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Specific subfields within the hippocampus have shown vulnerability to chronic stress, highlighting the importance of looking regionally within the hippocampus to understand the role of psychosocial factors in the development of neurodegenerative diseases. A systematic review on psychosocial factors and hippocampal subfield volumes was performed and showed inconsistent results, highlighting the need for future studies to explore this relationship. The current study aimed to explore the association of psychosocial factors with hippocampal (subfield) volumes, using high-field 7T MRI. Data were from the Memory Depression and Aging (Medea)-7T study, which included 333 participants without dementia. Hippocampal subfields were automatically segmented from T2-weighted images using ASHS software. Generalized linear models accounting for correlated outcomes were used to assess the association between subfields (i.e., entorhinal cortex, subiculum, Cornu Ammonis [CA]1, CA2, CA3, dentate gyrus, and tail) and each psychosocial factor (i.e., depressive symptoms, anxiety symptoms, childhood maltreatment, recent stressful life events, and social support), adjusted for age, sex, and intracranial volume. Neither depression nor anxiety was associated with specific hippocampal (subfield) volumes. A trend for lower total hippocampal volume was found in those reporting childhood maltreatment, and a trend for higher total hippocampal volume was found in those who experienced a recent stressful life event. Among subfields, low social support was associated with lower volume in the CA3 (B = -0.43, 95% CI: -0.72; -0.15). This study suggests possible differential effects among hippocampal (subfield) volumes and psychosocial factors., (© 2022 The Authors. Human Brain Mapping published by Wiley Periodicals LLC.)

Background: Vascular risk factors have been associated with risk of Alzheimer's disease (AD) and volume loss of the hippocampus, but the associations with subfields of the hippocampus are understudied. Knowing if vascular risk factors contribute to hippocampal subfield atrophy may improve our understanding of vascular contributions to neurodegenerative diseases., Objective: To investigate the associations between age, sex, and vascular risk factors with hippocampal subfields volumes on 7T MRI in older persons without dementia., Methods: From the Medea 7T study, 283 participants (67±9 years, 68% men) without dementia had 7T brain MRI and hippocampal subfield segmentation. Subfields were automatically segmented on the 3D T2-weighted 7T images with ASHS software. Using linear mixed models, we estimated adjusted associations of age, sex, and vascular risk factors with z-scores of volumes of the entorhinal cortex (ERC), subiculum (SUB), Cornu Ammonis (CA)1, CA2, CA3, CA4, and dentate gyrus (DG), and tail as multivariate correlated outcomes., Results: Increasing age was associated with smaller volumes in all subfields, except CA4/DG. Current smoking was associated with smaller ERC and SUB volumes; moderate alcohol use with smaller CA1 and CA4/DG, obesity with smaller volumes of ERC, SUB, CA2, CA3, and tail; and diabetes mellitus with smaller SUB volume. Sex, former smoking, and hypertension were not associated with subfield volumes. When formally tested, no risk factor affected the subfield volumes differentially., Conclusion: Several vascular risk factors were associated with smaller volumes of specific hippocampal subfields. However, no statistical evidence was found that subfields were differentially affected by these risk factors.

Hippocampal sulcal cavities (HSCs) are frequently observed on MRI, but their etiology and relevance is unclear. HSCs may be anatomical variations, or result from pathology. We assessed the presence of HSCs, and their cross-sectional association with demographics, vascular risk factors and cognitive functioning in two study samples. Within a random sample of 92 patients with vascular disease from the SMART-Medea study (mean age = 62, SD = 9 years) and 83 primary care patients from the PREDICT-MR study (mean age = 62, SD = 12 years) one rater manually scored HSCs at 1.5 T 3D T1-weighted coronal images blind to patient information. We estimated relative risks of age, sex and vascular risk factors with presence of HSCs using Poisson regression with log-link function and robust standard errors adjusted for age and sex. Using ANCOVA adjusted for age, sex, and education we estimated the association of the number of HSCs with memory, executive functioning, speed, and working memory. In the SMART-Medea study HSCs were present in 65% and in 52% in the PREDICT-MR study (χ 2  = 2.99, df = 1, p = 0.08). In both samples, no significant associations were observed between presence of HSCs and age (SMART-Medea: RR = 1.00; 95%CI 0.98-1.01; PREDICT-MR: RR = 1.01; 95%CI 0.99-1.03), sex, or vascular risk factors. Also, no associations between HSCs and cognitive functioning were found in either sample. HSCs are frequently observed on 1.5 T MRI. Our findings suggest that, in patients with a history of vascular disease and primary care attendees, HSCs are part of normal anatomic variation of the human hippocampus rather than markers of pathology.

Background: Multi-atlas segmentation, a popular technique implemented in the Automated Segmentation of Hippocampal Subfields (ASHS) software, utilizes multiple expert-labelled images ("atlases") to delineate medial temporal lobe substructures. This multi-atlas method is increasingly being employed in early Alzheimer's disease (AD) research, it is therefore becoming important to know how the construction of the atlas set in terms of proportions of controls and patients with mild cognitive impairment (MCI) and/or AD affects segmentation accuracy., Objective: To evaluate whether the proportion of controls in the training sets affects the segmentation accuracy of both controls and patients with MCI and/or early AD at 3T and 7T., Methods: We performed cross-validation experiments varying the proportion of control subjects in the training set, ranging from a patient-only to a control-only set. Segmentation accuracy of the test set was evaluated by the Dice similarity coeffiecient (DSC). A two-stage statistical analysis was applied to determine whether atlas composition is linked to segmentation accuracy in control subjects and patients, for 3T and 7T., Results: The different atlas compositions did not significantly affect segmentation accuracy at 3T and for patients at 7T. For controls at 7T, including more control subjects in the training set significantly improves the segmentation accuracy, but only marginally, with the maximum of 0.0003 DSC improvement per percent increment of control subject in the training set., Conclusion: ASHS is robust in this study, and the results indicate that future studies investigating hippocampal subfields in early AD populations can be flexible in the selection of their atlas compositions.

Extinction learning refers to the phenomenon that a previously learned response to an environmental stimulus, for example, the expression of an aversive behaviour upon exposure to a specific context, is reduced when the stimulus is repeatedly presented in the absence of a previously paired aversive event. Extinction of fear memories has been implicated with the treatment of anxiety disease but the molecular processes that underlie fear extinction are only beginning to emerge. Here, we show that fear extinction initiates upregulation of hippocampal insulin-growth factor 2 (Igf2) and downregulation of insulin-growth factor binding protein 7 (Igfbp7). In line with this observation, we demonstrate that IGF2 facilitates fear extinction, while IGFBP7 impairs fear extinction in an IGF2-dependent manner. Furthermore, we identify one cellular substrate of altered IGF2 signalling during fear extinction. To this end, we show that fear extinction-induced IGF2/IGFBP7 signalling promotes the survival of 17-19-day-old newborn hippocampal neurons. In conclusion, our data suggest that therapeutic strategies that enhance IGF2 signalling and adult neurogenesis might be suitable to treat disease linked to excessive fear memory.