An Imbalance in the Autism Spectrum Disorder Brain?


  • A balance between excitation and inhibition (E/I) in the brain is necessary for optimal neural signal formation, synchrony, and transmission, which as a result supports information processing driving both simple and complex behaviours.
  • Recent theories propose that the neurodevelopmental consequences displayed by autistic individuals can be ascribed to the imbalance between excitation and inhibition (E/I) in the brain.
  • There is a proposed theory that an E/I imbalance in ASD is caused by increased excitation and/or decreased inhibition.
  • This imbalance may be due to how the inhibitory neurotransmitter GABA is used or how it interacts with other neurotransmitters.
  • Some potential consequences of E/I imbalance and increased noise in the ASD brain are a higher incidence of epilepsy, and atypically large fluctuations in neural responses, which may create unstable and unpredictable perceptions of the environment.
  • High neural noise may also produce some benefits in the ASD brain (covered in future NeuroBlog posts)!

The Diagnostic and Statistical Manual of Mental Disorders (DSM-5) describes Autism Spectrum Disorder (ASD) as a developmental disorder that is generally characterised by specific behavioural symptoms that include social communication difficulties, abnormal sensory sensitivities, and repetitive behaviours. Recent theories propose that the deficits displayed by autistic individuals can be ascribed to the imbalance between excitation and inhibition (E/I) in the brain (Foss-Feig et al., 2017). But what does E/I balance mean?

For neurons (and circuits) E/I balance is important for circuit function and stability (He & Cline, 2019). More specifically, E/I balance is necessary for optimal neural signal formation, synchrony, and transmission, which as a result supports information processing driving both simple and complex behaviours (Foss-Feig et al., 2017). The E/I balance is predominantly driven by two neurotransmitter inputs: glutamate and gamma-aminobutyric acid (or GABA) respectively. Glutamate is the most abundant excitatory neurotransmitter in the nervous system (neurotransmitters are chemical messengers that transmit signals between neurons). GABA is the primary inhibitory neurotransmitter in the brain, that is also the second most prevalent neurotransmitter in the nervous system, and is synthesised from glutamate (Gazzaniga et al., 2018). Without excitation (glutamate) neurons would not fire, and without inhibition (GABA) the brain would become epileptogenic – at risk for epilepsy (Foss-Feig., 2017). We need excitation and inhibition for optimal functioning, and an imbalance between the two may result in many neurodevelopmental disorders, such as Autism, which will be the focus of this blog post.

E/I Imbalance in ASD

In ASD, the assumption is that the E/I imbalance is caused due to increased excitation and/or decreased inhibition. For instance, Robertson et al. (2016) employed a binocular rivalry task to test this hypothesis. A binocular rivalry task is a task where two separate images are presented to each eye and therefore, the two eyes compete for dominance/control for perception. During this task, we perceive one image and then switch to the other after a few seconds. The neural inputs in each eye appear to compete for dominance, seemingly warranting competition between excitation and inhibition in the visual cortex (Gazzaniga et al., 2018). Robertson et al. (2016) found that autistic individuals showed a slower rate of switching between the two images when compared to neurotypical individuals. To further test whether autistic individuals’ performance on the binocular rivalry task was linked to the reduced action of excitatory (glutamate) or inhibitory (GABA) neurotransmitters in the brain, the authors employed magnetic resonance spectroscopy (MRS) to measure the concentrations of these two neurotransmitters in the visual cortex. Note: MRS is a tool that is used to measure the chemical compositions of tissues.

From the MRS, the authors found that the control group showed a correlation between GABA concentration in the visual cortex and the rate of switching. This relationship was absent in the autistic group. The researchers did not find this difference between the groups for glutamate. In the image below, Image A illustrates a voxel (a 3D version of a pixel) from the visual cortex. Image B depicts GABA and glutamate levels strongly predicting perceptual suppression during binocular rivalry, the proportion of each trial spent viewing the dominant percept (Robertson et al., 2016). The lack of correlation between perception and GABA levels may be linked to the lower switching rates in the group (Image B top right corner).

This image is taken from Robertson et al. (2016)

Robertson et al. (2016) suggested that it may not be the amount of neurotransmitter that is abnormal in the autistic group, but perhaps how the neurotransmitter is used or how it interacts with other neurotransmitters. Edmondson et al. (2020) also found similar results where the amount of GABA did not differ between the ASD and control group. Interestingly, the authors showed a correlation where greater GABA concentrations in a specific region, such as the visual cortex, were related to more efficient searches in visual search experiments. Moreover, lower GABA levels in the visual cortex were associated with increased social impairments.

Recently, Kolodny et al. (2020) published a paper where they used MRS to quantify signals associated with GABA and glutamate in multiple regions in the sensory and somatosensory cortex in autistic and typically developed individuals. These authors found that the levels of GABA and Glx (glutamate, glutamine, and glutathione) in sensory and sensory motor cortex was comparable in ASD and typically developed individuals, suggesting no differences between the groups. Also, the authors observed a correlation between GABA levels and self-reported sensory atypicalities, but this was subjected to only one brain region (left MT+). Further research is required to validate this finding.

Although this research showed contradictory results, the E/I imbalance hypothesis cannot be easily discredited, due to the copious amount of research evidence supporting this hypothesis.

Regions of interest measured in Kolodny et al. (2020) . This image is taken from Kolodny et al. (2020)

What are the consequences of E/I imbalance in ASD?

A sequalae of E/I imbalance is elevated levels of internal/neural noise in the nervous system. Moreover, a ‘noisy’ (hyperexcitable) cortex is unstable and is susceptible to epilepsy (Rubenstein & Merzenich, 2003). This could also explain why 30% of autistic individuals develop clinically apparent seizures (Rubenstein & Merzenich, 2003). High neural noise in the ASD brain is also linked to atypically large fluctuations in neural responses (Park et al., 2017). This excessive neural variability is associated with the core social, sensory, and repetitive behaviour symptoms that define autism. Dinstein et al. (2015) speculated that unreliable neural responses in multiple sensory and associative brain areas during early development in autistic individuals may create an unstable and unpredictable perception of the environment. As a result, individuals with autism may struggle to learn the correct probabilities and statistics of the external events and, hence indicating difficulties in predicting their environment (Dinstein et al., 2015). This is more prominent in social situations where humans (unlike objects) display a wide range of social and emotional cues, which need to be perceived using multiple sensory modalities. Under these circumstances, an autistic infant may be motivated to retract from social interactions and engage in repetitive behaviours that are more likely to produce predictable outcomes (Dinstein et al., 2015).

Dinstein et al. (2015) used the idea of neural variability theory to explain the fundamental functional differences between neurodivergent and neutotypical brains. However, the idea of high neural noise in the autistic brain can also explain symptomology in ASD, and I will cover that in the near future. Moreover, high neural noise in ASD can also be used to explain superior performance by autistic individuals in certain tasks when compared to typically developed individuals, which is actually the focus of my research, and I will be talking about this on the NeuroBlog as well!


Dinstein, I., Heeger, D. J., & Behrmann, M. (2015). Neural variability: friend or foe?. Trends in cognitive sciences19(6), 322-328.

Edmondson, D. A., Xia, P., McNally Keehn, R., Dydak, U., & Keehn, B. (2020). A magnetic resonance spectroscopy study of superior visual search abilities in children with autism spectrum disorder. Autism Research, 13(4), 550-562.

Foss-Feig, J. H., Adkinson, B. D., Ji, J. L., Yang, G., Srihari, V. H., McPartland, J. C., … & Anticevic, A. (2017). Searching for cross-diagnostic convergence: neural mechanisms governing excitation and inhibition balance in schizophrenia and autism spectrum disorders. Biological psychiatry81(10), 848-861.

Gazzaniga, M., Ivry, R. B., & Mangun, G. R. (2018). Cognitive Neuroscience: Fifth International Student Edition. W.W. Norton.

Kolodny, T., Schallmo, M. P., Gerdts, J., Edden, R. A., Bernier, R. A., & Murray, S. O. (2020). Concentrations of cortical GABA and glutamate in young adults with autism spectrum disorder. Autism Research13(7), 1111-1129.

Pankevich, D. E., Davis, M., & Altevogt, B. M. (2011). Glutamate-related biomarkers in drug development for disorders of the nervous system. The National Academies Press, Washington.

Park, W. J., Schauder, K. B., Zhang, R., Bennetto, L., & Tadin, D. (2017). High internal noise and poor external noise filtering characterize perception in autism spectrum disorder. Scientific reports, 7(1), 1-12.

Robertson, C. E., Ratai, E. M., & Kanwisher, N. (2016). Reduced GABAergic action in the autistic brain. Current Biology26(1), 80-85.

Rubenstein, J. L. R., & Merzenich, M. M. (2003). Model of autism: increased ratio of excitation/inhibition in key neural systems. Genes, Brain and Behavior, 2(5), 255-267.

Featured image credit Lightspring/Shutterstock

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