Confounders and caveats in connectivity research and brain-based predictions

COEX 

In-vivo analyses of structural, functional, and more recently molecular connectivity in the human brain as well as their changes in patients with neurological or psychiatric disorders have opened new avenues for understanding large-scale integration in the human brain and its dysfunction. Moreover, combined with machine-learning methods for predicting individual phenotypes from such data, they open the possibility for inference on unobserved traits in single-subjects. While the perspectives of these approaches for clinical and neuropsychological assessment are substantial, the major part of this talk will be focused on several critical yet often underappreciated challenges for such endeavours. These include on the one hand technical and biological aspects that may undermine the validity of prediction results, in particular due to the inherent low-dimensional structure of biological variability. On the other hand, ethical, legal, and societal aspects will ultimately shape practical adaptation but need stronger consideration in the development of new pipelines if these are to move beyond proof-of-concept work.