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Taspinar G, Ozkurt N. A review of ADHD detection studies with machine learning methods using rsfMRI data. NMR IN BIOMEDICINE 2024; 37:e5138. [PMID: 38472163 DOI: 10.1002/nbm.5138] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/14/2023] [Revised: 02/05/2024] [Accepted: 02/11/2024] [Indexed: 03/14/2024]
Abstract
Attention deficit hyperactivity disorder (ADHD) is a common mental health condition that significantly affects school-age children, causing difficulties with learning and daily functioning. Early identification is crucial, and reliable and objective diagnostic tools are necessary. However, current clinical evaluations of behavioral symptoms can be inconsistent and subjective. Functional magnetic resonance imaging (fMRI) is a non-invasive technique that has proven effective in detecting brain abnormalities in individuals with ADHD. Recent studies have shown promising outcomes in using resting state fMRI (rsfMRI)-based brain functional networks to diagnose various brain disorders, including ADHD. Several review papers have examined the detection of other diseases using fMRI data and machine learning or deep learning methods. However, no review paper has specifically addressed ADHD. Therefore, this study aims to contribute to the literature by reviewing the use of rsfMRI data and machine learning methods for detection of ADHD. The study provides general information about fMRI databases and detailed knowledge of the ADHD-200 database, which is commonly used for ADHD detection. It also emphasizes the importance of examining all stages of the process, including network and atlas selection, feature extraction, and feature selection, before the classification stage. The study compares the performance, advantages, and disadvantages of previous studies in detail. This comprehensive approach may be a useful starting point for new researchers in this area.
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Affiliation(s)
| | - Nalan Ozkurt
- Electric and Electronic Engineering, Yasar University Izmir, Izmir, Turkey
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2
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Cao C, Fu H, Li G, Wang M, Gao X. ADHD diagnosis guided by functional brain networks combined with domain knowledge. Comput Biol Med 2024; 177:108611. [PMID: 38788375 DOI: 10.1016/j.compbiomed.2024.108611] [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: 01/04/2024] [Revised: 04/13/2024] [Accepted: 05/11/2024] [Indexed: 05/26/2024]
Abstract
Utilizing functional magnetic resonance imaging (fMRI) to model functional brain networks (FBNs) is increasingly prominent in attention-deficit/hyperactivity disorder (ADHD) research, revealing neural impact and mechanisms through the exploration of activated brain regions. However, current FBNs-based methods face two major challenges. The primary challenge stems from the limitations of existing modeling methods in accurately capturing both regional correlations and long-distance dependencies (LDDs) within the dynamic brain, thereby affecting the diagnostic accuracy of FBNs as biomarkers. Additionally, limited sample size and class imbalance also pose a challenge to the learning performance of the model. To address the issues, we propose an automated diagnostic framework, which integrates modeling, multimodal fusion, and classification into a unified process. It aims to extract representative FBNs and efficiently incorporate domain knowledge to guide ADHD classification. Our work mainly includes three-fold: 1) A multi-head attention-based region-enhancement module (MAREM) is designed to simultaneously capture regional correlations and LDDs across the entire sequence of brain activity, which facilitates the construction of representative FBNs. 2) The multimodal supplementary learning module (MSLM) is proposed to integrate domain knowledge from phenotype data with FBNs from neuroimaging data, achieving information complementarity and alleviating the problems of insufficient medical data and unbalanced sample categories. 3) An ADHD automatic diagnosis framework guided by FBNs and domain knowledge (ADF-FAD) is proposed to help doctors make more accurate decisions, which is applied to the ADHD-200 dataset to confirm its effectiveness. The results indicate that the FBNs extracted by MAREM perform well in modeling and classification. After with MSLM, the model achieves accuracy of 92.4%, 74.4%, and 80% at NYU, PU, and KKI, respectively, demonstrating its ability to effectively capture crucial information related to ADHD diagnosis. Codes are available at https://github.com/zhuimengxuebao/ADF-FAD.
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Affiliation(s)
- Chunhong Cao
- MOE Key Laboratory of Intelligent Computing and Information Processing, Xiangtan University, Xiangtan, 411100, China
| | - Huawei Fu
- MOE Key Laboratory of Intelligent Computing and Information Processing, Xiangtan University, Xiangtan, 411100, China
| | - Gai Li
- MOE Key Laboratory of Intelligent Computing and Information Processing, Xiangtan University, Xiangtan, 411100, China
| | - Mengyang Wang
- MOE Key Laboratory of Intelligent Computing and Information Processing, Xiangtan University, Xiangtan, 411100, China
| | - Xieping Gao
- Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing, Hunan Normal University, Changsha, 410081, China.
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Tian L, Zheng H, Zhang K, Qiu J, Song X, Li S, Zeng Z, Ran B, Deng X, Cai J. Structural or/and functional MRI-based machine learning techniques for attention-deficit/hyperactivity disorder diagnosis: A systematic review and meta-analysis. J Affect Disord 2024; 355:459-469. [PMID: 38580035 DOI: 10.1016/j.jad.2024.03.111] [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: 10/30/2023] [Revised: 03/21/2024] [Accepted: 03/23/2024] [Indexed: 04/07/2024]
Abstract
BACKGROUND The aim of this study was to investigate the diagnostic value of ML techniques based on sMRI or/and fMRI for ADHD. METHODS We conducted a comprehensive search (from database creation date to March 2024) for relevant English articles on sMRI or/and fMRI-based ML techniques for diagnosing ADHD. The pooled sensitivity, specificity, positive likelihood ratio (LR+), negative likelihood ratio (LR-), summary receiver operating characteristic (SROC) curve and area under the curve (AUC) were calculated to assess the diagnostic value of sMRI or/and fMRI-based ML techniques. The I2 test was used to assess heterogeneity and the source of heterogeneity was investigated by performing a meta-regression analysis. Publication bias was assessed using the Deeks funnel plot asymmetry test. RESULTS Forty-three studies were included in the systematic review, 27 of which were included in our meta-analysis. The pooled sensitivity and specificity of sMRI or/and fMRI-based ML techniques for the diagnosis of ADHD were 0.74 (95 % CI 0.65-0.81) and 0.75 (95 % CI 0.67-0.81), respectively. SROC curve showed that AUC was 0.81 (95 % CI 0.77-0.84). Based on these findings, the sMRI or/and fMRI-based ML techniques have relatively good diagnostic value for ADHD. LIMITATIONS Our meta-analysis specifically focused on ML techniques based on sMRI or/and fMRI studies. Since EEG-based ML techniques are also used for diagnosing ADHD, further systematic analyses are necessary to explore ML methods based on multimodal medical data. CONCLUSION sMRI or/and fMRI-based ML technique is a promising objective diagnostic method for ADHD.
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Affiliation(s)
- Lu Tian
- Department of Radiology, Children's Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Pediatric Metabolism and Inflammatory Diseases, Chongqing 400014, China
| | - Helin Zheng
- Department of Radiology, Children's Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Pediatric Metabolism and Inflammatory Diseases, Chongqing 400014, China
| | - Ke Zhang
- Department of Radiology, Children's Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Pediatric Metabolism and Inflammatory Diseases, Chongqing 400014, China
| | - Jiawen Qiu
- Department of Radiology, Children's Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Pediatric Metabolism and Inflammatory Diseases, Chongqing 400014, China
| | - Xuejuan Song
- Department of Radiology, Children's Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Pediatric Metabolism and Inflammatory Diseases, Chongqing 400014, China
| | - Siwei Li
- Department of Radiology, Children's Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Pediatric Metabolism and Inflammatory Diseases, Chongqing 400014, China
| | - Zhao Zeng
- Department of Radiology, Children's Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Pediatric Metabolism and Inflammatory Diseases, Chongqing 400014, China
| | - Baosheng Ran
- Department of Radiology, Children's Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Pediatric Metabolism and Inflammatory Diseases, Chongqing 400014, China
| | - Xin Deng
- Department of Radiology, Children's Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Pediatric Metabolism and Inflammatory Diseases, Chongqing 400014, China
| | - Jinhua Cai
- Department of Radiology, Children's Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Pediatric Metabolism and Inflammatory Diseases, Chongqing 400014, China.
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Li H, Wang J, Li Z, Cecil KM, Altaye M, Dillman JR, Parikh NA, He L. Supervised contrastive learning enhances graph convolutional networks for predicting neurodevelopmental deficits in very preterm infants using brain structural connectome. Neuroimage 2024; 291:120579. [PMID: 38537766 PMCID: PMC11059107 DOI: 10.1016/j.neuroimage.2024.120579] [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: 11/28/2023] [Revised: 02/15/2024] [Accepted: 03/15/2024] [Indexed: 04/13/2024] Open
Abstract
Very preterm (VPT) infants (born at less than 32 weeks gestational age) are at high risk for various adverse neurodevelopmental deficits. Unfortunately, most of these deficits cannot be accurately diagnosed until the age of 2-5 years old. Given the benefits of early interventions, accurate diagnosis and prediction soon after birth are urgently needed for VPT infants. Previous studies have applied deep learning models to learn the brain structural connectome (SC) to predict neurodevelopmental deficits in the preterm population. However, none of these models are specifically designed for graph-structured data, and thus may potentially miss certain topological information conveyed in the brain SC. In this study, we aim to develop deep learning models to learn the SC acquired at term-equivalent age for early prediction of neurodevelopmental deficits at 2 years corrected age in VPT infants. We directly treated the brain SC as a graph, and applied graph convolutional network (GCN) models to capture complex topological information of the SC. In addition, we applied the supervised contrastive learning (SCL) technique to mitigate the effects of the data scarcity problem, and enable robust training of GCN models. We hypothesize that SCL will enhance GCN models for early prediction of neurodevelopmental deficits in VPT infants using the SC. We used a regional prospective cohort of ∼280 VPT infants who underwent MRI examinations at term-equivalent age from the Cincinnati Infant Neurodevelopment Early Prediction Study (CINEPS). These VPT infants completed neurodevelopmental assessment at 2 years corrected age to evaluate cognition, language, and motor skills. Using the SCL technique, the GCN model achieved mean areas under the receiver operating characteristic curve (AUCs) in the range of 0.72∼0.75 for predicting three neurodevelopmental deficits, outperforming several competing models. Our results support our hypothesis that the SCL technique is able to enhance the GCN model in our prediction tasks.
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Affiliation(s)
- Hailong Li
- Imaging Research Center, Department of Radiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA; Neurodevelopmental Disorders Prevention Center, Perinatal Institute, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA; Artificial Intelligence Imaging Research Center, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA; Department of Radiology, University of Cincinnati College of Medicine, Cincinnati, OH, USA
| | - Junqi Wang
- Imaging Research Center, Department of Radiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
| | - Zhiyuan Li
- Imaging Research Center, Department of Radiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA; Department of Computer Science, University of Cincinnati, Cincinnati, OH, USA
| | - Kim M Cecil
- Imaging Research Center, Department of Radiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA; Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, USA; Department of Radiology, University of Cincinnati College of Medicine, Cincinnati, OH, USA
| | - Mekibib Altaye
- Neurodevelopmental Disorders Prevention Center, Perinatal Institute, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA; Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, USA; Division of Biostatistics and Epidemiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
| | - Jonathan R Dillman
- Imaging Research Center, Department of Radiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA; Artificial Intelligence Imaging Research Center, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA; Department of Radiology, University of Cincinnati College of Medicine, Cincinnati, OH, USA
| | - Nehal A Parikh
- Neurodevelopmental Disorders Prevention Center, Perinatal Institute, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA; Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, USA
| | - Lili He
- Imaging Research Center, Department of Radiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA; Neurodevelopmental Disorders Prevention Center, Perinatal Institute, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA; Artificial Intelligence Imaging Research Center, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA; Department of Computer Science, University of Cincinnati, Cincinnati, OH, USA; Department of Biomedical Engineering, University of Cincinnati, Cincinnati, OH, USA; Department of Biomedical Informatics, University of Cincinnati College of Medicine, Cincinnati, OH, USA; Department of Radiology, University of Cincinnati College of Medicine, Cincinnati, OH, USA.
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Chen Y, Wang L, Li Z, Tang Y, Huan Z. Unveiling critical ADHD biomarkers in limbic system and cerebellum using a binary hypothesis testing approach. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2024; 21:5803-5825. [PMID: 38872559 DOI: 10.3934/mbe.2024256] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2024]
Abstract
Attention deficit hyperactivity disorder (ADHD) is a common childhood developmental disorder. In recent years, pattern recognition methods have been increasingly applied to neuroimaging studies of ADHD. However, these methods often suffer from limited accuracy and interpretability, impeding their contribution to the identification of ADHD-related biomarkers. To address these limitations, we applied the amplitude of low-frequency fluctuation (ALFF) results for the limbic system and cerebellar network as input data and conducted a binary hypothesis testing framework for ADHD biomarker detection. Our study on the ADHD-200 dataset at multiple sites resulted in an average classification accuracy of 93%, indicating strong discriminative power of the input brain regions between the ADHD and control groups. Moreover, our approach identified critical brain regions, including the thalamus, hippocampal gyrus, and cerebellum Crus 2, as biomarkers. Overall, this investigation uncovered potential ADHD biomarkers in the limbic system and cerebellar network through the use of ALFF realizing highly credible results, which can provide new insights for ADHD diagnosis and treatment.
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Affiliation(s)
- Ying Chen
- School of Microelectronics and Control Engineering, Changzhou University, Changzhou 213159, China
| | - Lele Wang
- School of Microelectronics and Control Engineering, Changzhou University, Changzhou 213159, China
| | - Zhixin Li
- School of Microelectronics and Control Engineering, Changzhou University, Changzhou 213159, China
| | - Yibin Tang
- College of Information Science and Engineering, Hohai University, Changzhou 213200, China
| | - Zhan Huan
- School of Microelectronics and Control Engineering, Changzhou University, Changzhou 213159, China
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Ji J, Zhang Y. Deep Hashing Mutual Learning for Brain Network Classification. IEEE J Biomed Health Inform 2023; 27:4489-4499. [PMID: 37318974 DOI: 10.1109/jbhi.2023.3286421] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/17/2023]
Abstract
Recently, clinical phenotypic semantic information has begun to play an important role in some brain network classification methods based on deep learning. However, most current methods only consider the phenotypic semantic information of individual brain networks but ignore the potential phenotypic characteristics among group brain networks. To address this problem, we present a deep hashing mutual learning (DHML)-based brain network classification method. Specifically, we first design a separable CNN-based deep hashing learning to extract individual topological features of brain networks and map them into hash codes. Secondly, we construct a group brain network relationship graph based on the similarity of phenotypic semantic information, in which each node is a brain network, and the properties of the nodes are the individual features extracted in the previous step. Then, we adopt a GCN-based deep hashing learning to extract the group topological features of the brain network and map them to hash codes. Finally, the two deep hashing learning models perform mutual learning by measuring the distribution differences between the hash codes to achieve the interaction of individual and group features. The experimental results on the three commonly used brain atlases (AAL Atlas, Dosenbach160 Atlas, and CC200 Atlas) of the ABIDE I dataset show that our proposed DHML method achieves optimal classification performance compared with some state-of-the-art methods.
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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 Y, Gao Y, Jiang A, Tang Y, Wang C. ADHD classification combining biomarker detection with attention auto-encoding neural network. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2023.104733] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/05/2023]
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Liu G, Lu W, Qiu J, Shi L. Identifying individuals with attention‐deficit/hyperactivity disorder based on multisite resting‐state functional magnetic resonance imaging: A radiomics analysis. Hum Brain Mapp 2023; 44:3433-3445. [PMID: 36971664 PMCID: PMC10171499 DOI: 10.1002/hbm.26290] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2021] [Revised: 12/17/2022] [Accepted: 03/13/2023] [Indexed: 03/29/2023] Open
Abstract
Attention-deficit/hyperactivity disorder (ADHD) is one of the most common neurodevelopmental disorders, characterized by symptoms of age-inappropriate inattention, hyperactivity, and impulsivity. Apart from behavioral symptoms investigated by psychiatric methods, there is no standard biological test to diagnose ADHD. This study aimed to explore whether the radiomics features based on resting-state functional magnetic resonance (rs-fMRI) have more discriminative power for the diagnosis of ADHD. The rs-fMRI of 187 subjects with ADHD and 187 healthy controls were collected from 5 sites of ADHD-200 Consortium. A total of four preprocessed rs-fMRI images including regional homogeneity (ReHo), amplitude of low-frequency fluctuation (ALFF), voxel-mirrored homotopic connectivity (VMHC) and network degree centrality (DC) were used in this study. From each of the four images, we extracted 93 radiomics features within each of 116 automated anatomical labeling brain areas, resulting in a total of 43,152 features for each subject. After dimension reduction and feature selection, 19 radiomics features were retained (5 from ALFF, 9 from ReHo, 3 from VMHC and 2 from DC). By training and optimizing a support vector machine model using the retained features of training dataset, we achieved the accuracy of 76.3% and 77.0% (areas under curve = 0.811 and 0.797) in the training and testing datasets, respectively. Our findings demonstrate that radiomics can be a novel strategy for fully utilizing rs-fMRI information to distinguish ADHD from healthy controls. The rs-fMRI-based radiomics features have the potential to be neuroimaging biomarkers for ADHD.
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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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Wang Z, Zhou X, Gui Y, Liu M, Lu H. Multiple measurement analysis of resting-state fMRI for ADHD classification in adolescent brain from the ABCD study. Transl Psychiatry 2023; 13:45. [PMID: 36746929 PMCID: PMC9902465 DOI: 10.1038/s41398-023-02309-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/08/2022] [Revised: 12/22/2022] [Accepted: 01/06/2023] [Indexed: 02/08/2023] Open
Abstract
Attention deficit hyperactivity disorder (ADHD) is one of the most common psychiatric disorders in school-aged children. Its accurate diagnosis looks after patients' interests well with effective treatment, which is important to them and their family. Resting-state functional magnetic resonance imaging (rsfMRI) has been widely used to characterize the abnormal brain function by computing the voxel-wise measures and Pearson's correlation (PC)-based functional connectivity (FC) for ADHD diagnosis. However, exploring the powerful measures of rsfMRI to improve ADHD diagnosis remains a particular challenge. To this end, this paper proposes an automated ADHD classification framework by fusion of multiple measures of rsfMRI in adolescent brain. First, we extract the voxel-wise measures and ROI-wise time series from the brain regions of rsfMRI after preprocessing. Then, to extract the multiple functional connectivities, we compute the PC-derived FCs including the topographical information-based high-order FC (tHOFC) and dynamics-based high-order FC (dHOFC), the sparse representation (SR)-derived FCs including the group SR (GSR), the strength and similarity guided GSR (SSGSR), and sparse low-rank (SLR). Finally, these measures are combined with multiple kernel learning (MKL) model for ADHD classification. The proposed method is applied to the Adolescent Brain and Cognitive Development (ABCD) dataset. The results show that the FCs of dHOFC and SLR perform better than the others. Fusing multiple measures achieves the best classification performance (AUC = 0.740, accuracy = 0.6916), superior to those from the single measure and the previous studies. We have identified the most discriminative FCs and brain regions for ADHD diagnosis, which are consistent with those of published literature.
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Affiliation(s)
- Zhaobin Wang
- grid.16821.3c0000 0004 0368 8293State Key Lab of Microbial Metabolism, Joint International Research Laboratory of Metabolic Developmental Sciences, Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China ,grid.16821.3c0000 0004 0368 8293SJTU-Yale Joint Center of Biostatistics and Data Science, National Center for Translational Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Xiaocheng Zhou
- grid.16821.3c0000 0004 0368 8293State Key Lab of Microbial Metabolism, Joint International Research Laboratory of Metabolic Developmental Sciences, Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China
| | - Yuanyuan Gui
- grid.16821.3c0000 0004 0368 8293State Key Lab of Microbial Metabolism, Joint International Research Laboratory of Metabolic Developmental Sciences, Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China ,grid.16821.3c0000 0004 0368 8293SJTU-Yale Joint Center of Biostatistics and Data Science, National Center for Translational Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Manhua Liu
- MoE Key Laboratory of Artificial Intelligence, AI Institute, School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China.
| | - Hui Lu
- State Key Lab of Microbial Metabolism, Joint International Research Laboratory of Metabolic Developmental Sciences, Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China. .,SJTU-Yale Joint Center of Biostatistics and Data Science, National Center for Translational Medicine, Shanghai Jiao Tong University, Shanghai, China. .,Shanghai Engineering Research Center for Big Data in Pediatric Precision Medicine, Center for Biomedical Informatics, Shanghai Children's Hospital, Shanghai, China.
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12
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Zhang-James Y, Razavi AS, Hoogman M, Franke B, Faraone SV. Machine Learning and MRI-based Diagnostic Models for ADHD: Are We There Yet? J Atten Disord 2023; 27:335-353. [PMID: 36651494 DOI: 10.1177/10870547221146256] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/19/2023]
Abstract
OBJECTIVE Machine learning (ML) has been applied to develop magnetic resonance imaging (MRI)-based diagnostic classifiers for attention-deficit/hyperactivity disorder (ADHD). This systematic review examines this literature to clarify its clinical significance and to assess the implications of the various analytic methods applied. METHODS A comprehensive literature search on MRI-based diagnostic classifiers for ADHD was performed and data regarding the utilized models and samples were gathered. RESULTS We found that, although most studies reported the classification accuracies, they varied in choice of MRI modalities, ML models, cross-validation and testing methods, and sample sizes. We found that the accuracies of cross-validation methods inflated the performance estimation compared with those of a held-out test, compromising the model generalizability. Test accuracies have increased with publication year but were not associated with training sample sizes. Improved test accuracy over time was likely due to the use of better ML methods along with strategies to deal with data imbalances. CONCLUSION Ultimately, large multi-modal imaging datasets, and potentially the combination with other types of data, like cognitive data and/or genetics, will be essential to achieve the goal of developing clinically useful imaging classification tools for ADHD in the future.
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Affiliation(s)
| | | | - Martine Hoogman
- Radboud University Medical Center, Nijmegen, The Netherlands
- Donders Institute for Brain, Cognition and Behavior, Nijmegen, The Netherlands
| | - Barbara Franke
- Radboud University Medical Center, Nijmegen, The Netherlands
- Donders Institute for Brain, Cognition and Behavior, Nijmegen, The Netherlands
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13
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Ji J, Zhang Y. Functional Brain Network Classification Based on Deep Graph Hashing Learning. IEEE TRANSACTIONS ON MEDICAL IMAGING 2022; 41:2891-2902. [PMID: 35533175 DOI: 10.1109/tmi.2022.3173428] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Brain network classification using resting-state functional magnetic resonance imaging (rs-fMRI) is an effective analytical method for diagnosing brain diseases. In recent years, brain network classification methods based on deep learning have attracted increasing attention. However, these methods only consider the spatial topological characteristics of the brain network but ignore its proximity relationships in semantic space. To overcome this problem, we propose a novel brain network classification method based on deep graph hashing learning named BNC-DGHL. Specifically, we first extract the deep features of the brain network and then learn a graph hash function based on clinical phenotype labels and the similarity of diagnostic labels. Secondly, we use the learned graph hash function to convert deep features into hash codes, which can maintain the original semantic spatial relationships. Finally, we calculate the distance between hash codes to obtain the predicted category of the brain network. Experimental results on ABIDE I, ABIDE II, and ADHD-200 datasets demonstrate that our method achieves better classification performance of brain diseases compared with some state-of-the-art methods, and the abnormal functional connectivities between brain regions identified may serve as biomarkers associated with related brain diseases.
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14
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An fMRI Sequence Representation Learning Framework for Attention Deficit Hyperactivity Disorder Classification. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12126211] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
For attention deficit hyperactivity disorder (ADHD), a common neurological disease, accurate identification is the basis for treatment. In this paper, a novel end-to-end representation learning framework for ADHD classification of functional magnetic resonance imaging (fMRI) sequences is proposed. With such a framework, the complexity of the sequence representation learning neural network decreases, the overfitting problem of deep learning for small samples cases is solved effectively, and superior classification performance is achieved. Specifically, a data conversion module was designed to convert a two-dimensional sequence into a three-dimensional image, which expands the modeling area and greatly reduces the computational complexity. The transfer learning method was utilized to freeze or fine-tune the parameters of the pre-trained neural network to reduce the risk of overfitting in the cases with small samples. Hierarchical feature extraction can be performed automatically by combining the sequence representation learning modules with a weighted cross-entropy loss. Experiments were conducted both with individual imaging sites and combining them, and the results showed that the classification average accuracies with the proposed framework were 73.73% and 72.02%, respectively, which are much higher than those of the existing methods.
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15
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Cibrian FL, Monteiro E, Schuck SEB, Nelson M, Hayes GR, Lakes KD. Interdisciplinary Tensions When Developing Digital Interventions Supporting Individuals With ADHD. Front Digit Health 2022; 4:876039. [PMID: 35633736 PMCID: PMC9133410 DOI: 10.3389/fdgth.2022.876039] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2022] [Accepted: 03/28/2022] [Indexed: 11/17/2022] Open
Affiliation(s)
- Franceli L. Cibrian
- Fowler School of Engineering, Chapman University, Orange, CA, United States
- *Correspondence: Franceli L. Cibrian
| | - Elissa Monteiro
- Graduate School of Education, University of California, Riverside, Riverside, CA, United States
| | - Sabrina E. B. Schuck
- Pediatrics Department, University of California, Irvine, Irvine, CA, United States
| | - Michele Nelson
- Department of Psychiatry and Neuroscience, University of California, Riverside, Riverside, CA, United States
| | - Gillian R. Hayes
- Pediatrics Department, University of California, Irvine, Irvine, CA, United States
- Informatics Department, University of California, Irvine, Irvine, CA, United States
| | - Kimberley D. Lakes
- Department of Psychiatry and Neuroscience, University of California, Riverside, Riverside, CA, United States
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16
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Loh HW, Ooi CP, Barua PD, Palmer EE, Molinari F, Acharya UR. Automated detection of ADHD: Current trends and future perspective. Comput Biol Med 2022; 146:105525. [DOI: 10.1016/j.compbiomed.2022.105525] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2022] [Revised: 03/31/2022] [Accepted: 04/04/2022] [Indexed: 12/25/2022]
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17
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Ingabire HN, Qu H, Li M, He S, Amos JT, Cui Y, Wang Q, Yao D, Ma D, Ren P. Stability Analysis of fMRI BOLD Signals for Disease Diagnosis. IEEE Trans Neural Syst Rehabil Eng 2022; 30:967-978. [PMID: 35363617 DOI: 10.1109/tnsre.2022.3164074] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
Previous studies have demonstrated that the stability changes in physiological signals can reflect individuals' pathological conditions. Apart from this, according to system science theory, a large-scale system can generally be divided into many subsystems whose stability level govern its overall performance. Therefore, this study attempts to investigate the possibility of analyzing the stability of decomposed subsystems of resting-state fMRI (rs-fMRI) BOLD signals in order to assess the overall characteristic of the human brain and individuals' health conditions. We used attention deficit/hyperactive disorder (ADHD) as an example to illustrate our method. Rs-fMRI BOLD signals were first decomposed into dynamic modes (DMs) which can illuminate the patterns of brain subsystems. Each DM is associated with one eigenvalue that determines its stability as well as oscillation frequency. Accordingly, we divided the DMs within common BOLD frequency bands into stable and unstable DMs. Then, the features related to the stability of those DMs were extracted, and nine common classifiers were used to differentiate healthy controls from ADHD patients taken from ADHD-200, a well-known dataset. The results showed that almost all features were statistically significant. Additionally, our proposed approach outperforms all existing methods with the highest possible precision, recall, and area under the receiver operating characteristic curve of 100%. In sum, we are the first to evaluate the stability of BOLD signals and demonstrate its possibility for disease diagnosis. This method can unveil new mechanisms of brain function, and could be widely used in medicine and engineering.
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18
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Dwyer D, Koutsouleris N. Annual Research Review: Translational machine learning for child and adolescent psychiatry. J Child Psychol Psychiatry 2022; 63:421-443. [PMID: 35040130 DOI: 10.1111/jcpp.13545] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 09/06/2021] [Indexed: 12/14/2022]
Abstract
Children and adolescents could benefit from the use of predictive tools that facilitate personalized diagnoses, prognoses, and treatment selection. Such tools have not yet been deployed using traditional statistical methods, potentially due to the limitations of the paradigm and the need to leverage large amounts of digital data. This review will suggest that a machine learning approach could address these challenges and is designed to introduce new readers to the background, methods, and results in the field. A rationale is first introduced followed by an outline of fundamental elements of machine learning approaches. To provide an overview of the use of the techniques in child and adolescent literature, a scoping review of broad trends is then presented. Selected studies are also highlighted in order to draw attention to research areas that are closest to translation and studies that exhibit a high degree of experimental innovation. Limitations to the research, and machine learning approaches generally, are outlined in the penultimate section highlighting issues related to sample sizes, validation, clinical utility, and ethical challenges. Finally, future directions are discussed that could enhance the possibility of clinical implementation and address specific questions relevant to the child and adolescent psychiatry. The review gives a broad overview of the machine learning paradigm in order to highlight the benefits of a shift in perspective towards practically oriented statistical solutions that aim to improve clinical care of children and adolescents.
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Affiliation(s)
- Dominic Dwyer
- Department of Psychiatry and Psychotherapy, Ludwig-Maximilian-University, Munich, Germany.,Orygen, The National Centre of Excellence in Youth Mental Health, Melbourne, Australia.,Centre for Youth Mental Health, University of Melbourne, Melbourne, Australia
| | - Nikolaos Koutsouleris
- Department of Psychiatry and Psychotherapy, Ludwig-Maximilian-University, Munich, Germany.,Max-Planck Institute of Psychiatry, Munich, Germany.,Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
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19
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Chen M, Li H, Fan H, Dillman JR, Wang H, Altaye M, Zhang B, Parikh NA, He L. ConCeptCNN: A novel multi-filter convolutional neural network for the prediction of neurodevelopmental disorders using brain connectome. Med Phys 2022; 49:3171-3184. [PMID: 35246986 DOI: 10.1002/mp.15545] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2021] [Revised: 01/31/2022] [Accepted: 02/09/2022] [Indexed: 11/11/2022] Open
Abstract
BACKGROUND Deep convolutional neural network (CNN) and its derivatives have recently shown great promise in the prediction of brain disorders using brain connectome data. Existing deep CNN methods using single global row and column convolutional filters have limited ability to extract discriminative information from brain connectome for prediction tasks. PURPOSE This paper presents a novel deep Connectome-Inception CNN (ConCeptCNN) model, which is developed based on multiple convolutional filters. The proposed model is used to extract topological features from brain connectome data for neurological disorders classification and analysis. METHODS The ConCeptCNN uses multiple vector-shaped filters extract topological information from the brain connectome at different levels for complementary feature embeddings of brain connectome. The proposed model is validated using two datasets: the Neuro Bureau ADHD-200 dataset and the Cincinnati Early Prediction Study (CINEPS) dataset. RESULTS In a cross-validation experiment, the ConCeptCNN achieved a prediction accuracy of 78.7% for the detection of ADHD in adolescents and an accuracy of 81.6% for the prediction of cognitive deficits at 2-year corrected age in very preterm infants. In addition to the classification tasks, the ConCeptCNN identified several brain regions that are discriminative to neurodevelopmental disorders. CONCLUSIONS We compared the ConCeptCNN with several peer CNN methods. The results demonstrated that proposed model improves overall classification performance of neurodevelopmental disorders prediction tasks. This article is protected by copyright. All rights reserved.
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Affiliation(s)
- Ming Chen
- Imaging Research Center, Department of Radiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA.,Department of Electrical Engineering and Computer Science, University of Cincinnati, Cincinnati, OH, USA
| | - Hailong Li
- Imaging Research Center, Department of Radiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA.,Center for Prevention of Neurodevelopmental Disorders, Perinatal Institute, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
| | - Howard Fan
- Department of Electrical Engineering and Computer Science, University of Cincinnati, Cincinnati, OH, USA
| | - Jonathan R Dillman
- Imaging Research Center, Department of Radiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA.,Department of Radiology, University of Cincinnati College of Medicine, Cincinnati, OH, USA
| | - Hui Wang
- Imaging Research Center, Department of Radiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA.,MR Clinical Science, Philips, Cincinnati, OH, USA
| | - Mekibib Altaye
- Division of Biostatistics and Epidemiology, Department of Pediatrics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA.,Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, USA
| | - Bin Zhang
- Division of Biostatistics and Epidemiology, Department of Pediatrics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA.,Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, USA
| | - Nehal A Parikh
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, USA.,Center for Prevention of Neurodevelopmental Disorders, Perinatal Institute, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
| | - Lili He
- Imaging Research Center, Department of Radiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA.,Department of Radiology, University of Cincinnati College of Medicine, Cincinnati, OH, USA.,Center for Prevention of Neurodevelopmental Disorders, Perinatal Institute, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
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20
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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.
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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
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21
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Machine learning models effectively distinguish attention-deficit/hyperactivity disorder using event-related potentials. Cogn Neurodyn 2022; 16:1335-1349. [PMID: 36408064 PMCID: PMC9666608 DOI: 10.1007/s11571-021-09746-2] [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: 12/18/2020] [Revised: 07/18/2021] [Accepted: 10/29/2021] [Indexed: 11/30/2022] Open
Abstract
Accurate diagnosis of Attention-Deficit/Hyperactivity Disorder (ADHD) is a significant challenge. Misdiagnosis has significant negative medical side effects. Due to the complex nature of this disorder, there is no computational expert system for diagnosis. Recently, automatic diagnosis of ADHD by machine learning analysis of brain signals has received an increased attention. This paper aimed to achieve an accurate model to discriminate between ADHD patients and healthy controls by pattern discovery. Event-Related Potentials (ERP) data were collected from ADHD patients and healthy controls. After pre-processing, ERP signals were decomposed and features were calculated for different frequency bands. The classification was carried out based on each feature using seven machine learning algorithms. Important features were then selected and combined. To find specific patterns for each model, the classification was repeated using the proposed patterns. Results indicated that the combination of complementary features can significantly improve the performance of the predictive models. The newly developed features, defined based on band power, were able to provide the best classification using the Generalized Linear Model, Logistic Regression, and Deep Learning with the average accuracy and Receiver operating characteristic curve > %99.85 and > 0.999, respectively. High and low frequencies (Beta, Delta) performed better than the mid, frequencies in the discrimination of ADHD from control. Altogether, this study developed a machine learning expert system that minimises misdiagnosis of ADHD and is beneficial for the evaluation of treatment efficacy.
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22
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A novel ADHD classification method based on resting state temporal templates (RSTT) using spatiotemporal attention auto-encoder. Neural Comput Appl 2022. [DOI: 10.1007/s00521-021-06868-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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23
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ADHD classification using auto-encoding neural network and binary hypothesis testing. Artif Intell Med 2022; 123:102209. [PMID: 34998510 DOI: 10.1016/j.artmed.2021.102209] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2021] [Revised: 10/11/2021] [Accepted: 11/03/2021] [Indexed: 11/21/2022]
Abstract
Attention Deficit Hyperactivity Disorder (ADHD) is a highly prevalent neurodevelopmental disease of school-age children. Early diagnosis is crucial for ADHD treatment, wherein its neurobiological diagnosis (or classification) is helpful and provides the objective evidence to clinicians. The existing ADHD classification methods suffer two problems, i.e., insufficient data and feature noise disturbance from other associated disorders. As an attempt to overcome these difficulties, a novel deep-learning classification architecture based on a binary hypothesis testing framework and a modified auto-encoding (AE) network is proposed in this paper. The binary hypothesis testing framework is introduced to cope with insufficient data of ADHD database. Brain functional connectivities (FCs) of test data (without seeing their labels) are incorporated during feature selection along with those of training data and affect the sequential deep learning procedure under binary hypotheses. On the other hand, the modified AE network is developed to capture more effective features from training data, such that the difference of inter- and intra-class variability scores between binary hypotheses can be enlarged and effectively alleviate the disturbance of feature noise. On the test of ADHD-200 database, our method significantly outperforms the existing classification methods. The average accuracy reaches 99.6% with the leave-one-out cross validation. Our method is also more robust and practically convenient for ADHD classification due to its uniform parameter setting across various datasets.
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24
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Zhang H, Zeng W, Deng J, Shi Y, Zhao L, Li Y. Brain Relatively Inert Network: Taking Adult Attention Deficit Hyperactivity Disorder as an Example. Front Neurosci 2021; 15:771947. [PMID: 34924940 PMCID: PMC8678527 DOI: 10.3389/fnins.2021.771947] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2021] [Accepted: 11/05/2021] [Indexed: 11/13/2022] Open
Abstract
Resting-state functional MRI (rs-fMRI) has been increasingly applied in the research of brain cognitive science and psychiatric diseases. However, previous studies only focused on specific activation areas of the brain, and there are few studies on the inactivation areas. This may overlook much information that explains the brain’s cognitive function. In this paper, we propose a relatively inert network (RIN) and try to explore its important role in understanding the cognitive mechanism of the brain and the study of mental diseases, using adult attention deficit hyperactivity disorder (ADHD) as an example. Here, we utilize methods based on group independent component analysis (GICA) and t-test to identify RIN and calculate its corresponding time series. Through experiments, alterations in the RIN and the corresponding activation network (AN) in adult ADHD patients are observed. And compared with those in the left brain, the activation changes in the right brain are greater. Further, when the RIN functional connectivity is introduced as a feature to classify adult ADHD patients from healthy controls (HCs), the classification accuracy rate is 12% higher than that of the original functional connectivity feature. This was also verified by testing on an independent public dataset. These findings confirm that the RIN of the brain contains much information that will probably be neglected. Moreover, this research provides an effective new means of exploring the information integration between brain regions and the diagnosis of mental illness.
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Affiliation(s)
- Hua Zhang
- Lab of Digital Image and Intelligent Computation, Shanghai Maritime University, Shanghai, China
| | - Weiming Zeng
- Lab of Digital Image and Intelligent Computation, Shanghai Maritime University, Shanghai, China
| | - Jin Deng
- College of Mathematics and Informatics, South China Agricultural University, Guangzhou, China
| | - Yuhu Shi
- Lab of Digital Image and Intelligent Computation, Shanghai Maritime University, Shanghai, China
| | - Le Zhao
- Lab of Digital Image and Intelligent Computation, Shanghai Maritime University, Shanghai, China
| | - Ying Li
- Lab of Digital Image and Intelligent Computation, Shanghai Maritime University, Shanghai, China
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25
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Periyasamy R, Vibashan VS, Varghese GT, Aleem MA. Machine Learning Techniques for the Diagnosis of Attention-Deficit/Hyperactivity Disorder from Magnetic Resonance Imaging: A Concise Review. Neurol India 2021; 69:1518-1523. [PMID: 34979636 DOI: 10.4103/0028-3886.333520] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Abstract
Background Attention-deficit/hyperactivity disorder (ADHD) is a neuro-developmental disease commonly seen in children and it is diagnosed via extensive interview procedures, behavioral studies, third-party observations, and comprehensive personal history. ADHD causes regional atrophy in brain regions and alters the pattern of functional brain connectivity networks. Automated/computerized methods based on magnetic resonance imaging (MRI) can replace subjective methods for the identification of ADHD. Objectives The aim of this study was to analyze various machine-learning algorithms for ADHD by feeding in vital input features extracted from functional brain connectivity and different existing methods and to review factors crucial for the diagnosis of ADHD. Methods This paper is a concise review of machine learning methods for the diagnosis of ADHD from MRI. Techniques for feature extraction, dimensionality reduction/feature selection, and classification, employed in the computerized techniques for the diagnosis of ADHD from MRI and the accuracy of classification offered by the individual methods, are focussed on the review. Conclusions Machine learning algorithms with features of functional brain connectivity networks as input, with hierarchical sparse feature elimination, exhibits the highest accuracy. Augmentation of the behavioral features does not contribute much to increased accuracy. The level of accuracy offered by the frameworks meant for the computer-aided diagnosis of ADHD, available in the literature, does not justify their feasibility in clinical practice. Computerized methods that exploit highly specific biomarkers of ADHD like brain iron concentration in Globus Pallidus, Putamen, Caudate nucleus, and thalamus as features are not available.
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Affiliation(s)
- R Periyasamy
- Department of Instrumentation and Control, National Institute of Technology, Tiruchirappalli, Tamil Nadu, India
| | - V S Vibashan
- Department of Instrumentation and Control, National Institute of Technology, Tiruchirappalli, Tamil Nadu, India
| | - George Tom Varghese
- Department of Electronics and Instrumentation, St. Joseph's College of Engineering and Technology, Palai, Kerala, India
| | - M A Aleem
- Department of Neurology, K. A. P Viswanatham Government Medical College and MGM Government Hospital, Tiruchirappalli, Tamil Nadu, India
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Montaleão Brum Alves R, Ferreira da Silva M, Assis Schmitz É, Juarez Alencar A. Trends, Limits, and Challenges of Computer Technologies in Attention Deficit Hyperactivity Disorder Diagnosis and Treatment. CYBERPSYCHOLOGY BEHAVIOR AND SOCIAL NETWORKING 2021; 25:14-26. [PMID: 34569852 DOI: 10.1089/cyber.2020.0867] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/13/2022]
Abstract
Attention deficit hyperactivity disorder (ADHD) is a neurobiological condition that appears during an individual's childhood and may follow her/him for life. The research objective was to understand better how and which computer technologies have been applied to support ADHD diagnosis and treatment. The research used the systematic literature review method: a rigorous, verifiable, and repeatable approach that follows well-defined steps. Six well-known academic data sources have been consulted, including search engines and bibliographic databases, from technology and health care areas. After a rigorous research protocol, 1,239 articles were analyzed. For the diagnosis, the use of machine learning techniques was verified in 61 percent of the articles. Neurofeedback was ranked second with 9.3 percent participation, followed by serious games and eye tracking with 5.6 percent each. For the treatment, neurofeedback was present in 50 percent of the articles, whereas some studies combined both approaches, accounting for 31 percent of the total. Nine percent of the articles reported remote assistance technology, whereas another 9 percent have used virtual reality. By highlighting the leading computer technologies used, their applications, results, and challenges, this literature review breaks ground for further investigations. Moreover, the study highlighted the lack of consensus on ADHD biomarkers. The approaches using machine learning call attention to the probable occurrence of overfitting in several studies, thus demonstrating limitations of this technology on small-sized bases. This research also presented the convergence of evidence from different studies on the persistence of long-term effects of using neurofeedback in treating ADHD.
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A Novel Knowledge Distillation-Based Feature Selection for the Classification of ADHD. Biomolecules 2021; 11:biom11081093. [PMID: 34439759 PMCID: PMC8393979 DOI: 10.3390/biom11081093] [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] [Subscribe] [Scholar Register] [Received: 06/17/2021] [Revised: 07/21/2021] [Accepted: 07/21/2021] [Indexed: 01/17/2023] Open
Abstract
Attention Deficit Hyperactivity Disorder (ADHD) is a brain disorder with characteristics such as lack of concentration, excessive fidgeting, outbursts of emotions, lack of patience, difficulty in organizing tasks, increased forgetfulness, and interrupting conversation, and it is affecting millions of people worldwide. There is, until now, not a gold standard test using which an ADHD expert can differentiate between an individual with ADHD and a healthy subject, making accurate diagnosis of ADHD a challenging task. We are proposing a Knowledge Distillation-based approach to search for discriminating features between the ADHD and healthy subjects. Learned embeddings from a large neural network, trained on the functional connectivity features, were fed to one hidden layer Autoencoder for reproduction of the embeddings using the same connectivity features. Finally, a forward feature selection algorithm was used to select a combination of most discriminating features between the ADHD and the Healthy Controls. We achieved promising classification results for each of the five individual sites. A combined accuracy of 81% in KKI, 60% Peking, 56% in NYU, 64% NI, and 56% OHSU and individual site wise accuracy of 72% in KKI, 60% Peking, 73% in NYU, 70% NI, and 71% OHSU were obtained using our extracted features. Our results also outperformed state-of-the-art methods in literature which validates the efficacy of our proposed approach.
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Preetha P, Mallika R. Normalization and deep learning based attention deficit hyperactivity disorder classification. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2021. [DOI: 10.3233/jifs-189581] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Attention Deficit Hyperactivity Disorder (ADHD) is one of the major mental-health disorders worldwide. ADHD is typically characterized by impaired executive function, impulsivity, hyperactivity and with respect to these behavioral symptoms, diagnosis of ADHD is performed. These symptoms are obviously seen at in early stage. Serious impairments and substantial burdens are induced for society as well as to families. However, for ADHD, there is no diagnostic laboratory in current scenario. Psychological tests like Brown Attention Deficit Disorder Scale (BADDS), Conners Parent Rating Scale and ADHD Rating Scale (ADHD-RS) are carried out for ADHD diagnosis. Tedious and complex clinical analysis are needed in this testing and this makes low efficiency of the diagnostic process. A traditional diagnosis technique of ADHD produces degraded results. So, enhanced extreme learning machine is incorporated with existing techniques for avoiding the issues of performance degradation. There is a need to enhance the classifier performance further and there is a chance for unwanted noise in input samples, which may degrade the performance of classifier. For avoiding these issues, an enhanced and automated ADHS diagnosis technique is proposed. First stage is pre-processing, and it is carried out based on min max normalization and feature extraction is a next stage, which is carried out through Fast Independent Component Analysis and third stage is a Deep Extreme Learning Machine (DELM) based ADHD identification and classification. Extreme Learning Machine with Kernel (KELM) and Multilayer Extreme Learning Machine (MLELM) algorithm are combined in this method and it is termed as deep extreme learning machine (DELM). Collection of neuro images are used for quantitative and qualitative analysis and with respect to f-measure, recall, precision and accuracy, robustness of proposed technique is demonstrated.
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Affiliation(s)
- P. Preetha
- Research & Development Centre, Bharathiar University, Coimbatore, India
| | - R. Mallika
- Department of Computer Science, CBM College, Coimbatore, India
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Beneath the surface: hyper-connectivity between caudate and salience regions in ADHD fMRI at rest. Eur Child Adolesc Psychiatry 2021; 30:619-631. [PMID: 32385695 DOI: 10.1007/s00787-020-01545-0] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/17/2019] [Accepted: 04/24/2020] [Indexed: 12/12/2022]
Abstract
Attention-Deficit/Hyperactivity Disorder (ADHD) comprises disturbances in attention, emotional regulation, and reward-related processes. In spite of the active efforts in researching neurofunctional correlates of these symptoms, how the activity of subcortical regions-such as basal ganglia-is related to ADHD has yet to be clarified. More specifically, how age may influence the critical changes observed in functional dynamics from childhood to adulthood remains relatively unexplored. We hence selected five core subcortical regions (amygdala, caudate, putamen, pallidum and hippocampus) as regions of interest from the previous literature, measuring their whole-brain voxel-wise rsFC in a sample of 95 ADHD and 90 neurotypical children and adolescents aged from 7 to 18. The only subcortical structure showing significant differences in rsFC was the caudate nucleus. Specifically, we measured increased rsFC with anterior cingulate and right insula, two mesolimbic regions pertaining to the Salience Network. The degree of hyper-rsFC positively correlated with ADHD symptomatology, and showed different patterns of evolution in ADHD vs neurotypical subjects. Finally, the rsFC scores allowed a fair discrimination of the ADHD group (Area Under the Curve ≥ 0.7). These findings shed further light on the fundamental role covered by subcortical structures in ADHD pathogenesis and neurodevelopment, providing new evidence to fill the gap between neurofunctional and clinical expressions of ADHD.
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Tang Y, Wang C, Chen Y, Sun N, Jiang A, Wang Z. Identifying ADHD Individuals From Resting-State Functional Connectivity Using Subspace Clustering and Binary Hypothesis Testing. J Atten Disord 2021; 25:736-748. [PMID: 30938224 DOI: 10.1177/1087054719837749] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
Abstract
Objective: This study focused on the ADHD classification through functional connectivity (FC) analysis. Method: An ADHD classification method was proposed with subspace clustering and binary hypothesis testing, wherein partial information of test data was adopted for training. By hypothesizing the binary label (ADHD or control) for the test data, two feature sets of training FC data were generated during the feature selection procedure that employed both training and test data. Then, a multi-affinity subspace clustering approach was performed to obtain the corresponding subspace-projected feature sets. With the energy comparison of projected feature sets, we finally identified ADHD individuals for the test data. Results: Our method outperformed several state-of-the-art methods with the above 90% average identification accuracy. By the discriminative FC contribution analysis, it also proved the reliability of our method. Conclusion: Results demonstrate the remarkable classification performance of our method and reveal some useful brain circuits to identify ADHD.
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Affiliation(s)
- Yibin Tang
- Hohai University, Changzhou, China.,Columbia University, New York, NY, USA
| | | | - Ying Chen
- Columbia University, New York, NY, USA
| | - Ning Sun
- Columbia University, New York, NY, USA.,Nanjing University of Posts and Telecommunications, China
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Zou H, Yang J. Exploring the Brain Lateralization in ADHD Based on Variability of Resting-State fMRI Signal. J Atten Disord 2021; 25:258-264. [PMID: 30520697 DOI: 10.1177/1087054718816170] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Objective: In this study, we investigate the brain lateralization in ADHD patients. Furthermore, we also explore the difference between male and female patients, and the difference among distinct ADHD subtypes, that is, ADHD-inattentive (ADHD-IA) and ADHD-combined (ADHD-C). Method: We employed the standard deviation to quantify the variability of resting-state functional magnetic resonance imaging (fMRI) signal and measure the lateralization index (LI). Results: ADHD patients showed significantly increased rightward lateralization in the inferior frontal gyrus (opercular), precuneus, and paracentral lobule, and decreased rightward lateralization in the insula. Compared with male patients, female patients showed significantly rightward lateralization in the putamen and lobule VII of cerebellar hemisphere. ADHD-C patients exhibited increased rightward lateralization in the inferior frontal gyrus (opercular), and decreased rightward lateralization in the inferior temporal gyrus, as compared with ADHD-IA. The LI was also found to be related to inattentive and hyper/impulsive scores. Conclusion: These key findings may aid in understanding the pathology of ADHD.
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Affiliation(s)
| | - Jian Yang
- Nanjing University of Science & Technology, China
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32
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Shao L, You Y, Du H, Fu D. Classification of ADHD with fMRI data and multi-objective optimization. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2020; 196:105676. [PMID: 32791440 DOI: 10.1016/j.cmpb.2020.105676] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/19/2020] [Accepted: 07/20/2020] [Indexed: 06/11/2023]
Abstract
BACKGROUND AND OBJECTIVE Dataset imbalance is an important problem in neuroimaging. Imbalanced datasets would cause the performance degradation of a classifier by utilizing imbalanced learning, which tends to overfocus on the majority class. In this paper, we consider an imbalanced neuroimaging classification problem, namely, classification of attention deficit hyperactivity disorder (ADHD) using resting-state functional magnetic resonance imaging. METHODS We propose a multi-objective classification scheme based on support vector machine (SVM). Our scheme addresses the imbalanced dataset problem by using a three objective SVM model with the positive and negative empirical errors being handled explicitly and separately. Moreover, an interactive multi-objective method incorporating the decision maker's preference is adopted, thus a preferred subset of pareto optimal classifiers for decision making can be obtained. RESULTS The proposed scheme is assessed on five datasets from the ADHD- 200 consortium. Numerical results show that the proposed multi-objective scheme considerably outperforms some traditional classification methods in the literature. CONCLUSION The proposed multi-objective classification scheme avoids hyper-parameter selection, it effectively addresses dataset imbalanced problem from algorithm level. The scheme can not only be used in the diagnosis of ADHD but also in the diagnosis of other diseases, such as Alzheimer and Autism etc.
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Affiliation(s)
- Lizhen Shao
- Key Laboratory of Knowledge Automation for Industrial Processes of Ministry of Education, School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China.
| | - Yang You
- Key Laboratory of Knowledge Automation for Industrial Processes of Ministry of Education, School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China.
| | - Haipeng Du
- Key Laboratory of Knowledge Automation for Industrial Processes of Ministry of Education, School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China
| | - Dongmei Fu
- Key Laboratory of Knowledge Automation for Industrial Processes of Ministry of Education, School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China
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33
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Convolution-GRU Based on Independent Component Analysis for fMRI Analysis with Small and Imbalanced Samples. APPLIED SCIENCES-BASEL 2020. [DOI: 10.3390/app10217465] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
Functional magnetic resonance imaging (fMRI) is a commonly used method of brain research. However, due to the complexity and particularity of the fMRI task, it is difficult to find enough subjects, resulting in a small and, often, imbalanced dataset. A dataset with small samples causes overfitting of the learning model, and the imbalance will make the model insensitive to the minority class, which has been a problem in classification. It is of great significance to classify fMRI data with small and imbalanced samples. In the present study, we propose a 3-step method on a small and imbalanced fMRI dataset from a word-scene memory task. The steps of the method are as follows: (1) An independent component analysis is performed to reduce the dimension of data; (2) The synthetic minority oversampling technique is used to generate new samples of the minority class to balance data; (3) A convolution-Gated Recurrent Unit (GRU) network is used to classify the independent component signals, indicating whether the subjects are performing episodic memory tasks. The accuracy of the proposed method is 72.2%, which improves the classification performance compared with traditional classifiers such as support vector machines (SVM), logistic regression (LGR), linear discriminant analysis (LDA) and k-nearest neighbor (KNN), and this study gives a biomarker for evaluating the reactivation of episodic memory.
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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: 78] [Impact Index Per Article: 19.5] [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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35
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Gao MS, Tsai FS, Lee CC. Learning a Phenotypic-Attribute Attentional Brain Connectivity Embedding for ADHD Classification using rs-fMRI. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2020; 2020:5472-5475. [PMID: 33019218 DOI: 10.1109/embc44109.2020.9175789] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Automated diagnosis of Attention Deficit/Hyperactivity Disorder (ADHD) from brain's functional imaging has gained more interest due to its high prevalence rates among children. While phenotypic information, such as age and gender, is known to be important in diagnosing ADHD and critically affects the representation derived from fMRI brain images, limited studies have integrated phenotypic information when learning discriminative embedding from brain imaging for such an automatic classification task. In this work, we propose to integrate age and gender attributes through attention mechanism that is jointly optimized when learning a brain connectivity embedding using convolutional variational autoencoder derived from resting state functional magnetic resonance imaging (rs-fMRI) data. Our proposed framework achieves a state-of-the-art average of 86.22% accuracy in ADHD vs. typical develop control (TDC) binary classification task evaluated across five public ADHD-200 competition datasets. Furthermore, our analysis points out that there are insufficient linked connections to the brain region of precuneus in the ADHD group.
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36
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Wang XH, Xu J, Li L. Estimating individual scores of inattention and impulsivity based on dynamic features of intrinsic connectivity network. Neurosci Lett 2020; 724:134874. [PMID: 32114120 DOI: 10.1016/j.neulet.2020.134874] [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: 10/29/2019] [Revised: 02/18/2020] [Accepted: 02/26/2020] [Indexed: 11/30/2022]
Abstract
Inattention and impulsivity are the two most important indices for evaluations of ADHD. Currently, inattention and impulsivity were evaluated by clinical scales. The intelligent evaluation of the two indices using machine learning remains largely unexplored. This paper aimed to build regression modes for inattention and impulsivity based on resting state fMRI and additional measures, and discover the associating features for the two indices. To achieve these goals, a cohort of 95 children with ADHD as well as 105 healthy controls were selected from the ADHD-200 database. The raw features were consisted of univariate dynamic estimators of intrinsic connectivity network (ICNs), head motion, and additional measures. The regression models were solved using support vector regression (SVR). The performance of the regression models was evaluated by cross-validations. The performance of regression models based on ICNs outperformed that based on regional measures. The estimated clinical scores were significantly correlated to inattention (r = 0.4 ± 0.02, p < 0.01) and impulsivity (r = 0.31 ± 0.02, p < 0.01). The most associating ICNs are sensorimotor network (SMN) for inattention and executive control network (ECN) for impulsivity. The results suggested that inattention and impulsivity could be estimated using machine learning, and the intra-ICN dynamics could be supplementary features for regression models of clinical scores of ADHD.
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Affiliation(s)
- Xun-Heng Wang
- Institute of Biomedical Engineering and Instrumentation, School of Automation, Hangzhou Dianzi University, Hangzhou, 310018, China
| | - Jie Xu
- Institute of Biomedical Engineering and Instrumentation, School of Automation, Hangzhou Dianzi University, Hangzhou, 310018, China
| | - Lihua Li
- Institute of Biomedical Engineering and Instrumentation, School of Automation, Hangzhou Dianzi University, Hangzhou, 310018, China.
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37
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Li J, Joshi AA, Leahy RM. A NETWORK-BASED APPROACH TO STUDY OF ADHD USING TENSOR DECOMPOSITION OF RESTING STATE FMRI DATA. PROCEEDINGS. IEEE INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING 2020; 2020:544-548. [PMID: 33500749 PMCID: PMC7831393 DOI: 10.1109/isbi45749.2020.9098584] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Identifying changes in functional connectivity in Attention Deficit Hyperactivity Disorder (ADHD) using functional magnetic resonance imaging (fMRI) can help us understand the neural substrates of this brain disorder. Many studies of ADHD using resting state fMRI (rs-fMRI) data have been conducted in the past decade with either manually crafted features that do not yield satisfactory performance, or automatically learned features that often lack interpretability. In this work, we present a tensor-based approach to identify brain networks and extract features from rs-fMRI data. Results show the identified networks are interpretable and consistent with our current understanding of ADHD conditions. The extracted features are not only predictive of ADHD score but also discriminative for classification of ADHD subjects from typically developed children.
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Affiliation(s)
- Jian Li
- Signal and Image Processing Institute, University of Southern California
| | - Anand A Joshi
- Signal and Image Processing Institute, University of Southern California
| | - Richard M Leahy
- Signal and Image Processing Institute, University of Southern California
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38
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DeepFMRI: End-to-end deep learning for functional connectivity and classification of ADHD using fMRI. J Neurosci Methods 2020; 335:108506. [DOI: 10.1016/j.jneumeth.2019.108506] [Citation(s) in RCA: 41] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2019] [Revised: 11/05/2019] [Accepted: 11/07/2019] [Indexed: 11/18/2022]
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39
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Guo X, Yao D, Cao Q, Liu L, Zhao Q, Li H, Huang F, Wang Y, Qian Q, Wang Y, Calhoun VD, Johnstone SJ, Sui J, Sun L. Shared and distinct resting functional connectivity in children and adults with attention-deficit/hyperactivity disorder. Transl Psychiatry 2020; 10:65. [PMID: 32066697 PMCID: PMC7026417 DOI: 10.1038/s41398-020-0740-y] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/24/2019] [Revised: 01/14/2020] [Accepted: 01/14/2020] [Indexed: 02/08/2023] Open
Abstract
Attention-deficit/hyperactivity disorder (ADHD) often persists into adulthood, with a shift of symptoms including less hyperactivity/impulsivity and more co-morbidity of affective disorders in ADHDadult. Many studies have questioned the stability in diagnosing of ADHD from childhood to adulthood, and the shared and distinct aberrant functional connectivities (FCs) between ADHDchild and ADHDadult remain unidentified. We aim to explore shared and distinct FC patterns in ADHDchild and ADHDadult, and further investigated the cross-cohort predictability using the identified FCs. After investigating the ADHD-discriminative FCs from healthy controls (HCs) in both child (34 ADHDchild, 28 HCs) and adult (112 ADHDadult,77 HCs) cohorts, we identified both shared and distinct aberrant FC patterns between cohorts and their association with clinical symptoms. Moreover, the cross-cohort predictability using the identified FCs were tested. The ADHD-HC classification accuracies were 84.4% and 81.0% for children and male adults, respectively. The ADHD-discriminative FCs shared in children and adults lie in the intra-network within default mode network (DMN) and the inter-network between DMN and ventral attention network, positively correlated with total scores of ADHD symptoms. Particularly, inter-network FC between somatomotor network and dorsal attention network was uniquely impaired in ADHDchild, positively correlated with hyperactivity index; whereas the aberrant inter-network FC between DMN and limbic network exhibited more adult-specific ADHD dysfunction. And their cross-cohort predictions were 70.4% and 75.6% between each other. This work provided imaging evidence for symptomatic changes and pathophysiological continuity in ADHD from childhood to adulthood, suggesting that FCs may serve as potential biomarkers for ADHD diagnosis.
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Affiliation(s)
- Xiaojie Guo
- grid.11135.370000 0001 2256 9319Peking University Sixth Hospital/Institute of Mental Health, 100191 Beijing, China ,grid.453135.50000 0004 1769 3691National Clinical Research Center for Mental Disorders & Key Laboratory of Mental Health, Ministry of Health (Peking University), 100191 Beijing, China
| | - Dongren Yao
- grid.9227.e0000000119573309Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, 100190 Beijing, China ,grid.410726.60000 0004 1797 8419University of Chinese Academy of Sciences, Beijing, China
| | - Qingjiu Cao
- grid.11135.370000 0001 2256 9319Peking University Sixth Hospital/Institute of Mental Health, 100191 Beijing, China ,grid.453135.50000 0004 1769 3691National Clinical Research Center for Mental Disorders & Key Laboratory of Mental Health, Ministry of Health (Peking University), 100191 Beijing, China
| | - Lu Liu
- grid.11135.370000 0001 2256 9319Peking University Sixth Hospital/Institute of Mental Health, 100191 Beijing, China ,grid.453135.50000 0004 1769 3691National Clinical Research Center for Mental Disorders & Key Laboratory of Mental Health, Ministry of Health (Peking University), 100191 Beijing, China
| | - Qihua Zhao
- grid.11135.370000 0001 2256 9319Peking University Sixth Hospital/Institute of Mental Health, 100191 Beijing, China ,grid.453135.50000 0004 1769 3691National Clinical Research Center for Mental Disorders & Key Laboratory of Mental Health, Ministry of Health (Peking University), 100191 Beijing, China
| | - Hui Li
- grid.11135.370000 0001 2256 9319Peking University Sixth Hospital/Institute of Mental Health, 100191 Beijing, China ,grid.453135.50000 0004 1769 3691National Clinical Research Center for Mental Disorders & Key Laboratory of Mental Health, Ministry of Health (Peking University), 100191 Beijing, China
| | - Fang Huang
- grid.11135.370000 0001 2256 9319Peking University Sixth Hospital/Institute of Mental Health, 100191 Beijing, China ,grid.453135.50000 0004 1769 3691National Clinical Research Center for Mental Disorders & Key Laboratory of Mental Health, Ministry of Health (Peking University), 100191 Beijing, China
| | - Yanfei Wang
- grid.11135.370000 0001 2256 9319Peking University Sixth Hospital/Institute of Mental Health, 100191 Beijing, China ,grid.453135.50000 0004 1769 3691National Clinical Research Center for Mental Disorders & Key Laboratory of Mental Health, Ministry of Health (Peking University), 100191 Beijing, China
| | - Qiujin Qian
- grid.11135.370000 0001 2256 9319Peking University Sixth Hospital/Institute of Mental Health, 100191 Beijing, China ,grid.453135.50000 0004 1769 3691National Clinical Research Center for Mental Disorders & Key Laboratory of Mental Health, Ministry of Health (Peking University), 100191 Beijing, China
| | - Yufeng Wang
- grid.11135.370000 0001 2256 9319Peking University Sixth Hospital/Institute of Mental Health, 100191 Beijing, China ,grid.453135.50000 0004 1769 3691National Clinical Research Center for Mental Disorders & Key Laboratory of Mental Health, Ministry of Health (Peking University), 100191 Beijing, China
| | - Vince D. Calhoun
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS) [Georgia State University, Georgia Institute of Technology, Emory University], Atlanta, GA 30303 USA
| | - Stuart J. Johnstone
- grid.1007.60000 0004 0486 528XBrain & Behaviour Research Institute, School of Psychology, University of Wollongong, Wollongong, Australia
| | - Jing Sui
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, 100190, Beijing, China. .,Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS) [Georgia State University, Georgia Institute of Technology, Emory University], Atlanta, GA, 30303, USA. .,CAS Center for Excellence in Brain Science, Institute of Automation, Chinese Academy of Sciences, Beijing, China.
| | - Li Sun
- Peking University Sixth Hospital/Institute of Mental Health, 100191, Beijing, China. .,National Clinical Research Center for Mental Disorders & Key Laboratory of Mental Health, Ministry of Health (Peking University), 100191, Beijing, China.
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40
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Chen Y, Tang Y, Wang C, Liu X, Zhao L, Wang Z. ADHD classification by dual subspace learning using resting-state functional connectivity. Artif Intell Med 2020; 103:101786. [PMID: 32143793 DOI: 10.1016/j.artmed.2019.101786] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2019] [Revised: 12/11/2019] [Accepted: 12/30/2019] [Indexed: 11/19/2022]
Abstract
As one of the most common neurobehavioral diseases in school-age children, Attention Deficit Hyperactivity Disorder (ADHD) has been increasingly studied in recent years. But it is still a challenge problem to accurately identify ADHD patients from healthy persons. To address this issue, we propose a dual subspace classification algorithm by using individual resting-state Functional Connectivity (FC). In detail, two subspaces respectively containing ADHD and healthy control features, called as dual subspaces, are learned with several subspace measures, wherein a modified graph embedding measure is employed to enhance the intra-class relationship of these features. Therefore, given a subject (used as test data) with its FCs, the basic classification principle is to compare its projected component energy of FCs on each subspace and then predict the ADHD or control label according to the subspace with larger energy. However, this principle in practice works with low efficiency, since the dual subspaces are unstably obtained from ADHD databases of small size. Thereby, we present an ADHD classification framework by a binary hypothesis testing of test data. Here, the FCs of test data with its ADHD or control label hypothesis are employed in the discriminative FC selection of training data to promote the stability of dual subspaces. For each hypothesis, the dual subspaces are learned from the selected FCs of training data. The total projected energy of these FCs is also calculated on the subspaces. Sequentially, the energy comparison is carried out under the binary hypotheses. The ADHD or control label is finally predicted for test data with the hypothesis of larger total energy. In the experiments on ADHD-200 dataset, our method achieves a significant classification performance compared with several state-of-the-art machine learning and deep learning methods, where our accuracy is about 90 % for most of ADHD databases in the leave-one-out cross-validation test.
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Affiliation(s)
- Ying Chen
- Key Laboratory of Underwater Acoustic Signal Processing of Ministry of Education, Southeast University, China; Department of Psychiatry and Translational Imaging, Columbia University & NYSPI, USA
| | - Yibin Tang
- Changzhou Key Laboratory of Robots & Intelligent Technology, Hohai University, China
| | - Chun Wang
- Mood Disorders Department, Nanjing Medical University, China
| | - Xiaofeng Liu
- Changzhou Key Laboratory of Robots & Intelligent Technology, Hohai University, China
| | - Li Zhao
- Key Laboratory of Underwater Acoustic Signal Processing of Ministry of Education, Southeast University, China.
| | - Zhishun Wang
- Department of Psychiatry and Translational Imaging, Columbia University & NYSPI, USA.
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Amado-Caballero P, Casaseca-de-la-Higuera P, Alberola-Lopez S, Andres-de-Llano JM, Villalobos JAL, Garmendia-Leiza JR, Alberola-Lopez C. Objective ADHD Diagnosis Using Convolutional Neural Networks Over Daily-Life Activity Records. IEEE J Biomed Health Inform 2020; 24:2690-2700. [PMID: 31905156 DOI: 10.1109/jbhi.2020.2964072] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Attention Deficit/Hyperactivity Disorder (ADHD) is the most common neurobehavioral disorder in children and adolescents. However, its etiology is still unknown, and this hinders the existence of reliable, fast and inexpensive standard diagnostic methods. OBJECTIVE This paper proposes an end-to-end methodology for automatic diagnosis of the combined type of ADHD. METHODS Diagnosis is based on the analysis of 24 hour-long activity records using Convolutional Neural Networks to classify spectrograms of activity windows. RESULTS We achieve up to [Formula: see text] average sensitivity, [Formula: see text] specificity and AUC values over [Formula: see text]. Overall, our figures overcome those obtained by actigraphy-based methods reported in the literature as well as others based on more expensive (and not so convenient) acquisition methods. CONCLUSION These results reinforce the idea that combining deep learning techniques together with actimetry can lead to a robust and efficient system for objective ADHD diagnosis. SIGNIFICANCE Reliance on simple activity measurements leads to an inexpensive and non-invasive objective diagn-ostic method, which can be easily implemented with daily devices.
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Qiang N, Dong Q, Ge F, Liang H, Ge B, Zhang S, Sun Y, Gao J, Liu T. Deep Variational Autoencoder for Mapping Functional Brain Networks. IEEE Trans Cogn Dev Syst 2020. [DOI: 10.1109/tcds.2020.3025137] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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Chen M, Li H, Wang J, Dillman JR, Parikh NA, He L. A Multichannel Deep Neural Network Model Analyzing Multiscale Functional Brain Connectome Data for Attention Deficit Hyperactivity Disorder Detection. Radiol Artif Intell 2019; 2:e190012. [PMID: 32076663 DOI: 10.1148/ryai.2019190012] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2019] [Revised: 07/18/2019] [Accepted: 07/25/2019] [Indexed: 02/06/2023]
Abstract
Purpose To develop a multichannel deep neural network (mcDNN) classification model based on multiscale brain functional connectome data and demonstrate the value of this model by using attention deficit hyperactivity disorder (ADHD) detection as an example. Materials and Methods In this retrospective case-control study, existing data from the Neuro Bureau ADHD-200 dataset consisting of 973 participants were used. Multiscale functional brain connectomes based on both anatomic and functional criteria were constructed. The mcDNN model used the multiscale brain connectome data and personal characteristic data (PCD) as joint features to detect ADHD and identify the most predictive brain connectome features for ADHD diagnosis. The mcDNN model was compared with single-channel deep neural network (scDNN) models and the classification performance was evaluated through cross-validation and hold-out validation with the metrics of accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC). Results In the cross-validation, the mcDNN model using combined features (fusion of the multiscale brain connectome data and PCD) achieved the best performance in ADHD detection with an AUC of 0.82 (95% confidence interval [CI]: 0.80, 0.83) compared with scDNN models using the features of the brain connectome at each individual scale and PCD, independently. In the hold-out validation, the mcDNN model achieved an AUC of 0.74 (95% CI: 0.73, 0.76). Conclusion An mcDNN model was developed for multiscale brain functional connectome data, and its utility for ADHD detection was demonstrated. By fusing the multiscale brain connectome data, the mcDNN model improved ADHD detection performance considerably over the use of a single scale.© RSNA, 2019.
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Affiliation(s)
- Ming Chen
- Department of Pediatrics, Perinatal Institute (M.C., H.L., N.A.P., L.H.) and Department of Electronic Engineering and Computing Science, University of Cincinnati, Cincinnati, Ohio (M.C.); and Department of Radiology (J.R.D.), Cincinnati Children's Hospital Medical Center, 3333 Burnet Ave, MLC 7009, Cincinnati, OH 45229; and Departments of Radiology (J.W., J.R.D.) and Pediatrics (N.A.P., L.H.), University of Cincinnati College of Medicine, Cincinnati, Ohio
| | - Hailong Li
- Department of Pediatrics, Perinatal Institute (M.C., H.L., N.A.P., L.H.) and Department of Electronic Engineering and Computing Science, University of Cincinnati, Cincinnati, Ohio (M.C.); and Department of Radiology (J.R.D.), Cincinnati Children's Hospital Medical Center, 3333 Burnet Ave, MLC 7009, Cincinnati, OH 45229; and Departments of Radiology (J.W., J.R.D.) and Pediatrics (N.A.P., L.H.), University of Cincinnati College of Medicine, Cincinnati, Ohio
| | - Jinghua Wang
- Department of Pediatrics, Perinatal Institute (M.C., H.L., N.A.P., L.H.) and Department of Electronic Engineering and Computing Science, University of Cincinnati, Cincinnati, Ohio (M.C.); and Department of Radiology (J.R.D.), Cincinnati Children's Hospital Medical Center, 3333 Burnet Ave, MLC 7009, Cincinnati, OH 45229; and Departments of Radiology (J.W., J.R.D.) and Pediatrics (N.A.P., L.H.), University of Cincinnati College of Medicine, Cincinnati, Ohio
| | - Jonathan R Dillman
- Department of Pediatrics, Perinatal Institute (M.C., H.L., N.A.P., L.H.) and Department of Electronic Engineering and Computing Science, University of Cincinnati, Cincinnati, Ohio (M.C.); and Department of Radiology (J.R.D.), Cincinnati Children's Hospital Medical Center, 3333 Burnet Ave, MLC 7009, Cincinnati, OH 45229; and Departments of Radiology (J.W., J.R.D.) and Pediatrics (N.A.P., L.H.), University of Cincinnati College of Medicine, Cincinnati, Ohio
| | - Nehal A Parikh
- Department of Pediatrics, Perinatal Institute (M.C., H.L., N.A.P., L.H.) and Department of Electronic Engineering and Computing Science, University of Cincinnati, Cincinnati, Ohio (M.C.); and Department of Radiology (J.R.D.), Cincinnati Children's Hospital Medical Center, 3333 Burnet Ave, MLC 7009, Cincinnati, OH 45229; and Departments of Radiology (J.W., J.R.D.) and Pediatrics (N.A.P., L.H.), University of Cincinnati College of Medicine, Cincinnati, Ohio
| | - Lili He
- Department of Pediatrics, Perinatal Institute (M.C., H.L., N.A.P., L.H.) and Department of Electronic Engineering and Computing Science, University of Cincinnati, Cincinnati, Ohio (M.C.); and Department of Radiology (J.R.D.), Cincinnati Children's Hospital Medical Center, 3333 Burnet Ave, MLC 7009, Cincinnati, OH 45229; and Departments of Radiology (J.W., J.R.D.) and Pediatrics (N.A.P., L.H.), University of Cincinnati College of Medicine, Cincinnati, Ohio
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Functional Brain Network Classification for Alzheimer’s Disease Detection with Deep Features and Extreme Learning Machine. Cognit Comput 2019. [DOI: 10.1007/s12559-019-09688-2] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
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45
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Oikonomou VP, Blekas K, Astrakas L. Identification of Brain Functional Networks Using a Model-Based Approach. INT J PATTERN RECOGN 2019. [DOI: 10.1142/s0218001420570049] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Functional MRI (fMRI) is a valuable brain imaging technique. A significant problem, when analyzing fMRI time series, is the finding of functional brain networks when the brain is at rest, i.e. no external stimulus is applied to the subject. In this work, we present a probabilistic method to estimate the Resting State Networks (RSNs) using a model-based approach. More specifically, RSNs are assumed to be the result of a clustering procedure. In order to perform the clustering, a mixture of regression models are used. Furthermore, special care has been given in order to incorporate important functionalities, such as spatial and embedded sparsity enforcing properties, through the use of informative priors over the model parameters. Another interesting feature of the proposed scheme is the flexibility to handle all the brain time series at once, allowing more robust solutions. We provide comparative experimental results, using an artificial fMRI dataset and two real resting state fMRI datasets, that empirically illustrate the efficiency of the proposed regression mixture model.
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Affiliation(s)
- Vangelis P. Oikonomou
- Information Technologies Institute, Centre for Research and Technology Hellas, CERTH-ITI, 6th km Charilaou-Thermi Road, 57001 Thermi-Thessaloniki, Greece
| | - Konstantinos Blekas
- Department of Computer Science, University of Ioannina, 45110 Ioannina, Greece
| | - Loukas Astrakas
- Medical School, University of Ioannina, 45110 Ioannina, Greece
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Sakai K, Yamada K. Machine learning studies on major brain diseases: 5-year trends of 2014–2018. Jpn J Radiol 2018; 37:34-72. [DOI: 10.1007/s11604-018-0794-4] [Citation(s) in RCA: 73] [Impact Index Per Article: 12.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2018] [Accepted: 11/14/2018] [Indexed: 12/17/2022]
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Zhang K, Liu X, Liu F, He L, Zhang L, Yang Y, Li W, Wang S, Liu L, Liu Z, Wu X, Lin H. An Interpretable and Expandable Deep Learning Diagnostic System for Multiple Ocular Diseases: Qualitative Study. J Med Internet Res 2018; 20:e11144. [PMID: 30429111 PMCID: PMC6301833 DOI: 10.2196/11144] [Citation(s) in RCA: 25] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2018] [Revised: 07/31/2018] [Accepted: 08/02/2018] [Indexed: 12/30/2022] Open
Abstract
Background Although artificial intelligence performs promisingly in medicine, few automatic disease diagnosis platforms can clearly explain why a specific medical decision is made. Objective We aimed to devise and develop an interpretable and expandable diagnosis framework for automatically diagnosing multiple ocular diseases and providing treatment recommendations for the particular illness of a specific patient. Methods As the diagnosis of ocular diseases highly depends on observing medical images, we chose ophthalmic images as research material. All medical images were labeled to 4 types of diseases or normal (total 5 classes); each image was decomposed into different parts according to the anatomical knowledge and then annotated. This process yields the positions and primary information on different anatomical parts and foci observed in medical images, thereby bridging the gap between medical image and diagnostic process. Next, we applied images and the information produced during the annotation process to implement an interpretable and expandable automatic diagnostic framework with deep learning. Results This diagnosis framework comprises 4 stages. The first stage identifies the type of disease (identification accuracy, 93%). The second stage localizes the anatomical parts and foci of the eye (localization accuracy: images under natural light without fluorescein sodium eye drops, 82%; images under cobalt blue light or natural light with fluorescein sodium eye drops, 90%). The third stage carefully classifies the specific condition of each anatomical part or focus with the result from the second stage (average accuracy for multiple classification problems, 79%-98%). The last stage provides treatment advice according to medical experience and artificial intelligence, which is merely involved with pterygium (accuracy, >95%). Based on this, we developed a telemedical system that can show detailed reasons for a particular diagnosis to doctors and patients to help doctors with medical decision making. This system can carefully analyze medical images and provide treatment advices according to the analysis results and consultation between a doctor and a patient. Conclusions The interpretable and expandable medical artificial intelligence platform was successfully built; this system can identify the disease, distinguish different anatomical parts and foci, discern the diagnostic information relevant to the diagnosis of diseases, and provide treatment suggestions. During this process, the whole diagnostic flow becomes clear and understandable to both doctors and their patients. Moreover, other diseases can be seamlessly integrated into this system without any influence on existing modules or diseases. Furthermore, this framework can assist in the clinical training of junior doctors. Owing to the rare high-grade medical resource, it is impossible that everyone receives high-quality professional diagnosis and treatment service. This framework can not only be applied in hospitals with insufficient medical resources to decrease the pressure on experienced doctors but also deployed in remote areas to help doctors diagnose common ocular diseases.
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Affiliation(s)
- Kai Zhang
- School of Computer Science and Technology, Xidian University, Xi'an, China.,State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China
| | - Xiyang Liu
- School of Computer Science and Technology, Xidian University, Xi'an, China.,School of Software, Xidian University, Xi'an, China.,Institute of Software Engineering, Xidian University, Xi'an, China
| | - Fan Liu
- School of Software, Xidian University, Xi'an, China
| | - Lin He
- School of Computer Science and Technology, Xidian University, Xi'an, China
| | - Lei Zhang
- School of Computer Science and Technology, Xidian University, Xi'an, China
| | - Yahan Yang
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China
| | - Wangting Li
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China
| | - Shuai Wang
- School of Software, Xidian University, Xi'an, China
| | - Lin Liu
- School of Computer Science and Technology, Xidian University, Xi'an, China
| | - Zhenzhen Liu
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China
| | - Xiaohang Wu
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China
| | - Haotian Lin
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China
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Du Y, Fu Z, Calhoun VD. Classification and Prediction of Brain Disorders Using Functional Connectivity: Promising but Challenging. Front Neurosci 2018; 12:525. [PMID: 30127711 PMCID: PMC6088208 DOI: 10.3389/fnins.2018.00525] [Citation(s) in RCA: 166] [Impact Index Per Article: 27.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2018] [Accepted: 07/12/2018] [Indexed: 12/13/2022] Open
Abstract
Brain functional imaging data, especially functional magnetic resonance imaging (fMRI) data, have been employed to reflect functional integration of the brain. Alteration in brain functional connectivity (FC) is expected to provide potential biomarkers for classifying or predicting brain disorders. In this paper, we present a comprehensive review in order to provide guidance about the available brain FC measures and typical classification strategies. We survey the state-of-the-art FC analysis methods including widely used static functional connectivity (SFC) and more recently proposed dynamic functional connectivity (DFC). Temporal correlations among regions of interest (ROIs), data-driven spatial network and functional network connectivity (FNC) are often computed to reflect SFC from different angles. SFC can be extended to DFC using a sliding-window framework, and intrinsic connectivity states along the time-varying connectivity patterns are typically extracted using clustering or decomposition approaches. We also briefly summarize window-less DFC approaches. Subsequently, we highlight various strategies for feature selection including the filter, wrapper and embedded methods. In terms of model building, we include traditional classifiers as well as more recently applied deep learning methods. Moreover, we review representative applications with remarkable classification accuracy for psychosis and mood disorders, neurodevelopmental disorder, and neurological disorders using fMRI data. Schizophrenia, bipolar disorder, autism spectrum disorder (ASD), attention deficit hyperactivity disorder (ADHD), Alzheimer's disease and mild cognitive impairment (MCI) are discussed. Finally, challenges in the field are pointed out with respect to the inaccurate diagnosis labeling, the abundant number of possible features and the difficulty in validation. Some suggestions for future work are also provided.
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Affiliation(s)
- Yuhui Du
- The Mind Research Network, Albuquerque, NM, United States
- School of Computer & Information Technology, Shanxi University, Taiyuan, China
| | - Zening Fu
- The Mind Research Network, Albuquerque, NM, United States
| | - Vince D. Calhoun
- The Mind Research Network, Albuquerque, NM, United States
- Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, NM, United States
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