Genetic overlap between in-scanner head motion and the default network connectivity

Abstract

The association between in-scanner head motion and intrinsic functional connectivity (iFC) may confound explanations for individual differences in functional connectomics. However, the etiology of the correlation between head motion and iFC has not been established. This study aimed to investigate genetic and environmental contributions on the association between head motion and iFC using a twin dataset (175 same-sex twin pairs, aged 14-23 years, 48% females). After establishing that both head motion and default network iFC are moderately heritable, we found large genetic correlations (-0.52 to -0.73) between head motion and the default network iFCs. Common genes can explain 48% -61% of the negative phenotypic correlation between the two phenotypes. These results advance our understanding of the relationship between head motion and iFC, and may have profound implications for interpreting individual differences in default network connectivity in clinical research and brain-behavior association.

DREAM: A Toolbox to Decode Rhythms of the Brain System

“…This might be an indication that this age range is a key period for developing the ability to apply effective cognitive control. Our findings are consistent with recent reports that head motion during fMRI scanning can be an important confounding factor (Power et al, 2015) while it also has neurobiological components related to individual motion traits (Zeng et al, 2014;Zhou et al, 2016), which are likely driven by brain systems operating within a multi-band frequency landscape. Our results demonstrate the necessity to study the characteristics of head motion especially in special cohorts like children, the elderly and patients with neurologic or psychiatric conditions, since differences of distancerelated functional connectivity that may be influenced by head motion have been observed between such special cohorts and healthy young adults (Andrews-Hanna et al, 2007;Fair et al, 2007;Satterthwaite et al, 2012;Fair et al, 2013).…”

“…Here, we introduce the N3L algorithm and its DREAM 40 implementation. Neural oscillations reflected by the human brain spontaneous activity 41 measured with resting-state functional MRI and head motion data during mock MRI 42 scans were employed as two worked examples to demonstrate the use of DREAM to 43 perform frequency analyses. 44 2 Methods and Algorithms 45 Neuronal brain signals are temporally continuous but they are almost always measured 46 as discrete data for practical reasons.…”

“…Our findings are 381 consistent with the previous observation that the head motion had more impacts on the 382 short-distance brain connectivity. While the head motion during fMRI scanning has 383 been treated an important confounding factor in the neural signal [27], some recent work 384 also argued its neurobiological components related to individual traits of the motor 385 behaviors (e.g., [41,43]). The current researh offers data for an alternative explanation 386 on such neurobehavioral trait likely driven by brain systems operating within a 387 multi-band frequency landscape.…”

Dissimilarity of functional connectivity uncovers the influence of participant's motion in functional magnetic resonance imaging studies

“…At the voxel level, disruption caused by motion decreases the BOLD signal and the magnitude of the signal loss is associated with the extent of motion (Satterthwaite et al, 2013). In addition, more studies indicate that head motion can be a neurobiological trait, contributing to the dissimilarity of FC in the region of default‐mode network (Zeng et al, 2014; Zhou et al, 2016). Thus, preprocessing methods for removing motion‐related artifacts improve fMRI data quality without necessarily totally correcting the data (Power et al, 2014).…”

The Beijing Twin Study (BeTwiSt): An Update

“…Researchers have shown that brain activation in cortical motor areas, the thalamus and the cerebellum was associated with complex motor sequencing and audiovisual integration (Li, Huang et al, 2018), and activation in the left insula, right stratum and right superior parietal lobule was risk-related and may be heritable (Rao et al, 2018). In addition, researchers scanned the resting state of adolescent twins and found large genetic correlations between head motion and the default network intrinsic functional connectivity, which had profound implications for interpreting individual differences in default network connectivity (Zhou et al, 2016). They also found that the subdivisions of diverse brain regions based on genetic correlations were generally consistent with functional connectivity patterns, indicating that the magnitude of the genetic covariance in brain anatomy could be used to portray the boundaries of functional subregions of the brain (Cui et al, 2016).…”