Estimating the confidence level of white matter connections obtained with MRI tractography

Abstract

Background: Since the emergence of diffusion tensor imaging, a lot of work has been done to better understand the properties of diffusion MRI tractography. However, the validation of the reconstructed fiber connections remains problematic in many respects. For example, it is difficult to assess whether a connection is the result of the diffusion coherence contrast itself or the simple result of other uncontrolled parameters like for example: noise, brain geometry and algorithmic characteristics.

Methodology/principal findings: In this work, we propose a method to estimate the respective contributions of diffusion coherence versus other effects to a tractography result by comparing data sets with and without diffusion coherence contrast. We use this methodology to assign a confidence level to every gray matter to gray matter connection and add this new information directly in the connectivity matrix.

Conclusions/significance: Our results demonstrate that whereas we can have a strong confidence in mid- and long-range connections obtained by a tractography experiment, it is difficult to distinguish between short connections traced due to diffusion coherence contrast from those produced by chance due to the other uncontrolled factors of the tractography methodology.

Figure 1. Overview of the whole process.

Overview of the whole process. (A) Acquisition of the diffusion MR images. (B) Tractography in the brain WM. (C) Partitioning of the WM-GM interface into small regions of interest (ROIs). (D) Creation of the original brain connectivity graph using the results of steps B and C. (E) Construction of randomized versions of the original brain connectivity graph (the same partition into ROIs is used). (F) Computation of the confidence level of every edge in the original brain connectivity graph.

Figure 2. Node and edge statistics for G_O and G_R.

Node and edge statistics for G_O and G_R. A: Node degree distribution. B: Node strength distribution. C: Edge distance distribution. D: Edge weight distribution.

Figure 3. Distribution of non-zero edges and mean edge weight vs. edge distance.

(A) Distribution of non-zero edges in G_O and G_R as a function of the edge distance l_e. (B) Mean edge weight in G_O and G_R as a function of the edge distance l_e.

Figure 4. Confidence level distribution and mean confidence level vs. edge distance.

(A) Distribution of the confidence level computed for non-zero edges. (B) Mean confidence level as a function of the edge distance l_e.

Figure 5. High-resolution structural connection matrix.

High-resolution structural connection matrix, representing the fiber density (upper triangular part) and the confidence level (lower triangular part). The matrix is organized as follows: the upper left block represents the connections in the right hemisphere and the lower right block shows the connections in the left hemisphere. The off-diagonal blocks map the inter-hemispheric connections. The color bar at the left and bottom of the matrix help to make the correspondence between the matrix entries and the cortical parcels as displayed on the left part of the figure.

Figure 6. Standard vs. optimized confidence levels: mean confidence level vs. edge distance.

Mean standard and optimized confidence levels as a function of the edge distance l_e.

Figure 7. Matrix of the differences between standard and optimized confidence level.

High-resolution matrix, representing the difference between the standard and optimized confidence level (absolute values). In the insert: distribution of the differences between standard and optimized confidence level (absolute values), computed for non-zero edges.