Identification of chronic mild traumatic brain injury using resting state functional MRI and machine learning techniques

Abstract

Mild traumatic brain injury (mTBI) is a major public health concern that can result in a broad spectrum of short-term and long-term symptoms. Recently, machine learning (ML) algorithms have been used in neuroscience research for diagnostics and prognostic assessment of brain disorders. The present study aimed to develop an automatic classifier to distinguish patients suffering from chronic mTBI from healthy controls (HCs) utilizing multilevel metrics of resting-state functional magnetic resonance imaging (rs-fMRI). Sixty mTBI patients and forty HCs were enrolled and allocated to training and testing datasets with a ratio of 80:20. Several rs-fMRI metrics including fractional amplitude of low-frequency fluctuation (fALFF), regional homogeneity (ReHo), degree centrality (DC), voxel-mirrored homotopic connectivity (VMHC), functional connectivity strength (FCS), and seed-based FC were generated from two main analytical categories: local measures and network measures. Statistical two-sample t-test was employed comparing between mTBI and HCs groups. Then, for each rs-fMRI metric the features were selected extracting the mean values from the clusters showing significant differences. Finally, the support vector machine (SVM) models based on separate and multilevel metrics were built and the performance of the classifiers were assessed using five-fold cross-validation and via the area under the receiver operating characteristic curve (AUC). Feature importance was estimated using Shapley additive explanation (SHAP) values. Among local measures, the range of AUC was 86.67–100% and the optimal SVM model was obtained based on combined multilevel rs-fMRI metrics and DC as a separate model with AUC of 100%. Among network measures, the range of AUC was 80.42–93.33% and the optimal SVM model was obtained based on the combined multilevel seed-based FC metrics. The SHAP analysis revealed the DC value in the left postcentral and seed-based FC value between the motor ventral network and right superior temporal as the most important local and network features with the greatest contribution to the classification models. Our findings demonstrated that different rs-fMRI metrics can provide complementary information for classifying patients suffering from chronic mTBI. Moreover, we showed that ML approach is a promising tool for detecting patients with mTBI and might serve as potential imaging biomarker to identify patients at individual level.Clinical trial registration[clinicaltrials.gov], identifier [NCT03241732].

Repetitive mild closed-head injury induced synapse loss and increased local BOLD-fMRI signal homogeneity

“…For human TBI, BOLD-fMRI measurements of FC strength show pronounced heterogeneity, matching the variability of ReHo (Table 1) [25][26][27][28][29][30]77,78 [31][32][33][34] . Of particular relevance to the present study, brain-wide increases in FC strength, alongside increases in ReHo and fALFF have been recently reported 30 .…”

“…To assess how decreases in synapse density affect local connectivity we measure BOLD-fMRI synchrony using ReHo 52 . In patients with TBI, ReHo, at various times after injury, shows markedly heterogeneous results in terms of the brain regions and circuits affected, as well as whether ReHo goes up or down (Table 1) [25][26][27][28][29][30]77,78 .…”

Treatment effects of N-acetyl cysteine on resting-state functional MRI and cognitive performance in patients with chronic mild traumatic brain injury: a longitudinal study

“…Multiple rs-fMRI metrics were measured including fALFF, DC, and FCS. fALFF was measured as follows: for each participant, spatial smoothing [Gaussian kernel of full-width half maximum (FWHM) = 6 mm] was performed ( 7 , 46 ). Then, with the FFT, the time courses of rs-fMRI signal were converted to frequency domain, and the square root of the power spectrum was measured and averaged across the 0.01–0.08 Hz domain.…”

“…Individuals with TBI are prone to develop cognitive, behavioral, and sensorimotor impairments including attention deficit, memory loss, lower executive ability that necessitate long-term access to health care and disability services ( 5 ). Furthermore, from a clinical perspective, they are more likely to develop neurodegenerative disorders such as Alzheimer’s disease (AD) and Parkinson’s disease (PD) later in the lifetime ( 6 , 7 ).…”

Long Noncoding RNA VLDLR-AS1 Levels in Serum Correlate with Combat-Related Chronic Mild Traumatic Brain Injury and Depression Symptoms in US Veterans

“…According to the updated Lancet Neurology Commission's report of 2022 [16], there is a growing interest in identifying patients who have a history of mTBI and who will benefit from personalized treatment plans during their long-term care. In addition to CT scans [17,18], a recent study by Vedaei et al described the use of functional MRI and machine-learning techniques to distinguish patients with chronic mTBI from healthy individuals with no TBI incidences [19]. However, these imaging techniques are costly and time-consuming, thereby dramatically decreasing their use in screening all patients.…”