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Comment
. 2020 Apr 16:9:e56640.
doi: 10.7554/eLife.56640.

The many facets of brain aging

Lars Nyberg  1   2   3 , Anders Wåhlin  1   3
Affiliations
Comment

The many facets of brain aging

Lars Nyberg et al. Elife. .

Abstract

Applying big-data analytic techniques to brain images from 18,707 individuals is shedding light on the influence of aging on the brain.

Keywords: UK Biobank; aging; brain; cognition; computational analysis; human; imaging; neuroscience.

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Conflict of interest statement

LN, AW No competing interests declared

Figures

Figure 1.
Figure 1.. Refining big-data analytic approaches to reveal the many facets of brain aging.
(A) Smith et al. used a technique called independent component analysis (ICA) to analyze MRI and fMRI data on brain structure, connectivity or activity from more than 18,000 individuals over the age of 45. This enabled them to identify 62 modes. Most of these modes co-varied with age across the sample, thus potentially reflecting biological processes affected by aging. (B) Schematic matrix in which each row represents an individual and each column represents a mode. The color scale represents the brain-age delta, the difference between the actual age of the individual and what age would have been expected for this person given the value of the mode. (C) The 62 modes can be grouped into six mode-clusters, such as one which captures the microstructure of brain white-matter. (D) Smith et al. were able to relate the brain-age deltas for specific modes and the mode-clusters to various phenotypes (for instance health, genetics and cognition).
See this image and copyright information in PMC

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