# connectomics

Brain-connectivity differences in autism and other neurodevelopmental disorders have mostly been read from group-average case-control contrasts, which flatten individual variation. BRIDGE combines a graph variational autoencoder with normative modeling to learn an age-varying reference trajectory of structural connectomes from 1,164 neurotypical participants, then reports a global deviation score from brain-age difference and an individualized regional deviation map from regional embeddings. In 229 autistic children, the global score correlated with ADOS (Spearman R=−0.291) and IQ (Pearson R=0.195) in males, and brain-age prediction error was 3%–9% lower than comparable deep-learning models.

Brain-connectivity differences in autism and other neurodevelopmental disorders have mostly been read from group-average case-control contrasts, which flatten individual variation. BRIDGE combines a graph variational autoencoder with normative modeling to learn an age-varying reference trajectory of structural connectomes from 1,164 neurotypical participants, then reports a global deviation score from brain-age difference and an individualized regional deviation map from regional embeddings. In 229 autistic children, the global score correlated with ADOS (Spearman R=−0.291) and IQ (Pearson R=0.195) in males, and brain-age prediction error was 3%–9% lower than comparable deep-learning models.

Serial electron microscopy is the workhorse of brain connectomics, yet imaging a cubic millimeter of brain takes months to years. ZoomSEM trains a super-resolution model on real low- and high-resolution SEM image pairs and recovers 4×, 8× and up to 16× higher-resolution images from fast low-resolution scans while preserving the fine neuronal structures connectomic analysis needs, reaching up to 30-fold effective acceleration. Experiments across datasets of diverse biological samples show robust generalization under realistic low-resolution acquisition.

Serial electron microscopy is the workhorse of brain connectomics, yet imaging a cubic millimeter of brain takes months to years. ZoomSEM trains a super-resolution model on real low- and high-resolution SEM image pairs and recovers 4×, 8× and up to 16× higher-resolution images from fast low-resolution scans while preserving the fine neuronal structures connectomic analysis needs, reaching up to 30-fold effective acceleration. Experiments across datasets of diverse biological samples show robust generalization under realistic low-resolution acquisition.