Multimodal imaging improves brain age prediction and reveals distinct abnormalities in patients with psychiatric and neurological disorders

Abstract

The deviation between chronological age and age predicted using brain MRI is a putative marker of overall brain health. Age prediction based on structural MRI data shows high accuracy in common brain disorders. However, brain aging is complex and heterogenous, both in terms of individual differences and the underlying biological processes. Here, we implemented a multimodal model to estimate brain age using different combinations of cortical area, thickness and sub‐cortical volumes, cortical and subcortical T1/T2‐weighted ratios, and cerebral blood flow (CBF) based on arterial spin labeling. For each of the 11 models we assessed the age prediction accuracy in healthy controls (HC, n = 750) and compared the obtained brain age gaps (BAGs) between age‐matched subsets of HC and patients with Alzheimer's disease (AD, n = 54), mild (MCI, n = 90) and subjective (SCI, n = 56) cognitive impairment, schizophrenia spectrum (SZ, n = 159) and bipolar disorder (BD, n = 135). We found highest age prediction accuracy in HC when integrating all modalities. Furthermore, two‐group case–control classifications revealed highest accuracy for AD using global T1‐weighted BAG, while MCI, SCI, BD and SZ showed strongest effects in CBF‐based BAGs. Combining multiple MRI modalities improves brain age prediction and reveals distinct deviations in patients with psychiatric and neurological disorders. The multimodal BAG was most accurate in predicting age in HC, while group differences between patients and HC were often larger for BAGs based on single modalities. These findings indicate that multidimensional neuroimaging of patients may provide a brain‐based mapping of overlapping and distinct pathophysiology in common disorders.

A history of previous childbirths is linked to women's white matter brain age in midlife and older age

“…Relative to more traditional MRI summary measures, age prediction models have the advantage of encoding normative trajectories of brain differences across age, and condensing a rich variety of brain characteristics into single estimates per individual. Hence, brain age prediction provides a useful summary measure that may serve as a proxy for brain integrity across normative and clinical populations (Cole & Franke, 2017 ; Cole, Marioni, et al, 2019 ; Kaufmann et al, 2019 ; Rokicki et al, 2020 ; Smith et al, 2020 ).…”

Multimodal Image Analysis of Apparent Brain Age Identifies Physical Fitness as Predictor of Brain Maintenance

“…2018 ; Liang et al. 2019 ; Rokicki et al. 2020 ), although there might be other underlying explanations as well, for example, a nonlinear relationship between age and brain measures that would be more effectively captured using a nonlinear modeling approach.…”

Brain age estimation at tract group level and its association with daily life measures, cardiac risk factors and genetic variants

“…In addition, most of these previous studies have demonstrated better results when including multimodal neuroimaging data rather than a single modality in the models 22 , 26 , 27 . In particular, findings from these multimodality studies suggest that dMRI measures have higher accuracy in predicting brain age compared to those derived from fMRI, SWI or even anatomical images in some cases 1 , 11 .…”