PAGE TITLE
Functional Brain Age Estimation Via Resting State EEG
PAGE SUMMARY
The early detection and quantification of abnormal brain changes are important for the prospective identification and subsequent treatment of individuals at risk for various neurological disorders, injuries, and cognitive decline. Discrepancies between an individual’s chronological age and his or her neuroimaging-based “brain age” can reveal a “brain-age gap” that is a biomarker for a range of neurological disorders, including Alzheimer’s disease and other forms of dementia. Previous techniques for brain-age estimation unfortunately require expensive MRI scanning, which limits their practical use.
Addressing this issue, researchers at Drexel have developed and validated a new robust, precise, reliable, and inexpensive EEG-based machine-learning technique for accurately assessing whether an individual’s brain is aging more quickly or more slowly than is typical for healthy individuals. Based on our large database of EEGs, our results show that brain-age estimation with low-cost EEG recordings can be performed with a precision comparable to, or better than, estimations obtained with MRI, thus providing a breakthrough technology for studying and screening for the effects of disease, injury, genetics, and lifestyle factors such as exercise, nutrition, stress, and sleep on brain aging. We have also produced a user-friendly software package that applies our machine-learning technology to estimate an individual’s brain age. Additionally, for a user who wishes to compare individuals to a different baseline group (e.g., soldiers, athletes, or students), the software can compute a new machine-learning brain-age model based on EEGs from that group.
Our innovative technology expands the practical use of brain-age estimation because EEGs can be easily and inexpensively recorded in the field, including educational and medical institutions. Consumer-grade EEG units can also be deployed for home use.
APPLICATIONS
TITLE: Applications
Assess rate of brain maturation and aging
Enables early detection of abnormal brain aging that may signal a presymptomatic phase of age-related neurological disorders, such as dementia
Useful tool for researchers and other individuals who wish to test potential interventions for slowing or reversing brain aging
ADVANTAGES
TITLE:Advantages
Low cost: Relatively inexpensive compared to currently used MRI-based brain-age estimation
High accuracy: Low error rate and accuracy comparable to that of MRI-based estimations
Accessibility: Easily deployed and accessible due to the wide availability of consumer-grade EEG systems
PUBLICATIONS
References
Liang, H., Zhang, F., and Niu, X. (2019) Investigating Systematic Bias in Brain Age Estimation with Application to PTSD. Human Brain Mapping 40(11), 3143-3152
https://onlinelibrary.wiley.com/doi/epdf/10.1002/hbm.24588
For Additional Information
Functional Brain-Age Estimation with EEG
https://sway.office.com/CPH5jYZM7b2H4qre?ref=Link
Commercialization Opportunities
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Harshith Reddy
Licensing Manager
Office of Technology Commercialization
Drexel University
3180 Chestnut Street, Ste. 104
The Left Bank
Philadelphia, PA 19104
Phone: 215-571-4290
E-mail: harshith@drexel.edu
For Technical Information:
John Kounios, Ph.D.
Director, Doctoral Program in Applied Cognitive & Brain Sciences
Director, Creativity Research Lab
Professor of Psychology
Office: Stratton 318
jk342@drexel.edu
Phone: 215.553.7105