Landrew S. Sevel

Effective connectivity predicts future placebo analgesic response: A dynamic causal modeling study of pain processing in healthy controls.

A better understanding of the neural mechanisms underlying pain processing and analgesia may aid in the development and personalization of effective treatments for chronic pain. Clarification of the neural predictors of individual variability in placebo analgesia (PA) could aid in this process. The present study examined whether the strength of effective connectivity (EC) among pain-related brain regions could predict future placebo analgesic response in healthy individuals. In Visit 1, fMRI data were collected from 24 healthy subjects (13 females, mean age=22.56, SD=2.94) while experiencing painful thermal stimuli. During Visit 2, subjects were conditioned to expect less pain via a surreptitiously lowered temperature applied at two of the four sites on their feet. They were subsequently scanned again using the Visit 1 (painful) temperature. Subjects used an electronic VAS to rate their pain following each stimulus. Differences in ratings at conditioned and unconditioned sites were used to measure placebo response (PA scores). Dynamic causal modeling was used to estimate the EC among a set of brain regions related to pain processing at Visit 1 (periaqueductal gray, thalamus, rostral anterior cingulate cortex, dorsolateral prefrontal cortex). Individual PA scores from Visit 2 were regressed on salient EC parameter estimates from Visit 1. Results indicate that both greater left hemisphere modulatory DLPFC➔PAG connectivity and right hemisphere, endogenous thalamus➔DLPFC connectivity were significantly predictive of future placebo response (R(2)=0.82). To our knowledge, this is the first study to identify the value of EC in understanding individual differences in PA, and may suggest the potential modifiability of endogenous pain modulation.

Test-retest reliability of pain-related functional brain connectivity compared with pain self-report.

Abstract Test-retest reliability, or reproducibility of results over time, is poorly established for functional brain connectivity (fcMRI) during painful stimulation. As reliability informs the validity of research findings, it is imperative to examine, especially given recent emphasis on using functional neuroimaging as a tool for biomarker development. Although proposed pain neural signatures have been derived using complex, multivariate algorithms, even the reliability of less complex fcMRI findings has yet to be reported. This study examined the test-retest reliability for fcMRI of pain-related brain regions, and self-reported pain (through visual analogue scales [VASs]). Thirty-two healthy individuals completed 3 consecutive fMRI runs of a thermal pain task. Functional connectivity analyses were completed on pain-related brain regions. Intraclass correlations were conducted on fcMRI values and VAS scores across the fMRI runs. Intraclass correlations coefficients for fcMRI values varied widely (range = −.174-.766), with fcMRI between right nucleus accumbens and medial prefrontal cortex showing the highest reliability (range = .649-.766). Intraclass correlations coefficients for VAS scores ranged from .906 to .947. Overall, self-reported pain was more reliable than fcMRI data. These results highlight that fMRI findings might be less reliable than inherently assumed and have implications for future studies proposing pain markers.

Biomarkers for Musculoskeletal Pain Conditions: Use of Brain Imaging and Machine Learning

Chronic musculoskeletal pain condition often shows poor correlations between tissue abnormalities and clinical pain. Therefore, classification of pain conditions like chronic low back pain, osteoarthritis, and fibromyalgia depends mostly on self report and less on objective findings like X-ray or magnetic resonance imaging (MRI) changes. However, recent advances in structural and functional brain imaging have identified brain abnormalities in chronic pain conditions that can be used for illness classification. Because the analysis of complex and multivariate brain imaging data is challenging, machine learning techniques have been increasingly utilized for this purpose. The goal of machine learning is to train specific classifiers to best identify variables of interest on brain MRIs (i.e., biomarkers). This report describes classification techniques capable of separating MRI-based brain biomarkers of chronic pain patients from healthy controls with high accuracy (70–92%) using machine learning, as well as critical scientific, practical, and ethical considerations related to their potential clinical application. Although self-report remains the gold standard for pain assessment, machine learning may aid in the classification of chronic pain disorders like chronic back pain and fibromyalgia as well as provide mechanistic information regarding their neural correlates.

Interhemispheric Dorsolateral Prefrontal Cortex Connectivity is Associated with Individual Differences in Pain Sensitivity in Healthy Controls.

The dorsolateral prefrontal cortex (DLPFC) is implicated in pain modulation through multiple psychological processes. Recent noninvasive brain stimulation studies suggest that interhemispheric DLPFC connectivity influences pain tolerance and discomfort by altering interhemispheric inhibition. The structure and role of interhemispheric DLPFC connectivity in pain processing have not been investigated. The present study used dynamic causal modeling (DCM) for fMRI to investigate transcallosal DLPFC connectivity during painful stimulation in healthy volunteers. DCM parameters were used to predict individual differences in sensitivity to noxious heat stimuli. Bayesian model selection results indicated that influences among the right DLPFC (rDLPFC) and left DLPFC (lDLPFC) are modulated during painful stimuli. Regression analyses revealed that greater rDLPFC→lDLPFC couplings were associated with higher suprathreshold pain temperatures. These results highlight the role of interhemispheric connectivity in pain modulation and support the preferential role of the right hemisphere in pain processing. Knowledge of these mechanisms may improve understanding of abnormal pain modulation in chronic pain populations.

Functional brain connectivity of remembered fatigue or happiness in healthy adults: Use of arterial spin labeling

ABSTRACT Introduction: Chronic fatiguing illnesses like cancer, multiple sclerosis, chronic fatigue syndrome, or depression are frequently associated with comorbidities including depression, pain, and insomnia, making the study of their neural correlates challenging. To study fatigue without such comorbidities, functional connectivity (FC) analyses were used in healthy individuals to study brain activity during recall of a fatiguing event inside the MRI scanner. A positive mood induction served as control condition. Method: Using SPM8 and the CONN toolbox, FC was tested using seed- and independent component- based (ICA) analyses. Differences in the FC correlations between seed-to-voxel and ICA clusters between conditions were assessed with permutation testing. Results: 17 participants (59% women) achieved mean (SD) in-scanner fatigue VAS ratings of 31.85 (20.61). Positive mood induction resulted in happiness ratings of 46.07 (18.99) VAS. Brain regions where alterations in FC correlated with fatigue included the globus pallidum, left lateral occipital cortex, and cuneus. FC of happiness involved the parahippocampal gyrus, both supplemental motor areas, as well as right superior frontal gyrus. Using data-driven ICA, we identified an intra-cerebellar network where several regions were significantly associated with fatigue, but not happiness ratings. Results of permutation testing provided evidence that the detected clusters correlated differentially with self-reported fatigue and happiness. Conclusions: Our study suggests that functional interactions between globus pallidum and occipital structures contribute to experimental fatigue in healthy individuals. They also highlight the important role of cortico-cerebellar interactions in producing feelings of fatigue. FC of occipital structures contributed to both experimental fatigue and happiness ratings.

Structural brain changes versus self-report: machine-learning classification of chronic fatigue syndrome patients

Chronic fatigue syndrome (CFS) is a disorder associated with fatigue, pain, and structural/functional abnormalities seen during magnetic resonance brain imaging (MRI). Therefore, we evaluated the performance of structural MRI (sMRI) abnormalities in the classification of CFS patients versus healthy controls and compared it to machine learning (ML) classification based upon self-report (SR). Participants included 18 CFS patients and 15 healthy controls (HC). All subjects underwent T1-weighted sMRI and provided visual analogue-scale ratings of fatigue, pain intensity, anxiety, depression, anger, and sleep quality. sMRI data were segmented using FreeSurfer and 61 regions based on functional and structural abnormalities previously reported in patients with CFS. Classification was performed in RapidMiner using a linear support vector machine and bootstrap optimism correction. We compared ML classifiers based on (1) 61 a priori sMRI regional estimates and (2) SR ratings. The sMRI model achieved 79.58% classification accuracy. The SR (accuracy = 95.95%) outperformed both sMRI models. Estimates from multiple brain areas related to cognition, emotion, and memory contributed strongly to group classification. This is the first ML-based group classification of CFS. Our findings suggest that sMRI abnormalities are useful for discriminating CFS patients from HC, but SR ratings remain most effective in classification tasks.