- Email: [email protected]
Autism Spectrum Disorders
C H A P T E R
26 Autism Spectrum Disorders Autism was originally defined as “an innate disturbance of affective contact” by Leo Kanner in 1943 and was included in the chapter on childhood schizophrenia in early versions of the Diagnostic and Statistical Manual of Mental Disorders (DSM) (see toolbox: Psychiatry). Today, autism is part of a new autism spectrum disorders (ASD) diagnostic label in the DSM-5 [1] that subsumes autism according to Kanner’s definition, Asperger’s syndrome, and other early-onset interaction and communication impairments. One of the most puzzling and concerning aspects is the 10-fold increase in the rate of ASD diagnoses over a 20-year interval. According to the Centers for Disease Control and Prevention, the prevalence of ASD was 1 in 68 children in 2010 [2]. It remains to be discovered what causes ASD. But progress has been made in understanding the neurobiology of ASD. In this chapter, we will first introduce the symptoms of ASD, then discuss ASD from the perspective of brain networks, and finally review what we know about the mechanisms and etiology of ASD. The toolbox “Psychiatry” provides helpful background about psychiatry in general.
SYMPTOMS OF AUTISM SPECTRUM DISORDERS Early diagnosis of ASD is crucial, because intense behavioral interventions early in life substantially improve overall outcome. ASD symptoms center on difficulties with social interactions and communications, and repetitive behaviors. Many people with autism also have other medical problems, such as epileptic seizures. There is a wide range of severity of manifestations of symptoms of ASD. Although the diagnosis is usually made in children who are a few years old, more research has revealed early (precursor) symptoms that manifest as early as a few months of life. For example, children with autism fail to respond to their name, exhibit reduced interest in interpersonal interaction, and display limited early vocalizations, such as babbling. These are early signs of a general difficulty connecting with other people and understanding social clues, such as smiles and subtleties, such as sarcasm. Overall, people with autism appear to fail to understand the world through someone else’s eyes. Repetitive behaviors, such as head banging or stereotyped hand movements are common in people with ASD. Such stereotypy is not limited to motor actionsdpatients with ASD also tend to obsess over (often very technical) subject matters with unrelenting focus. It has been noted that some of the classical behavioral symptoms such as obsessing over technical details render people
Network Neuroscience http://dx.doi.org/10.1016/B978-0-12-801560-5.00026-4
319
Copyright © 2016 Elsevier Inc. All rights reserved.
320
26. AUTISM SPECTRUM DISORDERS
with ASD much more adept at solving certain very difficult tasks than typically developing (TD) people.
DYNAMICS AND STRUCTURE IN BRAIN NETWORKS IN AUTISM SPECTRUM DISORDERS Macroscopic Network Dynamics: Electroencephalogram The most obvious and common finding of resting-state electroencephalogram (EEG) in people with autism is the occurrence of epileptiform discharges, even in the absence of clinical seizures. For resting-state EEG, heterogeneous findings of increased and decreased power levels and alterations of coherence (see chapter: Network Interactions) have been found [2a]. For adults with ASD, decreased alpha power and increased beta and gamma power have been reported quite consistently. Overall, evidence points toward changes in functional connectivity as a function of spatial scale [3]. Local connections appear to be enhanced, whereas more global connections (between more distant electrodes) are reduced [4]. Coherence in the alpha frequency band is reduced, suggesting an impaired functional integration of the frontal lobe with the remainder of the cortex (long-range hypoconnectivity). In contrast, local connectivity in the theta band is increased, suggesting local hyperconnectivity. Little is known about cortical oscillations in young children with ASD (or at risk). Likely, the deficits in network activity structure and functional connectivity differ from the ones found in adults [4a].
Histology This shift from global (long range) to local connections is also supported by histological and structural imaging findings [5,6]. At the microscopic level, the number of minicolumns is increased in the brain of people with ASD compared to TD humans. Minicolumns are radially oriented clusters of neurons that may represent a unitary functional module. Neuronal size is smaller in the brains of patients with autism. Together with the larger number of minicolumns, this biases cortical connectivity toward short-range connections, since smaller neurons are less able to maintain the energy demands of long-range connections. Therefore these microscopic findings support reduced long-range connectivity and pathologically enhanced short-distance connections. Patients with ASD exhibit a pathological head enlargement that may be caused by brain overgrowth early in life, which has been proposed to be associated with pathological local hyperconnectivity. White matter structure can be separated into two relatively distinct aspects, the superficial and the deep white matter. Superficial white matter is composed of fibers that run orthogonal to the cortical surface. Axons that form the deep white matter travel in parallel to the cortical surface. This distinction is relevant to understanding ASD, because the fibers that form the superficial white matter are local connections, whereas the fibers that constitute the deep white matter provide long-range connections. In ASD, superficial white matter is pathologically increased and deep white matter pathologically reduced [7].
IV. NETWORK DISORDERS
DYNAMICS AND STRUCTURE IN BRAIN NETWORKS
321
(Functional) Magnetic Resonance Imaging Functional imaging studies with (functional) magnetic resonance imaging (fMRI) have shown conflicting evidence of hyper- and hypoconnectivity in ASD. It is likely that the connectivity differences between TD children and children with ASD exhibit a developmental time course. Also detailed methodological concerns have arisen that need to be taken into account for correct data interpretation. Most prominently, head motions (more prevalent in children with ASD) can introduce artifactual biases in functional connectivity measures based on fMRI data (see chapter: Imaging Functional Networks With MRI). Even small amounts of movement can introduce spurious functional connectivity with spatial structure. In addition, the choice of analysis strategy for extracting functional connectivity from BOLD fMRI data can lead to findings of hyper- or hypoconnectivity for the same dataset. For example, functional connectivity based on coactivation, which reflects online engagement of sensorymotor and cognitive processes, appears to bias data toward findings of hyperconnectivity, possibly because of the task engagement of neuronal structures. In contrast, intrinsic functional connectivity analysis extracts spontaneous BOLD fluctuations by bandpass filtering the BOLD time series and removing task-evoked activity by regression. This method biases the results toward findings of hypoconnectivity. Despite these limitations of fMRI [8], impairment of medium- and long-distance functional connectivity is a relatively common finding in fMRI studies of people with ASD [9]. In agreement with such impairment of functional longdistance connections, the corpus callosum appears different in patients with ASD. The corpus callosum is one of the main gateways of long-range projections between cortical hemispheres. Pathologies of the corpus callosum have been found in people with ASD, including findings of lower volume, increased diffusion, and lower fractional anisotropy (poorer organization of fibers, see chapter: Imaging Structural Networks with MRI).
Behavioral Analogies of Altered Network Connectivity It can be speculated that this pattern of hypo- and hyperconnectivity correlates with the behavioral profile. Patients with autism usually surpass TD children in tasks that require local processing (such as sensory discrimination tasks), but exhibit reduced performance in tasks that require information integration across multiple systems to “see the big picture.” Along this line of speculation is the behavioral hyperspecificity and inferior generalization observed in people with ASD. For example, the classical Wechsler IQ test includes tasks that require the participant to reconstruct a global pattern by arranging individual blocks that have red, white, or diagonally separated redewhite faces. The test requires breaking up each design into local units followed by a target manipulation of individual blocks to reconstruct the design. The task involves both global processing (the percept of the overall pattern to be reproduced) and local processing, analyzing the individual elements (blocks) that contribute to the pattern. In cases where perception of the overall pattern conflicts with local processing, patients with ASD outperform TD participants [10]. If segmentation is provided to aid local processing of the figure, this superior performance is abolished. Therefore TD are impeded in solving the task by global processing, whereas it is likely that people with ASD fail to engage global processing, and so excel by only processing the local information about individual elements in the task [11].
IV. NETWORK DISORDERS
322
26. AUTISM SPECTRUM DISORDERS
MECHANISMS AND ETIOLOGY OF AUTISM SPECTRUM DISORDERS Genes, Environment, and Multisystem Pathologies ASD are neurodevelopmental disorders with onset in childhood. ASD are complex disorders probably caused by many common genetic variants acting in combination with unknown environmental risk factors [12]. Fragile X syndrome (FXS) has many similarities with ASD at the neurobiological and behavioral level. But the disorders differ in their etiology. FXS is a monogenetic disorder caused by an expansion of the fragile X mental retardation protein 1 gene. Both disorders are more prevalent in males. FSX is an inherited syndrome with an X-linked dominant pattern. This means that the mutated gene is located on the X chromosome. It is called “dominant” since girls (who have two X chromosomes) can develop the disorder with just one copy of the X chromosome affected. However, disease manifestation is more prevalent in boys than girls (by about a factor of two) and is typically more severe in boys. ASD is almost five times more common in boys than in girlsdthe reason is unknown. It is quite clear that environmental factors (probably through interaction with genetic vulnerabilities) contribute to autism etiology [13]. The early study of environmental causes was marred by academic fraudda fraudulent publication claimed a link between childhood vaccines and autism. Those findings were concocted by a fraudster and have been proven to be wrong in a long series of careful scientific studies. However, exposure to certain chemicals may increase the risk for autism. For example, valproic acid, an antiepileptic (anticonvulsant) drug, which is also prescribed as a mood stabilizer, increases the prevalence of autism in offspring when taken during pregnancy. The highest vulnerability occurs a few weeks after conception. Rat models of valproic acid exposure exhibit structural and behavioral phenotypes similar to people with autism. At the structural level, changes in dendritic organization agree with the local hyperconnectivity and long-range hypoconnectivity discussed earlier. At the behavioral level, exposed rats exhibit a range of symptoms reminiscent of ASD, such as reduced social interactions, increased sensitivity to sensory stimuli, and exaggerated fear responses and memories. Another example of an environmental factor is organophosphate insecticides, such as chlorpyrifos, which have been shown to be detrimental to neurodevelopmental outcomes in rats. Similarly, epidemiological studies have shown association between maternal pesticide exposure and autism prevalence. Understanding ASD and its cause(s) will require a broad approach that considers the interaction of multiple complex systems. For example, ASD has been hypothesized to relate to changes in the gut microbiota. This aligns with the poorly understood but very common finding of gastrointestinal symptoms in patients with ASD. Interestingly, decades of studies have been performed on the link between the gut-associated immune system, enteric nervous system, and gut-based endocrine system. However, these findings had not penetrated the fields of neurology and psychiatry until recently [14]. One of the earlier fundamental observations was that germ-free mice display an exaggerated response to stress that was reduced by colonization of the gut with bifidobacteria. Overall, recolonization
IV. NETWORK DISORDERS
SUMMARY AND OUTLOOK
323
of germ-free mice with normal microbiota or probiotic treatment reverses both biochemical (tryptophan metabolism) and behavioral (anxiety, sociability) deficits. Probiotic treatment has positive behavioral effects. Interestingly, these effects are strain specific, with Bifidobacterium and Lactobacillus appearing to be the most beneficial genera in terms of their effect on anxiety and depression-like behavior in mice. Despite the promise of these findings, it remains to be seen if changes in the microbiome cause CNS dysfunction. Despite the psychiatric and cognitive side effects of antibiotic use, the neuronal effects of changes in the microbiome induced by antibiotics remain mostly unstudied.
Developmental Origin of Connectivity Deficits Differences in connectivity schemes may be related to differential brain growth trajectory in children with ASD and TD children. A large fraction of children with ASD exhibit pathologically accelerated brain growth that is limited to the first 2 years of their lives. As discussed earlier, larger brains may have relatively higher proportions of short-range to long-range connectivity. These altered growth trajectories follow an anterioreposterior organization pattern in which frontal areas are the most affected while occipital areas are the least affected. White matter differences appear to be most prominent for intrahemispheric fibers immediately underneath frontal areas, such as the dorsolateral prefrontal cortex. In terms of developmental trajectories, differences in head size disappear later during development, although people with ASD maintain elevated gray matter volumes and only limited increase in white matter. This contrasts with TD individuals, where gray matter volume decreases and white matter increases quite dramatically during development. At the structural level, one of the most consistent findings is a poor differentiation of the junction between the white and the gray matter. This can be caused by excess superficial white matter or pathologically increased number of interstitial neurons, defined as neurons in the white matter. Such an increase in interstitial neurons can be caused by pathologically arrested migration or by excess remnants of neurons in the fetal subplate, from which cortical neurons originate. The subplate is a transient neuronal structure that is fully established in the second trimester of human pregnancy and dissolves by postnatal month 6. The subplate is not only the source of cortical neurons, but it also represents a transient intermediate relay station of cortical afferents that form synapses onto neurons in the subplate before advancing their axonal terminals into the newly developed cortical layers. Notably, interstitial neurons remain in place in TD humans into adult life, particularly in frontal and prefrontal areas.
SUMMARY AND OUTLOOK In this chapter, we learned that ASD affects a rapidly growing number of people, and that the underlying cause is likely to be a complex interaction between genetic and environmental factors. We reviewed evidence from different methods and levels of analysis that can be summarized in an overall model of increased short-range connectivity and decreased long-range connectivity. It is interesting to note that this overall pattern of long-range hypoconnectivity and short-range hyperconnectivity has also been reported in numerous other disease states.
IV. NETWORK DISORDERS
324
26. AUTISM SPECTRUM DISORDERS
In particular, “frontal disconnection” has been reported in many studies, including studies of patients with schizophrenia, attention deficit disorder, dyslexia, Down syndrome, depression, and HIV/AIDS.
References [1] American Psychiatric Association, DSM-5 Task Force. Diagnostic and statistical manual of mental disorders: DSM-5. 5th ed. Washington, DC: American Psychiatric Association; 2013. xliv. 947 p. [2] Centers for Disease Control and Prevention. Prevalence of autism spectrum disorder among children aged 8 years d autism and developmental disabilities monitoring network, 11 sites, United States, 2010. Morbidity and Mortality Weekly Report 2014. [2a] Wang J, Barstein J, Ethridge LE, Mosconi MW, Takarae Y, Sweeney JA. Resting state EEG abnormalities in autism spectrum disorders. J Neurodev Disord 2013;5:24. [3] Murias M, et al. Resting state cortical connectivity reflected in EEG coherence in individuals with autism. Biol Psychiatry 2007;62(3):270e3. [4] Coben R, et al. EEG power and coherence in autistic spectrum disorder. Clin Neurophysiol 2008;119(5):1002e9. [4a] Orekhova EV, Elsabbagh M, Jones EJ, Dawson G, Charman T, Johnson MH. The BASIS Team. EEG hyperconnectivity in high-risk infants is associated with later autism. J Neurodev Disord 2014;6:40. [5] Casanova MF, et al. Minicolumnar abnormalities in autism. Acta Neuropathol 2006;112(3):287e303. [6] Zikopoulos B, Barbas H. Altered neural connectivity in excitatory and inhibitory cortical circuits in autism. Front Hum Neurosci 2013;7. [7] McFadden K, Minshew NJ. Evidence for dysregulation of axonal growth and guidance in the etiology of ASD. Front Hum Neurosci 2013;7. [8] Nair A, et al. Impact of methodological variables on functional connectivity findings in autism spectrum disorders. Hum Brain Mapp 2014;35(8):4035e48. [9] Wass S. Distortions and disconnections: disrupted brain connectivity in autism. Brain Cogn 2011;75(1):18e28. [10] Shah A, Frith U. Why do autistic individuals show superior performance on the block design task? J Child Psychol Psychiatry 1993;34(8):1351e64. [11] Mottron L, et al. Enhanced perceptual functioning in autism: an update, and eight principles of autistic perception. J Autism Dev Disord 2006;36(1):27e43. [12] Parellada M, et al. The neurobiology of autism spectrum disorders. Eur Psychiatry 2014;29(1):11e9. [13] Landrigan PJ. What causes autism? Exploring the environmental contribution. Curr Opin Pediatr 2010;22(2):219e25. [14] Mayer EA, et al. Gut microbes and the brain: paradigm shift in neuroscience. J Neurosci 2014;34(46):15490e6.
IV. NETWORK DISORDERS
26 Autism Spectrum Disorders Autism was originally defined as “an innate disturbance of affective contact” by Leo Kanner in 1943 and was included in the chapter on childhood schizophrenia in early versions of the Diagnostic and Statistical Manual of Mental Disorders (DSM) (see toolbox: Psychiatry). Today, autism is part of a new autism spectrum disorders (ASD) diagnostic label in the DSM-5 [1] that subsumes autism according to Kanner’s definition, Asperger’s syndrome, and other early-onset interaction and communication impairments. One of the most puzzling and concerning aspects is the 10-fold increase in the rate of ASD diagnoses over a 20-year interval. According to the Centers for Disease Control and Prevention, the prevalence of ASD was 1 in 68 children in 2010 [2]. It remains to be discovered what causes ASD. But progress has been made in understanding the neurobiology of ASD. In this chapter, we will first introduce the symptoms of ASD, then discuss ASD from the perspective of brain networks, and finally review what we know about the mechanisms and etiology of ASD. The toolbox “Psychiatry” provides helpful background about psychiatry in general.
SYMPTOMS OF AUTISM SPECTRUM DISORDERS Early diagnosis of ASD is crucial, because intense behavioral interventions early in life substantially improve overall outcome. ASD symptoms center on difficulties with social interactions and communications, and repetitive behaviors. Many people with autism also have other medical problems, such as epileptic seizures. There is a wide range of severity of manifestations of symptoms of ASD. Although the diagnosis is usually made in children who are a few years old, more research has revealed early (precursor) symptoms that manifest as early as a few months of life. For example, children with autism fail to respond to their name, exhibit reduced interest in interpersonal interaction, and display limited early vocalizations, such as babbling. These are early signs of a general difficulty connecting with other people and understanding social clues, such as smiles and subtleties, such as sarcasm. Overall, people with autism appear to fail to understand the world through someone else’s eyes. Repetitive behaviors, such as head banging or stereotyped hand movements are common in people with ASD. Such stereotypy is not limited to motor actionsdpatients with ASD also tend to obsess over (often very technical) subject matters with unrelenting focus. It has been noted that some of the classical behavioral symptoms such as obsessing over technical details render people
Network Neuroscience http://dx.doi.org/10.1016/B978-0-12-801560-5.00026-4
319
Copyright © 2016 Elsevier Inc. All rights reserved.
320
26. AUTISM SPECTRUM DISORDERS
with ASD much more adept at solving certain very difficult tasks than typically developing (TD) people.
DYNAMICS AND STRUCTURE IN BRAIN NETWORKS IN AUTISM SPECTRUM DISORDERS Macroscopic Network Dynamics: Electroencephalogram The most obvious and common finding of resting-state electroencephalogram (EEG) in people with autism is the occurrence of epileptiform discharges, even in the absence of clinical seizures. For resting-state EEG, heterogeneous findings of increased and decreased power levels and alterations of coherence (see chapter: Network Interactions) have been found [2a]. For adults with ASD, decreased alpha power and increased beta and gamma power have been reported quite consistently. Overall, evidence points toward changes in functional connectivity as a function of spatial scale [3]. Local connections appear to be enhanced, whereas more global connections (between more distant electrodes) are reduced [4]. Coherence in the alpha frequency band is reduced, suggesting an impaired functional integration of the frontal lobe with the remainder of the cortex (long-range hypoconnectivity). In contrast, local connectivity in the theta band is increased, suggesting local hyperconnectivity. Little is known about cortical oscillations in young children with ASD (or at risk). Likely, the deficits in network activity structure and functional connectivity differ from the ones found in adults [4a].
Histology This shift from global (long range) to local connections is also supported by histological and structural imaging findings [5,6]. At the microscopic level, the number of minicolumns is increased in the brain of people with ASD compared to TD humans. Minicolumns are radially oriented clusters of neurons that may represent a unitary functional module. Neuronal size is smaller in the brains of patients with autism. Together with the larger number of minicolumns, this biases cortical connectivity toward short-range connections, since smaller neurons are less able to maintain the energy demands of long-range connections. Therefore these microscopic findings support reduced long-range connectivity and pathologically enhanced short-distance connections. Patients with ASD exhibit a pathological head enlargement that may be caused by brain overgrowth early in life, which has been proposed to be associated with pathological local hyperconnectivity. White matter structure can be separated into two relatively distinct aspects, the superficial and the deep white matter. Superficial white matter is composed of fibers that run orthogonal to the cortical surface. Axons that form the deep white matter travel in parallel to the cortical surface. This distinction is relevant to understanding ASD, because the fibers that form the superficial white matter are local connections, whereas the fibers that constitute the deep white matter provide long-range connections. In ASD, superficial white matter is pathologically increased and deep white matter pathologically reduced [7].
IV. NETWORK DISORDERS
DYNAMICS AND STRUCTURE IN BRAIN NETWORKS
321
(Functional) Magnetic Resonance Imaging Functional imaging studies with (functional) magnetic resonance imaging (fMRI) have shown conflicting evidence of hyper- and hypoconnectivity in ASD. It is likely that the connectivity differences between TD children and children with ASD exhibit a developmental time course. Also detailed methodological concerns have arisen that need to be taken into account for correct data interpretation. Most prominently, head motions (more prevalent in children with ASD) can introduce artifactual biases in functional connectivity measures based on fMRI data (see chapter: Imaging Functional Networks With MRI). Even small amounts of movement can introduce spurious functional connectivity with spatial structure. In addition, the choice of analysis strategy for extracting functional connectivity from BOLD fMRI data can lead to findings of hyper- or hypoconnectivity for the same dataset. For example, functional connectivity based on coactivation, which reflects online engagement of sensorymotor and cognitive processes, appears to bias data toward findings of hyperconnectivity, possibly because of the task engagement of neuronal structures. In contrast, intrinsic functional connectivity analysis extracts spontaneous BOLD fluctuations by bandpass filtering the BOLD time series and removing task-evoked activity by regression. This method biases the results toward findings of hypoconnectivity. Despite these limitations of fMRI [8], impairment of medium- and long-distance functional connectivity is a relatively common finding in fMRI studies of people with ASD [9]. In agreement with such impairment of functional longdistance connections, the corpus callosum appears different in patients with ASD. The corpus callosum is one of the main gateways of long-range projections between cortical hemispheres. Pathologies of the corpus callosum have been found in people with ASD, including findings of lower volume, increased diffusion, and lower fractional anisotropy (poorer organization of fibers, see chapter: Imaging Structural Networks with MRI).
Behavioral Analogies of Altered Network Connectivity It can be speculated that this pattern of hypo- and hyperconnectivity correlates with the behavioral profile. Patients with autism usually surpass TD children in tasks that require local processing (such as sensory discrimination tasks), but exhibit reduced performance in tasks that require information integration across multiple systems to “see the big picture.” Along this line of speculation is the behavioral hyperspecificity and inferior generalization observed in people with ASD. For example, the classical Wechsler IQ test includes tasks that require the participant to reconstruct a global pattern by arranging individual blocks that have red, white, or diagonally separated redewhite faces. The test requires breaking up each design into local units followed by a target manipulation of individual blocks to reconstruct the design. The task involves both global processing (the percept of the overall pattern to be reproduced) and local processing, analyzing the individual elements (blocks) that contribute to the pattern. In cases where perception of the overall pattern conflicts with local processing, patients with ASD outperform TD participants [10]. If segmentation is provided to aid local processing of the figure, this superior performance is abolished. Therefore TD are impeded in solving the task by global processing, whereas it is likely that people with ASD fail to engage global processing, and so excel by only processing the local information about individual elements in the task [11].
IV. NETWORK DISORDERS
322
26. AUTISM SPECTRUM DISORDERS
MECHANISMS AND ETIOLOGY OF AUTISM SPECTRUM DISORDERS Genes, Environment, and Multisystem Pathologies ASD are neurodevelopmental disorders with onset in childhood. ASD are complex disorders probably caused by many common genetic variants acting in combination with unknown environmental risk factors [12]. Fragile X syndrome (FXS) has many similarities with ASD at the neurobiological and behavioral level. But the disorders differ in their etiology. FXS is a monogenetic disorder caused by an expansion of the fragile X mental retardation protein 1 gene. Both disorders are more prevalent in males. FSX is an inherited syndrome with an X-linked dominant pattern. This means that the mutated gene is located on the X chromosome. It is called “dominant” since girls (who have two X chromosomes) can develop the disorder with just one copy of the X chromosome affected. However, disease manifestation is more prevalent in boys than girls (by about a factor of two) and is typically more severe in boys. ASD is almost five times more common in boys than in girlsdthe reason is unknown. It is quite clear that environmental factors (probably through interaction with genetic vulnerabilities) contribute to autism etiology [13]. The early study of environmental causes was marred by academic fraudda fraudulent publication claimed a link between childhood vaccines and autism. Those findings were concocted by a fraudster and have been proven to be wrong in a long series of careful scientific studies. However, exposure to certain chemicals may increase the risk for autism. For example, valproic acid, an antiepileptic (anticonvulsant) drug, which is also prescribed as a mood stabilizer, increases the prevalence of autism in offspring when taken during pregnancy. The highest vulnerability occurs a few weeks after conception. Rat models of valproic acid exposure exhibit structural and behavioral phenotypes similar to people with autism. At the structural level, changes in dendritic organization agree with the local hyperconnectivity and long-range hypoconnectivity discussed earlier. At the behavioral level, exposed rats exhibit a range of symptoms reminiscent of ASD, such as reduced social interactions, increased sensitivity to sensory stimuli, and exaggerated fear responses and memories. Another example of an environmental factor is organophosphate insecticides, such as chlorpyrifos, which have been shown to be detrimental to neurodevelopmental outcomes in rats. Similarly, epidemiological studies have shown association between maternal pesticide exposure and autism prevalence. Understanding ASD and its cause(s) will require a broad approach that considers the interaction of multiple complex systems. For example, ASD has been hypothesized to relate to changes in the gut microbiota. This aligns with the poorly understood but very common finding of gastrointestinal symptoms in patients with ASD. Interestingly, decades of studies have been performed on the link between the gut-associated immune system, enteric nervous system, and gut-based endocrine system. However, these findings had not penetrated the fields of neurology and psychiatry until recently [14]. One of the earlier fundamental observations was that germ-free mice display an exaggerated response to stress that was reduced by colonization of the gut with bifidobacteria. Overall, recolonization
IV. NETWORK DISORDERS
SUMMARY AND OUTLOOK
323
of germ-free mice with normal microbiota or probiotic treatment reverses both biochemical (tryptophan metabolism) and behavioral (anxiety, sociability) deficits. Probiotic treatment has positive behavioral effects. Interestingly, these effects are strain specific, with Bifidobacterium and Lactobacillus appearing to be the most beneficial genera in terms of their effect on anxiety and depression-like behavior in mice. Despite the promise of these findings, it remains to be seen if changes in the microbiome cause CNS dysfunction. Despite the psychiatric and cognitive side effects of antibiotic use, the neuronal effects of changes in the microbiome induced by antibiotics remain mostly unstudied.
Developmental Origin of Connectivity Deficits Differences in connectivity schemes may be related to differential brain growth trajectory in children with ASD and TD children. A large fraction of children with ASD exhibit pathologically accelerated brain growth that is limited to the first 2 years of their lives. As discussed earlier, larger brains may have relatively higher proportions of short-range to long-range connectivity. These altered growth trajectories follow an anterioreposterior organization pattern in which frontal areas are the most affected while occipital areas are the least affected. White matter differences appear to be most prominent for intrahemispheric fibers immediately underneath frontal areas, such as the dorsolateral prefrontal cortex. In terms of developmental trajectories, differences in head size disappear later during development, although people with ASD maintain elevated gray matter volumes and only limited increase in white matter. This contrasts with TD individuals, where gray matter volume decreases and white matter increases quite dramatically during development. At the structural level, one of the most consistent findings is a poor differentiation of the junction between the white and the gray matter. This can be caused by excess superficial white matter or pathologically increased number of interstitial neurons, defined as neurons in the white matter. Such an increase in interstitial neurons can be caused by pathologically arrested migration or by excess remnants of neurons in the fetal subplate, from which cortical neurons originate. The subplate is a transient neuronal structure that is fully established in the second trimester of human pregnancy and dissolves by postnatal month 6. The subplate is not only the source of cortical neurons, but it also represents a transient intermediate relay station of cortical afferents that form synapses onto neurons in the subplate before advancing their axonal terminals into the newly developed cortical layers. Notably, interstitial neurons remain in place in TD humans into adult life, particularly in frontal and prefrontal areas.
SUMMARY AND OUTLOOK In this chapter, we learned that ASD affects a rapidly growing number of people, and that the underlying cause is likely to be a complex interaction between genetic and environmental factors. We reviewed evidence from different methods and levels of analysis that can be summarized in an overall model of increased short-range connectivity and decreased long-range connectivity. It is interesting to note that this overall pattern of long-range hypoconnectivity and short-range hyperconnectivity has also been reported in numerous other disease states.
IV. NETWORK DISORDERS
324
26. AUTISM SPECTRUM DISORDERS
In particular, “frontal disconnection” has been reported in many studies, including studies of patients with schizophrenia, attention deficit disorder, dyslexia, Down syndrome, depression, and HIV/AIDS.
References [1] American Psychiatric Association, DSM-5 Task Force. Diagnostic and statistical manual of mental disorders: DSM-5. 5th ed. Washington, DC: American Psychiatric Association; 2013. xliv. 947 p. [2] Centers for Disease Control and Prevention. Prevalence of autism spectrum disorder among children aged 8 years d autism and developmental disabilities monitoring network, 11 sites, United States, 2010. Morbidity and Mortality Weekly Report 2014. [2a] Wang J, Barstein J, Ethridge LE, Mosconi MW, Takarae Y, Sweeney JA. Resting state EEG abnormalities in autism spectrum disorders. J Neurodev Disord 2013;5:24. [3] Murias M, et al. Resting state cortical connectivity reflected in EEG coherence in individuals with autism. Biol Psychiatry 2007;62(3):270e3. [4] Coben R, et al. EEG power and coherence in autistic spectrum disorder. Clin Neurophysiol 2008;119(5):1002e9. [4a] Orekhova EV, Elsabbagh M, Jones EJ, Dawson G, Charman T, Johnson MH. The BASIS Team. EEG hyperconnectivity in high-risk infants is associated with later autism. J Neurodev Disord 2014;6:40. [5] Casanova MF, et al. Minicolumnar abnormalities in autism. Acta Neuropathol 2006;112(3):287e303. [6] Zikopoulos B, Barbas H. Altered neural connectivity in excitatory and inhibitory cortical circuits in autism. Front Hum Neurosci 2013;7. [7] McFadden K, Minshew NJ. Evidence for dysregulation of axonal growth and guidance in the etiology of ASD. Front Hum Neurosci 2013;7. [8] Nair A, et al. Impact of methodological variables on functional connectivity findings in autism spectrum disorders. Hum Brain Mapp 2014;35(8):4035e48. [9] Wass S. Distortions and disconnections: disrupted brain connectivity in autism. Brain Cogn 2011;75(1):18e28. [10] Shah A, Frith U. Why do autistic individuals show superior performance on the block design task? J Child Psychol Psychiatry 1993;34(8):1351e64. [11] Mottron L, et al. Enhanced perceptual functioning in autism: an update, and eight principles of autistic perception. J Autism Dev Disord 2006;36(1):27e43. [12] Parellada M, et al. The neurobiology of autism spectrum disorders. Eur Psychiatry 2014;29(1):11e9. [13] Landrigan PJ. What causes autism? Exploring the environmental contribution. Curr Opin Pediatr 2010;22(2):219e25. [14] Mayer EA, et al. Gut microbes and the brain: paradigm shift in neuroscience. J Neurosci 2014;34(46):15490e6.
IV. NETWORK DISORDERS