Language abnormalities in Alzheimer's disease indicate reduced informativeness.

Objective: This study aims to elucidate the cognitive underpinnings of language abnormalities in Alzheimer's Disease (AD) using a computational cross-linguistic approach and ultimately enhance the understanding and diagnostic accuracy of the disease.

Methods: Computational analyses were conducted on language samples of 156 English and 50 Persian speakers, comprising both AD patients and healthy controls, to extract language indicators of AD. Furthermore, we introduced a machine learning-based metric, Language Informativeness Index (LII), to quantify empty speech.

Artificial intelligence classifies primary progressive aphasia from connected speech.

Neurodegenerative dementia syndromes, such as primary progressive aphasias (PPA), have traditionally been diagnosed based, in part, on verbal and non-verbal cognitive profiles. Debate continues about whether PPA is best divided into three variants and regarding the most distinctive linguistic features for classifying PPA variants. In this cross-sectional study, we initially harnessed the capabilities of artificial intelligence and natural language processing to perform unsupervised classification of short, connected speech samples from 78 pateints with PPA.

Using explainable artificial intelligence to identify linguistic biomarkers of amyloid pathology in primary progressive aphasia.

Introduction: Recent success has been achieved in Alzheimer's disease (AD) clinical trials targeting amyloid beta (β), demonstrating a reduction in the rate of cognitive decline. However, testing methods for amyloid-β positivity are currently costly or invasive, motivating the development of accessible screening approaches to steer patients toward appropriate diagnostic tests. Here, we employ a pre-trained language model (Distil-RoBERTa) to identify amyloid-β positivity from a short, connected speech sample.

Language Abnormalities in Alzheimer's Disease Arise from Reduced Informativeness: A Cross-Linguistic Study in English and Persian.

Introduction: This research investigates the psycholinguistic origins of language impairments in Alzheimer's Disease (AD), questioning if these impairments result from language-specific structural disruptions or from a universal deficit in generating meaningful content.

Methods: Cross-linguistic analysis was conducted on language samples from 184 English and 52 Persian speakers, comprising both AD patients and healthy controls, to extract various language features. Furthermore, we introduced a machine learning-based metric, Language Informativeness Index (LII), to quantify informativeness.

Language uncovers visuospatial dysfunction in posterior cortical atrophy: a natural language processing approach.

Introduction: Posterior Cortical Atrophy (PCA) is a syndrome characterized by a progressive decline in higher-order visuospatial processing, leading to symptoms such as space perception deficit, simultanagnosia, and object perception impairment. While PCA is primarily known for its impact on visuospatial abilities, recent studies have documented language abnormalities in PCA patients. This study aims to delineate the nature and origin of language impairments in PCA, hypothesizing that language deficits reflect the visuospatial processing impairments of the disease.

Using Generative Artificial Intelligence to Classify Primary Progressive Aphasia from Connected Speech.

Neurodegenerative dementia syndromes, such as Primary Progressive Aphasias (PPA), have traditionally been diagnosed based in part on verbal and nonverbal cognitive profiles. Debate continues about whether PPA is best subdivided into three variants and also regarding the most distinctive linguistic features for classifying PPA variants. In this study, we harnessed the capabilities of artificial intelligence (AI) and natural language processing (NLP) to first perform unsupervised classification of concise, connected speech samples from 78 PPA patients.

Language Uncovers Visuospatial Dysfunction in Posterior Cortical Atrophy: A Natural Language Processing Approach.

Introduction: Posterior Cortical Atrophy (PCA) is a syndrome characterized by a progressive decline in higher-order visuospatial processing, leading to symptoms such as space perception deficit, simultanagnosia, and object perception impairment. While PCA is primarily known for its impact on visuospatial abilities, recent studies have documented language abnormalities in PCA patients. This study aims to delineate the nature and origin of language impairments in PCA, hypothesizing that language deficits reflect the visuospatial processing impairments of the disease.

Automated analysis of written language in the three variants of primary progressive aphasia.

Despite the important role of written language in everyday life, abnormalities in functional written communication have been sparsely investigated in primary progressive aphasia. Prior studies have analysed written language separately in each of the three variants of primary progressive aphasia-but have rarely compared them to each other or to spoken language. Manual analysis of written language can be a time-consuming process.

Measuring Sentence Information via Surprisal: Theoretical and Clinical Implications in Nonfluent Aphasia.

Objective: Nonfluent aphasia is characterized by simplified sentence structures and word-level abnormalities, including reduced use of verbs and function words. The predominant belief about the disease mechanism is that a core deficit in syntax processing causes both structural and word-level abnormalities. Here, we propose an alternative view based on information theory to explain the symptoms of nonfluent aphasia.

Less is more in language production: an information-theoretic analysis of agrammatism in primary progressive aphasia.

Agrammatism is a disorder of language production characterized by short, simplified sentences, the omission of function words, an increased use of nouns over verbs and a higher use of heavy verbs. Despite observing these phenomena for decades, the accounts of agrammatism have not converged. Here, we propose and test the hypothesis that the lexical profile of agrammatism results from a process that opts for words with a lower frequency of occurrence to increase lexical information.

A syntax-lexicon trade-off in language production.

Spoken language production involves selecting and assembling words and syntactic structures to convey one's message. Here we probe this process by analyzing natural language productions of individuals with primary progressive aphasia (PPA) and healthy individuals. Based on prior neuropsychological observations, we hypothesize that patients who have difficulty producing complex syntax might choose semantically richer words to make their meaning clear, whereas patients with lexicosemantic deficits may choose more complex syntax.

Natural language processing in psychiatry: the promises and perils of a transformative approach.

A person's everyday language can indicate patterns of thought and emotion predictive of mental illness. Here, we discuss how natural language processing methods can be used to extract indicators of mental health from language to help address long-standing problems in psychiatry, along with the potential hazards of this new technology.

A machine learning approach to predicting psychosis using semantic density and latent content analysis.

Subtle features in people's everyday language may harbor the signs of future mental illness. Machine learning offers an approach for the rapid and accurate extraction of these signs. Here we investigate two potential linguistic indicators of psychosis in 40 participants of the North American Prodrome Longitudinal Study.

Emotional self-other voice processing in schizophrenia and its relationship with hallucinations: ERP evidence.

Abnormalities in self-other voice processing have been observed in schizophrenia, and may underlie the experience of hallucinations. More recent studies demonstrated that these impairments are enhanced for speech stimuli with negative content. Nonetheless, few studies probed the temporal dynamics of self versus nonself speech processing in schizophrenia and, particularly, the impact of semantic valence on self-other voice discrimination.

Is this my voice or yours? The role of emotion and acoustic quality in self-other voice discrimination in schizophrenia.

Introduction: Impairments in self-other voice discrimination have been consistently reported in schizophrenia, and associated with the severity of auditory verbal hallucinations (AVHs). This study probed the interactions between voice identity, voice acoustic quality, and semantic valence in a self-other voice discrimination task in schizophrenia patients compared with healthy subjects. The relationship between voice identity discrimination and AVH severity was also explored.

Did you or I say pretty, rude or brief? An ERP study of the effects of speaker's identity on emotional word processing.

During speech comprehension, multiple cues need to be integrated at a millisecond speed, including semantic information, as well as voice identity and affect cues. A processing advantage has been demonstrated for self-related stimuli when compared with non-self stimuli, and for emotional relative to neutral stimuli. However, very few studies investigated self-other speech discrimination and, in particular, how emotional valence and voice identity interactively modulate speech processing.

Abnormal interactions between context, memory structure, and mood in schizophrenia: an ERP investigation.

This study used event-related potentials to examine interactions between mood, sentence context, and semantic memory structure in schizophrenia. Seventeen male chronic schizophrenia and 15 healthy control subjects read sentence pairs after positive, negative, or neutral mood induction. Sentences ended with expected words (EW), within-category violations (WCV), or between-category violations (BCV).

Abnormalities in the processing of emotional prosody from single words in schizophrenia.

Background: Abnormalities in emotional prosody processing have been consistently reported in schizophrenia and are related to poor social outcomes. However, the role of stimulus complexity in abnormal emotional prosody processing is still unclear.

Method: We recorded event-related potentials in 16 patients with chronic schizophrenia and 16 healthy controls to investigate: 1) the temporal course of emotional prosody processing; and 2) the relative contribution of prosodic and semantic cues in emotional prosody processing.

Evaluation of chaperone-like activity of alginate: microcapsule and water-soluble forms.

To evaluate the chaperone-like activity of alginate stabilization and refolding of alkaline phosphatase (ALP) was investigated in the presence of alginate through two different approaches, the soluble form and microcapsule assisted methods. It was found that in the presence of microcapsules, ALP can be stabilized to a higher degree compared with the water-soluble form, whereas the denatured ALP is refolded with a higher yield through latter method. Lower refolding yields of alginate beads compared with its soluble form may be the result of lower refolding rate of ALP upon elution of the bound enzyme by dispersing the precipitate in NaCl which left the unfolded protein in an unsuitable environment, providing enough time for protein aggregation and leading finally to lower recovered activity compared with application of soluble form of alginate.

Interaction between cannabinoid compounds and diazepam on anxiety-like behaviour of mice.

Previous studies have suggested that cannabinoidergic system is involved in anxiety. However, a complete picture of cannabinoid association in the anxiety is still lacking. In the present study, we investigated the possible interaction between cannabinoidergic and GABAergic systems in the anxiety-like behaviour of mice.