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Zhang W, Zhai X, Zhang C, Cheng S, Zhang C, Bai J, Deng X, Ji J, Li T, Wang Y, Tong HHY, Li J, Li K. Regional brain structural network topology mediates the associations between white matter damage and disease severity in first-episode, Treatment-naïve pubertal children with major depressive disorder. Psychiatry Res Neuroimaging 2024; 344:111862. [PMID: 39153232 DOI: 10.1016/j.pscychresns.2024.111862] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Revised: 03/22/2024] [Accepted: 07/31/2024] [Indexed: 08/19/2024]
Abstract
Puberty is a vulnerable period for the onset of major depressive disorder (MDD) due to considerable neurodevelopmental changes. Prior diffusion tensor imaging (DTI) studies in depressed youth have had heterogeneous participants, making assessment of early pathology challenging due to illness chronicity and medication confounds. This study leveraged whole-brain DTI and graph theory approaches to probe white matter (WM) abnormalities and disturbances in structural network topology related to first-episode, treatment-naïve pediatric MDD. Participants included 36 first-episode, unmedicated adolescents with MDD (mean age 15.8 years) and 29 age- and sex-matched healthy controls (mean age 15.2 years). Compared to controls, the MDD group showed reduced fractional anisotropy in the internal and external capsules, unveiling novel regions of WM disruption in early-onset depression. The right thalamus and superior temporal gyrus were identified as network hubs where betweenness centrality changes mediated links between WM anomalies and depression severity. A diagnostic model incorporating demographics, DTI, and network metrics achieved an AUROC of 0.88 and a F1 score of 0.80 using a neural network algorithm. By examining first-episode, treatment-naïve patients, this work identified novel WM abnormalities and a potential causal pathway linking WM damage to symptom severity via regional structural network alterations in brain hubs.
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Affiliation(s)
- Wenjie Zhang
- Department of Radiology, Heping Hospital Affiliated to Changzhi Medical College, Changzhi, Shanxi, China; Changzhi Key Lab of Functional Imaging for Brain Diseases, Heping Hospital Affiliated to Changzhi Medical College, Changzhi, Shanxi, China
| | - Xiaobing Zhai
- Faculty of Applied Sciences, Macao Polytechnic University, Macao SAR, China
| | - Chan Zhang
- Department of Radiology, Heping Hospital Affiliated to Changzhi Medical College, Changzhi, Shanxi, China; Changzhi Key Lab of Functional Imaging for Brain Diseases, Heping Hospital Affiliated to Changzhi Medical College, Changzhi, Shanxi, China
| | - Song Cheng
- Department of Radiology, Heping Hospital Affiliated to Changzhi Medical College, Changzhi, Shanxi, China; Changzhi Key Lab of Functional Imaging for Brain Diseases, Heping Hospital Affiliated to Changzhi Medical College, Changzhi, Shanxi, China
| | - Chaoqing Zhang
- Department of Radiology, Heping Hospital Affiliated to Changzhi Medical College, Changzhi, Shanxi, China; Changzhi Key Lab of Functional Imaging for Brain Diseases, Heping Hospital Affiliated to Changzhi Medical College, Changzhi, Shanxi, China
| | - Jinji Bai
- Department of Radiology, Heping Hospital Affiliated to Changzhi Medical College, Changzhi, Shanxi, China; Changzhi Key Lab of Functional Imaging for Brain Diseases, Heping Hospital Affiliated to Changzhi Medical College, Changzhi, Shanxi, China
| | - Xuan Deng
- Department of Radiology, Heping Hospital Affiliated to Changzhi Medical College, Changzhi, Shanxi, China; Changzhi Key Lab of Functional Imaging for Brain Diseases, Heping Hospital Affiliated to Changzhi Medical College, Changzhi, Shanxi, China
| | - Junjun Ji
- Department of Radiology, Heping Hospital Affiliated to Changzhi Medical College, Changzhi, Shanxi, China; Changzhi Key Lab of Functional Imaging for Brain Diseases, Heping Hospital Affiliated to Changzhi Medical College, Changzhi, Shanxi, China; Faculty of Applied Sciences, Macao Polytechnic University, Macao SAR, China
| | - Ting Li
- Department of Radiology, Heping Hospital Affiliated to Changzhi Medical College, Changzhi, Shanxi, China; Changzhi Key Lab of Functional Imaging for Brain Diseases, Heping Hospital Affiliated to Changzhi Medical College, Changzhi, Shanxi, China
| | - Yu Wang
- Department of Psychiatry, Changzhi Mental Health Center, Changzhi, Shanxi, China
| | - Henry H Y Tong
- Faculty of Applied Sciences, Macao Polytechnic University, Macao SAR, China
| | - Junfeng Li
- Department of Radiology, Heping Hospital Affiliated to Changzhi Medical College, Changzhi, Shanxi, China; Changzhi Key Lab of Functional Imaging for Brain Diseases, Heping Hospital Affiliated to Changzhi Medical College, Changzhi, Shanxi, China
| | - Kefeng Li
- Faculty of Applied Sciences, Macao Polytechnic University, Macao SAR, China.
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Chang CW, Tan CH, Hong WP, Yu RL. GBA moderates cognitive reserve's effect on cognitive function in patients with Parkinson's disease. J Neurol 2024; 271:4392-4405. [PMID: 38656622 DOI: 10.1007/s00415-024-12374-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2023] [Revised: 04/02/2024] [Accepted: 04/05/2024] [Indexed: 04/26/2024]
Abstract
BACKGROUND Cognitive reserve (CR) involves an individual's ability to maintain cognitive vitality over their lifespan. Glucocerebrosidase (GBA) gene mutations contribute to additional effects on cognitive function in Parkinson's disease (PD) patients, but the interplay between GBA mutations and CR remains unclear. We investigated the interactions among CR, GBA, and diseases, aiming to examine whether the CR established at different stages interacts with specific genotypes to affect cognitive function. METHODS Three hundred and eighteen participants' CR indicators (i.e., education, occupation, and social function) and comprehensive neuropsychological function (i.e., tests for executive function, attention/working memory, visuospatial function, memory, and language) were evaluated. RESULTS We found that CR established in a specific life stage influences the individual's cognitive function, particularly in PD, based on their distinct GBA rs9628662 genotypes. Attention/working memory and memory performance are affected by occupational complexity in midlife in PD patients with the GG genotype (q < 0.0001; q < 0.0001) and healthy adults with the T genotype (q = 0.0440; q < 0.0001). Language is influenced by early education and occupation, and the effects of occupation are also observed in PD patients with the GG genotype (q = 0.0040) and in healthy adults carrying the T genotype (q = 0.0040). CONCLUSIONS CR, established at different life stages, can be influenced by the GBA rs9628662 genotype, impacting later-life cognition. Validating genotypes and incorporating genotype information when assessing cognitive reserve effects is crucial and can enhance targeted cognitive training.
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Affiliation(s)
- Chia-Wen Chang
- Institute of Behavioral Medicine, College of Medicine, National Cheng Kung University, Tainan, Taiwan
| | - Chun-Hsiang Tan
- Department of Neurology, Kaohsiung Medical University Hospital, Kaohsiung Medical University, Kaohsiung, Taiwan
- Graduate Institute of Clinical Medicine, College of Medicine, Kaohsiung Medical University, Kaohsiung, Taiwan
| | - Wei-Pin Hong
- Department of Neurology, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan, Taiwan
| | - Rwei-Ling Yu
- Institute of Behavioral Medicine, College of Medicine, National Cheng Kung University, Tainan, Taiwan.
- Office of Strategic Planning, National Cheng Kung University, Tainan, Taiwan.
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Yueh-Hsin L, Dadario NB, Tang SJ, Crawford L, Tanglay O, Dow HK, Young I, Ahsan SA, Doyen S, Sughrue ME. Discernible interindividual patterns of global efficiency decline during theoretical brain surgery. Sci Rep 2024; 14:14573. [PMID: 38914649 PMCID: PMC11196730 DOI: 10.1038/s41598-024-64845-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2023] [Accepted: 06/13/2024] [Indexed: 06/26/2024] Open
Abstract
The concept of functional localization within the brain and the associated risk of resecting these areas during removal of infiltrating tumors, such as diffuse gliomas, are well established in neurosurgery. Global efficiency (GE) is a graph theory concept that can be used to simulate connectome disruption following tumor resection. Structural connectivity graphs were created from diffusion tractography obtained from the brains of 80 healthy adults. These graphs were then used to simulate parcellation resection in every gross anatomical region of the cerebrum by identifying every possible combination of adjacent nodes in a graph and then measuring the drop in GE following nodal deletion. Progressive removal of brain parcellations led to patterns of GE decline that were reasonably predictable but had inter-subject differences. Additionally, as expected, there were deletion of some nodes that were worse than others. However, in each lobe examined in every subject, some deletion combinations were worse for GE than removing a greater number of nodes in a different region of the brain. Among certain patients, patterns of common nodes which exhibited worst GE upon removal were identified as "connectotypes". Given some evidence in the literature linking GE to certain aspects of neuro-cognitive abilities, investigating these connectotypes could potentially mitigate the impact of brain surgery on cognition.
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Affiliation(s)
- Lin Yueh-Hsin
- Centre for Minimally Invasive Neurosurgery, Prince of Wales Private Hospital, Suite 19, Level 7 Prince of Wales Private Hospital, Randwick, Sydney, NSW, 2031, Australia
| | - Nicholas B Dadario
- Robert Wood Johnson Medical School, Rutgers University, 125 Paterson St, New Brunswick, NJ, 08901, USA
| | - Si Jie Tang
- School of Medicine, 21772 University of California Davis Medical Center, 2315 Stockton Blvd., Sacramento, CA, 95817, USA
| | - Lewis Crawford
- Omniscient Neurotechnology, Level 10/580 George Street, Sydney, NSW, 2000, Australia
| | - Onur Tanglay
- Omniscient Neurotechnology, Level 10/580 George Street, Sydney, NSW, 2000, Australia
| | - Hsu-Kang Dow
- School of Computer Science and Engineering, University of New South Wales (UNSW), Building K17, Sydney, NSW, 2052, USA
| | - Isabella Young
- Omniscient Neurotechnology, Level 10/580 George Street, Sydney, NSW, 2000, Australia
| | - Syed Ali Ahsan
- Centre for Minimally Invasive Neurosurgery, Prince of Wales Private Hospital, Suite 19, Level 7 Prince of Wales Private Hospital, Randwick, Sydney, NSW, 2031, Australia
| | - Stephane Doyen
- Omniscient Neurotechnology, Level 10/580 George Street, Sydney, NSW, 2000, Australia
| | - Michael E Sughrue
- Centre for Minimally Invasive Neurosurgery, Prince of Wales Private Hospital, Suite 19, Level 7 Prince of Wales Private Hospital, Randwick, Sydney, NSW, 2031, Australia.
- Omniscient Neurotechnology, Level 10/580 George Street, Sydney, NSW, 2000, Australia.
- Centre for Minimally Invasive Neurosurgery, Prince of Wales Private Hospital, Suite 3, Level 7 Barker St, Randwick, NSW, 2031, USA.
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Pappalettera C, Carrarini C, Miraglia F, Vecchio F, Rossini PM. Cognitive resilience/reserve: Myth or reality? A review of definitions and measurement methods. Alzheimers Dement 2024; 20:3567-3586. [PMID: 38477378 PMCID: PMC11095447 DOI: 10.1002/alz.13744] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2023] [Revised: 12/27/2023] [Accepted: 12/28/2023] [Indexed: 03/14/2024]
Abstract
INTRODUCTION This review examines the concept of cognitive reserve (CR) in relation to brain aging, particularly in the context of dementia and its early stages. CR refers to an individual's ability to maintain or regain cognitive function despite brain aging, damage, or disease. Various factors, including education, occupation complexity, leisure activities, and genetics are believed to influence CR. METHODS We revised the literature in the context of CR. A total of 842 articles were identified, then we rigorously assessed the relevance of articles based on titles and abstracts, employing a systematic approach to eliminate studies that did not align with our research objectives. RESULTS We evaluate-also in a critical way-the methods commonly used to define and measure CR, including sociobehavioral proxies, neuroimaging, and electrophysiological and genetic measures. The challenges and limitations of these measures are discussed, emphasizing the need for more targeted research to improve the understanding, definition, and measurement of CR. CONCLUSIONS The review underscores the significance of comprehending CR in the context of both normal and pathological brain aging and emphasizes the importance of further research to identify and enhance this protective factor for cognitive preservation in both healthy and neurologically impaired older individuals. HIGHLIGHTS This review examines the concept of cognitive reserve in brain aging, in the context of dementia and its early stages. We have evaluated the methods commonly used to define and measure cognitive reserve. Sociobehavioral proxies, neuroimaging, and electrophysiological and genetic measures are discussed. The review emphasizes the importance of further research to identify and enhance this protective factor for cognitive preservation.
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Affiliation(s)
- Chiara Pappalettera
- Brain Connectivity LaboratoryDepartment of Neuroscience and NeurorehabilitationIRCCS San Raffaele RomaRomeItaly
- Department of Theoretical and Applied ScienceseCampus UniversityNovedrateItaly
| | - Claudia Carrarini
- Brain Connectivity LaboratoryDepartment of Neuroscience and NeurorehabilitationIRCCS San Raffaele RomaRomeItaly
- Department of NeuroscienceCatholic University of Sacred HeartRomeItaly
| | - Francesca Miraglia
- Brain Connectivity LaboratoryDepartment of Neuroscience and NeurorehabilitationIRCCS San Raffaele RomaRomeItaly
- Department of Theoretical and Applied ScienceseCampus UniversityNovedrateItaly
| | - Fabrizio Vecchio
- Brain Connectivity LaboratoryDepartment of Neuroscience and NeurorehabilitationIRCCS San Raffaele RomaRomeItaly
- Department of Theoretical and Applied ScienceseCampus UniversityNovedrateItaly
| | - Paolo M. Rossini
- Brain Connectivity LaboratoryDepartment of Neuroscience and NeurorehabilitationIRCCS San Raffaele RomaRomeItaly
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Ma J, Chen X, Gu Y, Li L, Lin Y, Dai Z. Trade-offs among cost, integration, and segregation in the human connectome. Netw Neurosci 2023; 7:604-631. [PMID: 37397887 PMCID: PMC10312266 DOI: 10.1162/netn_a_00291] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2022] [Accepted: 11/02/2022] [Indexed: 09/22/2024] Open
Abstract
The human brain structural network is thought to be shaped by the optimal trade-off between cost and efficiency. However, most studies on this problem have focused on only the trade-off between cost and global efficiency (i.e., integration) and have overlooked the efficiency of segregated processing (i.e., segregation), which is essential for specialized information processing. Direct evidence on how trade-offs among cost, integration, and segregation shape the human brain network remains lacking. Here, adopting local efficiency and modularity as segregation factors, we used a multiobjective evolutionary algorithm to investigate this problem. We defined three trade-off models, which represented trade-offs between cost and integration (Dual-factor model), and trade-offs among cost, integration, and segregation (local efficiency or modularity; Tri-factor model), respectively. Among these, synthetic networks with optimal trade-off among cost, integration, and modularity (Tri-factor model [Q]) showed the best performance. They had a high recovery rate of structural connections and optimal performance in most network features, especially in segregated processing capacity and network robustness. Morphospace of this trade-off model could further capture the variation of individual behavioral/demographic characteristics in a domain-specific manner. Overall, our results highlight the importance of modularity in the formation of the human brain structural network and provide new insights into the original cost-efficiency trade-off hypothesis.
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Affiliation(s)
- Junji Ma
- Department of Psychology, Sun Yat-sen University, Guangzhou, China
| | - Xitian Chen
- Department of Psychology, Sun Yat-sen University, Guangzhou, China
| | - Yue Gu
- Department of Psychology, Sun Yat-sen University, Guangzhou, China
| | - Liangfang Li
- Department of Psychology, Sun Yat-sen University, Guangzhou, China
| | - Cam-CAN
- Cambridge Centre for Ageing and Neuroscience (Cam-CAN), University of Cambridge and MRC Cognition and Brain Sciences Unit, Cambridge, United Kingdom
| | - Ying Lin
- Department of Psychology, Sun Yat-sen University, Guangzhou, China
| | - Zhengjia Dai
- Department of Psychology, Sun Yat-sen University, Guangzhou, China
- Guangdong Provincial Key Laboratory of Brain Function and Disease, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, China
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Genç E, Metzen D, Fraenz C, Schlüter C, Voelkle MC, Arning L, Streit F, Nguyen HP, Güntürkün O, Ocklenburg S, Kumsta R. Structural architecture and brain network efficiency link polygenic scores to intelligence. Hum Brain Mapp 2023; 44:3359-3376. [PMID: 37013679 PMCID: PMC10171514 DOI: 10.1002/hbm.26286] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2022] [Revised: 02/15/2023] [Accepted: 03/01/2023] [Indexed: 04/05/2023] Open
Abstract
Intelligence is highly heritable. Genome-wide association studies (GWAS) have shown that thousands of alleles contribute to variation in intelligence with small effect sizes. Polygenic scores (PGS), which combine these effects into one genetic summary measure, are increasingly used to investigate polygenic effects in independent samples. Whereas PGS explain a considerable amount of variance in intelligence, it is largely unknown how brain structure and function mediate this relationship. Here, we show that individuals with higher PGS for educational attainment and intelligence had higher scores on cognitive tests, larger surface area, and more efficient fiber connectivity derived by graph theory. Fiber network efficiency as well as the surface of brain areas partly located in parieto-frontal regions were found to mediate the relationship between PGS and cognitive performance. These findings are a crucial step forward in decoding the neurogenetic underpinnings of intelligence, as they identify specific regional networks that link polygenic predisposition to intelligence.
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Affiliation(s)
- Erhan Genç
- Department of Psychology and NeuroscienceLeibniz Research Centre for Working Environment and Human Factors (IfADo)DortmundGermany
| | - Dorothea Metzen
- Biopsychology, Institute for Cognitive Neuroscience, Faculty of PsychologyRuhr University BochumBochumGermany
| | - Christoph Fraenz
- Department of Psychology and NeuroscienceLeibniz Research Centre for Working Environment and Human Factors (IfADo)DortmundGermany
| | - Caroline Schlüter
- Biopsychology, Institute for Cognitive Neuroscience, Faculty of PsychologyRuhr University BochumBochumGermany
| | - Manuel C. Voelkle
- Psychological Research Methods Department of PsychologyHumboldt UniversityBerlinGermany
| | - Larissa Arning
- Department of Human Genetics, Faculty of MedicineRuhr University BochumBochumGermany
| | - Fabian Streit
- Department Genetic Epidemiology in Psychiatry, Central Institute of Mental Health, Medical Faculty MannheimUniversity of HeidelbergMannheimGermany
| | - Huu Phuc Nguyen
- Department of Human Genetics, Faculty of MedicineRuhr University BochumBochumGermany
| | - Onur Güntürkün
- Biopsychology, Institute for Cognitive Neuroscience, Faculty of PsychologyRuhr University BochumBochumGermany
| | - Sebastian Ocklenburg
- Biopsychology, Institute for Cognitive Neuroscience, Faculty of PsychologyRuhr University BochumBochumGermany
- Department of PsychologyMedical School HamburgHamburgGermany
- ICAN Institute for Cognitive and Affective NeuroscienceMedical School HamburgHamburgGermany
| | - Robert Kumsta
- Genetic Psychology, Faculty of PsychologyRuhr University BochumBochumGermany
- Department of Behavioural and Cognitive Sciences, Laboratory for Stress and Gene‐Environment InterplayUniversity of LuxembourgEsch‐sur‐AlzetteLuxembourg
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Khodaei M, Laurienti PJ, Dagenbach D, Simpson SL. Brain working memory network indices as landmarks of intelligence. NEUROIMAGE. REPORTS 2023; 3:100165. [PMID: 37425210 PMCID: PMC10327823 DOI: 10.1016/j.ynirp.2023.100165] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 07/11/2023]
Abstract
Identifying the neural correlates of intelligence has long been a goal in neuroscience. Recently, the field of network neuroscience has attracted researchers' attention as a means for answering this question. In network neuroscience, the brain is considered as an integrated system whose systematic properties provide profound insights into health and behavioral outcomes. However, most network studies of intelligence have used univariate methods to investigate topological network measures, with their focus limited to a few measures. Furthermore, most studies have focused on resting state networks despite the fact that brain activation during working memory tasks has been linked to intelligence. Finally, the literature is still missing an investigation of the association between network assortativity and intelligence. To address these issues, here we employ a recently developed mixed-modeling framework for analyzing multi-task brain networks to elucidate the most critical working memory task network topological properties corresponding to individuals' intelligence differences. We used a data set of 379 subjects (22-35 y/o) from the Human Connectome Project (HCP). Each subject's data included composite intelligence scores, and fMRI during resting state and a 2-back working memory task. Following comprehensive quality control and preprocessing of the minimally preprocessed fMRI data, we extracted a set of the main topological network features, including global efficiency, degree, leverage centrality, modularity, and clustering coefficient. The estimated network features and subject's confounders were then incorporated into the multi-task mixed-modeling framework to investigate how brain network changes between working memory and resting state relate to intelligence score. Our results indicate that the general intelligence score (cognitive composite score) is associated with a change in the relationship between connection strength and multiple network topological properties, including global efficiency, leverage centrality, and degree difference during working memory as it is compared to resting state. More specifically, we observed a higher increase in the positive association between global efficiency and connection strength for the high intelligence group when they switch from resting state to working memory. The strong connections might form superhighways for a more efficient global flow of information through the brain network. Furthermore, we found an increase in the negative association between degree difference and leverage centrality with connection strength during working memory tasks for the high intelligence group. These indicate higher network resilience and assortativity along with higher circuit-specific information flow during working memory for those with a higher intelligence score. Although the exact neurobiological implications of our results are speculative at this point, our results provide evidence for the significant association of intelligence with hallmark properties of brain networks during working memory.
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Affiliation(s)
- Mohammadreza Khodaei
- Virginia Tech-Wake Forest University School of Biomedical Engineering and Sciences, Wake Forest University School of Medicine, Winston-Salem, NC, USA
| | - Paul J. Laurienti
- Virginia Tech-Wake Forest University School of Biomedical Engineering and Sciences, Wake Forest University School of Medicine, Winston-Salem, NC, USA
- Department of Radiology, Wake Forest University School of Medicine, Winston-Salem, NC, USA
| | - Dale Dagenbach
- Department of Psychology, Wake Forest University, Winston-Salem, NC, USA
| | - Sean L. Simpson
- Virginia Tech-Wake Forest University School of Biomedical Engineering and Sciences, Wake Forest University School of Medicine, Winston-Salem, NC, USA
- Department of Biostatistics and Data Science, Wake Forest University School of Medicine, Winston-Salem, NC, USA
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Tang H, Guo L, Fu X, Wang Y, Mackin S, Ajilore O, Leow AD, Thompson PM, Huang H, Zhan L. Signed graph representation learning for functional-to-structural brain network mapping. Med Image Anal 2023; 83:102674. [PMID: 36442294 PMCID: PMC9904311 DOI: 10.1016/j.media.2022.102674] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2022] [Revised: 10/04/2022] [Accepted: 10/27/2022] [Indexed: 11/18/2022]
Abstract
MRI-derived brain networks have been widely used to understand functional and structural interactions among brain regions, and factors that affect them, such as brain development and diseases. Graph mining on brain networks can facilitate the discovery of novel biomarkers for clinical phenotypes and neurodegenerative diseases. Since brain functional and structural networks describe the brain topology from different perspectives, exploring a representation that combines these cross-modality brain networks has significant clinical implications. Most current studies aim to extract a fused representation by projecting the structural network to the functional counterpart. Since the functional network is dynamic and the structural network is static, mapping a static object to a dynamic object may not be optimal. However, mapping in the opposite direction (i.e., from functional to structural networks) are suffered from the challenges introduced by negative links within signed graphs. Here, we propose a novel graph learning framework, named as Deep Signed Brain Graph Mining or DSBGM, with a signed graph encoder that, from an opposite perspective, learns the cross-modality representations by projecting the functional network to the structural counterpart. We validate our framework on clinical phenotype and neurodegenerative disease prediction tasks using two independent, publicly available datasets (HCP and OASIS). Our experimental results clearly demonstrate the advantages of our model compared to several state-of-the-art methods.
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Affiliation(s)
- Haoteng Tang
- University of Pittsburgh, 3700 O'Hara St., Pittsburgh, 15261, PA, USA.
| | - Lei Guo
- University of Pittsburgh, 3700 O'Hara St., Pittsburgh, 15261, PA, USA
| | - Xiyao Fu
- University of Pittsburgh, 3700 O'Hara St., Pittsburgh, 15261, PA, USA
| | - Yalin Wang
- Arizona State University, 699 S Mill Ave., Tempe, 85281, AZ, USA
| | - Scott Mackin
- University of California San Francisco, 505 Parnassus Ave., San Francisco, 94143, CA, USA
| | - Olusola Ajilore
- University of Illinois Chicago, 1601 W. Taylor St., Chicago, 60612, IL, USA
| | - Alex D Leow
- University of Illinois Chicago, 1601 W. Taylor St., Chicago, 60612, IL, USA
| | - Paul M Thompson
- University of Southern California, 2001 N. Soto St., Los Angeles, 90032, CA, USA
| | - Heng Huang
- University of Pittsburgh, 3700 O'Hara St., Pittsburgh, 15261, PA, USA
| | - Liang Zhan
- University of Pittsburgh, 3700 O'Hara St., Pittsburgh, 15261, PA, USA.
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Talaei N, Ghaderi A. Integration of structural brain networks is related to openness to experience: A diffusion MRI study with CSD-based tractography. Front Neurosci 2022; 16:1040799. [PMID: 36570828 PMCID: PMC9775296 DOI: 10.3389/fnins.2022.1040799] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2022] [Accepted: 11/23/2022] [Indexed: 12/13/2022] Open
Abstract
Openness to experience is one of the big five traits of personality which recently has been the subject of several studies in neuroscience due to its importance in understanding various cognitive functions. However, the neural basis of openness to experience is still unclear. Previous studies have found largely heterogeneous results, suggesting that various brain regions may be involved in openness to experience. Here we suggested that performing structural connectome analysis may shed light on the neural underpinnings of openness to experience as it provides a more comprehensive look at the brain regions that are involved in this trait. Hence, we investigated the involvement of brain network structural features in openness to experience which has not yet been explored to date. The magnetic resonance imaging (MRI) data along with the openness to experience trait score from the self-reported NEO Five-Factor Inventory of 100 healthy subjects were evaluated from Human Connectome Project (HCP). CSD-based whole-brain probabilistic tractography was performed using diffusion-weighted images as well as segmented T1-weighted images to create an adjacency matrix for each subject. Using graph theoretical analysis, we computed global efficiency (GE) and clustering coefficient (CC) which are measures of two important aspects of network organization in the brain: functional integration and functional segregation respectively. Results revealed a significant negative correlation between GE and openness to experience which means that the higher capacity of the brain in combining information from different regions may be related to lower openness to experience.
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Affiliation(s)
- Nima Talaei
- Department of Psychology, Faculty of Literature and Human Sciences, Shahid Bahonar University, Kerman, Iran,*Correspondence: Nima Talaei,
| | - Amirhossein Ghaderi
- Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada,Department of Psychology, University of Calgary, Calgary, AB, Canada
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Rostami M, Faridi F, Khosrowabadi R. Brain Functional Correlates of Intelligence Score in ADHD Based on EEG. Basic Clin Neurosci 2022; 13:883-900. [PMID: 37323951 PMCID: PMC10262280 DOI: 10.32598/bcn.2021.1904.1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2020] [Revised: 09/16/2020] [Accepted: 09/28/2020] [Indexed: 11/02/2023] Open
Abstract
Introduction It has been shown that intelligence as a general mental ability is related to the structure and function of the brain regions. However, the specificity of these regional dependencies to the intelligence scores in the typical and atypical developed individuals needs to be well understood. In this study, we hypothesized that neural correlates of IQ should not have a fixed pattern rather they must follow a dynamic pattern to compensate for the functional deficits caused by a neurodevelopmental disorder. Therefore, electroencephalography (EEG) correlates of normal IQ in various subtypes of attention deficit hyperactive disorder (ADHD) were compared with a group of healthy controls. Methods Sixty-three ADHD subjects comprising combined, inattentive, and hyperactive individuals diagnosed by a psychiatrist using structural clinical interview for DSM-V, and 46 healthy controls with similar normal IQ scores were recruited in this study. The subjects' EEG data were then recorded during an eye-closed resting condition. The subjects' intelligence level was measured by Raven's standard progressive matrices. Then, the association between IQ and the power of the EEG signal was computed in the conventional frequency bands. Subsequently, topographical representations of these associations were compared between the groups. Results Our results demonstrated that the association between IQ score and EEG power is not the same in various ADHD subtypes and healthy controls. Conclusion This finding suggests a compensatory mechanism in ADHD individuals for changing the regional oscillatory pattern to maintain the IQ within a normal range.
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Affiliation(s)
- Mohammad Rostami
- Department of Psychology, Faculty of Psychology and Educational Sciences, Allameh Tabatabaie University, Tehran, Iran
- Institute for Cognitive and Brain Sciences, Shahid Beheshti University, Tehran, Iran
| | - Farnaz Faridi
- Institute for Cognitive and Brain Sciences, Shahid Beheshti University, Tehran, Iran
- Department of Psychology, Faculty of Humanities, Tarbiat Modares University, Tehran, Iran
| | - Reza Khosrowabadi
- Institute for Cognitive and Brain Sciences, Shahid Beheshti University, Tehran, Iran
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11
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Tang H, Guo L, Fu X, Qu B, Ajilore O, Wang Y, Thompson PM, Huang H, Leow AD, Zhan L. A Hierarchical Graph Learning Model for Brain Network Regression Analysis. Front Neurosci 2022; 16:963082. [PMID: 35903810 PMCID: PMC9315240 DOI: 10.3389/fnins.2022.963082] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2022] [Accepted: 06/22/2022] [Indexed: 11/29/2022] Open
Abstract
Brain networks have attracted increasing attention due to the potential to better characterize brain dynamics and abnormalities in neurological and psychiatric conditions. Recent years have witnessed enormous successes in deep learning. Many AI algorithms, especially graph learning methods, have been proposed to analyze brain networks. An important issue for existing graph learning methods is that those models are not typically easy to interpret. In this study, we proposed an interpretable graph learning model for brain network regression analysis. We applied this new framework on the subjects from Human Connectome Project (HCP) for predicting multiple Adult Self-Report (ASR) scores. We also use one of the ASR scores as the example to demonstrate how to identify sex differences in the regression process using our model. In comparison with other state-of-the-art methods, our results clearly demonstrate the superiority of our new model in effectiveness, fairness, and transparency.
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Affiliation(s)
- Haoteng Tang
- Department of Electrical and Computer Engineering, University of Pittsburgh, Pittsburgh, PA, United States
| | - Lei Guo
- Department of Electrical and Computer Engineering, University of Pittsburgh, Pittsburgh, PA, United States
| | - Xiyao Fu
- Department of Electrical and Computer Engineering, University of Pittsburgh, Pittsburgh, PA, United States
| | - Benjamin Qu
- Mission San Jose High School, Fremont, CA, United States
| | - Olusola Ajilore
- Department of Psychiatry, University of Illinois Chicago, Chicago, IL, United States
| | - Yalin Wang
- Department of Computer Science and Engineering, Arizona State University, Tempe, AZ, United States
| | - Paul M. Thompson
- Imaging Genetics Center, University of Southern California, Los Angeles, CA, United States
| | - Heng Huang
- Department of Electrical and Computer Engineering, University of Pittsburgh, Pittsburgh, PA, United States
| | - Alex D. Leow
- Department of Psychiatry, University of Illinois Chicago, Chicago, IL, United States
| | - Liang Zhan
- Department of Electrical and Computer Engineering, University of Pittsburgh, Pittsburgh, PA, United States
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Oyefiade A, Moxon-Emre I, Beera K, Bouffet E, Taylor M, Ramaswamy V, Laughlin S, Skocic J, Mabbott D. Structural connectivity and intelligence in brain-injured children. Neuropsychologia 2022; 173:108285. [PMID: 35690116 DOI: 10.1016/j.neuropsychologia.2022.108285] [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: 09/23/2021] [Revised: 05/28/2022] [Accepted: 05/31/2022] [Indexed: 11/29/2022]
Abstract
In children, higher general intelligence corresponds with better processing speed ability. However, the relationship between structural brain connectivity and processing speed in the context of intelligence is unclear. Furthermore, the impact of brain injury on this relationship is also unknown. Structural networks were constructed for 36 brain tumor patients (mean age: 13.45 ± 2.73, 58% males) and 35 typically developing children (13.30 ± 2.86, 51% males). Processing speed and general intelligence scores were acquired using standard batteries. The relationship between network properties, processing speed, and intelligence was assessed using a partial least squares analysis. Results indicated that structural networks in brain-injured children were less integrated (β = -.38, p = 0.001) and more segregated (β = 0.4, p = 0.0005) compared to typically developing children. There was an indirect effect of network segregation on general intelligence via processing speed, where greater network segregation predicted slower processing speed which in turn predicted worse general intelligence (GoF = 0.37). These findings provide the first evidence of relations between structural connectivity, processing speed, and intelligence in children. Injury-related disruption to the structural network may result in worse intelligence through impacts on information processing. Our findings are discussed in the context of a network approach to understanding brain-behavior relationships.
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Affiliation(s)
- Adeoye Oyefiade
- Neurosciences and Mental Health, The Hospital for Sick Children, Toronto, Ontario, CANADA; Department of Psychology, University of Toronto, Toronto, Ontario, CANADA
| | - Iska Moxon-Emre
- Neurosciences and Mental Health, The Hospital for Sick Children, Toronto, Ontario, CANADA
| | - Kiran Beera
- Neurosciences and Mental Health, The Hospital for Sick Children, Toronto, Ontario, CANADA
| | - Eric Bouffet
- Division of Hematology/Oncology, The Hospital for Sick Children, Toronto, Ontario, CANADA
| | - Michael Taylor
- Division of Neurosurgery, The Hospital for Sick Children, Toronto, Ontario, CANADA
| | - Vijay Ramaswamy
- Division of Hematology/Oncology, The Hospital for Sick Children, Toronto, Ontario, CANADA
| | - Suzanne Laughlin
- Division of Radiology, The Hospital for Sick Children, Toronto, Ontario, CANADA
| | - Jovanka Skocic
- Neurosciences and Mental Health, The Hospital for Sick Children, Toronto, Ontario, CANADA
| | - Donald Mabbott
- Neurosciences and Mental Health, The Hospital for Sick Children, Toronto, Ontario, CANADA; Division of Hematology/Oncology, The Hospital for Sick Children, Toronto, Ontario, CANADA; Department of Psychology, University of Toronto, Toronto, Ontario, CANADA.
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13
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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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14
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Krupnik R, Yovel Y, Assaf Y. Inner Hemispheric and Interhemispheric Connectivity Balance in the Human Brain. J Neurosci 2021; 41:8351-8361. [PMID: 34465598 PMCID: PMC8496194 DOI: 10.1523/jneurosci.1074-21.2021] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2021] [Revised: 07/25/2021] [Accepted: 08/08/2021] [Indexed: 11/21/2022] Open
Abstract
The connectome of the brain has a great impact on the function of the brain as the structure of the connectome affects the speed and efficiency of information transfer. As a highly energy-consuming organ, an efficient network structure is essential. A previous study has shown consistent overall brain connectivity across a large variety of species. This connectivity conservation was explained by a balance between interhemispheric and intrahemispheric connections; that is, spices with highly connected hemispheres appear to have weaker interhemisphere connections. This study examines this connectivity trade-off in the human brain using diffusion-based tractography and network analysis in the Human Connectome Project (970 subjects, 527 female). We explore the biological origins of this phenomenon, heritability, and the effect on cognitive measures.The proportion of commissural fibers in the brain had a negative correlation to hemispheric efficiency, pointing to a trade-off between inner hemispheric and interhemispheric connectivity. Network hubs including anterior and middle cingulate cortex, superior frontal areas, medial occipital areas, the parahippocampal gyrus, post- and precentral gyri, and the precuneus had the strongest contribution to this phenomenon. Other results show a high heritability as well as a strong connection to crystalized intelligence. This work presents cohort-based network analysis research, spanning a large variety of samples and exploring the overall architecture of the human connectome. Our results show a connectivity conservation phenomenon at the base of the overall brain network architecture. This network structure may explain much of the functional, behavioral, and cognitive variability among different brains.SIGNIFICANCE STATEMENT The network structure of the brain is at the basis of every brain function as it dictates the characteristics of information transfer. Understanding the patterns and mechanisms that guide the connectome structure is crucial to understanding the brain itself. Here we unravel the mechanism at the base of the connectivity conservation phenomenon by exploring the interaction between hemispheric and commissural connectivity in a large-scale cohort-based connectivity study. We describe the trade-off between the two components and examine the origins of the trade-off and observe the effect on cognitive abilities and behavior.
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Affiliation(s)
- Ronnie Krupnik
- Sagol School of Neuroscience, Tel Aviv University, Tel Aviv 69978, Israel
| | - Yossi Yovel
- Sagol School of Neuroscience, Tel Aviv University, Tel Aviv 69978, Israel
- School of Zoology, George S. Wise Faculty of Life Sciences, Tel Aviv University, Tel Aviv 69978, Israel
- Steinhardt Museum of Natural History, Tel Aviv University, Tel Aviv 69978, Israel
| | - Yaniv Assaf
- Sagol School of Neuroscience, Tel Aviv University, Tel Aviv 69978, Israel
- School of Neurobiology, Biochemistry and Biophysics, George S. Wise Faculty of Life Sciences, Tel Aviv University, Tel Aviv 69978, Israel
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15
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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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16
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Ji MH, He X, Shen JC, Yang JJ. Aging-Related Neural Disruption Might Predispose to Postoperative Cognitive Impairment Following Surgical Trauma. J Alzheimers Dis 2021; 81:1685-1699. [PMID: 33967044 DOI: 10.3233/jad-201590] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
BACKGROUND Accumulating evidence has demonstrated that aging is associated with an exaggerated response to surgical trauma together with cognitive impairments. This has significant implications for the development of clinical phenotype such as perioperative neurocognitive disorders (PND), which is a common complication following surgery, especially for the elderly. However, the mechanism by which aging brain is vulnerable to surgical trauma remains to be elucidated. OBJECTIVE To test whether age-related alterations in hippocampal network activities contribute to increased risk of PND following surgery. METHODS Thirty-two adult and seventy-two aged male C57BL/6 mice undergone sevoflurane anesthesia and exploratory laparotomy were used to mimic human abdominal surgery. For the interventional study, mice were treated with minocycline. Behavioral tests were performed post-surgery with open field, novel object recognition and fear conditioning tests, respectively. The brain tissues were then harvested and subjected to biochemistry studies. Local field potential (LFP) recording was performed in another separate experiment. RESULTS Aged mice displayed signs of neuroinflammation, as reflected by significantly increased proinflammatory mediators in the hippocampus. Also, aged mice displayed persistently decreased oscillation activities under different conditions, both before and after surgery. Further correlation analysis suggested that theta power was positively associated with time with novel object, while γ oscillation activity was positively associated with freezing time to context. Of note, downregulation of neuroinflammation by microglia inhibitor minocycline reversed some of these abnormities. CONCLUSION Our study highlights that age-related hippocampal oscillation dysregulation increases the risk of PND incidence, which might provide diagnostic/prognostic biomarkers for PND and possible other neurodegenerative diseases.
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Affiliation(s)
- Mu-Huo Ji
- Department of Anesthesiology, the Second Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Xue He
- Department of Anesthesiology, Jinling Hospital, School of Medicine, Nanjing University, Nanjing, China
| | - Jin-Chun Shen
- Department of Anesthesiology, Jinling Hospital, School of Medicine, Nanjing University, Nanjing, China
| | - Jian-Jun Yang
- Department of Anesthesiology, Pain and Perioperative Medicine, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
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17
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Messaritaki E, Foley S, Schiavi S, Magazzini L, Routley B, Jones DK, Singh KD. Predicting MEG resting-state functional connectivity from microstructural information. Netw Neurosci 2021; 5:477-504. [PMID: 34189374 PMCID: PMC8233113 DOI: 10.1162/netn_a_00187] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2020] [Accepted: 02/01/2021] [Indexed: 12/18/2022] Open
Abstract
Understanding how human brain microstructure influences functional connectivity is an important endeavor. In this work, magnetic resonance imaging data from 90 healthy participants were used to calculate structural connectivity matrices using the streamline count, fractional anisotropy, radial diffusivity, and a myelin measure (derived from multicomponent relaxometry) to assign connection strength. Unweighted binarized structural connectivity matrices were also constructed. Magnetoencephalography resting-state data from those participants were used to calculate functional connectivity matrices, via correlations of the Hilbert envelopes of beamformer time series in the delta, theta, alpha, and beta frequency bands. Nonnegative matrix factorization was performed to identify the components of the functional connectivity. Shortest path length and search-information analyses of the structural connectomes were used to predict functional connectivity patterns for each participant. The microstructure-informed algorithms predicted the components of the functional connectivity more accurately than they predicted the total functional connectivity. This provides a methodology to understand functional mechanisms better. The shortest path length algorithm exhibited the highest prediction accuracy. Of the weights of the structural connectivity matrices, the streamline count and the myelin measure gave the most accurate predictions, while the fractional anisotropy performed poorly. Overall, different structural metrics paint very different pictures of the structural connectome and its relationship to functional connectivity.
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Affiliation(s)
- Eirini Messaritaki
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Maindy Road, Cardiff, UK
| | - Sonya Foley
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Maindy Road, Cardiff, UK
| | - Simona Schiavi
- Department of Computer Science, University of Verona, Verona, Italy
| | - Lorenzo Magazzini
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Maindy Road, Cardiff, UK
| | - Bethany Routley
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Maindy Road, Cardiff, UK
| | - Derek K Jones
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Maindy Road, Cardiff, UK
| | - Krish D Singh
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Maindy Road, Cardiff, UK
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Coelho A, Fernandes HM, Magalhães R, Moreira PS, Marques P, Soares JM, Amorim L, Portugal‐Nunes C, Castanho T, Santos NC, Sousa N. Reorganization of brain structural networks in aging: A longitudinal study. J Neurosci Res 2021; 99:1354-1376. [PMID: 33527512 PMCID: PMC8248023 DOI: 10.1002/jnr.24795] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2020] [Accepted: 12/31/2020] [Indexed: 12/12/2022]
Abstract
Normal aging is characterized by structural and functional changes in the brain contributing to cognitive decline. Structural connectivity (SC) describes the anatomical backbone linking distinct functional subunits of the brain and disruption of this communication is thought to be one of the potential contributors for the age-related deterioration observed in cognition. Several studies already explored brain network's reorganization during aging, but most focused on average connectivity of the whole-brain or in specific networks, such as the resting-state networks. Here, we aimed to characterize longitudinal changes of white matter (WM) structural brain networks, through the identification of sub-networks with significantly altered connectivity along time. Then, we tested associations between longitudinal changes in network connectivity and cognition. We also assessed longitudinal changes in topological properties of the networks. For this, older adults were evaluated at two timepoints, with a mean interval time of 52.8 months (SD = 7.24). WM structural networks were derived from diffusion magnetic resonance imaging, and cognitive status from neurocognitive testing. Our results show age-related changes in brain SC, characterized by both decreases and increases in connectivity weight. Interestingly, decreases occur in intra-hemispheric connections formed mainly by association fibers, while increases occur mostly in inter-hemispheric connections and involve association, commissural, and projection fibers, supporting the last-in-first-out hypothesis. Regarding topology, two hubs were lost, alongside with a decrease in connector-hub inter-modular connectivity, reflecting reduced integration. Simultaneously, there was an increase in the number of provincial hubs, suggesting increased segregation. Overall, these results confirm that aging triggers a reorganization of the brain structural network.
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Affiliation(s)
- Ana Coelho
- Life and Health Sciences Research Institute (ICVS), School of MedicineUniversity of MinhoBragaPortugal
- ICVS/3B’s, PT Government Associate LaboratoryBraga/GuimarãesPortugal
- Clinical Academic Center – BragaBragaPortugal
| | - Henrique M. Fernandes
- Center for Music in the Brain (MIB)Aarhus UniversityAarhusDenmark
- Department of PsychiatryUniversity of OxfordOxfordUK
| | - Ricardo Magalhães
- Life and Health Sciences Research Institute (ICVS), School of MedicineUniversity of MinhoBragaPortugal
- ICVS/3B’s, PT Government Associate LaboratoryBraga/GuimarãesPortugal
- Clinical Academic Center – BragaBragaPortugal
| | - Pedro S. Moreira
- Life and Health Sciences Research Institute (ICVS), School of MedicineUniversity of MinhoBragaPortugal
- ICVS/3B’s, PT Government Associate LaboratoryBraga/GuimarãesPortugal
- Clinical Academic Center – BragaBragaPortugal
| | - Paulo Marques
- Life and Health Sciences Research Institute (ICVS), School of MedicineUniversity of MinhoBragaPortugal
- ICVS/3B’s, PT Government Associate LaboratoryBraga/GuimarãesPortugal
- Clinical Academic Center – BragaBragaPortugal
| | - José M. Soares
- Life and Health Sciences Research Institute (ICVS), School of MedicineUniversity of MinhoBragaPortugal
- ICVS/3B’s, PT Government Associate LaboratoryBraga/GuimarãesPortugal
- Clinical Academic Center – BragaBragaPortugal
| | - Liliana Amorim
- Life and Health Sciences Research Institute (ICVS), School of MedicineUniversity of MinhoBragaPortugal
- ICVS/3B’s, PT Government Associate LaboratoryBraga/GuimarãesPortugal
- Clinical Academic Center – BragaBragaPortugal
| | - Carlos Portugal‐Nunes
- Life and Health Sciences Research Institute (ICVS), School of MedicineUniversity of MinhoBragaPortugal
- ICVS/3B’s, PT Government Associate LaboratoryBraga/GuimarãesPortugal
- Clinical Academic Center – BragaBragaPortugal
| | - Teresa Castanho
- Life and Health Sciences Research Institute (ICVS), School of MedicineUniversity of MinhoBragaPortugal
- ICVS/3B’s, PT Government Associate LaboratoryBraga/GuimarãesPortugal
- Clinical Academic Center – BragaBragaPortugal
| | - Nadine Correia Santos
- Life and Health Sciences Research Institute (ICVS), School of MedicineUniversity of MinhoBragaPortugal
- ICVS/3B’s, PT Government Associate LaboratoryBraga/GuimarãesPortugal
- Clinical Academic Center – BragaBragaPortugal
| | - Nuno Sousa
- Life and Health Sciences Research Institute (ICVS), School of MedicineUniversity of MinhoBragaPortugal
- ICVS/3B’s, PT Government Associate LaboratoryBraga/GuimarãesPortugal
- Clinical Academic Center – BragaBragaPortugal
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19
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Fischer FU, Wolf D, Tüscher O, Fellgiebel A. Structural Network Efficiency Predicts Resilience to Cognitive Decline in Elderly at Risk for Alzheimer's Disease. Front Aging Neurosci 2021; 13:637002. [PMID: 33692682 PMCID: PMC7937862 DOI: 10.3389/fnagi.2021.637002] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2020] [Accepted: 01/25/2021] [Indexed: 01/10/2023] Open
Abstract
Introduction: Functional imaging studies have demonstrated the recruitment of additional neural resources as a possible mechanism to compensate for age and Alzheimer's disease (AD)-related cerebral pathology, the efficacy of which is potentially modulated by underlying structural network connectivity. Additionally, structural network efficiency (SNE) is associated with intelligence across the lifespan, which is a known factor for resilience to cognitive decline. We hypothesized that SNE may be a surrogate of the physiological basis of resilience to cognitive decline in elderly persons without dementia and with age- and AD-related cerebral pathology.Methods: We included 85 cognitively normal elderly subjects or mild cognitive impairment (MCI) patients submitted to baseline diffusion imaging, liquor specimens, amyloid-PET and longitudinal cognitive assessments. SNE was calculated from baseline MRI scans using fiber tractography and graph theory. Mixed linear effects models were estimated to investigate the association of higher resilience to cognitive decline with higher SNE and the modulation of this association by increased cerebral amyloid, liquor tau or WMHV. Results: For the majority of cognitive outcome measures, higher SNE was associated with higher resilience to cognitive decline (p-values: 0.011-0.039). Additionally, subjects with higher SNE showed more resilience to cognitive decline at higher cerebral amyloid burden (p-values: <0.001-0.036) and lower tau levels (p-values: 0.002-0.015).Conclusion: These results suggest that SNE to some extent may quantify the physiological basis of resilience to cognitive decline most effective at the earliest stages of AD, namely at increased amyloid burden and before increased tauopathy.
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Affiliation(s)
- Florian U. Fischer
- Department of Psychiatry and Psychotherapy, University Medical Center Mainz, Johannes Gutenberg University Mainz, Mainz, Germany
- Center for Mental Health in Old Age, Landeskrankenhaus (AöR), Mainz, Germany
| | - Dominik Wolf
- Department of Psychiatry and Psychotherapy, University Medical Center Mainz, Johannes Gutenberg University Mainz, Mainz, Germany
- Center for Mental Health in Old Age, Landeskrankenhaus (AöR), Mainz, Germany
| | - Oliver Tüscher
- Department of Psychiatry and Psychotherapy, University Medical Center Mainz, Johannes Gutenberg University Mainz, Mainz, Germany
- Leibniz Institute for Resilience Research (LIR), Mainz, Germany
| | - Andreas Fellgiebel
- Department of Psychiatry and Psychotherapy, University Medical Center Mainz, Johannes Gutenberg University Mainz, Mainz, Germany
- Center for Mental Health in Old Age, Landeskrankenhaus (AöR), Mainz, Germany
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Arithmetic success and gender-based characterization of brain connectivity across EEG bands. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2020.102222] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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21
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Satary Dizaji A, Vieira BH, Khodaei MR, Ashrafi M, Parham E, Hosseinzadeh GA, Salmon CEG, Soltanianzadeh H. Linking Brain Biology to Intellectual Endowment: A Review on the Associations of Human Intelligence With Neuroimaging Data. Basic Clin Neurosci 2021; 12:1-28. [PMID: 33995924 PMCID: PMC8114859 DOI: 10.32598/bcn.12.1.574.1] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2020] [Revised: 05/10/2020] [Accepted: 10/28/2020] [Indexed: 11/20/2022] Open
Abstract
Human intelligence has always been a fascinating subject for scientists. Since the inception of Spearman's general intelligence in the early 1900s, there has been significant progress towards characterizing different aspects of intelligence and its relationship with structural and functional features of the brain. In recent years, the invention of sophisticated brain imaging devices using Diffusion-Weighted Imaging (DWI) and functional Magnetic Resonance Imaging (fMRI) has allowed researchers to test hypotheses about neural correlates of intelligence in humans.This review summarizes recent findings on the associations of human intelligence with neuroimaging data. To this end, first, we review the literature that has related brain morphometry to intelligence. Next, we elaborate on the applications of DWI and restingstate fMRI on the investigation of intelligence. Then, we provide a survey of literature that has used multimodal DWI-fMRI to shed light on intelligence. Finally, we discuss the state-of-the-art of individualized prediction of intelligence from neuroimaging data and point out future strategies. Future studies hold promising outcomes for machine learning-based predictive frameworks using neuroimaging features to estimate human intelligence.
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Affiliation(s)
- Aslan Satary Dizaji
- Control and Intelligent Processing Center of Excellence (CIPCE), School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran
| | - Bruno Hebling Vieira
- Inbrain Lab, Department of Physics, FFCLRP, University of São Paulo, Ribeirao Preto, Brazil
| | - Mohmmad Reza Khodaei
- Control and Intelligent Processing Center of Excellence (CIPCE), School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran
| | - Mahnaz Ashrafi
- Control and Intelligent Processing Center of Excellence (CIPCE), School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran
| | - Elahe Parham
- Control and Intelligent Processing Center of Excellence (CIPCE), School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran
| | - Gholam Ali Hosseinzadeh
- Control and Intelligent Processing Center of Excellence (CIPCE), School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran
- School of Cognitive Sciences, Institute for Research in Fundamental Sciences (IPM), Tehran, Iran
| | | | - Hamid Soltanianzadeh
- Control and Intelligent Processing Center of Excellence (CIPCE), School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran
- School of Cognitive Sciences, Institute for Research in Fundamental Sciences (IPM), Tehran, Iran
- Radiology Image Analysis Laboratory, Henry Ford Health System, Detroit, USA
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Relationship between the disrupted topological efficiency of the structural brain connectome and glucose hypometabolism in normal aging. Neuroimage 2020; 226:117591. [PMID: 33248254 DOI: 10.1016/j.neuroimage.2020.117591] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2020] [Revised: 11/02/2020] [Accepted: 11/19/2020] [Indexed: 12/18/2022] Open
Abstract
Normal aging is accompanied by structural degeneration and glucose hypometabolism in the human brain. However, the relationship between structural network disconnections and hypometabolism in normal aging remains largely unknown. In the present study, by combining MRI and PET techniques, we investigated the metabolic mechanism of the structural brain connectome and its relationship with normal aging in a cross-sectional, community-based cohort of 42 cognitively normal elderly individuals aged 57-84 years. The structural connectome was constructed based on diffusion MRI tractography, and the network efficiency metrics were quantified using graph theory analyses. FDG-PET scanning was performed to evaluate the glucose metabolic level in the cortical regions of the individuals. The results of this study demonstrated that both network efficiency and cortical metabolism decrease with age (both p < 0.05). In the subregions of the bilateral thalamus, significant correlations between nodal efficiency and cortical metabolism could be observed across subjects. Individual-level analyses indicated that brain regions with higher nodal efficiency tend to exhibit higher metabolic levels, implying a tight coupling between nodal efficiency and glucose metabolism (r = 0.56, p = 1.15 × 10-21). Moreover, efficiency-metabolism coupling coefficient significantly increased with age (r = 0.44, p = 0.0046). Finally, the main findings were also reproducible in the ADNI dataset. Together, our results demonstrate a close coupling between structural brain connectivity and cortical metabolism in normal elderly individuals and provide new insight that improve the present understanding of the metabolic mechanisms of structural brain disconnections in normal aging.
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Fischer FU, Wolf D, Fellgiebel A. Connectivity and morphology of hubs of the cerebral structural connectome are associated with brain resilience in AD- and age-related pathology. Brain Imaging Behav 2020; 13:1650-1664. [PMID: 30980275 DOI: 10.1007/s11682-019-00090-y] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
The physiological basis of resilience to age-associated and AD-typical neurodegenerative pathology is still not well understood. So far, the established resilience factor intelligence has been shown to be associated with white matter network global efficiency, a key constituent of which are highly connected hubs. However, hub properties have also been shown to be impaired in AD. Individual predisposition or vulnerability of hub properties may thus modulate the impact of pathology on cognitive outcome and form part of the physiological basis of resilience. 85 cognitively normal elderly subjects and patients with MCI with DWI, MRI and AV45-PET scans were included from ADNI. We reconstructed the global WM networks in each subject and characterized hub-properties of GM regions using graph theory by calculating regional betweenness centrality. Subsequently, we investigated whether regional GM volume (GMV) and structural WM connectivity (WMC) of more hub-like regions was more associated with resilience, quantified as cognitive performance independent of amyloid burden, tau and WM lesions. Subjects with higher resilience showed higher increased regional GMV and WMC in more hub-like compared to less hub-like GM-regions. Additionally, this association was in some instances further increased at elevated amounts of brain pathology. Higher GMV and WMC of more hub-like regions may contribute more to resilience compared to less hub-like regions, reflecting their increased importance to brain network efficiency, and may thus form part of the neurophysiological basis of resilience. Future studies should investigate the factors leading to higher GMV and WMC of hubs in non-demented elderly with higher resilience.
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Affiliation(s)
- Florian U Fischer
- Department of Psychiatry and Psychotherapy, University Medical Center Mainz, Untere Zahlbacher Str. 8, 55131, Mainz, Germany. .,Center for Mental Health in Old Age, Landeskrankenhaus (AöR), Hartmühlenweg 2-4, 55122, Mainz, Germany.
| | - Dominik Wolf
- Department of Psychiatry and Psychotherapy, University Medical Center Mainz, Untere Zahlbacher Str. 8, 55131, Mainz, Germany.,Center for Mental Health in Old Age, Landeskrankenhaus (AöR), Hartmühlenweg 2-4, 55122, Mainz, Germany
| | - Andreas Fellgiebel
- Department of Psychiatry and Psychotherapy, University Medical Center Mainz, Untere Zahlbacher Str. 8, 55131, Mainz, Germany.,Center for Mental Health in Old Age, Landeskrankenhaus (AöR), Hartmühlenweg 2-4, 55122, Mainz, Germany
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Ahsan SA, Chendeb K, Briggs RG, Fletcher LR, Jones RG, Chakraborty AR, Nix CE, Jacobs CC, Lack AM, Griffin DT, Teo C, Sughrue ME. Beyond eloquence and onto centrality: a new paradigm in planning supratentorial neurosurgery. J Neurooncol 2020; 146:229-238. [PMID: 31894519 DOI: 10.1007/s11060-019-03327-4] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2019] [Accepted: 10/31/2019] [Indexed: 01/20/2023]
Abstract
PURPOSE Minimizing post-operational neurological deficits as a result of brain surgery has been one of the most pertinent endeavours of neurosurgical research. Studies have utilised fMRIs, EEGs and MEGs in order to delineate and establish eloquent areas, however, these methods have not been utilized by the wider neurosurgical community due to a lack of clinical endpoints. We sought to ascertain if there is a correlation between graph theory metrics and the neurosurgical notion of eloquent brain regions. We also wanted to establish which graph theory based nodal centrality measure performs the best in predicting eloquent areas. METHODS We obtained diffusion neuroimaging data from the Human Connectome Project (HCP) and applied a parcellation scheme to it. This enabled us to construct a weighted adjacency matrix which we then analysed. Our analysis looked at the correlation between PageRank centrality and eloquent areas. We then compared PageRank centrality to eigenvector centrality and degree centrality to see what the best measure of empirical neurosurgical eloquence was. RESULTS Areas that are considered neurosurgically eloquent tended to be predicted by high PageRank centrality. By using summary scores for the three nodal centrality measures we found that PageRank centrality best correlated to empirical neurosurgical eloquence. CONCLUSION The notion of eloquent areas is important to neurosurgery and graph theory provides a mathematical framework to predict these areas. PageRank centrality is able to consistently find areas that we consider eloquent. It is able to do so better than eigenvector and degree central measures.
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Affiliation(s)
- Syed Ali Ahsan
- Center for Minimally Invasive Neurosurgery, Prince of Wales Private Hospital, Suite 3, Level 7, Barker Street, Randwick, Sydney, NSW, 2031, Australia
| | - Kassem Chendeb
- Center for Minimally Invasive Neurosurgery, Prince of Wales Private Hospital, Suite 3, Level 7, Barker Street, Randwick, Sydney, NSW, 2031, Australia
| | - Robert G Briggs
- Department of Neurosurgery, University of Oklahoma Health Science Center, Oklahoma City, OK, USA
| | - Luke R Fletcher
- Department of Neurosurgery, University of Oklahoma Health Science Center, Oklahoma City, OK, USA
| | - Ryan G Jones
- Department of Neurosurgery, University of Oklahoma Health Science Center, Oklahoma City, OK, USA
| | - Arpan R Chakraborty
- Department of Neurosurgery, University of Oklahoma Health Science Center, Oklahoma City, OK, USA
| | - Cameron E Nix
- Department of Neurosurgery, University of Oklahoma Health Science Center, Oklahoma City, OK, USA
| | - Christina C Jacobs
- Department of Neurosurgery, University of Oklahoma Health Science Center, Oklahoma City, OK, USA
| | - Alison M Lack
- Department of Neurosurgery, University of Oklahoma Health Science Center, Oklahoma City, OK, USA
| | - Daniel T Griffin
- Department of Neurosurgery, University of Oklahoma Health Science Center, Oklahoma City, OK, USA
| | - Charles Teo
- Center for Minimally Invasive Neurosurgery, Prince of Wales Private Hospital, Suite 3, Level 7, Barker Street, Randwick, Sydney, NSW, 2031, Australia
| | - Michael Edward Sughrue
- Center for Minimally Invasive Neurosurgery, Prince of Wales Private Hospital, Suite 3, Level 7, Barker Street, Randwick, Sydney, NSW, 2031, Australia.
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25
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Genç E, Fraenz C, Schlüter C, Friedrich P, Voelkle MC, Hossiep R, Güntürkün O. The Neural Architecture of General Knowledge. EUROPEAN JOURNAL OF PERSONALITY 2019. [DOI: 10.1002/per.2217] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
Cognitive performance varies widely between individuals and is highly influenced by structural and functional properties of the brain. In the past, neuroscientific research was principally concerned with fluid intelligence, while neglecting its equally important counterpart crystallized intelligence. Crystallized intelligence is defined as the depth and breadth of knowledge and skills that are valued by one's culture. The accumulation of crystallized intelligence is guided by information storage capacities and is likely to be reflected in an individual's level of general knowledge. In spite of the significant role general knowledge plays for everyday life, its neural foundation largely remains unknown. In a large sample of 324 healthy individuals, we used standard magnetic resonance imaging along with functional magnetic resonance imaging and diffusion tensor imaging to examine different estimates of brain volume and brain network connectivity and assessed their predictive power with regard to both general knowledge and fluid intelligence. Our results demonstrate that an individual's level of general knowledge is associated with structural brain network connectivity beyond any confounding effects exerted by age or sex. Moreover, we found fluid intelligence to be best predicted by cortex volume in male subjects and functional network connectivity in female subjects. Combined, these findings potentially indicate different neural architectures for information storage and information processing. © 2019 European Association of Personality Psychology
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Affiliation(s)
- Erhan Genç
- Department of Psychology, Biopsychology, Ruhr University Bochum, Bochum, Germany
| | - Christoph Fraenz
- Department of Psychology, Biopsychology, Ruhr University Bochum, Bochum, Germany
| | - Caroline Schlüter
- Department of Psychology, Biopsychology, Ruhr University Bochum, Bochum, Germany
| | - Patrick Friedrich
- Department of Psychology, Biopsychology, Ruhr University Bochum, Bochum, Germany
| | - Manuel C. Voelkle
- Department of Psychology, Psychological Research Methods, Humboldt University Berlin, Berlin, Germany
| | - Rüdiger Hossiep
- Department of Psychology, Team Test Development, Ruhr University Bochum, Bochum, Germany
| | - Onur Güntürkün
- Department of Psychology, Biopsychology, Ruhr University Bochum, Bochum, Germany
- Stellenbosch Institute for Advanced Study (STIAS), Wallenberg Research Centre at Stellenbosch University, Stellenbosch, South Africa
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26
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Gicas KM, Jones AA, Panenka WJ, Giesbrecht C, Lang DJ, Vila-Rodriguez F, Leonova O, Barr AM, Procyshyn RM, Su W, Rauscher A, Vertinsky AT, Buchanan T, MacEwan GW, Thornton AE, Honer WG. Cognitive profiles and associated structural brain networks in a multimorbid sample of marginalized adults. PLoS One 2019; 14:e0218201. [PMID: 31194834 PMCID: PMC6564539 DOI: 10.1371/journal.pone.0218201] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2019] [Accepted: 05/28/2019] [Indexed: 11/18/2022] Open
Abstract
Introduction Cognition is impaired in homeless and vulnerably housed persons. Within this heterogeneous and multimorbid group, distinct profiles of cognitive dysfunction are evident. However, little is known about the underlying neurobiological substrates. Imaging structural covariance networks provides a novel investigative strategy to characterizing relationships between brain structure and function within these different cognitive subgroups. Method Participants were 208 homeless and vulnerably housed persons. Cluster analysis was used to group individuals on the basis of similarities in cognitive functioning in the areas of attention, memory, and executive functioning. The principles of graph theory were applied to construct two brain networks for each cognitive group, using measures of cortical thickness and gyrification. Global and regional network properties were compared across networks for each of the three cognitive clusters. Results Three cognitive groups were defined by: higher cognitive functioning across domains (Cluster 1); lower cognitive functioning with a decision-making strength (Cluster 3); and an intermediate group with a relative executive functioning weakness (Cluster 2). Between-group differences were observed for cortical thickness, but not gyrification networks. The lower functioning cognitive group exhibited higher segregation and reduced integration, higher centrality in select nodes, and less spatially compact modules compared with the two other groups. Conclusions The cortical thickness network differences of Cluster 3 suggest that major disruptions in structural connectivity underlie cognitive dysfunction in a subgroup of people who have a high multimorbid illness burden and who are vulnerably housed or homeless. The origins, and possible plasticity of these structure-function relationships identified with network analysis warrant further study.
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Affiliation(s)
- Kristina M. Gicas
- Department of Psychiatry, University of British Columbia, Vancouver, BC Canada
- * E-mail:
| | - Andrea A. Jones
- Department of Psychiatry, University of British Columbia, Vancouver, BC Canada
| | - William J. Panenka
- Department of Psychiatry, University of British Columbia, Vancouver, BC Canada
| | | | - Donna J. Lang
- Department of Radiology, University of British Columbia, Vancouver, BC Canada
| | | | - Olga Leonova
- Department of Psychiatry, University of British Columbia, Vancouver, BC Canada
| | - Alasdair M. Barr
- Department of Anesthesiology, Pharmacology and Therapeutics, University of British Columbia, Vancouver, BC Canada
| | - Ric M. Procyshyn
- Department of Psychiatry, University of British Columbia, Vancouver, BC Canada
| | - Wayne Su
- Department of Psychiatry, University of British Columbia, Vancouver, BC Canada
| | - Alexander Rauscher
- Department of Paediatrics, University of British Columbia, Vancouver, BC Canada
| | - A. Talia Vertinsky
- Department of Radiology, University of British Columbia, Vancouver, BC Canada
| | - Tari Buchanan
- Department of Psychiatry, University of British Columbia, Vancouver, BC Canada
| | - G. William MacEwan
- Department of Psychiatry, University of British Columbia, Vancouver, BC Canada
| | - Allen E. Thornton
- Department of Psychology, Simon Fraser University, Burnaby, BC Canada
| | - William G. Honer
- Department of Psychiatry, University of British Columbia, Vancouver, BC Canada
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27
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Optimization of graph construction can significantly increase the power of structural brain network studies. Neuroimage 2019; 199:495-511. [PMID: 31176831 PMCID: PMC6693529 DOI: 10.1016/j.neuroimage.2019.05.052] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2019] [Revised: 04/08/2019] [Accepted: 05/19/2019] [Indexed: 12/31/2022] Open
Abstract
Structural brain networks derived from diffusion magnetic resonance imaging data have been used extensively to describe the human brain, and graph theory has allowed quantification of their network properties. Schemes used to construct the graphs that represent the structural brain networks differ in the metrics they use as edge weights and the algorithms they use to define the network topologies. In this work, twenty graph construction schemes were considered. The schemes use the number of streamlines, the fractional anisotropy, the mean diffusivity or other attributes of the tracts to define the edge weights, and either an absolute threshold or a data-driven algorithm to define the graph topology. The test-retest data of the Human Connectome Project were used to compare the reproducibility of the graphs and their various attributes (edges, topologies, graph theoretical metrics) derived through those schemes, for diffusion images acquired with three different diffusion weightings. The impact of the scheme on the statistical power of the study and on the number of participants required to detect a difference between populations or an effect of an intervention was also calculated. The reproducibility of the graphs and their attributes depended heavily on the graph construction scheme. Graph reproducibility was higher for schemes that used thresholding to define the graph topology, while data-driven schemes performed better at topology reproducibility (mean similarities of 0.962 and 0.984 respectively, for graphs derived from diffusion images with b=2000 s/mm2). Additionally, schemes that used thresholding resulted in better reproducibility for local graph theoretical metrics (intra-class correlation coefficients (ICC) of the order of 0.8), compared to data-driven schemes. Thresholded and data-driven schemes resulted in high (0.86 or higher) ICCs only for schemes that use exclusively the number of streamlines to construct the graphs. Crucially, the number of participants required to detect a difference between populations or an effect of an intervention could change by a factor of two or more depending on the scheme used, affecting the power of studies to reveal the effects of interest.
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Mutual Information Better Quantifies Brain Network Architecture in Children with Epilepsy. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2018; 2018:6142898. [PMID: 30425750 PMCID: PMC6217888 DOI: 10.1155/2018/6142898] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/07/2018] [Revised: 08/06/2018] [Accepted: 09/18/2018] [Indexed: 01/01/2023]
Abstract
Purpose Metrics of the brain network architecture derived from resting-state fMRI have been shown to provide physiologically meaningful markers of IQ in children with epilepsy. However, traditional measures of functional connectivity (FC), specifically the Pearson correlation, assume a dominant linear relationship between BOLD time courses; this assumption may not be valid. Mutual information is an alternative measure of FC which has shown promise in the study of complex networks due to its ability to flexibly capture association of diverse forms. We aimed to compare network metrics derived from mutual information-defined FC to those derived from traditional correlation in terms of their capacity to predict patient-level IQ. Materials and Methods Patients were retrospectively identified with the following: (1) focal epilepsy; (2) resting-state fMRI; and (3) full-scale IQ by a neuropsychologist. Brain network nodes were defined by anatomic parcellation. Parcellation was performed at the size threshold of 350 mm2, resulting in networks containing 780 nodes. Whole-brain, weighted graphs were then constructed according to the pairwise connectivity between nodes. In the traditional condition, edges (connections) between each pair of nodes were defined as the absolute value of the Pearson correlation coefficient between their BOLD time courses. In the mutual information condition, edges were defined as the mutual information between time courses. The following metrics were then calculated for each weighted graph: clustering coefficient, modularity, characteristic path length, and global efficiency. A machine learning algorithm was used to predict the IQ of each individual based on their network metrics. Prediction accuracy was assessed as the fractional variation explained for each condition. Results Twenty-four patients met the inclusion criteria (age: 8-18 years). All brain networks demonstrated expected small-world properties. Network metrics derived from mutual information-defined FC significantly outperformed the use of the Pearson correlation. Specifically, fractional variation explained was 49% (95% CI: 46%, 51%) for the mutual information method; the Pearson correlation demonstrated a variation of 17% (95% CI: 13%, 19%). Conclusion Mutual information-defined functional connectivity captures physiologically relevant features of the brain network better than correlation. Clinical Relevance Optimizing the capacity to predict cognitive phenotypes at the patient level is a necessary step toward the clinical utility of network-based biomarkers.
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29
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Weiler M, Casseb RF, de Campos BM, de Ligo Teixeira CV, Carletti-Cassani AFMK, Vicentini JE, Magalhães TNC, de Almeira DQ, Talib LL, Forlenza OV, Balthazar MLF, Castellano G. Cognitive Reserve Relates to Functional Network Efficiency in Alzheimer's Disease. Front Aging Neurosci 2018; 10:255. [PMID: 30186154 PMCID: PMC6111617 DOI: 10.3389/fnagi.2018.00255] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2017] [Accepted: 08/02/2018] [Indexed: 12/15/2022] Open
Abstract
Alzheimer’s disease (AD) is the most common form of dementia, with no means of cure or prevention. The presence of abnormal disease-related proteins in the population is, in turn, much more common than the incidence of dementia. In this context, the cognitive reserve (CR) hypothesis has been proposed to explain the discontinuity between pathophysiological and clinical expression of AD, suggesting that CR mitigates the effects of pathology on clinical expression and cognition. fMRI studies of the human connectome have recently reported that AD patients present diminished functional efficiency in resting-state networks, leading to a loss in information flow and cognitive processing. No study has investigated, however, whether CR modifies the effects of the pathology in functional network efficiency in AD patients. We analyzed the relationship between CR, pathophysiology and network efficiency, and whether CR modifies the relationship between them. Fourteen mild AD, 28 amnestic mild cognitive impairment (aMCI) due to AD, and 28 controls were enrolled. We used education to measure CR, cerebrospinal fluid (CSF) biomarkers to evaluate pathophysiology, and graph metrics to measure network efficiency. We found no relationship between CR and CSF biomarkers; CR was related to higher network efficiency in all groups; and abnormal levels of CSF protein biomarkers were related to more efficient networks in the AD group. Education modified the effects of tau-related pathology in the aMCI and mild AD groups. Although higher CR might not protect individuals from developing AD pathophysiology, AD patients with higher CR are better able to cope with the effects of pathology—presenting more efficient networks despite pathology burden. The present study highlights that interventions focusing on cognitive stimulation might be useful to slow age-related cognitive decline or dementia and lengthen healthy aging.
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Affiliation(s)
- Marina Weiler
- Neurophysics Group, Institute of Physics Gleb Wataghin, Cosmic Rays and Chronology Department, University of Campinas (UNICAMP), Campinas, Brazil.,Neuroimaging Laboratory, School of Medical Sciences, University of Campinas (UNICAMP), Campinas, Brazil
| | - Raphael Fernandes Casseb
- Neurophysics Group, Institute of Physics Gleb Wataghin, Cosmic Rays and Chronology Department, University of Campinas (UNICAMP), Campinas, Brazil.,Neuroimaging Laboratory, School of Medical Sciences, University of Campinas (UNICAMP), Campinas, Brazil
| | - Brunno Machado de Campos
- Neuroimaging Laboratory, School of Medical Sciences, University of Campinas (UNICAMP), Campinas, Brazil
| | | | | | - Jéssica Elias Vicentini
- Neuroimaging Laboratory, School of Medical Sciences, University of Campinas (UNICAMP), Campinas, Brazil
| | | | - Débora Queiroz de Almeira
- Neuroimaging Laboratory, School of Medical Sciences, University of Campinas (UNICAMP), Campinas, Brazil
| | - Leda Leme Talib
- Laboratório de Neurociências (LIM-27), Departamento e Instituto de Psiquiatria, Hospital das Clínicas da Faculdade de Medicina, Universidade de São Paulo (USP), São Paulo, Brazil
| | - Orestes Vicente Forlenza
- Laboratório de Neurociências (LIM-27), Departamento e Instituto de Psiquiatria, Hospital das Clínicas da Faculdade de Medicina, Universidade de São Paulo (USP), São Paulo, Brazil
| | | | - Gabriela Castellano
- Neurophysics Group, Institute of Physics Gleb Wataghin, Cosmic Rays and Chronology Department, University of Campinas (UNICAMP), Campinas, Brazil.,Brazilian Institute of Neuroscience and Neurotechnology (BRAINN), Campinas, Brazil
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White matter network topology relates to cognitive flexibility and cumulative neurological risk in adult survivors of pediatric brain tumors. NEUROIMAGE-CLINICAL 2018; 20:485-497. [PMID: 30148064 PMCID: PMC6105768 DOI: 10.1016/j.nicl.2018.08.015] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/24/2018] [Revised: 07/13/2018] [Accepted: 08/09/2018] [Indexed: 01/08/2023]
Abstract
Adult survivors of pediatric brain tumors exhibit deficits in executive functioning. Given that brain tumors and medical treatments for brain tumors result in disruptions to white matter, a network analysis was used to explore the topological properties of white matter networks. This study used diffusion tensor imaging and deterministic tractography in 38 adult survivors of pediatric brain tumors (mean age in years = 23.11 (SD = 4.96), 54% female, mean years post diagnosis = 14.09 (SD = 6.19)) and 38 healthy peers matched by age, gender, handedness, and socioeconomic status. Nodes were defined using the Automated Anatomical Labeling (AAL) parcellation scheme, and edges were defined as the mean fractional anisotropy of streamlines that connected each node pair. Global efficiency and average clustering coefficient were reduced in survivors compared to healthy peers with preferential impact to hub regions. Global efficiency mediated differences in cognitive flexibility between survivors and healthy peers, as well as the relationship between cumulative neurological risk and cognitive flexibility. These results suggest that adult survivors of pediatric brain tumors, on average one and a half decades post brain tumor diagnosis and treatment, exhibit altered white matter topology in the form of suboptimal integration and segregation of large scale networks, and that disrupted topology may underlie executive functioning impairments. Network based studies provided important topographic insights on network organization in long-term survivors of pediatric brain tumor. Long term brain tumor survivorship is associated with altered white matter networks. Hub regions were preferentially impacted in survivors. Network properties explain cognitive flexibility differences between survivors and peers.
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31
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Hindle JV, Martin-Forbes PA, Martyr A, Bastable AJM, Pye KL, Mueller Gathercole VC, Thomas EM, Clare L. The effects of lifelong cognitive lifestyle on executive function in older people with Parkinson's disease. Int J Geriatr Psychiatry 2017; 32:e157-e165. [PMID: 28170111 DOI: 10.1002/gps.4677] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/19/2016] [Accepted: 01/11/2017] [Indexed: 11/07/2022]
Abstract
OBJECTIVE Active lifelong cognitive lifestyles increase cognitive reserve and have beneficial effects on global cognition, cognitive decline and dementia risk in Parkinson's disease (PD). Executive function is particularly impaired even in early PD, and this impacts on quality of life. The effects of lifelong cognitive lifestyle on executive function in PD have not been studied previously. This study examined the association between lifelong cognitive lifestyle, as a proxy measure of cognitive reserve, and executive function in people with PD. METHODS Sixty-nine people diagnosed with early PD without dementia were recruited as part of the Bilingualism as a protective factor in Age-related Neurodegenerative Conditions study. Participants completed a battery of tests of executive function. The Lifetime of Experiences Questionnaire was completed as a comprehensive assessment of lifelong cognitive lifestyle. Non-parametric correlations compared clinical measures with executive function scores. Cross-sectional analyses of covariance were performed comparing the performance of low and high cognitive reserve groups on executive function tests. RESULTS Correlational analyses showed that better executive function scores were associated with younger age, higher levodopa dose and higher Lifetime of Experiences Questionnaire scores. Higher cognitive reserve was associated with better motor function, but high and low cognitive reserve groups did not differ in executive function. CONCLUSIONS Cognitive reserve, although associated with global cognition, does not appear to be associated with executive function. This differential effect may reflect the specific cognitive profile of PD. The long-term effects of cognitive reserve on executive function in PD require further exploration. Copyright © 2017 John Wiley & Sons, Ltd.
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Affiliation(s)
- John V Hindle
- Bangor University, Bangor, Gwynedd, UK.,Betsi Cadwaladr University Health Board, Llandudno Hospital, Conwy, UK
| | - Pamela A Martin-Forbes
- Bangor University, Bangor, Gwynedd, UK.,NISCHR CRC North Wales Research Network, Bangor University, Bangor, Gwynedd, UK
| | - Anthony Martyr
- Centre for Research in Ageing and Cognitive Health, School of Psychology, Devon, UK.,PenCLAHRC, University of Exeter, Exeter, Devon, UK
| | | | | | | | | | - Linda Clare
- Centre for Research in Ageing and Cognitive Health, School of Psychology, Devon, UK.,PenCLAHRC, University of Exeter, Exeter, Devon, UK
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32
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Network attributes underlying intellectual giftedness in the developing brain. Sci Rep 2017; 7:11321. [PMID: 28900176 PMCID: PMC5596014 DOI: 10.1038/s41598-017-11593-3] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2017] [Accepted: 08/25/2017] [Indexed: 01/15/2023] Open
Abstract
Brain network is organized to maximize the efficiency of both segregated and integrated information processing that may be related to human intelligence. However, there have been surprisingly few studies that focus on the topological characteristics of brain network underlying extremely high intelligence that is intellectual giftedness, particularly in adolescents. Here, we examined the network topology in 25 adolescents with superior intelligence (SI-Adol), 25 adolescents with average intelligence (AI-Adol), and 27 young adults with AI (AI-Adult). We found that SI-Adol had network topological properties of high global efficiency as well as high clustering with a low wiring cost, relative to AI-Adol. However, contrary to the suggested role that brain hub regions play in general intelligence, the network efficiency of rich club connection matrix, which represents connections among brain hubs, was low in SI-Adol in comparison to AI-Adol. Rather, a higher level of local connection density was observed in SI-Adol than in AI-Adol. The highly intelligent brain may not follow this efficient yet somewhat stereotypical process of information integration entirely. Taken together, our results suggest that a highly intelligent brain may communicate more extensively, while being less dependent on rich club communications during adolescence.
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Coynel D, Gschwind L, Fastenrath M, Freytag V, Milnik A, Spalek K, Papassotiropoulos A, de Quervain DJF. Picture free recall performance linked to the brain's structural connectome. Brain Behav 2017; 7:e00721. [PMID: 28729929 PMCID: PMC5516597 DOI: 10.1002/brb3.721] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/03/2017] [Revised: 03/02/2017] [Accepted: 03/31/2017] [Indexed: 11/09/2022] Open
Abstract
INTRODUCTION Memory functions are highly variable between healthy humans. The neural correlates of this variability remain largely unknown. METHODS Here, we investigated how differences in free recall performance are associated with DTI-based properties of the brain's structural connectome and with grey matter volumes in 664 healthy young individuals tested in the same MR scanner. RESULTS Global structural connectivity, but not overall or regional grey matter volumes, positively correlated with recall performance. Moreover, a set of 22 inter-regional connections, including some with no previously reported relation to human memory, such as the connection between the temporal pole and the nucleus accumbens, explained 7.8% of phenotypic variance. CONCLUSIONS In conclusion, this large-scale study indicates that individual memory performance is associated with the level of structural brain connectivity.
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Affiliation(s)
- David Coynel
- Division of Cognitive Neuroscience Department of Psychology University of Basel Basel Switzerland.,Transfaculty Research Platform University of Basel Basel Switzerland
| | - Leo Gschwind
- Division of Cognitive Neuroscience Department of Psychology University of Basel Basel Switzerland.,Transfaculty Research Platform University of Basel Basel Switzerland.,Division of Molecular Neuroscience Department of Psychology University of Basel Basel Switzerland
| | - Matthias Fastenrath
- Division of Cognitive Neuroscience Department of Psychology University of Basel Basel Switzerland.,Transfaculty Research Platform University of Basel Basel Switzerland
| | - Virginie Freytag
- Transfaculty Research Platform University of Basel Basel Switzerland.,Division of Molecular Neuroscience Department of Psychology University of Basel Basel Switzerland
| | - Annette Milnik
- Transfaculty Research Platform University of Basel Basel Switzerland.,Division of Molecular Neuroscience Department of Psychology University of Basel Basel Switzerland.,Psychiatric University Clinics University of Basel Basel Switzerland
| | - Klara Spalek
- Division of Cognitive Neuroscience Department of Psychology University of Basel Basel Switzerland.,Transfaculty Research Platform University of Basel Basel Switzerland
| | - Andreas Papassotiropoulos
- Transfaculty Research Platform University of Basel Basel Switzerland.,Division of Molecular Neuroscience Department of Psychology University of Basel Basel Switzerland.,Psychiatric University Clinics University of Basel Basel Switzerland.,Department Biozentrum Life Sciences Training Facility University of Basel Basel Switzerland
| | - Dominique J-F de Quervain
- Division of Cognitive Neuroscience Department of Psychology University of Basel Basel Switzerland.,Transfaculty Research Platform University of Basel Basel Switzerland.,Psychiatric University Clinics University of Basel Basel Switzerland
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34
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Amidi A, Hosseini SMH, Leemans A, Kesler SR, Agerbæk M, Wu LM, Zachariae R. Changes in Brain Structural Networks and Cognitive Functions in Testicular Cancer Patients Receiving Cisplatin-Based Chemotherapy. J Natl Cancer Inst 2017; 109:3855270. [DOI: 10.1093/jnci/djx085] [Citation(s) in RCA: 56] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2016] [Accepted: 04/04/2017] [Indexed: 11/13/2022] Open
Affiliation(s)
- Ali Amidi
- Unit for Psychooncology and Health Psychology, Department of Oncology and Department of Psychology and Behavioural Sciences, Aarhus University Hospital and Aarhus University, Aarhus, Denmark
| | - S M Hadi Hosseini
- Department of Psychiatry and Behavioral Sciences, School of Medicine, Stanford University, Stanford, CA
| | - Alexander Leemans
- Image Sciences Institute, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Shelli R Kesler
- Department of Neuro-oncology, University of Texas MD Anderson Cancer Center, Houston, TX
| | - Mads Agerbæk
- Department of Oncology, Aarhus University Hospital, Aarhus, Denmark
| | - Lisa M Wu
- Department of Medical Social Sciences, Northwestern University Feinberg School of Medicine, Chicago, IL
| | - Robert Zachariae
- Unit for Psychooncology and Health Psychology, Department of Oncology and Department of Psychology and Behavioural Sciences, Aarhus University Hospital and Aarhus University, Aarhus, Denmark
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35
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Chen JH, Yao ZJ, Qin JL, Yan R, Hua LL, Lu Q. Aberrant Global and Regional Topological Organization of the Fractional Anisotropy-weighted Brain Structural Networks in Major Depressive Disorder. Chin Med J (Engl) 2017; 129:679-89. [PMID: 26960371 PMCID: PMC4804414 DOI: 10.4103/0366-6999.178002] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022] Open
Abstract
Background: Most previous neuroimaging studies have focused on the structural and functional abnormalities of local brain regions in major depressive disorder (MDD). Moreover, the exactly topological organization of networks underlying MDD remains unclear. This study examined the aberrant global and regional topological patterns of the brain white matter networks in MDD patients. Methods: The diffusion tensor imaging data were obtained from 27 patients with MDD and 40 healthy controls. The brain fractional anisotropy-weighted structural networks were constructed, and the global network and regional nodal metrics of the networks were explored by the complex network theory. Results: Compared with the healthy controls, the brain structural network of MDD patients showed an intact small-world topology, but significantly abnormal global network topological organization and regional nodal characteristic of the network in MDD were found. Our findings also indicated that the brain structural networks in MDD patients become a less strongly integrated network with a reduced central role of some key brain regions. Conclusions: All these resulted in a less optimal topological organization of networks underlying MDD patients, including an impaired capability of local information processing, reduced centrality of some brain regions and limited capacity to integrate information across different regions. Thus, these global network and regional node-level aberrations might contribute to understanding the pathogenesis of MDD from the view of the brain network.
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Affiliation(s)
| | - Zhi-Jian Yao
- Department of Psychiatry, Affiliated Nanjing Brain Hospital, Nanjing Medical University, Nanjing, Jiangsu 210029, China
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36
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Li X, Steffens DC, Potter GG, Guo H, Song S, Wang L. Decreased between-hemisphere connectivity strength and network efficiency in geriatric depression. Hum Brain Mapp 2017; 38:53-67. [PMID: 27503772 PMCID: PMC5899899 DOI: 10.1002/hbm.23343] [Citation(s) in RCA: 31] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2016] [Accepted: 07/29/2016] [Indexed: 12/25/2022] Open
Abstract
White matter (WM) lesions have been recognized as a key etiological factor in geriatric depression. However, little is known about the topological pattern changes of WM in geriatric depression in the remitted state (RGD) and its relationship to depressive episodes. To address these questions, we acquired diffusion tensor images in 24 RGD and 24 healthy participants. Among them, 10 patients and 19 healthy controls completed a 1-year follow up. Between-hemisphere connectivity and graph theoretical methods were used to analyze the data. We found significantly reduced WM connectivity between the left and right hemisphere in the RGD group compared with the control group. Those with multiple depression episodes had greater reduction in between-hemisphere connectivity strength than those with fewer episodes. In addition, the RGD group had a reduced global clustering coefficient, global efficiency, and network strength, and an increased shortest path length compared with the controls. A lower clustering coefficient was correlated with poorer memory function. The reduction of nodal clustering coefficient, global efficiency, and network strength in several regions were associated with slower information processing speed. At 1-year follow up, the network properties in the RGD subjects were significantly changed suggesting instability of WM network properties of depressed patients. Together, our study provides direct evidence of reduced between-hemisphere WM connectivity with greater depressive episodes, and of alterations of network properties with cognitive dysfunction in geriatric depression. Hum Brain Mapp 38:53-67, 2017. © 2016 Wiley Periodicals, Inc.
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Affiliation(s)
- Xuesong Li
- Department of Biomedical EngineeringSchool of Medicine, Tsinghua UniversityBeijingChina
| | - David C. Steffens
- Department of PsychiatryUniversity of Connecticut School of MedicineFarmingtonConnecticut
| | - Guy G. Potter
- Department of Psychiatry and Behavioral ScienceDuke UniversityDurhamNorth Carolina
| | - Hua Guo
- Department of Biomedical EngineeringSchool of Medicine, Tsinghua UniversityBeijingChina
| | - Sen Song
- Department of Biomedical EngineeringSchool of Medicine, Tsinghua UniversityBeijingChina
| | - Lihong Wang
- Department of PsychiatryUniversity of Connecticut School of MedicineFarmingtonConnecticut
- Department of Psychiatry and Behavioral ScienceDuke UniversityDurhamNorth Carolina
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37
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Efficient hubs in the intelligent brain: Nodal efficiency of hub regions in the salience network is associated with general intelligence. INTELLIGENCE 2017. [DOI: 10.1016/j.intell.2016.11.001] [Citation(s) in RCA: 66] [Impact Index Per Article: 9.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/28/2023]
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38
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Development of brain networks and relevance of environmental and genetic factors: A systematic review. Neurosci Biobehav Rev 2016; 71:215-239. [DOI: 10.1016/j.neubiorev.2016.08.024] [Citation(s) in RCA: 44] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2016] [Revised: 07/10/2016] [Accepted: 08/23/2016] [Indexed: 01/25/2023]
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39
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The effect of IDH1 mutation on the structural connectome in malignant astrocytoma. J Neurooncol 2016; 131:565-574. [PMID: 27848136 DOI: 10.1007/s11060-016-2328-1] [Citation(s) in RCA: 53] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2016] [Accepted: 11/08/2016] [Indexed: 12/11/2022]
Abstract
Mutation of the IDH1 gene is associated with differences in malignant astrocytoma growth characteristics that impact phenotypic severity, including cognitive impairment. We previously demonstrated greater cognitive impairment in patients with IDH1 wild type tumor compared to those with IDH1 mutant, and therefore we hypothesized that brain network organization would be lower in patients with wild type tumors. Volumetric, T1-weighted MRI scans were obtained retrospectively from 35 patients with IDH1 mutant and 32 patients with wild type malignant astrocytoma (mean age = 45 ± 14 years) and used to extract individual level, gray matter connectomes. Graph theoretical analysis was then applied to measure efficiency and other connectome properties for each patient. Cognitive performance was categorized as impaired or not and random forest classification was used to explore factors associated with cognitive impairment. Patients with wild type tumor demonstrated significantly lower network efficiency in several medial frontal, posterior parietal and subcortical regions (p < 0.05, corrected for multiple comparisons). Patients with wild type tumor also demonstrated significantly higher incidence of cognitive impairment (p = 0.03). Random forest analysis indicated that network efficiency was inversely, though nonlinearly associated with cognitive impairment in both groups (p < 0.0001). Cognitive reserve appeared to mediate this relationship in patients with mutant tumor suggesting greater neuroplasticity and/or benefit from neuroprotective factors. Tumor volume was the greatest contributor to cognitive impairment in patients with wild type tumor, supporting our hypothesis that greater lesion momentum between grades may cause more disconnection of core neurocircuitry and consequently lower efficiency of information processing.
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40
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Jiang Y, Liu W, Ming Q, Gao Y, Ma R, Zhang X, Situ W, Wang X, Yao S, Huang B. Disrupted Topological Patterns of Large-Scale Network in Conduct Disorder. Sci Rep 2016; 6:37053. [PMID: 27841320 PMCID: PMC5107936 DOI: 10.1038/srep37053] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2016] [Accepted: 10/24/2016] [Indexed: 01/10/2023] Open
Abstract
Regional abnormalities in brain structure and function, as well as disrupted connectivity, have been found repeatedly in adolescents with conduct disorder (CD). Yet, the large-scale brain topology associated with CD is not well characterized, and little is known about the systematic neural mechanisms of CD. We employed graphic theory to investigate systematically the structural connectivity derived from cortical thickness correlation in a group of patients with CD (N = 43) and healthy controls (HCs, N = 73). Nonparametric permutation tests were applied for between-group comparisons of graphical metrics. Compared with HCs, network measures including global/local efficiency and modularity all pointed to hypo-functioning in CD, despite of preserved small-world organization in both groups. The hubs distribution is only partially overlapped with each other. These results indicate that CD is accompanied by both impaired integration and segregation patterns of brain networks, and the distribution of highly connected neural network 'hubs' is also distinct between groups. Such misconfiguration extends our understanding regarding how structural neural network disruptions may underlie behavioral disturbances in adolescents with CD, and potentially, implicates an aberrant cytoarchitectonic profiles in the brain of CD patients.
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Affiliation(s)
- Yali Jiang
- Medical Psychological Institute, the Second Xiangya Hospital, Central South University, Changsha, Hunan, People’s Republic of China
| | - Weixiang Liu
- School of Biomedical Engineering, Health Science Centre, Shenzhen University, Shenzhen, Guangdong, People’s Republic of China
- Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, Shenzhen University, Shenzhen, Guangdong, People’s Republic of China
| | - Qingsen Ming
- Medical Psychological Institute, the Second Xiangya Hospital, Central South University, Changsha, Hunan, People’s Republic of China
| | - Yidian Gao
- Medical Psychological Institute, the Second Xiangya Hospital, Central South University, Changsha, Hunan, People’s Republic of China
| | - Ren Ma
- Medical Psychological Institute, the Second Xiangya Hospital, Central South University, Changsha, Hunan, People’s Republic of China
| | - Xiaocui Zhang
- Medical Psychological Institute, the Second Xiangya Hospital, Central South University, Changsha, Hunan, People’s Republic of China
| | - Weijun Situ
- Department of Radiology, the Second Xiangya Hospital, Central South University, Changsha, Hunan, People’s Republic of China
| | - Xiang Wang
- Medical Psychological Institute, the Second Xiangya Hospital, Central South University, Changsha, Hunan, People’s Republic of China
- National Technology Institute of Psychiatry, Central South University, Changsha, Hunan, People’s Republic of China
- Key Laboratory of Psychiatry and Mental Health of Hunan Province, Central South University, Changsha, Hunan, People’s Republic of China
| | - Shuqiao Yao
- Medical Psychological Institute, the Second Xiangya Hospital, Central South University, Changsha, Hunan, People’s Republic of China
- National Technology Institute of Psychiatry, Central South University, Changsha, Hunan, People’s Republic of China
- Key Laboratory of Psychiatry and Mental Health of Hunan Province, Central South University, Changsha, Hunan, People’s Republic of China
| | - Bingsheng Huang
- Medical Psychological Institute, the Second Xiangya Hospital, Central South University, Changsha, Hunan, People’s Republic of China
- Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, Shenzhen University, Shenzhen, Guangdong, People’s Republic of China
- Shenzhen Institute of Research and Innovation, University of Hong Kong, Shenzhen, Guangdong, People’s Republic of China
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41
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Schuck F, Wolf D, Fellgiebel A, Endres K. Increase of α-Secretase ADAM10 in Platelets Along Cognitively Healthy Aging. J Alzheimers Dis 2016; 50:817-26. [PMID: 26757187 DOI: 10.3233/jad-150737] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
Abstract
ADAM10 is one of the key players in ectodomain-shedding of the amyloid-β protein precursor (AβPP). Previous research with postmortem tissue has shown reduced expression and activity of ADAM10 within the central nervous system (CNS) of Alzheimer's disease (AD) patients. Determination of cerebral ADAM10 in living humans is hampered by its transmembrane property; only the physiological AβPP cleavage product generated by ADAM10, sAβPPα, can be assessed in cerebrospinal fluid. Establishment of surrogate markers in easily accessible material therefore is crucial. It has been demonstrated that ADAM10 is expressed in platelets and that platelet amount is decreased in AD patients. Just recently it has been shown that platelet ADAM10 and cognitive performance of AD patients positively correlate. In contrast to AD patients, to our knowledge almost no information has been published regarding ADAM10 expression during normal aging. We investigated ADAM10 amount and activity in platelets of cognitively healthy individuals from three different age groups ranging from 22-85 years. Interestingly, we observed an age-dependent increase in ADAM10 levels and activity in platelets.
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42
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Graph Metrics of Structural Brain Networks in Individuals with Schizophrenia and Healthy Controls: Group Differences, Relationships with Intelligence, and Genetics. J Int Neuropsychol Soc 2016; 22:240-9. [PMID: 26888620 DOI: 10.1017/s1355617715000867] [Citation(s) in RCA: 36] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
OBJECTIVES One of the most prominent features of schizophrenia is relatively lower general cognitive ability (GCA). An emerging approach to understanding the roots of variation in GCA relies on network properties of the brain. In this multi-center study, we determined global characteristics of brain networks using graph theory and related these to GCA in healthy controls and individuals with schizophrenia. METHODS Participants (N=116 controls, 80 patients with schizophrenia) were recruited from four sites. GCA was represented by the first principal component of a large battery of neurocognitive tests. Graph metrics were derived from diffusion-weighted imaging. RESULTS The global metrics of longer characteristic path length and reduced overall connectivity predicted lower GCA across groups, and group differences were noted for both variables. Measures of clustering, efficiency, and modularity did not differ across groups or predict GCA. Follow-up analyses investigated three topological types of connectivity--connections among high degree "rich club" nodes, "feeder" connections to these rich club nodes, and "local" connections not involving the rich club. Rich club and local connectivity predicted performance across groups. In a subsample (N=101 controls, 56 patients), a genetic measure reflecting mutation load, based on rare copy number deletions, was associated with longer characteristic path length. CONCLUSIONS Results highlight the importance of characteristic path lengths and rich club connectivity for GCA and provide no evidence for group differences in the relationships between graph metrics and GCA.
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43
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Hindle JV, Hurt CS, Burn DJ, Brown RG, Samuel M, Wilson KC, Clare L. The effects of cognitive reserve and lifestyle on cognition and dementia in Parkinson's disease--a longitudinal cohort study. Int J Geriatr Psychiatry 2016; 31:13-23. [PMID: 25781584 DOI: 10.1002/gps.4284] [Citation(s) in RCA: 57] [Impact Index Per Article: 7.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/29/2014] [Revised: 02/02/2015] [Accepted: 02/17/2015] [Indexed: 11/09/2022]
Abstract
OBJECTIVE Cognitive reserve theory seeks to explain the observed mismatch between the degree of brain pathology and clinical manifestations. Early-life education, midlife social and occupational activities and later-life cognitive and social interactions are associated with a more favourable cognitive trajectory in older people. Previous studies of Parkinson's disease (PD) have suggested a possible role for the effects of cognitive reserve, but further research into different proxies for cognitive reserve and longitudinal studies is required. This study examined the effects of cognitive lifestyle on cross-sectional and longitudinal measures of cognition and dementia severity in people with PD. METHODS Baseline assessments of cognition, and of clinical, social and demographic information, were completed by 525 participants with PD. Cognitive assessments were completed by 323 participants at 4-year follow-up. Cognition was assessed using the measures of global cognition dementia severity. Cross-sectional and longitudinal serial analyses of covariance for cognition and binomial regression for dementia were performed. RESULTS Higher educational level, socio-economic status and recent social engagement were associated with better cross-sectional global cognition. In those with normal cognition at baseline, higher educational level was associated with better global cognition after 4 years. Increasing age and low levels of a measure of recent social engagement were associated with an increased risk of dementia. CONCLUSIONS Higher cognitive reserve has a beneficial effect on performance on cognitive tests and a limited effect on cognitive decline and dementia risk in PD.
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Affiliation(s)
- John V Hindle
- Department of Care of the Elderly, Betsi Cadwaladr University Health Board, Llandudno, UK.,North Wales Organisation for Randomised Trials in Health (NWORTH), Bangor University, Bangor, UK
| | | | - David J Burn
- Institute for Ageing and Health, Newcastle University, Newcastle upon Tyne, UK
| | - Richard G Brown
- Department of Psychology, Institute of Psychiatry Psychology and Neuroscience, King's College London, London, UK
| | - Mike Samuel
- Department of Neurology, King's Health Partners, King's College Hospital, London, UK.,East Kent Hospitals NHS University Foundation Trust, Kent, UK
| | - Kenneth C Wilson
- EMI Academic Unit, St Catherine's Hospital, University of Liverpool, Wirral, UK
| | - Linda Clare
- School of Psychology, Exeter University, Devon, UK.,School of Psychology, Bangor University, Gwynedd, UK
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44
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Bohlken MM, Brouwer RM, Mandl RC, Hedman AM, van den Heuvel MP, van Haren NE, Kahn RS, Hulshoff Pol HE. Topology of genetic associations between regional gray matter volume and intellectual ability: Evidence for a high capacity network. Neuroimage 2016; 124:1044-1053. [DOI: 10.1016/j.neuroimage.2015.09.046] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2015] [Revised: 08/03/2015] [Accepted: 09/20/2015] [Indexed: 02/05/2023] Open
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45
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Romero-Garcia R, Atienza M, Cantero JL. Different Scales of Cortical Organization are Selectively Targeted in the Progression to Alzheimer's Disease. Int J Neural Syst 2015; 26:1650003. [PMID: 26790483 DOI: 10.1142/s0129065716500039] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Previous studies have shown that the topological organization of the cerebral cortex is altered in Alzheimer's disease (AD). However, it remains unknown whether different levels of the cortical hierarchy are homogeneously affected during disease progression, and which of these levels are mostly involved in the breakdown of metabolic (functional) connectivity. To fulfill these goals, we acquired structural magnetic resonance images (MRI) and positron emission tomography (PET) with the radiotracer 18F-fludeoxyglucose (FDG) in 29 healthy old (HO) adults, 29 amnestic mild cognitive impairment (aMCI) and 29 mild AD patients. Structural and metabolic connections were obtained from inter-regional correlations of cortical thickness and glucose consumption, respectively. Results showed that AD and HO groups differed at all levels of cortical organization (i.e. whole cortex, hemisphere, lobe and node), whereas differences among the three groups were only evident at the lobe and node levels. The correlation between structural and metabolic connectivity (F-S coupling) was also disturbed during AD progression, affecting to different connectivity scales: it decreased at the local level, revealing a progressive increase of metabolic connections in those local communities with fewer structural connections; whereas it increased at the global level, likely due to a parallel reduction of cortical thickness and glucose consumption between long-distance cortical regions. Collectively, these results reveal that different levels of cortical organization are selectively affected during the transition from normal aging to dementia, which could be helpful to track cortical dysfunctions in the progression to AD.
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Affiliation(s)
- Rafael Romero-Garcia
- Laboratory of Functional Neuroscience, Pablo de Olavide University, Seville, Spain
| | - Mercedes Atienza
- Laboratory of Functional Neuroscience, CIBERNED, Pablo de Olavide University, Seville, Spain
| | - Jose L. Cantero
- Laboratory of Functional Neuroscience, CIBERNED, Pablo de Olavide University, Seville, Spain
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46
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Simpson SL, Laurienti PJ. Disentangling Brain Graphs: A Note on the Conflation of Network and Connectivity Analyses. Brain Connect 2015; 6:95-8. [PMID: 26414952 DOI: 10.1089/brain.2015.0361] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/21/2023] Open
Abstract
Understanding the human brain remains the holy grail in biomedical science, and arguably in all of the sciences. Our brains represent the most complex systems in the world (and some contend the universe) comprising nearly 100 billion neurons with septillions of possible connections between them. The structure of these connections engenders an efficient hierarchical system capable of consciousness, as well as complex thoughts, feelings, and behaviors. Brain connectivity and network analyses have exploded over the last decade due to their potential in helping us understand both normal and abnormal brain function. Functional connectivity (FC) analysis examines functional associations between time series pairs in specified brain voxels or regions. Brain network analysis serves as a distinct subfield of connectivity analysis, in which associations are quantified for all time series pairs to create an interconnected representation of the brain (a brain network), which allows studying its systemic properties. While connectivity analyses underlie network analyses, the subtle distinction between the two research areas has generally been overlooked in the literature, with them often being referred to synonymously. However, developing more useful analytic methods and allowing for more precise biological interpretations require distinguishing these two complementary domains.
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Affiliation(s)
- Sean L Simpson
- 1 Department of Biostatistical Sciences, Wake Forest School of Medicine , Winston-Salem, North Carolina.,2 Laboratory for Complex Brain Networks, Wake Forest School of Medicine , Winston-Salem, North Carolina
| | - Paul J Laurienti
- 2 Laboratory for Complex Brain Networks, Wake Forest School of Medicine , Winston-Salem, North Carolina.,3 Department of Radiology, Wake Forest School of Medicine , Winston-Salem, North Carolina
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47
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Kim DJ, Davis EP, Sandman CA, Sporns O, O'Donnell BF, Buss C, Hetrick WP. Children's intellectual ability is associated with structural network integrity. Neuroimage 2015; 124:550-556. [PMID: 26385010 DOI: 10.1016/j.neuroimage.2015.09.012] [Citation(s) in RCA: 48] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2015] [Revised: 09/04/2015] [Accepted: 09/08/2015] [Indexed: 02/07/2023] Open
Abstract
Recent structural and functional neuroimaging studies of adults suggest that efficient patterns of brain connectivity are fundamental to human intelligence. Specifically, whole brain networks with an efficient small-world organization, along with specific brain regions (i.e., Parieto-Frontal Integration Theory, P-FIT) appear related to intellectual ability. However, these relationships have not been studied in children using structural network measures. This cross-sectional study examined the relation between non-verbal intellectual ability and structural network organization in 99 typically developing healthy preadolescent children. We showed a strong positive association between the network's global efficiency and intelligence, in which a subtest for visuo-spatial motor processing (Block Design, BD) was prominent in both global brain structure and local regions included within P-FIT as well as temporal regions involved with pattern and form processing. BD was also associated with rich club organization, which encompassed frontal, occipital, temporal, hippocampal, and neostriatal regions. This suggests that children's visual construction ability is significantly related to how efficiently children's brains are globally and locally integrated. Our findings indicate that visual construction and reasoning may make general demands on globally integrated processing by the brain.
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Affiliation(s)
- Dae-Jin Kim
- Department of Psychological and Brain Sciences, Indiana University, 1101 East 10th Street, Bloomington, IN 47405, USA
| | - Elysia Poggi Davis
- Department of Psychology, University of Denver, 2155 South Race Street, Denver, CO 80208, USA; Department of Psychiatry and Human Behavior, University of California Irvine, One University Drive, Orange, CA 92866, USA
| | - Curt A Sandman
- Department of Psychiatry and Human Behavior, University of California Irvine, One University Drive, Orange, CA 92866, USA
| | - Olaf Sporns
- Department of Psychological and Brain Sciences, Indiana University, 1101 East 10th Street, Bloomington, IN 47405, USA; Indiana University Network Science Institute, Indiana University, Bloomington, IN 47405, USA
| | - Brian F O'Donnell
- Department of Psychological and Brain Sciences, Indiana University, 1101 East 10th Street, Bloomington, IN 47405, USA
| | - Claudia Buss
- Institut für Medizinische Psychologie, Charité Centrum für Human-und Gesundheitswissenschaften, Charité Universitätsmedizin, Berlin, Germany
| | - William P Hetrick
- Department of Psychological and Brain Sciences, Indiana University, 1101 East 10th Street, Bloomington, IN 47405, USA.
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48
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Baggio HC, Segura B, Junque C, de Reus MA, Sala-Llonch R, Van den Heuvel MP. Rich Club Organization and Cognitive Performance in Healthy Older Participants. J Cogn Neurosci 2015; 27:1801-10. [DOI: 10.1162/jocn_a_00821] [Citation(s) in RCA: 48] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/21/2023]
Abstract
Abstract
The human brain is a complex network that has been noted to contain a group of densely interconnected hub regions. With a putative “rich club” of hubs hypothesized to play a central role in global integrative brain functioning, we assessed whether hub and rich club organizations are associated with cognitive performance in healthy participants and whether the rich club might be differentially involved in cognitive functions with a heavier dependence on global integration. A group of 30 relatively older participants (range = 39–79 years of age) underwent extensive neuropsychological testing, combined with diffusion-weighted magnetic resonance imaging to reconstruct individual structural brain networks. Rich club connectivity was found to be associated with general cognitive performance. More specifically, assessing the relationship between the rich club and performance in two specific cognitive domains, we found rich club connectivity to be differentially associated with attention/executive functions—known to rely on the integration of distributed brain areas—rather than with visuospatial/visuoperceptual functions, which have a more constrained neuroanatomical substrate. Our findings thus provide first empirical evidence of a relevant role played by the rich club in cognitive processes.
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49
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Fischer FU, Wolf D, Scheurich A, Fellgiebel A. Altered whole-brain white matter networks in preclinical Alzheimer's disease. NEUROIMAGE-CLINICAL 2015; 8:660-6. [PMID: 26288751 PMCID: PMC4536470 DOI: 10.1016/j.nicl.2015.06.007] [Citation(s) in RCA: 77] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/26/2015] [Revised: 06/17/2015] [Accepted: 06/20/2015] [Indexed: 11/28/2022]
Abstract
Surrogates of whole-brain white matter (WM) networks reconstructed using diffusion tensor imaging (DTI) are novel markers of structural brain connectivity. Global connectivity of networks has been found impaired in clinical Alzheimer's disease (AD) compared to cognitively healthy aging. We hypothesized that network alterations are detectable already in preclinical AD and investigated major global WM network properties. Other structural markers of neurodegeneration typically affected in prodromal AD but seeming largely unimpaired in preclinical AD were also examined. 12 cognitively healthy elderly with preclinical AD as classified by florbetapir-PET (mean age 73.4 ± 4.9) and 31 age-matched controls without cerebral amyloidosis (mean age 73.1 ± 6.7) from the ADNI were included. WM networks were reconstructed from DTI using tractography and graph theory. Indices of network capacity and the established imaging markers of neurodegeneration hippocampal volume, and cerebral glucose utilization as measured by fludeoxyglucose-PET were compared between the two groups. Additionally, we measured surrogates of global WM integrity (fractional anisotropy, mean diffusivity, volume). We found an increase of shortest path length and a decrease of global efficiency in preclinical AD. These results remained largely unchanged when controlling for WM integrity. In contrast, neither markers of neurodegeneration nor WM integrity were altered in preclinical AD subjects. Our results suggest an impairment of WM networks in preclinical AD that is detectable while other structural imaging markers do not yet indicate incipient neurodegeneration. Moreover, these findings are specific to WM networks and cannot be explained by other surrogates of global WM integrity.
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Affiliation(s)
- Florian Udo Fischer
- University Medical Center Mainz, Untere Zahlbacher Str. 8, Mainz 55131, Germany
| | - Dominik Wolf
- University Medical Center Mainz, Untere Zahlbacher Str. 8, Mainz 55131, Germany
| | - Armin Scheurich
- University Medical Center Mainz, Untere Zahlbacher Str. 8, Mainz 55131, Germany
| | - Andreas Fellgiebel
- University Medical Center Mainz, Untere Zahlbacher Str. 8, Mainz 55131, Germany
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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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