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

> **NIH NIH RF1** · MICHIGAN STATE UNIVERSITY · 2021 · $2,286,356

## Abstract

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 comple...

## Key facts

- **NIH application ID:** 10212669
- **Project number:** 1RF1AG072449-01
- **Recipient organization:** MICHIGAN STATE UNIVERSITY
- **Principal Investigator:** HIROKO Hayama DODGE
- **Activity code:** RF1 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $2,286,356
- **Award type:** 1
- **Project period:** 2021-04-15 → 2025-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10212669

## Citation

> US National Institutes of Health, RePORTER application 10212669, Identification of Mild Cognitive Impairment using Machine Learning from Language and Behavior Markers (1RF1AG072449-01). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10212669. Licensed CC0.

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