New Machine Learning Algorithms Aid Prognosis in Early Alzheimer’s
Update 13.12.2022
Scientists develop machine learning algorithms that can predict pathological “tau” protein buildup in the brains of patients with early Alzheimer’s disease
The cognitive
and functional decline in patients with Alzheimer’s disease is associated,
among other factors, with the aberrant accumulation of a protein called “tau”
inside the nerve cells of the brain. However, the current method to detect tau
accumulation is expensive, risky, and poorly available at healthcare centers. Now,
scientists from Korea have an innovative solution: machine-learning-based models
that not only beat these challenges but also open doors to better screening,
prognosis, and treatment.
Korean researchers successfully
develop machine learning algorithms that accurately predict the accumulation of
abnormal tau proteins in prodromal Alzheimer’s disease.
Photo
courtesy: Shutterstock.
Alzheimer’s disease, which makes slow progress and ultimately leads to
severe memory loss and cognitive decline, has considerably captured popular
pathos and even featured in classic films like “Still Alice.” This is also
fueled by the fact that the disease is difficult to diagnose and prognose and
still lacks a treatment. Fortunately, the research effort into understanding
the fundamentals of how the disease develops moves ahead at full steam.
This research effort has revealed the key role of the aberrant tangling of filaments
of a protein called “tau” in our brain during Alzheimer’s. These tangles
accumulate inside neurons (nerve cells) and disrupt their ability to
communicate with each other, leading to decline in cognitive or motor functions.
Studies have linked progressive tau accumulation with progressive cognitive
decline in Alzheimer’s disease, meaning that tau accumulation could be a
prognostic marker.
But the current technological standard for tau accumulation detection is
positron emission tomography (PET) scanning, which is limited by expense,
availability, and risk of excess radiation exposure. Scientists have,
therefore, been trying to find an improved detection method, or better, a way
to predict it in Alzheimer’s. “I believe
predicting tau accumulation is an important goal not just for prognosis but
because future treatment strategies may target the tau protein,” explains
Dr. Sang Won Seo from the Sungkyunkwan University School of Medicine, Korea,
who studies Alzheimer’s disease.
Dr. Seo’s team has an innovative solution to the tau observation problem:
machine learning. Their new breakthrough is published in Nature’s Scientific Reports. This study was supported by a fund
(2018-ER6203-02) by the Research of Korea Centers for Disease Control and
Prevention.
In their study, the scientists first recruited 64 patients with very early stage
Alzheimer’s, or prodromal Alzheimer’s, when there is definitive mild cognitive
decline due to Alzheimer’s but no functional decline. They then used various
combinations of multimodal biomarkers and clinical characteristics of
Alzheimer’s, such as brain structure and volume, memory, Braak stages
(indicative of Alzheimer’s pathology), Alzheimer’s scale scores, and genotype,
and patient demographics, such as age, gender, and education, as input
variables in six machine learning models to predict tau positivity (or the
presence of aberrant tau accumulation) based on the random forest and gradient
boosting machine algorithms. “Not all
clinical settings can acquire data on all biomarkers, so we tested various
combinations to make it more accessible, cost-effective, and thus widely applicable
to different clinical settings,” Dr. Seo explains.
Both types of models showed good prediction performance. In the gradient boosting machine based models, the
cortical thickness of the parietal lobe (the brain area processing sensory
information such as touch, taste, and temperature) was the most important
predictive feature, followed by performance on memory tests, cortical thickness
of the occipital lobe (brain area responsible for processing visual
information), and performance on the number cancellation test. In the random
forest–based
models, more or less the same features proved important, with word recognition
score turning out to be more important than number cancellation test scores.
Overall, clinical and neuropsychological data resulted in better performance
than demographic data did.
Speaking of the applications of these algorithms, Dr.
Seo says, “They can be useful for
screening study populations in clinical trials to receive targeted tau-based
treatments, helping to reduce screening failure. They can also help patients receive
better treatment via improved prediction of disease severity and progress
speed.”
While these are the immediate potential applications, with
validation in bigger datasets (much larger than 64 patients) and in other
medical conditions, the applications can be broadened further. “We believe our models could facilitate
better stratification or intervention design in not just Alzheimer’s but other
tauopathies as well,” says Prof. Seo. “Our
secondary goal was to enable the maximization of the potential applicability of
our models.”
Reference
Authors
Title of original paper
Journal |
Jaeho Kim1,2,4,5,
Yuhyun Park2,3, Seongbeom Park2, Hyemin Jang2,4,5,
Hee Jin Kim2,4,5, Duk L. Na2,4,5,6,7, Hyejoo Lee2,4,5,
and Sang Won Seo2,3,4,5,7
Prediction of tau
accumulation in prodromal Alzheimer’s disease using an ensemble machine learning
approach
Scientific
Reports |
|
|
DOI
Affiliations |
https://doi.org/10.1038/s41598-021-85165-x
1. Department of Neurology,
Dongtan Sacred Heart Hospital, Hallym University College of Medicine,
Hwaseong‑si, Gyeonggi‑do, Republic of Korea.
2. Department of
Neurology, Samsung Medical Center, Sungkyunkwan, University School of
Medicine, 81 Irwon‑ro, Gangnam‑gu, Seoul 06351,
Republic of Korea. 3. Department of
Intelligent Precision Healthcare Convergence, Sungkyunkwan University School
of Medicine, Suwon, Republic of Korea. 4. Neuroscience Center,
Samsung Medical Center, Seoul, Republic of Korea. 5. Samsung Alzheimer
Research Center, Samsung Medical Center, Seoul, Republic of Korea. 6. Stem Cell and
Regenerative Medicine Institute, Samsung Medical Center, Seoul, Republic of
Korea. 7. Department of Health
Sciences and Technology, SAIHST, Sungkyunkwan University, Seoul, Republic of
Korea. |
About National Institute of Health in Korea
The Korea National Institute of Health (KNIH), one
of the major operating components of the Ministry of Health and Welfare, leads
the nation’s medical research. Over the past seven decades, the KNIH has made
unwavering efforts to enhance the public’s health and innovate biomedical
research. The KNIH seeks to eradicate diseases and make people healthier. The
KNIH establishes a scientific basis and evidence underlying health policy as
well as provides national research infrastructures. We also promote public health
research. To this end, we make efforts to enrich a health research environment
by granting funds to research projects and keeping our resources, data, and
facilities more open and accessible to researchers.
Website:
http://www.nih.go.kr/eng/
About Dr. Sang Won Seo
Dr. Sang Won Seo is a Professor at the Department of Neurology,
Sungkyunkwan University School of Medicine, Samsung Medical Center. Dr. Seo’s lab
studies the relationship among Alzheimer’ disease, small vessel disease, and
cognition. He was a visiting scholar at Epidemiology, Johns
Hopkins Bloomberg School of Public Health (2014) and at Neuropathology, Memory
Aging Center University of California at San Francisco (2015). He received his
MD from Yonsei Medical School in 1997 and his Ph.D. in Neurology in 2008 from
Yonsei Medical School. He has published about 300 papers in international
peer-reviewed journals.