Connectivity-based Psychometric Prediction Framework

To better understand the relationships between interindividual variability in brain regions’ connectivity and behavioural phenotypes, we are developing a connectivity-based psychometric prediction framework (CBPP). Preliminary to the development of this region-wise machine learning approach, we performed an extensive assessment of the general connectivity-based psychometric prediction (CBPP) framework based on whole-brain connectivity information.
Because a systematic evaluation of different parameters was lacking from previous literature, we evaluated several approaches pertaining to the different steps of a CBPP study.
We hence tested 72 different approach combinations (3 types of preprocessing x 4 parcellation granularity x 2 connectivity methods x 3 regression methods = 72 combinations) in a cohort of over 900 healthy adults across 98 psychometric variables. Overall, our extensive evaluation combined to an innovative region-wise machine learning approach offer a framework that optimises both, prediction performance and neurobiological validity (and hence interpretability) for studying brain-behaviour relationships.