Predicting Functional Brain Connectivity with Context-Aware Deep Neural Networks

Alexander Ratzan, Sidharth Goel, Junhao Wen, Christos Davatzikos, Erdem Varol

Keywords: neuroscience, connectomics, transcriptomics, transformers, fMRI

TL;DR: We apply context-aware deep neural networks to predict functional connectivity from gene expression in human datasets.

Abstract:

Spatial location and molecular interactions have long been linked to the connectivity
patterns of neural circuits. Yet, at the macroscale of human brain networks,
the interplay between spatial position, gene expression, and connectivity remains
incompletely understood. Recent efforts to map the human transcriptome and
connectome have yielded spatially resolved brain atlases, however modeling the
relationship between high-dimensional transcriptomic data and connectivity while
accounting for inherent spatial confounds presents a significant challenge. In this
paper, we present the first deep learning approaches for predicting whole-brain
functional connectivity from gene expression and regional spatial coordinates, including
our proposed Spatiomolecular Transformer (SMT). SMT explicitly models
biological context by tokenizing genes based on their transcription start site (TSS)
order to capture multi-scale genomic organization, and incorporating regional
3D spatial location via a dedicated context [CLS] token within its multi-head
self-attention mechanism. We rigorously benchmark context-aware neural networks,
including SMT and a single-gene resolution Multilayer-Perceptron (MLP),
to established rules-based and bilinear methods. Crucially, to ensure that learned
relationships in any model are not mere artifacts of spatial proximity, we introduce
novel spatiomolecular null maps preserving key transcriptomic autocorrelation
structure. Context-aware neural networks outperform linear methods, significantly
exceed our stringent null map estimates, and generalize across diverse connectomic
datasets and parcellation resolutions. Together, these findings demonstrate a
strong, predictable link between the spatial distributions of gene expression and
functional brain network architecture, and establish a rigorously validated deep
learning framework for decoding this relationship. Code to reproduce our results is
available at: github.com/neuroinfolab/GeneEx2Conn.

Primary Area: Neuroscience and cognitive science (e.g., neural coding, brain-computer interfaces)

Submission Number: 26223