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000280962 1001_ $$0P:(DE-2719)2812537$$aNemali, A.$$b0$$eFirst author
000280962 245__ $$aSMAS: Structural MRI-based AD Score using Bayesian supervised VAE.
000280962 260__ $$aAmsterdam [u.a.]$$bElsevier Science$$c2025
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000280962 520__ $$aThis study introduces the Structural MRI-based Alzheimer's Disease Score (SMAS), a novel index intended to quantify Alzheimer's Disease (AD)-related morphometric patterns using a deep learning Bayesian-supervised Variational Autoencoder (Bayesian-SVAE). The SMAS index was constructed using baseline structural MRI data from the DELCODE study and evaluated longitudinally in two independent cohorts: DELCODE (n=415) and ADNI (n=190). Our findings indicate that SMAS has strong associations with cognitive performance (DELCODE: r=-0.83; ADNI: r=-0.62), age (DELCODE: r=0.50; ADNI: r=0.28), hippocampal volume (DELCODE: r=-0.44; ADNI: r=-0.66), and total gray matter volume (DELCODE: r=-0.42; ADNI: r=-0.47), suggesting its potential as a biomarker for AD-related brain atrophy. Moreover, our longitudinal studies indicated that SMAS may be useful for the early identification and tracking of AD. The model demonstrated significant predictive accuracy in distinguishing cognitively healthy individuals from those with AD (DELCODE: AUC=0.971 at baseline, 0.833 at 36 months; ADNI: AUC=0.817 at baseline, improving to 0.903 at 24 months). Notably, over 36 months, the SMAS index outperformed existing measures such as SPARE-AD and hippocampal volume. The relevance map analysis revealed significant morphological changes in key AD-related brain regions, including the hippocampus, posterior cingulate cortex, precuneus, and lateral parietal cortex, highlighting that SMAS is a sensitive and interpretable biomarker of brain atrophy, suitable for early AD detection and longitudinal monitoring of disease progression.
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000280962 650_7 $$2Other$$aAlzheimer’s disease
000280962 650_7 $$2Other$$aBayesian Supervised Variational Autoencoder
000280962 650_7 $$2Other$$aBayesian inference
000280962 650_7 $$2Other$$aBrain morphology indices
000280962 650_7 $$2Other$$aCognitive decline
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000280962 7001_ $$0P:(DE-2719)9003016$$aHellmann-Regen, J.$$b9$$udzne
000280962 7001_ $$0P:(DE-2719)9000703$$aPreis, Lukas$$b10$$udzne
000280962 7001_ $$0P:(DE-2719)2811122$$aPriller, J.$$b11$$udzne
000280962 7001_ $$0P:(DE-2719)2812446$$aSpruth, Eike Jakob$$b12
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000280962 7001_ $$0P:(DE-2719)9002557$$aJanowitz, Daniel$$b25$$udzne
000280962 7001_ $$0P:(DE-2719)9000543$$aEwers, Michael$$b26
000280962 7001_ $$0P:(DE-2719)2812234$$aPerneczky, R.$$b27$$udzne
000280962 7001_ $$0P:(DE-2719)9001808$$aRauchmann, Boris Stephan$$b28$$udzne
000280962 7001_ $$0P:(DE-2719)2000026$$aTeipel, S.$$b29$$udzne
000280962 7001_ $$0P:(DE-2719)2810394$$aKilimann, I.$$b30$$udzne
000280962 7001_ $$0P:(DE-2719)2812583$$aGoerss, D.$$b31$$udzne
000280962 7001_ $$0P:(DE-2719)2000055$$aLaske, C.$$b32$$udzne
000280962 7001_ $$0P:(DE-2719)9003152$$aSodenkamp, S.$$b33$$udzne
000280962 7001_ $$0P:(DE-2719)2811324$$aSpottke, A.$$b34$$udzne
000280962 7001_ $$0P:(DE-HGF)0$$aCoenjaerts, M.$$b35
000280962 7001_ $$0P:(DE-2719)2810593$$aBrosseron, F.$$b36$$udzne
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000280962 7001_ $$0P:(DE-2719)9001147$$aScheffler, K.$$b39
000280962 7001_ $$0P:(DE-HGF)0$$aHetzer, S.$$b40
000280962 7001_ $$0P:(DE-2719)2812139$$aKleineidam, L.$$b41$$udzne
000280962 7001_ $$0P:(DE-HGF)0$$aStark, M.$$b42
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000280962 7001_ $$0P:(DE-2719)2000005$$aDuzel, E.$$b44$$udzne
000280962 7001_ $$0P:(DE-2719)2814076$$aZiegler, G.$$b45$$eLast author$$udzne
000280962 773__ $$0PERI:(DE-600)1496984-1$$a10.1016/j.compbiomed.2025.110829$$gVol. 196, no. Pt C, p. 110829 -$$nPt C$$p110829$$tComputers in biology and medicine$$v196$$x0010-4825$$y2025
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