Nuclear technology for a more effective Alzheimer diagnosis
Currently, the anatomic analysis of the brain cortex through magnetic resonance imaging (MRI) supports the diagnosis of Alzheimer’s disease in 80% of the cases. Could results improve if a different brain structure is analyzed?
The answer is in the hands of a team of researchers from Inserm, a French public research organization dedicated to human health that cooperates with the University of Paris and the French Alternative Energies and Atomic Energy Commission (CEA). According to a study published on the scientific magazine Neurobiology of aging, the analysis of the morphology of the cortical groove would make it possible to recognize Alzheimer’s Disease in 91% of the cases. The size of these grooves seems to be associated to the progression stage of the disease and to cognitive impairment. The study shows the benefits of this method for the diagnosis and follow-up of patients.
The analysis of the morphology of the cortical groove would make it possible to recognize Alzheimer’s Disease in 91% of the cases
More effective measuring through MRI
An anatomical brain analysis through MRI usually means measuring the thickness of the cerebral cortex (the “grey matter” that covers both hemispheres) or the volume of several brain regions such as the hippocampus, which is usually one of the first signs of Alzheimer’s Disease when it presents atrophy. This method can correctly detect the disease in around 80% of cases. The research team at Inserm showed that cortical groove analysis through MRI is more effective.
The grooves are circunvolutions of the brain. In old age they tend to get wider while the thickness of the cortex surrounding them diminishes. In previous studies, the team already detected that in Alzheimer’s Disease this phenomenon accelerates much faster than the usual aging progress in healthy patients. This time, their goal was to verify whether the morphological analysis of the grooves could be a diagnostical marker of the disease and the stage of its evolution.
Tests with patient and control groups
Researchers conducted a brain MRI on 51 patients with Alzheimer’s Disease, both at the early and advanced stages, as well as on 21 control participants unaffected by the disease. The diagnosis was made after a biological evaluation based on a lumbar puncture to detect the presence of biomarkers of the disease and on a positron emission tomography (PET-scan) that shows amyloid deposits (accumulations of protein aggregates in plaques, a characteristic of certain neurodegenerative diseases).
The research team at Inserm showed that the MRI analysis of cortical grooves is more effective
Next they used the Morphologist software recently developed at NeuroSpin (CEA’s neuro image center) that recreates a negative “cast” of the brain from an RMI. They focused on 18 areas from each cerebral hemisphere to calculate the average width of each groove and the thickness of the surrounding cortex. At the same time, in order to compare these techniques, the researchers measured the volume of several brain regions and the thickness of the cortex with the usual means.
Afterwards they used an algorithm to correlate the health state of each participant (control or patient) with the measures obtained. The researchers found that the width of a group of these grooves, which correspond to the frontal and temporal lobes, was associated to Alzheimer’s Disease. This made it possible to determine the medical state of participants in 91% of the cases, while the usual anatomical measures only reach 80%. Additionally, the morphology of the grooves seems to change with the stages of the disease: the more advanced the cognitive decline, the greater the grooves.
Advances for better and easier diagnoses
According to project director Maxine Bertoux, these measures that reflect the evolution of the disease seem to correlate to cognitive performance, which could be especially useful in clinical tests that evaluate the effectivity of a potential medication. Additionally, they only require an RMI and a highly automatized analysis that can be done in many health centers. This technique has not yet been validated on bigger samples of patients, but it could be of great clinical interest. Researchers are using this new approach to detect specific signs of other neuro degenerative diseases, especially frontotemporal dementia.