Establishing diagnostic criteria: the role of clinical pragmatics

The study of pragmatic disorders is of interest to speech-language pathologists who have a professional responsibility to assess and treat communication impairments. However, these disorders, it will be argued in this paper, have a significance beyond the clinical management of clients with communication impairments. Specifically, pragmatic disorders can now make a contribution to the diagnosis of a range of clinical conditions in which communication is adversely affected. These conditions include attention deficit hyperactivity disorder (ADHD), the autistic spectrum disorders, schizophrenia and the dementias. Pragmatic disorders are already among the criteria used to diagnose some of these conditions (e.g. ADHD), although they are not described in these terms. In other conditions (e.g. the dementias), pragmatic disorders have potential diagnostic value in the absence of reliable biomarkers markers of these conditions and similar initial presenting symptoms. Using clinical data, and the findings of empirical studies, the case is made for the inclusion and/or greater integration of pragmatic disorders in the formal classificatory systems that are used to diagnose a range of disorders. A previously unrecognised role for pragmatic impairments in the nosology and diagnosis of clinical disorders is thereby established

The Language of Mental Illness

This paper surveys some philosophical issues with the language surrounding mental illness, but is especially focused on pejoratives relating to mental illness. I argue that though 'crazy' and similar mental illness-based epithets (MI-epithets) are not best understood as slurs, they do function to isolate, exclude, and marginalize members of the targeted group in ways similar to the harmfulness of slurs more generally. While they do not generally express the hate/contempt characteristic of weaponized uses of slurs, MI-epithets perpetuate epistemic injustice by portraying sufferers of mental illness as deserving minimal credibility. After outlining the ways in which these epithets can cause harm, I examine available legal and social remedies, and suggest that the best path going forward is to pursue a reclamation project rather than aiming to censure the use of MI-epithets

Hallucinated and spoken linguistic patterns as markers of psychiatric disorders

A person with alterations in the brain and cognitive functioning and whose language- and speech-related processes are affected might experience atypical linguistic phenomena. Hallucinated voices, also known as auditory verbal hallucinations, illustrate this: when they manifest, an individual can hear words, phrases or dialogues that can resemble actual human language production in the absence of an actual source of the voice in the outer world. Disorganized speech represents another example: an individual might express her/himself with a discourse whose associations of concepts and use of grammatical elements area typical, sometimes until the point in which the speech is not understandable anymore. Since these linguistic phenomena occur both in individuals with and without need for mental care, a main clinical problem consist in accurately and consistently identifying the patterns that differentiate between pathological and non-pathological hallucinated voices or disorganized speech. In this thesis, a combination of linguistic theory, computational methods, and artificial intelligence was implemented to unveil the linguistic patterns that distinguish between pathological and non-pathological hallucinated voices, as well as those that differentiate the speech of patients with schizophrenia-spectrum disorders from that of control individuals. More broadly, a consensual framework was developed regarding the potential use of this approach across psychiatric disorders and for a series of clinical actions. Lastly, pending obstacles and emerging questions related to this approach were underlined, followed by tentative theoretical venues that might point into solutions and answers about “Hallucinated and spoken linguistic patterns as markers of psychiatric disorders”

Graph theory applied to speech: insights on cognitive deficit diagnosis and dream research

In the past ten years, graph theory has been widely employed in the study of natural and technological phenomena. The representation of the relationships among the units of a network allow for a quantitative analysis of its overall structure, beyond what can be understood by considering only a few units. Here we discuss the application of graph theory to psychiatric diagnosis of psychoses and dementias. The aim is to quantify the flow of thoughts of psychiatric patients, as expressed by verbal reports of dream or waking events. This flow of thoughts is hard to measure but is at the roots of psychiatry as well as psychoanalysis. To this end, speech graphs were initially designed with nodes representing lexemes and edges representing the temporal sequence between consecutive words, leading to directed multigraphs. In a subsequent study, individual words were considered as nodes and their temporal sequence as edges; this simplification allowed for the automatization of the process, effected by the free software Speech Graphs. Using this approach, one can calculate local and global attributes that characterize the network structure, such as the total number of nodes and edges, the number of nodes present in the largest connected and the largest strongly connected components, measures of recurrence such as loops of 1, 2, and 3 nodes, parallel and repeated edges, and global measures such as the average degree, density, diameter, average shortest path, and clustering coefficient. Using these network attributes we were able to automatically sort schizophrenia and bipolar patients undergoing psychosis, and also to separate these psychotic patients from subjects without psychosis, with more than 90% sensitivity and specificity. In addition to the use of the method for strictly clinical purposes, we found that differences in the content of the verbal reports correspond to structural differences at the graph level. When reporting a dream, healthy subjects without psychosis and psychotic subjects with bipolar disorder produced more complex graphs than when reporting waking activities of the previous day; this difference was not observed in psychotic subjects with schizophrenia, which produced equally poor reports irrespective of the content. As a consequence, graphs of dream reports were more efficient for the differential diagnosis of psychosis than graphs of daily reports. Based on these results we can conclude that graphs from dream reports are more informative about mental states, echoing the psychoanalytic notion that dreams are a privileged window into thought.2019-07-3

The white matter connectome as an individualized biomarker of language impairment in temporal lobe epilepsy.

ObjectiveThe distributed white matter network underlying language leads to difficulties in extracting clinically meaningful summaries of neural alterations leading to language impairment. Here we determine the predictive ability of the structural connectome (SC), compared with global measures of white matter tract microstructure and clinical data, to discriminate language impaired patients with temporal lobe epilepsy (TLE) from TLE patients without language impairment.MethodsT1- and diffusion-MRI, clinical variables (CVs), and neuropsychological measures of naming and verbal fluency were available for 82 TLE patients. Prediction of language impairment was performed using a robust tree-based classifier (XGBoost) for three models: (1) a CV-model which included demographic and epilepsy-related clinical features, (2) an atlas-based tract-model, including four frontotemporal white matter association tracts implicated in language (i.e., the bilateral arcuate fasciculus, inferior frontal occipital fasciculus, inferior longitudinal fasciculus, and uncinate fasciculus), and (3) a SC-model based on diffusion MRI. For the association tracts, mean fractional anisotropy was calculated as a measure of white matter microstructure for each tract using a diffusion tensor atlas (i.e., AtlasTrack). The SC-model used measurement of cortical-cortical connections arising from a temporal lobe subnetwork derived using probabilistic tractography. Dimensionality reduction of the SC was performed with principal components analysis (PCA). Each model was trained on 49 patients from one epilepsy center and tested on 33 patients from a different center (i.e., an independent dataset). Randomization was performed to test the stability of the results.ResultsThe SC-model yielded a greater area under the curve (AUC; .73) and accuracy (79%) compared to both the tract-model (AUC: .54, p < .001; accuracy: 70%, p < .001) and the CV-model (AUC: .59, p < .001; accuracy: 64%, p < .001). Within the SC-model, lateral temporal connections had the highest importance to model performance, including connections similar to language association tracts such as links between the superior temporal gyrus to pars opercularis. However, in addition to these connections many additional connections that were widely distributed, bilateral and interhemispheric in nature were identified as contributing to SC-model performance.ConclusionThe SC revealed a white matter network contributing to language impairment that was widely distributed, bilateral, and lateral temporal in nature. The distributed network underlying language may be why the SC-model has an advantage in identifying sub-components of the complex fiber networks most relevant for aspects of language performance

Emotional and behavioral symptoms in neurodegenerative disease: a model for studying the neural bases of psychopathology.

Disruptions in emotional, cognitive, and social behavior are common in neurodegenerative disease and in many forms of psychopathology. Because neurodegenerative diseases have patterns of brain atrophy that are much clearer than those of psychiatric disorders, they may provide a window into the neural bases of common emotional and behavioral symptoms. We discuss five common symptoms that occur in both neurodegenerative disease and psychopathology (i.e., anxiety, dysphoric mood, apathy, disinhibition, and euphoric mood) and their associated neural circuitry. We focus on two neurodegenerative diseases (i.e., Alzheimer's disease and frontotemporal dementia) that are common and well characterized in terms of emotion, cognition, and social behavior and in patterns of associated atrophy. Neurodegenerative diseases provide a powerful model system for studying the neural correlates of psychopathological symptoms; this is supported by evidence indicating convergence with psychiatric syndromes (e.g., symptoms of disinhibition associated with dysfunction in orbitofrontal cortex in both frontotemporal dementia and bipolar disorder). We conclude that neurodegenerative diseases can play an important role in future approaches to the assessment, prevention, and treatment of mental illness

Crossing Borders Between Frontotemporal Dementia and Psychiatric Disorders: An Updated Overview

Frontotemporal dementia (FTD) includes a group of neurocognitive syndromes, clinically characterized by altered behaviors, impairment of language proficiency, and altered executive functioning. FTD is one of the most frequently observed forms of dementia in the elderly population and the most common in presenile age. As for other subtypes of dementia, FTD incidence is constantly on the rise due to the steadily increasing age of the population, and its recognition is now becoming a determinant for clinicians. FTD and psychiatric disorders can overlap in terms of clinical presentations by sharing a common genetic predisposition and neuropathological mechanism in some cases. Nonetheless, this association is often unclear and underestimated. Since its first reports, research into FTD has constantly grown, with the identification of recent findings related to its neuropathology, genetic, clinical, and therapeutic issues. Literature is thriving on this topic, with numerous research articles published in recent years. In the present review, we aimed to provide an updated description of the clinical manifestations that link and potentially confound the diagnosis of FTD and psychiatric disorders in order to improve their differential diagnosis and early detection. In particular, we systematically reviewed the literature, considering articles specifically focused on the behavioral variant FTD, published after 2015 on the PubMed database

Knowledge Rich Natural Language Queries over Structured Biological Databases

Increasingly, keyword, natural language and NoSQL queries are being used for information retrieval from traditional as well as non-traditional databases such as web, document, image, GIS, legal, and health databases. While their popularity are undeniable for obvious reasons, their engineering is far from simple. In most part, semantics and intent preserving mapping of a well understood natural language query expressed over a structured database schema to a structured query language is still a difficult task, and research to tame the complexity is intense. In this paper, we propose a multi-level knowledge-based middleware to facilitate such mappings that separate the conceptual level from the physical level. We augment these multi-level abstractions with a concept reasoner and a query strategy engine to dynamically link arbitrary natural language querying to well defined structured queries. We demonstrate the feasibility of our approach by presenting a Datalog based prototype system, called BioSmart, that can compute responses to arbitrary natural language queries over arbitrary databases once a syntactic classification of the natural language query is made