Typicality of Functional Connectivity robustly captures motion artifacts in rs-fMRI across datasets, atlases and preprocessing pipelines

Abstract

Functional connectivity analysis of resting state fMRI data has recently become one of the most common approaches to characterizing individual brain function. It has been widely suggested that the functional connectivity matrix, calculated by correlating signals from regions of interest, is a useful approximate representation of the brain's connectivity, potentially providing behaviorally or clinically relevant markers. However, functional connectivity estimates are known to be detrimentally affected by various artifacts, including those due to in-scanner head motion. Treatment of such artifacts poses a standing challenge because of their high variability. Moreover, as individual functional connections generally covary only very weakly with head motion estimates, motion influence is difficult to quantify robustly, and prone to be neglected in practice. Although the use of individual estimates of head motion, or group-level correlation of motion and functional connectivity has been suggested, a sufficiently sensitive measure of individual functional connectivity quality has not yet been established. We propose a new intuitive summary index, the Typicality of Functional Connectivity, to capture deviations from normal brain functional connectivity pattern. Based on results of resting state fMRI for 245 healthy subjects we show that this measure is significantly correlated with individual head motion metrics. The results were further robustly reproduced across atlas granularity and preprocessing options, as well as other datasets including 1081 subjects from the Human Connectome Project. The Typicality of Functional Connectivity provides individual proxy measure of motion effect on functional connectivity and is more sensitive to inter-individual variation of motion than individual functional connections. In principle it should be sensitive also to other types of artifacts, processing errors and possibly also brain pathology, allowing wide use in data quality screening and quantification in functional connectivity studies as well as methodological investigations.

Rich-club reorganization of functional brain networks in acute mild traumatic brain injury with cognitive impairment

“…Second, functional images were corrected for slice-timing by using slice interpolation to interpolate the voxel time, and head motion correction was performed. According to the criteria of spatial movement, participants with head motion >2.0° of rotation or 2.0 mm of translation in any direction were removed (32). Third, the fMRI images were spatially co-registered with their anatomical T1 image and then resampled to 3×3×3 mm 3 voxels (33).…”

Cholinergic modulation supports dynamic switching of resting state networks through selective DMN suppression

“…Of course, the observed SC-FC relation may differ depending on the specific pipelines used to estimate the SC and FC. For example, with more conservative preprocessing that aims to suppress potential artifact sources, the individual FC matrices resemble more the typical FC matrix [Kopal et al, 2020] and, in our case, leads to globally decreasing strength of the functional connectivity (for the effects of commonly used preprocessing components see [Bartoň et al, 2019]); at the same time different fiber tracking methods would also lead to varying estimates of SC [Schirner et al, 2015].…”

Promises and pitfalls of Topological Data Analysis for brain connectivity analysis

“…Furthermore, to remove possible signal drift, time-series were linearly detrended and filtered by band-pass filter [0.009-0.08Hz]. See Kopal et al (2020) and Oliver et al (2019) for detailed prepocessing description. To extract the time series for further analysis, the brain’s spatial domain was divided into 90 non-overlapping regions of interest (ROIs) according to the AAL atlas; from each ROI we extract one BOLD time series by averaging the time series of all voxels in the ROI.…”