Abstract: The present disclosure describes a method of Connectome Ensemble Transfer Learning (CETL), which makes connectome-based predictive models useful for precision mental healthcare. CETL comprises a novel transfer learning process that incrementally trains Connectome Ensemble Predictive Models (CEPMs) by leveraging information from source domains to improve predictive performance in target domains. The disclosed methods broadly comprise selecting target and source domains, obtaining network connectivity data from individual persons, sampling source ensemble representations of connectome “views” from the obtained network connectivity data of said persons in the source domain, reducing the dimensionality of the sampled connectome “views”, and transferring the distilled representations to the target domain to train more robust, generalizable, and clinically deployable CEPMs that predict diverse target mental health phenotypes.

Abstract: Existing methods for analyzing person-specific ‘connectomes’ are not computationally equipped for scalable, flexible, and integrated processing across multiple network resolutions and drawing from disparate data modalities-a major roadblock to utilizing ensembles and hierarchies of connectomes to solve person-specific machine-learning problems. The processes implemented in software described herein consists of an end-to-end pipeline for deploying ensembles and hierarchies of network-generating workflows that can utilize multimodal, person-specific data to sample networks, extracted from that data, across a grid of network-defining hyperparameters. In essence, this pipeline enables users to perform ensemble sampling of connectomes for given individual(s) based on any input phenotypic datatype, constructed from any data modality or hierarchy of modalities at any scale, and based on any set of network-defining hyperparameters.

Abstract: Existing methods for analyzing person-specific ‘connectomes’ are not computationally equipped for scalable, flexible, and integrated processing across multiple network resolutions and drawing from disparate data modalities—a major roadblock to utilizing ensembles and hierarchies of connectomes to solve person-specific machine-learning problems. The processes implemented in software described herein consists of an end-to-end pipeline for deploying ensembles and hierarchies of network-generating workflows that can utilize multimodal, person-specific data to sample networks, extracted from that data, across a grid of network-defining hyperparameters. In essence, this pipeline enables users to perform ensemble sampling of connectomes for given individual(s) based on any input phenotypic datatype, constructed from any data modality or hierarchy of modalities at any scale, and based on any set of network-defining hyperparameters.