Pallab Bhattacharyya1, Micheal Phillips1, Lael Stone2, and Mark Lowe1
1Imaging Institute, Cleveland Clinic, Cleveland, OH, United States, 2Neurological Institute, Cleveland Clinic, Cleveland, OH, United States
Synopsis
Gamma
aminobutyric acid (GABA), a major inhibitory neurotransmitter, has been
implicated as a metabolic marker of multiple sclerosis (MS). Previously it has
been shown that sensorimotor cortex GABA level is higher in relapsing remitting
MS (RRMS) patients with poorer motor performance. In this study, the association
between cortical GABA level and motor performance, as measured by 9-hole peg
test score, has been studied for groups of patients with RRMS with different
degrees of motor impairment. The results suggest that cortical GABA has more
involvement in motor performance in early stage of RRMS or in less impaired
patients.
Purpose
The major inhibitory neurotransmitter GABA has
recently been suggested to be a biomarker of multiple sclerosis (MS)1,2. Studying correlation between 9 hole peg test scores (9HPT), a measure of
motor performance) and sensorimotor cortex GABA level [GABA], motor impairment
has been reported to be associated with higher [GABA] in relapsing remitting MS
(RRMS)3 and lower GABA in secondary
progressive (SPMS)2.
This difference prompts to look for association of GABA in different levels of
motor impairment. Correlation of [GABA] and 9HPT of 2 groups of RRMS patients
(with 9HPT Z score < 0 and > 0) were investigated in this study. Methods
MR scans were performed using a 3 Tesla Siemens
whole body Tim-Trio scanner (Erlangen, Germany) under an Institutional Review
Board approved protocol. RRMS patients were scanned with a MEGA-PRESS sequence4 (voxel size =
2×2×2 cm3, TR=2700 ms, TE=68 ms, number of averages = 96 ON and 96
OFF, editing pulse frequency = 1.9 (ON) /1.5 (OFF) ppm to reduce macromolecule
contamination). A voxel at the motor cortex was selected prior to the
spectroscopy scan from the area of maximum activation (Siemens Neuro3D program)
following an fMRI scan in which each subject performed bilateral finger tapping
in a block interleaved 32 second ON and 32 second OFF pattern. After discarding
motion corrupted data as determined by interleaved water signal navigator
signal or residual water signal3 a total of 24 RRMS
patients’ (Age: 48±8 y, 6 M) data were used for analyses. PRESS scans with and
without water suppression were performed for absolute quantification following
the editing scans. For absolute quantification, the gray matter, white matter
and CSF contribution to the voxel composition was performed by using FAST5 of FSL library6
with the anatomical 3D MPRAGE as the base image, and applying a mask at the
voxel location. MRUI software was used for spectroscopy data analysis7. Absolute [GABA]
was determined by taking the product of [GABA]/[Cr]
(from MEGA-PRESS) and [Cr] using internal water reference (from a PRESS scan
run during the same session)1. For 9HPT, the time taken by a subject to pick up and place 9 dowels in 9 holes was
recorded8.
The Z
score for 9HPT score for each subject was calculated with methods delineated by
the National Multiple Sclerosis Society Clinical Outcomes Assessment Task Force
based on a pool of data involving a total of 4715 patients.9-11 Next, correlation coefficients of [GABA]
and 9HPT scores (Z scored) (N=24) were calculated. In addition, the subjects
were divided into 2 groups: (i) 9HPT<0 (N=9; Age: 48±8 y, 3 M), and (ii)
9HPT>0 (N=15; Age: 51±5 y, 3 M), and GABA
- 9HPT correlation coefficient in each group was calculated.Results and Discussion
The 9HPT score and [GABA] for all
subjects and the 2 groups (9HPT<0 and 9HPT>0) are shown in Table 1. Representative
spectra from 5 subjects are shown in Fig. 1. The 9HPT scores between the 2 groups (Z<0 and Z>0) are
significantly different (P < 0.0001 from unpaired t-test), which ensured a
significant difference in motor performance between the 2 groups. Unpaired
t-test showed no significant difference in [GABA] between the 2 groups. [GABA] –
9HPT correlation was significant (P<0.05) in all subjects combined group,
not significant in 9HPT<0 group, and strongly significant (P<0.005) in
9HPT>0 group (Fig. 2-4). The correlation coefficients between the 2 groups
are significantly different (P<0.05).
Inverse correlation between
[GABA] and 9HPT has previously been reported in RRMS3 suggesting a role
of GABA in the disease process and a potential biomarker of MS. This study
implies that GABA plays more significant role in motor functioning and thus the
disease process in early stage of RRMS, i.e., in patients with less impairment
(9HPT>0). On the other hand our data suggest that GABA does not play a
significant role in more impaired patients (9HPT<0) with RRMS. It should be
pointed out that the direct [GABA]-9HPT
correlation in contrast with the inverse correlations reported here could be a
result of (i) the GABA level reported in the secondary progressive MS study
including contribution from co-edited macromolecule, and (ii) loss of
compensatory adaptation process in secondary progressive MS.12Conclusion
Higher
sensorimotor cortex [GABA] was observed in motor cortex of MS patients with
more motor impairment. The correlation was significantly more in patients with
less motor impairment (9HPT>0) than in more impaired patients (9HPT<0).
Cortical GABA is suggested to have more involvement in motor performance in
early stage of RRMS or in less impaired patients.Acknowledgements
National Institutes of Health, National Multiple Sclerosis
Society, Research Programs Committee Cleveland Clinic, Siemens Medical
Solutions.References
1. Bhattacharyya PK, Phillips
MD, Stone LA, Lowe MJ. In vivo magnetic resonance spectroscopy measurement of
gray-matter and white-matter gamma-aminobutyric acid concentration in
sensorimotor cortex using a motion-controlled MEGA point-resolved spectroscopy
sequence. Magn Reson Imaging. 2011;29(3):374-379.
2. Cawley
N, Solanky BS, Muhlert N, Tur C, Edden RA, Wheeler-Kingshott CA, Miller DH,
Thompson AJ, Ciccarelli O. Reduced gamma-aminobutyric acid concentration is
associated with physical disability in progressive multiple sclerosis. Brain. 2015;138(Pt 9):2584-2595.
3. Bhattacharyya
PK, Phillips MD, Stone LA, Bermel RA, Lowe MJ. Sensorimotor cortex
gamma-aminobutyric acid concentration correlates with impaired performance in
patients with MS. AJNR Am J Neuroradiol. 2013;34(9):1733-1739.
4. Mescher
M, Merkle H, Kirsch J, Garwood M, Gruetter R. Simultaneous in vivo spectral
editing and water suppression. NMR
Biomed. 1998;11(6):266-272.
5. Zhang Y,
Brady M, Smith S. Segmentation of brain MR images through a hidden Markov
random field model and the expectation-maximization algorithm. IEEE Trans Med Imaging. 2001;20(1):45-57.
6. Smith
SM, Jenkinson M, Woolrich MW, Beckmann CF, Behrens TE, Johansen-Berg H,
Bannister PR, De Luca M, Drobnjak I, Flitney DE, Niazy RK, Saunders J, Vickers
J, Zhang Y, De Stefano N, Brady JM, Matthews PM. Advances in functional and
structural MR image analysis and implementation as FSL. Neuroimage. 2004;23 Suppl 1:S208-219.
7. http://www.mrui.uab.es/mrui/.
8. Mathiowetz
V, Volland G, Kashman N, Weber K. Adult norms for the Box and Block Test of
manual dexterity. Am J Occup Ther. 1985;39(6):386-391.
9. Cutter
GR, Baier ML, Rudick RA, Cookfair DL, Fischer JS, Petkau J, Syndulko K,
Weinshenker BG, Antel JP, Confavreux C, Ellison GW, Lublin F, Miller AE, Rao
SM, Reingold S, Thompson A, Willoughby E. Development of a multiple sclerosis
functional composite as a clinical trial outcome measure. Brain. 1999;122 ( Pt 5):871-882.
10. Rudick R, Antel J, Confavreux C, Cutter G, Ellison G, Fischer
J, Lublin F, Miller A, Petkau J, Rao S, Reingold S, Syndulko K, Thompson A,
Wallenberg J, Weinshenker B, Willoughby E. Recommendations from the National
Multiple Sclerosis Society Clinical Outcomes Assessment Task Force. Ann Neurol. 1997;42(3):379-382.
11. Fischer JS, Rudick RA, Cutter GR, Reingold SC. The Multiple
Sclerosis Functional Composite Measure (MSFC): an integrated approach to MS
clinical outcome assessment. National MS Society Clinical Outcomes Assessment
Task Force. Mult Scler. 1999;5(4):244-250.
12. Rocca MA, Colombo B, Falini A, Ghezzi A, Martinelli V, Scotti
G, Comi G, Filippi M. Cortical adaptation in patients with MS: a
cross-sectional functional MRI study of disease phenotypes. Lancet Neurol. 2005;4(10):618-626.