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Gamgam G, Yıldırım Z, Kabakçıoğlu A, Gurvit H, Demiralp T, Acar B. Siamese Graph Convolutional Network quantifies increasing structure-function discrepancy over the cognitive decline continuum. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2024; 254:108290. [PMID: 38954916 DOI: 10.1016/j.cmpb.2024.108290] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/27/2023] [Revised: 05/09/2024] [Accepted: 06/16/2024] [Indexed: 07/04/2024]
Abstract
BACKGROUND AND OBJECTIVE Alzheimer's disease dementia (ADD) is well known to induce alterations in both structural and functional brain connectivity. However, reported changes in connectivity are mostly limited to global/local network features, which have poor specificity for diagnostic purposes. Following recent advances in machine learning, deep neural networks, particularly Graph Neural Network (GNN) based approaches, have found applications in brain research as well. The majority of existing applications of GNNs employ a single network (uni-modal or structure/function unified), despite the widely accepted view that there is a nontrivial interdependence between the brain's structural connectivity and the neural activity patterns, which is hypothesized to be disrupted in ADD. This disruption is quantified as a discrepancy score by the proposed "structure-function discrepancy learning network" (sfDLN) and its distribution is studied over the spectrum of clinical cognitive decline. The measured discrepancy score is utilized as a diagnostic biomarker and is compared with state-of-the-art diagnostic classifiers. METHODS sfDLN is a GNN with a siamese architecture built on the hypothesis that the mismatch between structural and functional connectivity patterns increases over the cognitive decline spectrum, starting from subjective cognitive impairment (SCI), passing through a mid-stage mild cognitive impairment (MCI), and ending up with ADD. The structural brain connectome (sNET) built using diffusion MRI-based tractography and the novel, sparse (lean) functional brain connectome (ℓNET) built using fMRI are input to sfDLN. The siamese sfDLN is trained to extract connectome representations and a discrepancy (dissimilarity) score that complies with the proposed hypothesis and is blindly tested on an MCI group. RESULTS The sfDLN generated structure-function discrepancy scores show high disparity between ADD and SCI subjects. Leave-one-out experiments of SCI-ADD classification over a cohort of 42 subjects reach 88% accuracy, surpassing state-of-the-art GNN-based classifiers in the literature. Furthermore, a blind assessment over a cohort of 46 MCI subjects confirmed that it captures the intermediary character of the MCI group. GNNExplainer module employed to investigate the anatomical determinants of the observed discrepancy confirms that sfDLN attends to cortical regions neurologically relevant to ADD. CONCLUSION In support of our hypothesis, the harmony between the structural and functional organization of the brain degrades with increasing cognitive decline. This discrepancy, shown to be rooted in brain regions neurologically relevant to ADD, can be quantified by sfDLN and outperforms state-of-the-art GNN-based ADD classification methods when used as a biomarker.
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Affiliation(s)
- Gurur Gamgam
- VAVlab, Department of Electrical And Electronics Eng., Bogazici University, Istanbul, 34342, Turkiye
| | - Zerrin Yıldırım
- Department of Neuroscience, Aziz Sancar Institute of Experimental Medicine, Istanbul University, Istanbul, 34093, Turkiye
| | | | - Hakan Gurvit
- Department of Neurology, Istanbul Faculty of Medicine, Istanbul University, Istanbul, 34093, Turkiye
| | - Tamer Demiralp
- Hulusi Behçet Life Sciences Research Lab., Istanbul University, Istanbul, 34093, Turkiye
| | - Burak Acar
- VAVlab, Department of Electrical And Electronics Eng., Bogazici University, Istanbul, 34342, Turkiye.
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Zheng C, Zhao W, Yang Z, Guo S. Functional connectome hierarchy dysfunction in Alzheimer's disease and its relationship with cognition and gene expression profiling. J Neurosci Res 2024; 102:e25280. [PMID: 38284860 DOI: 10.1002/jnr.25280] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2023] [Revised: 10/21/2023] [Accepted: 11/16/2023] [Indexed: 01/30/2024]
Abstract
Numerous researches have shown that the human brain organizes as a continuum axis crossing from sensory motor to transmodal cortex. Functional network alterations were commonly found in Alzheimer's disease (AD). Whether the hierarchy of AD brain networks has changed and how these changes related to gene expression profiling and cognition is unclear. Using resting-state functional magnetic resonance imaging data from 233 subjects (185 AD patients and 48 healthy controls), we studied the changes in the functional network gradients in AD. Moreover, we investigated the relationships between gradient alterations and cognition, and gene expression profiling, respectively. We found that the second gradient organizes as a continuum axis crossing from the sensory motor to the transmodal cortex. Compared to the healthy controls, the secondary gradient scores of the visual and somatomotor network (SOM) increased significantly in AD, and the secondary gradient scores of default mode and frontoparietal network decreased significantly in AD. The secondary gradient scores of SOM and salience network (SAL) significantly positively correlated with memory function in AD. The secondary gradient in SAL also significantly positively correlated with language function. The AD-related second gradient alterations were spatially associated with the gene expression and the relevant genes enriched in neurobiology-related pathways, specially expressed in various tissues, cell types, and developmental stages. These findings suggested the changes in the functional network gradients in AD and deepened our understanding of the correlation between macroscopic gradient structure and microscopic gene expression profiling in AD.
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Affiliation(s)
- Chuchu Zheng
- School of Mathematics and Statistics, Hunan Normal University, Changsha, China
- Key Laboratory of Applied Statistics and Data Science, Hunan Normal University, College of Hunan Province, Changsha, China
| | - Wei Zhao
- School of Mathematics and Statistics, Hunan Normal University, Changsha, China
- Key Laboratory of Applied Statistics and Data Science, Hunan Normal University, College of Hunan Province, Changsha, China
| | - Zeyu Yang
- School of Mathematics and Statistics, Hunan Normal University, Changsha, China
- Key Laboratory of Applied Statistics and Data Science, Hunan Normal University, College of Hunan Province, Changsha, China
| | - Shuixia Guo
- School of Mathematics and Statistics, Hunan Normal University, Changsha, China
- Key Laboratory of Applied Statistics and Data Science, Hunan Normal University, College of Hunan Province, Changsha, China
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Yi C, Fan Y, Wu Y. Cross-module switching diversity of brain network nodes in resting and cognitive states. Cogn Neurodyn 2023; 17:1485-1499. [PMID: 37974588 PMCID: PMC10640499 DOI: 10.1007/s11571-022-09894-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2021] [Revised: 07/28/2022] [Accepted: 09/21/2022] [Indexed: 11/03/2022] Open
Abstract
Large-scale brain network dynamics reflect state change in brain activities and have potential effects on cognition. Such dynamics can be described by node temporal switching between modules; however, there are only a few studies on the influence of brain network node switching on brain cognition. Based on the functional magnetic resonance imaging (fMRI) data of resting and task states, we constructed dynamic functional networks using overlap sliding-time windows and applied multilayer network analysis to study the behaviours of nodes across brain modules. We found that (i) nodes with a high level switching rate in the resting-state mainly come from the default network, while nodes with a low level of switching rate mainly come from the visual network, (ii) nodes with a high switching rate have lower clustering coefficients and shorter characteristic path lengths, which are mainly affected by the somatomotor network and dorsal attention network; and (iii) in task states, there is still a negative correlation between switching rate, clustering coefficient and characteristic path length. However, the main subsystems that affect brain functions are regulated by the tasks. Our findings not only reveal the relevant characteristics of network node switching behaviours but also provide new insights for further understanding the complex functions of the brain. Supplementary Information The online version contains supplementary material available at 10.1007/s11571-022-09894-z.
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Affiliation(s)
- Chao Yi
- State Key Laboratory for Strength and Vibration of Mechanical Structures and School of Aerospace Engineering, Xi’an Jiaotong University, Xi’an, 710049 China
| | - Yongchen Fan
- State Key Laboratory for Strength and Vibration of Mechanical Structures and School of Aerospace Engineering, Xi’an Jiaotong University, Xi’an, 710049 China
| | - Ying Wu
- State Key Laboratory for Strength and Vibration of Mechanical Structures and School of Aerospace Engineering, Xi’an Jiaotong University, Xi’an, 710049 China
- National Demonstration Center for Experimental Mechanics Education, Xi’an Jiaotong University, Xi’an, 710049 China
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4
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Li Y, An S, Zhou T, Su C, Zhang S, Li C, Jiang J, Mu Y, Yao N, Huang ZG. Triple-network analysis of Alzheimer's disease based on the energy landscape. Front Neurosci 2023; 17:1171549. [PMID: 37287802 PMCID: PMC10242117 DOI: 10.3389/fnins.2023.1171549] [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: 02/22/2023] [Accepted: 04/13/2023] [Indexed: 06/09/2023] Open
Abstract
Introduction Research on the brain activity during resting state has found that brain activation is centered around three networks, including the default mode network (DMN), the salient network (SN), and the central executive network (CEN), and switches between multiple modes. As a common disease in the elderly, Alzheimer's disease (AD) affects the state transitions of functional networks in the resting state. Methods Energy landscape, as a new method, can intuitively and quickly grasp the statistical distribution of system states and information related to state transition mechanisms. Therefore, this study mainly uses the energy landscape method to study the changes of the triple-network brain dynamics in AD patients in the resting state. Results AD brain activity patterns are in an abnormal state, and the dynamics of patients with AD tend to be unstable, with an unusually high flexibility in switching between states. Also , the subjects' dynamic features are correlated with clinical index. Discussion The atypical balance of large-scale brain systems in patients with AD is associated with abnormally active brain dynamics. Our study are helpful for further understanding the intrinsic dynamic characteristics and pathological mechanism of the resting-state brain in AD patients.
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Affiliation(s)
- Youjun Li
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Institute of Health and Rehabilitation Science, School of Life Science and Technology, Xi'an Jiaotong University, The Key Laboratory of Neuro-informatics and Rehabilitation Engineering of Ministry of Civil Affairs, Xi'an, Shaanxi, China
- Research Center for Brain-inspired Intelligence, Xi'an Jiaotong University, Xi'an, Shaanxi, China
| | - Simeng An
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Institute of Health and Rehabilitation Science, School of Life Science and Technology, Xi'an Jiaotong University, The Key Laboratory of Neuro-informatics and Rehabilitation Engineering of Ministry of Civil Affairs, Xi'an, Shaanxi, China
- Research Center for Brain-inspired Intelligence, Xi'an Jiaotong University, Xi'an, Shaanxi, China
| | - Tianlin Zhou
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Institute of Health and Rehabilitation Science, School of Life Science and Technology, Xi'an Jiaotong University, The Key Laboratory of Neuro-informatics and Rehabilitation Engineering of Ministry of Civil Affairs, Xi'an, Shaanxi, China
- Research Center for Brain-inspired Intelligence, Xi'an Jiaotong University, Xi'an, Shaanxi, China
| | - Chunwang Su
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Institute of Health and Rehabilitation Science, School of Life Science and Technology, Xi'an Jiaotong University, The Key Laboratory of Neuro-informatics and Rehabilitation Engineering of Ministry of Civil Affairs, Xi'an, Shaanxi, China
- Research Center for Brain-inspired Intelligence, Xi'an Jiaotong University, Xi'an, Shaanxi, China
| | - Siping Zhang
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Institute of Health and Rehabilitation Science, School of Life Science and Technology, Xi'an Jiaotong University, The Key Laboratory of Neuro-informatics and Rehabilitation Engineering of Ministry of Civil Affairs, Xi'an, Shaanxi, China
- Research Center for Brain-inspired Intelligence, Xi'an Jiaotong University, Xi'an, Shaanxi, China
| | - Chenxi Li
- Department of Military Medical Psychology, Air Force Medical University, Xi'an, Shaanxi, China
| | - Junjie Jiang
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Institute of Health and Rehabilitation Science, School of Life Science and Technology, Xi'an Jiaotong University, The Key Laboratory of Neuro-informatics and Rehabilitation Engineering of Ministry of Civil Affairs, Xi'an, Shaanxi, China
- Research Center for Brain-inspired Intelligence, Xi'an Jiaotong University, Xi'an, Shaanxi, China
| | - Yunfeng Mu
- Department of Gynecological Oncology, Shaanxi Provincial Cancer Hospital, Xi'an, China
| | - Nan Yao
- Research Center for Brain-inspired Intelligence, Xi'an Jiaotong University, Xi'an, Shaanxi, China
- Department of Applied Physics, Xi'an University of Technology, Xi'an, China
| | - Zi-Gang Huang
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Institute of Health and Rehabilitation Science, School of Life Science and Technology, Xi'an Jiaotong University, The Key Laboratory of Neuro-informatics and Rehabilitation Engineering of Ministry of Civil Affairs, Xi'an, Shaanxi, China
- Research Center for Brain-inspired Intelligence, Xi'an Jiaotong University, Xi'an, Shaanxi, China
- The State Key Laboratory of Congnitive Neuroscience and Learning, Beijing Normal University, Beijing, China
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Yang M, Chen B, Zhou H, Mai N, Zhang M, Wu Z, Peng Q, Wang Q, Liu M, Zhang S, Lin G, Lao J, Zeng Y, Zhong X, Ning Y. Relationships Among Short Self-Reported Sleep Duration, Cognitive Impairment, and Insular Functional Connectivity in Late-Life Depression. J Alzheimers Dis 2023:JAD220968. [PMID: 37182865 DOI: 10.3233/jad-220968] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/16/2023]
Abstract
BACKGROUND Both late-life depression (LLD) and short sleep duration increase the risk of cognitive impairment. Increased insular resting-state functional connectivity (FC) has been reported in individuals with short sleep duration and dementia. OBJECTIVE This study aimed to investigate whether short sleep duration is associated with impaired cognition and higher insular FC in patients with LLD. METHODS This case- control study recruited 186 patients with LLD and 83 normal controls (NC), and comprehensive psychometric assessments, sleep duration reports and resting-state functional MRI scans (81 LLD patients and 54 NC) were conducted. RESULTS Patients with LLD and short sleep duration (LLD-SS patients) exhibited more severe depressive symptoms and worse cognitive function than those with normal sleep duration (LLD-NS patients) and NC. LLD-SS patients exhibited higher FC between the bilateral insula and inferior frontal gyrus (IFG) pars triangularis than LLD-NS patients and NC, while LLD-NS patients exhibited lower FC than NC. Increased insular FC was correlated with short sleep duration, severe depressive symptoms, and slower information processing speeds. Furthermore, an additive effect was found between sleep duration and LLD on global cognition and insular FC. CONCLUSION LLD-SS patients exhibited impaired cognition and increased insular FC. Abnormal FC in LLD-SS patients may be a therapeutic target for neuromodulation to improve sleep and cognitive performance and thus decrease the risk of dementia.
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Affiliation(s)
- Mingfeng Yang
- Department of Geriatric Psychiatry, The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, Guangdong Province, China
- The first School of Clinical Medicine, Southern Medical University, Guangzhou, Guangdong Province, China
| | - Ben Chen
- Department of Geriatric Psychiatry, The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, Guangdong Province, China
| | - Huarong Zhou
- Department of Geriatric Psychiatry, The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, Guangdong Province, China
| | - Naikeng Mai
- Department of Neurology, The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, Guangdong Province, China
| | - Min Zhang
- Department of Geriatric Psychiatry, The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, Guangdong Province, China
| | - Zhangying Wu
- Department of Geriatric Psychiatry, The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, Guangdong Province, China
| | - Qi Peng
- Department of Geriatric Psychiatry, The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, Guangdong Province, China
| | - Qiang Wang
- Department of Geriatric Psychiatry, The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, Guangdong Province, China
| | - Meiling Liu
- Department of Geriatric Psychiatry, The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, Guangdong Province, China
| | - Si Zhang
- Department of Geriatric Psychiatry, The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, Guangdong Province, China
| | - Gaohong Lin
- Department of Geriatric Psychiatry, The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, Guangdong Province, China
| | - Jingyi Lao
- Department of Geriatric Psychiatry, The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, Guangdong Province, China
| | - Yijie Zeng
- Department of Geriatric Psychiatry, The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, Guangdong Province, China
| | - Xiaomei Zhong
- Department of Geriatric Psychiatry, The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, Guangdong Province, China
| | - Yuping Ning
- Department of Geriatric Psychiatry, The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, Guangdong Province, China
- The first School of Clinical Medicine, Southern Medical University, Guangzhou, Guangdong Province, China
- Guangdong Engineering Technology Research Center for Translational Medicine of Mental Disorders, Guangzhou, China
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6
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Lopez S, Del Percio C, Lizio R, Noce G, Padovani A, Nobili F, Arnaldi D, Famà F, Moretti DV, Cagnin A, Koch G, Benussi A, Onofrj M, Borroni B, Soricelli A, Ferri R, Buttinelli C, Giubilei F, Güntekin B, Yener G, Stocchi F, Vacca L, Bonanni L, Babiloni C. Patients with Alzheimer's disease dementia show partially preserved parietal 'hubs' modeled from resting-state alpha electroencephalographic rhythms. Front Aging Neurosci 2023; 15:780014. [PMID: 36776437 PMCID: PMC9908964 DOI: 10.3389/fnagi.2023.780014] [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: 09/20/2021] [Accepted: 01/05/2023] [Indexed: 01/28/2023] Open
Abstract
Introduction Graph theory models a network by its nodes (the fundamental unit by which graphs are formed) and connections. 'Degree' hubs reflect node centrality (the connection rate), while 'connector' hubs are those linked to several clusters of nodes (mainly long-range connections). Methods Here, we compared hubs modeled from measures of interdependencies of between-electrode resting-state eyes-closed electroencephalography (rsEEG) rhythms in normal elderly (Nold) and Alzheimer's disease dementia (ADD) participants. At least 5 min of rsEEG was recorded and analyzed. As ADD is considered a 'network disease' and is typically associated with abnormal rsEEG delta (<4 Hz) and alpha rhythms (8-12 Hz) over associative posterior areas, we tested the hypothesis of abnormal posterior hubs from measures of interdependencies of rsEEG rhythms from delta to gamma bands (2-40 Hz) using eLORETA bivariate and multivariate-directional techniques in ADD participants versus Nold participants. Three different definitions of 'connector' hub were used. Results Convergent results showed that in both the Nold and ADD groups there were significant parietal 'degree' and 'connector' hubs derived from alpha rhythms. These hubs had a prominent outward 'directionality' in the two groups, but that 'directionality' was lower in ADD participants than in Nold participants. Discussion In conclusion, independent methodologies and hub definitions suggest that ADD patients may be characterized by low outward 'directionality' of partially preserved parietal 'degree' and 'connector' hubs derived from rsEEG alpha rhythms.
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Affiliation(s)
- Susanna Lopez
- Department of Physiology and Pharmacology “Vittorio Erspamer”, Sapienza University of Rome, Rome, Italy
| | - Claudio Del Percio
- Department of Physiology and Pharmacology “Vittorio Erspamer”, Sapienza University of Rome, Rome, Italy
| | - Roberta Lizio
- Department of Physiology and Pharmacology “Vittorio Erspamer”, Sapienza University of Rome, Rome, Italy
| | | | - Alessandro Padovani
- Neurology Unit, Department of Clinical and Experimental Sciences, University of Brescia, Brescia, Italy
| | - Flavio Nobili
- Clinica Neurologica, IRCCS Ospedale Policlinico San Martino, Genova, Italy
- Dipartimento di Neuroscienze, Oftalmologia, Genetica, Riabilitazione e Scienze Materno-infantili (DiNOGMI), Università di Genova, Genova, Italy
| | - Dario Arnaldi
- Clinica Neurologica, IRCCS Ospedale Policlinico San Martino, Genova, Italy
- Dipartimento di Neuroscienze, Oftalmologia, Genetica, Riabilitazione e Scienze Materno-infantili (DiNOGMI), Università di Genova, Genova, Italy
| | - Francesco Famà
- Dipartimento di Neuroscienze, Oftalmologia, Genetica, Riabilitazione e Scienze Materno-infantili (DiNOGMI), Università di Genova, Genova, Italy
| | - Davide V. Moretti
- Alzheimer’s Disease Rehabilitation Unit, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | | | - Giacomo Koch
- Non-Invasive Brain Stimulation Unit/Department of Behavioral and Clinical Neurology, Santa Lucia Foundation IRCCS, Rome, Italy
- Stroke Unit, Department of Neuroscience, Tor Vergata Policlinic, Rome, Italy
| | - Alberto Benussi
- Neurology Unit, Department of Clinical and Experimental Sciences, University of Brescia, Brescia, Italy
| | - Marco Onofrj
- Department of Neuroscience Imaging and Clinical Sciences and CESI, University “G. D’Annunzio” of Chieti-Pescara, Chieti, Italy
| | - Barbara Borroni
- Neurology Unit, Department of Clinical and Experimental Sciences, University of Brescia, Brescia, Italy
| | - Andrea Soricelli
- IRCCS Synlab SDN, Naples, Italy
- Department of Motor Sciences and Healthiness, University of Naples Parthenope, Naples, Italy
| | | | - Carla Buttinelli
- Department of Neuroscience, Mental Health and Sensory Organs, Sapienza University of Rome, Rome, Italy
| | - Franco Giubilei
- Department of Neuroscience, Mental Health and Sensory Organs, Sapienza University of Rome, Rome, Italy
| | - Bahar Güntekin
- Department of Biophysics, School of Medicine, Istanbul Medipol University, Istanbul, Türkiye
- Research Institute for Health Sciences and Technologies (SABITA), Istanbul Medipol University, Istanbul, Türkiye
| | - Görsev Yener
- Department of Neurology, Dokuz Eylül University Medical School, Izmir, Türkiye
- Faculty of Medicine, Izmir University of Economics, Izmir, Türkiye
| | - Fabrizio Stocchi
- Institute for Research and Medical Care, IRCCS San Raffaele Roma, Rome, Italy
- Telematic University San Raffaele, Rome, Italy
| | - Laura Vacca
- Institute for Research and Medical Care, IRCCS San Raffaele Roma, Rome, Italy
| | - Laura Bonanni
- Department of Medicine and Aging Sciences, University G. D’Annunzio of Chieti-Pescara, Chieti, Italy
| | - Claudio Babiloni
- Department of Physiology and Pharmacology “Vittorio Erspamer”, Sapienza University of Rome, Rome, Italy
- San Raffaele of Cassino, Cassino, Italy
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Lau CI, Yeh JH, Tsai YF, Hsiao CY, Wu YT, Jao CW. Decreased Brain Structural Network Connectivity in Patients with Mild Cognitive Impairment: A Novel Fractal Dimension Analysis. Brain Sci 2023; 13:brainsci13010093. [PMID: 36672073 PMCID: PMC9856782 DOI: 10.3390/brainsci13010093] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2022] [Revised: 12/18/2022] [Accepted: 12/30/2022] [Indexed: 01/06/2023] Open
Abstract
Mild cognitive impairment (MCI) is widely regarded to be the intermediate stage to Alzheimer's disease. Cerebral morphological alteration in cortical subregions can provide an accurate predictor for early recognition of MCI. Thirty patients with MCI and thirty healthy control subjects participated in this study. The Desikan-Killiany cortical atlas was applied to segment participants' cerebral cortex into 68 subregions. A complexity measure termed fractal dimension (FD) was applied to assess morphological changes in cortical subregions of participants. The MCI group revealed significantly decreased FD values in the bilateral temporal lobes, right parietal lobe including the medial temporal, fusiform, para hippocampal, and also the orbitofrontal lobes. We further proposed a novel FD-based brain structural network to compare network parameters, including intra- and inter-lobular connectivity between groups. The control group had five modules, and the MCI group had six modules in their brain networks. The MCI group demonstrated shrinkage of modular sizes with fewer components integrated, and significantly decreased global modularity in the brain network. The MCI group had lower intra- and inter-lobular connectivity in all lobes. Between cerebral lobes, the MCI patients may maintain nodal connections between both hemispheres to reduce connectivity loss in the lateral hemispheres. The method and results presented in this study could be a suitable tool for early detection of MCI.
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Affiliation(s)
- Chi Ieong Lau
- Institute of Biophotonics, National Yang Ming Chiao Tung University, Taipei 112, Taiwan
- School of Medicine, College of Medicine, Fu Jen Catholic University, New Taipei 242, Taiwan
- Dementia Center, Department of Neurology, Shin Kong Wu Ho-Su Memorial Hospital, Taipei 111, Taiwan
- Applied Cognitive Neuroscience Group, Institute of Cognitive Neuroscience, University College London, London WC1N 3AZ, UK
- Department of Neurology, University Hospital, Taipa 999078, Macau
| | - Jiann-Horng Yeh
- School of Medicine, College of Medicine, Fu Jen Catholic University, New Taipei 242, Taiwan
- Department of Neurology, Shin Kong Wu Ho-Su Memorial Hospital, Taipei 111, Taiwan
| | - Yuh-Feng Tsai
- School of Medicine, College of Medicine, Fu Jen Catholic University, New Taipei 242, Taiwan
- Department of Diagnostic Radiology, Shin Kong Wu Ho Su Memorial Hospital, Taipei 111, Taiwan
| | - Chen-Yu Hsiao
- Department of Diagnostic Radiology, Shin Kong Wu Ho Su Memorial Hospital, Taipei 111, Taiwan
| | - Yu-Te Wu
- Institute of Biophotonics, National Yang Ming Chiao Tung University, Taipei 112, Taiwan
- Brain Research Center, National Yang Ming Chiao Tung University, Taipei 112, Taiwan
- Correspondence: (Y.-T.W.); (C.-W.J.); Tel.: +886-02-28267169 (Y.-T.W.); +886-02-28267394 (C.-W.J.)
| | - Chi-Wen Jao
- Institute of Biophotonics, National Yang Ming Chiao Tung University, Taipei 112, Taiwan
- Department of Research, Shin Kong Wu Ho-Su Memorial Hospital, Taipei 111, Taiwan
- Correspondence: (Y.-T.W.); (C.-W.J.); Tel.: +886-02-28267169 (Y.-T.W.); +886-02-28267394 (C.-W.J.)
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Altered structural and functional homotopic connectivity associated with the progression from mild cognitive impairment to Alzheimer's disease. Psychiatry Res 2023; 319:115000. [PMID: 36502711 DOI: 10.1016/j.psychres.2022.115000] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/09/2022] [Revised: 11/28/2022] [Accepted: 12/04/2022] [Indexed: 12/12/2022]
Abstract
The progressive mild cognitive impairment (pMCI) is associated with an increased risk of Alzheimer's disease (AD). Many studies have reported the disrupted brain alteration during the imminent conversion from pMCI to AD. However, the subtle difference of structural and functional of inter-hemispheric between pMCI and stable mild cognitive impairment (sMCI) remains unknown. In the present study, we scanned the multimodal magnetic resonance imaging of 38 sMCI, 26 pMCI, and 50 healthy controls (HC) and assessed the cognitive function. The voxel-mirrored homotopic connectivity (VMHC) and volume of corpus callosum were calculated. A structural equation modeling (SEM) was established to determine the relationships between the corpus callosum, the inter-hemispheric connectivity, and cognitive assessment. Compared to sMCI, pMCI exhibited decreased VMHC in insular and thalamus, and reduced volume of corpus callosum. SEM results showed that decreased inter-hemispheric connectivity was directly associated with cognitive impairment and corpus callosum atrophy, and corpus callosum atrophy indirectly caused cognitive impairment by mediating inter-hemispheric connectivity in pMCI. In conclusion, the destruction of homotopic connectivity is related to cognitive impairment, and the corpus callosum atrophy partially mediates the association between the homotopic connectivity and cognitive impairment in pMCI.
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Wei X, Du X, Xie Y, Suo X, He X, Ding H, Zhang Y, Ji Y, Chai C, Liang M, Yu C, Liu Y, Qin W. Mapping cerebral atrophic trajectory from amnestic mild cognitive impairment to Alzheimer's disease. Cereb Cortex 2022; 33:1310-1327. [PMID: 35368064 PMCID: PMC9930625 DOI: 10.1093/cercor/bhac137] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2021] [Revised: 02/13/2022] [Accepted: 03/13/2022] [Indexed: 11/14/2022] Open
Abstract
Alzheimer's disease (AD) patients suffer progressive cerebral atrophy before dementia onset. However, the region-specific atrophic processes and the influences of age and apolipoprotein E (APOE) on atrophic trajectory are still unclear. By mapping the region-specific nonlinear atrophic trajectory of whole cerebrum from amnestic mild cognitive impairment (aMCI) to AD based on longitudinal structural magnetic resonance imaging data from Alzheimer's disease Neuroimaging Initiative (ADNI) database, we unraveled a quadratic accelerated atrophic trajectory of 68 cerebral regions from aMCI to AD, especially in the superior temporal pole, caudate, and hippocampus. Besides, interaction analyses demonstrated that APOE ε4 carriers had faster atrophic rates than noncarriers in 8 regions, including the caudate, hippocampus, insula, etc.; younger patients progressed faster than older patients in 32 regions, especially for the superior temporal pole, hippocampus, and superior temporal gyrus; and 15 regions demonstrated complex interaction among age, APOE, and disease progression, including the caudate, hippocampus, etc. (P < 0.05/68, Bonferroni correction). Finally, Cox proportional hazards regression model based on the identified region-specific biomarkers could effectively predict the time to AD conversion within 10 years. In summary, cerebral atrophic trajectory mapping could help a comprehensive understanding of AD development and offer potential biomarkers for predicting AD conversion.
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Affiliation(s)
| | | | | | | | - Xiaoxi He
- Department of Radiology, Tianjin Medical University General Hospital, Tianjin 300052, China,Tianjin Key Lab of Functional Imaging, Tianjin Medical University General Hospital, Tianjin 300052, China
| | - Hao Ding
- Department of Radiology, Tianjin Medical University General Hospital, Tianjin 300052, China,Tianjin Key Lab of Functional Imaging, Tianjin Medical University General Hospital, Tianjin 300052, China,School of Medical Imaging, Tianjin Medical University, Tianjin 300070, China
| | - Yu Zhang
- Department of Radiology, Tianjin Medical University General Hospital, Tianjin 300052, China,Tianjin Key Lab of Functional Imaging, Tianjin Medical University General Hospital, Tianjin 300052, China
| | - Yi Ji
- Department of Radiology, Tianjin Medical University General Hospital, Tianjin 300052, China,Tianjin Key Lab of Functional Imaging, Tianjin Medical University General Hospital, Tianjin 300052, China
| | - Chao Chai
- Department of Radiology, Tianjin Medical University General Hospital, Tianjin 300052, China,Tianjin Key Lab of Functional Imaging, Tianjin Medical University General Hospital, Tianjin 300052, China
| | - Meng Liang
- Department of Radiology, Tianjin Medical University General Hospital, Tianjin 300052, China,Tianjin Key Lab of Functional Imaging, Tianjin Medical University General Hospital, Tianjin 300052, China,School of Medical Imaging, Tianjin Medical University, Tianjin 300070, China
| | - Chunshui Yu
- Department of Radiology, Tianjin Medical University General Hospital, Tianjin 300052, China,Tianjin Key Lab of Functional Imaging, Tianjin Medical University General Hospital, Tianjin 300052, China,School of Medical Imaging, Tianjin Medical University, Tianjin 300070, China
| | - Yong Liu
- Corresponding author: Wen Qin, Department of Radiology, and Tianjin Key Lab of Functional Imaging, Tianjin Medical University General Hospital, Anshan Road No 154, Heping District, Tianjin 300052, China. ; Yong Liu, School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China.
| | - Wen Qin
- Corresponding author: Wen Qin, Department of Radiology, and Tianjin Key Lab of Functional Imaging, Tianjin Medical University General Hospital, Anshan Road No 154, Heping District, Tianjin 300052, China. ; Yong Liu, School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China.
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10
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Cho NS, Peck KK, Gene MN, Jenabi M, Holodny AI. Resting-state functional MRI language network connectivity differences in patients with brain tumors: exploration of the cerebellum and contralesional hemisphere. Brain Imaging Behav 2022; 16:252-262. [PMID: 34333725 DOI: 10.1007/s11682-021-00498-5] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/05/2021] [Indexed: 01/19/2023]
Abstract
Brain tumors can have far-reaching impacts on functional networks. Language processing is typically lateralized to the left hemisphere, but also involves the right hemisphere and cerebellum. This resting-state functional MRI study investigated the proximal and distal effects of left-hemispheric brain tumors on language network connectivity in the ipsilesional and contralesional hemispheres. Separate language resting-state networks were generated from seeding in ipsilesional (left) and contralesional (right) Broca's Area for 29 patients with left-hemispheric brain tumors and 13 controls. Inclusion criteria for all subjects included language left-dominance based on task-based functional MRI. Functional connectivity was analyzed in each network to the respective Wernicke's Area and contralateral cerebellum. Patients were assessed for language deficits prior to scanning. Compared to controls, patients exhibited decreased connectivity in the ipsilesional and contralesional hemispheres between the Broca's Area and Wernicke's Area homologs (mean connectivity for patients/controls: left 0.51/0.59, p < 0.002; right 0.52/0.59, p < 0.0002). No differences in mean connectivity to the contralateral cerebellum were observed between groups (p > 0.09). Crossed cerebro-cerebellar connectivity was correlated in controls (rho = 0.59, p < 0.05), patients without language deficits (rho = 0.74, p < 0.0002), and patients with high-grade gliomas (rho = 0.78, p < 0.0002), but not in patients with language deficits or low-grade gliomas (p > 0.l). These findings demonstrate that brain tumors impact the language network in the contralesional hemisphere and cerebellum, which may reflect neurological deficits and lesion-induced cortical reorganization.
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Affiliation(s)
- Nicholas S Cho
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA
- Medical Scientist Training Program, David Geffen UCLA School of Medicine, Los Angeles, CA, 90095, USA
| | - Kyung K Peck
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA.
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, 1275 York Avenue, New York, NY, 10065, USA.
| | - Madeleine N Gene
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA
| | - Mehrnaz Jenabi
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA
| | - Andrei I Holodny
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA
- Department of Radiology, Weill Medical College of Cornell University, New York, NY, 10065, USA
- Department of Neuroscience, Weill-Cornell Graduate School of the Medical Sciences, New York, NY, 10065, USA
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11
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Shu P, Zhu H, Jin W, Zhou J, Tong S, Sun J. The Resilience and Vulnerability of Human Brain Networks Across the Lifespan. IEEE Trans Neural Syst Rehabil Eng 2021; 29:1756-1765. [PMID: 34410925 DOI: 10.1109/tnsre.2021.3105991] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Resilience, the ability for a system to maintain its basic functionality when suffering from lesions, is a critical property for human brain, especially in the brain aging process. This study adopted a novel metric of network resilience, the Resilience Index (RI), to assess human brain resilience with three different lifespan datasets. Based on the structural brain networks constructed from diffusion tensor imaging (DTI), we observed an inverted-U relationship between RI and age, that is, RI increased during development and early adulthood, reached a peak at about 35 years old, and then decreased during aging, which suggested that brain resilience could be quantified by RI. Furthermore, we studied brain network vulnerability by the decreases in RI when virtual lesions occurred to nodes (i.e., brain regions) or edges (i.e., structural brain connectivity). We found that the strong edges were markedly vulnerable, and the homotopic edges were the most prominent representatives of vulnerable edges. In other words, an arbitrary attack on homotopic edges would have a high probability to degrade brain network resilience. These findings suggest the change of human brain resilience across the lifespan and provide a new perspective for exploring human brain vulnerability.
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12
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Huang Q, Ren S, Zhang T, Li J, Jiang D, Xiao J, Hua F, Xie F, Guan Y. Aging-Related Modular Architectural Reorganization of the Metabolic Brain Network. Brain Connect 2021; 12:432-442. [PMID: 34210172 DOI: 10.1089/brain.2021.0054] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/29/2023] Open
Abstract
Background: Modules in brain network represent groups of brain regions that are collectively involved in one or more cognitive domains. Exploring aging-related reorganization of the brain modular architecture using metabolic brain network could further our understanding about aging-related neuromechanism and neurodegenerations. Materials and Methods: In this study, 432 subjects who performed 18F-fluorodeoxyglucose (FDG) positron emission tomography (PET) were enrolled and divided into young and old adult groups, as well as female and male groups. The modular architecture was detected, and the connector and hub nodes were identified to explore the topological role of the brain regions based on the metabolic brain network. Results: This study revealed that human metabolic brain network was modular and could be clustered into three modules. The modular architecture was reorganized from young to old ages with regions related to sensorimotor function clustered into the same module; and the number of connector nodes was reduced and most connector nodes were localized in temporo-occipital areas related to visual and auditory functions in old ages. The major gender difference is that the metabolic brain network was delineated into four modules in old female group with the nodes related to sensorimotor function split into two modules. Discussion: Those findings suggest aging is associated with reorganized brain modular architecture. Clinical Trial Registration number: ChiCTR2000036842.
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Affiliation(s)
- Qi Huang
- PET Center, Huashan Hospital, Fudan University, Shanghai, China
| | - Shuhua Ren
- PET Center, Huashan Hospital, Fudan University, Shanghai, China
| | - Tianhao Zhang
- Beijing Engineering Research Center of Radiographic Techniques and Equipment, Institute of High Energy Physics, Chinese Academy of Sciences, Beijing, China
| | - Junpeng Li
- PET Center, Huashan Hospital, Fudan University, Shanghai, China
| | - Donglang Jiang
- PET Center, Huashan Hospital, Fudan University, Shanghai, China
| | - Jianfei Xiao
- PET Center, Huashan Hospital, Fudan University, Shanghai, China
| | - Fengchun Hua
- PET Center, Huashan Hospital, Fudan University, Shanghai, China
| | - Fang Xie
- PET Center, Huashan Hospital, Fudan University, Shanghai, China
| | - Yihui Guan
- PET Center, Huashan Hospital, Fudan University, Shanghai, China
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13
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Zhang L, Wang L, Gao J, Risacher SL, Yan J, Li G, Liu T, Zhu D. Deep Fusion of Brain Structure-Function in Mild Cognitive Impairment. Med Image Anal 2021; 72:102082. [PMID: 34004495 DOI: 10.1016/j.media.2021.102082] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2020] [Revised: 03/20/2021] [Accepted: 04/13/2021] [Indexed: 01/22/2023]
Abstract
Multimodal fusion of different types of neural image data provides an irreplaceable opportunity to take advantages of complementary cross-modal information that may only partially be contained in single modality. To jointly analyze multimodal data, deep neural networks can be especially useful because many studies have suggested that deep learning strategy is very efficient to reveal complex and non-linear relations buried in the data. However, most deep models, e.g., convolutional neural network and its numerous extensions, can only operate on regular Euclidean data like voxels in 3D MRI. The interrelated and hidden structures that beyond the grid neighbors, such as brain connectivity, may be overlooked. Moreover, how to effectively incorporate neuroscience knowledge into multimodal data fusion with a single deep framework is understudied. In this work, we developed a graph-based deep neural network to simultaneously model brain structure and function in Mild Cognitive Impairment (MCI): the topology of the graph is initialized using structural network (from diffusion MRI) and iteratively updated by incorporating functional information (from functional MRI) to maximize the capability of differentiating MCI patients from elderly normal controls. This resulted in a new connectome by exploring "deep relations" between brain structure and function in MCI patients and we named it as Deep Brain Connectome. Though deep brain connectome is learned individually, it shows consistent patterns of alteration comparing to structural network at group level. With deep brain connectome, our developed deep model can achieve 92.7% classification accuracy on ADNI dataset.
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Affiliation(s)
- Lu Zhang
- Department of Computer Science and Engineering, The University of Texas at Arlington, Arlington, TX 76019 USA
| | - Li Wang
- Department of Computer Science and Engineering, The University of Texas at Arlington, Arlington, TX 76019 USA; Department of Mathematics, The University of Texas at Arlington, Arlington, TX 76019 USA
| | - Jean Gao
- Department of Computer Science and Engineering, The University of Texas at Arlington, Arlington, TX 76019 USA
| | - Shannon L Risacher
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN 46202 USA
| | - Jingwen Yan
- School of Informatics and Computing, Indiana University School of Medicine, Indianapolis, IN 46202 USA
| | - Gang Li
- Biomedical Research Imaging Center and Department of Radiology, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599-7160, USA
| | - Tianming Liu
- Cortical Architecture Imaging and Discovery Lab, Department of Computer Science and Bioimaging Research Center, The University of Georgia, Athens, GA, USA
| | - Dajiang Zhu
- Department of Computer Science and Engineering, The University of Texas at Arlington, Arlington, TX 76019 USA.
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14
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Park HJ, Kang J. A Computational Framework for Controlling the Self-Restorative Brain Based on the Free Energy and Degeneracy Principles. Front Comput Neurosci 2021; 15:590019. [PMID: 33935674 PMCID: PMC8079648 DOI: 10.3389/fncom.2021.590019] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2020] [Accepted: 03/18/2021] [Indexed: 11/13/2022] Open
Abstract
The brain is a non-linear dynamical system with a self-restoration process, which protects itself from external damage but is often a bottleneck for clinical treatment. To treat the brain to induce the desired functionality, formulation of a self-restoration process is necessary for optimal brain control. This study proposes a computational model for the brain's self-restoration process following the free-energy and degeneracy principles. Based on this model, a computational framework for brain control is established. We posited that the pre-treatment brain circuit has long been configured in response to the environmental (the other neural populations') demands on the circuit. Since the demands persist even after treatment, the treated circuit's response to the demand may gradually approximate the pre-treatment functionality. In this framework, an energy landscape of regional activities, estimated from resting-state endogenous activities by a pairwise maximum entropy model, is used to represent the pre-treatment functionality. The approximation of the pre-treatment functionality occurs via reconfiguration of interactions among neural populations within the treated circuit. To establish the current framework's construct validity, we conducted various simulations. The simulations suggested that brain control should include the self-restoration process, without which the treatment was not optimal. We also presented simulations for optimizing repetitive treatments and optimal timing of the treatment. These results suggest a plausibility of the current framework in controlling the non-linear dynamical brain with a self-restoration process.
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Affiliation(s)
- Hae-Jeong Park
- Center for Systems and Translational Brain Science, Institute of Human Complexity and Systems Science, Yonsei University, Seoul, South Korea.,Department of Nuclear Medicine, Yonsei University College of Medicine, Seoul, South Korea.,Department of Psychiatry, Yonsei University College of Medicine, Seoul, South Korea.,Brain Korea 21 Project, Graduate School of Medical Science, Yonsei University College of Medicine, Seoul, South Korea.,Department of Cognitive Science, Yonsei University, Seoul, South Korea
| | - Jiyoung Kang
- Center for Systems and Translational Brain Science, Institute of Human Complexity and Systems Science, Yonsei University, Seoul, South Korea.,Department of Nuclear Medicine, Yonsei University College of Medicine, Seoul, South Korea
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15
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Stefanovski L, Meier JM, Pai RK, Triebkorn P, Lett T, Martin L, Bülau K, Hofmann-Apitius M, Solodkin A, McIntosh AR, Ritter P. Bridging Scales in Alzheimer's Disease: Biological Framework for Brain Simulation With The Virtual Brain. Front Neuroinform 2021; 15:630172. [PMID: 33867964 PMCID: PMC8047422 DOI: 10.3389/fninf.2021.630172] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2020] [Accepted: 03/08/2021] [Indexed: 12/18/2022] Open
Abstract
Despite the acceleration of knowledge and data accumulation in neuroscience over the last years, the highly prevalent neurodegenerative disease of AD remains a growing problem. Alzheimer's Disease (AD) is the most common cause of dementia and represents the most prevalent neurodegenerative disease. For AD, disease-modifying treatments are presently lacking, and the understanding of disease mechanisms continues to be incomplete. In the present review, we discuss candidate contributing factors leading to AD, and evaluate novel computational brain simulation methods to further disentangle their potential roles. We first present an overview of existing computational models for AD that aim to provide a mechanistic understanding of the disease. Next, we outline the potential to link molecular aspects of neurodegeneration in AD with large-scale brain network modeling using The Virtual Brain (www.thevirtualbrain.org), an open-source, multiscale, whole-brain simulation neuroinformatics platform. Finally, we discuss how this methodological approach may contribute to the understanding, improved diagnostics, and treatment optimization of AD.
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Affiliation(s)
- Leon Stefanovski
- Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Berlin, Germany
- Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Neurology with Experimental Neurology, Brain Simulation Section, Berlin, Germany
| | - Jil Mona Meier
- Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Berlin, Germany
- Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Neurology with Experimental Neurology, Brain Simulation Section, Berlin, Germany
| | - Roopa Kalsank Pai
- Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Berlin, Germany
- Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Neurology with Experimental Neurology, Brain Simulation Section, Berlin, Germany
- Bernstein Center for Computational Neuroscience Berlin, Berlin, Germany
| | - Paul Triebkorn
- Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Berlin, Germany
- Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Neurology with Experimental Neurology, Brain Simulation Section, Berlin, Germany
- Institut de Neurosciences des Systèmes, Aix Marseille Université, Marseille, France
| | - Tristram Lett
- Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Berlin, Germany
- Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Neurology with Experimental Neurology, Brain Simulation Section, Berlin, Germany
| | - Leon Martin
- Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Berlin, Germany
- Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Neurology with Experimental Neurology, Brain Simulation Section, Berlin, Germany
| | - Konstantin Bülau
- Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Berlin, Germany
- Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Neurology with Experimental Neurology, Brain Simulation Section, Berlin, Germany
| | - Martin Hofmann-Apitius
- Fraunhofer Institute for Algorithms and Scientific Computing SCAI, Sankt Augustin, Germany
| | - Ana Solodkin
- Behavioral and Brain Sciences, University of Texas at Dallas, Dallas, TX, United States
| | | | - Petra Ritter
- Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Berlin, Germany
- Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Neurology with Experimental Neurology, Brain Simulation Section, Berlin, Germany
- Bernstein Center for Computational Neuroscience Berlin, Berlin, Germany
- Einstein Center for Neuroscience Berlin, Berlin, Germany
- Einstein Center Digital Future, Berlin, Germany
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16
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Ridwan AR, Niaz MR, Wu Y, Qi X, Zhang S, Kontzialis M, Javierre-Petit C, Tazwar M, Bennett DA, Yang Y, Arfanakis K. Development and evaluation of a high performance T1-weighted brain template for use in studies on older adults. Hum Brain Mapp 2021; 42:1758-1776. [PMID: 33449398 PMCID: PMC7978143 DOI: 10.1002/hbm.25327] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2020] [Revised: 11/16/2020] [Accepted: 12/13/2020] [Indexed: 01/03/2023] Open
Abstract
Τhe accuracy of template-based neuroimaging investigations depends on the template's image quality and representativeness of the individuals under study. Yet a thorough, quantitative investigation of how available standardized and study-specific T1-weighted templates perform in studies on older adults has not been conducted. The purpose of this work was to construct a high-quality standardized T1-weighted template specifically designed for the older adult brain, and systematically compare the new template to several other standardized and study-specific templates in terms of image quality, performance in spatial normalization of older adult data and detection of small inter-group morphometric differences, and representativeness of the older adult brain. The new template was constructed with state-of-the-art spatial normalization of high-quality data from 222 older adults. It was shown that the new template (a) exhibited high image sharpness, (b) provided higher inter-subject spatial normalization accuracy and (c) allowed detection of smaller inter-group morphometric differences compared to other standardized templates, (d) had similar performance to that of study-specific templates constructed with the same methodology, and (e) was highly representative of the older adult brain.
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Affiliation(s)
- Abdur Raquib Ridwan
- Department of Biomedical Engineering, Illinois Institute of Technology, Chicago, Illinois, USA
| | - Mohammad Rakeen Niaz
- Department of Biomedical Engineering, Illinois Institute of Technology, Chicago, Illinois, USA
| | - Yingjuan Wu
- Department of Biomedical Engineering, Illinois Institute of Technology, Chicago, Illinois, USA
| | - Xiaoxiao Qi
- Department of Biomedical Engineering, Illinois Institute of Technology, Chicago, Illinois, USA
| | - Shengwei Zhang
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, Illinois, USA
| | - Marinos Kontzialis
- Department of Diagnostic Radiology and Nuclear Medicine, Rush University Medical Center, Chicago, Illinois, USA
| | - Carles Javierre-Petit
- Department of Biomedical Engineering, Illinois Institute of Technology, Chicago, Illinois, USA
| | - Mahir Tazwar
- Department of Biomedical Engineering, Illinois Institute of Technology, Chicago, Illinois, USA
| | | | - David A Bennett
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, Illinois, USA
| | - Yongyi Yang
- Department of Biomedical Engineering, Illinois Institute of Technology, Chicago, Illinois, USA
| | - Konstantinos Arfanakis
- Department of Biomedical Engineering, Illinois Institute of Technology, Chicago, Illinois, USA.,Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, Illinois, USA.,Department of Diagnostic Radiology and Nuclear Medicine, Rush University Medical Center, Chicago, Illinois, USA
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17
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Abdolalizadeh A, Ostadrahimi H, Mohajer B, Darvishi A, Sattarian M, Bayani Ershadi AS, Abbasi N. White Matter Microstructural Properties Associated with Impaired Attention in Chronic Schizophrenia: A Multi-Center Study. Psychiatry Res Neuroimaging 2020; 302:111105. [PMID: 32498000 DOI: 10.1016/j.pscychresns.2020.111105] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/29/2019] [Revised: 04/24/2020] [Accepted: 05/01/2020] [Indexed: 12/17/2022]
Abstract
Attention as a key cognitive function is impaired in schizophrenia, interfering with the normal daily life of the patients. Previous studies on the microstructural correlates of attention in schizophrenia were limited to single fibers, did not include a control group, or did not adjust for drug dosage. In the current study, we investigated the association between microstructural properties of the white matter fibers and attention tests in 81 patients and 79 healthy controls from the Mind Clinical Imaging Consortium database. Integrity measures of superior longitudinal fasciculus, cingulum, genu, and splenium were extracted after tractography. Using an interaction model between diagnosis and microstructural properties, and adjusting for age, gender, acquisition site, education, and cumulative drug usage dose, and after correcting for family-wise error, we showed decreased integrity in the patients and a significant negative association between fractional anisotropy of the tracts and trail making test part A with a greater expected decrease in the attention per unit of decrease of integrity in the patients compared to the healthy controls. Our findings suggest that decreased integrity of the bilateral cingulum, and splenium, are independent of the cumulative drug dosage, age, gender, and site, and may underlie the impaired attention in the schizophrenia.
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Affiliation(s)
| | - Hamidreza Ostadrahimi
- Students' Scientific Research Center, Tehran University of Medical Sciences, Tehran, Iran
| | - Bahram Mohajer
- Non-Communicable Diseases Research Center, Endocrinology and Metabolism Population Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran
| | - Asma Darvishi
- Students' Scientific Research Center, Tehran University of Medical Sciences, Tehran, Iran
| | - Mahta Sattarian
- Students' Scientific Research Center, Tehran University of Medical Sciences, Tehran, Iran
| | | | - Nooshin Abbasi
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Canada
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18
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Tosi G, Borsani C, Castiglioni S, Daini R, Franceschi M, Romano D. Complexity in neuropsychological assessments of cognitive impairment: A network analysis approach. Cortex 2020; 124:85-96. [DOI: 10.1016/j.cortex.2019.11.004] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2019] [Revised: 07/29/2019] [Accepted: 11/08/2019] [Indexed: 12/12/2022]
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19
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Pahapill PA, Chen G, Arocho-Quinones EV, Nencka AS, Li SJ. Functional connectivity and structural analysis of trial spinal cord stimulation responders in failed back surgery syndrome. PLoS One 2020; 15:e0228306. [PMID: 32074111 PMCID: PMC7029839 DOI: 10.1371/journal.pone.0228306] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2019] [Accepted: 01/13/2020] [Indexed: 01/02/2023] Open
Abstract
Background Chronic pain has been associated with alterations in brain structure and function that appear dependent on pain phenotype. Functional connectivity (FC) data on chronic back pain (CBP) is limited and based on heterogeneous pain populations. We hypothesize that failed back surgery syndrome (FBSS) patients being considered for spinal cord stimulation (SCS) therapy have altered resting state (RS) FC cross-network patterns that 1) specifically involve emotion and reward/aversion functions and 2) are related to pain scores. Methods RS functional MRI (fMRI) scans were obtained for 10 FBSS patients who are being considered for but who have not yet undergone implantation of a permanent SCS device and 12 healthy age-matched controls. Seven RS networks were analyzed including the striatum (STM). The Wilcoxon signed-rank test evaluated differences in cross-network FC strength (FCS). Differences in periaqueductal grey (PAG) FC were assessed with seed-based analysis. Results Cross-network FCS was decreased (p<0.05) between the STM and all other networks in these FBSS patients. There was a negative linear relationship (R2 = 0.76, p<0.0022) between STMFCS index and pain scores. The PAG showed decreased FC with network elements and amygdala but increased FC with the sensorimotor cortex and cingulate gyrus. Conclusions Decreased FC between STM and other RS networks in FBSS has not been previously reported. This STMFCS index may represent a more objective measure of chronic pain specific to FBSS which may help guide patient selection for SCS and subsequent management.
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Affiliation(s)
- Peter A. Pahapill
- Department of Neurosurgery, U.S. Department of Veterans Affairs Medical Center, Milwaukee, Wisconsin, United States of America
- Department of Neurosurgery, Medical College of Wisconsin, Milwaukee, Wisconsin, United States of America
| | - Guangyu Chen
- Department of Biophysics, Medical College of Wisconsin, Milwaukee, Wisconsin, United States of America
| | - Elsa V. Arocho-Quinones
- Department of Neurosurgery, U.S. Department of Veterans Affairs Medical Center, Milwaukee, Wisconsin, United States of America
- Department of Neurosurgery, Medical College of Wisconsin, Milwaukee, Wisconsin, United States of America
- * E-mail:
| | - Andrew S. Nencka
- Center for Imaging Research, Medical College of Wisconsin, Milwaukee, Wisconsin, United States of America
| | - Shi-Jiang Li
- Department of Biophysics, Medical College of Wisconsin, Milwaukee, Wisconsin, United States of America
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Harlalka V, Bapi RS, Vinod PK, Roy D. Atypical Flexibility in Dynamic Functional Connectivity Quantifies the Severity in Autism Spectrum Disorder. Front Hum Neurosci 2019; 13:6. [PMID: 30774589 PMCID: PMC6367662 DOI: 10.3389/fnhum.2019.00006] [Citation(s) in RCA: 46] [Impact Index Per Article: 9.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2018] [Accepted: 01/08/2019] [Indexed: 01/10/2023] Open
Abstract
Resting-state functional connectivity (FC) analyses have shown atypical connectivity in autism spectrum disorder (ASD) as compared to typically developing (TD). However, this view emerges from investigating static FC overlooking the whole brain transient connectivity patterns. In our study, we investigated how age and disease influence the dynamic changes in functional connectivity of TD and ASD. We used resting-state functional magnetic resonance imaging (rs-fMRI) data stratified into three cohorts: children (7-11 years), adolescents (12-17 years), and adults (18+ years) for the analysis. The dynamic variability in the connection strength and the modular organization in terms of measures such as flexiblity, cohesion strength, and disjointness were explored for each subject to characterize the differences between ASD and TD. In ASD, we observed significantly higher inter-subject dynamic variability in connection strength as compared to TD. This hyper-variability relates to the symptom severity in ASD. We also found that whole-brain flexibility correlates with static modularity only in TD. Further, we observed a core-periphery organization in the resting-state, with Sensorimotor and Visual regions in the rigid core; and DMN and attention areas in the flexible periphery. TD also develops a more cohesive organization of sensorimotor areas. However, in ASD we found a strong positive correlation of symptom severity with flexibility of rigid areas and with disjointness of sensorimotor areas. The regions of the brain showing high predictive power of symptom severity were distributed across the cortex, with stronger bearings in the frontal, motor, and occipital cortices. Our study demonstrates that the dynamic framework best characterizes the variability in ASD.
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Affiliation(s)
- Vatika Harlalka
- Center for Computational Natural Sciences and Bioinformatics, IIIT Hyderabad, Hyderabad, India
| | - Raju S. Bapi
- Cognitive Science Lab, IIIT Hyderabad, Hyderabad, India
- School of Computer and Information Sciences, University of Hyderabad, Hyderabad, India
| | - P. K. Vinod
- Center for Computational Natural Sciences and Bioinformatics, IIIT Hyderabad, Hyderabad, India
| | - Dipanjan Roy
- Cognitive Brain Dynamics Lab, National Brain Research Centre, Manesar, India
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21
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Li W, Yang C, Wu S, Nie Y, Zhang X, Lu M, Chu T, Shi F. Alterations of Graphic Properties and Related Cognitive Functioning Changes in Mild Alzheimer's Disease Revealed by Individual Morphological Brain Network. Front Neurosci 2018; 12:927. [PMID: 30618556 PMCID: PMC6295573 DOI: 10.3389/fnins.2018.00927] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2018] [Accepted: 11/26/2018] [Indexed: 01/30/2023] Open
Abstract
Alzheimer’s disease (AD) is one of the most common forms of dementia that has slowly negative impacts on memory and cognition. With the assistance of multimodal brain networks and graph-based analysis approaches, AD-related network disruptions support the hypothesis that AD can be identified as a dysconnectivity syndrome. However, as the recent emerging of individual-based morphological network research of AD, the utilization of multiple morphometric features may provide a broader horizon for locating the lesions. Therefore, the present study applied the newly proposed individual morphological brain network with five commonly used morphometric features (cortical thickness, regional volume, surface area, mean curvature, and fold index) to explore the topological aberrations and their relationship with cognitive functioning alterations in the early stage of AD. A total of 40 right-handed participants were selected from Open Access Series of Imaging Studies Database with 20 AD patients (age ranged from 70 to 79, CDR = 0.5) and 20 age/gender-matched healthy controls. The significantly affected connections (p < 0.05 with FDR correction) were observed across multiple regions, both enhanced and attenuated correlations, primarily related to the left entorhinal cortex (ENT). In addition, profoundly changed Mini Mental State Examination (MMSE) score and global efficiency (p < 0.05) were noted in the AD patients, as well as the pronounced inter-group distinctions of betweenness centrality, global and local efficiency (p < 0.05) in the higher MMSE score zone (28–30), which indicating the potential role of graphic properties in determination of early-stage AD patients. Moreover, the reservations (regions in the occipital and frontal lobes) and alterations (regions in the right temporal lobe and cingulate cortex) of hubs were also detected in the AD patients. Overall, the findings further confirm the selective AD-related disruptions in morphological brain networks and also suggest the feasibility of applying the morphological graphic properties in the discrimination of early-stage AD patients.
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Affiliation(s)
- Wan Li
- College of Life Science and Bioengineering, Beijing University of Technology, Beijing, China
| | - Chunlan Yang
- College of Life Science and Bioengineering, Beijing University of Technology, Beijing, China
| | - Shuicai Wu
- College of Life Science and Bioengineering, Beijing University of Technology, Beijing, China
| | - Yingnan Nie
- College of Life Science and Bioengineering, Beijing University of Technology, Beijing, China
| | - Xin Zhang
- College of Life Science and Bioengineering, Beijing University of Technology, Beijing, China
| | - Ming Lu
- College of Life Science and Bioengineering, Beijing University of Technology, Beijing, China
| | - Tongpeng Chu
- College of Life Science and Bioengineering, Beijing University of Technology, Beijing, China
| | - Feng Shi
- Department of Biomedical Sciences, Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, United States
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Chen D, Jiang J, Wu P, Guo Q, Zuo C. Module differences of glucose metabolic brain network among Alzheimer's disease, Parkinson's disease dementia, Lewy body dementia and health control. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2018; 2018:2036-2039. [PMID: 30440801 DOI: 10.1109/embc.2018.8512655] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
Dementia has become a serious disease in elderly population. Graph theory based brain network analysis method is considered as a popular and universal technique in the field of the neurosciences. It can help neuroscientists to investigate the neuropathology of dementia, especially to understand dementia subtypes. However, brain network analysis for various dementia subtypes at the same time is still limited. The aim at this study is to investigate the similarities and differences in brain network modular parameters of three dementia subtypes: Alzheimer's disease (AD), Parkinson's disease dementia (PDD), Lewy body dementia (DLB). Health control has also been compared. All subjects of health control(HC), AD, PDD and DLB groups were from Huashan Hospital. Using topological analysis algorithms, we found that HC brain was divided into 4 modules; AD brain was divided into 4 modules; PDD brain was divided into 6 modules; and DLB brain was divided into 10 modules, which demonstrated differences in these dementia subtypes. We also found that right thalamus area was scattered in all dementia subtypes. After using the seed point correlation analysis, it can be seen that the connections between right thalamus and subcortical, frontal gyrus were enhanced and the connections with the occipital gyrus was attenuated in dementia subtypes. As a result, findings in this paper are expected to be useful for neuroscientists to further understand the pathology of AD, PDD and DLB.
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Dai Z, Lin Q, Li T, Wang X, Yuan H, Yu X, He Y, Wang H. Disrupted structural and functional brain networks in Alzheimer's disease. Neurobiol Aging 2018; 75:71-82. [PMID: 30553155 DOI: 10.1016/j.neurobiolaging.2018.11.005] [Citation(s) in RCA: 105] [Impact Index Per Article: 17.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2018] [Revised: 11/08/2018] [Accepted: 11/09/2018] [Indexed: 12/22/2022]
Abstract
Studies have demonstrated that the clinical manifestations of Alzheimer's disease (AD) are associated with abnormal connections in either functional connectivity networks (FCNs) or structural connectivity networks (SCNs). However, the FCN and SCN of AD have usually been examined separately, and the results were inconsistent. In this multimodal study, we collected resting-state functional magnetic resonance imaging and diffusion magnetic resonance imaging data from 46 patients with AD and 39 matched healthy controls (HCs). Graph-theory analysis was used to investigate the topological organization of the FCN and SCN simultaneously. Compared with HCs, both the FCN and SCN of patients with AD showed disrupted network integration (i.e., increased characteristic path length) and segregation (i.e., decreased intramodular connections in the default mode network). Moreover, the FCN, but not the SCN, exhibited a reduced clustering coefficient and reduced rich club connections in AD. The coupling (i.e., correlation) of the FCN and SCN in AD was increased in connections of the default mode network and the rich club. These findings demonstrated overlapping and distinct network disruptions in the FCN and SCN and a strengthened correlation between FCNs and SCNs in AD, which provides a novel perspective for understanding the pathophysiological mechanisms underlying AD.
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Affiliation(s)
- Zhengjia Dai
- National Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China; Department of Psychology, Sun Yat-sen University, Guangzhou, China
| | - Qixiang Lin
- National Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China; Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
| | - Tao Li
- Dementia Care & Research Center, Peking University Institute of Mental Health (Sixth Hospital), Beijing, China; Beijing Key Laboratory for Translational Research on Diagnosis and Treatment of Dementia, Beijing, China; National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, China
| | - Xiao Wang
- Dementia Care & Research Center, Peking University Institute of Mental Health (Sixth Hospital), Beijing, China; Beijing Key Laboratory for Translational Research on Diagnosis and Treatment of Dementia, Beijing, China; National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, China
| | - Huishu Yuan
- Department of Radiology, Peking University Third Hospital, Beijing, China
| | - Xin Yu
- Dementia Care & Research Center, Peking University Institute of Mental Health (Sixth Hospital), Beijing, China; Beijing Key Laboratory for Translational Research on Diagnosis and Treatment of Dementia, Beijing, China; National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, China
| | - Yong He
- National Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China; Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China.
| | - Huali Wang
- Dementia Care & Research Center, Peking University Institute of Mental Health (Sixth Hospital), Beijing, China; Beijing Key Laboratory for Translational Research on Diagnosis and Treatment of Dementia, Beijing, China; National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, China.
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Dragomir A, Vrahatis AG, Bezerianos A. A Network-Based Perspective in Alzheimer's Disease: Current State and an Integrative Framework. IEEE J Biomed Health Inform 2018; 23:14-25. [PMID: 30080151 DOI: 10.1109/jbhi.2018.2863202] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
A major rise in the prevalence and impact of Alzheimer's disease (AD) is projected in the coming decades, resulting from increasing life expectancy, thus leading to substantially increased healthcare costs. While brain disfunctions at the time of diagnosis are irreversible, it is widely accepted that AD pathology develops decades before clinical symptoms onset. If incipient processes can be detected early in the disease progression, prospective intervention for preventing or slowing the disease can be designed. Currently, there is no noninvasive biomarker available to detect and monitor early stages of disease progression. The complex etiology of AD warrants a systems-based approach supporting the integration of multimodal and multilevel data, while network-based modeling provides the scaffolding for methods revealing complex systems-level disruptions initiated by the disease. In this work, we review current state-of-the-art, focusing on network-based biomarkers at molecular and brain functional connectivity levels. Particular emphasis is placed on outlining recent trends, which highlight the functional importance of modular substructures in molecular and connectivity networks and their potential biomarker value. Our perspective is rooted in network medicine and summarizes the pipelines for identifying network-based biomarkers, as well as the benefits of integrating genotype and brain phenotype information for a comprehensively noninvasive approach in the early diagnosis of AD. Finally, we propose a framework for integrating knowledge from molecular and brain connectivity levels, which has the potential to enable noninvasive diagnosis, provide support for monitoring therapies, and help understand heretofore unexamined deep level relations between genotype and brain phenotype.
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A Pilot Study Investigating a Novel Non-Linear Measure of Eyes Open versus Eyes Closed EEG
Synchronization in People with Alzheimer’s Disease and Healthy Controls. Brain Sci 2018; 8:brainsci8070134. [PMID: 30018264 PMCID: PMC6070980 DOI: 10.3390/brainsci8070134] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2018] [Revised: 06/27/2018] [Accepted: 07/16/2018] [Indexed: 12/14/2022] Open
Abstract
Background: The incidence of Alzheimer disease (AD) is increasing with the ageing population. The development of low cost non-invasive diagnostic aids for AD is a research priority. This pilot study investigated whether an approach based on a novel dynamic quantitative parametric EEG method could detect abnormalities in people with AD. Methods: 20 patients with probable AD, 20 matched healthy controls (HC) and 4 patients with probable fronto temporal dementia (FTD) were included. All had detailed neuropsychology along with structural, resting state fMRI and EEG. EEG data were analyzed using the Error Reduction Ratio-causality (ERR-causality) test that can capture both linear and nonlinear interactions between different EEG recording areas. The 95% confidence intervals of EEG levels of bi-centroparietal synchronization were estimated for eyes open (EO) and eyes closed (EC) states. Results: In the EC state, AD patients and HC had very similar levels of bi-centro parietal synchronization; but in the EO resting state, patients with AD had significantly higher levels of synchronization (AD = 0.44; interquartile range (IQR) 0.41 vs. HC = 0.15; IQR 0.17, p < 0.0001). The EO/EC synchronization ratio, a measure of the dynamic changes between the two states, also showed significant differences between these two groups (AD ratio 0.78 versus HC ratio 0.37 p < 0.0001). EO synchronization was also significantly different between AD and FTD (FTD = 0.075; IQR 0.03, p < 0.0001). However, the EO/EC ratio was not informative in the FTD group due to very low levels of synchronization in both states (EO and EC). Conclusion: In this pilot work, resting state quantitative EEG shows significant differences between healthy controls and patients with AD. This approach has the potential to develop into a useful non-invasive and economical diagnostic aid in AD.
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Zhang D, Huang J, Jie B, Du J, Tu L, Liu M. Ordinal Pattern: A New Descriptor for Brain Connectivity Networks. IEEE TRANSACTIONS ON MEDICAL IMAGING 2018; 37:1711-1722. [PMID: 29969421 DOI: 10.1109/tmi.2018.2798500] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
Brain connectivity networks based on magnetic resonance imaging (MRI) or functional MRI (fMRI) data provide a straightforward way to quantify the structural or functional systems of the brain. Currently, there are several network descriptors developed for representing and analyzing brain connectivity networks. However, most of them are designed for unweighted networks, regardless of the valuable weight information of edges, or do not take advantage of the ordinal relationship of weighted edges (even though they are designed for weighted networks). In this paper, we propose a new network descriptor (i.e., ordinal pattern that contains a sequence of weighted edges) for brain connectivity network analysis. Compared with previous network properties, the proposed ordinal patterns cannot only take advantage of the weight information of edges but also explicitly model the ordinal relationship of weighted edges in brain connectivity networks. We further develop an ordinal pattern-based learning framework for brain disease diagnosis using resting-state fMRI data. Specifically, we first construct a set of brain functional connectivity networks, where each network is corresponding to a particular subject. We then develop an algorithm to identify ordinal patterns that frequently appear in brain connectivity networks of patients and normal controls. We further perform discriminative ordinal pattern selection and extract feature representations for subjects based on the selected ordinal patterns, followed by a learning model for automated brain disease diagnosis. Experimental results on both Alzheimer's Disease Neuroimaging Initiative and attention deficit hyperactivity disorder-200 data sets demonstrate that our method outperforms the several state-of-the-art approaches in the tasks of disease classification and clinical score regression.
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27
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Ding J, Chen K, Zhang W, Li M, Chen Y, Yang Q, Lv Y, Guo Q, Han Z. Topological Alterations and Symptom-Relevant Modules in the Whole-Brain Structural Network in Semantic Dementia. J Alzheimers Dis 2018; 59:1283-1297. [PMID: 28731453 DOI: 10.3233/jad-170449] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/21/2023]
Abstract
BACKGROUND Semantic dementia (SD) is characterized by a selective decline in semantic processing. Although the neuropsychological pattern of this disease has been identified, its topological global alterations and symptom-relevant modules in the whole-brain anatomical network have not been fully elucidated. OBJECTIVE This study aims to explore the topological alteration of anatomical network in SD and reveal the modules associated with semantic deficits in this disease. METHODS We first constructed the whole-brain white-matter networks of 20 healthy controls and 19 patients with SD. Then, the network metrics of graph theory were compared between these two groups. Finally, we separated the network of SD patients into different modules and correlated the structural integrity of each module with the severity of the semantic deficits across patients. RESULTS The network of the SD patients presented a significantly reduced global efficiency, indicating that the long-distance connections were damaged. The network was divided into the following four distinctive modules: the left temporal/occipital/parietal, frontal, right temporal/occipital, and frontal/parietal modules. The first two modules were associated with the semantic deficits of SD. CONCLUSION These findings illustrate the skeleton of the neuroanatomical network of SD patients and highlight the key role of the left temporal/occipital/parietal module and the left frontal module in semantic processing.
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Affiliation(s)
- Junhua Ding
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Keliang Chen
- Department of Neurology, Huashan Hospital, Fudan University, Shanghai, China
| | - Weibin Zhang
- Department of Psychology, Beijing Normal University, Beijing, China
| | - Ming Li
- Department of Psychology, Beijing Normal University, Beijing, China
| | - Yan Chen
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Qing Yang
- Department of Neurology, Huashan Hospital, Fudan University, Shanghai, China
| | - Yingru Lv
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai, China
| | - Qihao Guo
- Department of Neurology, Huashan Hospital, Fudan University, Shanghai, China
| | - Zaizhu Han
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
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Hohenfeld C, Werner CJ, Reetz K. Resting-state connectivity in neurodegenerative disorders: Is there potential for an imaging biomarker? Neuroimage Clin 2018; 18:849-870. [PMID: 29876270 PMCID: PMC5988031 DOI: 10.1016/j.nicl.2018.03.013] [Citation(s) in RCA: 145] [Impact Index Per Article: 24.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2017] [Revised: 02/06/2018] [Accepted: 03/14/2018] [Indexed: 12/14/2022]
Abstract
Biomarkers in whichever modality are tremendously important in diagnosing of disease, tracking disease progression and clinical trials. This applies in particular for disorders with a long disease course including pre-symptomatic stages, in which only subtle signs of clinical progression can be observed. Magnetic resonance imaging (MRI) biomarkers hold particular promise due to their relative ease of use, cost-effectiveness and non-invasivity. Studies measuring resting-state functional MR connectivity have become increasingly common during recent years and are well established in neuroscience and related fields. Its increasing application does of course also include clinical settings and therein neurodegenerative diseases. In the present review, we critically summarise the state of the literature on resting-state functional connectivity as measured with functional MRI in neurodegenerative disorders. In addition to an overview of the results, we briefly outline the methods applied to the concept of resting-state functional connectivity. While there are many different neurodegenerative disorders cumulatively affecting a substantial number of patients, for most of them studies on resting-state fMRI are lacking. Plentiful amounts of papers are available for Alzheimer's disease (AD) and Parkinson's disease (PD), but only few works being available for the less common neurodegenerative diseases. This allows some conclusions on the potential of resting-state fMRI acting as a biomarker for the aforementioned two diseases, but only tentative statements for the others. For AD, the literature contains a relatively strong consensus regarding an impairment of the connectivity of the default mode network compared to healthy individuals. However, for AD there is no considerable documentation on how that alteration develops longitudinally with the progression of the disease. For PD, the available research points towards alterations of connectivity mainly in limbic and motor related regions and networks, but drawing conclusions for PD has to be done with caution due to a relative heterogeneity of the disease. For rare neurodegenerative diseases, no clear conclusions can be drawn due to the few published results. Nevertheless, summarising available data points towards characteristic connectivity alterations in Huntington's disease, frontotemporal dementia, dementia with Lewy bodies, multiple systems atrophy and the spinocerebellar ataxias. Overall at this point in time, the data on AD are most promising towards the eventual use of resting-state fMRI as an imaging biomarker, although there remain issues such as reproducibility of results and a lack of data demonstrating longitudinal changes. Improved methods providing more precise classifications as well as resting-state network changes that are sensitive to disease progression or therapeutic intervention are highly desirable, before routine clinical use could eventually become a reality.
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Affiliation(s)
- Christian Hohenfeld
- RWTH Aachen University, Department of Neurology, Aachen, Germany; JARA-BRAIN Institute Molecular Neuroscience and Neuroimaging, Forschungszentrum Jülich GmbH and RWTH Aachen University, Aachen, Germany
| | - Cornelius J Werner
- RWTH Aachen University, Department of Neurology, Aachen, Germany; RWTH Aachen University, Section Interdisciplinary Geriatrics, Aachen, Germany
| | - Kathrin Reetz
- RWTH Aachen University, Department of Neurology, Aachen, Germany; JARA-BRAIN Institute Molecular Neuroscience and Neuroimaging, Forschungszentrum Jülich GmbH and RWTH Aachen University, Aachen, Germany.
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Abidin AZ, DSouza AM, Nagarajan MB, Wang L, Qiu X, Schifitto G, Wismüller A. Alteration of brain network topology in HIV-associated neurocognitive disorder: A novel functional connectivity perspective. NEUROIMAGE-CLINICAL 2017. [PMID: 29527484 PMCID: PMC5842750 DOI: 10.1016/j.nicl.2017.11.025] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Abstract
HIV is capable of invading the brain soon after seroconversion. This ultimately can lead to deficits in multiple cognitive domains commonly referred to as HIV-associated neurocognitive disorders (HAND). Clinical diagnosis of such deficits requires detailed neuropsychological assessment but clinical signs may be difficult to detect during asymptomatic injury of the central nervous system (CNS). Therefore neuroimaging biomarkers are of particular interest in HAND. In this study, we constructed brain connectivity profiles of 40 subjects (20 HIV positive subjects and 20 age-matched seronegative controls) using two different methods: a non-linear mutual connectivity analysis approach and a conventional method based on Pearson's correlation. These profiles were then summarized using graph-theoretic methods characterizing their topological network properties. Standard clinical and laboratory assessments were performed and a battery of neuropsychological (NP) tests was administered for all participating subjects. Based on NP testing, 14 of the seropositive subjects exhibited mild neurologic impairment. Subsequently, we analyzed associations between the network derived measures and neuropsychological assessment scores as well as common clinical laboratory plasma markers (CD4 cell count, HIV RNA) after adjusting for age and gender. Mutual connectivity analysis derived graph-theoretic measures, Modularity and Small Worldness, were significantly (p < 0.05, FDR adjusted) associated with the Executive as well as Overall z-score of NP performance. In contrast, network measures derived from conventional correlation-based connectivity did not yield any significant results. Thus, changes in connectivity can be captured using advanced time-series analysis techniques. The demonstrated associations between imaging-derived graph-theoretic properties of brain networks with neuropsychological performance, provides opportunities to further investigate the evolution of HAND in larger, longitudinal studies. Our analysis approach, involving non-linear time-series analysis in conjunction with graph theory, is promising and it may prove to be useful not only in HAND but also in other neurodegenerative disorders. Currently, cognitive impairment in HIV positive individuals is detected using detailed neuropsychological testing. Analysis of fMRI data using MCA-GRBF method revealed significant associations with current clinical standards. In contrast, functional connectivity analysis using conventional correlation analysis does not produce any such associations. Nonlinear analysis using MCA-GRBF method can potentially capture relevant information when compared to conventional methods.
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Affiliation(s)
- Anas Z Abidin
- Department Biomedical Engineering, University of Rochester, NY, USA.
| | - Adora M DSouza
- Department of Electrical Engineering, University of Rochester, NY, USA
| | - Mahesh B Nagarajan
- Department of Radiological Sciences, University of California Los Angeles, Los Angeles, CA, USA
| | - Lu Wang
- Department of Biostatistics and Computational Biology, University of Rochester, NY, USA
| | - Xing Qiu
- Department of Biostatistics and Computational Biology, University of Rochester, NY, USA
| | - Giovanni Schifitto
- Department of Imaging Sciences, University of Rochester, NY, USA; Department of Neurology, University of Rochester, NY, USA
| | - Axel Wismüller
- Department Biomedical Engineering, University of Rochester, NY, USA; Department of Electrical Engineering, University of Rochester, NY, USA; Department of Imaging Sciences, University of Rochester, NY, USA; Faculty of Medicine and Institute of Clinical Radiology, Ludwig Maximilian University, Munich, Germany
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30
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Li J, Gong Y, Tang X. Hierarchical Subcortical Sub-Regional Shape Network Analysis in Alzheimer's Disease. Neuroscience 2017; 366:70-83. [PMID: 29037598 DOI: 10.1016/j.neuroscience.2017.10.011] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2017] [Revised: 09/11/2017] [Accepted: 10/07/2017] [Indexed: 01/06/2023]
Abstract
In this paper, by utilizing surface diffeomorphic deformations, we constructed and analyzed subcortical shape morphometric networks in 210 healthy control (HC) subjects and 175 subjects with Alzheimer's disease (AD), aiming to identify AD-induced abnormalities in the subcortical shape network. We quantitatively analyzed pertinent network attributes of the entire network and each node. Further to this, hierarchical analyses were performed; group comparisons were conducted at the structure level first and then the sub-region level. The bilateral amygdalae, hippocampi, as well as the thalamus were all divided into multiple functionally distinct sub-regions. From the structure level analysis, we found significant HC-vs-AD group differences in the average local efficiency and average global efficiency. In addition, the local nodal efficiencies between the right thalamus and all three of the right hippocampus, right amygdala, and left thalamus, as well as that between the left amygdala and left hippocampus, decreased significantly in AD. According to the sub-regional network analyses, we observed significant AD-induced local efficiency decreases between different sub-regions within the right hippocampus itself and between the subiculum of the right hippocampus and the sub-region of the right thalamus connecting to the temporal lobe, indicating a degradation of circuit between the hippocampus, thalamus, and temporal lobe. Statistical comparisons were performed using 40,000 non-parametric permutation tests, with false discovery rate correction employed for multiple comparison correction.
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Affiliation(s)
- Jingyuan Li
- Sun Yat-sen University-Carnegie Mellon University (SYSU-CMU) Joint Institute of Engineering, Sun Yat-sen University, Guangzhou, Guangdong, China; Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PA, USA
| | - Yujing Gong
- Sun Yat-sen University-Carnegie Mellon University (SYSU-CMU) Joint Institute of Engineering, Sun Yat-sen University, Guangzhou, Guangdong, China; Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PA, USA
| | - Xiaoying Tang
- Sun Yat-sen University-Carnegie Mellon University (SYSU-CMU) Joint Institute of Engineering, Sun Yat-sen University, Guangzhou, Guangdong, China; Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PA, USA; School of Electronics and Information Technology, Sun Yat-sen University, Guangzhou, Guangdong, China; Sun Yat-sen University-Carnegie Mellon University (SYSU-CMU) Shunde International Joint Research Institute, Shunde, Guangdong, China.
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Kaushal M, Oni-Orisan A, Chen G, Li W, Leschke J, Ward D, Kalinosky B, Budde M, Schmit B, Li SJ, Muqeet V, Kurpad S. Large-Scale Network Analysis of Whole-Brain Resting-State Functional Connectivity in Spinal Cord Injury: A Comparative Study. Brain Connect 2017; 7:413-423. [PMID: 28657334 DOI: 10.1089/brain.2016.0468] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
Network analysis based on graph theory depicts the brain as a complex network that allows inspection of overall brain connectivity pattern and calculation of quantifiable network metrics. To date, large-scale network analysis has not been applied to resting-state functional networks in complete spinal cord injury (SCI) patients. To characterize modular reorganization of whole brain into constituent nodes and compare network metrics between SCI and control subjects, fifteen subjects with chronic complete cervical SCI and 15 neurologically intact controls were scanned. The data were preprocessed followed by parcellation of the brain into 116 regions of interest (ROI). Correlation analysis was performed between every ROI pair to construct connectivity matrices and ROIs were categorized into distinct modules. Subsequently, local efficiency (LE) and global efficiency (GE) network metrics were calculated at incremental cost thresholds. The application of a modularity algorithm organized the whole-brain resting-state functional network of the SCI and the control subjects into nine and seven modules, respectively. The individual modules differed across groups in terms of the number and the composition of constituent nodes. LE demonstrated statistically significant decrease at multiple cost levels in SCI subjects. GE did not differ significantly between the two groups. The demonstration of modular architecture in both groups highlights the applicability of large-scale network analysis in studying complex brain networks. Comparing modules across groups revealed differences in number and membership of constituent nodes, indicating modular reorganization due to neural plasticity.
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Affiliation(s)
- Mayank Kaushal
- 1 Department of Biomedical Engineering, Marquette University , Milwaukee, Wisconsin
| | - Akinwunmi Oni-Orisan
- 2 Department of Neurosurgery, Medical College of Wisconsin , Milwaukee, Wisconsin
| | - Gang Chen
- 3 Department of Biophysics, Medical College of Wisconsin , Milwaukee, Wisconsin
| | - Wenjun Li
- 3 Department of Biophysics, Medical College of Wisconsin , Milwaukee, Wisconsin
| | - Jack Leschke
- 4 Department of Neurology, Medical College of Wisconsin , Milwaukee, Wisconsin
| | - Doug Ward
- 3 Department of Biophysics, Medical College of Wisconsin , Milwaukee, Wisconsin
| | - Benjamin Kalinosky
- 1 Department of Biomedical Engineering, Marquette University , Milwaukee, Wisconsin
| | - Matthew Budde
- 2 Department of Neurosurgery, Medical College of Wisconsin , Milwaukee, Wisconsin
| | - Brian Schmit
- 1 Department of Biomedical Engineering, Marquette University , Milwaukee, Wisconsin
| | - Shi-Jiang Li
- 3 Department of Biophysics, Medical College of Wisconsin , Milwaukee, Wisconsin
| | - Vaishnavi Muqeet
- 5 Department of Physical Medicine and Rehabilitation, Clement J. Zablocki Veterans Affairs Medical Center , Milwaukee, Wisconsin
| | - Shekar Kurpad
- 2 Department of Neurosurgery, Medical College of Wisconsin , Milwaukee, Wisconsin
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Abstract
This article presents a review of recent advances in neuroscience research in the specific area of brain connectivity as a potential biomarker of Alzheimer's disease with a focus on the application of graph theory. The review will begin with a brief overview of connectivity and graph theory. Then resent advances in connectivity as a biomarker for Alzheimer's disease will be presented and analyzed.
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Affiliation(s)
- Jon delEtoile
- 1 Biophysics Graduate Program, The Ohio State University, Columbus, OH, USA
| | - Hojjat Adeli
- 2 Departments of Biomedical Engineering, Biomedical Informatics, Neurological Surgery, and Neuroscience, and Biophysics Graduate Program, The Ohio State University, Columbus, OH, USA
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33
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Boehm-Sturm P, Füchtemeier M, Foddis M, Mueller S, Trueman RC, Zille M, Rinnenthal JL, Kypraios T, Shaw L, Dirnagl U, Farr TD. Neuroimaging Biomarkers Predict Brain Structural Connectivity Change in a Mouse Model of Vascular Cognitive Impairment. Stroke 2017; 48:468-475. [PMID: 28070001 PMCID: PMC5266417 DOI: 10.1161/strokeaha.116.014394] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2016] [Revised: 11/01/2016] [Accepted: 11/28/2016] [Indexed: 12/11/2022]
Abstract
BACKGROUND AND PURPOSE Chronic hypoperfusion in the mouse brain has been suggested to mimic aspects of vascular cognitive impairment, such as white matter damage. Although this model has attracted attention, our group has struggled to generate a reliable cognitive and pathological phenotype. This study aimed to identify neuroimaging biomarkers of brain pathology in aged, more severely hypoperfused mice. METHODS We used magnetic resonance imaging to characterize brain degeneration in mice hypoperfused by refining the surgical procedure to use the smallest reported diameter microcoils (160 μm). RESULTS Acute cerebral blood flow decreases were observed in the hypoperfused group that recovered over 1 month and coincided with arterial remodeling. Increasing hypoperfusion resulted in a reduction in spatial learning abilities in the water maze that has not been previously reported. We were unable to observe severe white matter damage with histology, but a novel approach to analyze diffusion tensor imaging data, graph theory, revealed substantial reorganization of the hypoperfused brain network. A logistic regression model from the data revealed that 3 network parameters were particularly efficient at predicting group membership (global and local efficiency and degrees), and clustering coefficient was correlated with performance in the water maze. CONCLUSIONS Overall, these findings suggest that, despite the autoregulatory abilities of the mouse brain to compensate for a sudden decrease in blood flow, there is evidence of change in the brain networks that can be used as neuroimaging biomarkers to predict outcome.
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Affiliation(s)
- Philipp Boehm-Sturm
- From the Department of Experimental Neurology, Center for Stroke Research Berlin (CSB) (P.B.-S., M.F., M.F., S.M., M.Z., U.D., T.D.F.), Charité Core Facility 7T Experimental MRIs (P.B.-S., S.M.), Department of Neuropathology (J.L.R.), and German Centre for Neurodegenerative Diseases (DZNE), Berlin site (M.F., U.D.), Charité University Medicine Berlin, Germany; and School of Life Sciences (R.C.T., T.D.F.) and School of Mathematics (T.K., L.S.), University of Nottingham, United Kingdom
| | - Martina Füchtemeier
- From the Department of Experimental Neurology, Center for Stroke Research Berlin (CSB) (P.B.-S., M.F., M.F., S.M., M.Z., U.D., T.D.F.), Charité Core Facility 7T Experimental MRIs (P.B.-S., S.M.), Department of Neuropathology (J.L.R.), and German Centre for Neurodegenerative Diseases (DZNE), Berlin site (M.F., U.D.), Charité University Medicine Berlin, Germany; and School of Life Sciences (R.C.T., T.D.F.) and School of Mathematics (T.K., L.S.), University of Nottingham, United Kingdom
| | - Marco Foddis
- From the Department of Experimental Neurology, Center for Stroke Research Berlin (CSB) (P.B.-S., M.F., M.F., S.M., M.Z., U.D., T.D.F.), Charité Core Facility 7T Experimental MRIs (P.B.-S., S.M.), Department of Neuropathology (J.L.R.), and German Centre for Neurodegenerative Diseases (DZNE), Berlin site (M.F., U.D.), Charité University Medicine Berlin, Germany; and School of Life Sciences (R.C.T., T.D.F.) and School of Mathematics (T.K., L.S.), University of Nottingham, United Kingdom
| | - Susanne Mueller
- From the Department of Experimental Neurology, Center for Stroke Research Berlin (CSB) (P.B.-S., M.F., M.F., S.M., M.Z., U.D., T.D.F.), Charité Core Facility 7T Experimental MRIs (P.B.-S., S.M.), Department of Neuropathology (J.L.R.), and German Centre for Neurodegenerative Diseases (DZNE), Berlin site (M.F., U.D.), Charité University Medicine Berlin, Germany; and School of Life Sciences (R.C.T., T.D.F.) and School of Mathematics (T.K., L.S.), University of Nottingham, United Kingdom
| | - Rebecca C Trueman
- From the Department of Experimental Neurology, Center for Stroke Research Berlin (CSB) (P.B.-S., M.F., M.F., S.M., M.Z., U.D., T.D.F.), Charité Core Facility 7T Experimental MRIs (P.B.-S., S.M.), Department of Neuropathology (J.L.R.), and German Centre for Neurodegenerative Diseases (DZNE), Berlin site (M.F., U.D.), Charité University Medicine Berlin, Germany; and School of Life Sciences (R.C.T., T.D.F.) and School of Mathematics (T.K., L.S.), University of Nottingham, United Kingdom
| | - Marietta Zille
- From the Department of Experimental Neurology, Center for Stroke Research Berlin (CSB) (P.B.-S., M.F., M.F., S.M., M.Z., U.D., T.D.F.), Charité Core Facility 7T Experimental MRIs (P.B.-S., S.M.), Department of Neuropathology (J.L.R.), and German Centre for Neurodegenerative Diseases (DZNE), Berlin site (M.F., U.D.), Charité University Medicine Berlin, Germany; and School of Life Sciences (R.C.T., T.D.F.) and School of Mathematics (T.K., L.S.), University of Nottingham, United Kingdom
| | - Jan Leo Rinnenthal
- From the Department of Experimental Neurology, Center for Stroke Research Berlin (CSB) (P.B.-S., M.F., M.F., S.M., M.Z., U.D., T.D.F.), Charité Core Facility 7T Experimental MRIs (P.B.-S., S.M.), Department of Neuropathology (J.L.R.), and German Centre for Neurodegenerative Diseases (DZNE), Berlin site (M.F., U.D.), Charité University Medicine Berlin, Germany; and School of Life Sciences (R.C.T., T.D.F.) and School of Mathematics (T.K., L.S.), University of Nottingham, United Kingdom
| | - Theodore Kypraios
- From the Department of Experimental Neurology, Center for Stroke Research Berlin (CSB) (P.B.-S., M.F., M.F., S.M., M.Z., U.D., T.D.F.), Charité Core Facility 7T Experimental MRIs (P.B.-S., S.M.), Department of Neuropathology (J.L.R.), and German Centre for Neurodegenerative Diseases (DZNE), Berlin site (M.F., U.D.), Charité University Medicine Berlin, Germany; and School of Life Sciences (R.C.T., T.D.F.) and School of Mathematics (T.K., L.S.), University of Nottingham, United Kingdom
| | - Laurence Shaw
- From the Department of Experimental Neurology, Center for Stroke Research Berlin (CSB) (P.B.-S., M.F., M.F., S.M., M.Z., U.D., T.D.F.), Charité Core Facility 7T Experimental MRIs (P.B.-S., S.M.), Department of Neuropathology (J.L.R.), and German Centre for Neurodegenerative Diseases (DZNE), Berlin site (M.F., U.D.), Charité University Medicine Berlin, Germany; and School of Life Sciences (R.C.T., T.D.F.) and School of Mathematics (T.K., L.S.), University of Nottingham, United Kingdom
| | - Ulrich Dirnagl
- From the Department of Experimental Neurology, Center for Stroke Research Berlin (CSB) (P.B.-S., M.F., M.F., S.M., M.Z., U.D., T.D.F.), Charité Core Facility 7T Experimental MRIs (P.B.-S., S.M.), Department of Neuropathology (J.L.R.), and German Centre for Neurodegenerative Diseases (DZNE), Berlin site (M.F., U.D.), Charité University Medicine Berlin, Germany; and School of Life Sciences (R.C.T., T.D.F.) and School of Mathematics (T.K., L.S.), University of Nottingham, United Kingdom
| | - Tracy D Farr
- From the Department of Experimental Neurology, Center for Stroke Research Berlin (CSB) (P.B.-S., M.F., M.F., S.M., M.Z., U.D., T.D.F.), Charité Core Facility 7T Experimental MRIs (P.B.-S., S.M.), Department of Neuropathology (J.L.R.), and German Centre for Neurodegenerative Diseases (DZNE), Berlin site (M.F., U.D.), Charité University Medicine Berlin, Germany; and School of Life Sciences (R.C.T., T.D.F.) and School of Mathematics (T.K., L.S.), University of Nottingham, United Kingdom.
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Tentolouris-Piperas V, Ryan NS, Thomas DL, Kinnunen KM. Brain imaging evidence of early involvement of subcortical regions in familial and sporadic Alzheimer's disease. Brain Res 2016; 1655:23-32. [PMID: 27847196 DOI: 10.1016/j.brainres.2016.11.011] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2016] [Revised: 11/08/2016] [Accepted: 11/09/2016] [Indexed: 12/15/2022]
Abstract
Recent brain imaging studies have found changes in subcortical regions in presymptomatic autosomal dominant Alzheimer's disease (ADAD). These regions are also affected in sporadic Alzheimer's disease (sAD), but whether such changes are seen in early-stage disease is still uncertain. In this review, we discuss imaging studies published in the past 12 years that have found evidence of subcortical involvement in early-stage ADAD and/or sAD. Several papers have reported amyloid deposition in the striatum of presymptomatic ADAD mutation carriers, prior to amyloid deposition elsewhere. Altered caudate volume has also been implicated in early-stage ADAD, but findings have been variable. Less is known about subcortical involvement in sAD: the thalamus and striatum have been found to be atrophied in symptomatic patients, but their involvement in the preclinical phase remains unclear, in part due to the difficulties of studying this stage in sporadic disease. Longitudinal imaging studies comparing ADAD mutation carriers with individuals at high-risk for sAD may be needed to elucidate the significance of subcortical involvement in different AD clinical stages.
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Affiliation(s)
| | - Natalie S Ryan
- Dementia Research Centre, UCL Institute of Neurology, University College London, Queen Square, London, UK
| | - David L Thomas
- Dementia Research Centre, UCL Institute of Neurology, University College London, Queen Square, London, UK; Neuroradiological Academic Unit, Department of Brain Repair and Rehabilitation, UCL Institute of Neurology, University College London, Queen Square, London, UK
| | - Kirsi M Kinnunen
- Dementia Research Centre, UCL Institute of Neurology, University College London, Queen Square, London, UK.
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Leh SE, Kälin AM, Schroeder C, Park MTM, Chakravarty MM, Freund P, Gietl AF, Riese F, Kollias S, Hock C, Michels L. Volumetric and shape analysis of the thalamus and striatum in amnestic mild cognitive impairment. J Alzheimers Dis 2016; 49:237-49. [PMID: 26444755 DOI: 10.3233/jad-150080] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
Alterations in brain structures, including progressive neurodegeneration, are a hallmark in patients with Alzheimer's disease (AD). However, pathological mechanisms, such as the accumulation of amyloid and the proliferation of tau, are thought to begin years, even decades, before the initial clinical manifestations of AD. In this study, we compare the brain anatomy of amnestic mild cognitive impairment patients (aMCI, n = 16) to healthy subjects (CS, n = 22) using cortical thickness, subcortical volume, and shape analysis, which we believe to be complimentary to volumetric measures. We were able to replicate "classical" cortical thickness alterations in aMCI in the hippocampus, amygdala, putamen, insula, and inferior temporal regions. Additionally, aMCI showed significant thalamic and striatal shape differences. We observed higher global amyloid deposition in aMCI, a significant correlation between striatal displacement and global amyloid, and an inverse correlation between executive function and right-hemispheric thalamic displacement. In contrast, no volumetric differences were detected in thalamic, striatal, and hippocampal regions. Our results provide new evidence for early subcortical neuroanatomical changes in patients with aMCI, which are linked to cognitive abilities and amyloid deposition. Hence, shape analysis may aid in the identification of structural biomarkers for identifying individuals at highest risk of conversion to AD.
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Affiliation(s)
- Sandra E Leh
- Division of Psychiatry Research and Psychogeriatric Medicine, University of Zurich, Switzerland
| | - Andrea M Kälin
- Division of Psychiatry Research and Psychogeriatric Medicine, University of Zurich, Switzerland
| | - Clemens Schroeder
- Division of Psychiatry Research and Psychogeriatric Medicine, University of Zurich, Switzerland
| | - Min Tae M Park
- Cerebral Imaging Centre, Douglas Mental Health University Institute, Montreal, Canada.,Schulich School of Medicine and Dentistry, The University of Western Ontario, London, Canada
| | - M Mallar Chakravarty
- Cerebral Imaging Centre, Douglas Mental Health University Institute, Montreal, Canada.,Departments of Psychiatry and Biomedical Engineering, McGill University, Montreal, Canada
| | - Patrick Freund
- Spinal Cord Injury Center, University Hospital Balgrist, Switzerland.,Department of Brain Repair and Rehabilitation, UCL Institute of Neurology, University College London, London, UK.,Wellcome Trust Centre for Neuroimaging, UCL Institute of Neurology, University College London, London, UK.,Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Anton F Gietl
- Division of Psychiatry Research and Psychogeriatric Medicine, University of Zurich, Switzerland
| | - Florian Riese
- Division of Psychiatry Research and Psychogeriatric Medicine, University of Zurich, Switzerland
| | - Spyros Kollias
- Institute of Neuroradiology, University of Zurich, Switzerland
| | - Christoph Hock
- Division of Psychiatry Research and Psychogeriatric Medicine, University of Zurich, Switzerland
| | - Lars Michels
- Institute of Neuroradiology, University of Zurich, Switzerland
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36
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Qiu Y, Liu S, Hilal S, Loke YM, Ikram MK, Xu X, Yeow Tan B, Venketasubramanian N, Chen CLH, Zhou J. Inter-hemispheric functional dysconnectivity mediates the association of corpus callosum degeneration with memory impairment in AD and amnestic MCI. Sci Rep 2016; 6:32573. [PMID: 27581062 PMCID: PMC5007647 DOI: 10.1038/srep32573] [Citation(s) in RCA: 34] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2016] [Accepted: 08/10/2016] [Indexed: 12/21/2022] Open
Abstract
Evidences suggested that both corpus callosum (CC) degeneration and alternations of homotopic inter-hemispheric functional connectivity (FC) are present in Alzheimer's disease (AD). However, the associations between region-specific CC degeneration and homotopic inter-hemispheric FC and their relationships with memory deficits in AD remain uncharacterized. We hypothesized that selective CC degeneration is associated with memory impairment in AD and amnestic mild cognitive impairment (aMCI), which is mediated by homotopic inter-hemispheric functional dysconnectivity. Using structural magnetic resonance imaging (MRI) and task-free functional MRI, we assessed the CC volume and inter-hemispheric FC in 66 healthy controls, 41 aMCI and 41 AD. As expected, AD had CC degeneration and attenuated inter-hemispheric homotopic FC. Nevertheless, aMCI had relatively less severe CC degeneration (mainly in mid-anterior, central, and mid-posterior) and no reduction in inter-hemispheric homotopic FC. The degeneration of each CC sub-region was associated with specific inter-hemispheric homotopic functional disconnections in AD and aMCI. More importantly, impairment of inter-hemispheric homotopic FC partially mediated the association between CC (particularly the central and posterior parts) degeneration and memory deficit. Notably, these results remained after controlling for hippocampal volume. Our findings shed light on how CC degeneration and the related inter-hemispheric FC impact memory impairment in early stage of AD.
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Affiliation(s)
- Yingwei Qiu
- Center for Cognitive Neuroscience, Neuroscience and Behavioral Disorders Program, Duke-National University of Singapore Graduate Medical School, Singapore
| | - Siwei Liu
- Center for Cognitive Neuroscience, Neuroscience and Behavioral Disorders Program, Duke-National University of Singapore Graduate Medical School, Singapore
| | - Saima Hilal
- Department of Pharmacology, National University Health System, Clinical Research Centre, Singapore
- Memory Aging & Cognition Centre, National University Health System, Singapore
| | - Yng Miin Loke
- Center for Cognitive Neuroscience, Neuroscience and Behavioral Disorders Program, Duke-National University of Singapore Graduate Medical School, Singapore
| | - Mohammad Kamran Ikram
- Memory Aging & Cognition Centre, National University Health System, Singapore
- Duke-NUS Graduate Medical School, National University of Singapore, Singapore
| | - Xin Xu
- Department of Pharmacology, National University Health System, Clinical Research Centre, Singapore
- Memory Aging & Cognition Centre, National University Health System, Singapore
| | | | | | - Christopher Li-Hsian Chen
- Department of Pharmacology, National University Health System, Clinical Research Centre, Singapore
- Memory Aging & Cognition Centre, National University Health System, Singapore
| | - Juan Zhou
- Center for Cognitive Neuroscience, Neuroscience and Behavioral Disorders Program, Duke-National University of Singapore Graduate Medical School, Singapore
- Clinical Imaging Research Centre, the Agency for Science, Technology and Research and National University of Singapore, Singapore
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37
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Peraza LR, Taylor JP, Kaiser M. Divergent brain functional network alterations in dementia with Lewy bodies and Alzheimer's disease. Neurobiol Aging 2015; 36:2458-67. [PMID: 26115566 PMCID: PMC4706129 DOI: 10.1016/j.neurobiolaging.2015.05.015] [Citation(s) in RCA: 52] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2015] [Revised: 05/05/2015] [Accepted: 05/23/2015] [Indexed: 01/29/2023]
Abstract
The clinical phenotype of dementia with Lewy bodies (DLB) is different from Alzheimer's disease (AD), suggesting a divergence between these diseases in terms of brain network organization. To fully understand this, we studied functional networks from resting-state functional magnetic resonance imaging in cognitively matched DLB and AD patients. The DLB group demonstrated a generalized lower synchronization compared with the AD and healthy controls, and this was more severe for edges connecting distant brain regions. Global network measures were significantly different between DLB and AD. For instance, AD showed lower small-worldness than healthy controls, while DLB showed higher small-worldness (AD < controls < DLB), and this was also the case for global efficiency (DLB > controls > AD) and clustering coefficient (DLB < controls < AD). Differences were also found for nodal measures at brain regions associated with each disease. Finally, we found significant associations between network performance measures and global cognitive impairment and severity of cognitive fluctuations in DLB. These results show network divergences between DLB and AD which appear to reflect their neuropathological differences.
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Affiliation(s)
- Luis R Peraza
- Institute of Neuroscience, Campus for Ageing and Vitality, Newcastle University, Newcastle upon Tyne, UK.
| | - John-Paul Taylor
- Institute of Neuroscience, Campus for Ageing and Vitality, Newcastle University, Newcastle upon Tyne, UK
| | - Marcus Kaiser
- Interdisciplinary Computing and Complex BioSystems (ICOS) research group, School of Computing Science, Newcastle University, Newcastle upon Tyne, UK; Institute of Neuroscience, Campus for Ageing and Vitality, Newcastle University, Newcastle upon Tyne, UK
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38
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He Y, Evans A. Magnetic resonance imaging of healthy and diseased brain networks. Front Hum Neurosci 2014; 8:890. [PMID: 25404910 PMCID: PMC4217377 DOI: 10.3389/fnhum.2014.00890] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2014] [Accepted: 10/16/2014] [Indexed: 11/21/2022] Open
Affiliation(s)
- Yong He
- State Key Laboratory of Cognitive Neuroscience and Learning, IDG/McGovern Institute for Brain Research, Beijing Normal University Beijing, China ; Center for Collaboration and Innovation in Brain and Learning Sciences, Beijing Normal University Beijing, China
| | - Alan Evans
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University Montreal Montreal, QC, Canada
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39
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Dai Z, Yan C, Li K, Wang Z, Wang J, Cao M, Lin Q, Shu N, Xia M, Bi Y, He Y. Identifying and Mapping Connectivity Patterns of Brain Network Hubs in Alzheimer's Disease. Cereb Cortex 2014; 25:3723-42. [PMID: 25331602 DOI: 10.1093/cercor/bhu246] [Citation(s) in RCA: 227] [Impact Index Per Article: 22.7] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022] Open
Affiliation(s)
- Zhengjia Dai
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research Center for Collaboration and Innovation in Brain and Learning Sciences, Beijing Normal University, Beijing 100875, China
| | - Chaogan Yan
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research Nathan Kline Institute for Psychiatric Research, Orangeburg, NY 10962, USA
| | - Kuncheng Li
- Department of Radiology, Xuanwu Hospital of Capital Medical University, Beijing 100053, China
| | - Zhiqun Wang
- Department of Radiology, Xuanwu Hospital of Capital Medical University, Beijing 100053, China
| | - Jinhui Wang
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research
| | - Miao Cao
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research Center for Collaboration and Innovation in Brain and Learning Sciences, Beijing Normal University, Beijing 100875, China
| | - Qixiang Lin
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research Center for Collaboration and Innovation in Brain and Learning Sciences, Beijing Normal University, Beijing 100875, China
| | - Ni Shu
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research Center for Collaboration and Innovation in Brain and Learning Sciences, Beijing Normal University, Beijing 100875, China
| | - Mingrui Xia
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research Center for Collaboration and Innovation in Brain and Learning Sciences, Beijing Normal University, Beijing 100875, China
| | - Yanchao Bi
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research Center for Collaboration and Innovation in Brain and Learning Sciences, Beijing Normal University, Beijing 100875, China
| | - Yong He
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research Center for Collaboration and Innovation in Brain and Learning Sciences, Beijing Normal University, Beijing 100875, China
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40
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Wehrl HF, Wiehr S, Divine MR, Gatidis S, Gullberg GT, Maier FC, Rolle AM, Schwenck J, Thaiss WM, Pichler BJ. Preclinical and Translational PET/MR Imaging. J Nucl Med 2014; 55:11S-18S. [PMID: 24833493 DOI: 10.2967/jnumed.113.129221] [Citation(s) in RCA: 50] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022] Open
Abstract
Combined PET and MR imaging (PET/MR imaging) has progressed tremendously in recent years. The focus of current research has shifted from technologic challenges to the application of this new multimodal imaging technology in the areas of oncology, cardiology, neurology, and infectious diseases. This article reviews studies in preclinical and clinical translation. The common theme of these initial results is the complementary nature of combined PET/MR imaging that often provides additional insights into biologic systems that were not clearly feasible with just one modality alone. However, in vivo findings require ex vivo validation. Combined PET/MR imaging also triggers a multitude of new developments in image analysis that are aimed at merging and using multimodal information that ranges from better tumor characterization to analysis of metabolic brain networks. The combination of connectomics information that maps brain networks derived from multiparametric MR data with metabolic information from PET can even lead to the formation of a new research field that we would call cometomics that would map functional and metabolic brain networks. These new methodologic developments also call for more multidisciplinarity in the field of molecular imaging, in which close interaction and training among clinicians and a variety of scientists is needed.
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Affiliation(s)
- Hans F Wehrl
- Werner Siemens Imaging Center, Department for Preclinical Imaging and Radiopharmacy, Eberhard Karls University Tuebingen, Tuebingen, Germany
| | - Stefan Wiehr
- Werner Siemens Imaging Center, Department for Preclinical Imaging and Radiopharmacy, Eberhard Karls University Tuebingen, Tuebingen, Germany
| | - Mathew R Divine
- Werner Siemens Imaging Center, Department for Preclinical Imaging and Radiopharmacy, Eberhard Karls University Tuebingen, Tuebingen, Germany
| | - Sergios Gatidis
- Department of Diagnostic and Interventional Radiology, Eberhard Karls University Tuebingen, Tuebingen, Germany
| | - Grant T Gullberg
- Department of Radiotracer Development and Imaging Technology, Ernest Orlando Lawrence Berkeley National Laboratory, Berkeley, California Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, California; and
| | - Florian C Maier
- Werner Siemens Imaging Center, Department for Preclinical Imaging and Radiopharmacy, Eberhard Karls University Tuebingen, Tuebingen, Germany
| | - Anna-Maria Rolle
- Werner Siemens Imaging Center, Department for Preclinical Imaging and Radiopharmacy, Eberhard Karls University Tuebingen, Tuebingen, Germany
| | - Johannes Schwenck
- Werner Siemens Imaging Center, Department for Preclinical Imaging and Radiopharmacy, Eberhard Karls University Tuebingen, Tuebingen, Germany Department of Nuclear Medicine, Eberhard Karls University Tuebingen, Tuebingen, Germany
| | - Wolfgang M Thaiss
- Werner Siemens Imaging Center, Department for Preclinical Imaging and Radiopharmacy, Eberhard Karls University Tuebingen, Tuebingen, Germany Department of Diagnostic and Interventional Radiology, Eberhard Karls University Tuebingen, Tuebingen, Germany
| | - Bernd J Pichler
- Werner Siemens Imaging Center, Department for Preclinical Imaging and Radiopharmacy, Eberhard Karls University Tuebingen, Tuebingen, Germany
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Dai Z, He Y. Disrupted structural and functional brain connectomes in mild cognitive impairment and Alzheimer's disease. Neurosci Bull 2014; 30:217-32. [PMID: 24733652 PMCID: PMC5562665 DOI: 10.1007/s12264-013-1421-0] [Citation(s) in RCA: 108] [Impact Index Per Article: 10.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2013] [Accepted: 01/23/2014] [Indexed: 12/25/2022] Open
Abstract
Alzheimer's disease (AD) is the most common type of dementia, comprising an estimated 60-80% of all dementia cases. It is clinically characterized by impairments of memory and other cognitive functions. Previous studies have demonstrated that these impairments are associated with abnormal structural and functional connections among brain regions, leading to a disconnection concept of AD. With the advent of a combination of non-invasive neuroimaging (structural magnetic resonance imaging (MRI), diffusion MRI, and functional MRI) and neurophysiological techniques (electroencephalography and magnetoencephalography) with graph theoretical analysis, recent studies have shown that patients with AD and mild cognitive impairment (MCI), the prodromal stage of AD, exhibit disrupted topological organization in large-scale brain networks (i.e., connectomics) and that this disruption is significantly correlated with the decline of cognitive functions. In this review, we summarize the recent progress of brain connectomics in AD and MCI, focusing on the changes in the topological organization of large-scale structural and functional brain networks using graph theoretical approaches. Based on the two different perspectives of information segregation and integration, the literature reviewed here suggests that AD and MCI are associated with disrupted segregation and integration in brain networks. Thus, these connectomics studies open up a new window for understanding the pathophysiological mechanisms of AD and demonstrate the potential to uncover imaging biomarkers for clinical diagnosis and treatment evaluation for this disease.
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Affiliation(s)
- Zhengjia Dai
- State Key Laboratory of Cognitive Neuroscience and Learning; IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, 100875 China
- Center for Collaboration and Innovation in Brain and Learning Sciences, Beijing Normal University, Beijing, 100875 China
| | - Yong He
- State Key Laboratory of Cognitive Neuroscience and Learning; IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, 100875 China
- Center for Collaboration and Innovation in Brain and Learning Sciences, Beijing Normal University, Beijing, 100875 China
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Baggio HC, Sala-Llonch R, Segura B, Marti MJ, Valldeoriola F, Compta Y, Tolosa E, Junqué C. Functional brain networks and cognitive deficits in Parkinson's disease. Hum Brain Mapp 2014; 35:4620-34. [PMID: 24639411 DOI: 10.1002/hbm.22499] [Citation(s) in RCA: 164] [Impact Index Per Article: 16.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2013] [Revised: 01/10/2014] [Accepted: 02/14/2014] [Indexed: 11/06/2022] Open
Abstract
Graph-theoretical analyses of functional networks obtained with resting-state functional magnetic resonance imaging (fMRI) have recently proven to be a useful approach for the study of the substrates underlying cognitive deficits in different diseases. We used this technique to investigate whether cognitive deficits in Parkinson's disease (PD) are associated with changes in global and local network measures. Thirty-six healthy controls (HC) and 66 PD patients matched for age, sex, and education were classified as having mild cognitive impairment (MCI) or not based on performance in the three mainly affected cognitive domains in PD: attention/executive, visuospatial/visuoperceptual (VS/VP), and declarative memory. Resting-state fMRI and graph theory analyses were used to evaluate network measures. We have found that patients with MCI had connectivity reductions predominantly affecting long-range connections as well as increased local interconnectedness manifested as higher measures of clustering, small-worldness, and modularity. The latter measures also tended to correlate negatively with cognitive performance in VS/VP and memory functions. Hub structure was also reorganized: normal hubs displayed reduced centrality and degree in MCI PD patients. Our study indicates that the topological properties of brain networks are changed in PD patients with cognitive deficits. Our findings provide novel data regarding the functional substrate of cognitive impairment in PD, which may prove to have value as a prognostic marker.
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Affiliation(s)
- Hugo-Cesar Baggio
- Departament de Psiquiatria i Psicobiologia Clinica, Universitat de Barcelona, Spain
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