Ms. Shiyu Wang Profile

Shiyu Wang

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Proceedings Article | 2 April 2024 Poster + Paper

Leveraging sinusoidal representation networks to predict fMRI signals from EEG

Yamin Li, Ange Lou, Ziyuan Xu, Shiyu Wang, Catie Chang

Proceedings Volume 12926, 129263A (2024) https://doi.org/10.1117/12.3007677

KEYWORDS: Functional magnetic resonance imaging, Electroencephalography, Data modeling, Brain, Machine learning, Deep learning, Neuroimaging, Performance modeling, Neural networks

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In modern neuroscience, functional magnetic resonance imaging (fMRI) has been a crucial and irreplaceable tool that provides a non-invasive window into the dynamics of whole-brain activity. Nevertheless, fMRI is limited by hemodynamic blurring as well as high cost, immobility, and incompatibility with metal implants. Electroencephalography (EEG) is complementary to fMRI and can directly record the cortical electrical activity at high temporal resolution, but has more limited spatial resolution and is unable to recover information about deep subcortical brain structures. The ability to obtain fMRI information from EEG would enable cost-effective, naturalistic imaging across a wider set of brain regions. Further, beyond augmenting the capabilities of EEG, cross-modality models would facilitate the interpretation of fMRI signals. However, as both EEG and fMRI are high-dimensional and prone to noise and artifacts, it is currently challenging to model fMRI from EEG. Indeed, although correlations between these two modalities have been widely investigated, few studies have successfully used EEG to directly reconstruct fMRI time series. To address this challenge, we propose a novel architecture that can predict fMRI signals directly from multi-channel EEG without explicit feature engineering. Our model achieves this by implementing a Sinusoidal Representation Network (SIREN) to learn frequency information in brain dynamics from EEG, which serves as the input to a subsequent encoder-decoder to effectively reconstruct the fMRI signal in a specific brain region. We evaluate our model using a simultaneous EEG-fMRI dataset with 8 subjects and investigate its potential for predicting subcortical fMRI signals. The present results reveal that our model outperforms a recent state-of-the-art model and indicate the potential of leveraging periodic activation functions in deep neural networks to model functional neuroimaging data.

Proceedings Article | 2 April 2024 Poster + Paper

Low-dimensional representation of fMRI disentangles temporal contributions of EEG-determined vigilance from other global effects

Haatef Pourmotabbed, Achyuth Vivek, Caroline Martin, Sarah Goodale, Shiyu Wang, Victoria Morgan, Dario Englot, Catie Chang

Proceedings Volume 12930, 129301I (2024) https://doi.org/10.1117/12.3005182

KEYWORDS: Dimension reduction, Functional magnetic resonance imaging, Electroencephalography, Independent component analysis, Physiology

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Global brain-wide signals in functional magnetic resonance imaging (fMRI) are influenced by temporal variations in vigilance, peripheral physiological processes, head motion, and other potential neuronal and non-neuronal sources. These effects are challenging to disentangle as fluctuations in vigilance and peripheral physiology are difficult to detect with fMRI alone. In this study, we leveraged multimodal neuroimaging data (simultaneous fMRI, EEG, respiratory, and cardiac recordings) to investigate the ability of dimensionality reduction techniques to separate influences of vigilance, physiology, and other global effects in fMRI. Our study included resting-state fMRI from 30 subjects, parcellated into 317 brain regions. Two different methods, temporal independent component analysis (tICA) and a fully connected autoencoder, were used to project the atlas-based data into a lower dimensional latent space. The correlation of each latent component with the EEG alpha/theta power ratio (a marker of vigilance), physiological signals (respiratory volume and heart rate), and the global fMRI signal was computed. LASSO regression was additionally employed to reconstruct the alpha/theta ratio from the latent components. Our results showed that tICA, but not the autoencoder, was able to disentangle a vigilance-related component from other global effects. Both the vigilance and global components exhibited a moderate relationship with physiological activity. Therefore, tICA is useful for isolating vigilance-related influences in fMRI, which may aid in discovering novel clinical biomarkers linked to vigilance dysregulation as well as assist in explaining intersubject variability due to in-scanner state.

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