Tuesday, April 16, 2024
4/16/2024
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.
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