Poster No:
1701
Submission Type:
Abstract Submission
Authors:
Kevin Sitek1, Vaishak Harish2, Jason Bohland2
Institutions:
1Northwestern University, Evanston, IL, 2University of Pittsburgh, Pittsburgh, PA
First Author:
Co-Author(s):
Introduction:
Motor-induced suppression (MIS) is a well-known effect in which sensory responses evoked by a self-generated motor act are reduced relative to responses to the same input presented externally (Blakemore et al., 1998). Motor control theory suggests this effect is driven by internal models that use efference copies to cancel or suppress sensory signals during movements (Wolpert et al., 2001). Motor-induced suppression of the auditory cortex has been studied using EEG/MEG in humans producing speech (Houde et al., 2002) or generating sounds via button presses (Martikainen et al., 2005; Aliu et al., 2010). The evoked potentials typically used to characterize MIS offer limited anatomical specificity. Here, we examined MIS of the auditory cortex in a button-press task using high-resolution 7T fMRI. This enables an anatomically specific description of the modulation of motor and sensory regions related to MIS.
Methods:
6 neurotypical adults participated and completed 4-6 ~8.5-minute task-based runs. Each run included 3 conditions, presented pseudorandomly in 30s blocks: (1) SELFGEN: participants pressed a button, which elicited an immediate auditory stimulus (amplitude modulated white noise, 700 ms duration), (2) LISTEN: participants heard the same stimuli without a motor act, (3) MOTOR: participants made button presses without corresponding sounds. Participants were instructed to press the button once per trial (3s) in SELFGEN and MOTOR conditions and to passively listen in the LISTEN condition. 15s rest blocks were interspersed through each run. Auditory stimuli were presented via Sensimetrics S15 earphones. Button presses were elicited using an MRI-compatible response glove.
Data were acquired on a 7T Siemens MAGNETOM scanner with custom Tic Tac Toe RF head coil (60-channel Tx, 32-channel Rx; Krishnamurthy et al., 2019). T1-weighted MP2RAGE images were obtained at 0.55mm resolution. fMRI data were collected using a multiband (MB) EPI sequence (MB factor = 2, TR = 3s, TE = 22.8ms, flip angle = 72°, voxel size = 1mm isotropic, 80 slices). Paired spin-echo images with opposing phase encoding directions were collected to map distortions. Preprocessing was performed using fMRIPrep (v24.0.0; Esteban et al., 2019). Functional images were corrected for susceptibility distortions, aligned, and co-registered to T1 images. Data were normalized to the MNI152NLin2009cAsym template. fMRI data were smoothed with a 3mm Gaussian kernel. Subject-level analyses were conducted using nilearn (Abraham et al., 2014). A general linear model included regressors for LISTEN, SELFGEN, and MOTOR conditions and 24 motion parameters. Scans with >0.5 mm framewise displacement or >1.5 standardized DVARS were scrubbed (Power et al., 2014). Univariate contrasts were computed for individual participants and evaluated at the group level using 1-sample t-tests.
Results:
Across individuals, we found superior temporal cortex responses in the LISTEN and SELFGEN conditions and distributed motor, premotor, somatosensory, and inferior parietal responses in the MOTOR and SELFGEN conditions (Figure 1). Foci of activations varied greatly in our relatively small sample. Figure 2 shows results from the participant with strongest overall effects, demonstrating characteristic auditory and motor responses in the three conditions. In this participant, we found increased, primarily left hemisphere superior temporal activity for SELFGEN compared to LISTEN, in addition to increased motor and medial premotor activity.

·Figure 1: Baseline contrasts for A) LISTEN, B) MOTOR, and C) SELFGEN conditions evaluated at the group level. Results are presented at p<0.05, uncorrected. Colorscale shows z-score.

·Figure 2: Individual participant results for baseline contrasts (A-C) and for SELFGEN vs. LISTEN (D-E). All images thresholded to control false discovery rate at q<0.05. Colorscale indicates z-score.
Conclusions:
We identified increased auditory cortical BOLD signal for self-generated sounds compared to passively presented sounds within a single participant using 7T fMRI. This contrasts with common findings with EEG/MEG, which show suppressed auditory responses for self-generated sounds. These data provide further opportunity to examine cortical depth-dependent responses to characterize the feedforward vs. feedback mechanisms contributing to auditory processing of self-generated sounds.
Language:
Speech Production
Motor Behavior:
Motor Planning and Execution 1
Perception, Attention and Motor Behavior:
Perception: Auditory/ Vestibular 2
Keywords:
FUNCTIONAL MRI
Motor
Other - auditory; motor-induced suppression; efference copy; internal models
1|2Indicates the priority used for review
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Please indicate below if your study was a "resting state" or "task-activation” study.
Task-activation
Healthy subjects only or patients (note that patient studies may also involve healthy subjects):
Healthy subjects
Was this research conducted in the United States?
Yes
Are you Internal Review Board (IRB) certified?
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Yes, I have IRB or AUCC approval
Were any human subjects research approved by the relevant Institutional Review Board or ethics panel?
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Yes
Were any animal research approved by the relevant IACUC or other animal research panel?
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Not applicable
Please indicate which methods were used in your research:
Functional MRI
Structural MRI
Behavior
For human MRI, what field strength scanner do you use?
7T
Which processing packages did you use for your study?
Free Surfer
Other, Please list
-
fmriprep, nilearn
Provide references using APA citation style.
1. Abraham, A., Pedregosa, F., Eickenberg, M., Gervais, P., Mueller, A., Kossaifi, J., Gramfort, A., Thirion, B., & Varoquaux, G. (2014). Machine learning for neuroimaging with scikit-learn. Frontiers in Neuroinformatics, 8, 14.
2. Aliu, S. O., Houde, J. F., & Nagarajan, S. S. (2009). Motor-induced suppression of the auditory cortex. Journal of Cognitive Neuroscience, 21(4), 791–802.
3. Blakemore, S. J., Wolpert, D. M., & Frith, C. D. (1998). Central cancellation of self-produced tickle sensation. Nature Neuroscience, 1(7), 635–640.
4. Esteban, O., Markiewicz, C. J., Blair, R. W., Moodie, C. A., Isik, A. I., Erramuzpe, A., Kent, J. D., Goncalves, M., DuPre, E., Snyder, M., Oya, H., Ghosh, S. S., Wright, J., Durnez, J., Poldrack, R. A., & Gorgolewski, K. J. (2019). fMRIPrep: a robust preprocessing pipeline for functional MRI. Nature Methods, 16(1), 111–116.
5. Houde, J. F., Nagarajan, S. S., Sekihara, K., & Merzenich, M. M. (2002). Modulation of the auditory cortex during speech: an MEG study. Journal of Cognitive Neuroscience, 14(8), 1125–1138.
6. Krishnamurthy, N., Santini, T., Wood, S., Kim, J., Zhao, T., Aizenstein, H. J., & Ibrahim, T. S. (2019). Computational and experimental evaluation of the Tic-Tac-Toe RF coil for 7 Tesla MRI. PloS ONE, 14(1), e0209663.
7. Martikainen, M. H., Kaneko, K., & Hari, R. (2005). Suppressed responses to self-triggered sounds in the human auditory cortex. Cerebral Cortex, 15(3), 299–302.
8. Power, J. D., Mitra, A., Laumann, T. O., Snyder, A. Z., Schlaggar, B. L., & Petersen, S. E. (2014). Methods to detect, characterize, and remove motion artifact in resting state fMRI. NeuroImage, 84, 320–341.
9. Wolpert, D. M., & Flanagan, J. R. (2001). Motor prediction. Current Biology, 11(18), R729–R732.
No