Recent views posited that negative parenting and attachment insecurity can be considered as general environmental factors of vulnerability for psychosis, specifically for individuals diagnosed with psychosis (PSY). Furthermore, evidence highlighted a tight relationship between attachment style and social cognition abilities, a key PSY behavioral phenotype. The aim of this study is to generate a machine learning algorithm based on the perceived quality of parenting and attachment style-related features to discriminate between PSY and healthy controls (HC) and to investigate its ability to track PSY early stages and risk conditions, as well as its association with social cognition performance. Perceived maternal and paternal parenting, as well as attachment anxiety and avoidance scores, were trained to separate 71 HC from 34 PSY (20 individuals diagnosed with schizophrenia + 14 diagnosed with bipolar disorder with psychotic manifestations) using support vector classification and repeated nested cross-validation. We then validated this model on independent datasets including individuals at the early stages of disease (ESD, i.e. first episode of psychosis or depression, or at-risk mental state for psychosis) and with familial high risk for PSY (FHR, i.e. having a first-degree relative suffering from psychosis). Then, we performed factorial analyses to test the group x classification rate interaction on emotion perception, social inference and managing of emotions abilities. The perceived parenting and attachment-based machine learning model discriminated PSY from HC with a Balanced Accuracy (BAC) of 72.2%. Slightly lower classification performance was measured in the ESD sample (HC-ESD BAC = 63.5%), while the model could not discriminate between FHR and HC (BAC = 44.2%). We observed a significant group x classification interaction in PSY and HC from the discovery sample on emotion perception and on the ability to manage emotions (both p = 0.02). The interaction on managing of emotion abilities was replicated in the ESD and HC validation sample (p = 0.03). Our results suggest that parenting and attachment-related variables bear significant classification power when applied to both PSY and its early stages and are associated with variability in emotion processing. These variables could therefore be useful in psychosis early recognition programs aimed at softening the psychosis-associated disability.

中文翻译：

---

通过基于父母和依恋相关变量的机器学习对精神病和早期疾病进行分类的能力与社会认知相关

最近的观点认为，负面的父母教养和依恋不安全感可以被认为是精神病易感性的一般环境因素，特别是对于被诊断为精神病的个体。此外，证据强调依恋风格与社交认知能力之间的紧密联系，社交认知能力是一种重要的PSY行为表型。这项研究的目的是基于对父母和依恋风格相关特征的感知质量来生成机器学习算法，以区分PSY和健康对照（HC），并研究其追踪PSY早期阶段和风险状况的能力，例如以及与社会认知表现的关系。知母和父母育儿，以及依恋焦虑和回避分数，使用支持向量分类法和重复嵌套交叉验证法，对来自34名PSY（20名被诊断为精神分裂症的人+ 14名被诊断为患有精神病性表现的躁郁症的人）中的71种HC进行了培训。然后，我们在独立的数据集上验证了该模型，该数据集包括处于疾病早期阶段（ESD，即精神病或抑郁的首发发作，或精神病的高危精神状态）且具有PSY家族高风险（FHR，即具有先天性精神分裂症）的个体度相对患有精神病的人）。然后，我们进行了析因分析，以测试在情感感知，社交推理和情感能力管理方面的x分组率交互作用。感知的基于育儿和依恋的机器学习模型将PSY与HC进行了区分，其平衡准确度（BAC）为72.2％。在ESD样本中测得的分类性能略低（HC-ESD BAC = 63.5％），而模型无法区分FHR和HC（BAC = 44.2％）。我们从发现样本中在情绪感知和控制情绪的能力上观察到了PSY和HC中显着的x组分类相互作用（p = 0.02）。在ESD和HC验证样本中重复了管理情绪能力的互动（p = 0.03）。我们的结果表明，父母养育和与依恋相关的变量在应用于PSY及其早期阶段时均具有显着的分类能力，并且与情绪处理的变异性相关。因此，这些变量在旨在减轻与精神病相关的残疾的精神病早期识别计划中可能有用。