by Keyword: Clinical neuroscience
Martinez-Torres S, Mesquida-Veny F, Del Rio JA, Hervera A, (2023). Injury-induced activation of the endocannabinoid system promotes axon regeneration Iscience 26, 106814
Regeneration after a peripheral nerve injury still remains a challenge, due to the limited regenerative potential of axons after injury. While the endocannabinoid system (ECS) has been widely studied for its neuroprotective and analgesic effects, its role in axonal regeneration and during the conditioning lesion remains unexplored. In this study, we observed that a peripheral nerve injury induces axonal regeneration through an increase in the endocannabinoid tone. We also enhanced the regenerative capacity of dorsal root ganglia (DRG) neurons through the inhibition of endocannabinoid degradative enzyme MAGL or a CB1R agonist. Our results suggest that the ECS, via CB1R and PI3K-pAkt pathway activation, plays an important role in promoting the intrinsic regenerative capacity of sensory neurons after injury.© 2023 The Author(s).
JTD Keywords: brain, gene-expression, lesion, nerve, receptors, targets, Clinical neuroscience, Drugs, Endogenous cannabinoid system, Molecular medicine
Arsiwalla, Xerxes D., Verschure, Paul, (2018). Measuring the complexity of consciousness Frontiers in Neuroscience 12, (424), Article 424
The grand quest for a scientific understanding of consciousness has given rise to many new theoretical and empirical paradigms for investigating the phenomenology of consciousness as well as clinical disorders associated to it. A major challenge in this field is to formalize computational measures that can reliably quantify global brain states from data. In particular, information-theoretic complexity measures such as integrated information have been proposed as measures of conscious awareness. This suggests a new framework to quantitatively classify states of consciousness. However, it has proven increasingly difficult to apply these complexity measures to realistic brain networks. In part, this is due to high computational costs incurred when implementing these measures on realistically large network dimensions. Nonetheless, complexity measures for quantifying states of consciousness are important for assisting clinical diagnosis and therapy. This article is meant to serve as a lookup table of measures of consciousness, with particular emphasis on clinical applicability. We consider both, principle-based complexity measures as well as empirical measures tested on patients. We address challenges facing these measures with regard to realistic brain networks, and where necessary, suggest possible resolutions. We address challenges facing these measures with regard to realistic brain networks, and where necessary, suggest possible resolutions.
JTD Keywords: Consciousness in the Clinic, Computational neuroscience, Complexity measures, Clinical Neuroscience, Measures of consciousness