fMRI as an outcome measure in clinical trials: A systematic review in clinicaltrials.gov

Abstract

Background: Functional magnetic resonance imaging (fMRI) is quickly becoming a significant outcome measure for clinical trials and as more than one thousand trials with fMRI as an outcome measure were registered in clinicaltrials.gov at the time of writing this article. However, 93% of these registered trials are still not completed with published results and there is no picture available about methodological dimensions of these ongoing trials with fMRI as an outcome measure. Methods: We collected trials that use fMRI as an outcome measure by searching "fMRI" in the ClinicalTrials.gov registry on October 13 2018 and reviewing each trial's record entry. Eligible trials' characteristics were extracted and summarized. Results: In total, 1386 clinical trials were identified that reported fMRI in their outcome measures with fMRI as the only primary outcome in 33% of them. 82% of fMRI trials were started after 2011. The most frequent intervention was drug (29%). 57% of trials had parallel assignment design and 20% were designed for cross over assignment. For task-based fMRI, cognitive systems (46%) based on RDoC was the most frequent domain of tasks, followed by positive valence systems (19%), systems for social processing (10%), and sensorimotor systems (5%). Less than one-third of trials (28%) registered at least one region of interest for their analysis. Food cue reactivity task, pain perception task, n-back task, and monetary incentive delay task were recruited in more than 25 registered trials. Conclusion: The number of fMRI trials (fMRI as an outcome measure) with both task and rest protocols is growing rapidly. Different RDoC domains are covered by various tasks in fMRI trials. However, our study suggests the need of greater harmony and better standardization for registration of fMRI details on both methods and analysis which would allow for more effective comparison across studies in systematic reviews and also help the validation of results towards having fMRI as a biomarker in the future.

Executive control network resting state fMRI functional and effective connectivity and delay discounting in cocaine dependent subjects compared to healthy controls

“…Subsequent further signal denoising was performed using aCompCor for CSF and white matter implemented in the CONN software [(43), RRID:SCR_009550]. 3 High pass filtering (cutoff period = 125 s) was performed as a final step after all other denoising steps had been completed. Furthermore, group ICA signal components with possible motion-related or other structured noise were regressed out as part of the FSL dual regression step [(37), pp.…”

“…One tool available for studying the brain of CD is resting state functional magnetic resonance imaging (fMRI). Resting state fMRI functional connectivity measures the statistical association of the fluctuations in the blood oxygen level dependent (BOLD) signal between distinct brain regions (2,3), allowing for the analysis of functional brain networks. However, functional connectivity does not inform the directional influence of one region upon another region (i.e., whether region A influences region B, whether B influences A, or both A and B influence each other) (4).…”

It is Never as Good the Second Time Around: Brain Areas Involved in Salience Processing Habituate During Repeated Drug Cue Exposure in Methamphetamine and Opioid Users

“…As of 2019, there are over 300 original published studies with FDCR. These studies reported positive findings that FDCR in certain brain areas is associated with severity of the drug addiction (Sjoerds, van den Brink, Beekman, Penninx, & Veltman, 2014;Smolka et al, 2006), defines prognosis of long-term recovery (Janes et al, 2010;Kosten et al, 2006), predicts the response to specific interventions (Courtney, Schacht, Hutchison, Roche, & Ray, 2016;Mann et al, 2014) and can be used as a proxy measure for effectiveness of different interventions in clinical trials (Lukas et al, 2013;Sadraee, Paulus, & Ekhtiari, 2019).…”

Brain Imaging Biomarkers for Chronic Pain

“…However, some evidence suggests that QST can predict response to chronic pain treatments ( 209 ). Compared to other biological markers of clinical pain such as genetic profiling, MRI based biomarkers have seen a faster progress ( 5 , 174 , 210 , 211 ) and are closer to adoption in clinical trials ( 145 , 191 , 198 , 212 ). As such progress in identifying reproducible diagnostic or prognostic genetic polymorphism for chronic pain has been limited so far to rare causes of chronic pain such as gain of function mutations of the sodium channel causing inherited erythromelalgia ( 213 ).…”