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Brain Imaging

Brain Imaging

Brain imaging technologies provide critical insights into both the structure and function of the human brain, enabling medical professionals and researchers to diagnose, study, and treat neurological disorders or psychiatric disorders more effectively.
These technologies include computerized axial tomography (CAT or CT scans), positron-emission tomography (PET scans), magnetic resonance imaging (MRI), functional magnetic resonance imaging (fMRI), and Transcranial Magnetic Stimulation (TMS).

Parallel Processing

Parallel Processing

The brain processes sensory information rapidly due to parallel processing, which involves sending data across multiple neural pathways at the same time. This method allows the brain to manage various sensory qualities, such as shapes, colors, movements, and locations, all concurrently. For instance, when observing a forest landscape, the brain simultaneously processes the movement of leaves, the shapes of trees, the depth between them, and the various shades of green. This enables a quick and...

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Related Experiment Video

Updated: Jun 21, 2026

Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example

Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example

Published on: October 24, 2012

Reconstructing shared visual experiences from human brain activity across individuals.

Jinke Li¹, Yuxiao Yang², Yanyan Huang³

¹School of Information Science and Engineering, Lanzhou University, Lanzhou, China.

Medical Image Analysis

|June 19, 2026

Summary

This summary is machine-generated.

MindShow reconstructs visual experiences from brain activity using functional magnetic resonance imaging (fMRI). This new framework enables scalable, shared-subject neural decoding by efficiently adapting to new individuals with limited data.

Keywords:

Brain decoding Neural representation alignment Shared-subject decoding fMRI-based visual reconstruction

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Brain Imaging Investigation of the Neural Correlates of Observing Virtual Social Interactions

Brain Imaging Investigation of the Neural Correlates of Observing Virtual Social Interactions

Published on: July 6, 2011

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Last Updated: Jun 21, 2026

Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example

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Published on: October 24, 2012

Brain Imaging Investigation of the Neural Correlates of Observing Virtual Social Interactions

Brain Imaging Investigation of the Neural Correlates of Observing Virtual Social Interactions

Published on: July 6, 2011

Area of Science:

Neuroscience
Computer Science
Artificial Intelligence

Background:

Reconstructing visual experiences from brain activity is key for advancing brain-computer interfaces and understanding perception.
Current functional magnetic resonance imaging (fMRI)-based image synthesis methods are often person-specific, limiting scalability due to extensive data requirements for new individuals.

Purpose of the Study:

To introduce MindShow, a unified generative framework for shared-subject fMRI-to-image reconstruction using cohort-level training.
To enable data-efficient adaptation to new subjects with limited calibration data.

Main Methods:

Developed a Hierarchically-Conditioned Mixture-of-Experts (HiCo-MoE) encoder to disentangle population-shared and subject-specific neural representations.
Utilized a Gated Perceiver Bottleneck (GPB) to map fMRI features into fixed-size image and text latent tokens, addressing multi-scale representational misalignment.
Introduced a multi-granular optimal transport loss (MOT-Align) for semantic and structural consistency by aligning brain-derived features with a pre-trained vision-language model.
Employed a frozen diffusion model guided by aligned embeddings to synthesize images preserving semantic content and coarse layout.

Main Results:

MindShow demonstrates improved high-level reconstruction metrics compared to existing methods.
The framework maintains competitive structural fidelity in synthesized images.
Achieved scalable shared-subject neural decoding with data-efficient adaptation.

Conclusions:

MindShow offers a significant methodological advancement for scalable neural decoding from fMRI data.
The framework facilitates the reconstruction of visual experiences across subjects more efficiently.
This approach holds promise for broader applications in brain-computer interfaces and cognitive neuroscience research.