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(324) Changes in resting-state functional connectivity that are related increased pain sensitivity in a model of acute low back pain
Abstracts
(324) Changes in resting-state functional connectivity that are related increased pain sensitivity in a model of acute low back pain C Gay, J Craggs, M Robinson, and M Bishop; University of Florida, Gainesville, FL
The Journal of Pain
S57
(326) Classification of brain activity response to painful heat stimuli in fibromyalgia patients and healthy controls M Borja, K Martucci, A Nilakantan, and S Mackey; Stanford University, Stanford, CA
The Pain Processing Network (PPN) has been studied as a system that underlies altered central pain processing such as central sensitization, leading to increased pain sensitivity. A shift in pain sensitivity, which manifests as lowered pressure pain thresholds, takes place during an acute muscular injury. To identify brain regions of the PPN where changes in pre-stimulus (i.e resting) interbrain-region activity may underlie this behavior in acute pain, we examined changes in resting-state functional connectivity (rs-FC) using a region of interest (ROI)-to-ROI approach in a model of acute endogenous lower back pain. We compared the rs-FC change over time in subjects (N=16) who underwent an exhaustive exercise protocol. Using a p-value of less than 0.05 without correcting for the number of comparisons, we found several inter-brain region connections demonstrating changes. However only one, the change between the right primary somatosensory cortex and the right anterior insular (r_SIr_aINS) (mean difference in Dr=0.45, t16=2.39, p-unc=0.03, p-FDR=0.48), was also significantly associated with local (i.e. over lumbar spine) and remote (i.e. over distal extremities) changes in pressure pain sensitivity (spearmanr=-0.59, p=0.02 and spearman-r=-0.78,p<0.01, respectively). These results indicate that increased local and remote pressure pain sensitivity are associated with greater inter-regional brain activity between sensory discriminate and sensory limbic regions. Further, the stronger correlation between the rs-FC changes and changes in pain sensitivity in remote locations is noteworthy as this location is not cofounded by local changes in peripheral nerves which contribute to local sensitivity changes. These data suggest that PPN indices are sensitive to acute changes in a clinical pain model, and may represent an adaptive neural system associated with the development of pain, and perhaps the transition from acute to chronic pain. Supported by grants awarded from the National Center for Alternative and Complementary Medicine: F32 AT007729-01A1 and R01 AT006334-01.
Hypersensitivity and other alterations in sensory processing occur in patients suffering from fibromyalgia syndrome, although the exact nature of how sensory processing contributes to these differences remains unclear. Investigating differences in acute pain processing in fibromyalgia could potentially help uncover mechanisms by which chronic pain processing is modified in fibromyalgia patients. Our recent study used a multivariate approach to demonstrate that brain activity can be used to validate various levels of painful and non-painful heat stimuli in healthy individuals.1 Our goal here was to extend this approach to classify levels of painful heat stimuli in a chronic pain population. Subjects (10 healthy controls, 9 fibromyalgia patients) completed a quantitative sensory testing (QST) session to determine individualized temperatures evoking pain levels of 5 and 7 (visual analog scale, VAS, 0-10). Functional MRI scans and VAS pain ratings were collected while subjects underwent painful heat stimuli on their lower right leg, using individualized temperatures to match heat pain levels across patients and healthy controls. Our preliminary conventional analyses show significant differences in brain activity in response to moderate (VAS pain level 5) and high (VAS pain level 7) levels of heat pain across all subjects. Further analyses will aim to use a multivariate approach (support vector machine, SVM) to classify levels of painful heat stimuli and distinguish between brain activity evoked by acute pain in fibromyalgia patients versus in healthy controls. Our future goal is to identify a signature pattern of brain activity response to acute heat pain that is able to differentiate between fibromyalgia patients and healthy controls. (1. Brown, J.E., et al., Towards a physiologybased measure of pain: patterns of human brain activity distinguish painful from non-painful thermal stimulation. PLoS One, 2011. 6(9): p. e24124.)
(325) fcMRI maps during pain tasks vary based on the inclusion of paradigm modeling in analysis
(327) Cognitive modulation of pain before and after real-time fMRI neurofeedback training: Improving brain state classification
J Ibinson and K Vogt; University of Pittsburgh, Pittsburgh, PA As an alternative to resting state functional connectivity (fcMRI) analysis, blockdesign fcMRI studies of pain have been increasingly performed. However, the effect of the stimulus-correlated MRI signal changes on fcMRI studies is still unknown. We compared analyses with and without modeling of the stimulation paradigm during a 30 s block-design experiment of electric nerve stimulation (ENS). Our primary hypothesis was that modeling the stimulation paradigm would result in reduced correlation, compared to an un-modeled analysis. fcMRI data was acquired in 14 healthy adults in an IRB-approved 3 T whole brain functional MRI study of ENS-generated pain in the right index finger (intensity self-adjusted to 7/10). A seed region was chosen in the left (contralateral) insula and modeled for each subject as the primary regressor with and without the stimulation paradigm included as an effect of no interest. These were compared to the fcMRI maps of a similar tonic pain task (without cyclic changes). Group average functional correlation maps were generated. Mean and maximum (max) Z-scores were tabulated from regions of interest (ROIs) with significant correlation. The left insula seed time course was found to be correlated to areas of the pain matrix (anterior cingulate cortex, bilateral primary and secondary somatosensory cortices, and right insula), as expected. Modeling the paradigm in this cyclic pain task decreased the strength of correlation in all significant brain areas, giving a map more similar to that of the tonic pain task. Thus, modeling the stimulus timing removed superimposed task-induced activation, giving more accurate fcMRI maps.
A Sentis, E Bagarinao, K Martucci, and S Mackey; Stanford University, Stanford , CA Many chronic low back pain (cLBP) cases show no skeletal pathology. Because of this, investigation of brain structure and function can give insights into the mechanisms underlying the chronic pain. Our group has previously found that patients can be trained to control activation of localized brain regions by using feedback derived from real-time functional MRI.1 Further, control of activation in the rostral anterior cingulate cortex region can change perception of pain and may provide a clinical approach to treatment of severe pain associated with cLBP. The purpose of this study is to understand the underlying neural mechanism in effectively using cognitive strategies to modulate pain (increase or decrease). Healthy controls (N=8) who are adept pain modulators underwent an fMRI scan (30 axial slices, 4mm thickness, 1mm gap, 3.4 mm in-plane voxel size, TR = 2000 ms, TE = 30 ms, flip angle = 61 degrees, FOV = 22x22 cm.) with the end goal of using the same approach with chronic pain patients. Using multivariate pattern analysis, specifically support vector machines (SVMs), we discriminated brain states associated with increased versus decreased perceived pain and we tested four alternative methods of preprocessing the data that are input to the SVMs. Using leave-one-out cross validation (LOOCV) analyses repeated for all 150 volumes, we correctly classified the test volume as either decreasing or increasing pain with 88.1% and 85.6% accuracy on two datasets. With preprocessing, the LOOCV accuracy on the two datasets improved to 96.3% and 97.4% on average across the four preprocessing methods. With optimal preprocessing methods, our SVM classified with near-perfect accuracy the brain states associated with cognitive strategies to increase or decrease pain. As further analysis, we intend to look at the effect of demographic information and pain modulation strategies on classification accuracy. (1. deCharms RC et al, PNAS, 2005.)
(324) Changes in resting-state functional connectivity that are related increased pain sensitivity in a model of acute low back pain C Gay, J Craggs, M Robinson, and M Bishop; University of Florida, Gainesville, FL
The Journal of Pain
S57
(326) Classification of brain activity response to painful heat stimuli in fibromyalgia patients and healthy controls M Borja, K Martucci, A Nilakantan, and S Mackey; Stanford University, Stanford, CA
The Pain Processing Network (PPN) has been studied as a system that underlies altered central pain processing such as central sensitization, leading to increased pain sensitivity. A shift in pain sensitivity, which manifests as lowered pressure pain thresholds, takes place during an acute muscular injury. To identify brain regions of the PPN where changes in pre-stimulus (i.e resting) interbrain-region activity may underlie this behavior in acute pain, we examined changes in resting-state functional connectivity (rs-FC) using a region of interest (ROI)-to-ROI approach in a model of acute endogenous lower back pain. We compared the rs-FC change over time in subjects (N=16) who underwent an exhaustive exercise protocol. Using a p-value of less than 0.05 without correcting for the number of comparisons, we found several inter-brain region connections demonstrating changes. However only one, the change between the right primary somatosensory cortex and the right anterior insular (r_SIr_aINS) (mean difference in Dr=0.45, t16=2.39, p-unc=0.03, p-FDR=0.48), was also significantly associated with local (i.e. over lumbar spine) and remote (i.e. over distal extremities) changes in pressure pain sensitivity (spearmanr=-0.59, p=0.02 and spearman-r=-0.78,p<0.01, respectively). These results indicate that increased local and remote pressure pain sensitivity are associated with greater inter-regional brain activity between sensory discriminate and sensory limbic regions. Further, the stronger correlation between the rs-FC changes and changes in pain sensitivity in remote locations is noteworthy as this location is not cofounded by local changes in peripheral nerves which contribute to local sensitivity changes. These data suggest that PPN indices are sensitive to acute changes in a clinical pain model, and may represent an adaptive neural system associated with the development of pain, and perhaps the transition from acute to chronic pain. Supported by grants awarded from the National Center for Alternative and Complementary Medicine: F32 AT007729-01A1 and R01 AT006334-01.
Hypersensitivity and other alterations in sensory processing occur in patients suffering from fibromyalgia syndrome, although the exact nature of how sensory processing contributes to these differences remains unclear. Investigating differences in acute pain processing in fibromyalgia could potentially help uncover mechanisms by which chronic pain processing is modified in fibromyalgia patients. Our recent study used a multivariate approach to demonstrate that brain activity can be used to validate various levels of painful and non-painful heat stimuli in healthy individuals.1 Our goal here was to extend this approach to classify levels of painful heat stimuli in a chronic pain population. Subjects (10 healthy controls, 9 fibromyalgia patients) completed a quantitative sensory testing (QST) session to determine individualized temperatures evoking pain levels of 5 and 7 (visual analog scale, VAS, 0-10). Functional MRI scans and VAS pain ratings were collected while subjects underwent painful heat stimuli on their lower right leg, using individualized temperatures to match heat pain levels across patients and healthy controls. Our preliminary conventional analyses show significant differences in brain activity in response to moderate (VAS pain level 5) and high (VAS pain level 7) levels of heat pain across all subjects. Further analyses will aim to use a multivariate approach (support vector machine, SVM) to classify levels of painful heat stimuli and distinguish between brain activity evoked by acute pain in fibromyalgia patients versus in healthy controls. Our future goal is to identify a signature pattern of brain activity response to acute heat pain that is able to differentiate between fibromyalgia patients and healthy controls. (1. Brown, J.E., et al., Towards a physiologybased measure of pain: patterns of human brain activity distinguish painful from non-painful thermal stimulation. PLoS One, 2011. 6(9): p. e24124.)
(325) fcMRI maps during pain tasks vary based on the inclusion of paradigm modeling in analysis
(327) Cognitive modulation of pain before and after real-time fMRI neurofeedback training: Improving brain state classification
J Ibinson and K Vogt; University of Pittsburgh, Pittsburgh, PA As an alternative to resting state functional connectivity (fcMRI) analysis, blockdesign fcMRI studies of pain have been increasingly performed. However, the effect of the stimulus-correlated MRI signal changes on fcMRI studies is still unknown. We compared analyses with and without modeling of the stimulation paradigm during a 30 s block-design experiment of electric nerve stimulation (ENS). Our primary hypothesis was that modeling the stimulation paradigm would result in reduced correlation, compared to an un-modeled analysis. fcMRI data was acquired in 14 healthy adults in an IRB-approved 3 T whole brain functional MRI study of ENS-generated pain in the right index finger (intensity self-adjusted to 7/10). A seed region was chosen in the left (contralateral) insula and modeled for each subject as the primary regressor with and without the stimulation paradigm included as an effect of no interest. These were compared to the fcMRI maps of a similar tonic pain task (without cyclic changes). Group average functional correlation maps were generated. Mean and maximum (max) Z-scores were tabulated from regions of interest (ROIs) with significant correlation. The left insula seed time course was found to be correlated to areas of the pain matrix (anterior cingulate cortex, bilateral primary and secondary somatosensory cortices, and right insula), as expected. Modeling the paradigm in this cyclic pain task decreased the strength of correlation in all significant brain areas, giving a map more similar to that of the tonic pain task. Thus, modeling the stimulus timing removed superimposed task-induced activation, giving more accurate fcMRI maps.
A Sentis, E Bagarinao, K Martucci, and S Mackey; Stanford University, Stanford , CA Many chronic low back pain (cLBP) cases show no skeletal pathology. Because of this, investigation of brain structure and function can give insights into the mechanisms underlying the chronic pain. Our group has previously found that patients can be trained to control activation of localized brain regions by using feedback derived from real-time functional MRI.1 Further, control of activation in the rostral anterior cingulate cortex region can change perception of pain and may provide a clinical approach to treatment of severe pain associated with cLBP. The purpose of this study is to understand the underlying neural mechanism in effectively using cognitive strategies to modulate pain (increase or decrease). Healthy controls (N=8) who are adept pain modulators underwent an fMRI scan (30 axial slices, 4mm thickness, 1mm gap, 3.4 mm in-plane voxel size, TR = 2000 ms, TE = 30 ms, flip angle = 61 degrees, FOV = 22x22 cm.) with the end goal of using the same approach with chronic pain patients. Using multivariate pattern analysis, specifically support vector machines (SVMs), we discriminated brain states associated with increased versus decreased perceived pain and we tested four alternative methods of preprocessing the data that are input to the SVMs. Using leave-one-out cross validation (LOOCV) analyses repeated for all 150 volumes, we correctly classified the test volume as either decreasing or increasing pain with 88.1% and 85.6% accuracy on two datasets. With preprocessing, the LOOCV accuracy on the two datasets improved to 96.3% and 97.4% on average across the four preprocessing methods. With optimal preprocessing methods, our SVM classified with near-perfect accuracy the brain states associated with cognitive strategies to increase or decrease pain. As further analysis, we intend to look at the effect of demographic information and pain modulation strategies on classification accuracy. (1. deCharms RC et al, PNAS, 2005.)