Based on our long-term accumulated healthy brain image database, we used meta-analysismultivariate analytical algorithms to extract the cognitive biological characteristics of individual subjects. By using machine learning / artificial intelligence algorithms, these features were further calculated for capturing individual cognition according to related brain networks. This biological index could be a potential marker for evaluating individual brain health, cognitive functions,risk for neurodegenerative diseases.

First, a unique multi-site neuroimaging database was constructed from Taiwan health residentsits constructed prediction models can be promoted to apply for Asia populations. Second, we obtain different cognitive function features from a huge multi-site neuroimaging dataset with optimized neuroimaging preprocess including meta-multivariate analysis. Finally, we provide an ensemble learning framework with machine learning algorithms to evaluate individual brain health in cognitive function.

This technique aims to provide an objective biological quantitative index to evaluate individual brain healthto assess the change of cognitive-related behavior during aging. This brain aging biomarker can be used in clinical diseases, including neurodegenerative diseases, neuropsychiatric diseases,geriatrics. Moreover, this individual health prediction framework also can be potential to apply in fields of cognition training intervention, personal health risk evaluation, preventive medicine,even insurance.

天使投資人、策略合作夥伴