Bayesian probabilistic density mapping of the decussating dentato-rubro-thalamic tract to predict clinical tremor improvement in MRgFUS

Probabilistic tractography successfully visualized the d-DRTT, and its location appears to capture the "tremor-relevant" neural pathway more accurately than either the indirect coordinate or the nd-DRTT.

Authors

Institutions

Publication Details

Journal
Journal of neurosurgery
Published
2026-02-01
DOI
https://doi.org/10.3171/2025.9.jns251021
Primary Topic
Neurological disorders and treatments
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Bayesian probabilistic density mapping of the decussating dentato-rubro-thalamic tract to predict clinical tremor improvement in MRgFUS

H Matsuzawa, Takuhito Narita, Masahito Kawabori, Miki Fujimura et al.
Journal of neurosurgery
Neurological disorders and treatments
article

Bayesian probabilistic density mapping of the decussating dentato-rubro-thalamic tract to predict clinical tremor improvement in MRgFUS

H Matsuzawa, Takuhito Narita, Masahito Kawabori, Miki Fujimura, Takeshi Muraki, Shunsuke Terasaka, Hiroyuki Kobayashi
article en

Abstract

Probabilistic tractography successfully visualized the d-DRTT, and its location appears to capture the "tremor-relevant" neural pathway more accurately than either the indirect coordinate or the nd-DRTT.

Journal of neurosurgery
Hokkaido University (JP), Keiyukai Sapporo Hospital (JP)
Good health and well-being
Openalex Percentile: Top 60%
Neurological disorders and treatments
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.