Automatic alignment of EEG/MEG and MRI data sets

Kozińska, Dorota; Carducci, Filippo; Nowiński, Krzysztof

doi:10.1016/s1388-2457(01)00556-9

Cited by 53 publications

(57 citation statements)

References 21 publications

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“…Prior to the source space (beamformer) analysis, individual participant's MEG data was co-registered onto that participant's structural MRI scan using surface-based alignment procedure (Kozinska et al, 2001). T1-weighted MR images were obtained with a GE 3.0-T Signa Excite HDx system (General Electric, Milwaukee, USA) using an 8-channel head coil and a sagittal-isotropic 3-D fast spoiled gradientrecalled sequence.…”

Section: Methodsmentioning

confidence: 99%

Interactions between visual and semantic processing during object recognition revealed by modulatory effects of age of acquisition

et al. 2014

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Northumbria University has developed Northumbria Research Link (NRL) to enable users to access the University's research output. Copyright © and moral rights for items on NRL are retained by the individual author(s) and/or other copyright owners. Single copies of full items can be reproduced, displayed or performed, and given to third parties in any format or medium for personal research or study, educational, or not-for-profit purposes without prior permission or charge, provided the authors, title and full bibliographic details are given, as well as a hyperlink and/or URL to the original metadata page. The content must not be changed in any way. Full items must not be sold commercially in any format or medium without formal permission of the copyright holder. The full policy is available online: http://nrl.northumbria.ac.uk/policies.html This document may differ from the final, published version of the research and has been made available online in accordance with publisher policies. To read and/or cite from the published version of the research, please visit the publisher's website (a subscription may be required.)Interactions between visual and semantic processing during object recognition revealed by modulatory effects of age of acquisition The age of acquisition (AoA) of objects and their names is a powerful determinant of processing speed in adulthood, with early-acquired objects being recognized and named faster than late-acquired objects. Previous research using fMRI . Traces of vocabulary acquisition in the brain: evidence from covert object naming. NeuroImage 33,[958][959][960][961][962][963][964][965][966][967][968] found that AoA modulated the strength of BOLD responses in both occipital and left anterior temporal cortex during object naming. We used magnetoencephalography (MEG) to explore in more detail the nature of the influence of AoA on activity in those two regions. Covert object naming recruited a network within the left hemisphere that is familiar from previous research, including visual, left occipito-temporal, anterior temporal and inferior frontal regions. Region of interest (ROI) analyses found that occipital cortex generated a rapid evoked response (~ 75-200 ms at 0-40 Hz) that peaked at 95 ms but was not modulated by AoA. That response was followed by a complex of later occipital responses that extended from ~ 300 to 850 ms and were stronger to early-than late-acquired items from ~ 325 to 675 ms at 10-20 Hz in the induced rather than the evoked component. Left anterior temporal cortex showed an evoked response that occurred significantly later than the first occipital response (~ 100-400 ms at 0-10 Hz with a peak at 191 ms) and was stronger to early-than late-acquired items from ~ 100 to 300 ms at 2-12 Hz. A later anterior temporal response from ~ 550 to 1050 ms at 5-20 Hz was not modulated by AoA. The results indicate that the initial analysis of object forms in visual cortex is not influenced by AoA. A fastforward sweep of activation from occipital and left anterior temporal cortex t...

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Section: Methodsmentioning

confidence: 99%

Interactions between visual and semantic processing during object recognition revealed by modulatory effects of age of acquisition

et al. 2014

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“…Source level analysis. MEG data were co-registered with each participant's structural MRI scan first by matching the surface of the head digitization maps to the surface of the structural MRI (27). A spatial filter (''beamformer'') technique was applied to localize sources related to each task (28).…”

Section: Methodsmentioning

confidence: 99%

MEG demonstrates a supra-additive response to facial and vocal emotion in the right superior temporal sulcus

Hagan

Woods²,

Johnson³

et al. 2009

Proc. Natl. Acad. Sci. U.S.A.

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An influential neural model of face perception suggests that the posterior superior temporal sulcus (STS) is sensitive to those aspects of faces that produce transient visual changes, including facial expression. Other researchers note that recognition of expression involves multiple sensory modalities and suggest that the STS also may respond to crossmodal facial signals that change transiently. Indeed, many studies of audiovisual (AV) speech perception show STS involvement in AV speech integration. Here we examine whether these findings extend to AV emotion. We used magnetoencephalography to measure the neural responses of participants as they viewed and heard emotionally congruent fear and minimally congruent neutral face and voice stimuli. We demonstrate significant supra-additive responses (i.e., where AV > [unimodal auditory ؉ unimodal visual]) in the posterior STS within the first 250 ms for emotionally congruent AV stimuli. These findings show a role for the STS in processing crossmodal emotive signals.audio-visual emotion ͉ emotional faces ͉ emotional voices ͉ fear ͉ gamma S ome aspects of faces, such as identity, are relatively fixed whereas other aspects, such as facial expression, can change from moment to moment. A highly influential neural model of face perception suggests that the aspects of faces that change visibly (e.g., eye gaze, lip movement, facial expression) are processed by a neural pathway leading to the posterior superior temporal sulcus (STS) (1, 2). However, both lip movement and facial expression are generally accompanied by changing auditory signals. Indeed, in daily life emotion commonly is conveyed through multiple signals involving the face, voice, and body. Although originating from different sensory modalities, these transient emotive signals are perceived and integrated within our social interactions with seemingly minimal effort. It therefore is possible that recognition of emotional expression is an intrinsically crossmodal process and that the sensitivity of the posterior STS to the changeable aspects of faces may extend to voices, as the posterior STS could serve to integrate emotional signals arising from faces and voices (3). During emotion perception, the role of the posterior STS may extend beyond the visual function of perceiving facial emotion to serve a wider purpose related to the crossmodal integration of facial and vocal signals.The STS is a prime candidate for the integration of visual and auditory emotive signals. The STS lies between primary auditory and visual cortices, and studies of audiovisual (AV) speech integration have demonstrated supra-additive responses in the STS to congruent speech stimuli (4, 5). Supra-additivity is a stringent criterion for multisensory integrative regions and is based on the known electrophysiological behavior of signal integration (6, 7). Supra-additivity occurs when the observed multisensory effect exceeds the sum of the unisensory components, i.e., when AV Ͼ [unimodal auditory (A) ϩ unimodal visual (V)]. The purpose of the pr...

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“…3 shows a head surface extracted from the MR volume image. We transformed the MEG sensor array to MRI coordinates by matching the reference points (measured on the subjectTs head) and the extracted head surface (Kozinska et al, 2001). Fig.…”

Section: Resultsmentioning

confidence: 99%

Controlled Support MEG imaging

et al. 2006

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In this paper, we present a novel approach to imaging sparse and focal neural current sources from MEG (magnetoencephalography) data. Using the framework of Tikhonov regularization theory, we introduce a new stabilizer that uses the concept of controlled support to incorporate a priori assumptions about the area occupied by focal sources. The paper discusses the underlying Tikhonov theory and its relationship to a Bayesian formulation which in turn allows us to interpret and better understand other related algorithms.

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Automatic alignment of EEG/MEG and MRI data sets

Cited by 53 publications

References 21 publications

Interactions between visual and semantic processing during object recognition revealed by modulatory effects of age of acquisition

Interactions between visual and semantic processing during object recognition revealed by modulatory effects of age of acquisition

MEG demonstrates a supra-additive response to facial and vocal emotion in the right superior temporal sulcus

Controlled Support MEG imaging

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