A pain in the bud? Implications of cross-modal sensitivity for pain experience

Neurophysiological and Psychosocial Mechanisms of Fibromyalgia: A Comprehensive Review and Call for An Integrative Model

Research into the neurobiological and psychosocial mechanisms involved in fibromyalgia has progressed remarkably in recent years. Despite this, current accounts of fibromyalgia fail to capture the complex, dynamic, and mutual crosstalk between neurophysiological and psychosocial domains. We conducted a comprehensive review of the existing literature in order to: a) synthesize current knowledge on fibromyalgia; b) explore and highlight multi-level links and pathways between different systems; and c) build bridges connecting disparate perspectives. An extensive panel of international experts in neurophysiological and psychosocial aspects of fibromyalgia discussed the collected evidence and progressively refined and conceptualized its interpretation. This work constitutes an essential step towards the development of a model capable of integrating the main factors implicated in fibromyalgia into a single, unified construct which appears indispensable to foster the understanding, assessment, and intervention for fibromyalgia.

Aberrant modulations of static functional connectivity and dynamic functional network connectivity in chronic migraine

Background

Chronic migraine (CM) is a common and disabling neurological disorder that affects 1-2% of the global population. The aim of the present study was to identify the functional characteristics of the CM brain using static functional connectivity (s-FC), static functional network connectivity (s-FNC), and dynamic functional network connectivity (d-FNC) analyses.

Methods

In the present study, 17 CM patients and 20 sex- and age-matched healthy controls (HCs) underwent resting-state functional magnetic resonance imaging. We utilized independent component (IC) analysis to identify 13 ICs. These 13 ICs were then classified into the following 6 resting-state networks (RSNs): the default mode network (DMN), executive control network (ECN), dorsal attention network, auditory network (AN), visual network (VN), and cerebellum network. Subsequently, s-FC, s-FNC, and d-FNC analyses of 13 ICs were employed for between-group comparisons. Three temporal metrics (fraction of time spent, mean dwell time, and number of transitions), which were derived from the state-transition vector, were calculated for group comparisons. In addition, correlation analyses were performed between these dynamic metrics and clinical characteristics [mean visual analog scale (VAS) scores, days with headache per month, days with migraine pain feature per month, and disease duration].

Results

In the comparison of s-FC of 13 ICs within RSNs between the CM and HC groups, increased connectivity was observed in the left angular gyrus (Angular_L) of the ECN (IC 2) and the right superior parietal gyrus (Parietal_Sup_R) of the AN (IC 5), and reduced connectivity was found in the left superior frontal gyrus (Frontal_Sup_2_L) of the AN (IC 5) and DMN (IC 19), the right calcarine sulcus (Calcarine_R) of the VN (IC 7), and the left precuneus (Precuneus_L) of the DMN (IC 17) in CM patients. In the comparison of the d-FNC of 13 IC pairs within RSNs between the two groups, the CM group exhibited significantly decreased connections between the DMN (IC 11) and AN (IC 5), and increased connections between the ECN (IC 2, IC 4) and DMN (IC 19), ECN (IC 4) and AN (IC 5), and ECN (IC 4) and VN (IC 13) in state 1. However, no significant differences in s-FNC were observed between the two groups during the s-FNC analysis. Between-group comparisons of three dynamic metrics between the CM and HC groups showed a longer fraction of time spent and mean dwell time in state 2 for CM patients. Furthermore, from the correlation analyses between these metrics and clinical characteristics, we observed a significant positive correlation between the number of transitions and mean VAS scores.

Conclusions

Our findings suggest that functional features of the CM brain may fluctuate over time instead of remaining static, and provide further evidence that migraine chronification may be related to abnormal pattern connectivity between sensory and cognitive brain networks.

Pain, Smell, and Taste in Adults: A Narrative Review of Multisensory Perception and Interaction

Every day our sensory systems perceive and integrate a variety of stimuli containing information vital for our survival. Pain acts as a protective warning system, eliciting a response to remove harmful stimuli; it may also be a symptom of an illness or present as a disease itself. There is a growing need for additional pain-relieving therapies involving the multisensory integration of smell and taste in pain modulation, an approach that may provide new strategies for the treatment and management of pain. While pain, smell, and taste share common features and are strongly linked to emotion and cognition, their interaction has been poorly explored. In this review, we provide an overview of the literature on pain modulation by olfactory and gustatory substances. It includes adult human studies investigating measures of pain threshold, tolerance, intensity, and/or unpleasantness. Due to the limited number of studies currently available, we have structured this review as a narrative in which we comment on experimentally induced and clinical pain separately on pain–smell and pain–taste interaction. Inconsistent study findings notwithstanding, pain, smell, and taste seem to interact at both the behavioral and the neural levels. Pain intensity and unpleasantness seem to be affected more by olfactory substances, whereas pain threshold and tolerance are influenced by gustatory substances. Few pilot studies to date have investigated these effects in clinical populations. While the current results are promising for the future, more evidence is needed to elucidate the link between the chemical senses and pain. Doing so has the potential to improve and develop novel options for pain treatment.

Visual network alterations in brain functional connectivity in chronic low back pain: A resting state functional connectivity and machine learning study

Chronic low back pain (cLBP) is associated with widespread functional and structural changes in the brain. This study aims to investigate the resting state functional connectivity (rsFC) changes of visual networks in cLBP patients and the feasibility of distinguishing cLBP patients from healthy controls using machine learning methods. cLBP (n = 90) and control individuals (n = 74) were enrolled and underwent resting-state BOLD fMRI scans. Primary, dorsal, and ventral visual networks derived from independent component analysis were used as regions of interest to compare resting state functional connectivity changes between the cLBP patients and healthy controls. We then applied a support vector machine classifier to distinguish the cLBP patients and control individuals. These results were further verified in a new cohort of subjects. We found that the functional connectivity between the primary visual network and the somatosensory/motor areas were significantly enhanced in cLBP patients. The rsFC between the primary visual network and S1 was negatively associated with duration of cLBP. In addition, we found that the rsFC of the visual network could achieve a classification accuracy of 79.3% in distinguishing cLBP patients from HCs, and these results were further validated in an independent cohort of subjects (accuracy = 66.7%). Our results demonstrate significant changes in the rsFC of the visual networks in cLBP patients. We speculate these alterations may represent an adaptation/self-adjustment mechanism and cross-model interaction between the visual, somatosensory, motor, attention, and salient networks in response to cLBP. Elucidating the role of the visual networks in cLBP may shed light on the pathophysiology and development of the disorder.

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We investigated rsFC changes of visual networks in cLBP patients.

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rsFC of the primary visual network with S1 and M1 increased in cLBP patients.

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rsFC of the visual networks can differentiate cLBP patients from controls (with 79.3% accuracy).

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Classification results can be validated in an independent cohort (with 66.7% accuracy).