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Identification of Subclinical Language Deficit Using Machine Learning Classification Based on Poststroke Functional Connectivity Derived from Low Frequency Oscillations
Post-stroke neuropsychological evaluation is time-intensive in assessing impairments in subjects without overt clinical deficits. We utilized functional connectivity (FC) from ten-minute non-invasive resting-state functional MRI (rs-fMRI) to identify stroke subjects at risk for subclinical language...
में बचाया:
| में प्रकाशित: | Brain Connect |
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| मुख्य लेखकों: | , , , , , |
| स्वरूप: | Artigo |
| भाषा: | Inglês |
| प्रकाशित: |
Mary Ann Liebert, Inc., publishers
2019
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| विषय: | |
| ऑनलाइन पहुंच: | https://ncbi.nlm.nih.gov/pmc/articles/PMC6445059/ https://ncbi.nlm.nih.gov/pubmed/30398379 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1089/brain.2018.0597 |
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