Category Archives: Autism research

A Mathematical Algorithm Uncovers Traces of Autism (part II)

Exposing patterns:

Study results are preliminary, but they offer proof of concept that the method can link certain behavior, or phenotype, to a specific genetic structure or genotype. The signatures of shared behavior may indicate shared gene pathways that lead to behaviors, which in turn could hint at the cause of autism.

“The power of the machine learning of the vector support system is that you can find hidden patterns, ie patterns that were not detected by statistical analysis without conventional supervision” says Hilgo Bruining, assistant professor of child and adolescent psychiatry at the University of Utrecht, who also led the study.

The researchers plan to sift through large sets of behavioral and genetic data of individuals with idiopathic autism. If the algorithm can identify new behavior signatures inside these sets of data, it may be able to divide into subgroups of autism and concentrate on the genome areas responsible with the disorder subtypes.

However, some experts call for caution with this line of reasoning.

Valsamma Eapen, professor of child and adolescent psychiatry at the University of New South Wales in Australia, who was not involved in the study, says: “As should have been expected, the phenotypes of autism could be spaced around a diverse set of genetic lesions, suggesting that these results mean different types of autism are an exaggeration”.

For their study, the researchers analyzed behavioral data in theĀ  medical records of 322 people at the University of Utrecht Medical Center in the Netherlands and the Institute of Psychiatry at Kings College London. The subgroups diagnosed with each of the six autism-related syndromes, comprised between 21 and 90 people.

In particular, researchers worked with data from 37 behaviors, including verbal rituals, imaginative and unusual occupations in their system.

On the task of distinguishing between the six syndromes, the algorithm correctly identified a behavioral syndrome signature 63% of the time.

“Sixty-three percent is a bit low, but it’s not intended to be an absolute,” says Wall.

The algorithm was more accurate when two syndromes were compared simultaneously. For example, it could distinguish the 22q11.2 deletion syndrome from the Chromosome 15 isodicentric with 97% accuracy, its best performance. It has been worse when it comes to distinguishing the syndrome from five other disorders.

To test the algorithm in cases of autism, researchers look at behavioral data of 1,261 persons selected in the Autism Genetic Research Exchange (AGRE). However, this data does not include information on whether people are diagnosed with one of the six syndromes, so the algorithm can only generate a probability that each identification is correct.

Within the first sample of 375 people from AGRE, the algorithm classified 255 people with behavior signing of the tuberous sclerosis complex with a 61 percent chance that this analysis is correct. The figures for the other syndromes were lower, with several of them in the range of 40 percent and with the Down syndrome, again the lowest, at 25 percent probability of accuracy.

The researchers then repeated the test with two sets of 443 people each, yielding similar results.

In general, they say, the pattern of social difficulties is the most useful to help identify syndromes. This suggests that the social impairment, such as extreme social avoidance seen in people with fragile X syndrome may be related to certain genetic risk factors, the researchers say.

Eapen says he expects to see studies in which the cause of the disorders and the underlying mechanisms are better outlined.

“In these deletion syndromes, no one is sure that the genes are linked to autism and how it affects the size of the demonstrations and duplications of genes or phenotypes,” said Eapen. “This study sets the stage for a closer look at diagnosis in cohort selection based on genetic damage.”

A Mathematical Algorithm Uncovers Traces of Autism (part I)

Math algorithm detects autism syndromeAutism is defined according to a variety of behavioral symptoms, but it is precisely this variation – along with a complex genetic background – which makes it difficult to connect behavior to the underlying genes.

A new algorithm can make this challenge a little easier to solve. The algorithm, which uses a form of artificial intelligence to learn as it goes, analyzed behavioral data and has learned to recognize six genetic disorders associated with autism, according to research published February 11 in the journal of Molecular Autism.

Researchers hope to use these behavioral signatures to narrow their search for the genetic bases of ‘idiopathic autism’, for which there is no known cause.

“There was a way to assume that genetic risk factors were likely to lead to a set of distinct behavioral phenotypes, but many researchers have never formally proven what they previously proposed,” says researcher Patrick Bolton, professor of child and adolescent psychiatry at Kings College London. “That was the motivation for this project.”

Previous studies have sought ways in which different syndromes may come from shared molecular or neurological pathways, but showed no clear consensus on how.

The new robot learning technique called support vector, performed screening through large volumes of data to find recognizable patterns that can be used to subdivide a group of people. The system searched through health information of individuals diagnosed with one of six genetic disorders linked to autism: 22q11.2 deletion syndrome (also called DiGeorge syndrome), Down syndrome, Prader-Willi, Tuberous Sclerosis, Klinefelter Syndrome and chromosome 15 isodicentric or idic, which is caused by a duplication of a segment of chromosome 15.

The method was able to identify specific behavioral signatures for each syndrome. To this end an algorithm was built that can find the same types of signatures in the behavioral data of individuals with idiopathic autism.

The researchers tested their algorithm on three samples with a total of 1,261 individuals with autism and found instances of all six signatures.

“What I find most impressive about this study is that it is expanding its classification for idiopathic autism and is showing these signatures,” says Dennis Wall, associate professor of Pediatrics at Stanford University, who has used the automated learning to develop diagnostic tools for autism, but was not involved in this research.

“It suggests that we might be able to use this, or methods like this, to find a firm genotype-phenotype correlation that can act as a gravitational force to make sense of what is now a very mixed and complex picture,” says Dennis Wall.