Predicting aging trajectories of decline in brain volume, cortical thickness and fractional anisotropy in schizophrenia

Abstract

Brain-age prediction is a novel approach to assessing deviated brain aging trajectories in different diseases. However, most studies have used an average brain age gap (BAG) of individuals with schizophrenia of different illness durations for comparison with healthy participants. Therefore, this study investigated whether declined brain structures as reflected by BAGs may be present in schizophrenia in terms of brain volume, cortical thickness, and fractional anisotropy across different illness durations. We used brain volume, cortical thickness, and fractional anisotropy as features to train three models from the training dataset. Three models were applied to predict brain ages in the hold-out test and schizophrenia datasets and calculate BAGs. We divided the schizophrenia dataset into multiple groups based on the illness duration using a sliding time window approach for ANCOVA analysis. The brain volume and cortical thickness models revealed that, in comparison with healthy controls, individuals with schizophrenia had larger BAGs across different illness durations, whereas the BAG in terms of fractional anisotropy did not differ from that of healthy controls after disease onset. Moreover, the BAG at the initial stage of schizophrenia was the largest in the cortical thickness model. In contrast, the BAG from approximately two decades after disease onset was the largest in the brain volume model. Our findings suggest that schizophrenia differentially affects the decline of different brain structures during the disease course. Moreover, different trends of decline in thickness and volume-based measures suggest a differential decline in dimensions of brain structure throughout the course of schizophrenia.

Investigating brain aging trajectory deviations in different brain regions of individuals with schizophrenia using multimodal magnetic resonance imaging and brain-age prediction: a multicenter study

“…Previous studies on brain-age prediction have also demonstrated that individuals with schizophrenia exhibit deviations in brain aging trajectories, as indicated in both T1-weighted magnetic resonance imaging (MRI) [16][17][18][19][20][21][22][23][24] and DTI [25,26] findings. The recent studies used multimodal MRI to evaluate the brain age gap in patients with schizophrenia and obtained results consistent with those of studies using a single neuroimaging modality [27][28][29][30].…”

“…Brain-age studies have indicated that individuals with schizophrenia had a brain age that was older than those of HCs in the FA-based model [25,26]. Our recent study suggested that there were nonsignificant differences in the global brain age gap between participants with schizophrenia and healthy controls across different illness durations in the FA model [30]. The possible reason is that computing the global brain age gap might reduce the sensitivity for detecting the deviation of aging trajectories of individual white matter tracts.…”

“…Kaufmann et al found that among the different brain regions, the frontal lobe of individuals with schizophrenia had the most pronounced acceleration of brain aging [20]. Zhu et al revealed that individuals with schizophrenia had significantly larger brain age gaps than those of healthy controls across different durations of illness in the brain volume and cortical thickness models [30].…”

Lower cortical thickness and increased brain aging in adults with cocaine use disorder

“…Previous research has demonstrated that cortical thickness age-dependently declines across the adult life span (64-67). Notably, both GM volume and cortical thickness are key features in many brain age models (25,68), and cortical thickness has shown some of the largest disparities in brain age across different durations of illnesses (69). Consequently, the regions with reduced cortical thickness identified in our study might be the main drivers of accelerated brain aging in individuals with CUD.…”

“…It is hypothesized that the deterioration of cortical thickness is a driving factor behind reductions in brain volume (69,70). However, it is important to note that these are speculative assumptions, as several other factors such as cortical surface area, gray/white matter intensity contrast, and curvature may collectively contribute to changes in brain volume (69,70). Further investigations are needed to comprehensively understand the complex interplay of these factors.…”