Perturbing the activity of the superior temporal gyrus during pain encoding prevents the exaggeration of pain memories: A virtual lesion study using single-pulse transcranial magnetic stimulation.

BACKGROUND: Past studies have shown that pain memories are often inaccurate, a phenomenon known as mnemonic pain bias. Pain memories are thought to play an important role on how future pain is felt. Recent evidence from our laboratory suggests that individuals who exaggerate past pain display increased superior temporal gyrus (STG) activity during the encoding of experimental painful stimulations, suggesting that this brain structure plays an important role in pain memories. OBJECTIVE: /hypothesis. To determine whether a virtual lesion paradigm, targeting the STG during pain encoding, can affect long-lasting pain memories. We hypothesized that interfering with the activity of the STG would attenuate mnemonic bias. METHODS: Randomized double-blind study with two parallel groups. Participants received either sham (n = 21) or real (n = 21) transcranial magnetic stimulation (TMS - virtual lesion paradigm) over the STG during pain encoding (milliseconds after the administration of a painful stimuli). Pain intensity and unpleasantness were evaluated using a visual analog scale (VAS; 0 to 10) immediately after the painful event, and at recall, 2 months later. The mnemonic pain bias (calculated by subtracting the pain scores obtained at recall from the pain score obtained during encoding) was compared between the two groups for both pain intensity and unpleasantness. RESULTS: Participants in both groups did not differ in terms of age and gender (real TMS = 27 years ±â€¯9, 43% female; sham TMS = 25 years ±â€¯4, 49% female; p > 0.64). The mnemonic bias related to pain intensity was similar in both groups (p = 0.83). However, the mnemonic bias related to pain unpleasantness was lower in the real TMS group (p = 0.04). CONCLUSIONS: Our results provide the first evidence that the STG, is causally involved in the formation of biased memories of pain unpleasantness.

Automatic segmentation of the spinal cord and intramedullary multiple sclerosis lesions with convolutional neural networks.

The spinal cord is frequently affected by atrophy and/or lesions in multiple sclerosis (MS) patients. Segmentation of the spinal cord and lesions from MRI data provides measures of damage, which are key criteria for the diagnosis, prognosis, and longitudinal monitoring in MS. Automating this operation eliminates inter-rater variability and increases the efficiency of large-throughput analysis pipelines. Robust and reliable segmentation across multi-site spinal cord data is challenging because of the large variability related to acquisition parameters and image artifacts. In particular, a precise delineation of lesions is hindered by a broad heterogeneity of lesion contrast, size, location, and shape. The goal of this study was to develop a fully-automatic framework - robust to variability in both image parameters and clinical condition - for segmentation of the spinal cord and intramedullary MS lesions from conventional MRI data of MS and non-MS cases. Scans of 1042 subjects (459 healthy controls, 471 MS patients, and 112 with other spinal pathologies) were included in this multi-site study (n = 30). Data spanned three contrasts (T1-, T2-, and T2∗-weighted) for a total of 1943 vol and featured large heterogeneity in terms of resolution, orientation, coverage, and clinical conditions. The proposed cord and lesion automatic segmentation approach is based on a sequence of two Convolutional Neural Networks (CNNs). To deal with the very small proportion of spinal cord and/or lesion voxels compared to the rest of the volume, a first CNN with 2D dilated convolutions detects the spinal cord centerline, followed by a second CNN with 3D convolutions that segments the spinal cord and/or lesions. CNNs were trained independently with the Dice loss. When compared against manual segmentation, our CNN-based approach showed a median Dice of 95% vs. 88% for PropSeg (p ≤ 0.05), a state-of-the-art spinal cord segmentation method. Regarding lesion segmentation on MS data, our framework provided a Dice of 60%, a relative volume difference of -15%, and a lesion-wise detection sensitivity and precision of 83% and 77%, respectively. In this study, we introduce a robust method to segment the spinal cord and intramedullary MS lesions on a variety of MRI contrasts. The proposed framework is open-source and readily available in the Spinal Cord Toolbox.

Automatic spinal cord localization, robust to MRI contrasts using global curve optimization.

During the last two decades, MRI has been increasingly used for providing valuable quantitative information about spinal cord morphometry, such as quantification of the spinal cord atrophy in various diseases. However, despite the significant improvement of MR sequences adapted to the spinal cord, automatic image processing tools for spinal cord MRI data are not yet as developed as for the brain. There is nonetheless great interest in fully automatic and fast processing methods to be able to propose quantitative analysis pipelines on large datasets without user bias. The first step of most of these analysis pipelines is to detect the spinal cord, which is challenging to achieve automatically across the broad range of MRI contrasts, field of view, resolutions and pathologies. In this paper, a fully automated, robust and fast method for detecting the spinal cord centerline on MRI volumes is introduced. The algorithm uses a global optimization scheme that attempts to strike a balance between a probabilistic localization map of the spinal cord center point and the overall spatial consistency of the spinal cord centerline (i.e. the rostro-caudal continuity of the spinal cord). Additionally, a new post-processing feature, which aims to automatically split brain and spine regions is introduced, to be able to detect a consistent spinal cord centerline, independently from the field of view. We present data on the validation of the proposed algorithm, known as "OptiC", from a large dataset involving 20 centers, 4 contrasts (T2-weighted n = 287, T1-weighted n = 120, T2∗-weighted n = 307, diffusion-weighted n = 90), 501 subjects including 173 patients with a variety of neurologic diseases. Validation involved the gold-standard centerline coverage, the mean square error between the true and predicted centerlines and the ability to accurately separate brain and spine regions. Overall, OptiC was able to cover 98.77% of the gold-standard centerline, with a mean square error of 1.02 mm. OptiC achieved superior results compared to a state-of-the-art spinal cord localization technique based on the Hough transform, especially on pathological cases with an averaged mean square error of 1.08 mm vs. 13.16 mm (Wilcoxon signed-rank test p-value < .01). Images containing brain regions were identified with a 99% precision, on which brain and spine regions were separated with a distance error of 9.37 mm compared to ground-truth. Validation results on a challenging dataset suggest that OptiC could reliably be used for subsequent quantitative analyses tasks, opening the door to more robust analysis on pathological cases.

Sex differences in the neural representation of pain unpleasantness.

UNLABELLED: Sex differences in pain perception are still poorly understood, but they may be related to the way the brains of men and women respond to the affective dimensions of pain. Using a matched pain intensity paradigm, where pain intensity was kept constant across participants but pain unpleasantness was left free to vary among participants, we studied the relationship between pain unpleasantness and pain-evoked brain activity in healthy men and women separately. Experimental pain was provoked using transcutaneous electrical stimulation of the sural nerve while pain-related brain activity was measured using somatosensory-evoked brain potentials with source localization. Cardiac responses to pain were also measured using electrocardiac recordings. Results revealed that subjective pain unpleasantness was strongly associated with increased perigenual anterior cingulate cortex activity in women, whereas it was strongly associated with decreased ventromedial prefrontal cortex activity in men. Only ventromedial prefrontal cortex deactivations in men were additionally associated with increased autonomic cardiac arousal. These results suggest that in order to deal with pain's objectionable properties, men preferentially deactivate prefrontal suppression regions, leading to the mobilization of threat-control circuits, whereas women recruit well-known emotion-processing areas of the brain. PERSPECTIVE: This article presents neuroimaging findings demonstrating that subjective pain unpleasantness ratings are associated with different pain-evoked brain responses in men and women, which has potentially important implications regarding sex differences in the risk of developing chronic pain.

Werner's syndrome helicase participates in transcription of phenobarbital-inducible CYP2B genes in rat and mouse liver.

Werner's syndrome (WS) is a rare human autosomal recessive segmental progeroid syndrome clinically characterized by atherosclerosis, cancer, osteoporosis, type 2 diabetes mellitus and ocular cataracts. The WRN gene codes for a RecQ helicase which is present in many tissues. Although the exact functions of the WRN protein remain unclear, accumulating evidence suggests that it participates in DNA repair, replication, recombination and telomere maintenance. It has also been proposed that WRN participates in RNA polymerase II-dependent transcription. However no promoter directly targeted by WRN has yet been identified. In this work, we report mammalian genes that are WRN targets. The rat CYP2B2 gene and its closely related mouse homolog, Cyp2b10, are both strongly induced in liver by phenobarbital. We found that there is phenobarbital-dependent recruitment of WRN to the promoter of the CYP2B2 gene as demonstrated by chromatin immunoprecipitation analysis. Mice homozygous for a Wrn mutation deleting part of the helicase domain showed a decrease in basal and phenobarbital-induced CYP2B10 mRNA levels compared to wild type animals. The phenobarbital-induced level of CYP2B10 protein was also reduced in the mutant mice. Electrophoretic mobility shift assays showed that WRN can participate in the formation of a complex with a specific sequence within the CYP2B2 basal promoter. Hence, there is a WRN binding site in a region of DNA sequence to which WRN is recruited in vivo. Taken together, these results suggest that WRN participates in transcription of CYP2B genes in liver and identifies the first physical interaction between a specific promoter sequence and WRN.

Glucocorticoids and phenobarbital induce murine CYP2B genes by independent mechanisms.

BACKGROUND: Genes for CYP of the 2B subfamily (CYP2B genes) have long been known to be inducible in murine liver by phenobarbital and phenobarbital-like inducers. More recently, it has become clear that glucocorticoids can also induce these genes by a mechanism independent of that of phenobarbital-like inducers. OBJECTIVE: To summarize the evidence for the existence of two distinct molecular mechanisms for induction of murine CYP2B genes and to analyze the wider implications of this situation for inducible xenobiotic metabolism. METHODS: The mechanism of action of phenobarbital-like inducers of murine CYP2B genes is first briefly summarized. The role of glucocorticoids in the induction of various proteins, particularly rat phosphoenolpyruvate carboxykinase, where transcriptional activation is achieved via a glucocorticoid response unit, is also discussed. Finally, recent results are presented on glucocorticoid induction of murine CYP2B genes, including evidence for the presence of a functional glucocorticoid response unit in the rat CYP2B2 gene and for the role of constitutive androstane receptor as an accessory factor in this response. RESULTS/CONCLUSION: Murine CYP2B genes are seen to respond to two distinct regulatory mechanisms, but much remains to be learned concerning the interactions between these two regulatory loops, as well as the details of glucocorticoid induction.