دورية أكاديمية
Personalized prediction of repetitive transcranial magnetic stimulation clinical response in medication-refractory depression data
| العنوان: | Personalized prediction of repetitive transcranial magnetic stimulation clinical response in medication-refractory depression data |
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| المؤلفون: | Helene Hopman, Sandra Chan, Winnie Chu, Hanna Lu, Chun-Yu Tse, Steven Chau, Linda Lam, Arthur Mak, Sebastiaan Neggers |
| المصدر: | Data in Brief, Vol 37, Iss , Pp 107264- (2021) |
| بيانات النشر: | Elsevier, 2021. |
| سنة النشر: | 2021 |
| المجموعة: | LCC:Computer applications to medicine. Medical informatics LCC:Science (General) |
| مصطلحات موضوعية: | Depression, Resting-state functional magnetic resonance imaging, Functional connectivity, Neuroimaging, Biomarkers, Transcranial magnetic stimulation, Computer applications to medicine. Medical informatics, R858-859.7, Science (General), Q1-390 |
| الوصف: | This article describes a dataset that was generated as part of the article: Personalized prediction of transcranial magnetic stimulation clinical response in patients with treatment-refractory depression using neuroimaging biomarkers and machine learning (DOI: 10.1016/j.jad.2021.04.081). We collected resting-state functional Magnetic Resonance Imaging data from 70 medication-refractory depressed subjects before undergoing four weeks of repetitive transcranial magnetic stimulation targeting the left dorsolateral prefrontal cortex. The data presented here include information about the seed-based analyses such as regions of interest, individual/group functional connectivity maps and contrast maps. The contrast maps are controlled for age, gender, duration of the current depressive episode, duration since the first depressive episode, and symptom scores. Demographics, clinical characteristics, and categorical treatment response variables are reported as well. Further, the individual connectivity values of the identified neuroimaging biomarkers of long-term clinical response were used as features in the support vector machine models are presented in combination with the trained classifiers of the support vector machine models. Post hoc analyses that were not published in the original analyses are presented as well. Finally, the R or MATLAB code scripts for all figures published in the co-submitted paper are included. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 2352-3409 |
| Relation: | http://www.sciencedirect.com/science/article/pii/S2352340921005485; https://doaj.org/toc/2352-3409 |
| DOI: | 10.1016/j.dib.2021.107264 |
| URL الوصول: | https://doaj.org/article/bd496d15323545618416c3383aefa0a5 |
| رقم الأكسشن: | edsdoj.bd496d15323545618416c3383aefa0a5 |
| قاعدة البيانات: | Directory of Open Access Journals |
Full Text Finder | تدمد: | 23523409 |
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| DOI: | 10.1016/j.dib.2021.107264 |