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

Abstract

Although many studies on brain-age prediction in patients with schizophrenia have been reported recently, none has predicted brain age based on different neuroimaging modalities and different brain regions in these patients. Here, we constructed brain-age prediction models with multimodal MRI and examined the deviations of aging trajectories in different brain regions of participants with schizophrenia recruited from multiple centers. The data of 230 healthy controls (HCs) were used for model training. Next, we investigated the differences in brain age gaps between participants with schizophrenia and HCs from two independent cohorts. A Gaussian process regression algorithm with fivefold cross-validation was used to train 90, 90, and 48 models for gray matter (GM), functional connectivity (FC), and fractional anisotropy (FA) maps in the training dataset, respectively. The brain age gaps in different brain regions for all participants were calculated, and the differences in brain age gaps between the two groups were examined. Our results showed that most GM regions in participants with schizophrenia in both cohorts exhibited accelerated aging, particularly in the frontal lobe, temporal lobe, and insula. The parts of the white matter tracts, including the cerebrum and cerebellum, indicated deviations in aging trajectories in participants with schizophrenia. However, no accelerated brain aging was noted in the FC maps. The accelerated aging in 22 GM regions and 10 white matter tracts in schizophrenia potentially exacerbates with disease progression. In individuals with schizophrenia, different brain regions demonstrate dynamic deviations of brain aging trajectories. Our findings provided more insights into schizophrenia neuropathology.

BrainAGE: Revisited and reframed machine learning workflow

“…The results of the SZ sample were consistent with the findings of the previous study on the same sample, where Nenadić et al (2015) found the most prominent neocortical thinning in the negative symptoms group and the least in the disorganised group. Similar to the studies of Kaufmann et al (2019) and Zhu et al (2023), our sample of SZ patients had the highest BrainAGE scores in the frontal and temporal regions. Additional studies are required to validate the regional BrainAGE model in various clinical groups.…”

BrainAGE: Revisited and reframed machine learning workflow

“…The results from the SZ sample were in accordance with the findings of the previous study on the same sample, where Nenadić et al (2015) found the most prominent neocortical thinning in the negative symptoms group and the least in the disorganised group. Similarly, to the studies of Kaufmann et al (2019) and Zhu et al (2023), SZ patients in our sample had the highest BrainAGE scores in the frontal and temporal parts. Additional studies are needed to further validate the regional BrainAGE model in various clinical groups.…”

Differentiation of the retinal morphology aging trajectories in schizophrenia and their associations with cognitive dysfunctions

“…In this perspective, structural brain changes, for example, thinning of selected cortical areas present in schizophrenia ( 15 ), may be the result of patients overtaking healthy subjects in the aging process comprising evaluated cortical regions. Several neuroimaging findings corroborated the existence of the “brain age gap” in this population, indicating that the aging of neuronal structures occurs faster than what would be expected considering the chronological age of patients ( 16 , 17 ). Accelerated aging is also associated with the specificity of cognitive dysfunctions present in schizophrenia ( 18 ).…”