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Mendeley readers
Attention Score in Context
| Title |
Head Motion and Inattention/Hyperactivity Share Common Genetic Influences: Implications for fMRI Studies of ADHD
|
|---|---|
| Published in |
PLOS ONE, January 2016
|
| DOI | 10.1371/journal.pone.0146271 |
| Pubmed ID | |
| Authors |
Baptiste Couvy-Duchesne, Jane L. Ebejer, Nathan A. Gillespie, David L. Duffy, Ian B. Hickie, Paul M. Thompson, Nicholas G. Martin, Greig I. de Zubicaray, Katie L. McMahon, Sarah E. Medland, Margaret J. Wright |
| 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. |
X Demographics
The data shown below were collected from the profiles of 5 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Australia | 1 | 20% |
| Unknown | 4 | 80% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 4 | 80% |
| Scientists | 1 | 20% |
Mendeley readers
The data shown below were compiled from readership statistics for 115 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 115 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Master | 24 | 21% |
| Researcher | 20 | 17% |
| Student > Ph. D. Student | 19 | 17% |
| Student > Bachelor | 10 | 9% |
| Student > Doctoral Student | 6 | 5% |
| Other | 12 | 10% |
| Unknown | 24 | 21% |
| Readers by discipline | Count | As % |
|---|---|---|
| Psychology | 23 | 20% |
| Neuroscience | 22 | 19% |
| Medicine and Dentistry | 12 | 10% |
| Biochemistry, Genetics and Molecular Biology | 5 | 4% |
| Nursing and Health Professions | 4 | 3% |
| Other | 18 | 16% |
| Unknown | 31 | 27% |
Attention Score in Context
This research output has an Altmetric Attention Score of 3. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 01 July 2016.
All research outputs
#13,175,336
of 23,577,654 outputs
Outputs from PLOS ONE
#105,720
of 202,084 outputs
Outputs of similar age
#179,795
of 397,321 outputs
Outputs of similar age from PLOS ONE
#2,123
of 4,949 outputs
Altmetric has tracked 23,577,654 research outputs across all sources so far. This one is in the 43rd percentile – i.e., 43% of other outputs scored the same or lower than it.
So far Altmetric has tracked 202,084 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 15.3. This one is in the 46th percentile – i.e., 46% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 397,321 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 54% of its contemporaries.
We're also able to compare this research output to 4,949 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 56% of its contemporaries.