Connected speech markers of amyloid burden in primary progressive aphasia. (December 2021)

• Record Type: Journal Article
• Authors: Slegers, Antoine; Chafouleas, Geneviève; Montembeault, Maxime; Bedetti, Christophe; Welch, Ariane E.; Rabinovici, Gil D.; Langlais, Philippe; Gorno-Tempini, Maria L.; Brambati, Simona M.
• Is Part Of: Cortex. Volume 145(2021)
• Page Start: 160
• Page End: 168
• Publication Date: 2021-12
• Subjects: Primary progressive aphasia -- Biomarkers -- Connected speech -- Natural language processing -- Alzheimer's disease -- Differential diagnosis -- Telemedicine; Neuropsychology -- Periodicals; Nervous system -- Periodicals; Neurology -- Periodicals; Psychophysiology -- Periodicals; Behavior -- Periodicals; Neurology -- Periodicals; 612.825
• DOI: 10.1016/j.cortex.2021.09.010
• Languages: English
• ISSNs: 0010-9452

Abstract

Abstract: Introduction: Positron emission tomography (PET) amyloid imaging has become an important part of the diagnostic workup for patients with primary progressive aphasia (PPA) and uncertain underlying pathology. Here, we employ a semi-automated analysis of connected speech (CS) with a twofold objective. First, to determine if quantitative CS features can help select primary progressive aphasia (PPA) patients with a higher probability of a positive PET amyloid imaging result. Second, to examine the relevant group differences from a clinical perspective. Methods: 117 CS samples from a well-characterised cohort of PPA patients who underwent PET amyloid imaging were collected. Expert consensus established PET amyloid status for each patient, and 40% of the sample was amyloid positive. Results: Leave-one-out cross-validation yields 77% classification accuracy (sensitivity: 74%, specificity: 79%). Discussion: Our results confirm the potential of CS analysis as a screening tool. Discriminant CS features from lexical, syntactic, pragmatic, and semantic domains are discussed.