# Machine Learning and rs-fMRI to Identify Potential Brain Regions Associated with Autism Severity
## 1 Introduction
Many of these works used magnetic resonance imaging (MRI) and ML combined, aiming for a positive or negative ASD diagnosis by classifying subjects between ASD and TD [21], as in [18; 19; 20; 22; 23; 24]. One of MRI's advantages is that it is a non-invasive procedure, being a prevalent method to scan the brain in living human beings [25]. There are two main uses for brain MRI: (1) the structural scan, which scans brain tissues and assesses their differences; and (2) the functional scan, which tracks the oxygen flow in the brain. This second method is usually called functional MRI (fMRI) and allows the indirect measurement of brain activities in regions of interest (ROIs). From the measured oxygen levels, it is possible to determine which regions are more activated than others [25; 26]. There are many tasks applied to a subject for an fMRI scan; they range from resting state to very narrow activities, such as watching a video. The resting-state fMRI is usually called rs-fMRI, which is a means to delimitate the activity for scan acquisition. However, the other activities, in general, do not have a specific nomination. The rs-fMRI is easiest to apply and is also easy to compare between multiple studies, as it is easier to reproduce in the same setup than any other activity. Additionally, other medical images are also combined with ML to diagnose ASD, as is the case of electroencephalograms (EEG), which try to measure brain activity by scanning magnetic signals originating from the brain. There are many different setups, but as in fMRI, many papers using resting-state scans are available, such as [27; 28; 29; 30]. However, some other setups, such as during the ADOS test [31] or while watching videos [32], are also available. However, there are few EEG data with ASD diagnosis that are publicly available. Meanwhile, on the fMRI side, some universities have worked together and created the Autism Brain Imaging Data Exchange (ABIDE) [33], an initiative that makes available more than 2000 brain fMRI scans for research purposes. In addition, all fMRI subjects gave consent to use their images. This initiative facilitates autism investigation by providing access to a database that otherwise would not be easily acquired. Moreover, the pre-processed data available on ABIDE I PREPROCESSED also contribute in this sense. Therefore, we take into account the following true propositions: (1) early diagnosis and interventions lead to better outcomes for autism treatment, as well as long-term cost reduction; (2) ADOS scores allow a rating of the ASD severity; (3) promising results of ML techniques classifying ASD vs. neurotypical through the use of rs-fMRI; and (4) the ADOS scores and ASD rs-fMRI data available at ABIDE. This work aims to investigate the functional differences between autism spectrum and autistic individuals, looking for potential brain regions that may be associated with autism severity. We used ML applied to brain segments from rs-fMRI data to classify individuals from the two groups to identify these regions, selecting the ones with the greatest differences as potential biomarkers that should be more deeply investigated in future works. The remainder of this paper is structured as follows: Section 2 presents the methodology employed. Sections 3 and 4 present and discuss our results, while Section 5 concludes this work.