# Machine Learning and rs-fMRI to Identify Potential Brain Regions Associated with Autism Severity
## 1 Introduction  
Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder characterized mainly by social impairments, commonly followed by communication challenges or restricted and repetitive patterns of behavior [1]. ASD is a substantially heterogeneous disorder in which two diagnosed subjects may have a completely different set of symptoms. Some researchers estimated that approximately one in 44 children aged eight years are in the spectrum [2]. Despite a possible gender bias regarding diagnosis, ASD seems to be a sex-related disorder, with a male-to-female ratio close to 3-4:1 [2; 3; 4]. Current research points to ASD as a primarily hereditary disorder. Approximately 80-83% of ASD cases are due to genetic inheritance. Close to 17-20% are due to environmental risk factors, including problems during the gestation period and the parents' age [5; 6; 7]. Children and adolescents with an ASD diagnosis have medical expenses up to 6.2 times greater than those with typical development (TD), with general costs from 8.4 to 9.5 times greater than the average [8]. In addition to medical expenses, intensive behavioral interventions needed for ASD treatment have costs from USD 40,000 to USD 60,000 per childper year [9]. Moreover, most ASD individuals live in low- or middle-income countries and receive no proper support from health or social care systems, suffering from the high costs of (1) proprietary tools for diagnosis; (2) evidence-based intervention techniques, and (3) training of parents and professionals to conduct the ASD treatment process [10]. Early diagnosis and proper interventions are critical factors in reversing the impairments generated by ASD in children. Unfortunately, there are no low-cost automated tests to identify the disorder. Instead, the ASD diagnosis is performed through clinical observation, which is challenging to accomplish in young children, especially in the early years of life [11]. Early treatments may result in improved cognitive, behavioral, and social functioning, allowing, for a subset of people, an evolution that may lead to healthy adult life, as well as significant long-term societal cost reductions [12]. However, most technological tools proposed to assist the ASD intervention process showed some common limitations [13]. It is critical to comprehend the severity of each individual with ASD to plan personalized treatments and conduct more effective intervention processes. Nowadays, there are many protocols used to support diagnosis, such as the Autism Diagnostic Observation Schedule (ADOS), Autism Diagnostic Interview--Revised (ADI-R), and Social Communication Questionnaire (SCQ). However, ADOS is currently one of the most used worldwide. ADOS divides ASD classification between autism--the ones with more severe symptoms--autism spectrum--the ones with less severe symptoms--and as non-spectrum--those diagnosed outside of the spectrum [14]. An ADOS diagnosis consists of standard evaluation on three main domains: communication, social relations, and behavior. Each domain has a set of tasks to be evaluated, with different total scores. The ADOS diagnosis comprises four modules for a specific range of ages and language skills, each with different cut-offs for each of the three classes [15]. Furthermore, current ASD diagnosis is performed by trained professionals, with the help of tools such as ADOS, which has both sensibility and specificity above 80% [16]. It is important to note that the current ADOS version mainly used is the ADOS-2 [14], but due to our available samples, we used the ADOS in its classic version. The last decade was marked by research looking for methods to take advantage of the recent evolution of machine learning (ML) to build automated ASD diagnosis processes [17; 18; 19; 20]. The first works in this field date from mid-2010 [21]. Since then, there has been an increase in the number of papers and improved outcomes.