ArticleComputational intelligence and neuroscience2016
MEG Connectivity and Power Detections with Minimum Norm Estimates Require Different Regularization Parameters.
Article in Computational intelligence and neuroscience, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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17 citing papers in PubMed.
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- Altered neuromagnetic activity under visual stimuli in migraine: a multi-frequency magnetoencephalography study.Frontiers in neurology · 2025Article
- Validating MEG estimated resting-state connectome with intracranial EEG.Network neuroscience (Cambridge, Mass.) · 2025Article
- Altered correlation of concurrently recorded EEG-fMRI connectomes in temporal lobe epilepsy.Network neuroscience (Cambridge, Mass.) · 2024Article
- Spontaneous blinking and brain health in aging: Large-scale evaluation of blink-related oscillations across the lifespan.Frontiers in aging neuroscience · 2024Article
- Identifying good practices for detecting inter-regional linear functional connectivity from EEG.NeuroImage · 2023Article
- Deep brain stimulation for parkinson's disease induces spontaneous cortical hypersynchrony in extended motor and cognitive networks.Cerebral cortex (New York, N.Y. : 1991) · 2022Article
- Alterations in the default mode network in rolandic epilepsy with mild spike-wave index in non-rapid eye movement sleep.Frontiers in neuroscience · 2022Article
- Altered Brain Criticality in Schizophrenia: New Insights From Magnetoencephalography.Frontiers in neural circuits · 2022Article
- Age-related hearing loss is associated with alterations in temporal envelope processing in different neural generators along the auditory pathway.Frontiers in neurology · 2022Article
- Brain mapping of auditory steady-state responses: A broad view of cortical and subcortical sources.Human brain mapping · 2021Article
- Patient, interrupted: MEG oscillation dynamics reveal temporal dysconnectivity in schizophrenia.NeuroImage. Clinical · 2020Article
- Neural Generators Underlying Temporal Envelope Processing Show Altered Responses and Hemispheric Asymmetry Across Age.Frontiers in aging neuroscience · 2020Article
- Lag-invariant detection of interactions in spatially-extended systems using linear inverse modeling.PloS one · 2020Article
- Connectivity of the Frontal Cortical Oscillatory Dynamics Underlying Inhibitory Control During a Go/No-Go Task as a Predictive Biomarker in Major Depression.Frontiers in psychiatry · 2020Article
- Alterations of Intrinsic Brain Connectivity Patterns in Depression and Bipolar Disorders: A Critical Assessment of Magnetoencephalography-Based Evidence.Frontiers in psychiatry · 2017Review
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Abstract
Minimum Norm Estimation (MNE) is an inverse solution method widely used to reconstruct the source time series that underlie magnetoencephalography (MEG) data. MNE addresses the ill-posed nature of MEG source estimation through regularization (e.g., Tikhonov regularization). Selecting the best regularization parameter is a critical step. Generally, once set, it is common practice to keep the same coefficient throughout a study. However, it is yet to be known whether the optimal lambda for spectral power analysis of MEG source data coincides with the optimal regularization for source-level oscillatory coupling analysis. We addressed this question via extensive Monte-Carlo simulations of MEG data, where we generated 21,600 configurations of pairs of coupled sources with varying sizes, signal-to-noise ratio (SNR), and coupling strengths. Then, we searched for the Tikhonov regularization coefficients (lambda) that maximize detection performance for (a) power and (b) coherence. For coherence, the optimal lambda was two orders of magnitude smaller than the best lambda for power. Moreover, we found that the spatial extent of the interacting sources and SNR, but not the extent of coupling, were the main parameters affecting the best choice for lambda. Our findings suggest using less regularization when measuring oscillatory coupling compared to power estimation.
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