Word-priming studies have suggested that the associative disturbance of schizophrenia may reflect aberrant spread of activation through the lexicon of the brain. To explore this, we examined lexical activation using a semantic word-priming paradigm coupled with functional magnetic resonance imaging (fMRI). We also wanted to determine whether brain activation to this paradigm correlated with relevant clinical symptom measures. In addition to completing clinical symptom measures, twelve chronic patients and twelve demographically matched control subjects completed a lexical-decision semantic-priming paradigm developed as an event-related BOLD fMRI task. This paradigm consisted of words that differed in connectivity. Words with many connections between shared semantic associates are considered high in connectivity and produce the largest behavioral semantic priming effects in control subjects, while words with few connections between shared semantic associates are considered low in connectivity and produce a relatively smaller amount of semantic priming. In fMRI, a respective step-wise increase in activation from high connectivity to low connectivity to unrelated word pairs was expected for normal subjects. Controls showed the expected pattern of activation to word connectivity; however, patients showed a less robust pattern of activation to word connectivity. Furthermore, this aberrant response correlated with measures of Auditory Hallucinations, Distractive Speech, Illogicality, and Incoherence. The patients did not display left frontal and temporal activation as a function of the degree of word connectivity as seen in healthy controls. This may reflect a disease-related disturbance in functional connectivity of lexical activation, which in turn may be associated with clinical symptomatology.
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http://dx.doi.org/10.1016/j.neuroimage.2006.11.029 | DOI Listing |
Sensors (Basel)
January 2025
Department of Electrical Engineering, American University of Sharjah, Sharjah 26666, United Arab Emirates.
Accurately identifying and discriminating between different brain states is a major emphasis of functional brain imaging research. Various machine learning techniques play an important role in this regard. However, when working with a small number of study participants, the lack of sufficient data and achieving meaningful classification results remain a challenge.
View Article and Find Full Text PDFNeurobiol Lang (Camb)
January 2025
Key Laboratory of Brain Functional Genomics (MOE & STCSM), Shanghai Changning-ECNU Mental Health Center, School of Psychology and Cognitive Science, East China Normal University, Shanghai, China.
Leftward language production and rightward spatial attention are salient features of functional organization in most humans, but their anatomical basis remains unclear. Interhemispheric connections and intrahemispheric white matter asymmetries have been proposed as important factors underlying functional lateralization. To investigate the role of white matter connectivity in functional lateralization, we first identified 96 left-handers using visual half field naming tasks.
View Article and Find Full Text PDFISA Trans
January 2025
State Key Laboratory of Mechanical System and Vibration, Shanghai Jiao Tong University, Shanghai 200240, China. Electronic address:
This paper addresses the critical challenge of interpretability in machine learning methods for machine fault diagnosis by introducing a novel ad hoc interpretable neural network structure called Sparse Temporal Logic Network (STLN). STLN conceptualizes network neurons as logical propositions and constructs formal connections between them using specified logical operators, which can be articulated and understood as a formal language called Weighted Signal Temporal Logic. The network includes a basic word network using wavelet kernels to extract intelligible features, a transformer encoder with sparse and structured neural attention to locate informative signal segments relevant to decision-making, and a logic network to synthesize a coherent language for fault explanation.
View Article and Find Full Text PDFNeuroscience
January 2025
Human Communication, Learning, and Development, Faculty of Education, The University of Hong Kong, China.
The human brain possesses the ability to automatically extract statistical regularities from environmental inputs, including visual-graphic symbols and printed units. However, the specific brain regions underlying the statistical learning of these visual-graphic symbols or artificial orthography remain unclear. This study utilized functional magnetic resonance imaging (fMRI) with an artificial orthography learning paradigm to measure brain activities associated with the statistical learning of radical positional regularities embedded in pseudocharacters containing high (100%), moderate (80%), and low (60%) levels of consistency, along with a series of random abstract figures.
View Article and Find Full Text PDFIowa Orthop J
January 2025
University of Tennessee Health Science Center-Campbell Clinic Department of Orthopaedic Surgery and Biomedical Engineering, Memphis, Tennessee, USA.
Background: Core curricula do not include courses on how to find employment after hand fellowships. Little data exists in literature regarding job selection in hand surgery. This study's purpose was to provide information to future hand surgeons on ways of finding a job that meets their expectations and to elucidate factors that should be considered before deciding on a hand practice.
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