Jonathan D Lynn1, Eric A Woodcock2,3, Chaitali Anand1, Dalal Khatib4, and Jeffrey A Stanley5
1Psychiatry and Behavioral Neurosciences, Wayne State University, Detroit, MI, United States, 2Yale University, New Haven, CT, United States, 3Wayne State University, Detroit, MI, United States, 4Department of Psychiatry and Behavioral Neurosciences, Wayne State University, Detroit, MI, United States, 5Psychiatry and Behavavioral Neurosciences, Wayne State University, Detroit, MI, United States
Synopsis
Proton functional
magnetic resonance spectroscopy (1H fMRS) is capable of detecting
dynamic changes in brain glutamate related to task engagement compared to a “non-task-active” control condition. The selection of an appropriate control
condition is critical, which may confound the magnitude change in glutamate
modulation. The purpose of this 1H fMRS study was to compare the steady-state
levels of glutamate and its variability in the left dorsolateral prefrontal
cortex during four different putative control conditions. Results show significant differences in the glutamate
level and variability between conditions, emphasizing the importance of the
control condition for the detection of task-evoked glutamate modulation.
Introduction
1H fMRS is a powerful technique for
characterizing dynamic changes in brain metabolite levels related to task
engagement with a temporal resolution of under a minute. Several recent ¹H fMRS
studies have consistently reported elevated glutamate – the primary excitatory
neurotransmitter – during neural-activity promoting, ‘task-active’ conditions
in focal brain regions, relative to control conditions1,2,3,4. The magnitude and direction of change in the task-evoked
glutamate modulation does depend on the comparison “non-task-active” control
condition in eliciting a stable, steady-state glutamate level. However, the ‘control’ condition from these
studies do vary with respect to constraining behavior and it is unclear whether
the acquired steady-state glutamate levels truly reflect a ‘non-task–active’
condition. Using 1H fMRS, this study compared steady-state levels
and variability of glutamate in the dlPFC between four different putative control
paradigms with the hypothesis that glutamate levels are highly susceptible to
the behavioral constraint imposed on the ‘control’ condition.
Methods
Sixteen healthy adult
volunteers (9 males; mean age: 24.3 ± 3.5years), participated in morning ¹H
fMRS scanning sessions (9:30-11:30am) using a 3T Siemens Verio system with a
32-channel volume head-coil. Following a structural T1- weighted
scan (MPRAGE), a single-voxel (15x20x15mm) was prescribed in the left dlPFC using
the AVP approach5, followed by B0-field shimming using FASTESTMAP.
Four consecutive ¹H
fMRS scans were acquired during different
behavioral constraints, including: a) relaxed with eyes closed; b) visual fixation
(centered crosshair); c) visual
fixation to a flashing
checkerboard (3Hz); and d) finger-tapping to periodic stimulus (1Hz;
right-hand index finger). The order of conditions b) and d) were
counter-balanced. 1H fMRS spectra (N=13) were continuously acquired
every 16s (PRESS with OVS and VAPOR, TE=23ms, TR=4.0s, 4 averages/spectrum;
2048 data points; 208s/condition). Prior to quantification, spectra from 2 to 13 were averaged into
consecutive pairs to give a 32s temporal resolution (Figure 2). LCModel (v6.3)
was used to quantify the metabolite levels, which were expressed in
institutional units relative to the unsuppressed water signal. The primary
outcome variables of interest were mean glutamate levels and percent coefficient
of variation (CV%) from the 6 paired measurements for each condition.
Results
Descriptive data are presented mean ± 1SD. Geometric voxel
overlap indicated highly precise (mean % overlap with the template voxel = 92.3±4.7%)
and reliable (overlap across all subjects = 88.6%) (Figure 3). Mean voxel grey
matter tissue composition was 36.8 ± 3.8%). Mean signal-to-noise (11.7 ± 2.0),
full-width half-maximum (4.8 ± 1.0 Hz), and Cramer-Rao lower bound (7 ± 1%) values
demonstrated 1H fMRS spectra were fit reliably
and did not differ across conditions.
Based on a repeated measure analysis, the
condition term for glutamate levels failed to reach significance (Χ2
= 5.09; p=.17). However, post-hoc
comparisons indicated glutamate levels were significantly lower during the
visual fixation crosshair condition (11.6 ± 0.9) compared to both the visual fixation
flashing checkerboard (12.2 ± 0.9; p=.0080)
and finger-tapping (12.0 ± 0.9; p=.023)
conditions (Figure 4). The condition
term for the glutamate variability (CV%) failed to reach significance (Χ2
= 6.60, p=.086) (Figure 5). However,
post-hoc analyses demonstrated significantly lower glutamate variability during
the visual fixation flashing checkerboard compared to both the eyes closed (p=.0037) and finger-tapping (p=.035) conditions. Finally, glutamate
levels during the visual fixation crosshair condition were significantly less
variable than the eyes closed condition (p=.0087)
(Figure 5).
Discussion
The motivation of
this study was to investigate whether differences in steady-state glutamate
levels and its temporal variability in the dlPFC can be detected across four putative
‘control’ conditions. These ‘control’
conditions were chosen because the dlPFC is not the dominant brain region
engaged in their implementation. We
found mean glutamate levels were qualitatively the lowest during the visual fixation
crosshair condition: significantly lower than the flashing checkerboard and
finger-tapping conditions. Moreover, the two visual fixation conditions (crosshair
and flashing checkerboard) produced the least glutamate variability: significantly lower than the eyes closed
condition. The differences in glutamate
levels were likely due to the shifting of the excitatory and inhibitory balance
(E/I balance) in the brain6,7,8,9 implying that the four ‘control’
conditions differed in their level of engagement of the left dlPFC by shifting
the E/I drive. This study provides
evidence that steady-state glutamate levels are sensitive to behavioral
constraints of ‘non-task-active’ control conditions, and highlights the importance
of behavioral paradigms for task-based ¹H fMRS.Acknowledgements
The authors would
like to thank Caroline Zajac-Benitez, and the participants in the scanning
sessions. This study was supported in part by the Lycaki-Young Funds from the
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