in neurological disorders, psychiatric disorders, and over the course of development.
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Mar 21, 2024 | 
biorxiv.org | Charles Ellis |Martina Lapera Sancho |Robyn Miller |Vince Calhoun
AbstractDeep learning methods are increasingly being applied to raw electroencephalogram (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 be used in research contexts, methods for combining explanations across large numbers of models must be developed to counteract the inherent randomness of existing training approaches.
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Feb 13, 2024 | 
biorxiv.org | Charles Ellis |Robyn Miller |Vince Calhoun |Martina Lapera
AbstractThe 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 generalizability of identified biomarkers in many machine learning-based studies is highly questionable given that most studies only analyze explanations from a small number of models.
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Jan 27, 2024 | 
biorxiv.org | Robyn Miller |Victor Vergara |Erik B Erhardt |Vince Calhoun
AbstractAlthough 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 both signal propagation and healthy functional integration remains almost entirely neglected.
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Dec 18, 2023 | 
biorxiv.org | Charles Ellis |Robyn Miller |Vince Calhoun
AbstractWhile 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, but the utility of EEG DA methods remains relatively underexplored in neuropsychiatric disorder diagnosis. In this study, we train a model for major depressive disorder diagnosis. We then evaluate the utility of 6 EEG DA approaches.
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Dec 15, 2023 | 
biorxiv.org | Robyn Miller |Victor Vergara |Vince Calhoun |Erik B Erhardt
AbstractWhile 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 and implications of spatial propagation within the 4D neurovascular BOLD signal has been almost entirely neglected.
