Diffusion MRI-based cortical connectome reconstruction: dependency on tractography procedures and neuroanatomical characteristics

Abstract

Diffusion MRI (dMRI)-based tractography offers unique abilities to map whole-brain structural connections in human and animal brains. However, dMRI-based tractography indirectly measures white matter tracts, with suboptimal accuracy and reliability. Recently, sophisticated methods including constrained spherical deconvolution (CSD) and global tractography have been developed to improve tract reconstructions through modeling of more complex fiber orientations. Our study aimed to determine the accuracy of connectome reconstruction for three dMRI-based tractography approaches: diffusion tensor (DT)-based, CSD-based and global tractography. Therefore, we validated whole brain structural connectome reconstructions based on ten ultrahigh-resolution dMRI rat brain scans and 106 cortical regions, from which varying tractography parameters were compared against standardized neuronal tracer data. All tested tractography methods generated considerable numbers of false positive and false negative connections. There was a parameter range trade-off between sensitivity: 0.06–0.63 interhemispherically and 0.22–0.86 intrahemispherically; and specificity: 0.99–0.60 interhemispherically and 0.99–0.23 intrahemispherically. Furthermore, performance of all tractography methods decreased with increasing spatial distance between connected regions. Similar patterns and trade-offs were found, when we applied spherical deconvolution informed filtering of tractograms, streamline thresholding and group-based average network thresholding. Despite the potential of CSD-based and global tractography to handle complex fiber orientations at voxel level, reconstruction accuracy, especially for long-distance connections, remains a challenge. Hence, connectome reconstruction benefits from varying parameter settings and combination of tractography methods to account for anatomical variation of neuronal pathways.Electronic supplementary materialThe online version of this article (10.1007/s00429-018-1628-y) contains supplementary material, which is available to authorized users.

Test–retest reproducibility of white matter parcellation using diffusion MRI tractography fiber clustering

“…While multiple studies have assessed test–retest reproducibility of white matter parcellations using cortical‐parcellation‐based strategies, for example, on the brain connectome network (Besson, Lopes, Leclerc, Derambure, & Tyvaert, ; Bonilha et al, ; Buchanan, Pernet, Gorgolewski, Storkey, & Bastin, ; Dennis et al, ; Duda, Cook, & Gee, ; Schumacher et al, ; Smith et al, ; Vaessen et al, ; Zhao et al, ; Zhang, Descoteaux, et al, ) and on anatomical fiber tracts (Besseling et al, ; Cousineau et al, ; Heiervang, Behrens, Mackay, Robson, & Johansen‐Berg, ; Kristo et al, ; Lin et al, ; Papinutto et al, ; Tensaouti, Lahlou, Clarisse, Lotterie, & Berry, ; Wang et al, ; Yendiki et al, ), there are no existing studies of fiber clustering, to our knowledge. Studies have suggested that fiber clustering approaches have advantages in parcellating the white matter in a highly consistent way, aiming to reconstruct fiber parcels/tracts corresponding to the white matter anatomy (Ge et al, ; Sydnor et al, ; Zhang et al, ; Zhang, Wu, Norton, et al, ; Ziyan, Sabuncu, Eric, Grimson, & Westin, ), while cortical‐parcellation‐based methods could be less consistent considering factors such as the variability of intersubject cortical anatomy, the dependence on registration between dMRI and structural images, and the presence of false positive/negative connections (Amunts et al, ; Fischl, Sereno, Tootell, & Dale, ; Maier‐Hein et al, ; Sinke et al, ; Zhang, Descoteaux, et al, ). However, to our knowledge, test–retest reproducibility of the cortical‐parcellation‐based and fiber clustering white matter parcellation strategies has not yet been quantitatively compared.…”

Comparison between diffusion MRI tractography and histological tract-tracing of cortico-cortical structural connectivity in the ferret brain

“…In the case of retrograde tracing, histological tracing also quantifies the number of axons in a projection, since each labeled projection neuron provides one axon. Studies performed in macaque (Azadbakht et al, 2015;Donahue et al, 2016;Schilling, Nath, et al, 2019;Zhang et al, 2018) , squirrel monkey (Gao et al, 2013; , pig (Knösche, Anwander, Liptrot, & Dyrby, 2015) , mouse (Calabrese, Badea, Cofer, Qi, & Johnson, 2015) and rat (Sinke et al, 2018) , have explored the relationship between tract-tracing experiments and tractography. Overall these studies have shown that diffusion MRI tractography provides a good estimate of structural brain connectivity.…”

“…Few explorations have been made on the ability of the different tractography approaches available to estimate structural connectivity weights (Gao et al, 2013) . Previous studies have mainly focused on the ability of tractography algorithms to properly estimate white-matter pathways by means of voxel-wise overlap (Knösche et al, 2015) , or on the detectability (presence or absence) of connections (Sinke et al, 2018) , or both Schilling, Nath, et al, 2019) .…”

The efficacy of different preprocessing steps in reducing motion-related confounds in diffusion MRI connectomics

“…This limitation makes it difficult to clearly establish the extent to which head motion is driving false-positive or false-negative connections. Previous studies examining the validity of different tractography algorithms have used synthetic DWI data where ground truth fibre bundles are known (Maier-Hein et al, 2017;Schilling, Daducci, et al, 2019;, or have relied on comparisons to axonal tract-tracing data (Sinke et al, 2018;Thomas et al, 2014). Simulations of head motion in a model where ground truths are known could allow for a more detailed characterisation of how streamline reconstructions are affected under such conditions, which in turn would allow for a better understanding of how preprocessing steps are or are not sensitive to residual motion-related artefacts.…”