Schoen Devin, Deutsch Skyler, Mehta Juhi, Wang Sarah, Kornak John, Starr Philip A, Wang Doris D, Ostrem Jill L, Bledsoe Ian O, Morrison Melanie A
Department of Radiology and Biomedical Imaging, University of California, San Francisco, USA.
UCSF-UC Berkeley Joint PhD Program in Bioengineering, Berkeley, USA.
Nat Commun. 2025 Jul 1;16(1):5590. doi: 10.1038/s41467-025-60695-4.
While deep brain stimulation (DBS) remains an effective therapy for Parkinson's disease (PD), sources of variance in patient outcomes are still not fully understood, underscoring a need for better prognostic criteria. Here, we leveraged routinely collected T1-weighted (T1-w) magnetic resonance imaging (MRI) data to derive patient-specific measures of brain structure and evaluate their usefulness in predicting changes in PD medications in response to DBS. Preoperative T1-w MRI data from 231 patients with PD were used to extract regional measures of fractal dimension (FD), sensitive to the structural complexities of cortical and subcortical brain. FD was validated as a biomarker of PD progression through comparison of patients with PD and healthy controls (HCs). This analysis revealed significant group differences in FD across nine brain regions, including frontal, occipital, insular, and basal ganglia areas, which supports its utility as a marker of PD. We evaluated the impact of adding imaging features (FD) to a clinical model that included demographics and clinical parameters (age, sex, total number and location of DBS electrodes), and preoperative motor response to levodopa. This model aimed to explain variance and predict changes in medication following DBS. Regression analysis revealed that inclusion of the FD of distributed brain areas correlated with post-DBS reductions in medication burden, explaining an additional 13.6% of outcome variance (R = 0.388) compared to clinical features alone (R = 0.252). Hypergraph-based classification learning tasks achieved an area under the receiver operating characteristic curve of 0.64 when predicting with clinical features alone, versus 0.76 when combining clinical and imaging features. These findings demonstrate that PD effects on brain morphology linked to disease progression influence DBS outcomes. The work also highlights FD as a potentially useful imaging biomarker to enhance DBS candidate selection criteria for optimized treatment planning.
虽然深部脑刺激（DBS）仍然是治疗帕金森病（PD）的有效方法，但患者治疗结果的差异来源仍未完全明确，这凸显了对更好的预后标准的需求。在此，我们利用常规收集的T1加权（T1-w）磁共振成像（MRI）数据，得出患者特异性的脑结构测量值，并评估其在预测PD药物对DBS反应变化方面的有用性。来自231例PD患者的术前T1-w MRI数据用于提取分形维数（FD）的区域测量值，该值对皮质和皮质下脑的结构复杂性敏感。通过比较PD患者和健康对照（HCs），FD被验证为PD进展的生物标志物。该分析揭示了九个脑区（包括额叶、枕叶、岛叶和基底神经节区域）的FD存在显著的组间差异，这支持了其作为PD标志物的效用。我们评估了将成像特征（FD）添加到一个临床模型中的影响，该临床模型包括人口统计学和临床参数（年龄、性别、DBS电极的总数和位置）以及术前对左旋多巴的运动反应。该模型旨在解释差异并预测DBS后药物的变化。回归分析显示，纳入分布脑区的FD与DBS后药物负担的减轻相关，与仅考虑临床特征相比（R = 0.252），额外解释了13.6%的结果差异（R = 0.388）。基于超图的分类学习任务在仅使用临床特征进行预测时，受试者操作特征曲线下面积为0.64，而在结合临床和成像特征时为0.76。这些发现表明，与疾病进展相关的PD对脑形态的影响会影响DBS结果。这项工作还强调了FD作为一种潜在有用的成像生物标志物，可用于加强DBS候选者选择标准，以优化治疗方案。
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