Functional MRI (fMRI) may be possible without a priori models of the cerebral hemodynamic response. First, such data-driven fMRI requires that all cerebral territories with distinct patterns be identified. Second, a systematic selection method is necessary to prevent the subjective interpretation of the identified territories. This paper addresses the second point by proposing a novel method for the automated interpretation of identified territories in data-driven fMRI. Selection criteria are formulated using: 1) the temporal cross-correlation between each identified territory and the paradigm and 2) the spatial contiguity of the corresponding voxel map. Ten event-design fMRI data sets are analyzed with one prominent algorithm, fuzzy c-means clustering, before applying the selection criteria. For comparison, these data are also analyzed with an established, model-based method: statistical parametric mapping. Both methods produced similar results and identified potential activation in the expected territory of the sensorimotor cortex in all ten data sets. Moreover, the proposed method classified distinct territories in separate clusters. Selected clusters have a mean temporal correlation coefficient of 0.39 ± 0.07 (n=19) with a mean 2.7 ± 1.4 second response delay. At most, four separate contiguous territories were observed in 87% of these clusters. These results suggest that the proposed method may be effective for exploratory fMRI studies where the hemodynamic response is perturbed during cerebrovascular disease.

Additional Metadata
Keywords Automated interpretation, Blood-oxygen-level dependent (BOLD), Cerebral functional MRI (fMRI), Data-driven analysis, Hemodynamic response, Spatial contiguity, Temporal cross correlation
Persistent URL dx.doi.org/10.1109/TBME.2008.926680
Journal IEEE Transactions on Biomedical Engineering
Citation
Gómez-Laberge, C. (Camille), Adler, A, Cameron, I. (Ian), Nguyen, T.B. (Thanh Binh), & Hogan, M.J. (Matthew J.). (2008). Selection criteria for the analysis of data-driven clusters in cerebral fMRI. IEEE Transactions on Biomedical Engineering, 55(10), 2372–2380. doi:10.1109/TBME.2008.926680