## 4 Discussion  
In the present study using TRS, we evaluated the relationship between the cognitive functions (i.e., the MMSE scores) and optical parameters of the head regions including the PFC in elderly subjects with systemic disorders. It is difficult to measure Hb concentrations in the cortex selectively by TRS; however, the following findings suggest that the Hb concentrations measured by TRS reflected CBO in the PFC. First, simultaneous measurements of TRS and PET demonstrated that \(C_{t\text{-}Hb}\) and SO\({}_{2}\) measured by TRS correlated with an increase of regional cerebral blood flow and volume induced by acetazolamide [18]. Second, the result of TRS functional study was consistent with the result obtained by fMRI; TRS demonstrated an increase in \(C_{deoxy\text{-}Hb}\) in the PFC during driving simulation while fMRI demonstrated a decrease in the Blood oxgenation level dependent (BOLD) signal (i.e., deactivation) in the PFC [19]. Third, TRS could detect cerebral ischemia caused by vasospasms after SAH by demonstrating a decrease in \(C_{oxy\text{-}Hb}\) and SO\({}_{2}\)[10]. These findings suggest that \(C_{oxy\text{-}Hb}\) and SO\({}_{2}\) measured by TRS at rest reflected the PFC activity at rest. The present study revealed positive correlations between the MMSE score and SO\({}_{2}\), \(C_{oxy\text{-}Hb}\) in the PFC, suggesting that the greater the degree of PFC activity at rest, the higher the cognitive function. These observations are consistent with our recent TRS study on elderly women, which demonstrated that mild cognitive impairment exhibited higher baseline \(C_{oxy\text{-}Hb}\) in the PFC than those in severe cognitive impairment [20]. Moreover, the negative correlation between the subject's age and SO\({}_{2}\) and \(C_{oxy\text{-}Hb}\) in the PFC suggest that the older the subject, the lower the PFC activity at rest, which is consistent with the studies on the effect of aging on regional cerebral blood flow in the PFC [21]. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & \multicolumn{4}{c}{**VSRAD**} \\ \cline{3-6}  & & **Severity** & **Whole Brain** & **External of VOI** & **Ratio** \\ \hline \multirow{3}{*}{Right PFC} & OPL1 & \(-\)0.305 * & 0.073 & \(-\)0.312 * & \(-\)0.396 ** \\ \cline{2-6}  & OPL2 & \(-\)0.284 * & 0.095 & \(-\)0.292 * & \(-\)0.386 ** \\ \cline{2-6}  & OPL3 & \(-\)0.306 * & 0.052 & \(-\)0.312 * & \(-\)0.395 ** \\ \hline \multirow{3}{*}{Left PFC} & OPL1 & \(-\)0.211 & \(-\)0.038 & \(-\)0.242 & \(-\)0.248 \\ \cline{2-6}  & OPL2 & \(-\)0.228 & \(-\)0.023 & \(-\)0.262 & \(-\)0.283 * \\ \cline{1-1} \cline{2-6}  & OPL3 & \(-\)0.240 & \(-\)0.038 & \(-\)0.276 * & \(-\)0.294 * \\ \hline \hline \end{tabular}
* indicates \(p<\) 0.05. ** indicates \(p<\) 0.01.  
\end{table} 
Table 6: Correlations between OPLs and VSRAD parameters. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Case**} & \multicolumn{4}{c}{**Right**} & \multicolumn{4}{c}{**Left**} & \multirow{2}{*}{**Average**} \\ \cline{2-2} \cline{4-7}  & **OPL1** & **OPL2** & **OPL3** & **OPL1** & **OPL2** & **OPL3** \\  & **(761 nm)** & **(791 nm)** & **(836 nm)** & **(761 nm)** & **(791 nm)** & **(836 nm)** & \\ \hline A & 217.9 & 220.8 & 208.4 & 226.2 & 227.2 & 211.4 & 218.7 (1.00) \\ \hline B & 177.1 & 179.0 & 169.6 & 171.8 & 174.0 & 162.9 & 172.4 (0.79) \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of OPLs (in mm) between no brain atrophy (Case A) and severe brain atrophy (Case B). The close correlations between the MMSE score and the TRS parameters suggest that machine learning may allow prediction of cognitive function based on the TRS parameters. For the prediction, deep learning, a subset of machine learning, may be useful since it allows analyzing regularity and relevance from a large amount of data, make judgments and predictions [22]. Indeed, the deep learning has been applied to imaging diagnosis [23], including computer- aided-diagnosis of Alzheimer's Disease (AD) based on MRI images [24].