Detecting structural changes in whole brain based on nonlinear deformations application to schizophrenia research

Research output: Contribution to journalResearch articleContributedpeer-review

Contributors

Abstract

This paper describes a new method for detecting structural brain differences based on the analysis of deformation fields. Deformations are obtained by an intensity-based nonlinear registration routine that transforms one brain onto another one. We present a general multivariate statistical approach to analyze deformation fields in different subjects. This method was applied to the brains of 85 schizophrenic patients and 75 healthy volunteers to examine whether low frequency deformations are sufficiently sensitive to detect regional deviations in the brains of both groups. We observed significant changes caused by volume reduction in brains of schizophrenics bilaterally in the thalamus and in the superior temporal gyrus. On the left side, the superior frontal gyrus and precentral gyrus are found to be changed, while on the right side, the middle frontal gyrus was altered. In addition, there were significant changes in the occipital lobe (left lingual gyrus) and in the left cerebellum. Volume enlargement in brains of schizophrenics was observed in the right putamen and in the adjacent white matter of the thalamic region. Our data suggest a disturbance in the nodes of a prefrontal-thalamic-cerebellar circuitry. This provides further support for the model of 'cognitive dysmetria,' which postulates a disruption in these nodes. We have demonstrated the application of deformation-based morphometry by detecting structural changes in the whole brain. This technique is fully automatic, thus allowing for the inclusion of large samples, with no user bias or a priori-defined regions of interest.

Details

Original languageEnglish
Pages (from-to)107-113
Number of pages7
JournalNeuroImage
Volume10
Issue number2
Publication statusPublished - Aug 1999
Peer-reviewedYes

External IDs

PubMed 10417245

Keywords

ASJC Scopus subject areas

Keywords

  • Brain, Morphometry, MRI, Nonlinear image registration, Schizophrenia, Statistical parametric map