Introduction
The MesoConnect Atlas is a probabilistic atlas of mesolimbic white-matter pathways derived from 7 T diffusion magnetic resonance imaging data of the Human Connectome Project (HCP; Vu et al., 2015). It comprises seven bilateral pathways connecting the ventral tegmental area (VTA), hippocampus, nucleus accumbens, ventral pallidum and amygdala, distributed as a single package (Downloads). This tutorial describes how to reconstruct those pathways in new participants using the atlas as an anatomical constraint on participant-level tractography, and how to quantify microstructure along the reconstructed bundles.
Scope
The procedure comprises nine steps. The atlas map for a tract is registered to each participant and dilated into a corridor; probabilistic tractography is run through the corridor from the pathway's seed region to its target; the resulting bundle is cleaned of outlying streamlines; and scalar maps, fractional anisotropy (FA) and the indices of neurite orientation dispersion and density imaging (NODDI; Zhang et al., 2012), are sampled at 100 equidistant points along the bundle. Group-level inference may be performed at three resolutions, the whole tract, quartiles of the tract, or individual nodes, and the tutorial describes the model and correction appropriate to each. Node-wise results may be inspected in the Node-wise Tract Explorer, a browser-based viewer distributed with this site that renders t-value profiles, clusters and hemispheric comparisons from a results file.
Two alternative uses of the atlas, averaging a scalar map within the warped atlas mask and sampling along an atlas-derived centerline, are described in the appendix. Corridor-constrained tractography is presented as the primary method because the streamlines are estimated from each participant's own data and support along-tract analysis.
Prerequisites
The tutorial assumes preprocessed diffusion data (denoised, corrected for susceptibility and eddy-current distortion, and brain-masked), a T1-weighted anatomical image, and a Unix environment with FSL (Jenkinson et al., 2012), ANTs (Avants et al., 2008), MRtrix3 (Tournier et al., 2019) and Python with DIPY (Garyfallidis et al., 2014) and pyAFQ (Kruper et al., 2021). Preprocessing is documented in the Diffusion MRI Preprocessing tutorial, which ends with the exact set of files this workflow expects; its list of required outputs lists them and names the step that produces each. QSIPrep, a containerized preprocessing pipeline for diffusion MRI, produces equivalent inputs.
Organization
Table 1 summarizes the sections of the site.
Table 1
Sections of the Tutorial
| Section | Contents |
|---|---|
| Atlas | The seven pathways, how the atlas was constructed, sources of the seed and target regions, the complete package for download |
| Workflow | The nine steps, each with parameters, verification criteria, the full script, and illustrative output from an example dataset |
| Explorer | The results viewer and its input format |
| Alternative approaches | Whole-tract extraction; atlas-guided synthetic streamlines |
| Reference | Consolidated parameters, troubleshooting, software, scripts, suggested methods text, abbreviations, citation, references |
Example dataset
Figures and illustrative values on the workflow pages are drawn from a single dataset of 57 adults scanned at 3 T with a multi-shell protocol (b = 1000, 2000, 3250 and 5000 s/mm²) and processed for the posterior and anterior VTA → hippocampus pathways. The atlas itself was constructed at 7 T. The example dataset is included to illustrate expected output at a field strength typical of most studies.