# Use of machine learning to quantify cognitive function in AD, FTD, and DLB

> **NIH NIH R21** · MASSACHUSETTS GENERAL HOSPITAL · 2021 · $203,241

## Abstract

Project Abstract / Summary
Cognitive assessment is a key element of the diagnostic evaluation of patients suspected of having
early symptomatic stages of neurodegenerative brain diseases, including Alzheimer’s disease (AD),
Frontotemporal Lobar Degeneration (FTLD), and Lewy Body Disease (LBD). As biomarkers mature, the
field is separating clinical syndromes arising from these diseases from the neuropathologic changes
themselves, leading to concerns about classification systems for the illnesses these diseases produce.
Many tests typically employed in cognitive assessment are verbal, often implemented by an
examiner asking the patient a question and the patient answering. These tests are typically scored by
hand, with the examiner counting correct or incorrect answers and a simple score being generated
against normative scores. Many of the tests still in use were developed 30+ years ago. An exciting
array of recent advances in artificial intelligence methods has begun to enable the measurement and
classification of language and other cognitive characteristics captured in audio recordings.
Here we introduce a new approach to accomplishing both of these possibilities. Recent
developments in Natural Language Processing (NLP) and Machine Learning (ML) have now made
possible the automated discovery and classification of features measurable in speech samples. Once
established, these feature sets can be connected to distributions of cortical atrophy, thus enabling links
between specific cognitive abnormalities and underlying neural networks. This approach to the analysis
of clinical dementia syndromes can be achieved through a sufficiently large number of cognitive test
measures recorded as speech samples. In addition, such analyses require the use of the latest
generation of artificial intelligence models, called transformer-networks, to be able to learn the unique
ways in which individuals with cognitive impairment or dementia respond to questions requiring
memory, executive function, emotion, or language. In Aim 1, we will investigate the validity of an
unsupervised artificial intelligence model for measuring cognitive abnormalities in patients with AD,
FTLD, or DLB against traditional clinical measures and against MRI measures of regional brain atrophy.
In Aim 2, we will investigate the performance of an unsupervised artificial intelligence model for
classifying cognitive abnormalities in AD, FTLD, and DLB patients into clusters. In Aim 3, we will
evaluate the reliability of these automated measures of cognitive abnormalities in AD, FTLD, and DLB.
Through a finer-grained analysis of cognition in people with AD, FTLD, or DLB, it should be possible to
develop better understanding of the overlapping and dissociable features of these dementias, aiming
for improved diagnostic classification and better monitoring.

## Key facts

- **NIH application ID:** 10288487
- **Project number:** 1R21AG073744-01
- **Recipient organization:** MASSACHUSETTS GENERAL HOSPITAL
- **Principal Investigator:** BRADFORD C DICKERSON
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $203,241
- **Award type:** 1
- **Project period:** 2021-08-01 → 2023-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10288487

## Citation

> US National Institutes of Health, RePORTER application 10288487, Use of machine learning to quantify cognitive function in AD, FTD, and DLB (1R21AG073744-01). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10288487. Licensed CC0.

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