Fuzzy clustering of gradient-echo functional MRI in the human visual cortex. Part I: reproducibility.

J Magn Reson Imaging

Arbeitsgruppe NMR, Institut fuer Medizinische Physik and Klinische MR-Einrichtung, University of Vienna, Austria.

Published: February 1998

Reproducibility of human functional MRI (fMRI) studies is essential for clinical and neuroresearch applications of this new human brain mapping method. Based on a recently presented study on reproducibility of gradient-echo fMRI in the human visual cortex (Moser et al. Magn Reson Imaging 1996; 14:567-579), comparing the performance of three different threshold strategies for correlation analysis, we demonstrate that (a) fuzzy clustering is a robust, model-independent method to extract functional information in time and space; (b) intertrial reproducibility of cortical activation is significantly improved by the capability of fuzzy clustering to separate signal contributions from larger vessels, running perpendicular to the slice orientation, from activation apparently close to the primary visual cortex; and (c) for repeated single subject studies, SDs of <20% for signal enhancement in approximately 80% of the studies and SDs of <30% for activated area size in approximately 65% of the studies are obtained. This, however, depends also on signal-to-noise ratio, (motion) artifacts, and subject cooperation.

Download full-text PDF

Source
http://dx.doi.org/10.1002/jmri.1880070623DOI Listing

Publication Analysis

Top Keywords

fuzzy clustering
12
visual cortex
12
functional mri
8
human visual
8
clustering gradient-echo
4
gradient-echo functional
4
human
4
mri human
4
reproducibility
4
cortex reproducibility
4

Similar Publications

This study develops an innovative method for analyzing and clustering tonal trends in Chinese Yue Opera to identify different vocal styles accurately. Linear interpolation is applied to process the time series data of vocal melodies, addressing inconsistent feature dimensions. The second-order difference method extracts tonal trend features.

View Article and Find Full Text PDF

Alzheimer's disease (AD) is a complex, progressive, and irreversible neurodegenerative disorder marked by cognitive decline and memory loss. Early diagnosis is the most effective strategy to slow the disease's progression. Mild Cognitive Impairment (MCI) is frequently viewed as a crucial stage before the onset of AD, making it the ideal period for therapeutic intervention.

View Article and Find Full Text PDF

Context: The timing of a woman's final menstrual period (FMP) in relation to her age is considered a valuable indicator of overall health, being associated with cardiovascular, bone health, reproductive, and general mortality outcomes.

Objective: This work aimed to evaluate the relationship between hormones and the "time to FMP" when daily hormone trajectories are characterized by their 1) entropy, and 2) deviation from premenopausal/stable cycle patterns (representing a textbook "gold standard"; GS).

Methods: As part of the Study of Women's Health Across the Nation, urinary luteinizing hormone (LH), follicle-stimulating hormone (FSH), estrogen conjugates (E1C), and pregnanediol glucuronide (PDG) were measured daily from a multiracial sample of 549 mid-life women for the duration of one menstrual cycle.

View Article and Find Full Text PDF

An autonomous vehicles' test case extraction method: Example of vehicle-to-pedestrian scenarios.

Heliyon

January 2025

Key Lab of Forensic Science, Ministry of Justice, China (Academy of Forensic Science), Shanghai, 200063, China.

Testing autonomous vehicles (AVs) in hazardous scenarios is a crucial technical approach to ensure their safety. A key aspect of this process is the generation of hazard scenarios. In general, such scenarios are generated through cluster analysis of traffic accident data.

View Article and Find Full Text PDF

Fuzzy bifocal disambiguation for partial multi-label learning.

Neural Netw

January 2025

College of Computer Science, Guangdong Polytechnic Normal University, Guangzhou, 510665, China. Electronic address:

In partial multi-label learning (PML), each instance is associated with multiple candidate labels, but only a subset is the ground-truth label. Due to the ambiguous label information, PML is more challenging than traditional multi-label learning. Conventional PML mainly focuses on learning a desired feature space or label space for disambiguation, ignoring the tight correlation between two spaces.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!