Surface-based Analysis
Surface-based Analysis - Related Videos
14:11
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0 Views •
Cited by 21 •
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10:16
Protocol for Microplastics Sampling on the Sea Surface and Sample Analysis
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The protocol below describes the methodology for: microplastics sampling on the sea surface, separation of microplastic and chemical identification of particles. This protocol is in line with the recommendations for microplastics monitoring published by the MSFD Technical Subgroup on Marine Litter.
09:56
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05:52
Observation and Analysis of Blinking Surface-enhanced Raman Scattering
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Cited by 4 •
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09:42
Acquisition and Semi-Automated Analysis of Respiratory Muscle Surface Electromyography
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2025
Here, we describe a protocol to record and analyze respiratory electromyography (EMG) signals. It includes the anatomic references for placing the EMG electrodes over several respiratory muscles, removing electrocardiographic noise from the EMG signals, and acquiring the EMG root mean square (RMS) and onset timing of activity.
FAQs
Surface-based measurements follow cortical folds rather than treating the cortex as a uniform volume. This representation preserves spatial relationships along gyri and sulci, allowing researchers to localize anatomical differences to particular parts of the cortical sheet. It is especially useful when a change is thin but spatially extended, or when neighboring regions have different folding patterns.
Cortical thickness, surface area, and curvature should not be treated as interchangeable outputs. Thickness describes the distance across the cortical sheet, whereas surface area captures its extent and curvature characterizes how that sheet bends. Sulcal patterns add information about the arrangement of folds. Examining these measures separately helps distinguish different forms of anatomical variation.
Aligning cortical surfaces across individuals creates anatomical correspondence despite differences in overall brain shape and folding. Computational alignment places comparable cortical locations into a common framework, so researchers can compare thickness, area, curvature, or sulcal patterns across participants. This step is central to group studies because it converts measurements from separate reconstructed surfaces into analyzable regional patterns.
A surface-based analysis workflow begins with structural MRI, followed by computational identification of the relevant gray matter boundaries. Those boundaries are reconstructed as a three-dimensional mesh, which supplies the geometric representation needed for measurements. The resulting surface can then be used to calculate thickness, area, curvature, and sulcal features, followed by alignment of corresponding regions across individuals.
It is particularly informative when the research question concerns where along the cortical sheet anatomy changes, rather than only how much tissue a broader volume contains. Because the approach preserves cortical geometry, it complements volume-based analysis by exposing localized anatomical patterns. Using both perspectives can provide a more complete account of structural differences in neuroscience studies.
In neuroscience, these measurements can be examined across development, aging, neurological disease, and cognitive function. Thickness, area, curvature, and sulcal features provide distinct structural readouts that may serve as anatomically precise biomarkers. Comparing these measures across groups or individuals helps researchers investigate whether cortical anatomy varies with a condition, life stage, or cognitive characteristic.