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Background: Early identification of dementia is crucial for prompt intervention and better outcomes for high‐risk individuals in the general population. The use of machine learning in dementia prediction has allowed for highly accurate models that could aid in early classification; however, they have focused on expensive predictors. Exploring predictors that are more accessible is crucial for the possible widespread use of machine learning models in clinical practice. Method: Data from 4,793 individuals without dementia or mild cognitive impairment at baseline were included from the population‐based AGES‐Reykjavik Study. Cognitive, biometric, and MRI assessments (total: 64 variables) were collected at baseline, with follow‐up of incident dementia diagnoses for a maximum of 12 years. Elastic net regression, random forest, support vector machine, Naïve Bayes, logistic regression, and elastic net Cox regression (for time‐to‐event analysis) were explored as possible algorithms. Model 1 was fit using all variables, model 2 after feature selection using the Boruta package, and model 3 without neuroimaging markers (clinically accessible model). Ten‐fold cross‐validation, repeated ten times, was implemented during training. Upsampling was used to account for imbalanced data. Tuning parameters were optimized for recalibration automatically using the caret package. Result: During training, the Model 2 elastic net regression had the highest AUC [0.78; 95% CI: 0.76‐0.80], sensitivity [72; 95% CI: 68‐75], and specificity [72; 95% CI: 70‐73]. For Model 3, excluding MRI markers, the AUC remained high [AUC 0.75; 95% CI: 0.73‐0.77]. Similar results were found in our test data for Model 3 [AUC 0.74; 95% CI: 0.71‐0.77]; thus, the risk of overfitting was low. Time‐to‐event analysis using elastic net Cox models showed similar discrimination [c‐statistic 0.79] during testing. The most important variables based on Boruta selection included the Activities of Daily Living, presence of APOE e4 allele, memory functioning, and sex. Conclusion: Supervised machine learning could be used to identify individuals at high‐risk for dementia in the general population using easily accessible variables. As dementia becomes a leading problem in developing countries, this clinically accessible model could be explored for use in these areas for better identification of individuals at risk in the community. Further external validation is needed. [ABSTRACT FROM AUTHOR]

Background: 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 risk factors in the development of neurodegenerative diseases. The current study had two aims: to perform a systematic review on psychosocial factors and hippocampal subfield volume and to explore the association of psychosocial factors with hippocampal volume, both total volume and among subfields, using high‐field 7T MRI. Method: Within the Memory Depression and Aging (Medea)‐7T study, 333 participants without dementia underwent 7T MRI and answered psychosocial questionnaires. 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 life events, and social support), adjusting for age, sex, and intracranial volume. Result: Neither depression nor anxiety was associated with specific hippocampal (sub)volumes. Recent life events were associated with greater total volume (B = 0.17, 95% CI: 0.07; 0.27). However, reporting childhood maltreatment was associated with smaller total hippocampal volume (B per standard deviation = ‐0.20, 95% CI: ‐0.31; ‐0.10). Among subfields, high social support was associated with greater volume in the CA3 (B = 0.47, 95% CI: 0.21; 0.73). Conclusion: This study suggests possible differential effects among hippocampal (sub)volumes and psychosocial factors, specifically reporting childhood maltreatment and social support. The systematic review highlighted inconsistencies in results, highlighting the need for future studies validating these results. [ABSTRACT FROM AUTHOR]

Hypertension has been related to structural and functional brain changes. In high-risk populations, hypertensive target organ damage might better represent exposure to high blood pressure than the blood pressure measurement itself. We examined the association of hypertensive target organ damage with longitudinal changes in brain structure and function within the Second Manifestations of Arterial Disease-Magnetic Resonance (SMART-MR) study. Renal function, albuminuria, and left ventricular hypertrophy on electrocardiography were measured in 663 patients with manifest arterial disease (mean age, 57±9 years; 81% men). Automated brain segmentation was used to quantify progression of global brain atrophy (change in brain parenchymal fraction) and progression of cerebral small vessel disease on 1.5T magnetic resonance imaging, and memory and executive functioning were assessed at baseline and after on average 3.9 years of follow-up. Regression analyses showed that an increasing number of signs of target organ damage was associated with more progression of global brain atrophy and more rapid decline in memory performance. Compared with no target organ damage, mean differences in change in brain parenchymal fraction (95% confidence interval) for 1 and ≥2 signs of organ damage were -0.12 (-0.30; 0.06) and -0.41 (-0.77; -0.05) % intracranial volume, and mean (95% confidence interval) differences in change in memory performance (z score) were -0.15 (-0.29; -0.00) and -0.27 (-0.54; -0.01). Results were independent of blood pressure, antihypertensive treatment, and other confounders. Hypertension target organ damage was not associated with progression of cerebral small vessel disease or change in executive functioning. Routinely assessed signs of hypertensive target organ damage, and in particular impaired renal function, could be used to identify patients at the highest risk of cognitive decline. [ABSTRACT FROM AUTHOR]

The nature and origin of ion-π and ion-σ interactions has been systematically investigated using dispersion-corrected density functional theory and the recently developed noncovalent interaction (NCI) method. A detailed analysis of these interactions is performed with the aim to identify the requirements that have to be fulfilled by the molecular system for strong ion-ligand interactions. Interestingly, our results indicate that aliphatic systems, such as cyclohexane, can interact as strong as aromatic ones with both cations and anions, despite of having a negligible quadrupole moment. In fact, cyclohexane binds anions stronger than benzene itself but slightly weaker that hexafluorobenzene. The NCI method reveals that the interaction between the ions and three C–H bonds of the saturated fragment are responsible for the surprisingly strong ion-σ interaction. A weakening of the ion-σ interactions is observed in the order: Li + > F − > Na + > Cl − > Br − ≈ K + . In addition, a complete Ziegler–Rauk type energy decomposition analysis has been carried out in order to reveal the origins of the thermodynamic driving force for complex formations. The electron density deformation upon complex formation has been scrutinized with a complementary NOCV analysis allowing the identification of molecular orbital interaction contributions to the stabilization. Based on these analysis, it is shown that the formally anion-π interaction is rather an anion-σ ∗ interaction. [ABSTRACT FROM AUTHOR]