Alladi Ramakrishnan Hall

Multivariate meta-analysis of functional brain imaging literature

Anjali Krishnan

Brooklyn College, City University of New York, New York City, USA

With current advances in brain imaging technology, it is safe to claim that cognitive neuroscience is a massive contributor to the ongoing ‘big data’ phenomenon. The exponential growth of the function brain imaging literature has resulted in the development of coordinate based meta-analytic (CBMA) tools that aim to consolidate major neuroscientific findings. CBMA tools collect and store study-wise brain coordinates and textual information, and perform statistical analyses that generate spatial meta-analytic maps of brain regions associated with only one specific term of interest (e.g., memory). However, one of the limitations of CBMA tools is that they do not provide meta-analytic comparisons of brain regions that are associated with multiple terms of interest. In this work, we present a novel approach to compare brain regions that are associated with three terms of interest identified from the neuroimaging literature. A key aspect to our approach is to represent complex information in an intuitive way, and provide a frame work for insightful data analytic practices infoWith current advances in brain imaging technology, it is safe to claim that cognitive neuroscience is a massive contributor to the ongoing `big data' phenomenon. The exponential growth of the function brain imaging literature has resulted in the development of coordinate based meta-analytic (CBMA) tools that aim to consolidate major neuroscientific findings. CBMA tools collect and store study-wise brain coordinates and textual information, and perform statistical analyses that generate spatial meta-analytic maps of brain regions associated with only one specific term of interest (e.g., memory). However, one of the limitations of CBMA tools is that they do not provide meta-analytic comparisons of brain regions that are associated with multiple terms of interest. In this work, we present a novel approach to compare brain regions that are associated with three terms of interest identified from the neuroimaging literature. A key aspect to our approach is to represent complex information in an intuitive way, and provide a frame work for insightful data analytic practices informed by cognitive neuroscience.rmed by cognitive neuroscience.