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Determining Significant Connectivity by 4D Spatiotemporal Wavelet Packet Resampling of Functional Neuroimaging Data

R.S. Patel, D. Van De Ville, F. DuBois Bowman

Proceedings of the Twelfth Annual Meeting of the Organization for Human Brain Mapping (HBM'06), Firenze, Italian Republic, June 11-15, 2006, pp. S42.



473 M-AM


Determining Significant Connectivity by 4D Spatiotemporal Wavelet Packet Resampling of Functional Neuroimaging Data

R.S. Patel1, D. Van De Ville2, F. DuBois Bowman3

1Amgen, Inc., 2Biomedical Imaging Group, École polytechnique fédérale de Lausanne (EPFL), 3Department of Biostatistics, Rollins School of Public Health, Emory University


Objective

To non-parametrically estimate the null distribution of the spatiotemporal correlation inherent in functional neuroimaging data using full 4D wavelet packet resampling techniques.


Methods

A single healthy subject was scanned at rest (90 volumes) and another healthy subject was scanned during an experimental motor task of alternating bilateral finger tapping (191 volumes) using T2-weighted fMRI.

Through 4D wavelet packet resampling, we construct null datasets with similar spatiotemporal correlation as the original data, but with the specific activations and correlations randomized spatiotemporally throughout. Through these resampled datasets we can estimate the null distribution of the correlation between any pair of intra-cranial voxels and subsequently calculate permutation-test type p-values of the corresponding correlation in the original data.

We construct 19 surrogate datasets for both the resting state and motor task datasets and thus are able to determine if permutation test p-values are less than 0.05, indicating that the observed correlation present in the experimental data is significantly greater than the null level. We examine correlations from the posterior cingulate cortex (PCC) for the resting state dataset and correlations from the primary motor cortex (PMC) for the motor task dataset.


Results & Discussion

Figure 1 reveals voxels statistically significantly (α = 0.05) correlated to the PCC in the resting state study. Consistent with previous resting state study findings [1], Brodmann's Area (BA) 6 and BA 10 of the superior frontal gyrus, BA 7 in the precuneus and BA 19 in the superior occipital gyrus are all significantly correlated with the PCC in the resting state.

Figure 1.

Figure 1: Map of significant neurophysiological motor task correlation to the PCC. Four significant clusters are labeled A-D: A—Superior frontal gyrus (Brodmann's Area (BA) 6), B—Precuneus (BA 7), C—Superior occipital gyrus (BA 19), D—Superior frontal gyrus (BA 10).

Figure 2 reveals voxels statistically significantly (α = 0.05) correlated to the primary motor cortex in the motor task study. The PMC (right hemisphere) is significantly positively correlated with the right cingulated gyrus, while being negatively correlated with the left cingulated gyrus. Furthermore, the PMC was positively correlated with the supramarginal gyrus and middle frontal gyrus in the left hemisphere.

Figure 2.

Figure 2: Map of significant neurophysiological motor task correlation to the PMC (Talairach coordinates: 35 -10 36) in the right hemisphere. Three significant clusters are labelled A-C: A—Cingulate gyrus (BA 24), B—Supramarginal gyrus (BA 40), C—middle frontal gyrus (BA 10).


Conclusions

4D wavelet packet resampling allows us to estimate the null spatiotemporal correlation inherent in functional neuroimaging data and consequently determine statistically significant voxel-pair correlations adjusting for the non-neurophysiological correlation in the data. Our method is the first to generate 4D neuroimaging data with similar spatiotemporal correlation as the original, but with the specific activations and correlations randomized throughout.


References

  1. M.D. Greicius, B. Krasnow, A.L. Reiss, V. Menon, "Functional Connectivity in the Resting Brain: A Network Analysis of the Default Mode Hypothesis," Proceedings of the National Academy of Sciences of the United States of America, vol. 100, no. 1, pp. 253-258, January 7, 2003.


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