Reference standard space hippocampus labels according to the European Alzheimer's Disease Consortium–Alzheimer's Disease Neuroimaging Initiative harmonized protocol: Utility in automated volumetry

Wolf, Dominik; Bocchetta, Martina; Preboske, Gregory M.; Boccardi, Marina; Grothe, Michel J.

doi:10.1016/j.jalz.2017.01.009

Cited by 37 publications

(34 citation statements)

References 42 publications

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“…4 An ROI mask for the hippocampus was obtained by manual delineation of the hippocampus in the reference template following the harmonized protocol for hippocampus segmentation. 22 The parahippocampal gyrus ROI was derived from the corresponding label in the "Hammers" maximum probability structural atlas, 23 which includes both the parahippocampal gyrus proper and the entorhinal cortex.…”

Section: Extraction Of Imaging Features From Bf and Hippocampal Roismentioning

confidence: 99%

Neuropathologic features associated with basal forebrain atrophy in Alzheimer disease

Teipel

Fritz²,

Grothe

2020

Neurology

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Objective:To study the neuropathological correlates of cholinergic basal forebrain atrophy as determined using ante-mortem MRI in the Alzheimer’s disease (AD) spectrum.Methods:We determined associations between basal forebrain (BF) volume from antemortem MRI brain scans and post-mortem assessment of neuropathological features, including neuritic plaques, neurofibrillary tangles (NFT), Lewy body (LB) pathology, and TDP-43, in 64 cases of the Alzheimer’s Disease Neuroimaging Initiative (ADNI) cohort. For comparison, we assessed neuropathological features associated with hippocampal and parahippocampal gyrus atrophy. In addition to region of interest-based analysis, we determined the association of neuropathological features with whole brain grey matter volume using regionally unbiased voxel-based volumetry.Results:BF atrophy was associated with Thal amyloid phases (95% confidence interval -0.49 - -0.01, p=0.049) and presence of LB pathology (95% CI -0.54 - -0.06, p=0.015), as well as with the degree of LB pathology within the Nucleus basalis Meynert (NbM) (95% CI -0.54 - -0.07, p=0.025). These effects were no more significant after false discovery rate (FDR) correction. Hippocampal atrophy was significantly associated with the presence of TDP-43 pathology (95% CI -0.61 - -0.17, p=0.003; surviving FDR correction), in addition to dentate gyrus NFT load (95% CI -0.49 - -0.01, p = 0.044; uncorrected). Voxel-based analysis confirmed spatially restricted effects of Thal phases and presence of LB pathology on BF volume.Conclusions:These findings indicate that neuropathological correlates of regional atrophy differ substantially between different brain regions that are typically involved in AD-related neurodegeneration, including different susceptibilities to common co-morbid pathologies.

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Section: Extraction Of Imaging Features From Bf and Hippocampal Roismentioning

confidence: 99%

Neuropathologic features associated with basal forebrain atrophy in Alzheimer disease

Teipel

Fritz²,

Grothe

2020

Neurology

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“…Further details can be found on the project's website (http://www.hippocampalprotocol.net/SOPs/index.php). More recently, the manual hippocampus labels were integrated into an automated volumetry pipeline to ease processing of larger numbers of cases [30]. A high correspondence between manual and automated segmented hippocampi based on the harmonized protocol was shown in 135 MRI scans that were measured using both manual segmentation and automated segmentation.…”

Section: Image Processingmentioning

confidence: 99%

“…Following this automated processing pipeline, the T1-weighed MPRAGE images were normalized to the MNI reference template from CAT12 using SPM12 new segment and the Diffeomorphic Anatomical Registration Through Exponentiated Lie algebra (DARTEL) algorithm [31]. Subsequently, hippocampus volume was automatically computed from all voxels within the harmonized hippocampus mask regions of the normalized and modulated grey matter maps [30]. The raw volume estimates were proportionally scaled to total intracranial volume to adjust for head size.…”

Section: Image Processingmentioning

confidence: 99%

Multicenter Tract-Based Analysis of Microstructural Lesions within the Alzheimer’s Disease Spectrum: Association with Amyloid Pathology and Diagnostic Usefulness

Teipel

Kuper-Smith

Bartels

et al. 2019

JAD

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Diffusion changes as determined by diffusion tensor imaging are potential indicators of microstructural lesions in people with mild cognitive impairment (MCI), prodromal Alzheimer's disease (AD), and AD dementia. Here we extended the scope of analysis toward subjective cognitive complaints as a pre-MCI at risk stage of AD. In a cohort of 271 participants of the prospective DELCODE study, including 93 healthy controls and 98 subjective cognitive decline (SCD), 45 MCI, and 35 AD dementia cases, we found reductions of fiber tract integrity in limbic and association fiber tracts in MCI and AD dementia compared with controls in a tract-based analysis (p < 0.05, family wise error corrected). In contrast, people with SCD showed spatially restricted white matter alterations only for the mode of anisotropy and only at an uncorrected level of significance. DTI parameters yielded a high cross-validated diagnostic accuracy of almost 80% for the clinical diagnosis of MCI and the discrimination of A␤ positive MCI cases from A␤ negative controls. In contrast, DTI parameters reached only random level accuracy for the discrimination between A␤ positive SCD and control cases from A␤ negative controls. These findings suggest that in prodromal stages of AD, such as in A␤ positive MCI, multicenter DTI with prospectively harmonized acquisition parameters yields diagnostic accuracy meeting the criteria for a useful biomarker. In contrast, automated tractbased analysis of DTI parameters is not useful for the identification of preclinical AD, including A␤ positive SCD and control cases.

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“…MRI images in ADNI are acquired at multiple sites using scanner-speci c T1-weighted sagittal 3D MPRAGE sequences and undergo standardized image pre-processing steps to improve uniformity across the scanners (http://adni.loni.usc.edu/methods/documents/). We extracted regional grey matter volumes from these scans using a previously described automated volumetry approach implemented in statistical parametric mapping software (SPM8, Wellcome Trust Center for Neuroimaging) and the VBM8-toolbox (22,23). Brie y, this involves automated tissue class segmentation and high-dimensional spatial normalization to an aging/AD-speci c reference template.…”

Section: Biomarkersmentioning

confidence: 99%

Data-driven FDG-PET subtypes of Alzheimer’s disease-related neurodegeneration

Levin

Ferreira

Lange

et al. 2020

Preprint

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BackgroundPrevious research has described distinct subtypes of Alzheimer’s disease (AD) based on differences in regional patterns of brain atrophy on MRI. We conducted a data-driven exploration of distinct AD neurodegeneration subtypes using FDG-PET as a sensitive molecular imaging marker of neurodegenerative processes.MethodsHierarchical clustering of voxel-wise FDG-PET data from 177 amyloid-positive patients with AD dementia enrolled in the Alzheimer’s Disease Neuroimaging Initiative (ADNI) was used to identify distinct hypometabolic subtypes of AD, which were then further characterized with respect to clinical and biomarker characteristics. We then classified FDG-PET scans of 217 amyloid-positive patients with mild cognitive impairment (‘prodromal AD’) according to the identified subtypes and studied their domain-specific cognitive trajectories and progression to dementia over a follow-up interval of up to 72 months. ResultsThree main hypometabolic subtypes were identified: (i) “typical” (48.6%), showing a classic posterior temporo-parietal hypometabolic pattern, (ii) “limbic-predominant” (44.6%), characterized by old age and a memory-predominant cognitive profile, and (iii) a relatively rare “cortical-predominant” subtype (6.8%) characterized by younger age and more severe executive dysfunction. Subtypes classified in the prodromal AD sample demonstrated similar subtype characteristics as in the AD dementia sample and further showed differential courses of cognitive decline. ConclusionsThese findings complement recent research efforts on MRI-based identification of distinct AD atrophy subtypes and may provide a potentially more sensitive molecular imaging tool for early detection and characterization of AD-related neurodegeneration variants at prodromal disease stages.

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Reference standard space hippocampus labels according to the European Alzheimer's Disease Consortium–Alzheimer's Disease Neuroimaging Initiative harmonized protocol: Utility in automated volumetry

Abstract: The consensual HarP hippocampus labels in the MNI152 template can serve as a reference standard for automated image analyses involving MNI standard space normalization.

Cited by 37 publications

References 42 publications

Neuropathologic features associated with basal forebrain atrophy in Alzheimer disease

Neuropathologic features associated with basal forebrain atrophy in Alzheimer disease

Multicenter Tract-Based Analysis of Microstructural Lesions within the Alzheimer’s Disease Spectrum: Association with Amyloid Pathology and Diagnostic Usefulness

Data-driven FDG-PET subtypes of Alzheimer’s disease-related neurodegeneration

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