Pattern classification of sad facial processing: Toward the development of neurobiological markers in depression
Pattern classification of sad facial processing: Toward the development of neurobiological markers in depression
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DOI：
10.1016/j.biopsych.2007.08.020
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发表时间：
2008-04-01
影响因子：
10.6
通讯作者：
Brammer, Michael J.
中科院分区：
文献类型：
--
作者：
Fu, Cynthia H. Y.;Mourao-Miranda, Janaina;Brammer, Michael J.
背景：检查整个大脑的大脑活动模式的分析方法已被用来识别和预测健康个体的神经认知状态。这些方法可应用于患者群体的功能神经影像数据，以帮助诊断精神疾病和预测治疗反应。我们试图检查抑郁症患者悲伤面部表情内隐处理的全脑模式分类的敏感性和特异性。方法：招募了 19 名未接受药物治疗的抑郁症患者和 19 名健康志愿者进行涉及连续扫描的功能磁共振成像 (fMRI) 研究。功能磁共振成像范式需要对悲伤的面部刺激进行偶然的情感处理，并调节情绪表达的强度（低、中和高强度）。使用支持向量机模式分类方法对每个情感强度水平的 fMRl 数据进行分析。 结果：悲伤面部处理期间的大脑活动模式正确分类了高达 84% 的患者（敏感性）和 89% 的对照受试者（特异性），相应的准确度为 86% (p < .0001)。在开始治疗之前，患者在基线时的临床反应的分类显示出显着的趋势。结论：通过fMR1数据的全脑模式分析实现了对急性抑郁发作的患者的显着分类。由于子样本功效的降低，治疗反应的预测显示出显着性趋势。这些方法可能为开发精神病学神经生物学标志物提供了第一步。
Background: Methods of analysis that examine the pattern of cerebral activity over the whole brain have been used to identify and predict neurocognitive states in healthy individuals. Such methods may be applied to functional neuroimaging data in patient groups to aid in the diagnosis of psychiatric disorders and the prediction of treatment response. We sought to examine the sensitivity and specificity of whole brain pattern classification of implicit processing of sad facial expressions in depression.Methods: Nineteen medication-free patients with depression and 19 healthy volunteers had been recruited for a functional magnetic resonance imaging (fMRI) study involving serial scans. The fMRI paradigm entailed incidental affective processing of sad facial stimuli with modulation of the intensity of the emotional expression (low, medium, and high intensity). The fMRl data were analyzed at each level of affective intensity with a support vector machine pattern classification method.Results: The pattern of brain activity during sad facial processing correctly classified up to 84% of patients (sensitivity) and 89% of control subjects (specificity), corresponding to an accuracy of 86% (p < .0001). Classification of patients' clinical response at baseline, prior to the initiation of treatment, showed a trend toward significance.Conclusions: Significant classification of patients in an acute depressive episode was achieved with whole brain pattern analysis of fMRl data. The prediction of treatment response showed a trend toward significance due to the reduced power of the subsample. Such methods may provide the first steps toward developing neurobiological markers in psychiatry.