BibTeX

@misc{gagliardi-etal-2021-editorial-307124,
	title        = {Editorial: Digital Linguistic Biomarkers: Beyond Paper and Pencil Test},
	abstract     = {Over the last decades, a growing body of linguistic studies have been devoted to the clinical domain (Perkins 2011), while the amount of experimental linguistic research focusing on neuroscience and mental health has increased exponentially during the last few years.
Considering that many of the factors underlying cognitive and neuropsychiatric disorders may yield to late symptoms that are hard to foresee, it is often difficult to predict the existence of a presence or risk of a disease, as well as the disease’s trajectory. In this context, interdisciplinary approaches gain increasing popularity, and the analysis of complex behaviour – such as speech and language – emerges as a natural candidate to identify and analyse the extent to which a given neuropathology can impact the cognitive system at the very early stages. In this context, the development of cognitive evaluation and intervention tools focusing on linguistic biomarkers becomes a critical scientific arena both in and outside the clinic and laboratory (see Petrizzo & Popolo, 2020).

Recent international research has demonstrated that automated collected and analysed quantitative linguistic features, easily extractable from a patient’s verbal productions, can be very useful in separating people with various cognitive or mental impairment from healthy subjects, even at a very early stage (see Bedi et al., 2015), and even to predict the outcomes of clinical interventions (see Carrillo et al., 2018). In this line, machine learning-based language technology methods and tools based on artificial intelligence are particularly promising to address this task (Locke et al. 2021; Sigman et al., 2021). Indeed, subtle language disruptions can be employed as digital linguistic 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. Compared to classical pen-and-paper neuropsychological tests, the use of these instruments shows many advantages – such as its non-intrusive and time-effective application – providing not only offline, but also online measures that serve as a proxy for cognitive processing and its underlying mechanisms.

The aim of the Research Topic Digital Linguistic Biomarkers: Beyond Paper and Pencil Tests is to provide a state-of-the-art overview of this multidisciplinary and constantly evolving area of research, bringing together contributions from different quarters of the cognitive sciences. The collection comprises one systematic review, six original research papers, and one opinion paper. The articles are based on empirical and theoretical research from several disciplines (i.e., linguistics, psychology, Artificial Intelligence), and they tackle a range of developmental and acquired disorders. Most probably, dementia assessment has been one of the most rapidly evolving domain of Natural Language Processing (NLP) application for medical science (Petti, Baker & Korhonen 2020), but this approach is spreading rapidly through the community, with encouraging results on both developmental and acquired pathologies, as shown in the current article collection (i.e., autism, developmental language disorder, attention-deficit hyperactivity disorder, Alzheimer’s disease and mild cognitive impairment, or Parkinson’s disease). Furthermore, this Research Topic covers a variety of test languages showing the degree of internationalization of the research on the analysis verbal productions (i.e., English, Italian, German, and Japanese).},
	author       = {Gagliardi, Gloria and Kokkinakis, Dimitrios and Dunabeitia, Jon Andoni},
	year         = {2021},
	volume       = {12},
	pages        = {752238},
}