Connectivity-based whole brain dual parcellation by group ICA reveals tract structures and decreased connectivity in schizophrenia

Lei Wu, Vince Daniel Calhoun, Rex E. Jung, Arvind Caprihan

Research output: Contribution to journalArticle

Abstract

Mapping brain connectivity based on neuroimaging data is a promising new tool for understanding brain structure and function. In this methods paper, we demonstrate that group independent component analysis (GICA) can be used to perform a dual parcellation of the brain based on its connectivity matrix (cmICA). This dual parcellation consists of a set of spatially independent source maps, and a corresponding set of paired dual maps that define the connectivity of each source map to the brain. These dual maps are called the connectivity profiles of the source maps. Traditional analysis of connectivity matrices has been used previously for brain parcellation, but the present method provides additional information on the connectivity of these segmented regions. In this paper, the whole brain structural connectivity matrices were calculated on a 5 mm3 voxel scale from diffusion imaging data based on the probabilistic tractography method. The effect of the choice of the number of components (30 and 100) and their stability were examined. This method generated a set of spatially independent components that are consistent with the canonical brain tracts provided by previous anatomic descriptions, with the high order model yielding finer segmentations. The corpus-callosum example shows how this method leads to a robust parcellation of a brain structure based on its connectivity properties. We applied cmICA to study structural connectivity differences between a group of schizophrenia subjects and healthy controls. The connectivity profiles at both model orders showed similar regions with reduced connectivity in schizophrenia patients. These regions included forceps major, right inferior fronto-occipital fasciculus, uncinate fasciculus, thalamic radiation, and corticospinal tract. This paper provides a novel unsupervised data-driven framework that summarizes the information in a large global connectivity matrix and tests for brain connectivity differences. It has the potential for capturing important brain changes related to disease in connectivity-based disorders.

Original languageEnglish (US)
Pages (from-to)4681-4701
Number of pages21
JournalHuman Brain Mapping
Volume36
Issue number11
DOIs
StatePublished - Nov 1 2015
Externally publishedYes

Fingerprint

Schizophrenia
Brain
Subthalamus
Brain Mapping
Pyramidal Tracts
Corpus Callosum
Surgical Instruments
Neuroimaging
Healthy Volunteers
Radiation

Keywords

  • Diffusion tensor imaging (DTI)
  • Independent component analysis (ICA)
  • Schizophrenia
  • Structural connectivity
  • Tractography

ASJC Scopus subject areas

  • Clinical Neurology
  • Anatomy
  • Neurology
  • Radiology Nuclear Medicine and imaging
  • Radiological and Ultrasound Technology

Cite this

Connectivity-based whole brain dual parcellation by group ICA reveals tract structures and decreased connectivity in schizophrenia. / Wu, Lei; Calhoun, Vince Daniel; Jung, Rex E.; Caprihan, Arvind.

In: Human Brain Mapping, Vol. 36, No. 11, 01.11.2015, p. 4681-4701.

Research output: Contribution to journalArticle

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