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Chen AA, Clark K, Dewey BE, DuVal A, Pellegrini N, Nair G, Jalkh Y, Khalil S, Zurawski J, Calabresi PA, Reich DS, Bakshi R, Shou H, Shinohara RT. PARE: A framework for removal of confounding effects from any distance-based dimension reduction method. PLoS Comput Biol 2024; 20:e1012241. [PMID: 38985831 DOI: 10.1371/journal.pcbi.1012241] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2023] [Accepted: 06/10/2024] [Indexed: 07/12/2024] Open
Abstract
Dimension reduction tools preserving similarity and graph structure such as t-SNE and UMAP can capture complex biological patterns in high-dimensional data. However, these tools typically are not designed to separate effects of interest from unwanted effects due to confounders. We introduce the partial embedding (PARE) framework, which enables removal of confounders from any distance-based dimension reduction method. We then develop partial t-SNE and partial UMAP and apply these methods to genomic and neuroimaging data. For lower-dimensional visualization, our results show that the PARE framework can remove batch effects in single-cell sequencing data as well as separate clinical and technical variability in neuroimaging measures. We demonstrate that the PARE framework extends dimension reduction methods to highlight biological patterns of interest while effectively removing confounding effects.
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Affiliation(s)
- Andrew A Chen
- Department of Public Health Sciences, Medical University of South Carolina, Charleston, South Carolina, United States of America
| | - Kelly Clark
- Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
| | - Blake E Dewey
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America
| | - Anna DuVal
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America
| | - Nicole Pellegrini
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America
| | - Govind Nair
- Translational Neuroradiology Section, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, Maryland, United States of America
| | - Youmna Jalkh
- Department of Neurology, Brigham and Women's Hospital, Harvard Medical School, Boston, Masschusetts, United States of America
| | - Samar Khalil
- Department of Neurology, Brigham and Women's Hospital, Harvard Medical School, Boston, Masschusetts, United States of America
| | - Jon Zurawski
- Department of Neurology, Brigham and Women's Hospital, Harvard Medical School, Boston, Masschusetts, United States of America
| | - Peter A Calabresi
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America
| | - Daniel S Reich
- Translational Neuroradiology Section, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, Maryland, United States of America
| | - Rohit Bakshi
- Department of Neurology, Brigham and Women's Hospital, Harvard Medical School, Boston, Masschusetts, United States of America
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Haochang Shou
- Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
| | - Russell T Shinohara
- Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
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Urrutia R, Espejo D, Evens N, Guerra M, Sühn T, Boese A, Hansen C, Fuentealba P, Illanes A, Poblete V. Clustering Methods for Vibro-Acoustic Sensing Features as a Potential Approach to Tissue Characterisation in Robot-Assisted Interventions. SENSORS (BASEL, SWITZERLAND) 2023; 23:9297. [PMID: 38067671 PMCID: PMC10708300 DOI: 10.3390/s23239297] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/06/2023] [Revised: 11/06/2023] [Accepted: 11/11/2023] [Indexed: 12/18/2023]
Abstract
This article provides a comprehensive analysis of the feature extraction methods applied to vibro-acoustic signals (VA signals) in the context of robot-assisted interventions. The primary objective is to extract valuable information from these signals to understand tissue behaviour better and build upon prior research. This study is divided into three key stages: feature extraction using the Cepstrum Transform (CT), Mel-Frequency Cepstral Coefficients (MFCCs), and Fast Chirplet Transform (FCT); dimensionality reduction employing techniques such as Principal Component Analysis (PCA), t-Distributed Stochastic Neighbour Embedding (t-SNE), and Uniform Manifold Approximation and Projection (UMAP); and, finally, classification using a nearest neighbours classifier. The results demonstrate that using feature extraction techniques, especially the combination of CT and MFCC with dimensionality reduction algorithms, yields highly efficient outcomes. The classification metrics (Accuracy, Recall, and F1-score) approach 99%, and the clustering metric is 0.61. The performance of the CT-UMAP combination stands out in the evaluation metrics.
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Affiliation(s)
- Robin Urrutia
- Instituto de Acústica, Facultad de Ciencias de la Ingeniería, Universidad Austral de Chile, Valdivia 5111187, Chile; (R.U.); (V.P.)
- Audio Mining Laboratory (AuMiLab), Instituto de Acústica, Universidad Austral de Chile, Valdivia 5111187, Chile;
| | - Diego Espejo
- Audio Mining Laboratory (AuMiLab), Instituto de Acústica, Universidad Austral de Chile, Valdivia 5111187, Chile;
| | - Natalia Evens
- Instituto de Anatomia, Histologia y Patologia, Facultad de Medicina, Universidad Austral de Chile, Valdivia 5111187, Chile; (N.E.); (M.G.)
| | - Montserrat Guerra
- Instituto de Anatomia, Histologia y Patologia, Facultad de Medicina, Universidad Austral de Chile, Valdivia 5111187, Chile; (N.E.); (M.G.)
| | - Thomas Sühn
- Department of Orthopaedic Surgery, Otto-von-Guericke University Magdeburg, 39120 Magdeburg, Germany;
- SURAG Medical GmbH, 39118 Magdeburg, Germany;
| | - Axel Boese
- INKA Innovation Laboratory for Image Guided Therapy, Otto-von-Guericke University Magdeburg, 39120 Magdeburg, Germany
| | - Christian Hansen
- Research Campus STIMULATE, Otto-von-Guericke University Magdeburg, 39106 Magdeburg, Germany;
| | - Patricio Fuentealba
- Instituto de Electricidad y Electrónica, Facultad de Ciencias de la Ingeniería, Universidad Austral de Chile, Valdivia 5111187, Chile;
| | | | - Victor Poblete
- Instituto de Acústica, Facultad de Ciencias de la Ingeniería, Universidad Austral de Chile, Valdivia 5111187, Chile; (R.U.); (V.P.)
- Audio Mining Laboratory (AuMiLab), Instituto de Acústica, Universidad Austral de Chile, Valdivia 5111187, Chile;
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Latypov TH, So MC, Hung PSP, Tsai P, Walker MR, Tohyama S, Tawfik M, Rudzicz F, Hodaie M. Brain imaging signatures of neuropathic facial pain derived by artificial intelligence. Sci Rep 2023; 13:10699. [PMID: 37400574 DOI: 10.1038/s41598-023-37034-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Accepted: 06/13/2023] [Indexed: 07/05/2023] Open
Abstract
Advances in neuroimaging have permitted the non-invasive examination of the human brain in pain. However, a persisting challenge is in the objective differentiation of neuropathic facial pain subtypes, as diagnosis is based on patients' symptom descriptions. We use artificial intelligence (AI) models with neuroimaging data to distinguish subtypes of neuropathic facial pain and differentiate them from healthy controls. We conducted a retrospective analysis of diffusion tensor and T1-weighted imaging data using random forest and logistic regression AI models on 371 adults with trigeminal pain (265 classical trigeminal neuralgia (CTN), 106 trigeminal neuropathic pain (TNP)) and 108 healthy controls (HC). These models distinguished CTN from HC with up to 95% accuracy, and TNP from HC with up to 91% accuracy. Both classifiers identified gray and white matter-based predictive metrics (gray matter thickness, surface area, and volume; white matter diffusivity metrics) that significantly differed across groups. Classification of TNP and CTN did not show significant accuracy (51%) but highlighted two structures that differed between pain groups-the insula and orbitofrontal cortex. Our work demonstrates that AI models with brain imaging data alone can differentiate neuropathic facial pain subtypes from healthy data and identify regional structural indicates of pain.
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Affiliation(s)
- Timur H Latypov
- Division of Brain, Imaging & Behaviour, Krembil Research Institute, University Health Network, Toronto, ON, Canada
- Institute of Medical Science, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
- Collaborative Program in Neuroscience, University of Toronto, Toronto, ON, Canada
| | - Matthew C So
- Max Rady College of Medicine, University of Manitoba, Winnipeg, MB, Canada
| | - Peter Shih-Ping Hung
- Division of Brain, Imaging & Behaviour, Krembil Research Institute, University Health Network, Toronto, ON, Canada
- Institute of Medical Science, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
- Collaborative Program in Neuroscience, University of Toronto, Toronto, ON, Canada
| | - Pascale Tsai
- Michael G. DeGroote School of Medicine, McMaster University, Hamilton, ON, Canada
| | - Matthew R Walker
- Division of Brain, Imaging & Behaviour, Krembil Research Institute, University Health Network, Toronto, ON, Canada
| | - Sarasa Tohyama
- A.A. Martinos Center for Biomedical Imaging, Harvard Medical School, Charlestown, MA, USA
| | - Marina Tawfik
- Collaborative Program in Neuroscience, University of Toronto, Toronto, ON, Canada
- Department of Computer Science, University of Toronto, Toronto, ON, Canada
- Vector Institute for Artificial Intelligence, Toronto, ON, Canada
| | - Frank Rudzicz
- Department of Computer Science, University of Toronto, Toronto, ON, Canada
- Vector Institute for Artificial Intelligence, Toronto, ON, Canada
- Faculty of Computer Science, Dalhousie University, Halifax, NS, Canada
| | - Mojgan Hodaie
- Division of Brain, Imaging & Behaviour, Krembil Research Institute, University Health Network, Toronto, ON, Canada.
- Institute of Medical Science, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada.
- Division of Neurosurgery, Department of Surgery, University of Toronto, Toronto, ON, Canada.
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Neural manifold analysis of brain circuit dynamics in health and disease. J Comput Neurosci 2023; 51:1-21. [PMID: 36522604 PMCID: PMC9840597 DOI: 10.1007/s10827-022-00839-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2022] [Revised: 08/30/2022] [Accepted: 10/29/2022] [Indexed: 12/23/2022]
Abstract
Recent developments in experimental neuroscience make it possible to simultaneously record the activity of thousands of neurons. However, the development of analysis approaches for such large-scale neural recordings have been slower than those applicable to single-cell experiments. One approach that has gained recent popularity is neural manifold learning. This approach takes advantage of the fact that often, even though neural datasets may be very high dimensional, the dynamics of neural activity tends to traverse a much lower-dimensional space. The topological structures formed by these low-dimensional neural subspaces are referred to as "neural manifolds", and may potentially provide insight linking neural circuit dynamics with cognitive function and behavioral performance. In this paper we review a number of linear and non-linear approaches to neural manifold learning, including principal component analysis (PCA), multi-dimensional scaling (MDS), Isomap, locally linear embedding (LLE), Laplacian eigenmaps (LEM), t-SNE, and uniform manifold approximation and projection (UMAP). We outline these methods under a common mathematical nomenclature, and compare their advantages and disadvantages with respect to their use for neural data analysis. We apply them to a number of datasets from published literature, comparing the manifolds that result from their application to hippocampal place cells, motor cortical neurons during a reaching task, and prefrontal cortical neurons during a multi-behavior task. We find that in many circumstances linear algorithms produce similar results to non-linear methods, although in particular cases where the behavioral complexity is greater, non-linear methods tend to find lower-dimensional manifolds, at the possible expense of interpretability. We demonstrate that these methods are applicable to the study of neurological disorders through simulation of a mouse model of Alzheimer's Disease, and speculate that neural manifold analysis may help us to understand the circuit-level consequences of molecular and cellular neuropathology.
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Saha DK, Calhoun VD, Du Y, Fu Z, Kwon SM, Sarwate AD, Panta SR, Plis SM. Privacy-preserving quality control of neuroimaging datasets in federated environments. Hum Brain Mapp 2022; 43:2289-2310. [PMID: 35243723 PMCID: PMC8996357 DOI: 10.1002/hbm.25788] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2021] [Revised: 12/10/2021] [Accepted: 12/13/2021] [Indexed: 11/18/2022] Open
Abstract
Privacy concerns for rare disease data, institutional or IRB policies, access to local computational or storage resources or download capabilities are among the reasons that may preclude analyses that pool data to a single site. A growing number of multisite projects and consortia were formed to function in the federated environment to conduct productive research under constraints of this kind. In this scenario, a quality control tool that visualizes decentralized data in its entirety via global aggregation of local computations is especially important, as it would allow the screening of samples that cannot be jointly evaluated otherwise. To solve this issue, we present two algorithms: decentralized data stochastic neighbor embedding, dSNE, and its differentially private counterpart, DP‐dSNE. We leverage publicly available datasets to simultaneously map data samples located at different sites according to their similarities. Even though the data never leaves the individual sites, dSNE does not provide any formal privacy guarantees. To overcome that, we rely on differential privacy: a formal mathematical guarantee that protects individuals from being identified as contributors to a dataset. We implement DP‐dSNE with AdaCliP, a method recently proposed to add less noise to the gradients per iteration. We introduce metrics for measuring the embedding quality and validate our algorithms on these metrics against their centralized counterpart on two toy datasets. Our validation on six multisite neuroimaging datasets shows promising results for the quality control tasks of visualization and outlier detection, highlighting the potential of our private, decentralized visualization approach.
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Affiliation(s)
- Debbrata K Saha
- Georgia Institute of Technology, Atlanta, Georgia, USA.,Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, Georgia, USA
| | - Vince D Calhoun
- Georgia Institute of Technology, Atlanta, Georgia, USA.,Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, Georgia, USA.,Georgia State University, Atlanta, Georgia, USA
| | - Yuhui Du
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, Georgia, USA
| | - Zening Fu
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, Georgia, USA
| | - Soo Min Kwon
- Rutgers, The State University of New Jersey, New Brunswick, New Jersey, USA
| | - Anand D Sarwate
- Rutgers, The State University of New Jersey, New Brunswick, New Jersey, USA
| | - Sandeep R Panta
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, Georgia, USA
| | - Sergey M Plis
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, Georgia, USA.,Georgia State University, Atlanta, Georgia, USA
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6
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Casanova R, Lyday RG, Bahrami M, Burdette JH, Simpson SL, Laurienti PJ. Embedding Functional Brain Networks in Low Dimensional Spaces Using Manifold Learning Techniques. Front Neuroinform 2021; 15:740143. [PMID: 35002665 PMCID: PMC8739961 DOI: 10.3389/fninf.2021.740143] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2021] [Accepted: 11/19/2021] [Indexed: 11/13/2022] Open
Abstract
Background: fMRI data is inherently high-dimensional and difficult to visualize. A recent trend has been to find spaces of lower dimensionality where functional brain networks can be projected onto manifolds as individual data points, leading to new ways to analyze and interpret the data. Here, we investigate the potential of two powerful non-linear manifold learning techniques for functional brain networks representation: (1) T-stochastic neighbor embedding (t-SNE) and (2) Uniform Manifold Approximation Projection (UMAP) a recent breakthrough in manifold learning. Methods: fMRI data from the Human Connectome Project (HCP) and an independent study of aging were used to generate functional brain networks. We used fMRI data collected during resting state data and during a working memory task. The relative performance of t-SNE and UMAP were investigated by projecting the networks from each study onto 2D manifolds. The levels of discrimination between different tasks and the preservation of the topology were evaluated using different metrics. Results: Both methods effectively discriminated the resting state from the memory task in the embedding space. UMAP discriminated with a higher classification accuracy. However, t-SNE appeared to better preserve the topology of the high-dimensional space. When networks from the HCP and aging studies were combined, the resting state and memory networks in general aligned correctly. Discussion: Our results suggest that UMAP, a more recent development in manifold learning, is an excellent tool to visualize functional brain networks. Despite dramatic differences in data collection and protocols, networks from different studies aligned correctly in the embedding space.
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Affiliation(s)
- Ramon Casanova
- Department of Biostatistics and Data Science, Wake Forest School of Medicine, Winston-Salem, NC, United States
| | - Robert G. Lyday
- Laboratory for Complex Brain Networks, Wake Forest School of Medicine, Winston-Salem, NC, United States
- Department of Radiology, Wake Forest School of Medicine, Winston-Salem, NC, United States
| | - Mohsen Bahrami
- Laboratory for Complex Brain Networks, Wake Forest School of Medicine, Winston-Salem, NC, United States
- Department of Radiology, Wake Forest School of Medicine, Winston-Salem, NC, United States
| | - Jonathan H. Burdette
- Laboratory for Complex Brain Networks, Wake Forest School of Medicine, Winston-Salem, NC, United States
- Department of Radiology, Wake Forest School of Medicine, Winston-Salem, NC, United States
| | - Sean L. Simpson
- Department of Biostatistics and Data Science, Wake Forest School of Medicine, Winston-Salem, NC, United States
- Laboratory for Complex Brain Networks, Wake Forest School of Medicine, Winston-Salem, NC, United States
| | - Paul J. Laurienti
- Laboratory for Complex Brain Networks, Wake Forest School of Medicine, Winston-Salem, NC, United States
- Department of Radiology, Wake Forest School of Medicine, Winston-Salem, NC, United States
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Parmar H, Nutter B, Long R, Antani S, Mitra S. Visualizing temporal brain-state changes for fMRI using t-distributed stochastic neighbor embedding. J Med Imaging (Bellingham) 2021; 8:046001. [PMID: 34423072 DOI: 10.1117/1.jmi.8.4.046001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2020] [Accepted: 08/02/2021] [Indexed: 11/14/2022] Open
Abstract
Purpose: Currently, functional magnetic resonance imaging (fMRI) is the most commonly used technique for obtaining dynamic information about the brain. However, because of the complexity of the data, it is often difficult to directly visualize the temporal aspect of the fMRI data. Approach: We outline a t -distributed stochastic neighbor embedding (t-SNE)-based postprocessing technique that can be used for visualization of temporal information from a 4D fMRI data. Apart from visualization, we also show its utility in detection of major changes in the brain meta-states during the scan duration. Results: The t-SNE approach is able to detect brain-state changes from task to rest and vice versa for single- and multitask fMRI data. A temporal visualization can also be obtained for task and resting state fMRI data for assessing the temporal dynamics during the scan duration. Additionally, hemodynamic delay can be quantified by comparison of the detected brain-state changes with the experiment paradigm for task fMRI data. Conclusion: The t-SNE visualization can visualize help identify major brain-state changes from fMRI data. Such visualization can provide information about the degree of involvement and attentiveness of the subject during the scan and can be potentially utilized as a quality control for subject's performance during the scan.
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Affiliation(s)
- Harshit Parmar
- Texas Tech University, Department of Electrical and Computer Engineering, Lubbock, Texas, United States
| | - Brian Nutter
- Texas Tech University, Department of Electrical and Computer Engineering, Lubbock, Texas, United States
| | - Rodney Long
- National Institutes of Health, National Library of Medicine, Lister Hill National Center for Biomedical Communications, Bethesda, Maryland, United States
| | - Sameer Antani
- National Institutes of Health, National Library of Medicine, Lister Hill National Center for Biomedical Communications, Bethesda, Maryland, United States
| | - Sunanda Mitra
- Texas Tech University, Department of Electrical and Computer Engineering, Lubbock, Texas, United States
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Eitel F, Schulz MA, Seiler M, Walter H, Ritter K. Promises and pitfalls of deep neural networks in neuroimaging-based psychiatric research. Exp Neurol 2021; 339:113608. [PMID: 33513353 DOI: 10.1016/j.expneurol.2021.113608] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2020] [Revised: 01/07/2021] [Accepted: 01/09/2021] [Indexed: 12/13/2022]
Abstract
By promising more accurate diagnostics and individual treatment recommendations, deep neural networks and in particular convolutional neural networks have advanced to a powerful tool in medical imaging. Here, we first give an introduction into methodological key concepts and resulting methodological promises including representation and transfer learning, as well as modelling domain-specific priors. After reviewing recent applications within neuroimaging-based psychiatric research, such as the diagnosis of psychiatric diseases, delineation of disease subtypes, normative modeling, and the development of neuroimaging biomarkers, we discuss current challenges. This includes for example the difficulty of training models on small, heterogeneous and biased data sets, the lack of validity of clinical labels, algorithmic bias, and the influence of confounding variables.
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Affiliation(s)
- Fabian Eitel
- Charité - Universitätsmedizin Berlin, Corporate Member Of Freie Universität Berlin, Humboldt-Universität zu Berlin; Department of Psychiatry and Psychotherapy, 10117 Berlin, Germany; Bernstein Center for Computational Neuroscience, 10117 Berlin, Germany
| | - Marc-André Schulz
- Charité - Universitätsmedizin Berlin, Corporate Member Of Freie Universität Berlin, Humboldt-Universität zu Berlin; Department of Psychiatry and Psychotherapy, 10117 Berlin, Germany; Bernstein Center for Computational Neuroscience, 10117 Berlin, Germany
| | - Moritz Seiler
- Charité - Universitätsmedizin Berlin, Corporate Member Of Freie Universität Berlin, Humboldt-Universität zu Berlin; Department of Psychiatry and Psychotherapy, 10117 Berlin, Germany; Bernstein Center for Computational Neuroscience, 10117 Berlin, Germany
| | - Henrik Walter
- Charité - Universitätsmedizin Berlin, Corporate Member Of Freie Universität Berlin, Humboldt-Universität zu Berlin; Department of Psychiatry and Psychotherapy, 10117 Berlin, Germany; Bernstein Center for Computational Neuroscience, 10117 Berlin, Germany
| | - Kerstin Ritter
- Charité - Universitätsmedizin Berlin, Corporate Member Of Freie Universität Berlin, Humboldt-Universität zu Berlin; Department of Psychiatry and Psychotherapy, 10117 Berlin, Germany; Bernstein Center for Computational Neuroscience, 10117 Berlin, Germany.
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Rashid B, Calhoun V. Towards a brain-based predictome of mental illness. Hum Brain Mapp 2020; 41:3468-3535. [PMID: 32374075 PMCID: PMC7375108 DOI: 10.1002/hbm.25013] [Citation(s) in RCA: 65] [Impact Index Per Article: 16.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2019] [Revised: 04/06/2020] [Accepted: 04/06/2020] [Indexed: 01/10/2023] Open
Abstract
Neuroimaging-based approaches have been extensively applied to study mental illness in recent years and have deepened our understanding of both cognitively healthy and disordered brain structure and function. Recent advancements in machine learning techniques have shown promising outcomes for individualized prediction and characterization of patients with psychiatric disorders. Studies have utilized features from a variety of neuroimaging modalities, including structural, functional, and diffusion magnetic resonance imaging data, as well as jointly estimated features from multiple modalities, to assess patients with heterogeneous mental disorders, such as schizophrenia and autism. We use the term "predictome" to describe the use of multivariate brain network features from one or more neuroimaging modalities to predict mental illness. In the predictome, multiple brain network-based features (either from the same modality or multiple modalities) are incorporated into a predictive model to jointly estimate features that are unique to a disorder and predict subjects accordingly. To date, more than 650 studies have been published on subject-level prediction focusing on psychiatric disorders. We have surveyed about 250 studies including schizophrenia, major depression, bipolar disorder, autism spectrum disorder, attention-deficit hyperactivity disorder, obsessive-compulsive disorder, social anxiety disorder, posttraumatic stress disorder, and substance dependence. In this review, we present a comprehensive review of recent neuroimaging-based predictomic approaches, current trends, and common shortcomings and share our vision for future directions.
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Affiliation(s)
- Barnaly Rashid
- Department of PsychiatryHarvard Medical SchoolBostonMassachusettsUSA
| | - Vince Calhoun
- Tri‐Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS)Georgia State University, Georgia Institute of Technology, and Emory UniversityAtlantaGeorgiaUSA
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10
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Luo YH, Xi IL, Wang R, Abdallah HO, Wu J, Vance AZ, Chang K, Kohi M, Jones L, Reddy S, Zhang ZS, Bai HX, Shlansky-Goldberg R. Deep Learning Based on MR Imaging for Predicting Outcome of Uterine Fibroid Embolization. J Vasc Interv Radiol 2020; 31:1010-1017.e3. [DOI: 10.1016/j.jvir.2019.11.032] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2019] [Revised: 11/23/2019] [Accepted: 11/27/2019] [Indexed: 01/09/2023] Open
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Shou G, Yuan H, Li C, Chen Y, Chen Y, Ding L. Whole-brain electrophysiological functional connectivity dynamics in resting-state EEG. J Neural Eng 2020; 17:026016. [PMID: 32106106 DOI: 10.1088/1741-2552/ab7ad3] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
OBJECTIVE Functional connectivity (FC) dynamics have been studied in functional magnetic resonance imaging (fMRI) data, while it is largely unknown in electrophysiological data, e.g. EEG. APPROACH The present study proposed a novel analytic framework to study spatiotemporal dynamics of FC (dFC) in resting-state human EEG data, including independent component analysis, cortical source imaging, sliding-window correlation analysis, and k-means clustering. MAIN RESULTS Our results confirm that major fMRI intrinsic connectivity networks (ICNs) can be successfully reconstructed from EEG using our analytic framework. Prominent spatial and temporal variability were revealed in these ICNs. The mean dFC spatial patterns of individual ICNs resemble their corresponding static FC (sFC) patterns but show fewer cross-talks among distinct ICNs. Our investigation unveils evidences of time-domain variations in individual ICNs comparable to their mean FC level in terms of magnitude. The major contributors to these variations are from the frequency below 0.0156 Hz, in the similar range of FC dynamics from fMRI data. Among different ICNs, larger temporal variabilities are observed in the frontal attention and auditory/visual ICNs, while sensorimotor, salience, and default model networks showed less. Our analytic framework for the first time revealed quasi-stable states within individual EEG ICNs, with various strengths or spatial patterns that were reliably detected at both group and individual levels. These states all together reveal a more complete picture of EEG ICNs: (1) quasi-stable state spatial patterns as a whole for each EEG ICN are more consistent with the corresponding fMRI ICN in terms of the bilateral distribution and multi-nodes structure; (2) EEG ICNs reveal more transient patterns about within-ICN between-node communications than fMRI ICNs. SIGNIFICANCE The present findings highlight the fact that rich temporal and spatial dynamics exist in ICN that can be detected from EEG data. Future studies might extend investigations towards spectral dynamics of EEG ICNs.
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Affiliation(s)
- Guofa Shou
- Stephenson School of Biomedical Engineering, University of Oklahoma, Norman, United States of America
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Bridwell DA, Cavanagh JF, Collins AGE, Nunez MD, Srinivasan R, Stober S, Calhoun VD. Moving Beyond ERP Components: A Selective Review of Approaches to Integrate EEG and Behavior. Front Hum Neurosci 2018; 12:106. [PMID: 29632480 PMCID: PMC5879117 DOI: 10.3389/fnhum.2018.00106] [Citation(s) in RCA: 44] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2018] [Accepted: 03/06/2018] [Indexed: 11/17/2022] Open
Abstract
Relationships between neuroimaging measures and behavior provide important clues about brain function and cognition in healthy and clinical populations. While electroencephalography (EEG) provides a portable, low cost measure of brain dynamics, it has been somewhat underrepresented in the emerging field of model-based inference. We seek to address this gap in this article by highlighting the utility of linking EEG and behavior, with an emphasis on approaches for EEG analysis that move beyond focusing on peaks or “components” derived from averaging EEG responses across trials and subjects (generating the event-related potential, ERP). First, we review methods for deriving features from EEG in order to enhance the signal within single-trials. These methods include filtering based on user-defined features (i.e., frequency decomposition, time-frequency decomposition), filtering based on data-driven properties (i.e., blind source separation, BSS), and generating more abstract representations of data (e.g., using deep learning). We then review cognitive models which extract latent variables from experimental tasks, including the drift diffusion model (DDM) and reinforcement learning (RL) approaches. Next, we discuss ways to access associations among these measures, including statistical models, data-driven joint models and cognitive joint modeling using hierarchical Bayesian models (HBMs). We think that these methodological tools are likely to contribute to theoretical advancements, and will help inform our understandings of brain dynamics that contribute to moment-to-moment cognitive function.
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Affiliation(s)
| | - James F Cavanagh
- Department of Psychology, University of New Mexico, Albuquerque, NM, United States
| | - Anne G E Collins
- Department of Psychology, University of California, Berkeley, Berkeley, CA, United States.,Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, CA, United States
| | - Michael D Nunez
- Department of Cognitive Sciences, University of California, Irvine, Irvine, CA, United States.,Department of Biomedical Engineering, University of California, Irvine, Irvine, CA, United States
| | - Ramesh Srinivasan
- Department of Cognitive Sciences, University of California, Irvine, Irvine, CA, United States.,Department of Biomedical Engineering, University of California, Irvine, Irvine, CA, United States
| | - Sebastian Stober
- Research Focus Cognitive Sciences, University of Potsdam, Potsdam, Germany
| | - Vince D Calhoun
- The Mind Research Network, Albuquerque, NM, United States.,Department of ECE, University of New Mexico, Albuquerque, NM, United States
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13
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Modelling neuroanatomical variation during childhood and adolescence with neighbourhood-preserving embedding. Sci Rep 2017; 7:17796. [PMID: 29259302 PMCID: PMC5736651 DOI: 10.1038/s41598-017-18253-6] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2017] [Accepted: 12/07/2017] [Indexed: 01/29/2023] Open
Abstract
Brain development is a dynamic process with tissue-specific alterations that reflect complex and ongoing biological processes taking place during childhood and adolescence. Accurate identification and modelling of these anatomical processes in vivo with MRI may provide clinically useful imaging markers of individual variability in development. In this study, we use manifold learning to build a model of age- and sex-related anatomical variation using multiple magnetic resonance imaging metrics. Using publicly available data from a large paediatric cohort (n = 768), we apply a multi-metric machine learning approach combining measures of tissue volume, cortical area and cortical thickness into a low-dimensional data representation. We find that neuroanatomical variation due to age and sex can be captured by two orthogonal patterns of brain development and we use this model to simultaneously predict age with a mean error of 1.5-1.6 years and sex with an accuracy of 81%. We validate this model in an independent developmental cohort. We present a framework for modelling anatomical development during childhood using manifold embedding. This model accurately predicts age and sex based on image-derived markers of cerebral morphology and generalises well to independent populations.
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14
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Ming J, Verner E, Sarwate A, Kelly R, Reed C, Kahleck T, Silva R, Panta S, Turner J, Plis S, Calhoun V. COINSTAC: Decentralizing the future of brain imaging analysis. F1000Res 2017; 6:1512. [PMID: 29123643 PMCID: PMC5657031 DOI: 10.12688/f1000research.12353.1] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 08/17/2017] [Indexed: 11/20/2022] Open
Abstract
In the era of Big Data, sharing neuroimaging data across multiple sites has become increasingly important. However, researchers who want to engage in centralized, large-scale data sharing and analysis must often contend with problems such as high database cost, long data transfer time, extensive manual effort, and privacy issues for sensitive data. To remove these barriers to enable easier data sharing and analysis, we introduced a new, decentralized, privacy-enabled infrastructure model for brain imaging data called COINSTAC in 2016. We have continued development of COINSTAC since this model was first introduced. One of the challenges with such a model is adapting the required algorithms to function within a decentralized framework. In this paper, we report on how we are solving this problem, along with our progress on several fronts, including additional decentralized algorithms implementation, user interface enhancement, decentralized regression statistic calculation, and complete pipeline specifications.
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Affiliation(s)
- Jing Ming
- Datalytic Solutions, Albuquerque, NM, 87106, USA.,The Mind Research Network, Albuquerque, NM, 87106, USA
| | - Eric Verner
- Datalytic Solutions, Albuquerque, NM, 87106, USA.,The Mind Research Network, Albuquerque, NM, 87106, USA
| | - Anand Sarwate
- Department of Electrical and Computer Engineering, Rutgers University, Piscataway, NJ, 08854, USA
| | - Ross Kelly
- The Mind Research Network, Albuquerque, NM, 87106, USA
| | - Cory Reed
- The Mind Research Network, Albuquerque, NM, 87106, USA
| | | | - Rogers Silva
- Datalytic Solutions, Albuquerque, NM, 87106, USA.,The Mind Research Network, Albuquerque, NM, 87106, USA
| | - Sandeep Panta
- Datalytic Solutions, Albuquerque, NM, 87106, USA.,The Mind Research Network, Albuquerque, NM, 87106, USA
| | - Jessica Turner
- The Mind Research Network, Albuquerque, NM, 87106, USA.,Department of Psychology and Neuroscience Institute, Georgia State University, Atlanta, GA, 30303, USA
| | - Sergey Plis
- The Mind Research Network, Albuquerque, NM, 87106, USA
| | - Vince Calhoun
- Datalytic Solutions, Albuquerque, NM, 87106, USA.,The Mind Research Network, Albuquerque, NM, 87106, USA.,Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, NM, 87131, USA
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15
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Enhancing studies of the connectome in autism using the autism brain imaging data exchange II. Sci Data 2017; 4:170010. [PMID: 28291247 PMCID: PMC5349246 DOI: 10.1038/sdata.2017.10] [Citation(s) in RCA: 297] [Impact Index Per Article: 42.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2016] [Accepted: 01/05/2017] [Indexed: 12/27/2022] Open
Abstract
The second iteration of the Autism Brain Imaging Data Exchange (ABIDE II) aims to enhance the scope of brain connectomics research in Autism Spectrum Disorder (ASD). Consistent with the initial ABIDE effort (ABIDE I), that released 1112 datasets in 2012, this new multisite open-data resource is an aggregate of resting state functional magnetic resonance imaging (MRI) and corresponding structural MRI and phenotypic datasets. ABIDE II includes datasets from an additional 487 individuals with ASD and 557 controls previously collected across 16 international institutions. The combination of ABIDE I and ABIDE II provides investigators with 2156 unique cross-sectional datasets allowing selection of samples for discovery and/or replication. This sample size can also facilitate the identification of neurobiological subgroups, as well as preliminary examinations of sex differences in ASD. Additionally, ABIDE II includes a range of psychiatric variables to inform our understanding of the neural correlates of co-occurring psychopathology; 284 diffusion imaging datasets are also included. It is anticipated that these enhancements will contribute to unraveling key sources of ASD heterogeneity.
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16
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Plis SM, Sarwate AD, Wood D, Dieringer C, Landis D, Reed C, Panta SR, Turner JA, Shoemaker JM, Carter KW, Thompson P, Hutchison K, Calhoun VD. COINSTAC: A Privacy Enabled Model and Prototype for Leveraging and Processing Decentralized Brain Imaging Data. Front Neurosci 2016; 10:365. [PMID: 27594820 PMCID: PMC4990563 DOI: 10.3389/fnins.2016.00365] [Citation(s) in RCA: 47] [Impact Index Per Article: 5.9] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2016] [Accepted: 07/22/2016] [Indexed: 01/17/2023] Open
Abstract
The field of neuroimaging has embraced the need for sharing and collaboration. Data sharing mandates from public funding agencies and major journal publishers have spurred the development of data repositories and neuroinformatics consortia. However, efficient and effective data sharing still faces several hurdles. For example, open data sharing is on the rise but is not suitable for sensitive data that are not easily shared, such as genetics. Current approaches can be cumbersome (such as negotiating multiple data sharing agreements). There are also significant data transfer, organization and computational challenges. Centralized repositories only partially address the issues. We propose a dynamic, decentralized platform for large scale analyses called the Collaborative Informatics and Neuroimaging Suite Toolkit for Anonymous Computation (COINSTAC). The COINSTAC solution can include data missing from central repositories, allows pooling of both open and "closed" repositories by developing privacy-preserving versions of widely-used algorithms, and incorporates the tools within an easy-to-use platform enabling distributed computation. We present an initial prototype system which we demonstrate on two multi-site data sets, without aggregating the data. In addition, by iterating across sites, the COINSTAC model enables meta-analytic solutions to converge to "pooled-data" solutions (i.e., as if the entire data were in hand). More advanced approaches such as feature generation, matrix factorization models, and preprocessing can be incorporated into such a model. In sum, COINSTAC enables access to the many currently unavailable data sets, a user friendly privacy enabled interface for decentralized analysis, and a powerful solution that complements existing data sharing solutions.
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Affiliation(s)
- Sergey M. Plis
- The Mind Research Network, Lovelace Biomedical and Environmental Research InstituteAlbuquerque, NM, USA
| | - Anand D. Sarwate
- Department of Electrical and Computer Engineering, Rutgers, The State University of New JerseyPiscataway, NJ, USA
| | - Dylan Wood
- The Mind Research Network, Lovelace Biomedical and Environmental Research InstituteAlbuquerque, NM, USA
| | - Christopher Dieringer
- The Mind Research Network, Lovelace Biomedical and Environmental Research InstituteAlbuquerque, NM, USA
| | - Drew Landis
- The Mind Research Network, Lovelace Biomedical and Environmental Research InstituteAlbuquerque, NM, USA
| | - Cory Reed
- The Mind Research Network, Lovelace Biomedical and Environmental Research InstituteAlbuquerque, NM, USA
| | - Sandeep R. Panta
- The Mind Research Network, Lovelace Biomedical and Environmental Research InstituteAlbuquerque, NM, USA
| | - Jessica A. Turner
- The Mind Research Network, Lovelace Biomedical and Environmental Research InstituteAlbuquerque, NM, USA
- Department of Psychology and Neuroscience Institute, Georgia State UniversityAtlanta, GA, USA
| | - Jody M. Shoemaker
- The Mind Research Network, Lovelace Biomedical and Environmental Research InstituteAlbuquerque, NM, USA
| | - Kim W. Carter
- Telethon Kids Institute, The University of Western AustraliaSubiaco, WA, Australia
| | - Paul Thompson
- Departments of Neurology, Psychiatry, Engineering, Radiology, and Pediatrics, Imaging Genetics Center, Enhancing Neuroimaging and Genetics through Meta-Analysis Center for Worldwide Medicine, Imaging, and Genomics, University of Southern CaliforniaMarina del Rey, CA, USA
| | - Kent Hutchison
- Department of Psychology and Neuroscience, University of Colorado BoulderBoulder, CO, USA
| | - Vince D. Calhoun
- The Mind Research Network, Lovelace Biomedical and Environmental Research InstituteAlbuquerque, NM, USA
- Department of Electrical and Computer Engineering, University of New MexicoAlbuquerque, NM, USA
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