Comparing surface-based and volume-based analyses of functional neuroimaging data in patients with schizophrenia

A major challenge in functional neuroimaging is to cope with individual variability in cortical structure and function. Most analyses of cortical function compensate for variability using affine or low-dimensional nonlinear volume-based registration (VBR) of individual subjects to an atlas, which do...

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Published inNeuroImage (Orlando, Fla.) Vol. 41; no. 3; pp. 835 - 848
Main Authors Anticevic, Alan, Dierker, Donna L., Gillespie, Sarah K., Repovs, Grega, Csernansky, John G., Van Essen, David C., Barch, Deanna M.
Format Journal Article
LanguageEnglish
Published United States Elsevier Inc 01.07.2008
Elsevier Limited
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Online AccessGet full text
ISSN1053-8119
1095-9572
DOI10.1016/j.neuroimage.2008.02.052

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Abstract A major challenge in functional neuroimaging is to cope with individual variability in cortical structure and function. Most analyses of cortical function compensate for variability using affine or low-dimensional nonlinear volume-based registration (VBR) of individual subjects to an atlas, which does not explicitly take into account the geometry of cortical convolutions. A promising alternative is to use surface-based registration (SBR), which capitalizes on explicit surface representations of cortical folding patterns in individual subjects. In this study, we directly compare results from SBR and affine VBR in a study of working memory in healthy controls and patients with schizophrenia (SCZ). Each subject's structural scan was used for cortical surface reconstruction using the SureFit method. fMRI data were mapped directly onto individual cortical surface models, and each hemisphere was registered to the population-average PALS-B12 atlas using landmark-constrained SBR. The precision with which cortical sulci were aligned was much greater for SBR than VBR. SBR produced superior alignment precision across the entire cortex, and this benefit was greater in patients with schizophrenia. We demonstrate that spatial smoothing on the surface provides better resolution and signal preservation than a comparable degree of smoothing in the volume domain. Lastly, the statistical power of functional activation in the working memory task was greater for SBR than for VBR. These results indicate that SBR provides significant advantages over affine VBR when analyzing cortical fMRI activations. Furthermore, these improvements can be even greater in disorders that have associated structural abnormalities.
AbstractList A major challenge in functional neuroimaging is to cope with individual variability in cortical structure and function. Most analyses of cortical function compensate for variability using affine or low-dimensional nonlinear volume-based registration (VBR) of individual subjects to an atlas, which does not explicitly take into account the geometry of cortical convolutions. A promising alternative is to use surface-based registration (SBR), which capitalizes on explicit surface representations of cortical folding patterns in individual subjects. In this study, we directly compare results from SBR and affine VBR in a study of working memory in healthy controls and patients with schizophrenia (SCZ). Each subject's structural scan was used for cortical surface reconstruction using the SureFit method. fMRI data were mapped directly onto individual cortical surface models, and each hemisphere was registered to the population-average PALS-B12 atlas using landmark-constrained SBR. The precision with which cortical sulci were aligned was much greater for SBR than VBR. SBR produced superior alignment precision across the entire cortex, and this benefit was greater in patients with schizophrenia. We demonstrate that spatial smoothing on the surface provides better resolution and signal preservation than a comparable degree of smoothing in the volume domain. Lastly, the statistical power of functional activation in the working memory task was greater for SBR than for VBR. These results indicate that SBR provides significant advantages over affine VBR when analyzing cortical fMRI activations. Furthermore, these improvements can be even greater in disorders that have associated structural abnormalities.
A major challenge in functional neuroimaging is to cope with individual variability in cortical structure and function. Most analyses of cortical function compensate for variability using affine or low-dimensional nonlinear volume-based registration (VBR) of individual subjects to an atlas, which does not explicitly take into account the geometry of cortical convolutions. A promising alternative is to use surface-based registration (SBR), which capitalizes on explicit surface representations of cortical folding patterns in individual subjects. In this study, we directly compare results from SBR and affine VBR in a study of working memory in healthy controls and patients with schizophrenia (SCZ). Each subject's structural scan was used for cortical surface reconstruction using the SureFit method. fMRI data were mapped directly onto individual cortical surface models, and each hemisphere was registered to the population-average PALS-B12 atlas using landmark-constrained SBR. The precision with which cortical sulci were aligned was much greater for SBR than VBR. SBR produced superior alignment precision across the entire cortex, and this benefit was greater in patients with schizophrenia. We demonstrate that spatial smoothing on the surface provides better resolution and signal preservation than a comparable degree of smoothing in the volume domain. Lastly, the statistical power of functional activation in the working memory task was greater for SBR than for VBR. These results indicate that SBR provides significant advantages over affine VBR when analyzing cortical fMRI activations. Furthermore, these improvements can be even greater in disorders that have associated structural abnormalities.A major challenge in functional neuroimaging is to cope with individual variability in cortical structure and function. Most analyses of cortical function compensate for variability using affine or low-dimensional nonlinear volume-based registration (VBR) of individual subjects to an atlas, which does not explicitly take into account the geometry of cortical convolutions. A promising alternative is to use surface-based registration (SBR), which capitalizes on explicit surface representations of cortical folding patterns in individual subjects. In this study, we directly compare results from SBR and affine VBR in a study of working memory in healthy controls and patients with schizophrenia (SCZ). Each subject's structural scan was used for cortical surface reconstruction using the SureFit method. fMRI data were mapped directly onto individual cortical surface models, and each hemisphere was registered to the population-average PALS-B12 atlas using landmark-constrained SBR. The precision with which cortical sulci were aligned was much greater for SBR than VBR. SBR produced superior alignment precision across the entire cortex, and this benefit was greater in patients with schizophrenia. We demonstrate that spatial smoothing on the surface provides better resolution and signal preservation than a comparable degree of smoothing in the volume domain. Lastly, the statistical power of functional activation in the working memory task was greater for SBR than for VBR. These results indicate that SBR provides significant advantages over affine VBR when analyzing cortical fMRI activations. Furthermore, these improvements can be even greater in disorders that have associated structural abnormalities.
Author Barch, Deanna M.
Repovs, Grega
Anticevic, Alan
Csernansky, John G.
Gillespie, Sarah K.
Dierker, Donna L.
Van Essen, David C.
Author_xml – sequence: 1
  givenname: Alan
  surname: Anticevic
  fullname: Anticevic, Alan
  email: aanticev@artsci.wustl.edu
  organization: Department of Psychology, Washington University in St. Louis, USA
– sequence: 2
  givenname: Donna L.
  surname: Dierker
  fullname: Dierker, Donna L.
  organization: Department of Anatomy and Neurobiology, Washington University in St. Louis, USA
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  givenname: Sarah K.
  surname: Gillespie
  fullname: Gillespie, Sarah K.
  organization: Department of Psychiatry, Washington University in St. Louis, USA
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  givenname: Grega
  surname: Repovs
  fullname: Repovs, Grega
  organization: Department of Psychology, Washington University in St. Louis, USA
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  givenname: John G.
  surname: Csernansky
  fullname: Csernansky, John G.
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  organization: Department of Anatomy and Neurobiology, Washington University in St. Louis, USA
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  givenname: Deanna M.
  surname: Barch
  fullname: Barch, Deanna M.
  organization: Department of Psychology, Washington University in St. Louis, USA
BackLink https://www.ncbi.nlm.nih.gov/pubmed/18434199$$D View this record in MEDLINE/PubMed
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Snippet A major challenge in functional neuroimaging is to cope with individual variability in cortical structure and function. Most analyses of cortical function...
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SubjectTerms Accuracy
Adult
Brain
Brain - anatomy & histology
Brain - physiology
Brain Mapping - methods
Female
Humans
Image Processing, Computer-Assisted - methods
Magnetic Resonance Imaging
Male
Memory
Memory - physiology
NMR
Nuclear magnetic resonance
Schizophrenia - pathology
Schizophrenia - physiopathology
Studies
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Title Comparing surface-based and volume-based analyses of functional neuroimaging data in patients with schizophrenia
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