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Glutamate Dysregulation in Cingulated Cortices Is Associated with Autism Spectrum Disorder Traits

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Carmen Jimenez-Espinoza, Francisco Marcano Serrano and José González-Mora

Submitted: 29 January 2024 Reviewed: 01 April 2024 Published: 18 June 2024

DOI: 10.5772/intechopen.1005336

Two Sides of the Same Coin - Glutamate in Health and Disease IntechOpen
Two Sides of the Same Coin - Glutamate in Health and Disease Edited by Kaneez Fatima-Shad

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Two Sides of the Same Coin - Glutamate in Health and Disease [Working Title]

Prof. Kaneez Fatima Shad

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Abstract

Autism spectrum disorder (ASD) is a severe developmental syndrome that arises largely as a disorder of the neural systems. Despite unclear etiology, one of the most studied causes is the increase in the excitation/inhibition relationship in the sensory and social systems which may explain certain phenotypic expressions in ASD. Glutamate (Glu) is the most important excitatory neurotransmitter in mammals, and the excessive activation of once N-methyl-D-aspartate (NMDA) receptors leads to neuronal death. Crucially, in this study, the finding of elevated Glu concentration [12.10 ± 3.92 (mM) *p = 0.02] by 1H-MRS in the anterior cingulate cortices (ACC) provides strong empirical support for increased arousal in ASD. The imbalance of Glu in cingulated cortices was correlated to dysfunction of social skills, attention switching/tolerance to change, attention to detail, communication, and imagination, (the five deficits present in ASD described in the Autism Quotient test), suggesting new therapeutic avenues.

Keywords

  • autism spectrum disorders
  • excitotoxicity
  • cingulated cortices
  • spectroscopy resonance magnetic
  • social skills
  • attention switching/tolerance to change
  • attention to detail
  • imagination
  • communication

1. Introduction

Autism spectrum disorder (ASD) is a severe developmental syndrome that arises largely as a disorder of neural systems in early childhood. Currently, the etiology remains unclear, and therapeutic options for ASD are limited; however, one of the most studied causes currently is the imbalance in the excitation/inhibition relationship in the sensory systems that can explain the extensive phenotypic variations in ASD, which are generally characterized by deficits in social reciprocity, communication, imagination, and restrictions interests and behaviors [1, 2]. Moreover, it might present secondary medical symptoms that include sleep and eating disorders, anxiety, depression, attention problems, aggressive behavior toward others or themselves, epilepsy, and gastrointestinal problems. Given this scenario, treating a person with ASD is a serious health problem for specialized medical care, not to mention the high costs they generate.

Through the different magnetic resonance techniques applied in research, many studies have been carried out to observe the involvement the glutamate (Glu) in people with ASD. However, currently, data referring to adults with ASD are scarce, given that most studies have been carried out in childhood, increasingly aggravating the lack of specific pharmacotherapies for adults. The specific pharmacotherapeutic approach in ASD is one of the main objectives of research studies, hence the need to delve deeper into the search for biomarkers that lead to the development of novel drugs for therapeutic application in the clinic. In this sense, the evidence suggests that an imbalance in glutamatergic metabolism and its products is linked to pathophysiological changes in ASD [3, 4, 5, 6, 7, 8, 9, 10, 11, 12]. The interest in the different resonance magnetic modalities highlights specific brain areas such as the involvement of the ACC, and posterior cingulate cortex (PCC) in ASD symptoms, specifically magnetic resonance spectroscopy which relates cingulated cortices dysfunction to deficits in joint attention and social skills in ASD [13, 14, 15, 16, 17, 18, 19, 20, 21]. Increasing of these, neuropathology advances [22, 23, 24, 25, 26, 27], structural MRI [28, 29, 30, 31, 32], fMRI [33, 34, 35, 36, 37, 38, 39, 40], PET [41, 42], and SPECT [43, 44], including EEG-evoked potentials [45, 46, 47, 48] indicate the cingulated cortices as one of the most affected areas of the brain responsible for the symptoms of the autism triad mainly [49].

Taking into account the plentiful evidence mentioned above, our objective is to study the role of Glu metabolism in the ACC and PCC, and its correlation with the five phenotypical characteristics as domains of interest (social, communication, imagination, attention to detail, and attention shifting/change tolerance), evaluated in the psychometric Autism Spectrum Quotient (AQ) test for adults, which we will describe later [2]. Here, we report an independent study of glutamate metabolism in the cingulated cortices in adults with ASD. A deeper follow-up investigation leads to larger when related to highlighted symptoms and/or severity within the AQ test for adults with ASD.

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2. The brain metabolism

From the physiological point of view, the metabolism of the brain is made up of a wide variety of molecules, including peptides, neurotransmitters, enzymes, etc., interacting with water. All these molecules, due to their functions and activity, can maintain the physiological balance necessary for the healthy functioning of the brain. A very controlled balance, which represents 80% of the mass of the brain and (2–3) % of the body weight that receives 15% of the blood flow at rest (quote), it is also one of the organs with greater energy demand, which consumes 20% of the oxygen and 25% of the glucose ingested by the body [50]. All this neurometabolic exchange must be rapid and effective, precisely because the brain has few energy reserves and only receives them through the cerebral vascular system, which implies a correct supply of cerebral circulation through the circle of Willis [51, 52].

There is a group of biomolecules in the brain that are detected by proton magnetic resonance spectroscopy, we refer to neurotransmitters, defined as endogenous substances that act as chemical messengers transmitting signals, and that are normally released by neurons into the synaptic space where they exert their function on other neurons or other target cells through a synapse. Although neurotransmitters are related to their overall excitatory or inhibitory activity, some neurotransmitters can exert both excitatory and inhibitory effects depending on the type of receptors that are present.

There are other molecules that can be released from the same axon terminals as neurotransmitters, and they are known as endogenous neuromodulators [53] of the central nervous system (CNS), also known as neuropeptides, which can increase, prolong, inhibit, or limit the effect of the main neurotransmitter on the postsynaptic membrane, which acts through a system of second messengers [54]. It has also been pointed out that defects in the synthesis, release, or degradation of some neurotransmitters are involved in the pathogenesis of many neurological, muscular, and psychiatric diseases [55]. Evaluating the dynamics of neurotransmitters and neuromodulators in both health and neurological diseases is a challenge, precisely due to the lack of in vivo tools to monitor them with high spatiotemporal resolution and thus have a clear understanding of their functions within the nervous system.

2.1 Glutamate

Glutamate is an amino acid that has the four basic criteria to be considered a neurotransmitter. It is one of the most important neurotransmitters in our nervous system and it has a very high concentration. Recognized for functions such as a mediator of memory formation, the management of attention, or the regulation of emotions, in addition to intervening in 80% of all synapses necessary in processes such as neuroplasticity, learning, or movement. The physiological role of glutamate and its dysfunction has gained importance in neurology and psychiatry to the extent that knowledge has deepened about its metabolism, types of receptors, transporters, and homeostasis mechanisms, whose dysfunction can lead to neuronal death.

Additionally, glutamate and aspartate are the brain’s dominant excitatory amino acids and constitute the main transmitters of pyramidal cells, the dominant neurons of the cortex where developmental changes occur capable of carrying out transient steps in the visual cortex and the hippocampus, especially during critical periods [56].

The glutamatergic system is distributed throughout the CNS, unlike other neurotransmission systems with more discrete metabolic pathways, which is why Glu is considered a general activator of the CNS [57, 58]. Under physiological conditions, endogenous Glu is one of the most abundant amino acids in our body and is the main excitatory neurotransmitter whose main purpose is to provide energy to the brain. However, exogenous Glu can be dangerous for our brain health in excessive concentrations, causing neurotoxicity. From a functional point of view, Glu acts as an “on switch” in nerve pathways and requires the neurotransmitter g-aminobutyric acid (GABA) as an “off switch” providing the necessary balance between these two neurotransmitters for at proper functioning of the CNS and essential for regulating cognition, learning, memory, and emotional behaviors. The imbalance between Glu excitation and GABA inhibition leads to hyperarousal of CNS related to ASD symptoms [59, 60].

2.1.1 Compartmentalization of glutamate in nervous system cells

One of the functional characteristics of glutamate is the establishment of compartmentalization in neurons and astrocytes. Within glutamatergic transmission, the release of gliotransmitters is an event that occurs by astrocytes through calcium waves [61, 62], where the elevation of intracellular calcium upon physiological neuronal stimulation is initiated by the activation of the mGluR5 receptor. This fact makes the difference in the vision of astrocytes as neuronal support cells to cells actively participating in neurotransmission and therefore in the processes mediated by neurotransmission [63, 64], glutamate itself being one of the gliotransmitters released [65, 66, 67].

This is an indication of the biochemical participation of glial cells in the glutamatergic system, also called the glutamate-glutamine cycle. Here, astrocytes express two key enzymes in glutamate metabolism that are not expressed in neurons and one enzyme that is not expressed in astrocytes but is expressed in neurons. One of the enzymes expressed in astrocytes but not in neurons is glutamate dehydrogenase (GD) [68], indicative of glutamate compartmentalization in the CNS. Thus, the glia-neuron glutamate-glutamine coupling mechanism demonstrates that the expression of the enzyme glutamine synthetase (GS) is glia-specific [69].

There is also another important compartmentalization establishment for Glu, we are referring to the synthesis of the neuromodulator N-acetyl-aspartyl-glutamate (NAAG), which has been the target of study in recent decades. This dipeptide is synthesized from N-acetyl-aspartate (NAA) and Glu by the enzyme NAA-synthase following the anabolic pathway, forming a reservoir of Glu that cannot be metabolized, and the NAAG produced is hydrolyzed, under the catabolic pathway by the NAAG enzyme-peptidase, which releases glutamate by activating the mGluR3 receptor, where its metabolic activity occurs by interconnecting neurons, astrocytes, and oligodendrocytes.

This dipeptide derivative of NAA and L-glutamate acts as a neurotransmitter and neuromodulator and is widely distributed in the CNS and peripheral in millimolar [mM] concentrations [70, 71]. Here, the establishment compartmentalization of Glu involves the participation of neurons, oligodendrocytes, and astrocytes where development an important metabolic activity develops known as the tri-cellular metabolism system [72]. This is the only metabolic cycle of amino acids in the brain known yet, that requires the three types of nervous cells. One of the main functions of NAAG is precisely the activation of glutamate (mGluRs) receptors that act as selective agonists of group II metabotropic receptors (mGluR, mGluR3) and involved the potential importance of metabotropic glutamate receptors, showing the neuromodulator NAAG a good candidate for elucidate the glutamatergic pathway ASD targeting. Considering that NAAG is distributed together with different neurotransmitters including Glu and GABA.

2.2 Autism spectrum quotient test (AQ)

The AQ is an instrument to measure the degree to which an adult with normal intelligence has the traits associated with the autism spectrum. Baron-Cohen and colleagues have described the adult test as a new self-assessment screening instrument to measure the degree to which an individual of normal intelligence displays autistic traits and was translated into Spanish by Betty Trabal, Editorial Amat, S.L., Barcelona, [73] with reasonable construct validity in that item that is intended to measure each one of the five domains of interest (social, communication, imagination, attention to detail, and attention shifting/change tolerance) showing moderate to high alpha coefficients.

To carry out this study, the AQ test was previously validated in an aleatory sample of adults with Spanish as their mother tongue [74], the Spanish version of the AQ has shown satisfactory levels of internal consistency and supports the use of the Spanish version of the AQ for the evaluation of ASD. In this sense, is considered a valuable instrument to quickly quantify where a given individual falls on the continuum from autism to normality.

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3. Materials and methods

To further understand the role of the glutamatergic imbalance in the cingulated cortices and their relationship with the development of ASD symptoms related to psychometric test AQ scores, we conducted a clinical study of proton magnetic resonance spectroscopy (1H-MRS) to explore the cerebral glutamate levels in ACC and PCC, and determine its correlation with the five undergo change characteristics in ASD development (social skills, attention to detail, attention shifting/tolerance of change, communication, and imagination) compared to control subjects.

3.1 Population recruitment, demographic, and behavioral evaluations

3.1.1 Participants

We recruited 61 adult participants: Nineteen subjects with ASD (3 females; mean ± age SD: 20.58 ± 0.71 years, range: 17.8–30.9 years) and 42 typical developmental (TD) control subjects (25 females; 23.2 ± 0.71 years, 18.4–31.5 years) free of psychiatric or developmental disorders participated (see Table 1). The 61 participants were further divided into four subtypes (AQ1, AQ2, AQ3, and AQ4) according to the AQ test for adult’s cause met the AQ test score cut-off criteria in all five characteristics domains. All participants with ASD were recruited through the research program through the faculty of Health Sciences. Dept. of Basic Medical Sciences of University of La Laguna (ULL), Tenerife, Spain. Potential participants were excluded if they had a comorbidity, psychiatric, or medical disorder that affects brain development (e.g., schizophrenia or psychosis), a history of head injury, or a genetic disorder associated with ASD, for example, tuberous sclerosis or fragile X syndrome [75]. The participants with ASD who suffered from anxiety or depressive disorders, gastrointestinal disorders, and muscular hypotonia were not excluded, given the high frequency of these comorbidities in ASD. In addition, based on participants’ self-report, all participants were without previous medication at the time of the examination.

Demographic characteristicsASD (n = 19) mean (S.D.)(TD) (n = 42) mean (S.D.)Statistics p value
Gender (male/female)16/316/25P = 0.016
Age (years)20.58 /0.71)23.19 (0.71)P = 0.049
AQ (0–50) points33.84 (6.36)11.67 (7.07)P < 0.0001
Social skills5.92 (2.54)1.22 (0.61)P < 0.0001
Attention switching/tolerance to change6.81 (1.41)3.49 (2.06)P < 0.0001
Attention to detail4.3 (1.91)5.03 (2.3)P < 0.0001
Communication7.5 (1.75)2.15 (1.63)P < 0.0001
Imagination5.9 (1.82)2.22 (1.59)P < 0.0001
Muscular hypotonia140P < 0.0001
Gastrointestinal disorders170P < 0.0001
Epilepsy50P < 0.0015
Familial hypothyroidism150P < 0.0001
Special education/transition to adult life60P < 0.0008
Elementary school1341P < 0.0002
Middle school641P < 0.0008
High school341P < 0.008

Table 1.

Demographic data, neuropsychological, and physical measures.

ASD (autism spectrum disorder), TD (Typical development). *p, 0.05 vs. controls; values for age and AQ are group mean ± standard deviation (range). AQ = Autism Quotient.

Informed written consent was obtained from all participants or from their legal guardians, as well as ethical approval for this study provided by the ethical standards and the Helsinki Declaration of 1964, revised in 2000 and approved by our local ethics committee. This study was approved by the Research Ethics and Animal Welfare Committee (CEIBA) (registration number: CEIBA2013–0056) of the University of La Laguna.

Additionally, participants were assessed and stratified into four subgroups according to established categories, empirically derived from Baron-Cohen & collaborators, AQ test scores, where the subgroup algorithm, which combines scores on the five domains of the AQ: Social skills, attention shifting/tolerance to change, attention to detail, communication, and imagination defined the cut-off threshold for producing reliable ASD subgroups [73]. This approach is consistent with previous publications on this sample, providing a description of participant characteristics [74].

The AQ test score for adult, included the five symptom domains and was divided into four subtypes, each with the following cutoff and meaning: AQ1 (0 to 10 points) = below average; AQ2 (11–21 points) = average values of the normal population (female mean is 15 and male mean is 17); AQ3 (22–31 points) = above average; AQ4 (32–50 points) = very high index of autistic characteristics (Asperger syndrome or high-functioning autism has an average score of 35). Due to the differences in the results of the AQ test domains, we carried out a follow-up to evaluate the neurometabolic pattern of the four subtypes (AQ1, AQ2, AQ3, and AQ4) in the studied population, according to the implication of autistic characteristics. Considering the AQ1 subtype as a typical development control group (TD).

3.2 Proton magnetic resonance spectroscopy (1H-MRS) data acquisition

Proton magnetic resonance spectroscopy (1H-MRS) is a non-invasive imaging method that provides spectroscopic information that allows us to infer the metabolic cellular activity of the individual studied. 1H-MRS data were acquired using a 3 T Signa-HD MR scanner (GE Healthcare, Waukesha, WI, USA). T2-weighted images were used for positioning the volumes of interest (VOIs). The single voxel acquisition used a spin-echo sequence recorded within the following parameters: TE = 23 ms, TR = 1070 ms, NEX = 2, flip angle = 90°, and 256 acquisitions with the point-resolved spectroscopy (PRESS) technique. During data acquisition, the same experienced neuroradiologist, blind to the clinical data, placed the voxels (2 × 2 × 2) cm3 at the ACC and PCC (See Figure 1) so careful to exclude contamination of signal from the skull and subcutaneous fat. The 1H-MRS data sets collected showed the quantification of the absolute concentrations of brain metabolites, expressed in millimoles per kilogram of wet weight, involving the correction of many factors, such as the tissue composition of the voxel (relative amounts of cerebrospinal fluid and gray and white matter), the T1 and T2 relaxation times of the metabolites in the patient, the location of the voxels and their relationship with the electromagnetic properties of the coil, and any temporary variation in the scanner [76].

Figure 1.

Locations of the volume studied in the anterior (ACC) and posterior (PCC) cingulated cortices. The single voxel acquisition used a spin-echo sequence recorded within the following parameters: Echo time (TE) = 23 ms, repetition time (TR) = 1070 ms, 2 NEX, flip angle = 90, and 256 acquisitions with the point-resolved spectroscopy (PRESS) technique. During data acquisition, the same experienced neuroradiologist blinded the clinical data to place the voxels at interesting brain areas.

3.2.1 Automatic quantitation of localized in vivo 1H spectra

LCModel is automatic (non-interactive) software version 6–1-0 (Stephen Provencher Incorporated, Oakville, Canada) [77] with no subjective input. Approximately maximum-likelihood estimates of the metabolite concentrations, phases, referencing shift, line shape, baseline, etc., and their uncertainties in the concentrations (Cramér-Rao lower bounds) are obtained [78]. The main metabolite resonances were limited for NAA, creatine and phosphocreatine, together abbreviated (Cr), choline-containing compounds phosphocholine, glycerophosphocholine, choline proper, and acetylcholine, together abbreviated (Cho), myo-inositol (mI), glutamate (Glu), glutamine (Gln), and the peak, “Glx,” was for the sum of glutamate and glutamine [79]. One possible weakness of our method is the reliance on accurate suppression of the NAAG signal in the NAA scan, particularly the NAA signal in the NAAG scan (due to the higher concentration of NAA) [80]. An intense peak at 2 ppm is generally assigned to NAA (which is responsible for the greater part of the signal), but in this work, it was assumed to correspond to NAA + NAAG. Considered also, any small N-acetyl molecules in the brain will contribute to the peak, and moreover, small contributions from other N-acetyl species (e.g., N-acetyl-glutamate) could result in overestimation of the NAA and NAAG concentrations. Notwithstanding, the paradigm used here allowed us to resolve the NAAG peak with a %SD of <20% based on the reliability indicators or lower levels of Cramér–Rao. Each spectrum was reviewed to ensure an adequate signal-to-noise ratio, as well as the absence of artifacts which allowed us to define previously which were the best times TE/TR for the spectra obtained to show the highest number of metabolites.

The different metabolites were resolved using Cr as an internal reference according to LCModel, following standard clinical practice, because it is considered the most stable metabolite in cell tissue, [78]. Nor can the ratios of a metabolite represent all possible differences, if a single specific denominator is used (as is the case for creatine), if the signals recorded in all subjects were obtained in the same scanner unit and using the same protocol. Therefore, the excellent reproducibility for each metabolite allows us to quantify their absolute concentration in the ACC and PCC (see Table 2), and consequently to observe the different alterations in ASD (see Figure 2).

Brain area [mM]ASD (n = 19) mean (S.D.)TD (n = 42) mean (S.D.)StatisticsP value
Anterior cingulate cortex
NAA + NAAG9.78 (0.49)10.44 (0.29)*p = 0.02
NAA9.37 (1.36)9.91 (0.68)n.s.
Glx(Glu+Gln)16.10 (6.87)15.19 (9.02)n.s.
Glu12.10 (3.92)10.54 (5.64)*p = 0.02
GPC+PCh2.08 (0.14)2.08 (0.13)n.s.
Cr+PCr6.98 (1.56)7.40 (1.87)n.s.
mI5.40 (0.78)5.25 (0.27)n.s.
Posterior cingulate cortex
NAA + NAAG10.80 (0.86)11.02 (0.68)n.s.
NAA10.47 (1.39)10.68 (0.20)n.s.
Glx(Glu+Gln)13.87 (4.09)14.08 (2.15)n.s.
Glu10.22 (3.19)10.71 (2.06)n.s.
GPC+PCh1.55 (0.44)1.61 (0.38)n.s.
Cr+PCr6.72 (0.90)6.99 (0.42)n.s.
mI4.98 (0.68)5.13 (1.94)n.s.

Table 2.

Absolute metabolic concentrations detected by 1H-MRS.

The different metabolites concentrations detectable in ASD vs. TD, N-acetyl-aspartate (NAA), N-acetyl-aspartate + N-acetylaspartyl-glutamate (NAA + NAAG), glutamate+ glutamine Glx = (Glu + Gln), glutamate (Glu), creatine (PCr), choline (PCG + PCh), and myo-inositol (mI). Brain areas = anterior cingulate cortex (ACC) and posterior cingulate cortex (PCC). values for metabolites’ absolute concentration are group mean ± standard deviation (range). *p < 0.05 considered significantly different, while n.s. represents non-significant results.

Figure 2.

The correlation matrix was used as a statistical technique to evaluate the relationship between two variables in metabolites’ absolute concentrations set present in ACC and PCC, represented by values for N-acetyl aspartyl-glutamate (NAA + NAAG), glutamate + glutamine (Glx = Glu + Gln), glutamate (Glu), creatine (Cr + PCr), N-acetyl-aspartate (NAA), choline (GPC + PCh), and myo-inositol (mI) in ASD (n = 19) vs. TD (42). In this sense, we can summarize a large amount of data to identify patterns. Every cell contains a correlation coefficient, where 1 is considered a strong relationship between variables, 0 is a neutral relationship, and −1 is a not strong relationship. Above, the observed metabolic pattern is evidence of differences between subjects with or without autism. (*p < 0.05) considers statistical significance.

Anteriorly, studies have also used the LCModel in Ref. to disorders in epilepsy, [81, 82, 83] multiple sclerosis, [84, 85, 86] tumors, [87, 88] Alzheimer’s disease, [89, 90] and other pathologies, [91, 92, 93, 94, 95] including the identification and quantitation of unusual metabolites, increasing the robustness of the results.

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4. Results

4.1 Demographic characteristics data

Following our objective of characterizing the glutamate dysfunctions in adult subjects with ASD by quantifying (1) glutamate levels in the anterior and posterior cingulate cortex using proton magnetic resonance spectroscopy and, (2) its correlation with the AQ test on their different domains, of characteristics of ASD compared to the TD group presented in materials and method section. The ASD group differed significantly from the TD in the total AQ , which prompted us to use this psychometric test to correlate the Glu concentrations with each domain evaluated in the test.

4.2 Cingulate cortices neuro metabolites pattern in ASD

The 1H-MRS results showed an overall increase in Glu concentration in adults with ASD, which was only observed in ACC. This suggests that functional changes in Glu concentration could reflect an adaptation to previous glutamatergic dysfunctions rather than being key to the pathophysiology of ASD (see Figure 3).

Figure 3.

Normalized data metabolic variability of the different metabolites present in ACC and PCC in the ASD group (n = 19) and the TD group (n = 42), allows bias problems to be minimized. Glutamate is significantly elevated in the ACC, revealing a dysfunction pattern in excitatory/inhibitory metabolism in ACC patients with autism. (*p < 0.05) considers statistical significance.

However, when we diversified the population using the AQ test, a pattern was observed in the variation of Glu concentration linked to the severity of autistic characteristics, evidencing the commitment of this neurotransmitter in the cingulated cortices. The compartmentalization of Glu with NAAG (see the introduction section) is also observed in ACC indicating a marked difference with PCC, which marks an important finding linked to ASD syndrome. (see Figure 4).

Figure 4.

Pattern of variability of neurometabolites present in the cingulate cortices, when the ASD and TD groups were stratified into the AQ groups (AQ1, AQ2, AQ3, and AQ4), according to autistic characteristics. Graphic representation of the metabolic differentiation pattern and symptoms’ severity between ACC and PCC. (*p < 0.05) considered significantly different.

4.3 Correlation of Glu with the autistic characteristics evaluated within the AQ

Here, interesting results were observed regarding Glu’s commitment to each one of the five domains of interest (social, communication, imagination, attention to detail, and attention shifting/tolerance to change), suggesting how much it affects TD and the ASD group. Observing a direct correlation between Glu concentration and social skills, communication, and imagination in the AQ1 group (autistic characteristics below average). In contrast to the AQ4 group (very high index of autistic characteristics), where this direct correlation was observed with social skills only. However, the AQ3 group (above average), showed a direct correlation to social skills, attention switching/tolerance of change, and communication in ACC (see Figure 5). Suggest a broad interpretation of the effect caused by Glu dysfunction on the development of autistic triad mainly characteristics.

Figure 5.

Graph of correlation pattern Pearson’s coefficient of [Glu] concentration present in ACC and PCC, and the different autistic characteristics represented by AQ2, AQ3, and AQ4 groups with the AQ1 group taken as a reference or control of healthy AQ index, which can be used as a marker of the severity of symptoms in ACC and PCC. *p < 0.05 considered significantly different.

However, the results obtained in the PCC reflect that [Glu] concentration is directly correlated only with communication in the AQ1 group. Compared to the AQ4 group, which correlates directly with attention to detail and imagination. However, the AQ3 group shows a direct correlation with social skills, imagination, attention to detail, and attention shifting/tolerance to change, highlighting the biggest compromising of Glu dysfunction in this group.

One of the biggest concerns in children with autism is the development of speech or communication. This leads us to consider the importance of Glu dysfunction in PCC for groups AQ3 and AQ4 where autistic characteristics are more exacerbated, and its correlation with the development of communication.

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5. Discussion

Herein, we presented evidence demonstrating a potential connection of ASD with glutamatergic dysfunction. We focused specifically on biochemical links, between ACC and PCC, and its functional connectivity.

The glutamatergic pathways in the brain are extensive. Glutamate is excreted into the synaptic cleft by the process of exocytosis or glutamatergic neurotransmission which involves processes of glial reuptake, presynaptic reuptake, AMPA agonism, NMDA agonism, and Kainate and Quisqualate receptor agonism.

In this 1H-MRS study, the finding of a significant increase in Glu concentration in the ACC, observed in adults with ASD, is in line with previous studies that reflect an imbalance of excitation/inhibition in the children and young brain [96, 97, 98], and support the hypothesis of excitatory/inhibitory imbalance in ASD.

The excitotoxicity induced by an increase in the level of Glu in the brain can have pathological consequences, due to the deregulation of intracellular Ca2+ concentrations. This delicate balance between the mechanisms that allow the entry of physiological Ca2+ and those that can limit the intracellular excess to avoid neurodegenerative processes are the ones that determine the loss of neuronal viability [99]. This statement allows us to conjecture, that this significant increase in glutamate levels in the ACC would be a possible cause of disorder within the autism spectrum, either due to deregulation of intracellular Ca2+ and/or, due to neurotoxicity due to excessive activation of NMDA receptors. Since they are the most permeable to calcium Ca2+, and act cooperatively with AMPA-kainate-type receptors, fundamentally permeable to sodium [100].

Other studies have found atypical undergrowth of auditory and visual networks which was associated with the severity of autistic core socio-communication symptoms, that of the visual network was correlated with the severity of restricted and repetitive behaviors in ASD adults [101].

In this sense, in deregulation due to excess glutamate, there is a risk of losing cognitive capacity and even cell death, even in adults. Therefore, due to the role that glutamate has in various neurodegenerative pathologies, it results in an important – although also complex – pharmacological target. In this study, it was observed how an excess of glutamate in the cingulate cortex intervenes in the cognitive development of adult subjects with autism from childhood, when comparing them with neurotypical subjects. From the cognitive impairment that affects the functioning of functions such as attention, imagination, communication, and detail, as well as tolerance to change, to the main and most studied to date in the autistic condition - social skills - as seen in Figure 5.

An important milestone is the finding presented here of the metabolic deregulation of Glu, reported in the cingulate cortices, that justifies the hypofunction of the principal networks: The salience network (SAN), the default network (DMN), and the frontotemporal visual networks; as well as the motor skills which confirm the functional and neurochemical differences between ACC and PCC in subjects with ASD [102]. In addition to this, the dendritic complexity of the PCC is much lower than that present in the ACC, as other authors have shown [103], which would explain the metabolic differences between both regions in ASD and that are balanced in neurotypical subjects.

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6. Conclusions

  • The effects of excess glutamate in the ACC in subjects with ASD are directly correlated with the three central characteristics within the autism spectrum.

  • The study of Glu receptors is ambitious in allowing greater knowledge of the functioning of the nervous system in people with ASD, which will open the doors for the development of more effective therapeutic strategies based on the regulation of glutamatergic neurotransmission.

  • Currently, numerous NMDA receptor antagonists have been synthesized to reduce the entry of cal2+ through the targets of the AMPA-Kainate and NMDA receptors and thus reduce the excitotoxic effect of glutamate in the brain, but their clinical development is limited and has been hampered by the appearance of important adverse effects that would make its implementation in therapeutics difficult.

  • Not surprisingly, drugs developed through animal models for ASD have not had enough success in human trials to justify their use in the clinic, and therefore there may be little enthusiasm for investing in Glu-regulating drugs as a possible therapeutic approach. Perhaps it is time to consider the investments that will bring this knowledge to the clinic.

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Acknowledgments

Mainly we want to thank the Roviralta Foundation (Barcelona, Spain) for financing this pioneering project in the Canary Islands. As well as all the people who voluntarily participated in this study, technical and medical staff from the Canary Islands University Hospital (HUC), and the Magnetic Resonance Service for Biomedical Research (SEGAI). To the associations (APANATE, ASPERTEN, and ALDIS) that represent and serve the population with ASD in Tenerife, Canary Islands, Spain; for giving me your trust and support and for making all the years of effort worth it. I would like to add it under acknowledgments as funding (“Examination of GABA/glutamate neurotransmitters and functional connectivity in cognitive inhibition tasks. PID2021-126172NB-I00”).

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Conflict of interest

The authors declare no conflict of interest.

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Written By

Carmen Jimenez-Espinoza, Francisco Marcano Serrano and José González-Mora

Submitted: 29 January 2024 Reviewed: 01 April 2024 Published: 18 June 2024

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