Large-scale Automated Synthesis of Functional Neuroimaging Data

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DESCRIPTION (provided by applicant): The explosive growth of the human neuroimaging literature has led to major advances in understanding of normal and abnormal human brain function, but has also made aggregation and synthesis of neuroimaging findings increasingly difficult. The goal of this project is to develop an automated software platform for large-scale synthesis of human functional neuroimaging studies. Our work builds directly on an existing software platform (NeuroSynth) and involves key extensions and improvements that focus on (i) aggregation, (ii) coding, (iii) synthesis, and (iv) sharing of functional neuroimaging data. In Aim 1, we will use computational linguistics and bioinformatics data mining techniques to develop new algorithms for automatically extracting activation foci and associated metadata from published neuroimaging articles. In Aim 2, we will use topic-modeling techniques such as Latent Dirichlet Analysis in combination with existing cognitive ontologies such as the Cognitive Atlas to develop structured representations of automatically extracted neuroimaging data. In Aim 3, we will improve the meta-analysis and classification capacities of our existing platform by implementing a state-of- the-art hierarchical Bayesian meta-analysis method recently developed by the research team. Finally, in Aim 4, we will develop a state-of-the-art web interface (://neurosynth.org) that supports real-time, in-browser access to the data, results, and tools produced in Aims 1 - 3. Realizing these objectives will introduce powerful new tools for organizing and synthesizing the neuroimaging literature on an unprecedented scale. These tools will be freely and publicly available to anyone with an internet connection, enabling rapid and efficient application to a broad range of clinical and basic research applications.