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脑分形维数与机器学习可预测首发精神病及向精神病转变的风险。

Brain Fractal Dimension and Machine Learning can predict first-episode psychosis and risk for transition to psychosis.

作者信息

Hu Yaxin, Frisman Marina, Andreou Christina, Avram Mihai, Riecher-Rössler Anita, Borgwardt Stefan, Barth Erhardt, Korda Alexandra

机构信息

Institute of Neuro- and Bioinformatics, University of Luebeck, Ratzeburger Allee 160, Luebeck, 23562, Schleswig-Holstein, Germany; Pattern Recognition Company GmbH, Maria-Goeppert-Straße 3, Luebeck, 23562, Schleswig-Holstein, Germany.

Schleswig-Holstein University Medical Center, Ratzeburger Allee 160, Luebeck, 23562, Schleswig-Holstein, Germany.

出版信息

Comput Biol Med. 2025 Jul;193:110333. doi: 10.1016/j.compbiomed.2025.110333. Epub 2025 May 26.

Abstract

Although there are notable structural abnormalities in the brain associated with psychotic diseases, it is still unclear how these abnormalities relate to clinical presentation. However, the fractal dimension (FD), which offers details on the complexity and irregularity of brain microstructures, may be a promising feature, as demonstrated by neuropsychiatric disorders such as Parkinson's and Alzheimer's. It may offer a possible biomarker for the detection and prognosis of psychosis when paired with machine learning. The purpose of this study is to investigate FD as a structural magnetic resonance imaging (sMRI) feature from individuals with a high clinical risk of psychosis who did not transit to psychosis (CHR_NT), clinical high risk who transit to psychosis (CHR_T), patients with first-episode psychosis (FEP) and healthy controls (HC). Using a machine learning approach that ultimately classifies sMRI images, the goals are (a) to evaluate FD as a potential biomarker and (b) to investigate its ability to predict a subsequent transition to psychosis from the high-risk clinical condition. We obtained sMRI images from 194 subjects, including 44 HCs, 77 FEPs, 16 CHR_Ts, and 57 CHR_NTs. We extracted the FD features and analyzed them using machine learning methods under five classification schemas (a) FEP vs. HC, (b) FEP vs. CHR_NT, (c) FEP vs. CHR_T, (d) CHR_NT vs. CHR_T, (d) CHR_NT vs. HC and (e) CHR_T vs. HC. In addition, the CHR_T group was used as external validation in (a), (b) and (d) comparisons to examine whether the progression of the disorder followed the FEP or CHR_NT patterns. The proposed algorithm resulted in a balanced accuracy greater than 0.77. This study has shown that FD can function as a predictive neuroimaging marker, providing fresh information on the microstructural alterations triggered throughout the course of psychosis. The effectiveness of FD in the detection of psychosis and transition to psychosis should be established by further research using larger datasets.

摘要

尽管与精神疾病相关的大脑存在明显的结构异常,但这些异常与临床表现之间的关系仍不明确。然而,分形维数(FD)能够提供有关脑微观结构复杂性和不规则性的细节,可能是一个有前景的特征,帕金森病和阿尔茨海默病等神经精神疾病已证明了这一点。当与机器学习相结合时,它可能为精神病的检测和预后提供一种可能的生物标志物。本研究的目的是调查FD作为一种结构磁共振成像(sMRI)特征在未转化为精神病的临床高风险个体(CHR_NT)、转化为精神病的临床高风险个体(CHR_T)、首发精神病患者(FEP)和健康对照(HC)中的情况。使用一种最终对sMRI图像进行分类的机器学习方法,目标是(a)评估FD作为一种潜在的生物标志物,以及(b)研究其从高风险临床状态预测随后转化为精神病的能力。我们从194名受试者那里获得了sMRI图像,包括44名HC、77名FEP、16名CHR_T和57名CHR_NT。我们提取了FD特征,并在五种分类模式下使用机器学习方法进行分析:(a)FEP与HC,(b)FEP与CHR_NT,(c)FEP与CHR_T,(d)CHR_NT与CHR_T,(d)CHR_NT与HC以及(e)CHR_T与HC。此外,在(a)、(b)和(d)比较中,CHR_T组用作外部验证,以检查疾病进展是否遵循FEP或CHR_NT模式。所提出的算法得出的平衡准确率大于0.77。本研究表明,FD可以作为一种预测性神经影像标志物,为精神病病程中引发的微观结构改变提供新信息。FD在精神病检测和转化为精神病方面的有效性应通过使用更大数据集的进一步研究来确定。

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