Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands.
Department of Research and Development, Epilepsy Centre Kempenhaeghe, Heeze, The Netherlands.
J Neuroimaging. 2022 Jul;32(4):582-595. doi: 10.1111/jon.13011. Epub 2022 May 21.
Objective diagnosis and prognosis in major depressive disorder (MDD) remains a challenge due to the absence of biomarkers based on physiological parameters or medical tests. Numerous studies have been conducted to identify functional magnetic resonance imaging-based biomarkers of depression that either objectively differentiate patients with depression from healthy subjects, predict personalized treatment outcome, or characterize biological subtypes of depression. While there are some findings of consistent functional biomarkers, there is still lack of robust data acquisition and analysis methodology. According to current findings, primarily, the anterior cingulate cortex, prefrontal cortex, and default mode network play a crucial role in MDD. Yet, there are also less consistent results and the involvement of other regions or networks remains ambiguous. We further discuss image acquisition, processing, and analysis limitations that might underlie these inconsistencies. Finally, the current review aims to address and discuss possible remedies and future opportunities that could improve the search for consistent functional imaging biomarkers of depression. Novel acquisition techniques, such as multiband and multiecho imaging, and neural network-based cleaning approaches can enhance the signal quality in limbic and frontal regions. More comprehensive analyses, such as directed or dynamic functional features or the identification of biological depression subtypes, can improve objective diagnosis or treatment outcome prediction and mitigate the heterogeneity of MDD. Overall, these improvements in functional MRI imaging techniques, processing, and analysis could advance the search for biomarkers and ultimately aid patients with MDD and their treatment course.
由于缺乏基于生理参数或医学测试的生物标志物，重度抑郁症（MDD）的客观诊断和预后仍然是一个挑战。许多研究都致力于识别基于功能磁共振成像的抑郁生物标志物，这些标志物可以客观地区分抑郁症患者和健康受试者，预测个性化的治疗效果，或描述抑郁症的生物学亚型。虽然有一些关于一致的功能生物标志物的发现，但仍然缺乏强大的数据采集和分析方法。根据目前的研究结果，主要是前扣带皮层、前额叶皮层和默认模式网络在 MDD 中起着关键作用。然而，也存在不一致的结果，并且涉及到其他区域或网络仍然不明确。我们进一步讨论了可能导致这些不一致的图像采集、处理和分析限制。最后，本综述旨在解决和讨论可能改善寻找一致的抑郁功能成像生物标志物的潜在方法和未来机会。新的采集技术，如多带和多回波成像，以及基于神经网络的清理方法，可以提高边缘和额叶区域的信号质量。更全面的分析，如定向或动态功能特征或识别生物学抑郁亚型，可以提高客观诊断或治疗效果预测，并减轻 MDD 的异质性。总的来说，这些在功能磁共振成像技术、处理和分析方面的改进可以推进生物标志物的研究，并最终帮助 MDD 患者及其治疗过程。
