Elsevier

NeuroImage

Volume 243, November 2021, 118519
NeuroImage

Ventral intermediate nucleus structural connectivity-derived segmentation: anatomical reliability and variability

https://doi.org/10.1016/j.neuroimage.2021.118519Get rights and content
Under a Creative Commons license
open access

Abstract

The Ventral intermediate nucleus (Vim) of thalamus is the most targeted structure for the treatment of drug-refractory tremors. Since methodological differences across existing studies are remarkable and no gold-standard pipeline is available, in this study, we tested different parcellation pipelines for tractography-derived putative Vim identification.

Thalamic parcellation was performed on a high quality, multi-shell dataset and a downsampled, clinical-like dataset using two different diffusion signal modeling techniques and two different voxel classification criteria, thus implementing a total of four parcellation pipelines. The most reliable pipeline in terms of inter-subject variability has been picked and parcels putatively corresponding to motor thalamic nuclei have been selected by calculating similarity with a histology-based mask of Vim. Then, spatial relations with optimal stimulation points for the treatment of essential tremor have been quantified. Finally, effect of data quality and parcellation pipelines on a volumetric index of connectivity clusters has been assessed.

We found that the pipeline characterized by higher-order signal modeling and threshold-based voxel classification criteria was the most reliable in terms of inter-subject variability regardless data quality. The maps putatively corresponding to Vim were those derived by precentral and dentate nucleus-thalamic connectivity. However, tractography-derived functional targets showed remarkable differences in shape and sizes when compared to a ground truth model based on histochemical staining on seriate sections of human brain. Thalamic voxels connected to contralateral dentate nucleus resulted to be the closest to literature-derived stimulation points for essential tremor but at the same time showing the most remarkable inter-subject variability. Finally, the volume of connectivity parcels resulted to be significantly influenced by data quality and parcellation pipelines. Hence, caution is warranted when performing thalamic connectivity-based segmentation for stereotactic targeting.

Keywords

Cerebellum
Cerebral cortex
dMRI
Thalamus
Tractography

Abbreviations

CBP
connectivity-based parcellation
COG
center of gravity
CSD
constrained spherical deconvolution
CSF
cerebrospinal fluid
DBS
deep brain stimulation
DTI
diffusion tensor imaging
DRTC
dento-rubro-thalamo-cortical
DWI
diffusion-weighted imaging
EPI
echo planar imaging
ET
essential tremor
fODF
fiber orientation distribution functions
GM
grey matter
GPi
internal globus pallidus
HARDI
high angular resolution diffusion imaging
HCP
human connectome project
MPMs
maximum probability maps
MRgFUS
magnetic resonance-guided focused ultrasounds
MSMT-CSD
multi-shell multi-tissue constrained spherical deconvolution
MRI
magnetic resonance imaging
OBL
overlap by label
PD
Parkinson's disease
PSM
probabilistic stimulation mapping
RF
response function
ROIs
regions of interest
SCP
superior cerebellar peduncle
SDI
streamline density index
SMA
supplementary motor area
SS3T-CSD
single-shell three-tissue constrained spherical deconvolution
SyN
symmetric diffeomorphic image registration
TAO
total accumulated overlap
Vim
ventral intermediate nucleus
Vop
ventro-oralis posterior
VTA
volume of tissue activated, WM, white matter

Cited by (0)

§

These authors equally contributed to the present work