Neurochemistry-enriched dynamic causal models of magnetoencephalography, using magnetic resonance spectroscopy

We present a hierarchical empirical Bayesian framework for testing hypotheses about neurotransmitters’ concertation as empirical prior for synaptic physiology using ultra-high field magnetic resonance spectroscopy (7T-MRS) and magnetoencephalography data (MEG). A first level dynamic causal modelling of cortical microcircuits is used to infer the connectivity parameters of a generative model of individuals’ neurophysiological observations. At the second level, individuals’ 7T-MRS estimates of regional neurotransmitter concentration supply empirical priors on synaptic connectivity. We compare the group-wise evidence for alternative empirical priors, defined by monotonic functions of spectroscopic estimates, on subsets of synaptic connections. For efficiency and reproducibility, we used Bayesian model reduction (BMR), parametric empirical Bayes and variational Bayesian inversion. In particular, we used Bayesian model reduction to compare alternative model evidence of how spectroscopic neurotransmitter measures inform estimates of synaptic connectivity. This identifies the subset of synaptic connections that are influenced by individual differences in neurotransmitter levels, as measured by 7T-MRS. We demonstrate the method using resting-state MEG (i.e., task-free recording) and 7T-MRS data from healthy adults. Our results confirm the hypotheses that GABA concentration influences local recurrent inhibitory intrinsic connectivity in deep and superficial cortical layers, while glutamate influences the excitatory connections between superficial and deep layers and connections from superficial to inhibitory interneurons. Using within-subject split-sampling of the MEG dataset (i.e., validation by means of a held-out dataset), we show that model comparison for hypothesis testing can be highly reliable. The method is suitable for applications with magnetoencephalography or electroencephalography, and is well-suited to reveal the mechanisms of neurological and psychiatric disorders, including responses to psychopharmacological interventions

Can neuroimaging predict dementia in Parkinson’s disease?

Dementia in Parkinson’s disease affects 50% of patients within 10 years of diagnosis but there is wide variation in severity and timing. Thus, robust neuroimaging prediction of cognitive involvement in Parkinson’s disease is important: (i) to identify at-risk individuals for clinical trials of potential new treatments; (ii) to provide reliable prognostic information for individuals and populations; and (iii) to shed light on the pathophysiological processes underpinning Parkinson’s disease dementia. To date, neuroimaging has not made major contributions to predicting cognitive involvement in Parkinson’s disease. This is perhaps unsurprising considering conventional methods rely on macroscopic measures of topographically distributed neurodegeneration, a relatively late event in Parkinson’s dementia. However, new technologies are now emerging that could provide important insights through detection of other potentially relevant processes. For example, novel MRI approaches can quantify magnetic susceptibility as a surrogate for tissue iron content, and increasingly powerful mathematical approaches can characterize the topology of brain networks at the systems level. Here, we present an up-to-date overview of the growing role of neuroimaging in predicting dementia in Parkinson’s disease. We discuss the most relevant findings to date, and consider the potential of emerging technologies to detect the earliest signs of cognitive involvement in Parkinson’s disease

New Developments in Cholinergic Imaging in Alzheimer and Lewy Body Disorders

© 2020, This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply. Purpose of Review: This paper aims to review novel trends in cholinergic neuroimaging in Alzheimer and Lewy body parkinsonian disorders. Recent Findings: The spectrum of cholinergic imaging is expanding with the availability of spatially more precise radioligands that allow assessment of previously less recognized subcortical and cortical structures with more dense cholinergic innervation. In addition, advances in MRI techniques now allow quantitative structural or functional assessment of both the cholinergic forebrain and the pedunculopontine nucleus, which may serve as non-invasive prognostic predictors. Multimodal imaging approaches, such as PET-MRI or multiligand PET, offer new insights into the dynamic and interactive roles of the cholinergic system at both local and larger-scale neural network levels. Summary: Our understanding of the heterogeneous roles of the cholinergic system in age-related diseases is evolving. Multimodal imaging approaches that provide complimentary views of the cholinergic system will be necessary to shed light on the impact of cholinergic degeneration on regional and large-scale neural networks that underpin clinical symptom manifestation in neurodegeneration

Search for B → μ μ And B0 → μ+ μ- Decays

A search for the rare decays B → μ μ and B → μ μ is performed in pp collisions at ps = 7TeV, with a data sample corresponding to an integrated luminosity of 5 fb collected by the CMS experiment at the LHC. In both decays, the number of events observed after all selection requirements is consistent with the expectation from background plus standard model signal predictions. The resulting upper limits on the branching fractions are B(B → μ μ-) < 7:7 × 10 and B(B → μ μ ) < 1:8 × 10 at 95% confidence level

The human motor cortex microcircuit: insights for neurodegenerative disease

The human motor cortex comprises a microcircuit of five interconnected layers with different cell types. In this Review, we use a layer-specific and cell-specific approach to integrate physiological accounts of this motor cortex microcircuit with the pathophysiology of neurodegenerative diseases affecting motor functions. In doing so we can begin to link motor microcircuit pathology to specific disease stages and clinical phenotypes. Based on microcircuit physiology, we can make future predictions of axonal loss and microcircuit dysfunction. With recent advances in high-resolution neuroimaging we can then test these predictions in humans in vivo, providing mechanistic insights into neurodegenerative disease