Surface-based analysis increases the specificity of cortical activation patterns and connectivity results

Abstract

Spatial smoothing of functional magnetic resonance imaging (fMRI) data can be performed on volumetric images and on the extracted surface of the brain. Smoothing on the unfolded cortex should theoretically improve the ability to separate signals between brain areas that are near together in the folded cortex but are more distant in the unfolded cortex. However, surface-based method approaches (SBA) are currently not utilized as standard procedure in the preprocessing of neuroimaging data. Recent improvements in the quality of cortical surface modeling and improvements in its usability nevertheless advocate this method. In the current study, we evaluated the benefits of an up-to-date surface-based smoothing in comparison to volume-based smoothing. We focused on the effect of signal contamination between different functional systems using the primary motor and primary somatosensory cortex as an example. We were particularly interested in how this signal contamination influences the results of activity and connectivity analyses for these brain regions. We addressed this question by performing fMRI on 19 subjects during a tactile stimulation paradigm and by using simulated BOLD responses. We demonstrated that volume-based smoothing causes contamination of the primary motor cortex by somatosensory cortical responses, leading to false positive motor activation. These false positive motor activations were not found by using surface-based smoothing for reasonable kernel sizes. Accordingly, volume-based smoothing caused an exaggeration of connectivity estimates between these regions. In conclusion, this study showed that surface-based smoothing decreases signal contamination considerably between neighboring functional brain regions and improves the validity of activity and connectivity results.

Brain functional network integrity sustains cognitive function despite atrophy in presymptomatic genetic frontotemporal dementia

“…The inclusion of study site as a covariate of no interest 61 and the nature of our multivariate approach to identify shared signals between brain and behavioral data reduce residual effects of scanner variance 38,62 . Future studies may use alternative brain measures that reflect differences in cortical surface and thickness estimates, 63,64 or which infer neural connectivity directly from neurophysiology or from the separation of neurovascular from neuronal contributors to blood oxygen level–dependent (BOLD) fMRI variance, 18,65 given the confounding effects of age, drug, or disease on neurovascular signals 66,67 …”

Brain functional network integrity sustains cognitive function despite atrophy in presymptomatic genetic frontotemporal dementia

“…The inclusion of study site as a covariate of no interest [61] and the nature of our multivariate approach to identify shared signals between brain and behavioural data reduce residual effects of scanner variance [38,62]. Future studies may use alternative brain measures that reflect differences in cortical surface and thickness estimates [63,64], or which infer neural connectivity directly from neurophysiology or from the separation of neurovascular from neuronal contributors to BOLD fMRI variance [18,65], given the confounding effects of age, drug or disease on neurovascular signals [66,67].…”

Improved correspondence of fMRI visual field localizer data after cortex-based macroanatomical alignment

“…Finally, surface-based pre-processing of functional MRI data included spatial smoothing using a nearest neighbor interpolation (1 iteration, approximating a 2D Gaussian smoothing kernel with a FWHM of 1 mm), temporal high-pass filtering (high-pass 0.00903 Hz) and linear trend removal. Spatial smoothing in surface space is clearly superior to spatial smoothing in volume space (Brodoehl, Gaser, Dahnke, Witte, & Klingner, 2020). However, we still opted for minimal spatial smoothing to minimize the loss of accuracy for our visual field localizer.…”