{"id":176,"date":"2020-08-16T03:00:07","date_gmt":"2020-08-16T03:00:07","guid":{"rendered":"https:\/\/groups.cs.umass.edu\/infofusion\/?page_id=176"},"modified":"2020-08-16T03:00:08","modified_gmt":"2020-08-16T03:00:08","slug":"4thought-early-forecasting-of-alzheimers-disease","status":"publish","type":"page","link":"https:\/\/groups.cs.umass.edu\/infofusion\/4thought-early-forecasting-of-alzheimers-disease\/","title":{"rendered":"4Thought: Early Forecasting of Alzheimer’s Disease"},"content":{"rendered":"\n

Project 4Thought aims to preemptively forecast Alzheimer\u2019s disease. We are looking to identify subjects who will get Alzheimer’s at least 2 years ahead of the standard diagnosis, based on brain structural MRIs and cognitive test scores.<\/p>\n\n\n\n

4Thought was homegrown here at CICS in the Information Fusion Lab, supported by the Manning\/IALS Innovation Award. 4Thought was initially led by Joie Wu, and is now led by Sidong Zhang. Our mentors from IALS are Peter Reinhart and Karen Utgoff.<\/p>\n\n\n\n

Detailed Description of Project 4Thought<\/em><\/strong><\/h4>\n\n\n\n

Alzheimer\u2019s disease (AD)  is a degenerative condition that causes major changes in the brain, leading to cognitive decline and death in a matter of a few years. AD is incurable, many treatments have been tried, immense effort has been expended into clinical trials and nothing has worked so far. The main reason is that, by the time AD is diagnosed, 60% of brain matter is gone and the process is irreversible. The sketch on the left in the image below shows this degeneration. In this project, we\u2019re trying to identify people that will almost definitely get Alzheimer\u2019s at least 2 years ahead of an official diagnostic. We\u2019ve used data from the ADNI study that monitors people every 6 months over a period of a few years. In that study, some people go from cognitively normal (CN), to cognitively impaired (MCI), to then being diagnosed with AD. ADNI contains brain MRIs and pet scans that document these transitions – these are shown on the right in the image below.<\/p>\n\n\n\n

<\/p>\n\n\n\n

The question we are trying to answer is: can we identify those that will get AD while they\u2019re still in the CN stage? If so, we could try to preemptively treat them.<\/p>\n\n\n\n

To achieve this, we came up with a framework called FLARe, which focuses on long-term forecasting. The idea behind it is that we learn some abstractions of the subject\u2019s health state and we forecast this abstraction. Then, we learn how to map the abstract health status to a diagnosis. Intuitively, FLARe works as the deep learning version of an HMM.<\/p>\n\n\n\n

<\/p>\n\n\n\n

<\/p>\n\n\n\n

Below are some preliminary results. First, we compared against the s.o.t.a. Most of the existing algorithms are only evaluated for a forecasting window of 6 months in advance, which we know is insufficient. The green bar shows the contender which does ok for a forecasting window of 6-12 months, but once we go beyond that the F1 score plummets. FLARe is better across the board, but especially over long-term time windows, which is exactly what we\u2019ve designed it for.<\/p>\n\n\n\n

<\/p>\n\n\n\n

More recently, we obtained preliminary results showing that the fusing of several modalities, cognitive test scores, volumetric features from the brain, raw brain images, in the forecasting framework results in performance improvement over relying on any single modality. We are researching different ways to integrate these modalities, some using the same principles as ShortFuse, while others considering the redundancy of information encoded with different modalities. We are also incorporating domain knowledge about affected areas of the brain and considering differences in scans between consecutive visits.<\/p>\n\n\n\n

<\/td><\/td><\/td><\/tr><\/tbody><\/table>
Areas of the brain that are affected by AD, as seen on MRIs.<\/figcaption><\/figure>\n\n\n\n

One of the long-term objectives of this study is to provide some analysis of the images to radiologists and identify some areas of interest in the MRIs.<\/p>\n\n\n\n

Also, once completed, our framework will be included in a multi-stage selection procedure of study subject cohorts for clinical trials of AD prevention drugs.<\/p>\n\n\n\n

<\/p>\n\n\n\n

Resources<\/em><\/strong><\/h4>\n\n\n\n