Shared Brain Connectivity Issues,Symptoms,and Comorbidities in Autism Spectrum Disorder,Attention Deficit/Hyperactivity Disorder,and Tourette Syndrome

Abstract

The prevalence of neurodevelopmental disorders, including autism spectrum disorder (ASD), attention deficit/hyperactivity disorder (ADHD), and Tourette syndrome (TS), has increased over the past two decades. Currently, about one in six children in the United States is diagnosed as having a neurodevelopmental disorder. Evidence suggests that ASD, ADHD, and TS have similar neuropathology, which includes long-range underconnectivity and short-range overconnectivity. They also share similar symptomatology with considerable overlap in their core and associated symptoms and a frequent overlap in their comorbid conditions. Consequently, it is apparent that ASD, ADHD, and TS diagnoses belong to a broader spectrum of neurodevelopmental illness. Biologically, long-range underconnectivity and short-range overconnectivity are plausibly related to neuronal insult (e.g., neurotoxicity, neuroinflammation, excitotoxicity, sustained microglial activation, proinflammatory cytokines, toxic exposure, and oxidative stress). Therefore, these disorders may a share a similar etiology. The main purpose of this review is to critically examine the evidence that ASD, ADHD, and TS belong to a broader spectrum of neurodevelopmental illness, an abnormal connectivity spectrum disorder, which results from neural long-range underconnectivity and short-range overconnectivity. The review also discusses the possible reasons for these neuropathological connectivity findings. In addition, this review examines the role and issue of axonal injury and regeneration in order to better understand the neuropathophysiological interplay between short- and long-range axons in connectivity issues.

Introduction

Autism spectrum disorder (ASD), attention deficit/hyperactivity disorder (ADHD), and Tourette syndrome (TS) are neurodevelopmental disorders that typically begin in early childhood and are considered chronic conditions, which last a lifetime. There has been an increase in the rates of these three neurodevelopmental disorders in the last two decades (Boyle et al., 2011; Cubo, 2012). To date, there is significant debate within the scientific/medical communities as to the causes and contributing factors related to the observed increases.

Although ASD, ADHD, and TS are considered distinct disorders, some researchers have called that separation into question. In the case of ASD and ADHD, many researchers noting shared psychopathological, neuropsychological, brain imaging, genetic, and medical findings have suggested that these disorders are part of a continuum (Banaschewski et al., 2011; Bradstreet et al., 2010; Grzadzinski et al., 2011; Hattori et al., 2006; Kern et al., 2012b). For example, Hattori and colleagues (2006) found that children with ASD and ADHD have higher scores than normal control children in the three ASD core features (social impairment, communication impairment, and restricted and repetitive behaviors) with no significant differences between ASD and ADHD in the domains of communication impairment and restricted and repetitive behavior. Similarly, several researchers have reported considerable overlap of ASD and ADHD symptoms with TS. For example, a study by Kadesjö and Gillberg (2000) reported that two-thirds of children with TS had attention deficit and autism spectrum comorbidities, and in most cases, the attention deficit and autism spectrum problems appeared to cause more suffering than the tics. This finding was later reiterated by Cohen and associates (2013) who stated that 90% of children with TS also have comorbid conditions and that these disorders often cause more problems for the child both at home and at school than tics do alone. In addition, Burd and colleagues (2009) examined 7288 participants from the Tourette Syndrome International Database Consortium and found that TS substantially increased the risk for pervasive developmental disorders by 13-fold across the reporting sites. The following narrative discusses more of the similarities between these three disorders.

First, males are considerably more likely to be diagnosed as having ASD, ADHD, or TS than females (Freeman et al., 2000; Knight et al., 2012). A review of automated medical records of children revealed the percentages of individuals evaluated in ASD, ADHD, and TS groups were 80.4% male, 77.7% male, and 76.2% male, respectively (Young et al., 2008). Second, all three of these disorders often manifest later during childhood. The respective median ages for a diagnosis of ASD, ADHD, or TS were 4.7, 6.4, and 6.0 years (Developmental Disabilities Monitoring Network Surveillance Year 2010 Principal Investigators; Centers for Disease Control and Prevention, 2014; Freeman et al., 2000; Young et al., 2008). Third, these disorders share symptomatology, showing considerable overlap in the core and associated symptoms, that is, issues with attention, impulsivity, repetitive behaviors, impairments in socialization and communication, anxiety, sleep disturbance, obsessive compulsive behaviors, sensory processing abnormalities, depression, and ritualistic behaviors, such as counting, repeating, or ordering and arranging (see Table 1 for a comparison of the shared symptomatology) (Grzadzinski et al., 2011; Hariz and Robertson, 2010; Hattori et al., 2006; Taylor, 2009; Terband et al., 2014; Tourette Syndrome Fact Sheet, 2013). Fourth, these disorders share neuropathology. Considerable evidence shows long-range neural underconnectivity and short-range overconnectivity in ASD, ADHD, and TS (Church et al., 2009; Tomasi and Volkow, 2012; Wass, 2011). Importantly, the issues of connectivity correlate with symptom severity in all three disorders (Liu et al., 2014; Peterson et al., 2011; Rudie et al., 2012; Yeatman et al., 2012).

Table 1.

Shared Symptomatology Among Autism Spectrum Disorder, Attention Deficit/Hyperactivity Disorder, and Tourette Syndrome

Overlap of symptoms
Symptom	ASD	ADHD	TS	References (ASD, ADHD, TS)
Impairment in socialization	Core feature	Frequently reported	Frequently reported	DSM-V, Hattori et al. (2006), Grzadzinski et al. (2011)
Impairment in communication	Core feature	Frequently reported	Frequently reported	DSM-V, Hattori et al. (2006), Grzadzinski et al. (2011)
Restricted patterns of behavior	Core feature	Frequently reported	Frequently reported	DSM-V, Hattori et al. (2006), Grzadzinski et al. (2011)
Repetitive patterns of behavior	Core feature	Frequently reported	Frequently reported	DSM-V, Hattori et al. (2006), Grzadzinski et al. (2011)
Impairment in attention	Associated symptom	Core feature	Associated symptom	Banaschewski et al. (2011), DSM-V, Tourette Syndrome Fact Sheet (2013)
Impairment in control	Associated symptom	Core feature	Associated symptom	Banaschewski et al. (2011), DSM-V, Tourette Syndrome Fact Sheet (2013)
Hyperactivity	Associated symptom	Core feature	Associated symptom	Banaschewski et al. (2011), DSM-V, Tourette Syndrome Fact Sheet (2013)
Anxiety/fear	Frequently reported	Frequently reported	Associated symptom	Geier et al. (2012), Taurines et al. (2010), Tourette Syndrome Fact Sheet (2013), Cohen et al. (2013)
Behavior problems	Frequently reported	Frequently reported	Associated symptom	Geier et al. (2012), Taurines et al. (2010), Tourette Syndrome Fact Sheet (2013)
Obsessive-compulsive behaviors	Frequently reported	Frequently reported	Associated symptom	Banaschewski et al. (2011), Tourette Syndrome Fact Sheet (2013), Cohen et al. (2013)
Sensory processing issues	Associated symptom	Frequently reported	Frequently reported	Kern et al. (2008), Mangeot et al. (2001), Tourette Syndrome Fact Sheet (2013), Cohen et al. (2013)
Depression	Frequently reported	Frequently reported	Associated symptom	Ozsivadjian et al. (2013), Taurines et al. (2010), Tourette Syndrome Fact Sheet (2013)
Sleep disturbance	Frequently reported	Associated symptom	Frequently reported	Hodge et al. (2014), Hvolby (2014), Taurines et al. (2010), Ghosh et al. (2014)

DSM V, Diagnostic and Statistical Manual of Mental Disorders (Fifth edition) (American Psychiatric Association, 2013); ASD, autism spectrum disorder; ADHD, attention deficit/hyperactivity disorder; TS, Tourette syndrome.

The main purpose of this review is to critically examine the evidence that ASD, ADHD, and TS are part of a broader neurodevelopmental illness spectrum, an abnormal connectivity spectrum disorder (ACSD), which results from processes that cause neural long-range underconnectivity and short-range overconnectivity. This review will also (1) examine the role and issue of axonal injury and regeneration; (2) summarize the shared symptomatology in ASD, ADHD, and TS; and (3) discuss the possible reasons for the consistent neuropathological pattern shared by those disorders and this pattern's implications. The review begins with the issue of axonal injury and regeneration in order to better understand the neuropathophysiological interplay between short- and long-range axons in connectivity issues.

Axonal Injury and Regeneration in the Central Nervous System

The central nervous system (CNS), which includes the brain and spinal cord, is made of two general types of cells: nerve cells (or neurons) and glial cells (or glia). While glia provide support to the neurons, the neurons are the basic building blocks of the nervous system, which send and receive information. Neurons connect to one another forming a network of communication through electrical and chemical signals. A typical neuron has dendrites (branches, which receive information), a cell body, and an axon (a thread-like slender projection, which transmits information). Although a neuron may have many dendrites, it has only one axon (Kandel et al., 2013; Lalli, 2012).

Axons can vary in size from ∼0.2 to 20 μm in diameter and from ∼1 mm to 1 m in length. Shorter axons (short-range axons) are required for communication within a brain region; however, axons linking distinct brain regions are required to be longer (long-range axons). Examples of neurons with long, projecting axonal processes are pyramidal cells, Purkinje cells, and anterior horn cells. Axons are considered the primary transmission lines in the nervous system, having to traverse, in many cases, long distances to connect different brain regions (Kandel et al., 2013).

When an axon is injured such that the axon breaks apart or its connection to another neuron is severed, it does not necessarily lead to the death of the neuron (Huebner and Strittmatter, 2009). In some cases, neurons are capable of axonal regeneration, ultimately reconnecting with other cells. Considerable science has been devoted to gaining insight into the mechanisms involved in axonal regeneration, knowing that it has the potential to promote an injured neuron's functional recovery. What is known from research is that neurons in the peripheral nervous system are capable of regenerating their axons, frequently restoring the functional connections of their damaged or severed peripheral axons. However, neurons in the CNS have only a limited capacity to regenerate their axons (Huebner and Strittmatter, 2009). For reasons, some known and some unknown, the CNS loses much of its ability to regenerate early during its developmental period (Goldberg et al., 2002).

In the CNS, regenerative sprouting (when an injured neuron attempts to reform an axon) is usually referred to as abortive sprouting (Schwartz and Flanders, 2006) because of the inability of injured axons to cross the lesion site, elongate, and undergo true axonal regeneration (Meyer et al., 2009). This is particularly difficult for long-range axons (Kunik et al., 2011). Evidence shows that the shorter the distance between the regeneration site and its distal target, the more successful regeneration of the axon is likely to be because postnatal, mature neuronal axons can only regenerate for very short distances in the CNS (Fawcett, 1992; Glenn and Zhigang, 2006) regardless of the original length of the axon. Chew and colleagues (2012), who wrote about the challenges of long-distance axon regeneration in the injured CNS, stated that long-tract axonal damage typically leads to permanent functional deficits in areas innervated at, and below, the level of the lesion. The initial ischemia, inflammation, and neurodegeneration are followed by a progressive generation of scar tissue, dieback of transected axons, and demyelination, creating an area inhibitory to regrowth and recovery.

In a study by Kunik and colleagues (2011), which examined the retinal neuronal RGC-5 cell line (after laser transaction), the researchers found that longer axons were less likely to begin a regenerative program after axotomy compared with cells with shorter axons. Measuring the axon length, they were able to calculate that the mean axon length of cells, which regenerated their axons, was 60±16 μm compared with 90±35 μm of cells, which did not regenerate their axons.

Consequently, as the brain tries to regenerate after long-range axon connectivity loss, that regeneration process often simply results in an increase in the number of short thin axons. As a result, whenever long-range connectivity is lost, that loss can lead to an increase in short-range connectivity. This subsequent shift from long-range connectivity to short-range connectivity has been shown in research studies. For example, Barttfeld and colleagues (2011), in assessing dynamic brain connectivity using electroencephalography (EEG), found that as long-range connections decreased, there was an increase in short-range connections.

Importantly, in normal development, the opposite occurs. In normal brain development, there appears to be a shift from local processing to more global processing (from short-range connectivity to long-range connectivity). For example, Uddin and colleagues (2010), used resting-state functional magnetic resonance imaging (fMRI) and revealed simultaneous pruning of local (short-range) connectivity and strengthening of long-range connectivity with age.

Similarly, Fair and associates (2007) found that development of the proposed adult control networks involves both segregation (i.e., decreased short-range connections) and integration (i.e., increased long-range connections) of the brain regions that comprise them. Fair and associates (2007) stated that a compromise in this developmental process of segregation and integration may play a role in disorders of control, such as ASD, ADHD, and TS. To illuminate this point, this review will examine the evidence of neural long-range underconnectivity and short-range overconnectivity in ASD, ADHD, and TS after first examining the shared clinical symptomatology between these disorders.

Shared Symptomatology

Autism spectrum disorder

ASD is defined by qualitative impairments in both communication and social interaction and in restricted and repetitive patterns of behavior, interests, and activities (American Psychiatric Association, 2013). In addition, recent studies reveal that individuals diagnosed as having ASD have a high prevalence of (1) inattention, impulsivity, and hyperactivity (Banaschewski et al., 2011); (2) sensory processing abnormalities (Kern et al., 2008); (3) anxiety/fear; (4) behavior problems; (5) obsessive-compulsive behaviors; (6) sleep disturbance (Geier et al., 2012); and (7) depression (Ozsivadjian et al., 2014). Importantly, the core features of ADHD (attention, impulsivity, and hyperactivity) are among the most frequently associated symptoms of ASD (Banaschewski et al., 2011). Table 1 shows the symptoms ASD shares with ADHD and TS.

Attention deficit/hyperactivity disorder

ADHD is defined by features of inattention, hyperactivity, and impulsivity (Banaschewski et al., 2011). In addition, often reported in ADHD are (1) impairments in socialization, social cognition, and communication (Grzadzinski et al., 2011; Hattori et al., 2006; Taurines et al., 2010); (2) restricted repetitive and stereotyped patterns of behavior, interests, and activities (Grzadzinski et al., 2011); (3) sensory processing abnormalities (Ghanizadeh, 2011; Lin et al., 2013); (4) anxiety; (5) disruptive behavior; (6) obsessive-compulsive disorder (OCD); (7) sleep disturbance; and (8) depression (Taurines et al., 2010; Yüce et al., 2013). Table 1 shows the symptoms ADHD shares with ASD and TS.

Tourette syndrome

TS is characterized by multiple motor tics and one or more vocal/phonic tics (Hariz and Robertson, 2010). However, only a small minority of TS patients presents exclusively with a tic disorder (Martino et al., 2013). Psychopathology and comorbidity occur in ∼80–90% of clinical cohorts evaluated (Freeman et al., 2000). Common comorbid conditions are (1) ADHD, (2) OCD, (3) depression, (4) anxiety, (5) sleep disturbance, (6) sensory processing abnormalities, and (7) behavioral issues (Cohen et al., 2013; Hariz and Robertson, 2010; Saccomani et al., 2005). According to Cohen and colleagues (2013), 90% of children with tic disorder also exhibit attention, hyperactivity, obsessive-compulsive, and impulsive behaviors. They urge that the diagnosis and treatment of TS should involve appropriate evaluation and recognition of not only tics but also of these associated problems. Table 1 shows the symptoms TS shares with ASD and ADHD.

As mentioned, Table 1 shows the overlap of symptoms shared by these three disorders. However, although there is considerable overlap, differences do exist. In addition, within each disorder there is also heterogeneity in presentation. Furthermore, there is also a spectrum of symptom severity in each of these disorders ranging from mild to severe.

Evidence of Long-Range Underconnectivity and Short-Range Overconnectivity in ASD, ADHD, and TS

Evidence suggests that neural long-range underconnectivity and short-range overconnectivity are part of the pathology of ASD, ADHD, and TS. The following three sections describe some of the relevant research, and Table 2 summarizes examples of the studies, which support this finding. Evidence also suggests that neural long-range underconnectivity and short-range overconnectivity are found in OCD, a common comorbidity found in those with a diagnosis of ASD, ADHD, and/or TS. However, less research has been conducted on these connectivity issues in individuals with OCD (Zhang et al., 2011). Table 2 also includes this information.

Table 2.

Examples of Studies that Show Long-Range Underconnectivity and/or Short-Range Overconnectivity in Autism Spectrum Disorder, Attention Deficit/Hyperactivity Disorder, Tourette Syndrome, or Obsessive-Compulsive Disorder

Study	Disorder	Type of study	(N) exp:control	Method: examining	Reported findings	Study conclusion
Aoki et al. (2013)	ASD	Meta-analysis	NA	DTI: Structural/functional connectivity	Fractional anisotropy reductions in corpus callosum, left uncinate fasciculus, and left superior longitudinal fasciculus; significant increases of mean diffusivity in the corpus callosum and the superior longitudinal fasciculus bilaterally	LRU
Casanova et al. (2009)	ASD	Original	14:28	MRI: structural connectivity	Reduction in aperture for afferent/efferent cortical connections, that is, gyral window	LRU, SRO
Barnea-Goraly et al. (2004)	ASD	Original	7:9	DTI: white matter structure	Reduced FA in white matter adjacent to the ventromedial prefrontal cortices, anterior cingulate gyri, temporoparietal junctions, adjacent to the superior temporal sulcus bilaterally, temporal lobes approaching the amygdala bilaterally, occipitotemporal tracts, and corpus callosum	LRU
Zikopoulos and Barbas (2010)	ASD	Original	5:4	Postmortem: structural connectivity	Decrease in the largest axons that communicate over long distances and excessive number of thin axons in anterior cingulate cortex	LRU, SRO
Just et al. (2007)	ASD	Original	18:18	fMRI: functional/anatomical connectivity	Smaller corpus callosum and size of genu correlated with frontal–parietal functional connectivity	LRU
Keown et al. (2013)	ASD	Original	29:29	MRI and graph theory: functional connectivity	Increased local connectivity in temporo-occipital regions bilaterally	SRO
Pardini et al. (2009)	ASD	Original	10:10	DTI-MRI scanning: structural connectivity	Reduced tract volume and lower mean FA values in the left orbitofrontal cortex	LRU
Wass (2011)	ASD	Review	NA	Imaging and other sources: structural/functional connectivity	Evidence both of local overconnectivity and of long-distance underconnectivity	LRU, SRO
Barttfeld et al. (2011)	ASD	Original	10:9	EEG: functional connectivity	Long-range connection deficit in fronto-occipital connections. Increased short-range connections in lateral-frontal electrodes. As ASD severity increased, short-range coherence was more pronounced and long-range coherence decreased	LRU, SRO
Peters et al. (2013)	ASD	Original	30^a:46	EEG: functional connectivity	In ASD, both with and without a concurrent diagnosis of TSC, decreased long- over short-range coherence	LRU, SRO
Kikuchi et al. (2014)	ASD	Original	50:50	MEG: functional connectivity	Reduced connectivity between left-anterior and right-posterior areas of cortex. Reduction in coherence correlated with clinical severity in right-handed children with ASD	LRU
Schaer et al. (2013)	ASD	Original	11:11	DTI: structural connectivity	Imbalance between short- and long-range white matter tracts	LRU, SRO
Cao et al. (2014)	ADHD	Review	NA	Graphic analysis of large-scale brain systems: functional/structural brain connectomics	Higher local clustering and lower global integrity	LRU, SRO
Castellanos et al. (2008)	ADHD	Original	20:20	Resting-state BOLD fMRI: functional connectivity (authors state findings also suggestive of structural connectivity)	Decreases in connectivity between precuneus and other default-mode network components (ventromedial prefrontal cortex and posterior cingulate)	LRU
Liu et al. (2014)	ADHD	Original	13:13	Multisource interference task using EEG: functional connectivity	Increased local characteristics combined with decreased global characteristics in ADHD	LRU, SRO
Tomasi and Volkow (2012)	ADHD	Original	247:304	Compared functional connectivity density from a public MRI database	Lower connectivity (short- and long-range) in superior parietal cortex and default-mode (precuneus) networks and cerebellum. Higher connectivity (short-range) (ventral striatum and orbitofrontal cortex)	LRU, SRO, SRU
Wang et al. (2009)	ADHD	Original	19:20	fMRI; brain functional networks were constructed by thresholding correlation matrix between 90 cortical and subcortical regions	Increased local efficiencies combined with a decreasing tendency in global efficiencies (functional networks)	LRU, SRO
Bäumer et al. (2010)	TS	Original	14:15	DTI: structural/functional connectivity	Abnormal functional interhemispheric connectivity (structural and functional)	LRU
Cheng et al. (2013)	TS	Original	15:0	DTI: structural connectivity	Intrahemispheric white matter connectivity in cortico-subcortical circuits engaged in motor control showed widespread structural connectivity deficits (in tracts connecting supplementary motor areas (SMA) with basal ganglia (pre-SMA-putamen, SMA-putamen) and in frontal cortico-cortical circuits). Widespread structural connectivity deficits.	LRU
Church et al. (2009)	TS	Original	39:33	rs-fcMRI: functional connectivity	Less effective functional communication between distant areas of cortex, and, in contrast, over communication between regions in close proximity	LRU, SRO
Neuner et al. (2010)	TS	Original	19:19	DTI: structural connectivity	Altered interhemispheric connectivity as indicated by an FA-decrease in the corpus callosum; association fibers, such as the inferior fronto-occipitalis fascicle, the superior longitudinal fascicle, and fascicle uncinatus, were affected as well	LRU
Worbe et al. (2012)	TS	Original	59:27	3T magnetic resonance imaging: functional connectivity	Stronger functional integration (more interactions among anatomical regions) and a global functional disorganization of cortico-basal ganglia networks. All networks were characterized by a shorter path length, a higher number of and stronger functional connections among the regions, and by a loss of pivotal regions of information transfer (hubs)	LRU, SRO
Plessen et al. (2004)	TS	Original	158:121	High-resolution MRI: corpus callosum size and anatomical connectivity	Children with TS had smaller overall corpus callosum size (adults with Tourette's disorder on average had larger corpus callosum size). Corpus callosum size correlated positively with tic severity. Corpus callosum size also correlated inversely with dorsolateral prefrontal and orbitofrontal cortical volumes.	LRU
Zhang et al. (2011)	OCD	Original	18:16	rs-fcMRI: functional connectivity	Visual examination indicated OCD-related increases in functional connections were short-range, whereas the OCD-related decreases in connections were mainly associated with different lobes (i.e., long-range connections, especially between frontal and temporal lobes, and the frontal and parietal lobes)	LRU, SRO

Fourteen of the thirty with ASD also with tuberous sclerosis complex (TSC).

DTI, diffusion tensor imaging; MRI, magnetic resonance images; FA, fractional anisotropy; fMRI, functional magnetic resonance imaging; EEG, electroencephalography; MEG, magnetoencephalography; rs-fcMRI, resting-state functional connectivity MRI; LRU, long-range underconnectivity; SRO, short-range overconnectivity; SRU, short-range underconnectivity; NA, not applicable.

The main focus of this review is structural connectivity. However, functional connectivity studies are also included because neurodegenerative diseases tend to show that structural and functional network degeneration are similar (Schipul et al., 2012; Schmidt et al., 2014). However, studies show that structural and functional findings do not necessarily coincide (Ponten et al., 2010).

Evidence of long-range underconnectivity and short-range overconnectivity in ASD

There are numerous studies that suggest issues with neuronal connectivity in persons with an ASD diagnosis. In a review of connectivity in ASD, Wass (2011) stated that there is considerable convergent evidence, suggesting that connectivity is disrupted in ASD, and that evidence indicates both local overconnectivity and long-distance underconnectivity, with disruptions appearing more severe in the later-developing cortical regions. According to Kana and associates (2011), cortical underconnectivity between brain regions, especially the frontal cortex and more posterior areas, is relatively well established in autism, supporting the idea that there is weaker coordination between different parts of the brain. Currently, numerous studies support the view of local overconnectivity and long-distance disconnection in ASD (Anagnostou and Taylor, 2011; Aoki et al., 2013; Assaf et al., 2010; Barnea-Goraly et al., 2004; Bernardi et al., 2011; Casanova, 2004; Casanova et al., 2002; Courchesne and Pierce, 2005; Girgis et al., 2007; Herbert et al., 2004; Hyde et al., 2010; Just et al., 2007; Kana et al., 2006; Keown et al., 2013; Pardini et al., 2009; Wass, 2011; Zikopoulos and Barbas, 2013). Both the functional and structural connectivity between regions of the brains of those diagnosed as having ASD are diminished (Herbert, 2005; Herbert et al., 2004), and the limits of structural connectivity appear to be related to functional connectivity (Schipul et al., 2012).

As stated by Palau-Baduell and associates (2012), findings from brain function studies show that patients with an ASD diagnosis have deficits in long-distance connections (under-connectivity), with a most prominent deficit in frontoposterior connections. With regard to structural connectivity, they also stated that there is evidence of disruption to the interhemispheric white matter structures and that functional studies reveal an excess of local connections (short-range overconnectivity) in patients with ASD.

Importantly, studies show that long-range underconnectivity and short-range overconnectivity are associated with ASD severity such that the greater the short-range overconnectivity and long-range underconnectivity, the worse the ASD symptoms (Barttfeld et al., 2011; Keown et al., 2013; Kikuchi et al., 2014). For example, Barttfeld and colleagues (2011), using EEG to assess dynamic brain connectivity in ASD, found that as ASD severity increased, short-range coherence was more pronounced and long-range coherence decreased. Kikuchi and associates (2014) noninvasively measured brain activity during consciousness in 50 young children with an ASD diagnosis and 50 age- and gender-matched typically developing children using a custom child-sized magnetoencephalography system. They found reduced long-range functional connectivity in conscious young children with ASD. They also found that the reduction in coherence was significantly correlated with clinical severity. Keown and colleagues (2013), using functional connectivity MRI and graph theory, found that local functional overconnectivity in posterior brain regions in ASD is associated with symptom severity.

However, although long-range underconnectivity is a consistent finding in ASD, findings on short-range overconnectivity appear to be somewhat mixed, depending on the areas of the brain examined (Khan et al., 2013; Maximo et al., 2013). In those with an ASD diagnosis, Maximo and colleagues (2013), for example, found local overconnectivity in the occipital and posterior temporal regions and local underconnectivity in the middle/posterior cingulate and medial prefrontal regions.

Evidence of long-range underconnectivity and short-range overconnectivity in ADHD

As in ASD, neural connectivity is an issue in ADHD. Konrad and Eickhoff (2010) completed a review of the structural and functional connectivity in individuals with an ADHD diagnosis and found numerous studies that described abnormal structural and functional connectivity in ADHD in many parts of the brain. Moreover, as in ASD, there is evidence of local overconnectivity and long-range underconnectivity. As described by Cao and colleagues (2014) in their recent review (where they summarize the recent progresses of functional and structural brain connectomics in ADHD), convergent evidence suggests that children with ADHD have abnormal small-world properties in both functional and structural brain networks characterized by higher local clustering and lower global integrity. They also stated that the magnitude of these ADHD-associated alterations was significantly correlated with behavioral disturbances (e.g., inattention and hyperactivity/impulsivity symptoms).

There are several examples of specific studies that provide evidence of local overconnectivity and long-range underconnectivity in ADHD using various methods. Liu and associates (2014) investigated the topologic properties of human brain attention-related functional networks associated with Multisource Interference Task performance using EEG from 13 children diagnosed as having ADHD and 13 normal control children. They reported increased local characteristics (short-range overconnectivity) combined with decreased global characteristics (long-range underconnectivity) in ADHD. In harmony with the theory described by Fair and colleagues (2007), mentioned earlier, these authors stated that the results were consistent with a hypothesis of dysfunctional segregation and integration of the brain in ADHD.

Tomasi and Volkow (2012) also found long-range underconnectivity and short-range overconnectivity in ADHD. In a large study (255 children with ADHD and 316 typically developing control children), they found that the ventral striatum, caudate, and orbitofrontal cortex [brain regions that are implicated in motivation and reward (Wise, 2002)] demonstrated higher strength of short-range connectivity for children diagnosed as having ADHD than for typically developing children. In addition, they found lower long-range functional connectivity density in the superior parietal cortices and the cerebellum in those with an ADHD diagnosis than in typically developing children. Moreover, the decreased functional connectivity density in these regions showed significant correlation with inattention and impulsivity/hyperactivity for ADHD-diagnosed children. Quantitatively, they stated that ADHD-diagnosed children demonstrated 15% higher short-range connectivity in regions classically associated with reward and motivation and 33% lower long-range connectivity in regions classically associated with cognitive processing (parietal cortex).

Castellanos and colleagues (2008) found abnormalities in functional connectivity in ADHD using resting-state blood oxygen level-dependent fMRI scans. Comparing 20 adults diagnosed as having ADHD with controls, they reported decreases in the long-range connections linking dorsal anterior cingulate to posterior cingulate and precuneus.

As described in their review of the structural and functional connectivity in ADHD, Konrad and Eickhoff (2010) explain that recent advances in graph theory enable the characterization of topological properties of complex neural networks. Using these approaches, dense local connections and few long connections can be characterized as small-world networks. Using the small-world network typology, Wang and colleagues (2009) examined the correlation matrix between 90 cortical and subcortical regions and demonstrated that neural networks of the ADHD-diagnosed group were altered compared with those of the control group. A tendency toward decreased global efficiency of the brain networks was found in ADHD. Wang and associates (2009) concluded that there is an increase in local efficiencies combined with a decreasing tendency in global efficiencies in those diagnosed as having ADHD, suggesting local (or short-range) overconnectivity and long-range underconnectivity.

As with ASD, several studies found that connectivity abnormalities correlated with symptom severity in ADHD (Cao et al., 2014; Peterson et al., 2011; Tomasi and Volkow, 2012). For example, in children diagnosed as having ADHD compared with controls, Peterson and associates (2011) found that the decreased connectivity in the long-range tracts connecting the temporal lobe and other distant cortical regions was positively associated with symptom severity in the ADHD-diagnosed group.

In addition, as with the ASD case, so in ADHD, the issue of local connectivity appears to be more mixed depending on the brain area. For example, in a study by Tomasi and Volkow (2012), they reported that children with an ADHD diagnosis had lower connectivity (short- and long-range) in the superior parietal cortex, precuneus, and cerebellum and higher connectivity (short-range) in the ventral striatum and orbitofrontal cortex than their control subjects.

Evidence of long-range underconnectivity and short-range overconnectivity in TS

As with ASD and ADHD, there are widespread structural connectivity deficits in TS (Cheng et al., 2013; Neuner et al., 2010). Similarly, the evidence shows short-range overconnectivity and long-range underconnectivity and these deficits were found to correlate with symptom severity (Cheng et al., 2014). For example, Church and colleagues (2009) measured resting-state functional connectivity using MRI between 39 previously defined putative control regions in 33 adolescents diagnosed as having TS. They found evidence of overcommunication between regions in close proximity (short-range overconnectivity) located in areas such as the dorsolateral prefrontal cortex and the anterior prefrontal cortex. They found decreased long-range functional connectivity between control regions such as the dorsolateral prefrontal cortex and the posterior parietal cortex.

Worbe and colleagues (2012) had similar findings. They evaluated the global integrative state and organization of functional connections of sensorimotor, associative, and limbic cortico-basal ganglia networks, which are postulated to be involved in tics and behavioral expressions of those with a diagnosis of TS. What they found was that all networks were characterized by a shorter path length, a higher number of stronger functional connections within the regions (short-range overconnectivity), and a loss of regions of information transfer (long-range connections). They also found that the functional abnormalities correlated with tic severity in all cortico-basal ganglia networks, namely in the premotor, sensorimotor, parietal and cingulate cortices, and medial thalamus.

In addition, Neuner and colleagues (2010) found evidence of altered interhemispheric connectivity as indicated by a fractional anisotropy decrease in the corpus callosum in those with an ADHD diagnosis. Their results indicated that TS-related effects were not restricted to motor pathways, but association fibers, such as the inferior fronto-occipitalis fascicle, the superior longitudinal fascicle, and the fascicle uncinatus, were involved as well. They further stated that alterations in the long association fiber tracts and the corpus callosum were a part of the hindered interhemispheric and transhemispheric interaction in those diagnosed as having TS. Altered interhemispheric connectivity has been corroborated by others (Bäumer et al., 2010). Reduced transcallosal connectivity represents long-range connectivity abnormalities and has been found to be associated with behavioral abnormalities in autism and with tic severity in TS (Anderson et al., 2011; Plessen et al., 2004).

Connectivity and Behavior

Several of the aforementioned studies found that the worse the connectivity issues in these disorders (where the short-range coherence was more pronounced and long-range coherence was decreased), the more severe the symptomatology (Barttfeld et al., 2011; Cheng et al., 2014; Tomasi and Volkow, 2012). Because long-range axons are critical for many functions in the brain, it is not surprising that the symptomatology in these three disorders is so broad. Conscious processing occurs when incoming information is made globally available to multiple brain systems through a network of neurons with long-range axons (Dehaene and Changeux, 2011). More specifically, long-range axons have been shown to be involved in central coherence, information processing and integration (Dehaene and Changeux, 2011), attention, sensory processing, social skills (Chang et al., 2014), communication, and language (Jeong et al., 2011). It is theorized that the long-range underconnectivity may contribute to the lack of central coherence, limited executive function, difficulty with attention, and sensory processing difficulties, and the short-range overconnectivity may contribute to the narrowed restricted interests, repetitive behaviors, and obsessive behaviors (Zhang et al., 2011). However, it may be a complex combination and interplay between these two factors of long-range underconnectivity and short-range overconnectivity that contributes to these abnormal behaviors. More research is needed on this topic.

Discussion/Implications

When considering the implications of this similar neuropathological finding (long-range underconnectivity and short-range overconnectivity) in individuals diagnosed as having these three disorders (ASD, ADHD, and TS), one must also consider the other similarities (mentioned earlier) that are noted in these disorders. These other similarities include (1) shared symptomatology in the core and associated features, (2) a higher ratio of males affected than females affected, (3) the disorders manifest later during childhood, and (4) an increase in prevalence in the last two decades.

Even though long-range underconnectivity and short-range overconnectivity and the general disorganization of the neuronal pathways are a form of neuropathology, it has been suggested by some that the brain abnormalities are purely developmental with a genetic basis, implying the brain changes are not a result of neuronal insult (Stoner et al., 2014). However, the biological plausibility of neuronal insult is supported by the fact that large-caliber long-range axons are vulnerable to insult (Coleman, 2013; Glynn, 2006; Spencer and Schaumburg, 1975; Stankovic, 2006; Wang and Michaelis, 2010). Studies show that large myelinated axons and long CNS axon tracts are particularly vulnerable to insult and damage (Englander et al., 2013; Koliatsos et al., 2011; Shaia et al., 2005; Spencer and Schaumburg, 1975). It is important to note that projections or long-range neurons have, in general, larger cell bodies and axons than local circuit neurons (Jacquin et al., 1989; Taylor, 1996).

In their review of the selective vulnerability of neurons, Wang and Michaelis (2010) found that, in general, vulnerable neurons are large in size, with axons projecting over long distances to their targets. The authors listed the possible reasons for the susceptibility of large neurons, which include (1) a high demand for energy and mitochondrial activity, (2) dependence on long-distance axonal transport, (3) high content of neurofilaments, which tend to form aggregates, and (4) a relatively large surface area for increased exposure to toxicants in the extracellular environment. These findings have been corroborated by others (Shaw and Eggette, 2000). In addition, there are increased demands in regard to axonal transport, signaling, glial–axonal interaction, and other support functions (Coleman, 2013; Glynn, 2006). For instance, higher metabolic demands make the large-caliber long-range axons more vulnerable to oxidative stress (Wang and Michaelis, 2010).

To that point, large-caliber long-range axons are shown to be selectively vulnerable to several toxins. For example, large-caliber long-range axons have been found to be selectively vulnerable to (1) organophosphates (Glynn, 2006), (2) mercury (Stankovic, 2006), and (3) bilirubin (Shaia et al., 2005). Acrylamide has also been shown to affect long-range axons (Spencer and Schaumburg, 1975), however, it predominately affects peripheral axons.

Specifically, according to Glynn (2006), organophosphate's direct effect on long-range axons has been hypothesized to result from a transient loss of neuropathy target esterase activity, putatively disrupting membrane phospholipid homeostasis and endoplasmic reticulum functions, including axonal transport and glial–axonal interaction; the distal parts of long axons will be particularly vulnerable to loss of these support functions. It is also thought that the axonopathy caused by organophosphates is due to disruption of Ca(2+) homeostasis and the activation of calpain (Song and Xie, 2012).

One of the main effects of mercury is axonal degeneration, particularly of large-caliber axons, which tend to be long-range axons (Jacquin et al., 1989; Kern et al., 2012a; Taylor, 1996). Large neurons have a higher metabolic demand and are more vulnerable to oxidative stress (Wang and Michaelis, 2010), and mercury causes significant oxidative stress. In addition, the large-caliber axons take up more toxins at the neuromuscular junction as seen in studies using autometallography on mice exposed to mercury vapor (Stankovic, 2006). Furthermore, mercury binds to the cysteine on the cytoskeletal structure of the axon and causes it to depolymerize (Aschner et al., 2010; Leong et al., 2001). This is a form of axonal degeneration, which is unique to mercury. Other toxic metals, for example, lead, manganese, cadmium, and aluminum, do not show this effect (Leong et al., 2001).

Bilirubin toxicity appears to result in misshapen thin axons with myelin clusters noted in the axoplasm (Can et al., 2004). Both CNS and peripheral nervous system neurons are affected (Brito et al., 2013; Can et al., 2004). The exact mechanisms are not clear (Can et al., 2004).

As with most toxins, the effects of these aforementioned toxins are broad and diffuse, including, but are not limited to, causing long-range axon loss. Ultimately these toxins cause other types of pathology in the brain as well. Importantly, a primary affect of axon loss can cause secondary effects such as microglial activation (if for no other reason, than just debris) that can also cause loss of connectivity. This effect will be discussed further in the following section.

Microglial activation

Another aspect important to this discussion is the role of neuroglia cells in interneuronal connectivity. In particular, microglia, a phagocytic cell in the brain, plays a role in connectivity. Microglia has been shown to engulf and clear damaged cellular debris after brain insult (Hughes, 2012; Paolicelli et al., 2011). However, when microglial activation is sustained for a long period of time (i.e., prolonged brain inflammation), the microglia can begin to engulf healthy tissue (Hughes, 2012; Rodriquez and Kern, 2011). Thus, sustained microglial activation can and has been shown to significantly contribute to axon loss and loss of connectivity.

In regard to that point, there is evidence to suggest microglial activation in ASD, ADHD, and TS. Numerous studies show microglial activation in ASD. According to a review by Mitchell and Goldstein (2014), who conducted a systematic review of studies that examined proinflammatory markers in children and adolescents with neuropsychiatric disorders, the evidence was strongest for ASD. However, they also found that proinflammatory biomarkers are also evident in ADHD and TS, as well as other neuropsychiatric disorders in children and adolescents. However, they also stated that the data were inconsistent.

Although direct postmortem evidence for microglial dysregulation (positive or negative) in TS is limited, Morer and associates (2010) reported postmortem evidence of increased expression of monocyte chemotactic factor-1 (MCP-1, a marker of chronic inflammation) and interleukin-2 (IL-2, a growth factor derived from T lymphocytes) in adult TS patients. The authors stated that the elevated expression of MCP-1 and IL-2 supports the notion of sustained neuroinflammation in the basal ganglia of TS patients. [The induction of neuronal MCP-1 has been found to cause microglial recruitment/activation, thus exacerbating neurodegeneration (Mizutani et al., 2012)]. In addition, Kumar and colleagues (2014) applied positron emission tomography scanning to evaluate neuroinflammatory changes in basal ganglia and thalami in children with TS. Activated microglia-mediated neuroinflammation was found to be increased in bilateral caudate nuclei in the TS group compared with the control group.

As in TS, direct postmortem evidence for microglial dysregulation (positive or negative) in ADHD is limited. However, there is other evidence of neuroinflammation (Oades et al., 2010). Oades and associates (2010), for example, examined systematic associations of eight cytokines (indicators of pro/anti-inflammatory function) with symptom rating scores (e.g., anxiety, opposition, inattention) in 35 ADHD (14 on medication) and 21 control children. The researchers reported that the total symptom ratings were associated with increases of the interleukin 16 (IL-16) and interleukin 13 (IL-13), where relations of IL-16 (along with decreased S100B) with hyperactivity and IL-13 with inattention were notable. [IL-16 protein present in neurons is found to promote recruitment of microglial cells to the injured axons (Croq et al., 2010)]. In addition, increased response time variability was associated with higher interferon-gamma (IFNγ) levels. [IFNγ is an inflammatory cytokine, which has been shown to change the protective response of microglia and macrophages into a robust proinflammatory classical response (Bsibsi et al., 2014).]

In a previous section, the possibility of toxins playing a critical role in the axon loss in these three disorders was discussed. All of the toxins listed previously that can cause long-range axon loss can also trigger microglial activation: bilirubin toxicity (Brites 2012), organophosphates (Binukumar et al., 2011), and mercury (Kern et al., 2012a).

Increase in toxic exposure

Notably, along with the increase in neurodevelopmental disorders in the past two decades, there has also been a concomitant increase in toxic exposures. Many studies show an increase, not only of toxicants in the environment but also in humans. For example, Laks (2009) examined National Health and Nutritional Examination Survey (NHANES) data and reported on time trends on blood inorganic mercury levels in 6174 women, aged 18–49, in the NHANES 1999–2006 data sets. Laks (2009) found that, in the United States population, the population percentage with concerning levels of blood inorganic mercury rose sharply from 2% in 1999–2000 to 30% in 2005–2006 (with average mercury levels increasing from 0.33 to 0.39 μg/L). Moreover, the level of blood inorganic mercury was significantly associated with subject's age (increasing levels of mercury with increasing age), suggesting bioaccumulation.

In another example, Woodruff and colleagues (2011) analyzed data for 268 pregnant women from the NHANES 2003–2004 study group. They found that 99% of pregnant women tested positive for at least 43 different chemicals, including polychlorinated biphenyls, organochlorine pesticides, perfluorinated compounds, phenols, polybrominated diphenyl ethers, phthalates, polycyclic aromatic hydrocarbons, and perchlorate. Similar findings are reported by others (Leino et al., 2013). A recent study commissioned by Moms Across America and Sustainable Pulse (momsacrossamerica.com) found that the levels of the herbicide and pesticide glyphosate were up to 1600 times higher than what is allowed in European drinking water (Sustainable Pulse, 2014).

In a benchmark study conducted by the Environmental Working Group (EWG, 2005) in conjunction with Commonweal, researchers found an average of 200 industrial chemicals and pollutants in umbilical cord blood from 10 babies born in August and September of 2004 in U.S. hospitals (a total of 287 non-natural chemicals were found in the infants). Of the 287 chemicals they detected in umbilical cord blood, 217 of them are toxic to the brain and nervous system.

Because of the large numbers of toxins that children are now exposed to, the issue of combined effects in toxicology may also have to be considered, that is, coexposure, synergism, and/or additivity. Evidence shows that toxins can potentiate each other. For example, lead has been found to be more toxic in the presence of mercury than by itself (Schubert et al., 1978).

It is possible that clinical manifestations of long-range axon loss are resulting from the increasing toxic pollutant exposure in utero, postnatally, and/or during childhood. The long-range underconnectivity and short-range overconnectivity may indicate brain damage as a result of neuronal insult (e.g., neurotoxicity, neuroinflammation, excitotoxicity, sustained microglial activation, proinflammatory cytokines, toxic exposure, and oxidative stress).

A possible scenario could be as follows. Neurotoxic exposure results in long-range axon loss and microglial activation in the brain. The microglial activation starts engulfing damaged neuron debris. If the toxin remains present over a period of time, this continued presence of the toxin results in a sustained neuroinflammatory state in the brain where the microglia starts engulfing not only damaged neuron debris but also healthy brain tissue (Rodriguez and Kern, 2011). This leads to cell loss and reduced connectivity, particularly involving the more vulnerable long-range axons. Then, as the brain tries to regain what was lost, unable to regenerate long-range axons, it regrows short-range axons. The results would correspond to what the authors see in the brains in ASD, ADHD, and TS: long-range underconnectivity and short-range overconnectivity. Ultimately, the short-range overconnectivity may be a compensatory mechanism triggered by neuronal insult.

Differential effects

It could be argued that there are different effects and degrees of effects in the brain areas, which predominate in regard to connectivity issues, in these three disorders. For example, Maximo and colleagues (2013), mentioned earlier, found local overconnectivity in the occipital and posterior temporal regions and local underconnectivity in the middle/posterior cingulate and medial prefrontal regions.

Response to damage in the brain would predictably be nonuniform and not controlled simply by the nature of neuronal insult. To expect a uniform and controlled level of damage and level of compensatory repair is not realistic. Damage done to the brain from mercury, for example, is notably found to be idiosyncratic, being dependent upon the individual's thiol availability and redox capacity in various brain regions, with a hierarchy of damage related to areas of cellular vulnerability (Kern et al., 2013). For example, research clearly shows that the cerebellum is vulnerable to mercury damage and that certain types of neurons within the cerebellum are especially vulnerable (Kern, 2003). The ability of the brain to respond to insult would also be dependent upon the extent of the damage, with some areas being so affected that even a compensatory mechanism (e.g., short-range axon regrowth) could not be accomplished. Thus, some damaged areas of the brain may not be able to accomplish a compensatory response. Considering how various areas have unique vulnerabilities and varying ability to respond to toxins, it stands to reason that some brain areas would show long-range underconnectivity with little or no short-range overconnectivity. The brain is not uniform and therefore damage and response to damage will not be uniform.

Conclusion

Children with a diagnosis of ASD, ADHD, and TS share substantial neuropathological findings of long-range underconnectivity and short-range overconnectivity. They also share similar symptomatology, showing considerable overlap in core and associated symptoms. The shared neuropathology and symptomatology described in this review of the science support the hypothesis that each of the diagnoses is part of a broader ACSD and may a share a similar etiology.

To date, there is significant debate within the scientific/medical communities as to the causes or contributing factors related to the increase in these developmental disorders. Many questions regarding the potential weight of genetic inheritance and susceptibility, gene/environment interaction, and epigenetic and environmental factors for causing specific deficits remain unanswered. However, children's nervous systems are now developing in an increasingly toxic world. Moreover, biologically, it is evident from the scientific literature that long-range underconnectivity and short-range overconnectivity are plausibly related to neuronal insult. This information suggests that future studies need to focus on external/environmental factors, which may be causing or contributing to the brain connectivity changes in these ACSDs.

Footnotes

Acknowledgments

This study was supported by the nonprofit 501(c)(3) Institute of Chronic Illnesses, Inc., and the nonprofit 501(c)(3) CoMeD, Inc.

Author Disclosure Statement

There are no competing financial interests. Five of the six authors have been involved in vaccine/biological litigation.

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