Abstract
Objectives To investigate the classification performance of support vector machine in mild traumatic brain injury (mTBI) from normal controls. Methods Twenty-four mTBI patients (15 males and 9 females; mean age, 38.88 ± 13.33 years) and 24 age and sex-matched normal controls (13 males and 11 females; mean age, 40.46 ± 11.4 years) underwent resting-state functional MRI examination. Seven imaging parameters, including amplitude of low-frequency fluctuation (ALFF), fractional amplitude of low-frequency fluctuation (fALFF), regional homogeneity (ReHo), degree centrality (DC), voxel-mirrored homotopic connectivity (VMHC), long-range functional connectivity density (FCD), and short-range FCD, were entered into the classification model to distinguish the mTBI from normal controls. Results The ability for any single imaging parameters to distinguish the two groups is lower than multiparameter combinations. The combination of ALFF, fALFF, DC, VMHC, and short-range FCD showed the best classification performance for distinguishing the two groups with optimal AUC value of 0.778, accuracy rate of 81.11%, sensitivity of 88%, and specificity of 75%. The brain regions with the highest contributions to this classification mainly include bilateral cerebellum, left orbitofrontal cortex, left cuneus, left temporal pole, right inferior occipital cortex, bilateral parietal lobe, and left supplementary motor area. Conclusions Multiparameter combinations could improve the classification performance of mTBI from normal controls by using the brain regions associated with emotion and cognition.
Highlights
Traumatic brain injury (TBI), a major public health problem and a leading cause of disability, affects half the world’s population [1]
We found that the combination with amplitude of low-frequency fluctuation (ALFF), fractional amplitude of low-frequency fluctuation (fALFF), degree centrality (DC), voxel-mirrored homotopic connectivity (VMHC), and short-term functional connectivity density (FCD) significantly reached up the classification accuracy, sensitivity, and specificity and received the highest classification performances among all combination with classification accuracy of 81.1% (p < 0:001), sensitivity of 88.0% (p < 0:001), and specificity of 75.0% (p < 0:001) (Figure 1)
We found that the combination with ALFF, fALFF, DC, VMHC, and short-term FCD received the highest classification performances among all combination
Summary
Traumatic brain injury (TBI), a major public health problem and a leading cause of disability, affects half the world’s population [1]. 70%-90% of TBI patients are mild TBI (mTBI), and 30-40% of whom cannot fully recover even at 6 months postinjury [1, 2]. Patients with mild head injury often manifest as dizziness, headache, and memory and attention deficit, which was considered to be associated with abnormal changes of brain networks [3]. Functional and structural neuroimaging methods have been widely used to address the functional and morphological changes of mTBI [4,5,6,7,8,9,10,11]. Zhou et al found abnormal functional connectivity within the default mode network in mTBI patients, which was associated with cognitive neurological dysfunction and posttraumatic symptoms (i.e., depression, anxiety, fatigue, and postconcussion syndrome). Nakamura et al found that mTBI was associated with changes in the “small world” networks [13].
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