scholarly journals Brain aging comprises many modes of structural and functional change with distinct genetic and biophysical associations

eLife ◽  
2020 ◽  
Vol 9 ◽  
Author(s):  
Stephen M Smith ◽  
Lloyd T Elliott ◽  
Fidel Alfaro-Almagro ◽  
Paul McCarthy ◽  
Thomas E Nichols ◽  
...  
Keyword(s):  
Brain Imaging ◽  
Brain Aging ◽  
Functional Change ◽  
Genetic Associations ◽  
Imaging Data ◽  
Brain Health ◽  
Health And Disease ◽  
Brain Imaging Data ◽  
Structural Brain Change ◽  
Brain Age

Brain imaging can be used to study how individuals’ brains are aging, compared against population norms. This can inform on aspects of brain health; for example, smoking and blood pressure can be seen to accelerate brain aging. Typically, a single ‘brain age’ is estimated per subject, whereas here we identified 62 modes of subject variability, from 21,407 subjects’ multimodal brain imaging data in UK Biobank. The modes represent different aspects of brain aging, showing distinct patterns of functional and structural brain change, and distinct patterns of association with genetics, lifestyle, cognition, physical measures and disease. While conventional brain-age modelling found no genetic associations, 34 modes had genetic associations. We suggest that it is important not to treat brain aging as a single homogeneous process, and that modelling of distinct patterns of structural and functional change will reveal more biologically meaningful markers of brain aging in health and disease.

scholarly journals Brain aging comprises many modes of structural and functional change with distinct genetic and biophysical associations

2019 ◽  
Author(s):  
Stephen M. Smith ◽  
Lloyd T. Elliott ◽  
Fidel Alfaro-Almagro ◽  
Paul McCarthy ◽  
Thomas E. Nichols ◽  
...  
Keyword(s):  
Brain Imaging ◽  
Brain Aging ◽  
Functional Change ◽  
Genetic Associations ◽  
Imaging Data ◽  
Brain Health ◽  
Health And Disease ◽  
Brain Imaging Data ◽  
Structural Brain Change ◽  
Brain Age

AbstractBrain imaging can be used to study how individuals’ brains are aging, compared against population norms. This can inform on aspects of brain health; for example, smoking and blood pressure can be seen to accelerate brain aging. Typically, a single “brain age” is estimated per subject, whereas here we we identified 62 modes of subject variability, from 21,407 subjects’ multimodal brain imaging data in UK Biobank. The modes represent different aspects of brain aging, showing distinct patterns of functional and structural brain change, and distinct patterns of association with genetics, lifestyle, cognition, physical measures and disease. While conventional brain-age modelling found no genetic associations, 34 modes had genetic associations. We suggest that it is important not to treat brain aging as a single homogeneous process, and that modelling of distinct patterns of structural and functional change will reveal more biologically meaningful markers of brain aging in health and disease.

scholarly journals Improving brain age estimates with deep learning leads to identification of novel genetic factors associated with brain aging

2020 ◽  
Author(s):  
Kaida Ning ◽  
Ben A. Duffy ◽  
Meredith Franklin ◽  
Will Matloff ◽  
Lu Zhao ◽  
...  
Keyword(s):  
Genome Wide Association Study ◽  
Brain Aging ◽  
Nucleotide Polymorphisms ◽  
Genetic Associations ◽  
Imaging Data ◽  
Factors Associated ◽  
Genome Wide ◽  
A Genome ◽  
Validation Tests ◽  
Brain Age

AbstractBrain aging trajectories among those of the same chronological age can vary significantly. Statistical models have been created for estimating the apparent age of the brain, or predicted brain age, with imaging data. Recently, convolutional neural networks (CNNs) have shown the potential to more accurately predict brain age. We trained a CNN on 16,998 UK Biobank subjects, and in validation tests found that it was more accurate than a regression model for predicting brain age. A genome-wide association study was conducted on CNN-derived predicted brain age whereby we identified single nucleotide polymorphisms from four independent loci significantly associated with brain aging. One locus has been previously reported to be associated with brain aging. The three other loci were novel. Our results suggest that a more accurate brain age prediction enables the discovery of novel genetic associations, which may be valuable for identifying other lifestyle factors associated with brain aging.

scholarly journals The Open Brain Consent : Informing research participants and obtaining consent to share brain imaging data

2021 ◽  
Author(s):  
Elise Bannier ◽  
Gareth Barker ◽  
Valentina Borghesani ◽  
Nils Broeckx ◽  
Patricia Clement ◽  
...  
Keyword(s):  
Brain Imaging ◽  
Imaging Data ◽  
Research Participants ◽  
Brain Imaging Data

Learning from Enhanced Contextual Similarity in Brain Imaging Data for Classification of Schizophrenia

2017 ◽  
pp. 265-275 ◽  
Author(s):  
Tewodros Mulugeta Dagnew ◽  
Letizia Squarcina ◽  
Massimo W. Rivolta ◽  
Paolo Brambilla ◽  
Roberto Sassi
Keyword(s):  
Brain Imaging ◽  
Imaging Data ◽  
Brain Imaging Data

scholarly journals Chronic subdural hematomas: challenges and solutions. Part I. Clinical variants and diagnosis

2021 ◽  
Vol 2 (2) ◽  
pp. 21-27
Author(s):  
Leonid B. Likhterman ◽  
◽  
Aleksandr D. Kravchuk ◽  
Vladimir A. Okhlopkov ◽  
◽  
...  
Keyword(s):  
Brain Imaging ◽  
Intracranial Hemorrhage ◽  
Subdural Hematoma ◽  
Clinical Course ◽  
Chronic Subdural Hematoma ◽  
Imaging Data ◽  
Clinical Variants ◽  
Brain Compression ◽  
Brain Imaging Data ◽  
Ct And Mri

Chronic subdural hematoma (cSDH) is a multifactorial extensive intracranial hemorrhage, causing the local and/or general brain compression. Hematoma has a delimiting capsule, which defines all pathophysiological features, clinical course and treatment tactics. The paper reports contemporary views on ethiology and clinical course of cSDH. Emphasis is placed on the diagnosis. Based on the analysis of 558 verified cSDH observations, the phasal course and brain imaging data are reported. CT and MRI signs of cSDH are defined.

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