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Guan Y, Li J, Wei Y, Shi PT, Yang C, Yun X, Quan Q, Wang WJ, Yu XG, Wei M. Brain functional connectivity alterations in patients with anterior cruciate ligament injury. Brain Res 2024; 1836:148956. [PMID: 38657888 DOI: 10.1016/j.brainres.2024.148956] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2024] [Revised: 04/16/2024] [Accepted: 04/21/2024] [Indexed: 04/26/2024]
Abstract
Recent advancements in neuroimaging have illustrated that anterior cruciate ligament (ACL) injuries could impact the central nervous system (CNS), causing neuroplastic changes in the brain beyond the traditionally understood biomechanical consequences. While most of previous functional magnetic resonance imaging (fMRI) studies have focused on localized cortical activity changes post-injury, emerging research has suggested disruptions in functional connectivity across the brain. However, these prior investigations, albeit pioneering, have been constrained by two limitations: a reliance on small-sample participant cohorts, often limited to two to three patients, potentially limiting the generalizability of findings, and an adherence to region of interest based analysis, which may overlook broader network interactions. To address these limitations, our study employed resting-state fMRI to assess whole-brain functional connectivity in 15 ACL-injured patients, comparing them to matched controls using two distinct network analysis methods. Using Network-Based Statistics, we identified widespread reductions in connectivity that spanned across multiple brain regions. Further modular connectivity analysis showed significant decreases in inter-modular connectivity between the sensorimotor and cerebellar modules, and intra-modular connectivity within the default-mode network in ACL-injured patients. Our results thus highlight a shift from localized disruptions to network-wide dysfunctions, suggesting that ACL injuries induce widespread CNS changes. This enhanced understanding has the potential to stimulate the development of strategies aiming to restore functional connectivity and improve recovery outcomes.
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Affiliation(s)
- Yu Guan
- Department of Orthopedics, The Fourth Medical Center, Chinese PLA General Hospital, Beijing 100142, China; Medical School of Chinese PLA, Beijing 100853, China
| | - Ji Li
- Department of Orthopedics, The Fourth Medical Center, Chinese PLA General Hospital, Beijing 100142, China
| | - Yu Wei
- Department of Orthopedics, The Fourth Medical Center, Chinese PLA General Hospital, Beijing 100142, China
| | - Peng-Tao Shi
- Department of Orthopedics, The Fourth Medical Center, Chinese PLA General Hospital, Beijing 100142, China; Medical School of Chinese PLA, Beijing 100853, China
| | - Chen Yang
- Department of Orthopedics, The Fourth Medical Center, Chinese PLA General Hospital, Beijing 100142, China; Medical School of Chinese PLA, Beijing 100853, China
| | - Xing Yun
- Department of Orthopedics, The Fourth Medical Center, Chinese PLA General Hospital, Beijing 100142, China; Medical School of Chinese PLA, Beijing 100853, China
| | - Qi Quan
- Department of Orthopedics, The Fourth Medical Center, Chinese PLA General Hospital, Beijing 100142, China; Department of Orthopedic Surgery, Key Laboratory of Musculoskeletal Trauma &War Injuries PLA, Beijing Key Lab of Regenerative Medicine in Orthopedics, Chinese PLA General Hospital, Beijing 100853, China
| | - Wen-Juan Wang
- Department of Orthopedics, The Fourth Medical Center, Chinese PLA General Hospital, Beijing 100142, China
| | - Xin-Guang Yu
- Department of Neurosurgery, The First Medical Center, Chinese PLA General Hospital, Beijing 100853, China
| | - Min Wei
- Department of Orthopedics, The Fourth Medical Center, Chinese PLA General Hospital, Beijing 100142, China; Medical School of Chinese PLA, Beijing 100853, China.
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2
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Gregorich M, Simpson SL, Heinze G. Flexible parametrization of graph-theoretical features from individual-specific networks for prediction. Stat Med 2024; 43:2592-2606. [PMID: 38664934 DOI: 10.1002/sim.10091] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2023] [Revised: 03/15/2024] [Accepted: 04/15/2024] [Indexed: 05/24/2024]
Abstract
Statistical techniques are needed to analyze data structures with complex dependencies such that clinically useful information can be extracted. Individual-specific networks, which capture dependencies in complex biological systems, are often summarized by graph-theoretical features. These features, which lend themselves to outcome modeling, can be subject to high variability due to arbitrary decisions in network inference and noise. Correlation-based adjacency matrices often need to be sparsified before meaningful graph-theoretical features can be extracted, requiring the data analysts to determine an optimal threshold. To address this issue, we propose to incorporate a flexible weighting function over the full range of possible thresholds to capture the variability of graph-theoretical features over the threshold domain. The potential of this approach, which extends concepts from functional data analysis to a graph-theoretical setting, is explored in a plasmode simulation study using real functional magnetic resonance imaging (fMRI) data from the Autism Brain Imaging Data Exchange (ABIDE) Preprocessed initiative. The simulations show that our modeling approach yields accurate estimates of the functional form of the weight function, improves inference efficiency, and achieves a comparable or reduced root mean square prediction error compared to competitor modeling approaches. This assertion holds true in settings where both complex functional forms underlie the outcome-generating process and a universal threshold value is employed. We demonstrate the practical utility of our approach by using resting-state fMRI data to predict biological age in children. Our study establishes the flexible modeling approach as a statistically principled, serious competitor to ad-hoc methods with superior performance.
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Affiliation(s)
- Mariella Gregorich
- Medical University of Vienna, Center for Medical Data Science, Institute of Clinical Biometrics, Vienna, Austria
| | - Sean L Simpson
- Department of Biostatistics and Data Science, Wake Forest University School of Medicine, Winston-Salem, North Carolina, USA
| | - Georg Heinze
- Medical University of Vienna, Center for Medical Data Science, Institute of Clinical Biometrics, Vienna, Austria
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3
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Zhao CL, Hou W, Jia Y, Sahakian BJ, Luo Q. Sex differences of signal complexity at resting-state functional magnetic resonance imaging and their associations with the estrogen-signaling pathway in the brain. Cogn Neurodyn 2024; 18:973-986. [PMID: 38826661 PMCID: PMC11143120 DOI: 10.1007/s11571-023-09954-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2022] [Revised: 01/27/2023] [Accepted: 03/08/2023] [Indexed: 06/04/2024] Open
Abstract
Sex differences in the brain have been widely reported and may hold the key to elucidating sex differences in many medical conditions and drug response. However, the molecular correlates of these sex differences in structural and functional brain measures in the human brain remain unclear. Herein, we used sample entropy (SampEn) to quantify the signal complexity of resting-state functional magnetic resonance imaging (rsfMRI) in a large neuroimaging cohort (N = 1,642). The frontoparietal control network and the cingulo-opercular network had high signal complexity while the cerebellar and sensory motor networks had low signal complexity in both men and women. Compared with those in male brains, we found greater signal complexity in all functional brain networks in female brains with the default mode network exhibiting the largest sex difference. Using the gene expression data in brain tissues, we identified genes that were significantly associated with sex differences in brain signal complexity. The significant genes were enriched in the gene sets that were differentially expressed between the brain cortex and other tissues, the estrogen-signaling pathway, and the biological function of neural plasticity. In particular, the G-protein-coupled estrogen receptor 1 gene in the estrogen-signaling pathway was expressed more in brain regions with greater sex differences in SampEn. In conclusion, greater complexity in female brains may reflect the interactions between sex hormone fluctuations and neuromodulation of estrogen in women. Supplementary Information The online version contains supplementary material available at 10.1007/s11571-023-09954-y.
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Affiliation(s)
- Cheng-li Zhao
- College of Science, National University of Defense Technology, Changsha, 410073 China
| | - Wenjie Hou
- National Clinical Research Center for Aging and Medicine at Huashan Hospital, State Key Laboratory of Medical Neurobiology and Ministry of Education Frontiers Center for Brain Science, Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, 200433 China
- Center for Computational Psychiatry, MOE Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Human Phenome Institute, Fudan University, Shanghai, 200438 China
| | - Yanbing Jia
- School of Mathematics and Statistics, Henan University of Science and Technology, Luoyang, 471000 China
| | - Barbara J. Sahakian
- Department of Psychiatry, University of Cambridge, Cambridge, CB2 0SZ UK
- Behavioural and Clinical Neuroscience Institute, University of Cambridge, Cambridge, CB2 3EB UK
| | - the DIRECT Consortium
- College of Science, National University of Defense Technology, Changsha, 410073 China
- National Clinical Research Center for Aging and Medicine at Huashan Hospital, State Key Laboratory of Medical Neurobiology and Ministry of Education Frontiers Center for Brain Science, Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, 200433 China
- Center for Computational Psychiatry, MOE Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Human Phenome Institute, Fudan University, Shanghai, 200438 China
- School of Mathematics and Statistics, Henan University of Science and Technology, Luoyang, 471000 China
- Department of Psychiatry, University of Cambridge, Cambridge, CB2 0SZ UK
- Behavioural and Clinical Neuroscience Institute, University of Cambridge, Cambridge, CB2 3EB UK
| | - Qiang Luo
- National Clinical Research Center for Aging and Medicine at Huashan Hospital, State Key Laboratory of Medical Neurobiology and Ministry of Education Frontiers Center for Brain Science, Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, 200433 China
- Center for Computational Psychiatry, MOE Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Human Phenome Institute, Fudan University, Shanghai, 200438 China
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4
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Sorooshyari SK. Brain age monotonicity and functional connectivity differences of healthy subjects. PLoS One 2024; 19:e0300720. [PMID: 38814972 PMCID: PMC11139261 DOI: 10.1371/journal.pone.0300720] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2023] [Accepted: 03/04/2024] [Indexed: 06/01/2024] Open
Abstract
Alterations in the brain's connectivity or the interactions among brain regions have been studied with the aid of resting state (rs)fMRI data attained from large numbers of healthy subjects of various demographics. This has been instrumental in providing insight into how a phenotype as fundamental as age affects the brain. Although machine learning (ML) techniques have already been deployed in such studies, novel questions are investigated in this work. We study whether young brains develop properties that progressively resemble those of aged brains, and if the aging dynamics of older brains provide information about the aging trajectory in young subjects. The degree of a prospective monotonic relationship will be quantified, and hypotheses of brain aging trajectories will be tested via ML. Furthermore, the degree of functional connectivity across the age spectrum of three datasets will be compared at a population level and across sexes. The findings scrutinize similarities and differences among the male and female subjects at greater detail than previously performed.
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Affiliation(s)
- Siamak K. Sorooshyari
- Department of Statistics, Stanford University, Stanford, CA, United States of America
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5
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Fleming LL, Defenderfer M, Demirayak P, Stewart P, Decarlo DK, Visscher KM. Impact of deprivation and preferential usage on functional connectivity between early visual cortex and category selective visual regions. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.17.593020. [PMID: 38798355 PMCID: PMC11118586 DOI: 10.1101/2024.05.17.593020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/29/2024]
Abstract
Human behavior can be remarkably shaped by experience, such as the removal of sensory input. Many studies of conditions such as stroke, limb amputation, and vision loss have examined how the removal of input changes brain function. However, an important question has yet to be answered: when input is lost, does the brain change its connectivity to preferentially use some remaining inputs over others? In individuals with healthy vision, the central portion of the retina is preferentially used for everyday visual tasks, due to its ability to discriminate fine details. However, when central vision is lost in conditions like macular degeneration, peripheral vision must be relied upon for those everyday tasks, with certain portions receiving "preferential" usage over others. Using resting-state fMRI collected during total darkness, we examined how deprivation and preferential usage influence the intrinsic functional connectivity of sensory cortex by studying individuals with selective vision loss due to late stages of macular degeneration. We found that cortical regions representing spared portions of the peripheral retina, regardless of whether they are preferentially used, exhibit plasticity of intrinsic functional connectivity in macular degeneration. Cortical representations of spared peripheral retinal locations showed stronger connectivity to MT, a region involved in processing motion. These results suggest that long-term loss of central vision can produce widespread effects throughout spared representations in early visual cortex, regardless of whether those representations are preferentially used. These findings support the idea that connections to visual cortex maintain the capacity for change well after critical periods of visual development. Highlights Portions of early visual cortex representing central vs. peripheral vision exhibit different patterns of connectivity to category-selective visual regions.When central vision is lost, cortical representations of peripheral vision display stronger functional connections to MT than central representations.When central vision is lost, connectivity to regions selective for tasks that involve central vision (FFA and PHA) are not significantly altered.These effects do not depend on which locations of peripheral vision are used more.
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6
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Li J, Cao Y, Huang M, Li Z, Qin Z, Lang J. The alterations of functional brain networks and its relationship with sport decision-making and training duration in soccer players across different skill levels. Neurosci Lett 2024; 831:137788. [PMID: 38642882 DOI: 10.1016/j.neulet.2024.137788] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2023] [Revised: 04/03/2024] [Accepted: 04/17/2024] [Indexed: 04/22/2024]
Abstract
Studies have indicated that skilled soccer players possess superior decision-making abilities compared to their less-skilled counterparts. However, the underlying neural mechanism for this phenomenon remains incompletely understood. In our investigation, we explored distinctions in the topology of functional brain networks between skilled and less-skilled soccer players. Employing mediating analysis, we scrutinized the relationships among functional brain network parameters, training duration, and decision-making accuracy. Our findings revealed that skilled soccer players demonstrated significantly higher decision-making accuracy compared to their less-skilled counterparts. Skilled players also exhibited increased values in the cluster coefficient, characteristic path length and local efficiency but lower global efficiency. Moreover, we observed enhanced functional brain connectivity within the occipital and cingulo-opercular networks, as well as between the fronto-parietal and cingulo-opercular networks in skilled soccer players. Cluster coefficient and functional connectivity between fronto-parietal and cingulo-opercular networks had positive mediating effects on the association between training duration and sport decision-making accuracy. In conclusion, our study provides initial evidence for distinctions in functional brain network parameters between soccer players with varying skill levels and their relationship with sport decision-making accuracy.
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Affiliation(s)
- Ju Li
- College of P.E. and Sports, Beijing Normal University, Beijing 100875, China.
| | - Yaping Cao
- College of P.E. and Sports, Beijing Normal University, Beijing 100875, China
| | - Minghao Huang
- College of P.E. and Sports, Northwest Normal University, Gansu 730070, China.
| | - Zhongcheng Li
- College of P.E. and Sports, Beijing Normal University, Beijing 100875, China.
| | - Zhe Qin
- College of P.E. and Sports, Northwest Normal University, Gansu 730070, China
| | - Jian Lang
- College of P.E. and Sports, Beijing Normal University, Beijing 100875, China.
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7
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Wang W, Xiao L, Qu G, Calhoun VD, Wang YP, Sun X. Multiview hyperedge-aware hypergraph embedding learning for multisite, multiatlas fMRI based functional connectivity network analysis. Med Image Anal 2024; 94:103144. [PMID: 38518530 DOI: 10.1016/j.media.2024.103144] [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: 08/01/2023] [Revised: 03/17/2024] [Accepted: 03/18/2024] [Indexed: 03/24/2024]
Abstract
Recently, functional magnetic resonance imaging (fMRI) based functional connectivity network (FCN) analysis via graph convolutional networks (GCNs) has shown promise for automated diagnosis of brain diseases by regarding the FCNs as irregular graph-structured data. However, multiview information and site influences of the FCNs in a multisite, multiatlas fMRI scenario have been understudied. In this paper, we propose a Class-consistency and Site-independence Multiview Hyperedge-Aware HyperGraph Embedding Learning (CcSi-MHAHGEL) framework to integrate FCNs constructed on multiple brain atlases in a multisite fMRI study. Specifically, for each subject, we first model brain network as a hypergraph for every brain atlas to characterize high-order relations among multiple vertexes, and then introduce a multiview hyperedge-aware hypergraph convolutional network (HGCN) to extract a multiatlas-based FCN embedding where hyperedge weights are adaptively learned rather than employing the fixed weights precalculated in traditional HGCNs. In addition, we formulate two modules to jointly learn the multiatlas-based FCN embeddings by considering the between-subject associations across classes and sites, respectively, i.e., a class-consistency module to encourage both compactness within every class and separation between classes for promoting discrimination in the embedding space, and a site-independence module to minimize the site dependence of the embeddings for mitigating undesired site influences due to differences in scanning platforms and/or protocols at multiple sites. Finally, the multiatlas-based FCN embeddings are fed into a few fully connected layers followed by the soft-max classifier for diagnosis decision. Extensive experiments on the ABIDE demonstrate the effectiveness of our method for autism spectrum disorder (ASD) identification. Furthermore, our method is interpretable by revealing ASD-relevant brain regions that are biologically significant.
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Affiliation(s)
- Wei Wang
- MoE Key Laboratory of Brain-inspired Intelligent Perception and Cognition, University of Science and Technology of China, Hefei 230052, China
| | - Li Xiao
- MoE Key Laboratory of Brain-inspired Intelligent Perception and Cognition, University of Science and Technology of China, Hefei 230052, China; Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei 230088, China.
| | - Gang Qu
- Department of Biomedical Engineering, Tulane University, New Orleans, LA 70118, USA
| | - Vince D Calhoun
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA 30030, USA
| | - Yu-Ping Wang
- Department of Biomedical Engineering, Tulane University, New Orleans, LA 70118, USA
| | - Xiaoyan Sun
- MoE Key Laboratory of Brain-inspired Intelligent Perception and Cognition, University of Science and Technology of China, Hefei 230052, China; Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei 230088, China
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8
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Qin K, Lei D, Zhu Z, Li W, Tallman MJ, Rodrigo Patino L, Fleck DE, Aghera V, Gong Q, Sweeney JA, McNamara RK, DelBello MP. Different brain functional network abnormalities between attention-deficit/hyperactivity disorder youth with and without familial risk for bipolar disorder. Eur Child Adolesc Psychiatry 2024; 33:1395-1405. [PMID: 37336861 DOI: 10.1007/s00787-023-02245-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/08/2023] [Accepted: 06/07/2023] [Indexed: 06/21/2023]
Abstract
Attention-deficit/hyperactivity disorder (ADHD) commonly precedes the initial onset of mania in youth with familial risk for bipolar disorder (BD). Although ADHD youth with and without BD familial risk exhibit different clinical features, associated neuropathophysiological mechanisms remain poorly understood. This study aimed to identify brain functional network abnormalities associated with ADHD in youth with and without familial risk for BD. Resting-state functional magnetic resonance imaging scans were acquired from 37 ADHD youth with a family history of BD (high-risk), 45 ADHD youth without a family history of BD (low-risk), and 32 healthy controls (HC). Individual whole-brain functional networks were constructed, and graph theory analysis was applied to estimate network topological metrics. Topological metrics, including network efficiency, small-worldness and nodal centrality, were compared across groups, and associations between topological metrics and clinical ratings were evaluated. Compared to HC, low-risk ADHD youth exhibited weaker global integration (i.e., decreased global efficiency and increased characteristic path length), while high-risk ADHD youth showed a disruption of localized network components with decreased frontoparietal and frontolimbic connectivity. Common topological deficits were observed in the medial superior frontal gyrus between low- and high-risk ADHD. Distinct network deficits were found in the inferior parietal lobule and corticostriatal circuitry. Associations between global topological metrics and externalizing symptoms differed significantly between the two ADHD groups. Different patterns of functional network topological abnormalities were found in high- as compared to low-risk ADHD, suggesting that ADHD in youth with BD familial risk may represent a phenotype that is different from ADHD alone.
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Affiliation(s)
- Kun Qin
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, 610041, Sichuan, China
- Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati College of Medicine, Cincinnati, OH, 45219, USA
- Department of Radiology, Taihe Hospital, Hubei University of Medicine, Shiyan, 442000, Hubei, China
| | - Du Lei
- College of Medical Informatics, Chongqing Medical University, Chongqing, 400016, China.
| | - Ziyu Zhu
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, 610041, Sichuan, China
- Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati College of Medicine, Cincinnati, OH, 45219, USA
| | - Wenbin Li
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450000, Henan, China
| | - Maxwell J Tallman
- Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati College of Medicine, Cincinnati, OH, 45219, USA
| | - L Rodrigo Patino
- Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati College of Medicine, Cincinnati, OH, 45219, USA
| | - David E Fleck
- Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati College of Medicine, Cincinnati, OH, 45219, USA
| | - Veronica Aghera
- Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati College of Medicine, Cincinnati, OH, 45219, USA
| | - Qiyong Gong
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, 610041, Sichuan, China.
| | - John A Sweeney
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, 610041, Sichuan, China
- Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati College of Medicine, Cincinnati, OH, 45219, USA
| | - Robert K McNamara
- Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati College of Medicine, Cincinnati, OH, 45219, USA
| | - Melissa P DelBello
- Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati College of Medicine, Cincinnati, OH, 45219, USA
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9
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Luo Z, Yin E, Yan Y, Zhao S, Xie L, Shen H, Zeng LL, Wang L, Hu D. Sleep deprivation changes frequency-specific functional organization of the resting human brain. Brain Res Bull 2024; 210:110925. [PMID: 38493835 DOI: 10.1016/j.brainresbull.2024.110925] [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: 11/29/2023] [Revised: 02/13/2024] [Accepted: 03/10/2024] [Indexed: 03/19/2024]
Abstract
Previous resting-state functional magnetic resonance imaging (rs-fMRI) studies have widely explored the temporal connection changes in the human brain following long-term sleep deprivation (SD). However, the frequency-specific topological properties of sleep-deprived functional networks remain virtually unclear. In this study, thirty-seven healthy male subjects underwent resting-state fMRI during rested wakefulness (RW) and after 36 hours of SD, and we examined frequency-specific spectral connection changes (0.01-0.08 Hz, interval = 0.01 Hz) caused by SD. First, we conducted a multivariate pattern analysis combining linear SVM classifiers with a robust feature selection algorithm, and the results revealed that accuracies of 74.29%-84.29% could be achieved in the classification between RW and SD states in leave-one-out cross-validation at different frequency bands, moreover, the spectral connection at the lowest and highest frequency bands exhibited higher discriminative power. Connection involving the cingulo-opercular network increased most, while connection involving the default-mode network decreased most following SD. Then we performed a graph-theoretic analysis and observed reduced low-frequency modularity and high-frequency global efficiency in the SD state. Moreover, hub regions, which were primarily situated in the cerebellum and the cingulo-opercular network after SD, exhibited high discriminative power in the aforementioned classification consistently. The findings may indicate the frequency-dependent effects of SD on the functional network topology and its efficiency of information exchange, providing new insights into the impact of SD on the human brain.
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Affiliation(s)
- Zhiguo Luo
- Defense Innovation Institute, Academy of Military Sciences (AMS), Beijing 100071, China; Intelligent Game and Decision Laboratory, Beijing 100071, China; Tianjin Artificial Intelligence Innovation Center (TAIIC), Tianjin 300450, China; College of Intelligence Science and Technology, National University of Defense Technology, Changsha, Hunan 410073, China
| | - Erwei Yin
- Defense Innovation Institute, Academy of Military Sciences (AMS), Beijing 100071, China; Intelligent Game and Decision Laboratory, Beijing 100071, China; Tianjin Artificial Intelligence Innovation Center (TAIIC), Tianjin 300450, China.
| | - Ye Yan
- Defense Innovation Institute, Academy of Military Sciences (AMS), Beijing 100071, China; Intelligent Game and Decision Laboratory, Beijing 100071, China; Tianjin Artificial Intelligence Innovation Center (TAIIC), Tianjin 300450, China
| | - Shaokai Zhao
- Defense Innovation Institute, Academy of Military Sciences (AMS), Beijing 100071, China; Intelligent Game and Decision Laboratory, Beijing 100071, China; Tianjin Artificial Intelligence Innovation Center (TAIIC), Tianjin 300450, China
| | - Liang Xie
- Defense Innovation Institute, Academy of Military Sciences (AMS), Beijing 100071, China; Intelligent Game and Decision Laboratory, Beijing 100071, China; Tianjin Artificial Intelligence Innovation Center (TAIIC), Tianjin 300450, China
| | - Hui Shen
- College of Intelligence Science and Technology, National University of Defense Technology, Changsha, Hunan 410073, China
| | - Ling-Li Zeng
- College of Intelligence Science and Technology, National University of Defense Technology, Changsha, Hunan 410073, China
| | - Lubin Wang
- The Brain Science Center, Beijing Institute of Basic Medical Sciences, Beijing 102206, China
| | - Dewen Hu
- College of Intelligence Science and Technology, National University of Defense Technology, Changsha, Hunan 410073, China.
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10
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Madar A, Kurtz-David V, Hakim A, Levy DJ, Tavor I. Pre-acquired Functional Connectivity Predicts Choice Inconsistency. J Neurosci 2024; 44:e0453232024. [PMID: 38508713 PMCID: PMC11063819 DOI: 10.1523/jneurosci.0453-23.2024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2023] [Revised: 01/29/2024] [Accepted: 01/31/2024] [Indexed: 03/22/2024] Open
Abstract
Economic choice theories usually assume that humans maximize utility in their choices. However, studies have shown that humans make inconsistent choices, leading to suboptimal behavior, even without context-dependent manipulations. Previous studies showed that activation in value and motor networks are associated with inconsistent choices at the moment of choice. Here, we investigated if the neural predispositions, measured before a choice task, can predict choice inconsistency in a later risky choice task. Using functional connectivity (FC) measures from resting-state functional magnetic resonance imaging (rsfMRI), derived before any choice was made, we aimed to predict subjects' inconsistency levels in a later-performed choice task. We hypothesized that rsfMRI FC measures extracted from value and motor brain areas would predict inconsistency. Forty subjects (21 females) completed a rsfMRI scan before performing a risky choice task. We compared models that were trained on FC that included only hypothesized value and motor regions with models trained on whole-brain FC. We found that both model types significantly predicted inconsistency levels. Moreover, even the whole-brain models relied mostly on FC between value and motor areas. For external validation, we used a neural network pretrained on FC matrices of 37,000 subjects and fine-tuned it on our data and again showed significant predictions. Together, this shows that the tendency for choice inconsistency is predicted by predispositions of the nervous system and that synchrony between the motor and value networks plays a crucial role in this tendency.
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Affiliation(s)
- Asaf Madar
- Sagol School of Neuroscience, Tel Aviv University, Tel Aviv 69978, Israel
| | - Vered Kurtz-David
- Coller School of Management, Tel Aviv University, Tel Aviv 69978, Israel
- Grossman School of Medicine, New York University, New York, New York 10016
| | - Adam Hakim
- Sagol School of Neuroscience, Tel Aviv University, Tel Aviv 69978, Israel
| | - Dino J Levy
- Sagol School of Neuroscience, Tel Aviv University, Tel Aviv 69978, Israel
- Coller School of Management, Tel Aviv University, Tel Aviv 69978, Israel
| | - Ido Tavor
- Sagol School of Neuroscience, Tel Aviv University, Tel Aviv 69978, Israel
- Department of Anatomy and Anthropology, Faculty of Medicine, Tel Aviv University, Tel Aviv 69978, Israel
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11
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Shan X, Uddin LQ, Ma R, Xu P, Xiao J, Li L, Huang X, Feng Y, He C, Chen H, Duan X. Disentangling the Individual-Shared and Individual-Specific Subspace of Altered Brain Functional Connectivity in Autism Spectrum Disorder. Biol Psychiatry 2024; 95:870-880. [PMID: 37741308 DOI: 10.1016/j.biopsych.2023.09.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/21/2023] [Revised: 08/25/2023] [Accepted: 09/15/2023] [Indexed: 09/25/2023]
Abstract
BACKGROUND Despite considerable effort toward understanding the neural basis of autism spectrum disorder (ASD) using case-control analyses of resting-state functional magnetic resonance imaging data, findings are often not reproducible, largely due to biological and clinical heterogeneity among individuals with ASD. Thus, exploring the individual-shared and individual-specific altered functional connectivity (AFC) in ASD is important to understand this complex, heterogeneous disorder. METHODS We considered 254 individuals with ASD and 295 typically developing individuals from the Autism Brain Imaging Data Exchange to explore the individual-shared and individual-specific subspaces of AFC. First, we computed AFC matrices of individuals with ASD compared with typically developing individuals. Then, common orthogonal basis extraction was used to project AFC of ASD onto 2 subspaces: an individual-shared subspace, which represents altered connectivity patterns shared across ASD, and an individual-specific subspace, which represents the remaining individual characteristics after eliminating the individual-shared altered connectivity patterns. RESULTS Analysis yielded 3 common components spanning the individual-shared subspace. Common components were associated with differences of functional connectivity at the group level. AFC in the individual-specific subspace improved the prediction of clinical symptoms. The default mode network-related and cingulo-opercular network-related magnitudes of AFC in the individual-specific subspace were significantly correlated with symptom severity in social communication deficits and restricted, repetitive behaviors in ASD. CONCLUSIONS Our study decomposed AFC of ASD into individual-shared and individual-specific subspaces, highlighting the importance of capturing and capitalizing on individual-specific brain connectivity features for dissecting heterogeneity. Our analysis framework provides a blueprint for parsing heterogeneity in other prevalent neurodevelopmental conditions.
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Affiliation(s)
- Xiaolong Shan
- Sichuan Provincial Center for Mental Health, Sichuan Provincial People's Hospital, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China; Ministry of Education Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, China
| | - Lucina Q Uddin
- Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, Los Angeles, California
| | - Rui Ma
- Sichuan Provincial Center for Mental Health, Sichuan Provincial People's Hospital, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China; Ministry of Education Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, China
| | - Pengfei Xu
- Sichuan Provincial Center for Mental Health, Sichuan Provincial People's Hospital, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China; Ministry of Education Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, China
| | - Jinming Xiao
- Sichuan Provincial Center for Mental Health, Sichuan Provincial People's Hospital, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China; Ministry of Education Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, China
| | - Lei Li
- Sichuan Provincial Center for Mental Health, Sichuan Provincial People's Hospital, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China; Ministry of Education Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, China
| | - Xinyue Huang
- Sichuan Provincial Center for Mental Health, Sichuan Provincial People's Hospital, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China; Ministry of Education Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, China
| | - Yu Feng
- Sichuan Provincial Center for Mental Health, Sichuan Provincial People's Hospital, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China; Ministry of Education Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, China
| | - Changchun He
- College of Blockchain Industry, Chengdu University of Information Technology, Chengdu, China
| | - Huafu Chen
- Sichuan Provincial Center for Mental Health, Sichuan Provincial People's Hospital, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China; Ministry of Education Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, China.
| | - Xujun Duan
- Sichuan Provincial Center for Mental Health, Sichuan Provincial People's Hospital, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China; Ministry of Education Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, China.
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12
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Ashburn SM, Matejko AA, Eden GF. Activation and functional connectivity of cerebellum during reading and during arithmetic in children with combined reading and math disabilities. Front Neurosci 2024; 18:1135166. [PMID: 38741787 PMCID: PMC11090247 DOI: 10.3389/fnins.2024.1135166] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2022] [Accepted: 02/06/2024] [Indexed: 05/16/2024] Open
Abstract
Background Reading and math constitute important academic skills, and as such, reading disability (RD or developmental dyslexia) and math disability (MD or developmental dyscalculia) can have negative consequences for children's educational progress. Although RD and MD are different learning disabilities, they frequently co-occur. Separate theories have implicated the cerebellum and its cortical connections in RD and in MD, suggesting that children with combined reading and math disability (RD + MD) may have altered cerebellar function and disrupted functional connectivity between the cerebellum and cortex during reading and during arithmetic processing. Methods Here we compared Control and RD + MD groups during a reading task as well as during an arithmetic task on (i) activation of the cerebellum, (ii) background functional connectivity, and (iii) task-dependent functional connectivity between the cerebellum and the cortex. Results The two groups (Control, RD + MD) did not differ for either task (reading, arithmetic) on any of the three measures (activation, background functional connectivity, task-dependent functional connectivity). Conclusion These results do not support theories that children's deficits in reading and math originate in the cerebellum.
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Affiliation(s)
| | | | - Guinevere F. Eden
- Center for the Study of Learning, Department of Pediatrics, Georgetown University Medical Center, Washington, DC, United States
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13
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Graves AJ, Danoff JS, Kim M, Brindley SR, Skyberg AM, Giamberardino SN, Lynch ME, Straka BC, Lillard TS, Gregory SG, Connelly JJ, Morris JP. Accelerated epigenetic age is associated with whole-brain functional connectivity and impaired cognitive performance in older adults. Sci Rep 2024; 14:9646. [PMID: 38671048 PMCID: PMC11053089 DOI: 10.1038/s41598-024-60311-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2024] [Accepted: 04/21/2024] [Indexed: 04/28/2024] Open
Abstract
While chronological age is a strong predictor for health-related risk factors, it is an incomplete metric that fails to fully characterize the unique aging process of individuals with different genetic makeup, neurodevelopment, and environmental experiences. Recent advances in epigenomic array technologies have made it possible to generate DNA methylation-based biomarkers of biological aging, which may be useful in predicting a myriad of cognitive abilities and functional brain network organization across older individuals. It is currently unclear which cognitive domains are negatively correlated with epigenetic age above and beyond chronological age, and it is unknown if functional brain organization is an important mechanism for explaining these associations. In this study, individuals with accelerated epigenetic age (i.e. AgeAccelGrim) performed worse on tasks that spanned a wide variety of cognitive faculties including both fluid and crystallized intelligence (N = 103, average age = 68.98 years, 73 females, 30 males). Additionally, fMRI connectome-based predictive models suggested a mediating mechanism of functional connectivity on epigenetic age acceleration-cognition associations primarily in medial temporal lobe and limbic structures. This research highlights the important role of epigenetic aging processes on the development and maintenance of healthy cognitive capacities and function of the aging brain.
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Affiliation(s)
| | | | - Minah Kim
- University of Virginia, Charlottesville, USA
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14
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Xu Q, Zhou LL, Xing C, Xu X, Feng Y, Lv H, Zhao F, Chen YC, Cai Y. Tinnitus classification based on resting-state functional connectivity using a convolutional neural network architecture. Neuroimage 2024; 290:120566. [PMID: 38467345 DOI: 10.1016/j.neuroimage.2024.120566] [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: 10/05/2023] [Revised: 03/02/2024] [Accepted: 03/04/2024] [Indexed: 03/13/2024] Open
Abstract
OBJECTIVES Many studies have investigated aberrant functional connectivity (FC) using resting-state functional MRI (rs-fMRI) in subjective tinnitus patients. However, no studies have verified the efficacy of resting-state FC as a diagnostic imaging marker. We established a convolutional neural network (CNN) model based on rs-fMRI FC to distinguish tinnitus patients from healthy controls, providing guidance and fast diagnostic tools for the clinical diagnosis of subjective tinnitus. METHODS A CNN architecture was trained on rs-fMRI data from 100 tinnitus patients and 100 healthy controls using an asymmetric convolutional layer. Additionally, a traditional machine learning model and a transfer learning model were included for comparison with the CNN, and each of the three models was tested on three different brain atlases. RESULTS Of the three models, the CNN model outperformed the other two models with the highest area under the curve, especially on the Dos_160 atlas (AUC = 0.944). Meanwhile, the model with the best classification performance highlights the crucial role of the default mode network, salience network, and sensorimotor network in distinguishing between normal controls and patients with subjective tinnitus. CONCLUSION Our CNN model could appropriately tackle the diagnosis of tinnitus patients using rs-fMRI and confirmed the diagnostic value of FC as measured by rs-fMRI.
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Affiliation(s)
- Qianhui Xu
- Department of Otolaryngology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, 107 West Yanjiang Road, Guangzhou, Guangdong Province 510120, China
| | - Lei-Lei Zhou
- Department of Radiology, Nanjing First Hospital, Nanjing Medical University, No.68, Changle Road, Nanjing 210006, China
| | - Chunhua Xing
- Department of Radiology, Nanjing First Hospital, Nanjing Medical University, No.68, Changle Road, Nanjing 210006, China
| | - Xiaomin Xu
- Department of Radiology, Nanjing First Hospital, Nanjing Medical University, No.68, Changle Road, Nanjing 210006, China
| | - Yuan Feng
- Department of Radiology, Nanjing First Hospital, Nanjing Medical University, No.68, Changle Road, Nanjing 210006, China
| | - Han Lv
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing, China
| | - Fei Zhao
- Department of Speech and Language Therapy and Hearing Science, Cardiff Metropolitan University, Cardiff, UK
| | - Yu-Chen Chen
- Department of Radiology, Nanjing First Hospital, Nanjing Medical University, No.68, Changle Road, Nanjing 210006, China.
| | - Yuexin Cai
- Department of Otolaryngology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, 107 West Yanjiang Road, Guangzhou, Guangdong Province 510120, China.
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15
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Chen Y, He H, Ding Y, Tao W, Guan Q, Krueger F. Connectome-based prediction of decreased trust propensity in older adults with mild cognitive impairment: A resting-state functional magnetic resonance imaging study. Neuroimage 2024; 292:120605. [PMID: 38615705 DOI: 10.1016/j.neuroimage.2024.120605] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2023] [Revised: 03/19/2024] [Accepted: 04/08/2024] [Indexed: 04/16/2024] Open
Abstract
Trust propensity (TP) relies more on social than economic rationality to transform the perceived probability of betrayal into positive reciprocity expectations in older adults with normal cognition. While deficits in social rationality have been observed in older adults with mild cognitive impairment (MCI), there is limited research on TP and its associated resting-state functional connectivity (RSFC) mechanisms in this population. To measure TP and related psychological functions (affect, motivation, executive cognition, and social cognition), MCI (n = 42) and normal healthy control (NHC, n = 115) groups completed a one-shot trust game and additional assessments of related psychological functions. RSFC associated with TP was analyzed using connectome-based predictive modeling (CPM) and lesion simulations. Our behavioral results showed that the MCI group trusted less (i.e., had lower TP) than the NHC group, with lower TP associated with higher sensitivity to the probability of betrayal in the MCI group. In the MCI group, only negative CPM models (RSFC negatively correlated with TP) significantly predicted TP, with a high salience network (SN) contribution. In contrast, in the NHC group, positive CPM models (RSFC positively correlated with TP) significantly predicted TP, with a high contribution from the default mode network (DMN). In addition, the total network strength of the NHC-specific positive network was lower in the MCI group than in the NHC group. Our findings demonstrated a decrease in TP in the MCI group compared to the NHC group, which is associated with deficits in social rationality (social cognition, associated with DMN) and increased sensitivity to betrayal (affect, associated with SN) in a trust dilemma. In conclusion, our study contributes to understanding MCI-related alterations in trust and their underlying neural mechanisms.
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Affiliation(s)
- Yiqi Chen
- School of Psychology, Shenzhen University, Shenzhen 518060, China; Department of Psychology, University of Mannheim, Mannheim 68131, Germany
| | - Hao He
- School of Psychology, Shenzhen University, Shenzhen 518060, China; Shenzhen-Hong Kong Institute of Brain Science-Shenzhen Fundamental Research Institutions, Shenzhen 518055, China
| | - Yiyang Ding
- School of Psychology, Shenzhen University, Shenzhen 518060, China
| | - Wuhai Tao
- School of Psychology, Shenzhen University, Shenzhen 518060, China; Shenzhen-Hong Kong Institute of Brain Science-Shenzhen Fundamental Research Institutions, Shenzhen 518055, China.
| | - Qing Guan
- School of Psychology, Shenzhen University, Shenzhen 518060, China; Shenzhen-Hong Kong Institute of Brain Science-Shenzhen Fundamental Research Institutions, Shenzhen 518055, China.
| | - Frank Krueger
- Department of Psychology, University of Mannheim, Mannheim 68131, Germany; School of Systems Biology, George Mason University, Fair, VA, USA
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16
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Shao Z, Tan Y, Zhan Y, He L. Modular organization of functional brain networks in patients with degenerative cervical myelopathy. Sci Rep 2024; 14:8593. [PMID: 38615051 PMCID: PMC11016091 DOI: 10.1038/s41598-024-58764-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2023] [Accepted: 04/03/2024] [Indexed: 04/15/2024] Open
Abstract
Previous studies have indicated that brain functional plasticity and reorganization in patients with degenerative cervical myelopathy (DCM). However, the effects of cervical cord compression on the functional integration and separation between and/or within modules remain unclear. This study aimed to address these questions using graph theory. Functional MRI was conducted on 46 DCM patients and 35 healthy controls (HCs). The intra- and inter-modular connectivity properties of the whole-brain functional network and nodal topological properties were then calculated using theoretical graph analysis. The difference in categorical variables between groups was compared using a chi-squared test, while that between continuous variables was evaluated using a two-sample t-test. Correlation analysis was conducted between modular connectivity properties and clinical parameters. Modules interaction analyses showed that the DCM group had significantly greater inter-module connections than the HCs group (DMN-FPN: t = 2.38, p = 0.02); inversely, the DCM group had significantly lower intra-module connections than the HCs group (SMN: t = - 2.13, p = 0.036). Compared to HCs, DCM patients exhibited higher nodal topological properties in the default-mode network and frontal-parietal network. In contrast, DCM patients exhibited lower nodal topological properties in the sensorimotor network. The Japanese Orthopedic Association (JOA) score was positively correlated with inter-module connections (r = 0.330, FDR p = 0.029) but not correlated with intra-module connections. This study reported alterations in modular connections and nodal centralities in DCM patients. Decreased nodal topological properties and intra-modular connection in the sensory-motor regions may indicate sensory-motor dysfunction. Additionally, increased nodal topological properties and inter-modular connection in the default mode network and frontal-parietal network may serve as a compensatory mechanism for sensory-motor dysfunction in DCM patients. This could provide an implicative neural basis to better understand alterations in brain networks and the patterns of changes in brain plasticity in DCM patients.
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Affiliation(s)
- Ziwei Shao
- Department of Radiology, The First Affiliated Hospital of Nanchang University, Nanchang, 330006, China
- Clinical Research Center for Medical Imaging In Jiangxi Province, Nanchang, China
| | - Yongming Tan
- Department of Radiology, The First Affiliated Hospital of Nanchang University, Nanchang, 330006, China
- Clinical Research Center for Medical Imaging In Jiangxi Province, Nanchang, China
| | - Yaru Zhan
- Department of Radiology, The First Affiliated Hospital of Nanchang University, Nanchang, 330006, China
- Clinical Research Center for Medical Imaging In Jiangxi Province, Nanchang, China
| | - Laichang He
- Department of Radiology, The First Affiliated Hospital of Nanchang University, Nanchang, 330006, China.
- Clinical Research Center for Medical Imaging In Jiangxi Province, Nanchang, China.
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17
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Garcia-Cordero I, Vasilevskaya A, Taghdiri F, Khodadadi M, Mikulis D, Tarazi A, Mushtaque A, Anssari N, Colella B, Green R, Rogaeva E, Sato C, Grinberg M, Moreno D, Hussain MW, Blennow K, Zetterberg H, Davis KD, Wennberg R, Tator C, Tartaglia MC. Functional connectivity changes in neurodegenerative biomarker-positive athletes with repeated concussions. J Neurol 2024:10.1007/s00415-024-12340-1. [PMID: 38589629 DOI: 10.1007/s00415-024-12340-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2023] [Revised: 02/27/2024] [Accepted: 03/19/2024] [Indexed: 04/10/2024]
Abstract
Multimodal biomarkers may identify former contact sports athletes with repeated concussions and at risk for dementia. Our study aims to investigate whether biomarker evidence of neurodegeneration in former professional athletes with repetitive concussions (ExPro) is associated with worse cognition and mood/behavior, brain atrophy, and altered functional connectivity. Forty-one contact sports athletes with repeated concussions were divided into neurodegenerative biomarker-positive (n = 16) and biomarker-negative (n = 25) groups based on positivity of serum neurofilament light-chain. Six healthy controls (negative for biomarkers) with no history of concussions were also analyzed. We calculated cognitive and mood/behavior composite scores from neuropsychological assessments. Gray matter volume maps and functional connectivity of the default mode, salience, and frontoparietal networks were compared between groups using ANCOVAs, controlling for age, and total intracranial volume. The association between the connectivity networks and sports characteristics was analyzed by multiple regression analysis in all ExPro. Participants presented normal-range mean performance in executive function, memory, and mood/behavior tests. The ExPro groups did not differ in professional years played, age at first participation in contact sports, and number of concussions. There were no differences in gray matter volume between groups. The neurodegenerative biomarker-positive group had lower connectivity in the default mode network (DMN) compared to the healthy controls and the neurodegenerative biomarker-negative group. DMN disconnection was associated with increased number of concussions in all ExPro. Biomarkers of neurodegeneration may be useful to detect athletes that are still cognitively normal, but with functional connectivity alterations after concussions and at risk of dementia.
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Affiliation(s)
- Indira Garcia-Cordero
- Tanz Centre for Research in Neurodegenerative Diseases, University of Toronto, Toronto, Canada
- Canadian Concussion Centre, Toronto Western Hospital, University Health Network, Toronto, Canada
| | - Anna Vasilevskaya
- Tanz Centre for Research in Neurodegenerative Diseases, University of Toronto, Toronto, Canada
| | - Foad Taghdiri
- Tanz Centre for Research in Neurodegenerative Diseases, University of Toronto, Toronto, Canada
| | - Mozhgan Khodadadi
- Canadian Concussion Centre, Toronto Western Hospital, University Health Network, Toronto, Canada
| | - David Mikulis
- Canadian Concussion Centre, Toronto Western Hospital, University Health Network, Toronto, Canada
| | - Apameh Tarazi
- Canadian Concussion Centre, Toronto Western Hospital, University Health Network, Toronto, Canada
| | - Asma Mushtaque
- Canadian Concussion Centre, Toronto Western Hospital, University Health Network, Toronto, Canada
| | - Neda Anssari
- Canadian Concussion Centre, Toronto Western Hospital, University Health Network, Toronto, Canada
- Brain Vision and Concussion Clinic, Winnipeg, Canada
| | - Brenda Colella
- Canadian Concussion Centre, Toronto Western Hospital, University Health Network, Toronto, Canada
| | - Robin Green
- Canadian Concussion Centre, Toronto Western Hospital, University Health Network, Toronto, Canada
| | - Ekaterina Rogaeva
- Tanz Centre for Research in Neurodegenerative Diseases, University of Toronto, Toronto, Canada
| | - Christine Sato
- Tanz Centre for Research in Neurodegenerative Diseases, University of Toronto, Toronto, Canada
| | - Mark Grinberg
- Tanz Centre for Research in Neurodegenerative Diseases, University of Toronto, Toronto, Canada
| | - Danielle Moreno
- Tanz Centre for Research in Neurodegenerative Diseases, University of Toronto, Toronto, Canada
| | - Mohammed W Hussain
- Tanz Centre for Research in Neurodegenerative Diseases, University of Toronto, Toronto, Canada
| | - Kaj Blennow
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy at the University of Gothenburg, Mölndal, Sweden
- Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden
| | - Henrik Zetterberg
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy at the University of Gothenburg, Mölndal, Sweden
- Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden
- Department of Neurodegenerative Disease, UCL Institute of Neurology, Queen Square, London, UK
- UK Dementia Research Institute at UCL, London, UK
- Hong Kong Center for Neurodegenerative Diseases, Clear Water Bay, Hong Kong, China
| | - Karen D Davis
- Canadian Concussion Centre, Toronto Western Hospital, University Health Network, Toronto, Canada
- Krembil Brain Institute, University Health Network, Toronto, Canada
- Department of Surgery, University of Toronto, Toronto, Canada
- Institute of Medical Science, University of Toronto, Toronto, Canada
| | - Richard Wennberg
- Canadian Concussion Centre, Toronto Western Hospital, University Health Network, Toronto, Canada
| | - Charles Tator
- Canadian Concussion Centre, Toronto Western Hospital, University Health Network, Toronto, Canada
| | - Maria C Tartaglia
- Tanz Centre for Research in Neurodegenerative Diseases, University of Toronto, Toronto, Canada.
- Canadian Concussion Centre, Toronto Western Hospital, University Health Network, Toronto, Canada.
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18
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Garcia-Cordero I, Anastassiadis C, Khoja A, Morales-Rivero A, Thapa S, Vasilevskaya A, Davenport C, Sumra V, Couto B, Multani N, Taghdiri F, Anor C, Misquitta K, Vandevrede L, Heuer H, Tang-Wai D, Dickerson B, Pantelyat A, Litvan I, Boeve B, Rojas JC, Ljubenkov P, Huey E, Fox S, Kovacs GG, Boxer A, Lang A, Tartaglia MC. Evaluating the Effect of Alzheimer's Disease-Related Biomarker Change in Corticobasal Syndrome and Progressive Supranuclear Palsy. Ann Neurol 2024. [PMID: 38578117 DOI: 10.1002/ana.26930] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2023] [Revised: 02/26/2024] [Accepted: 03/11/2024] [Indexed: 04/06/2024]
Abstract
OBJECTIVES To evaluate the effect of Alzheimer's disease (AD) -related biomarker change on clinical features, brain atrophy and functional connectivity of patients with corticobasal syndrome (CBS) and progressive supranuclear palsy (PSP). METHODS Data from patients with a clinical diagnosis of CBS, PSP, and AD and healthy controls were obtained from the 4-R-Tauopathy Neuroimaging Initiative 1 and 2, the Alzheimer's Disease Neuroimaging Initiative, and a local cohort from the Toronto Western Hospital. Patients with CBS and PSP were divided into AD-positive (CBS/PSP-AD) and AD-negative (CBS/PSP-noAD) groups based on fluid biomarkers and amyloid PET scans. Cognitive, motor, and depression scores; AD fluid biomarkers (cerebrospinal p-tau, t-tau, and amyloid-beta, and plasma ptau-217); and neuroimaging data (amyloid PET, MRI and fMRI) were collected. Clinical features, whole-brain gray matter volume and functional networks connectivity were compared across groups. RESULTS Data were analyzed from 87 CBS/PSP-noAD and 23 CBS/PSP-AD, 18 AD, and 30 healthy controls. CBS/PSP-noAD showed worse performance in comparison to CBS/PSP-AD in the PSPRS [mean(SD): 34.8(15.8) vs 23.3(11.6)] and the UPDRS scores [mean(SD): 34.2(17.0) vs 21.8(13.3)]. CBS/PSP-AD demonstrated atrophy in AD signature areas and brainstem, while CBS/PSP-noAD patients displayed atrophy in frontal and temporal areas, globus pallidus, and brainstem compared to healthy controls. The default mode network showed greatest disconnection in CBS/PSP-AD compared with CBS/PSP-no AD and controls. The thalamic network connectivity was most affected in CBS/PSP-noAD. INTERPRETATION AD biomarker positivity may modulate the clinical presentation of CBS/PSP, with evidence of distinctive structural and functional brain changes associated with the AD pathology/co-pathology. ANN NEUROL 2024.
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Affiliation(s)
- Indira Garcia-Cordero
- Tanz Centre for Research in Neurodegenerative Diseases, University of Toronto, Toronto, ON, Canada
| | - Chloe Anastassiadis
- Tanz Centre for Research in Neurodegenerative Diseases, University of Toronto, Toronto, ON, Canada
| | - Abeer Khoja
- University Health Network Memory Clinic, Toronto, ON, Canada
- Neurology Division, Medical Department, Faculty of Medicine, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Alonso Morales-Rivero
- University Health Network Memory Clinic, Toronto, ON, Canada
- ABC Medical Center, Mexico City, Mexico
| | - Simrika Thapa
- Tanz Centre for Research in Neurodegenerative Diseases, University of Toronto, Toronto, ON, Canada
| | - Anna Vasilevskaya
- Tanz Centre for Research in Neurodegenerative Diseases, University of Toronto, Toronto, ON, Canada
| | - Carly Davenport
- Tanz Centre for Research in Neurodegenerative Diseases, University of Toronto, Toronto, ON, Canada
| | - Vishaal Sumra
- Tanz Centre for Research in Neurodegenerative Diseases, University of Toronto, Toronto, ON, Canada
| | - Blas Couto
- Rossy PSP Program, University Health Network and the University of Toronto, Toronto, ON, Canada
- The Edmond J. Safra Program in Parkinson's Disease and Morton and Gloria Shulman Movement Disorders Clinic, Toronto, ON, Canada
- Institute of Cognitive and Translational Neuroscience (INCyT-INECO-CONICET), Favaloro University Hospital, Buenos Aires, Argentina
| | - Namita Multani
- Tanz Centre for Research in Neurodegenerative Diseases, University of Toronto, Toronto, ON, Canada
| | - Foad Taghdiri
- Tanz Centre for Research in Neurodegenerative Diseases, University of Toronto, Toronto, ON, Canada
| | - Cassandra Anor
- Tanz Centre for Research in Neurodegenerative Diseases, University of Toronto, Toronto, ON, Canada
| | - Karen Misquitta
- Tanz Centre for Research in Neurodegenerative Diseases, University of Toronto, Toronto, ON, Canada
| | - Lawren Vandevrede
- Memory and Aging Center, Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, CA, USA
| | - Hilary Heuer
- Memory and Aging Center, Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, CA, USA
| | - David Tang-Wai
- Krembil Brain Institute, University Health Network, Toronto, ON, Canada
| | - Bradford Dickerson
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | | | - Irene Litvan
- Department of Neurosciences, University of California San Diego, La Jolla, CA, USA
| | - Bradley Boeve
- Department of Neurology, Mayo Clinic, Rochester, MN, USA
| | - Julio C Rojas
- Memory and Aging Center, Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, CA, USA
| | - Peter Ljubenkov
- Memory and Aging Center, Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, CA, USA
| | - Edward Huey
- Department of Psychiatry and Human Behavior, Brown University, Providence, RI, USA
| | - Susan Fox
- Rossy PSP Program, University Health Network and the University of Toronto, Toronto, ON, Canada
- The Edmond J. Safra Program in Parkinson's Disease and Morton and Gloria Shulman Movement Disorders Clinic, Toronto, ON, Canada
| | - Gabor G Kovacs
- Tanz Centre for Research in Neurodegenerative Diseases, University of Toronto, Toronto, ON, Canada
- Rossy PSP Program, University Health Network and the University of Toronto, Toronto, ON, Canada
- The Edmond J. Safra Program in Parkinson's Disease and Morton and Gloria Shulman Movement Disorders Clinic, Toronto, ON, Canada
- Krembil Brain Institute, University Health Network, Toronto, ON, Canada
| | - Adam Boxer
- Memory and Aging Center, Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, CA, USA
| | - Anthony Lang
- Tanz Centre for Research in Neurodegenerative Diseases, University of Toronto, Toronto, ON, Canada
- Rossy PSP Program, University Health Network and the University of Toronto, Toronto, ON, Canada
- The Edmond J. Safra Program in Parkinson's Disease and Morton and Gloria Shulman Movement Disorders Clinic, Toronto, ON, Canada
- Krembil Brain Institute, University Health Network, Toronto, ON, Canada
| | - M Carmela Tartaglia
- Tanz Centre for Research in Neurodegenerative Diseases, University of Toronto, Toronto, ON, Canada
- University Health Network Memory Clinic, Toronto, ON, Canada
- Rossy PSP Program, University Health Network and the University of Toronto, Toronto, ON, Canada
- Krembil Brain Institute, University Health Network, Toronto, ON, Canada
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19
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Jiang A, Ma X, Li S, Wang L, Yang B, Wang S, Li M, Dong G. Age-atypical brain functional networks in autism spectrum disorder: a normative modeling approach. Psychol Med 2024:1-12. [PMID: 38563297 DOI: 10.1017/s0033291724000138] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
BACKGROUND Despite extensive research into the neural basis of autism spectrum disorder (ASD), the presence of substantial biological and clinical heterogeneity among diagnosed individuals remains a major barrier. Commonly used case‒control designs assume homogeneity among subjects, which limits their ability to identify biological heterogeneity, while normative modeling pinpoints deviations from typical functional network development at individual level. METHODS Using a world-wide multi-site database known as Autism Brain Imaging Data Exchange, we analyzed individuals with ASD and typically developed (TD) controls (total n = 1218) aged 5-40 years, generating individualized whole-brain network functional connectivity (FC) maps of age-related atypicality in ASD. We then used local polynomial regression to estimate a networkwise normative model of development and explored correlations between ASD symptoms and brain networks. RESULTS We identified a subset exhibiting highly atypical individual-level FC, exceeding 2 standard deviation from the normative value. We also identified clinically relevant networks (mainly default mode network) at cohort level, since the outlier rates decreased with age in TD participants, but increased in those with autism. Moreover, deviations were linked to severity of repetitive behaviors and social communication symptoms. CONCLUSIONS Individuals with ASD exhibit distinct, highly individualized trajectories of brain functional network development. In addition, distinct developmental trajectories were observed among ASD and TD individuals, suggesting that it may be challenging to identify true differences in network characteristics by comparing young children with ASD to their TD peers. This study enhances understanding of the biological heterogeneity of the disorder and can inform precision medicine.
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Affiliation(s)
- Anhang Jiang
- Department of Psychology, Yunnan Normal University, Kunming, Yunnan Province, P.R. China
- Center for Cognition and Brain Disorders, the Affiliated Hospital of Hangzhou Normal University, Hangzhou, Zhejiang Province, P.R. China
| | - Xuefeng Ma
- Center for Cognition and Brain Disorders, the Affiliated Hospital of Hangzhou Normal University, Hangzhou, Zhejiang Province, P.R. China
| | - Shuang Li
- Center for Cognition and Brain Disorders, the Affiliated Hospital of Hangzhou Normal University, Hangzhou, Zhejiang Province, P.R. China
| | - Lingxiao Wang
- Center for Cognition and Brain Disorders, the Affiliated Hospital of Hangzhou Normal University, Hangzhou, Zhejiang Province, P.R. China
- Institutes of Psychological Sciences, Hangzhou Normal University, Hangzhou, Zhejiang Province, China
| | - Bo Yang
- Department of Psychology, Yunnan Normal University, Kunming, Yunnan Province, P.R. China
| | - Shizhen Wang
- Center for Cognition and Brain Disorders, the Affiliated Hospital of Hangzhou Normal University, Hangzhou, Zhejiang Province, P.R. China
| | - Mei Li
- Center for Cognition and Brain Disorders, the Affiliated Hospital of Hangzhou Normal University, Hangzhou, Zhejiang Province, P.R. China
- Center for Mental Health Education and Counselling, Hangzhou Normal University, Hangzhou, Zhejiang Province, P.R. China
| | - Guangheng Dong
- Department of Psychology, Yunnan Normal University, Kunming, Yunnan Province, P.R. China
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20
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El-Sayed MM, Hawash MM, Khedr MA, Hafez SA, Salem ESAEH, Essa SA, Sayyd SM, El-Ashry AM. Cognitive flexibility's role in shaping self-perception of aging, body appreciation, and self-efficacy among community-dwelling older women. BMC Nurs 2024; 23:220. [PMID: 38561732 PMCID: PMC10983730 DOI: 10.1186/s12912-024-01874-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2023] [Accepted: 03/17/2024] [Indexed: 04/04/2024] Open
Abstract
BACKGROUND Cognitive flexibility, the capacity to adjust to new information, affects how aging is perceived. In Egyptian culture, older women's views on aging are shaped by societal gender roles and expectations. These views influence their body image and belief in their abilities, all melded by cultural standards and values. AIM Investigate the mediating role of cognitive flexibility in the relationship between self-aging perception, body appreciation, and self-efficacy among community-dwelling older women. METHODS A correlational analytical design was used on 200 women aged 60 years or older using the Cognitive Flexibility Inventory, Self-Perceptions of Aging, General Self-Efficacy Scale, and Body Appreciation Scales. Structural equation modeling was used in the analysis. RESULTS The study found that cognitive flexibility is positively related to self-perception of aging and body appreciation and is also significantly related to general self-efficacy. However, no significant relationship was found between body appreciation and general self-efficacy. Additionally, the study found that cognitive flexibility partially mediates the relationship between self-perception of aging and body appreciation and fully mediates the relationship between body appreciation and self-efficacy. CONCLUSION Cognitive flexibility is vital in the relationships between self-perceptions of aging, body appreciation, and self-efficacy among older women. Therefore, nursing interventions targeting cognitive flexibility are recommended to promote positive self-aging perceptions, body appreciation, and self-efficacy in this population.
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Affiliation(s)
- Mona Metwally El-Sayed
- Psychiatric and Mental Health Nursing Department, Faculty of Nursing, Alexandria University, Alexandria, Egypt.
| | - Manal Mohammed Hawash
- Gerontological Nursing Department, Faculty of Nursing, Alexandria University, Alexandria, Egypt
| | - Mahmoud Abdelwahab Khedr
- Psychiatric and Mental Health Nursing Department, Faculty of Nursing, Alexandria University, Alexandria, Egypt
| | - Sarah Ali Hafez
- Gerontological Nursing Department, Faculty of Nursing, Alexandria University, Alexandria, Egypt
| | - El-Saied Abd El-Hamid Salem
- Department of Fitness, Gymnastics, and Sports Shows, Faculty of Physical Education for Men, Abu Qir, Alexandria University, Alexandria, Egypt
| | - Samir Abdelnaby Essa
- Department of Physical Education and Sports Sciences, Faculty of Education, Taibah University, Madinah, 41477, Saudi Arabia
| | - Sameer Mohammed Sayyd
- Department of Physical Education and Sports Sciences, Faculty of Education, Taibah University, Madinah, 41477, Saudi Arabia
| | - Ayman Mohamed El-Ashry
- Psychiatric and Mental Health Nursing Department, Faculty of Nursing, Alexandria University, Alexandria, Egypt
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21
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Mittal P, Sao AK, Biswal B, Di X, Dileep AD. Network-wise analysis of movie-specific information in dynamic functional connectivity using COBE. Cereb Cortex 2024; 34:bhae170. [PMID: 38679477 DOI: 10.1093/cercor/bhae170] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2023] [Revised: 03/29/2024] [Accepted: 04/01/2024] [Indexed: 05/01/2024] Open
Abstract
Movie watching during functional magnetic resonance imaging has emerged as a promising tool to measure the complex behavior of the brain in response to a stimulus similar to real-life situations. It has been observed that presenting a movie (sequence of events) as a stimulus will lead to a unique time course of dynamic functional connectivity related to movie stimuli that can be compared across the participants. We assume that the observed dynamic functional connectivity across subjects can be divided into following 2 components: (i) specific to a movie stimulus (depicting group-level behavior in functional connectivity) and (ii) individual-specific behavior (not necessarily common across the subjects). In this work, using the dynamic time warping distance measure, we have shown the extent of similarity between the temporal sequences of functional connectivity while the underlying movie stimuli were same and different. Further, the temporal sequence of functional connectivity patterns related to a movie is enhanced by suppressing the subject-specific components of dynamic functional connectivity using common and orthogonal basis extraction. Quantitative analysis using the F-ratio measure reveals significant differences in dynamic functional connectivity within the somatomotor network and default mode network, as well as between the occipital network and somatomotor networks.
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Affiliation(s)
- Priyanka Mittal
- School of Computing and Electrical Engineering, Indian Institute of Technology Mandi 175005, Himachal Pradesh, India
| | - Anil K Sao
- Electrical Engineering and Computer Science, Indian Institute of Technology Bhilai, Raipur 492015, Chhattisgarh, India
| | - Bharat Biswal
- Department of Biomedical Engineering, New Jersey Institute of Technology, Newark 07102, NJ, United States
| | - Xin Di
- Department of Biomedical Engineering, New Jersey Institute of Technology, Newark 07102, NJ, United States
| | - Aroor Dinesh Dileep
- School of Computing and Electrical Engineering, Indian Institute of Technology Mandi 175005, Himachal Pradesh, India
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22
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Xiao P, Li Q, Gui H, Xu B, Zhao X, Wang H, Tao L, Chen H, Wang H, Lv F, Luo T, Cheng O, Luo J, Man Y, Xiao Z, Fang W. Combined brain topological metrics with machine learning to distinguish essential tremor and tremor-dominant Parkinson's disease. Neurol Sci 2024:10.1007/s10072-024-07472-1. [PMID: 38528280 DOI: 10.1007/s10072-024-07472-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2023] [Accepted: 03/14/2024] [Indexed: 03/27/2024]
Abstract
BACKGROUND Essential tremor (ET) and Parkinson's disease (PD) are the two most prevalent movement disorders, sharing several overlapping tremor clinical features. Although growing evidence pointed out that changes in similar brain network nodes are associated with these two diseases, the brain network topological properties are still not very clear. OBJECTIVE The combination of graph theory analysis with machine learning (ML) algorithms provides a promising way to reveal the topological pathogenesis in ET and tremor-dominant PD (tPD). METHODS Topological metrics were extracted from Resting-state functional images of 86 ET patients, 86 tPD patients, and 86 age- and sex-matched healthy controls (HCs). Three steps were conducted to feature dimensionality reduction and four frequently used classifiers were adopted to discriminate ET, tPD, and HCs. RESULTS A support vector machine classifier achieved the best classification performance of four classifiers for discriminating ET, tPD, and HCs with 89.0% mean accuracy (mACC) and was used for binary classification. Particularly, the binary classification performances among ET vs. tPD, ET vs. HCs, and tPD vs. HCs were with 94.2% mACC, 86.0% mACC, and 86.3% mACC, respectively. The most power discriminative features were mainly located in the default, frontal-parietal, cingulo-opercular, sensorimotor, and cerebellum networks. Correlation analysis results showed that 2 topological features negatively and 1 positively correlated with clinical characteristics. CONCLUSIONS These results demonstrated that combining topological metrics with ML algorithms could not only achieve high classification accuracy for discrimination ET, tPD, and HCs but also help to reveal the potential brain topological network pathogenesis in ET and tPD.
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Affiliation(s)
- Pan Xiao
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, No. 1 Youyi Road, Yuzhong District, Chongqing, 400016, China
| | - Qin Li
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, No. 1 Youyi Road, Yuzhong District, Chongqing, 400016, China
| | - Honge Gui
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, No. 1 Youyi Road, Yuzhong District, Chongqing, 400016, China
| | - Bintao Xu
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, No. 1 Youyi Road, Yuzhong District, Chongqing, 400016, China
| | - Xiaole Zhao
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, No. 1 Youyi Road, Yuzhong District, Chongqing, 400016, China
| | - Hongyu Wang
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, No. 1 Youyi Road, Yuzhong District, Chongqing, 400016, China
| | - Li Tao
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, No. 1 Youyi Road, Yuzhong District, Chongqing, 400016, China
| | - Huiyue Chen
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, No. 1 Youyi Road, Yuzhong District, Chongqing, 400016, China
| | - Hansheng Wang
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, No. 1 Youyi Road, Yuzhong District, Chongqing, 400016, China
| | - Fajin Lv
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, No. 1 Youyi Road, Yuzhong District, Chongqing, 400016, China
| | - Tianyou Luo
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, No. 1 Youyi Road, Yuzhong District, Chongqing, 400016, China
| | - Oumei Cheng
- Department of Neurology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Jin Luo
- Department of Neurology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Yun Man
- Department of Neurology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Zheng Xiao
- Department of Neurology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Weidong Fang
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, No. 1 Youyi Road, Yuzhong District, Chongqing, 400016, China.
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23
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Xia H, Li T, Hou Y, Liu Z, Chen A. Age-related decline in cognitive flexibility and inadequate preparation: evidence from task-state network analysis. GeroScience 2024:10.1007/s11357-024-01135-x. [PMID: 38514520 DOI: 10.1007/s11357-024-01135-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2023] [Accepted: 03/12/2024] [Indexed: 03/23/2024] Open
Abstract
Behavioral evidence showed decreased cognitive flexibility in older adults. However, task-based network mechanisms of cognitive flexibility in aging (CFA) remain unclear. Here, we provided the first task-state network evidence that CFA was associated with inadequate preparation for switching trials by revealing age-related changes in functional integration. We examined functional integration in a letter-number switch task that distinguished between the cue and target stages. Both young and older adults showed decreased functional integration from the cue stage to the target stage, indicating that control-related processes were executed as the task progressed. However, compared to young adults, older adults showed less cue-to-target reduction in functional integration, which was primarily driven by higher network integration in the target stage. Moreover, less cue-to-target reductions were correlated with age-related decreases in task performance in the switch task. To sum up, compared to young adults, older adults pre-executed less control-related processes in the cue stage and more control-related processes in the target stage. Therefore, the decline in cognitive flexibility in older adults was associated with inadequate preparation for the impending demands of cognitive switching. This study offered novel insights into network mechanisms underlying CFA. Furthermore, we highlighted that training the function of brain networks, in conjunction with providing more preparation time for older adults, may be beneficial to their cognitive flexibility.
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Affiliation(s)
- Haishuo Xia
- Faculty of Psychology, Southwest University, Chongqing, China
| | - Ting Li
- Faculty of Psychology, Beijing Normal University, Beijing, China
| | - Yongqing Hou
- Faculty of Psychology, Southwest University, Chongqing, China
| | - Zijin Liu
- School of Psychology, Shanghai University of Sport, Shanghai, 200438, China
| | - Antao Chen
- School of Psychology, Shanghai University of Sport, Shanghai, 200438, China.
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24
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Nakuci J, Yeon J, Kim JH, Kim SP, Rahnev D. Behavior can be decoded across the cortex when individual differences are considered. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.12.584674. [PMID: 38559114 PMCID: PMC10979965 DOI: 10.1101/2024.03.12.584674] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
Group-level analyses have typically associated behavioral signatures with a constrained set of brain areas. Here we show that two behavioral metrics - reaction time (RT) and confidence - can be decoded across the cortex when each individual is considered separately. Subjects (N=50) completed a perceptual decision-making task with confidence. We built models decoding trial-level RT and confidence separately for each subject using the activation patterns in one brain area at a time after splitting the entire cortex into 200 regions of interest (ROIs). At the group level, we replicated previous results by showing that both RT and confidence could be decoded from a small number of ROIs (12.0% and 3.5%, respectively). Critically, at the level of the individual, both RT and confidence could be decoded from most brain regions even after Bonferroni correction (90.0% and 72.5%, respectively). Surprisingly, we observed that many brain regions exhibited opposite brain-behavior relationships across individuals, such that, for example, higher activations predicted fast RTs in some subjects but slow RTs in others. These results were further replicated in a second dataset. Lastly, we developed a simple test to determine the robustness of decoding performance, which showed that several hundred trials per subject are required for robust decoding. These results show that behavioral signatures can be decoded from a much broader range of cortical areas than previously recognized and suggest the need to study the brain-behavior relationship at both the group and the individual level.
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Affiliation(s)
- Johan Nakuci
- School of Psychology, Georgia Institute of Technology, Atlanta, Georgia, 30332, USA
| | - Jiwon Yeon
- School of Psychology, Georgia Institute of Technology, Atlanta, Georgia, 30332, USA
- Department of Psychology, Stanford University, Stanford, California, 94305, USA
| | - Ji-Hyun Kim
- Department of Biomedical Engineering, Ulsan National Institute of Science and Technology, Ulsan, South Korea
| | - Sung-Phil Kim
- Department of Biomedical Engineering, Ulsan National Institute of Science and Technology, Ulsan, South Korea
| | - Dobromir Rahnev
- School of Psychology, Georgia Institute of Technology, Atlanta, Georgia, 30332, USA
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25
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Chen Y, Xia X, Zhou Z, Yuan M, Peng Y, Liu Y, Tang J, Fu Y. Interleukin-6 is correlated with amygdala volume and depression severity in adolescents and young adults with first-episode major depressive disorder. Brain Imaging Behav 2024:10.1007/s11682-024-00871-0. [PMID: 38467915 DOI: 10.1007/s11682-024-00871-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/04/2024] [Indexed: 03/13/2024]
Abstract
Inflammatory mechanisms may play crucial roles in the pathophysiology of major depressive disorder (MDD), and cytokine concentrations are correlated with brain alterations. Adolescents and young adults with MDD have higher recurrence and suicide rates than adults, but there has been limited research on the underlying mechanisms. In this study, we aimed to investigate the potential correlations among cytokines, depression severity, and the volumes of the amygdala, hippocampus, and nucleus accumbens in Han Chinese adolescents and young adults with first-episode MDD. Nineteen patients with MDD aged 10-21 years were enrolled from the Psychiatry Department of the First Affiliated Hospital of Chongqing Medical University, along with 18 age-matched healthy controls from a local school. We measured the concentrations of interleukin (IL)-4, IL-6, IL-8, and IL-10 in the peripheral blood, along with the volumes of the amygdala, hippocampus, and nucleus accumbens, as determined by magnetic resonance imaging. We observed that patients with MDD had higher concentrations of IL-6 and a trend towards reduced left amygdala and bilateral hippocampus volumes than healthy controls. Additionally, the concentration of IL-6 was correlated with the left amygdala volume and depression severity, while the left hippocampus volume was correlated with depression severity. This study suggests that inflammation is an underlying neurobiological change and implies that IL-6 could serve as a potential biomarker for identifying early stage MDD in adolescents and young adults.
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Affiliation(s)
- Yingying Chen
- Department of Psychiatry, The First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, China
| | - Xiaodi Xia
- Department of Psychiatry, The First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, China
| | - Zheyi Zhou
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, 100875, China
| | - Meng Yuan
- Department of Psychiatry, The First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, China
| | - Yadong Peng
- Department of Psychiatry, The First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, China
| | - Ying Liu
- Department of Psychiatry, The First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, China
| | - Jinxiang Tang
- Department of Psychiatry, The First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, China
| | - Yixiao Fu
- Department of Psychiatry, The First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, China.
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26
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Hoheisel L, Kambeitz-Ilankovic L, Wenzel J, Haas SS, Antonucci LA, Ruef A, Penzel N, Schultze-Lutter F, Lichtenstein T, Rosen M, Dwyer DB, Salokangas RKR, Lencer R, Brambilla P, Borgwardt S, Wood SJ, Upthegrove R, Bertolino A, Ruhrmann S, Meisenzahl E, Koutsouleris N, Fink GR, Daun S, Kambeitz J. Alterations of Functional Connectivity Dynamics in Affective and Psychotic Disorders. BIOLOGICAL PSYCHIATRY. COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2024:S2451-9022(24)00065-X. [PMID: 38461964 DOI: 10.1016/j.bpsc.2024.02.013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/30/2023] [Revised: 01/23/2024] [Accepted: 02/15/2024] [Indexed: 03/12/2024]
Abstract
BACKGROUND Psychosis and depression patients exhibit widespread neurobiological abnormalities. The analysis of dynamic functional connectivity (dFC), allows for the detection of changes in complex brain activity patterns, providing insights into common and unique processes underlying these disorders. METHODS In the present study, we report the analysis of dFC in a large patient sample including 127 clinical high-risk patients (CHR), 142 recent-onset psychosis (ROP) patients, 134 recent-onset depression (ROD) patients, and 256 healthy controls (HC). A sliding window-based technique was used to calculate the time-dependent FC in resting-state MRI data, followed by clustering to reveal recurrent FC states in each diagnostic group. RESULTS We identified five unique FC states, which could be identified in all groups with high consistency (rmean = 0.889, sd = 0.116). Analysis of dynamic parameters of these states showed a characteristic increase in the lifetime and frequency of a weakly-connected FC state in ROD patients (p < 0.0005) compared to most other groups, and a common increase in the lifetime of a FC state characterised by high sensorimotor and cingulo-opercular connectivities in all patient groups compared to the HC group (p < 0.0002). Canonical correlation analysis revealed a mode which exhibited significant correlations between dFC parameters and clinical variables (r = 0.617, p < 0.0029), which was associated with positive psychosis symptom severity and several dFC parameters. CONCLUSIONS Our findings indicate diagnosis-specific alterations of dFC and underline the potential of dynamic analysis to characterize disorders such as depression, psychosis and clinical risk states.
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Affiliation(s)
- Linnea Hoheisel
- Cognitive Neuroscience, Institute of Neuroscience and Medicine (INM-3), Forschungszentrum Jülich, Jülich, Germany; Department of Psychiatry and Psychotherapy, Faculty of Medicine and University Hospital, University of Cologne, Cologne, Germany
| | - Lana Kambeitz-Ilankovic
- Department of Psychiatry and Psychotherapy, Faculty of Medicine and University Hospital, University of Cologne, Cologne, Germany; Department of Psychiatry and Psychotherapy, Ludwig Maximilians University, Munich, Germany
| | - Julian Wenzel
- Department of Psychiatry and Psychotherapy, Faculty of Medicine and University Hospital, University of Cologne, Cologne, Germany
| | - Shalaila S Haas
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Linda A Antonucci
- Department of Translational Biomedicine and Neuroscience, University of Bari Aldo Moro, Bari, Italy
| | - Anne Ruef
- Department of Psychiatry and Psychotherapy, Ludwig Maximilians University, Munich, Germany
| | - Nora Penzel
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA
| | - Frauke Schultze-Lutter
- Department of Psychiatry and Psychotherapy, University of Düsseldorf, Düsseldorf, Germany; University Hospital of Child and Adolescent Psychiatry and Psychotherapy, University of Bern, Bern, Switzerland; Department of Psychology and Mental Health, Faculty of Psychology, Airlangga University, Surabaya, Indonesia
| | - Theresa Lichtenstein
- Department of Psychiatry and Psychotherapy, Faculty of Medicine and University Hospital, University of Cologne, Cologne, Germany
| | - Marlene Rosen
- Department of Psychiatry and Psychotherapy, Faculty of Medicine and University Hospital, University of Cologne, Cologne, Germany
| | - Dominic B Dwyer
- Centre for Youth Mental Health, The University of Melbourne, Parkville, VIC, Australia; Orygen, Parkville, VIC, Australia
| | | | - Rebekka Lencer
- Institute for Translational Psychiatry, University of Münster, Münster, Germany; Department of Psychiatry and Psychotherapy, Lübeck University, Lübeck, Germany
| | - Paolo Brambilla
- Department of Pathophysiology and Transplantation, University of Milan, Milan, Italy; Department of Neurosciences and Mental Health, IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy
| | - Stephan Borgwardt
- Department of Psychiatry and Psychotherapy, University of Lübeck, Lübeck, Germany
| | - Stephen J Wood
- Institute for Mental Health, University of Birmingham, Birmingham, United Kingdom; Orygen, Parkville, Victoria, Australia; Centre for Youth Mental Health, University of Melbourne, Melbourne, Victoria, Australia
| | - Rachel Upthegrove
- Institute for Mental Health, University of Birmingham, Birmingham, United Kingdom; Centre for Human Brain Health, University of Birmingham, Birmingham, United Kingdom; Birmingham Early Interventions Service, Birmingham Women's and Children's NHS Foundation Trust, Birmingham, United Kingdom
| | - Alessandro Bertolino
- Department of Basic Medical Science, Neuroscience, and Sense Organs, University of Bari Aldo Moro, Bari, Italy
| | - Stephan Ruhrmann
- Department of Psychiatry and Psychotherapy, Faculty of Medicine and University Hospital, University of Cologne, Cologne, Germany
| | - Eva Meisenzahl
- Department of Psychiatry and Psychotherapy, University of Düsseldorf, Düsseldorf, Germany
| | - Nikolaos Koutsouleris
- Department of Psychiatry and Psychotherapy, Ludwig Maximilians University, Munich, Germany
| | - Gereon R Fink
- Cognitive Neuroscience, Institute of Neuroscience and Medicine (INM-3), Forschungszentrum Jülich, Jülich, Germany; Department of Neurology, Faculty of Medicine and University Hospital, University of Cologne, Cologne, Germany
| | - Silvia Daun
- Cognitive Neuroscience, Institute of Neuroscience and Medicine (INM-3), Forschungszentrum Jülich, Jülich, Germany; Institute of Zoology, University of Cologne, Cologne, Germany
| | - Joseph Kambeitz
- Cognitive Neuroscience, Institute of Neuroscience and Medicine (INM-3), Forschungszentrum Jülich, Jülich, Germany; Department of Psychiatry and Psychotherapy, Faculty of Medicine and University Hospital, University of Cologne, Cologne, Germany.
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27
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Wu YK, Su YA, Li L, Zhu LL, Li K, Li JT, Mitchell PB, Yan CG, Si TM. Brain functional changes across mood states in bipolar disorder: from a large-scale network perspective. Psychol Med 2024; 54:763-774. [PMID: 38084586 DOI: 10.1017/s0033291723002453] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 03/05/2024]
Abstract
BACKGROUND Exploring the neural basis related to different mood states is a critical issue for understanding the pathophysiology underlying mood switching in bipolar disorder (BD), but research has been scarce and inconsistent. METHODS Resting-state functional magnetic resonance imaging data were acquired from 162 patients with BD: 33 (hypo)manic, 64 euthymic, and 65 depressive, and 80 healthy controls (HCs). The differences of large-scale brain network functional connectivity (FC) between the four groups were compared and correlated with clinical characteristics. To validate the generalizability of our findings, we recruited a small longitudinal independent sample of BD patients (n = 11). In addition, we examined topological nodal properties across four groups as exploratory analysis. RESULTS A specific strengthened pattern of network FC, predominantly involving the default mode network (DMN), was observed in (hypo)manic patients when compared with HCs and bipolar patients in other mood states. Longitudinal observation revealed an increase in several network FCs in patients during (hypo)manic episode. Both samples evidenced an increase in the FC between the DMN and ventral attention network, and between the DMN and limbic network (LN) related to (hypo)mania. The altered network connections were correlated with mania severity and positive affect. Bipolar depressive patients exhibited decreased FC within the LN compared with HCs. The exploratory analysis also revealed an increase in degree in (hypo)manic patients. CONCLUSIONS Our findings identify a distributed pattern of large-scale network disturbances in the unique context of (hypo)mania and thus provide new evidence for our understanding of the neural mechanism of BD.
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Affiliation(s)
- Yan-Kun 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), Beijing, China
| | - Yun-Ai Su
- 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), Beijing, China
| | - Le Li
- Key Laboratory of Behavioral Science, Institute of Psychology, Chinese Academy of Sciences, Beijing, China
- Center for Cognitive Science of Language, Beijing Language and Culture University, Beijing, China
| | - Lin-Lin Zhu
- 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), Beijing, China
| | - Ke Li
- PLA Strategic Support Force Characteristic Medical Center, Beijing, China
| | - Ji-Tao Li
- 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), Beijing, China
| | - Philip B Mitchell
- School of Psychiatry, University of New South Wales, Sydney, Australia
- Black Dog Institute, Prince of Wales Hospital, Sydney, Australia
| | - Chao-Gan Yan
- Key Laboratory of Behavioral Science, Institute of Psychology, Chinese Academy of Sciences, Beijing, China
| | - Tian-Mei 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), Beijing, China
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Orlichenko A, Su KJ, Shen H, Deng HW, Wang YP. Somatomotor-visual resting state functional connectivity increases after 2 years in the UK Biobank longitudinal cohort. J Med Imaging (Bellingham) 2024; 11:024010. [PMID: 38618171 PMCID: PMC11009525 DOI: 10.1117/1.jmi.11.2.024010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2023] [Revised: 01/26/2024] [Accepted: 03/29/2024] [Indexed: 04/16/2024] Open
Abstract
Purpose Functional magnetic resonance imaging (fMRI) and functional connectivity (FC) have been used to follow aging in both children and older adults. Robust changes have been observed in children, in which high connectivity among all brain regions changes to a more modular structure with maturation. We examine FC changes in older adults after 2 years of aging in the UK Biobank (UKB) longitudinal cohort. Approach We process fMRI connectivity data using the Power264 atlas and then test whether the average internetwork FC changes in the 2722-subject longitudinal cohort are statistically significant using a Bonferroni-corrected t -test. We also compare the ability of Power264 and UKB-provided, independent component analysis (ICA)-based FC to determine which of a longitudinal scan pair is older. Finally, we investigate cross-sectional FC changes as well as differences due to differing scanner tasks in the UKB, Philadelphia Neurodevelopmental Cohort, and Alzheimer's Disease Neuroimaging Initiative datasets. Results We find a 6.8% average increase in somatomotor network (SMT)-visual network (VIS) connectivity from younger to older scans (corrected p < 10 - 15 ) that occurs in male, female, older subject (> 65 years old), and younger subject (< 55 years old) groups. Among all internetwork connections, the average SMT-VIS connectivity is the best predictor of relative scan age. Using the full FC and a training set of 2000 subjects, one is able to predict which scan is older 82.5% of the time using either the full Power264 FC or the UKB-provided ICA-based FC. Conclusions We conclude that SMT-VIS connectivity increases with age in the UKB longitudinal cohort and that resting state FC increases with age in the UKB cross-sectional cohort.
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Affiliation(s)
- Anton Orlichenko
- Tulane University, Department of Biomedical Engineering, New Orleans, Louisiana, United States
| | - Kuan-Jui Su
- Tulane University, School of Medicine, Center for Biomedical Informatics and Genomics, New Orleans, Louisiana, United States
| | - Hui Shen
- Tulane University, School of Medicine, Center for Biomedical Informatics and Genomics, New Orleans, Louisiana, United States
| | - Hong-Wen Deng
- Tulane University, School of Medicine, Center for Biomedical Informatics and Genomics, New Orleans, Louisiana, United States
| | - Yu-Ping Wang
- Tulane University, Department of Biomedical Engineering, New Orleans, Louisiana, United States
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29
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Li Y, Zhu W, Zhou S, Li H, Gao Z, Huang Z, Li X, Yu Y, Li X. Sex differences in functional connectivity and the predictive role of the connectome-based predictive model in Alzheimer's disease. J Neurosci Res 2024; 102:e25307. [PMID: 38444265 DOI: 10.1002/jnr.25307] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2023] [Revised: 01/23/2024] [Accepted: 01/31/2024] [Indexed: 03/07/2024]
Abstract
Alzheimer's disease (AD) is a neurodegenerative disease characterized by cognitive decline. Sex differences in the progression of AD exist, but the neural mechanisms are not well understood. The purpose of the current study was to explore sex differences in brain functional connectivity (FC) at different stages of AD and their predictive ability on Montreal Cognitive Assessment (MoCA) scores using connectome-based predictive modeling (CPM). Resting-state functional magnetic resonance imaging was collected from 81 AD patients (44 females), 78 amnestic mild cognitive impairment patients (44 females), and 92 healthy controls (50 females). The FC analysis was conducted and the interaction effect between sex and group was investigated using two-factor variance analysis. The CPM was used to predict MoCA scores. There were sex-by-group interaction effects on FC between the left dorsolateral superior frontal gyrus and left middle temporal gyrus, left precuneus and right calcarine fissure surrounding cortex, left precuneus and left middle occipital gyrus, left middle temporal gyrus and left precentral gyrus, and between the left middle temporal gyrus and right cuneus. In the CPM, the positive network predictive model significantly predicted MoCA scores in both males and females. There were significant sex-by-group interaction effects on FC between the left precuneus and left middle occipital gyrus, and between the left middle temporal gyrus and right cuneus could predict MoCA scores in female patients. Our results suggest that there are sex differences in FC at different stages of AD. The sex-specific FC can further predict MoCA scores at individual level.
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Affiliation(s)
- Yuqing Li
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Wanqiu Zhu
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Shanshan Zhou
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Hui Li
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Ziwen Gao
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Ziang Huang
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Xiaohu Li
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Yongqiang Yu
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Xiaoshu Li
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, China
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30
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Thomson AR, Hwa H, Pasanta D, Hopwood B, Powell HJ, Lawrence R, Tabuenca ZG, Arichi T, Edden RAE, Chai X, Puts NA. The developmental trajectory of 1H-MRS brain metabolites from childhood to adulthood. Cereb Cortex 2024; 34:bhae046. [PMID: 38430105 PMCID: PMC10908220 DOI: 10.1093/cercor/bhae046] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2023] [Revised: 01/15/2024] [Accepted: 01/16/2024] [Indexed: 03/03/2024] Open
Abstract
Human brain development is ongoing throughout childhood, with for example, myelination of nerve fibers and refinement of synaptic connections continuing until early adulthood. 1H-Magnetic Resonance Spectroscopy (1H-MRS) can be used to quantify the concentrations of endogenous metabolites (e.g. glutamate and γ -aminobutyric acid (GABA)) in the human brain in vivo and so can provide valuable, tractable insight into the biochemical processes that support postnatal neurodevelopment. This can feasibly provide new insight into and aid the management of neurodevelopmental disorders by providing chemical markers of atypical development. This study aims to characterize the normative developmental trajectory of various brain metabolites, as measured by 1H-MRS from a midline posterior parietal voxel. We find significant non-linear trajectories for GABA+ (GABA plus macromolecules), Glx (glutamate + glutamine), total choline (tCho) and total creatine (tCr) concentrations. Glx and GABA+ concentrations steeply decrease across childhood, with more stable trajectories across early adulthood. tCr and tCho concentrations increase from childhood to early adulthood. Total N-acetyl aspartate (tNAA) and Myo-Inositol (mI) concentrations are relatively stable across development. Trajectories likely reflect fundamental neurodevelopmental processes (including local circuit refinement) which occur from childhood to early adulthood and can be associated with cognitive development; we find GABA+ concentrations significantly positively correlate with recognition memory scores.
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Affiliation(s)
- Alice R Thomson
- Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, 16 De Crespigny Park, London, SE5 8AF, United Kingdom
- MRC Centre for Neurodevelopmental Disorders, Department of Neurodevelopmental Disorders, New Hunt's House, Guy's Campus, King's College London, London, SE1 1UL, United Kingdom
| | - Hannah Hwa
- Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, 16 De Crespigny Park, London, SE5 8AF, United Kingdom
| | - Duanghathai Pasanta
- Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, 16 De Crespigny Park, London, SE5 8AF, United Kingdom
| | - Benjamin Hopwood
- Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, 16 De Crespigny Park, London, SE5 8AF, United Kingdom
| | - Helen J Powell
- Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, 16 De Crespigny Park, London, SE5 8AF, United Kingdom
| | - Ross Lawrence
- Division of Cognitive Neurology, Department of Neurology, Johns Hopkins University, 1629 Thames Street Suite 350, Baltimore, MD 21231, United States
| | - Zeus G Tabuenca
- Department of Statistical Methods, University of Zaragoza, Pedro Cerbuna 12, Zaragoza, 50009, Spain
| | - Tomoki Arichi
- MRC Centre for Neurodevelopmental Disorders, Department of Neurodevelopmental Disorders, New Hunt's House, Guy's Campus, King's College London, London, SE1 1UL, United Kingdom
- Centre for the Developing Brain, Department of Perinatal Imaging & Health, 1st Floor, South Wing, St Thomas’ Hospital, London, SE1 7EH, United Kingdom
| | - Richard A E Edden
- Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, 601 North Caroline Street, Baltimore, MD 21287, United States
- F.M. Kirby Research Centre for Functional Brain Imaging, Kennedy Krieger Institute, 707 North Broadway, Baltimore, MD 21205, United States
| | - Xiaoqian Chai
- Department of Neurology and Neurosurgery, McGill University, QC H3A2B4, Canada
| | - Nicolaas A Puts
- Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, 16 De Crespigny Park, London, SE5 8AF, United Kingdom
- MRC Centre for Neurodevelopmental Disorders, Department of Neurodevelopmental Disorders, New Hunt's House, Guy's Campus, King's College London, London, SE1 1UL, United Kingdom
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31
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Yan S, Zhao G, Zhang Q, Liu L, Bai X, Jin H. Altered resting-state brain function in endurance athletes. Cereb Cortex 2024; 34:bhae076. [PMID: 38494416 DOI: 10.1093/cercor/bhae076] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2023] [Revised: 02/01/2024] [Accepted: 02/02/2024] [Indexed: 03/19/2024] Open
Abstract
Previous research has confirmed significant differences in regional brain activity and functional connectivity between endurance athletes and non-athletes. However, no studies have investigated the differences in topological efficiency of the brain functional network between endurance athletes and non-athletes. Here, we compared differences in regional activities, functional connectivity, and topological properties to explore the functional basis associated with endurance training. The results showed significant correlations between Regional Homogeneity in the motor cortex, visual cortex, cerebellum, and the training intensity parameters. Alterations in functional connectivity among the motor cortex, visual cortex, cerebellum, and the inferior frontal gyrus and cingulate gyrus were significantly correlated with training intensity parameters. In addition, the graph theoretical analysis results revealed a significant reduction in global efficiency among athletes. This decline is mainly caused by decreased nodal efficiency and nodal local efficiency of the cerebellar regions. Notably, the sensorimotor regions, such as the precentral gyrus and supplementary motor areas, still exhibit increased nodal efficiency and nodal local efficiency. This study not only confirms the improvement of regional activity in brain regions related to endurance training, but also offers novel insights into the mechanisms through which endurance athletes undergo changes in the topological efficiency of the brain functional network.
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Affiliation(s)
- Shizhen Yan
- School of Health, Fujian Medical University, Fuzhou 350122, China
| | - Guang Zhao
- Faculty of Psychology, Tianjin Normal University, Tianjin 300387, China
- Key Research Base of Humanities and Social Sciences of the Ministry of Education, Academy of Psychology and Behavior, Tianjin Normal University, Tianjin 300387, China
| | - Qihan Zhang
- Faculty of Psychology, Tianjin Normal University, Tianjin 300387, China
- Key Research Base of Humanities and Social Sciences of the Ministry of Education, Academy of Psychology and Behavior, Tianjin Normal University, Tianjin 300387, China
| | - Liqing Liu
- Faculty of Psychology, Tianjin Normal University, Tianjin 300387, China
- Key Research Base of Humanities and Social Sciences of the Ministry of Education, Academy of Psychology and Behavior, Tianjin Normal University, Tianjin 300387, China
| | - Xuejun Bai
- Faculty of Psychology, Tianjin Normal University, Tianjin 300387, China
- Key Research Base of Humanities and Social Sciences of the Ministry of Education, Academy of Psychology and Behavior, Tianjin Normal University, Tianjin 300387, China
| | - Hua Jin
- Faculty of Psychology, Tianjin Normal University, Tianjin 300387, China
- Key Research Base of Humanities and Social Sciences of the Ministry of Education, Academy of Psychology and Behavior, Tianjin Normal University, Tianjin 300387, China
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32
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Feng S, Zheng S, Dong L, Li Z, Zhu H, Liu S, Li X, Ning Y, Jia H. Effects of aripiprazole on resting-state functional connectivity of large-scale brain networks in first-episode drug-naïve schizophrenia patients. J Psychiatr Res 2024; 171:215-221. [PMID: 38309211 DOI: 10.1016/j.jpsychires.2024.01.020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/24/2023] [Revised: 01/04/2024] [Accepted: 01/15/2024] [Indexed: 02/05/2024]
Abstract
Aripiprazole modulates functional connectivity (FC) between several brain regions in first-episode schizophrenia patients, contributing to improvement in clinical symptoms. However, the effects of aripiprazole on abnormal connections among extensive brain networks in schizophrenia patients remain unclear. We aimed to investigate the effects of 12 weeks of aripiprazole treatment on the FC of large-scale brain networks. Forty-five first-episode drug-naïve schizophrenia patients and 45 healthy controls were recruited for this longitudinal study. Resting-state functional magnetic resonance imaging (fMRI) data were collected at baseline and after 12 weeks of aripiprazole treatment. The patients were classified into those in response (SCHr group) and non-response (SCHnr group) according to the improvement of clinical symptoms after 12-weeks treatment. The FC were evaluated for seven large-scale brain networks. In addition, correlation analysis was performed to investigate associations between changes FC of large-scale brain networks and clinical symptoms. Before aripiprazole treatment, schizophrenia patients showed decreased FC of extensive brain networks compared to healthy controls. The 12-week aripiprazole treatment significantly prevented the constantly decreased FC of subcortical network, default mode network and other brain networks in patients with SCHr, in association with the improvement of clinical symptoms. Taken together, these findings have revealed the effects of aripiprazole on FC in large-scale networks in schizophrenia patients, which could provide new insight on interpreting symptom improvement in SCH.
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Affiliation(s)
- Sitong Feng
- Beijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders & National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China; Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
| | - Sisi Zheng
- Beijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders & National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China; Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
| | - Linrui Dong
- Beijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders & National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China; Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
| | - Ziyan Li
- Beijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders & National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China; Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
| | - Hong Zhu
- Beijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders & National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China; Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
| | - Shanshan Liu
- Beijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders & National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China; Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
| | - Xue Li
- Beijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders & National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China; Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
| | - Yanzhe Ning
- Beijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders & National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China; Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China.
| | - Hongxiao Jia
- Beijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders & National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China; Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China.
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33
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Guan X, Zheng W, Fan K, Han X, Hu B, Li X, Yan Z, Lu Z, Gong J. Structural and functional changes following brain surgery in pediatric patients with intracranial space-occupying lesions. Brain Imaging Behav 2024:10.1007/s11682-023-00799-x. [PMID: 38376714 DOI: 10.1007/s11682-023-00799-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/06/2023] [Indexed: 02/21/2024]
Abstract
We explored the structural and functional changes of the healthy hemisphere of the brain after surgery in children with intracranial space-occupying lesions. We enrolled 32 patients with unilateral intracranial space-occupying lesions for brain imaging and cognitive assessment. Voxel-based morphometry and surface-based morphometry analyses were used to investigate the structural images of the healthy hemisphere. Functional images were analyzed using regional homogeneity, amplitude of low-frequency fluctuations, and fractional-amplitude of low-frequency fluctuations. Voxel-based morphometry and surface-based morphometry analysis used the statistical model built into the CAT 12 toolbox. Paired t-tests were used for functional image and cognitive test scores. For structural image analysis, we used family-wise error correction of peak level (p < 0.05), and for functional image analysis, we use Gaussian random-field theory correction (voxel p < 0.001, cluster p < 0.05). We found an increase in gray matter volume in the healthy hemisphere within six months postoperatively, mainly in the frontal lobe. Regional homogeneity and fractional-amplitude of low-frequency fluctuations also showed greater functional activity in the frontal lobe. The results of cognitive tests showed that psychomotor speed and motor speed decreased significantly after surgery, and reasoning increased significantly after surgery. We concluded that in children with intracranial space-occupying lesions, the healthy hemisphere exhibits compensatory structural and functional effects within six months after surgery. This effect occurs mainly in the frontal lobe and is responsible for some higher cognitive compensation. This may provide some guidance for the rehabilitation of children after brain surgery.
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Affiliation(s)
- Xueyi Guan
- Department of Pediatric Neurosurgery Beijing Tiantan Hospital, Capital Medical University, Beijing, 100070, China
| | - Wenjian Zheng
- Department of Pediatric Neurosurgery Beijing Tiantan Hospital, Capital Medical University, Beijing, 100070, China
| | - Kaiyu Fan
- Department of Pediatric Neurosurgery Beijing Tiantan Hospital, Capital Medical University, Beijing, 100070, China
| | - Xu Han
- Department of Pediatric Neurosurgery Beijing Tiantan Hospital, Capital Medical University, Beijing, 100070, China
| | - Bohan Hu
- Department of Pediatric Neurosurgery Beijing Tiantan Hospital, Capital Medical University, Beijing, 100070, China
| | - Xiang Li
- Department of Pediatric Neurosurgery Beijing Tiantan Hospital, Capital Medical University, Beijing, 100070, China
| | - Zihan Yan
- Department of Pediatric Neurosurgery Beijing Tiantan Hospital, Capital Medical University, Beijing, 100070, China
| | - Zheng Lu
- Department of Pediatric Neurosurgery Beijing Tiantan Hospital, Capital Medical University, Beijing, 100070, China
| | - Jian Gong
- Department of Pediatric Neurosurgery Beijing Tiantan Hospital, Capital Medical University, Beijing, 100070, China.
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Busch EL, Rapuano KM, Anderson KM, Rosenberg MD, Watts R, Casey BJ, Haxby JV, Feilong M. Dissociation of Reliability, Heritability, and Predictivity in Coarse- and Fine-Scale Functional Connectomes during Development. J Neurosci 2024; 44:e0735232023. [PMID: 38148152 PMCID: PMC10866091 DOI: 10.1523/jneurosci.0735-23.2023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2023] [Revised: 10/09/2023] [Accepted: 11/16/2023] [Indexed: 12/28/2023] Open
Abstract
The functional connectome supports information transmission through the brain at various spatial scales, from exchange between broad cortical regions to finer-scale, vertex-wise connections that underlie specific information processing mechanisms. In adults, while both the coarse- and fine-scale functional connectomes predict cognition, the fine scale can predict up to twice the variance as the coarse-scale functional connectome. Yet, past brain-wide association studies, particularly using large developmental samples, focus on the coarse connectome to understand the neural underpinnings of individual differences in cognition. Using a large cohort of children (age 9-10 years; n = 1,115 individuals; both sexes; 50% female, including 170 monozygotic and 219 dizygotic twin pairs and 337 unrelated individuals), we examine the reliability, heritability, and behavioral relevance of resting-state functional connectivity computed at different spatial scales. We use connectivity hyperalignment to improve access to reliable fine-scale (vertex-wise) connectivity information and compare the fine-scale connectome with the traditional parcel-wise (coarse scale) functional connectomes. Though individual differences in the fine-scale connectome are more reliable than those in the coarse-scale, they are less heritable. Further, the alignment and scale of connectomes influence their ability to predict behavior, whereby some cognitive traits are equally well predicted by both connectome scales, but other, less heritable cognitive traits are better predicted by the fine-scale connectome. Together, our findings suggest there are dissociable individual differences in information processing represented at different scales of the functional connectome which, in turn, have distinct implications for heritability and cognition.
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Affiliation(s)
- Erica L Busch
- Department of Psychology, Yale University, New Haven, Connecticut, 06510
| | - Kristina M Rapuano
- Department of Psychology, Yale University, New Haven, Connecticut, 06510
| | - Kevin M Anderson
- Department of Psychology, Yale University, New Haven, Connecticut, 06510
| | - Monica D Rosenberg
- Department of Psychology, University of Chicago, Chicago, Illinois, 60637
| | - Richard Watts
- Department of Psychology, Yale University, New Haven, Connecticut, 06510
| | - B J Casey
- Department of Psychology, Yale University, New Haven, Connecticut, 06510
| | - James V Haxby
- Department of Psychological and Brain Sciences, Dartmouth College, Hanover, New Hampshire, 03755
| | - Ma Feilong
- Department of Psychological and Brain Sciences, Dartmouth College, Hanover, New Hampshire, 03755
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Chen M, Wang Y, Shi Y, Feng J, Feng R, Guan X, Xu X, Zhang Y, Jin C, Wei H. Brain Age Prediction Based on Quantitative Susceptibility Mapping Using the Segmentation Transformer. IEEE J Biomed Health Inform 2024; 28:1012-1021. [PMID: 38090820 DOI: 10.1109/jbhi.2023.3341629] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2023]
Abstract
The process of brain aging is intricate, encompassing significant structural and functional changes, including myelination and iron deposition in the brain. Brain age could act as a quantitative marker to evaluate the degree of the individual's brain evolution. Quantitative susceptibility mapping (QSM) is sensitive to variations in magnetically responsive substances such as iron and myelin, making it a favorable tool for estimating brain age. In this study, we introduce an innovative 3D convolutional network named Segmentation-Transformer-Age-Network (STAN) to predict brain age based on QSM data. STAN employs a two-stage network architecture. The first-stage network learns to extract informative features from the QSM data through segmentation training, while the second-stage network predicts brain age by integrating the global and local features. We collected QSM images from 712 healthy participants, with 548 for training and 164 for testing. The results demonstrate that the proposed method achieved a high accuracy brain age prediction with a mean absolute error (MAE) of 4.124 years and a coefficient of determination (R2) of 0.933. Furthermore, the gaps between the predicted brain age and the chronological age of Parkinson's disease patients were significantly higher than those of healthy subjects (P<0.01). We thus believe that using QSM-based predicted brain age offers a more reliable and accurate phenotype, with the potentiality to serve as a biomarker to explore the process of advanced brain aging.
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36
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Guan S, Jiang R, Meng C, Biswal B. Brain age prediction across the human lifespan using multimodal MRI data. GeroScience 2024; 46:1-20. [PMID: 37733220 PMCID: PMC10828281 DOI: 10.1007/s11357-023-00924-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2023] [Accepted: 08/22/2023] [Indexed: 09/22/2023] Open
Abstract
Measuring differences between an individual's age and biological age with biological information from the brain have the potential to provide biomarkers of clinically relevant neurological syndromes that arise later in human life. To explore the effect of multimodal brain magnetic resonance imaging (MRI) features on the prediction of brain age, we investigated how multimodal brain imaging data improved age prediction from more imaging features of structural or functional MRI data by using partial least squares regression (PLSR) and longevity data sets (age 6-85 years). First, we found that the age-predicted values for each of these ten features ranged from high to low: cortical thickness (R = 0.866, MAE = 7.904), all seven MRI features (R = 0.8594, MAE = 8.24), four features in structural MRI (R = 0.8591, MAE = 8.24), fALFF (R = 0.853, MAE = 8.1918), gray matter volume (R = 0.8324, MAE = 8.931), three rs-fMRI feature (R = 0.7959, MAE = 9.744), mean curvature (R = 0.7784, MAE = 10.232), ReHo (R = 0.7833, MAE = 10.122), ALFF (R = 0.7517, MAE = 10.844), and surface area (R = 0.719, MAE = 11.33). In addition, the significance of the volume and size of brain MRI data in predicting age was also studied. Second, our results suggest that all multimodal imaging features, except cortical thickness, improve brain-based age prediction. Third, we found that the left hemisphere contributed more to the age prediction, that is, the left hemisphere showed a greater weight in the age prediction than the right hemisphere. Finally, we found a nonlinear relationship between the predicted age and the amount of MRI data. Combined with multimodal and lifespan brain data, our approach provides a new perspective for chronological age prediction and contributes to a better understanding of the relationship between brain disorders and aging.
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Affiliation(s)
- Sihai Guan
- College of Electronic and Information, Southwest Minzu University, Chengdu, 610041, China.
- Key Laboratory of Electronic and Information Engineering, State Ethnic Affairs Commission, Chengdu, 610041, China.
| | - Runzhou Jiang
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 611731, China
- Medical Equipment Department, Xiangyang No. 1 People's Hospital, Xiangyang, 441000, China
| | - Chun Meng
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 611731, China
| | - Bharat Biswal
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 611731, China.
- Department of Biomedical Engineering, New Jersey Institute of Technology, Newark, NJ, 07102, USA.
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Li XY, Yoncheva Y, Yan CG, Castellanos FX, St-Onge MP. Chronic Mild Sleep Restriction Does Not Lead to Marked Neuronal Alterations Compared With Maintained Adequate Sleep in Adults. J Nutr 2024; 154:446-454. [PMID: 38104943 PMCID: PMC10900194 DOI: 10.1016/j.tjnut.2023.12.016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2023] [Revised: 12/07/2023] [Accepted: 12/11/2023] [Indexed: 12/19/2023] Open
Abstract
BACKGROUND Sleep restriction (SR) has been shown to upregulate neuronal reward networks in response to food stimuli, but prior studies were short-term and employed severe SR paradigms. OBJECTIVE Our goal was to determine whether mild SR, achieved by delaying bedtimes by 1.5 h, influences neuronal networks responsive to food stimuli compared with maintained adequate sleep (AS) >7 h/night. METHODS A randomized controlled crossover study with 2 6-wk phases, AS (≥7 h sleep/night) and SR (-1.5 h/night relative to screening), was conducted. Adults with AS duration, measured using wrist actigraphy over a 2-wk screening period, and self-reported good sleep quality were enrolled. Resting-state and food-stimulated functional neuroimaging (fMRI) was performed at the endpoint of each phase. Resting-state fMRI data analyses included a priori region-of-interest seed-based functional connectivity, whole-brain voxel-wise analyses, and network analyses. Food task-fMRI analyses compared brain activity patterns in response to food cues between conditions. Paired-sample t tests tested differences between conditions. RESULTS Twenty-six participants (16 males; age 29.6 ± 5.3 y, body mass index 26.9 ± 4.0 kg/m2) contributed complete data. Total sleep time was 7 h 30 ± 28 min/night during AS compared with 6 h 12 ± 26 min/night during SR. We employed different statistical approaches to replicate prior studies in the field and to apply more robust approaches that are currently advocated in the field. Using uncorrected P value of <0.01, cluster ≥10-voxel thresholds, we replicated prior findings of increased activation in response to foods in reward networks after SR compared with AS (right insula, right inferior frontal gyrus, and right supramarginal gyrus). These findings did not survive more rigorous analytical approaches (Gaussian Random Field theory correction at 2-tailed voxel P < 0.001, cluster P < 0.05). CONCLUSIONS The results suggest that mild SR leads to increased reward responsivity to foods but with low confidence given the failure to meet significance from rigorous statistical analyses. Further research is necessary to inform the mechanisms underlying the role of sleep on food intake regulation. This trial was registered at clinicaltrials.gov as NCT02960776.
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Affiliation(s)
- Xue-Ying Li
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing, China; Department of Psychology, University of Chinese Academy of Sciences, Beijing, China
| | - Yuliya Yoncheva
- New York University Grossman School of Medicine, New York, NY, United States
| | - Chao-Gan Yan
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing, China; Department of Psychology, University of Chinese Academy of Sciences, Beijing, China; Magnetic Resonance Imaging Research Center, Institute of Psychology, Chinese Academy of Sciences, Beijing, China; International Big-Data Center for Depression Research, Chinese Academy of Sciences, Beijing, China
| | - Francisco Xavier Castellanos
- New York University Grossman School of Medicine, New York, NY, United States; Nathan Kline Institute for Psychiatric Research, Orangeburg, NY, United States
| | - Marie-Pierre St-Onge
- Division of General Medicine and Center of Excellence for Sleep & Circadian Research, Department of Medicine, Columbia University Irving Medical Center, New York, NY, United States.
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Wenzel J, Badde L, Haas SS, Bonivento C, Van Rheenen TE, Antonucci LA, Ruef A, Penzel N, Rosen M, Lichtenstein T, Lalousis PA, Paolini M, Stainton A, Dannlowski U, Romer G, Brambilla P, Wood SJ, Upthegrove R, Borgwardt S, Meisenzahl E, Salokangas RKR, Pantelis C, Lencer R, Bertolino A, Kambeitz J, Koutsouleris N, Dwyer DB, Kambeitz-Ilankovic L. Transdiagnostic subgroups of cognitive impairment in early affective and psychotic illness. Neuropsychopharmacology 2024; 49:573-583. [PMID: 37737273 PMCID: PMC10789737 DOI: 10.1038/s41386-023-01729-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/12/2023] [Revised: 08/03/2023] [Accepted: 08/26/2023] [Indexed: 09/23/2023]
Abstract
Cognitively impaired and spared patient subgroups were identified in psychosis and depression, and in clinical high-risk for psychosis (CHR). Studies suggest differences in underlying brain structural and functional characteristics. It is unclear whether cognitive subgroups are transdiagnostic phenomena in early stages of psychotic and affective disorder which can be validated on the neural level. Patients with recent-onset psychosis (ROP; N = 140; female = 54), recent-onset depression (ROD; N = 130; female = 73), CHR (N = 128; female = 61) and healthy controls (HC; N = 270; female = 165) were recruited through the multi-site study PRONIA. The transdiagnostic sample and individual study groups were clustered into subgroups based on their performance in eight cognitive domains and characterized by gray matter volume (sMRI) and resting-state functional connectivity (rsFC) using support vector machine (SVM) classification. We identified an impaired subgroup (NROP = 79, NROD = 30, NCHR = 37) showing cognitive impairment in executive functioning, working memory, processing speed and verbal learning (all p < 0.001). A spared subgroup (NROP = 61, NROD = 100, NCHR = 91) performed comparable to HC. Single-disease subgroups indicated that cognitive impairment is stronger pronounced in impaired ROP compared to impaired ROD and CHR. Subgroups in ROP and ROD showed specific symptom- and functioning-patterns. rsFC showed superior accuracy compared to sMRI in differentiating transdiagnostic subgroups from HC (BACimpaired = 58.5%; BACspared = 61.7%, both: p < 0.01). Cognitive findings were validated in the PRONIA replication sample (N = 409). Individual cognitive subgroups in ROP, ROD and CHR are more informative than transdiagnostic subgroups as they map onto individual cognitive impairment and specific functioning- and symptom-patterns which show limited overlap in sMRI and rsFC. CLINICAL TRIAL REGISTRY NAME: German Clinical Trials Register (DRKS). Clinical trial registry URL: https://www.drks.de/drks_web/ . Clinical trial registry number: DRKS00005042.
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Affiliation(s)
- Julian Wenzel
- Department of Psychiatry and Psychotherapy, Faculty of Medicine and University Hospital, University of Cologne, Cologne, Germany.
| | - Luzie Badde
- Department of Psychiatry and Psychotherapy, Faculty of Medicine and University Hospital, University of Cologne, Cologne, Germany
| | - Shalaila S Haas
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, New York, NY, USA
| | | | - Tamsyn E Van Rheenen
- Centre for Mental Health, School of Health Sciences, Swinburne University of Technology, Melbourne, VIC, Australia
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, University of Melbourne & Melbourne Health, Melbourne, VIC, Australia
| | - Linda A Antonucci
- Department of Psychiatry and Psychotherapy, Ludwig-Maximilian University, Munich, Germany
- Department of Translational Biomedicine and Neuroscience - University of Bari Aldo Moro, Bari, Italy
| | - Anne Ruef
- Department of Psychiatry and Psychotherapy, Ludwig-Maximilian University, Munich, Germany
| | - Nora Penzel
- Department of Psychiatry and Psychotherapy, Faculty of Medicine and University Hospital, University of Cologne, Cologne, Germany
- Department of Translational Biomedicine and Neuroscience - University of Bari Aldo Moro, Bari, Italy
| | - Marlene Rosen
- Department of Psychiatry and Psychotherapy, Faculty of Medicine and University Hospital, University of Cologne, Cologne, Germany
| | - Theresa Lichtenstein
- Department of Psychiatry and Psychotherapy, Faculty of Medicine and University Hospital, University of Cologne, Cologne, Germany
| | - Paris Alexandros Lalousis
- Institute of Psychiatry, Psychology & Neuroscience, Department of Psychosis Studies, King's College London, London, UK
| | - Marco Paolini
- Department of Radiology, University Hospital, Ludwig-Maximilian University, Munich, Germany
| | - Alexandra Stainton
- Orygen, Melbourne, VIC, Australia
- Centre for Youth Mental Health, University of Melbourne, Melbourne, VIC, Australia
| | - Udo Dannlowski
- Institute for Translational Psychiatry, University of Muenster, Münster, Germany
| | - Georg Romer
- Department of Child and Adolescent Psychiatry, University of Münster, Münster, Germany
| | - Paolo Brambilla
- Department of Neuosciences and Mental Health, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy
- Department of Pathophysiology and Mental Health, University of Milan, Milan, Italy
| | - Stephen J Wood
- Orygen, Melbourne, VIC, Australia
- Centre for Youth Mental Health, University of Melbourne, Melbourne, VIC, Australia
- School of Psychology, University of Birmingham, Birmingham, UK
| | - Rachel Upthegrove
- School of Psychology, University of Birmingham, Birmingham, UK
- Institute of Mental Health and Centre for Human Brain Health, University of Birmingham, Birmingham, UK
| | - Stefan Borgwardt
- Translational Psychiatry Unit (TPU), Department of Psychiatry and Psychotherapy, University of Luebeck, Luebeck, Germany
| | - Eva Meisenzahl
- Department of Psychiatry and Psychotherapy, Medical Faculty, Heinrich-Heine University, Düsseldorf, Germany
| | | | - Christos Pantelis
- Melbourne Neuropsychiatry Centre, University of Melbourne & Western Health, Melbourne, VIC, Australia
| | - Rebekka Lencer
- Institute for Translational Psychiatry, University of Muenster, Münster, Germany
- Translational Psychiatry Unit (TPU), Department of Psychiatry and Psychotherapy, University of Luebeck, Luebeck, Germany
| | - Alessandro Bertolino
- Department of Translational Biomedicine and Neuroscience - University of Bari Aldo Moro, Bari, Italy
| | - Joseph Kambeitz
- Department of Psychiatry and Psychotherapy, Faculty of Medicine and University Hospital, University of Cologne, Cologne, Germany
| | - Nikolaos Koutsouleris
- Department of Psychiatry and Psychotherapy, Ludwig-Maximilian University, Munich, Germany
- Institute of Psychiatry, Psychology & Neuroscience, Department of Psychosis Studies, King's College London, London, UK
- Max Planck Institute for Psychiatry, Munich, Germany
| | - Dominic B Dwyer
- Department of Psychiatry and Psychotherapy, Ludwig-Maximilian University, Munich, Germany
- Orygen, Melbourne, VIC, Australia
| | - Lana Kambeitz-Ilankovic
- Department of Psychiatry and Psychotherapy, Faculty of Medicine and University Hospital, University of Cologne, Cologne, Germany
- Faculty of Psychology and Educational Sciences, Department of Psychology, Ludwig-Maximilian University, Munich, Germany
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Han L, Chan MY, Agres PF, Winter-Nelson E, Zhang Z, Wig GS. Measures of resting-state brain network segregation and integration vary in relation to data quantity: implications for within and between subject comparisons of functional brain network organization. Cereb Cortex 2024; 34:bhad506. [PMID: 38385891 PMCID: PMC10883417 DOI: 10.1093/cercor/bhad506] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2023] [Revised: 12/05/2023] [Accepted: 12/16/2023] [Indexed: 02/23/2024] Open
Abstract
Measures of functional brain network segregation and integration vary with an individual's age, cognitive ability, and health status. Based on these relationships, these measures are frequently examined to study and quantify large-scale patterns of network organization in both basic and applied research settings. However, there is limited information on the stability and reliability of the network measures as applied to functional time-series; these measurement properties are critical to understand if the measures are to be used for individualized characterization of brain networks. We examine measurement reliability using several human datasets (Midnight Scan Club and Human Connectome Project [both Young Adult and Aging]). These datasets include participants with multiple scanning sessions, and collectively include individuals spanning a broad age range of the adult lifespan. The measurement and reliability of measures of resting-state network segregation and integration vary in relation to data quantity for a given participant's scan session; notably, both properties asymptote when estimated using adequate amounts of clean data. We demonstrate how this source of variability can systematically bias interpretation of differences and changes in brain network organization if appropriate safeguards are not included. These observations have important implications for cross-sectional, longitudinal, and interventional comparisons of functional brain network organization.
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Affiliation(s)
- Liang Han
- Center for Vital Longevity and School of Behavioral and Brain Sciences, University of Texas at Dallas, Dallas, TX 75235, United States
| | - Micaela Y Chan
- Center for Vital Longevity and School of Behavioral and Brain Sciences, University of Texas at Dallas, Dallas, TX 75235, United States
| | - Phillip F Agres
- Center for Vital Longevity and School of Behavioral and Brain Sciences, University of Texas at Dallas, Dallas, TX 75235, United States
| | - Ezra Winter-Nelson
- Center for Vital Longevity and School of Behavioral and Brain Sciences, University of Texas at Dallas, Dallas, TX 75235, United States
| | - Ziwei Zhang
- Center for Vital Longevity and School of Behavioral and Brain Sciences, University of Texas at Dallas, Dallas, TX 75235, United States
| | - Gagan S Wig
- Center for Vital Longevity and School of Behavioral and Brain Sciences, University of Texas at Dallas, Dallas, TX 75235, United States
- Department of Psychiatry, University of Texas Southwestern Medical Center, Dallas, TX 75390, United States
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40
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Huang C, Li A, Pang Y, Yang J, Zhang J, Wu X, Mei L. How the intrinsic functional connectivity patterns of the semantic network support semantic processing. Brain Imaging Behav 2024:10.1007/s11682-024-00849-y. [PMID: 38261218 DOI: 10.1007/s11682-024-00849-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/05/2024] [Indexed: 01/24/2024]
Abstract
Semantic processing, a core of language comprehension, involves the activation of brain regions dispersed extensively across the frontal, temporal, and parietal cortices that compose the semantic network. To comprehend the functional structure of this semantic network and how it prepares for semantic processing, we investigated its intrinsic functional connectivity (FC) and the relation between this pattern and semantic processing ability in a large sample from the Human Connectome Project (HCP) dataset. We first defined a well-studied brain network for semantic processing, and then we characterized the within-network connectivity (WNC) and the between-network connectivity (BNC) within this network using a voxel-based global brain connectivity (GBC) method based on resting-state functional magnetic resonance imaging (fMRI). The results showed that 97.73% of the voxels in the semantic network displayed considerably greater WNC than BNC, demonstrating that the semantic network is a fairly encapsulated network. Moreover, multiple connector hubs in the semantic network were identified after applying the criterion of WNC > 1 SD above the mean WNC of the semantic network. More importantly, three of these connector hubs (i.e., the left anterior temporal lobe, angular gyrus, and orbital part of the inferior frontal gyrus) were reliably associated with semantic processing ability. Our findings suggest that the three identified regions use WNC as the central mechanism for supporting semantic processing and that task-independent spontaneous connectivity in the semantic network is essential for semantic processing.
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Affiliation(s)
- Chengmei Huang
- Philosophy and Social Science Laboratory of Reading and Development in Children and Adolescents (South China Normal University), Ministry of Education, Guangzhou, 510631, China
- School of Psychology, South China Normal University, Guangzhou, 510631, China
- Center for Studies of Psychological Application, South China Normal University, Guangzhou, 510631, China
| | - Aqian Li
- Philosophy and Social Science Laboratory of Reading and Development in Children and Adolescents (South China Normal University), Ministry of Education, Guangzhou, 510631, China
- School of Psychology, South China Normal University, Guangzhou, 510631, China
- Center for Studies of Psychological Application, South China Normal University, Guangzhou, 510631, China
| | - Yingdan Pang
- Philosophy and Social Science Laboratory of Reading and Development in Children and Adolescents (South China Normal University), Ministry of Education, Guangzhou, 510631, China
- School of Psychology, South China Normal University, Guangzhou, 510631, China
- Center for Studies of Psychological Application, South China Normal University, Guangzhou, 510631, China
| | - Jiayi Yang
- Philosophy and Social Science Laboratory of Reading and Development in Children and Adolescents (South China Normal University), Ministry of Education, Guangzhou, 510631, China
- School of Psychology, South China Normal University, Guangzhou, 510631, China
- Center for Studies of Psychological Application, South China Normal University, Guangzhou, 510631, China
| | - Jingxian Zhang
- Philosophy and Social Science Laboratory of Reading and Development in Children and Adolescents (South China Normal University), Ministry of Education, Guangzhou, 510631, China
- School of Psychology, South China Normal University, Guangzhou, 510631, China
- Center for Studies of Psychological Application, South China Normal University, Guangzhou, 510631, China
| | - Xiaoyan Wu
- Philosophy and Social Science Laboratory of Reading and Development in Children and Adolescents (South China Normal University), Ministry of Education, Guangzhou, 510631, China
- School of Psychology, South China Normal University, Guangzhou, 510631, China
- Center for Studies of Psychological Application, South China Normal University, Guangzhou, 510631, China
| | - Leilei Mei
- Philosophy and Social Science Laboratory of Reading and Development in Children and Adolescents (South China Normal University), Ministry of Education, Guangzhou, 510631, China.
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41
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Zhou Y, Zhu Y, Ye H, Jiang W, Zhang Y, Kong Y, Yuan Y. Abnormal changes of dynamic topological characteristics in patients with major depressive disorder. J Affect Disord 2024; 345:349-357. [PMID: 37884195 DOI: 10.1016/j.jad.2023.10.143] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/17/2023] [Revised: 10/20/2023] [Accepted: 10/23/2023] [Indexed: 10/28/2023]
Abstract
BACKGROUND Most studies have detected abnormalities of static topological characteristics in major depressive disorder (MDD). However, whether dynamic alternations in brain topology are influenced by MDD remains unknown. METHODS An approach was proposed to capture the dynamic topological characteristics with sliding-window and graph theory for a large data sample from the REST-meta-MDD project. RESULTS It was shown that patients with MDD were characterized by decreased nodal efficiency of the left orbitofrontal cortex. The temporal variability of topological characteristics was focused on the left opercular part of inferior frontal gyrus, and the right part of middle frontal gyrus, inferior parietal gyrus, precuneus and thalamus. LIMITATIONS Future studies need larger and diverse samples to explore the relationship between dynamic topological network characteristics and MDD symptoms. CONCLUSIONS The results support that the altered dynamic topology in cortex of frontal and parietal lobes and thalamus during resting-state activity may be involved in the neuropathological mechanism of MDD.
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Affiliation(s)
- Yue Zhou
- Department of Psychosomatics and Psychiatry, Zhongda Hospital, School of Medicine, Southeast University, Nanjing 210009, China
| | - Yihui Zhu
- Jiangsu Provincial Joint International Research Laboratory of Medical Information Processing, School of Computer Science and Engineering, Southeast University, Nanjing, Jiangsu Province 210096, China
| | - Hongting Ye
- Jiangsu Provincial Joint International Research Laboratory of Medical Information Processing, School of Computer Science and Engineering, Southeast University, Nanjing, Jiangsu Province 210096, China
| | - Wenhao Jiang
- Department of Psychosomatics and Psychiatry, Zhongda Hospital, School of Medicine, Southeast University, Nanjing 210009, China
| | - Yubo Zhang
- Department of Psychosomatics and Psychiatry, Zhongda Hospital, School of Medicine, Southeast University, Nanjing 210009, China
| | - Youyong Kong
- Jiangsu Provincial Joint International Research Laboratory of Medical Information Processing, School of Computer Science and Engineering, Southeast University, Nanjing, Jiangsu Province 210096, China.
| | - Yonggui Yuan
- Department of Psychosomatics and Psychiatry, Zhongda Hospital, School of Medicine, Southeast University, Nanjing 210009, China; Jiangsu Provincial Key Laboratory of Critical Care Medicine, Southeast University, Nanjing 210009, China.
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42
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Bi S, Guan Y, Tian L. Prediction of individual brain age using movie and resting-state fMRI. Cereb Cortex 2024; 34:bhad407. [PMID: 37885127 DOI: 10.1093/cercor/bhad407] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2023] [Revised: 10/07/2023] [Accepted: 10/09/2023] [Indexed: 10/28/2023] Open
Abstract
Brain age is a promising biomarker for predicting chronological age based on brain imaging data. Although movie and resting-state functional MRI techniques have attracted much research interest for the investigation of brain function, whether the 2 different imaging paradigms show similarities and differences in terms of their capabilities and properties for predicting brain age remains largely unexplored. Here, we used movie and resting-state functional MRI data from 528 participants aged from 18 to 87 years old in the Cambridge Centre for Ageing and Neuroscience data set for functional network construction and further used elastic net for age prediction model building. The connectivity properties of movie and resting-state functional MRI were evaluated based on the connections supporting predictive model building. We found comparable predictive abilities of movie and resting-state connectivity in estimating brain age of individuals, as evidenced by correlation coefficients of 0.868 and 0.862 between actual and predicted age, respectively. Despite some similarities, notable differences in connectivity properties were observed between the predictive models using movie and resting-state functional MRI data, primarily involving components of the default mode network. Our results highlight that both movie and resting-state functional MRI are effective and promising techniques for predicting brain age. Leveraging its data acquisition advantages, such as improved child and patient compliance resulting in reduced motion artifacts, movie functional MRI is emerging as an important paradigm for studying brain function in pediatric and clinical populations.
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Affiliation(s)
- Suyu Bi
- School of International Journalism and Communication, Beijing Foreign Studies University, Beijing 100081, China
| | - Yun Guan
- School of Computer and Information Technology, Beijing Jiaotong University, Beijing 100044, China
- Beijing Key Laboratory of Traffic Data Analysis and Mining, Beijing Jiaotong University, Beijing 100044, China
| | - Lixia Tian
- School of Computer and Information Technology, Beijing Jiaotong University, Beijing 100044, China
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43
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Xu X, Chen P, Li W, Xiang Y, Xie Z, Yu Q, Tang Y, Wang P. Topological properties analysis and identification of mild cognitive impairment based on individual morphological brain network connectome. Cereb Cortex 2024; 34:bhad450. [PMID: 38012122 DOI: 10.1093/cercor/bhad450] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2023] [Revised: 11/01/2023] [Accepted: 11/02/2023] [Indexed: 11/29/2023] Open
Abstract
Mild cognitive impairment is considered the prodromal stage of Alzheimer's disease. Accurate diagnosis and the exploration of the pathological mechanism of mild cognitive impairment are extremely valuable for targeted Alzheimer's disease prevention and early intervention. In all, 100 mild cognitive impairment patients and 86 normal controls were recruited in this study. We innovatively constructed the individual morphological brain networks and derived multiple brain connectome features based on 3D-T1 structural magnetic resonance imaging with the Jensen-Shannon divergence similarity estimation method. Our results showed that the most distinguishing morphological brain connectome features in mild cognitive impairment patients were consensus connections and nodal graph metrics, mainly located in the frontal, occipital, limbic lobes, and subcortical gray matter nuclei, corresponding to the default mode network. Topological properties analysis revealed that mild cognitive impairment patients exhibited compensatory changes in the frontal lobe, while abnormal cortical-subcortical circuits associated with cognition were present. Moreover, the combination of multidimensional brain connectome features using multiple kernel-support vector machine achieved the best classification performance in distinguishing mild cognitive impairment patients and normal controls, with an accuracy of 84.21%. Therefore, our findings are of significant importance for developing potential brain imaging biomarkers for early detection of Alzheimer's disease and understanding the neuroimaging mechanisms of the disease.
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Affiliation(s)
- Xiaowen Xu
- Department of Medical Imaging, Tongji Hospital, School of Medicine, Tongji University, Shanghai 200065, China
- Institute of Medical Imaging Artificial Intelligence, Tongji University School of Medicine, Shanghai 200065, China
| | - Peiying Chen
- Department of Medical Imaging, Tongji Hospital, School of Medicine, Tongji University, Shanghai 200065, China
- Institute of Medical Imaging Artificial Intelligence, Tongji University School of Medicine, Shanghai 200065, China
| | - Weikai Li
- School of Mathematics and Statistics, Chongqing Jiaotong University, Chongqing 400064, China
- MIIT Key Laboratory of Pattern Analysis and Machine Intelligence, Nanjing University of Aeronautics and Astronautics, Nanjing 276800, China
| | - Yongsheng Xiang
- Department of Medical Imaging, Tongji Hospital, School of Medicine, Tongji University, Shanghai 200065, China
- Institute of Medical Imaging Artificial Intelligence, Tongji University School of Medicine, Shanghai 200065, China
| | - Zhongfeng Xie
- Department of Medical Imaging, Tongji Hospital, School of Medicine, Tongji University, Shanghai 200065, China
- Institute of Medical Imaging Artificial Intelligence, Tongji University School of Medicine, Shanghai 200065, China
| | - Qiang Yu
- Department of Medical Imaging, Tongji Hospital, School of Medicine, Tongji University, Shanghai 200065, China
- Institute of Medical Imaging Artificial Intelligence, Tongji University School of Medicine, Shanghai 200065, China
| | - Ying Tang
- Department of Electrical and Computer Engineering, Rowan University, Glassboro, New Jersey 08028, USA
| | - Peijun Wang
- Department of Medical Imaging, Tongji Hospital, School of Medicine, Tongji University, Shanghai 200065, China
- Institute of Medical Imaging Artificial Intelligence, Tongji University School of Medicine, Shanghai 200065, China
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Dan XJ, Wang YW, Sun JY, Gao LL, Chen X, Yang XY, Xu EH, Ma JH, Yan CG, Wu T, Chan P. Reorganization of intrinsic functional connectivity in early-stage Parkinson's disease patients with probable REM sleep behavior disorder. NPJ Parkinsons Dis 2024; 10:5. [PMID: 38172178 PMCID: PMC10764752 DOI: 10.1038/s41531-023-00617-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2022] [Accepted: 12/07/2023] [Indexed: 01/05/2024] Open
Abstract
REM sleep behavior disorder (RBD) symptoms in Parkinson's disease (PD) suggest both a clinically and pathologically malignant subtype. However, whether RBD symptoms are associated with alterations in the organization of whole-brain intrinsic functional networks in PD, especially at early disease stages, remains unclear. Here we use resting-state functional MRI, coupled with graph-theoretical approaches and network-based statistics analyses, and validated with large-scale network analyses, to characterize functional brain networks and their relationship with clinical measures in early PD patients with probable RBD (PD+pRBD), early PD patients without probable RBD (PD-pRBD) and healthy controls. Thirty-six PD+pRBD, 57 PD-pRBD and 71 healthy controls were included in the final analyses. The PD+pRBD group demonstrated decreased global efficiency (t = -2.036, P = 0.0432) compared to PD-pRBD, and decreased network efficiency, as well as comprehensively disrupted nodal efficiency and whole-brain networks (all eight networks, but especially in the sensorimotor, default mode and visual networks) compared to healthy controls. The PD-pRBD group showed decreased nodal degree in right ventral frontal cortex and more affected edges in the frontoparietal and ventral attention networks compared to healthy controls. Furthermore, the assortativity coefficient was negatively correlated with Montreal cognitive assessment scores in the PD+pRBD group (r = -0.365, P = 0.026, d = 0.154). The observation of altered whole-brain functional networks and its correlation with cognitive function in PD+pRBD suggest reorganization of the intrinsic functional connectivity to maintain the brain function in the early stage of the disease. Future longitudinal studies following these alterations along disease progression are warranted.
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Affiliation(s)
- Xiao-Juan Dan
- Department of Neurology, Xuanwu Hospital of Capital Medical University, 100053, Beijing, China
- Key Laboratory on Neurodegenerative Disorders of Ministry of Education, Key Laboratory on Parkinson's Disease of Beijing, 100053, Beijing, China
| | - Yu-Wei Wang
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, 100101, Beijing, China
- Department of Psychology, University of Chinese Academy of Sciences, 100101, Beijing, China
| | - Jun-Yan Sun
- Center for Movement Disorders, Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, 100070, Beijing, China
| | - Lin-Lin Gao
- Department of Neurobiology, Xuanwu Hospital of Capital Medical University, 100053, Beijing, China
| | - Xiao Chen
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, 100101, Beijing, China
- Department of Psychology, University of Chinese Academy of Sciences, 100101, Beijing, China
| | - Xue-Ying Yang
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, 100101, Beijing, China
- Department of Psychology, University of Chinese Academy of Sciences, 100101, Beijing, China
| | - Er-He Xu
- Department of Neurology, Xuanwu Hospital of Capital Medical University, 100053, Beijing, China
| | - Jing-Hong Ma
- Department of Neurology, Xuanwu Hospital of Capital Medical University, 100053, Beijing, China
| | - Chao-Gan Yan
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, 100101, Beijing, China
- Department of Psychology, University of Chinese Academy of Sciences, 100101, Beijing, China
| | - Tao Wu
- Center for Movement Disorders, Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, 100070, Beijing, China.
| | - Piu Chan
- Department of Neurology, Xuanwu Hospital of Capital Medical University, 100053, Beijing, China.
- Key Laboratory on Neurodegenerative Disorders of Ministry of Education, Key Laboratory on Parkinson's Disease of Beijing, 100053, Beijing, China.
- National Clinical Research Center for Geriatric Disorders, 100053, Beijing, China.
- Beijing Institute for Brain Disorders Parkinson's Disease Center, Advanced Innovation Center for Human Brain Protection, Capital Medical University, 100069, Beijing, China.
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45
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Nguyen TT, Qian X, Ng EKK, Ong MQW, Ngoh ZM, Yeo SSP, Lau JM, Tan AP, Broekman BFP, Law EC, Gluckman PD, Chong YS, Cortese S, Meaney MJ, Zhou JH. Variations in Cortical Functional Gradients Relate to Dimensions of Psychopathology in Preschool Children. J Am Acad Child Adolesc Psychiatry 2024; 63:80-89. [PMID: 37394176 DOI: 10.1016/j.jaac.2023.05.029] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/09/2022] [Revised: 05/26/2023] [Accepted: 06/23/2023] [Indexed: 07/04/2023]
Abstract
OBJECTIVE It is unclear how the functional brain hierarchy is organized in preschool-aged children, and whether alterations in the brain organization are linked to mental health in this age group. Here, we assessed whether preschool-aged children exhibit a brain organizational structure similar to that of older children, how this structure might change over time, and whether it might reflect mental health. METHOD This study derived functional gradients using diffusion embedding from resting state functional magnetic resonance imaging data of 4.5-year-old children (N = 100, 42 male participants) and 6.0-year-old children (N = 133, 62 male participants) from the longitudinal Growing Up in Singapore Towards healthy Outcomes (GUSTO) cohort. We then conducted partial least-squares correlation analyses to identify the association between the impairment ratings of different mental disorders and network gradient values. RESULTS The main organizing axis of functional connectivity (ie, principal gradient) separated the visual and somatomotor regions (ie, unimodal) in preschool-aged children, whereas the second axis delineated the unimodal-transmodal gradient. This pattern of organization was stable from 4.5 to 6 years of age. The second gradient separating the high- and low-order networks exhibited a diverging pattern across mental health severity, differentiating dimensions related to attention-deficit/hyperactivity disorder and phobic disorders. CONCLUSION This study characterized, for the first time, the functional brain hierarchy in preschool-aged children. A divergence in functional gradient pattern across different disease dimensions was found, highlighting how perturbations in functional brain organization can relate to the severity of different mental health disorders.
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Affiliation(s)
- Thuan Tinh Nguyen
- Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore; Integrative Sciences and Engineering Programme (ISEP), NUS Graduate School, National University of Singapore, Singapore, Singapore
| | - Xing Qian
- Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Eric Kwun Kei Ng
- Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Marcus Qin Wen Ong
- Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Zhen Ming Ngoh
- Singapore Institute for Clinical Sciences (SICS), A∗STAR Research Entities (ARES), Singapore
| | - Shayne S P Yeo
- Singapore Institute for Clinical Sciences (SICS), A∗STAR Research Entities (ARES), Singapore
| | - Jia Ming Lau
- Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Ai Peng Tan
- Singapore Institute for Clinical Sciences (SICS), A∗STAR Research Entities (ARES), Singapore; National University Hospital, Singapore, Singapore
| | - Birit F P Broekman
- OLVG, Amsterdam, the Netherlands, and Amsterdam University Medical Centre, Vrije Universiteit, Amsterdam, the Netherlands
| | - Evelyn C Law
- Singapore Institute for Clinical Sciences (SICS), A∗STAR Research Entities (ARES), Singapore; National University Health System, Singapore
| | - Peter D Gluckman
- Singapore Institute for Clinical Sciences (SICS), A∗STAR Research Entities (ARES), Singapore; Liggins Institute, University of Auckland, Auckland, New Zealand
| | - Yap-Seng Chong
- Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore; Singapore Institute for Clinical Sciences (SICS), A∗STAR Research Entities (ARES), Singapore; National University Health System, Singapore
| | - Samuele Cortese
- Liggins Institute, University of Auckland, Auckland, New Zealand; School of Psychology, Faculty of Environmental and Life Sciences, University of Southampton, Southampton, United Kingdom; Clinical and Experimental Sciences (CNS and Psychiatry), University of Southampton, Southampton, United Kingdom; Solent NHS Trust, Southampton, United Kingdom; Hassenfeld Children's Hospital at NYU Langone, New York University Child Study Center, New York City, New York; University of Nottingham, Nottingham, United Kingdom
| | - Michael J Meaney
- Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore; Singapore Institute for Clinical Sciences (SICS), A∗STAR Research Entities (ARES), Singapore; Douglas Mental Health University Institute, McGill University, Montreal, Quebec, Canada, and the Strategic Research Program, A∗STAR Research Entities (ARES), Singapore
| | - Juan Helen Zhou
- Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore; Integrative Sciences and Engineering Programme (ISEP), NUS Graduate School, National University of Singapore, Singapore, Singapore.
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Yu H, Kim W, Park DK, Phi JH, Lim BC, Chae JH, Kim SK, Kim KJ, Provenzano FA, Khodagholy D, Gelinas JN. Interaction of interictal epileptiform activity with sleep spindles is associated with cognitive deficits and adverse surgical outcome in pediatric focal epilepsy. Epilepsia 2024; 65:190-203. [PMID: 37983643 PMCID: PMC10873110 DOI: 10.1111/epi.17810] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2023] [Revised: 10/20/2023] [Accepted: 10/20/2023] [Indexed: 11/22/2023]
Abstract
OBJECTIVE Temporal coordination between oscillations enables intercortical communication and is implicated in cognition. Focal epileptic activity can affect distributed neural networks and interfere with these interactions. Refractory pediatric epilepsies are often accompanied by substantial cognitive comorbidity, but mechanisms and predictors remain mostly unknown. Here, we investigate oscillatory coupling across large-scale networks in the developing brain. METHODS We analyzed large-scale intracranial electroencephalographic recordings in children with medically refractory epilepsy undergoing presurgical workup (n = 25, aged 3-21 years). Interictal epileptiform discharges (IEDs), pathologic high-frequency oscillations (HFOs), and sleep spindles were detected. Spatiotemporal metrics of oscillatory coupling were determined and correlated with age, cognitive function, and postsurgical outcome. RESULTS Children with epilepsy demonstrated significant temporal coupling of both IEDs and HFOs to sleep spindles in discrete brain regions. HFOs were associated with stronger coupling patterns than IEDs. These interactions involved tissue beyond the clinically identified epileptogenic zone and were ubiquitous across cortical regions. Increased spatial extent of coupling was most prominent in older children. Poor neurocognitive function was significantly correlated with high IED-spindle coupling strength and spatial extent; children with strong pathologic interactions additionally had decreased likelihood of postoperative seizure freedom. SIGNIFICANCE Our findings identify pathologic large-scale oscillatory coupling patterns in the immature brain. These results suggest that such intercortical interactions could predict risk for adverse neurocognitive and surgical outcomes, with the potential to serve as novel therapeutic targets to restore physiologic development.
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Affiliation(s)
- Han Yu
- Department of Electrical Engineering, Columbia University, New York, NY, USA
| | - Woojoong Kim
- Division of Pediatric Neurology, Department of Pediatrics, Pediatric Clinical Neuroscience Center, Seoul National University Children's Hospital, Seoul, South Korea
| | - David K. Park
- Department of Biomedical Engineering, Columbia University, New York, NY, USA
| | - Ji Hoon Phi
- Division of Pediatric Neurosurgery, Department of Neurosurgery, Pediatric Clinical Neuroscience Center, Seoul National University Children's Hospital, Seoul, South Korea
| | - Byung Chan Lim
- Division of Pediatric Neurology, Department of Pediatrics, Pediatric Clinical Neuroscience Center, Seoul National University Children's Hospital, Seoul, South Korea
| | - Jong-Hee Chae
- Division of Pediatric Neurology, Department of Pediatrics, Pediatric Clinical Neuroscience Center, Seoul National University Children's Hospital, Seoul, South Korea
| | - Seung-Ki Kim
- Division of Pediatric Neurosurgery, Department of Neurosurgery, Pediatric Clinical Neuroscience Center, Seoul National University Children's Hospital, Seoul, South Korea
| | - Ki Joong Kim
- Division of Pediatric Neurology, Department of Pediatrics, Pediatric Clinical Neuroscience Center, Seoul National University Children's Hospital, Seoul, South Korea
| | | | - Dion Khodagholy
- Department of Electrical Engineering, Columbia University, New York, NY, USA
| | - Jennifer N. Gelinas
- Departments of Neurology, Columbia University, New York, NY, USA
- Institute for Genomic Medicine, Columbia University Medical Center, New York, NY, USA
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47
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Chen DY, Di X, Biswal B. Cerebrovascular reactivity increases across development in multiple networks as revealed by a breath-holding task: A longitudinal fMRI study. Hum Brain Mapp 2024; 45:e26515. [PMID: 38183372 PMCID: PMC10789211 DOI: 10.1002/hbm.26515] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2023] [Revised: 09/15/2023] [Accepted: 09/29/2023] [Indexed: 01/08/2024] Open
Abstract
Functional magnetic resonance imaging (fMRI) has been widely used to understand the neurodevelopmental changes that occur in cognition and behavior across childhood. The blood-oxygen-level-dependent (BOLD) signal obtained from fMRI is understood to be comprised of both neuronal and vascular information. However, it is unclear whether the vascular response is altered across age in studies investigating development in children. Since the breath-hold (BH) task is commonly used to understand cerebrovascular reactivity (CVR) in fMRI studies, it can be used to account for developmental differences in vascular response. This study examines how the cerebrovascular response changes over age in a longitudinal children's BH data set from the Nathan Kline Institute (NKI) Rockland Sample (aged 6-18 years old at enrollment). A general linear model approach was applied to derive CVR from BH data. To model both the longitudinal and cross-sectional effects of age on BH response, we used mixed-effects modeling with the following terms: linear, quadratic, logarithmic, and quadratic-logarithmic, to find the best-fitting model. We observed increased BH BOLD signals in multiple networks across age, in which linear and logarithmic mixed-effects models provided the best fit with the lowest Akaike information criterion scores. This shows that the cerebrovascular response increases across development in a brain network-specific manner. Therefore, fMRI studies investigating the developmental period should account for cerebrovascular changes that occur with age.
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Affiliation(s)
- Donna Y. Chen
- Department of Biomedical EngineeringNew Jersey Institute of TechnologyNewarkNew JerseyUSA
- Rutgers Biomedical and Health SciencesRutgers School of Graduate StudiesNewarkNew JerseyUSA
| | - Xin Di
- Department of Biomedical EngineeringNew Jersey Institute of TechnologyNewarkNew JerseyUSA
| | - Bharat Biswal
- Department of Biomedical EngineeringNew Jersey Institute of TechnologyNewarkNew JerseyUSA
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48
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Xie JQ, Tian Y, Hu J, Yin MZ, Sun YD, Shan YJ, Chen K, Feng G, Qiu J. The neural correlates of value hierarchies: a prospective typology based on personal value profiles of emerging adults. Front Psychol 2023; 14:1224911. [PMID: 38164257 PMCID: PMC10758175 DOI: 10.3389/fpsyg.2023.1224911] [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: 05/18/2023] [Accepted: 11/28/2023] [Indexed: 01/03/2024] Open
Abstract
Introduction Value hierarchies, as motivational goals anchored in the self-schema, may be correlated with spontaneous activity in the resting brain, especially those involving self-relevance. This study aims to investigate the neural correlates of value hierarchies from the perspective of typology. Methods A total of 610 Chinese college students (30.31% women), aged 18 to 23, completed the personal values questionnaire and underwent resting-state functional magnetic resonance imaging. Results The latent profile analysis revealed three personal value profiles: traditional social orientation, modernized orientation, and undifferentiated orientation. Neuroimaging results revealed that individuals with modernized orientation prioritized openness to change value, and this personal-focus is related to the higher low-frequency amplitude of the posterior insula; individuals with traditional social orientation prioritized self-transcendence and conservation values, and this social-focus is related to the stronger functional connectivity of the middle insula with the inferior temporal gyrus, temporal gyrus, posterior occipital cortex, and basal ganglia, as well as weaker functional connections within the right middle insula. Discussion Taken together, these findings potentially indicate the intra-generational differentiation of contemporary Chinese emerging adults' value hierarchies. At the neural level, these are correlated with brain activities involved in processing self- and other-relevance.
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Affiliation(s)
- Jia-Qiong Xie
- Faculty of Social Sciences, Chongqing University, Chongqing, China
| | - Yun Tian
- Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing, China
- Sleep and NeuroImaging Center, Faculty of Psychology, Southwest University, Chongqing, China
| | - Jia Hu
- Institute for Advanced Studies in Humanities and Social Sciences, Chongqing University, Chongqing, China
| | - Ming-Ze Yin
- Faculty of Education, Southwest University, Chongqing, China
- Office of Social Sciences, Chongqing University, Chongqing, China
| | - Ya-Dong Sun
- Faculty of Social Sciences, Chongqing University, Chongqing, China
| | - Yan-Jie Shan
- Faculty of Social Sciences, Chongqing University, Chongqing, China
| | - Ke Chen
- Faculty of Social Sciences, Chongqing University, Chongqing, China
| | - Gang Feng
- School of Marxism, Beijing Normal University, Beijing, China
| | - Jiang Qiu
- Faculty of Psychology, Southwest University, Chongqing, China
- Southwest University Branch, Collaborative Innovation Center of Assessment Toward Basic Education Quality at Beijing Normal University, Beijing, China
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49
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Sundermann B, Feldmann R, Mathys C, Rau JMH, Garde S, Braje A, Weglage J, Pfleiderer B. Functional connectivity of cognition-related brain networks in adults with fetal alcohol syndrome. BMC Med 2023; 21:496. [PMID: 38093292 PMCID: PMC10720228 DOI: 10.1186/s12916-023-03208-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/22/2023] [Accepted: 12/01/2023] [Indexed: 12/17/2023] Open
Abstract
BACKGROUND Fetal alcohol syndrome (FAS) can result in cognitive dysfunction. Cognitive functions affected are subserved by few functional brain networks. Functional connectivity (FC) in these networks can be assessed with resting-state functional MRI (rs-fMRI). Alterations of FC have been reported in children and adolescents prenatally exposed to alcohol. Previous reports varied substantially regarding the exact nature of findings. The purpose of this study was to assess FC of cognition-related networks in young adults with FAS. METHODS Cross-sectional rs-fMRI study in participants with FAS (n = 39, age: 20.9 ± 3.4 years) and healthy participants without prenatal alcohol exposure (n = 44, age: 22.2 ± 3.4 years). FC was calculated as correlation between cortical regions in ten cognition-related sub-networks. Subsequent modelling of overall FC was based on linear models comparing FC between FAS and controls. Results were subjected to a hierarchical statistical testing approach, first determining whether there is any alteration of FC in FAS in the full cognitive connectome, subsequently resolving these findings to the level of either FC within each network or between networks based on the Higher Criticism (HC) approach for detecting rare and weak effects in high-dimensional data. Finally, group differences in single connections were assessed using conventional multiple-comparison correction. In an additional exploratory analysis, dynamic FC states were assessed. RESULTS Comparing FAS participants with controls, we observed altered FC of cognition-related brain regions globally, within 7 out of 10 networks, and between networks employing the HC statistic. This was most obvious in attention-related network components. Findings also spanned across subcomponents of the fronto-parietal control and default mode networks. None of the single FC alterations within these networks yielded statistical significance in the conventional high-resolution analysis. The exploratory time-resolved FC analysis did not show significant group differences of dynamic FC states. CONCLUSIONS FC in cognition-related networks was altered in adults with FAS. Effects were widely distributed across networks, potentially reflecting the diversity of cognitive deficits in FAS. However, no altered single connections could be determined in the most detailed analysis level. Findings were pronounced in networks in line with attentional deficits previously reported.
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Affiliation(s)
- Benedikt Sundermann
- Institute of Radiology and Neuroradiology, Evangelisches Krankenhaus Oldenburg, Universitätsmedizin Oldenburg, Oldenburg, Germany
- Clinic of Radiology, Medical Faculty, University of Münster, Albert- Schweitzer-Campus 1, Building A1, 48149, Münster, Germany
- Research Center Neurosensory Science, Carl von Ossietzky Universität Oldenburg, Oldenburg, Germany
| | - Reinhold Feldmann
- Department of General Pediatrics, University Hospital Münster, Münster, Germany
| | - Christian Mathys
- Institute of Radiology and Neuroradiology, Evangelisches Krankenhaus Oldenburg, Universitätsmedizin Oldenburg, Oldenburg, Germany
- Research Center Neurosensory Science, Carl von Ossietzky Universität Oldenburg, Oldenburg, Germany
| | - Johanna M H Rau
- Clinic of Radiology, Medical Faculty, University of Münster, Albert- Schweitzer-Campus 1, Building A1, 48149, Münster, Germany
- Department of Neurology With Institute of Translational Neurology, University Hospital Münster, Münster, Germany
| | - Stefan Garde
- Clinic of Radiology, Medical Faculty, University of Münster, Albert- Schweitzer-Campus 1, Building A1, 48149, Münster, Germany
| | - Anna Braje
- Clinic of Radiology, Medical Faculty, University of Münster, Albert- Schweitzer-Campus 1, Building A1, 48149, Münster, Germany
| | - Josef Weglage
- Department of General Pediatrics, University Hospital Münster, Münster, Germany
| | - Bettina Pfleiderer
- Clinic of Radiology, Medical Faculty, University of Münster, Albert- Schweitzer-Campus 1, Building A1, 48149, Münster, Germany.
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50
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Zhu Y, Huang T, Li R, Yang Q, Zhao C, Yang M, Lin B, Li X. Distinct resting-state effective connectivity of large-scale networks in first-episode and recurrent major depression disorder: evidence from the REST-meta-MDD consortium. Front Neurosci 2023; 17:1308551. [PMID: 38148946 PMCID: PMC10750394 DOI: 10.3389/fnins.2023.1308551] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2023] [Accepted: 11/24/2023] [Indexed: 12/28/2023] Open
Abstract
Introduction Previous studies have shown disrupted effective connectivity in the large-scale brain networks of individuals with major depressive disorder (MDD). However, it is unclear whether these changes differ between first-episode drug-naive MDD (FEDN-MDD) and recurrent MDD (R-MDD). Methods This study utilized resting-state fMRI data from 17 sites in the Chinese REST-meta-MDD project, consisting of 839 patients with MDD and 788 normal controls (NCs). All data was preprocessed using a standardized protocol. Then, we performed a granger causality analysis to calculate the effectivity connectivity (EC) within and between brain networks for each participant, and compared the differences between the groups. Results Our findings revealed that R-MDD exhibited increased EC in the fronto-parietal network (FPN) and decreased EC in the cerebellum network, while FEDN-MDD demonstrated increased EC from the sensorimotor network (SMN) to the FPN compared with the NCs. Importantly, the two MDD subgroups displayed significant differences in EC within the FPN and between the SMN and visual network. Moreover, the EC from the cingulo-opercular network to the SMN showed a significant negative correlation with the Hamilton Rating Scale for Depression (HAMD) score in the FEDN-MDD group. Conclusion These findings suggest that first-episode and recurrent MDD have distinct effects on the effective connectivity in large-scale brain networks, which could be potential neural mechanisms underlying their different clinical manifestations.
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Affiliation(s)
- Yao Zhu
- School of Psychology and Cognitive Science, East China Normal University, Shanghai, China
| | - Tianming Huang
- Department of General Psychiatry, Shanghai Changning Mental Health Center, Shanghai, China
| | - Ruolin Li
- Department of Radiology, Children’s Hospital of Philadelphia, Philadelphia, PA, United States
| | - Qianrong Yang
- Department of General Psychiatry, Shanghai Changning Mental Health Center, Shanghai, China
| | - Chaoyue Zhao
- School of Psychology and Cognitive Science, East China Normal University, Shanghai, China
| | - Ming Yang
- School of Psychology and Cognitive Science, East China Normal University, Shanghai, China
| | - Bin Lin
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | | | - Xuzhou Li
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
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