Identifiability in Functional Connectivity May Unintentionally Inflate Prediction Results.

ABSTRACT: Functional magnetic resonance (fMRI) is an invaluable tool in studying cognitive processes in vivo. Many recent studies use functional connectivity (FC), partial correlation connectivity (PC), or fMRI-derived brain networks to predict phenotypes with results that sometimes cannot be replicated. At the same time, FC can be used to identify the same subject from different scans with great accuracy. In this paper, we show a method by which one can unknowingly inflate classification results from 61% accuracy to 86% accuracy by treating longitudinal or contemporaneous scans of the same subject as independent data points. Using the UK Biobank dataset, we find one can achieve the same level of variance explained with 50 training subjects by exploiting identifiability as with 10,000 training subje

SUBMITTER: Orlichenko A 

PROVIDER: S-EPMC10418521 | biostudies-literature | 2023 Aug

REPOSITORIES: biostudies-literature