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Baghernezhad S, Daliri MR. Age-related changes in human brain functional connectivity using graph theory and machine learning techniques in resting-state fMRI data. GeroScience 2024; 46:5303-5320. [PMID: 38499956 PMCID: PMC11336041 DOI: 10.1007/s11357-024-01128-w] [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: 11/05/2023] [Accepted: 03/08/2024] [Indexed: 03/20/2024] Open
Abstract
Aging is the basis of neurodegeneration and dementia that affects each endemic in the body. Normal aging in the brain is associated with progressive slowdown and disruptions in various abilities such as motor ability, cognitive impairment, decreasing information processing speed, attention, and memory. With the aggravation of global aging, more research focuses on brain changes in the elderly adult. The graph theory, in combination with functional magnetic resonance imaging (fMRI), makes it possible to evaluate the brain network functional connectivity patterns in different conditions with brain modeling. We have evaluated the brain network communication model changes in three different age groups (including 8 to 15 years, 25 to 35 years, and 45 to 75 years) in lifespan pilot data from the human connectome project (HCP). Initially, Pearson correlation-based connectivity networks were calculated and thresholded. Then, network characteristics were compared between the three age groups by calculating the global and local graph measures. In the resting state brain network, we observed decreasing global efficiency and increasing transitivity with age. Also, brain regions, including the amygdala, putamen, hippocampus, precuneus, inferior temporal gyrus, anterior cingulate gyrus, and middle temporal gyrus, were selected as the most affected brain areas with age through statistical tests and machine learning methods. Using feature selection methods, including Fisher score and Kruskal-Wallis, we were able to classify three age groups using SVM, KNN, and decision-tree classifier. The best classification accuracy is in the combination of Fisher score and decision tree classifier obtained, which was 82.2%. Thus, by examining the measures of functional connectivity using graph theory, we will be able to explore normal age-related changes in the human brain, which can be used as a tool to monitor health with age.
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Affiliation(s)
- Sepideh Baghernezhad
- Neuroscience & Neuroengineering Research Lab, Biomedical Engineering Department, School of Electrical Engineering, Iran University of Science and Technology (IUST), Tehran, Iran
| | - Mohammad Reza Daliri
- Neuroscience & Neuroengineering Research Lab, Biomedical Engineering Department, School of Electrical Engineering, Iran University of Science and Technology (IUST), Tehran, Iran.
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2
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Sun L, Zhao T, Liang X, Xia M, Li Q, Liao X, Gong G, Wang Q, Pang C, Yu Q, Bi Y, Chen P, Chen R, Chen Y, Chen T, Cheng J, Cheng Y, Cui Z, Dai Z, Deng Y, Ding Y, Dong Q, Duan D, Gao JH, Gong Q, Han Y, Han Z, Huang CC, Huang R, Huo R, Li L, Lin CP, Lin Q, Liu B, Liu C, Liu N, Liu Y, Liu Y, Lu J, Ma L, Men W, Qin S, Qiu J, Qiu S, Si T, Tan S, Tang Y, Tao S, Wang D, Wang F, Wang J, Wang P, Wang X, Wang Y, Wei D, Wu Y, Xie P, Xu X, Xu Y, Xu Z, Yang L, Yuan H, Zeng Z, Zhang H, Zhang X, Zhao G, Zheng Y, Zhong S, He Y. Functional connectome through the human life span. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.09.12.557193. [PMID: 37745373 PMCID: PMC10515818 DOI: 10.1101/2023.09.12.557193] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/26/2023]
Abstract
The lifespan growth of the functional connectome remains unknown. Here, we assemble task-free functional and structural magnetic resonance imaging data from 33,250 individuals aged 32 postmenstrual weeks to 80 years from 132 global sites. We report critical inflection points in the nonlinear growth curves of the global mean and variance of the connectome, peaking in the late fourth and late third decades of life, respectively. After constructing a fine-grained, lifespan-wide suite of system-level brain atlases, we show distinct maturation timelines for functional segregation within different systems. Lifespan growth of regional connectivity is organized along a primary-to-association cortical axis. These connectome-based normative models reveal substantial individual heterogeneities in functional brain networks in patients with autism spectrum disorder, major depressive disorder, and Alzheimer's disease. These findings elucidate the lifespan evolution of the functional connectome and can serve as a normative reference for quantifying individual variation in development, aging, and neuropsychiatric disorders.
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Affiliation(s)
- Lianglong Sun
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Tengda Zhao
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Xinyuan Liang
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Mingrui Xia
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Qiongling Li
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Xuhong Liao
- School of Systems Science, Beijing Normal University, Beijing, China
| | - Gaolang Gong
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
- Chinese Institute for Brain Research, Beijing, China
| | - Qian Wang
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Chenxuan Pang
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Qian Yu
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Yanchao Bi
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
- Chinese Institute for Brain Research, Beijing, China
| | - Pindong Chen
- Brainnetome Center & National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Rui Chen
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Yuan Chen
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Taolin Chen
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
| | - Jingliang Cheng
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Yuqi Cheng
- Department of Psychiatry, First Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Zaixu Cui
- Chinese Institute for Brain Research, Beijing, China
| | - Zhengjia Dai
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Yao Deng
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Yuyin Ding
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Qi Dong
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Dingna Duan
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Jia-Hong Gao
- Center for MRI Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China
- Beijing City Key Laboratory for Medical Physics and Engineering, Institute of Heavy Ion Physics, School of Physics, Peking University, Beijing, China
- IDG/McGovern Institute for Brain Research, Peking University, Beijing, China
| | - Qiyong Gong
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China
| | - Ying Han
- Department of Neurology, Xuanwu Hospital of Capital Medical University, Beijing, China
| | - Zaizhu Han
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Chu-Chung Huang
- Key Laboratory of Brain Functional Genomics (Ministry of Education), Affiliated Mental Health Center (ECNU), School of Psychology and Cognitive Science, East China Normal University, Shanghai, China
| | - Ruiwang Huang
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Ran Huo
- Department of Radiology, Peking University Third Hospital, Beijing, China
| | - Lingjiang Li
- Department of Psychiatry, and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, China
- Mental Health Institute of Central South University, China National Technology Institute on Mental Disorders, Hunan Technology Institute of Psychiatry, Hunan Key Laboratory of Psychiatry and Mental Health, Hunan Medical Center for Mental Health, Changsha, China
| | - Ching-Po Lin
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
- Institute of Neuroscience, National Yang Ming Chiao Tung University, Taipei, China
- Department of Education and Research, Taipei City Hospital, Taipei, China
| | - Qixiang Lin
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Bangshan Liu
- Department of Psychiatry, and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, China
- Mental Health Institute of Central South University, China National Technology Institute on Mental Disorders, Hunan Technology Institute of Psychiatry, Hunan Key Laboratory of Psychiatry and Mental Health, Hunan Medical Center for Mental Health, Changsha, China
| | - Chao Liu
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Ningyu Liu
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Ying Liu
- Department of Radiology, Peking University Third Hospital, Beijing, China
| | - Yong Liu
- Center for Artificial Intelligence in Medical Imaging, School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China
| | - Jing Lu
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Leilei Ma
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Weiwei Men
- Center for MRI Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China
- Beijing City Key Laboratory for Medical Physics and Engineering, Institute of Heavy Ion Physics, School of Physics, Peking University, Beijing, China
| | - Shaozheng Qin
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
- Chinese Institute for Brain Research, Beijing, China
| | - Jiang Qiu
- Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing, China
- Department of Psychology, Southwest University, Chongqing, China
| | - Shijun Qiu
- Department of Radiology, The First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Tianmei Si
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Peking University, Beijing, China
| | - Shuping Tan
- Beijing Huilongguan Hospital, Peking University Huilongguan Clinical Medical School, Beijing, China
| | - Yanqing Tang
- Department of Psychiatry, The First Affiliated Hospital of China Medical University, Shenyang, China
| | - Sha Tao
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Dawei Wang
- Department of Radiology, Qilu Hospital of Shandong University, Ji’nan, China
| | - Fei Wang
- Department of Psychiatry, The First Affiliated Hospital of China Medical University, Shenyang, China
| | - Jiali Wang
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Pan Wang
- Department of Neurology, Tianjin Huanhu Hospital, Tianjin University, Tianjin, China
| | - Xiaoqin Wang
- Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing, China
- Department of Psychology, Southwest University, Chongqing, China
| | - Yanpei Wang
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Dongtao Wei
- Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing, China
- Department of Psychology, Southwest University, Chongqing, China
| | - Yankun Wu
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Peking University, Beijing, China
| | - Peng Xie
- Chongqing Key Laboratory of Neurobiology, Chongqing, China
- Department of Neurology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Xiufeng Xu
- Department of Psychiatry, First Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Yuehua Xu
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Zhilei Xu
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Liyuan Yang
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Huishu Yuan
- Department of Radiology, Peking University Third Hospital, Beijing, China
| | - Zilong Zeng
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Haibo Zhang
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Xi Zhang
- Department of Neurology, the Second Medical Centre, National Clinical Research Centre for Geriatric Diseases, Chinese PLA General Hospital, Beijing, China
| | - Gai Zhao
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Yanting Zheng
- Department of Radiology, The First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Suyu Zhong
- Center for Artificial Intelligence in Medical Imaging, School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China
| | | | | | | | | | | | | | - Yong He
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
- Chinese Institute for Brain Research, Beijing, China
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3
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Yan ZX, He Z, Jiang LH, Zou X. Age-related trajectories of the development of social cognition. Front Psychol 2024; 15:1348781. [PMID: 38711752 PMCID: PMC11071648 DOI: 10.3389/fpsyg.2024.1348781] [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: 12/03/2023] [Accepted: 03/20/2024] [Indexed: 05/08/2024] Open
Abstract
Age-related trajectories of intrinsic functional connectivity (iFC), which represent the interconnections between discrete regions of the human brain, for processes related to social cognition (SC) provide evidence for social development through neural imaging and can guide clinical interventions when such development is atypical. However, due to the lack of studies investigating brain development over a wide range of ages, the neural mechanisms of SC remain poorly understood, although considerable behavior-related evidence is available. The present study mapped vortex-wise iFC features between SC networks and the entire cerebral cortex by using common functional networks, creating the corresponding age-related trajectories. Three networks [moral cognition, theory of mind (ToM), and empathy] were selected as representative SC networks. The Enhanced Nathan Kline Institute-Rockland Sample (NKI-RS, N = 316, ages 8-83 years old) was employed delineate iFC characteristics and construct trajectories. The results showed that the SC networks display unique and overlapping iFC profiles. The iFC of the empathy network, an age-sensitive network, with dorsal attention network was found to exhibit a linear increasing pattern, that of the ventral attention network was observed to exhibit a linear decreasing pattern, and that of the somatomotor and dorsal attention networks was noted to exhibit a quadric-concave iFC pattern. Additionally, a sex-specific effect was observed for the empathy network as it exhibits linear and quadric sex-based differences in iFC with the frontoparietal and vision networks, respectively. The iFC of the ToM network with the ventral attention network exhibits a pronounced quadric-convex (inverted U-shape) trajectory. No linear or quadratic trajectories were noted in the iFC of the moral cognition network. These findings indicate that SC networks exhibit iFC with both low-level (somatomotor, vision) and high-level (attention and control) networks along specific developmental trajectories. The age-related trajectories determined in this study advance our understanding of the neural mechanisms of SC, providing valuable references for identification and intervention in cases of development of atypical SC.
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Affiliation(s)
- Zhi-Xiong Yan
- Guangxi Center of Developmental Population Neuroscience, Nanning Normal University, Nanning, China
| | - Zhe He
- Guangxi Center of Developmental Population Neuroscience, Nanning Normal University, Nanning, China
| | - Ling-Hui Jiang
- Guangxi Center of Developmental Population Neuroscience, Nanning Normal University, Nanning, China
| | - Xia Zou
- Continuing Education School, Guangxi College for Preschool Education, Nanning, China
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4
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Johansson J, Nordin K, Pedersen R, Karalija N, Papenberg G, Andersson M, Korkki SM, Riklund K, Guitart-Masip M, Rieckmann A, Bäckman L, Nyberg L, Salami A. Biphasic patterns of age-related differences in dopamine D1 receptors across the adult lifespan. Cell Rep 2023; 42:113107. [PMID: 37676765 DOI: 10.1016/j.celrep.2023.113107] [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: 04/17/2023] [Revised: 07/14/2023] [Accepted: 08/22/2023] [Indexed: 09/09/2023] Open
Abstract
Age-related alterations in D1-like dopamine receptor (D1DR) have distinct implications for human cognition and behavior during development and aging, but the timing of these periods remains undefined. Enabled by a large sample of in vivo assessments (n = 180, age 20 to 80 years of age, 50% female), we discover that age-related D1DR differences pivot at approximately 40 years of age in several brain regions. Focusing on the most age-sensitive dopamine-rich region, we observe opposing pre- and post-forties interrelations among caudate D1DR, cortico-striatal functional connectivity, and memory. Finally, particularly caudate D1DR differences in midlife and beyond, but not in early adulthood, associate with manifestation of white matter lesions. The present results support a model by which excessive dopamine modulation in early adulthood and insufficient modulation in aging are deleterious to brain function and cognition, thus challenging a prevailing view of monotonic D1DR function across the adult lifespan.
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Affiliation(s)
- Jarkko Johansson
- Department of Radiation Sciences, Diagnostic Radiology, Umeå University, 90187 Umeå, Sweden; Umeå Center for Functional Brain Imaging (UFBI), Umeå University, 90187 Umeå, Sweden.
| | - Kristin Nordin
- Aging Research Center, Karolinska Institutet & Stockholm University, Tomtebodavägen 18A, 17165 Stockholm, Sweden
| | - Robin Pedersen
- Umeå Center for Functional Brain Imaging (UFBI), Umeå University, 90187 Umeå, Sweden; Department of Integrative Medical Biology, Umeå University, 90187 Umeå, Sweden; Wallenberg Center for Molecular Medicine, Umeå University, Umeå, Sweden
| | - Nina Karalija
- Department of Radiation Sciences, Diagnostic Radiology, Umeå University, 90187 Umeå, Sweden; Umeå Center for Functional Brain Imaging (UFBI), Umeå University, 90187 Umeå, Sweden
| | - Goran Papenberg
- Aging Research Center, Karolinska Institutet & Stockholm University, Tomtebodavägen 18A, 17165 Stockholm, Sweden
| | - Micael Andersson
- Umeå Center for Functional Brain Imaging (UFBI), Umeå University, 90187 Umeå, Sweden; Department of Integrative Medical Biology, Umeå University, 90187 Umeå, Sweden
| | - Saana M Korkki
- Aging Research Center, Karolinska Institutet & Stockholm University, Tomtebodavägen 18A, 17165 Stockholm, Sweden
| | - Katrine Riklund
- Department of Radiation Sciences, Diagnostic Radiology, Umeå University, 90187 Umeå, Sweden; Umeå Center for Functional Brain Imaging (UFBI), Umeå University, 90187 Umeå, Sweden
| | - Marc Guitart-Masip
- Aging Research Center, Karolinska Institutet & Stockholm University, Tomtebodavägen 18A, 17165 Stockholm, Sweden; Max Planck UCL Centre for Computational Psychiatry and Ageing Research, University College London, London, UK
| | - Anna Rieckmann
- Department of Radiation Sciences, Diagnostic Radiology, Umeå University, 90187 Umeå, Sweden; Umeå Center for Functional Brain Imaging (UFBI), Umeå University, 90187 Umeå, Sweden; Department of Integrative Medical Biology, Umeå University, 90187 Umeå, Sweden; The Munich Center for the Economics of Aging, Max Planck Institute for Social Law and Social Policy, 80799 Munich, Germany
| | - Lars Bäckman
- Aging Research Center, Karolinska Institutet & Stockholm University, Tomtebodavägen 18A, 17165 Stockholm, Sweden
| | - Lars Nyberg
- Department of Radiation Sciences, Diagnostic Radiology, Umeå University, 90187 Umeå, Sweden; Umeå Center for Functional Brain Imaging (UFBI), Umeå University, 90187 Umeå, Sweden; Department of Integrative Medical Biology, Umeå University, 90187 Umeå, Sweden; Wallenberg Center for Molecular Medicine, Umeå University, Umeå, Sweden
| | - Alireza Salami
- Umeå Center for Functional Brain Imaging (UFBI), Umeå University, 90187 Umeå, Sweden; Aging Research Center, Karolinska Institutet & Stockholm University, Tomtebodavägen 18A, 17165 Stockholm, Sweden; Department of Integrative Medical Biology, Umeå University, 90187 Umeå, Sweden; Wallenberg Center for Molecular Medicine, Umeå University, Umeå, Sweden
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Hormovas J, Dadario NB, Tang SJ, Nicholas P, Dhanaraj V, Young I, Doyen S, Sughrue ME. Parcellation-Based Connectivity Model of the Judgement Core. J Pers Med 2023; 13:1384. [PMID: 37763153 PMCID: PMC10532823 DOI: 10.3390/jpm13091384] [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: 08/14/2023] [Revised: 09/12/2023] [Accepted: 09/12/2023] [Indexed: 09/29/2023] Open
Abstract
Judgement is a higher-order brain function utilized in the evaluation process of problem solving. However, heterogeneity in the task methodology based on the many definitions of judgement and its expansive and nuanced applications have prevented the identification of a unified cortical model at a level of granularity necessary for clinical translation. Forty-six task-based fMRI studies were used to generate activation-likelihood estimations (ALE) across moral, social, risky, and interpersonal judgement paradigms. Cortical parcellations overlapping these ALEs were used to delineate patterns in neurocognitive network engagement for the four judgement tasks. Moral judgement involved the bilateral superior frontal gyri, right temporal gyri, and left parietal lobe. Social judgement demonstrated a left-dominant frontoparietal network with engagement of right-sided temporal limbic regions. Moral and social judgement tasks evoked mutual engagement of the bilateral DMN. Both interpersonal and risk judgement were shown to involve a right-sided frontoparietal network with accompanying engagement of the left insular cortex, converging at the right-sided CEN. Cortical activation in normophysiological judgement function followed two separable patterns involving the large-scale neurocognitive networks. Specifically, the DMN was found to subserve judgement centered around social inferences and moral cognition, while the CEN subserved tasks involving probabilistic reasoning, risk estimation, and strategic contemplation.
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Affiliation(s)
- Jorge Hormovas
- Centre for Minimally Invasive Neurosurgery, Prince of Wales Private Hospital, Level 7 Prince of Wales Private Hospital, Randwick, NSW 2031, Australia; (J.H.); (V.D.)
| | - Nicholas B. Dadario
- Robert Wood Johnson Medical School, Rutgers University, 125 Paterson St., New Brunswick, NJ 08901, USA;
| | - Si Jie Tang
- School of Medicine, 21772 University of California Davis Medical Center, 2315 Stockton Blvd., Sacramento, CA 95817, USA
| | - Peter Nicholas
- Omniscient Neurotechnology, Level 10/580 George Street, Haymarket, NSW 2000, Australia; (P.N.); (I.Y.); (S.D.)
| | - Vukshitha Dhanaraj
- Centre for Minimally Invasive Neurosurgery, Prince of Wales Private Hospital, Level 7 Prince of Wales Private Hospital, Randwick, NSW 2031, Australia; (J.H.); (V.D.)
| | - Isabella Young
- Omniscient Neurotechnology, Level 10/580 George Street, Haymarket, NSW 2000, Australia; (P.N.); (I.Y.); (S.D.)
| | - Stephane Doyen
- Omniscient Neurotechnology, Level 10/580 George Street, Haymarket, NSW 2000, Australia; (P.N.); (I.Y.); (S.D.)
| | - Michael E. Sughrue
- Centre for Minimally Invasive Neurosurgery, Prince of Wales Private Hospital, Level 7 Prince of Wales Private Hospital, Randwick, NSW 2031, Australia; (J.H.); (V.D.)
- Omniscient Neurotechnology, Level 10/580 George Street, Haymarket, NSW 2000, Australia; (P.N.); (I.Y.); (S.D.)
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6
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Wang Z, Yang J, Zheng Z, Cao W, Dong L, Li H, Wen X, Luo C, Cai Q, Jian W, Yao D. Trait- and State-Dependent Changes in Cortical-Subcortical Functional Networks Across the Adult Lifespan. J Magn Reson Imaging 2023; 58:720-731. [PMID: 36637029 DOI: 10.1002/jmri.28599] [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: 10/07/2022] [Revised: 12/29/2022] [Accepted: 01/03/2023] [Indexed: 01/14/2023] Open
Abstract
BACKGROUND How the functional interactions of the basal ganglia/thalamus with the cerebral cortex and the cerebellum change over the adult lifespan in movie-watching and resting-state is less clear. PURPOSE To investigate the functional changes in the organization of the human cortical-subcortical functional networks over the adult lifespan using movie-watching and resting-state fMRI data. STUDY TYPE Cohort. SUBJECTS Healthy 467 adults (cross-sectional individuals aged 18-88 years) from the Cambridge Centre for Ageing and Neuroscience (www.cam-can.com). FIELD STRENGTH/SEQUENCE: fMRI using a gradient-echo echo-planar imaging (EPI) sequence at 3 T. ASSESSMENT Functional connectivities (FCs) of the subcortical subregions (i.e. the basal ganglia and thalamus) with both the cerebral cortex and cerebellum were examined in fMRI data acquired during resting state and movie-watching. And, fluid intelligence scores were also assessed. STATISTICAL TESTS Student's t-tests, false discovery rate (FDR) corrected. RESULTS As age increased, FCs that mainly within the basal ganglia and thalamus, and between the basal ganglia/thalamus and cortical networks (including the dorsal attention, ventral attention, and limbic networks) were both increased/decreased during movie-watching and resting states. However, FCs showed a state-dependent component with advancing age. During the movie-watching state, the FCs between the basal ganglia/thalamus and cerebellum/frontoparietal control networks were mainly increased with age, and the FCs in the somatomotor network were decreased with age. During the resting state, the FCs between the basal ganglia/thalamus and default mode/visual networks were mainly increased with age, and the FCs in the cerebellum were mainly decreased with age. Moreover, inverse relationships between FCs and fluid intelligence were mainly found in these network regions. DATA CONCLUSION Our study may suggest that changes in cortical-subcortical functional networks across the adult lifespan were both state-dependent and stable traits, and that aging fMRI studies should consider the effects of both physiological characteristics and individual situations. EVIDENCE LEVEL 2. TECHNICAL EFFICACY Stage 3.
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Affiliation(s)
- Ziqi Wang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Jie Yang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Zihao Zheng
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Weifang Cao
- Department of Radiology, Shandong First Medical University & Shandong Academy of Medical Sciences, Tai'an, China
| | - Li Dong
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
- Research Unit of NeuroInformation, Chinese Academy of Medical Sciences, Chengdu, China
- School of Electrical Engineering, Zhengzhou University, Zhengzhou, China
- Sichuan Institute for Brain Science and Brain-Inspired Intelligence, Chengdu, China
| | - Hechun Li
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Xin Wen
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Cheng Luo
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
- Research Unit of NeuroInformation, Chinese Academy of Medical Sciences, Chengdu, China
| | - Qingyan Cai
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Wei Jian
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Dezhong Yao
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
- Research Unit of NeuroInformation, Chinese Academy of Medical Sciences, Chengdu, China
- School of Electrical Engineering, Zhengzhou University, Zhengzhou, China
- Sichuan Institute for Brain Science and Brain-Inspired Intelligence, Chengdu, China
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7
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Frank LE, Zeithamova D. Evaluating methods for measuring background connectivity in slow event-related functional magnetic resonance imaging designs. Brain Behav 2023; 13:e3015. [PMID: 37062880 PMCID: PMC10275534 DOI: 10.1002/brb3.3015] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/02/2022] [Revised: 03/10/2023] [Accepted: 03/20/2023] [Indexed: 04/18/2023] Open
Abstract
INTRODUCTION Resting-state functional magnetic resonance imaging (fMRI) is widely used for measuring functional interactions between brain regions, significantly contributing to our understanding of large-scale brain networks and brain-behavior relationships. Furthermore, idiosyncratic patterns of resting-state connections can be leveraged to identify individuals and predict individual differences in clinical symptoms, cognitive abilities, and other individual factors. Idiosyncratic connectivity patterns are thought to persist across task states, suggesting task-based fMRI can be similarly leveraged for individual differences analyses. METHOD Here, we tested the degree to which functional interactions occurring in the background of a task during slow event-related fMRI parallel or differ from those captured during resting-state fMRI. We compared two approaches for removing task-evoked activity from task-based fMRI: (1) applying a low-pass filter to remove task-related frequencies in the signal, or (2) extracting residuals from a general linear model (GLM) that accounts for task-evoked responses. RESULT We found that the organization of large-scale cortical networks and individual's idiosyncratic connectivity patterns are preserved during task-based fMRI. In contrast, individual differences in connection strength can vary more substantially between rest and task. Compared to low-pass filtering, background connectivity obtained from GLM residuals produced idiosyncratic connectivity patterns and individual differences in connection strength that more resembled rest. However, all background connectivity measures were highly similar when derived from the low-pass-filtered signal or GLM residuals, indicating that both methods are suitable for measuring background connectivity. CONCLUSION Together, our results highlight new avenues for the analysis of task-based fMRI datasets and the utility of each background connectivity method.
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Affiliation(s)
- Lea E. Frank
- Department of PsychologyUniversity of OregonEugeneOregonUSA
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8
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Deery HA, Di Paolo R, Moran C, Egan GF, Jamadar SD. The older adult brain is less modular, more integrated, and less efficient at rest: A systematic review of large-scale resting-state functional brain networks in aging. Psychophysiology 2023; 60:e14159. [PMID: 36106762 PMCID: PMC10909558 DOI: 10.1111/psyp.14159] [Citation(s) in RCA: 32] [Impact Index Per Article: 32.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2022] [Revised: 07/22/2022] [Accepted: 07/25/2022] [Indexed: 12/23/2022]
Abstract
The literature on large-scale resting-state functional brain networks across the adult lifespan was systematically reviewed. Studies published between 1986 and July 2021 were retrieved from PubMed. After reviewing 2938 records, 144 studies were included. Results on 11 network measures were summarized and assessed for certainty of the evidence using a modified GRADE method. The evidence provides high certainty that older adults display reduced within-network and increased between-network functional connectivity. Older adults also show lower segregation, modularity, efficiency and hub function, and decreased lateralization and a posterior to anterior shift at rest. Higher-order functional networks reliably showed age differences, whereas primary sensory and motor networks showed more variable results. The inflection point for network changes is often the third or fourth decade of life. Age effects were found with moderate certainty for within- and between-network altered patterns and speed of dynamic connectivity. Research on within-subject bold variability and connectivity using glucose uptake provides low certainty of age differences but warrants further study. Taken together, these age-related changes may contribute to the cognitive decline often seen in older adults.
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Affiliation(s)
- Hamish A. Deery
- Turner Institute for Brain and Mental HealthMonash UniversityMelbourneVictoriaAustralia
- Monash Biomedical ImagingMonash UniversityMelbourneVictoriaAustralia
| | - Robert Di Paolo
- Turner Institute for Brain and Mental HealthMonash UniversityMelbourneVictoriaAustralia
- Monash Biomedical ImagingMonash UniversityMelbourneVictoriaAustralia
| | - Chris Moran
- Peninsula Clinical School, Central Clinical SchoolMonash UniversityFrankstonVictoriaAustralia
- Department of Geriatric MedicinePeninsula HealthFrankstonVictoriaAustralia
| | - Gary F. Egan
- Turner Institute for Brain and Mental HealthMonash UniversityMelbourneVictoriaAustralia
- Monash Biomedical ImagingMonash UniversityMelbourneVictoriaAustralia
- Australian Research Council Centre of Excellence for Integrative Brain FunctionMelbourneVictoriaAustralia
| | - Sharna D. Jamadar
- Turner Institute for Brain and Mental HealthMonash UniversityMelbourneVictoriaAustralia
- Monash Biomedical ImagingMonash UniversityMelbourneVictoriaAustralia
- Australian Research Council Centre of Excellence for Integrative Brain FunctionMelbourneVictoriaAustralia
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9
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Xiao L, Cai B, Qu G, Zhang G, Stephen JM, Wilson TW, Calhoun VD, Wang YP. Distance Correlation-Based Brain Functional Connectivity Estimation and Non-Convex Multi-Task Learning for Developmental fMRI Studies. IEEE Trans Biomed Eng 2022; 69:3039-3050. [PMID: 35316180 PMCID: PMC9594860 DOI: 10.1109/tbme.2022.3160447] [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] [Indexed: 11/07/2022]
Abstract
OBJECTIVE Resting-state functional magnetic resonance imaging (rs-fMRI)-derived functional connectivity (FC) patterns have been extensively used to delineate global functional organization of the human brain in healthy development and neuropsychiatric disorders. In this paper, we investigate how FC in males and females differs in an age prediction framework. METHODS We first estimate FC between regions-of-interest (ROIs) using distance correlation instead of Pearson's correlation. Distance correlation, as a multivariate statistical method, explores spatial relations of voxel-wise time courses within individual ROIs and measures both linear and nonlinear dependence, capturing more complex between-ROI interactions. Then, we propose a novel non-convex multi-task learning (NC-MTL) model to study age-related gender differences in FC, where age prediction for each gender group is viewed as one task, and a composite regularizer with a combination of the non-convex l2,1-2 and l1-2 terms is introduced for selecting both common and task-specific features. RESULTS AND CONCLUSION We validate the effectiveness of our NC-MTL model with distance correlation-based FC derived from rs-fMRI for predicting ages of both genders. The experimental results on the Philadelphia Neurodevelopmental Cohort demonstrate that our NC-MTL model outperforms several other competing MTL models in age prediction. We also compare the age prediction performance of our NC-MTL model using FC estimated by Pearson's correlation and distance correlation, which shows that distance correlation-based FC is more discriminative for age prediction than Pearson's correlation-based FC. SIGNIFICANCE This paper presents a novel framework for functional connectome developmental studies, characterizing developmental gender differences in FC patterns.
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10
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Wilms M, Bannister JJ, Mouches P, MacDonald ME, Rajashekar D, Langner S, Forkert ND. Invertible Modeling of Bidirectional Relationships in Neuroimaging With Normalizing Flows: Application to Brain Aging. IEEE TRANSACTIONS ON MEDICAL IMAGING 2022; 41:2331-2347. [PMID: 35324436 DOI: 10.1109/tmi.2022.3161947] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Many machine learning tasks in neuroimaging aim at modeling complex relationships between a brain's morphology as seen in structural MR images and clinical scores and variables of interest. A frequently modeled process is healthy brain aging for which many image-based brain age estimation or age-conditioned brain morphology template generation approaches exist. While age estimation is a regression task, template generation is related to generative modeling. Both tasks can be seen as inverse directions of the same relationship between brain morphology and age. However, this view is rarely exploited and most existing approaches train separate models for each direction. In this paper, we propose a novel bidirectional approach that unifies score regression and generative morphology modeling and we use it to build a bidirectional brain aging model. We achieve this by defining an invertible normalizing flow architecture that learns a probability distribution of 3D brain morphology conditioned on age. The use of full 3D brain data is achieved by deriving a manifold-constrained formulation that models morphology variations within a low-dimensional subspace of diffeomorphic transformations. This modeling idea is evaluated on a database of MR scans of more than 5000 subjects. The evaluation results show that our bidirectional brain aging model (1) accurately estimates brain age, (2) is able to visually explain its decisions through attribution maps and counterfactuals, (3) generates realistic age-specific brain morphology templates, (4) supports the analysis of morphological variations, and (5) can be utilized for subject-specific brain aging simulation.
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11
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Qing T, Chen J, Xue L, Tan Y, Huang Z, Yang S, Chen Y, Wang J, Zou Q, Lv Y, Zhao J. Decreasing integration within face network and segregation beyond the face network in the aging brain. Psych J 2022; 11:448-459. [PMID: 35599334 DOI: 10.1002/pchj.560] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2021] [Accepted: 04/10/2022] [Indexed: 11/07/2022]
Affiliation(s)
- Tianying Qing
- Center for Cognition and Brain Disorders The Affiliated Hospital of Hangzhou Normal University Zhejiang China
- Institute of Psychological Science Hangzhou Normal University Zhejiang China
- Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments Zhejiang China
| | - Jing Chen
- Center for Cognition and Brain Disorders The Affiliated Hospital of Hangzhou Normal University Zhejiang China
- Institute of Psychological Science Hangzhou Normal University Zhejiang China
- Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments Zhejiang China
| | - Licheng Xue
- Institute for Brain Research and Rehabilitation, Center for Studies of Psychological Application South China Normal University Guangzhou China
| | - Yufei Tan
- Laboratoire de Psychologie Cognitive Aix‐Marseille Université and CNRS Marseille France
| | - Zehao Huang
- Center for Cognition and Brain Disorders The Affiliated Hospital of Hangzhou Normal University Zhejiang China
- Institute of Psychological Science Hangzhou Normal University Zhejiang China
- Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments Zhejiang China
| | - Shimeng Yang
- Center for Cognition and Brain Disorders The Affiliated Hospital of Hangzhou Normal University Zhejiang China
- Institute of Psychological Science Hangzhou Normal University Zhejiang China
- Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments Zhejiang China
| | - Yuqiu Chen
- Center for Cognition and Brain Disorders The Affiliated Hospital of Hangzhou Normal University Zhejiang China
- Institute of Psychological Science Hangzhou Normal University Zhejiang China
- Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments Zhejiang China
| | - Jinhui Wang
- Institute for Brain Research and Rehabilitation, Center for Studies of Psychological Application South China Normal University Guangzhou China
| | - Qihong Zou
- Center for MRI Research, Academy for Advanced Interdisciplinary Studies Peking University Beijing China
| | - Yating Lv
- Center for Cognition and Brain Disorders The Affiliated Hospital of Hangzhou Normal University Zhejiang China
- Institute of Psychological Science Hangzhou Normal University Zhejiang China
- Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments Zhejiang China
| | - Jing Zhao
- Center for Cognition and Brain Disorders The Affiliated Hospital of Hangzhou Normal University Zhejiang China
- Institute of Psychological Science Hangzhou Normal University Zhejiang China
- Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments Zhejiang China
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12
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Xu J, Zhang J, Li J, Wang H, Chen J, Lyu H, Hu Q. Structural and Functional Trajectories of Middle Temporal Gyrus Sub-Regions During Life Span: A Potential Biomarker of Brain Development and Aging. Front Aging Neurosci 2022; 14:799260. [PMID: 35572140 PMCID: PMC9094684 DOI: 10.3389/fnagi.2022.799260] [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: 10/21/2021] [Accepted: 02/15/2022] [Indexed: 11/13/2022] Open
Abstract
Although previous studies identified a similar topography pattern of structural and functional delineations in human middle temporal gyrus (MTG) using healthy adults, trajectories of MTG sub-regions across lifespan remain largely unknown. Herein, we examined gray matter volume (GMV) and resting-state functional connectivity (RSFC) using datasets from the Nathan Kline Institute (NKI), and aimed to (1) investigate structural and functional trajectories of MTG sub-regions across the lifespan; and (2) assess whether these features can be used as biomarkers to predict individual’s chronological age. As a result, GMV of all MTG sub-regions followed U-shaped trajectories with extreme age around the sixth decade. The RSFC between MTG sub-regions and many cortical brain regions showed inversed U-shaped trajectories, whereas RSFC between MTG sub-regions and sub-cortical regions/cerebellum showed U-shaped way, with extreme age about 20 years earlier than those of GMV. Moreover, GMV and RSFC of MTG sub-regions could be served as useful features to predict individual age with high estimation accuracy. Together, these results not only provided novel insights into the dynamic process of structural and functional roles of MTG sub-regions across the lifespan, but also served as useful biomarkers to age prediction.
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Affiliation(s)
- Jinping Xu
- Institute of Biomedical and Health Engineering, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Jinhuan Zhang
- Institute of Biomedical and Health Engineering, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Jiaying Li
- Institute of Biomedical and Health Engineering, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Haoyu Wang
- Institute of Biomedical and Health Engineering, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Jianxiang Chen
- Department of Radiology, Shenzhen Traditional Chinese Medicine Hospital, The Fourth Clinical Medical College, Guangzhou University of Chinese Medicine, Shenzhen, China
| | - Hanqing Lyu
- Department of Radiology, Shenzhen Traditional Chinese Medicine Hospital, The Fourth Clinical Medical College, Guangzhou University of Chinese Medicine, Shenzhen, China
- *Correspondence: Hanqing Lyu,
| | - Qingmao Hu
- Institute of Biomedical and Health Engineering, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
- CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
- Qingmao Hu,
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13
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Moretto M, Silvestri E, Zangrossi A, Corbetta M, Bertoldo A. Unveiling whole-brain dynamics in normal aging through Hidden Markov Models. Hum Brain Mapp 2021; 43:1129-1144. [PMID: 34783122 PMCID: PMC8764474 DOI: 10.1002/hbm.25714] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2021] [Revised: 10/24/2021] [Accepted: 10/31/2021] [Indexed: 11/27/2022] Open
Abstract
During normal aging, the brain undergoes structural and functional changes. Many studies applied static functional connectivity (FC) analysis on resting state functional magnetic resonance imaging (rs‐fMRI) data showing a link between aging and the increase of between‐networks connectivity. However, it has been demonstrated that FC is not static but varies over time. By employing the dynamic data‐driven approach of Hidden Markov Models, this study aims to investigate how aging is related to specific characteristics of dynamic brain states. Rs‐fMRI data of 88 subjects, equally distributed in young and old were analyzed. The best model resulted to be with six states, which we characterized not only in terms of FC and mean BOLD activation, but also uncertainty of the estimates. We found two states were mostly occupied by young subjects, whereas three other states by old subjects. A graph‐based analysis revealed a decrease in strength with the increase of age, and an overall more integrated topology of states occupied by old subjects. Indeed, while young subjects tend to cycle in a loop of states characterized by a high segregation of the networks, old subjects' loops feature high integration, with a crucial intermediary role played by the dorsal attention network. These results suggest that the employed mathematical approach captures the complex and rich brain's dynamics underpinning the aging process.
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Affiliation(s)
- Manuela Moretto
- Department of Information Engineering, University of Padova, Padova, Italy.,Padova Neuroscience Center, University of Padova, Padova, Italy
| | - Erica Silvestri
- Department of Information Engineering, University of Padova, Padova, Italy.,Padova Neuroscience Center, University of Padova, Padova, Italy
| | | | - Maurizio Corbetta
- Padova Neuroscience Center, University of Padova, Padova, Italy.,Department of Neuroscience, University of Padova, Padova, Italy
| | - Alessandra Bertoldo
- Department of Information Engineering, University of Padova, Padova, Italy.,Padova Neuroscience Center, University of Padova, Padova, Italy
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14
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Kupis L, Goodman ZT, Kornfeld S, Hoang S, Romero C, Dirks B, Dehoney J, Chang C, Spreng RN, Nomi JS, Uddin LQ. Brain Dynamics Underlying Cognitive Flexibility Across the Lifespan. Cereb Cortex 2021; 31:5263-5274. [PMID: 34145442 PMCID: PMC8491685 DOI: 10.1093/cercor/bhab156] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2021] [Revised: 05/13/2021] [Accepted: 05/16/2021] [Indexed: 11/14/2022] Open
Abstract
The neural mechanisms contributing to flexible cognition and behavior and how they change with development and aging are incompletely understood. The current study explored intrinsic brain dynamics across the lifespan using resting-state fMRI data (n = 601, 6-85 years) and examined the interactions between age and brain dynamics among three neurocognitive networks (midcingulo-insular network, M-CIN; medial frontoparietal network, M-FPN; and lateral frontoparietal network, L-FPN) in relation to behavioral measures of cognitive flexibility. Hierarchical multiple regression analysis revealed brain dynamics among a brain state characterized by co-activation of the L-FPN and M-FPN, and brain state transitions, moderated the relationship between quadratic effects of age and cognitive flexibility as measured by scores on the Delis-Kaplan Executive Function System (D-KEFS) test. Furthermore, simple slope analyses of significant interactions revealed children and older adults were more likely to exhibit brain dynamic patterns associated with poorer cognitive flexibility compared with younger adults. Our findings link changes in cognitive flexibility observed with age with the underlying brain dynamics supporting these changes. Preventative and intervention measures should prioritize targeting these networks with cognitive flexibility training to promote optimal outcomes across the lifespan.
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Affiliation(s)
- Lauren Kupis
- Department of Psychology, University of Miami, Coral Gables, FL 33124, USA
| | - Zachary T Goodman
- Department of Psychology, University of Miami, Coral Gables, FL 33124, USA
| | - Salome Kornfeld
- Department of Psychology, University of Miami, Coral Gables, FL 33124, USA
| | - Stephanie Hoang
- Department of Psychology, University of Miami, Coral Gables, FL 33124, USA
| | - Celia Romero
- Department of Psychology, University of Miami, Coral Gables, FL 33124, USA
| | - Bryce Dirks
- Department of Psychology, University of Miami, Coral Gables, FL 33124, USA
| | - Joseph Dehoney
- Department of Psychology, University of Miami, Coral Gables, FL 33124, USA
| | - Catie Chang
- Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, TN 37235, USA
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN 37235, USA
- Vanderbilt University Institute of Imaging Science, Vanderbilt University, Nashville, TN 37232, USA
| | - R Nathan Spreng
- Montreal Neurological Institute, Department of Neurology and Neurosurgery, McGill University, Montreal, QC H3A 2B4, Canada
| | - Jason S Nomi
- Department of Psychology, University of Miami, Coral Gables, FL 33124, USA
| | - Lucina Q Uddin
- Department of Psychology, University of Miami, Coral Gables, FL 33124, USA
- Neuroscience Program, University of Miami Miller School of Medicine, Miami, FL 33136, USA
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15
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Rao B, Xu D, Zhao C, Wang S, Li X, Sun W, Gang Y, Fang J, Xu H. Development of functional connectivity within and among the resting-state networks in anesthetized rhesus monkeys. Neuroimage 2021; 242:118473. [PMID: 34390876 DOI: 10.1016/j.neuroimage.2021.118473] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2021] [Revised: 08/08/2021] [Accepted: 08/11/2021] [Indexed: 01/22/2023] Open
Abstract
OBJECTIVE The age-related changes in the resting-state networks (RSNs) exhibited temporally specific patterns in humans, and humans and rhesus monkeys have similar RSNs. We hypothesized that the RSNs in rhesus monkeys experienced similar developmental patterns as humans. METHODS We acquired resting-state fMRI data from 62 rhesus monkeys, which were divided into childhood, adolescence, and early adulthood groups. Group independent component analysis (ICA) was used to identify monkey RSNs. We detected the between-group differences in the RSNs and static, dynamic, and effective functional network connections (FNCs) using one-way variance analysis (ANOVA) and post-hoc analysis. RESULTS Eight rhesus RSNs were identified, including cerebellum (CN), left and right lateral visual (LVN and RVN), posterior default mode (pDMN), visuospatial (VSN), frontal (FN), salience (SN), and sensorimotor networks (SMN). In internal connections, the CN, SN, FN, and SMN mainly matured in early adulthood. The static FNCs associated with FN, SN, pDMN primarily experienced fast descending slow ascending type (U-shaped) developmental patterns for maturation, and the dynamic FNCs related to pDMN (RVN, CN, and SMN) and SMN (CN) were mature in early adulthood. The effective FNC results showed that the pDMN and VSN (stimulated), SN (inhibited), and FN (first inhibited then stimulated) chiefly matured in early adulthood. CONCLUSION We identified eight monkey RSNs, which exhibited similar development patterns as humans. All the RSNs and FNCs in monkeys were not widely changed but fine-tuned. Our study clarified that the progressive synchronization, exploration, and regulation of cognitive RSNs within the pDMN, FN, SN, and VSN denoted potential maturation of the RSNs throughout development. We confirmed the development patterns of RSNs and FNCs would support the use of monkeys as a best animal model for human brain function.
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Affiliation(s)
- Bo Rao
- Department of Radiology, Zhongnan Hospital of Wuhan University, Wuhan University, Wuchang District, Wuhan, Hubei 430071, China.
| | - Dan Xu
- Department of Radiology, Zhongnan Hospital of Wuhan University, Wuhan University, Wuchang District, Wuhan, Hubei 430071, China.
| | - Chaoyang Zhao
- Department of Radiology, Zhongnan Hospital of Wuhan University, Wuhan University, Wuchang District, Wuhan, Hubei 430071, China.
| | - Shouchao Wang
- Department of Radiology, Zhongnan Hospital of Wuhan University, Wuhan University, Wuchang District, Wuhan, Hubei 430071, China
| | - Xuan Li
- Department of Radiology, Zhongnan Hospital of Wuhan University, Wuhan University, Wuchang District, Wuhan, Hubei 430071, China
| | - Wenbo Sun
- Department of Radiology, Zhongnan Hospital of Wuhan University, Wuhan University, Wuchang District, Wuhan, Hubei 430071, China
| | - Yadong Gang
- Department of Radiology, Zhongnan Hospital of Wuhan University, Wuhan University, Wuchang District, Wuhan, Hubei 430071, China
| | - Jian Fang
- Department of Radiology, Zhongnan Hospital of Wuhan University, Wuhan University, Wuchang District, Wuhan, Hubei 430071, China
| | - Haibo Xu
- Department of Radiology, Zhongnan Hospital of Wuhan University, Wuhan University, Wuchang District, Wuhan, Hubei 430071, China.
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16
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Gorbach T, Lundquist A, de Luna X, Nyberg L, Salami A. A Hierarchical Bayesian Mixture Model Approach for Analysis of Resting-State Functional Brain Connectivity: An Alternative to Thresholding. Brain Connect 2021; 10:202-211. [PMID: 32308015 PMCID: PMC7310299 DOI: 10.1089/brain.2020.0740] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023] Open
Abstract
This article proposes a Bayesian hierarchical mixture model to analyze functional brain connectivity where mixture components represent “positively connected” and “non-connected” brain regions. Such an approach provides a data-informed separation of reliable and spurious connections in contrast to arbitrary thresholding of a connectivity matrix. The hierarchical structure of the model allows simultaneous inferences for the entire population as well as for each individual subject. A new connectivity measure, the posterior probability of a given pair of brain regions of a specific subject to be connected given the observed correlation of regions' activity, can be computed from the model fit. The posterior probability reflects the connectivity of a pair of regions relative to the overall connectivity pattern of an individual, which is overlooked in traditional correlation analyses. This article demonstrates that using the posterior probability might diminish the effect of spurious connections on inferences, which is present when a correlation is used as a connectivity measure. In addition, simulation analyses reveal that the sparsification of the connectivity matrix using the posterior probabilities might outperform the absolute thresholding based on correlations. Therefore, we suggest that posterior probability might be a beneficial measure of connectivity compared with the correlation. The applicability of the introduced method is exemplified by a study of functional resting-state brain connectivity in older adults.
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Affiliation(s)
- Tetiana Gorbach
- Department of Statistics, Umeå School of Business, Economics and Statistics, Umeå University, Umeå, Sweden.,Umeå Center for Functional Brain Imaging, Umeå University, Umeå, Sweden.,Department of Integrative Medical Biology, Umeå University, Umeå, Sweden
| | - Anders Lundquist
- Department of Statistics, Umeå School of Business, Economics and Statistics, Umeå University, Umeå, Sweden.,Umeå Center for Functional Brain Imaging, Umeå University, Umeå, Sweden
| | - Xavier de Luna
- Department of Statistics, Umeå School of Business, Economics and Statistics, Umeå University, Umeå, Sweden
| | - Lars Nyberg
- Umeå Center for Functional Brain Imaging, Umeå University, Umeå, Sweden.,Department of Radiation Sciences, Umeå University, Umeå, Sweden.,Department of Integrative Medical Biology, Umeå University, Umeå, Sweden
| | - Alireza Salami
- Umeå Center for Functional Brain Imaging, Umeå University, Umeå, Sweden.,Department of Radiation Sciences, Umeå University, Umeå, Sweden.,Department of Integrative Medical Biology, Umeå University, Umeå, Sweden.,Aging Research Center, Karolinska Institutet and Stockholm University, Stockholm, Sweden.,Wallenberg Centre for Molecular Medicine, Umeå University, Umeå, Sweden
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17
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Kucikova L, Goerdten J, Dounavi ME, Mak E, Su L, Waldman AD, Danso S, Muniz-Terrera G, Ritchie CW. Resting-state brain connectivity in healthy young and middle-aged adults at risk of progressive Alzheimer's disease. Neurosci Biobehav Rev 2021; 129:142-153. [PMID: 34310975 DOI: 10.1016/j.neubiorev.2021.07.024] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2020] [Revised: 05/18/2021] [Accepted: 07/21/2021] [Indexed: 11/15/2022]
Abstract
Functional brain connectivity of the resting-state networks has gained recent attention as a possible biomarker of Alzheimer's Disease (AD). In this paper, we review the literature of functional connectivity differences in young adults and middle-aged cognitively intact individuals with non-modifiable risk factors of AD (n = 17). We focus on three main intrinsic resting-state networks: The Default Mode network, Executive network, and the Salience network. Overall, the evidence from the literature indicated early vulnerability of functional connectivity across different at-risk groups, particularly in the Default Mode Network. While there was little consensus on the interpretation on directionality, the topography of the findings showed frequent overlap across studies, especially in regions that are characteristic of AD (i.e., precuneus, posterior cingulate cortex, and medial prefrontal cortex areas). We conclude that while resting-state functional connectivity markers have great potential to identify at-risk individuals, implementing more data-driven approaches, further longitudinal and cross-validation studies, and the analysis of greater sample sizes are likely to be necessary to fully establish the effectivity and utility of resting-state network-based analyses.
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Affiliation(s)
- Ludmila Kucikova
- Edinburgh Dementia Prevention and Centre for Clinical Brain Sciences, Edinburgh Medical School, University of Edinburgh, Edinburgh, United Kingdom.
| | - Jantje Goerdten
- Department of Epidemiological Methods and Etiological Research, Leibniz Institute for Prevention Research and Epidemiology - BIPS, Bremen, Germany
| | - Maria-Eleni Dounavi
- Department of Psychiatry, School of Clinical Medicine, University of Cambridge, Cambridge, United Kingdom
| | - Elijah Mak
- Department of Psychiatry, School of Clinical Medicine, University of Cambridge, Cambridge, United Kingdom
| | - Li Su
- Department of Psychiatry, School of Clinical Medicine, University of Cambridge, Cambridge, United Kingdom
| | - Adam D Waldman
- Edinburgh Dementia Prevention and Centre for Clinical Brain Sciences, Edinburgh Medical School, University of Edinburgh, Edinburgh, United Kingdom
| | - Samuel Danso
- Edinburgh Dementia Prevention and Centre for Clinical Brain Sciences, Edinburgh Medical School, University of Edinburgh, Edinburgh, United Kingdom
| | - Graciela Muniz-Terrera
- Edinburgh Dementia Prevention and Centre for Clinical Brain Sciences, Edinburgh Medical School, University of Edinburgh, Edinburgh, United Kingdom
| | - Craig W Ritchie
- Edinburgh Dementia Prevention and Centre for Clinical Brain Sciences, Edinburgh Medical School, University of Edinburgh, Edinburgh, United Kingdom
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18
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Relationship Between Age and Cerebral Hemodynamic Response to Breath Holding: A Functional Near-Infrared Spectroscopy Study. Brain Topogr 2021; 34:154-166. [PMID: 33544290 DOI: 10.1007/s10548-021-00818-4] [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: 05/23/2020] [Accepted: 01/06/2021] [Indexed: 10/22/2022]
Abstract
Cerebrovascular reactivity (CVR) is routinely measured as a predictor of stroke in people with a high risk of ischemic attack. Neuroimaging techniques such as emission tomography, magnetic resonance imaging, and transcranial doppler are frequently used to measure CVR even though each technique has its limitations. Functional near-infrared spectroscopy (fNIRS), also based on the principle of neurovascular coupling, is relatively inexpensive, portable, and allows for the quantification of oxy- and deoxy-hemoglobin concentration changes at a high temporal resolution. This study examines the relationship between age and CVR using fNIRS in 45 young healthy adult participants aged 18-41 years (6 females, 26.64 ± 5.49 years) performing a simple breath holding task. Eighteen of the 45 participants were scanned again after a week to evaluate the feasibility of fNIRS in reliably measuring CVR. Results indicate (a) a negative relationship between age and hemodynamic measures of breath holding task in the sensorimotor cortex of 45 individuals and (b) widespread positive coactivation within medial sensorimotor regions and between medial sensorimotor regions with supplementary motor area and prefrontal cortex during breath holding with increasing age. The intraclass correlation coefficient (ICC) indicated only a low to fair/good reliability of the breath hold hemodynamic measures from sensorimotor and prefrontal cortices. However, the average hemodynamic response to breath holding from the two sessions were found to be temporally and spatially in correspondence. Future improvements in the sensitivity and reliability of fNIRS metrics could facilitate fNIRS-based assessment of cerebrovascular function as a potential clinical tool.
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19
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Sadiq MU, Langella S, Giovanello KS, Mucha PJ, Dayan E. Accrual of functional redundancy along the lifespan and its effects on cognition. Neuroimage 2021; 229:117737. [PMID: 33486125 PMCID: PMC8022200 DOI: 10.1016/j.neuroimage.2021.117737] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2020] [Revised: 01/04/2021] [Accepted: 01/05/2021] [Indexed: 12/01/2022] Open
Abstract
Despite the necessity to understand how the brain endures the initial stages of age-associated cognitive decline, no brain mechanism has been quantitatively specified to date. The brain may withstand the effects of cognitive aging through redundancy, a design feature in engineered and biological systems, which entails the presence of substitute elements to protect it against failure. Here, we investigated the relationship between functional network redundancy and age over the human lifespan and their interaction with cognition, analyzing resting-state functional MRI images and cognitive measures from 579 subjects. Network-wide redundancy was significantly associated with age, showing a stronger link with age than other major topological measures, presenting a pattern of accumulation followed by old-age decline. Critically, redundancy significantly mediated the association between age and executive function, with lower anti-correlation between age and cognition in subjects with high redundancy. The results suggest that functional redundancy accrues throughout the lifespan, mitigating the effects of age on cognition.
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Affiliation(s)
- Muhammad Usman Sadiq
- Biomedical Research Imaging Center (BRIC), UNC-Chapel Hill, Chapel Hill, NC 27599, United States
| | - Stephanie Langella
- Department of Psychology and Neuroscience, UNC-Chapel Hill, Chapel Hill, NC 27599, United States
| | - Kelly S Giovanello
- Biomedical Research Imaging Center (BRIC), UNC-Chapel Hill, Chapel Hill, NC 27599, United States; Department of Psychology and Neuroscience, UNC-Chapel Hill, Chapel Hill, NC 27599, United States
| | - Peter J Mucha
- Department of Mathematics, UNC-Chapel Hill, Chapel Hill, NC 27599, United States; Department of Applied Physical Sciences, UNC-Chapel Hill, Chapel Hill, NC 27599, United States
| | - Eran Dayan
- Biomedical Research Imaging Center (BRIC), UNC-Chapel Hill, Chapel Hill, NC 27599, United States; Department of Radiology, UNC-Chapel Hill, Chapel Hill, NC 27599, United States.
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20
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Zhang W, Ji G, Manza P, Li G, Hu Y, Wang J, Lv G, He Y, von Deneen KM, Han Y, Cui G, Tomasi D, Volkow ND, Nie Y, Wang GJ, Zhang Y. Connectome-Based Prediction of Optimal Weight Loss Six Months After Bariatric Surgery. Cereb Cortex 2020; 31:2561-2573. [PMID: 33350441 DOI: 10.1093/cercor/bhaa374] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2020] [Revised: 10/06/2020] [Accepted: 11/09/2020] [Indexed: 12/19/2022] Open
Abstract
Despite bariatric surgery being the most effective treatment for obesity, a proportion of subjects have suboptimal weight loss post-surgery. Therefore, it is necessary to understand the mechanisms behind the variance in weight loss and identify specific baseline biomarkers to predict optimal weight loss. Here, we employed functional magnetic resonance imaging (fMRI) with baseline whole-brain resting-state functional connectivity (RSFC) and a multivariate prediction framework integrating feature selection, feature transformation, and classification to prospectively identify obese patients that exhibited optimal weight loss at 6 months post-surgery. Siamese network, which is a multivariate machine learning method suitable for small sample analysis, and K-nearest neighbor (KNN) were cascaded as the classifier (Siamese-KNN). In the leave-one-out cross-validation, the Siamese-KNN achieved an accuracy of 83.78%, which was substantially higher than results from traditional classifiers. RSFC patterns contributing to the prediction consisted of brain networks related to salience, reward, self-referential, and cognitive processing. Further RSFC feature analysis indicated that the connection strength between frontal and parietal cortices was stronger in the optimal versus the suboptimal weight loss group. These findings show that specific RSFC patterns could be used as neuroimaging biomarkers to predict individual weight loss post-surgery and assist in personalized diagnosis for treatment of obesity.
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Affiliation(s)
- Wenchao Zhang
- Center for Brain Imaging, School of Life Science and Technology, Xidian University, Xi'an, Shaanxi 710071, China
| | - Gang Ji
- State Key Laboratory of Cancer Biology, National Clinical Research Center for Digestive Diseases and Xijing Hospital of Digestive Diseases, Fourth Military Medical University, Xi'an, Shaanxi 710032, China
| | - Peter Manza
- Laboratory of Neuroimaging, National Institute on Alcohol Abuse and Alcoholism, Bethesda, MD 20892, USA
| | - Guanya Li
- Center for Brain Imaging, School of Life Science and Technology, Xidian University, Xi'an, Shaanxi 710071, China
| | - Yang Hu
- Center for Brain Imaging, School of Life Science and Technology, Xidian University, Xi'an, Shaanxi 710071, China
| | - Jia Wang
- Center for Brain Imaging, School of Life Science and Technology, Xidian University, Xi'an, Shaanxi 710071, China
| | - Ganggang Lv
- Center for Brain Imaging, School of Life Science and Technology, Xidian University, Xi'an, Shaanxi 710071, China
| | - Yang He
- Center for Brain Imaging, School of Life Science and Technology, Xidian University, Xi'an, Shaanxi 710071, China
| | - Karen M von Deneen
- Center for Brain Imaging, School of Life Science and Technology, Xidian University, Xi'an, Shaanxi 710071, China
| | - Yu Han
- Department of Radiology, Tangdu Hospital, The Fourth Military Medical University, Xi'an, Shaanxi 710038, China
| | - Guangbin Cui
- Department of Radiology, Tangdu Hospital, The Fourth Military Medical University, Xi'an, Shaanxi 710038, China
| | - Dardo Tomasi
- Laboratory of Neuroimaging, National Institute on Alcohol Abuse and Alcoholism, Bethesda, MD 20892, USA
| | - Nora D Volkow
- Laboratory of Neuroimaging, National Institute on Alcohol Abuse and Alcoholism, Bethesda, MD 20892, USA
| | - Yongzhan Nie
- State Key Laboratory of Cancer Biology, National Clinical Research Center for Digestive Diseases and Xijing Hospital of Digestive Diseases, Fourth Military Medical University, Xi'an, Shaanxi 710032, China
| | - Gene-Jack Wang
- Laboratory of Neuroimaging, National Institute on Alcohol Abuse and Alcoholism, Bethesda, MD 20892, USA
| | - Yi Zhang
- Center for Brain Imaging, School of Life Science and Technology, Xidian University, Xi'an, Shaanxi 710071, China
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21
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Nenning KH, Xu T, Schwartz E, Arroyo J, Woehrer A, Franco AR, Vogelstein JT, Margulies DS, Liu H, Smallwood J, Milham MP, Langs G. Joint embedding: A scalable alignment to compare individuals in a connectivity space. Neuroimage 2020; 222:117232. [PMID: 32771618 PMCID: PMC7779372 DOI: 10.1016/j.neuroimage.2020.117232] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2020] [Revised: 07/31/2020] [Accepted: 08/02/2020] [Indexed: 11/15/2022] Open
Abstract
A common coordinate space enabling comparison across individuals is vital to understanding human brain organization and individual differences. By leveraging dimensionality reduction algorithms, high-dimensional fMRI data can be represented in a low-dimensional space to characterize individual features. Such a representative space encodes the functional architecture of individuals and enables the observation of functional changes across time. However, determining comparable functional features across individuals in resting-state fMRI in a way that simultaneously preserves individual-specific connectivity structure can be challenging. In this work we propose scalable joint embedding to simultaneously embed multiple individual brain connectomes within a common space that allows individual representations across datasets to be aligned. Using Human Connectome Project data, we evaluated the joint embedding approach by comparing it to the previously established orthonormal alignment model. Alignment using joint embedding substantially increased the similarity of functional representations across individuals while simultaneously capturing their distinct profiles, allowing individuals to be more discriminable from each other. Additionally, we demonstrated that the common space established using resting-state fMRI provides a better overlap of task-activation across participants. Finally, in a more challenging scenario - alignment across a lifespan cohort aged from 6 to 85 - joint embedding provided a better prediction of age (r2 = 0.65) than the prior alignment model. It facilitated the characterization of functional trajectories across lifespan. Overall, these analyses establish that joint embedding can simultaneously capture individual neural representations in a common connectivity space aligning functional data across participants and populations and preserve individual specificity.
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Affiliation(s)
- Karl-Heinz Nenning
- Computational Imaging Research Lab, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria.
| | - Ting Xu
- Center for the Developing Brain, Child Mind Institute, New York, NY, USA.
| | - Ernst Schwartz
- Computational Imaging Research Lab, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Jesus Arroyo
- Center for Imaging Science, Johns Hopkins University, Baltimore, MD, USA
| | - Adelheid Woehrer
- Division of Neuropathology and Neurochemistry, Department of Neurology, Medical University of Vienna, Vienna, Austria
| | - Alexandre R Franco
- Center for the Developing Brain, Child Mind Institute, New York, NY, USA; Center for Biomedical Imaging and Neuromodulation, Nathan Kline Institute, Orangeburg, NY, USA; Department of Psychiatry, NYU Langone School of Medicine, New York, NY, USA
| | - Joshua T Vogelstein
- Department of Biomedical Engineering, Institute for Computational Medicine, Kavli Neuroscience Discovery Institute, Johns Hopkins University, Baltimore, MD, USA
| | - Daniel S Margulies
- Centre National de la Recherche Scientifique, Frontlab, Institut du Cerveau et de la Moelle Epinière, Paris, France
| | - Hesheng Liu
- A.A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | | | - Michael P Milham
- Center for the Developing Brain, Child Mind Institute, New York, NY, USA; Center for Biomedical Imaging and Neuromodulation, Nathan Kline Institute, Orangeburg, NY, USA
| | - Georg Langs
- Computational Imaging Research Lab, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria; Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA.
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22
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Lund MJ, Alnæs D, Schwab S, van der Meer D, Andreassen OA, Westlye LT, Kaufmann T. Differences in directed functional brain connectivity related to age, sex and mental health. Hum Brain Mapp 2020; 41:4173-4186. [PMID: 32613721 PMCID: PMC7502836 DOI: 10.1002/hbm.25116] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2020] [Revised: 06/07/2020] [Accepted: 06/16/2020] [Indexed: 01/09/2023] Open
Abstract
Functional interconnections between brain regions define the "connectome" which is of central interest for understanding human brain function. Resting-state functional magnetic resonance (rsfMRI) work has revealed changes in static connectivity related to age, sex, cognitive abilities and psychiatric symptoms, yet little is known how these factors may alter the information flow. The commonly used approach infers functional brain connectivity using stationary coefficients yielding static estimates of the undirected connection strength between brain regions. Dynamic graphical models (DGMs) are a multivariate model with dynamic coefficients reflecting directed temporal associations between nodes, and can yield novel insight into directed functional connectivity. Here, we leveraged this approach to test for associations between edge-wise estimates of direction flow across the functional connectome and age, sex, intellectual abilities and mental health. We applied DGM to investigate patterns of information flow in data from 984 individuals from the Human Connectome Project (HCP) and 10,249 individuals from the UK Biobank. Our analysis yielded patterns of directed connectivity in independent HCP and UK Biobank data similar to those previously reported, including that the cerebellum consistently receives information from other networks. We show robust associations between information flow and age and sex for several connections, with strongest effects of age observed in the sensorimotor network. Visual, auditory and sensorimotor nodes were also linked to mental health. Our findings support the use of DGM as a measure of directed connectivity in rsfMRI data and provide new insight into the shaping of the connectome during aging.
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Affiliation(s)
- Martina J. Lund
- Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and AddictionOslo University Hospital, and Institute of Clinical Medicine, University of OsloOsloNorway
| | - Dag Alnæs
- Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and AddictionOslo University Hospital, and Institute of Clinical Medicine, University of OsloOsloNorway
- Bjørknes University CollegeOsloNorway
| | - Simon Schwab
- Center for Reproducible Science (CRS) & Epidemiology, Biostatistics and Prevention Institute (EBPI)University of ZürichZurichSwitzerland
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Population HealthUniversity of OxfordOxfordUK
| | - Dennis van der Meer
- Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and AddictionOslo University Hospital, and Institute of Clinical Medicine, University of OsloOsloNorway
- School of Mental Health and Neuroscience, Faculty of Health, Medicine and Life SciencesMaastricht UniversityMaastrichtThe Netherlands
| | - Ole A. Andreassen
- Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and AddictionOslo University Hospital, and Institute of Clinical Medicine, University of OsloOsloNorway
- KG Jebsen Centre for neurodevelopmental disorders, University of OsloOsloNorway
| | - Lars T. Westlye
- Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and AddictionOslo University Hospital, and Institute of Clinical Medicine, University of OsloOsloNorway
- KG Jebsen Centre for neurodevelopmental disorders, University of OsloOsloNorway
- Department of PsychologyUniversity of OsloOsloNorway
| | - Tobias Kaufmann
- Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and AddictionOslo University Hospital, and Institute of Clinical Medicine, University of OsloOsloNorway
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23
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MacCormack JK, Stein AG, Kang J, Giovanello KS, Satpute AB, Lindquist KA. Affect in the Aging Brain: A Neuroimaging Meta-Analysis of Older Vs. Younger Adult Affective Experience and Perception. AFFECTIVE SCIENCE 2020; 1:128-154. [PMID: 36043210 PMCID: PMC9382982 DOI: 10.1007/s42761-020-00016-8] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/24/2020] [Accepted: 08/20/2020] [Indexed: 05/15/2023]
Abstract
We report the first functional neuroimaging meta-analysis on age-related differences in adult neural activity during affect. We identified and coded experimental contrasts from 27 studies (published 1997-2018) with 490 older adults (55-87 years, M age = 69 years) and 470 younger adults (18-39 years, M age = 24 years). Using multilevel kernel density analysis, we assessed functional brain activation contrasts for older vs. younger adult affect across in-scanner tasks (i.e., affect induction and perception). Relative to older adults, younger adults showed more reliable activation in subcortical structures (e.g., amygdala, thalamus, caudate) and in relatively more posterior aspects of specific brain structures (e.g., posterior insula, mid- and posterior cingulate). In contrast, older adults exhibited more reliable activation in the prefrontal cortex and more anterior aspects of specific brain structures (e.g., anterior insula, anterior cingulate). Meta-analytic coactivation network analyses further revealed that in younger adults, the amygdala and mid-cingulate were more central, locally efficient network nodes, whereas in older adults, regions in the superior and medial prefrontal cortex were more central, locally efficient network nodes. Collectively, these findings help characterize age differences in the brain basis of affect and provide insights for future investigations into the neural mechanisms underlying affective aging.
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Affiliation(s)
- Jennifer K. MacCormack
- Department of Psychiatry, University of Pittsburgh, 506 Old Engineering Hall, 3943 O’Hara St, Pittsburgh, PA 15213 USA
- Department of Psychology and Neuroscience, University of North Carolina at Chapel Hill, Chapel Hill, NC USA
| | - Andrea G. Stein
- Department of Psychology, University of Wisconsin-Madison, Madison, WI USA
| | - Jian Kang
- Department of Biostatistics, University of Michigan, Ann Arbor, MI USA
| | - Kelly S. Giovanello
- Department of Psychology and Neuroscience, University of North Carolina at Chapel Hill, Chapel Hill, NC USA
- Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC USA
| | - Ajay B. Satpute
- Department of Psychology, Northeastern University, Boston, MA USA
| | - Kristen A. Lindquist
- Department of Psychology and Neuroscience, University of North Carolina at Chapel Hill, Chapel Hill, NC USA
- Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC USA
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24
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Wang L, Li X, Zhu Y, Lin B, Bo Q, Li F, Wang C. Discriminative Analysis of Symptom Severity and Ultra-High Risk of Schizophrenia Using Intrinsic Functional Connectivity. Int J Neural Syst 2020; 30:2050047. [PMID: 32689843 DOI: 10.1142/s0129065720500471] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
Abstract
Past studies have consistently shown functional dysconnectivity of large-scale brain networks in schizophrenia. In this study, we aimed to further assess whether multivariate pattern analysis (MVPA) could yield a sensitive predictor of patient symptoms, as well as identify ultra-high risk (UHR) stage of schizophrenia from intrinsic functional connectivity of whole-brain networks. We first combined rank-based feature selection and support vector machine methods to distinguish between 43 schizophrenia patients and 52 healthy controls. The constructed classifier was then applied to examine functional connectivity profiles of 18 UHR individuals. The classifier indicated reliable relationship between MVPA measures and symptom severity, with higher classification accuracy in more severely affected schizophrenia patients. The UHR subjects had classification scores falling between those of healthy controls and patients, suggesting an intermediate level of functional brain abnormalities. Moreover, UHR individuals with schizophrenia-like connectivity profiles at baseline presented higher rate of conversion to full-blown illness in the follow-up visits. Spatial maps of discriminative brain regions implicated increases of functional connectivity in the default mode network, whereas decreases of functional connectivity in the cerebellum, thalamus and visual areas in schizophrenia. The findings may have potential utility in the early diagnosis and intervention of schizophrenia.
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Affiliation(s)
- Lubin Wang
- Institute of Military Cognition and Brain Sciences, Academy of Military Medical Sciences, Beijing 100850, P. R. China
| | - Xianbin Li
- The National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing 100088, P. R. China
| | - Yuyang Zhu
- Institute of Military Cognition and Brain Sciences, Academy of Military Medical Sciences, Beijing 100850, P. R. China
| | - Bei Lin
- Institute of Military Cognition and Brain Sciences, Academy of Military Medical Sciences, Beijing 100850, P. R. China
| | - Qijing Bo
- The National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing 100088, P. R. China
| | - Feng Li
- The National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing 100088, P. R. China
| | - Chuanyue Wang
- The National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing 100088, P. R. China
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25
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Wen X, He H, Dong L, Chen J, Yang J, Guo H, Luo C, Yao D. Alterations of local functional connectivity in lifespan: A resting-state fMRI study. Brain Behav 2020; 10:e01652. [PMID: 32462815 PMCID: PMC7375100 DOI: 10.1002/brb3.1652] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/14/2019] [Revised: 04/08/2020] [Accepted: 04/13/2020] [Indexed: 12/24/2022] Open
Abstract
INTRODUCTION As aging attracted attention globally, revealing changes in brain function across the lifespan was largely concerned. In this study, we aimed to reveal the changes of functional networks of the brain (via local functional connectivity, local FC) in lifespan and explore the mechanism underlying them. MATERIALS AND METHODS A total of 523 healthy participants (258 males and 265 females) aged 18-88 years from part of the Cambridge Center for Ageing and Neuroscience (CamCAN) were involved in this study. Next, two data-driven measures of local FC, local functional connectivity density (lFCD) and four-dimensional spatial-temporal consistency of local neural activity (FOCA), were calculated, and then, general linear models were used to assess the changes of them in lifespan. RESULTS Local functional connectivity (lFCD and FOCA) within visual networks (VN), sensorimotor network (SMN), and default mode network (DMN) decreased across the lifespan, while within basal ganglia network (BGN), local connectivity was increased across the lifespan. And, the fluid intelligence decreased within BGN while increased within VN, SMN, and DMN. CONCLUSION These results might suggest that the decline of executive control and intrinsic cognitive ability in the aging population was related to the decline of functional connectivity in VN, SMN, and DMN. Meanwhile, BGN might play a regulatory role in the aging process to compensate for the dysfunction of other functional systems. Our findings may provide important neuroimaging evidence for exploring the brain functional mechanism in lifespan.
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Affiliation(s)
- Xin Wen
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, China
| | - Hui He
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China
| | - Li Dong
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China.,School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Junjie Chen
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, China
| | - Jie Yang
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Hao Guo
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, China
| | - Cheng Luo
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China.,School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Dezhong Yao
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China.,School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
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26
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Faghiri A, Stephen JM, Wang YP, Wilson TW, Calhoun VD. Brain Development Includes Linear and Multiple Nonlinear Trajectories: A Cross-Sectional Resting-State Functional Magnetic Resonance Imaging Study. Brain Connect 2020; 9:777-788. [PMID: 31744324 DOI: 10.1089/brain.2018.0641] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/30/2023] Open
Abstract
Studies of brain structure have shown that the cortex matures in both a linear and nonlinear manner depending on the time window and specific region studied. In addition, it has been shown that socioeconomic status can impact brain development throughout childhood. However, very few studies have evaluated these patterns using functional measures. To this end, in this study we used cross-sectional resting-state functional magnetic resonance imaging data of 368 subjects, age 3-21 years, to examine the linear and nonlinear development of brain connectivity. We employed a clustering approach to characterize these developmental patterns into different linear and nonlinear groups. Our results showed that functional brain development exhibits multiple types of linear and nonlinear patterns, and assuming that brain connectivity values reach a stable state after a specific age might be misleading.
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Affiliation(s)
- Ashkan Faghiri
- The Mind Research Network, Albuquerque, New Mexico.,Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, New Mexico
| | | | - Yu-Ping Wang
- Department of Biomedical Engineering, Tulane University, New Orleans, Louisiana.,Center for Genomics and Bioinformatics, Tulane University, New Orleans, Louisiana
| | - Tony W Wilson
- Department of Neurological Sciences, University of Nebraska Medical Center, Omaha, Nebraska.,Center for Magnetoencephalography, University of Nebraska Medical Center, Omaha, Nebraska
| | - Vince D Calhoun
- The Mind Research Network, Albuquerque, New Mexico.,Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, New Mexico
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27
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Luo N, Sui J, Abrol A, Lin D, Chen J, Vergara VM, Fu Z, Du Y, Damaraju E, Xu Y, Turner JA, Calhoun VD. Age-related structural and functional variations in 5,967 individuals across the adult lifespan. Hum Brain Mapp 2020; 41:1725-1737. [PMID: 31876339 PMCID: PMC7267948 DOI: 10.1002/hbm.24905] [Citation(s) in RCA: 37] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2019] [Revised: 11/24/2019] [Accepted: 12/10/2019] [Indexed: 12/13/2022] Open
Abstract
Exploring brain changes across the human lifespan is becoming an important topic in neuroscience. Though there are multiple studies which investigated the relationship between age and brain imaging, the results are heterogeneous due to small sample sizes and relatively narrow age ranges. Here, based on year-wise estimation of 5,967 subjects from 13 to 72 years old, we aimed to provide a more precise description of adult lifespan variation trajectories of gray matter volume (GMV), structural network correlation (SNC), and functional network connectivity (FNC) using independent component analysis and multivariate linear regression model. Our results revealed the following relationships: (a) GMV linearly declined with age in most regions, while parahippocampus showed an inverted U-shape quadratic relationship with age; SNC presented a U-shape quadratic relationship with age within cerebellum, and inverted U-shape relationship primarily in the default mode network (DMN) and frontoparietal (FP) related correlation. (b) FNC tended to linearly decrease within resting-state networks (RSNs), especially in the visual network and DMN. Early increase was revealed between RSNs, primarily in FP and DMN, which experienced a decrease at older ages. U-shape relationship was also revealed to compensate for the cognition deficit in attention and subcortical related connectivity at late years. (c) The link between middle occipital gyrus and insula, as well as precuneus and cerebellum, exhibited similar changing trends between SNC and FNC across the adult lifespan. Collectively, these results highlight the benefit of lifespan study and provide a precise description of age-related regional variation and SNC/FNC changes based on a large dataset.
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Affiliation(s)
- Na Luo
- Brainnetome Center and National Laboratory of Pattern RecognitionInstitute of Automation, Chinese Academy of SciencesBeijingChina
- University of Chinese Academy of SciencesBeijingChina
| | - Jing Sui
- Brainnetome Center and National Laboratory of Pattern RecognitionInstitute of Automation, Chinese Academy of SciencesBeijingChina
- University of Chinese Academy of SciencesBeijingChina
- CAS Center for Excellence in Brain Science and Intelligence TechnologyInstitute of Automation, Chinese Academy of SciencesBeijingChina
| | - Anees Abrol
- Tri‐Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS): Georgia State University, Georgia Institute of Technology, and Emory UniversityAtlantaGeorgia
| | - Dongdong Lin
- Tri‐Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS): Georgia State University, Georgia Institute of Technology, and Emory UniversityAtlantaGeorgia
| | - Jiayu Chen
- Tri‐Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS): Georgia State University, Georgia Institute of Technology, and Emory UniversityAtlantaGeorgia
| | - Victor M. Vergara
- CAS Center for Excellence in Brain Science and Intelligence TechnologyInstitute of Automation, Chinese Academy of SciencesBeijingChina
| | - Zening Fu
- Tri‐Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS): Georgia State University, Georgia Institute of Technology, and Emory UniversityAtlantaGeorgia
| | - Yuhui Du
- Tri‐Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS): Georgia State University, Georgia Institute of Technology, and Emory UniversityAtlantaGeorgia
- School of Computer and Information TechnologyShanxi UniversityTaiyuanChina
| | - Eswar Damaraju
- Tri‐Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS): Georgia State University, Georgia Institute of Technology, and Emory UniversityAtlantaGeorgia
| | - Yong Xu
- Department of PsychiatryFirst Clinical Medical College/ First Hospital of Shanxi Medical UniversityTaiyuanChina
| | - Jessica A. Turner
- Department of PsychologyNeuroscience Institute, Georgia State UniversityAtlantaGeorgia
| | - Vince D. Calhoun
- Tri‐Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS): Georgia State University, Georgia Institute of Technology, and Emory UniversityAtlantaGeorgia
- Department of PsychiatryYale University, School of MedicineNew HavenConnecticut
- Department of Psychology, Computer ScienceNeuroscience Institute, and Physics, Georgia State UniversityAtlantaGeorgia
- Department of Electrical and Computer EngineeringGeorgia Institute of TechnologyAtlantaGeorgia
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28
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Zhang J, Andreano JM, Dickerson BC, Touroutoglou A, Barrett LF. Stronger Functional Connectivity in the Default Mode and Salience Networks Is Associated With Youthful Memory in Superaging. Cereb Cortex 2020; 30:72-84. [PMID: 31058917 PMCID: PMC7029690 DOI: 10.1093/cercor/bhz071] [Citation(s) in RCA: 36] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2018] [Revised: 03/08/2019] [Accepted: 03/10/2019] [Indexed: 12/19/2022] Open
Abstract
"Superagers" are older adults who, despite their advanced age, maintain youthful memory. Previous morphometry studies revealed multiple default mode network (DMN) and salience network (SN) regions whose cortical thickness is greater in superagers and correlates with memory performance. In this study, we examined the intrinsic functional connectivity within DMN and SN in 41 young (24.5 ± 3.6 years old) and 40 older adults (66.9 ± 5.5 years old). Superaging was defined as youthful performance on a memory recall task, the California Verbal Learning Test (CVLT). Participants underwent a resting-state functional magnetic resonance imaging (fMRI) scan and performed a separate visual-verbal recognition memory task. As predicted, within both DMN and SN, superagers had stronger connectivity compared with typical older adults and similar connectivity compared with young adults. Superagers also performed similarly to young adults and better than typical older adults on the recognition task, demonstrating youthful episodic memory that generalized across memory tasks. Stronger connectivity within each network independently predicted better performance on both the CVLT and recognition task in older adults. Variation in intrinsic connectivity explained unique variance in memory performance, above and beyond youthful neuroanatomy. These results extend our understanding of the neural basis of superaging as a model of successful aging.
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Affiliation(s)
- Jiahe Zhang
- Department of Psychology, Northeastern University, Boston, MA, USA
| | - Joseph M Andreano
- Psychiatric Neuroimaging Division, Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA, USA
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA, USA
| | - Bradford C Dickerson
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA, USA
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA
- Frontotemporal Disorders Unit, Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA, USA
| | - Alexandra Touroutoglou
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA, USA
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA
- Frontotemporal Disorders Unit, Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA, USA
| | - Lisa Feldman Barrett
- Department of Psychology, Northeastern University, Boston, MA, USA
- Psychiatric Neuroimaging Division, Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA, USA
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA, USA
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29
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Straathof M, Sinke MRT, Roelofs TJM, Blezer ELA, Sarabdjitsingh RA, van der Toorn A, Schmitt O, Otte WM, Dijkhuizen RM. Distinct structure-function relationships across cortical regions and connectivity scales in the rat brain. Sci Rep 2020; 10:56. [PMID: 31919379 PMCID: PMC6952407 DOI: 10.1038/s41598-019-56834-9] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2019] [Accepted: 12/16/2019] [Indexed: 01/08/2023] Open
Abstract
An improved understanding of the structure-function relationship in the brain is necessary to know to what degree structural connectivity underpins abnormal functional connectivity seen in disorders. We integrated high-field resting-state fMRI-based functional connectivity with high-resolution macro-scale diffusion-based and meso-scale neuronal tracer-based structural connectivity, to obtain an accurate depiction of the structure-function relationship in the rat brain. Our main goal was to identify to what extent structural and functional connectivity strengths are correlated, macro- and meso-scopically, across the cortex. Correlation analyses revealed a positive correspondence between functional and macro-scale diffusion-based structural connectivity, but no significant correlation between functional connectivity and meso-scale neuronal tracer-based structural connectivity. Zooming in on individual connections, we found strong functional connectivity in two well-known resting-state networks: the sensorimotor and default mode network. Strong functional connectivity within these networks coincided with strong short-range intrahemispheric structural connectivity, but with weak heterotopic interhemispheric and long-range intrahemispheric structural connectivity. Our study indicates the importance of combining measures of connectivity at distinct hierarchical levels to accurately determine connectivity across networks in the healthy and diseased brain. Although characteristics of the applied techniques may affect where structural and functional networks (dis)agree, distinct structure-function relationships across the brain could also have a biological basis.
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Affiliation(s)
- Milou Straathof
- Biomedical MR Imaging and Spectroscopy Group, Center for Image Sciences, University Medical Center Utrecht and Utrecht University, Utrecht, The Netherlands.
| | - Michel R T Sinke
- Biomedical MR Imaging and Spectroscopy Group, Center for Image Sciences, University Medical Center Utrecht and Utrecht University, Utrecht, The Netherlands
| | - Theresia J M Roelofs
- Biomedical MR Imaging and Spectroscopy Group, Center for Image Sciences, University Medical Center Utrecht and Utrecht University, Utrecht, The Netherlands.,Department of Translational Neuroscience, UMC Utrecht Brain Center, University Medical Center Utrecht and Utrecht University, Utrecht, the Netherlands
| | - Erwin L A Blezer
- Biomedical MR Imaging and Spectroscopy Group, Center for Image Sciences, University Medical Center Utrecht and Utrecht University, Utrecht, The Netherlands
| | - R Angela Sarabdjitsingh
- Department of Translational Neuroscience, UMC Utrecht Brain Center, University Medical Center Utrecht and Utrecht University, Utrecht, the Netherlands
| | - Annette van der Toorn
- Biomedical MR Imaging and Spectroscopy Group, Center for Image Sciences, University Medical Center Utrecht and Utrecht University, Utrecht, The Netherlands
| | - Oliver Schmitt
- Department of Anatomy, University of Rostock, Rostock, Germany
| | - Willem M Otte
- Biomedical MR Imaging and Spectroscopy Group, Center for Image Sciences, University Medical Center Utrecht and Utrecht University, Utrecht, The Netherlands.,Department of Pediatric Neurology, UMC Utrecht Brain Center, University Medical Center Utrecht and Utrecht University, Utrecht, the Netherlands
| | - Rick M Dijkhuizen
- Biomedical MR Imaging and Spectroscopy Group, Center for Image Sciences, University Medical Center Utrecht and Utrecht University, Utrecht, The Netherlands.
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30
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Khosla M, Jamison K, Ngo GH, Kuceyeski A, Sabuncu MR. Machine learning in resting-state fMRI analysis. Magn Reson Imaging 2019; 64:101-121. [PMID: 31173849 PMCID: PMC6875692 DOI: 10.1016/j.mri.2019.05.031] [Citation(s) in RCA: 100] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2018] [Revised: 05/20/2019] [Accepted: 05/21/2019] [Indexed: 12/13/2022]
Abstract
Machine learning techniques have gained prominence for the analysis of resting-state functional Magnetic Resonance Imaging (rs-fMRI) data. Here, we present an overview of various unsupervised and supervised machine learning applications to rs-fMRI. We offer a methodical taxonomy of machine learning methods in resting-state fMRI. We identify three major divisions of unsupervised learning methods with regard to their applications to rs-fMRI, based on whether they discover principal modes of variation across space, time or population. Next, we survey the algorithms and rs-fMRI feature representations that have driven the success of supervised subject-level predictions. The goal is to provide a high-level overview of the burgeoning field of rs-fMRI from the perspective of machine learning applications.
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Affiliation(s)
- Meenakshi Khosla
- School of Electrical and Computer Engineering, Cornell University, United States of America
| | - Keith Jamison
- Radiology, Weill Cornell Medical College, United States of America
| | - Gia H Ngo
- School of Electrical and Computer Engineering, Cornell University, United States of America
| | - Amy Kuceyeski
- Radiology, Weill Cornell Medical College, United States of America; Brain and Mind Research Institute, Weill Cornell Medical College, United States of America
| | - Mert R Sabuncu
- School of Electrical and Computer Engineering, Cornell University, United States of America; Nancy E. & Peter C. Meinig School of Biomedical Engineering, Cornell University, United States of America.
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31
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Functional brain networks in never-treated and treated long-term Ill schizophrenia patients. Neuropsychopharmacology 2019; 44:1940-1947. [PMID: 31163450 PMCID: PMC6784906 DOI: 10.1038/s41386-019-0428-2] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/29/2019] [Revised: 05/19/2019] [Accepted: 05/23/2019] [Indexed: 02/05/2023]
Abstract
This study compared the topological organization of brain function in never-treated and treated long-term schizophrenia patients. In a cross-sectional study, 21 never-treated schizophrenia patients with illness duration over 5 years, 26 illness duration-matched antipsychotic-treated patients and 24 demographically-matched healthy controls underwent a resting-state functional magnetic resonance imaging (MRI) scan. The topological properties of brain functional networks were compared across groups, and then we tested for differential age-related effects in regions with significant group differences. Both never-treated and antipsychotic-treated schizophrenia patient groups showed altered nodal centralities in left pre-/postcentral gyri relative to controls. Never-treated patients demonstrated reduced global efficacy, decreased nodal centralities in right amygdala/hippocampus and bilateral putamen/caudate relative to antipsychotic-treated patients and controls. No significant relationships of age and altered functional metrics were seen in either patient group, and no alterations were greater in the treated group. These findings provide insight into brain function deficits over the longer-term course of schizophrenia independent from potential effects of antipsychotic medication. The presence of greater alterations in never-treated than treated patients suggests that long-term antipsychotic treatment may partially protect or enhance brain global and nodal topological function over the course of schizophrenia, notably involving the amygdala, hippocampus, and striatum that have long been associated with the disorder.
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32
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Colon E, Ludwick A, Wilcox SL, Youssef AM, Danehy A, Fair DA, Lebel AA, Burstein R, Becerra L, Borsook D. Migraine in the Young Brain: Adolescents vs. Young Adults. Front Hum Neurosci 2019; 13:87. [PMID: 30967767 PMCID: PMC6438928 DOI: 10.3389/fnhum.2019.00087] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2018] [Accepted: 02/20/2019] [Indexed: 12/14/2022] Open
Abstract
Migraine is a disease that peaks in late adolescence and early adulthood. The aim of this study was to evaluate age-related brain changes in resting state functional connectivity (rs-FC) in migraineurs vs. age-sex matched healthy controls at two developmental stages: adolescence vs. young adulthood. The effect of the disease was assessed within each developmental group and age- and sex-matched healthy controls and between developmental groups (migraine-related age effects). Globally the within group comparisons indicated more widespread abnormal rs-FC in the adolescents than in the young adults and more abnormal rs-FC associated with sensory networks in the young adults. Direct comparison of the two groups showed a number of significant changes: (1) more connectivity changes in the default mode network in the adolescents than in the young adults; (2) stronger rs-FC in the cerebellum network in the adolescents in comparison to young adults; and (3) stronger rs-FC in the executive and sensorimotor network in the young adults. The duration and frequency of the disease were differently associated with baseline intrinsic connectivity in the two groups. fMRI resting state networks demonstrate significant changes in brain function at critical time point of brain development and that potentially different treatment responsivity for the disease may result.
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Affiliation(s)
- Elisabeth Colon
- Department of Anesthesiology, Perioperative and Pain Medicine, Center for Pain and the Brain, Boston Children's Hospital, Harvard Medical School, Boston, MA, United States
| | - Allison Ludwick
- Department of Anesthesiology, Perioperative and Pain Medicine, Center for Pain and the Brain, Boston Children's Hospital, Harvard Medical School, Boston, MA, United States
| | - Sophie L Wilcox
- Department of Anesthesiology, Perioperative and Pain Medicine, Center for Pain and the Brain, Boston Children's Hospital, Harvard Medical School, Boston, MA, United States
| | - Andrew M Youssef
- Department of Anesthesiology, Perioperative and Pain Medicine, Center for Pain and the Brain, Boston Children's Hospital, Harvard Medical School, Boston, MA, United States
| | - Amy Danehy
- Department of Radiology, Boston Children's Hospital, Boston, MA, United States
| | - Damien A Fair
- Department of Behavioral Neuroscience, Oregon Health & Science University, Portland, OR, United States
| | - Alyssa A Lebel
- Department of Anesthesiology, Perioperative and Pain Medicine, Center for Pain and the Brain, Boston Children's Hospital, Harvard Medical School, Boston, MA, United States.,Pediatric Headache Program, Department of Anesthesiology, Perioperative and Pain Medicine, Boston Children's Hospital, Waltham, MA, United States.,Department of Neurology, Boston Children's Hospital, Waltham, MA, United States
| | - Rami Burstein
- Department of Anesthesia, Critical Care and Pain Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, United States
| | - Lino Becerra
- Department of Anesthesiology, Perioperative and Pain Medicine, Center for Pain and the Brain, Boston Children's Hospital, Harvard Medical School, Boston, MA, United States
| | - David Borsook
- Department of Anesthesiology, Perioperative and Pain Medicine, Center for Pain and the Brain, Boston Children's Hospital, Harvard Medical School, Boston, MA, United States.,Pediatric Headache Program, Department of Anesthesiology, Perioperative and Pain Medicine, Boston Children's Hospital, Waltham, MA, United States.,Department of Neurology, Boston Children's Hospital, Waltham, MA, United States
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33
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Zhai J, Li K. Predicting Brain Age Based on Spatial and Temporal Features of Human Brain Functional Networks. Front Hum Neurosci 2019; 13:62. [PMID: 30863296 PMCID: PMC6399206 DOI: 10.3389/fnhum.2019.00062] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2018] [Accepted: 02/05/2019] [Indexed: 12/01/2022] Open
Abstract
The organization of human brain networks can be measured by capturing correlated brain activity with functional MRI data. There have been a variety of studies showing that human functional connectivities undergo an age-related change over development. In the present study, we employed resting-state functional MRI data to construct functional network models. Principal component analysis was performed on the FC matrices across all the subjects to explore meaningful components especially correlated with age. Coefficients across the components, edge features after a newly proposed feature reduction method as well as temporal features based on fALFF, were extracted as predictor variables and three different regression models were learned to make prediction of brain age. We observed that individual's functional network architecture was shaped by intrinsic component, age-related component and other components and the predictive models extracted sufficient information to provide comparatively accurate predictions of brain age.
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Affiliation(s)
- Jian Zhai
- School of Mathematical Science, Zhejiang University, Hangzhou, China
| | - Ke Li
- School of Mathematical Science, Zhejiang University, Hangzhou, China
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34
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Quantification of the Biological Age of the Brain Using Neuroimaging. HEALTHY AGEING AND LONGEVITY 2019. [DOI: 10.1007/978-3-030-24970-0_19] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
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35
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Touroutoglou A, Zhang J, Andreano JM, Dickerson BC, Barrett LF. Dissociable Effects of Aging on Salience Subnetwork Connectivity Mediate Age-Related Changes in Executive Function and Affect. Front Aging Neurosci 2018; 10:410. [PMID: 30618717 PMCID: PMC6304391 DOI: 10.3389/fnagi.2018.00410] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2018] [Accepted: 11/28/2018] [Indexed: 12/18/2022] Open
Abstract
Aging is associated with both changes in affective experience and attention. An intrinsic brain network subserving these functions, the salience network, has not shown clear evidence of a corresponding age-related change. We propose a solution to this discrepancy: that aging differentially affects the connectivity of two dissociated subsystems of the salience network identified in our prior research (Touroutoglou et al., 2012). We examined the age-related changes in intrinsic connectivity between a dorsal and a ventral salience subsystem in a sample of 111 participants ranging in age from 18 years to 81 years old. We predicted that connectivity within the ventral subsystem is relatively preserved with age, while connectivity in the dorsal subsystem declines. Our findings showed that the connectivity within the ventral subsystem was not only preserved but it actually increased with age, whereas the connectivity within the dorsal subsystem decreased with age. Furthermore, age-related increase in arousal experience was partially mediated by age-related increases in ventral salience subsystem, whereas age-related decline in executive function was fully mediated by age-related decreases in dorsal salience subsystem connectivity. These findings explain previously conflicting results on age-related changes in the salience network, and suggest a mechanism for relatively preserved affective function in the elderly.
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Affiliation(s)
- Alexandra Touroutoglou
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States
| | - Jiahe Zhang
- Department of Psychology, Northeastern University, Boston, MA, United States
| | - Joseph M. Andreano
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States
- Psychiatric Neuroimaging Division, Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States
| | - Bradford C. Dickerson
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States
- Frontotemporal Disorders Unit, Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States
| | - Lisa Feldman Barrett
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States
- Department of Psychology, Northeastern University, Boston, MA, United States
- Psychiatric Neuroimaging Division, Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States
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36
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Joseph JE, Vanderweyen D, Swearingen J, Vaughan BK, Novo D, Zhu X, Gebregziabher M, Bonilha L, Bhatt R, Naselaris T, Dean B. Tracking the Development of Functional Connectomes for Face Processing. Brain Connect 2018; 9:231-239. [PMID: 30489152 DOI: 10.1089/brain.2018.0607] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Face processing capacities become more specialized and advanced during development, but neural underpinnings of these processes are not fully understood. The present study applied graph theory-based network analysis to task-negative (resting blocks) and task-positive (viewing faces) functional magnetic resonance imaging data in children (5-17 years) and adults (18-42 years) to test the hypothesis that the development of a specialized network for face processing is driven by task-positive processing (face viewing) more than by task-negative processing (visual fixation) and by both progressive and regressive changes in network properties. Predictive modeling was used to predict age from node-based network properties derived from task-positive and task-negative states in a whole-brain network (WBN) and a canonical face network (FN). The best-fitting model indicated that FN maturation was marked by both progressive and regressive changes in information diffusion (eigenvector centrality) in the task-positive state, with regressive changes outweighing progressive changes. Hence, FN maturation was characterized by reductions in information diffusion potentially reflecting the development of more specialized modules. In contrast, WBN maturation was marked by a balance of progressive and regressive changes in hub-connectivity (betweenness centrality) in the task-negative state. These findings suggest that the development of specialized networks like the FN depends on dynamic developmental changes associated with domain-specific information (e.g., face processing), but maturation of the brain as a whole can be predicted from task-free states.
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Affiliation(s)
- Jane E Joseph
- 1 Department of Neuroscience, Medical University of South Carolina, Charleston, South Carolina
| | - Davy Vanderweyen
- 1 Department of Neuroscience, Medical University of South Carolina, Charleston, South Carolina
| | - Joshua Swearingen
- 1 Department of Neuroscience, Medical University of South Carolina, Charleston, South Carolina
| | - Brandon K Vaughan
- 1 Department of Neuroscience, Medical University of South Carolina, Charleston, South Carolina
| | - Derek Novo
- 1 Department of Neuroscience, Medical University of South Carolina, Charleston, South Carolina
| | - Xun Zhu
- 1 Department of Neuroscience, Medical University of South Carolina, Charleston, South Carolina
| | - Mulugeta Gebregziabher
- 2 Department of Public Health Sciences, and Medical University of South Carolina, Charleston, South Carolina
| | - Leonardo Bonilha
- 3 Department of Neurology, Medical University of South Carolina, Charleston, South Carolina
| | - Ramesh Bhatt
- 4 Department of Psychology, University of Kentucky, Lexington, Kentucky
| | - Thomas Naselaris
- 1 Department of Neuroscience, Medical University of South Carolina, Charleston, South Carolina
| | - Brian Dean
- 5 Division of Computer Science, School of Computing, Clemson, South Carolina
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37
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Abstract
Qualitative and quantitative structural magnetic resonance imaging offer objective measures of the underlying neurodegeneration in atypical parkinsonism. Regional changes in tissue volume, signal changes and increased deposition of iron as assessed with different structural MRI techniques are surrogate markers of underlying neurodegeneration and may reflect cell loss, microglial proliferation and astroglial activation. Structural MRI has been explored as a tool to enhance diagnostic accuracy in differentiating atypical parkinsonian disorders (APDs). Moreover, the longitudinal assessment of serial structural MRI-derived parameters offers the opportunity for robust inferences regarding the progression of APDs. This review summarizes recent research findings as (1) a diagnostic tool for APDs as well as (2) as a tool to assess longitudinal changes of serial MRI-derived parameters in the different APDs.
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38
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Vij SG, Nomi JS, Dajani DR, Uddin LQ. Evolution of spatial and temporal features of functional brain networks across the lifespan. Neuroimage 2018; 173:498-508. [PMID: 29518568 PMCID: PMC6613816 DOI: 10.1016/j.neuroimage.2018.02.066] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2017] [Revised: 02/27/2018] [Accepted: 02/28/2018] [Indexed: 01/15/2023] Open
Abstract
Development and aging are associated with functional changes in the brain across the lifespan. These changes manifest in a variety of spatial and temporal features of resting state functional MRI (rs-fMRI) but have seldom been explored exhaustively. We present a comprehensive study assessing age-related changes in spatial and temporal features of blind-source separated components identified by independent vector analysis (IVA) in a cross-sectional lifespan sample (ages 6-85 years). We show that while large-scale network configurations remain consistent throughout the lifespan, changes persist in both local and global organization of these networks. We show that the spatial extent of the majority of functional networks exhibits linear decreases and both positive and negative quadratic trajectories across the lifespan. Network connectivity revealed nuanced patterns of linear and quadratic relationships with age, primarily in higher order cognitive networks. We also show divergent age-related patterns across the frequency spectrum in lower and higher frequencies. Taken together, these results point to the presence of sophisticated patterns of age-related changes that have previously not been considered collectively. We suggest that established patterns of lifespan changes in rs-fMRI features may be driven by changes in the spectral composition of BOLD signals.
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Affiliation(s)
- Shruti G Vij
- Department of Psychology, University of Miami, Coral Gables, FL 33124 USA.
| | - Jason S Nomi
- Department of Psychology, University of Miami, Coral Gables, FL 33124 USA
| | - Dina R Dajani
- Department of Psychology, University of Miami, Coral Gables, FL 33124 USA
| | - Lucina Q Uddin
- Department of Psychology, University of Miami, Coral Gables, FL 33124 USA; Neuroscience Program, University of Miami Miller School of Medicine, Miami, FL 33136, USA.
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Curvilinear locus coeruleus functional connectivity trajectories over the adult lifespan: a 7T MRI study. Neurobiol Aging 2018; 69:167-176. [PMID: 29908415 DOI: 10.1016/j.neurobiolaging.2018.05.021] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2017] [Revised: 05/16/2018] [Accepted: 05/17/2018] [Indexed: 10/16/2022]
Abstract
The locus coeruleus (LC) plays a crucial role in modulating several higher order cognitive functions via its widespread projections to the entire brain. We set out to investigate the hypothesis that LC functional connectivity (FC) may fluctuate nonlinearly with age and explored its relation to memory function. To that end, 49 cognitively healthy individuals (19-74 years) underwent ultra high-resolution 7T resting-state functional magnetic resonance imaging and cognitive testing. FC patterns from the LC to regions of the isodendritic core network and cortical regions were examined using region of interest-to-region of interest analyses. Curvilinear patterns with age were observed for FC between the left LC and cortical regions and the nucleus basalis of Meynert. A linear negative association was observed between age and LC-FC and ventral tegmental area. Higher levels of FC between the LC and nucleus basalis of Meynert or ventral tegmental area were associated with lower memory performance from age of 40 years onward. Thus, different LC-FC patterns early in life can signal subtle memory deficits. Furthermore, these results highlight the importance of intact interactions between neurotransmitter systems for optimal cognitive aging.
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40
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Rohr CS, Arora A, Cho IYK, Katlariwala P, Dimond D, Dewey D, Bray S. Functional network integration and attention skills in young children. Dev Cogn Neurosci 2018; 30:200-211. [PMID: 29587178 PMCID: PMC6969078 DOI: 10.1016/j.dcn.2018.03.007] [Citation(s) in RCA: 42] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2017] [Revised: 02/12/2018] [Accepted: 03/15/2018] [Indexed: 12/17/2022] Open
Abstract
Children acquire attention skills rapidly during early childhood as their brains undergo vast neural development. Attention is well studied in the adult brain, yet due to the challenges associated with scanning young children, investigations in early childhood are sparse. Here, we examined the relationship between age, attention and functional connectivity (FC) during passive viewing in multiple intrinsic connectivity networks (ICNs) in 60 typically developing girls between 4 and 7 years whose sustained, selective and executive attention skills were assessed. Visual, auditory, sensorimotor, default mode (DMN), dorsal attention (DAN), ventral attention (VAN), salience, and frontoparietal ICNs were identified via Independent Component Analysis and subjected to a dual regression. Individual spatial maps were regressed against age and attention skills, controlling for age. All ICNs except the VAN showed regions of increasing FC with age. Attention skills were associated with FC in distinct networks after controlling for age: selective attention positively related to FC in the DAN; sustained attention positively related to FC in visual and auditory ICNs; and executive attention positively related to FC in the DMN and visual ICN. These findings suggest distributed network integration across this age range and highlight how multiple ICNs contribute to attention skills in early childhood.
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Affiliation(s)
- Christiane S Rohr
- Department of Radiology, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada; Child and Adolescent Imaging Research Program, University of Calgary, Calgary, Alberta, Canada; Alberta Children's Hospital Research Institute, University of Calgary, Calgary, Alberta, Canada; Hotchkiss Brain Institute, University of Calgary, Calgary, Alberta, Canada.
| | - Anish Arora
- Department of Radiology, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada; Child and Adolescent Imaging Research Program, University of Calgary, Calgary, Alberta, Canada; Alberta Children's Hospital Research Institute, University of Calgary, Calgary, Alberta, Canada
| | - Ivy Y K Cho
- Department of Radiology, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada; Child and Adolescent Imaging Research Program, University of Calgary, Calgary, Alberta, Canada; Alberta Children's Hospital Research Institute, University of Calgary, Calgary, Alberta, Canada
| | - Prayash Katlariwala
- Department of Radiology, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada; Child and Adolescent Imaging Research Program, University of Calgary, Calgary, Alberta, Canada; Alberta Children's Hospital Research Institute, University of Calgary, Calgary, Alberta, Canada
| | - Dennis Dimond
- Department of Neuroscience, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada; Child and Adolescent Imaging Research Program, University of Calgary, Calgary, Alberta, Canada; Alberta Children's Hospital Research Institute, University of Calgary, Calgary, Alberta, Canada
| | - Deborah Dewey
- Department of Paediatrics, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada; Department of Community Health Sciences, University of Calgary, Calgary, Alberta, Canada; Alberta Children's Hospital Research Institute, University of Calgary, Calgary, Alberta, Canada
| | - Signe Bray
- Department of Radiology, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada; Department of Paediatrics, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada; Child and Adolescent Imaging Research Program, University of Calgary, Calgary, Alberta, Canada; Alberta Children's Hospital Research Institute, University of Calgary, Calgary, Alberta, Canada; Hotchkiss Brain Institute, University of Calgary, Calgary, Alberta, Canada.
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41
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Sorrentino P, Nieboer D, Twisk JWR, Stam CJ, Douw L, Hillebrand A. The Hierarchy of Brain Networks Is Related to Insulin Growth Factor-1 in a Large, Middle-Aged, Healthy Cohort: An Exploratory Magnetoencephalography Study. Brain Connect 2018; 7:321-330. [PMID: 28520468 DOI: 10.1089/brain.2016.0469] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
Recently, a large study demonstrated that lower serum levels of insulin growth factor-1 (IGF-1) relate to brain atrophy and to a greater risk for developing Alzheimer's disease in a healthy elderly population. We set out to test if functional brain networks relate to IGF-1 levels in the middle aged. Hence, we studied the association between IGF-1 and magnetoencephalography-based functional network characteristics in a middle-aged population. The functional connections between brain areas were estimated for six frequency bands (delta, theta, alpha1, alpha2, beta, gamma) using the phase lag index. Subsequently, the topology of the frequency-specific functional networks was characterized using the minimum spanning tree. Our results showed that lower levels of serum IGF-1 relate to a globally less integrated functional network in the beta and theta band. The associations remained significant when correcting for gender and systemic effects of IGF-1 that might indirectly affect the brain. The value of this exploratory study is the demonstration that lower levels of IGF-1 are associated with brain network topology in the middle aged.
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Affiliation(s)
- Pierpaolo Sorrentino
- 1 Department of Clinical Neurophysiology and MEG Center, VU University Medical Center , Amsterdam, The Netherlands .,2 Istituto di Diagnosi e Cura Hermitage Capodimonte , Naples, Italy
| | - Dagmar Nieboer
- 3 Department of Methodology and Applied Biostatistics, Faculty of Earth and Life Sciences, VU University Amsterdam , Amsterdam, The Netherlands
| | - Jos W R Twisk
- 4 Department of Epidemiology and Biostatistics, VU University Medical Center , Amsterdam, The Netherlands
| | - Cornelis J Stam
- 1 Department of Clinical Neurophysiology and MEG Center, VU University Medical Center , Amsterdam, The Netherlands
| | - Linda Douw
- 5 Department of Anatomy and Neurosciences, VU University Medical Center , Amsterdam, The Netherlands .,6 Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging/Massachusetts General Hospital , Boston, Massachusetts
| | - Arjan Hillebrand
- 1 Department of Clinical Neurophysiology and MEG Center, VU University Medical Center , Amsterdam, The Netherlands
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42
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Neuronal Connectivity, General Anesthesia, and the Elderly. CURRENT ANESTHESIOLOGY REPORTS 2017. [DOI: 10.1007/s40140-017-0241-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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43
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Machine Learning Applications to Resting-State Functional MR Imaging Analysis. Neuroimaging Clin N Am 2017; 27:609-620. [DOI: 10.1016/j.nic.2017.06.010] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
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44
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Grayson DS, Fair DA. Development of large-scale functional networks from birth to adulthood: A guide to the neuroimaging literature. Neuroimage 2017; 160:15-31. [PMID: 28161313 PMCID: PMC5538933 DOI: 10.1016/j.neuroimage.2017.01.079] [Citation(s) in RCA: 253] [Impact Index Per Article: 36.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2016] [Revised: 01/16/2017] [Accepted: 01/31/2017] [Indexed: 02/08/2023] Open
Abstract
The development of human cognition results from the emergence of coordinated activity between distant brain areas. Network science, combined with non-invasive functional imaging, has generated unprecedented insights regarding the adult brain's functional organization, and promises to help elucidate the development of functional architectures supporting complex behavior. Here we review what is known about functional network development from birth until adulthood, particularly as understood through the use of resting-state functional connectivity MRI (rs-fcMRI). We attempt to synthesize rs-fcMRI findings with other functional imaging techniques, with macro-scale structural connectivity, and with knowledge regarding the development of micro-scale structure. We highlight a number of outstanding conceptual and technical barriers that need to be addressed, as well as previous developmental findings that may need to be revisited. Finally, we discuss key areas ripe for future research in order to (1) better characterize normative developmental trajectories, (2) link these trajectories to biologic mechanistic events, as well as component behaviors and (3) better understand the clinical implications and pathophysiological basis of aberrant network development.
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Affiliation(s)
- David S Grayson
- The MIND Institute, University of California Davis, Sacramento, CA 95817, USA; Center for Neuroscience, University of California Davis, Davis, CA 95616, USA; Department of Behavioral Neuroscience, Oregon Health and Science University, Portland, OR 97239, USA
| | - Damien A Fair
- Department of Behavioral Neuroscience, Oregon Health and Science University, Portland, OR 97239, USA; Department of Psychiatry, Oregon Health and Science University, Portland, OR 97239, USA; Advanced Imaging Research Center, Oregon Health and Science University, Portland, OR 97239, USA.
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45
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Lifespan Development of the Human Brain Revealed by Large-Scale Network Eigen-Entropy. ENTROPY 2017. [DOI: 10.3390/e19090471] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
Abstract
Imaging connectomics based on graph theory has become an effective and unique methodological framework for studying functional connectivity patterns of the developing and aging brain. Normal brain development is characterized by continuous and significant network evolution through infancy, childhood, and adolescence, following specific maturational patterns. Normal aging is related to some resting state brain networks disruption, which are associated with certain cognitive decline. It is a big challenge to design an integral metric to track connectome evolution patterns across the lifespan, which is to understand the principles of network organization in the human brain. In this study, we first defined a brain network eigen-entropy (NEE) based on the energy probability (EP) of each brain node. Next, we used the NEE to characterize the lifespan orderness trajectory of the whole-brain functional connectivity of 173 healthy individuals ranging in age from 7 to 85 years. The results revealed that during the lifespan, the whole-brain NEE exhibited a significant non-linear decrease and that the EP distribution shifted from concentration to wide dispersion, implying orderness enhancement of functional connectome over age. Furthermore, brain regions with significant EP changes from the flourishing (7–20 years) to the youth period (23–38 years) were mainly located in the right prefrontal cortex and basal ganglia, and were involved in emotion regulation and executive function in coordination with the action of the sensory system, implying that self-awareness and voluntary control performance significantly changed during neurodevelopment. However, the changes from the youth period to middle age (40–59 years) were located in the mesial temporal lobe and caudate, which are associated with long-term memory, implying that the memory of the human brain begins to decline with age during this period. Overall, the findings suggested that the human connectome shifted from a relatively anatomical driven state to an orderly organized state with lower entropy.
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46
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Heim B, Krismer F, De Marzi R, Seppi K. Magnetic resonance imaging for the diagnosis of Parkinson's disease. J Neural Transm (Vienna) 2017; 124:915-964. [PMID: 28378231 PMCID: PMC5514207 DOI: 10.1007/s00702-017-1717-8] [Citation(s) in RCA: 134] [Impact Index Per Article: 19.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2017] [Accepted: 03/22/2017] [Indexed: 12/11/2022]
Abstract
The differential diagnosis of parkinsonian syndromes is considered one of the most challenging in neurology and error rates in the clinical diagnosis can be high even at specialized centres. Despite several limitations, magnetic resonance imaging (MRI) has undoubtedly enhanced the diagnostic accuracy in the differential diagnosis of neurodegenerative parkinsonism over the last three decades. This review aims to summarize research findings regarding the value of the different MRI techniques, including advanced sequences at high- and ultra-high-field MRI and modern image analysis algorithms, in the diagnostic work-up of Parkinson's disease. This includes not only the exclusion of alternative diagnoses for Parkinson's disease such as symptomatic parkinsonism and atypical parkinsonism, but also the diagnosis of early, new onset, and even prodromal Parkinson's disease.
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Affiliation(s)
- Beatrice Heim
- Department of Neurology, Medical University of Innsbruck, Anichstraße 35, 6020, Innsbruck, Austria
| | - Florian Krismer
- Department of Neurology, Medical University of Innsbruck, Anichstraße 35, 6020, Innsbruck, Austria.
| | - Roberto De Marzi
- Department of Neurology, Medical University of Innsbruck, Anichstraße 35, 6020, Innsbruck, Austria
| | - Klaus Seppi
- Department of Neurology, Medical University of Innsbruck, Anichstraße 35, 6020, Innsbruck, Austria.
- Neuroimaging Research Core Facility, Medical University Innsbruck, Innsbruck, Austria.
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47
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Tsang A, Lebel CA, Bray SL, Goodyear BG, Hafeez M, Sotero RC, McCreary CR, Frayne R. White Matter Structural Connectivity Is Not Correlated to Cortical Resting-State Functional Connectivity over the Healthy Adult Lifespan. Front Aging Neurosci 2017; 9:144. [PMID: 28572765 PMCID: PMC5435815 DOI: 10.3389/fnagi.2017.00144] [Citation(s) in RCA: 45] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2016] [Accepted: 04/30/2017] [Indexed: 11/13/2022] Open
Abstract
Structural connectivity (SC) of white matter (WM) and functional connectivity (FC) of cortical regions undergo changes in normal aging. As WM tracts form the underlying anatomical architecture that connects regions within resting state networks (RSNs), it is intuitive to expect that SC and FC changes with age are correlated. Studies that investigated the relationship between SC and FC in normal aging are rare, and have mainly compared between groups of elderly and younger subjects. The objectives of this work were to investigate linear SC and FC changes across the healthy adult lifespan, and to define relationships between SC and FC measures within seven whole-brain large scale RSNs. Diffusion tensor imaging (DTI) and resting-state functional MRI (rs-fMRI) data were acquired from 177 healthy participants (male/female = 69/108; aged 18-87 years). Forty cortical regions across both hemispheres belonging to seven template-defined RSNs were considered. Mean diffusivity (MD), fractional anisotropy (FA), mean tract length, and number of streamlines derived from DTI data were used as SC measures, delineated using deterministic tractography, within each RSN. Pearson correlation coefficients of rs-fMRI-obtained BOLD signal time courses between cortical regions were used as FC measure. SC demonstrated significant age-related changes in all RSNs (decreased FA, mean tract length, number of streamlines; and increased MD), and significant FC decrease was observed in five out of seven networks. Among the networks that showed both significant age related changes in SC and FC, however, SC was not in general significantly correlated with FC, whether controlling for age or not. The lack of observed relationship between SC and FC suggests that measures derived from DTI data that are commonly used to infer the integrity of WM microstructure are not related to the corresponding changes in FC within RSNs. The possible temporal lag between SC and FC will need to be addressed in future longitudinal studies to better elucidate the links between SC and FC changes in normal aging.
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Affiliation(s)
- Adrian Tsang
- Department of Radiology, University of CalgaryCalgary, AB, Canada.,Hotchkiss Brain Institute, University of CalgaryCalgary, AB, Canada.,Seaman Family MR Research Centre, Foothills Medical Centre, Alberta Health ServicesCalgary, AB, Canada
| | - Catherine A Lebel
- Department of Radiology, University of CalgaryCalgary, AB, Canada.,Alberta Children's Hospital Research Institute, University of CalgaryCalgary, AB, Canada.,Child and Adolescent Imaging Research Program, Alberta Children's Hospital, Alberta Health ServicesCalgary, AB, Canada
| | - Signe L Bray
- Department of Radiology, University of CalgaryCalgary, AB, Canada.,Alberta Children's Hospital Research Institute, University of CalgaryCalgary, AB, Canada.,Child and Adolescent Imaging Research Program, Alberta Children's Hospital, Alberta Health ServicesCalgary, AB, Canada
| | - Bradley G Goodyear
- Department of Radiology, University of CalgaryCalgary, AB, Canada.,Hotchkiss Brain Institute, University of CalgaryCalgary, AB, Canada.,Seaman Family MR Research Centre, Foothills Medical Centre, Alberta Health ServicesCalgary, AB, Canada
| | - Moiz Hafeez
- Department of Radiology, University of CalgaryCalgary, AB, Canada.,Hotchkiss Brain Institute, University of CalgaryCalgary, AB, Canada.,Seaman Family MR Research Centre, Foothills Medical Centre, Alberta Health ServicesCalgary, AB, Canada
| | - Roberto C Sotero
- Department of Radiology, University of CalgaryCalgary, AB, Canada
| | - Cheryl R McCreary
- Department of Radiology, University of CalgaryCalgary, AB, Canada.,Hotchkiss Brain Institute, University of CalgaryCalgary, AB, Canada.,Seaman Family MR Research Centre, Foothills Medical Centre, Alberta Health ServicesCalgary, AB, Canada
| | - Richard Frayne
- Department of Radiology, University of CalgaryCalgary, AB, Canada.,Hotchkiss Brain Institute, University of CalgaryCalgary, AB, Canada.,Seaman Family MR Research Centre, Foothills Medical Centre, Alberta Health ServicesCalgary, AB, Canada
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48
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Zhang H, Lee A, Qiu A. A posterior-to-anterior shift of brain functional dynamics in aging. Brain Struct Funct 2017; 222:3665-3676. [PMID: 28417233 DOI: 10.1007/s00429-017-1425-z] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2017] [Accepted: 04/10/2017] [Indexed: 10/19/2022]
Abstract
Convergent evidence from task-based functional magnetic resonance imaging (fMRI) studies suggests a posterior-to-anterior shift as an adaptive compensatory scaffolding mechanism for aging. This study aimed to investigate whether brain functional dynamics at rest follow the same scaffolding mechanism for aging using a large Chinese sample aged from 22 to 79 years (n = 277). We defined a probability of brain regions being hubs over a period of time to characterize functional hub dynamic, and defined variability of the functional connectivity to characterize dynamic functional connectivity using resting-state fMRI. Our results revealed that both functional hub dynamics and dynamic functional connectivity posited an age-related posterior-to-anterior shift. Specifically, the posterior brain region showed attenuated dynamics, while the anterior brain regions showed augmented dynamics in aging. Interestingly, our analysis further indicated that the age-related episodic memory decline was associated with the age-related decrease in the brain functional dynamics of the posterior regions. Hence, these findings provided a new dimension to view the scaffolding mechanism for aging based on the brain functional dynamics.
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Affiliation(s)
- Han Zhang
- Department of Biomedical Engineering, National University of Singapore, Singapore, 117576, Singapore
| | - Annie Lee
- Department of Biomedical Engineering, National University of Singapore, Singapore, 117576, Singapore
| | - Anqi Qiu
- Department of Biomedical Engineering, National University of Singapore, Singapore, 117576, Singapore. .,Clinical Imaging Research Center, National University of Singapore, Singapore, 117456, Singapore. .,Singapore Institute for Clinical Sciences, The Agency for Science, Technology and Research, Singapore, 117609, Singapore.
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49
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Schlesinger KJ, Turner BO, Lopez BA, Miller MB, Carlson JM. Age-dependent changes in task-based modular organization of the human brain. Neuroimage 2017; 146:741-762. [DOI: 10.1016/j.neuroimage.2016.09.001] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2016] [Revised: 08/14/2016] [Accepted: 09/01/2016] [Indexed: 02/08/2023] Open
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50
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Dørum ES, Kaufmann T, Alnæs D, Andreassen OA, Richard G, Kolskår KK, Nordvik JE, Westlye LT. Increased sensitivity to age-related differences in brain functional connectivity during continuous multiple object tracking compared to resting-state. Neuroimage 2017; 148:364-372. [PMID: 28111190 DOI: 10.1016/j.neuroimage.2017.01.048] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2016] [Revised: 12/16/2016] [Accepted: 01/18/2017] [Indexed: 11/28/2022] Open
Abstract
Age-related differences in cognitive agility vary greatly between individuals and cognitive functions. This heterogeneity is partly mirrored in individual differences in brain network connectivity as revealed using resting-state functional magnetic resonance imaging (fMRI), suggesting potential imaging biomarkers for age-related cognitive decline. However, although convenient in its simplicity, the resting state is essentially an unconstrained paradigm with minimal experimental control. Here, based on the conception that the magnitude and characteristics of age-related differences in brain connectivity is dependent on cognitive context and effort, we tested the hypothesis that experimentally increasing cognitive load boosts the sensitivity to age and changes the discriminative network configurations. To this end, we obtained fMRI data from younger (n=25, mean age 24.16±5.11) and older (n=22, mean age 65.09±7.53) healthy adults during rest and two load levels of continuous multiple object tracking (MOT). Brain network nodes and their time-series were estimated using independent component analysis (ICA) and dual regression, and the edges in the brain networks were defined as the regularized partial temporal correlations between each of the node pairs at the individual level. Using machine learning based on a cross-validated regularized linear discriminant analysis (rLDA) we attempted to classify groups and cognitive load from the full set of edge-wise functional connectivity indices. While group classification using resting-state data was highly above chance (approx. 70% accuracy), functional connectivity (FC) obtained during MOT strongly increased classification performance, with 82% accuracy for the young and 95% accuracy for the old group at the highest load level. Further, machine learning revealed stronger differentiation between rest and task in young compared to older individuals, supporting the notion of network dedifferentiation in cognitive aging. Task-modulation in edgewise FC was primarily observed between attention- and sensorimotor networks; with decreased negative correlations between attention- and default mode networks in older adults. These results demonstrate that the magnitude and configuration of age-related differences in brain functional connectivity are partly dependent on cognitive context and load, which emphasizes the importance of assessing brain connectivity differences across a range of cognitive contexts beyond the resting-state.
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Affiliation(s)
- Erlend S Dørum
- Sunnaas Rehabilitation Hospital HT, Nesodden, Norway; NORMENT, KG Jebsen Centre for Psychosis Research, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Norway; Department of Psychology, University of Oslo, Norway
| | - Tobias Kaufmann
- NORMENT, KG Jebsen Centre for Psychosis Research, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Norway
| | - Dag Alnæs
- NORMENT, KG Jebsen Centre for Psychosis Research, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Norway
| | - Ole A Andreassen
- NORMENT, KG Jebsen Centre for Psychosis Research, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Norway
| | - Geneviève Richard
- Sunnaas Rehabilitation Hospital HT, Nesodden, Norway; NORMENT, KG Jebsen Centre for Psychosis Research, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Norway; Department of Psychology, University of Oslo, Norway
| | - Knut K Kolskår
- Sunnaas Rehabilitation Hospital HT, Nesodden, Norway; NORMENT, KG Jebsen Centre for Psychosis Research, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Norway; Department of Psychology, University of Oslo, Norway
| | | | - Lars T Westlye
- NORMENT, KG Jebsen Centre for Psychosis Research, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Norway; Department of Psychology, University of Oslo, Norway.
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