Parkinson’s disease: deep learning with a parameter-weighted structural connectome matrix for diagno...
Parkinson’s disease: deep learning with a parameter-weighted structural connectome matrix for diagnosis and neural circuit disorder investigation
About this item
Full title
Author / Creator
Publisher
Berlin/Heidelberg: Springer Berlin Heidelberg
Journal title
Language
English
Formats
Publication information
Publisher
Berlin/Heidelberg: Springer Berlin Heidelberg
Subjects
More information
Scope and Contents
Contents
Purpose
To investigate whether Parkinson’s disease (PD) can be differentiated from healthy controls and to identify neural circuit disorders in PD by applying a deep learning technique to parameter-weighted and number of streamlines (NOS)–based structural connectome matrices calculated from diffusion-weighted MRI.
Methods
In this prospecti...
Alternative Titles
Full title
Parkinson’s disease: deep learning with a parameter-weighted structural connectome matrix for diagnosis and neural circuit disorder investigation
Authors, Artists and Contributors
Identifiers
Primary Identifiers
Record Identifier
TN_cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_8376710
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_8376710
Other Identifiers
ISSN
0028-3940
E-ISSN
1432-1920
DOI
10.1007/s00234-021-02648-4
