1
|
Shoeibi A, Ghassemi N, Khodatars M, Moridian P, Khosravi A, Zare A, Gorriz JM, Chale-Chale AH, Khadem A, Rajendra Acharya U. Automatic diagnosis of schizophrenia and attention deficit hyperactivity disorder in rs-fMRI modality using convolutional autoencoder model and interval type-2 fuzzy regression. Cogn Neurodyn 2023; 17:1501-1523. [PMID: 37974583 PMCID: PMC10640504 DOI: 10.1007/s11571-022-09897-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] [Received: 06/12/2022] [Revised: 09/23/2022] [Accepted: 10/04/2022] [Indexed: 11/13/2022] Open
Abstract
Nowadays, many people worldwide suffer from brain disorders, and their health is in danger. So far, numerous methods have been proposed for the diagnosis of Schizophrenia (SZ) and attention deficit hyperactivity disorder (ADHD), among which functional magnetic resonance imaging (fMRI) modalities are known as a popular method among physicians. This paper presents an SZ and ADHD intelligent detection method of resting-state fMRI (rs-fMRI) modality using a new deep learning method. The University of California Los Angeles dataset, which contains the rs-fMRI modalities of SZ and ADHD patients, has been used for experiments. The FMRIB software library toolbox first performed preprocessing on rs-fMRI data. Then, a convolutional Autoencoder model with the proposed number of layers is used to extract features from rs-fMRI data. In the classification step, a new fuzzy method called interval type-2 fuzzy regression (IT2FR) is introduced and then optimized by genetic algorithm, particle swarm optimization, and gray wolf optimization (GWO) techniques. Also, the results of IT2FR methods are compared with multilayer perceptron, k-nearest neighbors, support vector machine, random forest, and decision tree, and adaptive neuro-fuzzy inference system methods. The experiment results show that the IT2FR method with the GWO optimization algorithm has achieved satisfactory results compared to other classifier methods. Finally, the proposed classification technique was able to provide 72.71% accuracy.
Collapse
Affiliation(s)
- Afshin Shoeibi
- FPGA Lab, Faculty of Electrical Engineering, K. N. Toosi University of Technology, Tehran, Iran
| | - Navid Ghassemi
- Computer Engineering Department, Ferdowsi University of Mashhad, Mashhad, Iran
| | - Marjane Khodatars
- Department of Medical Engineering, Mashhad Branch, Islamic Azad University, Mashhad, Iran
| | - Parisa Moridian
- Faculty of Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
| | - Abbas Khosravi
- Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Geelong, Australia
| | - Assef Zare
- Faculty of Electrical Engineering, Gonabad Branch, Islamic Azad University, Gonabad, Iran
| | - Juan M. Gorriz
- Department of Signal Theory, Networking and Communications, Universidad de Granada, Granada, Spain
| | | | - Ali Khadem
- Faculty of Electrical Engineering, K. N. Toosi University of Technology, Tehran, Iran
| | - U. Rajendra Acharya
- Ngee Ann Polytechnic, Singapore, 599489 Singapore
- Department of Biomedical Informatics and Medical Engineering, Asia University, Taichung, Taiwan
- Department of Biomedical Engineering, School of Science and Technology, Singapore University of Social Sciences, Singapore, Singapore
| |
Collapse
|
2
|
Giangreco B, Dwir D, Klauser P, Jenni R, Golay P, Cleusix M, Baumann PS, Cuénod M, Conus P, Toni N, Do KQ. Characterization of early psychosis patients carrying a genetic vulnerability to redox dysregulation: a computational analysis of mechanism-based gene expression profile in fibroblasts. Mol Psychiatry 2023; 28:1983-1994. [PMID: 37002404 PMCID: PMC10575782 DOI: 10.1038/s41380-023-02034-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/03/2021] [Revised: 02/21/2023] [Accepted: 03/10/2023] [Indexed: 06/19/2023]
Abstract
In view of its heterogeneity, schizophrenia needs new diagnostic tools based on mechanistic biomarkers that would allow early detection. Complex interaction between genetic and environmental risk factors may lead to NMDAR hypofunction, inflammation and redox dysregulation, all converging on oxidative stress. Using computational analysis, the expression of 76 genes linked to these systems, known to be abnormally regulated in schizophrenia, was studied in skin-fibroblasts from early psychosis patients and age-matched controls (N = 30), under additional pro-oxidant challenge to mimic environmental stress. To evaluate the contribution of a genetic risk related to redox dysregulation, we investigated the GAG trinucleotide polymorphism in the key glutathione (GSH) synthesizing enzyme, glutamate-cysteine-ligase-catalytic-subunit (gclc) gene, known to be associated with the disease. Patients and controls showed different gene expression profiles that were modulated by GAG-gclc genotypes in combination with oxidative challenge. In GAG-gclc low-risk genotype patients, a global gene expression dysregulation was observed, especially in the antioxidant system, potentially induced by other risks. Both controls and patients with GAG-gclc high-risk genotype (gclcGAG-HR) showed similar gene expression profiles. However, under oxidative challenge, a boosting of other antioxidant defense, including the master regulator Nrf2 and TRX systems was observed only in gclcGAG-HR controls, suggesting a protective compensation against the genetic GSH dysregulation. Moreover, RAGE (redox/inflammation interaction) and AGMAT (arginine pathway) were increased in the gclcGAG-HR patients, suggesting some additional risk factors interacting with this genotype. Finally, the use of a machine-learning approach allowed discriminating patients and controls with an accuracy up to 100%, paving the way towards early detection of schizophrenia.
Collapse
Affiliation(s)
- Basilio Giangreco
- Center for Psychiatric Neuroscience, Department of Psychiatry, Lausanne University Hospital and University of Lausanne (CHUV-UNIL), Lausanne, Switzerland
| | - Daniella Dwir
- Center for Psychiatric Neuroscience, Department of Psychiatry, Lausanne University Hospital and University of Lausanne (CHUV-UNIL), Lausanne, Switzerland
| | - Paul Klauser
- Center for Psychiatric Neuroscience, Department of Psychiatry, Lausanne University Hospital and University of Lausanne (CHUV-UNIL), Lausanne, Switzerland
- Service of Child and Adolescent Psychiatry, Department of Psychiatry, Lausanne University Hospital and University of Lausanne (CHUV-UNIL), Lausanne, Switzerland
| | - Raoul Jenni
- Center for Psychiatric Neuroscience, Department of Psychiatry, Lausanne University Hospital and University of Lausanne (CHUV-UNIL), Lausanne, Switzerland
| | - Philippe Golay
- Service of Community Psychiatry, Department of Psychiatry, Lausanne University Hospital and University of Lausanne (CHUV-UNIL), Lausanne, Switzerland
- Service of General Psychiatry, Department of Psychiatry, Lausanne University Hospital and University of Lausanne (CHUV-UNIL), Lausanne, Switzerland
| | - Martine Cleusix
- Center for Psychiatric Neuroscience, Department of Psychiatry, Lausanne University Hospital and University of Lausanne (CHUV-UNIL), Lausanne, Switzerland
- Service of General Psychiatry, Department of Psychiatry, Lausanne University Hospital and University of Lausanne (CHUV-UNIL), Lausanne, Switzerland
| | - Philipp S Baumann
- Service of General Psychiatry, Department of Psychiatry, Lausanne University Hospital and University of Lausanne (CHUV-UNIL), Lausanne, Switzerland
| | - Michel Cuénod
- Center for Psychiatric Neuroscience, Department of Psychiatry, Lausanne University Hospital and University of Lausanne (CHUV-UNIL), Lausanne, Switzerland
| | - Philippe Conus
- Service of General Psychiatry, Department of Psychiatry, Lausanne University Hospital and University of Lausanne (CHUV-UNIL), Lausanne, Switzerland
| | - Nicolas Toni
- Center for Psychiatric Neuroscience, Department of Psychiatry, Lausanne University Hospital and University of Lausanne (CHUV-UNIL), Lausanne, Switzerland
| | - Kim Q Do
- Center for Psychiatric Neuroscience, Department of Psychiatry, Lausanne University Hospital and University of Lausanne (CHUV-UNIL), Lausanne, Switzerland.
| |
Collapse
|
3
|
Neuroimaging biomarkers for detecting schizophrenia: A resting-state functional MRI-based radiomics analysis. Heliyon 2022; 8:e12276. [PMID: 36582679 PMCID: PMC9793282 DOI: 10.1016/j.heliyon.2022.e12276] [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: 02/18/2022] [Revised: 05/19/2022] [Accepted: 12/02/2022] [Indexed: 12/14/2022] Open
Abstract
Schizophrenia (SZ) is a common psychiatric disorder that is difficult to accurately diagnose in clinical practice. Quantifiable biomarkers are urgently required to explore the potential physiological mechanism of SZ and improve its diagnostic accuracy. Thus, this study aimed to identify biomarkers that classify SZ patients and healthy control subjects and investigate the potential neural mechanisms of SZ using degree centrality (DC)- and voxel-mirrored homotopic connectivity (VMHC)-based radiomics. Radiomics features were extracted from DC and VMHC metrics generated via resting-state functional magnetic resonance imaging, and significant features were selected and dimensionality was reduced using t-tests and least absolute shrinkage and selection operator. Subsequently, we built our model using a support vector machine classifier. We observed that our method obtained great classification performance (area under the curve, 0.808; accuracy, 74.02%), and it could be generalized to different brain atlases. The regions that we identified as discriminative features mainly included bilateral dorsal caudate and front-parietal, somatomotor, limbic, and default mode networks. Our findings showed that the radiomics-based machine learning method could facilitate us to understand the potential pathological mechanism of SZ more comprehensively and contribute to the accurate diagnosis of patients with SZ.
Collapse
|
4
|
Ferrara M, Franchini G, Funaro M, Cutroni M, Valier B, Toffanin T, Palagini L, Zerbinati L, Folesani F, Murri MB, Caruso R, Grassi L. Machine Learning and Non-Affective Psychosis: Identification, Differential Diagnosis, and Treatment. Curr Psychiatry Rep 2022; 24:925-936. [PMID: 36399236 PMCID: PMC9780131 DOI: 10.1007/s11920-022-01399-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 10/12/2022] [Indexed: 11/19/2022]
Abstract
PURPOSE OF REVIEW This review will cover the most relevant findings on the use of machine learning (ML) techniques in the field of non-affective psychosis, by summarizing the studies published in the last three years focusing on illness detection and treatment. RECENT FINDINGS Multiple ML tools that include mostly supervised approaches such as support vector machine, gradient boosting, and random forest showed promising results by applying these algorithms to various sources of data: socio-demographic information, EEG, language, digital content, blood biomarkers, neuroimaging, and electronic health records. However, the overall performance, in the binary classification case, varied from 0.49, which is to be considered very low (i.e., noise), to over 0.90. These results are fully justified by different factors, some of which may be attributable to the preprocessing of the data, the wide variety of the data, and the a-priori setting of hyperparameters. One of the main limitations of the field is the lack of stratification of results based on biological sex, given that psychosis presents differently in men and women; hence, the necessity to tailor identification tools and data analytic strategies. Timely identification and appropriate treatment are key factors in reducing the consequences of psychotic disorders. In recent years, the emergence of new analytical tools based on artificial intelligence such as supervised ML approaches showed promises as a potential breakthrough in this field. However, ML applications in everyday practice are still in its infancy.
Collapse
Affiliation(s)
- Maria Ferrara
- Department of Neuroscience and Rehabilitation, Institute of Psychiatry, University of Ferrara, via Fossato di Mortara 64/A, Ferrara, Italy.
- Department of Psychiatry, Yale School of Medicine, 34 Park Street, New Haven, CT, USA.
| | - Giorgia Franchini
- Department of Physics, Informatics and Mathematics, University of Modena and Reggio Emilia, Via Campi 213/B, Modena, Italy
- Department of Mathematics and Computer Science, University of Ferrara, Via Macchiavelli 33, Ferrara, Italy
| | - Melissa Funaro
- Harvey Cushing/John Hay Whitney Medical Library, Yale University, 333 Cedar St., New Haven, CT, USA
| | - Marcello Cutroni
- Department of Neuroscience and Rehabilitation, Institute of Psychiatry, University of Ferrara, via Fossato di Mortara 64/A, Ferrara, Italy
| | - Beatrice Valier
- Department of Neuroscience and Rehabilitation, Institute of Psychiatry, University of Ferrara, via Fossato di Mortara 64/A, Ferrara, Italy
| | - Tommaso Toffanin
- Department of Neuroscience and Rehabilitation, Institute of Psychiatry, University of Ferrara, via Fossato di Mortara 64/A, Ferrara, Italy
| | - Laura Palagini
- Department of Neuroscience and Rehabilitation, Institute of Psychiatry, University of Ferrara, via Fossato di Mortara 64/A, Ferrara, Italy
| | - Luigi Zerbinati
- Department of Neuroscience and Rehabilitation, Institute of Psychiatry, University of Ferrara, via Fossato di Mortara 64/A, Ferrara, Italy
| | - Federica Folesani
- Department of Neuroscience and Rehabilitation, Institute of Psychiatry, University of Ferrara, via Fossato di Mortara 64/A, Ferrara, Italy
| | - Martino Belvederi Murri
- Department of Neuroscience and Rehabilitation, Institute of Psychiatry, University of Ferrara, via Fossato di Mortara 64/A, Ferrara, Italy
| | - Rosangela Caruso
- Department of Neuroscience and Rehabilitation, Institute of Psychiatry, University of Ferrara, via Fossato di Mortara 64/A, Ferrara, Italy
| | - Luigi Grassi
- Department of Neuroscience and Rehabilitation, Institute of Psychiatry, University of Ferrara, via Fossato di Mortara 64/A, Ferrara, Italy
| |
Collapse
|
5
|
Sadeghi D, Shoeibi A, Ghassemi N, Moridian P, Khadem A, Alizadehsani R, Teshnehlab M, Gorriz JM, Khozeimeh F, Zhang YD, Nahavandi S, Acharya UR. An overview of artificial intelligence techniques for diagnosis of Schizophrenia based on magnetic resonance imaging modalities: Methods, challenges, and future works. Comput Biol Med 2022; 146:105554. [DOI: 10.1016/j.compbiomed.2022.105554] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2021] [Revised: 04/11/2022] [Accepted: 04/11/2022] [Indexed: 12/21/2022]
|
6
|
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.
Collapse
|
7
|
Zhao L, Sun YK, Xue SW, Luo H, Lu XD, Zhang LH. Identifying Boys With Autism Spectrum Disorder Based on Whole-Brain Resting-State Interregional Functional Connections Using a Boruta-Based Support Vector Machine Approach. Front Neuroinform 2022; 16:761942. [PMID: 35273487 PMCID: PMC8901599 DOI: 10.3389/fninf.2022.761942] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2021] [Accepted: 01/12/2022] [Indexed: 11/13/2022] Open
Abstract
An increasing number of resting-state functional magnetic resonance neuroimaging (R-fMRI) studies have used functional connections as discriminative features for machine learning to identify patients with brain diseases. However, it remains unclear which functional connections could serve as highly discriminative features to realize the classification of autism spectrum disorder (ASD). The aim of this study was to find ASD-related functional connectivity patterns and examine whether these patterns had the potential to provide neuroimaging-based information to clinically assist with the diagnosis of ASD by means of machine learning. We investigated the whole-brain interregional functional connections derived from R-fMRI. Data were acquired from 48 boys with ASD and 50 typically developing age-matched controls at NYU Langone Medical Center from the publicly available Autism Brain Imaging Data Exchange I (ABIDE I) dataset; the ASD-related functional connections identified by the Boruta algorithm were used as the features of support vector machine (SVM) to distinguish patients with ASD from typically developing controls (TDC); a permutation test was performed to assess the classification performance. Approximately, 92.9% of participants were correctly classified by a combined SVM and leave-one-out cross-validation (LOOCV) approach, wherein 95.8% of patients with ASD were correctly identified. The default mode network (DMN) exhibited a relatively high network degree and discriminative power. Eight important brain regions showed a high discriminative power, including the posterior cingulate cortex (PCC) and the ventrolateral prefrontal cortex (vlPFC). Significant correlations were found between the classification scores of several functional connections and ASD symptoms (p < 0.05). This study highlights the important role of DMN in ASD identification. Interregional functional connections might provide useful information for the clinical diagnosis of ASD.
Collapse
Affiliation(s)
- Lei Zhao
- Centre for Cognition and Brain Disorders, The Affiliated Hospital of Hangzhou Normal University, Hangzhou, China
- Institute of Psychological Science, Hangzhou Normal University, Hangzhou, China
- Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou, China
| | - Yun-Kai Sun
- Department of Psychiatry, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Shao-Wei Xue
- Centre for Cognition and Brain Disorders, The Affiliated Hospital of Hangzhou Normal University, Hangzhou, China
- Institute of Psychological Science, Hangzhou Normal University, Hangzhou, China
- Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou, China
- *Correspondence: Shao-Wei Xue
| | - Hong Luo
- Centre for Cognition and Brain Disorders, The Affiliated Hospital of Hangzhou Normal University, Hangzhou, China
- Institute of Psychological Science, Hangzhou Normal University, Hangzhou, China
- Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou, China
| | - Xiao-Dong Lu
- Centre for Cognition and Brain Disorders, The Affiliated Hospital of Hangzhou Normal University, Hangzhou, China
- Institute of Psychological Science, Hangzhou Normal University, Hangzhou, China
- Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou, China
| | - Lan-Hua Zhang
- College of Medical Information and Engineering, Shandong First Medical University and Shandong Academy of Medical Sciences, Tai'an, China
- Lan-Hua Zhang
| |
Collapse
|
8
|
Shi AP, Yu Y, Hu B, Li YT, Wang W, Cui GB. Large-scale functional connectivity predicts cognitive impairment related to type 2 diabetes mellitus. World J Diabetes 2022; 13:110-125. [PMID: 35211248 PMCID: PMC8855139 DOI: 10.4239/wjd.v13.i2.110] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/23/2021] [Revised: 12/10/2021] [Accepted: 01/06/2022] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND Large-scale functional connectivity (LSFC) patterns in the brain have unique intrinsic characteristics. Abnormal LSFC patterns have been found in patients with dementia, as well as in those with mild cognitive impairment (MCI), and these patterns predicted their cognitive performance. It has been reported that patients with type 2 diabetes mellitus (T2DM) may develop MCI that could progress to dementia. We investigated whether we could adopt LSFC patterns as discriminative features to predict the cognitive function of patients with T2DM, using connectome-based predictive modeling (CPM) and a support vector machine.
AIM To investigate the utility of LSFC for predicting cognitive impairment related to T2DM more accurately and reliably.
METHODS Resting-state functional magnetic resonance images were derived from 42 patients with T2DM and 24 healthy controls. Cognitive function was assessed using the Montreal Cognitive Assessment (MoCA). Patients with T2DM were divided into two groups, according to the presence (T2DM-C; n = 16) or absence (T2DM-NC; n = 26) of MCI. Brain regions were marked using Harvard Oxford (HOA-112), automated anatomical labeling (AAL-116), and 264-region functional (Power-264) atlases. LSFC biomarkers for predicting MoCA scores were identified using a new CPM technique. Subsequently, we used a support vector machine based on LSFC patterns for among-group differentiation. The area under the receiver operating characteristic curve determined the appearance of the classification.
RESULTS CPM could predict the MoCA scores in patients with T2DM (Pearson’s correlation coefficient between predicted and actual MoCA scores, r = 0.32, P=0.0066 [HOA-112 atlas]; r = 0.32, P=0.0078 [AAL-116 atlas]; r = 0.42, P=0.0038 [Power-264 atlas]), indicating that LSFC patterns represent cognition-level measures in these patients. Positive (anti-correlated) LSFC networks based on the Power-264 atlas showed the best predictive performance; moreover, we observed new brain regions of interest associated with T2DM-related cognition. The area under the receiver operating characteristic curve values (T2DM-NC group vs. T2DM-C group) were 0.65-0.70, with LSFC matrices based on HOA-112 and Power-264 atlases having the highest value (0.70). Most discriminative and attractive LSFCs were related to the default mode network, limbic system, and basal ganglia.
CONCLUSION LSFC provides neuroimaging-based information that may be useful in detecting MCI early and accurately in patients with T2DM.
Collapse
Affiliation(s)
- An-Ping Shi
- Department of Radiology, Department of Radiology and Functional and Molecular Imaging Key Lab of Shaanxi Province, The Affiliated Tangdu Hospital of Air Force Medical University (Fourth Military Medical University), Xi'an 710038, Shaanxi Province, China
| | - Ying Yu
- Department of Radiology, Department of Radiology and Functional and Molecular Imaging Key Lab of Shaanxi Province, The Affiliated Tangdu Hospital of Air Force Medical University (Fourth Military Medical University), Xi'an 710038, Shaanxi Province, China
| | - Bo Hu
- Department of Radiology, Department of Radiology and Functional and Molecular Imaging Key Lab of Shaanxi Province, The Affiliated Tangdu Hospital of Air Force Medical University (Fourth Military Medical University), Xi'an 710038, Shaanxi Province, China
| | - Yu-Ting Li
- Department of Radiology, Department of Radiology and Functional and Molecular Imaging Key Lab of Shaanxi Province, The Affiliated Tangdu Hospital of Air Force Medical University (Fourth Military Medical University), Xi'an 710038, Shaanxi Province, China
| | - Wen Wang
- Department of Radiology, Department of Radiology and Functional and Molecular Imaging Key Lab of Shaanxi Province, The Affiliated Tangdu Hospital of Air Force Medical University (Fourth Military Medical University), Xi'an 710038, Shaanxi Province, China
| | - Guang-Bin Cui
- Department of Radiology, Department of Radiology and Functional and Molecular Imaging Key Lab of Shaanxi Province, The Affiliated Tangdu Hospital of Air Force Medical University (Fourth Military Medical University), Xi'an 710038, Shaanxi Province, China
| |
Collapse
|
9
|
Zang J, Huang Y, Kong L, Lei B, Ke P, Li H, Zhou J, Xiong D, Li G, Chen J, Li X, Xiang Z, Ning Y, Wu F, Wu K. Effects of Brain Atlases and Machine Learning Methods on the Discrimination of Schizophrenia Patients: A Multimodal MRI Study. Front Neurosci 2021; 15:697168. [PMID: 34385901 PMCID: PMC8353157 DOI: 10.3389/fnins.2021.697168] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2021] [Accepted: 07/07/2021] [Indexed: 11/24/2022] Open
Abstract
Recently, machine learning techniques have been widely applied in discriminative studies of schizophrenia (SZ) patients with multimodal magnetic resonance imaging (MRI); however, the effects of brain atlases and machine learning methods remain largely unknown. In this study, we collected MRI data for 61 first-episode SZ patients (FESZ), 79 chronic SZ patients (CSZ) and 205 normal controls (NC) and calculated 4 MRI measurements, including regional gray matter volume (GMV), regional homogeneity (ReHo), amplitude of low-frequency fluctuation and degree centrality. We systematically analyzed the performance of two classifications (SZ vs NC; FESZ vs CSZ) based on the combinations of three brain atlases, five classifiers, two cross validation methods and 3 dimensionality reduction algorithms. Our results showed that the groupwise whole-brain atlas with 268 ROIs outperformed the other two brain atlases. In addition, the leave-one-out cross validation was the best cross validation method to select the best hyperparameter set, but the classification performances by different classifiers and dimensionality reduction algorithms were quite similar. Importantly, the contributions of input features to both classifications were higher with the GMV and ReHo features of brain regions in the prefrontal and temporal gyri. Furthermore, an ensemble learning method was performed to establish an integrated model, in which classification performance was improved. Taken together, these findings indicated the effects of these factors in constructing effective classifiers for psychiatric diseases and showed that the integrated model has the potential to improve the clinical diagnosis and treatment evaluation of SZ.
Collapse
Affiliation(s)
- Jinyu Zang
- Department of Biomedical Engineering, School of Material Science and Engineering, South China University of Technology, Guangzhou, China.,Guangdong Engineering Technology Research Center for Translational Medicine of Mental Disorders, Guangzhou, China.,National Engineering Research Center for Tissue Restoration and Reconstruction, South China University of Technology, Guangzhou, China
| | - Yuanyuan Huang
- Guangdong Engineering Technology Research Center for Translational Medicine of Mental Disorders, Guangzhou, China.,The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou Huiai Hospital, Guangzhou, China
| | - Lingyin Kong
- Department of Biomedical Engineering, School of Material Science and Engineering, South China University of Technology, Guangzhou, China.,Guangdong Engineering Technology Research Center for Translational Medicine of Mental Disorders, Guangzhou, China.,National Engineering Research Center for Tissue Restoration and Reconstruction, South China University of Technology, Guangzhou, China
| | - Bingye Lei
- Department of Biomedical Engineering, School of Material Science and Engineering, South China University of Technology, Guangzhou, China.,Guangdong Engineering Technology Research Center for Translational Medicine of Mental Disorders, Guangzhou, China.,National Engineering Research Center for Tissue Restoration and Reconstruction, South China University of Technology, Guangzhou, China
| | - Pengfei Ke
- Department of Biomedical Engineering, School of Material Science and Engineering, South China University of Technology, Guangzhou, China.,Guangdong Engineering Technology Research Center for Translational Medicine of Mental Disorders, Guangzhou, China.,National Engineering Research Center for Tissue Restoration and Reconstruction, South China University of Technology, Guangzhou, China
| | - Hehua Li
- The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou Huiai Hospital, Guangzhou, China
| | - Jing Zhou
- Department of Biomedical Engineering, School of Material Science and Engineering, South China University of Technology, Guangzhou, China.,Guangdong Engineering Technology Research Center for Translational Medicine of Mental Disorders, Guangzhou, China.,National Engineering Research Center for Tissue Restoration and Reconstruction, South China University of Technology, Guangzhou, China
| | - Dongsheng Xiong
- Department of Biomedical Engineering, School of Material Science and Engineering, South China University of Technology, Guangzhou, China.,Guangdong Engineering Technology Research Center for Translational Medicine of Mental Disorders, Guangzhou, China.,National Engineering Research Center for Tissue Restoration and Reconstruction, South China University of Technology, Guangzhou, China
| | - Guixiang Li
- Guangdong Engineering Technology Research Center for Diagnosis and Rehabilitation of Dementia, Guangzhou, China.,National Engineering Research Center for Healthcare Devices, Guangzhou, China
| | - Jun Chen
- Guangdong Engineering Technology Research Center for Diagnosis and Rehabilitation of Dementia, Guangzhou, China.,National Engineering Research Center for Healthcare Devices, Guangzhou, China
| | - Xiaobo Li
- Department of Biomedical Engineering, New Jersey Institute of Technology, Newark, NJ, United States
| | - Zhiming Xiang
- Guangdong Engineering Technology Research Center for Diagnosis and Rehabilitation of Dementia, Guangzhou, China.,Department of Radiology, Panyu Central Hospital of Guangzhou, Guangzhou, China
| | - Yuping Ning
- Guangdong Engineering Technology Research Center for Translational Medicine of Mental Disorders, Guangzhou, China.,The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou Huiai Hospital, Guangzhou, China
| | - Fengchun Wu
- The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou Huiai Hospital, Guangzhou, China
| | - Kai Wu
- Department of Biomedical Engineering, School of Material Science and Engineering, South China University of Technology, Guangzhou, China.,Guangdong Engineering Technology Research Center for Translational Medicine of Mental Disorders, Guangzhou, China.,National Engineering Research Center for Tissue Restoration and Reconstruction, South China University of Technology, Guangzhou, China.,The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou Huiai Hospital, Guangzhou, China.,Guangdong Engineering Technology Research Center for Diagnosis and Rehabilitation of Dementia, Guangzhou, China.,National Engineering Research Center for Healthcare Devices, Guangzhou, China.,Key Laboratory of Biomedical Engineering of Guangdong Province, South China University of Technology, Guangzhou, China.,Department of Nuclear Medicine and Radiology, Institute of Development, Aging and Cancer, Tohoku University, Sendai, Japan
| |
Collapse
|
10
|
Shi D, Li Y, Zhang H, Yao X, Wang S, Wang G, Ren K. Machine Learning of Schizophrenia Detection with Structural and Functional Neuroimaging. DISEASE MARKERS 2021; 2021:9963824. [PMID: 34211615 PMCID: PMC8208855 DOI: 10.1155/2021/9963824] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/09/2021] [Accepted: 06/03/2021] [Indexed: 01/10/2023]
Abstract
Schizophrenia (SZ) is a severe psychiatric illness, and it affects around 1% of the general population; however, its reliable diagnosis is challenging. Functional MRI (fMRI) and structural MRI (sMRI) are useful techniques for investigating the functional and structural abnormalities of the human brain, and a growing number of studies have reported that multimodal brain data can improve diagnostic accuracy. Machine learning (ML) is widely used in the diagnosis of neuroscience and neuropsychiatry diseases, and it can obtain high accuracy. However, the conventional ML which concatenated the features into a longer feature vector could not be sufficiently effective to combine different features from different modalities. There are considerable controversies over the use of global signal regression (GSR), and few studies have explored the role of GSR in ML in diagnosing neurological diseases. The current study utilized fMRI and sMRI data to implement a new method named multimodal imaging and multilevel characterization with multiclassifier (M3) to classify SZs and healthy controls (HCs) and investigate the influence of GSR in SZ classification. We found that when we used Brainnetome 246 atlas and without performed GSR, our method obtained a classification accuracy of 83.49%, with a sensitivity of 68.69%, a specificity of 93.75%, and an AUC of 0.8491, respectively. We also got great classification performances with different processing methods (with/without GSR and different brain parcellation schemes). We found that the accuracy and specificity of the models without GSR were higher than that of the models with GSR. Our findings indicate that the M3 method is an effective tool to distinguish SZs from HCs, and it can identify discriminative regions to detect SZ to explore the neural mechanisms underlying SZ. The global signal may contain important neuronal information; it can improve the accuracy and specificity of SZ detection.
Collapse
Affiliation(s)
- Dafa Shi
- Department of Radiology, Xiang'an Hospital of Xiamen University, Xiamen 361002, China
| | - Yanfei Li
- Department of Radiology, Xiang'an Hospital of Xiamen University, Xiamen 361002, China
| | - Haoran Zhang
- Department of Radiology, Xiang'an Hospital of Xiamen University, Xiamen 361002, China
| | - Xiang Yao
- Department of Radiology, Xiang'an Hospital of Xiamen University, Xiamen 361002, China
| | - Siyuan Wang
- Department of Radiology, Xiang'an Hospital of Xiamen University, Xiamen 361002, China
| | - Guangsong Wang
- Department of Radiology, Xiang'an Hospital of Xiamen University, Xiamen 361002, China
| | - Ke Ren
- Department of Radiology, Xiang'an Hospital of Xiamen University, Xiamen 361002, China
| |
Collapse
|
11
|
Lai JW, Ang CKE, Acharya UR, Cheong KH. Schizophrenia: A Survey of Artificial Intelligence Techniques Applied to Detection and Classification. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:6099. [PMID: 34198829 PMCID: PMC8201065 DOI: 10.3390/ijerph18116099] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/01/2021] [Revised: 05/26/2021] [Accepted: 05/28/2021] [Indexed: 02/07/2023]
Abstract
Artificial Intelligence in healthcare employs machine learning algorithms to emulate human cognition in the analysis of complicated or large sets of data. Specifically, artificial intelligence taps on the ability of computer algorithms and software with allowable thresholds to make deterministic approximate conclusions. In comparison to traditional technologies in healthcare, artificial intelligence enhances the process of data analysis without the need for human input, producing nearly equally reliable, well defined output. Schizophrenia is a chronic mental health condition that affects millions worldwide, with impairment in thinking and behaviour that may be significantly disabling to daily living. Multiple artificial intelligence and machine learning algorithms have been utilized to analyze the different components of schizophrenia, such as in prediction of disease, and assessment of current prevention methods. These are carried out in hope of assisting with diagnosis and provision of viable options for individuals affected. In this paper, we review the progress of the use of artificial intelligence in schizophrenia.
Collapse
Affiliation(s)
- Joel Weijia Lai
- Science, Mathematics and Technology, Singapore University of Technology and Design, 8 Somapah Road, Singapore 487372, Singapore; (J.W.L.); (C.K.E.A.)
| | - Candice Ke En Ang
- Science, Mathematics and Technology, Singapore University of Technology and Design, 8 Somapah Road, Singapore 487372, Singapore; (J.W.L.); (C.K.E.A.)
- MOH Holdings Pte Ltd, 1 Maritime Square, Singapore 099253, Singapore
| | - U. Rajendra Acharya
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Clementi 599489, Singapore;
- Department of Biomedical Engineering, School of Science and Technology, Singapore University of Social Sciences, Clementi 599491, Singapore
- Department of Biomedical Informatics and Medical Engineering, Asia University, Taichung 41354, Taiwan
| | - Kang Hao Cheong
- Science, Mathematics and Technology, Singapore University of Technology and Design, 8 Somapah Road, Singapore 487372, Singapore; (J.W.L.); (C.K.E.A.)
| |
Collapse
|
12
|
Abstract
Neurophysiological signals are crucial intermediaries, through which brain activity can be quantitatively measured and brain mechanisms are able to be revealed. In particular, non‐invasive neurophysiological signals, such as electroencephalogram (EEG) and functional magnetic resonance imaging (fMRI), are welcomed and frequently utilised in various studies since these signals can be non‐invasively recorded without harming the human brain while they convey abundant information pertaining to brain activity. The recorded neurophysiological signals are analysed to mine meaningful information for the understanding of brain mechanisms or are classified to distinguish different patterns (e.g., different cognitive states, brain diseases versus healthy controls). To date, remarkable progress has been made in both the analysis and classification of neurophysiological signals, but scholars are not feeling complacent. Consistent effort ought to be paid to advance the research of analysis and classification based on neurophysiological signals. In this paper, I express my thoughts regarding promising future directions in neurophysiological signal analysis and classification based on current developments and accomplishments. I will elucidate the thoughts after brief summaries of relevant backgrounds, accomplishments, and tendencies. According to my personal selection and preference, I mainly focus on brain connectivity, multidimensional array (tensor), multi‐modality, multiple task classification, deep learning, big data, and naturalistic experiment. Hopefully, my thoughts could give a little help to inspire new ideas and contribute to the research of the analysis and classification of neurophysiological signals in some way.
Collapse
Affiliation(s)
- Junhua Li
- Laboratory for Brain–Bionic Intelligence and Computational Neuroscience, Wuyi University, Jiangmen 529020, Guangdong, China
- Centre for Multidisciplinary Convergence Computing, School of Computer Science and Engineering, Northwestern Polytechnical University, Xi’an 710072, Shaanxi, China
- School of Computer Science and Electronic Engineering, University of Essex, Colchester, CO4 3SQ, UK
| |
Collapse
|
13
|
Potvin S, Giguère CÉ, Mendrek A. Functional Connectivity During Visuospatial Processing in Schizophrenia: A Classification Study Using Lasso Regression. Neuropsychiatr Dis Treat 2021; 17:1077-1087. [PMID: 33888984 PMCID: PMC8055358 DOI: 10.2147/ndt.s304434] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/29/2021] [Accepted: 03/16/2021] [Indexed: 11/23/2022] Open
Abstract
BACKGROUND Robust evidence shows that schizophrenia is associated with significant cognitive impairments, including deficits in visuospatial abilities. While other cognitive domains have sparked several functional neuroimaging studies in schizophrenia, only a few brain activation studies have examined the neural correlates of visuospatial abilities in schizophrenia. PURPOSE Here, we propose to perform a functional connectivity study on visuospatial processing in schizophrenia, and to determine the classification accuracy of the observed alterations. METHODS Thirty-nine schizophrenia patients and 42 healthy controls were scanned using functional magnetic resonance imaging while performing a mental rotation task. Task-based functional connectivity was examined using a region-of-interest (ROI) to ROI approach, as implemented in the CONN Toolbox. ROIs were selected from a previous meta-analysis on mental rotation. Logistic regression with Lasso regularization was performed, using train-test cross-validation. RESULTS Schizophrenia was associated with a complex pattern of dysconnectivity between the superior, middle and inferior frontal gyrus, the precentral gyrus, the superior parietal lobule (SPL) and the inferior lateral occipital cortex. The classification accuracy was 86.1%. Mental rotation performance was predicted by the dysconnectivity between the right and left superior frontal gyrus (SFG), as well as between the left SFG and left SPL. CONCLUSION The results of the current study highlight that visuospatial processing is useful for examining the widespread dysconnectivity between executive, motor and visual brain regions in schizophrenia. We also demonstrate that very good classification accuracy can be achieved using visuospatial-related functional connectivity data.
Collapse
Affiliation(s)
- Stéphane Potvin
- Centre de recherche de l'Institut Universitaire en Santé Mentale de Montréal, Montreal, Quebec, Canada.,Department of Psychiatry and Addiction, Faculty of Medicine, University of Montreal, Montreal, Quebec, Canada
| | - Charles-Édouard Giguère
- Centre de recherche de l'Institut Universitaire en Santé Mentale de Montréal, Montreal, Quebec, Canada
| | - Adrianna Mendrek
- Department of Psychology, Bishop's University, Lennoxville, Quebec, Canada
| |
Collapse
|
14
|
Segato A, Marzullo A, Calimeri F, De Momi E. Artificial intelligence for brain diseases: A systematic review. APL Bioeng 2020; 4:041503. [PMID: 33094213 PMCID: PMC7556883 DOI: 10.1063/5.0011697] [Citation(s) in RCA: 43] [Impact Index Per Article: 10.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2020] [Accepted: 09/09/2020] [Indexed: 12/15/2022] Open
Abstract
Artificial intelligence (AI) is a major branch of computer science that is fruitfully used for analyzing complex medical data and extracting meaningful relationships in datasets, for several clinical aims. Specifically, in the brain care domain, several innovative approaches have achieved remarkable results and open new perspectives in terms of diagnosis, planning, and outcome prediction. In this work, we present an overview of different artificial intelligent techniques used in the brain care domain, along with a review of important clinical applications. A systematic and careful literature search in major databases such as Pubmed, Scopus, and Web of Science was carried out using "artificial intelligence" and "brain" as main keywords. Further references were integrated by cross-referencing from key articles. 155 studies out of 2696 were identified, which actually made use of AI algorithms for different purposes (diagnosis, surgical treatment, intra-operative assistance, and postoperative assessment). Artificial neural networks have risen to prominent positions among the most widely used analytical tools. Classic machine learning approaches such as support vector machine and random forest are still widely used. Task-specific algorithms are designed for solving specific problems. Brain images are one of the most used data types. AI has the possibility to improve clinicians' decision-making ability in neuroscience applications. However, major issues still need to be addressed for a better practical use of AI in the brain. To this aim, it is important to both gather comprehensive data and build explainable AI algorithms.
Collapse
Affiliation(s)
- Alice Segato
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan 20133, Italy
| | - Aldo Marzullo
- Department of Mathematics and Computer Science, University of Calabria, Rende 87036, Italy
| | - Francesco Calimeri
- Department of Mathematics and Computer Science, University of Calabria, Rende 87036, Italy
| | - Elena De Momi
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan 20133, Italy
| |
Collapse
|
15
|
Liang S, Deng W, Li X, Wang Q, Greenshaw AJ, Guo W, Kong X, Li M, Zhao L, Meng Y, Zhang C, Yu H, Li XM, Ma X, Li T. Aberrant posterior cingulate connectivity classify first-episode schizophrenia from controls: A machine learning study. Schizophr Res 2020; 220:187-193. [PMID: 32220502 DOI: 10.1016/j.schres.2020.03.022] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/15/2019] [Revised: 02/23/2020] [Accepted: 03/10/2020] [Indexed: 02/08/2023]
Abstract
BACKGROUND Posterior cingulate cortex (PCC) is a key aspect of the default mode network (DMN). Aberrant PCC functional connectivity (FC) is implicated in schizophrenia, but the potential for PCC related changes as biological classifier of schizophrenia has not yet been evaluated. METHODS We conducted a data-driven approach using resting-state functional MRI data to explore differences in PCC-based region- and voxel-wise FC patterns, to distinguish between patients with first-episode schizophrenia (FES) and demographically matched healthy controls (HC). Discriminative PCC FCs were selected via false discovery rate estimation. A gradient boosting classifier was trained and validated based on 100 FES vs. 93 HC. Subsequently, classification models were tested in an independent dataset of 87 FES patients and 80 HC using resting-state data acquired on a different MRI scanner. RESULTS Patients with FES had reduced connectivity between PCC and frontal areas, left parahippocampal regions, left anterior cingulate cortex, and right inferior parietal lobule, but hyperconnectivity with left lateral temporal regions. Predictive voxel-wise clusters were similar to region-wise selected brain areas functionally connected with PCC in relation to discriminating FES from HC subject categories. Region-wise analysis of FCs yielded a relatively high predictive level for schizophrenia, with an average accuracy of 72.28% in the independent samples, while selected voxel-wise connectivity yielded an accuracy of 68.72%. CONCLUSION FES exhibited a pattern of both increased and decreased PCC-based connectivity, but was related to predominant hypoconnectivity between PCC and brain areas associated with DMN, that may be a useful differential feature revealing underpinnings of neuropathophysiology for schizophrenia.
Collapse
Affiliation(s)
- Sugai Liang
- Mental Health Center, Psychiatric Laboratory, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu 610041, Sichuan, China; West China Brain Research Centre, West China Hospital, Sichuan University, Chengdu 610041, Sichuan, China
| | - Wei Deng
- Mental Health Center, Psychiatric Laboratory, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu 610041, Sichuan, China; West China Brain Research Centre, West China Hospital, Sichuan University, Chengdu 610041, Sichuan, China
| | - Xiaojing Li
- Mental Health Center, Psychiatric Laboratory, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu 610041, Sichuan, China
| | - Qiang Wang
- Mental Health Center, Psychiatric Laboratory, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu 610041, Sichuan, China
| | - Andrew J Greenshaw
- Department of Psychiatry, University of Alberta, Edmonton T6G 2B7, AB, Canada
| | - Wanjun Guo
- Mental Health Center, Psychiatric Laboratory, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu 610041, Sichuan, China; West China Brain Research Centre, West China Hospital, Sichuan University, Chengdu 610041, Sichuan, China
| | - Xiangzhen Kong
- Language and Genetics Department, Max Planck Institute for Psycholinguistics, Nijmegen 6525, XD, Netherlands
| | - Mingli Li
- Mental Health Center, Psychiatric Laboratory, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu 610041, Sichuan, China
| | - Liansheng Zhao
- Mental Health Center, Psychiatric Laboratory, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu 610041, Sichuan, China
| | - Yajing Meng
- Mental Health Center, Psychiatric Laboratory, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu 610041, Sichuan, China
| | - Chengcheng Zhang
- Mental Health Center, Psychiatric Laboratory, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu 610041, Sichuan, China
| | - Hua Yu
- Mental Health Center, Psychiatric Laboratory, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu 610041, Sichuan, China
| | - Xin-Min Li
- Department of Psychiatry, University of Alberta, Edmonton T6G 2B7, AB, Canada
| | - Xiaohong Ma
- Mental Health Center, Psychiatric Laboratory, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu 610041, Sichuan, China
| | - Tao Li
- Mental Health Center, Psychiatric Laboratory, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu 610041, Sichuan, China; West China Brain Research Centre, West China Hospital, Sichuan University, Chengdu 610041, Sichuan, China.
| |
Collapse
|
16
|
Xiang Y, Wang J, Tan G, Wu FX, Liu J. Schizophrenia Identification Using Multi-View Graph Measures of Functional Brain Networks. Front Bioeng Biotechnol 2020; 7:479. [PMID: 32010682 PMCID: PMC6974443 DOI: 10.3389/fbioe.2019.00479] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2019] [Accepted: 12/23/2019] [Indexed: 01/06/2023] Open
Abstract
Schizophrenia (SZ) is a functional mental disorder that seriously affects the social life of patients. Therefore, accurate diagnosis of SZ has raised extensive attention of researchers. At present, study of brain network based on resting-state functional magnetic resonance imaging (rs-fMRI) has provided promising results for SZ identification by studying functional network alteration. However, previous studies based on brain network analysis are not very effective for SZ identification. Therefore, we propose an improved SZ identification method using multi-view graph measures of functional brain networks. Firstly, we construct an individual functional connectivity network based on Brainnetome atlas for each subject. Then, multi-view graph measures are calculated by the brain network analysis method as feature representations. Next, in order to consider the relationships between measures within the same brain region in feature selection, multi-view measures are grouped according to the corresponding regions and Sparse Group Lasso is applied to identify discriminative features based on this feature grouping structure. Finally, a support vector machine (SVM) classifier is employed to perform SZ identification task. To evaluate our proposed method, computational experiments are conducted on 145 subjects (71 schizophrenic patients and 74 healthy controls) using a leave-one-out cross-validation (LOOCV) scheme. The results show that our proposed method can obtain an accuracy of 93.10% for SZ identification. By comparison, our method is more effective for SZ identification than some existing methods.
Collapse
Affiliation(s)
- Yizhen Xiang
- School of Computer Science and Engineering, Central South University, Changsha, China
| | - Jianxin Wang
- School of Computer Science and Engineering, Central South University, Changsha, China.,Hunan Provincial Key Lab on Bioinformatics, Central South University, Changsha, China
| | - Guanxin Tan
- School of Computer Science and Engineering, Central South University, Changsha, China
| | - Fang-Xiang Wu
- Division of Biomedical Engineering and Department of Mechanical Engineering, University of Saskatchewan, Saskatoon, SK, Canada
| | - Jin Liu
- School of Computer Science and Engineering, Central South University, Changsha, China
| |
Collapse
|
17
|
Steardo L, Carbone EA, de Filippis R, Pisanu C, Segura-Garcia C, Squassina A, De Fazio P, Steardo L. Application of Support Vector Machine on fMRI Data as Biomarkers in Schizophrenia Diagnosis: A Systematic Review. Front Psychiatry 2020; 11:588. [PMID: 32670113 PMCID: PMC7326270 DOI: 10.3389/fpsyt.2020.00588] [Citation(s) in RCA: 50] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/31/2020] [Accepted: 06/08/2020] [Indexed: 01/06/2023] Open
Abstract
Non-invasive measurements of brain function and structure as neuroimaging in patients with mental illnesses are useful and powerful tools for studying discriminatory biomarkers. To date, functional MRI (fMRI), structural MRI (sMRI) represent the most used techniques to provide multiple perspectives on brain function, structure, and their connectivity. Recently, there has been rising attention in using machine-learning (ML) techniques, pattern recognition methods, applied to neuroimaging data to characterize disease-related alterations in brain structure and function and to identify phenotypes, for example, for translation into clinical and early diagnosis. Our aim was to provide a systematic review according to the PRISMA statement of Support Vector Machine (SVM) techniques in making diagnostic discrimination between SCZ patients from healthy controls using neuroimaging data from functional MRI as input. We included studies using SVM as ML techniques with patients diagnosed with Schizophrenia. From an initial sample of 660 papers, at the end of the screening process, 22 articles were selected, and included in our review. This technique can be a valid, inexpensive, and non-invasive support to recognize and detect patients at an early stage, compared to any currently available assessment or clinical diagnostic methods in order to save crucial time. The higher accuracy of SVM models and the new integrated methods of ML techniques could play a decisive role to detect patients with SCZ or other major psychiatric disorders in the early stages of the disease or to potentially determine their neuroimaging risk factors in the near future.
Collapse
Affiliation(s)
- Luca Steardo
- Department of Health Sciences, School of Medicine and Surgery, University Magna Graecia of Catanzaro, Catanzaro, Italy
| | - Elvira Anna Carbone
- Department of Health Sciences, School of Medicine and Surgery, University Magna Graecia of Catanzaro, Catanzaro, Italy
| | - Renato de Filippis
- Department of Health Sciences, School of Medicine and Surgery, University Magna Graecia of Catanzaro, Catanzaro, Italy
| | - Claudia Pisanu
- Section of Neuroscience and Clinical Pharmacology, Department of Biomedical Sciences, Faculty of Medicine and Surgery, University of Cagliari, Cagliari, Italy
| | - Cristina Segura-Garcia
- Department of Medical and Surgical Science, University of Magna Graecia, Catanzaro, Italy
| | - Alessio Squassina
- Section of Neuroscience and Clinical Pharmacology, Department of Biomedical Sciences, Faculty of Medicine and Surgery, University of Cagliari, Cagliari, Italy.,Department of Psychiatry, Faculty of Medicine, Dalhousie University, Halifax, NS, Canada
| | - Pasquale De Fazio
- Department of Health Sciences, School of Medicine and Surgery, University Magna Graecia of Catanzaro, Catanzaro, Italy
| | - Luca Steardo
- Department of Physiology and Pharmacology, Faculty of Pharmacy and Medicine, Sapienza University of Rome, Rome, Italy.,Department of Psychiatry, Giustino Fortunato University, Benevento, Italy
| |
Collapse
|
18
|
Sun Y, Zhao L, Lan Z, Jia XZ, Xue SW. Differentiating Boys with ADHD from Those with Typical Development Based on Whole-Brain Functional Connections Using a Machine Learning Approach. Neuropsychiatr Dis Treat 2020; 16:691-702. [PMID: 32210565 PMCID: PMC7071874 DOI: 10.2147/ndt.s239013] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/18/2019] [Accepted: 03/01/2020] [Indexed: 12/20/2022] Open
Abstract
PURPOSE In recent years, machine learning techniques have received increasing attention as a promising approach to differentiating patients from healthy subjects. Therefore, some resting-state functional magnetic resonance neuroimaging (R-fMRI) studies have used interregional functional connections as discriminative features. The aim of this study was to investigate ADHD-related spatially distributed discriminative features derived from whole-brain resting-state functional connectivity patterns using machine learning. PATIENTS AND METHODS We measured the interregional functional connections of the R-fMRI data from 40 ADHD patients and 28 matched typically developing controls. Machine learning was used to discriminate ADHD patients from controls. Classification performance was assessed by permutation tests. RESULTS The results from the model with the highest classification accuracy showed that 85.3% of participants were correctly identified using leave-one-out cross-validation (LOOV) with support vector machine (SVM). The majority of the most discriminative functional connections were located within or between the cerebellum, default mode network (DMN) and frontoparietal regions. Approximately half of the most discriminative connections were associated with the cerebellum. The cerebellum, right superior orbitofrontal cortex, left olfactory cortex, left gyrus rectus, right superior temporal pole, right calcarine gyrus and bilateral inferior occipital cortex showed the highest discriminative power in classification. Regarding the brain-behaviour relationships, some functional connections between the cerebellum and DMN regions were significantly correlated with behavioural symptoms in ADHD (P < 0.05). CONCLUSION This study indicated that whole-brain resting-state functional connections might provide potential neuroimaging-based information for clinically assisting the diagnosis of ADHD.
Collapse
Affiliation(s)
- Yunkai Sun
- Center for Cognition and Brain Disorders, Institute of Psychological Sciences and the Affiliated Hospital, Hangzhou Normal University, Hangzhou 311121, People's Republic of China.,Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou 311121, People's Republic of China
| | - Lei Zhao
- Center for Cognition and Brain Disorders, Institute of Psychological Sciences and the Affiliated Hospital, Hangzhou Normal University, Hangzhou 311121, People's Republic of China.,Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou 311121, People's Republic of China
| | - Zhihui Lan
- Center for Cognition and Brain Disorders, Institute of Psychological Sciences and the Affiliated Hospital, Hangzhou Normal University, Hangzhou 311121, People's Republic of China.,Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou 311121, People's Republic of China
| | - Xi-Ze Jia
- Center for Cognition and Brain Disorders, Institute of Psychological Sciences and the Affiliated Hospital, Hangzhou Normal University, Hangzhou 311121, People's Republic of China.,Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou 311121, People's Republic of China
| | - Shao-Wei Xue
- Center for Cognition and Brain Disorders, Institute of Psychological Sciences and the Affiliated Hospital, Hangzhou Normal University, Hangzhou 311121, People's Republic of China.,Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou 311121, People's Republic of China
| |
Collapse
|
19
|
Chang J, Yu R. Hippocampal connectivity in the aftermath of acute social stress. Neurobiol Stress 2019; 11:100195. [PMID: 31832509 PMCID: PMC6889252 DOI: 10.1016/j.ynstr.2019.100195] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2019] [Revised: 08/06/2019] [Accepted: 09/13/2019] [Indexed: 12/12/2022] Open
Abstract
The hippocampus is a core brain region that responds to stress. Previous studies have found a dysconnectivity between hippocampus and other brain regions under acute and chronic stress. However, whether and how acute social stress influences the directed connectivity patterns from and to the hippocampus remains unclear. In this study, using a within-subject design and Granger causal analysis (GCA), we investigated the alterations of resting state effective connectivity from and to hippocampal subregions after an acute social stressor (the Trier Social Stress Test). Participants were engaged in stress and control conditions spaced approximately one month apart. Our findings showed that stress altered the information flows in the thalamus-hippocampus-insula/midbrain circuit. The changes in this circuit could also predict with high accuracy the stress and control conditions at the subject level. These hippocampus-related brain networks have been documented to be involved in emotional information processing and storage, as well as habitual responses. We speculate that alterations of the effective connectivity between these brain regions may be associated with the registering and encoding of threatening stimuli under stress. Our investigation of hippocampal functional connectivity at a subregional level may help elucidate the functional neurobiology of stress-related psychiatric disorders.
Collapse
Affiliation(s)
- Jingjing Chang
- Guangdong Key Laboratory of Mental Health and Cognitive Science, Center for Studies of Psychological Application, School of Psychology, South China Normal University, Guangzhou, China
| | - Rongjun Yu
- Department of Psychology, National University of Singapore, Singapore
| |
Collapse
|
20
|
Fujiwara H, Ueno T, Yoshimura S, Kobayashi K, Miyagi T, Oishi N, Murai T. Martial Arts " Kendo" and the Motivation Network During Attention Processing: An fMRI Study. Front Hum Neurosci 2019; 13:170. [PMID: 31191277 PMCID: PMC6539200 DOI: 10.3389/fnhum.2019.00170] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2019] [Accepted: 05/08/2019] [Indexed: 11/13/2022] Open
Abstract
Japanese martial arts, Budo, have been reported to improve cognitive function, especially attention. However, the underlying neural mechanisms of the effect of Budo on attention processing has not yet been investigated. Kendo, a type of fencing using bamboo swords, is one of the most popular forms of Budo worldwide. We investigated the difference in functional connectivity (FC) between Kendo players (KPs) and non-KPs (NKPs) during an attention-related auditory oddball paradigm and during rest. The analyses focused on the brain network related to “motivation.” Resting-state functional magnetic resonance imaging (rs-fMRI) and task-based fMRI using the oddball paradigm were performed in healthy male volunteers (14 KPs and 11 NKPs). Group differences in FC were tested using CONN-software within the motivation network, which consisted of 22 brain regions defined by a previous response-conflict task-based fMRI study with a reward cue. Daily general physical activities were assessed using the International Physical Activity Questionnaire (IPAQ). We also investigated the impact of major confounders, namely, smoking habits, alcohol consumption, IPAQ score, body mass index (BMI), and reaction time (RT) in the oddball paradigm. Resting-state fMRI revealed that KPs had a significantly lower FC than NKPs between the right nucleus accumbens and right frontal eye field (FEF) within the motivation network. Conversely, KPs exhibited a significantly higher FC than NKPs between the left intraparietal sulcus (IPS) and the left precentral gyrus (PCG) within the network during the auditory oddball paradigm [statistical thresholds, False Discovery Rate (FDR) < 0.05]. These results remained significant after controlling for major covariates. Our results suggest that attenuated motivation network integrity at rest together with enhanced motivation network integrity during attentional demands might underlie the instantaneous concentration abilities of KPs.
Collapse
Affiliation(s)
- Hironobu Fujiwara
- Integrated Clinical Education Center, Kyoto University Hospital, Kyoto, Japan.,Department of Neuropsychiatry, Graduate School of Medicine, Kyoto University, Kyoto, Japan.,Artificial Intelligence Ethics and Society Team, RIKEN Center for Advanced Intelligence Project, Tokyo, Japan
| | - Tsukasa Ueno
- Department of Neuropsychiatry, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Sayaka Yoshimura
- Department of Neurodevelopmental Psychiatry, Habilitation and Rehabilitation, Kyoto University, Kyoto, Japan
| | - Kei Kobayashi
- Department of Neuropsychiatry, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Takashi Miyagi
- Department of Neuropsychiatry, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Naoya Oishi
- Medical Innovation Center, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Toshiya Murai
- Department of Neuropsychiatry, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| |
Collapse
|