Month: July 2012
I thought we were over this neuroimaging blobology reporting?Newsweek writes:
The brains of Internet addicts, it turns out, look like the brains of drug and alcohol addicts. In a study published in January, Chinese researchers found ???abnormal white matter??????essentially extra nerve cells built for speed???in the areas charged with attention, control, and executive function. A parallel study found similar changes in the brains of videogame addicts. And both studies come on the heels of other Chinese results that link Internet addiction to ???structural abnormalities in gray matter,??? namely shrinkage of 10 to 20 percent in the area of the brain responsible for processing of speech, memory, motor control, emotion, sensory, and other information. And worse, the shrinkage never stopped: the more time online, the more the brain showed signs of ???atrophy.???White matter: extra new cells? hmmm… The Chinese study referenced seems to be this one: Abnormal White Matter Integrity in Adolescents with Internet Addiction Disorder: A Tract-Based Spatial Statistics Study “The other Chinese results” may be this one: Microstructure Abnormalities in Adolescents with Internet Addiction Disorder Another Chinese one is here: Gray matter abnormalities in Internetaddiction: A voxel-based morphometry study. Individual neuroimaging studies should as a rule not be used as “facts” in news reports, – or at least with great caution. Reviews and meta-analyses may possibly be. Reporting on “look like” and “similar changes” are difficult. How do you define “similar”? Do you just choose an single local reported point in the brain (Talairach or MNI coordinate) and look in other articles to see if there is a nearby location reported? You could regard the the similarity problem as an information retrieval problem which I wrote about back in 2004: Finding related functional neuroimaging volumes. Using this technique and looking at “Related – positive correlated volumes” in the Brede Database, see, e.g., here, you immediately see that the neuroimaging results highly vary. Much news reporting runs straight to a interpretation, – that may possibly be an overinterpretation.
I was looking for a value of how clustered a network is. I thought that somewhere in graph spectrum was a good place to start and that in the Python package NetworkX there would be some useful methods. However, I couldn’t immediately see any good methods in NetworkX. Then Morten Mørup mentioned something about community detection and modularity and I became diverged, but now I am back again at the graph spectrum.
The second smallest eigenvalue of the Laplacian matrix of the graph seems to represent reasonably well what I was looking for. Apparently that eigenvalue is called the Algebraic connectivity.
NetworkX has a number of graph generators, and for small test cases the algebraic connectivity seems to give an ok value for how clustered the network is, – or rather how non-clustered it is.