Alternative Treatment to Reduce Chronicity in OCD: Research Into Brain Response and Adequacy of Treatment
Condition: Obsessive-Compulsive Disorder
MeSH terms: Compulsive Personality Disorder; Obsessive-Compulsive Disorder
Study type: Interventional

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.
    

Intervention name(s): Inference Based Approach (IBA); Cognitive Behavioural Therapy (CBT)
Intervention type(s): Behavioral; Behavioral
Intervention description(s): 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.; CBT teaches the patient with Obsessive-Compulsive Disorder to refrain from compulsive acts.
Other intervention name(s): Psychotherapy; Psychotherapy
Allocation: Randomized
Intervention model: Parallel Assignment
Intervention model description: This is a multicenter, randomized controlled non-inferiority trial. 203 OCD patients will be recruited. During intake for treatment, patients willing to participate, will be invited for a baseline visit, after the Informed Consent process (IC). Then the patient will be randomized to 20 sessions IBA or 20 sessions CBT. Randomization ratio is 1:1, stratified by site. OCD symptom severity will be assessed by a blind assessor at baseline, after 20 sessions, and 6 and 12 months after posttest. Further, OCD symptom severity and insight in OCD symptoms will be assessed (self-report) weekly. To explore working mechanisms, 43 patients of each treatment arm will receive pre- and posttreatment brain imaging and neuropsychological assessment. After randomization, patients are screened for MRI eligibility, until the sufficient amount of participants is reached. A complementary IC will be signed. 43 healthy controls will receive a single brain imaging and neuropsychological testing session.
Masking: Single (Investigator)
Primary purpose: Treatment
Keywords: Obsessive-Compulsive Disorder; Inference Based Approach (IBA); Cognitive Behavioral Therapy (CBT); Personalized care; Neurocognitive working mechanisms of treatment; Non-inferiority; Prediction of treatment efficiency