Personalized prediction of repetitive transcranial magnetic stimulation clinical response in medication-refractory depression data
| Autor: | Chun-Yu Tse, Sebastiaan F. W. Neggers, Sandra S. M. Chan, Arthur D. P. Mak, Helene J. Hopman, Steven W H Chau, Hanna Lu, Linda Lam, Winnie C.W. Chu |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
medicine.medical_specialty
Science (General) Resting-state functional magnetic resonance imaging Support vector machine medicine.medical_treatment Computer applications to medicine. Medical informatics R858-859.7 Neuroimaging Q1-390 Functional connectivity Physical medicine and rehabilitation Refractory Machine learning Medicine Categorical variable Depression (differential diagnoses) Data Article Multidisciplinary medicine.diagnostic_test business.industry Depression Contrast (statistics) Transcranial magnetic stimulation business Functional magnetic resonance imaging Biomarkers |
| Zdroj: | Data in Brief Data in Brief, Vol 37, Iss, Pp 107264-(2021) |
| ISSN: | 2352-3409 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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