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Fall AB, Preti MG, Eshmawey M, Kagerer SM, Van De Ville D, Unschuld PG. Functional network centrality indicates interactions between APOE4 and age across the clinical spectrum of AD. Neuroimage Clin 2024; 43:103635. [PMID: 38941766 PMCID: PMC11260379 DOI: 10.1016/j.nicl.2024.103635] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2024] [Revised: 06/07/2024] [Accepted: 06/18/2024] [Indexed: 06/30/2024]
Abstract
Advanced age is the most important risk factor for Alzheimer's disease (AD), and carrier-status of the Apolipoprotein E4 (APOE4) allele is the strongest known genetic risk factor. Many studies have consistently shown a link between APOE4 and synaptic dysfunction, possibly reflecting pathologically accelerated biological aging in persons at risk for AD. To test the hypothesis that distinct functional connectivity patterns characterize APOE4 carriers across the clinical spectrum of AD, we investigated 128 resting state functional Magnetic Resonance Imaging (fMRI) datasets from the Alzheimer's Disease Neuroimaging Initiative database (ADNI), representing all disease stages from cognitive normal to clinical dementia. Brain region centralities within functional networks, computed as eigenvector centrality, were tested for multivariate associations with chronological age, APOE4 carrier status and clinical stage (as well as their interactions) by partial least square analysis (PLSC). By PLSC analysis two distinct brain activity patterns could be identified, which reflected interactive effects of age, APOE4 and clinical disease stage. A first component including sensorimotor regions and parietal regions correlated with age and AD clinical stage (p < 0.001). A second component focused on medial-frontal regions and was specifically related to the interaction between age and APOE4 (p = 0.032). Our findings are consistent with earlier reports on altered network connectivity in APOE4 carriers. Results of our study highlight promise of graph-theory based network centrality to identify brain connectivity linked to genetic risk, clinical stage and age. Our data suggest the existence of brain network activity patterns that characterize APOE4 carriers across clinical stages of AD.
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Affiliation(s)
- Aïda B Fall
- Faculty of Medicine, University of Geneva (UNIGE), Geneva, Switzerland; Geriatric Psychiatry Service, University Hospitals of Geneva (HUG), Thônex, Switzerland; CIBM Center for Biomedical Imaging, Switzerland.
| | - Maria Giulia Preti
- Faculty of Medicine, University of Geneva (UNIGE), Geneva, Switzerland; CIBM Center for Biomedical Imaging, Switzerland; Neuro-X Institute, École Polytechnique Fédérale de Lausanne, Geneva, Switzerland
| | - Mohamed Eshmawey
- Geriatric Psychiatry Service, University Hospitals of Geneva (HUG), Thônex, Switzerland
| | - Sonja M Kagerer
- Faculty of Medicine, University of Geneva (UNIGE), Geneva, Switzerland; Institute for Regenerative Medicine (IREM), University of Zurich, Zurich, Switzerland; Psychogeriatric Medicine, Psychiatric University Hospital Zurich (PUK), Zurich, Switzerland
| | - Dimitri Van De Ville
- Faculty of Medicine, University of Geneva (UNIGE), Geneva, Switzerland; CIBM Center for Biomedical Imaging, Switzerland; Neuro-X Institute, École Polytechnique Fédérale de Lausanne, Geneva, Switzerland
| | - Paul G Unschuld
- Faculty of Medicine, University of Geneva (UNIGE), Geneva, Switzerland; Geriatric Psychiatry Service, University Hospitals of Geneva (HUG), Thônex, Switzerland
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2
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Fu Z, Sui J, Iraji A, Liu J, Calhoun V. Cognitive and Psychiatric Relevance of Dynamic Functional Connectivity States in a Large (N>10,000) Children Population. RESEARCH SQUARE 2024:rs.3.rs-3586731. [PMID: 38260417 PMCID: PMC10802706 DOI: 10.21203/rs.3.rs-3586731/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/24/2024]
Abstract
Children's brains dynamically adapt to the stimuli from the internal state and the external environment, allowing for changes in cognitive and mental behavior. In this work, we performed a large-scale analysis of dynamic functional connectivity (DFC) in children aged 9 ~ 11 years, investigating how brain dynamics relate to cognitive performance and mental health at an early age. A hybrid independent component analysis framework was applied to the Adolescent Brain Cognitive Development (ABCD) data containing 10,988 children. We combined a sliding-window approach with k-means clustering to identify five brain states with distinct DFC patterns. Interestingly, the occurrence of a strongly connected state was negatively correlated with cognitive performance and positively correlated with dimensional psychopathology in children. Meanwhile, opposite relationships were observed for a sparsely connected state. The composite cognitive score and the ADHD score were the most significantly correlated with the DFC states. The mediation analysis further showed that attention problems mediated the effect of DFC states on cognitive performance. This investigation unveils the neurological underpinnings of DFC states, which suggests that tracking the transient dynamic connectivity may help to characterize cognitive and mental problems in children and guide people to provide early intervention to buffer adverse influences.
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Affiliation(s)
- Zening Fu
- Georgia Institute of Technology, Emory University and Georgia State University
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3
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Zheng W, Mu R, Liu F, Qin X, Li X, Yang P, Li X, Liang Y, Zhu X. Textural features of the frontal white matter could be used to discriminate amnestic mild cognitive impairment patients from the normal population. Brain Behav 2023; 13:e3222. [PMID: 37587901 PMCID: PMC10636424 DOI: 10.1002/brb3.3222] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/16/2023] [Revised: 08/03/2023] [Accepted: 08/04/2023] [Indexed: 08/18/2023] Open
Abstract
OBJECTIVE We aim to develop a radiomics model based on 3-dimensional (3D)-T1WI images to discriminate amnestic mild cognitive impairment (aMCI) patients from the normal population by measuring changes in frontal white matter. METHODS In this study, 126 patients with aMCI and 174 normal controls (NC) were recruited from the local community. All subjects underwent routine magnetic resonance imaging examination (including 3D-T1WI ). Participants were randomly divided into a training set (n = 242, aMCI:102, NC:140) and a testing set (n = 58, aMCI:24, NC:34). Texture features of the frontal lobe were extracted from 3D-T1WI images. The least absolute shrinkage and selection operator (LASSO) was used to reduce feature dimensions and develop a radiomics signature model. Diagnostic performance was assessed in the training and testing sets using the receiver operating characteristic (ROC) curve analysis. The area under the ROC curve (AUC), sensitivity, and specificity were also calculated. The efficacy of the radiomics model in discriminating aMCI patients from the normal population was assessed by decision curve analysis (DCA). RESULTS A total of 108 frontal lobe texture features were extracted from 3D-T1WI images. LASSO selected 58 radiomic features for the final model, including log-sigma (n = 18), original (n = 8), and wavelet (n = 32) features. The performance of radiomic features extracted from 3D T1 imaging for distinguishing aMCI patients from controls was: in the training set, AUC was 1.00, and the accuracy, sensitivity, and specificity were 100%, 98%, and 100%, respectively. In the testing set, AUC was 0.82 (95% CI:0.69-0.95), and the accuracy, sensitivity, and specificity were 69%, 92%, and 55%, respectively. The DCA demonstrated that the model had favorable clinical predictive value. CONCLUSIONS Textural features of white matter in the frontal lobe showed potential for distinguishing aMCI from the normal population, which could be a surrogate protocol to aid aMCI screening in clinical setting.
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Affiliation(s)
- Wei Zheng
- Department of Clinical MedicineGuilin Medical universityGuilinChina
- Department of Medical ImagingNanxishan Hospital of Guangxi ZhuangAutonomous RegionGuilinChina
| | - Ronghua Mu
- Department of Medical ImagingNanxishan Hospital of Guangxi ZhuangAutonomous RegionGuilinChina
| | - Fuzhen Liu
- Department of Medical ImagingNanxishan Hospital of Guangxi ZhuangAutonomous RegionGuilinChina
| | - Xiaoyan Qin
- Department of Medical ImagingNanxishan Hospital of Guangxi ZhuangAutonomous RegionGuilinChina
| | - Xin Li
- Department of Medical ImagingNanxishan Hospital of Guangxi ZhuangAutonomous RegionGuilinChina
| | - Peng Yang
- Department of Medical ImagingNanxishan Hospital of Guangxi ZhuangAutonomous RegionGuilinChina
| | - Xin Li
- Department of Medical ImagingNanxishan Hospital of Guangxi ZhuangAutonomous RegionGuilinChina
| | | | - Xiqi Zhu
- Department of Medical ImagingNanxishan Hospital of Guangxi ZhuangAutonomous RegionGuilinChina
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4
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Dennison JB, Tepfer LJ, Smith DV. Tensorial independent component analysis reveals social and reward networks associated with major depressive disorder. Hum Brain Mapp 2023; 44:2905-2920. [PMID: 36880638 PMCID: PMC10089091 DOI: 10.1002/hbm.26254] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2022] [Revised: 02/09/2023] [Accepted: 02/10/2023] [Indexed: 03/08/2023] Open
Abstract
Major depressive disorder (MDD) has been associated with changes in functional brain connectivity. Yet, typical analyses of functional connectivity, such as spatial independent components analysis (ICA) for resting-state data, often ignore sources of between-subject variability, which may be crucial for identifying functional connectivity patterns associated with MDD. Typically, methods like spatial ICA will identify a single component to represent a network like the default mode network (DMN), even if groups within the data show differential DMN coactivation. To address this gap, this project applies a tensorial extension of ICA (tensorial ICA)-which explicitly incorporates between-subject variability-to identify functionally connected networks using functional MRI data from the Human Connectome Project (HCP). Data from the HCP included individuals with a diagnosis of MDD, a family history of MDD, and healthy controls performing a gambling and social cognition task. Based on evidence associating MDD with blunted neural activation to rewards and social stimuli, we predicted that tensorial ICA would identify networks associated with reduced spatiotemporal coherence and blunted social and reward-based network activity in MDD. Across both tasks, tensorial ICA identified three networks showing decreased coherence in MDD. All three networks included ventromedial prefrontal cortex, striatum, and cerebellum and showed different activation across the conditions of their respective tasks. However, MDD was only associated with differences in task-based activation in one network from the social task. Additionally, these results suggest that tensorial ICA could be a valuable tool for understanding clinical differences in relation to network activation and connectivity.
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Affiliation(s)
- Jeff B Dennison
- Department of Psychology & Neuroscience, Temple University, Philadelphia, Pennsylvania, USA
| | - Lindsey J Tepfer
- Department of Psychological and Brain Science, Dartmouth University, Hanover, New Hampshire, USA
| | - David V Smith
- Department of Psychology & Neuroscience, Temple University, Philadelphia, Pennsylvania, USA
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Castanho EN, Aidos H, Madeira SC. Biclustering fMRI time series: a comparative study. BMC Bioinformatics 2022; 23:192. [PMID: 35606701 PMCID: PMC9126639 DOI: 10.1186/s12859-022-04733-8] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2021] [Accepted: 05/13/2022] [Indexed: 12/12/2022] Open
Abstract
BACKGROUND The effectiveness of biclustering, simultaneous clustering of rows and columns in a data matrix, was shown in gene expression data analysis. Several researchers recognize its potentialities in other research areas. Nevertheless, the last two decades have witnessed the development of a significant number of biclustering algorithms targeting gene expression data analysis and a lack of consistent studies exploring the capacities of biclustering outside this traditional application domain. RESULTS This work evaluates the potential use of biclustering in fMRI time series data, targeting the Region × Time dimensions by comparing seven state-in-the-art biclustering and three traditional clustering algorithms on artificial and real data. It further proposes a methodology for biclustering evaluation beyond gene expression data analysis. The results discuss the use of different search strategies in both artificial and real fMRI time series showed the superiority of exhaustive biclustering approaches, obtaining the most homogeneous biclusters. However, their high computational costs are a challenge, and further work is needed for the efficient use of biclustering in fMRI data analysis. CONCLUSIONS This work pinpoints avenues for the use of biclustering in spatio-temporal data analysis, in particular neurosciences applications. The proposed evaluation methodology showed evidence of the effectiveness of biclustering in finding local patterns in fMRI time series data. Further work is needed regarding scalability to promote the application in real scenarios.
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Affiliation(s)
| | - Helena Aidos
- LASIGE, Faculdade de Ciências, Universidade de Lisboa, Lisbon, Portugal
| | - Sara C Madeira
- LASIGE, Faculdade de Ciências, Universidade de Lisboa, Lisbon, Portugal.
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6
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Kok TE, Domingo D, Hassan J, Vuong A, Hordacre B, Clark C, Katrakazas P, Shekhawat GS. Resting-state Networks in Tinnitus : A Scoping Review. Clin Neuroradiol 2022; 32:903-922. [PMID: 35556148 PMCID: PMC9744700 DOI: 10.1007/s00062-022-01170-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2022] [Accepted: 04/07/2022] [Indexed: 12/16/2022]
Abstract
Chronic subjective tinnitus is the constant perception of a sound that has no physical source. Brain imaging studies show alterations in tinnitus patients' resting-state networks (RSNs). This scoping review aims to provide an overview of resting-state fMRI studies in tinnitus, and to evaluate the evidence for changes in different RSNs. A total of 29 studies were included, 26 of which found alterations in networks such as the auditory network, default mode network, attention networks, and visual network; however, there is a lack of reproducibility in the field which can be attributed to the use of different regions of interest and analytical methods per study, and tinnitus heterogeneity. Future studies should focus on replication by using the same regions of interest in their analysis of resting-state data, and by controlling adequately for potential confounds. These efforts could potentially lead to the identification of a biomarker for tinnitus in the future.
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Affiliation(s)
- Tori Elyssa Kok
- grid.83440.3b0000000121901201Ear Institute, University College London, London, UK
| | - Deepti Domingo
- grid.1014.40000 0004 0367 2697College of Medicine and Public Health, Flinders University, Adelaide, Australia
| | - Joshua Hassan
- grid.1014.40000 0004 0367 2697College of Medicine and Public Health, Flinders University, Adelaide, Australia
| | - Alysha Vuong
- grid.1014.40000 0004 0367 2697College of Medicine and Public Health, Flinders University, Adelaide, Australia
| | - Brenton Hordacre
- grid.1026.50000 0000 8994 5086Innovation, IMPlementation and Clinical Translation (IIMPACT) in Health, Allied Health and Human Performance, University of South Australia, Adelaide, Australia
| | - Chris Clark
- grid.83440.3b0000000121901201Great Ormond Street Institute of Child Health, Department of Developmental Imaging and Biophysics, University College London, London, UK
| | | | - Giriraj Singh Shekhawat
- grid.83440.3b0000000121901201Ear Institute, University College London, London, UK ,grid.1014.40000 0004 0367 2697College of Medicine and Public Health, Flinders University, Adelaide, Australia ,Tinnitus Research Initiative, Regensburg, Germany
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7
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Yu L, Kazinka R, Pratt D, Kwashie A, MacDonald AW. Resting-State Networks Associated with Behavioral and Self-Reported Measures of Persecutory Ideation in Psychosis. Brain Sci 2021; 11:brainsci11111490. [PMID: 34827489 PMCID: PMC8615751 DOI: 10.3390/brainsci11111490] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2021] [Revised: 10/29/2021] [Accepted: 11/03/2021] [Indexed: 11/17/2022] Open
Abstract
Persecutory ideations are self-referential delusions of being the target of malevolence despite a lack of evidence. Wisner et al. (2021) found that reduced connectivity between the left frontoparietal (lFP) network and parts of the orbitofrontal cortex (OFC) correlated with increased persecutory behaviors among psychotic patients performing in an economic social decision-making task that can measure the anticipation of a partner’s spiteful behavior. If this pattern could be observed in the resting state, it would suggest a functional-structural prior predisposing individuals to persecutory ideation. Forty-four patients in the early course of a psychotic disorder provided data for resting-state functional connectivity magnetic resonance imaging across nine brain networks that included the FP network and a similar OFC region. As predicted, we found a significant and negative correlation between the lFP–OFC at rest and the level of suspicious mistrust on the decision-making task using a within-group correlational design. Additionally, self-reported persecutory ideation correlated significantly with the connectivity between the right frontoparietal (rFP) network and the OFC. We extended the previous finding of reduced connectivity between the lFP network and the OFC in psychosis patients to the resting state, and observed a possible hemispheric difference, such that greater rFP–OFC connectivity predicted elevated self-reported persecutory ideation, suggesting potential differences between the lFP and rFP roles in persecutory social interactions.
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8
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Roy M, Rheault F, Croteau E, Castellano CA, Fortier M, St-Pierre V, Houde JC, Turcotte ÉE, Bocti C, Fulop T, Cunnane SC, Descoteaux M. Fascicle- and Glucose-Specific Deterioration in White Matter Energy Supply in Alzheimer's Disease. J Alzheimers Dis 2021; 76:863-881. [PMID: 32568202 DOI: 10.3233/jad-200213] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
BACKGROUND White matter energy supply to oligodendrocytes and the axonal compartment is crucial for normal axonal function. Although gray matter glucose hypometabolism is extensively reported in Alzheimer's disease (AD), glucose and ketones, the brain's two main fuels, are rarely quantified in white matter in AD. OBJECTIVE Using a dual-tracer PET method combined with a fascicle-specific diffusion MRI approach, robust to white matter hyper intensities and crossing fibers, we aimed to quantify both glucose and ketone metabolism in specific white matter fascicles associated with mild cognitive impairment (MCI; n = 51) and AD (n = 13) compared to cognitively healthy age-matched controls (Controls; n = 14). METHODS Eight white matter fascicles of the limbic lobe and corpus callosum were extracted and analyzed into fascicle profiles of five sections. Glucose (18F-fluorodeoxyglucose) and ketone (11C-acetoacetate) uptake rates, corrected for partial volume effect, were calculated along each fascicle. RESULTS The only fascicle with significantly lower glucose uptake in AD compared to Controls was the left posterior cingulate segment of the cingulum (-22%; p = 0.016). Non-significantly lower glucose uptake in this fascicle was also observed in MCI. In contrast to glucose, ketone uptake was either unchanged or higher in sections of the fornix and parahippocampal segment of the cingulum in AD. CONCLUSION To our knowledge, this is the first report of brain fuel uptake calculated along white matter fascicles in humans. Energetic deterioration in white matter in AD appears to be specific to glucose and occurs first in the posterior cingulum.
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Affiliation(s)
- Maggie Roy
- Research Center on Aging, CIUSSS de l'Estrie - CHUS, Sherbrooke, QC, Canada.,Department of Pharmacology and Physiology, Université de Sherbrooke, Sherbrooke, QC, Canada
| | - François Rheault
- Department of Computer Science, Université de Sherbrooke, Sherbrooke, QC, Canada
| | - Etienne Croteau
- CR-CHUS, CIUSSS de l'Estrie - CHUS, Sherbrooke, QC, Canada.,Sherbrooke Molecular Imaging Center, Université de Sherbrooke, Sherbrooke, QC, Canada
| | | | - Mélanie Fortier
- Research Center on Aging, CIUSSS de l'Estrie - CHUS, Sherbrooke, QC, Canada
| | - Valérie St-Pierre
- Research Center on Aging, CIUSSS de l'Estrie - CHUS, Sherbrooke, QC, Canada
| | | | - Éric E Turcotte
- CR-CHUS, CIUSSS de l'Estrie - CHUS, Sherbrooke, QC, Canada.,Sherbrooke Molecular Imaging Center, Université de Sherbrooke, Sherbrooke, QC, Canada.,Department of Nuclear Medicine, Université de Sherbrooke, Sherbrooke, QC, Canada.,Department of Radiobiology, Université de Sherbrooke, Sherbrooke, QC, Canada
| | - Christian Bocti
- Research Center on Aging, CIUSSS de l'Estrie - CHUS, Sherbrooke, QC, Canada.,Department of Medicine, Université de Sherbrooke, Sherbrooke, QC, Canada
| | - Tamas Fulop
- Research Center on Aging, CIUSSS de l'Estrie - CHUS, Sherbrooke, QC, Canada.,Department of Medicine, Université de Sherbrooke, Sherbrooke, QC, Canada
| | - Stephen C Cunnane
- Research Center on Aging, CIUSSS de l'Estrie - CHUS, Sherbrooke, QC, Canada.,Department of Pharmacology and Physiology, Université de Sherbrooke, Sherbrooke, QC, Canada.,Department of Medicine, Université de Sherbrooke, Sherbrooke, QC, Canada
| | - Maxime Descoteaux
- Department of Computer Science, Université de Sherbrooke, Sherbrooke, QC, Canada
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9
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Ibrahim B, Suppiah S, Ibrahim N, Mohamad M, Hassan HA, Nasser NS, Saripan MI. Diagnostic power of resting-state fMRI for detection of network connectivity in Alzheimer's disease and mild cognitive impairment: A systematic review. Hum Brain Mapp 2021; 42:2941-2968. [PMID: 33942449 PMCID: PMC8127155 DOI: 10.1002/hbm.25369] [Citation(s) in RCA: 70] [Impact Index Per Article: 23.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2020] [Revised: 01/29/2021] [Accepted: 02/01/2021] [Indexed: 12/20/2022] Open
Abstract
Resting‐state fMRI (rs‐fMRI) detects functional connectivity (FC) abnormalities that occur in the brains of patients with Alzheimer's disease (AD) and mild cognitive impairment (MCI). FC of the default mode network (DMN) is commonly impaired in AD and MCI. We conducted a systematic review aimed at determining the diagnostic power of rs‐fMRI to identify FC abnormalities in the DMN of patients with AD or MCI compared with healthy controls (HCs) using machine learning (ML) methods. Multimodal support vector machine (SVM) algorithm was the commonest form of ML method utilized. Multiple kernel approach can be utilized to aid in the classification by incorporating various discriminating features, such as FC graphs based on “nodes” and “edges” together with structural MRI‐based regional cortical thickness and gray matter volume. Other multimodal features include neuropsychiatric testing scores, DTI features, and regional cerebral blood flow. Among AD patients, the posterior cingulate cortex (PCC)/Precuneus was noted to be a highly affected hub of the DMN that demonstrated overall reduced FC. Whereas reduced DMN FC between the PCC and anterior cingulate cortex (ACC) was observed in MCI patients. Evidence indicates that the nodes of the DMN can offer moderate to high diagnostic power to distinguish AD and MCI patients. Nevertheless, various concerns over the homogeneity of data based on patient selection, scanner effects, and the variable usage of classifiers and algorithms pose a challenge for ML‐based image interpretation of rs‐fMRI datasets to become a mainstream option for diagnosing AD and predicting the conversion of HC/MCI to AD.
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Affiliation(s)
- Buhari Ibrahim
- Department of Radiology, Faculty of Medicine and Health Sciences, Universiti Putra Malaysia, Serdang, Selangor, Malaysia.,Department of Physiology, Faculty of Basic Medical Sciences, Bauchi State University Gadau, Gadau, Nigeria
| | - Subapriya Suppiah
- Department of Radiology, Faculty of Medicine and Health Sciences, Universiti Putra Malaysia, Serdang, Selangor, Malaysia
| | - Normala Ibrahim
- Department of Psychiatry, Faculty of Medicine and Health Sciences, Universiti Putra Malaysia, Serdang, Selangor, Malaysia
| | - Mazlyfarina Mohamad
- Centre for Diagnostic and Applied Health Sciences, Faculty of Health Sciences, Universiti Kebangsaan Malaysia, Kuala Lumpur, Malaysia
| | - Hasyma Abu Hassan
- Department of Radiology, Faculty of Medicine and Health Sciences, Universiti Putra Malaysia, Serdang, Selangor, Malaysia
| | - Nisha Syed Nasser
- Department of Radiology, Faculty of Medicine and Health Sciences, Universiti Putra Malaysia, Serdang, Selangor, Malaysia
| | - M Iqbal Saripan
- Department of Computer and Communication System Engineering, Universiti Putra Malaysia, Serdang, Selangor, Malaysia
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10
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Yang W, Pilozzi A, Huang X. An Overview of ICA/BSS-Based Application to Alzheimer's Brain Signal Processing. Biomedicines 2021; 9:386. [PMID: 33917280 PMCID: PMC8067382 DOI: 10.3390/biomedicines9040386] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2021] [Revised: 03/29/2021] [Accepted: 03/30/2021] [Indexed: 11/16/2022] Open
Abstract
Alzheimer's disease (AD) is by far the most common cause of dementia associated with aging. Early and accurate diagnosis of AD and ability to track progression of the disease is increasingly important as potential disease-modifying therapies move through clinical trials. With the advent of biomedical techniques, such as computerized tomography (CT), electroencephalography (EEG), magnetoencephalography (MEG), positron emission tomography (PET), magnetic resonance imaging (MRI), and functional magnetic resonance imaging (fMRI), large amounts of data from Alzheimer's patients have been acquired and processed from which AD-related information or "signals" can be assessed for AD diagnosis. It remains unknown how best to mine complex information from these brain signals to aid in early diagnosis of AD. An increasingly popular technique for processing brain signals is independent component analysis or blind source separation (ICA/BSS) that separates blindly observed signals into original signals that are as independent as possible. This overview focuses on ICA/BSS-based applications to AD brain signal processing.
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Affiliation(s)
- Wenlu Yang
- Department of Electrical Engineering, Information Engineering College, Shanghai Maritime University, Shanghai 200135, China;
| | - Alexander Pilozzi
- Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA 02129, USA;
| | - Xudong Huang
- Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA 02129, USA;
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11
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Cao J, Huang Y, Meshberg N, Hodges SA, Kong J. Neuroimaging-Based Scalp Acupuncture Locations for Dementia. J Clin Med 2020; 9:E2477. [PMID: 32752265 PMCID: PMC7463942 DOI: 10.3390/jcm9082477] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2020] [Revised: 07/21/2020] [Accepted: 07/29/2020] [Indexed: 12/12/2022] Open
Abstract
Scalp acupuncture is a modality of acupuncture in which acupuncture needles are inserted into a certain layer of the scalp in order to affect the function of corresponding areas of the cerebral cortex and relieve symptoms. Clinical studies have demonstrated the potential of scalp acupuncture as a non-pharmacological treatment for dementia. Unfortunately, recent findings from brain neuroimaging studies on dementia have not been incorporated into scalp acupuncture. This study aims to integrate meta-analysis, resting-state functional connectivity, and diffusion tensor imaging (DTI) to identify potential locations of scalp acupuncture for treatment of dementia. We found that the prefrontal cortex, the medial prefrontal cortex, the middle and superior temporal gyrus, the temporal pole, the supplementary motor area, the inferior occipital gyrus, and the precuneus are involved in the pathophysiology of dementia and, therefore, may be the target areas of scalp acupuncture for dementia treatment. The neuroimaging-based scalp acupuncture protocol developed in this study may help to refine the locations for the treatment of dementia. Integrating multidisciplinary methods to identify key surface cortical areas associated with a certain disorder may shed light on the development of scalp acupuncture and other neuromodulation methods such as transcranial electrical current stimulation, particularly in the domain of identifying stimulation locations.
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Affiliation(s)
- Jin Cao
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Building 120, 2nd AVE, Charlestown, MA 02129, USA
| | - Yiting Huang
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Building 120, 2nd AVE, Charlestown, MA 02129, USA
| | - Nathaniel Meshberg
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Building 120, 2nd AVE, Charlestown, MA 02129, USA
| | - Sierra A Hodges
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Building 120, 2nd AVE, Charlestown, MA 02129, USA
| | - Jian Kong
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Building 120, 2nd AVE, Charlestown, MA 02129, USA
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12
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Intrinsic functional connectivity, CSF biomarker profiles and their relation to cognitive function in mild cognitive impairment. Acta Neuropsychiatr 2020; 32:206-213. [PMID: 31801648 DOI: 10.1017/neu.2019.49] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/19/2023]
Abstract
Mild cognitive impairment (MCI) often precedes Alzheimer's Dementia (AD), and in a high proportion of individuals affected by MCI, there are already neuropathological processes ongoing that become more evident when patients progress to AD. Accordingly, there is a need for reliable biomarkers to distinguish between normal aging and incipient AD. Recent research suggests that, in addition to established biomarkers such as CSF Aß42, total tau and hyperphosphorylated tau, resting state connectivity established by functional magnetic resonance imaging might also be a feasible biomarker for prodromal stages of AD. In order to explore this possibility, we investigated resting state functional connectivity as well as cerebrospinal fluid (CSF) biomarker profiles in patients with MCI (n = 30; age 66.43 ± 7.06 years) and cognitively healthy controls (n = 38; age 66.89 ± 7.12 years). CSF Aß42, total tau and hyperphosphorylated tau concentrations were correlated with measures of cognitive performance (immediate and delayed recall, global cognition, processing speed). Moreover, MCI-related alterations in intrinsic functional connectivity within the default mode network were investigated using functional resting state MRI. As expected, MCI patients showed decreased CSF Aß42 and increased total tau concentrations. These alterations were associated with cognitive performance. However, there were no differences between MCI patients and cognitively healthy controls regarding intrinsic functional connectivity. In conclusion, our results indicate that CSF protein profiles seem to be more closely related to cognitive decline than alterations in resting state activity. Thus, resting state connectivity might not be a reliable biomarker for early stages of AD.
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13
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Ranasinghe KG, Cha J, Iaccarino L, Hinkley LB, Beagle AJ, Pham J, Jagust WJ, Miller BL, Rankin KP, Rabinovici GD, Vossel KA, Nagarajan SS. Neurophysiological signatures in Alzheimer's disease are distinctly associated with TAU, amyloid-β accumulation, and cognitive decline. Sci Transl Med 2020; 12:eaaz4069. [PMID: 32161102 PMCID: PMC7138514 DOI: 10.1126/scitranslmed.aaz4069] [Citation(s) in RCA: 50] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2019] [Accepted: 02/03/2020] [Indexed: 12/31/2022]
Abstract
Neural synchrony is intricately balanced in the normal resting brain but becomes altered in Alzheimer's disease (AD). To determine the neurophysiological manifestations associated with molecular biomarkers of AD neuropathology, in patients with AD, we used magnetoencephalographic imaging (MEGI) and positron emission tomography with amyloid-beta (Aβ) and TAU tracers. We found that alpha oscillations (8 to 12 Hz) were hyposynchronous in occipital and posterior temporoparietal cortices, whereas delta-theta oscillations (2 to 8 Hz) were hypersynchronous in frontal and anterior temporoparietal cortices, in patients with AD compared to age-matched controls. Regional patterns of alpha hyposynchrony were unique in each neurobehavioral phenotype of AD, whereas the regional patterns of delta-theta hypersynchrony were similar across the phenotypes. Alpha hyposynchrony strongly colocalized with TAU deposition and was modulated by the degree of TAU tracer uptake. In contrast, delta-theta hypersynchrony colocalized with both TAU and Aβ depositions and was modulated by both TAU and Aβ tracer uptake. Furthermore, alpha hyposynchrony but not delta-theta hypersynchrony was correlated with the degree of global cognitive dysfunction in patients with AD. The current study demonstrates frequency-specific neurophysiological signatures of AD pathophysiology and suggests that neurophysiological measures from MEGI are sensitive indices of network disruptions mediated by TAU and Aβ and associated cognitive decline. These findings facilitate the pursuit of novel therapeutic approaches toward normalizing network synchrony in AD.
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Affiliation(s)
- Kamalini G Ranasinghe
- Memory and Aging Center, Department of Neurology, University of California, San Francisco, San Francisco, CA 94158, USA.
| | - Jungho Cha
- Memory and Aging Center, Department of Neurology, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Leonardo Iaccarino
- Memory and Aging Center, Department of Neurology, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Leighton B Hinkley
- Department Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA 94143, USA
| | - Alexander J Beagle
- Memory and Aging Center, Department of Neurology, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Julie Pham
- Memory and Aging Center, Department of Neurology, University of California, San Francisco, San Francisco, CA 94158, USA
| | - William J Jagust
- Helen Wills Neuroscience Institute, UC Berkeley, Berkeley, CA 94720, USA
| | - Bruce L Miller
- Memory and Aging Center, Department of Neurology, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Katherine P Rankin
- Memory and Aging Center, Department of Neurology, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Gil D Rabinovici
- Memory and Aging Center, Department of Neurology, University of California, San Francisco, San Francisco, CA 94158, USA
- Department Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA 94143, USA
| | - Keith A Vossel
- Memory and Aging Center, Department of Neurology, University of California, San Francisco, San Francisco, CA 94158, USA
- N. Bud Grossman Center for Memory Research and Care, Institute for Translational Neuroscience, and Department of Neurology, University of Minnesota, Minneapolis, MN 55455, USA
| | - Srikantan S Nagarajan
- Department Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA 94143, USA
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14
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Mohanty R, Sethares WA, Nair VA, Prabhakaran V. Rethinking Measures of Functional Connectivity via Feature Extraction. Sci Rep 2020; 10:1298. [PMID: 31992762 PMCID: PMC6987226 DOI: 10.1038/s41598-020-57915-w] [Citation(s) in RCA: 57] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2019] [Accepted: 01/08/2020] [Indexed: 11/17/2022] Open
Abstract
Functional magnetic resonance imaging (fMRI)-based functional connectivity (FC) commonly characterizes the functional connections in the brain. Conventional quantification of FC by Pearson's correlation captures linear, time-domain dependencies among blood-oxygen-level-dependent (BOLD) signals. We examined measures to quantify FC by investigating: (i) Is Pearson's correlation sufficient to characterize FC? (ii) Can alternative measures better quantify FC? (iii) What are the implications of using alternative FC measures? FMRI analysis in healthy adult population suggested that: (i) Pearson's correlation cannot comprehensively capture BOLD inter-dependencies. (ii) Eight alternative FC measures were similarly consistent between task and resting-state fMRI, improved age-based classification and provided better association with behavioral outcomes. (iii) Formulated hypotheses were: first, in lieu of Pearson’s correlation, an augmented, composite and multi-metric definition of FC is more appropriate; second, canonical large-scale brain networks may depend on the chosen FC measure. A thorough notion of FC promises better understanding of variations within a given population.
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Affiliation(s)
- Rosaleena Mohanty
- Department of Radiology, University of Wisconsin-Madison, Madison, WI, USA. .,Electrical Engineering, University of Wisconsin-Madison, Madison, WI, USA. .,Karolinska Institutet, Huddinge, Sweden.
| | - William A Sethares
- Electrical Engineering, University of Wisconsin-Madison, Madison, WI, USA
| | - Veena A Nair
- Department of Radiology, University of Wisconsin-Madison, Madison, WI, USA
| | - Vivek Prabhakaran
- Department of Radiology, University of Wisconsin-Madison, Madison, WI, USA.,Department of Psychiatry, University of Wisconsin-Madison, Madison, USA.,Department of Medical Physics, University of Wisconsin-Madison, Madison, WI, USA
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15
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Nalci A, Luo W, Liu TT. Nuisance effects in inter-scan functional connectivity estimates before and after nuisance regression. Neuroimage 2019; 202:116005. [PMID: 31336189 DOI: 10.1016/j.neuroimage.2019.07.018] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2019] [Revised: 06/06/2019] [Accepted: 07/08/2019] [Indexed: 12/13/2022] Open
Abstract
In resting-state functional MRI, the correlation between blood-oxygenation-level-dependent (BOLD) signals across brain regions is used to estimate the functional connectivity (FC) of the brain. FC estimates are prone to the influence of nuisance factors including scanner-related artifacts and physiological modulations of the BOLD signal. Nuisance regression is widely performed to reduce the effect of nuisance factors on FC estimates on a per-scan basis. However, a dedicated analysis of nuisance effects on the variability of FC metrics across a collection of scans has been lacking. This work investigates the effects of nuisance factors on the variability of FC estimates across a collection of scans both before and after nuisance regression. Inter-scan variations in FC estimates are shown to be significantly correlated with the geometric norms of various nuisance terms, including head motion measurements, signals derived from white-matter and cerebrospinal regions, and the whole-brain global signal (GS) both before and after nuisance regression. In addition, it is shown that GS regression (GSR) can introduce GS norm-related fluctuations that are negatively correlated with inter-scan FC estimates. The empirical results are shown to be largely consistent with the predictions of a theoretical framework previously developed for the characterization of dynamic FC measures. This work shows that caution must be exercised when interpreting inter-scan FC measures across scans both before and after nuisance regression.
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Affiliation(s)
- Alican Nalci
- Center for Functional MRI, University of California San Diego, 9500 Gilman Drive MC 0677, La Jolla, CA, 92093, USA; Department of Electrical and Computer Engineering, University of California San Diego, 9500 Gilman Drive, La Jolla, CA, 92093, USA.
| | - Wenjing Luo
- Center for Functional MRI, University of California San Diego, 9500 Gilman Drive MC 0677, La Jolla, CA, 92093, USA
| | - Thomas T Liu
- Center for Functional MRI, University of California San Diego, 9500 Gilman Drive MC 0677, La Jolla, CA, 92093, USA; Departments of Radiology, Psychiatry, and Bioengineering, University of California San Diego, 9500 Gilman Drive, La Jolla, CA, 92093, USA.
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16
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Characteristic changes in the default mode network in hypertensive patients with cognitive impairment. Hypertens Res 2018; 42:530-540. [PMID: 30573810 DOI: 10.1038/s41440-018-0176-4] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2018] [Revised: 08/24/2018] [Accepted: 09/17/2018] [Indexed: 02/04/2023]
Abstract
Hypertension has a close affinity to brain degeneration and cognitive decline during the aging process. The default mode network (DMN) is usually affected in various diseases related to cognitive impairment (CI). The present research aimed to explore the alterations in the DMN and its subcomponents in hypertensive patients with and without CI and to investigate the associations between cognitive performance and network abnormalities. Resting-state functional magnetic resonance imaging and neuropsychological tests were performed in 74 subjects, namely, 30 hypertensive patients with normal cognition (HTN-NC), 25 hypertensive patients with CI (HTN-CI), and 19 healthy controls. Seed-based functional connectivity (FC) analysis was performed to identify the DMN patterns. The group differences in the DMN were mainly shown in brain regions related to the core subsystem and the dorsal medial subsystem of the DMN. Post hoc analysis revealed a trend of dissociation among the DMN subsystems in the HTN-NC group. In contrast, the HTN-CI group displayed extensively increased FC in both subsystems. Importantly, increased FC of the dorsal medial subsystem in the HTN-CI patients was associated with poor cognitive performance, such as scores on Mini-Mental State Examination (ρ = -0.438, P = 0.029) and Montreal Cognitive Assessment (ρ = -0.449, P = 0.025). The findings suggest that extensively increased connectivities in the core subsystem and the dorsal media subsystem of the DMN may distinguish hypertension with CI from hypertension with normal cognition. The characteristic change in the dorsal medial subsystem may become an early imaging biomarker for the diagnosis and treatment of cognitive impairment associated with hypertension.
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17
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Exploring brain functional connectivity in rest and sleep states: a fNIRS study. Sci Rep 2018; 8:16144. [PMID: 30385843 PMCID: PMC6212555 DOI: 10.1038/s41598-018-33439-2] [Citation(s) in RCA: 35] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2018] [Accepted: 09/19/2018] [Indexed: 11/16/2022] Open
Abstract
This study investigates the brain functional connectivity in the rest and sleep states. We collected EEG, EOG, and fNIRS signals simultaneously during rest and sleep phases. The rest phase was defined as a quiet wake-eyes open (w_o) state, while the sleep phase was separated into three states; quiet wake-eyes closed (w_c), non-rapid eye movement sleep stage 1 (N1), and non-rapid eye movement sleep stage 2 (N2) using the EEG and EOG signals. The fNIRS signals were used to calculate the cerebral hemodynamic responses (oxy-, deoxy-, and total hemoglobin). We grouped 133 fNIRS channels into five brain regions (frontal, motor, temporal, somatosensory, and visual areas). These five regions were then used to form fifteen brain networks. A network connectivity was computed by calculating the Pearson correlation coefficients of the hemodynamic responses between fNIRS channels belonging to the network. The fifteen networks were compared across the states using the connection ratio and connection strength calculated from the normalized correlation coefficients. Across all fifteen networks and three hemoglobin types, the connection ratio was high in the w_c and N1 states and low in the w_o and N2 states. In addition, the connection strength was similar between the w_c and N1 states and lower in the w_o and N2 states. Based on our experimental results, we believe that fNIRS has a high potential to be a main tool to study the brain connectivity in the rest and sleep states.
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18
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Wink AM, Tijms BM, Ten Kate M, Raspor E, de Munck JC, Altena E, Ecay-Torres M, Clerigue M, Estanga A, Garcia-Sebastian M, Izagirre A, Martinez-Lage Alvarez P, Villanua J, Barkhof F, Sanz-Arigita E. Functional brain network centrality is related to APOE genotype in cognitively normal elderly. Brain Behav 2018; 8:e01080. [PMID: 30136422 PMCID: PMC6160659 DOI: 10.1002/brb3.1080] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/11/2018] [Revised: 06/20/2018] [Accepted: 06/25/2018] [Indexed: 01/19/2023] Open
Abstract
INTRODUCTION Amyloid plaque deposition in the brain is an early pathological change in Alzheimer's disease (AD), causing disrupted synaptic connections. Brain network disruptions in AD have been demonstrated with eigenvector centrality (EC), a measure that identifies central regions within networks. Carrying an apolipoprotein (APOE)-ε4 allele is a genetic risk for AD, associated with increased amyloid deposition. We studied whether APOE-ε4 carriership is associated with EC disruptions in cognitively normal individuals. METHODS A total of 261 healthy middle-aged to older adults (mean age 56.6 years) were divided into high-risk (APOE-ε4 carriers) and low-risk (noncarriers) groups. EC was computed from resting-state functional MRI data. Clusters of between-group differences were assessed with a permutation-based method. Correlations between cluster mean EC with brain volume, CSF biomarkers, and psychological test scores were assessed. RESULTS Decreased EC in the visual cortex was associated with APOE-ε4 carriership, a genetic risk factor for AD. EC differences were correlated with age, CSF amyloid levels, and scores on the trail-making and 15-object recognition tests. CONCLUSION Our findings suggest that the APOE-ε4 genotype affects brain connectivity in regions previously found to be abnormal in AD as a sign of very early disease-related pathology. These differences were too subtle in healthy elderly to use EC for single-subject prediction of APOE genotype.
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Affiliation(s)
- Alle Meije Wink
- Department of Radiology, Nuclear Medicine and PET Research, Neuroscience Campus Amsterdam, VU University Medical Centre, Amsterdam, The Netherlands
| | - Betty M Tijms
- Department of Neurology, Alzheimer Centre, Neuroscience Campus Amsterdam, VU University Medical Centre, Amsterdam, The Netherlands
| | - Mara Ten Kate
- Department of Neurology, Alzheimer Centre, Neuroscience Campus Amsterdam, VU University Medical Centre, Amsterdam, The Netherlands
| | - Eva Raspor
- Department of Radiology, Nuclear Medicine and PET Research, Neuroscience Campus Amsterdam, VU University Medical Centre, Amsterdam, The Netherlands
| | - Jan C de Munck
- Department of Physics and Medical Technology, Neuroscience Campus Amsterdam, VU University Medical Centre, Amsterdam, The Netherlands
| | - Ellemarije Altena
- Université Bordeaux, Bordeaux, France.,CNRS, SANPSY, USR 3413, Bordeaux, France
| | - Mirian Ecay-Torres
- CITA Alzheimer Foundation, Donostia University Hospital, San Sebastian, Spain
| | - Montserrat Clerigue
- CITA Alzheimer Foundation, Donostia University Hospital, San Sebastian, Spain
| | - Ainara Estanga
- CITA Alzheimer Foundation, Donostia University Hospital, San Sebastian, Spain
| | | | - Andrea Izagirre
- CITA Alzheimer Foundation, Donostia University Hospital, San Sebastian, Spain
| | | | - Jorge Villanua
- CITA Alzheimer Foundation, Donostia University Hospital, San Sebastian, Spain.,Donostia Unit, Osatek, Donostia University Hospital, San Sebastian, Spain
| | - Frederik Barkhof
- Department of Radiology, Nuclear Medicine and PET Research, Neuroscience Campus Amsterdam, VU University Medical Centre, Amsterdam, The Netherlands.,Institutes of Neurology and Healthcare Engineering, University College London, London, UK
| | - Ernesto Sanz-Arigita
- Department of Radiology, Nuclear Medicine and PET Research, Neuroscience Campus Amsterdam, VU University Medical Centre, Amsterdam, The Netherlands.,Université Bordeaux, Bordeaux, France
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19
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Karunamuni RJ, Kong L, Tu W. Efficient robust doubly adaptive regularized regression with applications. Stat Methods Med Res 2018; 28:2210-2226. [PMID: 29451086 DOI: 10.1177/0962280218757560] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
We consider the problem of estimation and variable selection for general linear regression models. Regularized regression procedures have been widely used for variable selection, but most existing methods perform poorly in the presence of outliers. We construct a new penalized procedure that simultaneously attains full efficiency and maximum robustness. Furthermore, the proposed procedure satisfies the oracle properties. The new procedure is designed to achieve sparse and robust solutions by imposing adaptive weights on both the decision loss and the penalty function. The proposed method of estimation and variable selection attains full efficiency when the model is correct and, at the same time, achieves maximum robustness when outliers are present. We examine the robustness properties using the finite-sample breakdown point and an influence function. We show that the proposed estimator attains the maximum breakdown point. Furthermore, there is no loss in efficiency when there are no outliers or the error distribution is normal. For practical implementation of the proposed method, we present a computational algorithm. We examine the finite-sample and robustness properties using Monte Carlo studies. Two datasets are also analyzed.
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Affiliation(s)
- Rohana J Karunamuni
- Department of Mathematical and Statistical Sciences, University of Alberta, Alberta, Canada
| | - Linglong Kong
- Department of Mathematical and Statistical Sciences, University of Alberta, Alberta, Canada
| | - Wei Tu
- Department of Mathematical and Statistical Sciences, University of Alberta, Alberta, Canada
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20
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Yoneda T, Rush J, Berg AI, Johansson B, Piccinin AM. Trajectories of Personality Traits Preceding Dementia Diagnosis. J Gerontol B Psychol Sci Soc Sci 2017; 72:922-931. [PMID: 26945005 PMCID: PMC5927080 DOI: 10.1093/geronb/gbw006] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2015] [Accepted: 01/13/2016] [Indexed: 11/12/2022] Open
Abstract
BACKGROUND Several retrospective studies using informant report have shown that individuals with dementia demonstrate considerable personality change. Two prospective studies, also using informant report, have shown that individuals who develop dementia show some personality changes prior to diagnosis. The current study is the first to assess personality trait change prior to dementia diagnosis using self-report measures from longitudinal data. METHOD This study used data from the Swedish OCTO-Twin Study, a longitudinal panel of 702 twins aged 80 and older. Analysis was restricted to 86 individuals who completed the Eysenck Personality Inventory and received a dementia diagnosis during follow-up occasions. Latent growth curve analyses were used to examine trajectories of extraversion and neuroticism preceding dementia diagnosis. RESULTS Controlling for sex, age, education, depressive symptoms, and the interaction between age and education, growth curve analyses revealed a linear increase in neuroticism and stability in extraversion. Individuals who were eventually diagnosed with dementia showed a significant increase in neuroticism preceding diagnosis of dementia. DISCUSSION Personality change, specifically an increase in neuroticism, may be an early indicator of dementia. Identification of early indicators of dementia may facilitate development of screening assessments and aid in early care strategies and planning.
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Affiliation(s)
- Tomiko Yoneda
- Department of Psychology, University of Victoria, British Columbia, Canada
- Anne Ingeborg Berg and Boo Johansson, Department of Psychology, University of Gothenburg, Sweden
| | - Jonathan Rush
- Department of Psychology, University of Victoria, British Columbia, Canada
- Anne Ingeborg Berg and Boo Johansson, Department of Psychology, University of Gothenburg, Sweden
| | - Anne Ingeborg Berg
- Anne Ingeborg Berg and Boo Johansson, Department of Psychology, University of Gothenburg, Sweden
| | - Boo Johansson
- Anne Ingeborg Berg and Boo Johansson, Department of Psychology, University of Gothenburg, Sweden
| | - Andrea M Piccinin
- Department of Psychology, University of Victoria, British Columbia, Canada
- Anne Ingeborg Berg and Boo Johansson, Department of Psychology, University of Gothenburg, Sweden
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21
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A whole-brain computational modeling approach to explain the alterations in resting-state functional connectivity during progression of Alzheimer's disease. NEUROIMAGE-CLINICAL 2017; 16:343-354. [PMID: 28861336 PMCID: PMC5568172 DOI: 10.1016/j.nicl.2017.08.006] [Citation(s) in RCA: 45] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/24/2017] [Revised: 07/22/2017] [Accepted: 08/07/2017] [Indexed: 12/20/2022]
Abstract
Alzheimer's disease (AD) is the most common dementia with dramatic consequences. The research in structural and functional neuroimaging showed altered brain connectivity in AD. In this study, we investigated the whole-brain resting state functional connectivity (FC) of the subjects with preclinical Alzheimer's disease (PAD), mild cognitive impairment due to AD (MCI) and mild dementia due to Alzheimer's disease (AD), the impact of APOE4 carriership, as well as in relation to variations in core AD CSF biomarkers. The synchronization in the whole-brain was monotonously decreasing during the course of the disease progression. Furthermore, in AD patients we found widespread significant decreases in functional connectivity (FC) strengths particularly in the brain regions with high global connectivity. We employed a whole-brain computational modeling approach to study the mechanisms underlying these alterations. To characterize the causal interactions between brain regions, we estimated the effective connectivity (EC) in the model. We found that the significant EC differences in AD were primarily located in left temporal lobe. Then, we systematically manipulated the underlying dynamics of the model to investigate simulated changes in FC based on the healthy control subjects. Furthermore, we found distinct patterns involving CSF biomarkers of amyloid-beta (Aβ1 - 42) total tau (t-tau) and phosphorylated tau (p-tau). CSF Aβ1 - 42 was associated to the contrast between healthy control subjects and clinical groups. Nevertheless, tau CSF biomarkers were associated to the variability in whole-brain synchronization and sensory integration regions. These associations were robust across clinical groups, unlike the associations that were found for CSF Aβ1 - 42. APOE4 carriership showed no significant correlations with the connectivity measures.
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22
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Olivito G, Cercignani M, Lupo M, Iacobacci C, Clausi S, Romano S, Masciullo M, Molinari M, Bozzali M, Leggio M. Neural substrates of motor and cognitive dysfunctions in SCA2 patients: A network based statistics analysis. NEUROIMAGE-CLINICAL 2017; 14:719-725. [PMID: 28393013 PMCID: PMC5377430 DOI: 10.1016/j.nicl.2017.03.009] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/12/2017] [Revised: 03/07/2017] [Accepted: 03/24/2017] [Indexed: 01/04/2023]
Abstract
Spinocerebellar ataxia type 2 (SCA2) is an autosomal dominant neurodegenerative disease characterized by a progressive cerebellar syndrome, which can be isolated or associated with extracerebellar signs. It has been shown that patients affected by SCA2 present also cognitive impairments and psychiatric symptoms. The cerebellum is known to modulate cortical activity and to contribute to distinct functional networks related to higher-level functions beyond motor control. It is therefore conceivable that one or more networks, rather than isolated regions, may be dysfunctional in cerebellar degenerative diseases and that an abnormal connectivity within specific cerebello-cortical regions might explain the widespread deficits typically observed in patients. In the present study, the network-based statistics (NBS) approach was used to assess differences in functional connectivity between specific cerebellar and cerebral “nodes” in SCA2 patients. Altered inter-nodal connectivity was found between more posterior regions in the cerebellum and regions in the cerebral cortex clearly related to cognition and emotion. Furthermore, more anterior cerebellar lobules showed altered inter-nodal connectivity with motor and somatosensory cerebral regions. The present data suggest that in SCA2 a cerebellar dysfunction affects long-distance cerebral regions and that the clinical symptoms may be specifically related with connectivity changes between motor and non-motor cerebello-cortical nodes. A cerebellar dysfunction affects long-distance cerebral regions in SCA2 patients. Connectivity changes affect sensorimotor and cognitive cerebello-cortical nodes. Cerebellar symptoms may be related to altered cerebello-cerebral connectivity.
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Affiliation(s)
- G Olivito
- Ataxia Laboratory, IRCCS Santa Lucia Foundation, Rome, Italy; Neuroimaging Laboratory, IRCCS Santa Lucia Foundation, Rome, Italy
| | - M Cercignani
- Neuroimaging Laboratory, IRCCS Santa Lucia Foundation, Rome, Italy; Clinical Imaging Science Center, Brighton and Sussex Medical School, Brighton, UK
| | - M Lupo
- Ataxia Laboratory, IRCCS Santa Lucia Foundation, Rome, Italy
| | - C Iacobacci
- Ataxia Laboratory, IRCCS Santa Lucia Foundation, Rome, Italy; Department of Psychology, Faculty of Medicine and Psychology, "Sapienza" University of Rome, Rome, Italy
| | - S Clausi
- Ataxia Laboratory, IRCCS Santa Lucia Foundation, Rome, Italy; Department of Psychology, Faculty of Medicine and Psychology, "Sapienza" University of Rome, Rome, Italy
| | - S Romano
- Department of Neurosciences, Mental Health and Sensory Organs (NESMOS), "Sapienza" University of Rome-Sant'Andrea Hospital, Rome, Italy
| | - M Masciullo
- SPInal REhabilitation Lab, IRCCS Fondazione Santa Lucia,Rome, Italy
| | - M Molinari
- Neurorehabilitation 1 and Spinal Center, Robotic Neurorehabilitation Lab, IRCCS Santa Lucia Foundation, Rome, Italy
| | - M Bozzali
- Neuroimaging Laboratory, IRCCS Santa Lucia Foundation, Rome, Italy
| | - M Leggio
- Ataxia Laboratory, IRCCS Santa Lucia Foundation, Rome, Italy; Department of Psychology, Faculty of Medicine and Psychology, "Sapienza" University of Rome, Rome, Italy
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23
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Pagani M, Giuliani A, Öberg J, De Carli F, Morbelli S, Girtler N, Arnaldi D, Accardo J, Bauckneht M, Bongioanni F, Chincarini A, Sambuceti G, Jonsson C, Nobili F. Progressive Disintegration of Brain Networking from Normal Aging to Alzheimer Disease: Analysis of Independent Components of 18F-FDG PET Data. J Nucl Med 2017; 58:1132-1139. [PMID: 28280223 DOI: 10.2967/jnumed.116.184309] [Citation(s) in RCA: 32] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2016] [Accepted: 12/01/2016] [Indexed: 12/13/2022] Open
Abstract
Brain connectivity has been assessed in several neurodegenerative disorders investigating the mutual correlations between predetermined regions or nodes. Selective breakdown of brain networks during progression from normal aging to Alzheimer disease dementia (AD) has also been observed. Methods: We implemented independent-component analysis of 18F-FDG PET data in 5 groups of subjects with cognitive states ranging from normal aging to AD-including mild cognitive impairment (MCI) not converting or converting to AD-to disclose the spatial distribution of the independent components in each cognitive state and their accuracy in discriminating the groups. Results: We could identify spatially distinct independent components in each group, with generation of local circuits increasing proportionally to the severity of the disease. AD-specific independent components first appeared in the late-MCI stage and could discriminate converting MCI and AD from nonconverting MCI with an accuracy of 83.5%. Progressive disintegration of the intrinsic networks from normal aging to MCI to AD was inversely proportional to the conversion time. Conclusion: Independent-component analysis of 18F-FDG PET data showed a gradual disruption of functional brain connectivity with progression of cognitive decline in AD. This information might be useful as a prognostic aid for individual patients and as a surrogate biomarker in intervention trials.
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Affiliation(s)
- Marco Pagani
- Institute of Cognitive Sciences and Technologies, CNR, Rome, Italy .,Department of Nuclear Medicine, Karolinska Hospital, Stockholm, Sweden
| | - Alessandro Giuliani
- Environment and Health Department, Istituto Superiore di Sanità, Rome, Italy
| | - Johanna Öberg
- Department of Hospital Physics, Karolinska Hospital, Stockholm, Sweden
| | | | - Silvia Morbelli
- Departments of Nuclear Medicine and Health Science, University of Genoa, and IRCCS AOU San Martino-IST, Genoa, Italy
| | - Nicola Girtler
- Clinical Neurology, Department of Neuroscience, University of Genoa, and IRCCS AOU San Martino-IST, Genoa, Italy.,Clinical Psychology, IRCCS AOU San Martino-IST, Genoa, Italy; and
| | - Dario Arnaldi
- Clinical Neurology, Department of Neuroscience, University of Genoa, and IRCCS AOU San Martino-IST, Genoa, Italy
| | - Jennifer Accardo
- Clinical Neurology, Department of Neuroscience, University of Genoa, and IRCCS AOU San Martino-IST, Genoa, Italy
| | - Matteo Bauckneht
- Departments of Nuclear Medicine and Health Science, University of Genoa, and IRCCS AOU San Martino-IST, Genoa, Italy
| | - Francesca Bongioanni
- Departments of Nuclear Medicine and Health Science, University of Genoa, and IRCCS AOU San Martino-IST, Genoa, Italy
| | | | - Gianmario Sambuceti
- Departments of Nuclear Medicine and Health Science, University of Genoa, and IRCCS AOU San Martino-IST, Genoa, Italy
| | - Cathrine Jonsson
- Department of Hospital Physics, Karolinska Hospital, Stockholm, Sweden
| | - Flavio Nobili
- Clinical Neurology, Department of Neuroscience, University of Genoa, and IRCCS AOU San Martino-IST, Genoa, Italy
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Hege MA, Stingl KT, Veit R, Preissl H. Modulation of attentional networks by food-related disinhibition. Physiol Behav 2017; 176:84-92. [PMID: 28237551 DOI: 10.1016/j.physbeh.2017.02.023] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2016] [Revised: 02/16/2017] [Accepted: 02/17/2017] [Indexed: 10/20/2022]
Abstract
The risk of weight gain is especially related to disinhibition, which indicates the responsiveness to external food stimuli with associated disruptions in eating control. We adapted a food-related version of the attention network task and used functional magnetic resonance imaging to study the effects of disinhibition on attentional networks in 19 normal-weight participants. High disinhibition scores were associated with a rapid reorienting response to food pictures after invalid cueing and with an enhanced alerting effect of a warning cue signalizing the upcoming appearance of a food picture. Imaging data revealed activation of a right-lateralized ventral attention network during reorienting. The faster the reorienting and the higher the disinhibition score, the less activation of this network was observed. The alerting contrast showed activation in visual, temporo-parietal and anterior sites. These modulations of attentional networks by food-related disinhibition might be related to an attentional bias to energy dense and palatable food and increased intake of food in disinhibited individuals.
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Affiliation(s)
- Maike A Hege
- Institute for Diabetes Research and Metabolic Diseases of the Helmholtz Center Munich at the University of Tübingen, German Center for Diabetes Research (DZD e.V.), Tübingen, Germany.
| | - Krunoslav T Stingl
- Pupil Research Group at the Centre for Ophthalmology, University of Tübingen, Tübingen, Germany
| | - Ralf Veit
- Institute for Diabetes Research and Metabolic Diseases of the Helmholtz Center Munich at the University of Tübingen, German Center for Diabetes Research (DZD e.V.), Tübingen, Germany; Institute of Medical Psychology and Behavioural Neurobiology, University of Tübingen, Tübingen, Germany
| | - Hubert Preissl
- Institute for Diabetes Research and Metabolic Diseases of the Helmholtz Center Munich at the University of Tübingen, German Center for Diabetes Research (DZD e.V.), Tübingen, Germany; Institute for Diabetes and Obesity, Helmholtz Diabetes Center, Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH), Neuherberg, Germany; Department of Internal Medicine, Division of Endocrinology, Diabetology, Angiology, Nephrology and Clinical Chemistry, Eberhard Karls University Tübingen, Tübingen, Germany; Institute of Pharmaceutical Sciences, Department of Pharmacy and Biochemistry, Interfaculty Centre for Pharmacogenomics and Pharma Research, Eberhard Karls University Tübingen, Tübingen, Germany
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25
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Zhu X, Cortes CR, Mathur K, Tomasi D, Momenan R. Model-free functional connectivity and impulsivity correlates of alcohol dependence: a resting-state study. Addict Biol 2017; 22:206-217. [PMID: 26040546 DOI: 10.1111/adb.12272] [Citation(s) in RCA: 106] [Impact Index Per Article: 15.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
Alcohol dependence is characterized by impulsiveness toward consumption despite negative consequences. Although neuro-imaging studies have implicated some regions underlying this disorder, there is little information regarding its large-scale connectivity pattern. This study investigated the within- and between-network functional connectivity (FC) in alcohol dependence and examined its relationship with clinical impulsivity measures. Using probabilistic independent component analysis on resting-state functional magnetic resonance imaging (rs-fMRI) data from 25 alcohol-dependent (AD) and 26 healthy control (HC) participants, we compared the within- and between-network FC between AD and HC. Then, the relationship between FC and impulsiveness as measured by the Barratt Impulsiveness Scale (BIS-11), the UPPS-P Impulsive Scale and the delay discounting task (DDT), was explored. Compared with HC, AD exhibited increased within-network FC in salience (SN), default mode (DMN), orbitofrontal cortex (OFCN), left executive control (LECN) and amygdala-striatum (ASN) networks. Increased between-network FC was found among LECN, ASN and SN. Between-network FC correlations were significantly negative between Negative-Urgency and OFCN pairs with right executive control network (RECN), anterior DMN (a-DMN) and posterior DMN (p-DMN) in AD. DDT was significantly correlated with the between-network FC among the LECN, a-DMN and SN in AD. These findings add evidence to the concept of altered within-network FC and also highlight the role of between-network FC in the pathophysiology of AD. Additionally, this study suggests differential neurobiological bases for different clinical measures of impulsivity that may be used as a systems-level biomarker for alcohol dependence severity and treatment efficacy.
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Affiliation(s)
- Xi Zhu
- Section on Brain Electrophysiology and Imaging; LCTS; National Institute on Alcohol Abuse and Alcoholism; National Institutes of Health; Bethesda MD USA
| | - Carlos R. Cortes
- Section on Brain Electrophysiology and Imaging; LCTS; National Institute on Alcohol Abuse and Alcoholism; National Institutes of Health; Bethesda MD USA
| | - Karan Mathur
- Section on Brain Electrophysiology and Imaging; LCTS; National Institute on Alcohol Abuse and Alcoholism; National Institutes of Health; Bethesda MD USA
| | - Dardo Tomasi
- Laboratory of Neuroimaging; National Institute on Alcohol Abuse and Alcoholism; National Institutes of Health; Bethesda MD USA
| | - Reza Momenan
- Section on Brain Electrophysiology and Imaging; LCTS; National Institute on Alcohol Abuse and Alcoholism; National Institutes of Health; Bethesda MD USA
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26
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Disentangling subgroups of participants recruiting shared as well as different brain regions for the execution of the verb generation task: A data-driven fMRI study. Cortex 2016; 86:247-259. [PMID: 28010939 DOI: 10.1016/j.cortex.2016.11.017] [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] [Received: 12/31/2015] [Revised: 08/19/2016] [Accepted: 11/29/2016] [Indexed: 11/23/2022]
Abstract
The spatial pattern of task-related brain activity in fMRI studies might be expected to change according to several variables such as handedness and age. However this spatial heterogeneity might also be due to other unmodeled sources of inter-subject variability. Since group-level results reflect patterns of task-evoked brain activity common to most of the subjects in the sample, they could conceal the presence of subgroups recruiting other brain regions beyond the common pattern. To deal with these issues, data-driven methods can be used to detect the presence of sources of inter-subject variability that might be hard to identify and therefore model a priori. Here we assess the potential of Independent Component Analysis (ICA) to detect the presence of unexpected subgroups of participants. To this end, we acquired task-evoked fMRI data on 45 healthy adults using the verb generation (VGEN) task, in which participants are visually presented with the noun of an object of everyday use, and asked to covertly generate a verb describing the corresponding action. As expected, the task elicited activity in a temporo-parieto-frontal network typically found in previous VGEN experiments. We then quantified the contribution of every subject to nine task-related spatio-temporal processes identified by ICA. A cluster analysis of this quantity yielded three subgroups of participants. Differences between the three identified subgroups were distributed in left and right prefrontal, posterior parietal and extrastriate occipital regions. These results could not be explained by differences in sex, age or handedness across the participants. Furthermore, some regions where a significant difference was found between subgroups were not present in the group-level pattern of task-related activity. We discuss the potential application of this approach for characterizing brain activity in different subgroups of patients with neuropsychiatric or neurological conditions.
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27
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Pagani M, Giuliani A, Öberg J, Chincarini A, Morbelli S, Brugnolo A, Arnaldi D, Picco A, Bauckneht M, Buschiazzo A, Sambuceti G, Nobili F. Predicting the transition from normal aging to Alzheimer's disease: A statistical mechanistic evaluation of FDG-PET data. Neuroimage 2016; 141:282-290. [PMID: 27453158 DOI: 10.1016/j.neuroimage.2016.07.043] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2016] [Revised: 06/28/2016] [Accepted: 07/20/2016] [Indexed: 12/13/2022] Open
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28
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Coppen EM, van der Grond J, Hafkemeijer A, Rombouts SARB, Roos RAC. Early grey matter changes in structural covariance networks in Huntington's disease. NEUROIMAGE-CLINICAL 2016; 12:806-814. [PMID: 27830113 PMCID: PMC5094265 DOI: 10.1016/j.nicl.2016.10.009] [Citation(s) in RCA: 35] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/10/2016] [Revised: 09/27/2016] [Accepted: 10/11/2016] [Indexed: 01/18/2023]
Abstract
Background Progressive subcortical changes are known to occur in Huntington's disease (HD), a hereditary neurodegenerative disorder. Less is known about the occurrence and cohesion of whole brain grey matter changes in HD. Objectives We aimed to detect network integrity changes in grey matter structural covariance networks and examined relationships with clinical assessments. Methods Structural magnetic resonance imaging data of premanifest HD (n = 30), HD patients (n = 30) and controls (n = 30) was used to identify ten structural covariance networks based on a novel technique using the co-variation of grey matter with independent component analysis in FSL. Group differences were studied controlling for age and gender. To explore whether our approach is effective in examining grey matter changes, regional voxel-based analysis was additionally performed. Results Premanifest HD and HD patients showed decreased network integrity in two networks compared to controls. One network included the caudate nucleus, precuneous and anterior cingulate cortex (in HD p < 0.001, in pre-HD p = 0.003). One other network contained the hippocampus, premotor, sensorimotor, and insular cortices (in HD p < 0.001, in pre-HD p = 0.023). Additionally, in HD patients only, decreased network integrity was observed in a network including the lingual gyrus, intracalcarine, cuneal, and lateral occipital cortices (p = 0.032). Changes in network integrity were significantly associated with scores of motor and neuropsychological assessments. In premanifest HD, voxel-based analyses showed pronounced volume loss in the basal ganglia, but less prominent in cortical regions. Conclusion Our results suggest that structural covariance might be a sensitive approach to reveal early grey matter changes, especially for premanifest HD. Identification of anatomical networks in Huntington's disease (HD). Independent component analysis was used to examine structural covariance networks. HD patients showed changes in subcortical and cortical covariance networks. A network-based approach is sensitive to reveal early grey matter changes.
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Key Words
- CAG, cytosine-adenine-guanine
- Grey matter
- HD, Huntington's disease
- HTT, Huntingtin
- Huntington's disease
- ICA, Independent Component Analysis
- MMSE, Mini Mental State Examination
- MNI, Montreal Neurological Institute
- SDMT, Symbol Digit Modality Test
- Structural MRI
- Structural covariance networks
- TFC, Total Functional Capacity
- TMS, Total Motor Score
- TMT, Trail-Making Test
- UHDRS, Unified Huntington's Disease Rating Scale
- VBM, Voxel-Based Morphometry
- Voxel-based morphometry
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Affiliation(s)
- Emma M Coppen
- Department of Neurology, Leiden University Medical Center, PO Box 9600, 2300 RC Leiden, The Netherlands
| | - Jeroen van der Grond
- Department of Radiology, Leiden University Medical Center, PO Box 9600, 2300 RC Leiden, The Netherlands
| | - Anne Hafkemeijer
- Department of Radiology, Leiden University Medical Center, PO Box 9600, 2300 RC Leiden, The Netherlands; Department of Methodology and Statistics, Institute of Psychology, Leiden University, PO Box 9555, 2300 RB Leiden, The Netherlands; Leiden Institute for Brain and Cognition, Leiden University, PO Box 9600, 2300 RC Leiden, The Netherlands
| | - Serge A R B Rombouts
- Department of Radiology, Leiden University Medical Center, PO Box 9600, 2300 RC Leiden, The Netherlands; Department of Methodology and Statistics, Institute of Psychology, Leiden University, PO Box 9555, 2300 RB Leiden, The Netherlands; Leiden Institute for Brain and Cognition, Leiden University, PO Box 9600, 2300 RC Leiden, The Netherlands
| | - Raymund A C Roos
- Department of Neurology, Leiden University Medical Center, PO Box 9600, 2300 RC Leiden, The Netherlands
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29
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Dong L, Luo C, Zhu Y, Hou C, Jiang S, Wang P, Biswal BB, Yao D. Complex discharge-affecting networks in juvenile myoclonic epilepsy: A simultaneous EEG-fMRI study. Hum Brain Mapp 2016; 37:3515-29. [PMID: 27159669 DOI: 10.1002/hbm.23256] [Citation(s) in RCA: 35] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2015] [Revised: 04/28/2016] [Accepted: 04/29/2016] [Indexed: 02/03/2023] Open
Abstract
Juvenile myoclonic epilepsy (JME) is a common subtype of idiopathic generalized epilepsies (IGEs) and is characterized by myoclonic jerks, tonic-clonic seizures and infrequent absence seizures. The network notion has been proposed to better characterize epilepsy. However, many issues remain not fully understood in JME, such as the associations between discharge-affecting networks and the relationships among resting-state networks. In this project, eigenspace maximal information canonical correlation analysis (emiCCA) and functional network connectivity (FNC) analysis were applied to simultaneous EEG-fMRI data from JME patients. The main findings of our study are as follows: discharge-affecting networks comprising the default model (DMN), self-reference (SRN), basal ganglia (BGN) and frontal networks have linear and nonlinear relationships with epileptic discharge information in JME patients; the DMN, SRN and BGN have dense/specific associations with discharge-affecting networks as well as resting-state networks; and compared with controls, significantly increased FNCs between the salience network (SN) and resting-state networks are found in JME patients. These findings suggest that the BGN, DMN and SRN may play intermediary roles in the modulation and propagation of epileptic discharges. These roles further tend to disturb the switching function of the SN in JME patients. We also postulate that emiCCA and FNC analysis may provide a potential analysis platform to provide insights into our understanding of the pathophysiological mechanism of epilepsy subtypes such as JME. Hum Brain Mapp 37:3515-3529, 2016. © 2016 Wiley Periodicals, Inc.
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Affiliation(s)
- Li Dong
- Key Laboratory for NeuroInformation of Ministry of Education, Center for Information in Medicine, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, China
| | - Cheng Luo
- Key Laboratory for NeuroInformation of Ministry of Education, Center for Information in Medicine, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, China
| | - Yutian Zhu
- Key Laboratory for NeuroInformation of Ministry of Education, Center for Information in Medicine, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, China.,Department of Neurology, Chongzhou People's Hospital, Chengdu, China
| | - Changyue Hou
- Key Laboratory for NeuroInformation of Ministry of Education, Center for Information in Medicine, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, China
| | - Sisi Jiang
- Key Laboratory for NeuroInformation of Ministry of Education, Center for Information in Medicine, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, China
| | - Pu Wang
- Key Laboratory for NeuroInformation of Ministry of Education, Center for Information in Medicine, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, China.,Department of Neurology, Chongzhou People's Hospital, Chengdu, China
| | - Bharat B Biswal
- Department of Biomedical Engineering, New Jersey Institute of Technology, Newark, New Jersey
| | - Dezhong Yao
- Key Laboratory for NeuroInformation of Ministry of Education, Center for Information in Medicine, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, China
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30
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Taylor KS, Kucyi A, Millar PJ, Murai H, Kimmerly DS, Morris BL, Bradley TD, Floras JS. Association between resting-state brain functional connectivity and muscle sympathetic burst incidence. J Neurophysiol 2016; 115:662-73. [PMID: 26538607 PMCID: PMC4752303 DOI: 10.1152/jn.00675.2015] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2015] [Accepted: 10/31/2015] [Indexed: 12/14/2022] Open
Abstract
The insula (IC) and cingulate are key components of the central autonomic network and central nodes of the salience network (SN), a set of spatially distinct but temporally correlated brain regions identified with resting-state (task free) functional MRI (rsMRI). To examine the SN's involvement in sympathetic outflow, we tested the hypothesis that individual differences in intrinsic connectivity of the SN correlate positively with resting postganglionic muscle sympathetic nerve activity (MSNA) burst incidence (BI) in subjects without and with obstructive sleep apnea (OSA). Overnight polysomnography, 5-min rsMRI, and fibular MSNA recording were performed in 36 subjects (mean age 57 yr; 10 women, 26 men). Independent component analysis (ICA) of the entire cohort identified the SN as including bilateral IC, pregenual anterior cingulate cortex (pgACC), midcingulate cortex (MCC), and the temporoparietal junction (TPJ). There was a positive correlation between BI and the apnea-hypopnea index (AHI) (P < 0.001), but dual-regression analysis identified no differences in SN functional connectivity between subjects with no or mild OSA (n = 17) and moderate or severe (n = 19) OSA. Correlation analysis relating BI to the strength of connectivity within the SN revealed large (i.e., spatial extent) and strong correlations for the left IC (P < 0.001), right pgACC/MCC (P < 0.006), left TPJ (P < 0.004), thalamus (P < 0.035), and cerebellum (P < 0.013). Indexes of sleep apnea were unrelated to BI and the strength of SN connectivity. There were no relationships between BI and default or sensorimotor network connectivity. This study links connectivity within the SN to MSNA, demonstrating several of its nodes to be key sympathoexcitatory regions.
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Affiliation(s)
- Keri S Taylor
- University Health Network and Mount Sinai Hospital Department of Medicine, University of Toronto, Toronto, Ontario, Canada;
| | - Aaron Kucyi
- Department of Psychiatry, Harvard Medical School and Massachusetts General Hospital, Boston, Massachusetts; and
| | - Philip J Millar
- University Health Network and Mount Sinai Hospital Department of Medicine, University of Toronto, Toronto, Ontario, Canada
| | - Hisayoshi Murai
- University Health Network and Mount Sinai Hospital Department of Medicine, University of Toronto, Toronto, Ontario, Canada
| | - Derek S Kimmerly
- University Health Network and Mount Sinai Hospital Department of Medicine, University of Toronto, Toronto, Ontario, Canada; School of Health and Human Performance, Faculty of Health Professions, Dalhousie University, Halifax, Nova Scotia, Canada
| | - Beverley L Morris
- University Health Network and Mount Sinai Hospital Department of Medicine, University of Toronto, Toronto, Ontario, Canada
| | - T Douglas Bradley
- University Health Network and Mount Sinai Hospital Department of Medicine, University of Toronto, Toronto, Ontario, Canada
| | - John S Floras
- University Health Network and Mount Sinai Hospital Department of Medicine, University of Toronto, Toronto, Ontario, Canada
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31
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Abstract
The difficulty to understand, diagnose, and treat neurological disorders stems from the great complexity of the central nervous system on different levels of physiological granularity. The individual components, their interactions, and dynamics involved in brain development and function can be represented as molecular, cellular, or functional networks, where diseases are perturbations of networks. These networks can become a useful research tool in investigating neurological disorders if they are properly tailored to reflect corresponding mechanisms. Here, we review approaches to construct networks specific for neurological disorders describing disease-related pathology on different scales: the molecular, cellular, and brain level. We also briefly discuss cross-scale network analysis as a necessary integrator of these scales.
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32
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Soddu A, Gómez F, Heine L, Di Perri C, Bahri MA, Voss HU, Bruno MA, Vanhaudenhuyse A, Phillips C, Demertzi A, Chatelle C, Schrouff J, Thibaut A, Charland-Verville V, Noirhomme Q, Salmon E, Tshibanda JFL, Schiff ND, Laureys S. Correlation between resting state fMRI total neuronal activity and PET metabolism in healthy controls and patients with disorders of consciousness. Brain Behav 2016; 6:e00424. [PMID: 27110443 PMCID: PMC4834945 DOI: 10.1002/brb3.424] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/05/2015] [Revised: 11/23/2015] [Accepted: 11/26/2015] [Indexed: 02/05/2023] Open
Abstract
INTRODUCTION The mildly invasive 18F-fluorodeoxyglucose positron emission tomography (FDG-PET) is a well-established imaging technique to measure 'resting state' cerebral metabolism. This technique made it possible to assess changes in metabolic activity in clinical applications, such as the study of severe brain injury and disorders of consciousness. OBJECTIVE We assessed the possibility of creating functional MRI activity maps, which could estimate the relative levels of activity in FDG-PET cerebral metabolic maps. If no metabolic absolute measures can be extracted, our approach may still be of clinical use in centers without access to FDG-PET. It also overcomes the problem of recognizing individual networks of independent component selection in functional magnetic resonance imaging (fMRI) resting state analysis. METHODS We extracted resting state fMRI functional connectivity maps using independent component analysis and combined only components of neuronal origin. To assess neuronality of components a classification based on support vector machine (SVM) was used. We compared the generated maps with the FDG-PET maps in 16 healthy controls, 11 vegetative state/unresponsive wakefulness syndrome patients and four locked-in patients. RESULTS The results show a significant similarity with ρ = 0.75 ± 0.05 for healthy controls and ρ = 0.58 ± 0.09 for vegetative state/unresponsive wakefulness syndrome patients between the FDG-PET and the fMRI based maps. FDG-PET, fMRI neuronal maps, and the conjunction analysis show decreases in frontoparietal and medial regions in vegetative patients with respect to controls. Subsequent analysis in locked-in syndrome patients produced also consistent maps with healthy controls. CONCLUSIONS The constructed resting state fMRI functional connectivity map points toward the possibility for fMRI resting state to estimate relative levels of activity in a metabolic map.
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Affiliation(s)
- Andrea Soddu
- Department of Physics & Astronomy, Brain and Mind Institute Western University London Ontario Canada
| | - Francisco Gómez
- Department of Computer Science Universidad Central de Colombia Bogotá Colombia
| | - Lizette Heine
- GIGA-Research & Cyclotron Research Centre University of Liège Liège Belgium
| | - Carol Di Perri
- GIGA-Research & Cyclotron Research Centre University of Liège Liège Belgium
| | - Mohamed Ali Bahri
- GIGA-Research & Cyclotron Research Centre University of Liège Liège Belgium
| | - Henning U Voss
- Department of Radiology Weill Cornell Medical College New York New York
| | | | - Audrey Vanhaudenhuyse
- Department of Algology and Palliative Care University Hospital of Liège Liège Belgium
| | | | - Athena Demertzi
- GIGA-Research & Cyclotron Research Centre University of Liège Liège Belgium; Brain and Spine InstituteInstitut du Cerveau et de la Moelle épinière (ICM) Hôpital Pitié-Salpêtrière Paris France
| | - Camille Chatelle
- GIGA-Research & Cyclotron Research Centre University of Liège Liège Belgium
| | - Jessica Schrouff
- GIGA-Research & Cyclotron Research Centre University of Liège Liège Belgium
| | - Aurore Thibaut
- GIGA-Research & Cyclotron Research Centre University of Liège Liège Belgium
| | | | - Quentin Noirhomme
- GIGA-Research & Cyclotron Research Centre University of Liège Liège Belgium; Brain Innovation B.V. Maastricht the Netherlands
| | - Eric Salmon
- GIGA-Research & Cyclotron Research Centre University of Liège Liège Belgium
| | | | - Nicholas D Schiff
- Department of Radiology Weill Cornell Medical College New York New York
| | - Steven Laureys
- GIGA-Research & Cyclotron Research Centre University of Liège Liège Belgium; Department of Neurology University Hospital of Liège Liège Belgium
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33
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Firbank M, Kobeleva X, Cherry G, Killen A, Gallagher P, Burn DJ, Thomas AJ, O'Brien JT, Taylor JP. Neural correlates of attention-executive dysfunction in lewy body dementia and Alzheimer's disease. Hum Brain Mapp 2015; 37:1254-70. [PMID: 26705763 PMCID: PMC4784171 DOI: 10.1002/hbm.23100] [Citation(s) in RCA: 40] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2015] [Revised: 11/18/2015] [Accepted: 12/13/2015] [Indexed: 12/12/2022] Open
Abstract
Attentional and executive dysfunction contribute to cognitive impairment in both Lewy body dementia and Alzheimer's disease. Using functional MRI, we examined the neural correlates of three components of attention (alerting, orienting, and executive/conflict function) in 23 patients with Alzheimer's disease, 32 patients with Lewy body dementia (19 with dementia with Lewy bodies and 13 with Parkinson's disease with dementia), and 23 healthy controls using a modified Attention Network Test. Although the functional MRI demonstrated a similar fronto-parieto-occipital network activation in all groups, Alzheimer's disease and Lewy body dementia patients had greater activation of this network for incongruent and more difficult trials, which were also accompanied by slower reaction times. There was no recruitment of additional brain regions or, conversely, regional deficits in brain activation. The default mode network, however, displayed diverging activity patterns in the dementia groups. The Alzheimer's disease group had limited task related deactivations of the default mode network, whereas patients with Lewy body dementia showed heightened deactivation to all trials, which might be an attempt to allocate neural resources to impaired attentional networks. We posit that, despite a common endpoint of attention-executive disturbances in both dementias, the pathophysiological basis of these is very different between these diseases.
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Affiliation(s)
- Michael Firbank
- Institute of Neuroscience, Newcastle University, Campus for Ageing and Vitality, Newcastle upon Tyne, NE4 5PL, United Kingdom
| | - Xenia Kobeleva
- Institute of Neuroscience, Newcastle University, Campus for Ageing and Vitality, Newcastle upon Tyne, NE4 5PL, United Kingdom.,Department of Neurology and Neurophysiology, Medical School Hannover, Carl-Neuberg-Straße 1, Hannover, 30625, Germany
| | - George Cherry
- School of Medical Science, Newcastle University, Newcastle-upon-Tyne, NE2 4HH, United Kingdom
| | - Alison Killen
- Institute of Neuroscience, Newcastle University, Campus for Ageing and Vitality, Newcastle upon Tyne, NE4 5PL, United Kingdom
| | - Peter Gallagher
- Institute of Neuroscience, Newcastle University, Campus for Ageing and Vitality, Newcastle upon Tyne, NE4 5PL, United Kingdom
| | - David J Burn
- Institute of Neuroscience, Newcastle University, Campus for Ageing and Vitality, Newcastle upon Tyne, NE4 5PL, United Kingdom
| | - Alan J Thomas
- Institute of Neuroscience, Newcastle University, Campus for Ageing and Vitality, Newcastle upon Tyne, NE4 5PL, United Kingdom
| | - John T O'Brien
- Department of Psychiatry, University of Cambridge School of Clinical Medicine, Cambridge, CB2 0SP, United Kingdom
| | - John-Paul Taylor
- Institute of Neuroscience, Newcastle University, Campus for Ageing and Vitality, Newcastle upon Tyne, NE4 5PL, United Kingdom
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34
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Hafkemeijer A, Möller C, Dopper EGP, Jiskoot LC, van den Berg-Huysmans AA, van Swieten JC, van der Flier WM, Vrenken H, Pijnenburg YAL, Barkhof F, Scheltens P, van der Grond J, Rombouts SARB. Differences in structural covariance brain networks between behavioral variant frontotemporal dementia and Alzheimer's disease. Hum Brain Mapp 2015; 37:978-88. [PMID: 26660857 DOI: 10.1002/hbm.23081] [Citation(s) in RCA: 41] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2015] [Accepted: 11/30/2015] [Indexed: 12/24/2022] Open
Abstract
Disease-specific patterns of gray matter atrophy in Alzheimer's disease (AD) and behavioral variant frontotemporal dementia (bvFTD) overlap with distinct structural covariance networks (SCNs) in cognitively healthy controls. This suggests that both types of dementia target specific structural networks. Here, we study SCNs in AD and bvFTD. We used structural magnetic resonance imaging data of 31 AD patients, 24 bvFTD patients, and 30 controls from two centers specialized in dementia. Ten SCNs were defined based on structural covariance of gray matter density using independent component analysis. We studied group differences in SCNs using F-tests, with Bonferroni corrected t-tests, adjusted for age, gender, and study center. Associations with cognitive performance were studied using linear regression analyses. Cross-sectional group differences were found in three SCNs (all P < 0.0025). In bvFTD, we observed decreased anterior cingulate network integrity compared with AD and controls. Patients with AD showed decreased precuneal network integrity compared with bvFTD and controls, and decreased hippocampal network and anterior cingulate network integrity compared with controls. In AD, we found an association between precuneal network integrity and global cognitive performance (P = 0.0043). Our findings show that AD and bvFTD target different SCNs. The comparison of both types of dementia showed decreased precuneal (i.e., default mode) network integrity in AD and decreased anterior cingulate (i.e., salience) network integrity in bvFTD. This confirms the hypothesis that AD and bvFTD have distinct anatomical networks of degeneration and shows that structural covariance gives valuable insights in the understanding of network pathology in dementia.
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Affiliation(s)
- Anne Hafkemeijer
- Department of Methodology and Statistics, Institute of Psychology, Leiden University, 2300 RB, Leiden, the Netherlands.,Department of Radiology, Leiden University Medical Center, Postzone C2-S, 2300 RC, Leiden, the Netherlands.,Leiden Institute for Brain and Cognition, Leiden University, 2300 RC, Leiden, the Netherlands
| | - Christiane Möller
- Alzheimer Center & Department of Neurology, VU University Medical Center, 1007 MB, Amsterdam, the Netherlands
| | - Elise G P Dopper
- Department of Radiology, Leiden University Medical Center, Postzone C2-S, 2300 RC, Leiden, the Netherlands.,Alzheimer Center & Department of Neurology, VU University Medical Center, 1007 MB, Amsterdam, the Netherlands.,Alzheimer Center & Department of Neurology, Erasmus Medical Center, 3000 CA, Rotterdam, the Netherlands
| | - Lize C Jiskoot
- Department of Radiology, Leiden University Medical Center, Postzone C2-S, 2300 RC, Leiden, the Netherlands.,Alzheimer Center & Department of Neurology, Erasmus Medical Center, 3000 CA, Rotterdam, the Netherlands.,Department of Neuropsychology, Erasmus Medical Center, 3000 CA, Rotterdam, the Netherlands
| | | | - John C van Swieten
- Alzheimer Center & Department of Neurology, Erasmus Medical Center, 3000 CA, Rotterdam, the Netherlands.,Department of Clinical Genetics, VU University Medical Center, 1007 MB, Amsterdam, the Netherlands
| | - Wiesje M van der Flier
- Alzheimer Center & Department of Neurology, VU University Medical Center, 1007 MB, Amsterdam, the Netherlands.,Department of Epidemiology and Biostatistics, VU University Medical Center, 1007 MB, Amsterdam, the Netherlands
| | - Hugo Vrenken
- Department of Radiology and Nuclear Medicine, VU University Medical Center, 1007 MB, Amsterdam, the Netherlands.,Department of Physics and Medical Technology, VU University Medical Center, 1007 MB, Amsterdam, the Netherlands
| | - Yolande A L Pijnenburg
- Alzheimer Center & Department of Neurology, VU University Medical Center, 1007 MB, Amsterdam, the Netherlands
| | - Frederik Barkhof
- Department of Radiology and Nuclear Medicine, VU University Medical Center, 1007 MB, Amsterdam, the Netherlands
| | - Philip Scheltens
- Alzheimer Center & Department of Neurology, VU University Medical Center, 1007 MB, Amsterdam, the Netherlands
| | - Jeroen van der Grond
- Department of Radiology, Leiden University Medical Center, Postzone C2-S, 2300 RC, Leiden, the Netherlands
| | - Serge A R B Rombouts
- Department of Methodology and Statistics, Institute of Psychology, Leiden University, 2300 RB, Leiden, the Netherlands.,Department of Radiology, Leiden University Medical Center, Postzone C2-S, 2300 RC, Leiden, the Netherlands.,Leiden Institute for Brain and Cognition, Leiden University, 2300 RC, Leiden, the Netherlands
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35
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Zhang Y, Liu F, Chen H, Li M, Duan X, Xie B, Chen H. Intranetwork and internetwork functional connectivity alterations in post-traumatic stress disorder. J Affect Disord 2015; 187:114-21. [PMID: 26331685 DOI: 10.1016/j.jad.2015.08.043] [Citation(s) in RCA: 53] [Impact Index Per Article: 5.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/25/2015] [Revised: 08/01/2015] [Accepted: 08/20/2015] [Indexed: 10/23/2022]
Abstract
BACKGROUND A large number of previous neuroimaging studies have explored the functional alterations of post-traumatic stress disorder (PTSD). However, abnormalities in the functional architecture of resting-state networks in PTSD were rarely elucidated. METHODS This study used independent component analysis to explore the resting-state intranetwork and internetwork functional connectivity differences between 20 PTSD patients and 20 matched healthy controls (HCs). RESULTS Selective alterations of intranetwork and internetwork intrinsic functional connectivities were found in the PTSD patients. Compared with HCs, the PTSD patients exhibited significantly decreased network connectivity within the anterior default mode network, posterior default mode network (pDMN), salience network (SN), sensory-motor network, and auditory network. Furthermore, the PTSD patients exhibited increased internetwork connectivity between SN and pDMN. LIMITATIONS This study lacked recruitment of trauma-exposed HCs, which limits our ability to determine whether the alterations are caused by PTSD or trauma exposure. CONCLUSION The findings suggested that the PTSD patients exhibited abnormal functional connectivity at the brain network level. Notably, the enhanced internetwork connectivity between SN and pDMN in the PTSD patients may be associated with hyperarousal and heightened anxiety in PTSD.
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Affiliation(s)
- Youxue Zhang
- Key Laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Feng Liu
- Key Laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Heng Chen
- Key Laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Meiling Li
- Key Laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Xujun Duan
- Key Laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Bing Xie
- Key Laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China; Department of Anatomy, Third Military Medical University, 30 Gaotanyan Street, Chongqing 400038, China.
| | - Huafu Chen
- Key Laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China.
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36
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Adriaanse SM, Wink AM, Tijms BM, Ossenkoppele R, Verfaillie SCJ, Lammertsma AA, Boellaard R, Scheltens P, van Berckel BNM, Barkhof F. The Association of Glucose Metabolism and Eigenvector Centrality in Alzheimer's Disease. Brain Connect 2015; 6:1-8. [PMID: 26414628 DOI: 10.1089/brain.2014.0320] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Both fluorine-18-labeled fluorodeoxyglucose ([(18)F]FDG) positron emission tomography, examining glucose metabolism, and resting-state functional magnetic resonance imaging (rs-fMRI), using covarying blood oxygen levels, can be used to explore neuronal dysfunction in Alzheimer's disease (AD). Both measures are reported to identify similar brain regions affected in AD patients. The spatial overlap and association of [(18)F]FDG with rs-fMRI in AD patients and controls were examined to investigate whether these two measures are associated, and if so, to what extent. For 24 AD patients and 18 controls, [(18)F]FDG and rs-fMRI data were available. [(18)F]FDG standardized uptake value ratios (SUVr), with cerebellar gray matter (GM) as reference tissue, were calculated. Eigenvector centrality (EC) mapping was used to spatially analyze the functional brain network. Group differences were calculated for [(18)F]FDG and eigenvector centrality mapping (ECM) values in four cortical regions (occipital, parietal, frontal, and temporal) and across voxels, with age, gender, and GM as covariates. Correlation of [(18)F]FDG with ECM was calculated within groups. Both lowered [(18)F]FDG SUVr and EC values were seen in the parietal and occipital cortex of AD patients. However, [(18)F]FDG yielded more robust and widespread brain areas affected in AD patients; hypometabolism was also observed in the temporal cortex and regions within frontal brain areas. Poor spatial overlap of both measures was observed. No associations were found between local [(18)F]FDG SUVr and ECM. In conclusion, agreement of [(18)F]FDG and ECM in AD patients seems moderate at best. [(18)F]FDG was most accurate in distinguishing AD patients from controls.
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Affiliation(s)
- Sofie M Adriaanse
- 1 Department of Radiology and Nuclear Medicine, VU University Medical Center , Amsterdam, The Netherlands
| | - Alle Meije Wink
- 1 Department of Radiology and Nuclear Medicine, VU University Medical Center , Amsterdam, The Netherlands
| | - Betty M Tijms
- 2 Department of Neurology and Alzheimer Center, VU University Medical Center , Amsterdam, The Netherlands
| | - Rik Ossenkoppele
- 1 Department of Radiology and Nuclear Medicine, VU University Medical Center , Amsterdam, The Netherlands .,2 Department of Neurology and Alzheimer Center, VU University Medical Center , Amsterdam, The Netherlands
| | - Sander C J Verfaillie
- 2 Department of Neurology and Alzheimer Center, VU University Medical Center , Amsterdam, The Netherlands
| | - Adriaan A Lammertsma
- 1 Department of Radiology and Nuclear Medicine, VU University Medical Center , Amsterdam, The Netherlands
| | - Ronald Boellaard
- 1 Department of Radiology and Nuclear Medicine, VU University Medical Center , Amsterdam, The Netherlands
| | - Philip Scheltens
- 2 Department of Neurology and Alzheimer Center, VU University Medical Center , Amsterdam, The Netherlands
| | | | - Frederik Barkhof
- 1 Department of Radiology and Nuclear Medicine, VU University Medical Center , Amsterdam, The Netherlands
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37
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Bordier C, Macaluso E. Time-resolved detection of stimulus/task-related networks, via clustering of transient intersubject synchronization. Hum Brain Mapp 2015; 36:3404-25. [PMID: 26095530 PMCID: PMC5008218 DOI: 10.1002/hbm.22852] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2014] [Revised: 05/08/2015] [Accepted: 05/11/2015] [Indexed: 11/06/2022] Open
Abstract
Several methods are available for the identification of functional networks of brain areas using functional magnetic resonance imaging (fMRI) time-series. These typically assume a fixed relationship between the signal of the areas belonging to the same network during the entire time-series (e.g., positive correlation between the areas belonging to the same network), or require a priori information about when this relationship may change (task-dependent changes of connectivity). We present a fully data-driven method that identifies transient network configurations that are triggered by the external input and that, therefore, include only regions involved in stimulus/task processing. Intersubject synchronization with short sliding time-windows was used to identify if/when any area showed stimulus/task-related responses. Next, a first clustering step grouped together areas that became engaged concurrently and repetitively during the time-series (stimulus/task-related networks). Finally, for each network, a second clustering step grouped together all the time-windows with the same BOLD signal. The final output consists of a set of network configurations that show stimulus/task-related activity at specific time-points during the fMRI time-series. We label these configurations: "brain modes" (bModes). The method was validated using simulated datasets and a real fMRI experiment with multiple tasks and conditions. Future applications include the investigation of brain functions using complex and naturalistic stimuli.
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Affiliation(s)
- Cécile Bordier
- Neuroimaging LaboratoryIRCCS, Santa Lucia Foundationvia Ardeatina 306Rome00179Italy
| | - Emiliano Macaluso
- Neuroimaging LaboratoryIRCCS, Santa Lucia Foundationvia Ardeatina 306Rome00179Italy
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38
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Arenivas A, Diaz-Arrastia R, Spence J, Cullum CM, Krishnan K, Bosworth C, Culver C, Kennard B, Marquez de la Plata C. Three approaches to investigating functional compromise to the default mode network after traumatic axonal injury. Brain Imaging Behav 2015; 8:407-19. [PMID: 22847713 DOI: 10.1007/s11682-012-9191-2] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
The default mode network (DMN) is a reliably elicited functional neural network with potential clinical implications. Its discriminant and prognostic utility following traumatic axonal injury (TAI) have not been previously investigated. The present study used three approaches to analyze DMN functional connectedness, including a whole-brain analysis [A1], network-specific analysis [A2], and between-node (edge) analysis [A3]. The purpose was to identify the utility of each method in distinguishing between healthy and brain-injured individuals, and determine whether observed differences have clinical significance. Resting-state fMRI was acquired from 25 patients with TAI and 17 healthy controls. Patients were scanned 6-11 months post-injury, and functional and neurocognitive outcomes were assessed the same day. Using all three approaches, TAI subjects revealed significantly weaker functional connectivity (FC) than controls, and binary logistic regressions demonstrated all three approaches have discriminant value. Clinical outcomes were not correlated with FC using any approach. Results suggest that compromise to the functional connectedness of the DMN after TAI can be identified using resting-state FC; however, the degree of functional compromise to this network, as measured in this study, may not have clinical implications in chronic TAI.
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Affiliation(s)
- Ana Arenivas
- Department of Psychiatry, University of Texas Southwestern Medical Center, Dallas, TX, USA
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39
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Deco G, Kringelbach ML. Great expectations: using whole-brain computational connectomics for understanding neuropsychiatric disorders. Neuron 2015; 84:892-905. [PMID: 25475184 DOI: 10.1016/j.neuron.2014.08.034] [Citation(s) in RCA: 231] [Impact Index Per Article: 25.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
Abstract
The study of human brain networks with in vivo neuroimaging has given rise to the field of connectomics, furthered by advances in network science and graph theory informing our understanding of the topology and function of the healthy brain. Here our focus is on the disruption in neuropsychiatric disorders (pathoconnectomics) and how whole-brain computational models can help generate and predict the dynamical interactions and consequences of brain networks over many timescales. We review methods and emerging results that exhibit remarkable accuracy in mapping and predicting both spontaneous and task-based healthy network dynamics. This raises great expectations that whole-brain modeling and computational connectomics may provide an entry point for understanding brain disorders at a causal mechanistic level, and that computational neuropsychiatry can ultimately be leveraged to provide novel, more effective therapeutic interventions, e.g., through drug discovery and new targets for deep brain stimulation.
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Affiliation(s)
- Gustavo Deco
- Center for Brain and Cognition, Computational Neuroscience Group, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Roc Boronat 138, Barcelona 08018, Spain; Institució Catalana de la Recerca i Estudis Avançats (ICREA), Universitat Pompeu Fabra, Passeig Lluís Companys 23, Barcelona 08010, Spain.
| | - Morten L Kringelbach
- Department of Psychiatry, University of Oxford, OX3 7JX Oxford, UK; Center of Functionally Integrative Neuroscience (CFIN), Aarhus University, 8000 Aarhus C, Denmark
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40
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Goto M, Abe O, Aoki S, Hayashi N, Ohtsu H, Takao H, Miyati T, Matsuda H, Yamashita F, Iwatsubo T, Mori H, Kunimatsu A, Ino K, Yano K, Ohtomo K. Longitudinal gray-matter volume change in the default-mode network: utility of volume standardized with global gray-matter volume for Alzheimer's disease: a preliminary study. Radiol Phys Technol 2014; 8:64-72. [PMID: 25261344 DOI: 10.1007/s12194-014-0295-9] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2014] [Revised: 09/04/2014] [Accepted: 09/05/2014] [Indexed: 11/25/2022]
Abstract
Our aim was to show whether sensitivity for detecting volume changes in regional gray matter in default mode network (DMN) at converted [from mild cognitive impairment to Alzheimer's disease (from MCI to AD)] phase was improved by use of a standardized volume with global gray-matter volume. T1-weighted MR images (T1WI) of seven normal subjects and seven converted (from MCI to AD) patients were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. Gray-matter images segmented with Statistical Parametric Mapping 5 were measured by the atlas-based method. We focused on five nodes of the DMN. For each phase, region of interest (ROI) volumes in the five nodes were standardized by two methods: (1) the ratio to the screening phase (S_volume) and (2) the ratio to the screening phase after both volumes were standardized by the global gray-matter volume (S_N_volume). Significant group differences between longitudinal gray-matter volume change of the converted (from MCI to AD) group and that of the normal group were found in lateral temporal cortex by S_N_volume, and precuneus by S_N_volume. These findings are useful for improving the understanding of DMN volume changes at the converted (from MCI to AD) phase.
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Affiliation(s)
- Masami Goto
- Department of Radiological Technology, University of Tokyo Hospital, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8655, Japan,
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41
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Pluta A, Wolak T, Czajka N, Lewandowska M, Cieśla K, Rusiniak M, Grudzień D, Skarżyński H. Reduced resting-state brain activity in the default mode network in children with (central) auditory processing disorders. Behav Brain Funct 2014; 10:33. [PMID: 25261349 PMCID: PMC4236576 DOI: 10.1186/1744-9081-10-33] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2014] [Accepted: 09/15/2014] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND In recent years, there has been a growing interest in Central Auditory Processing Disorder (C)APD. However, the neural correlates of (C)APD are poorly understood. Previous neuroimaging experiments have shown changes in the intrinsic activity of the brain in various cognitive deficits and brain disorders. The present study investigated the spontaneous brain activity in (C)APD subjects with resting-state fMRI (rs-fMRI). METHODS Thirteen children diagnosed with (C)APD and fifteen age and gender-matched controls participated in a rs-fMRI study during which they were asked to relax keeping their eyes open. Two different techniques of the rs-fMRI data analysis were used: Regional Homogeneity (ReHo) and Independent Component Analysis (ICA), which approach is rare. RESULTS Both methods of data analysis showed comparable results in the pattern of DMN activity within groups. Additionally, ReHo analysis revealed increased co-activation of the superior frontal gyrus, the posterior cingulate cortex/the precuneus in controls, compared to the (C)APD group. ICA yielded inconsistent results across groups. CONCLUSIONS Our ReHo results suggest that (C)APD children seem to present reduced regional homogeneity in brain regions considered a part of the default mode network (DMN). These findings might contribute to a better understanding of neural mechanisms of (C)APD.
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Affiliation(s)
- Agnieszka Pluta
- World Hearing Center of the Institute of Physiology and Pathology of Hearing, Mokra 17 street, 05-830 Nadarzyn, Warsaw/Kajetany, Poland.
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Sun L, Liang P, Jia X, Qi Z, Li K. Age-related increase in brain activity during task-related and -negative networks and numerical inductive reasoning. INTERNATIONAL JOURNAL OF CLINICAL AND EXPERIMENTAL PATHOLOGY 2014; 7:5960-5967. [PMID: 25337240 PMCID: PMC4203211] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Subscribe] [Scholar Register] [Received: 07/09/2014] [Accepted: 08/21/2014] [Indexed: 06/04/2023]
Abstract
OBJECTIVE Recent neuroimaging studies have shown that elderly adults exhibit increased and decreased activation on various cognitive tasks, yet little is known about age-related changes in inductive reasoning. METHODS To investigate the neural basis for the aging effect on inductive reasoning, 15 young and 15 elderly subjects performed numerical inductive reasoning while in a magnetic resonance (MR) scanner. RESULTS Functional magnetic resonance imaging (fMRI) analysis revealed that numerical inductive reasoning, relative to rest, yielded multiple frontal, temporal, parietal, and some subcortical area activations for both age groups. In addition, the younger participants showed significant regions of task-induced deactivation, while no deactivation occurred in the elderly adults. Direct group comparisons showed that elderly adults exhibited greater activity in regions of task-related activation and areas showing task-induced deactivation (TID) in the younger group. CONCLUSIONS Our findings suggest an age-related deficiency in neural function and resource allocation during inductive reasoning.
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Affiliation(s)
- Li Sun
- Department of Radiology, Xuan Wu Hospital, Capital Medical University Beijing 100053, China
| | - Peipeng Liang
- Department of Radiology, Xuan Wu Hospital, Capital Medical University Beijing 100053, China
| | - Xiuqin Jia
- Department of Radiology, Xuan Wu Hospital, Capital Medical University Beijing 100053, China
| | - Zhigang Qi
- Department of Radiology, Xuan Wu Hospital, Capital Medical University Beijing 100053, China
| | - Kuncheng Li
- Department of Radiology, Xuan Wu Hospital, Capital Medical University Beijing 100053, China
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Dey S, Rao AR, Shah M. Attributed graph distance measure for automatic detection of attention deficit hyperactive disordered subjects. Front Neural Circuits 2014; 8:64. [PMID: 24982615 PMCID: PMC4058754 DOI: 10.3389/fncir.2014.00064] [Citation(s) in RCA: 47] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2013] [Accepted: 05/27/2014] [Indexed: 11/13/2022] Open
Abstract
Attention Deficit Hyperactive Disorder (ADHD) is getting a lot of attention recently for two reasons. First, it is one of the most commonly found childhood disorders and second, the root cause of the problem is still unknown. Functional Magnetic Resonance Imaging (fMRI) data has become a popular tool for the analysis of ADHD, which is the focus of our current research. In this paper we propose a novel framework for the automatic classification of the ADHD subjects using their resting state fMRI (rs-fMRI) data of the brain. We construct brain functional connectivity networks for all the subjects. The nodes of the network are constructed with clusters of highly active voxels and edges between any pair of nodes represent the correlations between their average fMRI time series. The activity level of the voxels are measured based on the average power of their corresponding fMRI time-series. For each node of the networks, a local descriptor comprising of a set of attributes of the node is computed. Next, the Multi-Dimensional Scaling (MDS) technique is used to project all the subjects from the unknown graph-space to a low dimensional space based on their inter-graph distance measures. Finally, the Support Vector Machine (SVM) classifier is used on the low dimensional projected space for automatic classification of the ADHD subjects. Exhaustive experimental validation of the proposed method is performed using the data set released for the ADHD-200 competition. Our method shows promise as we achieve impressive classification accuracies on the training (70.49%) and test data sets (73.55%). Our results reveal that the detection rates are higher when classification is performed separately on the male and female groups of subjects.
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Affiliation(s)
- Soumyabrata Dey
- Department of Electrical Engineering and Computer Science, Center for Research in Computer Vision, University of Central Florida Orlando, FL, USA
| | | | - Mubarak Shah
- Department of Electrical Engineering and Computer Science, Center for Research in Computer Vision, University of Central Florida Orlando, FL, USA
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44
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Cabral J, Kringelbach ML, Deco G. Exploring the network dynamics underlying brain activity during rest. Prog Neurobiol 2014; 114:102-31. [DOI: 10.1016/j.pneurobio.2013.12.005] [Citation(s) in RCA: 238] [Impact Index Per Article: 23.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2012] [Revised: 11/04/2013] [Accepted: 12/17/2013] [Indexed: 11/17/2022]
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45
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Hanoğlu L, Özkara Ç, Yalçiner B, Nani A, Cavanna AE. Epileptic qualia and self-awareness: a third dimension for consciousness. Epilepsy Behav 2014; 30:62-5. [PMID: 24100248 DOI: 10.1016/j.yebeh.2013.09.010] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/03/2013] [Accepted: 09/05/2013] [Indexed: 11/18/2022]
Abstract
Over the last few decades, there has been increasing awareness among epileptologists about the need to refine our understanding and assessment of ictal consciousness, focusing on both subjective and behavioral aspects of seizures. Specifically, there have been suggestions that both the internal and external milieux - the former related to the phenomenal qualia of experience, the latter related to behavior - must be taken into account for a better understanding of altered states of consciousness in epilepsy. It has been proposed that clinical and experimental data from patients experiencing alterations of consciousness during epileptic seizures could be better understood within a bidimensional model, in which any manifestation of conscious experience can be plotted according to the level and contents of consciousness. The 'level' axis measures the degree of alertness/arousal, whereas the 'contents' axis measures the vividness of specific experiential phenomena reported by the patient. We argue that certain seizure types might require more rigorous conceptual models for their characterization, and we highlight the potential usefulness of a more refined framework which includes a further dimension related to the 'self', in addition to those of 'level' and 'contents'. This model could be visualized in a three-dimensional space to allow fine-grained distinctions between epileptic seizures.
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Affiliation(s)
- Lütfü Hanoğlu
- Department of Neurology, Medical Faculty, Medipol University, Istanbul, Turkey
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46
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Abou Elseoud A, Nissilä J, Liettu A, Remes J, Jokelainen J, Takala T, Aunio A, Starck T, Nikkinen J, Koponen H, Zang YF, Tervonen O, Timonen M, Kiviniemi V. Altered resting-state activity in seasonal affective disorder. Hum Brain Mapp 2014; 35:161-72. [PMID: 22987670 PMCID: PMC6869738 DOI: 10.1002/hbm.22164] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2011] [Revised: 05/15/2012] [Accepted: 06/19/2012] [Indexed: 12/14/2022] Open
Abstract
At present, our knowledge about seasonal affective disorder (SAD) is based mainly up on clinical symptoms, epidemiology, behavioral characteristics and light therapy. Recently developed measures of resting-state functional brain activity might provide neurobiological markers of brain disorders. Studying functional brain activity in SAD could enhance our understanding of its nature and possible treatment strategies. Functional network connectivity (measured using ICA-dual regression), and amplitude of low-frequency fluctuations (ALFF) were measured in 45 antidepressant-free patients (39.78 ± 10.64, 30 ♀, 15 ♂) diagnosed with SAD and compared with age-, gender- and ethnicity-matched healthy controls (HCs) using resting-state functional magnetic resonance imaging. After correcting for Type 1 error at high model orders (inter-RSN correction), SAD patients showed significantly increased functional connectivity in 11 of the 47 identified RSNs. Increased functional connectivity involved RSNs such as visual, sensorimotor, and attentional networks. Moreover, our results revealed that SAD patients compared with HCs showed significant higher ALFF in the visual and right sensorimotor cortex. Abnormally altered functional activity detected in SAD supports previously reported attentional and psychomotor symptoms in patients suffering from SAD. Further studies, particularly under task conditions, are needed in order to specifically investigate cognitive deficits in SAD.
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Affiliation(s)
- Ahmed Abou Elseoud
- Department of Diagnostic Radiology, Oulu University Hospital, Oulu, Finland
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Cabral J, Fernandes HM, Van Hartevelt TJ, James AC, Kringelbach ML, Deco G. Structural connectivity in schizophrenia and its impact on the dynamics of spontaneous functional networks. CHAOS (WOODBURY, N.Y.) 2013; 23:046111. [PMID: 24387590 DOI: 10.1063/1.4851117] [Citation(s) in RCA: 42] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/03/2023]
Abstract
The neuropathology of schizophrenia remains unclear. Some insight has come from modern neuroimaging techniques, which offer an unparalleled opportunity to explore in vivo the structure and function of the brain. Using functional magnetic resonance imaging, it has been found that the large-scale resting-state functional connectivity (rsFC) in schizophrenia--measured as the temporal correlations of the blood-oxygen-level-dependent (BOLD) signal--exhibit altered network topology, with lower small-world index. The origin of these rsFC alterations and link with the underlying structural connectivity remain unclear. In this work, we used a computational model of spontaneous large-scale brain activity to explore the role of the structural connectivity in the large-scale dynamics of the brain in health and schizophrenia. The structural connectomes from 15 adolescent patients with early-onset schizophrenia and 15 age- and gender-matched controls were built from diffusion tensor imaging data to detect the white matter tracts between 90 brain areas. Brain areas, simulated using a reduced dynamic mean-field model, receive excitatory input from other areas in proportion to the number of fibre tracts between them. The simulated mean field activity was transformed into BOLD signal, and the properties of the simulated functional networks were analyzed. Our results suggest that the functional alterations observed in schizophrenia are not directly linked to alterations in the structural topology. Instead, subtly randomized and less small-world functional networks appear when the brain operates with lower global coupling, which shifts the dynamics from the optimal healthy regime.
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Affiliation(s)
- Joana Cabral
- Theoretical and Computational Neuroscience Group, Center of Brain and Cognition, Universitat Pompeu Fabra, Barcelona 08018, Spain
| | | | - Tim J Van Hartevelt
- Department of Psychiatry, University of Oxford, Oxford OX3 7JX, United Kingdom
| | - Anthony C James
- Department of Psychiatry, University of Oxford, Oxford OX3 7JX, United Kingdom
| | | | - Gustavo Deco
- Theoretical and Computational Neuroscience Group, Center of Brain and Cognition, Universitat Pompeu Fabra, Barcelona 08018, Spain
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Characterizing functional integrity: intraindividual brain signal variability predicts memory performance in patients with medial temporal lobe epilepsy. J Neurosci 2013; 33:9855-65. [PMID: 23739982 DOI: 10.1523/jneurosci.3009-12.2013] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022] Open
Abstract
Computational modeling suggests that variability in brain signals provides important information regarding the system's capacity to adopt different network configurations that may promote optimal responding to stimuli. Although there is limited empirical work on this construct, a recent study indicates that age-related decreases in variability across the adult lifespan correlate with less efficient and less accurate performance. Here, we extend this construct to the assessment of cerebral integrity by comparing fMRI BOLD variability and fMRI BOLD amplitude in their ability to account for differences in functional capacity in patients with focal unilateral medial temporal dysfunction. We were specifically interested in whether either of these BOLD measures could identify a link between the affected medial temporal region and memory performance (as measured by a clinical test of verbal memory retention). Using partial least-squares analyses, we found that variability in a set of regions including the left hippocampus predicted verbal retention and, furthermore, this relationship was similar across a range of cognitive tasks measured during scanning (i.e., the same pattern was seen in fixation, autobiographical recall, and word generation). In contrast, signal amplitude in the hippocampus did not predict memory performance, even for a task that reliably activates the medial temporal lobes (i.e., autobiographical recall). These findings provide a powerful validation of the concept that variability in brain signals reflects functional integrity. Furthermore, this measure can be characterized as a robust biomarker in this clinical setting because it reveals the same pattern regardless of cognitive challenge or task engagement during scanning.
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Alichniewicz KK, Brunner F, Klünemann HH, Greenlee MW. Neural correlates of saccadic inhibition in healthy elderly and patients with amnestic mild cognitive impairment. Front Psychol 2013; 4:467. [PMID: 23898312 PMCID: PMC3721022 DOI: 10.3389/fpsyg.2013.00467] [Citation(s) in RCA: 39] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2013] [Accepted: 07/04/2013] [Indexed: 11/25/2022] Open
Abstract
Performance on tasks that require saccadic inhibition declines with age and altered inhibitory functioning has also been reported in patients with Alzheimer's disease. Although mild cognitive impairment (MCI) is assumed to be a high-risk factor for conversion to AD, little is known about changes in saccadic inhibition and its neural correlates in this condition. Our study determined whether the neural activation associated with saccadic inhibition is altered in persons with amnestic mild cognitive impairment (aMCI). Functional magnetic resonance imaging (fMRI) revealed decreased activation in parietal lobe in healthy elderly persons compared to young persons and decreased activation in frontal eye fields in aMCI patients compared to healthy elderly persons during the execution of anti-saccades. These results illustrate that the decline in inhibitory functions is associated with impaired frontal activation in aMCI. This alteration in function might reflect early manifestations of AD and provide new insights in the neural activation changes that occur in pathological ageing.
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Affiliation(s)
- K K Alichniewicz
- Institute of Experimental Psychology, University of Regensburg Regensburg, Germany
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50
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Nemeth D, Janacsek K, Király K, Londe Z, Németh K, Fazekas K, Adám I, Elemérné K, Csányi A. Probabilistic sequence learning in mild cognitive impairment. Front Hum Neurosci 2013; 7:318. [PMID: 23847493 PMCID: PMC3696838 DOI: 10.3389/fnhum.2013.00318] [Citation(s) in RCA: 31] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2013] [Accepted: 06/10/2013] [Indexed: 11/26/2022] Open
Abstract
Mild Cognitive Impairment (MCI) causes slight but noticeable disruption in cognitive systems, primarily executive and memory functions. However, it is not clear if the development of sequence learning is affected by an impaired cognitive system and, if so, how. The goal of our study was to investigate the development of probabilistic sequence learning, from the initial acquisition to consolidation, in MCI and healthy elderly control groups. We used the Alternating Serial Reaction Time task (ASRT) to measure probabilistic sequence learning. Individuals with MCI showed weaker learning performance than the healthy elderly group. However, using the reaction times only from the second half of each learning block—after the reactivation phase—we found intact learning in MCI. Based on the assumption that the first part of each learning block is related to reactivation/recall processes, we suggest that these processes are affected in MCI. The 24-h offline period showed no effect on sequence-specific learning in either group but did on general skill learning: the healthy elderly group showed offline improvement in general reaction times while individuals with MCI did not. Our findings deepen our understanding regarding the underlying mechanisms and time course of sequence acquisition and consolidation.
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Affiliation(s)
- Dezso Nemeth
- Department of Clinical Psychology and Addiction, Institute of Psychology, Eötvös Loránd University Budapest, Hungary
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