Comparison of fiber tracts derived from in-vivo DTI tractography with 3D histological neural tract tracer reconstruction on a macaque brain

Dauguet, Julien; Peled, Sharon; Березовский, В. К.; Delzescaux, Thierry; Warfield, Simon K.; Born, Richard T.; Westin, Carl‐Fredrik

doi:10.1016/j.neuroimage.2007.04.067

Cited by 227 publications

(190 citation statements)

References 30 publications

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“…The present work extends and underlines other in vivo MRI studies of the rhesus monkey brain, which -apart from an earlier feasibility study [29] -mainly focused on the somatosensory and motor system using similar tract tracing techniques [30] or on prefrontal areas using probabilistic fiber tractography [31]. Although the latter approach yields a comprehensive list of all possible connections, it does not offer a straightforward mechanism for identifying individual fiber pathways as attempted here.…”

Section: Discussionmentioning

confidence: 64%

In Vivo Mapping of Fiber Pathways in the Rhesus Monkey Brain

Hofer¹,

Frahm²

2008

TOMIJ

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The study of complex fiber systems in relation to the cognitive abilities of humans is a long-standing challenge for neuroscientists. With the development of diffusion tensor imaging (DTI) it is now possible to visualize large fiber bundles non-invasively. The existing knowledge of the white matter architecture largely stems from either lesion studies of human patients or, in more detail, tracer injection studies of non-human primates. Hence, it seems mandatory to compare DTI results with histochemical findings obtained for the same species. Using a geometrically undistorted DTI technique and fiber tractography, we examined the fiber anatomy of the macaque brain in vivo and related the results to fiber pathways previously identified in monkeys with conventional tract tracing. The approach identified multiple fiber tracts including the main association and projection pathways as well as fibers of the limbic system, commissural system, optic system, and cerebellar system. In conclusion, in vivo fiber tractography based on current-state DTI allows for a comprehensive analysis of major fiber pathways in the intact macaque brain.

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Section: Discussionmentioning

confidence: 64%

In Vivo Mapping of Fiber Pathways in the Rhesus Monkey Brain

Hofer¹,

Frahm²

2008

TOMIJ

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“…However, in these studies the validation was based primarily on qualitative observations. Studies that assessed the accuracy of DWI tractography by quantifying the overlap fraction between tracer-labeled regions and tractography (39,40) found that DWI tractography missed some of the tracer-labeled target regions (i.e., false negatives). However, these previous studies differ from the present study in three ways.…”

Section: Discussionmentioning

confidence: 99%

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

Thomas

İrfanoğlu

et al. 2014

Proc. Natl. Acad. Sci. U.S.A.

708

633

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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.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 indirec...

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“…For example, we observed that the potential connection between posterior cingulate cortices (which was found to have a high partial correlation in two subjects when using the graphical lasso) was absent in our estimates. In order to prevent the exclusion of important connections due to biases associated with probabilistic streamlining (Dauguet et al, 2007;Li et al, 2012), a more lenient threshold may be used to estimate the structural graph, preventing false negatives at the expense of false positives. In addition, connections known to be absent may simply be ruled out in the prior.…”

Section: Discussionmentioning

confidence: 99%

Structurally-informed Bayesian functional connectivity analysis

et al. 2014

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Functional connectivity refers to covarying activity between spatially segregated brain regions and can be studied by measuring correlation between functional magnetic resonance imaging (fMRI) timeseries. These correlations can be caused either by direct communication via active axonal pathways or indirectly via the interaction with other regions. It is not possible to discriminate between these two kinds of functional interaction simply by considering the covariance matrix. However, the non-diagonal elements of its inverse, the precision matrix, can be naturally related to direct communication between brain areas and interpreted in terms of partial correlations. In this paper, we propose a Bayesian model for functional connectivity analysis which allows estimation of a posterior density over precision matrices, and, consequently, allows one to quantify the uncertainty about estimated partial correlations. In order to make model estimation feasible it is assumed that the sparseness structure of the precision matrices is given by an estimate of structural connectivity obtained using diffusion imaging data. The model was tested on simulated data as well as resting-state fMRI data and compared with a graphical lasso analysis. The presented approach provides a theoretically solid foundation for quantifying functional connectivity in the presence of uncertainty.

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Comparison of fiber tracts derived from in-vivo DTI tractography with 3D histological neural tract tracer reconstruction on a macaque brain

Cited by 227 publications

References 30 publications

In Vivo Mapping of Fiber Pathways in the Rhesus Monkey Brain

In Vivo Mapping of Fiber Pathways in the Rhesus Monkey Brain

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

Structurally-informed Bayesian functional connectivity analysis

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