Euromedia24 on Play Store Euromedia24 on App Sore
BNB

$574.18

BTC

$64748

ADA

$0.440159

ETH

$3457.74

SOL

$159.4

32 °

Yerevan

27 °

Moscow

40 °

Dubai

24 °

London

33 °

Beijing

26 °

Brussels

22 °

Rome

32 °

Madrid

BNB

$574.18

BTC

$64748

ADA

$0.440159

ETH

$3457.74

SOL

$159.4

32 °

Yerevan

27 °

Moscow

40 °

Dubai

24 °

London

33 °

Beijing

26 °

Brussels

22 °

Rome

32 °

Madrid

A neural network was created that predicts dementia with 80% accuracy. NMH:


British scientists from Queen Mary University of London have created a new method of predicting dementia nine years before diagnosis. According to experts, the tool outperforms conventional memory tests. The study was published in the scientific journal Nature Mental Health (NMH). Dementia is a collective term used to describe a variety of conditions characterized by a decline in cognitive function severe enough to interfere with daily life and independent functioning. It affects memory, thinking, orientation, perception, calculation, learning ability, language and judgment. The experts used data from the UK Biobank. The researchers focused on the subgroup of participants who underwent functional magnetic resonance imaging (fMRI) and either already had a diagnosis of dementia or received it later. The sample consisted of 148 dementia cases and 1,030 controls, which provided a reliable comparison group according to age, sex, ethnicity, guiding hand, and geographic location of the MRI scanning center. Participants underwent a resting-state fMRI scan (rs-fMRI), which measures brain activity by detecting changes in blood flow. The researchers focused on the passive mode network of brain activity ( DMN), a brain structure that is active at rest and is involved in higher-level cognitive functions such as social cognition and self-referential thinking. Using a technique called dynamic causal modeling (DCM), the researchers examined rs-fMRI data to assess effective connectivity in the DMN. between different regions. The experts then used those connectivity scores for a machine learning model. The goal of the model was to distinguish between people who later develop dementia and those who do not. Neuronetwork identified 15 important features of connections in the DMN that differed significantly between future dementia cases and control groups. Among them, the most notable changes included high inhibition in the ventromedial prefrontal cortex (vmPFC), left parahippocampus (lPHF), and left intraparietal cortex (lIPC) to lPHF, as well as weak inhibition in the right parahippocampus (rPHF) in relation to the dorsomedial frontal cortex (dmPFC). To supplement the possibilities, the researchers created a model to predict the time until a dementia diagnosis is made. The predictive power of these models is 80 percent and suggests that changes in the DMN may serve as early biomarkers of dementia, opening a window into the disease process years before clinical symptoms appear.