Speech Analysis by Natural Language Processing Techniques: A Possible Tool for Very Early Detection of Cognitive Decline?

The discovery of early, non-invasive biomarkers for the identification of "preclinical” or “pre-symptomatic” Alzheimer’s disease and other dementias is a key issue, especially for research purposes, the design of preventive clinical trials, and drafting population-based health care policies. Complex behaviors are natural candidates for this. In particular, recent studies have suggested that speech alterations might be one of the earliest signs of cognitive decline, frequently noticeable years before other cognitive deficits become apparent. Traditional neuropsychological language tests provide ambiguous results in this contest. In contrast, the analysis of spoken language productions by Natural Language Processing (NLP) techniques can ecologically pinpoint language modifications in potential patients. This interdisciplinary study aimed at using NLP to identify early linguistic signs of cognitive decline in a population of elderly individuals. We enrolled 96 participants (age range 50-75): 48 healthy controls and 48 impaired participants: 16 participants with single domain amnestic Mild Cognitive Impairment (a-MCI), 16 with multiple domain MCI (md-MCI) and 16 with early Dementia (eD). Each subject underwent a brief neuropsychological screening composed by MMSE, MoCA, GPCog, CDT and verbal fluency (phonemic and semantic). The spontaneous speech during three tasks (explaining a complex picture, a typical working day and recalling a last remembered dream) was then recorded, transcribed and annotated at various linguistic levels. A multidimensional parameter computation was performed by a quantitative analysis of spoken texts, computing rhythmic, acoustic, lexical, morpho-syntactic and syntactic features. Neuropsychological tests showed significant differences between controls and md-MCI, and between controls and eD participants; MoCA, phonemic fluency and GPCog discriminated between controls and a-MCI, while MMSE, CDT and semantic fluency did not differentiate between the two groups. In the linguistic experiments, a number of features regarding lexical, acoustic and syntactic aspects were significant (non-parametric statistical analysis) in differentiating between all the considered subject groups. Linguistic features of spontaneous speech transcribed and analyzed by NLP techniques show significant differences between controls and pathological states and seems to be a promising approach for the identification of preclinical stages of dementia. Long duration follow-up studies are needed to confirm this assumption.