Potential of brain age in identifying early cognitive impairment in subcortical small-vessel disease patients

Abstract

BackgroundReliable and individualized biomarkers are crucial for identifying early cognitive impairment in subcortical small-vessel disease (SSVD) patients. Personalized brain age prediction can effectively reflect cognitive impairment. Thus, the present study aimed to investigate the association of brain age with cognitive function in SSVD patients and assess the potential value of brain age in clinical assessment of SSVD.Materials and methodsA prediction model for brain age using the relevance vector regression algorithm was developed using 35 healthy controls. Subsequently, the prediction model was tested using 51 SSVD patients [24 subjective cognitive impairment (SCI) patients and 27 mild cognitive impairment (MCI) patients] to identify brain age-related imaging features. A support vector machine (SVM)-based classification model was constructed to differentiate MCI from SCI patients. The neurobiological basis of brain age-related imaging features was also investigated based on cognitive assessments and oxidative stress biomarkers.ResultsThe gray matter volume (GMV) imaging features accurately predicted brain age in individual patients with SSVD (R2 = 0.535, p < 0.001). The GMV features were primarily distributed across the subcortical system (e.g., thalamus) and dorsal attention network. SSVD patients with age acceleration showed significantly poorer Mini-Mental State Examination and Montreal Cognitive Assessment (MoCA) scores. The classification model based on GMV features could accurately distinguish MCI patients from SCI patients (area under the curve = 0.883). The classification outputs of the classification model exhibited significant associations with MoCA scores, Trail Making Tests A and B scores, Stroop Color and Word Test C scores, information processing speed total scores, and plasma levels of total antioxidant capacity in SSVD patients.ConclusionBrain age can be accurately quantified using GMV imaging data and shows potential clinical value for identifying early cognitive impairment in SSVD patients.

Association between behavioral and psychological symptoms and cerebral small vessel disease MRI findings in memory clinic patients

“…The TMT is used to evaluate visual attention, psychomotor speed, and executive function. Shi et al reported that patients with SVD showed executive dysfunction, which was determined using the TMT score (Shi et al, 2022). Processing speed has been associated with BPS in patients with CAA and likewise, our study revealed that the time taken to complete TMT set-A was longer in patients with BPS.…”

Macrophage Migration Inhibitory Factor as a Potential Plasma Biomarker of Cognitive Impairment in Cerebral Small Vessel Disease

“…As previously described, − the raw test scores of each scale were standardized through Z -transformation using the mean and standard deviation for subsequent analysis. Information processing speed scores were calculated based on the composite TMT-A, Stroop-A, and Stroop-B scale scores using the Z -transformed averages.…”

“…Further details are provided in the Supporting Information and our previous studies. 27,37,38 Statistical Analyses. Statistical analyses were carried out using SPSS version 22.0 (SPSS Inc. Chicago, Illinois) and R software (version 4.2.1).…”

“…Brain regions with significant differences in mALFF values were visualized using BrainNet Viewer software. Further details are provided in the Supporting Information and our previous studies. ,, …”

Assessing Brain Involvement in Fabry Disease with Deep Learning and the Brain-Age Paradigm

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