Automatic classification of AD pathology in FTD phenotypes using natural speech.

INTRODUCTION: Screening for Alzheimer's disease neuropathologic change (ADNC) in individuals with atypical presentations is challenging but essential for clinical management. We trained automatic speech-based classifiers to distinguish frontotemporal dementia (FTD) patients with ADNC from those with frontotemporal lobar degeneration (FTLD). METHODS: We trained automatic classifiers with 99 speech features from 1 minute speech samples of 179 participants (ADNC = 36, FTLD = 60, healthy controls [HC] = 89). Patients' pathology was assigned based on autopsy or cerebrospinal fluid analytes. Structural network-based magnetic resonance imaging analyses identified anatomical correlates of distinct speech features. RESULTS: Our classifier showed 0.88 ± $ \pm $ 0.03 area under the curve (AUC) for ADNC versus FTLD and 0.93 ± $ \pm $ 0.04 AUC for patients versus HC. Noun frequency and pause rate correlated with gray matter volume loss in the limbic and salience networks, respectively. DISCUSSION: Brief naturalistic speech samples can be used for screening FTD patients for underlying ADNC in vivo. This work supports the future development of digital assessment tools for FTD. HIGHLIGHTS: We trained machine learning classifiers for frontotemporal dementia patients using natural speech. We grouped participants by neuropathological diagnosis (autopsy) or cerebrospinal fluid biomarkers. Classifiers well distinguished underlying pathology (Alzheimer's disease vs. frontotemporal lobar degeneration) in patients. We identified important features through an explainable artificial intelligence approach. This work lays the groundwork for a speech-based neuropathology screening tool.

Automated Measures of Syntactic Complexity in Natural Speech Production: Older and Younger Adults as a Case Study.

PURPOSE: Multiple methods have been suggested for quantifying syntactic complexity in speech. We compared eight automated syntactic complexity metrics to determine which best captured verified syntactic differences between old and young adults. METHOD: We used natural speech samples produced in a picture description task by younger (n = 76, ages 18-22 years) and older (n = 36, ages 53-89 years) healthy participants, manually transcribed and segmented into sentences. We manually verified that older participants produced fewer complex structures. We developed a metric of syntactic complexity using automatically extracted syntactic structures as features in a multidimensional metric. We compared our metric to seven other metrics: Yngve score, Frazier score, Frazier-Roark score, developmental level, syntactic frequency, mean dependency distance, and sentence length. We examined the success of each metric in identifying the age group using logistic regression models. We repeated the analysis with automatic transcription and segmentation using an automatic speech recognition (ASR) system. RESULTS: Our multidimensional metric was successful in predicting age group (area under the curve [AUC] = 0.87), and it performed better than the other metrics. High AUCs were also achieved by the Yngve score (0.84) and sentence length (0.84). However, in a fully automated pipeline with ASR, the performance of these two metrics dropped (to 0.73 and 0.46, respectively), while the performance of the multidimensional metric remained relatively high (0.81). CONCLUSIONS: Syntactic complexity in spontaneous speech can be quantified by directly assessing syntactic structures and considering them in a multivariable manner. It can be derived automatically, saving considerable time and effort compared to manually analyzing large-scale corpora, while maintaining high face validity and robustness. SUPPLEMENTAL MATERIAL: https://doi.org/10.23641/asha.24964179.

Changes in Digital Speech Measures in Asymptomatic Carriers of Pathogenic Variants Associated With Frontotemporal Degeneration.

BACKGROUND AND OBJECTIVES: Clinical trials developing therapeutics for frontotemporal degeneration (FTD) focus on pathogenic variant carriers at preclinical stages. Objective, quantitative clinical assessment tools are needed to track stability and delayed disease onset. Natural speech can serve as an accessible, cost-effective assessment tool. We aimed to identify early changes in the natural speech of FTD pathogenic variant carriers before they become symptomatic. METHODS: In this cohort study, speech samples of picture descriptions were collected longitudinally from healthy participants in observational studies at the University of Pennsylvania and Columbia University between 2007 and 2020. Participants were asymptomatic but at risk for familial FTD. Status as "carrier" or "noncarrier" was based on screening for known pathogenic variants in the participant's family. Thirty previously validated digital speech measures derived from automatic speech processing pipelines were selected a priori based on previous studies in patients with FTD and compared between asymptomatic carriers and noncarriers cross-sectionally and longitudinally. RESULTS: A total of 105 participants, all asymptomatic, included 41 carriers: 12 men [30%], mean age 43 ± 13 years; education, 16 ± 2 years; MMSE 29 ± 1; and 64 noncarriers: 27 men [42%]; mean age, 48 ± 14 years; education, 15 ± 3 years; MMSE 29 ± 1. We identified 4 speech measures that differed between carriers and noncarriers at baseline: mean speech segment duration (mean difference -0.28 seconds, 95% CI -0.55 to -0.02, p = 0.04); word frequency (mean difference 0.07, 95% CI 0.008-0.14, p = 0.03); word ambiguity (mean difference 0.02, 95% CI 0.0008-0.05, p = 0.04); and interjection count per 100 words (mean difference 0.33, 95% CI 0.07-0.59, p = 0.01). Three speech measures deteriorated over time in carriers only: particle count per 100 words per month (ß = -0.02, 95% CI -0.03 to -0.004, p = 0.009); total narrative production time in seconds per month (ß = -0.24, 95% CI -0.37 to -0.12, p < 0.001); and total number of words per month (ß = -0.48, 95% CI -0.78 to -0.19, p = 0.002) including in 3 carriers who later converted to symptomatic disease. DISCUSSION: Using automatic processing pipelines, we identified early changes in the natural speech of FTD pathogenic variant carriers in the presymptomatic stage. These findings highlight the potential utility of natural speech as a digital clinical outcome assessment tool in FTD, where objective and quantifiable measures for abnormal behavior and language are lacking.