# Machine Learning and rs-fMRI to Identify Potential Brain Regions Associated with Autism Severity
## 3 Results  
This section presents the results of our ASD vs. autism classification experiments. All SoRs can be seen in Table 8 and each ROI used by these sets can be seen in Table 1. Inthis paper, we used specificity (SPC) related to the ASD classification and sensitivity (SNS) associated with the autistic classification. Our experiments worked with a total of 202 subjects, which comprised 36 with ASD and 166 with autism, according to the ADOS scores. Table 9 shows the SoRs with the ACC, SNS, and SPC greater than or equal to 60%. ACC ranged from 60.9% (SoRs 27) to 73.8% (SoRs 11). SNS ranged from 60.8% (SoRs 27) to 76.5% (SoRs 11). SPC ranged from 60.0% (SoRs 27) to 70.8% (SoRs 30). This shows the existence of a non-random separation when considering five brain regions. The \(t\)-test of each feature allows us to understand the difference between the ASD and autistic groups. The \(t\)-test results are a statistical difference between any two given groups, and positive values mean that the group 1 average is larger than group 2, while negative values mean that the group 2 average is larger than group 1. Table 10 shows the \(t\)-test result for each feature on each SoRs for which SVM had above threshold results, and the positive values mean that the ASD group average is larger than the autistic group for that feature, while negative values mean that the autistic group average is higher. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Feature**} & \multicolumn{5}{c}{**SoRs**} \\ \cline{2-7}  & **1** & **11** & **23** & **27** & **30** \\ \hline
1 st ROI max & \(-2.2285\) & \(-1.7574\) & \(-1.3936\) & \(-2.0293\) & \(-1.9078\) \\ \hline
1 st ROI min & \(2.0665\) & \(1.9254\) & \(1.7192\) & \(2.2895\) & \(1.8749\) \\ \hline
1 st ROI mean & \(-0.2457\) & \(-0.3619\) & \(-0.1699\) & \(0.4010\) & \(-0.4500\) \\ \hline
1 st ROI STD & \(0.2434\) & \(0.2758\) & \(0.0988\) & \(-0.4227\) & \(0.4915\) \\ \hline
2 nd ROI max & \(-1.6051\) & \(-1.7618\) & \(-1.4630\) & \(-1.8003\) & \(-1.9181\) \\ \hline
2 nd ROI min & \(1.9787\) & \(1.6766\) & \(1.4059\) & \(2.0074\) & \(1.8686\) \\ \hline
2 nd ROI mean & \(0.3057\) & \(-0.4697\) & \(-0.0915\) & \(-0.8066\) & \(0.8104\) \\ \hline
2 nd ROI STD & \(-0.2794\) & \(0.4066\) & \(0.0596\) & \(0.8234\) & \(-0.6772\) \\ \hline
3 rd ROI max & \(-1.8155\) & \(-1.7295\) & - & - & - \\ \hline
3 rd ROI min & \(1.7308\) & \(1.6808\) & - & - & - \\ \hline
3 rd ROI mean & \(0.0548\) & \(0.4520\) & - & - & - \\ \hline
3 rd ROI STD & \(-0.2010\) & \(-0.5442\) & - & - & - \\ \hline
4 th ROI max & \(-1.6348\) & \(-1.7266\) & - & - & - \\ \hline
4 th ROI min & \(1.8527\) & \(1.9396\) & - & - & - \\ \hline
4 th ROI mean & \(-0.1745\) & \(1.8407\) & - & - & - \\ \hline
4 th ROI STD & \(0.1780\) & \(-1.9850\) & - & - & - \\ \hline \hline \end{tabular}
\end{table} 
Table 10: The \(t\)-test for features on SoRs with values above required threshold. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**SoRs ID** & **ACC** & **SNS** & **SPC** \\ \hline
11 & 73.85\% & 76.50\% & 60.83\% \\ \hline
23 & 66.28\% & 67.38\% & 60.83\% \\ \hline
1 & 64.88\% & 65.69\% & 63.33\% \\ \hline
30 & 63.38\% & 61.47\% & 70.83\% \\ \hline
27 & 60.90\% & 60.84\% & 60.00\% \\ \hline \hline \end{tabular}
\end{table} 
Table 9: SoRs above the required threshold. Furthermore, reinforcing the \(t\)-test result, the \(p\)-value (scale [0,1]) of each feature from SoRs above the required threshold is plotted in Table 11. The higher \(p\)-value was 0.96 for the mean on ROI 4 (Frontal Sup. Orb. Left), the third ROI from SoRs 1, with high values indicating a risk of not being able to distinguish the two groups from each other. On the other hand, lower values indicate a high possibility of discerning the two groups using the feature. The lower \(p\)-value was 0.02 from the min on ROI 72 (Putamen Left), the first ROI from SoRs 27.