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Neuroimage Clin. 2018 Mar 15;19:30-37. doi: 10.1016/j.nicl.2018.03.017. eCollection 2018.

Dynamic functional network connectivity discriminates mild traumatic brain injury through machine learning.

Author information

1
The Mind Research Network and Lovelace Biomedical and Environmental Research Institute, 1101 Yale Blvd. NE, Albuquerque, NM 87106, United States. Electronic address: vvergara@mrn.org.
2
The Mind Research Network and Lovelace Biomedical and Environmental Research Institute, 1101 Yale Blvd. NE, Albuquerque, NM 87106, United States; Department of Psychology, University of New Mexico, Albuquerque, NM 87131, United States; Department of Neurology, University of New Mexico School of Medicine, Albuquerque, NM 87131, United States; Department of Psychiatry and Behavioral Sciences, University of New Mexico School of Medicine, Albuquerque, NM 87131, United States. Electronic address: amayer@mrn.org.
3
The Mind Research Network and Lovelace Biomedical and Environmental Research Institute, 1101 Yale Blvd. NE, Albuquerque, NM 87106, United States; Department of Psychology, University of New Mexico, Albuquerque, NM 87131, United States. Electronic address: kkiehl@mrn.org.
4
The Mind Research Network and Lovelace Biomedical and Environmental Research Institute, 1101 Yale Blvd. NE, Albuquerque, NM 87106, United States; Dept of ECE, University of New Mexico, Albuquerque, NM 87131, United States. Electronic address: vcalhoun@mrn.org.

Abstract

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.

KEYWORDS:

Dynamic functional network connectivity; Magnetic resonance imaging; Traumatic brain injury

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