Predicting Brain Age Based on Spatial and Temporal Features of Human Brain Functional Networks
The organization of human brain networks can be measured by capturing correlated brain activity with functional MRI data. There have been a variety of studies showing that human functional connectivities undergo an age-related change over development. In the present study, we employed resting-state...
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Frontiers Media S.A.
2019-02-01
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Online Access: | https://www.frontiersin.org/article/10.3389/fnhum.2019.00062/full |
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doaj-706b88656b78493a8f0e060c8edd0af02020-11-25T03:14:11ZengFrontiers Media S.A.Frontiers in Human Neuroscience1662-51612019-02-011310.3389/fnhum.2019.00062415335Predicting Brain Age Based on Spatial and Temporal Features of Human Brain Functional NetworksJian ZhaiKe LiThe organization of human brain networks can be measured by capturing correlated brain activity with functional MRI data. There have been a variety of studies showing that human functional connectivities undergo an age-related change over development. In the present study, we employed resting-state functional MRI data to construct functional network models. Principal component analysis was performed on the FC matrices across all the subjects to explore meaningful components especially correlated with age. Coefficients across the components, edge features after a newly proposed feature reduction method as well as temporal features based on fALFF, were extracted as predictor variables and three different regression models were learned to make prediction of brain age. We observed that individual's functional network architecture was shaped by intrinsic component, age-related component and other components and the predictive models extracted sufficient information to provide comparatively accurate predictions of brain age.https://www.frontiersin.org/article/10.3389/fnhum.2019.00062/fullfMRIresting statefunctional connectivitylifespanpredictive modelprincipal component |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Jian Zhai Ke Li |
spellingShingle |
Jian Zhai Ke Li Predicting Brain Age Based on Spatial and Temporal Features of Human Brain Functional Networks Frontiers in Human Neuroscience fMRI resting state functional connectivity lifespan predictive model principal component |
author_facet |
Jian Zhai Ke Li |
author_sort |
Jian Zhai |
title |
Predicting Brain Age Based on Spatial and Temporal Features of Human Brain Functional Networks |
title_short |
Predicting Brain Age Based on Spatial and Temporal Features of Human Brain Functional Networks |
title_full |
Predicting Brain Age Based on Spatial and Temporal Features of Human Brain Functional Networks |
title_fullStr |
Predicting Brain Age Based on Spatial and Temporal Features of Human Brain Functional Networks |
title_full_unstemmed |
Predicting Brain Age Based on Spatial and Temporal Features of Human Brain Functional Networks |
title_sort |
predicting brain age based on spatial and temporal features of human brain functional networks |
publisher |
Frontiers Media S.A. |
series |
Frontiers in Human Neuroscience |
issn |
1662-5161 |
publishDate |
2019-02-01 |
description |
The organization of human brain networks can be measured by capturing correlated brain activity with functional MRI data. There have been a variety of studies showing that human functional connectivities undergo an age-related change over development. In the present study, we employed resting-state functional MRI data to construct functional network models. Principal component analysis was performed on the FC matrices across all the subjects to explore meaningful components especially correlated with age. Coefficients across the components, edge features after a newly proposed feature reduction method as well as temporal features based on fALFF, were extracted as predictor variables and three different regression models were learned to make prediction of brain age. We observed that individual's functional network architecture was shaped by intrinsic component, age-related component and other components and the predictive models extracted sufficient information to provide comparatively accurate predictions of brain age. |
topic |
fMRI resting state functional connectivity lifespan predictive model principal component |
url |
https://www.frontiersin.org/article/10.3389/fnhum.2019.00062/full |
work_keys_str_mv |
AT jianzhai predictingbrainagebasedonspatialandtemporalfeaturesofhumanbrainfunctionalnetworks AT keli predictingbrainagebasedonspatialandtemporalfeaturesofhumanbrainfunctionalnetworks |
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1724644169616457728 |