Dense mode clustering in brain maps.

A mode-based clustering method is developed for identifying spatially dense clusters in brain maps. This type of clustering focuses on identifying clusters in brain maps independent of their shape or overall variance. This can be useful for both localization in terms of interpretation and for subsequent graphical analysis that might require more coherent or dense regions of interest as starting points. The method automatically does signal/noise sharpening through density mode seeking. We also discuss the problem of parameter selection with this method and propose a new method involving 2-parameter control surface, in which we show that the same cluster solution results from tradeoff of these 2 parameters (the local density k and the radius r of the spherical kernel). We benchmark the new dense mode clustering by using several artificially created data sets and brain imaging data sets from an event perception task by perturbing the data set with noise and measuring three kinds of deviation from the original cluster solution. We present benchmark results that demonstrate that the mode clustering method consistently outperforms the commonly used single-linkage clustering, k means method (centroid method) and Ward's method (variance method).

[1]  S. C. Johnson Hierarchical clustering schemes , 1967, Psychometrika.

[2]  Thomas E. Nichols,et al.  Nonstationary cluster-size inference with random field and permutation methods , 2004, NeuroImage.

[3]  Karl J. Friston,et al.  Detecting Activations in PET and fMRI: Levels of Inference and Power , 1996, NeuroImage.

[4]  K. Murase,et al.  Determination of arterial input function using fuzzy clustering for quantification of cerebral blood flow with dynamic susceptibility contrast‐enhanced MR imaging , 2001, Journal of magnetic resonance imaging : JMRI.

[5]  Karl J. Friston,et al.  Dynamic causal modelling , 2003, NeuroImage.

[6]  Kai-Hsiang Chuang,et al.  Model-free functional MRI analysis using Kohonen clustering neural network and fuzzy C-means , 1999, IEEE Transactions on Medical Imaging.

[7]  Stephen M. Smith,et al.  A global optimisation method for robust affine registration of brain images , 2001, Medical Image Anal..

[8]  R Baumgartner,et al.  Comparison of two exploratory data analysis methods for fMRI: fuzzy clustering vs. principal component analysis. , 2000, Magnetic resonance imaging.

[9]  Stephen M. Smith,et al.  Improved Optimization for the Robust and Accurate Linear Registration and Motion Correction of Brain Images , 2002, NeuroImage.

[10]  Stephen M. Smith,et al.  Temporal Autocorrelation in Univariate Linear Modeling of FMRI Data , 2001, NeuroImage.

[11]  J. Talairach,et al.  Co-Planar Stereotaxic Atlas of the Human Brain: 3-Dimensional Proportional System: An Approach to Cerebral Imaging , 1988 .

[12]  Karl J. Friston,et al.  Combining Spatial Extent and Peak Intensity to Test for Activations in Functional Imaging , 1997, NeuroImage.

[13]  Brian Everitt,et al.  Cluster analysis , 1974 .

[14]  A. Tversky,et al.  Extended similarity trees , 1986 .

[15]  Karl J. Friston,et al.  Assessing the significance of focal activations using their spatial extent , 1994, Human brain mapping.

[16]  Patrick Suppes,et al.  Foundations of measurement , 1971 .

[17]  T. Matsuka,et al.  Bottom-up and top-down brain functional connectivity underlying comprehension of everyday visual action , 2007, Brain Structure and Function.

[18]  Teuvo Kohonen,et al.  Self-Organizing Maps , 2010 .

[19]  Stephen José Hanson,et al.  Combinatorial codes in ventral temporal lobe for object recognition: Haxby (2001) revisited: is there a “face” area? , 2004, NeuroImage.

[20]  Smadar Shiffman,et al.  Interactive specification of regions of interest on brain surfaces , 2003, NeuroImage.

[21]  Karl J. Friston Analysing brain images: principles and overview , 1997 .

[22]  Jaap Van Brakel,et al.  Foundations of measurement , 1983 .

[23]  R. Edelman,et al.  Magnetic resonance imaging (2) , 1993, The New England journal of medicine.

[24]  B M Bly,et al.  The distribution of BOLD susceptibility effects in the brain is non-Gaussian , 2001, Neuroreport.

[25]  Thomas E. Nichols,et al.  Validating cluster size inference: random field and permutation methods , 2003, NeuroImage.

[26]  Jean-Baptiste Poline,et al.  Dealing with the shortcomings of spatial normalization: Multi‐subject parcellation of fMRI datasets , 2006, Human brain mapping.

[27]  Karl J. Friston,et al.  Human Brain Function , 1997 .

[28]  Kurt Hornik,et al.  A quantitative comparison of functional MRI cluster analysis , 2004, Artif. Intell. Medicine.

[29]  Heidi A. Baseler,et al.  Statistical properties of BOLD magnetic resonance activity in the human brain , 2003, NeuroImage.

[30]  Jonathan D. Cohen,et al.  Improved Assessment of Significant Activation in Functional Magnetic Resonance Imaging (fMRI): Use of a Cluster‐Size Threshold , 1995, Magnetic resonance in medicine.

[31]  John A. Hartigan,et al.  Clustering Algorithms , 1975 .

[33]  Thomas E. Nichols,et al.  Combining voxel intensity and cluster extent with permutation test framework , 2004, NeuroImage.

[34]  Rajesh Nandy,et al.  Cluster analysis of fMRI data using dendrogram sharpening , 2003, Human brain mapping.

[35]  Anthony Randal McIntosh,et al.  Towards a network theory of cognition , 2000, Neural Networks.

[36]  P. Boesiger,et al.  A new correlation‐based fuzzy logic clustering algorithm for FMRI , 1998, Magnetic resonance in medicine.

[37]  Michael Brady,et al.  Improved Optimization for the Robust and Accurate Linear Registration and Motion Correction of Brain Images , 2002, NeuroImage.

[38]  Mark W. Woolrich,et al.  Advances in functional and structural MR image analysis and implementation as FSL , 2004, NeuroImage.

[39]  Bernard Mazoyer,et al.  Cluster analysis in individual functional brain images: Some new techniques to enhance the sensitivity of activation detection methods , 1994 .

[40]  L. K. Hansen,et al.  Feature‐space clustering for fMRI meta‐analysis , 2001, Human brain mapping.

[41]  R W Cox,et al.  AFNI: software for analysis and visualization of functional magnetic resonance neuroimages. , 1996, Computers and biomedical research, an international journal.