Over the last few years, a growing body of linguistic studies have been devoted to the clinical domain, and neuroscience and mental health are no exception to this. However, many of the factors underlying cognitive and neuropsychiatric symptoms are hard to foresee; furthermore, it is often difficult to predict the disease 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.
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. Moreover, language technology methods and tools have proved particularly promising to address this task. 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 measures, but also online measures that serve as a proxy for cognitive processing and the underlying mechanisms.
Dementia assessment is one rapidly evolving domain of Natural Language Processing (NLP) application for medical science, but this approach is spreading rapidly through the community, with encouraging results on both developmental and acquired pathologies.
This Research Topic aims at bringing together research on digital linguistic biomarkers from different quarters of the cognitive sciences. We welcome original research or systematic reviews on the use of NLP tools for the clinical diagnosis or the evaluation of disease severity and prognosis.
Topics may include, but are not limited to:
• Cognitive frailty screening and clinical phenotypization (e.g. Mild Cognitive Impairment and dementia);
• Automatic analysis of dysarthric speech (e.g. Parkinson’s disease, Huntington's disease, Multiple Sclerosis, Amyotrophic Lateral Sclerosis);
• Mental illness diagnosis (e.g. Major Depressive Disorder, Bipolar Disorder, Eating Disorders, Schizophrenia);
• Early detection of Neurodevelopmental Disorders (e.g. Autism Spectrum Disorder, Developmental Language Disorder);
• Data collection of novel data samples in the domain of brain and mental health (e.g. neuropsychological assessment, self-reported behavioural and/or physiological data).
Over the last few years, a growing body of linguistic studies have been devoted to the clinical domain, and neuroscience and mental health are no exception to this. However, many of the factors underlying cognitive and neuropsychiatric symptoms are hard to foresee; furthermore, it is often difficult to predict the disease 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.
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. Moreover, language technology methods and tools have proved particularly promising to address this task. 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 measures, but also online measures that serve as a proxy for cognitive processing and the underlying mechanisms.
Dementia assessment is one rapidly evolving domain of Natural Language Processing (NLP) application for medical science, but this approach is spreading rapidly through the community, with encouraging results on both developmental and acquired pathologies.
This Research Topic aims at bringing together research on digital linguistic biomarkers from different quarters of the cognitive sciences. We welcome original research or systematic reviews on the use of NLP tools for the clinical diagnosis or the evaluation of disease severity and prognosis.
Topics may include, but are not limited to:
• Cognitive frailty screening and clinical phenotypization (e.g. Mild Cognitive Impairment and dementia);
• Automatic analysis of dysarthric speech (e.g. Parkinson’s disease, Huntington's disease, Multiple Sclerosis, Amyotrophic Lateral Sclerosis);
• Mental illness diagnosis (e.g. Major Depressive Disorder, Bipolar Disorder, Eating Disorders, Schizophrenia);
• Early detection of Neurodevelopmental Disorders (e.g. Autism Spectrum Disorder, Developmental Language Disorder);
• Data collection of novel data samples in the domain of brain and mental health (e.g. neuropsychological assessment, self-reported behavioural and/or physiological data).