Predicting future cognitive decline from non-brain and multimodal brain imaging data in healthy and pathological aging

Abstract

Cognitive decline occurs in healthy and pathological aging, and both may be preceded by subtle changes in the brain — offering a basis for cognitive predictions. Previous work has largely focused on predicting a diagnostic label from structural brain imaging. Our study broadens the scope of applications to cognitive decline in healthy aging by predicting future decline as a continuous trajectory, rather than a diagnostic label. Furthermore, since brain structure as well as function changes in aging, it is reasonable to expect predictive gains when using multiple brain imaging modalities. Here, we tested whether baseline multimodal neuroimaging data improve the prediction of future cognitive decline in healthy and pathological aging. Non-brain data (including demographics and clinical and neuropsychological scores) were combined with structural and functional connectivity MRI data from the OASIS-3 project (N = 662; age = 46 – 96y). The combined input data was entered into cross-validated multi-target random forest models to predict future cognitive decline (measured by the Clinical Dementia Rating and the Mini-Mental State Examination), on average 5.8y into the future. The analysis was preregistered and all analysis code is publicly available. We found that combining non-brain with structural data improved the continuous prediction of future cognitive decline (best test-set performance: R2 = 0.42) and that cognitive performance, daily functioning, and subcortical volume drove the performance of our model. In contrast, including functional connectivity did not improve predictive accuracy. In the future, the prognosis of age-related cognitive decline may enable earlier and more effective cognitive, pharmacological, and behavioral interventions to be tailored to the individual.

Classification and prediction of cognitive performance differences in older age based on brain network patterns using a machine learning approach

“…Table S42). While the obtained MAEs across samples were not competitive with those reported in the literature, results from the validation analyses, nevertheless, generally support the view that the current pipeline may yield reasonable prediction and classification performances (Liem et al, 2017(Liem et al, , 2020Pläschke et al, 2017;Vergun et al, 2013). Thus, the low ML performance estimates may be specific to the setting of classifying and predicting cognitive performance differences from RSFC strength measures in healthy older adults rather than a general finding pertained to the ML setup, parcellation granularity, sampling or features.…”

“…To adapt this to our classification setting, we examined the classification of extreme age groups (old vs. young, see Suppl. Table S4-5) in feature set 421 (Liem et al, 2020). In the prediction setting, age was predicted continuously.…”

“…To contextualize ML performance and obtain a chance-level prediction equivalent, we compared ML model estimations to those from a reference model, i.e. Dummy Classifier and Regressor, given the low computational costs of dummy estimates and their similarity in distribution to approaches based on permutation (Engemann et al, 2020;Liem et al, 2020). In this case, the percentage of folds, for which the ML models were better than the reference model in terms of accuracy (classification) and R 2 (regression), was calculated with higher percentages (> 80%) indicating robust outperformance of the reference model.…”

“…While some older adults seem to maintain their cognitive abilities until old age, others show higher rates of cognitive decline during the aging process (Cabeza, 2001;Damoiseaux et al, 2008;Hedden & Gabrieli, 2004;Raz, 2000;Raz & Rodrigue, 2006). In light of the continuously growing aging population, the impact of cognitive decline on everyday functioning of older adults has gained momentum in research (Avery et al, 2020;Deary et al, 2009;Depp & Jeste, 2006;Fountain-Zaragoza et al, 2019;Liem et al, 2020;Luciano et al, 2009).…”

Baseline Cognition Is the Best Predictor of 4-year Cognitive Change in Cognitively Intact Older Adults

“…In a recent study including cognitively healthy individuals as well as patients who were cognitively impaired (lowest MMSE = 16) (Liem et al, 2020), the strongest predictor for cognitive decline (measured based on MMSE and CDR) were baseline episodic memory scores (Wechsler Memory Scale Logical Memory test) rather than MRI volumetric measures. The current findings confirm the predictive power of baseline cognitive scores in predicting cognitive decline in cognitively intact older adults, outweighing amyloid or volumetric MRI measures.…”