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Accurately reflect cognitive decline and the risk of Alzheimer's disease, and you can rely on this method!

 Last Update: 2023-02-01
 Source: Internet
 Author: User

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Early identification of this neuroanatomical change helps screen individuals based on their risk of AD
.

Executive Summary

The human brain holds many clues
about a person's long-term health.
Previous studies have shown that a person's brain age is a more useful and accurate predictor of health risks and future diseases
than their date of birth.
A new artificial intelligence (AI) model developed by researchers at the University of Southern California that analyzes magnetic resonance imaging (MRI) brain scans that can be used to accurately capture cognitive decline associated with neurodegenerative diseases such as Alzheimer's, was published in PNAS on January 3
, 2023.

Study screenshots

status quo

Brain aging is considered a reliable biomarker of neurodegenerative disease risk
.
This risk increases
when a person's brain exhibits characteristics that are "older" than expected by someone of that person's age.

Andrei Irimia, assistant professor of biomedical engineering, quantitative and computational biology and neuroscience at the University of Southern California's Leonard Davis School of Gerontology, said

Our research harnesses the power of deep learning to identify areas of aging in the brain that reflect cognitive decline that may contribute to Alzheimer's;
People age at different rates, as do
the types of tissue in the body.
For example, when we say, "So-and-so is forty years old, but looks thirty," the same idea applies to the brain
.
A forty-year-old brain may look as 'young' as a thirty-year-old brain, or look as 'old'
as a sixty-year-old brain.

conclusion

The researchers collated brain MRIs of 4681 cognitively normal participants, some of whom went on to develop cognitive decline or Alzheimer's disease
later in life.

Using this data, the researchers created an artificial intelligence model called a neural network that predicted the age
of participants through MRIs of their brains.
First, the researchers trained the network to generate detailed anatomical brain maps that revealed aging patterns
in specific objects.
They then compared
the perceived (bio)brain age with the actual (actual) age of the study participants.
The greater the difference between the two, the worse the participants' cognitive scores, which reflects the risk
of Alzheimer's.

The results of the study found:

The brain age (BA) estimation error is significantly lower than in previous studies;
At the individual and cohort levels, convolutional neural networks (CNNs) provide detailed anatomical maps of brain aging patterns, revealing gender dimorphisms and neurocognitive trajectories in adults with mild cognitive impairment (MCI, N = 351) and Alzheimer's disease (AD, N = 359);
In patients with MCI (54% of whom were diagnosed with dementia within 10.
9 years of MRI acquisition), BA significantly outperformed CA in capturing dementia symptom severity, dysfunction, and executive function;
The profile of sex dimorphism and lateralization in brain aging also maps to
neuroanatomical patterns of change reflecting cognitive decline.

prospect

That said, a significant association between BA and neurocognitive measures suggests that the proposed framework can systematically map the relationship
between CN individuals and aging-related neuroanatomical changes in MCI or AD participants.
Early identification of this neuroanatomical change helps screen individuals based on their risk of AD
.

Andrei Irimia said:

Explainable AI can be a powerful tool for assessing the risk of Alzheimer's and other neurocognitive diseases, and the sooner we identify people at high risk for Alzheimer's, the sooner clinicians can intervene in treatment options, monitoring, and disease management;
AI is particularly powerful because of its ability to spot subtle and complex features of aging that other methods cannot, and which are key to identifying risk years before a person develops a disease;
One of the most important applications of our work is its potential to pave the way for customized interventions that address each person's unique aging patterns, and many are interested in knowing their true aging rate;
This information can provide us with tips on different lifestyle changes or interventions that a person can take to improve their overall health and well-being;
Our approach can be used to design patient-centered treatment plans and personalized brain aging maps that may be of interest to people with different health needs and goals
.

Source: Medical Neurology Channel

Responsible editor: Chen Lijin

Proofreader: Zang Hengjia

Editor: Zhao Jing

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