Fuzzy clustering of gradient‐echo functional MRI in the human visual cortex. Part I: Reproducibility

Baumgartner, Richard N.; Scarth, Gordon B.; Teichtmeister, C.; Somorjai, Ray L.; Moser, Ewald

doi:10.1002/jmri.1880070623

Cited by 127 publications

(64 citation statements)

References 20 publications

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“…2, and Table 1. clustering (3,17), have been applied previously to brain functional MRI data. However, we know of only one prior implementation of model-independent activation detection that used mfMRI data (28).…”

Section: Discussionmentioning

confidence: 99%

Cluster analysis of muscle functional MRI data

Damon¹,

Wigmore²,

Ding³

et al. 2003

Journal of Applied Physiology

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magnetic resonance imaging (mfMRI) is frequently used to determine spatial patterns of muscle involvement in exercising humans. A frequent finding in mfMRI is that, even within synergistic muscle groups, signal intensity (SI) data from individual voxels can be quite heterogeneous. The purpose of this study was to develop a novel method for organizing heterogeneous mfMRI data into clusters whose members behave similarly to each other but distinctly from members of other clusters and apply it in studies of functional compartmentalization in the anterior compartment of the leg. An algorithm was developed that compared the SI time courses of adjacent voxels and grouped together voxels that were sufficiently similar. The algorithm's performance was verified by using simulated data sets with known regional differences in SI time courses that were then applied to experimental mfMRI data acquired from six male subjects (age 22.6 Ϯ 0.9 yr, mean Ϯ SE) who sustained isometric contractions of the dorsiflexors at 40% of maximum voluntary contraction. The experimental data were also characterized by using a traditional analysis (userspecified regions of interest from a single image), in which the relative change in SI and the contrast-to-noise ratio [CNR; 100%ϫ(SI RESTING Ϫ SIACTIVE)/(noise standard deviation)] were measured. In general, clusters were found in areas in which the CNR exceeded 5. Cluster analysis made functional distinctions between regions of muscle that were not seen with traditional analysis. In conclusion, cluster analysis's use of the full SI time course provides more sensitivity to muscle functional compartmentation than traditional analysis.transverse relaxation time constant; image processing; time series; exercise; dorsiflexors DURING THE PAST 15 YEARS, muscle functional magnetic resonance imaging (mfMRI) has emerged as a promising tool for studying muscle involvement during exercise because it is sensitive to the spatial pattern and extent of muscle utilization. For example, mfMRI has been used to identify different muscle utilization patterns in novice and elite rowers (9) and in uphill and horizontal running (25). It has also been used to detect changes in muscle function consequent to clinical conditions such as peripheral vascular disease (31). The interpretation of these data, however, is limited by the absence of a full theoretical understanding of which physiological variables produce the mfMRI response and how their contributions may differ during and after exercise.Multiexponential relaxation analysis has shown that the major contribution to signal intensity (SI) changes in mfMRI is made by the increase in the transverse relaxation time constant (T2) of intracellular water protons (4,22,23). Proposed explanations for intracellular T2 increases include the accumulation of end products of cellular energy metabolism, which cause water to move into the cell (4, 21), decreased intracellular pH (4, 8), and water shifts from an intracellular compartment possessing a short T2 (ϳ20 ms) to a second int...

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Section: Discussionmentioning

confidence: 99%

Cluster analysis of muscle functional MRI data

Damon¹,

Wigmore²,

Ding³

et al. 2003

Journal of Applied Physiology

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“…All these methods have been extensively explored in the contexts of regression-based detection (Baumgartner, Scarth, Teichtmeister, Somorjai, & Moser, 1997;Beckmann & Smith, 2004Fadili, Ruan, Bloyet, & Mazoyer, 2000;Filzmoser, Baumgartner, & Moser, 1999;Friston et al, 1993;Golay et al, 1998;Goutte, Toft, Rostrup, Nielsen, & Hansen, 1999;McKeown et al, 1998;Moser, Diemling, & Baumgartner, 1997;Thirion & Faugeras, 2004;Voultsidou, Dodel, & Herrmann, 2005). The fuzzy variants of clustering Moser et al, 1997), PCA (Friston et al, 1993) and ICA (Beckmann & Smith, 2004McKeown et al, 1998) were successfully used to extract and eliminate components that contain physiological noise unrelated to the activation protocol, substantially improving performance of the standard, regression-based detection.…”

Section: Other Data-driven Approachesmentioning

confidence: 99%

Data-driven clustering reveals a fundamental subdivision of the human cortex into two global systems

Golland²,

et al. 2008

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Global organizational principles are critical for understanding cortical functionality. Recently we proposed a global sub-division of the posterior cortex into two large-scale systems. One system, labeled extrinsic, comprises the sensory-motor cortex, and is associated with the external environment. The second system, labeled intrinsic, overlaps substantially with the previously described "default-mode" network, and is likely associated with inner-oriented processing. This global partition of the cerebral cortex emerged from hemodynamic imaging data the analysis of which was constrained by pre-determined hypotheses. Here we applied a hypothesis-free, unsupervised two-class clustering algorithm (k-means) to a large set of fMRI data. The two clusters delineated by this unsupervised hypothesis-free procedure showed high anatomical consistency across individuals, and their cortical topography coincided largely with the previously determined extrinsic and intrinsic systems. These new clustering-based results confirm that the intrinsic-extrinsic subdivision constitutes a fundamental cortical divide.

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“…FMRI activation pattern reproducibility has been reported at clinical [1][2][3][4][5][6] and high [7,8] magnetic field strengths, using well characterised visual, motor and cognitive paradigms. All studies report high within-subject and variable between-subject reproducibility, depending on the method of analysis.…”

Section: Introductionmentioning

confidence: 95%

Effect of slice orientation on reproducibility of fMRI motor activation at 3 Tesla

Gustard

Fadili

Williams

et al. 2001

Magnetic Resonance Imaging

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The effect of slice orientation on reproducibility and sensitivity of 3T fMRI activation using a motor task has been investigated in six normal volunteers. Four slice orientations were used; axial, oblique axial, coronal and sagittal. We applied analysis of variance (ANOVA) to suprathreshold voxel statistics to quantify variability in activation between orientations and between subjects. We also assessed signal detection accuracy in voxels across the whole brain by using a finite mixture model to fit receiver operating characteristic (ROC) curves to the data. Preliminary findings suggest that suprathreshold cluster characteristics demonstrate high motor reproducibility across subjects and orientations, although a significant difference between slice orientations in number of activated voxels was demonstrated in left motor cortex but not cerebellum. Subtle inter-orientation differences are highlighted in the ROC analyses, which are not obvious by ANOVA; the oblique axial slice orientation offers the highest signal detection accuracy, whereas coronal slices give the lowest.

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Fuzzy clustering of gradient‐echo functional MRI in the human visual cortex. Part I: Reproducibility

Cited by 127 publications

References 20 publications

Cluster analysis of muscle functional MRI data

Cluster analysis of muscle functional MRI data

Data-driven clustering reveals a fundamental subdivision of the human cortex into two global systems

Effect of slice orientation on reproducibility of fMRI motor activation at 3 Tesla

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