Narrative and the Origins of Law

In order to understand these distinct narratives of legal origin through the tools of narratology, we will proceed in several steps. First, we will define more precisely the set of social contract theories that we consider. We will discuss our decision to narrow the focus down to two social contract theorists in particular, one contemporary and one classical, John Rawls and Jean-Jacques Rousseau. These two theorists seem worlds apart in many respects—yet the tools of narratology will enable us to see their shared enterprise. Second, the tools of narratology will help us to identify and discuss the component parts that define social contract narratives, namely, the creation of an original space and situation, the description of the act of contracting, and the way in which endings are used to frame and position all of the moving pieces into one linear and evolutionary storyline. We will then examine why narratology yields an interesting and fruitful analysis of these narratives. In concluding, we will link this endeavour to future ones, in which we intend to use the tools of ‘law and literature’ to make sense of the narratives, in theory and in practice, that give rise to and sustain legal and political order

MEG cortical microstates: spatiotemporal characteristics, dynamic functional connectivity and stimulus-evoked responses

EEG microstate analysis is an approach to study brain states and their fast transitions in healthy cognition and disease. A key limitation of conventional microstate analysis is that it must be performed at the sensor level, and therefore gives limited anatomical insight. Here, we generalise the microstate methodology to be applicable to source-reconstructed electrophysiological data. Using simulations of a neural-mass network model, we first established the validity and robustness of the proposed method. Using MEG resting-state data, we uncovered ten microstates with distinct spatial distributions of cortical activation. Multivariate pattern analysis demonstrated that source-level microstates were associated with distinct functional connectivity patterns. We further demonstrated that the occurrence probability of MEG microstates were altered by auditory stimuli, exhibiting a hyperactivity of the microstate including the auditory cortex. Our results support the use of source-level microstates as a method for investigating brain dynamic activity and connectivity at the millisecond scale

+microstate: A MATLAB toolbox for brain microstate analysis in sensor and cortical EEG/MEG

+microstate is a MATLAB toolbox for brain functional microstate analysis. It builds upon previous EEG microstate literature and toolboxes by including algorithms for source-space microstate analysis. +microstate includes codes for performing individual- and group-level brain microstate analysis in resting-state and task-based data including event-related potentials/fields. Functions are included to visualise and perform statistical analysis of microstate sequences, including novel advanced statistical approaches such as statistical testing for associated functional connectivity patterns, cluster-permutation topographic ANOVAs, an

A systematic evaluation of source reconstruction of resting MEG of the human brain with a new high-resolution atlas: performance, precision, and parcellation

Noninvasive functional neuroimaging of the human brain can give crucial insight into the mechanisms that underpin healthy cognition and neurological disorders. Magnetoencephalography (MEG) measures extracranial magnetic fields originating from neuronal activity with high temporal resolution, but requires source reconstruction to make neuroanatomical inferences from these signals. Many source reconstruction algorithms are available, and have been widely evaluated in the context of localizing task-evoked activities. However, no consensus yet exists on the optimum algorithm for resting-state data. Here, we evaluated the performance of six commonly-used source reconstruction algorithms based on minimum-norm and beamforming estimates. Using human resting-state MEG, we compared the algorithms using quantitative metrics, including resolution properties of inverse solutions and explained variance in sensor-level data. Next, we proposed a data-driven approach to reduce the atlas from the Human Connectome Project's multi-modal parcellation of the human cortex based on metrics such as MEG signal-to-noise-ratio and resting-state functional connectivity gradients. This procedure produced a reduced cortical atlas with 230 regions, optimized to match the spatial resolution and the rank of MEG data from the current generation of MEG scanners. Our results show that there is no “one size fits all” algorithm, and make recommendations on the appropriate algorithms depending on the data and aimed analyses. Our comprehensive comparisons and recommendations can serve as a guide for choosing appropriate methodologies in future studies of resting-state MEG

The Constraint on FCNC Coupling of the Top Quark with a Gluon from ep Collisions

Using the constraint on the single top production cross-section obtained at the HERA collider, $\sigma(ep \to e t X)$, we evaluate an upper limit on oupling constant of the anomalous top quark interaction with a gluon via flavor-changing neutral current: $|\kappa_{tgq}/\Lambda| \le 0.4 {TeV}^{-1}$, BR$(t \to gq) < 13$Comment: Latex, 3 figures, missed references were adde

Substance P-Botulinum Mediates Long-term Silencing of Pain Pathways that can be Re-instated with a Second Injection of the Construct in Mice

Chronic pain presents an enormous personal and economic burden and there is an urgent need for effective treatments. In a mouse model of chronic neuropathic pain, selective silencing of key neurons in spinal pain signalling networks with botulinum constructs resulted in a reduction of pain behaviours associated with the peripheral nerve. However, to establish clinical relevance it was important to know how long this silencing period lasted. Now, we show that neuronal silencing and the concomitant reduction of neuropathic mechanical and thermal hypersensitivity lasts for up to 120d following a single injection of botulinum construct. Crucially, we show that silencing and analgesia can then be reinstated with a second injection of the botulinum conjugate. Here we demonstrate that single doses of botulinum-toxin conjugates are a powerful new way of providing long-term neuronal silencing and pain relief

Differential Effects of Whole Red Raspberry Polyphenols and Their Gut Metabolite Urolithin A on Neuroinflammation in BV-2 Microglia

Whole red raspberry polyphenols (RRW), including ellagic acid, and their gut-derived metabolite, urolithin A (UroA), attenuate inflammation and confer health benefits. Although results from recent studies indicate that polyphenols and UroA also provide neuroprotective effects, these compounds differ in their bioavailability and may, therefore, have unique effects on limiting neuroinflammation. Accordingly, we aimed to compare the neuroprotective effects of RRW and UroA on BV-2 microglia under both 3 h and 12 and 24 h inflammatory conditions. In inflammation induced by lipopolysaccharide (LPS) and ATP stimulation after 3 h, RRW and UroA suppressed pro-inflammatory cytokine gene expression and regulated the JNK/c-Jun signaling pathway. UroA also reduced inducible nitric oxide synthase gene expression and promoted M2 microglial polarization. During inflammatory conditions induced by either 12 or 24 h stimulation with LPS, UroA—but not RRW—dampened pro-inflammatory cytokine gene expression and suppressed JNK/c-Jun signaling. Taken together, these results demonstrate that RRW and its gut-derived metabolite UroA differentially regulate neuroprotective responses in microglia during 3 h versus 12 and 24 h inflammatory conditions

Network substrates of cognitive impairment in Alzheimer's Disease

This is the final version. Available on open access from Elsevier via the DOI in this recordObjectives: Functional and structural disconnection of the brain is a prevailing hypothesis to explain cognitive impairment in Alzheimer's Disease (AD). We aim to understand the link between alterations to networks and cognitive impairment using functional connectivity analysis and modelling. Methods: EEG was recorded from 21 AD patients and 26 controls, mapped into source space using eLORETA, and functional connectivity was calculated using phase locking factor. The mini-mental state exam (MMSE) was used to assess cognitive impairment. A computational model was used to uncover mechanisms of altered functional connectivity. Results: Small-worldness (SW) of functional networks decreased in AD and was positively correlated with MMSE score and the language sub-score. Reduced SW was a result of increased path lengths, predominantly localized to the temporal lobes. Combining observed differences in local oscillation frequency with reduced temporal lobe effective connectivity in the model could account for observed functional network differences. Conclusions: Temporal lobe disconnection plays a key role in cognitive impairment in AD. Significance: We combine electrophysiology, neuropsychological scores, and computational modelling to provide novel insight into the relationships between the disconnection hypothesis and cognitive decline in AD.Engineering and Physical Sciences Research Council (EPSRC)Wellcome TrustAlzheimer's SocietyGarfield Weston FoundationUniversity of Bristo

A large-scale brain network mechanism for increased seizure propensity in Alzheimer's disease

People with Alzheimer’s disease (AD) are 6-10 times more likely to develop seizures than the healthy aging population. Leading hypotheses largely consider hyperexcitability of local cortical tissue as primarily responsible for increased seizure prevalence in AD. However, in the general population of people with epilepsy, large-scale brain network organization additionally plays a role in determining seizure likelihood and phenotype. Here, we propose that alterations to large-scale brain network organization seen in AD may contribute to increased seizure likelihood. To test this hypothesis, we combine computational modelling with electrophysiological data using an approach that has proved informative in clinical epilepsy cohorts without AD. EEG was recorded from 21 people with probable AD and 26 healthy controls. At the time of EEG acquisition, all participants were free from seizures. Whole brain functional connectivity derived from source-reconstructed EEG recordings was used to build subject-specific brain network models of seizure transitions. As cortical tissue excitability was increased in the simulations, AD simulations were more likely to transition into seizures than simulations from healthy controls, suggesting an increased group-level probability of developing seizures at a future time for AD participants. We subsequently used the model to assess seizure propensity of different regions across the cortex. We found the most important regions for seizure generation were those typically burdened by amyloid-beta at the early stages of AD, as previously reported by in-vivo and post-mortem staging of amyloid plaques. Analysis of these spatial distributions also give potential insight into mechanisms of increased susceptibility to generalized (as opposed to focal) seizures in AD vs controls. This research suggests avenues for future studies testing patients with seizures, e.g. co-morbid AD/epilepsy patients, and comparisons with PET and MRI scans to relate regional seizure propensity with AD pathologies

Resting-state EEG signatures of Alzheimer's disease are driven by periodic but not aperiodic changes

Electroencephalography (EEG) has shown potential for identifying early-stage biomarkers of neurocognitive dysfunction associated with dementia due to Alzheimer's disease (AD). A large body of evidence shows that, compared to healthy controls (HC), AD is associated with power increases in lower EEG frequencies (delta and theta) and decreases in higher frequencies (alpha and beta), together with slowing of the peak alpha frequency. However, the pathophysiological processes underlying these changes remain unclear. For instance, recent studies have shown that apparent shifts in EEG power from high to low frequencies can be driven either by frequency specific periodic power changes or rather by non-oscillatory (aperiodic) changes in the underlying 1/f slope of the power spectrum. Hence, to clarify the mechanism(s) underlying the EEG alterations associated with AD, it is necessary to account for both periodic and aperiodic characteristics of the EEG signal. Across two independent datasets, we examined whether resting-state EEG changes linked to AD reflect true oscillatory (periodic) changes, changes in the aperiodic (non-oscillatory) signal, or a combination of both. We found strong evidence that the alterations are purely periodic in nature, with decreases in oscillatory power at alpha and beta frequencies (AD &lt; HC) leading to lower (alpha + beta) / (delta + theta) power ratios in AD. Aperiodic EEG features did not differ between AD and HC. By replicating the findings in two cohorts, we provide robust evidence for purely oscillatory pathophysiology in AD and against aperiodic EEG changes. We therefore clarify the alterations underlying the neural dynamics in AD and emphasize the robustness of oscillatory AD signatures, which may further be used as potential prognostic or interventional targets in future clinical investigations.</p