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      Building better biomarkers: brain models in translational neuroimaging

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      Nature Neuroscience
      Springer Nature

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          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Neuroimaging and pattern recognition are being combined to develop brain models of clinical disorders. Such models yield biomarkers that can be shared and validated across populations, narrowing the gap between neuroscience and clinical applications. The authors summarize 475 translational modeling studies, highlighting challenges and ways to improve biomarker development.

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          Most cited references90

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          Distributed and overlapping representations of faces and objects in ventral temporal cortex.

          The functional architecture of the object vision pathway in the human brain was investigated using functional magnetic resonance imaging to measure patterns of response in ventral temporal cortex while subjects viewed faces, cats, five categories of man-made objects, and nonsense pictures. A distinct pattern of response was found for each stimulus category. The distinctiveness of the response to a given category was not due simply to the regions that responded maximally to that category, because the category being viewed also could be identified on the basis of the pattern of response when those regions were excluded from the analysis. Patterns of response that discriminated among all categories were found even within cortical regions that responded maximally to only one category. These results indicate that the representations of faces and objects in ventral temporal cortex are widely distributed and overlapping.
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            Information-based functional brain mapping.

            The development of high-resolution neuroimaging and multielectrode electrophysiological recording provides neuroscientists with huge amounts of multivariate data. The complexity of the data creates a need for statistical summary, but the local averaging standardly applied to this end may obscure the effects of greatest neuroscientific interest. In neuroimaging, for example, brain mapping analysis has focused on the discovery of activation, i.e., of extended brain regions whose average activity changes across experimental conditions. Here we propose to ask a more general question of the data: Where in the brain does the activity pattern contain information about the experimental condition? To address this question, we propose scanning the imaged volume with a "searchlight," whose contents are analyzed multivariately at each location in the brain.
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              Neural Networks and the Bias/Variance Dilemma

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                Author and article information

                Journal
                Nature Neuroscience
                Nat Neurosci
                Springer Nature
                1097-6256
                1546-1726
                February 23 2017
                February 23 2017
                : 20
                : 3
                : 365-377
                Article
                10.1038/nn.4478
                5988350
                28230847
                13c76e8c-a8aa-4d6a-8670-8cc42a41201e
                © 2017
                History

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