The Animal Model of Spinal Cord Injury as an Experimental Pain Model

Pain, which remains largely unsolved, is one of the most crucial problems for spinal cord injury patients. Due to sensory problems, as well as motor dysfunctions, spinal cord injury research has proven to be complex and difficult. Furthermore, many types of pain are associated with spinal cord injury, such as neuropathic, visceral, and musculoskeletal pain. Many animal models of spinal cord injury exist to emulate clinical situations, which could help to determine common mechanisms of pathology. However, results can be easily misunderstood and falsely interpreted. Therefore, it is important to fully understand the symptoms of human spinal cord injury, as well as the various spinal cord injury models and the possible pathologies. The present paper summarizes results from animal models of spinal cord injury, as well as the most effective use of these models

An Evolutionarily Threat-Relevant Odor Strengthens Human Fear Memory.

Olfaction is an evolutionary ancient sense, but it remains unclear to what extent it can influence routine human behavior. We examined whether a threat-relevant predator odor (2-methyl-2-thiazoline) would contextually enhance the formation of human fear memory associations. Participants who learned to associate visual stimuli with electric shock in this predator odor context later showed stronger fear responses to the visual stimuli than participants who learned in an aversiveness-matched control odor context. This effect generalized to testing in another odor context, even after extinction training. Results of a separate experiment indicate that a possible biological mechanism for this effect may be increased cortisol levels in a predator odor context. These results suggest that innate olfactory processes can play an important role in human fear learning. Modulatory influences of odor contexts may partly explain the sometimes maladaptive persistence of human fear memory, e.g., in post-traumatic stress disorders

Nonclassic lipoid congenital adrenal hyperplasia masquerading as familial glucocorticoid deficiency

Context: Familial glucocorticoid deficiency (FGD) is an autosomal recessive disorder resulting from resistance to the action of ACTH on the adrenal cortex. Affected individuals are deficient in cortisol and, if untreated, are likely to succumb to hypoglycemia and/or overwhelming infection. Mutations of the ACTH receptor (MC2R) and the melanocortin 2 receptor accessory protein (MRAP), FGD types 1 and 2 respectively, account for approximately 45% of cases. Objective: A locus on chromosome 8 has previously been linked to the disease in three families, but no underlying gene defect has to date been identified. Design: The study design comprised single-nucleotide polymorphism genotyping and mutation detection. Setting: The study was conducted at secondary and tertiary referral centers. Patients: Eighty probands from families referred for investigation of the genetic cause of FGD participated in the study. Interventions: There were no interventions. Results: Analysis by single-nucleotide polymorphism array of the genotype of one individual with FGD previously linked to chromosome 8 revealed a large region of homozygosity encompassing the steroidogenic acute regulatory protein gene, STAR. We identified homozygous STAR mutations in this patient and his affected siblings. Screening of our total FGD patient cohort revealed homozygous STAR mutations in a further nine individuals from four other families. Conclusions: Mutations in STAR usually cause lipoid congenital adrenal hyperplasia, a disorder characterized by both gonadal and adrenal steroid deficiency. Our results demonstrate that certain mutations in STAR (R192C and the previously reported R188C) can present with a phenotype indistinguishable from that seen in FGD

Classification and characterisation of brain network changes in chronic back pain: A multicenter study [version 2; referees: 3 approved]

Background. Chronic pain is a common, often disabling condition thought to involve a combination of peripheral and central neurobiological factors. However, the extent and nature of changes in the brain is poorly understood. Methods. We investigated brain network architecture using resting-state fMRI data in chronic back pain patients in the UK and Japan (41 patients, 56 controls), as well as open data from USA. We applied machine learning and deep learning (conditional variational autoencoder architecture) methods to explore classification of patients/controls based on network connectivity. We then studied the network topology of the data, and developed a multislice modularity method to look for consensus evidence of modular reorganisation in chronic back pain. Results. Machine learning and deep learning allowed reliable classification of patients in a third, independent open data set with an accuracy of 63%, with 68% in cross validation of all data. We identified robust evidence of network hub disruption in chronic pain, most consistently with respect to clustering coefficient and betweenness centrality. We found a consensus pattern of modular reorganisation involving extensive, bilateral regions of sensorimotor cortex, and characterised primarily by negative reorganisation - a tendency for sensorimotor cortex nodes to be less inclined to form pairwise modular links with other brain nodes. Furthermore, these regions were found to display increased connectivity with the pregenual anterior cingulate cortex, a region known to be involved in endogenous pain control. In contrast, intraparietal sulcus displayed a propensity towards positive modular reorganisation, suggesting that it might have a role in forming modules associated with the chronic pain state. Conclusion. The results provide evidence of consistent and characteristic brain network changes in chronic pain, characterised primarily by extensive reorganisation of the network architecture of the sensorimotor cortex