Neuron: a new mode of dementia transmission in brain neural network

October 16, 2019 / Biovalley BIOON / -- in a new study, scientists at the University of California, San Francisco, used brain Connection maps to predict the spread of brain atrophy in patients with frontotemporal dementia (FTD) Their latest evidence shows that the loss of brain cells associated with dementia is spread through brain networks established by synaptic connections The results improve scientists' understanding of how neurodegenerative diseases spread, and help to develop new therapies and new assessment methods to effectively alleviate the deterioration of such diseases Dr Jesse brown, assistant professor of Neuroscience at UCSF, said: "our research reveals how dementia spreads, opening a window to understand the biological mechanism of the disease, that is, to be able to find the most vulnerable part of our brain cells or neural circuits." (photo source: www Pixabay Com) FTD is the most common type of dementia in people under 60 years old, whose symptoms are reflected in language and behavior As with Alzheimer's disease, the diversity of FTD symptoms reflects a significant difference in the diffusion pattern of neurodegenerative diseases in patients' brains This difference makes it difficult for scientists to find drugs to solve brain atrophy, or to carry out clinical trials to evaluate whether a new treatment can effectively prevent the deterioration of the disease Dr William Seeley, senior author of the study and professor of Neurology and pathology, has previously revealed multiple patterns of brain atrophy in dementia patients, which has greatly influenced the field of dementia research In their new study, published October 14 in the journal Neuron, Seeley and his colleagues studied neural networks based on brain scans to predict the spread of brain atrophy in FTD patients The researchers recruited 42 patients with behavioral variant frontotemporal dementia (bvftd), a form of FTD that results in poor social behavior, and 30 patients with semantic variant primary progressive aphasia (svppa), another form of FTD that primarily affects speech Before the start of the experiment, these patients received "baseline" MRI scan to assess the degree of existing brain degeneration, and then a follow-up scan about one year later to measure the progress of their disease The researchers first hypothesized that brain degeneration starts in particularly vulnerable locations and then spreads to anatomically connected brain regions To this end, they first estimated the starting site of brain atrophy seen in each patient's baseline scan They then used the same standardized connection chart to predict the most likely location of brain atrophy in a follow-up scan one year later, and compared the accuracy of this prediction with that of other prediction methods They found that two special connectivity measures significantly improved their accuracy in predicting the likelihood of brain atrophy between baseline and subsequent brain scans in a given brain region One is called the "nodal hazard" and the other is called the "nodal hazard" On average, these two network connectivity indicators predict the possibility of disease spreading to new brain regions more accurately than existing prediction methods, the researchers show In many cases, the disease bypasses areas of the brain that are adjacent to, but not anatomically connected to, atrophic areas and jumps to areas that are more functionally connected In addition to providing biological insights into the mechanism by which FTD spreads brain atrophy, the findings will also provide information for the development of treatments, such as predicting whether scientists can effectively change disease progression Researchers can also better predict how atrophy will spread through the brain to help patients and their families adapt to the symptoms that may occur as the disease progresses Er, Howard J Rosen, William W Seeley Patient-Tailored, Connectivity-Based Forecasts of Spreading Brain Atrophy Neuron , 2019; DOI: 10.1016/j.neuron.2019.08.037