Manual segmentation of the fornix, fimbria, and alveus on high-resolution 3T MRI: Application via fully-automated mapping of the human memory circuit white and grey matter in healthy and pathological aging.
Neuroimage
; 170: 132-150, 2018 04 15.
Article
en En
| MEDLINE
| ID: mdl-27765611
ABSTRACT
Recently, much attention has been focused on the definition and structure of the hippocampus and its subfields, while the projections from the hippocampus have been relatively understudied. Here, we derive a reliable protocol for manual segmentation of hippocampal white matter regions (alveus, fimbria, and fornix) using high-resolution magnetic resonance images that are complementary to our previous definitions of the hippocampal subfields, both of which are freely available at https//github.com/cobralab/atlases. Our segmentation methods demonstrated high inter- and intra-rater reliability, were validated as inputs in automated segmentation, and were used to analyze the trajectory of these regions in both healthy aging (OASIS), and Alzheimer's disease (AD) and mild cognitive impairment (MCI; using ADNI). We observed significant bilateral decreases in the fornix in healthy aging while the alveus and cornu ammonis (CA) 1 were well preserved (all p's<0.006). MCI and AD demonstrated significant decreases in fimbriae and fornices. Many hippocampal subfields exhibited decreased volume in both MCI and AD, yet no significant differences were found between MCI and AD cohorts themselves. Our results suggest a neuroprotective or compensatory role for the alveus and CA1 in healthy aging and suggest that an improved understanding of the volumetric trajectories of these structures is required.
Palabras clave
Texto completo:
1
Colección:
01-internacional
Banco de datos:
MEDLINE
Asunto principal:
Envejecimiento
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Fórnix
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Enfermedad de Alzheimer
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Neuroimagen
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Disfunción Cognitiva
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Sustancia Gris
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Sustancia Blanca
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Hipocampo
Límite:
Adolescent
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Adult
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Aged
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Aged80
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Female
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Humans
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Male
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Middle aged
Idioma:
En
Revista:
Neuroimage
Asunto de la revista:
DIAGNOSTICO POR IMAGEM
Año:
2018
Tipo del documento:
Article