# An eLORETA Longitudinal Analysis of Resting State EEG Rhythms in Alzheimer's Disease
## 4 Discussion and Conclusions  
Alzheimer's disease is a neurological disorder which is the most common cause of dementia. As there is no cure for AD, early diagnosis is very important. In fact, a pharmacological treatment  
Figure 9: Boxplot of the power current density of patient 184 at time T0 and T1 for each frequency sub-band and each region of interest (frontal, occipital, parietal, and temporal).  
in the early stage (MCI) can slow down the evolution of the disease. The potential of EEG as a tool for studying AD has been widely explored and proved in the last years. In fact, the use of the EEG is advisable as compared to imaging techniques because it is easy-to-use, not expensive, comfortable, and safe for the patient.  
Over the years, several studies have revealed that EEG signals of AD patients become slower, meaning that the spectral power is shifted towards lower frequencies [49]. In our paper, we conducted a longitudinal study involving eight patients who converted from MCI to AD after four months. The doctors made the diagnosis according to the MoCA scores. The novelty is that the spectral power analysis was carried out by means of eLORETA algorithm, which should provide a better source localization accuracy compared to LORETA. The previous works, both cross-sectional and longitudinal, were all carried out using LORETA [26; 27; 28; 29; 30; 31; 32; 33; 34; 35; 36]. The time period of follow-up was relatively short, so the condition at time T1 should be considered as slight worse in comparison with MCI condition at time T0. Finally, unlike the three longitudinal studies reported in [34; 35; 36], whose results are at group level, our work wants to investigate whether resting state EEG sources can be a marker to follow the progression of the disease in a single AD subject. In our study, five patients (03, 32, 41, 180, and 184) out of eight showed an increasing power in delta and theta bands. All patients, except for 180, exhibited a lower activation in alpha 1 and beta 2 bands. Finally, all patients, except for 03 and 180, revealed a decreased power in alpha 2 and beta 1 bands. Most of our findings are consistent with those of previous studies. On the other hand, the discrepancy of some outcomes could be related to the not yet severe stage of the disease at time T1. This topic needs to be further explored. As for the future development, it could be interesting to apply the eLORETA method to high-density EEG, which should improve the source localization accuracy [50]. In addition, it would be advisable to test the method on a larger number of patients for the validation of results. In order to further confirm the better performance of eLORETA as compared to both LORETA and sLORETA, as already proved in [46; 47], a comparative methodological study could be carried out. Moreover, as in recent years several new algorithms for solving the EEG inverse problem have been developed, a comparison between them and eLORETA would be essential for testing the eLORETA effectiveness [51]. Finally, the combination of source reconstruction with complex network analysis could also provide a further instrument for a better understanding of the evolution of the disease. In conclusion, our work could represent a first step for more detailed investigations, in order to make the analysis of power current density a valid tool for evaluating the cognitive decline in AD.  
Conceptualization, S.D. and F.L.F.; Methodology, S.D. and F.L.F.; Formal Analysis, S.D.; Software, S.D.; Project administration, F.L.F.; Writing--original draft preparation, S.D.; Writing--review and editing, F.L.F.; Supervision, F.L.F. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors thank the doctors of IRCCS Centro Neurolesi Bonino-Pulejo of Messina (Italy) for their insightful comments and suggestions.  
The authors declare no conflict of interest.