Faculty Bibliography

The brain is a spatially heterogeneous and temporally dynamic organ, with different regions requiring different amount of blood supply at different time. Therefore, the ability of the blood vessels to dilate or constrict, known as Cerebral-Vascular-Reactivity (CVR), represents an important domain of vascular function. An imaging marker representing this dynamic property will provide new information of cerebral vessels under normal and diseased conditions such as stroke, dementia, atherosclerosis, small vessel diseases, brain tumor, traumatic brain injury, and multiple sclerosis. In order to perform this type of measurement in humans, it is necessary to deliver a vasoactive stimulus such as CO2 and/or O2 gas mixture while quantitative brain magnetic resonance images (MRI) are being collected. In this work, we presented a MR compatible gas-delivery system and the associated protocol that allow the delivery of special gas mixtures (e.g., O2, CO2, N2, and their combinations) while the subject is lying inside the MRI scanner. This system is relatively simple, economical, and easy to use, and the experimental protocol allows accurate mapping of CVR in both healthy volunteers and patients with neurological disorders. This approach has the potential to be used in broad clinical applications and in better understanding of brain vascular pathophysiology. In the video, we demonstrate how to set up the system inside an MRI suite and how to perform a complete experiment on a human participant.

Importance: Cerebrovascular reactivity (CVR) is an inherent indicator of the dilatory capacity of cerebral arterioles for a vasomotor stimulus for maintaining a spontaneous and instant increase of cerebral blood flow (CBF) in response to neural activation. The integrity of this mechanism is essential to preserving healthy neurovascular coupling; however, to our knowledge, no studies have investigated whether there are CVR abnormalities in multiple sclerosis (MS). Objective: To use hypercapnic perfusion magnetic resonance imaging to assess CVR impairment in patients with MS. Design, Setting, and Participants: A total of 19 healthy volunteers and 19 patients with MS underwent perfusion magnetic resonance imaging based on pseudocontinuous arterial spin labeling to measure CBF at normocapnia (ie, breathing room air) and hypercapnia. The hypercapnia condition is achieved by breathing 5% carbon dioxide gas mixture, which is a potent vasodilator causing an increase of CBF. Main Outcomes and Measures: Cerebrovascular reactivity was calculated as the percent increase of normocapnic to hypercapnic CBF normalized by the change in end-tidal carbon dioxide, which was recorded during both conditions. Group analysis was performed for regional and global CVR comparison between patients and controls. Regression analysis was also performed between CVR values, lesion load, and brain atrophy measures in patients with MS. Results: A significant decrease of mean (SD) global gray matter CVR was found in patients with MS (3.56 [0.81]) compared with healthy controls (5.08 [1.56]; P = .001). Voxel-by-voxel analysis showed diffuse reduction of CVR in multiple regions of patients with MS. There was a significant negative correlation between gray matter CVR and lesion volume (R = 0.6, P = .004) and a significant positive correlation between global gray matter CVR and gray matter atrophy index (R = 0.5, P = .03). Conclusions and Relevance: Our quantitative imaging findings suggest impairment in functional cerebrovascular pathophysiology, by measuring a diffuse decrease in CVR, which may be the underlying cause of neurodegeneration in MS.

OBJECTIVE: The purpose of this study was to develop an algorithm incorporating MRI metrics to classify patients with mild traumatic brain injury (mTBI) and controls. METHODS: This was an institutional review board-approved, Health Insurance Portability and Accountability Act-compliant prospective study. We recruited patients with mTBI and healthy controls through the emergency department and general population. We acquired data on a 3.0T Siemens Trio magnet including conventional brain imaging, resting-state fMRI, diffusion-weighted imaging, and magnetic field correlation (MFC), and performed multifeature analysis using the following MRI metrics: mean kurtosis (MK) of thalamus, MFC of thalamus and frontal white matter, thalamocortical resting-state networks, and 5 regional gray matter and white matter volumes including the anterior cingulum and left frontal and temporal poles. Feature selection was performed using minimal-redundancy maximal-relevance. We used classifiers including support vector machine, naive Bayesian, Bayesian network, radial basis network, and multilayer perceptron to test maximal accuracy. RESULTS: We studied 24 patients with mTBI and 26 controls. Best single-feature classification uses thalamic MK yielding 74% accuracy. Multifeature analysis yields 80% accuracy using the full feature set, and up to 86% accuracy using minimal-redundancy maximal-relevance feature selection (MK thalamus, right anterior cingulate volume, thalamic thickness, thalamocortical resting-state network, thalamic microscopic MFC, and sex). CONCLUSION: Multifeature analysis using diffusion-weighted imaging, MFC, fMRI, and volumetrics may aid in the classification of patients with mTBI compared with controls based on optimal feature selection and classification methods. CLASSIFICATION OF EVIDENCE: This study provides Class III evidence that classification algorithms using multiple MRI features accurately identifies patients with mTBI as defined by American Congress of Rehabilitation Medicine criteria compared with healthy controls.