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Wang S, Li Y, Qiu S, Zhang C, Wang G, Xian J, Li T, He H. Reorganization of rich-clubs in functional brain networks during propofol-induced unconsciousness and natural sleep. NEUROIMAGE-CLINICAL 2020; 25:102188. [PMID: 32018124 PMCID: PMC6997627 DOI: 10.1016/j.nicl.2020.102188] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/18/2019] [Revised: 12/31/2019] [Accepted: 01/18/2020] [Indexed: 12/20/2022]
Abstract
BACKGROUND General anesthesia (GA) provides an invaluable experimental tool to understand the essential neural mechanisms underlying consciousness. Previous neuroimaging studies have shown the functional integration and segregation of brain functional networks during anesthetic-induced alteration of consciousness. However, the organization pattern of hubs in functional brain networks remains unclear. Moreover, comparisons with the well-characterized physiological unconsciousness can help us understand the neural mechanisms of anesthetic-induced unconsciousness. METHODS Resting-state functional magnetic resonance imaging was performed during wakefulness, mild propofol-induced sedation (m-PIS), and deep PIS (d-PIS) with clinical unconsciousness on 8 healthy volunteers and wakefulness and natural sleep on 9 age- and sex-matched healthy volunteers. Large-scale functional brain networks of each volunteer were constructed based on 160 regions of interest. Then, rich-club organizations in brain functional networks and nodal properties (nodal strength and efficiency) were assessed and analyzed among the different states and groups. RESULTS Rich-clubs in the functional brain networks were reorganized during alteration of consciousness induced by propofol. Firstly, rich-club nodes were switched from the posterior cingulate cortex (PCC), angular gyrus, and anterior and middle insula to the inferior parietal lobule (IPL), inferior parietal sulcus (IPS), and cerebellum. When sedation was deepened to unconsciousness, the rich-club nodes were switched to the occipital and angular gyrus. These results suggest that the rich-club nodes were switched among the high-order cognitive function networks (default mode network [DMN] and fronto-parietal network [FPN]), sensory networks (occipital network [ON]), and cerebellum network (CN) from consciousness (wakefulness) to propofol-induced unconsciousness. At the same time, compared with wakefulness, local connections were switched to rich-club connections during propofol-induced unconsciousness, suggesting a strengthening of the overall information commutation of networks. Nodal efficiency of the anterior and middle insula and ventral frontal cortex was significantly decreased. Additionally, from wakefulness to natural sleep, a similar pattern of rich-club reorganization with propofol-induced unconsciousness was observed: rich-club nodes were switched from the DMN (including precuneus and PCC) to the sensorimotor network (SMN, including part of the frontal and temporal gyrus). Compared with natural sleep, nodal efficiency of the insula, frontal gyrus, PCC, and cerebellum significantly decreased during propofol-induced unconsciousness. CONCLUSIONS Our study demonstrated that the rich-club reorganization in functional brain networks is characterized by switching of rich-club nodes between the high-order cognitive and sensory and motor networks during propofol-induced alteration of consciousness and natural sleep. These findings will help understand the common neurological mechanism of pharmacological and physiological unconsciousness.
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Affiliation(s)
- Shengpei Wang
- Research Center for Brain-inspired Intelligence and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China; University of Chinese Academy of Sciences, Beijing, China
| | - Yun Li
- Department of Anesthesia, Beijing Tongren Hospital, Capital Medical University, Beijing, China
| | - Shuang Qiu
- Research Center for Brain-inspired Intelligence and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Chuncheng Zhang
- Research Center for Brain-inspired Intelligence and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Guyan Wang
- Department of Anesthesia, Beijing Tongren Hospital, Capital Medical University, Beijing, China
| | - Junfang Xian
- Department of Radiology, Beijing Tongren Hospital, Capital Medical University, Beijing, China
| | - Tianzuo Li
- Department of Anesthesia, Beijing Tongren Hospital, Capital Medical University, Beijing, China; Beijing Shijitan Hospital, Capital Medical University, Beijing, China.
| | - Huiguang He
- Research Center for Brain-inspired Intelligence and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China; University of Chinese Academy of Sciences, Beijing, China; Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Beijing, China.
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Longitudinal structural connectomic and rich-club analysis in adolescent mTBI reveals persistent, distributed brain alterations acutely through to one year post-injury. Sci Rep 2019; 9:18833. [PMID: 31827105 PMCID: PMC6906376 DOI: 10.1038/s41598-019-54950-0] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2018] [Accepted: 11/20/2019] [Indexed: 12/28/2022] Open
Abstract
The diffuse nature of mild traumatic brain injury (mTBI) impacts brain white-matter pathways with potentially long-term consequences, even after initial symptoms have resolved. To understand post-mTBI recovery in adolescents, longitudinal studies are needed to determine the interplay between highly individualised recovery trajectories and ongoing development. To capture the distributed nature of mTBI and recovery, we employ connectomes to probe the brain’s structural organisation. We present a diffusion MRI study on adolescent mTBI subjects scanned one day, two weeks and one year after injury with controls. Longitudinal global network changes over time suggests an altered and more ‘diffuse’ network topology post-injury (specifically lower transitivity and global efficiency). Stratifying the connectome by its back-bone, known as the ‘rich-club’, these network changes were driven by the ‘peripheral’ local subnetwork by way of increased network density, fractional anisotropy and decreased diffusivities. This increased structural integrity of the local subnetwork may be to compensate for an injured network, or it may be robust to mTBI and is exhibiting a normal developmental trend. The rich-club also revealed lower diffusivities over time with controls, potentially indicative of longer-term structural ramifications. Our results show evolving, diffuse alterations in adolescent mTBI connectomes beginning acutely and continuing to one year.
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Connectomics-Based Functional Network Alterations in both Depressed Patients with Suicidal Behavior and Healthy Relatives of Suicide Victims. Sci Rep 2019; 9:14330. [PMID: 31586117 PMCID: PMC6778100 DOI: 10.1038/s41598-019-50881-y] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2018] [Accepted: 09/19/2019] [Indexed: 01/08/2023] Open
Abstract
Understanding the neural mechanisms of suicidal behavior is crucial. While regional brain alterations have previously been reported, knowledge about brain functional connectomics is currently limited. Here, we investigated differences in global topologic network properties and local network-based functional organization in both suicide attempters and suicide relatives. Two independent samples of depressed suicide attempters (N = 42), depressed patient controls (N = 43), healthy controls (N = 66) as well as one sample of healthy relatives of suicide victims (N = 16) and relatives of depressed patients (N = 16) were investigated with functional magnetic resonance imaging in the resting-state condition. Graph theory analyses were performed. Assortativity, clustering coefficients, global efficiency, and rich-club coefficients were calculated. A network-based statistic approach was finally used to examine functional connectivity matrices. In comparison to healthy controls, both patient groups showed significant reduction in assortativity, and decreased functional connectivity in largely central and posterior brain networks. Suicide attempters only differed from patient controls in terms of higher rich-club coefficients for the highest degree nodes. Compared to patient relatives and healthy controls, suicide relatives showed reduced assortativity, reduced clustering coefficients, increased global efficiency, and increased rich-club coefficients for the highest degree nodes. Suicide relatives also showed reduced functional connectivity in one anterior and one posterior sub-network in comparison to healthy controls, and in a largely anterior brain network in comparison to patient relatives. In conclusion, these results suggest that the vulnerability to suicidal behavior may be associated with heritable deficits in global brain functioning – characterized by weak resilience and poor segregation - and in functional organization with reduced connectivities affecting the ventral and dorsal prefrontal cortex, the anterior cingulate, thalamus, striatum, and possibly the insula, fusiform gyrus and the cerebellum.
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54
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Roy A, Ghosal S, Prescott J, Roy Choudhury K. Bayesian modeling of the structural connectome for studying Alzheimer’s disease. Ann Appl Stat 2019. [DOI: 10.1214/19-aoas1257] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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55
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Chen J, Huang X, Liu S, Lu C, Dai Y, Yao Z, Chen Y. Disrupted topological properties of brain networks in erectile dysfunction patients owing predominantly to psychological factors: a structural and functional neuroimaging study. Andrology 2019; 8:381-391. [PMID: 31468742 DOI: 10.1111/andr.12684] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2019] [Revised: 06/27/2019] [Accepted: 06/28/2019] [Indexed: 12/14/2022]
Affiliation(s)
- J. Chen
- Department of Andrology Jiangsu Province Hospital of Chinese MedicineAffiliated Hospital of Nanjing University of Chinese Medicine Nanjing China
| | - X. Huang
- Department of Andrology Jiangsu Province Hospital of Chinese MedicineAffiliated Hospital of Nanjing University of Chinese Medicine Nanjing China
| | - S. Liu
- Department of Radiology Jiangsu Province Hospital of Chinese MedicineAffiliated Hospital of Nanjing University of Chinese Medicine Nanjing China
| | - C. Lu
- Department of Radiology Jiangsu Province Hospital of Chinese MedicineAffiliated Hospital of Nanjing University of Chinese Medicine Nanjing China
| | - Y. Dai
- Department of Andrology Nanjing Drum Tower HospitalAffiliated Hospital of Nanjing University Medical School Nanjing China
| | - Z. Yao
- Department of Psychiatry Nanjing Brain Hospital Affiliated Hospital of Nanjing Medical University Nanjing China
| | - Y. Chen
- Department of Andrology Jiangsu Province Hospital of Chinese MedicineAffiliated Hospital of Nanjing University of Chinese Medicine Nanjing China
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Abstract
Technologies for imaging the pathophysiology of Alzheimer disease (AD) now permit studies of the relationships between the two major proteins deposited in this disease - amyloid-β (Aβ) and tau - and their effects on measures of neurodegeneration and cognition in humans. Deposition of Aβ in the medial parietal cortex appears to be the first stage in the development of AD, although tau aggregates in the medial temporal lobe (MTL) precede Aβ deposition in cognitively healthy older people. Whether aggregation of tau in the MTL is the first stage in AD or a fairly benign phenomenon that may be transformed and spread in the presence of Aβ is a major unresolved question. Despite a strong link between Aβ and tau, the relationship between Aβ and neurodegeneration is weak; rather, it is tau that is associated with brain atrophy and hypometabolism, which, in turn, are related to cognition. Although there is support for an interaction between Aβ and tau resulting in neurodegeneration that leads to dementia, the unknown nature of this interaction, the strikingly different patterns of brain Aβ and tau deposition and the appearance of neurodegeneration in the absence of Aβ and tau are challenges to this model that ultimately must be explained.
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Cai S, Huang K, Kang Y, Jiang Y, von Deneen KM, Huang L. Potential biomarkers for distinguishing people with Alzheimer’s disease from cognitively intact elderly based on the rich-club hierarchical structure of white matter networks. Neurosci Res 2019; 144:56-66. [DOI: 10.1016/j.neures.2018.07.005] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2018] [Revised: 06/29/2018] [Accepted: 07/10/2018] [Indexed: 01/26/2023]
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Schirmer MD, Chung AW, Grant PE, Rost NS. Network structural dependency in the human connectome across the life-span. Netw Neurosci 2019; 3:792-806. [PMID: 31410380 PMCID: PMC6663353 DOI: 10.1162/netn_a_00081] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2018] [Accepted: 02/07/2019] [Indexed: 01/23/2023] Open
Abstract
Principles of network topology have been widely studied in the human connectome. Of particular interest is the modularity of the human brain, where the connectome is divided into subnetworks from which changes with development, aging or disease can be investigated. We present a weighted network measure, the Network Dependency Index (NDI), to identify an individual region’s importance to the global functioning of the network. Importantly, we utilize NDI to differentiate four subnetworks (Tiers) in the human connectome following Gaussian mixture model fitting. We analyze the topological aspects of each subnetwork with respect to age and compare it to rich club-based subnetworks (rich club, feeder, and seeder). Our results first demonstrate the efficacy of NDI to identify more consistent, central nodes of the connectome across age groups, when compared with the rich club framework. Stratifying the connectome by NDI led to consistent subnetworks across the life-span, revealing distinct patterns associated with age where, for example, the key relay nuclei and cortical regions are contained in a subnetwork with highest NDI. The divisions of the human connectome derived from our data-driven NDI framework have the potential to reveal topological alterations described by network measures through the life-span.
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Affiliation(s)
- Markus D Schirmer
- Stroke Division & Massachusetts General Hospital, J. Philip Kistler Stroke Research Center, Harvard Medical School, Boston, MA, USA
| | - Ai Wern Chung
- Fetal-Neonatal Neuroimaging & Developmental Science Center, Division of Newborn Medicine, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - P Ellen Grant
- Fetal-Neonatal Neuroimaging & Developmental Science Center, Division of Newborn Medicine, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - Natalia S Rost
- Stroke Division & Massachusetts General Hospital, J. Philip Kistler Stroke Research Center, Harvard Medical School, Boston, MA, USA
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59
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Kuang L, Zhao D, Xing J, Chen Z, Xiong F, Han X. Metabolic Brain Network Analysis of FDG-PET in Alzheimer's Disease Using Kernel-Based Persistent Features. Molecules 2019; 24:E2301. [PMID: 31234358 PMCID: PMC6630461 DOI: 10.3390/molecules24122301] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2019] [Revised: 06/03/2019] [Accepted: 06/20/2019] [Indexed: 12/11/2022] Open
Abstract
Recent research of persistent homology in algebraic topology has shown that the altered network organization of human brain provides a promising indicator of many neuropsychiatric disorders and neurodegenerative diseases. However, the current slope-based approach may not accurately characterize changes of persistent features over graph filtration because such curves are not strictly linear. Moreover, our previous integrated persistent feature (IPF) works well on an rs-fMRI cohort while it has not yet been studied on metabolic brain networks. To address these issues, we propose a novel univariate network measurement, kernel-based IPF (KBI), based on the prior IPF, to quantify the difference between IPF curves. In our experiments, we apply the KBI index to study fluorodeoxyglucose positron emission tomography (FDG-PET) imaging data from 140 subjects with Alzheimer's disease (AD), 280 subjects with mild cognitive impairment (MCI), and 280 healthy normal controls (NC). The results show the disruption of network integration in the progress of AD. Compared to previous persistent homology-based measures, as well as other standard graph-based measures that characterize small-world organization and modular structure, our proposed network index KBI possesses more significant group difference and better classification performance, suggesting that it may be used as an effective preclinical AD imaging biomarker.
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Affiliation(s)
- Liqun Kuang
- School of Data Science and Technology, North University of China, Taiyuan 030051, China.
| | - Deyu Zhao
- School of Data Science and Technology, North University of China, Taiyuan 030051, China.
| | - Jiacheng Xing
- School of Software, Nanchang University, Nanchang 330047, China.
| | - Zhongyu Chen
- School of Software, East China Jiaotong University, Nanchang 330013, China.
| | - Fengguang Xiong
- School of Data Science and Technology, North University of China, Taiyuan 030051, China.
| | - Xie Han
- School of Data Science and Technology, North University of China, Taiyuan 030051, China.
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60
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Wu Z, Xu D, Potter T, Zhang Y. Effects of Brain Parcellation on the Characterization of Topological Deterioration in Alzheimer's Disease. Front Aging Neurosci 2019; 11:113. [PMID: 31164815 PMCID: PMC6536693 DOI: 10.3389/fnagi.2019.00113] [Citation(s) in RCA: 26] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2018] [Accepted: 04/30/2019] [Indexed: 12/11/2022] Open
Abstract
Alzheimer's disease (AD) causes the progressive deterioration of neural connections, disrupting structural connectivity (SC) networks within the brain. Graph-based analyses of SC networks have shown that topological properties can reveal the course of AD propagation. Different whole-brain parcellation schemes have been developed to define the nodes of these SC networks, although it remains unclear which scheme can best describe the AD-related deterioration of SC networks. In this study, four whole-brain parcellation schemes with different numbers of parcels were used to define SC network nodes. SC networks were constructed based on high angular resolution diffusion imaging (HARDI) tractography for a mixed cohort that includes 20 normal controls (NC), 20 early mild cognitive impairment (EMCI), 20 late mild cognitive impairment (LMCI), and 20 AD patients, from the Alzheimer's Disease Neuroimaging Initiative. Parcellation schemes investigated in this study include the OASIS-TRT-20 (62 regions), AAL (116 regions), HCP-MMP (180 regions), and Gordon-rsfMRI (333 regions), which have all been widely used for the construction of brain structural or functional connectivity networks. Topological characteristics of the SC networks, including the network strength, global efficiency, clustering coefficient, rich-club, characteristic path length, k-core, rich-club coefficient, and modularity, were fully investigated at the network level. Statistical analyses were performed on these metrics using Kruskal-Wallis tests to examine the group differences that were apparent at different stages of AD progression. Results suggest that the HCP-MMP scheme is the most robust and sensitive to AD progression, while the OASIS-TRT-20 scheme is sensitive to group differences in network strength, global efficiency, k-core, and rich-club coefficient at k-levels from 18 and 39. With the exception of the rich-club and modularity coefficients, AAL could not significantly identify group differences on other topological metrics. Further, the Gordon-rsfMRI atlas only significantly differentiates the groups on network strength, characteristic path length, k-core, and rich-club coefficient. Results show that the topological examination of SC networks with different parcellation schemes can provide important complementary AD-related information and thus contribute to a more accurate and earlier diagnosis of AD.
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Affiliation(s)
- Zhanxiong Wu
- School of Electronic Information, Hangzhou Dianzi University, Hangzhou, China.,Department of Biomedical Engineering, University of Houston, Houston, TX, United States
| | - Dong Xu
- School of Electronic Information, Hangzhou Dianzi University, Hangzhou, China.,Zhejiang Key Laboratory of Equipment Electronics, Hangzhou, China
| | - Thomas Potter
- Department of Biomedical Engineering, University of Houston, Houston, TX, United States
| | - Yingchun Zhang
- Department of Biomedical Engineering, University of Houston, Houston, TX, United States
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Guillon J, Chavez M, Battiston F, Attal Y, La Corte V, Thiebaut de Schotten M, Dubois B, Schwartz D, Colliot O, De Vico Fallani F. Disrupted core-periphery structure of multimodal brain networks in Alzheimer's disease. Netw Neurosci 2019; 3:635-652. [PMID: 31157313 PMCID: PMC6542619 DOI: 10.1162/netn_a_00087] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2018] [Accepted: 04/02/2019] [Indexed: 11/20/2022] Open
Abstract
In Alzheimer's disease (AD), the progressive atrophy leads to aberrant network reconfigurations both at structural and functional levels. In such network reorganization, the core and peripheral nodes appear to be crucial for the prediction of clinical outcome because of their ability to influence large-scale functional integration. However, the role of the different types of brain connectivity in such prediction still remains unclear. Using a multiplex network approach we integrated information from DWI, fMRI, and MEG brain connectivity to extract an enriched description of the core-periphery structure in a group of AD patients and age-matched controls. Globally, the regional coreness-that is, the probability of a region to be in the multiplex core-significantly decreased in AD patients as result of a random disconnection process initiated by the neurodegeneration. Locally, the most impacted areas were in the core of the network-including temporal, parietal, and occipital areas-while we reported compensatory increments for the peripheral regions in the sensorimotor system. Furthermore, these network changes significantly predicted the cognitive and memory impairment of patients. Taken together these results indicate that a more accurate description of neurodegenerative diseases can be obtained from the multimodal integration of neuroimaging-derived network data.
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Affiliation(s)
- Jeremy Guillon
- Institut du Cerveau et de la Moelle Epiniere, ICM, Inserm, U 1127, CNRS, UMR 7225, Sorbonne Universite, Paris, France
- Inria Paris, Aramis Project Team, Paris, France
| | | | - Federico Battiston
- Inria Paris, Aramis Project Team, Paris, France
- CNRS, UMR 7225, Paris, France
- Department of Network and Data Science, Central European University, Budapest, Hungary
| | | | - Valentina La Corte
- Department of Neurology, Institute of Memory and Alzheimer’s Disease, Assistance Publique - Hopitaux de Paris, Pitié-Salpêtrière Hospital, Paris, France
- Inserm, UMR 894, Center of Psychiatry and Neurosciences, Memory and Cognition Laboratory, Paris, France
- Institute of Psychology, University Paris Descartes, Sorbonne Paris Cite, France
| | - Michel Thiebaut de Schotten
- Institut du Cerveau et de la Moelle Epiniere, ICM, Inserm, U 1127, CNRS, UMR 7225, Sorbonne Universite, Paris, France
| | - Bruno Dubois
- Institut de la Memoire et de la Maladie d’Alzheimer - IM2A, AP-HP, Sorbonne Universite, Paris, France
| | - Denis Schwartz
- Institut du Cerveau et de la Moelle Epiniere, ICM, Inserm, U 1127, CNRS, UMR 7225, Sorbonne Universite, Ecole Normale Superieure, ENS, Centre MEG-EEG, Paris, France
| | - Olivier Colliot
- Institut du Cerveau et de la Moelle Epiniere, ICM, Inserm, U 1127, CNRS, UMR 7225, Sorbonne Universite, Paris, France
- Inria Paris, Aramis Project Team, Paris, France
| | - Fabrizio De Vico Fallani
- Institut du Cerveau et de la Moelle Epiniere, ICM, Inserm, U 1127, CNRS, UMR 7225, Sorbonne Universite, Paris, France
- Inria Paris, Aramis Project Team, Paris, France
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Pietzuch M, King AE, Ward DD, Vickers JC. The Influence of Genetic Factors and Cognitive Reserve on Structural and Functional Resting-State Brain Networks in Aging and Alzheimer's Disease. Front Aging Neurosci 2019; 11:30. [PMID: 30894813 PMCID: PMC6414800 DOI: 10.3389/fnagi.2019.00030] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2018] [Accepted: 02/01/2019] [Indexed: 01/22/2023] Open
Abstract
Magnetic resonance imaging (MRI) offers significant insight into the complex organization of neural networks within the human brain. Using resting-state functional MRI data, topological maps can be created to visualize changes in brain activity, as well as to represent and assess the structural and functional connections between different brain regions. Crucially, Alzheimer's disease (AD) is associated with progressive loss in this connectivity, which is particularly evident within the default mode network. In this paper, we review the recent literature on how factors that are associated with risk of dementia may influence the organization of the brain network structures. In particular, we focus on cognitive reserve and the common genetic polymorphisms of APOE and BDNF Val66Met.
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Affiliation(s)
- Manuela Pietzuch
- Wicking Dementia Research and Education Centre, College of Health and Medicine, University of Tasmania, Hobart, TAS, Australia
| | - Anna E. King
- Wicking Dementia Research and Education Centre, College of Health and Medicine, University of Tasmania, Hobart, TAS, Australia
| | - David D. Ward
- Population Health Sciences, German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
| | - James C. Vickers
- Wicking Dementia Research and Education Centre, College of Health and Medicine, University of Tasmania, Hobart, TAS, Australia
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Wang B, Zhan Q, Yan T, Imtiaz S, Xiang J, Niu Y, Liu M, Wang G, Cao R, Li D. Hemisphere and Gender Differences in the Rich-Club Organization of Structural Networks. Cereb Cortex 2019; 29:4889-4901. [PMID: 30810159 DOI: 10.1093/cercor/bhz027] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2018] [Revised: 01/23/2019] [Accepted: 02/03/2019] [Indexed: 02/07/2023] Open
Abstract
AbstractStructural and functional differences in brain hemispheric asymmetry have been well documented between female and male adults. However, potential differences in the connectivity patterns of the rich-club organization of hemispheric structural networks in females and males remain to be determined. In this study, diffusion tensor imaging was used to construct hemispheric structural networks in healthy subjects, and graph theoretical analysis approaches were applied to quantify hemisphere and gender differences in rich-club organization. The results showed that rich-club organization was consistently observed in both hemispheres of female and male adults. Moreover, a reduced level of connectivity was found in the left hemisphere. Notably, rightward asymmetries were mainly observed in feeder and local connections among one hub region and peripheral regions, many of which are implicated in visual processing and spatial attention functions. Additionally, significant gender differences were revealed in the rich-club, feeder, and local connections in rich-club organization. These gender-related hub and peripheral regions are involved in emotional, sensory, and cognitive control functions. The topological changes in rich-club organization provide novel insight into the hemisphere and gender effects on white matter connections and underlie a potential network mechanism of hemisphere- and gender-based differences in visual processing, spatial attention and cognitive control.
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Affiliation(s)
- Bin Wang
- Department of Information and Computer, Taiyuan University of Technology, No. 79, Yingze West Street, Taiyuan, Shanxi, China
- Department of Pathology and Shanxi Key Laboratory of Carcinogenesis and Translational Research on Esophageal Cancer, Shanxi Medical University, Taiyuan, Shanxi, China
| | - Qionghui Zhan
- Department of Information and Computer, Taiyuan University of Technology, No. 79, Yingze West Street, Taiyuan, Shanxi, China
| | - Ting Yan
- Department of Pathology and Shanxi Key Laboratory of Carcinogenesis and Translational Research on Esophageal Cancer, Shanxi Medical University, Taiyuan, Shanxi, China
| | - Sumaira Imtiaz
- Department of Information and Computer, Taiyuan University of Technology, No. 79, Yingze West Street, Taiyuan, Shanxi, China
| | - Jie Xiang
- Department of Information and Computer, Taiyuan University of Technology, No. 79, Yingze West Street, Taiyuan, Shanxi, China
| | - Yan Niu
- Department of Information and Computer, Taiyuan University of Technology, No. 79, Yingze West Street, Taiyuan, Shanxi, China
| | - Miaomiao Liu
- Graduate School of Natural Science and Technology, Okayama University, 1-1-1Tsushimanaka, Kita-ku, Okayama, Japan
| | - Gongshu Wang
- Department of Information and Computer, Taiyuan University of Technology, No. 79, Yingze West Street, Taiyuan, Shanxi, China
| | - Rui Cao
- Department of Information and Computer, Taiyuan University of Technology, No. 79, Yingze West Street, Taiyuan, Shanxi, China
| | - Dandan Li
- Department of Information and Computer, Taiyuan University of Technology, No. 79, Yingze West Street, Taiyuan, Shanxi, China
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Tan X, Zhou Z, Gao J, Meng F, Yu Y, Zhang J, He F, Wei R, Wang J, Peng G, Zhang X, Pan G, Luo B. Structural connectome alterations in patients with disorders of consciousness revealed by 7-tesla magnetic resonance imaging. NEUROIMAGE-CLINICAL 2019; 22:101702. [PMID: 30711681 PMCID: PMC6360803 DOI: 10.1016/j.nicl.2019.101702] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/03/2018] [Revised: 01/25/2019] [Accepted: 01/28/2019] [Indexed: 02/04/2023]
Abstract
Although the functional connectivity of patients with disorders of consciousness (DOC) has been widely examined, less is known about brain white matter connectivity. The aim of this study was to explore structural network alterations for the diagnosis and prognosis of patients with chronic DOC. Eleven DOC patients and 11 sex- and age-matched controls were included in the study. Participants underwent diffusion magnetic resonance imaging (MRI) and T1-weighted structural MRI at 7 tesla (7 T). Graph-theoretical analysis and network-based statistics were used to analyze the group differences. Two patients were scanned twice for a longitudinal study to examine the relationship between connectome metrics and the patients' prognoses. Compared with healthy controls, DOC patients showed significantly elevated transitivity (p < .001), local efficiency (p = .009), and clustering coefficient (p = .039). When comparing the connectome metrics within the three groups (healthy controls, minimally conscious state (MCS), and vegetative state/unresponsive wakefulness syndrome (VS/UWS)), significant group differences were observed in transitivity (p < .001) and local efficiency (p = .031). Significantly increased transitivity was observed in vegetative state/unresponsive wakefulness syndrome compared with minimally conscious state (p = .0217, Bonferroni corrected). Transitivity showed significant negative correlations with the Coma Recovery Scale-Revised score (r = -0.6902, p = .023), consistent with the longitudinal study results. A subnetwork with significantly decreased structural connections was identified using network-based statistical analysis comparing DOC patients with healthy controls, which was mainly located in the frontal cortex, limbic system, and occipital and parietal lobes. This preliminary study suggests that graph theoretical approaches for assessing white matter connectivity may enable various states of DOC to be distinguished. Of the metrics analyzed, transitivity had a critical role in distinguishing the diagnostic groups. Larger cohorts will be necessary to confirm the predictive value of 7 T MRI in the prognosis of DOC patients.
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Affiliation(s)
- Xufei Tan
- Department of Neurology, Brain Medical Centre, the First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Zhen Zhou
- State Key Lab of CAD&CG, Zhejiang University, Hangzhou, China; College of Computer Science and Technology, Zhejiang University, Hangzhou, China
| | - Jian Gao
- Department of Rehabilitation, Hangzhou Hospital of Zhejiang CAPR, Hangzhou, China
| | - Fanxia Meng
- Department of Neurology, Brain Medical Centre, the First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Yamei Yu
- Department of Neurology, Brain Medical Centre, the First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Jie Zhang
- Department of Rehabilitation Medicine, Zhejiang Provincial People's Hospital, People's Hospital of Hangzhou Medical College, Hangzhou, China
| | - Fangping He
- Department of Neurology, Brain Medical Centre, the First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Ruili Wei
- Department of Neurology, Brain Medical Centre, the First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Junyang Wang
- Department of Neurology, Brain Medical Centre, the First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Guoping Peng
- Department of Neurology, Brain Medical Centre, the First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Xiaotong Zhang
- Interdisciplinary Institute of Neuroscience and Technology, Qiushi Academy for Advanced Studies, Zhejiang University, Hangzhou, China; Center for Brain Imaging Science and Technology, College of Biomedical Engineering and Instrumental Science, Zhejiang University, Hangzhou, China
| | - Gang Pan
- State Key Lab of CAD&CG, Zhejiang University, Hangzhou, China; College of Computer Science and Technology, Zhejiang University, Hangzhou, China.
| | - Benyan Luo
- Department of Neurology, Brain Medical Centre, the First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China; School of Medicine, Zhejiang University, Collaborative Innovation Center for Brain Science, Hangzhou, China.
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65
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Sun Y, Bi Q, Wang X, Hu X, Li H, Li X, Ma T, Lu J, Chan P, Shu N, Han Y. Prediction of Conversion From Amnestic Mild Cognitive Impairment to Alzheimer's Disease Based on the Brain Structural Connectome. Front Neurol 2019; 9:1178. [PMID: 30687226 PMCID: PMC6335339 DOI: 10.3389/fneur.2018.01178] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2018] [Accepted: 12/20/2018] [Indexed: 12/12/2022] Open
Abstract
Background: Early prediction of disease progression in patients with amnestic mild cognitive impairment (aMCI) is important for early diagnosis and intervention of Alzheimer's disease (AD). Previous brain network studies have suggested topological disruptions of the brain connectome in aMCI patients. However, whether brain connectome markers at baseline can predict longitudinal conversion from aMCI to AD remains largely unknown. Methods: In this study, 52 patients with aMCI and 26 demographically matched healthy controls from a longitudinal cohort were evaluated. During 2 years of follow-up, 26 patients with aMCI were retrospectively classified as aMCI converters and 26 patients remained stable as aMCI non-converters based on whether they were subsequently diagnosed with AD. For each participant, diffusion tensor imaging at baseline and deterministic tractography were used to map the whole-brain white matter structural connectome. Graph theoretical analysis was applied to investigate the convergent and divergent connectivity patterns of structural connectome between aMCI converters and non-converters. Results: Disrupted topological organization of the brain structural connectome were identified in both aMCI converters and non-converters. More severe disruptions of structural connectivity in aMCI converters compared with non-converters were found, especially in the default-mode network regions and connections. Finally, a support vector machine-based classification demonstrated the good discriminative ability of structural connectivity in differentiating aMCI patients from controls with an accuracy of 98%, and in discriminating converters from non-converters with an accuracy of 81%. Conclusion: Our study provides potential structural connectome/connectivity-based biomarkers for predicting disease progression in aMCI, which is important for the early diagnosis of AD.
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Affiliation(s)
- Yu Sun
- Department of Neurology, XuanWu Hospital of Capital Medical University, Beijing, China
| | - Qiuhui Bi
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China.,Center for Collaboration and Innovation in Brain and Learning Sciences, Beijing Normal University, Beijing, China.,Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
| | - Xiaoni Wang
- Department of Neurology, XuanWu Hospital of Capital Medical University, Beijing, China
| | - Xiaochen Hu
- Department of Psychiatry and Psychotherapy, Medical Faculty, University of Cologne, Cologne, Germany
| | - Huijie Li
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, China.,CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing, China
| | - Xiaobo Li
- Department of Biomedical Engineering, New Jersey Institute of Technology, Newark, NJ, United States
| | - Ting Ma
- Department of Electronic and Information Engineering, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, China
| | - Jie Lu
- Department of Radiology, XuanWu Hospital of Capital Medical University, Beijing, China
| | - Piu Chan
- Department of Neurology, XuanWu Hospital of Capital Medical University, Beijing, China.,Beijing Institute of Geriatrics, XuanWu Hospital of Capital Medical University, Beijing, China.,National Clinical Research Center for Geriatric Disorders, Beijing, China
| | - Ni Shu
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China.,Center for Collaboration and Innovation in Brain and Learning Sciences, Beijing Normal University, Beijing, China.,Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
| | - Ying Han
- Department of Neurology, XuanWu Hospital of Capital Medical University, Beijing, China.,Beijing Institute of Geriatrics, XuanWu Hospital of Capital Medical University, Beijing, China.,National Clinical Research Center for Geriatric Disorders, Beijing, China.,Center of Alzheimer's Disease, Beijing Institute for Brain Disorders, Beijing, China
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66
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Chen Z, Hu X, Chen Q, Feng T. Altered structural and functional brain network overall organization predict human intertemporal decision-making. Hum Brain Mapp 2019; 40:306-328. [PMID: 30240495 PMCID: PMC6865623 DOI: 10.1002/hbm.24374] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2018] [Revised: 08/14/2018] [Accepted: 08/15/2018] [Indexed: 11/06/2022] Open
Abstract
Intertemporal decision-making is naturally ubiquitous to us: individuals always make a decision with different consequences occurring at different moments. These choices are invariably involved in life-changing outcomes regarding marriage, education, fertility, long-term well-being, and even public policy. Previous studies have clearly uncovered the neurobiological mechanism of the intertemporal decision in the schemes of regional location or sub-network. However, it still remains unclear how to characterize intertemporal behavior with multimodal whole-brain network metrics to date. Here, we combined diffusion tensor image and resting-state functional connectivity MRI technology, in conjunction with graph-theoretical analysis, to explore the link between topological properties of integrated structural and functional whole-brain networks and intertemporal decision-making. Graph-theoretical analysis illustrated that the participants with steep discounting rates exhibited the decreased global topological organizations including small-world and rich-club regimes in both functional and structural connectivity networks, and reflected the dreadful local topological dynamics in the modularity of functional connectome. Furthermore, in the cross-modalities configuration, the same relationship was predominantly observed for the coupling of structural-functional connectivity as well. Above topological metrics are commonly indicative of the communication pattern of simultaneous global and local parallel information processing, and it thus reshapes our accounts on intertemporal decision-making from functional regional/sub-network scheme to multimodal brain overall organization.
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Affiliation(s)
- Zhiyi Chen
- Faculty of PsychologySouthwest UniversityChongqingChina
| | - Xingwang Hu
- Institute of EducationSichuan Normal UniversityChengduChina
| | - Qi Chen
- School of PsychologySouth China Normal UniversityGuangzhouChina
| | - Tingyong Feng
- Faculty of PsychologySouthwest UniversityChongqingChina
- Key Laboratory of Cognition and Personality, Ministry of EducationChongqingChina
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67
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Neural Correlates of Cognitive Impairment in Parkinson's Disease: A Review of Structural MRI Findings. INTERNATIONAL REVIEW OF NEUROBIOLOGY 2019; 144:1-28. [DOI: 10.1016/bs.irn.2018.09.009] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/01/2022]
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68
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Dai Z, Lin Q, Li T, Wang X, Yuan H, Yu X, He Y, Wang H. Disrupted structural and functional brain networks in Alzheimer's disease. Neurobiol Aging 2018; 75:71-82. [PMID: 30553155 DOI: 10.1016/j.neurobiolaging.2018.11.005] [Citation(s) in RCA: 105] [Impact Index Per Article: 17.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2018] [Revised: 11/08/2018] [Accepted: 11/09/2018] [Indexed: 12/22/2022]
Abstract
Studies have demonstrated that the clinical manifestations of Alzheimer's disease (AD) are associated with abnormal connections in either functional connectivity networks (FCNs) or structural connectivity networks (SCNs). However, the FCN and SCN of AD have usually been examined separately, and the results were inconsistent. In this multimodal study, we collected resting-state functional magnetic resonance imaging and diffusion magnetic resonance imaging data from 46 patients with AD and 39 matched healthy controls (HCs). Graph-theory analysis was used to investigate the topological organization of the FCN and SCN simultaneously. Compared with HCs, both the FCN and SCN of patients with AD showed disrupted network integration (i.e., increased characteristic path length) and segregation (i.e., decreased intramodular connections in the default mode network). Moreover, the FCN, but not the SCN, exhibited a reduced clustering coefficient and reduced rich club connections in AD. The coupling (i.e., correlation) of the FCN and SCN in AD was increased in connections of the default mode network and the rich club. These findings demonstrated overlapping and distinct network disruptions in the FCN and SCN and a strengthened correlation between FCNs and SCNs in AD, which provides a novel perspective for understanding the pathophysiological mechanisms underlying AD.
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Affiliation(s)
- Zhengjia Dai
- National Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China; Department of Psychology, Sun Yat-sen University, Guangzhou, China
| | - Qixiang Lin
- National Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China; Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
| | - Tao Li
- Dementia Care & Research Center, Peking University Institute of Mental Health (Sixth Hospital), Beijing, China; Beijing Key Laboratory for Translational Research on Diagnosis and Treatment of Dementia, Beijing, China; National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, China
| | - Xiao Wang
- Dementia Care & Research Center, Peking University Institute of Mental Health (Sixth Hospital), Beijing, China; Beijing Key Laboratory for Translational Research on Diagnosis and Treatment of Dementia, Beijing, China; National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, China
| | - Huishu Yuan
- Department of Radiology, Peking University Third Hospital, Beijing, China
| | - Xin Yu
- Dementia Care & Research Center, Peking University Institute of Mental Health (Sixth Hospital), Beijing, China; Beijing Key Laboratory for Translational Research on Diagnosis and Treatment of Dementia, Beijing, China; National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, China
| | - Yong He
- National Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China; Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China.
| | - Huali Wang
- Dementia Care & Research Center, Peking University Institute of Mental Health (Sixth Hospital), Beijing, China; Beijing Key Laboratory for Translational Research on Diagnosis and Treatment of Dementia, Beijing, China; National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, China.
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69
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Amoroso N, La Rocca M, Bruno S, Maggipinto T, Monaco A, Bellotti R, Tangaro S. Multiplex Networks for Early Diagnosis of Alzheimer's Disease. Front Aging Neurosci 2018; 10:365. [PMID: 30487745 PMCID: PMC6247675 DOI: 10.3389/fnagi.2018.00365] [Citation(s) in RCA: 25] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2018] [Accepted: 10/23/2018] [Indexed: 12/18/2022] Open
Abstract
Analysis and quantification of brain structural changes, using Magnetic Resonance Imaging (MRI), are increasingly used to define novel biomarkers of brain pathologies, such as Alzheimer's disease (AD). Several studies have suggested that brain topological organization can reveal early signs of AD. Here, we propose a novel brain model which captures both intra- and inter-subject information within a multiplex network approach. This model localizes brain atrophy effects and summarizes them with a diagnostic score. On an independent test set, our multiplex-based score segregates (i) normal controls (NC) from AD patients with a 0.86±0.01 accuracy and (ii) NC from mild cognitive impairment (MCI) subjects that will convert to AD (cMCI) with an accuracy of 0.84±0.01. The model shows that illness effects are maximally detected by parceling the brain in equal volumes of 3, 000 mm3 ("patches"), without any a priori segmentation based on anatomical features. The multiplex approach shows great sensitivity in detecting anomalous changes in the brain; the robustness of the obtained results is assessed using both voxel-based morphometry and FreeSurfer morphological features. Because of its generality this method can provide a reliable tool for clinical trials and a disease signature of many neurodegenerative pathologies.
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Affiliation(s)
- Nicola Amoroso
- Dipartimento Interateneo di Fisica “M. Merlin”, Università degli studi di Bari “A. Moro”, Bari, Italy
- Dipartimento Interateneo di Fisica “M. Merlin”, Istituto Nazionale di Fisica Nucleare, Sezione di Bari, Bari, Italy
| | - Marianna La Rocca
- Laboratory of Neuro Imaging, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, United States
| | - Stefania Bruno
- Blackheath Brain Injury Rehabilitation Centre, London, United Kingdom
| | - Tommaso Maggipinto
- Dipartimento Interateneo di Fisica “M. Merlin”, Università degli studi di Bari “A. Moro”, Bari, Italy
- Dipartimento Interateneo di Fisica “M. Merlin”, Istituto Nazionale di Fisica Nucleare, Sezione di Bari, Bari, Italy
| | - Alfonso Monaco
- Dipartimento Interateneo di Fisica “M. Merlin”, Istituto Nazionale di Fisica Nucleare, Sezione di Bari, Bari, Italy
| | - Roberto Bellotti
- Dipartimento Interateneo di Fisica “M. Merlin”, Università degli studi di Bari “A. Moro”, Bari, Italy
- Dipartimento Interateneo di Fisica “M. Merlin”, Istituto Nazionale di Fisica Nucleare, Sezione di Bari, Bari, Italy
| | - Sabina Tangaro
- Dipartimento Interateneo di Fisica “M. Merlin”, Istituto Nazionale di Fisica Nucleare, Sezione di Bari, Bari, Italy
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70
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Battiston F, Guillon J, Chavez M, Latora V, De Vico Fallani F. Multiplex core-periphery organization of the human connectome. J R Soc Interface 2018; 15:20180514. [PMID: 30209045 PMCID: PMC6170773 DOI: 10.1098/rsif.2018.0514] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2018] [Accepted: 08/16/2018] [Indexed: 01/16/2023] Open
Abstract
What is the core of the human brain is a fundamental question that has been mainly addressed by studying the anatomical connections between differently specialized areas, thus neglecting the possible contributions from their functional interactions. While many methods are available to identify the core of a network when connections between nodes are all of the same type, a principled approach to define the core when multiple types of connectivity are allowed is still lacking. Here, we introduce a general framework to define and extract the core-periphery structure of multi-layer networks by explicitly taking into account the connectivity patterns at each layer. We first validate our algorithm on synthetic networks of different size and density, and with tunable overlap between the cores at different layers. We then use our method to merge information from structural and functional brain networks, obtaining in this way an integrated description of the core of the human connectome. Results confirm the role of the main known cortical and subcortical hubs, but also suggest the presence of new areas in the sensori-motor cortex that are crucial for intrinsic brain functioning. Taken together these findings provide fresh evidence on a fundamental question in modern neuroscience and offer new opportunities to explore the mesoscale properties of multimodal brain networks.
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Affiliation(s)
- Federico Battiston
- Inria Paris, Aramis project-team, 75013 Paris, France
- CNRS, Sorbonne Universites, UPMC Univ Paris 06, Inserm, Institut du cerveau et la moelle epiniere (ICM), Hopital Pitie-Salpetriere, 75013 Paris, France
- School of Mathematical Sciences, Queen Mary University of London, London E1 4NS, UK
| | - Jeremy Guillon
- Inria Paris, Aramis project-team, 75013 Paris, France
- CNRS, Sorbonne Universites, UPMC Univ Paris 06, Inserm, Institut du cerveau et la moelle epiniere (ICM), Hopital Pitie-Salpetriere, 75013 Paris, France
| | - Mario Chavez
- CNRS, Sorbonne Universites, UPMC Univ Paris 06, Inserm, Institut du cerveau et la moelle epiniere (ICM), Hopital Pitie-Salpetriere, 75013 Paris, France
| | - Vito Latora
- School of Mathematical Sciences, Queen Mary University of London, London E1 4NS, UK
- Dipartimento di Fisica ed Astronomia, Università di Catania and INFN, 95123 Catania, Italy
| | - Fabrizio De Vico Fallani
- Inria Paris, Aramis project-team, 75013 Paris, France
- CNRS, Sorbonne Universites, UPMC Univ Paris 06, Inserm, Institut du cerveau et la moelle epiniere (ICM), Hopital Pitie-Salpetriere, 75013 Paris, France
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71
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Yan T, Wang W, Yang L, Chen K, Chen R, Han Y. Rich club disturbances of the human connectome from subjective cognitive decline to Alzheimer's disease. Theranostics 2018; 8:3237-3255. [PMID: 29930726 PMCID: PMC6010989 DOI: 10.7150/thno.23772] [Citation(s) in RCA: 123] [Impact Index Per Article: 20.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2017] [Accepted: 04/08/2018] [Indexed: 12/13/2022] Open
Abstract
Alzheimer's disease (AD) has a preclinical phase that can last for decades prior to clinical dementia onset. Subjective cognitive decline (SCD) is regarded as the last preclinical AD stage prior to the development of amnestic mild cognitive decline (aMCI) and AD dementia (d-AD). The analysis of brain structural networks based on diffusion tensor imaging (DTI) has identified the so-called 'rich club', a set of cortical regions highly connected to each other, with other regions referred to as peripheral. It has been reported that rich club architecture is affected by regional atrophy and connectivity, which are reduced in patients with aMCI and d-AD. Methods: We recruited 62 normal controls, 47 SCD patients, 60 aMCI patients and 55 d-AD patients and collected DTI data to analyze rich-club organization. Results: We demonstrated that rich club organization was disrupted, with reduced structural connectivity among rich club nodes, in aMCI and d-AD patients but remained stable in SCD patients. In addition, SCD, aMCI and d-AD patients showed similar patterns of disrupted peripheral regions and reduced connectivity involving these regions, suggesting that peripheral regions might contribute to cognitive decline and that disruptions here could be regarded as an early marker of SCD. This organization could provide the fundamental structural architecture for complex cognitive functions and explain the low prevalence of cognitive problems in SCD patients. Conclusions: These findings reveal a disrupted pattern of the AD connectome that starts in peripheral regions and then hierarchically propagates to rich club regions, when patients show clinical symptoms. This pattern provides evidence that disruptions in rich club organization are a key factor in the progression of AD that can dynamically reflect the progression of AD, thus representing a potential biomarker for early diagnosis.
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Affiliation(s)
- Tianyi Yan
- School of Life Science, Beijing Institute of Technology, Beijing, China
- Key Laboratory of Convergence Medical Engineering System and Healthcare Technology, The Ministry of Industry and Information Technology, Beijing Institute of Technology, Beijing, China
| | - Wenhui Wang
- School of Life Science, Beijing Institute of Technology, Beijing, China
- Key Laboratory of Convergence Medical Engineering System and Healthcare Technology, The Ministry of Industry and Information Technology, Beijing Institute of Technology, Beijing, China
| | - Liu Yang
- Department of Neurology, XuanWu Hospital of Capital Medical University, Beijing, China
| | - Kewei Chen
- Banner Alzheimer's Institute and Banner Good Samaritan PET center, Phoenix, AZ, USA
| | - Rong Chen
- Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland, Baltimore, USA
| | - Ying Han
- Department of Neurology, XuanWu Hospital of Capital Medical University, Beijing, China
- Center of Alzheimer's Disease, Beijing Institute for Brain Disorders, Beijing, China
- Beijing Institute of Geriatrics, Beijing, China
- National Clinical Research Center for Geriatric Disorders, Beijing, China
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72
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Lee WJ, Han CE, Aganj I, Seo SW, Seong JK. Distinct Patterns of Rich Club Organization in Alzheimer’s Disease and Subcortical Vascular Dementia: A White Matter Network Study. J Alzheimers Dis 2018; 63:977-987. [DOI: 10.3233/jad-180027] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Affiliation(s)
- Wha Jin Lee
- School of Biomedical Engineering, Korea University, Seoul, Republic of Korea
| | - Cheol E. Han
- Department of Electronics and Information Engineering, Korea University, Sejong, Republic of Korea
| | - Iman Aganj
- Department of Radiology, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Sang Won Seo
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University, School of Medicine, Seoul, Republic of Korea
| | - Joon-Kyung Seong
- School of Biomedical Engineering, Korea University, Seoul, Republic of Korea
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73
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Perea RD, Rabin JS, Fujiyoshi MG, Neal TE, Smith EE, Van Dijk KRA, Hedden T. Connectome-derived diffusion characteristics of the fornix in Alzheimer's disease. NEUROIMAGE-CLINICAL 2018; 19:331-342. [PMID: 30013916 PMCID: PMC6044183 DOI: 10.1016/j.nicl.2018.04.029] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/21/2017] [Revised: 04/03/2018] [Accepted: 04/23/2018] [Indexed: 02/06/2023]
Abstract
The fornix bundle is a major white matter pathway of the hippocampus. While volume of the hippocampus has been a primary imaging biomarker of Alzheimer's disease progression, recent research has suggested that the volume and microstructural characteristics of the fornix bundle connecting the hippocampus could add relevant information for diagnosing and staging Alzheimer's disease. Using a robust fornix bundle isolation technique in native diffusion space, this study investigated whether diffusion measurements of the fornix differed between normal older adults and Alzheimer's disease patients when controlling for volume measurements. Data were collected using high gradient multi-shell diffusion-weighted MRI from a Siemens CONNECTOM scanner in 23 Alzheimer's disease and 23 age- and sex-matched control older adults (age range = 53–92). These data were used to reconstruct a continuous fornix bundle in every participant's native diffusion space, from which tract-derived volumetric and diffusion metrics were extracted and compared between groups. Diffusion metrics included those from a tensor model and from a generalized q-sampling imaging model. Results showed no significant differences in tract-derived fornix volumes but did show altered diffusion metrics within tissue classified as the fornix in the Alzheimer's disease group. Comparisons to a manual tracing method indicated the same pattern of results and high correlations between the methods. These results suggest that in Alzheimer's disease, diffusion characteristics may provide more sensitive measures of fornix degeneration than do volume measures and may be a potential early marker for loss of medial temporal lobe connectivity. An enhanced method for measurement of continuous fornix bundles is described. Diffusion characteristics of the fornix were degraded in Alzheimer's disease. Alzheimer's disease primarily affected the crus and body of the fornix. Diffusion differences were observed controlling for fornix volume differences.
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Affiliation(s)
- Rodrigo D Perea
- Athinoula A. Martinos Center for Biomedical Imaging, Dept. of Radiology, Massachusetts General Hospital, Charlestown, MA, United States; Dept. of Radiology, Harvard Medical School, Boston, MA, United States
| | - Jennifer S Rabin
- Dept. of Psychiatry, Massachusetts General Hospital, Boston, MA, United States; Dept. of Psychiatry, Harvard Medical School, Boston, MA, United States
| | - Megan G Fujiyoshi
- Dept. of Psychiatry, Massachusetts General Hospital, Boston, MA, United States
| | - Taylor E Neal
- Dept. of Psychiatry, Massachusetts General Hospital, Boston, MA, United States
| | - Emily E Smith
- Dept. of Psychiatry, Massachusetts General Hospital, Boston, MA, United States; Dept. of Psychiatry, University of Texas Southwestern Medical Center, Dallas, TX, United States
| | - Koene R A Van Dijk
- Athinoula A. Martinos Center for Biomedical Imaging, Dept. of Radiology, Massachusetts General Hospital, Charlestown, MA, United States
| | - Trey Hedden
- Athinoula A. Martinos Center for Biomedical Imaging, Dept. of Radiology, Massachusetts General Hospital, Charlestown, MA, United States; Dept. of Radiology, Harvard Medical School, Boston, MA, United States.
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74
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Lee H, Chung MK, Kang H, Choi H, Kim YK, Lee DS. ABNORMAL HOLE DETECTION IN BRAIN CONNECTIVITY BY KERNEL DENSITY OF PERSISTENCE DIAGRAM AND HODGE LAPLACIAN. PROCEEDINGS. IEEE INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING 2018; 2018:20-23. [PMID: 30319734 PMCID: PMC6181146 DOI: 10.1109/isbi.2018.8363514] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
Community and rich-club detection are a well-known method to extract functionally specialized subnetwork in brain connectivity analysis. They find densely connected subregions with large modularity or high degree in brain connectivity studies. However, densely connected nodes are not the only representation of network shape. In this study, we propose a new method to extract abnormal holes, which are another representation of network shape. While densely connected component characterizes network's efficiency, abnormal holes characterize inefficiency. The proposed method differs from the existing hole detection in two respects. One is to use Hodge Laplacian to obtain a harmonic hole in the linear combination of edges, rather than a subset of edges. The other is to use the kernel density estimation of persistence diagram of random networks to determine the significance of a hole, rather than using the persistence of a hole. We applied the proposed method to find the abnormality of metabolic connectivity in the FDG PET data of ADNI. We found that, as AD severely progressed, the brain network had more abnormal holes. The localized holes showed how inefficient the structure of brain network became as the disease progressed.
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Affiliation(s)
| | - Moo K Chung
- University of Wisconsin, Madison, WI 53706 USA
| | | | - Hongyoon Choi
- Cheonan Public Health Center, Chungnam, Republic of Korea
| | - Yu Kyeong Kim
- Seoul National University Boramae Medical Center, Seoul
| | - Dong Soo Lee
- Seoul National University Hospital
- Seoul National University
- Korea Brain Research Institute, Daegu
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75
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Colon-Perez LM, Tanner JJ, Couret M, Goicochea S, Mareci TH, Price CC. Cognition and connectomes in nondementia idiopathic Parkinson's disease. Netw Neurosci 2018; 2:106-124. [PMID: 29911667 PMCID: PMC5989988 DOI: 10.1162/netn_a_00027] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2017] [Accepted: 09/18/2017] [Indexed: 01/01/2023] Open
Abstract
In this study, we investigate the organization of the structural connectome in cognitively well participants with Parkinson’s disease (PD-Well; n = 31) and a subgroup of participants with Parkinson’s disease who have amnestic disturbances (PD-MI; n = 9). We explore correlations between connectome topology and vulnerable cognitive domains in Parkinson’s disease relative to non-Parkinson’s disease peers (control, n = 40). Diffusion-weighted MRI data and deterministic tractography were used to generate connectomes. Connectome topological indices under study included weighted indices of node strength, path length, clustering coefficient, and small-worldness. Relative to controls, node strength was reduced 4.99% for PD-Well (p = 0.041) and 13.2% for PD-MI (p = 0.004). We found bilateral differences in the node strength between PD-MI and controls for inferior parietal, caudal middle frontal, posterior cingulate, precentral, and rostral middle frontal. Correlations between connectome and cognitive domains of interest showed that topological indices of global connectivity negatively associated with working memory and displayed more and larger negative correlations with neuropsychological indices of memory in PD-MI than in PD-Well and controls. These findings suggest that indices of network connectivity are reduced in PD-MI relative to PD-Well and control participants. Parkinson’s disease (PD) patients with amnestic mild cognitive impairment (e.g., primary processing-speed impairments or primary memory impairments) are at greater risk of developing dementia. Recent evidence suggests that patients with PD and mild cognitive impairment present an altered connectome connectivity. In this work, we further explore the structural connectome of PD patients to provide clues to identify possible sensitive markers of disease progression, and cognitive impairment, in susceptible PD patients. We employed a weighted network framework that yields more stable topological results than the binary network framework and is robust despite graph density differences, hence it does not require thresholding to analyze the connectomes. As Supplementary Information (Colon-Perez et al., 2017), we include databases sharing the results of the network data.
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Affiliation(s)
| | - Jared J Tanner
- Department of Clinical and Health Psychology, University of Florida, Gainesville, FL, USA
| | - Michelle Couret
- Department of Medicine, Columbia University, New York, NY, USA
| | - Shelby Goicochea
- Department of Medicine, University of Florida, Gainesville, FL, USA
| | - Thomas H Mareci
- Department of Biochemistry and Molecular Biology, University of Florida, Gainesville, FL, USA
| | - Catherine C Price
- Department of Clinical and Health Psychology, University of Florida, Gainesville, FL, USA
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76
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Abnormal organization of white matter networks in patients with subjective cognitive decline and mild cognitive impairment. Oncotarget 2018; 7:48953-48962. [PMID: 27418146 PMCID: PMC5226483 DOI: 10.18632/oncotarget.10601] [Citation(s) in RCA: 32] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2015] [Accepted: 06/29/2016] [Indexed: 11/25/2022] Open
Abstract
Network analysis has been widely used in studying Alzheimer's disease (AD). However, how the white matter network changes in cognitive impaired patients with subjective cognitive decline (SCD) (a symptom emerging during early stage of AD) and amnestic mild cognitive impairment (aMCI) (a pre-dementia stage of AD) is still unclear. Here, structural networks were constructed respectively based on FA and FN for 36 normal controls, 21 SCD patients, and 33 aMCI patients by diffusion tensor imaging and graph theory. Significantly lower efficiency was found in aMCI patients than normal controls (NC). Though not significant, the values in those with SCD were intermediate between aMCI and NC. In addition, our results showed significantly altered betweenness centrality located in right precuneus, calcarine, putamen, and left anterior cingulate in aMCI patients. Furthermore, association was found between network metrics and cognitive impairment. Our study suggests that the structural network properties might be preserved in SCD stage and disrupted in aMCI stage, which may provide novel insights into pathological mechanisms of AD.
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77
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Li C, Huang B, Zhang R, Ma Q, Yang W, Wang L, Wang L, Xu Q, Feng J, Liu L, Zhang Y, Huang R. Impaired topological architecture of brain structural networks in idiopathic Parkinson's disease: a DTI study. Brain Imaging Behav 2018; 11:113-128. [PMID: 26815739 DOI: 10.1007/s11682-015-9501-6] [Citation(s) in RCA: 45] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Parkinson's disease (PD) is considered as a neurodegenerative disorder of the brain central nervous system. But, to date, few studies adopted the network model to reveal topological changes in brain structural networks in PD patients. Additionally, although the concept of rich club organization has been widely used to study brain networks in various brain disorders, there is no study to report the changed rich club organization of brain networks in PD patients. Thus, we collected diffusion tensor imaging (DTI) data from 35 PD patients and 26 healthy controls and adopted deterministic tractography to construct brain structural networks. During the network analysis, we calculated their topological properties, and built the rich club organization of brain structural networks for both subject groups. By comparing the between-group differences in topological properties and rich club organizations, we found that the connectivity strength of the feeder and local connections are lower in PD patients compared to those of the healthy controls. Furthermore, using a network-based statistic (NBS) approach, we identified uniformly significantly decreased connections in two modules, the limbic/paralimbic/subcortical module and the cognitive control/attention module, in patients compared to controls. In addition, for the topological properties of brain network topology in the PD patients, we found statistically increased shortest path length and decreased global efficiency. Statistical comparisons of nodal properties were also widespread in the frontal and parietal regions for the PD patients. These findings may provide useful information to better understand the abnormalities of brain structural networks in PD patients.
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Affiliation(s)
- Changhong Li
- Center for the Study of Applied Psychology, Key Laboratory of Mental Health and Cognitive Science of Guangdong Province, School of Psychology, Brain Study Institute, South China Normal University, Guangzhou, 510631, China
| | - Biao Huang
- Department of Radiology, Guangdong Academy of Medical Sciences, Guangdong General Hospital, Guangzhou, China.
| | - Ruibin Zhang
- Center for the Study of Applied Psychology, Key Laboratory of Mental Health and Cognitive Science of Guangdong Province, School of Psychology, Brain Study Institute, South China Normal University, Guangzhou, 510631, China
| | - Qing Ma
- Center for the Study of Applied Psychology, Key Laboratory of Mental Health and Cognitive Science of Guangdong Province, School of Psychology, Brain Study Institute, South China Normal University, Guangzhou, 510631, China
| | - Wanqun Yang
- Department of Radiology, Guangdong Academy of Medical Sciences, Guangdong General Hospital, Guangzhou, China
| | - Lijuan Wang
- Department of Neurology, Guangdong Academy of Medical Sciences, Guangdong General Hospital, Guangzhou, China
| | - Limin Wang
- Department of Neurology, Guangdong Academy of Medical Sciences, Guangdong General Hospital, Guangzhou, China
| | - Qin Xu
- Center for the Study of Applied Psychology, Key Laboratory of Mental Health and Cognitive Science of Guangdong Province, School of Psychology, Brain Study Institute, South China Normal University, Guangzhou, 510631, China
| | - Jieying Feng
- Department of Radiology, Guangdong Academy of Medical Sciences, Guangdong General Hospital, Guangzhou, China
| | - Liqing Liu
- Center for the Study of Applied Psychology, Key Laboratory of Mental Health and Cognitive Science of Guangdong Province, School of Psychology, Brain Study Institute, South China Normal University, Guangzhou, 510631, China
| | - Yuhu Zhang
- Department of Neurology, Guangdong Academy of Medical Sciences, Guangdong General Hospital, Guangzhou, China
| | - Ruiwang Huang
- Center for the Study of Applied Psychology, Key Laboratory of Mental Health and Cognitive Science of Guangdong Province, School of Psychology, Brain Study Institute, South China Normal University, Guangzhou, 510631, China.
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78
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Wierenga LM, van den Heuvel MP, Oranje B, Giedd JN, Durston S, Peper JS, Brown TT, Crone EA. A multisample study of longitudinal changes in brain network architecture in 4-13-year-old children. Hum Brain Mapp 2018; 39:157-170. [PMID: 28960629 PMCID: PMC5783977 DOI: 10.1002/hbm.23833] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2017] [Revised: 09/15/2017] [Accepted: 09/19/2017] [Indexed: 01/21/2023] Open
Abstract
Recent advances in human neuroimaging research have revealed that white-matter connectivity can be described in terms of an integrated network, which is the basis of the human connectome. However, the developmental changes of this connectome in childhood are not well understood. This study made use of two independent longitudinal diffusion-weighted imaging data sets to characterize developmental changes in the connectome by estimating age-related changes in fractional anisotropy (FA) for reconstructed fibers (edges) between 68 cortical regions. The first sample included 237 diffusion-weighted scans of 146 typically developing children (4-13 years old, 74 females) derived from the Pediatric Longitudinal Imaging, Neurocognition, and Genetics (PLING) study. The second sample included 141 scans of 97 individuals (8-13 years old, 62 females) derived from the BrainTime project. In both data sets, we compared edges that had the most substantial age-related change in FA to edges that showed little change in FA. This allowed us to investigate if developmental changes in white matter reorganize network topology. We observed substantial increases in edges connecting peripheral and a set of highly connected hub regions, referred to as the rich club. Together with the observed topological differences between regions connecting to edges showing the smallest and largest changes in FA, this indicates that changes in white matter affect network organization, such that highly connected regions become even more strongly imbedded in the network. These findings suggest that an important process in brain development involves organizing patterns of inter-regional interactions. Hum Brain Mapp 39:157-170, 2018. © 2017 Wiley Periodicals, Inc.
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Affiliation(s)
- Lara M Wierenga
- Institute of psychology, Leiden University, Leiden, RB 2300, The Netherlands.,Leiden Institute for Brain and Cognition, Leiden, RB 2300, The Netherlands
| | - Martijn P van den Heuvel
- Department of Psychiatry, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, CX 3584, The Netherlands
| | - Bob Oranje
- NICHE Lab, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, CX 3584, The Netherlands
| | - Jay N Giedd
- Department of Psychiatry, University of California, San Diego, La Jolla, California
| | - Sarah Durston
- NICHE Lab, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, CX 3584, The Netherlands
| | - Jiska S Peper
- Institute of psychology, Leiden University, Leiden, RB 2300, The Netherlands.,Leiden Institute for Brain and Cognition, Leiden, RB 2300, The Netherlands
| | - Timothy T Brown
- Department of Neurosciences, University of California, San Diego, School of Medicine, La Jolla, Califoria
| | - Eveline A Crone
- Institute of psychology, Leiden University, Leiden, RB 2300, The Netherlands.,Leiden Institute for Brain and Cognition, Leiden, RB 2300, The Netherlands
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79
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Powell F, Tosun D, Sadeghi R, Weiner M, Raj A. Preserved Structural Network Organization Mediates Pathology Spread in Alzheimer's Disease Spectrum Despite Loss of White Matter Tract Integrity. J Alzheimers Dis 2018; 65:747-764. [PMID: 29578480 PMCID: PMC6152926 DOI: 10.3233/jad-170798] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Models of Alzheimer's disease (AD) hypothesize stereotyped progression via white matter (WM) fiber connections, most likely via trans-synaptic transmission of toxic proteins along neuronal pathways. An important question in the field is whether and how organization of fiber pathways is affected by disease. It remains unknown whether fibers act as conduits of degenerative pathologies, or if they also degenerate with the gray matter network. This work uses graph theoretic modeling in a longitudinal design to investigate the impact of WM network organization on AD pathology spread. We hypothesize if altered WM network organization mediates disease progression, then a previously published network diffusion model will yield higher prediction accuracy using subject-specific connectomes in place of a healthy template connectome. Neuroimaging data in 124 subjects from ADNI were assessed. Graph topology metrics show preserved network organization in patients compared to controls. Using a published diffusion model, we further probe the effect of network alterations on degeneration spread in AD. We show that choice of connectome does not significantly impact the model's predictive ability. These results suggest that, despite measurable changes in integrity of specific fiber tracts, WM network organization in AD is preserved. Further, there is no difference in the mediation of putative pathology spread between healthy and AD-impaired networks. This conclusion is somewhat at variance with previous results, which report global topological disturbances in AD. Our data indicates the combined effect of edge thresholding, binarization, and inclusion of subcortical regions to network graphs may be responsible for previously reported effects.
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Affiliation(s)
- Fon Powell
- Department of Radiology, Weill Cornell Medicine, New York, NY, USA
| | - Duygu Tosun
- Department of Radiology, University of California San Francisco, San Francisco, CA, USA
| | - Roksana Sadeghi
- Department of Radiology, University of California San Francisco, San Francisco, CA, USA
| | - Michael Weiner
- Department of Radiology, University of California San Francisco, San Francisco, CA, USA
| | - Ashish Raj
- Department of Radiology, Weill Cornell Medicine, New York, NY, USA
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80
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Senden M, Reuter N, van den Heuvel MP, Goebel R, Deco G, Gilson M. Task-related effective connectivity reveals that the cortical rich club gates cortex-wide communication. Hum Brain Mapp 2017; 39:1246-1262. [PMID: 29222818 DOI: 10.1002/hbm.23913] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2017] [Revised: 11/23/2017] [Accepted: 11/30/2017] [Indexed: 12/20/2022] Open
Abstract
Higher cognition may require the globally coordinated integration of specialized brain regions into functional networks. A collection of structural cortical hubs-referred to as the rich club-has been hypothesized to support task-specific functional integration. In the present paper, we use a whole-cortex model to estimate directed interactions between 68 cortical regions from functional magnetic resonance imaging activity for four different tasks (reflecting different cognitive domains) and resting state. We analyze the state-dependent input and output effective connectivity (EC) of the structural rich club and relate these to whole-cortex dynamics and network reconfigurations. We find that the cortical rich club exhibits an increase in outgoing EC during task performance as compared with rest while incoming connectivity remains constant. Increased outgoing connectivity targets a sparse set of peripheral regions with specific regions strongly overlapping between tasks. At the same time, community detection analyses reveal massive reorganizations of interactions among peripheral regions, including those serving as target of increased rich club output. This suggests that while peripheral regions may play a role in several tasks, their concrete interplay might nonetheless be task-specific. Furthermore, we observe that whole-cortex dynamics are faster during task as compared with rest. The decoupling effects usually accompanying faster dynamics appear to be counteracted by the increased rich club outgoing EC. Together our findings speak to a gating mechanism of the rich club that supports fast-paced information exchange among relevant peripheral regions in a task-specific and goal-directed fashion, while constantly listening to the whole network.
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Affiliation(s)
- Mario Senden
- Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, 6201BC Maastricht, The Netherlands.,Maastricht Brain Imaging Centre, Faculty of Psychology and Neuroscience, Maastricht University, P.O. Box 616, 6200 MD Maastricht, The Netherlands
| | - Niels Reuter
- Institute of Systems Neuroscience and Institute of Clinical Neuroscience & Medical Psychology, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany.,Institute of Neuroscience and Medicine (INM-1, INM-7), Research Centre Jülich, Jülich, Germany
| | - Martijn P van den Heuvel
- Brain Center Rudolf Magnus, Department of Psychiatry, University Medical Center Utrecht, Brain Center Rudolf Magnus, 3508 GA Utrecht, The Netherlands
| | - Rainer Goebel
- Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, 6201BC Maastricht, The Netherlands.,Maastricht Brain Imaging Centre, Faculty of Psychology and Neuroscience, Maastricht University, P.O. Box 616, 6200 MD Maastricht, The Netherlands.,Department of Neuroimaging and Neuromodeling, Netherlands Institute for Neuroscience, an Institute of the Royal Netherlands Academy of Arts and Sciences (KNAW), 1105BA Amsterdam, The Netherlands
| | - 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.,Department of Neuropsychology, Max Planck Institute for Human Cognitive and Brain Sciences, 04103 Leipzig, Germany.,School of Psychological Sciences, Monash University, Melbourne, Clayton VIC 3800, Australia
| | - Matthieu Gilson
- Center for Brain and Cognition, Computational Neuroscience Group, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Roc Boronat 138, Barcelona 08018, Spain
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81
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Daianu M, Mendez MF, Baboyan VG, Jin Y, Melrose RJ, Jimenez EE, Thompson PM. An advanced white matter tract analysis in frontotemporal dementia and early-onset Alzheimer's disease. Brain Imaging Behav 2017; 10:1038-1053. [PMID: 26515192 PMCID: PMC5167220 DOI: 10.1007/s11682-015-9458-5] [Citation(s) in RCA: 32] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Cortical and subcortical nuclei degenerate in the dementias, but less is known about changes in the white matter tracts that connect them. To better understand white matter changes in behavioral variant frontotemporal dementia (bvFTD) and early-onset Alzheimer’s disease (EOAD), we used a novel approach to extract full 3D profiles of fiber bundles from diffusion-weighted MRI (DWI) and map white matter abnormalities onto detailed models of each pathway. The result is a spatially complex picture of tract-by-tract microstructural changes. Our atlas of tracts for each disease consists of 21 anatomically clustered and recognizable white matter tracts generated from whole-brain tractography in 20 patients with bvFTD, 23 with age-matched EOAD, and 33 healthy elderly controls. To analyze the landscape of white matter abnormalities, we used a point-wise tract correspondence method along the 3D profiles of the tracts and quantified the pathway disruptions using common diffusion metrics – fractional anisotropy, mean, radial, and axial diffusivity. We tested the hypothesis that bvFTD and EOAD are associated with preferential degeneration in specific neural networks. We mapped axonal tract damage that was best detected with mean and radial diffusivity metrics, supporting our network hypothesis, highly statistically significant and more sensitive than widely studied fractional anisotropy reductions. From white matter diffusivity, we identified abnormalities in bvFTD in all 21 tracts of interest but especially in the bilateral uncinate fasciculus, frontal callosum, anterior thalamic radiations, cingulum bundles and left superior longitudinal fasciculus. This network of white matter alterations extends beyond the most commonly studied tracts, showing greater white matter abnormalities in bvFTD versus controls and EOAD patients. In EOAD, network alterations involved more posterior white matter – the parietal sector of the corpus callosum and parahipoccampal cingulum bilaterally. Widespread but distinctive white matter alterations are a key feature of the pathophysiology of these two forms of dementia.
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Affiliation(s)
- Madelaine Daianu
- Imaging Genetics Center, Mark & Mary Stevens Institute for Neuroimaging & Informatics, University of Southern California, Marina del Rey, CA, USA.,Department of Neurology, UCLA School of Medicine, Los Angeles, CA, USA
| | - Mario F Mendez
- Behavioral Neurology Program, Department of Neurology, UCLA, Los Angeles, CA, USA
| | - Vatche G Baboyan
- Imaging Genetics Center, Mark & Mary Stevens Institute for Neuroimaging & Informatics, University of Southern California, Marina del Rey, CA, USA
| | - Yan Jin
- Imaging Genetics Center, Mark & Mary Stevens Institute for Neuroimaging & Informatics, University of Southern California, Marina del Rey, CA, USA
| | - Rebecca J Melrose
- Brain, Behavior, and Aging Research Center, VA Greater Los Angeles Healthcare System, Los Angeles, CA, USA.,Departments of Psychiatry and Biobehavioral Sciences, UCLA School of Medicine, Los Angeles, CA, USA
| | - Elvira E Jimenez
- Behavioral Neurology Program, Department of Neurology, UCLA, Los Angeles, CA, USA
| | - Paul M Thompson
- Imaging Genetics Center, Mark & Mary Stevens Institute for Neuroimaging & Informatics, University of Southern California, Marina del Rey, CA, USA. .,Department of Neurology, UCLA School of Medicine, Los Angeles, CA, USA. .,Departments of Neurology, Psychiatry, Radiology, Engineering, Pediatrics, and Ophthalmology, University of Southern California, Los Angeles, CA, USA.
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82
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Mai N, Zhong X, Chen B, Peng Q, Wu Z, Zhang W, Ouyang C, Ning Y. Weight Rich-Club Analysis in the White Matter Network of Late-Life Depression with Memory Deficits. Front Aging Neurosci 2017; 9:279. [PMID: 28878666 PMCID: PMC5572942 DOI: 10.3389/fnagi.2017.00279] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2017] [Accepted: 08/07/2017] [Indexed: 11/13/2022] Open
Abstract
Patients with late-life depression (LLD) have a higher incident of developing dementia, especially individuals with memory deficits. However, little is known about the white matter characteristics of LLD with memory deficits (LLD-MD) in the human connectome, especially for the rich-club coefficient, which is an indicator that describes the organization pattern of hub in the network. To address this question, diffusion tensor imaging of 69 participants [15 LLD-MD patients; 24 patients with LLD with intact memory (LLD-IM); and 30 healthy controls (HC)] was applied to construct a brain network for each individual. A full-scale battery of neuropsychological tests were used for grouping, and evaluating executive function, processing speed and memory. Rich-club analysis and global network properties were utilized to describe the topological features in each group. Network-based statistics (NBS) were calculated to identify the impaired subnetwork in the LLD-MD group relative to that in the LLD-IM group. We found that compared with HC participants, patients with LLD (LLD-MD and LLD-IM) had relatively impaired rich-club organizations and rich-club connectivity. In addition, LLD-MD group exhibited lower feeder and local connective average strength than LLD-IM group. Furthermore, global network properties, such as the shortest path length, connective strength, efficiency and fault tolerant efficiency, were significantly decreased in the LLD-MD group relative to those in the LLD-IM and HC groups. According to NBS analysis, a subnetwork, including right cognitive control network (CCN) and corticostriatal circuits, were disrupted in LLD-MD patients. In conclusion, the disease effects of LLD were prevalent in rich-club organization. Feeder and local connections, especially in the subnetwork including right CCN and corticostriatal circuits, were further impaired in those with memory deficits. Global network properties were disrupted in LLD-MD patients relative to those in LLD-IM patients.
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Affiliation(s)
- Naikeng Mai
- Department of Neurology, Southern Medical UniversityGuangdong, China.,Department of Neurology, The Affiliated Brain Hospital of Guangzhou Medical University (Guangzhou Huiai Hospital)Guangdong, China
| | - Xiaomei Zhong
- Department of Neurology, The Affiliated Brain Hospital of Guangzhou Medical University (Guangzhou Huiai Hospital)Guangdong, China
| | - Ben Chen
- Department of Neurology, The Affiliated Brain Hospital of Guangzhou Medical University (Guangzhou Huiai Hospital)Guangdong, China
| | - Qi Peng
- Department of Neurology, The Affiliated Brain Hospital of Guangzhou Medical University (Guangzhou Huiai Hospital)Guangdong, China
| | - Zhangying Wu
- Department of Neurology, The Affiliated Brain Hospital of Guangzhou Medical University (Guangzhou Huiai Hospital)Guangdong, China
| | - Weiru Zhang
- Department of Neurology, The Affiliated Brain Hospital of Guangzhou Medical University (Guangzhou Huiai Hospital)Guangdong, China
| | - Cong Ouyang
- Department of Neurology, The Affiliated Brain Hospital of Guangzhou Medical University (Guangzhou Huiai Hospital)Guangdong, China
| | - Yuping Ning
- Department of Neurology, Southern Medical UniversityGuangdong, China.,Department of Neurology, The Affiliated Brain Hospital of Guangzhou Medical University (Guangzhou Huiai Hospital)Guangdong, China
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83
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Shu N, Wang X, Bi Q, Zhao T, Han Y. Disrupted Topologic Efficiency of White Matter Structural Connectome in Individuals with Subjective Cognitive Decline. Radiology 2017; 286:229-238. [PMID: 28799862 DOI: 10.1148/radiol.2017162696] [Citation(s) in RCA: 88] [Impact Index Per Article: 12.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
Purpose To determine whether individuals with subjective cognitive decline (SCD), which is defined by memory complaints with normal performance at objective neuropsychologic examinations, exhibit disruptions of white matter (WM) connectivity and topologic alterations of the brain structural connectome. Materials and Methods Diffusion-tensor magnetic resonance imaging and graph theory approaches were used to investigate the topologic organization of the brain structural connectome in 36 participants with SCD (21 women: mean age, 62.0 years ± 8.6 [standard deviation]; age range, 42-76 years; 15 men: mean age, 65.5 years ± 8.9; age range, 51-80 years) and 51 age-, sex-, and years of education-matched healthy control participants (33 women: mean age, 63.7 years ± 8.8; age range, 46-83 years; 18 men: mean age, 59.4 years ± 9.3; age range, 43-75 years). Individual WM networks were constructed for each participant, and the network properties between two groups were compared with a linear regression model. Results Graph theory analyses revealed that the participants with SCD had less global efficiency (P = .001) and local efficiency (P = .008) compared with the healthy control participants. Lower regional efficiency was mainly distributed in the bilateral prefrontal regions and left thalamus (P < .05, corrected). Furthermore, a disrupted subnetwork was observed that consisted of widespread anatomic connections (P < .05, corrected), which has the potential to discriminate individuals with SCD from control participants. Moreover, similar hub distributions and less connection strength between the hub regions (P = .023) were found in SCD. Importantly, diminished strength of the rich-club and local connections was correlated with the impaired memory performance in patients with SCD (rich-club connection: r = 0.43, P = .011; local connection: r = 0.36, P = .037). Conclusion This study demonstrated disrupted topologic efficiency of the brain's structural connectome in participants with SCD and provided potential connectome-based biomarkers for the early detection of cognitive impairment in elderly individuals. © RSNA, 2017 Online supplemental material is available for this article.
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Affiliation(s)
- Ni Shu
- From the State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research (N.S., Q.B., T.Z.), Center for Collaboration and Innovation in Brain and Learning Sciences (N.S., Q.B., T.Z.), and Beijing Key Laboratory of Brain Imaging and Connectomics (N.S., Q.B., T.Z.), Beijing Normal University, Beijing, P. R. China; Department of Neurology, XuanWu Hospital of Capital Medical University, Beijing 100053, P. R. China (X.W., Y.H.); Center of Alzheimer's Disease, Beijing Institute for Brain Disorders, Beijing, P. R. China (X.W., Y.H.); National Clinical Research Center for Geriatric Disorders, Beijing, P. R. China (Y.H.); and PKU Care Rehabilitation Hospital, Beijing, P. R. China (Y.H.)
| | - Xiaoni Wang
- From the State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research (N.S., Q.B., T.Z.), Center for Collaboration and Innovation in Brain and Learning Sciences (N.S., Q.B., T.Z.), and Beijing Key Laboratory of Brain Imaging and Connectomics (N.S., Q.B., T.Z.), Beijing Normal University, Beijing, P. R. China; Department of Neurology, XuanWu Hospital of Capital Medical University, Beijing 100053, P. R. China (X.W., Y.H.); Center of Alzheimer's Disease, Beijing Institute for Brain Disorders, Beijing, P. R. China (X.W., Y.H.); National Clinical Research Center for Geriatric Disorders, Beijing, P. R. China (Y.H.); and PKU Care Rehabilitation Hospital, Beijing, P. R. China (Y.H.)
| | - Qiuhui Bi
- From the State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research (N.S., Q.B., T.Z.), Center for Collaboration and Innovation in Brain and Learning Sciences (N.S., Q.B., T.Z.), and Beijing Key Laboratory of Brain Imaging and Connectomics (N.S., Q.B., T.Z.), Beijing Normal University, Beijing, P. R. China; Department of Neurology, XuanWu Hospital of Capital Medical University, Beijing 100053, P. R. China (X.W., Y.H.); Center of Alzheimer's Disease, Beijing Institute for Brain Disorders, Beijing, P. R. China (X.W., Y.H.); National Clinical Research Center for Geriatric Disorders, Beijing, P. R. China (Y.H.); and PKU Care Rehabilitation Hospital, Beijing, P. R. China (Y.H.)
| | - Tengda Zhao
- From the State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research (N.S., Q.B., T.Z.), Center for Collaboration and Innovation in Brain and Learning Sciences (N.S., Q.B., T.Z.), and Beijing Key Laboratory of Brain Imaging and Connectomics (N.S., Q.B., T.Z.), Beijing Normal University, Beijing, P. R. China; Department of Neurology, XuanWu Hospital of Capital Medical University, Beijing 100053, P. R. China (X.W., Y.H.); Center of Alzheimer's Disease, Beijing Institute for Brain Disorders, Beijing, P. R. China (X.W., Y.H.); National Clinical Research Center for Geriatric Disorders, Beijing, P. R. China (Y.H.); and PKU Care Rehabilitation Hospital, Beijing, P. R. China (Y.H.)
| | - Ying Han
- From the State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research (N.S., Q.B., T.Z.), Center for Collaboration and Innovation in Brain and Learning Sciences (N.S., Q.B., T.Z.), and Beijing Key Laboratory of Brain Imaging and Connectomics (N.S., Q.B., T.Z.), Beijing Normal University, Beijing, P. R. China; Department of Neurology, XuanWu Hospital of Capital Medical University, Beijing 100053, P. R. China (X.W., Y.H.); Center of Alzheimer's Disease, Beijing Institute for Brain Disorders, Beijing, P. R. China (X.W., Y.H.); National Clinical Research Center for Geriatric Disorders, Beijing, P. R. China (Y.H.); and PKU Care Rehabilitation Hospital, Beijing, P. R. China (Y.H.)
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84
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Zhao T, Sheng C, Bi Q, Niu W, Shu N, Han Y. Age-related differences in the topological efficiency of the brain structural connectome in amnestic mild cognitive impairment. Neurobiol Aging 2017; 59:144-155. [PMID: 28882420 DOI: 10.1016/j.neurobiolaging.2017.08.005] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2016] [Revised: 08/01/2017] [Accepted: 08/02/2017] [Indexed: 01/07/2023]
Abstract
Amnestic mild cognitive impairment (aMCI) is accompanied by the accelerated cognitive decline and rapid brain degeneration with aging. However, the age-related alterations of the topological organization of the brain connectome in aMCI patients remained largely unknown. In this study, we constructed the brain structural connectome in 51 aMCI patients and 51 healthy controls by diffusion magnetic resonance imaging and deterministic tractography. The different age-related alteration patterns of the global and regional network metrics between aMCI patients and healthy controls were assessed by a linear regression model. Compared with healthy controls, significantly decreased global and local network efficiency in aMCI patients were found. When correlating network efficiency with age, we observed a significant decline in network efficiency with aging in the aMCI patients, while not in the healthy controls. The age-related decreases of nodal efficiency in aMCI patients were mainly distributed in the key regions of the default-mode network, such as precuneus, anterior cingulate gyrus, and parahippocampal gyrus. In addition, age-related decreases in the connection strength of the edges between peripheral nodes were observed in aMCI patients. Moreover, the decreased regional efficiency of the parahippocampal gyrus was correlated with impaired memory performances in patients. The present study suggests an age-related disruption of the topological organization of the brain structural connectome in aMCI patients, which may provide evidence for different neural mechanisms underlying aging in aMCI and may serve as a potential imaging marker for the early diagnosis of Alzheimer's disease.
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Affiliation(s)
- Tengda Zhao
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, P. R. China; Center for Collaboration and Innovation in Brain and Learning Sciences, Beijing Normal University, Beijing, P. R. China; Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, P. R. China
| | - Can Sheng
- Department of Neurology, XuanWu Hospital of Capital Medical University, Beijing, P. R. China; Center of Alzheimer's Disease, Beijing Institute for Brain Disorders, Beijing, P. R. China
| | - Qiuhui Bi
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, P. R. China; Center for Collaboration and Innovation in Brain and Learning Sciences, Beijing Normal University, Beijing, P. R. China; Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, P. R. China
| | - Weili Niu
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, P. R. China; Center for Collaboration and Innovation in Brain and Learning Sciences, Beijing Normal University, Beijing, P. R. China; Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, P. R. China
| | - Ni Shu
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, P. R. China; Center for Collaboration and Innovation in Brain and Learning Sciences, Beijing Normal University, Beijing, P. R. China; Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, P. R. China.
| | - Ying Han
- Department of Neurology, XuanWu Hospital of Capital Medical University, Beijing, P. R. China; Center of Alzheimer's Disease, Beijing Institute for Brain Disorders, Beijing, P. R. China; National Clinical Research Center for Geriatric Disorders, Beijing, P. R. China; PKU Care Rehabilitation Hospital, Beijing, P. R. China.
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85
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Yang C, Zhong S, Zhou X, Wei L, Wang L, Nie S. The Abnormality of Topological Asymmetry between Hemispheric Brain White Matter Networks in Alzheimer's Disease and Mild Cognitive Impairment. Front Aging Neurosci 2017; 9:261. [PMID: 28824422 PMCID: PMC5545578 DOI: 10.3389/fnagi.2017.00261] [Citation(s) in RCA: 40] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2017] [Accepted: 07/24/2017] [Indexed: 12/20/2022] Open
Abstract
A large number of morphology-based studies have previously reported a variety of regional abnormalities in hemispheric asymmetry in Alzheimer’s disease (AD). Recently, neuroimaging studies have revealed changes in the topological organization of the structural network in AD. However, little is known about the alterations in topological asymmetries. In the present study, we used diffusion tensor image tractography to construct the hemispheric brain white matter networks of 25 AD patients, 95 mild cognitive impairment (MCI) patients, and 48 normal control (NC) subjects. Graph theoretical approaches were then employed to estimate hemispheric topological properties. Rightward asymmetry in both global and local network efficiencies were observed between the two hemispheres only in AD patients. The brain regions/nodes exhibiting increased rightward asymmetry in both AD and MCI patients were primarily located in the parahippocampal gyrus and cuneus. The observed rightward asymmetry was attributed to changes in the topological properties of the left hemisphere in AD patients. Finally, we found that the abnormal hemispheric asymmetries of brain network properties were significantly correlated with memory performance (Rey’s Auditory Verbal Learning Test). Our findings provide new insights into the lateralized nature of hemispheric disconnectivity and highlight the potential for using hemispheric asymmetry of brain network measures as biomarkers for AD.
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Affiliation(s)
- Cheng Yang
- Institute of Medical Imaging Engineering, School of Medical Instrument and Food Engineering, University of Shanghai for Science and TechnologyShanghai, China
| | - Suyu Zhong
- State Key Laboratory of Cognitive Neuroscience and Learning and IDG/McGovern Institute for Brain Research, Beijing Normal UniversityBeijing, China
| | - Xiaolong Zhou
- Institute of Medical Imaging Engineering, School of Medical Instrument and Food Engineering, University of Shanghai for Science and TechnologyShanghai, China
| | - Long Wei
- Institute of Medical Imaging Engineering, School of Medical Instrument and Food Engineering, University of Shanghai for Science and TechnologyShanghai, China.,Laiwu Vocational and Technical CollegeShandong, China
| | - Lijia Wang
- Institute of Medical Imaging Engineering, School of Medical Instrument and Food Engineering, University of Shanghai for Science and TechnologyShanghai, China
| | - Shengdong Nie
- Institute of Medical Imaging Engineering, School of Medical Instrument and Food Engineering, University of Shanghai for Science and TechnologyShanghai, China
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86
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O'Donoghue S, Kilmartin L, O'Hora D, Emsell L, Langan C, McInerney S, Forde NJ, Leemans A, Jeurissen B, Barker GJ, McCarthy P, Cannon DM, McDonald C. Anatomical integration and rich-club connectivity in euthymic bipolar disorder. Psychol Med 2017; 47:1609-1623. [PMID: 28573962 DOI: 10.1017/s0033291717000058] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
Abstract
BACKGROUND Although repeatedly associated with white matter microstructural alterations, bipolar disorder (BD) has been relatively unexplored using complex network analysis. This method combines structural and diffusion magnetic resonance imaging (MRI) to model the brain as a network and evaluate its topological properties. A group of highly interconnected high-density structures, termed the 'rich-club', represents an important network for integration of brain functioning. This study aimed to assess structural and rich-club connectivity properties in BD through graph theory analyses. METHOD We obtained structural and diffusion MRI scans from 42 euthymic patients with BD type I and 43 age- and gender-matched healthy volunteers. Weighted fractional anisotropy connections mapped between cortical and subcortical structures defined the neuroanatomical networks. Next, we examined between-group differences in features of graph properties and sub-networks. RESULTS Patients exhibited significantly reduced clustering coefficient and global efficiency, compared with controls globally and regionally in frontal and occipital regions. Additionally, patients displayed weaker sub-network connectivity in distributed regions. Rich-club analysis revealed subtly reduced density in patients, which did not withstand multiple comparison correction. However, hub identification in most participants indicated differentially affected rich-club membership in the BD group, with two hubs absent when compared with controls, namely the superior frontal gyrus and thalamus. CONCLUSIONS This graph theory analysis presents a thorough investigation of topological features of connectivity in euthymic BD. Abnormalities of global and local measures and network components provide further neuroanatomically specific evidence for distributed dysconnectivity as a trait feature of BD.
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Affiliation(s)
- S O'Donoghue
- The Centre for Neuroimaging & Cognitive Genomics (NICOG) and NCBES Galway Neuroscience Centre, National University of Ireland Galway,Galway,Republic of Ireland
| | - L Kilmartin
- College of Engineering and Informatics, National University of Ireland Galway,Galway,Republic of Ireland
| | - D O'Hora
- School of Psychology, National University of Ireland Galway,Galway,Republic of Ireland
| | - L Emsell
- Translational MRI, Department of Imaging & Pathology,KU Leuven & Radiology, University Hospitals Leuven,Leuven,Belgium
| | - C Langan
- The Centre for Neuroimaging & Cognitive Genomics (NICOG) and NCBES Galway Neuroscience Centre, National University of Ireland Galway,Galway,Republic of Ireland
| | - S McInerney
- Department of Psychiatry,St Michael's Hospital,Toronto,Ontario,Canada
| | - N J Forde
- Department of Psychiatry,University Medical Centre Groningen,Groningen,The Netherlands
| | - A Leemans
- Image Sciences Institute, University Medical Center Utrecht,Utrecht,The Netherlands
| | - B Jeurissen
- Vision Lab,University of Antwerp,Antwerp,Belgium
| | - G J Barker
- Institute of Psychiatry, Psychology and Neuroscience,London,UK
| | - P McCarthy
- Radiology, University Hospital Galway,Galway,Republic of Ireland
| | - D M Cannon
- The Centre for Neuroimaging & Cognitive Genomics (NICOG) and NCBES Galway Neuroscience Centre, National University of Ireland Galway,Galway,Republic of Ireland
| | - C McDonald
- The Centre for Neuroimaging & Cognitive Genomics (NICOG) and NCBES Galway Neuroscience Centre, National University of Ireland Galway,Galway,Republic of Ireland
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87
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Research of Hubs Location Method for Weighted Brain Network Based on NoS-FA. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2017; 2017:6174090. [PMID: 28717361 PMCID: PMC5499242 DOI: 10.1155/2017/6174090] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/09/2017] [Revised: 04/28/2017] [Accepted: 05/15/2017] [Indexed: 11/28/2022]
Abstract
As a complex network of many interlinked brain regions, there are some central hub regions which play key roles in the structural human brain network based on T1 and diffusion tensor imaging (DTI) technology. Since most studies about hubs location method in the whole human brain network are mainly concerned with the local properties of each single node but not the global properties of all the directly connected nodes, a novel hubs location method based on global importance contribution evaluation index is proposed in this study. The number of streamlines (NoS) is fused with normalized fractional anisotropy (FA) for more comprehensive brain bioinformation. The brain region importance contribution matrix and information transfer efficiency value are constructed, respectively, and then by combining these two factors together we can calculate the importance value of each node and locate the hubs. Profiting from both local and global features of the nodes and the multi-information fusion of human brain biosignals, the experiment results show that this method can detect the brain hubs more accurately and reasonably compared with other methods. Furthermore, the proposed location method is used in impaired brain hubs connectivity analysis of schizophrenia patients and the results are in agreement with previous studies.
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88
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Weiner MW, Veitch DP, Aisen PS, Beckett LA, Cairns NJ, Green RC, Harvey D, Jack CR, Jagust W, Morris JC, Petersen RC, Saykin AJ, Shaw LM, Toga AW, Trojanowski JQ. Recent publications from the Alzheimer's Disease Neuroimaging Initiative: Reviewing progress toward improved AD clinical trials. Alzheimers Dement 2017; 13:e1-e85. [PMID: 28342697 DOI: 10.1016/j.jalz.2016.11.007] [Citation(s) in RCA: 170] [Impact Index Per Article: 24.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2016] [Revised: 11/21/2016] [Accepted: 11/28/2016] [Indexed: 01/31/2023]
Abstract
INTRODUCTION The Alzheimer's Disease Neuroimaging Initiative (ADNI) has continued development and standardization of methodologies for biomarkers and has provided an increased depth and breadth of data available to qualified researchers. This review summarizes the over 400 publications using ADNI data during 2014 and 2015. METHODS We used standard searches to find publications using ADNI data. RESULTS (1) Structural and functional changes, including subtle changes to hippocampal shape and texture, atrophy in areas outside of hippocampus, and disruption to functional networks, are detectable in presymptomatic subjects before hippocampal atrophy; (2) In subjects with abnormal β-amyloid deposition (Aβ+), biomarkers become abnormal in the order predicted by the amyloid cascade hypothesis; (3) Cognitive decline is more closely linked to tau than Aβ deposition; (4) Cerebrovascular risk factors may interact with Aβ to increase white-matter (WM) abnormalities which may accelerate Alzheimer's disease (AD) progression in conjunction with tau abnormalities; (5) Different patterns of atrophy are associated with impairment of memory and executive function and may underlie psychiatric symptoms; (6) Structural, functional, and metabolic network connectivities are disrupted as AD progresses. Models of prion-like spreading of Aβ pathology along WM tracts predict known patterns of cortical Aβ deposition and declines in glucose metabolism; (7) New AD risk and protective gene loci have been identified using biologically informed approaches; (8) Cognitively normal and mild cognitive impairment (MCI) subjects are heterogeneous and include groups typified not only by "classic" AD pathology but also by normal biomarkers, accelerated decline, and suspected non-Alzheimer's pathology; (9) Selection of subjects at risk of imminent decline on the basis of one or more pathologies improves the power of clinical trials; (10) Sensitivity of cognitive outcome measures to early changes in cognition has been improved and surrogate outcome measures using longitudinal structural magnetic resonance imaging may further reduce clinical trial cost and duration; (11) Advances in machine learning techniques such as neural networks have improved diagnostic and prognostic accuracy especially in challenges involving MCI subjects; and (12) Network connectivity measures and genetic variants show promise in multimodal classification and some classifiers using single modalities are rivaling multimodal classifiers. DISCUSSION Taken together, these studies fundamentally deepen our understanding of AD progression and its underlying genetic basis, which in turn informs and improves clinical trial design.
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Affiliation(s)
- Michael W Weiner
- Department of Veterans Affairs Medical Center, Center for Imaging of Neurodegenerative Diseases, San Francisco, CA, USA; Department of Radiology, University of California, San Francisco, CA, USA; Department of Medicine, University of California, San Francisco, CA, USA; Department of Psychiatry, University of California, San Francisco, CA, USA; Department of Neurology, University of California, San Francisco, CA, USA.
| | - Dallas P Veitch
- Department of Veterans Affairs Medical Center, Center for Imaging of Neurodegenerative Diseases, San Francisco, CA, USA
| | - Paul S Aisen
- Alzheimer's Therapeutic Research Institute, University of Southern California, San Diego, CA, USA
| | - Laurel A Beckett
- Division of Biostatistics, Department of Public Health Sciences, University of California, Davis, CA, USA
| | - Nigel J Cairns
- Knight Alzheimer's Disease Research Center, Washington University School of Medicine, Saint Louis, MO, USA; Department of Neurology, Washington University School of Medicine, Saint Louis, MO, USA
| | - Robert C Green
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Danielle Harvey
- Division of Biostatistics, Department of Public Health Sciences, University of California, Davis, CA, USA
| | | | - William Jagust
- Helen Wills Neuroscience Institute, University of California Berkeley, Berkeley, CA, USA
| | - John C Morris
- Alzheimer's Therapeutic Research Institute, University of Southern California, San Diego, CA, USA
| | | | - Andrew J Saykin
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA; Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Leslie M Shaw
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Arthur W Toga
- Laboratory of Neuroimaging, Institute of Neuroimaging and Informatics, Keck School of Medicine of University of Southern California, Los Angeles, CA, USA
| | - John Q Trojanowski
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Institute on Aging, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Alzheimer's Disease Core Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Udall Parkinson's Research Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
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89
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Amoroso N, Monaco A, Tangaro S, Neuroimaging Initiative AD. Topological Measurements of DWI Tractography for Alzheimer's Disease Detection. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2017; 2017:5271627. [PMID: 28352290 PMCID: PMC5352968 DOI: 10.1155/2017/5271627] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/04/2016] [Accepted: 10/27/2016] [Indexed: 12/20/2022]
Abstract
Neurodegenerative diseases affect brain morphology and connectivity, making complex networks a suitable tool to investigate and model their effects. Because of its stereotyped pattern Alzheimer's disease (AD) is a natural benchmark for the study of novel methodologies. Several studies have investigated the network centrality and segregation changes induced by AD, especially with a single subject approach. In this work, a holistic perspective based on the application of multiplex network concepts is introduced. We define and assess a diagnostic score to characterize the brain topology and measure the disease effects on a mixed cohort of 52 normal controls (NC) and 47 AD patients, from Alzheimer's Disease Neuroimaging Initiative (ADNI). The proposed topological score allows an accurate NC-AD classification: the average area under the curve (AUC) is 95% and the 95% confidence interval is 92%-99%. Besides, the combination of topological information and structural measures, such as the hippocampal volumes, was also investigated. Topology is able to capture the disease signature of AD and, as the methodology is general, it can find interesting applications to enhance our insight into disease with more heterogeneous patterns.
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Affiliation(s)
- Nicola Amoroso
- Università degli Studi di Bari “A. Moro”, Via Orabona 4, 70123 Bari, Italy
- Istituto Nazionale di Fisica Nucleare, Sezione di Bari, Via Orabona 4, 70123 Bari, Italy
| | - Alfonso Monaco
- Istituto Nazionale di Fisica Nucleare, Sezione di Bari, Via Orabona 4, 70123 Bari, Italy
| | - Sabina Tangaro
- Istituto Nazionale di Fisica Nucleare, Sezione di Bari, Via Orabona 4, 70123 Bari, Italy
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90
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O'Donoghue S, Holleran L, Cannon DM, McDonald C. Anatomical dysconnectivity in bipolar disorder compared with schizophrenia: A selective review of structural network analyses using diffusion MRI. J Affect Disord 2017; 209:217-228. [PMID: 27930915 DOI: 10.1016/j.jad.2016.11.015] [Citation(s) in RCA: 62] [Impact Index Per Article: 8.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/17/2016] [Revised: 08/16/2016] [Accepted: 11/14/2016] [Indexed: 11/15/2022]
Abstract
BACKGROUND The dysconnectivity hypothesis suggests that psychotic illnesses arise not from regionally specific focal pathophysiology, but rather from impaired neuroanatomical integration across networks of brain regions. Decreased white matter organization has been hypothesized to be a feature of psychotic illnesses in general, which is supported by meta-analyses of DTI studies in bipolar disorder and schizophrenia. Although many diffusion MRI studies investigate bipolar disorder and schizophrenia alone, relatively few studies directly compare structural features in these psychotic illnesses. Recently, the application of graph theory analyses to DTI data has supported the dysconnectivity hypothesis in bipolar disorder and schizophrenia, employing topological properties to assess neuroanatomical dysconnectivity. METHODS This selective review evaluates white matter alterations using Diffusion Tensor Imaging (DTI) in bipolar disorder and schizophrenia, with a focus upon direct comparison DTI studies in both psychotic illnesses. We then expand in more detail on the development of network analyses and the application of these techniques in bipolar disorder and schizophrenia. RESULTS Converging evidence indicates that frontal connectivity alterations are common to both disorders, with prominent fronto-temporal deficits identified in schizophrenia and inter-hemispheric and limbic alterations reported in bipolar disorder. LIMITATIONS In bipolar disorder, most connectome reports use cortical maps alone, which given the importance of the limbic system in emotional regulation may limit the scope of network approaches in mood disorders. CONCLUSIONS Further direct connectivity comparisons between these psychotic illnesses may assist in unravelling the neuroanatomical deviations underpinning the overlapping features of psychosis and cognitive impairment, and the more diagnostically distinctive features of affective disturbance in bipolar disorder and deficit syndrome in schizophrenia.
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Affiliation(s)
- Stefani O'Donoghue
- The Centre for Neuroimaging & Cognitive Genomics (NICOG) and NCBES Galway Neuroscience Centre, National University of Ireland Galway, Galway, Ireland.
| | - Laurena Holleran
- The Centre for Neuroimaging & Cognitive Genomics (NICOG) and NCBES Galway Neuroscience Centre, National University of Ireland Galway, Galway, Ireland
| | - Dara M Cannon
- The Centre for Neuroimaging & Cognitive Genomics (NICOG) and NCBES Galway Neuroscience Centre, National University of Ireland Galway, Galway, Ireland
| | - Colm McDonald
- The Centre for Neuroimaging & Cognitive Genomics (NICOG) and NCBES Galway Neuroscience Centre, National University of Ireland Galway, Galway, Ireland
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91
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Berlot R, O'Sullivan MJ. What can the topology of white matter structural networks tell us about mild cognitive impairment? FUTURE NEUROLOGY 2017. [DOI: 10.2217/fnl-2016-0022] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
The focus of investigation in cognitive disorders has shifted from single regional motifs toward brain networks. White matter connections collectively form the connectome, and provide the underpinnings of distributed patterns of brain activity. We examine findings about large-scale properties of structural networks in mild cognitive impairment (MCI), discuss these in terms of the mechanism of cognitive decline and evaluate potential clinical implications. Networks of patients with MCI exhibit reduced global efficiency, which associates with cognitive performance. The structural core of the connectome remains relatively unperturbed. Some global measures of network structure in MCI lie on a spectrum between healthy aging and Alzheimer's dementia. Connectomics seems ill-equipped to guide diagnosis, but provides measures suitable for monitoring disease progression and treatment effect.
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Affiliation(s)
- Rok Berlot
- Department of Basic & Clinical Neuroscience, Institute of Psychology, Psychiatry & Neuroscience, King's College London, 5 Cutcombe Road, London SE5 9RX, UK
- Department of Neurology, University Medical Centre Ljubljana, Zaloška 2, 1000 Ljubljana, Slovenia
| | - Michael J O'Sullivan
- Department of Basic & Clinical Neuroscience, Institute of Psychology, Psychiatry & Neuroscience, King's College London, 5 Cutcombe Road, London SE5 9RX, UK
- Mater Centre for Neuroscience & Queensland Brain Institute, University of Queensland, St Lucia QLD 4072, Brisbane, Australia
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92
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Batalle D, Hughes EJ, Zhang H, Tournier JD, Tusor N, Aljabar P, Wali L, Alexander DC, Hajnal JV, Nosarti C, Edwards AD, Counsell SJ. Early development of structural networks and the impact of prematurity on brain connectivity. Neuroimage 2017; 149:379-392. [PMID: 28153637 PMCID: PMC5387181 DOI: 10.1016/j.neuroimage.2017.01.065] [Citation(s) in RCA: 158] [Impact Index Per Article: 22.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2016] [Revised: 12/19/2016] [Accepted: 01/26/2017] [Indexed: 12/30/2022] Open
Abstract
Preterm infants are at high risk of neurodevelopmental impairment, which may be due to altered development of brain connectivity. We aimed to (i) assess structural brain development from 25 to 45 weeks gestational age (GA) using graph theoretical approaches and (ii) test the hypothesis that preterm birth results in altered white matter network topology. Sixty-five infants underwent MRI between 25+3 and 45+6 weeks GA. Structural networks were constructed using constrained spherical deconvolution tractography and were weighted by measures of white matter microstructure (fractional anisotropy, neurite density and orientation dispersion index). We observed regional differences in brain maturation, with connections to and from deep grey matter showing most rapid developmental changes during this period. Intra-frontal, frontal to cingulate, frontal to caudate and inter-hemispheric connections matured more slowly. We demonstrated a core of key connections that was not affected by GA at birth. However, local connectivity involving thalamus, cerebellum, superior frontal lobe, cingulate gyrus and short range cortico-cortical connections was related to the degree of prematurity and contributed to altered global topology of the structural brain network. The relative preservation of core connections at the expense of local connections may support more effective use of impaired white matter reserve following preterm birth. First characterisation of preterm brain networks weighted by microstructural features. Preterm brain is resistant to disruptions in development of core connections. Peripheral connections associated with cognition and behaviour are more vulnerable.
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Affiliation(s)
- Dafnis Batalle
- Centre for the Developing Brain, Division of Imaging Sciences & Biomedical Engineering, King's College London, SE1 7EH London, United Kingdom
| | - Emer J Hughes
- Centre for the Developing Brain, Division of Imaging Sciences & Biomedical Engineering, King's College London, SE1 7EH London, United Kingdom
| | - Hui Zhang
- Department of Computer Science & Centre for Medical Image Computing, University College London, United Kingdom
| | - J-Donald Tournier
- Centre for the Developing Brain, Division of Imaging Sciences & Biomedical Engineering, King's College London, SE1 7EH London, United Kingdom
| | - Nora Tusor
- Centre for the Developing Brain, Division of Imaging Sciences & Biomedical Engineering, King's College London, SE1 7EH London, United Kingdom
| | - Paul Aljabar
- Centre for the Developing Brain, Division of Imaging Sciences & Biomedical Engineering, King's College London, SE1 7EH London, United Kingdom
| | - Luqman Wali
- Centre for the Developing Brain, Division of Imaging Sciences & Biomedical Engineering, King's College London, SE1 7EH London, United Kingdom
| | - Daniel C Alexander
- Department of Computer Science & Centre for Medical Image Computing, University College London, United Kingdom
| | - Joseph V Hajnal
- Centre for the Developing Brain, Division of Imaging Sciences & Biomedical Engineering, King's College London, SE1 7EH London, United Kingdom
| | - Chiara Nosarti
- Centre for the Developing Brain, Division of Imaging Sciences & Biomedical Engineering, King's College London, SE1 7EH London, United Kingdom
| | - A David Edwards
- Centre for the Developing Brain, Division of Imaging Sciences & Biomedical Engineering, King's College London, SE1 7EH London, United Kingdom.
| | - Serena J Counsell
- Centre for the Developing Brain, Division of Imaging Sciences & Biomedical Engineering, King's College London, SE1 7EH London, United Kingdom
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93
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Yang X, Shi L, Daianu M, Tong H, Liu Q, Thompson P. Blockwise Human Brain Network Visual Comparison Using NodeTrix Representation. IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS 2017; 23:181-190. [PMID: 27514058 PMCID: PMC5293509 DOI: 10.1109/tvcg.2016.2598472] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/20/2023]
Abstract
Visually comparing human brain networks from multiple population groups serves as an important task in the field of brain connectomics. The commonly used brain network representation, consisting of nodes and edges, may not be able to reveal the most compelling network differences when the reconstructed networks are dense and homogeneous. In this paper, we leveraged the block information on the Region Of Interest (ROI) based brain networks and studied the problem of blockwise brain network visual comparison. An integrated visual analytics framework was proposed. In the first stage, a two-level ROI block hierarchy was detected by optimizing the anatomical structure and the predictive comparison performance simultaneously. In the second stage, the NodeTrix representation was adopted and customized to visualize the brain network with block information. We conducted controlled user experiments and case studies to evaluate our proposed solution. Results indicated that our visual analytics method outperformed the commonly used node-link graph and adjacency matrix design in the blockwise network comparison tasks. We have shown compelling findings from two real-world brain network data sets, which are consistent with the prior connectomics studies.
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Affiliation(s)
- Xinsong Yang
- Chinese Academy of Sciences, SKLCSInstitute of Software
| | - Lei Shi
- Chinese Academy of Sciences, SKLCSInstitute of Software
| | - Madelaine Daianu
- Imaging Genetics CenterMark & Mary Stevens Institute for Neuroimaging & InformaticsUniversity of Southern California
| | - Hanghang Tong
- School of Computing, Informatics and Decision Systems EngineeringArizona State University
| | - Qingsong Liu
- Chinese Academy of Sciences, SKLCSInstitute of Software
| | - Paul Thompson
- Imaging Genetics CenterMark & Mary Stevens Institute for Neuroimaging & InformaticsUniversity of Southern California
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94
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Mak E, Colloby SJ, Thomas A, O'Brien JT. The segregated connectome of late-life depression: a combined cortical thickness and structural covariance analysis. Neurobiol Aging 2016; 48:212-221. [PMID: 27721203 PMCID: PMC5096887 DOI: 10.1016/j.neurobiolaging.2016.08.013] [Citation(s) in RCA: 31] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2016] [Revised: 08/02/2016] [Accepted: 08/13/2016] [Indexed: 12/22/2022]
Abstract
Late-life depression (LLD) has been associated with both generalized and focal neuroanatomical changes including gray matter atrophy and white matter abnormalities. However, previous literature has not been consistent and, in particular, its impact on the topology organization of brain networks remains to be established. In this multimodal study, we first examined cortical thickness, and applied graph theory to investigate structural covariance networks in LLD. Thirty-three subjects with LLD and 25 controls underwent T1-weighted, fluid-attenuated inversion recovery and clinical assessments. Freesurfer was used to perform vertex-wise comparisons of cortical thickness, whereas the Graph Analysis Toolbox (GAT) was implemented to construct and analyze the structural covariance networks. LLD showed a trend of lower thickness in the left insular region (p < 0.001 uncorrected). In addition, the structural network of LLD was characterized by greater segregation, particularly showing higher transitivity (i.e., measure of clustering) and modularity (i.e., tendency for a network to be organized into subnetworks). It was also less robust against random failure and targeted attacks. Despite relative cortical preservation, the topology of the LLD network showed significant changes particularly in segregation. These findings demonstrate the potential for graph theoretical approaches to complement conventional structural imaging analyses and provide novel insights into the heterogeneous etiology and pathogenesis of LLD.
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Affiliation(s)
- Elijah Mak
- Department of Psychiatry, University of Cambridge, Cambridge, UK
| | - Sean J Colloby
- Institute of Neuroscience, Newcastle University, Newcastle, UK
| | - Alan Thomas
- Institute of Neuroscience, Newcastle University, Newcastle, UK
| | - John T O'Brien
- Department of Psychiatry, University of Cambridge, Cambridge, UK.
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95
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Gaiteri C, Mostafavi S, Honey CJ, De Jager PL, Bennett DA. Genetic variants in Alzheimer disease - molecular and brain network approaches. Nat Rev Neurol 2016; 12:413-27. [PMID: 27282653 PMCID: PMC5017598 DOI: 10.1038/nrneurol.2016.84] [Citation(s) in RCA: 69] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
Genetic studies in late-onset Alzheimer disease (LOAD) are aimed at identifying core disease mechanisms and providing potential biomarkers and drug candidates to improve clinical care of AD. However, owing to the complexity of LOAD, including pathological heterogeneity and disease polygenicity, extraction of actionable guidance from LOAD genetics has been challenging. Past attempts to summarize the effects of LOAD-associated genetic variants have used pathway analysis and collections of small-scale experiments to hypothesize functional convergence across several variants. In this Review, we discuss how the study of molecular, cellular and brain networks provides additional information on the effects of LOAD-associated genetic variants. We then discuss emerging combinations of these omic data sets into multiscale models, which provide a more comprehensive representation of the effects of LOAD-associated genetic variants at multiple biophysical scales. Furthermore, we highlight the clinical potential of mechanistically coupling genetic variants and disease phenotypes with multiscale brain models.
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Affiliation(s)
- Chris Gaiteri
- Rush Alzheimer's Disease Center, Rush University Medical Center, 600 S Paulina Street, Chicago, Illinois 60612, USA
| | - Sara Mostafavi
- Department of Statistics, and Medical Genetics; Centre for Molecular and Medicine and Therapeutics, University of British Columbia, 950 West 28th Avenue, Vancouver, British Columbia V5Z 4H4, Canada
| | - Christopher J Honey
- Department of Psychology, University of Toronto, 100 St. George Street, 4th Floor Sidney Smith Hall, Toronto, Ontario M5S 3G3, Canada
| | - Philip L De Jager
- Program in Translational NeuroPsychiatric Genomics, Institute for the Neurosciences, Departments of Neurology and Psychiatry, Brigham and Women's Hospital, 75 Francis Street, Boston MA 02115, USA
| | - David A Bennett
- Rush Alzheimer's Disease Center, Rush University Medical Center, 600 S Paulina Street, Chicago, Illinois 60612, USA
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96
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Wirsich J, Perry A, Ridley B, Proix T, Golos M, Bénar C, Ranjeva JP, Bartolomei F, Breakspear M, Jirsa V, Guye M. Whole-brain analytic measures of network communication reveal increased structure-function correlation in right temporal lobe epilepsy. NEUROIMAGE-CLINICAL 2016; 11:707-718. [PMID: 27330970 PMCID: PMC4909094 DOI: 10.1016/j.nicl.2016.05.010] [Citation(s) in RCA: 43] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/18/2015] [Revised: 03/15/2016] [Accepted: 05/18/2016] [Indexed: 12/13/2022]
Abstract
The in vivo structure-function relationship is key to understanding brain network reorganization due to pathologies. This relationship is likely to be particularly complex in brain network diseases such as temporal lobe epilepsy, in which disturbed large-scale systems are involved in both transient electrical events and long-lasting functional and structural impairments. Herein, we estimated this relationship by analyzing the correlation between structural connectivity and functional connectivity in terms of analytical network communication parameters. As such, we targeted the gradual topological structure-function reorganization caused by the pathology not only at the whole brain scale but also both in core and peripheral regions of the brain. We acquired diffusion (dMRI) and resting-state fMRI (rsfMRI) data in seven right-lateralized TLE (rTLE) patients and fourteen healthy controls and analyzed the structure-function relationship by using analytical network communication metrics derived from the structural connectome. In rTLE patients, we found a widespread hypercorrelated functional network. Network communication analysis revealed greater unspecific branching of the shortest path (search information) in the structural connectome and a higher global correlation between the structural and functional connectivity for the patient group. We also found evidence for a preserved structural rich-club in the patient group. In sum, global augmentation of structure-function correlation might be linked to a smaller functional repertoire in rTLE patients, while sparing the central core of the brain which may represent a pathway that facilitates the spread of seizures.
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Key Words
- CSD, constrained spherical deconvolution
- CSF, cerebrospinal fluid
- FA, fractional anisotropy
- FCA, analytic functional connectivity
- FCD, functional connectivity dynamics
- FOD, fiber orientation distribution
- Functional connectivity
- NBS, network based statistics
- Network based statistics
- Network communication
- Rich club
- Structural connectivity
- Temporal lobe epilepsy
- dMRI, diffusion magnetic resonance imaging
- rTLE, right temporal lobe epilepsy
- rsfMRI, resting state functional magnetic resonance imaging
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Affiliation(s)
- Jonathan Wirsich
- Aix-Marseille Université, CNRS, CRMBM UMR 7339, 13385 Marseille, France; APHM, Hôpitaux de la Timone, Pôle d'imagerie Médicale, CEMEREM, 13005 Marseille, France; Aix-Marseille Université, Institut de Neurosciences des Systèmes, 13385 Marseille, France; INSERM, UMR_S 1106, 13385 Marseille, France.
| | - Alistair Perry
- Centre for Healthy Brain Ageing (CHeBA), School of Psychiatry, University of New South Wales, Sydney, NSW, Australia; School of Psychiatry, University of New South Wales, Sydney, NSW 2052, Australia; Systems Neuroscience Group, QIMR Berghofer Medical Research Institute, 300 Herston Road, Herston, QLD 4006, Australia.
| | - Ben Ridley
- Aix-Marseille Université, CNRS, CRMBM UMR 7339, 13385 Marseille, France; APHM, Hôpitaux de la Timone, Pôle d'imagerie Médicale, CEMEREM, 13005 Marseille, France.
| | - Timothée Proix
- Aix-Marseille Université, Institut de Neurosciences des Systèmes, 13385 Marseille, France; INSERM, UMR_S 1106, 13385 Marseille, France.
| | - Mathieu Golos
- Aix-Marseille Université, Institut de Neurosciences des Systèmes, 13385 Marseille, France; INSERM, UMR_S 1106, 13385 Marseille, France.
| | - Christian Bénar
- Aix-Marseille Université, Institut de Neurosciences des Systèmes, 13385 Marseille, France; INSERM, UMR_S 1106, 13385 Marseille, France.
| | - Jean-Philippe Ranjeva
- Aix-Marseille Université, CNRS, CRMBM UMR 7339, 13385 Marseille, France; APHM, Hôpitaux de la Timone, Pôle d'imagerie Médicale, CEMEREM, 13005 Marseille, France.
| | - Fabrice Bartolomei
- Aix-Marseille Université, Institut de Neurosciences des Systèmes, 13385 Marseille, France; INSERM, UMR_S 1106, 13385 Marseille, France; APHM, Hôpitaux de la Timone, Pôle de Neurosciences Cliniques, Service de Neurophysiologie Clinique, 13005 Marseille, France.
| | - Michael Breakspear
- School of Psychiatry, University of New South Wales, Sydney, NSW 2052, Australia; Systems Neuroscience Group, QIMR Berghofer Medical Research Institute, 300 Herston Road, Herston, QLD 4006, Australia; Metro North Mental Health Services, Brisbane, QLD 4006, Australia.
| | - Viktor Jirsa
- Aix-Marseille Université, Institut de Neurosciences des Systèmes, 13385 Marseille, France; INSERM, UMR_S 1106, 13385 Marseille, France.
| | - Maxime Guye
- Aix-Marseille Université, CNRS, CRMBM UMR 7339, 13385 Marseille, France; APHM, Hôpitaux de la Timone, Pôle d'imagerie Médicale, CEMEREM, 13005 Marseille, France.
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97
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Daianu M, Mezher A, Mendez MF, Jahanshad N, Jimenez EE, Thompson PM. Disrupted rich club network in behavioral variant frontotemporal dementia and early-onset Alzheimer's disease. Hum Brain Mapp 2016; 37:868-83. [PMID: 26678225 PMCID: PMC4883024 DOI: 10.1002/hbm.23069] [Citation(s) in RCA: 45] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2015] [Revised: 11/05/2015] [Accepted: 11/18/2015] [Indexed: 11/12/2022] Open
Abstract
In network analysis, the so-called "rich club" describes the core areas of the brain that are more densely interconnected among themselves than expected by chance, and has been identified as a fundamental aspect of the human brain connectome. This is the first in-depth diffusion imaging study to investigate the rich club along with other organizational changes in the brain's anatomical network in behavioral frontotemporal dementia (bvFTD), and a matched cohort with early-onset Alzheimer's disease (EOAD). Our study sheds light on how bvFTD and EOAD affect connectivity of white matter fiber pathways in the brain, revealing differences and commonalities in the connectome among the dementias. To analyze the breakdown in connectivity, we studied three groups: 20 bvFTD, 23 EOAD, and 37 healthy elderly controls. All participants were scanned with diffusion-weighted magnetic resonance imaging (MRI), and based on whole-brain probabilistic tractography and cortical parcellations, we analyzed the rich club of the brain's connectivity network. This revealed distinct patterns of disruption in both forms of dementia. In the connectome, we detected less disruption overall in EOAD than in bvFTD [false discovery rate (FDR) critical Pperm = 5.7 × 10(-3) , 10,000 permutations], with more involvement of richly interconnected areas of the brain (chi-squared P = 1.4 × 10(-4) )-predominantly posterior cognitive alterations. In bvFTD, we found a greater spread of disruption including the rich club (FDR critical Pperm = 6 × 10(-4) ), but especially more peripheral alterations (chi-squared P = 6.5 × 10(-3) ), particularly in medial frontal areas of the brain, in line with the known behavioral socioemotional deficits seen in these patients.
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Affiliation(s)
- Madelaine Daianu
- Imaging Genetics Center, Mark & Mary Stevens Neuroimaging & Informatics InstituteUniversity of Southern CaliforniaMarina del ReyCalifornia
- Department of NeurologyUCLA School of MedicineLos AngelesCalifornia
| | - Adam Mezher
- Imaging Genetics Center, Mark & Mary Stevens Neuroimaging & Informatics InstituteUniversity of Southern CaliforniaMarina del ReyCalifornia
| | - Mario F. Mendez
- Department of NeurologyBehavioral Neurology Program, UCLALos AngelesCalifornia
| | - Neda Jahanshad
- Imaging Genetics Center, Mark & Mary Stevens Neuroimaging & Informatics InstituteUniversity of Southern CaliforniaMarina del ReyCalifornia
| | - Elvira E. Jimenez
- Department of NeurologyBehavioral Neurology Program, UCLALos AngelesCalifornia
| | - Paul M. Thompson
- Imaging Genetics Center, Mark & Mary Stevens Neuroimaging & Informatics InstituteUniversity of Southern CaliforniaMarina del ReyCalifornia
- Department of NeurologyBehavioral Neurology Program, UCLALos AngelesCalifornia
- Departments of Neurology, Psychiatry, Radiology, Engineering, Pediatrics, and OphthalmologyUniversity of Southern CaliforniaLos AngelesCalifornia
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98
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Measuring Cortical Connectivity in Alzheimer's Disease as a Brain Neural Network Pathology: Toward Clinical Applications. J Int Neuropsychol Soc 2016; 22:138-63. [PMID: 26888613 DOI: 10.1017/s1355617715000995] [Citation(s) in RCA: 78] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
OBJECTIVES The objective was to review the literature on diffusion tensor imaging as well as resting-state functional magnetic resonance imaging and electroencephalography (EEG) to unveil neuroanatomical and neurophysiological substrates of Alzheimer's disease (AD) as a brain neural network pathology affecting structural and functional cortical connectivity underlying human cognition. METHODS We reviewed papers registered in PubMed and other scientific repositories on the use of these techniques in amnesic mild cognitive impairment (MCI) and clinically mild AD dementia patients compared to cognitively intact elderly individuals (Controls). RESULTS Hundreds of peer-reviewed (cross-sectional and longitudinal) papers have shown in patients with MCI and mild AD compared to Controls (1) impairment of callosal (splenium), thalamic, and anterior-posterior white matter bundles; (2) reduced correlation of resting state blood oxygen level-dependent activity across several intrinsic brain circuits including default mode and attention-related networks; and (3) abnormal power and functional coupling of resting state cortical EEG rhythms. Clinical applications of these measures are still limited. CONCLUSIONS Structural and functional (in vivo) cortical connectivity measures represent a reliable marker of cerebral reserve capacity and should be used to predict and monitor the evolution of AD and its relative impact on cognitive domains in pre-clinical, prodromal, and dementia stages of AD.
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99
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Collin G, Turk E, van den Heuvel MP. Connectomics in Schizophrenia: From Early Pioneers to Recent Brain Network Findings. BIOLOGICAL PSYCHIATRY: COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2016; 1:199-208. [PMID: 29560880 DOI: 10.1016/j.bpsc.2016.01.002] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/04/2015] [Revised: 01/15/2016] [Accepted: 01/19/2016] [Indexed: 12/15/2022]
Abstract
Schizophrenia has been conceptualized as a brain network disorder. The historical roots of connectomics in schizophrenia go back to the late 19th century, when influential scholars such as Theodor Meynert, Carl Wernicke, Emil Kraepelin, and Eugen Bleuler worked on a theoretical understanding of the multifaceted syndrome that is currently referred to as schizophrenia. Their work contributed to the understanding that symptoms such as psychosis and cognitive disorganization might stem from abnormal integration or dissociation due to disruptions in the brain's association fibers. As methods to test this hypothesis were long lacking, the claims of these early pioneers remained unsupported by empirical evidence for almost a century. In this review, we revisit and pay tribute to the old masters and, discussing recent findings from the developing field of disease connectomics, we examine how their pioneering hypotheses hold up in light of current evidence.
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Affiliation(s)
- Guusje Collin
- Department of Psychiatry, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, Netherlands.
| | - Elise Turk
- Department of Psychiatry, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, Netherlands
| | - Martijn P van den Heuvel
- Department of Psychiatry, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, Netherlands
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100
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Daianu M, Jacobs RE, Weitz TM, Town TC, Thompson PM. Multi-Shell Hybrid Diffusion Imaging (HYDI) at 7 Tesla in TgF344-AD Transgenic Alzheimer Rats. PLoS One 2015; 10:e0145205. [PMID: 26683657 PMCID: PMC4687716 DOI: 10.1371/journal.pone.0145205] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2015] [Accepted: 12/01/2015] [Indexed: 11/18/2022] Open
Abstract
Diffusion weighted imaging (DWI) is widely used to study microstructural characteristics of the brain. Diffusion tensor imaging (DTI) and high-angular resolution imaging (HARDI) are frequently used in radiology and neuroscience research but can be limited in describing the signal behavior in composite nerve fiber structures. Here, we developed and assessed the benefit of a comprehensive diffusion encoding scheme, known as hybrid diffusion imaging (HYDI), composed of 300 DWI volumes acquired at 7-Tesla with diffusion weightings at b = 1000, 3000, 4000, 8000 and 12000 s/mm2 and applied it in transgenic Alzheimer rats (line TgF344-AD) that model the full clinico-pathological spectrum of the human disease. We studied and visualized the effects of the multiple concentric "shells" when computing three distinct anisotropy maps-fractional anisotropy (FA), generalized fractional anisotropy (GFA) and normalized quantitative anisotropy (NQA). We tested the added value of the multi-shell q-space sampling scheme, when reconstructing neural pathways using mathematical frameworks from DTI and q-ball imaging (QBI). We show a range of properties of HYDI, including lower apparent anisotropy when using high b-value shells in DTI-based reconstructions, and increases in apparent anisotropy in QBI-based reconstructions. Regardless of the reconstruction scheme, HYDI improves FA-, GFA- and NQA-aided tractography. HYDI may be valuable in human connectome projects and clinical research, as well as magnetic resonance research in experimental animals.
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Affiliation(s)
- Madelaine Daianu
- Imaging Genetics Center, Mark & Mary Stevens Neuroimaging & Informatics Institute, University of Southern California, Marina del Rey, CA, United States of America
- Department of Neurology, UCLA School of Medicine, Los Angeles, CA, United States of America
| | - Russell E. Jacobs
- Division of Biology and Biological Engineering, Beckman Institute, California Institute of Technology, Pasadena, CA, United States of America
| | - Tara M. Weitz
- Department of Physiology & Biophysics, Zilkha Neurogenetic Institute, University of Southern California, Los Angeles, CA, United States of America
| | - Terrence C. Town
- Department of Physiology & Biophysics, Zilkha Neurogenetic Institute, University of Southern California, Los Angeles, CA, United States of America
| | - Paul M. Thompson
- Imaging Genetics Center, Mark & Mary Stevens Neuroimaging & Informatics Institute, University of Southern California, Marina del Rey, CA, United States of America
- Department of Neurology, UCLA School of Medicine, Los Angeles, CA, United States of America
- Departments of Neurology, Psychiatry, Radiology, Engineering, Pediatrics, and Ophthalmology, University of Southern California, Los Angeles, CA, United States of America
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