‘Big Data’ study discovers earliest sign of Alzheimer’s development

Late-onset Alzheimer’s disease has baffled researchers since long due to its incredibly complex nature. The complexity is attributed to the fact that it is not caused due to fault in one neurological mechanism but is a result of several associated mechanism in the brain.

In a pioneering study published recently in the journal ˜Nature Communications', researchers at the Montreal Neurological Institute and Hospital have used a powerful tool that can help in better understanding the progression of late-onset Alzheimer’s disease (LOAD), and identify its first physiological signs. In this research which is led by Dr. Alan Evans, a professor of neurology, neurosurgery and biomedical engineering at the Neuro, more than 7,700 brain images from 1,171 people in various stages of Alzheimer’s progression were analyzed. The techniques used in this analysis include magnetic resonance imaging (MRI) and Positron Emission Tomography (PET). Also, the level of cognition of the subjects involved and their blood and cerebrospinal fluid were analyzed.

It was believed that an increase in amyloid protein was the first detectable sign of Alzheimer’s. However, this research revealed that a decrease in blood flow in the brain is the first physiological sign of Alzheimer’s disease. Undoubtedly, amyloid does play a role, but, this study has revealed that changes in blood flow are the earliest warning sign of Alzheimer’s. It was also found that changes in cognition start earlier in the progression than previously known.

Late-onset Alzheimer’s disease is the most common cause of human dementia. Hence, it is necessary to understand the interactions between its various mechanisms in order to develop viable treatments.

Previous studies on LOAD were limited in their scope and didn’t shed light on various facets of this complex disease. This study is the most exhaustive research that has factored in the pattern of amyloid concentration, glucose metabolism, cerebral blood flow, functional activity and brain atrophy in 78 regions of the brain, covering all grey matter.

Yasser Iturria Medina, first author of the paper and a post-doctoral fellow at the MNI opined that lack of an integrative understanding of LOAD pathology, its multifactorial mechanisms, is a hindrance in the path of developing effective, disease-modifying therapeutic agents.

For this research, the trajectory of each biological factor of LOAD was recorded using data from each patient taken over a 30-year period. This process was then repeated 500 times to improve robustness of estimations and stability of the results. In order to compute such mammoth data thousands of compute hours were spent. It was all possible with the help of sophisticated software and terabytes of hard drive space. Evans opines that such a data-driven approach to neurology is important in today’s time. He adds that all the data about the brain will be useful only if we can deduce information from it and make sense of it. This creates complex mathematical and statistical challenges but that’s where the future of clinical research in the brain lies.

Another important thing this research underlines is the necessity of data sharing across institutions, known as the Open Science model. For this study, patient data came from the Alzheimer’s Disease Neuroimaging Initiative (ADNI), a partnership of more than 30 institutions across Canada and the United States. Had it not been for data sharing, the knowledge that this study has added to the understanding of LOAD would still be undiscovered.

This study is perhaps one of the most thorough works ever published on the subject of Alzheimer’s disease progression. But, it just scratches the surface “ there is a need to delve further, to determine the causes of each mechanism, which could be the key to unlocking better treatments. Evans adds that it is a computational, mathematical challenge that goes beyond anything they have done so far. Their goal is to go to a high-level, causal modeling of the interactions amongst all of the factors of disease, but you need huge computational power to do that. Development of hardware that is capable of doing it is crucial.

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References

https://medicalxpress.com/news/2016-07-big-earliest-alzheimer.html

https://www.mcgill.ca/neuro/Alzheimers-Big-Data-Physiology