# Assessment of fNIRS Signal Processing Pipelines: Towards Clinical Applications
## 3 Results
### Block Average and Group-Level Results
The SNR across task conditions over the contralateral motor region was within the 8.99-14.67 dB range for \(\Delta[HbO_{2}]\) and 9.48-11.84 dB range for \(\Delta[HbR]\), while ipsilateral activation presented reduced values in the 3.69-8.22 dB range for \(\Delta[HbO_{2}]\) and 2.43-6.04 dB range for \(\Delta[HbR]\). Overall, median  
Figure 8: Subject-specific block-averaged responses across pipelines of \(\Delta[HbO_{2}]\) and \(\Delta[HRR]\) related to left-hand motor grasp and a measurement channel placed over the left motor region (i.e., ipsilateral hemisphere). (**a**) SC-MARA-spline, (**b**) MC-MARA-spline, (**c**) SC-MARA-tPCA, (**d**) MC-MARA-tPCA, (**e**) HybridMARA-spline SG, (**f**) HybridMARA-tPCA SG, (**g**) TDDR, and (**h**) Wavelet method. Figure 9: Visual comparison of subject-specific block-averaged response (dashed line) and GLM fitting (bold line) across pipelines. These results are related to left-hand motor grasp and a measurement channel placed over the right motor region (i.e., contralateral hemisphere) in accordance with Figure 7.  
SNR values do not vary significantly across pipelines, confirming the graphical results provided in Figures 7 and 8. Among MARA approaches, SC-MARA often presents the highest median SNR value, both for tPCA and spline correction methods, while the lowest values are mostly attributed to the MC-MARA algorithm. In general, even lower SNR values are attributed to TDDR, since the decreased response amplitude compared to other algorithms reduces the total energy in the activation range of 6-12 s. Conversely, Wavelet methods provide SNR values more comparable to MARA methods, most probably due to a lower total energy in the baseline range of 24-30 s.  
The lack of substantial differences among different pipelines in the final single step and cumulative ERD, block-averaged responses, and SNR over the motor area is mainly related to the correspondence between the functional tasks and expected elicited brain areas. The temporal course and statistical significance were not affected. Notwithstanding, we suggest adopting preprocessing algorithms for MA reduction that present a channel-wise approach, such as SC-MARA, HybridMARA, TDDR, and Wavelet methods to prevent invasive interpolation over MA tracts which could lead to the loss of valuable signals if occurring at high rates. Among them, we suggest adopting the TDDR and Wavelet methods to increase the reproducibility since fewer parameters need to be set, supporting non-expert users in the signal processing. For a better quantification of the above comparisons, [PERSON]'s correlation and RMSE were computed between the block average and the GLM fitting curve in each subject and measurement channels, adapting the proposed approach by [PERSON] et al. (2018). All methods showed a significant linear correlation and [PERSON]'s coefficient \(r\) across task and chromophores above the 0.6 (median in 0.613-0.682 range, IQR in the 0.287-0.328 range). Conversely, RMSE values associated with significant \(r\)-values were higher for \(\Delta[HbO_{2}]\) (median in 0.022-0.031 A.U. range, IQR in 0.020-0.030 A.U. range) than \(\Delta[HbR]\) (median in 0.009-0.014 A.U. range, IQR in 0.008-0.012 A.U. range). The reduced RMSE values in \(\Delta[HbR]\) compared to \(\Delta[HbO_{2}]\) could be attributed to the amplitude inequality between chromophores of block-averaged responses (Figures 7 and 9). Passing to the whole HYA group analysis (alias, second level) of activation vs. resting condition significance maps, all methods were able to localize the activated contralateral motor region and the ipsilateral inhibition, as shown in Figures 10 and 11 for \(\Delta[HbO_{2}]\) and \(\Delta[HbR]\), respectively. The shown maps report significant \(t\)-values corrected for multiple comparison according to the Benjamini-Hochberg False Discovery Rate (\(p_{FDR}<0.05\)). The \(t\)-test compared the average signal difference between the task and rest to the noise level at rest.