Mapping the subcortical connectome using in vivo diffusion MRI: Feasibility and reliability

Jason Kai (1) , Ali R Khan (2) , Roy Am Haast (2, 3) , Jonathan C Lau (2)

1 Department of Medical Biophysics, Schulich School of Medicine & Dentistry, The University of Western Ontario, London, Ontario
2 Center for Functional and Metabolic Mapping, Robarts Institute, The University of Western Ontario, London, Ontario
3 CRMBM-CEMEREM - Aix Marseille Univ, UMR 7339 CNRS, Centre de Résonance Magnétique Biologique et Médicale,

Tractography combined with regions of interest (ROIs) has been used to non-invasively study the structural connectivity of the cortex as well as to assess the reliability of these connections. However, the subcortical connectome (subcortex to subcortex) has not been comprehensively examined, in part due to the difficulty of performing tractography in this complex and compact region. In this study, we performed an in vivo investigation using tractography to assess the feasibility and reliability of mapping known connections between structures of the subcortex using the test-retest dataset from the Human Connectome Project (HCP). We further validated our observations using a separate unrelated subjects dataset from the HCP. Quantitative assessment was performed by computing tract densities and spatial overlap of identified connections between subcortical ROIs. Further, known connections between structures of the basal ganglia and thalamus were identified and visually inspected, comparing tractography reconstructed trajectories with descriptions from tract-tracing studies. Our observations demonstrate both the feasibility and reliability of using a data-driven tractography-based approach to map the subcortical connectome in vivo .

Keywords: Diffusion MRI Tractography Subcortical connectivity Reliability

Domains: Life Sciences [q-bio]; Neurons and Cognition [q-bio.NC]

Jason Kai, Ali R Khan, Roy Am Haast, Jonathan C Lau. Mapping the subcortical connectome using in vivo diffusion MRI: Feasibility and reliability. NeuroImage, 2022, 262, ⟨10.1016/j.neuroimage.2022.119553⟩. ⟨hal-03960452⟩