Neuro-VisAge: a visual and statistical strategy to investigate age effect on brain activity

The last decade was marked by a spike in the use of Machine Learning (ML) andfunctional magnetic resonance image for neurological disorders diagnoses. How-ever, there is a limitation considering age bias when designing their experiments,which can impact the final result. Here, we investigate the effects of age bias ona sample of typical neurological subjects, looking for patterns in brain activity.We also suggest that age groups be used in the ML training and classificationfor future works. Our results show that for the five brain regions investigated(Frontal Gyrus, Cingulum Bundle, Putamen, Angular Gyrus, and Heschl Gyrus),using a 10-year span would increase the reliability of ML experiments aiming todiagnose neural disorders by reducing the age bias effect on the models.