Abstract
Asperger syndrome (AS) is subtype of autism spectrum disorder (ASD). Diagnosis and pathological analysis of AS through resting-state fMRI data is one of the hot topics in brain science. We employed a new model called the genetic-evolutionary random Support Vector Machine cluster (GE-RSVMC) to classify AS and normal people, and search for lesions. The model innovatively integrates the methods of the cluster and genetic evolution to improve the performance of the model. We randomly selected samples and sample features to construct GE-RSVMC, and then used the cluster to classify and extract lesions according to classification results. The model was validated by data of 157 participants (86 AS and 71 health controls) in ABIDE database. The classification accuracy of the model reached to 97.5% and we discovered the brain regions with significant differences, such as the Angular gyrus (ANG.R), Precuneus (PCUN.R), Caudate nucleus (CAU.R), Cuneus (CUN.R) and so on. Our method provides a new perspective for the diagnosis and treatment of AS, and a universal framework for other brain science research as the model has excellent generalization performance.
Highlights
Asperger syndrome (AS), as a common neurological disease (Gillberg et al, 2016), has an increasing incidence (Jensen et al, 2014) and is usually considered as one of the autism spectrum disorders (ASDs; Lugnegård et al, 2015) or pervasive developmental disorder (Ghaziuddin, 2010; Bucaille et al, 2016)
We innovatively integrates the methods of cluster and genetic evolution to improve the performance of the model, which called GE-RSVMC
Automatic Anatomical Labeling (AAL) template was used to split the preprocessed brain images into 90 brain regions which are called as regions of interest (ROI)
Summary
Asperger syndrome (AS), as a common neurological disease (Gillberg et al, 2016), has an increasing incidence (Jensen et al, 2014) and is usually considered as one of the autism spectrum disorders (ASDs; Lugnegård et al, 2015) or pervasive developmental disorder (Ghaziuddin, 2010; Bucaille et al, 2016). In the early exploration of AS, researchers have found that AS and ASD have some similar features including limited interest and repetition, stereotyped activity and communication difficulties (Kamp-Becker et al, 2010; Lugnegård et al, 2013; Woods et al, 2013). Neurological soft signs were applied to discriminate AS and other ASD patients but the performance was unsatisfactory (Hirjak et al, 2014)
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