Walter Nina, Wenzel Julian, Haas Shalaila S, Squarcina Letizia, Bonivento Carolina, Ruef Anne, Dwyer Dominic, Lichtenstein Theresa, Bastrük Öznur, Stainton Alexandra, Antonucci Linda A, Brambilla Paolo, Wood Stephen J, Upthegrove Rachel, Borgwardt Stefan, Lencer Rebekka, Meisenzahl Eva, Salokangas Raimo K R, Pantelis Christos, Bertolino Alessandro, Koutsouleris Nikolaos, Kambeitz Joseph, Kambeitz-Ilankovic Lana
Department of Psychiatry and Psychotherapy, Faculty of Medicine and University Hospital, University of Cologne, Kerpenerstr.62, 50931, Cologne, Germany.
Department of Psychiatry, Icahn School of Medicine at Mount Sinai, NY, New York, United States of America.
Prog Neuropsychopharmacol Biol Psychiatry. 2024 Jan 10;128:110864. doi: 10.1016/j.pnpbp.2023.110864. Epub 2023 Sep 17.
Clinical and neuroimaging data has been increasingly used in recent years to disentangle heterogeneity of treatment response to cognitive training (CT) and predict which individuals may achieve the highest benefits. CT has small to medium effects on improving cognitive and social functioning in recent onset psychosis (ROP) patients, who show the most profound cognitive and social functioning deficits among psychiatric patients. We employed multivariate pattern analysis (MVPA) to investigate the potential of cognitive data to predict social functioning improvement in response to 10 h of CT in patients with ROP. A support vector machine (SVM) classifier was trained on the naturalistic data of the Personalized Prognostic Tools for Early Psychosis Management (PRONIA) study sample to predict functioning in an independent sample of 70 ROP patients using baseline cognitive data. PRONIA is a part of a FP7 EU grant program that involved 7 sites across 5 European countries, designed and conducted with the main aim of identifying (bio)markers associated with an enhanced risk of developing psychosis in order to improve early detection and prognosis. Social functioning was predicted with a balanced accuracy (BAC) of 66.4% (Sensitivity 78.8%; Specificity 54.1%; PPV 60.5%; NPV 74.1%; AUC 0.64; P = 0.01). The most frequently selected cognitive features (mean feature weights > ± 0.2) included the (1) correct number of symbol matchings within the Digit Symbol Substitution Test, (2) the number of distracting stimuli leading to an error within 300 and 200 trials in the Continuous Performance Test and (3) the dynamics of verbal fluency between 15 and 30 s within the Verbal Fluency Test, phonetic part. Next, the SVM classifier generated on the PRONIA sample was applied to the intervention sample, that obtained 54 ROP patients who were randomly assigned to a social cognitive training (SCT) or treatment as usual (TAU) group and dichotomized into good (GF-S ≥ 7) and poor (GF-S < 7) functioning patients based on their level of Global Functioning-Social (GF-S) score at follow-up (FU). By applying the initial PRONIA classifier, using out-of-sample cross-validation (OOCV) to the sample of ROP patients who have undergone the CT intervention, a BAC of 59.3% (Sensitivity 70.4%; Specificity 48.1%; PPV 57.6%; NPV 61.9%; AUC 0.63) was achieved at T0 and a BAC of 64.8% (Sensitivity 66.7%; Specificity 63.0%; PPV 64.3%; NPV 65.4%; AUC 0.66) at FU. After SCT intervention, a significant improvement in predicted social functioning values was observed in the SCT compared to TAU group (P ≤0.05; ES[Cohens' d] = 0.18). Due to a small sample size and modest variance of social functioning of the intervention sample it was not feasible to predict individual response to SCT in the current study. Our findings suggest that the use of baseline cognitive data could provide a robust individual estimate of future social functioning, while prediction of individual response to SCT using cognitive data that can be generated in the routine patient care remains to be addressed in large-scale cognitive training trials.
近年来，临床和神经影像学数据越来越多地被用于剖析认知训练（CT）治疗反应的异质性，并预测哪些个体可能获得最大益处。CT对近期发病的精神病（ROP）患者的认知和社会功能改善有小到中等程度的效果，这些患者在精神病患者中表现出最严重的认知和社会功能缺陷。我们采用多变量模式分析（MVPA）来研究认知数据预测ROP患者在接受10小时CT治疗后社会功能改善的潜力。使用早期精神病管理个性化预后工具（PRONIA）研究样本的自然主义数据训练支持向量机（SVM）分类器，以使用基线认知数据预测70名ROP患者独立样本中的功能。PRONIA是欧盟第七框架计划资助项目的一部分，该项目涉及5个欧洲国家的7个地点，设计和开展的主要目的是识别与患精神病风险增加相关的（生物）标志物，以改善早期检测和预后。预测社会功能的平衡准确率（BAC）为66.4%（敏感性78.8%；特异性54.1%；阳性预测值60.5%；阴性预测值74.1%；曲线下面积0.64；P = 0.01）。最常选择的认知特征（平均特征权重>±0.2）包括：（1）数字符号替换测试中正确的符号匹配数量；（2）连续性能测试中在300次和200次试验内导致错误的干扰刺激数量；（3）言语流畅性测试语音部分中15至30秒内言语流畅性的动态变化。接下来，将在PRONIA样本上生成的SVM分类器应用于干预样本，该样本有54名ROP患者，他们被随机分配到社会认知训练（SCT）组或常规治疗（TAU）组，并根据随访时的全球功能 - 社会（GF - S）评分水平分为功能良好（GF - S≥7）和功能较差（GF - S < 7）的患者。通过将最初的PRONIA分类器应用于接受CT干预的ROP患者样本，使用样本外交叉验证（OOCV），在T0时获得的BAC为59.3%（敏感性70.4%；特异性48.1%；阳性预测值57.6%；阴性预测值61.9%；曲线下面积0.63），随访时为64.8%（敏感性66.7%；特异性63.0%；阳性预测值64.3%；阴性预测值65.4%；曲线下面积0.66）。SCT干预后，与TAU组相比观察到SCT组预测的社会功能值有显著改善（P≤0.05；效应量[科恩d] = 0.18）。由于干预样本的样本量小且社会功能方差适中，在当前研究中预测个体对SCT的反应是不可行的。我们的研究结果表明使用基线认知数据可以对未来社会功能提供可靠的个体估计，而使用常规患者护理中可生成的认知数据预测个体对SCT的反应仍有待在大规模认知训练试验中解决。
