Department of Computer Science, University College London, United Kingdom.
PLoS One. 2012;7(2):e29482. doi: 10.1371/journal.pone.0029482. Epub 2012 Feb 15.
There are no known biological measures that accurately predict future development of psychiatric disorders in individual at-risk adolescents. We investigated whether machine learning and fMRI could help to: 1. differentiate healthy adolescents genetically at-risk for bipolar disorder and other Axis I psychiatric disorders from healthy adolescents at low risk of developing these disorders; 2. identify those healthy genetically at-risk adolescents who were most likely to develop future Axis I disorders.
16 healthy offspring genetically at risk for bipolar disorder and other Axis I disorders by virtue of having a parent with bipolar disorder and 16 healthy, age- and gender-matched low-risk offspring of healthy parents with no history of psychiatric disorders (12-17 year-olds) performed two emotional face gender-labeling tasks (happy/neutral; fearful/neutral) during fMRI. We used Gaussian Process Classifiers (GPC), a machine learning approach that assigns a predictive probability of group membership to an individual person, to differentiate groups and to identify those at-risk adolescents most likely to develop future Axis I disorders.
Using GPC, activity to neutral faces presented during the happy experiment accurately and significantly differentiated groups, achieving 75% accuracy (sensitivity = 75%, specificity = 75%). Furthermore, predictive probabilities were significantly higher for those at-risk adolescents who subsequently developed an Axis I disorder than for those at-risk adolescents remaining healthy at follow-up.
We show that a combination of two promising techniques, machine learning and neuroimaging, not only discriminates healthy low-risk from healthy adolescents genetically at-risk for Axis I disorders, but may ultimately help to predict which at-risk adolescents subsequently develop these disorders.
目前尚无已知的生物学指标可以准确预测个体高危青少年未来精神障碍的发展。我们研究了机器学习和 fMRI 是否有助于：1. 将患有双相情感障碍和其他 I 类精神障碍的高危青少年与低发病风险的健康青少年区分开来；2. 识别出那些最有可能在未来发展出 I 类障碍的健康高危青少年。
16 名健康的双相情感障碍和其他 I 类障碍的高危后代（因父母患有双相情感障碍）和 16 名健康的、年龄和性别匹配的无精神病史的健康父母的低风险后代（12-17 岁）在 fMRI 期间执行了两个情绪面孔性别标签任务（快乐/中性；恐惧/中性）。我们使用了高斯过程分类器（GPC），这是一种机器学习方法，它为个体分配了群体成员的预测概率，以区分群体，并识别出最有可能在未来发展出 I 类障碍的高危青少年。
使用 GPC，在快乐实验中呈现的中性面孔的活动可以准确且显著地区分群体，准确率达到 75%（敏感性=75%，特异性=75%）。此外，对于那些随后发展出 I 类障碍的高危青少年，其预测概率明显高于那些在随访中保持健康的高危青少年。
我们表明，机器学习和神经影像学这两种有前途的技术的结合，不仅可以区分健康的低风险和高危青少年，而且可能最终有助于预测哪些高危青少年随后会发展出这些障碍。
