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      Building better biomarkers: brain models in translational neuroimaging


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          Despite its great promise, neuroimaging has yet to substantially impact clinical practice and public health. However, a developing synergy between emerging analysis techniques and data-sharing initiatives has the potential to transform the role of neuroimaging in clinical applications. We review the state of translational neuroimaging and outline an approach to developing brain signatures that can be shared, tested in multiple contexts and applied in clinical settings. The approach rests on three pillars: (i) the use of multivariate pattern-recognition techniques to develop brain signatures for clinical outcomes and relevant mental processes; (ii) assessment and optimization of their diagnostic value; and (iii) a program of broad exploration followed by increasingly rigorous assessment of generalizability across samples, research contexts and populations. Increasingly sophisticated models based on these principles will help to overcome some of the obstacles on the road from basic neuroscience to better health and will ultimately serve both basic and applied goals.

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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


                Author and article information

                Nat Neurosci
                Nat. Neurosci.
                Nature neuroscience
                22 May 2018
                23 February 2017
                05 June 2018
                : 20
                : 3
                : 365-377
                [1 ]Center for Neuroscience Imaging Research, Institute for Basic Science, Suwon, Republic of Korea
                [2 ]Department of Biomedical Engineering, Sungkyunkwan University, Suwon, Republic of Korea
                [3 ]Department of Psychology and Neuroscience, University of Colorado, Boulder, Colorado, USA
                [4 ]Institute of Cognitive Science, University of Colorado, Boulder, Colorado, USA
                [5 ]Department of Psychological and Brain Sciences, Dartmouth College, Hanover, New Hampshire, USA
                [6 ]Department of Biostatistics, Johns Hopkins University, Baltimore, Maryland, USA
                Author notes
                Correspondence should be addressed to T.D.W. ( tor.wager@ 123456colorado.edu )
                PMC5988350 PMC5988350 5988350 nihpa969554

                Reprints and permissions information is available online at http://www.nature.com/reprints/index.html.



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