E. Bagarinao

Real-time fMRI applied to pain management

Current views recognize the brain as playing a pivotal role in the arising and maintenance of pain experience. Real-time fMRI (rtfMRI) feedback is a potential tool for pain modulation that directly targets the brain with the goal of restoring regulatory function. Though still relatively new, rtfMRI is a rapidly developing technology that has evolved in the last 15 years from simple proof of concept experiments to demonstrations of learned control of single and multiple brain areas. Numerous studies indicate rtfMRI feedback assisted control over specific brain areas may have applications including mood regulation, language processing, neurorehabilitation in stroke, enhancement of perception and learning, and pain management. We discuss in detail earlier work from our lab in which rtfMRI feedback was used to train both healthy controls and chronic pain patients to modulate anterior cingulate cortex (ACC) activation for the purposes of altering pain experience. Both groups improved in their ability to control ACC activation and modulate their pain with rtfMRI feedback training. Furthermore, the degree to which participants were able to modulate their pain correlated with the degree of control over ACC activation. We additionally review current advances in rtfMRI feedback, such as real-time pattern classification, that bring the technology closer to more comprehensive control over neural function. Finally, remaining methodological questions concerning the further development of rtfMRI feedback and its implications for the future of pain research are also discussed.

Preliminary structural MRI based brain classification of chronic pelvic pain: A MAPP Network Study

Summary A preliminary classifier of brain structure was identified in chronic pelvic pain using a support vector machine learning algorithm suggesting distributed regional gray matter increases. ABSTRACT Neuroimaging studies have shown that changes in brain morphology often accompany chronic pain conditions. However, brain biomarkers that are sensitive and specific to chronic pelvic pain (CPP) have not yet been adequately identified. Using data from the Trans‐MAPP Research Network, we examined the changes in brain morphology associated with CPP. We used a multivariate pattern classification approach to detect these changes and to identify patterns that could be used to distinguish participants with CPP from age‐matched healthy controls. In particular, we used a linear support vector machine (SVM) algorithm to differentiate gray matter images from the 2 groups. Regions of positive SVM weight included several regions within the primary somatosensory cortex, pre‐supplementary motor area, hippocampus, and amygdala were identified as important drivers of the classification with 73% overall accuracy. Thus, we have identified a preliminary classifier based on brain structure that is able to predict the presence of CPP with a good degree of predictive power. Our regional findings suggest that in individuals with CPP, greater gray matter density may be found in the identified distributed brain regions, which are consistent with some previous investigations in visceral pain syndromes. Future studies are needed to improve upon our identified preliminary classifier with integration of additional variables and to assess whether the observed differences in brain structure are unique to CPP or generalizable to other chronic pain conditions.

The posterior medial cortex in urologic chronic pelvic pain syndrome: detachment from default mode network-a resting-state study from the MAPP Research Network.

Abstract Altered resting-state (RS) brain activity, as a measure of functional connectivity (FC), is commonly observed in chronic pain. Identifying a reliable signature pattern of altered RS activity for chronic pain could provide strong mechanistic insights and serve as a highly beneficial neuroimaging-based diagnostic tool. We collected and analyzed RS functional magnetic resonance imaging data from female patients with urologic chronic pelvic pain syndrome (N = 45) and matched healthy participants (N = 45) as part of an NIDDK-funded multicenter project (www.mappnetwork.org). Using dual regression and seed-based analyses, we observed significantly decreased FC of the default mode network to 2 regions in the posterior medial cortex (PMC): the posterior cingulate cortex (PCC) and the left precuneus (threshold-free cluster enhancement, family-wise error corrected P < 0.05). Further investigation revealed that patients demonstrated increased FC between the PCC and several brain regions implicated in pain, sensory, motor, and emotion regulation processes (eg, insular cortex, dorsolateral prefrontal cortex, thalamus, globus pallidus, putamen, amygdala, hippocampus). The left precuneus demonstrated decreased FC to several regions of pain processing, reward, and higher executive functioning within the prefrontal (orbitofrontal, anterior cingulate, ventromedial prefrontal) and parietal cortices (angular gyrus, superior and inferior parietal lobules). The altered PMC connectivity was associated with several phenotype measures, including pain and urologic symptom intensity, depression, anxiety, quality of relationships, and self-esteem levels in patients. Collectively, these findings indicate that in patients with urologic chronic pelvic pain syndrome, regions of the PMC are detached from the default mode network, whereas neurological processes of self-referential thought and introspection may be joined to pain and emotion regulatory processes.

Effects of salience-network-node neurofeedback training on affective biases in major depressive disorder

Neural models of major depressive disorder (MDD) posit that over-response of components of the brains salience network (SN) to negative stimuli plays a crucial role in the pathophysiology of MDD. In the present proof-of-concept study, we tested this formulation directly by examining the affective consequences of training depressed persons to down-regulate response of SN nodes to negative material. Ten participants in the real neurofeedback group saw, and attempted to learn to down-regulate, activity from an empirically identified node of the SN. Ten other participants engaged in an equivalent procedure with the exception that they saw SN-node neurofeedback indices from participants in the real neurofeedback group. Before and after scanning, all participants completed tasks assessing emotional responses to negative scenes and to negative and positive self-descriptive adjectives. Compared to participants in the sham-neurofeedback group, from pre- to post-training, participants in the real-neurofeedback group showed a greater decrease in SN-node response to negative stimuli, a greater decrease in self-reported emotional response to negative scenes, and a greater decrease in self-reported emotional response to negative self-descriptive adjectives. Our findings provide support for a neural formulation in which the SN plays a primary role in contributing to negative cognitive biases in MDD.