Deep Collaborative Learning With Application to the Study of Multimodal Brain Development
Year: 2019
Type: article
Abstract: OBJECTIVE: Multi-modal functional magnetic resonance imaging has been widely used for brain research. Conventional data-fusion methods cannot capture complex relationship (e.g., nonlinear predictive relationship) between multiple data. This paper aims to develop a neural network framework to extract phenotype related cross-data relationships and use it to study the brain development. METHODS: We propose a novel method, deep collaborative learning (DCL), to address the limitation of existing methods. DCL first uses a deep network to represent original data and then seeks their correlations, while also linking the data representation with phenotypical information. RESULTS: We studied the difference of functional connectivity (FCs) between different age groups and also use FCs as a fingerprint to predict cognitive abilities. Our experiments demonstrated higher accuracy of using DCL over other conventional models when classifying populations of different ages and cognitive scores. Moreover, DCL revealed that brain connections became stronger at adolescence stage. Furthermore, DCL detected strong correlations between default mode network and other networks which were overlooked by linear canonical correlation analysis, demonstrating DCL's ability of detecting nonlinear correlations. CONCLUSION: The results verified the superiority of DCL over conventional data-fusion methods. In addition, the stronger brain connection demonstrated the importance of adolescence stage for brain development. SIGNIFICANCE: DCL can better combine complex correlations between multiple data sets in addition to their fitting to phenotypes, with the potential to overcome the limitations of several current data-fusion models.
Institutions: Tulane University, Mind Research Network, University of New Mexico
Language: English
Subfield: Cognitive Neuroscience
Field: Neuroscience
Domain: Life Sciences
Open Access status: green
Awards: Unified multivariate data-driven solutions for static and dynamic brain connectivity, Integration of fMRI imaging, genomics, network and biological knowledge, Integration of brain imaging with genomic and epigenomic data, Integration of multiscale genomic data for comprehensive analysis of complex dise, Multimodal Imaging of Neuropsychiatric Disorders (MIND): Mechanisms &, Dynamic imaging-genomic models for characterizing and predicting psychosis and mood disorders, Multivariate methods for identifying multitask/multimodal brain imaging biomarkers
OpenAlex ID: W2921060387