Individual Differences in Impulsivity Predict Head Motion during Magnetic Resonance Imaging

Kong, Xiangzhen; Zhen, Zonglei; Li, Xueting; Lu, Hanqing; Ruosi, Wang; Liu, Ling; He, Yong; Zang, Yu‐Feng; Liu, Jia

doi:10.1371/journal.pone.0104989

Cited by 103 publications

(97 citation statements)

References 41 publications

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“…Our findings align with the (phenotypic) association of HM with (self reported) impulsivity in a similar age group (r=0.10) and in children (r=0.34) [321]. We also found a significant phenotypic correlation between HM and inattention, which has not been reported before [321].…”

Section: Discussionsupporting

confidence: 90%

“…We also found a significant phenotypic correlation between HM and inattention, which has not been reported before [321]. We expanded prior findings [321] by showing that the association was driven by genetic factors common to HM and ADHD rather than by experimental conditions (environmental). Indeed, the genetic correlation coefficients were around 0.2 between HM and the SWAN scores.…”

Section: Discussionsupporting

confidence: 82%

“…Recently, HM (in both fMRI and DTI) was shown to be greater in impulsive individuals, in line with the findings from externalising disorders such as Attention Deficit Hyperactivity Disorder (ADHD) and conduct disorders [321]. In many studies the frequency of exclusions for gross motion was greater in ADHD [269,271,273] and autism cases [272,322] compared to controls.…”

Section: Introductionsupporting

confidence: 52%

“…Another limitation of our study was that we could not separate hyperactivity from impulsivity in the analysis, nor could we investigate subtypes of impulsivity that might not contribute to the HM increase [321]. In addition, RS-fMRI was acquired last in the ~50 minutes scanning session, which could potentially accentuate the strength of the relationship between HM and Impulsivity or Hyperactivity by increasing movement in the impulsive or hyperactive group.…”

Section: Figure 19: Structural Equation Model That Can Disentangle Thmentioning

confidence: 99%

“…Here, we chose to focus on the relationship of HM with ADHD, following a recent study showing an association between HM and impulsivity [321]. We used the twin design to investigate how much of the association between these traits is due to environmental or This provides a computationally fast and direct measurement of the mean displacement over all brain voxels [62,262,282].…”

Section: Introductionmentioning

confidence: 99%

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Breaking down the genetics of depression using brain endophenotypes

Couvy‐Duchesne¹

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The Queensland Brain Institute 2 Thesis AbstractThe objective of the work presented in this thesis was to extend our understanding of depression aetiology and to identify brain MRI endophenotypes of depression. Our work builds on previous research that highlighted the genetic relationships between stress, sex and depression. In addition, MRI measurements are heritable and do not relying on selfreport. As such, they represent promising endophenotypes that could help break down further the genetics of depression.Preliminary work involved reporting depression prevalence in a young adult Australian twin sample (N=2,773) as well as comorbidity with affective and substance use disorders (Chapter 1). In addition, we demonstrated the good psychometric properties of the SPHERE questionnaire, which provides self-reported measures of anxiety-depression and chronic fatigue. Properties of the scores include: i) stochastic ordering on the sum score, ii) limited sex and age bias through adolescence, iii) high internal consistency, iv) good 3 months test-retest, v) moderate heritability, vi) significant association with lifetime MDD, social anxiety and alcohol dependence diagnoses (Chapter 2).In an older sample of Australian twins (N=5,221, Chapter 3), we performed a direct test of the diathesis-stress hypothesis in depression, which states that stressors can mediate the effect of genetic predisposition (diathesis) on the disease risk. We used Polygenic Risk Scores as a measure of diathesis and report a significant interaction with personal stressful life events, supporting the idea that stress and predisposition have a multiplicative effect on the depression liability scale. This interaction may be different between sexes as suggested by the stratified analysis.We then performed 3 analyses of resting-state fMRI from which we showed that: 1) Head motion (HM) is reliable and heritable in addition to being a major confound in fMRI analyses. However, this is unlikely to greatly confound twin studies of resting-state functional connectivity (FC). As a by-product of the analysis we showed that FC in the Broca's areas networks exhibited low to moderate test-retest and heritability (Chapter 4).2) HM is phenotypically and genetically associated with ADHD scores of Inattention and Hyperactivity/Impulsivity. This leads to most head motion image processing options to reduce power and/or to induce a sampling bias in RS-fMRI studies of ADHD. We discussed ways of circumventing this problem, via volume pruning or multivariate analysis (Chapter 5). 33) Nuisance regression (global signal regression, CompCor and HM regression) options in RS-fMRI processing have a moderate impact on the test-retest of regional homogeneity. Independently of the processing options, the low to moderate intra-class coefficients estimated in our sample may be due to insufficient scanning time (5mins 20s) and would impair our statistical power to detect RS-fMRI endophenotypes of depression (Chapter 6).Rather, we focused on structural brain images and contributed sample...

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Section: Discussionsupporting

confidence: 90%

Section: Discussionsupporting

confidence: 82%

Section: Introductionsupporting

confidence: 52%

Section: Figure 19: Structural Equation Model That Can Disentangle Thmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Breaking down the genetics of depression using brain endophenotypes

Couvy‐Duchesne¹

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Functional integration of the posterior superior temporal sulcus correlates with facial expression recognition

Wang

Song

Zhen

et al. 2016

Human Brain Mapping

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Face perception is essential for daily and social activities. Neuroimaging studies have revealed a distributed face network (FN) consisting of multiple regions that exhibit preferential responses to invariant or changeable facial information. However, our understanding about how these regions work collaboratively to facilitate facial information processing is limited. Here, we focused on changeable facial information processing, and investigated how the functional integration of the FN is related to the performance of facial expression recognition. To do so, we first defined the FN as voxels that responded more strongly to faces than objects, and then used a voxel-based global brain connectivity method based on resting-state fMRI to characterize the within-network connectivity (WNC) of each voxel in the FN. By relating the WNC and performance in the "Reading the Mind in the Eyes" Test across participants, we found that individuals with stronger WNC in the right posterior superior temporal sulcus (rpSTS) were better at recognizing facial expressions. Further, the resting-state functional connectivity (FC) between the rpSTS and right occipital face area (rOFA), early visual cortex (EVC), and bilateral STS were positively correlated with the ability of facial expression recognition, and the FCs of EVC-pSTS and OFA-pSTS contributed independently to facial expression recognition. In short, our study highlights the behavioral significance of intrinsic functional integration of the FN in facial expression processing, and provides evidence for the hub-like role of the rpSTS for facial expression recognition. Hum Brain Mapp 37:1930-1940, 2016. © 2016 Wiley Periodicals, Inc.

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Assessing the performance of different DTI motion correction strategies in the presence of EPI distortion correction

Taylor

Alhamud

Kouwe

et al. 2016

Human Brain Mapping

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Diffusion tensor imaging (DTI) is susceptible to several artifacts due to eddy currents, echo planar imaging (EPI) distortion and subject motion. While several techniques correct for individual distortion effects, no optimal combination of DTI acquisition and processing has been determined. Here, the effects of several motion correction techniques are investigated while also correcting for EPI distortion: prospective correction, using navigation; retrospective correction, using two different popular packages (FSL and TORTOISE); and the combination of both methods. Data from a pediatric group that exhibited incidental motion in varying degrees are analyzed. Comparisons are carried while implementing eddy current and EPI distortion correction. DTI parameter distributions, white matter maps and probabilistic tractography are examined. The importance of prospective correction during data acquisition is demonstrated. In contrast to some previous studies, results also show that the inclusion of retrospective processing also improved ellipsoid fits and both the sensitivity and specificity of group tractographic results, even for navigated data. Matches with anatomical white matter maps are highest throughout the brain for data that have been both navigated and processed using TORTOISE. The inclusion of both prospective and retrospective motion correction with EPI distortion correction is important for DTI analysis, particularly when studying subject populations that are prone to motion.

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Individual Differences in Impulsivity Predict Head Motion during Magnetic Resonance Imaging

Cited by 103 publications

References 41 publications

Breaking down the genetics of depression using brain endophenotypes

Breaking down the genetics of depression using brain endophenotypes

Functional integration of the posterior superior temporal sulcus correlates with facial expression recognition

Assessing the performance of different DTI motion correction strategies in the presence of EPI distortion correction

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