# Machine Learning and rs-fMRI to Identify Potential Brain Regions Associated with Autism Severity
## 3 Results
The SoRs 1 has a mean \(p\)-value of 0.45 (0.43 STD), while SoRs 11 has a mean \(p\)-value of 0.32 (0.14 STD); for SoRs 23, 27, and 30, the mean \(p\)-value is 0.53 (0.51 STD), 0.30 (0.24 STD), and 0.30 (0.24 STD), respectively. Therefore, SoRs 11 has the lowest \(p\)-value STD and one of the lowest \(p\)-value means, which indicates a high probability of containing the largest set of features to classify ASD severity. It is worth noting that these values reflect only our sample and should not be used as a diagnostic tool as further research is needed to either confirm or deny our findings. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Feature**} & \multicolumn{5}{c}{**SoRs**} \\ \cline{2-6}  & **1** & **11** & **23** & **27** & **30** \\ \hline
5 th ROI max & - & \(-\)1.8367 & - & - & - \\ \hline
5 th ROI min & - & 1.3644 & - & - & - \\ \hline
5 th ROI mean & - & \(-\)0.5116 & - & - & - \\ \hline
5 th ROI STD & - & 0.5581 & - & - & - \\ \hline
6 th ROI max & - & \(-\)1.5904 & - & - & - \\ \hline
6 th ROI min & - & 1.3676 & - & - & - \\ \hline
6 th ROI mean & - & 0.5552 & - & - & - \\ \hline
6 th ROI STD & - & \(-\)0.5744 & - & - & - \\ \hline \hline \end{tabular}
\end{table} 
Table 10: _Cont._ \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Feature**} & \multicolumn{5}{c}{**SoRs**} \\ \cline{2-6}  & **1** & **11** & **23** & **27** & **30** \\ \hline
1 st ROI max & **0.02696** & 0.08038 & 0.16498 & **0.04375** & 0.05785 \\ \hline
1 st ROI min & **0.04007** & 0.05560 & 0.08713 & **0.02309** & 0.06227 \\ \hline
1 st ROI mean & 0.80617 & 0.71784 & 0.86524 & 0.68888 & 0.65319 \\ \hline
1 st ROI STD & 0.80794 & 0.78296 & 0.92141 & 0.67295 & 0.62359 \\ \hline
2 nd ROI max & 0.11005 & 0.07963 & 0.14504 & 0.07332 & 0.05652 \\ \hline
2 nd ROI min & **0.04922** & 0.09519 & 0.16131 & **0.04605** & 0.06314 \\ \hline
2 nd ROI mean & 0.76015 & 0.63907 & 0.92717 & 0.42088 & 0.41870 \\ \hline
2 nd ROI STD & 0.78026 & 0.68474 & 0.95254 & 0.41129 & 0.49906 \\ \hline
3 rd ROI max & 0.07095 & 0.08527 & - & - & - \\ \hline
3 rd ROI min & 0.08502 & 0.09437 & - & - & - \\ \hline
3 rd ROI mean & 0.95639 & 0.65175 & - & - & - \\ \hline
3 rd ROI STD & 0.84090 & 0.58691 & - & - & - \\ \hline
4 th ROI max & 0.10366 & 0.08578 & - & - & - \\ \hline \hline \end{tabular}
\end{table} 
Table 11: \(p\)-values for features on SoRs with values above required threshold. Moreover, we performed other trials adding phenotype information (age, sex, and full IQ). We used the same features and added the phenotype data in the vector sent to the ML algorithm. We executed the test for the three phenotypes together, one at a time, and all combinations of two phenotypes. We used the same process for the main experiment; the results that reached the threshold defined in Section 2.7, as well as the ACC gain, using the phenotype for each SoRs are shown in Table 12. However, as shown by [21], these features did not show a significant improvement, if any, in the sample. Finally, we show the mean result for each of the features with high ACC both for ASD and autistic in Tables 13 and 14, respectively. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Feature**} & \multicolumn{5}{c}{**SoRs**} \\ \cline{2-6}  & **1** & **11** & **23** & **27** & **30** \\ \hline
1 st ROI max & 0.9605 & 1.6019 & 2.6961 & 1.5552 & 3.0939 \\ \hline
1 st ROI min & \(-1.0676\) & \(-1.5430\) & \(-2.5440\) & \(-1.4001\) & \(-3.0682\) \\ \hline
1 st ROI mean & 0.0000 & 0.0009 & \(-0.0004\) & 0.0005 & \(-0.0019\) \\ \hline
1 st ROI STD & \(-0.0341\) & \(-0.4287\) & 0.1747 & \(-0.2329\) & 0.9709 \\ \hline
2 nd ROI max & 1.1802 & 2.8490 & 2.5306 & 1.3805 & 2.3672 \\ \hline \hline \end{tabular}
\end{table} 
Table 13: Mean values for features on SoRs from ASD sample.