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Wang H, Zhu Z, Bi H, Jiang Z, Cao Y, Wang S, Zou L. Changes in Community Structure of Brain Dynamic Functional Connectivity States in Mild Cognitive Impairment. Neuroscience 2024; 544:1-11. [PMID: 38423166 DOI: 10.1016/j.neuroscience.2024.02.026] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2023] [Revised: 01/22/2024] [Accepted: 02/24/2024] [Indexed: 03/02/2024]
Abstract
Recent researches have noted many changes of short-term dynamic modalities in mild cognitive impairment (MCI) patients' brain functional networks. In this study, the dynamic functional brain networks of 82 MCI patients and 85 individuals in the normal control (NC) group were constructed using the sliding window method and Pearson correlation. The window size was determined using single-scale time-dependent (SSTD) method. Subsequently, k-means was applied to cluster all window samples, identifying three dynamic functional connectivity (DFC) states. Collective sparse symmetric non-negative matrix factorization (cssNMF) was then used to perform community detection on these states and quantify differences in brain regions. Finally, metrics such as within-community connectivity strength, community strength, and node diversity were calculated for further analysis. The results indicated high similarity between the two groups in state 2, with no significant differences in optimal community quantity and functional segregation (p < 0.05). However, for state 1 and state 3, the optimal community quantity was smaller in MCI patients compared to the NC group. In state 1, MCI patients had lower within-community connectivity strength and overall strength than the NC group, whereas state 3 showed results opposite to state 1. Brain regions with statistical difference included MFG.L, ORBinf.R, STG.R, IFGtriang.L, CUN.L, CUN.R, LING.R, SOG.L, and PCUN.R. This study on DFC states explores changes in the brain functional networks of patients with MCI from the perspective of alterations in the community structures of DFC states. The findings could provide new insights into the pathological changes in the brains of MCI patients.
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Affiliation(s)
- Hongwei Wang
- School of Computer Science and Artificial Intelligence, Aliyun School of Big Data, School of Software, Changzhou University, Changzhou, Jiangsu 213164, China
| | - Zhihao Zhu
- School of Microelectronics and Control Engineering, Changzhou University, Changzhou, Jiangsu 213164, China
| | - Hui Bi
- School of Computer Science and Artificial Intelligence, Aliyun School of Big Data, School of Software, Changzhou University, Changzhou, Jiangsu 213164, China
| | - Zhongyi Jiang
- School of Computer Science and Artificial Intelligence, Aliyun School of Big Data, School of Software, Changzhou University, Changzhou, Jiangsu 213164, China
| | - Yin Cao
- The Affiliated Changzhou Second People's Hospital of Nanjing Medical University, Changzhou, Jiangsu 213164, China
| | - Suhong Wang
- Clinical Psychology, The Third Affiliated Hospital of Soochow University, Juqian Road No. 185, Changzhou, Jiangsu 213164, China
| | - Ling Zou
- School of Microelectronics and Control Engineering, Changzhou University, Changzhou, Jiangsu 213164, China; The Key Laboratory of Brain Machine Collaborative Intelligence Foundation of Zhejiang Province, Hangzhou, Zhejiang 310018, China.
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Xia J, Chen N, Qiu A. Multi-level and joint attention networks on brain functional connectivity for cross-cognitive prediction. Med Image Anal 2023; 90:102921. [PMID: 37666116 DOI: 10.1016/j.media.2023.102921] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2022] [Revised: 05/15/2023] [Accepted: 07/31/2023] [Indexed: 09/06/2023]
Abstract
Deep learning on resting-state functional MRI (rs-fMRI) has shown great success in predicting a single cognition or mental disease. Nevertheless, cognitive functions or mental diseases may share neural mechanisms that can benefit their prediction/classification. We propose a multi-level and joint attention (ML-Joint-Att) network to learn high-order representations of brain functional connectivities that are specific and shared across multiple tasks. We design the ML-Joint-Att network with edge and node convolutional operators, an adaptive inception module, and three attention modules, including network-wise, region-wise, and region-wise joint attention modules. The adaptive inception learns brain functional connectivity at multiple spatial scales. The network-wise and region-wise attention modules take the multi-scale functional connectivities as input and learn features at the network and regional levels for individual tasks. Moreover, the joint attention module is designed as region-wise joint attention to learn shared brain features that contribute to and compensate for the prediction of multiple tasks. We employed the Adolescent Brain Cognitive Development (ABCD) dataset (n =9092) to evaluate the ML-Joint-Att network for the prediction of cognitive flexibility and inhibition. Our experiments demonstrated the usefulness of the three attention modules and identified brain functional connectivities and regions specific and common between cognitive flexibility and inhibition. In particular, the joint attention module can significantly improve the prediction of both cognitive functions. Moreover, leave-one-site cross-validation showed that the ML-Joint-Att network is robust to independent samples obtained from different sites of the ABCD study. Our network outperformed existing machine learning techniques, including Brain Bias Set (BBS), spatio-temporal graph convolution network (ST-GCN), and BrainNetCNN. We demonstrated the generalization of our method to other applications, such as the prediction of fluid intelligence and crystallized intelligence, which also outperformed the ST-GCN and BrainNetCNN.
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Affiliation(s)
- Jing Xia
- Department of Biomedical Engineering, National University of Singapore, Singapore
| | - Nanguang Chen
- Department of Biomedical Engineering, National University of Singapore, Singapore
| | - Anqi Qiu
- Department of Biomedical Engineering, National University of Singapore, Singapore; The N.1 Institute for Health, National University of Singapore, Singapore; NUS (Suzhou) Research Institute, National University of Singapore, China; Institute of Data Science, National University of Singapore, Singapore; Department of Health Technology and Informatics, Hong Kong Polytechnic University, Hong Kong; Department of Biomedical Engineering, the Johns Hopkins University, USA.
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Munro CE, Boyle R, Chen X, Coughlan G, Gonzalez C, Jutten RJ, Martinez J, Orlovsky I, Robinson T, Weizenbaum E, Pluim CF, Quiroz YT, Gatchel JR, Vannini P, Amariglio R. Recent contributions to the field of subjective cognitive decline in aging: A literature review. Alzheimers Dement (Amst) 2023; 15:e12475. [PMID: 37869044 PMCID: PMC10585124 DOI: 10.1002/dad2.12475] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 12/21/2022] [Revised: 06/23/2023] [Accepted: 08/04/2023] [Indexed: 10/24/2023]
Abstract
Subjective cognitive decline (SCD) is defined as self-experienced, persistent concerns of decline in cognitive capacity in the context of normal performance on objective cognitive measures. Although SCD was initially thought to represent the "worried well," these concerns can be linked to subtle brain changes prior to changes in objective cognitive performance and, therefore, in some individuals, SCD may represent the early stages of an underlying neurodegenerative disease process (e.g., Alzheimer's disease). The field of SCD research has expanded rapidly over the years, and this review aims to provide an update on new advances in, and contributions to, the field of SCD in key areas and themes identified by researchers in this field as particularly important and impactful. First, we highlight recent studies examining sociodemographic and genetic risk factors for SCD, including explorations of SCD across racial and ethnic minoritized groups, and examinations of sex and gender considerations. Next, we review new findings on relationships between SCD and in vivo markers of pathophysiology, utilizing neuroimaging and biofluid data, as well as associations between SCD and objective cognitive tests and neuropsychiatric measures. Finally, we summarize recent work on interventions for SCD and areas of future growth in the field of SCD.
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Affiliation(s)
| | - Rory Boyle
- Department of NeurologyMassachusetts General HospitalHarvard Medical SchoolBostonMassachusettsUSA
| | - Xi Chen
- Helen Wills Neuroscience InstituteUniversity of California BerkeleyBerkeleyCaliforniaUSA
| | - Gillian Coughlan
- Department of NeurologyMassachusetts General HospitalHarvard Medical SchoolBostonMassachusettsUSA
| | - Christopher Gonzalez
- Department of PsychologyIllinois Institute of TechnologyChicagoIllinoisUSA
- Department of PsychiatryMassachusetts General HospitalHarvard Medical SchoolBostonMassachusettsUSA
| | - Roos J. Jutten
- Department of NeurologyMassachusetts General HospitalHarvard Medical SchoolBostonMassachusettsUSA
| | - Jairo Martinez
- Department of PsychiatryMassachusetts General HospitalHarvard Medical SchoolBostonMassachusettsUSA
- Department of Psychological and Brain SciencesBoston UniversityBostonMassachusettsUSA
| | - Irina Orlovsky
- Department of Psychological and Brain SciencesUniversity of Massachusetts AmherstAmherstMassachusettsUSA
| | | | - Emma Weizenbaum
- Department of PsychiatryMassachusetts General HospitalHarvard Medical SchoolBostonMassachusettsUSA
| | - Celina F. Pluim
- Brigham and Women's HospitalBostonMassachusettsUSA
- Department of PsychiatryMassachusetts General HospitalHarvard Medical SchoolBostonMassachusettsUSA
- Department of Psychological and Brain SciencesBoston UniversityBostonMassachusettsUSA
| | - Yakeel T. Quiroz
- Department of NeurologyMassachusetts General HospitalHarvard Medical SchoolBostonMassachusettsUSA
- Department of PsychiatryMassachusetts General HospitalHarvard Medical SchoolBostonMassachusettsUSA
| | - Jennifer R. Gatchel
- Department of PsychiatryMassachusetts General HospitalHarvard Medical SchoolBostonMassachusettsUSA
| | - Patrizia Vannini
- Brigham and Women's HospitalBostonMassachusettsUSA
- Department of NeurologyMassachusetts General HospitalHarvard Medical SchoolBostonMassachusettsUSA
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Cao Y, Kuai H, Liang P, Pan JS, Yan J, Zhong N. BNLoop-GAN: a multi-loop generative adversarial model on brain network learning to classify Alzheimer's disease. Front Neurosci 2023; 17:1202382. [PMID: 37424996 PMCID: PMC10326383 DOI: 10.3389/fnins.2023.1202382] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2023] [Accepted: 05/09/2023] [Indexed: 07/11/2023] Open
Abstract
Recent advancements in AI, big data analytics, and magnetic resonance imaging (MRI) have revolutionized the study of brain diseases such as Alzheimer's Disease (AD). However, most AI models used for neuroimaging classification tasks have limitations in their learning strategies, that is batch training without the incremental learning capability. To address such limitations, the systematic Brain Informatics methodology is reconsidered to realize evidence combination and fusion computing with multi-modal neuroimaging data through continuous learning. Specifically, we introduce the BNLoop-GAN (Loop-based Generative Adversarial Network for Brain Network) model, utilizing multiple techniques such as conditional generation, patch-based discrimination, and Wasserstein gradient penalty to learn the implicit distribution of brain networks. Moreover, a multiple-loop-learning algorithm is developed to combine evidence with better sample contribution ranking during training processes. The effectiveness of our approach is demonstrated through a case study on the classification of individuals with AD and healthy control groups using various experimental design strategies and multi-modal brain networks. The BNLoop-GAN model with multi-modal brain networks and multiple-loop-learning can improve classification performance.
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Affiliation(s)
- Yu Cao
- Faculty of Information Technology, Beijing University of Technology, Beijing, China
- Beijing International Collaboration Base on Brain Informatics and Wisdom Services, Beijing, China
| | - Hongzhi Kuai
- Faculty of Engineering, Maebashi Institute of Technology, Maebashi, Gunma, Japan
| | - Peipeng Liang
- School of Psychology and Beijing Key Laboratory of Learning and Cognition, Capital Normal University, Beijing, China
| | - Jeng-Shyang Pan
- College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao, China
| | - Jianzhuo Yan
- Faculty of Information Technology, Beijing University of Technology, Beijing, China
- Beijing International Collaboration Base on Brain Informatics and Wisdom Services, Beijing, China
| | - Ning Zhong
- Beijing International Collaboration Base on Brain Informatics and Wisdom Services, Beijing, China
- Faculty of Engineering, Maebashi Institute of Technology, Maebashi, Gunma, Japan
- School of Psychology and Beijing Key Laboratory of Learning and Cognition, Capital Normal University, Beijing, China
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Fan Y, Liu W, Chen S, Li M, Zhao L, Wu C, Liu H, Zhu M. Association Between High Serum Tetrahydrofolate and Low Cognitive Functions in the United States: A Cross-Sectional Study. J Alzheimers Dis 2022; 89:163-179. [PMID: 35871329 DOI: 10.3233/jad-220058] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Background: The relationship between serum folate status and cognitive functions is still controversial. Objective: To evaluate the association between serum tetrahydrofolate and cognitive functions. Methods: A total of 3,132 participants (60–80 years old) from the 2011–2014 NHANES were included in this cross-sectional study. The primary outcome measure was cognitive function assessment, determined by the Consortium to Establish a Registry for Alzheimer’s Disease Word Learning Test (CERAD-WL), CERAD-Delayed Recall Test (CERAD-DR), Animal Fluency Test (AF), Digit Symbol Substitution Test (DSST), and global cognitive score. Generalized linear model (GLM), multivariate logistic regression models, weighted generalized additive models (GAM), and subgroup analyses were performed to evaluate the association between serum tetrahydrofolate and low cognitive functions. Results: In GLM, and the crude model, model 1, model 2 of multivariate logistic regression models, increased serum tetrahydrofolate was associated with reduced cognitive functions via AF, DSST, CERAD-WL, CERAD-DR, and global cognitive score (p < 0.05). In GAM, the inflection points were 1.1, 2.8, and 2.8 nmol/L tetrahydrofolate, determined by a two-piece wise linear regression model of AF, DSST, and global cognitive score, respectively. Also, in GAM, there were no non-linear relationship between serum tetrahydrofolate and low cognitive functions, as determined by CERAD-WL or CERAD-DR. The results of subgroup analyses found that serum tetrahydrofolate levels and reduced cognitive functions as determined by AF had significant interactions for age and body mass index. The association between high serum tetrahydrofolate level and reduced cognitive functions as determined using DSST, CERAD-WL, CERAD-DR, or global cognitive score had no interaction with the associations between cognition and gender, or age, or so on. Conclusion: High serum tetrahydrofolate level is associated with significantly reduced cognitive function.
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Affiliation(s)
- Yaohua Fan
- Shenzhen Hospital of Integrated Traditional Chinese and Western Medicine, Guangzhou University of Chinese Medicine, Shenzhen, China
| | - Wen Liu
- Department of OphthalmologyGuangzhou Women and Children’s Medical Center, Guangzhou Medical University, Guangzhou, China
| | - Si Chen
- The First Clinical College, Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Mengzhu Li
- Shenzhen Hospital of Integrated Traditional Chinese and Western Medicine, Guangzhou University of Chinese Medicine, Shenzhen, China
| | - Lijun Zhao
- Shenzhen Hospital of Integrated Traditional Chinese and Western Medicine, Guangzhou University of Chinese Medicine, Shenzhen, China
| | - Chunxiao Wu
- Shenzhen Hospital of Integrated Traditional Chinese and Western Medicine, Guangzhou University of Chinese Medicine, Shenzhen, China
| | - Helu Liu
- Shenzhen Hospital of Integrated Traditional Chinese and Western Medicine, Guangzhou University of Chinese Medicine, Shenzhen, China
| | - Meiling Zhu
- Shenzhen Hospital of Integrated Traditional Chinese and Western Medicine, Guangzhou University of Chinese Medicine, Shenzhen, China
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Meng X, Liu J, Fan X, Bian C, Wei Q, Wang Z, Liu W, Jiao Z. Multi-Modal Neuroimaging Neural Network-Based Feature Detection for Diagnosis of Alzheimer’s Disease. Front Aging Neurosci 2022; 14:911220. [PMID: 35651528 PMCID: PMC9149574 DOI: 10.3389/fnagi.2022.911220] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2022] [Accepted: 04/19/2022] [Indexed: 11/29/2022] Open
Abstract
Alzheimer’s disease (AD) is a neurodegenerative brain disease, and it is challenging to mine features that distinguish AD and healthy control (HC) from multiple datasets. Brain network modeling technology in AD using single-modal images often lacks supplementary information regarding multi-source resolution and has poor spatiotemporal sensitivity. In this study, we proposed a novel multi-modal LassoNet framework with a neural network for AD-related feature detection and classification. Specifically, data including two modalities of resting-state functional magnetic resonance imaging (rs-fMRI) and diffusion tensor imaging (DTI) were adopted for predicting pathological brain areas related to AD. The results of 10 repeated experiments and validation experiments in three groups prove that our proposed framework outperforms well in classification performance, generalization, and reproducibility. Also, we found discriminative brain regions, such as Hippocampus, Frontal_Inf_Orb_L, Parietal_Sup_L, Putamen_L, Fusiform_R, etc. These discoveries provide a novel method for AD research, and the experimental study demonstrates that the framework will further improve our understanding of the mechanisms underlying the development of AD.
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Affiliation(s)
- Xianglian Meng
- School of Computer Information and Engineering, Changzhou Institute of Technology, Changzhou, China
| | - Junlong Liu
- School of Computer Information and Engineering, Changzhou Institute of Technology, Changzhou, China
| | - Xiang Fan
- School of Computer Information and Engineering, Changzhou Institute of Technology, Changzhou, China
| | - Chenyuan Bian
- Shandong Provincial Key Laboratory of Digital Medicine and Computer-Assisted Surgery, Affiliated Hospital of Qingdao University, Qingdao, China
| | - Qingpeng Wei
- School of Computer Information and Engineering, Changzhou Institute of Technology, Changzhou, China
| | - Ziwei Wang
- 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
- *Correspondence: Wenjie Liu,
| | - Zhuqing Jiao
- School of Computer Science and Artificial Intelligence, Changzhou University, Changzhou, China
- Zhuqing Jiao,
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