The tactile digit representations in the primary somatosensory cortex have so far been mapped for either the left or the right hand. This study localized all ten digit representations in right-handed subjects and compared them within and across the left and right hands to assess potential differences in the functional organization of the digit map between hands and in the structural organization between hemispheres. Functional magnetic resonance imaging of tactile stimulation of each fingertip in BA 3b confirmed the expected lateral-anterior-inferior to medial-posterior-superior succession from thumb to little-finger representation, located in the post-central gyrus opposite to the motor hand knob. While the more functionally related measures, such as the extent and strength of activation as well as the Euclidean distance between neighboring digit representations, showed significant differences between the digits, no side difference was detected: the layout of the functional digit-representation map did not consistently differ between the left, non-dominant, and the right, dominant hand. Comparing the absolute spatial coordinates also revealed a significant difference for the digits, but not between the left and right hand digits. Estimating the individual subject's digit coordinates of one hand by within-subject mirroring of the other-hand digit coordinates across hemispheres yielded a larger estimation error distance than using averaged across-subjects coordinates from within the same hemisphere. However, both methods should only be used with care for single-subject clinical evaluation, as an average estimation error of around 9 mm was observed, being slightly higher than the average distance between neighboring digits.
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http://dx.doi.org/10.3389/fnhum.2018.00492 | DOI Listing |
Front Neurosci
January 2025
Department of Mathematics, University of Antwerp-Interuniversity Microelectronics Centre (imec), Antwerp, Belgium.
Introduction: The study of attention has been pivotal in advancing our comprehension of cognition. The goal of this study is to investigate which EEG data representations or features are most closely linked to attention, and to what extent they can handle the cross-subject variability.
Methods: We explore the features obtained from the univariate time series from a single EEG channel, such as time domain features and recurrence plots, as well as representations obtained directly from the multivariate time series, such as global field power or functional brain networks.
Digit Health
January 2025
Department of Interventional Radiology, The First Affiliated Hospital of Soochow University, Suzhou, China.
Introduction: This study aims to critically assess the appropriateness and limitations of two prominent large language models (LLMs), enhanced representation through knowledge integration (ERNIE Bot) and chat generative pre-trained transformer (ChatGPT), in answering questions about liver cancer interventional radiology. Through a comparative analysis, the performance of these models will be evaluated based on their responses to questions about transarterial chemoembolization and hepatic arterial infusion chemotherapy in both English and Chinese contexts.
Methods: A total of 38 questions were developed to cover a range of topics related to transarterial chemoembolization (TACE) and hepatic arterial infusion chemotherapy (HAIC), including foundational knowledge, patient education, and treatment and care.
Eur Heart J Digit Health
January 2025
Department of Cardiovascular Surgery of Zhongshan Hospital, Fudan University, Shanghai 200032, China.
Aims: Accurate heart function estimation is vital for detecting and monitoring cardiovascular diseases. While two-dimensional echocardiography (2DE) is widely accessible and used, it requires specialized training, is prone to inter-observer variability, and lacks comprehensive three-dimensional (3D) information. We introduce CardiacField, a computational echocardiography system using a 2DE probe for precise, automated left ventricular (LV) and right ventricular (RV) ejection fraction (EF) estimations, which is especially easy to use for non-cardiovascular healthcare practitioners.
View Article and Find Full Text PDFSci Rep
January 2025
Department of General Psychology and Padova Neuroscience Center, University of Padova, Padova, Italy.
Hierarchical generative models can produce data samples based on the statistical structure of their training distribution. This capability can be linked to current theories in computational neuroscience, which propose that spontaneous brain activity at rest is the manifestation of top-down dynamics of generative models detached from action-perception cycles. A popular class of hierarchical generative models is that of Deep Belief Networks (DBNs), which are energy-based deep learning architectures that can learn multiple levels of representations in a completely unsupervised way exploiting Hebbian-like learning mechanisms.
View Article and Find Full Text PDFLancet Digit Health
January 2025
Department of Psychiatry, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA. Electronic address:
Large language models (LLMs) offer promising applications in mental health care to address gaps in treatment and research. By leveraging clinical notes and transcripts as data, LLMs could improve diagnostics, monitoring, prevention, and treatment of mental health conditions. However, several challenges persist, including technical costs, literacy gaps, risk of biases, and inequalities in data representation.
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