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Here we took several stimulus configurations that have the same numerosity progression but vary considerably in their non-numerical visual features. We collected responses to these stimuli using ultra-high-field (7T) fMRI in a posterior parietal area that responds to changes in these stimuli. We first quantify the relationships between numerosity and several non-numerical visual features in each stimulus configuration. We then use population receptive field (pRF) modeling to quantify how well responses to each of these visual features predicts the observed responses to each stimulus configuration, and observed responses to all stimulus configurations together. We compare the predictive accuracy of responses to numerosity and to non-numerical visual features in explaining the observed responses. This provides the details of the analysis outcomes summarized in an accompanying article (10.1016/j.neuroimage.2017.02.012, NIMG-16-1350).
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http://dx.doi.org/10.1016/j.dib.2017.11.022 | DOI Listing |
Front Neurosci
December 2024
Graduate Program in Cognitive Science, Yonsei University, Seoul, Republic of Korea.
Introduction: Functional magnetic resonance imaging (fMRI) data is highly complex and high-dimensional, capturing signals from regions of interest (ROIs) with intricate correlations. Analyzing such data is particularly challenging, especially in resting-state fMRI, where patterns are less identifiable without task-specific contexts. Nonetheless, interconnections among ROIs provide essential insights into brain activity and exhibit unique characteristics across groups.
View Article and Find Full Text PDFFront Pharmacol
December 2024
Beijing Tongren Eye Center, Beijing Ophthalmology and Visual Science Key Lab, Beijing Tongren Hospital, Capital Medical University, Beijing, China.
Background: Corticosteroids are extensively used in ophthalmology, particularly for treating various inflammatory conditions. Despite their effectiveness, prolonged or high-dose corticosteroid use is associated with significant adverse drug reactions (ADRs), such as increased intraocular pressure, cataract formation, and secondary infections. However, there is currently no systematic study comparing the side effects of ophthalmic corticosteroids.
View Article and Find Full Text PDFBrain Commun
December 2024
Dipartimento Universitario di Neuroscienze, Università Cattolica del Sacro Cuore, 00168 Roma, Italy.
Familial hemiplegic migraine type 2 results from pathogenic variants in the gene, which encodes for a catalytic subunit of sodium/potassium ATPase. This extremely rare autosomal dominant disorder manifests with a spectrum of symptoms, most commonly pure hemiplegic phenotype, epilepsy, and/or intellectual disability. In this study, we detail the clinical features and genetic analysis of nine patients from a large family spanning four generations, with all carrying a previously unreported likely pathogenic variant, p.
View Article and Find Full Text PDFFront Psychiatry
December 2024
Rehabilitation Medical Center, Kangci Hospital, Jiaxing, Zhejiang, China.
Objective: Cognitive impairment is notably prevalent among schizophrenic individuals and is acknowledged as one of the core features of the disorder. Despite the proliferation of literature on cognitive rehabilitation treatments for schizophrenia in recent years, there remains a dearth of systematic reviews and selections of research in this area. From a bibliometric perspective, this study aims to analyze and discuss the current state, developmental trends, and potential research hotspots of cognitive rehabilitation in schizophrenia over the past two decades.
View Article and Find Full Text PDFFront Med (Lausanne)
December 2024
Department of Pathology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi, China.
Introduction: Thymoma classification is challenging due to its diverse morphology. Accurate classification is crucial for diagnosis, but current methods often struggle with complex tumor subtypes. This study presents an AI-assisted diagnostic model that combines weakly supervised learning with a divide-and-conquer multi-instance learning (MIL) approach to improve classification accuracy and interpretability.
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