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scale automated synthesis of human functional neuroimaging data

The rapid growth of the literature on neuroimaging in humans has led to major advances in our understanding of human brain function but has also made it increasingly difficult to aggregate and synthesize neuroimaging findings. 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 show that our approach can be used to automatically conduct large scale, high quality neuroimaging meta analyses, address long standing inferential problems in the neuroimaging literature and support accurate of broad cognitive states from brain activity in both entire studies and individual human subjects. Collectively, our results have validated a powerful and generative framework for synthesizing human neuroimaging data on an unprecedented scale.

(a) Outline of the NeuroSynth approach. The full text of a large corpus of articles is retrieved and terms of scientific interest are stored in a database. Articles are retrieved from the database on the basis of a user entered search string (for example, and peak coordinates from the associated articles are extracted from tables. A meta analysis of the peak coordinates is automatically performed, producing a whole brain map of the posterior probability of the term given activation at each voxel (P(painactivation)). (b) Outlines of forward and reverse inference in brain imaging. Given a known psychological manipulation, one can quantify the corresponding changes in brain activity and generate a forward inference, but given an observed pattern of activity, drawing a reverse inference about associated cognitive states is more difficult because multiple cognitive states could have similar neural signatures. (c) Given meta analytic posterior probability maps for multiple terms (for example, working memory, emotion and pain), one can classify a new activation map by identifying the class with the highest probability, P, given the new data (in this example, pain).

(a) Meta analytic maps produced manually in previous studies14, 15, 16. (b) Automatically generated forward inference maps showing the probability of activation given the presence of the term (P(act.term)). (c) Automatically generated reverse inference maps showing the probability of the term given observed activation (P(termact.)). Meta analyses were carried out for working memory (top), emotion (middle) and physical pain (bottom) and mapped to the PALS B12 atlas30. Regions in b were consistently associated with the term and regions in c were selectively associated with the term. To account for base differences in term frequencies, reverse inference maps assumed uniform priors (equal 50% probabilities of and term Activation in orange or red regions implies a high probability that a term is present, and activation in blue regions implies a high probability that a term is not present. Values for all images are shown only for regions that survived a test of association between term and activation, with a whole brain correction for multiple comparisons (false discovery rate was 0.05). DLPFC, dorsolateral prefrontal cortex; DACC, dorsal anterior cingulate cortex; AI, anterior insula.

(a) Naive Bayes classifier performance when cross validated on studies in the database (left) or applied to individual subjects from studies not in the database (right). knock off van cleef green necklace (b) Whole brain maximum posterior probability map; each voxel is colored by van cleef four leaf clover necklace imitation the term with the highest associated probability. (c) Whole brain maps showing the proportion of individual subjects in the three pain studies (n = 79 subjects total) who showed activation at each voxel (P 0.05, uncorrected), averaged separately for subjects who were classified correctly (n = 51 subjects; top) or incorrectly (n = 28 subjects; bottom). conceived the project and carried out most of the software implementation, data analysis and writing. provided data and performed analyses. provided statistical advice, reviewed all statistical procedures and contributed to the implementation of the naive Bayes classifier. provided data, contributed to automated data van cleef and arpels mini alhambra necklace copy extraction and coordinated data validation. conceived the classification analyses, wrote part of the software, provided data and suggested and performed analyses.

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