Ever since the first measurements of the human electroencephalogram, it is known that neural activity in the alpha frequency (~10Hz) is dominant during relaxed wakefulness. The frequency peak of alpha varies between individuals but is fairly consistent within each person and, highly heritable. This is known as the Individual Alpha Frequency (IAF). The IAF is related to a wide variety of cognitive processes and neuropathologies, being a biomarker for diseases such as schizophrenia, Alzheimer’s disease or autism. Remarkably, the predisposition and incidence for some neuropathologies differs between male and female. However, when it comes to these sex differences, there is still a big gap in research in neuroscience. Nowadays, it is becoming increasingly clear that neglecting differences in the expression of neuropathologies across sexes can lead to the loss of relevant information for prevention, diagnosis and treatment. So far, only a few studies have focused on sex differences in alpha oscillations, and the results are mixed. Here we extracted the individual alpha frequency peak of a large sample of male and female subjects (N>350) gathered from different pre-existing datasets. Although the IAF is a widely used signal from the human EEG, there is no consensus on the method of extraction. This thesis applies a novel, automated method that takes into account the quality of the recording channels. Once estimated, the IAF peaks were analysed using a mixed model ANOVA to reveal potential differences between sexes, taking into account the variability that may arise from the fact that the datasets were recorded separately. Our results show a clear slowing down of the IAF after the age of 55, but we found no significant differences between sexes.

Tutors: Mireia Torralba Cuello, Salvador Soto Faraco, Alice Drew