Multi-modal MRI for objective diagnosis and outcome prediction in depression

IF 3.4 2区医学 Q2 NEUROIMAGING

Neuroimage-Clinical Pub Date : 2024-01-01 DOI:10.1016/j.nicl.2024.103682

Jesper Pilmeyer , Rolf Lamerichs , Sjir Schielen , Faroeq Ramsaransing , Vivianne van Kranen-Mastenbroek , Jacobus F.A. Jansen , Marcel Breeuwer , Svitlana Zinger

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引用次数: 0

Abstract

Research Purpose

The low treatment effectiveness in major depressive disorder (MDD) may be caused by the subjectiveness in clinical examination and the lack of quantitative tests. Objective biomarkers derived from magnetic resonance imaging (MRI) may support clinical experts during decision-making. Numerous studies have attempted to identify such MRI-based biomarkers. However, the majority is uni-modal (based on a single MRI modality) and focus on either MDD diagnosis or outcome. Uncertainty remains regarding whether key features or classification models for diagnosis may also be used for outcome prediction. Therefore, we aim to find multi-modal predictors of both, MDD diagnosis and outcome. By addressing these research questions using the same dataset, we eliminate between-study confounding factors.

Various structural (T₁-weighted, T₂-weighted, diffusion tensor imaging (DTI)) and functional (resting-state and task-based functional MRI) scans were acquired from 32 MDD and 31 healthy control (HC) subjects during the first visit at the investigational site (baseline). Depression severity was assessed at baseline and 6 months later. Features were extracted from the baseline MRI images with different modalities. Binary 6-months negative and positive outcome (NO; PO) classes were defined based on relative (to baseline) change in depression severity. Support vector machine models were employed to separate MDD from HC (diagnosis) and NO from PO subjects (outcome). Classification was performed through a uni-modal (features from a single MRI modality) and multi-modal (combination of features from different modalities) approach.

Principal Results

Our results show that DTI features yielded the highest uni-modal performance for diagnosis and outcome prediction: mean diffusivity (AUC (area under the curve) = 0.701) and the sum of streamline weights (AUC = 0.860), respectively. Multi-modal ensemble classifiers with T₁-weighted, resting-state functional MRI and DTI features improved classification performance for both diagnosis and outcome (AUC = 0.746 and 0.932, respectively). Feature analyses revealed that the most important features were located in frontal, limbic and parietal areas. However, the modality or location of these features was different between diagnostic and prognostic models.

Major Conclusions

Our findings suggest that combining features from different MRI modalities predict MDD diagnosis and outcome with higher performance. Furthermore, we demonstrated that the most important features for MDD diagnosis were different and located in other brain regions than those for outcome. This longitudinal study contributes to the identification of objective biomarkers of MDD and its outcome. Follow-up studies may further evaluate the generalizability of our models in larger or multi-center cohorts.

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多模态磁共振成像用于抑郁症的客观诊断和结果预测

研究目的重度抑郁障碍（MDD）的治疗效果不佳可能是由于临床检查的主观性和缺乏定量检测所致。通过磁共振成像（MRI）获得的客观生物标志物可为临床专家的决策提供支持。许多研究都试图找出这种基于磁共振成像的生物标志物。然而，大多数研究都是单模态的（基于单一磁共振成像模式），并且侧重于 MDD 诊断或结果。诊断的关键特征或分类模型是否也可用于结果预测仍存在不确定性。因此，我们的目标是找到 MDD 诊断和结果的多模态预测因子。32 名 MDD 受试者和 31 名健康对照（HC）受试者在研究机构的首次就诊（基线）期间接受了各种结构（T1 加权、T2 加权、弥散张量成像（DTI））和功能（静息态和基于任务的功能 MRI）扫描。抑郁严重程度在基线和 6 个月后进行评估。从基线 MRI 图像中提取了不同模式的特征。根据抑郁严重程度的相对（与基线相比）变化，定义了 6 个月后的二元阴性和阳性结果（NO; PO）类别。采用支持向量机模型将 MDD 与 HC（诊断）和 NO 与 PO 受试者（结果）区分开来。主要结果我们的研究结果表明，DTI 特征在诊断和结果预测方面的单模态性能最高：分别为平均弥散度（AUC（曲线下面积）= 0.701）和流线权重总和（AUC = 0.860）。具有 T1 加权、静息态功能 MRI 和 DTI 特征的多模态集合分类器提高了诊断和预后的分类性能（AUC 分别为 0.746 和 0.932）。特征分析表明，最重要的特征位于额叶、边缘和顶叶区域。主要结论：我们的研究结果表明，结合不同 MRI 模式的特征可以预测 MDD 的诊断和预后，而且性能更高。此外，我们还证明了对 MDD 诊断最重要的特征与对预后最重要的特征不同，而且位于其他脑区。这项纵向研究有助于确定 MDD 及其预后的客观生物标志物。后续研究可进一步评估我们的模型在更大规模或多中心队列中的推广性。

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

Neuroimage-Clinical NEUROIMAGING-

CiteScore

7.50

自引率

4.80%

发文量

368

审稿时长

52 days

期刊介绍： NeuroImage: Clinical, a journal of diseases, disorders and syndromes involving the Nervous System, provides a vehicle for communicating important advances in the study of abnormal structure-function relationships of the human nervous system based on imaging. The focus of NeuroImage: Clinical is on defining changes to the brain associated with primary neurologic and psychiatric diseases and disorders of the nervous system as well as behavioral syndromes and developmental conditions. The main criterion for judging papers is the extent of scientific advancement in the understanding of the pathophysiologic mechanisms of diseases and disorders, in identification of functional models that link clinical signs and symptoms with brain function and in the creation of image based tools applicable to a broad range of clinical needs including diagnosis, monitoring and tracking of illness, predicting therapeutic response and development of new treatments. Papers dealing with structure and function in animal models will also be considered if they reveal mechanisms that can be readily translated to human conditions.