## 4 Discussion
Post-lingually deafened individuals have robust language systems to interpret degraded auditory signals via the CI device. On the other hand, prelingually deafened patients construe the reduced auditory input from the CI using language systems that were deprived during development. However, the similarities between current and previous studies suggest a common neural basis for speech processing in temporal regions across CI populations. Moreover, the significant correlation that we found between SRTn and cortical activity in the right and left temporal ROIs across all our participants, both CI and NH, may further suggest neural markers in the right and left MTG for speech understanding, regardless of the auditory background. Notably, high mean beta-values were also shown across participants in channel(s) covering part of the right DLPFC in response to the SIQ and SIN, and in channels covering the left fusiform gyrus in response to SIN, with no significant differences in activation between the groups. The elevated activation in the right DLPFC aligns with reports on the sensitivity of this area to the acoustic envelope of the speech stimuli [90], specifically to the sound's onsets [91]. This sensitivity was suggested to reflect an increase in cognitive control processes that plausibly prepare the listener to be attentive to the following stimulation [90; 91]. Regarding the left fusiform gyrus, although it was previously described mainly as a visual area involved in face perception, reading, and object recognition [92; 93], several reports suggest that it also plays a critical role in the integration of multiple stimuli [94], and in semantic decoding [95]. Both these skills are relevant for SIN understanding. Channel-wise contrast analyses were conducted separately for each group; these did not yield significant differences between conditions (SO, SIN, NO), contrary to the previous fNIRS reports in NH participants [42; 45]. This inconsistency may be related to the fact that most previous studies employed less efficient techniques to separate spurious scalp signals from cortical signals (e.g., SSC regression, as used in the present study). Methods to correct for systemic hemodynamic responses using SSC have been shown to improve signal quality and provide a more precise estimate of the evoked HRF [96; 97; 98]. Alternatively, the fabrication errors, assumed to be created by inter-subject variations in the thickness of the skull and nearby tissues, which were larger in the temporal areas (Figure 3), might have masked channel-based differences across conditions. Analyses of the HbO data based on the pre-determined anatomical ROIs yielded no significant differences between conditions or groups. This lack of differences may be attributed to the fact that each fNIRS channel may reflect a combined response from more than just one anatomical area (as reflected by the specificity values, Table 2) and therefore there may be overlapping between ROIs, introducing variability to the data. No significant differences across conditions or groups were also shown for the ROI-base analyses of the HbR data. This may result from a higher noise level in the HbR data than in the HbO data when regressing SSCs. Using a single SSC regression (as was done in this study, using the most correlated channel) was shown to improve HbO noise reduction by 33%, while only improving HbR noise reduction by 3% [99]. This may also explain why many previous studies have chosen to analyze only HbO data and/or have used HbR data merely for cross-correlations with HbO during signal preprocessing [32; 38; 44; 45; 48]. Assuming that a smaller but detectable change may, nevertheless, be expected in HbR values in response to auditory stimuli (e.g., [100]), it is worthwhile considering to enlarge statistical power by including larger groups of participants in future studies.