The Autistic Place

A review of: Molly Helt, Elizabeth Kelley, Marcel Kinsbourne, Juhi Pandey, Hilary Boorstein, Martha Herbert, Deborah Fein (2008). Can Children with Autism Recover? If So, How? Neuropsychology Review DOI: 10.1007/s11065-008-9075-9

In this fascinating analytical review of the scientific literature in autism, the authors examined empirical evidence that some people with autism “recover” and no longer meet the diagnosis of autism. The general view in the scientific community has been that autism is a life-long condition and that treatment interventions are most often designed to 1) minimizing maladaptive symptoms (e.g., self-injurious behaviors) and 2) maximizing cognitive/behavioral/social functioning (e.g., increasing adaptive social behaviors or language). But what happens when these interventions are sufficiently effective in minimizing or eliminating symptoms to the extent that the person no longer meets the DSM-IV criteria for autism? Has the person recovered?

The authors first provided a specific working definition of "recovery" before embarking on an exploration of the available literature. The authors argued that to be considered “recovered” a person:

1. has a clear history of autism
2. must be learning and applying skills at a developmental level that is within the range expected in typically developing persons of the same age.
3. must not meet DSM-IV symptom criteria for ASDs

Based on their current research program, they further expanded this definition to include a more detailed set of criteria, namely, a "recovered" person:

1. must not meet ASD cutoff on social or communication domain on the ADOS
2. must not receive special education services targeted to remediate key features of autism (if any special education services are received they must target non-autism features such as attention, learning disabilities, etc)
3. must be functioning within a regular education classroom WITHOUT individualized assistance
4. IQ must be 80 or greater
5. Vineland communication and socialization scales must be within the normal range (78 or above)

A critical component of this definition of recovery is that it is not limited to DSM-IV based symptoms of autism, but it also addresses key areas of functioning. This is critical, because it prevents using the label of ‘recovered’ in cases when the effects of the treatment are limited to reducing symptoms, with little change in the functional limitations of the condition.

The authors then conducted a narrative review of the available scientific literature and concluded that there is compelling evidence showing that between 3% and 20% of children diagnosed with autism may eventually meet the criteria for “recovered”. This wide range is likely the result in differences between the studies in the nature of the population examined, and the presence or absence of factors that are believed to contribute, or hamper, the possibility of recovery. That is, the authors also found that a number of baseline factors (i.e. present while the person met diagnostic criteria) facilitated recovery rates, including:
1. high intelligence
2. presence of receptive language
3. verbal and motor imitation
4. early age of diagnosis
5. PDD-NOS diagnosis

A number of factors limited the possibility of recovery, including:
1. seizures
2. mental retardation
3. genetic syndromes

Although these findings are interesting, I believe an additional contribution of this study is their presentation of the possible mechanisms by which treatment interventions may lead to recovery. These mechanisms can be seen as a road map that helps parents, educators, and clinicians better understand how treatment modalities can be targeted to the specific deficits of each child. These mechanisms include:

1. Normalizing input through attention
2. Promoting the reinforcement value of social stimuli
3. Early intervention provides enriched environments
4. Early intervention provides mass practice and trials
5. Compensatory processes
6. Suppression of interfering behaviors

Next week I will describe these 6 processes in detail.

A review of: Emma J. Grinter, Pia L. Beek, Murray T. Maybery, David R. Badcock (2008). Brief Report: Visuospatial Analysis and Self-Rated Autistic-Like Traits Journal of Autism and Developmental Disorders DOI: 10.1007/s10803-008-0658-3


Although most psychiatric, developmental, and neurological disorders are usually conceptualized as discrete categories (e.g., you either have autism or you don’t), these conditions can also be conceptualized as occurring in a continuum. For example, we all feel sad sometimes. Such sadness, and related symptoms, can range from very mild to incapacitating. Somewhere in this continuum there is a theoretical line that separates sadness from clinical depression.

In autism, a number of researchers have proposed the concept of the Broader Autism Phenotype. This concept refers to mild traits of autism found in the general typically developing population, and especially among relatives of people with Autism. You can read a couple of summaries of research on the Broader Autism Phenotype here and here.

In this study, the authors were interested in examining whether typically developing young adults (college students) who have high levels autism-like traits also have relative strengths on tasks of visospatial skills – a finding that is common among people with some autism spectrum disorders. The authors presented two studies. In the first study they examined 548 college students in Australia. The students completed the Autism-spectrum Quotient (AQ), which is a measure of autistic traits. A subsample of these students then completed two visuospatial tasks: the block design of the Wechsler Adult Intelligence Scales and the Embedded Figures Test. The authors reported that students with high AQ scores outperformed those with low AQ scores in both the block design task and the Embedded Figures Test. In a second study, the authors examined the effects of general IQ on these results. They showed that IQ partially explained the group differences in the Embedded Figures Test, but IQ did not explain the differences in the block design task. That is, these two groups continued to show differences in performance on the block design task even when considering differences in IQ between the groups.

The authors explained their results in the context of the Weak Central Coherence Theory. This theory suggests that some individuals (e.g., people with autism) have a “lack of distraction by holistic configurations and relative facilitation in dealing with piecemeal components.” This in turn would facilitate visuospatial tasks that require significant attention to detail.

There is one methodological issue I wish you briefly note. The authors initially examined 548 students. Then only those who scores significantly low and high in the AQ scales were invited to complete the visuospatial tasks. These groups included only 20 students in the low AQ group and 19 students in the high AQ group. This represents the extreme 4% of the sample. I wish that the entire sample of 548 students had completed all tasks and that the data were presented without categorizing the sample as low and high AQ. Instead the data could have been presented in a continuum to show whether high scores in the AQ were also associated (correlated) with high scores on the block design task and the Embedded Figures Test. This would provide a much stronger argument for the fluidity of autism-like traits in the general population and its association with other cognitive skills observed in autism.

A summary of: Michael Waldman, PhD; Sean Nicholson, PhD; Nodir Adilov, PhD; John Williams, MD, MBA (2008). Autism Prevalence and Precipitation Rates in California, Oregon, and Washington Counties Arch Pediatr Adolesc Med, 162 (11), 1026-1034

The authors briefly reviewed epidemiological data suggesting that the highest autism rates in the USA are found on Northern and Western States, while the lowest autism rates are found in the deep south (e.g., Alabama). They concluded from these findings that an environmental trigger in these Northern and Western States (such as bad weather) may be a risk factor for the development of autism. To test this hypothesis, the authors examined precipitation rates in various counties within California, Oregon, and Washington and compared these rates to the prevalence of autism in said counties. The authors found that high rate of annual precipitation was associated with high rates of autism cases, even after controlling for variables such as county level income, population size, or access to specialized services. The authors argue that this association may be to a number of factors including 1) higher rates of television viewing in very young children (although no evidence was presented suggesting that young children in rainy counties watch more television than young children in less rainy counties), 2) vitamin D deficiencies due to less sun exposure (although no evidence was presenting suggesting that vitamin D is associated with autism, or that the rates of autism are higher in polar regions given the reduced sun exposure during winter months), 3) environmental triggers associated with playing indoors instead of outdoors, and 4) possible harmful chemicals transported by the rain.

Given the preliminary/speculative nature of this study, the manuscript was accompanied by a letter from Dr. Noel Weiss from the Department of Epidemiology of the University of Washington that, while arguing that these results may not advance our understanding of the causes of autism, lauds the editorial decision to accept this article for publication. Below I provide some excerpts of Dr. Waiss’ arguments:

First, Dr. Waiss provides a sensible interpretation of the findings:

…there are other possible explanations for the association with precipitation that they have observed. First, the criteria used to diagnose autism, and the completeness with which such diagnoses are identified by state agencies and regional centers, likely vary to a considerable extent across counties. Possibly, the degree of completeness of reporting itself is associated with levels of precipitation. In Oregon and Washington, for example, could it be that state agencies in the western, rainy, relatively urbanized counties have enumerated a greater proportion of children with autism than their counterparts in the eastern, arid, relatively more rural counties?

However, in response to concerns regarding the potential misinterpretation and misuse of the findings by the public, Dr. Waiss states that:

The primary audience for the article of Waldman et al is not the practicing pediatrician, and certainly, it is not a member of the public at large. These individuals cannot take away any practical message from it. Rather, the primary target is an investigator interested in the causes of autism, someone who might be able to test one or more of the etiologic hypotheses that derive from the research of Waldman et al.

I do not agree with Dr. Waiss on this last point. It is no longer the case that most scientific research is read mostly by relevant scientists. The audience of scientific peer reviewed articles has expanded dramatically, mostly due to the internet and the new level of activism and involvement with research by relevant communities (e.g., autism ).

Finally, in regards to the findings, the study does not in any way show or suggest that rain causes autism. It only states that there is an association between rainy counties and autism rates. This may be due to a large number of factors, most of which (e.g., differences in how diagnostic data is collected between counties) have nothing to do with rain.

A review of: S. Ozonoff, S. Macari, G. S. Young, S. Goldring, M. Thompson, S. J. Rogers (2008). Atypical object exploration at 12 months of age is associated with autism in a prospective sample Autism, 12 (5), 457-472 DOI: 10.1177/1362361308096402

A number of research programs are currently examining very early signs of autism. Researchers believe that if they are able to identify behaviors or symptoms that reliably predict the development of autism, these kids could receive early intervention and we may be able to better understand how autism emerges during key developmental periods. I have reviewed some of these studies (see for example this review of a study of infancy motor asymmetry and autism). The current study comes to us from the MIND institute at the University of California at Davis. The researchers wanted to examine atypical use of objects during infancy (12 month of age) and explore whether object use during this time predicted the development of autism 1 to 2 years later.

The sample included a high-risk group consisting of 35 non-affected siblings of children with autism and 31 siblings of typically developing kids. 62% of the sample were males. All children were 12 month of age at the time of the first assessment. These children were further evaluated at 24 or 36 month of age via a comprehensive neurodevelopmental battery of tests which included the ADOS.

At 12 months of age, the infants were observed interacting with 4 objects (metal lid, plastic ring, rattle, baby bottle). The interactions lasted 30-seconds each. These interactions were coded for typical and atypical object use. Typical uses at this age would include shaking, banging, mouthing, and throwing. More infrequent, and thus atypical, uses of these toys would include spinning, rolling, rotating, and unusual visual exploration (looking at the toy from odd angles or peripherally).

From the initial pool of 66 infants, 9 eventually met diagnostic criteria for autism (based on ADOS), 10 met criteria for non-asd developmental delay, 47 did not receive any diagnosis (no concern group). There were no differences among these 3 outcome groups in regards to gestational age, ethnicity, or income. However, the percentage of males in the autism group (100%) was significantly higher than in the developmental delay (70% males) and the no concern group (49% males).

In regards to typical object use, the only group difference observed was for “throwing”. Infants who would eventually be classified into the developmental delayed group were significantly more likely to throw the objects than children eventually classified into the autism or no concern groups. However, in regards to atypical object use, the findings were striking. Infants who would eventually developed autism were significantly more likely to engage in all 4 atypical uses (spinning, rolling, rotating, and unusual visual exploration) than kids classified into the no concerns group. In addition, 3 specific atypical object behaviors differentiated kids with autism from those who would develop a non-asd developmental delay. Specifically, kids with autism were more likely to spin, rotate, or engage in atypical visual inspection of the objects, than developmentally delayed kids.

These results indicate that, among children at familial risk for autism, atypical object use during infancy seems to be associated with autism diagnosis during early childhood. Furthermore, this effect appears to be specific to austim --and not to general developmental delays.

A review of: Stephen M. Kanne, Jena K. Randolph, Janet E. Farmer (2008). Diagnostic and Assessment Findings: A Bridge to Academic Planning for Children with Autism Spectrum Disorders Neuropsychology Review DOI: 10.1007/s11065-008-9072-z

While working at a pediatric neuropsychology division of a large urban hospital in Michigan, I conducted weekly neuropsychological evaluations of children with Autism Spectrum Disorders. Most often, the main purpose of these evaluations was to confirm the diagnosis and to provide a picture of neurocognitive strengths and weaknesses that would guide and facilitate the implementation of educational services. By “Neurocognitive” functions we refer to cognitive processes that are linked to neurological functioning, such as attention, memory, language, visual-motor coordination, auditory processing, etc. I always wondered, however, whether my recommendations were truly reviewed by the child’s educational planning team, and whether they were implemented by the child’s teachers and therapists.

In this very insightful and needed review, the authors present a model that would facilitate the implementation of recommendations resulting from neuropsychological evaluations. The authors indicate that one of the biggest obstacles preventing the proper implementation of these recommendations is the tendency by educators to see the neuropsychological evaluation as, primarily, a diagnostic procedure; one that is only useful to guide decisions regarding eligibility for special education services. However, good neuropsychological evaluations (and reports) provide very detailed information that addresses all 6 areas outlined by the National Research Council as the key areas of focus for IEP committees.

These 6 areas include:
1. Cognitive and Functional Academics
2. Self-Management
3. Communication: Verbal and Non-Verbal
4. Motor Skills
5. Daily Living Skills
6. Social Functioning (including play)

These 6 areas are then integrated into 3 target domains: Environment, Instruction, and Behavior. That is, EIPs should discuss how each of the 6 areas above will be addressed in regards to environmental modifications, specific instruction, and targeted behaviors.

The authors argue that neuropsychologists should complement their reports with a ‘bridge document” which would provide very specific recommendations regarding environmental changes, instructional changes, and targeted behaviors for all six areas of functioning used by the IEP committee.

I recommend to parents who will be obtaining a pediatric neuropsychology evaluation for their children to discuss with their neuropsychologist whether they will include a “bridge document” that could be used during the IEP meeting. This will significantly maximize the utility of neuropsychological evaluation reports.

A review of S. Hossein Fatemi, Teri J. Reutiman, Timothy D. Folsom, Paul D. Thuras (2008). GABAA Receptor Downregulation in Brains of Subjects with Autism Journal of Autism and Developmental Disorders DOI: 10.1007/s10803-008-0646-7

Neurotransmitters are chemicals that transfer information from one neuron to another. Although we often hear about how specific neurotransmitters are associated with specific behaviors or disorders (serotonin and depression), at the most basic level, neurotransmitters do mostly two things: they tell the receiving neuron to “fire” (excitatory) or they tell the receiving neuron to stop firing (inhibitory). The neurotransmitters are able to do this because of the nature of the receptors that receive the neurotransmitter in the receiving neuron. GABA, for example, attaches to receptors that are primarily inhibitory in nature. Thus, most anti-anxiety medications work by increasing GABA transmission so more GABA receptors are activated and consequently “shutting down” the central nervous system (thus relaxation).

In this study the authors examined key GABA receptors in the brains of 8 person with autism (1 female) and 8 comparison participants (1 female) matched for age and gender. The participants range from 19 to 56 years of age. The cause of death varied, from car accidents to myocardial infarction. The authors found significant reductions in GABA receptors in the parietal cortex, cerebellum, and superior frontal cortex of the subjects with autism when compared to the control group. Most importantly, this group differences were not due to group differences in seizure disorders. The authors suggest that the noted cerebellar abnormalities may explain dysfunction with motor systems usually observed in autism, parietal cortex abnormalities may explain disturbances in visuo-spatial integration, language, and attention, and frontal cortex abnormalities may explain disruptions in emotion, cognition, and language. These findings may also explain why many parents report significant improvement when their children take anti-anxiety medications.

One note about studies using very small samples. I often see a tendency to dismiss the results of such studies because they have too few participants (“the sample size was too small”). This criticism, while valid in some cases, is most appropriate for studies that fail to find significant differences between the groups. That is, having a small sample makes it more difficult to find statistically significant differences between the groups. Thus, finding such differences in small samples (such as in this case) may speak to the robustness of the differences: The differences are so large we are able to see them even in this small sample. However, small samples are also more sensitive to unusual cases (outliers) and that the sample may be unique and not really representative of the population it is supposed to represent, thus possibly making the results less reliable (but not necessarily less real).

A review of: D GEIER, J KERN, C GARVER, J ADAMS, T AUDHYA, R NATAF, M GEIER (2008). Biomarkers of environmental toxicity and susceptibility in autism☆ Journal of the Neurological Sciences DOI: 10.1016/j.jns.2008.08.021

One bio-social model of the etiology of autism suggests that a genetic predisposition, in the form of limited ability to remove toxins from the body (e.g., mercury), combined with exposure to such toxins, leads to an increased risk for developing autism. As I previously described in this study, researchers have examined urinary porphyrins as a measure of mercury exposure, and some studies have found increased levels of porphyrins in children with autism. In this study, the authors examined urinary porphyrin metabolites (a proposed measure of heavy metal toxicity) and plasma sulfates (a proposed measure of detoxification capacity) among children with autism.

The sample included 28 children between the ages of 2 and 16 recruited from the Dallas/Fort Worth community. These children had a diagnosis of autism (Childhood Autism Rating Scale scores above 30). This group was categorized into a mild (CARS < 38.5) and severe (CARS > 38.5) autism. Blood and urine samples were collected for analysis. A laboratory standard control sample of typically developing children was also included in the plasma analysis only.

The mild ASD group had significantly lower ratios of key porphyrin levels when compared with kids with severe ASD. The following group means are statistically significantly different from each other:

Porphyrin
Pentacarboxyporphyrin
MILD ASD = 4.92±2.79 VS. SEVERE ASD = 6.0±1.58

Precoproporphyrin
MILD ASD = 17.3±8.9 VS. SEVERE ASD = 24.05±7.28

Ratios:
Pentacarboxyporphyrin/uroporphyrins I and III
MILD ASD = 0.19±0.06 VS. SEVERE ASD = 0.27±0.08

Precoprophyrin/uroporphyrins I and III
MILD ASD = 0.68±0.28 VS. SEVERE ASD = 1.09±0.36

Coproporphyrins I and III/ uroporphyrins I & and III
MILD ASD = 9.54±2.97 VS. SEVERE ASD = 12.64±3.47

(Precoproporphyrin+ pentacarboxyporphyrin) / (uroporphyrins I & and III+ heptacarboxyporphryin)
MILD ASD = 0.73±0.26 VS. SEVERE ASD = 1.1±0.29

The authors concluded that increased urinary porphyrin metabolites were associated with more severe autism based on CARS scores. Urinary porphyrin are believed to represent mercury toxicity.

Furthermore, the authors found significantly lower levels of plasma sulfates (Plasma cysteine, Plasma reduced glutathione, Plasma oxidized glutathione) among children with autism when compared to typically developing kids. Decreased sulfation (low plasma levels of sulfates) can indicate limited detoxification capacity, especially of heavy metals such as mercury.

In conclusion, the study presents some compelling evidence regarding differences in urinary porphyrin metabolites between kids with mild and severe autism symptoms, as well as differences in plasma sulfates between kids with autism when compared to neurotypical kids. This is consistent with the author’s theory that mercury exposure plays a role in autism. However, given the controversy regarding the mercury-autism association, I would like to clarify that these results do not indicate that mercury causes autism. Yes, it is possible that mercury toxicity and reduced detoxification capacity plays a role in autism, both, in the onset and severity. However, it is also possible that Autism itself results in reduced detoxification capacity and mercury toxicity via other mechanisms, but that such toxicity is not associated at all with the development of the disorder. For example, erythrocyte sedimentation rate and C-reactive protein are tests used in arthritis. Atypical results on these tests are found in people with arthritis. Yet, neither one is related to any of the proposed causes of arthritis, and instead reflect a consequence of the disorder itself (inflammation).

A review of Efrosini Kalyva (2008). Comparison of Eating Attitudes between Adolescent Girls with and without Asperger Syndrome: Daughters’ and Mothers’ Reports Journal of Autism and Developmental Disorders DOI: 10.1007/s10803-008-0648-5

The marked gender difference in the rate of autism spectrum diagnoses has resulted in a major gender disparity in research. That is, the overwhelming majority of studies on autism are conducted with boys, and studies examining mostly girls are very rare. The study I'm reviewing today was conducted at the University of Sheffield in Greece. The author wanted to examine the rates of eating problems reported by girls with Asperger’s syndrome when compared to typically developing girls. The study included 56 girls with AS and 56 typically developing girls (Age 12 to 18). The AS was diagnosed by a multidisciplinary team via ADI and ADOS using USA cutoff points. The comparison sample was selected from the local school system. The AS and the typically developing girls were match for Body Mass Index (BMI). The girls and their mothers completed the Eating Attitude Test. This test assesses a wide range of eating behaviors (“I have gone on eating binges”) and attitudes (“I’m terrified about being overweight”) associated with several eating disorders.

The results indicated that, when compared to typically developing girls, girls with AS were more likely to endorse more symptoms associated with bulimia and food preoccupation. However, no difference between the groups were noted on dieting behaviors. The author reached the same conclusion when examining mother’s reports of their daughters eating habits and attitudes.

Please note that these results suggest increased symptoms of eating disorders among the AS girls, but this may not necessarily translate to actual diagnoses of eating disorders. That is, the study did not include a comprehensive evaluation for actual eating disorders. Thus, the differences in rates of eating disorder among the two groups of girls is unknown.

A review of: Laudan B. Jahromi, Connie L. Kasari, James T. McCracken, Lisa S-Y. Lee, Michael G. Aman, Christopher J. McDougle, Lawrence Scahill, Elaine Tierney, L. Eugene Arnold, Benedetto Vitiello, Louise Ritz, Andrea Witwer, Erin Kustan, Jaswinder Ghuman, David J. Posey (2008). Positive Effects of Methylphenidate on Social Communication and Self-Regulation in Children with Pervasive Developmental Disorders and Hyperactivity Journal of Autism and Developmental Disorders DOI: 10.1007/s10803-008-0636-9


Methylphenidate is a psycho-stimulant that is used extensively in the treatment of children with attention deficit hyperactivity disorder (ADHD). Given the high co-morbidity between ADHD and autism, a few studies have examined the efficacy and effectiveness of Ritalin in the treatment of autism (e.g., Aman & Langworthy, 2000). However, most these studies examined the effects of Ritalin on attention and hyperactivity symptoms among kids with autism spectrum disorders (ASD). The present study attempted to examine the effects of Methylphenidate on social-communication and self-regulation behaviors among kids with ASDs.

The sample included 33 children with pervasive developmental disorder (29 boys) with a mean age of 6.93 years (range 5-13). This was a 4-week randomized, double-blind, cross-over placebo study, with treatment changing each week between 4 conditions: placebo, low dose, medium dose, and high dose. In this design, neither the experimenters nor the families know which of the 4 treatments the child is receiving at any given time. In addition, the treatment condition changes randomly each week, without anyone knowing the nature of the old or new condition. This allows the experimenters to assume that consistent changes in behaviors that occur during a particular treatment is truly due to the effect of that treatment and not to the expectation of the treatment (placebo effect).

The results indicate that children showed significantly more joint attention behaviors when receiving Methylphenidate than when receiving the placebo (although the most effective dosage varied by individual). Furthermore, at a group level, the low dose of Methylphenidate resulted in significantly improved joint attention behaviors when compared to the placebo, but no differences were noted between the low, medium, and high doses. Low and medium doses of Methylphenidate also resulted in improved self-regulation behavior when compared to placebo.

The study presents compelling preliminary evidence suggesting that Methylphenidate is effective in improving some social behaviors among children with ASDs.

A review of: Losh, M., Sullivan, P.F., Trembath, D., Piven, J. (2008). Current Developments in the Genetics of Autism: From Phenome to Genome. Journal of Neuropathology and Experimental Neurology, 67(9), 829-837. DOI: 10.1097/NEN.0b013e318184482d

Dr. Molly Losh and a team at the University of North Carolina at Chapel Hill just published a comprehensive review of the scientific literature on the genetics of autism. Here I present the cliffsnotes version of the article, although I highly recommend that everyone with access to this manuscript should read it, for it is a very well crafted analysis of the status of the autism-genetics science to date.

Evidence for a genetics basis of autism: family and twin studies
- Among monozygotic twins, there is a 60% concordance rate of autism, compared to only a 3 to 5% among dizygotic twins. This means that if a monozygotic twin has autism, 60% of the time his or her twin will also have autism. In contrast, when a dizygotic twin has autism, only 3-5% of the time his or her twin will have autism.
- The recurrence rate among families (changes that more than one member of the family will have autism) is between 5 to 8%, which is 25 to 40 times greater than the rate in the general the population.

Specific Target Genes:

MET: The MET gene is associated with the receptor to tyrosine kinase, a protein associated with neural growth, organization, immunological functioning, and gastrointestinal functioning. At least one study has found that a specific variant (SNIP) of this gene is over transmitted among families with autism (Campbell et al., 2006).

SLC6A4: This is the famous serotonin transporter gene associated with depression. The data on the association between this gene and autism appears to be very inconsistent and somewhat contradictory.

RELN: Reelin is a protein that is associated with neural migration during brain development. At least five studies have found an association between variants of the RELN gene and autism (Ashley-Koch et al., 2007; Perisco et al., 2002, 2001; Zhang et al., 2002; Dutta et al., 2007). Furthermore, mutant mice that do not have a section of the RELN gene have the same atypical cortical organization that is found in post-mortem studies of autism.

PTEN: The PTEN is a tumor suppressor gene associated with the prevention of uncontrolled cell growth. Mutations in the PTEN gene is associated with macrocephaly. Four studies have documented an association between the PTEN gene and autism among children with macrocephaly (Butler et al., 2005; Buxbaum et al., 2007; Boccone et al., 2006; Herman et al., 2007).

NLGN3 and NLGN4: These genes are related to Neuroligins, molecules that are associated with cell adhesion and neural development. Four studies have identified mutations within these genes among families or individuals with autism (Jamain et al., 2006; Laumonnier et al., 2004; Yan et al., 2005; Lawson-Yuen et al., 2008).

CNTNAP2: This is another gene associated with neural development and one that has shown significant promise in the search for genetic links in autism. CNTNAP2 was associated with autism in an Amish community affected with cortical dysplasia-focal epilepsy (Strauss et al., 2006). Another three studies found an association between variants of the CNTNAP2 gene and autism, including a specific autism phenotype (Arkin et al., 2008; Bakkaloglu et al., 2008; Alarcon et al., 2008). Specifically, this gene was associated with severity of language delays among children with autism – a significant finding given that CNTNAP2 appears to be largely expressed in the language centers of the brain.

SHANK3: Is a gene related to CNTNAP2 also involved in brain development. One study (Durand et al., 2007) found mutations in the SHANK3 gene among 3 of 226 families affected with autism. This is significantly larger than the rate of mutations found in the general population. Another study (Moessner et al., 2007) also found increased rates of SHANK3 mutations among people with autism.

One thing to keep in mind when reading genetic studies: Although the rates of specific genetic mutations or variants may be higher in affected families than in the general population, in most studies the majority of affected families do not show such gene anomaly. That is, the proposed genetic marker identifies only a small portion of affected individuals. This is not a problem that is unique to autism, as it is a common finding when trying to link genetic variants to complex and heterogeneous disorders. Thus, the research usually progresses towards: 1) the identification of phenotypes (specific types of autism) that may be associated with specific genetic variants, and 2) the identification of specific factors (environmental exposures for example) that when interacting with specific genetic variants may lead to the development of some types of autism.

A review of: Austin, D., Shandley, K. (2008). An Investigation of Porphyrinuria in Australian Children with Autism. Journal of Toxicology and Environmental Health, Part A, 71(20), 1349-1351. DOI: 10.1080/15287390802271723

The journal of Toxicology and Environmental Health just published a study conducted in Australia examining the possible link between mercury and autism. The study examined urinary porphyrins as a measure of mercury exposure in children with autism. Porphyrinuria, or the excess urinary excretion of porphyrin, is purported to reflect heavy metal exposure and in particularly mercury. Two previous studies (Nataf et al., 2006, and Geier & Geier, 2007) used this urinary measure and reported increased levels of porphyrins in children with autism when compared to typically developing children. In the current study the authors examined urinary samples of 41 patients with ASD (detailed diagnostic procedure information was not provided). The age of the sample ranged from 1 to 16 (average 6). There were 30 boys and 11 girls. The authors did not include a control group. That is, no local comparison group of typically developing children was used. Instead, the authors compared the levels of porphyrins of the Australian sample with the control samples (typically developing kids) used by the two previous studies as well as to ‘normative’ laboratory ranges obtained from a French laboratory and the normative ranges published in a 1996 European study (Minder and Schneider-Yin, 1996).

The authors reported that the ratio of uroporphyrin to coproporphyrin (CP to UP) was statistically significantly higher in the Australian ASD group when compared to all control samples of the previous studies. The comparison effect sizes were very large, namely: 1.60 when compared to the Geier & Geier sample, 1.54 when compared to the Nataf et al sample, and 1.46 when compared to the 1996 normative sample. Effect size is a measure of the differences between the means of two groups that is not as sensitive (not affected by) the very large differences in sample sizes between these studies. Effect sizes in this range (1.46 to 1.60) indicate that the differences between the groups were very substantial.

A final thought. I can not discuss the merits of this particular urinary measure as reflective of mercury exposure or mercury toxicity, since this is beyond my understanding of heavy metal metabolism, thus I would simple make one final comment regarding the methodology of this study. I am very surprised that the authors did not include a local comparison sample, especially since their intention was to replicate the previous findings within a local Australian group. This is a strong limitation of the study. The results indicate that the Australian ASD group is statistically significantly different than the typically developing group used in the two previous samples (one from the USA and another from France) in regards to CP to UP ratio, but the results provide no information as how this ASD group compares to typically developing children in the sample geographical regions.

However, the data is compelling in showing a strong difference in the CP to UP ratio between the Australian sample and typically developing children in the USA and France. Does this mean that mercury causes autism? Not at all. Readers should be careful not to make conclusions about causal mechanisms from these type of association studies. There are multiple possible interpretations of the data, including the possibility that heavy metal exposure may play a role in the development of ASD. However, it is also possible that CP/UP ratio differences between these groups are the result, rather than the cause, of physiological differences in Autism. The results of this study do not support one interpretation more than the other. The results simply indicate that the groups are different in CP to UP ratio. The data do not tell us why these groups are different, or whether mercury causes autism.



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A review of:Roos, E.M., McDuffie, A.S., Weismer, S.E., Gernsbacher, M.A. (2008). A comparison of contexts for assessing joint attention in toddlers on the autism spectrum. Autism, 12(3), 275-291. DOI: 10.1177/1362361307089521

Earlier this year I reported on a study that showed significant improvement in joint attention after systematic training. Limited joint attention behaviors is seen as an early indicator of autism. In this new study, a team from the University of Wisconsin – Madison compared naturalistic (play interactions) and structured (Early Social Communication Scale protocol - ESCS) joint attention behaviors in toddlers, in order to examine the utility and viability of using naturalistic observations as an alternative to, or in support of, structured protocols such as the ESCS.

The ESCS assesses for two aspects of joint attention, namely: initiation of joint attention (IJA) and response to joint attention (RJA).

RJA refers to the child’s use of attention-following behaviors, such as head turns and eye gaze to follow the visual focus of a communicative partner. IJA refers to the child’s use of attention-directing behaviors, such as pointing or showing to coordinate attention with a social partner with reference to an object or event.

The authors wanted to explore IJA and RJA in multiple contexts in order to determine how stable these behaviors are across settings. To this end, the authors examined 20 toddlers with symptoms of autism (16 boys, four girls, mean age = 33.2 months). The experimenters conducted the ESCS during a first home visit and a play session during a second visit approximately one week apart. Both sessions were coded for IJA and RJA events. The authors found that the ESCS elicited significantly more IJA when compared to naturalistic play. However, the naturalistic play session elicited significantly more RJA. This indicates that the ESCS may underestimate a child’s use of RJA, and that conducting assessments in multiple contexts may provide a more valid measure of the child’s use of joint attention behaviors.