Landmark study reveals earlier sign of Alzheimer's than amyloid build-up
July 13, 2016
Alzheimers/Neurology
Health IT
MRI
Decrease in blood flow is first sign
of disease, not increase in amyloid
Montreal Neurological Institute
of disease, not increase in amyloid
Montreal Neurological Institute
By Christina Hwang and Gus Iversen
Contrary to popular belief, an increase of amyloid protein in the brain may not be the earliest evidence of late-onset Alzheimer’s disease.
By leveraging big data analytics on an enormous quantity of patient information, (including MR and PET exams) researchers from the Montreal Neurological Institute (MNI) and Hospital have shown that decreased blood flow through the brain is the earliest discernible physiological clue that a patient may have the most common cause of dementia in humans on the planet.
Thanks to the Alzheimer’s Disease Neuroimaging Initiative (ADNI) — a partnership of over 30 institutions across Canada and the U.S. — they were able to analyze more than 7,700 brain images from 1,171 people in various stages of Alzheimer’s.
“ADNI database has the original and preprocessed PET/MRI images. In addition, they have tables with the imaging values for some specific regions and measures,” first author Yasser Iturria Medina, a postdoctoral fellow at the MNI, told HCB News.
Besides showing that the first physiological sign of Alzheimer’s is a decrease in blood flow in the brain, their research — published in Nature Communications — also indicates that changes in a patient’s cognitive abilities begins earlier than is commonly believed.
In a general sense, the study provides a glimpse of what 'big data' can tell us when it's properly utilized. Dr. Alan Evans, lead researcher and professor of neurology, neurosurgery and biomedical engineering at MNI, said that compiling and analyzing the data took thousands of hours and could not be accomplished without sophisticated software and terabytes of hard drive space.
"We have many ways to capture data about the brain, but what are you supposed to do with all this data?" he asked. "Increasingly, neurology is limited by the ability to take all this information together 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."
For Medina, overcoming those computational limitations was intrinsic to their discoveries. “We wanted to analyze not only a specific set of regions or measures (defined by ADNI experts), but all the information that could be associated with changes in the brain gray matter," he said.
Contrary to popular belief, an increase of amyloid protein in the brain may not be the earliest evidence of late-onset Alzheimer’s disease.
By leveraging big data analytics on an enormous quantity of patient information, (including MR and PET exams) researchers from the Montreal Neurological Institute (MNI) and Hospital have shown that decreased blood flow through the brain is the earliest discernible physiological clue that a patient may have the most common cause of dementia in humans on the planet.
Thanks to the Alzheimer’s Disease Neuroimaging Initiative (ADNI) — a partnership of over 30 institutions across Canada and the U.S. — they were able to analyze more than 7,700 brain images from 1,171 people in various stages of Alzheimer’s.
“ADNI database has the original and preprocessed PET/MRI images. In addition, they have tables with the imaging values for some specific regions and measures,” first author Yasser Iturria Medina, a postdoctoral fellow at the MNI, told HCB News.
Besides showing that the first physiological sign of Alzheimer’s is a decrease in blood flow in the brain, their research — published in Nature Communications — also indicates that changes in a patient’s cognitive abilities begins earlier than is commonly believed.
In a general sense, the study provides a glimpse of what 'big data' can tell us when it's properly utilized. Dr. Alan Evans, lead researcher and professor of neurology, neurosurgery and biomedical engineering at MNI, said that compiling and analyzing the data took thousands of hours and could not be accomplished without sophisticated software and terabytes of hard drive space.
"We have many ways to capture data about the brain, but what are you supposed to do with all this data?" he asked. "Increasingly, neurology is limited by the ability to take all this information together 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."
For Medina, overcoming those computational limitations was intrinsic to their discoveries. “We wanted to analyze not only a specific set of regions or measures (defined by ADNI experts), but all the information that could be associated with changes in the brain gray matter," he said.
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