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A unique pattern of cortical connectivity characterizes patients with attention deficit disorders: a large electroencephalographic coherence study

Overview of attention for article published in BMC Medicine, March 2017
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About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (94th percentile)
  • High Attention Score compared to outputs of the same age and source (80th percentile)

Mentioned by

news
3 news outlets
blogs
3 blogs
twitter
8 tweeters

Citations

dimensions_citation
15 Dimensions

Readers on

mendeley
166 Mendeley
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Title
A unique pattern of cortical connectivity characterizes patients with attention deficit disorders: a large electroencephalographic coherence study
Published in
BMC Medicine, March 2017
DOI 10.1186/s12916-017-0805-9
Pubmed ID
Authors

Frank H. Duffy, Aditi Shankardass, Gloria B. McAnulty, Heidelise Als

Abstract

Attentional disorders (ADD) feature decreased attention span, impulsivity, and over-activity interfering with successful lives. Childhood onset ADD frequently persists to adulthood. Etiology may be hereditary or disease associated. Prevalence is 5% but recognition may be 'overshadowed' by comorbidities (brain injury, mood disorder) thereby escaping formal recognition. Blinded diagnosis by MRI has failed. ADD may not itself manifest a single anatomical pattern of brain abnormality but may reflect multiple, unique responses to numerous and diverse etiologies. Alternatively, a stable ADD-specific brain pattern may be better detected by brain physiology. EEG coherence, measuring cortical connectivity, is used to explore this possibility. Participants: Ages 2 to 22 years; 347 ADD and 619 neurotypical controls (CON). Following artifact reduction, principal components analysis (PCA) identifies coherence factors with unique loading patterns. Discriminant function analysis (DFA) determines discrimination success differentiating ADD from CON. Split-half and jackknife analyses estimate prospective diagnostic success. Coherence factor loading constitutes an ADD-specific pattern or 'connectome'.  RESULTS: PCA identified 40 factors explaining 50% of total variance. DFA on CON versus ADD groups utilizing all factors was highly significant (p≤0.0001). ADD subjects were separated into medication and comorbidity subgroups. DFA (stepping allowed) based on CON versus ADD without comorbidities or medication treatment successfully classified the correspondingly held out ADD subjects in every instance. Ten randomly generated split-half replications of the entire population demonstrated high-average classification success for each of the left out test-sets (overall: CON, 83.65%; ADD, 90.07%). Higher success was obtained with more restricted age sub-samples using jackknifing: 2-8 year olds (CON, 90.0%; ADD, 90.6%); 8-14 year olds (CON, 96.8%; ADD 95.9%); and 14-20 year-olds (CON, 100.0%; ADD, 97.1%). The connectome manifested decreased and increased coherence. Patterns were complex and bi-hemispheric; typically reported front-back and left-right loading patterns were not observed. Subtemporal electrodes (seldom utilized) were prominently involved.  CONCLUSIONS: Results demonstrate a stable coherence connectome differentiating ADD from CON subjects including subgroups with and without comorbidities and/or medications. This functional 'connectome', constitutes a diagnostic ADD phenotype. Split-half replications support potential for EEG-based ADD diagnosis, with increased accuracy using limited age ranges. Repeated studies could assist recognition of physiological change from interventions (pharmacological, behavioral).

Twitter Demographics

The data shown below were collected from the profiles of 8 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 166 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Malaysia 1 <1%
Spain 1 <1%
Unknown 164 99%

Demographic breakdown

Readers by professional status Count As %
Student > Master 27 16%
Researcher 23 14%
Student > Ph. D. Student 22 13%
Student > Bachelor 20 12%
Student > Doctoral Student 11 7%
Other 30 18%
Unknown 33 20%
Readers by discipline Count As %
Psychology 40 24%
Neuroscience 21 13%
Medicine and Dentistry 18 11%
Engineering 12 7%
Social Sciences 7 4%
Other 27 16%
Unknown 41 25%

Attention Score in Context

This research output has an Altmetric Attention Score of 44. 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 19 April 2017.
All research outputs
#798,227
of 22,881,154 outputs
Outputs from BMC Medicine
#562
of 3,438 outputs
Outputs of similar age
#18,333
of 307,558 outputs
Outputs of similar age from BMC Medicine
#14
of 67 outputs
Altmetric has tracked 22,881,154 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 96th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 3,438 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 43.6. This one has done well, scoring higher than 83% of its peers.
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 307,558 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 94% of its contemporaries.
We're also able to compare this research output to 67 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 80% of its contemporaries.