Source-space EEG functional connectivity and prediction of cognition in Parkinsons disease: No added benefit of individualized head models over standard templates

Tetereva, A.; Hall-McMaster, G.; Slater, N.; Harris, A.; Shoorangiz, R.; Le Heron, C.; Keenan, R.; Myall, D.; Pitcher, T.; Kirk, I.; Meissner, W.; Anderson, T.; Melzer, T.; Pat, N.; Dalrymple-Alford, J.

doi:10.64898/2026.05.07.723671

bioRxiv · 10.64898/2026.05.07.723671

Source-space EEG functional connectivity and prediction of cognition in Parkinsons disease: No added benefit of individualized head models over standard templates

Tetereva, A.·Hall-McMaster, G.·Slater, N.·Harris, A.·Shoorangiz, R.·Le Heron, C.·Keenan, R.·Myall, D.·Pitcher, T.·Kirk, I.·Meissner, W.·Anderson, T.·Melzer, T.·Pat, N.·Dalrymple-Alford, J.

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Abstract

IntroductionCognitive decline is a major non-motor feature of Parkinsons disease (PD), but reliable and accessible biomarkers remain limited. Resting-state electroencephalography (EEG) is a promising candidate because it is low-cost, portable, and well suited to repeated assessment. Recent work has increasingly focused on source-space functional connectivity (FC) for the prediction of cognition. However, the influence of source modelling based on an individualized MRI-based head model relative to that based on standard template model is unknown. MethodsTo compare these two source-space EEG FC methods, we analysed EEG data from the New Zealand Parkinsons Progression Programme, including 136 people with PD and 51 age-similar controls. Source space resting-state EEG, parcellated with the HCP-MMP1 atlas, was used to derive amplitude envelope correlation (AEC) and debiased weighted phase lag index (dwPLI) across six canonical frequency bands. The resulting twenty-four FC modalities were evaluated using six machine-learning regression algorithms within a nested cross-validation framework. ResultsTheta-, alpha-, and beta-band FC showed the most consistent prediction of global cognition. The strongest performance was observed for theta- and alpha-band AEC and dwPLI features (max R{superscript 2} = 0.170, 95% CI = 0.067-0.262; max r = 0.439, 95% CI = 0.328-0.537). Standard and individualized head models showed comparable predictive performance across nearly all modalities. The feature-importance neuroanatomical patterns for Cole-Anticevic networks were also similar between the two head-model options. ConclusionsWe found that source-space resting-state EEG FC can predict cognitive performance in PD. The comparability of the two head models suggests that the more user-friendly and less resource-intensive standard template head model is sufficient for this purpose. This supports feasible, scalable, and clinically accessible EEG-based FC biomarkers of cognition in PD.

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Tetereva, A., Hall-McMaster, G., Slater, N., Harris, A., Shoorangiz, R., Le Heron, C., Keenan, R., Myall, D., Pitcher, T., Kirk, I., Meissner, W., Anderson, T., Melzer, T., Pat, N., Dalrymple-Alford, J.. 2026-05-12. Source-space EEG functional connectivity and prediction of cognition in Parkinsons disease: No added benefit of individualized head models over standard templates. https://doi.org/10.64898/2026.05.07.723671

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