Lyda Hill Department of Bioinformatics, University of Texas Southwestern Medical Center, Dallas, TX, USA.
University of Texas at Dallas, Dallas, TX, USA.
Parkinsonism Relat Disord. 2021 Apr;85:44-51. doi: 10.1016/j.parkreldis.2021.02.026. Epub 2021 Mar 7.
Predictive biomarkers of Parkinson's Disease progression are needed to expedite neuroprotective treatment development and facilitate prognoses for patients. This work uses measures derived from resting-state functional magnetic resonance imaging, including regional homogeneity (ReHo) and fractional amplitude of low frequency fluctuations (fALFF), to predict an individual's current and future severity over up to 4 years and to elucidate the most prognostic brain regions.
ReHo and fALFF are measured for 82 Parkinson's Disease subjects and used to train machine learning predictors of baseline clinical and future severity at 1 year, 2 years, and 4 years follow-up as measured by the Movement Disorder Society Unified Parkinson's Disease Rating Scale (MDS-UPDRS). Predictive performance is measured with nested cross-validation, validated on an external dataset, and again validated through leave-one-site-out cross-validation. Important predictive features are identified.
The models explain up to 30.4% of the variance in current MDS-UPDRS scores, 55.8% of the variance in year 1 scores, and 47.1% of the variance in year 2 scores (p < 0.0001). For distinguishing high and low-severity individuals at each timepoint (MDS-UPDRS score above or below the median, respectively), the models achieve positive predictive values up to 79% and negative predictive values up to 80%. Higher ReHo and fALFF in several regions, including components of the default motor network, predicted lower severity across current and future timepoints.
These results identify an accurate prognostic neuroimaging biomarker which may be used to better inform enrollment in trials of neuroprotective treatments and enable physicians to counsel their patients.
需要预测帕金森病进展的生物标志物，以加快神经保护治疗的开发并为患者提供预后。本研究使用静息态功能磁共振成像（rs-fMRI）得到的指标，包括局部一致性（ReHo）和低频振幅（fALFF），预测个体当前和未来长达 4 年的严重程度，并阐明最具预后价值的脑区。
对 82 名帕金森病患者进行 ReHo 和 fALFF 测量，并用其训练机器学习预测基线临床和 1 年、2 年和 4 年随访时的未来严重程度，采用运动障碍协会统一帕金森病评定量表（MDS-UPDRS）进行测量。通过嵌套交叉验证评估预测性能，在外部数据集上进行验证，并通过留一站点交叉验证再次验证。确定重要的预测特征。
该模型可解释当前 MDS-UPDRS 评分的 30.4%、第 1 年评分的 55.8%和第 2 年评分的 47.1%的方差（p<0.0001）。对于区分每个时间点的高严重程度和低严重程度个体（分别为 MDS-UPDRS 评分高于或低于中位数），模型的阳性预测值高达 79%，阴性预测值高达 80%。包括默认运动网络在内的几个区域的 ReHo 和 fALFF 较高，预示着当前和未来时间点的严重程度较低。
这些结果确定了一种准确的预后神经影像学生物标志物，可用于更好地告知神经保护治疗试验的入组情况，并使医生能够为患者提供咨询。
