Alternative Treatment to Reduce Chronicity in OCD: Research Into Brain Response and Adequacy of Treatment

NCT ID: NCT03929081

Last Updated: 2023-10-24

Basic Information

• Recruitment Status: COMPLETED
• Clinical Phase: NA
• Total Enrollment: 246 participants
• Study Classification: INTERVENTIONAL
• Study Start Date: 2019-03-20
• Study Completion Date: 2023-10-17

Brief Summary

Rationale: Obsessive-Compulsive Disorder (OCD) is a disabling neuropsychiatric disorder that often has a chronic disease course. The standard psychotherapeutic treatment Cognitive Behavioural Therapy (CBT) is unable to redeem about half of all patients and is rejected by many because of its anxiety provoking methods. A promising alternative is the Interference Based Approach (IBA), which appears to be as effective as CBT, and more effective for patients with poor insight. The current study will investigate the proposed IBA non-inferiority to CBT. Furthermore, the neurobiological working mechanisms of both treatments will be investigated. Both treatment modalities are expected to alter activity and connectivity in different functional brain networks. In order to lead the way towards personalized care for OCD patients, clinical and neurobiological predictors of response to treatment will be studied. The eventual aim of this study is to prevent the demoralizing effect of undergoing an ineffective treatment by future prediction of whether an individual patient will respond better to IBA or CBT. This also contributes to solving the costs and waiting times for CBT.

Objective: To investigate non-inferiority of IBA compared to CBT and to unravel the neurobiological working mechanisms of both treatment modalities.

Study design: Multicentre randomized controlled trial.

Study population: 203 adults with a primary diagnosis of OCD and 43 healthy controls, matched on gender, age and educational level.

Intervention: The 203 adults with the primary diagnosis of OCD will be divided into the experimental- (IBA) and control intervention (CBT). Healthy controls will not receive an intervention.

Main study parameters/endpoints: Clinical measures (e.g. severity of OCD symptoms, disease insight), neurocognitive capabilities (performance on neuropsychological tests), neural correlates on brain structure (i.e. white matter integrity, grey matter volume) and brain function (i.e., activation and connectivity during resting state and symptom provocation) using 3 Tesla magnetic resonance imaging.

Detailed Description

Sample size calculation: for the primary study objective, the 95-95 approach to determine the non-inferiority margin was used. Results of a meta-analysis on the effectiveness of CBT for OCD indicated that the mean (95 percent Confidence Interval (1.04) generates a non-inferiority margin of .42. Expressed in scores on the primary outcome measure Yale Brown Obsessive Compulsive Scale (YBOCS), this would be a loss of about 2 points on this scale with a range of 0-40, which is clinically almost negligible. Applying an alpha of 0.05, a beta of 80%, and a .42 non-inferiority margin, a sample size of 71 per arm is adequate. Since analyses have to be run on the per protocol (P)- and the intention-to-treat (ITT) sample, 203 participants have to be recruited (anticipating 30 percent drop-out). Moreover, this sample size allows testing the secondary hypothesis: IBA is more effective than CBT for patients with OCD with poor insight (approximately 25 percent of the population). Applying a two-sided alpha of 0.05 and a beta of 80 percent and effect size of Cohen's d =1, a sample of 34 participants is needed (in a previous study a between groups effect of 1.7 was found), anticipating 30 percent drop-out, 48 participants with poor insight at baseline are needed.

Previous studies into the influence of psychotherapy for OCD on brain structure and functioning have used sample sizes of 9-45. Based on their findings, analysing data for a minimum of 30 participants per condition is planned to explore the working mechanisms of the treatment modalities. Anticipating an upper limit of 30 percent drop-out rate, 2 x 43 respondents will be included, as well as 43 control subjects.

Adherence to treatment protocol: the therapists will be extensively trained in adherence to the protocol and the use of the premade forms for exercises and homework assignments. All treatment sessions will be audiotaped. Each therapist will receive supervision. During supervision, the supervisor will listen to parts of the audiotapes to give feedback and to make sure that the therapist does not drift from the protocol.

Furthermore, after the study is completed, there will be an adherence check. A trained blinded assessor will sample-wise listen to audiotapes of the session to score whether it was an IBA of CBT session.

Nature and extent of the burden and risks associated with participation, benefit and group relatedness: patients will be assessed during five time points, including two follow ups. Further, patients will fill in two questionnaires after each treatment session. Participants of the additional imaging part will be subjected to two extra sessions for MRI scanning and cognitive testing. Healthy controls will be measured once.

Safety Committee: the current study has been classified as low risk. No risk is expected of administering IBA, compared to receiving standard clinical care (CBT). IBA treatment will be conducted by well-trained clinicians. Some psychological discomfort may occur with respect to discussing emotional symptoms, these will be constraint to the minimum. No extra discomfort regarding to standard clinical care is expected. MRI scanning, after extensive screening for eligibility, is classified as a non-significant to low risk.

Statistical analyses: statistical analyses will be performed on the intention-to-treat (ITT) as well as the per-protocol sample (PP). Missing data will be replaced using the multiple imputation method. For all analyses, corrected p-values of <0.05 will be regarded as statistically significant. Safety data (e.g. side effects) will be collected and summarized in the study results. A description of the analyses for the primary and secondary objectives is provided below.

Primary study parameter(s): the YBOCS, a validated questionnaire with 10 items and a maximum score of 40, will be used as the primary outcome parameter. To conclude non-inferiority, the upper margin of the 95 percent confidence interval, i.e. the maximum difference between groups within this 95 percent confidence interval, should not exceed the non-inferiority margin of 2 points on the YBOCS. To determine non-inferiority immediately following treatment and at follow-up, one-sided tests will be conducted at post-treatment, at six month follow-up and one year follow-up assessment. Linear mixed-effects models will be used, regressing the continuous outcome measures on intervention by time interaction terms, both in the ITT- and the PP-sample.

Secondary study parameter(s): mixed-effects models will be used to test the treatment effects between the two treatment conditions for the secondary outcome measures level of insight, using the Overvalued Ideas Scale (OVIS); severity of depressive symptoms, using the Beck Depression Inventory (BDI); severity of anxiety symptoms, using the Beck Anxiety Inventory (BAI); social functioning, using the Sheehan Disability Scale (SDS); quality of life (using the Euroquol); relationship satisfaction, using the Relationship Satisfaction Scale (RSS) and tolerability of treatment, using the Treatment Acceptability/Adherence Scale (TAAS). Exploratively a multiple regression analysis will be used to determine which factors predict treatment outcome.

A linear mixed model will be used to determine whether insight improves more or earlier in patients who underwent IBA compared to those who underwent CBT. A time-lag model (a linear mixed model) will be used to determine whether change in insight is related to symptom reduction.

Neuroimage (pre)processing: fMRI data will be analysed using Statistical parametric mapping (SPM) software or FMRIB Software Library (FSL). Standard group comparisons will be conducted to analyse structural and task-related (symptom provocation) (f)MRI. Full factorial analyses with the factors group (IBA vs CBT) and time (pre- vs posttreatment) will be conducted to determine the effect of treatment on task-related brain activity. Independent component analysis (ICA; FSL MELODIC) and dual regression analyses will be conducted to investigate changes in functional connectivity within networks (e.g. Salience Network, Default Mode Network, CEN). Brain regions with fluctuations concurrent over time in blood-oxygen level dependent (BOLD; proxy for brain activity) will automatically per person be assigned to a component. After filtering and a clean-up of the components (components that seem to result from movement or scanner artefacts), the mean components per group will be determined. By means of non-parametric permutation tests (FSL randomise), functional connectivity of the networks will be compared between the intervention groups and over time. To conduct whole-brain network analyses, the structural MRI will be parcellated into 225 separate regions of interest (ROIs) based on the Brainnetome atlas. These parcellations will be transformed to the resting-state fMRI and the time series will be extracted per ROI and correlated to each other to arrive at a whole-brain network connectivity matrix per subject. Using a similar network approach, the DTI images will allow for tractography in order to measure structural connectivity. The Brain connectivity toolbox (BCT) will be used to calculate topological indices of the networks (e.g. modularity, betweenness centrality, clustering coefficient and efficiency) from these network connectivity matrices. Network topological indices reduce the abundant amount of information resulting from the brain scan to only a few neurobiologically meaningful measures. Global efficiency, for instance, provides a measure of how efficiently information can travel through a network and betweenness centrality of the relative importance of a brain region for the information flow within a network. These topological network measures will be calculated per assessment and condition and subsequently compared by means of permutation tests.

Statistical Neuroimage analysing: to investigate whether treatment modality (IBA/CBT) predicts changes in resting state and task-based functional networks, a multivariate regression analysis will be conducted with treatment modality as independent variable and pre-to posttreatment changes in functional networks as dependent variables. To investigate the association with changes in YBOCS scores and neurocognitive performance, these variables will be entered in the first block. Possible confounders (e.g. medication use, age, disease duration) will be controlled for.

Machine learning techniques will be conducted to investigate whether multimodal pre-treatment brain characteristics (using T-1, DTI and rs- and task-based fMRI) have predictive value for treatment response in the CBT and IBA group separately. The neuroimaging characteristics will be entered into a supervised multivariate classification procedure using a linear support vector machine (SVM) algorithm.

The procedure consists of a training, validation and testing phase. During the training phase, a hyperplane will be estimated that maximally separates the remitters from the non-remitters based on all available data points (features) that show a difference between the groups. The SVM validation phase will consist of a leave-one-per-group-out cross-validation. Then the accuracy will be tested with which the determined hyperplane could classify other patients during the classification stage with independent data (20%) not used for training. This procedure will be iterated 10.000 times for each network. This results in an accuracy measure, per subject, based on the number of times the subject was included in the test sample and correctly classified. The accuracy will be tested on significance. If predictors of response to IBA differ from those to CBT, a post hoc analysis will be carried out with predictors for both IBA and CBT response within the entire OCD group.

Conditions

Obsessive-Compulsive Disorder

Study Design

• Allocation Method: RANDOMIZED
• Intervention Model: PARALLEL
• Primary Study Purpose: TREATMENT
• Blinding Strategy: SINGLE

Study Groups

Inference Based Approach (IBA)

The IBA treatment, a focused form of psychotherapy consists of twenty 45-minutes sessions, delivered weekly. The IBA model is based on the assumption that patients with OCD feel the need to perform compulsive acts because they misjudge the actual state of affairs, for example fearing that an appliance is on when it is visibly off. It is assumed that certain reasoning processes lead to these erroneous conclusions and distract the patient's attention from observable reality. IBA teaches patients how to defend themselves against the absorbing and confusing effect of obsessive reasoning processes and how to stay in touch with reality by actively relying on the sensory information of the very moment. As a consequence, the patient realizes that any compulsive act is superfluous and feels able to omit it.

Group Type: EXPERIMENTAL

Inference Based Approach (IBA)

Intervention Type: BEHAVIORAL

The Inference Based Approach aims at strengthening reality testing in patients with Obsessive-Compulsive Disorder, by teaching the patient to actively rely on sensory information.

Cognitive Behavior Therapy (CBT)

In the control condition, the patients will receive twenty 45-minutes sessions of CBT consisting of self-guided exposure in vivo with response prevention (ERP) and cognitive therapy (CT), both standardized according to evidence-based session-by-session protocols, containing standardized forms for exercises and homework assignments.

Group Type: ACTIVE_COMPARATOR

Cognitive Behavioural Therapy (CBT)

Intervention Type: BEHAVIORAL

CBT teaches the patient with Obsessive-Compulsive Disorder to refrain from compulsive acts.

No intervention (healthy controls)

The healthy control group will receive no intervention.

Group Type: NO_INTERVENTION

No interventions assigned to this group

Interventions

Inference Based Approach (IBA)

The Inference Based Approach aims at strengthening reality testing in patients with Obsessive-Compulsive Disorder, by teaching the patient to actively rely on sensory information.

Intervention Type: BEHAVIORAL

Cognitive Behavioural Therapy (CBT)

CBT teaches the patient with Obsessive-Compulsive Disorder to refrain from compulsive acts.

Intervention Type: BEHAVIORAL

Other Intervention Names

Psychotherapy; Psychotherapy

Eligibility Criteria

Inclusion Criteria

Participants

• Referred to one of the participating sites for OCD treatment
• Age 18 or above
• Primary Diagnostic Statistical Manual (DSM)-5 diagnosis of OCD (established by the Structured Clinical Interview for DSM-5 (SCID)
• Moderate to severe OCD symptoms (expressed as a minimum score of 16 on the Yale Brown Obsessive Compulsive Scale (YBOCS)
• Not currently using psychotropic medication, or on a stable dose for at least 12 weeks prior to randomisation with no plans to change the dose during the course of the study (this will be monitored during the study)
• If CBT already has been received for OCD, treatment has ended at least 26 weeks before study participation.

Controls

• Age 18 or above

Exclusion Criteria

Patients

• Suffering from a current psychotic disorder, organic mental disorder, substance use disorder or mental retardation
• No sufficient command of the Dutch language

• Use of psychotropic medication other than Selective Serotonin Reuptake Inhibitor/Selective Norepinephrine Reuptake Inhibitor/clomipramine (e.g. antipsychotics). Occasional (not daily, a maximal equivalent of 10 mg. diazepam at a time) use of benzodiazepines/sleeping medication is allowed, if the participant is willing to tolerate to refrain from use for at least a week before the MRI scanning session, and able to tolerate this period of refrainment.
• Pregnancy
• Iron in the body
• Claustrophobia
• Any known neurological diseases (including epilepsy) or brain surgery
• Head trauma that resulted in unconsciousness for at least 1 hour
• Age 65 or above
• Controls
• Age 65 or above
• Current DSM-5 diagnosis (established by the SCID)
• Personal history of DSM-5 diagnosis, except for depressive or anxiety disorder longer than 12 months ago
• Personal history or current use of psychotropic medication (excluding sporadic use of sedatives/benzodiazepines, not having been used the week prior to participation
• First-degree relative (parent/sibling/child) with OCD or tic-disorder
• Insufficient command of the Dutch language
• Pregnancy
• Iron in the body
• Claustrophobia
• Any known neurological diseases (including epilepsy), or past brain surgery
• Head trauma that resulted in unconsciousness for at least 1 hour

• Minimum Eligible Age: 18 Years
• Maximum Eligible Age: 85 Years
• Eligible Sex: ALL
• Accepts Healthy Volunteers: Yes

Sponsors

Amsterdam UMC, location VUmc

OTHER

Sponsor Role: collaborator

GGZ inGeest

OTHER

Sponsor Role: collaborator

PsyQ

OTHER

Sponsor Role: collaborator

Radboud University Medical Center

OTHER

Sponsor Role: collaborator

GGZ Centraal

OTHER

Sponsor Role: lead

Responsible Party

Responsibility Role: SPONSOR

Principal Investigators

Henny Visser, PhD

Role: PRINCIPAL_INVESTIGATOR

GGZ Centraal

Locations

GGz Drenthe

Assen, Drenthe, Netherlands

GGz Centraal

Ermelo, Gelderland, Netherlands

Pro Persona

Nijmegen, Gelderland, Netherlands

Mondriaan

Maastricht, Limburg, Netherlands

GGz InGeest

Amsterdam, North Holland, Netherlands

Amsterdam UMC, VUmc

Amsterdam, North Holland, Netherlands

PsyQ Amsterdam

Amsterdam, North Holland, Netherlands

Countries

Netherlands

References

Abramowitz JS. The psychological treatment of obsessive-compulsive disorder. Can J Psychiatry. 2006 Jun;51(7):407-16. doi: 10.1177/070674370605100702.

Reference Type: BACKGROUND

PMID: 16838822 (View on PubMed)

Abramowitz JS, Taylor S, McKay D. Potentials and limitations of cognitive treatments for obsessive-compulsive disorder. Cogn Behav Ther. 2005;34(3):140-7. doi: 10.1080/16506070510041202.

Reference Type: BACKGROUND

PMID: 16195053 (View on PubMed)

Adler CM, McDonough-Ryan P, Sax KW, Holland SK, Arndt S, Strakowski SM. fMRI of neuronal activation with symptom provocation in unmedicated patients with obsessive compulsive disorder. J Psychiatr Res. 2000 Jul-Oct;34(4-5):317-24. doi: 10.1016/s0022-3956(00)00022-4.

Reference Type: BACKGROUND

PMID: 11104844 (View on PubMed)

Andrews-Hanna JR, Smallwood J, Spreng RN. The default network and self-generated thought: component processes, dynamic control, and clinical relevance. Ann N Y Acad Sci. 2014 May;1316(1):29-52. doi: 10.1111/nyas.12360. Epub 2014 Feb 6.

Reference Type: BACKGROUND

PMID: 24502540 (View on PubMed)

Banks SJ, Eddy KT, Angstadt M, Nathan PJ, Phan KL. Amygdala-frontal connectivity during emotion regulation. Soc Cogn Affect Neurosci. 2007 Dec;2(4):303-12. doi: 10.1093/scan/nsm029.

Reference Type: BACKGROUND

PMID: 18985136 (View on PubMed)

Bari A, Robbins TW. Inhibition and impulsivity: behavioral and neural basis of response control. Prog Neurobiol. 2013 Sep;108:44-79. doi: 10.1016/j.pneurobio.2013.06.005. Epub 2013 Jul 13.

Reference Type: BACKGROUND

PMID: 23856628 (View on PubMed)

Boedhoe PS, Schmaal L, Abe Y, Ameis SH, Arnold PD, Batistuzzo MC, Benedetti F, Beucke JC, Bollettini I, Bose A, Brem S, Calvo A, Cheng Y, Cho KI, Dallaspezia S, Denys D, Fitzgerald KD, Fouche JP, Gimenez M, Gruner P, Hanna GL, Hibar DP, Hoexter MQ, Hu H, Huyser C, Ikari K, Jahanshad N, Kathmann N, Kaufmann C, Koch K, Kwon JS, Lazaro L, Liu Y, Lochner C, Marsh R, Martinez-Zalacain I, Mataix-Cols D, Menchon JM, Minuzzi L, Nakamae T, Nakao T, Narayanaswamy JC, Piras F, Piras F, Pittenger C, Reddy YC, Sato JR, Simpson HB, Soreni N, Soriano-Mas C, Spalletta G, Stevens MC, Szeszko PR, Tolin DF, Venkatasubramanian G, Walitza S, Wang Z, van Wingen GA, Xu J, Xu X, Yun JY, Zhao Q; ENIGMA OCD Working Group; Thompson PM, Stein DJ, van den Heuvel OA. Distinct Subcortical Volume Alterations in Pediatric and Adult OCD: A Worldwide Meta- and Mega-Analysis. Am J Psychiatry. 2017 Jan 1;174(1):60-69. doi: 10.1176/appi.ajp.2016.16020201. Epub 2016 Sep 9.

Reference Type: BACKGROUND

PMID: 27609241 (View on PubMed)

Eisen JL, Rasmussen SA, Phillips KA, Price LH, Davidson J, Lydiard RB, Ninan P, Piggott T. Insight and treatment outcome in obsessive-compulsive disorder. Compr Psychiatry. 2001 Nov-Dec;42(6):494-7. doi: 10.1053/comp.2001.27898.

Reference Type: BACKGROUND

PMID: 11704942 (View on PubMed)

O'Connor KP, Aardema F, Bouthillier D, Fournier S, Guay S, Robillard S, Pelissier MC, Landry P, Todorov C, Tremblay M, Pitre D. Evaluation of an inference-based approach to treating obsessive-compulsive disorder. Cogn Behav Ther. 2005;34(3):148-63. doi: 10.1080/16506070510041211.

Reference Type: BACKGROUND

PMID: 16195054 (View on PubMed)

van Oppen P, van Balkom AJ, de Haan E, van Dyck R. Cognitive therapy and exposure in vivo alone and in combination with fluvoxamine in obsessive-compulsive disorder: a 5-year follow-up. J Clin Psychiatry. 2005 Nov;66(11):1415-22. doi: 10.4088/jcp.v66n1111.

Reference Type: BACKGROUND

PMID: 16420079 (View on PubMed)

Visser HA, van Oppen P, van Megen HJ, Eikelenboom M, van Balkom AJ. Obsessive-compulsive disorder; chronic versus non-chronic symptoms. J Affect Disord. 2014 Jan;152-154:169-74. doi: 10.1016/j.jad.2013.09.004. Epub 2013 Sep 13.

Reference Type: BACKGROUND

PMID: 24084621 (View on PubMed)

Schumi J, Wittes JT. Through the looking glass: understanding non-inferiority. Trials. 2011 May 3;12:106. doi: 10.1186/1745-6215-12-106.

Reference Type: BACKGROUND

PMID: 21539749 (View on PubMed)

Olatunji BO, Davis ML, Powers MB, Smits JA. Cognitive-behavioral therapy for obsessive-compulsive disorder: a meta-analysis of treatment outcome and moderators. J Psychiatr Res. 2013 Jan;47(1):33-41. doi: 10.1016/j.jpsychires.2012.08.020. Epub 2012 Sep 20.

Reference Type: BACKGROUND

PMID: 22999486 (View on PubMed)

Visser HA, van Megen H, van Oppen P, Eikelenboom M, Hoogendorn AW, Kaarsemaker M, van Balkom AJ. Inference-Based Approach versus Cognitive Behavioral Therapy in the Treatment of Obsessive-Compulsive Disorder with Poor Insight: A 24-Session Randomized Controlled Trial. Psychother Psychosom. 2015;84(5):284-93. doi: 10.1159/000382131. Epub 2015 Aug 6.

Reference Type: BACKGROUND

PMID: 26278470 (View on PubMed)

Thorsen AL, van den Heuvel OA, Hansen B, Kvale G. Neuroimaging of psychotherapy for obsessive-compulsive disorder: A systematic review. Psychiatry Res. 2015 Sep 30;233(3):306-13. doi: 10.1016/j.pscychresns.2015.05.004. Epub 2015 May 16.

Reference Type: BACKGROUND

PMID: 26228566 (View on PubMed)

Goodman WK, Price LH, Rasmussen SA, Mazure C, Fleischmann RL, Hill CL, Heninger GR, Charney DS. The Yale-Brown Obsessive Compulsive Scale. I. Development, use, and reliability. Arch Gen Psychiatry. 1989 Nov;46(11):1006-11. doi: 10.1001/archpsyc.1989.01810110048007.

Reference Type: BACKGROUND

PMID: 2684084 (View on PubMed)

Goodman WK, Price LH, Rasmussen SA, Mazure C, Delgado P, Heninger GR, Charney DS. The Yale-Brown Obsessive Compulsive Scale. II. Validity. Arch Gen Psychiatry. 1989 Nov;46(11):1012-6. doi: 10.1001/archpsyc.1989.01810110054008.

Reference Type: BACKGROUND

PMID: 2510699 (View on PubMed)

Rubinov M, Sporns O. Complex network measures of brain connectivity: uses and interpretations. Neuroimage. 2010 Sep;52(3):1059-69. doi: 10.1016/j.neuroimage.2009.10.003. Epub 2009 Oct 9.

Reference Type: BACKGROUND

PMID: 19819337 (View on PubMed)

Bullmore E, Sporns O. Complex brain networks: graph theoretical analysis of structural and functional systems. Nat Rev Neurosci. 2009 Mar;10(3):186-98. doi: 10.1038/nrn2575. Epub 2009 Feb 4.

Reference Type: BACKGROUND

PMID: 19190637 (View on PubMed)

Other Identifiers

NL66299.029.18

Identifier Type: -

Identifier Source: org_study_id