Wang Lei, Heywood Ashley, Stocks Jane, Bae Jinhyeong, Ma Da, Popuri Karteek, Toga Arthur W, Kantarci Kejal, Younes Laurent, Mackenzie Ian R, Zhang Fengqing, Beg Mirza Faisal, Rosen Howard
Northwestern University Feinberg School of Medicine, Chicago, 60611 IL, USA.
School of Engineering Science, Simon Fraser University, Burnaby, V6A1S6 BC, Canada.
J Psychiatr Brain Sci. 2019;4. doi: 10.20900/jpbs.20190017. Epub 2019 Oct 30.
We report on the ongoing project "PREDICT-ADFTD: Multimodal Imaging Prediction of AD/FTD and Differential Diagnosis" describing completed and future work supported by this grant. This project is a multi-site, multi-study collaboration effort with research spanning seven sites across the US and Canada. The overall goal of the project is to study neurodegeneration within Alzheimer's Disease, Frontotemporal Dementia, and related neurodegenerative disorders, using a variety of brain imaging and computational techniques to develop methods for the early and accurate prediction of disease and its course. The overarching goal of the project is to develop the earliest and most accurate biomarker that can differentiate clinical diagnoses to inform clinical trials and patient care. In its third year, this project has already completed several projects to achieve this goal, focusing on (1) structural MRI (2) machine learning and (3) FDG-PET and multimodal imaging. Studies utilizing structural MRI have identified key features of underlying pathology by studying hippocampal deformation that is unique to clinical diagnosis and also post-mortem confirmed neuropathology. Several machine learning experiments have shown high classification accuracy in the prediction of disease based on Convolutional Neural Networks utilizing MRI images as input. In addition, we have also achieved high accuracy in predicting conversion to DAT up to five years in the future. Further, we evaluated multimodal models that combine structural and FDG-PET imaging, in order to compare the predictive power of multimodal to unimodal models. Studies utilizing FDG-PET have shown significant predictive ability in the prediction and progression of disease.
我们报告了正在进行的项目“PREDICT - ADFTD：AD/FTD的多模态成像预测与鉴别诊断”，描述了本资助所支持的已完成工作和未来工作。该项目是一项多地点、多研究的合作项目，研究范围横跨美国和加拿大的七个地点。该项目的总体目标是研究阿尔茨海默病、额颞叶痴呆及相关神经退行性疾病中的神经退行性变，运用多种脑成像和计算技术来开发疾病早期准确预测及其病程的方法。该项目的首要目标是开发最早且最准确的生物标志物，以区分临床诊断，为临床试验和患者护理提供依据。在项目的第三年，为实现这一目标已完成了多个项目，重点关注（1）结构磁共振成像（2）机器学习和（3）氟代脱氧葡萄糖正电子发射断层显像（FDG - PET）及多模态成像。利用结构磁共振成像的研究通过研究海马体变形确定了潜在病理学的关键特征，这种变形是临床诊断所特有的，且经尸检证实为神经病理学特征。多项机器学习实验表明，基于以磁共振成像图像为输入的卷积神经网络，在疾病预测方面具有较高的分类准确率。此外，我们在预测未来长达五年向痴呆症的转化方面也取得了高精度。此外，我们评估了结合结构和FDG - PET成像的多模态模型，以比较多模态模型与单模态模型的预测能力。利用FDG - PET的研究在疾病预测和进展方面显示出显著的预测能力。
