Increasing the power of functional maps of the medial temporal lobe by using large deformation diffeomorphic metric mapping

Abstract

The functional magnetic resonance imagery responses of declarative memory tasks in the medial temporal lobe (MTL) are examined by using large deformation diffeomorphic metric mapping (LDDMM) to remove anatomical variations across subjects. LDDMM is used to map the structures of the MTL in multiple subjects into extrinsic atlas coordinates; these same diffeomorphic mappings are used to transfer the corresponding functional data activation to the same extrinsic coordinates. The statistical power in the averaged LDDMM mapped signals is significantly increased over conventional Talairach-Tournoux averaging. Activation patterns are highly localized within the MTL. Whereas the present demonstration has been aimed at enhancing alignment within the MTL, this technique is general and can be applied throughout the brain.

Fig. 1.

Functional, structural, and atlas domains connected by means of diffeomorphic mapping.

Fig. 2.

ROI rough hand segmentations of the hippocampus (Left) and other temporal lobe structures (Right). Red, boundaries of ROI segmentations used for the mapping algorithm; blue, the gold standard segmentations used for quantifying the accuracy of the mapping algorithms only.

Fig. 3.

Errors depicted in MRI volume for LDDMM and Talairach alignment. (A and D) The original target hippocampuses in each subject. (B and E) The discrepancy between the template mapped to the target by using the MRI-LDDMM. Errors are shown in red; notice the small errors near the boundary. (C and F) The discrepancy for the Talairach alignment, which exhibits errors (red) over the entire hippocampus.

Fig. 4.

Comparisons of alignment accuracy visualizing the results of averaging the rough segmentations of 15 new participants' medial temporal lobes by using Talairach (Upper) and ROI-LDDMM (Lower) alignment in both coronal (Left) and sagittal (Right) cropped slices. The average structural and the average segmentations across participants are shown. The overlaid colors range from red (all participants agree this voxel is in the MTL) to blue (one participant labels this voxel as part of the MTL).

Fig. 5.

L₁ error rate percentages of multiple subject hippocampi (A) and temporopolar cortices (B) for different registration methods. Error rates shown are L₁ errors interpolated as fractions between 0 and 1 and normalized by target volume. Error rates are ordered according to the Talairach alignment (highest dark blue), the ROI-Affine mapping (light blue), the MRI-LDDMM segmentation (green), and the rough segmentation based ROI-LDDMM (orange). As a gold standard comparison (baseline), the lowest error bars result from using the gold standard segmentation (which is never available) as the true registration function for the LDDMM.

Fig. 6.

Areas showing significant activity during the recognition memory task associated with incidental encoding, shown as colored overlays on cropped coronal slices through the MTL (left of each image is left side of brain; there is a 4-mm gap between successive slices). Upper two rows show activity for two memory-related contrasts after Talairach alignment. Lower two rows show the same after ROI-LDDMM alignment. RvF, remembered vs. forgotten; CR-R v CR-F, correct rejections later remembered vs. correct rejections later forgotten.

Fig. 7.

Plotted are the hemodynamic responses (as represented by the set of beta coefficients calculated by multiple regression in a deconvolution-style analysis) for the single voxel within the right perirhinal cortex that had the strongest (highest t value) signal for each technique. The three selected voxels were within several millimeters of each other.