Group of Psychiatric Neuroscience, Department of Basic Medical Science, Neuroscience, and Sense Organs - University of Bari Aldo Moro, Bari, Italy.
Lieber Institute for Brain Development, Johns Hopkins Medical Campus - Baltimore, MD, USA.
Psychol Med. 2020 Jul;50(9):1501-1509. doi: 10.1017/S0033291719001430. Epub 2019 Jul 30.
Previous models suggest biological and behavioral continua among healthy individuals (HC), at-risk condition, and full-blown schizophrenia (SCZ). Part of these continua may be captured by schizotypy, which shares subclinical traits and biological phenotypes with SCZ, including thalamic structural abnormalities. In this regard, previous findings have suggested that multivariate volumetric patterns of individual thalamic nuclei discriminate HC from SCZ. These results were obtained using machine learning, which allows case-control classification at the single-subject level. However, machine learning accuracy is usually unsatisfactory possibly due to phenotype heterogeneity. Indeed, a source of misclassification may be related to thalamic structural characteristics of those HC with high schizotypy, which may resemble structural abnormalities of SCZ. We hypothesized that thalamic structural heterogeneity is related to schizotypy, such that high schizotypal burden would implicate misclassification of those HC whose thalamic patterns resemble SCZ abnormalities.
Following a previous report, we used Random Forests to predict diagnosis in a case-control sample (SCZ = 131, HC = 255) based on thalamic nuclei gray matter volumes estimates. Then, we investigated whether the likelihood to be classified as SCZ (π-SCZ) was associated with schizotypy in 174 HC, evaluated with the Schizotypal Personality Questionnaire.
Prediction accuracy was 72.5%. Misclassified HC had higher positive schizotypy scores, which were correlated with π-SCZ. Results were specific to thalamic rather than whole-brain structural features.
These findings strengthen the relevance of thalamic structural abnormalities to SCZ and suggest that multivariate thalamic patterns are correlates of the continuum between schizotypy in HC and the full-blown disease.
先前的模型表明，健康个体（HC）、高危状态和精神分裂症（SCZ）之间存在生物学和行为连续体。这些连续体的一部分可能被精神分裂症特质所捕捉，它与 SCZ 共享亚临床特征和生物学表型，包括丘脑结构异常。在这方面，先前的研究结果表明，个体丘脑核的多变量体积模式可以区分 HC 和 SCZ。这些结果是使用机器学习获得的，它允许在单个受试者水平进行病例对照分类。然而，机器学习的准确性通常并不令人满意，可能是由于表型异质性。事实上，分类错误的一个来源可能与那些具有高精神分裂症特质的 HC 的丘脑结构特征有关，这些特征可能与 SCZ 的结构异常相似。我们假设，丘脑结构异质性与精神分裂症特质有关，因此，高精神分裂症特质负担会导致那些丘脑模式类似于 SCZ 异常的 HC 被错误分类。
根据先前的报告，我们使用随机森林根据丘脑核灰质体积估计值，在病例对照样本（SCZ=131，HC=255）中预测诊断。然后，我们在 174 名 HC 中研究了以精神分裂症人格问卷评估的精神分裂症特质负担与被归类为 SCZ 的可能性（π-SCZ）之间的关系。
预测准确率为 72.5%。被错误分类的 HC 的阳性精神分裂症特质评分较高，与 π-SCZ 相关。结果与丘脑结构而非全脑结构特征有关。
这些发现加强了丘脑结构异常与 SCZ 的相关性，并表明多变量丘脑模式是 HC 中的精神分裂症特质与完全疾病之间连续体的相关指标。
