The last decade was marked by a spike in the use of Machine Learning (ML) and functional magnetic resonance image for neurological disorders diagnoses. However, we suggest additional precautions to be considered regard the age bias when designing experiments, which can impact the final result. Here, we investigate the effects of age bias on a sample of typical neurological subjects, looking for patterns in brain activity. We also suggest that age groups be used in the ML training and classification for future works. Our results show that for the five brain regions investigated (Frontal Gyrus, Cingulum Bundle, Putamen, Angular Gyrus, and Heschl Gyrus), the reliability of ML experiments aiming to diagnose neurological disorders can be impacted by age bias.