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Cortical surface statistical parametric mapping
NeuroImage
11, Number
5, 2000,
Part 2 of 2 Parts 10
METHODS
E kl@
- ACQUISITION
CORTICAL SURFACE STATISTICAL PARAMETRIC MAPPING Alexandre Andrade*t, Ferath KheriPE, Jean-Fraqois Mangin*, K.J. Worsleyl, Stanislas Dehaeneg, Denis Le Bihan*, Jean-Baptiste Poline*
Olivier Simon&
*Service Hospitalier Frkdkric Joliot, CEA, Orsay, France tlnstituto de BiojiXca e Engenharia Biomkdica, Fat. de Ci&cias da Univ. de Lisboa, Lisboa, Portugal SDepartment of Mathematics and Statistics, McGill University, Montrtfal, Qutfbec, Canada $INSERM lJ 334, Service Hospitalier Frkdkric Joliot, CEA, Orsay, France Introduction Functional magnetic resonance data analysis is typically conducted over volumes that reflect the BOLD response at regularly distributed spatial locations in the brain. This volume-based analysis does not usually take anatomical information into consideration. Performing analysis in a more anatomically-oriented fashion could lead to major improvements, among which: 1) the use of a 2D topology to describe the functional organisation of the cortex (surface-based analysis); 2) an enhanced sensitivity due to tissue-specific spatial smoothing (avoiding unwanted averaging effects between different tissues); 3) improved spatial location of activated foci (provided that coregistration between Tl and T2* images is good and that distortion is negligible). We propose to implement the SPM method (General Linear Model + Random Field theory) (1) on the cortical surface for fMRI data. This approach would therefore retain the efficiency and flexibility of SPM, allowing it to be used in a wide range of experimental paradigms while incorporating anatomical information. Methods The proposed methodology (CSPM, from Cortical Surface Parametric Mapping) comprises the following steps: 1) the cortical sheet (white/grey matter triangulated interface) is extracted from the Tl images (2); 2) a functional time series is computed for each node of the extracted suface using interpolation of the T2*-weighted voxel data: 3) spatial smoothing is performed over the cortical lattice. Smoothing is based on a local resolution of the heat equation, equivalent to convolution with a gaussian kernel (3). Surface-based smoothing ensures that the resulting correlation structure depends on geodesic rather than euclidean distance; 4) resolution of the GLM is carried out over the surface nodes; 5) smoothness estimation for multiple comparisons correction (4) is done locally, by means of statistical flattening (5), allowing for possible situations of non-stationarity in the resulting smoothness structure of the field. We applied CSPM on both simulated noise and actual data, and compared the results with standard 3D SPM analysis on several subjects who underwent a simple motor activation fMR1 protocol. The smoothing kernel was chosen to be 8 mm FWHM for both CSPM and 3D SPM. Results The false positive rate (spurious activations due to noise) was found to be according to expectations on the simulated data, thereby ensuring adequate protection against type I error. CSPM analysis of subjects issuing from the activation protocol showed good reproductibility with regard to standard SPM analysis: roughly 70 % of the regions highlighted by 3D SPM (p=O.O5 corrected) were also found using surface-based analysis. On the other hand, a number of regions was detected with CSPM but not with the standard method. Relative sensitivity to activation varied markedly between methods, within the set of detected activation clusters. Conclusions CSPM is presented as a way of undertaking functional brain imaging data analysis in a way that is more adapted to the topological characteristics of the human cortex (sheet-lie. highly convoluted region), and able to preserve in a more effective way the signal of interest, minimising spurious contributions from surrounding tissues. Noise simulations and application to data from a real protocol demonstrated its robustness and teproductibility with respect to standard analysis. Discrepancies between the msuhs from both methods seem to indicate that the different smoothing approaches have important consequences in the final outcome: in some cases, pseudoactivated foci enhanced by averaging with actual signal spilling over across a sulcus will not show up if surface-based smoothing is used. The performance of the proposed method is likely to be improved with finer design of the segmented surfaces, or the use of a more suitable interpolation process. Furthermore, future hardware improvements (e.g. resolution of T2 images) should constitute a particularly welcome boon for this kind of approach. References (1) Friston, KJ: Holmes, A; Worsley, KJ; Poline, JB; Frith, CD; Frackowiak, RSJ; Human Brain Mapping, 2: 189-210. (2) Mangin, JF, Regis, J; Bloch, I; Frouin, V; Samson, Y; L6pez-Krahe, J; 1st International Conference on Computer Vision Virtual Reality and Robotics in Medicine. Lecture Notes in Computer Science, pp. 177-183. Springer-Verlag, NY, 1995. (3) Perona. P; Malik, J; IEEE Transactions on Pattern Analysis and Machine Intelligence, 12: 629-639. (4) Worsley, KJ; Evans, AC; Marrett, S; Neelin, P; Journal of Cerebral Blood Flow and Metabolism, 12: 900-918. (5) Worsley, KJ; Andermann, M; Koulis, T; MacDonald, D; Evans, AC; NemoImage, 9:Sll.
S504
,
11, Number
5, 2000,
Part 2 of 2 Parts 10
METHODS
E kl@
- ACQUISITION
CORTICAL SURFACE STATISTICAL PARAMETRIC MAPPING Alexandre Andrade*t, Ferath KheriPE, Jean-Fraqois Mangin*, K.J. Worsleyl, Stanislas Dehaeneg, Denis Le Bihan*, Jean-Baptiste Poline*
Olivier Simon&
*Service Hospitalier Frkdkric Joliot, CEA, Orsay, France tlnstituto de BiojiXca e Engenharia Biomkdica, Fat. de Ci&cias da Univ. de Lisboa, Lisboa, Portugal SDepartment of Mathematics and Statistics, McGill University, Montrtfal, Qutfbec, Canada $INSERM lJ 334, Service Hospitalier Frkdkric Joliot, CEA, Orsay, France Introduction Functional magnetic resonance data analysis is typically conducted over volumes that reflect the BOLD response at regularly distributed spatial locations in the brain. This volume-based analysis does not usually take anatomical information into consideration. Performing analysis in a more anatomically-oriented fashion could lead to major improvements, among which: 1) the use of a 2D topology to describe the functional organisation of the cortex (surface-based analysis); 2) an enhanced sensitivity due to tissue-specific spatial smoothing (avoiding unwanted averaging effects between different tissues); 3) improved spatial location of activated foci (provided that coregistration between Tl and T2* images is good and that distortion is negligible). We propose to implement the SPM method (General Linear Model + Random Field theory) (1) on the cortical surface for fMRI data. This approach would therefore retain the efficiency and flexibility of SPM, allowing it to be used in a wide range of experimental paradigms while incorporating anatomical information. Methods The proposed methodology (CSPM, from Cortical Surface Parametric Mapping) comprises the following steps: 1) the cortical sheet (white/grey matter triangulated interface) is extracted from the Tl images (2); 2) a functional time series is computed for each node of the extracted suface using interpolation of the T2*-weighted voxel data: 3) spatial smoothing is performed over the cortical lattice. Smoothing is based on a local resolution of the heat equation, equivalent to convolution with a gaussian kernel (3). Surface-based smoothing ensures that the resulting correlation structure depends on geodesic rather than euclidean distance; 4) resolution of the GLM is carried out over the surface nodes; 5) smoothness estimation for multiple comparisons correction (4) is done locally, by means of statistical flattening (5), allowing for possible situations of non-stationarity in the resulting smoothness structure of the field. We applied CSPM on both simulated noise and actual data, and compared the results with standard 3D SPM analysis on several subjects who underwent a simple motor activation fMR1 protocol. The smoothing kernel was chosen to be 8 mm FWHM for both CSPM and 3D SPM. Results The false positive rate (spurious activations due to noise) was found to be according to expectations on the simulated data, thereby ensuring adequate protection against type I error. CSPM analysis of subjects issuing from the activation protocol showed good reproductibility with regard to standard SPM analysis: roughly 70 % of the regions highlighted by 3D SPM (p=O.O5 corrected) were also found using surface-based analysis. On the other hand, a number of regions was detected with CSPM but not with the standard method. Relative sensitivity to activation varied markedly between methods, within the set of detected activation clusters. Conclusions CSPM is presented as a way of undertaking functional brain imaging data analysis in a way that is more adapted to the topological characteristics of the human cortex (sheet-lie. highly convoluted region), and able to preserve in a more effective way the signal of interest, minimising spurious contributions from surrounding tissues. Noise simulations and application to data from a real protocol demonstrated its robustness and teproductibility with respect to standard analysis. Discrepancies between the msuhs from both methods seem to indicate that the different smoothing approaches have important consequences in the final outcome: in some cases, pseudoactivated foci enhanced by averaging with actual signal spilling over across a sulcus will not show up if surface-based smoothing is used. The performance of the proposed method is likely to be improved with finer design of the segmented surfaces, or the use of a more suitable interpolation process. Furthermore, future hardware improvements (e.g. resolution of T2 images) should constitute a particularly welcome boon for this kind of approach. References (1) Friston, KJ: Holmes, A; Worsley, KJ; Poline, JB; Frith, CD; Frackowiak, RSJ; Human Brain Mapping, 2: 189-210. (2) Mangin, JF, Regis, J; Bloch, I; Frouin, V; Samson, Y; L6pez-Krahe, J; 1st International Conference on Computer Vision Virtual Reality and Robotics in Medicine. Lecture Notes in Computer Science, pp. 177-183. Springer-Verlag, NY, 1995. (3) Perona. P; Malik, J; IEEE Transactions on Pattern Analysis and Machine Intelligence, 12: 629-639. (4) Worsley, KJ; Evans, AC; Marrett, S; Neelin, P; Journal of Cerebral Blood Flow and Metabolism, 12: 900-918. (5) Worsley, KJ; Andermann, M; Koulis, T; MacDonald, D; Evans, AC; NemoImage, 9:Sll.
S504
,