INTRODUCTION

1. SFN 2008 Washington 175.2 INTRODUCTION Timing of BOLD signals across brain regions during pain rating task Pain and visual magnitude ratings Brain regions encoding variance of pain and visual rating tasks 1 3 5 • Assessing the size of objects rapidly and accurately clearly has survival value. Thus, a central multi-sensory module for magnitude assessment is highly likely and is suggested by psychophysical studies • In this study, we use fMRI to examine brain areas that are activated in healthy subjects using a finger-spanning device to rate and log the magnitude of their perception of thermal pain and the length of a visual bar. • We specifically compare brain responses to painful and visual stimuli because there is little ambiguity as to their disparate perceptions and assumed unique representations in the brain. Therefore, observing a common representation of magnitudes for both modalities would provide strong evidence for a central module. a c d c b a. Individual pain and visual magnitude ratings. Left column shows individual online subjective pain ratings. Right column shows the corresponding visual online ratings using the same scale. Variance for the pain and visual tasks are indicated. b. Time course of the thermal stimulus applied to the participants. c. Thevariances for pain and visual rating tasks across 14 subjects tightly correlate. a. Time course of average BOLD responses for pain specific areas (left column) and brain areas derived from the conjunction of variance related maps (right column). b. Top panel shows stimulus-rating indices for mINS and pINS as a function of peak response latency for each subject. The distributions of pINS and mINS show no overlap as indicated by color markings. The mean stimulus-rating indices for each area across all subjects (n=14) are shown in the lower panel. METHODS • In the scanner, 15 healthy subjects used the finger-span device to rate the perceived magnitudes of painful and visual (Panel 1). • GLM model (FSL software; fmrib, Smith et al. 2001) was used to identify brain areas that were activated during the rating tasks (Panel 2). • Covariate analysis was performed to determine cortical regions encoding amount of information in both tasks (Panel 3). • Percent BOLD change was extracted from ROIs and submitted for correlation (Panel 4) and time course analyses (Panel 5). • Functional networks were produced by extracting the BOLD time series from a seed region and then computing the correlation coefficient with all other brain voxels (Panel 6). • 12 subjects underwent whole brain diffusion tensor imaging (DTI). Data was analyzed using the Diffusion Toolbox (FDT) of FSL (FMRIB, Oxford). a. Whole brain covariate analysis between variance and brain activity for pain and visual rating tasks. b. Conjunction analyses for the pain and visual covariate maps c. Topological maps of insular activity showing spatial dissociation for pain specific activation (pINS, red contour) and magnitude encoding areas (mINS, blue contour). d. Scatter plots depict the relationship between brain activity from mINS and pINS (mean z-score) and variance for pain (circle) and visual (triangle) tasks. 2 Brain activity maps for pain and visual rating tasks Functional and anatomical dissociation in the insula 6 Brain regions encoding magnitude for visual and pain stimuli 4 Random-effects analysis for pain and visual rating tasks. Many cortical areas were commonly activated. The conjunction map between pain and visual rating is shown in blue and represents voxels commonly activated for both tasks. The contrast map shows regions significantly more active for pain rating and include bilateral thalamus and basal ganglia, and parts of insula and mACC. There were no regions that were more active for the visual rating. CONCLUSION a. Functional connectivity maps for left pINS and mINS. PINS exhibit strong connectivity to pain specific regions, while mINS show strong connectivity to task related regions. b. DTI for left mINS and pINS. Right graph show the number of connections average across all subjects as a function of threshold of connectivity. c. Right colored brain regions illustrate the targets used in connectivity. Bar graph displays the target connectivity for mINS and pINS. • The above analyses distinguishes between brain areas involved in nociception (ACC, BG, Th, pINS), task performance (PPc, DPc, SMA), and magnitude estimation (mINS). • mINS in contiguity with VPc is distinct from pINS in: 1. encoding pain and visual magnitudes; 2. capturing variance for pain and visual tasks; 3. functional connectivity; 4. anatomical connectivity. • We cannot identify a brain region specific for pain perception, instead we propose that perceived pain magnitude is due to transformation of nociceptive information to a magnitude through mINS/VPc, which assesses ‘how much’ for at least visual and painful stimuli equally well. Funded by NIH NINDS NS35115 a. Example ofBOLD signal and rating in standard units from one subjects. Peak BOLD and rating were extracted for each stimulation epoch and submitted for a correlational analysis. b. Correlation of BOLD with magnitude for 2 regions derived from the conjunction of variance related maps (blue) and contrast map (red). Scatter plots depict the degree of association between individuals’ ROI signal and magnitude. . c. Correlation between magnitude and BOLD for pain and visual stimuli across task and pain specific regions. * p < 0.01; ** p < 0.001. a Central cortical module for magnitude estimation in the human brain M.N. Baliki, P.Y. Geha, D.R. Chialvo, A.V. Apkarian Department of Physiology, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611