Population EEG Dynamics Uncovered Using Over 6,000 Subjects
Material below summarizes the article Characterizing Population EEG Dynamics Throughout Adulthood, published on November 30, 2016, in eNeuro and authored by Ali Hashemi, Lou J. Pino, Graeme Moffat, Karen J. Mathewson, Chris Aimone, Patrick J. Bennett, Louis A. Schmidt, and Allison B. Sekuler.
Non-invasive measures of brain activity provide valuable insights to the functioning of the brain and the mental processes underlying perception and cognition. Until recently, the most accessible method to measure brain activity was via expensive, lab-based electroencephalography (EEG) systems operated by trained researchers or technicians.
Decades of EEG research have revealed much about how brain activity is modulated from sleep to awake states, during cognitive and perceptual tasks, and after various training paradigms, as well as how these dynamics change during typical and atypical development. However, use of EEG has, until now, largely been restricted to controlled environments with time- and labor-intensive studies using research-grade equipment with relatively small sample sizes.
In our study, we used a novel EEG database obtained using Muse™, a consumer EEG headset created by InteraXon™ and intended to provide auditory neurofeedback during mindfulness-based exercises. Muse records EEG at two frontal and two temporoparietal sites, referenced to another frontal electrode. Users are given the option of sharing their results with InteraXon to advance research. On every Muse session, consumers complete an active-mind exercise, followed by a calm-mind (mindful) exercise. Although user data is anonymous, it is tagged with some demographics, including the two traits we used to characterize our results: age and sex.
We analyzed how several EEG parameters change as a function of age and sex for 6,029 unique individuals: 4,379 men and 1,650 women ranging from 18 to 88 years old. The EEG parameters we extracted included power in the delta, theta, alpha, and beta frequency bands. These components are present in everyone’s EEG, but changes in power for each of them has been associated with different cognitive and affect-related changes.
The results showed how, on average, the adult EEG changes across the adult lifespan. For example, slow age-related shifts towards less delta and theta power are accompanied by increases in alpha and beta power, which may represent a fundamental change in how the brain operates with increasing age.
This overall shift in power from lower to higher frequency bands underlies the importance in understanding how these changes are associated with behavioral or physiological differences; previous research from other labs has demonstrated the role of alpha and beta modulation in attention regulation. Research has yet to establish if baseline power in these frequencies is predictive of attention capabilities, but modulation during tasks is certainly correlated with performance on attention and memory tasks.
Our results also highlight a very strong, year-by-year slowing in the peak alpha frequency. From early to late adulthood, peak alpha frequency decreases by over 1 Hz, which is astounding, given that peak alpha frequency is relatively stable per individual. An age-related slowing in peak alpha frequency is likely associated with age-related declines in cognitive abilities, as researchers have demonstrated that peak alpha frequency can be changed with neurofeedback training. Older adults who learned to increase their peak alpha frequency also showed improved executive functions after training.
Another point of interest in our current study comes from the measure of frontal alpha asymmetry. Alpha asymmetry may index overall cortical activity, with relatively greater alpha power in one hemisphere reflecting lower overall activity in that hemisphere due to alpha’s inhibitory role. Our large sample size enabled us to see a small but significant rightward bias in overall cortical activity for our entire sample, especially for females.
Some research suggests that a rightward bias in alpha asymmetry is associated with a bias toward using one’s behavioral inhibition system, in which behavior is dictated by negative emotion and a general tendency to withdraw. This finding may be appropriate in the sample we used, as we restricted our analyses to the first five sessions of Muse use. Consumers use Muse to improve their mental well-being, so they may have more negative emotions at the outset of Muse training.
This result highlights the importance of tracking the progress of these consumers across time to see if mindfulness training elicits significant changes in their alpha asymmetry, amongst other EEG parameters, to better understand mindfulness-induced brain changes, and how they may interact with subtle, but important, age- and sex-related differences.
Overall, our study highlights the power of big data, population-based approaches to neuroscience, and points to increased opportunities when foundational neuroscientists collaborate with industry partners. EEG collected in uncontrolled environments using Muse captures many fundamental patterns of EEG power distribution.
We replicated previous small-scaled laboratory-based findings, and, with our 6,000-plus sample size, we revealed novel gradual age-related changes and sex differences that are largely understudied and that would take many years to study using traditional methods. We believe these age- and sex-related patterns in the EEG likely are present in many existing and yet-to-be collected datasets, and that it is worthwhile to not only account for age and sex in our sampling approaches, but also to actually study what these differences represent functionally.
Characterizing Population EEG Dynamics throughout Adulthood. Ali Hashemi, Lou J. Pino, Graeme Moffat, Karen J. Mathewson, Chris Aimone, Patrick J. Bennett, Louis A. Schmidt, Allison B. Sekuler. eNeur 3(6), DOI:10.1523/ENEURO.0275-16.2016