Synopsis
In this study, we measured regional neurochemical variations in rat prelimbic cortex (PrL)/infralimbic cortex (IL), cingulate cortex (Cg) and retrosplenial cortex (RSC) with in vivo 1H-MRS at 7T. It was found that the regional metabolic variations follow cytoarchitectural/receptor-architectonical
organization in these brain regions.Introduction
Medial prefrontal cortex (mPFC) and cingulate cortex are important nodes
of brain networks known to be involved in many psychiatric disorders [1]. A
recent
in vivo 1H-MRS study
has demonstrated that regional variations in glutamate (Glu) and γ-aminobutyric
acid (GABA) concentrations in human cingulate cortex may reflect receptor-architectonical
and functional segregation of the cingulate cortex along the rostral-caudal
axis [2]. To further elucidate the relationship between the metabolic profile
and cytoarchitectural/receptor-architectonical organization in the prefrontal
and cingulate cortex, we measured regional neurochemical variations in rat prelimbic
cortex (PrL)/infralimbic cortex (IL), cingulate cortex (Cg) and retrosplenial
cortex (RSC) with
in vivo 1H-MRS
at 7 T.
Materials and methods
Eleven adult male Sprague-Dawley rats (about 3.6 months old, weighting
525±32 g) were used. All
spectra were acquired on a 7 T/20 cm Bruker Biospec scanner with a volume coil
for transmission and a quadrature surface coil for detection. The animals were
anesthetized with 1.8-2.5% isoflurane. Localized spectra were acquired from PrL+IL
(2.5 mm×2.4 mm×2.0 mm), Cg (2.3 mm×2.0 mm×2.5 mm) and RSC (2.0 mm×2.0 mm×2.5
mm) of each animal with a PRESS sequence (Fig. 1), VAPOR water suppression, TR/TE
4000/15 ms, spectral bandwidth 4 kHz, 2048 data points and 512 averages.
LCModel was used for quantification, and only the results with fitting CRLBs
less than 20% were reported. Paired
t-test
was used for statistical analysis. Bonferroni correction was applied for
multiple comparisons among different brain regions. A corrected p<0.05 was
considered to be statistically significant.
Results
Figure 1 shows the voxels for
in
vivo 1H-MRS, representative spectra acquired and the
corresponding LCModel fits. Figure 2 plots quantitative regional metabolic
variations. RSC had the highest tNAA/tCr, but the lowest Glu/tCr and Glu/tNAA
among the three regions. PrL+IL had the highest Ins/tCr and Gln/tCr. The three
regions showed similar Glu/Gln.
Discussion
The observation that the RSC had higher tNAA/tCr is consistent with the
results from a previous in vivo
1H-MRS
study on mice brain [3]. Although the three brain regions measured had similar density
of total neurons [4], the RSC is known to have higher density of neuropil (i.e.,
apical dendrites of layer V pyramidal neurons) than the other two regions [4,
5]. This might explain the higher level of tNAA observed in the region. The rostrocaudal
gradients of Glu/tCr and Glu/tNAA corroborated with the known distribution of N-methyl-D-aspartate
(NMDA) receptors in rat brain [6]. For example, the RSC had the lowest Glu/tCr
and Glu/tNAA, and was also reported to have the lowest NMDA-sensitive
L-glutamate binding sites among the three brain region [6]. RSC had a prominent
granular layer (i.e., layer IV), while the other two brain regions did not have
[4]. The local excitatory/inhibitory network within the RSC and the external
excitatory disinhibition inputs of RSC could be different from those in the Cg
and PrL/IL [7]. Our results are in line with these previous findings. Ins and
Gln are metabolites mainly located in glia cells [8, 9]. Regional variations in
these metabolites are consistent with the previous finding that the density of s-100β positive glial cells in the PrL/IL is higher
than that in the Cg [10]. Glu/Gln did not show significant regional variations,
perhaps indicating that the overall neuronal-glial interaction is similar among
these brain regions.
Acknowledgements
Supported
by National Basic Research Program of China (2011CB707802), and Natural Science
Foundation of China (21221064 and 81000598).References
[1]Lu H et al, Proc Natl
Acad Sci U S A, 2012, 109: 3979-84. [2]Dow W et al, J Neurosci, 2013, 33:12698-704.
[3]Kulak A et al, J Neurochem, 2010, 115: 1466-77. [4]Vogt BA et al, J Comp
Neuro, 1981, 195: 603-25. [5]Monaghan DT et al, Brain Res, 1984, 324: 160-4. [6]Monaghan
DT et al, J Neurosci, 1985, 5: 2909-19. [7]Li Q et al, J Neurosci, 2002, 22:
3070-80. [8]Brand A et al, Dev Neurosci, 1993, 15: 289-98. [9]Urenjak J et al,
J Neurosci, 1993, 13: 981-9. [10]Gosselin RD et al, Neuroscience, 2009, 15:
915-25.