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He P, Shi Z, Cui Y, Wang R, Wu D. A spatiotemporal graph transformer approach for Alzheimer's disease diagnosis with rs-fMRI. Comput Biol Med 2024; 178:108762. [PMID: 38908359 DOI: 10.1016/j.compbiomed.2024.108762] [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: 11/18/2023] [Revised: 05/24/2024] [Accepted: 06/13/2024] [Indexed: 06/24/2024]
Abstract
Alzheimer's disease (AD) is a neurodegenerative disease accompanied by cognitive impairment. Early diagnosis is crucial for the timely treatment and intervention of AD. Resting-state functional magnetic resonance imaging (rs-fMRI) records the temporal dynamics and spatial dependency in the brain, which have been utilized for automatically diagnosis of AD in the community. Existing approaches of AD diagnosis using rs-fMRI only assess functional connectivity, ignoring the spatiotemporal dependency mining of rs-fMRI. In addition, it is difficult to increase diagnosis accuracy due to the shortage of rs-fMRI sample and the poor anti-noise ability of model. To deal with these problems, this paper proposes a novel approach for the automatic diagnosis of AD, namely spatiotemporal graph transformer network (STGTN). The proposed STGTN can effectively extract spatiotemporal features of rs-fMRI. Furthermore, to solve the sample-limited problem and to improve the anti-noise ability of the proposed model, an adversarial training strategy is adopted for the proposed STGTN to generate adversarial examples (AEs) and augment training samples with AEs. Experimental results indicate that the proposed model achieves the classification accuracy of 92.58%, and 85.27% with the adversarial training strategy for AD vs. normal control (NC), early mild cognitive impairment (eMCI) vs. late mild cognitive impairment (lMCI) respectively, outperforming the state-of-the-art methods. Besides, the spatial attention coefficients reflected from the designed model reveal the importance of brain connections under different classification tasks.
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Affiliation(s)
- Peng He
- School of Communications and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing, 400065, China; Advanced Network and Intelligent Connection Technology Key Laboratory of Chongqing Education Commission of China, Chongqing, 400065, China; Chongqing Key Laboratory of Ubiquitous Sensing and Networking, Chongqing, 400065, China.
| | - Zhan Shi
- School of Communications and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing, 400065, China; Advanced Network and Intelligent Connection Technology Key Laboratory of Chongqing Education Commission of China, Chongqing, 400065, China; Chongqing Key Laboratory of Ubiquitous Sensing and Networking, Chongqing, 400065, China
| | - Yaping Cui
- School of Communications and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing, 400065, China; Advanced Network and Intelligent Connection Technology Key Laboratory of Chongqing Education Commission of China, Chongqing, 400065, China; Chongqing Key Laboratory of Ubiquitous Sensing and Networking, Chongqing, 400065, China
| | - Ruyan Wang
- School of Communications and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing, 400065, China; Advanced Network and Intelligent Connection Technology Key Laboratory of Chongqing Education Commission of China, Chongqing, 400065, China; Chongqing Key Laboratory of Ubiquitous Sensing and Networking, Chongqing, 400065, China
| | - Dapeng Wu
- School of Communications and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing, 400065, China; Advanced Network and Intelligent Connection Technology Key Laboratory of Chongqing Education Commission of China, Chongqing, 400065, China; Chongqing Key Laboratory of Ubiquitous Sensing and Networking, Chongqing, 400065, China
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2
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Lei B, Li Y, Fu W, Yang P, Chen S, Wang T, Xiao X, Niu T, Fu Y, Wang S, Han H, Qin J. Alzheimer's disease diagnosis from multi-modal data via feature inductive learning and dual multilevel graph neural network. Med Image Anal 2024; 97:103213. [PMID: 38850625 DOI: 10.1016/j.media.2024.103213] [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: 09/12/2023] [Revised: 05/13/2024] [Accepted: 05/17/2024] [Indexed: 06/10/2024]
Abstract
Multi-modal data can provide complementary information of Alzheimer's disease (AD) and its development from different perspectives. Such information is closely related to the diagnosis, prevention, and treatment of AD, and hence it is necessary and critical to study AD through multi-modal data. Existing learning methods, however, usually ignore the influence of feature heterogeneity and directly fuse features in the last stages. Furthermore, most of these methods only focus on local fusion features or global fusion features, neglecting the complementariness of features at different levels and thus not sufficiently leveraging information embedded in multi-modal data. To overcome these shortcomings, we propose a novel framework for AD diagnosis that fuses gene, imaging, protein, and clinical data. Our framework learns feature representations under the same feature space for different modalities through a feature induction learning (FIL) module, thereby alleviating the impact of feature heterogeneity. Furthermore, in our framework, local and global salient multi-modal feature interaction information at different levels is extracted through a novel dual multilevel graph neural network (DMGNN). We extensively validate the proposed method on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset and experimental results demonstrate our method consistently outperforms other state-of-the-art multi-modal fusion methods. The code is publicly available on the GitHub website. (https://github.com/xiankantingqianxue/MIA-code.git).
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Affiliation(s)
- Baiying Lei
- National-Regional Key Technology Engineering Lab. for Medical Ultrasound, Guangdong Key Lab. for Biomedical Measurements and Ultrasound Imaging, Marshall Lab. of Biomedical Engineering, School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen University, Shenzhen, 518060, China
| | - Yafeng Li
- National-Regional Key Technology Engineering Lab. for Medical Ultrasound, Guangdong Key Lab. for Biomedical Measurements and Ultrasound Imaging, Marshall Lab. of Biomedical Engineering, School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen University, Shenzhen, 518060, China
| | - Wanyi Fu
- Department of Electronic Engineering, Tsinghua University, Beijing Key Laboratory of Magnetic Resonance Imaging Devices and Technology, China
| | - Peng Yang
- National-Regional Key Technology Engineering Lab. for Medical Ultrasound, Guangdong Key Lab. for Biomedical Measurements and Ultrasound Imaging, Marshall Lab. of Biomedical Engineering, School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen University, Shenzhen, 518060, China
| | - Shaobin Chen
- National-Regional Key Technology Engineering Lab. for Medical Ultrasound, Guangdong Key Lab. for Biomedical Measurements and Ultrasound Imaging, Marshall Lab. of Biomedical Engineering, School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen University, Shenzhen, 518060, China
| | - Tianfu Wang
- National-Regional Key Technology Engineering Lab. for Medical Ultrasound, Guangdong Key Lab. for Biomedical Measurements and Ultrasound Imaging, Marshall Lab. of Biomedical Engineering, School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen University, Shenzhen, 518060, China
| | - Xiaohua Xiao
- The First Affiliated Hospital of Shenzhen University, Shenzhen University Medical School, Shenzhen University, Shenzhen Second People's Hospital, Shenzhen, 530031, China
| | - Tianye Niu
- Shenzhen Bay Laboratory, Shenzhen, 518067, China
| | - Yu Fu
- Department of Neurology, Peking University Third Hospital, No. 49, North Garden Rd., Haidian District, Beijing, 100191, China.
| | - Shuqiang Wang
- Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China.
| | - Hongbin Han
- Institute of Medical Technology, Peking University Health Science Center, Department of Radiology, Peking University Third Hospital, Beijing Key Laboratory of Magnetic Resonance Imaging Devices and Technology, Beijing, 100191, China; The second hospital of Dalian Medical University,Research and developing center of medical technology, Dalian, 116027, China.
| | - Jing Qin
- Center for Smart Health, School of Nursing, The Hong Kong Polytechnic University, Hong Kong, China
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3
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Jomeiri A, Navin AH, Shamsi M. Longitudinal MRI analysis using a hybrid DenseNet-BiLSTM method for Alzheimer's disease prediction. Behav Brain Res 2024; 463:114900. [PMID: 38341100 DOI: 10.1016/j.bbr.2024.114900] [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: 07/30/2023] [Revised: 12/16/2023] [Accepted: 02/06/2024] [Indexed: 02/12/2024]
Abstract
Alzheimer's disease is a progressive neurological disorder characterized by brain atrophy and cell death, leading to cognitive decline and impaired functioning. Previous research has primarily focused on using cross-sectional data for Alzheimer's disease identification, but analyzing longitudinal sequential MR images is crucial for improved diagnostic accuracy and understanding disease progression. However, existing deep learning models face challenges in learning spatial and temporal features from such data. To address these challenges, this study presents a novel hybrid DenseNet-BiLSTM method for Alzheimer's disease prediction using longitudinal MRI analysis. The proposed framework combines Convolutional DenseNet for spatial information extraction and joined BiLSTM layers for capturing temporal characteristics and relationships between longitudinal images at different time points. This approach overcomes issues like overfitting, vanishing gradients, and incomplete patient data. We evaluated the model on 684 longitudinal MRI images from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database, including normal controls, individuals with mild cognitive impairment, and Alzheimer's disease patients. The results demonstrate high classification accuracy, with 95.28% for AD/CN, 88.19% for NC/MCI, 83.51% for sMCI/pMCI, and 92.14% for MCI/AD. These findings highlight the substantial improvement in Alzheimer's disease diagnosis achieved through the utilization of longitudinal MRI images. The contributions of this study lie in both the deep learning and medical domains. In the deep learning domain, our hybrid framework effectively learns spatial and temporal features from longitudinal data, addressing the challenges associated with multi-dimensional and sequential time series data. In the medical domain, our study emphasizes the importance of analyzing baseline and longitudinal MR images for accurate diagnosis and understanding disease progression.
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Affiliation(s)
- Alireza Jomeiri
- Department of Computer Engineering, Qom Branch, Islamic Azad University, Qom, Iran
| | - Ahmad Habibizad Navin
- Department of Computer Engineering, Tabriz Branch, Islamic Azad University, Tabriz, Iran.
| | - Mahboubeh Shamsi
- Department of Engineering, Qom University of Technology, Qom, Iran
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Andargoli AE, Ulapane N, Nguyen TA, Shuakat N, Zelcer J, Wickramasinghe N. Intelligent decision support systems for dementia care: A scoping review. Artif Intell Med 2024; 150:102815. [PMID: 38553156 DOI: 10.1016/j.artmed.2024.102815] [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: 12/03/2022] [Revised: 12/11/2023] [Accepted: 02/12/2024] [Indexed: 04/02/2024]
Abstract
In the context of dementia care, Artificial Intelligence (AI) powered clinical decision support systems have the potential to enhance diagnosis and management. However, the scope and challenges of applying these technologies remain unclear. This scoping review aims to investigate the current state of AI applications in the development of intelligent decision support systems for dementia care. We conducted a comprehensive scoping review of empirical studies that utilised AI-powered clinical decision support systems in dementia care. The results indicate that AI applications in dementia care primarily focus on diagnosis, with limited attention to other aspects outlined in the World Health Organization (WHO) Global Action Plan on the Public Health Response to Dementia 2017-2025 (GAPD). A trifecta of challenges, encompassing data availability, cost considerations, and AI algorithm performance, emerges as noteworthy barriers in adoption of AI applications in dementia care. To address these challenges and enhance AI reliability, we propose a novel approach: a digital twin-based patient journey model. Future research should address identified gaps in GAPD action areas, navigate data-related obstacles, and explore the implementation of digital twins. Additionally, it is imperative to emphasize that addressing trust and combating the stigma associated with AI in healthcare should be a central focus of future research directions.
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Affiliation(s)
| | | | - Tuan Anh Nguyen
- Swinburne University of Technology, Melbourne, Australia; National Ageing Research Institute, Australia
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5
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Ma H, Wang Y, Hao Z, Yu Y, Jia X, Li M, Chen L. Classification of Alzheimer's disease: application of a transfer learning deep Q-network method. Eur J Neurosci 2024; 59:2118-2127. [PMID: 38282277 DOI: 10.1111/ejn.16261] [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: 10/09/2023] [Revised: 12/25/2023] [Accepted: 01/08/2024] [Indexed: 01/30/2024]
Abstract
Early diagnosis is crucial to slowing the progression of Alzheimer's disease (AD), so it is urgent to find an effective diagnostic method for AD. This study intended to investigate whether the transfer learning approach of deep Q-network (DQN) could effectively distinguish AD patients using local metrics of resting-state functional magnetic resonance imaging (rs-fMRI) as features. This study included 1310 subjects from the Consortium for Reliability and Reproducibility (CoRR) and 50 subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI) GO/2. The amplitude of low-frequency fluctuation (ALFF), fractional ALFF (fALFF) and percent amplitude of fluctuation (PerAF) were extracted as features using the Power 264 atlas. Based on gender bias in AD, we searched for transferable similar parts between the CoRR feature matrix and the ADNI feature matrix, resulting in the CoRR similar feature matrix served as the source domain and the ADNI similar feature matrix served as the target domain. A DQN classifier was pre-trained in the source domain and transferred to the target domain. Finally, the transferred DQN classifier was used to classify AD and healthy controls (HC). A permutation test was performed. The DQN transfer learning achieved a classification accuracy of 86.66% (p < 0.01), recall of 83.33% and precision of 83.33%. The findings suggested that the transfer learning approach using DQN could be an effective way to distinguish AD from HC. It also revealed the potential value of local brain activity in AD clinical diagnosis.
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Affiliation(s)
- Huibin Ma
- School of Information and Electronics Technology, Jiamusi University, Jiamusi, China
- Key Laboratory of Autonomous Intelligence and Information Processing in Heilongjiang Province, Jiamusi, China
| | - Yadan Wang
- School of Information and Electronics Technology, Jiamusi University, Jiamusi, China
- Key Laboratory of Autonomous Intelligence and Information Processing in Heilongjiang Province, Jiamusi, China
| | - Zeqi Hao
- School of Psychology, Zhejiang Normal University, Jinhua, China
| | - Yang Yu
- Department of Psychiatry, the second affiliated hospital of Zhejiang University school of Medicine, Zhejiang, China
| | - Xize Jia
- Department of Radiology, Changshu No. 2 People's Hospital, The Affiliated Changshu Hospital of Xuzhou Medical University, Changshu, China
| | - Mengting Li
- School of Psychology, Zhejiang Normal University, Jinhua, China
| | - Lanfen Chen
- School of Medical Imaging, Weifang Medical University, Weifang, China
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6
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Newby D, Orgeta V, Marshall CR, Lourida I, Albertyn CP, Tamburin S, Raymont V, Veldsman M, Koychev I, Bauermeister S, Weisman D, Foote IF, Bucholc M, Leist AK, Tang EYH, Tai XY, Llewellyn DJ, Ranson JM. Artificial intelligence for dementia prevention. Alzheimers Dement 2023; 19:5952-5969. [PMID: 37837420 PMCID: PMC10843720 DOI: 10.1002/alz.13463] [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: 04/12/2023] [Revised: 08/01/2023] [Accepted: 08/07/2023] [Indexed: 10/16/2023]
Abstract
INTRODUCTION A wide range of modifiable risk factors for dementia have been identified. Considerable debate remains about these risk factors, possible interactions between them or with genetic risk, and causality, and how they can help in clinical trial recruitment and drug development. Artificial intelligence (AI) and machine learning (ML) may refine understanding. METHODS ML approaches are being developed in dementia prevention. We discuss exemplar uses and evaluate the current applications and limitations in the dementia prevention field. RESULTS Risk-profiling tools may help identify high-risk populations for clinical trials; however, their performance needs improvement. New risk-profiling and trial-recruitment tools underpinned by ML models may be effective in reducing costs and improving future trials. ML can inform drug-repurposing efforts and prioritization of disease-modifying therapeutics. DISCUSSION ML is not yet widely used but has considerable potential to enhance precision in dementia prevention. HIGHLIGHTS Artificial intelligence (AI) is not widely used in the dementia prevention field. Risk-profiling tools are not used in clinical practice. Causal insights are needed to understand risk factors over the lifespan. AI will help personalize risk-management tools for dementia prevention. AI could target specific patient groups that will benefit most for clinical trials.
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Affiliation(s)
- Danielle Newby
- University of Oxford, Department of Psychiatry, Warneford Hospital, Oxford, OX3 7JX, UK
| | - Vasiliki Orgeta
- Division of Psychiatry, University College London, London, W1T 7BN, UK
| | - Charles R Marshall
- Preventive Neurology Unit, Wolfson Institute of Population Health, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, E1 4NS, UK
- Department of Neurology, Royal London Hospital, London, E1 1BB, UK
| | - Ilianna Lourida
- Population Health Sciences Institute, Newcastle University, Newcastle, NE2 4AX, UK
- University of Exeter Medical School, Exeter, EX1 2HZ, UK
| | - Christopher P Albertyn
- Department of Old Age Psychiatry, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, SE5 8AF, UK
| | - Stefano Tamburin
- Department of Neurosciences, Biomedicine and Movement Sciences, University of Verona, Verona, 37129, Italy
| | - Vanessa Raymont
- University of Oxford, Department of Psychiatry, Warneford Hospital, Oxford, OX3 7JX, UK
| | - Michele Veldsman
- Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, OX3 9DU, UK
- Department of Experimental Psychology, University of Oxford, Oxford, OX2 6GG, UK
| | - Ivan Koychev
- University of Oxford, Department of Psychiatry, Warneford Hospital, Oxford, OX3 7JX, UK
| | - Sarah Bauermeister
- University of Oxford, Department of Psychiatry, Warneford Hospital, Oxford, OX3 7JX, UK
| | - David Weisman
- Abington Neurological Associates, Abington, PA 19001, USA
| | - Isabelle F Foote
- Preventive Neurology Unit, Wolfson Institute of Population Health, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, E1 4NS, UK
- Institute for Behavioral Genetics, University of Colorado Boulder, Boulder, CO 80309, USA
| | - Magda Bucholc
- Cognitive Analytics Research Lab, School of Computing, Engineering & Intelligent Systems, Ulster University, Derry, BT48 7JL, UK
| | - Anja K Leist
- Institute for Research on Socio-Economic Inequality (IRSEI), Department of Social Sciences, University of Luxembourg, L-4365, Luxembourg
| | - Eugene Y H Tang
- Population Health Sciences Institute, Newcastle University, Newcastle, NE2 4AX, UK
| | - Xin You Tai
- Nuffield Department of Clinical Neuroscience, University of Oxford, Oxford, OX3 9DU, UK
- Division of Clinical Neurology, John Radcliffe Hospital, Oxford University Hospitals Trust, Oxford, OX3 9DU, UK
| | | | - David J. Llewellyn
- University of Exeter Medical School, Exeter, EX1 2HZ, UK
- The Alan Turing Institute, London, NW1 2DB, UK
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Dai Y, Zou B, Zhu C, Li Y, Chen Z, Ji Z, Kui X, Zhang W. DE-JANet: A unified network based on dual encoder and joint attention for Alzheimer's disease classification using multi-modal data. Comput Biol Med 2023; 165:107396. [PMID: 37703717 DOI: 10.1016/j.compbiomed.2023.107396] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2023] [Revised: 07/28/2023] [Accepted: 08/26/2023] [Indexed: 09/15/2023]
Abstract
Structural magnetic resonance imaging (sMRI), which can reflect cerebral atrophy, plays an important role in the early detection of Alzheimer's disease (AD). However, the information provided by analyzing only the morphological changes in sMRI is relatively limited, and the assessment of the atrophy degree is subjective. Therefore, it is meaningful to combine sMRI with other clinical information to acquire complementary diagnosis information and achieve a more accurate classification of AD. Nevertheless, how to fuse these multi-modal data effectively is still challenging. In this paper, we propose DE-JANet, a unified AD classification network that integrates image data sMRI with non-image clinical data, such as age and Mini-Mental State Examination (MMSE) score, for more effective multi-modal analysis. DE-JANet consists of three key components: (1) a dual encoder module for extracting low-level features from the image and non-image data according to specific encoding regularity, (2) a joint attention module for fusing multi-modal features, and (3) a token classification module for performing AD-related classification according to the fused multi-modal features. Our DE-JANet is evaluated on the ADNI dataset, with a mean accuracy of 0.9722 and 0.9538 for AD classification and mild cognition impairment (MCI) classification, respectively, which is superior to existing methods and indicates advanced performance on AD-related diagnosis tasks.
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Affiliation(s)
- Yulan Dai
- School of Computer Science and Engineering, Central South University, Changsha, China; Hunan Engineering Research Center of Machine Vision and Intelligent Medicine, Changsha, China
| | - Beiji Zou
- School of Computer Science and Engineering, Central South University, Changsha, China; Hunan Engineering Research Center of Machine Vision and Intelligent Medicine, Changsha, China
| | - Chengzhang Zhu
- School of Computer Science and Engineering, Central South University, Changsha, China; Hunan Engineering Research Center of Machine Vision and Intelligent Medicine, Changsha, China.
| | - Yang Li
- School of Computer Science and Engineering, Central South University, Changsha, China; Hunan Engineering Research Center of Machine Vision and Intelligent Medicine, Changsha, China
| | - Zhi Chen
- School of Computer Science and Engineering, Central South University, Changsha, China; Hunan Engineering Research Center of Machine Vision and Intelligent Medicine, Changsha, China
| | - Zexin Ji
- School of Computer Science and Engineering, Central South University, Changsha, China; Hunan Engineering Research Center of Machine Vision and Intelligent Medicine, Changsha, China
| | - Xiaoyan Kui
- School of Computer Science and Engineering, Central South University, Changsha, China
| | - Wensheng Zhang
- Institute of Automation, Chinese Academy of Sciences, Beijing, China
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8
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Gupta R, Kumari S, Senapati A, Ambasta RK, Kumar P. New era of artificial intelligence and machine learning-based detection, diagnosis, and therapeutics in Parkinson's disease. Ageing Res Rev 2023; 90:102013. [PMID: 37429545 DOI: 10.1016/j.arr.2023.102013] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2023] [Revised: 06/26/2023] [Accepted: 07/06/2023] [Indexed: 07/12/2023]
Abstract
Parkinson's disease (PD) is characterized by the loss of neuronal cells, which leads to synaptic dysfunction and cognitive defects. Despite the advancements in treatment strategies, the management of PD is still a challenging event. Early prediction and diagnosis of PD are of utmost importance for effective management of PD. In addition, the classification of patients with PD as compared to normal healthy individuals also imposes drawbacks in the early diagnosis of PD. To address these challenges, artificial intelligence (AI) and machine learning (ML) models have been implicated in the diagnosis, prediction, and treatment of PD. Recent times have also demonstrated the implication of AI and ML models in the classification of PD based on neuroimaging methods, speech recording, gait abnormalities, and others. Herein, we have briefly discussed the role of AI and ML in the diagnosis, treatment, and identification of novel biomarkers in the progression of PD. We have also highlighted the role of AI and ML in PD management through altered lipidomics and gut-brain axis. We briefly explain the role of early PD detection through AI and ML algorithms based on speech recordings, handwriting patterns, gait abnormalities, and neuroimaging techniques. Further, the review discuss the potential role of the metaverse, the Internet of Things, and electronic health records in the effective management of PD to improve the quality of life. Lastly, we also focused on the implementation of AI and ML-algorithms in neurosurgical process and drug discovery.
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Affiliation(s)
- Rohan Gupta
- Molecular Neuroscience and Functional Genomics Laboratory, Department of Biotechnology, Delhi Technological, University, USA.
| | - Smita Kumari
- Molecular Neuroscience and Functional Genomics Laboratory, Department of Biotechnology, Delhi Technological, University, USA
| | | | - Rashmi K Ambasta
- Molecular Neuroscience and Functional Genomics Laboratory, Department of Biotechnology, Delhi Technological, University, USA
| | - Pravir Kumar
- Molecular Neuroscience and Functional Genomics Laboratory, Department of Biotechnology, Delhi Technological, University, USA.
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9
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Teng J, Mi C, Shi J, Li N. Brain disease research based on functional magnetic resonance imaging data and machine learning: a review. Front Neurosci 2023; 17:1227491. [PMID: 37662098 PMCID: PMC10469689 DOI: 10.3389/fnins.2023.1227491] [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: 05/23/2023] [Accepted: 07/13/2023] [Indexed: 09/05/2023] Open
Abstract
Brain diseases, including neurodegenerative diseases and neuropsychiatric diseases, have long plagued the lives of the affected populations and caused a huge burden on public health. Functional magnetic resonance imaging (fMRI) is an excellent neuroimaging technology for measuring brain activity, which provides new insight for clinicians to help diagnose brain diseases. In recent years, machine learning methods have displayed superior performance in diagnosing brain diseases compared to conventional methods, attracting great attention from researchers. This paper reviews the representative research of machine learning methods in brain disease diagnosis based on fMRI data in the recent three years, focusing on the most frequent four active brain disease studies, including Alzheimer's disease/mild cognitive impairment, autism spectrum disorders, schizophrenia, and Parkinson's disease. We summarize these 55 articles from multiple perspectives, including the effect of the size of subjects, extracted features, feature selection methods, classification models, validation methods, and corresponding accuracies. Finally, we analyze these articles and introduce future research directions to provide neuroimaging scientists and researchers in the interdisciplinary fields of computing and medicine with new ideas for AI-aided brain disease diagnosis.
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Affiliation(s)
- Jing Teng
- School of Control and Computer Engineering, North China Electric Power University, Beijing, China
| | - Chunlin Mi
- School of Control and Computer Engineering, North China Electric Power University, Beijing, China
| | - Jian Shi
- Department of Hematology and Critical Care Medicine, The Third Xiangya Hospital of Central South University, Changsha, China
| | - Na Li
- Department of Radiology, The Third Xiangya Hospital of Central South University, Changsha, China
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10
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Zhang J, He X, Liu Y, Cai Q, Chen H, Qing L. Multi-modal cross-attention network for Alzheimer's disease diagnosis with multi-modality data. Comput Biol Med 2023; 162:107050. [PMID: 37269680 DOI: 10.1016/j.compbiomed.2023.107050] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2022] [Revised: 04/26/2023] [Accepted: 05/03/2023] [Indexed: 06/05/2023]
Abstract
Alzheimer's disease (AD) is a neurodegenerative disorder, the most common cause of dementia, so the accurate diagnosis of AD and its prodromal stage mild cognitive impairment (MCI) is significant. Recent studies have demonstrated that multiple neuroimaging and biological measures contain complementary information for diagnosis. Many existing multi-modal models based on deep learning simply concatenate each modality's features despite substantial differences in representation spaces. In this paper, we propose a novel multi-modal cross-attention AD diagnosis (MCAD) framework to learn the interaction between modalities for better playing their complementary roles for AD diagnosis with multi-modal data including structural magnetic resonance imaging (sMRI), fluorodeoxyglucose-positron emission tomography (FDG-PET) and cerebrospinal fluid (CSF) biomarkers. Specifically, the imaging and non-imaging representations are learned by the image encoder based on cascaded dilated convolutions and CSF encoder, respectively. Then, a multi-modal interaction module is introduced, which takes advantage of cross-modal attention to integrate imaging and non-imaging information and reinforce relationships between these modalities. Moreover, an extensive objective function is designed to reduce the discrepancy between modalities for effectively fusing the features of multi-modal data, which could further improve the diagnosis performance. We evaluate the effectiveness of our proposed method on the ADNI dataset, and the extensive experiments demonstrate that our MCAD achieves superior performance for multiple AD-related classification tasks, compared to several competing methods. Also, we investigate the importance of cross-attention and the contribution of each modality to the diagnostics performance. The experimental results demonstrate that combining multi-modality data via cross-attention is helpful for accurate AD diagnosis.
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Affiliation(s)
- Jin Zhang
- College of Electronics and Information Engineering, Sichuan University, Chengdu, Sichuan, 610065, China
| | - Xiaohai He
- College of Electronics and Information Engineering, Sichuan University, Chengdu, Sichuan, 610065, China.
| | - Yan Liu
- Department of Neurology, The Affiliated Hospital of Southwest Jiaotong University, The Third People's Hospital of Chengdu, Chengdu, Sichuan, 610031, China
| | - Qingyan Cai
- Department of Geriatric Medicine, The Fourth People's Hospital of Chengdu, Chengdu, Sichuan, 610036, China
| | - Honggang Chen
- College of Electronics and Information Engineering, Sichuan University, Chengdu, Sichuan, 610065, China
| | - Linbo Qing
- College of Electronics and Information Engineering, Sichuan University, Chengdu, Sichuan, 610065, China
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11
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Chen H. Enterprise marketing strategy using big data mining technology combined with XGBoost model in the new economic era. PLoS One 2023; 18:e0285506. [PMID: 37276212 DOI: 10.1371/journal.pone.0285506] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2023] [Accepted: 04/24/2023] [Indexed: 06/07/2023] Open
Abstract
The technological development in the new economic era has brought challenges to enterprises. Enterprises need to use massive and effective consumption information to provide customers with high-quality customized services. Big data technology has strong mining ability. The relevant theories of computer data mining technology are summarized to optimize the marketing strategy of enterprises. The application of data mining in precision marketing services is analyzed. Extreme Gradient Boosting (XGBoost) has shown strong advantages in machine learning algorithms. In order to help enterprises to analyze customer data quickly and accurately, the characteristics of XGBoost feedback are used to reverse the main factors that can affect customer activation cards, and effective analysis is carried out for these factors. The data obtained from the analysis points out the direction of effective marketing for potential customers to be activated. Finally, the performance of XGBoost is compared with the other three methods. The characteristics that affect the top 7 prediction results are tested for differences. The results show that: (1) the accuracy and recall rate of the proposed model are higher than other algorithms, and the performance is the best. (2) The significance p values of the features included in the test are all less than 0.001. The data shows that there is a very significant difference between the proposed features and the results of activation or not. The contributions of this paper are mainly reflected in two aspects. 1. Four precision marketing strategies based on big data mining are designed to provide scientific support for enterprise decision-making. 2. The improvement of the connection rate and stickiness between enterprises and customers has played a huge driving role in overall customer marketing.
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12
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El-Sappagh S, Alonso-Moral JM, Abuhmed T, Ali F, Bugarín-Diz A. Trustworthy artificial intelligence in Alzheimer’s disease: state of the art, opportunities, and challenges. Artif Intell Rev 2023. [DOI: 10.1007/s10462-023-10415-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/28/2023]
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13
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Zhao Z, Chuah JH, Lai KW, Chow CO, Gochoo M, Dhanalakshmi S, Wang N, Bao W, Wu X. Conventional machine learning and deep learning in Alzheimer's disease diagnosis using neuroimaging: A review. Front Comput Neurosci 2023; 17:1038636. [PMID: 36814932 PMCID: PMC9939698 DOI: 10.3389/fncom.2023.1038636] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2022] [Accepted: 01/13/2023] [Indexed: 02/08/2023] Open
Abstract
Alzheimer's disease (AD) is a neurodegenerative disorder that causes memory degradation and cognitive function impairment in elderly people. The irreversible and devastating cognitive decline brings large burdens on patients and society. So far, there is no effective treatment that can cure AD, but the process of early-stage AD can slow down. Early and accurate detection is critical for treatment. In recent years, deep-learning-based approaches have achieved great success in Alzheimer's disease diagnosis. The main objective of this paper is to review some popular conventional machine learning methods used for the classification and prediction of AD using Magnetic Resonance Imaging (MRI). The methods reviewed in this paper include support vector machine (SVM), random forest (RF), convolutional neural network (CNN), autoencoder, deep learning, and transformer. This paper also reviews pervasively used feature extractors and different types of input forms of convolutional neural network. At last, this review discusses challenges such as class imbalance and data leakage. It also discusses the trade-offs and suggestions about pre-processing techniques, deep learning, conventional machine learning methods, new techniques, and input type selection.
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Affiliation(s)
- Zhen Zhao
- Department of Electrical Engineering, Faculty of Engineering, Universiti Malaya, Kuala Lumpur, Malaysia
| | - Joon Huang Chuah
- Department of Electrical Engineering, Faculty of Engineering, Universiti Malaya, Kuala Lumpur, Malaysia,*Correspondence: Joon Huang Chuah ✉
| | - Khin Wee Lai
- Department of Biomedical Engineering, Faculty of Engineering, Universiti Malaya, Kuala Lumpur, Malaysia,Khin Wee Lai ✉
| | - Chee-Onn Chow
- Department of Electrical Engineering, Faculty of Engineering, Universiti Malaya, Kuala Lumpur, Malaysia
| | - Munkhjargal Gochoo
- Department of Computer Science and Software Engineering, United Arab Emirates University, Al Ain, United Arab Emirates
| | - Samiappan Dhanalakshmi
- Department of Electronics and Communication Engineering, SRM Institute of Science and Technology, Chennai, India
| | - Na Wang
- School of Automation, Guangdong Polytechnic Normal University, Guangzhou, China
| | - Wei Bao
- China Electronics Standardization Institute, Beijing, China,Wei Bao ✉
| | - Xiang Wu
- School of Medical Information Engineering, Xuzhou Medical University, Xuzhou, China
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14
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Wang S, Zheng K, Kong W, Huang R, Liu L, Wen G, Yu Y. Multimodal data fusion based on IGERNNC algorithm for detecting pathogenic brain regions and genes in Alzheimer's disease. Brief Bioinform 2023; 24:6887308. [PMID: 36502428 DOI: 10.1093/bib/bbac515] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2022] [Revised: 09/28/2022] [Accepted: 10/30/2022] [Indexed: 12/14/2022] Open
Abstract
At present, the study on the pathogenesis of Alzheimer's disease (AD) by multimodal data fusion analysis has been attracted wide attention. It often has the problems of small sample size and high dimension with the multimodal medical data. In view of the characteristics of multimodal medical data, the existing genetic evolution random neural network cluster (GERNNC) model combine genetic evolution algorithm and neural network for the classification of AD patients and the extraction of pathogenic factors. However, the model does not take into account the non-linear relationship between brain regions and genes and the problem that the genetic evolution algorithm can fall into local optimal solutions, which leads to the overall performance of the model is not satisfactory. In order to solve the above two problems, this paper made some improvements on the construction of fusion features and genetic evolution algorithm in GERNNC model, and proposed an improved genetic evolution random neural network cluster (IGERNNC) model. The IGERNNC model uses mutual information correlation analysis method to combine resting-state functional magnetic resonance imaging data with single nucleotide polymorphism data for the construction of fusion features. Based on the traditional genetic evolution algorithm, elite retention strategy and large variation genetic algorithm are added to avoid the model falling into the local optimal solution. Through multiple independent experimental comparisons, the IGERNNC model can more effectively identify AD patients and extract relevant pathogenic factors, which is expected to become an effective tool in the field of AD research.
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Affiliation(s)
- Shuaiqun Wang
- School of Information Engineering, Shanghai Maritime University, Shanghai, China
| | - Kai Zheng
- School of Information Engineering, Shanghai Maritime University, Shanghai, China
| | - Wei Kong
- School of Information Engineering, Shanghai Maritime University, Shanghai, China
| | - Ruiwen Huang
- School of Information Engineering, Shanghai Maritime University, Shanghai, China
| | - Lulu Liu
- School of Information Engineering, Shanghai Maritime University, Shanghai, China
| | - Gen Wen
- School of Information Engineering, Shanghai Maritime University, Shanghai, China
| | - Yaling Yu
- School of Information Engineering, Shanghai Maritime University, Shanghai, China
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15
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Xi Z, Liu T, Shi H, Jiao Z. Hypergraph representation of multimodal brain networks for patients with end-stage renal disease associated with mild cognitive impairment. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2023; 20:1882-1902. [PMID: 36899513 DOI: 10.3934/mbe.2023086] [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: 06/18/2023]
Abstract
The structure and function of brain networks (BN) may be altered in patients with end-stage renal disease (ESRD). However, there are relatively few attentions on ESRD associated with mild cognitive impairment (ESRDaMCI). Most studies focus on the pairwise relationships between brain regions, without taking into account the complementary information of functional connectivity (FC) and structural connectivity (SC). To address the problem, a hypergraph representation method is proposed to construct a multimodal BN for ESRDaMCI. First, the activity of nodes is determined by connection features extracted from functional magnetic resonance imaging (fMRI) (i.e., FC), and the presence of edges is determined by physical connections of nerve fibers extracted from diffusion kurtosis imaging (DKI) (i.e., SC). Then, the connection features are generated through bilinear pooling and transformed into an optimization model. Next, a hypergraph is constructed according to the generated node representation and connection features, and the node degree and edge degree of the hypergraph are calculated to obtain the hypergraph manifold regularization (HMR) term. The HMR and L1 norm regularization terms are introduced into the optimization model to achieve the final hypergraph representation of multimodal BN (HRMBN). Experimental results show that the classification performance of HRMBN is significantly better than that of several state-of-the-art multimodal BN construction methods. Its best classification accuracy is 91.0891%, at least 4.3452% higher than that of other methods, verifying the effectiveness of our method. The HRMBN not only achieves better results in ESRDaMCI classification, but also identifies the discriminative brain regions of ESRDaMCI, which provides a reference for the auxiliary diagnosis of ESRD.
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Affiliation(s)
- Zhengtao Xi
- School of Microelectronics and Control Engineering, Changzhou University, Changzhou 213164, China
| | - Tongqiang Liu
- Department of Nephrology, The Affiliated Changzhou No.2 People's Hospital of Nanjing Medical University, Changzhou 213003, China
| | - Haifeng Shi
- Department of Radiology, The Affiliated Changzhou No.2 People's Hospital of Nanjing Medical University, Changzhou 213003, China
| | - Zhuqing Jiao
- School of Computer Science and Artificial Intelligence, Changzhou University, Changzhou 213164, China
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16
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Lin L, Liu S, Chen Z, Xia Y, Xie J, Fu M, Lu D, Wu Y, Shen H, Yang P, Qian J. Anatomically resolved transcriptome and proteome landscapes reveal disease‐relevant molecular signatures and systematic changes in heart function of end‐stage dilated cardiomyopathy. VIEW 2022. [DOI: 10.1002/viw.20220040] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022] Open
Affiliation(s)
- Ling Lin
- Institutes of Biomedical Sciences of Shanghai Medical School & Minhang Hospital Fudan University Shanghai China
- Department of Cardiology Shanghai Institute of Cardiovascular Diseases Zhongshan Hospital Fudan University Shanghai China
| | - Shanshan Liu
- Institutes of Biomedical Sciences of Shanghai Medical School & Minhang Hospital Fudan University Shanghai China
| | - Zhangwei Chen
- Department of Cardiology Shanghai Institute of Cardiovascular Diseases Zhongshan Hospital Fudan University Shanghai China
| | - Yan Xia
- Department of Cardiology Shanghai Institute of Cardiovascular Diseases Zhongshan Hospital Fudan University Shanghai China
| | - Juanjuan Xie
- Institutes of Biomedical Sciences of Shanghai Medical School & Minhang Hospital Fudan University Shanghai China
| | - Mingqiang Fu
- Department of Cardiology Shanghai Institute of Cardiovascular Diseases Zhongshan Hospital Fudan University Shanghai China
| | - Danbo Lu
- Department of Cardiology Shanghai Institute of Cardiovascular Diseases Zhongshan Hospital Fudan University Shanghai China
| | - Yuan Wu
- Department of Cardiology Shanghai Institute of Cardiovascular Diseases Zhongshan Hospital Fudan University Shanghai China
| | - Huali Shen
- Institutes of Biomedical Sciences of Shanghai Medical School & Minhang Hospital Fudan University Shanghai China
| | - Pengyuan Yang
- Institutes of Biomedical Sciences of Shanghai Medical School & Minhang Hospital Fudan University Shanghai China
- Department of chemistry Fudan University Shanghai China
| | - Juying Qian
- Department of Cardiology Shanghai Institute of Cardiovascular Diseases Zhongshan Hospital Fudan University Shanghai China
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17
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Detection of Association Features Based on Gene Eigenvalues and MRI Imaging Using Genetic Weighted Random Forest. Genes (Basel) 2022; 13:genes13122344. [PMID: 36553611 PMCID: PMC9777775 DOI: 10.3390/genes13122344] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Revised: 12/07/2022] [Accepted: 12/07/2022] [Indexed: 12/14/2022] Open
Abstract
In the studies of Alzheimer's disease (AD), jointly analyzing imaging data and genetic data provides an effective method to explore the potential biomarkers of AD. AD can be separated into healthy controls (HC), early mild cognitive impairment (EMCI), late mild cognitive impairment (LMCI) and AD. In the meantime, identifying the important biomarkers of AD progression, and analyzing these biomarkers in AD provide valuable insights into understanding the mechanism of AD. In this paper, we present a novel data fusion method and a genetic weighted random forest method to mine important features. Specifically, we amplify the difference among AD, LMCI, EMCI and HC by introducing eigenvalues calculated from the gene p-value matrix for feature fusion. Furthermore, we construct the genetic weighted random forest using the resulting fused features. Genetic evolution is used to increase the diversity among decision trees and the decision trees generated are weighted by weights. After training, the genetic weighted random forest is analyzed further to detect the significant fused features. The validation experiments highlight the performance and generalization of our proposed model. We analyze the biological significance of the results and identify some significant genes (CSMD1, CDH13, PTPRD, MACROD2 and WWOX). Furthermore, the calcium signaling pathway, arrhythmogenic right ventricular cardiomyopathy and the glutamatergic synapse pathway were identified. The investigational findings demonstrate that our proposed model presents an accurate and efficient approach to identifying significant biomarkers in AD.
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18
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Li J, Xu H, Yu H, Jiang Z, Zhu L. Multi-modal feature selection with anchor graph for Alzheimer's disease. Front Neurosci 2022; 16:1036244. [DOI: 10.3389/fnins.2022.1036244] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2022] [Accepted: 10/21/2022] [Indexed: 11/10/2022] Open
Abstract
In Alzheimer's disease, the researchers found that if the patients were treated at the early stage of the disease, it could effectively delay the development of the disease. At present, multi-modal feature selection is widely used in the early diagnosis of Alzheimer's disease. However, existing multi-modal feature selection algorithms focus on learning the internal information of multiple modalities. They ignore the relationship between modalities, the importance of each modality and the local structure in the multi-modal data. In this paper, we propose a multi-modal feature selection algorithm with anchor graph for Alzheimer's disease. Specifically, we first use the least square loss and l2,1−norm to obtain the weight of the feature under each modality. Then we embed a modal weight factor into the objective function to obtain the importance of each modality. Finally, we use anchor graph to quickly learn the local structure information in multi-modal data. In addition, we also verify the validity of the proposed algorithm on the published ADNI dataset.
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19
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Kline A, Wang H, Li Y, Dennis S, Hutch M, Xu Z, Wang F, Cheng F, Luo Y. Multimodal machine learning in precision health: A scoping review. NPJ Digit Med 2022; 5:171. [PMID: 36344814 PMCID: PMC9640667 DOI: 10.1038/s41746-022-00712-8] [Citation(s) in RCA: 65] [Impact Index Per Article: 32.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2022] [Accepted: 10/14/2022] [Indexed: 11/09/2022] Open
Abstract
Machine learning is frequently being leveraged to tackle problems in the health sector including utilization for clinical decision-support. Its use has historically been focused on single modal data. Attempts to improve prediction and mimic the multimodal nature of clinical expert decision-making has been met in the biomedical field of machine learning by fusing disparate data. This review was conducted to summarize the current studies in this field and identify topics ripe for future research. We conducted this review in accordance with the PRISMA extension for Scoping Reviews to characterize multi-modal data fusion in health. Search strings were established and used in databases: PubMed, Google Scholar, and IEEEXplore from 2011 to 2021. A final set of 128 articles were included in the analysis. The most common health areas utilizing multi-modal methods were neurology and oncology. Early fusion was the most common data merging strategy. Notably, there was an improvement in predictive performance when using data fusion. Lacking from the papers were clear clinical deployment strategies, FDA-approval, and analysis of how using multimodal approaches from diverse sub-populations may improve biases and healthcare disparities. These findings provide a summary on multimodal data fusion as applied to health diagnosis/prognosis problems. Few papers compared the outputs of a multimodal approach with a unimodal prediction. However, those that did achieved an average increase of 6.4% in predictive accuracy. Multi-modal machine learning, while more robust in its estimations over unimodal methods, has drawbacks in its scalability and the time-consuming nature of information concatenation.
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Affiliation(s)
- Adrienne Kline
- Department of Preventive Medicine, Northwestern University, Chicago, 60201, IL, USA
| | - Hanyin Wang
- Department of Preventive Medicine, Northwestern University, Chicago, 60201, IL, USA
| | - Yikuan Li
- Department of Preventive Medicine, Northwestern University, Chicago, 60201, IL, USA
| | - Saya Dennis
- Department of Preventive Medicine, Northwestern University, Chicago, 60201, IL, USA
| | - Meghan Hutch
- Department of Preventive Medicine, Northwestern University, Chicago, 60201, IL, USA
| | - Zhenxing Xu
- Department of Population Health Sciences, Cornell University, New York, 10065, NY, USA
| | - Fei Wang
- Department of Population Health Sciences, Cornell University, New York, 10065, NY, USA
| | - Feixiong Cheng
- Cleveland Clinic Lerner College of Medicine, Case Western Reserve University, Cleveland, 44195, OH, USA
| | - Yuan Luo
- Department of Preventive Medicine, Northwestern University, Chicago, 60201, IL, USA.
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20
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Review on Machine Learning Techniques for Medical Data Classification and Disease Diagnosis. REGENERATIVE ENGINEERING AND TRANSLATIONAL MEDICINE 2022. [DOI: 10.1007/s40883-022-00273-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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21
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Integrated Prediction Framework for Clinical Scores of Cognitive Functions in ESRD Patients. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:8124053. [PMID: 35983157 PMCID: PMC9381242 DOI: 10.1155/2022/8124053] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/30/2022] [Revised: 06/27/2022] [Accepted: 07/05/2022] [Indexed: 12/03/2022]
Abstract
The clinical scores are applied to determine the stage of cognitive function in patients with end-stage renal disease (ESRD). However, accurate clinical scores are hard to come by. This paper proposed an integrated prediction framework with GPLWLSV to predict clinical scores of cognitive functions in ESRD patients. GPLWLSV incorporated three parts, graph theoretic algorithm (GTA) and principal component analysis (PCA), whale optimization algorithm with Levy flight (LWOA), and least squares support vector regression machine (LSSVRM). GTA was adopted to extract features from the brain functional networks in ESRD patients, while PCA was used to select features. LSSVRM was built to explore the relationship between the selected features and the clinical scores of ESRD patients. Whale optimization algorithm (WOA) was introduced to select better parameters of the kernel function in LSSVRM; it aims to improve the exploration competence of LSSVRM. Levy flight was used to optimize the ability to jump out of local optima in WOA and improve the convergence of coefficient vectors in WOA, which lead to an increase in the generalization ability and convergence speed of WOA. The results validated that the prediction accuracy of GPLWLSV was higher than that of several comparable frameworks, such as GPSV, GPLSV, and GPWLSV. In particular, the average of root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) between the predicted scores and the actual scores of ESRD patients was 2.40, 2.06, and 9.83%, respectively. The proposed framework not only can predict the clinical scores more accurately but also can capture imaging markers associated with decline of cognitive function. It helps to understand the potential relationship between structural changes in the brain and cognitive function of ESRD patients.
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22
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Wen C, Huai T, Zhang Q, Song Z, Cao F. A new rotation forest ensemble algorithm. INT J MACH LEARN CYB 2022. [DOI: 10.1007/s13042-022-01613-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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23
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Lei B, Zhang Y, Liu D, Xu Y, Yue G, Cao J, Hu H, Yu S, Yang P, Wang T, Qiu Y, Xiao X, Wang S. Longitudinal study of early mild cognitive impairment via similarity-constrained group learning and self-attention based SBi-LSTM. Knowl Based Syst 2022. [DOI: 10.1016/j.knosys.2022.109466] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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24
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Zamani J, Sadr A, Javadi AH. Classification of early-MCI patients from healthy controls using evolutionary optimization of graph measures of resting-state fMRI, for the Alzheimer's disease neuroimaging initiative. PLoS One 2022; 17:e0267608. [PMID: 35727837 PMCID: PMC9212187 DOI: 10.1371/journal.pone.0267608] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2021] [Accepted: 04/11/2022] [Indexed: 11/21/2022] Open
Abstract
Identifying individuals with early mild cognitive impairment (EMCI) can be an effective strategy for early diagnosis and delay the progression of Alzheimer's disease (AD). Many approaches have been devised to discriminate those with EMCI from healthy control (HC) individuals. Selection of the most effective parameters has been one of the challenging aspects of these approaches. In this study we suggest an optimization method based on five evolutionary algorithms that can be used in optimization of neuroimaging data with a large number of parameters. Resting-state functional magnetic resonance imaging (rs-fMRI) measures, which measure functional connectivity, have been shown to be useful in prediction of cognitive decline. Analysis of functional connectivity data using graph measures is a common practice that results in a great number of parameters. Using graph measures we calculated 1155 parameters from the functional connectivity data of HC (n = 72) and EMCI (n = 68) extracted from the publicly available database of the Alzheimer's disease neuroimaging initiative database (ADNI). These parameters were fed into the evolutionary algorithms to select a subset of parameters for classification of the data into two categories of EMCI and HC using a two-layer artificial neural network. All algorithms achieved classification accuracy of 94.55%, which is extremely high considering single-modality input and low number of data participants. These results highlight potential application of rs-fMRI and efficiency of such optimization methods in classification of images into HC and EMCI. This is of particular importance considering that MRI images of EMCI individuals cannot be easily identified by experts.
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Affiliation(s)
- Jafar Zamani
- School of Electrical Engineering, Iran University of Science and Technology, Tehran, Iran
| | - Ali Sadr
- School of Electrical Engineering, Iran University of Science and Technology, Tehran, Iran
| | - Amir-Homayoun Javadi
- School of Psychology, University of Kent, Canterbury, United Kingdom
- School of Rehabilitation, Tehran University of Medical Sciences, Tehran, Iran
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25
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Chen Z, Liu Y, Zhang Y, Jin R, Tao J, Chen L. Low-rank sparse feature selection with incomplete labels for Alzheimer's disease progression prediction. Comput Biol Med 2022; 147:105705. [PMID: 35717935 DOI: 10.1016/j.compbiomed.2022.105705] [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: 03/10/2022] [Revised: 05/16/2022] [Accepted: 06/04/2022] [Indexed: 11/29/2022]
Abstract
BACKGROUND How to predict the cognitive performance of Alzheimer's disease (AD) and identify the informative neuroimaging markers is essential for timely treatment and possible delay of the disease. However, incomplete labeled samples and noises in neuroimaging data pose challenges to building reliable and robust prediction models. In this paper, we present a model named Low-rank Sparse Feature Selection with Incomplete Labels (LSFSIL) for predicting cognitive performance and identifying informative neuroimaging markers with MRI data and incomplete cognitive scores. METHOD We propose a sparse matrix decomposition method to decompose the incomplete cognitive score matrix into two parts for recovering missing scores and utilizing incomplete labeled data. The former is the recovered cognitive score matrix without missing values. To make the recovered scores close to the real ones, a manifold regularizer is devised to fit the label distribution for capturing the label correlations locally. The latter is a ℓ1-norm regularized matrix which represents the associated errors. Next, a low-rank regression model that regards the recovered matrix as the target is developed to increase the robustness to noises and outliers. Besides, ℓ2,1-norm is introduced into the objective function as a sparse regularization to identify the important features. RESULTS Experimental results demonstrate that LSFSIL achieves higher performance and outperforms several state-of-the-art feature selection approaches. Moreover, the neuroimaging markers selected by LSFSIL are consistent with the previous AD studies. CONCLUSIONS LSFSIL is effective in informative neuroimaging marker identification for cognitive performance prediction with incomplete labeled data.
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Affiliation(s)
- Zhi Chen
- Knowledge and Data Engineering Laboratory of Chinese Medicine, School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu, 610054, China
| | - Yongguo Liu
- Knowledge and Data Engineering Laboratory of Chinese Medicine, School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu, 610054, China.
| | - Yun Zhang
- Knowledge and Data Engineering Laboratory of Chinese Medicine, School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu, 610054, China
| | - Rongjiang Jin
- College of Health Preservation and Rehabilitation, Chengdu University of Traditional Chinese Medicine, Chengdu, 610075, China
| | - Jing Tao
- College of Rehabilitation Medicine, Fujian University of Traditional Chinese Medicine, Fuzhou, 350122, China
| | - Lidian Chen
- College of Rehabilitation Medicine, Fujian University of Traditional Chinese Medicine, Fuzhou, 350122, China
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26
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Wang G, Wu W, Xu Y, Yang Z, Xiao B, Long L. Imaging Genetics in Epilepsy: Current Knowledge and New Perspectives. Front Mol Neurosci 2022; 15:891621. [PMID: 35706428 PMCID: PMC9189397 DOI: 10.3389/fnmol.2022.891621] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2022] [Accepted: 05/06/2022] [Indexed: 12/11/2022] Open
Abstract
Epilepsy is a neurological network disease with genetics playing a much greater role than was previously appreciated. Unfortunately, the relationship between genetic basis and imaging phenotype is by no means simple. Imaging genetics integrates multidimensional datasets within a unified framework, providing a unique opportunity to pursue a global vision for epilepsy. This review delineates the current knowledge of underlying genetic mechanisms for brain networks in different epilepsy syndromes, particularly from a neural developmental perspective. Further, endophenotypes and their potential value are discussed. Finally, we highlight current challenges and provide perspectives for the future development of imaging genetics in epilepsy.
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Affiliation(s)
- Ge Wang
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
- Clinical Research Center for Epileptic Disease of Hunan Province, Central South University, Changsha, China
| | - Wenyue Wu
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, China
- Department of Neurology, The Second Affiliated Hospital of Nanchang University, Jiangxi, China
| | - Yuchen Xu
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, China
- Department of Neurology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Zhuanyi Yang
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
- Clinical Research Center for Epileptic Disease of Hunan Province, Central South University, Changsha, China
- Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha, China
| | - Bo Xiao
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
- Clinical Research Center for Epileptic Disease of Hunan Province, Central South University, Changsha, China
| | - Lili Long
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
- Clinical Research Center for Epileptic Disease of Hunan Province, Central South University, Changsha, China
- *Correspondence: Lili Long
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Feature Fusion and Detection in Alzheimer’s Disease Using a Novel Genetic Multi-Kernel SVM Based on MRI Imaging and Gene Data. Genes (Basel) 2022; 13:genes13050837. [PMID: 35627222 PMCID: PMC9140721 DOI: 10.3390/genes13050837] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2022] [Revised: 05/03/2022] [Accepted: 05/05/2022] [Indexed: 01/27/2023] Open
Abstract
Voxel-based morphometry provides an opportunity to study Alzheimer’s disease (AD) at a subtle level. Therefore, identifying the important brain voxels that can classify AD, early mild cognitive impairment (EMCI) and healthy control (HC) and studying the role of these voxels in AD will be crucial to improve our understanding of the neurobiological mechanism of AD. Combining magnetic resonance imaging (MRI) imaging and gene information, we proposed a novel feature construction method and a novel genetic multi-kernel support vector machine (SVM) method to mine important features for AD detection. Specifically, to amplify the differences among AD, EMCI and HC groups, we used the eigenvalues of the top 24 Single Nucleotide Polymorphisms (SNPs) in a p-value matrix of 24 genes associated with AD for feature construction. Furthermore, a genetic multi-kernel SVM was established with the resulting features. The genetic algorithm was used to detect the optimal weights of 3 kernels and the multi-kernel SVM was used after training to explore the significant features. By analyzing the significance of the features, we identified some brain regions affected by AD, such as the right superior frontal gyrus, right inferior temporal gyrus and right superior temporal gyrus. The findings proved the good performance and generalization of the proposed model. Particularly, significant susceptibility genes associated with AD were identified, such as CSMD1, RBFOX1, PTPRD, CDH13 and WWOX. Some significant pathways were further explored, such as the calcium signaling pathway (corrected p-value = 1.35 × 10−6) and cell adhesion molecules (corrected p-value = 5.44 × 10−4). The findings offer new candidate abnormal brain features and demonstrate the contribution of these features to AD.
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Wu J, Cao Y, Li M, Li B, Jia X, Cao L. Altered intrinsic brain activity in patients with CSF1R-related leukoencephalopathy. Brain Imaging Behav 2022; 16:1842-1853. [PMID: 35389179 DOI: 10.1007/s11682-022-00646-5] [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] [Accepted: 01/24/2022] [Indexed: 11/26/2022]
Abstract
CSF1R-related leukoencephalopathy is an adult-onset white matter disease with high disability and mortality, while little is known about its pathogenesis. This study introduced amplitude of low-frequency fluctuations (ALFF) and regional homogeneity (ReHo) based on resting-state functional magnetic resonance imaging(rsfMRI) to compare the spontaneous brain activities of patients and healthy controls, aiming to enhance our understanding of the disease. RsfMRI was performed on 16 patients and 23 healthy controls, and preprocessed for calculation of ALFF and ReHo. Permutation tests with threshold free cluster enhancement (TFCE) was applied for comparison (number of permutations = 5,000). The TFCE significance threshold was set at [Formula: see text] < 0.05. In addition, 10 was set as the minimum cluster size. Compared to healthy controls, the patient group showed decreased ALFF in right paracentral lobule, and increased ALFF in bilateral insula, hippocampus, thalamus, supramarginal and precentral gyrus, right inferior, middle and superior frontal gyrus, right superior and middle occipital gyrus, as well as left parahippocampal gyrus, fusiform, middle occipital gyrus and angular gyrus. ReHo was decreased in right supplementary motor area, paracentral lobule and precentral gyrus, while increased in right superior occipital gyrus and supramarginal gyrus, left parahippocampal gyrus, hippocampus, fusiform, middle occipital gyrus and angular gyrus, as well as bilateral middle occipital gyrus and midbrain. These results revealed altered spontaneous brain activities in CSF1R-related leukoencephalopathy, especially in limbic system and motor cortex, which may shed light on underlying mechanisms.
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Affiliation(s)
- Jingying Wu
- Department of Neurology, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai, China
- Department of Neurology and Institute of Neurology, Ruijin Hospital Affiliated To Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yikang Cao
- School of Information and Electronics Technology, Jiamusi University, Jiamusi, China
| | - Mengting Li
- School of Teacher Education, Zhejiang Normal University, Jinhua, China
| | - Binyin Li
- Department of Neurology and Institute of Neurology, Ruijin Hospital Affiliated To Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Xize Jia
- Centre for Cognition and Brain Disorders, The Affiliated Hospital of Hangzhou Normal University, Hangzhou, China
| | - Li Cao
- Department of Neurology, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai, China.
- Department of Neurology and Institute of Neurology, Ruijin Hospital Affiliated To Shanghai Jiao Tong University School of Medicine, Shanghai, China.
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Meng X, Wu Y, Liu W, Wang Y, Xu Z, Jiao Z. Research on Voxel-Based Features Detection and Analysis of Alzheimer’s Disease Using Random Survey Support Vector Machine. Front Neuroinform 2022; 16:856295. [PMID: 35418845 PMCID: PMC8995748 DOI: 10.3389/fninf.2022.856295] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2022] [Accepted: 02/08/2022] [Indexed: 11/13/2022] Open
Abstract
Alzheimer’s disease (AD) is a degenerative disease of the central nervous system characterized by memory and cognitive dysfunction, as well as abnormal changes in behavior and personality. The research focused on how machine learning classified AD became a recent hotspot. In this study, we proposed a novel voxel-based feature detection framework for AD. Specifically, using 649 voxel-based morphometry (VBM) methods obtained from MRI in Alzheimer’s Disease Neuroimaging Initiative (ADNI), we proposed a feature detection method according to the Random Survey Support Vector Machines (RS-SVM) and combined the research process based on image-, gene-, and pathway-level analysis for AD prediction. Particularly, we constructed 136, 141, and 113 novel voxel-based features for EMCI (early mild cognitive impairment)-HC (healthy control), LMCI (late mild cognitive impairment)-HC, and AD-HC groups, respectively. We applied linear regression model, least absolute shrinkage and selection operator (Lasso), partial least squares (PLS), SVM, and RS-SVM five methods to test and compare the accuracy of these features in these three groups. The prediction accuracy of the AD-HC group using the RS-SVM method was higher than 90%. In addition, we performed functional analysis of the features to explain the biological significance. The experimental results using five machine learning indicate that the identified features are effective for AD and HC classification, the RS-SVM framework has the best classification accuracy, and our strategy can identify important brain regions for AD.
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Affiliation(s)
- Xianglian Meng
- School of Computer Information and Engineering, Changzhou Institute of Technology, Changzhou, China
| | - Yue Wu
- School of Computer Information and Engineering, Changzhou Institute of Technology, Changzhou, China
| | - Wenjie Liu
- School of Computer Information and Engineering, Changzhou Institute of Technology, Changzhou, China
| | - Ying Wang
- School of Computer Science and Engineering, Changshu Institute of Technology, Changshu, China
| | - Zhe Xu
- School of Computer Information and Engineering, Changzhou Institute of Technology, Changzhou, China
| | - Zhuqing Jiao
- School of Computer Science and Artificial Intelligence, Changzhou University, Changzhou, China
- *Correspondence: Zhuqing Jiao,
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Agarwal D, Randall P, White Z, Bisnette B, Dickson J, Allen C, Chamani F, Prakash P, Ade C, Natarajan B. A Non-Invasive Hydration Monitoring Technique Using Microwave Transmission and Data-Driven Approaches. SENSORS (BASEL, SWITZERLAND) 2022; 22:2536. [PMID: 35408154 PMCID: PMC9003514 DOI: 10.3390/s22072536] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/17/2022] [Revised: 03/20/2022] [Accepted: 03/20/2022] [Indexed: 06/14/2023]
Abstract
Dehydration in the human body arises due to inadequate replenishment of fluids. An appropriate level of hydration is essential for optimal functioning of the human body, and complications ranging from mild discomfort to, in severe cases, death, could result from a neglected imbalance in fluid levels. Regular and accurate monitoring of hydration status can provide meaningful information for people operating in stressful environmental conditions, such as athletes, military professionals and the elderly. In this study, we propose a non-invasive hydration monitoring technique employing non-ionizing electromagnetic power in the microwave band to estimate the changes in the water content of the whole body. Specifically, we investigate changes in the attenuation coefficient in the frequency range 2-3.5 GHz between a pair of planar antennas positioned across a participant's arm during various states of hydration. Twenty healthy young adults (10M, 10F) underwent controlled hypohydration and euhydration control bouts. The attenuation coefficient was compared among trials and used to predict changes in body mass. Volunteers lost 1.50±0.44% and 0.49±0.54% body mass during hypohydration and euhydration, respectively. The microwave transmission-based attenuation coefficient (2-3.5 GHz) was accurate in predicting changes in hydration status. The corresponding regression analysis demonstrates that building separate estimation models for dehydration and rehydration phases offer better predictive performance (88%) relative to a common model for both the phases (76%).
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Affiliation(s)
- Deepesh Agarwal
- Department of Electrical and Computer Engineering, Kansas State University, Manhattan, KS 66506, USA; (D.A.); (P.R.); (B.B.); (J.D.); (C.A.); (F.C.); (B.N.)
| | - Philip Randall
- Department of Electrical and Computer Engineering, Kansas State University, Manhattan, KS 66506, USA; (D.A.); (P.R.); (B.B.); (J.D.); (C.A.); (F.C.); (B.N.)
| | - Zachary White
- Department of Kinesiology, Kansas State University, Manhattan, KS 66506, USA; (Z.W.); (C.A.)
| | - Bayleigh Bisnette
- Department of Electrical and Computer Engineering, Kansas State University, Manhattan, KS 66506, USA; (D.A.); (P.R.); (B.B.); (J.D.); (C.A.); (F.C.); (B.N.)
| | - Jenalee Dickson
- Department of Electrical and Computer Engineering, Kansas State University, Manhattan, KS 66506, USA; (D.A.); (P.R.); (B.B.); (J.D.); (C.A.); (F.C.); (B.N.)
| | - Cross Allen
- Department of Electrical and Computer Engineering, Kansas State University, Manhattan, KS 66506, USA; (D.A.); (P.R.); (B.B.); (J.D.); (C.A.); (F.C.); (B.N.)
| | - Faraz Chamani
- Department of Electrical and Computer Engineering, Kansas State University, Manhattan, KS 66506, USA; (D.A.); (P.R.); (B.B.); (J.D.); (C.A.); (F.C.); (B.N.)
| | - Punit Prakash
- Department of Electrical and Computer Engineering, Kansas State University, Manhattan, KS 66506, USA; (D.A.); (P.R.); (B.B.); (J.D.); (C.A.); (F.C.); (B.N.)
| | - Carl Ade
- Department of Kinesiology, Kansas State University, Manhattan, KS 66506, USA; (Z.W.); (C.A.)
| | - Balasubramaniam Natarajan
- Department of Electrical and Computer Engineering, Kansas State University, Manhattan, KS 66506, USA; (D.A.); (P.R.); (B.B.); (J.D.); (C.A.); (F.C.); (B.N.)
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Clinical Text Data Categorization and Feature Extraction Using Medical-Fissure Algorithm and Neg-Seq Algorithm. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:5759521. [PMID: 35295284 PMCID: PMC8920702 DOI: 10.1155/2022/5759521] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/07/2022] [Revised: 01/22/2022] [Accepted: 01/27/2022] [Indexed: 12/19/2022]
Abstract
A large amount of patient information has been gathered in Electronic Health Records (EHRs) concerning their conditions. An EHR, as an unstructured text document, serves to maintain health by identifying, treating, and curing illnesses. In this research, the technical complexities in extracting the clinical text data are removed by using machine learning and natural language processing techniques, in which an unstructured clinical text data with low data quality is recognized by Halve Progression, which uses Medical-Fissure Algorithm which provides better data quality and makes diagnosis easier by using a cross-validation approach. Moreover, to enhance the accuracy in extracting and mapping clinical text data, Clinical Data Progression uses Neg-Seq Algorithm in which the redundancy in clinical text data is removed. Finally, the extracted clinical text data is stored in the cloud with a secret key to enhance security. The proposed technique improves the data quality and provides an efficient data extraction with high accuracy of 99.6%.
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Predictive classification of Alzheimer’s disease using brain imaging and genetic data. Sci Rep 2022; 12:2405. [PMID: 35165327 PMCID: PMC8844076 DOI: 10.1038/s41598-022-06444-9] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2021] [Accepted: 01/24/2022] [Indexed: 02/06/2023] Open
Abstract
For now, Alzheimer’s disease (AD) is incurable. But if it can be diagnosed early, the correct treatment can be used to delay the disease. Most of the existing research methods use single or multi-modal imaging features for prediction, relatively few studies combine brain imaging with genetic features for disease diagnosis. In order to accurately identify AD, healthy control (HC) and the two stages of mild cognitive impairment (MCI: early MCI, late MCI) combined with brain imaging and genetic characteristics, we proposed an integrated Fisher score and multi-modal multi-task feature selection research method. We learned first genetic features with Fisher score to perform dimensionality reduction in order to solve the problem of the large difference between the feature scales of genetic and brain imaging. Then we learned the potential related features of brain imaging and genetic data, and multiplied the selected features with the learned weight coefficients. Through the feature selection program, five imaging and five genetic features were selected to achieve an average classification accuracy of 98% for HC and AD, 82% for HC and EMCI, 86% for HC and LMCI, 80% for EMCI and LMCI, 88% for EMCI and AD, and 72% for LMCI and AD. Compared with only using imaging features, the classification accuracy has been improved to a certain extent, and a set of interrelated features of brain imaging phenotypes and genetic factors were selected.
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Zhang Y, Xi Z, Zheng J, Shi H, Jiao Z. GWLS: A Novel Model for Predicting Cognitive Function Scores in Patients With End-Stage Renal Disease. Front Aging Neurosci 2022; 14:834331. [PMID: 35185530 PMCID: PMC8850953 DOI: 10.3389/fnagi.2022.834331] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2021] [Accepted: 01/05/2022] [Indexed: 01/03/2023] Open
Abstract
The scores of the cognitive function of patients with end-stage renal disease (ESRD) are highly subjective, which tend to affect the results of clinical diagnosis. To overcome this issue, we proposed a novel model to explore the relationship between functional magnetic resonance imaging (fMRI) data and clinical scores, thereby predicting cognitive function scores of patients with ESRD. The model incorporated three parts, namely, graph theoretic algorithm (GTA), whale optimization algorithm (WOA), and least squares support vector regression machine (LSSVRM). It was called GTA-WOA-LSSVRM or GWLS for short. GTA was adopted to calculate the area under the curve (AUC) of topological parameters, which were extracted as the features from the functional networks of the brain. Then, the statistical method and Pearson correlation analysis were used to select the features. Finally, the LSSVRM was built according to the selected features to predict the cognitive function scores of patients with ESRD. Besides, WOA was introduced to optimize the parameters in the LSSVRM kernel function to improve the prediction accuracy. The results validated that the prediction accuracy obtained by GTA-WOA-LSSVRM was higher than several comparable models, such as GTA-SVRM, GTA-LSSVRM, and GTA-WOA-SVRM. In particular, the root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) between the predicted scores and the actual scores of patients with ESRD were 0.92, 0.88, and 4.14%, respectively. The proposed method can more accurately predict the cognitive function scores of ESRD patients and thus helps to understand the pathophysiological mechanism of cognitive dysfunction associated with ESRD.
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Affiliation(s)
- Yutao Zhang
- School of Microelectronics and Control Engineering, Changzhou University, Changzhou, China
| | - Zhengtao Xi
- School of Microelectronics and Control Engineering, Changzhou University, Changzhou, China
| | - Jiahui Zheng
- Department of Radiology, Changzhou Second People’s Hospital Affiliated to Nanjing Medical University, Changzhou, China
| | - Haifeng Shi
- Department of Radiology, Changzhou Second People’s Hospital Affiliated to Nanjing Medical University, Changzhou, China
- *Correspondence: Haifeng Shi,
| | - Zhuqing Jiao
- School of Microelectronics and Control Engineering, Changzhou University, Changzhou, China
- School of Computer Science and Artificial Intelligence, Changzhou University, Changzhou, China
- Zhuqing Jiao,
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Liu Z, Lu H, Pan X, Xu M, Lan R, Luo X. Diagnosis of Alzheimer’s disease via an attention-based multi-scale convolutional neural network. Knowl Based Syst 2022. [DOI: 10.1016/j.knosys.2021.107942] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
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Liu W, Cao L, Luo H, Wang Y. Research on Pathogenic Hippocampal Voxel Detection in Alzheimer's Disease Using Clustering Genetic Random Forest. Front Psychiatry 2022; 13:861258. [PMID: 35463515 PMCID: PMC9022175 DOI: 10.3389/fpsyt.2022.861258] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/24/2022] [Accepted: 02/22/2022] [Indexed: 11/16/2022] Open
Abstract
Alzheimer's disease (AD) is an age-related neurological disease, which is closely associated with hippocampus, and subdividing the hippocampus into voxels can capture subtle signals that are easily missed by region of interest (ROI) methods. Therefore, studying interpretable associations between voxels can better understand the effect of voxel set on the hippocampus and AD. In this study, by analyzing the hippocampal voxel data, we propose a novel method based on clustering genetic random forest to identify the important voxels. Specifically, we divide the left and right hippocampus into voxels to constitute the initial feature set. Moreover, the random forest is constructed using the randomly selected samples and features. The genetic evolution is used to amplify the difference in decision trees and the clustering evolution is applied to generate offspring in genetic evolution. The important voxels are the features that reach the peak classification. The results demonstrate that our method has good classification and stability. Particularly, through biological analysis of the obtained voxel set, we find that they play an important role in AD by affecting the function of the hippocampus. These discoveries demonstrate the contribution of the voxel set to AD.
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Affiliation(s)
- Wenjie Liu
- School of Computer Information and Engineering, Changzhou Institute of Technology, Changzhou, China
| | - Luolong Cao
- College of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin, China
| | - Haoran Luo
- College of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin, China
| | - Ying Wang
- School of Computer Science and Engineering, Changshu Institute of Technology, Suzhou, China
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37
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Zeng A, Rong H, Pan D, Jia L, Zhang Y, Zhao F, Peng S. Discovery of Genetic Biomarkers for Alzheimer's Disease Using Adaptive Convolutional Neural Networks Ensemble and Genome-Wide Association Studies. Interdiscip Sci 2021; 13:787-800. [PMID: 34410590 DOI: 10.1007/s12539-021-00470-3] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2021] [Revised: 07/01/2021] [Accepted: 08/01/2021] [Indexed: 06/13/2023]
Abstract
OBJECTIVE To identify candidate neuroimaging and genetic biomarkers for Alzheimer's disease (AD) and other brain disorders, especially for little-investigated brain diseases, we advocate a data-driven approach which incorporates an adaptive classifier ensemble model acquired by integrating Convolutional Neural Network (CNN) and Ensemble Learning (EL) with Genetic Algorithm (GA), i.e., the CNN-EL-GA method, into Genome-Wide Association Studies (GWAS). METHODS Above all, a large number of CNN models as base classifiers were trained using coronal, sagittal, or transverse magnetic resonance imaging slices, respectively, and the CNN models with strong discriminability were then selected to build a single classifier ensemble with the GA for classifying AD, with the help of the CNN-EL-GA method. While the acquired classifier ensemble exhibited the highest generalization capability, the points of intersection were determined with the most discriminative coronal, sagittal, and transverse slices. Finally, we conducted GWAS on the genotype data and the phenotypes, i.e., the gray matter volumes of the top ten most discriminative brain regions, which contained the ten most points of intersection. RESULTS Six genes of PCDH11X/Y, TPTE2, LOC107985902, MUC16 and LINC01621 as well as Single-Nucleotide Polymorphisms, e.g., rs36088804, rs34640393, rs2451078, rs10496214, rs17016520, rs2591597, rs9352767 and rs5941380, were identified. CONCLUSION This approach overcomes the limitations associated with the impact of subjective factors and dependence on prior knowledge while adaptively achieving more robust and effective candidate biomarkers in a data-driven way. SIGNIFICANCE The approach is promising to facilitate discovering effective candidate genetic biomarkers for brain disorders, as well as to help improve the effectiveness of identified candidate neuroimaging biomarkers for brain diseases.
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Affiliation(s)
- An Zeng
- Faculty of Computer, Guangdong University of Technology, Guangzhou, 510006, People's Republic of China
| | - Huabin Rong
- Faculty of Computer, Guangdong University of Technology, Guangzhou, 510006, People's Republic of China
| | - Dan Pan
- School of Electronics and Information, Guangdong Polytechnic Normal University, Guangzhou, 510665, People's Republic of China.
| | - Longfei Jia
- Faculty of Computer, Guangdong University of Technology, Guangzhou, 510006, People's Republic of China
| | - Yiqun Zhang
- Faculty of Computer, Guangdong University of Technology, Guangzhou, 510006, People's Republic of China
| | - Fengyi Zhao
- Faculty of Computer, Guangdong University of Technology, Guangzhou, 510006, People's Republic of China
| | - Shaoliang Peng
- College of Computer Science and Electronic Engineering, Hunan University, School of Computer Science, National University of Defense Technology, Peng Cheng Lab, Shenzhen, 518000, People's Republic of China.
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Song J, Zheng J, Li P, Lu X, Zhu G, Shen P. An Effective Multimodal Image Fusion Method Using MRI and PET for Alzheimer's Disease Diagnosis. Front Digit Health 2021; 3:637386. [PMID: 34713109 PMCID: PMC8521941 DOI: 10.3389/fdgth.2021.637386] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2020] [Accepted: 02/05/2021] [Indexed: 11/13/2022] Open
Abstract
Alzheimer's disease (AD) is an irreversible brain disease that severely damages human thinking and memory. Early diagnosis plays an important part in the prevention and treatment of AD. Neuroimaging-based computer-aided diagnosis (CAD) has shown that deep learning methods using multimodal images are beneficial to guide AD detection. In recent years, many methods based on multimodal feature learning have been proposed to extract and fuse latent representation information from different neuroimaging modalities including magnetic resonance imaging (MRI) and 18-fluorodeoxyglucose positron emission tomography (FDG-PET). However, these methods lack the interpretability required to clearly explain the specific meaning of the extracted information. To make the multimodal fusion process more persuasive, we propose an image fusion method to aid AD diagnosis. Specifically, we fuse the gray matter (GM) tissue area of brain MRI and FDG-PET images by registration and mask coding to obtain a new fused modality called "GM-PET." The resulting single composite image emphasizes the GM area that is critical for AD diagnosis, while retaining both the contour and metabolic characteristics of the subject's brain tissue. In addition, we use the three-dimensional simple convolutional neural network (3D Simple CNN) and 3D Multi-Scale CNN to evaluate the effectiveness of our image fusion method in binary classification and multi-classification tasks. Experiments on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset indicate that the proposed image fusion method achieves better overall performance than unimodal and feature fusion methods, and that it outperforms state-of-the-art methods for AD diagnosis.
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Affiliation(s)
- Juan Song
- School of Computer Science and Technology, Xidian University, Shaanxi, China
| | - Jian Zheng
- School of Computer Science and Technology, Xidian University, Shaanxi, China
| | - Ping Li
- Data and Virtual Research Room, Shanghai Broadband Network Center, Shanghai, China
| | - Xiaoyuan Lu
- Data and Virtual Research Room, Shanghai Broadband Network Center, Shanghai, China
| | - Guangming Zhu
- School of Computer Science and Technology, Xidian University, Shaanxi, China
| | - Peiyi Shen
- School of Computer Science and Technology, Xidian University, Shaanxi, China
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Zhang S, Li H, Xu Q, Wang C, Li X, Sun J, Wang Y, Sun T, Wang Q, Zhang C, Wang J, Jia X, Sun X. Regional homogeneity alterations in multi-frequency bands in tension-type headache: a resting-state fMRI study. J Headache Pain 2021; 22:129. [PMID: 34711175 PMCID: PMC8555254 DOI: 10.1186/s10194-021-01341-4] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2021] [Accepted: 10/11/2021] [Indexed: 01/03/2023] Open
Abstract
OBJECTIVES In this study, we aimed to investigate the spontaneous neural activity in the conventional frequency band (0.01-0.08 Hz) and two sub-frequency bands (slow-4: 0.027-0.073 Hz, and slow-5: 0.01-0.027 Hz) in tension-type headache (TTH) patients with regional homogeneity (ReHo) analyses. METHODS Thirty-eight TTH patients and thirty-eight healthy controls (HCs) underwent resting-state functional magnetic resonance imaging (RS-fMRI) scanning to investigate abnormal spontaneous neural activity using ReHo analysis in conventional frequency band (0.01-0.08 Hz) and two sub-frequency bands (slow-4: 0.027-0.073 Hz and slow-5: 0.01-0.027 Hz). RESULTS In comparison with the HC group, patients with TTH exhibited ReHo increases in the right medial superior frontal gyrus in the conventional frequency band (0.01-0.08 Hz). The between group differences in the slow-5 band (0.01-0.027 Hz) highly resembled the differences in the conventional frequency band (0.01-0.08 Hz); even the voxels with increased ReHo were spatially more extensive, including the right medial superior frontal gyrus and the middle frontal gyrus. In contrast, no region showed significant between-group differences in the slow-4 band (0.027-0.073 Hz). The correlation analyses showed no correlation between the ReHo values in TTH patients and VAS scores, course of disease and number of seizures per month in conventional band (0.01-0.08 Hz), slow-4 band (0.027-0.073 Hz), as well as in slow-5 band (0.01-0.027 Hz). CONCLUSIONS The results showed that the superior frontal gyrus and middle frontal gyrus were involved in the integration and processing of pain signals. In addition, the abnormal spontaneous neural activity in TTH patients was frequency-specific. Namely, slow-5 band (0.01-0.027 Hz) might contain additional useful information in comparison to slow-4 band (0.027-0.073 Hz). This preliminary exploration might provide an objective imaging basis for the understanding of the pathophysiological mechanism of TTH.
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Affiliation(s)
- Shuxian Zhang
- Affiliated Hospital of Weifang Medical University, Weifang, Shandong Province, China
| | - Huayun Li
- College of Teacher Education, Zhejiang Normal University, Jinhua, China
- Key Laboratory of Intelligent Education Technology and Application of Zhejiang Province, Zhejiang Normal University, Jinhua, China
| | - Qinyan Xu
- Affiliated Hospital of Weifang Medical University, Weifang, Shandong Province, China
| | - Chao Wang
- Affiliated Hospital of Weifang Medical University, Weifang, Shandong Province, China
| | - Xue Li
- School of Information and Electronics Technology, Jiamusi University, Jiamusi, China
| | - Jiawei Sun
- School of Information and Electronics Technology, Jiamusi University, Jiamusi, China
| | - Yaqi Wang
- Weifang Hospital of Traditional Chinese Medicine, Weifang, Shandong Province, China
| | - Tong Sun
- Weifang Hospital of Traditional Chinese Medicine, Weifang, Shandong Province, China
| | - Qianqian Wang
- College of Teacher Education, Zhejiang Normal University, Jinhua, China
| | - Chengcheng Zhang
- Department of Medical Imaging, Weifang Medical University, Weifang, Shandong Province, China
| | - Jili Wang
- Department of Medical Imaging, Weifang Medical University, Weifang, Shandong Province, China
| | - Xize Jia
- Centre for Cognition and Brain disorders, the Affiliated Hospital of Hangzhou Normal University, Hangzhou, China.
| | - Xihe Sun
- Affiliated Hospital of Weifang Medical University, Weifang, Shandong Province, China.
- Department of Medical Imaging, Weifang Medical University, Weifang, Shandong Province, China.
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Jiao Z, Gao P, Ji Y, Shi H. Integration and Segregation of Dynamic Functional Connectivity States for Mild Cognitive Impairment Revealed by Graph Theory Indicators. CONTRAST MEDIA & MOLECULAR IMAGING 2021; 2021:6890024. [PMID: 34366726 PMCID: PMC8313367 DOI: 10.1155/2021/6890024] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/19/2021] [Revised: 06/14/2021] [Accepted: 07/01/2021] [Indexed: 02/06/2023]
Abstract
Mild cognitive impairment (MCI) is an intermediate stage between normal aging and dementia. Researchers tend to discuss its early state (early MCI, eMCI) due to its high conversion rate of dementia and poor treatment effect in the middle and late stages. Currently, the research on the disease evolution of the brain functional networks of patients with MCI has gradually become a research hotspot. In this study, we compare the differences in dynamic functional connectivity among eMCI, late MCI (lMCI), and normal control (NC) groups, and their graph theory indicators reveal the integration and segregation of functional connectivity states. Firstly, dynamic functional network windows were constructed based on the sliding time window method, and then these window samples were clustered by k-means to extract the functional connectivity states. The differences in the three groups were compared by analyzing the graph theory indicators, such as the participation coefficient, module degree distribution, clustering coefficient, global efficiency, and local efficiency, which distinguish the functional connectivity states. The results reveal that the NC group has the strongest integration and segregation, followed by the eMCI group, and the lMCI group has the weakest integration and segregation. We conclude that with the aggravation of MCI, the integration and segregation of dynamic functional connectivity states tend to decline. The results also reflect that the lMCI group has significantly more brain functional connections in some states, such as IPL.L-MTG.R and DCG.R-SMG.L, than the eMCI group, while the lMCI group has significantly less OLF.L-SPG.L than the NC group.
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Affiliation(s)
- Zhuqing Jiao
- School of Computer Science and Artificial Intelligence, Changzhou University, Changzhou 213164, China
- School of Microelectronics and Control Engineering, Changzhou University, Changzhou 213164, China
| | - Peng Gao
- School of Computer Science and Artificial Intelligence, Changzhou University, Changzhou 213164, China
| | - Yixin Ji
- School of Computer Science and Artificial Intelligence, Changzhou University, Changzhou 213164, China
| | - Haifeng Shi
- Department of Radiology, Changzhou Second People's Hospital Affiliated to Nanjing Medical University, Changzhou 213003, China
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41
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Zhang Z, Cui L, Huang Y, Chen Y, Li Y, Guo Q. Changes of Regional Neural Activity Homogeneity in Preclinical Alzheimer's Disease: Compensation and Dysfunction. Front Neurosci 2021; 15:646414. [PMID: 34220418 PMCID: PMC8248345 DOI: 10.3389/fnins.2021.646414] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2020] [Accepted: 05/26/2021] [Indexed: 12/23/2022] Open
Abstract
INTRODUCTION Subjective cognitive decline (SCD) is the preclinical stage of Alzheimer's disease and may develop into amnestic mild cognitive impairment (aMCI). Finding suitable biomarkers is the key to accurately identifying SCD. Previous resting-state functional magnetic resonance imaging (rs-fMRI) studies on SCD patients showed functional connectivity disorders. Our goal was to explore whether local neurological homogeneity changes in SCD patients, the relationship between these changes and cognitive function, and similarities of neurological homogeneity changes between SCD and aMCI patients. MATERIALS AND METHODS 37 cases of the healthy control (HC) group, 39 cases of the SCD group, and 28 cases of the aMCI group were included. Participants underwent rs-fMRI examination and a set of neuropsychological test batteries. Regional homogeneity (ReHo) was calculated and compared between groups. ReHo values were extracted from meaningful regions in the SCD group, and the correlation between ReHo values with the performance of neuropsychological tests was analyzed. RESULTS Our results showed significant changes in the ReHo among groups. In the SCD group compared with the HC group, part of the parietal lobe, frontal lobe, and occipital lobe showed decreased ReHo, and the temporal lobe, part of the parietal lobe and the frontal lobe showed increased ReHo. The increased area of ReHo was negatively correlated with the decreased area, and was related to decrease on multiple neuropsychological tests performance. Simultaneously, the changed areas of ReHo in SCD patients are similar to aMCI patients, while aMCI group's neuropsychological test performance was significantly lower than that of the SCD group. CONCLUSION There are significant changes in local neurological homogeneity in SCD patients, and related to the decline of cognitive function. The increase of neurological homogeneity in the temporal lobe and adjacent area is negatively correlated with cognitive function, reflecting compensation for local neural damage. These changes in local neurological homogeneity in SCD patients are similar to aMCI patients, suggesting similar neuropathy in these two stages. However, the aMCI group's cognitive function was significantly worse than that of the SCD group, suggesting that this compensation is limited. In summary, regional neural activity homogeneity may be a potential biomarker for identifying SCD and measuring the disease severity.
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Affiliation(s)
- Zhen Zhang
- Department of Gerontology, Shanghai Jiao Tong University Affiliated Sixth People’s Hospital, Shanghai, China
| | - Liang Cui
- Department of Gerontology, Shanghai Jiao Tong University Affiliated Sixth People’s Hospital, Shanghai, China
| | - Yanlu Huang
- Department of Gerontology, Shanghai Jiao Tong University Affiliated Sixth People’s Hospital, Shanghai, China
| | - Yu Chen
- The Brain Cognition and Brain Disease Institute, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen–Hong Kong Institute of Brain Science-Shenzhen Fundamental Research Institutions, Shenzhen, China
| | - Yuehua Li
- Department of Radiology, Shanghai Jiao Tong University Affiliated Sixth People’s Hospital, Shanghai, China
| | - Qihao Guo
- Department of Gerontology, Shanghai Jiao Tong University Affiliated Sixth People’s Hospital, Shanghai, China
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Cui L, Zhang Z, Zac Lo CY, Guo Q. Local Functional MR Change Pattern and Its Association With Cognitive Function in Objectively-Defined Subtle Cognitive Decline. Front Aging Neurosci 2021; 13:684918. [PMID: 34177559 PMCID: PMC8232526 DOI: 10.3389/fnagi.2021.684918] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2021] [Accepted: 05/10/2021] [Indexed: 11/13/2022] Open
Abstract
Introduction: To identify individuals with preclinical cognitive impairment, researchers proposed the concept of objectively-defined subtle cognitive decline (Obj-SCD). However, it is not clear whether Obj-SCD has characteristic brain function changes. In this study, we aimed at exploring the changing pattern of brain function activity in Obj-SCD individuals and the similarities and differences with mild cognitive impairments (MCI). Method: 37 healthy control individuals, 25 Obj-SCD individuals (with the impairment in memory and language domain), and 28 aMCI individuals were included. Resting-state fMRI and neuropsychological tests were performed. fALFF was used to reflect the local functional activity and compared between groups. Finally, we analyzed the correlation between the fALFF values of significantly changed regions and neuropsychological performance. Results: We found similar functional activity enhancements in some local brain regions in the Obj-SCD and aMCI groups, including the left orbital part of the inferior frontal gyrus and the left median cingulate and paracingulate gyri. However, some changes in local functional activities of the Obj-SCD group showed different patterns from the aMCI group. Compared with healthy control (HC), the Obj-SCD group showed increased local functional activity in the right middle occipital gyrus, decreased local functional activity in the left precuneus and the left inferior temporal gyrus. In the Obj-SCD group, in normal band, the fALFF value of the right middle occipital gyrus was significantly negatively correlated with Mini-Mental State Examination (MMSE) score (r = -0.450, p = 0.024) and Animal Verbal Fluency Test (AFT) score (r = -0.402, p = 0.046); the left inferior temporal gyrus was significantly positively correlated with MMSE score (r = 0.588, p = 0.002). In slow-4 band, the fALFF value of the left precuneus was significantly positively correlated with MMSE score (r = 0.468, p = 0.018) and AFT score (r = 0.600, p = 0.002). In the aMCI group, the fALFF value of the left orbital part of the inferior frontal gyrus was significantly positively correlated with Auditory Verbal Learning Test (AVLT) long delay cued recall score (r = 0.506, p = 0.006). Conclusion: The Obj-SCD group showed a unique changing pattern; the functional changes of different brain regions have a close but different correlation with cognitive impairment, indicating that there may be a complex pathological basis inside. This suggests that Obj-SCD may be a separate stage of cognitive decline before aMCI and is helpful to the study of preclinical cognitive decline.
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Affiliation(s)
- Liang Cui
- Department of Gerontology, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai, China
| | - Zhen Zhang
- Department of Gerontology, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai, China
| | - Chun-Yi Zac Lo
- Institute of Science and Technology for Brain Inspired Intelligence, Fudan University, Shanghai, China
| | - Qihao Guo
- Department of Gerontology, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai, China
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Zheng X, Sun J, Lv Y, Wang M, Du X, Jia X, Ma J. Frequency-specific alterations of the resting-state BOLD signals in nocturnal enuresis: an fMRI Study. Sci Rep 2021; 11:12042. [PMID: 34103549 PMCID: PMC8187680 DOI: 10.1038/s41598-021-90546-3] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2020] [Accepted: 05/07/2021] [Indexed: 02/05/2023] Open
Abstract
Resting state functional magnetic resonance imaging studies of nocturnal enuresis have focused primarily on regional metrics in the blood oxygen level dependent (BOLD) signal ranging from 0.01 to 0.08 Hz. However, it remains unclear how local metrics show in sub-frequency band. 129 children with nocturnal enuresis (NE) and 37 healthy controls were included in this study. The patients were diagnosed by the pediatricians in Shanghai Children's Medical Center affiliated to Shanghai Jiao Tong University School of Medicine, according to the criteria from International Children's Continence Society (ICCS). Questionnaires were used to evaluate the symptoms of enuresis and completed by the participants. In this study, fALFF, ReHo and PerAF were calculated within five different frequency bands: typical band (0.01-0.08 Hz), slow-5 (0.01-0.027 Hz), slow-4 (0.027-0.073 Hz), slow-3 (0.073-0.198 Hz), and slow-2 (0.198-0.25 Hz). In the typical band, ReHo increased in the left insula and the right thalamus, while fALFF decreased in the right insula in children with NE. Besides, PerAF was increased in the right middle temporal gyrus in these children. The results regarding ReHo, fALFF and PerAF in the typical band was similar to those in slow-5 band, respectively. A correlation was found between the PerAF value of the right middle temporal gyrus and scores of the urinary intention-related wakefulness. Results in other bands were either negative or in white matter. NE children might have abnormal intrinsic neural oscillations mainly on slow-5 bands.
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Affiliation(s)
- Xiangyu Zheng
- Department of Developmental and Behavioral Pediatrics, Shanghai Children's Medical Center, School of Medicine, Shanghai Jiao Tong University, 1678 Dong-Fang Road, Shanghai, 200127, China
| | - Jiawei Sun
- School of Information and Electronics Technology, Jiamusi University, Jiamusi, Heilongjiang, China
| | - Yating Lv
- Institute of Psychological Sciences, Hangzhou Normal University, Hangzhou, 311121, Zhejiang, China
- Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou, 311121, China
| | - Mengxing Wang
- College of Medical Imaging, Shanghai University of Medicine & Health Sciences, Shanghai, 201318, China
| | - Xiaoxia Du
- Department of Physics, Shanghai Key Laboratory of Magnetic Resonance, East China Normal University, 3663 North Zhong-Shan Road, Shanghai, 200062, China
| | - Xize Jia
- Institute of Psychological Sciences, Hangzhou Normal University, Hangzhou, 311121, Zhejiang, China.
- Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou, 311121, China.
| | - Jun Ma
- Department of Developmental and Behavioral Pediatrics, Shanghai Children's Medical Center, School of Medicine, Shanghai Jiao Tong University, 1678 Dong-Fang Road, Shanghai, 200127, China.
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44
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Hippocampal Subregion and Gene Detection in Alzheimer's Disease Based on Genetic Clustering Random Forest. Genes (Basel) 2021; 12:genes12050683. [PMID: 34062866 PMCID: PMC8147351 DOI: 10.3390/genes12050683] [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: 03/24/2021] [Revised: 04/29/2021] [Accepted: 04/29/2021] [Indexed: 01/18/2023] Open
Abstract
The distinguishable subregions that compose the hippocampus are differently involved in functions associated with Alzheimer's disease (AD). Thus, the identification of hippocampal subregions and genes that classify AD and healthy control (HC) groups with high accuracy is meaningful. In this study, by jointly analyzing the multimodal data, we propose a novel method to construct fusion features and a classification method based on the random forest for identifying the important features. Specifically, we construct the fusion features using the gene sequence and subregions correlation to reduce the diversity in same group. Moreover, samples and features are selected randomly to construct a random forest, and genetic algorithm and clustering evolutionary are used to amplify the difference in initial decision trees and evolve the trees. The features in resulting decision trees that reach the peak classification are the important "subregion gene pairs". The findings verify that our method outperforms well in classification performance and generalization. Particularly, we identified some significant subregions and genes, such as hippocampus amygdala transition area (HATA), fimbria, parasubiculum and genes included RYR3 and PRKCE. These discoveries provide some new candidate genes for AD and demonstrate the contribution of hippocampal subregions and genes to AD.
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45
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Cui L, Chen K, Huang L, Sun J, Lv Y, Jia X, Guo Q. Changes in local brain function in mild cognitive impairment due to semantic dementia. CNS Neurosci Ther 2021; 27:587-602. [PMID: 33650764 PMCID: PMC8025655 DOI: 10.1111/cns.13621] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2020] [Revised: 01/24/2021] [Accepted: 01/28/2021] [Indexed: 11/30/2022] Open
Abstract
AIMS Mild cognitive impairment due to semantic dementia represents the preclinical stage, involving cognitive decline dominated by semantic impairment below the semantic dementia standard. Therefore, studying mild cognitive impairment due to semantic dementia may identify changes in patients before progression to dementia. However, whether changes in local functional activity occur in preclinical stages of semantic dementia remains unknown. Here, we explored local functional changes in patients with mild cognitive impairment due to semantic dementia using resting-state functional MRI. METHODS We administered a battery of neuropsychological tests to twenty-two patients with mild cognitive impairment due to semantic dementia (MCI-SD group) and nineteen healthy controls (HC group). We performed structural MRI to compare gray matter volumes, and resting-state functional MRI with multiple sub-bands and indicators to evaluate functional activity. RESULTS Neuropsychological tests revealed a significant decline in semantic performance in the MCI-SD group, but no decline in other cognitive domains. Resting-state functional MRI revealed local functional changes in multiple brain regions in the MCI-SD group, distributed in different sub-bands and indicators. In the normal band, local functional changes were only in the gray matter atrophic area. In the other sub-bands, more regions with local functional changes outside atrophic areas were found across various indicators. Among these, the degree centrality of the left precuneus in the MCI-SD group was positively correlated with general semantic tasks (oral sound naming, word-picture verification). CONCLUSION Our study revealed local functional changes in mild cognitive impairment due to semantic dementia, some of which were located outside the atrophic gray matter. Driven by functional connectivity changes, the left precuneus might play a role in preclinical semantic dementia. The study proved the value of frequency-dependent sub-bands, especially the slow-2 and slow-3 sub-bands.
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Affiliation(s)
- Liang Cui
- Department of GerontologyShanghai Jiao Tong University Affiliated Sixth People’s HospitalShanghaiChina
| | - Keliang Chen
- Department of NeurologyHuashan HospitalFudan UniversityShanghaiChina
| | - Lin Huang
- Department of GerontologyShanghai Jiao Tong University Affiliated Sixth People’s HospitalShanghaiChina
| | - Jiawei Sun
- School of Information and Electronics TechnologyJiamusi UniversityJiamusiChina
| | - Yating Lv
- Institute of Psychological SciencesHangzhou Normal UniversityHangzhouChina
- Zhejiang Key Laboratory for Research in Assessment of Cognitive ImpairmentsHangzhouChina
| | - Xize Jia
- Institute of Psychological SciencesHangzhou Normal UniversityHangzhouChina
- Zhejiang Key Laboratory for Research in Assessment of Cognitive ImpairmentsHangzhouChina
| | - Qihao Guo
- Department of GerontologyShanghai Jiao Tong University Affiliated Sixth People’s HospitalShanghaiChina
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46
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Zhao X, Yao H, Li X. Unearthing of Key Genes Driving the Pathogenesis of Alzheimer's Disease via Bioinformatics. Front Genet 2021; 12:641100. [PMID: 33936168 PMCID: PMC8085575 DOI: 10.3389/fgene.2021.641100] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2020] [Accepted: 03/15/2021] [Indexed: 01/23/2023] Open
Abstract
Alzheimer’s disease (AD) is a neurodegenerative disease with unelucidated molecular pathogenesis. Herein, we aimed to identify potential hub genes governing the pathogenesis of AD. The AD datasets of GSE118553 and GSE131617 were collected from the NCBI GEO database. The weighted gene coexpression network analysis (WGCNA), differential gene expression analysis, and functional enrichment analysis were performed to reveal the hub genes and verify their role in AD. Hub genes were validated by machine learning algorithms. We identified modules and their corresponding hub genes from the temporal cortex (TC), frontal cortex (FC), entorhinal cortex (EC), and cerebellum (CE). We obtained 33, 42, 42, and 41 hub genes in modules associated with AD in TC, FC, EC, and CE tissues, respectively. Significant differences were recorded in the expression levels of hub genes between AD and the control group in the TC and EC tissues (P < 0.05). The differences in the expressions of FCGRT, SLC1A3, PTN, PTPRZ1, and PON2 in the FC and CE tissues among the AD and control groups were significant (P < 0.05). The expression levels of PLXNB1, GRAMD3, and GJA1 were statistically significant between the Braak NFT stages of AD. Overall, our study uncovered genes that may be involved in AD pathogenesis and revealed their potential for the development of AD biomarkers and appropriate AD therapeutics targets.
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Affiliation(s)
- Xingxing Zhao
- Department of Neurology, Bethune Hospital Affiliated to Shanxi Medical University, Taiyuan, China.,Department of Cardiology, First Hospital of Shanxi Medical University, Taiyuan, China
| | - Hongmei Yao
- Department of Cardiology, First Hospital of Shanxi Medical University, Taiyuan, China
| | - Xinyi Li
- Department of Neurology, Bethune Hospital Affiliated to Shanxi Medical University, Taiyuan, China
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47
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Ji Y, Zhang Y, Shi H, Jiao Z, Wang SH, Wang C. Constructing Dynamic Brain Functional Networks via Hyper-Graph Manifold Regularization for Mild Cognitive Impairment Classification. Front Neurosci 2021; 15:669345. [PMID: 33867931 PMCID: PMC8047143 DOI: 10.3389/fnins.2021.669345] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2021] [Accepted: 03/11/2021] [Indexed: 12/15/2022] Open
Abstract
Brain functional networks (BFNs) constructed via manifold regularization (MR) have emerged as a powerful tool in finding new biomarkers for brain disease diagnosis. However, they only describe the pair-wise relationship between two brain regions, and cannot describe the functional interaction between multiple brain regions, or the high-order relationship, well. To solve this issue, we propose a method to construct dynamic BFNs (DBFNs) via hyper-graph MR (HMR) and employ it to classify mild cognitive impairment (MCI) subjects. First, we construct DBFNs via Pearson's correlation (PC) method and remodel the PC method as an optimization model. Then, we use k-nearest neighbor (KNN) algorithm to construct the hyper-graph and obtain the hyper-graph manifold regularizer based on the hyper-graph. We introduce the hyper-graph manifold regularizer and the L1-norm regularizer into the PC-based optimization model to optimize DBFNs and obtain the final sparse DBFNs (SDBFNs). Finally, we conduct classification experiments to classify MCI subjects from normal subjects to verify the effectiveness of our method. Experimental results show that the proposed method achieves better classification performance compared with other state-of-the-art methods, and the classification accuracy (ACC), the sensitivity (SEN), the specificity (SPE), and the area under the curve (AUC) reach 82.4946 ± 0.2827%, 77.2473 ± 0.5747%, 87.7419 ± 0.2286%, and 0.9021 ± 0.0007, respectively. This method expands the MR method and DBFNs with more biological significance. It can effectively improve the classification performance of DBFNs for MCI, and has certain reference value for the research and auxiliary diagnosis of Alzheimer's disease (AD).
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Affiliation(s)
- Yixin Ji
- School of Microelectronics and Control Engineering, Changzhou University, Changzhou, China
- School of Computer Science and Artificial Intelligence, Changzhou University, Changzhou, China
| | - Yutao Zhang
- School of Microelectronics and Control Engineering, Changzhou University, Changzhou, China
| | - Haifeng Shi
- Department of Radiology, Changzhou Second People’s Hospital Affiliated to Nanjing Medical University, Changzhou, China
| | - Zhuqing Jiao
- School of Microelectronics and Control Engineering, Changzhou University, Changzhou, China
- School of Computer Science and Artificial Intelligence, Changzhou University, Changzhou, China
| | - Shui-Hua Wang
- School of Informatics, University of Leicester, Leicester, United Kingdom
| | - Chuang Wang
- School of Medicine, Ningbo University, Ningbo, China
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48
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Du L, Zhang J, Liu F, Wang H, Guo L, Han J, Disease Neuroimaging Initiative TA. Identifying associations among genomic, proteomic and imaging biomarkers via adaptive sparse multi-view canonical correlation analysis. Med Image Anal 2021; 70:102003. [PMID: 33735757 DOI: 10.1016/j.media.2021.102003] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2020] [Revised: 02/10/2021] [Accepted: 02/15/2021] [Indexed: 12/13/2022]
Abstract
To uncover the genetic underpinnings of brain disorders, brain imaging genomics usually jointly analyzes genetic variations and imaging measurements. Meanwhile, other biomarkers such as proteomic expressions can also carry valuable complementary information. Therefore, it is necessary yet challenging to investigate the underlying relationships among genetic variations, proteomic expressions, and neuroimaging measurements, which stands a chance of gaining new insights into the pathogenesis of brain disorders. Given multiple types of biomarkers, using sparse multi-view canonical correlation analysis (SMCCA) and its variants to identify the multi-way associations is straightforward. However, due to the gradient domination issue caused by the naive fusion of multiple SCCA objectives, SMCCA is suboptimal. In this paper, we proposed two adaptive SMCCA (AdaSMCCA) methods, i.e. the robustness-aware AdaSMCCA and the uncertainty-aware AdaSMCCA, to analyze the complicated associations among genetic, proteomic, and neuroimaging biomarkers. We also imposed a data-driven feature grouping penalty to the genetic data with aim to uncover the joint inheritance of neighboring genetic variations. An efficient optimization algorithm, which is guaranteed to converge, was provided. Using two state-of-the-art SMCCA as benchmarks, we evaluated robustness-aware AdaSMCCA and uncertainty-aware AdaSMCCA on both synthetic data and real neuroimaging, proteomics, and genetic data. Both proposed methods obtained higher associations and cleaner canonical weight profiles than comparison methods, indicating their promising capability for association identification and feature selection. In addition, the subsequent analysis showed that the identified biomarkers were related to Alzheimer's disease, demonstrating the power of our methods in identifying multi-way bi-multivariate associations among multiple heterogeneous biomarkers.
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Affiliation(s)
- Lei Du
- School of Automation, Northwestern Polytechnical University, Xi'an 710072, China.
| | - Jin Zhang
- School of Automation, Northwestern Polytechnical University, Xi'an 710072, China
| | - Fang Liu
- School of Automation, Northwestern Polytechnical University, Xi'an 710072, China
| | - Huiai Wang
- School of Automation, Northwestern Polytechnical University, Xi'an 710072, China
| | - Lei Guo
- School of Automation, Northwestern Polytechnical University, Xi'an 710072, China
| | - Junwei Han
- School of Automation, Northwestern Polytechnical University, Xi'an 710072, China
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Chang CH, Lin CH, Liu CY, Huang CS, Chen SJ, Lin WC, Yang HT, Lane HY. Plasma d-glutamate levels for detecting mild cognitive impairment and Alzheimer's disease: Machine learning approaches. J Psychopharmacol 2021; 35:265-272. [PMID: 33586518 DOI: 10.1177/0269881120972331] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
BACKGROUND d-glutamate, which is involved in N-methyl-d-aspartate receptor modulation, may be associated with cognitive ageing. AIMS This study aimed to use peripheral plasma d-glutamate levels to differentiate patients with mild cognitive impairment (MCI) and Alzheimer's disease (AD) from healthy individuals and to evaluate its prediction ability using machine learning. METHODS Overall, 31 healthy controls, 21 patients with MCI and 133 patients with AD were recruited. Serum d-glutamate levels were measured using high-performance liquid chromatography (HPLC). Cognitive deficit severity was assessed using the Clinical Dementia Rating scale and the Mini-Mental Status Examination (MMSE). We employed four machine learning algorithms (support vector machine, logistic regression, random forest and naïve Bayes) to build an optimal predictive model to distinguish patients with MCI or AD from healthy controls. RESULTS The MCI and AD groups had lower plasma d-glutamate levels (1097.79 ± 283.99 and 785.10 ± 720.06 ng/mL, respectively) compared to healthy controls (1620.08 ± 548.80 ng/mL). The naïve Bayes model and random forest model appeared to be the best models for determining MCI and AD susceptibility, respectively (area under the receiver operating characteristic curve: 0.8207 and 0.7900; sensitivity: 0.8438 and 0.6997; and specificity: 0.8158 and 0.9188, respectively). The total MMSE score was positively correlated with d-glutamate levels (r = 0.368, p < 0.001). Multivariate regression analysis indicated that d-glutamate levels were significantly associated with the total MMSE score (B = 0.003, 95% confidence interval 0.002-0.005, p < 0.001). CONCLUSIONS Peripheral plasma d-glutamate levels were associated with cognitive impairment and may therefore be a suitable peripheral biomarker for detecting MCI and AD. Rapid and cost-effective HPLC for biomarkers and machine learning algorithms may assist physicians in diagnosing MCI and AD in outpatient clinics.
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Affiliation(s)
- Chun-Hung Chang
- Institute of Clinical Medical Science, China Medical University, Taichung, Taiwan.,Department of Psychiatry and Brain Disease Research Centre, China Medical University Hospital, Taichung, Taiwan.,An Nan Hospital, China Medical University, Tainan, Taiwan
| | - Chieh-Hsin Lin
- Institute of Clinical Medical Science, China Medical University, Taichung, Taiwan.,Graduate Institute of Biomedical Sciences, China Medical University, Taichung, Taiwan.,Kaohsiung Chang Gung Memorial Hospital, Chang Gung University College of Medicine, Kaohsiung, Taiwan
| | - Chieh-Yu Liu
- Biostatistical Consulting Lab, Department of Speech Language Pathology and Audiology, National Taipei University of Nursing and Health Sciences, Taipei, Taiwan
| | - Chih-Sheng Huang
- Artificial Intelligence Research and Development Department, ELAN Microelectronics Corporation, Hsinchu, Taiwan
| | - Shaw-Ji Chen
- Department of Psychiatry, Mackay Memorial Hospital Taitung Branch, Taitung, Taiwan.,Department of Medicine, Mackay Medical College, New Taipei, Taiwan
| | - Wen-Cheng Lin
- Department of Medical Informatics, Tzu Chi University, Hualien, Taiwan
| | - Hui-Ting Yang
- School of Food Safety, Taipei Medical University, Taipei, Taiwan
| | - Hsien-Yuan Lane
- Institute of Clinical Medical Science, China Medical University, Taichung, Taiwan.,Department of Psychiatry and Brain Disease Research Centre, China Medical University Hospital, Taichung, Taiwan.,Graduate Institute of Biomedical Sciences, China Medical University, Taichung, Taiwan.,Department of Psychology, College of Medical and Health Sciences, Asia University, Taichung, Taiwan
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50
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Asadi S, Roshan S, Kattan MW. Random forest swarm optimization-based for heart diseases diagnosis. J Biomed Inform 2021; 115:103690. [PMID: 33540075 DOI: 10.1016/j.jbi.2021.103690] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2020] [Revised: 01/21/2021] [Accepted: 01/23/2021] [Indexed: 11/29/2022]
Abstract
Heart disease has been one of the leading causes of death worldwide in recent years. Among diagnostic methods for heart disease, angiography is one of the most common methods, but it is costly and has side effects. Given the difficulty of heart disease prediction, data mining can play an important role in predicting heart disease accurately. In this paper, by combining the multi-objective particle swarm optimization (MOPSO) and Random Forest, a new approach is proposed to predict heart disease. The main goal is to produce diverse and accurate decision trees and determine the (near) optimal number of them simultaneously. In this method, an evolutionary multi-objective approach is used instead of employing a commonly used approach, i.e., bootstrap, feature selection in the Random Forest, and random number selection of training sets. By doing so, different training sets with different samples and features for training each tree are generated. Also, the obtained solutions in Pareto-optimal fronts determine the required number of training sets to build the random forest. By doing so, the random forest's performance can be enhanced, and consequently, the prediction accuracy will be improved. The proposed method's effectiveness is investigated by comparing its performance over six heart datasets with individual and ensemble classifiers. The results suggest that the proposed method with the (near) optimal number of classifiers outperforms the random forest algorithm with different classifiers.
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Affiliation(s)
- Shahrokh Asadi
- Data Mining Laboratory, Department of Engineering, College of Farabi, University of Tehran, Tehran, Iran.
| | - SeyedEhsan Roshan
- Data Mining Laboratory, Department of Engineering, College of Farabi, University of Tehran, Tehran, Iran
| | - Michael W Kattan
- Department of Quantitative Health Sciences, Cleveland Cancer Foundation, Cleveland, OH, United States
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