Oxy-Hb was selected as the focus of measurements due to its sensitivity to task-associated changes (Sato et al., 2006; Cui, 2011). Probes that were not in good contact with the scalp may have resulted in rapid large changes (such as high amplitude spikes) in oxy-Hb signals. An fNIRS channel was considered to be “noisy” if there were very large spikes (changes in oxy-Hb larger than 0.5 mM·mm in amplitude) and all noisy channels were excluded from subsequent analyses. All data analyses were performed using a software Platform for Optical Topography Analysis Tools (Sutoko et al., 2016). A Butterworth bandpass filter with cut-off frequencies of 0.01–0.8 Hz was applied to remove instrumental or physiological noise (Luu and Chau, 2009).

Three categories of SVFT were represented by three 100-s blocks of data for analysis. As discussed in the task paradigm, each block consisted of a 20-s pre-task rest period, a 60-s task period followed by a 20-s post-task period (see to Figure 3). A moving average filter with a window size of 50 data points (5 s) was applied to remove high frequency noise from the measured oxy-Hb signals (Ishii-Takahashi et al., 2014). With respect to the start of each block, the oxy-Hb signals were then baseline-corrected to a zero baseline. Blocks with a large spike noise in the oxy-Hb signal (changes in oxy-Hb larger than 0.5 mM·mm in amplitude) were excluded from further analyses. Subsequently, fNIRS signals from each channel were meaned over all the remaining blocks so that each subject should have had only one average fNIRS signal per channel. Subsequent analyses were performed using these average fNIRS signals. Previous fNIRS studies have suggested that earliest activation starts from 5 s after task onset (Sato et al., 2006) and sharp increases in activation are often observed at around 5–10 s after onset (Maki et al., 1995). Therefore, the level of activation at each channel was determined for each individual using the percentage signal change, which was calculated using the following formula:

where oxy − Hb_{t = 5:65 (avg)} is the average oxy-Hb signal during the task period, after accounting for hemodynamic delay, and oxy − Hb_{t = −10:0 (avg)} is the average oxy-Hb signal during the rest period (−10–0 s of the pre-task rest period; see Figure 3). Channels located in the PFC that showed a percentage signal change of larger than 50% were empirically considered to be activated and were further divided into left and right PFC. Hence, it was possible for each subject to have a different number of activated channels, but they were all within the regions of interest (the left and right PFC).

Due to hemodynamic delay, the period between 5 and 25 s after task onset was defined as the activation period (see Figure 3). The activation signal was defined as the difference between the average oxy-Hb signal during the activation period and during the rest period. For each participant, the mean activation signal in the left and right PFC was calculated by averaging the signals obtained in activated measurement channels located in the left and right PFC, respectively. Based on the mean activation signals, for both the left and right PFC, the time taken to achieve the activation level was determined and the slope in the first 5 s after task onset was calculated using the following formula:

where oxy-Hbleft PFC/right PFCt=0 and oxy-HbleftPFC/rightPFCt=5 denote the mean activation signals in the left or right PFC at the onset of the task and 5 s after the onset of the task, respectively.

The number of activated measurement channels, the mean activation signals in both the left and right PFC, and the time taken to achieve the activation level and the slope in the first 5 s after task onset were then statistically assessed.

Differences between all three groups in MMSE scores were first tested using the multiple non-parametric Mann–Whitney U tests without multiple testing correction, followed by Mann–Whitney U tests with Bonferroni-Holm correction. Both tests were repeated to assess whether there were any within-category group differences in the number of words generated in the three categories of SVFT. The Bonferroni-Holm correction would test each individual hypothesis in a sequential rejective manner at α/[n − rank number of the pair (by degree of significance) + 1], where α is the desired significance level (0.05) and n is the number of comparisons. fNIRS data that were statistically assessed included the number of activated measurement channels, the mean activation signals in both the left and right PFC, the time taken to achieve the activation level and the slope in the first 5 s after task onset. For each of these four fNIRS parameters, statistical significance was estimated using multiple comparisons between the three different groups at each category level, with two sets of independent two-sample t-tests—one without multiple testing correction and one with similar Bonferroni-Holm correction. Finally, to investigate the relationship between the MMSE score and various fNIRS parameters, correlation and simple linear regression analyses were performed using the MMSE score as a continuous independent variable.

The results suggested that there were two fNIRS parameters that were significantly different between groups when the subjects were engaged in SVFT, only when no multiple testing correction was used. The first parameter is the t_{left PFC/right PFC}. In the left PFC, HC took a shorter time in achieving the targeted activation level, compared to patients with MCI and mild AD. However, contrasting with the belief that MCI would have a faster activation than mild AD, patients with mild AD actually took a shorter time. Conversely, with respect to the right PFC, MCI demonstrated the shortest time taken, followed by HC and mild AD. This is suggestive of the faster hemodynamic response in the right PFC of the MCI possibly being a compensatory response for the loss of the left PFC (Yeung et al., 2016). Taken separately, the poorer performance in SVFT and smaller activation in conjunction with the shorter time taken for left PFC activation in patients with mild AD might suggest that the compensatory mechanism is compromised. The second parameter examined was the m_{left PFC/right PFC}. Experimental results showed that the m_{right PFC} was significantly greater in MCI compared to mild AD, further suggesting its importance to describing compensatory mechanisms. While the Bonferroni-Holm correction demonstrated that there were no significant differences, it is worthwhile explaining here the underlying mechanisms for these results.

Two possible explanations have been proposed for the underlying mechanism. The first explanation was consistent with that of the scaffolding theory of aging and cognition, in which additional circuitry is recruited to support declining brain function that has become inefficient (Park and Bischof, 2013). This is commonly manifested in older adults that show increased contralateral right PFC recruitment for both working memory and episodic encoding (Reuter-Lorenz et al., 2000; Cabeza et al., 2002), which is consistent with our results. Such bilateral activation may be a form of interhemispheric interaction that has been claimed to be vital in neural compensatory mechanisms (Banich, 1998; Cabeza, 2002). Additionally, the results presented here imply that compensation and neuroplasticity might be present in the PFC of MCI, but not in mild AD. It has also been suggested that such compensatory ability might be reduced or lost in the progression of MCI toward AD, as neurodegeneration suffered by AD patients is severe enough to halt natural compensation (Clement et al., 2013). This is supportive of the results presented here, where patients with mild AD tended to forget which words they had already given added to the lack of activation of their right PFC, which is responsible for monitoring the immediate information during a task (Hayama and Rugg, 2009). This is further supported as the right PFC is specifically involved in semantic aspects of lexico-semantic processing (Joanette and Goulet, 1986).

We propose further, that the additional right PFC involvement and poorer memory performance can be explained using the inhibitory hypothesis. According to the inhibitory hypothesis, the non-dominant right PFC is normally suppressed by its dominant contra-lateral counterpart (Cox et al., 2015). Such transcallosal inhibition might be impaired with left PFC or anterior corpus callosum atrophy, which might result in extra recruitment of the right PFC. It has been found that additional non-dominant right PFC activity might reflect age-related changes in the brain and has been reported to be negatively correlated with memory performance (de Chastelaine et al., 2011). However, it has also been suggested that such disinhibition could reflect an attempted compensatory process, which is insufficient to fully compensate for age-related neurodegeneration. Our results point more toward the first explanation as we found that MCI, with right PFC activation relatively higher than both HC and mild AD, actually performed better than the latter in the SVFT. Hence, we suggest that the identification of a compensatory role for the right PFC might offer a potential target area for neurorehabilitation (Cotelli et al., 2008). It is expected that in the future the residual plasticity in the right PFC of cognitively impaired patients, particularly those with MCI, might be effectively harnessed by neurorehabilitation and other interventional techniques. At this juncture, it is necessary to examine other parameters to identify differences in task-related activities across different populations.

Considering the number of activated fNIRS channels, there were no significant differences between both the right and left PFC for all three groups. The left PFC, which is responsible for semantic memory, was activated in the SVFT, as expected (Grossman et al., 2002). The activation of the right PFC, however, may be suggestive of participants being engaged in object imagery prior to recalling names during the task. In addition, it might also indicate ongoing monitoring of semantic information (Hayama and Rugg, 2009); the episodic memory located in the right PFC was engaged to ensure that participants did not repeat names during the task. As compared with HC, a smaller region of right PFC activation was found in the other two groups (MCI and mild AD). This could indicate the lack of a monitoring process, thus partially explaining poorer performance in SVFT in these groups compared to HC. However, this cannot explain the differences in SVFT performances between MCI and mild AD.

MCI demonstrated the greatest oxygenation levels of PFC activation, in both the left and right hemispheres, during SVFT, followed by HC, and mild AD. Despite not being statistically significant, this result might be of clinical significance. The result is in agreement with previous studies, utilizing various neuroimaging modalities, which have demonstrated similar findings (Johnson et al., 2000; Arai et al., 2006; Sperling, 2007; Driscoll et al., 2009; Woodard et al., 2009). As the dorsolateral PFC has been suggested to be associated with compensatory mechanisms (Erickson et al., 2007), the brain region-specific hyperactivation and hypoactivation observed in MCI and mild AD, respectively, might indicate the presence of neural compensation in the former, and the inability to compensate in the latter (Prvulovic et al., 2005; Clement and Belleville, 2012). The differences in the level of activation across groups might also explain the differences in performance in SVFT, particularly in the left hemisphere (Fallgatter et al., 1997; Arai et al., 2006; Herrmann et al., 2008). However, contrary to current opinion, in the present study activation in the right PFC was greater compared to the left PFC, and was consistent between all the groups. This might be for the following reasons: (a) the activation of right PFC in monitoring cognitive processes during SVFT might implicate a greater role compared to the left PFC; (b) the difference might be due to methodological differences, including the differences in recruitment strategies, brain region investigated and variations in research design and measurements used to describe the outcome. More specifically, the variations in research design refer to different assessment tools, inclusion and exclusion criteria (Fallgatter et al., 1997; Yeung et al., 2016), the numbers of groups (e.g., between normal aging and AD; Herrmann et al., 2008) or between normal aging and MCI (Yeung et al., 2016), and the focus on a brain region (e.g., frontal and parietal lobe). Similarly, differences in the outcomes measured might exert a significant impact on the results, such as separate analyses of oxy-Hb and deoxy-Hb (Herrmann et al., 2008) and different baseline-corrected values (Fallgatter et al., 1997). The high variance observed here might be due to placement of the probes i.e., the position of the optodes relative to the skin (Strangman et al., 2003). Inter-subject anatomical variability, such as the thicknesses of the skull and the cerebrospinal fluid layers could have also caused the large variation in the fNIRS measurements (Okada and Delpy, 2003).

Finally, we propose an explanation for the moderately positive (R = 0.5886) linear relationship between the oxygenation level in the left PFC and the MMSE score, which was found only in MCI subjects, but not in HC and mild AD. This result reflects the fact that increasing activation actually contributed to the cognitive status in MCI, while both HC and mild AD did not show a similar trend. We suggest that in HC, the behavioral ceiling effect might have been achieved, as represented by the maximum score in MMSE (30) and this does not necessarily imply the presence of a parallel ceiling in brain activation (continuous increases in left PFC activation) (Hagenbeek et al., 2007). This might also indicate that the neural network is intact in HC. In addition, MMSE might be a relatively easy task for HC and the maximum score of 30 points may, therefore, not be a sensitive measure of cognitive status. MMSE is not designed to measure the cognitive ability of a healthy person. However, cognitive status in MCI is well below the ceiling. The pathogenesis of AD is characterized pathologically by brain accumulation of amyloid β-protein (Aβ) in the early stages (Jack et al., 2013) and Aβ is thought to be the cause of neuronal dysfunction in AD (Palop and Mucke, 2010), which necessitates neural activity. Previous studies have reported that, relative to younger people or older people without Aβ, both cognitively normal older people with Aβ deposition (Mormino et al., 2012) and MCI patients (Dickerson et al., 2004) exhibit higher neuronal activity during cognitive task performance. This phenomenon might be evidence of functional compensation keeping older people with Aβ and MCI patients cognitively stable. In agreement with these results, the increase in oxygenation levels might represent an attempted compensatory response, and hence it is proportionate to the improvement in cognitive status in MCI. However, it is not possible to draw any conclusions with respect to the interpretation due to the small MCI sample size here. Meanwhile, for mild AD, no linear relationship was found between the oxygenation levels in the left PFC and the MMSE score. The degree of Aβ deposition in the brain might reduce neural efficiency, which eventually causes progression to a more severe stage of AD (Landau et al., 2012). In such a situation, it is possible that patients eventually decline cognitively as any compensatory ability has been compromised (O'Brien et al., 2010). So, when patients progress into mild AD, their neural compensation ability might have been weakened or the neural networks might have been compromised to the point where higher oxygenation levels coupled with compensatory mechanisms are no longer enough to maintain cognitive status, unlike with MCI. Although the explanations above might fit with the observations, it is important to note that various non-neural factors might confound such interpretations; disrupted neurovascular coupling that is associated with pathological conditions e.g., aging and disease (Buckner et al., 2000; D'Esposito et al., 2003) is one of the many factors. Other factors include alterations in perfusion and metabolism (El Fakhri et al., 2003), and vascular physiology (Mueggler et al., 2002). It has also been reported that employing different verbal memory strategies led to different patterns of cortical activation (Logan et al., 2002). A final factor that might have influenced PFC activation is the administration of medication e.g., cholinergic stimulation (Rombouts et al., 2002) and donepezil (McGeown et al., 2010).