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Alterations in Default Mode Network Connectivity During Pain Processing in Borderline Personality Disorder

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Alterations in Default Mode Network Connectivity During Pain Processing in Borderline Personality Disorder Supplementary Online Content Kluetsch RC, Schmahl C, Niedtfeld I, Densmore M, Calhoun VD, Daniels J, Kraus A, Ludaescher P, Bohus M, Lanius RA. Alterations in default mode network connectivity during pain processing in borderline personality disorder. Arch Gen Psychiatry. Published online June 4, 2012. doi:10.1001/archgenpsychiatry.2012.476. eAppendix. Methods eTable 1. Brain regions identified in each of the default mode subnetwork components eTable 2. Brain regions identified in each of the default mode subnetwork components eTable 3. Brain regions identified in each of the default mode subnetwork components eTable 4. Areas showing connectivity with the two seed regions during “neutral” greater than “pain” – within-group results eTable 5. Areas showing connectivity with the two seed regions during “pain” greater than “neutral” – within-group results This supplementary material has been provided by the authors to give readers additional information about their work. © 2012 American Medical Association. All rights reserved. Downloaded From: https://jamanetwork.com/ on 09/30/2021 Methods Image Analysis Component Identification The components related to the DMN were selected following visual inspection and methods previously described.1-3 First, we systematically excluded components that showed significant correlations (r ≥ 0.1) with a priori localized cerebral spinal fluid (CSF) and/or low correlations with a priori localized gray matter (r ≤ 0.1), as these are likely to be artifactual.4 The remaining component images were then correlated with a binary DMN mask derived from a previous study.5 This mask contained the PCC/PrC, mPFC, bilateral lateral parietal cortices, and bilateral temporal gyri. We selected those components that showed a significant correlation (r ≥ 0.25) with our template and included regions previously implicated in the DMN.3,5,6 _ENREF_54_ENREF_25 Statistical Comparison of Images For the selected components, the individual subject maps were entered into second-level analyses in SPM8 (http://www.fil.ion.ucl.ac.uk/spm/). First, separate one-sample t-tests were conducted for patients and controls to generate group-level maps of the selected components. Second, an overall component spatial map3,7_ENREF_53 was created for each sub-component by entering the maps of all subjects (collapsed across groups) into a one-sample t- test. Third, two-sample t-tests, masked with the respective overall component spatial map,53,55 were performed to test for between-group differences. p < 0.005 (uncorrected) at the voxel-level. Additionally, to correct for multiple comparisons across the whole brain, we used a cluster extent correction procedure to compute the number of expected voxels per cluster according to random field theory.8 Thus, only clusters exceeding the respective number of voxels are presented. _ENREF_58To control for differences in subjective pain intensity ratings © 2012 American Medical Association. All rights reserved. Downloaded From: https://jamanetwork.com/ on 09/30/2021 during the fMRI acquisition, we entered each subject’s average rating for the five “pain” blocks as a covariate of no interest into the two-sample t-tests comparing the component images of BPD patients and HC. Statistical Comparison of Time Courses Multiple regression analysis, using the temporal sorting function in GIFT, was performed on the ICA time courses (considered the dependent variables) with the general linear model (GLM) design matrix taken from SPM8.9,10,11 The design matrix contained three regressors corresponding to the different types of thermal stimulation, namely (1) the individually adjusted temperature (subsequently referred to as “pain”), (2) the neutral temperature (subsequently referred to as “neutral”), and (3) a fixed temperature of 43°C that was of no interest to the current study. This procedure resulted in a set of beta-weights (= slopes of regressors) for every regressor, subject and component. These beta- weights were then entered into second-level analyses to draw inferences about the degree of task-relatedness.10,12 To account for our explicit baseline, we subtracted the beta-weights of “neutral” from “pain” and entered their difference scores into separate one-sample t-tests per group. Testing these difference scores against the null hypothesis of no change reveals whether a component exhibits significant pain-related signal change relative to “neutral”. Next, two- sample t-tests were performed for each component and its associated difference scores to test for group differences in task-modulation. The aforementioned group statistics on beta-weights were conducted in SPSS for Windows (Rel. 18.0.0. 2009. Chicago: SPSS Inc.), and thresholded at p < 0.05.10 References 1. Stevens MC, Kiehl KA, Pearlson G, Calhoun VD. Functional neural circuits for mental timekeeping. Hum Brain Mapp. May 2007;28(5):394‐408. 2. Jafri MJ, Pearlson GD, Stevens M, Calhoun VD. A method for functional network connectivity among spatially independent resting‐state components in schizophrenia. Neuroimage. Feb 15 2008;39(4):1666‐1681. © 2012 American Medical Association. All rights reserved. Downloaded From: https://jamanetwork.com/ on 09/30/2021 3. Assaf M, Jagannathan K, Calhoun VD, Miller L, Stevens MC, Sahl R, O'Boyle JG, Schultz RT, Pearlson GD. Abnormal functional connectivity of default mode sub‐networks in autism spectrum disorder patients. Neuroimage. Oct 15 2010;53(1):247‐256. 4. Maldjian JA, Laurienti PJ, Kraft RA, Burdette JH. An automated method for neuroanatomic and cytoarchitectonic atlas‐based interrogation of fMRI data sets. Neuroimage. Jul 2003;19(3):1233‐1239. 5. Bluhm RL, Osuch EA, Lanius RA, Boksman K, Neufeld RW, Théberge J, Williamson P. Default mode network connectivity: effects of age, sex, and analytic approach. Neuroreport. May 28 2008;19(8):887‐891. 6. Buckner RL, Andrews‐Hanna JR, Schacter DL. The brain's default network: anatomy, function, and relevance to disease. Ann N Y Acad Sci. Mar 2008;1124:1‐38. 7. Garrity AG, Pearlson GD, McKiernan K, Lloyd D, Kiehl KA, Calhoun VD. Aberrant "default mode" functional connectivity in schizophrenia. Am J Psychiatry. Mar 2007;164(3):450‐457. 8. Hayasaka S, Nichols TE. Combining voxel intensity and cluster extent with permutation test framework. Neuroimage. Sep 2004;23(1):54‐63. 9. Calhoun VD, Liu J, Adali T. A review of group ICA for fMRI data and ICA for joint inference of imaging, genetic, and ERP data. Neuroimage. Mar 2009;45(1 Suppl):S163‐ 172. 10. Otti A, Guendel H, Laer L, Wohlschlaeger AM, Lane RD, Decety J, Zimmer C, Henningsen P, Noll‐Hussong M. I know the pain you feel‐how the human brain's default mode predicts our resonance to another's suffering. Neuroscience. Aug 11 2010;169(1):143‐148. 11. Kim DI, Mathalon DH, Ford JM, Mannell M, Turner JA, Brown GG, Belger A, Gollub R, Lauriello J, Wible C, O'Leary D, Lim K, Toga A, Potkin SG, Birn F, Calhoun VD. Auditory oddball deficits in schizophrenia: an independent component analysis of the fMRI multisite function BIRN study. Schizophr Bull. Jan 2009;35(1):67‐81. 12. Kim DI, Manoach DS, Mathalon DH, Turner JA, Mannell M, Brown GG, Ford JM, Gollub RL, White T, Wible C, Belger A, Bockholt HJ, Clark VP, Lauriello J, O'Leary D, Mueller BA, Lim KO, Andreasen N, Potkin SG, Calhoun VD. Dysregulation of working memory and default‐mode networks in schizophrenia using independent component analysis, an fBIRN and MCIC study. Hum Brain Mapp. Nov 2009;30(11):3795‐3811. © 2012 American Medical Association. All rights reserved. Downloaded From: https://jamanetwork.com/ on 09/30/2021 eTable 1: Brain regions identified in each of the default mode subnetwork components All participants (n = 47) Brain region* MNI coordinates z Score Component 28 Bilateral posterior cingulate/precuneus (BA 23, 29, 30, 31) -6, -57, 21 Inf Right superior/middle temporal gyrus, angular gyrus (BA 19, 39) 48, -72, 24 Inf Left superior/middle temporal gyrus, angular gyrus (BA 19, 39) -45, -69, 18 Inf Bilateral medial frontal gyrus (BA 10, 11) -3, 48, -15 Inf Left superior/middle frontal gyrus (BA 6, 8) -27, 33, 48 7.68 Right superior/middle frontal gyrus (BA 6, 8) 27, 30, 54 7.34 Right insula 36, -9, 6 7.05 Cerebellum 36, -69, -51 6.95 Component 27 Bilateral posterior cingulate/precuneus (BA 23, 30, 31) -3, -54, 24 Inf Bilateral superior/medial frontal gyrus (BA 6, 8, 9, 10) -6, 54, 36 Inf Left superior/middle temporal gyrus, angular gyrus (BA 39, 40) -51, -66, 27 Inf Left inferior parietal lobule (BA 40) -54, -60, 42 Inf Right cerebellum 30, -84, -33 Inf Right insula 36, -15, 12 Inf Right inferior temporal gyrus (BA 19, 37) 48, -72, -9 7.78 Right superior/middle temporal gyrus, angular gyrus (BA 39, 40) 57, -60, 30 Inf Right precentral/postcentral gyrus (BA 3, 4) 33, -21, 51 Inf Left inferior/middle/superior temporal gyrus (BA 20, 21, 38) -54, -6, -30 Inf Right inferior/middle/superior temporal gyrus (BA 20, 21, 38) 63, -6, -27 Inf Left inferior frontal gyrus (BA 45, 47) -33, 27, -21 Inf Bilateral anterior cingulate gyrus (BA 24) 0, -15, 42 7.80 Component 13 Bilateral posterior cingulate gyrus/ precuneus (BA 7, 23, 31) 6, -45, 36 Inf Right superior temporal gyrus, angular gyrus, inferior parietal 42, -66, 39 Inf Left angular gyrus, inferior parietal lobule (BA 19, 39) -36, -69, 39 Inf Bilateral mid-cingulate gyrus (BA 24) -3, 12, 36 7.73 Abbreviations: BA = Brodmann area; BPD = Borderline Personality Disorder; MNI = Montreal Neurological Institute. * Peak activation voxels are thresholded at p < 0.005 (uncorrected) at the voxel-level, in addition to multiple
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