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      Using connectome-based predictive modeling to predict individual behavior from brain connectivity

      , , , , , ,

      Nature Protocols

      Springer Nature

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          Abstract

          This protocol describes how to develop linear models to predict individual behavior from brain connectivity data with proper cross-validation, and how to use an online tool to visualize the most predictive features of the models.

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          Most cited references 16

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          A whole brain fMRI atlas generated via spatially constrained spectral clustering.

          Connectivity analyses and computational modeling of human brain function from fMRI data frequently require the specification of regions of interests (ROIs). Several analyses have relied on atlases derived from anatomical or cyto-architectonic boundaries to specify these ROIs, yet the suitability of atlases for resting state functional connectivity (FC) studies has yet to be established. This article introduces a data-driven method for generating an ROI atlas by parcellating whole brain resting-state fMRI data into spatially coherent regions of homogeneous FC. Several clustering statistics are used to compare methodological trade-offs as well as determine an adequate number of clusters. Additionally, we evaluate the suitability of the parcellation atlas against four ROI atlases (Talairach and Tournoux, Harvard-Oxford, Eickoff-Zilles, and Automatic Anatomical Labeling) and a random parcellation approach. The evaluated anatomical atlases exhibit poor ROI homogeneity and do not accurately reproduce FC patterns present at the voxel scale. In general, the proposed functional and random parcellations perform equivalently for most of the metrics evaluated. ROI size and hence the number of ROIs in a parcellation had the greatest impact on their suitability for FC analysis. With 200 or fewer ROIs, the resulting parcellations consist of ROIs with anatomic homology, and thus offer increased interpretability. Parcellation results containing higher numbers of ROIs (600 or 1,000) most accurately represent FC patterns present at the voxel scale and are preferable when interpretability can be sacrificed for accuracy. The resulting atlases and clustering software have been made publicly available at: http://www.nitrc.org/projects/cluster_roi/. Copyright © 2011 Wiley Periodicals, Inc.
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            Puzzlingly High Correlations in fMRI Studies of Emotion, Personality, and Social Cognition.

            Functional magnetic resonance imaging (fMRI) studiesofemotion, personality, and social cognition have drawn much attention in recent years, with high-profile studies frequently reporting extremely high (e.g., >.8) correlations between brain activation and personality measures. We show that these correlations are higher than should be expected given the (evidently limited) reliability of both fMRI and personality measures. The high correlations are all the more puzzling because method sections rarely contain much detail about how the correlations were obtained. We surveyed authors of 55 articles that reported findings of this kind to determine a few details on how these correlations were computed. More than half acknowledged using a strategy that computes separate correlations for individual voxels and reports means of only those voxels exceeding chosen thresholds. We show how this nonindependent analysis inflates correlations while yielding reassuring-looking scattergrams. This analysis technique was used to obtain the vast majority of the implausibly high correlations in our survey sample. In addition, we argue that, in some cases, other analysis problems likely created entirely spurious correlations. We outline how the data from these studies could be reanalyzed with unbiased methods to provide accurate estimates of the correlations in question and urge authors to perform such reanalyses. The underlying problems described here appear to be common in fMRI research of many kinds-not just in studies of emotion, personality, and social cognition.
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              On the interpretation of weight vectors of linear models in multivariate neuroimaging.

              The increase in spatiotemporal resolution of neuroimaging devices is accompanied by a trend towards more powerful multivariate analysis methods. Often it is desired to interpret the outcome of these methods with respect to the cognitive processes under study. Here we discuss which methods allow for such interpretations, and provide guidelines for choosing an appropriate analysis for a given experimental goal: For a surgeon who needs to decide where to remove brain tissue it is most important to determine the origin of cognitive functions and associated neural processes. In contrast, when communicating with paralyzed or comatose patients via brain-computer interfaces, it is most important to accurately extract the neural processes specific to a certain mental state. These equally important but complementary objectives require different analysis methods. Determining the origin of neural processes in time or space from the parameters of a data-driven model requires what we call a forward model of the data; such a model explains how the measured data was generated from the neural sources. Examples are general linear models (GLMs). Methods for the extraction of neural information from data can be considered as backward models, as they attempt to reverse the data generating process. Examples are multivariate classifiers. Here we demonstrate that the parameters of forward models are neurophysiologically interpretable in the sense that significant nonzero weights are only observed at channels the activity of which is related to the brain process under study. In contrast, the interpretation of backward model parameters can lead to wrong conclusions regarding the spatial or temporal origin of the neural signals of interest, since significant nonzero weights may also be observed at channels the activity of which is statistically independent of the brain process under study. As a remedy for the linear case, we propose a procedure for transforming backward models into forward models. This procedure enables the neurophysiological interpretation of the parameters of linear backward models. We hope that this work raises awareness for an often encountered problem and provides a theoretical basis for conducting better interpretable multivariate neuroimaging analyses. Copyright © 2013 The Authors. Published by Elsevier Inc. All rights reserved.
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                Author and article information

                Journal
                Nature Protocols
                Nat Protoc
                Springer Nature
                1754-2189
                1750-2799
                February 9 2017
                February 9 2017
                : 12
                : 3
                : 506-518
                Article
                10.1038/nprot.2016.178
                5526681
                28182017
                © 2017

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