A team of international researchers have discovered a specific network of brain regions through MRI that is more vulnerable to unhealthy aging, such as Alzheimer's disease, and disorders in young people, such as schizophrenia.
The first-of-its-kind research, published online on 24 November in the Proceedings of the National Academy of Sciences, also concluded that these brain areas in healthy people are the last to develop and the first to show signs of neurodegeneration.
Led by Medical Research Council (MRC) funded researcher Dr. Gwenaëlle Douaud at the Oxford University Functional MRI of the Brain (FMRIB) Center, the study evaluated MR images and patterns in the brain structure of 484 healthy people, ranging in age from 8 to 85.
MRI unveiled a network within nerve cells, also known as gray matter, linking mostly "higher order" regions that connect information from different senses that does not develop until late adolescence or early adulthood. That delayed development can affect intellectual ability and long-term memory and later become schizophrenia in young people or Alzheimer's in adults. In addition, this network was found to develop later than the rest of the brain and was the first to degenerate in older age.
"These complex regions, which combine information coming from various senses, seem to be more vulnerable than the rest of the brain to both schizophrenia and Alzheimer's, even though these two diseases have different origins and appear at very different, almost opposite, times of life," the authors noted.
Hugh Perry, PhD, chairman of the MRC's Neurosciences and Mental Health Board, which funded the work, said the findings raise important issues about possible genetic and environmental factors that may occur in early life and then have lifelong consequences.
![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)
