Please can you describe the most typical characteristics of autism?
Autism does not come in just one flavour. It is a spectrum of disorders that share several features: impaired social interactions; impaired verbal and non-verbal communication; and restricted, repetitive behaviours.
What was previously known as the reason for the withdrawal into one’s own inner world?
The apparent withdrawal into self is linked to the lack of interest in social interactions but the reason is not known. Rather than providing an ultimate explanation for this form of behaviour, our study shows that it correlates with specific features of brain activity.
Your recent research found that the brains of autistic children generate more information at rest. How did you make this finding?
In a previous study that we published last year, we developed and successfully tested a new method for analyzing brain activity. The method is based on the fact that brain activity at rest can be accurately described by a universal mathematical model.
The model is actually fairly simple. In fact, it is commonly used by engineers to design and simulate electronic devices. In its simplest interpretation, the model depicts the brain as a black box that transforms an input into an output. The output is the activity that we record from each brain region and the input is obtained by fitting the recorded data to the model.
We can then proceed as engineers and ask how much information is generated in the black box, or in other words, how much information contained in the output cannot be accounted for by information contained in the input. So that is what we computed and found that on average an autistic brain at rest generates 42% more information than a non-autistic brain.
Just to clarify, information in engineering is a measure of the complexity of a signal, in our case, the recordings of brain activity. It does not tell us what the brain is thinking. However, it is reasonable to assume that the complexity of brain activity reflects the complexity of the underlying cognitive processes.
Which brain regions were particularly active at rest in autistic children and how does this compare to non-autistic children?
The level of activity itself was not different in any specific region. What were different were the interactions between certain regions, that is, the functional connectivity of the brain.
By far, the most significant change was between frontal and parietal regions. Frontal regions are associated with decision making and executive functions, while parietal regions primarily process sensory information. In autistic children the interaction between these areas was much stronger.
How far do your results go to explain the typical lack of interest in external stimuli experienced by autistic children?
I always like to make a clear distinction between hard data and the interpretation thereof. The changes in information and functional connectivity that we were talking about before are hard data; objective results of our study. But the ultimate goal of collecting and analyzing data is obviously to interpret them in the context of what it is already known.
In that regard, our quantitative results fit very nicely with the classical view on autism as withdrawal into self, because if autistic brains generate more information at rest they may not need to interact with the external world as much as non-autistic brains to achieve the same level of stimulation.
Our results also fit very nicely with a more recent theory on autism, the Intense World Theory, put forth by Drs. Kamila and Henry Markram a few years ago.
Could you please outline the “Intense World Theory” of autism? Do your results support this theory?
In a nutshell, it describes autism as a disorder resulting from hyper-functioning neural circuitry, which leads to a state of over-arousal. According to this view, one would expect that autistic brains at rest generate more information than non-autistic brains. And that is indeed what we found.
What further research is needed to advance our understanding of autism?
Research on autism has mainly focused on two very different scales: the molecular scale and the scale of the whole brain.
Studies at the molecular level have identified various genetic mutations linked to different forms of autism. By inducing those mutations in mice, researchers have created animal models of autism that recapitulate some features of the disorder in humans.
In parallel, studies of the anatomy and activity of the brain with non-invasive techniques have shown differences between autistic and non-autistic brains. The current picture is that autism is caused by an abnormal growth rate of the brain, which in turn leads to altered neuronal circuitry.
Thus, research on autism needs to bridge the gap between the molecular and the whole brain levels by focusing on neuronal circuitry in different parts of the brain. We need to understand not only how neurons are interconnected but also how information flows in those circuits.
In fact, understanding how neuronal circuits work is not a specific challenge in autism research; it is probably the major challenge in current neuroscience.
Do you have plans to apply your research techniques to other conditions?
The analytic method we have developed has great potential as a biomarker for different mental disorders. We would like to apply the same methodology to schizophrenia and depression.
In the early days of autism research, autism was mistakenly referred to as children’s schizophrenia. In fact, schizophrenic people also withdraw into their own world and they do it more often, the more advanced the disease is.
Where can readers find more information?
In the following three papers:
Pérez Velázquez JL and Galán RF (2013) Information gain in the brain's resting state: A new perspective on autism. Front. Neuroinform. 7:37.
Domínguez LG, Velázquez JLP, Galán RF (2013) A Model of Functional Brain Connectivity and Background Noise as a Biomarker for Cognitive Phenotypes: Application to Autism. PLoS ONE 8(4): e61493.
Markram K and Markram H (2010) The Intense World Theory – a unifying theory of the neurobiology of autism. Front. Hum. Neurosci. 4:224.
About Dr. Roberto Fernández Galán
Dr. Galán studied fundamental physics at the Universidad Autónoma de Madrid. After receiving his Master's degree he moved to Berlin where he received graduate training in theoretical biology and a PhD in computational neuroscience from the Humboldt Universität.
As a postdoctoral researcher at Carnegie Mellon University in Pittsburgh he combined theory and experiments to investigate the biophysical mechanisms for neuronal synchronization; a phenomenon that generates EEG oscillations associated with high cognitive processes.
One of his papers was chosen by Scientific American as one of fifty emerging trends in research, business and policy in 2005.
Since 2008 Dr. Galán is an assistant professor in the department of neurosciences at Case Western Reserve University where he combines theoretical and computational studies with experimental research on various topics, including autism and epilepsy.
Thus far, Dr. Galán has published 30 peer-reviewed publications and book chapters. He is a scholar of The Mt. Sinai Health Care Foundation and a former fellow of The Alfred P. Sloan Foundation. In 2011 he was nominated for a Diekhoff Mentoring Award at CWRU.