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Thalamic resting-state functional networks: disruption in patients with mild traumatic brain injury.

ABSTRACT: To explore the neural correlates of the thalamus by using resting-state functional magnetic resonance (MR) imaging and to investigate whether thalamic resting-state networks (RSNs) are disrupted in patients with mild traumatic brain injury (MTBI).This HIPAA-compliant study was approved by the institutional review board, and written informed consent was obtained from 24 patients with MTBI and 17 healthy control subjects. The patients had varying degrees of symptoms, with a mean disease duration of 22 days. The resting-state functional MR imaging data were analyzed by using a standard seed-based whole-brain correlation method to characterize thalamic RSNs. Student t tests were used to perform comparisons. The association between thalamic RSNs and performance on neuropsychologic and neurobehavioral measures was also investigated in patients with MTBI by using Spearman rank correlation.A normal pattern of thalamic RSNs was demonstrated in healthy subjects. This pattern was characterized as representing relatively symmetric and restrictive functional thalamocortical connectivity, suggesting an inhibitory property of the thalamic neurons during the resting state. This pattern was disrupted, with significantly increased thalamic RSNs (P ≤ .005) and decreased symmetry (P = .03) in patients with MTBI compared with healthy control subjects. Increased functional thalamocortical redistributive connectivity was correlated with diminished neurocognitive functions and clinical symptoms in patients with MTBI.These findings of abnormal thalamic RSNs lend further support to the presumed subtle thalamic injury in patients with MTBI. Resting-state functional MR imaging can be used as an additional imaging modality for detection of thalamocortical connectivity abnormalities and for better understanding of the complex persistent postconcussive syndrome.

SUBMITTER: Tang L

PROVIDER: S-EPMC3157002 | biostudies-other | 2011 Sep

REPOSITORIES: biostudies-other

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Project description: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].

Project description:Mild traumatic brain injury (mTBI) is one of the most common neurological disorders worldwide. Posttraumatic complaints are frequently reported, interfering with outcome. However, a consistent neural substrate has not yet been found. We used graph analysis to further unravel the complex interactions between functional brain networks, complaints, anxiety and depression in the sub-acute stage after mTBI. This study included 54 patients with uncomplicated mTBI and 20 matched healthy controls. Posttraumatic complaints, anxiety and depression were measured at two weeks post-injury. Patients were selected based on presence (n = 34) or absence (n = 20) of complaints. Resting-state fMRI scans were made approximately four weeks post-injury. High order independent component analysis resulted in 89 neural components that were included in subsequent graph analyses. No differences in graph measures were found between patients with mTBI and healthy controls. Regarding the two patient subgroups, degree, strength, local efficiency and eigenvector centrality of the bilateral posterior cingulate/precuneus and bilateral parahippocampal gyrus were higher, and eigenvector centrality of the frontal pole/ bilateral middle & superior frontal gyrus was lower in patients with complaints compared to patients without complaints. In patients with mTBI, higher degree, strength and eigenvector centrality of default mode network components were related to higher depression scores, and higher degree and eigenvector centrality of executive network components were related to lower depression scores. In patients without complaints, one extra module was found compared to patients with complaints and healthy controls, consisting of the cingulate areas. In conclusion, this research extends the knowledge of functional network connectivity after mTBI. Specifically, our results suggest that an imbalance in the function of the default mode- and executive network plays a central role in the interaction between emotion regulation and the persistence of posttraumatic complaints.

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Thalamic resting-state functional networks: disruption in patients with mild traumatic brain injury.

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