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

Content Provider	Springer Nature : BioMed Central
Author	Leming, Matthew J. Baron-Cohen, Simon Suckling, John
Abstract	Background Autism has previously been characterized by both structural and functional differences in brain connectivity. However, while the literature on single-subject derivations of functional connectivity is extensively developed, similar methods of structural connectivity or similarity derivation from T1 MRI are less studied. Methods We introduce a technique of deriving symmetric similarity matrices from regional histograms of grey matter volumes estimated from T1-weighted MRIs. We then validated the technique by inputting the similarity 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. Limitations While this study used a large sample size, the majority of data was from a young age group; furthermore, to make a viable machine learning study, we treated autism, a highly heterogeneous condition, as a binary label. Thus, these results are not necessarily generalizable to all subtypes and age groups in autism. Results Our models gave AUROCs of 0.7298 (69.71% accuracy) when classifying by only structural similarity, 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 similarity and functional connectivity gave an AUROC of 0.7354 (69.40% accuracy). Analysis of classification performance across age revealed the greatest accuracy in adolescents, in which most data were present. 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 similarity. Conclusion This study provides a simple means of feature extraction for inputting large numbers of structural MRIs into machine learning models. Our methods revealed a unique emphasis of the deep learning model on the structure of the bilateral Heschl’s gyrus when characterizing autism.
Related Links	https://molecularautism.biomedcentral.com/counter/pdf/10.1186/s13229-021-00439-5.pdf
Ending Page	15
Page Count	15
Starting Page	1
File Format	HTM / HTML
ISSN	20402392
DOI	10.1186/s13229-021-00439-5
Journal	Molecular Autism
Issue Number	1
Volume Number	12
Language	English
Publisher	BioMed Central
Publisher Date	2021-05-10
Access Restriction	Open
Subject Keyword	Neurology Neurosciences Neuropsychology Psychiatry Pediatrics Human Genetics Autism Deep learning Functional connectivity Structural similarity
Content Type	Text
Resource Type	Article
Subject	Psychiatry and Mental Health Molecular Biology Developmental Biology Developmental Neuroscience
Journal Impact Factor	6.2/2023
5-Year Journal Impact Factor	6.5/2023

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