Head Title

The Effect of Ageing on Neuro-Magnetic Power Spectra: A Big-Data Perspective with GLM-Spectrum

Register to watch this free webinar on-demand. A link to watch the recording will then be emailed to you shortly after registration. ​

This masterclass is presented by Andrew Quinn, Assistant Professor at the University of Birmingham, and discusses: "An increasing number of large open-access MEG datasets are becoming available and are supporting the literature towards a consensus on how the MEG signal changes with age. Our statisicial and analysis methods must continue to develop to make the most of this emerging opportunity. This webinar introduces the GLM-Spectrum,  a flexible analysis framework for multilevel and multivariate regression modelling of power spectrum effects. This naturally combines the flexibility of modern regression modelling and permutation-statistics with established frequency spectrum estimation. 

Using the GLM-Spectrum, we estimate the effect of ageing across several large datasets whilst controlling for a range of potentially interfering covariates at the first- and group-level. The results show that the MEG power spectrum is consistently different in older compared to younger adults with differential effects in low frequencies, alpha and beta. These effects are robust across sensor and source space, magnetometers and gradiometers, and different MEG manufacturers. Covariates and confounds such as eye-blinks, head size, head position and heart rate do change the power spectrum but do not obscure the age effect at the group level. Yet, these confounding variables do remain critical for modelling the spectra of individual subjects’ recordings. Finally, we show that the covariate structure of large datasets can vary both due to systematic differences (eg acquisition set-up) or by chance due to sampling. As such, robust methods for multivariate modelling are a critical step towards consensus about how ageing changes the MEG signal."


Dr Andrew Quinn completed his PhD in 2014 at the University of York developing methods for estimating time-varying functional connectivity during visual word recognition from Magnetoencephalography data. He then worked as a postdoctoral scientist at the Oxford Centre for Human Brain Activity (OHBA) where he developed a range of novel time-series analysis techniques targeted at analysis of brain changes in neurodegenerative disorders.  

Dr Quinn started as an Assistant Professor at the University of Birmingham in 2022. His research continues to explore novel analysis of electrophysiological time series in the context of visual and auditory perception, as well as how brain dynamics change across the lifespan and into neurodegeneration.


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