Cognitive neuroimaging methods are well regarded as powerful research tools for studying the neural correlates of both health and disease, and have led to the generation of enormous amounts of data. As a result, quantitative meta-analysis methods have been developed and adopted by the community as a means to organize and synthesize large-scale data sets. Meta-analyses allow diverse results regarding cognitive and cortical dysfunction in disease and treatment studies to be assessed, and the most reliable patterns of results to be determined. However, the most labor-intensive step of these procedures is identification of the appropriate literature. Currently, researchers manually execute multiple PubMed searches utilizing different keywords from alternate terminologies to capture the entirety of the studies they seek. The Cognitive Paradigm Ontology (CogPO) was created in 2009 to address the non-standard vocabulary that exists for describing behavioral tasks or paradigms in brain mapping experiments. Here, we propose to leverage the National Center for Biomedical Ontologies'bioinformatics tools to integrate CogPO and the NCBO Annotator, to develop and test a new computational resource, BrainMap Tracker, that will enable automatic identification of candidate studies for neuroimaging meta-analyses. This tool will allow rapid filtering of PubMed abstracts to identify what paradigms have been utilized to study brain activations for a given disease, or vice versa. The proposed system will be a novel web-based resource for cognitive and clinical neuroscientists to provide coherent groupings of studies suitable for meta-analysis. BrainMap Tracker will alleviate the problem of incomplete neuroimaging meta-analyses, provide additional informatics support through semantic annotations for large scale text-mining and data visualization efforts, and offer initial recommendations for new annotation terms for better categorization of new studies.
The integration of results across the thousands of neuroimaging papers being published every year in neuropsychiatric diseases is needed to quickly and reliably identify the brain circuitry underlying cognitive and emotional dysfunction across different diagnostic categories. The BrainMap Tracker uses NCBO tools and semantic information to allow researchers to search PubMed and identify cross-disease groupings of studies using similar experimental methods or similar studies within a disease for meta-analysis.
|Eickhoff, Simon; Nichols, Thomas E; Van Horn, John D et al. (2016) Sharing the wealth: Neuroimaging data repositories. Neuroimage 124:1065-8|
|Ãkos SzabÃ³, C; Salinas, Felipe S; Li, Karl et al. (2016) Modeling the effective connectivity of the visual network in healthy and photosensitive, epileptic baboons. Brain Struct Funct 221:2023-33|
|Sutherland, Matthew T; Riedel, Michael C; Flannery, Jessica S et al. (2016) Chronic cigarette smoking is linked with structural alterations in brain regions showing acute nicotinic drug-induced functional modulations. Behav Brain Funct 12:16|
|Sutherland, Matthew T; Ray, Kimberly L; Riedel, Michael C et al. (2015) Neurobiological impact of nicotinic acetylcholine receptor agonists: an activation likelihood estimation meta-analysis of pharmacologic neuroimaging studies. Biol Psychiatry 78:711-20|
|Riedel, Michael C; Ray, Kimberly L; Dick, Anthony S et al. (2015) Meta-analytic connectivity and behavioral parcellation of the human cerebellum. Neuroimage 117:327-42|
|Laird, Angela R; Riedel, Michael C; Sutherland, Matthew T et al. (2015) Neural architecture underlying classification of face perception paradigms. Neuroimage 119:70-80|
|Ray, K L; Zald, D H; Bludau, S et al. (2015) Co-activation based parcellation of the human frontal pole. Neuroimage 123:200-11|
|Chakrabarti, Chayan; Jones, Thomas B; Luger, George F et al. (2014) Statistical algorithms for ontology-based annotation of scientific literature. J Biomed Semantics 5:S2|
|Burns, Gully A P C; Turner, Jessica A (2013) Modeling functional Magnetic Resonance Imaging (fMRI) experimental variables in the Ontology of Experimental Variables and Values (OoEVV). Neuroimage 82:662-70|
|Ray, Kimberly L; McKay, D Reese; Fox, Peter M et al. (2013) ICA model order selection of task co-activation networks. Front Neurosci 7:237|
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