AI to track cognitive deviation in aging brains

Date:
June 23, 2021
Source:
Radiological Society of North America
Summary:
Researchers have developed an artificial intelligence-based
brain age prediction model to quantify deviations from a healthy
brain-aging trajectory in patients with mild cognitive impairment,
according to a new study. The model has the potential to aid in
early detection of cognitive impairment at an individual level.



FULL STORY ========================================================================== Researchers have developed an artificial intelligence (AI)-based brain
age prediction model to quantify deviations from a healthy brain-aging trajectory in patients with mild cognitive impairment, according to a
study published in Radiology: Artificial Intelligence. The model has
the potential to aid in early detection of cognitive impairment at an individual level.


========================================================================== Amnestic mild cognitive impairment (aMCI) is a transition phase from
normal aging to Alzheimer's disease (AD). People with aMCI have memory
deficits that are more serious than normal for their age and education,
but not severe enough to affect daily function.

For the study, Ni Shu, Ph.D., from State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, in Beijing, China,
and colleagues used a machine learning approach to train a brain age
prediction model based on the T1-weighted MR images of 974 healthy adults
aged from 49.3 to 95.4 years. The trained model was applied to estimate
the predicted age difference (predicted age vs. actual age) of aMCI
patients in the Beijing Aging Brain Rejuvenation Initiative (616 healthy controls and 80 aMCI patients) and the Alzheimer's Disease Neuroimaging Initiative (589 healthy controls and 144 aMCI patients) datasets.

The researchers also examined the associations between the predicted age difference and cognitive impairment, genetic risk factors, pathological biomarkers of AD, and clinical progression in aMCI patients.

The results showed that aMCI patients had brain-aging trajectories
distinct from the typical normal aging trajectory, and the proposed
brain age prediction model could quantify individual deviations from
the typical normal aging trajectory in these patients. The predicted
age difference was significantly associated with individual cognitive impairment of aMCI patients in several domains, specifically including
memory, attention and executive function.

"The predictive model we generated was highly accurate at estimating chronological age in healthy participants based on only the appearance
of MRI scans," the researchers wrote. "In contrast, for aMCI, the model estimated brain age to be greater than 2.7 years older on average than the patient's chronological age." The model further showed that progressive
aMCI patients exhibit more deviations from typical normal aging than
stable aMCI patients, and the use of the predicted age difference
score along with other AD-specific biomarkers could better predict the progression of aMCI. Apolipoprotein E (APOE) e4 carriers showed larger predicted age differences than non-carriers, and amyloid-positive patients showed larger predicted age differences than amyloid-negative patients.

Combining the predicted age difference with other biomarkers of AD showed
the best performance in differentiating progressive aMCI from stable aMCI.

"This work indicates that predicted age difference has the potential to
be a robust, reliable and computerized biomarker for early diagnosis of cognitive impairment and monitoring response to treatment," the authors concluded.

========================================================================== Story Source: Materials provided by
Radiological_Society_of_North_America. Note: Content may be edited for
style and length.


========================================================================== Journal Reference:
1. Weijie Huang, Xin Li, He Li, Wenxiao Wang, Kewei Chen, Kai Xu,
Junying
Zhang, Yaojing Chen, Dongfeng Wei, Ni Shu, Zhanjun
Zhang. Accelerated Brain Aging in Amnestic Mild Cognitive
Impairment: Relationships with Individual Cognitive Decline, Risk
Factors for Alzheimer Disease and Clinical Progression. Radiology:
Artificial Intelligence, 2021; e200171 DOI: 10.1148/ryai.2021200171 ==========================================================================

Link to news story: https://www.sciencedaily.com/releases/2021/06/210623100257.htm

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