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Predicting Human Brain States with Transformer (2412.19814v1)

Published 11 Dec 2024 in q-bio.NC, cs.AI, and cs.LG

Abstract: The human brain is a complex and highly dynamic system, and our current knowledge of its functional mechanism is still very limited. Fortunately, with functional magnetic resonance imaging (fMRI), we can observe blood oxygen level-dependent (BOLD) changes, reflecting neural activity, to infer brain states and dynamics. In this paper, we ask the question of whether the brain states rep-resented by the regional brain fMRI can be predicted. Due to the success of self-attention and the transformer architecture in sequential auto-regression problems (e.g., LLMling or music generation), we explore the possi-bility of the use of transformers to predict human brain resting states based on the large-scale high-quality fMRI data from the human connectome project (HCP). Current results have shown that our model can accurately predict the brain states up to 5.04s with the previous 21.6s. Furthermore, even though the prediction error accumulates for the prediction of a longer time period, the gen-erated fMRI brain states reflect the architecture of functional connectome. These promising initial results demonstrate the possibility of developing gen-erative models for fMRI data using self-attention that learns the functional or-ganization of the human brain. Our code is available at: https://github.com/syf0122/brain_state_pred.

Summary

  • The paper demonstrates that transformer models effectively predict dynamic brain states using high-resolution rs-fMRI data from 1003 subjects.
  • The study employs an autoregressive time series transformer with an encoder-decoder framework optimized via cross-validation and MSE metrics.
  • Empirical results reveal accurate short-term forecasts and reliable functional connectivity patterns, paving the way for clinical fMRI and BCI applications.

Predicting Human Brain States with Transformer

The paper explores the application of transformer architecture in predicting human brain states by analyzing functional magnetic resonance imaging (fMRI) data. Utilized are the capabilities of transformer models, renowned for their success in sequential data tasks, to forecast the dynamic nature of brain states observed in resting-state fMRI (rs-fMRI) scans. This approach stands distinct in its ability to model brain activity over time, leveraging large-scale datasets from the Human Connectome Project (HCP).

Methodological Approach

Consistent with the autoregressive nature of brain activity prediction, the paper adopts a time series transformer architecture with adaptations tailored for brain state prediction. The dataset consists of high-resolution rs-fMRI data acquired from 1003 young adults, allowing for robust model training and validation. Data preprocessing involved spatial smoothing and filtering to enhance signal integrity before input into the transformer model, refined further to predict states of 379 distinct gray matter regions.

The model architecture incorporates both transformer encoders and decoders, optimized through cross-validation to determine productive window sizes and epoch numbers for model training. In pursuit of accurately detecting temporal dependencies in the rs-fMRI data, the encoder-decoder framework captures and predicts subsequent brain states, an endeavor quantified via mean squared error (MSE) metrics.

Empirical Findings

Evaluation of the model demonstrated the transformer's proficiency in predicting brain states, with favorable results up to a 5.04-second forecasting window utilizing only 21.6 seconds of preceding fMRI data. This predictive capability is noteworthy, given the intricacy and temporal dynamism inherent to human brain states. When tested against randomized input sequences, the model exhibited significantly heightened prediction errors, reinforcing the model's ability to discern and exploit temporal sequential information.

Further analysis involved synthetic time series prediction, where the model exhibited an incremental rise in prediction error due to error propagation akin to a Markovian process. Despite this, initial sequential predictions remained accurate, affirming the model's efficacy for short-term forecasting.

Moreover, the paper scrutinized the functional connectivity (FC) derived from the predicted brain states. The transformer was adept at capturing group-level connectivity patterns, with low mean absolute differences and strong spatial correlations between predicted and true FC matrices.

Implications and Future Directions

The implications of this research extend to the reduction of scanning time in clinical fMRI applications, potentially alleviating the challenges faced by patients incapable of prolonged scanning sessions. The research paves the way for further exploration into the integration of this predictive modeling into brain-computer interfaces (BCI) and personalized medicine. Furthermore, understanding these predictive mechanisms could illuminate the functional organization of the brain at both individual and group levels.

Future research aims to refine prediction accuracy and counteract error accumulation via architectural adjustments to the transformer model. Additionally, advancements in personalized modeling, possibly through transfer learning, and enhancing the interpretability of predictions are prospective trajectories to decipher the underpinnings of brain function.

In summary, the paper is a significant contribution to leveraging self-attention and transformer networks for brain state prediction, harnessing the intricacies of fMRI data. It envisions a new frontier in neuroscience and clinical applications of AI, facilitating deeper insights into human brain dynamics.

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