Multimodal Data
Multimodal Data - Related Videos
06:48
A Multimodal Wide-Field Fourier-Transform Raman Microscope
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2025
A wide-field Fourier-transform microscope, based on a compact and ultra-stable birefringent interferometer, allows the parallel acquisition of spectra for all pixels of a 2D detector. The time-domain approach enables the disentanglement of photoluminescence and Raman signals, and allows rapid Raman mapping (~5 ms/pixel) with ~1-µm spatial and 23-cm-1 spectral resolution.
12:38
4D Multimodality Imaging of Citrobacter rodentium Infections in Mice
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Cited by 19 •
2013
Multi-modality imaging is a valuable approach for studying bacterial colonization in small animal models. This protocol outlines infection of mice with bioluminescent Citrobacter rodentium and the longitudinal monitoring of bacterial colonization using composite 3D diffuse light imaging tomography with μCT imaging to create a 4D movie of C. rodentium infection.
19:32
Preterm EEG: A Multimodal Neurophysiological Protocol
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Cited by 39 •
2012
This video explains the background theory of the neonatal EEG activity and the sensory responses, followed by a live demonstration of their recording in neonatal intensive care unit.
11:28
Biofunctionalized Prussian Blue Nanoparticles for Multimodal Molecular Imaging Applications
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Cited by 17 •
2015
This protocol describes the synthesis of biofunctionalized Prussian blue nanoparticles and their use as multimodal, molecular imaging agents. The nanoparticles have a core-shell design where gadolinium or manganese ions within the nanoparticle core generate MRI contrast. The biofunctional shell contains fluorophores for fluorescence imaging and targeting ligands for molecular targeting.
06:51
Multimodal Behavioral Phenotyping of Stress-Induced Depression-Like States in Drosophila melanogaster
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2026
This protocol provides a framework for assessing stress-induced behavioral changes in Drosophila melanogaster. Combining complementary assays enables the quantification of activity, exploration, and decision-making for individual flies. The approach is flexible and can be adapted to a wide range of studies investigating stress biology, metabolism, and neurobehavioral function.
FAQs
Analysis begins by preprocessing each modality and aligning measurements that refer to the same phenomenon. This step makes numerical, image, text, audio, or video representations comparable enough for joint analysis while preserving modality-specific structure. Without careful alignment, apparent associations may reflect mismatched observations rather than meaningful relationships, weakening prediction, classification, and inference.
Different scales, structures, and patterns of missingness influence how evidence should be combined. A statistical model must account for these differences rather than treating every measurement as interchangeable. Doing so helps distinguish a genuine shared pattern from an artifact of representation or incomplete observations, improving the credibility of multimodal conclusions.
These methods provide different ways to identify relationships across modalities. Feature extraction focuses analysis on informative representations, dimensionality reduction summarizes complex information, and correlation analysis examines statistical associations between measurements. Together, they can reveal shared patterns while reducing the difficulty of comparing data with different structures and measurement characteristics.
A typical workflow starts by preprocessing the separate data sources and aligning observations that describe the same phenomenon. Researchers then select methods such as feature extraction, dimensionality reduction, correlation analysis, or data fusion according to the analytical goal. The resulting patterns can support prediction, classification, or inference, provided differences among modalities remain explicit.
They are particularly useful when one measurement type cannot capture the phenomenon adequately. Biomedical studies, social science research, and engineering can combine complementary evidence to support richer prediction, classification, and inference. The main benefit is broader characterization of the same subject or process, rather than relying on the limitations of a single data source.
Statistical modeling can evaluate how each modality contributes to a joint result while quantifying uncertainty around the analysis. This distinction matters because additional data do not automatically provide useful evidence. Examining contribution and uncertainty helps researchers judge whether observed improvements in prediction, classification, or inference are supported by the data.