Classifying Resting State Connectivity: Lag-One Autocorrelation and Pattern Differentiability

Resting state functional connectivity (rsFC) and resting state effective connectivity (rsEC) are two of the most common measures that can be extracted from resting state functional magnetic resonance imaging (rs-fMRI) data. RSFC is often used to indicate the statistical dependencies among different brain regions of interest, whereas rsEC describes the causal influences among them. Many studies have explored utilities of rsFC and rsEC measures for classifying psychiatric conditions. Several studies showed that rsEC were better than rsFC features for classifying major depression (Frassle et al., 2020; Geng et al., 2018) and schizophrenia ((Brodersen et al., 2014)). However, no study to-date has investigated whether rsEC is inherently better than rsFC for classifying psychiatric conditions or the impact of autocorrelation on classifying rsFC, even though autocorrelation is known to be present in rs-fMRI data. To fill these gaps, we performed a series of computational experiments, by varying the size of the network and the number of participants, to gain some insight into these two aspects of supervised classification with resting state connectivity. Contrary to what has been reported in the literature, the results from our study suggest that rsEC cannot be, in principle, better than rsFC features for classification. In fact, rsEC measures led to systematically worse classification results, compared to rsFC features. In terms of the impact of autocorrelation, we found that lag-one autocorrelation could lead to both false negative and false positive classification results for studies with a small sample size.