Cross Atlas Remapping via Optimal Transport (CAROT): Creating connectomes for different atlases when raw data is not available

Harmonizing network-based statistics across different atlases in brain connectome analysis

Incorporating summary statistics across neuroimaging studies is important for enhancing translatability but poses challenges to connectomic analyses due to diverse methodological pipelines and brain atlases. We present TACOS (Transform brAin COnnectomes across atlaSes), a novel tool that translates network-based statistics across different atlases without requiring individual raw data. TACOS employs linear models based on anatomical information from brain parcellations and white matter fibers. Testing across 17 atlases, we show TACOS-transformed t-statistics to correlate well to the ground truth for both structural (r = 0.32-0.95) and functional networks (r = 0.57-0.95) using HCP surrogate statistics. These correlations remain consistent when tested with independent data from populations of different ancestries. Furthermore, TACOS effectively harmonizes connectomic results across multi-site schizophrenia data cohorts (r = 0.57-0.94 and 0.75-0.95 for structural and functional networks, respectively). This tool enables cross-atlas transformations of network-based statistics, showing great potential for downstream applications that share and combine multi-site connectomic data.

Krakencoder: a unified brain connectome translation and fusion tool

Brain connectivity can be estimated in many ways, depending on modality and processing strategy. Here, we present the Krakencoder, a joint connectome mapping tool that simultaneously bidirectionally translates between structural and functional connectivity, and between different atlases and processing choices via a common latent representation. These mappings demonstrate exceptional accuracy and individual-level identifiability; the mapping between structural and functional connectivity has identifiability 42-54% higher than existing models. The Krakencoder combines all connectome flavors via a shared low-dimensional latent space. This fusion representation better reflects familial relatedness, preserves age- and sex-relevant information, and enhances cognition-relevant information. The Krakencoder can be applied, without retraining, to new out-of-distribution data while still preserving inter-individual differences in the connectome predictions and familial relationships in the latent representations. The Krakencoder is a notable leap forward in capturing the relationship between multimodal brain connectomes in an individualized, behaviorally and demographically relevant way.

Release the Krakencoder: A unified brain connectome translation and fusion tool

Brain connectivity can be estimated in many ways, depending on modality and processing strategy. Here we present the Krakencoder, a joint connectome mapping tool that simultaneously, bidirectionally translates between structural (SC) and functional connectivity (FC), and across different atlases and processing choices via a common latent representation. These mappings demonstrate unprecedented accuracy and individual-level identifiability; the mapping between SC and FC has identifiability 42-54% higher than existing models. The Krakencoder combines all connectome flavors via a shared low-dimensional latent space. This "fusion" representation i) better reflects familial relatedness, ii) preserves age- and sex-relevant information and iii) enhances cognition-relevant information. The Krakencoder can be applied without retraining to new, out-of-age-distribution data while still preserving inter-individual differences in the connectome predictions and familial relationships in the latent representations. The Krakencoder is a significant leap forward in capturing the relationship between multi-modal brain connectomes in an individualized, behaviorally- and demographically-relevant way.