Comprehensive analysis of epigenetic clocks reveals associations between disproportionate biological ageing and hippocampal volume

The concept of age acceleration, the difference between biological age and chronological age, is of growing interest, particularly with respect to age-related disorders, such as Alzheimer’s Disease (AD). Whilst studies have reported associations with AD risk and related phenotypes, there remains a lack of consensus on these associations. Here we aimed to comprehensively investigate the relationship between five recognised measures of age acceleration, based on DNA methylation patterns (DNAm age), and cross-sectional and longitudinal cognition and AD-related neuroimaging phenotypes (volumetric MRI and Amyloid-β PET) in the Australian Imaging, Biomarkers and Lifestyle (AIBL) and the Alzheimer’s Disease Neuroimaging Initiative (ADNI). Significant associations were observed between age acceleration using the Hannum epigenetic clock and cross-sectional hippocampal volume in AIBL and replicated in ADNI. In AIBL, several other findings were observed cross-sectionally, including a significant association between hippocampal volume and the Hannum and Phenoage epigenetic clocks. Further, significant associations were also observed between hippocampal volume and the Zhang and Phenoage epigenetic clocks within Amyloid-β positive individuals. However, these were not validated within the ADNI cohort. No associations between age acceleration and other Alzheimer’s disease-related phenotypes, including measures of cognition or brain Amyloid-β burden, were observed, and there was no association with longitudinal change in any phenotype. This study presents a link between age acceleration, as determined using DNA methylation, and hippocampal volume that was statistically significant across two highly characterised cohorts. The results presented in this study contribute to a growing literature that supports the role of epigenetic modifications in ageing and AD-related phenotypes

Uncovering the heterogeneity and temporal complexity of neurodegenerative diseases with Subtype and Stage Inference

The heterogeneity of neurodegenerative diseases is a key confound to disease understanding and treatment development, as study cohorts typically include multiple phenotypes on distinct disease trajectories. Here we introduce a machine-learning technique\u2014Subtype and Stage Inference (SuStaIn)\u2014able to uncover data-driven disease phenotypes with distinct temporal progression patterns, from widely available cross-sectional patient studies. Results from imaging studies in two neurodegenerative diseases reveal subgroups and their distinct trajectories of regional neurodegeneration. In genetic frontotemporal dementia, SuStaIn identifies genotypes from imaging alone, validating its ability to identify subtypes; further the technique reveals within-genotype heterogeneity. In Alzheimer\u2019s disease, SuStaIn uncovers three subtypes, uniquely characterising their temporal complexity. SuStaIn provides fine-grained patient stratification, which substantially enhances the ability to predict conversion between diagnostic categories over standard models that ignore subtype (p = 7.18 7 10 124 ) or temporal stage (p = 3.96 7 10 125 ). SuStaIn offers new promise for enabling disease subtype discovery and precision medicine

Reducing computation time by Monte Carlo method: an application in determining axonal orientation distribution function

Diffusion MRI (dMRI) is highly sensitive in detecting early cerebral ischemic changes in acute stroke, and in pre-clinical assessment of white matter (WM) anatomy using tractography, thus being an important component of health informatics. In clinical settings, the computation time is critical, and so finding forms of reducing the processing time in high computation processes such as Diffusion Spectrum Imaging (DSI) dMRI data processing is extremely relevant. We analyse here a method for reducing the computation of the dMRI-based axonal orientation distribution function h by using a Monte Carlo sampling-based methods for voxel selection, and so obtained a reduction in required data sampling of about 20%. In this work we show that the convergence to the correct value in this type of dMRI data-processing is linear and not exponential, implying that the Monte Carlo approach in this type of dMRI data processing improves its speed, but further improvements are needed.We thank the financial support by QREN, FEDER, COMPETE, Investigador FCT, FCT Ciencia 2007, FCT PTDC/SAU-BEB/100147/2008, FCT Project Scope UID/CEC/00319/2013, and the ERASMUS projects (FCT stands for “Fundação para a Ciência e Tecnologia”). We are thankful the relevamt scientific conversations with Alard Roebroeck, Rainer Goebel, Van Wedeen, ReducingComputation Time byMonte Carlo Method ...103 and Gina Caetano. Data collection for this work was in part from the ”Human Connectome Project” (HCP; Principal Investigators: Bruce Rosen, M.D., Ph.D., Arthur W. Toga, Ph.D., Van J. Weeden, MD). HCP funding was provided by the National Institute of Dental and Craniofacial Research (NIDCR), the National Institute of Mental Health (NIMH), and the National Institute of Neurological Disorders and Stroke (NINDS). HCP data are disseminated by the Laboratory of Neuro Imaging at the University of Southern Californiainfo:eu-repo/semantics/publishedVersio

Outcomes and Treatment Strategies for Autoimmunity and Hyperinflammation in Patients with RAG Deficiency

Background: Although autoimmunity and hyperinflammation secondary to recombination activating gene (RAG) deficiency have been associated with delayed diagnosis and even death, our current understanding is limited primarily to small case series. Objective: Understand the frequency, severity, and treatment responsiveness of autoimmunity and hyperinflammation in RAG deficiency. Methods: In reviewing the literature and our own database, we identified 85 patients with RAG deficiency, reported between 2001 and 2016, and compiled the largest case series to date of 63 patients with prominent autoimmune and/or hyperinflammatory pathology. Results: Diagnosis of RAG deficiency was delayed a median of 5 years from the first clinical signs of immune dysregulation. Most patients (55.6%) presented with more than 1 autoimmune or hyperinflammatory complication, with the most common etiologies being cytopenias (84.1%), granulomas (23.8%), and inflammatory skin disorders (19.0%). Infections, including live viral vaccinations, closely preceded the onset of autoimmunity in 28.6% of cases. Autoimmune cytopenias had early onset (median, 1.9, 2.1, and 2.6 years for autoimmune hemolytic anemia, immune thrombocytopenia, and autoimmune neutropenia, respectively) and were refractory to intravenous immunoglobulin, steroids, and rituximab in most cases (64.7%, 73.7%, and 71.4% for autoimmune hemolytic anemia, immune thrombocytopenia, and autoimmune neutropenia, respectively). Evans syndrome specifically was associated with lack of response to first-line therapy. Treatment-refractory autoimmunity/hyperinflammation prompted hematopoietic stem cell transplantation in 20 patients. Conclusions: Autoimmunity/hyperinflammation can be a presenting sign of RAG deficiency and should prompt further evaluation. Multilineage cytopenias are often refractory to immunosuppressive treatment and may require hematopoietic cell transplantation for definitive management. © 2019 The Author

Spectral and non-linear analysis of thalamocortical neural mass model oscillatory dynamics

The chapter is organised in two parts: In the first part, the focus is on a combined power spectral and non-linear behavioural analysis of a neural mass model of the thalamocortical circuitry. The objective is to study the effectiveness of such ‘multi-modal’ analytical techniques in model-based studies investigating the neural correlates of abnormal brain oscillations in Alzheimer’s disease (AD). The power spectral analysis presented here is a study of the ‘slowing’ (decreasing dominant frequency of oscillation) within the alpha frequency band (8 – 13 Hz), a hallmark of Electroencephalogram (EEG) dynamics in AD. Analysis of the nonlinear dynamical behaviour focuses on the bifurcating property of the model. The results show that the alpha rhythmic content is maximal at close proximity to the bifurcation point — an observation made possible by the ‘multi-modal’ approach adopted herein. Furthermore, a slowing in alpha rhythm is observed for increasing inhibitory connectivity — a consistent feature of our research into neuropathological oscillations associated with AD. In the second part, we have presented power spectral analysis on a model that implements multiple feed-forward and feed-back connectivities in the thalamo-cortico-thalamic circuitry, and is thus more advanced in terms of biological plausibility. This study looks at the effects of synaptic connectivity variation on the power spectra within the delta (1 – 3 Hz), theta (4 – 7 Hz), alpha (8 – 13 Hz) and beta (14 – 30 Hz) bands. An overall slowing of EEG with decreasing synaptic connectivity is observed, indicated by a decrease of power within alpha and beta bands and increase in power within the theta and delta bands. Thus, the model behaviour conforms to longitudinal studies in AD indicating an overall slowing of EEG