NEW YORK (Reuters Health), Jun 20 - Assessment of regional atrophy using voxel-based morphometry (VBM) can be used to predict the rate of decline in patients with mild Alzheimer's disease, according to a report in the June 3 issue of Neurology.
"A better knowledge of the factors which can predict the severity of disease progression may have important implications for patient care, for the development of clinical trials with new therapeutic agents, and for public health," the authors of the report explain.
Dr. Marie Sarazin from Hopital de la Salpetriere, Paris, and associates analyzed the respective prognostic power of neuropsychological tests and regional atrophy using VBM in 23 patients with mild Alzheimer's disease.
Baseline global cognitive efficiency did not differ between patients who became slow decliners or fast decliners over the subsequent three years, the authors report.
Both slow and fast decliners showed significant clusters of reduced gray matter concentration by VBM, the researchers note, particularly in the temporal lobes, left occipital lobe and fusiform gyrus, left frontal lobe, and the insula bilaterally.
VBM revealed significantly greater gray matter reductions in the fast decliner group bilaterally in the medial occipitoparietal lobes covering the calcarine fissure and surrounding cortex, the cuneus, precuneus, and lingual gyrus.
Regional differences in gray matter concentration corresponded to less gray matter concentration in the fast decliners rather than more gray matter in the slow decliners, the investigators say.
"VBM, more than neuropsychology, is able to detect a future rapid cognitive decline and to differentiate two close stages of the disease, even in a small sample of patients with mild Alzheimer's disease," the authors conclude. "Medial parieto-occipital atrophy is a good prognostic marker for Alzheimer's disease."
"The recent development of powerful image acquisition and analysis tools has dramatically enhanced our ability to probe into the neurobiology of Alzheimer's disease," write Dr. Giovanni B. Frisoni from IRCCS Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy, and Dr. Jennifer J. Whitwell from the Mayo Clinic, Rochester, MN, in a related editorial.
"However, we should be constantly vigilant about Mark Twain's maxim that 'to a man with a hammer, everything looks like a nail' and be careful not to overstretch the hammers available to date."
"A large amount of prospective data are currently being collected worldwide that will in the next few years allow us to more accurately outline the natural history of the neurobiology of Alzheimer's disease and refine tools for individual predictions," the editorialists conclude.
Neurology 2008;70:2194-2195,2201-2211.
Last Updated: 2008-06-18 19:30:30 -0400 (Reuters Health)
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![Overview of the study design. (A) The fully automated deep learning framework was developed to estimate body composition (BC) (defined as subcutaneous adipose tissue [SAT] in liters; visceral adipose tissue [VAT] in liters; skeletal muscle [SM] in liters; SM fat fraction [SMFF] as a percentage; and intramuscular adipose tissue [IMAT] in deciliters) from MRI. The fully automated framework comprised one model (model 1) to quantify different BC measures (SAT, VAT, SM, SMFF, and IMAT) as three-dimensional (3D) measures from whole-body MRI scans. The second model (model 2) was trained to identify standardized anatomic landmarks along the craniocaudal body axis (z coordinate field), which allowed for subdividing the whole-body measures into different subregions typically examined on clinical routine MRI scans (chest, abdomen, and pelvis). (B) BC was quantified from whole-body MRI in over 66,000 individuals from two large population-based cohort studies, the UK Biobank (UKB) (36,317 individuals) and the German National Cohort (NAKO) (30,291 individuals). Bar graphs show age distribution by sex and cohort. BMI = body mass index. (C) After the performance assessment of the fully automated framework, the change in BC measures, distributions, and profiles across age decades were investigated. Age-, sex-, and height-adjusted body composition reference curves were calculated and made publicly available in a web-based z-score calculator (https://circ-ml.github.io).](https://img.auntminnieeurope.com/mindful/smg/workspaces/default/uploads/2026/05/body-comp.XgAjTfPj1W.jpg?auto=format%2Ccompress&dpr=2&fit=crop&h=167&q=70&w=250)
