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Brain-wide functional connectome analysis of 40,000 individuals reveals brain networks that show aging effects in older adults.
Pan, Yezhi; Bi, Chuan; Kochunov, Peter; Shardell, Michelle; Smith, J Carson; McCoy, Rozalina G; Ye, Zhenyao; Yu, Jiaao; Lu, Tong; Yang, Yifan; Lee, Hwiyoung; Liu, Song; Gao, Si; Ma, Yizhou; Li, Yiran; Chen, Chixiang; Ma, Tianzhou; Wang, Ze; Nichols, Thomas; Hong, L Elliot; Chen, Shuo.
Afiliação
  • Pan Y; Maryland Psychiatric Research Center, Department of Psychiatry, School of Medicine, University of Maryland, Baltimore, Maryland, United States of America.
  • Bi C; Maryland Psychiatric Research Center, Department of Psychiatry, School of Medicine, University of Maryland, Baltimore, Maryland, United States of America.
  • Kochunov P; Department of Psychiatry and Behavioral Science, University of Texas Health Science Center Houston, Houston, United States of America.
  • Shardell M; Department of Epidemiology and Public Health and Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, Maryland, United States of America.
  • Smith JC; Department of Kinesiology, University of Maryland, College Park, Maryland, United States of America.
  • McCoy RG; Division of Endocrinology, Diabetes, & Nutrition, Department of Medicine, School of Medicine, University of Maryland, Baltimore, Maryland, United States of America.
  • Ye Z; Maryland Psychiatric Research Center, Department of Psychiatry, School of Medicine, University of Maryland, Baltimore, Maryland, United States of America.
  • Yu J; Division of Biostatistics and Bioinformatics, Department of Epidemiology and Public Health, School of Medicine, University of Maryland, Baltimore, Maryland, United States of America.
  • Lu T; Department of Mathematics, University of Maryland, College Park, Maryland, United States of America.
  • Yang Y; Department of Mathematics, University of Maryland, College Park, Maryland, United States of America.
  • Lee H; Department of Mathematics, University of Maryland, College Park, Maryland, United States of America.
  • Liu S; Maryland Psychiatric Research Center, Department of Psychiatry, School of Medicine, University of Maryland, Baltimore, Maryland, United States of America.
  • Gao S; School of Computer Science and Technology, Qilu University of Technology (Shandong Academy of Sciences), Jinan, Shandong, China.
  • Ma Y; Maryland Psychiatric Research Center, Department of Psychiatry, School of Medicine, University of Maryland, Baltimore, Maryland, United States of America.
  • Li Y; Maryland Psychiatric Research Center, Department of Psychiatry, School of Medicine, University of Maryland, Baltimore, Maryland, United States of America.
  • Chen C; Department of Diagnostic Radiology & Nuclear Medicine, University of Maryland School of Medicine, Baltimore, Maryland, United States of America.
  • Ma T; Division of Biostatistics and Bioinformatics, Department of Epidemiology and Public Health, School of Medicine, University of Maryland, Baltimore, Maryland, United States of America.
  • Wang Z; Department of Epidemiology and Biostatistics, School of Public Health, University of Maryland, College Park, Maryland, United States of America.
  • Nichols T; Department of Diagnostic Radiology & Nuclear Medicine, University of Maryland School of Medicine, Baltimore, Maryland, United States of America.
  • Hong LE; Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom.
  • Chen S; Department of Psychiatry and Behavioral Science, University of Texas Health Science Center Houston, Houston, United States of America.
bioRxiv ; 2024 May 17.
Article em En | MEDLINE | ID: mdl-38798606
ABSTRACT
The functional connectome changes with aging. We systematically evaluated aging related alterations in the functional connectome using a whole-brain connectome network analysis in 39,675 participants in UK Biobank project. We used adaptive dense network discovery tools to identify networks directly associated with aging from resting-state fMRI data. We replicated our findings in 499 participants from the Lifespan Human Connectome Project in Aging study. The results consistently revealed two motor-related subnetworks (both permutation test p-values <0.001) that showed a decline in resting-state functional connectivity (rsFC) with increasing age. The first network primarily comprises sensorimotor and dorsal/ventral attention regions from precentral gyrus, postcentral gyrus, superior temporal gyrus, and insular gyrus, while the second network is exclusively composed of basal ganglia regions, namely the caudate, putamen, and globus pallidus. Path analysis indicates that white matter fractional anisotropy mediates 19.6% (p<0.001, 95% CI [7.6% 36.0%]) and 11.5% (p<0.001, 95% CI [6.3% 17.0%]) of the age-related decrease in both networks, respectively. The total volume of white matter hyperintensity mediates 32.1% (p<0.001, 95% CI [16.8% 53.0%]) of the aging-related effect on rsFC in the first subnetwork.
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Texto completo: 1 Base de dados: MEDLINE Idioma: En Ano de publicação: 2024 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Idioma: En Ano de publicação: 2024 Tipo de documento: Article