Feature space clustering for fMRI meta-analysis.

Clustering fMRI time series has emerged in recent years as a possible alternative to parametric modelling approaches. Most of the work has been so far concerned with clustering raw time series. In this contribution we investigate the applicability of a clustering method applied to features extracted from the data. This approach is extremely versatile and encompasses previously published results (Goutte et al., 1999) as special cases. A typical application is in data reduction: as the increase in temporal resolution of fMRI experiments routinely yields fMRI sequences containing several hundreds of images, it is sometimes necessary to invoke feature extraction to reduce the dimensionality of the data space. A second interesting application is in the meta-analysis of fMRI experiment, where features are obtained from a possibly large number of single-voxel analyses. This allows in particular to check the differences and agreements between different methods of analysis. Both approaches are ..

Comparison of thresholding tractography results, obtained with and without ICE-T, in a human in vivo subject.

<p>Both results are generated from a cubic seed (dark green) placed approximately in the left MC region. Tractography without ICE-T used the original cubic seed ROI as the seed (25,000 streamlines, blue, top row). Tractography with ICE-T used the ICE-T ROI_I as seed (ICE-T_threshold 0.01, ICE-T_streams 20, purple, bottom row), shown here at various rendering thresholds (0.02, 0.01, 0.005, 0.001).The path-length dependency is very pronounced in the tractography results without ICE-T (top row), evidenced by the movement of the end-of-tract point (green arrows) as a function of the applied threshold. Probable false-positives are seen in tractography both with and without ICE-T around the descending portion of the contralateral CST (red arrows). These can be addressed in the conventional manner by the introduction of exclusion masks (dark green box and plane) that terminate and remove any streamlines that propagate through them. Here two are shown for both methods (last column) - one along the mid-sagittal plane and one in the contralateral CST. The former is to prevent streamlines crossing between the hemispheres at the cortical level dorsal to the corpus callosum due to the high partial volume effect. The latter is to prevent segmentation of a known false-positive branch of the contralateral CST.</p

Illustration of the ability of ICE-T to penetrate through a complex region.

<p>The figure shows how ICE-T successfully propagates through a known crossing-fibre region (centrum semi-ovale, light blue dotted circle, upper left panel) when seeded from the SC region (green region, upper left panel) of dataset P1. The dark blue region (upper left panel) shows the results using ICE-T_threshold of 0.02, and the red region (upper left panel) for ICE-T_threshold of 0.015. The graph (lower panel) shows the spatial extent of the ICE-T_ROI_I, sampled along the canonical streamline, from the seed region (defined as Distance  = 0) as a function of both distance from the seed region, and of the value of the ICE-T_threshold parameter. Here a coloured voxel represents that the segmented ROI was present at the given threshold and distance from the seed. Each threshold level is coloured differently for clarity. Once the ICE-T_threshold parameter falls to 0.015 and below (lower three rows), the region-growing penetrates past the complexity and continues on to extract the distal portion of the tract. The 3D rendering (upper right panel) shows the ICE-T results at the same two thresholds (0.02 in blue and 0.015 in orange). The seed region is located at the site of the green arrow (upper right panel).</p

The impact of the ICE-T_threshold parameter.

<p>Curves show the parameter's effect upon the size of ICE-T_ROI_I with the ICE-T_streams parameter fixed at 20 streamlines, shown for each of the three ROIs (MC, PFC and SC) in each of the three ex-vivo datasets (P1, P2 and P3).</p

The ICE-T Framework as Pseudo-Code.

<p>ICE-T_threshold: The level of “connection probability” (typically scaled between 0 and 1), above which a voxel must rise for it to be considered part of a significant connection, and therefore to be appended to the seed region from which the streamlines were initiated. Applied at each iteration of the feedback loop.</p><p>ICE-T_streams: The number of streamlines emitted from each voxel in the seed region during the region-growing steps.</p><p>ICE-T_iterations, I: The number of times that the seed region is iteratively grown.</p

Tractography with ICE-T from each of the three ROIs (MC, PFC and SC), for each pig brain (P1, P2, P3).

<p>Parameters: ICE-T_streams = 20, ICE-T_threshold = 0.005, results rendered at 0.005. Data show the glass brain of the unweighted diffusion image as anatomical reference.</p

Comparison of tractography with and without ICE-T.

<p>Tractography is seeded from both the MC & PFC seeds (shown in green) of dataset P1.(Left Panel) Tractography without ICE-T (i.e. directly with the seed ROI) using N = 64000 streamlines per voxel and then visualised using the following thresholds (from top)  = 0.050, 0.020, 0.010, 0.005, 0.002.(Right Panel) Tractography with ICE-T ROI_I (generated using ICE-T_streams = 20, ICE-T_threshold = 0.01) used as seed, and then visualised at the following thresholds (from top)  = 0.015, 0.012, 0.010, 0.005, 0.002.Green arrows indicate areas demonstrating “near-seed flare”. Red arrows indicate premature termination of the tract ROI due to the PLD effect causing the PICo values to fall below the applied threshold.</p