Visualizing functional pathways in the human brain using correlation tensors and magnetic resonance imaging

Abstract

Functional magnetic resonance imaging usually detects changes in blood oxygenation level dependent (BOLD) signals from T2*-sensitive acquisitions, and is most effective in detecting activity in brain cortex which is irrigated by rich vasculature to meet high metabolic demands. We recently demonstrated that MRI signals from T2*-sensitive acquisitions in a resting state exhibit structure-specific temporal correlations along white matter tracts. In this report we validate our preliminary findings and introduce spatio-temporal functional correlation tensors to characterize the directional preferences of temporal correlations in MRI signals acquired at rest. The results bear a remarkable similarity to data obtained by diffusion tensor imaging but without any diffusion-encoding gradients. Just as in gray matter, temporal correlations in resting state signals may reflect intrinsic synchronizations of neural activity in white matter. Here we demonstrate that functional correlation tensors are able to visualize long range white matter tracts as well as short range sub-cortical fibers imaged at rest, and that evoked functional activities alter these structures and enhance the visualization of relevant neural circuitry. Furthermore, we explore the biophysical mechanisms underlying these phenomena by comparing pulse sequences, which suggest that white matter signal variations are consistent with hemodynamic (BOLD) changes associated with neural activity. These results suggest new ways to evaluate MRI signal changes within white matter.

Learning-based structurally-guided construction of resting-state functional correlation tensors

“…in [21], the FCT T i for the voxel V i in the input rs-fMRI can be represented using a 3×3 symmetric matrix. The elements in T i are written as follows: $${\mathit{T}}_i=\left[\begin{array}{ccc}{T}_{xx}& T_{xy}& T_{xz}\\ T_{xy}& T_{yy}& T_{yz}\\ T_{xz}& T_{yz}& T_{zz}\end{array}\right].$$…”

“…In [21], T i is obtained using the following steps: The voxel V i computes its Pearson’s linear correlation coefficient C ij with its surrounding 26 voxels V j by comparing their time courses. The equation is given as C ij = f corr ( V i , V j ), where f corr is a function to compute the Pearson’s correlation coefficient of the time courses of V i and V j .…”

“…They captured this anisotropy using a local spatiotemporal correlation tensor and demonstrated that the realistic fiber bundle can be obtained by tracing the directions of FCs. They subsequently introduced “functional correlation tensor (FCT)” in [21] and demonstrated the ability of FCT in revealing several major WM fiber bundles, indicating that FCT is coherent with DTI. However, our experiments indicate that FCTs are sensitive to noise.…”

“…The aim is to reduce the influence of noise in WM to the FCT estimation, which is referred from the finding of the consistency between the WM fiber direction and the spatiotemporal correlation pattern of the WM BOLD signals [20, 21]. The main contribution in this paper is four-fold: First , we develop a novel strategy, aiming at increasing the noise robustness of FCT using a patch-based approach.…”

Improving Functional MRI Registration Using Whole-Brain Functional Correlation Tensors

“…Motivated by the feasibility of using fMRI to detect WM activations [9] and connectivities not only over a long distance [10] but also in a local range [11], we propose a novel functional registration algorithm by incorporating functional information in both GM and WM to guide the subsequent registration. There are at least three aspects of contribution in our proposed method.…”