7T quantitative magnetization transfer (qMT) of cortical gray matter in multiple sclerosis correlates with cognitive impairment

Abstract

Cognitive impairment (CI) is a major manifestation of multiple sclerosis (MS) and is responsible for extensively hindering patient quality of life. Cortical gray matter (cGM) damage is a significant contributor to CI, but is poorly characterized by conventional MRI let alone with quantitative MRI, such as quantitative magnetization transfer (qMT). Here we employed high-resolution qMT at 7T via the selective inversion recovery (SIR) method, which provides tissue-specific indices of tissue macromolecular content, such as the pool size ratio (PSR) and the rate of MT exchange (kmf). These indices could represent expected demyelination that occurs in the presence of gray matter damage. We utilized selective inversion recovery (SIR) qMT which provides a low SAR estimate of macromolecular-bulk water interactions using a tailored, B1 and B0 robust inversion recovery (IR) sequence acquired at multiple inversion times (TI) at 7T and fit to a two-pool model of magnetization exchange. Using this sequence, we evaluated qMT indices across relapsing-remitting multiple sclerosis patients (N = 19) and healthy volunteers (N = 37) and derived related associations with neuropsychological measures of cognitive impairment. We found a significant reduction in k_mf in cGM of MS patients (15.5%, p = 0.002), unique association with EDSS (ρ = -0.922, p = 0.0001), and strong correlation with cognitive performance (ρ = -0.602, p = 0.0082). Together these findings indicate that the rate of MT exchange (k_mf) may be a significant biomarker of cGM damage relating to CI in MS.

Fig. 1.

Location of 10mm thick slice shown above, composed of 5 adjacent 2mm slices with a slice gap of 0mm (partial brain coverage). The center of the volume was placed approximately 20mm above the anterior commissure – posterior commissure (AC-PC) line.

Fig. 2.

Matlab generated maps for SIR qMT indices (kmf, PSR, R1f) for a subject from each cohort (healthy control, MS patient with low EDSS, MS patient with high EDSS), exhibiting visible changes most notably for the k_mf measurement, consistent with numerical findings.

Fig. 3.

Maps derived using SPM12 segment tool for gray matter (A), white matter (B), and cerebrospinal fluid (C) in a healthy control subject.

Fig. 4.

Maps derived from multi-atlas segmentation creating volumes for over 130 brain regions (A) and further grouped into 7 regions (B), blue = prefrontal, red = motor and somatosensory, teal = parietal, green = occipital.

Fig. 5.

Gray matter maps applied to SIR qMT indices for kmf (A), PSR (B), R1f (C).

Fig. 6.

k_mf maps for healthy control, female, age 47 (left) and patient, EDSS 3.5, female, age 38 (right). The white arrows point out how the contrast between the gray and white matter is very evident in the healthy control, but is highly diminished in the patient with high disability.

Fig. 7.

Box plots comparing mean kmf in cortical gray matter for each cohort (healthy controls, all MS patients, MS patients with high EDSS scores (>2)). Significant differences (indicated by *), were found for k_mf in parietal, motor, and somatosensory regions for patients with high EDSS compared to healthy controls and for k_mf in parietal lobe for all MS patients compared to healthy controls.

Fig. 8.

Overlaid histograms for values for every cGM voxel contained in all the healthy controls (blue) and all the patients (red) for qMT indices, k_mf (left), PSR (middle), R_1f (right). This demonstrates the deviation of the distribution of k_mf values in the patient cohort. Correlation between k_mf and Expanded Disability Status (EDSS).

Fig. 9.

In cohort of patients with disability (EDSS >0), the mean k_mf value in all cortical gray matter (1), parietal (2), motor (3), and somatosensory (4) regions were found to be significantly correlated with patient EDSS scores. Correlation between k_mf and Choice Reaction Time (CRT).

Fig. 10.

In the cohort of all MS patients, the mean k_mf value in all cortical gray matter (1), parietal (2), motor (3), and somatosensory (4) regions were found to be significantly correlated with patient CRT scores.