Multimodal Data
Multimodal Data - Related Videos
04:47
Multimodal Optical Imaging Platform for Studying Cellular Metabolism
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2025
Utilizing a multimodal platform combining label-free optical imaging modalities, we have developed a protocol for visualizing and quantifying cellular dynamics and metabolism. Through imaging via multiphoton fluorescence, second harmonic generation, and stimulated Raman scattering microscopy, we can generate a holistic overview of the cellular and molecular environment.
07:13
Biomolecular Imaging of Cellular Uptake of Nanoparticles using Multimodal Nonlinear Optical Microscopy
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Cited by 1 •
2022
This article presents the integration of a spectral-focusing module and a dual-output pulse laser, enabling rapid hyperspectral imaging of gold nanoparticles and cancer cells. This work aims to demonstrate the details of multimodal nonlinear optical techniques on a standard laser scanning microscope.
07:13
Multimodal Cross-Device and Marker-Free Co-Registration of Preclinical Imaging Modalities
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Cited by 3 •
2023
The combination of multiple imaging modalities is often necessary to gain a comprehensive understanding of pathophysiology. This approach utilizes phantoms to generate a differential transformation between the coordinate systems of two modalities, which is then applied for co-registration. This method eliminates the need for fiducials in production scans.
08:28
Automated Multimodal Stimulation and Simultaneous Neuronal Recording from Multiple Small Organisms
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Cited by 2 •
2023
We present a method for the flexible chemical and multimodal stimulation and recording of simultaneous neural activity from many Caenorhabditis elegans worms. This method uses microfluidics, open-source hardware and software, and supervised automated data analysis to enable the measurement of neuronal phenomena such as adaptation, temporal inhibition, and stimulus crosstalk.
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.
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.