Surface-based Analysis
Surface-based Analysis - Related Videos
14:11
Quantification of Hydrogen Concentrations in Surface and Interface Layers and Bulk Materials through Depth Profiling with Nuclear Reaction Analysis
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Cited by 21 •
2016
We illustrate the application of 1H(15N,αγ)12C resonant nuclear reaction analysis (NRA) to quantitatively evaluate the density of hydrogen atoms on the surface, in the volume, and at an interfacial layer of solid materials. The near-surface hydrogen depth profiling of a Pd(110) single crystal and of SiO2/Si(100) stacks is described.
10:16
Protocol for Microplastics Sampling on the Sea Surface and Sample Analysis
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Cited by 194 •
2016
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
A Stepwise Guide to the Isolation and Analysis of Leaf Surface and Apoplastic RNA Using Arabidopsis Rosettes
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2025
We describe a simple yet highly reproducible method to isolate extracellular RNA from the leaf surface and apoplast of Arabidopsis plants.
10:21
Evanescent Field Based Photoacoustics: Optical Property Evaluation at Surfaces
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Cited by 2 •
2016
Here we present a protocol to estimate material and surface optical properties using the photoacoustic effect combined with total internal reflection. This technique evanescent field-based photoacoustics can be used to create a photoacoustic metrology system to estimate materials' thicknesses, bulk and thin film refractive indices, and explore their optical properties.
06:19
Label-Free Surface-Enhanced Raman Scattering Bioanalysis Based on Au@Carbon Dot Nanoprobes
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Cited by 4 •
2023
In this study, we developed a low-cost surface-enhanced Raman scattering (SERS)-based fingerprint nanoprobe with favorable biocompatibility to show label-free live cell bioimaging and detect two bacterial strains, showing in detail how to get SERS spectra of living cells in a non-destructive method.
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.