Dynamic functional network connectivity discriminates mild traumatic brain injury through machine learning.
Dynamic functional network connectivity discriminates mild traumatic brain injury through machine learning.
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动态功能网络连通性通过机器学习区分了轻度的脑损伤。
DOI：
10.1016/j.nicl.2018.03.017
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发表时间：
2018
期刊：
影响因子：
--
通讯作者：
Calhoun VD
中科院分区：
文献类型：
--
作者：
Vergara VM;Mayer AR;Kiehl KA;Calhoun VD
Mild traumatic brain injury (mTBI) can result in symptoms that affect a person's cognitive and social abilities. Improvements in diagnostic methodologies are necessary given that current clinical techniques have limited accuracy and are solely based on self-reports. Recently, resting state functional network connectivity (FNC) has shown potential as an important imaging modality for the development of mTBI biomarkers. The present work explores the use of dynamic functional network connectivity (dFNC) for mTBI detection. Forty eight mTBI patients (24 males) and age-gender matched healthy controls were recruited. We identified a set of dFNC states and looked at the possibility of using each state to classify subjects in mTBI patients and healthy controls. A linear support vector machine was used for classification and validated using leave-one-out cross validation. One of the dFNC states achieved a high classification performance of 92% using the area under the curve method. A series of t-test analysis revealed significant dFNC increases between cerebellum and sensorimotor networks. This significant increase was detected in the same dFNC state useful for classification. Results suggest that dFNC can be used to identify optimal dFNC states for classification excluding those that does not contain useful features. Dynamic functional connectivity and support vector machine classified traumatic brain injury patients and healthy controls. Out of 4 dynamic brain states, we identified 1 state useful for classification. Classification performance of the dynamic state of interest achieved a performance of 92% area under the curve method.
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DOI：
10.1097/htr.0b013e318289ede5
发表时间：
2014-03
期刊：
The Journal of head trauma rehabilitation
影响因子：
--
作者：
Arenth PM;Russell KC;Scanlon JM;Kessler LJ;Ricker JH
通讯作者：
Ricker JH
DOI：
10.1109/tbme.2011.2167149
发表时间：
2011-12
期刊：
IEEE transactions on bio-medical engineering
影响因子：
--
作者：
Ma S;Correa NM;Li XL;Eichele T;Calhoun VD;Adalı T
通讯作者：
Adalı T
影响因子：
3.7
作者：
Allen, Elena A.;Damaraju, Eswar;Calhoun, Vince D.
通讯作者：
Calhoun, Vince D.
影响因子：
9.9
作者：
Kasahara, M.;Menon, D. K.;Stamatakis, E. A.
通讯作者：
Stamatakis, E. A.
DOI：
10.1016/j.apmr.2013.04.026
发表时间：
2014-03-01
影响因子：
4.3
作者：
Kristman, Vicki L;Borg, Jorgen;Cassidy, J David
通讯作者：
Cassidy, J David