Di Camillo Fabio, Grimaldi David Antonio, Cattarinussi Giulia, Di Giorgio Annabella, Locatelli Clara, Khuntia Adyasha, Enrico Paolo, Brambilla Paolo, Koutsouleris Nikolaos, Sambataro Fabio
Department of Neuroscience (DNS), University of Padova, Padua, Italy.
Padova Neuroscience Center, University of Padova, Padua, Italy.
Psychiatry Clin Neurosci. 2024 Dec;78(12):732-743. doi: 10.1111/pcn.13736. Epub 2024 Sep 18.
Recent advances in multivariate pattern recognition have fostered the search for reliable neuroimaging-based biomarkers in psychiatric conditions, including schizophrenia. These approaches consider the complex pattern of alterations in brain function and structure, overcoming the limitations of traditional univariate methods. To assess the reliability of neuroimaging-based biomarkers and the contribution of study characteristics in distinguishing individuals with schizophrenia spectrum disorder (SSD) from healthy controls (HCs), we conducted a systematic review of the studies that used multivariate pattern recognition for this objective.
We systematically searched PubMed, Scopus, and Web of Science for studies on SSD classification using multivariate pattern analysis on magnetic resonance imaging data. We employed a bivariate random-effects meta-analytic model to explore the classification of sensitivity (SE) and specificity (SP) across studies while also evaluating the moderator effects of clinical and non-clinical variables.
A total of 119 studies (with 12,723 patients with SSD and 13,196 HCs) were identified. The meta-analysis estimated a SE of 79.1% (95% confidence interval [CI], 77.1%-81.0%) and a SP of 80.0% (95% CI, 77.8%-82.0%). In particular, the Positive and Negative Syndrome Scale and the Global Assessment of Functioning scores, age, age of onset, duration of untreated psychosis, deep learning, algorithm type, features selection, and validation methods had significant effects on classification performance.
Multivariate pattern analysis reliably identifies neuroimaging-based biomarkers of SSD, achieving ∼80% SE and SP. Despite clinical heterogeneity, discernible brain modifications effectively differentiate SSD from HCs. Classification performance depends on patient-related and methodological factors crucial for the development, validation, and application of prospective models in clinical settings.
多元模式识别的最新进展推动了在包括精神分裂症在内的精神疾病中寻找基于神经影像学的可靠生物标志物。这些方法考虑了脑功能和结构改变的复杂模式，克服了传统单变量方法的局限性。为了评估基于神经影像学的生物标志物的可靠性以及研究特征在区分精神分裂症谱系障碍（SSD）患者与健康对照（HCs）中的作用，我们对为此目的使用多元模式识别的研究进行了系统综述。
我们系统地检索了PubMed、Scopus和Web of Science，以查找使用磁共振成像数据进行多元模式分析的SSD分类研究。我们采用双变量随机效应荟萃分析模型来探索各研究中的敏感性（SE）和特异性（SP）分类，同时还评估临床和非临床变量的调节效应。
共识别出119项研究（涉及12,723例SSD患者和13,196例HCs）。荟萃分析估计SE为79.1%（95%置信区间[CI]，77.1%-81.0%），SP为80.0%（95%CI，77.8%-82.0%）。特别是，阳性和阴性症状量表、功能总体评定分数、年龄、发病年龄、未治疗精神病持续时间、深度学习、算法类型、特征选择和验证方法对分类性能有显著影响。
多元模式分析可靠地识别出SSD基于神经影像学的生物标志物，实现了约80%的SE和SP。尽管存在临床异质性，但可辨别的脑改变有效地将SSD与HCs区分开来。分类性能取决于对临床环境中前瞻性模型的开发、验证和应用至关重要的患者相关因素和方法学因素。
