Connectopic mapping techniques do not reflect functional gradients in the brain

Functional gradients, in which response properties change gradually across a brain region, have been proposed as a key organising principle of the brain. Recent studies using both resting-state and natural viewing paradigms have indicated that these gradients may be reconstructed from functional connectivity patterns via connectopic mapping analyses. However, local connectivity patterns may be confounded by spatial autocorrelations arti-ficially introduced during data analysis, for instance by spatial smoothing or interpolation between coordinate spaces. Here, we investigate whether such confounds can produce illusory connectopic gradients. We generated datasets comprising random white noise in subjects' functional volume spaces, then optionally applied spatial smoothing and/or interpolated the data to a different volume or surface space. Both smoothing and interpolation induced spatial autocorrelations sufficient for connectopic mapping to produce both volume-and surface-based local gradients in numerous brain regions. Furthermore, these gradients appeared highly similar to those obtained from real natural viewing data, although gradients generated from real and random data were statistically differ-ent in certain scenarios. We also reconstructed global gradients across the whole-brain - while these appeared less susceptible to artificial spatial autocorrelations, the ability to reproduce previously reported gradients was closely linked to specific features of the analysis pipeline. These results indicate that previously reported gradients iden-tified by connectopic mapping techniques may be confounded by artificial spatial autocorrelations introduced during the analysis, and in some cases may reproduce poorly across different analysis pipelines. These findings imply that connectopic gradients need to be interpreted with caution.

Automated summary

Functional gradients, a key organizing principle of the brain, can be reconstructed from functional connectivity patterns. However, the local connectivity patterns may be confounded by artificial spatial autocorrelations introduced during data analysis, resulting in illusory gradients. Connectopic mapping techniques need to be interpreted with caution due to the potential confounding effect of artificial spatial autocorrelations.