Quantifying inter-subject agreement in brain-imaging analyses

Wong, Dik Kin; Grosenick, Logan; Uy, E. Timothy; Guimaraes, Marcos Perreau; Carvalhaes, Claudio G.; Desain, Peter; Suppes, Patrick

doi:10.1016/j.neuroimage.2007.07.064

Cited by 7 publications

(9 citation statements)

References 63 publications

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“…In addition to facilitating the use of the four-sphere model in EEG signal analysis (see, e.g., Peraza et al (2012); Wong et al (2008); Chu et al (2012)), the present formulas and scripts will also be a resource for benchmarking comprehensive numerical schemes for computing EEG signals based on detailed head reconstructions such as the Finite Element Method (FEM) (Larson and Bengzon, 2013). The FEM approach is not restricted to specific head symmetry assumptions and can take into account an arbitrarily complex spatial distribution of electrical conductivity representing the electrical properties of the head.…”

Section: Discussionmentioning

confidence: 99%

“…The Poisson equation, which describes the electric fields of the brain within volume-conductor theory, is solved for each layer separately, and the mathematical solutions are matched at the layer interfaces to obtain an analytical expression for the EEG signal as set up by a current dipole in the brain tissue. This model has been extensively used in analysis of EEG signals, see, e.g., Peraza et al (2012); Wong et al (2008); Chu et al (2012), but it is also useful for validation of general numerical schemes, such as the Finite Element Method (FEM) (Larson and Bengzon, 2013). The FEM approach is not limited by specific assumptions on head symmetry and can, in principle, take into account an arbitrarily complex spatial distribution of electrical conductivity representing the electrical properties of the head (Bangera et al, 2010; Huang et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

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Four-sphere head model for EEG signals revisited

Næss

Chintaluri

Ness

et al. 2017

Preprint

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4Electric potential recorded at the scalp (EEG) is dominated by contributions from BY 4.0 International license not peer-reviewed) is the author/funder. It is made available under aThe copyright holder for this preprint (which was . http://dx.doi.org/10.1101/124875 doi: bioRxiv preprint first posted online Apr. 6, 2017; errors. We here derive and present the correct analytical formulas for future reference. 17They are compared with the results of FEM simulations of four-sphere model. We 18 also provide scripts for computing EEG potentials in this model with the correct 19 analytical formula and using FEM.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Four-sphere head model for EEG signals revisited

Næss

Chintaluri

Ness

et al. 2017

Preprint

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“…. (p. 1434) Constructive statistical approaches that take more of a "full information" approach are also starting to be developed in the specialist literature, such as Bowman et al (2008) and Wong et al (2008), but none of these concerns seem to filter through to qualify the conclusions of the neuroeconomics literature.…”

Section: Questions About Proceduresmentioning

confidence: 99%

Neuroeconomics: A Critical Reconsideration

Harrison

2008

Economics and Philosophy

147

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Abstract. Understanding more about how the brain functions should help us understand economic behaviour. But some would have us believe that it has done this already, and that insights from neuroscience have already provided insights in economics that we would not otherwise have. Much of this is just academic marketing hype, and to get down to substantive issues we need to identify that fluff for what it is. After we clear away the distractions, what is left? The answer is that a lot is left, but it is still all potential. That is not a bad thing, or a reason to stop the effort, but it does point to the need for a serious reconsideration of what neuroeconomics is and what passes for explanation in this literature. I argue that neuroeconomics can be a valuable field, but not the way it is being developed and "sold" now. The same is true more generally of behavioural economics, which shares many of the methodological flaws of neuroeconomics.

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“…Similarly, the following questions were answered. Can we train a classifier on some participants and use it to make predictions for other participants [5], [13]? And, can we train a classifier using words and use that classifier to make predictions for pictures depicting what the words refer to (and vice versa) [14]?…”

Section: Introductionmentioning

confidence: 99%

Structural Similarities between Brain and Linguistic Data Provide Evidence of Semantic Relations in the Brain

2013

Self Cite

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This paper presents a new method of analysis by which structural similarities between brain data and linguistic data can be assessed at the semantic level. It shows how to measure the strength of these structural similarities and so determine the relatively better fit of the brain data with one semantic model over another. The first model is derived from WordNet, a lexical database of English compiled by language experts. The second is given by the corpus-based statistical technique of latent semantic analysis (LSA), which detects relations between words that are latent or hidden in text. The brain data are drawn from experiments in which statements about the geography of Europe were presented auditorily to participants who were asked to determine their truth or falsity while electroencephalographic (EEG) recordings were made. The theoretical framework for the analysis of the brain and semantic data derives from axiomatizations of theories such as the theory of differences in utility preference. Using brain-data samples from individual trials time-locked to the presentation of each word, ordinal relations of similarity differences are computed for the brain data and for the linguistic data. In each case those relations that are invariant with respect to the brain and linguistic data, and are correlated with sufficient statistical strength, amount to structural similarities between the brain and linguistic data. Results show that many more statistically significant structural similarities can be found between the brain data and the WordNet-derived data than the LSA-derived data. The work reported here is placed within the context of other recent studies of semantics and the brain. The main contribution of this paper is the new method it presents for the study of semantics and the brain and the focus it permits on networks of relations detected in brain data and represented by a semantic model.

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Quantifying inter-subject agreement in brain-imaging analyses

Cited by 7 publications

References 63 publications

Four-sphere head model for EEG signals revisited

Four-sphere head model for EEG signals revisited

Neuroeconomics: A Critical Reconsideration

Structural Similarities between Brain and Linguistic Data Provide Evidence of Semantic Relations in the Brain

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