[ { "key": "VZQXJXA8", "version": 8199, "library": { "type": "group", "id": 16389, "name": "MEEG SourceLoc", "links": { "alternate": { "href": "https://www.zotero.org/groups/meeg_sourceloc", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/16389/items/VZQXJXA8", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/meeg_sourceloc/items/VZQXJXA8", "type": "text/html" } }, "meta": { "createdByUser": { "id": 224520, "username": "ErickOrtiz", "name": "", "links": { "alternate": { "href": "https://www.zotero.org/erickortiz", "type": "text/html" } } }, "creatorSummary": "Henson et al.", "parsedDate": "2009-05-15", "numChildren": 0 }, "data": { "key": "VZQXJXA8", "version": 8199, "itemType": "journalArticle", "title": "Selecting forward models for MEG source-reconstruction using model-evidence", "creators": [ { "creatorType": "author", "firstName": "R.N.", "lastName": "Henson" }, { "creatorType": "author", "firstName": "J.", "lastName": "Mattout" }, { "creatorType": "author", "firstName": "C.", "lastName": "Phillips" }, { "creatorType": "author", "firstName": "K.J.", "lastName": "Friston" } ], "abstractNote": "We investigated four key aspects of forward models for distributed solutions to the MEG inverse problem: 1) the nature of the cortical mesh constraining sources (derived from an individual's MRI, or inverse-normalised from a template mesh); 2) the use of single-sphere, overlapping spheres, or Boundary Element Model (BEM) head-models; 3) the density of the cortical mesh (3000 vs. 7000 vertices); and 4) whether source orientations were constrained to be normal to that mesh. These were compared within the context of two types of spatial prior on the sources: a single prior corresponding to a standard L2-minimum-norm (MNM) inversion, or multiple sparse priors (MSP). The resulting generative models were compared using a free-energy approximation to the Bayesian model-evidence after fitting multiple epochs of responses to faces or scrambled faces. Statistical tests of the free-energy, across nine participants, showed clear superiority of MSP over MNM models; with the former reconstructing deeper sources. Furthermore, there was 1) no evidence that an individually-defined cortical mesh was superior to an inverse-normalised canonical mesh, but 2) clear evidence that a BEM was superior to spherical head-models, provided individually-defined inner skull and scalp meshes were used. Finally, for MSP models, there was evidence that the combination of 3) higher density cortical meshes and 4) dipoles constrained to be normal to the mesh was superior to lower-density or freely-oriented sources (in contrast to the MNM models, in which free-orientation was optimal). These results have practical implications for MEG source reconstruction, particularly in the context of group studies.", "publicationTitle": "NeuroImage", "publisher": "", "place": "", "date": "May 15, 2009", "volume": "46", "issue": "1", "section": "", "partNumber": "", "partTitle": "", "pages": "168-176", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "", "DOI": "10.1016/j.neuroimage.2009.01.062", "citationKey": "", "url": "http://www.sciencedirect.com/science/article/pii/S1053811909000925", "accessDate": "2012-04-17T16:05:27Z", "PMID": "", "PMCID": "", "ISSN": "1053-8119", "archive": "", "archiveLocation": "", "shortTitle": "", "language": "", "libraryCatalog": "ScienceDirect", "callNumber": "0025", "rights": "", "extra": "", "tags": [ { "tag": "Bayesian", "type": 1 }, { "tag": "Inverse problem", "type": 1 }, { "tag": "Magnetoencephalography", "type": 1 } ], "collections": [], "relations": {}, "dateAdded": "2012-04-17T16:05:41Z", "dateModified": "2013-05-23T05:48:10Z" } }, { "key": "VT29P6UV", "version": 8199, "library": { "type": "group", "id": 16389, "name": "MEEG SourceLoc", "links": { "alternate": { "href": "https://www.zotero.org/groups/meeg_sourceloc", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/16389/items/VT29P6UV", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/meeg_sourceloc/items/VT29P6UV", "type": "text/html" } }, "meta": { "createdByUser": { "id": 224520, "username": "ErickOrtiz", "name": "", "links": { "alternate": { "href": "https://www.zotero.org/erickortiz", "type": "text/html" } } }, "creatorSummary": "Brookes et al.", "parsedDate": "2011-06-01", "numChildren": 0 }, "data": { "key": "VT29P6UV", "version": 8199, "itemType": "journalArticle", "title": "Measuring functional connectivity using MEG: Methodology and comparison with fcMRI", "creators": [ { "creatorType": "author", "firstName": "Matthew J.", "lastName": "Brookes" }, { "creatorType": "author", "firstName": "Joanne R.", "lastName": "Hale" }, { "creatorType": "author", "firstName": "Johanna M.", "lastName": "Zumer" }, { "creatorType": "author", "firstName": "Claire M.", "lastName": "Stevenson" }, { "creatorType": "author", "firstName": "Susan T.", "lastName": "Francis" }, { "creatorType": "author", "firstName": "Gareth R.", "lastName": "Barnes" }, { "creatorType": "author", "firstName": "Julia P.", "lastName": "Owen" }, { "creatorType": "author", "firstName": "Peter G.", "lastName": "Morris" }, { "creatorType": "author", "firstName": "Srikantan S.", "lastName": "Nagarajan" } ], "abstractNote": "Functional connectivity (FC) between brain regions is thought to be central to the way in which the brain processes information. Abnormal connectivity is thought to be implicated in a number of diseases. The ability to study FC is therefore a key goal for neuroimaging. Functional connectivity (fc) MRI has become a popular tool to make connectivity measurements but the technique is limited by its indirect nature. A multimodal approach is therefore an attractive means to investigate the electrodynamic mechanisms underlying hemodynamic connectivity. In this paper, we investigate resting state FC using fcMRI and magnetoencephalography (MEG). In fcMRI, we exploit the advantages afforded by ultra high magnetic field. In MEG we apply envelope correlation and coherence techniques to source space projected MEG signals. We show that beamforming provides an excellent means to measure FC in source space using MEG data. However, care must be taken when interpreting these measurements since cross talk between voxels in source space can potentially lead to spurious connectivity and this must be taken into account in all studies of this type. We show good spatial agreement between FC measured independently using MEG and fcMRI; FC between sensorimotor cortices was observed using both modalities, with the best spatial agreement when MEG data are filtered into the β band. This finding helps to reduce the potential confounds associated with each modality alone: while it helps reduce the uncertainties in spatial patterns generated by MEG (brought about by the ill posed inverse problem), addition of electrodynamic metric confirms the neural basis of fcMRI measurements. Finally, we show that multiple MEG based FC metrics allow the potential to move beyond what is possible using fcMRI, and investigate the nature of electrodynamic connectivity. Our results extend those from previous studies and add weight to the argument that neural oscillations are intimately related to functional connectivity and the BOLD response.", "publicationTitle": "NeuroImage", "publisher": "", "place": "", "date": "June 1, 2011", "volume": "56", "issue": "3", "section": "", "partNumber": "", "partTitle": "", "pages": "1082-1104", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "", "DOI": "10.1016/j.neuroimage.2011.02.054", "citationKey": "", "url": "http://www.sciencedirect.com/science/article/pii/S1053811911002102", "accessDate": "2012-02-10T14:40:55Z", "PMID": "", "PMCID": "", "ISSN": "1053-8119", "archive": "", "archiveLocation": "", "shortTitle": "Measuring functional connectivity using MEG", "language": "", "libraryCatalog": "ScienceDirect", "callNumber": "0000", "rights": "", "extra": "", "tags": [ { "tag": "7T", "type": 1 }, { "tag": "BOLD", "type": 1 }, { "tag": "Coherence", "type": 1 }, { "tag": "Envelope correlation", "type": 1 }, { "tag": "Functional connectivity", "type": 1 }, { "tag": "Imaginary coherence", "type": 1 }, { "tag": "MEG", "type": 1 }, { "tag": "Neural oscillations", "type": 1 }, { "tag": "Resting state", "type": 1 }, { "tag": "fMRI", "type": 1 } ], "collections": [], "relations": {}, "dateAdded": "2012-02-10T14:58:47Z", "dateModified": "2013-05-23T05:48:09Z" } }, { "key": "VGFC7WC9", "version": 8199, "library": { "type": "group", "id": 16389, "name": "MEEG SourceLoc", "links": { "alternate": { "href": "https://www.zotero.org/groups/meeg_sourceloc", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/16389/items/VGFC7WC9", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/meeg_sourceloc/items/VGFC7WC9", "type": "text/html" } }, "meta": { "createdByUser": { "id": 224520, "username": "ErickOrtiz", "name": "", "links": { "alternate": { "href": "https://www.zotero.org/erickortiz", "type": "text/html" } } }, "creatorSummary": "Friston et al.", "parsedDate": "2006-07", "numChildren": 0 }, "data": { "key": "VGFC7WC9", "version": 8199, "itemType": "journalArticle", "title": "A free energy principle for the brain", "creators": [ { "creatorType": "author", "firstName": "Karl", "lastName": "Friston" }, { "creatorType": "author", "firstName": "James", "lastName": "Kilner" }, { "creatorType": "author", "firstName": "Lee", "lastName": "Harrison" } ], "abstractNote": "By formulating Helmholtz's ideas about perception, in terms of modern-day theories, one arrives at a model of perceptual inference and learning that can explain a remarkable range of neurobiological facts: using constructs from statistical physics, the problems of inferring the causes of sensory input and learning the causal structure of their generation can be resolved using exactly the same principles. Furthermore, inference and learning can proceed in a biologically plausible fashion. The ensuing scheme rests on Empirical Bayes and hierarchical models of how sensory input is caused. The use of hierarchical models enables the brain to construct prior expectations in a dynamic and context-sensitive fashion. This scheme provides a principled way to understand many aspects of cortical organisation and responses.\nIn this paper, we show these perceptual processes are just one aspect of emergent behaviours of systems that conform to a free energy principle. The free energy considered here measures the difference between the probability distribution of environmental quantities that act on the system and an arbitrary distribution encoded by its configuration. The system can minimise free energy by changing its configuration to affect the way it samples the environment or change the distribution it encodes. These changes correspond to action and perception respectively and lead to an adaptive exchange with the environment that is characteristic of biological systems. This treatment assumes that the system's state and structure encode an implicit and probabilistic model of the environment. We will look at the models entailed by the brain and how minimisation of its free energy can explain its dynamics and structure.", "publicationTitle": "Journal of Physiology-Paris", "publisher": "", "place": "", "date": "July 2006", "volume": "100", "issue": "1-3", "section": "", "partNumber": "", "partTitle": "", "pages": "70-87", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "", "DOI": "10.1016/j.jphysparis.2006.10.001", "citationKey": "", "url": "http://www.sciencedirect.com/science/article/B6VMC-4MBC500-1/2/5a0b34eb07e1bee0fb388ff38997dcd9", "accessDate": "2010-04-30T11:44:32Z", "PMID": "", "PMCID": "", "ISSN": "0928-4257", "archive": "", "archiveLocation": "", "shortTitle": "", "language": "", "libraryCatalog": "ScienceDirect", "callNumber": "0132", "rights": "", "extra": "", "tags": [ { "tag": "Action", "type": 1 }, { "tag": "Attention", "type": 1 }, { "tag": "Free energy", "type": 1 }, { "tag": "Hierarchical", "type": 1 }, { "tag": "Inference", "type": 1 }, { "tag": "Learning", "type": 1 }, { "tag": "Perception", "type": 1 }, { "tag": "Selection", "type": 1 }, { "tag": "Variational Bayes", "type": 1 } ], "collections": [], "relations": {}, "dateAdded": "2010-04-30T11:57:32Z", "dateModified": "2013-05-23T05:48:08Z" } }, { "key": "UURGQPUB", "version": 8199, "library": { "type": "group", "id": 16389, "name": "MEEG SourceLoc", "links": { "alternate": { "href": "https://www.zotero.org/groups/meeg_sourceloc", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/16389/items/UURGQPUB", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/meeg_sourceloc/items/UURGQPUB", "type": "text/html" } }, "meta": { "createdByUser": { "id": 224520, "username": "ErickOrtiz", "name": "", "links": { "alternate": { "href": "https://www.zotero.org/erickortiz", "type": "text/html" } } }, "creatorSummary": "Wipf et al.", "parsedDate": "2010-01-01", "numChildren": 0 }, "data": { "key": "UURGQPUB", "version": 8199, "itemType": "journalArticle", "title": "Robust Bayesian estimation of the location, orientation, and time course of multiple correlated neural sources using MEG", "creators": [ { "creatorType": "author", "firstName": "David P.", "lastName": "Wipf" }, { "creatorType": "author", "firstName": "Julia P.", "lastName": "Owen" }, { "creatorType": "author", "firstName": "Hagai T.", "lastName": "Attias" }, { "creatorType": "author", "firstName": "Kensuke", "lastName": "Sekihara" }, { "creatorType": "author", "firstName": "Srikantan S.", "lastName": "Nagarajan" } ], "abstractNote": "The synchronous brain activity measured via MEG (or EEG) can be interpreted as arising from a collection (possibly large) of current dipoles or sources located throughout the cortex. Estimating the number, location, and time course of these sources remains a challenging task, one that is significantly compounded by the effects of source correlations and unknown orientations and by the presence of interference from spontaneous brain activity, sensor noise, and other artifacts. This paper derives an empirical Bayesian method for addressing each of these issues in a principled fashion. The resulting algorithm guarantees descent of a cost function uniquely designed to handle unknown orientations and arbitrary correlations. Robust interference suppression is also easily incorporated. In a restricted setting, the proposed method is shown to produce theoretically zero reconstruction error estimating multiple dipoles even in the presence of strong correlations and unknown orientations, unlike a variety of existing Bayesian localization methods or common signal processing techniques such as beamforming and sLORETA. Empirical results on both simulated and real data sets verify the efficacy of this approach.", "publicationTitle": "NeuroImage", "publisher": "", "place": "", "date": "January 1, 2010", "volume": "49", "issue": "1", "section": "", "partNumber": "", "partTitle": "", "pages": "641-655", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "", "DOI": "10.1016/j.neuroimage.2009.06.083", "citationKey": "", "url": "http://www.sciencedirect.com/science/article/B6WNP-4WR66FW-1/2/455e6ca52793ab9a75781acb786fa8db", "accessDate": "2010-04-21T13:12:16Z", "PMID": "", "PMCID": "", "ISSN": "1053-8119", "archive": "", "archiveLocation": "", "shortTitle": "", "language": "", "libraryCatalog": "ScienceDirect", "callNumber": "0000", "rights": "", "extra": "", "tags": [], "collections": [], "relations": {}, "dateAdded": "2010-04-21T14:14:11Z", "dateModified": "2013-05-23T05:48:07Z" } }, { "key": "UTKDFT3P", "version": 8199, "library": { "type": "group", "id": 16389, "name": "MEEG SourceLoc", "links": { "alternate": { "href": "https://www.zotero.org/groups/meeg_sourceloc", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/16389/items/UTKDFT3P", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/meeg_sourceloc/items/UTKDFT3P", "type": "text/html" } }, "meta": { "createdByUser": { "id": 224520, "username": "ErickOrtiz", "name": "", "links": { "alternate": { "href": "https://www.zotero.org/erickortiz", "type": "text/html" } } }, "creatorSummary": "Diwakar et al.", "parsedDate": "2011-01-01", "numChildren": 0 }, "data": { "key": "UTKDFT3P", "version": 8199, "itemType": "journalArticle", "title": "Dual-Core Beamformer for obtaining highly correlated neuronal networks in MEG", "creators": [ { "creatorType": "author", "firstName": "Mithun", "lastName": "Diwakar" }, { "creatorType": "author", "firstName": "Ming-Xiong", "lastName": "Huang" }, { "creatorType": "author", "firstName": "Ramesh", "lastName": "Srinivasan" }, { "creatorType": "author", "firstName": "Deborah L.", "lastName": "Harrington" }, { "creatorType": "author", "firstName": "Ashley", "lastName": "Robb" }, { "creatorType": "author", "firstName": "Annemarie", "lastName": "Angeles" }, { "creatorType": "author", "firstName": "Laura", "lastName": "Muzzatti" }, { "creatorType": "author", "firstName": "Reza", "lastName": "Pakdaman" }, { "creatorType": "author", "firstName": "Tao", "lastName": "Song" }, { "creatorType": "author", "firstName": "Rebecca J.", "lastName": "Theilmann" }, { "creatorType": "author", "firstName": "Roland R.", "lastName": "Lee" } ], "abstractNote": "The \"Dual-Core Beamformer\" (DCBF) is a new lead-field based MEG inverse-modeling technique designed for localizing highly correlated networks from noisy MEG data. Conventional beamformer techniques are successful in localizing neuronal sources that are uncorrelated under poor signal-to-noise ratio (SNR) conditions. However, they fail to reconstruct multiple highly correlated sources. Though previously published dual-beamformer techniques can successfully localize multiple correlated sources, they are computationally expensive and impractical, requiring a priori information. The DCBF is able to automatically calculate optimal amplitude-weighting and dipole orientation for reconstruction, greatly reducing the computational cost of the dual-beamformer technique. Paired with a modified Powell algorithm, the DCBF can quickly identify multiple sets of correlated sources contributing to the MEG signal. Through computer simulations, we show that the DCBF quickly and accurately reconstructs source locations and their time-courses under widely varying SNR, degrees of correlation, and source strengths. Simulations also show that the DCBF identifies multiple simultaneously active correlated networks. Additionally, DCBF performance was tested using MEG data in humans. In an auditory task, the DCBF localized and reconstructed highly correlated left and right auditory responses. In a median-nerve stimulation task, the DCBF identified multiple meaningful networks of activation without any a priori information. Altogether, our results indicate that the DCBF is an effective and valuable tool for reconstructing correlated networks of neural activity from MEG recordings.", "publicationTitle": "NeuroImage", "publisher": "", "place": "", "date": "January 1, 2011", "volume": "54", "issue": "1", "section": "", "partNumber": "", "partTitle": "", "pages": "253-263", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "", "DOI": "10.1016/j.neuroimage.2010.07.023", "citationKey": "", "url": "http://www.sciencedirect.com/science/article/B6WNP-50JHBVM-1/2/580d15bd15d5a692cbf59ef275f17421", "accessDate": "2011-01-25T10:23:02Z", "PMID": "", "PMCID": "", "ISSN": "1053-8119", "archive": "", "archiveLocation": "", "shortTitle": "", "language": "", "libraryCatalog": "ScienceDirect", "callNumber": "0000", "rights": "", "extra": "", "tags": [], "collections": [], "relations": {}, "dateAdded": "2011-01-25T10:44:55Z", "dateModified": "2013-05-23T05:48:06Z" } }, { "key": "UNKJ2P7F", "version": 8199, "library": { "type": "group", "id": 16389, "name": "MEEG SourceLoc", "links": { "alternate": { "href": "https://www.zotero.org/groups/meeg_sourceloc", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/16389/items/UNKJ2P7F", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/meeg_sourceloc/items/UNKJ2P7F", "type": "text/html" } }, "meta": { "createdByUser": { "id": 224520, "username": "ErickOrtiz", "name": "", "links": { "alternate": { "href": "https://www.zotero.org/erickortiz", "type": "text/html" } } }, "creatorSummary": "Haueisen et al.", "parsedDate": "2002-01", "numChildren": 0 }, "data": { "key": "UNKJ2P7F", "version": 8199, "itemType": "journalArticle", "title": "The Influence of Brain Tissue Anisotropy on Human EEG and MEG", "creators": [ { "creatorType": "author", "firstName": "J.", "lastName": "Haueisen" }, { "creatorType": "author", "firstName": "D.S.", "lastName": "Tuch" }, { "creatorType": "author", "firstName": "C.", "lastName": "Ramon" }, { "creatorType": "author", "firstName": "P.H.", "lastName": "Schimpf" }, { "creatorType": "author", "firstName": "V.J.", "lastName": "Wedeen" }, { "creatorType": "author", "firstName": "J.S.", "lastName": "George" }, { "creatorType": "author", "firstName": "J.W.", "lastName": "Belliveau" } ], "abstractNote": "The influence of gray and white matter tissue anisotropy on the human electroencephalogram (EEG) and magnetoencephalogram (MEG) was examined with a high resolution finite element model of the head of an adult male subject. The conductivity tensor data for gray and white matter were estimated from magnetic resonance diffusion tensor imaging. Simulations were carried out with single dipoles or small extended sources in the cortical gray matter. The inclusion of anisotropic volume conduction in the brain was found to have a minor influence on the topology of EEG and MEG (and hence source localization). We found a major influence on the amplitude of EEG and MEG (and hence source strength estimation) due to the change in conductivity and the inclusion of anisotropy. We expect that inclusion of tissue anisotropy information will improve source estimation procedures.", "publicationTitle": "NeuroImage", "publisher": "", "place": "", "date": "January 2002", "volume": "15", "issue": "1", "section": "", "partNumber": "", "partTitle": "", "pages": "159-166", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "", "DOI": "10.1006/nimg.2001.0962", "citationKey": "", "url": "http://www.sciencedirect.com/science/article/pii/S1053811901909620", "accessDate": "2012-02-01T13:46:49Z", "PMID": "", "PMCID": "", "ISSN": "1053-8119", "archive": "", "archiveLocation": "", "shortTitle": "", "language": "", "libraryCatalog": "ScienceDirect", "callNumber": "0000", "rights": "", "extra": "", "tags": [], "collections": [], "relations": {}, "dateAdded": "2012-02-01T13:52:13Z", "dateModified": "2013-05-23T05:47:03Z" } }, { "key": "UN24KE3R", "version": 8199, "library": { "type": "group", "id": 16389, "name": "MEEG SourceLoc", "links": { "alternate": { "href": "https://www.zotero.org/groups/meeg_sourceloc", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/16389/items/UN24KE3R", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/meeg_sourceloc/items/UN24KE3R", "type": "text/html" } }, "meta": { "createdByUser": { "id": 224520, "username": "ErickOrtiz", "name": "", "links": { "alternate": { "href": "https://www.zotero.org/erickortiz", "type": "text/html" } } }, "creatorSummary": "Darvas et al.", "parsedDate": "2004", "numChildren": 0 }, "data": { "key": "UN24KE3R", "version": 8199, "itemType": "journalArticle", "title": "Mapping human brain function with MEG and EEG: methods and validation", "creators": [ { "creatorType": "author", "firstName": "F.", "lastName": "Darvas" }, { "creatorType": "author", "firstName": "D.", "lastName": "Pantazis" }, { "creatorType": "author", "firstName": "E.", "lastName": "Kucukaltun-Yildirim" }, { "creatorType": "author", "firstName": "R.M.", "lastName": "Leahy" } ], "abstractNote": "We survey the field of magnetoencephalography (MEG) and electroencephalography (EEG) source estimation. These modalities offer the potential for functional brain mapping with temporal resolution in the millisecond range. However, the limited number of spatial measurements and the ill-posedness of the inverse problem present significant limits to our ability to produce accurate spatial maps from these data without imposing major restrictions on the form of the inverse solution. Here we describe approaches to solving the forward problem of computing the mapping from putative inverse solutions into the data space. We then describe the inverse problem in terms of low dimensional solutions, based on the equivalent current dipole (ECD), and high dimensional solutions, in which images of neural activation are constrained to the cerebral cortex. We also address the issue of objective assessment of the relative performance of inverse procedures by the free-response receiver operating characteristic (FROC) curve. We conclude with a discussion of methods for assessing statistical significance of experimental results through use of the bootstrap for determining confidence regions in dipole-fitting methods, and random field (RF) and permutation methods for detecting significant activation in cortically constrained imaging studies.", "publicationTitle": "NeuroImage", "publisher": "", "place": "", "date": "2004", "volume": "23", "issue": "Supplement 1", "section": "", "partNumber": "", "partTitle": "", "pages": "S289-S299", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "", "DOI": "10.1016/j.neuroimage.2004.07.014", "citationKey": "", "url": "http://www.sciencedirect.com/science/article/B6WNP-4DD8GN8-1/2/8da7e6085e6f0b2a1fd72bb84640a4e6", "accessDate": "2010-07-26T16:01:36Z", "PMID": "", "PMCID": "", "ISSN": "1053-8119", "archive": "", "archiveLocation": "", "shortTitle": "Mapping human brain function with MEG and EEG", "language": "", "libraryCatalog": "ScienceDirect", "callNumber": "0000", "rights": "", "extra": "", "tags": [ { "tag": "Dipole fitting", "type": 1 }, { "tag": "EEG", "type": 1 }, { "tag": "Imaging methods", "type": 1 }, { "tag": "Inverse methods", "type": 1 }, { "tag": "MEG", "type": 1 }, { "tag": "ROC analysis", "type": 1 }, { "tag": "Statistical parametric tests", "type": 1 } ], "collections": [], "relations": {}, "dateAdded": "2010-07-26T16:01:53Z", "dateModified": "2013-05-23T05:47:02Z" } }, { "key": "U4XBNTTP", "version": 8199, "library": { "type": "group", "id": 16389, "name": "MEEG SourceLoc", "links": { "alternate": { "href": "https://www.zotero.org/groups/meeg_sourceloc", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/16389/items/U4XBNTTP", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/meeg_sourceloc/items/U4XBNTTP", "type": "text/html" } }, "meta": { "createdByUser": { "id": 224520, "username": "ErickOrtiz", "name": "", "links": { "alternate": { "href": "https://www.zotero.org/erickortiz", "type": "text/html" } } }, "creatorSummary": "Gross et al.", "parsedDate": "2004", "numChildren": 0 }, "data": { "key": "U4XBNTTP", "version": 8199, "itemType": "journalArticle", "title": "Modulation of long-range neural synchrony reflects temporal limitations of visual attention in humans", "creators": [ { "creatorType": "author", "firstName": "Joachim", "lastName": "Gross" }, { "creatorType": "author", "firstName": "Frank", "lastName": "Schmitz" }, { "creatorType": "author", "firstName": "Irmtraud", "lastName": "Schnitzler" }, { "creatorType": "author", "firstName": "Klaus", "lastName": "Kessler" }, { "creatorType": "author", "firstName": "Kimron", "lastName": "Shapiro" }, { "creatorType": "author", "firstName": "Bernhard", "lastName": "Hommel" }, { "creatorType": "author", "firstName": "Alfons", "lastName": "Schnitzler" } ], "abstractNote": "Because of attentional limitations, the human visual system can process for awareness and response only a fraction of the input received. Lesion and functional imaging studies have identified frontal, temporal, and parietal areas as playing a major role in the attentional control of visual processing, but very little is known about how these areas interact to form a dynamic attentional network. We hypothesized that the network communicates by means of neural phase synchronization, and we used magnetoencephalography to study transient long-range interarea phase coupling in a well studied attentionally taxing dual-target task (attentional blink). Our results reveal that communication within the fronto-parieto-temporal attentional network proceeds via transient long-range phase synchronization in the beta band. Changes in synchronization reflect changes in the attentional demands of the task and are directly related to behavioral performance. Thus, we show how attentional limitations arise from the way in which the subsystems of the attentional network interact.", "publicationTitle": "Proceedings of the National Academy of Sciences of the United States of America", "publisher": "", "place": "", "date": "2004", "volume": "101", "issue": "35", "section": "", "partNumber": "", "partTitle": "", "pages": "13050 -13055", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "", "DOI": "10.1073/pnas.0404944101", "citationKey": "", "url": "http://www.pnas.org/content/101/35/13050.abstract", "accessDate": "2011-11-14T09:59:06Z", "PMID": "", "PMCID": "", "ISSN": "", "archive": "", "archiveLocation": "", "shortTitle": "", "language": "", "libraryCatalog": "Highwire 2.0", "callNumber": "0000", "rights": "", "extra": "", "tags": [], "collections": [], "relations": {}, "dateAdded": "2011-11-14T09:59:13Z", "dateModified": "2013-05-23T05:47:01Z" } }, { "key": "TZ3B2HFK", "version": 8199, "library": { "type": "group", "id": 16389, "name": "MEEG SourceLoc", "links": { "alternate": { "href": "https://www.zotero.org/groups/meeg_sourceloc", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/16389/items/TZ3B2HFK", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/meeg_sourceloc/items/TZ3B2HFK", "type": "text/html" } }, "meta": { "createdByUser": { "id": 224520, "username": "ErickOrtiz", "name": "", "links": { "alternate": { "href": "https://www.zotero.org/erickortiz", "type": "text/html" } } }, "creatorSummary": "Hillebrand and Barnes", "parsedDate": "2005", "numChildren": 0 }, "data": { "key": "TZ3B2HFK", "version": 8199, "itemType": "bookSection", "title": "Beamformer Analysis of MEG Data", "creators": [ { "creatorType": "author", "firstName": "Arjan", "lastName": "Hillebrand" }, { "creatorType": "author", "firstName": "Gareth R.", "lastName": "Barnes" }, { "creatorType": "contributor", "firstName": "", "lastName": "Hubert Preissl" } ], "abstractNote": "", "bookTitle": "Magnetoencephalography", "series": "", "seriesNumber": "", "volume": "Volume 68", "numberOfVolumes": "", "edition": "", "date": "2005", "publisher": "Academic Press", "place": "", "originalDate": "", "originalPublisher": "", "originalPlace": "", "format": "", "pages": "149-171", "ISBN": "0074-7742", "DOI": "", "citationKey": "", "url": "http://www.sciencedirect.com/science/article/pii/S0074774205680063", "accessDate": "2012-06-28T08:29:34Z", "ISSN": "", "archive": "", "archiveLocation": "", "shortTitle": "", "language": "", "libraryCatalog": "ScienceDirect", "callNumber": "0000", "rights": "", "extra": "", "tags": [], "collections": [], "relations": {}, "dateAdded": "2012-06-28T08:30:30Z", "dateModified": "2013-05-23T05:47:00Z" } }, { "key": "TWP3DS4D", "version": 8199, "library": { "type": "group", "id": 16389, "name": "MEEG SourceLoc", "links": { "alternate": { "href": "https://www.zotero.org/groups/meeg_sourceloc", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/16389/items/TWP3DS4D", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/meeg_sourceloc/items/TWP3DS4D", "type": "text/html" } }, "meta": { "createdByUser": { "id": 224520, "username": "ErickOrtiz", "name": "", "links": { "alternate": { "href": "https://www.zotero.org/erickortiz", "type": "text/html" } } }, "creatorSummary": "Hui et al.", "parsedDate": "2010-02-15", "numChildren": 0 }, "data": { "key": "TWP3DS4D", "version": 8199, "itemType": "journalArticle", "title": "Identifying true cortical interactions in MEG using the nulling beamformer", "creators": [ { "creatorType": "author", "firstName": "Hua Brian", "lastName": "Hui" }, { "creatorType": "author", "firstName": "Dimitrios", "lastName": "Pantazis" }, { "creatorType": "author", "firstName": "Steven L.", "lastName": "Bressler" }, { "creatorType": "author", "firstName": "Richard M.", "lastName": "Leahy" } ], "abstractNote": "Modeling functional brain interaction networks using non-invasive EEG and MEG data is more challenging than using intracranial recording data. This is because most interaction measures are not robust to the cross-talk (interference) between cortical regions, which may arise due to the limited spatial resolution of EEG/MEG inverse procedures. In this article, we describe a modified beamforming approach to accurately measure cortical interactions from EEG/MEG data, designed to suppress cross-talk between cortical regions. We estimate interaction measures from the output of the modified beamformer and test for statistical significance using permutation tests. Since the underlying neuronal sources and their interactions are unknown in real MEG data, we demonstrate the performance of the proposed beamforming method in a novel simulation scheme, where intracranial recordings from a macaque monkey are used as neural sources to simulate realistic MEG signals. The advantage of this approach is that local field potentials are more realistic representations of true neuronal sources than simulation models and therefore are more suitable to indicate the performance of our nulling beamforming method.", "publicationTitle": "NeuroImage", "publisher": "", "place": "", "date": "February 15, 2010", "volume": "49", "issue": "4", "section": "", "partNumber": "", "partTitle": "", "pages": "3161-3174", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "", "DOI": "10.1016/j.neuroimage.2009.10.078", "citationKey": "", "url": "http://www.sciencedirect.com/science/article/B6WNP-4XMD5K4-2/2/326cc2688eeca14393583e89097717b2", "accessDate": "2010-11-05T13:17:03Z", "PMID": "", "PMCID": "", "ISSN": "1053-8119", "archive": "", "archiveLocation": "", "shortTitle": "", "language": "", "libraryCatalog": "ScienceDirect", "callNumber": "0000", "rights": "", "extra": "", "tags": [ { "tag": "Coherence", "type": 1 }, { "tag": "Cortical interaction", "type": 1 }, { "tag": "Functional connectivity", "type": 1 }, { "tag": "Granger causality", "type": 1 }, { "tag": "LCMV", "type": 1 }, { "tag": "MVAR", "type": 1 }, { "tag": "Nulling beamformer", "type": 1 }, { "tag": "Phase synchrony", "type": 1 } ], "collections": [], "relations": {}, "dateAdded": "2011-01-25T10:44:26Z", "dateModified": "2013-05-23T05:46:59Z" } }, { "key": "TW597Q87", "version": 8199, "library": { "type": "group", "id": 16389, "name": "MEEG SourceLoc", "links": { "alternate": { "href": "https://www.zotero.org/groups/meeg_sourceloc", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/16389/items/TW597Q87", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/meeg_sourceloc/items/TW597Q87", "type": "text/html" } }, "meta": { "createdByUser": { "id": 224520, "username": "ErickOrtiz", "name": "", "links": { "alternate": { "href": "https://www.zotero.org/erickortiz", "type": "text/html" } } }, "creatorSummary": "Baillet et al.", "parsedDate": "1999", "numChildren": 0 }, "data": { "key": "TW597Q87", "version": 8199, "itemType": "journalArticle", "title": "Combined MEG and EEG source imaging by minimization of mutual information", "creators": [ { "creatorType": "author", "firstName": "S.", "lastName": "Baillet" }, { "creatorType": "author", "firstName": "L.", "lastName": "Garnero" }, { "creatorType": "author", "firstName": "G.", "lastName": "Marin" }, { "creatorType": "author", "firstName": "J.-P.", "lastName": "Hugonin" } ], "abstractNote": "Though very frequently assumed, the necessity to operate a joint processing of simultaneous magnetoencephalography (MEG) and electroencephalography (EEG) recordings for functional brain imaging has never been clearly demonstrated. However, the very last generation of MEG instruments allows the simultaneous recording of brain magnetic fields and electrical potentials on the scalp. But the general fear regarding the fusion between MEG and EEG data is that the drawbacks from one modality will systematically spoil the performances of the other one without any consequent improvement. This is the case for instance for the estimation of deeper or radial sources with MEG. In this paper, the authors propose a method for a cooperative processing of MEG and EEG in a distributed source model. First, the evaluation of the respective performances of each modality for the estimation of every dipole in the source pattern is made using a conditional entropy criterion. Then, the algorithm operates a preprocessing of the MEG and EEG gain matrices which minimizes the mutual information between these two transfer functions, by a selective weighting of the MEG and EEG lead fields. This new combined EEG/MEG modality brings major improvements to the localization of active sources, together with reduced sensitivity to perturbations on data.", "publicationTitle": "Biomedical Engineering, IEEE Transactions on", "publisher": "", "place": "", "date": "1999", "volume": "46", "issue": "5", "section": "", "partNumber": "", "partTitle": "", "pages": "522-534", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "Biomedical Engineering, IEEE Transactions on", "DOI": "10.1109/10.759053", "citationKey": "", "url": "", "accessDate": "", "PMID": "", "PMCID": "", "ISSN": "0018-9294", "archive": "", "archiveLocation": "", "shortTitle": "", "language": "", "libraryCatalog": "IEEE Xplore", "callNumber": "0000", "rights": "", "extra": "", "tags": [ { "tag": "Bayes Theorem", "type": 1 }, { "tag": "Brain", "type": 1 }, { "tag": "Electroencephalography", "type": 1 }, { "tag": "Electromagnetic Fields", "type": 1 }, { "tag": "Humans", "type": 1 }, { "tag": "Magnetic Resonance Imaging", "type": 1 }, { "tag": "Magnetoencephalography", "type": 1 }, { "tag": "Models, Neurological", "type": 1 }, { "tag": "Nonlinear dynamics", "type": 1 }, { "tag": "Signal Processing, Computer-Assisted", "type": 1 }, { "tag": "Skull", "type": 1 }, { "tag": "brain functional information", "type": 1 }, { "tag": "combined MEG/EEG source imaging", "type": 1 }, { "tag": "conditional entropy criterion", "type": 1 }, { "tag": "cooperative processing", "type": 1 }, { "tag": "distributed source model", "type": 1 }, { "tag": "entropy", "type": 1 }, { "tag": "medical diagnostic imaging", "type": 1 }, { "tag": "medical image processing", "type": 1 }, { "tag": "minimisation", "type": 1 }, { "tag": "mutual information minimization", "type": 1 }, { "tag": "perturbations sensitivity", "type": 1 }, { "tag": "source pattern dipole", "type": 1 }, { "tag": "source reconstruction", "type": 1 } ], "collections": [], "relations": {}, "dateAdded": "2011-01-20T15:31:17Z", "dateModified": "2013-05-23T05:46:58Z" } }, { "key": "TSVPR52E", "version": 8199, "library": { "type": "group", "id": 16389, "name": "MEEG SourceLoc", "links": { "alternate": { "href": "https://www.zotero.org/groups/meeg_sourceloc", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/16389/items/TSVPR52E", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/meeg_sourceloc/items/TSVPR52E", "type": "text/html" } }, "meta": { "createdByUser": { "id": 224628, "username": "pbela", "name": "", "links": { "alternate": { "href": "https://www.zotero.org/pbela", "type": "text/html" } } }, "lastModifiedByUser": { "id": 224520, "username": "ErickOrtiz", "name": "", "links": { "alternate": { "href": "https://www.zotero.org/erickortiz", "type": "text/html" } } }, "creatorSummary": "Hauk", "parsedDate": "2004", "numChildren": 0 }, "data": { "key": "TSVPR52E", "version": 8199, "itemType": "journalArticle", "title": "Keep it simple: a case for using classical minimum norm estimation in the analysis of EEG and MEG data", "creators": [ { "creatorType": "author", "firstName": "O.", "lastName": "Hauk" } ], "abstractNote": "", "publicationTitle": "Neuroimage", "publisher": "", "place": "", "date": "2004", "volume": "21", "issue": "4", "section": "", "partNumber": "", "partTitle": "", "pages": "1612-1621", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "", "DOI": "", "citationKey": "", "url": "", "accessDate": "", "PMID": "", "PMCID": "", "ISSN": "", "archive": "", "archiveLocation": "", "shortTitle": "", "language": "", "libraryCatalog": "", "callNumber": "0000", "rights": "", "extra": "", "tags": [], "collections": [], "relations": {}, "dateAdded": "2010-07-28T12:24:29Z", "dateModified": "2013-05-23T05:46:57Z" } }, { "key": "T3X6CKKT", "version": 8199, "library": { "type": "group", "id": 16389, "name": "MEEG SourceLoc", "links": { "alternate": { "href": "https://www.zotero.org/groups/meeg_sourceloc", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/16389/items/T3X6CKKT", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/meeg_sourceloc/items/T3X6CKKT", "type": "text/html" } }, "meta": { "createdByUser": { "id": 224520, "username": "ErickOrtiz", "name": "", "links": { "alternate": { "href": "https://www.zotero.org/erickortiz", "type": "text/html" } } }, "creatorSummary": "Friston et al.", "parsedDate": "2007-01-01", "numChildren": 0 }, "data": { "key": "T3X6CKKT", "version": 8199, "itemType": "journalArticle", "title": "Variational free energy and the Laplace approximation", "creators": [ { "creatorType": "author", "firstName": "Karl", "lastName": "Friston" }, { "creatorType": "author", "firstName": "Jérémie", "lastName": "Mattout" }, { "creatorType": "author", "firstName": "Nelson", "lastName": "Trujillo-Barreto" }, { "creatorType": "author", "firstName": "John", "lastName": "Ashburner" }, { "creatorType": "author", "firstName": "Will", "lastName": "Penny" } ], "abstractNote": "This note derives the variational free energy under the Laplace approximation, with a focus on accounting for additional model complexity induced by increasing the number of model parameters. This is relevant when using the free energy as an approximation to the log-evidence in Bayesian model averaging and selection. By setting restricted maximum likelihood (ReML) in the larger context of variational learning and expectation maximisation (EM), we show how the ReML objective function can be adjusted to provide an approximation to the log-evidence for a particular model. This means ReML can be used for model selection, specifically to select or compare models with different covariance components. This is useful in the context of hierarchical models because it enables a principled selection of priors that, under simple hyperpriors, can be used for automatic model selection and relevance determination (ARD). Deriving the ReML objective function, from basic variational principles, discloses the simple relationships among Variational Bayes, EM and ReML. Furthermore, we show that EM is formally identical to a full variational treatment when the precisions are linear in the hyperparameters. Finally, we also consider, briefly, dynamic models and how these inform the regularisation of free energy ascent schemes, like EM and ReML.", "publicationTitle": "NeuroImage", "publisher": "", "place": "", "date": "January 1, 2007", "volume": "34", "issue": "1", "section": "", "partNumber": "", "partTitle": "", "pages": "220-234", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "", "DOI": "10.1016/j.neuroimage.2006.08.035", "citationKey": "", "url": "http://www.sciencedirect.com/science/article/B6WNP-4M57H65-1/2/e0c2e1ec4be9a4bb669a5a791685cc6e", "accessDate": "2010-04-30T11:44:53Z", "PMID": "", "PMCID": "", "ISSN": "1053-8119", "archive": "", "archiveLocation": "", "shortTitle": "", "language": "", "libraryCatalog": "ScienceDirect", "callNumber": "0000", "rights": "", "extra": "", "tags": [ { "tag": "Automatic relevance determination", "type": 1 }, { "tag": "Expectation maximisation", "type": 1 }, { "tag": "Free energy", "type": 1 }, { "tag": "Model selection", "type": 1 }, { "tag": "Relevance vector machines", "type": 1 }, { "tag": "Restricted maximum likelihood", "type": 1 }, { "tag": "Variational Bayes", "type": 1 } ], "collections": [], "relations": {}, "dateAdded": "2010-04-30T11:57:35Z", "dateModified": "2013-05-23T05:46:56Z" } }, { "key": "RRPQAHZ4", "version": 8199, "library": { "type": "group", "id": 16389, "name": "MEEG SourceLoc", "links": { "alternate": { "href": "https://www.zotero.org/groups/meeg_sourceloc", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/16389/items/RRPQAHZ4", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/meeg_sourceloc/items/RRPQAHZ4", "type": "text/html" } }, "meta": { "createdByUser": { "id": 224628, "username": "pbela", "name": "", "links": { "alternate": { "href": "https://www.zotero.org/pbela", "type": "text/html" } } }, "lastModifiedByUser": { "id": 224520, "username": "ErickOrtiz", "name": "", "links": { "alternate": { "href": "https://www.zotero.org/erickortiz", "type": "text/html" } } }, "creatorSummary": "Romani", "parsedDate": "1987", "numChildren": 0 }, "data": { "key": "RRPQAHZ4", "version": 8199, "itemType": "journalArticle", "title": "The inverse problem in MEG studies: an instrumental and analytical perspective", "creators": [ { "creatorType": "author", "firstName": "G. L.", "lastName": "Romani" } ], "abstractNote": "", "publicationTitle": "Physics in Medicine and Biology", "publisher": "", "place": "", "date": "1987", "volume": "32", "issue": "", "section": "", "partNumber": "", "partTitle": "", "pages": "23", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "", "DOI": "", "citationKey": "", "url": "", "accessDate": "", "PMID": "", "PMCID": "", "ISSN": "", "archive": "", "archiveLocation": "", "shortTitle": "", "language": "", "libraryCatalog": "", "callNumber": "0000", "rights": "", "extra": "", "tags": [], "collections": [], "relations": {}, "dateAdded": "2010-07-30T08:33:03Z", "dateModified": "2013-05-23T05:46:55Z" } }, { "key": "RK6C5GCN", "version": 8199, "library": { "type": "group", "id": 16389, "name": "MEEG SourceLoc", "links": { "alternate": { "href": "https://www.zotero.org/groups/meeg_sourceloc", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/16389/items/RK6C5GCN", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/meeg_sourceloc/items/RK6C5GCN", "type": "text/html" } }, "meta": { "createdByUser": { "id": 224520, "username": "ErickOrtiz", "name": "", "links": { "alternate": { "href": "https://www.zotero.org/erickortiz", "type": "text/html" } } }, "creatorSummary": "Hamalainen and Sarvas", "parsedDate": "1987-01-01", "numChildren": 0 }, "data": { "key": "RK6C5GCN", "version": 8199, "itemType": "journalArticle", "title": "Feasibility of the homogeneous head model in the interpretation of neuromagnetic fields", "creators": [ { "creatorType": "author", "firstName": "M S", "lastName": "Hamalainen" }, { "creatorType": "author", "firstName": "J", "lastName": "Sarvas" } ], "abstractNote": "", "publicationTitle": "Physics in Medicine and Biology", "publisher": "", "place": "", "date": "1987-01-01", "volume": "32", "issue": "1", "section": "", "partNumber": "", "partTitle": "", "pages": "91-97", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "", "DOI": "10.1088/0031-9155/32/1/014", "citationKey": "", "url": "http://iopscience.iop.org/0031-9155/32/1/014", "accessDate": "2012-02-02T15:00:38Z", "PMID": "", "PMCID": "", "ISSN": "0031-9155, 1361-6560", "archive": "", "archiveLocation": "", "shortTitle": "", "language": "", "libraryCatalog": "Intitute of Physics", "callNumber": "0000", "rights": "", "extra": "", "tags": [], "collections": [], "relations": {}, "dateAdded": "2012-02-02T15:01:44Z", "dateModified": "2013-05-23T05:46:54Z" } }, { "key": "QWHAQ5MD", "version": 8199, "library": { "type": "group", "id": 16389, "name": "MEEG SourceLoc", "links": { "alternate": { "href": "https://www.zotero.org/groups/meeg_sourceloc", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/16389/items/QWHAQ5MD", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/meeg_sourceloc/items/QWHAQ5MD", "type": "text/html" } }, "meta": { "createdByUser": { "id": 224520, "username": "ErickOrtiz", "name": "", "links": { "alternate": { "href": "https://www.zotero.org/erickortiz", "type": "text/html" } } }, "creatorSummary": "Wolters et al.", "parsedDate": "2006-04-15", "numChildren": 0 }, "data": { "key": "QWHAQ5MD", "version": 8199, "itemType": "journalArticle", "title": "Influence of tissue conductivity anisotropy on EEG/MEG field and return current computation in a realistic head model: A simulation and visualization study using high-resolution finite element modeling", "creators": [ { "creatorType": "author", "firstName": "C.H.", "lastName": "Wolters" }, { "creatorType": "author", "firstName": "A.", "lastName": "Anwander" }, { "creatorType": "author", "firstName": "X.", "lastName": "Tricoche" }, { "creatorType": "author", "firstName": "D.", "lastName": "Weinstein" }, { "creatorType": "author", "firstName": "M.A.", "lastName": "Koch" }, { "creatorType": "author", "firstName": "R.S.", "lastName": "MacLeod" } ], "abstractNote": "To achieve a deeper understanding of the brain, scientists, and clinicians use electroencephalography (EEG) and magnetoencephalography (MEG) inverse methods to reconstruct sources in the cortical sheet of the human brain. The influence of structural and electrical anisotropy in both the skull and the white matter on the EEG and MEG source reconstruction is not well understood.\n\nIn this paper, we report on a study of the sensitivity to tissue anisotropy of the EEG/MEG forward problem for deep and superficial neocortical sources with differing orientation components in an anatomically accurate model of the human head.\n\nThe goal of the study was to gain insight into the effect of anisotropy of skull and white matter conductivity through the visualization of field distributions, isopotential surfaces, and return current flow and through statistical error measures. One implicit premise of the study is that factors that affect the accuracy of the forward solution will have at least as strong an influence over solutions to the associated inverse problem.\n\nMajor findings of the study include (1) anisotropic white matter conductivity causes return currents to flow in directions parallel to the white matter fiber tracts; (2) skull anisotropy has a smearing effect on the forward potential computation; and (3) the deeper a source lies and the more it is surrounded by anisotropic tissue, the larger the influence of this anisotropy on the resulting electric and magnetic fields. Therefore, for the EEG, the presence of tissue anisotropy both for the skull and white matter compartment substantially compromises the forward potential computation and as a consequence, the inverse source reconstruction. In contrast, for the MEG, only the anisotropy of the white matter compartment has a significant effect. Finally, return currents with high amplitudes were found in the highly conducting cerebrospinal fluid compartment, underscoring the need for accurate modeling of this space.", "publicationTitle": "NeuroImage", "publisher": "", "place": "", "date": "April 15, 2006", "volume": "30", "issue": "3", "section": "", "partNumber": "", "partTitle": "", "pages": "813-826", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "", "DOI": "10.1016/j.neuroimage.2005.10.014", "citationKey": "", "url": "http://www.sciencedirect.com/science/article/pii/S1053811905007871", "accessDate": "2012-02-01T13:50:58Z", "PMID": "", "PMCID": "", "ISSN": "1053-8119", "archive": "", "archiveLocation": "", "shortTitle": "Influence of tissue conductivity anisotropy on EEG/MEG field and return current computation in a realistic head model", "language": "", "libraryCatalog": "ScienceDirect", "callNumber": "0202", "rights": "", "extra": "", "tags": [ { "tag": "CSF", "type": 1 }, { "tag": "EEG", "type": 1 }, { "tag": "Finite element method", "type": 1 }, { "tag": "Forward problem", "type": 1 }, { "tag": "MEG", "type": 1 }, { "tag": "Return current", "type": 1 }, { "tag": "Tissue conductivity anisotropy", "type": 1 }, { "tag": "Visualization", "type": 1 }, { "tag": "source reconstruction", "type": 1 } ], "collections": [], "relations": {}, "dateAdded": "2012-02-01T13:52:00Z", "dateModified": "2013-05-23T05:46:37Z" } }, { "key": "Q4PI3JXW", "version": 8199, "library": { "type": "group", "id": 16389, "name": "MEEG SourceLoc", "links": { "alternate": { "href": "https://www.zotero.org/groups/meeg_sourceloc", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/16389/items/Q4PI3JXW", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/meeg_sourceloc/items/Q4PI3JXW", "type": "text/html" } }, "meta": { "createdByUser": { "id": 224520, "username": "ErickOrtiz", "name": "", "links": { "alternate": { "href": "https://www.zotero.org/erickortiz", "type": "text/html" } } }, "creatorSummary": "Litvak et al.", "parsedDate": "2011", "numChildren": 0 }, "data": { "key": "Q4PI3JXW", "version": 8199, "itemType": "journalArticle", "title": "EEG and MEG Data Analysis in SPM8", "creators": [ { "creatorType": "author", "firstName": "Vladimir", "lastName": "Litvak" }, { "creatorType": "author", "firstName": "Jérémie", "lastName": "Mattout" }, { "creatorType": "author", "firstName": "Stefan", "lastName": "Kiebel" }, { "creatorType": "author", "firstName": "Christophe", "lastName": "Phillips" }, { "creatorType": "author", "firstName": "Richard", "lastName": "Henson" }, { "creatorType": "author", "firstName": "James", "lastName": "Kilner" }, { "creatorType": "author", "firstName": "Gareth", "lastName": "Barnes" }, { "creatorType": "author", "firstName": "Robert", "lastName": "Oostenveld" }, { "creatorType": "author", "firstName": "Jean", "lastName": "Daunizeau" }, { "creatorType": "author", "firstName": "Guillaume", "lastName": "Flandin" }, { "creatorType": "author", "firstName": "Will", "lastName": "Penny" }, { "creatorType": "author", "firstName": "Karl", "lastName": "Friston" } ], "abstractNote": "", "publicationTitle": "Computational Intelligence and Neuroscience", "publisher": "", "place": "", "date": "2011", "volume": "2011", "issue": "", "section": "", "partNumber": "", "partTitle": "", "pages": "1-32", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "Computational Intelligence and Neuroscience", "DOI": "10.1155/2011/852961", "citationKey": "", "url": "http://www.hindawi.com/journals/cin/2011/852961/", "accessDate": "2011-03-08T13:05:42Z", "PMID": "", "PMCID": "", "ISSN": "1687-5265", "archive": "", "archiveLocation": "", "shortTitle": "", "language": "", "libraryCatalog": "CrossRef", "callNumber": "0047", "rights": "", "extra": "", "tags": [], "collections": [], "relations": {}, "dateAdded": "2011-03-08T16:54:42Z", "dateModified": "2013-05-23T05:46:36Z" } }, { "key": "PTTG4T2W", "version": 8199, "library": { "type": "group", "id": 16389, "name": "MEEG SourceLoc", "links": { "alternate": { "href": "https://www.zotero.org/groups/meeg_sourceloc", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/16389/items/PTTG4T2W", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/meeg_sourceloc/items/PTTG4T2W", "type": "text/html" } }, "meta": { "createdByUser": { "id": 224520, "username": "ErickOrtiz", "name": "", "links": { "alternate": { "href": "https://www.zotero.org/erickortiz", "type": "text/html" } } }, "creatorSummary": "Sekihara et al.", "parsedDate": "2002", "numChildren": 0 }, "data": { "key": "PTTG4T2W", "version": 8199, "itemType": "journalArticle", "title": "Application of an MEG eigenspace beamformer to reconstructing spatio-temporal activities of neural sources", "creators": [ { "creatorType": "author", "firstName": "Kensuke", "lastName": "Sekihara" }, { "creatorType": "author", "firstName": "Srikantan S.", "lastName": "Nagarajan" }, { "creatorType": "author", "firstName": "David", "lastName": "Poeppel" }, { "creatorType": "author", "firstName": "Alec", "lastName": "Marantz" }, { "creatorType": "author", "firstName": "Yasushi", "lastName": "Miyashita" } ], "abstractNote": "We have applied the eigenspace-based beamformer to reconstruct spatio-temporal activities of neural sources from MEG data. The weight vector of the eigenspace-based beamformer is obtained by projecting the weight vector of the minimum-variance beamformer onto the signal subspace of a measurement covariance matrix. This projection removes the residual noise-subspace component that considerably degrades the signal-to-noise ratio (SNR) of the beamformer output when errors in estimating the sensor lead field exist. Therefore, the eigenspace-based beamformer produces a SNR considerably higher than that of the minimum-variance beamformer in practical situations. The effectiveness of the eigenspace-based beamformer was validated in our numerical experiments and experiments using auditory responses. We further extended the eigenspace-based beamformer so that it incorporates the information regarding the noise covariance matrix. Such a prewhitened eigenspace beamformer was experimentally demonstrated to be useful when large background activity exists. Hum. Brain Mapping 15:199-215, 2002. © 2002 Wiley-Liss, Inc.", "publicationTitle": "Human Brain Mapping", "publisher": "", "place": "", "date": "2002", "volume": "15", "issue": "4", "section": "", "partNumber": "", "partTitle": "", "pages": "199-215", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "", "DOI": "10.1002/hbm.10019", "citationKey": "", "url": "http://dx.doi.org/10.1002/hbm.10019", "accessDate": "2010-06-16T08:51:43Z", "PMID": "", "PMCID": "", "ISSN": "", "archive": "", "archiveLocation": "", "shortTitle": "", "language": "", "libraryCatalog": "Wiley InterScience", "callNumber": "0081", "rights": "", "extra": "", "tags": [], "collections": [], "relations": {}, "dateAdded": "2010-06-16T09:01:07Z", "dateModified": "2013-05-23T05:46:35Z" } }, { "key": "PJEI7VIF", "version": 8199, "library": { "type": "group", "id": 16389, "name": "MEEG SourceLoc", "links": { "alternate": { "href": "https://www.zotero.org/groups/meeg_sourceloc", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/16389/items/PJEI7VIF", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/meeg_sourceloc/items/PJEI7VIF", "type": "text/html" } }, "meta": { "createdByUser": { "id": 224520, "username": "ErickOrtiz", "name": "", "links": { "alternate": { "href": "https://www.zotero.org/erickortiz", "type": "text/html" } } }, "creatorSummary": "Tesche et al.", "parsedDate": "1995-09", "numChildren": 0 }, "data": { "key": "PJEI7VIF", "version": 8199, "itemType": "journalArticle", "title": "Signal-space projections of MEG data characterize both distributed and well-localized neuronal sources", "creators": [ { "creatorType": "author", "firstName": "C. D.", "lastName": "Tesche" }, { "creatorType": "author", "firstName": "M. A.", "lastName": "Uusitalo" }, { "creatorType": "author", "firstName": "R. J.", "lastName": "Ilmoniemi" }, { "creatorType": "author", "firstName": "M.", "lastName": "Huotilainen" }, { "creatorType": "author", "firstName": "M.", "lastName": "Kajola" }, { "creatorType": "author", "firstName": "O.", "lastName": "Salonen" } ], "abstractNote": "We describe the use of signal-space projection (SSP) for the detection and characterization of simultaneous and/or sequential activation of neuronal source distributions. In this analysis, a common signal space is used to represent both the signals measured by an array of detectors and the underlying brain sources. This presents distinct advantages for the analysis of EEG and MEG data. Both highly localized and distributed sources are characterized by the components of the field patterns which are measured by the detectors. As a result, a unified description of arbitrary source configurations is obtained which permits the consistent implementation of a variety of analysis techniques. The method is illustrated by the application of SSP to auditory, visual and somatosensory evoked-response MEG data. Single-trace evoked responses obtained by SSP of spontaneous activity demonstrate that a considerable discrimination against both system noise and uncorrelated brain activity may be achieved. Application of signal-space projections determined in the frequency domain to spontaneous activity illustrates the possibility of including temporal relationships into the analysis. Finally, we demonstrate that SSP is particularly useful for the description of multiple sources of distributed activity and for the comparison of the strengths of specific neuronal sources under a variety of different paradigms or subject conditions.", "publicationTitle": "Electroencephalography and Clinical Neurophysiology", "publisher": "", "place": "", "date": "September 1995", "volume": "95", "issue": "3", "section": "", "partNumber": "", "partTitle": "", "pages": "189-200", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "", "DOI": "10.1016/0013-4694(95)00064-6", "citationKey": "", "url": "http://www.sciencedirect.com/science/article/B6SYX-4037WP9-9Y/2/f45c44156f2128901a49050fc3fd0638", "accessDate": "2011-01-25T14:35:07Z", "PMID": "", "PMCID": "", "ISSN": "0013-4694", "archive": "", "archiveLocation": "", "shortTitle": "", "language": "", "libraryCatalog": "ScienceDirect", "callNumber": "0216", "rights": "", "extra": "", "tags": [ { "tag": "Brain mapping", "type": 1 }, { "tag": "Distributed sources", "type": 1 }, { "tag": "Electroencephalography", "type": 1 }, { "tag": "Evoked activity", "type": 1 }, { "tag": "Human", "type": 1 }, { "tag": "Magnetoencephalography", "type": 1 }, { "tag": "Single trial", "type": 1 }, { "tag": "Spontaneous activity", "type": 1 } ], "collections": [], "relations": {}, "dateAdded": "2011-01-25T14:37:37Z", "dateModified": "2013-05-23T05:46:34Z" } }, { "key": "PI9DVXP6", "version": 8199, "library": { "type": "group", "id": 16389, "name": "MEEG SourceLoc", "links": { "alternate": { "href": "https://www.zotero.org/groups/meeg_sourceloc", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/16389/items/PI9DVXP6", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/meeg_sourceloc/items/PI9DVXP6", "type": "text/html" } }, "meta": { "createdByUser": { "id": 224520, "username": "ErickOrtiz", "name": "", "links": { "alternate": { "href": "https://www.zotero.org/erickortiz", "type": "text/html" } } }, "creatorSummary": "Lopes da Silva", "parsedDate": "2004-12", "numChildren": 0 }, "data": { "key": "PI9DVXP6", "version": 8199, "itemType": "journalArticle", "title": "Functional localization of brain sources using EEG and/or MEG data: volume conductor and source models", "creators": [ { "creatorType": "author", "firstName": "Fernando", "lastName": "Lopes da Silva" } ], "abstractNote": "In this overview we examine the basic principles of properties of electroencephalogram and magnetoencephalogram and the corresponding models of sources and of the volume conductor. In particular we show how the dipolar model is anchored in neurophysiological findings and how the different conductivities of the brain and the tissue surrounding it can be estimated. Using these basic models as tools we show how the functional localization of the neural sources of rhythmic activities (alpha and mu rhythms and sleep spindles) and of epileptiform activities can be estimated and integrated with structural data of the brain obtained with MRI.", "publicationTitle": "Magnetic Resonance Imaging", "publisher": "", "place": "", "date": "December 2004", "volume": "22", "issue": "10", "section": "", "partNumber": "", "partTitle": "", "pages": "1533-1538", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "", "DOI": "10.1016/j.mri.2004.10.010", "citationKey": "", "url": "http://www.sciencedirect.com/science/article/B6T9D-4FF9547-N/2/8cbcc6a6b3c3a94514cb49a8b122f7c8", "accessDate": "2011-01-24T16:45:15Z", "PMID": "", "PMCID": "", "ISSN": "0730-725X", "archive": "", "archiveLocation": "", "shortTitle": "Functional localization of brain sources using EEG and/or MEG data", "language": "", "libraryCatalog": "ScienceDirect", "callNumber": "0058", "rights": "", "extra": "", "tags": [ { "tag": "EEG", "type": 1 }, { "tag": "Functional localization", "type": 1 }, { "tag": "MEG", "type": 1 }, { "tag": "Volume conductor", "type": 1 }, { "tag": "fMRI", "type": 1 } ], "collections": [], "relations": {}, "dateAdded": "2011-01-24T16:48:05Z", "dateModified": "2013-05-23T05:46:33Z" } }, { "key": "PHAWFKUX", "version": 8199, "library": { "type": "group", "id": 16389, "name": "MEEG SourceLoc", "links": { "alternate": { "href": "https://www.zotero.org/groups/meeg_sourceloc", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/16389/items/PHAWFKUX", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/meeg_sourceloc/items/PHAWFKUX", "type": "text/html" } }, "meta": { "createdByUser": { "id": 224520, "username": "ErickOrtiz", "name": "", "links": { "alternate": { "href": "https://www.zotero.org/erickortiz", "type": "text/html" } } }, "creatorSummary": "Hillebrand and Barnes", "numChildren": 0 }, "data": { "key": "PHAWFKUX", "version": 8199, "itemType": "journalArticle", "title": "Practical constraints on estimation of source extent with MEG beamformers", "creators": [ { "creatorType": "author", "firstName": "Arjan", "lastName": "Hillebrand" }, { "creatorType": "author", "firstName": "Gareth R.", "lastName": "Barnes" } ], "abstractNote": "We aimed to determine practical constraints on the estimation of the spatial extent of neuronal activation using MEG beamformers. Correct estimation of spatial extent is a pre-requisite for accurate models of electrical activity, allows one to estimate current density, and enables non-invasive monitoring of functional recovery following stroke.\nThe output of an MEG beamformer is maximum when the correct source model is used, so that the spatial extent of a source can in principal be determined through evaluation of different source models with the beamformer. Here, we simulated 275-channel MEG data using sources of varying spatial extents that followed the cortical geometry. These data were subsequently used to estimate the spatial extent of generic disc elements without knowledge of the underlying surface, and we compared these results to estimates based on cortical surface geometry (with and without error in surface location).\nWe found that disc-shaped source models are too simplistic, particularly for areas with high curvature. For areas with low curvature spatial extent was underestimated, although on average there was a linear relationship between the true and estimated extent. In contrast, cortical surface models gave accurate predictions of spatial extent. However, adding small errors (> 2 mm) to the estimated location of the cortical surface abolished this relationship between true and estimated extent, implying that accurate co-registration is needed with such models.\nOur results show that models exploiting surface information are necessary in order to model spatial extent and in turn current density, but in order to render such models applicable in practical situations, the accuracy of the cortical surface model itself needs to improve.", "publicationTitle": "NeuroImage", "publisher": "", "place": "", "date": "", "volume": "In Press, Uncorrected Proof", "issue": "", "section": "", "partNumber": "", "partTitle": "", "pages": "", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "", "DOI": "10.1016/j.neuroimage.2010.10.036", "citationKey": "", "url": "http://www.sciencedirect.com/science/article/B6WNP-518TY9G-3/2/0c6d14412fbddabbced200467730e432", "accessDate": "2010-11-12T13:22:37Z", "PMID": "", "PMCID": "", "ISSN": "1053-8119", "archive": "", "archiveLocation": "", "shortTitle": "", "language": "", "libraryCatalog": "ScienceDirect", "callNumber": "0009", "rights": "", "extra": "", "tags": [ { "tag": "Beamforming", "type": 1 }, { "tag": "Current density", "type": 1 }, { "tag": "Magnetoencephalography", "type": 1 }, { "tag": "Neuronal activity", "type": 1 }, { "tag": "Source modelling", "type": 1 }, { "tag": "Spatial extent", "type": 1 } ], "collections": [], "relations": {}, "dateAdded": "2010-11-12T13:23:09Z", "dateModified": "2013-05-23T05:46:32Z" } }, { "key": "P92Z7Q5Q", "version": 8199, "library": { "type": "group", "id": 16389, "name": "MEEG SourceLoc", "links": { "alternate": { "href": "https://www.zotero.org/groups/meeg_sourceloc", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/16389/items/P92Z7Q5Q", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/meeg_sourceloc/items/P92Z7Q5Q", "type": "text/html" } }, "meta": { "createdByUser": { "id": 224520, "username": "ErickOrtiz", "name": "", "links": { "alternate": { "href": "https://www.zotero.org/erickortiz", "type": "text/html" } } }, "creatorSummary": "Friston et al.", "parsedDate": "2002-06", "numChildren": 1 }, "data": { "key": "P92Z7Q5Q", "version": 8199, "itemType": "journalArticle", "title": "Classical and Bayesian Inference in Neuroimaging: Theory", "creators": [ { "creatorType": "author", "firstName": "K. J.", "lastName": "Friston" }, { "creatorType": "author", "firstName": "W.", "lastName": "Penny" }, { "creatorType": "author", "firstName": "C.", "lastName": "Phillips" }, { "creatorType": "author", "firstName": "S.", "lastName": "Kiebel" }, { "creatorType": "author", "firstName": "G.", "lastName": "Hinton" }, { "creatorType": "author", "firstName": "J.", "lastName": "Ashburner" } ], "abstractNote": "This paper reviews hierarchical observation models, used in functional neuroimaging, in a Bayesian light. It emphasizes the common ground shared by classical and Bayesian methods to show that conventional analyses of neuroimaging data can be usefully extended within an empirical Bayesian framework. In particular we formulate the procedures used in conventional data analysis in terms of hierarchical linear models and establish a connection between classical inference and parametric empirical Bayes (PEB) through covariance component estimation. This estimation is based on an expectation maximization or EM algorithm. The key point is that hierarchical models not only provide for appropriate inference at the highest level but that one can revisit lower levels suitably equipped to make Bayesian inferences. Bayesian inferences eschew many of the difficulties encountered with classical inference and characterize brain responses in a way that is more directly predicated on what one is interested in. The motivation for Bayesian approaches is reviewed and the theoretical background is presented in a way that relates to conventional methods, in particular restricted maximum likelihood (ReML). This paper is a technical and theoretical prelude to subsequent papers that deal with applications of the theory to a range of important issues in neuroimaging. These issues include; (i) Estimating nonsphericity or variance components in fMRI time-series that can arise from serial correlations within subject, or are induced by multisubject (i.e., hierarchical) studies. (ii) Spatiotemporal Bayesian models for imaging data, in which voxels-specific effects are constrained by responses in other voxels. (iii) Bayesian estimation of nonlinear models of hemodynamic responses and (iv) principled ways of mixing structural and functional priors in EEG source reconstruction. Although diverse, all these estimation problems are accommodated by the PEB framework described in this paper.", "publicationTitle": "NeuroImage", "publisher": "", "place": "", "date": "June 2002", "volume": "16", "issue": "2", "section": "", "partNumber": "", "partTitle": "", "pages": "465-483", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "", "DOI": "10.1006/nimg.2002.1090", "citationKey": "", "url": "http://www.sciencedirect.com/science/article/B6WNP-4603732-J/2/f0811215167a0592e7e7048e2f252409", "accessDate": "2010-06-07T09:19:43Z", "PMID": "", "PMCID": "", "ISSN": "1053-8119", "archive": "", "archiveLocation": "", "shortTitle": "Classical and Bayesian Inference in Neuroimaging", "language": "", "libraryCatalog": "ScienceDirect", "callNumber": "0374", "rights": "", "extra": "", "tags": [ { "tag": "fMRI; PET; random effects; EM algorithm; ReML; Bayesian inference; hierarchical models", "type": 1 } ], "collections": [], "relations": {}, "dateAdded": "2010-06-07T13:15:32Z", "dateModified": "2013-05-23T05:46:31Z" } }, { "key": "NUUTUXRS", "version": 8199, "library": { "type": "group", "id": 16389, "name": "MEEG SourceLoc", "links": { "alternate": { "href": "https://www.zotero.org/groups/meeg_sourceloc", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/16389/items/NUUTUXRS", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/meeg_sourceloc/items/NUUTUXRS", "type": "text/html" } }, "meta": { "createdByUser": { "id": 224520, "username": "ErickOrtiz", "name": "", "links": { "alternate": { "href": "https://www.zotero.org/erickortiz", "type": "text/html" } } }, "creatorSummary": "Litvak et al.", "parsedDate": "2010-05-01", "numChildren": 0 }, "data": { "key": "NUUTUXRS", "version": 8199, "itemType": "journalArticle", "title": "Optimized beamforming for simultaneous MEG and intracranial local field potential recordings in deep brain stimulation patients", "creators": [ { "creatorType": "author", "firstName": "Vladimir", "lastName": "Litvak" }, { "creatorType": "author", "firstName": "Alexandre", "lastName": "Eusebio" }, { "creatorType": "author", "firstName": "Ashwani", "lastName": "Jha" }, { "creatorType": "author", "firstName": "Robert", "lastName": "Oostenveld" }, { "creatorType": "author", "firstName": "Gareth R.", "lastName": "Barnes" }, { "creatorType": "author", "firstName": "William D.", "lastName": "Penny" }, { "creatorType": "author", "firstName": "Ludvic", "lastName": "Zrinzo" }, { "creatorType": "author", "firstName": "Marwan I.", "lastName": "Hariz" }, { "creatorType": "author", "firstName": "Patricia", "lastName": "Limousin" }, { "creatorType": "author", "firstName": "Karl J.", "lastName": "Friston" }, { "creatorType": "author", "firstName": "Peter", "lastName": "Brown" } ], "abstractNote": "Insight into how brain structures interact is critical for understanding the principles of functional brain architectures and may lead to better diagnosis and therapy for neuropsychiatric disorders. We recorded, simultaneously, magnetoencephalographic (MEG) signals and subcortical local field potentials (LFP) in a Parkinson's disease (PD) patient with bilateral deep brain stimulation (DBS) electrodes in the subthalamic nucleus (STN). These recordings offer a unique opportunity to characterize interactions between the subcortical structures and the neocortex. However, high-amplitude artefacts appeared in the MEG. These artefacts originated from the percutaneous extension wire, rather than from the actual DBS electrode and were locked to the heart beat. In this work, we show that MEG beamforming is capable of suppressing these artefacts and quantify the optimal regularization required. We demonstrate how beamforming makes it possible to localize cortical regions whose activity is coherent with the STN-LFP, extract artefact-free virtual electrode time-series from regions of interest and localize cortical areas exhibiting specific task-related power changes. This furnishes results that are consistent with previously reported results using artefact-free MEG data. Our findings demonstrate that physiologically meaningful information can be extracted from heavily contaminated MEG signals and pave the way for further analysis of combined MEG-LFP recordings in DBS patients.", "publicationTitle": "NeuroImage", "publisher": "", "place": "", "date": "May 1, 2010", "volume": "50", "issue": "4", "section": "", "partNumber": "", "partTitle": "", "pages": "1578-1588", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "", "DOI": "10.1016/j.neuroimage.2009.12.115", "citationKey": "", "url": "http://www.sciencedirect.com/science/article/B6WNP-4Y34K4V-H/2/8244920f7500c29d289e4fe70d6a832d", "accessDate": "2010-06-16T17:34:08Z", "PMID": "", "PMCID": "", "ISSN": "1053-8119", "archive": "", "archiveLocation": "", "shortTitle": "", "language": "", "libraryCatalog": "ScienceDirect", "callNumber": "0026", "rights": "", "extra": "", "tags": [], "collections": [], "relations": {}, "dateAdded": "2010-06-16T17:34:49Z", "dateModified": "2013-05-23T05:46:30Z" } }, { "key": "NTAKMQDF", "version": 8199, "library": { "type": "group", "id": 16389, "name": "MEEG SourceLoc", "links": { "alternate": { "href": "https://www.zotero.org/groups/meeg_sourceloc", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/16389/items/NTAKMQDF", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/meeg_sourceloc/items/NTAKMQDF", "type": "text/html" } }, "meta": { "createdByUser": { "id": 224520, "username": "ErickOrtiz", "name": "", "links": { "alternate": { "href": "https://www.zotero.org/erickortiz", "type": "text/html" } } }, "creatorSummary": "Wipf and Nagarajan", "parsedDate": "2009-02-01", "numChildren": 0 }, "data": { "key": "NTAKMQDF", "version": 8199, "itemType": "journalArticle", "title": "A unified Bayesian framework for MEG/EEG source imaging", "creators": [ { "creatorType": "author", "firstName": "David", "lastName": "Wipf" }, { "creatorType": "author", "firstName": "Srikantan", "lastName": "Nagarajan" } ], "abstractNote": "The ill-posed nature of the MEG (or related EEG) source localization problem requires the incorporation of prior assumptions when choosing an appropriate solution out of an infinite set of candidates. Bayesian approaches are useful in this capacity because they allow these assumptions to be explicitly quantified using postulated prior distributions. However, the means by which these priors are chosen, as well as the estimation and inference procedures that are subsequently adopted to affect localization, have led to a daunting array of algorithms with seemingly very different properties and assumptions. From the vantage point of a simple Gaussian scale mixture model with flexible covariance components, this paper analyzes and extends several broad categories of Bayesian inference directly applicable to source localization including empirical Bayesian approaches, standard MAP estimation, and multiple variational Bayesian (VB) approximations. Theoretical properties related to convergence, global and local minima, and localization bias are analyzed and fast algorithms are derived that improve upon existing methods. This perspective leads to explicit connections between many established algorithms and suggests natural extensions for handling unknown dipole orientations, extended source configurations, correlated sources, temporal smoothness, and computational expediency. Specific imaging methods elucidated under this paradigm include the weighted minimum l2-norm, FOCUSS, minimum current estimation, VESTAL, sLORETA, restricted maximum likelihood, covariance component estimation, beamforming, variational Bayes, the Laplace approximation, and automatic relevance determination, as well as many others. Perhaps surprisingly, all of these methods can be formulated as particular cases of covariance component estimation using different concave regularization terms and optimization rules, making general theoretical analyses and algorithmic extensions/improvements particularly relevant.", "publicationTitle": "NeuroImage", "publisher": "", "place": "", "date": "February 1, 2009", "volume": "44", "issue": "3", "section": "", "partNumber": "", "partTitle": "", "pages": "947-966", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "", "DOI": "10.1016/j.neuroimage.2008.02.059", "citationKey": "", "url": "http://www.sciencedirect.com/science/article/B6WNP-4S33N67-1/2/5387f67d68d6022ad90faff60b744605", "accessDate": "2010-04-21T13:44:21Z", "PMID": "", "PMCID": "", "ISSN": "1053-8119", "archive": "", "archiveLocation": "", "shortTitle": "", "language": "", "libraryCatalog": "ScienceDirect", "callNumber": "0081", "rights": "", "extra": "", "tags": [], "collections": [], "relations": {}, "dateAdded": "2010-04-21T14:14:03Z", "dateModified": "2013-05-23T05:46:29Z" } }, { "key": "JZQI3D3K", "version": 8199, "library": { "type": "group", "id": 16389, "name": "MEEG SourceLoc", "links": { "alternate": { "href": "https://www.zotero.org/groups/meeg_sourceloc", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/16389/items/JZQI3D3K", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/meeg_sourceloc/items/JZQI3D3K", "type": "text/html" } }, "meta": { "createdByUser": { "id": 224520, "username": "ErickOrtiz", "name": "", "links": { "alternate": { "href": "https://www.zotero.org/erickortiz", "type": "text/html" } } }, "creatorSummary": "Lin et al.", "parsedDate": "2006", "numChildren": 0 }, "data": { "key": "JZQI3D3K", "version": 8199, "itemType": "journalArticle", "title": "Distributed current estimates using cortical orientation constraints", "creators": [ { "creatorType": "author", "firstName": "Fa-Hsuan", "lastName": "Lin" }, { "creatorType": "author", "firstName": "John W.", "lastName": "Belliveau" }, { "creatorType": "author", "firstName": "Anders M.", "lastName": "Dale" }, { "creatorType": "author", "firstName": "Matti S.", "lastName": "Hämäläinen" } ], "abstractNote": "", "publicationTitle": "Human Brain Mapping", "publisher": "", "place": "", "date": "01/2006", "volume": "27", "issue": "1", "section": "", "partNumber": "", "partTitle": "", "pages": "1-13", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "Hum. Brain Mapp.", "DOI": "10.1002/hbm.20155", "citationKey": "", "url": "http://onlinelibrary.wiley.com/doi/10.1002/hbm.20155/full", "accessDate": "2011-03-28T14:57:08Z", "PMID": "", "PMCID": "", "ISSN": "1065-9471", "archive": "", "archiveLocation": "", "shortTitle": "", "language": "", "libraryCatalog": "CrossRef", "callNumber": "0109", "rights": "", "extra": "", "tags": [], "collections": [], "relations": {}, "dateAdded": "2012-02-01T13:19:19Z", "dateModified": "2013-05-23T05:46:28Z" } } ]