Cognitive biomarker prioritization in Alzheimer’s Disease using brain morphometric data

Abstract

Background Cognitive assessments represent the most common clinical routine for the diagnosis of Alzheimer’s Disease (AD). Given a large number of cognitive assessment tools and time-limited office visits, it is important to determine a proper set of cognitive tests for different subjects. Most current studies create guidelines of cognitive test selection for a targeted population, but they are not customized for each individual subject. In this manuscript, we develop a machine learning paradigm enabling personalized cognitive assessments prioritization. Method We adapt a newly developed learning-to-rank approach $${\mathtt {PLTR}}$$ PLTR to implement our paradigm. This method learns the latent scoring function that pushes the most effective cognitive assessments onto the top of the prioritization list. We also extend $${\mathtt {PLTR}}$$ PLTR to better separate the most effective cognitive assessments and the less effective ones. Results Our empirical study on the ADNI data shows that the proposed paradigm outperforms the state-of-the-art baselines on identifying and prioritizing individual-specific cognitive biomarkers. We conduct experiments in cross validation and level-out validation settings. In the two settings, our paradigm significantly outperforms the best baselines with improvement as much as 22.1% and 19.7%, respectively, on prioritizing cognitive features. Conclusions The proposed paradigm achieves superior performance on prioritizing cognitive biomarkers. The cognitive biomarkers prioritized on top have great potentials to facilitate personalized diagnosis, disease subtyping, and ultimately precision medicine in AD.

Barriers to Effective Memory Assessments for Alzheimer’s Disease

“…P. Capitalise on secondary data approaches relying on datasets such as ADNI [72], API [73], DIAN [74], and Population Studies [62] Q. Develop AI models to identify domainspecific cognitive tests that can enhance both effectiveness of assessments and precision medicine [28,63] The Visual Short-Term Memory Binding Test (VSTMBT) can be accessed for free on this page: https://www.strath.ac.uk/research/subjects/psychology/cognition/appliedcognitionlab/visu alshort-termmemorybindingtestvstmbt/. Relevant publications reporting on sensitivity, specificity and where these tests stand regarding the identified barriers can be found in the above suggested webpage (see also Martínez et al [75] for a recent review on these methodologies).…”

“…Recent diagnostic approaches, which focus on costly biomarkers [25,26], are not to going to be readily and globally available any time soon [27]. We need affordable assessment (e.g., cognitive biomarkers) to screen individuals at risk who are seeking help [27,28]. To that aim, we need to better understand the impact of risk and protective factors on memory and cognition across diverse populations [29,30].…”

Applications of Artificial Intelligence in the Neuropsychological Assessment of Dementia: A Systematic Review

“…However, choosing the most appropriate set of cognitive tests for patients is also challenging due to the large number of available options. A study introduced a ML approach for personalized cognitive assessment prioritization, tailoring the assessment selection process to an individual's brain morphometric characteristics for AD [46]. The method identifies the most effective assessments and prioritizes them at the top of the list.…”

The Road to Personalized Medicine in Alzheimer’s Disease: The Use of Artificial Intelligence

“…Another important component of biomarkers studies is a neuropsychological assessment [ 59 ]. One key component of the generation of classes for supervised learning models are based on the score obtained by the Clinical Dementia Rating (CDR).…”