Machine learning could help to find new treatments for dementia, according to researchers at UCL. A new algorithm that can automatically disentangle different patterns of progression in patients with a range of different dementias, including Alzheimer's disease, will enable individuals to be identified that may respond best to different treatments. For the paper, published in Nature Communications, researchers devised and applied a new algorithm called SuStaIn (Subtype and Stage Inference) to routinely acquired MRI scans from patients with dementia. The algorithm was able to identify three separate subtypes of Alzheimer's disease, which broadly match those observed in post-mortems of brain tissue, and several different subtypes of frontotemporal dementia. Critically, however, this subtyping could be done in life, using brain scanning, and very early in the disease process.
Researchers face a challenge in understanding the brain changes during the long course of Alzheimer's disease. It's not possible to track neurodegeneration continuously in individual people for up to 30 years, so instead scientists collect snapshots of the disease from different people in all stages of the disease. Now, using advanced computational approaches and a massive trove of MRI brain volume data, scientists have stitched together a series of these snapshots. This way, they identified disease subtypes with distinct progression patterns in people with Alzheimer's disease or with mutations that cause frontotemporal dementia (FTD) and amyotrophic lateral sclerosis (ALS). They dubbed their method SuStaIn, for Subtype and Stage Inference.
Alzheimer's disease (AD) is a degenerative brain disease impairing a person's ability to perform day to day activities. The clinical manifestations of Alzheimer's disease are characterized by heterogeneity in age, disease span, progression rate, impairment of memory and cognitive abilities. Due to these variabilities, personalized care and treatment planning, as well as patient counseling about their individual progression is limited. Recent developments in machine learning to detect hidden patterns in complex, multi-dimensional datasets provides significant opportunities to address this critical need. In this work, we use unsupervised and supervised machine learning approaches for subtype identification and prediction. We apply machine learning methods to the extensive clinical observations available at the Alzheimer's Disease Neuroimaging Initiative (ADNI) data set to identify patient subtypes and to predict disease progression. Our analysis depicts the progression space for the Alzheimer's disease into low, moderate and high disease progression zones. The proposed work will enable early detection and characterization of distinct disease subtypes based on clinical heterogeneity. We anticipate that our models will enable patient counseling, clinical trial design, and ultimately individualized clinical care.
A simple eye test can quickly detect the early signs of a rare type of dementia, a new study found. The thinning of the outer retina indicates the patient has frontotemporal dementia, an uncommon type of dementia that mainly affects the front and sides of the brain. It is hard to diagnose because it does not initially cause memory problems and like other types of dementia, it tends to develop slowly and get gradually worse over several years. Now scientists at the University of Pennsylvania School of Medicine have found eye changes may signal frontotemporal dementia, also known as frontotemporal lobe degeneration (FTD). FTD affects around 16,300 people or two percent of all dementia cases and signs include personality and behavior changes and problems with language, mental abilities and memory.
Iron accumulation in the outer layer of the brain could lead to mental deterioration in people with Alzheimer's disease, scientists report. MRI scans over the course of 17 years showed people with Alzheimer's have higher levels of iron in some regions of the brain – including deep grey matter, temporal lobes and neo-cortex – than those of the same age without the disease. Iron concentrations correlate with a key protein known as amyloid beta, which clumps in and around brain cells and causes Alzheimer's. The results suggest that drugs that reduce the iron burden in the brain, known as chelators, could have a potential role in Alzheimer's disease treatment. Iron from the blood is essential for the brain's neurological function, but the chemical element needs to be tightly regulated to prevent adverse effects.