BOLD fMRI effects of concurrent tDCS at the pre-SMA on inhibition in OCD patients.
Poster No:
1358
Submission Type:
Abstract Submission
Authors:
Daniela Rodriguez Manrique1, Kathrin Koch2
Institutions:
1Technical University Munich, Munich, Bavaria, 2Neuroradiology, Klinikum rechts der Isar der Technischen Universität München, Munich, Bavaria
First Author:
Co-Author:
Kathrin Koch, Prof.
Neuroradiology, Klinikum rechts der Isar der Technischen Universität München
Munich, Bavaria
Neuroradiology, Klinikum rechts der Isar der Technischen Universität München
Munich, Bavaria
Introduction:
OCD Patients have difficulty inhibiting obsessive thoughts and reoccurring compulsive behaviors, two core symptoms underlying the disorder. Hence, studies have investigated brain areas and networks involved in response inhibition in OCD patients. They find the inferior frontal gyrus and the pre-supplementary motor area (pre-SMA) as key regions involved in inhibition (5, 6, 9).
Previously, the pre-SMA has been chose as a target in inhibition performance tDCS studies. Post stimulation OCD studies showed modulated pre-SMA-vmPFC connectivity (7).
tDCS is an attractive method due to its cheap and mobile nature, however its underlying mechanisms are poorly understood and rarely studied in combination with imaging. Studies rarely detail the conditions between stimulation and task results. Performing concurrent fMRI-tDCS allows increased insight into the immediate and postponed blood oxygen dependent (BOLD) changes in brain regions and networks and their second-hand effects (1). Thus, allowing studies to better investigate the neuronal basis for behavioural and symptomatic changes in patients. This study aimed to test whether tDCS at the pre-SMA could improve inhibition performance and related brain connectivity.
Previously, the pre-SMA has been chose as a target in inhibition performance tDCS studies. Post stimulation OCD studies showed modulated pre-SMA-vmPFC connectivity (7).
tDCS is an attractive method due to its cheap and mobile nature, however its underlying mechanisms are poorly understood and rarely studied in combination with imaging. Studies rarely detail the conditions between stimulation and task results. Performing concurrent fMRI-tDCS allows increased insight into the immediate and postponed blood oxygen dependent (BOLD) changes in brain regions and networks and their second-hand effects (1). Thus, allowing studies to better investigate the neuronal basis for behavioural and symptomatic changes in patients. This study aimed to test whether tDCS at the pre-SMA could improve inhibition performance and related brain connectivity.
Methods:
54 OCD patients recruited from four clinics in and around Munich, a double-blinded crossover study, the patients received 20 minutes of tDCS during one appointment and a 30 second sham during another.
Patients were stimulated in the scanner during which they performed the stop-signal task and the Stroop task (each 10min). Electrodes were placed on the FC1 and FC2 10-20 EEG positions. 46 Patients with OCD were included in the analysis upon excluding participants with outlying task performance.
The imaging data recorded during the Stroop task were analysed using McIntosh's event-related approach with partial least squares (2). This multivariate analysis technique identifies whole-brain patterns of covariance related to conditions of an experiment. Each brain voxel has a weight, referred to as salience, which specifies how strongly the voxel contributes to the covariance explained by that latent variable (LV).
In the non-rotated mean-centred event-related PLS, the analysis looks for LVs which explain the maximum percentage of variance between the contrast and the BOLD signal. The LVs were determined with a permutation test using 2000 permutations, each event had a temporal window size of 14 time-points post-stimulus onset, called Lags. To answer our hypothesis on the effect of tDCS on the inhibition (incongruent) condition, we contrasted between task conditions and timepoints.
Furthermore, the reliability of each voxel's contribution to a particular LV was tested by submitting all saliences to a bootstrap estimation of the standard errors (SEs), using 2,000 bootstraps. The bootstrap ratio (BSR) is calculated by dividing salience with standard error. Peak voxels with a salience/SE ratio ≥3.0 or ≤-3 for negative correlations (p< .001) were deemed as reliable (3).
Patients were stimulated in the scanner during which they performed the stop-signal task and the Stroop task (each 10min). Electrodes were placed on the FC1 and FC2 10-20 EEG positions. 46 Patients with OCD were included in the analysis upon excluding participants with outlying task performance.
The imaging data recorded during the Stroop task were analysed using McIntosh's event-related approach with partial least squares (2). This multivariate analysis technique identifies whole-brain patterns of covariance related to conditions of an experiment. Each brain voxel has a weight, referred to as salience, which specifies how strongly the voxel contributes to the covariance explained by that latent variable (LV).
In the non-rotated mean-centred event-related PLS, the analysis looks for LVs which explain the maximum percentage of variance between the contrast and the BOLD signal. The LVs were determined with a permutation test using 2000 permutations, each event had a temporal window size of 14 time-points post-stimulus onset, called Lags. To answer our hypothesis on the effect of tDCS on the inhibition (incongruent) condition, we contrasted between task conditions and timepoints.
Furthermore, the reliability of each voxel's contribution to a particular LV was tested by submitting all saliences to a bootstrap estimation of the standard errors (SEs), using 2,000 bootstraps. The bootstrap ratio (BSR) is calculated by dividing salience with standard error. Peak voxels with a salience/SE ratio ≥3.0 or ≤-3 for negative correlations (p< .001) were deemed as reliable (3).
Results:
No significant differences in stroop ansd stop-signal task performance between the stimulation and sham timepoints were found. Figure 1 displays the brain scores for each condition and over time. All three timepoints included in Fig 2. Had their most significant cluster in the supramarginal gyrus. The stimulation condition did show an increase connectivity in the expected inferior frontal gyrus (Fig. 2)(6, 9).
·Figure 1
·Figure 2
Conclusions:
Correlations with behavioural performance scores including accuracy and response time will be included in further analysis. Additionally, the question whether timepoint sequence influences brain patterns remains to be answered. Finally, a separate analysis will be conducted where the effect of electric field magnitude patterns calculated for each participant using a method developed last year will be considered (8).
Brain Stimulation:
TDCS 2
Modeling and Analysis Methods:
Activation (eg. BOLD task-fMRI) 1
Multivariate Approaches
Keywords:
FUNCTIONAL MRI
1|2Indicates the priority used for review
Provide references using author date format
2. McIntosh, A. R. (2004) ‘Spatiotemporal analysis of event-related fMRI data using partial least squares’, NeuroImage, vol. 23, 764-775.
3. Sampson, P. D. (1989). ‘Neurobehavioral effects of prenatal alcohol: Part II. Partial Least Squares Analysis.’, Neurotoxicology and Teratology, vol. 11, 477–491.
4. Madan, C. R. (2015) ‘Creating 3D visualisations of MRI data: A brief guide [version 1; peer review:3 approved]’, F1000Research, 4:466.
5. Cai, W. (2012) ‘The role of the right pre-supplementary motor area in stopping action: two studies with event-related transcranial magnetic stimulation.’, J Neurophysiology, vol. 108, 380-389.
6. De Wit, S. J. (2012) ‘Presupplementary Motor Area Hyperactivity During Response Inhibition: A Candidate Endophenotype of Obsessive-Compulsive Disorder’, Am J Psychiatry, vol. 169, 1100-1108.
7. Yu, J. (2015) ‘Brain Stimulation Improves Cognitive Control by Modulating Medial-Frontal Activity and preSMA-vmPFC Functional Connectivity’, Human Brain Mapping, vol. 36, 4004-4015.
8. Rodriguez-Manrique, D. (2022) ‘Electrode localisation for electrical field modelling in transcranial direct current stimulation (tDCS) studies using magnetic resonance imaging (MRI).’, OHBM Abstract
9. Chambers, C. D. (2006) ‘Insights into the neural basis of response inhibition from cognitive and clinical neuroscience’, Neuroscience & Biobehavioural Reviews, vol. 33, 631-646.