Vince D. Calhoun

Yuxiang Wei, Yanteng Zhang, Xi Xiao et al.

Recent advances in multimodal large language models (LLMs) have enabled unified reasoning across images, audio, and video, but extending such capability to brain imaging remains largely unexplored. Bridging this gap is essential to link neural activity with semantic cognition and to develop cross-modal brain representations. To this end, we present fMRI-LM, a foundational model that bridges functional MRI (fMRI) and language through a three-stage framework. In Stage 1, we learn a neural tokenizer that maps fMRI into discrete tokens embedded in a language-consistent space. In Stage 2, a pretrained LLM is adapted to jointly model fMRI tokens and text, treating brain activity as a sequence that can be temporally predicted and linguistically described. To overcome the lack of natural fMRI-text pairs, we construct a large descriptive corpus that translates diverse imaging-based features into structured textual descriptors, capturing the low-level organization of fMRI signals. In Stage 3, we perform multi-task, multi-paradigm instruction tuning to endow fMRI-LM with high-level semantic understanding, supporting diverse downstream applications. Across various benchmarks, fMRI-LM achieves strong zero-shot and few-shot performance, and adapts efficiently with parameter-efficient tuning (LoRA), establishing a scalable pathway toward a language-aligned, universal model for structural and semantic understanding of fMRI.

Yi-Ting Hung, Li-Hsiang Lin, Vince D. Calhoun

Recent advances in deep learning highlight the need for personalized models that can learn from small or moderate samples, handle high dimensional features, and remain interpretable. To address this challenge, we propose the Sparse Deep Additive Model with Interactions (SDAMI), a framework that combines sparsity driven feature selection with deep subnetworks for flexible function approximation. Unlike conventional deep learning models, which often function as black boxes, SDAMI explicitly disentangles main effects and interaction effects to enhance interpretability. At the same time, its deep additive structure achieves higher predictive accuracy than classical additive models. Central to SDAMI is the concept of an Effect Footprint, which assumes that higher order interactions project marginally onto main effects. Guided by this principle, SDAMI adopts a two stage strategy: first, identify strong main effects that implicitly carry information about important interactions. second, exploit this information through structured regularization such as group lasso to distinguish genuine main effects from interaction effects. For each selected main effect, SDAMI constructs a dedicated subnetwork, enabling nonlinear function approximation while preserving interpretability and providing a structured foundation for modeling interactions. Extensive simulations with comparisons confirm SDAMI$'$s ability to recover effect structures across diverse scenarios, while applications in reliability analysis, neuroscience, and medical diagnostics further demonstrate its versatility in addressing real-world high-dimensional modeling challenges.

Badhan Mazumder, Lei Wu, Vince D. Calhoun et al.

Gaining insights into the structural and functional mechanisms of the brain has been a longstanding focus in neuroscience research, particularly in the context of understanding and treating neuropsychiatric disorders such as Schizophrenia (SZ). Nevertheless, most of the traditional multimodal deep learning approaches fail to fully leverage the complementary characteristics of structural and functional connectomics data to enhance diagnostic performance. To address this issue, we proposed ConneX, a multimodal fusion method that integrates cross-attention mechanism and multilayer perceptron (MLP)-Mixer for refined feature fusion. Modality-specific backbone graph neural networks (GNNs) were firstly employed to obtain feature representation for each modality. A unified cross-modal attention network was then introduced to fuse these embeddings by capturing intra- and inter-modal interactions, while MLP-Mixer layers refined global and local features, leveraging higher-order dependencies for end-to-end classification with a multi-head joint loss. Extensive evaluations demonstrated improved performance on two distinct clinical datasets, highlighting the robustness of our proposed framework.