Dajiang Zhu, Qingyang Li, Brandalyn Riedel, Neda Jahanshad, Derrek Hibar, Ilya Veer, Henrik Walter, Lianne Schmaal, Dick Veltman, Dominik Grotegerd, Udo Dannlowski, Matthew Sacchet, Ian Gotlib, Jieping Ye, Paul Thompson
KEYWORDS: Data centers, Brain, Magnetic resonance imaging, Feature selection, Data analysis, Control systems, Psychiatry, Diagnostics, Neuroimaging, Data modeling
Compared to many neurological disorders, for which imaging biomarkers are often available, there are no accepted imaging biomarkers to assist in the diagnosis of major depressive disorder (MDD). One major barrier to understanding MDD has been the lack of a practical and efficient platform for collaborative efforts across multiple data centers; integrating the knowledge from different centers should make it easier to identify characteristic measures that are consistently associated with the illness. Here we applied our newly developed “distributed Lasso” method to brain MRI data from multiple centers to perform feature selection and classification. Over 1,000 participants were involved in the study; our results indicate the potential of the proposed framework to enable large-scale collaborative data analysis in the future.
Sparse learning enables dimension reduction and efficient modeling of high dimensional signals and images, but it may need to be tailored to best suit specific applications and datasets. Here we used sparse learning to efficiently represent functional magnetic resonance imaging (fMRI) data from the human brain. We propose a novel embedded sparse representation (ESR), to identify the most consistent dictionary atoms across different brain datasets via an iterative group-wise dictionary optimization procedure. In this framework, we introduced additional criteria to make the learned dictionary atoms more consistent across different subjects. We successfully identified four common dictionary atoms that follow the external task stimuli with very high accuracy. After projecting the corresponding coefficient vectors back into the 3-D brain volume space, the spatial patterns are also consistent with traditional fMRI analysis results. Our framework reveals common features of brain activation in a population, as a new, efficient fMRI analysis method.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.