Alterations in regional subcortical brain volumes have been investigated as part of the efforts of an international consortium, ENIGMA, to identify reliable neural correlates of major depressive ...disorder (MDD). Given that subcortical structures are comprised of distinct subfields, we sought to build significantly from prior work by precisely mapping localized MDD‐related differences in subcortical regions using shape analysis. In this meta‐analysis of subcortical shape from the ENIGMA‐MDD working group, we compared 1,781 patients with MDD and 2,953 healthy controls (CTL) on individual measures of shape metrics (thickness and surface area) on the surface of seven bilateral subcortical structures: nucleus accumbens, amygdala, caudate, hippocampus, pallidum, putamen, and thalamus. Harmonized data processing and statistical analyses were conducted locally at each site, and findings were aggregated by meta‐analysis. Relative to CTL, patients with adolescent‐onset MDD (≤ 21 years) had lower thickness and surface area of the subiculum, cornu ammonis (CA) 1 of the hippocampus and basolateral amygdala (Cohen's d = −0.164 to −0.180). Relative to first‐episode MDD, recurrent MDD patients had lower thickness and surface area in the CA1 of the hippocampus and the basolateral amygdala (Cohen's d = −0.173 to −0.184). Our results suggest that previously reported MDD‐associated volumetric differences may be localized to specific subfields of these structures that have been shown to be sensitive to the effects of stress, with important implications for mapping treatments to patients based on specific neural targets and key clinical features.
White matter hyperintensities are associated with increased risk of dementia and cognitive decline. The current study investigates the relationship between white matter hyperintensities burden and ...patterns of brain atrophy associated with brain ageing and Alzheimer's disease in a large populatison-based sample (n = 2367) encompassing a wide age range (20-90 years), from the Study of Health in Pomerania. We quantified white matter hyperintensities using automated segmentation and summarized atrophy patterns using machine learning methods resulting in two indices: the SPARE-BA index (capturing age-related brain atrophy), and the SPARE-AD index (previously developed to capture patterns of atrophy found in patients with Alzheimer's disease). A characteristic pattern of age-related accumulation of white matter hyperintensities in both periventricular and deep white matter areas was found. Individuals with high white matter hyperintensities burden showed significantly (P < 0.0001) lower SPARE-BA and higher SPARE-AD values compared to those with low white matter hyperintensities burden, indicating that the former had more patterns of atrophy in brain regions typically affected by ageing and Alzheimer's disease dementia. To investigate a possibly causal role of white matter hyperintensities, structural equation modelling was used to quantify the effect of Framingham cardiovascular disease risk score and white matter hyperintensities burden on SPARE-BA, revealing a statistically significant (P < 0.0001) causal relationship between them. Structural equation modelling showed that the age effect on SPARE-BA was mediated by white matter hyperintensities and cardiovascular risk score each explaining 10.4% and 21.6% of the variance, respectively. The direct age effect explained 70.2% of the SPARE-BA variance. Only white matter hyperintensities significantly mediated the age effect on SPARE-AD explaining 32.8% of the variance. The direct age effect explained 66.0% of the SPARE-AD variance. Multivariable regression showed significant relationship between white matter hyperintensities volume and hypertension (P = 0.001), diabetes mellitus (P = 0.023), smoking (P = 0.002) and education level (P = 0.003). The only significant association with cognitive tests was with the immediate recall of the California verbal and learning memory test. No significant association was present with the APOE genotype. These results support the hypothesis that white matter hyperintensities contribute to patterns of brain atrophy found in beyond-normal brain ageing in the general population. White matter hyperintensities also contribute to brain atrophy patterns in regions related to Alzheimer's disease dementia, in agreement with their known additive role to the likelihood of dementia. Preventive strategies reducing the odds to develop cardiovascular disease and white matter hyperintensities could decrease the incidence or delay the onset of dementia.
Purpose
Associations between thyroid diseases and depression have been described since the 1960s but there is a lack of population-based studies investigating associations of thyroid diseases with ...depression and anxiety defined by gold-standard methods. Thus, the aim was to investigate the association of diagnosed thyroid disorders, serum thyroid-stimulating hormone (TSH) levels, and anti-thyroid-peroxidase antibodies (TPO-abs) with depression and anxiety.
Methods
We used data from 2142 individuals, who participated in the first follow-up of the Study of Health in Pomerania (SHIP-1) and in the Life-Events and Gene-Environment Interaction in Depression (LEGEND). DSM-VI diagnoses of major depression disorder and anxiety were defined using the Munich-Composite International Diagnostic Interview; the Beck depression inventory (BDI-II) was used for the assessment of current depressive symptoms. Thyroid diseases were assessed by interviews and by biomarkers and were associated with depression and anxiety using Poisson regression adjusted for age, sex, marital status, educational level, smoking status, BMI, and the log-transformed time between SHIP-1 and LEGEND.
Results
Untreated diagnosed hypothyroidism was positively associated with the BDI-II-score and with anxiety, while untreated diagnosed hyperthyroidism was significantly related to MDD during the last 12 months. Serum TSH levels and TPO-Abs were not significantly associated with depression and anxiety. In sub-analyses, distinct interactions were found between childhood maltreatment and thyroid disorders in modifying the association on depression and anxiety disorders.
Conclusions
Our results substantiate evidence that diagnosed untreated hypothyroidism is associated with depression and anxiety, and that diagnosed untreated hyperthyroidism is associated with depression.
One of the features that distinguishes modern humans from our extinct relatives and ancestors is a globular shape of the braincase 1–4. As the endocranium closely mirrors the outer shape of the ...brain, these differences might reflect altered neural architecture 4, 5. However, in the absence of fossil brain tissue, the underlying neuroanatomical changes as well as their genetic bases remain elusive. To better understand the biological foundations of modern human endocranial shape, we turn to our closest extinct relatives: the Neandertals. Interbreeding between modern humans and Neandertals has resulted in introgressed fragments of Neandertal DNA in the genomes of present-day non-Africans 6, 7. Based on shape analyses of fossil skull endocasts, we derive a measure of endocranial globularity from structural MRI scans of thousands of modern humans and study the effects of introgressed fragments of Neandertal DNA on this phenotype. We find that Neandertal alleles on chromosomes 1 and 18 are associated with reduced endocranial globularity. These alleles influence expression of two nearby genes, UBR4 and PHLPP1, which are involved in neurogenesis and myelination, respectively. Our findings show how integration of fossil skull data with archaic genomics and neuroimaging can suggest developmental mechanisms that may contribute to the unique modern human endocranial shape.
•We use fossil skull data to derive an index of endocranial shape in human MRI scans•In 4,468 Europeans, we screen introgressed Neandertal SNPs for association with the index•Lead SNPs consistently associate with reduced globularity in five separate subsamples•These SNPs affect neural expression of two genes linked to neurogenesis and myelination
Gunz, Tilot et al. combine paleoanthropology, archaic genomics, neuroimaging, and gene expression to study biological foundations of the characteristic modern human endocranial shape. They find introgressed Neandertal alleles that associate with reduced endocranial globularity and affect expression of genes linked to neurogenesis and myelination.
Abstract
Advanced brain aging is commonly regarded as a risk factor for neurodegenerative diseases, for example, Alzheimer’s dementia, and it was suggested that sleep disorders such as obstructive ...sleep apnea (OSA) are significantly contributing factors to these neurodegenerative processes. To determine the association between OSA and advanced brain aging, we investigated the specific effect of two indices quantifying OSA, namely the apnea–hypopnea index (AHI) and the oxygen desaturation index (ODI), on brain age, a score quantifying age-related brain patterns in 169 brain regions, using magnetic resonance imaging and overnight polysomnography data from 690 participants (48.8% women, mean age 52.5 ± 13.4 years) of the Study of Health in Pomerania. We additionally investigated the mediating effect of subclinical inflammation parameters on these associations via a causal mediation analysis. AHI and ODI were both positively associated with brain age (AHI std. effect 95% CI: 0.07 0.03; 0.12, p-value: 0.002; ODI std. effect 95% CI: 0.09 0.04; 0.13, p-value: < 0.0003). The effects remained stable in the presence of various confounders such as diabetes and were partially mediated by the white blood cell count, indicating a subclinical inflammation process. Our results reveal an association between OSA and brain age, indicating subtle but widespread age-related changes in regional brain structures, in one of the largest general population studies to date, warranting further examination of OSA in the prevention of neurodegenerative diseases.
•Levels of renin and aldosterone were altered in relation to childhood and adulthood trauma in the general population.•Exposure to and severity of childhood trauma were associated with increased ...levels of aldosterone.•This association was carried by all dimensions of childhood abuse, but not neglect.•Adulthood trauma and PTSD were associated with enhanced renin levels.•Renin and aldosterone were increased in subjects with exposure to childhood and adulthood.
Previous evidence suggested lasting and cumulative effects of traumatization on the renin-angiotensin-aldosterone-system (RAAS). However, it is unclear whether traumas during childhood and those experienced in adulthood differentially impact the RAAS. In this study, we sought to investigate main and putative interactive effects of childhood and adulthood trauma on RAAS functioning.
Plasma concentrations of renin and aldosterone were measured in a general population sample (n = 2016). Childhood trauma was assessed using the Childhood Trauma Questionnaire (CTQ), adulthood trauma was measured using the PTSD module of the Structured Clinical Interview of the DSM-IV. Linear regression models were calculated to assess the relations between childhood or adulthood traumatization with renin and aldosterone concentrations.
Exposure to (ß = 0.094; p = 0.01), severity of childhood trauma (ß = 0.004; p = 0.01) were associated with increased aldosterone, but not renin levels. Results were carried by all dimensions of abuse, while childhood neglect was not associated with altered RAAS activity. In contrast, adulthood traumas (ß = 0.113; p < 0.01) were significantly associated with increased renin concentrations. Subjects with PTSD (renin: ß = 0.345; p = 0.01; aldosterone: ß = 0.232; p = 0.04) and those who had been exposed to both childhood and adulthood trauma showed increases in renin (ß = 0.180; p < 0.01) and aldosterone (ß = 0.340; p < 0.01) levels.
These findings indicate that trauma is associated with differential alterations of the RAAS depending on the time of traumatization. Moreover, exposure to childhood or adulthood trauma may act synergistically on the RAAS, resulting in severe dysregulation of the RAAS. The results contribute to explain associations between trauma and enhanced risk for physical disease.
In this work, we introduce an entirely data-driven and automated approach to reveal disease-associated biomarker and risk factor networks from heterogeneous and high-dimensional healthcare data. Our ...workflow is based on Bayesian networks, which are a popular tool for analyzing the interplay of biomarkers. Usually, data require extensive manual preprocessing and dimension reduction to allow for effective learning of Bayesian networks. For heterogeneous data, this preprocessing is hard to automatize and typically requires domain-specific prior knowledge. We here combine Bayesian network learning with hierarchical variable clustering in order to detect groups of similar features and learn interactions between them entirely automated. We present an optimization algorithm for the adaptive refinement of such group Bayesian networks to account for a specific target variable, like a disease. The combination of Bayesian networks, clustering, and refinement yields low-dimensional but disease-specific interaction networks. These networks provide easily interpretable, yet accurate models of biomarker interdependencies. We test our method extensively on simulated data, as well as on data from the Study of Health in Pomerania (SHIP-TREND), and demonstrate its effectiveness using non-alcoholic fatty liver disease and hypertension as examples. We show that the group network models outperform available biomarker scores, while at the same time, they provide an easily interpretable interaction network.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Deep learning has emerged as a powerful approach to constructing imaging signatures of normal brain ageing as well as of various neuropathological processes associated with brain diseases. In ...particular, MRI-derived brain age has been used as a comprehensive biomarker of brain health that can identify both advanced and resilient ageing individuals via deviations from typical brain ageing. Imaging signatures of various brain diseases, including schizophrenia and Alzheimer's disease, have also been identified using machine learning. Prior efforts to derive these indices have been hampered by the need for sophisticated and not easily reproducible processing steps, by insufficiently powered or diversified samples from which typical brain ageing trajectories were derived, and by limited reproducibility across populations and MRI scanners. Herein, we develop and test a sophisticated deep brain network (DeepBrainNet) using a large (n = 11 729) set of MRI scans from a highly diversified cohort spanning different studies, scanners, ages and geographic locations around the world. Tests using both cross-validation and a separate replication cohort of 2739 individuals indicate that DeepBrainNet obtains robust brain-age estimates from these diverse datasets without the need for specialized image data preparation and processing. Furthermore, we show evidence that moderately fit brain ageing models may provide brain age estimates that are most discriminant of individuals with pathologies. This is not unexpected as tightly-fitting brain age models naturally produce brain-age estimates that offer little information beyond age, and loosely fitting models may contain a lot of noise. Our results offer some experimental evidence against commonly pursued tightly-fitting models. We show that the moderately fitting brain age models obtain significantly higher differentiation compared to tightly-fitting models in two of the four disease groups tested. Critically, we demonstrate that leveraging DeepBrainNet, along with transfer learning, allows us to construct more accurate classifiers of several brain diseases, compared to directly training classifiers on patient versus healthy control datasets or using common imaging databases such as ImageNet. We, therefore, derive a domain-specific deep network likely to reduce the need for application-specific adaptation and tuning of generic deep learning networks. We made the DeepBrainNet model freely available to the community for MRI-based evaluation of brain health in the general population and over the lifespan.
OBJECTIVESTo investigate spatial heterogeneity of white matter lesions or hyperintensities (WMH).
METHODSMRI scans of 1,836 participants (median age 52.2 ± 13.16 years) encompassing a wide age range ...(22–84 years) from the cross-sectional Study of Health in Pomerania (Germany) were included as discovery set identifying spatially distinct components of WMH using a structural covariance approach. Scans of 307 participants (median age 73.8 ± 10.2 years, with 747 observations) from the Baltimore Longitudinal Study of Aging (United States) were included to examine differences in longitudinal progression of these components. The associations of these components with vascular risk factors, cortical atrophy, Alzheimer disease (AD) genetics, and cognition were then investigated using linear regression.
RESULTSWMH were found to occur nonuniformly, with higher frequency within spatially heterogeneous patterns encoded by 4 components, which were consistent with common categorizations of deep and periventricular WMH, while further dividing the latter into posterior, frontal, and dorsal components. Temporal trends of the components differed both cross-sectionally and longitudinally. Frontal periventricular WMH were most distinctive as they appeared in the fifth decade of life, whereas the other components appeared later in life during the sixth decade. Furthermore, frontal WMH were associated with systolic blood pressure and with pronounced atrophy including AD-related regions. AD polygenic risk score was associated with the dorsal periventricular component in the elderly. Cognitive decline was associated with the dorsal component.
CONCLUSIONSThese results support the hypothesis that the appearance of WMH follows age and disease-dependent regional distribution patterns, potentially influenced by differential underlying pathophysiologic mechanisms, and possibly with a differential link to vascular and neurodegenerative changes.
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