A famous British neuroscientist and popular-science writer, the Baroness Susan Greenfield, who is a Professor of Synaptic Pharmacology at Lincoln College, Oxford, a member of the House of Lords, and until recently was the Director of the Royal Institution of Great Britain, has suggested that widespread Internet use might have something to do with the increasing prevalence of autism.
Here she is, quoted in an article in the Guardian:
"It could be the case that this different environment is changing the brain in an unprecedented way. It's such an important issue and I'm just putting it before people to discuss." Greenfield said there was a need for work to be done, but measuring subtle changes in the brain was extremely difficult. She cited an article in Scientific American that showed US teenagers may be losing their ability to feel empathy. "When you are social networking online and not learning how to connect face to face or how to hug, not out there rehearsing those things, then could that mean a child goes on to exhibit autistic-like behaviour?"Asked in this interview with New Scientist for any evidence supporting this hypothesis, she cites two studies: this one describing differences in brain anatomy between healthy and Internet-addicted Chinese university students, and this review (full text here) of the psychological and neurological literature on the effects of various electronic media.
"I point to the increase in autism and I point to internet use. That's all. Establishing a causal relationship is very hard but there are trends out there that we must think about. I have not said that internet use causes autism and I would apologise to any family who is upset by anything I have said."
She added: "I have never, ever said that the internet is bad for the brain. But if the environment is changing, then the brain will change to adapt. All I have ever said is, let's talk about this. The internet has become the central, iconic feature of young people's lives and to say our brains will not be affected by that is to deny our evolutionary heritage."
She also cited this article, mentioned in this Scientific American podcast, which is a meta-analysis of 72 studies of empathy in American college students, all conducted between 1979 and 2009, and all using the same measure of empathy: the Interpersonal Reactivity Index. I will talk about that study in another post; for now, I'd like to focus on the Internet-addiction study.)
The Chinese study used diffusion tensor imaging (a type of magnetic resonance imaging that measures the movement of water molecules along a tract of fibrous tissue and uses the three-dimensional vectors, or tensors, describing the water molecule's motion to build a three-dimensional computer model of the tissue) to look for differences in gray-matter volume and white-matter structure between two groups of eighteen young people (twelve young men and six young women in each group), one consisting of people meeting diagnostic criteria for Internet addiction (using the Young Diagnostic Questionnaire for Internet addiction), and the other consisting of healthy, age- and sex-matched control subjects who said they used the Internet for less than two hours per day.
(Figure 1A, in Yuan et al., 2011 - orange blotches represent brain regions where the 18 Internet-addicted young people in this study had less gray matter than non-addicted people of the same age and sex)
They found that the Internet-addicted people had less gray matter, on average, than the non-Internet-addicted people in a few areas: the dorsolateral prefrontal cortex (i.e., the part of the prefrontal cortex that's on top and to the sides), the rostral anterior cingulate cortex (on the left side only), the orbitofrontal cortex, the cerebellum and the supplementary motor area straddling the boundary between the frontal and parietal lobes. For a few of these areas (the left anterior cingulate, the right dorsolateral prefrontal cortex, and the right supplementary motor area), the degree of atrophy correlated with how long the person had been addicted --- i.e., the longer they'd been addicted, the smaller those brain areas would be.
They also found differences in white-matter distribution between the two groups in two regions: the left posterior limb of the internal capsule and the right parahippocampal gyrus.
(Figure 2, in Yuan et al., 2011 - top series of images show major white-matter tracts in study participants' brains; image (b) gives a side view of the brain and shows an area in blue where Internet-addicted subjects are thought to have less white matter than control subjects; image (c) shows an area in orange where the Internet-addicted subjects are thought to have more white matter than controls. Bottom graph shows the relationship between their measure of white-matter density, taken at the blue area, and the duration of individual study participants' Internet addictions.)
The measure they used, fractional anisotropy, reflects the degree to which the diffusion of water is constrained by the presence of linear fibers. If there were nothing there, water molecules would diffuse outward in a sphere; the degree to which the diffusion pattern deviates from a perfect sphere, and the direction in which the most diffusion occurs, give you an idea of where the fibers are and how many of them there are.
This short, but very technical, article describes how this measure is typically used in neuroimaging studies*:
Much progress has been made in modeling more complex diffusion geometries that a single tensor fails to model, but most clinical studies still rely on simple [diffusion tensor imaging]-derived scalar measures. Some of these, such as the trace of the covariance matrix or mean diffusivity (MD) can adequately describe isotropic water diffusion, but this only occurs in the cerebrospinal fluid spaces of the brain. In the white matter, myelinated fibers resist water diffusion orthogonal to the local dominant fiber orientation, and diffusion occurs preferentially along local fiber tracts. In clinical research, white matter fiber integrity is commonly assessed by determining how strongly diffusion is directionally constrained. One common scalar measure of directional diffusion, the fractional anisotropy (FA), is computed from the diffusion tensor's eigenvalues, and quantifies the magnitude of this directional preference. Clinical studies now routinely use FA as an index of white matter integrity, sensitive to white matter deterioration in aging and neurodegenerative diseases.
Using this explanation of what fractional anisotropy values are understood to mean --- higher numbers mean more diffusion occurring in the same direction, which is taken to mean that fibers running in that direction are numerous and thick; lower numbers imply fewer and/or thinner fibers --- you can see that the researchers are saying they think their Internet-addicted subjects have less white matter (lower FA value) than their non-addicted subjects in the parahippocampal gyrus, and also more white matter (higher FA value) in the left posterior limb of the internal capsule.
The researchers believe that these changes are a result of the Internet addiction, although they can't totally rule out the possibility that they existed before the onset of addiction, and may actually have been part of the reason those young people were susceptible to addiction in the first place. In favor of their brain-changes-as-effect-of-Internet-addiction model, they cite the positive correlations they found between several of the changes they found (decreased gray matter in the left anterior cingulate, right dorsolateral prefrontal cortex, and right supplementary motor area, along with increased white matter in the left posterior limb of the internal capsule) and duration of Internet addiction. But not all of the changes showed such a correlation --- less than half, to be exact.
When you take into account brain lateralization, you come up with eleven areas that differed between groups: four of the five regions showing loss of gray matter were affected on both sides, for a total of eight affected areas, plus one more (the left anterior cingulate) and the two regions showing white-matter changes (both confined to one side of the brain). That gives you only four out of eleven affected regions where the change showed any relationship to how long the person had been addicted; if the relation to time was meaningful, you'd expect it to show up more consistently. As is, it leaves open the possibility that whatever correlations were observed are just statistical noise.
Does extensive Internet use physically change the brain? It's certainly possible, given what we know about neuroplasticity, but this study doesn't shed a whole lot of light on it. Its design makes it impossible to know whether the brain differences observed between Internet-addicted and non-addicted students were effects or potential causes of Internet addiction.
Even granting that possibility (that extensive Internet use can, over time, remodel an Internet user's brain), though, it's still very, very unlikely that there's any connection between widespread Internet use and increasing prevalence of autism. The timing is wrong, for one thing --- most autism diagnoses are given in early childhood, and a lot of parents begin to ask questions and look into it when their child is an infant or toddler. Given the text-based nature of the Internet, it is highly unlikely that pre-literate children are doing a lot of web surfing on their own.
For more about this weirdness, see Neuroskeptic, Jon Brock, Dorothy Bishop, Neuroskeptic again, and this hilarious website riffing on Greenfield's gnomic comment "I point to the increase in autism and I point to the Internet. That's all."
*The article also criticizes the measure as being too vague, and not a very good predictor of actual fiber density and location. It describes an alternative measure that the authors consider more accurate.
Yuan, K., Qin, W., Wang, G., Zeng, F., Zhao, L., Yang, X., Liu, P., Liu, J., Sun, J., von Deneen, K., Gong, Q., Liu, Y., & Tian, J. (2011). Microstructure Abnormalities in Adolescents with Internet Addiction Disorder PLoS ONE, 6 (6) DOI: 10.1371/journal.pone.0020708
