The heritability of multi-modal connectivity in human brain activity

Patterns of intrinsic human brain activity exhibit a profile of functional connectivity that is associated with behaviour and cognitive performance, and deteriorates with disease. This paper investigates the relative importance of genetic factors and the common environment between twins in determining this functional connectivity profile. Using functional magnetic resonance imaging (fMRI) on 820 subjects from the Human Connectome Project, and magnetoencephalographic (MEG) recordings from a subset, the heritability of connectivity between 39 cortical regions was estimated. On average over all connections, genes account for about 15% of the observed variance in fMRI connectivity (and about 10% in alpha-band and 20% in beta-band oscillatory power synchronisation), which substantially exceeds the contribution from the environment shared between twins. Therefore, insofar as twins share a common upbringing, it appears that genes, rather than the developmental environment, play a dominant role in determining the coupling of neuronal activity

Genetic and environmental influences on MRI scan quantity and quality.

The current study provides an overview of quantity and quality of MRI data in a large developmental twin sample (N = 512, aged 7–9), and investigated to what extent scan quantity and quality were influenced by genetic and environmental factors. This was examined in a fixed scan protocol consisting of two functional MRI tasks, high resolution structural anatomy (3DT1) and connectivity (DTI) scans, and a resting state scan. Overall, scan quantity was high (88% of participants completed all runs), while scan quality decreased with increasing session length. Scanner related distress was negatively associated with scan quantity (i.e., completed runs), but not with scan quality (i.e., included runs). In line with previous studies, behavioral genetic analyses showed that genetics explained part of the variation in head motion, with heritability estimates of 29% for framewise displacement and 65% for absolute displacement. Additionally, our results revealed that subtle head motion (after exclusion of excessive head motion) showed lower heritability estimates (0–14%), indicating that findings of motion-corrected and quality-controlled MRI data may be less confounded by genetic factors. These findings provide insights in factors contributing to scan quality in children, an issue that is highly relevant for the field of developmental neuroscience

Distinctive heritability patterns of subcortical-prefrontal cortex resting state connectivity in childhood: A twin study

Connectivity between limbic/subcortical and prefrontal-cortical brain regions develops considerably across childhood, but less is known about the heritability of these networks at this age. We tested the heritability of limbic/subcortical-cortical and limbic/subcortical-subcortical functional brain connectivity in 7- to 9-year-old twins (N = 220), focusing on two key limbic/subcortical structures: the ventral striatum and the amygdala, given their combined influence on changing incentivised behavior during childhood and adolescence. Whole brain analyses with ventral striatum (VS) and amygdala as seeds in genetically independent groups showed replicable functional connectivity patterns. The behavioral genetic analyses revealed that in general VS and amygdala connectivity showed distinct influences of genetics and environment. VS-prefrontal cortex connections were best described by genetic and unique environmental factors (the latter including measurement error), whereas amygdala-prefrontal cortex connectivity was mainly explained by environmental influences. Similarities were also found: connectivity between both the VS and amygdala and ventral anterior cingulate cortex (vACC) showed influences of shared environment, while connectivity with the orbitofrontal cortex (OFC) showed heritability. These findings may inform future interventions that target behavioral control and emotion regulation, by taking into account genetic dispositions as well as shared and unique environmental factors such as child rearing.Development Psychopathology in context: famil

Head motion in children with ADHD during resting-state brain imaging

Although head motion during scanning has been largely considered to reflect simply technical artifacts, there is growing evidence showing that the variable of head motion reflects valuable information regarding individual’s psychological and/or clinical factors. Detailed studies would not only help to deal with the head motion biases, but they also help researchers in understanding the mental disorders. In this study, children with ADHD and demographically-matched typically developing control (TDC) participants underwent rs-fMRI examination without any specific task, and six mean single head motion parameters (three translational and three rotational) and a summary motion index for each participant were obtained. We found that patients with ADHD showed specific patterns of head motion during scanning: motion was significantly increased in the ADHD group, which was mainly contributed by the motion around and along the superior-to-inferior direction. Furthermore, the cross-validation classification analyses showed that the head motion could accurately distinguish children with ADHD from the healthy controls. These results suggest that head motion during scanning reflects useful information about the participants and accounting for head motion from MRI data may be helpful for ADHD diagnosing and treatment with neuroimaging

Investigating the genetic and environmental basis of head micromovements during MRI

Introduction Head motion during magnetic resonance imaging is heritable. Further, it shares phenotypical and genetic variance with body mass index (BMI) and impulsivity. Yet, to what extent this trait is related to single genetic variants and physiological or behavioral features is unknown. We investigated the genetic basis of head motion in a meta-analysis of genome-wide association studies. Further, we tested whether physiological or psychological measures, such as respiratory rate or impulsivity, mediated the relationship between BMI and head motion.Methods We conducted a genome-wide association meta-analysis for mean and maximal framewise head displacement (FD) in seven population neuroimaging cohorts (UK Biobank, LIFE-Adult, Rotterdam Study cohort 1-3, Austrian Stroke Prevention Family Study, Study of Health in Pomerania; total N = 35.109). We performed a pre-registered analysis to test whether respiratory rate, respiratory volume, self-reported impulsivity and heart rate mediated the relationship between BMI and mean FD in LIFE-Adult.Results No variant reached genome-wide significance for neither mean nor maximal FD. Neither physiological nor psychological measures mediated the relationship between BMI and head motion.Conclusion Based on these findings from a large meta-GWAS and pre-registered follow-up study, we conclude that the previously reported genetic correlation between BMI and head motion relies on polygenic variation, and that neither psychological nor simple physiological parameters explain a substantial amount of variance in the association of BMI and head motion. Future imaging studies should thus rigorously control for head motion at acquisition and during preprocessing

Individual Differences in Human Brain Functional Network Organization

The human brain is organized at many spatial scales, including the level of areas and systems. Resting-state functional magnetic resonance imaging is a non-invasive technique that allows for the study of areal- and systems-level brain organization in vivo. Over two decades of research has sought to identify and characterize the functional communities that comprise the brain’s network architecture. Consequently, a convergent description of group-average functional network organization in healthy adults has emerged. Recent advances have allowed for the study of such organization in single individuals. Investigation of functional network organization in highly sampled individuals has revealed brain regions that deviate from the group-level description, i.e. individual differences in human brain functional network organization. This dissertation work characterizes individual differences in functional network organization, referred to as network variants, across a large sample of healthy adults. Network variants appear to be stable over time within an individual and organized systematically across individuals. They occur in characteristic cortical locations and associate with characteristic functional networks. Further, their task-evoked activity is consistent with their idiosyncratic functional network association. Finally, individuals may be sub-typed into one of two groups, where individuals in the same sub-group have a similar distribution of network variants. The sub-group phenomenon is heritable and relates to differences in neuropsychological measures of behavior. Network variants appear to be trait-like, functionally-relevant components of individual human brain functional network organization

Quality control in resting-state fMRI: the benefits of visual inspection

Background: A variety of quality control (QC) approaches are employed in resting-state functional magnetic resonance imaging (rs-fMRI) to determine data quality and ultimately inclusion or exclusion of a fMRI data set in group analysis. Reliability of rs-fMRI data can be improved by censoring or “scrubbing” volumes affected by motion. While censoring preserves the integrity of participant-level data, including excessively censored data sets in group analyses may add noise. Quantitative motion-related metrics are frequently reported in the literature; however, qualitative visual inspection can sometimes catch errors or other issues that may be missed by quantitative metrics alone. In this paper, we describe our methods for performing QC of rs-fMRI data using software-generated quantitative and qualitative output and trained visual inspection. Results: The data provided for this QC paper had relatively low motion-censoring, thus quantitative QC resulted in no exclusions. Qualitative checks of the data resulted in limited exclusions due to potential incidental findings and failed pre-processing scripts. Conclusion: Visual inspection in addition to the review of quantitative QC metrics is an important component to ensure high quality and accuracy in rs-fMRI data analysis