Previous Talks

Abstract Recent advances in noninvasive neuroimaging have set the stage for the systematic exploration of human brain circuits in health and disease. One such effort is the Human Connectome Project (HCP), which will characterize brain circuitry and its variability in a large population of healthy adults. This talk will review recent progress by a consortium of investigators at Washington University, University of Minnesota, University of Oxford, and 7 other institutions, who are engaged in a 5-year project to characterize the human connectome in 1,200 individuals (twins and their non-twin siblings). Information about structural and functional connectivity is being acquired using diffusion MRI and resting-state fMRI, respectively. Additional modalities include task-evoked fMRI and MEG/EEG, plus extensive behavioral testing and genotyping. Each of these methods is powerful, yet faces significant technical limitations that are important to characterize and be mindful of when interpreting neuroimaging data.
Advanced visualization and analysis methods developed by the HCP enable characterization of brain circuits in individuals and group averages at high spatial resolution and at the level of functionally distinct brain parcels. Comparisons across subjects will reveal aspects of brain circuitry which are related to particular behavioral capacities and which are heritable or related to specific genetic variants. Data from the HCP is being made freely available to the neuroscience community via a user-friendly informatics platform. Altogether, the HCP will provide invaluable information about the healthy human brain and its variability and will set the stage for characterizing abnormal brain connectivity in a variety of brain disorders and diseases.

• Reference PDF Article (Yarkoni T, Poldrack RA, Van Essen DC, Wager TD (2010). Cognitive neuroscience 2.0: building a cumulative science of human brain function. Trends Cogn Sci. 14 (11): 489-96. )
• Slideshow Click here to view
• Lab Website http://www.brainvis.wustl.edu