Anatomical accuracy of brain connections derived from diffusion MRI tractography is inherently limited

Abstract

Tractography based on diffusion-weighted MRI (DWI) is widely used for mapping the structural connections of the human brain. Its accuracy is known to be limited by technical factors affecting in vivo data acquisition, such as noise, artifacts, and data undersampling resulting from scan time constraints. It generally is assumed that improvements in data quality and implementation of sophisticated tractography methods will lead to increasingly accurate maps of human anatomical connections. However, assessing the anatomical accuracy of DWI tractography is difficult because of the lack of independent knowledge of the true anatomical connections in humans. Here we investigate the future prospects of DWI-based connectional imaging by applying advanced tractography methods to an ex vivo DWI dataset of the macaque brain. The results of different tractography methods were compared with maps of known axonal projections from previous tracer studies in the macaque. Despite the exceptional quality of the DWI data, none of the methods demonstrated high anatomical accuracy. The methods that showed the highest sensitivity showed the lowest specificity, and vice versa. Additionally, anatomical accuracy was highly dependent upon parameters of the tractography algorithm, with different optimal values for mapping different pathways. These results suggest that there is an inherent limitation in determining long-range anatomical projections based on voxel-averaged estimates of local fiber orientation obtained from DWI data that is unlikely to be overcome by improvements in data acquisition and analysis alone.

Keywords: diffusion MRI; tracer; tractography; validation; white matter.

Fig. 1.

A representative illustration of the procedure used for assessing the anatomical accuracy of various diffusion tractography approaches. (A) The red dots indicate the topography of axonal pathways following the injection of an anterograde tracer in the left PCG, redrawn from the reference atlas (data from ref. 17, p. 324) to the corresponding coronal slice of the DWI volume, which was rotated and resliced to match the histology slices used in the reference atlas. (B) For each DWI slice that was anatomically matched with the histology slice from the reference atlas, the gray matter (black lines) and white matter (white lines) regions were manually segmented and parcellated into discrete ROIs. The red dots in B represent the location of axonal pathways as determined with the Q-ball tractography method based on the left PCG as the seed region and an angular threshold of 80°. (C) Using the gray and white matter ROIs illustrated in B as a grid, the agreement between tracer and tractography results was computed for each slice. The colors indicate ROIs that were categorized as true positives (TP), false negatives (FN), false positives (FP), and true negatives (TN). (D) Histogram showing the TP, FN, FP, and TN for the specific slice, which were used to compute the specificity [TN/(TN + FP)] and sensitivity [TP/(TP + FN)] values for the single slice shown here. AS, arcuate sulcus; Cd, caudate; Cl, claustrum; LF, lateral fissure; Put, putamen; SLF, superior longitudinal fasciculus; StB, striatal bundle; STS, superior temporal sulcus.

Fig. 2.

The specificity and sensitivity of diffusion tractography techniques differ by diffusion model and location of the seed ROI. Specificity and sensitivity for seed ROI-PCG (A) and seed ROI-V4v (B). For all four types of diffusion models tested, the seed ROI was a sphere with a radius of 10 voxels, and the default angular threshold was used for deterministic (45°) and probabilistic (80°) tractography techniques. The Youden index value (J), which summarizes the performance of each tractography technique, is noted.

Fig. 3.

The effect of changing the angular threshold on the specificity and sensitivity of results from the four tractography techniques across the two seed ROIs. (A–H) For both ROIs, the Youden index value (J) for each angular threshold is noted, and the threshold that yields the most optimal J value is depicted by a filled symbol. Note that for the B&S method, the specificity and sensitivity are presented only for the optimal threshold. See Figs. S1 and S2 for the ROC curves for each angular threshold.

Fig. 4.

Sensitivity and specificity of the four tractography techniques in seed location PCG (A) and V4v (B) when the seed ROI was restricted within gray matter. For all four types of diffusion models tested, the angular threshold was set to 80°. See Fig. S3 for a summary of the true positives and false negatives for the two seed regions.