Single-participant structural connectivity matrices lead to greater accuracy in classification of participants than function in autism in MRI

16 May 2020Matthew LemingSimon Baron-CohenJohn Suckling

In this work, we introduce a technique of deriving symmetric connectivity matrices from regional histograms of grey-matter volume estimated from T1-weighted MRIs. We then validated the technique by inputting the connectivity matrices into a convolutional neural network (CNN) to classify between participants with autism and age-, motion-, and intracranial-volume-matched controls from six different databases (29,288 total connectomes, mean age = 30.72, range 0.42-78.00, including 1555 subjects with autism)... (read more)

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