Selective effect of age, Apo e4, and Alzheimer's disease on hippocampal subfields
Open accessAbstract
Histopathological studies and animal models suggest that different physiological and pathophysiological processes exert different subfield specific effects on the hippocampus. High‐resolution images at 4T depict details of the internal structure of the hippocampus allowing for in vivo volumetry of hippocampal subfields. The aims of this study were (1) to determine patterns of hippocampal subfield volume loss due to normal aging and Apo e4 carrier state, (2) to determine subfield specific volume losses due to preclinical (MCI) and clinical Alzheimer's disease (AD) and their modification due to age and Apo e4 carrier state. One hundred fifty seven subjects (119 cognitively healthy elderly controls, 20 MCI and 18 AD) were studied with a high resolution T2 weighted imaging sequence obtained at 4T aimed at the hippocampus. Apo e4 carrier state was known in 95 subjects (66 controls, 14 MCI, 15 AD). Subiculum (SUB), CA1, CA1‐CA2 transition zone (CA1‐2 transition), CA3‐ dentate gyrus (CA3&DG) were manually marked. Multiple linear regression analysis was used to test for effects of age, Apo e4 carrier state and effects of MCI and AD on different hippocampal subfields. Age had a significant negative effect on CA1 and CA3&DG volumes in controls ( P < 0.05). AD had significantly smaller volumes of SUB, CA1, CA1‐2 transition, and MCI had smaller CA1‐2 transition volumes than controls ( P < 0.05). Apo e4 carrier state was associated with volume loss in CA3&DG compared to non‐Apo e4 carriers in healthy controls and AD. Based on these findings, we conclude that subfield volumetry provides regional selective information that allows to distinguish between different normal and pathological processes affecting the hippocampus and thus for an improved differential diagnosis of neurodegenerative diseases affecting the hippocampus. © 2009 Wiley‐Liss, Inc.
AI snapshot
Written from the paper’s full text.
TL;DR
High-resolution 4T MRI subfield volumetry detected selective hippocampal subfield effects of age, Apo e4 carrier state, and Alzheimer’s disease/MCI. The authors conclude that subfield volumetry provides regional information beyond total hippocampal volume for differentiating normal and pathological processes.
Key findings
- In cognitively intact controls, age had a significant negative effect on CA1 (β = −1.02, P < 0.0001) and CA3&DG (β = −1.15, P < 0.001); CA1 was stable until the 6th decade and CA3&DG until the 5th decade before declining.
- AD had significantly smaller SUB (94.9 ± 31.4 vs. 119.2 ± 23.96), CA1 (165.6 ± 38.6 vs. 203.3 ± 29.3), and CA1–2 transition volumes (8.5 ± 2.0 vs. 12.1 ± 3.3) than controls, while CA3&DG was not different; MCI showed significant volume loss only in CA1–2 transition (9.02 ± 1.64 vs. controls).
- Apo e4 carrier state was associated with smaller CA3&DG volumes in healthy controls (Apo e4 negative 146 ± 24.1 vs. positive 134.06 ± 25.1) and in AD (Apo e4 negative 159.0 ± 29.6 vs. positive 124.8 ± 26.8); MCI Apo e4 carriers also had smaller CA3&DG (132.0 ± 12.5 vs. 141.9 ± 35.3) but not significantly.
Methods
- One hundred fifty-seven subjects were studied: 119 cognitively healthy elderly controls, 20 MCI, and 18 AD; Apo e4 carrier state was known in 95 subjects (66 controls, 14 MCI, 15 AD).
- High-resolution T2-weighted fast spin echo images were acquired at 4T; SUB, CA1, CA1–CA2 transition zone, and CA3&DG were manually marked, with intracranial volume used as a covariate.
- Multiple linear regression tested effects of age, Apo e4, and disease group; post hoc analyses used Duncan’s tests or Mann–Whitney tests.
Limitations
- The paper states that the AD and MCI samples were relatively small, that findings in the small Apo e4 subgroup are preliminary and need confirmation in a larger population, and that histopathological correlation studies are necessary to verify the subfield findings.
- The paper notes that manual subfield labeling is time-consuming and requires an experienced rater, limiting usefulness for large studies; automated methods have their own sensitivity to shape variants, intensity changes, and image quality.
- Evident from the text: the study is cross-sectional, Apo e4 genotype was available only in a subset, and no direct histopathological validation was performed.
Model-written summary. Check it against the paper before relying on it.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
0 of 11 cited sources checked · 0 supported · 0 flagged
- Findingown data
Elderly controls (60-85 years) with Apo e4 have smaller CA3&DG volumes than those without (127.0 ± 24.7 vs 144.0 ± 24.8).
“as did the group of elderly (60–85 yrs) controls (127.0 ± 24.7 vs. 144.0 ± 24.8).”
Quote in the paperSubstantial claim - Findingown data
In controls with known Apo e4 status, the interaction between Apo e4 and age on CA3&DG is not significant.
“however, the interaction between Apo e4 and age was not significant.”
Quote in the paperNot a substantial claim - Findingown data
In controls with known Apo e4 status, CA1 volume is not influenced by Apo e4 carrier state (P = 0.81).
“CA1 volume was not influenced by Apo e4 carrier state (P = 0.81)”
Quote in the paperSubstantial claim - Findingown data
In controls with known Apo e4 status, age has a significant negative effect on CA3&DG volume (β = −0.78, P = 0.02).
“Similarly as in the total population, there was also a significant effect for age on CA3&DG (β = −0.78, P = 0.02)”
Quote in the paperSubstantial claim - Findingown data
In the subgroup with known Apo e4 carrier state, CA3&DG has a significant Apo e4 effect (Apo e4 negative β = 15.7, P = 0.0031).
“and Apo e4 effect (Apo e4 negative β = 15.7, P = 0.0031).”
Quote in the paperSubstantial claim
- Findingown data
AD does not have significantly different CA3&DG volumes compared to controls.
“whereas CA3&DG volumes were not different (137.1 ± 29.9 vs. 141.0 ± 26.2).”
Quote in the paperSubstantial claim - Findingown data
In controls with known Apo e4 status, Apo e4 carrier state is associated with smaller CA3&DG volumes (non-carriers 146 ± 24.1 vs carriers 134.06 ± 25.1).
“There was significant effect for Apo e4 (β = 9.45, P = 0.042) on CA3&DG, indicating that subjects without Apo e4 had larger CA3&DG volumes (146 ± 24.1) than subjects with Apo e4 (134.06 ± 25.1).”
Quote in the paperSubstantial claim - Findingown data
There are no significant differences in subfield volumes between AD and MCI.
“There were no significant differences between AD and MCI”
Quote in the paperSubstantial claim - Findingown data
Age has a significant negative effect on CA3&DG volume in cognitively healthy controls.
“A significant negative age effect was found for CA1 (β = −1.02, P < 0.0001) and CA3&DG (β = −1.15, P < 0.001).”
Quote in the paperSubstantial claim - Findingown data
MCI has significantly reduced CA1-2 transition volume compared to controls.
“In contrast, the atrophy was more restricted in MCI who showed significant volume losses in CA1–2 transition (9.02 ± 1.64) compared to controls”
Quote in the paperSubstantial claim - Findingown data
AD has significantly reduced SUB, CA1, and CA1-2 transition volumes compared to controls.
“Post-hoc analyses showed that AD had significantly reduced SUB (94.9 ± 31.4 vs. 119.2 ± 23.96), CA1 (165.6 ± 38.6 vs. 203.3 ± 29.3), and CA1–2 transition volumes (8.5 ± 2.0 vs.12.1 ± 3.3) compared to controls”
Quote in the paperSubstantial claim - Findingown data
In controls with known Apo e4 status, CA1 volume shows a significant negative age effect (β = −1.69, P = 0.0002).
“but showed a significant age effect (β = −1.69; P = 0.0002) as in the previous analysis”
Quote in the paperSubstantial claim - Findingown data
In the subgroup with known Apo e4 carrier state, MCI has smaller CA1-2 transition volume than controls.
“The results regarding group effect remained unchanged, that is, AD had smaller SUB, CA1, CA1–2 transition volumes and MCI had smaller CA1–2 transition volumes than controls.”
Quote in the paperSubstantial claim - Findingown data
AD subjects with Apo e4 have smaller CA3&DG volumes than AD subjects without Apo e4 (124.8 ± 26.8 vs 159.0 ± 29.6).
“Posthoc tests showed that AD with Apo e4 had smaller CA3&DG than those without (124.8 ± 26.8 vs. 159.0 ± 29.6)”
Quote in the paperSubstantial claim - Findingown data
In the subgroup with known Apo e4 carrier state, CA3&DG has a significant negative age effect (β = −1.58, P = 0.024).
“CA3&DG had a significant age effect (β = −1.58, P = 0.024)”
Quote in the paperSubstantial claim - Findingown data
In the subgroup with known Apo e4 carrier state, AD has smaller SUB, CA1, and CA1-2 transition volumes than controls.
“The results regarding group effect remained unchanged, that is, AD had smaller SUB, CA1, CA1–2 transition volumes and MCI had smaller CA1–2 transition volumes than controls.”
Quote in the paperSubstantial claim - Findingown data
Age has a significant negative effect on CA1 volume in cognitively healthy controls.
“A significant negative age effect was found for CA1 (β = −1.02, P < 0.0001)”
Quote in the paperSubstantial claim - Interpretationreasoning
Neuroimaging methods able to obtain subfield-specific information might allow for a better differentiation of different disease processes than total hippocampal volume, which is currently the gold standard for in vivo assessment of hippocampal structural damage.
“These findings suggest that neuroimaging methods able to obtain subfield specific information might allow for a better differentiation of different disease processes than total hippocampal volume that is currently the gold standard for the in vivo assessment of hippocampal structural damage.”
Quote in the paperSubstantial claim - Interpretationown data
In vivo subfield volumetry using high-resolution MR images and a manual parcellation scheme can identify regional selective effects of age, Apo e4, and AD on the hippocampus.
“The most important finding of this study was that in vivo subfield volumetry obtained using high resolution MR images and a manual parcellation scheme was able to identify regional selective effects of age and Apo e4 and AD on the hippocampus.”
Quote in the paperSubstantial claim
- May combine two claims. 12
- The quote only partly states this claim. 1
- Classified as probably not a substantial claim. 10
May combine two claims.
Findingown dataIn the analysis of AD and MCI with age- and gender-matched controls, disease group has significant effects on SUB, CA1, and CA1-2 transition volumes but not on CA3&DG.
“Multiple regression analysis showed significant effects for disease group (defined as control, MCI, and AD) for SUB (P = 0.003), CA1 (P = 0.0001), and CA1–2 transition (P < 0.0001) but not for CA3&DG.”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataIn controls, CA1 volumes are stable until the 6th decade, after 60 years they become increasingly smaller.
“CA1 volumes were stable until the 6th decade, after 60 yrs of age; CA1 volumes started getting increasingly smaller.”
Quote in the paperSubstantial claimThe quote only partly states this claim.
Findingown dataIn controls, CA3&DG volumes are stable up to the 5th decade and then become increasingly smaller.
“CA3&DG volumes stayed stable up to the 5th decade and then became increasingly smaller”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataMCI subjects with Apo e4 have smaller CA3&DG volumes than those without (132.0 ± 12.5 vs 141.9 ± 35.3), but the difference is not significant.
“MCI with the Apo e4 also had smaller CA3&DG than those without (132.0 ± 12.5 vs. 141.9 ± 35.3); however, the difference was not significant”
Quote in the paperNot a substantial claimMay combine two claims.
Findingown dataMCI does not have significantly reduced SUB, CA1, or CA3&DG volumes compared to controls.
“but not in SUB (112.9 ± 23.4), CA1 (181.2 ± 30.3), or CA3&DG (140.9 ± 24.4).”
Quote in the paperSubstantial claimClassified as probably not a substantial claim. May combine two claims.
Study design and conductown dataThe findings in the small AD and MCI sample are preliminary and need confirmation in a larger population.
“The findings in this small sample therefore have to be considered preliminary and have to be confirmed in a larger population.”
Quote in the paperSubstantial claimClassified as probably not a substantial claim.
PremisecitationDifferent physiological and pathophysiological processes exert different subfield-specific effects on the hippocampus.
“Histopathological studies and animal models suggest that different physiological and pathophysiological processes exert different subfield specific effects on the hippocampus.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
PremisecitationApo e4 is the best documented genetic risk factor for sporadic AD, although it is insufficient to explain all cases of sporadic late onset AD as only 40-80% of all AD patients carry the Apo e4 allele.
“The best documented genetic risk factor for sporadic AD is apolipoprotein E4 (Apo e4), although it is insufficient to explain all cases of sporadic late onset AD as only 40–80% of all AD patients carry the Apo e4 allele (Waring and Rosenberg, 2008).”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Waring and Rosenberg, 2008
Not checked in the free run.
May combine two claims.
Study design and conductreasoningThe sample size of the AD and MCI group was relatively small, so it cannot be excluded that the study lacked statistical power to differentiate between strong AD effects and more subtle Apo e4 effects.
“But again, the sample size of the AD and MCI group was relatively small and thus it cannot be excluded that we lacked the statistical power to differentiate between the strong AD effects active in CA1, CA1–2, SUB and ERC, and the probably more subtle effect of Apo e4.”
Quote in the paperSubstantial claimClassified as probably not a substantial claim.
PremisecitationThe hippocampus is not a homogeneous structure but consists of several histologically and functionally specialized but tightly interconnected subfields: the subiculum (SUB), the cornu ammonis sectors (CA) 1-3, and the dentate gyrus (DG).
“However, the hippocampus is not a homogeneous structure but consists of several histologically and functionally specialized but tightly interconnected subfields: the subiculum (SUB), the cornu ammonis sectors (CA) 1–3, and the dentate gyrus (DG) (Duvernoy, 2005).”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Duvernoy, 2005 source
Not checked in the free run.
May combine two claims.
PremisecitationAnimal and histopathological studies have shown that different pathological conditions exert regional selective effects on hippocampal subfields, for example, AD and hypoxia damage preferentially CA1, schizophrenia CA2, and post-traumatic stress syndrome CA3.
“Animal and histopathological studies have shown that different pathological conditions exert regional selective effects on hippocampal subfields, for example, AD and hypoxia damage preferentially CA1 (West et al., 2004), schizophrenia CA2 (Harrison, 2004), and post-traumatic stress syndrome CA3 (McEwen and Magarinos, 1997), and so forth.”
Quote in the paperSubstantial claim0 of 3 sources checked- cites West et al., 2004
Not checked in the free run.
- cites Harrison, 2004
Not checked in the free run.
- cites McEwen and Magarinos, 1997
Not checked in the free run.
Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
PremisecitationAmnestic MCI is the most relevant subtype for AD, characterized by subjective and objective memory deficits but otherwise intact cognitive functions.
“The most relevant subtype for AD is amnestic MCI, which is characterized by subjective and objective memory deficits but otherwise intact cognitive functions.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Petersen et al., 1999
Not checked in the free run.
Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
PremisecitationMCI is a clinical concept defining the transition from normal aging to possible AD; subjects with MCI are not demented but have significant deficits in one or several cognitive domains and an increased risk to develop dementia.
“The concept of mild cognitive impairment (MCI) is an attempt to clinically define this transition from normal aging to possible AD. Subjects diagnosed with MCI are not demented, but have significant deficits in one or several cognitive domains and an increased risk to develop dementia.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Petersen et al., 1999
Not checked in the free run.
Classified as probably not a substantial claim.
PremisecitationMolecular pathomechanisms leading to AD are active several years before the patient becomes cognitively impaired.
“There is increasing evidence that the molecular pathomechanisms leading to AD are active several years before the patient becomes cognitively impaired (DeKosky and Marek, 2003).”
Quote in the paperSubstantial claim0 of 1 sources checked- cites DeKosky and Marek, 2003
Not checked in the free run.
May combine two claims.
Interpretationown dataSubfield volumetry provides regional selective information that allows distinguishing between different normal and pathological processes affecting the hippocampus and thus improves differential diagnosis of neurodegenerative diseases affecting the hippocampus.
“Based on these findings, we conclude that subfield volumetry provides regional selective information that allows to distinguish between different normal and pathological processes affecting the hippocampus and thus for an improved differential diagnosis of neurodegenerative diseases affecting the hippocampus.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Study design and conductcitationThe method used for subfield marking, including assessment of measurement reliability, has been described in detail previously.
“The method used for subfield marking including assessment of measurement reliability has been described in detail previously (Mueller et al., 2007, 2008).”
Quote in the paperNot a substantial claim0 of 2 sources checked- cites Mueller et al., 2007
Not checked in the free run.
- cites Mueller et al., 2008
Not checked in the free run.
Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
PremisecitationAge is the most consistent epidemiological risk factor for sporadic AD, with prevalence increasing from 1% at age 65 to 40-50% by age 95.
“Age is the most consistent epidemiological risk factor with the prevalence of the disease increasing from 1% at age 65 to 40–50% by the age of 95 (Wang and Ding, 2008).”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Wang and Ding, 2008
Not checked in the free run.
Classified as probably not a substantial claim.
Study design and conductreasoningICV was used as a covariate in all analyses to account for volumetric differences due to different head sizes.
“ICV in ccm was used as a covariate in all these analyses to account for volumetric differences due to different head sizes.”
Quote in the paperSubstantial claim
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The CA1 region is one of the earliest and more affected brain regions in AD patients.
“In contrast to 5xFAD, other FAD mouse models show clear intraneuronal Aβ accumulation and neuronal loss in the CA1 region [7, 16, 88], which has also been shown to be one of the earliest and more affected brain regions in AD patients [4, 27, 68, 82].”
From Senescence-related impairment of autophagy induces toxic intraneuronal amyloid-β accumulation in a mouse model of amyloid pathology (2023) · cites this paper as [82]
Controversial data were obtained particularly in studies that relied on manual versus automated segmentation methods.
“As for the second point of concern, controversial data were obtained particularly in studies that relied on manual versus automated segmentation methods.”
From Atrophy of presubiculum and subiculum is the earliest hippocampal anatomical marker of Alzheimer's disease (2015) · cites this paper as [35]
The volume of the subiculum was reported to be normal in MCI in a manual segmentation study and an automated segmentation study but significantly reduced in two other studies using manual or automated segmentation.
“Thus, for example, the volume of the subiculum was reported to be normal in MCI in a study that relied on manual segmentation [35] and in another study that used an automated segmentation method [38] but was significantly reduced in two other studies that also used a method of manual [39] or automated segmentation [40].”
From Atrophy of presubiculum and subiculum is the earliest hippocampal anatomical marker of Alzheimer's disease (2015) · cites this paper as [35]
The most critical issues explaining contrasting data regard the diagnostic criteria adopted in sample selection and the reliability of segmentation techniques for discriminating between individual hippocampal subfields.
“Probably, the most critical issues regard the diagnostic criteria adopted in sample selection and the reliability of the segmentation techniques used for discriminating between individual hippocampal subfields.”
From Atrophy of presubiculum and subiculum is the earliest hippocampal anatomical marker of Alzheimer's disease (2015) · cites this paper as [35]
Literature on individuals with MCI is more controversial, with discrepant data resulting from adoption of the same subfield segmentation procedure.
“Analogous literature on individuals with MCI is still more controversial, with discrepant data resulting from adoption of the same subfield segmentation procedure.”
From Atrophy of presubiculum and subiculum is the earliest hippocampal anatomical marker of Alzheimer's disease (2015) · cites this paper as [35]
Cites
Mild Cognitive Impairment: Clinical Characterization and Outcome
Petersen, R. C., Smith, G. E., Waring, S. C., Ivnik, R. J., Tangalos, E. G., & Kokmen, E. (1999) · Archives of Neurology · doi:10.1001/archneur.56.3.303 · Cited by 8,980 · see citing works
Increased hippocampal neurogenesis in Alzheimer's disease
Jin, K., Peel, A. L., Mao, X. O., Xie, L., Cottrell, B. A., Henshall, D. C., & Greenberg, D. A. (2004) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2634794100 · Cited by 971 · see citing works
Mahley, R. W., Weisgraber, K. H., & Huang, Y. (2006) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0600549103 · Cited by 912 · see citing works
Selective vulnerability of the hippocampus in brain ischemia
Schmidt‐Kastner, R., & Freund, T. F. (1991) · Neuroscience · doi:10.1016/0306-4522(91)90001-5 · Cited by 890 · see citing works
Sex, stress and the hippocampus: allostasis, allostatic load and the aging process
MCEWEN, B. (2002) · Neurobiology of Aging · doi:10.1016/s0197-4580(02)00027-1 · Cited by 753 · see citing works
Harrison, P. (2004) · Psychopharmacology · doi:10.1007/s00213-003-1761-y · Cited by 693 · see citing works
Petrovitch, H., White, L. R., Izmirilian, G., Ross, G. W., Havlik, R. J., Markesbery, W., Nelson, J., Davis, D. G., Hardman, J., Foley, D. J., et al. (2000) · Neurobiology of Aging · doi:10.1016/s0197-4580(00)00106-8 · Cited by 468 · see citing works
The Aging Hippocampus: Cognitive, Biochemical and Structural Findings
Driscoll, I. F. (2003) · Cerebral Cortex · doi:10.1093/cercor/bhg081 · Cited by 449 · see citing works
Looking Backward to Move Forward: Early Detection of Neurodegenerative Disorders
DeKosky, S. T., & MAREK, K. L. (2003) · Science · doi:10.1126/science.1090349 · Cited by 403 · see citing works
Stress Effects on Morphology and Function of the Hippocampusa
McEwen, B. S., & Magariños, A. M. (1997) · Annals of the New York Academy of Sciences · doi:10.1111/j.1749-6632.1997.tb48286.x · Cited by 393 · see citing works
Regional hippocampal atrophy in multiple sclerosis.
Sicotte, N., Kern, K. C., Giesser, B., Arshanapalli, A., Schultz, A., Montag, M. J., Wang, H., & Bookheimer, S. (2008) · Brain : a journal of neurology · doi:10.1093/brain/awn030 · Cited by 376 · see citing works
The Human Hippocampus: Functional Anatomy, Vascularization and Serial Sections with MRI
Duvernoy, H. M. (2005) · doi:10.1007/b138576 · Cited by 350 · see citing works
Hippocampal neurons in pre-clinical Alzheimer's disease.
West, M. J., Kawas, C. H., Stewart, W. F., Rudow, G. L., & Troncoso, J. C. (2004) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2003.12.005 · Cited by 317 · cited 7× · see citing works
“The mild, nonsignificant volume loss in SUB, CA1, CA3&DG is in good agreement with the mild, nonsignificant neuronal cell described in a histopathological study of MCI by West et al. (2004).”
BackgroundResultInfluentialMeasurement of hippocampal subfields and age-related changes with high resolution MRI at 4T
Mueller, S. G., Stables, L. A., Du, A., Schuff, N. W., Truran, D., Cashdollar, N. M., & Weiner, M. W. (2006) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2006.03.007 · Cited by 266 · see citing works
Self-citationWang, L., Swank, J. S., Glick, I. E., Gado, M. H., Miller, M. I., Morris, J. C., & Csernansky, J. G. (2003) · NeuroImage · doi:10.1016/s1053-8119(03)00361-6 · Cited by 263 · see citing works
Correlates of hippocampal neuron number in Alzheimer's disease and ischemic vascular dementia
Zarow, C., Vinters, H. V., Ellis, W. G., Weiner, M. W., Mungas, D., White, L., & Chui, H. C. (2005) · Annals of Neurology · doi:10.1002/ana.20503 · Cited by 241 · see citing works
Self-citationGenome-Wide Association Studies in Alzheimer Disease
Waring, S. C., & Rosenberg, R. N. (2008) · Archives of Neurology · doi:10.1001/archneur.65.3.329 · Cited by 238 · see citing works
Ho, L., Purohit, D. P., Haroutunian, V. H., Luterman, J. D., Willis, F., Näslund, J., Buxbaum, J. D., Mohs, R. C., Aisen, P. S. S., & Pasinetti, G. M. (2001) · Archives of Neurology · doi:10.1001/archneur.58.3.487 · Cited by 196 · see citing works
Substantial Archaeocortical Atrophy and Neuronal Loss in Multiple Sclerosis
Papadopoulos, D., Dukes, S., Patel, R., Nicholas, R., Vora, A., & Reynolds, R. (2009) · Brain Pathology · doi:10.1111/j.1750-3639.2008.00177.x · Cited by 192 · see citing works
Brain Aging in Very Old Men With Type 2 Diabetes
Korf, E. S. C., White, L. R., Scheltens, P., & Launer, L. J. (2006) · Diabetes Care · doi:10.2337/dc06-0243 · Cited by 190 · see citing works
Ji, Y., Gong, Y., Gan, W., Beach, T., Holtzman, D. M., & Wisniewski, T. (2003) · Neuroscience · doi:10.1016/j.neuroscience.2003.08.007 · Cited by 189 · see citing works
Molecular neuropathology of human mesial temporal lobe epilepsy
Blümcke, I., Beck, H., Lie, A. A., & Wiestler, O. D. (1999) · Epilepsy Research · doi:10.1016/s0920-1211(99)00052-2 · Cited by 188 · see citing works
Yushkevich, P. A., Avants, B., Pluta, J. B., Das, S. R., Minkoff, D. L., Mechanic-Hamilton, D. J., Glynn, S. M., Pickup, S., LIU, W., & Gee, J. C. (2008) · NeuroImage · doi:10.1016/j.neuroimage.2008.08.042 · Cited by 179 · see citing works
Increased Neurogenesis in Young Transgenic Mice Overexpressing Human APPSw,Ind
López-Toledano, M. A., & Shelanski, M. L. (2007) · Journal of Alzheimer s Disease · doi:10.3233/jad-2007-12304 · Cited by 177 · see citing works
Age-related alterations in hippocampal spines and deficiencies in spatial memory in mice
Halbach, O. V. B. U., Zacher, C., Gass, P., & Unsicker, K. (2006) · Journal of Neuroscience Research · doi:10.1002/jnr.20759 · Cited by 177 · see citing works
Aging, spatial learning, and total synapse number in the rat CA1 stratum radiatum
Geinisman, Y., Ganeshina, O., Yoshida, R., Berry, R. W., Disterhoft, J. F., & Gallagher, M. (2004) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2003.12.001 · Cited by 161 · see citing works
Hippocampal Cell Loss in Posttraumatic Human Epilepsy
Swartz, B. E., Houser, C. R., Tomiyasu, U., Walsh, G. O., DeSalles, A. A. F., Rich, J., & Delgado‐Escueta, A. V. (2006) · Epilepsia · doi:10.1111/j.1528-1167.2006.00602.x · Cited by 142 · see citing works
White, F., Nicoll, J. A. R., Roses, A. D., & Horsburgh, K. (2001) · Neurobiology of Disease · doi:10.1006/nbdi.2001.0401 · Cited by 128 · see citing works
Mueller, S. G., Schuff, N., Raptentsetsang, S., Elman, J., & Weiner, M. W. (2008) · NeuroImage · doi:10.1016/j.neuroimage.2008.04.174 · Cited by 116 · see citing works
Self-citationUnfolding the human hippocampus with high resolution structural and functional MRI
Zeineh, M., Engel, S. A., Thompson, P. M., & Bookheimer, S. Y. (2001) · The Anatomical Record · doi:10.1002/ar.1061 · Cited by 107 · see citing works
Hayakawa, N., Kato, H., & Araki, T. (2007) · Mechanisms of Ageing and Development · doi:10.1016/j.mad.2007.01.005 · Cited by 102 · see citing works
Levi, O., & Michaelson, D. M. (2006) · Journal of Neurochemistry · doi:10.1111/j.1471-4159.2006.04189.x · Cited by 94 · see citing works
Fukutani, Y., Cairns, N. J., Shiozawa, M., Sasaki, K., Sudo, S., Isaki, K., & Lantos, P. L. (2000) · Psychiatry and Clinical Neurosciences · doi:10.1046/j.1440-1819.2000.00747.x · Cited by 70 · see citing works
Lin, J. J., Salamon, N., Dutton, R. A., Lee, A. D., Geaga, J. A., Hayashi, K. M., Toga, A. W., Engel, J., & Thompson, P. M. (2005) · Neurology · doi:10.1212/01.wnl.0000179003.95838.71 · Cited by 67 · see citing works
Alzheimer’s disease: epidemiology, genetics, and beyond
Wang, X., & Ding, H. (2008) · Neuroscience Bulletin · doi:10.1007/s12264-008-0105-7 · Cited by 66 · see citing works
Cambon, K., Davies, H. A., & Stewart, M. G. (2000) · Neuroscience · doi:10.1016/s0306-4522(00)00065-8 · Cited by 63 · see citing works
Shi, L., Argenta, A. E., Winseck, A. K., & Brunso‐Bechtold, J. K. (2004) · The Journal of Comparative Neurology · doi:10.1002/cne.20303 · Cited by 61 · see citing works
The length of hippocampal cholinergic fibers is reduced in the aging brain
Ypsilanti, A. R., da Cruz, M. T. G., Burgess, A., & Aubert, I. (2007) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2007.04.001 · Cited by 50 · see citing works
Capillary changes in hippocampal CA1 and CA3 areas of the aging rhesus monkey
Keuker, J. I. H., Luiten, P. G. M., & Fuchs, E. (2000) · Acta Neuropathologica · doi:10.1007/s004010000227 · Cited by 42 · see citing works
Davies, H. A., Kelly, A. M., Dhanrajan, T. M., Lynch, M. A., guez, J. J. R. X., & Stewart, M. G. (2003) · Brain Research · doi:10.1016/s0006-8993(03)03251-7 · Cited by 37 · see citing works
Model-Based Segmentation of Hippocampal Subfields in Ultra-High Resolution In Vivo MRI
Leemput, K. V., Bakkour, A., Benner, T., Wiggins, G. C., Wald, L. L., Augustinack, J. C., Dickerson, B. C., Golland, P., & Fischl, B. R. (2008) · Lecture notes in computer science · doi:10.1007/978-3-540-85988-8_29 · Cited by 32 · see citing works
Levi, O., Dolev, I., Belinson, H., & Michaelson, D. M. (2007) · Journal of Neurochemistry · doi:10.1111/j.1471-4159.2007.04810.x · Cited by 22 · see citing works
Takayama, N., Iseki, E., Yamamoto, T., & Kosaka, K. (2002) · Neuropathology · doi:10.1046/j.1440-1789.2002.t01-1-00433.x · Cited by 13 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
The clinical use of structural MRI in Alzheimer disease
Frisoni, G. B., Fox, N. C., Jack, C. R., Scheltens, P., & Thompson, P. M. (2010) · Nature Reviews Neurology · doi:10.1038/nrneurol.2009.215 · Cited by 2,113 · see citing works
A pathophysiological framework of hippocampal dysfunction in ageing and disease
Small, S. A., Schobel, S., Buxton, R. B., Witter, M. P., & Barnes, C. A. (2011) · Nature reviews. Neuroscience · doi:10.1038/nrn3085 · Cited by 920 · see citing works
Brain atrophy in Alzheimer’s Disease and aging
Pini, L., Pievani, M., Bocchetta, M., Altomare, D., Bosco, P., Cavedo, E., Galluzzi, S., Marizzoni, M., & Frisoni, G. B. (2016) · Ageing Research Reviews · doi:10.1016/j.arr.2016.01.002 · Cited by 874 · see citing works
Multi-Atlas Segmentation with Joint Label Fusion
Wang, H., Suh, J. W., Das, S. R., Pluta, J. B., Craige, C., & Yushkevich, P. A. (2013) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2012.143 · Cited by 796 · see citing works
Yushkevich, P. A., Pluta, J. B., Wang, H., Xie, L., Ding, S., Gertje, E. C., Mancuso, L., Kliot, D., Das, S. R., & Wolk, D. A. (2014) · Human Brain Mapping · doi:10.1002/hbm.22627 · Cited by 587 · see citing works
Yassa, M. A., Stark, S. M., Bakker, A., Albert, M. S., Gallagher, M., & Stark, C. E. L. (2010) · NeuroImage · doi:10.1016/j.neuroimage.2010.03.040 · Cited by 523 · see citing works
The hippocampus in aging and disease: From plasticity to vulnerability
Bartsch, T., & Wulff, P. (2015) · Neuroscience · doi:10.1016/j.neuroscience.2015.07.084 · Cited by 466 · see citing works
Structural imaging of hippocampal subfields in healthy aging and Alzheimer’s disease
de Florès, R., Joie, R. L., & Chételat, G. (2015) · Neuroscience · doi:10.1016/j.neuroscience.2015.08.033 · Cited by 363 · see citing works
Apostolova, L. G., Green, A. E., Babakchanian, S., Hwang, K., Chou, Y., Toga, A. W., & Thompson, P. M. (2011) · Alzheimer Disease & Associated Disorders · doi:10.1097/wad.0b013e3182163b62 · Cited by 363 · see citing works
Pipitone, J., Park, M. T. M., Winterburn, J. L., Lett, T. A. P., Lerch, J. P., Pruessner, J. C., Lepage, M., Voineskos, A. N., & Chakravarty, M. M. (2014) · NeuroImage · doi:10.1016/j.neuroimage.2014.04.054 · Cited by 340 · see citing works
Yushkevich, P. A., Amaral, R. S. C., Augustinack, J. C., Bender, A. R., Bernstein, J. D., Boccardi, M., Bocchetta, M., Burggren, A. C., Carr, V. A., Chakravarty, M. M., et al. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2015.01.004 · Cited by 342 · see citing works
Self-citationVulnerable Neural Systems and the Borderland of Brain Aging and Neurodegeneration
Jagust, W. J. (2013) · Neuron · doi:10.1016/j.neuron.2013.01.002 · Cited by 321 · see citing works
Novel genetic loci associated with hippocampal volume
Hibar, D. P., Adams, H. H. H., Jahanshad, N., Chauhan, G., Stein, J. L., Hofer, E., Renteria, M. E., Bis, J. C., Arias-Vasquez, A., Ikram, M. K., et al. (2017) · Nature Communications · doi:10.1038/ncomms13624 · Cited by 299 · see citing works
Self-citationAisen, P. S., Gauthier, S., Ferris, S. H., Saumier, D., Haine, D., Garceau, D., Duong, A., Suhy, J., Oh, J., Lau, W. C., et al. (2011) · Archives of Medical Science · doi:10.5114/aoms.2011.20612 · Cited by 295 · see citing works
Neuroimaging biomarkers for Alzheimer’s disease.
Márquez, F., & Yassa, M. A. (2019) · Molecular Neurodegeneration · doi:10.1186/s13024-019-0325-5 · Cited by 289 · see citing works
Winterburn, J. L., Pruessner, J. C., Chavez, S., Schira, M. M., Lobaugh, N. J., Voineskos, A. N., & Chakravarty, M. M. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.02.003 · Cited by 271 · see citing works
Nearly automatic segmentation of hippocampal subfields in in vivo focal T2-weighted MRI
Yushkevich, P. A., Wang, H., Pluta, J. B., Das, S. R., Craige, C. P., Avants, B., Weiner, M. W., & Mueller, S. G. (2010) · NeuroImage · doi:10.1016/j.neuroimage.2010.06.040 · Cited by 231 · see citing works
Self-citationHippocampal Subfield Volumes: Age, Vascular Risk, and Correlation with Associative Memory
Shing, Y. L., Rodrigue, K. M., Kennedy, K. M., Fandakova, Y., Bodammer, N., Werkle‐Bergner, M., Lindenberger, U., & Raz, N. (2011) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2011.00002 · Cited by 200 · see citing works
Relevance of Magnetic Resonance Imaging for Early Detection and Diagnosis of Alzheimer Disease
Teipel, S. J., Grothe, M. J., Lista, S., Toschi, N., Garaci, F., & Hampel, H. J. (2013) · Medical Clinics of North America · doi:10.1016/j.mcna.2012.12.013 · Cited by 192 · see citing works
Reduced Hippocampal Volume in Healthy Young ApoE4 Carriers: An MRI Study
O’Dwyer, L., Lamberton, F., Matura, S., Tanner, C. J., Scheibe, M., Miller, J., Rujescu, D., Prvulovic, D., & Hampel, H. J. (2012) · PLoS ONE · doi:10.1371/journal.pone.0048895 · Cited by 190 · see citing works
Hippocampal subfield volumes at 7T in early Alzheimer's disease and normal aging
Wisse, L. E. M., Biessels, G. J., Heringa, S. M., Kuijf, H. J., Koek, D., Luijten, P. R., & Geerlings, M. I. (2014) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2014.02.021 · Cited by 171 · see citing works
Joie, R. L., Fouquet, M., Mézenge, F., Landeau, B., Villain, N., Mevel, K., Pèlerin, A., Eustache, F., Desgranges, B., & Chételat, G. (2010) · NeuroImage · doi:10.1016/j.neuroimage.2010.06.024 · Cited by 168 · see citing works
Wisse, L. E. M., Daugherty, A. M., Olsen, R. K., Berron, D., Carr, V. A., Stark, C. E. L., Amaral, R. S. C., Amunts, K., Augustinack, J. C., Bender, A. R., et al. (2016) · Hippocampus · doi:10.1002/hipo.22671 · Cited by 159 · see citing works
Self-citationCarlesimo, G. A., Piras, F., Orfei, M. D., Iorio, M., Caltagirone, C., & Spalletta, G. (2015) · Alzheimer's & Dementia: Diagnosis, Assessment & Disease Monitoring · doi:10.1016/j.dadm.2014.12.001 · Cited by 159 · see citing works
Episodic memory on the path to Alzheimer's disease
Gallagher, M., & Koh, M. T. (2011) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2011.10.021 · Cited by 156 · see citing works
The Human Dentate Gyrus Plays a Necessary Role in Discriminating New Memories
Baker, S., Vieweg, P., Gao, F., Gilboa, A., Wolbers, T., Black, S. E., & Rosenbaum, R. S. (2016) · Current Biology · doi:10.1016/j.cub.2016.07.081 · Cited by 148 · see citing works
APOE Genotype and Neuroimaging Markers of Alzheimer’s Disease: Systematic Review and Meta-Analysis
Liu, Y., Yu, J.-T., Wang, H.-F., Han, P.-R., Tan, C.-C., Wang, C., Meng, X.-F., Risacher, S. L., Saykin, A. J., & Tan, L. (2015) · Journal of Neurology, Neurosurgery & Psychiatry · doi:10.1136/jnnp-2014-307719 · Cited by 152 · see citing works
Histology-derived volumetric annotation of the human hippocampal subfields in postmortem MRI
Adler, D. H., Pluta, J. B., Kadivar, S., Craige, C. P., Gee, J. C., Avants, B., & Yushkevich, P. A. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.08.067 · Cited by 152 · see citing works
Age differences in hippocampal subfield volumes from childhood to late adulthood
Daugherty, A. M., Bender, A. R., Raz, N., & Ofen, N. (2015) · Hippocampus · doi:10.1002/hipo.22517 · Cited by 149 · see citing works
Mueller, S. G., Chao, L. L., Berman, B. D., & Weiner, M. W. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2011.03.028 · Cited by 146 · see citing works
Self-citationA Critical Appraisal of the Hippocampal Subfield Segmentation Package in FreeSurfer
Wisse, L. E. M., Biessels, G. J., & Geerlings, M. I. (2014) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2014.00261 · Cited by 145 · see citing works
Maruszak, A., & Thuret, S. (2014) · Frontiers in Cellular Neuroscience · doi:10.3389/fncel.2014.00095 · Cited by 140 · see citing works
Volume of hippocampal subfields and episodic memory in childhood and adolescence
Lee, J. K., Ekstrom, A. D., & Ghetti, S. (2014) · NeuroImage · doi:10.1016/j.neuroimage.2014.03.019 · Cited by 142 · see citing works
Hampel, H. J., Lista, S., Teipel, S. J., Garaci, F., Nisticò, R., Blennow, K., Zetterberg, H., Bertram, L., Duyckaerts, C. J., Bakardjian, H., et al. (2013) · Biochemical Pharmacology · doi:10.1016/j.bcp.2013.11.009 · Cited by 141 · see citing works
Stanley, J. A., & Raz, N. (2018) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2018.00076 · Cited by 141 · see citing works
Effects of age and Alzheimer's disease on hippocampal subfields
de Florès, R., Joie, R. L., Landeau, B., Perrotin, A., Mézenge, F., Sayette, V. D. L., Eustache, F., Desgranges, B., & Chételat, G. (2014) · Human Brain Mapping · doi:10.1002/hbm.22640 · Cited by 142 · see citing works
3D PIB and CSF biomarker associations with hippocampal atrophy in ADNI subjects
Apostolova, L. G., Hwang, K., Andrawis, J. P., Green, A. E., Babakchanian, S., Morra, J. H., Cummings, J. L., Toga, A. W., Trojanowski, J. Q., Shaw, L. M. J., et al. (2010) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2010.05.003 · Cited by 137 · see citing works
Self-citationSämann, P. G., Iglesias, J. E., Gutman, B. A., Grotegerd, D., Leenings, R., Flint, C., Dannlowski, U., Clarke‐Rubright, E. K., Morey, R. A., van Erp, T. G. M., et al. (2020) · Human Brain Mapping · doi:10.1002/hbm.25326 · Cited by 135 · see citing works
APOE4 is associated with greater atrophy of the hippocampal formation in Alzheimer's disease
Pievani, M., Galluzzi, S., Thompson, P. M., Rasser, P. E., Bonetti, M., & Frisoni, G. B. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2010.12.081 · Cited by 134 · see citing works
Zeineh, M., Palomero‐Gallagher, N., Axer, M., Gräβel, D., Goubran, M., Wree, A., Woods, R. P., Amunts, K., & Zilles, K. (2016) · Cerebral Cortex · doi:10.1093/cercor/bhw010 · Cited by 113 · see citing works
Perturbations of neural circuitry in aging, mild cognitive impairment, and Alzheimer's disease
Leal, S. L., & Yassa, M. A. (2013) · Ageing Research Reviews · doi:10.1016/j.arr.2013.01.006 · Cited by 110 · see citing works
Mild Cognitive Impairment: Differential Atrophy in the Hippocampal Subfields
Hanseeuw, B. J., Van Leemput, K., Kavec, M., Grandin, C., Seron, X., & Ivanoiu, A. (2011) · American Journal of Neuroradiology · doi:10.3174/ajnr.a2589 · Cited by 110 · see citing works
Olsen, R. K., Yeung, L., Noly‐Gandon, A., D’Angelo, M. C., Kacollja, A., Smith, V. M., Ryan, J. D., & Barense, M. D. (2017) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2017.04.025 · Cited by 106 · see citing works
Pluta, J., Yushkevich, P., Das, S., & Wolk, D. (2012) · Journal of Alzheimer's Disease · doi:10.3233/jad-2012-111931 · Cited by 107 · see citing works
Arnold, S. J. M., Ivleva, E. I., Gopal, T. A., Reddy, A. P., Jeon‐Slaughter, H., Sacco, C. B., Francis, A. N., Tandon, N., Bidesi, A. S., Witte, B. P., et al. (2014) · Schizophrenia Bulletin · doi:10.1093/schbul/sbu009 · Cited by 105 · see citing works
In vivo imaging of adult human hippocampal neurogenesis: progress, pitfalls and promise
Ho, N. F., Hooker, J. M., Sahay, A., Holt, D. J., & Roffman, J. L. (2013) · Molecular Psychiatry · doi:10.1038/mp.2013.8 · Cited by 97 · see citing works
Shen, K., Fripp, J., Mériaudeau, F., Chételat, G., Salvado, O., & Bourgeat, P. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2011.10.014 · Cited by 98 · see citing works
Voineskos, A. N., Winterburn, J. L., Felsky, D., Pipitone, J., Rajji, T. K., Mulsant, B. H., & Chakravarty, M. M. (2015) · Human Brain Mapping · doi:10.1002/hbm.22825 · Cited by 93 · see citing works
Medial temporal lobe subregional morphometry using high resolution MRI in Alzheimer's disease
Wolk, D. A., Das, S. R., Mueller, S. G., Weiner, M. W., & Yushkevich, P. A. (2016) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2016.09.011 · Cited by 92 · see citing works
Self-citationSpatial navigation—a unique window into physiological and pathological aging
Gažová, I., Vlček, K., Laczó, J., Nedelská, Z., Hynčicová, E., Mokrišová, I., Sheardová, K., & Hort, J. (2012) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2012.00016 · Cited by 92 · see citing works
Shi, J., Thompson, P. M., Gutman, B. A., & Wang, Y. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.04.018 · Cited by 90 · see citing works
Malykhin, N. V., Huang, Y., Hrybouski, S., & Olsen, F. (2017) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2017.08.001 · Cited by 86 · see citing works
Khan, W., Westman, E., Jones, N., Wahlund, L., Mecocci, P., Vellas, B. J., Tsolaki, M., Kłoszewska, I., Soininen, H. S., Spenger, C., et al. (2014) · Brain Topography · doi:10.1007/s10548-014-0415-1 · Cited by 88 · see citing works
Family History of Alzheimer's Disease and Hippocampal Structure in Healthy People
Donix, M., Burggren, A. C., Suthana, N., Siddarth, P., Ekstrom, A. D., Krupa, A. K., Jones, M., Martin‐Harris, L., Ercoli, L. M., Miller, K. J., et al. (2010) · American Journal of Psychiatry · doi:10.1176/appi.ajp.2010.09111575 · Cited by 86 · see citing works
APOE ε4 worsens hippocampal CA1 apical neuropil atrophy and episodic memory
Kerchner, G., Berdnik, D., Shen, J., Bernstein, J. D., Fenesy, M. C., Deutsch, G., Wyss-Coray, T., & Rutt, B. (2014) · Neurology · doi:10.1212/wnl.0000000000000154 · Cited by 82 · see citing works
Regional vulnerability of hippocampal subfields to aging measured by structural and diffusion MRI
Pereira, J. B., Valls‐Pedret, C., Ros, E., Palacios, E. M., Falcón, C., Bargalló, N., Bartrés‐Faz, D., Wahlund, L., Westman, E., & Junqué, C. (2013) · Hippocampus · doi:10.1002/hipo.22234 · Cited by 81 · see citing works
Imaging Brain Effects of APOE4 in Cognitively Normal Individuals Across the Lifespan
Fouquet, M., Besson, F. L., Gonneaud, J., Joie, R. L., & Chételat, G. (2014) · Neuropsychology Review · doi:10.1007/s11065-014-9263-8 · Cited by 83 · see citing works
Unraveling the complexity of human brain: Structure, function in healthy and disease states
Sultana, O. F., Bandaru, M., Islam, M. A., & Reddy, P. H. (2024) · Ageing Research Reviews · doi:10.1016/j.arr.2024.102414 · Cited by 88 · see citing works
Amaral, R. S. C., Park, M. T. M., Devenyi, G. A., Lynn, V., Pipitone, J., Winterburn, J. L., Chavez, S., Schira, M. M., Lobaugh, N. J., Voineskos, A. N., et al. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2016.10.027 · Cited by 82 · see citing works
Age-related differences in sleep-based memory consolidation: a meta-analysis
Gui, W.-J., Li, H.-J., Guo, Y.-H., Peng, P., Lei, X., & Yu, J. (2017) · Neuropsychologia · doi:10.1016/j.neuropsychologia.2017.02.001 · Cited by 78 · see citing works
How Aging Affects Sleep-Dependent Memory Consolidation?
Harand, C., Bertran, F., Doidy, F., Guénolé, F., Desgranges, B., Eustache, F., & Rauchs, G. (2012) · Frontiers in Neurology · doi:10.3389/fneur.2012.00008 · Cited by 83 · see citing works
Daugherty, A. M., Flinn, R., & Ofen, N. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.03.047 · Cited by 77 · see citing works
Apostolova, L. G., Zarow, C., Biado, K., Hurtz, S., Boccardi, M., Somme, J. H., Honarpisheh, H., Blanken, A. E., Brook, J. K., Tung, S., et al. (2015) · Alzheimer s & Dementia · doi:10.1016/j.jalz.2015.01.001 · Cited by 77 · see citing works
Mak, E., Su, L., Williams, G. B., Watson, R., Firbank, M., Blamire, A., & O’Brien, J. (2016) · The American Journal of Geriatric Psychiatry · doi:10.1016/j.jagp.2015.06.006 · Cited by 79 · see citing works
Schoene‐Bake, J., Keller, S. S., Niehusmann, P., Volmering, E., Elger, C. E., Deppe, M., & Weber, B. (2014) · Human Brain Mapping · doi:10.1002/hbm.22506 · Cited by 78 · see citing works
Merluzzi, A. P., Dean, D. C., Adluru, N., Suryawanshi, G. S., Okonkwo, O. C., Oh, J. M., Hermann, B. P., Sager, M. A., Asthana, S., Zhang, H., et al. (2016) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2016.03.026 · Cited by 78 · see citing works
Raz, N., Daugherty, A. M., Bender, A. R., Dahle, C. L., & Land, S. J. (2014) · Brain Structure and Function · doi:10.1007/s00429-014-0817-6 · Cited by 76 · see citing works
From perception to memory: Changes in memory systems across the lifespan
Ofen, N., & Shing, Y. L. (2013) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2013.04.006 · Cited by 76 · see citing works
Subcortical surface morphometry in substance dependence: An ENIGMA addiction working group study
Chye, Y., Mackey, S., Gutman, B. A., Ching, C. R. K., Batalla, A., Blaine, S. K., Brooks, S. J., Caparelli, E. C., Cousijn, J., Dagher, A., et al. (2019) · Addiction Biology · doi:10.1111/adb.12830 · Cited by 79 · see citing works
Ho, A. J., Hua, X., Lee, S., Leow, A., Yanovsky, I., Gutman, B. A., Dinov, I. D., Leporé, N., Stein, J. L., Toga, A. W., et al. (2009) · Human Brain Mapping · doi:10.1002/hbm.20882 · Cited by 75 · see citing works
Self-citationRegional vulnerability of hippocampal subfields and memory deficits in Parkinson's disease
Pereira, J. B., Junqué, C., Bartrés‐Faz, D., Ramírez‐Ruiz, B., Martı́, M. J., & TOLOSA, E. S. (2013) · Hippocampus · doi:10.1002/hipo.22131 · Cited by 74 · see citing works
Shi, J., Leporé, N., Gutman, B. A., Thompson, P. M., Baxter, L. C., Caselli, R. J., Wang, Y., & Initiative, A. D. N. (2014) · Human Brain Mapping · doi:10.1002/hbm.22447 · Cited by 72 · see citing works
Myelin in Alzheimer’s disease: culprit or bystander?
Maitre, M., Jeltsch-David, H., Okechukwu, N. G., Klein, C., Patte-Mensah, C., & Mensah-Nyagan, A.-G. (2023) · Acta Neuropathologica Communications · doi:10.1186/s40478-023-01554-5 · Cited by 66 · see citing works
Vascular Risk Moderates Associations between Hippocampal Subfield Volumes and Memory
Bender, A. R., Daugherty, A. M., & Raz, N. (2013) · Journal of Cognitive Neuroscience · doi:10.1162/jocn_a_00435 · Cited by 69 · see citing works
Beaujoin, J., Palomero‐Gallagher, N., Boumezbeur, F., Axer, M., Bernard, J., Poupon, F., Schmitz, D., Mangin, J., & Poupon, C. (2018) · Brain Structure and Function · doi:10.1007/s00429-018-1617-1 · Cited by 68 · see citing works
Free-water imaging of the hippocampus is a sensitive marker of Alzheimer's disease
Ofori, E., DeKosky, S. T., Febo, M., Colón-Pérez, L. M., Chakrabarty, P., Duara, R., Adjouadi, M., Golde, T. E., & Vaillancourt, D. E. (2019) · NeuroImage Clinical · doi:10.1016/j.nicl.2019.101985 · Cited by 67 · see citing works
Harrison, T. M., Mahmood, Z., Lau, E., Karacozoff, A. M., Burggren, A., Small, G., & Bookheimer, S. (2016) · eNeuro · doi:10.1523/eneuro.0098-16.2016 · Cited by 67 · see citing works
Striepens, N., Scheef, L., Wind, A., Meiberth, D. U., Popp, J., Spottke, A. E., Kölsch, H., Wagner, M., & Jessen, F. (2011) · Psychological Medicine · doi:10.1017/s0033291711000067 · Cited by 63 · see citing works
Interactive effects of chronic cigarette smoking and age on hippocampal volumes
Durazzo, T. C., Meyerhoff, D. J., & Nixon, S. J. (2013) · Drug and Alcohol Dependence · doi:10.1016/j.drugalcdep.2013.08.020 · Cited by 65 · see citing works
Kälin, A. M., Park, M. T. M., Chakravarty, M. M., Lerch, J. P., Michels, L., Schroeder, C., Broicher, S. D., Kollias, S., Nitsch, R. M., Gietl, A. F., et al. (2017) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2017.00038 · Cited by 62 · see citing works
Hippocampal Transcriptomic Profiles: Subfield Vulnerability to Age and Cognitive Impairment
Ianov, L., Both, M. D. D., Chawla, M. K., Rani, A., Kennedy, A. J., Piras, I. S., Day, J. J., Siniard, A. L., Kumar, A., Sweatt, J. D., et al. (2017) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2017.00383 · Cited by 55 · see citing works
Apostolova, L. G., Hwang, K., Omid Kohannim, A., Avila, D., Elashoff, D. A., Jack, C. R., Shaw, L. M. J., Trojanowski, J. Q., Weiner, M. W., & Thompson, P. M. (2014) · NeuroImage Clinical · doi:10.1016/j.nicl.2013.12.012 · Cited by 56 · see citing works
Self-citationQiao, J., Zhao, J., Chang, S., Sun, Q., Liu, N., Dong, J., Chen, Y., Yang, D., Ye, D., Liu, X., et al. (2019) · Cell Death and Differentiation · doi:10.1038/s41418-019-0388-4 · Cited by 55 · see citing works
Stark, S. M., Frithsen, A., & Stark, C. E. L. (2020) · Hippocampus · doi:10.1002/hipo.23259 · Cited by 52 · see citing works
Effects of ApoE4 and maternal history of dementia on hippocampal atrophy
Andrawis, J. P., Hwang, K. S., Green, A. E., Kotlerman, J., Elashoff, D., Morra, J. H., Cummings, J. L., Toga, A. W., Thompson, P. M., & Apostolova, L. G. (2012) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2010.07.020 · Cited by 54 · see citing works
Brain lipid metabolism, apolipoprotein E and the pathophysiology of Alzheimer's disease
Bales, K. R. (2010) · Neuropharmacology · doi:10.1016/j.neuropharm.2010.01.005 · Cited by 53 · see citing works
Automated Segmentation of Hippocampal Subfields in Drug-Naïve Patients with Alzheimer Disease
Lim, H. K., Hong, S. C., Jung, W. S., Ahn, K. J., Won, W. Y., Hahn, C., Kim, I. S., & Lee, C. U. (2013) · American Journal of Neuroradiology · doi:10.3174/ajnr.a3293 · Cited by 52 · see citing works
Hsu, P. J., Shou, H., Benzinger, T. L. S., Marcus, D., Durbin, T. J., Morris, J. C., & Sheline, Y. I. (2015) · Journal of Alzheimer s Disease · doi:10.3233/jad-141743 · Cited by 51 · see citing works
A Focus on Structural Brain Imaging in the Alzheimer’s Disease Neuroimaging Initiative
Braskie, M. N., & Thompson, P. M. (2013) · Biological Psychiatry · doi:10.1016/j.biopsych.2013.11.020 · Cited by 51 · see citing works
Parker, T. D., Slattery, C. F., Yong, K. X. X., Nicholas, J. M., Paterson, R. W., Foulkes, A., Malone, I. B., Thomas, D. L., Cash, D. M., Crutch, S. J., et al. (2018) · NeuroImage Clinical · doi:10.1016/j.nicl.2018.101632 · Cited by 51 · see citing works
Neuroimaging in aging and neurologic diseases
Risacher, S. L., & Saykin, A. J. (2019) · Handbook of clinical neurology · doi:10.1016/b978-0-12-804766-8.00012-1 · Cited by 54 · see citing works
The human hippocampus and its subfield volumes across age, sex and APOE e4 status
Veldsman, M., Nobis, L., Alfaro‐Almagro, F., Smith, S. M., Manohar, S., & Husain, M. (2020) · Brain Communications · doi:10.1093/braincomms/fcaa219 · Cited by 47 · see citing works
Hoseth, E. Z., Westlye, L. T., Hope, S., Dieset, I., Aukrust, P., Melle, I. S., Haukvik, U. K., Agartz, I., Ueland, T., Ueland, T., et al. (2015) · Acta Psychiatrica Scandinavica · doi:10.1111/acps.12467 · Cited by 51 · see citing works
Wolf, D. G. F., Fischer, F. U., de Florès, R., Chételat, G., & Fellgiebel, A. (2015) · Human Brain Mapping · doi:10.1002/hbm.22880 · Cited by 45 · see citing works
Reduced age-related degeneration of the hippocampal subiculum in long-term meditators
Kurth, F., Cherbuin, N., & Eileen Luders, A. (2015) · Psychiatry Research Neuroimaging · doi:10.1016/j.pscychresns.2015.03.008 · Cited by 49 · see citing works
Nichols, L. M., Masdeu, J. C., Mattay, V. S., Kohn, P. D., Emery, M. R., Sambataro, F., Kolachana, B. S., Elvevåg, B., Kippenhan, S., Weinberger, D. R., et al. (2012) · Archives of General Psychiatry · doi:10.1001/archgenpsychiatry.2011.1893 · Cited by 48 · see citing works
Does age worsen sleep‐dependent memory consolidation?
Cherdieu, M., Reynaud, E., Uhlrich, J., Versace, R., & Mazza, S. (2013) · Journal of Sleep Research · doi:10.1111/jsr.12100 · Cited by 46 · see citing works
Optimization and validation of automated hippocampal subfield segmentation across the lifespan
Bender, A. R., Keresztes, A., Bodammer, N., Shing, Y. L., Werkle‐Bergner, M., Daugherty, A. M., Yu, Q., Kühn, S., Lindenberger, U., & Raz, N. (2017) · Human Brain Mapping · doi:10.1002/hbm.23891 · Cited by 43 · see citing works
Bhagwat, N., Pipitone, J., Voineskos, A. N., Chakravarty, M. M., & Initiative, A. D. N. (2019) · Journal of Psychiatry and Neuroscience · doi:10.1503/jpn.180016 · Cited by 44 · see citing works
Human Hippocampal Subfields in Young Adults at 7.0 T: Feasibility of Imaging
Prudent, V., Kumar, A., Liu, S., Wiggins, G. C., Malaspina, D., & Gonen, O. (2010) · Radiology · doi:10.1148/radiol.09090897 · Cited by 43 · see citing works
- Authors
- Mueller, S. G., & Weiner, M. W.
- Affiliations
- San Francisco VA Medical Center (US)
- Corresponding author
- Mueller, Susanne G.
- Venue
- Hippocampus
- Publisher
- Wiley
- Published
- 2009-04-29
- Volume
- 19
- Issue
- 6
- Pages
- 558-564
- ISSN
- 1050-9631 (ISSN-L)
- 1098-1063
- PMC
- PMC2802542
- OpenAlex
- W1984748043
- Cited by
- 248
- References
- 43
- Citations by year
- 2026: 5 · 2025: 9 · 2024: 6 · 2023: 12 · 2022: 10 · 2021: 15 · 2020: 16 · 2019: 13 · 2018: 12 · 2017: 17 · 2016: 17 · 2015: 25 · 2014: 27 · 2013: 22 · 2012: 14
- FWCI
- 8.63
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Hippocampus (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Institute on Aging (P01AG 12435, R01AG010897, P01AG012435)
- Topics
- Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
- Health Sciences › Medicine › Physiology › Alzheimer's disease research and treatments
- Life Sciences › Neuroscience › Cognitive Neuroscience › Memory and Neural Mechanisms
- Keywords
- hippocampal subfields, Alzheimer's disease, mild cognitive impairment, APOE ε4, normal aging, CA1, CA3, dentate gyrus, subiculum, differential diagnosis of neurodegenerative diseases
- MeSH terms
- Adult, Age Factors, Aged, Aged, 80 and over, Aging, Alzheimer Disease, Cognition Disorders, Female, Hippocampus, Humans, Magnetic Resonance Imaging, Male, Middle Aged, Organ Size, Linear Models, Sequence Analysis, DNA, Apolipoprotein E4, Young Adult
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Claims read by deepseek-v4.1-flash under definition version 3 (extract-v3) on 2026-10-10.
Classified by jev-1.13-20260917 (gold-guideline-v0.2) on 2026-10-10.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review