Identification of Asperger's from healthy individuals: Using a graph theoretical approach on the task-free fMRI data

Asperger’s syndrome is generally known as a neurodevelopmental disorder. The main features of this syndrome are the lack of social interaction, non-verbal communication, unusual repetitive behavior, restricted interests, and may have an inherent talent such as mathematics, music, etc. Nonetheless, their brain structural and functional variations as compared to healthy individuals require to be well understood.

This study intends to identify differences of the task-free fMRI data in Asperger’s syndrome as compared to healthy individuals using the graph-theoretical approach. In this approach, graph local and global measures are calculated from the functional network, which estimated through taking the correlation between activities in different parts of the brain. Subsequently, the differential pattern of local and global measures in Asperger’s syndrome as compared to healthy control group is investigated. Two groups of the subjects are matched in terms of age, gender, handedness, and IQ scores.

Results revealed the significant differences in local measures at temporal, amygdala, thalamus, and heschl regions. Classification of the tf-fMRI data based on the identified measures shows an accuracy of 84% to discriminate Asperger's individuals from the healthy group.

Accordingly, local measures extracted from the graph of the task-free functional connectivity network have a good potential for screening of Asperger's syndrome that can be used as an automatically-diagnosed method of this disorder.