Hippocampus co-atrophy pattern in dementia deviates from covariance patterns across the lifespan

Plachti, Anna; Kharabian, Shahrzad; Eickhoff, Simon B.; Balajoo, Somayeh Maleki; Hoffstaedter, Felix; Varikuti, Deepthi P.; Jockwitz, Christiane; Caspers, Svenja; Amunts, Katrin; Genon, Sarah

doi:10.1093/brain/awaa222

Cited by 22 publications

(25 citation statements)

References 45 publications

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“…1 and 3) which is also consistent with the structural covariation pattern of the lateral posterior subregion (similarly corresponding to Posterior-CA(Green)) previously demonstrated in healthy elderly (Supplementary-Fig. 8D) [15]. This pattern seems to be related to the fornix path which plays a signi cant role in interconnecting the hippocampal formation with the subcortical structures.…”

Section: Different Hippocampal Metabolic Network Corresponding To Different Behavioral and Neurophysiological Systemssupporting

confidence: 88%

“…The mediolateral differentiation in the body and tail regions then strikingly follow the cytoarchitectonic differentiation between the CA and subiculum sub elds. Interestingly, the obtained metabolic differentiation pattern within the hippocampus in this study closely resembles the hippocampus parcellation based on structural covariance [15,31] suggesting a close interplay between metabolic features and macrostructural changes. In the next paragraphs, we rst discuss the metabolic networks driving this differentiation with their related physiological and behavioral engagement.…”

Section: Metabolic Pro Les Differ Across the Longitudinal Axis And Sub Eldssupporting

confidence: 70%

“…Our recent examination of hippocampal structural covariance networks in healthy populations, including older populations, revealed three hippocampal subregions with speci c structural covariance networks (Fig. 1B) [15] the hippocampal head as part of an anterior network, the lateral posterior hippocampus (corresponding to the body and tail of Cornu Ammonis (CA) elds) as part of a subcortical network and the medial posterior hippocampus (corresponding to the body and tail of the subiculum) showing a very vast cortical structural covariance pattern. As illustrated in Fig.…”

Section: Introductionmentioning

confidence: 92%

“…Hence, the metabolic covariance pattern of Posteriorsubiculum(Blue) with a very extended cortical network is congruent with its central role in the hippocampal output pathways. We have recently shown that the medial posterior subregion (similarly corresponding to Posterior-subiculum(Blue)) in healthy elderly subjects has a very broad pattern of structural covariance [15], especially with the posterior brain regions, but also with frontal regions (Supplementary-Fig. 8D).…”

Section: Different Hippocampal Metabolic Network Corresponding To Different Behavioral and Neurophysiological Systemsmentioning

confidence: 99%

“…1 (C and D), these structural covariance networks appear, to a great extent, to follow white matter tracts which connect the medial temporal lobe to subcortical and cortical regions suggesting that morphological covariance across the brain in a healthy population is, to a great extent, in uenced by anatomical connection [16,17]. Nevertheless and importantly, our previous study also suggested that in patients with dementia, the posterior regions differentiate together from the more anterior regions in their co-atrophy patterns suggesting that the complex pathophysiological process in Alzheimer's disease would not result in atrophy patterns following speci cally structural covariance networks seen in the healthy population (such as a speci c alteration within the cortical network), but rather, would affect several subregions [15]. Because atrophy patterns that can be observed at the macroscale in dementia are the ultimate consequences of a long and complex pathological process, a rst step towards understanding how this process nally leads to complex macrostructural atrophy patterns is to identify the metabolic changes that affect the hippocampal metabolic networks.…”

Section: Introductionmentioning

confidence: 96%

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Hippocampal Metabolic Subregions and Networks: Behavioral, Molecular and Pathological Aging Profiles

Balajoo

Eickhoff

Masouleh

et al. 2021

Preprint

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Purpose: Hippocampal dysfunction happens across many neuropsychiatric disorders and is the hallmark of Alzheimer’s disease with evidenced metabolic alterations. However, while metabolic changes are a key aspect of Alzheimer’s disease, hippocampal metabolic networks, as defined by metabolic covariance, haven’t been identified in healthy populations. As the hippocampus portrays cytoarchitectural, connectional, and functional heterogeneity, heterogeneous patterns of metabolic covariance could be expected. Methods: We first characterized this heterogeneity with a data-driven approach by identifying the spatial pattern of hippocampus differentiation based on metabolic covariance with the rest of the brain in FDG-PET data of large healthy elderly cohort (n=362). Then, we characterized the metabolic networks of the robustly defined subregions. In the following, we characterized the disentangled hippocampal metabolic networks with regards to behavioral and neurotransmitter systems using quantitative decoding. Finally, we examined how the local metabolism in the hippocampal subregions is influenced by Alzheimer’s disease pathology in a cohort of ADNI participants (n = 580). Results: Based on hippocampal-brain metabolic covariance in a healthy elderly cohort, we found a differentiation into primarily anterior vs. posterior and secondarily Cornu Ammonis (CA) vs. subiculum subregions. Characterizing the associated metabolic networks revealed that the anterior-subiculum network including temporal-pole and orbitofrontal regions relates to self, motivation and mentalizing behavior and is influenced by dopaminergic systems. In contrast, the posterior-subiculum shows a wide cortical network engaged in action- and world-oriented cognition targeted by serotoninergic systems. The anterior- and posterior-CA, connected respectively to amygdala and broader subcortical networks, are associated to several transporters release. Local metabolism comparison between Alzheimer’s disease-related diagnosis groups revealed early CA’s alterations while posterior subicular alterations appear at advanced stages in line with broader cortical atrophy and behavioral dysfunctions.Conclusion: Future studies should delineate patients’ individual profiles according to hippocampal subregions and networks.

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Section: Different Hippocampal Metabolic Network Corresponding To Different Behavioral and Neurophysiological Systemssupporting

confidence: 88%

Section: Metabolic Pro Les Differ Across the Longitudinal Axis And Sub Eldssupporting

confidence: 70%

Section: Introductionmentioning

confidence: 92%

Section: Different Hippocampal Metabolic Network Corresponding To Different Behavioral and Neurophysiological Systemsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 96%

See 3 more Smart Citations

Hippocampal Metabolic Subregions and Networks: Behavioral, Molecular and Pathological Aging Profiles

Balajoo

Eickhoff

Masouleh

et al. 2021

Preprint

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Hippocampal metabolic subregions and networks: Behavioral, molecular, and pathological aging profiles

Balajoo

Eickhoff

Masouleh

et al. 2023

Alzheimer's & Dementia

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INTRODUCTIONHippocampal local and network dysfunction is the hallmark of Alzheimer's disease (AD).METHODSWe characterized the spatial patterns of hippocampus differentiation based on brain co‐metabolism in healthy elderly participants and demonstrated their relevance to study local metabolic changes and associated dysfunction in pathological aging.RESULTSThe hippocampus can be differentiated into anterior/posterior and dorsal cornu ammonis (CA)/ventral (subiculum) subregions. While anterior/posterior CA show co‐metabolism with different regions of the subcortical limbic networks, the anterior/posterior subiculum are parts of cortical networks supporting object‐centered memory and higher cognitive demands, respectively. Both networks show relationships with the spatial patterns of gene expression pertaining to cell energy metabolism and AD's process. Finally, while local metabolism is generally lower in posterior regions, the anterior–posterior imbalance is maximal in late mild cognitive impairment with the anterior subiculum being relatively preserved.DISCUSSIONFuture studies should consider bidimensional hippocampal differentiation and in particular the posterior subicular region to better understand pathological aging.

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Mapping the macrostructure and microstructure of the in vivo human hippocampus using diffusion MRI

Karat,

DeKraker,

Hussain

et al. 2023

Human Brain Mapping

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The hippocampus is classically divided into mesoscopic subfields which contain varying microstructure that contribute to their unique functional roles. It has been challenging to characterize this microstructure with current magnetic resonance based neuroimaging techniques. In this work, we used diffusion magnetic resonance imaging (dMRI) and a novel surface‐based approach in the hippocampus which revealed distinct microstructural distributions of neurite density and dispersion, T1w/T2w ratio as a proxy for myelin content, fractional anisotropy, and mean diffusivity. We used the neurite orientation dispersion and density imaging (NODDI) model optimized for grey matter diffusivity to characterize neurite density and dispersion. We found that neurite dispersion was highest in the cornu ammonis (CA) 1 and subiculum subfields which likely captures the large heterogeneity of tangential and radial fibres, such as the Schaffer collaterals, perforant path, and pyramidal neurons. Neurite density and T1w/T2w were highest in the subiculum and CA3 and lowest in CA1, which may reflect known myeloarchitectonic differences between these subfields. Using a simple logistic regression model, we showed that neurite density, dispersion, and T1w/T2w measures were separable across the subfields, suggesting that they may be sensitive to the known variability in subfield cyto‐ and myeloarchitecture. We report macrostructural measures of gyrification, thickness, and curvature that were in line with ex vivo descriptions of hippocampal anatomy. We employed a multivariate orthogonal projective non‐negative matrix factorization (OPNNMF) approach to capture co‐varying regions of macro‐ and microstructure across the hippocampus. The clusters were highly variable along the medial–lateral (proximal–distal) direction, likely reflecting known differences in morphology, cytoarchitectonic profiles, and connectivity. Finally, we show that by examining the main direction of diffusion relative to canonical hippocampal axes, we could identify regions with stereotyped microstructural orientations that may map onto specific fibre pathways, such as the Schaffer collaterals, perforant path, fimbria, and alveus. These results highlight the value of combining in vivo dMRI with computational approaches for capturing hippocampal microstructure, which may provide useful features for understanding cognition and for diagnosis of disease states.

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Hippocampus co-atrophy pattern in dementia deviates from covariance patterns across the lifespan

Cited by 22 publications

References 45 publications

Hippocampal Metabolic Subregions and Networks: Behavioral, Molecular and Pathological Aging Profiles

Hippocampal Metabolic Subregions and Networks: Behavioral, Molecular and Pathological Aging Profiles

Hippocampal metabolic subregions and networks: Behavioral, molecular, and pathological aging profiles

Mapping the macrostructure and microstructure of the in vivo human hippocampus using diffusion MRI

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