Multivariate Classification of Adolescent Major Depressive Disorder Using Whole-brain Functional Connectivity.

IF 3.8 2区医学 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

Academic Radiology Pub Date : 2025-03-29 DOI:10.1016/j.acra.2025.02.052

Zhong Li, Yanrui Shen, Meng Zhang, Xuekun Li, Baolin Wu

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引用次数: 0

Abstract

Rationale and objectives: Adolescent major depressive disorder (MDD) is a serious mental health condition that has been linked to abnormal functional connectivity (FC) patterns within the brain. However, whether FC could be used as a potential biomarker for diagnosis of adolescent MDD is still unclear. The aim of our study was to investigate the potential diagnostic value of whole-brain FC in adolescent MDD.

Methods: Resting-state functional magnetic resonance imaging data were obtained from 94 adolescents with MDD and 78 healthy adolescents. The whole brain was segmented into 90 regions of interest (ROIs) using the automated anatomical labeling atlas. FC was assessed by calculating the Pearson correlation coefficient of the average time series between each pair of ROIs. A multivariate pattern analysis was employed to classify patients from controls using the whole-brain FC as input features.

Results: The linear support vector machine classifier achieved an accuracy of 69.18% using the optimal functional connection features. The consensus functional connections were mainly located within and between large-scale brain networks. The top 10 nodes with the highest weight in the classification model were mainly located in the default mode, salience, auditory, and sensorimotor networks.

Conclusion: Our findings highlighted the importance of functional network connectivity in the neurobiology of adolescent MDD, and suggested the possibility of altered FC and high-weight regions as complementary diagnostic markers in adolescents with depression.

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理由和目标：青少年重度抑郁障碍（MDD）是一种严重的精神疾病，与大脑内异常的功能连接（FC）模式有关。然而，FC 能否作为诊断青少年重度抑郁症的潜在生物标志物尚不清楚。我们的研究旨在探讨全脑功能连接在青少年 MDD 中的潜在诊断价值：静息态功能磁共振成像数据来自 94 名患有 MDD 的青少年和 78 名健康青少年。使用自动解剖标记图谱将整个大脑分割成90个感兴趣区（ROI）。通过计算每对 ROI 之间平均时间序列的皮尔逊相关系数来评估 FC。采用多变量模式分析，以全脑FC作为输入特征，对患者和对照组进行分类：使用最佳功能连接特征，线性支持向量机分类器的准确率达到了 69.18%。达成共识的功能连接主要位于大规模脑网络内部和之间。分类模型中权重最高的前 10 个节点主要位于默认模式、显著性、听觉和感觉运动网络中：我们的研究结果强调了功能网络连接在青少年多发性抑郁症神经生物学中的重要性，并提出了功能网络连接和高权重区域的改变作为青少年抑郁症互补诊断标志物的可能性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Academic Radiology 医学-核医学

CiteScore

7.60

自引率

10.40%

发文量

432

审稿时长

18 days

期刊介绍： Academic Radiology publishes original reports of clinical and laboratory investigations in diagnostic imaging, the diagnostic use of radioactive isotopes, computed tomography, positron emission tomography, magnetic resonance imaging, ultrasound, digital subtraction angiography, image-guided interventions and related techniques. It also includes brief technical reports describing original observations, techniques, and instrumental developments; state-of-the-art reports on clinical issues, new technology and other topics of current medical importance; meta-analyses; scientific studies and opinions on radiologic education; and letters to the Editor.