Ashish Raj

Nuutti Barron, Heng Rao, Urmi Saha et al.

Mechanistic modeling provides a biophysically grounded framework for studying the spread of pathological tau protein in tauopathies like Alzheimer's disease. Existing approaches typically model tau propagation as a diffusive process on the brain's structural connectome, reproducing macroscopic patterns but neglecting microscale cellular transport and reaction mechanisms. The Network Transport Model (NTM) was introduced to fill this gap, explaining how region-level progression of tau emerges from microscale biophysical processes. However, the NTM faces a common challenge for complex models defined by large systems of partial differential equations: the inability to perform parameter inference and mechanistic discovery due to high computational burden and slow model simulations. To overcome this barrier, we propose Tau-BNO, a Brain Neural Operator surrogate framework for rapidly approximating NTM dynamics that captures both intra-regional reaction kinetics and inter-regional network transport. Tau-BNO combines a function operator that encodes kinetic parameters with a query operator that preserves initial state information, while approximating anisotropic transport through a spectral kernel that retains directionality. Empirical evaluations demonstrate high predictive accuracy ($R^2\approx$ 0.98) across diverse biophysical regimes and an 89\% performance improvement over state-of-the-art sequence models like Transformers and Mamba, which lack inherent structural priors. By reducing simulation time from hours to seconds, we show that the surrogate model is capable of producing new insights and generating new hypotheses. This framework is readily extensible to a broader class of connectome-based biophysical models, showcasing the transformative value of deep learning surrogates to accelerate analysis of large-scale, computationally intensive dynamical systems.

Shihao Yang, Xiying Huang, Danilo Bernardo et al.

The advent of large-scale artificial intelligence (AI) models has a transformative effect on neuroscience research, which represents a paradigm shift from the traditional computational methods through the facilitation of end-to-end learning from raw brain signals and neural data. In this paper, we explore the transformative effects of large-scale AI models on five major neuroscience domains: neuroimaging and data processing, brain-computer interfaces and neural decoding, molecular neuroscience and genomic modeling, clinical assistance and translational frameworks, and disease-specific applications across neurological and psychiatric disorders. These models are demonstrated to address major computational neuroscience challenges, including multimodal neural data integration, spatiotemporal pattern interpretation, and the derivation of translational frameworks for clinical deployment. Moreover, the interaction between neuroscience and AI has become increasingly reciprocal, as biologically informed architectural constraints are now incorporated to develop more interpretable and computationally efficient models. This review highlights both the notable promise of such technologies and key implementation considerations, with particular emphasis on rigorous evaluation frameworks, effective domain knowledge integration, and comprehensive ethical guidelines for clinical use. Finally, a systematic listing of critical neuroscience datasets used to derive and validate large-scale AI models across diverse research applications is provided.

Xizheng Yu, Justin Torok, Sneha Pandya et al.

In this paper, we study the efficacy and utility of recent advances in non-local, non-linear image interpolation and extrapolation algorithms, specifically, ideas based on Implicit Neural Representations (INR), as a tool for analysis of spatial transcriptomics data. We seek to utilize the microarray gene expression data sparsely sampled in the healthy human brain, and produce fully resolved spatial maps of any given gene across the whole brain at a voxel-level resolution. To do so, we first obtained the 100 top AD risk genes, whose baseline spatial transcriptional profiles were obtained from the Allen Human Brain Atlas (AHBA). We adapted Implicit Neural Representation models so that the pipeline can produce robust voxel-resolution quantitative maps of all genes. We present a variety of experiments using interpolations obtained from Abagen as a baseline/reference.