98%
921
2 minutes
20
Purpose: Niemann-Pick disease type C (NPC) is a rare lysosomal storage disease characterized by progressive neurodegeneration and neuropsychiatric symptoms. This study investigated pathophysiological mechanisms underlying motor deficits, particularly speech production, and cognitive impairment.
Methods: We prospectively phenotyped 8 adults with NPC and age-sex-matched healthy controls using a comprehensive assessment battery, encompassing clinical presentation, plasma biomarkers, hand-motor skills, speech production, cognitive tasks, and (micro-)structural and functional central nervous system properties through magnetic resonance imaging.
Results: Patients with NPC demonstrated deficits in fine-motor skills, speech production timing and coordination, and cognitive performance. Magnetic resonance imaging revealed reduced cortical thickness and volume in cerebellar subdivisions (lobule VI and crus I), cortical (frontal, temporal, and cingulate gyri) and subcortical (thalamus and basal ganglia) regions, and increased choroid plexus volumes in NPC. White matter fractional anisotropy was reduced in specific pathways (intracerebellar input and Purkinje tracts), whereas diffusion tensor imaging graph theory analysis identified altered structural connectivity. Patients with NPC exhibited altered activity in sensorimotor and cognitive processing hubs during resting-state and speech production. Canonical component analysis highlighted the role of cerebellar-cerebral circuitry in NPC and its integration with behavioral performance and disease severity.
Conclusion: This deep phenotyping approach offers a comprehensive systems neuroscience understanding of NPC motor and cognitive impairments, identifying potential central nervous system biomarkers.
Download full-text PDF |
Source |
---|---|
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11995604 | PMC |
http://dx.doi.org/10.1016/j.gim.2023.101053 | DOI Listing |
Res Integr Peer Rev
September 2025
Centre for Journalology, Ottawa Methods Centre, Ottawa Hospital Research Institute, Ottawa, ON, Canada.
Background: Artificial intelligence chatbots (AICs) are designed to mimic human conversations through text or speech, offering both opportunities and challenges in scholarly publishing. While journal policies of AICs are becoming more defined, there is still a limited understanding of how Editors in chief (EiCs) of biomedical journals' view these tools. This survey examined EiCs' attitudes and perceptions, highlighting positive aspects, such as language and grammar support, and concerns regarding setup time, training requirements, and ethical considerations towards the use of AICs in the scholarly publishing process.
View Article and Find Full Text PDFJ Voice
September 2025
School of Music, University of Minnesota, Minneapolis, MN 55455. Electronic address:
Introduction: Due to its tonal and syllabic structures, Chinese speakers may encounter unique difficulties when learning native Western operatic techniques. These challenges are particularly evident in balancing pitch control, subglottic pressure, and vowel production. The present study examines how native language influences vocal performance, using the Italian art song Caro mio ben as a test piece for singers from different language backgrounds.
View Article and Find Full Text PDFJ Ethnopharmacol
September 2025
Key Laboratory of Basic Pharmacology of Ministry of Education and Joint International Research Laboratory of Ethnomedicine of Ministry of Education, Zunyi Medical University, Zunyi, Guizhou Province, China. Electronic address:
Ethnopharmacological Relevance: Gastrodia elata, also known as Chijian, belongs to the Orchidaceae family of plants. The "Compendium of Materia Medica" records that Gastrodia elata treats "confused speech, excessive fear, and loss of willpower". Gastrodin (GAS) is the main bioactive component of Gastrodia elata.
View Article and Find Full Text PDFBrain Res
September 2025
Institute of Cognitive Neuroscience and Psychology, HUN-REN Research Centre for Natural Sciences, Hungary.
Identifying early predictors of language development is essential for understanding how infants acquire vocabulary during the first years of life. While previous studies have established the importance of infant-directed speech (IDS) and neural speech processing, this longitudinal study introduces a novel approach by combining EEG-based functional connectivity analysis and machine learning to assess the joint contribution of maternal and infant neural factors to language outcomes. Data were collected at birth and nine months, including maternal personality and speech characteristics, alongside infant EEG responses during speech processing.
View Article and Find Full Text PDFCell Rep
September 2025
Department of Neurosurgery, Mental Health and Neuroscience Research Institute, Maastricht, the Netherlands. Electronic address:
Speech brain-computer interfaces (BCIs) offer a solution for those affected by speech impairments by decoding brain activity into speech. Current neuroprosthetics focus on the motor cortex, which might not be suitable for all patient populations. We investigate potential alternative targets for a speech BCI across a brain-wide distribution.
View Article and Find Full Text PDF