Improved correspondence of resting-state networks after macroanatomical alignment

Resting state brain activity, as measured with functional magnetic resonance imaging (fMRI) in the absence of stimulation, is widely investigated in clinical, pharmacological, developmental and cross-species neuroscience research. However, despite the general and broad interest in understating the nature of resting state networks (RSNs), there has not been a thorough investigation into the relationship between these functional networks and their adherence to underling brain anatomy. We acquired resting state fMRI data from 10 subjects and extracted individual and group RSN maps respectively using independent component analysis (ICA) and self organising group-level ICA (sogICA). Cortex based alignment (CBA), an advanced surface based alignment technique which uses individual curvature information to align individual subjects' brains to a dynamic group average, was used to maximise anatomical correspondence across subjects. Cross subject spatial correlations of the RSN maps (independent components) were carried out with and without CBA. Seven RSNs, which are amongst the most reported and studied networks, were identified. We observed a systematic gain in the spatial correlation in all of them following CBA, although this gain was not uniform across RSNs. The observed increase in similarity of the functional RSNs after anatomical alignment illustrates that these functional networks are indeed related to underlying macroanatomical features. Moreover, our results demonstrate that by correcting for individual anatomical differences, advanced surface based alignment techniques increase the overlap of corresponding resting state networks across subjects, thereby providing a useful means to improve resting state group statistics with no need for substantial smoothing.