Lavagnino L, Amianto F, Mwangi B, D'Agata F, Spalatro A, Zunta-Soares G B, Abbate Daga G, Mortara P, Fassino S, Soares J C
UT Center of Excellence on Mood Disorders,Department of Psychiatry and Behavioral Sciences,UT Houston Medical School,Houston,TX,USA.
Department of Neuroscience,AOU San Giovanni Battista,Turin,Italy.
Psychol Med. 2015 Oct;45(13):2805-12. doi: 10.1017/S0033291715000768. Epub 2015 May 20.
There are currently no neuroanatomical biomarkers of anorexia nervosa (AN) available to make clinical inferences at an individual subject level. We present results of a multivariate machine learning (ML) approach utilizing structural neuroanatomical scan data to differentiate AN patients from matched healthy controls at an individual subject level.
Structural neuroimaging scans were acquired from 15 female patients with AN (age = 20, s.d. = 4 years) and 15 demographically matched female controls (age = 22, s.d. = 3 years). Neuroanatomical volumes were extracted using the FreeSurfer software and input into the Least Absolute Shrinkage and Selection Operator (LASSO) multivariate ML algorithm. LASSO was 'trained' to identify 'novel' individual subjects as either AN patients or healthy controls. Furthermore, the model estimated the probability that an individual subject belonged to the AN group based on an individual scan.
The model correctly predicted 25 out of 30 subjects, translating into 83.3% accuracy (sensitivity 86.7%, specificity 80.0%) (p < 0.001; χ 2 test). Six neuroanatomical regions (cerebellum white matter, choroid plexus, putamen, accumbens, the diencephalon and the third ventricle) were found to be relevant in distinguishing individual AN patients from healthy controls. The predicted probabilities showed a linear relationship with drive for thinness clinical scores (r = 0.52, p < 0.005) and with body mass index (BMI) (r = -0.45, p = 0.01).
The model achieved a good predictive accuracy and drive for thinness showed a strong neuroanatomical signature. These results indicate that neuroimaging scans coupled with ML techniques have the potential to provide information at an individual subject level that might be relevant to clinical outcomes.
目前尚无神经性厌食症（AN）的神经解剖学生物标志物可用于在个体水平上进行临床推断。我们展示了一种多变量机器学习（ML）方法的结果，该方法利用结构性神经解剖扫描数据在个体水平上区分AN患者和匹配的健康对照。
对15名患有AN的女性患者（年龄 = 20岁，标准差 = 4岁）和15名人口统计学匹配的女性对照（年龄 = 22岁，标准差 = 3岁）进行结构性神经成像扫描。使用FreeSurfer软件提取神经解剖体积，并将其输入到最小绝对收缩和选择算子（LASSO）多变量ML算法中。LASSO经过“训练”以将“新的”个体受试者识别为AN患者或健康对照。此外，该模型根据个体扫描估计个体受试者属于AN组的概率。
该模型在30名受试者中正确预测了25名，准确率为83.3%（敏感性86.7%，特异性80.0%）（p < 0.001；χ²检验）。发现六个神经解剖区域（小脑白质、脉络丛、壳核、伏隔核、间脑和第三脑室）在区分个体AN患者和健康对照方面具有相关性。预测概率与瘦身驱动力临床评分呈线性关系（r = 0.52，p < 0.005），与体重指数（BMI）呈线性关系（r = -0.45，p = 0.01）。
该模型具有良好的预测准确性，且瘦身驱动力显示出强烈的神经解剖学特征。这些结果表明，神经成像扫描与ML技术相结合有潜力在个体水平上提供可能与临床结果相关的信息。
