Computerized natural language processing (NLP) allows for objective and sensitive detection of speech disturbance, a hallmark of schizophrenia spectrum disorders (SSD). We explored several methods for characterizing speech changes in SSD (n = 20) compared to healthy control (HC) participants (n = 11) and approached linguistic phenotyping on three levels: individual words, parts-of-speech (POS), and sentence-level coherence. NLP features were compared with a clinical gold standard, the Scale for the Assessment of Thought, Language and Communication (TLC). We utilized Bidirectional Encoder Representations from Transformers (BERT), a state-of-the-art embedding algorithm incorporating bidirectional context. Through the POS approach, we found that SSD used more pronouns but fewer adverbs, adjectives, and determiners (e.g., “the,” “a,”). Analysis of individual word usage was notable for more frequent use of first-person singular pronouns among individuals with SSD and first-person plural pronouns among HC. There was a striking increase in incomplete words among SSD. Sentence-level analysis using BERT reflected increased tangentiality among SSD with greater sentence embedding distances. The SSD sample had low speech disturbance on average and there was no difference in group means for TLC scores. However, NLP measures of language disturbance appear to be sensitive to these subclinical differences and showed greater ability to discriminate between HC and SSD than a model based on clinical ratings alone. These intriguing exploratory results from a small sample prompt further inquiry into NLP methods for characterizing language disturbance in SSD and suggest that NLP measures may yield clinically relevant and informative biomarkers.

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Background and Objectives:We compared digital speech and language features of patients with amnestic Alzheimer’s disease (aAD) or logopenic variant primary progressive aphasia (lvPPA) in a biologically confirmed cohort and related these features to neuropsychiatric test scores and CSF analytes.Methods:We included patients with aAD or lvPPA with cerebrospinal fluid (CSF) (phosphorylated Tau (p-Tau)/Aβ≥ 0.09 and total Tau/Aβ≥ 0.34) or autopsy confirmation of AD pathology and age-matched healthy controls (HC) recruited at the Frontotemporal Degeneration Center of the University of Pennsylvania for a cross-sectional study. We extracted speech and language variables with automated lexical and acoustic pipelines from participants’ oral picture descriptions. We compared the groups and correlated distinct features with clinical ratings and CSF p-Tau levels.Results:We examined patients with aAD (n=44; 62±8 years; 24 females; Mini-Mental State Exam (MMSE)=21.1±4.8) or lvPPA (n=21; 64.1±8.2 years; 11 females; MMSE=23.0±4.2), and healthy controls (HC) (n=28; 65.9±5.9 years, 15 females; MMSE=29±1). Patients with lvPPA produced fewer verbs (10.5±2.3; p=0.001), adjectives (2.7±1.3, p=0.019), and more fillers (7.4±3.9; p=0.022) with lower lexical diversity (0.84±0.1; p=0.05) and higher pause rate (54.2±19.2; p=0.015) than aAD (verbs: 12.5±2; adjectives: 3.8±2; fillers: 4.9±4.5; lexical diversity: 0.87±0.1; pause rate: 45.3±12.8). Both groups showed some shared language impairments compared with HC. Word frequency (MMSE: β=-1.6, p=0.009, BNT: β=-4.36, p<0.001), adverbs (MMSE: β=-1.9, p=0.003, BNT: β=-2.41, p=0.041), pause rate (MMSE: β=-1.21, p=0.041, BNT: β=-2.09, p=0.041), and word length (MMSE: β=1.75, p=0.001, BNT: β=2.94, p=0.003) were significantly correlated with both MMSE and BNT, but other measures were not correlated with MMSE and/or BNT. Prepositions (r=-0.36, p=0.019), nouns (r=-0.31, p=0.047), speech segment duration (r=-0.33, p=0.032), word frequency (r=0.33, p=0.036), and pause rate (r=0.34, p=0.026) were correlated with patients’ CSF p-Tau levels.Discussion:Our measures captured language and speech differences between the two phenotypes that traditional language-based clinical assessments failed to identify. This work demonstrates the potential of natural speech in reflecting underlying variants with AD pathology.

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