1. Neuroimage. 2012 Jan 10. [Epub ahead of print]
The future of the human connectome.
Van Essen DC, Ugurbil K.
Washington University School of Medicine, Anatomy & Neurobiology, 660 S. Euclid Avenue, St. Louis, MO 63128, USA.
Abstract
The opportunity to explore the human connectome using cutting-edge neuroimaging methods has elicited widespread interest. How far will the field be able to progress in deciphering long-distance connectivity patterns and in relating differences in connectivity to phenotypic characteristics in health and disease? We discuss the daunting nature of this challenge in relation to specific complexities of brain circuitry and known limitations of in vivo imaging methods. We also discuss the excellent prospects for continuing improvements in data acquisition and analysis. Accordingly, we are optimistic that major insights will emerge from human connectomics in the coming decade.
2. Neuroimage. 2012 Jan 10. [Epub ahead of print]
A short history of causal modeling of fMRI data.
Stephan KE, Roebroeck A.
Laboratory for Social and Neural Systems Research, Dept. of Economics, University of Zurich, Switzerland; Wellcome Trust Centre for Neuroimaging, Institute of Neurology, University College London, UK.
Abstract
Twenty years ago, the discovery of the blood oxygen level dependent (BOLD) contrast and invention of functional magnetic resonance imaging (MRI) not only allowed for enhanced analyses of regional brain activity, but also laid the foundation for novel approaches to studying effective connectivity, which is essential for mechanistically interpretable accounts of neuronal systems. Dynamic causal modeling (DCM) and Granger causality (G-causality) modeling have since become the most frequently used techniques for inferring effective connectivity from fMRI data. In this paper, we provide a short historical overview of these approaches, describing milestones of their development from our subjective perspectives.
3. Neuroimage. 2012 Jan 10. [Epub ahead of print]
The future of FMRI connectivity.
Smith SM.
Abstract
"FMRI connectivity" encompasses many areas of research, including resting-state networks, biophysical modelling of task-FMRI data and bottom-up simulation of multiple individual neurons interacting with each other. In this brief paper I discuss several outstanding areas that I believe will see exciting developments in the next few years, in particular concentrating on how I think the currently separate approaches will increasingly need to take advantage of each others' respective complementarities.