کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
6027041 | 1580907 | 2014 | 14 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Subject-specific functional parcellation via Prior Based Eigenanatomy
ترجمه فارسی عنوان
تقسیم کارکردی موضوعی خاص از طریق پیشگفتار هسته اصلی
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کلمات کلیدی
موضوعات مرتبط
علوم زیستی و بیوفناوری
علم عصب شناسی
علوم اعصاب شناختی
چکیده انگلیسی
We show that while still maintaining correspondence across subjects, p-Eigen extracts biologically-relevant and patient-specific functional parcels that facilitate hypothesis-driven network analysis. We construct default mode network (DMN) connectivity graphs using p-Eigen refined ROIs and use them in a classification paradigm. Our results show that the functional connectivity graphs derived from p-Eigen significantly aid classification of mild cognitive impairment (MCI) as well as the prediction of scores in a Delayed Recall memory task when compared to graph metrics derived from 1) standard registration-based seed ROI definitions, 2) totally data-driven ROIs, 3) a model based on standard demographics plus hippocampal volume as covariates, and 4) Ward Clustering based data-driven ROIs. In summary, p-Eigen incarnates a new class of prior-constrained dimensionality reduction tools that may improve our understanding of the relationship between MCI and functional connectivity.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: NeuroImage - Volume 99, 1 October 2014, Pages 14-27
Journal: NeuroImage - Volume 99, 1 October 2014, Pages 14-27
نویسندگان
Paramveer S. Dhillon, David A. Wolk, Sandhitsu R. Das, Lyle H. Ungar, James C. Gee, Brian B. Avants,