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.

Modified mRNA-Mediated CCN5 Gene Transfer Ameliorates Cardiac Dysfunction and Fibrosis without Adverse Structural Remodeling.

Modified mRNAs (modRNAs) are an emerging delivery method for gene therapy. The success of modRNA-based COVID-19 vaccines has demonstrated that modRNA is a safe and effective therapeutic tool. Moreover, modRNA has the potential to treat various human diseases, including cardiac dysfunction. Acute myocardial infarction (MI) is a major cardiac disorder that currently lacks curative treatment options, and MI is commonly accompanied by fibrosis and impaired cardiac function. Our group previously demonstrated that the matricellular protein CCN5 inhibits cardiac fibrosis (CF) and mitigates cardiac dysfunction. However, it remains unclear whether early intervention of CF under stress conditions is beneficial or more detrimental due to potential adverse effects such as left ventricular (LV) rupture. We hypothesized that CCN5 would alleviate the adverse effects of myocardial infarction (MI) through its anti-fibrotic properties under stress conditions. To induce the rapid expression of CCN5, ModRNA-CCN5 was synthesized and administrated directly into the myocardium in a mouse MI model. To evaluate CCN5 activity, we established two independent experimental schemes: (1) preventive intervention and (2) therapeutic intervention. Functional analyses, including echocardiography and magnetic resonance imaging (MRI), along with molecular assays, demonstrated that modRNA-mediated CCN5 gene transfer significantly attenuated cardiac fibrosis and improved cardiac function in both preventive and therapeutic models, without causing left ventricular rupture or any adverse cardiac remodeling. In conclusion, early intervention in CF by ModRNA-CCN5 gene transfer is an efficient and safe therapeutic modality for treating MI-induced heart failure.

Conventional Method of Cardiac Ischemia/Reperfusion Injury in Mice.

Myocardial ischemia-reperfusion injury (IRI) after myocardial ischemia, cardiac surgery, or circulatory arrest leads to adverse cardiovascular outcomes. Primarily, no blood flow to the heart causes an imbalance between oxygen demand and supply, namely, ischemia, resulting in damage or dysfunction of the cardiac tissue. Early restoration of blood flow has been established to be the treatment of choice to prevent further tissue injury. Indeed, the use of thrombolytic therapy or primary percutaneous coronary intervention is the most effective strategy for reducing the size of a myocardial infarct and improving the clinical outcome. Unfortunately, restoring blood flow to the ischemic myocardium, named reperfusion, can also contribute to injury. This phenomenon was therefore termed myocardial IRI. Subsequent studies in animal models of acute myocardial infarction suggest that myocardial IRI accounts for up to 50% of the final size of a myocardial infarct. Consequently, many researchers aim to understand the underlying molecular mechanism of myocardial IRI to find therapeutic strategies that ultimately reduce the final infarct size. Despite numerous therapeutic strategies identified in laboratories, no clinical medicine specifically targeting IRI has yet been approved. Therefore, more relevant research is needed to develop promising therapeutic agents. In this respect, we will introduce a solid and reproducible experimental protocol to induce myocardial IRI in mice and test potent drug transfer during this surgical procedure.

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.

Speech markers of depression dimensions across cognitive status.

Introduction: Depression and its components significantly impact dementia prediction and severity, necessitating reliable objective measures for quantification. Methods: We investigated associations between emotion-based speech measures (valence, arousal, and dominance) during picture descriptions and depression dimensions derived from the geriatric depression scale (GDS, dysphoria, withdrawal-apathy-vigor (WAV), anxiety, hopelessness, and subjective memory complaint). Results: Higher WAV was associated with more negative valence (estimate = -0.133, p = 0.030). While interactions of apolipoprotein E (APOE) 4 status with depression dimensions on emotional valence did not reach significance, there was a trend for more negative valence with higher dysphoria in those with at least one APOE4 allele (estimate = -0.404, p = 0.0846). Associations were similar irrespective of dementia severity. Discussion: Our study underscores the potential utility of speech biomarkers in characterizing depression dimensions. In future research, using emotionally charged stimuli may enhance emotional measure elicitation. The role of APOE on the interaction of speech markers and depression dimensions warrants further exploration with greater sample sizes. Highlights: Participants reporting higher apathy used more negative words to describe a neutral picture.Those with higher dysphoria and at least one APOE4 allele also tended to use more negative words.Our results suggest the potential use of speech biomarkers in characterizing depression dimensions.

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.

Automatically Predicting Perceived Conversation Quality in a Pediatric Sample Enriched for Autism.

Social interaction quality ratings derived from short natural conversations can differentiate children with and without autism at the group level. In this work, we explored conversations between children and an unfamiliar adult who rated their social interaction success on six dimensions. Using hand-crafted acoustic and lexical features, we built different classifiers to predict children's dimensional conversation quality. The best classifier achieved 61% accuracy, which outperformed human raters (49%). Follow-up analyses revealed that a subset of features determined communication quality scores. Additionally, we extracted acoustic features using a pretrained audio transformer and improved our prediction to 68%. This study suggests that automatically predicting conversation quality could be an inexpensive and objective way to monitor intervention progress in children with communication challenges, and could be used to identify intervention targets for improving conversational success.