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Combined structural and functional patterns discriminating upper limb motor disability in multiple sclerosis using multivariate approaches

Overview of attention for article published in Brain Imaging and Behavior, May 2016
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3 tweeters

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4 Dimensions

Readers on

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35 Mendeley
Title
Combined structural and functional patterns discriminating upper limb motor disability in multiple sclerosis using multivariate approaches
Published in
Brain Imaging and Behavior, May 2016
DOI 10.1007/s11682-016-9551-4
Pubmed ID
Authors

Jidan Zhong, David Qixiang Chen, Julia C. Nantes, Scott A. Holmes, Mojgan Hodaie, Lisa Koski

Abstract

A structural or functional pattern of neuroplasticity that could systematically discriminate between people with impaired and preserved motor performance could help us to understand the brain networks contributing to preservation or compensation of behavior in multiple sclerosis (MS). This study aimed to (1) investigate whether a machine learning-based technique could accurately classify MS participants into groups defined by upper extremity function (i.e. motor function preserved (MP) vs. motor function impaired (MI)) based on their regional grey matter measures (GMM, cortical thickness and deep grey matter volume) and inter-regional functional connection (FC), (2) investigate which features (GMM, FC, or GMM + FC) could classify groups more accurately, and (3) identify the multivariate patterns of GMM and FCs that are most discriminative between MP and MI participants, and between each of these groups and the healthy controls (HCs). With 26 MP, 25 MI, and 21 HCs (age and sex matched) underwent T1-weighted and resting-state functional MRI at 3 T, we applied support vector machine (SVM) based classification to learn discriminant functions indicating regions in which GMM or between which FCs were most discriminative between groups. This study demonstrates that there exist structural and FC patterns sufficient for correct classification of upper limb motor ability of people with MS. The classifier with GMM + FC features yielded the highest accuracy of 85.61 % (p < 0.001) to distinguish between the MS groups using leave-one-out cross-validation. It suggests that a machine-learning approach combining structural and functional features is useful for identifying the specific neural substrates that are necessary and sufficient to preserve motor function among people with MS.

Twitter Demographics

The data shown below were collected from the profiles of 3 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 35 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Canada 1 3%
Unknown 34 97%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 8 23%
Researcher 7 20%
Student > Master 5 14%
Student > Postgraduate 3 9%
Other 3 9%
Other 9 26%
Readers by discipline Count As %
Medicine and Dentistry 10 29%
Neuroscience 8 23%
Unspecified 7 20%
Engineering 3 9%
Nursing and Health Professions 2 6%
Other 5 14%

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 08 May 2016.
All research outputs
#7,224,372
of 12,517,134 outputs
Outputs from Brain Imaging and Behavior
#323
of 714 outputs
Outputs of similar age
#122,000
of 262,749 outputs
Outputs of similar age from Brain Imaging and Behavior
#21
of 35 outputs
Altmetric has tracked 12,517,134 research outputs across all sources so far. This one is in the 40th percentile – i.e., 40% of other outputs scored the same or lower than it.
So far Altmetric has tracked 714 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.2. This one is in the 49th percentile – i.e., 49% 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 262,749 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 50% of its contemporaries.
We're also able to compare this research output to 35 others from the same source and published within six weeks on either side of this one. This one is in the 40th percentile – i.e., 40% of its contemporaries scored the same or lower than it.