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In this work, we introduce a technique of deriving symmetric connectivitymatrices from regional histograms of grey-matter volume estimated fromT1-weighted MRIs. We then validated the technique by inputting the connectivitymatrices into a convolutional neural network (CNN) to classify betweenparticipants with autism and age-, motion-, and intracranial-volume-matchedcontrols from six different databases (29,288 total connectomes, mean age =30.72, range 0.42-78.00, including 1555 subjects with autism). We compared thismethod to similar classifications of the same participants using fMRIconnectivity matrices as well as univariate estimates of grey-matter volumes.We further applied graph-theoretical metrics on output class activation maps toidentify areas of the matrices that the CNN preferentially used to make theclassification, focusing particularly on hubs. Our results gave AUROCs of0.7298 (69.71% accuracy) when classifying by only structural connectivity,0.6964 (67.72% accuracy) when classifying by only functional connectivity, and0.7037 (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 nodistinguishable network patterns for functional inputs, but did reveallocalized differences between groups in bilateral Heschl's gyrus and uppervermis for structural connectivity. This work provides a simple means offeature extraction for inputting large numbers of structural MRIs into machinelearning models.

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