The goals of this proposal are to further investigate and evaluate recent new discoveries about the nature of temporal variations in magnetic resonance imaging (MRI) signals from the human brain, acquired in a resting state, which potentially provide a completely new basis for quantifying the functional architecture of the brain We have recently shown that local inter-voxel correlations between resting state signal fluctuations within both white and gray matters are spatially anisotropic. Measurements of these anisotropic correlations and subsequent analyses permit the derivation of a new mathematical descriptor, a functional connectivity tensor (FCT), that quantifies the functional relationships between neighboring voxels but which also delineates longer range dynamic connections. Some of the features of FCTs superficially closely resemble the appearance of diffusion tensor imaging (DTI) data across large brain regions but without the use of any diffusion gradients. This discovery demonstrates there exist strong correlations between static white matter structures and functional, temporal dynamics which in turn suggest a highly original approach for integrating functional and structural information. The construction and analysis of functional connectivity tensors permits tractography of functional pathways, in similar manner to DTI, which appear to follow white matter tracts, but their interpretation is not clear. Furthermore, the functional tensors appear to change in response to neural stimulation, even though task-activation of white matter has proven elusive. Resting state connectivity has been extensively used to delineate functional circuits within the cortex but to date has been completely overlooked in white matter, and current opinions are biased against being able to detect neural activity in white matter using MRI. The objectives of this research therefore are to construct functional connectivity tensors in normal brains at rest, compare these to underlying structural features, and elucidate the underlying biophysical mechanisms that account for their origins.
Our specific aims are (i) to measure and characterize functional connectivity tensors in a resting state in normal subjects, assess their reproducibility, and determine how they depend on specific technical factors, including field strength and spatial resolution; (ii) to quantitatively compare and relate FCT with DTI data, which may be achieved by appropriate analyses of atlases created from a population of normal subjects co-registered to the same space; and (iii) to investigate the biophysical basis of resting state correlational anisotropy, and perform experiments to determine whether such correlations originate from BOLD (blood oxygenation level dependent) -type hemodynamic processes and how they relate to underlying neural electrical activity. Overall, these will be the first comprehensive evaluations of our novel observations of anisotropy of correlations in resting state MRI signals from white matter. The proposed use of FCTs for detailed mapping of brain functional connectivity is compelling and offers the potential of advancing our understanding of the functional organization of complex neural networks in the brain.
Functional MRI has been widely used to delineate the functional architecture of the brain, and resting state methods, in which subjects perform no task, have allowed the detection of multiple neural circuits via analyses of synchronized signal variations from different parts of the cortex that are of hemodynamic origins. These methods provide important measures of the integrity of critical brain functions and are altered in various disorders, but they have never been applied to white matter because it is generally believed that neural activity provides no detectable changes in MRI signals from white matter. This research builds on our recent demonstrations that signal variations within white matter from neighboring regions are highly correlated and the correlations are anisotropic and reveal an underlying structure that may be used to delineate functionally connected circuits spanning large sections of the brain that in turn may provide an indication of abnormal brain function.
