Development and application of a multi-task fusion model using susceptibility-weighted imaging-based substantia nigra and adjacent structures for prognostic prediction of subthalamic nucleus deep brain stimulation in Parkinson's disease.
The 2 matches
- [1] § Materials and methods › Model development ↔ PD_model.py, lines 410–460 · score 0.56 · Conv3d, MaxPool3d, ReLU, layers, softmax, blocks
- [2] § Materials and methods › Model development ↔ PD_model.py, lines 113–176 · score 0.52 · logistic regression, MaxPool3d, ReLU, linear, Model
Paper
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The authors' code
Python · 561 lines · 19 KB · no license · 2 matches
PD_model.py at commit 8d75c50, no license · at the source
Overview
- Institute of Intelligent Machines, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, P.R. China
- University of Science and Technology of China, Hefei 230026, P.R. China
- Department of Neurosurgery, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei 230001, P.R. China
Abstract
We developed and evaluated a deep learning framework using susceptibility-weighted imaging signatures of the substantia nigra and adjacent structures to predict motor outcomes following subthalamic nucleus deep brain stimulation in Parkinson’s disease. This retrospective study included patients undergoing bilateral subthalamic nucleus deep brain stimulation, and motor response was defined as the percentage improvement in the original Unified Parkinson’s Disease Rating Scale Part III score from the preoperative medication-off condition to the 1-year postoperative stimulation-on/
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ceasorcai/PD
8d75c50f0ae0a640a7a3f7e9bc94457f96bd5cd7, 27 May 2026Availability: 1 check, the latest on 26 September 2026: the link answers
- 26 September 2026: the link answers
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- main33.py — Python, 121 lines, shown from its source
The paper's code and data availability statement is in the Data section.
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Data
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Data availability
The clinical and imaging data supporting the findings of this study are available from the corresponding author upon reasonable request, subject to institutional and ethical restrictions. The code used for model training, performance evaluation, statistical analysis, and DBS-related metric extraction is available at https://
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Version 1, 27 September 2026: the first record
Recorded: type, language, journal, volume, issue, pages, dates, 7 authors, 5 keywords, 4 funders, 38 references.
Cite
This paper
Xiong, C., Wang, K., Cheng, E., Sun, Z., Cai, B., Niu, C., & Song, B. (2026). Development and application of a multi-task fusion model using susceptibility-weighted imaging-based substantia nigra and adjacent structures for prognostic prediction of subthalamic nucleus deep brain stimulation in Parkinson's disease. Brain communications, 8(5), fcag333. https://
BibTeX
@article{xiong2026develo
author = {Xiong, Chi and Wang, Kun and Cheng, Erkang and Sun, Zhiyong and Cai, Bin and Niu, Chaoshi and Song, Bo},
title = {{Development and application of a multi-task fusion model using susceptibility-weighted imaging-based substantia nigra and adjacent structures for prognostic prediction of subthalamic nucleus deep brain stimulation in Parkinson's disease}},
journal = {Brain communications},
year = {2026},
month = sep,
volume = {8},
number = {5},
pages = {fcag333},
publisher = {Oxford University Press},
issn = {2632-1297},
doi = {10.1093/
url = {https://
pmid = {42729974},
pmcid = {PMC13563298}
}
RIS
TY - JOUR
AU - Xiong, Chi
AU - Wang, Kun
AU - Cheng, Erkang
AU - Sun, Zhiyong
AU - Cai, Bin
AU - Niu, Chaoshi
AU - Song, Bo
TI - Development and application of a multi-task fusion model using susceptibility-weighted imaging-based substantia nigra and adjacent structures for prognostic prediction of subthalamic nucleus deep brain stimulation in Parkinson's disease
T2 - Brain communications
J2 - Brain Commun
PY - 2026
DA - 2026/
VL - 8
IS - 5
SP - fcag333
SN - 2632-1297
PB - Oxford University Press
DO - 10.1093/
UR - https://
LA - en
ER -
CSL-JSON
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