No registrations found.
ID
Source
Brief title
Health condition
Obsessive-compulsive disorder (OCD)
Obsessieve-compulsieve stoornis (OCS)
Sponsors and support
Intervention
Outcome measures
Primary outcome
Classifier accuracy as the proportion of patients correctly classified as responder (sensitivity) and non-responder (specificity)
Secondary outcome
Differences in the proportion of responders between the randomized (first) and fMRI biomarker allocated (second) cohort
Background summary
Obsessive-compulsive disorder (OCD) is a severely debilitating psychiatric disorder that is characterized by repetitive behaviour such as washing or cleaning, which may take up the entire day. First-line treatment for OCD consists of pharmacological treatment with selective serotonin reuptake inhibitors (SSRIs) or psychological treatment with cognitive-behavioral therapy (CBT) (van Balkom et al. 2013). Both these treatments are effective, but 40-60% of patients do not benefit sufficiently (Pallanti et al. 2002; Eddy et al. 2004). We recently found that machine learning analysis of resting-state functional MRI obtained prior to treatment can reliably predict treatment outcome in depression (van Waarde et al. 2015).
Here, we aim to apply these methods to OCD to develop a treatment selection biomarker that enables the allocation of patients to the treatment with the largest chance of success. In addition, we aim to determine the common and specific neural mechanisms underlying treatment efficacy. The analysis of CBT and SSRI-related changes at the level of brain areas and circuits will provide more perspective on the pathophysiology of OCD and the response to different treatments. Patients in the first cohort are randomized to SSRI or CBT to develop and validate a treatment selection fMRI biomarker for allocating OCD patients and to determine the divergent longitudinal effects on brain measures of treatment in patients with OCD. In the second cohort, patients will be allocated to SSRI or CBT based on fMRI biomarkers identified in the first cohort.
Study objective
We recently found that machine learning analysis of resting-state functional MRI obtained prior to treatment can reliably predict treatment outcome in depression (van Waarde et al. 2015). Here, we aim to apply these methods to OCD to develop a treatment selection biomarker that enables the allocation of patients to the treatment with the largest chance of success. In addition, we aim to determine the common and specific neural mechanisms underlying treatment efficacy.
Study design
Before treatment and 16 weeks after treatment.
Intervention
The subjects will be randomized to pharmacological treatment or cognitive behavioral therapy (CBT).
Meibergdreef 5
Amsterdam 1105 AZ
The Netherlands
+31 20 8913661
a.l.vanderstraten@amc.nl
Meibergdreef 5
Amsterdam 1105 AZ
The Netherlands
+31 20 8913661
a.l.vanderstraten@amc.nl
Inclusion criteria
Patients:
• Diagnosis of obsessive compulsive disorder (OCD) according to the DSM-IV
• 18-70 years of age
• Willingness and ability to give written informed consent and willingness and ability to understand, to participate and to comply with the study requirements
Exclusion criteria
Patients:
• Bipolar disorder, current or past psychosis, primary alcohol or drug abuse assessed by the MINI
• Contraindication for MRI, such as metal implants, claustrophobia, and
pregnancy
• Major head trauma or neurological disease, current or in history
• Adequate treatment of OCD with high dosed SSRI or CBT at the moment of screening or within 4 weeks before screening. Current treatment with tricyclic antidepressant or antipsychotic medication.
Design
Recruitment
Followed up by the following (possibly more current) registration
Other (possibly less up-to-date) registrations in this register
No registrations found.
In other registers
| Register | ID |
|---|---|
| NTR-new | NL6400 |
| NTR-old | NTR6575 |
| CCMO | NL57808.018.16 |
| OMON | NL-OMON45849 |