Decoding Neural Representations from Cross-Subject fMRI Data

Core Concepts

The author proposes a method, STTM, that leverages cross-subject fMRI data to learn transferable neural representations shared across human brains. By pre-training on the NSD dataset and performing transfer learning on the GOD dataset, the approach achieves comparable or superior decoding performance across various tasks.

Abstract

The content discusses a novel method, STTM, that utilizes cross-subject fMRI data for decoding neural representations. The approach involves pre-training on the NSD dataset and transfer learning on the GOD dataset to achieve promising results in image retrieval, text retrieval, brain imaging retrieval, and image reconstruction tasks. The integration of high-level and low-level perception guidance enhances overall performance by mimicking bottom-up and top-down processes observed in the human brain. Key points: Proposal of STTM method for decoding neural representations from fMRI data. Pre-training on NSD dataset and transfer learning on GOD dataset. Achieving superior performance in various tasks like image retrieval and reconstruction. Integration of high-level and low-level perception guidance for enhanced results.

Stats

Our model integrates a high-level perception decoding pipeline and a pixel-wise reconstruction pipeline guided by high-level perceptions. Empirical experiments demonstrate robust neural representation learning across subjects for both pipelines. Compared to previous state-of-the-art methods, notably pre-training-based methods (Mind-Vis and fMRI-PTE), our approach achieves comparable or superior results across diverse tasks.

Quotes

"Our model integrates a high-level perception decoding pipeline and a pixel-wise reconstruction pipeline guided by high-level perceptions." "Empirical experiments demonstrate robust neural representation learning across subjects for both pipelines." "Compared to previous state-of-the-art methods, notably pre-training-based methods (Mind-Vis and fMRI-PTE), our approach achieves comparable or superior results across diverse tasks."

Key Insights Distilled From

See Through Their Minds

by Yulong Liu,Y... at arxiv.org 03-12-2024

https://arxiv.org/pdf/2403.06361.pdf

Deeper Inquiries

How does training with cross-subject fMRI data enhance model generalizability?

Training with cross-subject fMRI data enhances model generalizability by exposing the shared decoding model to a more diverse data distribution. When models are trained on data from multiple subjects, they are better able to capture essential features present across different individuals. This exposure helps in extracting more robust and transferable fMRI representations that can be applied across various brains. By incorporating information from multiple subjects, the model learns to generalize well beyond individual idiosyncrasies and specific response patterns, leading to improved performance on diverse tasks.

What are the implications of combining global visual-linguistic contrastive learning in multi-modal brain decoding?

Combining global visual-linguistic contrastive learning in multi-modal brain decoding has several implications for enhancing decoding performance. Firstly, this approach allows for leveraging both visual and textual modalities simultaneously, enabling a richer understanding of neural representations related to stimuli perception. By aligning fMRI data with CLIP embeddings from both visual and textual encoders through contrastive learning, the model can bridge brain activity with semantic information effectively. Additionally, integrating global visual-linguistic contrastive learning improves text retrieval performance significantly while also positively impacting image reconstruction tasks. This approach not only enhances semantic recognition but also facilitates a deeper understanding of multi-sensory processing within the human brain. The combination of these modalities provides a foundation for advancing generic multi-modal brain decoding research by capturing complex relationships between different types of information.

How does integrating high-level and low-level perception guidance improve overall reconstruction performance?

Integrating high-level and low-level perception guidance improves overall reconstruction performance by mimicking the interaction between bottom-up and top-down processes observed in the human brain. The high-level pipeline focuses on capturing semantic perceptions underlying fMRI data by aligning it with visual and textual modalities using subject-specific adapters and shared decoding modules. On the other hand, the low-level pipeline is designed for pixel-wise reconstruction guided by high-level perceptions processed through a residual MLP backbone before being up-sampled using latent space transformations like VAEs or diffusion models. By combining these two pipelines, where high-level features guide pixel-wise reconstructions based on low-level details such as color distribution or spatial position, an img2img setting simulates natural cognitive processes effectively improving final reconstructions' quality significantly compared to using each pipeline independently.