Standardizing T1-w/T2-w ratio images in trigeminal neuralgia to estimate the degree of demyelination in vivo.

BACKGROUND: Novel magnetic resonance (MR) imaging techniques have led to the development of T1-w/T2-w ratio images or "myelin-sensitive maps (MMs)" to estimate and compare myelin content in vivo. Currently, raw image intensities in conventional MR images are unstandardized, preventing meaningful quantitative comparisons. We propose an improved workflow to standardize the MMs, which was applied to patients with classic trigeminal neuralgia (CTN) and trigeminal neuralgia secondary to multiple sclerosis (MSTN), to assess the validity and feasibility of this clinical tool. METHODS: T1-w and T2-w images were obtained for 17 CTN patients and 17 MSTN patients using a 3 T scanner. Template images were obtained from ICBM152. Multiple sclerosis (MS) plaques in the pons were labelled in MSTN patients. For each patient image, a Gaussian curve was fitted to the histogram of its intensity distribution, and transformed to match the Gaussian curve of its template image. RESULTS: After standardization, the structural contrast of the patient image and its histogram more closely resembled the ICBM152 template. Moreover, there was reduced variability in the histogram peaks of the gray and white matter between patients after standardization (p < 0.001). MM intensities were decreased within MS plaques, compared to normal-appearing white matter (NAWM) in MSTN patients (p < 0.001) and its corresponding regions in CTN patients (p < 0.001). CONCLUSIONS: Images intensities are calibrated according to a mathematic relationship between the intensities of the patient image and its template. Reduced variability among histogram peaks allows for interpretation of tissue-specific intensity and facilitates quantitative analysis. The resultant MMs facilitate comparisons of myelin content between different regions of the brain and between different patients in vivo. MM analysis revealed reduced myelin content in MS plaques compared to its corresponding regions in CTN patients and its surrounding NAWM in MSTN patients. Thus, the standardized MM serves as a non-invasive, easily-automated tool that can be feasibly applied to clinical populations for quantitative analyses of myelin content.

Trigeminal neuralgia diffusivities using Gaussian process classification and merged group tractography.

ABSTRACT: Imaging of trigeminal neuralgia (TN) has demonstrated key diffusion tensor imaging-based diffusivity alterations in the trigeminal nerve; however, imaging has primarily focused on the peripheral nerve segment because of previous limitations in reliably segmenting small fiber bundles across multiple subjects. We used Selective Automated Group Integrated Tractography to study 36 subjects with TN (right-sided pain) and 36 sex-matched controls to examine the trigeminal nerve (fifth cranial nerve [CN V]), pontine decussation (TPT), and thalamocortical fibers (S1). Gaussian process classifiers were trained by scrolling a moving window over CN V, TPT, and S1 tractography centroids. Fractional anisotropy (FA), generalized FA, radial diffusivity, axial diffusivity, and mean diffusivity metrics were evaluated for both groups, analyzing TN vs control groups and affected vs unaffected sides. Classifiers that performed at greater-than-or-equal-to 70% accuracy were included. Gaussian process classifier consistently demonstrated bilateral trigeminal changes, differentiating them from controls with an accuracy of 80%. Affected and unaffected sides could be differentiated from each other with 75% accuracy. Bilateral TPT could be distinguished from controls with at least 85% accuracy. TPT left-right classification achieved 98% accuracy. Bilateral S1 could be differentiated from controls, where the affected S1 radial diffusivity classifier achieved 87% accuracy. This is the first TN study that combines group-wise merged tractography, machine learning classification, and analysis of the complete trigeminal pathways from the peripheral fibers to S1 cortex. This analysis demonstrates that TN is characterized by bilateral abnormalities throughout the trigeminal pathway compared with controls and abnormalities between affected and unaffected sides. This full pathway tractography study of TN demonstrates bilateral changes throughout the trigeminal pathway and changes between affected and unaffected sides.

Characterizing the modulation of resting-state fMRI metrics by baseline physiology.

The blood-oxygenation level dependent (BOLD) functional magnetic resonance imaging (fMRI) signal is commonly used to assess functional connectivity across brain regions, particularly in the resting state (rs-fMRI). However, the BOLD fMRI signal is not merely a representation of neural activity, but a combination of neural activity and vascular response. These aspects of the BOLD signal are easily influenced by systemic physiology, potentially biasing BOLD-based functional connectivity measurements. In this work, we focus on the following physiological modulators of the BOLD signal: cerebral blood flow (CBF), venous blood oxygenation, and cerebrovascular reactivity (CVR). We use simulations and experiments to examine the relationship between the physiological parameters and rs-fMRI functional connectivity measurements in three resting-state networks: default mode network, somatosensory network and visual network. By using the general linear model, we demonstrate that physiological modulators significantly impact functional connectivity measurements in these regions, but in a manner that depends on the interplay between signal- and noise-driven correlations. Moreover, we find that the physiological effects vary by brain region and depend on the range of physiological conditions probed; the associations are more complex than previously reported. The results confirm that it is important to account for the effect of physiological modulators when comparing resting-state fMRI metrics. We note that such modulatory effects may be amplified by disease conditions, which will warrant future investigations.