Research Progress in Characterizing Brain Activity of Autism Spectrum Disorder Patients with Optical Brain Imaging and Machine-Learning Classification Algorithms

Diagnosis for autism spectrum disorder (ASD) has always relied on behavioral observations. However, recent studies with brain imaging show significant alterations in brain structure and function associated with ASD, which opens a new avenue for imaging-based diagnosis. Accurate diagnosis depends on accurate characterization on the alterations. In this regard, multi-contrast imaging can provide more complete and complementary information for characterizing autistic brain. Among various brain imaging modalities, optical brain imaging has good temporal and acceptable spatial resolution. It is also less sensitive to head motion and thus very suitable for studies on children, in particular, children with ASD. Near-infrared spectroscopy (NIRS) probes cerebral blood oxygenation, while diffuse correlation spectroscopy (DCS) measures cerebral blood flow. These hemodynamic variables may provide complementary information on the cerebral hemodynamics. Therefore by using more efficient machine-learning classification algorithm, it is anticipated that more accurate characterization and classification on ASD can be achieved. Based on extensive literature search and our on-going study, we reviewed in this paper the progress on using optical brain imaging to investigate characteristics of autistic brain, and the classification on ASD with various machine-learning algorithms. The prospect of using multimodal optical imaging (combined NIRS with DCS) to study ASD is also discussed. This systematic review and outlook might be beneficial to scientists who are working in ASD-related field.