51
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Wu Y, Besson P, Azcona EA, Bandt SK, Parrish TB, Breiter HC, Katsaggelos AK. A multicohort geometric deep learning study of age dependent cortical and subcortical morphologic interactions for fluid intelligence prediction. Sci Rep 2022; 12:17760. [PMID: 36273036 PMCID: PMC9588039 DOI: 10.1038/s41598-022-22313-x] [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: 12/03/2021] [Accepted: 10/12/2022] [Indexed: 01/19/2023] Open
Abstract
The relationship of human brain structure to cognitive function is complex, and how this relationship differs between childhood and adulthood is poorly understood. One strong hypothesis suggests the cognitive function of Fluid Intelligence (Gf) is dependent on prefrontal cortex and parietal cortex. In this work, we developed a novel graph convolutional neural networks (gCNNs) for the analysis of localized anatomic shape and prediction of Gf. Morphologic information of the cortical ribbons and subcortical structures was extracted from T1-weighted MRIs within two independent cohorts, the Adolescent Brain Cognitive Development Study (ABCD; age: 9.93 ± 0.62 years) of children and the Human Connectome Project (HCP; age: 28.81 ± 3.70 years). Prediction combining cortical and subcortical surfaces together yielded the highest accuracy of Gf for both ABCD (R = 0.314) and HCP datasets (R = 0.454), outperforming the state-of-the-art prediction of Gf from any other brain measures in the literature. Across both datasets, the morphology of the amygdala, hippocampus, and nucleus accumbens, along with temporal, parietal and cingulate cortex consistently drove the prediction of Gf, suggesting a significant reframing of the relationship between brain morphology and Gf to include systems involved with reward/aversion processing, judgment and decision-making, motivation, and emotion.
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Affiliation(s)
- Yunan Wu
- Department of Electrical Computer Engineering, Northwestern University, Evanston, IL, USA.
| | - Pierre Besson
- grid.16753.360000 0001 2299 3507Department of Radiology, Northwestern University, Feinberg School of Medicine, Chicago, IL USA
| | - Emanuel A. Azcona
- grid.16753.360000 0001 2299 3507Department of Electrical Computer Engineering, Northwestern University, Evanston, IL USA
| | - S. Kathleen Bandt
- grid.16753.360000 0001 2299 3507Department of Neurosurgery, Northwestern University, Feinberg School of Medicine, Chicago, IL USA
| | - Todd B. Parrish
- grid.16753.360000 0001 2299 3507Department of Radiology, Northwestern University, Feinberg School of Medicine, Chicago, IL USA
| | - Hans C. Breiter
- grid.24827.3b0000 0001 2179 9593Departments of Computer Science and Biomedical Engineering, University of Cincinnati, Cincinnat, OH USA ,grid.32224.350000 0004 0386 9924Laboratory of Neuroimaging and Genetics, Department of Psychiatry, Massachusetts General Hospital and Harvard School of Medicine, Boston, MA USA
| | - Aggelos K. Katsaggelos
- grid.16753.360000 0001 2299 3507Department of Electrical Computer Engineering, Northwestern University, Evanston, IL USA ,grid.16753.360000 0001 2299 3507Department of Radiology, Northwestern University, Feinberg School of Medicine, Chicago, IL USA ,grid.16753.360000 0001 2299 3507Department of Computer Science, Northwestern University, Evanston, IL USA
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Dhamala E, Ooi LQR, Chen J, Kong R, Anderson KM, Chin R, Yeo BTT, Holmes AJ. Proportional intracranial volume correction differentially biases behavioral predictions across neuroanatomical features, sexes, and development. Neuroimage 2022; 260:119485. [PMID: 35843514 PMCID: PMC9425854 DOI: 10.1016/j.neuroimage.2022.119485] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2022] [Revised: 07/08/2022] [Accepted: 07/13/2022] [Indexed: 01/03/2023] Open
Abstract
Individual differences in brain anatomy can be used to predict variations in cognitive ability. Most studies to date have focused on broad population-level trends, but the extent to which the observed predictive features are shared across sexes and age groups remains to be established. While it is standard practice to account for intracranial volume (ICV) using proportion correction in both regional and whole-brain morphometric analyses, in the context of brain-behavior predictions the possible differential impact of ICV correction on anatomical features and subgroups within the population has yet to be systematically investigated. In this work, we evaluate the effect of proportional ICV correction on sex-independent and sex-specific predictive models of individual cognitive abilities across multiple anatomical properties (surface area, gray matter volume, and cortical thickness) in healthy young adults (Human Connectome Project; n = 1013, 548 females) and typically developing children (Adolescent Brain Cognitive Development study; n = 1823, 979 females). We demonstrate that ICV correction generally reduces predictive accuracies derived from surface area and gray matter volume, while increasing predictive accuracies based on cortical thickness in both adults and children. Furthermore, the extent to which predictive models generalize across sexes and age groups depends on ICV correction: models based on surface area and gray matter volume are more generalizable without ICV correction, while models based on cortical thickness are more generalizable with ICV correction. Finally, the observed neuroanatomical features predictive of cognitive abilities are unique across age groups regardless of ICV correction, but whether they are shared or unique across sexes (within age groups) depends on ICV correction. These findings highlight the importance of considering individual differences in ICV, and show that proportional ICV correction does not remove the effects of cranial volume from anatomical measurements and can introduce ICV bias where previously there was none. ICV correction choices affect not just the strength of the relationships captured, but also the conclusions drawn regarding the neuroanatomical features that underlie those relationships.
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Affiliation(s)
- Elvisha Dhamala
- Department of Psychology, Yale University, New Haven, United States; Kavli Institute for Neuroscience, Yale University, New Haven, United States.
| | - Leon Qi Rong Ooi
- Centre for Sleep & Cognition & Centre for Translational Magnetic Resonance Research, Yong Loo Lin School of Medicine, Singapore, National University of Singapore, Singapore; Department of Electrical and Computer Engineering, National University of Singapore, Singapore; N.1 Institute for Health & Institute for Digital Medicine, National University of Singapore, Singapore; Integrative Sciences and Engineering Programme (ISEP), National University of Singapore, Singapore
| | - Jianzhong Chen
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore; N.1 Institute for Health & Institute for Digital Medicine, National University of Singapore, Singapore; Integrative Sciences and Engineering Programme (ISEP), National University of Singapore, Singapore
| | - Ru Kong
- Centre for Sleep & Cognition & Centre for Translational Magnetic Resonance Research, Yong Loo Lin School of Medicine, Singapore, National University of Singapore, Singapore; Department of Electrical and Computer Engineering, National University of Singapore, Singapore; Integrative Sciences and Engineering Programme (ISEP), National University of Singapore, Singapore
| | - Kevin M Anderson
- Department of Psychology, Yale University, New Haven, United States
| | - Rowena Chin
- Department of Psychology, Yale University, New Haven, United States
| | - B T Thomas Yeo
- Centre for Sleep & Cognition & Centre for Translational Magnetic Resonance Research, Yong Loo Lin School of Medicine, Singapore, National University of Singapore, Singapore; Department of Electrical and Computer Engineering, National University of Singapore, Singapore; N.1 Institute for Health & Institute for Digital Medicine, National University of Singapore, Singapore; Integrative Sciences and Engineering Programme (ISEP), National University of Singapore, Singapore; Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, United States
| | - Avram J Holmes
- Department of Psychology, Yale University, New Haven, United States; Kavli Institute for Neuroscience, Yale University, New Haven, United States; Department of Psychiatry, Yale University, New Haven, United States; Wu Tsai Institute, Yale University, New Haven, United States.
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53
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Ooi LQR, Chen J, Zhang S, Kong R, Tam A, Li J, Dhamala E, Zhou JH, Holmes AJ, Yeo BTT. Comparison of individualized behavioral predictions across anatomical, diffusion and functional connectivity MRI. Neuroimage 2022; 263:119636. [PMID: 36116616 DOI: 10.1016/j.neuroimage.2022.119636] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2022] [Revised: 08/24/2022] [Accepted: 09/15/2022] [Indexed: 10/31/2022] Open
Abstract
A fundamental goal across the neurosciences is the characterization of relationships linking brain anatomy, functioning, and behavior. Although various MRI modalities have been developed to probe these relationships, direct comparisons of their ability to predict behavior have been lacking. Here, we compared the ability of anatomical T1, diffusion and functional MRI (fMRI) to predict behavior at an individual level. Cortical thickness, area and volume were extracted from anatomical T1 images. Diffusion Tensor Imaging (DTI) and approximate Neurite Orientation Dispersion and Density Imaging (NODDI) models were fitted to the diffusion images. The resulting metrics were projected to the Tract-Based Spatial Statistics (TBSS) skeleton. We also ran probabilistic tractography for the diffusion images, from which we extracted the stream count, average stream length, and the average of each DTI and NODDI metric across tracts connecting each pair of brain regions. Functional connectivity (FC) was extracted from both task and resting-state fMRI. Individualized prediction of a wide range of behavioral measures were performed using kernel ridge regression, linear ridge regression and elastic net regression. Consistency of the results were investigated with the Human Connectome Project (HCP) and Adolescent Brain Cognitive Development (ABCD) datasets. In both datasets, FC-based models gave the best prediction performance, regardless of regression model or behavioral measure. This was especially true for the cognitive component. Furthermore, all modalities were able to predict cognition better than other behavioral components. Combining all modalities improved prediction of cognition, but not other behavioral components. Finally, across all behaviors, combining resting and task FC yielded prediction performance similar to combining all modalities. Overall, our study suggests that in the case of healthy children and young adults, behaviorally-relevant information in T1 and diffusion features might reflect a subset of the variance captured by FC.
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Affiliation(s)
- Leon Qi Rong Ooi
- Integrative Sciences and Engineering Programme (ISEP), National University of Singapore, Singapore, Singapore; Centre for Sleep & Cognition & Centre for Translational Magnetic Resonance Research, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore; Department of Electrical and Computer Engineering, National University of Singapore, Singapore, Singapore; N.1 Institute for Health & Institute for Digital Medicine, National University of Singapore, Singapore, Singapore
| | - Jianzhong Chen
- Centre for Sleep & Cognition & Centre for Translational Magnetic Resonance Research, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore; Department of Electrical and Computer Engineering, National University of Singapore, Singapore, Singapore; N.1 Institute for Health & Institute for Digital Medicine, National University of Singapore, Singapore, Singapore
| | - Shaoshi Zhang
- Integrative Sciences and Engineering Programme (ISEP), National University of Singapore, Singapore, Singapore; Centre for Sleep & Cognition & Centre for Translational Magnetic Resonance Research, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore; Department of Electrical and Computer Engineering, National University of Singapore, Singapore, Singapore; N.1 Institute for Health & Institute for Digital Medicine, National University of Singapore, Singapore, Singapore
| | - Ru Kong
- Integrative Sciences and Engineering Programme (ISEP), National University of Singapore, Singapore, Singapore; Centre for Sleep & Cognition & Centre for Translational Magnetic Resonance Research, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore; Department of Electrical and Computer Engineering, National University of Singapore, Singapore, Singapore; N.1 Institute for Health & Institute for Digital Medicine, National University of Singapore, Singapore, Singapore
| | - Angela Tam
- Centre for Sleep & Cognition & Centre for Translational Magnetic Resonance Research, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore; Department of Electrical and Computer Engineering, National University of Singapore, Singapore, Singapore; N.1 Institute for Health & Institute for Digital Medicine, National University of Singapore, Singapore, Singapore
| | - Jingwei Li
- Institute of Neuroscience and Medicine, Brain & Behavior (INM-7), Research Center Jülich, Jülich, Germany; Institute of Systems Neuroscience, Medical Faculty, Heinrich-Heine University Düsseldorf, Düsseldorf, Germany
| | - Elvisha Dhamala
- Yale University, Departments of Psychology and Psychiatry, New Haven, CT, United States; Kavli Institute for Neuroscience, Yale University, New Haven, CT, United States
| | - Juan Helen Zhou
- Integrative Sciences and Engineering Programme (ISEP), National University of Singapore, Singapore, Singapore; Centre for Sleep & Cognition & Centre for Translational Magnetic Resonance Research, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore; Department of Electrical and Computer Engineering, National University of Singapore, Singapore, Singapore
| | - Avram J Holmes
- Yale University, Departments of Psychology and Psychiatry, New Haven, CT, United States; Wu Tsai Institute, Yale University, New Haven, CT, United States
| | - B T Thomas Yeo
- Integrative Sciences and Engineering Programme (ISEP), National University of Singapore, Singapore, Singapore; Centre for Sleep & Cognition & Centre for Translational Magnetic Resonance Research, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore; Department of Electrical and Computer Engineering, National University of Singapore, Singapore, Singapore; N.1 Institute for Health & Institute for Digital Medicine, National University of Singapore, Singapore, Singapore.
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54
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Spisak T. Statistical quantification of confounding bias in machine learning models. Gigascience 2022; 11:6676500. [PMID: 36017878 PMCID: PMC9412867 DOI: 10.1093/gigascience/giac082] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2022] [Revised: 07/07/2022] [Accepted: 07/28/2022] [Indexed: 11/12/2022] Open
Abstract
BACKGROUND The lack of nonparametric statistical tests for confounding bias significantly hampers the development of robust, valid, and generalizable predictive models in many fields of research. Here I propose the partial confounder test, which, for a given confounder variable, probes the null hypotheses of the model being unconfounded. RESULTS The test provides a strict control for type I errors and high statistical power, even for nonnormally and nonlinearly dependent predictions, often seen in machine learning. Applying the proposed test on models trained on large-scale functional brain connectivity data (N= 1,865) (i) reveals previously unreported confounders and (ii) shows that state-of-the-art confound mitigation approaches may fail preventing confounder bias in several cases. CONCLUSIONS The proposed test (implemented in the package mlconfound; https://mlconfound.readthedocs.io) can aid the assessment and improvement of the generalizability and validity of predictive models and, thereby, fosters the development of clinically useful machine learning biomarkers.
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Affiliation(s)
- Tamas Spisak
- Center for Translational Neuro- and Behavioral Sciences, Institute for Diagnostic and Interventional Radiology and Neuroradiology, Center University Hospital Essen, Essen, D-45147, Germany
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55
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Żygierewicz J, Janik RA, Podolak IT, Drozd A, Malinowska U, Poziomska M, Wojciechowski J, Ogniewski P, Niedbalski P, Terczynska I, Rogala J. Decoding working memory-related information from repeated psychophysiological EEG experiments using convolutional and contrastive neural networks. J Neural Eng 2022; 19. [PMID: 35985292 DOI: 10.1088/1741-2552/ac8b38] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2022] [Accepted: 08/19/2022] [Indexed: 11/12/2022]
Abstract
OBJECTIVE Extracting reliable information from EEG signals is difficult because the low signal-to-noise ratio and significant intersubject variability seriously hinder statistical analyses. However, recent advances in explainable machine learning open a new strategy to address this problem. APPROACH The current study evaluates this approach using results from the classification and decoding of electrical brain activity associated with information retention. We designed four neural network models differing in architecture, training strategies, and input representation to classify single experimental trials of a working memory task. MAIN RESULTS Our best models achieved an accuracy of 65.29$±0.76 and Matthews correlation coefficient of 0.288±0.018, outperforming the reference model trained on the same data. The highest correlation between classification score and behavioral performance was 0.36 (p=0.0007). Using analysis of input perturbation, we estimated the importance of EEG channels and frequency bands in the task at hand. The set of essential features identified for each network varies. We identified a subset of features common to all models that identified brain regions and frequency bands consistent with current neurophysiological knowledge of the processes critical to attention and working memory. Finally, we proposed sanity checks to examine further the robustness of each model's set of features. SIGNIFICANCE Our results indicate that explainable deep learning is a powerful tool for decoding information from EEG signals. It is crucial to train and analyze a range of models to identify stable and reliable features. Our results highlight the need for explainable modeling as the model with the highest accuracy appeared to use residual artifactual activity.
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Affiliation(s)
- Jarosław Żygierewicz
- Biomedical Physics, University of Warsaw Faculty of Physics, Pasteura 5, Warszawa, 02-093, POLAND
| | - Romuald A Janik
- Institute of Theoretical Physics, Jagiellonian University in Krakow Faculty of Physics Astronomy and Applied Computer Science, Łojasiewicza 6, Krakow, Małopolskie, 30-348, POLAND
| | - Igor T Podolak
- Faculty of Mathematics and Computer Science, Jagiellonian University in Krakow, Łojasiewicza 6, Krakow, Małopolska, 30-348, POLAND
| | - Alan Drozd
- Nencki Institute of Experimental Biology PAS, Pasteura 3, Warszawa, Mazowieckie, 02-093, POLAND
| | - Urszula Malinowska
- Nencki Institute of Experimental Biology PAS, Pasteura 3, Warszawa, Mazowieckie, 02-093, POLAND
| | - Martyna Poziomska
- University of Warsaw Faculty of Physics, Pasteura 5, Warszawa, 02-093, POLAND
| | - Jakub Wojciechowski
- Nencki Institute of Experimental Biology PAS, Pasteura 3, Warszawa, Mazowieckie, 02-093, POLAND
| | - Paweł Ogniewski
- ELMIKO BIOSIGNALS LTD, Sportowa 3, Milanowek, 05-822, POLAND
| | | | - Iwona Terczynska
- Institute of Mother and Child, Kasprzaka 17A, Warszawa, 01-211, POLAND
| | - Jacek Rogala
- Nencki Institute of Experimental Biology PAS, Pasteura 3, Warszawa, Mazowieckie, 02-093, POLAND
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56
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Yoo K, Rosenberg MD, Kwon YH, Scheinost D, Constable RT, Chun MM. A cognitive state transformation model for task-general and task-specific subsystems of the brain connectome. Neuroimage 2022; 257:119279. [PMID: 35577026 PMCID: PMC9307138 DOI: 10.1016/j.neuroimage.2022.119279] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2021] [Revised: 04/11/2022] [Accepted: 05/02/2022] [Indexed: 11/07/2022] Open
Abstract
The human brain flexibly controls different cognitive behaviors, such as memory and attention, to satisfy contextual demands. Much progress has been made to reveal task-induced modulations in the whole-brain functional connectome, but we still lack a way to model context-dependent changes. Here, we present a novel connectome-to-connectome (C2C) transformation framework that enables us to model the brain's functional reorganization from one connectome state to another in response to specific task goals. Using functional magnetic resonance imaging data from the Human Connectome Project, we demonstrate that the C2C model accurately generates an individual's task-related connectomes from their task-free (resting-state) connectome with a high degree of specificity across seven different cognitive states. Moreover, the C2C model amplifies behaviorally relevant individual differences in the task-free connectome, thereby improving behavioral predictions with increased power, achieving similar performance with just a third of the subjects needed when relying on resting-state data alone. Finally, the C2C model reveals how the brain reorganizes between cognitive states. Our observations support the existence of reliable state-specific subsystems in the brain and demonstrate that we can quantitatively model how the connectome reconfigures to different cognitive states, enabling more accurate predictions of behavior with fewer subjects.
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Affiliation(s)
- Kwangsun Yoo
- Department of Psychology, Yale University, New Haven, CT 06520, United States of America.
| | - Monica D Rosenberg
- Department of Psychology, Yale University, New Haven, CT 06520, United States of America,Department of Psychology, University of Chicago, Chicago, IL 60637, United States of America
| | - Young Hye Kwon
- Department of Psychology, Yale University, New Haven, CT 06520, United States of America
| | - Dustin Scheinost
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT 06510, United States of America
| | - R Todd Constable
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT 06510, United States of America,Interdepartmental Neuroscience Program, Yale University, New Haven, CT 06520, United States of America,Department of Neurosurgery, Yale School of Medicine, New Haven, CT 06510, United States of America
| | - Marvin M Chun
- Department of Psychology, Yale University, New Haven, CT 06520, United States of America,Interdepartmental Neuroscience Program, Yale University, New Haven, CT 06520, United States of America,Department of Neuroscience, Yale School of Medicine, New Haven, CT 06510, United States of America
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57
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(PSE) early identification of process deviation based on convolutional neural network. Chin J Chem Eng 2022. [DOI: 10.1016/j.cjche.2022.07.034] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
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58
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Ji J, Ren Y, Lei M. FC–HAT: Hypergraph attention network for functional brain network classification. Inf Sci (N Y) 2022. [DOI: 10.1016/j.ins.2022.07.041] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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59
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Lin K, Jie B, Dong P, Ding X, Bian W, Liu M. Convolutional Recurrent Neural Network for Dynamic Functional MRI Analysis and Brain Disease Identification. Front Neurosci 2022; 16:933660. [PMID: 35873806 PMCID: PMC9298744 DOI: 10.3389/fnins.2022.933660] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2022] [Accepted: 06/13/2022] [Indexed: 12/12/2022] Open
Abstract
Dynamic functional connectivity (dFC) networks derived from resting-state functional magnetic resonance imaging (rs-fMRI) help us understand fundamental dynamic characteristics of human brains, thereby providing an efficient solution for automated identification of brain diseases, such as Alzheimer's disease (AD) and its prodromal stage. Existing studies have applied deep learning methods to dFC network analysis and achieved good performance compared with traditional machine learning methods. However, they seldom take advantage of sequential information conveyed in dFC networks that could be informative to improve the diagnosis performance. In this paper, we propose a convolutional recurrent neural network (CRNN) for automated brain disease classification with rs-fMRI data. Specifically, we first construct dFC networks from rs-fMRI data using a sliding window strategy. Then, we employ three convolutional layers and long short-term memory (LSTM) layer to extract high-level features of dFC networks and also preserve the sequential information of extracted features, followed by three fully connected layers for brain disease classification. Experimental results on 174 subjects with 563 rs-fMRI scans from the Alzheimer's Disease Neuroimaging Initiative (ADNI) demonstrate the effectiveness of our proposed method in binary and multi-category classification tasks.
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Affiliation(s)
- Kai Lin
- School of Computer and Information, Anhui Normal University, Wuhu, China
| | - Biao Jie
- School of Computer and Information, Anhui Normal University, Wuhu, China
| | - Peng Dong
- School of Computer and Information, Anhui Normal University, Wuhu, China
| | - Xintao Ding
- School of Computer and Information, Anhui Normal University, Wuhu, China
| | - Weixin Bian
- School of Computer and Information, Anhui Normal University, Wuhu, China
| | - Mingxia Liu
- Department of Radiology and Biomedical Research Imaging Center (BRIC), University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
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60
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Zhang Z, Jiang R, Zhang C, Williams B, Jiang Z, Li CT, Chazot P, Pavese N, Bouridane A, Beghdadi A. Robust Brain Age Estimation based on sMRI via Nonlinear Age-Adaptive Ensemble Learning. IEEE Trans Neural Syst Rehabil Eng 2022; 30:2146-2156. [PMID: 35830403 DOI: 10.1109/tnsre.2022.3190467] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Precise prediction on brain age is urgently needed by many biomedical areas including mental rehabilitation prognosis as well as various medicine or treatment trials. People began to realize that contrasting physical (real) age and predicted brain age can help to highlight brain issues and evaluate if patients' brains are healthy or not. Such age prediction is often challenging for single model-based prediction, while the conditions of brains vary drastically over age. In this work, we present an age-adaptive ensemble model that is based on the combination of four different machine learning algorithms, including a support vector machine (SVR), a convolutional neural network (CNN) model, and the popular GoogLeNet and ResNet deep networks. The ensemble model proposed here is nonlinearly adaptive, where age is taken as a key factor in the nonlinear combination of various single-algorithm-based independent models. In our age-adaptive ensemble method, the weights of each model are learned automatically as nonlinear functions over age instead of fixed values, while brain age estimation is based on such an age-adaptive integration of various single models. The quality of the model is quantified by the mean absolute errors (MAE) and spearman correlation between the predicted age and the actual age, with the least MAE and the highest Spearman correlation representing the highest accuracy in age prediction. By testing on the Predictive Analysis Challenge 2019 (PAC 2019) dataset, our novel ensemble model has achieved a MAE down to 3.19, which is a significantly increased accuracy in this brain age competition. If deployed in the real world, our novel ensemble model having an improved accuracy could potentially help doctors to identify the risk of brain diseases more accurately and quickly, thus helping pharmaceutical companies develop drugs or treatments precisely, and potential offer a new powerful tool for researchers in the field of brain science.
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Vieira BH, Pamplona GSP, Fachinello K, Silva AK, Foss MP, Salmon CEG. On the prediction of human intelligence from neuroimaging: A systematic review of methods and reporting. INTELLIGENCE 2022. [DOI: 10.1016/j.intell.2022.101654] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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63
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Batta I, Abrol A, Fu Z, Preda A, van Erp TG, Calhoun VD. Building Models of Functional Interactions Among Brain Domains that Encode Varying Information Complexity: A Schizophrenia Case Study. Neuroinformatics 2022; 20:777-791. [PMID: 35267145 PMCID: PMC9463406 DOI: 10.1007/s12021-022-09563-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/12/2022] [Indexed: 12/31/2022]
Abstract
Revealing associations among various structural and functional patterns of the brain can yield highly informative results about the healthy and disordered brain. Studies using neuroimaging data have more recently begun to utilize the information within as well as across various functional and anatomical domains (i.e., groups of brain networks). However, most whole-brain approaches assume similar complexity of interactions throughout the brain. Here we investigate the hypothesis that interactions between brain networks capture varying amounts of complexity, and that we can better capture this information by varying the complexity of the model subspace structure based on available training data. To do this, we employ a Bayesian optimization-based framework known as the Tree Parzen Estimator (TPE) to identify, exploit and analyze patterns of variation in the information encoded by temporal information extracted from functional magnetic resonance imaging (fMRI) subdomains of the brain. Using a repeated cross-validation procedure on a schizophrenia classification task, we demonstrate evidence that interactions between specific functional subdomains are better characterized by more sophisticated model architectures compared to less complicated ones required by the others for optimally contributing towards classification and understanding the brain's functional interactions. We show that functional subdomains known to be involved in schizophrenia require more complex architectures to optimally unravel discriminatory information about the disorder. Our study points to the need for adaptive, hierarchical learning frameworks that cater differently to the features from different subdomains, not only for a better prediction but also for enabling the identification of features predicting the outcome of interest.
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Affiliation(s)
- Ishaan Batta
- Center for Translational Research in Neuroimaging and Data Science (TReNDS): Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, USA,Dept. of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, USA,Corresponding Author: Ishaan Batta,
| | - Anees Abrol
- Center for Translational Research in Neuroimaging and Data Science (TReNDS): Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, USA
| | - Zening Fu
- Center for Translational Research in Neuroimaging and Data Science (TReNDS): Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, USA
| | - Adrian Preda
- Department of Psychiatry and Human Behavior, University of California Irvine, Irvine, California, USA
| | - Theo G.M. van Erp
- Department of Psychiatry and Human Behavior, University of California Irvine, Irvine, California, USA
| | - Vince D. Calhoun
- Center for Translational Research in Neuroimaging and Data Science (TReNDS): Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, USA,Dept. of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, USA
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Liu M, Amey RC, Backer RA, Simon JP, Forbes CE. Behavioral Studies Using Large-Scale Brain Networks – Methods and Validations. Front Hum Neurosci 2022; 16:875201. [PMID: 35782044 PMCID: PMC9244405 DOI: 10.3389/fnhum.2022.875201] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2022] [Accepted: 05/17/2022] [Indexed: 11/13/2022] Open
Abstract
Mapping human behaviors to brain activity has become a key focus in modern cognitive neuroscience. As methods such as functional MRI (fMRI) advance cognitive scientists show an increasing interest in investigating neural activity in terms of functional connectivity and brain networks, rather than activation in a single brain region. Due to the noisy nature of neural activity, determining how behaviors are associated with specific neural signals is not well-established. Previous research has suggested graph theory techniques as a solution. Graph theory provides an opportunity to interpret human behaviors in terms of the topological organization of brain network architecture. Graph theory-based approaches, however, only scratch the surface of what neural connections relate to human behavior. Recently, the development of data-driven methods, e.g., machine learning and deep learning approaches, provide a new perspective to study the relationship between brain networks and human behaviors across the whole brain, expanding upon past literatures. In this review, we sought to revisit these data-driven approaches to facilitate our understanding of neural mechanisms and build models of human behaviors. We start with the popular graph theory approach and then discuss other data-driven approaches such as connectome-based predictive modeling, multivariate pattern analysis, network dynamic modeling, and deep learning techniques that quantify meaningful networks and connectivity related to cognition and behaviors. Importantly, for each topic, we discuss the pros and cons of the methods in addition to providing examples using our own data for each technique to describe how these methods can be applied to real-world neuroimaging data.
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Affiliation(s)
- Mengting Liu
- School of Biomedical Engineering, Sun Yat-sen University, Shenzhen, China
- Mengting Liu,
| | - Rachel C. Amey
- Department of Psychological and Brain Sciences, University of Delaware, Newark, DE, United States
- *Correspondence: Rachel C. Amey,
| | - Robert A. Backer
- Department of Psychological and Brain Sciences, University of Delaware, Newark, DE, United States
| | - Julia P. Simon
- Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
| | - Chad E. Forbes
- Department of Psychology, Florida Atlantic University, Boca Raton, FL, United States
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65
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He T, An L, Chen P, Chen J, Feng J, Bzdok D, Holmes AJ, Eickhoff SB, Yeo BTT. Meta-matching as a simple framework to translate phenotypic predictive models from big to small data. Nat Neurosci 2022; 25:795-804. [PMID: 35578132 PMCID: PMC9202200 DOI: 10.1038/s41593-022-01059-9] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2020] [Accepted: 03/23/2022] [Indexed: 12/12/2022]
Abstract
We propose a simple framework-meta-matching-to translate predictive models from large-scale datasets to new unseen non-brain-imaging phenotypes in small-scale studies. The key consideration is that a unique phenotype from a boutique study likely correlates with (but is not the same as) related phenotypes in some large-scale dataset. Meta-matching exploits these correlations to boost prediction in the boutique study. We apply meta-matching to predict non-brain-imaging phenotypes from resting-state functional connectivity. Using the UK Biobank (N = 36,848) and Human Connectome Project (HCP) (N = 1,019) datasets, we demonstrate that meta-matching can greatly boost the prediction of new phenotypes in small independent datasets in many scenarios. For example, translating a UK Biobank model to 100 HCP participants yields an eight-fold improvement in variance explained with an average absolute gain of 4.0% (minimum = -0.2%, maximum = 16.0%) across 35 phenotypes. With a growing number of large-scale datasets collecting increasingly diverse phenotypes, our results represent a lower bound on the potential of meta-matching.
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Affiliation(s)
- Tong He
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore, Singapore.,Centre for Sleep and Cognition (CSC) & Centre for Translational Magnetic Resonance Research (TMR), National University of Singapore, Singapore, Singapore.,N.1 Institute for Health & Institute for Digital Medicine (WisDM), National University of Singapore, Singapore, Singapore
| | - Lijun An
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore, Singapore.,Centre for Sleep and Cognition (CSC) & Centre for Translational Magnetic Resonance Research (TMR), National University of Singapore, Singapore, Singapore.,N.1 Institute for Health & Institute for Digital Medicine (WisDM), National University of Singapore, Singapore, Singapore
| | - Pansheng Chen
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore, Singapore.,Centre for Sleep and Cognition (CSC) & Centre for Translational Magnetic Resonance Research (TMR), National University of Singapore, Singapore, Singapore.,N.1 Institute for Health & Institute for Digital Medicine (WisDM), National University of Singapore, Singapore, Singapore
| | - Jianzhong Chen
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore, Singapore.,Centre for Sleep and Cognition (CSC) & Centre for Translational Magnetic Resonance Research (TMR), National University of Singapore, Singapore, Singapore.,N.1 Institute for Health & Institute for Digital Medicine (WisDM), National University of Singapore, Singapore, Singapore
| | | | - Danilo Bzdok
- Department of Biomedical Engineering, McConnell Brain Imaging Centre (BIC), Montreal Neurological Institute (MNI), Faculty of Medicine, School of Computer Science, McGill University, Montreal QC, Canada.,Mila - Quebec Artificial Intelligence Institute, Montreal, QC, Canada
| | - Avram J Holmes
- Departments of Psychology and Psychiatry, Yale University, New Haven, CT, USA
| | - Simon B Eickhoff
- Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany.,Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre Jülich, Jülich, Germany
| | - B T Thomas Yeo
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore, Singapore. .,Centre for Sleep and Cognition (CSC) & Centre for Translational Magnetic Resonance Research (TMR), National University of Singapore, Singapore, Singapore. .,N.1 Institute for Health & Institute for Digital Medicine (WisDM), National University of Singapore, Singapore, Singapore. .,NUS Graduate School for Integrative Sciences and Engineering, National University of Singapore, Singapore, Singapore. .,Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA.
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66
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Poeppl TB, Dimas E, Sakreida K, Kernbach JM, Markello RD, Schöffski O, Dagher A, Koellinger P, Nave G, Farah MJ, Mišić B, Bzdok D. Pattern learning reveals brain asymmetry to be linked to socioeconomic status. Cereb Cortex Commun 2022; 3:tgac020. [PMID: 35702547 PMCID: PMC9188625 DOI: 10.1093/texcom/tgac020] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2021] [Revised: 05/13/2022] [Accepted: 05/15/2022] [Indexed: 11/16/2022] Open
Abstract
Socioeconomic status (SES) anchors individuals in their social network layers. Our embedding in the societal fabric resonates with habitus, world view, opportunity, and health disparity. It remains obscure how distinct facets of SES are reflected in the architecture of the central nervous system. Here, we capitalized on multivariate multi-output learning algorithms to explore possible imprints of SES in gray and white matter structure in the wider population (n ≈ 10,000 UK Biobank participants). Individuals with higher SES, compared with those with lower SES, showed a pattern of increased region volumes in the left brain and decreased region volumes in the right brain. The analogous lateralization pattern emerged for the fiber structure of anatomical white matter tracts. Our multimodal findings suggest hemispheric asymmetry as an SES-related brain signature, which was consistent across six different indicators of SES: degree, education, income, job, neighborhood and vehicle count. Hence, hemispheric specialization may have evolved in human primates in a way that reveals crucial links to SES.
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Affiliation(s)
- Timm B Poeppl
- Department of Psychiatry, Psychotherapy and Psychosomatics, Faculty of Medicine, RWTH Aachen University, Aachen, Germany
- Department of Health Management, School of Business, Economics and Society, Friedrich-Alexander-Universität Erlangen-Nürnberg, Nürnberg, Germany
| | - Emile Dimas
- Department of Biomedical Engineering, McConnell Brain Imaging Center (BIC), Montreal Neurological Institute (MNI), Faculty of Medicine, School of Computer Science, McGill University, Montreal, Quebec, Canada
| | - Katrin Sakreida
- Department of Psychiatry, Psychotherapy and Psychosomatics, Faculty of Medicine, RWTH Aachen University, Aachen, Germany
| | - Julius M Kernbach
- Department of Neurosurgery, Faculty of Medicine, RWTH Aachen University, Aachen, Germany
| | - Ross D Markello
- McConnell Brain Imaging Center (BIC), Montreal Neurological Institute (MNI), Faculty of Medicine, McGill University, Montreal, Quebec, Canada
| | - Oliver Schöffski
- Department of Health Management, School of Business, Economics and Society, Friedrich-Alexander-Universität Erlangen-Nürnberg, Nürnberg, Germany
| | - Alain Dagher
- Montreal Neurological Institute (MNI), McGill University, Montreal, Quebec, Canada
| | - Philipp Koellinger
- Department of Economics, School of Business and Economics, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- La Follette School of Public Affairs, University of Wisconsin-Madison, Madison, Wisconsin, United States of America
| | - Gideon Nave
- Marketing Department, The Wharton School, University of Pennsylvania, Philadelphia, United States of America
| | - Martha J Farah
- Center for Neuroscience & Society, University of Pennsylvania, Philadelphia, United States of America
| | - Bratislav Mišić
- McConnell Brain Imaging Center (BIC), Montreal Neurological Institute (MNI), Faculty of Medicine, McGill University, Montreal, Quebec, Canada
| | - Danilo Bzdok
- Department of Biomedical Engineering, McConnell Brain Imaging Center (BIC), Montreal Neurological Institute (MNI), Faculty of Medicine, School of Computer Science, McGill University, Montreal, Quebec, Canada
- Mila – Quebec Artificial Intelligence Institute, Montreal, Quebec, Canada
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67
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Yeung AWK, More S, Wu J, Eickhoff SB. Reporting details of neuroimaging studies on individual traits prediction: A literature survey. Neuroimage 2022; 256:119275. [PMID: 35513295 DOI: 10.1016/j.neuroimage.2022.119275] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Revised: 04/27/2022] [Accepted: 04/29/2022] [Indexed: 11/17/2022] Open
Abstract
Using machine-learning tools to predict individual phenotypes from neuroimaging data is one of the most promising and hence dynamic fields in systems neuroscience. Here, we perform a literature survey of the rapidly work on phenotype prediction in healthy subjects or general population to sketch out the current state and ongoing developments in terms of data, analysis methods and reporting. Excluding papers on age-prediction and clinical applications, which form a distinct literature, we identified a total 108 papers published since 2007. In these, memory, fluid intelligence and attention were most common phenotypes to be predicted, which resonates with the observation that roughly a quarter of the papers used data from the Human Connectome Project, even though another half recruited their own cohort. Sample size (in terms of training and external test sets) and prediction accuracy (from internal and external validation respectively) did not show significant temporal trends. Prediction accuracy was negatively correlated with sample size of the training set, but not the external test set. While known to be optimistic, leave-one-out cross-validation (LOO CV) was the prevalent strategy for model validation (n = 48). Meanwhile, 27 studies used external validation with external test set. Both numbers showed no significant temporal trends. The most popular learning algorithm was connectome-based predictive modeling introduced by the Yale team. Other common learning algorithms were linear regression, relevance vector regression (RVR), support vector regression (SVR), least absolute shrinkage and selection operator (LASSO), and elastic net. Meanwhile, the amount of data from self-recruiting studies (but not studies using open, shared dataset) was positively correlated with internal validation prediction accuracy. At the same time, self-recruiting studies also reported a significantly higher internal validation prediction accuracy than those using open, shared datasets. Data type and participant age did not significantly influence prediction accuracy. Confound control also did not influence prediction accuracy after adjusted for other factors. To conclude, most of the current literature is probably quite optimistic with internal validation using LOO CV. More efforts should be made to encourage the use of external validation with external test sets to further improve generalizability of the models.
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Affiliation(s)
- Andy Wai Kan Yeung
- Oral and Maxillofacial Radiology, Applied Oral Sciences and Community Dental Care, Faculty of Dentistry, The University of Hong Kong, Hong Kong, China.
| | - Shammi More
- Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre Jülich, Jülich, Germany; Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Jianxiao Wu
- Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre Jülich, Jülich, Germany; Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Simon B Eickhoff
- Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre Jülich, Jülich, Germany; Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany.
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68
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Al Zoubi O, Misaki M, Tsuchiyagaito A, Zotev V, White E, Paulus M, Bodurka J. Machine Learning Evidence for Sex Differences Consistently Influences Resting-State Functional Magnetic Resonance Imaging Fluctuations Across Multiple Independently Acquired Data Sets. Brain Connect 2022; 12:348-361. [PMID: 34269609 PMCID: PMC9131354 DOI: 10.1089/brain.2020.0878] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
Background/Introduction: Sex classification using functional connectivity from resting-state functional magnetic resonance imaging (rs-fMRI) has shown promising results. This suggested that sex difference might also be embedded in the blood-oxygen-level-dependent properties such as the amplitude of low-frequency fluctuation (ALFF) and the fraction of ALFF (fALFF). This study comprehensively investigates sex differences using a reliable and explainable machine learning (ML) pipeline. Five independent cohorts of rs-fMRI with over than 5500 samples were used to assess sex classification performance and map the spatial distribution of the important brain regions. Methods: Five rs-fMRI samples were used to extract ALFF and fALFF features from predefined brain parcellations and then were fed into an unbiased and explainable ML pipeline with a wide range of methods. The pipeline comprehensively assessed unbiased performance for within-sample and across-sample validation. In addition, the parcellation effect, classifier selection, scanning length, spatial distribution, reproducibility, and feature importance were analyzed and evaluated thoroughly in the study. Results: The results demonstrated high sex classification accuracies from healthy adults (area under the curve >0.89), while degrading for nonhealthy subjects. Sex classification showed moderate to good intraclass correlation coefficient based on parcellation. Linear classifiers outperform nonlinear classifiers. Sex differences could be detected even with a short rs-fMRI scan (e.g., 2 min). The spatial distribution of important features overlaps with previous results from studies. Discussion: Sex differences are consistent in rs-fMRI and should be considered seriously in any study design, analysis, or interpretation. Features that discriminate males and females were found to be distributed across several different brain regions, suggesting a complex mosaic for sex differences in rs-fMRI. Impact statement The presented study unraveled that sex differences are embedded in the blood-oxygen-level dependent (BOLD) and can be predicted using unbiased and explainable machine learning pipeline. The study revealed that psychiatric disorders and demographics might influence the BOLD signal and interact with the classification of sex. The spatial distribution of the important features presented here supports the notion that the brain is a mosaic of male and female features. The findings emphasize the importance of controlling for sex when conducting brain imaging analysis. In addition, the presented framework can be adapted to classify other variables from resting-state BOLD signals.
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Affiliation(s)
- Obada Al Zoubi
- Laureate Institute for Brain Research, Tulsa, Oklahoma, USA
- Department of Psychiatry, Harvard Medical School/McLean Hospital, Boston, Massachusetts, USA
| | - Masaya Misaki
- Laureate Institute for Brain Research, Tulsa, Oklahoma, USA
| | | | - Vadim Zotev
- Laureate Institute for Brain Research, Tulsa, Oklahoma, USA
| | - Evan White
- Laureate Institute for Brain Research, Tulsa, Oklahoma, USA
| | - Martin Paulus
- Laureate Institute for Brain Research, Tulsa, Oklahoma, USA
| | - Jerzy Bodurka
- Laureate Institute for Brain Research, Tulsa, Oklahoma, USA
- Stephenson School of Biomedical Engineering, University of Oklahoma, Norman, Oklahoma, USA
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69
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Feng G, Wang Y, Huang W, Chen H, Dai Z, Ma G, Li X, Zhang Z, Shu N. Methodological evaluation of individual cognitive prediction based on the brain white matter structural connectome. Hum Brain Mapp 2022; 43:3775-3791. [PMID: 35475571 PMCID: PMC9294303 DOI: 10.1002/hbm.25883] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2021] [Revised: 03/22/2022] [Accepted: 04/05/2022] [Indexed: 11/18/2022] Open
Abstract
An emerging trend is to use regression‐based machine learning approaches to predict cognitive functions at the individual level from neuroimaging data. However, individual prediction models are inherently influenced by the vast options for network construction and model selection in machine learning pipelines. In particular, the brain white matter (WM) structural connectome lacks a systematic evaluation of the effects of different options in the pipeline on predictive performance. Here, we focused on the methodological evaluation of brain structural connectome‐based predictions. For network construction, we considered two parcellation schemes for defining nodes and seven strategies for defining edges. For the regression algorithms, we used eight regression models. Four cognitive domains and brain age were targeted as predictive tasks based on two independent datasets (Beijing Aging Brain Rejuvenation Initiative [BABRI]: 633 healthy older adults; Human Connectome Projects in Aging [HCP‐A]: 560 healthy older adults). Based on the results, the WM structural connectome provided a satisfying predictive ability for individual age and cognitive functions, especially for executive function and attention. Second, different parcellation schemes induce a significant difference in predictive performance. Third, prediction results from different data sets showed that dMRI with distinct acquisition parameters may plausibly result in a preference for proper fiber reconstruction algorithms and different weighting options. Finally, deep learning and Elastic‐Net models are more accurate and robust in connectome‐based predictions. Together, significant effects of different options in WM network construction and regression algorithms on the predictive performances are identified in this study, which may provide important references and guidelines to select suitable options for future studies in this field.
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Affiliation(s)
- Guozheng Feng
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China.,BABRI Centre, Beijing Normal University, Beijing, China.,Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
| | - Yiwen Wang
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China.,BABRI Centre, Beijing Normal University, Beijing, China.,Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
| | - Weijie Huang
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China.,BABRI Centre, Beijing Normal University, Beijing, China.,Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
| | - Haojie Chen
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China.,BABRI Centre, Beijing Normal University, Beijing, China.,Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
| | - Zhengjia Dai
- Department of Psychology, Sun Yat-sen University, Guangzhou, China
| | - Guolin Ma
- Department of Radiology, China-Japan Friendship Hospital, Beijing, China
| | - Xin Li
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China.,BABRI Centre, Beijing Normal University, Beijing, China
| | - Zhanjun Zhang
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China.,BABRI Centre, Beijing Normal University, Beijing, China
| | - Ni Shu
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China.,BABRI Centre, Beijing Normal University, Beijing, China.,Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
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70
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Chen J, Tam A, Kebets V, Orban C, Ooi LQR, Asplund CL, Marek S, Dosenbach NUF, Eickhoff SB, Bzdok D, Holmes AJ, Yeo BTT. Shared and unique brain network features predict cognitive, personality, and mental health scores in the ABCD study. Nat Commun 2022; 13:2217. [PMID: 35468875 PMCID: PMC9038754 DOI: 10.1038/s41467-022-29766-8] [Citation(s) in RCA: 56] [Impact Index Per Article: 28.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2020] [Accepted: 03/18/2022] [Indexed: 12/30/2022] Open
Abstract
How individual differences in brain network organization track behavioral variability is a fundamental question in systems neuroscience. Recent work suggests that resting-state and task-state functional connectivity can predict specific traits at the individual level. However, most studies focus on single behavioral traits, thus not capturing broader relationships across behaviors. In a large sample of 1858 typically developing children from the Adolescent Brain Cognitive Development (ABCD) study, we show that predictive network features are distinct across the domains of cognitive performance, personality scores and mental health assessments. On the other hand, traits within each behavioral domain are predicted by similar network features. Predictive network features and models generalize to other behavioral measures within the same behavioral domain. Although tasks are known to modulate the functional connectome, predictive network features are similar between resting and task states. Overall, our findings reveal shared brain network features that account for individual variation within broad domains of behavior in childhood.
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Affiliation(s)
- Jianzhong Chen
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore, Singapore.,Centre for Sleep and Cognition, National University of Singapore, Singapore, Singapore.,Centre for Translational MR Research, National University of Singapore, Singapore, Singapore.,N.1 Institute for Health & Institute for Digital Medicine (WisDM), National University of Singapore, Singapore, Singapore
| | - Angela Tam
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore, Singapore.,Centre for Sleep and Cognition, National University of Singapore, Singapore, Singapore.,Centre for Translational MR Research, National University of Singapore, Singapore, Singapore.,N.1 Institute for Health & Institute for Digital Medicine (WisDM), National University of Singapore, Singapore, Singapore
| | - Valeria Kebets
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore, Singapore.,Centre for Sleep and Cognition, National University of Singapore, Singapore, Singapore.,Centre for Translational MR Research, National University of Singapore, Singapore, Singapore.,N.1 Institute for Health & Institute for Digital Medicine (WisDM), National University of Singapore, Singapore, Singapore
| | - Csaba Orban
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore, Singapore.,Centre for Sleep and Cognition, National University of Singapore, Singapore, Singapore.,Centre for Translational MR Research, National University of Singapore, Singapore, Singapore.,N.1 Institute for Health & Institute for Digital Medicine (WisDM), National University of Singapore, Singapore, Singapore
| | - Leon Qi Rong Ooi
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore, Singapore.,Centre for Sleep and Cognition, National University of Singapore, Singapore, Singapore.,Centre for Translational MR Research, National University of Singapore, Singapore, Singapore.,N.1 Institute for Health & Institute for Digital Medicine (WisDM), National University of Singapore, Singapore, Singapore.,Integrative Sciences and Engineering Programme (ISEP), National University of Singapore, Singapore, Singapore
| | - Christopher L Asplund
- Centre for Sleep and Cognition, National University of Singapore, Singapore, Singapore.,Centre for Translational MR Research, National University of Singapore, Singapore, Singapore.,N.1 Institute for Health & Institute for Digital Medicine (WisDM), National University of Singapore, Singapore, Singapore.,Division of Social Sciences, Yale-NUS College, Singapore, Singapore.,Department of Psychology, National University of Singapore, Singapore, Singapore.,Duke-NUS Medical School, Singapore, Singapore
| | - Scott Marek
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA
| | - Nico U F Dosenbach
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA.,Department of Radiology, Washington University School of Medicine, St. Louis, MO, USA.,Department of Biomedical Engineering, Washington University School of Medicine, St. Louis, MO, USA.,Department of Pediatrics, Washington University School of Medicine, St. Louis, MO, USA
| | - Simon B Eickhoff
- Institute for Systems Neuroscience, Medical Faculty, Heinrich-Heine University Düsseldorf, Düsseldorf, Germany.,Institute of Neuroscience and Medicine, Brain & Behaviours (INM-7), Research Center Jülich, Jülich, Germany
| | - Danilo Bzdok
- Department of Biomedical Engineering, Montreal Neurological Institute, McGill University, Montreal, QC, Canada.,Mila - Quebec AI Institute, Montreal, QC, Canada
| | - Avram J Holmes
- Yale University, Departments of Psychology and Psychiatry, New Haven, CT, USA
| | - B T Thomas Yeo
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore, Singapore. .,Centre for Sleep and Cognition, National University of Singapore, Singapore, Singapore. .,Centre for Translational MR Research, National University of Singapore, Singapore, Singapore. .,N.1 Institute for Health & Institute for Digital Medicine (WisDM), National University of Singapore, Singapore, Singapore. .,Integrative Sciences and Engineering Programme (ISEP), National University of Singapore, Singapore, Singapore. .,Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA.
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71
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Li Y, Zhuang K, Yi Z, Wei D, Sun J, Qiu J. The trait and state negative affect can be separately predicted by stable and variable resting-state functional connectivity. Psychol Med 2022; 52:813-823. [PMID: 32654675 DOI: 10.1017/s0033291720002391] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
BACKGROUND Many emotional experiences such as anxiety and depression are influenced by negative affect (NA). NA has both trait and state features, which play different roles in physiological and mental health. Attending to NA common to various emotional experiences and their trait-state features might help deepen the understanding of the shared foundation of related emotional disorders. METHODS The principal component of five measures was calculated to indicate individuals' NA level. Applying the connectivity-based correlation analysis, we first identified resting-state functional connectives (FCs) relating to NA in sample 1 (n = 367), which were validated through an independent sample (n = 232; sample 2). Next, based on the variability of FCs across large timescale, we further divided the NA-related FCs into high- and low-variability groups. Finally, FCs in different variability groups were separately applied to predict individuals' neuroticism level (which is assumed to be the core trait-related factor underlying NA), and the change of NA level (which represents the state-related fluctuation of NA). RESULTS The low-variability FCs were primarily within the default mode network (DMN) and between the DMN and dorsal attention network/sensory system and significantly predicted trait rather than state NA. The high-variability FCs were primarily between the DMN and ventral attention network, the fronto-parietal network and DMN/sensory system, and significantly predicted the change of NA level. CONCLUSIONS The trait and state NA can be separately predicted by stable and variable spontaneous FCs with different attentional processes and emotion regulatory mechanisms, which could deepen our understanding of NA.
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Affiliation(s)
- Yu Li
- Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing, China
- Department of Psychology, Southwest University, Chongqing, China
| | - Kaixiang Zhuang
- Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing, China
- Department of Psychology, Southwest University, Chongqing, China
| | - Zili Yi
- Beibei Mental Health Center, Chongqing400715, China
| | - Dongtao Wei
- Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing, China
- Department of Psychology, Southwest University, Chongqing, China
| | - Jiangzhou Sun
- Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing, China
- Department of Psychology, Southwest University, Chongqing, China
| | - Jiang Qiu
- Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing, China
- Department of Psychology, Southwest University, Chongqing, China
- Southwest University Branch, Collaborative Innovation Center of Assessment Toward Basic Education Quality at Beijing Normal University
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72
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Li J, Bzdok D, Chen J, Tam A, Ooi LQR, Holmes AJ, Ge T, Patil KR, Jabbi M, Eickhoff SB, Yeo BTT, Genon S. Cross-ethnicity/race generalization failure of behavioral prediction from resting-state functional connectivity. SCIENCE ADVANCES 2022; 8:eabj1812. [PMID: 35294251 PMCID: PMC8926333 DOI: 10.1126/sciadv.abj1812] [Citation(s) in RCA: 34] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/14/2023]
Abstract
Algorithmic biases that favor majority populations pose a key challenge to the application of machine learning for precision medicine. Here, we assessed such bias in prediction models of behavioral phenotypes from brain functional magnetic resonance imaging. We examined the prediction bias using two independent datasets (preadolescent versus adult) of mixed ethnic/racial composition. When predictive models were trained on data dominated by white Americans (WA), out-of-sample prediction errors were generally higher for African Americans (AA) than for WA. This bias toward WA corresponds to more WA-like brain-behavior association patterns learned by the models. When models were trained on AA only, compared to training only on WA or an equal number of AA and WA participants, AA prediction accuracy improved but stayed below that for WA. Overall, the results point to the need for caution and further research regarding the application of current brain-behavior prediction models in minority populations.
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Affiliation(s)
- Jingwei Li
- Institute of Neuroscience and Medicine, Brain and
Behavior (INM-7), Research Center Jülich, Jülich, Germany
- Institute for Systems Neuroscience, Medical Faculty,
Heinrich-Heine University Düsseldorf, Düsseldorf, Germany
- Department of Electrical and Computer Engineering,
Centre for Sleep and Cognition, Centre for Translational Magnetic Resonance
Research, and N.1 Institute for Health and Institute for Digital Medicine,
National University of Singapore, Singapore, Singapore
- Corresponding author. (J.L.); (S.G.);
(B.T.T.Y.)
| | - Danilo Bzdok
- Department of Biomedical Engineering, Montreal
Neurological Institute (MNI), McConnell Brain Imaging Institute (BIC), McGill
University, Montreal, QC, Canada
- Mila-Quebec Artificial Intelligence Institute,
Montreal, QC, Canada
| | - Jianzhong Chen
- Department of Electrical and Computer Engineering,
Centre for Sleep and Cognition, Centre for Translational Magnetic Resonance
Research, and N.1 Institute for Health and Institute for Digital Medicine,
National University of Singapore, Singapore, Singapore
| | - Angela Tam
- Department of Electrical and Computer Engineering,
Centre for Sleep and Cognition, Centre for Translational Magnetic Resonance
Research, and N.1 Institute for Health and Institute for Digital Medicine,
National University of Singapore, Singapore, Singapore
| | - Leon Qi Rong Ooi
- Department of Electrical and Computer Engineering,
Centre for Sleep and Cognition, Centre for Translational Magnetic Resonance
Research, and N.1 Institute for Health and Institute for Digital Medicine,
National University of Singapore, Singapore, Singapore
| | - Avram J. Holmes
- Departments of Psychology and Psychiatry, Yale
University, New Haven, CT, USA
- Psychiatric and Neurodevelopmental Genetics Unit,
Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA,
USA
| | - Tian Ge
- Psychiatric and Neurodevelopmental Genetics Unit,
Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA,
USA
- Stanley Center for Psychiatric Research, Broad
Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Psychiatry, Massachusetts General
Hospital, Harvard Medical School, Boston, MA, USA
| | - Kaustubh R. Patil
- Institute of Neuroscience and Medicine, Brain and
Behavior (INM-7), Research Center Jülich, Jülich, Germany
- Institute for Systems Neuroscience, Medical Faculty,
Heinrich-Heine University Düsseldorf, Düsseldorf, Germany
| | - Mbemba Jabbi
- Department of Psychiatry, Dell Medical School,
University of Texas at Austin, Austin, TX, USA
- The Mulva Clinic for Neurosciences, Dell Medical
School, University of Texas at Austin, Austin, TX, USA
- Institute of Neuroscience, University of Texas at
Austin, Austin, TX, USA
- Department of Psychology, University of Texas at
Austin, Austin, TX, USA
| | - Simon B. Eickhoff
- Institute of Neuroscience and Medicine, Brain and
Behavior (INM-7), Research Center Jülich, Jülich, Germany
- Institute for Systems Neuroscience, Medical Faculty,
Heinrich-Heine University Düsseldorf, Düsseldorf, Germany
| | - B. T. Thomas Yeo
- Department of Electrical and Computer Engineering,
Centre for Sleep and Cognition, Centre for Translational Magnetic Resonance
Research, and N.1 Institute for Health and Institute for Digital Medicine,
National University of Singapore, Singapore, Singapore
- Integrative Sciences and Engineering Programme
(ISEP), National University of Singapore, Singapore, Singapore
- Corresponding author. (J.L.); (S.G.);
(B.T.T.Y.)
| | - Sarah Genon
- Institute of Neuroscience and Medicine, Brain and
Behavior (INM-7), Research Center Jülich, Jülich, Germany
- Institute for Systems Neuroscience, Medical Faculty,
Heinrich-Heine University Düsseldorf, Düsseldorf, Germany
- Corresponding author. (J.L.); (S.G.);
(B.T.T.Y.)
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73
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Chen M, Li H, Fan H, Dillman JR, Wang H, Altaye M, Zhang B, Parikh NA, He L. ConCeptCNN: A novel multi-filter convolutional neural network for the prediction of neurodevelopmental disorders using brain connectome. Med Phys 2022; 49:3171-3184. [PMID: 35246986 DOI: 10.1002/mp.15545] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2021] [Revised: 01/31/2022] [Accepted: 02/09/2022] [Indexed: 11/11/2022] Open
Abstract
BACKGROUND Deep convolutional neural network (CNN) and its derivatives have recently shown great promise in the prediction of brain disorders using brain connectome data. Existing deep CNN methods using single global row and column convolutional filters have limited ability to extract discriminative information from brain connectome for prediction tasks. PURPOSE This paper presents a novel deep Connectome-Inception CNN (ConCeptCNN) model, which is developed based on multiple convolutional filters. The proposed model is used to extract topological features from brain connectome data for neurological disorders classification and analysis. METHODS The ConCeptCNN uses multiple vector-shaped filters extract topological information from the brain connectome at different levels for complementary feature embeddings of brain connectome. The proposed model is validated using two datasets: the Neuro Bureau ADHD-200 dataset and the Cincinnati Early Prediction Study (CINEPS) dataset. RESULTS In a cross-validation experiment, the ConCeptCNN achieved a prediction accuracy of 78.7% for the detection of ADHD in adolescents and an accuracy of 81.6% for the prediction of cognitive deficits at 2-year corrected age in very preterm infants. In addition to the classification tasks, the ConCeptCNN identified several brain regions that are discriminative to neurodevelopmental disorders. CONCLUSIONS We compared the ConCeptCNN with several peer CNN methods. The results demonstrated that proposed model improves overall classification performance of neurodevelopmental disorders prediction tasks. This article is protected by copyright. All rights reserved.
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Affiliation(s)
- Ming Chen
- Imaging Research Center, Department of Radiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA.,Department of Electrical Engineering and Computer Science, University of Cincinnati, Cincinnati, OH, USA
| | - Hailong Li
- Imaging Research Center, Department of Radiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA.,Center for Prevention of Neurodevelopmental Disorders, Perinatal Institute, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
| | - Howard Fan
- Department of Electrical Engineering and Computer Science, University of Cincinnati, Cincinnati, OH, USA
| | - Jonathan R Dillman
- Imaging Research Center, Department of Radiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA.,Department of Radiology, University of Cincinnati College of Medicine, Cincinnati, OH, USA
| | - Hui Wang
- Imaging Research Center, Department of Radiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA.,MR Clinical Science, Philips, Cincinnati, OH, USA
| | - Mekibib Altaye
- Division of Biostatistics and Epidemiology, Department of Pediatrics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA.,Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, USA
| | - Bin Zhang
- Division of Biostatistics and Epidemiology, Department of Pediatrics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA.,Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, USA
| | - Nehal A Parikh
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, USA.,Center for Prevention of Neurodevelopmental Disorders, Perinatal Institute, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
| | - Lili He
- Imaging Research Center, Department of Radiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA.,Department of Radiology, University of Cincinnati College of Medicine, Cincinnati, OH, USA.,Center for Prevention of Neurodevelopmental Disorders, Perinatal Institute, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
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74
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Hahn S, Owens MM, Yuan D, Juliano AC, Potter A, Garavan H, Allgaier N. Performance scaling for structural MRI surface parcellations: a machine learning analysis in the ABCD Study. Cereb Cortex 2022; 33:176-194. [PMID: 35238352 PMCID: PMC9758581 DOI: 10.1093/cercor/bhac060] [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: 11/08/2021] [Revised: 01/21/2022] [Accepted: 01/22/2022] [Indexed: 11/13/2022] Open
Abstract
The use of predefined parcellations on surface-based representations of the brain as a method for data reduction is common across neuroimaging studies. In particular, prediction-based studies typically employ parcellation-driven summaries of brain measures as input to predictive algorithms, but the choice of parcellation and its influence on performance is often ignored. Here we employed preprocessed structural magnetic resonance imaging (sMRI) data from the Adolescent Brain Cognitive Development Study® to examine the relationship between 220 parcellations and out-of-sample predictive performance across 45 phenotypic measures in a large sample of 9- to 10-year-old children (N = 9,432). Choice of machine learning (ML) pipeline and use of alternative multiple parcellation-based strategies were also assessed. Relative parcellation performance was dependent on the spatial resolution of the parcellation, with larger number of parcels (up to ~4,000) outperforming coarser parcellations, according to a power-law scaling of between 1/4 and 1/3. Performance was further influenced by the type of parcellation, ML pipeline, and general strategy, with existing literature-based parcellations, a support vector-based pipeline, and ensembling across multiple parcellations, respectively, as the highest performing. These findings highlight the choice of parcellation as an important influence on downstream predictive performance, showing in some cases that switching to a higher resolution parcellation can yield a relatively large boost to performance.
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Affiliation(s)
- Sage Hahn
- Corresponding author: Departments of Complex Systems and Psychiatry, University of Vermont, Burlington, 100 South Prospect Street Burlington, Vermont 05401, United States.
| | - Max M Owens
- Departments of Complex Systems and Psychiatry, University of Vermont, Burlington, VT 05401, United States
| | - DeKang Yuan
- Departments of Complex Systems and Psychiatry, University of Vermont, Burlington, VT 05401, United States
| | - Anthony C Juliano
- Departments of Complex Systems and Psychiatry, University of Vermont, Burlington, VT 05401, United States
| | - Alexandra Potter
- Departments of Complex Systems and Psychiatry, University of Vermont, Burlington, VT 05401, United States
| | - Hugh Garavan
- Departments of Complex Systems and Psychiatry, University of Vermont, Burlington, VT 05401, United States
| | - Nicholas Allgaier
- Departments of Complex Systems and Psychiatry, University of Vermont, Burlington, VT 05401, United States
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75
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Gupta S, Lim M, Rajapakse JC. Decoding task specific and task general functional architectures of the brain. Hum Brain Mapp 2022; 43:2801-2816. [PMID: 35224817 PMCID: PMC9120557 DOI: 10.1002/hbm.25817] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2021] [Revised: 02/01/2022] [Accepted: 02/13/2022] [Indexed: 11/06/2022] Open
Abstract
Functional magnetic resonance imaging (fMRI) is used to capture complex and dynamic interactions between brain regions while performing tasks. Task related alterations in the brain have been classified as task specific and task general, depending on whether they are particular to a task or common across multiple tasks. Using recent attempts in interpreting deep learning models, we propose an approach to determine both task specific and task general architectures of the functional brain. We demonstrate our methods with a reference‐based decoder on deep learning classifiers trained on 12,500 rest and task fMRI samples from the Human Connectome Project (HCP). The decoded task general and task specific motor and language architectures were validated with findings from previous studies. We found that unlike intersubject variability that is characteristic of functional pathology of neurological diseases, a small set of connections are sufficient to delineate the rest and task states. The nodes and connections in the task general architecture could serve as potential disease biomarkers as alterations in task general brain modulations are known to be implicated in several neuropsychiatric disorders.
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Affiliation(s)
- Sukrit Gupta
- School of Computer Science and Engineering Nanyang Technological University Singapore
| | - Marcus Lim
- School of Computer Science and Engineering Nanyang Technological University Singapore
| | - Jagath C. Rajapakse
- School of Computer Science and Engineering Nanyang Technological University Singapore
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76
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Jiang Z, Wang Y, Shi C, Wu Y, Hu R, Chen S, Hu S, Wang X, Qiu B. Attention module improves both performance and interpretability of four-dimensional functional magnetic resonance imaging decoding neural network. Hum Brain Mapp 2022; 43:2683-2692. [PMID: 35212436 PMCID: PMC9057093 DOI: 10.1002/hbm.25813] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2021] [Revised: 01/29/2022] [Accepted: 02/09/2022] [Indexed: 11/15/2022] Open
Abstract
Decoding brain cognitive states from neuroimaging signals is an important topic in neuroscience. In recent years, deep neural networks (DNNs) have been recruited for multiple brain state decoding and achieved good performance. However, the open question of how to interpret the DNN black box remains unanswered. Capitalizing on advances in machine learning, we integrated attention modules into brain decoders to facilitate an in‐depth interpretation of DNN channels. A four‐dimensional (4D) convolution operation was also included to extract temporo‐spatial interaction within the fMRI signal. The experiments showed that the proposed model obtains a very high accuracy (97.4%) and outperforms previous researches on the seven different task benchmarks from the Human Connectome Project (HCP) dataset. The visualization analysis further illustrated the hierarchical emergence of task‐specific masks with depth. Finally, the model was retrained to regress individual traits within the HCP and to classify viewing images from the BOLD5000 dataset, respectively. Transfer learning also achieves good performance. Further visualization analysis shows that, after transfer learning, low‐level attention masks remained similar to the source domain, whereas high‐level attention masks changed adaptively. In conclusion, the proposed 4D model with attention module performed well and facilitated interpretation of DNNs, which is helpful for subsequent research.
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Affiliation(s)
- Zhoufan Jiang
- Center for Biomedical Imaging, University of Science and Technology of China, Hefei, Anhui, China
| | - Yanming Wang
- Center for Biomedical Imaging, University of Science and Technology of China, Hefei, Anhui, China
| | - ChenWei Shi
- Center for Biomedical Imaging, University of Science and Technology of China, Hefei, Anhui, China
| | - Yueyang Wu
- Center for Biomedical Imaging, University of Science and Technology of China, Hefei, Anhui, China
| | - Rongjie Hu
- Center for Biomedical Imaging, University of Science and Technology of China, Hefei, Anhui, China
| | - Shishuo Chen
- Center for Biomedical Imaging, University of Science and Technology of China, Hefei, Anhui, China
| | - Sheng Hu
- Center for Biomedical Imaging, University of Science and Technology of China, Hefei, Anhui, China
| | - Xiaoxiao Wang
- Center for Biomedical Imaging, University of Science and Technology of China, Hefei, Anhui, China.,Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei, Anhui, China
| | - Bensheng Qiu
- Center for Biomedical Imaging, University of Science and Technology of China, Hefei, Anhui, China.,Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei, Anhui, China
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77
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Dhamala E, Jamison KW, Jaywant A, Kuceyeski A. Shared functional connections within and between cortical networks predict cognitive abilities in adult males and females. Hum Brain Mapp 2022; 43:1087-1102. [PMID: 34811849 PMCID: PMC8764478 DOI: 10.1002/hbm.25709] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2021] [Revised: 10/14/2021] [Accepted: 10/21/2021] [Indexed: 01/25/2023] Open
Abstract
A thorough understanding of sex-independent and sex-specific neurobiological features that underlie cognitive abilities in healthy individuals is essential for the study of neurological illnesses in which males and females differentially experience and exhibit cognitive impairment. Here, we evaluate sex-independent and sex-specific relationships between functional connectivity and individual cognitive abilities in 392 healthy young adults (196 males) from the Human Connectome Project. First, we establish that sex-independent models comparably predict crystallised abilities in males and females, but only successfully predict fluid abilities in males. Second, we demonstrate sex-specific models comparably predict crystallised abilities within and between sexes, and generally fail to predict fluid abilities in either sex. Third, we reveal that largely overlapping connections between visual, dorsal attention, ventral attention, and temporal parietal networks are associated with better performance on crystallised and fluid cognitive tests in males and females, while connections within visual, somatomotor, and temporal parietal networks are associated with poorer performance. Together, our findings suggest that shared neurobiological features of the functional connectome underlie crystallised and fluid abilities across the sexes.
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Affiliation(s)
- Elvisha Dhamala
- Department of RadiologyWeill Cornell MedicineNew YorkNew YorkUSA
- Brain and Mind Research Institute, Weill Cornell MedicineNew YorkNew YorkUSA
- Present address:
Department of PsychologyYale UniversityNew HavenConnecticutUSA
| | - Keith W. Jamison
- Department of RadiologyWeill Cornell MedicineNew YorkNew YorkUSA
| | - Abhishek Jaywant
- Department of Psychiatry, Weill Cornell MedicineNew YorkNew YorkUSA
- Department of Rehabilitation Medicine, Weill Cornell MedicineNew YorkNew YorkUSA
- NewYork‐Presbyterian Hospital/Weill Cornell Medical CenterNew YorkNew YorkUSA
| | - Amy Kuceyeski
- Department of RadiologyWeill Cornell MedicineNew YorkNew YorkUSA
- Brain and Mind Research Institute, Weill Cornell MedicineNew YorkNew YorkUSA
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78
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Spatio-Temporal Directed Acyclic Graph Learning with Attention Mechanisms on Brain Functional Time Series and Connectivity. Med Image Anal 2022; 77:102370. [DOI: 10.1016/j.media.2022.102370] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2021] [Revised: 01/08/2022] [Accepted: 01/11/2022] [Indexed: 11/22/2022]
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79
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80
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Ngo GH, Khosla M, Jamison K, Kuceyeski A, Sabuncu MR. Predicting Individual Task Contrasts From Resting-state Functional Connectivity using a Surface-based Convolutional Network. Neuroimage 2021; 248:118849. [PMID: 34965456 PMCID: PMC10155599 DOI: 10.1016/j.neuroimage.2021.118849] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2021] [Revised: 11/20/2021] [Accepted: 12/21/2021] [Indexed: 12/23/2022] Open
Abstract
Task-based and resting-state represent the two most common experimental paradigms of functional neuroimaging. While resting-state offers a flexible and scalable approach for characterizing brain function, task-based techniques provide superior localization. In this paper, we build on recent deep learning methods to create a model that predicts task-based contrast maps from resting-state fMRI scans. Specifically, we propose BrainSurfCNN, a surface-based fully-convolutional neural network model that works with a representation of the brain's cortical sheet. BrainSurfCNN achieves exceptional predictive accuracy on independent test data from the Human Connectome Project, which is on par with the repeat reliability of the measured subject-level contrast maps. Conversely, our analyses reveal that a previously published benchmark is no better than group-average contrast maps. Finally, we demonstrate that BrainSurfCNN can generalize remarkably well to novel domains with limited training data.
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Affiliation(s)
- Gia H Ngo
- School of Electrical & Computer Engineering, Cornell University and Cornell Tech, United States
| | - Meenakshi Khosla
- School of Electrical & Computer Engineering, Cornell University and Cornell Tech, United States
| | | | | | - Mert R Sabuncu
- School of Electrical & Computer Engineering, Cornell University and Cornell Tech, United States; Radiology, Weill Cornell Medicine, United States.
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81
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Hebling Vieira B, Dubois J, Calhoun VD, Garrido Salmon CE. A deep learning based approach identifies regions more relevant than resting-state networks to the prediction of general intelligence from resting-state fMRI. Hum Brain Mapp 2021; 42:5873-5887. [PMID: 34587333 PMCID: PMC8596958 DOI: 10.1002/hbm.25656] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2021] [Revised: 08/26/2021] [Accepted: 08/27/2021] [Indexed: 11/30/2022] Open
Abstract
Prediction of cognitive ability latent factors such as general intelligence from neuroimaging has elucidated questions pertaining to their neural origins. However, predicting general intelligence from functional connectivity limit hypotheses to that specific domain, being agnostic to time‐distributed features and dynamics. We used an ensemble of recurrent neural networks to circumvent this limitation, bypassing feature extraction, to predict general intelligence from resting‐state functional magnetic resonance imaging regional signals of a large sample (n = 873) of Human Connectome Project adult subjects. Ablating common resting‐state networks (RSNs) and measuring degradation in performance, we show that model reliance can be mostly explained by network size. Using our approach based on the temporal variance of saliencies, that is, gradients of outputs with regards to inputs, we identify a candidate set of networks that more reliably affect performance in the prediction of general intelligence than similarly sized RSNs. Our approach allows us to further test the effect of local alterations on data and the expected changes in derived metrics such as functional connectivity and instantaneous innovations.
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Affiliation(s)
- Bruno Hebling Vieira
- InBrain Lab, Departamento de Física, Universidade de São Paulo, Ribeirão Preto, Brazil.,Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, Georgia, USA
| | - Julien Dubois
- Cedars-Sinai Medical Center, Los Angeles, California, USA.,Caltech, Pasadena, California, USA
| | - Vince D Calhoun
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, Georgia, USA.,The Mind Research Network, Albuquerque, New Mexico, USA.,School of Electrical & Computer Engineering, Georgia Institute of Technology, Atlanta, Georgia, USA
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82
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Ikeda S, Kawano K, Watanabe S, Yamashita O, Kawahara Y. Predicting behavior through dynamic modes in resting-state fMRI data. Neuroimage 2021; 247:118801. [PMID: 34896588 DOI: 10.1016/j.neuroimage.2021.118801] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2021] [Revised: 12/03/2021] [Accepted: 12/09/2021] [Indexed: 11/20/2022] Open
Abstract
Dynamic properties of resting-state functional connectivity (FC) provide rich information on brain-behavior relationships. Dynamic mode decomposition (DMD) has been used as a method to characterize FC dynamics. However, it remains unclear whether dynamic modes (DMs), spatial-temporal coherent patterns computed by DMD, provide information about individual behavioral differences. This study established a methodological approach to predict individual differences in behavior using DMs. Furthermore, we investigated the contribution of DMs within each of seven specific frequency bands (0-0.1,...,0.6-0.7 Hz) for prediction. To validate our approach, we tested whether each of 59 behavioral measures could be predicted by performing multivariate pattern analysis on a Gram matrix, which was created using subject-specific DMs computed from resting-state functional magnetic resonance imaging (rs-fMRI) data of individuals. DMD successfully predicted behavior and outperformed temporal and spatial independent component analysis, which is the conventional data decomposition method for extracting spatial activity patterns. Most of the behavioral measures that were predicted with significant accuracy in a permutation test were related to cognition. We found that DMs within frequency bands <0.2 Hz primarily contributed to prediction and had spatial structures similar to several common resting-state networks. Our results indicate that DMD is efficient in extracting spatiotemporal features from rs-fMRI data.
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Affiliation(s)
- Shigeyuki Ikeda
- RIKEN Center for Advanced Intelligence Project, Tokyo 103-0027, Japan; ATR Neural Information Analysis Laboratories, Kyoto 619-0288, Japan.
| | - Koki Kawano
- RIKEN Center for Advanced Intelligence Project, Tokyo 103-0027, Japan
| | - Soichi Watanabe
- RIKEN Center for Advanced Intelligence Project, Tokyo 103-0027, Japan
| | - Okito Yamashita
- RIKEN Center for Advanced Intelligence Project, Tokyo 103-0027, Japan; ATR Neural Information Analysis Laboratories, Kyoto 619-0288, Japan
| | - Yoshinobu Kawahara
- RIKEN Center for Advanced Intelligence Project, Tokyo 103-0027, Japan; Institute of Mathematics for Industry, Kyushu University, Fukuoka 819-0395, Japan
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83
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Predicting cognitive scores with graph neural networks through sample selection learning. Brain Imaging Behav 2021; 16:1123-1138. [PMID: 34757563 PMCID: PMC9107424 DOI: 10.1007/s11682-021-00585-7] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/06/2021] [Indexed: 10/29/2022]
Abstract
Analyzing the relation between intelligence and neural activity is of the utmost importance in understanding the working principles of the human brain in health and disease. In existing literature, functional brain connectomes have been used successfully to predict cognitive measures such as intelligence quotient (IQ) scores in both healthy and disordered cohorts using machine learning models. However, existing methods resort to flattening the brain connectome (i.e., graph) through vectorization which overlooks its topological properties. To address this limitation and inspired from the emerging graph neural networks (GNNs), we design a novel regression GNN model (namely RegGNN) for predicting IQ scores from brain connectivity. On top of that, we introduce a novel, fully modular sample selection method to select the best samples to learn from for our target prediction task. However, since such deep learning architectures are computationally expensive to train, we further propose a learning-based sample selection method that learns how to choose the training samples with the highest expected predictive power on unseen samples. For this, we capitalize on the fact that connectomes (i.e., their adjacency matrices) lie in the symmetric positive definite (SPD) matrix cone. Our results on full-scale and verbal IQ prediction outperforms comparison methods in autism spectrum disorder cohorts and achieves a competitive performance for neurotypical subjects using 3-fold cross-validation. Furthermore, we show that our sample selection approach generalizes to other learning-based methods, which shows its usefulness beyond our GNN architecture.
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84
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Deep Neural Networks to Detect Weeds from Crops in Agricultural Environments in Real-Time: A Review. REMOTE SENSING 2021. [DOI: 10.3390/rs13214486] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
Automation, including machine learning technologies, are becoming increasingly crucial in agriculture to increase productivity. Machine vision is one of the most popular parts of machine learning and has been widely used where advanced automation and control have been required. The trend has shifted from classical image processing and machine learning techniques to modern artificial intelligence (AI) and deep learning (DL) methods. Based on large training datasets and pre-trained models, DL-based methods have proven to be more accurate than previous traditional techniques. Machine vision has wide applications in agriculture, including the detection of weeds and pests in crops. Variation in lighting conditions, failures to transfer learning, and object occlusion constitute key challenges in this domain. Recently, DL has gained much attention due to its advantages in object detection, classification, and feature extraction. DL algorithms can automatically extract information from large amounts of data used to model complex problems and is, therefore, suitable for detecting and classifying weeds and crops. We present a systematic review of AI-based systems to detect weeds, emphasizing recent trends in DL. Various DL methods are discussed to clarify their overall potential, usefulness, and performance. This study indicates that several limitations obstruct the widespread adoption of AI/DL in commercial applications. Recommendations for overcoming these challenges are summarized.
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85
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Abrol A, Hassanzadeh R, Plis S, Calhoun V. Deep learning in resting-state fMRI . ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2021; 2021:3965-3969. [PMID: 34892099 DOI: 10.1109/embc46164.2021.9630257] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Modeling the rich, dynamic spatiotemporal variations captured by human brain functional magnetic resonance imaging (fMRI) data is a complicated task. Analysis at the brain's regional and connection levels provides more straightforward biological interpretation for fMRI data and has been instrumental in characterizing the brain thus far. Here we hypothesize that spatiotemporal learning directly in the four-dimensional (4D) fMRI voxel-time space could result in enhanced discriminative brain representations compared to widely used, pre-engineered fMRI temporal transformations, and brain regional and connection-level fMRI features. Motivated by this, we extend our recently reported structural MRI (sMRI) deep learning (DL) pipeline to additionally capture temporal variations, training the proposed 4D DL model end-to-end on preprocessed fMRI data. Results validate that the complex non-linear functions of the used deep spatiotemporal approach generate discriminative encodings for the studied learning task, outperforming both standard machine learning (SML) and DL methods on the widely used fMRI voxel/region/connection features, except the relatively simplistic measure of central tendency - the temporal mean of the fMRI data. Additionally, we identify the fMRI features for which DL significantly outperformed SML methods for voxel-level fMRI features. Overall, our results support the efficiency and potential of DL models trainable at the voxel level fMRI data and highlight the importance of developing auxiliary tools to facilitate interpretation of such flexible models.
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86
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Kim JH, Zhang Y, Han K, Wen Z, Choi M, Liu Z. Representation learning of resting state fMRI with variational autoencoder. Neuroimage 2021; 241:118423. [PMID: 34303794 PMCID: PMC8485214 DOI: 10.1016/j.neuroimage.2021.118423] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2020] [Revised: 07/18/2021] [Accepted: 07/21/2021] [Indexed: 11/24/2022] Open
Abstract
Resting state functional magnetic resonance imaging (rsfMRI) data exhibits complex but structured patterns. However, the underlying origins are unclear and entangled in rsfMRI data. Here we establish a variational auto-encoder, as a generative model trainable with unsupervised learning, to disentangle the unknown sources of rsfMRI activity. After being trained with large data from the Human Connectome Project, the model has learned to represent and generate patterns of cortical activity and connectivity using latent variables. The latent representation and its trajectory represent the spatiotemporal characteristics of rsfMRI activity. The latent variables reflect the principal gradients of the latent trajectory and drive activity changes in cortical networks. Representational geometry captured as covariance or correlation between latent variables, rather than cortical connectivity, can be used as a more reliable feature to accurately identify subjects from a large group, even if only a short period of data is available in each subject. Our results demonstrate that VAE is a valuable addition to existing tools, particularly suited for unsupervised representation learning of resting state fMRI activity.
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Affiliation(s)
- Jung-Hoon Kim
- Department of Biomedical Engineering, University of Michigan, United States; Weldon School of Biomedical Engineering, Purdue University, United States
| | - Yizhen Zhang
- Department of Electrical Engineering and Computer Science, University of Michigan, United States
| | - Kuan Han
- Department of Electrical Engineering and Computer Science, University of Michigan, United States
| | - Zheyu Wen
- Department of Electrical Engineering and Computer Science, University of Michigan, United States
| | - Minkyu Choi
- Department of Electrical Engineering and Computer Science, University of Michigan, United States
| | - Zhongming Liu
- Department of Biomedical Engineering, University of Michigan, United States; Department of Electrical Engineering and Computer Science, University of Michigan, United States.
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87
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Starke G, De Clercq E, Borgwardt S, Elger BS. Computing schizophrenia: ethical challenges for machine learning in psychiatry. Psychol Med 2021; 51:2515-2521. [PMID: 32536358 DOI: 10.1017/s0033291720001683] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Recent advances in machine learning (ML) promise far-reaching improvements across medical care, not least within psychiatry. While to date no psychiatric application of ML constitutes standard clinical practice, it seems crucial to get ahead of these developments and address their ethical challenges early on. Following a short general introduction concerning ML in psychiatry, we do so by focusing on schizophrenia as a paradigmatic case. Based on recent research employing ML to further the diagnosis, treatment, and prediction of schizophrenia, we discuss three hypothetical case studies of ML applications with view to their ethical dimensions. Throughout this discussion, we follow the principlist framework by Tom Beauchamp and James Childress to analyse potential problems in detail. In particular, we structure our analysis around their principles of beneficence, non-maleficence, respect for autonomy, and justice. We conclude with a call for cautious optimism concerning the implementation of ML in psychiatry if close attention is paid to the particular intricacies of psychiatric disorders and its success evaluated based on tangible clinical benefit for patients.
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Affiliation(s)
- Georg Starke
- Institute for Biomedical Ethics, University of Basel, Basel, Switzerland
| | - Eva De Clercq
- Institute for Biomedical Ethics, University of Basel, Basel, Switzerland
| | - Stefan Borgwardt
- Department of Psychiatry, University of Basel, Basel, Switzerland
- Department of Psychiatry and Psychotherapy, University of Lübeck, Lübeck, Germany
| | - Bernice Simone Elger
- Institute for Biomedical Ethics, University of Basel, Basel, Switzerland
- University Center of Legal Medicine, University of Geneva, Geneva, Switzerland
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88
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Tian Y, Zalesky A. Machine learning prediction of cognition from functional connectivity: Are feature weights reliable? Neuroimage 2021; 245:118648. [PMID: 34673248 DOI: 10.1016/j.neuroimage.2021.118648] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2021] [Revised: 10/03/2021] [Accepted: 10/10/2021] [Indexed: 10/20/2022] Open
Abstract
Cognitive performance can be predicted from an individual's functional brain connectivity with modest accuracy using machine learning approaches. As yet, however, predictive models have arguably yielded limited insight into the neurobiological processes supporting cognition. To do so, feature selection and feature weight estimation need to be reliable to ensure that important connections and circuits with high predictive utility can be reliably identified. We comprehensively investigate feature weight test-retest reliability for various predictive models of cognitive performance built from resting-state functional connectivity networks in healthy young adults (n=400). Despite achieving modest prediction accuracies (r=0.2-0.4), we find that feature weight reliability is generally poor for all predictive models (ICC< 0.3), and significantly poorer than predictive models for overt biological attributes such as sex (ICC≈0.5). Larger sample sizes (n=800), the Haufe transformation, non-sparse feature selection/regularization and smaller feature spaces marginally improve reliability (ICC< 0.4). We elucidate a tradeoff between feature weight reliability and prediction accuracy and find that univariate statistics are marginally more reliable than feature weights from predictive models. Finally, we show that measuring agreement in feature weights between cross-validation folds provides inflated estimates of feature weight reliability. We thus recommend for reliability to be estimated out-of-sample, if possible. We argue that rebalancing focus from prediction accuracy to model reliability may facilitate mechanistic understanding of cognition with machine learning approaches.
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Affiliation(s)
- Ye Tian
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne, Australia.
| | - Andrew Zalesky
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne, Australia; Department of Biomedical Engineering, Faculty of Engineering and Information Technology, The University of Melbourne, Australia.
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89
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Eickhoff CR, Hoffstaedter F, Caspers J, Reetz K, Mathys C, Dogan I, Amunts K, Schnitzler A, Eickhoff SB. Advanced brain ageing in Parkinson's disease is related to disease duration and individual impairment. Brain Commun 2021; 3:fcab191. [PMID: 34541531 PMCID: PMC8445399 DOI: 10.1093/braincomms/fcab191] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2021] [Revised: 06/22/2021] [Accepted: 06/30/2021] [Indexed: 11/13/2022] Open
Abstract
Machine learning can reliably predict individual age from MRI data, revealing that patients with neurodegenerative disorders show an elevated biological age. A surprising gap in the literature, however, pertains to Parkinson's disease. Here, we evaluate brain age in two cohorts of Parkinson's patients and investigated the relationship between individual brain age and clinical characteristics. We assessed 372 patients with idiopathic Parkinson's disease, newly diagnosed cases from the Parkinson's Progression Marker Initiative database and a more chronic local sample, as well as age- and sex-matched healthy controls. Following morphometric preprocessing and atlas-based compression, individual brain age was predicted using a multivariate machine learning model trained on an independent, multi-site reference sample. Across cohorts, healthy controls were well predicted with a mean error of 4.4 years. In turn, Parkinson's patients showed a significant (controlling for age, gender and site) increase in brain age of ∼3 years. While this effect was already present in the newly diagnosed sample, advanced biological age was significantly related to disease duration as well as worse cognitive and motor impairment. While biological age is increased in patients with Parkinson's disease, the effect is at the lower end of what is found for other neurological and psychiatric disorders. We argue that this may reflect a heterochronicity between forebrain atrophy and small but behaviourally salient midbrain pathology. Finally, we point to the need to disentangle physiological ageing trajectories, lifestyle effects and core pathological changes.
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Affiliation(s)
- Claudia R Eickhoff
- Institute of Neuroscience and Medicine (INM-1, INM-7, INM-11), Jülich, Germany.,Institute of Clinical Neuroscience and Medical Psychology, Medical Faculty, Heinrich-Heine University Düsseldorf, Düsseldorf, Germany
| | - Felix Hoffstaedter
- Institute of Neuroscience and Medicine (INM-1, INM-7, INM-11), Jülich, Germany.,Institute of Systems Neuroscience, Medical Faculty, Heinrich-Heine University Düsseldorf, Düsseldorf, Germany
| | - Julian Caspers
- Institute of Diagnostic and Interventional Radiology, Medical Faculty, Heinrich-Heine University Düsseldorf, Düsseldorf, Germany
| | - Kathrin Reetz
- Institute of Neuroscience and Medicine (INM-1, INM-7, INM-11), Jülich, Germany.,Department of Neurology, University Hospital RWTH Aachen, Aachen, Germany
| | | | - Imis Dogan
- Institute of Neuroscience and Medicine (INM-1, INM-7, INM-11), Jülich, Germany.,Department of Neurology, University Hospital RWTH Aachen, Aachen, Germany
| | - Katrin Amunts
- Institute of Neuroscience and Medicine (INM-1, INM-7, INM-11), Jülich, Germany.,Institute of Clinical Neuroscience and Medical Psychology, Medical Faculty, Heinrich-Heine University Düsseldorf, Düsseldorf, Germany
| | - Alfons Schnitzler
- Institute of Clinical Neuroscience and Medical Psychology, Medical Faculty, Heinrich-Heine University Düsseldorf, Düsseldorf, Germany
| | - Simon B Eickhoff
- Institute of Neuroscience and Medicine (INM-1, INM-7, INM-11), Jülich, Germany.,Institute of Systems Neuroscience, Medical Faculty, Heinrich-Heine University Düsseldorf, Düsseldorf, Germany
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90
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Saha S, Pagnozzi A, Bradford D, Fripp J. Predicting fluid intelligence in adolescence from structural MRI with deep learning methods. INTELLIGENCE 2021. [DOI: 10.1016/j.intell.2021.101568] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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91
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Wu D, Li X, Feng J. Connectome-based individual prediction of cognitive behaviors via graph propagation network reveals directed brain network topology. J Neural Eng 2021; 18. [PMID: 34181582 DOI: 10.1088/1741-2552/ac0f4d] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2021] [Accepted: 06/28/2021] [Indexed: 11/11/2022]
Abstract
Objective. Brain connectivity network supports the information flow underlying human cognitions and should reflect the individual variability in human cognitive behaviors. Various studies have utilized brain connectivity to predict individual differences in human behaviors. However, traditional studies viewed brain connectivity network as a one-dimensional vector, a method which neglects topological properties of brain connectivity network.Approach. To utilize these topological properties, we proposed that graph neural network (GNN) which combines graph theory and neural network can be adopted. Different from previous node-driven GNNs that parameterize on the node feature transformation, we designed an edge-driven GNN named graph propagation network (GPN) that parameterizes on the information propagation within brain connectivity network.Main results.Edge-driven GPN outperforms various baseline models such as node-driven GNN and traditional partial least square regression in predicting the individual total cognition based on the resting-state functional connectome. GPN also reveals a directed network topology encoding the information flow, indicating that higher-order association cortices such as dorsolateral prefrontal, inferior frontal and inferior parietal cortices are responsible for the information integration underlying total cognition.Significance. These results suggest that edge-driven GPN can better explore topological structures of brain connectivity network and can serve as a new method to associate brain connectome and human behaviors.
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Affiliation(s)
- Dongya Wu
- School of Information Science and Technology, Northwest University, Xi'an 710127, People's Republic of China
| | - Xin Li
- School of Mathematics, Northwest University, Xi'an 710127, People's Republic of China
| | - Jun Feng
- School of Information Science and Technology, Northwest University, Xi'an 710127, People's Republic of China.,State-Province Joint Engineering and Research Center of Advanced Networking and Intelligent Information Services, School of Information Science and Technology, Northwest University, Xi'an 710127, People's Republic of China
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92
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Dhamala E, Jamison KW, Jaywant A, Dennis S, Kuceyeski A. Distinct functional and structural connections predict crystallised and fluid cognition in healthy adults. Hum Brain Mapp 2021; 42:3102-3118. [PMID: 33830577 PMCID: PMC8193532 DOI: 10.1002/hbm.25420] [Citation(s) in RCA: 38] [Impact Index Per Article: 12.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2020] [Revised: 03/03/2021] [Accepted: 03/10/2021] [Indexed: 12/24/2022] Open
Abstract
White matter pathways between neurons facilitate neuronal coactivation patterns in the brain. Insight into how these structural and functional connections underlie complex cognitive functions provides an important foundation with which to delineate disease‐related changes in cognitive functioning. Here, we integrate neuroimaging, connectomics, and machine learning approaches to explore how functional and structural brain connectivity relate to cognition. Specifically, we evaluate the extent to which functional and structural connectivity predict individual crystallised and fluid cognitive abilities in 415 unrelated healthy young adults (202 females) from the Human Connectome Project. We report three main findings. First, we demonstrate functional connectivity is more predictive of cognitive scores than structural connectivity, and, furthermore, integrating the two modalities does not increase explained variance. Second, we show the quality of cognitive prediction from connectome measures is influenced by the choice of grey matter parcellation, and, possibly, how that parcellation is derived. Third, we find that distinct functional and structural connections predict crystallised and fluid abilities. Taken together, our results suggest that functional and structural connectivity have unique relationships with crystallised and fluid cognition and, furthermore, studying both modalities provides a more comprehensive insight into the neural correlates of cognition.
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Affiliation(s)
- Elvisha Dhamala
- Department of Radiology, Weill Cornell Medicine, New York, New York, USA.,Brain and Mind Research Institute, Weill Cornell Medicine, New York, New York, USA
| | - Keith W Jamison
- Department of Radiology, Weill Cornell Medicine, New York, New York, USA
| | - Abhishek Jaywant
- Department of Psychiatry, Weill Cornell Medicine, New York, New York, USA.,Department of Rehabilitation Medicine, Weill Cornell Medicine, New York, New York, USA.,NewYork-Presbyterian Hospital/Weill Cornell Medical Center, New York, New York, USA
| | - Sarah Dennis
- Sarah Lawrence College, Bronxville, New York, USA
| | - Amy Kuceyeski
- Department of Radiology, Weill Cornell Medicine, New York, New York, USA.,Brain and Mind Research Institute, Weill Cornell Medicine, New York, New York, USA
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93
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Ball G, Kelly CE, Beare R, Seal ML. Individual variation underlying brain age estimates in typical development. Neuroimage 2021; 235:118036. [PMID: 33838267 DOI: 10.1016/j.neuroimage.2021.118036] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2020] [Revised: 03/19/2021] [Accepted: 03/26/2021] [Indexed: 12/14/2022] Open
Abstract
Typical brain development follows a protracted trajectory throughout childhood and adolescence. Deviations from typical growth trajectories have been implicated in neurodevelopmental and psychiatric disorders. Recently, the use of machine learning algorithms to model age as a function of structural or functional brain properties has been used to examine advanced or delayed brain maturation in healthy and clinical populations. Termed 'brain age', this approach often relies on complex, nonlinear models that can be difficult to interpret. In this study, we use model explanation methods to examine the cortical features that contribute to brain age modelling on an individual basis. In a large cohort of n = 768 typically-developing children (aged 3-21 years), we build models of brain development using three different machine learning approaches. We employ SHAP, a model-agnostic technique to identify sample-specific feature importance, to identify regional cortical metrics that explain errors in brain age prediction. We find that, on average, brain age prediction and the cortical features that explain model predictions are consistent across model types and reflect previously reported patterns of regions brain development. However, while several regions are found to contribute to brain age prediction error, we find little spatial correspondence between individual estimates of feature importance, even when matched for age, sex and brain age prediction error. We also find no association between brain age error and cognitive performance in this typically-developing sample. Overall, this study shows that, while brain age estimates based on cortical development are relatively robust and consistent across model types and preprocessing strategies, significant between-subject variation exists in the features that explain erroneous brain age predictions on an individual level.
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Affiliation(s)
- Gareth Ball
- Developmental Imaging, Murdoch Children's Research Institute, The Royal Children's Hospital, Melbourne, 3052 VIC, Australia; Department of Paediatrics, University of Melbourne, Australia.
| | - Claire E Kelly
- Developmental Imaging, Murdoch Children's Research Institute, The Royal Children's Hospital, Melbourne, 3052 VIC, Australia; Victorian Infant Brain Studies (VIBeS), Murdoch Children's Research Institute, Australia
| | - Richard Beare
- Developmental Imaging, Murdoch Children's Research Institute, The Royal Children's Hospital, Melbourne, 3052 VIC, Australia
| | - Marc L Seal
- Developmental Imaging, Murdoch Children's Research Institute, The Royal Children's Hospital, Melbourne, 3052 VIC, Australia; Department of Paediatrics, University of Melbourne, Australia
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94
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Vidaurre D, Llera A, Smith SM, Woolrich MW. Behavioural relevance of spontaneous, transient brain network interactions in fMRI. Neuroimage 2021; 229:117713. [PMID: 33421594 PMCID: PMC7994296 DOI: 10.1016/j.neuroimage.2020.117713] [Citation(s) in RCA: 33] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2020] [Accepted: 12/26/2020] [Indexed: 12/19/2022] Open
Abstract
How spontaneously fluctuating functional magnetic resonance imaging (fMRI) signals in different brain regions relate to behaviour has been an open question for decades. Correlations in these signals, known as functional connectivity, can be averaged over several minutes of data to provide a stable representation of the functional network architecture for an individual. However, associations between these stable features and behavioural traits have been shown to be dominated by individual differences in anatomy. Here, using kernel learning tools, we propose methods to assess and compare the relation between time-varying functional connectivity, time-averaged functional connectivity, structural brain data, and non-imaging subject behavioural traits. We applied these methods to Human Connectome Project resting-state fMRI data to show that time-varying fMRI functional connectivity, detected at time-scales of a few seconds, has associations with some behavioural traits that are not dominated by anatomy. Despite time-averaged functional connectivity accounting for the largest proportion of variability in the fMRI signal between individuals, we found that some aspects of intelligence could only be explained by time-varying functional connectivity. The finding that time-varying fMRI functional connectivity has a unique relationship to population behavioural variability suggests that it might reflect transient neuronal communication fluctuating around a stable neural architecture.
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Affiliation(s)
- D Vidaurre
- Center for Functionally Integrative Neuroscience, Department of Clinical Health, Aarhus University, 8000 Denmark; Department of Psychiatry, University of Oxford, OX37JX UK; Wellcome Trust Center for Integrative Neuroimaging, University of Oxford, OX37JX UK,.
| | - A Llera
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, 6525 Netherlands
| | - S M Smith
- Wellcome Trust Center for Integrative Neuroimaging, University of Oxford, OX37JX UK
| | - M W Woolrich
- Department of Psychiatry, University of Oxford, OX37JX UK; Wellcome Trust Center for Integrative Neuroimaging, University of Oxford, OX37JX UK
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95
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Caicedo-Acosta J, Castaño GA, Acosta-Medina C, Alvarez-Meza A, Castellanos-Dominguez G. Deep Neural Regression Prediction of Motor Imagery Skills Using EEG Functional Connectivity Indicators. SENSORS (BASEL, SWITZERLAND) 2021; 21:1932. [PMID: 33801817 PMCID: PMC7999933 DOI: 10.3390/s21061932] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/05/2021] [Revised: 02/20/2021] [Accepted: 02/25/2021] [Indexed: 11/16/2022]
Abstract
Motor imaging (MI) induces recovery and neuroplasticity in neurophysical regulation. However, a non-negligible portion of users presents insufficient coordination skills of sensorimotor cortex control. Assessments of the relationship between wakefulness and tasks states are conducted to foster neurophysiological and mechanistic interpretation in MI-related applications. Thus, to understand the organization of information processing, measures of functional connectivity are used. Also, models of neural network regression prediction are becoming popular, These intend to reduce the need for extracting features manually. However, predicting MI practicing's neurophysiological inefficiency raises several problems, like enhancing network regression performance because of the overfitting risk. Here, to increase the prediction performance, we develop a deep network regression model that includes three procedures: leave-one-out cross-validation combined with Monte Carlo dropout layers, subject clustering of MI inefficiency, and transfer learning between neighboring runs. Validation is performed using functional connectivity predictors extracted from two electroencephalographic databases acquired in conditions close to real MI applications (150 users), resulting in a high prediction of pretraining desynchronization and initial training synchronization with adequate physiological interpretability.
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Affiliation(s)
- Julian Caicedo-Acosta
- Signal Processing and Recognition Group, Universidad Nacional de Colombia, Manizales 170001, Colombia; (C.A.-M.); (A.A.-M.); (G.C.-D.)
| | - German A. Castaño
- Grupo de investigación Cultura de la Calidad en la Educación, Universidad Nacional de Colombia, Manizales 170001, Colombia;
| | - Carlos Acosta-Medina
- Signal Processing and Recognition Group, Universidad Nacional de Colombia, Manizales 170001, Colombia; (C.A.-M.); (A.A.-M.); (G.C.-D.)
| | - Andres Alvarez-Meza
- Signal Processing and Recognition Group, Universidad Nacional de Colombia, Manizales 170001, Colombia; (C.A.-M.); (A.A.-M.); (G.C.-D.)
| | - German Castellanos-Dominguez
- Signal Processing and Recognition Group, Universidad Nacional de Colombia, Manizales 170001, Colombia; (C.A.-M.); (A.A.-M.); (G.C.-D.)
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96
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Anderson KM, Ge T, Kong R, Patrick LM, Spreng RN, Sabuncu MR, Yeo BTT, Holmes AJ. Heritability of individualized cortical network topography. Proc Natl Acad Sci U S A 2021; 118:e2016271118. [PMID: 33622790 PMCID: PMC7936334 DOI: 10.1073/pnas.2016271118] [Citation(s) in RCA: 38] [Impact Index Per Article: 12.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022] Open
Abstract
Human cortex is patterned by a complex and interdigitated web of large-scale functional networks. Recent methodological breakthroughs reveal variation in the size, shape, and spatial topography of cortical networks across individuals. While spatial network organization emerges across development, is stable over time, and is predictive of behavior, it is not yet clear to what extent genetic factors underlie interindividual differences in network topography. Here, leveraging a nonlinear multidimensional estimation of heritability, we provide evidence that individual variability in the size and topographic organization of cortical networks are under genetic control. Using twin and family data from the Human Connectome Project (n = 1,023), we find increased variability and reduced heritability in the size of heteromodal association networks (h2 : M = 0.34, SD = 0.070), relative to unimodal sensory/motor cortex (h2 : M = 0.40, SD = 0.097). We then demonstrate that the spatial layout of cortical networks is influenced by genetics, using our multidimensional estimation of heritability (h2-multi; M = 0.14, SD = 0.015). However, topographic heritability did not differ between heteromodal and unimodal networks. Genetic factors had a regionally variable influence on brain organization, such that the heritability of network topography was greatest in prefrontal, precuneus, and posterior parietal cortex. Taken together, these data are consistent with relaxed genetic control of association cortices relative to primary sensory/motor regions and have implications for understanding population-level variability in brain functioning, guiding both individualized prediction and the interpretation of analyses that integrate genetics and neuroimaging.
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Affiliation(s)
| | - Tian Ge
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA 02114
- Stanley Center for Psychiatric Research, Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA 02142
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114
| | - Ru Kong
- Department of Electrical and Computer Engineering, Centre for Sleep and Cognition, National University of Singapore, Singapore 119077
- Department of Electrical and Computer Engineering, Centre for Translational Magnetic Resonance Research, National University of Singapore, Singapore 119077
- N.1 Institute for Health, National University of Singapore, Singapore 119077
- Institute for Digital Medicine, National University of Singapore, Singapore 119077
| | | | - R Nathan Spreng
- Department of Neurology and Neurosurgery, Montreal Neurological Institute, McGill University, Montreal, QC H3A 0G4, Canada
- McConnell Brain Imaging Centre, McGill University, Montreal, QC H3A 0G4, Canada
| | - Mert R Sabuncu
- School of Electrical and Computer Engineering, Cornell University, Ithaca, NY 14850
- Meinig School of Biomedical Engineering, Cornell University, Ithaca, NY 14850
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA 02129
| | - B T Thomas Yeo
- Department of Electrical and Computer Engineering, Centre for Sleep and Cognition, National University of Singapore, Singapore 119077
- Department of Electrical and Computer Engineering, Centre for Translational Magnetic Resonance Research, National University of Singapore, Singapore 119077
- N.1 Institute for Health, National University of Singapore, Singapore 119077
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA 02129
- National University of Singapore Graduate School for Integrative Sciences and Engineering, National University of Singapore, Singapore 119077
| | - Avram J Holmes
- Department of Psychology, Yale University, New Haven, CT 06520
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114
- Department of Psychiatry, Yale University, New Haven, CT 06520
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97
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Tavakol S, Li Q, Royer J, Vos de Wael R, Larivière S, Lowe A, Paquola C, Jefferies E, Hartley T, Bernasconi A, Bernasconi N, Smallwood J, Bohbot V, Caciagli L, Bernhardt B. A Structure-Function Substrate of Memory for Spatial Configurations in Medial and Lateral Temporal Cortices. Cereb Cortex 2021; 31:3213-3225. [PMID: 33667310 DOI: 10.1093/cercor/bhab001] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2020] [Revised: 12/21/2020] [Accepted: 12/27/2020] [Indexed: 01/30/2023] Open
Abstract
Prior research has shown a role of the medial temporal lobe, particularly the hippocampal-parahippocampal complex, in spatial cognition. Here, we developed a new paradigm, the conformational shift spatial task (CSST), which examines the ability to encode and retrieve spatial relations between unrelated items. This task is short, uses symbolic cues, incorporates two difficulty levels, and can be administered inside the scanner. A cohort of 48 healthy young adults underwent the CSST, together with a set of behavioral measures and multimodal magnetic resonance imaging (MRI). Inter-individual differences in CSST performance correlated with scores on an established spatial memory paradigm, but neither with episodic memory nor mnemonic discrimination, supporting specificity. Analyzing high-resolution structural MRI data, individuals with better spatial memory showed thicker medial and lateral temporal cortices. Functional relevance of these findings was supported by task-based functional MRI analysis in the same participants and ad hoc meta-analysis. Exploratory resting-state functional MRI analyses centered on clusters of morphological effects revealed additional modulation of intrinsic network integration, particularly between lateral and medial temporal structures. Our work presents a novel spatial memory paradigm and supports an integrated structure-function substrate in the human temporal lobe. Task paradigms are programmed in python and made open access.
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Affiliation(s)
- Shahin Tavakol
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec H3A 2B4, Canada
| | - Qiongling Li
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec H3A 2B4, Canada
| | - Jessica Royer
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec H3A 2B4, Canada
| | - Reinder Vos de Wael
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec H3A 2B4, Canada
| | - Sara Larivière
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec H3A 2B4, Canada
| | - Alex Lowe
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec H3A 2B4, Canada
| | - Casey Paquola
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec H3A 2B4, Canada
| | | | | | - Andrea Bernasconi
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec H3A 2B4, Canada
| | - Neda Bernasconi
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec H3A 2B4, Canada
| | | | - Veronique Bohbot
- Douglas Mental Health University Institute, McGill University, Montreal, Quebec H4H 1R3, Canada
| | - Lorenzo Caciagli
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, WC1N 3BG London, United Kingdom.,Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Boris Bernhardt
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec H3A 2B4, Canada
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98
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Quaak M, van de Mortel L, Thomas RM, van Wingen G. Deep learning applications for the classification of psychiatric disorders using neuroimaging data: Systematic review and meta-analysis. Neuroimage Clin 2021; 30:102584. [PMID: 33677240 PMCID: PMC8209481 DOI: 10.1016/j.nicl.2021.102584] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2020] [Revised: 01/18/2021] [Accepted: 01/29/2021] [Indexed: 12/20/2022]
Abstract
Deep learning (DL) methods have been increasingly applied to neuroimaging data to identify patients with psychiatric and neurological disorders. This review provides an overview of the different DL applications within psychiatry and compares DL model accuracy to standard machine learning (SML). Fifty-three articles were included for qualitative analysis, primarily investigating autism spectrum disorder (ASD; n = 22), schizophrenia (SZ; n = 22) and attention-deficit/hyperactivity disorder (ADHD; n = 9). Thirty-two of the thirty-five studies that directly compared DL to SML reported a higher accuracy for DL. Only sixteen studies could be included in a meta-regression to quantitatively compare DL and SML performance. This showed a higher odds ratio for DL models, though the comparison attained significance only for ASD. Our results suggest that deep learning of neuroimaging data is a promising tool for the classification of individual psychiatric patients. However, it is not yet used to its full potential: most studies use pre-engineered features, whereas one of the main advantages of DL is its ability to learn representations of minimally processed data. Our current evaluation is limited by minimal reporting of performance measures to enable quantitative comparisons, and the restriction to ADHD, SZ and ASD as current research focusses on large publicly available datasets. To truly uncover the added value of DL, we need carefully designed comparisons of SML and DL models which are yet rarely performed.
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Affiliation(s)
- Mirjam Quaak
- Amsterdam UMC, University of Amsterdam, Department of Psychiatry, Meibergdreef 5, 1105 AZ Amsterdam, The Netherlands
| | - Laurens van de Mortel
- Amsterdam UMC, University of Amsterdam, Department of Psychiatry, Meibergdreef 5, 1105 AZ Amsterdam, The Netherlands
| | - Rajat Mani Thomas
- Amsterdam UMC, University of Amsterdam, Department of Psychiatry, Meibergdreef 5, 1105 AZ Amsterdam, The Netherlands
| | - Guido van Wingen
- Amsterdam UMC, University of Amsterdam, Department of Psychiatry, Meibergdreef 5, 1105 AZ Amsterdam, The Netherlands.
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99
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Lee EE, Torous J, De Choudhury M, Depp CA, Graham SA, Kim HC, Paulus MP, Krystal JH, Jeste DV. Artificial Intelligence for Mental Health Care: Clinical Applications, Barriers, Facilitators, and Artificial Wisdom. BIOLOGICAL PSYCHIATRY: COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2021; 6:856-864. [PMID: 33571718 DOI: 10.1016/j.bpsc.2021.02.001] [Citation(s) in RCA: 47] [Impact Index Per Article: 15.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/16/2020] [Revised: 02/01/2021] [Accepted: 02/02/2021] [Indexed: 12/19/2022]
Abstract
Artificial intelligence (AI) is increasingly employed in health care fields such as oncology, radiology, and dermatology. However, the use of AI in mental health care and neurobiological research has been modest. Given the high morbidity and mortality in people with psychiatric disorders, coupled with a worsening shortage of mental health care providers, there is an urgent need for AI to help identify high-risk individuals and provide interventions to prevent and treat mental illnesses. While published research on AI in neuropsychiatry is rather limited, there is a growing number of successful examples of AI's use with electronic health records, brain imaging, sensor-based monitoring systems, and social media platforms to predict, classify, or subgroup mental illnesses as well as problems such as suicidality. This article is the product of a study group held at the American College of Neuropsychopharmacology conference in 2019. It provides an overview of AI approaches in mental health care, seeking to help with clinical diagnosis, prognosis, and treatment, as well as clinical and technological challenges, focusing on multiple illustrative publications. Although AI could help redefine mental illnesses more objectively, identify them at a prodromal stage, personalize treatments, and empower patients in their own care, it must address issues of bias, privacy, transparency, and other ethical concerns. These aspirations reflect human wisdom, which is more strongly associated than intelligence with individual and societal well-being. Thus, the future AI or artificial wisdom could provide technology that enables more compassionate and ethically sound care to diverse groups of people.
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Affiliation(s)
- Ellen E Lee
- Department of Psychiatry, University of California San Diego, San Diego, California; Sam and Rose Stein Institute for Research on Aging, University of California San Diego, San Diego, California; VA San Diego Healthcare System, San Diego, California
| | - John Torous
- Department of Psychiatry, Beth Israel Deaconess Medical Center and Harvard University, Boston, Massachusetts
| | - Munmun De Choudhury
- School of Interactive Computing, Georgia Institute of Technology, Atlanta, Georgia
| | - Colin A Depp
- Department of Psychiatry, University of California San Diego, San Diego, California; Sam and Rose Stein Institute for Research on Aging, University of California San Diego, San Diego, California; VA San Diego Healthcare System, San Diego, California
| | - Sarah A Graham
- Department of Psychiatry, University of California San Diego, San Diego, California; Sam and Rose Stein Institute for Research on Aging, University of California San Diego, San Diego, California
| | - Ho-Cheol Kim
- AI and Cognitive Software, IBM Research-Almaden, San Jose, California
| | | | - John H Krystal
- Department of Psychiatry, Yale University, New Haven, Connecticut
| | - Dilip V Jeste
- Department of Psychiatry, University of California San Diego, San Diego, California; Department of Neurosciences, University of California San Diego, San Diego, California; Sam and Rose Stein Institute for Research on Aging, University of California San Diego, San Diego, California.
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100
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Peng H, Gong W, Beckmann CF, Vedaldi A, Smith SM. Accurate brain age prediction with lightweight deep neural networks. Med Image Anal 2021; 68:101871. [PMID: 33197716 PMCID: PMC7610710 DOI: 10.1016/j.media.2020.101871] [Citation(s) in RCA: 156] [Impact Index Per Article: 52.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2019] [Revised: 07/24/2020] [Accepted: 10/05/2020] [Indexed: 11/23/2022]
Abstract
Deep learning has huge potential for accurate disease prediction with neuroimaging data, but the prediction performance is often limited by training-dataset size and computing memory requirements. To address this, we propose a deep convolutional neural network model, Simple Fully Convolutional Network (SFCN), for accurate prediction of brain age using T1-weighted structural MRI data. Compared with other popular deep network architectures, SFCN has fewer parameters, so is more compatible with small dataset size and 3D volume data. The network architecture was combined with several techniques for boosting performance, including data augmentation, pre-training, model regularization, model ensemble and prediction bias correction. We compared our overall SFCN approach with several widely-used machine learning models. It achieved state-of-the-art performance in UK Biobank data (N = 14,503), with mean absolute error (MAE) = 2.14y in brain age prediction and 99.5% in sex classification. SFCN also won (both parts of) the 2019 Predictive Analysis Challenge for brain age prediction, involving 79 competing teams (N = 2,638, MAE = 2.90y). We describe here the details of our approach, and its optimisation and validation. Our approach can easily be generalised to other tasks using different image modalities, and is released on GitHub.
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Affiliation(s)
- Han Peng
- Wellcome Centre for Integrative Neuroimaging (WIN FMRIB), University of Oxford, Oxford, OX3 9DU, United Kingdom; Visual Geometry Group (VGG), University of Oxford, Oxford, OX2 6NN, United Kingdom; Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen, Nijmegen, 6525 EN, the Netherlands.
| | - Weikang Gong
- Wellcome Centre for Integrative Neuroimaging (WIN FMRIB), University of Oxford, Oxford, OX3 9DU, United Kingdom
| | - Christian F Beckmann
- Wellcome Centre for Integrative Neuroimaging (WIN FMRIB), University of Oxford, Oxford, OX3 9DU, United Kingdom; Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen, Nijmegen, 6525 EN, the Netherlands
| | - Andrea Vedaldi
- Visual Geometry Group (VGG), University of Oxford, Oxford, OX2 6NN, United Kingdom
| | - Stephen M Smith
- Wellcome Centre for Integrative Neuroimaging (WIN FMRIB), University of Oxford, Oxford, OX3 9DU, United Kingdom
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