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Identification of Mild Cognitive Impairment using Machine Learning from Language and Behavior Markers

Award Number: RF1AG072449
ORGANIZATION: NATIONAL INSTITUTE ON AGING
OPDIV: NIH
AWARD CLASS: DISCRETIONARY
AWARD ACTIVITY TYPE: SCIENTIFIC/HEALTH RESEARCH (INCLUDES SURVEYS)
PERIOD OF PERFORMANCE START DATE: 04/15/2021
PERIOD OF PERFORMANCE END DATE: 03/31/2025

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Identification of Mild Cognitive Impairment using Machine Learning from Language and Behavior Markers - Project Summary Recent estimates indicate that Alzheimer’s disease (AD) may rank as the third leading cause of death for older people in the United States, just behind heart disease and cancer. While scientists know that AD involves a progressive brain cell failure, the reason why cells fail is still not clear. To understand the progression of the disease, one of the keys is to investigate the cognitive changes in patients with mild cognitive impairment (MCI). Even though biomarkers such as imaging and clinical functions are found to be outstanding in differentiating AD patients from those with normal cognition (NC), studies suggest that their discriminative power in early-stage MCI are rather limited. Detecting signals which distinguish subjects with MCI from those with NC is challenging due to the low sensitivity and high variability of current clinical measures such as annually assessed neuropsychological test results and self-reported functional measurements. Moreover, even though in-vivo biomarkers such as beta-amyloid and tau can be used as indicators of pathological progression towards AD, the screening of biomarkers are prohibitively expensive to be widely used among pre-symptomatic individuals in the outpatient setting. We hypothesize that progressive cognitive impact from MCI has elicited detectable changes in the way people talk and behave, which can be sensed by inexpensive and accessible sensors and leveraged by machine learning (ML) algorithms to build predictive models for quantifying the risk of MCI. Our preliminary results on a small cohort indicated that there are significant differences between MCI and NC subjects during a semi-structured conversation, and ML algorithms can use such differences for differentiating MCI and NC with promising performance. Our preliminary results in behavior monitoring also suggest highly predictive performance using temporal patterns of behavior signals. In this project, we plan to build upon our initial success and conduct comprehensive studies on language and behavior markers in larger-scale cohorts to build high-performance and interpretable ML models for screening MCI. Our three Specific Aims are: (1) Discover language markers and develop predictive models characterizing MCI. Using interview recordings from the I-CONECT project, we will use natural language processing and ML algorithms to extract linguistic and acoustic markers and develop multi- modal learning algorithms to fuse the two types of information. (2) Discover behavior markers and develop predictive models characterizing MCI. Using the in-home monitoring data from ORCATECH, we will extract short-term and long-term behavior patterns and integrate multi-granularity behavior markers to differentiate MCI and NC. (3) Linking language and behavior markers with an information framework. We will use demographics and common clinical information to profile the patients and match the two cohorts via certain similarity metrics, creating complementary features for improved prediction.


Issue Date FY	Funding FY	Legal Entity Name	Legal Entity Address	Legal Entity City	Legal Entity State	Legal Entity Zip Code	Legal Entity COUNTY	Legal Entity COUNTRY	Assistance Listing	Award Code	Budget Year	Action Date	Action Type	Action Amount

Issue Date FY: 2025 ( Subtotal = $0 )
2025	2021	MICHIGAN STATE UNIVERSITY	426 AUDITORIUM RD RM 360	EAST LANSING	MI	48824	INGHAM	USA	Aging Research	000	1	7/29/2025	NEW	$0
														Subtotal = $0

Issue Date FY: 2024 ( Subtotal = $0 )
2024	2021	MICHIGAN STATE UNIVERSITY	426 AUDITORIUM RD RM 2	EAST LANSING	MI	48824	INGHAM	USA	Aging Research	000	1	2/13/2024	NEW	$0
														Subtotal = $0

Issue Date FY: 2023 ( Subtotal = $330,271 )
2023	2023	MICHIGAN STATE UNIVERSITY	426 AUDITORIUM RD RM 2	EAST LANSING	MI	48824	INGHAM	USA	Aging Research	000	1	5/4/2023	SUPPLEMENT FOR EXPANSION	$330,271
														Subtotal = $330,271

Issue Date FY: 2021 ( Subtotal = $2,286,356 )
2021	2021	MICHIGAN STATE UNIVERSITY	426 AUDITORIUM RD RM 2	EAST LANSING	MI	48824	INGHAM	USA	Aging Research	000	1	4/7/2021	NEW	$2,286,356
														Subtotal = $2,286,356

Grand Total All Awards = $2,616,627

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Identification of Mild Cognitive Impairment using Machine Learning from Language and Behavior Markers

Award Number: RF1AG072449

ORGANIZATION: NATIONAL INSTITUTE ON AGING

OPDIV: NIH

AWARD CLASS: DISCRETIONARY

AWARD ACTIVITY TYPE: SCIENTIFIC/HEALTH RESEARCH (INCLUDES SURVEYS)

PERIOD OF PERFORMANCE START DATE: 04/15/2021

PERIOD OF PERFORMANCE END DATE: 03/31/2025

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