Ignite Creation Date:
2026-03-26 @ 3:15 PM
Ignite Modification Date:
2026-03-30 @ 10:13 PM
Study NCT ID:
NCT07479966
Status:
ENROLLING_BY_INVITATION
Last Update Posted:
2026-03-23
First Post:
2026-03-13
Is NOT Gene Therapy:
True
Has Adverse Events:
False
Brief Title:
Resting-state Connectivity and Individual Differences in Affective Placebo Responsiveness
Sponsor:
Organization:
Raw JSON
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These rsFC measures will be tested for associations with participant-level placebo-response indices derived from prior placebo/control sessions.'}], 'secondaryOutcomes': [{'measure': 'Within-network and between-network resting-state functional connectivity across canonical large-scale networks', 'timeFrame': 'During one separate post-paradigm MRI session lasting approximately 45 minutes, after completion of the preceding placebo study protocol.', 'description': 'Resting-state fMRI will be used to derive within-network and between-network connectivity measures for the frontoparietal control, salience, and default mode networks. These measures will be tested for associations with participant-level placebo-response indices derived from prior placebo/control sessions.'}]}, 'oversightModule': {'isFdaRegulatedDrug': False, 'isFdaRegulatedDevice': False}, 'conditionsModule': {'conditions': ['Condition: Healthy']}, 'referencesModule': {'references': [{'pmid': '15955494', 'type': 'BACKGROUND', 'citation': 'Ashburner J, Friston KJ. Unified segmentation. Neuroimage. 2005 Jul 1;26(3):839-51. doi: 10.1016/j.neuroimage.2005.02.018. Epub 2005 Apr 1.'}, {'pmid': '35351936', 'type': 'BACKGROUND', 'citation': 'Baker J, Gamer M, Rauh J, Brassen S. Placebo induced expectations of mood enhancement generate a positivity effect in emotional processing. Sci Rep. 2022 Mar 29;12(1):5345. doi: 10.1038/s41598-022-09342-2.'}, {'pmid': '17560126', 'type': 'BACKGROUND', 'citation': 'Behzadi Y, Restom K, Liau J, Liu TT. A component based noise correction method (CompCor) for BOLD and perfusion based fMRI. Neuroimage. 2007 Aug 1;37(1):90-101. doi: 10.1016/j.neuroimage.2007.04.042. Epub 2007 May 3.'}, {'pmid': '8524021', 'type': 'BACKGROUND', 'citation': 'Biswal B, Yetkin FZ, Haughton VM, Hyde JS. Functional connectivity in the motor cortex of resting human brain using echo-planar MRI. Magn Reson Med. 1995 Oct;34(4):537-41. doi: 10.1002/mrm.1910340409.'}, {'pmid': '18400922', 'type': 'BACKGROUND', 'citation': "Buckner RL, Andrews-Hanna JR, Schacter DL. The brain's default network: anatomy, function, and relevance to disease. Ann N Y Acad Sci. 2008 Mar;1124:1-38. doi: 10.1196/annals.1440.011."}, {'pmid': '27138646', 'type': 'BACKGROUND', 'citation': 'Dubois J, Adolphs R. Building a Science of Individual Differences from fMRI. Trends Cogn Sci. 2016 Jun;20(6):425-443. doi: 10.1016/j.tics.2016.03.014. Epub 2016 Apr 30.'}, {'pmid': '19897823', 'type': 'BACKGROUND', 'citation': 'Faul F, Erdfelder E, Buchner A, Lang AG. Statistical power analyses using G*Power 3.1: tests for correlation and regression analyses. Behav Res Methods. 2009 Nov;41(4):1149-60. doi: 10.3758/BRM.41.4.1149.'}, {'pmid': '17695343', 'type': 'BACKGROUND', 'citation': 'Faul F, Erdfelder E, Lang AG, Buchner A. G*Power 3: a flexible statistical power analysis program for the social, behavioral, and biomedical sciences. Behav Res Methods. 2007 May;39(2):175-91. doi: 10.3758/bf03193146.'}, {'pmid': '26457551', 'type': 'BACKGROUND', 'citation': 'Finn ES, Shen X, Scheinost D, Rosenberg MD, Huang J, Chun MM, Papademetris X, Constable RT. Functional connectome fingerprinting: identifying individuals using patterns of brain connectivity. Nat Neurosci. 2015 Nov;18(11):1664-71. doi: 10.1038/nn.4135. Epub 2015 Oct 12.'}, {'pmid': '17704812', 'type': 'BACKGROUND', 'citation': 'Fox MD, Raichle ME. Spontaneous fluctuations in brain activity observed with functional magnetic resonance imaging. Nat Rev Neurosci. 2007 Sep;8(9):700-11. doi: 10.1038/nrn2201.'}, {'pmid': '26468196', 'type': 'BACKGROUND', 'citation': 'Geerligs L, Rubinov M, Cam-Can, Henson RN. State and Trait Components of Functional Connectivity: Individual Differences Vary with Mental State. J Neurosci. 2015 Oct 14;35(41):13949-61. doi: 10.1523/JNEUROSCI.1324-15.2015.'}, {'pmid': '29673485', 'type': 'BACKGROUND', 'citation': 'Gratton C, Laumann TO, Nielsen AN, Greene DJ, Gordon EM, Gilmore AW, Nelson SM, Coalson RS, Snyder AZ, Schlaggar BL, Dosenbach NUF, Petersen SE. Functional Brain Networks Are Dominated by Stable Group and Individual Factors, Not Cognitive or Daily Variation. Neuron. 2018 Apr 18;98(2):439-452.e5. doi: 10.1016/j.neuron.2018.03.035.'}, {'pmid': '39805555', 'type': 'BACKGROUND', 'citation': 'Handoko K, Neppach A, Snyder I, Karim HT, Dombrovski AY, Pecina M. Expectancy-Mood Neural Dynamics Predict Mechanisms of Short- and Long-Term Antidepressant Placebo Effects. Biol Psychiatry Cogn Neurosci Neuroimaging. 2025 Aug;10(8):794-803. doi: 10.1016/j.bpsc.2025.01.002. Epub 2025 Jan 11.'}, {'pmid': '23352757', 'type': 'BACKGROUND', 'citation': 'Kong J, Jensen K, Loiotile R, Cheetham A, Wey HY, Tan Y, Rosen B, Smoller JW, Kaptchuk TJ, Gollub RL. Functional connectivity of the frontoparietal network predicts cognitive modulation of pain. Pain. 2013 Mar;154(3):459-467. doi: 10.1016/j.pain.2012.12.004. Epub 2012 Dec 20.'}, {'pmid': '21908230', 'type': 'BACKGROUND', 'citation': 'Menon V. Large-scale brain networks and psychopathology: a unifying triple network model. Trends Cogn Sci. 2011 Oct;15(10):483-506. doi: 10.1016/j.tics.2011.08.003. Epub 2011 Sep 9.'}, {'pmid': '40097556', 'type': 'BACKGROUND', 'citation': 'Mostauli A, Rauh J, Gamer M, Buchel C, Rief W, Brassen S. Placebo treatment entails resource-dependent downregulation of negative inputs. Sci Rep. 2025 Mar 17;15(1):9088. doi: 10.1038/s41598-025-93589-y.'}, {'pmid': '31494250', 'type': 'BACKGROUND', 'citation': 'Noble S, Scheinost D, Constable RT. A decade of test-retest reliability of functional connectivity: A systematic review and meta-analysis. Neuroimage. 2019 Dec;203:116157. doi: 10.1016/j.neuroimage.2019.116157. Epub 2019 Sep 5.'}, {'pmid': '25281800', 'type': 'BACKGROUND', 'citation': 'Reineberg AE, Andrews-Hanna JR, Depue BE, Friedman NP, Banich MT. Resting-state networks predict individual differences in common and specific aspects of executive function. Neuroimage. 2015 Jan 1;104:69-78. doi: 10.1016/j.neuroimage.2014.09.045. Epub 2014 Oct 2.'}, {'pmid': '26709390', 'type': 'BACKGROUND', 'citation': 'Sikora M, Heffernan J, Avery ET, Mickey BJ, Zubieta JK, Pecina M. Salience Network Functional Connectivity Predicts Placebo Effects in Major Depression. Biol Psychiatry Cogn Neurosci Neuroimaging. 2016 Jan;1(1):68-76. doi: 10.1016/j.bpsc.2015.10.002.'}, {'pmid': '27788130', 'type': 'BACKGROUND', 'citation': 'Tetreault P, Mansour A, Vachon-Presseau E, Schnitzer TJ, Apkarian AV, Baliki MN. Brain Connectivity Predicts Placebo Response across Chronic Pain Clinical Trials. PLoS Biol. 2016 Oct 27;14(10):e1002570. doi: 10.1371/journal.pbio.1002570. eCollection 2016 Oct.'}, {'pmid': '23294010', 'type': 'BACKGROUND', 'citation': 'Vaidya CJ, Gordon EM. Phenotypic variability in resting-state functional connectivity: current status. Brain Connect. 2013;3(2):99-120. doi: 10.1089/brain.2012.0110.'}, {'pmid': '18799601', 'type': 'BACKGROUND', 'citation': 'Vincent JL, Kahn I, Snyder AZ, Raichle ME, Buckner RL. Evidence for a frontoparietal control system revealed by intrinsic functional connectivity. J Neurophysiol. 2008 Dec;100(6):3328-42. doi: 10.1152/jn.90355.2008. Epub 2008 Sep 17.'}, {'pmid': '22642651', 'type': 'BACKGROUND', 'citation': 'Whitfield-Gabrieli S, Nieto-Castanon A. Conn: a functional connectivity toolbox for correlated and anticorrelated brain networks. Brain Connect. 2012;2(3):125-41. doi: 10.1089/brain.2012.0073. Epub 2012 Jul 19.'}], 'seeAlsoLinks': [{'url': 'https://clinicaltrials.gov/study/NCT07031804?term=szabo&rank=4', 'label': 'Szabo et al., in preperation; Expectation Effects on Emotional Processing ClinicalTrials.gov ID NCT07031804'}]}, 'descriptionModule': {'briefSummary': 'Expectation effects on affective states are central to mechanisms relevant for mood disorders and their treatment. This study will test whether inter-individual differences in resting-state functional connectivity (rsFC) within emotion regulation networks is associated with affective placebo responsiveness in healthy volunteers. Approximately 50 healthy participants will be pooled from two cross-over placebo studies in which participants receive intranasal saline either labeled as "oxytocin" in the placebo condition or as saline in the control condition.\n\nPrimary indices of placebo responsiveness are placebo-control difference scores of mood state, emotional paradigm responses, as well as expectation and experience ratings.\n\nrsFC will be estimated using the CONN toolbox. Confirmatory analyses will use an a priori seed-based approach, with prefrontal seeds derived from previous task-based fMRI findings on affective placebo effects. These analyses will be complemented by secondary summaries of within- and between-network connectivity across the saliency network, the fronto-parietal control network, and the default mode network.\n\nWe hypothesize that interindividual differences in target rsFC networks will be associated with affective placebo effects, indicating general, trait-like mechanisms of placebo responsiveness.', 'detailedDescription': "Background Understanding the neurobiological mechanisms underlying expectation effects in the affective domain can provide important insights into therapeutic principles relevant to mood disorders.\n\nPlacebo and expectancy effects are substantial in mood-related interventions and can be elicited experimentally in healthy samples using standardized, instruction-based expectation inductions combined with sham treatment. In related experimental paradigms based on the same broader framework, experimentally induced positive expectations have been shown to enhance subjective mood and bias emotional information processing toward positivity even in the absence of an active pharmacological agent.\n\nRecent work further emphasizes marked interindividual variability in these effects, suggesting that affective placebo effects are stronger when more cognitive resources are available and are accompanied by lateral prefrontal engagement together with stronger coupling in prefrontal-limbic networks.\n\nThis pattern motivates an individual-differences approach in which expectancy effects depend, at least in part, on the capacity to initiate and maintain instructed treatment beliefs and to translate them into downstream affective processing. Resting-state fMRI (rsfMRI) offers a well-established way to quantify intrinsic functional coupling among large-scale brain systems. Resting-state functional connectivity (rsFC) is commonly used to characterize individual-specific and partly trait-like components of functional network organization, while also encompassing state-dependent variance and measurement constraints.\n\nSuch individual differences in rsFC have been linked to variability in executive functioning and broader phenotypic differences across healthy and clinical populations.\n\nIn the context of placebo mechanisms, rsFC involving frontoparietal control circuitry has been associated with individual differences in cue- and expectancy-related modulation (e.g., cognitive modulation of pain). In clinical settings, baseline connectivity patterns can prospectively predict placebo response magnitude in chronic pain trials and placebo-related symptom change in depression, supporting the premise that intrinsic network organization may shape responsiveness to expectancy manipulations.\n\nExtending these baseline-predictor accounts, recent work in antidepressant placebo models highlights salience network-default mode network (SN-DMN) coupling as a candidate mechanism linking learned expectancies to both acute mood responses and longer-term belief-related symptom trajectories.\n\nIn the present study, rsFC will be used to test whether interindividual differences in the intrinsic functional architecture of prefrontal-downstream affective circuitry are associated with affective placebo effects.\n\nIn addition, canonical large-scale networks will be examined to characterize intrinsic functional architecture relevant to salience detection, cognitive control, and self-referential or internally oriented processing.\n\nAll participants included in this study have completed one of the preceding fMRI placebo studies and agreed to attend a separate MRI session including structural and rsfMRI measurements.\n\nObjectives and hypotheses This rsfMRI study will investigate whether interindividual differences in intrinsic functional coupling are related to primary markers of affective placebo responses.\n\nThe lateral PFC will serve as the seed region due to its proposed role in maintaining instructed beliefs and translating treatment expectations into limbic valuation networks.\n\nCanonical large-scale network summaries (SN, FPCN, DMN) will additionally be used as complementary, systems-level descriptors of the broader organizational context in which these targeted pathways are embedded. The following hypotheses are proposed:\n\n* Interindividual differences in rsFC within prefrontal-emotion-related circuitry are associated with placebo changes in mood.\n* Interindividual differences in rsFC within prefrontal-emotion-related circuitry are associated with placebo changes in task-derived emotional processing.\n* Interindividual differences in rsFC within prefrontal-emotion-related circuitry are associated with placebo changes in expectancy and experienced benefit.\n* Interindividual differences in within-network and between-network rsFC across canonical large-scale networks implicated in cognitive control, salience processing, and internally oriented or self-referential processing are associated with primary outcomes of placebo treatment.\n\nMethods Ethics information The study was approved by the Ethics Committee of the Hamburg Medical Association (PV 7141) and will be conducted in accordance with the Declaration of Helsinki, with written informed consent, financial compensation, and authorized deception as part of the expectancy manipulation.\n\nMRI acquisition parameters MRI data will be acquired on a Siemens Prisma\\_fit 3T scanner using a 64-channel head/neck coil. Resting-state fMRI will be acquired with a multiband T2\\*-weighted gradient-echo EPI sequence (TR = 1.33 s, TE = 30 ms, flip angle = 60 degrees, multiband factor = 2, 44 slices, matrix = 76 x 76, voxel size = 3 x 3 x 3 mm). Structural images will be acquired using a high-resolution T1-weighted 3D MPRAGE sequence (TR = 2.53 s, TE = 2.34 ms, TI = 1.10 s, flip angle = 7 degrees, voxel size = 1 mm isotropic, matrix = 256 x 256) for anatomical localization, coregistration, and normalization.\n\nDuring resting-state acquisition, participants will be instructed to keep their eyes open, fixate on a central cross, remain still, and not perform any explicit task.\n\nSampling plan The pooled sample is expected to comprise approximately 50 eligible volunteers across the two matched cross-over datasets. An a priori G\\*Power analysis showed that N = 44 provides approximately 80% power to detect medium effect sizes (r = .40) at alpha = .05.\n\nAnalysis plan Resting-state fMRI data will be processed in CONN (RRID: SCR\\_009550; v25.b, 22 Feb 2026) in combination with SPM12 (RRID: SCR\\_007037; revision r7771). Structural images will be segmented and normalized using SPM's unified segmentation, and functional images will undergo standard preprocessing, including realignment, slice-timing correction, coregistration, normalization to MNI space, and 8 mm FWHM smoothing. Denoising will use CompCor with white matter and cerebrospinal fluid components, motion regressors and their derivatives, ART-based scrubbing regressors, and band-pass filtering (0.008-0.09 Hz).\n\nGeneral linear models will be used to examine associations between placebo-related outcomes and interindividual differences in resting-state functional connectivity, behavioral indices, and standardized neural measures. Continuous variables will be mean-centered or z-standardized as appropriate, and standardized effect estimates with 95% confidence intervals will be reported.\n\nResting-state functional connectivity will be quantified as Fisher r-to-z-transformed correlations between BOLD time series and examined using seed-to-voxel and ROI-to-ROI approaches based on prespecified seed and ROI definitions.\n\nSeed-to-voxel maps will be tested using cluster-based inference with a voxel-level threshold of p \\< .001, uncorrected, and cluster-level FWE correction at p \\< .05. ROI-to-ROI analyses will assess connectivity between prespecified prefrontal seeds and downstream mesolimbic regions of interest, with significance evaluated using small-volume-corrected FWE control at p \\< .05.\n\nComplementary systems-level analyses will derive within-network and between-network connectivity summaries for the salience, frontoparietal control, and default mode networks from pairwise Fisher z-transformed edges.\n\nThe study is part of the collaborative research center (CRC) SFB/TRR289 and is funded by the Deutsche Forschungsgemeinschaft (DFG, ID: 422744262)."}, 'eligibilityModule': {'sex': 'ALL', 'stdAges': ['ADULT'], 'maximumAge': '35 Years', 'minimumAge': '18 Years', 'samplingMethod': 'NON_PROBABILITY_SAMPLE', 'studyPopulation': 'This rs-fMRI study will include healthy volunteers who completed an expectation-manipulation study and agreed to an additional rs-fMRI session.\n\nThe sample will be drawn from the ongoing ClinicalTrials.gov-registered study "Expectation Effects on Emotional Processing" (NCT07031804) (Szabo et al., in prep) and a previously collected cohort reported by Mostauli et al. (2025).\n\nIn both datasets, participants complete a counterbalanced cross-over fMRI design with sessions approximately one week apart, receiving intranasal saline labeled as "oxytocin" or "saline". In the scanner, participants perform an emotion classification task (Szabo et al., in prep.) or a spatial cueing task with emotional distractors (Mostauli et al., 2025). After finishing the study, participants are invited to an additional MRI session including resting-state fMRI and a T1-weighted scan.', 'healthyVolunteers': True, 'eligibilityCriteria': 'Inclusion Criteria:\n\n* Aged 18-35 years\n* MRI compatible\n* Medical information and signed declaration of consent\n* Normal or corrected to normal vision\n* German speaking\n\nExclusion Criteria:\n\n* No informed consent\n* Current intake of central nervous system active drugs\n* Under influence of alcohol\n* BDI score above 12\n* Significant acute somatic or neurological diseases\n* History of psychiatric or neurological disorders\n* Pregnancy/ breastfeeding\n* Acute nasal diseases or injuries\n* MR-specific exclusion criteria (claustrophobia, pacemaker, non-MR compatible metallic objects)\n* fMRI data with strong artefacts or excessive movement will be excluded from analysis'}, 'identificationModule': {'nctId': 'NCT07479966', 'briefTitle': 'Resting-state Connectivity and Individual Differences in Affective Placebo Responsiveness', 'organization': {'class': 'OTHER', 'fullName': 'Universitätsklinikum Hamburg-Eppendorf'}, 'officialTitle': 'Resting-state Connectivity and Individual Differences in Affective Placebo Responsiveness', 'orgStudyIdInfo': {'id': 'Z03-ReCAP'}, 'secondaryIdInfos': [{'id': 'Project Number 422744262 (Z03)', 'type': 'OTHER_GRANT', 'domain': 'Deutsche Forschungsgemeinschaft (DFG)'}]}, 'armsInterventionsModule': {'armGroups': [{'label': 'Healthy volunteers', 'description': 'This rs-fMRI study will include healthy volunteers who completed an expectation-manipulation study and agreed to an additional rs-fMRI session.\n\nThe sample will be drawn from the ongoing ClinicalTrials.gov-registered study "Expectation Effects on Emotional Processing" (NCT07031804) (Szabo et al., in prep) and a previously collected cohort reported by Mostauli et al. (2025).', 'interventionNames': ['Other: No intervention']}], 'interventions': [{'name': 'No intervention', 'type': 'OTHER', 'description': 'No intervention', 'armGroupLabels': ['Healthy volunteers']}]}, 'contactsLocationsModule': {'locations': [{'zip': '20246', 'city': 'Hamburg', 'state': 'Hamburg', 'country': 'Germany', 'facility': 'Department of Systems Neuroscience, University Medical Center Hamburg-Eppendorf', 'geoPoint': {'lat': 53.55073, 'lon': 9.99302}}]}, 'ipdSharingStatementModule': {'infoTypes': ['STUDY_PROTOCOL', 'SAP', 'ANALYTIC_CODE'], 'ipdSharing': 'YES', 'description': 'With Publication'}, 'sponsorCollaboratorsModule': {'leadSponsor': {'name': 'Stefanie Brassen', 'class': 'OTHER'}, 'responsibleParty': {'type': 'SPONSOR_INVESTIGATOR', 'investigatorTitle': 'Prof. Dr.', 'investigatorFullName': 'Stefanie Brassen', 'investigatorAffiliation': 'Universitätsklinikum Hamburg-Eppendorf'}}}}