Spatiotemporal Learning of Brain Dynamics from fMRI Using Frequency-Specific Multi-Band Attention for Cognitive and Psychiatric Applications

Understanding how the brain's complex nonlinear dynamics give rise to adaptive cognition and behavior is a central challenge in neuroscience. These dynamics exhibit scale-free and multifractal properties, influencing the reconfiguration of neural networks. However, conventional neuroimaging models are constrained by linear and stationary assumptions, limiting their ability to capture these processes. Transformer-based architectures, known for capturing long-range dependencies, align well with the brain's hierarchical and temporal organization. We introduce Multi-Band Brain Net (MBBN), a transformer-based framework that models frequency-specific spatiotemporal brain dynamics from fMRI by integrating scale-free network principles with frequency-resolved multi-band self-attention. Trained on three large-scale neuroimaging cohorts (UK Biobank, ABCD, ABIDE) totaling 45,951 individuals, MBBN reveals previously undetectable frequency-dependent network interactions, shedding light on connectivity disruptions in psychiatric conditions (ADHD, ASD, depression). This validation shows robust generalizability and highlights core neural principles conserved across populations. MBBN achieves up to 30.59% higher predictive accuracy than state-of-the-art methods, demonstrating the advantage of frequency-informed spatiotemporal modeling in capturing latent neural computations. MBBN's interpretability uncovers novel frequency-specific biomarkers for neurodevelopmental disorders, providing insights into the hierarchical organization of brain function. By offering an interpretable framework for spatiotemporal learning, MBBN provides insights into how neural computations underpin cognitive function and psychiatric vulnerability, with implications for brain decoding, cognitive neuroscience, and precision psychiatry.

Macro2Micro: Cross-modal Magnetic Resonance Imaging Synthesis Leveraging Multi-scale Brain Structures

Spanning multiple scales-from macroscopic anatomy down to intricate microscopic architecture-the human brain exemplifies a complex system that demands integrated approaches to fully understand its complexity. Yet, mapping nonlinear relationships between these scales remains challenging due to technical limitations and the high cost of multimodal Magnetic Resonance Imaging (MRI) acquisition. Here, we introduce Macro2Micro, a deep learning framework that predicts brain microstructure from macrostructure using a Generative Adversarial Network (GAN). Grounded in the scale-free, self-similar nature of brain organization-where microscale information can be inferred from macroscale patterns-Macro2Micro explicitly encodes multiscale brain representations into distinct processing branches. To further enhance image fidelity and suppress artifacts, we propose a simple yet effective auxiliary discriminator and learning objective. Our results show that Macro2Micro faithfully translates T1-weighted MRIs into corresponding Fractional Anisotropy (FA) images, achieving a 6.8% improvement in the Structural Similarity Index Measure (SSIM) compared to previous methods, while preserving the individual neurobiological characteristics.

Quantum Privacy Aggregation of Teacher Ensembles (QPATE) for Privacy-preserving Quantum Machine Learning

The utility of machine learning has rapidly expanded in the last two decades and presents an ethical challenge. Papernot et. al. developed a technique, known as Private Aggregation of Teacher Ensembles (PATE) to enable federated learning in which multiple teacher models are trained on disjoint datasets. This study is the first to apply PATE to an ensemble of quantum neural networks (QNN) to pave a new way of ensuring privacy in quantum machine learning (QML) models.

SwiFT: Swin 4D fMRI Transformer

The modeling of spatiotemporal brain dynamics from high-dimensional data, such as 4D functional MRI, is a formidable task in neuroscience. To address this challenge, we present SwiFT (Swin 4D fMRI Transformer), a Swin Transformer architecture that can learn brain dynamics directly from 4D functional brain MRI data in a memory and computation-efficient manner. SwiFT achieves this by implementing a 4D window multi-head self-attention mechanism and absolute positional embeddings. We evaluate SwiFT using multiple largest-scale human functional brain imaging datasets in tasks such as predicting sex, age, and cognitive intelligence. Our experimental outcomes reveal that SwiFT consistently outperforms recent state-of-the-art models. To the best of our knowledge, SwiFT is the first Swin Transformer architecture that can process dimensional spatiotemporal brain functional data in an end-to-end fashion. Furthermore, due to the end-to-end learning capability, we also show that contrastive loss-based self-supervised pre-training of SwiFT is also feasible for achieving improved performance on a downstream task. We believe that our work holds substantial potential in facilitating scalable learning of functional brain imaging in neuroscience research by reducing the hurdles associated with applying Transformer models to high-dimensional fMRI.