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 voxelaveraged estimates of local fiber orientation obtained from DWI data that is unlikely to be overcome by improvements in data acquisition and analysis alone. diffusion MRI | tractography | white matter | tracer | validation T he creation of a comprehensive map of the connectional neuroanatomy of the human brain would be a fundamental achievement in neuroscience. However, despite the numerous efforts to date (for a historical review, see ref. 1), creating this map remains a challenge. A major limitation is that the current goldstandard technique for mapping structural connections, which requires the injection of axonal tracers, cannot be used in humans. The introduction of diffusion-weighted MRI (DWI) (2-4) and the subsequent advent of diffusion tensor MRI (DTI) (5) opened the possibility of exploring the structural properties of white matter in the living human brain (6). Local DWI measures are used clinically for the early detection of stroke and for the characterization of neurological disorders such as multiple sclerosis, epilepsy, and brain gliomas, among others (7). In addition, tractography approaches (8-12) that can infer structural brain connectivity based on brain-wide local DWI measurement have been developed (for reviews, see refs. 13 and 14). The success of DWI tractography as a method for studying fiber trajectories has led to a systematic characterization of large white-matter pathways of the living human brain (e.g., ref. 15), and now it is used routinely to provide a structural explanation for aspects of human brain function (16). A major limitation of DWI tractography is that its characterization of axonal pathways is based on indirect information and numerous assumptions. Local white matter orientation profiles are based on the statistical displacement profile (i.e., diffusion propagator) of water molecules in brain tissue on the coarse scale of a voxel, and fiber trajectories are inferred based on the adjacency of similar diffusion profiles. This approach differs fundamentally from conventional tract-tracing approaches in animals, which involve the physical transport of traceable molecules through the cells' axoplasm over a large distance. Because these molecules occupy positions within the axon, it sometimes is possible to reconstruct the trajectory of individual neurons through the white matter (e.g., ref. 17). Given the inherent coarseness of DWI tractography, it can be argued that the prospect of using this method to reconstruct complex axonal pathways accurately in the human brain, in a manner similar to that used for molecular tracers in animals, is likely to be intrinsically problematic. Indeed, the limitations of DWI tractography techniques have been noted since their inception (8), and the anatomical accuracy of results from tractography based on the tensor model has been shown to be mixed (18). This inaccuracy has been attributed to two main factors. The first relates to the assumptions underlying tractography algorithms. For example, it has long been recognized that a simple tensor model (19) of local diffusion leads to problems in certain white matter regions where fibers cross within individual voxels. As a remedy, high angular resolution diffusion imaging (HARDI) methods (e.g., refs. 20-24) have been developed to enable better characterization of the diffusion displacement profile and to improve the accuracy of tractography. The second factor limiting accuracy stems from the low quality of clinical DWI data because of various sources of noise. Eddy current distortions, Significance Diffusion-weighted MRI (DWI) tractography is widely used to map structural connections of the human brain in vivo and has been adopted by large-scale initiatives such as the human connectome project. Our results indicate that, even with highquality data, DWI tractography alone is unlikely to provide an anatomically accurate map of the brain connectome. It is crucial to complement tractography results with a combination of histological or neurophysiological methods to map structural connectivity accurately. Our findings, however, do not diminish the importance of diffusion MRI as a noninvasive tool that offers important quantitative measures related to brain tissue microstructure and white matter architecture.
In brain networks, even the most advanced forms of imaging cannot provide an accurate or complete description of structural connectivity, see REF for example.
Anatomical accuracy of brain connections derived from diffusion MRI tractography is inherently limited
Proceedings of the National Academy of Sciences of the United States of America
Proceedings of the National Academy of Sciences of the United States of America
Medicine Psychology