Raguž Marina, Marčinković Petar, Chudy Hana, Galkowski Valentina, Majdak Maja, Orešković Darko, Chudy Darko
Department of Neurosurgery, Dubrava University Hospital, Zagreb, Croatia.
Catholic University of Croatia, Zagreb, Croatia.
Front Neurol. 2025 Aug 28;16:1629319. doi: 10.3389/fneur.2025.1629319. eCollection 2025.
Disorders of consciousness (DoC) encompass a spectrum of clinical conditions with often indistinct boundaries, making accurate diagnosis and therapeutic decision-making particularly challenging. While advanced imaging techniques such as fMRI and PET reduce misdiagnosis risk, their limited availability in routine clinical settings underscores the need for alternative approaches. This study investigates whether the integration of qualitative and quantitative parameters derived from conventional MRI can improve diagnostic precision and support more accurate deep brain stimulation (DBS) candidate selection in DoC patients.
Fifty consecutive DoC patients underwent comprehensive clinical, neurophysiological, and MRI assessment. Based on an integrated assessment of these findings, patients were classified as DBS candidates or non-candidates. MRI scans were qualitatively assessed for cortical and subcortical atrophy (including diffuse cortical, thalamic, and brainstem degeneration), ventricular enlargement, sulcal widening, leukoaraiosis, corpus callosum damage, gray-white matter border effacement, and extensive lesions (e.g., global ischemia or porencephalic cavities). Quantitative volumetric analysis was performed using the FreeSurfer pipeline.
Qualitative features such as leukoaraiosis, thalamic and cortical atrophy, ventricular enlargement, and corpus callosum lesions were significantly associated with DBS candidacy. Quantitative predictors included striatal volume, total gray matter, ventricular volume, CSF, and supratentorial volume. A combined model incorporating both qualitative and quantitative MRI data achieved high predictive accuracy () for DBS candidacy.
Integrating conventional MRI-based qualitative and quantitative assessments with clinical and neurophysiological evaluation may substantially improve DBS candidate selection in DoC patients, especially where functional imaging is unavailable. These findings support the development of practical MRI-based decision frameworks and call for multicenter validation. Despite increasing research on imaging and neuromodulation in DoC, studies directly comparing qualitative and quantitative structural MRI in the context of DBS candidacy remain scarce, highlighting a critical gap in the field.
意识障碍（DoC）涵盖一系列临床状况，其界限往往不清晰，这使得准确诊断和治疗决策极具挑战性。虽然功能磁共振成像（fMRI）和正电子发射断层扫描（PET）等先进成像技术降低了误诊风险，但它们在常规临床环境中的可用性有限，凸显了采用替代方法的必要性。本研究调查了源自传统磁共振成像（MRI）的定性和定量参数整合是否能提高诊断精度，并支持对意识障碍患者更准确地选择深部脑刺激（DBS）候选者。
连续50例意识障碍患者接受了全面的临床、神经生理学和MRI评估。基于对这些结果的综合评估，将患者分为DBS候选者或非候选者。对MRI扫描进行定性评估，包括皮质和皮质下萎缩（包括弥漫性皮质、丘脑和脑干变性）、脑室扩大、脑沟增宽、脑白质疏松、胼胝体损伤、灰白质边界模糊以及广泛病变（如全脑缺血或脑穿通畸形腔）。使用FreeSurfer管道进行定量体积分析。
脑白质疏松、丘脑和皮质萎缩、脑室扩大以及胼胝体病变等定性特征与DBS候选资格显著相关。定量预测指标包括纹状体体积、总灰质、脑室体积、脑脊液和幕上体积。结合定性和定量MRI数据的联合模型对DBS候选资格具有较高的预测准确性（）。
将基于传统MRI的定性和定量评估与临床及神经生理学评估相结合，可能会显著改善意识障碍患者的DBS候选者选择，尤其是在无法进行功能成像的情况下。这些发现支持基于MRI的实用决策框架的开发，并呼吁进行多中心验证。尽管关于意识障碍的成像和神经调节研究不断增加，但在DBS候选资格背景下直接比较定性和定量结构MRI的研究仍然很少，凸显了该领域的一个关键空白。
