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Head Motion and Inattention/Hyperactivity Share Common Genetic Influences: Implications for fMRI Studies of ADHD.

ABSTRACT: Head motion (HM) is a well known confound in analyses of functional MRI (fMRI) data. Neuroimaging researchers therefore typically treat HM as a nuisance covariate in their analyses. Even so, it is possible that HM shares a common genetic influence with the trait of interest. Here we investigate the extent to which this relationship is due to shared genetic factors, using HM extracted from resting-state fMRI and maternal and self report measures of Inattention and Hyperactivity-Impulsivity from the Strengths and Weaknesses of ADHD Symptoms and Normal Behaviour (SWAN) scales. Our sample consisted of healthy young adult twins (N = 627 (63% females) including 95 MZ and 144 DZ twin pairs, mean age 22, who had mother-reported SWAN; N = 725 (58% females) including 101 MZ and 156 DZ pairs, mean age 25, with self reported SWAN). This design enabled us to distinguish genetic from environmental factors in the association between head movement and ADHD scales. HM was moderately correlated with maternal reports of Inattention (r = 0.17, p-value = 7.4E-5) and Hyperactivity-Impulsivity (r = 0.16, p-value = 2.9E-4), and these associations were mainly due to pleiotropic genetic factors with genetic correlations [95% CIs] of rg = 0.24 [0.02, 0.43] and rg = 0.23 [0.07, 0.39]. Correlations between self-reports and HM were not significant, due largely to increased measurement error. These results indicate that treating HM as a nuisance covariate in neuroimaging studies of ADHD will likely reduce power to detect between-group effects, as the implicit assumption of independence between HM and Inattention or Hyperactivity-Impulsivity is not warranted. The implications of this finding are problematic for fMRI studies of ADHD, as failing to apply HM correction is known to increase the likelihood of false positives. We discuss two ways to circumvent this problem: censoring the motion contaminated frames of the RS-fMRI scan or explicitly modeling the relationship between HM and Inattention or Hyperactivity-Impulsivity.

SUBMITTER: Couvy-Duchesne B

PROVIDER: S-EPMC4712830 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature

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Head Motion and Inattention/Hyperactivity Share Common Genetic Influences: Implications for fMRI Studies of ADHD.

Couvy-Duchesne Baptiste B Ebejer Jane L JL Gillespie Nathan A NA Duffy David L DL Hickie Ian B IB Thompson Paul M PM Martin Nicholas G NG de Zubicaray Greig I GI McMahon Katie L KL Medland Sarah E SE Wright Margaret J MJ

PloS one 20160108 1

Head motion (HM) is a well known confound in analyses of functional MRI (fMRI) data. Neuroimaging researchers therefore typically treat HM as a nuisance covariate in their analyses. Even so, it is possible that HM shares a common genetic influence with the trait of interest. Here we investigate the extent to which this relationship is due to shared genetic factors, using HM extracted from resting-state fMRI and maternal and self report measures of Inattention and Hyperactivity-Impulsivity from t ...[more]

PMID: 26745144

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Project description:The quality of functional MRI (fMRI) data is affected by head motion. It has been shown that fMRI data quality can be improved by prospectively updating the gradients and radio-frequency pulses in response to head motion during image acquisition by using an MR-compatible optical tracking system (prospective motion correction, or PMC). Recent studies showed that PMC improves the temporal Signal to Noise Ratio (tSNR) of resting state fMRI data (rs-fMRI) acquired from subjects not moving intentionally. Besides that, the time courses of Independent Components (ICs), resulting from Independent Component Analysis (ICA), were found to present significant temporal correlation with the motion parameters recorded by the camera. However, the benefits of applying PMC for improving the quality of rs-fMRI acquired under large head movements and its effects on resting state networks (RSN) and connectivity matrices are still unknown. In this study, subjects were instructed to cross their legs at will while rs-fMRI data with and without PMC were acquired, which generated head motion velocities ranging from 4 to 30 mm/s. We also acquired fMRI data without intentional motion. Independent component analysis of rs-fMRI was performed to evaluate IC maps and time courses of RSNs. We also calculated the temporal correlation among different brain regions and generated connectivity matrices for the different motion and PMC conditions. In our results we verified that the crossing leg movements reduced the tSNR of sessions without and with PMC by 45 and 20%, respectively, when compared to sessions without intentional movements. We have verified an interaction between head motion speed and PMC status, showing stronger attenuation of tSNR for acquisitions without PMC than for those with PMC. Additionally, the spatial definition of major RSNs, such as default mode, visual, left and right central executive networks, was improved when PMC was enabled. Furthermore, motion altered IC-time courses by decreasing power at low frequencies and increasing power at higher frequencies (typically associated with artefacts). PMC partially reversed these alterations of the power spectra. Finally, we showed that PMC provides temporal correlation matrices for data acquired under motion conditions more comparable to those obtained by fMRI sessions where subjects were instructed not to move.

Project description:BackgroundTeens with attention deficit-hyperactivity disorder (ADHD) are at increased risk for motor vehicle collisions. A computerized skills-training program to reduce long glances away from the roadway, a contributor to collision risk, may ameliorate driving risks among teens with ADHD.MethodsWe evaluated a computerized skills-training program designed to reduce long glances (lasting ≥2 seconds) away from the roadway in drivers 16 to 19 years of age with ADHD. Participants were randomly assigned in a 1:1 ratio to undergo either enhanced Focused Concentration and Attention Learning, a program that targets reduction in the number of long glances (intervention) or enhanced conventional driver's education (control). The primary outcomes were the number of long glances away from the roadway and the standard deviation of lane position, a measure of lateral movements away from the center of the lane, during two 15-minute simulated drives at baseline and at 1 month and 6 months after training. Secondary outcomes were the rates of long glances and collisions or near-collisions involving abrupt changes in vehicle momentum (g-force event), as assessed with in-vehicle recordings over the 1-year period after training.ResultsDuring simulated driving after training, participants in the intervention group had a mean of 16.5 long glances per drive at 1 month and 15.7 long glances per drive at 6 months, as compared with 28.0 and 27.0 long glances, respectively, in the control group (incidence rate ratio at 1 month, 0.64; 95% confidence interval [CI], 0.52 to 0.76; P<0.001; incidence rate ratio at 6 months, 0.64; 95% CI, 0.52 to 0.76; P<0.001). The standard deviation of lane position (in feet) was 0.98 SD at 1 month and 0.98 SD at 6 months in the intervention group, as compared with 1.20 SD and 1.20 SD, respectively, in the control group (difference at 1 month, -0.21 SD; 95% CI, -0.29 to -0.13; difference at 6 months, -0.22 SD; 95% CI, -0.31 to -0.13; P<0.001 for interaction for both comparisons). During real-world driving over the year after training, the rate of long glances per g-force event was 18.3% in the intervention group and 23.9% in the control group (relative risk, 0.76; 95% CI, 0.61 to 0.92); the rate of collision or near-collision per g-force event was 3.4% and 5.6%, respectively (relative risk, 0.60, 95% CI, 0.41 to 0.89).ConclusionsIn teens with ADHD, a specially designed computerized simulated-driving program with feedback to reduce long glances away from the roadway reduced the frequency of long glances and lessened variation in lane position as compared with a control program. During real-world driving in the year after training, the rate of collisions and near-collisions was lower in the intervention group. (Funded by the National Institutes of Health; ClinicalTrials.gov number, NCT02848092.).

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Head Motion and Inattention/Hyperactivity Share Common Genetic Influences: Implications for fMRI Studies of ADHD.

Publications

Head Motion and Inattention/Hyperactivity Share Common Genetic Influences: Implications for fMRI Studies of ADHD.

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