# Computational psycholinguistic analysis of speech samples in PPA and AD and FTD

> **NIH NIH R21** · MASSACHUSETTS GENERAL HOSPITAL · 2022 · $203,425

## Abstract

Abstract
Primary Progressive Aphasia (PPA) is a clinical neurodegenerative syndrome characterized by abnormalities
in language with initial relative sparing of other cognitive processes. The syndrome may result from several
kinds of neuropathology, including Alzheimer's disease (AD) or Frontotemporal Lobar Degeneration (FTLD).
The different neuropathological causes are associated with specific variants of the disease. Individuals with the
non-fluent variant of PPA (nfvPPA) tend to show effortful speech and agrammatism, in some cases with motor
speech dysfunction. Impairments in sentence repetition and lexical retrieval are exhibited by those with
the logopenic variant of PPA (lvPPA). Difficulties in object naming and word comprehension are experienced in
individuals with the semantic variant of PPA (svPPA). While widely used, the current system of classification is
challenged by the occurrence of individuals with overlapping profiles of linguistic behavior and by an
inconsistent alignment of linguistic profiles and patterns of cortical atrophy. In addition, some of these same
linguistic or anatomic abnormalities can be seen in patients with non-PPA clinical phenotypes of AD or FTLD.
That is, these PPA subtypes may represent one way of classifying a multidimensional spectrum of cognitive-
behavioral anatomic abnormalities arising from a set of neurodegenerative pathologies; we need new ways
of quantifying these abnormalities, and we need to consider alternative classification schemes. 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 linguistic features. Once established, these feature sets can be connected to distributions
of cortical atrophy, thus enabling links between specific linguistic behavioral abnormalities and underlying
neural networks. This approach to the analysis of PPA subtypes, and their contextualization with other
clinical types of AD and FTLD, can be achieved through a sufficiently large number of language samples
collected in ways that highlight both the production and comprehension aspects of the language system. In
addition, such analyses require the use of the latest generation of artificial intelligence models, called
transformer-networks. The result will be a new understanding of the PPA syndrome and the language network
that it affects. In Aim 1, we will investigate the performance of an unsupervised artificial intelligence model for
measuring and classifying language abnormalities in PPA patients. In Aim 2, we will investigate the how these
models can be used to measure and classify language abnormalities in AD and FTD patients. In Aim 3, we will
evaluate the reliability of these automated measures of language abnormalities in PPA, AD, and FTD. Through
a finer-grained analysis of language in people with PPA and other forms of AD or FTLD, it sh...

## Key facts

- **NIH application ID:** 10373191
- **Project number:** 1R21DC019567-01A1
- **Recipient organization:** MASSACHUSETTS GENERAL HOSPITAL
- **Principal Investigator:** BRADFORD C DICKERSON
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $203,425
- **Award type:** 1
- **Project period:** 2022-02-04 → 2024-01-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10373191

## Citation

> US National Institutes of Health, RePORTER application 10373191, Computational psycholinguistic analysis of speech samples in PPA and AD and FTD (1R21DC019567-01A1). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/10373191. Licensed CC0.

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