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Song B, Yoshida S. Explainability of three-dimensional convolutional neural networks for functional magnetic resonance imaging of Alzheimer's disease classification based on gradient-weighted class activation mapping. PLoS One 2024; 19:e0303278. [PMID: 38771733 PMCID: PMC11108152 DOI: 10.1371/journal.pone.0303278] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2023] [Accepted: 04/22/2024] [Indexed: 05/23/2024] Open
Abstract
Currently, numerous studies focus on employing fMRI-based deep neural networks to diagnose neurological disorders such as Alzheimer's Disease (AD), yet only a handful have provided results regarding explainability. We address this gap by applying several prevalent explainability methods such as gradient-weighted class activation mapping (Grad-CAM) to an fMRI-based 3D-VGG16 network for AD diagnosis to improve the model's explainability. The aim is to explore the specific Region of Interest (ROI) of brain the model primarily focuses on when making predictions, as well as whether there are differences in these ROIs between AD and normal controls (NCs). First, we utilized multiple resting-state functional activity maps including ALFF, fALFF, ReHo, and VMHC to reduce the complexity of fMRI data, which differed from many studies that utilized raw fMRI data. Compared to methods utilizing raw fMRI data, this manual feature extraction approach may potentially alleviate the model's burden. Subsequently, 3D-VGG16 were employed for AD classification, where the final fully connected layers were replaced with a Global Average Pooling (GAP) layer, aimed at mitigating overfitting while preserving spatial information within the feature maps. The model achieved a maximum of 96.4% accuracy on the test set. Finally, several 3D CAM methods were employed to interpret the models. In the explainability results of the models with relatively high accuracy, the highlighted ROIs were primarily located in the precuneus and the hippocampus for AD subjects, while the models focused on the entire brain for NC. This supports current research on ROIs involved in AD. We believe that explaining deep learning models would not only provide support for existing research on brain disorders, but also offer important referential recommendations for the study of currently unknown etiologies.
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Affiliation(s)
- Boyue Song
- Graduate School of Engineering, Kochi University of Technology, Kami City, Kochi Prefecture, Japan
| | - Shinichi Yoshida
- School of Information, Kochi University of Technology, Kami City, Kochi Prefecture, Japan
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2
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Valizadeh A, Moassefi M, Nakhostin-Ansari A, Heidari Some'eh S, Hosseini-Asl H, Saghab Torbati M, Aghajani R, Maleki Ghorbani Z, Menbari-Oskouie I, Aghajani F, Mirzamohamadi A, Ghafouri M, Faghani S, Memari AH. Automated diagnosis of autism with artificial intelligence: State of the art. Rev Neurosci 2024; 35:141-163. [PMID: 37678819 DOI: 10.1515/revneuro-2023-0050] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2023] [Accepted: 07/28/2023] [Indexed: 09/09/2023]
Abstract
Autism spectrum disorder (ASD) represents a panel of conditions that begin during the developmental period and result in impairments of personal, social, academic, or occupational functioning. Early diagnosis is directly related to a better prognosis. Unfortunately, the diagnosis of ASD requires a long and exhausting subjective process. We aimed to review the state of the art for automated autism diagnosis and recognition in this research. In February 2022, we searched multiple databases and sources of gray literature for eligible studies. We used an adapted version of the QUADAS-2 tool to assess the risk of bias in the studies. A brief report of the methods and results of each study is presented. Data were synthesized for each modality separately using the Split Component Synthesis (SCS) method. We assessed heterogeneity using the I 2 statistics and evaluated publication bias using trim and fill tests combined with ln DOR. Confidence in cumulative evidence was assessed using the GRADE approach for diagnostic studies. We included 344 studies from 186,020 participants (51,129 are estimated to be unique) for nine different modalities in this review, from which 232 reported sufficient data for meta-analysis. The area under the curve was in the range of 0.71-0.90 for all the modalities. The studies on EEG data provided the best accuracy, with the area under the curve ranging between 0.85 and 0.93. We found that the literature is rife with bias and methodological/reporting flaws. Recommendations are provided for future research to provide better studies and fill in the current knowledge gaps.
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Affiliation(s)
- Amir Valizadeh
- Neuroscience Institute, Tehran University of Medical Sciences, PO: 1419733141, Tehran, Iran
| | - Mana Moassefi
- Neuroscience Institute, Tehran University of Medical Sciences, PO: 1419733141, Tehran, Iran
| | - Amin Nakhostin-Ansari
- Sports Medicine Research Center, Neuroscience Institute, Tehran University of Medical Sciences, PO: 14395578, Tehran, Iran
| | - Soheil Heidari Some'eh
- Sports Medicine Research Center, Neuroscience Institute, Tehran University of Medical Sciences, PO: 14395578, Tehran, Iran
- Students' Scientific Research Center, Tehran University of Medical Sciences, PO: 1417755331, Tehran, Iran
| | - Hossein Hosseini-Asl
- Sports Medicine Research Center, Neuroscience Institute, Tehran University of Medical Sciences, PO: 14395578, Tehran, Iran
- Students' Scientific Research Center, Tehran University of Medical Sciences, PO: 1417755331, Tehran, Iran
| | | | - Reyhaneh Aghajani
- Sports Medicine Research Center, Neuroscience Institute, Tehran University of Medical Sciences, PO: 14395578, Tehran, Iran
- Students' Scientific Research Center, Tehran University of Medical Sciences, PO: 1417755331, Tehran, Iran
| | - Zahra Maleki Ghorbani
- Sports Medicine Research Center, Neuroscience Institute, Tehran University of Medical Sciences, PO: 14395578, Tehran, Iran
- Students' Scientific Research Center, Tehran University of Medical Sciences, PO: 1417755331, Tehran, Iran
| | - Iman Menbari-Oskouie
- Sports Medicine Research Center, Neuroscience Institute, Tehran University of Medical Sciences, PO: 14395578, Tehran, Iran
| | - Faezeh Aghajani
- Sports Medicine Research Center, Neuroscience Institute, Tehran University of Medical Sciences, PO: 14395578, Tehran, Iran
- Research Development Center, Arash Women's Hospital, Tehran University of Medical Sciences, PO: 14695542, Tehran, Iran
| | - Alireza Mirzamohamadi
- Sports Medicine Research Center, Neuroscience Institute, Tehran University of Medical Sciences, PO: 14395578, Tehran, Iran
- Students' Scientific Research Center, Tehran University of Medical Sciences, PO: 1417755331, Tehran, Iran
| | - Mohammad Ghafouri
- Sports Medicine Research Center, Neuroscience Institute, Tehran University of Medical Sciences, PO: 14395578, Tehran, Iran
| | - Shahriar Faghani
- Shariati Hospital, Department of Radiology, Tehran University of Medical Sciences, PO: 1411713135, Tehran, Iran
- Interdisciplinary Neuroscience Research Program (INRP), Tehran University of Medical Sciences, PO: 1416634793, Tehran, Iran
| | - Amir Hossein Memari
- Sports Medicine Research Center, Neuroscience Institute, Tehran University of Medical Sciences, PO: 14395578, Tehran, Iran
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3
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Xu Z, Tang S, Liu C, Zhang Q, Gu H, Li X, Di Z, Li Z. Temporal segmentation of EEG based on functional connectivity network structure. Sci Rep 2023; 13:22566. [PMID: 38114604 PMCID: PMC10730570 DOI: 10.1038/s41598-023-49891-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2023] [Accepted: 12/13/2023] [Indexed: 12/21/2023] Open
Abstract
In the study of brain functional connectivity networks, it is assumed that a network is built from a data window in which activity is stationary. However, brain activity is non-stationary over sufficiently large time periods. Addressing the analysis electroencephalograph (EEG) data, we propose a data segmentation method based on functional connectivity network structure. The goal of segmentation is to ensure that within a window of analysis, there is similar network structure. We designed an intuitive and flexible graph distance measure to quantify the difference in network structure between two analysis windows. This measure is modular: a variety of node importance indices can be plugged into it. We use a reference window versus sliding window comparison approach to detect changes, as indicated by outliers in the distribution of graph distance values. Performance of our segmentation method was tested in simulated EEG data and real EEG data from a drone piloting experiment (using correlation or phase-locking value as the functional connectivity strength metric). We compared our method under various node importance measures and against matrix-based dissimilarity metrics that use singular value decomposition on the connectivity matrix. The results show the graph distance approach worked better than matrix-based approaches; graph distance based on partial node centrality was most sensitive to network structural changes, especially when connectivity matrix values change little. The proposed method provides EEG data segmentation tailored for detecting changes in terms of functional connectivity networks. Our study provides a new perspective on EEG segmentation, one that is based on functional connectivity network structure differences.
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Affiliation(s)
- Zhongming Xu
- The International Academic Center of Complex Systems, Beijing Normal University, Zhuhai, 519087, China
- The Center for Cognition and Neuroergonomics, State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Zhuhai, 519087, China
- The School of Systems Science, Beijing Normal University, Beijing, 100875, China
| | - Shaohua Tang
- The Center for Cognition and Neuroergonomics, State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Zhuhai, 519087, China
- The School of Systems Science, Beijing Normal University, Beijing, 100875, China
| | - Chuancai Liu
- The State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, 100875, China
| | - Qiankun Zhang
- The State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, 100875, China
| | - Heng Gu
- The State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, 100875, China
| | - Xiaoli Li
- The State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, 100875, China
| | - Zengru Di
- The International Academic Center of Complex Systems, Beijing Normal University, Zhuhai, 519087, China
| | - Zheng Li
- The Center for Cognition and Neuroergonomics, State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Zhuhai, 519087, China.
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4
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Chen X, Onur OA, Richter N, Fassbender R, Gramespacher H, Befahr Q, von Reutern B, Dillen K, Jacobs HIL, Kukolja J, Fink GR, Dronse J. Concordance of Intrinsic Brain Connectivity Measures Is Disrupted in Alzheimer's Disease. Brain Connect 2023; 13:344-355. [PMID: 34269605 DOI: 10.1089/brain.2020.0918] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022] Open
Abstract
Background: Recently, a new resting-state functional magnetic resonance imaging (rs-fMRI) measure to evaluate the concordance between different rs-fMRI metrics has been proposed and has not been investigated in Alzheimer's disease (AD). Methods: 3T rs-fMRI data were obtained from healthy young controls (YC, n = 26), healthy senior controls (SC, n = 29), and AD patients (n = 35). The fractional amplitude of low-frequency fluctuations (fALFF), regional homogeneity (ReHo), and degree centrality (DC) were analyzed, followed by the calculation of their concordance using Kendall's W for each brain voxel across time. Group differences in the concordance were compared globally, within seven intrinsic brain networks, and on a voxel-by-voxel basis with covariates of age, sex, head motion, and gray matter volume. Results: The global concordance was lowest in AD among the three groups, with similar differences for the single metrics. When comparing AD to SC, reductions of concordance were detected in each of the investigated networks apart from the limbic network. For SC in comparison to YC, lower global concordance without any network-level difference was observed. Voxel-wise analyses revealed lower concordance in the right middle temporal gyrus in AD compared to SC and lower concordance in the left middle frontal gyrus in SC compared to YC. Lower fALFF were observed in the right angular gyrus in AD in comparison to SC, but ReHo and DC showed no group differences. Conclusions: The concordance of resting-state measures differentiates AD from healthy aging and may represent a novel imaging marker in AD.
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Affiliation(s)
- Xiangliang Chen
- Cognitive Neuroscience, Institute of Neuroscience and Medicine (INM-3), Jülich Research Centre, Jülich, Germany
- Department of Neurology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
- Department of Neurology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
| | - Oezguer A Onur
- Cognitive Neuroscience, Institute of Neuroscience and Medicine (INM-3), Jülich Research Centre, Jülich, Germany
- Department of Neurology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Nils Richter
- Cognitive Neuroscience, Institute of Neuroscience and Medicine (INM-3), Jülich Research Centre, Jülich, Germany
- Department of Neurology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Ronja Fassbender
- Department of Neurology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Hannes Gramespacher
- Department of Neurology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Qumars Befahr
- Cognitive Neuroscience, Institute of Neuroscience and Medicine (INM-3), Jülich Research Centre, Jülich, Germany
- Department of Neurology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Boris von Reutern
- Cognitive Neuroscience, Institute of Neuroscience and Medicine (INM-3), Jülich Research Centre, Jülich, Germany
- Department of Neurology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Kim Dillen
- Cognitive Neuroscience, Institute of Neuroscience and Medicine (INM-3), Jülich Research Centre, Jülich, Germany
| | - Heidi I L Jacobs
- Department of Radiology, Gordon Center for Medical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
- Department of Psychiatry and Neuropsychology, Alzheimer Centre, Limburg, School for Mental Health and Neuroscience, Maastricht University, Maastricht, the Netherlands
| | - Juraj Kukolja
- Department of Neurology and Clinical Neurophysiology, Helios University Hospital Wuppertal, Wuppertal, Germany
- Department of Neurology, Faculty of Health, Witten/Herdecke University, Witten, Germany
| | - Gereon R Fink
- Cognitive Neuroscience, Institute of Neuroscience and Medicine (INM-3), Jülich Research Centre, Jülich, Germany
- Department of Neurology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Julian Dronse
- Cognitive Neuroscience, Institute of Neuroscience and Medicine (INM-3), Jülich Research Centre, Jülich, Germany
- Department of Neurology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
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5
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Chen Z, Hu B, Liu X, Becker B, Eickhoff SB, Miao K, Gu X, Tang Y, Dai X, Li C, Leonov A, Xiao Z, Feng Z, Chen J, Chuan-Peng H. Sampling inequalities affect generalization of neuroimaging-based diagnostic classifiers in psychiatry. BMC Med 2023; 21:241. [PMID: 37400814 DOI: 10.1186/s12916-023-02941-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/10/2023] [Accepted: 06/13/2023] [Indexed: 07/05/2023] Open
Abstract
BACKGROUND The development of machine learning models for aiding in the diagnosis of mental disorder is recognized as a significant breakthrough in the field of psychiatry. However, clinical practice of such models remains a challenge, with poor generalizability being a major limitation. METHODS Here, we conducted a pre-registered meta-research assessment on neuroimaging-based models in the psychiatric literature, quantitatively examining global and regional sampling issues over recent decades, from a view that has been relatively underexplored. A total of 476 studies (n = 118,137) were included in the current assessment. Based on these findings, we built a comprehensive 5-star rating system to quantitatively evaluate the quality of existing machine learning models for psychiatric diagnoses. RESULTS A global sampling inequality in these models was revealed quantitatively (sampling Gini coefficient (G) = 0.81, p < .01), varying across different countries (regions) (e.g., China, G = 0.47; the USA, G = 0.58; Germany, G = 0.78; the UK, G = 0.87). Furthermore, the severity of this sampling inequality was significantly predicted by national economic levels (β = - 2.75, p < .001, R2adj = 0.40; r = - .84, 95% CI: - .41 to - .97), and was plausibly predictable for model performance, with higher sampling inequality for reporting higher classification accuracy. Further analyses showed that lack of independent testing (84.24% of models, 95% CI: 81.0-87.5%), improper cross-validation (51.68% of models, 95% CI: 47.2-56.2%), and poor technical transparency (87.8% of models, 95% CI: 84.9-90.8%)/availability (80.88% of models, 95% CI: 77.3-84.4%) are prevailing in current diagnostic classifiers despite improvements over time. Relating to these observations, model performances were found decreased in studies with independent cross-country sampling validations (all p < .001, BF10 > 15). In light of this, we proposed a purpose-built quantitative assessment checklist, which demonstrated that the overall ratings of these models increased by publication year but were negatively associated with model performance. CONCLUSIONS Together, improving sampling economic equality and hence the quality of machine learning models may be a crucial facet to plausibly translating neuroimaging-based diagnostic classifiers into clinical practice.
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Affiliation(s)
- Zhiyi Chen
- Experimental Research Center for Medical and Psychological Science (ERC-MPS), School of Psychology, Third Military Medical University, Chongqing, China.
- Faculty of Psychology, Southwest University, Chongqing, China.
| | - Bowen Hu
- Faculty of Psychology, Southwest University, Chongqing, China
| | - Xuerong Liu
- Experimental Research Center for Medical and Psychological Science (ERC-MPS), School of Psychology, Third Military Medical University, Chongqing, China
| | - Benjamin Becker
- The Center of Psychosomatic Medicine, Sichuan Provincial Center for Mental Health, Sichuan Provincial People's Hospital, Chengdu, China
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China
| | - Simon B Eickhoff
- Institute of Systems Neuroscience, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Kuan Miao
- Experimental Research Center for Medical and Psychological Science (ERC-MPS), School of Psychology, Third Military Medical University, Chongqing, China
| | - Xingmei Gu
- Experimental Research Center for Medical and Psychological Science (ERC-MPS), School of Psychology, Third Military Medical University, Chongqing, China
| | - Yancheng Tang
- School of Business and Management, Shanghai International Studies University, Shanghai, China
| | - Xin Dai
- Faculty of Psychology, Southwest University, Chongqing, China
| | - Chao Li
- Department of Radiology, The Third Affiliated Hospital, Sun Yat-Sen University, Guangdong, China
| | - Artemiy Leonov
- School of Psychology, Clark University, Worcester, MA, USA
| | - Zhibing Xiao
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Zhengzhi Feng
- Experimental Research Center for Medical and Psychological Science (ERC-MPS), School of Psychology, Third Military Medical University, Chongqing, China
| | - Ji Chen
- Department of Psychology and Behavioral Sciences, Zhejiang University, Hangzhou, China.
- Department of Psychiatry, The Fourth Affiliated Hospital, Zhejiang University School of Medicine, Yiwu, Zhejiang, China.
| | - Hu Chuan-Peng
- School of Psychology, Nanjing Normal University, Nanjing, China
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Chen Z, Liu X, Yang Q, Wang YJ, Miao K, Gong Z, Yu Y, Leonov A, Liu C, Feng Z, Chuan-Peng H. Evaluation of Risk of Bias in Neuroimaging-Based Artificial Intelligence Models for Psychiatric Diagnosis: A Systematic Review. JAMA Netw Open 2023; 6:e231671. [PMID: 36877519 PMCID: PMC9989906 DOI: 10.1001/jamanetworkopen.2023.1671] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 03/07/2023] Open
Abstract
IMPORTANCE Neuroimaging-based artificial intelligence (AI) diagnostic models have proliferated in psychiatry. However, their clinical applicability and reporting quality (ie, feasibility) for clinical practice have not been systematically evaluated. OBJECTIVE To systematically assess the risk of bias (ROB) and reporting quality of neuroimaging-based AI models for psychiatric diagnosis. EVIDENCE REVIEW PubMed was searched for peer-reviewed, full-length articles published between January 1, 1990, and March 16, 2022. Studies aimed at developing or validating neuroimaging-based AI models for clinical diagnosis of psychiatric disorders were included. Reference lists were further searched for suitable original studies. Data extraction followed the CHARMS (Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modeling Studies) and PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-analyses) guidelines. A closed-loop cross-sequential design was used for quality control. The PROBAST (Prediction Model Risk of Bias Assessment Tool) and modified CLEAR (Checklist for Evaluation of Image-Based Artificial Intelligence Reports) benchmarks were used to systematically evaluate ROB and reporting quality. FINDINGS A total of 517 studies presenting 555 AI models were included and evaluated. Of these models, 461 (83.1%; 95% CI, 80.0%-86.2%) were rated as having a high overall ROB based on the PROBAST. The ROB was particular high in the analysis domain, including inadequate sample size (398 of 555 models [71.7%; 95% CI, 68.0%-75.6%]), poor model performance examination (with 100% of models lacking calibration examination), and lack of handling data complexity (550 of 555 models [99.1%; 95% CI, 98.3%-99.9%]). None of the AI models was perceived to be applicable to clinical practices. Overall reporting completeness (ie, number of reported items/number of total items) for the AI models was 61.2% (95% CI, 60.6%-61.8%), and the completeness was poorest for the technical assessment domain with 39.9% (95% CI, 38.8%-41.1%). CONCLUSIONS AND RELEVANCE This systematic review found that the clinical applicability and feasibility of neuroimaging-based AI models for psychiatric diagnosis were challenged by a high ROB and poor reporting quality. Particularly in the analysis domain, ROB in AI diagnostic models should be addressed before clinical application.
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Affiliation(s)
- Zhiyi Chen
- School of Psychology, Third Military Medical University, Chongqing, China
- Experimental Research Center for Medical and Psychological Science, Third Military Medical University, Chongqing, China
| | - Xuerong Liu
- School of Psychology, Third Military Medical University, Chongqing, China
- Experimental Research Center for Medical and Psychological Science, Third Military Medical University, Chongqing, China
| | - Qingwu Yang
- Department of Neurology, Daping Hospital, Third Military Medical University, Chongqing, China
| | - Yan-Jiang Wang
- Department of Neurology, Daping Hospital, Third Military Medical University, Chongqing, China
| | - Kuan Miao
- School of Psychology, Third Military Medical University, Chongqing, China
- Experimental Research Center for Medical and Psychological Science, Third Military Medical University, Chongqing, China
| | - Zheng Gong
- School of Psychology, Third Military Medical University, Chongqing, China
- Experimental Research Center for Medical and Psychological Science, Third Military Medical University, Chongqing, China
| | - Yang Yu
- School of Psychology, Third Military Medical University, Chongqing, China
| | - Artemiy Leonov
- Department of Psychology, Clark University, Worcester, Massachusetts
| | - Chunlei Liu
- School of Psychology, Qufu Normal University, Qufu, China
| | - Zhengzhi Feng
- School of Psychology, Third Military Medical University, Chongqing, China
- Experimental Research Center for Medical and Psychological Science, Third Military Medical University, Chongqing, China
| | - Hu Chuan-Peng
- School of Psychology, Nanjing Normal University, Nanjing, China
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7
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Shirwaikar RD, Sarwari I, Najam M, M SH. Has Machine Learning Enhanced the Diagnosis of Autism Spectrum Disorder? Crit Rev Biomed Eng 2023; 51:1-14. [PMID: 37522537 DOI: 10.1615/critrevbiomedeng.v51.i1.10] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/01/2023]
Abstract
Autism spectrum disorder (ASD) is a complex neurological condition that limits an individual's capacity for communication and learning throughout their life. Although symptoms of Autism can be diagnosed in individuals of different ages, it is labeled as a developmental disorder because symptoms typically start to show up in the initial 2 years of childhood. Autism has no single known cause but multiple factors contribute to its etiology in children. Because symptoms and severity of ASD vary in every individual, there could be many causes. Detection of ASD in the early stages is crucial for providing a path for rehabilitation that enhances the quality of life and integrates the ASD person into the social, family, and professional spheres. Assessment of ASD includes experienced observers in neutral environments, which brings constraints and biases to a lack of credibility and fails to accurately reflect performance in terms of real-world scenarios. To get around these limitations, the conducted review offers a thorough analysis of the impact on the individual and the ones living around them and most recent research on how these techniques are implemented in the diagnosis of ASD. As a result of improvements in technology, assessments now include processing unconventional data than can be collected from measurements arising out of laboratory chemistry or of electrophysiological origin. Examples of these technologies include virtual reality and sensors including eye-tracking imaging. Studies have been conducted towards recognition of emotion and brain networks to identify functional connectivity and discriminate between people with ASD and people who are thought to be typically developing. Diagnosis of Autism has recently made substantial use of long short term memory (LSTM), convolutional neural network (CNN) and its variants, the random forest (RF) and naive Bayes (NB) machine learning techniques. It is hoped that researchers will develop methodologies that increase the probability of identification of ASD in its varied forms and contribute towards improved lifestyle for patients with ASD and those affected by the pathology.
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Affiliation(s)
- Rudresh Deepak Shirwaikar
- Department of Computer Engineering, Agnel Institute of Technology and Design (AITD), Goa University, Assagao, Goa, India, 403507
| | - Iram Sarwari
- Department of Information Science and Engineering, Ramaiah Institute of Technology (RIT), Bangalore, Karnataka, India 560064
| | - Mehwish Najam
- Department of Information Science and Engineering, Ramaiah Institute of Technology (RIT), Bangalore, Karnataka, India 560064
| | - Shama H M
- BMS Institute of Technology and Management (BMSIT), Bangalore, Karnataka, India 560064
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8
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Warren SL, Moustafa AA. Functional magnetic resonance imaging, deep learning, and Alzheimer's disease: A systematic review. J Neuroimaging 2023; 33:5-18. [PMID: 36257926 PMCID: PMC10092597 DOI: 10.1111/jon.13063] [Citation(s) in RCA: 18] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2022] [Revised: 09/30/2022] [Accepted: 09/30/2022] [Indexed: 02/01/2023] Open
Abstract
Alzheimer's disease (AD) is currently diagnosed using a mixture of psychological tests and clinical observations. However, these diagnoses are not perfect, and additional diagnostic tools (e.g., MRI) can help improve our understanding of AD as well as our ability to detect the disease. Accordingly, a large amount of research has been invested into innovative diagnostic methods for AD. Functional MRI (fMRI) is a form of neuroimaging technology that has been used to diagnose AD; however, fMRI is incredibly noisy, complex, and thus lacks clinical use. Nonetheless, recent innovations in deep learning technology could enable the simplified and streamlined analysis of fMRI. Deep learning is a form of artificial intelligence that uses computer algorithms based on human neural networks to solve complex problems. For example, in fMRI research, deep learning models can automatically denoise images and classify AD by detecting patterns in participants' brain scans. In this systematic review, we investigate how fMRI (specifically resting-state fMRI) and deep learning methods are used to diagnose AD. In turn, we outline the common deep neural network, preprocessing, and classification methods used in the literature. We also discuss the accuracy, strengths, limitations, and future direction of fMRI deep learning methods. In turn, we aim to summarize the current field for new researchers, suggest specific areas for future research, and highlight the potential of fMRI to aid AD diagnoses.
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Affiliation(s)
- Samuel L Warren
- School of Psychology, Faculty of Society and Design, Bond University, Gold Coast, Queensland, Australia
| | - Ahmed A Moustafa
- School of Psychology, Faculty of Society and Design, Bond University, Gold Coast, Queensland, Australia.,Department of Human Anatomy and Physiology, Faculty of Health Sciences, University of Johannesburg, Johannesburg, South Africa
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9
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rs-fMRI and machine learning for ASD diagnosis: a systematic review and meta-analysis. Sci Rep 2022; 12:6030. [PMID: 35411059 PMCID: PMC9001715 DOI: 10.1038/s41598-022-09821-6] [Citation(s) in RCA: 26] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2021] [Accepted: 03/23/2022] [Indexed: 02/08/2023] Open
Abstract
AbstractAutism Spectrum Disorder (ASD) diagnosis is still based on behavioral criteria through a lengthy and time-consuming process. Much effort is being made to identify brain imaging biomarkers and develop tools that could facilitate its diagnosis. In particular, using Machine Learning classifiers based on resting-state fMRI (rs-fMRI) data is promising, but there is an ongoing need for further research on their accuracy and reliability. Therefore, we conducted a systematic review and meta-analysis to summarize the available evidence in the literature so far. A bivariate random-effects meta-analytic model was implemented to investigate the sensitivity and specificity across the 55 studies that offered sufficient information for quantitative analysis. Our results indicated overall summary sensitivity and specificity estimates of 73.8% and 74.8%, respectively. SVM stood out as the most used classifier, presenting summary estimates above 76%. Studies with bigger samples tended to obtain worse accuracies, except in the subgroup analysis for ANN classifiers. The use of other brain imaging or phenotypic data to complement rs-fMRI information seems promising, achieving higher sensitivities when compared to rs-fMRI data alone (84.7% versus 72.8%). Finally, our analysis showed AUC values between acceptable and excellent. Still, given the many limitations indicated in our study, further well-designed studies are warranted to extend the potential use of those classification algorithms to clinical settings.
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Distinctive Alterations of Functional Connectivity Strength between Vascular and Amnestic Mild Cognitive Impairment. Neural Plast 2021; 2021:8812490. [PMID: 34104193 PMCID: PMC8159649 DOI: 10.1155/2021/8812490] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2020] [Revised: 11/02/2020] [Accepted: 04/30/2021] [Indexed: 11/18/2022] Open
Abstract
Widespread structural and functional alterations have been reported in the two highly prevalent mild cognitive impairment (MCI) subtypes, amnestic MCI (aMCI) and vascular MCI (VaMCI). However, the changing pattern in functional connectivity strength (FCS) remains largely unclear. The aim of the present study is to detect the differences of FCS and to further explore the detailed resting-state functional connectivity (FC) alterations among VaMCI subjects, aMCI subjects, and healthy controls (HC). Twenty-six aMCI subjects, 31 VaMCI participants, and 36 HC participants underwent cognitive assessments and resting-state functional MRI scans. At first, one-way ANCOVA and post hoc analysis indicated significant decreased FCS in the left middle temporal gyrus (MTG) in aMCI and VaMCI groups compared to HC, especially in the VaMCI group. Then, we selected the left MTG as a seed to further explore the detailed resting-state FC alterations among the three groups, and the results indicated that FC between the left MTG and some frontal brain regions were significantly decreased mainly in VaMCI. Finally, partial correlation analysis revealed that the FC values between the left MTG and left inferior frontal gyrus were positively correlated with the cognitive performance episodic memory and negatively related to the living status. The present study demonstrated that different FCS alterations existed in aMCI and VaMCI. These findings may provide a novel insight into the understanding of pathophysiological mechanisms underlying different MCI subtypes.
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Iterative consensus spectral clustering improves detection of subject and group level brain functional modules. Sci Rep 2020; 10:7590. [PMID: 32371990 PMCID: PMC7200822 DOI: 10.1038/s41598-020-63552-0] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2019] [Accepted: 03/31/2020] [Indexed: 11/29/2022] Open
Abstract
Specialized processing in the brain is performed by multiple groups of brain regions organized as functional modules. Although, in vivo studies of brain functional modules involve multiple functional Magnetic Resonance Imaging (fMRI) scans, the methods used to derive functional modules from functional networks of the brain ignore individual differences in the functional architecture and use incomplete functional connectivity information. To correct this, we propose an Iterative Consensus Spectral Clustering (ICSC) algorithm that detects the most representative modules from individual dense weighted connectivity matrices derived from multiple scans. The ICSC algorithm derives group-level modules from modules of multiple individuals by iteratively minimizing the consensus-cost between the two. We demonstrate that the ICSC algorithm can be used to derive biologically plausible group-level (for multiple subjects) and subject-level (for multiple subject scans) brain modules, using resting-state fMRI scans of 589 subjects from the Human Connectome Project. We employed a multipronged strategy to show the validity of the modularizations obtained from the ICSC algorithm. We show a heterogeneous variability in the modular structure across subjects where modules involved in visual and motor processing were highly stable across subjects. Conversely, we found a lower variability across scans of the same subject. The performance of our algorithm was compared with existing functional brain modularization methods and we show that our method detects group-level modules that are more representative of the modules of multiple individuals. Finally, the experiments on synthetic images quantitatively demonstrate that the ICSC algorithm detects group-level and subject-level modules accurately under varied conditions. Therefore, besides identifying functional modules for a population of subjects, the proposed method can be used for applications in personalized neuroscience. The ICSC implementation is available at https://github.com/SCSE-Biomedical-Computing-Group/ICSC.
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