Tract-based magnetic resonance spectroscopy of the cingulum bundles at 7 T

Abstract

The cingulum bundle is a white matter fiber bundle in the human brain that is believed to be implicated in various neurological and psychiatric diseases. Subtle disease-related differences in metabolite concentrations in the cingulum tracts that may underlie these diseases may be detected using MR spectroscopic information. However, to date, limited signal to noise and lack of spatial resolution have prevented a reliable and reproducible measurement of metabolites in the cingulum bundle in vivo. Here we propose a new method that combines MR spectroscopic imaging at 7 T with fiber tracking to select only those MR spectroscopy voxels that are actually part of the cingulum bundles. The spectra of the selected spectroscopy voxels are processed per voxel and then combined yielding one spectrum at high spectral resolution for each cingulum bundle. In this way sensitivity is increased, as large parts of the cingulum are included while partial volume effects with both gray matter and white matter from other tracts is kept to a minimum. Three healthy volunteers were scanned to assess the feasibility of the method. For all three healthy volunteers spectra for the left and right cingulum tracts were computed, partial volume fractions calculated and metabolite fractions were quantified yielding similar results suggesting that tract-based MR spectroscopy allows us to study metabolic concentrations of individual white matter fiber bundles with high sensitivity and high specificity.

Figure 1

Example of the reconstructed left cingulum bundle seen from the left (A) and top (B).

Figure 2

Tract‐based MR spectroscopy. The spectrum (y) is automatically computed for each of the n CSI voxels from the cingulum [depicted in red in (A)]. For each frequency f a model (B) was fitted to estimate the contribution of white matter from the cingulum (represented by a in the model) to the measured signal. The fractions of the different tissue types in the selected CSI voxels used in the model namely white matter in the cingulum, white matter outside the cingulum, and gray matter are represented by WMC, WME, and GM. The resulting spectra for the different tissue types are shown in (C).

Figure 3

Example of metabolite estimation and spectra results for all healthy participants. The results of fitting simulated metabolite spectra using AQSES to the measured spectrum of the white matter from the left cingulum tract from Subject 1 are shown in (A). The measured spectra for the white matter of the left and right cingulum tracts for all three healthy participants are shown in (B).

Figure 4

Metabolite fractions computed using tract‐based MR spectroscopy and segmented spectroscopy approach. The results of estimating metabolite concentrations using AQSES for the left and right cingulum tracts for all three subjects using the tract‐based MR spectroscopy approach (A) and the segmented spectroscopy approach (B). For both approaches, a significant difference (P < 0.05, uncorrected for multiple comparisons) in estimated glutamate concentrations was found between left and right cingulum tracts.

Figure 5

Distributions of left and right glutamate mean values at a CSI voxel level. For each subject, one voxel was randomly selected from the set of CSI voxels that contain at least 50% of cingulum tissue. For the left and right cingulum CSI voxels, the average estimated glutamate concentrations were computed over all three subjects. This procedure was repeated 10,000 times yielding a distribution of average estimated glutamate concentrations for the left (blue) and right cingulum tract (green).