Background: 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 in the field, 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 context. In contrast, the analysis of spoken language productions by Natural Language Processing (NLP) techniques can 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. Methods: We enrolled 96 participants (age range 50–75): 48 healthy controls (CG) and 48 cognitively impaired participants: 16 participants with single domain amnestic Mild Cognitive Impairment (aMCI), 16 with multiple domain MCI (mdMCI) 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 (describing 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. Results: Neuropsychological tests showed significant differences between controls and mdMCI, and between controls and eD participants; GPCog, MoCA, PF, and SF also discriminated between controls and aMCI. In the linguistic experiments, a number of features regarding lexical, acoustic and syntactic aspects were significant in differentiating between mdMCI, eD, and CG (non-parametric statistical analysis). Some features, mainly in the acoustic domain also discriminated between CG and aMCI. Conclusions: Linguistic features of spontaneous speech transcribed and analyzed by NLP techniques show significant differences between controls and pathological states (not only eD but also MCI) 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.

Background: In the past few years there has been a growing interest in the employment of verbal productions as digital biomarkers, namely objective, quantifiable behavioural data that can be collected and measured by means of digital devices, allowing for a low‐cost pathology detection, classification and monitoring. Numerous research papers have been published on the automatic detection of subtle verbal alteration, starting from written texts, raw speech recordings and transcripts, and such linguistic analysis has been singled out as a cost‐effective method for diagnosing dementia and other medical conditions common among elderly patients (e.g., cognitive dysfunctions associated with metabolic disorders, dysarthria).Aims: To provide a critical appraisal and synthesis of evidence concerning the application of natural language processing (NLP) techniques for clinical purposes in the geriatric population. In particular, we discuss the state of the art on studying language in healthy and pathological ageing, focusing on the latest research efforts to build non‐intrusive language‐based tools for the early identification of cognitive frailty due to dementia. We also discuss some challenges and open problems raised by this approach.Methods & Procedures: We performed a scoping review to examine emerging evidence about this novel domain. Potentially relevant studies published up to November 2021 were identified from the databases of MEDLINE, Cochrane and Web of Science. We also browsed the proceedings of leading international conferences (e.g., ACL, COLING, Interspeech, LREC) from 2017 to 2021, and checked the reference lists of relevant studies and reviews.Main Contribution: The paper provides an introductory, but complete, overview of the application of NLP techniques for studying language disruption due to dementia. We also suggest that this technique can be fruitfully applied to other medical conditions (e.g., cognitive dysfunctions associated with dysarthria, cerebrovascular disease and mood disorders).Conclusions & Implications: Despite several critical points need to be addressed by the scientific community, a growing body of empirical evidence shows that NLP techniques can represent a promising tool for studying language changes in pathological aging, with a high potential to lead a significant shift in clinical practice.WHAT THIS PAPER ADDS: What is already known on this subject: Speech and languages abilities change due to non‐pathological neurocognitive ageing and neurodegenerative processes. These subtle verbal modifications can be measured through NLP techniques and used as biomarkers for screening/diagnostic purposes in the geriatric population (i.e., digital linguistic biomarkers—DLBs). What this paper adds to existing knowledge: The review shows that DLBs can represent a promising clinical tool, with a high potential to spark a major shift to dementia assessment in the elderly. Some challenges and open problems are also discussed. What are the potential or actual clinical implications of this work?: This methodological review represents a starting point for clinicians approaching the DLB research field for studying language in healthy and pathological ageing. It summarizes the state of the art and future research directions of this novel approach.

The study presented in this article aims at investigating the clinical usefulness of a novel test, called T-PEC, for the diagnosis of Developmental Language Disorder in Italian preschool children. The instrument exploits the production of clitic pronouns, in particular third person direct object clitics (3PDO-CL), as a clinical marker for the disorder. Psychometric properties and normative data were computed on a sample of 70 children ranging in age from 4.6 to 5.8 years: 22 children diagnosed as language-impaired by expert clinicians according to international guidelines, and 48 typically developing peers. The statistical analysis of the collected data revealed good internal consistency (Cronbach’s α = 0.86) and confirmed the effectiveness of the T-PEC test in distinguishing typically developing and DLD children, especially when the latter showed morphosyntactic deficits (AUC = 79.9%). Its high accuracy, combined with the rapidity and easiness of its administration, makes the T-PEC test suitable for use in clinical settings.

Purpose: Attention has recently been paid to Clinical Linguistics for the detection and support of clinical conditions. Many works have been published on the “linguistic profile” of various clinical populations, but very few papers have been devoted to linguistic changes in patients with eating disorders. Patients with Anorexia Nervosa (AN) share similar psychological features such as disturbances in self-perceived body image, inflexible and obsessive thinking and anxious or depressive traits. We hypothesize that these characteristics can result in altered linguistic patterns and be detected using the Natural Language Processing tools.Methods: We enrolled 51 young participants from December 2019 to February 2020 (age range: 14–18): 17 girls with a clinical diagnosis of AN, and 34 normal-weighted peers, matched by gender, age and educational level. Participants in each group were asked to produce three written texts (around 10–15 lines long). A rich set of linguistic features was extracted from the text samples and the statistical significance in pinpointing the pathological process was measured.Results: Comparison between the two groups showed several linguistics indexes as statistically significant, with syntactic reduction as the most relevant trait of AN productions. In particular, the following features emerge as statistically significant in distinguishing AN girls and their normal-weighted peers: the length of the sentences, the complexity of the noun phrase, and the global syntactic complexity. This peculiar pattern of linguistic erosion may be due to the severe metabolic impairment also affecting the central nervous system in AN.Conclusion: These preliminary data showed the existence of linguistic parameters as probable linguistic markers of AN. However, the analysis of a bigger cohort, still ongoing, is needed to consolidate this assumption.Level of evidence III: Evidence obtained from case–control analytic studies.

Autistic children are frequently said to speak with accents that markedly differ from those of their linguistic communities. To date, these anecdotal reports have never been tested or explained. We ran two perception studies using short audio recordings of autistic and typically developing children from the Campania region in Italy. The variety of Italian to which children are exposed in this region markedly differs from those spoken in the rest of Italy. Participant responses about the children's geographical origin show: (a) That autistic children's accent is devoid of the regional features of their community; (b) resembles the standard variety used in cartoons and child television programs. The judgments about children's accents are, furthermore, independent of the overall perception of speech atypicality. This paper shows that the accent of autistic children may diverge from that of their caregivers and peers because of the lasting influence of non‐interactional, screen sources on their speech.