Assessing Quantitative Changes in Intrinsic Thalamic Networks in Blast and Nonblast Mild Traumatic Brain Injury: Implications for Mechanisms of Injury.

In the global war on terror, the increased use of improvised explosive devices has resulted in increased incidence of blast-related mild traumatic brain injury (mTBI). Diagnosing mTBI is both challenging and controversial due to heterogeneity of injury location, trauma intensity, transient symptoms, and absence of focal biomarkers on standard clinical imaging modalities. The goal of this study is to identify a brain biomarker that is sensitive to mTBI injury. Research suggests the thalamus may be sensitive to changes induced by mTBI. A significant number of connections to and from various brain regions converge at the thalamus. In addition, the thalamus is involved in information processing, integration, and regulation of specific behaviors and mood. In this study, changes in task-free thalamic networks as quantified by graph theory measures in mTBI blast (N = 186), mTBI nonblast (N = 80), and controls (N = 21) were compared. Results show that the blast mTBI group had significant hyper-connectivity compared with the controls and nonblast mTBI group. However, after controlling for post-traumatic stress symptoms (PTSS), the blast mTBI group was not different from the controls, but the nonblast mTBI group showed significant hypo-connectivity. The results suggest that there are differences in the mechanisms of injury related to mTBI as reflected in the architecture of the thalamic networks. However, the effect of PTSS and its relationship to mTBI is difficult to distinguish and warrants more research.

Cerebrovascular reactivity mapping in patients with low grade gliomas undergoing presurgical sensorimotor mapping with BOLD fMRI.

PURPOSE: (i) to validate blood oxygenation level dependent (BOLD) breathhold cerebrovascular reactivity (BH CVR) mapping as an effective technique for potential detection of neurovascular uncoupling (NVU) in a cohort of patients with perirolandic low grade gliomas undergoing presurgical functional MRI (fMRI) for sensorimotor mapping, and (ii) to determine whether NVU potential, as assessed by BH CVR mapping, is prevalent in this tumor group. MATERIALS AND METHODS: We retrospectively evaluated 12 patients, with histological diagnosis of grade II glioma, who performed multiple motor tasks and a BH task. Sensorimotor activation maps and BH CVR maps were compared in two automatically defined regions of interest (ROIs), ipsilateral to the lesion (i.e., ipsilesional) and contralateral to the lesion (i.e., contralesional). RESULTS: Motor task mean T-value was significantly higher in the contralesional ROIs (6.00 ± 1.74 versus 4.34 ± 1.68; P = 0.00004) as well as the BH mean T-value (4.74 ± 2.30 versus 4.09 ± 2.50; P = 0.009). The number of active voxels was significantly higher in the contralesional ROIs (Z = 2.99; P = 0.03). Actual NVU prevalence was 75%. CONCLUSION: Presurgical sensorimotor fMRI mapping can be affected by NVU-related false negative activation in low grade gliomas (76% of analyzed tasks).

Graph theoretical analysis of resting magnetoencephalographic functional connectivity networks.

Complex networks have been observed to comprise small-world properties, believed to represent an optimal organization of local specialization and global integration of information processing at reduced wiring cost. Here, we applied magnitude squared coherence to resting magnetoencephalographic time series in reconstructed source space, acquired from controls and patients with schizophrenia, and generated frequency-dependent adjacency matrices modeling functional connectivity between virtual channels. After configuring undirected binary and weighted graphs, we found that all human networks demonstrated highly localized clustering and short characteristic path lengths. The most conservatively thresholded networks showed efficient wiring, with topographical distance between connected vertices amounting to one-third as observed in surrogate randomized topologies. Nodal degrees of the human networks conformed to a heavy-tailed exponentially truncated power-law, compatible with the existence of hubs, which included theta and alpha bilateral cerebellar tonsil, beta and gamma bilateral posterior cingulate, and bilateral thalamus across all frequencies. We conclude that all networks showed small-worldness, minimal physical connection distance, and skewed degree distributions characteristic of physically-embedded networks, and that these calculations derived from graph theoretical mathematics did not quantifiably distinguish between subject populations, independent of bandwidth. However, post-hoc measurements of edge computations at the scale of the individual vertex revealed trends of reduced gamma connectivity across the posterior medial parietal cortex in patients, an observation consistent with our prior resting activation study that found significant reduction of synthetic aperture magnetometry gamma power across similar regions. The basis of these small differences remains unclear.

Higher-order spectrum in understanding nonlinearity in EEG rhythms.

The fundamental nature of the brain's electrical activities recorded as electroencephalogram (EEG) remains unknown. Linear stochastic models and spectral estimates are the most common methods for the analysis of EEG because of their robustness, simplicity of interpretation, and apparent association with rhythmic behavioral patterns in nature. In this paper, we extend the use of higher-order spectrum in order to indicate the hidden characteristics of EEG signals that simply do not arise from random processes. The higher-order spectrum is an extension Fourier spectrum that uses higher moments for spectral estimates. This essentially nullifies all Gaussian random effects, therefore, can reveal non-Gaussian and nonlinear characteristics in the complex patterns of EEG time series. The paper demonstrates the distinguishing features of bispectral analysis for chaotic systems, filtered noises, and normal background EEG activity. The bispectrum analysis detects nonlinear interactions; however, it does not quantify the coupling strength. The squared bicoherence in the nonredundant region has been estimated to demonstrate nonlinear coupling. The bicoherence values are minimal for white Gaussian noises (WGNs) and filtered noises. Higher bicoherence values in chaotic time series and normal background EEG activities are indicative of nonlinear coupling in these systems. The paper shows utility of bispectral methods as an analytical tool in understanding neural process underlying human EEG patterns.