School of Computer Science and Engineering, Central South University, Changsha, Hunan, China.
School of Computer Science and Engineering, Central South University, Changsha, Hunan, China.
J Affect Disord. 2025 Jan 15;369:364-372. doi: 10.1016/j.jad.2024.10.006. Epub 2024 Oct 6.
Major depressive disorder (MDD) is a severe and common mental illness. The first-episode drugs-naive MDD (FEDN-MDD) patients, who have not undergone medication intervention, contribute to understanding the biological basis of MDD. Multimodal Magnetic Resonance Imaging can provide a comprehensive understanding of brain functional and structural abnormalities in MDD. However, most MDD studies use single-modal, small-scale MRI data. And several multimodal studies of MDD are limited to simple linear combinations of functional and structural features.
We screened a large sample of FEDN-MDD patients and healthy controlsmultimodal MRI data. Extracting the fractional amplitude of low-frequency fluctuations (fALFF) feature from functional magnetic resonance imaging and the gray matter volume (GMV) feature from structural magnetic resonance imaging. The mCCA-jICA method was used to integrate these two modal features to investigate the functional-structural co-variation abnormalities in MDD. To validate the stability of the extracted functional-structural covariant abnormalities features, we apply them to identify FEDN-MDD patients.
The results show that compared to healthy controls, FEDN-MDD patients exhibit joint group-discriminative independent component and modality-specific group-discriminative independent component, suggesting functional-structural covariant abnormalities in MDD patients. Using lightGBM classifier, we achieve a classification accuracy of 99.84 %.
We use GMV and fALFF for multimodal fusion shows promise, but requires further validation with other datasets and exploration of additional multimodal features.
This may indicate that multimodal fusion features can effectively explore information between different modalities and can accurately identify FEDN-MDD patients, suggesting their potential as multimodal brain imaging biomarkers for MDD.
重度抑郁症（MDD）是一种严重且常见的精神疾病。未经药物干预的首发药物-naive MDD（FEDN-MDD）患者有助于了解 MDD 的生物学基础。多模态磁共振成像可以提供对 MDD 大脑功能和结构异常的全面了解。然而，大多数 MDD 研究使用单模态、小规模 MRI 数据。并且，几项 MDD 的多模态研究仅限于功能和结构特征的简单线性组合。
我们筛选了大量 FEDN-MDD 患者和健康对照者的多模态 MRI 数据。从功能磁共振成像中提取分数低频波动（fALFF）特征，从结构磁共振成像中提取灰质体积（GMV）特征。使用 mCCA-jICA 方法整合这两种模态特征，以研究 MDD 中的功能-结构协变异常。为了验证提取的功能-结构协变异常特征的稳定性，我们将其应用于识别 FEDN-MDD 患者。
结果表明，与健康对照组相比，FEDN-MDD 患者表现出联合组判别独立成分和模态特定组判别独立成分，表明 MDD 患者存在功能-结构协变异常。使用 lightGBM 分类器，我们实现了 99.84%的分类准确率。
我们使用 GMV 和 fALFF 进行多模态融合显示出前景，但需要使用其他数据集进行进一步验证，并探索其他多模态特征。
这可能表明多模态融合特征可以有效地探索不同模态之间的信息，并且可以准确地识别 FEDN-MDD 患者，表明它们可能成为 MDD 的多模态脑成像生物标志物。
