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Multivariate resting-state functional connectivity predicts response to cognitive behavioral therapy in obsessive–compulsive disorder

Overview of attention for article published in Proceedings of the National Academy of Sciences of the United States of America, February 2018
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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 (98th percentile)
  • High Attention Score compared to outputs of the same age and source (86th percentile)

Citations

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46 Dimensions

Readers on

mendeley
155 Mendeley
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1 CiteULike
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Title
Multivariate resting-state functional connectivity predicts response to cognitive behavioral therapy in obsessive–compulsive disorder
Published in
Proceedings of the National Academy of Sciences of the United States of America, February 2018
DOI 10.1073/pnas.1716686115
Pubmed ID
Authors

Nicco Reggente, Teena D. Moody, Francesca Morfini, Courtney Sheen, Jesse Rissman, Joseph O’Neill, Jamie D. Feusner

Abstract

Cognitive behavioral therapy (CBT) is an effective treatment for many with obsessive-compulsive disorder (OCD). However, response varies considerably among individuals. Attaining a means to predict an individual's potential response would permit clinicians to more prudently allocate resources for this often stressful and time-consuming treatment. We collected resting-state functional magnetic resonance imaging from adults with OCD before and after 4 weeks of intensive daily CBT. We leveraged machine learning with cross-validation to assess the power of functional connectivity (FC) patterns to predict individual posttreatment OCD symptom severity. Pretreatment FC patterns within the default mode network and visual network significantly predicted posttreatment OCD severity, explaining up to 67% of the variance. These networks were stronger predictors than pretreatment clinical scores. Results have clinical implications for developing personalized medicine approaches to identifying individual OCD patients who will maximally benefit from intensive CBT.

Twitter Demographics

The data shown below were collected from the profiles of 35 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 155 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 155 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 40 26%
Researcher 26 17%
Student > Bachelor 16 10%
Student > Master 16 10%
Student > Doctoral Student 11 7%
Other 25 16%
Unknown 21 14%
Readers by discipline Count As %
Psychology 43 28%
Neuroscience 34 22%
Medicine and Dentistry 12 8%
Engineering 4 3%
Agricultural and Biological Sciences 3 2%
Other 18 12%
Unknown 41 26%

Attention Score in Context

This research output has an Altmetric Attention Score of 120. 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 04 January 2019.
All research outputs
#201,177
of 17,521,884 outputs
Outputs from Proceedings of the National Academy of Sciences of the United States of America
#4,320
of 89,630 outputs
Outputs of similar age
#7,122
of 379,084 outputs
Outputs of similar age from Proceedings of the National Academy of Sciences of the United States of America
#132
of 969 outputs
Altmetric has tracked 17,521,884 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 98th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 89,630 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 30.9. This one has done particularly well, scoring higher than 94% 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 379,084 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 98% of its contemporaries.
We're also able to compare this research output to 969 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 86% of its contemporaries.