Title |
Characterizing Functional Connectivity Differences in Aging Adults using Machine Learning on Resting State fMRI Data
|
---|---|
Published in |
Frontiers in Computational Neuroscience, January 2013
|
DOI | 10.3389/fncom.2013.00038 |
Pubmed ID | |
Authors |
Svyatoslav Vergun, Alok S. Deshpande, Timothy B. Meier, Jie Song, Dana L. Tudorascu, Veena A. Nair, Vikas Singh, Bharat B. Biswal, M. Elizabeth Meyerand, Rasmus M. Birn, Vivek Prabhakaran |
Abstract |
The brain at rest consists of spatially distributed but functionally connected regions, called intrinsic connectivity networks (ICNs). Resting state functional magnetic resonance imaging (rs-fMRI) has emerged as a way to characterize brain networks without confounds associated with task fMRI such as task difficulty and performance. Here we applied a Support Vector Machine (SVM) linear classifier as well as a support vector machine regressor to rs-fMRI data in order to compare age-related differences in four of the major functional brain networks: the default, cingulo-opercular, fronto-parietal, and sensorimotor. A linear SVM classifier discriminated between young and old subjects with 84% accuracy (p-value < 1 × 10(-7)). A linear SVR age predictor performed reasonably well in continuous age prediction (R (2) = 0.419, p-value < 1 × 10(-8)). These findings reveal that differences in intrinsic connectivity as measured with rs-fMRI exist between subjects, and that SVM methods are capable of detecting and utilizing these differences for classification and prediction. |
X Demographics
As of 1 July 2024, you may notice a temporary increase in the numbers of X profiles with Unknown location. Click here to learn more.
Geographical breakdown
Country | Count | As % |
---|---|---|
Unknown | 1 | 100% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 1 | 100% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 4 | 3% |
Finland | 1 | <1% |
United Kingdom | 1 | <1% |
Taiwan | 1 | <1% |
Singapore | 1 | <1% |
China | 1 | <1% |
Poland | 1 | <1% |
Unknown | 129 | 93% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 38 | 27% |
Researcher | 22 | 16% |
Student > Master | 20 | 14% |
Professor > Associate Professor | 10 | 7% |
Student > Bachelor | 8 | 6% |
Other | 19 | 14% |
Unknown | 22 | 16% |
Readers by discipline | Count | As % |
---|---|---|
Psychology | 30 | 22% |
Neuroscience | 28 | 20% |
Engineering | 12 | 9% |
Agricultural and Biological Sciences | 10 | 7% |
Computer Science | 8 | 6% |
Other | 27 | 19% |
Unknown | 24 | 17% |