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Brain morphology is altered in both anorexia nervosa and obesity. However, it is yet unclear if the relationship between Body Mass Index-Standard Deviation Score (BMI-SDS) and brain morphology exists across the BMI-SDS spectrum, or is present only in the extremes. The study involved 3160 9-to-11 year-old children (50.3% female) who participate in Generation R, a population-based study. Structural MRI scans were obtained from all children and FreeSurfer was used to quantify both global and surface-based measures of gyrification and cortical thickness. Body length and weight were measured to calculate BMI. Dutch growth curves were used to calculate BMI-SDS. BMI-SDS was analyzed continuously and in two categories (median split). The relationship between BMI-SDS (range - 3.82 to 3.31) and gyrification showed an inverted-U shape curve in children with both lower and higher BMI-SDS values having lower gyrification in widespread areas of the brain. BMI-SDS had a positive linear association with cortical thickness in multiple brain regions. This study provides evidence for an association between BMI-SDS and brain morphology in a large sample of children from the general population and suggests that a normal BMI during childhood is important for brain development. Future studies could determine whether lifestyle modifications optimize BMI-SDS result in return to more typical patterns of brain morphology.

Enlarged perivascular spaces (PVS) are structural brain changes visible in MRI, are common in aging, and are considered a reflection of cerebral small vessel disease. As such, assessing the burden of PVS has promise as a brain imaging marker. Visual and manual scoring of PVS is a tedious and observer-dependent task. Automated methods would advance research into the etiology of PVS, could aid to assess what a "normal" burden is in aging, and could evaluate the potential of PVS as a biomarker of cerebral small vessel disease. In this work, we propose and evaluate an automated method to quantify PVS in the midbrain, hippocampi, basal ganglia and centrum semiovale. We also compare associations between (earlier established) determinants of PVS and visual PVS scores versus the automated PVS scores, to verify whether automated PVS scores could replace visual scoring of PVS in epidemiological and clinical studies. Our approach is a deep learning algorithm based on convolutional neural network regression, and is contingent on successful brain structure segmentation. In our work we used FreeSurfer segmentations. We trained and validated our method on T2-contrast MR images acquired from 2115 subjects participating in a population-based study. These scans were visually scored by an expert rater, who counted the number of PVS in each brain region. Agreement between visual and automated scores was found to be excellent for all four regions, with intraclass correlation coefficients (ICCs) between 0.75 and 0.88. These values were higher than the inter-observer agreement of visual scoring (ICCs between 0.62 and 0.80). Scan-rescan reproducibility was high (ICCs between 0.82 and 0.93). The association between 20 determinants of PVS, including aging, and the automated scores were similar to those between the same 20 determinants of PVS and visual scores. We conclude that this method may replace visual scoring and facilitate large epidemiological and clinical studies of PVS., (Copyright © 2018 The Authors. Published by Elsevier Inc. All rights reserved.)

Both normal aging and neurodegenerative disorders such as Alzheimer's disease (AD) cause morphological changes of the brain. It is generally difficult to distinguish these two causes of morphological change by visual inspection of magnetic resonance (MR) images. To facilitate making this distinction and thus aid the diagnosis of neurodegenerative disorders, we propose a method for developing a spatio-temporal model of morphological differences in the brain due to normal aging. The method utilizes groupwise image registration to characterize morphological variation across brain scans of people with different ages. To extract the deformations that are due to normal aging we use partial least squares regression, which yields modes of deformations highly correlated with age, and corresponding scores for each input subject. Subsequently, we determine a distribution of morphologies as a function of age by fitting smooth percentile curves to these scores. This distribution is used as a reference to which a person's morphology score can be compared. We validate our method on two different datasets, using images from both cognitively normal subjects and patients with Alzheimer disease (AD). Results show that the proposed framework extracts the expected atrophy patterns. Moreover, the morphology scores of cognitively normal subjects are on average lower than the scores of AD subjects, indicating that morphology differences between AD subjects and healthy subjects can be partly explained by accelerated aging. With our methods we are able to assess accelerated brain aging on both population and individual level. A spatio-temporal aging brain model derived from 988 T1-weighted MR brain scans from a large population imaging study (age range 45.9-91.7y, mean age 68.3y) is made publicly available at www.agingbrain.nl., (Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.)

18FDG PET-CT is useful for early and differential diagnosis of dementia. ASL-MRI seems an attractive alternative. Visual agreement was calculated in 21 brain regions bilaterally in 9/11 patients with suspected early-stage dementia, excluding 2 patients due to ASL-MRI motion artefacts. Overall gross agreement was almost perfect and identical between and within modalities (κ=0.85-0.86, p<0.05). Intermodality regional agreement was variable, moderate (κ=0,56 in posterior cingulate) to perfect (including in precuneus), not different from intramodality agreement. Diagnostic accuracy was 5/9 (56%) for ASL-MRI and 7/9 (78%) for FDG PETCT. (p=0.31). Clinical experience and optimisation may further improve ASL-MRI's performance., (Copyright © 2017 Elsevier Inc. All rights reserved.)

The pattern of structural brain alterations associated with major depressive disorder (MDD) remains unresolved. This is in part due to small sample sizes of neuroimaging studies resulting in limited statistical power, disease heterogeneity and the complex interactions between clinical characteristics and brain morphology. To address this, we meta-analyzed three-dimensional brain magnetic resonance imaging data from 1728 MDD patients and 7199 controls from 15 research samples worldwide, to identify subcortical brain volumes that robustly discriminate MDD patients from healthy controls. Relative to controls, patients had significantly lower hippocampal volumes (Cohen's d=-0.14, % difference=-1.24). This effect was driven by patients with recurrent MDD (Cohen's d=-0.17, % difference=-1.44), and we detected no differences between first episode patients and controls. Age of onset ⩽21 was associated with a smaller hippocampus (Cohen's d=-0.20, % difference=-1.85) and a trend toward smaller amygdala (Cohen's d=-0.11, % difference=-1.23) and larger lateral ventricles (Cohen's d=0.12, % difference=5.11). Symptom severity at study inclusion was not associated with any regional brain volumes. Sample characteristics such as mean age, proportion of antidepressant users and proportion of remitted patients, and methodological characteristics did not significantly moderate alterations in brain volumes in MDD. Samples with a higher proportion of antipsychotic medication users showed larger caudate volumes in MDD patients compared with controls. This currently largest worldwide effort to identify subcortical brain alterations showed robust smaller hippocampal volumes in MDD patients, moderated by age of onset and first episode versus recurrent episode status.

Background and Purpose: Central sensitization in chronic pain involves structural brain changes that influence vulnerability to pain. Identifying brain regions involved in pain processing and sensitization can provide more insight into chronic pain. This study examines structural brain changes in chronic pain and experimental pain in a large population-based study., Materials and Methods: For 3892 participants in the Rotterdam study, global and regional MR imaging brain volumes were automatically segmented and quantified. Chronic joint pain was defined as pain for more than half of all days during the past 6 weeks. Heat pain thresholds were measured in a subset of 1538 individuals. The association between the presence of chronic joint pain and global and lobar brain volumes was studied. Subsequently, literature was reviewed and the association of chronic pain and heat pain thresholds with 11 brain regions associated with musculoskeletal pain in previous publications was studied., Results: Total gray matter volume was smaller in women with chronic pain (β = -0.066, P = .016). This effect was primarily driven by lower gray matter volume in the temporal lobe (β = 0.086, P = .005), the frontal lobe (β = -0.060, P = .039), and the hippocampus (β = -0.099, P = .002). In addition, we observed that a lower heat pain threshold was associated with smaller volumes of the hippocampus (β = 0.017, P = .048), the thalamus (β = 0.018, P = .009), and the anterior cingulate cortex (β = -0.016, P = .037). In men, no significant associations were observed., Conclusions: The primary identified brain areas, the temporal and frontal lobes and the hippocampus, indicated involvement of emotional processing. The volumetric differences found indicated a sex-specific neuroplasticity in chronic pain. These results emphasized sex-specific and multidisciplinary pain treatment., (© 2016 by American Journal of Neuroradiology.)

Background: Several psychosocial risk factors for complicated grief have been described. However, the association of complicated grief with cognitive and biological risk factors is unclear. The present study examined whether complicated grief and normal grief are related to cognitive performance or structural brain volumes in a large population-based study., Method: The present research comprised cross-sectional analyses embedded in the Rotterdam Study. The study included 5501 non-demented persons. Participants were classified as experiencing no grief (n = 4731), normal grief (n = 615) or complicated grief (n = 155) as assessed with the Inventory of Complicated Grief. All persons underwent cognitive testing (Mini-Mental State Examination, Letter-Digit Substitution Test, Stroop Test, Word Fluency Task, word learning test - immediate and delayed recall), and magnetic resonance imaging to measure general brain parameters (white matter, gray matter), and white matter lesions. Total brain volume was defined as the sum of gray matter plus normal white matter and white matter lesion volume. Persons with depressive disorders were excluded and analyses were adjusted for depressive symptoms., Results: Compared with no-grief participants, participants with complicated grief had lower scores for the Letter-Digit Substitution Test [Z-score -0.16 v. 0.04, 95% confidence interval (CI) -0.36 to -0.04, p = 0.01] and Word Fluency Task (Z-score -0.15 v. 0.03, 95% CI -0.35 to -0.02, p = 0.02) and smaller total volumes of brain matter (933.53 ml v. 952.42 ml, 95% CI -37.6 to -0.10, p = 0.04)., Conclusions: Participants with complicated grief performed poorly in cognitive tests and had a smaller total brain volume. Although the effect sizes were small, these findings suggest that there may be a neurological correlate of complicated grief, but not of normal grief, in the general population.

The importance of macrostructural white matter changes, including white matter lesions and atrophy, in intact brain functioning is increasingly being recognized. Diffusion tensor imaging (DTI) enables measurement of the microstructural integrity of white matter. Loss of white matter integrity in aging has been reported, but whether this is inherent to the aging process itself or results from specific white matter pathology is unknown. In 832 persons aged 60 years and older from the population-based Rotterdam Study, we measured fractional anisotropy (FA) and directional diffusivities in normal-appearing white matter using DTI. All subjects' DTI measures were projected onto a common white matter skeleton to enable robust voxelwise comparison. With increasing age, multiple regions showed significant decreases in FA or increases in axial or radial diffusivity in normal-appearing white matter. However, nearly all of these regional changes were explained by either white matter atrophy or by white matter lesions; each of which related to changes in distinct brain regions. These results indicate that loss of white matter integrity in aging is primarily explained by atrophy and lesion formation and not by the aging process itself. Furthermore, white matter atrophy and white matter lesion formation relate to loss of integrity in distinct brain regions, indicating the two processes are pathophysiologically different.