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Chen Y, Zhao W, Yi S, Liu J. The diagnostic performance of machine learning based on resting-state functional magnetic resonance imaging data for major depressive disorders: a systematic review and meta-analysis. Front Neurosci 2023; 17:1174080. [PMID: 37811326 PMCID: PMC10559726 DOI: 10.3389/fnins.2023.1174080] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2023] [Accepted: 08/11/2023] [Indexed: 10/10/2023] Open
Abstract
Objective Machine learning (ML) has been widely used to detect and evaluate major depressive disorder (MDD) using neuroimaging data, i.e., resting-state functional magnetic resonance imaging (rs-fMRI). However, the diagnostic efficiency is unknown. The aim of the study is to conduct an updated meta-analysis to evaluate the diagnostic performance of ML based on rs-fMRI data for MDD. Methods English databases were searched for relevant studies. The Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) was used to assess the methodological quality of the included studies. A random-effects meta-analytic model was implemented to investigate the diagnostic efficiency, including sensitivity, specificity, diagnostic odds ratio (DOR), and area under the curve (AUC). Regression meta-analysis and subgroup analysis were performed to investigate the cause of heterogeneity. Results Thirty-one studies were included in this meta-analysis. The pooled sensitivity, specificity, DOR, and AUC with 95% confidence intervals were 0.80 (0.75, 0.83), 0.83 (0.74, 0.82), 14.00 (9, 22.00), and 0.86 (0.83, 0.89), respectively. Substantial heterogeneity was observed among the studies included. The meta-regression showed that the leave-one-out cross-validation (loocv) (sensitivity: p < 0.01, specificity: p < 0.001), graph theory (sensitivity: p < 0.05, specificity: p < 0.01), n > 100 (sensitivity: p < 0.001, specificity: p < 0.001), simens equipment (sensitivity: p < 0.01, specificity: p < 0.001), 3.0T field strength (Sensitivity: p < 0.001, specificity: p = 0.04), and Beck Depression Inventory (BDI) (sensitivity: p = 0.04, specificity: p = 0.06) might be the sources of heterogeneity. Furthermore, the subgroup analysis showed that the sample size (n > 100: sensitivity: 0.71, specificity: 0.72, n < 100: sensitivity: 0.81, specificity: 0.79), the different levels of disease evaluated by the Hamilton Depression Rating Scale (HDRS/HAMD) (mild vs. moderate vs. severe: sensitivity: 0.52 vs. 0.86 vs. 0.89, specificity: 0.62 vs. 0.78 vs. 0.82, respectively), the depression scales in patients with comparable levels of severity. (BDI vs. HDRS/HAMD: sensitivity: 0.86 vs. 0.87, specificity: 0.78 vs. 0.80, respectively), and the features (graph vs. functional connectivity: sensitivity: 0.84 vs. 0.86, specificity: 0.76 vs. 0.78, respectively) selected might be the causes of heterogeneity. Conclusion ML showed high accuracy for the automatic diagnosis of MDD. Future studies are warranted to promote the potential use of these classification algorithms in clinical settings.
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Affiliation(s)
- Yanjing Chen
- Department of Radiology, Second Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Wei Zhao
- Department of Radiology, Second Xiangya Hospital, Central South University, Changsha, Hunan, China
- Clinical Research Center for Medical Imaging in Hunan Province, Changsha, Hunan, China
| | - Sijie Yi
- Department of Radiology, Second Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Jun Liu
- Department of Radiology, Second Xiangya Hospital, Central South University, Changsha, Hunan, China
- Clinical Research Center for Medical Imaging in Hunan Province, Changsha, Hunan, China
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Long Z, Li J, Fan J, Li B, Du Y, Qiu S, Miao J, Chen J, Yin J, Jing B. Identifying Alzheimer's disease and mild cognitive impairment with atlas-based multi-modal metrics. Front Aging Neurosci 2023; 15:1212275. [PMID: 37719872 PMCID: PMC10501142 DOI: 10.3389/fnagi.2023.1212275] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2023] [Accepted: 08/21/2023] [Indexed: 09/19/2023] Open
Abstract
Introduction Multi-modal neuroimaging metrics in combination with advanced machine learning techniques have attracted more and more attention for an effective multi-class identification of Alzheimer's disease (AD), mild cognitive impairment (MCI) and health controls (HC) recently. Methods In this paper, a total of 180 subjects consisting of 44 AD, 66 MCI and 58 HC subjects were enrolled, and the multi-modalities of the resting-state functional magnetic resonance imaging (rs-fMRI) and the structural MRI (sMRI) for all participants were obtained. Then, four kinds of metrics including the Hurst exponent (HE) metric and bilateral hippocampus seed independently based connectivity metrics generated from fMRI data, and the gray matter volume (GMV) metric obtained from sMRI data, were calculated and extracted in each region of interest (ROI) based on a newly proposed automated anatomical Labeling (AAL3) atlas after data pre-processing. Next, these metrics were selected with a minimal redundancy maximal relevance (MRMR) method and a sequential feature collection (SFC) algorithm, and only a subset of optimal features were retained after this step. Finally, the support vector machine (SVM) based classification methods and artificial neural network (ANN) algorithm were utilized to identify the multi-class of AD, MCI and HC subjects in single modal and multi-modal metrics respectively, and a nested ten-fold cross-validation was utilized to estimate the final classification performance. Results The results of the SVM and ANN based methods indicated the best accuracies of 80.36 and 74.40%, respectively, by utilizing all the multi-modal metrics, and the optimal accuracies for AD, MCI and HC were 79.55, 78.79 and 82.76%, respectively, in the SVM based method. In contrast, when using single modal metric, the SVM based method obtained a best accuracy of 72.62% with the HE metric, and the accuracies for AD, MCI and HC subjects were just 56.82, 80.30 and 75.86%, respectively. Moreover, the overlapping abnormal brain regions detected by multi-modal metrics were mainly located at posterior cingulate gyrus, superior frontal gyrus and cuneus. Conclusion Taken together, the SVM based method with multi-modal metrics could provide effective diagnostic information for identifying AD, MCI and HC subjects.
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Affiliation(s)
- Zhuqing Long
- Medical Apparatus and Equipment Deployment, Hunan Children’s Hospital, Changsha, Hunan Province, China
- School of Biomedical Engineering, Capital Medical University, Beijing, China
| | - Jie Li
- Medical Apparatus and Equipment Deployment, Hunan Children’s Hospital, Changsha, Hunan Province, China
| | - Jianghua Fan
- Department of Pediatric Emergency Center, Hunan Children’s Hospital, Changsha, Hunan Province, China
| | - Bo Li
- Department of Traditional Chinese Medicine, Beijing Chest Hospital, Capital Medical University, Beijing Tuberculosis and Thoracic Tumor Research Institute, Beijing, China
| | - Yukeng Du
- Medical Apparatus and Equipment Deployment, Hunan Children’s Hospital, Changsha, Hunan Province, China
| | - Shuang Qiu
- Medical Apparatus and Equipment Deployment, Hunan Children’s Hospital, Changsha, Hunan Province, China
| | - Jichang Miao
- Department of Medical Devices, Nanfang Hospital, Guangzhou, China
| | - Jian Chen
- School of Electronic, Electrical Engineering and Physics, Fujian University of Technology, Fuzhou, Fujian, China
- Beijing Key Laboratory of Fundamental Research on Biomechanics in Clinical Application, Beijing, China
| | - Juanwu Yin
- Medical Apparatus and Equipment Deployment, Hunan Children’s Hospital, Changsha, Hunan Province, China
| | - Bin Jing
- School of Biomedical Engineering, Capital Medical University, Beijing, China
- Beijing Key Laboratory of Fundamental Research on Biomechanics in Clinical Application, Beijing, China
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Guo ZP, Chen L, Tang LR, Gao Y, Chand T, Sen ZD, Li M, Walter M, Wang L, Liu CH. Association between decreased interhemispheric functional connectivity of the insula and duration of illness in recurrent depression. J Affect Disord 2023; 329:88-95. [PMID: 36841304 DOI: 10.1016/j.jad.2023.02.083] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/09/2022] [Revised: 02/14/2023] [Accepted: 02/19/2023] [Indexed: 02/27/2023]
Abstract
OBJECTIVE To investigate the altered interhemispheric functional connectivity in the resting state in patients with recurrent major depressive disorder (MDD). METHODS Voxel-mirrored homotopic connectivity (VMHC), a measure of the functional connectivity between any pair of symmetrical interhemispheric voxels, and pattern classification were examined in 41 recurrent MDD patients (22 during the depressive state and 19 during the remitted state) and 60 age, sex, and education level-matched healthy controls (HC) using resting-state functional magnetic resonance imaging (fMRI). RESULTS Compared with HC, the recurrent MDD patients exhibited decreased VMHC values in the bilateral fusiform, inferior occipital gyrus, posterior insula, precentral gyrus, precuneus, superior temporal gyrus, and thalamus. A significant negative correlation between the VMHC value of the bilateral posterior insula and illness duration in recurrent MDD was identified. Support vector machine (SVM) analysis showed that VMHC in the fusiform and posterior insula could be used to distinguish recurrent MDD patients from HC with a sensitivity and accuracy >0.6. CONCLUSION Our findings revealed a reduction in the resting-state brain activity across several neural networks in patients with recurrent MDD, including within the posterior insula. Lower VMHC values in the posterior insula were associated with longer illness duration, suggesting that impairment in interhemispheric synchronization within the salience network may be due to the accumulated pathology of depression and may contribute to future depression relapse. VMHC changes in the posterior insula may serve as a potential imaging marker to discriminate recurrent MDD patients from HC.
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Affiliation(s)
- Zhi-Peng Guo
- Beijing Hospital of Traditional Chinese Medicine, Capital Medical University, Beijing 100010, China
| | - Lei Chen
- Beijing Hospital of Traditional Chinese Medicine, Capital Medical University, Beijing 100010, China
| | - Li-Rong Tang
- Beijing Hospital of Anding, Capital Medical University, Beijing 100088, China
| | - Yue Gao
- Beijing Hospital of Anding, Capital Medical University, Beijing 100088, China
| | - Tara Chand
- Department of Psychiatry and Psychotherapy, Jena University Hospital, Jena 07743, Germany; Clinical Affective Neuroimaging Laboratory (CANLAB), Magdeburg 39120, Germany; Department of Clinical Psychology, Friedrich Schiller University, Jena, Germany
| | - Zümrüt Duygu Sen
- Department of Psychiatry and Psychotherapy, Jena University Hospital, Jena 07743, Germany; Center for Intervention and Research on adaptive and maladaptive brain Circuits underlying mental health (C-I-R-C), Jena-Magdeburg-Halle, Germany
| | - Meng Li
- Department of Psychiatry and Psychotherapy, Jena University Hospital, Jena 07743, Germany; Clinical Affective Neuroimaging Laboratory (CANLAB), Magdeburg 39120, Germany; Center for Intervention and Research on adaptive and maladaptive brain Circuits underlying mental health (C-I-R-C), Jena-Magdeburg-Halle, Germany
| | - Martin Walter
- Department of Psychiatry and Psychotherapy, Jena University Hospital, Jena 07743, Germany; Clinical Affective Neuroimaging Laboratory (CANLAB), Magdeburg 39120, Germany; Center for Intervention and Research on adaptive and maladaptive brain Circuits underlying mental health (C-I-R-C), Jena-Magdeburg-Halle, Germany; German Center for Mental Health (DZPG), Site Halle-Jena-Magdeburg, Germany; Department of Psychiatry and Psychotherapy, University of Tuebingen, Tuebingen 72074, Germany; Department Systems Physiology of Learning, Leibniz Institute for Neurobiology, Magdeburg, Germany
| | - Lihong Wang
- Department of Psychiatry, University of Connecticut Health Center, Farmington, CT 06030, USA.
| | - Chun-Hong Liu
- Beijing Hospital of Traditional Chinese Medicine, Capital Medical University, Beijing 100010, China.
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Gao W, Biswal B, Yang J, Li S, Wang Y, Chen S, Yuan J. Temporal dynamic patterns of the ventromedial prefrontal cortex underlie the association between rumination and depression. Cereb Cortex 2023; 33:969-982. [PMID: 35462398 DOI: 10.1093/cercor/bhac115] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2022] [Revised: 02/21/2022] [Accepted: 02/22/2022] [Indexed: 11/14/2022] Open
Abstract
As a major contributor to the development of depression, rumination has proven linked with aberrant default-mode network (DMN) activity. However, it remains unclear how the spontaneous spatial and temporal activity of DMN underlie the association between rumination and depression. To illustrate this issue, behavioral measures and resting-state functional magnetic resonance images were connected in 2 independent samples (NSample1 = 100, NSample2 = 95). Fractional amplitude of low-frequency fluctuations (fALFF) and regional homogeneity (ReHo) were used to assess spatial characteristic patterns, while voxel-wise functional concordance (across time windows) (VC) and Hurst exponent (HE) were used to assess temporal dynamic patterns of brain activity. Results from both samples consistently show that temporal dynamics but not spatial patterns of DMN are associated with rumination. Specifically, rumination is positively correlated with HE and VC (but not fALFF and ReHo) values, reflecting more consistent and regular temporal dynamic patterns in DMN. Moreover, subregion analyses indicate that temporal dynamics of the ventromedial prefrontal cortex (VMPFC) reliably predict rumination scores. Furthermore, mediation analyses show that HE and VC of VMPFC mediate the association between rumination and depression. These findings shed light on neural mechanisms of individual differences in rumination and corresponding risk for depression.
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Affiliation(s)
- Wei Gao
- Institute of Brain and Psychological Science, Sichuan Normal University, Chengdu, Sichuan, China.,Faculty of Psychology, Southwest University, Chongqing, China
| | - Bharat Biswal
- Department of Biomedical Engineering, New Jersey Institute of Technology, Newark, NJ, United States
| | - Jiemin Yang
- Institute of Brain and Psychological Science, Sichuan Normal University, Chengdu, Sichuan, China
| | - Songlin Li
- School of Educational Science, Sichuan Normal University, Chengdu, Sichuan, China
| | - YanQing Wang
- Institute of Psychology, Chinese Academy of Sciences, Beijing, China
| | - Shengdong Chen
- School of Psychology, Qufu Normal University, Qufu, Shandong, China
| | - JiaJin Yuan
- Institute of Brain and Psychological Science, Sichuan Normal University, Chengdu, Sichuan, China
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Resting-State fMRI Whole Brain Network Function Plasticity Analysis in Attention Deficit Hyperactivity Disorder. Neural Plast 2022; 2022:4714763. [PMID: 36199291 PMCID: PMC9529483 DOI: 10.1155/2022/4714763] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2022] [Accepted: 09/08/2022] [Indexed: 12/03/2022] Open
Abstract
Attention deficit hyperactivity disorder (ADHD) is a common mental disorder in children, which is related to inattention and hyperactivity. These symptoms are associated with abnormal interactions of brain networks. We used resting-state functional magnetic resonance imaging (rs-fMRI) based on the graph theory to explore the topology property changes of brain networks between an ADHD group and a normal group. The more refined AAL_1024 atlas was used to construct the functional networks with high nodal resolution, for detecting more subtle changes in brain regions and differences among groups. We compared altered topology properties of brain network between the groups from multilevel, mainly including modularity at mesolevel. Specifically, we analyzed the similarities and differences of module compositions between the two groups. The results found that the ADHD group showed stronger economic small-world network property, while the clustering coefficient was significantly lower than the normal group; the frontal and occipital lobes showed smaller node degree and global efficiency between disease statuses. The modularity results also showed that the module number of the ADHD group decreased, and the ADHD group had short-range overconnectivity within module and long-range underconnectivity between modules. Moreover, modules containing long-range connections between the frontal and occipital lobes disappeared, indicating that there was lack of top-down control information between the executive control region and the visual processing region in the ADHD group. Our results suggested that these abnormal regions were related to executive control and attention deficit of ADHD patients. These findings helped to better understand how brain function correlates with the ADHD symptoms and complement the fewer modularity elaboration of ADHD research.
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6
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Long Z, Li J, Liao H, Deng L, Du Y, Fan J, Li X, Miao J, Qiu S, Long C, Jing B. A Multi-Modal and Multi-Atlas Integrated Framework for Identification of Mild Cognitive Impairment. Brain Sci 2022; 12:brainsci12060751. [PMID: 35741636 PMCID: PMC9221217 DOI: 10.3390/brainsci12060751] [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: 04/09/2022] [Revised: 05/29/2022] [Accepted: 06/03/2022] [Indexed: 11/16/2022] Open
Abstract
Background: Multi-modal neuroimaging with appropriate atlas is vital for effectively differentiating mild cognitive impairment (MCI) from healthy controls (HC). Methods: The resting-state functional magnetic resonance imaging (rs-fMRI) and structural MRI (sMRI) of 69 MCI patients and 61 HC subjects were collected. Then, the gray matter volumes obtained from the sMRI and Hurst exponent (HE) values calculated from rs-fMRI data in the Automated Anatomical Labeling (AAL-90), Brainnetome (BN-246), Harvard–Oxford (HOA-112) and AAL3-170 atlases were extracted, respectively. Next, these characteristics were selected with a minimal redundancy maximal relevance algorithm and a sequential feature collection method in single or multi-modalities, and only the optimal features were retained after this procedure. Lastly, the retained characteristics were served as the input features for the support vector machine (SVM)-based method to classify MCI patients, and the performance was estimated with a leave-one-out cross-validation (LOOCV). Results: Our proposed method obtained the best 92.00% accuracy, 94.92% specificity and 89.39% sensitivity with the sMRI in AAL-90 and the fMRI in HOA-112 atlas, which was much better than using the single-modal or single-atlas features. Conclusion: The results demonstrated that the multi-modal and multi-atlas integrated method could effectively recognize MCI patients, which could be extended into various neurological and neuropsychiatric diseases.
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Affiliation(s)
- Zhuqing Long
- Medical Apparatus and Equipment Deployment, Hunan Children’s Hospital, Changsha 410007, China; (Z.L.); (J.L.); (H.L.); (Y.D.); (S.Q.)
- School of Biomedical Engineering, Capital Medical University, Beijing 100069, China
| | - Jie Li
- Medical Apparatus and Equipment Deployment, Hunan Children’s Hospital, Changsha 410007, China; (Z.L.); (J.L.); (H.L.); (Y.D.); (S.Q.)
| | - Haitao Liao
- Medical Apparatus and Equipment Deployment, Hunan Children’s Hospital, Changsha 410007, China; (Z.L.); (J.L.); (H.L.); (Y.D.); (S.Q.)
| | - Li Deng
- Department of Data Assessment and Examination, Hunan Children’s Hospital, Changsha 410007, China;
| | - Yukeng Du
- Medical Apparatus and Equipment Deployment, Hunan Children’s Hospital, Changsha 410007, China; (Z.L.); (J.L.); (H.L.); (Y.D.); (S.Q.)
| | - Jianghua Fan
- Department of Pediatric Emergency Center, Emergency Generally Department I, Hunan Children’s Hospital, Changsha 410007, China;
| | - Xiaofeng Li
- Hunan Guangxiu Hospital, Hunan Normal University, Changsha 410006, China;
| | - Jichang Miao
- Department of Medical Devices, Nanfang Hospital, Guangzhou 510515, China;
| | - Shuang Qiu
- Medical Apparatus and Equipment Deployment, Hunan Children’s Hospital, Changsha 410007, China; (Z.L.); (J.L.); (H.L.); (Y.D.); (S.Q.)
| | - Chaojie Long
- Medical Apparatus and Equipment Deployment, Hunan Children’s Hospital, Changsha 410007, China; (Z.L.); (J.L.); (H.L.); (Y.D.); (S.Q.)
- Correspondence: (C.L.); (B.J.); Tel./Fax: +86-731-8560-0908 (C.L.); +86-10-8391-1552 (B.J.)
| | - Bin Jing
- School of Biomedical Engineering, Capital Medical University, Beijing 100069, China
- Correspondence: (C.L.); (B.J.); Tel./Fax: +86-731-8560-0908 (C.L.); +86-10-8391-1552 (B.J.)
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A neural network approach to optimising treatments for depression using data from specialist and community psychiatric services in Australia, New Zealand and Japan. Neural Comput Appl 2022; 35:11497-11516. [PMID: 35039718 PMCID: PMC8754538 DOI: 10.1007/s00521-021-06710-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2021] [Accepted: 10/27/2021] [Indexed: 01/10/2023]
Abstract
This study investigated the application of a recurrent neural network for optimising pharmacological treatment for depression. A clinical dataset of 458 participants from specialist and community psychiatric services in Australia, New Zealand and Japan were extracted from an existing custom-built, web-based tool called Psynary . This data, which included baseline and self-completed reviews, was used to train and refine a novel algorithm which was a fully connected network feature extractor and long short-term memory algorithm was firstly trained in isolation and then integrated and annealed using slow learning rates due to the low dimensionality of the data. The accuracy of predicting depression remission before processing patient review data was 49.8%. After processing only 2 reviews, the accuracy was 76.5%. When considering a change in medication, the precision of changing medications was 97.4% and the recall was 71.4% . The medications with predicted best results were antipsychotics (88%) and selective serotonin reuptake inhibitors (87.9%). This is the first study that has created an all-in-one algorithm for optimising treatments for all subtypes of depression. Reducing treatment optimisation time for patients suffering with depression may lead to earlier remission and hence reduce the high levels of disability associated with the condition. Furthermore, in a setting where mental health conditions are increasing strain on mental health services, the utilisation of web-based tools for remote monitoring and machine/deep learning algorithms may assist clinicians in both specialist and primary care in extending specialist mental healthcare to a larger patient community.
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Zhang Z, Li G, Xu Y, Tang X. Application of Artificial Intelligence in the MRI Classification Task of Human Brain Neurological and Psychiatric Diseases: A Scoping Review. Diagnostics (Basel) 2021; 11:1402. [PMID: 34441336 PMCID: PMC8392727 DOI: 10.3390/diagnostics11081402] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2021] [Revised: 07/21/2021] [Accepted: 07/21/2021] [Indexed: 12/12/2022] Open
Abstract
Artificial intelligence (AI) for medical imaging is a technology with great potential. An in-depth understanding of the principles and applications of magnetic resonance imaging (MRI), machine learning (ML), and deep learning (DL) is fundamental for developing AI-based algorithms that can meet the requirements of clinical diagnosis and have excellent quality and efficiency. Moreover, a more comprehensive understanding of applications and opportunities would help to implement AI-based methods in an ethical and sustainable manner. This review first summarizes recent research advances in ML and DL techniques for classifying human brain magnetic resonance images. Then, the application of ML and DL methods to six typical neurological and psychiatric diseases is summarized, including Alzheimer's disease (AD), Parkinson's disease (PD), major depressive disorder (MDD), schizophrenia (SCZ), attention-deficit/hyperactivity disorder (ADHD), and autism spectrum disorder (ASD). Finally, the limitations of the existing research are discussed, and possible future research directions are proposed.
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Affiliation(s)
- Zhao Zhang
- 715-3 Teaching Building No.5, Department of Biomedical Engineering, School of Life Sciences, Beijing Institute of Technology, 5 South Zhongguancun Road, Haidian District, Beijing 100081, China; (Z.Z.); (G.L.)
| | - Guangfei Li
- 715-3 Teaching Building No.5, Department of Biomedical Engineering, School of Life Sciences, Beijing Institute of Technology, 5 South Zhongguancun Road, Haidian District, Beijing 100081, China; (Z.Z.); (G.L.)
| | - Yong Xu
- Department of Cardiology, Chinese PLA General Hospital, Beijing 100853, China;
| | - Xiaoying Tang
- 715-3 Teaching Building No.5, Department of Biomedical Engineering, School of Life Sciences, Beijing Institute of Technology, 5 South Zhongguancun Road, Haidian District, Beijing 100081, China; (Z.Z.); (G.L.)
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Mousavian M, Chen J, Traylor Z, Greening S. Depression detection from sMRI and rs-fMRI images using machine learning. J Intell Inf Syst 2021. [DOI: 10.1007/s10844-021-00653-w] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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10
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Translational application of neuroimaging in major depressive disorder: a review of psychoradiological studies. Front Med 2021; 15:528-540. [PMID: 33511554 DOI: 10.1007/s11684-020-0798-1] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2019] [Accepted: 04/25/2020] [Indexed: 02/05/2023]
Abstract
Major depressive disorder (MDD) causes great decrements in health and quality of life with increments in healthcare costs, but the causes and pathogenesis of depression remain largely unknown, which greatly prevent its early detection and effective treatment. With the advancement of neuroimaging approaches, numerous functional and structural alterations in the brain have been detected in MDD and more recently attempts have been made to apply these findings to clinical practice. In this review, we provide an updated summary of the progress in translational application of psychoradiological findings in MDD with a specified focus on potential clinical usage. The foreseeable clinical applications for different MRI modalities were introduced according to their role in disorder classification, subtyping, and prediction. While evidence of cerebral structural and functional changes associated with MDD classification and subtyping was heterogeneous and/or sparse, the ACC and hippocampus have been consistently suggested to be important biomarkers in predicting treatment selection and treatment response. These findings underlined the potential utility of brain biomarkers for clinical practice.
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Jun E, Na K, Kang W, Lee J, Suk H, Ham B. Identifying
resting‐state
effective connectivity abnormalities in
drug‐naïve
major depressive disorder diagnosis via graph convolutional networks. Hum Brain Mapp 2020. [DOI: 10.1002/hbm.25175 10.1002/hbm.25175] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Affiliation(s)
- Eunji Jun
- Department of Brain and Cognitive Engineering Korea University Seoul Republic of Korea
| | - Kyoung‐Sae Na
- Department of Psychiatry Gachon University Gil Medical Center Incheon Republic of Korea
| | - Wooyoung Kang
- Department of Biomedical Sciences Korea University College of Medicine Seoul Republic of Korea
| | - Jiyeon Lee
- Department of Brain and Cognitive Engineering Korea University Seoul Republic of Korea
| | - Heung‐Il Suk
- Department of Brain and Cognitive Engineering Korea University Seoul Republic of Korea
- Department of Artificial Intelligence Korea University Seoul Republic of Korea
| | - Byung‐Joo Ham
- Department of Psychiatry Korea University Anam Hospital, Korea University College of Medicine Seoul Republic of Korea
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Jun E, Na KS, Kang W, Lee J, Suk HI, Ham BJ. Identifying resting-state effective connectivity abnormalities in drug-naïve major depressive disorder diagnosis via graph convolutional networks. Hum Brain Mapp 2020; 41:4997-5014. [PMID: 32813309 PMCID: PMC7643383 DOI: 10.1002/hbm.25175] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2019] [Revised: 07/13/2020] [Accepted: 08/01/2020] [Indexed: 02/06/2023] Open
Abstract
Major depressive disorder (MDD) is a leading cause of disability; its symptoms interfere with social, occupational, interpersonal, and academic functioning. However, the diagnosis of MDD is still made by phenomenological approach. The advent of neuroimaging techniques allowed numerous studies to use resting-state functional magnetic resonance imaging (rs-fMRI) and estimate functional connectivity for brain-disease identification. Recently, attempts have been made to investigate effective connectivity (EC) that represents causal relations among regions of interest. In the meantime, to identify meaningful phenotypes for clinical diagnosis, graph-based approaches such as graph convolutional networks (GCNs) have been leveraged recently to explore complex pairwise similarities in imaging/nonimaging features among subjects. In this study, we validate the use of EC for MDD identification by estimating its measures via a group sparse representation along with a structured equation modeling approach in a whole-brain data-driven manner from rs-fMRI. To distinguish drug-naïve MDD patients from healthy controls, we utilize spectral GCNs based on a population graph to successfully integrate EC and nonimaging phenotypic information. Furthermore, we devise a novel sensitivity analysis method to investigate the discriminant connections for MDD identification in our trained GCNs. Our experimental results validated the effectiveness of our method in various scenarios, and we identified altered connectivities associated with the diagnosis of MDD.
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Affiliation(s)
- Eunji Jun
- Department of Brain and Cognitive Engineering, Korea University, Seoul, Republic of Korea
| | - Kyoung-Sae Na
- Department of Psychiatry, Gachon University Gil Medical Center, Incheon, Republic of Korea
| | - Wooyoung Kang
- Department of Biomedical Sciences, Korea University College of Medicine, Seoul, Republic of Korea
| | - Jiyeon Lee
- Department of Brain and Cognitive Engineering, Korea University, Seoul, Republic of Korea
| | - Heung-Il Suk
- Department of Brain and Cognitive Engineering, Korea University, Seoul, Republic of Korea.,Department of Artificial Intelligence, Korea University, Seoul, Republic of Korea
| | - Byung-Joo Ham
- Department of Psychiatry, Korea University Anam Hospital, Korea University College of Medicine, Seoul, Republic of Korea
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Rashid B, Calhoun V. Towards a brain-based predictome of mental illness. Hum Brain Mapp 2020; 41:3468-3535. [PMID: 32374075 PMCID: PMC7375108 DOI: 10.1002/hbm.25013] [Citation(s) in RCA: 65] [Impact Index Per Article: 16.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2019] [Revised: 04/06/2020] [Accepted: 04/06/2020] [Indexed: 01/10/2023] Open
Abstract
Neuroimaging-based approaches have been extensively applied to study mental illness in recent years and have deepened our understanding of both cognitively healthy and disordered brain structure and function. Recent advancements in machine learning techniques have shown promising outcomes for individualized prediction and characterization of patients with psychiatric disorders. Studies have utilized features from a variety of neuroimaging modalities, including structural, functional, and diffusion magnetic resonance imaging data, as well as jointly estimated features from multiple modalities, to assess patients with heterogeneous mental disorders, such as schizophrenia and autism. We use the term "predictome" to describe the use of multivariate brain network features from one or more neuroimaging modalities to predict mental illness. In the predictome, multiple brain network-based features (either from the same modality or multiple modalities) are incorporated into a predictive model to jointly estimate features that are unique to a disorder and predict subjects accordingly. To date, more than 650 studies have been published on subject-level prediction focusing on psychiatric disorders. We have surveyed about 250 studies including schizophrenia, major depression, bipolar disorder, autism spectrum disorder, attention-deficit hyperactivity disorder, obsessive-compulsive disorder, social anxiety disorder, posttraumatic stress disorder, and substance dependence. In this review, we present a comprehensive review of recent neuroimaging-based predictomic approaches, current trends, and common shortcomings and share our vision for future directions.
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Affiliation(s)
- Barnaly Rashid
- Department of PsychiatryHarvard Medical SchoolBostonMassachusettsUSA
| | - Vince Calhoun
- Tri‐Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS)Georgia State University, Georgia Institute of Technology, and Emory UniversityAtlantaGeorgiaUSA
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14
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Resting-State Functional Network Scale Effects and Statistical Significance-Based Feature Selection in Machine Learning Classification. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2019; 2019:9108108. [PMID: 31781290 PMCID: PMC6875180 DOI: 10.1155/2019/9108108] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/18/2019] [Revised: 08/04/2019] [Accepted: 09/06/2019] [Indexed: 12/17/2022]
Abstract
In recent years, functional brain network topological features have been widely used as classification features. Previous studies have found that network node scale differences caused by different network parcellation definitions significantly affect the structure of the constructed network and its topological properties. However, we still do not know how network scale differences affect the classification accuracy, performance of classification features, and effectiveness of the feature selection strategy using P values in terms of the machine learning method. This study used five scale parcellations, involving 90, 256, 497, 1003, and 1501 nodes. Three local properties of resting-state functional brain networks were selected (degree, betweenness centrality, and nodal efficiency), and the support vector machine method was used to construct classifiers to identify patients with major depressive disorder. We analyzed the impact of the five scales on classification accuracy. In addition, the effectiveness and redundancy of features obtained by the different scale parcellations were compared. Finally, traditional statistical significance (P value) was verified as a feature selection criterion. The results showed that the feature effectiveness of different scales was similar; in other words, parcellation with more regions did not provide more effective discriminative features. Nevertheless, parcellation with more regions did provide a greater quantity of discriminative features, which led to an improvement in the accuracy of the classification. However, due to the close distance between brain regions, the redundancy of parcellation with more regions was also greater. The traditional P value feature selection strategy is feasible with different scales, but our analysis showed that the traditional P < 0.05 threshold was too strict for feature selection. This study provides an important reference for the selection of network scales when applying topological properties of brain networks to machine learning methods.
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Long Z, Huang J, Li B, Li Z, Li Z, Chen H, Jing B. A Comparative Atlas-Based Recognition of Mild Cognitive Impairment With Voxel-Based Morphometry. Front Neurosci 2018; 12:916. [PMID: 30574064 PMCID: PMC6291519 DOI: 10.3389/fnins.2018.00916] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2018] [Accepted: 11/21/2018] [Indexed: 01/18/2023] Open
Abstract
An accurate and reliable brain partition atlas is vital to quantitatively investigate the structural and functional abnormalities in mild cognitive impairment (MCI), generally considered to be a prodromal phase of Alzheimer's disease. In this paper, we proposed an automated structural classification method to identify MCI from healthy controls (HC) and investigated whether the classification performance was dependent on the brain parcellation schemes, including Automated Anatomical Labeling (AAL-90) atlas, Brainnetome (BN-246) atlas, and AAL-1024 atlas. In detail, structural magnetic resonance imaging (sMRI) data of 69 MCI patients and 63 HC matched well on gender, age, and education level were collected and analyzed with voxel-based morphometry method first, then the volume features of every region of interest (ROI) belonging to the above-mentioned three atlases were calculated and compared between MCI and HC groups, respectively. At last, the abnormal volume features were selected as the classification features for a proposed support vector machine based identification method. After the leave-one-out cross-validation to estimate the classification performance, our results reported accuracies of 83, 92, and 89% with AAL-90, BN-246, and AAL-1024 atlas, respectively, suggesting that future studies should pay more attention to the selection of brain partition schemes in the atlas-based studies. Furthermore, the consistent atrophic brain regions among three atlases were predominately located at bilateral hippocampus, bilateral parahippocampal, bilateral amygdala, bilateral cingulate gyrus, left angular gyrus, right superior frontal gyrus, right middle frontal gyrus, left inferior frontal gyrus, and left precentral gyrus.
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Affiliation(s)
- Zhuqing Long
- Medical Apparatus and Equipment Deployment, Nanfang Hospital, Southern Medical University, Guangzhou, China.,School of Biomedical Engineering, Capital Medical University, Beijing, China
| | - Jinchang Huang
- Department of Acupuncture and Minimally Invasive Oncology, Beijing University of Chinese Medicine Third Affiliated Hospital, Beijing, China
| | - Bo Li
- Department of Traditional Chinese Medicine, Beijing Chest Hospital, Capital Medical University, Beijing, China.,Beijing Tuberculosis and Thoracic Tumor Research Institute, Beijing, China
| | - Zuojia Li
- Medical Apparatus and Equipment Deployment, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Zihao Li
- Medical Apparatus and Equipment Deployment, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Hongwen Chen
- Medical Apparatus and Equipment Deployment, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Bin Jing
- School of Biomedical Engineering, Capital Medical University, Beijing, China
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Gao S, Calhoun VD, Sui J. Machine learning in major depression: From classification to treatment outcome prediction. CNS Neurosci Ther 2018; 24:1037-1052. [PMID: 30136381 DOI: 10.1111/cns.13048] [Citation(s) in RCA: 145] [Impact Index Per Article: 24.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2018] [Revised: 07/19/2018] [Accepted: 07/21/2018] [Indexed: 01/10/2023] Open
Abstract
AIMS Major depression disorder (MDD) is the single greatest cause of disability and morbidity, and affects about 10% of the population worldwide. Currently, there are no clinically useful diagnostic biomarkers that are able to confirm a diagnosis of MDD from bipolar disorder (BD) in the early depressive episode. Therefore, exploring translational biomarkers of mood disorders based on machine learning is in pressing need, though it is challenging, but with great potential to improve our understanding of these disorders. DISCUSSIONS In this study, we review popular machine-learning methods used for brain imaging classification and predictions, and provide an overview of studies, specifically for MDD, that have used magnetic resonance imaging data to either (a) classify MDDs from controls or other mood disorders or (b) investigate treatment outcome predictors for individual patients. Finally, challenges, future directions, and potential limitations related to MDD biomarker identification are also discussed, with a goal of offering a comprehensive overview that may help readers to better understand the applications of neuroimaging data mining in depression. CONCLUSIONS We hope such efforts may highlight the need for an urgently needed paradigm shift in treatment, to guide personalized optimal clinical care.
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Affiliation(s)
- Shuang Gao
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China.,University of Chinese Academy of Sciences, Beijing, China
| | - Vince D Calhoun
- The Mind Research Network, Albuquerque, New Mexico.,Department of Electrical and Computer Engineering, The University of New Mexico, Albuquerque, New Mexico
| | - Jing Sui
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China.,University of Chinese Academy of Sciences, Beijing, China.,CAS Centre for Excellence in Brain Science and Intelligence Technology, Institute of Automation, Chinese Academy of Sciences, Beijing, China
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Long Z, Jing B, Guo R, Li B, Cui F, Wang T, Chen H. A Brainnetome Atlas Based Mild Cognitive Impairment Identification Using Hurst Exponent. Front Aging Neurosci 2018; 10:103. [PMID: 29692721 PMCID: PMC5902491 DOI: 10.3389/fnagi.2018.00103] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2017] [Accepted: 03/27/2018] [Indexed: 11/15/2022] Open
Abstract
Mild cognitive impairment (MCI), which generally represents the transition state between normal aging and the early changes related to Alzheimer’s disease (AD), has drawn increasing attention from neuroscientists due that efficient AD treatments need early initiation ahead of irreversible brain tissue damage. Thus effective MCI identification methods are desperately needed, which may be of great importance for the clinical intervention of AD. In this article, the range scaled analysis, which could effectively detect the temporal complexity of a time series, was utilized to calculate the Hurst exponent (HE) of functional magnetic resonance imaging (fMRI) data at a voxel level from 64 MCI patients and 60 healthy controls (HCs). Then the average HE values of each region of interest (ROI) in brainnetome atlas were extracted and compared between MCI and HC. At last, the abnormal average HE values were adopted as the classification features for a proposed support vector machine (SVM) based identification algorithm, and the classification performance was estimated with leave-one-out cross-validation (LOOCV). Our results indicated 83.1% accuracy, 82.8% sensitivity and 83.3% specificity, and an area under curve of 0.88, suggesting that the HE index could serve as an effective feature for the MCI identification. Furthermore, the abnormal HE brain regions in MCI were predominately involved in left middle frontal gyrus, right hippocampus, bilateral parahippocampal gyrus, bilateral amygdala, left cingulate gyrus, left insular gyrus, left fusiform gyrus, left superior parietal gyrus, left orbital gyrus and left basal ganglia.
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Affiliation(s)
- Zhuqing Long
- Medical Apparatus and Equipment Deployment, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Bin Jing
- School of Biomedical Engineering, Capital Medical University, Beijing, China
| | - Ru Guo
- Department of Tuberculosis, Beijing Chest Hospital Capital Medical University, Beijing, China
| | - Bo Li
- Department of Traditional Chinese Medicine, Beijing Chest Hospital, Capital Medical University, Beijing Tuberculosis and Thoracic Tumor Research Institute, Beijing, China
| | - Feiyi Cui
- Medical Apparatus and Equipment Deployment, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Tingting Wang
- Medical Apparatus and Equipment Deployment, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Hongwen Chen
- Medical Apparatus and Equipment Deployment, Nanfang Hospital, Southern Medical University, Guangzhou, China
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