Thirty right-handed subjects (16 APOE-3/3; 14 APOE-4/3) gave written informed consent to participate in this study, which was approved by the University of California, Los Angeles Office for the Protection of Research Subjects. All subjects were given a diagnostic evaluation to rule out medical and psychiatric problems and screened for any history of neurological problems or psychiatric conditions including hypertension or cardiovascular disease. Subjects were required to score 0 (no dementia) on the Clinical Dementia Rating (CDR) Scale (Hughes et al., 1982
) and must not have met diagnostic criteria for possible or probable AD (McKhann et al., 1984
). To ensure that we did not include subjects in the early stages of dementia, we only included subjects who scored ≥ 27 on the Mini-Mental State Exam (MMSE) (Bleecker et al., 1988
). Subjects were genotyped for APOE using standard methods (Wenham et al., 1991
). To avoid experimental bias, the investigators performing the scanning and unfolding procedures were blind to each subject’s genetic status. The two genetic groups were similar across all neuropsychological tests and clinical characteristics, and we detected no significant differences between the groups ().
Demographic And Clinical Characteristics Of Subject Groups.
MRI scanning was performed on a 3-Tesla scanner (General Electric, Waukesha, Wis.). Echo-planar shimming sequence achieved approximately 0.3 ppm r.m.s. field inhomogeneity. A Fast Spin Echo (FSE) sagittal sequence [TR 6000/TE 17&85/4 mm thick, 1 mm spacing/FOV 20 cm/1 NEX/ 26 slices] was used for spatial localization. A high-resolution oblique coronal T2 FSE structural sequence was acquired for structural segmentation and unfolding procedures [TR 3000/TE 41/ 3mm thick, 0 mm spacing/FOV 20 cm/ 2 NEX/16 slices, in-plane voxel size 0.39×0.39mm]. These images were acquired perpendicular to the long axis of the hippocampus to minimize slice-to-slice variation in anatomy (Zeineh et al., 2001
). A T-1 weighted 3-dimensional volume scan [SPGR, T1 500/TE 3.7/FOV 20/1 NEX] was also acquired to visually serve as a guide in sulcal visualization during segmentation procedures in the same way an atlas is used as a visual reference.
Structural unfolding was used to create a continuous gray matter strip within the MTL using the high-resolution structural images, described in detail by Zeineh and colleagues (Zeineh et al., 2000
; Zeineh et al., 2003
). The unfolding procedure is designed to take the entire volume of pixels within the MTL defined as gray matter in a 3-D strip and flatten it so the volume is visible as a single 2-D sheet. First, we manually defined white matter and cerebrospinal fluid (CSF) within the MTL (). We segmented the following as “white matter”: (1) white matter in the parahippocampal gyrus throughout the fill rostrocaudal extent, (2) white matter on the medial aspect of the occipitotemporal sulcus, (3) CSF in the inferior horn of the lateral ventricle, and (4) the fornix. We segmented the following as “CSF”: (5) CSF in the collateral sulcus and the hippocampal fissure, (6) the ambient cistern (including the wing) and the adjacent posterior cerebral artery and basal vein, and (7) the boundaries of the segmentation. We next used a region-expansion algorithm to grow 18 connected layers of gray matter from the white-matter edge, stopping at the boundary of CSF and covering all of the gray matter pixels of interest. The final product was a gray matter ribbon that included cornu ammonis (CA) fields 1, 2, and 3, the dentate gyrus (DG), subiculum (Sub), ERC, perirhinal cortex (PRC), parahippocampal cortex (PHC), and the fusiform gyrus (Fus). As in previous studies using the unfolding procedure (Eldridge et al., 2005; Zeineh et al., 2000
; Zeineh et al., 2003
), we identified these regions using a rule-based decision schema in concert with histological and MRI atlases (Amaral and Insausti, 1990
; Duvernoy, 1998
; Mai, 1997
). Unfolding computations were performed using modified versions of mrUnfold software [available at http://sourceforge.net/projects/mtl-unfolding/
] (Engel et al., 1997
). As shown in color on the MRI image () and explained in detail in the figure legend, we delineated the boundaries of the following subregions on the in-plane image based on reference to atlases (Amaral and Insausti, 1990
; Duvernoy, 1998
; Mai, 1997
) : Fus, PHC, PRC, ERC, Sub, CA1 and CA fields 2, 3 and dentate gyrus (CA23DG). Because of the convoluted shape of CA fields 2, 3 and dentate gyrus in the anterior hippocampus the regions were treated as a single entity. The thickness map of one subject in 3-dimensional space, superimposed on an anatomical image is shown in .
All demarcations of structural boundaries were traced on the original coronal images and projected mathematically to the corresponding coordinates in flat map space. We performed reverse transformations for each subject and visually compared this reverse transformation to the original data to confirm that the data was in the correct space (Zeineh et al., 2000
). Prior work has confirmed that this algorithm produces topographically correct unfoldings with minimal levels of distortion (Wandell et al., 1996; Zeineh et al., 2000
; Zeineh et al., 2003
Thickness maps were produced in 3-dimensional (3D) space as follows: for each gray matter voxel in each layer of the manifold we computed the distance to the closest non-gray matter voxel (white matter or CSF). The middle layer of any given section of cortex will be equidistant from the adjacent white matter and CSF and will have the greatest distance values to the closest non-gray voxel. For each 2-dimensional (2D) flat voxel, we took the maximum of the distance values of the corresponding 3D voxels across all layers (thus effectively extracting this middle layer which will be the maximum) and multiplied by two to arrive at a thickness value. In the 2D maps, thickness was represented in voxel intensity.
We conducted a region of interest (ROI) analysis of cortical thickness for the following subregions: Fus, PHC, PRC, ERC, Sub, CA1, and CA23DG. We computed the mean thickness in each subregion by averaging the thickness across all 2-D voxels within each ROI. To average across hemispheres in each subject, we weighted the mean thickness according to the number of 2D voxels within that subregion in order to minimize the potentially confounding effects of surface area asymmetries. This weighted mean thickness for each subregion was used in our statistical analyses. We calculated the total hippocampal thickness in each subject by performing a similarly weighted average across all of the subregions. In order to assess volume, we calculated the volume of the voxels for each given region in 3-D space. In addition, the total mean cortical volume was calculated by averaging across all of these subregions within the MTL.
Two sample t-tests assuming unequal variance determined the significance of cortical thickness and volume differences between the two genetic groups. We used a corrected Bonferroni threshold of p=0.007 to adjust for multiple comparisons between the subregions. In order to assess the reliability of this technique for use in calculating cortical thickness, a single rater repeated the entire analysis on 6 subjects (3 APOE-3/3 and 3 APOE-3/4) and calculated reliability for both thickness and volumetric measurements. We used the intra-class correlation coefficient (ICC) to evaluate intra-rater reliability, reporting also the standard error of the mean (SEM).