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

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.

SUBMITTER: Vergara VM

PROVIDER: S-EPMC6051314 | biostudies-other | 2018

REPOSITORIES: biostudies-other

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

Vergara Victor M VM Mayer Andrew R AR Kiehl Kent A KA Calhoun Vince D VD

NeuroImage. Clinical 20180315

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 ( ...[more]

PMID: 30034999

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Project description:IntroductionDynamic functional network connectivity (dFNC), derived from magnetic resonance imaging (fMRI), is an important technique in the search for biomarkers of brain diseases such as mild traumatic brain injury (mTBI). At the individual level, mTBI can affect cognitive functions and change personality traits. Previous research aimed at detecting significant changes in the dFNC of mTBI subjects. However, one of the main concerns in dFNC analysis is the appropriateness of methods used to correct for subject movement. In this work, we focus on the effect that rearranging movement correction at different points of the processing pipeline has in dFNC analysis utilizing mTBI data.MethodsThe sample cohort consists of 50 mTBI patients and matched healthy controls. A 5-min resting-state run was completed by each participant. Data were preprocessed using different pipeline alternatives varying with the place where motion-related variance was removed. In all pipelines, group-independent component analysis (gICA) followed by dFNC analysis was performed. Additional tests were performed varying the detection of temporal spikes, the number of gICA components, and the sliding-window size. A linear support vector machine was used to test how each pipeline affects classification accuracy.ResultsResults suggest that correction for motion variance before spatial smoothing, but leaving correction for spiky time courses after gICA produced the best mean classification performance. The number of gICA components and the sliding-window size were also important in determining classification performance. Variance in spikes correction affected some pipelines more than others with fewer significant differences than the other parameters.ConclusionThe sequence of preprocessing steps motion regression, smoothing, gICA, and despiking produced data most suitable for differentiating mTBI from healthy subjects. However, the selection of optimal preprocessing parameters strongly affected the final results.

Dataset Information

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

Publications

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

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