利用因果连通性对帕金森病的纵向预后进行分析

IF 3.4 2区 医学 Q2 NEUROIMAGING
Cooper J. Mellema , Kevin P. Nguyen , Alex Treacher , Aixa X. Andrade , Nader Pouratian , Vibhash D. Sharma , Padraig O'Suileabhain , Albert A. Montillo
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引用次数: 0

摘要

尽管帕金森病(Parkinson's disease,PD)的发病率很高,但目前还没有临床公认的神经影像生物标志物来预测运动或认知功能衰退的轨迹,或将帕金森病与非典型进行性帕金森病区分开来。由于运动回路和基底神经节的异常连接先前已被证明是神经退行性变的早期标志物,我们假设区域间的连接模式可能有助于形成特定患者的疾病状态和帕金森病进展的预测模型。我们利用多系统萎缩症(MSA)、进行性核上性麻痹(PSP)、特发性帕金森病和健康对照组受试者的 fMRI 数据,构建了运动和认知能力下降的预测模型,并区分了这四个亚组。此外,我们还确定了对病情发展和诊断最有参考价值的特定联系。在预测MDS-UPDRS-III1*和蒙特利尔认知评估(MoCA)的一年进展时,我们取得了最先进的平均绝对误差性能。此外,我们在诊断帕金森病、多发性硬化症、帕金森病和健康对照组时所达到的平衡准确性也超过了大多数诊所的水平,这凸显了大脑连接特征的相关性。我们的模型显示,深部核团、运动区和丘脑之间的连通性对预测最为重要。总之,这些结果证明了 fMRI 连接性作为帕金森病预后生物标志物的潜力,并增加了我们对这种疾病的了解。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Longitudinal prognosis of Parkinson’s outcomes using causal connectivity

Despite the prevalence of Parkinson’s disease (PD), there are no clinically-accepted neuroimaging biomarkers to predict the trajectory of motor or cognitive decline or differentiate Parkinson’s disease from atypical progressive parkinsonian diseases. Since abnormal connectivity in the motor circuit and basal ganglia have been previously shown as early markers of neurodegeneration, we hypothesize that patterns of interregional connectivity could be useful to form patient-specific predictive models of disease state and of PD progression. We use fMRI data from subjects with Multiple System Atrophy (MSA), Progressive Supranuclear Palsy (PSP), idiopathic PD, and healthy controls to construct predictive models for motor and cognitive decline and differentiate between the four subgroups. Further, we identify the specific connections most informative for progression and diagnosis. When predicting the one-year progression in the MDS-UPDRS-III1* and Montreal Cognitive assessment (MoCA), we achieve new state-of-the-art mean absolute error performance. Additionally, the balanced accuracy we achieve in the diagnosis of PD, MSA, PSP, versus healthy controls surpasses that attained in most clinics, underscoring the relevance of the brain connectivity features. Our models reveal the connectivity between deep nuclei, motor regions, and the thalamus as the most important for prediction. Collectively these results demonstrate the potential of fMRI connectivity as a prognostic biomarker for PD and increase our understanding of this disease.

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来源期刊
Neuroimage-Clinical
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.
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