  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). We compared this
method to similar classifications of the same participants using fMRI
connectivity matrices as well as univariate estimates of grey-matter volumes.
We further applied graph-theoretical metrics on output class activation maps to
identify areas of the matrices that the CNN preferentially used to make the
classification, focusing particularly on hubs. Our results gave AUROCs of
0.7298 (69.71% accuracy) when classifying by only structural connectivity,
0.6964 (67.72% accuracy) when classifying by only functional connectivity, and
0.7037 (66.43% accuracy) when classifying by univariate grey matter volumes.
Combining structural and functional connectivities gave an AUROC of 0.7354
(69.40% accuracy). Graph analysis of class activation maps revealed no
distinguishable network patterns for functional inputs, but did reveal
localized differences between groups in bilateral Heschl's gyrus and upper
vermis for structural connectivity. This work provides a simple means of
feature extraction for inputting large numbers of structural MRIs into machine
learning models.
