Research in Autistic Spectrum Disorders (ASD) has
greatly increased within the past decade. DSM IV TR (APA, 2000) has
grouped autism, Rett’s disorder, Asperger’s Disorder (AS), and
childhood disintegrative disorder under the umbrella term,
Pervasive Developmental Disorder (PDD). PDD-NOS (not otherwise
specified) is a term and diagnosis with no specific criteria which
is often used when a child does not meet full criteria for either
diagnosis and is generally used when the child shows some, but not
all of the symptoms of either AS or autism.
The main hallmark of these disorders is a severe
impairment across situations in social interaction skills as well
as significant problems with communication or stereotyped
behaviors, interests, and activities. PDD may also be seen with
medical and chromosomal abnormalities and has been particularly
associated with tuberous sclerosis. Common comorbid diagnoses with
ASD are seizures (Volkmar, Klin, & Pauls, 1998), Tourette’s syndrome (Baron-Cohen,
Scahill, Izaguirre, Hornsey, & Robertson, 1999), ADHD (Ghaziuddin, 2002), anxiety, and mood disorders (Kim,
Szatmari, Bryson, Streiner, & Wilson, 2000). Case histories suggest that some children
with autism spectrum disorders (ASD) were socially unresponsive
from early infancy (Dahlgren, Ehlers, Hagberg, & Gillberg,
2000) while others report the
onset of symptoms sometime after the second year of life (Volkmar,
Lord, Bailey, Schultz, & Klin, 2004).
Social interaction difficulties such as poor eye
contact and difficulty understanding nonverbal communication and
social reciprocity are the hallmarks for the diagnosis of autistic
spectrum disorder (Semrud-Clikeman, 2007). Delays are often found in spoken and
receptive language, pragmatic language, the presence of stereotyped
and echolalic speech. A narrow pattern of interests and behavior is
frequently present, coupled with repetitive behaviors and
preoccupation with objects and items.
Incidence and Prevalence
The overall prevalence of the Autistic Spectrum
Disorders (ASD) is approximately 26.1 per 10,000 (Fombonne,
2001) with estimates of prevalence
for ASD of 12.7 cases per 10,000 (Fombonne, 2003b). The incidence of ASD appears to be
increasing due, in part, to improved diagnostic measures and the
tendency for children with autism to be eligible for more services
through the public schools than those with mental retardation. It
is not uncommon for neuropsychologists, school psychologists and
clinical psychologists to be pressured into making a diagnosis of
autism to receive these additional services. Children with autism
are generally identified earlier compared to diagnoses of PDD or
AS, approximately by the age of 30.0 months of age, compared to
37.2 for PDD-NOS and AS.
Epidemiological data reports indicate that the
incidence of AS is approximately 8.4 per 10,000 children
(Chakrabarti & Fombonne, 2001), while Rett’s disorder and Childhood
Disintegrative Disorder have lower rates (<1 per 10,000 and 1
per 50,000, respectively). The prevalence of PDD-NOS is more
problematic to estimate due to the difficulty with the diagnostic
criteria. It has been estimated from an epidemiological stay at
36.1 cases per 10,000 (Chakrabarti & Fombonne, 2001).
Racial and ethnic differences have not been
substantiated for the diagnosis of ASD or between social class and
ASD (Dyches, Wilder, & Obiakor, 2001; Fombonne, 2003a). More males are identified with autism
than girls with the ratios approaching 2:1 (Fombonne,
2003b). There appears to be a
difference based on cognitive ability with average or higher
ability being related to a higher incidence of ASD in males. When
the ability level is in the mentally handicapped range, the ratios
approach each other (Volkmar et al., 2004). It has been suggested that boys may be
at higher risk for autism while girls require more neurological
compromise for autism to be confirmed.
Neuropsychological Aspects of ASD
Asperger Disorder and High Functioning Autism
A more recent conceptualization of autism is that
impairments in social reciprocity, communication, and stereotyped
behaviors lie on a severity continuum ranging from severely
autistic to those individuals classified as high functioning (HFA)
(Barrett, Prior, & Manjiviona, 2004; Prior & Ozonoff, 1998). Children with ASD have difficulties with
planning, cognitive flexibility, working memory, and verbal
fluency, with few differences between HFA and AS found on these
measures (Klin, Saulnier, Tsatsanis, & Volkmar, 2005; Klin, Sparrow, Cicchetti, & Rourke,
1995; Miller & Ozonoff,
2000; Ozonoff & Griffith,
2000; Verte et al.,
2006).
Presently, the differentiation between AS and
children who are classified with HFA is not clear given the overlap
in the areas of social reciprocity, communication, and perspective
taking found in both disorders (Gillberg, 1999; Macintosh & Dissanayake,
2004). Some neuropsychological
differences have been identified between HFA and AS. Strengths on
visual-spatial tasks and perceptual reasoning have been found in
HFA with weaknesses in obtaining knowledge that requires
inferential thinking (Ehlers et al., 1997). In contrast, the children with AS exhibit
the opposite pattern. Further analysis found that these differences
were due to higher overall cognitive ability and language skills in
the children with AS compared to those with HFA.
Others have found that children with HFA show a
higher performance IQ than verbal IQ, with the opposite present for
children with AS (Klin et al., 1995). This finding continues to be of
interest, but has not been replicated with larger groups of
children. Similarly, executive function and social-cognitive
abilities were not found to discriminate between HFA and AS
(Manjiviona & Prior, 1999;
Miller & Ozonoff, 2000). Some
behavioral differences do appear to be present. Children with AS
have fewer stereotyped behaviors, but more abnormal preoccupations
than children with HFA (Kugler, 1998; McLaughlin-Cheng, 1998). Others have suggested that HFA and AS
are part of the same disorder, but may differ in severity.
Social Understanding
Children with ASD generally have difficulty with
social reciprocity, likely related to challenges in social
information processing. The encoding of these social-emotional cues
includes processing of nonverbal cues such as facial expressions,
gestures, and voice intonation. Nonverbal, novel stimuli are
generally processed in the right hemisphere in typically developing
children and in adults, while the lexical aspects of language are
processed in the left hemisphere. Children with ASD frequently
utilize language to process social information and, thus, may use
left hemispheric pathways more than children without ASD. Such
processing generally requires longer latencies and is not as
efficient.
Ashwin, Wheelwright, and Baron-Cohen
(2006) studied latency to
Stroop-like pictures of emotional faces in adults with AS and
controls. Findings indicated that response latencies to angry faces
were the longest, followed by response to neutral faces, and
fastest for looking at an object (a chair). The control group
showed the longest latency for the angry faces while the AS group
showed no difference between angry and neutral faces. As predicted,
the controls showed more attention to faces that could be
considered threatening while the ASD group showed longer latencies
to all faces versus the object. These differences may be related to
difficulties in encoding any face in participants with ASD, and
suggest a threat vulnerability to facial expressions of any type
(Schultz, Gauthier et al., 2000).
Ashwin, Wheelwright et al. (2006 ) suggest that any face may produce anxiety
for participants with ASD and that these participants are actually
biased toward attempting to decode facial expressions. Thus, it may
be that these latency differences are due to perceptual decoding
difficulties as well as an innate problem for understanding facial
expressions. Williams, Goldstein, and Minshew (2005) studied adults with HFA on measures of
auditory and visual memory. Compared to the control sample,
deficits were found for memory of faces and the social scenes
compared to the controls. These findings implicate difficulty in
the area of recalling faces and social scenes that may interfere
with social performance in more naturalistic settings.
Children with ASD may have difficulty
understanding emotions from standardized facial expressions such as
anger, fear, happiness, and sadness when matched to same-aged peers
(Castelli, 2005b). Some have
hypothesized that these differences are due partially to
difficulties with social interaction in children with ASD while
others suggest that a visual perceptual deficit contributes to this
problem (Behrman, Thomas, & Humphreys, 2006). For some children simple identification
may be related to low ability. When children with ASD are matched
to peers at the same developmental language level, these
differences disappear (Ozonoff, Pennington, & Rogers,
1990), except for young children
(Klin et al., 1999).
Castelli (2005) studied children with HFA and
found they were as able as control children to identify static
pictures of complex and simple emotions. This study differs from
other studies because it included only HFA and AS children rather
than the full range of abilities that was utilized in earlier
studies. It is possible that children with HFA and AS have
developed compensatory techniques to identify emotions based on
intensive intervention generally provided in early and middle
childhood.
Support for this hypothesis comes from an
event-related potential study evaluating face processing in
children aged 3–4 years prior to having experienced significant
amounts of intervention pointed toward emotional identification. In
that study slower brain responses to faces and higher activation to
objects were found for children with ASD, compared to children who
were typically developing and those who were developmentally
delayed but not autistic (Webb, Dawson, Bernier, &
Panagiotides, 2006). There was a
preference for objects over faces implicating both perceptual
differences and, possibly, motivation to process social
interactions. For this reason it is important to control for
ability level as well as age level when studying the ability of a
child with ASD to recognize facial expression from pictures.
It has also been hypothesized that children with
ASD may prefer fragmented and detail-oriented processing of visual
material which interferes with processing of the whole picture;
this is also referred to as weak central coherence (Happe &
Frith, 2006). Thus, the difficulty
present in emotion identification in faces may be due to problems
with paying attention to the whole rather than the parts (Mann
& Walker, 2003). In support
of this hypothesis Baron-Cohen, Wheelwright, and Jolliffe
(1997) found that adults with ASD
have a problem recognizing feelings when shown the eye region, as
compared to the whole face, as well as in understanding more
complex emotions such as interest or surprise. These complex
emotions require more processing as well as perspective taking as
they are related to a metacognitive understanding of why the person
may feel what he/she is feeling compared to simple identification
(“she’s happy”). It may be that the child with ASD is centered on
details rather than the whole.
Similarly, Grossman, Klin, Carter, and Volkmar
(2000) found that when the label
conflicted with the pictured emotion, children with ASD used the
verbal label rather than the nonverbal information to define the
emotion depicted. Again, the emphasis was on details rather than
generalizing or understanding the whole of the nonverbal
information presented. Castelli (2005) suggests that these
difficulties may be related to executive functioning deficits as
well as in perspective taking.
Thus, these studies are of interest to understand
facial recognition, but do not provide information about how the
child performs in a more naturalistic setting which is, by
definition, more fluid and dynamic. While there may not be
consistent difficulties for children with ASD in facial
identification in a controlled clinical environment, these
difficulties are likely to present when the child is faced with a
threatening experience or when the facial expressions change
quickly as happens in everyday social interactions.
Rett’s Disorder/Childhood Disintegrative Disorder
Rett’s disorder and childhood disintegrative
disorder are included under the PDD umbrella. Rett’s disorder is a
neurodegenerative disorder and seems out of place in the ASD
category. Some have suggested that it is grouped within ASD as a
place marker (Volkmar et al., 2004). Rett’s disorder is found only in girls
and is usually not identified until the child is at least
five-months-old (frequently later), but generally before the age of
three (Swaiman & Dyken, 1999). Initially the child appears to have
difficult with hand control and becomes less interested in
observing or interacting with others. Neurological examinations,
with MRI confirmation, generally find that the head stops growing
due to a lack of brain growth. The child appears to lose language
and show significant cognitive decline (Ozonoff & Rogers,
2003). With age the child begins
to wring his/her hands, and to clap or rub his/her hands together.
In addition, the cognitive decline continues. It is believed that
Rett’s syndrome is a mutation of the X chromosome (Kerr,
2002).
Childhood Disintegrative Disorder
Childhood disintegrative disorder (CDD) is a rare
condition. The child shows a pattern of regression after normal
development. It is present in both genders, but more commonly seen
in males. In this disorder the regression occurs without warning,
is quite severe, and can occur anywhere between the ages of two and
10. Prior to this time the child’s development appears normal. The
child’s ability and adaptive behaviors decrease significantly and
communication and social interaction become nonexistent. This
process lasts approximately 1–2 months with the child becoming very
agitated and difficult to control. After this period the child
appears to have severe autism and mental retardation.
Unfortunately, there is little improvement with treatment and the
condition is irreversible. The cause of this disorder is not
presently clear, but it is believed to be genetic (Ozonoff &
Rogers, 2003).
Pervasive Developmental Disorders-Not Otherwise Specified (PDD-NOS)
PDD-NOS is very difficult to reliably diagnose
and is frequently a fallback diagnosis when the criteria for AS or
autism is not met. For the most part, a diagnosis of PDD-NOS
indicates that two of the three symptom clusters that identify
children with ASD or AS have been met (Ozonoff & Rogers,
2003). These clusters are the
social responsiveness cluster, communication skill difficulty, and
stereotyped or repetitive behaviors. The diagnosis of PDD-NOS
requires that the child have difficulty with social reciprocity and
either social communication problems or stereotyped/repetitive
behaviors. The incidence of mental retardation is much lower than
in autism and is generally around 7.3 percent of the PDD-NOS
population (Chakrabarti & Fombonne, 2001). Most children with a diagnosis of PDD-NOS
show some autistic-like symptoms, but do not qualify for a
diagnosis of autism, or have a language delay and so do not qualify
for a diagnosis of AS.
The DSM IV field trials found that the diagnosis
of PDD-NOS is one of the more unreliable diagnostic categories
(Volkmar et al., 1994). In the
field trials one-third of the children diagnosed with PDD-NOS met
criteria for autism, while another one-third did not qualify for
any diagnosis in the autistic spectrum. The children who did not
qualify were generally found to have language and learning problems
or had significant symptoms of ADHD.
Developmental Course of Autistic Spectrum Disorders
For most children with ASD onset occurs prior to
age three, particularly for those children with a more severe
presentation of the disorder. As discussed earlier a sizable
majority does not show the disorder until after the age of two,
particularly when diagnosed with AS or PDD-NOS. Retrospectively the
children who are diagnosed later are reported to show
irregularities and delays in development from infancy until
diagnosis. Generally these problems are related to difficulty with
nonverbal communication, inappropriate responses to facial
expressions, and a lack of social responsiveness to caretakers.
Approximately one-third of children with autism show regression of
skills between the ages of 1–2 years, and some have hypothesized
that this regression is due to infections and immunological factors
(Hornig & Lipkin, 2001) or to
genetic influences (Lainhart et al., 2002).
Qualitative differences are present in the
expression of some symptoms at different ages. Stereotyped and
repetitive behaviors are most commonly seen in preschool and either
improve or significantly decline in elementary school
(Semrud-Clikeman, 2007). For
children receiving early intervention, it is estimated that 50
percent approach normal functioning by adolescence (McEachin,
Smith, & Lovass, 1993).
Although improvement is noted, most of these adolescents continue
to have difficulty with social interaction and few are able to
establish an independent lifestyle in adulthood (Howlin,
2000). The most predictive
variable for a positive outcome is the level of intelligence
present by the age of five (Howlin, 2000).
Prenatal and Postnatal Factors
An increase in the incidence of prenatal and
perinatal complications in autistic individuals compared to normal
children has been found (Meyer et al., 2008). Some of the more frequent complications
are meconium in the amniotic fluid, bleeding during pregnancy, and
use of doctor-prescribed hormones (National Institute of Mental
Health, 2006). In a study of mice
and their offspring, Meyer et al. (2008) found that prenatal events predispose the
child to autism or schizophrenia more so than postnatal
events.
Seven studies that met stringent criteria for
selection were reviewed to evaluate prenatal and perinatal risk
factors for autism (Kolevzon, Gross, & Reichenberg,
2008). Selected studies needed to
have a well-defined sample standardized and data collected during
and after pregnancy, a group of comparison subjects who also
experienced obstetric complications without resulting autism, and a
standardized report of the findings to allow comparisons across
studies. Characteristics that were selected were those associated
with a 50 percent increase or larger in risk. Factors that emerged
for parental characteristics included advanced maternal age,
advanced paternal age, and maternal birthplace. Of the seven
studies, three showed maternal age to be a significant predictor
for autism when confounding variables were covaried. Paternal age
has also been identified as a risk factor with a two-fold risk
found for each 10-year increase in paternal age (Reichenberg et
al., 2006).
Other factor that emerged included birth weight
and prematurity as well as hypoxia at birth. Low birth weight
(defined as less than 2,500 g) was not associated with an
increased risk of autism. Four studies reported prematurity with a
birth at less than 35 weeks to be at higher risk for autism in two
studies. Apgar scores below seven were also associated with autism
in all four studies that examined this variable. The authors
concluded that hypoxia-related complications appeared to increase
the risk of autism. They also concluded that low birth weight and
prematurity were not strongly tied to a heightened risk for autism
and that further study is needed to more carefully examine these
issues (Kolevzon et al., 2008).
Autism and Vaccines
An additional issue that has arisen in the past
decade is the relationship between autism and vaccines that contain
thimerosal. The media has reported a possible link between autism
and these vaccines. The empirical support for such a link is
tenuous at best. Thimerosol was removed from vaccines, except for
trace amounts, by 2001. The Immunization Safety Review Committee of
the Institute of Medicine from the National Academies reviewed the
data about thimerosal and autism and did not find a causal
relationship (Immunization Safety Review Committee: Board on Health
Promotion and Disease Prevention, 2004). Studies that have evaluated the incidence
of autism since thimerosal’s removal from the vaccines have not
found a drop in the incidence of the disorder (Fombonne,
2008; Schechter & Grether,
2008).
Studies that evaluated early thimerosal exposure
and neuropsychological functioning in mid-childhood have been
conducted in children without autism (Thompson, Price, Goodson,
& Shay, 2007).
One-thousand-forty-seven children were enrolled in the study and
administered several neuropsychological measures. The study did not
find a causal association between early exposure to thimerosal and
neuropsychological deficits. Thus, the findings, taken as a whole,
do not support a link between thimerosal and autism.
Genetics
The heritability of autism is supported by two
important findings: (1) the rate of autism in siblings of autistic
individuals is approximately 50 times that of the general
population (Bailey, Pelferman, & Heavey, 1998), and (2) there is a high concordance rate
of autism in monozygotic twins compared to dyzygotic twins (Ozonoff
& Rogers, 2003). Recent
advances in genetic analysis have found that autism recurs in
families at an approximate rate of 3–6 percent, which is higher
than the rate found in the general population (Bailey, Le Couteur,
& Gottesman, 1995). Twin
studies have found that monozygotic twins have a concordance rate
for a diagnosis of autism of 60 percent, while the rate was 5
percent for dyzygotic pairs (Bailey et al., 1995). When all types of PDD were included the
concordance rate increased to 90 percent for monozygotic pairs,
yielding a heritability estimate greater than 0.90 (LeCouteur et
al., 1996). In studies that have evaluated familial risk factors in
autism, these families have a higher rate of psychiatric and
developmental illnesses compared to the general population. In
addition, these families also show a higher incidence of medical
disorders leading one to suggest that the genetic structures in
these families leaves the members vulnerable to many types of
disorders (Brimacombe, Ming, & Parikh, 2007)
Ozonoff and Rogers (2003) further point out that
autism frequently co-occurs with other chromosomal abnormalities
including tuberous sclerosis, fragile X syndrome and in deletion
syndromes including chromosomes 7, 15, and 18 (Semrud-Clikeman
& Schaefer, 2000). Fragile X
syndrome is a commonly inherited cause of mental retardation that
is transmitted by the mother’s contribution to the sex chromosomes.
Approximately 2–8 percent of boys with autism and Fragile X
syndrome are also mentally retarded (Reiss & Hall,
2007; Wassnik, Piven, &
Vieland, 2001).
Tuberous Sclerosis (TS) is a genetic disorder
where tubers or lesions are present throughout the body,
particularly in the brain. Approximately 2–4 percent of children
with autism have TS (Hansen & Hagerman, 2003). Although the majority of children with TS
are not diagnosed with autism, approximately 43–61 percent show
autistic symptoms with a higher than expected percentage showing
brain lesions in the temporal lobe, an area of the brain
particularly involved in language and emotion recognition (Gillberg
& Billstedt, 2000). A possible
link between ASD and TS was evaluated in a PET study. Patients with
TS, both with and without autism, showed a higher metabolic rate in
areas of the brain associated with impaired social interactions,
language problems, and stereotyped behaviors (Asano, Chugani, &
Muzik, 2001). These are the areas
that are specifically problematic for children with ASD.
Neurological Features
Neurodevelopmental anomalies have been identified
in children with autism, particularly in the frontal lobes with
neural circuits differing as well as frontal lobe enlargement and
atypical patterns of brain connectivity (Courchesne, Carper, &
Akshoomoff, 2003; Courchesne &
Pierce, 2005; Hill, 2004; Murphy et al., 2002). Although the exact etiology of autism is
still unknown, results from electrophysiological and dichotic
listening techniques suggest that autistic children may not show
the expected pattern of hemispheric specialization. Research has
documented that normally the two hemispheres are functionally and
structurally asymmetric at birth (Gazzaniga, Ivry, & Mangun,
2002). Autistic children do not
show such hemispheric specialization and may show less functional
asymmetry as evidenced by dichotic listening techniques as well as
through electroencephalograms (Coben, Clarke, Hudspeth, &
Barry, 2008).
Data from electrophysiological studies indicate
that children with autism tended to either have dominant
right-hemisphere response to linguistic stimuli with impairment in
the left hemisphere or did not show a dominant language hemisphere
(Tanguay, 2000). When EEG
recordings are made during completion of cognitive tasks, a
reversed pattern of brain activity during language tasks and use of
the right hand (normally left-hemispheric-mediated tasks) has been
found (Dawson, Finley, Phillips, & Galpert, 1986). Moreover, children with autism have been
found to show differences when told not to attend to stimuli
compared to typically developing children. These differences
indicate that children with autism may process auditory signals
(i.e., words and sounds) differently than those without autism and
that this difference leads to difficulty in processing of
information (Dunn, Gomes, & Gravel, 2008).
As you will recall from Chapter
3, the P300 component has been associated with
the detection of novel and unpredictable stimuli. In individuals
with autism this component has an extended latency; that is, it
occurs later than expected and the amplitude (degree of response)
is smaller (Dawson et al., 1986). Additional work in this area has
led researchers to hypothesize that the foregoing results may be
due to the possibility that autistic children react to novel
stimuli as aversive and/or as overstimulating (Dawson et al.,
2005). Moreover, there is emerging
evidence that autistic individuals may be chronically over-aroused
(Wolf, Fein, & Akshoomoff, 2007).
It may well be that the connectivity of the brain
in children with autism interferes with the crucial aspects of
language processing that are so important for social interactions.
Differences in the ability to process emotional and non-emotional
words as well as possible perceptual difficulty likely interfere
with the autistic child’s ability to understand the social and
general world. Atypical patterns of brain connectivity have
indicated an underconnectivity for both inter- and intrahemispheric
neuronal signals (Rippon, Brock, Brown, & Boucher,
2007). This underconnectivity has
been associated with problems with social cognition (Barnea-Gorly
et al., 2004), frontal lobe
connectivity (Belmonte et al., 2004), and facial processing (Dawson & Webb,
2005).
Neuroimaging
Neuroimaging techniques have made it possible to
view the developing brain while the child completes various tasks.
Thus, the ability to compare brain activity among groups as well as
for different tasks allows us to understand some of the differences
that are present that may account for problems in reasoning, social
interaction, and with executive functioning. One of the more common
techniques used to study children and adolescents with autistic
spectrum disorders is functional magnetic resonance imaging
(fMRI).
Because the behaviors associated with autism vary
from social reciprocity/understanding to language, to stereotyped
and repetitive behaviors, it is likely that many brain systems are
involved and will vary depending on the severity of the autistic
symptoms as well as the level of cognitive involvement. Children
with autism tend to have larger heads than the general population
(Aylward, Minshew, Field, Sparks, & Singh, 2002). Brains of autistic toddlers have measured
10 percent larger than same-aged peers; the largeness of the head
decreases with age, but continues to be larger than matched aged
peers throughout life (Courchesne et al., 2003). Interestingly, there is no difference in
head size at birth (Lainhart, 1997) and the brain growth that later occurs
may be due to early overgrowth of neurons, glial cells, and a lack
of synaptic pruning (Courchesne & Pierce, 2005). Findings have suggested that this
increased brain size indicates that the extra tissue is not well
utilized or organized, thus resulting in poorer skill development
(Aylward et al., 2002). Specific
findings indicate an increase in gray matter volume, particularly
in the temporal lobes (Herbert et al., 2002; Rojas et al., 2004). Autopsy studies have found that the
cellular columns that make up the frontal and temporal gray matter
areas were disrupted, possibly resulting in an inability to inhibit
neuronal activity in these areas and, thus, produce cognitive
dysfunction and possibly behavioral overflow (Casanova, Buxhoeven,
& Brown, 2002; Casanova,
Buxhoeven, Switala, & Roy, 2002).
Using structural MRI analyses, Courchesne et al.
(2001) found smaller measures of
white matter compared to gray matter in toddlers and adolescents.
Other studies of adults with autism have found reduced measures of
the corpus callosum (Hardan, Minshew, & Keshavan,
2000), a structure that connects
the two hemispheres, as well as difficulties with interregional
integration. Some have suggested that the larger brain, higher
white matter volume, and disrupted gray matter cellular columns may
contribute to an autistic person’s difficulty in integrating
information and generalizing this information to new situations
(Schultz, Romanski, & Tsatsanis, 2000). These difficulties may interfere with
the person’s ability to put information together into an
understandable whole—or interfere with establishing central
coherence—a theory discussed in an earlier chapter.
Schultz et al. (2003) suggested that the social brain
incorporates frontal, limbic, and temporal connectivity and that
these regions are integral to socialization. In children with ASD,
findings have included hypoactivation in the areas of the superior
temporal gyrus (STG), the fusiform face gyrus of the temporal lobe
(FG), and regions of the temporal and occipital lobes. These areas
are the hypothesized regions for social understanding and
comprehension. Schultz et al. (2003) suggest that this hypoactivation is not
causative of autism, but rather may be an outcome of autism--less
practice may mean less growth in this region.
The amygdala has also been implicated in autism
(Adolphs, 2001; Baron-Cohen et al.,
2000; Sparks et al.,
2002). Patients with damage to
the amygdala experience difficulties with some aspects of social
impairment including lack of emotional response and problems in
recognizing fearful stimuli (Zirlinger & Anderson,
2003). Postmortem analysis of
autistic brains have found increased neuronal density in the
amygdala in people with autism (Kemper & Bauman, 1993). When the amygdala has been ablated in
rhesus monkeys and neonatal rats, social behaviors are poorly
developed, particularly if the damage occurred before birth
(Baron-Cohen et al., 2000;
Wolterink et al., 2001). The
amygdala has extensive connections to the cortex, the striatum, and
the hippocampus (Amaral, Price, Pitkanan, & Carmichael,
1992). This connectivity may
influence perception as well as perspective taking abilities. If
there is a reduction in the functional connectivity for social
processing, then there should be differences in these regions on
fMRI as well as on Diffusion Tensor Imaging (DTI), a procedure that
examines white matter tracts.
MRI Findings in ASD
Structural imaging in ASD has found a variety of
cerebral anomalies. Some findings indicate differences in the
corpus callosum, particularly in thinning and in the midsagittal
area and in white matter density in persons with ASD (Chung,
Dalton, Alexander, & Davidson, 2004; Hardan et al., 2000; Vidal et al., 2006). These differences have been linked to
difficulties with language processing and in working memory (Just,
Cherkassky, Keller, & Minshew, 2004). Structural imaging findings have found
increased brain size in children with ASD, specifically enlargement
in the regions of the parietal, temporal, and occipital lobes
(Courchesne et al., 2003; Piven,
Arndt, Bailey, & Andreasen, 1996). The enlargement is due to greater
volumes of white matter, but not gray (Filipek, 1999). For adolescents and adults with autism,
increased brain size was not found, but increased head
circumference was identified (Aylward et al., 2002).
Findings have also implicated the caudate in
children with ASD. Sears et al. (1999) found increased volume bilaterally in the
caudate of adolescents and young adults with HFA. Caudate
enlargement was proportional to the increased total brain volumes
also identified. To determine whether these findings were specific
to the sample studied, the results were replicated with a different
sample of participants with ASD. Previous studies have found that
the caudate volume in typically developing children decreases at
puberty (Giedd, 2004). For the
Sears et al. (1999) study this
decrease did not occur.
In typically developing adults, right-sided
activation has been found for the FG, the STG, and the amygdala
when viewing social faces (Schultz et al., 2003) and social interaction (Semrud-Clikeman,
Fine, & Zhu, submitted). These findings seem to corroborate the
hypothesis that the right hemisphere is more heavily involved in
social processing than the left (Winner, Brownell, Happe, Blum,
& Pincus, 1998). Studies of
adults with HFA or AS have found differences in these regions using
structural and functional imaging that found less activation or
smaller volumes (Abell et al., 1999; Baron-Cohen, Ring et al., 1999).
Some studies have found underactivation in the
prefrontal areas in participants with ASD (Schultz et al.,
2000) with higher activation in
controls particularly in the left prefrontal regions. Significant
metabolic reduction has been found in the anterior cingulate gyrus,
an area important for error monitoring, and in the frontostriatal
networks involved in visuospatial processing (Ohnishi et al.,
2000; Silk et al., 2006). The amygdala and striatal regions have
been implicated in processing of emotional facial expressions
(Canli, Sivers, Whitfield, Gotlib, & Gabrieli, 2002; Killgore & Yurgelun-Todd,
2005; Yang et al., 2002).
Studies involving the amygdala have found that
patients with bilateral lesions show difficulty in judging
emotional facial expressions (Bechara, Damasio, & Damasio,
2003). Faces that were negative in
nature were found to produce slower activation than those which
were neutral in controls (Monk et al., 2003; Simpson, Öngür, Akbudak, Conturo, &
Ollinger, 2000). Positive stimuli
increased activity in the amygdala with happy expressions,
resulting in faster response times (Canli et al., 2002; Pessoa, Kastner, & Ungerleider,
2002; Somerville, Kim, Johnstone,
Alexander, & Whalen, 2004).
Hare, Tottenham, Davidson, Glover, and Casey (2005) found longer latencies for negative facial
expressions in activation of the amygdala in normal adult controls,
while activation of the caudate was present when the participant
was avoiding positive information.
These findings are important because they point
to the neural networks that underlie the processing of positive and
negative emotions. It is crucial to determine how emotional cues
may influence behavior that interferes with this processing which
is frequently seen in children and adults with ASD. Similarly,
Ashwin, Baron-Cohen, Wheelwright, O'Riordan, and Bullmore
(2006) studied the amygdala in
adults with HFA and AS using fMRI when viewing fearful (high and
low fear) and neutral faces. Findings indicated that the control
sample showed more activation in the left amygdala and left
orbitofrontal regions compared to the HFA/AS group when viewing the
fearful faces. In contrast, the HFA/AS group showed more activation
in the anterior cingulate and the superior temporal cortex when
viewing all of the faces.
Specific areas implicated in ASD using fMRI
include the superior temporal gyrus (STG) (Allison, Puce, &
McCarthy, 2000; Baron-Cohen, Ring
et al., 1999) and the amygdala (Amaral et al., 1992; Carmichael & Price, 1995). When a child with ASD is asked to view
faces, functional imaging has found less activation in the right
fusiform face gyrus (FG) (Critchley et al., 2000; Dierks, Bolte, Hubl, Lanfermann, &
Poustka, 2004; Pierce, Muller,
Ambrose, Allen, & Courchesne, 2001). The fusiform face gyrus appears to be
selectively engaged when the person views faces. Children with ASD
appear to pay less attention to the face and may not activate this
region in the same manner as typically developing children (Schultz
et al., 2003).
Differences in activation have not been found
solely for viewing of faces. Just et al. (2004) found more cortical activation in children
with HFA when processing verbal material compared to a control
group. This finding may indicate that more brain resources are
needed to process verbal information in children with ASD. This
inefficiency may slow down their processing and take resources away
from understanding the intent of the speaker’s prosody or
interpreting facial expressions. Kana, Keller, Cherkassky, Minshew,
and Just (2006) found that
children with HFA show poorly synchronized neural connectivity when
asked to process material using their imagination. Moreover,
children with ASD were also found to use parietal and occipital
regions associated with imagery for tasks that had low imagery as
well as high imagery requirements. Controls only utilized these
regions for the high imagery tasks.
The difference in activation that ASD
participants have when viewing faces is important for our
understanding of ASD. Animal studies have found that the temporal
cortex responds to the perceptual aspects of social stimuli
(Hasselmo, Rolls, & Baylis, 1989; Perrett & Mistlin, 1990). Human studies have also found a
relationship between the perception of eyes and mouths in the
superior temporal cortex (Adolphs, 2001; Haxby, Hoffman, & Gobbini,
2000). The superior temporal lobe
is thought to be involved in perceiving social stimuli with the
connections to the amygdala and frontal lobes to interpret these
stimuli. Findings from participants with ASD have repeatedly shown
that these areas are important for the processing of facial
expressions. An area of the temporal lobe that is important for
recognizing faces has been studied in children with autism. This
area is underactive in people with autism, and the degree of
underactivation is highly correlated with the degree of social
impairment (Schultz et al., 2001). Additionally, this area of the temporal
lobe has also been implicated in successfully solving Theory of
Mind tasks, skills that are also impaired in people with autism
(Castelli, 2005a; Martin &
Weisberg, 2003).
Few studies have used DTI in children with ASD.
Alexander et al. (2007) used DTI
with children with ASD and found reduced volumes in the corpus
callosum and reduced fractional anisotropy (FA) of the genu,
splenium, and total corpus callosum in children with ASD.
Medication status and the presence of comorbid diagnoses were not
found to have an effect on the DTI measures. In a similar vein to
these findings, Boger-Megiddo et al. (2006) scanned young children with ASD and found
that the corpus callosum area was disproportionately small compared
to total brain volume for these children, compared to typically
developing children. Children with a more severe form of ASD showed
the smallest corpus callosal area. Future research needs to utilize
quantitative imaging to more fully capture the relation between the
corpus callosum connectivity and ASD. Taken together these findings
suggest that children with ASD may be inefficient in their use of
neural networks and that their networks show poor connectivity
compared to same-aged peers.
The amygdala, anterior cingulate, and hippocampus
have also been studied in children with ASD. These two structures
are part of the limbic system of the brain—or the emotional part of
the brain. The amygdala is important in emotional arousal as well
as processing social information. The hippocampus allows for the
short-term and eventual long-term storage of information while the
anterior cingulate works as a type of central executive directing
attention where it is most required. Abnormalities have been found
in these parts of the brain during autopsy with reduced size and
fewer connections present (Kemper & Bauman, 1993). These abnormalities may contribute to the
behavioral difficulties seen in people with autism in social
reciprocity and social awareness. Further study is needed in these
areas. Some have suggested that the amygdala may be important for
mediating physiological arousal and, if it is not as active, the
person may not be as motivated to participate in social activities
(Dawson, Meltzoff, Osterling, & Rinaldi, 1998; Klin & Volkmar, 2003).
Areas of the frontal lobes have also been studied
in patients with autism. Both the frontal lobes and the superior
area of the temporal lobes are important for understanding and
perceiving social interactions as well as interpreting facial
expressions. The frontal lobes have been particularly implicated in
the ability to take another’s perspective, or in social cognition.
These areas are intimately connected to the limbic system as well
as the temporal lobe areas discussed earlier in this section.
Studies of brain metabolism have found reduced activity in these
regions of the brain in autistic patients, particularly when
individuals are asked to perform tasks that tap social cognition
and perception (Castelli et al., 2002; Ehlers et al., 1997;
Haznedar et al., 2000).
The neuroimaging findings are intriguing and
suggest that there are significant differences structurally as well
as in neural connectivity of cognitive systems. These differences
likely result in neuropsychological challenges for these children
as well as for the neuropsychologist. The following section
discusses the neuropsychological factors involved in autism.
Neuropsychological Aspects in Diagnosis
The cognitive ability of children within the ASD
umbrella varies widely, ranging from significant mental retardation
to giftedness. There is no pattern of strengths and abilities
within the cognitive measures, although some have found a pattern
of performance IQ being stronger than verbal IQ (Akshoomoff,
2005). Some have suggested that
children with autism show a VIQ < PIQ profile, and children with
AS show the opposite pattern (Klin et al., 1995), while others have not replicated this
finding. Instead, children with AS have higher verbal scores
compared to those with HFA, but not performance ability (Ghaziuddin
& Mountain-Kimchi, 2004).
Ability is generally found to be higher in HFA children with some
suggesting the neuropsychological differences between AS and HFA
are due to ability and not true neuropsychological variation
(Miller & Ozonoff, 2000).
A comprehensive neuropsychological evaluation of
a child suspected of having an ASD needs to include a measure of
communication ability including both receptive and expressive
language skills (Kjelgaard & Tager-Flusberg, 2001). Consistent with the discussion presented
in earlier sections, children with autism also have difficulty with
executive functioning, particularly in cognitive flexibility and
working memory (Lord et al., 2006). The difficulties in executive
functioning have been equivocal with some studies which have
identified problems in these areas (Kleinhans, Akshoomoff, &
Delis, 2005; Ozonoff & Jensen,
1999), while others have not
(Griffith, Pennington, Wehner, & Rogers, 1999; Liss et al., 2001). Further evaluation of the differences
among these studies is that some were with young children who may
not evidence executive functioning deficits on standardized
measures until a later age (Semrud-Clikeman & Schaefer,
2000), while others did not match
the groups on ability (Wolf et al., 2007). These areas require additional study
with groups matched by ability as well as evaluating the effect of
age on the performance of the child on standardized measures of
executive functioning. It is certainly appropriate for an
evaluation to include measures of executive functioning as well as
problem solving skills. These skills have been tied to the ability
to adapt to changing environments; an area of difficulty for many
children with ASD.
As discussed earlier in this chapter, many
children with ASD have difficulty discerning the whole from the
parts. Tests such as block design or copying of figures (VMI,
Rey-Osterreith Complex figure) may be particularly difficult for
them. It is important to evaluate these skills for the individual.
Memory skills is another area that requires evaluation in children
with ASD. Verbal memory and spatial memory skills have been
reported as an area of difficulty (Lord et al., 2006; Luna et al., 2002; Toichi & Kamio, 1998) while visual memory skills for designs
have been relatively intact. Working memory skills may also be an
area of weakness for children with ASD and appear to be very
dependent on the context and nature of the skills required (Ozonoff
& Strayer, 2001).
Attention is a variable area in the population of
children with ASD. Few studies have evaluated the co-occurrence of
ADHD and ASD partly due to the exclusion of ADHD in the diagnostic
criteria for AS. However, Ghaziuddin, Weidmer-Mikhail, &
Ghaziuddin (1998) found a high
co-occurrence of ADHD in a sample of children with AS followed by a
higher than expected incidence of depression. A significant number
of attentional symptoms were found in approximately 33 percent–50
percent of the ASD population in this study. In contrast, the
co-occurrence of ASD and hyperactivity/impulsivity symptoms is less
(approximately 7–10%). Other studies have found that children
diagnosed with HFA and AS appear to share similar attentional
difficulties with ADHD children (Ehlers et al., 1997; Nyden, Gillberg, Hjelmquist, & Heiman,
1999).
Leyfer et al. (2006) examined 109 children with autism for
comorbid diagnoses. Phobias were the most common co-occurring
disorder, followed by obsessive- compulsive disorder and ADHD. Of
the 109 children in the sample, 20 percent qualified for a
diagnosis of ADHD: predominately inattentive (ADHD:PI). When cases
that were one symptom short of diagnosis were included, the rate
increased to 55 percent. It is not clear from this study how many
of the children with co-occurring ASD and ADHD also had other
diagnoses. Studies have found that 40 percent of children aged 3–5,
and 50 percent of those aged 6–12 referred to a clinic with ASD
showed some form of ADHD (Gadow, DeVincent, & Pomeroy,
2006; Gadow, DeVincent, Pomeroy,
& Azizian, 2005). Findings
also indicated that these rates of ADHD in children with ASD were
similar to children without ASD who were also referred for a
clinical evaluation.
One of the concerns about the comorbidity of ADHD
and ASD is whether the attentional and hyperactive/impulsive
symptoms are part of the ASD diagnosis or are reflective of the ASD
diagnosis. Gadow et al. (2006)
sought to study DSM IV psychiatric symptoms in children with ASD
who referred to a clinic using parent and teacher rating scales.
Findings indicated that 20 percent of the younger children
qualified for a diagnosis of ADHD:PI, and 12 percent for ADHD:C
along with a diagnosis of ASD. For the older group 36 percent
qualified for a diagnosis of ADHD:PI and 20 percent for ADHD:C, as
well as a diagnosis of ASD. Importantly, there were no differences
in severity between the groups of children with ASD and those with
a sole diagnosis of ADHD, suggesting that the expression of
significant attentional difficulties is similar in the two
populations. Gadow et al. (2006)
conclude that DSM IV should be considered a blueprint for
diagnosing ADHD in this population and not as a firm standard given
the dearth of studies with this population. In addition, these
findings also lend support to the hypothesis that ADHD co-occurs
with ASD and is not just part of the ASD diagnosis. The existing
studies used parent and teacher rating scales to determine the
existence of ADHD.
Given the hypothesis that children with ASD may
have attentional differences, it is important to rule out
significant attentional problems for these children. ADHD may
interact with ASD and stretch the already depleted attentional
resources to the limit. These children’s ability to understand
social interactions may be further compounded by difficulties with
response inhibition as well as with impulse control. These concerns
about the comorbidity of ASD and ADHD raise the question about
previously equivocal findings of executive functioning in children
with ASD. It may be that attentional issues were not controlled in
these studies, thus confounding the findings.
As discussed in
Chapter 6, many of the neuropsychological
measures for memory, attention, and language are presented and are
appropriate for use with children with ASD depending on the child’s
ability level and age. The following section discusses particular
instruments that have been developed for children with autism. Most
of these measures are parent interview and behavior rating scales.
The findings discussed above indicate that there is no current
“pattern” of functioning for a child with ASD and, thus, a
comprehensive evaluation needs to include measures from the various
domains as well as the interviews discussed below.
Diagnostic Instruments
Diagnosing ASD has been somewhat problematic, but
the advent of fairly well-standardized measures has improved our
ability to reliably diagnose children with ASD. Three diagnostic
instruments are considered to be reliable for diagnosis (Wolf et
al., 2007): the Childhood Autism
Rating Scale (CARS) (Schopler & Reichler, 2004), Autism Diagnostic Observation System
(ADOS) (Lord, Rutter, DiLavore, & Risi, 1999), and Autism Diagnostic Interview-Revised
(ADI-R) (Rutter, Le Couteur, & Lord, 2003).
Although many children with autism have been
diagnosed with mental retardation, there are more children now
diagnosed in the average to above average range with autism partly
due to improved diagnostic skills as well as in our understanding
of the disorder’s scope (Volkmar et al., 2004). Previously these children were not
diagnosed because they were able to adapt somewhat to the situation
at hand or their parents had arranged for early and intense
interventions. During the past several years, studies have found
that less than half of children with autism now qualify for an
additional diagnosis of mental retardation when adequately
evaluated (Chakrabarti & Fombonne, 2001, 2005;
Howlin, 2000; Tsatsania,
2003).
PsychopharmacologicaI Treatment
Psychopharmacological treatment for ASD has
increased by approximately 50 percent in the past 15 years (Aman,
Lam, & Van Bourgondein, 2005). Antidepressant medication is the
most commonly prescribed medication in children with ASD, followed
by psychostimulants and antihypertensive drugs (Aman et al.,
2005). Medication to reduce anxiety
and compulsive behaviors includes anticonvulsants, stimulants,
neuroleptics, fluoxetine and clomipramine, fenfluramine, and most
recently opiate antagonists (Wilens, 2004). These medications may assist with
obsessions and compulsions as well as anxiety and irritability.
Fenfluramine, a serotonin reducer, has had a good effects on early
autistic symptoms, but this response diminishes with time and an
increasing the dosage has been only moderately helpful (Erickson,
Stigler, Posey, & McDougle, 2007). Another type of psychopharmacological
intervention is the use of opiate receptor antagonists such as
naloxone or naltreione. Opiate receptor antagonists do not allow
the postsynaptic receptors to absorb the brain endorphins.
Beginning evidence shows that low doses of this agent have reduced
many maladaptive behaviors including self-injurious behavior
(Benjamin, Seek, Tresise, Price, & Gagnon, 1995), while high doses improve the child's
ability to relate to others (Feldman, Kolman, & Gonzaga,
1999).
Atypical antipsychotics have also been studied in
children with ASD. These medications, such as haloperidol, have
been found to improve symptoms in ASD as well as attentional
difficulties, but have also increased dyskinesias and so are used
sparingly due to this serious side effect (Barnard, Young, Pearson,
Geddes, & O'Brien, 2002;
Remington, Sloman, Konstantareas, Parker, & Gow, 2001). Risperidone has also been utilized for
children with ASD to control difficult behavior. While the
medication has been very helpful in reducing behavioral
difficulties in children with ASD (Hanft & Hendren,
2004), side effects such as
decreased appetite, weight gain, fatigue, and drooling may
counterindicate the use of this medication (Findling et al.,
2004; Stigler, Posey, &
McDougle, 2004).
Attentional problems are also seen frequently
with autistic spectrum disorders.While not diagnosed in the
presence of such a disorder, the difficulty they cause for the
child is often treated through medication. Approximately 60 percent
of children with ASD exhibit attentional problems, and 40 percent
of these also have hyperactivity (Hazell, 2007). Improvement in activity level and
attention has been found for a majority of children with ASD with
attention/hyperactivity symptoms following administration of
methylphenidate (Handen, Johnson, & Lubetsky, 2000; Research Units on Pediatric
Psychopharmacology Autistic Disorder Network, 2005), but not for dexamphetamine (Handen et
al., 2000).
Behavioral Treatments
The treatment options available for autistic
children has grown, but no one solution has been found to be
appropriate for all children with autism. Areas that require
intervention include the domains of language, attention, social
cognitive, cognition, learning, and adaptive behavior. These areas
require multifaceted treatment that includes working with families,
schools, and individuals in order to improve functioning as well as
develop skills (Iovannone, Dunlap, Huber, & Kincaid,
2003). There has been an explosion
in literature in the area of autism and many of the clinicians and
researchers suggest that, based on the literature, no single
approach is appropriate for all children with autism. Therefore,
programs needs to be tailored to the individual child’s needs
(National Research Council, 2001). It has been strongly suggested that a
combination of approaches be used to support the child’s
acquisition of basic skills and allow the child to develop more
complex social skills and social reciprocity (Volkmar et al.,
2004).
Behavioral treatment has by far the most support
for interventions for children with autism, although improvement
has not been as good as previously was predicted from initial
studies (Mudford, Martin, Eikeseth, & Bibby, 2001; Smith, Buch, & Gamby, 2000; Smith, Groen, & Wynn, 2000). Although behavioral treatment improved
targeted behaviors, the children required continual support to
maintain these gains and cognitive skills did not improve (Bibby,
Eikeseth, Martin, Mudford, & Reeves, 2001). Parent satisfaction has been generally
good with applied behavior analysis techniques as well as other
behavioral treatments, and the maintenance and increase in gains
have not been significant when the children are followed 2–3 years
later (Volkmar et al., 2004).
One important finding is that to be successful,
the child needs to be motivated to participate in the program and
that the program needs to be tailored to the specific family’s
needs (Lord & McGee, 2001;
Moes, 1998). Teaching individual
skills such as joint attention and social communication skills has
led to improved social skills (Drew et al., 2002; Whalen & Schreibman, 2002). However, these skills are often not
generalized to the natural setting without directly teaching those
skills in that setting (Hwang & Hughes, 2000; Strain & Hoyson, 2000).
A review of 10 studies on the efficacy of
communication intervention found that teaching social communication
was most successful when it was directly related to everyday
situations and possible communications to which the child would be
exposed (Delprato, 2001). When the
communication skills are tied to an interest of the child’s (i.e.,
weather, dinosaurs), language skills improvement is dramatic and
appears to be present in long-lasting follow-up evaluations (Siller
& Sigman, 2002).
Similar to the findings for language, Gutstein
& Whitney (2002) suggest that
interventions need to include specific occasions for the child to
learn social skills that are apart from the traditionally scripted
approaches most commonly seen with social skills programs. Tasks
that directly teach appropriate social interaction as well as
perspective taking, and are guided by adults, have been most
appropriate. These authors suggest that the initial interventions
need to be ritualistic and predictable with inconsistency and
novelty kept to a minimum. As the child learns these skills, more
complexity and novelty is introduced, assisting the child with
generalization. The intervention needs to be in a quiet and
distraction-free environment where the child feels safe. It is
important to restrict the sensory input so that the child does not
become overwhelmed. Actions, emotions, intonations, and gestures
need to be overdone and amplified so that the child learns what is
important. Emotions and environmental input that are subtle may be
too difficult for the child to process and will most likely be
ignored. Finally the adult coaches need to move the child to a peer
coach as readiness is perceived. Using these techniques can assist
the child in developing the abilities that are needed, but more
importantly to generalize the skills to the more dynamic nature
seen in most social exchanges.
Due to the difficult nature of intervention
studies, most empirical evidence comes from case studies or small
groups. Further study is needed to determine the parameters for the
success of many of these interventions, and to move much of the
work into public schools. Corbett (2003) has developed a video modeling
intervention based on Bandura’s social learning theory. Video
modeling emphasizes the ability to learn new behaviors by observing
a model engaging in a behavior the child would like to emulate.
Video modeling involves the child watching a videotape of a
desirable model producing a behavior. The behavior is then imitated
and practiced with a therapist. The behaviors are repeated and
reinforced over time.
Corbett (2003)
studied the efficacy of video modeling on social skill acquisition
using a single case study design. The child was eight-years,
three-months-old and was diagnosed with autism. His IQ was measured
at 60 with adaptive behavior being significantly lower at 37. The
child watched the videotape showing expressions denoting happy,
sad, angry, or afraid. The videotape was shown for 10–15 minutes
daily for two weeks. The child quickly mastered identification of
the happy face with improvement seen in the other categories over
the treatment duration. Certainly this study shows promise and
replication, and follow-up of the length of these gains is
needed.
Social stories have also been used fairly
successfully with ASD children. This method describes a social
situation to the child and explains what the child should do and
what the appropriate feelings should be. The child is asked for his
or her interpretation. Gray & Garand (1993) use four types of sentences for the social
stories. The descriptive sentence describes where the situation is
occurring and who the main character is. The perspective sentences
describe the reactions of others and the feelings that the
characters have. The directive sentences provide information about
what the child is expected to do or say. Finally, the control
sentence is generally written by the child to solve the situation.
Most stories have 0–1 control and/or directive sentences for every
2–5 descriptive and perspective sentences. For those children who
cannot read the stories can be read to them and discussed as the
therapist reads the story.
An example of a social story would be the
following:
John rides the school bus to school every day (descriptive). He sits in the front seat next to the bus driver (descriptive). The ride takes about 20 minutes to get to John’s school (descriptive). He knows that he can ask the bus driver for help if he needs it (perspective). John also knows that he can’t always have the same seat on the bus if someone gets on before him (perspective). He can ask the other child if he can sit on the seat (descriptive). He is afraid to ask the child to move over (perspective). John asks the child and sits next to her (perspective). Buses have large seats (control). His mother will be proud that he solved his problem (perspective).
Social stories can help the child solve a current
problem or resolve an issue that is troublesome to him/her. They
can also be tailored to fit the child’s particular interests to
keep the story relevant to him/her. As with other interventions,
this appears promising, but empirical support has not been
developed at this time.
Conclusion
The etiology, effective psychopharmacological
treatments, and productive educational interventions for autism are
currently being developed and researched. Much is unknown about
this disorder, but gains are being made in our understanding. It is
likely that a transactional approach, utilizing neurological and
neuropsychological knowledge of this disorder in conjunction with
work in the child's two major environments, namely home and school,
will be most effective. Evidence of an exquisite sensitivity to
environmental stimulation has led to a new understanding of the
autistic child's behavior and language skills. Effective
pharmacological treatment continues to elude practitioners, and
additional work is needed in that regard. Researchers are beginning
to develop programs across the life span for individuals with
autism, and promising vocationally based programs have been started
for adolescents and adults.
The following case study is an example of a
complex scenario that is often referred to a neuropsychologist. The
school has repeatedly attempted an evaluation, but the school
personnel do not have the requisite knowledge to structure an
evaluation. This case was selected to illustrate the difficulties
that may be present when evaluating a significantly compromised
child, and the need to pursue serial evaluations in order to obtain
the most comprehensive and useful assessment of the child’s
abilities.
A Child with Severe Expressive Aphasia and Motor Apraxia with Pervasive Developmental Delay
This case illustrates the complexity of
diagnosing children with severe expressive aphasia with motor
apraxia and pervasive developmental delays at an early age. It
demonstrates the need for collaboration between neuropsychologists
and public school staff so that neuropsychological findings can be
integrated into the classroom. Initial contact with the child came
at the family's request to verify previous evaluations and
diagnoses conducted at a different clinic. A comprehensive
neuropsychological evaluation was conducted to determine the
child's neuropsychological, academic, cognitive and
social-emotional functioning, and to develop an appropriate
intervention program. Serial evaluation results were presented at
the school's multidisciplinary team meetings.
Teddy
First and Second Evaluations
Information gathered from the first (age 5 1/2
years) and second (age 7 1/2 years) evaluations was based on
observational methods because Teddy was unable to respond
adequately to verbal or nonverbal test items. A number of methods
were attempted during the first and second evaluations, including
items from the Stanford-Binet, the NEPSY II, the Wechsler Scales,
the Reitan-Indiana Battery, the French Pictorial Test of
Intelligence, and the Leiter International Scales. Severe motor
apraxia affected his ability to complete nonverbal measures, and
his severe expressive language deficits interfered with verbal
measures. On several subtests of the Leiter Scales, however, Teddy
showed near average abilities at the age of five and one-half
years.
Teddy was enrolled in a preschool program for
children with significant developmental delays. He was transferred
to a regular elementary school in first grade and received special
education services. Speech-language, occupational, and physical
therapy were the major interventions in the special education
program at the public school. Although he was taught sign language,
which he readily picked up, articulating vowels and consonant-vowel
combinations was the major focus of speech therapy over a four-year
period.
At the time of the last evaluation, the therapy
goal was to attain expressive language to the five-year-level.
After four years of speech therapy, Teddy had not reached his
milestone. Teddy was mainstreamed for part of the day for
socialization, but received services for learning disabilities 55
percent to 60 percent of the day, 12 percent to 15 percent of which
were for speech. Although he learned to read, Teddy appeared
hyperlexic as his rate and speed of reading outpaced his reading
comprehension skills.
Despite two years of extensive physical therapy
and a full-time aide who helped Teddy trace, cut paper, and draw
lines and letters, he was unable to print his name, cut with
scissors, or draw simple figures. While he did not approach
age-appropriate levels for fine motor skills, Teddy did learn to
use a computer and was able to type (one-two finger technique) on
the keyboard. Gross motor skills were also delayed, but were not as
significantly affected.
Attempts to increase his socialization skills
showed some progress in that Teddy became more responsive to
others. Although he was encouraged to use a combination of speech
and sign language for communication, Teddy remained isolated
despite attempts to increase socialization with normal and
handicapped peers.
Recommendations at the end of the second
evaluation urged the school to make a transition into computer
technology so that Teddy could make better progress with
communication and academic skills.
Third Evaluation
The third evaluation was conducted when Teddy was
9 years –3 months of age, and in the third grade. This was the
first evaluation where Teddy was able to complete subtests on
standardized assessment measures. During individual testing
sessions, Teddy was able to work for periods up to about 45 minutes
long. Teddy's attention span was best when activities required
reading or when pictures were used to elicit a response. Teddy was
able to indicate when he did not know an answer, and occasionally
it appeared that he simply said he didn't know when the answer
required a complex verbal response. His expressive language was
difficult to understand, and Teddy became frustrated when the
examiner could not understand what he was saying. He always
repeated his answer, but as the session progressed this was
obviously frustrating for him. When speaking, Teddy tried hard to
enunciate individual letter sounds, especially the T and C sounds.
On numerous occasions, he used the proper speech inflection and
rhythm, but it was difficult to determine if his verbal responses
were correct. Because of severe limitations in responding, formal
evaluations may underestimate Teddy's actual level of academic and
cognitive development. Portions of the Tests of Cognitive Ability
from the Woodcock-Johnson Psychoeducational Test Battery (WJIII)
were administered in an attempt to determine Teddy's cognitive
potential. The following scores were taken from age-based norms and
must be analyzed with caution because of Teddy's severe expressive
language delays secondary to motor apraxia.
|
Measure
|
Standard score
|
Percentile
|
|
Memory for Names
|
109
|
73
|
|
Memory for Names(delayed)
|
105
|
63
|
|
Memory for Words
|
89
|
17
|
|
Picture vocabulary
|
86
|
16
|
|
Visual Closure
|
79
|
8
|
Teddy's most outstanding strengths were revealed
on tests of associational learning and long-term retrieval.
Specifically, Teddy was able to learn associations between
unfamiliar auditory and visual stimuli, and scores on the subtest
reached the average to high average range of ability. He was able
to remember these auditory-visual associations after a four-day
delay, and his scores fell within the average range compared to
same-age peers. Further, his associational learning was much better
when pictures were used instead of more abstract visual stimuli,
like rebus figures.
On another measure of short-term memory, Teddy
scored within the average range of ability as he was able to recall
a series of unrelated words in the proper order. Teddy also showed
at least average potential on a task measuring
comprehension-knowledge or crystallized intelligence (standard
score = 86). When asked to name familiar and unfamiliar pictured
objects Teddy identified “waterfall,” “grasshopper,” “magnet,” and
“theater.” While Teddy scored within the average range on this
subtest, several of his verbal responses were unintelligible. Thus,
his academic potential may be higher than can be measured at this
time.
Teddy had more difficulty on tasks that required
visual processing when objects were distorted or superimposed on
other patterns. He also had trouble on a test where he had to
remember a series of objects when similar or "distractor" pictures
were included (standard score = 73). While this test is a measure
of visual processing, Teddy often did not study the pictures for
the full five-second interval before the distractors were
presented, so his lack of interest in or attention to this task
reduced his score. Subtest scores on the Stanford Binet
Intelligence Scale-Fifth Edition revealed similar patterns as did
the WJ III Cognitive subtests. Teddy scored above age level on
terms where short-term memory for objects was required, and had
difficulty on items requiring complex verbal or motor
responses.
Educational Implications
Academically, Teddy appears to show improvement
in the areas of reading recognition and comprehension. On
standardized measures, Teddy identified words such as special , straight, powder, and couple. While he was able to read
sentences, he made errors when responding to questions. For
example, he clapped five times instead of two and identified his
"eyebrow" instead of his elbow. At other times it was impossible to
understand his verbal responses; as a result, Teddy's reading
comprehension may be slightly higher than reported. He also showed
slightly higher comprehension scores on the Woodcock Reading
Mastery Test than on the K-ABC II test. According to his teacher,
Teddy is reading and understanding vocabulary slightly above grade
level and is reading from a fourth-grade book at school.
|
Test
|
Standard score
|
Percentile
|
|
WJ III
|
||
|
Letter-Word ID
|
93
|
18
|
|
Reading Comprehension
|
79
|
8
|
|
Mathematics Calculation
|
60
|
1
|
|
Mathematics Reasoning
|
Unable to complete
|
Teddy's math skills are less well developed than
his reading abilities. While math skills are emerging, this area is
a weakness for Teddy (below the 1.0 grade level). Teddy was able to
identify numbers and to determine if there were "just as many"
puppets and people in different pictures. However, he could not
complete simple addition or subtraction, and he was unable to
identify the "third" position in a line. Visual-perceptual deficits
appear to be having an impact on his ability to develop math
skills.
Spelling skills were not formerly measured
because severe apraxia interferes with Teddy's printing skills.
However, Teddy can spell some words with his Touch Talker that he
is unable to write. For example he was able to type the words
to and be. At this point he spontaneously
remarked, "To be or not to be. Shakespeare." He obviously
associated this phrase with something he had learned at home, and
he was able to further associate it in the testing session. Severe
motor apraxia severely limits Teddy's written expressive skills,
and he remains virtually a non-writer because of his motor
limitations.
Classroom Observations
Because standardized assessment tools may be
underestimating Teddy's academic progress, a school-based
observation was conducted. Teddy seemed aware of the routine and
reacted appropriately to directions for small group activities. In
large open classroom settings, Teddy was less adaptable, showing
more distractibility and off-task behaviors. He was unable to
complete worksheets without the help of a teacher's aide, who
helped Teddy print the answers on his paper. When working in pairs,
Teddy attempted to answer the questions his partner read aloud to
him and was more successful when questions were not too complex.
Teddy was able to answer 21 percent (3 out of 14) of the questions
read to him and guessed at others. He was unable to print any
answers on his worksheet, although his aide traced all the answers
with him. In less structured story time, Teddy sat on the floor
with the rest of his class and showed appropriate behaviors.
Generally, Teddy's behaviors were not
distinguishable from those of his peers during story time. He did,
however, move close to his teacher and sit up on his knees to see
the pictures of the book better. When his classmates signaled for
him to sit down, he complied. On several occasions Teddy rested his
elbows on his teacher's lap, and he seemed comfortable to be close
to her during story time. He smiled spontaneously, and he seemed
very attentive throughout this session, which was 25 minutes
long.
In speech therapy Teddy was careful when
articulating and appeared highly motivated and spontaneous in his
interaction with the speech therapist. His articulation of
individual sounds was clear, distinct, and at a more normal pace.
He played the message on the Touch Talker, and he seemed genuinely
spontaneous and interested in interacting. Teddy was able to
introduce the neuropsychologist to his teacher when prompted. He
practiced muscle control in front of the mirror, and he used the
proper hand and finger cues to remind himself where his tongue
belonged when reproducing individual sounds. His speech production
of individual sounds was about 80 percent intelligible, while his
production of c-v-c combinations and words were about 25 percent to
30 percent intelligible. He produced the K sound at the beginning
and end of words, he read out of a "book" he created about Mickey
Mouse, and he played a Mickey Mouse game.
Teddy showed some signs of anxiety during the
speech therapy session that was not present at any other time
during the day. Teddy repeatedly stomped his feet, clapped his
hands, and laughed out loud. He would stop for a short time when
his teacher told him to, but he would continue to act out when
frustrated. Despite these problems, Teddy was more intelligible,
happy, interactive, and spontaneous during speech therapy then at
any other time during the day.
Teddy showed appropriate lunchtime behaviors. He
turned in his lunch ticket by himself, carried his tray with some
assistance from his aide, ate quietly, and cleaned up by himself.
He used his utensils correctly and opened his milk carton on his
own. He sat next to his homeroom "buddy" and smiled, but did not
communicate further with him. During recess another child gave
Teddy a "high five" and he responded appropriately. Other than this
5- or 10-second interchange, Teddy did not interact with anyone
else. When left by himself, he walked up and down a square of
asphalt. His aide told him to play on the equipment, and he
methodically walked over to each piece and climbed up a ladder and
over two arches. Before climbing over the ladder, Teddy walked
around a square area for seven minutes waiting for two children to
leave. Once they left, he climbed the ladder once and then
left.
Although he went through the motions of play,
Teddy did not appear enthusiastic or involved. In the afternoon
sessions, Teddy worked on a worksheet counting the number of units
of 10 and units of one. He was able to count the units by himself,
but his aide had to trace the numbers on the worksheet with him.
When he played a game of Addition Bingo, the aide helped him count
problems like 7 + 3, 1 + 1, 3 + 0 , 4 + 5, 3 + 4 , 2 + 3 , and 5 +
1. Teddy was not independent on any of these problems, but he was
able to count out loud when the aide pointed to the edge of each
number. No other concrete objects were used during this
lesson.
In language arts, Teddy did one computer reading
lesson in which he had to read a short passage and answer
questions. He was able to turn the computer on, type his first and
last name, and select the correct sequence to start the lesson. His
accuracy rate was about 75 percent for the comprehension questions
following a story about dinosaurs.
In a small group reading lesson with his LD
teacher and one other student, Teddy read and answered questions in
a fourth-grade book. His intelligibility continues to be a problem,
but his teacher appears to understand his responses. His
inflections and rhythm follow the sentence structure, which
suggests that he is processing written material. He was able to
answer questions showing that he understood the difference between
fiction and nonfiction, and he was able to answer questions such as
“Which animal is extinct?”, “Why was the place terrible?”, and
“Where did the bugs go?” His teacher also reported that his
recognition vocabulary is at the level of the fourth-grade reading
book, and his comprehension skills are improving.
Developmental Progress
There have been remarkable gains in several areas
since Teddy's initial evaluation. First, the most apparent
developmental progress is in the area of behavior and classroom
adjustment. Teddy seems to be very much aware of classroom
expectations, although he is not always able to respond to
everything requested of him, especially writing on worksheets. He
is able to walk to his different classes without getting lost, make
transitions with relative ease, and sit in his seat and attend to
lessons for longer periods of time.
Second, Teddy appears to initiate interactions
with teachers and several peers at a rate higher than previously
observed. Although Teddy is still isolated because of his
communication difficulties, he spontaneously interacts and seeks to
communicate more often. Third, Teddy seems more tuned into and
aware of his surroundings. He responds more appropriately to the
directions of his teachers and he redirects quickly when reminded.
Fourth, Teddy spends less time daydreaming and staring off.
Although he still has a tendency to watch others, he is more
attentive to his lessons and to his teachers. Fifth, Teddy has made
remarkable academic gains, especially in reading and in reading
comprehension. Math continues to be a weakness.
Sixth, Teddy's expressive skills are improving.
His sound repertoire is larger than before and his intelligibility
is much better. Teddy is also using speech to communicate. Seventh,
Teddy has learned to use the Touch Talker to communicate. This
involves sequential associative learning whereby icons and words or
sentences are combined to communicate. Finally, Teddy shows more
independence for everyday activities, such as going to the bathroom
and eating lunch. Gains are apparent in speech production and Teddy
is communicating with a higher frequency than previously noted. His
articulation has improved, although he still has a tendency to
speak quickly, which reduces his intelligibility.
Teddy also appears more mature, and his classroom
behaviors are more age- appropriate. Although he still is not fully
integrated into his surroundings because of his developmental
problems, Teddy responds more appropriately to classroom rules, is
more attentive, and is more independent for everyday self-help
skills at school. Although Teddy remains isolated from his peers,
he is more spontaneous than ever and he initiates interactions more
frequently. He spontaneously interacts with his teachers more
often, and he smiles and laughs appropriately. Although he remains
delayed in overall social interaction skills, his progress over the
last year has been substantial.
Specific Recommendations
- 1.
The school staff was encouraged to focus on functional communication skills with an integrated speech-language program rather than primarily a therapy-articulation focus.
- 2.
Augmented speech technology should be incorporated throughout Teddy's academic lessons. At this time, the Touch Talker is used as a secondary rather than primary method for inputting and outputting communication. This should be reversed.
- 3.
The educational environment and expectations should be modified to include computer technology for written and reading lessons. Computer technologies are only intermittently used at present. Again, this should be shifted, with the majority of assignments produced on the computer. Efforts to encourage Teddy in social interactions with peers were recommended. Social skills training may be initiated to teach appropriate communication and joining-in skills. Teddy should be reinforced for social interactions and should be assigned a "buddy" for portions of the day during recess or lunchtime. Other cooperative learning experiences should also be incorporated into daily lessons. Socialization should be closely monitored and reassessed periodically to determine whether goals and recommendations are realistic.
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