Poster No:
349
Submission Type:
Abstract Submission
Authors:
Maria Baida1, Sana Vaziri1, Carly Demopoulos1, Yan Li1
Institutions:
1University of California San Francisco, San Francisco, CA
First Author:
Maria Baida
University of California San Francisco
San Francisco, CA
Co-Author(s):
Sana Vaziri
University of California San Francisco
San Francisco, CA
Yan Li
University of California San Francisco
San Francisco, CA
Introduction:
Autism spectrum disorder (ASD) affects social interaction and communication, with one neurobiological theory suggesting an imbalance in excitatory/inhibitory (E/I) neurotransmission (1). Glutamate (excitatory) and γ-aminobutyric acid (GABA, inhibitory) can be quantified using in vivo proton magnetic resonance spectroscopy (¹H MRS). Prior studies on these metabolites varied among different brain regions (2,3,4,5,6). In this study, we used an automated spectral prescription method to investigate neurotransmitters and their ratios in the auditory cortex in children with ASD versus typically developing controls (TDC) and explored their associations with age and speech measures like articulation, rapid naming, and motor function to understand the role of neurochemistry in ASD speech and language profiles.
Methods:
The study included 76 children with ASD-27F/49M, Age (Mean=12.2, STD=2.7) and 34 TDC-18F/16M, Age (Mean=11.97, STD==2.32). The characteristics of study population are illustrated in Figure 1. Speech measures included age scaled scores for the Goldman-Fristoe Test of Articulation Sounds-in-words subtest (articulation), NEPSY-II Inhibition Naming Time (rapid naming), and Diadochokinetic Period (DDK) for repetitions of "Puh-Tuh-Kuh" (speech motor function) administered according to Oral Speech Motor Screening Exam-3rd Edition procedures.MR data was acquired using a 32-channel coil on a 3T MR scanner (GE Healthcare). GABA-edited BASING-PRESS MRS was performed using TE/TR = 68/2000 ms, and voxel size of 2.5x2.5x2.5 cm in the left and right auditory cortex (AUD) using atlas-based automatic prescription (7,8). Spectral data were processed with Osprey (9). An example of voxel placement for the left auditory cortex and spectra data is shown in Figure 2a. GABA+ and Glx (glutathione+glutamate+glutamine) concentrations were quantified using LCModel after tissue corrections on T1 images. Comparisons within and across groups were performed using GLM with age as a covariate, and group differences were examined with age-adjusted linear regression on the concentrations of GABA, Glx, and GABA/Glx.
Results:
There was no significant difference in age or sex between the ASD and TDC groups. Significant differences were found in speech motor function, rapid naming, and GFTA-3, as illustrated in Figure 1. Within the TDC, higher age was significantly associated with lower GABA/Glx in L Aud (coef=-0.006, p=0.006). No significant differences were found in metabolite parameters in the left auditory or right auditory cortex between ASD and TDC after controlling for age; however, when ASD participants with impaired rapid naming (scaled score <6) were compared to TDC participants, the TDC participants had significantly higher GABA concentrations in left auditory cortex, t(45)=-1.952, p=.029. Among the subjects with ASD, better rapid naming was associated with higher GABA in L AUD (coef=2.3, p=0.004), higher Glx in L AUD (coef=0.6, p=0.03), and higher GABA/Glx in both L (coef=29.9, p=0.047) and R AUD (coef=33.9, p=0.04). A scatterplot illustrating the association between better rapid naming and higher GABA levels in L AUD is shown in Figure 2b.
Conclusions:
Unlike previous studies reporting lower GABA levels in individuals with ASD (4,5,6), we did not find overall group differences in neurotransmitter concentrations; however, the ASD participants with impaired rapid naming performance did present with significantly lower GABA concentrations in left auditory cortex. Indeed, in the ASD group as a whole, these left lateralized higher concentrations of GABA were associated with better rapid naming. These are consistent with prior work associating auditory system dysfunction with speech and language impairment in ASD.
Disorders of the Nervous System:
Neurodevelopmental/ Early Life (eg. ADHD, autism) 1
Language:
Language Comprehension and Semantics
Speech Perception
Speech Production
Novel Imaging Acquisition Methods:
MR Spectroscopy 2
Keywords:
Autism
GABA
Glutamate
Language
MR SPECTROSCOPY
Neurotransmitter
1|2Indicates the priority used for review
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Was this research conducted in the United States?
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Are you Internal Review Board (IRB) certified?
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Were any human subjects research approved by the relevant Institutional Review Board or ethics panel?
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Please indicate which methods were used in your research:
Neuropsychological testing
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MR Spectroscopy
For human MRI, what field strength scanner do you use?
3.0T
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Osprey
Provide references using APA citation style.
1. Rubenstein, J. L., & Merzenich, M. M. (2003). Model of autism: Increased ratio of excitation/inhibition in key neural systems. Genes, Brain and Behavior, 2(5), 255–267.
2. Harada, M., Taki, M. M., & Nose, A. (2011). Magnetic resonance spectroscopy studies of glutamate and GABA in autism. NeuroReport, 22(9), 430–435.
3. Port, R. G., et al. (2017). Exploring the relationship between cortical GABA concentrations, auditory gamma-band responses and development in ASD: Evidence for an altered maturational trajectory in ASD. Autism Research, 10(4), 593–607.
4. Gaetz, W., et al. (2014). GABA estimation in the brains of children on the autism spectrum: Measurement precision and regional cortical variation. NeuroImage, 86, 1–9.
5. Rojas, D. C., Becker, K. M., & Wilson, L. B. (2015). Magnetic resonance spectroscopy studies of glutamate and GABA in autism: Implications for excitation-inhibition imbalance theory. Current Developmental Disorders Reports, 2(1), 46–57.
6. Rojas, D. C., Singel, D., Steinmetz, S., Hepburn, S., & Brown, M. S. (2014). Decreased left perisylvian GABA concentration in children with autism and unaffected siblings. NeuroImage, 86, 28–34.
7. Provencher, S. W. (2001). Automatic quantitation of localized in vivo 1H spectra with LCModel. NMR in Biomedicine, 14(4), 260–264.
8. Mullins, P. G., McGonigle, D. J., O'Gorman, R. L., Puts, N. A., Vidyasagar, R., Evans, C. J., & Edden, R. A. E. (2014). Current practice in the use of MEGA-PRESS spectroscopy for the detection of GABA. NeuroImage, 86, 43–52.
9. Oeltzschner, G., Zöllner, H. J., Hui, S. C. N., Mikkelsen, M., Saleh, M. G., Tapper, S., & Near, J. (2020). Osprey: Open-source processing, reconstruction & estimation of magnetic resonance spectroscopy data. NeuroImage, 211, 116889.
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