To establish the frequency, severity, relationship to bulbar symptoms, and neural correlates of syntactic comprehension deficits across the frontotemporal dementia-amyotrophic lateral sclerosis (FTD-ALS) disease spectrum. In total, 85 participants were included in the study; 20 amyotrophic lateral sclerosis (ALS), 15 FTD-ALS, 27 progressive nonfluent aphasia (PNFA), and 23 controls. Syntactic comprehension was evaluated in ALS, FTD-ALS, PNFA, and controls using the Test for Reception of Grammar. Voxel-based morphometry examined neuroanatomical correlates of performance. Syntactic comprehension deficits were detected in 25% of ALS (p = 0.011), 92.9% of FTD-ALS (p < 0.001), and 81.5% of PNFA (p < 0.001) patients. FTD-ALS was disproportionately impaired compared to PNFA. Impaired Test for Reception of Grammar performance was frequent in ALS with early bulbar involvement but did not correlate with bulbar impairment overall. Left peri-insular atrophy correlated with syntactic comprehension deficits. Syntactic comprehension deficits are frequent in FTD-ALS, more severe than in PNFA, and related to left peri-insular atrophy. A significant minority of ALS patients are impaired, but the relationship between bulbar symptoms and syntactic impairment is not understood.
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http://dx.doi.org/10.1016/j.neurobiolaging.2016.02.002 | DOI Listing |
Front Psychol
January 2025
School of Government and Public Transformation, Tecnologico de Monterrey, Monterrey, Mexico.
This study explores the impact of integrative complexity (IC) and syntactic complexity (SC) on decision-making under uncertainty. The research addresses how cognitive structures contribute to decision quality in ambiguous situations. A modified Ellsberg experiment was conducted using an online platform.
View Article and Find Full Text PDFData Brief
February 2025
Arabic Department, University of Sharjah, UAE.
This paper introduces the Morphologically-Analyzed and Syntactically-Annotated Quran (MASAQ) dataset, a comprehensive resource designed to address the scarcity of annotated Quranic Arabic corpora and facilitate the development of advanced Natural Language Processing (NLP) models. The Quran, being a cornerstone of classical Arabic, presents unique challenges for NLP due to its sacred nature and complex linguistic features. MASAQ provides a detailed syntactic and morphological annotation of the entire Quranic text, utilizing a rigorously verified text from Tanzil.
View Article and Find Full Text PDFBrain Lang
January 2025
Department of Linguistics, Graduate School of Arts & Letters, Tohoku University, Sendai, Japan. Electronic address:
This study examines the neural mechanisms behind integrating syntactic and information structures during sentence comprehension using functional Magnetic Resonance Imaging. Focusing on Japanese sentences with canonical (SOV) and non-canonical (OSV) word orders, the study revealed distinct neural networks responsible for processing these linguistic structures. The left opercular part of the inferior frontal gyrus, left premotor area, and left posterior superior/middle temporal gyrus were primarily involved in syntactic processing.
View Article and Find Full Text PDFBrain Struct Funct
January 2025
CHRIST (Deemed to be University), Bangalore, Karnataka, India.
In this investigation, we delve into the neural underpinnings of auditory processing of Sanskrit verse comprehension, an area not previously explored by neuroscientific research. Our study examines a diverse group of 44 bilingual individuals, including both proficient and non-proficient Sanskrit speakers, to uncover the intricate neural patterns involved in processing verses of this ancient language. Employing an integrated neuroimaging approach that combines functional connectivity-multivariate pattern analysis (fc-MVPA), voxel-based univariate analysis, seed-based connectivity analysis, and the use of sparse fMRI techniques to minimize the interference of scanner noise, we highlight the brain's adaptability and ability to integrate multiple types of information.
View Article and Find Full Text PDFSensors (Basel)
December 2024
Department of Information Convergence Engineering, Pusan National University, Busan 46241, Republic of Korea.
Dialogue systems must understand children's utterance intentions by considering their unique linguistic characteristics, such as syntactic incompleteness, pronunciation inaccuracies, and creative expressions, to enable natural conversational engagement in child-robot interactions. Even state-of-the-art large language models (LLMs) for language understanding and contextual awareness cannot comprehend children's intent as accurately as humans because of their distinctive features. An LLM-based dialogue system should acquire the manner by which humans understand children's speech to enhance its intention reasoning performance in verbal interactions with children.
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