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

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

Lei Wang, Ashley Heywood, Jane Stocks, Jinhyeong Bae, Da Ma, Karteek Popuri, Arthur W. Toga, Kejal Kantarci, Laurent Younes, Ian R. Mackenzie, …
Journal of psychiatry and brain science, v 4
01 Jan 2019
DOI: https://doi.org/10.20900/jpbs.20190017
PMID: 31754634
url
https://doi.org/10.20900/jpbs.20190017View
Published, Version of Record (VoR)CC BY V4.0,  Open

Abstract

Alzheimer’s Disease brain imaging C9orf72 Convolutional Neural Networks dementia FDG-PET machine learning morphometry sex-differences
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.

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