Anna Min Wang1,2, Subechhya Pradhan1,2, Stephanie Korenic3, S. Andrea Wijtenburg3, Laura M. Rowland3, and Peter B. Barker1,2
1Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, United States, 2Kennedy Krieger Institute, Baltimore, MD, United States, 3Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD, United States
Synopsis
Brain metabolism was investigated
in 38 patients with schizophrenia (SZ), 38 healthy control (HC) subjects, and
11 first degree relatives of SZ patients using 7T MRS in 5
brain regions. Multiple metabolic abnormalities were found in SZ patients,
including increases in the ratio of glutamine to glutamate, increased levels of
brain lactate, and decreased levels of γ-aminobuytric acid (GABA) and N-acetylaspartate-glutamate (NAAG).
Many of these changes also correlated with measures of cognitive performance
and negative symptom severity. 7T MRS is an excellent tool for the non-invasive
investigation of brain pathophysiology in SZ.
Purpose
Schizophrenia (SZ) is a severe psychiatric disorder
characterized by positive, negative, and cognitive symptoms. The glutamatergic
and GABAergic neurotransmitter systems may play important roles in the pathophysiology
of schizophrenia (1),
and thus are potential targets for the novel interventions. Previous MRS
studies reported glutamate (Glu) and glutamine (Gln) changes in SZ patients
with variable results (2). Effective separation and
quantification of Glu and Gln under high field strength (7T) benefits the study
of the glutamatergic system in SZ, and other important molecules such as
lactate. Brain lactate concentration is used as the biomarker for mitochondrial
dysfunction and may be altered in SZ (3).
This study utilized 7T MRS to investigate these neurochemical differences
between SZ and healthy subjects.
Methods
Subjects: 38 SZ patients (SZ, 21 males, 33.9±12.6 y.o.),
38 healthy controls (HC, 19 males, 30.5±10.4 y.o.) and 11 first-degree
relatives of SZ patients (FDR, 2 males, 35.9±12.0 y.o.) were recruited. General
cognitive function was assessed in all subjects with the MATRICS Consensus
Cognitive Battery (MCCB). Psychiatric symptom severity in patients was assessed
with the Scale for the Assessment of Negative Symptoms (SANS).
MR protocols: All participants were scanned using a
7T scanner (Philips ‘Achieva’, Netherlands) with a 32-channel head coil. T
1W
images were acquired using an MPRAGE sequence (0.8 mm isotropic resolution).
Spectra were recorded from anterior cingulate cortex (ACC; 30×20×20 mm
3),
left centrum semiovale (CSO; 40×20×15 mm
3), left dorsolateral
prefrontal cortex (DLPFC; 25×20×20 mm
3), left hippocampus (Hippo;
20×20×20 mm
3), and bilateral thalamus (Thal, 20×30×15 mm
3)
using a STEAM sequence (TE/TM/TR = 14/33/3000 ms). VAPOR water suppression was
used, and a water-unsuppressed reference was acquired from each voxel.
Data Analysis: Spectra were analyzed using the LCModel
software and a basis set simulated using the ‘VESPA’ package. Metabolite
concentrations were normalized using the unsuppressed water reference.
Metabolite concentrations were only included in statistical analyses when the
corresponding Cramér-Rao low bound was below 20% except for Lactate (Lac) and N-acetylaspartate-glutamate
(NAAG) (<30%) (4). MPRAGE images was segmented
using ‘SPM8’ and gray matter, white matter and cerebrospinal fluid (CSF)
fractions were calculated for each MRS voxel. Metabolite concentrations were
CSF corrected except for Lac. Between group differences for γ-aminobuytric acid
(GABA), Lac, N-acetylaspartate (NAA), NAAG and the ratio of Gln/Glu between SZ,
HC and FDR groups were assessed using one-way ANOVAs. Metabolite concentrations
(and ratio) were also compared between SZ and HC groups only using two-tailed t-tests.
The relationships between the metabolite concentrations (or ratio) and the MCCB
or SANS scores were analyzed with Pearson's correlations.
Results
Figure 1 presents the T1W images from a HC
subject with the voxel localization in five brain regions and a representative spectrum
from the CSO with LCModel fit. Significant inter-group differences were found
for the concentrations of GABA in Hippo (p=0.032), Lac in Thal (p=0.019) and
the Gln/Glu ratio in CSO (p=0.021). Comparing the SZ to HC group, the Gln/Glu
ratio was significantly increased in ACC, CSO and Thal, and Lac concentration
was increased in ACC and Thal (Figure 2). The concentrations of NAAG in CSO (p=0.038)
and GABA in Hippo (p=0.009) were significantly decreased.
In all subjects, the Gln/Glu ratio was significantly negatively
correlated with MCCB scores in the CSO and DLPFC. Lac concentrations were
significantly negatively correlated with MCCB scores in ACC, CSO and Thal
(Figure 3). The concentrations of NAA in ACC, DLPFC and Thal, and GABA in Hippo,
were all significantly positively correlated with MCCB scores. In SZ patients,
GABA and NAA concentrations in ACC were significantly negatively correlated
with SANS scores.
Discussion
The widespread increase in the Gln/Glu ratio in SZ
suggests increased Gln synthesis in patients with SZ, but could also reflect
abnormal Gln/Glu cycling. Moreover, the negative correlation between Gln/Glu
ratios and MCCB scores suggests that Gln/Glu cycling is linked with the cognitive
function. Along with the finding of GABA and NAAG concentration decrease in SZ,
the study echoes the previous theories of SZ, which predicts the dysfunction of
both glutamatergic and GABAergic systems (5, 6).
Lactate levels were also increased in two of five of the brain regions studied,
and correlated negatively with MCCB scores, suggesting that anaerobic
glycolysis (perhaps secondary to mitochondrial dysfunction (7)) is associated
with SZ and related to cognitive performance.
Conclusion
This study revealed multiple metabolic alterations in SZ patients, most notably increased Gln/Glu ratios and increased levels of lactate. The underlying mechanisms for these changes remain to be determined. Nevertheless, 7T MRS is a valuable tool for the non-invasive investigation of brain metabolism and pathophysiology in patients with SZ.Acknowledgements
The authors acknowledge the funding support from NIH grant: R01 MH096263.References
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