| Autor: |
Mike M. Schmitgen, Inga Niedtfeld, Ruth Schmitt, Falk Mancke, Dorina Winter, Christian Schmahl, Sabine C. Herpertz |
| Jazyk: |
angličtina |
| Rok vydání: |
2019 |
| Předmět: |
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| Zdroj: |
Brain and Behavior, Vol 9, Iss 9, Pp n/a-n/a (2019) |
| Druh dokumentu: |
article |
| ISSN: |
2162-3279 |
| DOI: |
10.1002/brb3.1384 |
| Popis: |
Abstract Introduction Individualized treatment prediction is crucial for the development and selection of personalized psychiatric interventions. Here, we use random forest classification via pretreatment clinical and demographical (CD), functional, and structural magnetic resonance imaging (MRI) data from patients with borderline personality disorder (BPD) to predict individual treatment response. Methods Before dialectical behavior therapy (DBT), 31 female patients underwent functional (three different emotion regulation tasks) and structural MRI. DBT response was predicted using CD and MRI data in previously identified anatomical regions, which have been reported to be multimodally affected in BPD. Results Amygdala and parahippocampus activation during a cognitive reappraisal task (in contrasts displaying neural activation for emotional challenge and for regulation), along with severity measures of BPD psychopathology and gray matter volume of the amygdala, provided best predictive power with neuronal hyperractivities in nonresponders. All models, except one model using CD data solely, achieved significantly better accuracy (>70.25%) than a simple all‐respond model, with sensitivity and specificity of >0.7 and >0.7, as well as positive and negative likelihood ratios of >2.74 and |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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