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 in | NeuroImage (Orlando, Fla.) Vol. 41; no. 3; pp. 835 - 848 |
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Main Authors | , , , , , , |
Format | Journal Article |
Language | English |
Published |
United States
Elsevier Inc
01.07.2008
Elsevier Limited |
Subjects | |
Online Access | Get full text |
ISSN | 1053-8119 1095-9572 |
DOI | 10.1016/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. |
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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 – sequence: 3 givenname: Sarah K. surname: Gillespie fullname: Gillespie, Sarah K. organization: Department of Psychiatry, Washington University in St. Louis, USA – sequence: 4 givenname: Grega surname: Repovs fullname: Repovs, Grega organization: Department of Psychology, Washington University in St. Louis, USA – sequence: 5 givenname: John G. surname: Csernansky fullname: Csernansky, John G. organization: Department of Psychiatry, Washington University in St. Louis, USA – sequence: 6 givenname: David C. surname: Van Essen fullname: Van Essen, David C. organization: Department of Anatomy and Neurobiology, Washington University in St. Louis, USA – sequence: 7 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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