AI and Alzheimer's Disease

Blog Author: Archita Khaire

Image Credit: https://doi.org/10.1002/alz.12328

Alzheimer's is the most common form of Dementia. ​​It is progressive neurodegenerative disease that damages the healthy cells in the brain causing cognitive impairment and functional disability. As the disease progresses it starts impacting day to day functions such as walking, swallowing. Patients require round-the clock care. It is ultimately fatal. Lack of both diagnostic tools and effective treatments, Alzheimer’s disease (AD) is a growing public health and societal concern worldwide. Its impact on patients, their families and carers is devastating. 

Common symptoms of Alzheimer's include:

• Difficulties with everyday tasks
• Confusion in familiar environments
• Difficulty with words and numbers
• Memory loss
• Changes in mood and behavior

AD Identification:
Early identification of patients suffering from Alzheimer's is the biggest challenge as it is often confused with Dementia. While some forms of Dementia could be reversed, Alzheimer's is irreversible. Emerging blood-based biomarkers offer opportunities for screening patients before performing lumbar puncture tests to check for amyloid proteins in cerebrospinal fluid. 

Image Credit: DOI: 10.7717/peerj.6543/fig-

Machine learning models such as SVM, SHMR (Sparse High-order Interaction Model with Rejection option) could be used to detect AD patients using inexpensive and easily accessible biomarkers (e.g., Plasma). Only those patients difficult to diagnosis are recommended for invasive and/or more expensive screening (e.g., CSF).
There are image based deep learning algorithms that can detect the progression of Alzheimer's disease based on MRI or positron emission tomography (PET) images of patient. 

Image Credit: https://doi.org/10.3389/fninf.2018.00035

​In the early stages of Alzheimer's disease, an MRI scan of the brain may be normal. In later stages, MRI may show a decrease in the size of different areas of the brain (mainly affecting the temporal and parietal lobes)

Image Credit: Journal of Nanobiotechnology volume 19, Article number: 72 (2021) 

​Researchers are now working on ultrasensitive non-invasive micro-biomarkers which in combination with deep learning algorithms could be used to detect Alzheimer's disease.

AD Treatment:
Scientists are using artificial intelligence to screen 80 FDA-approved drugs and reveal which could be used as Alzheimer’s treatments.

Image Credit: Nature Communications volume 12, Article number: 1033 (2021) 

Image Credit: https://ec.europa.eu/programmes/horizon2020/en/news/neurotwin-proposes-novel-therapy-alzheimer%E2%80%99s-disease

​Researchers are building computational framework to represent the mechanisms of interaction of electric fields with personalized brain networks and assimilate neuroimaging data in order to design personalized optimization strategies to treat Alzheimer’s disease.
Using neuroimaging data from Alzheimer's disease, scientists will build a model that recapitulates the networks and the dynamical landscape of the individual brain. The objective is to employ this model to design and test personalized neuromodulation protocols capable of restoring healthy dynamics.

References:

• Xu J, Kochanek KD, Sherry L, Murphy BS, Tejada-Vera B. Deaths: final data for 2007. National vital statistics reports; vol. 58, no. 19. Hyattsville, MD: National Center for Health Statistics. 2010
• https://www.efpia.eu/about-medicines/use-of-medicines/disease-specific-groups/wewontrest-until-alzheimer-s-patients-have-a-brighter-future/
• Hassan, S.A., & Khan, T. (2017). A Machine Learning Model to Predict the Onset of Alzheimer Disease using Potential Cerebrospinal Fluid (CSF) Biomarkers. International Journal of Advanced Computer Science and Applications, 8.
• Eke CS, Jammeh E, Li X, Carroll C, Pearson S, Ifeachor E. Identification of Optimum Panel of Blood-based Biomarkers for Alzheimer's Disease Diagnosis Using Machine Learning. Annu Int Conf IEEE Eng Med Biol Soc. 2018 Jul;2018:3991-3994. doi: 10.1109/EMBC.2018.8513293. PMID: 30441233.
• Hampel, H., O’Bryant, S.E., Molinuevo, J.L. et al. Blood-based biomarkers for Alzheimer disease: mapping the road to the clinic. Nat Rev Neurol 14, 639–652 (2018). https://doi.org/10.1038/s41582-018-0079-7
• https://peerj.com/articles/6543/
• Karki, H.P., Jang, Y., Jung, J. et al. Advances in the development paradigm of biosample‐based biosensors for early ultrasensitive detection of alzheimer’s disease. J Nanobiotechnol 19, 72 (2021). https://doi.org/10.1186/s12951-021-00814-7
• https://ec.europa.eu/programmes/horizon2020/en/news/neurotwin-proposes-novel-therapy-alzheimer%E2%80%99s-disease
• https://www.drugtargetreview.com/news/84915/ai-based-method-used-to-screen-for-alzheimers-disease-drugs/
• https://news.harvard.edu/gazette/story/2021/03/ai-reveals-current-drugs-that-may-help-combat-alzheimers/
• Rodriguez, S., Hug, C., Todorov, P. et al. Machine learning identifies candidates for drug repurposing in Alzheimer’s disease. Nat Commun 12, 1033 (2021). https://doi.org/10.1038/s41467-021-21330-0