MEG Connectivity and Power Detections with Minimum Norm Estimates Require Different Regularization Parameters

المؤلفون المشاركون

Sébastien, Daligault
Hincapié, Ana-Sofía
Kujala, Jan
Delpuech, Claude
Mery, Domingo
Cosmelli, Diego
Jerbi, Karim
Mattout, Jérémie

المصدر

Computational Intelligence and Neuroscience

العدد

المجلد 2016، العدد 2016 (31 ديسمبر/كانون الأول 2015)، ص ص. 1-11، 11ص.

الناشر

Hindawi Publishing Corporation

تاريخ النشر

2016-03-22

دولة النشر

مصر

عدد الصفحات

11

التخصصات الرئيسية

الأحياء

الملخص EN

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.

نمط استشهاد جمعية علماء النفس الأمريكية (APA)

Hincapié, Ana-Sofía& Kujala, Jan& Mattout, Jérémie& Sébastien, Daligault& Delpuech, Claude& Mery, Domingo…[et al.]. 2016. MEG Connectivity and Power Detections with Minimum Norm Estimates Require Different Regularization Parameters. Computational Intelligence and Neuroscience،Vol. 2016, no. 2016, pp.1-11.
https://search.emarefa.net/detail/BIM-1099671

نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)

Hincapié, Ana-Sofía…[et al.]. MEG Connectivity and Power Detections with Minimum Norm Estimates Require Different Regularization Parameters. Computational Intelligence and Neuroscience Vol. 2016, no. 2016 (2015), pp.1-11.
https://search.emarefa.net/detail/BIM-1099671

نمط استشهاد الجمعية الطبية الأمريكية (AMA)

Hincapié, Ana-Sofía& Kujala, Jan& Mattout, Jérémie& Sébastien, Daligault& Delpuech, Claude& Mery, Domingo…[et al.]. MEG Connectivity and Power Detections with Minimum Norm Estimates Require Different Regularization Parameters. Computational Intelligence and Neuroscience. 2016. Vol. 2016, no. 2016, pp.1-11.
https://search.emarefa.net/detail/BIM-1099671

نوع البيانات

مقالات

لغة النص

الإنجليزية

الملاحظات

Includes bibliographical references

رقم السجل

BIM-1099671