Intrinsic functional relations between human cerebral cortex and thalamus

Washington University, Department of Radiology, Campus Box 8225, 510 South Kingshighway Blvd., St. Louis, MO 63110, USA.
Journal of Neurophysiology (Impact Factor: 3.04). 09/2008; 100(4):1740-8. DOI: 10.1152/jn.90463.2008
Source: PubMed

ABSTRACT The brain is active even in the absence of explicit stimuli or overt responses. This activity is highly correlated within specific networks of the cerebral cortex when assessed with resting-state functional magnetic resonance imaging (fMRI) blood oxygen level-dependent (BOLD) imaging. The role of the thalamus in this intrinsic activity is unknown despite its critical role in the function of the cerebral cortex. Here we mapped correlations in resting-state activity between the human thalamus and the cerebral cortex in adult humans using fMRI BOLD imaging. Based on this functional measure of intrinsic brain activity we partitioned the thalamus into nuclear groups that correspond well with postmortem human histology and connectional anatomy inferred from nonhuman primates. This structure/function correspondence in resting-state activity was strongest between each cerebral hemisphere and its ipsilateral thalamus. However, each hemisphere was also strongly correlated with the contralateral thalamus, a pattern that is not attributable to known thalamocortical monosynaptic connections. These results extend our understanding of the intrinsic network organization of the human brain to the thalamus and highlight the potential of resting-state fMRI BOLD imaging to elucidate thalamocortical relationships.

  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Traditionally studies of brain function have focused on task-evoked responses. By their very nature such experiments tacitly encourage a reflexive view of brain function. While such an approach has been remarkably productive at all levels of neuroscience, it ignores the alternative possibility that brain functions are mainly intrinsic and ongoing, involving information processing for interpreting, responding to and predicting environmental demands. I suggest that the latter view best captures the essence of brain function, a position that accords well with the allocation of the brain's energy resources, its limited access to sensory information and a dynamic, intrinsic functional organization. The nature of this intrinsic activity, which exhibits a surprising level of organization with dimensions of both space and time, is revealed in the ongoing activity of the brain and its metabolism. As we look to the future, understanding the nature of this intrinsic activity will require integrating knowledge from cognitive and systems neuroscience with cellular and molecular neuroscience where ion channels, receptors, components of signal transduction and metabolic pathways are all in a constant state of flux. The reward for doing so will be a much better understanding of human behaviour in health and disease.
    Philosophical Transactions of The Royal Society B Biological Sciences 05/2015; 370(1668). DOI:10.1098/rstb.2014.0172 · 6.31 Impact Factor
  • Source
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Functional connectivity patterns are known to exist in the human brain at the millimeter scale, but the standard fMRI connectivity measure only computes functional correlations at a coarse level. We present a method for identifying fine-grained functional connectivity between any two brain regions by simultaneously learning voxel-level connectivity maps over both regions. We show how to formulate this problem as a constrained least-squares optimization, which can be solved using a trust region approach. Our method can automatically discover multiple correspondences between distinct voxel clusters in the two regions, even when these clusters have correlated timecourses. We validate our method by identifying a known division in the lateral occipital complex using only functional connectivity, thus demonstrating that we can successfully learn subregion connectivity structures from a small amount of training data.
    Machine Learning and Interpretation in Neuroimaging Workshop (MLINI), Advances in Neural Information Processing Systems (NIPS); 12/2012