Predicting Prodromal Dementia Using Linguistic Patterns and Deficits

Language deficiency is evident in the onset of several neurodegenerative disorders yet has barely been investigated when first occurs on the continuum of cognitive impairment for the purpose of early diagnoses. Alzheimers disease (AD) is a neurodegenerative pathology that develops years prior to clinical manifestations and typically preceded by prodromal stages such as Mild Cognitive Impairment (MCI). Currently, the manual diagnostic procedures of both types are time consuming, following certain clinical criteria and neuropsychological examinations. Our study aims to establish state-of-the-art performance in the automatic identification of different dementia etiologies, including AD, MCI, and Possible AD (PoAD), and to determine whether patients with initial cognitive declines exhibit language deficits through the analysis of language samples deduced with the cookie theft picture description task. Data was derived from the cookie theft picture corpus of DementiaBank, from which all language samples of the identified etiologies were used, with a random subsampling technique that handles the skewness of the classes. Several original lexical and syntactic (i.e., lexicosyntactic) features were introduced and used alongside previously established lexicosyntactics to train machine learning (ML) classifiers against these etiologies. Further, a statistical analysis was conducted to uncover the deficiency across these etiologies. Our models resulted in benchmarks for differentiating all the identified classes with accuracies ranging between 95 to 98% and corresponding F1 values falling between 94 and 98%. The statistical analysis of our lexicosyntactic biomarkers shows that linguistic deviations are associated with prodromal as well as advanced neurodegenerative pathologies, being greatly impacted as cognitive decline increases and suggesting that language biomarkers may aid the early diagnosis of these pathologies.