Predicting Response to Repetitive Transcranial Magnetic Stimulation in Patients With Schizophrenia U...
Predicting Response to Repetitive Transcranial Magnetic Stimulation in Patients With Schizophrenia Using Structural Magnetic Resonance Imaging: A Multisite Machine Learning Analysis
About this item
Full title
Author / Creator
Koutsouleris, Nikolaos , Wobrock, Thomas , Guse, Birgit , Langguth, Berthold , Landgrebe, Michael , Eichhammer, Peter , Frank, Elmar , Cordes, Joachim , Wölwer, Wolfgang , Musso, Francesco , Winterer, Georg , Gaebel, Wolfgang , Hajak, Göran , Ohmann, Christian , Verde, Pablo E , Rietschel, Marcella , Ahmed, Raees , Honer, William G , Dwyer, Dominic , Ghaseminejad, Farhad , Dechent, Peter , Malchow, Berend , Kreuzer, Peter M , Poeppl, Tim B , Schneider-Axmann, Thomas , Falkai, Peter and Hasan, Alkomiet
Publisher
US: Oxford University Press
Journal title
Language
English
Formats
Publication information
Publisher
US: Oxford University Press
Subjects
More information
Scope and Contents
Contents
Abstract
Background
The variability of responses to plasticity-inducing repetitive transcranial magnetic stimulation (rTMS) challenges its successful application in psychiatric care. No objective means currently exists to individually predict the patients’ response to rTMS.
Methods
We used machine learning to develop and validate such tools using the pre-treatment structural Magnetic Resonance Images (sMRI) of 92 patients with schizophrenia enrolled in the multisite RESIS trial (http://clinicaltrials.gov, NCT00783120): patients were randomized to either active (N = 45) or sham (N = 47) 10-Hz rTMS applied to the left dorsolateral prefrontal cortex 5 days per week for 21 days. The prediction target was nonresponse vs response defined by a ≥20% pre-post Positive and Negative Syndrome Scale (PANSS) negative score reduction.
Results
Our model...
Alternative Titles
Full title
Predicting Response to Repetitive Transcranial Magnetic Stimulation in Patients With Schizophrenia Using Structural Magnetic Resonance Imaging: A Multisite Machine Learning Analysis
Authors, Artists and Contributors
Author / Creator
Wobrock, Thomas
Guse, Birgit
Langguth, Berthold
Landgrebe, Michael
Eichhammer, Peter
Frank, Elmar
Cordes, Joachim
Wölwer, Wolfgang
Musso, Francesco
Winterer, Georg
Gaebel, Wolfgang
Hajak, Göran
Ohmann, Christian
Verde, Pablo E
Rietschel, Marcella
Ahmed, Raees
Honer, William G
Dwyer, Dominic
Ghaseminejad, Farhad
Dechent, Peter
Malchow, Berend
Kreuzer, Peter M
Poeppl, Tim B
Schneider-Axmann, Thomas
Falkai, Peter
Hasan, Alkomiet
Identifiers
Primary Identifiers
Record Identifier
TN_cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_6101524
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_6101524
Other Identifiers
ISSN
0586-7614
E-ISSN
1745-1701
DOI
10.1093/schbul/sbx114
