On the possibility of magneto-structural correlations: detailed studies of di-nickel carboxylate complexes

A series of water-bridged dinickel complexes of the general formula [Ni<sub>2</sub>(μ<sub>2</sub>-OH<sub>2</sub>)(μ2- O<sub>2</sub>C<sup>t</sup>Bu)<sub>2</sub>(O<sub>2</sub>C<sup>t</sup>Bu)2(L)(L0)] (L = HO<sub>2</sub>C<sup>t</sup>Bu, L0 = HO<sub>2</sub>C<sup>t</sup>Bu (1), pyridine (2), 3-methylpyridine (4); L = L0 = pyridine (3), 3-methylpyridine (5)) has been synthesized and structurally characterized by X-ray crystallography. The magnetic properties have been probed by magnetometry and EPR spectroscopy, and detailed measurements show that the axial zero-field splitting, D, of the nickel(ii) ions is on the same order as the isotropic exchange interaction, J, between the nickel sites. The isotropic exchange interaction can be related to the angle between the nickel centers and the bridging water molecule, while the magnitude of D can be related to the coordination sphere at the nickel sites

Plasma and cerebrospinal fluid glial fibrillary acidic protein levels in adults with Down syndrome: a longitudinal cohort study

Background: The diagnosis of symptomatic Alzheimer's disease is a clinical challenge in adults with Down syndrome. Blood biomarkers would be of particular clinical importance in this population. The astrocytic Glial Fibrillary Acidic Protein (GFAP) is a marker of astrogliosis associated with amyloid pathology, but its longitudinal changes, association with other biomarkers and cognitive performance have not been studied in individuals with Down syndrome. Methods: We performed a three-centre study of adults with Down syndrome, autosomal dominant Alzheimer's disease and euploid individuals enrolled in Hospital Sant Pau, Barcelona (Spain), Hospital Clinic, Barcelona (Spain) and Ludwig-Maximilians-Universität, Munich (Germany). Cerebrospinal fluid (CSF) and plasma GFAP concentrations were quantified using Simoa. A subset of participants had PET 18F-fluorodeoxyglucose, amyloid tracers and MRI measurements. Findings: This study included 997 individuals, 585 participants with Down syndrome, 61 Familial Alzheimer's disease mutation carriers and 351 euploid individuals along the Alzheimer's disease continuum, recruited between November 2008 and May 2022. Participants with Down syndrome were clinically classified at baseline as asymptomatic, prodromal Alzheimer's disease and Alzheimer's disease dementia. Plasma GFAP levels were significantly increased in prodromal and Alzheimer's disease dementia compared to asymptomatic individuals and increased in parallel to CSF Aβ changes, ten years prior to amyloid PET positivity. Plasma GFAP presented the highest diagnostic performance to discriminate symptomatic from asymptomatic groups (AUC = 0.93, 95% CI 0.9−0.95) and its concentrations were significantly higher in progressors vs non-progressors (p < 0.001), showing an increase of 19.8% (11.8–33.0) per year in participants with dementia. Finally, plasma GFAP levels were highly correlated with cortical thinning and brain amyloid pathology. Interpretation: Our findings support the utility of plasma GFAP as a biomarker of Alzheimer's disease in adults with Down syndrome, with possible applications in clinical practice and clinical trials. Funding: AC Immune, La Caixa Foundation, Instituto de Salud Carlos III, National Institute on Aging, Wellcome Trust, Jérôme Lejeune Foundation, Medical Research Council, Alzheimer's Association, National Institute for Health Research, EU Joint Programme–Neurodegenerative Disease Research, Alzheimer's Society, Deutsche Forschungsgemeinschaft, Stiftung für die Erforschung von Verhaltens, Fundación Tatiana Pérez de Guzmán el Bueno & European Union's Horizon 2020 und Umwelteinflüssen auf die menschliche Gesundheit

Cerebral Amyloid Angiopathy in Down Syndrome and Sporadic and Autosomal-Dominant Alzheimer\u27s Disease

Introduction—We aimed to investigate if cerebral amyloid angiopathy (CAA) is more frequent in genetically determined than in sporadic early-onset forms of Alzheimer\u27s disease (AD) (early-onset AD [EOAD]). Methods—Neuroimaging features of CAA, APOE, and cerebrospinal fluid-Aβ40 levels were studied in subjects with Down syndrome (DS, n = 117), autosomal-dominant AD (ADAD, n = 29), sporadic EOAD (n = 42), and healthy controls (n = 68). Results—CAA was present in 31%, 38%, and 12% of cognitively impaired DS, symptomatic ADAD, and sporadic EOAD subjects and in 13% and 4% of cognitively unimpaired DS individuals and healthy controls, respectively. APOE-ε4 genotype was borderline significantly associated with CAA in sporadic EOAD (p = .06) but not with DS or ADAD. There were no differences in Aβ040 levels between groups or between subjects with and without CAA. Discussion—CAA is more frequently found in genetically determined AD than in sporadic EOAD. Cerebrospinal fluid-Aβ40 levels are not a useful biomarker for CAA in AD

Delineation of Diverse Macrophage Activation Programs in Response to Intracellular Parasites and Cytokines

Macrophages are a type of immune cell that engulf and digest microorganisms. Despite their role in protecting the host from infection, many pathogens have developed ways to hijack the macrophage and use the cell for their own survival and proliferation. This includes the parasites Trypanosoma cruzi and Leishmania mexicana. In order to gain further understanding of how these pathogens interact with the host macrophage, we compared macrophages that have been infected with these parasites to macrophages that have been stimulated in a number of different ways. Macrophages can be activated by a wide variety of stimuli, including common motifs found on pathogens (known as pathogen associated molecular patterns or PAMPs) and cytokines secreted by other immune cells. In this study, we have delineated the relationships between the macrophage activation programs elicited by a number of cytokines and PAMPs. Furthermore, we have placed the macrophage responses to T. cruzi and L. mexicana into the context of these activation programs, providing a better understanding of the interactions between these pathogens and macrophages

Increased power by harmonizing structural MRI site differences with the ComBat batch adjustment method in ENIGMA

A common limitation of neuroimaging studies is their small sample sizes. To overcome this hurdle, the Enhancing Neuro Imaging Genetics through Meta-Analysis (ENIGMA) Consortium combines neuroimaging data from many institutions worldwide. However, this introduces heterogeneity due to different scanning devices and sequences. ENIGMA projects commonly address this heterogeneity with random-effects meta-analysis or mixed-effects mega-analysis. Here we tested whether the batch adjustment method, ComBat, can further reduce site-related heterogeneity and thus increase statistical power. We conducted random-effects meta-analyses, mixed-effects mega-analyses and ComBat mega-analyses to compare cortical thickness, surface area and subcortical volumes between 2897 individuals with a diagnosis of schizophrenia and 3141 healthy controls from 33 sites. Specifically, we compared the imaging data between individuals with schizophrenia and healthy controls, covarying for age and sex. The use of ComBat substantially increased the statistical significance of the findings as compared to random-effects meta-analyses. The findings were more similar when comparing ComBat with mixed-effects mega-analysis, although ComBat still slightly increased the statistical significance. ComBat also showed increased statistical power when we repeated the analyses with fewer sites. Results were nearly identical when we applied the ComBat harmonization separately for cortical thickness, cortical surface area and subcortical volumes. Therefore, we recommend applying the ComBat function to attenuate potential effects of site in ENIGMA projects and other multi-site structural imaging work. We provide easy-to-use functions in R that work even if imaging data are partially missing in some brain regions, and they can be trained with one data set and then applied to another (a requirement for some analyses such as machine learning)

Genome sequencing analysis identifies new loci associated with Lewy body dementia and provides insights into its genetic architecture

The genetic basis of Lewy body dementia (LBD) is not well understood. Here, we performed whole-genome sequencing in large cohorts of LBD cases and neurologically healthy controls to study the genetic architecture of this understudied form of dementia, and to generate a resource for the scientific community. Genome-wide association analysis identified five independent risk loci, whereas genome-wide gene-aggregation tests implicated mutations in the gene GBA. Genetic risk scores demonstrate that LBD shares risk profiles and pathways with Alzheimer's disease and Parkinson's disease, providing a deeper molecular understanding of the complex genetic architecture of this age-related neurodegenerative condition

Principal component analysis as an efficient method for capturing multivariate brain signatures of complex disorders—ENIGMA study in people with bipolar disorders and obesity

Multivariate techniques better fit the anatomy of complex neuropsychiatric disorders which are characterized not by alterations in a single region, but rather by variations across distributed brain networks. Here, we used principal component analysis (PCA) to identify patterns of covariance across brain regions and relate them to clinical and demographic variables in a large generalizable dataset of individuals with bipolar disorders and controls. We then compared performance of PCA and clustering on identical sample to identify which methodology was better in capturing links between brain and clinical measures. Using data from the ENIGMA-BD working group, we investigated T1-weighted structural MRI data from 2436 participants with BD and healthy controls, and applied PCA to cortical thickness and surface area measures. We then studied the association of principal components with clinical and demographic variables using mixed regression models. We compared the PCA model with our prior clustering analyses of the same data and also tested it in a replication sample of 327 participants with BD or schizophrenia and healthy controls. The first principal component, which indexed a greater cortical thickness across all 68 cortical regions, was negatively associated with BD, BMI, antipsychotic medications, and age and was positively associated with Li treatment. PCA demonstrated superior goodness of fit to clustering when predicting diagnosis and BMI. Moreover, applying the PCA model to the replication sample yielded significant differences in cortical thickness between healthy controls and individuals with BD or schizophrenia. Cortical thickness in the same widespread regional network as determined by PCA was negatively associated with different clinical and demographic variables, including diagnosis, age, BMI, and treatment with antipsychotic medications or lithium. PCA outperformed clustering and provided an easy-to-use and interpret method to study multivariate associations between brain structure and system-level variables. Practitioner Points: In this study of 2770 Individuals, we confirmed that cortical thickness in widespread regional networks as determined by principal component analysis (PCA) was negatively associated with relevant clinical and demographic variables, including diagnosis, age, BMI, and treatment with antipsychotic medications or lithium. Significant associations of many different system-level variables with the same brain network suggest a lack of one-to-one mapping of individual clinical and demographic factors to specific patterns of brain changes. PCA outperformed clustering analysis in the same data set when predicting group or BMI, providing a superior method for studying multivariate associations between brain structure and system-level variables.</p

Principal component analysis as an efficient method for capturing multivariate brain signatures of complex disorders—ENIGMA study in people with bipolar disorders and obesity

Multivariate techniques better fit the anatomy of complex neuropsychiatric disorders which are characterized not by alterations in a single region, but rather by variations across distributed brain networks. Here, we used principal component analysis (PCA) to identify patterns of covariance across brain regions and relate them to clinical and demographic variables in a large generalizable dataset of individuals with bipolar disorders and controls. We then compared performance of PCA and clustering on identical sample to identify which methodology was better in capturing links between brain and clinical measures. Using data from the ENIGMA-BD working group, we investigated T1-weighted structural MRI data from 2436 participants with BD and healthy controls, and applied PCA to cortical thickness and surface area measures. We then studied the association of principal components with clinical and demographic variables using mixed regression models. We compared the PCA model with our prior clustering analyses of the same data and also tested it in a replication sample of 327 participants with BD or schizophrenia and healthy controls. The first principal component, which indexed a greater cortical thickness across all 68 cortical regions, was negatively associated with BD, BMI, antipsychotic medications, and age and was positively associated with Li treatment. PCA demonstrated superior goodness of fit to clustering when predicting diagnosis and BMI. Moreover, applying the PCA model to the replication sample yielded significant differences in cortical thickness between healthy controls and individuals with BD or schizophrenia. Cortical thickness in the same widespread regional network as determined by PCA was negatively associated with different clinical and demographic variables, including diagnosis, age, BMI, and treatment with antipsychotic medications or lithium. PCA outperformed clustering and provided an easy-to-use and interpret method to study multivariate associations between brain structure and system-level variables. Practitioner Points: In this study of 2770 Individuals, we confirmed that cortical thickness in widespread regional networks as determined by principal component analysis (PCA) was negatively associated with relevant clinical and demographic variables, including diagnosis, age, BMI, and treatment with antipsychotic medications or lithium. Significant associations of many different system-level variables with the same brain network suggest a lack of one-to-one mapping of individual clinical and demographic factors to specific patterns of brain changes. PCA outperformed clustering analysis in the same data set when predicting group or BMI, providing a superior method for studying multivariate associations between brain structure and system-level variables.</p