Functional MRI Time Series Generation via Wavelet-Based Image Transform and Spectral Flow Matching for Brain Disorder Identification
arXiv:2605.30387v1 Announce Type: new Abstract: Functional Magnetic Resonance Imaging (fMRI) provides non-invasive access to dynamic brain activity by measuring blood oxygen level-dependent (BOLD) signals over time. However, the resource-intensive nature of fMRI acquisition limits the availability of high-fidelity samples required for data-driven brain analysis models. While modern generative models can synthesize fMRI data, they often remain challenging in replicating their inherent non-stationarity, intricate spatiotemporal dynamics, and physiological variations of raw BOLD signals. To addre
The increasing availability of advanced generative AI models coincides with growing demand for high-fidelity synthetic data to overcome limitations in real fMRI acquisition.
This breakthrough could significantly accelerate neuroscience research and clinical applications by providing scalable and diverse fMRI data for training sophisticated brain analysis models.
The ability to accurately synthesize complex fMRI data mitigates resource constraints in medical imaging, potentially leading to faster development of diagnostic tools for neurological disorders.
- · Neuroscience researchers
- · AI developers in medical imaging
- · Biotech and pharmaceutical companies
- · Patients with brain disorders
- · Traditional fMRI data acquisition services (potentially less critical over time)
Improved diagnosis and understanding of brain disorders through enhanced data availability for AI models.
Accelerated development of personalized treatment plans and drug discovery for neurological conditions.
Ethical and regulatory discussions around synthetic medical data usage and its impact on data privacy and research integrity.
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