| pubmed-article:18356081 | rdf:type | pubmed:Citation | lld:pubmed |
| pubmed-article:18356081 | lifeskim:mentions | umls-concept:C0007776 | lld:lifeskim |
| pubmed-article:18356081 | lifeskim:mentions | umls-concept:C0022655 | lld:lifeskim |
| pubmed-article:18356081 | lifeskim:mentions | umls-concept:C0001613 | lld:lifeskim |
| pubmed-article:18356081 | lifeskim:mentions | umls-concept:C0441655 | lld:lifeskim |
| pubmed-article:18356081 | lifeskim:mentions | umls-concept:C0475264 | lld:lifeskim |
| pubmed-article:18356081 | lifeskim:mentions | umls-concept:C0679575 | lld:lifeskim |
| pubmed-article:18356081 | pubmed:issue | 4 | lld:pubmed |
| pubmed-article:18356081 | pubmed:dateCreated | 2008-4-14 | lld:pubmed |
| pubmed-article:18356081 | pubmed:abstractText | The spatiotemporal dynamics of cortical oscillations across human brain regions remain poorly understood because of a lack of adequately validated methods for reconstructing such activity from noninvasive electrophysiological data. In this paper, we present a novel adaptive spatial filtering algorithm optimized for robust source time-frequency reconstruction from magnetoencephalography (MEG) and electroencephalography (EEG) data. The efficacy of the method is demonstrated with simulated sources and is also applied to real MEG data from a self-paced finger movement task. The algorithm reliably reveals modulations both in the beta band (12-30 Hz) and high gamma band (65-90 Hz) in sensorimotor cortex. The performance is validated by both across-subjects statistical comparisons and by intracranial electrocorticography (ECoG) data from two epilepsy patients. Interestingly, we also reliably observed high frequency activity (30-300 Hz) in the cerebellum, although with variable locations and frequencies across subjects. The proposed algorithm is highly parallelizable and runs efficiently on modern high-performance computing clusters. This method enables the ultimate promise of MEG and EEG for five-dimensional imaging of space, time, and frequency activity in the brain and renders it applicable for widespread studies of human cortical dynamics during cognition. | lld:pubmed |
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| pubmed-article:18356081 | pubmed:language | eng | lld:pubmed |
| pubmed-article:18356081 | pubmed:journal | http://linkedlifedata.com/r... | lld:pubmed |
| pubmed-article:18356081 | pubmed:citationSubset | IM | lld:pubmed |
| pubmed-article:18356081 | pubmed:status | MEDLINE | lld:pubmed |
| pubmed-article:18356081 | pubmed:month | May | lld:pubmed |
| pubmed-article:18356081 | pubmed:issn | 1053-8119 | lld:pubmed |
| pubmed-article:18356081 | pubmed:author | pubmed-author:KnightRobert... | lld:pubmed |
| pubmed-article:18356081 | pubmed:author | pubmed-author:SekiharaKensu... | lld:pubmed |
| pubmed-article:18356081 | pubmed:author | pubmed-author:NagarajanSrik... | lld:pubmed |
| pubmed-article:18356081 | pubmed:author | pubmed-author:BergerMitchel... | lld:pubmed |
| pubmed-article:18356081 | pubmed:author | pubmed-author:BarbaroNichol... | lld:pubmed |
| pubmed-article:18356081 | pubmed:author | pubmed-author:GuggisbergAdr... | lld:pubmed |
| pubmed-article:18356081 | pubmed:author | pubmed-author:KirschHeidi... | lld:pubmed |
| pubmed-article:18356081 | pubmed:author | pubmed-author:EdwardsErikE | lld:pubmed |
| pubmed-article:18356081 | pubmed:author | pubmed-author:DalalSarang... | lld:pubmed |
| pubmed-article:18356081 | pubmed:author | pubmed-author:FindlayAnne... | lld:pubmed |
| pubmed-article:18356081 | pubmed:author | pubmed-author:CanoltyRyan... | lld:pubmed |
| pubmed-article:18356081 | pubmed:issnType | Print | lld:pubmed |
| pubmed-article:18356081 | pubmed:day | 1 | lld:pubmed |
| pubmed-article:18356081 | pubmed:volume | 40 | lld:pubmed |
| pubmed-article:18356081 | pubmed:owner | NLM | lld:pubmed |
| pubmed-article:18356081 | pubmed:authorsComplete | Y | lld:pubmed |
| pubmed-article:18356081 | pubmed:pagination | 1686-700 | lld:pubmed |
| pubmed-article:18356081 | pubmed:dateRevised | 2011-5-23 | lld:pubmed |
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