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Yang Q, Cai S, Chen G, Yu X, Cattell RF, Raviv TR, Huang C, Zhang N, Gao Y. Fine scale hippocampus morphology variation cross 552 healthy subjects from age 20 to 80. Front Neurosci 2023; 17:1162096. [PMID: 37719158 PMCID: PMC10501455 DOI: 10.3389/fnins.2023.1162096] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2023] [Accepted: 07/26/2023] [Indexed: 09/19/2023] Open
Abstract
The cerebral cortex varies over the course of a person's life span: at birth, the surface is smooth, before becoming more bumpy (deeper sulci and thicker gyri) in middle age, and thinner in senior years. In this work, a similar phenomenon was observed on the hippocampus. It was previously believed the fine-scale morphology of the hippocampus could only be extracted only with high field scanners (7T, 9.4T); however, recent studies show that regular 3T MR scanners can be sufficient for this purpose. This finding opens the door for the study of fine hippocampal morphometry for a large amount of clinical data. In particular, a characteristic bumpy and subtle feature on the inferior aspect of the hippocampus, which we refer to as hippocampal dentation, presents a dramatic degree of variability between individuals from very smooth to highly dentated. In this report, we propose a combined method joining deep learning and sub-pixel level set evolution to efficiently obtain fine-scale hippocampal segmentation on 552 healthy subjects. Through non-linear dentation extraction and fitting, we reveal that the bumpiness of the inferior surface of the human hippocampus has a clear temporal trend. It is bumpiest between 40 and 50 years old. This observation should be aligned with neurodevelopmental and aging stages.
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Affiliation(s)
- Qinzhu Yang
- School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen University, Shenzhen, Guangdong, China
| | - Shuxiu Cai
- School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen University, Shenzhen, Guangdong, China
| | - Guojing Chen
- School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen University, Shenzhen, Guangdong, China
| | - Xiaxia Yu
- School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen University, Shenzhen, Guangdong, China
| | - Renee F. Cattell
- Department of Biomedical Engineering, Stony Brook University, Stony Brook, NY, United States
- Department of Radiation Oncology, Renaissance School of Medicine, Stony Brook University, Stony Brook, NY, United States
| | - Tammy Riklin Raviv
- The School of Electrical and Computer Engineering, Ben-Gurion University of the Negev, Be'er Sheva, Israel
| | - Chuan Huang
- Department of Psychiatry, Stony Brook University, Stony Brook, NY, United States
- Department of Radiology, Stony Brook University, Stony Brook, NY, United States
| | - Nu Zhang
- Department of Neurosurgery, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Yi Gao
- School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen University, Shenzhen, Guangdong, China
- Shenzhen Key Laboratory of Precision Medicine for Hematological Malignancies, Shenzhen, China
- Marshall Laboratory of Biomedical Engineering, Shenzhen University, Shenzhen, China
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2
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Dichio V, De Vico Fallani F. Statistical models of complex brain networks: a maximum entropy approach. REPORTS ON PROGRESS IN PHYSICS. PHYSICAL SOCIETY (GREAT BRITAIN) 2023; 86:102601. [PMID: 37437559 DOI: 10.1088/1361-6633/ace6bc] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/10/2022] [Accepted: 07/12/2023] [Indexed: 07/14/2023]
Abstract
The brain is a highly complex system. Most of such complexity stems from the intermingled connections between its parts, which give rise to rich dynamics and to the emergence of high-level cognitive functions. Disentangling the underlying network structure is crucial to understand the brain functioning under both healthy and pathological conditions. Yet, analyzing brain networks is challenging, in part because their structure represents only one possible realization of a generative stochastic process which is in general unknown. Having a formal way to cope with such intrinsic variability is therefore central for the characterization of brain network properties. Addressing this issue entails the development of appropriate tools mostly adapted from network science and statistics. Here, we focus on a particular class of maximum entropy models for networks, i.e. exponential random graph models, as a parsimonious approach to identify the local connection mechanisms behind observed global network structure. Efforts are reviewed on the quest for basic organizational properties of human brain networks, as well as on the identification of predictive biomarkers of neurological diseases such as stroke. We conclude with a discussion on how emerging results and tools from statistical graph modeling, associated with forthcoming improvements in experimental data acquisition, could lead to a finer probabilistic description of complex systems in network neuroscience.
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Affiliation(s)
- Vito Dichio
- Sorbonne Universite, Paris Brain Institute-ICM, CNRS, Inria, Inserm, AP-HP, Hopital de la Pitie Salpêtriere, F-75013 Paris, France
| | - Fabrizio De Vico Fallani
- Sorbonne Universite, Paris Brain Institute-ICM, CNRS, Inria, Inserm, AP-HP, Hopital de la Pitie Salpêtriere, F-75013 Paris, France
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3
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Chen H, Zha R, Lai X, Liu Y, Wei Z, Wang M, Zuo H, Hong W, Fan C, Jin C, Cui G, Tao R, Liang P, Zhang X. Internet gaming disorder and tobacco use disorder share neural connectivity patterns between the subcortical and the motor network. Hum Brain Mapp 2023; 44:2607-2619. [PMID: 36807959 PMCID: PMC10028654 DOI: 10.1002/hbm.26233] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2022] [Revised: 01/09/2023] [Accepted: 01/26/2023] [Indexed: 02/22/2023] Open
Abstract
Internet gaming disorder (IGD) and tobacco use disorder (TUD) are globally common, non-substance-related disorders and substance-related disorders worldwide, respectively. Recognizing the commonalities between IGD and TUD will deepen understanding of the underlying mechanisms of addictive behavior and excessive online gaming. Using node strength, 141 resting-state data were collected in this study to compute network homogeneity. The participants included participants with IGD (PIGD: n = 34, male = 29, age: 15-25 years), participants with TUD (PTUD: n = 33, male = 33, age: 19-42 years), and matched healthy controls (control-for-IGD: n = 41, male = 38, age: 17-32 years; control-for-TUD: n = 33, age: 21-27 years). PIGD and PTUD exhibited common enhanced node strength between the subcortical and motor networks. Additionally, a common enhanced resting-state functional connectivity (RSFC) was found between the right thalamus and right postcentral gyrus in PIGD and PTUD. Node strength and RSFC were used to distinguish PIGD and PTUD from their respective healthy controls. Interestingly, models trained on PIGD versus controls could classify PTUD versus controls and vice versa, suggesting that these disorders share common neurological patterns. Enhanced connectivity may indicate a greater association between rewards and behaviors, inducing addiction behaviors without flexible and complex regulation. This study discovered that the connectivity between the subcortical and motor networks is a potential biological target for developing addiction treatment in the future.
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Affiliation(s)
- Hui Chen
- Hefei National Research Center for Physical Sciences at the Microscale, and Department of Radiology, the First Affiliated Hospital of USTC, Division of Life Science and Medicine, University of Science and Technology of China, Hefei, Anhui, China
- Department of Psychology, School of Humanities & Social Science, University of Science and Technology of China, Hefei, Anhui, China
| | - Rujing Zha
- Hefei National Research Center for Physical Sciences at the Microscale, and Department of Radiology, the First Affiliated Hospital of USTC, Division of Life Science and Medicine, University of Science and Technology of China, Hefei, Anhui, China
- Department of Psychology, School of Humanities & Social Science, University of Science and Technology of China, Hefei, Anhui, China
| | - Xin Lai
- Hefei National Research Center for Physical Sciences at the Microscale, and Department of Radiology, the First Affiliated Hospital of USTC, Division of Life Science and Medicine, University of Science and Technology of China, Hefei, Anhui, China
- Department of Psychology, School of Humanities & Social Science, University of Science and Technology of China, Hefei, Anhui, China
| | - Yan Liu
- Application Technology Center of Physical Therapy to Brain Disorders, Institute of Advanced Technology, University of Science and Technology of China, Hefei, Anhui, China
| | - Zhengde Wei
- Hefei National Research Center for Physical Sciences at the Microscale, and Department of Radiology, the First Affiliated Hospital of USTC, Division of Life Science and Medicine, University of Science and Technology of China, Hefei, Anhui, China
- Department of Psychology, School of Humanities & Social Science, University of Science and Technology of China, Hefei, Anhui, China
| | - Min Wang
- Hefei National Research Center for Physical Sciences at the Microscale, and Department of Radiology, the First Affiliated Hospital of USTC, Division of Life Science and Medicine, University of Science and Technology of China, Hefei, Anhui, China
- Department of Psychology, School of Humanities & Social Science, University of Science and Technology of China, Hefei, Anhui, China
| | - Huilin Zuo
- Hefei National Research Center for Physical Sciences at the Microscale, and Department of Radiology, the First Affiliated Hospital of USTC, Division of Life Science and Medicine, University of Science and Technology of China, Hefei, Anhui, China
- Department of Psychology, School of Humanities & Social Science, University of Science and Technology of China, Hefei, Anhui, China
| | - Wei Hong
- Hefei National Research Center for Physical Sciences at the Microscale, and Department of Radiology, the First Affiliated Hospital of USTC, Division of Life Science and Medicine, University of Science and Technology of China, Hefei, Anhui, China
- Department of Psychology, School of Humanities & Social Science, University of Science and Technology of China, Hefei, Anhui, China
| | - Chuan Fan
- Hefei National Research Center for Physical Sciences at the Microscale, and Department of Radiology, the First Affiliated Hospital of USTC, Division of Life Science and Medicine, University of Science and Technology of China, Hefei, Anhui, China
- Department of Psychology, School of Humanities & Social Science, University of Science and Technology of China, Hefei, Anhui, China
| | - Chen Jin
- Hefei National Research Center for Physical Sciences at the Microscale, and Department of Radiology, the First Affiliated Hospital of USTC, Division of Life Science and Medicine, University of Science and Technology of China, Hefei, Anhui, China
- Department of Psychology, School of Humanities & Social Science, University of Science and Technology of China, Hefei, Anhui, China
| | - Guanbao Cui
- Hefei National Research Center for Physical Sciences at the Microscale, and Department of Radiology, the First Affiliated Hospital of USTC, Division of Life Science and Medicine, University of Science and Technology of China, Hefei, Anhui, China
- Department of Psychology, School of Humanities & Social Science, University of Science and Technology of China, Hefei, Anhui, China
| | - Ran Tao
- Beijing Shijian Integrated Medicine Science Institute, Beijing, China
| | - Peipeng Liang
- School of Psychology, Beijing Key Laboratory of Learning and Cognition, Capital Normal University, Beijing, China
| | - Xiaochu Zhang
- Hefei National Research Center for Physical Sciences at the Microscale, and Department of Radiology, the First Affiliated Hospital of USTC, Division of Life Science and Medicine, University of Science and Technology of China, Hefei, Anhui, China
- Department of Psychology, School of Humanities & Social Science, University of Science and Technology of China, Hefei, Anhui, China
- Application Technology Center of Physical Therapy to Brain Disorders, Institute of Advanced Technology, University of Science and Technology of China, Hefei, Anhui, China
- Institute of Health and Medicine, Hefei Comprehensive National Science Center, Hefei, Anhui, China
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4
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Familiar AM, Fathi Kazerooni A. Editorial for "Age-Related Differences of Cortical Topology Across the Adult Lifespan: Evidence From a Multisite MRI Study With 1427 Individuals". J Magn Reson Imaging 2023; 57:444-445. [PMID: 35924785 DOI: 10.1002/jmri.28375] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2022] [Accepted: 07/15/2022] [Indexed: 01/20/2023] Open
Affiliation(s)
- Ariana M Familiar
- Center for Data-Driven Discovery in Biomedicine (D3b), Division of Neurosurgery, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA
| | - Anahita Fathi Kazerooni
- Center for Data-Driven Discovery in Biomedicine (D3b), Division of Neurosurgery, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA.,Department of Neurosurgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
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5
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Deery HA, Di Paolo R, Moran C, Egan GF, Jamadar SD. The older adult brain is less modular, more integrated, and less efficient at rest: A systematic review of large-scale resting-state functional brain networks in aging. Psychophysiology 2023; 60:e14159. [PMID: 36106762 PMCID: PMC10909558 DOI: 10.1111/psyp.14159] [Citation(s) in RCA: 32] [Impact Index Per Article: 32.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2022] [Revised: 07/22/2022] [Accepted: 07/25/2022] [Indexed: 12/23/2022]
Abstract
The literature on large-scale resting-state functional brain networks across the adult lifespan was systematically reviewed. Studies published between 1986 and July 2021 were retrieved from PubMed. After reviewing 2938 records, 144 studies were included. Results on 11 network measures were summarized and assessed for certainty of the evidence using a modified GRADE method. The evidence provides high certainty that older adults display reduced within-network and increased between-network functional connectivity. Older adults also show lower segregation, modularity, efficiency and hub function, and decreased lateralization and a posterior to anterior shift at rest. Higher-order functional networks reliably showed age differences, whereas primary sensory and motor networks showed more variable results. The inflection point for network changes is often the third or fourth decade of life. Age effects were found with moderate certainty for within- and between-network altered patterns and speed of dynamic connectivity. Research on within-subject bold variability and connectivity using glucose uptake provides low certainty of age differences but warrants further study. Taken together, these age-related changes may contribute to the cognitive decline often seen in older adults.
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Affiliation(s)
- Hamish A. Deery
- Turner Institute for Brain and Mental HealthMonash UniversityMelbourneVictoriaAustralia
- Monash Biomedical ImagingMonash UniversityMelbourneVictoriaAustralia
| | - Robert Di Paolo
- Turner Institute for Brain and Mental HealthMonash UniversityMelbourneVictoriaAustralia
- Monash Biomedical ImagingMonash UniversityMelbourneVictoriaAustralia
| | - Chris Moran
- Peninsula Clinical School, Central Clinical SchoolMonash UniversityFrankstonVictoriaAustralia
- Department of Geriatric MedicinePeninsula HealthFrankstonVictoriaAustralia
| | - Gary F. Egan
- Turner Institute for Brain and Mental HealthMonash UniversityMelbourneVictoriaAustralia
- Monash Biomedical ImagingMonash UniversityMelbourneVictoriaAustralia
- Australian Research Council Centre of Excellence for Integrative Brain FunctionMelbourneVictoriaAustralia
| | - Sharna D. Jamadar
- Turner Institute for Brain and Mental HealthMonash UniversityMelbourneVictoriaAustralia
- Monash Biomedical ImagingMonash UniversityMelbourneVictoriaAustralia
- Australian Research Council Centre of Excellence for Integrative Brain FunctionMelbourneVictoriaAustralia
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6
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Wang M, Cheng X, Shi Q, Xu B, Hou X, Zhao H, Gui Q, Wu G, Dong X, Xu Q, Shen M, Cheng Q, Xue S, Feng H, Ding Z. Brain diffusion tensor imaging reveals altered connections and networks in epilepsy patients. Front Hum Neurosci 2023; 17:1142408. [PMID: 37033907 PMCID: PMC10073437 DOI: 10.3389/fnhum.2023.1142408] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2023] [Accepted: 02/28/2023] [Indexed: 04/11/2023] Open
Abstract
Introduction Accumulating evidence shows that epilepsy is a disease caused by brain network dysfunction. This study explored changes in brain network structure in epilepsy patients based on graph analysis of diffusion tensor imaging data. Methods The brain structure networks of 42 healthy control individuals and 26 epilepsy patients were constructed. Using graph theory analysis, global and local network topology parameters of the brain structure network were calculated, and changes in global and local characteristics of the brain network in epilepsy patients were quantitatively analyzed. Results Compared with the healthy control group, the epilepsy patient group showed lower global efficiency, local efficiency, clustering coefficient, and a longer shortest path length. Both healthy control individuals and epilepsy patients showed small-world attributes, with no significant difference between groups. The epilepsy patient group showed lower nodal local efficiency and nodal clustering coefficient in the right olfactory cortex and right rectus and lower nodal degree centrality in the right olfactory cortex and the left paracentral lobular compared with the healthy control group. In addition, the epilepsy patient group showed a smaller fiber number of edges in specific regions of the frontal lobe, temporal lobe, and default mode network, indicating reduced connection strength. Discussion Epilepsy patients exhibited lower global and local brain network properties as well as reduced white matter fiber connectivity in key brain regions. These findings further support the idea that epilepsy is a brain network disorder.
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Affiliation(s)
- Meixia Wang
- Department of Neurology, Suzhou Hospital Affiliated to Nanjing Medical University, Suzhou Municipal Hospital, Suzhou, China
| | - Xiaoyu Cheng
- Department of Neurology and Clinical Research Center of Neurological Disease, The Second Affiliated Hospital of Soochow University, Suzhou, China
| | - Qianru Shi
- Department of Neurology, Suzhou Hospital Affiliated to Nanjing Medical University, Suzhou Municipal Hospital, Suzhou, China
| | - Bo Xu
- Department of Neurology, Suzhou Hospital Affiliated to Nanjing Medical University, Suzhou Municipal Hospital, Suzhou, China
| | - Xiaoxia Hou
- Department of Neurology, Suzhou Hospital Affiliated to Nanjing Medical University, Suzhou Municipal Hospital, Suzhou, China
| | - Huimin Zhao
- Department of Neurology, Suzhou Hospital Affiliated to Nanjing Medical University, Suzhou Municipal Hospital, Suzhou, China
| | - Qian Gui
- Department of Neurology, Suzhou Hospital Affiliated to Nanjing Medical University, Suzhou Municipal Hospital, Suzhou, China
| | - Guanhui Wu
- Department of Neurology, Suzhou Hospital Affiliated to Nanjing Medical University, Suzhou Municipal Hospital, Suzhou, China
| | - Xiaofeng Dong
- Department of Neurology, Suzhou Hospital Affiliated to Nanjing Medical University, Suzhou Municipal Hospital, Suzhou, China
| | - Qinrong Xu
- Department of Neurology, Suzhou Hospital Affiliated to Nanjing Medical University, Suzhou Municipal Hospital, Suzhou, China
| | - Mingqiang Shen
- Department of Neurology, Suzhou Hospital Affiliated to Nanjing Medical University, Suzhou Municipal Hospital, Suzhou, China
| | - Qingzhang Cheng
- Department of Neurology, Suzhou Hospital Affiliated to Nanjing Medical University, Suzhou Municipal Hospital, Suzhou, China
| | - Shouru Xue
- Department of Neurology, The First Affiliated Hospital of Soochow University, Suzhou, China
| | - Hongxuan Feng
- Department of Neurology, Suzhou Hospital Affiliated to Nanjing Medical University, Suzhou Municipal Hospital, Suzhou, China
- *Correspondence: Hongxuan Feng,
| | - Zhiliang Ding
- Department of Neurology, Suzhou Hospital Affiliated to Nanjing Medical University, Suzhou Municipal Hospital, Suzhou, China
- Zhiliang Ding,
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Yang D, Chen J, Yan C, Kim M, Laurienti PJ, Styner M, Wu G. Group-Wise Hub Identification by Learning Common Graph Embeddings on Grassmannian Manifold. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2022; 44:8249-8260. [PMID: 34010126 PMCID: PMC9596171 DOI: 10.1109/tpami.2021.3081744] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Human brain is a complex yet economically organized system, where a small portion of critical hub regions support the majority of brain functions. The identification of common hub nodes in a population of networks is often simplified as a voting procedure on the set of identified hub nodes across individual brain networks, which ignores the intrinsic data geometry and partially lacks the reproducible findings in neuroscience. Hence, we propose a first-ever group-wise hub identification method to identify hub nodes that are common across a population of individual brain networks. Specifically, the backbone of our method is to learn common graph embedding that can represent the majority of local topological profiles. By requiring orthogonality among the graph embedding vectors, each graph embedding as a data element is residing on the Grassmannian manifold. We present a novel Grassmannian manifold optimization scheme that allows us to find the common graph embeddings, which not only identify the most reliable hub nodes in each network but also yield a population-based common hub node map. Results of the accuracy and replicability on both synthetic and real network data show that the proposed manifold learning approach outperforms all hub identification methods employed in this evaluation.
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8
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Networks behind the morphology and structural design of living systems. Phys Life Rev 2022; 41:1-21. [DOI: 10.1016/j.plrev.2022.03.001] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2022] [Accepted: 03/04/2022] [Indexed: 01/06/2023]
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9
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Javaid H, Kumarnsit E, Chatpun S. Age-Related Alterations in EEG Network Connectivity in Healthy Aging. Brain Sci 2022; 12:brainsci12020218. [PMID: 35203981 PMCID: PMC8870284 DOI: 10.3390/brainsci12020218] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2022] [Revised: 01/28/2022] [Accepted: 02/01/2022] [Indexed: 02/01/2023] Open
Abstract
Emerging studies have reported that functional brain networks change with increasing age. Graph theory is applied to understand the age-related differences in brain behavior and function, and functional connectivity between the regions is examined using electroencephalography (EEG). The effect of normal aging on functional networks and inter-regional synchronization during the working memory (WM) state is not well known. In this study, we applied graph theory to investigate the effect of aging on network topology in a resting state and during performing a visual WM task to classify aging EEG signals. We recorded EEGs from 20 healthy middle-aged and 20 healthy elderly subjects with their eyes open, eyes closed, and during a visual WM task. EEG signals were used to construct the functional network; nodes are represented by EEG electrodes; and edges denote the functional connectivity. Graph theory matrices including global efficiency, local efficiency, clustering coefficient, characteristic path length, node strength, node betweenness centrality, and assortativity were calculated to analyze the networks. We applied the three classifiers of K-nearest neighbor (KNN), a support vector machine (SVM), and random forest (RF) to classify both groups. The analyses showed the significantly reduced network topology features in the elderly group. Local efficiency, global efficiency, and clustering coefficient were significantly lower in the elderly group with the eyes-open, eyes-closed, and visual WM task states. KNN achieved its highest accuracy of 98.89% during the visual WM task and depicted better classification performance than other classifiers. Our analysis of functional network connectivity and topological characteristics can be used as an appropriate technique to explore normal age-related changes in the human brain.
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Affiliation(s)
- Hamad Javaid
- Department of Biomedical Sciences and Biomedical Engineering, Faculty of Medicine, Prince of Songkla University, Hat Yai, Songkhla 90110, Thailand;
| | - Ekkasit Kumarnsit
- Physiology Program, Division of Health and Applied Science, Faculty of Science, Prince of Songkla University, Hat Yai, Songkhla 90112, Thailand;
- Biosignal Research Centre for Health, Prince of Songkla University, Hat Yai, Songkhla 90112, Thailand
| | - Surapong Chatpun
- Department of Biomedical Sciences and Biomedical Engineering, Faculty of Medicine, Prince of Songkla University, Hat Yai, Songkhla 90110, Thailand;
- Biosignal Research Centre for Health, Prince of Songkla University, Hat Yai, Songkhla 90112, Thailand
- Institute of Biomedical Engineering, Faculty of Medicine, Prince of Songkla University, Hat Yai, Songkhla 90110, Thailand
- Correspondence:
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10
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Li W, Yang D, Yan C, Chen M, Li Q, Zhu W, Wu G. Characterizing Network Selectiveness to the Dynamic Spreading of Neuropathological Events in Alzheimer's Disease. J Alzheimers Dis 2022; 86:1805-1816. [PMID: 35253761 PMCID: PMC9482760 DOI: 10.3233/jad-215596] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
BACKGROUND Mounting evidence shows that the neuropathological burdens manifest preference in affecting brain regions during the dynamic progression of Alzheimer's disease (AD). Since the distinct brain regions are physically wired by white matter fibers, it is reasonable to hypothesize the differential spreading pattern of neuropathological burdens may underlie the wiring topology, which can be characterized using neuroimaging and network science technologies. OBJECTIVE To study the dynamic spreading patterns of neuropathological events in AD. METHODS We first examine whether hub nodes with high connectivity in the brain network (assemble of white matter wirings) are susceptible to a higher level of pathological burdens than other regions that are less involved in the process of information exchange in the network. Moreover, we propose a novel linear mixed-effect model to characterize the multi-factorial spreading process of neuropathological burdens from hub nodes to non-hub nodes, where age, sex, and APOE4 indicators are considered as confounders. We apply our statistical model to the longitudinal neuroimaging data of amyloid-PET and tau-PET, respectively. RESULTS Our meta-data analysis results show that 1) AD differentially affects hub nodes with a significantly higher level of pathology, and 2) the longitudinal increase of neuropathological burdens on non-hub nodes is strongly correlated with the connectome distance to hub nodes rather than the spatial proximity. CONCLUSION The spreading pathway of AD neuropathological burdens might start from hub regions and propagate through the white matter fibers in a prion-like manner.
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Affiliation(s)
- Wenchao Li
- Intelligent Information Processing Laboratory, Hangzhou Dianzi University, Hangzhou, China
| | - Defu Yang
- Intelligent Information Processing Laboratory, Hangzhou Dianzi University, Hangzhou, China
- Research Center for Healthcare Data Science, Zhejiang Lab, Hangzhou, Zhejiang, China
| | - Chenggang Yan
- Intelligent Information Processing Laboratory, Hangzhou Dianzi University, Hangzhou, China
| | - Minghan Chen
- Department of Computer Science, Wake Forest University, Winston-Salem, NC, USA
| | - Quefeng Li
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Wentao Zhu
- Research Center for Healthcare Data Science, Zhejiang Lab, Hangzhou, Zhejiang, China
| | - Guorong Wu
- Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
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11
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Frau-Pascual A, Augustinack J, Varadarajan D, Yendiki A, Salat DH, Fischl B, Aganj I. Conductance-Based Structural Brain Connectivity in Aging and Dementia. Brain Connect 2021; 11:566-583. [PMID: 34042511 PMCID: PMC8558081 DOI: 10.1089/brain.2020.0903] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/29/2023] Open
Abstract
Background: Structural brain connectivity has been shown to be sensitive to the changes that the brain undergoes during Alzheimer's disease (AD) progression. Methods: In this work, we used our recently proposed structural connectivity quantification measure derived from diffusion magnetic resonance imaging, which accounts for both direct and indirect pathways, to quantify brain connectivity in dementia. We analyzed data from the second phase of Alzheimer's Disease Neuroimaging Initiative and third release in the Open Access Series of Imaging Studies data sets to derive relevant information for the study of the changes that the brain undergoes in AD. We also compared these data sets to the Human Connectome Project data set, as a reference, and eventually validated externally on two cohorts of the European DTI Study in Dementia database. Results: Our analysis shows expected trends of mean conductance with respect to age and cognitive scores, significant age prediction values in aging data, and regional effects centered among subcortical regions, and cingulate and temporal cortices. Discussion: Results indicate that the conductance measure has prediction potential, especially for age, that age and cognitive scores largely overlap, and that this measure could be used to study effects such as anticorrelation in structural connections. Impact statement This work presents a methodology and a set of analyses that open new possibilities in the study of healthy and pathological aging. The methodology used here is sensitive to direct and indirect pathways in deriving brain connectivity measures from diffusion-weighted magnetic resonance imaging, and therefore provides information that many state-of-the-art methods do not account for. As a result, this technique may provide the research community with ways to detect subtle effects of healthy aging and Alzheimer's disease.
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Affiliation(s)
- Aina Frau-Pascual
- Athinoula A. Martinos Center for Biomedical Imaging, Harvard Medical School, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Jean Augustinack
- Athinoula A. Martinos Center for Biomedical Imaging, Harvard Medical School, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Divya Varadarajan
- Athinoula A. Martinos Center for Biomedical Imaging, Harvard Medical School, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Anastasia Yendiki
- Athinoula A. Martinos Center for Biomedical Imaging, Harvard Medical School, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - David H. Salat
- Athinoula A. Martinos Center for Biomedical Imaging, Harvard Medical School, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Bruce Fischl
- Athinoula A. Martinos Center for Biomedical Imaging, Harvard Medical School, Massachusetts General Hospital, Boston, Massachusetts, USA
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Iman Aganj
- Athinoula A. Martinos Center for Biomedical Imaging, Harvard Medical School, Massachusetts General Hospital, Boston, Massachusetts, USA
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
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12
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Song X, García-Saldivar P, Kindred N, Wang Y, Merchant H, Meguerditchian A, Yang Y, Stein EA, Bradberry CW, Ben Hamed S, Jedema HP, Poirier C. Strengths and challenges of longitudinal non-human primate neuroimaging. Neuroimage 2021; 236:118009. [PMID: 33794361 PMCID: PMC8270888 DOI: 10.1016/j.neuroimage.2021.118009] [Citation(s) in RCA: 6] [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: 07/21/2020] [Revised: 03/16/2021] [Accepted: 03/23/2021] [Indexed: 01/20/2023] Open
Abstract
Longitudinal non-human primate neuroimaging has the potential to greatly enhance our understanding of primate brain structure and function. Here we describe its specific strengths, compared to both cross-sectional non-human primate neuroimaging and longitudinal human neuroimaging, but also its associated challenges. We elaborate on factors guiding the use of different analytical tools, subject-specific versus age-specific templates for analyses, and issues related to statistical power.
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Affiliation(s)
- Xiaowei Song
- Preclinical Pharmacology Section, Intramural Research Program, NIDA, NIH, Baltimore, MD 21224, USA
| | - Pamela García-Saldivar
- Instituto de Neurobiología, UNAM, Campus Juriquilla. Boulevard Juriquilla No. 3001 Querétaro, Qro. 76230, México
| | - Nathan Kindred
- Biosciences Institute & Centre for Behaviour and Evolution, Faculty of Medical Sciences, Newcastle University, United Kingdom
| | - Yujiang Wang
- CNNP Lab (www.cnnp-lab.com), Interdisciplinary Complex Systems Group, School of Computing, Newcastle University, United Kingdom
| | - Hugo Merchant
- Instituto de Neurobiología, UNAM, Campus Juriquilla. Boulevard Juriquilla No. 3001 Querétaro, Qro. 76230, México
| | - Adrien Meguerditchian
- Laboratoire de Psychologie Cognitive, UMR7290, Université Aix-Marseille/CNRS, Institut Language, Communication and the Brain 13331 Marseille, France
| | - Yihong Yang
- Neuroimaging Research Branch, Intramural Research Program, NIDA, NIH, Baltimore, MD 21224, USA
| | - Elliot A Stein
- Neuroimaging Research Branch, Intramural Research Program, NIDA, NIH, Baltimore, MD 21224, USA
| | - Charles W Bradberry
- Preclinical Pharmacology Section, Intramural Research Program, NIDA, NIH, Baltimore, MD 21224, USA
| | - Suliann Ben Hamed
- Institut des Sciences Cognitives Marc Jeannerod, UMR 5229, Université de Lyon - CNRS, France
| | - Hank P Jedema
- Preclinical Pharmacology Section, Intramural Research Program, NIDA, NIH, Baltimore, MD 21224, USA.
| | - Colline Poirier
- Biosciences Institute & Centre for Behaviour and Evolution, Faculty of Medical Sciences, Newcastle University, United Kingdom.
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13
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Poor speech recognition, sound localization and reorganization of brain activity in children with unilateral microtia-atresia. Brain Imaging Behav 2021; 16:78-90. [PMID: 34245431 PMCID: PMC8825362 DOI: 10.1007/s11682-021-00478-9] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/17/2021] [Indexed: 10/31/2022]
Abstract
Microtia-atresia is a congenital malformation of the external ear, often affecting one side and being associated with severe-to-profound unilateral conductive hearing loss (UCHL). Although the impact of unilateral hearing loss (UHL) on speech recognition, sound localization and brain plasticity has been intensively investigated, less is known about the subjects with unilateral microtia-atresia (UMA). Considering these UMA subjects have hearing loss from birth, we hypothesize it has a great effect on brain organization. A questionnaire on speech recognition and spatial listening ability was administered to 40 subjects with UMA and 40 age- and sex-matched controls. UMA subjects showed poorer speech recognition in laboratory and poorer spatial listening ability. However, cognitive scores determined by the Montreal Cognitive Assessment (MoCA) and Wechsler Intelligence Scale for Children (WISC-IV) did not differ significantly in these two groups. The impact of hearing loss in UMA on brain functional organization was examined by comparing resting-state fMRIs (rs-fMRI) in 27 subjects with right-sided UMA and 27 matched controls. UMA subjects had increased nodal betweenness in visual networks and DMN but decreases in auditory and attention networks. These results indicate that UCHL in UMA causes significant abnormalities in brain organization. The impact of UCHL on cognition should be further examined with a battery of tests that are more challenging and better focused on the cognitive networks identified.
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14
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Xu S, Yao X, Han L, Lv Y, Bu X, Huang G, Fan Y, Yu T, Huang G. Brain network analyses of diffusion tensor imaging for brain aging. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2021; 18:6066-6078. [PMID: 34517523 DOI: 10.3934/mbe.2021303] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
The approach of graph-based diffusion tensor imaging (DTI) networks has been used to explore the complicated structural connectivity of brain aging. In this study, the changes of DTI networks of brain aging were quantitatively and qualitatively investigated by comparing the characteristics of brain network. A cohort of 60 volunteers was enrolled and equally divided into young adults (YA) and older adults (OA) groups. The network characteristics of critical nodes, path length (Lp), clustering coefficient (Cp), global efficiency (Eglobal), local efficiency (Elocal), strength (Sp), and small world attribute (σ) were employed to evaluate the DTI networks at the levels of whole brain, bilateral hemispheres and critical brain regions. The correlations between each network characteristic and age were predicted, respectively. Our findings suggested that the DTI networks produced significant changes in network configurations at the critical nodes and node edges for the YA and OA groups. The analysis of whole brains network revealed that Lp, Cp increased (p < 0.05, positive correlation), Eglobal, Elocal, Sp decreased (p < 0.05, negative correlation), and σ unchanged (p ≥ 0.05, non-correlation) between the YA and OA groups. The analyses of bilateral hemispheres and brain regions showed similar results as that of the whole-brain analysis. Therefore the proposed scheme of DTI networks could be used to evaluate the WM changes of brain aging, and the network characteristics of critical nodes exhibited valuable indications for WM degeneration.
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Affiliation(s)
- Song Xu
- College of Medical Imaging, Shanghai University of Medicine and Health Sciences, Shanghai 201318, China
- School of Medical Instrument and Food Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China
| | - Xufeng Yao
- College of Medical Imaging, Shanghai University of Medicine and Health Sciences, Shanghai 201318, China
- Shanghai Key Laboratory of Molecular Imaging, Shanghai University of Medicine and Health Sciences, Shanghai 201318, China
- School of Medical Instrument and Food Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China
| | - Liting Han
- College of Medical Imaging, Shanghai University of Medicine and Health Sciences, Shanghai 201318, China
- School of Medical Instrument and Food Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China
| | - Yuting Lv
- College of Medical Imaging, Shanghai University of Medicine and Health Sciences, Shanghai 201318, China
- School of Medical Instrument and Food Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China
| | - Xixi Bu
- College of Medical Imaging, Shanghai University of Medicine and Health Sciences, Shanghai 201318, China
- School of Medical Instrument and Food Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China
| | - Gan Huang
- College of Medical Imaging, Shanghai University of Medicine and Health Sciences, Shanghai 201318, China
- School of Medical Instrument and Food Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China
| | - Yifeng Fan
- School of Medical Imaging, Hangzhou Medical College, Hangzhou 310053, China
| | - Tonggang Yu
- Shanghai Gamma Knife Hospital, Fudan University, Shanghai 200235, China
| | - Gang Huang
- College of Medical Imaging, Shanghai University of Medicine and Health Sciences, Shanghai 201318, China
- Shanghai Key Laboratory of Molecular Imaging, Shanghai University of Medicine and Health Sciences, Shanghai 201318, China
- School of Medical Instrument and Food Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China
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15
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Relationship Between Age and Cerebral Hemodynamic Response to Breath Holding: A Functional Near-Infrared Spectroscopy Study. Brain Topogr 2021; 34:154-166. [PMID: 33544290 DOI: 10.1007/s10548-021-00818-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2020] [Accepted: 01/06/2021] [Indexed: 10/22/2022]
Abstract
Cerebrovascular reactivity (CVR) is routinely measured as a predictor of stroke in people with a high risk of ischemic attack. Neuroimaging techniques such as emission tomography, magnetic resonance imaging, and transcranial doppler are frequently used to measure CVR even though each technique has its limitations. Functional near-infrared spectroscopy (fNIRS), also based on the principle of neurovascular coupling, is relatively inexpensive, portable, and allows for the quantification of oxy- and deoxy-hemoglobin concentration changes at a high temporal resolution. This study examines the relationship between age and CVR using fNIRS in 45 young healthy adult participants aged 18-41 years (6 females, 26.64 ± 5.49 years) performing a simple breath holding task. Eighteen of the 45 participants were scanned again after a week to evaluate the feasibility of fNIRS in reliably measuring CVR. Results indicate (a) a negative relationship between age and hemodynamic measures of breath holding task in the sensorimotor cortex of 45 individuals and (b) widespread positive coactivation within medial sensorimotor regions and between medial sensorimotor regions with supplementary motor area and prefrontal cortex during breath holding with increasing age. The intraclass correlation coefficient (ICC) indicated only a low to fair/good reliability of the breath hold hemodynamic measures from sensorimotor and prefrontal cortices. However, the average hemodynamic response to breath holding from the two sessions were found to be temporally and spatially in correspondence. Future improvements in the sensitivity and reliability of fNIRS metrics could facilitate fNIRS-based assessment of cerebrovascular function as a potential clinical tool.
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16
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Chen J, Han G, Cai H, Yang D, Laurienti PJ, Styner M, Wu G. Learning Common Harmonic Waves on Stiefel Manifold - A New Mathematical Approach for Brain Network Analyses. IEEE TRANSACTIONS ON MEDICAL IMAGING 2021; 40:419-430. [PMID: 33021935 PMCID: PMC7838011 DOI: 10.1109/tmi.2020.3029063] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
Converging evidence shows that disease-relevant brain alterations do not appear in random brain locations, instead, their spatial patterns follow large-scale brain networks. In this context, a powerful network analysis approach with a mathematical foundation is indispensable to understand the mechanisms of neuropathological events as they spread through the brain. Indeed, the topology of each brain network is governed by its native harmonic waves, which are a set of orthogonal bases derived from the Eigen-system of the underlying Laplacian matrix. To that end, we propose a novel connectome harmonic analysis framework that provides enhanced mathematical insights by detecting frequency-based alterations relevant to brain disorders. The backbone of our framework is a novel manifold algebra appropriate for inference across harmonic waves. This algebra overcomes the limitations of using classic Euclidean operations on irregular data structures. The individual harmonic differences are measured by a set of common harmonic waves learned from a population of individual Eigen-systems, where each native Eigen-system is regarded as a sample drawn from the Stiefel manifold. Specifically, a manifold optimization scheme is tailored to find the common harmonic waves, which reside at the center of the Stiefel manifold. To that end, the common harmonic waves constitute a new set of neurobiological bases to understand disease progression. Each harmonic wave exhibits a unique propagation pattern of neuropathological burden spreading across brain networks. The statistical power of our novel connectome harmonic analysis approach is evaluated by identifying frequency-based alterations relevant to Alzheimer's disease, where our learning-based manifold approach discovers more significant and reproducible network dysfunction patterns than Euclidean methods.
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17
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Carradus AJ, Mougin O, Hunt BAE, Tewarie PK, Geades N, Morris PG, Brookes MJ, Gowland PA, Madan CR. Age-related differences in myeloarchitecture measured at 7 T. Neurobiol Aging 2020; 96:246-254. [PMID: 33049517 DOI: 10.1016/j.neurobiolaging.2020.08.009] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2020] [Revised: 08/06/2020] [Accepted: 08/10/2020] [Indexed: 01/01/2023]
Abstract
We have used the magnetisation transfer (MT) MRI measure as a primary measure of myelination in both the gray matter (GM) of the 78 cortical automated anatomical labeling (AAL) regions of the brain, and the underlying white matter in each region, in a cohort of healthy adults (aged 19-62 year old). The results revealed a significant quadratic trend in myelination with age, with average global myelination peaking at 42.9 year old in gray matter, and at 41.7 year old in white matter. We also explored the possibility of using the Nuclear Overhauser Enhancement (NOE) effect, which is acquired in a similar method to MT, as an additional measure of myelination. We found that the MT and NOE signals were strongly correlated in the brain and that the NOE effects displayed similar (albeit weaker) parabolic trends with age. We also investigated differences in cortical thickness with age, and confirmed a previous result of a linear decline of 4.5 ± 1.2 μm/y.
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Affiliation(s)
- Andrew J Carradus
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, Nottingham, UK
| | - Olivier Mougin
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, Nottingham, UK
| | - Benjamin A E Hunt
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, Nottingham, UK
| | - Prejaas K Tewarie
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, Nottingham, UK
| | - Nicolas Geades
- Philips Clinical Science, Philips Healthcare, Eindhoven, the Netherlands
| | - Peter G Morris
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, Nottingham, UK
| | - Matthew J Brookes
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, Nottingham, UK
| | - Penny A Gowland
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, Nottingham, UK
| | - Christopher R Madan
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, Nottingham, UK; School of Psychology, University of Nottingham, Nottingham, UK.
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18
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Dicks E, Vermunt L, van der Flier WM, Barkhof F, Scheltens P, Tijms BM. Grey matter network trajectories across the Alzheimer's disease continuum and relation to cognition. Brain Commun 2020; 2:fcaa177. [PMID: 33376987 PMCID: PMC7751002 DOI: 10.1093/braincomms/fcaa177] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2019] [Revised: 06/23/2020] [Accepted: 07/02/2020] [Indexed: 11/13/2022] Open
Abstract
Biomarkers are needed to monitor disease progression in Alzheimer's disease. Grey matter network measures have such potential, as they are related to amyloid aggregation in cognitively unimpaired individuals and to future cognitive decline in predementia Alzheimer's disease. Here, we investigated how grey matter network measures evolve over time within individuals across the entire Alzheimer's disease cognitive continuum and whether such changes relate to concurrent decline in cognition. We included 190 cognitively unimpaired, amyloid normal (controls) and 523 individuals with abnormal amyloid across the cognitive continuum (preclinical, prodromal, Alzheimer's disease dementia) from the Alzheimer's Disease Neuroimaging Initiative and calculated single-subject grey matter network measures (median of five networks per individual over 2 years). We fitted linear mixed models to investigate how network measures changed over time and whether such changes were associated with concurrent changes in memory, language, attention/executive functioning and on the Mini-Mental State Examination. We further assessed whether associations were modified by baseline disease stage. We found that both cognitive functioning and network measures declined over time, with steeper rates of decline in more advanced disease stages. In all cognitive stages, decline in network measures was associated with concurrent decline on the Mini-Mental State Examination, with stronger effects for individuals closer to Alzheimer's disease dementia. Decline in network measures was associated with concurrent cognitive decline in different cognitive domains depending on disease stage: In controls, decline in networks was associated with decline in memory and language functioning; preclinical Alzheimer's disease showed associations of decline in networks with memory and attention/executive functioning; prodromal Alzheimer's disease showed associations of decline in networks with cognitive decline in all domains; Alzheimer's disease dementia showed associations of decline in networks with attention/executive functioning. Decline in grey matter network measures over time accelerated for more advanced disease stages and was related to concurrent cognitive decline across the entire Alzheimer's disease cognitive continuum. These associations were disease stage dependent for the different cognitive domains, which reflected the respective cognitive stage. Our findings therefore suggest that grey matter measures are helpful to track disease progression in Alzheimer's disease.
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Affiliation(s)
- Ellen Dicks
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, 1081 HV Amsterdam, The Netherlands
| | - Lisa Vermunt
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, 1081 HV Amsterdam, The Netherlands
| | - Wiesje M van der Flier
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, 1081 HV Amsterdam, The Netherlands
| | - Frederik Barkhof
- Department of Radiology and Nuclear Medicine, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, 1081 HV Amsterdam, The Netherlands; Institutes of Neurology & Healthcare Engineering, UCL London, London WC1E, UK
| | - Philip Scheltens
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, 1081 HV Amsterdam, The Netherlands
| | - Betty M Tijms
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, 1081 HV Amsterdam, The Netherlands
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19
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Kundu S, Lukemire J, Wang Y, Guo Y. A Novel Joint Brain Network Analysis Using Longitudinal Alzheimer's Disease Data. Sci Rep 2019; 9:19589. [PMID: 31863067 PMCID: PMC6925181 DOI: 10.1038/s41598-019-55818-z] [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: 03/26/2019] [Accepted: 11/26/2019] [Indexed: 12/14/2022] Open
Abstract
There is well-documented evidence of brain network differences between individuals with Alzheimer's disease (AD) and healthy controls (HC). To date, imaging studies investigating brain networks in these populations have typically been cross-sectional, and the reproducibility of such findings is somewhat unclear. In a novel study, we use the longitudinal ADNI data on the whole brain to jointly compute the brain network at baseline and one-year using a state of the art approach that pools information across both time points to yield distinct visit-specific networks for the AD and HC cohorts, resulting in more accurate inferences. We perform a multiscale comparison of the AD and HC networks in terms of global network metrics as well as at the more granular level of resting state networks defined under a whole brain parcellation. Our analysis illustrates a decrease in small-worldedness in the AD group at both the time points and also identifies more local network features and hub nodes that are disrupted due to the progression of AD. We also obtain high reproducibility of the HC network across visits. On the other hand, a separate estimation of the networks at each visit using standard graphical approaches reveals fewer meaningful differences and lower reproducibility.
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Affiliation(s)
- Suprateek Kundu
- Department of Biostatistics and Bioinformatics, Emory University, Atlanta, Ga, 30322, USA.
| | - Joshua Lukemire
- Department of Biostatistics and Bioinformatics, Emory University, Atlanta, Ga, 30322, USA
| | - Yikai Wang
- Department of Biostatistics and Bioinformatics, Emory University, Atlanta, Ga, 30322, USA
| | - Ying Guo
- Department of Biostatistics and Bioinformatics, Emory University, Atlanta, Ga, 30322, USA
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20
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Koirala N, Anwar AR, Ciolac D, Glaser M, Pintea B, Deuschl G, Muthuraman M, Groppa S. Alterations in White Matter Network and Microstructural Integrity Differentiate Parkinson's Disease Patients and Healthy Subjects. Front Aging Neurosci 2019; 11:191. [PMID: 31404311 PMCID: PMC6676803 DOI: 10.3389/fnagi.2019.00191] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2018] [Accepted: 07/15/2019] [Indexed: 01/15/2023] Open
Abstract
Parkinson’s disease (PD) is a neurodegenerative disease, neuropathologically characterized by progressive loss of neurons in distinct brain areas. We hypothesize that quantifiable network alterations are caused by neurodegeneration. The primary motivation of this study was to assess the specific network alterations in PD patients that are distinct but appear in conjunction with physiological aging. 178 subjects (130 females) stratified into PD patients, young, middle-aged and elderly healthy controls (age- and sex-matched with PD patients), were analyzed using 3D-T1 magnetization-prepared rapid gradient-echo (MPRAGE) and diffusion weighted images acquired in 3T MRI scanner. Diffusion modeling and probabilistic tractography analysis were applied for generating voxel-based connectivity index maps from each seed voxel. The obtained connectivity matrices were analyzed using graph theoretical tools for characterization of involved network. By network-based statistic (NBS) the interregional connectivity differences between the groups were assessed. Measures evaluating local diffusion properties for anisotropy and diffusivity were computed for characterization of white matter microstructural integrity. The graph theoretical analysis showed a significant decrease in distance measures – eccentricity and characteristic path length – in PD patients in comparison to healthy subjects. Both measures as well were lower in PD patients when compared to young and middle-aged healthy controls. NBS analysis demonstrated lowered structural connectivity in PD patients in comparison to young and middle-aged healthy subject groups, mainly in frontal, cingulate, olfactory, insula, thalamus, and parietal regions. These specific network differences were distinct for PD and were not observed between the healthy subject groups. Microstructural analysis revealed diffusivity alterations within the white matter tracts in PD patients, predominantly in the body, splenium and tapetum of corpus callosum, corticospinal tract, and corona radiata, which were absent in normal aging. The identified alterations of network connectivity presumably caused by neurodegeneration indicate the disruption in global network integration in PD patients. The microstructural changes identified within the white matter could endorse network reconfiguration. This study provides a clear distinction between the network changes occurring during aging and PD. This will facilitate a better understanding of PD pathophysiology and the direct link between white matter changes and their role in the restructured network topology.
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Affiliation(s)
- Nabin Koirala
- Department of Neurology, University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany
| | - Abdul Rauf Anwar
- Centre for Biomedical Engineering, University of Engineering and Technology, Lahore, Pakistan
| | - Dumitru Ciolac
- Department of Neurology, University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany.,Department of Neurology, Institute of Emergency Medicine, Chisinau, Moldova.,Laboratory of Neurobiology and Medical Genetics, State University of Medicine and Pharmacy "Nicolae Testemit̨anu", Chisinau, Moldova
| | - Martin Glaser
- Department of Neurosurgery, University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany
| | - Bogdan Pintea
- Department of Neurosurgery, Bergmannsheil Clinic, Ruhr University Bochum, Bochum, Germany
| | - Günther Deuschl
- Department of Neurology, Christian-Albrechts-University, Kiel, Germany
| | - Muthuraman Muthuraman
- Department of Neurology, University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany
| | - Sergiu Groppa
- Department of Neurology, University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany
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Abstract
The global population is ageing at an accelerating speed. The ability to perform working memory tasks together with rapid processing becomes increasingly difficult with increases in age. With increasing national average life spans and a rise in the prevalence of age-related disease, it is pertinent to discuss the unique perspectives that can be gained from imaging the aged brain. Differences in structure, function, blood flow, and neurovascular coupling are present in both healthy aged brains and in diseased brains and have not yet been explored to their full depth in contemporary imaging studies. Imaging methods ranging from optical imaging to magnetic resonance imaging (MRI) to newer technologies such as photoacoustic tomography each offer unique advantages and challenges in imaging the aged brain. This paper will summarize first the importance and challenges of imaging the aged brain and then offer analysis of potential imaging modalities and their representative applications. The potential breakthroughs in brain imaging are also envisioned.
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Affiliation(s)
- Hannah Humayun
- Photoacoustic Imaging Laboratory, Department of Biomedical Engineering, Duke University, Durham, NC, USA
| | - Junjie Yao
- Photoacoustic Imaging Laboratory, Department of Biomedical Engineering, Duke University, Durham, NC, USA
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22
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van Montfort SJT, van Dellen E, Stam CJ, Ahmad AH, Mentink LJ, Kraan CW, Zalesky A, Slooter AJC. Brain network disintegration as a final common pathway for delirium: a systematic review and qualitative meta-analysis. NEUROIMAGE-CLINICAL 2019; 23:101809. [PMID: 30981940 PMCID: PMC6461601 DOI: 10.1016/j.nicl.2019.101809] [Citation(s) in RCA: 30] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/01/2019] [Revised: 03/25/2019] [Accepted: 03/31/2019] [Indexed: 01/05/2023]
Abstract
Delirium is an acute neuropsychiatric syndrome characterized by altered levels of attention and awareness with cognitive deficits. It is most prevalent in elderly hospitalized patients and related to poor outcomes. Predisposing risk factors, such as older age, determine the baseline vulnerability for delirium, while precipitating factors, such as use of sedatives, trigger the syndrome. Risk factors are heterogeneous and the underlying biological mechanisms leading to vulnerability for delirium are poorly understood. We tested the hypothesis that delirium and its risk factors are associated with consistent brain network changes. We performed a systematic review and qualitative meta-analysis and included 126 brain network publications on delirium and its risk factors. Findings were evaluated after an assessment of methodological quality, providing N=99 studies of good or excellent quality on predisposing risk factors, N=10 on precipitation risk factors and N=7 on delirium. Delirium was consistently associated with functional network disruptions, including lower EEG connectivity strength and decreased fMRI network integration. Risk factors for delirium were associated with lower structural connectivity strength and less efficient structural network organization. Decreased connectivity strength and efficiency appear to characterize structural brain networks of patients at risk for delirium, possibly impairing the functional network, while functional network disintegration seems to be a final common pathway for the syndrome. Delirium is consistently associated with functional network impairments. Risk factors are associated with lower structural connectivity strength. Risk factors are associated with a less efficient structural network organization. Structural impairments make the functional network more vulnerable to deterioration. Functional network disintegration seems to be a final common pathway for delirium.
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Affiliation(s)
- S J T van Montfort
- Department of Intensive Care Medicine and Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht University, Heidelberglaan 100, 3584 CX Utrecht, The Netherlands.
| | - E van Dellen
- Department of Psychiatry and Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht University, Heidelberglaan 100, 3584 CX Utrecht, The Netherlands; Melbourne Neuropsychiatry Center, Department of Psychiatry, Level 3, Alan Gilbert Building, 161 Barry Street, Carlton South, 3053 Victoria, University of Melbourne and Melbourne Health, Australia
| | - C J Stam
- Department of Clinical Neurophysiology and MEG Center, Neuroscience Campus Amsterdam, VU University Medical Center, De Boelelaan 1117, 1081 HV Amsterdam, The Netherlands
| | - A H Ahmad
- Department of Intensive Care Medicine and Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht University, Heidelberglaan 100, 3584 CX Utrecht, The Netherlands; Faculty of Psychology, Utrecht University, Heidelberglaan 1, 3584 CS Utrecht, The Netherlands
| | - L J Mentink
- Department of Intensive Care Medicine and Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht University, Heidelberglaan 100, 3584 CX Utrecht, The Netherlands; Faculty of Science and Technology, University of Twente, Drienerlolaan 5, 7522 NB Enschede, The Netherlands
| | - C W Kraan
- Department of Intensive Care Medicine and Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht University, Heidelberglaan 100, 3584 CX Utrecht, The Netherlands; Faculty of Science and Technology, University of Twente, Drienerlolaan 5, 7522 NB Enschede, The Netherlands
| | - A Zalesky
- Melbourne Neuropsychiatry Center, Department of Psychiatry, Level 3, Alan Gilbert Building, 161 Barry Street, Carlton South, 3053 Victoria, University of Melbourne and Melbourne Health, Australia
| | - A J C Slooter
- Department of Intensive Care Medicine and Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht University, Heidelberglaan 100, 3584 CX Utrecht, The Netherlands
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23
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Wang Y, Guo Y. A hierarchical independent component analysis model for longitudinal neuroimaging studies. Neuroimage 2019; 189:380-400. [PMID: 30639837 PMCID: PMC6422710 DOI: 10.1016/j.neuroimage.2018.12.024] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2018] [Revised: 10/15/2018] [Accepted: 12/11/2018] [Indexed: 01/10/2023] Open
Abstract
In recent years, longitudinal neuroimaging study has become increasingly popular in neuroscience research to investigate disease-related changes in brain functions, to study neurodevelopment or to evaluate treatment effects on neural processing. One of the important goals in longitudinal imaging analysis is to study changes in brain functional networks across time and how the changes are modulated by subjects' clinical or demographic variables. In current neuroscience literature, one of the most commonly used tools to extract and characterize brain functional networks is independent component analysis (ICA), which separates multivariate signals into linear mixture of independent components. However, existing ICA methods are only applicable to cross-sectional studies and not suited for modeling repeatedly measured imaging data. In this paper, we propose a novel longitudinal independent component model (L-ICA) which provides a formal modeling framework for extending ICA to longitudinal studies. By incorporating subject-specific random effects and visit-specific covariate effects, L-ICA is able to provide more accurate estimates of changes in brain functional networks on both the population- and individual-level, borrow information across repeated scans within the same subject to increase statistical power in detecting covariate effects on the networks, and allow for model-based prediction for brain networks changes caused by disease progression, treatment or neurodevelopment. We develop a fully traceable exact EM algorithm to obtain maximum likelihood estimates of L-ICA. We further develop a subspace-based approximate EM algorithm which greatly reduce the computation time while still retaining high accuracy. Moreover, we present a statistical testing procedure for examining covariate effects on brain network changes. Simulation results demonstrate the advantages of our proposed methods. We apply L-ICA to ADNI2 study to investigate changes in brain functional networks in Alzheimer disease. Results from the L-ICA provide biologically insightful findings which are not revealed using existing methods.
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Affiliation(s)
- Yikai Wang
- Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, 1518 Clifton rd., Atlanta, 30322, Georgia, USA
| | - Ying Guo
- Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, 1518 Clifton rd., Atlanta, 30322, Georgia, USA.
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Frau-Pascual A, Fogarty M, Fischl B, Yendiki A, Aganj I. Quantification of structural brain connectivity via a conductance model. Neuroimage 2019; 189:485-496. [PMID: 30677502 PMCID: PMC6585945 DOI: 10.1016/j.neuroimage.2019.01.033] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2018] [Revised: 11/27/2018] [Accepted: 01/12/2019] [Indexed: 02/07/2023] Open
Abstract
Connectomics has proved promising in quantifying and understanding the effects of development, aging and an array of diseases on the brain. In this work, we propose a new structural connectivity measure from diffusion MRI that allows us to incorporate direct brain connections, as well as indirect ones that would not be otherwise accounted for by standard techniques and that may be key for the better understanding of function from structure. From our experiments on the Human Connectome Project dataset, we find that our measure of structural connectivity better correlates with functional connectivity than streamline tractography does, meaning that it provides new structural information related to function. Through additional experiments on the ADNI-2 dataset, we demonstrate the ability of this new measure to better discriminate different stages of Alzheimer's disease. Our findings suggest that this measure is useful in the study of the normal brain structure, and for quantifying the effects of disease on the brain structure.
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Affiliation(s)
- Aina Frau-Pascual
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, USA.
| | - Morgan Fogarty
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, USA
| | - Bruce Fischl
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, USA; Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Anastasia Yendiki
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, USA
| | - Iman Aganj
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, USA; Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA
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25
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Alam MA, Lin HY, Deng HW, Calhoun VD, Wang YP. A kernel machine method for detecting higher order interactions in multimodal datasets: Application to schizophrenia. J Neurosci Methods 2018; 309:161-174. [PMID: 30184473 DOI: 10.1016/j.jneumeth.2018.08.027] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2018] [Revised: 08/12/2018] [Accepted: 08/30/2018] [Indexed: 12/20/2022]
Abstract
BACKGROUND Technological advances are enabling us to collect multimodal datasets at an increasing depth and resolution while with decreasing labors. Understanding complex interactions among multimodal datasets, however, is challenging. NEW METHOD In this study, we tested the interaction effect of multimodal datasets using a novel method called the kernel machine for detecting higher order interactions among biologically relevant multimodal data. Using a semiparametric method on a reproducing kernel Hilbert space, we formulated the proposed method as a standard mixed-effects linear model and derived a score-based variance component statistic to test higher order interactions between multimodal datasets. RESULTS The method was evaluated using extensive numerical simulation and real data from the Mind Clinical Imaging Consortium with both schizophrenia patients and healthy controls. Our method identified 13-triplets that included 6 gene-derived SNPs, 10 ROIs, and 6 gene-specific DNA methylations that are correlated with the changes in hippocampal volume, suggesting that these triplets may be important for explaining schizophrenia-related neurodegeneration. COMPARISON WITH EXISTING METHOD(S) The performance of the proposed method is compared with the following methods: test based on only first and first few principal components followed by multiple regression, and full principal component analysis regression, and the sequence kernel association test. CONCLUSIONS With strong evidence (p-value ≤0.000001), the triplet (MAGI2, CRBLCrus1.L, FBXO28) is a significant biomarker for schizophrenia patients. This novel method can be applicable to the study of other disease processes, where multimodal data analysis is a common task.
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Affiliation(s)
- Md Ashad Alam
- Department of Biomedical Engineering, Tulane University, New Orleans, LA 70118, USA.
| | - Hui-Yi Lin
- Biostatistics Program, School of Public Health, Louisiana State University Health Sciences Center, New Orleans, LA 70112, USA
| | - Hong-Wen Deng
- Center for Bioinformatics and Genomics, Department of Global Biostatistics and Data Science, Tulane University, New Orleans, LA 70112, USA
| | - Vince D Calhoun
- Department of Electrical and Computer Engineering, The University of New Mexico, Albuquerque, NM 87131, USA
| | - Yu-Ping Wang
- Department of Biomedical Engineering, Tulane University, New Orleans, LA 70118, USA
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26
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Zhang W, Shu K, Wang S, Liu H, Wang Y. Multimodal Fusion of Brain Networks with Longitudinal Couplings. MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION : MICCAI ... INTERNATIONAL CONFERENCE ON MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION 2018; 11072:3-11. [PMID: 30272058 DOI: 10.1007/978-3-030-00931-1_1] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
In recent years, brain network analysis has attracted considerable interests in the field of neuroimaging analysis. It plays a vital role in understanding biologically fundamental mechanisms of human brains. As the upward trend of multi-source in neuroimaging data collection, effective learning from the different types of data sources, e.g. multimodal and longitudinal data, is much in demand. In this paper, we propose a general coupling framework, the multimodal neuroimaging network fusion with longitudinal couplings (MMLC), to learn the latent representations of brain networks. Specifically, we jointly factorize multimodal networks, assuming a linear relationship to couple network variance across time. Experimental results on two large datasets demonstrate the effectiveness of the proposed framework. The new approach integrates information from longitudinal, multimodal neuroimaging data and boosts statistical power to predict psychometric evaluation measures.
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Affiliation(s)
- Wen Zhang
- School of Computing, Informatics and Decision Systems Engineering, Arizona State University, Tempe, AZ, USA
| | - Kai Shu
- School of Computing, Informatics and Decision Systems Engineering, Arizona State University, Tempe, AZ, USA
| | - Suhang Wang
- School of Computing, Informatics and Decision Systems Engineering, Arizona State University, Tempe, AZ, USA
| | - Huan Liu
- School of Computing, Informatics and Decision Systems Engineering, Arizona State University, Tempe, AZ, USA
| | - Yalin Wang
- School of Computing, Informatics and Decision Systems Engineering, Arizona State University, Tempe, AZ, USA
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He P, Xu X, Zhang H, Li G, Nie J, Yap PT, Shen D. Spatiotemporal Analysis of Developing Brain Networks. Front Neuroinform 2018; 12:48. [PMID: 30108496 PMCID: PMC6080575 DOI: 10.3389/fninf.2018.00048] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2018] [Accepted: 07/10/2018] [Indexed: 11/13/2022] Open
Abstract
Recent advances in MRI have made it easier to collect data for studying human structural and functional connectivity networks. Computational methods can reveal complex spatiotemporal dynamics of the human developing brain. In this paper, we propose a Developmental Meta-network Decomposition (DMD) method to decompose a series of developmental networks into a set of Developmental Meta-networks (DMs), which reveal the underlying changes in connectivity over development. DMD circumvents the limitations of traditional static network decomposition methods by providing a novel exploratory approach to capture the spatiotemporal dynamics of developmental networks. We apply this method to structural correlation networks of cortical thickness across subjects at 3-20 years of age, and identify four DMs that smoothly evolve over three stages, i.e., 3-6, 7-12, and 13-20 years of age. We analyze and highlight the characteristic connections of each DM in relation to brain development.
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Affiliation(s)
- Ping He
- Department of Computer Science, Yangzhou University, Yangzhou, China
| | - Xiaohua Xu
- Department of Computer Science, Yangzhou University, Yangzhou, China
| | - Han Zhang
- Department of Radiology and BRIC, University of North Carolina, Chapel Hill, NC, United States
| | - Gang Li
- Department of Radiology and BRIC, University of North Carolina, Chapel Hill, NC, United States
| | - Jingxin Nie
- School of Psychology, South China Normal University, Guangzhou, China
| | - Pew-Thian Yap
- Department of Radiology and BRIC, University of North Carolina, Chapel Hill, NC, United States
| | - Dinggang Shen
- Department of Radiology and BRIC, University of North Carolina, Chapel Hill, NC, United States
- Department of Brain and Cognitive Engineering, Korea University, Seoul, South Korea
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28
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Wang L, Durante D, Jung RE, Dunson DB. Bayesian network-response regression. Bioinformatics 2018; 33:1859-1866. [PMID: 28165112 DOI: 10.1093/bioinformatics/btx050] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2016] [Accepted: 01/28/2017] [Indexed: 01/22/2023] Open
Abstract
Motivation There is increasing interest in learning how human brain networks vary as a function of a continuous trait, but flexible and efficient procedures to accomplish this goal are limited. We develop a Bayesian semiparametric model, which combines low-rank factorizations and flexible Gaussian process priors to learn changes in the conditional expectation of a network-valued random variable across the values of a continuous predictor, while including subject-specific random effects. Results The formulation leads to a general framework for inference on changes in brain network structures across human traits, facilitating borrowing of information and coherently characterizing uncertainty. We provide an efficient Gibbs sampler for posterior computation along with simple procedures for inference, prediction and goodness-of-fit assessments. The model is applied to learn how human brain networks vary across individuals with different intelligence scores. Results provide interesting insights on the association between intelligence and brain connectivity, while demonstrating good predictive performance. Availability and Implementation Source code implemented in R and data are available at https://github.com/wangronglu/BNRR. Contact rl.wang@duke.edu. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Lu Wang
- Department of Statistical Science, Duke University, Durham, NC, USA
| | - Daniele Durante
- Department of Statistical Sciences, University of Padova, Padova, Italy
| | - Rex E Jung
- Department of Psychology, University of New Mexico, Albuquerque, NM, USA
| | - David B Dunson
- Department of Statistical Science, Duke University, Durham, NC, USA
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29
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Bagarinao E, Watanabe H, Maesawa S, Mori D, Hara K, Kawabata K, Yoneyama N, Ohdake R, Imai K, Masuda M, Yokoi T, Ogura A, Wakabayashi T, Kuzuya M, Ozaki N, Hoshiyama M, Isoda H, Naganawa S, Sobue G. An unbiased data-driven age-related structural brain parcellation for the identification of intrinsic brain volume changes over the adult lifespan. Neuroimage 2018; 169:134-144. [DOI: 10.1016/j.neuroimage.2017.12.014] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2017] [Revised: 11/20/2017] [Accepted: 12/06/2017] [Indexed: 10/18/2022] Open
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30
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Mirzakhalili E, Gourgou E, Booth V, Epureanu B. Synaptic Impairment and Robustness of Excitatory Neuronal Networks with Different Topologies. Front Neural Circuits 2017; 11:38. [PMID: 28659765 PMCID: PMC5468411 DOI: 10.3389/fncir.2017.00038] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2016] [Accepted: 05/22/2017] [Indexed: 11/13/2022] Open
Abstract
Synaptic deficiencies are a known hallmark of neurodegenerative diseases, but the diagnosis of impaired synapses on the cellular level is not an easy task. Nonetheless, changes in the system-level dynamics of neuronal networks with damaged synapses can be detected using techniques that do not require high spatial resolution. This paper investigates how the structure/topology of neuronal networks influences their dynamics when they suffer from synaptic loss. We study different neuronal network structures/topologies by specifying their degree distributions. The modes of the degree distribution can be used to construct networks that consist of rich clubs and resemble small world networks, as well. We define two dynamical metrics to compare the activity of networks with different structures: persistent activity (namely, the self-sustained activity of the network upon removal of the initial stimulus) and quality of activity (namely, percentage of neurons that participate in the persistent activity of the network). Our results show that synaptic loss affects the persistent activity of networks with bimodal degree distributions less than it affects random networks. The robustness of neuronal networks enhances when the distance between the modes of the degree distribution increases, suggesting that the rich clubs of networks with distinct modes keep the whole network active. In addition, a tradeoff is observed between the quality of activity and the persistent activity. For a range of distributions, both of these dynamical metrics are considerably high for networks with bimodal degree distribution compared to random networks. We also propose three different scenarios of synaptic impairment, which may correspond to different pathological or biological conditions. Regardless of the network structure/topology, results demonstrate that synaptic loss has more severe effects on the activity of the network when impairments are correlated with the activity of the neurons.
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Affiliation(s)
- Ehsan Mirzakhalili
- Department of Mechanical Engineering, University of MichiganAnn Arbor, MI, United States
| | - Eleni Gourgou
- Department of Mechanical Engineering, University of MichiganAnn Arbor, MI, United States.,Division of Geriatrics, Department of Internal Medicine, Medical School, University of MichiganAnn Arbor, MI, United States
| | - Victoria Booth
- Department of Mathematics, University of MichiganAnn Arbor, MI, United States.,Department of Anesthesiology, Medical School, University of MichiganAnn Arbor, MI, United States
| | - Bogdan Epureanu
- Department of Mechanical Engineering, University of MichiganAnn Arbor, MI, United States
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31
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Zimmermann J, Ritter P, Shen K, Rothmeier S, Schirner M, McIntosh AR. Structural architecture supports functional organization in the human aging brain at a regionwise and network level. Hum Brain Mapp 2016; 37:2645-61. [PMID: 27041212 PMCID: PMC6867479 DOI: 10.1002/hbm.23200] [Citation(s) in RCA: 58] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2015] [Revised: 03/18/2016] [Accepted: 03/20/2016] [Indexed: 12/13/2022] Open
Abstract
Functional interactions in the brain are constrained by the underlying anatomical architecture, and structural and functional networks share network features such as modularity. Accordingly, age-related changes of structural connectivity (SC) may be paralleled by changes in functional connectivity (FC). We provide a detailed qualitative and quantitative characterization of the SC-FC coupling in human aging as inferred from resting-state blood oxygen-level dependent functional magnetic resonance imaging and diffusion-weighted imaging in a sample of 47 adults with an age range of 18-82. We revealed that SC and FC decrease with age across most parts of the brain and there is a distinct age-dependency of regionwise SC-FC coupling and network-level SC-FC relations. A specific pattern of SC-FC coupling predicts age more reliably than does regionwise SC or FC alone (r = 0.73, 95% CI = [0.7093, 0.8522]). Hence, our data propose that regionwise SC-FC coupling can be used to characterize brain changes in aging. Hum Brain Mapp 37:2645-2661, 2016. © 2016 Wiley Periodicals, Inc.
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Affiliation(s)
- Joelle Zimmermann
- Baycrest Health SciencesRotman Research Institute3560 Bathurst StTorontoOntarioM6A 2E1Canada
| | - Petra Ritter
- Department of NeurologyCharité ‐ University MedicineCharitéplatz 1Berlin13353Germany
- BrainModes Minerva Research Group Max‐Planck Institute for Cognitive and Brain Science LeipzigCharitéplatz 1Berlin13353Germany
- Bernstein Focus State Dependencies of Learning & Bernstein Center for Computational NeuroscienceCharitéplatz 1Berlin10117Germany
- Berlin School of Mind and Brain & Mind and Brain Institute, Humboldt UniversityLuisenstraße 56, Haus 110117BerlinGermany
| | - Kelly Shen
- Baycrest Health SciencesRotman Research Institute3560 Bathurst StTorontoOntarioM6A 2E1Canada
| | - Simon Rothmeier
- Department of NeurologyCharité ‐ University MedicineCharitéplatz 1Berlin13353Germany
- BrainModes Minerva Research Group Max‐Planck Institute for Cognitive and Brain Science LeipzigCharitéplatz 1Berlin13353Germany
| | - Michael Schirner
- Department of NeurologyCharité ‐ University MedicineCharitéplatz 1Berlin13353Germany
| | - Anthony R. McIntosh
- Baycrest Health SciencesRotman Research Institute3560 Bathurst StTorontoOntarioM6A 2E1Canada
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32
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Li K, Laird AR, Price LR, McKay DR, Blangero J, Glahn DC, Fox PT. Progressive Bidirectional Age-Related Changes in Default Mode Network Effective Connectivity across Six Decades. Front Aging Neurosci 2016; 8:137. [PMID: 27378909 PMCID: PMC4905965 DOI: 10.3389/fnagi.2016.00137] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2016] [Accepted: 05/27/2016] [Indexed: 12/31/2022] Open
Abstract
The default mode network (DMN) is a set of regions that is tonically engaged during the resting state and exhibits task-related deactivation that is readily reproducible across a wide range of paradigms and modalities. The DMN has been implicated in numerous disorders of cognition and, in particular, in disorders exhibiting age-related cognitive decline. Despite these observations, investigations of the DMN in normal aging are scant. Here, we used blood oxygen level dependent (BOLD) functional magnetic resonance imaging (fMRI) acquired during rest to investigate age-related changes in functional connectivity of the DMN in 120 healthy normal volunteers comprising six, 20-subject, decade cohorts (from 20–29 to 70–79). Structural equation modeling (SEM) was used to assess age-related changes in inter-regional connectivity within the DMN. SEM was applied both using a previously published, meta-analytically derived, node-and-edge model, and using exploratory modeling searching for connections that optimized model fit improvement. Although the two models were highly similar (only 3 of 13 paths differed), the sample demonstrated significantly better fit with the exploratory model. For this reason, the exploratory model was used to assess age-related changes across the decade cohorts. Progressive, highly significant changes in path weights were found in 8 (of 13) paths: four rising, and four falling (most changes were significant by the third or fourth decade). In all cases, rising paths and falling paths projected in pairs onto the same nodes, suggesting compensatory increases associated with age-related decreases. This study demonstrates that age-related changes in DMN physiology (inter-regional connectivity) are bidirectional, progressive, of early onset and part of normal aging.
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Affiliation(s)
- Karl Li
- Research Imaging Institute, University of Texas Health Science Center San Antonio San Antonio, TX, USA
| | - Angela R Laird
- Department of Physics, Florida International University Miami, FL, USA
| | - Larry R Price
- Department of Mathematics and College of Education, Texas State University San Marcos, TX, USA
| | - D Reese McKay
- Department of Psychiatry, Yale University School of MedicineNew Haven, CT, USA; Olin Neuropsychiatry Research Center, Institute of Living, Hartford HospitalHartford, CT, USA
| | - John Blangero
- Genomics Computing Center, South Texas Diabetes and Obesity Institute, University of Texas Rio Grande Valley School of Medicine Edinburg, TX, USA
| | - David C Glahn
- Department of Psychiatry, Yale University School of MedicineNew Haven, CT, USA; Olin Neuropsychiatry Research Center, Institute of Living, Hartford HospitalHartford, CT, USA
| | - Peter T Fox
- Research Imaging Institute, University of Texas Health Science Center San AntonioSan Antonio, TX, USA; Research Service, South Texas Veterans Health Care SystemSan Antonio, TX, USA; Neuroimaging Laboratory, Shenzhen University School of MedicineShenzhen, Guangdong, China
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Sun Y, Chen Y, Lee R, Bezerianos A, Collinson SL, Sim K. Disruption of brain anatomical networks in schizophrenia: A longitudinal, diffusion tensor imaging based study. Schizophr Res 2016; 171:149-57. [PMID: 26811255 DOI: 10.1016/j.schres.2016.01.025] [Citation(s) in RCA: 40] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/07/2015] [Revised: 01/08/2016] [Accepted: 01/12/2016] [Indexed: 01/12/2023]
Abstract
Despite convergent neuroimaging evidence indicating a wide range of brain abnormalities in schizophrenia, our understanding of alterations in the topological architecture of brain anatomical networks and how they are modulated over time, is still rudimentary. Here, we employed graph theoretical analysis of longitudinal diffusion tensor imaging data (DTI) over a 5-year period to investigate brain network topology in schizophrenia and its relationship with clinical manifestations of the illness. Using deterministic tractography, weighted brain anatomical networks were constructed from 31 patients experiencing schizophrenia and 28 age- and gender-matched healthy control subjects. Although the overall small-world characteristics were observed at both baseline and follow-up, a scan-point independent significant deficit of global integration was found in patients compared to controls, suggesting dysfunctional integration of the brain and supporting the notion of schizophrenia as a disconnection syndrome. Specifically, several brain regions (e.g., the inferior frontal gyrus and the bilateral insula) that are crucial for cognitive and emotional integration were aberrant. Furthermore, a significant group-by-longitudinal scan interaction was revealed in the characteristic path length and global efficiency, attributing to a progressive aberration of global integration in patients compared to healthy controls. Moreover, the progressive disruptions of the brain anatomical network topology were associated with the clinical symptoms of the patients. Together, our findings provide insights into the substrates of anatomical dysconnectivity patterns for schizophrenia and highlight the potential for connectome-based metrics as neural markers of illness progression and clinical change with treatment.
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Affiliation(s)
- Yu Sun
- Singapore Institute for Neurotechnology (SINAPSE), Centre for Life Sciences, National University of Singapore, Singapore.
| | - Yu Chen
- Singapore Institute for Neurotechnology (SINAPSE), Centre for Life Sciences, National University of Singapore, Singapore
| | - Renick Lee
- Department of Bioengineering, National University of Singapore, Singapore
| | - Anastasios Bezerianos
- Singapore Institute for Neurotechnology (SINAPSE), Centre for Life Sciences, National University of Singapore, Singapore
| | - Simon L Collinson
- Department of Psychology, National University of Singapore, Singapore
| | - Kang Sim
- Department of General Psychiatry, Institute of Mental Health (IMH), Singapore; Department of Research, Institute of Mental Health (IMH), Singapore
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Functional Reorganizations of Brain Network in Prelingually Deaf Adolescents. Neural Plast 2015; 2016:9849087. [PMID: 26819781 PMCID: PMC4706957 DOI: 10.1155/2016/9849087] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2015] [Accepted: 07/22/2015] [Indexed: 12/14/2022] Open
Abstract
Previous neuroimaging studies suggested structural or functional brain reorganizations occurred in prelingually deaf subjects. However, little is known about the reorganizations of brain network architectures in prelingually deaf adolescents. The present study aims to investigate alterations of whole-brain functional network using resting-state fMRI and graph theory analysis. We recruited 16 prelingually deaf adolescents (10~18 years) and 16 normal controls matched in age and gender. Brain networks were constructed from mean time courses of 90 regions. Widely distributed network was observed in deaf subjects, with increased connectivity between the limbic system and regions involved in visual and language processing, suggesting reinforcement of the processing for the visual and verbal information in deaf adolescents. Decreased connectivity was detected between the visual regions and language regions possibly due to inferior reading or speaking skills in deaf subjects. Using graph theory analysis, we demonstrated small-worldness property did not change in prelingually deaf adolescents relative to normal controls. However, compared with healthy adolescents, eight regions involved in visual, language, and auditory processing were identified as hubs only present in prelingually deaf adolescents. These findings revealed reorganization of brain functional networks occurred in prelingually deaf adolescents to adapt to deficient auditory input.
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35
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Aldana EM, Valverde JL, Fábregas N. Consciousness, cognition and brain networks: New perspectives. REVISTA ESPANOLA DE ANESTESIOLOGIA Y REANIMACION 2015; 63:459-70. [PMID: 26143337 DOI: 10.1016/j.redar.2015.04.005] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/01/2014] [Revised: 03/15/2015] [Accepted: 04/02/2015] [Indexed: 11/26/2022]
Abstract
A detailed analysis of the literature on consciousness and cognition mechanisms based on the neural networks theory is presented. The immune and inflammatory response to the anesthetic-surgical procedure induces modulation of neuronal plasticity by influencing higher cognitive functions. Anesthetic drugs can cause unconsciousness, producing a functional disruption of cortical and thalamic cortical integration complex. The external and internal perceptions are processed through an intricate network of neural connections, involving the higher nervous activity centers, especially the cerebral cortex. This requires an integrated model, formed by neural networks and their interactions with highly specialized regions, through large-scale networks, which are distributed throughout the brain collecting information flow of these perceptions. Functional and effective connectivity between large-scale networks, are essential for consciousness, unconsciousness and cognition. It is what is called the "human connectome" or map neural networks.
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Affiliation(s)
- E M Aldana
- Servicio de Anestesiología y Reanimación, Hospital Vithas Xanit Internacional, Benalmádena, Málaga, España.
| | - J L Valverde
- Servicio de Anestesiología y Reanimación, Hospital Vithas Xanit Internacional, Benalmádena, Málaga, España
| | - N Fábregas
- Servicio de Anestesiología y Reanimación, Hospital Clínic, Universidad de Barcelona, Barcelona, España
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36
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Age-related differences in electroencephalogram connectivity and network topology. Neurobiol Aging 2015; 36:1849-59. [DOI: 10.1016/j.neurobiolaging.2015.02.007] [Citation(s) in RCA: 48] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2014] [Revised: 01/11/2015] [Accepted: 02/05/2015] [Indexed: 01/08/2023]
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37
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Kesler SR, Watson CL, Blayney DW. Brain network alterations and vulnerability to simulated neurodegeneration in breast cancer. Neurobiol Aging 2015; 36:2429-42. [PMID: 26004016 DOI: 10.1016/j.neurobiolaging.2015.04.015] [Citation(s) in RCA: 69] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2014] [Revised: 04/26/2015] [Accepted: 04/27/2015] [Indexed: 01/11/2023]
Abstract
Breast cancer and its treatments are associated with mild cognitive impairment and brain changes that could indicate an altered or accelerated brain aging process. We applied diffusion tensor imaging and graph theory to measure white matter organization and connectivity in 34 breast cancer survivors compared with 36 matched healthy female controls. We also investigated how brain networks (connectomes) in each group responded to simulated neurodegeneration based on network attack analysis. Compared with controls, the breast cancer group demonstrated significantly lower fractional anisotropy, altered small-world connectome properties, lower brain network tolerance to systematic region (node), and connection (edge) attacks and significant cognitive impairment. Lower tolerance to network attack was associated with cognitive impairment in the breast cancer group. These findings provide further evidence of diffuse white matter pathology after breast cancer and extend the literature in this area with unique data demonstrating increased vulnerability of the post-breast cancer brain network to future neurodegenerative processes.
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Affiliation(s)
- Shelli R Kesler
- Department of Neuro-oncology, University of Texas MD Anderson Cancer Center, Houston, TX, USA.
| | - Christa L Watson
- Memory and Aging Center, Department of Neurology, University of California at San Francisco, San Francisco, CA, USA
| | - Douglas W Blayney
- Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA
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Progressive gender differences of structural brain networks in healthy adults: a longitudinal, diffusion tensor imaging study. PLoS One 2015; 10:e0118857. [PMID: 25742013 PMCID: PMC4350987 DOI: 10.1371/journal.pone.0118857] [Citation(s) in RCA: 78] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2014] [Accepted: 01/18/2015] [Indexed: 12/31/2022] Open
Abstract
Sexual dimorphism in the brain maturation during childhood and adolescence has been repeatedly documented, which may underlie the differences in behaviors and cognitive performance. However, our understanding of how gender modulates the development of structural connectome in healthy adults is still not entirely clear. Here we utilized graph theoretical analysis of longitudinal diffusion tensor imaging data over a five-year period to investigate the progressive gender differences of brain network topology. The brain networks of both genders showed prominent economical “small-world” architecture (high local clustering and short paths between nodes). Additional analysis revealed a more economical “small-world” architecture in females as well as a greater global efficiency in males regardless of scan time point. At the regional level, both increased and decreased efficiency were found across the cerebral cortex for both males and females, indicating a compensation mechanism of cortical network reorganization over time. Furthermore, we found that weighted clustering coefficient exhibited significant gender-time interactions, implying different development trends between males and females. Moreover, several specific brain regions (e.g., insula, superior temporal gyrus, cuneus, putamen, and parahippocampal gyrus) exhibited different development trajectories between males and females. Our findings further prove the presence of sexual dimorphism in brain structures that may underlie gender differences in behavioral and cognitive functioning. The sex-specific progress trajectories in brain connectome revealed in this work provide an important foundation to delineate the gender related pathophysiological mechanisms in various neuropsychiatric disorders, which may potentially guide the development of sex-specific treatments for these devastating brain disorders.
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39
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Hahn A, Kranz GS, Sladky R, Ganger S, Windischberger C, Kasper S, Lanzenberger R. Individual diversity of functional brain network economy. Brain Connect 2014; 5:156-65. [PMID: 25411715 DOI: 10.1089/brain.2014.0306] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022] Open
Abstract
On average, brain network economy represents a trade-off between communication efficiency, robustness, and connection cost, although an analogous understanding on an individual level is largely missing. Evaluating resting-state networks of 42 healthy participants with seven Tesla functional magnetic resonance imaging and graph theory revealed that not even half of all possible connections were common across subjects. The strongest similarities among individuals were observed for interhemispheric and/or short-range connections, which may relate to the essential feature of the human brain to develop specialized systems within each hemisphere. Despite this marked variability in individual network architecture, all subjects exhibited equal small-world properties. Furthermore, interdependency between four major network economy metrics was observed across healthy individuals. The characteristic path length was associated with the clustering coefficient (peak correlation r=0.93), the response to network attacks (r=-0.97), and the physical connection cost in three-dimensional space (r=-0.62). On the other hand, clustering was negatively related to attack response (r=-0.75) and connection cost (r=-0.59). Finally, increased connection cost was associated with better response to attacks (r=0.65). This indicates that functional brain networks with high global information transfer also exhibit strong network resilience. However, it seems that these advantages come at the cost of decreased local communication efficiency and increased physical connection cost. Except for wiring length, the results were replicated on a subsample at three Tesla (n=20). These findings highlight the finely tuned interrelationships between different parameters of brain network economy. Moreover, the understanding of the individual diversity of functional brain network economy may provide further insights in the vulnerability to mental and neurological disorders.
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Affiliation(s)
- Andreas Hahn
- 1 Department of Psychiatry and Psychotherapy, Medical University of Vienna , Vienna, Austria
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40
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Hafkemeijer A, Altmann‐Schneider I, Craen AJM, Slagboom PE, Grond J, Rombouts SARB. Associations between age and gray matter volume in anatomical brain networks in middle-aged to older adults. Aging Cell 2014; 13:1068-74. [PMID: 25257192 PMCID: PMC4326918 DOI: 10.1111/acel.12271] [Citation(s) in RCA: 87] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 08/21/2014] [Indexed: 12/25/2022] Open
Abstract
Aging is associated with cognitive decline, diminished brain function, regional brain atrophy, and disrupted structural and functional brain connectivity. Understanding brain networks in aging is essential, as brain function depends on large-scale distributed networks. Little is known of structural covariance networks to study inter-regional gray matter anatomical associations in aging. Here, we investigate anatomical brain networks based on structural covariance of gray matter volume among 370 middle-aged to older adults of 45-85 years. For each of 370 subjects, we acquired a T1-weighted anatomical MRI scan. After segmentation of structural MRI scans, nine anatomical networks were defined based on structural covariance of gray matter volume among subjects. We analyzed associations between age and gray matter volume in anatomical networks using linear regression analyses. Age was negatively associated with gray matter volume in four anatomical networks (P < 0.001, corrected): a subcortical network, sensorimotor network, posterior cingulate network, and an anterior cingulate network. Age was not significantly associated with gray matter volume in five networks: temporal network, auditory network, and three cerebellar networks. These results were independent of gender and white matter hyperintensities. Gray matter volume decreases with age in networks containing subcortical structures, sensorimotor structures, posterior, and anterior cingulate cortices. Gray matter volume in temporal, auditory, and cerebellar networks remains relatively unaffected with advancing age.
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Affiliation(s)
- Anne Hafkemeijer
- Institute of Psychology Leiden University Leiden The Netherlands
- Department of Radiology Leiden University Medical Center Leiden The Netherlands
- Leiden Institute for Brain and Cognition Leiden University Leiden The Netherlands
| | - Irmhild Altmann‐Schneider
- Department of Radiology Leiden University Medical Center Leiden The Netherlands
- Department of Molecular Epidemiology Netherlands Consortium for Healthy Ageing Leiden University Medical Center Leiden The Netherlands
| | - Anton J. M. Craen
- Department of Molecular Epidemiology Netherlands Consortium for Healthy Ageing Leiden University Medical Center Leiden The Netherlands
- Department of Gerontology and Geriatrics Leiden University Medical Center Leiden The Netherlands
| | - P. Eline Slagboom
- Department of Molecular Epidemiology Netherlands Consortium for Healthy Ageing Leiden University Medical Center Leiden The Netherlands
- Department of Molecular Epidemiology Leiden University Medical Center Leiden The Netherlands
| | - Jeroen Grond
- Department of Radiology Leiden University Medical Center Leiden The Netherlands
| | - Serge A. R. B. Rombouts
- Institute of Psychology Leiden University Leiden The Netherlands
- Department of Radiology Leiden University Medical Center Leiden The Netherlands
- Leiden Institute for Brain and Cognition Leiden University Leiden The Netherlands
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41
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He Y, Evans A. Magnetic resonance imaging of healthy and diseased brain networks. Front Hum Neurosci 2014; 8:890. [PMID: 25404910 PMCID: PMC4217377 DOI: 10.3389/fnhum.2014.00890] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2014] [Accepted: 10/16/2014] [Indexed: 11/21/2022] Open
Affiliation(s)
- Yong He
- State Key Laboratory of Cognitive Neuroscience and Learning, IDG/McGovern Institute for Brain Research, Beijing Normal University Beijing, China ; Center for Collaboration and Innovation in Brain and Learning Sciences, Beijing Normal University Beijing, China
| | - Alan Evans
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University Montreal Montreal, QC, Canada
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42
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Guo X, Wang Y, Chen K, Wu X, Zhang J, Li K, Jin Z, Yao L. Characterizing structural association alterations within brain networks in normal aging using Gaussian Bayesian networks. Front Comput Neurosci 2014; 8:122. [PMID: 25324771 PMCID: PMC4179716 DOI: 10.3389/fncom.2014.00122] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2014] [Accepted: 09/11/2014] [Indexed: 12/22/2022] Open
Abstract
Recent multivariate neuroimaging studies have revealed aging-related alterations in brain structural networks. However, the sensory/motor networks such as the auditory, visual and motor networks, have obtained much less attention in normal aging research. In this study, we used Gaussian Bayesian networks (BN), an approach investigating possible inter-regional directed relationship, to characterize aging effects on structural associations between core brain regions within each of these structural sensory/motor networks using volumetric MRI data. We then further examined the discriminability of BN models for the young (N = 109; mean age =22.73 years, range 20-28) and old (N = 82; mean age =74.37 years, range 60-90) groups. The results of the BN modeling demonstrated that structural associations exist between two homotopic brain regions from the left and right hemispheres in each of the three networks. In particular, compared with the young group, the old group had significant connection reductions in each of the three networks and lesser connection numbers in the visual network. Moreover, it was found that the aging-related BN models could distinguish the young and old individuals with 90.05, 73.82, and 88.48% accuracy for the auditory, visual, and motor networks, respectively. Our findings suggest that BN models can be used to investigate the normal aging process with reliable statistical power. Moreover, these differences in structural inter-regional interactions may help elucidate the neuronal mechanism of anatomical changes in normal aging.
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Affiliation(s)
- Xiaojuan Guo
- Information Processing Lab, College of Information Science and Technology, Beijing Normal University Beijing, China ; State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University Beijing, China
| | - Yan Wang
- Information Processing Lab, College of Information Science and Technology, Beijing Normal University Beijing, China
| | - Kewei Chen
- Computational Image Analysis Lab, Banner Alzheimer's Institute, Banner Health Phoenix, AZ, USA
| | - Xia Wu
- Information Processing Lab, College of Information Science and Technology, Beijing Normal University Beijing, China ; State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University Beijing, China
| | - Jiacai Zhang
- Information Processing Lab, College of Information Science and Technology, Beijing Normal University Beijing, China
| | - Ke Li
- Laboratory of Magnetic Resonance Imaging, Beijing 306 Hospital Beijing, China
| | - Zhen Jin
- Laboratory of Magnetic Resonance Imaging, Beijing 306 Hospital Beijing, China
| | - Li Yao
- Information Processing Lab, College of Information Science and Technology, Beijing Normal University Beijing, China ; State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University Beijing, China
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43
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Kesler SR. Default mode network as a potential biomarker of chemotherapy-related brain injury. Neurobiol Aging 2014; 35 Suppl 2:S11-9. [PMID: 24913897 PMCID: PMC4120757 DOI: 10.1016/j.neurobiolaging.2014.03.036] [Citation(s) in RCA: 70] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2013] [Revised: 03/11/2014] [Accepted: 03/14/2014] [Indexed: 01/01/2023]
Abstract
Chronic medical conditions and/or their treatments may interact with aging to alter or even accelerate brain senescence. Adult onset cancer, for example, is a disease associated with advanced aging and emerging evidence suggests a profile of subtle but diffuse brain injury following cancer chemotherapy. Breast cancer is currently the primary model for studying these "chemobrain" effects. Given the widespread changes to brain structure and function as well as the common impairment of integrated cognitive skills observed following breast cancer chemotherapy, it is likely that large-scale brain networks are involved. Default mode network (DMN) is a strong candidate considering its preferential vulnerability to aging and sensitivity to toxicity and disease states. Additionally, chemotherapy is associated with several physiological effects including increased inflammation and oxidative stress that are believed to elevate toxicity in the DMN. Biomarkers of DMN connectivity could aid in the development of treatments for chemotherapy-related cognitive decline.
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Affiliation(s)
- Shelli R Kesler
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, USA.
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44
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Alvarim LT, Nucci LP, Mamani JB, Marti LC, Aguiar MF, Silva HR, Silva GS, Nucci-da-Silva MP, DelBel EA, Gamarra LF. Therapeutics with SPION-labeled stem cells for the main diseases related to brain aging: a systematic review. Int J Nanomedicine 2014; 9:3749-70. [PMID: 25143726 PMCID: PMC4137998 DOI: 10.2147/ijn.s65616] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
Abstract
The increase in clinical trials assessing the efficacy of cell therapy for structural and functional regeneration of the nervous system in diseases related to the aging brain is well known. However, the results are inconclusive as to the best cell type to be used or the best methodology for the homing of these stem cells. This systematic review analyzed published data on SPION (superparamagnetic iron oxide nanoparticle)-labeled stem cells as a therapy for brain diseases, such as ischemic stroke, Parkinson’s disease, amyotrophic lateral sclerosis, and dementia. This review highlights the therapeutic role of stem cells in reversing the aging process and the pathophysiology of brain aging, as well as emphasizing nanotechnology as an important tool to monitor stem cell migration in affected regions of the brain.
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Affiliation(s)
- Larissa T Alvarim
- Hospital Israelita Albert Einstein, São Paulo, Brazil ; Faculdade de Ciências Médicas da Santa Casa de São Paulo, São Paulo, Brazil
| | | | | | | | - Marina F Aguiar
- Hospital Israelita Albert Einstein, São Paulo, Brazil ; Universidade Federal de São Paulo, UNIFESP, São Paulo, Brazil
| | - Helio R Silva
- Hospital Israelita Albert Einstein, São Paulo, Brazil ; Faculdade de Ciências Médicas da Santa Casa de São Paulo, São Paulo, Brazil
| | | | | | - Elaine A DelBel
- Universidade de São Paulo-Faculdade de Odontologia de Ribeirão Preto, São Paulo, Brazil ; NAPNA-Núcleo de Apoio a Pesquisa em Neurociências Aplicadas, São Paulo, Brazil
| | - Lionel F Gamarra
- Hospital Israelita Albert Einstein, São Paulo, Brazil ; Universidade Federal de São Paulo, UNIFESP, São Paulo, Brazil ; Faculdade de Ciências Médicas da Santa Casa de São Paulo, São Paulo, Brazil
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45
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Rotem-Kohavi N, Hilderman CGE, Liu A, Makan N, Wang JZ, Virji-Babul N. Network analysis of perception-action coupling in infants. Front Hum Neurosci 2014; 8:209. [PMID: 24778612 PMCID: PMC3986511 DOI: 10.3389/fnhum.2014.00209] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2013] [Accepted: 03/24/2014] [Indexed: 12/27/2022] Open
Abstract
The functional networks that support action observation are of great interest in understanding the development of social cognition and motor learning. How infants learn to represent and understand the world around them remains one of the most intriguing questions in developmental cognitive neuroscience. Recently, mathematical measures derived from graph theory have been used to study connectivity networks in the developing brain. Thus far, this type of analysis in infancy has only been applied to the resting state. In this study, we recorded electroencephalography (EEG) from infants (ages 4–11 months of age) and adults while they observed three types of actions: (a) reaching for an object; (b) walking; and (c) object motion. Graph theory based analysis was applied to these data to evaluate changes in brain networks. Global metrics that provide measures of the structural properties of the network (characteristic path, density, global efficiency, and modularity) were calculated for each group and for each condition. We found statistically significant differences in measures for the observation of walking condition only. Specifically, in comparison to adults, infants showed increased density and global efficiency in combination with decreased modularity during observation of an action that is not within their motor repertoire (i.e., independent walking), suggesting a less structured organization. There were no group differences in global metric measures for observation of object motion or for observation of actions that are within the repertoire of infants (i.e., reaching). These preliminary results suggest that infants and adults may share a basic functional network for action observation that is sculpted by experience. Motor experience may lead to a shift towards a more efficient functional network.
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Affiliation(s)
- Naama Rotem-Kohavi
- Neuroscience Program, University of British Columbia Vancouver, BC, Canada
| | | | - Aiping Liu
- Electrical and Computer Engineering, University of British Columbia Vancouver, BC, Canada
| | - Nadia Makan
- Neuroscience Program, University of British Columbia Vancouver, BC, Canada
| | - Jane Z Wang
- Electrical and Computer Engineering, University of British Columbia Vancouver, BC, Canada
| | - Naznin Virji-Babul
- Department of Physical Therapy, University of British Columbia Vancouver, BC, Canada ; Department of Developmental Neurosciences and Child Health, Child and Family Research Institute Vancouver, BC, Canada
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46
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Collin G, van den Heuvel MP. The ontogeny of the human connectome: development and dynamic changes of brain connectivity across the life span. Neuroscientist 2013; 19:616-28. [PMID: 24047610 DOI: 10.1177/1073858413503712] [Citation(s) in RCA: 116] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
The human brain comprises distributed cortical regions that are structurally and functionally connected into a network that is known as the human connectome. Elaborate developmental processes starting in utero herald connectome genesis, with dynamic changes in its architecture continuing throughout life. Connectome changes during development, maturation, and aging may be governed by a set of biological rules or algorithms, forming and shaping the macroscopic architecture of the brain's wiring network. To explore the presence of developmental patterns indicative of such rules, this review considers insights from studies on the cellular and the systems level into macroscopic connectome genesis and dynamics across the life span. We observe that in parallel with synaptogenesis, macroscopic connectome formation and transformation is characterized by an initial overgrowth and subsequent elimination of cortico-cortical axonal projections. Furthermore, dynamic changes in connectome organization throughout the life span are suggested to follow an inverted U-shaped pattern, with an increasingly integrated topology during development, a plateau lasting for the majority of adulthood and an increasingly localized topology in late life. Elucidating developmental patterns in brain connectivity is crucial for our understanding of the human connectome and how it may give rise to brain function, including the occurrence of brain network disorders across the life span.
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Affiliation(s)
- Guusje Collin
- 1Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, Netherlands
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47
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Siebenhühner F, Weiss SA, Coppola R, Weinberger DR, Bassett DS. Intra- and inter-frequency brain network structure in health and schizophrenia. PLoS One 2013; 8:e72351. [PMID: 23991097 PMCID: PMC3753323 DOI: 10.1371/journal.pone.0072351] [Citation(s) in RCA: 42] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2013] [Accepted: 07/08/2013] [Indexed: 01/22/2023] Open
Abstract
Empirical studies over the past two decades have provided support for the hypothesis that schizophrenia is characterized by altered connectivity patterns in functional brain networks. These alterations have been proposed as genetically mediated diagnostic biomarkers and are thought to underlie altered cognitive functions such as working memory. However, the nature of this dysconnectivity remains far from understood. In this study, we perform an extensive analysis of functional connectivity patterns extracted from MEG data in 14 subjects with schizophrenia and 14 healthy controls during a 2-back working memory task. We investigate uni-, bi- and multivariate properties of sensor time series by computing wavelet entropy of and correlation between time series, and by constructing binary networks of functional connectivity both within and between classical frequency bands ([Formula: see text], [Formula: see text], [Formula: see text], and [Formula: see text]). Networks are based on the mutual information between wavelet time series, and estimated for each trial window separately, enabling us to consider both network topology and network dynamics. We observed significant decreases in time series entropy and significant increases in functional connectivity in the schizophrenia group in comparison to the healthy controls and identified an inverse relationship between these measures across both subjects and sensors that varied over frequency bands and was more pronounced in controls than in patients. The topological organization of connectivity was altered in schizophrenia specifically in high frequency [Formula: see text] and [Formula: see text] band networks as well as in the [Formula: see text]-[Formula: see text] cross-frequency networks. Network topology varied over trials to a greater extent in patients than in controls, suggesting disease-associated alterations in dynamic network properties of brain function. Our results identify signatures of aberrant neurophysiological behavior in schizophrenia across uni-, bi- and multivariate scales and lay the groundwork for further clinical studies that might lead to the discovery of new intermediate phenotypes.
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Affiliation(s)
- Felix Siebenhühner
- Department of Physics, University of California Santa Barbara, Santa Barbara, California, United States of America
- Neuroscience Center, University of Helsinki, Helsinki, Finland
| | - Shennan A. Weiss
- Department of Neurology, Columbia University, New York, New York, United States of America
| | - Richard Coppola
- MEG Core Facility, National Institute of Mental Health, Bethesda, Maryland, United States of America
| | - Daniel R. Weinberger
- Genes, Cognition and Psychosis Program, Clinical Brain Disorders Branch, National Institute of Mental Health, Bethesda, Maryland, United States of America
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, Maryland, United States of America
| | - Danielle S. Bassett
- Department of Physics, University of California Santa Barbara, Santa Barbara, California, United States of America
- Sage Center for the Study of the Mind, University of California Santa Barbara, Santa Barbara, California, United States of America
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