Morphometry of medial temporal lobe subregions using high-resolution T2-weighted MRI in ADNI3: Why, how, and what's next?

Abstract

This paper for the 20th anniversary of the Alzheimer's Disease Neuroimaging Initiative (ADNI) provides an overview of magnetic resonance imaging (MRI) of medial temporal lobe (MTL) subregions in ADNI using a dedicated high-resolution T2-weighted sequence. A review of the work that supported the inclusion of this imaging modality into ADNI Phase 3 is followed by a brief description of the ADNI MTL imaging and analysis protocols and a summary of studies that have used these data. This review is supplemented by a new study that uses novel surface-based tools to characterize MTL neurodegeneration across biomarker-defined AD stages. This analysis reveals a pattern of spreading cortical thinning associated with increasing levels of tau pathology in the presence of elevated amyloid beta, with apparent epicenters in the transentorhinal region and inferior hippocampal subfields. The paper concludes with an outlook for high-resolution imaging of the MTL in ADNI Phase 4. HIGHLIGHTS: As of Phase 3, the Alzheimer's Disease Neuroimaging Initiative (ADNI) magnetic resonance imaging (MRI) protocol includes a high-resolution T2-weighted MRI scan optimized for imaging hippocampal subfields and medial temporal lobe (MTL) subregions. These scans are processed by the ADNI core to obtain automatic segmentations of MTL subregions and to derive morphologic measurements. More detailed granular examination of MTL neurodegeneration in response to disease progression is achieved by applying surface-based modeling techniques. Surface-based analysis of gray matter loss in MTL subregions reveals increasing and spatially expanding patterns of neurodegeneration with advancing stages of Alzheimer's disease (AD), as defined based on amyloid and tau positron emission tomography biomarkers in accordance with recently proposed criteria. These patterns closely align with post mortem literature on spread of pathological tau in AD, supporting the role of tau pathology in the presence of elevated levels of amyloid beta as the driver of neurodegeneration.

Keywords: Alzheimer's Disease Neuroimaging Initiative; hippocampal subfields; medial temporal lobe; morphometry; segmentation.

FIGURE 1

Overview of the HF‐T2 MRI in ADNI3. A, Sagittal view of a T1‐weighted MRI scan from ADNI3 (face removed) with the purple overlay indicating the position and orientation of the slab imaged with HF‐T2 MRI. The slab is approximately aligned with the hippocampal long axes. B, Coronal view of the HF‐T2 MRI scan from ADNI3. C, Zoomed‐in view of the right MTL in the HF‐T2 scan (corresponds to the dotted white box in [B]). D, Sagittal view of the right MTL. E, F, ASHS segmentation of MTL subregions in coronal and sagittal sections. G, 3D rendering of the ASHS segmentation. ADNI, Alzheimer's Disease Neuroimaging Initiative; ASHS, Automated Segmentation of Hippocampal Subfields algorithm; BA, Brodmann area; CA, cornu ammonis; DG, dentate gyrus; ERC, entorhinal cortex; HF‐T2, T2‐weighted MRI focused on the hippocampal region; Misc., miscellaneous label, includes hippocampal cerebrospinal fluid and cysts; MRI, magnetic resonance imaging; MTL, medial temporal lobe; PHC, parahippocampal cortex; SUB, subiculum.

FIGURE 2

Volumes of hippocampal subfields (CA1, CA2, CA3, DG, subiculum) and median thickness of MTL cortical regions (ERC, BA35, BA36, PHC) are plotted for the control group (N), biomarker‐defined progressive AD stages A–D, and the indeterminate stage group (I). Hippocampal subfield volumes are normalized by intracranial volume. The area under the receiver operating characteristic curve (AUC) is labeled for pairwise comparisons between each stage (A–D, I) and the control group (N). AUC values that are significant after correction for multiple comparisons using a permutation test are marked (***: P < 0.001 **: P < 0.01; *: P < 0.05; †: trend, i.e., uncorrected P < 0.05; ns, not significant). AD, Alzheimer's disease; BA, Brodmann area; CA, cornu ammonis; DG, dentate gyrus; ERC, entorhinal cortex; MTL, medial temporal lobe; PHC, parahippocampal cortex; SUB, subiculum.

FIGURE 3

Surface‐based analysis of MTL cortical thickness across biomarker‐defined AD stages. The left and right halves of the figure show different views (from inferior, looking at the MTL cortex, and from superior, looking at the hippocampus) of the same 3D models. The CRASHS surface template is shown in the top row, with MTL subregions labeled. The next four rows show results from a general linear model in which gray matter thickness is the dependent variable, AD stage (A to D, or N for the control group) is the categorical predictor of interest, and age and sex are nuisance predictors. Each row corresponds to the contrast between one of the A–D stages and the N group. T statistic maps are plotted on the left, and log‐transformed P values corrected for multiple comparisons using the threshold‐free cluster enhancement (TFCE) method with 10,000 permutations are plotted on the right. Larger values of the t statistic indicate greater relative differences in thickness between a given stage and the control group N, while accounting for differences in age and sex. The white outlines on the blue‐to‐red P value plots correspond to $p_{\mathrm{FWER}}=0.05$. The black outlines indicate subregion boundaries. It is important to note that in TFCE analysis, a small P value at a point on the template does not imply that there is a significant difference at that precise location, but rather that the point belongs to a set of clusters in the t statistic map, taken at different thresholds, that is significantly larger in terms of threshold‐weighted cluster area than under the null distribution of the permutation test. The last row shows a general linear model fitted to the thickness data in which participants were assigned to high‐cognition and low‐cognition groups based on terciles of the logical memory delayed recall score in each stage; the high‐versus‐low group was the categorical predictor of interest, and stage, age, and sex were used as nuisance predictors. AD, Alzheimer's disease; CRASHS, Cortical Reconstruction for Automated Segmentation of Hippocampal Subfields algorithm; MTL, medial temporal lobe.