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      Large-scale automated synthesis of human functional neuroimaging data

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          Abstract

          The explosive growth of the human neuroimaging literature has led to major advances in understanding of human brain function, but has also made aggregation and synthesis of neuroimaging findings increasingly difficult. Here we describe and validate an automated brain mapping framework that uses text mining, meta-analysis and machine learning techniques to generate a large database of mappings between neural and cognitive states. We demonstrate the capacity of our approach to automatically conduct large-scale, high-quality neuroimaging meta-analyses, address long-standing inferential problems in the neuroimaging literature, and support accurate ‘decoding’ of broad cognitive states from brain activity in both entire studies and individual human subjects. Collectively, our results validate a powerful and generative framework for synthesizing human neuroimaging data on an unprecedented scale.

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          Most cited references30

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          Functional grouping and cortical-subcortical interactions in emotion: a meta-analysis of neuroimaging studies.

          We performed an updated quantitative meta-analysis of 162 neuroimaging studies of emotion using a novel multi-level kernel-based approach, focusing on locating brain regions consistently activated in emotional tasks and their functional organization into distributed functional groups, independent of semantically defined emotion category labels (e.g., "anger," "fear"). Such brain-based analyses are critical if our ways of labeling emotions are to be evaluated and revised based on consistency with brain data. Consistent activations were limited to specific cortical sub-regions, including multiple functional areas within medial, orbital, and inferior lateral frontal cortices. Consistent with a wealth of animal literature, multiple subcortical activations were identified, including amygdala, ventral striatum, thalamus, hypothalamus, and periaqueductal gray. We used multivariate parcellation and clustering techniques to identify groups of co-activated brain regions across studies. These analyses identified six distributed functional groups, including medial and lateral frontal groups, two posterior cortical groups, and paralimbic and core limbic/brainstem groups. These functional groups provide information on potential organization of brain regions into large-scale networks. Specific follow-up analyses focused on amygdala, periaqueductal gray (PAG), and hypothalamic (Hy) activations, and identified frontal cortical areas co-activated with these core limbic structures. While multiple areas of frontal cortex co-activated with amygdala sub-regions, a specific region of dorsomedial prefrontal cortex (dmPFC, Brodmann's Area 9/32) was the only area co-activated with both PAG and Hy. Subsequent mediation analyses were consistent with a pathway from dmPFC through PAG to Hy. These results suggest that medial frontal areas are more closely associated with core limbic activation than their lateral counterparts, and that dmPFC may play a particularly important role in the cognitive generation of emotional states.
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            The visual word form area: expertise for reading in the fusiform gyrus.

            Brain imaging studies reliably localize a region of visual cortex that is especially responsive to visual words. This brain specialization is essential to rapid reading ability because it enhances perception of words by becoming specifically tuned to recurring properties of a writing system. The origin of this specialization poses a challenge for evolutionary accounts involving innate mechanisms for functional brain organization. We propose an alternative account, based on studies of other forms of visual expertise (i.e. bird and car experts) that lead to functional reorganization. We argue that the interplay between the unique demands of word reading and the structural constraints of the visual system lead to the emergence of the Visual Word Form Area.
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              Predicting human brain activity associated with the meanings of nouns.

              The question of how the human brain represents conceptual knowledge has been debated in many scientific fields. Brain imaging studies have shown that different spatial patterns of neural activation are associated with thinking about different semantic categories of pictures and words (for example, tools, buildings, and animals). We present a computational model that predicts the functional magnetic resonance imaging (fMRI) neural activation associated with words for which fMRI data are not yet available. This model is trained with a combination of data from a trillion-word text corpus and observed fMRI data associated with viewing several dozen concrete nouns. Once trained, the model predicts fMRI activation for thousands of other concrete nouns in the text corpus, with highly significant accuracies over the 60 nouns for which we currently have fMRI data.
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                Author and article information

                Journal
                101215604
                32338
                Nat Methods
                Nature methods
                1548-7091
                1548-7105
                6 June 2011
                26 June 2011
                1 February 2012
                : 8
                : 8
                : 665-670
                Affiliations
                [1 ]Department of Psychology and Neuroscience, University of Colorado at Boulder, Boulder, CO 80309, USA
                [2 ]Imaging Research Center and Departments of Psychology and Neurobiology, University of Texas at Austin, Austin, TX 78759, USA
                [3 ]Department of Statistics & Warwick Manufacturing Group, University of Warwick, Coventry, CV4 7AL, UK
                [4 ]Department of Anatomy & Neurobiology, Washington University School of Medicine, St. Louis MO 63110, USA
                Author notes
                [* ] Corresponding author: Tal Yarkoni, Department of Psychology and Neuroscience, UCB 345, University of Colorado at Boulder, Telephone: (303) 492-4299, tal.yarkoni@ 123456colorado.edu

                Author Contributions

                TY conceived the project and carried out most of the software implementation, data analysis, and writing. RAP provided data and performed analyses. TEN provided statistical advice, reviewed all statistical procedures, and contributed to the implementation of the naïve Bayes classifier. DCVE provided data, contributed to automated data extraction, and coordinated data validation. TDW conceived the classification analyses, wrote part of the software, provided data, and suggested and performed analyses. All authors contributed to the writing and editing of the manuscript at all stages.

                Article
                nihpa300972
                10.1038/nmeth.1635
                3146590
                21706013
                e4a47ce8-91c1-4eba-a3bf-0b6efee9a925

                Users may view, print, copy, download and text and data- mine the content in such documents, for the purposes of academic research, subject always to the full Conditions of use: http://www.nature.com/authors/editorial_policies/license.html#terms

                History
                Funding
                Funded by: National Institute on Drug Abuse : NIDA
                Award ID: RC1 DA028608-01 || DA
                Funded by: National Institute of Mental Health : NIMH
                Award ID: R01 MH082795-01 || MH
                Funded by: National Institute of Mental Health : NIMH
                Award ID: R01 MH076136-01A1 || MH
                Funded by: National Institute of Mental Health : NIMH
                Award ID: R01 MH060974-18 || MH
                Funded by: National Institute on Drug Abuse : NIDA
                Award ID: R01 DA027794-01 || DA
                Funded by: National Institute of Nursing Research : NINR
                Award ID: F32 NR012081-01A1 || NR
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                Life sciences
                Life sciences

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