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Zheng J, Cheng Y, Wu X, Li X, Fu Y, Yang Z. Rich-club organization of whole-brain spatio-temporal multilayer functional connectivity networks. Front Neurosci 2024; 18:1405734. [PMID: 38855440 PMCID: PMC11157044 DOI: 10.3389/fnins.2024.1405734] [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: 03/23/2024] [Accepted: 05/07/2024] [Indexed: 06/11/2024] Open
Abstract
Objective In this work, we propose a novel method for constructing whole-brain spatio-temporal multilayer functional connectivity networks (FCNs) and four innovative rich-club metrics. Methods Spatio-temporal multilayer FCNs achieve a high-order representation of the spatio-temporal dynamic characteristics of brain networks by combining the sliding time window method with graph theory and hypergraph theory. The four proposed rich-club scales are based on the dynamic changes in rich-club node identity, providing a parameterized description of the topological dynamic characteristics of brain networks from both temporal and spatial perspectives. The proposed method was validated in three independent differential analysis experiments: male-female gender difference analysis, analysis of abnormality in patients with autism spectrum disorders (ASD), and individual difference analysis. Results The proposed method yielded results consistent with previous relevant studies and revealed some innovative findings. For instance, the dynamic topological characteristics of specific white matter regions effectively reflected individual differences. The increased abnormality in internal functional connectivity within the basal ganglia may be a contributing factor to the occurrence of repetitive or restrictive behaviors in ASD patients. Conclusion The proposed methodology provides an efficacious approach for constructing whole-brain spatio-temporal multilayer FCNs and conducting analysis of their dynamic topological structures. The dynamic topological characteristics of spatio-temporal multilayer FCNs may offer new insights into physiological variations and pathological abnormalities in neuroscience.
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Affiliation(s)
- Jianhui Zheng
- College of Electronic Engineering, Chengdu University of Information Technology, Chengdu, China
| | - Yuhao Cheng
- Huaxi Molecular Imaging Research Laboratory, Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
| | - Xi Wu
- Department of Computer Science, Chengdu University of Information Technology, Chengdu, China
| | - Xiaojie Li
- Department of Computer Science, Chengdu University of Information Technology, Chengdu, China
| | - Ying Fu
- Department of Computer Science, Chengdu University of Information Technology, Chengdu, China
| | - Zhipeng Yang
- College of Electronic Engineering, Chengdu University of Information Technology, Chengdu, China
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2
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Li M, Schilling KG, Gao F, Xu L, Choi S, Gao Y, Zu Z, Anderson AW, Ding Z, Landman BA, Gore JC. Quantification of mediation effects of white matter functional characteristics on cognitive decline in aging. Cereb Cortex 2024; 34:bhae114. [PMID: 38517178 PMCID: PMC10958767 DOI: 10.1093/cercor/bhae114] [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: 12/12/2023] [Revised: 02/29/2024] [Accepted: 03/03/2024] [Indexed: 03/23/2024] Open
Abstract
Cognitive decline with aging involves multifactorial processes, including changes in brain structure and function. This study focuses on the role of white matter functional characteristics, as reflected in blood oxygenation level-dependent signals, in age-related cognitive deterioration. Building on previous research confirming the reproducibility and age-dependence of blood oxygenation level-dependent signals acquired via functional magnetic resonance imaging, we here employ mediation analysis to test if aging affects cognition through white matter blood oxygenation level-dependent signal changes, impacting various cognitive domains and specific white matter regions. We used independent component analysis of resting-state blood oxygenation level-dependent signals to segment white matter into coherent hubs, offering a data-driven view of white matter's functional architecture. Through correlation analysis, we constructed a graph network and derived metrics to quantitatively assess regional functional properties based on resting-state blood oxygenation level-dependent fluctuations. Our analysis identified significant mediators in the age-cognition relationship, indicating that aging differentially influences cognitive functions by altering the functional characteristics of distinct white matter regions. These findings enhance our understanding of the neurobiological basis of cognitive aging, highlighting the critical role of white matter in maintaining cognitive integrity and proposing new approaches to assess interventions targeting cognitive decline in older populations.
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Affiliation(s)
- Muwei Li
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN 37232, United States
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN 37232, United States
| | - Kurt G Schilling
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN 37232, United States
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN 37232, United States
| | - Fei Gao
- Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan 250021, China
| | - Lyuan Xu
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN 37232, United States
- Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN 37235, United States
| | - Soyoung Choi
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN 37232, United States
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN 37232, United States
| | - Yurui Gao
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN 37232, United States
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN 37240, United States
| | - Zhongliang Zu
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN 37232, United States
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN 37232, United States
| | - Adam W Anderson
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN 37232, United States
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN 37240, United States
| | - Zhaohua Ding
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN 37232, United States
- Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN 37235, United States
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN 37240, United States
- Department of Computer Science, Vanderbilt University, Nashville, TN 37240, United States
| | - Bennett A Landman
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN 37232, United States
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN 37232, United States
- Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN 37235, United States
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN 37240, United States
- Department of Computer Science, Vanderbilt University, Nashville, TN 37240, United States
| | - John C Gore
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN 37232, United States
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN 37232, United States
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN 37240, United States
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3
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Li Y, Peng J, Yang Z, Zhang F, Liu L, Wang P, Biswal BB. Altered white matter functional pathways in Alzheimer's disease. Cereb Cortex 2024; 34:bhad505. [PMID: 38436465 DOI: 10.1093/cercor/bhad505] [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: 10/13/2023] [Revised: 10/13/2023] [Accepted: 12/03/2023] [Indexed: 03/05/2024] Open
Abstract
Alzheimer's disease (AD) is associated with functional disruption in gray matter (GM) and structural damage to white matter (WM), but the relationship to functional signal in WM is unknown. We performed the functional connectivity (FC) and graph theory analysis to investigate abnormalities of WM and GM functional networks and corpus callosum among different stages of AD from a publicly available dataset. Compared to the controls, AD group showed significantly decreased FC between the deep WM functional network (WM-FN) and the splenium of corpus callosum, between the sensorimotor/occipital WM-FN and GM visual network, but increased FC between the deep WM-FN and the GM sensorimotor network. In the clinical groups, the global assortativity, modular interaction between occipital WM-FN and visual network, nodal betweenness centrality, degree centrality, and nodal clustering coefficient in WM- and GM-FNs were reduced. However, modular interaction between deep WM-FN and sensorimotor network, and participation coefficients of deep WM-FN and splenium of corpus callosum were increased. These findings revealed the abnormal integration of functional networks in different stages of AD from a novel WM-FNs perspective. The abnormalities of WM functional pathways connect downward to the corpus callosum and upward to the GM are correlated with AD.
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Affiliation(s)
- Yilu Li
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, NO. 2006, Xiyuan Ave, West Hi-Tech Zone, 611731, Chengdu, China
| | - Jinzhong Peng
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, NO. 2006, Xiyuan Ave, West Hi-Tech Zone, 611731, Chengdu, China
| | - Zhenzhen Yang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, NO. 2006, Xiyuan Ave, West Hi-Tech Zone, 611731, Chengdu, China
| | - Fanyu Zhang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, NO. 2006, Xiyuan Ave, West Hi-Tech Zone, 611731, Chengdu, China
| | - Lin Liu
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, NO. 2006, Xiyuan Ave, West Hi-Tech Zone, 611731, Chengdu, China
| | - Pan Wang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, NO. 2006, Xiyuan Ave, West Hi-Tech Zone, 611731, Chengdu, China
| | - Bharat B Biswal
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, NO. 2006, Xiyuan Ave, West Hi-Tech Zone, 611731, Chengdu, China
- Department of Biomedical Engineering, New Jersey Institute of Technology, 154 Summit Street, Newark 07102, NJ, United States
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Qin T, Wang L, Xu H, Liu C, Shao Y, Li F, Wang Y, Jiang J, Lin H. rTMS concurrent with cognitive training rewires AD brain by enhancing GM-WM functional connectivity: a preliminary study. Cereb Cortex 2024; 34:bhad460. [PMID: 38037857 DOI: 10.1093/cercor/bhad460] [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: 06/28/2023] [Revised: 11/03/2023] [Accepted: 11/09/2023] [Indexed: 12/02/2023] Open
Abstract
Repetitive transcranial magnetic stimulation (rTMS) and cognitive training for patients with Alzheimer's disease (AD) can change functional connectivity (FC) within gray matter (GM). However, the role of white matter (WM) and changes of GM-WM FC under these therapies are still unclear. To clarify this problem, we applied 40 Hz rTMS over angular gyrus (AG) concurrent with cognitive training to 15 mild-moderate AD patients and analyzed the resting-state functional magnetic resonance imaging before and after treatment. Through AG-based FC analysis, corona radiata and superior longitudinal fasciculus (SLF) were identified as activated WM tracts. Compared with the GM results with AG as seed, more GM regions were found with activated WM tracts as seeds. The averaged FC, fractional amplitude of low-frequency fluctuation (fALFF), and regional homogeneity (ReHo) of the above GM regions had stronger clinical correlations (r/P = 0.363/0.048 vs 0.299/0.108, 0.351/0.057 vs 0.267/0.153, 0.420/0.021 vs 0.408/0.025, for FC/fALFF/ReHo, respectively) and better classification performance to distinguish pre-/post-treatment groups (AUC = 0.91 vs 0.88, 0.65 vs 0.63, 0.87 vs 0.82, for FC/fALFF/ReHo, respectively). Our results indicated that rTMS concurrent with cognitive training could rewire brain network by enhancing GM-WM FC in AD, and corona radiata and SLF played an important role in this process.
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Affiliation(s)
- Tong Qin
- Department of Neurology, Xuanwu Hospital, Capital Medical University, No. 45 Changchun Street, Xicheng District, Beijing 100053, China
| | - Luyao Wang
- School of Life Science, Shanghai University, No. 99 Shangda Road, Baoshan District, Shanghai 200444, China
| | - Huanyu Xu
- School of Communication and Information Engineering, Shanghai University, No. 99 Shangda Road, Baoshan District, Shanghai 200444, China
| | - Chunyan Liu
- Department of Neurology, Xuanwu Hospital, Capital Medical University, No. 45 Changchun Street, Xicheng District, Beijing 100053, China
| | - Yuxuan Shao
- Department of Neurology, Xuanwu Hospital, Capital Medical University, No. 45 Changchun Street, Xicheng District, Beijing 100053, China
| | - Fangjie Li
- School of Acupuncture-Moxibustion and Tuina, Shanghai University of Traditional Chinese Medicine, No. 1200 Cailun Road, Pudong New Area, Shanghai 201203, China
| | - Yuping Wang
- Department of Neurology, Xuanwu Hospital, Capital Medical University, No. 45 Changchun Street, Xicheng District, Beijing 100053, China
| | - Jiehui Jiang
- School of Life Science, Shanghai University, No. 99 Shangda Road, Baoshan District, Shanghai 200444, China
| | - Hua Lin
- Department of Neurology, Xuanwu Hospital, Capital Medical University, No. 45 Changchun Street, Xicheng District, Beijing 100053, China
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Bolla G, Berente DB, Andrássy A, Zsuffa JA, Hidasi Z, Csibri E, Csukly G, Kamondi A, Kiss M, Horvath AA. Comparison of the diagnostic accuracy of resting-state fMRI driven machine learning algorithms in the detection of mild cognitive impairment. Sci Rep 2023; 13:22285. [PMID: 38097674 PMCID: PMC10721802 DOI: 10.1038/s41598-023-49461-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2023] [Accepted: 12/08/2023] [Indexed: 12/17/2023] Open
Abstract
Mild cognitive impairment (MCI) is a potential therapeutic window in the prevention of dementia; however, automated detection of early cognitive deterioration is an unresolved issue. The aim of our study was to compare various classification approaches to differentiate MCI patients from healthy controls, based on rs-fMRI data, using machine learning (ML) algorithms. Own dataset (from two centers) and ADNI database were used during the analysis. Three fMRI parameters were applied in five feature selection algorithms: local correlation, intrinsic connectivity, and fractional amplitude of low frequency fluctuations. Support vector machine (SVM) and random forest (RF) methods were applied for classification. We achieved a relatively wide range of 78-87% accuracy for the various feature selection methods with SVM combining the three rs-fMRI parameters. In the ADNI datasets case we can also see even 90% accuracy scores. RF provided a more harmonized result among the feature selection algorithms in both datasets with 80-84% accuracy for our local and 74-82% for the ADNI database. Despite some lower performance metrics of some algorithms, most of the results were positive and could be seen in two unrelated datasets which increase the validity of our methods. Our results highlight the potential of ML-based fMRI applications for automated diagnostic techniques to recognize MCI patients.
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Affiliation(s)
- Gergo Bolla
- Neurocognitive Research Center, National Institute of Mental Health, Neurology and Neurosurgery, Budapest, Hungary
- School of PhD Studies, Semmelweis University, Budapest, Hungary
| | - Dalida Borbala Berente
- Neurocognitive Research Center, National Institute of Mental Health, Neurology and Neurosurgery, Budapest, Hungary
- School of PhD Studies, Semmelweis University, Budapest, Hungary
| | - Anita Andrássy
- Neurocognitive Research Center, National Institute of Mental Health, Neurology and Neurosurgery, Budapest, Hungary
| | - Janos Andras Zsuffa
- Neurocognitive Research Center, National Institute of Mental Health, Neurology and Neurosurgery, Budapest, Hungary
- Department of Family Medicine, Semmelweis University, Budapest, Hungary
| | - Zoltan Hidasi
- Department of Psychiatry and Psychotherapy, Semmelweis University, Budapest, Hungary
| | - Eva Csibri
- Department of Psychiatry and Psychotherapy, Semmelweis University, Budapest, Hungary
| | - Gabor Csukly
- Neurocognitive Research Center, National Institute of Mental Health, Neurology and Neurosurgery, Budapest, Hungary
- Department of Psychiatry and Psychotherapy, Semmelweis University, Budapest, Hungary
| | - Anita Kamondi
- Neurocognitive Research Center, National Institute of Mental Health, Neurology and Neurosurgery, Budapest, Hungary
- Department of Neurology, Semmelweis University, Budapest, Hungary
| | - Mate Kiss
- Siemens Healthcare, Budapest, Hungary
| | - Andras Attila Horvath
- Department of Anatomy Histology and Embryology, Semmelweis University, Budapest, Hungary.
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6
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Khokhar SK, Kumar M, Kumar S, Manae T, Thanissery N, Ramakrishnan S, Arshad F, Nagaraj C, Mangalore S, Alladi S, Gandhi TK, Bharath RD. Alzheimer's Disease Is Associated with Increased Network Assortativity: Evidence from Metabolic Connectivity. Brain Connect 2023; 13:610-620. [PMID: 37930734 DOI: 10.1089/brain.2023.0024] [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] [Indexed: 11/07/2023] Open
Abstract
Introduction: Unraveling the network pathobiology in neurodegenerative disorders is a popular and promising field in research. We use a relatively newer network measure of assortativity in metabolic connectivity to understand network differences in patients with Alzheimer's Disease (AD), compared with those with mild cognitive impairment (MCI). Methods: Eighty-three demographically matched patients with dementia (56 AD and 27 MCI) who underwent positron emission tomography-magnetic resonance imaging (PET-MRI) study were recruited for this exploratory study. Global and nodal network measures obtained using the BRain Analysis using graPH theory toolbox were used to derive group-level differences (corrected p < 0.05). The methods were validated in age, and gender-matched 23 cognitively normal, 25 MCI, and 53 AD patients from the publicly available Alzheimer's Disease Neuroimaging Initiative (ADNI) data. Regions that revealed significant differences were correlated with the Addenbrooke's Cognitive Examination-III (ACE-III) scores. Results: Patients with AD revealed significantly increased global assortativity compared with the MCI group. In addition, they also revealed increased modularity and decreased participation coefficient. These findings were validated in the ADNI data. We also found that the regional standard uptake values of the right superior parietal and left superior temporal lobes were proportional to the ACE-III memory subdomain scores. Conclusion: Global errors associated with network assortativity are found in patients with AD, making the networks more regular and less resilient. Since the regional measures of these network errors were proportional to memory deficits, these measures could be useful in understanding the network pathobiology in AD.
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Affiliation(s)
- Sunil Kumar Khokhar
- Department of Neuroimaging and Interventional Radiology, and National Institute of Mental Health and Neurosciences (NIMHANS), Bengaluru, Karnataka, India
| | - Manoj Kumar
- Department of Neuroimaging and Interventional Radiology, and National Institute of Mental Health and Neurosciences (NIMHANS), Bengaluru, Karnataka, India
| | - Sandeep Kumar
- Department of Neurology, National Institute of Mental Health and Neurosciences (NIMHANS), Bengaluru, Karnataka, India
| | - Tejaswini Manae
- Department of Neurology, National Institute of Mental Health and Neurosciences (NIMHANS), Bengaluru, Karnataka, India
| | - Nithin Thanissery
- Department of Neurology, National Institute of Mental Health and Neurosciences (NIMHANS), Bengaluru, Karnataka, India
| | - Subasree Ramakrishnan
- Department of Neurology, National Institute of Mental Health and Neurosciences (NIMHANS), Bengaluru, Karnataka, India
| | - Faheem Arshad
- Department of Neurology, National Institute of Mental Health and Neurosciences (NIMHANS), Bengaluru, Karnataka, India
| | - Chandana Nagaraj
- Department of Neuroimaging and Interventional Radiology, and National Institute of Mental Health and Neurosciences (NIMHANS), Bengaluru, Karnataka, India
| | - Sandhya Mangalore
- Department of Neuroimaging and Interventional Radiology, and National Institute of Mental Health and Neurosciences (NIMHANS), Bengaluru, Karnataka, India
| | - Suvarna Alladi
- Department of Neurology, National Institute of Mental Health and Neurosciences (NIMHANS), Bengaluru, Karnataka, India
| | - Tapan K Gandhi
- Department of Electrical Engineering, Indian Institute of Technology (IIT) Delhi, New Delhi, Delhi, India
| | - Rose Dawn Bharath
- Department of Neuroimaging and Interventional Radiology, and National Institute of Mental Health and Neurosciences (NIMHANS), Bengaluru, Karnataka, India
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7
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Sengupta A, Wang F, Mishra A, Reed JL, Chen LM, Gore JC. Detection and characterization of resting state functional networks in squirrel monkey brain. Cereb Cortex Commun 2023; 4:tgad018. [PMID: 37753115 PMCID: PMC10518810 DOI: 10.1093/texcom/tgad018] [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: 06/02/2023] [Revised: 08/22/2023] [Accepted: 08/23/2023] [Indexed: 09/28/2023] Open
Abstract
Resting-state fMRI based on analyzing BOLD signals is widely used to derive functional networks in the brain and how they alter during disease or injury conditions. Resting-state networks can also be used to study brain functional connectomes across species, which provides insights into brain evolution. The squirrel monkey (SM) is a non-human primate (NHP) that is widely used as a preclinical model for experimental manipulations to understand the organization and functioning of the brain. We derived resting-state networks from the whole brain of anesthetized SMs using Independent Component Analysis of BOLD acquisitions. We detected 15 anatomically constrained resting-state networks localized in the cortical and subcortical regions as well as in the white-matter. Networks encompassing visual, somatosensory, executive control, sensorimotor, salience and default mode regions, and subcortical networks including the Hippocampus-Amygdala, thalamus, basal-ganglia and brainstem region correspond well with previously detected networks in humans and NHPs. The connectivity pattern between the networks also agrees well with previously reported seed-based resting-state connectivity of SM brain. This study demonstrates that SMs share remarkable homologous network organization with humans and other NHPs, thereby providing strong support for their suitability as a translational animal model for research and additional insight into brain evolution across species.
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Affiliation(s)
- Anirban Sengupta
- Vanderbilt University Institute of Imaging Science, Nashville, Vanderbilt University Medical Center, Nashville, TN, United States of America
- Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, United States of America
| | - Feng Wang
- Vanderbilt University Institute of Imaging Science, Nashville, Vanderbilt University Medical Center, Nashville, TN, United States of America
- Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, United States of America
| | - Arabinda Mishra
- Vanderbilt University Institute of Imaging Science, Nashville, Vanderbilt University Medical Center, Nashville, TN, United States of America
- Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, United States of America
| | - Jamie L Reed
- Vanderbilt University Institute of Imaging Science, Nashville, Vanderbilt University Medical Center, Nashville, TN, United States of America
- Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, United States of America
- Department of Psychology, Vanderbilt University, Nashville, TN, United States of America
| | - Li Min Chen
- Vanderbilt University Institute of Imaging Science, Nashville, Vanderbilt University Medical Center, Nashville, TN, United States of America
- Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, United States of America
- Biomedical Engineering, Vanderbilt University, Nashville, TN, United States of America
| | - John C Gore
- Vanderbilt University Institute of Imaging Science, Nashville, Vanderbilt University Medical Center, Nashville, TN, United States of America
- Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, United States of America
- Biomedical Engineering, Vanderbilt University, Nashville, TN, United States of America
- Department of Physics and Astronomy, Vanderbilt University, Nashville, TN, United States of America
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8
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Gao Y, Zhao Y, Li M, Lawless RD, Schilling KG, Xu L, Shafer AT, Beason-Held LL, Resnick SM, Rogers BP, Ding Z, Anderson AW, Landman BA, Gore JC. Functional alterations in bipartite network of white and grey matters during aging. Neuroimage 2023; 278:120277. [PMID: 37473978 PMCID: PMC10529380 DOI: 10.1016/j.neuroimage.2023.120277] [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: 04/14/2023] [Revised: 06/23/2023] [Accepted: 07/11/2023] [Indexed: 07/22/2023] Open
Abstract
The effects of normal aging on functional connectivity (FC) within various brain networks of gray matter (GM) have been well-documented. However, the age effects on the networks of FC between white matter (WM) and GM, namely WM-GM FC, remains unclear. Evaluating crucial properties, such as global efficiency (GE), for a WM-GM FC network poses a challenge due to the absence of closed triangle paths which are essential for assessing network properties in traditional graph models. In this study, we propose a bipartite graph model to characterize the WM-GM FC network and quantify these challenging network properties. Leveraging this model, we assessed the WM-GM FC network properties at multiple scales across 1,462 cognitively normal subjects aged 22-96 years from three repositories (ADNI, BLSA and OASIS-3) and investigated the age effects on these properties throughout adulthood and during late adulthood (age ≥70 years). Our findings reveal that (1) heterogeneous alterations occurred in region-specific WM-GM FC over the adulthood and decline predominated during late adulthood; (2) the FC density of WM bundles engaged in memory, executive function and processing speed declined with age over adulthood, particularly in later years; and (3) the GE of attention, default, somatomotor, frontoparietal and limbic networks reduced with age over adulthood, and GE of visual network declined during late adulthood. These findings provide unpresented insights into multi-scale alterations in networks of WM-GM functional synchronizations during normal aging. Furthermore, our bipartite graph model offers an extendable framework for quantifying WM-engaged networks, which may contribute to a wide range of neuroscience research.
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Affiliation(s)
- Yurui Gao
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA; Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA.
| | - Yu Zhao
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA; Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Muwei Li
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA; Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Richard D Lawless
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA; Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, USA
| | - Kurt G Schilling
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA; Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Lyuan Xu
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA; Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, USA
| | - Andrea T Shafer
- Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
| | - Lori L Beason-Held
- Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
| | - Susan M Resnick
- Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
| | - Baxter P Rogers
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA; Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA; Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA; Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Zhaohua Ding
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA; Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA; Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, USA; Department of Computer Science, Vanderbilt University, Nashville, TN, USA
| | - Adam W Anderson
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA; Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA
| | - Bennett A Landman
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA; Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA; Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA; Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, USA; Department of Computer Science, Vanderbilt University, Nashville, TN, USA
| | - John C Gore
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA; Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA; Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA.
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9
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Li M, Gao Y, Lawless RD, Xu L, Zhao Y, Schilling KG, Ding Z, Anderson AW, Landman BA, Gore JC. Changes in white matter functional networks across late adulthood. Front Aging Neurosci 2023; 15:1204301. [PMID: 37455933 PMCID: PMC10347529 DOI: 10.3389/fnagi.2023.1204301] [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: 04/12/2023] [Accepted: 06/14/2023] [Indexed: 07/18/2023] Open
Abstract
Introduction The aging brain is characterized by decreases in not only neuronal density but also reductions in myelinated white matter (WM) fibers that provide the essential foundation for communication between cortical regions. Age-related degeneration of WM has been previously characterized by histopathology as well as T2 FLAIR and diffusion MRI. Recent studies have consistently shown that BOLD (blood oxygenation level dependent) effects in WM are robustly detectable, are modulated by neural activities, and thus represent a complementary window into the functional organization of the brain. However, there have been no previous systematic studies of whether or how WM BOLD signals vary with normal aging. We therefore performed a comprehensive quantification of WM BOLD signals across scales to evaluate their potential as indicators of functional changes that arise with aging. Methods By using spatial independent component analysis (ICA) of BOLD signals acquired in a resting state, WM voxels were grouped into spatially distinct functional units. The functional connectivities (FCs) within and among those units were measured and their relationships with aging were assessed. On a larger spatial scale, a graph was reconstructed based on the pair-wise connectivities among units, modeling the WM as a complex network and producing a set of graph-theoretical metrics. Results The spectral powers that reflect the intensities of BOLD signals were found to be significantly affected by aging across more than half of the WM units. The functional connectivities (FCs) within and among those units were found to decrease significantly with aging. We observed a widespread reduction of graph-theoretical metrics, suggesting a decrease in the ability to exchange information between remote WM regions with aging. Discussion Our findings converge to support the notion that WM BOLD signals in specific regions, and their interactions with other regions, have the potential to serve as imaging markers of aging.
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Affiliation(s)
- Muwei Li
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, United States
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Yurui Gao
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, United States
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, United States
| | - Richard D. Lawless
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, United States
- Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, United States
| | - Lyuan Xu
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, United States
- Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, United States
| | - Yu Zhao
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, United States
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Kurt G. Schilling
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, United States
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Zhaohua Ding
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, United States
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, United States
- Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, United States
- Department of Computer Science, Vanderbilt University, Nashville, TN, United States
| | - Adam W. Anderson
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, United States
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, United States
| | - Bennett A. Landman
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, United States
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, United States
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, United States
- Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, United States
| | - John C. Gore
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, United States
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, United States
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, United States
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10
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Huang Y, Wei PH, Xu L, Chen D, Yang Y, Song W, Yi Y, Jia X, Wu G, Fan Q, Cui Z, Zhao G. Intracranial electrophysiological and structural basis of BOLD functional connectivity in human brain white matter. Nat Commun 2023; 14:3414. [PMID: 37296147 PMCID: PMC10256794 DOI: 10.1038/s41467-023-39067-3] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2022] [Accepted: 05/25/2023] [Indexed: 06/12/2023] Open
Abstract
While functional MRI (fMRI) studies have mainly focused on gray matter, recent studies have consistently found that blood-oxygenation-level-dependent (BOLD) signals can be reliably detected in white matter, and functional connectivity (FC) has been organized into distributed networks in white matter. Nevertheless, it remains unclear whether this white matter FC reflects underlying electrophysiological synchronization. To address this question, we employ intracranial stereotactic-electroencephalography (SEEG) and resting-state fMRI data from a group of 16 patients with drug-resistant epilepsy. We find that BOLD FC is correlated with SEEG FC in white matter, and this result is consistent across a wide range of frequency bands for each participant. By including diffusion spectrum imaging data, we also find that white matter FC from both SEEG and fMRI are correlated with white matter structural connectivity, suggesting that anatomical fiber tracts underlie the functional synchronization in white matter. These results provide evidence for the electrophysiological and structural basis of white matter BOLD FC, which could be a potential biomarker for psychiatric and neurological disorders.
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Affiliation(s)
- Yali Huang
- Chinese Institute for Brain Research, Beijing, 102206, China
| | - Peng-Hu Wei
- Department of Neurosurgery, Xuanwu Hospital Capital Medical University, Beijing, 100053, China
| | - Longzhou Xu
- Chinese Institute for Brain Research, Beijing, 102206, China
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, 100875, China
| | - Desheng Chen
- Department of Neurosurgery, Xuanwu Hospital Capital Medical University, Beijing, 100053, China
| | - Yanfeng Yang
- Department of Neurosurgery, Xuanwu Hospital Capital Medical University, Beijing, 100053, China
| | - Wenkai Song
- Chinese Institute for Brain Research, Beijing, 102206, China
| | - Yangyang Yi
- Chinese Institute for Brain Research, Beijing, 102206, China
| | - Xiaoli Jia
- Chinese Institute for Brain Research, Beijing, 102206, China
| | - Guowei Wu
- Chinese Institute for Brain Research, Beijing, 102206, China
| | - Qingchen Fan
- Chinese Institute for Brain Research, Beijing, 102206, China
| | - Zaixu Cui
- Chinese Institute for Brain Research, Beijing, 102206, China.
| | - Guoguang Zhao
- Department of Neurosurgery, Xuanwu Hospital Capital Medical University, Beijing, 100053, China.
- National Medical Center for Neurological Diseases, Beijing, 100053, China.
- Beijing Municipal Geriatric Medical Research Center, Beijing, 100053, China.
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11
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Zhang X, Li Y, Guan Q, Dong D, Zhang J, Meng X, Chen F, Luo Y, Zhang H. Distance-dependent reconfiguration of hubs in Alzheimer's disease: a cross-tissue functional network study. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.03.24.532772. [PMID: 36993290 PMCID: PMC10055319 DOI: 10.1101/2023.03.24.532772] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/30/2023]
Abstract
The hubs of the intra-grey matter (GM) network were sensitive to anatomical distance and susceptible to neuropathological damage. However, few studies examined the hubs of cross-tissue distance-dependent networks and their changes in Alzheimer's disease (AD). Using resting-state fMRI data of 30 AD patients and 37 normal older adults (NC), we constructed the cross-tissue networks based on functional connectivity (FC) between GM and white matter (WM) voxels. In the full-ranged and distance-dependent networks (characterized by gradually increased Euclidean distances between GM and WM voxels), their hubs were identified with weight degree metrics (frWD and ddWD). We compared these WD metrics between AD and NC; using the resultant abnormal WDs as the seeds, we performed seed-based FC analysis. With increasing distance, the GM hubs of distance-dependent networks moved from the medial to lateral cortices, and the WM hubs spread from the projection fibers to longitudinal fascicles. Abnormal ddWD metrics in AD were primarily located in the hubs of distance-dependent networks around 20-100mm. Decreased ddWDs were located in the left corona radiation (CR), which had decreased FCs with the executive network's GM regions in AD. Increased ddWDs were located in the posterior thalamic radiation (PTR) and the temporal-parietal-occipital junction (TPO), and their FCs were larger in AD. Increased ddWDs were shown in the sagittal striatum, which had larger FCs with the salience network's GM regions in AD. The reconfiguration of cross-tissue distance-dependent networks possibly reflected the disruption in the neural circuit of executive function and the compensatory changes in the neural circuits of visuospatial and social-emotional functions in AD.
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Affiliation(s)
- Xingxing Zhang
- Center for Brain Disorders and Cognitive Sciences, Shenzhen University, Shenzhen, China
- School of Psychology, Shenzhen University, Shenzhen, China
| | - Yingjia Li
- Center for Brain Disorders and Cognitive Sciences, Shenzhen University, Shenzhen, China
- School of Psychology, Shenzhen University, Shenzhen, China
| | - Qing Guan
- Center for Brain Disorders and Cognitive Sciences, Shenzhen University, Shenzhen, China
- School of Psychology, Shenzhen University, Shenzhen, China
- Center for Neuroimaging, Shenzhen Institute of Neuroscience, Shenzhen, China
| | - Debo Dong
- Key Laboratory of Cognition and Personality of Ministry of Education, Faculty of Psychology, Southwest University, Chongqing, 400715, China
- Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre Jülich, Jülich, Germany
| | - Jianfeng Zhang
- Center for Brain Disorders and Cognitive Sciences, Shenzhen University, Shenzhen, China
- School of Psychology, Shenzhen University, Shenzhen, China
| | - Xianghong Meng
- Department of Neurosurgery, Shenzhen University General Hospital, Shenzhen University, Shenzhen, China
| | - Fuyong Chen
- Department of Neurosurgery, Shenzhen Hospital of University of Hong Kong, Shenzhen, China
| | - Yuejia Luo
- Center for Brain Disorders and Cognitive Sciences, Shenzhen University, Shenzhen, China
- School of Psychology, Shenzhen University, Shenzhen, China
| | - Haobo Zhang
- Center for Brain Disorders and Cognitive Sciences, Shenzhen University, Shenzhen, China
- School of Psychology, Shenzhen University, Shenzhen, China
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12
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Gao Y, Lawless RD, Li M, Zhao Y, Schilling KG, Xu L, Shafer AT, Beason-Held LL, Resnick SM, Rogers BP, Ding Z, Anderson AW, Landman BA, Gore JC. Automatic Preprocessing Pipeline for White Matter Functional Analyses of Large-Scale Databases. PROCEEDINGS OF SPIE--THE INTERNATIONAL SOCIETY FOR OPTICAL ENGINEERING 2023; 12464:124640U. [PMID: 37600506 PMCID: PMC10437151 DOI: 10.1117/12.2653132] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/22/2023]
Abstract
Recently, increasing evidence suggests that fMRI signals in white matter (WM), conventionally ignored as nuisance, are robustly detectable using appropriate processing methods and are related to neural activity, while changes in WM with aging and degeneration are also well documented. These findings suggest variations in patterns of BOLD signals in WM should be investigated. However, existing fMRI analysis tools, which were designed for processing gray matter signals, are not well suited for large-scale processing of WM signals in fMRI data. We developed an automatic pipeline for high-performance preprocessing of fMRI images with emphasis on quantifying changes in BOLD signals in WM in an aging population. At the image processing level, the pipeline integrated existing software modules with fine parameter tunings and modifications to better extract weaker WM signals. The preprocessing results primarily included whole-brain time-courses, functional connectivity, maps and tissue masks in a common space. At the job execution level, this pipeline exploited a local XNAT to store datasets and results, while using DAX tool to automatic distribute batch jobs that run on high-performance computing clusters. Through the pipeline, 5,034 fMRI/T1 scans were preprocessed. The intraclass correlation coefficient (ICC) of test-retest experiment based on the preprocessed data is 0.52 - 0.86 (N=1000), indicating a high reliability of our pipeline, comparable to previously reported ICC in gray matter experiments. This preprocessing pipeline highly facilitates our future analyses on WM functional alterations in aging and may be of benefit to a larger community interested in WM fMRI studies.
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Affiliation(s)
- Yurui Gao
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA
- Biomedical Engineering, Vanderbilt University, Nashville, TN, USA
| | - Richard D Lawless
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA
- Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, USA
| | - Muwei Li
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA
- Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Yu Zhao
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA
- Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Kurt G Schilling
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA
- Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Lyuan Xu
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA
- Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, USA
| | - Andrea T Shafer
- Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
| | - Lori L Beason-Held
- Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
| | - Susan M Resnick
- Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
| | - Baxter P Rogers
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA
- Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Zhaohua Ding
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA
- Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, USA
| | - Adam W Anderson
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA
- Biomedical Engineering, Vanderbilt University, Nashville, TN, USA
| | - Bennett A Landman
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA
- Biomedical Engineering, Vanderbilt University, Nashville, TN, USA
- Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, USA
- Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
| | - John C Gore
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA
- Biomedical Engineering, Vanderbilt University, Nashville, TN, USA
- Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
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13
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Ma J, Liu F, Wang Y, Ma L, Niu Y, Wang J, Ye Z, Zhang J. Frequency-dependent white-matter functional network changes associated with cognitive deficits in subcortical vascular cognitive impairment. Neuroimage Clin 2022; 36:103245. [PMID: 36451351 PMCID: PMC9668649 DOI: 10.1016/j.nicl.2022.103245] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2022] [Revised: 10/07/2022] [Accepted: 10/21/2022] [Indexed: 11/11/2022]
Abstract
Vascular cognitive impairment (VCI) refers to all forms of cognitive decline associated with cerebrovascular diseases, in which white matter (WM) is highly vulnerable. Although previous studies have shown that blood oxygen level-dependent (BOLD) signals inside WM can effectively reflect neural activities, whether WM BOLD signal alterations are present and their roles underlying cognitive impairment in VCI remain largely unknown. In this study, 36 subcortical VCI (SVCI) patients and 36 healthy controls were enrolled to evaluate WM dysfunction. Specifically, fourteen distinct WM networks were identified from resting-state functional MRI using K-means clustering analysis. Subsequently, between-network functional connectivity (FC) and within-network BOLD signal amplitude of WM networks were calculated in three frequency bands (band A: 0.01-0.15 Hz, band B: 0.08-0.15 Hz, and band C: 0.01-0.08 Hz). Patients with SVCI manifested decreased FC mainly in bilateral parietal WM regions, forceps major, superior and inferior longitudinal fasciculi. These connections extensively linked with distinct WM networks and with gray-matter networks such as frontoparietal control, dorsal and ventral attention networks, which exhibited frequency-specific alterations in SVCI. Additionally, extensive amplitude reductions were found in SVCI, showing frequency-dependent properties in parietal, anterior corona radiate, pre/post central, superior and inferior longitudinal fasciculus networks. Furthermore, these decreased FC and amplitudes showed significant positive correlations with cognitive performances in SVCI, and high diagnostic performances for SVCI especially combining all bands. Our study indicated that VCI-related cognitive deficits were characterized by frequency-dependent WM functional abnormalities, which offered novel applicable neuromarkers for VCI.
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Affiliation(s)
- Juanwei Ma
- Department of Radiology, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China,National Clinical Research Center for Cancer, Tianjin, China,Key Laboratory of Cancer Prevention and Therapy, Tianjin, China,Tianjin’s Clinical Research Center for Cancer, Tianjin, China,Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Feng Liu
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Yang Wang
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Lin Ma
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Yali Niu
- Department of Rehabilitation, Tianjin Medical University General Hospital, Tianjin 300052, China
| | - Jing Wang
- Department of Rehabilitation, Tianjin Medical University General Hospital, Tianjin 300052, China
| | - Zhaoxiang Ye
- Department of Radiology, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China,National Clinical Research Center for Cancer, Tianjin, China,Key Laboratory of Cancer Prevention and Therapy, Tianjin, China,Tianjin’s Clinical Research Center for Cancer, Tianjin, China,Corresponding authors at: Department of Radiology, Tianjin Medical University General Hospital, No. 154, Anshan Road, Heping District, Tianjin 300052, China (J. Zhang). Department of Radiology, Tianjin Medical University Cancer Institute and Hospital, Huan-Hu-West Road, Ti-Yuan-Bei, Hexi District, Tianjin 300060, China (Z. Ye).
| | - Jing Zhang
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China,Corresponding authors at: Department of Radiology, Tianjin Medical University General Hospital, No. 154, Anshan Road, Heping District, Tianjin 300052, China (J. Zhang). Department of Radiology, Tianjin Medical University Cancer Institute and Hospital, Huan-Hu-West Road, Ti-Yuan-Bei, Hexi District, Tianjin 300060, China (Z. Ye).
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14
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Xiao D, Wang K, Theriault L, Charbel E. White matter integrity and key structures affected in Alzheimer's disease characterized by diffusion tensor imaging. Eur J Neurosci 2022; 56:5319-5331. [PMID: 36048971 DOI: 10.1111/ejn.15815] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2021] [Revised: 08/13/2022] [Accepted: 08/22/2022] [Indexed: 12/14/2022]
Abstract
White matter (WM) degeneration is suggested to predict the early signs of Alzheimer's disease (AD). The exact structural regions of brain circuitry involved are not known. This study aims to examine the associations between WM tract integrity, represented by the diffusion tensor imaging (DTI) measures, and AD diagnosis and to denote the key substrates in predicting AD. It included DTI measures of mean diffusivity (MD), fractional anisotropy, radial diffusivity and axial diffusivity of 18 main WM tracts in 84 non-Hispanic white participants from the Alzheimer's Disease Neuroimaging Initiative dataset. The multivariable general linear model was used to examine the association of AD diagnosis with each DTI measure adjusting for age, gender and education. The corpus callosum, fornix, cingulum hippocampus, uncinate fasciculus, sagittal striatum, left posterior thalamic radiation and fornix-stria terminalis showed significant increases in MD, radial and axial diffusivity, whereas the splenium of corpus callosum and the fornix showed significant decreases in fractional anisotropy among AD patients. Variable cluster analysis identified that hippocampus volume, mini-mental state examination (MMSE), cingulate gyrus/hippocampus, inferior fronto-occipital fasciculus and uncinate fasciculus are highly correlated in one cluster with MD measures. In conclusion, there were significant differences in DTI measures between the brain WM of AD patients and controls. Age is the risk factor associated with AD, not gender or education. Right cingulum gyrus and right uncinate fasciculus are particularly affected, correlating well with a cognitive test MMSE and MD measures for dementia in AD patients and could be a region of focus for AD staging.
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Affiliation(s)
- Danqing Xiao
- Department of STEM, School of Arts and Sciences, Regis College, Weston, Massachusetts, USA.,Neuroimaging Center, McLean Hospital, Belmont, Massachusetts, USA
| | - Kesheng Wang
- Department of Family and Community Health, School of Nursing, Health Sciences Center, West Virginia University, Morgantown, West Virginia, USA
| | - Luke Theriault
- Department of STEM, School of Arts and Sciences, Regis College, Weston, Massachusetts, USA.,School of Medicine, St. George's University, Saint George's, Grenada
| | - Elhelou Charbel
- Department of STEM, School of Arts and Sciences, Regis College, Weston, Massachusetts, USA
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15
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Classification and Interpretability of Mild Cognitive Impairment Based on Resting-State Functional Magnetic Resonance and Ensemble Learning. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:2535954. [PMID: 36035823 PMCID: PMC9417789 DOI: 10.1155/2022/2535954] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/08/2022] [Revised: 06/12/2022] [Accepted: 07/06/2022] [Indexed: 11/22/2022]
Abstract
The combination and integration of multimodal imaging and clinical markers have introduced numerous classifiers to improve diagnostic accuracy in detecting and predicting AD; however, many studies cannot ensure the homogeneity of data sets and consistency of results. In our study, the XGBoost algorithm was used to classify mild cognitive impairment (MCI) and normal control (NC) populations through five rs-fMRI analysis datasets. Shapley Additive exPlanations (SHAP) is used to analyze the interpretability of the model. The highest accuracy for diagnosing MCI was 65.14% (using the mPerAF dataset). The characteristics of the left insula, right middle frontal gyrus, and right cuneus correlated positively with the output value using DC datasets. The characteristics of left cerebellum 6, right inferior frontal gyrus, opercular part, and vermis 6 correlated positively with the output value using fALFF datasets. The characteristics of the right middle temporal gyrus, left middle temporal gyrus, left temporal pole, and middle temporal gyrus correlated positively with the output value using mPerAF datasets. The characteristics of the right middle temporal gyrus, left middle temporal gyrus, and left hippocampus correlated positively with the output value using PerAF datasets. The characteristics of left cerebellum 9, vermis 9, and right precentral gyrus, right amygdala, and left middle occipital gyrus correlated positively with the output value using Wavelet-ALFF datasets. We found that the XGBoost algorithm constructed from rs-fMRI data is effective for the diagnosis and classification of MCI. The accuracy rates obtained by different rs-fMRI data analysis methods are similar, but the important features are different and involve multiple brain regions, which suggests that MCI may have a negative impact on brain function.
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16
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Combined functional and structural imaging of brain white matter reveals stage-dependent impairment in multiple system atrophy of cerebellar type. NPJ Parkinsons Dis 2022; 8:105. [PMID: 35977953 PMCID: PMC9385720 DOI: 10.1038/s41531-022-00371-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2021] [Accepted: 07/26/2022] [Indexed: 12/04/2022] Open
Abstract
Advances in fMRI of brain white matter (WM) have established the feasibility of understanding how functional signals of WM evolve with brain diseases. By combining functional signals with structural features of WM, the current study characterizes functional and structural impairments of WM in cerebelar type multiple system atrophy, with the goal to derive new mechanistic insights into the pathological progression of this disease. Our analysis of 30 well-diagnosed patients revealed pronounced decreases in functional connectivity in WM bundles of the cerebellum and brainstem, and concomitant local structural alterations that depended on the disease stage. The novel findings implicate a critical time point in the pathological evolution of the disease, which could guide optimal therapeutic interventions. Furthermore, fMRI signals of impaired WM bundles exhibited superior sensitivity in differentiating initial disease development, which demonstrates great potential of using these signals to inform disease management.
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17
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Jiang Y, Wang P, Wen J, Wang J, Li H, Biswal BB. Hippocampus-based static functional connectivity mapping within white matter in mild cognitive impairment. Brain Struct Funct 2022; 227:2285-2297. [PMID: 35864361 DOI: 10.1007/s00429-022-02521-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2021] [Accepted: 06/04/2022] [Indexed: 11/28/2022]
Abstract
Mild cognitive impairment (MCI) is clinically characterized by memory loss and cognitive impairment closely associated with the hippocampal atrophy. Accumulating studies have confirmed the presence of neural signal changes within white matter (WM) in resting-state functional magnetic resonance imaging (fMRI). However, it remains unclear how abnormal hippocampus activity affects the WM regions in MCI. The current study employs 43 MCI, 71 very MCI (VMCI) and 87 age-, gender-, and education-matched healthy controls (HCs) from the public OASIS-3 dataset. Using the left and right hippocampus as seed points, we obtained the whole-brain functional connectivity (FC) maps for each subject. We then perform one-way ANOVA analysis to investigate the abnormal FC regions among HCs, VMCI, and MCI. We further performed probabilistic tracking to estimate whether the abnormal FC correspond to structural connectivity disruptions. Compared to HCs, MCI and VMCI groups exhibited reduced FC in the right middle temporal gyrus within gray matter, and right temporal pole, right inferior frontal gyrus within white matter. Specific dysconnectivity is shown in the cerebellum Crus II, left inferior temporal gyrus within gray matter, and right frontal gyrus within white matter. In addition, the fiber bundles connecting the left hippocampus and right temporal pole within white matter show abnormally increased mean diffusivity in MCI. The current study proposes a new functional imaging direction for exploring the mechanism of memory decline and pathophysiological mechanisms in different stages of Alzheimer's disease.
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Affiliation(s)
- Yuan Jiang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Pan Wang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China.
| | - Jiaping Wen
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Jianlin Wang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Hongyi Li
- The Fourth People's Hospital of Chengdu, Chengdu, China
| | - Bharat B Biswal
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China. .,Department of Biomedical Engineering, New Jersey Institute of Technology, Newark, NJ, 07102, USA.
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18
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Latency structure of BOLD signals within white matter in resting-state fMRI. Magn Reson Imaging 2022; 89:58-69. [PMID: 34999161 PMCID: PMC9851671 DOI: 10.1016/j.mri.2021.12.010] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2021] [Revised: 12/23/2021] [Accepted: 12/27/2021] [Indexed: 01/22/2023]
Abstract
PURPOSE Previous studies have demonstrated that BOLD signals in gray matter in resting-state functional MRI (RSfMRI) have variable time lags, representing apparent propagations of fMRI BOLD signals in gray matter. We complemented existing findings and explored the corresponding variations of signal latencies in white matter. METHODS We used data from the Brain Genomics Superstruct Project, consisting of 1412 subjects (both sexes included) and divided the dataset into ten equal groups to study both the patterns and reproducibility of latency estimates within white matter. We constructed latency matrices by computing cross-covariances between voxel pairs. We also applied a clustering analysis to identify functional networks within white matter, based on which latency analysis was also performed to investigate lead/lag relationship at network level. A dataset consisting of various sensory states (eyes closed, eyes open and eyes open with fixation) was also included to examine the relationship between latency structure and different states. RESULTS Projections of voxel latencies from the latency matrices were highly correlated (average Pearson correlation coefficient = 0.89) across the subgroups, confirming the reproducibility and structure of signal lags in white matter. Analysis of latencies within and between networks revealed a similar pattern of inter- and intra-network communication to that reported for gray matter. Moreover, a dominant direction, from inferior to superior regions, of BOLD signal propagation was revealed by higher resolution clustering. The variations of lag structure within white matter are associated with different sensory states. CONCLUSIONS These findings provide additional insight into the character and roles of white matter BOLD signals in brain functions.
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Li M, Gao Y, Anderson AW, Ding Z, Gore JC. Dynamic variations of resting-state BOLD signal spectra in white matter. Neuroimage 2022; 250:118972. [PMID: 35131432 PMCID: PMC8915948 DOI: 10.1016/j.neuroimage.2022.118972] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2021] [Revised: 01/27/2022] [Accepted: 02/03/2022] [Indexed: 12/03/2022] Open
Abstract
Recent studies have demonstrated that the mathematical model used for analyzing and interpreting fMRI data in gray matter (GM) is inappropriate for detecting or describing blood-oxygenation-level-dependent (BOLD) signals in white matter (WM). In particular the hemodynamic response function (HRF) which serves as the regressor in general linear models is different in WM compared to GM. We recently reported measurements of the frequency contents of resting-state signal time courses in WM that showed distinct power spectra which depended on local structural-vascular-functional associations. In addition, multiple studies of GM have revealed how functional connectivity between regions, as measured by the correlation between BOLD time series, varies dynamically over time. We therefore investigated whether and how BOLD signals from WM in a resting state varied over time. We measured voxel-wise spectrograms, which reflect the time-varying spectral patterns of WM time courses. The results suggest that the spectral patterns are non-stationary but could be categorized into five modes that recurred over time. These modes showed distinct spatial distributions of their occurrences and durations, and the distributions were highly consistent across individuals. In addition, one of the modes exhibited a strong coupling of its occurrence between GM and WM across individuals, and two communities of WM voxels were identified according to the hierarchical structures of transitions among modes. Moreover, these modes are coupled to the shape of instantaneous HRFs. Our findings extend previous studies and reveal the non-stationary nature of spectral patterns of BOLD signals over time, providing a spatial-temporal-frequency characterization of resting-state signals in WM.
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Affiliation(s)
- Muwei Li
- Vanderbilt University Institute of Imaging Science, Vanderbilt University, 1161 21st Ave. S, Medical Center North, AA-1105, Nashville, TN 37232-2310, United States; Department of Radiology and Radiological Sciences, Vanderbilt University, Nashville, TN 37232, United States.
| | - Yurui Gao
- Vanderbilt University Institute of Imaging Science, Vanderbilt University, 1161 21st Ave. S, Medical Center North, AA-1105, Nashville, TN 37232-2310, United States,Department of Biomedical Engineering, Vanderbilt University, Nashville, TN 37235, United States
| | - Adam W. Anderson
- Vanderbilt University Institute of Imaging Science, Vanderbilt University, 1161 21st Ave. S, Medical Center North, AA-1105, Nashville, TN 37232-2310, United States,Department of Radiology and Radiological Sciences, Vanderbilt University, Nashville, TN 37232, United States,Department of Biomedical Engineering, Vanderbilt University, Nashville, TN 37235, United States
| | - Zhaohua Ding
- Vanderbilt University Institute of Imaging Science, Vanderbilt University, 1161 21st Ave. S, Medical Center North, AA-1105, Nashville, TN 37232-2310, United States,Department of Biomedical Engineering, Vanderbilt University, Nashville, TN 37235, United States,Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN 37235, United States
| | - John C. Gore
- Vanderbilt University Institute of Imaging Science, Vanderbilt University, 1161 21st Ave. S, Medical Center North, AA-1105, Nashville, TN 37232-2310, United States,Department of Radiology and Radiological Sciences, Vanderbilt University, Nashville, TN 37232, United States,Department of Biomedical Engineering, Vanderbilt University, Nashville, TN 37235, United States,Department of Physics and Astronomy, Vanderbilt University, Nashville, TN 37232, United State
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20
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The Road to Personalized Medicine in Alzheimer’s Disease: The Use of Artificial Intelligence. Biomedicines 2022; 10:biomedicines10020315. [PMID: 35203524 PMCID: PMC8869403 DOI: 10.3390/biomedicines10020315] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2021] [Revised: 01/21/2022] [Accepted: 01/24/2022] [Indexed: 02/05/2023] Open
Abstract
Dementia remains an extremely prevalent syndrome among older people and represents a major cause of disability and dependency. Alzheimer’s disease (AD) accounts for the majority of dementia cases and stands as the most common neurodegenerative disease. Since age is the major risk factor for AD, the increase in lifespan not only represents a rise in the prevalence but also adds complexity to the diagnosis. Moreover, the lack of disease-modifying therapies highlights another constraint. A shift from a curative to a preventive approach is imminent and we are moving towards the application of personalized medicine where we can shape the best clinical intervention for an individual patient at a given point. This new step in medicine requires the most recent tools and analysis of enormous amounts of data where the application of artificial intelligence (AI) plays a critical role on the depiction of disease–patient dynamics, crucial in reaching early/optimal diagnosis, monitoring and intervention. Predictive models and algorithms are the key elements in this innovative field. In this review, we present an overview of relevant topics regarding the application of AI in AD, detailing the algorithms and their applications in the fields of drug discovery, and biomarkers.
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21
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Qin Z, Liang HB, Li M, Hu Y, Wu J, Qiao Y, Liu JR, Du X. Disrupted White Matter Functional Connectivity With the Cerebral Cortex in Migraine Patients. Front Neurosci 2022; 15:799854. [PMID: 35095401 PMCID: PMC8793828 DOI: 10.3389/fnins.2021.799854] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2021] [Accepted: 12/23/2021] [Indexed: 11/18/2022] Open
Abstract
Background: In attempts to understand the migraine patients’ overall brain functional architecture, blood oxygenation level-dependent (BOLD) signals in the white matter (WM) and gray matter (GM) were considered in the current study. Migraine, a severe and multiphasic brain condition, is characterized by recurrent attacks of headaches. BOLD fluctuations in a resting state exhibit similar temporal and spectral profiles in both WM and GM. It is feasible to explore the functional interactions between WM tracts and GM regions in migraine. Methods: Forty-eight migraineurs without aura (MWoA) and 48 healthy controls underwent resting-state functional magnetic resonance imaging. Pearson’s correlations between the mean time courses of 48 white matter (WM) bundles and 82 gray matter (GM) regions were computed for each subject. Two-sample t-tests were performed on the Pearson’s correlation coefficients (CC) to compare the differences between the MWoA and healthy controls in the GM-averaged CC of each bundle and the WM-averaged CC of each GM region. Results: The MWoAs exhibited an overall decreased average temporal CC between BOLD signals in 82 GM regions and 48 WM bundles compared with healthy controls, while little was increased. In particular, WM bundles such as left anterior corona radiata, left external capsule and bilateral superior longitudinal fasciculus had significantly decreased mean CCs with GM in MWoA. On the other hand, 16 GM regions had significantly decreased mean CCs with WM in MWoA, including some areas that are parts of the somatosensory regions, auditory cortex, temporal areas, frontal areas, cingulate cortex, and parietal cortex. Conclusion: Decreased functional connections between WM bundles and GM regions might contribute to disrupted functional connectivity between the parts of the pain processing pathway in MWoAs, which indicated that functional and connectivity abnormalities in cortical regions may not be limited to GM regions but are instead associated with functional abnormalities in WM tracts.
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Affiliation(s)
- Zhaoxia Qin
- School of Psychology, Shanghai University of Sport, Shanghai, China
- Department of Medical Imaging, The Affiliated Hospital of Yangzhou University, Yangzhou, China
| | - Huai-Bin Liang
- Department of Neurology, Jiuyuan Municipal Stroke Center, Shanghai Ninth People’s Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Clinical Research Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Muwei Li
- Vanderbilt University Institute of Imaging Science, Nashville, TN, United States
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Yue Hu
- Department of Neurology, Jiuyuan Municipal Stroke Center, Shanghai Ninth People’s Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Clinical Research Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Jing Wu
- Department of Neurology, Jiuyuan Municipal Stroke Center, Shanghai Ninth People’s Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yuan Qiao
- Department of Neurology, Jiuyuan Municipal Stroke Center, Shanghai Ninth People’s Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Clinical Research Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Jian-Ren Liu
- Department of Neurology, Jiuyuan Municipal Stroke Center, Shanghai Ninth People’s Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Clinical Research Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- *Correspondence: Jian-Ren Liu,
| | - Xiaoxia Du
- School of Psychology, Shanghai University of Sport, Shanghai, China
- Xiaoxia Du,
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22
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Zhou Y, Song Z, Han X, Li H, Tang X. Prediction of Alzheimer's Disease Progression Based on Magnetic Resonance Imaging. ACS Chem Neurosci 2021; 12:4209-4223. [PMID: 34723463 DOI: 10.1021/acschemneuro.1c00472] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
Abstract
The neuroimaging method of multimodal magnetic resonance imaging (MRI) can identify the changes in brain structure and function caused by Alzheimer's disease (AD) at different stages, and it is a practical method to study the mechanism of AD progression. This paper reviews the studies of methods and biomarkers for predicting AD progression based on multimodal MRI. First, different approaches for predicting AD progression are analyzed and summarized, including machine learning, deep learning, regression, and other MRI analysis methods. Then, the effective biomarkers of AD progression under structural magnetic resonance imaging, diffusion tensor imaging, functional magnetic resonance imaging, and arterial spin labeling modes of MRI are summarized. It is believed that the brain changes shown on MRI may be related to the cognitive decline in different prodrome stages of AD, which is conducive to the further realization of early intervention and prevention of AD. Finally, the deficiencies of the existing studies are analyzed in terms of data set size, data heterogeneity, processing methods, and research depth. More importantly, future research directions are proposed, including enriching data sets, simplifying biomarkers, utilizing multimodal magnetic resonance, etc. In the future, the study of AD progression by multimodal MRI will still be a challenge but also a significant research hotspot.
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Affiliation(s)
- Ying Zhou
- School of Life Science, Beijing Institute of Technology, 5 South Zhongguancun Street, Beijing 100081, P.R. China
| | - Zeyu Song
- School of Life Science, Beijing Institute of Technology, 5 South Zhongguancun Street, Beijing 100081, P.R. China
| | - Xiao Han
- School of Life Science, Beijing Institute of Technology, 5 South Zhongguancun Street, Beijing 100081, P.R. China
| | - Hanjun Li
- School of Life Science, Beijing Institute of Technology, 5 South Zhongguancun Street, Beijing 100081, P.R. China
| | - Xiaoying Tang
- School of Life Science, Beijing Institute of Technology, 5 South Zhongguancun Street, Beijing 100081, P.R. China
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23
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Power spectra reveal distinct BOLD resting-state time courses in white matter. Proc Natl Acad Sci U S A 2021; 118:2103104118. [PMID: 34716261 DOI: 10.1073/pnas.2103104118] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2021] [Accepted: 09/24/2021] [Indexed: 11/18/2022] Open
Abstract
Accurate characterization of the time courses of blood-oxygen-level-dependent (BOLD) signal changes is crucial for the analysis and interpretation of functional MRI data. While several studies have shown that white matter (WM) exhibits distinct BOLD responses evoked by tasks, there have been no comprehensive investigations into the time courses of spontaneous signal fluctuations in WM. We measured the power spectra of the resting-state time courses in a set of regions within WM identified as showing synchronous signals using independent components analysis. In each component, a clear separation between voxels into two categories was evident, based on their power spectra: one group exhibited a single peak, and the other had an additional peak at a higher frequency. Their groupings are location specific, and their distributions reflect unique neurovascular and anatomical configurations. Importantly, the two categories of voxels differed in their engagement in functional integration, revealed by differences in the number of interregional connections based on the two categories separately. Taken together, these findings suggest WM signals are heterogeneous in nature and depend on local structural-vascular-functional associations.
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24
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Wang P, Wang Z, Wang J, Jiang Y, Zhang H, Li H, Biswal BB. Altered Homotopic Functional Connectivity Within White Matter in the Early Stages of Alzheimer's Disease. Front Neurosci 2021; 15:697493. [PMID: 34630008 PMCID: PMC8492970 DOI: 10.3389/fnins.2021.697493] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2021] [Accepted: 08/13/2021] [Indexed: 11/13/2022] Open
Abstract
Alzheimer's disease (AD) is a progressive neurodegenerative disorder with memory loss and cognitive impairment. The white matter (WM) BOLD signal has recently been shown to provide an important role in understanding the intrinsic cerebral activity. Although the altered homotopic functional connectivity within gray matter (GM-HFC) has been examined in AD, the abnormal HFC to WM remains unknown. The present study sought to identify changes in the WM-HFC and anatomic characteristics by combining functional magnetic resonance imaging with diffusion tensor imaging (DTI). Resting-state and DTI magnetic resonance images were collected from the OASIS-3 dataset and consisted of 53 mild cognitive impairment (MCI) patients, 90 very MCI (VMCI), and 100 normal cognitive (NC) subjects. Voxel-mirrored HFC was adopted to examine whether WM-HFC was disrupted in VMCI and MCI participants. Moreover, the DTI technique was used to investigate whether specific alterations of WM-HFC were associated with anatomic characteristics. Support vector machine analyses were used to identify the MCI and VMCI participants using the abnormal WM-HFC as the features. Compared with NC, MCI, and VMCI participants showed significantly decreased GM-HFC in the middle occipital gyrus and inferior parietal gyrus and decreased WM-HFC in the bilateral middle occipital and parietal lobe-WM. In addition, specific WM-functional network alteration for the bilateral sub-lobar-WM was found in MCI subjects. MCI subjects showed abnormal anatomic characteristics for bilateral sub-lobar and parietal lobe-WM. Results of GM-HFC mainly showed common neuroimaging features for VMCI and MCI subjects, whereas analysis of WM-HFC showed specific clinical neuromarkers and effectively compensated for the lack of GM-HFC to distinguish NC, VMCI, and MCI subjects.
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Affiliation(s)
- Pan Wang
- Ministry of Education (MOE) Key Laboratory for Neuroinformation, Center for Information in Medicine, School of Life Sciences and Technology, The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu, China
| | - Zedong Wang
- Ministry of Education (MOE) Key Laboratory for Neuroinformation, Center for Information in Medicine, School of Life Sciences and Technology, The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu, China
| | - Jianlin Wang
- Ministry of Education (MOE) Key Laboratory for Neuroinformation, Center for Information in Medicine, School of Life Sciences and Technology, The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu, China
| | - Yuan Jiang
- Ministry of Education (MOE) Key Laboratory for Neuroinformation, Center for Information in Medicine, School of Life Sciences and Technology, The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu, China
| | - Hong Zhang
- Ministry of Education (MOE) Key Laboratory for Neuroinformation, Center for Information in Medicine, School of Life Sciences and Technology, The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu, China
| | - Hongyi Li
- The Fourth People's Hospital of Chengdu, Chengdu, China
| | - Bharat B Biswal
- Ministry of Education (MOE) Key Laboratory for Neuroinformation, Center for Information in Medicine, School of Life Sciences and Technology, The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu, China
- Department of Biomedical Engineering, New Jersey Institute of Technology, Newark, NJ, United States
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25
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Gao Y, Li M, Huang AS, Anderson AW, Ding Z, Heckers SH, Woodward ND, Gore JC. Lower functional connectivity of white matter during rest and working memory tasks is associated with cognitive impairments in schizophrenia. Schizophr Res 2021; 233:101-110. [PMID: 34215467 PMCID: PMC8442250 DOI: 10.1016/j.schres.2021.06.013] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/20/2020] [Revised: 06/17/2021] [Accepted: 06/18/2021] [Indexed: 01/24/2023]
Abstract
BACKGROUND Schizophrenia can be understood as a disturbance of functional connections within brain networks. However, functional alterations that involve white matter (WM) specifically, or their cognitive correlates, have seldomly been investigated, especially during tasks. METHODS Resting state and task fMRI images were acquired on 84 patients and 67 controls. Functional connectivities (FC) between 46 WM bundles and 82 cortical regions were compared between the groups under two conditions (i.e., resting state and during working memory retention period). The FC density of each WM bundle was then compared between groups. Associations of FC with cognitive scores were evaluated. RESULTS FC measures were lower in schizophrenia relative to controls for external capsule, cingulum (cingulate and hippocampus), uncinate fasciculus, as well as corpus callosum (genu and body) under the rest or the task condition, and were higher in the posterior corona radiata and posterior thalamic radiation during the task condition. FC for specific WM bundles was correlated with cognitive performance assessed by working memory and processing speed metrics. CONCLUSIONS The findings suggest that the functional abnormalities in patients' WM are heterogeneous, possibly reflecting several underlying mechanisms such as structural damage, functional compensation and excessive effort on task, and that WM FC disruption may contribute to the impairments of working memory and processing speed. This is the first report on WM FC abnormalities in schizophrenia relative to controls and their cognitive associates during both rest and task and highlights the need to consider WM functions as components of brain functional networks in schizophrenia.
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Affiliation(s)
- Yurui Gao
- Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA; Biomedical Engineering, Vanderbilt University, Nashville, TN, USA
| | - Muwei Li
- Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA; Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Anna S Huang
- Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Adam W Anderson
- Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA; Biomedical Engineering, Vanderbilt University, Nashville, TN, USA; Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Zhaohua Ding
- Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA; Biomedical Engineering, Vanderbilt University, Nashville, TN, USA; Electrical Engineering and Computer Science, Vanderbilt University, Nashville, TN, USA
| | - Stephan H Heckers
- Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA; Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Neil D Woodward
- Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, TN, USA.
| | - John C Gore
- Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA; Biomedical Engineering, Vanderbilt University, Nashville, TN, USA; Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA.
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26
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Behjat H, Aganj I, Abramian D, Eklund A, Westin CF. CHARACTERIZATION OF SPATIAL DYNAMICS OF FMRI DATA IN WHITE MATTER USING DIFFUSION-INFORMED WHITE MATTER HARMONICS. PROCEEDINGS. IEEE INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING 2021; 2021:1586-1590. [PMID: 34084267 DOI: 10.1109/isbi48211.2021.9433958] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
In this work, we leverage the Laplacian eigenbasis of voxel-wise white matter (WM) graphs derived from diffusion-weighted MRI data, dubbed WM harmonics, to characterize the spatial structure of WM fMRI data. Our motivation for such a characterization is based on studies that show WM fMRI data exhibit a spatial correlational anisotropy that coincides with underlying fiber patterns. By quantifying the energy content of WM fMRI data associated with subsets of WM harmonics across multiple spectral bands, we show that the data exhibits notable subtle spatial modulations under functional load that are not manifested during rest. WM harmonics provide a novel means to study the spatial dynamics of WM fMRI data, in such way that the analysis is informed by the underlying anatomical structure.
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Affiliation(s)
- Hamid Behjat
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, USA.,Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, USA.,Department of Biomedical Engineering, Lund University, Lund, Sweden
| | - Iman Aganj
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, USA.,Computer Science and Artificial Intelligence Lab, Massachusetts Institute of Technology, Cambridge, USA
| | - David Abramian
- Department of Biomedical Engineering, Linköping University, Linköping, Sweden.,Center for Medical Image Science and Visualization, Linköping University, Linköping, Sweden
| | - Anders Eklund
- Department of Biomedical Engineering, Linköping University, Linköping, Sweden.,Center for Medical Image Science and Visualization, Linköping University, Linköping, Sweden.,Department of Computer and Information Science, Linköping University, Linköping, Sweden
| | - Carl-Fredrik Westin
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, USA
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