Subjective memory complaints (SMCs) are a memory disorder that often precedes mild cognitive impairment (MCI) or Alzheimer\'s disease (AD). Both individual alpha rhythms and cognitive reserve (CR) represent key features of SMCs and provide useful tools to characterize and predict the course of the disorder. We studied whether older people with SMCs may also present some abnormal resting state electroencephalogram (rsEEG) alpha rhythms, and whether alpha rhythms are associated with CR. To do this, eyes-closed rsEEG were recorded in 68 older people with and without SMCs. The individual alpha indexes alpha/theta transition frequency (TF) and individual alpha frequency peak (IAFp) were computed. TF and IAFp were also used to determine the alpha1, alpha2, and alpha3 power frequency. Results indicated no differences in TF or IAFp between older people with SMCs and controls. The SMCs group showed a reduction in alpha3 power in comparison with controls. Specifically, women with SMCs were characterized by a significant decrease in alpha3 power compared to control women. Furthermore, only in SMCs group, greater CR was associated with slow IAFp. In sum, these results suggest that TF and IAFp are two stable indexes that are not influenced by the presence of SMCs. However, the reduction in alpha3, as observed in women with SMCs, shows an abnormal posterior rsEEG at alpha power. Finally, the compensatory mechanisms of CR appear to interact with the neurophysiological mechanisms that underlie the regulation of alpha rhythms.

A classic approach to estimate individual theta-to-alpha transition frequency (TF) requires two electroencephalographic (EEG) recordings, one acquired in a resting state condition and one showing alpha desynchronisation due, for example, to task execution. This translates into long recording sessions that may be cumbersome in studies involving patients. Moreover, an incomplete desynchronisation of the alpha rhythm may compromise TF estimates. Here we present transfreq, a publicly available Python library that allows TF computation from resting state data by clustering the spectral profiles associated to the EEG channels based on their content in alpha and theta bands. A detailed overview of transfreq core algorithm and software architecture is provided. Its effectiveness and robustness across different experimental setups are demonstrated on a publicly available EEG data set and on in-house recordings, including scenarios where the classic approach fails to estimate TF. We conclude with a proof of concept of the predictive power of transfreq TF as a clinical marker. Specifically, we present a scenario where transfreq TF shows a stronger correlation with the mini mental state examination score than other widely used EEG features, including individual alpha peak and median/mean frequency. The documentation of transfreq and the codes for reproducing the analysis of the article with the open-source data set are available online at https://elisabettavallarino.github.io/transfreq/. Motivated by the results showed in this article, we believe our method will provide a robust tool for discovering markers of neurodegenerative diseases.