Recent advances in In-vivo spectroscopy methods at the CNI

Background

A growing number of people at the CNI are interested in measuring metabolic changes in the brain using in-vivo spectroscopy (MRS) techniques.  Often, they combine this information with functional MRI measurements. The special interest spectroscopy group at CNI  develops and supports new MRS data acquisition and data analysis capabilities, following the recommendations of the at-large MRS community. This message describes the initiatives for education, support of experimental design, guided analyses, and interpretation of MRS data.

Recent spectroscopy results were shared in a poster that we presented at the 64^th ENC, the premier conference for nuclear magnetic research (April 16 – 20, 2023). Examples of studies at the CNI using in-vivo spectroscopy techniques include characterization of biomarkers following transcranial magnetic stimulation, and metabolite characterization for conditions such as addiction, pain, depression, and various forms of dementia. Published, collaborative studies performed at CNI include

• method applications (DeSouza DD, Stimpson K, Baltusis L, Sacchet MD, Gu M, Hurd R, Wu H, Yeomans DC, Williams N, Spiegel D. Association between anterior cingulate neurochemical concentration and individual differences in hypnotizability. Cerebral Cortex, Volume 30, Issue 6, June 2020, Pages 3644–3654),
• new method developments (Gu M, Hurd R, Noeske R, Baltusis L, Hancock R, Sacchet MD, Gotlib IH, Chin FT, Spielman DM (2018) GABA editing with macromolecule suppression using an improved MEGA-SPECIAL sequence, Magnetic Resonance in Medicine 79:41-47),
• participation in multi-site spectroscopy studies (Hui et al. Frequency drift in MR spectroscopy at 3T, NeuroImage 241(2021) 118430), and
• a recent paper regarding voxel placement method development (James H. Bishop, Andrew Geoly, Naushaba Khan, Claudia Tischler, Ruben Krueger, Poorvi Keshava, Heer Amin, Laima Baltusis, Hua Wu, David Spiegel, Nolan Williams, Matthew D. Sacchet Real-Time Semi-Automated and Automated Voxel Placement using fMRI Targets for Repeated Acquisition Magnetic Resonance Spectroscopy, Journal of Neuroscience Methods 392 (2023) 109853.

Technical notes

Here is a description of the specific methods we now support.  If you are interested in learning more about these methods, or using them, please consult with Laima Baltusis.

1. Experimental spectroscopy data acquisition methods continue to evolve. Currently the semi-LASER sequence is the ISMRM (International Society for Magnetic Resonance in Medicine) consensus method, particularly for multi-site 3T MRS studies. We have, therefore, transitioned studies at CNI to the semi-LASER sequence (a GE WIP (Works-in-Progress)) for both single voxel and to MRSI studies. The ENC poster presents both single voxel and new focal 2D MRSI results using the semi-LASER sequence in challenging and therefore less well studied but important areas of the human brain such as the basal ganglia regions. This region is often studied by investigators trying to understand movement, cognition, and emotion, and the single voxel data in this region has been of lower quality. To improve the data quality, and measure small metabolite differences in subregions of interest, we optimized a focal 2D MRSI data acquisition method (presented in the poster). The improvements for focal 2D MRSI and single-voxel MRS includes B0 shimming, encoding and acceleration methods, spatial selection, water suppression, extracranial lipid suppression, and RF (Radio Frequency) performance.
2. In addition, current basic data reconstruction and visualization can employ SAGE, a GE proprietary analysis and visualization tool with LCModel used as a separate module for data fitting. The next phase of this particular research project will continue to explore additional optimizations of data collection but will focus more on data processing for optimal quantification of metabolites. Areas of study will include improvements in data analysis using LCModel by (1) mitigation of baseline and macromolecular contributions for data analysis with LCModel and (2) improvement of the accuracy of the LCModel basis set by the addition and validation of experimental lower concentration metabolites (glutathione and ascorbate, as examples) to an otherwise synthetic LCModel basis set. The use of Bayesian analysis will also be explored as a technique to remove the broad baseline and macromolecule content without bias. We anticipate presenting these findings at the 2024 ENC conference.