Migraine and restless legs syndrome: is there an association?

Occasional clinical reports have suggested a link between migraine and restless legs syndrome. We undertook a systematic review of the evidence, which supports this association, and consider possible shared pathogenic mechanisms and the implications for current clinical practice

Genetics of migraine in the age of genome-wide association studies

Genetic factors importantly contribute to migraine. However, unlike for rare monogenic forms of migraine, approaches to identify genes for common forms of migraine have been of limited success. Candidate gene association studies were often negative and positive results were often not replicated or replication failed. Further, the significance of positive results from linkage studies remains unclear owing to the inability to pinpoint the genes under the peaks that may be involved in migraine. Problems hampering these studies include limited sample sizes, methods of migraine ascertainment, and the heterogeneous clinical phenotype. Three genome-wide association studies are available now and have successfully identified four new genetic variants associated with migraine. One new variant (rs1835740) modulates glutamate homeostasis, thus integrates well with current concepts of neurotransmitter disturbances. This variant may be more specific for severe forms of migraine such as migraine with aura than migraine without aura. Another variant (rs11172113) implicates the lipoprotein receptor LRP1, which may interact with neuronal glutamate receptors, thus also providing a link to the glutamate pathway. In contrast, rs10166942 is in close proximity to TRPM8, which codes for a cold and pain sensor. For the first time this links a gene explicitly implicated in pain related pathways to migraine. The potential function of the fourth variant rs2651899 (PRDM16) in migraine is unclear. All these variants only confer a small to moderate change in risk for migraine, which concurs with migraine being a heterogeneous disorder. Ongoing large international collaborations will likely identify additional gene variants for migraine

The primary headaches: genetics, epigenetics and a behavioural genetic model

The primary headaches, migraine with (MA) and without aura (MO) and cluster headache, all carry a substantial genetic liability. Familial hemiplegic migraine (FHM), an autosomal dominant mendelian disorder classified as a subtype of MA, is due to mutations in genes encoding neural channel subunits. MA/MO are considered multifactorial genetic disorders, and FHM has been proposed as a model for migraine aetiology. However, a review of the genetic studies suggests that the FHM genes are not involved in the typical migraines and that FHM should be considered as a syndromic migraine rather than a subtype of MA. Adopting the concept of syndromic migraine could be useful in understanding migraine pathogenesis. We hypothesise that epigenetic mechanisms play an important role in headache pathogenesis. A behavioural model is proposed, whereby the primary headaches are construed as behaviours, not symptoms, evolutionarily conserved for their adaptive value and engendered out of a genetic repertoire by a network of pattern generators present in the brain and signalling homeostatic imbalance. This behavioural model could be incorporated into migraine genetic research

Active SLAM using Connectivity Graphs as Priors

Mobile robots can be considered completely autonomous if they embed active algorithms for Simultaneous Localization And Mapping (SLAM). This means that the robot is able to autonomously, or actively, explore and create a reliable map of the environment, while simultaneously estimating its pose. In this paper, we propose a novel framework to robustly solve the active SLAM problem, in scenarios in which some prior information about the environment is available in the form of a topo-metric graph. This information is typically available or can be easily developed in industrial environments, but it is usually affected by uncertainties. In particular, the distinguishing features of our approach are: the inclusion of prior information for solving the active SLAM problem; the exploitation of this information to pursue active loop closure; the on-line correction of the inconsistencies in the provided data. We present some experiments, that are performed in different simulated environments: the results suggest that our method improves on state-of-the-art approaches, as it is able to deal with a wide variety of possibly large uncertainties