The progression of Alzheimer's disease (AD) is a continuous process in cognitive and biomarker changes, with only a fraction of mild cognitive impairment (MCI) patients eventually advancing to AD. There is no definite biomarker signature to determine this eventual progress. The discovery and development of prognostic biomarker signatures for AD is essential to address the challenges of AD drug discovery and development. The deep learning (DL) technique is a recent breakthrough in data science that enables researchers to discover previously unknown features comprised by complicated patterns learnt from large datasets. It has outperformed many traditional machine learning methods in computer vision tasks. In this article, we evaluated the performance of DL algorithms in differentiating patients with diagnosis of AD, MCI, or no evidence of dementia using baseline MRI data of the brain, and integrated features extracted from the neural network with other baseline biomarkers to develop an AD prognostic signature. This signature can be used to understand the patient heterogeneity in a study cohort and provide enrichment strategies for AD clinical trial design.

Liuqing Yang

Xifeng Wang

Qi Guo

Scott Gladstein

Dustin Wooten

Tengfei Li

Weining Z Robieson

Yan Sun

Xin Huang

Statistics in Biopharmaceutical Research

STATISTICS IN BIOPHARMACEUTICAL RESEARCH (SBR)，发表关注生物制药行业研究人员和应用统计学家需求的文章；来自医学院、兽医学院、公共卫生学院和药学院的学术生物统计学家；在监管机构工作的统计学家和定量分析师（例如，美国食品和药物管理局及其在其他国家的对应机构）；有兴趣将本期刊中介绍的方法应用于自己的领域的统计学家；以及对将统计方法应用于生物制药问题感兴趣的非统计学家。

The journal "Statistics in Biopharmaceutical Research" focuses on the biopharmaceutical industry, publishing articles that meet the needs of researchers and applied statisticians. We welcome academic biostatisticians from medical schools, veterinary schools, schools of public health, and schools of pharmacy, as well as statisticians and quantitative analysts working in regulatory agencies such as the U.S. Food and Drug Administration and its counterparts in other countries. Additionally, we encourage statisticians interested in applying the methods presented in this journal to their own fields, as well as non-statisticians who are interested in statistical methods applied to biopharmaceutical issues.

《Statistics in Biopharmaceutical Research》期刊专注于生物制药行业的研究，发表满足研究人员和应用统计学家需求的文章。我们欢迎来自医学院、兽医学院、公共卫生学院和药学院的学术生物统计学家，以及在监管机构（如美国食品和药物管理局及其国际对应机构）工作的统计学家和定量分析师。此外，我们也鼓励有兴趣将本期刊中介绍的方法应用于自身领域的统计学家，以及对生物制药问题中的统计方法感兴趣的非统计学家投稿。

Deep Learning Based Multimodal Progression Modeling for Alzheimer’s Disease

The progression of Alzheimer’s disease (AD) is a continuous process in cognitive and biomarker changes, with only a fraction of Mild Cognitive Impairment patients eventually advancing to AD.

Category	Quartile
医学	4
医学, 数学与计算生物学	3
医学, 统计学与概率论	4

Deep Learning Based Multimodal Progression Modeling for Alzheimer’s Disease

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