Recent years have seen dramatic advances in the technical capacity to image the internal workings of the human brain. As humans engage in mental processes, functional magnetic resonance imaging (fMRI) measurements of the activity of the entire human brain -- hundreds of thousands of data points -- can be acquired each second. Developments in the ability to collect such enormous and complex datasets have outstripped methods for analyzing them. One critical gap is that whereas the brain is organized in terms of functional pathways connecting multiple brain regions, no adequate, widely-available methods exist for identifying and testing the integrity of functional pathways in humans. With support from the National Science Foundation, Dr. Tor Wager and colleagues at Columbia University will conduct research aimed at developing analysis techniques for modeling functional connectivity in the human brain and produce high-quality software for this purpose that will be made publicly available. The multilevel mediation/moderation (M3) framework that they intend to develop will extend recent advances in multilevel modeling of multi-equation systems and expands their functionality to examine connectivity among many brain regions. The M3 framework will be the first method that can provide population inference about brain connectivity spanning more than two regions. Neural responses to pain will be used as a model system to develop and test the framework because pain pathways are well-characterized. The M3 framework will be used identify ascending thalamocortical pathways - an innovation not afforded by existing methods - and to examine how their activity is regulated by "control" circuits in the frontal cortex. Together, the method and application are expected to shed light on how functional pathways and internal feedback may be investigated in the human brain.

This research will lead to advances in structural equation modeling, including the modeling of covariances as random variables in a multi-level framework, robust estimation, and the fusion of structural models and dimension reduction techniques. Another innovative aspect of this work will be the development of recursive algorithms that permit variable selection in structural models, which will allow for hybrid confirmatory/model-building approaches necessary for application to large datasets. User-friendly software and documentation will make the new tools accessible to researchers in diverse fields, including neuroscience, economics, bioengineering, and psychology, and will encourage broader participation in brain-based analysis of human cognitive and affective processes. The methods will also be disseminated in courses and workshops, through scientific interactions between multi-disciplinary research groups in the U. S. and internationally, and through software posted for public download on Columbia's website. Finally, undergraduate and graduate cross-training in statistics and cognitive neuroscience will allow students in both areas to benefit from exposure to new concepts and techniques.

This award was supported as part of the fiscal year 2006 Mathematical Sciences priority area special competition on Mathematical Social and Behavioral Sciences (MSBS).

National Science Foundation (NSF)
Division of Behavioral and Cognitive Sciences (BCS)
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Lynne Bernstein
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Columbia University
New York
United States
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