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Liu R, Huang ZA, Hu Y, Zhu Z, Wong KC, Tan KC. Attention-Like Multimodality Fusion With Data Augmentation for Diagnosis of Mental Disorders Using MRI. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2024; 35:7627-7641. [PMID: 36374900 DOI: 10.1109/tnnls.2022.3219551] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
The globally rising prevalence of mental disorders leads to shortfalls in timely diagnosis and therapy to reduce patients' suffering. Facing such an urgent public health problem, professional efforts based on symptom criteria are seriously overstretched. Recently, the successful applications of computer-aided diagnosis approaches have provided timely opportunities to relieve the tension in healthcare services. Particularly, multimodal representation learning gains increasing attention thanks to the high temporal and spatial resolution information extracted from neuroimaging fusion. In this work, we propose an efficient multimodality fusion framework to identify multiple mental disorders based on the combination of functional and structural magnetic resonance imaging. A multioutput conditional generative adversarial network (GAN) is developed to address the scarcity of multimodal data for augmentation. Based on the augmented training data, the multiheaded gating fusion model is proposed for classification by extracting the complementary features across different modalities. The experiments demonstrate that the proposed model can achieve robust accuracies of 75.1 ± 1.5 %, 72.9 ± 1.1 %, and 87.2 ± 1.5 % for autism spectrum disorder (ASD), attention deficit/hyperactivity disorder, and schizophrenia, respectively. In addition, the interpretability of our model is expected to enable the identification of remarkable neuropathology diagnostic biomarkers, leading to well-informed therapeutic decisions.
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Luo Y, Chen W, Zhan L, Qiu J, Jia T. Multi-feature concatenation and multi-classifier stacking: An interpretable and generalizable machine learning method for MDD discrimination with rsfMRI. Neuroimage 2024; 285:120497. [PMID: 38142755 DOI: 10.1016/j.neuroimage.2023.120497] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2023] [Revised: 11/21/2023] [Accepted: 12/11/2023] [Indexed: 12/26/2023] Open
Abstract
Major depressive disorder (MDD) is a serious and heterogeneous psychiatric disorder that needs accurate diagnosis. Resting-state functional MRI (rsfMRI), which captures multiple perspectives on brain structure, function, and connectivity, is increasingly applied in the diagnosis and pathological research of MDD. Different machine learning algorithms are then developed to exploit the rich information in rsfMRI and discriminate MDD patients from normal controls. Despite recent advances reported, the MDD discrimination accuracy has room for further improvement. The generalizability and interpretability of the discrimination method are not sufficiently addressed either. Here, we propose a machine learning method (MFMC) for MDD discrimination by concatenating multiple features and stacking multiple classifiers. MFMC is tested on the REST-meta-MDD data set that contains 2428 subjects collected from 25 different sites. MFMC yields 96.9% MDD discrimination accuracy, demonstrating a significant improvement over existing methods. In addition, the generalizability of MFMC is validated by the good performance when the training and testing subjects are from independent sites. The use of XGBoost as the meta classifier allows us to probe the decision process of MFMC. We identify 13 feature values related to 9 brain regions including the posterior cingulate gyrus, superior frontal gyrus orbital part, and angular gyrus, which contribute most to the classification and also demonstrate significant differences at the group level. The use of these 13 feature values alone can reach 87% of MFMC's full performance when taking all feature values. These features may serve as clinically useful diagnostic and prognostic biomarkers for MDD in the future.
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Affiliation(s)
- Yunsong Luo
- College of Computer and Information Science, Southwest University, Chongqing, 400715, PR China.
| | - Wenyu Chen
- College of Computer and Information Science, Southwest University, Chongqing, 400715, PR China.
| | - Ling Zhan
- College of Computer and Information Science, Southwest University, Chongqing, 400715, PR China.
| | - Jiang Qiu
- Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing, 400715, PR China; School of Psychology, Southwest University (SWU), Chongqing, 400715, PR China; Southwest University Branch, Collaborative Innovation Center of Assessment Toward Basic Education Quality at Beijing Normal University, Chongqing, 400715, PR China.
| | - Tao Jia
- College of Computer and Information Science, Southwest University, Chongqing, 400715, PR China.
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Alves CL, Toutain TGLDO, Porto JAM, Aguiar PMDC, de Sena EP, Rodrigues FA, Pineda AM, Thielemann C. Analysis of functional connectivity using machine learning and deep learning in different data modalities from individuals with schizophrenia. J Neural Eng 2023; 20:056025. [PMID: 37673060 DOI: 10.1088/1741-2552/acf734] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2022] [Accepted: 09/06/2023] [Indexed: 09/08/2023]
Abstract
Objective. Schizophrenia(SCZ) is a severe mental disorder associated with persistent or recurrent psychosis, hallucinations, delusions, and thought disorders that affect approximately 26 million people worldwide, according to the World Health Organization. Several studies encompass machine learning (ML) and deep learning algorithms to automate the diagnosis of this mental disorder. Others study SCZ brain networks to get new insights into the dynamics of information processing in individuals suffering from the condition. In this paper, we offer a rigorous approach with ML and deep learning techniques for evaluating connectivity matrices and measures of complex networks to establish an automated diagnosis and comprehend the topology and dynamics of brain networks in SCZ individuals.Approach.For this purpose, we employed an functional magnetic resonance imaging (fMRI) and electroencephalogram (EEG) dataset. In addition, we combined EEG measures, i.e. Hjorth mobility and complexity, with complex network measurements to be analyzed in our model for the first time in the literature.Main results.When comparing the SCZ group to the control group, we found a high positive correlation between the left superior parietal lobe and the left motor cortex and a positive correlation between the left dorsal posterior cingulate cortex and the left primary motor. Regarding complex network measures, the diameter, which corresponds to the longest shortest path length in a network, may be regarded as a biomarker because it is the most crucial measure in different data modalities. Furthermore, the SCZ brain networks exhibit less segregation and a lower distribution of information. As a result, EEG measures outperformed complex networks in capturing the brain alterations associated with SCZ.Significance. Our model achieved an area under receiver operating characteristic curve (AUC) of 100% and an accuracy of 98.5% for the fMRI, an AUC of 95%, and an accuracy of 95.4% for the EEG data set. These are excellent classification results. Furthermore, we investigated the impact of specific brain connections and network measures on these results, which helped us better describe changes in the diseased brain.
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Affiliation(s)
- Caroline L Alves
- University of São Paulo (USP), Institute of Mathematical and Computer Sciences (ICMC), São Paulo, Brazil
- BioMEMS Lab, Aschaffenburg University of Applied Sciences, Aschaffenburg, Germany
| | | | | | - Patrícia Maria de Carvalho Aguiar
- Hospital Israelita Albert Einstein, São Paulo, Brazil
- Federal University of São Paulo, Department of Neurology and Neurosurgery, São Paulo, Brazil
| | | | - Francisco A Rodrigues
- University of São Paulo (USP), Institute of Mathematical and Computer Sciences (ICMC), São Paulo, Brazil
| | - Aruane M Pineda
- University of São Paulo (USP), Institute of Mathematical and Computer Sciences (ICMC), São Paulo, Brazil
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Wang T, Bezerianos A, Cichocki A, Li J. Multikernel Capsule Network for Schizophrenia Identification. IEEE TRANSACTIONS ON CYBERNETICS 2022; 52:4741-4750. [PMID: 33259321 DOI: 10.1109/tcyb.2020.3035282] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Schizophrenia seriously affects the quality of life. To date, both simple (e.g., linear discriminant analysis) and complex (e.g., deep neural network) machine-learning methods have been utilized to identify schizophrenia based on functional connectivity features. The existing simple methods need two separate steps (i.e., feature extraction and classification) to achieve the identification, which disables simultaneous tuning for the best feature extraction and classifier training. The complex methods integrate two steps and can be simultaneously tuned to achieve optimal performance, but these methods require a much larger amount of data for model training. To overcome the aforementioned drawbacks, we proposed a multikernel capsule network (MKCapsnet), which was developed by considering the brain anatomical structure. Kernels were set to match partition sizes of the brain anatomical structure in order to capture interregional connectivities at the varying scales. With the inspiration of the widely used dropout strategy in deep learning, we developed capsule dropout in the capsule layer to prevent overfitting of the model. The comparison results showed that the proposed method outperformed the state-of-the-art methods. Besides, we compared performances using different parameters and illustrated the routing process to reveal characteristics of the proposed method. MKCapsnet is promising for schizophrenia identification. Our study first utilized the capsule neural network for analyzing functional connectivity of magnetic resonance imaging (MRI) and proposed a novel multikernel capsule structure with the consideration of brain anatomical parcellation, which could be a new way to reveal brain mechanisms. In addition, we provided useful information in the parameter setting, which is informative for further studies using a capsule network for other neurophysiological signal classification.
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Wu Y, Ren P, Chen R, Xu H, Xu J, Zeng L, Wu D, Jiang W, Tang N, Liu X. Detection of functional and structural brain alterations in female schizophrenia using elastic net logistic regression. Brain Imaging Behav 2022; 16:281-290. [PMID: 34313906 PMCID: PMC8825615 DOI: 10.1007/s11682-021-00501-z] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/05/2021] [Indexed: 12/19/2022]
Abstract
Neuroimaging technique is a powerful tool to characterize the abnormality of brain networks in schizophrenia. However, the neurophysiological substrate of schizophrenia is still unclear. Here we investigated the patterns of brain functional and structural changes in female patients with schizophrenia using elastic net logistic regression analysis of resting-state functional magnetic resonance imaging data. Data from 52 participants (25 female schizophrenia patients and 27 healthy controls) were obtained. Using an elastic net penalty, the brain regions most relevant to schizophrenia pathology were defined in the models using the amplitude of low-frequency fluctuations (ALFF) and gray matter, respectively. The receiver operating characteristic analysis showed reliable classification accuracy with 85.7% in ALFF analysis, and 77.1% in gray matter analysis. Notably, our results showed eight common regions between the ALFF and gray matter analyses, including the Frontal-Inf-Orb-R, Rolandic-Oper-R, Olfactory-R, Angular-L, Precuneus-L, Precuenus-R, Heschl-L, and Temporal-Pole-Mid-R. In addition, the severity of symptoms was found positively associated with the ALFF within the Rolandic-Oper-R and Frontal-Inf-Orb-R. Our findings indicated that elastic net logistic regression could be a useful tool to identify the characteristics of schizophrenia -related brain deterioration, which provides novel insights into schizophrenia diagnosis and prediction.
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Affiliation(s)
- Ying Wu
- Key Lab of Statistical Modeling and Data Analysis of Yunnan, Yunnan University, Kunming, China
| | - Ping Ren
- Lab of Brain Health Assessment and Research, Shenzhen Mental Health Center, Shenzhen, China
- Department of Geriatric Psychiatry, Shenzhen Kangning Hospital, Shenzhen, China
| | - Rong Chen
- Female Ward of Acute Psychiatric Department, Shenzhen Kangning Hospital, Shenzhen, China
| | - Hong Xu
- Female Ward of Acute Psychiatric Department, Shenzhen Kangning Hospital, Shenzhen, China
| | - Jianxing Xu
- Neuropsychiatry Imaging Center, Shenzhen Mental Health Center, Shenzhen, China
- Department of Radiology, Shenzhen Kangning Hospital, Shenzhen, China
| | - Lin Zeng
- Neuropsychiatry Imaging Center, Shenzhen Mental Health Center, Shenzhen, China
- Department of Radiology, Shenzhen Kangning Hospital, Shenzhen, China
| | - Donghui Wu
- Department of Geriatric Psychiatry, Shenzhen Kangning Hospital, Shenzhen, China
- School of Mental Health, Jining Medical University, Jining, China
| | - Wentao Jiang
- Neuropsychiatry Imaging Center, Shenzhen Mental Health Center, Shenzhen, China
- Department of Radiology, Shenzhen Kangning Hospital, Shenzhen, China
| | - NianSheng Tang
- Key Lab of Statistical Modeling and Data Analysis of Yunnan, Yunnan University, Kunming, China.
| | - Xia Liu
- Neuropsychiatry Imaging Center, Shenzhen Mental Health Center, Shenzhen, China.
- Department of Radiology, Shenzhen Kangning Hospital, Shenzhen, China.
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Masoudi B, Daneshvar S, Razavi SN. Multi-modal neuroimaging feature fusion via 3D Convolutional Neural Network architecture for schizophrenia diagnosis. INTELL DATA ANAL 2021. [DOI: 10.3233/ida-205113] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
Early and precise diagnosis of schizophrenia disorder (SZ) has an essential role in the quality of a patient’s life and future treatments. Structural and functional neuroimaging provides robust biomarkers for understanding the anatomical and functional changes associated with SZ. Each of the neuroimaging techniques shows only a different perspective on the functional or structural of the brain, while multi-modal fusion can reveal latent connections in the brain. In this paper, we propose an approach for the fusion of structural and functional brain data with a deep learning-based model to take advantage of data fusion and increase the accuracy of schizophrenia disorder diagnosis. The proposed method consists of an architecture of 3D convolutional neural networks (CNNs) that applied to magnetic resonance imaging (MRI), functional magnetic resonance imaging (fMRI), and diffusion tensor imaging (DTI) extracted features. We use 3D MRI patches, fMRI spatial independent component analysis (ICA) map, and DTI fractional anisotropy (FA) as model inputs. Our method is validated on the COBRE dataset, and an average accuracy of 99.35% is obtained. The proposed method demonstrates promising classification performance and can be applied to real data.
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Affiliation(s)
- Babak Masoudi
- Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz, Iran
| | - Sabalan Daneshvar
- Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz, Iran
- Department of Electronic and Computer Engineering, College of Engineering, Design and Physical Sciences, Brunel University, London, UK
| | - Seyed Naser Razavi
- Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz, Iran
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7
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Convolution-GRU Based on Independent Component Analysis for fMRI Analysis with Small and Imbalanced Samples. APPLIED SCIENCES-BASEL 2020. [DOI: 10.3390/app10217465] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
Functional magnetic resonance imaging (fMRI) is a commonly used method of brain research. However, due to the complexity and particularity of the fMRI task, it is difficult to find enough subjects, resulting in a small and, often, imbalanced dataset. A dataset with small samples causes overfitting of the learning model, and the imbalance will make the model insensitive to the minority class, which has been a problem in classification. It is of great significance to classify fMRI data with small and imbalanced samples. In the present study, we propose a 3-step method on a small and imbalanced fMRI dataset from a word-scene memory task. The steps of the method are as follows: (1) An independent component analysis is performed to reduce the dimension of data; (2) The synthetic minority oversampling technique is used to generate new samples of the minority class to balance data; (3) A convolution-Gated Recurrent Unit (GRU) network is used to classify the independent component signals, indicating whether the subjects are performing episodic memory tasks. The accuracy of the proposed method is 72.2%, which improves the classification performance compared with traditional classifiers such as support vector machines (SVM), logistic regression (LGR), linear discriminant analysis (LDA) and k-nearest neighbor (KNN), and this study gives a biomarker for evaluating the reactivation of episodic memory.
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8
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Cai X, Xie D, Madsen KH, Wang Y, Bögemann SA, Cheung EFC, Møller A, Chan RCK. Generalizability of machine learning for classification of schizophrenia based on resting-state functional MRI data. Hum Brain Mapp 2020; 41:172-184. [PMID: 31571320 PMCID: PMC7268030 DOI: 10.1002/hbm.24797] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2019] [Revised: 08/19/2019] [Accepted: 09/04/2019] [Indexed: 12/11/2022] Open
Abstract
Machine learning has increasingly been applied to classification of schizophrenia in neuroimaging research. However, direct replication studies and studies seeking to investigate generalizability are scarce. To address these issues, we assessed within-site and between-site generalizability of a machine learning classification framework which achieved excellent performance in a previous study using two independent resting-state functional magnetic resonance imaging data sets collected from different sites and scanners. We established within-site generalizability of the classification framework in the main data set using cross-validation. Then, we trained a model in the main data set and investigated between-site generalization in the validated data set using external validation. Finally, recognizing the poor between-site generalization performance, we updated the unsupervised algorithm to investigate if transfer learning using additional unlabeled data were able to improve between-site classification performance. Cross-validation showed that the published classification procedure achieved an accuracy of 0.73 using majority voting across all selected components. External validation found a classification accuracy of 0.55 (not significant) and 0.70 (significant) using the direct and transfer learning procedures, respectively. The failure of direct generalization from one site to another demonstrates the limitation of within-site cross-validation and points toward the need to incorporate efforts to facilitate application of machine learning across multiple data sets. The improvement in performance with transfer learning highlights the importance of taking into account the properties of data when constructing predictive models across samples and sites. Our findings suggest that machine learning classification result based on a single study should be interpreted cautiously.
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Affiliation(s)
- Xin‐Lu Cai
- Neuropsychology and Applied Cognitive Neuroscience Laboratory, CAS Key Laboratory of Mental HealthInstitute of PsychologyBeijingChina
- Sino‐Danish College, University of Chinese Academy of SciencesBeijingChina
- Sino‐Danish Center for Education and ResearchBeijingChina
| | - Dong‐Jie Xie
- Neuropsychology and Applied Cognitive Neuroscience Laboratory, CAS Key Laboratory of Mental HealthInstitute of PsychologyBeijingChina
- Hangzhou College of Preschool Teacher EducationZhejiang Normal UniversityHangzhouChina
| | - Kristoffer H. Madsen
- Sino‐Danish Center for Education and ResearchBeijingChina
- Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and ResearchCopenhagen University Hospital HvidovreCopenhagenDenmark
- Department of Applied Mathematics and Computer ScienceTechnical University of DenmarkKongens LyngbyDenmark
| | - Yong‐Ming Wang
- Neuropsychology and Applied Cognitive Neuroscience Laboratory, CAS Key Laboratory of Mental HealthInstitute of PsychologyBeijingChina
- Sino‐Danish College, University of Chinese Academy of SciencesBeijingChina
- Sino‐Danish Center for Education and ResearchBeijingChina
| | - Sophie Alida Bögemann
- Neuropsychology and Applied Cognitive Neuroscience Laboratory, CAS Key Laboratory of Mental HealthInstitute of PsychologyBeijingChina
- Sino‐Danish College, University of Chinese Academy of SciencesBeijingChina
- Sino‐Danish Center for Education and ResearchBeijingChina
| | | | - Arne Møller
- Sino‐Danish Center for Education and ResearchBeijingChina
- Department of Nuclear Medicine and PET CentreAarhus University HospitalAarhusDenmark
| | - Raymond C. K. Chan
- Neuropsychology and Applied Cognitive Neuroscience Laboratory, CAS Key Laboratory of Mental HealthInstitute of PsychologyBeijingChina
- Sino‐Danish College, University of Chinese Academy of SciencesBeijingChina
- Sino‐Danish Center for Education and ResearchBeijingChina
- Department of PsychologyUniversity of Chinese Academy of SciencesBeijingChina
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9
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DSouza AM, Abidin AZ, Schifitto G, Wismüller A. A multivoxel pattern analysis framework with mutual connectivity analysis investigating changes in resting state connectivity in patients with HIV associated neurocognitve disorder. Magn Reson Imaging 2019; 62:121-128. [PMID: 31189074 DOI: 10.1016/j.mri.2019.06.001] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2018] [Revised: 05/09/2019] [Accepted: 06/02/2019] [Indexed: 01/19/2023]
Abstract
Functional MRI (fMRI) quantifies brain activity non-invasively by measuring the blood oxygen level dependent (BOLD) response to neuronal activity. It was recently demonstrated, on realistic fMRI simulations, that nonlinear connectivity approaches, such as Mutual Connectivity Analysis with Local Models (MCA-LM), are better suited for extracting connectivity measures than conventional techniques of cross-correlating time-series pairs. In this work, we investigate the application of MCA-LM in extracting meaningful connectivity measures aiding in distinguishing healthy controls from individuals presenting with symptoms of HIV Associated Neurocognitive Disorder (HAND), which occurs as a result of HIV infection of the central nervous system. The pairwise connectivity measures provide a high-dimensional representation of connectivity profiles for subjects and are used as features for classification. We adopt feature selection (FS) techniques reducing the number of redundant and noisy features, while also controlling the complexity of the classifiers. We investigate three FS techniques: 1) Kendall's τ, 2) Information Gain Attribute selection 3) ReliefF and two classifiers:1) AdaBoost and 2) Random Forests. Our results demonstrate that MCA-LM consistently outperforms correlation in terms of Area under the Receiver Operating Characteristic Curve and accuracy. Improved performance with MCA-LM suggests that such a nonlinear approach is better at capturing meaningful connectivity relationships between brain regions. This demonstrates potential for developing novel neuroimaging-derived biomarkers for HAND. Furthermore, FS helps identify connections between anatomical regions that are affected by HAND. In this work, we show that the regions of the basal ganglia and frontal cortex, which are known to be affected by HAND according to current literature, are identified as most discriminative.
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Affiliation(s)
- Adora M DSouza
- Department of Electrical Engineering, University of Rochester, Rochester, NY, USA.
| | - Anas Z Abidin
- Department of Biomedical Engineering, University of Rochester, Rochester, NY, USA
| | - Giovanni Schifitto
- Department of Neurology, University of Rochester Medical Center, Rochester, NY, USA; Department of Imaging Sciences, University of Rochester, NY, USA
| | - Axel Wismüller
- Department of Electrical Engineering, University of Rochester, Rochester, NY, USA; Department of Biomedical Engineering, University of Rochester, Rochester, NY, USA; Department of Imaging Sciences, University of Rochester, NY, USA; Faculty of Medicine and Institute of Clinical Radiology, Ludwig Maximilians University, Munich, Germany
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10
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Bi-objective approach for computer-aided diagnosis of schizophrenia patients using fMRI data. MULTIMEDIA TOOLS AND APPLICATIONS 2018. [DOI: 10.1007/s11042-018-5901-0] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
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11
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Multivariate brain network graph identification in functional MRI. Med Image Anal 2017; 42:228-240. [PMID: 28866433 DOI: 10.1016/j.media.2017.08.007] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2016] [Revised: 08/24/2017] [Accepted: 08/28/2017] [Indexed: 11/23/2022]
Abstract
Motivated by recent interest in identification of functional brain networks, we develop a new multivariate approach for functional brain network identification and name it as Multivariate Vector Regression-based Connectivity (MVRC). The proposed MVRC method regresses time series of all regions to those of other regions simultaneously and estimates pairwise association between two regions with consideration of influence of other regions and builds the adjacency matrix. Next, modularity method is applied on the adjacency matrix to detect communities or functional brain networks. We compare the proposed MVRC method with existing methods ranging from simple Pearson correlation to advanced Multivariate Adaptive Sparse Representation (ASR) methods. Experimental results on simulated and real fMRI dataset demonstrate that MVRC is able to extract functional brain networks that are consistent with the literature. Also, the proposed MVRC method is 650-750 times faster compared to the existing ASR method on 90 node network.
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12
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Rahim M, Thirion B, Bzdok D, Buvat I, Varoquaux G. Joint prediction of multiple scores captures better individual traits from brain images. Neuroimage 2017; 158:145-154. [PMID: 28676298 DOI: 10.1016/j.neuroimage.2017.06.072] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2017] [Revised: 06/23/2017] [Accepted: 06/26/2017] [Indexed: 01/09/2023] Open
Abstract
To probe individual variations in brain organization, population imaging relates features of brain images to rich descriptions of the subjects such as genetic information or behavioral and clinical assessments. Capturing common trends across these measurements is important: they jointly characterize the disease status of patient groups. In particular, mapping imaging features to behavioral scores with predictive models opens the way toward more precise diagnosis. Here we propose to jointly predict all the dimensions (behavioral scores) that make up the individual profiles, using so-called multi-output models. This approach often boosts prediction accuracy by capturing latent shared information across scores. We demonstrate the efficiency of multi-output models on two independent resting-state fMRI datasets targeting different brain disorders (Alzheimer's Disease and schizophrenia). Furthermore, the model with joint prediction generalizes much better to a new cohort: a model learned on one study is more accurately transferred to an independent one. Finally, we show how multi-output models can easily be extended to multi-modal settings, combining heterogeneous data sources for a better overall accuracy.
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Affiliation(s)
- Mehdi Rahim
- Parietal Team - Inria, CEA - Paris Saclay University, France.
| | | | - Danilo Bzdok
- Department of Psychiatry, Psychotherapy and Psychosomatics, RWTH Aachen University, Germany
| | - Irène Buvat
- IMIV Group - Inserm, CEA, Univ. Paris Sud - Paris Saclay University, France
| | - Gaël Varoquaux
- Parietal Team - Inria, CEA - Paris Saclay University, France
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13
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Zhang Y, Wang S, Phillips P, Yang J, Yuan TF. Three-Dimensional Eigenbrain for the Detection of Subjects and Brain Regions Related with Alzheimer's Disease. J Alzheimers Dis 2016; 50:1163-79. [PMID: 26836190 DOI: 10.3233/jad-150988] [Citation(s) in RCA: 42] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/03/2023]
Abstract
BACKGROUND Considering that Alzheimer's disease (AD) is untreatable, early diagnosis of AD from the healthy elderly controls (HC) is pivotal. However, computer-aided diagnosis (CAD) systems were not widely used due to its poor performance. OBJECTIVE Inspired from the eigenface approach for face recognition problems, we proposed an eigenbrain to detect AD brains. Eigenface is only for 2D image processing and is not suitable for volumetric image processing since faces are usually obtained as 2D images. METHODS We extended the eigenbrain to 3D. This 3D eigenbrain (3D-EB) inherits the fundamental strategies in either eigenface or 2D eigenbrain (2D-EB). All the 3D brains were transferred to a feature space, which encoded the variation among known 3D brain images. The feature space was named as the 3D-EB, and defined as eigenvectors on the set of 3D brains. We compared four different classifiers: feed-forward neural network, support vector machine (SVM) with linear kernel, polynomial (Pol) kernel, and radial basis function kernel. RESULTS The 50x10-fold stratified cross validation experiments showed that the proposed 3D-EB is better than the 2D-EB. SVM with Pol kernel performed the best among all classifiers. Our "3D-EB + Pol-SVM" achieved an accuracy of 92.81% ± 1.99% , a sensitivity of 92.07% ± 2.48% , a specificity of 93.02% ± 2.22% , and a precision of 79.03% ± 2.37% . Based on the most important 3D-EB U1, we detected 34 brain regions related with AD. The results corresponded to recent literature. CONCLUSIONS We validated the effectiveness of the proposed 3D-EB by detecting subjects and brain regions related to AD.
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Affiliation(s)
- Yudong Zhang
- School of Computer Science and Technology & School of Psychology, Nanjing Normal University, Nanjing, Jiangsu, China.,Guangxi Key Laboratory of Manufacturing System & Advanced Manufacturing Technology, Guilin, Guangxi, China
| | - Shuihua Wang
- School of Computer Science and Technology & School of Psychology, Nanjing Normal University, Nanjing, Jiangsu, China
| | - Preetha Phillips
- School of Natural Sciences and Mathematics, Shepherd University, Shepherdstown, WV, USA
| | - Jiquan Yang
- Jiangsu Key Laboratory of 3d Printing Equipment And Manufacturing, Nanjing, Jiangsu, China
| | - Ti-Fei Yuan
- School of Computer Science and Technology & School of Psychology, Nanjing Normal University, Nanjing, Jiangsu, China
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14
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Wang S, Zhang Y, Liu G, Phillips P, Yuan TF. Detection of Alzheimer's Disease by Three-Dimensional Displacement Field Estimation in Structural Magnetic Resonance Imaging. J Alzheimers Dis 2016; 50:233-48. [PMID: 26682696 DOI: 10.3233/jad-150848] [Citation(s) in RCA: 38] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
BACKGROUND Within the past decade, computer scientists have developed many methods using computer vision and machine learning techniques to detect Alzheimer's disease (AD) in its early stages. OBJECTIVE However, some of these methods are unable to achieve excellent detection accuracy, and several other methods are unable to locate AD-related regions. Hence, our goal was to develop a novel AD brain detection method. METHODS In this study, our method was based on the three-dimensional (3D) displacement-field (DF) estimation between subjects in the healthy elder control group and AD group. The 3D-DF was treated with AD-related features. The three feature selection measures were used in the Bhattacharyya distance, Student's t-test, and Welch's t-test (WTT). Two non-parallel support vector machines, i.e., generalized eigenvalue proximal support vector machine and twin support vector machine (TSVM), were then used for classification. A 50 × 10-fold cross validation was implemented for statistical analysis. RESULTS The results showed that "3D-DF+WTT+TSVM" achieved the best performance, with an accuracy of 93.05 ± 2.18, a sensitivity of 92.57 ± 3.80, a specificity of 93.18 ± 3.35, and a precision of 79.51 ± 2.86. This method also exceled in 13 state-of-the-art approaches. Additionally, we were able to detect 17 regions related to AD by using the pure computer-vision technique. These regions include sub-gyral, inferior parietal lobule, precuneus, angular gyrus, lingual gyrus, supramarginal gyrus, postcentral gyrus, third ventricle, superior parietal lobule, thalamus, middle temporal gyrus, precentral gyrus, superior temporal gyrus, superior occipital gyrus, cingulate gyrus, culmen, and insula. These regions were reported in recent publications. CONCLUSIONS The 3D-DF is effective in AD subject and related region detection.
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Affiliation(s)
- Shuihua Wang
- School of Computer Science and Technology & School of Psychology, Nanjing Normal University, Nanjing, Jiangsu, China.,School of Electronic Science and Engineering, Nanjing University, Nanjing, Jiangsu, China.,Jiangsu Key Laboratory of 3D Printing Equipment and Manufacturing, Nanjing, Jiangsu, China
| | - Yudong Zhang
- School of Computer Science and Technology & School of Psychology, Nanjing Normal University, Nanjing, Jiangsu, China.,Jiangsu Key Laboratory of 3D Printing Equipment and Manufacturing, Nanjing, Jiangsu, China
| | - Ge Liu
- Translational Imaging Division & MRI Unit, Columbia University & New York State Psychiatric Institute, New York, NY, USA
| | - Preetha Phillips
- School of Natural Sciences and Mathematics, Shepherd University, Shepherdstown, WV, USA
| | - Ti-Fei Yuan
- School of Computer Science and Technology & School of Psychology, Nanjing Normal University, Nanjing, Jiangsu, China
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15
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Peng JX, Rafferty K, Ferguson S. Building support vector machines in the context of regularized least squares. Neurocomputing 2016. [DOI: 10.1016/j.neucom.2016.03.087] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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16
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Lu X, Yang Y, Wu F, Gao M, Xu Y, Zhang Y, Yao Y, Du X, Li C, Wu L, Zhong X, Zhou Y, Fan N, Zheng Y, Xiong D, Peng H, Escudero J, Huang B, Li X, Ning Y, Wu K. Discriminative analysis of schizophrenia using support vector machine and recursive feature elimination on structural MRI images. Medicine (Baltimore) 2016; 95:e3973. [PMID: 27472673 PMCID: PMC5265810 DOI: 10.1097/md.0000000000003973] [Citation(s) in RCA: 61] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/20/2016] [Revised: 05/16/2016] [Accepted: 05/26/2016] [Indexed: 12/11/2022] Open
Abstract
Structural abnormalities in schizophrenia (SZ) patients have been well documented with structural magnetic resonance imaging (MRI) data using voxel-based morphometry (VBM) and region of interest (ROI) analyses. However, these analyses can only detect group-wise differences and thus, have a poor predictive value for individuals. In the present study, we applied a machine learning method that combined support vector machine (SVM) with recursive feature elimination (RFE) to discriminate SZ patients from normal controls (NCs) using their structural MRI data. We first employed both VBM and ROI analyses to compare gray matter volume (GMV) and white matter volume (WMV) between 41 SZ patients and 42 age- and sex-matched NCs. The method of SVM combined with RFE was used to discriminate SZ patients from NCs using significant between-group differences in both GMV and WMV as input features. We found that SZ patients showed GM and WM abnormalities in several brain structures primarily involved in the emotion, memory, and visual systems. An SVM with a RFE classifier using the significant structural abnormalities identified by the VBM analysis as input features achieved the best performance (an accuracy of 88.4%, a sensitivity of 91.9%, and a specificity of 84.4%) in the discriminative analyses of SZ patients. These results suggested that distinct neuroanatomical profiles associated with SZ patients might provide a potential biomarker for disease diagnosis, and machine-learning methods can reveal neurobiological mechanisms in psychiatric diseases.
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Affiliation(s)
- Xiaobing Lu
- Department of Psychiatry, Guangzhou Brain Hospital (GBH)/(Guangzhou Huiai Hospital, The Affiliated Brain Hospital of Guangzhou Medical University), Guangzhou, China
- GBH-SCUT Joint Research Centre for Neuroimaging, Guangzhou, China
| | - Yongzhe Yang
- Department of Biomedical Engineering, School of Materials Science and Engineering, South China University of Technology (SCUT), Guangzhou, China
- School of Medicine, South China University of Technology (SCUT), Guangzhou, China
- Department of Radiology, Guangdong Academy of Medical Sciences, Guangdong General Hospital, Guangzhou, China
| | - Fengchun Wu
- Department of Psychiatry, Guangzhou Brain Hospital (GBH)/(Guangzhou Huiai Hospital, The Affiliated Brain Hospital of Guangzhou Medical University), Guangzhou, China
- GBH-SCUT Joint Research Centre for Neuroimaging, Guangzhou, China
| | - Minjian Gao
- School of Computer Science and Engineering, South China University of Technology (SCUT), Guangzhou, China
| | - Yong Xu
- School of Computer Science and Engineering, South China University of Technology (SCUT), Guangzhou, China
| | - Yue Zhang
- Department of Biomedical Engineering, School of Materials Science and Engineering, South China University of Technology (SCUT), Guangzhou, China
| | - Yongcheng Yao
- Department of Biomedical Engineering, School of Materials Science and Engineering, South China University of Technology (SCUT), Guangzhou, China
| | - Xin Du
- Department of Biomedical Engineering, School of Materials Science and Engineering, South China University of Technology (SCUT), Guangzhou, China
| | - Chengwei Li
- Department of Biomedical Engineering, School of Materials Science and Engineering, South China University of Technology (SCUT), Guangzhou, China
| | - Lei Wu
- Department of Biomedical Engineering, School of Materials Science and Engineering, South China University of Technology (SCUT), Guangzhou, China
- School of Medicine, South China University of Technology (SCUT), Guangzhou, China
- Department of Radiology, Guangdong Academy of Medical Sciences, Guangdong General Hospital, Guangzhou, China
| | - Xiaomei Zhong
- Department of Psychiatry, Guangzhou Brain Hospital (GBH)/(Guangzhou Huiai Hospital, The Affiliated Brain Hospital of Guangzhou Medical University), Guangzhou, China
- GBH-SCUT Joint Research Centre for Neuroimaging, Guangzhou, China
| | - Yanling Zhou
- Department of Psychiatry, Guangzhou Brain Hospital (GBH)/(Guangzhou Huiai Hospital, The Affiliated Brain Hospital of Guangzhou Medical University), Guangzhou, China
| | - Ni Fan
- Department of Psychiatry, Guangzhou Brain Hospital (GBH)/(Guangzhou Huiai Hospital, The Affiliated Brain Hospital of Guangzhou Medical University), Guangzhou, China
| | - Yingjun Zheng
- Department of Psychiatry, Guangzhou Brain Hospital (GBH)/(Guangzhou Huiai Hospital, The Affiliated Brain Hospital of Guangzhou Medical University), Guangzhou, China
| | - Dongsheng Xiong
- Department of Biomedical Engineering, School of Materials Science and Engineering, South China University of Technology (SCUT), Guangzhou, China
| | - Hongjun Peng
- Department of Clinical Psychology, Guangzhou Brain Hospital (GBH)/ (Guangzhou Huiai Hospital, The Affiliated Brain Hospital of Guangzhou Medical University), Guangzhou, China
| | - Javier Escudero
- Institute for Digital Communications, School of Engineering, The University of Edinburgh, Edinburgh EH9 3JL, UK
| | - Biao Huang
- School of Medicine, South China University of Technology (SCUT), Guangzhou, China
- Department of Radiology, Guangdong Academy of Medical Sciences, Guangdong General Hospital, Guangzhou, China
| | - Xiaobo Li
- Department of Biomedical Engineering, New Jersey Institute of Technology, NJ, US
- Department of Electric and Computer Engineering, New Jersey Institute of Technology, NJ, US
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, NY, US
| | - Yuping Ning
- Department of Psychiatry, Guangzhou Brain Hospital (GBH)/(Guangzhou Huiai Hospital, The Affiliated Brain Hospital of Guangzhou Medical University), Guangzhou, China
- GBH-SCUT Joint Research Centre for Neuroimaging, Guangzhou, China
| | - Kai Wu
- Department of Psychiatry, Guangzhou Brain Hospital (GBH)/(Guangzhou Huiai Hospital, The Affiliated Brain Hospital of Guangzhou Medical University), Guangzhou, China
- Department of Biomedical Engineering, School of Materials Science and Engineering, South China University of Technology (SCUT), Guangzhou, China
- GBH-SCUT Joint Research Centre for Neuroimaging, Guangzhou, China
- Department of Nuclear Medicine and Radiology, Institute of Development, Aging and Cancer, Tohoku University, Sendai, Japan
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17
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Pathological Brain Detection by a Novel Image Feature—Fractional Fourier Entropy. ENTROPY 2015. [DOI: 10.3390/e17127877] [Citation(s) in RCA: 60] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
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