Deep Independence Network Analysis of Structural Brain Imaging: Application to Schizophrenia

Eduardo Castro, R. Devon Hjelm, Sergey M. Plis, Laurent Dinh, Jessica A. Turner, Vince D. Calhoun

Research output: Contribution to journalArticlepeer-review

Abstract

Linear independent component analysis (ICA) is a standard signal processing technique that has been extensively used on neuroimaging data to detect brain networks with coherent brain activity (functional MRI) or covarying structural patterns (structural MRI). However, its formulation assumes that the measured brain signals are generated by a linear mixture of the underlying brain networks and this assumption limits its ability to detect the inherent nonlinear nature of brain interactions. In this paper, we introduce nonlinear independent component estimation (NICE) to structural MRI data to detect abnormal patterns of gray matter concentration in schizophrenia patients. For this biomedical application, we further addressed the issue of model regularization of nonlinear ICA by performing dimensionality reduction prior to NICE, together with an appropriate control of the complexity of the model and the usage of a proper approximation of the probability distribution functions of the estimated components. We show that our results are consistent with previous findings in the literature, but we also demonstrate that the incorporation of nonlinear associations in the data enables the detection of spatial patterns that are not identified by linear ICA. Specifically, we show networks including basal ganglia, cerebellum and thalamus that show significant differences in patients versus controls, some of which show distinct nonlinear patterns.

Original languageEnglish (US)
Article number7405347
Pages (from-to)1729-1740
Number of pages12
JournalIEEE transactions on medical imaging
Volume35
Issue number7
DOIs
StatePublished - Jul 2016

Keywords

  • Deep learning
  • NICE
  • nonlinear ICA
  • schizophrenia
  • structural MRl

ASJC Scopus subject areas

  • Software
  • Radiological and Ultrasound Technology
  • Computer Science Applications
  • Electrical and Electronic Engineering

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