Exploring Heterogeneity in Structural Imaging Patterns of Aging Brains Using Machine Learning - Details

Author：

Lin, Lan (Lin, Lan.) | Liu, Lingyu (Liu, Lingyu.) | Wu, Shuicai (Wu, Shuicai.)

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Abstract：

The　impact　of　age-related　brain　atrophy　on　the　decline　of　cognitive　function　in　older　adults　has　been　widely　recognized.　To　investigate　this　issue,　we　conducted　a　study　using　machine　learning　algorithms　to　analyze　magnetic　resonance　imaging　(MRI)　data　collected　from　a　large　UK　Biobank　sample　of　6,　000　individuals,　ranging　in　age　from　48　to　84　years.　Our　methodology　consisted　of　splitting　the　entire　cohort　into　two　groups　based　on　brain　age　gap　estimation　from　three　image　modalities:　the　advanced　brain　age　group　(ABAG)　and　the　normal　and　healthy　brain　aging　group　(NHBAG).　Subsequently,　using　a　mixture　of　experts　to　provide　more　clarity　on　the　group　heterogeneity　of　NHBAG,　we　identified　four　subtypes.　These　included　the　temporal　lobe　preservation　subtype,　the　minimal　atrophy　subtype,　the　diffusion　atrophy　subtype,　and　the　frontal　lobe　preservation　subtype.　These　results　emphasize　the　importance　of　exploring　the　complexity　of　age-related　brain　changes　and　their　subtypes　through　advanced　data-driven　techniques.　©　2023　SPIE.　All　rights　reserved.

Keyword：

Machine learning Brain Learning algorithms Magnetic resonance imaging

Author Community：

[ 1 ] [Lin, Lan]Department of Biomedical Engineering, Faculty of Environment and Life Sciences, Beijing University of Technology, Beijing; 100124, China
[ 2 ] [Liu, Lingyu]Department of Biomedical Engineering, Faculty of Environment and Life Sciences, Beijing University of Technology, Beijing; 100124, China
[ 3 ] [Wu, Shuicai]Department of Biomedical Engineering, Faculty of Environment and Life Sciences, Beijing University of Technology, Beijing; 100124, China

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Source ：

ISSN： 0277-786X

Year： 2023

Volume： 12941

Language： English

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WoS CC Cited Count： 0

SCOPUS Cited Count：

ESI Highly Cited Papers on the List： 0 Unfold All

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Chinese Cited Count：

30 Days PV： 4

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