| Title: |
White matter brain age as a biomarker of cerebrovascular burden in the ageing brain |
| Authors: |
Du, J; Pan, Y; Jiang, J; Lam, BCP; Thalamuthu, A; Chen, R; Tsang, IW; Sachdev, PS; Wen, W |
| Source: |
urn:ISSN:0940-1334 ; urn:ISSN:1433-8491 ; European Archives of Psychiatry and Clinical Neuroscience, 275, 8, 2203-2213 |
| Publisher Information: |
Springer Nature |
| Publication Year: |
2025 |
| Collection: |
UNSW Sydney (The University of New South Wales): UNSWorks |
| Subject Terms: |
5202 Biological Psychology; 52 Psychology; Acquired Cognitive Impairment; Vascular Cognitive Impairment/Dementia; Alzheimer's Disease Related Dementias (ADRD); Machine Learning and Artificial Intelligence; Diabetes; Prevention; Cerebrovascular; Alzheimer's Disease including Alzheimer's Disease Related Dementias (AD/ADRD); Basic Behavioral and Social Science; Clinical Research; Cardiovascular; Neurodegenerative; Neurosciences; Biomedical Imaging; Behavioral and Social Science; Dementia; Brain Disorders; Aging; Humans; White Matter; Male; Female; Aged; Middle Aged; Cross-Sectional Studies; Hypertension; Risk Factors; Brain |
| Description: |
As the brain ages, it almost invariably accumulates vascular pathology, which differentially affects the cerebral white matter. A rich body of research has investigated the link between vascular risk factors and the brain. One of the less studied questions is that among various modifiable vascular risk factors, which is the most debilitating one for white matter health? A white matter specific brain age was developed to evaluate the overall white matter health from diffusion weighted imaging, using a three-dimensional convolutional neural network deep learning model in both cross-sectional UK biobank participants (n = 37,327) and a longitudinal subset (n = 1409). White matter brain age gap (WMBAG) was the difference between the white matter age and the chronological age. Participants with one, two, and three or more vascular risk factors, compared to those without any, showed an elevated WMBAG of 0.54, 1.23, and 1.94 years, respectively. Diabetes was most strongly associated with an increased WMBAG (1.39 years, p < 0.001) among all risk factors followed by hypertension (0.87 years, p < 0.001) and smoking (0.69 years, p < 0.001). Baseline WMBAG was associated significantly with processing speed, executive and global cognition. Significant associations of diabetes and hypertension with poor processing speed and executive function were found to be mediated through the WMBAG. White matter specific brain age can be successfully targeted for the examination of the most relevant risk factors and cognition, and for tracking an individual’s cerebrovascular ageing process. It also provides clinical basis for the better management of specific risk factors. |
| Document Type: |
article in journal/newspaper |
| File Description: |
application/pdf |
| Language: |
unknown |
| Relation: |
http://purl.org/au-research/grants/nhmrc/APP1093083; https://hdl.handle.net/1959.4/unsworks_85678 |
| DOI: |
10.1007/s00406-024-01758-3 |
| Availability: |
https://hdl.handle.net/1959.4/unsworks_85678; https://unsworks.unsw.edu.au/bitstreams/275183fa-434b-4a61-9bb0-4aef0fd929fa/download; https://doi.org/10.1007/s00406-024-01758-3 |
| Rights: |
open access ; https://purl.org/coar/access_right/c_abf2 ; CC BY ; https://creativecommons.org/licenses/by/4.0/ ; free_to_read |
| Accession Number: |
edsbas.8431B6A8 |
| Database: |
BASE |