Background

This paper attempts to review the endophenotype concept and its application in understanding the etiopathogenesis of Autism spectrum disorders (ASD). Therefore, we first review the endophenotype concept and various endophenotypes in ASD. We then present a brief overview of the predominant neuroanatomical endophenotypes in ASD with evidence from neuroimaging and discuss the potential relevance of the neural endophenotypes to ASD.

Measurable biological (physiological, biochemical, and anatomical features), behavioral (psychometric pattern), or cognitive markers are found more often in individuals with a disease than in the general population. Phenotype not visible from outside (internal phenotype) which is detectable using biochemical tests or microscopic examination is termed an endophenotype [1]. Endophenotype must fulfill the following criteria, (a) it should be associated with a disease of interest in the general population, (b) it should be heritable, and (c) trait dependent which means it should be detected in high-risk individuals irrespective of whether he or she is in an acute state of illness, (d) co-segregation of illness and endophenotypes in families and (e) Reported in both affected and nonaffected family members more than the general population [2]. Autism endophenotype is grouped under seven categories: neuroanatomical, neurophysiological, biochemical, morphological, immunological, hormonal, and behavioral [3]. Most psychiatric disorders are polygenic and multifactorial in origin which limits, an in-depth understanding of the pathophysiology of the illness. Identification of endophenotypes may be a more compelling approach to understanding ASD, their intermediate position between genotype and behavior may help researchers in classifying autistic patients based on the underlying pathophysiology [4]. However, the endophenotypes are not specific to ASD and can be reported in other developmental disorders [5, 6].

ASD despite heterogeneity in clinical presentation, share two essential features: (a) deficit in social interaction and communication and (b) behavioral abnormalities in the form of stereotypies (repetitive movements), restricted interests, and inability to adapt to a change [7].

Various factors contribute to the heterogeneity in ASD, which include a high incidence of comorbid medical (sleep disorders, gastrointestinal dysfunction, autoimmune disorders), neurological (seizures), and psychiatric conditions (ADHD, anxiety disorders). Furthermore, intellectual ability is another contributor to the heterogeneity leading to the concept of high-functioning and low-functioning ASD. There is no evidence to suggest the common co-occurrences of specific disorders are an independent phenotype of ASD or true comorbidity [8].

The estimated heritability for ASD is 80–90% [9, 10]. Evidence from twin pair studies reported 60% concordance in monozygotic (MZ) twins and 0% in dizygotic (DZ) twins for autism and 92% of MZ twins were concordant for broad-spectrum autism-related cognitive and social abnormalities compared to 10% DZ twins [11]. The concordance rate in siblings varies between 3 and 14% [12,13,14,15]. Moreover, 10–20% of individuals with known genetic disorders (Fragile X syndrome, Rett’s syndrome) may present with autistic features which further supports the genetic liability of ASD [16, 17]. Linkage analysis and candidate gene analysis found mutations in genes that are very important for brain development. These include genes important for synaptogenesis and pruning (such as SHANK, neurexin, and neuroligin families) others that regulate growth (RELIN, HOXA1, and PTEN), or are involved in other aspects of brain development such as signaling pathways. These genetic factors are likely to play an important role in the development of the brain in ASD [18].

The current theoretical approach to autism includes executive dysfunction, weak central coherence (difficulties integrating information into meaningful wholes), and impaired ability to understand mental states (theory of mind) [19]. The social impairment in ASD is related to misinterpretation of social signals. Proximity to others can trigger autonomic symptoms that helps maintains distance between themselves and others exhibiting the critical role of social signal interpretation in social interaction [20]. Abnormal social behavior in children with ASD is associated with a set of brain regions constituting the so-called “social brain”. The social brain includes the superior temporal sulcus (STS) and fusiform gyrus that recognize facial expression and understands the eye gaze, amygdala and ventromedial prefrontal cortex (vmPFC) that are involved in processing the emotional significance of social stimuli, temporoparietal junction (TPJ) and mPFC that infers goals, intentions, and desires of other people. From a perspective of cognitive functions, abnormalities of the PFC (required for attentional control) may lead to deficits in both social and non-social behavior. This dysfunction in attentional control may lead to a lack of learning of social behaviors and interactions. Evidence showing structural and/or functional abnormalities in these regions in individuals with ASD may underlie behavioral and social problems in ASD [21]. See Table 1, correlating the symptoms of ASD with atypicality in specific brain areas).

Table 1 Evidence of structural brain alterations (MRI) in children with ASD along with associated symptoms
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Methods

Search strategy

The evidence was researched at the following electronic databases: Pubmed, PsycINFO, Scopus, Web of Science, and EMBASE. The following descriptors registered in Mesh were considered Autism spectrum disorders[MeSH]OR Autism AND neuroimaging OR “Brain Mapping” [MeSH Terms]OR “Magnetic Resonance Imaging” [MeSH] OR Diffusion tensor imaging.

Result

To understand the neuroimaging changes in ASD, it is imperative to recognize the normal brain development and brain development in children with ASD.

Normal brain development

Brain development is a dynamic interplay between genes and environmental factors. Although brain development starts in utero synaptogenesis and myelination peak in toddlerhood forming early neural circuits and genetically programmed pruning continues in childhood and adolescence [22, 23]. The brain attains 80% of its size at 2 years and adult size at 5 years, followed by a reduction in grey matter GM after 12 years and an increase in white matter (WM) during childhood and adolescence. There is variation in the maturation of cortical area with primary cortices maturing earlier than the association cortex. The cortical centers for higher cognitive functions such as the prefrontal cortex mature at a later time [24, 25].

Brain development in Autistic spectrum disorders

Evidence from the last three and a half decades has proved that the typical course of brain development is altered in children with ASD, which starts prenatally [26, 27] and extends into adulthood [28, 29]. Even though the brain size is normal or smaller at birth compared to typically developing (TD) children [30], there is accelerated growth starting at 6 months and extending into toddlerhood [31,32,33,34] followed by a slowing in brain growth by school-age and stabilization. The early abnormalities include abnormal brain growth patterns and aberrant neural architecture and connectivity [28]. Sometimes, there may be a normalization of brain size during the late teenage called pseudo-normalization [35]. Disturbances in the development of neural circuitry were typically noted in frontal, temporal, and cerebellar regions contributing to the early clinical features of ASD [29, 35, 36]. Given the fact that there is accelerated brain growth in infants and toddlers with ASD [37, 38], macrocephaly is one of the most replicated findings reported in both children with ASD and their family members [32, 39,40,41,42].

Neuroimaging in autism

Remarkable advances in neuroimaging techniques have revealed typical and atypical neurodevelopmental abnormalities. Since ASD has a strong genetic underpinning, neuroimaging may help identify neuro-endophenotypes. Any consistent structural or functional neuroimaging findings may presumably represent neuro-endophenotype. Structural neuroimaging addresses the neuroanatomical changes such as the size of cerebral structures, whereas functional imaging detects the abnormalities in the functioning of the brain. MRI has proven its ubiquity in identifying both neuroanatomical and neurophysiological endophenotypes. Both structural and functional imaging using MRI has proven to be safe in children owing to their non-invasive nature. Nevertheless, neuroimaging in children has inherent limitations such as (a) difficulty in training children to stay still during the procedure, (b) reliability of the findings depending on image resolution, and (c) findings may not be generalizable as most subjects involved are high functioning children as it is not easy to train low functioning autistic children. However, functional near-infrared spectroscopy (fNIRS) is a relatively new technique that allows the measurement to be taken in any body position while retaining good temporal resolution making it an easier technique to be used for children [43]. The following is a brief overview of some consistent neuroimaging changes which may fit the criteria for neuro-endophenotype.

Structural neuroimaging–structural MRI

Structural MRI (sMRI) studies have been instrumental in finding the neuroanatomical changes and their progression concerning brain development.

Total brain volumes

The estimation of total brain volume (TBV) and volumes of specific brain areas have been done using structural MRI (sMRI). Voxel (volumetric pixel)-based morphometry (VBM) has been instrumental in measuring cortical thickness, surface area, grey matter volume (GMV), and white matter volume (WMV). Evidence suggests an increase in TBV including both GMV and WMV in children with ASD when compared to TD subjects [37, 44]. Mean cortical volumes (both GM and WM) of frontal and temporal lobes were found to be increased with no effect on parietal and occipital lobes [45, 46]. Other regional variations include enlarged dorsolateral prefrontal cortex and medial frontal regions with limited change in the precentral cortex [47]. Dissociation between volumes of the cerebrum and the subcortical structures was noted in some studies. For example, a study of 15-year-old autistic children compared to TD subjects reported a relatively smaller cerebral cortex, the hippocampus–amygdala when compared to larger cerebral white matter volumes [48]. Lower GMV was noted especially in right inferior occipital gyrus, left fusiform gyrus, right middle temporal gyrus, bilateral amygdala, right inferior frontal gyrus, right orbitofrontal cortex, and left dorsomedial prefrontal. Reduced GMV and white matter connectivity in these areas of social cognition represents the neural underpinnings of behavioral and social malfunctioning in ASD [49,50,51,52,53,54] (see Table 1).

Amygdala

The amygdala plays an important role in attachments, social behavior, and understanding the recognizing emotions making it an important area of research in ASD. Inconsistency in amygdala volume has been reported across studies. Some studies have found decreased volumes [55,56,57,58,59,60], while others have found increased volumes [56, 57] in children and not adolescents [57], and some studies did not find differences [58] (see Table 1).

Hippocampus

The hippocampus plays an important role in learning and memory. This could be a key area of research about the difficulty in learning encountered in ASD. Although postmortem studies have found the abnormal size of neurons in the hippocampus [59], MRI studies have shown inconsistent results. Few studies have reported a decrease in the volume of the hippocampus [55,56,57,58,59,60], while others have reported an increase in volume [61] or no significant difference [62]. However, the smaller volumes of the amygdala and hippocampus are normalized by adolescents and adulthood [60] (see Table 1).

Basal ganglia

Basal ganglia play a major role in cognition and modulation of movements via the cortico-striatal–thalamic loops and limbic circuits [63]. It is a key area of research about the repetitive behavior encountered in ASD [63, 64]. Increased volume of caudate has been the most consistent finding which was proportionately related to an increase in TBV [65,66,67,68,69]. However, changes in other basal ganglia structures such as globus palladium and putamen have not been reported [70]. Abnormal shapes of basal ganglia have been reported in boys which correlated with motor and social and communicative defects in ASD [71] (see Table 1).

Cerebellum

Cerebellum has dense interconnections with cortical regions important for cognition, language, executive and socioemotional functions [72]. VBM of ASD subjects has found increased total volumes of the cerebellum which correlated with TBV [73]. However, hypoplasia of posterior vermis [74,75,76,77,78] and reduced SA of vermal lobules (VI and VII) [75, 79, 80] were noted in a few studies. Decreases in GM in right Crus I, lobule VIII, and lobule IX of the cerebellum was reported in a meta-analysis of various VBM studies [81,82,83]. Others reported decreased GM in the left crus and an overall increase in GM of the cerebellum [81, 82] (see Table 1).

Corpus callosum

Corpus callosum (CC) is the thick white matter tract that connects the right and left hemisphere and helps in interhemispheric communication [83]. Although increased CC volume was reported in ASD children when compared to TD subjects [84], a more consistent finding was decreased CC size in youth with ASD compared to controls [85, 86]. Table 1 enumerates the various changes in the brain as evident from MRI and its correlation with the symptoms of ASD.

White matter abnormalities

Literature suggests abnormalities in genetically programmed synaptogenesis, myelination, and pruning in children with ASD, thus compromising WM integrity [87]. Diffusion tensor imaging (DTI) is a technique of MRI which is utilized to detect changes in white matter tracts. Measures such as fractional anisotropy (FA) and mean diffusion (MD) are used to assess the directionality and diffusion of water, respectively, in a particular area of interest or voxel [88]. Therefore, DTI is used to detect structural white matter changes in ASD [89]. Ample literature suggests reduced FA indicating increased WM abnormalities, especially in the frontal and temporal lobe areas [90,91,92,93,94,95]. DTI studies found that there are more abnormalities in the long tract when compared to short tracts [95]. Abnormalities of FA and MD wad reported in right inferior, superior longitudinal fasciculi [96], and bilateral anterior thalamic radiation [96, 97]. Uncinate fasciculus, arcuate fasciculus, inferior fronto-occipital fasciculus and corpus callosum [98,99,100]. Evidence from the last three decades has shown left-hemispheric abnormalities and atypical hemispheric dominance in children with ASD [101,102,103]. Two approaches to DTI, namely, whole-brain voxel-based analysis (VBA) and the region of interest (ROI) approach, reported less overlap of such findings using the same data set. However, overall results obtained by two methods reported lesser overall brain connectivity in both long and short-range connections in ASD subjects, while corpus callosum and ventral tracts turned out to be the main connections affected [104]. Few studies revealed abnormalities in FA and MD in age-matched unaffected siblings of children with ASD as compared to TD, which represent impairment in white matter integrity suggesting that it may be a potential endophenotype [105, 106]. However, unaffected siblings exhibited less severe and fewer affected tracts when compared to children with autism [106].

Functional imaging—fMRI

Functional MRI (fMRI) is instrumental in studying the functionality of neural circuits and interregional, cortico-cortical, and cortico-subcortical connectivity. ASD is considered a disconnection syndrome due to aberrant, atypical, and decreased functional connectivity as evidenced through fMRI studies. fMRI is primordial in finding the correlation of the neurocognitive abnormalities of ASD with anatomical and functional connectivity.

Task-dependent fMRI measures the region-specific deficit based on the neurocognitive task assigned to the subject. A range of region-specific deficits can be found with various tasks such as face processing, emotional processing, attention, language, imitation, and sensory and motor processing [107]. fMRI has been instrumental in studying the core features of ASD such as social cognition, language, and repetitive behavior. However, few studies reported impaired amygdala modulation rather than just hypoactivation in a social context [108].

Fusiform gyrus has been implicated in visual processing, particularly information related to faces [109, 110] and bodies [111, 112]. Possibly the most replicated finding is the hypoactivation of fusiform gyrus to facial expression [109,110,111,112,113]. Studies have reported hypoactivation of other areas involved in facial processing such as the amygdala and superior temporal sulcus [114]. The neurological substrate for imitation, the mirror neuron system (MNS) is dysfunctional in ASD leading to impaired imitation and contributing to communication deficit. A meta-analysis of 24 experimental studies reported hyperactivation of MNS in ASD, which might underlie the imitation impairments in these individuals [115] (see Table 2—summary of fMRI changes in autism [116,117,118,119,120,121]).

Table 2 Summary of fMRI changes in ASD
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fNIRS is a non-invasive neuroimaging technology that maps the functioning of the cerebral cortex by measuring hemodynamics via absorption of light. Different light absorption rates of oxy-Hb and deoxy-Hb allow the mapping of the activated and deactivated areas. In comparison with fMRI, fNIRS is low cost, portable, and tolerant to motion artifacts and electromagnetic noise [122]. fNIRS has shown abnormalities in fPFC, prefrontal cortex, TC: temporal cortex; STS, superior temporal sulcus; pSTS: posterior superior temporal sulcus; AC, auditory cortex [123,124,125,126,127,128,129,130] (see Table 3—summary of fNIRS changes in ASD).

Table 3 Summary of fNIRS changes in autistic spectrum disorders
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Discussion

As mentioned above, structural and functional neuroimaging studies have shown various changes in the brain in ASD which may be translated into endophenotypes. Regrettably, many of these findings have not been consistent. Therefore, translating these findings into neural endophenotype is by far an attempt in its budding stage. Aspiring to grow such a concept may require combined efforts of ASD researchers focused on different aspects of basic, genetic, neuroimaging, and clinical research [131]. To uncover the endophenotype rooted in the genetic program of a child, neuroimaging studies shall be matched with genetic studies to bridge the gap. Such endophenotype-oriented neuroimaging research will be a huge contribution. To correlate the genetic basis to an observed endophenotype, genetic studies shall be paired with neuroimaging findings observed in syndromic ASD when compared with “idiopathic” autism or in a non-autistic population. The most compelling area of investigation is to elucidate whether the comorbid conditions in ASD are true comorbidity or a distinct endophenotype of ASD. ADHD and ASD are commonly occurring comorbid conditions with genetic overlap [132]. However, it remains to be clarified whether these co-occurring conditions are separate neural endophenotypes of ASD. Similarly, 40–50% of children with ASD have comorbid anxiety disorder treated with psychotropics [133]. Certainly, some anxiety can be considered a part of ADS core symptoms such as a preference for sameness, although it remains unclear as to when Obsessive–compulsive symptoms should be considered beyond mere repetitive behavior of ASD. Subtyping ASD phenotypes with comorbid conditions using neuroimaging (for example, studying limbic system connectivity in case of anxiety disorders) may help in the development of more targeted and effective treatment strategies. Furthermore, subtyping ASD based upon neuro-endophenotypes may help find pre-treatment abnormalities, develop more personalized and effective treatment and monitor psychotropic response. One such approach would be to highlight the mitochondrial dysfunction as a cause for most psychiatric disorders. The prevalence of mitochondrial diseases is higher in the population of ASD than in general population and up to a half of children with ASD showed evidence of mitochondrial dysfunction. Insertion of mitochondrial DNA missense mutation exhibits ASD endophenotypes, including autism-like behaviors and electroencephalographic profiles, and correlates with mitochondrial abnormalities. On the other hand the alteration of the tryptophan (Trp)–kynurenine (KYN) metabolic system was also observed in patients with ASD. KYN is neuroprotective and evidence suggest its lower levels in neurodegenerative diseases and ASD. Growing evidence has revealed that mitochondria have a close link to KYN metabolism and that mitochondrial dysfunction and the activation of the KYN system contribute to the pathogenesis of neurodevelopmental disorders [134]. Thus, potential strategies of clinical neuroprotection using novel kynurenine metabolites and analogues could be a new approach toward treatment of ASD [135]. Moreover, a previous study has shown improvement in memory component of cognitive domain with KYN analogue [136].

Despite, the need to integrate neuroimaging studies with genetic studies, the pragmatic difficulties in recruiting unaffected relatives of children with ASD besides the children themselves have made it an arduous task. Moreover, neuroimaging itself has limitations such as spatial and temporal resolution, inability to visualize microscopic changes, difficulty in training children for the procedure, and small sample size which might potentially overestimate the effect sizes. Using large-scale neuroimaging in endophenotype studies could be a daunting task in terms of cost-effectiveness and recruitment of a large number of non-autistic relatives to ensure adequate statistical power. Therefore, the possibility of large population-based studies remains bleak. Nevertheless, it is important to disentangle the complexities of the atypical neurodevelopment in ASD in the light of neuro-endophenotype research to provide greater insight into etiopathogenesis translating into better therapies.

Future research shall employ machine-learning approaches particularly suited to the search for autism biomarkers. Structural and functional MRI findings can be used to classify ASD based on the neuroanatomical changes using machine learning techniques such as support vector machines. Identification of autism biomarkers would help detect autism in infants and young children who are at increased risk of developing autism. This approach of early detection would hold good, given the body of work on the efficacy of an early intervention. Moreover, ASD is a complex disorder not linked to a single biomarker. Instead, ASD biomarkers are complex involving many genes and many neurobiological abnormalities. However, matching the genetic studies and neuroimaging studies to find conclusive neuro-endophenotypes of ASD may help. Although such a classification holds good for various clinical application several issues needs to be addressed before its use in clinical practice. The high cost of MRI, problems with image resolution, difficulty to train children to stay still, and portability are among the few issues that need to be addressed. However, these are among the few issues which can be resolved by the use of fNIRS.

Conclusions

An internal biomarker representing a set of genes is an endophenotype. The structural and functional neuroimaging changes may represent neural endophenotypes unique to ASD. However, the inconsistencies in the finding from neuroimaging could not pave the way for categorizing ASD based on neural endophenotype. Nevertheless, endophenotype-oriented research remains a hope which can help classify ASD and throw light on etiopathogenesis translating into more personalized and effective therapy. In addition, identifying the biomarkers of ASD early in the course will help change neural connectivity through early intervention techniques.