期刊
BIOLOGICAL PSYCHIATRY
卷 78, 期 4, 页码 278-286出版社
ELSEVIER SCIENCE INC
DOI: 10.1016/j.biopsych.2014.11.018
关键词
Clinical information; Course trajectory; Magnetic resonance imaging; Major depressive disorder; Prediction; Probabilistic pattern recognition analysis
资金
- Geestkracht program of the Netherlands Organisation for Health Research and Development [10-000-1002]
- VU University Medical Center
- GGZ inGeest
- Arkin
- Leiden University Medical Center
- GGZ Rivierduinen
- University Medical Center Groningen
- Lentis
- GGZ Friesland
- GGZ Drenthe
- Scientific Institute for Quality of Healthcare [IQ healthcare]
- Netherlands Institute for Health Services Research
- Netherlands Institute of Mental Health and Addiction [Trimbos Institute]
- King's College London Centre of Excellence in Medical Engineering - Wellcome Trust
- Engineering and Physical Sciences Research Council [WT088641/Z/09/Z]
- Netherlands Organisation for Scientific Research under the Language in Interaction project
- Netherlands Organisation for Scientific Research Vici Grant [91811602]
- Netherlands Organisation for Scientific Research/Netherlands Organisation for Health Research and Development Veni Grant [016.126.059]
- Netherlands Brain Foundation [F2014(1)-24]
- Neuroscience Campus Amsterdam Grant [PoC-2014-NMH-02, PoC-2014-BIT-04]
BACKGROUND: A chronic course of major depressive disorder (MDD) is associated with profound alterations in brain volumes and emotional and cognitive processing. However, no neurobiological markers have been identified that prospectively predict MDD course trajectories. This study evaluated the prognostic value of different neuroimaging modalities, clinical characteristics, and their combination to classify MDD course trajectories. METHODS: One hundred eighteen MDD patients underwent structural and functional magnetic resonance imaging (MRI) (emotional facial expressions and executive functioning) and were clinically followed-up at 2 years. Three MDD trajectories (chronic n = 23, gradual improving n = 36, and fast remission n = 59) were identified based on Life Chart Interview measuring the presence of symptoms each month. Gaussian process classifiers were employed to evaluate prognostic value of neuroimaging data and clinical characteristics (including baseline severity, duration, and comorbidity). RESULTS: Chronic patients could be discriminated from patients with more favorable trajectories from neural responses to various emotional faces (up to 73% accuracy) but not from structural MRI and functional MRI related to executive functioning. Chronic patients could also be discriminated from remitted patients based on clinical characteristics (accuracy 69%) but not when age differences between the groups were taken into account. Combining different task contrasts or data sources increased prediction accuracies in some but not all cases. CONCLUSIONS: Our findings provide evidence that the prediction of naturalistic course of depression over 2 years is improved by considering neuroimaging data especially derived from neural responses to emotional facial expressions. Neural responses to emotional salient faces more accurately predicted outcome than clinical data.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据