ArticleNeuroImage2017
Using generative models to make probabilistic statements about hippocampal engagement in MEG.
Article in NeuroImage, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers, 1 of them a synthesis that pooled it.
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Who cites it
27 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Characterizing hippocampal dynamics with MEG: A systematic review and evidence-based guidelines.Human brain mapping · 2019Pooled it
- Multimodal MEG and Microstructure-MRI Investigations of the Human Hippocampal Scene Network.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2025Article
- Exploring the neuromagnetic signatures of cognitive decline from mild cognitive impairment to Alzheimer's disease dementia.EBioMedicine · 2025Article
- Modulation of alpha oscillations by attention is predicted by hemispheric asymmetry of subcortical regions.eLife · 2024Article
- EEG/MEG source imaging of deep brain activity within the maximum entropy on the mean framework: Simulations and validation in epilepsy.Human brain mapping · 2024Article
- The role of epidemic spreading in seizure dynamics and epilepsy surgery.Network neuroscience (Cambridge, Mass.) · 2023Article
- Preliminary Observations of Resting-State Magnetoencephalography in Nonmedicated Children with Obsessive-Compulsive Disorder.Journal of child and adolescent psychopharmacology · 2022Article
- Magnetoencephalography can reveal deep brain network activities linked to memory processes.Human brain mapping · 2022Article
- Cortical oscillatory dysrhythmias in visual snow syndrome: a magnetoencephalography study.Brain communications · 2022Article
- Testing covariance models for MEG source reconstruction of hippocampal activity.Scientific reports · 2021Article
- Flexible brain dynamics underpins complex behaviours as observed in Parkinson's disease.Scientific reports · 2021Article
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- Pragmatic spatial sampling for wearable MEG arrays.Scientific reports · 2020Article
- vmPFC Drives Hippocampal Processing during Autobiographical Memory Recall Regardless of Remoteness.Cerebral cortex (New York, N.Y. : 1991) · 2020Article
- Spatiotemporal and spectral dynamics of multi-item working memory as revealed by the n-back task using MEG.Human brain mapping · 2020Article
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- High-precision magnetoencephalography for reconstructing amygdalar and hippocampal oscillations during prediction of safety and threat.Human brain mapping · 2019Article
- Deep brain activities can be detected with magnetoencephalography.Nature communications · 2019Article
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Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Magnetoencephalography (MEG) enables non-invasive real time characterization of brain activity. However, convincing demonstrations of signal contributions from deeper sources such as the hippocampus remain controversial and are made difficult by its depth, structural complexity and proximity to neocortex. Here, we demonstrate a method for quantifying hippocampal engagement probabilistically using simulated hippocampal activity and realistic anatomical and electromagnetic source modelling. We construct two generative models, one which supports neuronal current flow on the cortical surface, and one which supports neuronal current flow on both the cortical and hippocampal surface. Using Bayesian model comparison, we then infer which of the two models provides a more likely explanation of the dataset at hand. We also carry out a set of control experiments to rule out bias, including simulating medial temporal lobe sources to assess the risk of falsely positive results, and adding different types of displacements to the hippocampal portion of the mesh to test for anatomical specificity of the results. In addition, we test the robustness of this inference by adding co-registration error and sensor level noise. We find that the model comparison framework is sensitive to hippocampal activity when co-registration error is <3 mm and the sensor-level signal-to-noise ratio (SNR) is >-20 dB. These levels of co-registration error and SNR can now be achieved empirically using recently developed subject-specific head-casts.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.