According to a study published in Neurobiology of Aging, the analysis of the morphology of the cortical furrows would make it possible to recognize Alzheimer's disease in 91% of cases.

(Translation by Google)

Currently, anatomical analysis of the cerebral cortex by MRI, supports the diagnosis of Alzheimer's disease in 80% of cases. And if the analysis of another brain structure, could give better results?

This is shown by the team of researchers associating Inserm, the University of Paris and the CEA under the supervision of Maxime Bertoux, now Inserm researcher in Unit 1171 Degenerative and Vascular Cognitive Disorders (Inserm / Université de Paris). Lille / Lille University Hospital).

According to his study published in Neurobiology of Aging , the analysis of the morphology of the cortical furrow would allow to recognize the Alzheimer's disease in 91% of the cases. In addition, the size of these furrows appears to be associated with stage of disease progression and cognitive decline. This work suggests the benefit of this method in the diagnosis and follow-up of patients.

Anatomical analysis of the brain by magnetic resonance imaging (MRI) usually involves measuring the thickness of the cerebral cortex (the so-called "gray matter", which covers both hemispheres of the brain) or the volume of several brain regions. like the hippocampus, whose atrophy is one of the first signs of Alzheimer's disease. This method can detect the disease correctly in about 80% of cases. The team of Maxime Bertoux, researcher Inserm shows that the analysis of cortical furrows by MRI is better.

The furrows are convolutions of the brain which tend, during aging, to widen. This evolution of the grooves is accompanied by a decrease in the thickness of the cortex which borders them. The acceleration of this phenomenon in Alzheimer's disease had been highlighted by the same team in previous works. The team this time sought to verify whether the morphological analysis of the furrows could constitute a diagnostic marker of the disease and its stage of evolution.

The researchers performed brain MRI in 51 patients with Alzheimer's disease including some at an early stage and others at an advanced stage, as well as 29 control participants not affected by the disease. The diagnosis was made after a biological assessment, based on a lumbar puncture for the presence of the biomarkers of the disease and on a positron emission imaging (PET-scan), showing the amyloid deposits, accumulations of protein aggregates in the form of plaques characteristic of certain neurodegenerative diseases.

The researchers then used the recently developed Morphologist software at NeuroSpin (CEA's Neuroimaging Center), which recreates a negative "mold" of the brain from an MRI. The software then extracted in 18 regions of each cerebral hemisphere, an average value of the width of each groove and the thickness of the cortex bordering them. In parallel, the researchers performed the usual measurements of the volume of several brain regions and the thickness of the cortex, in order to compare these techniques.

An algorithm was then used to correlate the state of health of each participant (control or patient) to the measurements obtained. The researchers then found that the width of a group of few furrows, belonging in particular to the frontal and temporal lobes, was associated with Alzheimer's disease. It allowed to determine the state of health of the participants in 91% of the cases, against only 80% for the usual anatomical measurements. In addition, the morphology of the furrows seems to change with the stages of the disease: they were larger in patients with the most advanced cognitive declines.

"These measures reflecting the evolution of the disease appear correlated to cognitive performance, says Maxime Bertoux, which can be very useful in clinical trials evaluating the effectiveness of a potential drug. In addition, these measurements require only an MRI and a largely automated analysis that can be performed in many care centers. This technique has yet to be validated on larger patient samples, but it could be of great clinical interest, " he concludes. The researcher is already using this new approach to detect specific signatures of other neurodegenerative diseases, particularly frontotemporal dementia.

(Source: Inserm / Auzias, William / Baillet, Sylvie / Colliot, Olivier)