NeuroImage: Multivariate Decoding and Brain Reading Posted By: David Kennedy - Nov 2, 2009Tool/Resource: Journals Special Issue Call for Original Papers: Multivariate Decoding and Brain Reading Multivariate decoding has recently emerged as a novel and powerful analysis tool in functional neuroimaging. The application of multivariate pattern recognition techniques has several important advantages over more conventional analyses based on "mass-univariate" approaches. Pattern recognition can help increase the sensitivity for detecting experimental effects. It can assess the amount of information "encoded" in a particular brain region under various cognitive tasks, even for fine-grained representations that are often assumed to be inaccessible to current neuroimaging techniques. It provides a more powerful framework for analysing neural representations that takes into account their distributed nature. It can also be extended to reveal the encoding of similarity structures and representational spaces. Furthermore, its increased sensitivity makes simple forms of "brain reading" possible, where mental states are decoded from neuroimaging signals. This opens up a window for potential applications such as biofeedback with real-time-fMRI, clinical diagnostics, detection of deception and neuromarketing. Because of the immense interest in this new approach, NeuroImage will be publishing a special issue on "Multivariate Decoding and Brain Reading". The core of the special issue will be six review articles that provide an overview of recent developments in the field. Professor John-Dylan Haynes from the Bernstein Center for Computational Neuroscience in Berlin will serve as Guest Editor for this special issue. In addition to the review articles listed below, we also invite submissions for original papers for the Special Issue. The topics can cover: * Application of pattern recognition techniques to functional and structural neuroimaging data in cognitive neuroscience. * Decoding-based neurotechnological applications such as brain-computer-interfaces and "brain-reading", real-time biofeedback, clinical diagnostics, lie detection, neuromarketing. * Novel classification algorithms and methodological / statistical issues. * Imaging sequences that are optimised for pattern classification (e.g. optimising speed or resolution). All papers will be subject to normal peer review and must comply with the Guide for Authors. To submit a paper to this Special Issue, please go to http://ees.elsevier.com/ynimg/ and click on "Submit new manuscript". It is important that authors select "Decoding fMRI signals" as the article type. Manuscripts should be submitted by 30th November 2009. Please address any questions to NI@Elsevier.com The Special Issue will include six authoritative state of the art review articles: 1. John-Dylan Haynes - "Multivariate decoding in neuroimaging: Introduction, overview of main questions" 2. Klaus-Robert Müller - "A brief tutorial on machine learning and pattern recognition" 3. Tom Mitchell - "Model-based approaches to fMRI-decoding" 4. Nikolaus Kriegeskorte - "Representational similarity analysis" 5. Stephen LaConte - "Real-time fMRI pattern classification" 6. Stefan Klöppel & John Ashburner - "Using pattern recognition for disease classification from neuroimaging data" |
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