DRUMS – A Flexible New Deep Learning Tool for Precise Cortical Surface Alignment across Individuals

Abstract

Accurate inter-individual alignment of human cerebral cortex is challenging because of the high variability of human cortical folding patterns and the regionally non-uniform and inconsistent spatial relationships across individuals of cortical folds versus the functional networks and cortical areas that we wish to study. To achieve precise alignment across individuals, an algorithm must use multi-modal neuroimaging features related to cortical areas and functional networks, ideally as inputs to surface-based registration, but at least during training if only folding patterns will be available during inference, so that it can learn where folding patterns are trustworthy and learn a spatially non-uniform regularization function that reflects the true gamut of human inter-individual variability in cortical organization. Additionally, an algorithm ideally will be capable of denoising its own input registration features to avoid overfitting to noise, enabling reproducible registration in test-retest data, and will not require precise hand tuning of input regularization parameters. To address these challenges, we developed Deep-learning Registration Using U-Net with Multimodal Supervision (DRUMS), a novel framework for multi-modal cortical surface registration. DRUMS applies its deep-learning approach to cortical surface registration using multi-resolution spheres, a well-validated approach used in other registration algorithms such as Multi-modal Surface Matching (MSM) (Robinson, et al., 2014; Robinson, et al., 2018). It also includes the same physically inspired strain energy regularization that we pioneered for MSM and the same precise barycentric interpolation on spherical surface meshes. DRUMS has a three-stage architecture: (1) multiscale feature extraction, (2) multiscale feature integration, and (3) deformation field generation. The framework’s multimodal design supports flexible integration of diverse imaging modalities, enabling registration using either folding features or multi-modal features as inputs with independent control over supervision during training (e.g., training DRUMS with folding inputs and multi-modal supervision to learn which folds best correlate with multi-modal features). DRUMS outperforms Multi-modal Surface Matching (MSM), the current state-of-the-art method used in the Human Connectome Project (HCP) pipelines, over a wide range of input regularization settings in both registration accuracy and test-retest reproducibility of registration results for both supervised folding registrations and multi-modal registrations across all tested modalities, including held out modalities. DRUMS further learns a biologically plausible spatially non-uniform regularization function, with registration induced distortion correctly matched to known human inter-individual cortical variability. These results suggest that DRUMS should replace MSM in the HCP Pipelines and position DRUMS as a versatile and reliable tool for cortical surface registration in neuroimaging research.