An untargeted metabolomics approach to MRS in the human brain: a comparison between LCModel and MRS-based classifier development system The pattern recognition process successfully caught changes where glucose concertation increase was around 1% estimated by LCModel. High spectral quality and reliable data acquisition techniques resulted in similar estimates of the glucose signal for LCModel and SpectralClassifier.

1. An untargeted metabolomics approach to MRS in the human brain: a comparison between LCModel and
MRS-based classifier development system
Srijyotsna Volety1
, Elizabeth Seaquist2
, Gulin Oz3
, Uzay Emir1,4
1School of Health Sciences, Purdue University, West Lafayette, IN, United States, 2Department of Medicine, Medical School, University of Minnesota, Minneapolis, Minnesota, 3Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis, Minnesota, 4Weldon School of Biomedical Engineering, Purdue University, West Lafayette, IN, United States
Methods Results
Conclusion
Introduction
References
• Current MR spectroscopy quantification tools require
prior knowledge and can be time-consuming
• Untargeted metabolomic approaches allow for automatic
analysis of unresolved in vivo spectra using pattern
recognition
• We utilized an untargeted metabolomics approach to
measure in-vivo changes of brain glucose concentrations
& compared to estimates from LCModel
• Simulated spectra with varying noise and linewidths
were developed to test accuracy of classifier
MRS Acquisition
• Localization achieved with STEAM (TE = 5 ms, TR = 4.5 s) on a 4 T
scanner
• 1H MR spectra of occipital lobe obtained from 5 volunteers (3M/2F,
31 ± 16 years old) during continuous glucose infusion for ~2h
Simulated Dataset
• Modeled in VeSPA to match in-vivo spectral quality and average
metabolite concentrations at different intervals of glucose infusion
• Subsequent spectra were simulated with 5 noise multiplier levels
and linewidths
LCModel Quantification
• In-vivo spectra were phase and frequency corrected & summed over
every 16 scans to provide 1.25 min resolution
• LCModel metabolite analysis performed between 0.5 and 4.2 ppm for
in-vivo and simulated spectra
SpectralClassifier
• Processing steps detailed in Figure 2
• Sensitivity of LCModel and SpectralClassifier was determined using
a two tailed t-test for comparison between consecutive time intervals
• The pattern recognition process successfully caught
changes where glucose concertation increase was around
1% estimated by LCModel.
• High spectral quality and reliable data acquisition
techniques resulted in similar estimates of the glucose
signal for LCModel and SpectralClassifier.
T-test
Meta Analysis
p values
Individual Analysis
p values
LCModel
individual analysis
p values
Glucose
Concentration
estimated by
LCModel [umo/g]
Baseline latent space 0.4435
Baseline
vs
1st interval
6.97706E-04 8.74703E-04 3.07775E-04 1.532960938
1st
vs
2nd interval
6.98801E-07 5.88104E-07 1.26690E-05 2.721089844
2nd
vs
3rd interval
3.02236E-06 9.37928E-06 5.33784E-06 3.437492188
3rd
vs
4th interval
1.38315E-04 4.02346E-06 6.52065E-01 3.479550781
1.25
3.75
6.25
8.75
11.25
13.75
16.25
18.75
21.25
23.75
26.25
28.75
31.25
33.75
36.25
38.75
41.25
43.75
46.25
48.75
51.25
53.75
56.25
58.75
61.25
63.75
66.25
68.75
71.25
73.75
76.25
78.75
m et a-an alysi s
0710
l cm od el
0710
Time (min)
Meta-Analysis
LCModel
Individual Analysis
Baseline
t = 50 min
t = 30 min
t = 10 min
Glc Glc
Figure 1: 1H MR spectra acquired at 4T from a volunteer before and after glucose infusion (Left),
Simulated VeSPA spectra (Right)
Figure 2: The procedure followed for the pattern recognition analysis using SpectraClassifier.
Table 1: One-tailed t-test
for comparison
between consecutive time
intervals and LCModel
glucose estimation. P-
values of meta and
individual analyses are
lower than LCModel
Figure 3: Glucose signal determined using LCModel and latent space data of
SpectraClassifier using both meta and individual analyses. Signals were references to
first time point (1.25 min) then normalized to the maximum concentration.
Baseline
1st
Interval
2nd
Interval
3rd
Interval
4th
Interval
1. Bingol K. Recent advances in targeted and untargeted metabolomics by NMR and MS/NMR methods. High Throughput. 2018; 7(2): 9; 2. Griffin, JL &
Kauppinen RA. A metabolomics perspective of human brain tumors. FEBS J. 2007. 274(5):1132-9; 3. Verma A, Kumar I, Verma N, Aggarwal P, Ohja R.
Magnetic resonance spectroscopy – revisiting the biochemical and molecular milieu of brain tumors. BBA Clin. 2016; 5: 170-178.; 4. Vingara LK, Yu
HJ, Wagshul ME, Serafin D, Christodoulou C, Pelczer I, Krupp LB, Savatic MM. Metabolomic approach to human brain spectroscopy identifies
associations between clinical features and the frontal lobe metabolome in multiple sclerosis. NMR Biomed. 2007. 20:763.; 5. Shestov AA, Emir UE, Kumar
A, Henry PG, Seaquist ER, Öz G. Simultaneous measurement of glucose transport and utilization in the human brain. Am J Physiol Endocrinol Metab.
2011;301(5):E1040–E1049.; 6. Provencher SW. Automatic quantitation of localized in vivo 1H spectra with LCModel. NMR Biomed. 2001;14(4):260-4.; 7.
Provencher SW. Estimation of metabolite concentrations from localized in vivo proton NMR spectra. Magn. Reson. Med. 1993;30:672–679.; 8. Ortega-
Martorell S, Olier I, Julià-Sapé M, et al. SpectraClassifier 1.0: a user friendly, automated MRS-based classifier-development system. BMC Bioinformatics.
2010. 11:106.
Supported by Grants: R01-NS-035192, P41-EB-0270601, and P30-NS-076408