Department of Education, Psychology, Communication, University of Bari Aldo Moro, Via Scipione Crisanzio 42, 70122, Bari, Italy.
Department of Basic Medical Science, Neuroscience and Sense Organs, University of Bari Aldo Moro, Bari, Italy.
BMC Psychol. 2021 Mar 23;9(1):47. doi: 10.1186/s40359-021-00552-3.
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.
最近的观点认为，消极的育儿方式和依恋不安全感可以被视为精神病的一般环境脆弱性因素，特别是对于被诊断为精神病的个体（PSY）。此外，有证据表明，依恋风格和社会认知能力之间存在紧密关系，这是 PSY 的一个关键行为表型。本研究的目的是生成一种基于感知育儿质量和依恋风格相关特征的机器学习算法，以区分 PSY 和健康对照组（HC），并研究其跟踪 PSY 早期阶段和风险状况的能力，以及与社会认知表现的关联。
使用支持向量分类和重复嵌套交叉验证，对 71 名 HC 和 34 名 PSY（20 名被诊断为精神分裂症+14 名被诊断为伴有精神病症状的双相情感障碍）的感知母婴育儿方式、依恋焦虑和回避评分进行训练。然后，我们在包括疾病早期阶段（即精神病首次发作或抑郁症，或精神病高危心理状态）和有 PSY 家族高风险（即有一级亲属患有精神病）的独立数据集上验证了该模型。然后，我们进行了因子分析，以测试情绪感知、社会推理和情绪管理能力方面的组 x 分类率交互作用。
基于感知育儿和依恋的机器学习模型以 72.2%的平衡准确性（BAC）区分 PSY 和 HC。在疾病早期阶段的样本中，分类性能略有下降（HC-ESD 的 BAC=63.5%），而该模型无法区分 FHR 和 HC（BAC=44.2%）。我们在发现样本中观察到 PSY 和 HC 之间存在显著的组 x 分类交互作用，表现在情绪感知和情绪管理能力方面（均 p=0.02）。在疾病早期阶段和 HC 的验证样本中，管理情绪能力的交互作用得到了复制（p=0.03）。
我们的结果表明，育儿和依恋相关变量在 PSY 及其早期阶段具有显著的分类能力，并与情绪处理的可变性相关。因此，这些变量在旨在减轻精神病相关残疾的精神病早期识别计划中可能很有用。
