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Neuropathology of Autism Spectrum Disorders: Postmortem Studies 

Neuropathology of Autism Spectrum Disorders: Postmortem Studies

Chapter:
Neuropathology of Autism Spectrum Disorders: Postmortem Studies
Author(s):

Cynthia M. Schumann

, Steven C. Noctor

, and David G. Amaral

DOI:
10.1093/med/9780195371826.003.0100
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Points of Interest

  • Compared to disorders such as Alzheimer’s disease, relatively few autistic brains have been neuropathologically investigated, and even fewer have been subjected to quantitative analysis.

  • Lower numbers of neurons have been reported in the amygdala, the fusiform gyrus of the temporal lobe, and the cerebellum.

  • One current line of research emphasizes alterations in the basic columnar organization of the neocortex.

  • Provocative data signs of ongoing inflammation are observed in the autistic brain.

Considerable progress in understanding the behavioral impairments of autism has been made over the last 65 years since Kanner (1943) first described a group of children with autistic-like disturbances, though the underlying neuropathology of the disorder remains elusive. Autism is a clinically defined disorder marked by impairments in reciprocal social interaction, abnormal development and use of language, and repetitive and ritualized behaviors, and a narrow range of interests that manifest by 3 years of age. These behavioral disturbances suggest that certain parts of the brain may be more pathological than others. Regions that constitute the “social brain” for example, might be preferentially impacted by autism. We recently compiled a list of neural systems involved in the functions that could be most affected by the core behavioral features of autism (Figure 31–1) to suggest where one might expect to find neuropathology (Amaral et al., 2008). Several brain regions have been implicated in social behavior through experimental animal studies, lesion studies in human patients, or functional imaging studies (Adolphs, 2001). These include regions of the frontal lobe, the cingulate cortex, the superior temporal cortex, the parietal cortex, and the amygdala. Language function is distributed throughout several cortical and subcortical regions. Foremost for expressive language function is Broca’s area in the inferior frontal gyrus and portions of the supplementary motor cortex. Wernicke’s area is essential for receptive language function, and the superior temporal sulcus plays a role in both language processing and social attention (Redcay, 2008). Finally, the repetitive or stereotyped behaviors of autism share many similarities with the abnormal of obsessive-compulsive disorder that implicate regions such as the orbitofrontal cortex and caudate nucleus.

Figure 31–1. Brain areas that have been implicated in the mediation of the three core behaviors that are impaired in autism: social behavior, language and communication, and repetitive and stereotyped behaviors. Adapted from Amaral, D. G., Schumann, C. M., & Nordahl, C. W. (2008). Neuroanatomy of autism. Trends in Neuroscience, 31, 137–145, with permission from Trends in Neurosciences.

Figure 31–1.
Brain areas that have been implicated in the mediation of the three core behaviors that are impaired in autism: social behavior, language and communication, and repetitive and stereotyped behaviors. Adapted from Amaral, D. G., Schumann, C. M., & Nordahl, C. W. (2008). Neuroanatomy of autism. Trends in Neuroscience, 31, 137–145, with permission from Trends in Neurosciences.

Autism is increasingly considered to be a heterogeneous disorder with multiple causes and courses. Because there is a great range in the severity of symptoms associated with autism, it is described as a spectrum disorder. Mental retardation is a common correlate of autism representing the “low” end of the spectrum, and there are a number of comorbid symptoms, such as epilepsy, that have neurological underpinnings and affect some, but not all, individuals with autism. The comorbid symptoms of autism must be taken into consideration for the interpretation of the neuropathology of autism. Epilepsy, for example, is associated with pathology of the cerebral cortex, amygdala, cerebellum, and hippocampal formation, all of which have also been implicated in autism. Thus, if one analyzes the brain of an individual who has autism as well as epilepsy, it is not clear whether the observed pathology is related to the core features of autism, to the cause, effects or treatment of epilepsy, or perhaps to some peculiar convergence of the two disorders. Unfortunately, the majority of cases evaluated in earlier postmortem studies of the autistic brain (Table 31-1) involved brains from individuals who had comorbid seizure disorders and mental retardation.

Table 31–1. Neuropathological studies of autism using postmortem brain from 1980–2010

Author

Year

Sample Size and Characteristics

Approach and Region of Interest

Major Findings

Williams et al.

1980

4A (3M, 1F; ages 4, 14, 27, 33; 2S; 4MR); no C

qualitative observation—whole brain

Nerve cell loss and replacement gliosis in atrophic orbitofrontal and temporal regions in 2 cases; smaller neurons in CA4; ↓ Purkinje cell density in 1 case

Bauman & Kemper

1985

1A (1M; age 29; no S; 1MR); 1C (1M; age 25)

qualitative observation—whole brain

↑ cell density and ↓ cell size in hippocampus, subiculum, entorhinal cortex, septal nuclei, mammillary body and amygdala. ↓ density of Purkinje cells, neurons small and pale

Coleman et al.

1985

1A (1F; age 21; no S; 1MR); 2C (2F; ages 18, 25)

2D cell counts auditory cortex and Broca’s area

No differences, except for ↓ glia in left auditory cortex and ↓ density of pyramidal neurons in right auditory association cortex

Ritvo et al.

1986

4A (4M; ages 10, 19, 19, 22; no S; 3MR); 3C (3M; ages 3, 10, 13)

2D cell counts in cerebellum

↓ Purkinje cell density in cerebellar hemisphere and vermis

Bauman & Kemper

1987

1A (1F; age 11; no S; ?MR); 2C (?M; ?F; age-matched)

qualitative observation—amygdala & hippocampus

↑ cell density in amygdala & hippocampus

Bauman & Kemper

1990

1A (1M; age 12; no S; no MR); 2C (2M; age-matched)

qualitative observation—amygdala, hippocampus & cerebellum

↑ cell density of smaller neurons in limbic system; ↓ density of Purkinje cells; enlarged neurons in deep cerebellar nuclei and inferior olive

Bauman

1991

5A (4M, 1F; ages 9–29; 4S; 4MR)

review of earlier findings

↑ cell density in limbic system (4/5) and ↓ Purkinje cell density in cerebellum (5/5)

Hof et al.

1991

1A (1F; age 24; no S; 1MR)

qualitative observation—cerebral cortex and limbic system

Microcephaly (773 g); neurofibrillary tangles, especially in layer II and III of temporal cortex, probably due to severe head banging

Kemper & Bauman

1993

6A (5M, 1F; ages 9–29; 4S; 5MR); 6C (6M; age- and sex-matched)

qualitative/density measures—limbic system and cerebellum

Small and densely packed neurons in limbic system (6/6); anterior cingulate coarse and poorly laminated in 5/6; ↓ Purkinje cell #s in cerebellum (6/6)

Guerin et al.

1996

1A (1F; age 16; 1S 1MR)

qualitative observations—whole brain

Microcephaly, ↑ ventricular dilation, thin corpus callosum

Raymond et al.

1996

2A (1M; ages 7, 9; no S; 2MR); 2C (?M, ?F; ages 8, 13)

Golgi analysis in hippocampus

Smaller neurons in CA4; less dendritic branching in CA1 and CA4

Rodier et al.

1996

1A (1F; age 21; 1S; 1MR); 1C (1M; age 80)

qualitative observation—pons, medulla and cerebellum

Near-complete absence of facial nucleus and superior olive and shortening of the brainstem

Bailey et al.

1998

6A (6M; ages 4, 20–27 years; 3S; 6MR); 7C (5M, 2F; age-matched)

qualitative/density measures—whole brain

Megalencephaly (4/6); abnormalities in inferior olives

(4/6); ↓ Purkinje cells in all adults; cortical dysgenesis in at least 50%

Blatt et al.

2001

4A (4M; ages 19–22; 2S; 4MR); 3C (3M; ages 16, 19, 24)

GABAergic, serotonergic, cholinergic, glutamatergic autoradiography in hippocampus

↓ GABA(A) receptor binding

Perry et al.

2001

7A (6M, 1F; ages ∼24; ∼4S; ∼7MR); 10C (8M, 2F; ages ∼32); 9MR (5M, 4F; ages ∼32)

cholinergic immunohistochemistry in frontal and parietal cortex and basal forebrain

30% ↓ M1 receptor binding in parietal cortex; 65–73% ↓ α4 nicotinic receptor binding in frontal and parietal cortex; ↑ BDNF in forebrain

Casanova et al.

2002

2AS (2M; ages 22 and 79 years; ?S; no MR); 18C (18M; ages 9–98)

minicolumn analyses Layer III of prefrontal and temporal cortex

Cell columns were more numerous, smaller, and less compact in prefrontal layer III

Casanova et al.

2002

9A (7M, 1F; ages ∼12; 5S; 7MR); 9C (?M, ?F; ages ∼15)

minicolumn analyses Layer III of prefrontal and temporal cortex

Cell columns more numerous, smaller and less compact in prefrontal layer III

Fatemi et al.

2002

5A (5M; ages ∼25; ?S; ?MR); 5C (≥ 4M; ages ∼24)

density and size measure of Purkinje cells in cerebellum

24% smaller Purkinje cells in cerebellum; no differences in density

Mukaetova-Ladinska et al.

2004

2A (2M; ages 29, 31; ?S; ?MR); 2C (1M, 1F; ages 19, 34)

MAP2 immunohistochemistry & Nissl in dorsolateral prefrontal cortex

“Ill defined neurocortical layers” & ↓ MAP2 immunoreactive neurons

Ray et al.

2005

3A (3M; ages 29, 31, 32; ?S; ?MR); 3C (2M, 1F; ages 12, 29, 72)

nAchR immunohistochemistry in thalamus

↓ α7 and β2 immunoreactive neurons in paraventricular nucleus & nucleus reuniens

Vargas et al.

2005

15A (12M, 3F; ages 5–44; 6S; 12MR); 12C (9M, 3F; ages 5–46)

neuroinflammation in cerebellum, mid frontal, & cingulate (immunostained for HLA-DR, etc.)

↑ activated microglia and astroglia and qualitative loss of Purkinje cells in cerebellum

Buxhoeveden

2006

2A (2M; ages 3, 41; ≥1S, ≥MR); 5C (5M; ages 2, 21, 34, 44, 75)

minicolumnar width in layer III in frontal cortex

↓ minicolumnar width in dorsal and orbital frontal cortex

Casanova

2006

6A (4M, 2F; ages 4–24; 2S; ?MR); 6C (4M, 2F; ages 4–25)

minicolumnar width in layer III of S1, BA4,9, 17

↓ minicolumnar width; smaller neurons; 23% ↑ in neuron density in layer III of BA9

Hutsler et al.

2006

8A (8M; ages 15–45, no S; ?MR); 8C (8M; ages 14–45)

cortical thickness on postmortem MRI

no difference in cortical thickness

Martchek et al.

2006

5A (?M, ?F; ages 19–54; ≥ 1S; ?MR); 4C (?M, ?F; ages 25–55)

stereological neuron # in locus coeruleus

no difference in neuron # in locus coeruleus

Schumann & Amaral

2006

9A (9M; ages 11–44; no S; ?MR); 10C (10M; ages 10–44)

stereological neuron # and size in amygdala

↓ neuron # in whole amygdala and lateral nucleus

Kennedy

2007

4A (4M; ages 3, 15, 34, 41; 1S; 3MR); 5C (5M; ages 2, 16, 21, 44, 75)

stereological spindle neuron # in frontal insula

no difference in spindle neuron #

Guptill et al.

2007

4A (4M; ages 19–22; 3S; 3MR); 3C (3M; ages 16–24)

multiple-concentration GABAergic autoradiography in the hipppocampus

Non-significant 20% ↓ in # of benzodiazepine binding sites

Yip et al.

2007

8A (6M, 2F; ages 16–30); 8C (8M; ages 16–30)

in situ GAD67 mRNA in cerebellum

40% reduction in GAD67 mRNA in Purkinje cells

Kulesza & Mangunay

2008

5A (5M; ages 8–32); 2C (2M; ages 26, 29)

morphology of neurons in the medial superior olive

↓ cell body size; abnormal shape and orientation

Van Kooten et al.

2008

7A(4M, 3F; ages 4–23; 4S; ?MR); 10C (8M, 2F; ages 4–65)

stereological neuron # in fusiform gyrus, V1 & cortical gray

↓ neuron densities in layer III, total neuron # in layers III,V, and VI, and mean perikaryal volumes in layers V/VI in fusiform. No differences in V1 or total cortical gray

Whitney et al.

2008

6A (5M, 1F; ages 13–54; ≥1S; ?MR); 4C (3M, 1F; ages 17–53)

calbindin-D28k immunohistochemistry in cerebellum

No difference in density of Purkinje cells

Yip et al.

2008

same as Yip et al., 2007

in situ hybridization for GAD67 mRNA in cerebellum

28% upregulated GAD67 in basket cells of cerebellum

Yip et al.

2009

same as Yip et al., 2007

in situ hybridization for GAD65 mRNA in cerebellum

↓ GAD65 mRNA levels in cerebellar dentate nuclei

Whitney et al.

2009

same as Whitney et al., 2008

parvalbumin immunohistochemistry in cerebellum

No difference in basket or stellate cells

Simms et al.

2009

9A (9M; ages 15–54; ≥5S; ≥4MR); 4C (4M; ages 20–53)

density and size of neurons in anterior cingulate

↓ cell size in layers I–III and layers V–VI of area 24b and cell packing density in layers V–VI of area 24c

Oblak et al.

2009

7A (6M, 1F; ages 19–30; 4S; 9MR); 9C (9M; ages 19–43)

multiple-concentration ligand-binding study of GABA(A) in anterior cingulate

↓ GABA(A) receptors and benzodiazepine binding sites in anterior cingulate cortex

Lawrence et al.

2010

5A (5M; ages 13–54; ≥2S; ?MR); 5C (5M; ages 14–63)

density of GABAergic interneurons immunostained with Ca+ binding proteins in hippocampus

↑ immunoreactive interneuron density for calbindin in dentate gyrus, ↑ calretinin in CA1, ↑ parvalbumin in CA1 and CA3

Avino et al.

2010

8A (8M; ages 10–45; ≥2S; ≥7MR); 8C (8M; ages 11–51)

Spatial extent of gray-white transition in BA 7, 9, 21

“indistinct” boundary of layer VI and underlying white matter

Casanova et al.

2010

7A (5M, 2F; ages 4–67; ?S; ?MR); 7C (5M; 2F; ages 4–65)

minicolumnar width in BA 4, 9, 10, 11, 17, 24, 43, 44

↓ minicolumnar width in supra- and infragranular layers, most notable in BA 44.

Hutsler & Zhang

2010

10A (10M; ages 10–44; 4S; ≥6MR); 15C (15M; ages 11–15)

Golgi analysis of spine density of pyramidal neurons in BA 7, 9, 21

↑ apical dendrite spine density in layer II of BA 7, 9, 21 and Layer V of BA 21

Kulesza et al

2010

9A (8M, 1F; ages 2–36; ≥1S; ?MR); 4C (1M, 3F; ages 4–32)

neuronal morphology and density in superior olivary complex

↓ neuron density in superior olive; ↓ cell size in medial superior olive

Morgan et al.

2010

13A (13M; ages 3–41; ≥6S; ≥2MR); 9C (13M; ages 2–44)

microglia activation in BA 9/46 (immunostained for iba-1)

↑ microglial density in BA 9/46 and ↑ cell size in underlying white matter

Oblak et al.

2010a

*16A (15M, 1F; ages 3–30; 7S; ?MR); 19C (18M, 1F; ages 16–43)

density of GABA(A) receptors in anterior and posterior cingulate and fusiform gyrus

↓ GABA(B) receptor density

Oblak et al.

2010b

*15A (13M, 2F; ages 14–37; 7S; ?MR); 17C (16M, 1F; ages 16–43)

ligand-binding autoradiography of GABA(B) posterior cingulate and fusiform

↓ GABA(A) receptors and benzodiazepine binding sites and ↑ binding affinity

Santos et al.

2010

4A (2M, 2F; ages 4–11; ≥1S; ?MR); 3C (2M, 1F; ages 4–14)

stereological von Economo and pyramidal neuron # in frontal insula layer V

↑ ratio of von Economo to pyramidal neurons

Wegiel et al.

2010

13A (9M, 4F; ages 4–62; ≥6S; ≥8MR); 14C (9M, 5F; ages 4–64)

qualitative neuropathological exam

multiregional heteropias (4/13) and flocculonodular, subependymal, or multifocal cerebral dysplasia (12/13)

Zikopoulos & Barbas

2010

5A (4M, 1F; ages 30–44; ≥1S; ?MR); 4C (2M, 2F; ages 36–42)

Light and electron microscopy of myelinated axons in frontal white matter

↓ # large axons, ↑ GAP-43 expression, ↑ # of thin axons below BA 32; ↓ myelin thickness below BA 11.

* regions analyzed varied by case (mean n = 8 per group per region)

A, autism spectrum disorder; C, control; M, male; F, female; S, seizure disorder; MR, mental retardation.

There is also substantial heterogeneity in the onset of autism. Some children have signs of developmental delays within the first 18 months of life. However, 25–40% of children with autism initially demonstrate near-normal development until 18–24 months, when they regress into an autism that is generally indistinguishable from early onset autism (Werner & Dawson, 2005; Hansen et al., 2008; Ozonoff et al., 2008). The possibility that there is early-onset versus regressive phenotypes of autism might have important implications for the types and time courses of neuropathology that one might expect to encounter. Increasingly, researchers refer to “the autisms” rather than a single autism phenotype (Geschwind & Levitt, 2007; Amaral et al., 2008). There is no consistent genetic etiology of autism even though estimates of heritability are as high as 90% (Levitt & Campbell, 2009). The autism spectrum disorders are likely to involve multiple genes and complex interactions between genetic risk and environmental factors. When one takes all of these into consideration, it would be surprising if the neuropathology of autism were identical across all affected individuals. Whether there is a core neuropathology that is common to all, or at least most, individuals with autism remains to be determined.

Magnetic resonance imaging (MRI) studies have provided the greatest contribution to our understanding of how the brains in people with autism deviate from typical development and function. As discussed elsewhere in this book, there is substantial evidence indicating that the brain is undergoing an abnormal developmental time course that appears to include a period of early overgrowth in some individuals with autism, particularly noted in the frontal, temporal, and cingulate cortices and the amygdala (Courchesne et al., 2007; Amaral et al., 2008). Structural MRI is well equipped to deal with the phenotypically diverse nature of autism spectrum disorders by providing a reliable method for studying brain growth and function in large numbers of subjects over time. However, if brain size is an indication of aberrant neurological development, what does this really tell us about the neuropathology of autism? If the brain is larger in young children with autism, are there too many neurons, glia, synapses, and so forth? Does the underlying neuropathology differ between brain regions and tissue types? If the difference in brain size does not persist into adulthood, what neuropathological underpinnings account for this phenomenon of an abnormal growth trajectory?

Studies analyzing postmortem brain tissue acquired from individuals with autism provide an approach for understanding the underlying neuropathology. But, due to the limited number of cases and documentation available, these studies are currently not well positioned to deal with the heterogeneity of autism spectrum disorders. It may be obvious, but critical to point out, that postmortem studies are limited to observing the end result of each case’s neuropathology due to their particular type of autism, comorbid symptoms, and individualized exposure to environmental factors. Well-designed postmortem studies must control for confounding factors such as age and gender, and excluding or segregating comorbid conditions such as epilepsy. The use of postmortem techniques as a tool for studying the neuropathology of autism is still very much in its infancy. Efforts have historically been hindered by poor tissue quality and small sample sizes, with fewer than 100 autism cases studied to date and a mean sample size of 6 autism cases per study (Table 31-1). In addition, nearly all of the brains studied have been from adults with autism, which is well after the time of peak aberrant neurological growth highlighted by MRI studies. Despite these limitations, with the availability of more abundant, high-quality postmortem tissue and by employing modern neuroanatomical techniques such as stereological methods for counting neurons and in situ hybridization for evaluating expression levels of genes, providing a more complete picture of the neuropathology of the autism spectrum disorders may be possible in the near future.

In this chapter, we first review the studies of the last 30 years that have utilized postmortem tissue and contributed to our current understanding of the neuropathology of autism (Table 31-1). Since autism, at its core, is a disorder of early development, we then review the pre- and postnatal stages of brain development. During this brief review of ontogenesis we speculate about how disruptions in the progressive and regressive events of brain development might result in the known pathology of autism. We next review data indicating that autism may involve ongoing inflammatory processes in the central nervous system. Finally, we discuss our ideas about the future direction of postmortem neuropathological studies, the need for better preparation of brain tissue to maximize the use of each precious case, and how best to serve the needs of multiple research specialties that must accommodate both classical histology and modern molecular approaches.

Historical Perspective

Margaret Bauman and Tom Kemper carried out the initial groundbreaking studies during the mid-1980s to early 1990s to systematically describe pathology in a sample of postmortem brains from people with autism during life. Before that time, most reports were case studies in which autism was not clearly identified, and in most cases, comorbid pathology such as seizure disorder, environmental insult (e.g., thalidomide exposure), severe mental retardation, self-injurious behavior, or another known neurodevelopmental disorder (e.g., phenylketonuria) was also present (Williams et al., 1980; Bauman & Kemper, 1985, 1990; Coleman et al., 1985; Hof et al., 1991; Guerin et al., 1996; Raymond et al., 1996; Rodier et al., 1996). Concurrently, during the early 1990s, a more consistent clinical definition of autism was beginning to emerge (DSM-IV, American Psychiatric Association, 1994) which enabled Kemper and Bauman (Kemper & Bauman, 1993) to collect and describe six cases of idiopathic autism, five of which had mental retardation and four of which had epilepsy. In a side-by-side comparison of each autism case with an age- and sex-matched control (see Box 31–1), the most consistent finding Kemper and Bauman reported was in the cerebellum. In all six autism cases they examined, there were fewer Purkinje cells in the cerebellar hemispheres. This observation had also been reported by Ritvo and colleagues (Ritvo et al., 1986) in a study in which they examined the brains from four males with autism, three of whom also had mental retardation, compared to three male controls; they found a lower Purkinje cell density in the cerebellar hemispheres and vermis in the autism group. In all six of the cases they studied, Kemper and Bauman (1993) also observed that olivary neurons tended to cluster at the periphery of the nuclear complex. In three of the young autism cases, the neurons in the inferior olive were large, whereas the neurons were small and pale in the adult autism cases. A near absence of the facial nucleus and superior olive was also reported in a female autism case study (Rodier et al., 1996). In addition to the cerebellum, Kemper and Bauman (1993) also observed that neurons in the amygdala and hippocampus of most autism cases appeared unusually small and more densely packed than in age-matched controls. The only area of consistent abnormality in the cerebral cortex of their cases was the anterior cingulate cortex, which appeared unusually coarse and poorly laminated.

In the late 1990s, Anthony Bailey and colleagues (1998) carried out a similar comprehensive qualitative and semiquantitative study of six cases of autism with mental retardation, four of which had seizures, compared to seven controls. In four of the six autism brains, the brain weights were approximately 20% greater than normal for their age; two of the cases also had indications of macrocephaly in childhood clinical records. Cortical dysgenesis, which is a general term to describe malformation of cortical development, was observed in four of the six autism cases, particularly in the cerebellum and frontal cortex. These observations included increased cortical thickness in the frontal, cingulate, temporal, and parietal cortices; high neuronal density in the hippocampus and frontal and cingulate cortices; neurons present in the molecular layer of the frontal cortex; irregular laminar patterns in the superior frontal gyrus; and poor gray-white matter boundaries in the frontal cortex. Ectopic (misplaced) gray matter and an increased number of neurons were observed in the white matter of the inferior cerebellar peduncle and/or frontal cortex in three of the autism cases. Olivary dysplasia was present in three autism cases, as well as olivary ectopic neurons in two other cases. As described by both Kemper and Bauman (1993) and Ritvo (1986), Bailey et al. (1998) also reported lower numbers of Purkinje cells in the cerebellum in all five of the adult cases with autism, but not in the 4-year-old child with autism and mental retardation.

At the turn of the century, three major factors changed the way postmortem studies of autism were conducted. The first was the widespread adoption of the Autism Diagnostic Interview-Revised (ADI-R; Lord et al., 1994) as a tool to confirm autism in a postmortem case by interviewing the parents or caregivers. Autism became more clearly defined as a clinical disorder in which cases were categorized as autistic disorder or autism spectrum disorder, which includes Pervasive Developmental Disorder Not-Otherwise-Specified (PDD-NOS) and the more loosely defined diagnosis of Asperger’s syndrome. Another important change was the establishment of the Autism Tissue Program (ATP) by the National Alliance for Autism Research (NAAR) (now under the direction of Autism Speaks), which supports the collection and distribution of autism brain tissue. The ATP has defined cases of autism by the ADI-R diagnosis thus facilitating modern studies utilizing postmortem autism brain tissue.

The second factor that altered the course of neuropathological studies of autism was that analytic tools, such as modern three-dimensional stereological quantitative techniques, replaced the use of density measures to become the standard for evaluating cellular pathology in postmortem brain tissue. Prior to 2006, reports on the neuropathology of autism (Table 31-1, Box 31–1) were based on two-dimensional observations to describe alterations in neuronal density, or neurons per unit volume, in a given region of brain tissue. However, it had become abundantly clear that density measurements were prone to a number of methodological artifacts, leading to interpretive problems. Haug et al. (1984), for example, found that the process of tissue fixation results in differential shrinkage of brains at different ages; shrinkage was found to be inversely proportional to age. The implication of this finding is that differences in the density of neurons may reflect changes in the volume of the tissue rather than changes in total cell number. Many investigators concluded that the only way to unambiguously interpret pathological changes in neuron number was to actually count a representative sample of neurons in a defined volume (West et al., 1991). This led to the genesis of modern “unbiased” stereological techniques that have now become standard practice for studies to describe cytoarchitectural abnormalities in human brain tissue (Box 31–1). These techniques require larger samples of brain tissue to obtain reliable measures and consequently, with better clinical definition, the sample sizes reported by postmortem autism studies are beginning to increase.

The third major factor influencing the course of neuropathological studies of autism over the last ten years was the dramatic increase in MRI studies reported in the literature that helped to guide postmortem studies on where and when in the brain pathology might be present. MRI studies have consistently found increases in brain size in younger children with autism, particularly in frontal and temporal cortices as well as the amygdala and cerebellum, followed by an abnormal growth pattern through adolescence and adulthood (Courchesne et al., 2007; Amaral et al., 2008). Below we discuss the postmortem quantitative studies of the last ten years by the major areas of interest: amygdala, cerebellum, and the frontal, cingulate, and temporal cortices.

Amygdala

Schumann and Amaral (2006) were the first to carry out a neuropathology study of the brain in individuals with autism using unbiased stereological methods. They measured the number and size of neurons in the entire amygdaloid complex and in individual nuclei in nine male postmortem cases of autism, without seizure disorder, compared to ten typically developing age-matched male controls ranging in age from 10 to 44 years at death. They found that the autism group had significantly fewer neurons in the total amygdala and in the lateral nucleus than the controls (Figure 31-2). Whereas the average number of neurons in the control amygdala was about 12.2 million, the average in the amygdala in the autistic brains was about 10.8 million or about 85% of the total in the control brains. They did not find increased neuronal density or decreased size of neurons as Kemper and Bauman (1993) had initially reported. These findings have yet to be replicated in an independent sample of brains, which is an important step in confirming decreased neuron numbers in the amygdala as a characteristic feature of autism. It is important to emphasize that this is a population difference with both the control brains and the autistic brains demonstrating a wide range of neuronal numbers.

Figure 31–2. (A) Brightfield photomicrograph of Nissl-stained coronal sections through rostral (a), midrostrocaudal (b), and caudal (c) levels of the amygdala. AAA, Anterior amygdaloid area; AB, accessory basal nucleus; AHA, amygdalohippocampal area; B, basal nucleus; C, central nucleus; COa, anterior cortical nucleus; COp, posterior cortical nucleus; EC, entorhinal cortex; H, hippocampus; I, intercalated nuclei; L, lateral nucleus; M, medial nucleus; NLOT, nucleus of the lateral olfactory tract; OT, optic tract; PAC, periamygdaloid cortex; PL, paralaminar nucleus; PU, putamen; SAS, semiannular sulcus; VC, ventral claustrum. Scale bar, 2 mm. (B) Number of neurons in five subdivisions of the amygdala of autism (filled circle) and control (open circle) brains. The asterisk indicates significant difference (p < 0.03) in neuron number between autism and control lateral nuclei. (C) Bivariate scattergram of the number of neurons in the total amygdala of autism (solid black line) and control (dotted black line) brains by age. Adapted from Schumann & Amaral (2006), with permission from Journal of Neuroscience.

Figure 31–2.
(A) Brightfield photomicrograph of Nissl-stained coronal sections through rostral (a), midrostrocaudal (b), and caudal (c) levels of the amygdala. AAA, Anterior amygdaloid area; AB, accessory basal nucleus; AHA, amygdalohippocampal area; B, basal nucleus; C, central nucleus; COa, anterior cortical nucleus; COp, posterior cortical nucleus; EC, entorhinal cortex; H, hippocampus; I, intercalated nuclei; L, lateral nucleus; M, medial nucleus; NLOT, nucleus of the lateral olfactory tract; OT, optic tract; PAC, periamygdaloid cortex; PL, paralaminar nucleus; PU, putamen; SAS, semiannular sulcus; VC, ventral claustrum. Scale bar, 2 mm. (B) Number of neurons in five subdivisions of the amygdala of autism (filled circle) and control (open circle) brains. The asterisk indicates significant difference (p < 0.03) in neuron number between autism and control lateral nuclei. (C) Bivariate scattergram of the number of neurons in the total amygdala of autism (solid black line) and control (dotted black line) brains by age. Adapted from Schumann & Amaral (2006), with permission from Journal of Neuroscience.

Taken together with the findings from previous magnetic resonance imaging studies, the autistic amygdala shows multiple signs of pathology. It appears to undergo an abnormal pattern of postnatal development that includes precocious enlargement and ultimately a lower number of neurons. It will be important to determine in future studies whether decreased neuronal numbers in the amygdala is a unique characteristic of autism or whether cell loss occurs in other brain regions as well. If the lower number of neurons in the amygdala is found to be a reliable characteristic of autism, what might account for this finding? Two possible hypotheses are: (1) fewer neurons were generated during early development, or (2) a normal or even excessive number of neurons was generated initially, which would be consistent with MRI findings of a larger amygdala in early childhood (Schumann et al., 2004; Schumann, Barnes, Lord, & Courchesne, 2009), but some of these neurons have subsequently been eliminated during adulthood. Unfortunately, there is currently no evidence to support or reject either of these possibilities, and systematic stereological studies on younger autism and control cases are needed.

Cerebellum

Of the 32 postmortem cases of autism reported in the literature in which the cerebellum was studied, 21 (or 66%) showed lower density of Purkinje cells, particularly in the more lateral parts or hemispheres of the structure (Ritvo et al., 1986; Kemper & Bauman, 1993; Bailey et al., 1998, Palmen et al., 2004; Whitney et al., 2008). Even though this is one of the most striking and consistent descriptive findings in neuropathological analyses in the autistic brain, a comprehensive stereological study of the actual number of Purkinje neurons in the whole cerebellum has yet to be carried out.

Recent studies from Blatt and colleagues have found that some, but not all, cases of autism show a reduction in the density of Purkinje neurons (using immunostaining for calbindin-D28k rather than standard Nissl staining), as well as a decrease in basket and stellate cell densities (Whitney et al., 2008; Whitney et al., 2009) (Figure 31-3). Fatemi and colleagues (2002) reported no differences in the density of Purkinje cells in the cerebellum in five adult cases of autism compared to five adult controls, but found a 24% decrease in the size of Purkinje neurons in the autism group. Blatt and colleagues have also reported substantial alterations in the GABAergic system of the cerebellum, including a 40% reduction in GAD67 mRNA in Purkinje cells, 28% upregulation of GAD67 in basket cells, and decreased levels of GAD65 mRNA in cerebellar dentate nuclei of eight individuals with autism compared to eight controls (Yip, Soghomonian, & Blatt, 2007, 2008, 2009).

Figure 31–3. (A) Number of Purkinje neurons per millimeter in control (light gray bars) and autistic (black bars) cases from calbindin-D28k-immunostained series. Photographs illustrate the variability in Purkinje cell density within autism group in (B) 4414 with reduced Purkinje cell density and (C) 3611 with Purkinje cell density in the normal range. Adapted from Whitney et al., 2008, with kind permission from Springer Science+Business Media.

Figure 31–3.
(A) Number of Purkinje neurons per millimeter in control (light gray bars) and autistic (black bars) cases from calbindin-D28k-immunostained series. Photographs illustrate the variability in Purkinje cell density within autism group in (B) 4414 with reduced Purkinje cell density and (C) 3611 with Purkinje cell density in the normal range. Adapted from Whitney et al., 2008, with kind permission from Springer Science+Business Media.

The observation of decreased Purkinje cell density appears to be in stark contrast to some reports from MRI studies of an enlarged cerebellum in autism (Courchesne et al., 2007) implying potentially a higher number of neurons. Several factors, however, make it impossible to compare findings from the two methods. At least 20 of the 32 brains examined in postmortem studies came from individuals who also had mental retardation (Ritvo et al., 1986; Kemper & Bauman, 1993; Bailey et al., 1998; Palmen et al., 2004; Whitney et al., 2008). Almost half of the brains were from individuals with epilepsy, and some were from individuals who were taking anticonvulsive medications that might themselves damage Purkinje cells. By contrast, most of the MRI studies were conducted with high-functioning autistic individuals and typically excluded subjects with seizure disorders. So, two very different cohorts of subjects are being studied with these different techniques.

Whether the observations of lower Purkinje cell density actually reflect fewer Purkinje cell numbers in the autistic brain awaits confirmation with unbiased stereological neuron-counting methods. As discussed in detail later in this chapter, an interesting hypothesis has emerged that neuroinflammation in the brain of individuals with autism, indicated by the presence of an increased number of activated microglia, may be associated with the loss of Purkinje cells in the cerebellum (Vargas et al., 2005).

Frontal Cortex

Although the frontal cortex is one of the most prominent areas of study in the search for potential neuropathology in autism, very little work has been conducted with postmortem brain tissue in this region. Structural MRI studies suggest that the frontal lobe, and in particular, the prefrontal cortex, may show the greatest degree of aberrant development in the brains of children with autism (Carper et al., 2002; Carper & Courchesne, 2005). Yet, no systematic postmortem study to date has explored the underlying neurobiology of the identified aberrant growth in the frontal cortex. Seven recent studies (Table 31-1) have utilized frontocortical tissue; findings include an increased spine density on the apical dendrite of pyramidal neurons in layer II (Hutsler & Zhang, 2010), the presence of ill-defined cortical layers (Mukaetova-Ladinska et al., 2004), and an indistinct boundary between gray and white matter (Avino & Hutsler, 2010) in the dorsolateral prefrontal region. An electron microscopy study of the white matter underlying the orbitofrontal cortex found decreased myelin thickness in 5 autism cases relative to 4 controls (Zikopoulos & Barbas, 2010).

Von Economo (a.k.a. spindle) neurons have been recent focus of interest in autism, although clear evidence of pathology has yet to be found. Von Economo neurons are unique, large cells localized to the anterior cingulate and frontal insular region of great apes and man, and are suspected to play a role in higher order cognitive function and emotional behavior (Allman et al., 2001; Allman et al., 2005). Two recent studies with small sample sizes have failed to find a difference in the number of von Economo neurons in the frontal insular region in autism cases relative to controls (Kennedy et al., 2007; Santos et al., 2010) However, a recent stereological study found a higher ratio of von Economo neurons to pyramidal neurons in layer V of the frontal insula in four autism cases relative to three controls (Santos et al., 2010), suggesting that von Economo neuron pathology may become evident in autism with larger sample sizes.

The frontal cortex has also been the focus of studies of minicolumnar organization of neurons, finding reduced intercolumnar width and increased cell density in dorsolateral prefrontal cortex (Casanova et al., 2002; Buxhoeveden et al., 2006; Casanova et al., 2006; Casanova et al., 2010). These findings are discussed in greater detail later in this chapter.

Anterior Cingulate Cortex

As described above, the anterior cingulate cortex (ACC) was the only region of neocortex noted by Kemper and Bauman (1993) to show abnormalities; they observed the cortex of autism cases to be unusually coarse and poorly laminated compared to controls. Abnormalities in the ACC have since been reported by Blatt and colleagues, including decreases in cell size in layers I–III and layers V–VI of area 24b and in cell packing density in layers V–VI of area 24c (Simms et al., 2009). The authors also observed irregular lamination in three of nine autism brains and increased density of neurons in the subcortical white matter in the remaining cases. A preliminary study of von Economo (a.k.a. spindle) neuron density was also carried out, but no significant differences in the autism cases were detected (Simms et al., 2009). Instead, the study appeared to uncover two subsets of autism cases, one that displayed an increase in von Economo neuron density and another with reduced density compared to controls. In another study by the same group, decreases in the mean density of GABA(A) and GABA(B) receptors and benzodiazepine binding sites were found in the ACC (Oblak et al., 2009; 2010), suggesting an alteration in GABAergic innervation of the ACC; this could potentially lead to a disturbance of the delicate balance between excitation and inhibition in this cortical area.

Another prominent theory, postulated by Herbert et al. (2004) and Courchesne & Pierce (2005), suggests excessive short-range and diminished long-range connectivity in the white matter underlying the frontal and anterior cingulate cortices. Herbert et al. (2004) reported that increased white matter volume in children with autism is restricted to superficial radiate white matter (i.e., corona radiata and “U” fibers), with no difference reported in deep and bridging white matter (e.g., corpus callosum, internal capsule, etc.). A recent post-mortem study of single axons in white matter underlying the anterior cingulate cortex of autism cases found an excessive number of thin axons, which link neighboring areas together, and a decreased number of large axons, that communicate over long distances (Zikopoulos & Barbas, 2010). Interestingly, this study also found an over expression of growth-associated protein (GAP-43 kDa), which is expressed at high levels during rapid axon growth (Benowitz & Routtenberg, 1997), in the white matter underlying the ACC in the autism cases relative to controls. Although this study was carried out in a small sample and included cases with other neurological conditions (i.e. schizophrenia, epilepsy, depression), the findings have important implications for understanding the underlying neurobiology of aberrant connectivity in autism as reported by structural MRI and diffusion tensor imaging (DTI) studies.

Temporal Cortex

In a recent longitudinal MRI study of young children, the temporal cortex was found to undergo an abnormal growth trajectory in children with autism as well as display the greatest degree of aberrant enlargement among the cortical lobes (Schumann et al., 2010). Postmortem studies designed to examine the underlying pathology of this abnormal growth have yet to be carried out. The only study of temporal cortex, and the only other study of stereological neuron number in the autism brain (besides Schumann & Amaral, 2006, on the amygdala) was carried out by Van Kooten, Schmitz, and colleagues (2008) on the fusiform gyrus, a visual area implicated in processing faces and located midrostrocaudal on the inferior surface of the temporal lobe. In seven postmortem brains from patients with autism compared to ten controls, they found that the autism group showed significantly lower neuronal densities within layer III; lower total neuron numbers in layers III, V, and VI; and smaller mean perikaryal volumes of neurons in layers V and VI (Figure 31-4). Although the findings of this study were based on a relatively small sample size, and require replication, the results have implications for the cellular basis of abnormalities of face perception in people with autism. Pathology may originate from events in the fusiform directly, or from pathology in other brain regions with which the fusiform connects, such as the amygdala, which has also been shown to have a reduction in neuron number in autism patients (Schumann & Amaral, 2006). It will be important to determine in future studies whether decreased neuronal numbers in both the fusiform gyrus and amygdala result in the functional impairments associated with autism or, alternatively, are caused by hypoactivation or underuse during early development.

Figure 31–4. Photomicrographs of 200 µm thick coronal sections of the brain hemispheres through the level of the fusiform gyrus from a control (A, C, E) and a patient with autism (B, D, F), a cross-section of cortex from the fusiform gyrus (C, D; scale bar = 400µm), and layer III of the fusiform gyrus from each subject at high magnification (E, F; scale bar = 50 µm). Adapted with permission from van Kooten et al., 2008.

Figure 31–4.
Photomicrographs of 200 µm thick coronal sections of the brain hemispheres through the level of the fusiform gyrus from a control (A, C, E) and a patient with autism (B, D, F), a cross-section of cortex from the fusiform gyrus (C, D; scale bar = 400µm), and layer III of the fusiform gyrus from each subject at high magnification (E, F; scale bar = 50 µm). Adapted with permission from van Kooten et al., 2008.

Blatt and colleagues have proposed widespread abnormalities in the GABAergic system of the brain in individuals with autism, including the fusiform gyrus and hippocampus. Reductions in the mean density of GABA(A) and GABA(B) receptors and benzodiazepine binding sites were recently reported in the fusiform gyrus (Oblak et al., 2010a; 2010b). Decreased GABA(A) receptor binding has also been reported in the hippocampus (Blatt et al., 2001); the decreased binding is likely due to a decrease in the density of binding sites rather than an altered affinity (Guptill et al., 2007). In a recent study, a subpopulation of GABAergic interneurons immunoreactive to calcium binding proteins were found to be decreased in the hippocampus of autism cases relative to controls (Lawrence et al., 2010). However, alterations in the GABAergic system in autism are not specific to the temporal cortex, as Blatt and colleagues have reported similar pathology in the cerebellum and cingulate cortices (Oblak et al., 2009, 2010a, 2010b; Yip et al., 2007, 2008, 2009), but instead appear to be a common feature that may disrupt global inhibitory control in the autistic brain.

Alterations of the Columnar Structure of the Neocortex: The Minicolumn Hypothesis

Increasing interest has been placed on the notion, advanced by Casanova and colleagues (Casanova et al., 2002; Buxhoeveden et al., 2006; Casanova et al., 2006; Casanova et al., 2010), that there are an abnormal number and width of minicolumns (Figure 31-5) in the cortex of individuals with autism. As described in further detail below, minicolumn formation has been associated with early stages of cortical development when postmitotic neurons ascend in linear arrays along a radial glial scaffolding (Rakic, 1988). Within the first year of life, there is a dramatic increase in dendritic growth. By 2 years of age, the minicolumns are spaced further apart with a lower cell density in a given region of cortex. Dendritic bundles and axonal fascicles that extend throughout several layers of the cortex occupy the space between minicolumns (Jones, 2000; Rockland & Ichinohe, 2004). Casanova and colleagues (Casanova et al., 2002; Buxhoeveden et al., 2006; Casanova et al., 2006) have posed the reasonable question of whether there is perturbation in the fundamental organization of minicolumns in the autistic brain.

Figure 31–5. Model of the minicolumnar hypothesis proposed by Casanova et al. (2002, 2006) that there are an abnormal number and width of minicolumns in the cortex of individuals with autism. Each so-called minicolumn contains approximately 80–100 neurons (see schematic of minicolumn formation in Figure 31–8). (A) Model depicts a decreased intercolumnar width in the autism case relative to controls. (B) Nissl images were adapted with permission from Casanova et al. (2006), in which the distance between cell-body defined minicolumns was found to be reduced in layer III of dorsolateral prefrontal cortex (BA 9) in a 4-year-old autism case relative to a 5-year-old control. Given the narrower neuropil area between columns, one would also predict a decrease in the dendritic arborization of BA 9 Neurons (as depicted in the model, A).

Figure 31–5.
Model of the minicolumnar hypothesis proposed by Casanova et al. (2002, 2006) that there are an abnormal number and width of minicolumns in the cortex of individuals with autism. Each so-called minicolumn contains approximately 80–100 neurons (see schematic of minicolumn formation in Figure 31–8). (A) Model depicts a decreased intercolumnar width in the autism case relative to controls. (B) Nissl images were adapted with permission from Casanova et al. (2006), in which the distance between cell-body defined minicolumns was found to be reduced in layer III of dorsolateral prefrontal cortex (BA 9) in a 4-year-old autism case relative to a 5-year-old control. Given the narrower neuropil area between columns, one would also predict a decrease in the dendritic arborization of BA 9 Neurons (as depicted in the model, A).

Sixteen cases of autism (at least 9 with seizures and at least 10 with mental retardation) have been examined for minicolumnar pathology in cortical layer III in three independent studies using varying techniques (Casanova et al., 2002; Buxhoeveden et al., 2006; Casanova et al., 2006). The most consistent finding in these studies is reduced intercolumnar width of the minicolumns in dorsolateral prefrontal cortex (BA 9/46). In a recent follow-up study, Casanova and colleagues (2010) determined that diminished minicolumnar width is not limited to layer III, but instead is present across supra- and infragranular cell layers, most notably in BA 44 (pars opercularis) in the frontal cortex of autism cases. These findings, coupled with increases in neuronal density on the order of 23% noted by Casanova et al. (2006), imply that there should be a greater number of neurons in BA 9 of the autistic cortex. Given the narrower neuropil area between columns, one would also predict a decrease in the dendritic arborization of dorsolateral prefrontal cortical neurons. These neuropathological questions are ripe for analysis using systematic stereological methods. One would predict an increased number of neurons in the prefrontal cortex in autism cases, given evidence of increased volume at young ages and reduction of minicolumnar spacing; however such a study has yet to be carried out.

Neuroinflammation in Autism

There is emerging evidence that an anomalous immune response during vulnerable periods of brain development could contribute to the neuropathology associated with autism (Ashwood & Van de Water, 2004; Pardo et al., 2005; Ashwood et al., 2006). Interactions between the immune and nervous systems begin early in embryogenesis and persist throughout an individual’s lifetime, with successful neurodevelopment contingent on a normal and balanced immune response (Boulanger & Shatz, 2004; Wrona, 2006). Two cell populations act as directors and effectors of the immune response. Microglia primarily act as the resident phagocytes, constantly eliminating damaged neurons, accumulated debris, and infectious agents. Astroglia, meanwhile, are classically considered to act as directors of the immune response via detection and release of a wide array of cytokines and chemokines. Both microglia and astroglia participate in several additional functions during development, including histogenesis, synaptogenesis, neuronal dendritic arborization, and regulation of neuron numbers (Marin-Teva et al., 2004; Schmitz & Rezaie, 2008). In adulthood, microglia play a dual role; in addition to cytotoxic, phagocytic, and antigen-presenting capabilities, they also demonstrate cytoprotective and neurotrophic functions (for review see Pardo et al., 2005).

Microglia undergo morphological changes based on the current state of the immune response. “Resting” ramified microglia (Figure 31-6), with small cell bodies and long thin processes, are commonly found throughout the typically developing brain across life span for constant surveillance of the local environment to detect inflammatory signals (Nimmerjahn et al., 2005). When infection is presented, microglia may be “activated” by a variety of factors, including pro-inflammatory cytokines, necrotic factors, glutamate receptor agonists, and changes in extracellular potassium concentration. Once the microglial cell is activated, the processes thicken and retract to move rapidly to the site of the insult. The cell body has the appearance of swelling with the uptake of MHC class II proteins and secretes cytotoxic and pro-inflammatory signaling molecules including cytokines and chemokines. These activated microglia interact with neurons to fight off infection, typically with minimal damage to healthy brain cells. “Ameboid” microglia in the activated state are commonly observed in the typically developing brain in high concentrations during the prenatal period responding to large amounts of extracellular debris and apoptotic cells that is a consequence of programmed death (Rezaie & Male, 1999).

Figure 31–6. (A) Normal appearance of the cerebellum in a control patient; (B–C) Atrophic folia and marked loss of  Purkinje and granular cells in the cerebellum of an autistic patient (H&E Stain); (D) Microglia activation seen with anti-MHC class II immunostaining. Adapted with permission from Vargas et al., 2005.

Figure 31–6.
(A) Normal appearance of the cerebellum in a control patient; (B–C) Atrophic folia and marked loss of  Purkinje and granular cells in the cerebellum of an autistic patient (H&E Stain); (D) Microglia activation seen with anti-MHC class II immunostaining. Adapted with permission from Vargas et al., 2005.

Although microglial and astroglial activation is often a beneficial response, dysfunction of the immune system resulting in a chronic state of neuroinflammation could have detrimental effects on the developing brain and potentially lead to neuronal cell death and alterations in neuronal connectivity (Streit et al., 2005). Ongoing neuroinflammatory processes, as evidenced by the presence of activated microglia and astroglia in cases of autism, was first described by Vargas et al. (2005). They observed excessive microglial activation in the cerebellum and frontal cortex in postmortem brain tissue in a subset of 11 autistic patients (age range 5–44 years) using immunocytochemical staining for MHC class II markers (HLA-DR). The excessive microglial activation was particularly prominent in the granular cell layer of the cerebellum. Vargas and colleagues speculated that the presence of activated microglia near Purkinje neurons in the cerebellum may be related to the commonly noted reduction of Purkinje cells in autism patients, given the regulatory role that microglia play in normal Purkinje neuron death (Marin-Teva et al., 2004) (Figure 31-6). Recently, Morgan and colleagues reported increases in activated microglia number and size in some, but not all, cases of autism in dorsolateral prefrontal cortex and amygdala using an antibody to ionized calcium binding adaptor molecule-1 (Iba-1); a marker that visualizes both resting and activated microglia (Morgan et al., 2010; Morgan et al., personal communication).

At present, the role of microglial activation in autism remains unclear. It is possible that aberrant microglial activity directly contributes to abnormal neurodevelopment, with activation triggered by genetic or environmental factors that otherwise would not be significantly disruptive. Alternately, it may be a largely healthy response to an exogenous insult such as viral infection, or a genetic alteration that, for example, produces an excessively large population of neurons that must be reduced. Interestingly, elevations in pro-inflammatory cytokine and chemokine levels have also been reported in the brain tissue and cerebrospinal fluid of patients with autism (Vargas et al., 2005, Li et al., 2009). An emerging hypothesis is that cytokine levels may be impacted by prenatal maternal infection (see Patterson, 2009, for review). Further evaluation is necessary to define the precise role of the immune response and microglial activation in the pathogenesis of autism. If glial activation in autism is demonstrated to be deleterious, anti-inflammatory agents represent a highly promising future avenue for biotheraputic research.

Future Perspective

Autism is clearly a pathological disorder of neural development, but when and how the pathology occurs remains elusive. Typical brain development is comprised of several stages, including the proliferation and migration of neurons, synaptic growth, and eventually cell death and dendritic pruning. Any deviation at one or more of these stages could produce catastrophic downstream effects. As evident from the previous section, the field of postmortem human brain research in autism is still young and we have few clues to point to a particular stage in which development goes awry. We therefore now ask the reverse question, if a particular stage of development did go awry, what would the resulting pathology look like? Below we describe the normal stages of brain development and speculate, based on the current findings of autistic neuropathology, about the particular stage at which brain development may deviate from normal, leading to autism. However, we would like to reiterate that it is unlikely that a single neuropathological event will account for all autism; future studies will need to be powered and designed to detect various patterns that underlie the “autisms.”

Fundamentals of Brain Development

The human central nervous system (CNS) is the most complex organ system in vertebrates and contains a greater variety of cell types than is found in any other organ system. The mature brain is comprised of approximately 100 billion neurons, and perhaps 10 times as many glial cells. Remarkably, the generation of nearly one trillion diverse, complex cell types is accomplished during a brief span of intense proliferation that encompasses approximately 3 months during gestation. Not surprisingly, this period of development is sensitive to genetic abnormalities and/or environmental interference. In fact, it is not surprising that there are occasionally errors of development leading to disorders such as autism; what is surprising given the complexity of the enterprise, is that neurodevelopment is generally successful and results in an individual that is “typically developing.”

CNS cells are generated in two proliferative zones that line the ventricular system of the developing brain. Each proliferative zone is home to a distinct class of precursor cell. The primary proliferative zone, the ventricular zone (VZ) is directly adjacent to the lumen of the ventricle. Radial glial cells are the principle precursor cell type in the VZ. The secondary proliferative zone, the subventricular zone (SVZ), appears at the onset of neurogenesis in many CNS regions (Boulder Committee, 1970). SVZ precursor cells are generated by radial glial cells in the VZ, and then migrate radially to establish the SVZ compartment superficial to the VZ (see Figure 31-7). Both VZ and SVZ precursor cells produce neurons in the embryonic forebrain (Noctor et al., 2007). The VZ proliferative zone becomes depleted during development and is not present in the adult brain, but the SVZ remains as a neurogenic compartment in the mature and a similar structure is present in the adult dentate gyrus (Ihrie & Alvarez-Buylla, 2008).

Figure 31–7. (A) Scheme depicting precursor cell types in the embryonic brain. After neural tube closure, the embryonic proliferative zone is composed of a single population of neuroepithelial cells which transition into radial glial cells at the onset of neurogenesis in the ventricular zone (VZ). They undergo division at the surface of the lateral ventricle and possess a long thin pial fiber that reaches the pial surface. Radial glial cell divisions produce neurons and intermediate progenitor cells. Intermediate progenitor cell bodies are primarily located in the subventricular zone (SVZ); Divisions occur away from the surface of the lateral ventricle and produce multiple neurons. At the conclusion of neurogenesis radial glial cells begin producing astrocytes, and Translocate to the SVZ, where gliogenesis continues after birth. (B) Micrograph showing an example of a radial glial cell labeled with fluorescent reporter gene. Radial glia are bipolar cells with a single process that contacts the ventricular surface (dotted line) and a single pial process that ascends toward the pial surface (small arrowheads). The cell body (arrow) is located in the VZ. (C) Micrograph showing a radial glial cell (arrow) and a daughter intermediate progenitor cell (large arrowhead) labeled with fluorescent reporter gene. The intermediate progenitor cell maintains contact with the radial glial cell pial fiber (small arrowheads).

Figure 31–7.
(A) Scheme depicting precursor cell types in the embryonic brain. After neural tube closure, the embryonic proliferative zone is composed of a single population of neuroepithelial cells which transition into radial glial cells at the onset of neurogenesis in the ventricular zone (VZ). They undergo division at the surface of the lateral ventricle and possess a long thin pial fiber that reaches the pial surface. Radial glial cell divisions produce neurons and intermediate progenitor cells. Intermediate progenitor cell bodies are primarily located in the subventricular zone (SVZ); Divisions occur away from the surface of the lateral ventricle and produce multiple neurons. At the conclusion of neurogenesis radial glial cells begin producing astrocytes, and Translocate to the SVZ, where gliogenesis continues after birth. (B) Micrograph showing an example of a radial glial cell labeled with fluorescent reporter gene. Radial glia are bipolar cells with a single process that contacts the ventricular surface (dotted line) and a single pial process that ascends toward the pial surface (small arrowheads). The cell body (arrow) is located in the VZ. (C) Micrograph showing a radial glial cell (arrow) and a daughter intermediate progenitor cell (large arrowhead) labeled with fluorescent reporter gene. The intermediate progenitor cell maintains contact with the radial glial cell pial fiber (small arrowheads).

Proliferation in the Developing Brain

Advances in the field of molecular biology have provided exciting new tools that can be applied to investigations of the developing CNS. New molecular tools allow researchers to control the expression of specific genes both regionally and temporally in the CNS. For example, the gene sequence for fluorescent reporter proteins has proven invaluable for detailed characterization of specific cell types, such as neural precursor cells in the developing brain. Fluorescent reporter proteins have allowed researchers to visualize neural precursor cells in living tissue. This capability has been used to identify neuronal and glial precursor cells in the developing brain, to characterize their patterns of cell division, cell production potential, and finally to characterize the daughter cells produced by precursor cells.

Glial cells and neurons had long been considered distinct cell types that were produced by separate precursor cell types in the developing brain. However, recent studies have demonstrated that glial functions are much broader, and that astrocytes in particular have much closer functional and lineal relationships with neurons than previously realized. Not only are astrocytes crucial partners with neurons in synaptic communication in the mature brain (Stevens, 2008), but they also generate neurons in several regions of the adult brain including the cortical SVZ (Doetsch et al., 1999), and the dentate gyrus (Seri et al., 2001). Astrocytes are lineally descended from embryonic radial glial cells (Noctor et al., 2004); the two cell types can be considered as representing different generations in the same immediate family. Radial glia are a specialized astroglial cell that is transiently present in the developing brain (Misson et al., 1988). Radial glia are bipolar cells that have a soma in the proliferative ventricular zone, a short descending process that contacts the ventricular lumen, and an ascending process that spans the wall of the developing brain to contact the pial membrane (Figure 31-7). Radial glial pial processes radiate outward from the ventricle to the surface of the brain, a pattern that dominates the appearance of the brain during early stages of development, and to a degree dictates organization of the mature CNS. Radial glial cells are the principle neuronal precursor cell type in the embryonic CNS (Malatesta et al., 2000; Miyata et al., 2001; Noctor et al., 2001; Noctor et al., 2002; Anthony et al., 2004). Radial glia generate neurons through one of two mechanisms: directly through an asymmetric division that produces a single neuron, or indirectly through a division that produces an SVZ intermediate progenitor cell that subsequently divides symmetrically to produce two neurons (Martínez-Cerdeño et al., 2006b). Most SVZ intermediate progenitor cells appear to undergo a single symmetric division to produce two neurons. But, in some cases they undergo multiple divisions that produce four or more neurons (Haubensak et al., 2004; Miyata et al., 2004; Noctor et al., 2004). A given radial glial cell division can therefore produce either a single neuron or multiple neurons. The exact number of radial glial cells in the embryonic brain has not been determined, nor has the total number of radial glial or intermediate progenitor divisions, but it is likely that billions of cell divisions are required to produce the 20 billion cortical neurons that populate the human cerebral cortex.

Most regions of the adult CNS are organized into discrete functional units. The cerebral cortex is a laminated sheet of tissue that is 2 to 4 mm thick, and organized into six horizontally arranged layers. This elaborate organization is further broken down into functional units that are arranged as radial columns, or cortical columns, that span across the six cortical layers (Mountcastle, 1997) (Figure 31-8). Each cortical column comprises multiple “minicolumns” in which a narrow column of approximately 80–100 radially arranged neurons span the cortical layers (Mountcastle, 1997). Neurons in each cortical column respond to similar stimuli and perform similar functions. The columnar, or radial organization of the cerebral cortex, is thought to derive from the organization of precursor cells in the embryonic ventricular zone. Rakic’s Radial Unit Hypothesis proposes that precursor cells in the VZ (radial glia) are organized into discrete proliferative units that provide a proto-map of cortical columns in the adult cerebral cortex (Rakic, 1988). The Radial Unit Hypothesis predicts that each proliferative unit in the VZ produces the cortical neurons that populate a cortical column; each proliferative radial glial cell produces the cortical neurons that populate a single minicolumn (Figure 31-8). More recent data that shows substantial neurogenesis in the embryonic SVZ (Haubensak et al., 2004; Miyata et al., 2004; Noctor et al., 2004), has lead to modifications of the Radial Unit Hypothesis to account for the contribution of additional proliferative precursor cells in the SVZ (Kriegstein et al., 2006; see Figure 31-8). Subtle changes in the pattern of radial glial division and/or SVZ precursor cell division could produce measurable changes in the total number of neurons in the adult brain. Thus, aberrant regulation of cell proliferation within the VZ or SVZ may contribute to changes in cell number (Schumann et al., 2006; van Kooten et al., 2008), cell density and minicolumnar width (Casanova et al., 2002; Buxhoeveden et al., 2006; Casanova et al., 2006) (Figure 31-5), and ultimately increased brain size (Courchesne et al., 2007; Schumann et al., 2010) as reported in some cases of autism.

Figure 31–8. Scheme depicting the formation of minicolumn and cortical column functional units in the cerebral cortex. Each minicolumn contains approximately 80–100 neurons. Multiple minicolumns together form a single cortical column. As proposed by Rakic (1988), proliferative units in the ventricular zone (VZ) of the dorsal forebrain produce neurons that are destined for a single mini-column functional unit (dark column) in the cortical plate (CP). Each proliferative unit in the VZ may comprise 5–10 radial glial cells. Each radial glial cell possesses a single pial fiber that stretches across the cortical wall to the pial surface of the brain. The pial fiber guides the migration of newborn neurons from the proliferative zones to their respective minicolumn functional units in the CP. Neighboring proliferative units in the VZ produce neurons that are destined for different minicolumns in the CP (grey columns). Radial glial cells produce both neurons and intermediate progenitor cells, which migrate away from the ventricle along their parental pial fiber. recent work shows that intermediate progenitor cells undergo additional rounds of division in the subventricular zone (SVZ) to produce multiple neurons. Excitatory projection neurons are derived from the VZ of the dorsal forebrain, and migrate radially along radial glial cell fibers. Inhibitory interneurons are derived from the VZ of the medial ganglionic eminence (MGE) in the basal forebrain, and migrate tangentially into the overlying cerebral cortex. Mechanisms that guide migrating interneurons to specific minicolumns remain to be determined.

Figure 31–8.
Scheme depicting the formation of minicolumn and cortical column functional units in the cerebral cortex. Each minicolumn contains approximately 80–100 neurons. Multiple minicolumns together form a single cortical column. As proposed by Rakic (1988), proliferative units in the ventricular zone (VZ) of the dorsal forebrain produce neurons that are destined for a single mini-column functional unit (dark column) in the cortical plate (CP). Each proliferative unit in the VZ may comprise 5–10 radial glial cells. Each radial glial cell possesses a single pial fiber that stretches across the cortical wall to the pial surface of the brain. The pial fiber guides the migration of newborn neurons from the proliferative zones to their respective minicolumn functional units in the CP. Neighboring proliferative units in the VZ produce neurons that are destined for different minicolumns in the CP (grey columns). Radial glial cells produce both neurons and intermediate progenitor cells, which migrate away from the ventricle along their parental pial fiber. recent work shows that intermediate progenitor cells undergo additional rounds of division in the subventricular zone (SVZ) to produce multiple neurons. Excitatory projection neurons are derived from the VZ of the dorsal forebrain, and migrate radially along radial glial cell fibers. Inhibitory interneurons are derived from the VZ of the medial ganglionic eminence (MGE) in the basal forebrain, and migrate tangentially into the overlying cerebral cortex. Mechanisms that guide migrating interneurons to specific minicolumns remain to be determined.

Regulation of Precursor Cell Proliferation

Proliferation is tightly regulated during brain development. Precursor cells follow a specific program that generates the proper number of neurons and glia. A wide variety of factors, including neurotransmitter substances, growth factors, and hormones, bind with receptors expressed by embryonic precursor cells and regulate cell division in the developing brain. For example, the classical neurotransmitters GABA and glutamate differentially regulate proliferation in the ventricular and subventricular zones during neocortical development (LoTurco et al., 1995; Haydar et al., 2000). Neurosteroids, such as estradiol, are also present in the embryonic proliferative zones and induce proliferation of precursor cells in the embryonic and adult brain (Martínez-Cerdeño et al., 2006a). Radial glial cells are coupled to one another through connexin gap junction channels that transmit electrical signals (LoTurco & Kriegstein, 1991). New evidence indicates that waves of calcium activity are also transmitted through gap junction channels and regulate radial glial cell proliferation (Weissman et al., 2004). Proteins such as beta-catenin promote proliferation versus differentiation of progenitor cells during cortical development (Chenn & Walsh, 2002). Blood vessels also appear to play an important role in proliferation. Precursor cells are often located in niches along the border of blood vessels in neurogenic regions of the adult brain (Palmer et al., 2000; Seri et al., 2004), as well as in the embryonic brain (Javaherian & Kriegstein, 2009; Stubbs et al., 2009). Furthermore, Temple and colleagues have found that endothelial cells release soluble factors that stimulate self-renewal of embryonic and adult precursor cells (Shen et al., 2004). These findings indicate that endothelial cells, and perhaps factors circulating in the blood, regulate cell genesis during brain development.

Mutations in specific genes that regulate precursor cell behavior have been identified in Autism Spectrum Disorders. The genes MCPH1 and ASPM are required for proper levels of proliferation during brain growth, and mutations in these genes produce microcephalic brains (Shen et al., 2005). Gene mutations that produce macrocephalic brains have also been identified. For example, mutations in the phosphatase and tensin homologue deleted on the chromosome 10 (PTEN) gene have been associated with large head size and autism (Zori et al., 1998; Goffin et al., 2001). PTEN is a tumor suppressor gene that controls cell size and number (Kwon et al., 2006). PTEN instructs cells to stop dividing, prevents cells from growing too rapidly, and in some cases instructs cells to undergo programmed cell death (Chu & Tarnawski, 2004). Together these functions prevent the formation of tumors and regulate the size of organs during development. Mice in which the PTEN gene has been deleted present with larger brains and hypertrophied neurons with abnormal cellular processes (Kwon et al., 2006). As described above, larger brain size has been reported in autism (Amaral et al., 2008). A recent study identified PTEN mutations in autism spectrum disorders, mentral retardation and developmental delays (Varga et al., 2009; McBride et al., 2010). Additional studies have reported that PTEN mutations are associated with some cases of autism spectrum disorders (Abrahams & Geschwind, 2008; see Chapters by State and colleagues, Lamb, and Morrow & Walsh in this volume). The cause of larger brain size observed in individuals with PTEN mutations has not been determined but may result from dysregulated precursor cell proliferation during development, hypertrophied neurons, decreased cell death, or a combination of the three.

Determination of Cell Fate

The determination of cell fate occurs at regional, local, and cellular levels. The regional expression patterns of different transcription factors along the rostrocaudal axis of the developing nervous system reveals one mechanism by which cortical cells acquire specific identities (Schuurmans & Guillemot, 2002). While all radial glial cells share characteristic morphological features, they nonetheless constitute a heterogeneous population based on protein expression patterns (Kriegstein & Gotz, 2003). This may explain how different regions of the developing telencephalon generate different classes of cortical cells. For example, excitatory pyramidal cells and astrocytes are generated by Pax6 expressing cells in the dorsal telencephalon, while inhibitory interneurons are generated by Dlx-1/2 expressing cells in the ganglionic eminences of the embryonic ventral telencephalon (Anderson et al., 1997; Figure 31-8). The expression of transcription factors such as Dlx-1/2 is likely an early step in the commitment of telencephalic cells to a specific fate. The expression of these factors may even correlate with the phenotype of specific neuronal subtypes. Indeed, new evidence indicates that subregions of the ganglionic eminences express different transcription factors and give rise to interneuron subtypes (Nery et al., 2002). Local environmental factors also play a role in determining the fate of neurons in the developing cortex. Transplantation studies demonstrate that the laminar fate of cortical neurons can be altered when transplantation of cortical precursor cells occurs during specific phases of the cell cycle (McConnell & Kaznowski, 1991). Furthermore, expression of transcription factors is crucial for the normal differentiation of cortical neurons. For example, the absence of the transcription factor Foxg1 during cortical neurogenesis induces deep layer cortical neurons to adopt the phenotype of Cajal-Retzius neurons that are normally found in the superficial layer 1 of the neocortex (Hanashima et al., 2004).

The generation of sufficient numbers of the diverse cell types in the nervous system is accomplished through two basic types of progenitor cell divisions, symmetric and asymmetric (Gotz & Huttner, 2005). Symmetric divisions generate two daughter cells that are similar, while asymmetric divisions generate two daughter cells that differ from one another. Recent evidence indicates that these types of divisions might occur in different proliferative zones, asymmetric divisions occur more frequently at the surface of the ventricular lumen, while symmetric divisions occur more frequently in the SVZ (Noctor et al., 2004). Radial glial cells divide asymmetrically at the ventricular lumen to generate either a single neuron, or a subventricular zone progenitor cell that subsequently generates two neurons. Therefore, each radial glial cell division can generate either one neuron directly, or two neurons indirectly. Thus, determination of daughter cell fate after radial glial divisions impacts the total number of neurons generated at a given time during development. A shift toward production of single neurons would decrease the total numbers of neurons being generated, while a shift toward the production of SVZ progenitor cells would double the neuronal output at a given time. Therefore, regulation of daughter cell fate during neurogenesis impacts the neuronal density for a given neocortical layer or structure. Invertebrate studies have revealed a number of molecules that are differentially segregated in nascent daughter cells during progenitor cell divisions. Some of these fate-determining molecules, such as Notch and Numb, are also expressed in mammalian proliferative zones and research into their role in determining daughter cell fate continues (Pearson & Doe, 2004; Bultje et al., 2009).

Migration in the Developing Brain

Neurons in the adult brain are organized into complex, intricately interconnected groups of nuclei and laminae. One of the remarkable aspects of brain development is that neurons are not born in their adult location, but instead must migrate distances of 7000 µm or more, from the proliferative zones to reach their final destination (Rakic, 1988). To put this into perspective, this is comparable to a person scaling a wall greater in height than Yosemite’s 3,000 foot high El Capitan. Despite the complexity of the task, this feat is achieved with such regularity and precision that there is little variation in the architectonic pattern of brain structures from one person to the next. In the developing neocortex, excitatory cortical neurons are generated in an inside-out sequence such that the deepest layers of the cerebral cortex are generated and migrate into the cortical mantle before the superficial layers. Thus, as development proceeds neurons must migrate progressively longer distances and through an increasing number of cortical cells. The radial glial cells that generate neurons play an important role in the migration of newborn cortical neurons. Radial glia are bipolar cells that have a long pial process that ascends from the cell body in the proliferative VZ, to the pial surface of the developing brain. Newborn excitatory neurons attach themselves to the pial process and migrate toward the cortical plate using the pial process as a directional guide (Rakic, 1971). In fact, many neurons piggy-back along the pial process of their parental radial glial cell (Noctor et al., 2001; Noctor et al., 2004; Noctor et al., 2008), demonstrating the importance of lineage relationships in the developing brain. Newborn cortical neurons extend a leading process toward the overlying cortical plate and migrate radially along the pial fiber of the parent radial glial cell until they reach their destination in the cortical plate, at which point they detach from the pial fiber. This form of migration relies on cell-cell adhesion molecules that interact between radial glial fibers and migrating neurons (Hatten, 1990). Recent data demonstrates that gap junctions play an important role in mediating the adhesion between migrating neurons and radial glial processes (Elias et al., 2007). Neuronal migration is also regulated by a number of extracellular signaling molecules such as neurotransmitter substances acting through the NMDA receptor (Komuro & Rakic, 1993). Migrating neurons depend on additional signaling molecules to reach the correct location in the developing brain. For example, the reelin protein acts through its constituent receptor molecules to guide migrating neurons to the cortical plate, and arrest migration once the neurons have reached their proper location (Tissir & Goffinet, 2003). Reelin may play a distinct role in neuronal functioning in the mature brain. Reductions in reelin protein have been reported in a few select regions of the cerebral cortex and in the cerebellum (Fatemi et al., 2005). Given current understanding of reelin function in the developing mammalian brain, a reduction in reelin protein could be predicted to alter the trajectory of migratory neurons, resulting in the ectopic location of neurons in the forebrain. Examination of the reelin gene (RELN) has detected missense mutations in some cases of autism, but at a very low frequency, leading some to suggest that reelin may not play a major role in the etiology of autism (Bonora et al., 2003). Future studies on the regulation of reelin protein expression may shed light on the role that reelin protein plays in the autistic brain.

Recent experiments employing time-lapse imaging of fluorescently labeled cells in cultured brain tissue have revealed that the patterns of neuronal migration in the neocortex are more complex than originally thought. Experiments in the 1990s discovered that inhibitory cortical neurons are generated in the ventral forebrain and migrate tangentially into the overlying dorsal neocortex (De Carlos et al., 1996; Anderson et al., 1997; Tamamaki et al., 1997; Wichterle et al., 1999). Interneurons do not appear to migrate along radial glial fibers during their journey from the ventral into the dorsal cortex. In fact, they appear to travel perpendicular to the radial glial matrix. It has yet to be determined whether they rely on cellular guides such as developing axonal pathways, or rather are guided solely along gradients of chemoattractive and repulsive factors (Marin & Rubenstein, 2003). Some syndromic forms of autism, for example mutations of CNTNAP2 in an Amish family with epilepsy, MR, and autism, appear to involve disruption of neuronal migration (Strauss et al., 2006), which likely occurs focally in the anterior frontal and temporal lobes (Alarcon et al., 2008). Thus, regional specificity may arise from either differences in protomap related genes, or regional cell adhesion molecules involved in migration.

Recent work in rodents shows that excitatory neurons undergo four distinct stages of migration that can be identified based on the morphology and position of the neurons. After being generated, cortical neurons enter stage one of migration, leaving the ventricular surface and rapidly ascending to the SVZ. During stage two, cortical neurons acquire a multipolar morphology and remain stationary in the SVZ for one day or longer. After sojourning in the SVZ (Bayer & Altman, 1991), many cortical neurons enter stage three, and make a retrograde movement back toward the ventricular lumen. Finally neurons enter stage four, during which they reverse polarity back toward the cortical plate and commence radial migration along the pial fiber (Noctor et al., 2004). Similar ventricular directed movements have been reported for GABAergic interneurons after they have migrated into the dorsal cortex (Nadarajah & Parnavelas, 2002). The ventricle directed movements exhibited by these distinct cell types suggests that both excitatory and inhibitory neurons can respond to some of the same cues during their cortical migrations, and also hints at a potential source of important migration guidance molecules located near the ventricular lumen of the developing brain. Yet another form of migration, termed chain migration, has been identified for olfactory bulb interneurons as they migrate from their birthplace in the cortical subventricular zone along the rostral migratory stream into the olfactory bulb (Lois et al., 1996). Despite differences in the identified forms of migration, all neurons appear to rely on a shared set of intracellular molecules that are involved in extension of the leading process and transport of cellular structures such as the nucleus (Feng & Walsh, 2001; Schaar et al., 2004).

Abnormal Cell Migration

Neuronal migration is thus a complex interplay between the migrating cell and its environment that relies on intracellular machinery as well as extrinsic signaling factors. Given the complexity of this task, it is not surprising that a number of nervous system malformations have been identified that result from defects in neuronal migration (Feng & Walsh, 2001). Neurons depend on different molecules for the successful progression from one stage of migration to the next. For example, deletion or mutation in the doublecortin gene prevents neurons from transitioning from stage two to stage three. Doublecortin is an X-linked gene, and males with a deletion or mutation in this gene are affected with lissencephaly, i.e., a greatly reduced and smooth cortical surface. Heterozygous females present with a milder form of neuropathology called “double cortex.” In this condition, a second band of grey matter is located below the normal location of the cortical laminae (des Portes et al., 1998; Gleeson et al., 1998). This secondary band of tissue consists of neurons that did not progress beyond stage two of migration (LoTurco, 2004). These neurons can fire action potentials, but they do not appear to elaborate the initial axonal process that precedes stage three of migration in the embryonic neocortex (Bai et al., 2003). Another molecule that has been associated with migration failure, Filamin A, is required for leading process extension that precedes transition to stage four of migration. Individuals with a mutation in this gene present with a form of neuropathology termed “periventricular nodular heterotopia,” in which large clusters or nodules of cortical neurons are found along the surface of the lateral ventricles. Periventricular nodular heterotopias have also been identified in subjects with fragile X syndrome (Moro et al., 2006), which results from CGG repeat expansion in the FMR1 gene. The range of neuropathology associated with migration failure varies from severe brain malformations found in lissencephaly, to small ectopic clusters of neurons. In each case, varying proportions of neurons fail to migrate to their proper destination. Afflicted individuals present with mental retardation in severe cases, but even mild malformations are often associated with epilepsy. It is of interest that Bailey and colleagues reported a number of instances of ectopic cortical neuronal clusters in their survey of neuropathology in the autistic brain (Bailey et al., 1998). Recent work identifying alterations in cortical column structure in some autistic brains may also be related (Casanova et al., 2002; Buxhoeveden et al., 2006; Casanova et al., 2006). The etiology of altered minicolumn structure has not been determined, but may result from altered dendritic arborization, increased numbers of proliferative units in the VZ.

Axonal Outgrowth

Neurons are polarized cells that possess a single axon and, typically, multiple dendrites. The proper development of axons and dendrites is crucial for normal functioning, since a single neuron in the adult brain can make thousands of connections with neighboring and distant cells. Neurons begin elaborating processes immediately after being generated. During migration neurons extend and retract multiple leading processes as they migrate through the developing brain tissue toward their destination. These temporary processes serve specific purposes during development but are not retained by the neurons as they differentiate to assume their adult morphology. However, neurons do elaborate some processes during development that are retained as the cells mature, and these processes can determine with which cells a given neuron will communicate. Work in the embryonic cortex shows that sister neurons develop and maintain multiple contacts with one another during migration (Noctor et al., 2001; Noctor et al., 2004; Noctor et al., 2008), and that these contacts presage adults patterns of connectivity (Yu et al., 2009). In the cerebral cortex, most excitatory neurons elaborate an axonal process before initiating radial migration to the cortical plate (Noctor et al., 2004), and these processes can grow substantial distances across the cerebral hemispheres during gestation (Schwartz & Goldman-Rakic, 1991). While migrating cells express and rely on functional neurotransmitter receptors (Komuro & Rakic, 1993; Flint et al., 1998; Komuro & Rakic, 1998), they do not appear to form synaptic connections until after they have reached their destination. Migrating neurons do not have synapses, which precludes the synaptic release of neurotransmitter on these cells, but nonsynaptic release of transmitters before synapse development has been described in the developing brain, including the hippocampus (Demarque et al., 2002). Axon outgrowth can be summarized as occurring in four distinct stages: initial axonal outgrowth, axon pathfinding, pruning, and stabilization (Hedin-Pereira et al., 1999). Numerous molecules that regulate these processes have been identified and are well characterized, including those that guide axon pathfinding such as slit/robo (Dickson & Gilestro, 2006).

A number of MRI studies suggest that there are alterations in connectivity in the autistic brain. Herbert et al. (2003) postulated that the abnormal brain enlargement observed in children with autism is disproportionately accounted for by increased white matter based on their findings of large increases in white matter, but no difference in gray matter, in 7- to 11-year-old boys. Of six studies investigating cerebral gray and white matter volumes in autism, three in very young children (1.5–4 years) have reported findings that are consistent with Herbert’s suggestion (Courchesne et al., 2001; Hazlett et al., 2005; Schumann et al., 2010). However, studies carried out with subjects who are in later childhood and adolescence are less consistent with Herbert’s hypothesis. Two studies found no difference in white matter in later childhood and adolescence (7–18 years; Lotspeich et al., 2004; Palmen et al., 2005), and one found no difference in adolescence and early adulthood (13–29 years; Hazlett et al., 2006). Herbert et al. examined a narrower age range restricted to preadolescent children (7–11 years) and reported a 13% increase in white matter (Herbert et al., 2003), which is restricted to superficial radiate white matter, with no difference reported in deep and bridging white matter Herbert et al. (2004). As discussed earlier, a recent study of post-mortem human tissue found a decreased number of large axons that project long distances beneath the anterior cingulate cortex, an increased number of thin local projection axons, and increased expression of GAP-43 (Zikopoulos & Barbas, 2010). While provocative, these observations should be confirmed and extended in studies that include a larger sample size.

Synaptogenesis is initiated at points of axodendritic contact between neurons. Functional synapses can form relatively quickly, in some cases within minutes after required pre- and postsynaptic proteins and material have been transported to the site (McAllister, 2007). Synapse development requires the presence of postsynaptic density, receptors, active zone proteins, synaptic vesicles, and transsynaptic adhesion molecules. Transsynaptic molecules in particular are thought to regulate early stages of synapse formation. Two classes of transsynaptic proteins, neuroligins and β-neurexins, have received considerable attention after the discovery that mutated forms of these proteins are found in some autistic patients (Jamain et al., 2003; Comoletti et al., 2004; Szatmari et al., 2007). Neuroligins are postsynaptic proteins that bind with high affinity to members of the presynaptic β-neurexin family of proteins. The formation of the neuroligin–β-neurexin complex is thought to be sufficient and necessary for synapse formation (McAllister, 2007). Multiple isoforms of neuroligin and neurexin have been identified, and each is associated with different types of synapses. The neuroligin-3 and neuroligin-4 isoforms garnered attention after the discovery that mutations in the genes that code these proteins were identified in some patients with autism (Jamain et al., 2003; Comoletti et al., 2004). The mutated forms of the protein can still induce synapse formation, but bind with their transsynaptic partners at a lower affinity (Jamain et al., 2003), which suggests the possibility that some synapses may be altered in these autistic patients. Mutated genes that code for proteins in the β-neuroexin family have also been identified and are linked to some cases of autism (Szatmari et al., 2007). Furthermore, mutated genes that code for additional proteins required for synaptogenesis, such as shank3 (Durand et al., 2007), and protocadherin 10 (Morrow et al., 2008), have been identified in families with autistic children. These data highlight the importance of proper synaptic formation and point toward the potential etiology of some types of autism.

The Role of Cell Death

Some reports indicate that as many as 50% of all neurons that are generated during the development of the CNS die soon after the formation of synapses. The discovery that neuron–target cell interactions are crucial for cell survival suggested that young neurons receive trophic support from their target cells with which they connect (Levi-Montalcini & Booker, 1960a, 1960b). The subsequent isolation and characterization of nerve growth factor led to the discovery of a large family of survival factors, called neurotrophins, which are secreted by target tissues. Most immature neurons depend on access to neurotrophins for survival; neurons that fail to make proper synaptic connections do not receive sufficient trophic support and do not survive. In addition, some neurons also depend on trophic support from cells that innervate them (Raff et al., 1993). The neurotrophic theory predicts that the developing nervous system can “correct” for some errors in proliferation or migration by eliminating those cells that do not succeed in making proper connections. Furthermore, the embryonic proliferative zones produce a greater number of cells than is generally required to ensure a higher degree of success during formation of CNS structures. Newborn neurons must therefore compete with one another for access to trophic support from target tissues in much the same way that neighboring trees compete with one another for access to sunlight. Additional signaling pathways also play a role in the programmed cell death of young neurons, such as the caspase family of cysteine proteases (Kuan et al., 2000).

As mentioned above and described in further detail elsewhere in this book, MRI studies have reported increased brain volume in some regions of the frontal lobes in young autistic children, but decreased brain volume in the same areas later in adolescence (for review see Courchesne et al., 2007). Additional groups have weighed in on this issue and generally concur that there is an enlargement of cortical brain volume in young autistic children, but it is not yet clear whether these changes are retained beyond childhood (Amaral et al., 2008). Although the etiology underlying these changes in cortical neuroanatomy has not been determined, the early overgrowth of brain tissue followed by possible degeneration is reminiscent of the neurotrophic theory. This suggests that an alteration in two distinct developmental processes could be associated with some forms of autism. An initial phase of increased proliferation in the ventricular and/or subventricular zone would increase cell numbers and could explain increased volume of grey cortical matter. Failure of these cells to form proper synaptic connections, and thus failure to receive sufficient trophic support, would then reduce cell numbers in affected structures and could explain the reduced brain volume reported for older children and adolescents. Furthermore, some cells in the brain appear to undergo programmed cell death, or apoptosis. Factors that promote and inhibit apoptosis have been identified. Interestingly, expression of the anti-apoptotic factor bcl2 is reduced in some cortical regions of individuals with autism, while the pro-apoptotic factor p53 may be overexpressed in the autistic brain (Fatemi & Halt, 2001). These patterns of expression would both lead to an increase in cell death, which could explain the smaller brain volumes that have been reported in some studies. Neurotrophins can regulate the activity of apoptotic factors such as p53, pointing to specific intracellular signaling pathways that might be activated in the autistic brain.

Establishment and Maintenance of Dendritic and Axonal Arbors

Just as the developing brain produces a greater number of cells than is needed, many neurons initially make an excessive number of synaptic connections (Scheiffele, 2003). The changes in grey matter volume reported in autistic children discussed above could also result from an exuberant production of neuronal processes and cell-cell interactions, followed by excessive pruning of these processes and synapse retraction. The assembly of mature neural networks relies on tightly controlled cell-cell interactions, and candidate molecules that play crucial roles in these processes have been identified. For example, the α1-chimaerin family of proteins, which are expressed in neurons during differentiation and synaptogenesis (Lim et al., 1992), regulate process growth and pruning along dendritic arbors during brain development (Buttery et al., 2006; Beg et al., 2007). These molecules are crucial for normal development. Neurons, in which expression of α1-chimaerin protein has been decreased, sprout overabundant dendritic processes (Buttery et al., 2006). In contrast neurons in which α1-chimaerin protein is overexpressed have longer dendrites with a greater number of processes. Genes that code for proteins such as α1-chimaerin are candidates for consideration in neurodevelopmental disorders, such as autism, that may result from abnormal patterns of connectivity in the CNS. Indeed, a genomewide screen for linkage with autism conducted in 2001 identified α1-chimaerin as a potential gene of interest (IMGSAC, 2001), which, although speculative, may contribute to diminished long-range connectivity (Courchesne & Pierce, 2005; Geschwind & Levitt, 2007).

Conclusions

Research into the neuropathology of autism is still in its infancy. Progress has been hindered, in part, by the lack of availability of a large number of postmortem brains, particularly from early postnatal periods. Given the inherent heterogeneity in the etiologies and trajectories of the autisms, it is likely that various patterns of neuropathology will be observed if an adequate number of brains become available for analyses. It is fair to say that the technology for sophisticated neuropathological analyses is available once the brains have been obtained. Indications that there are fewer neurons in the fusiform gyrus and amygdala beg the question of whether there were always fewer neurons or whether a neurodegenerative process occurs in autism. While the minicolumn hypothesis is important in focusing future neuropathological efforts, it will be critical to combine this research with estimates of cell number, size, and complexity. Similarly, studies indicating persistent inflammation of the autistic brain are provocative, but must be replicated across multiple brain regions and in larger samples of autistic brains. In the end, the relatively subtle neuropathology observed thus far in the autistic brain provides some hope that the behavioral impairments associated with the syndrome are not due to massive neural alteration, but rather to a more subtle, although pervasive, modulation of brain activity. If so, intervention leading to normalized behavior may be more feasible.

Challenges and Future Directions

  • Progress into the neuropathology of autism will require an international collaborative effort to acquire an adequate number of brain specimens for analysis.

  • Fundamental studies of neuronal organization using modern quantitative, stereological techniques as well as classical methods (e.g., Golgi) will need to be carried out to understand whether the autistic brain is characterized by abnormal cell proliferation and/or cell loss and if there are fundamental differences in the structure of neurons in the autism brain.

  • Sophisticated clinical phenotyping of donors will be essential to account for neuropathology associated with comorbid syndromes and the heterogeneity of the autisms in the neuropathological findings that are obtained from postmortem studies.

Suggested Readings

Amaral, D. G., Schumann, C. M., & Nordahl, C. W. (2008) Neuroanatomy of autism. Trends in Neurosciences, 31, 137–145.

American Psychiatric Association. (1994). Diagnostic and statistical manual of mental disorders (4th ed.). Washington, DC: Author.

Bailey, A. (2008). Postmortem studies of autism. Autism Research, 1(5), 265.

Schmitz, C., & Rezaie, P. (2008). The neuropathology of autism: Where do we stand? Neuropathology and Applied Neurobiology, 34(1), 4–11.

Acknowledgment

Original research described in this chapter by the authors was supported by grants from the National Institute of Mental Health (R01 MH41479-18) and by the M.I.N.D. Institute. As always, we are grateful to the individuals and their families who contribute autism research.

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