کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
6023622 | 1580872 | 2016 | 39 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Prediction of brain maturity in infants using machine-learning algorithms
ترجمه فارسی عنوان
پیش بینی بلوغ مغز در نوزادان با استفاده از الگوریتم های یادگیری ماشین
دانلود مقاله + سفارش ترجمه
دانلود مقاله ISI انگلیسی
رایگان برای ایرانیان
کلمات کلیدی
SVRrs-fMRIMVPAMNIEPIRSNBOLDLOOCVROIPMA - LDC هاResting state network - استراحت شبکه ایالتFunctional MRI - افامآرآی یا تصویرسازی تشدید مغناطیسی کارکردیNICU - بخش NICUmultivariate pattern analysis - تجزیه و تحلیل الگوی چند متغیرهSupport vector regression - رگرسیون بردار پشتیبانیprematurity - زودرسblood oxygen level dependent - سطح اکسیژن خون وابسته استgestational age - سن حاملگیPostmenstrual age - سن پس از قاعدگیdevelopmental neuroimaging - عصب شناسی رشدیSVM - ماشین بردار پشتیبانیSupport vector machine - ماشین بردار پشتیبانیregion of interest - منطقه مورد نظرMontreal Neurological Institute - موسسه عصبی مونترالInfant - نوزادneonatal intensive care unit - واحد مراقبت های ویژه نوزادان
موضوعات مرتبط
علوم زیستی و بیوفناوری
علم عصب شناسی
علوم اعصاب شناختی
چکیده انگلیسی
Recent resting-state functional MRI investigations have demonstrated that much of the large-scale functional network architecture supporting motor, sensory and cognitive functions in older pediatric and adult populations is present in term- and prematurely-born infants. Application of new analytical approaches can help translate the improved understanding of early functional connectivity provided through these studies into predictive models of neurodevelopmental outcome. One approach to achieving this goal is multivariate pattern analysis, a machine-learning, pattern classification approach well-suited for high-dimensional neuroimaging data. It has previously been adapted to predict brain maturity in children and adolescents using structural and resting state-functional MRI data. In this study, we evaluated resting state-functional MRI data from 50 preterm-born infants (born at 23-29 weeks of gestation and without moderate-severe brain injury) scanned at term equivalent postmenstrual age compared with data from 50 term-born control infants studied within the first week of life. Using 214 regions of interest, binary support vector machines distinguished term from preterm infants with 84% accuracy (p < 0.0001). Inter- and intra-hemispheric connections throughout the brain were important for group categorization, indicating that widespread changes in the brain's functional network architecture associated with preterm birth are detectable by term equivalent age. Support vector regression enabled quantitative estimation of birth gestational age in single subjects using only term equivalent resting state-functional MRI data, indicating that the present approach is sensitive to the degree of disruption of brain development associated with preterm birth (using gestational age as a surrogate for the extent of disruption). This suggests that support vector regression may provide a means for predicting neurodevelopmental outcome in individual infants.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: NeuroImage - Volume 136, 1 August 2016, Pages 1-9
Journal: NeuroImage - Volume 136, 1 August 2016, Pages 1-9
نویسندگان
Christopher D. Smyser, Nico U.F. Dosenbach, Tara A. Smyser, Abraham Z. Snyder, Cynthia E. Rogers, Terrie E. Inder, Bradley L. Schlaggar, Jeffrey J. Neil,