Grant Report on PREDICT-ADFTD: Multimodal Imaging Prediction of AD/FTD and Differential Diagnosis-Journal of Psychiatry and Brain Science-CSCIED科技核心评价数据库-手机版

Grant Report on PREDICT-ADFTD: Multimodal Imaging Prediction of AD/FTD and Differential Diagnosis

Grant Report on PREDICT-ADFTD: Multimodal Imaging Prediction of AD/FTD and Differential Diagnosis

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DOI	10.20900/jpbs.20190017
刊名	Journal of Psychiatry and Brain Science JPBS
年，卷(期)	2019, 4(5)
作者	Lei Wang*,Ashley Heywood,Jane Stocks,Jinhyeong Bae,Da Ma,Karteek Popuri,Arthur W. Toga,Kejal Kantarci,Laurent Younes,Ian R. Mackenzie,Fengqing Zhang,Mirza Faisal Beg,Howard Rosen,Alzheimer’s Disease Neuroimaging Initiative,
作者单位	1 Northwestern University Feinberg School of Medicine, Chicago, 60611 IL, USA; 2 School of Engineering Science, Simon Fraser University, Burnaby, V6A1S6 BC, Canada; 3 Keck School of Medicine of University of Southern California, Los Angeles, 90033 CA, USA; 4 Departments of Neurology and Radiology, Mayo Clinic, Rochester, 55905 MN, USA; 5 Department of Applied Mathematics and Statistics, Johns Hopkins University, Baltimore, 21218 MD, USA; 6 Department of Pathology and Lab Medicine, University of British Columbia, Vancouver, B6T1Z4 BC, Canada; 7 Department of Psychology, Drexel University, Philadelphia, 19104 PA, USA; 8 Department of Neurology, University of California, San Francisco, 94143 CA, USA
Abstract	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.
KeyWord	dementia; Alzheimer’s Disease; brain imaging; FDG-PET; machine learning; Convolutional Neural Networks; morphometry; sex-differences; C9orf72
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引用本文

Lei Wang*,Ashley Heywood,Jane Stocks,Jinhyeong Bae,Da Ma,Karteek Popuri,Arthur W. Toga,Kejal Kantarci,Laurent Younes,Ian R. Mackenzie,Fengqing Zhang,Mirza Faisal Beg,Howard Rosen,Alzheimer’s Disease Neuroimaging Initiative. Grant Report on PREDICT-ADFTD: Multimodal Imaging Prediction of AD/FTD and Differential Diagnosis, Journal of Psychiatry and Brain Science. 2019; 4; (5). https://doi.org/10.20900/jpbs.20190017.

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