PROJECT SUMMARY (See instructions):
The various forms of cognitive decline cost an estimated $305 billion in the United States alone. It
is anticipated that, by 2050, the number of older adults with cognitive decline will double. The impact
of cognitive decline goes beyond the financial costs but also presents physical, mental, and
emotional burdens to older adults, their caregivers, and society. Recent studies reveal that the
prevalence of mild cognitive impairment (MCI) ranges from 10% to 20% in older adults and no
medications have been proven to be effective for treating MCI. Thus, accurate diagnosis, assessment, and
detection of cognitive decline in older adults are essential for developing effective, precise, and
individualized management and treatment procedures.
In this project, we will develop a toolchain for the assessment of cognitive decline using
multimodal neuroimaging and machine learning (ML) methods. We propose three specific aims: (1)
to develop a comprehensive test battery selective of MCI in a mobile software synchronized with
multimodal functional near infrared spectroscopy and electroencephalography (fNIRS-EEG) based
neuroimaging system that can concurrently provide electrophysiological, hemodynamic and behavioral
measures; (2) to extract, select, and validate the multitude of within and across modality biomarkers
from fNIRS-EEG data in temporal, spatial, spectral, and complexity domains together with the
behavioral ones; (3) to develop a comprehensive multimodal ML approach to detect MCI based on
fNIRS-EEG and behavioral features.
The finding of this project can lead to an unprecedented transformation to the study, assessment,
diagnosis and monitoring of cognitive decline in older adults. Developing a mobile application that
combines functional near-infrared-spectroscopy (fNIRS) and electroencephalography (EEG) on one
platform that could be used in less expensive and restrictive testing environments to determine
functional brain alterations in patients with mild cognitive impairment (MCI) is very innovative and will
have major impact on knowledge. Furthermore, utilizing the mobile application and cutting-edge machine
learning methods, will allow us to determine novel functional brain biomarkers that distinguish older
adults with MCI from healthy controls, which in turn can have major impact on diagnostic procedures of
older adults with MCI.
