Investigating cortical neural correlates of ipsilateral Silent Period
Poster No:
1915
Submission Type:
Abstract Submission
Authors:
Costanza Iester1, Alice Bellosta1, Elena Monteleone1, Monica Biggio1, Ludovico Pedullà2, Ambra Bisio3, Sabrina Brigadoi4, Simone Cutini4, Laura Bonzano1, Marco Bove5
Institutions:
1Department of Neuroscience, DINOGMI, University of Genoa, Genoa, Italy, 2Italian Multiple Sclerosis Foundation, Genoa, Italy, 3DIMES, Section of Human Physiology, University of Genoa, Genova, Liguria, Italy, Genova, Italy, 4Department of Developmental Psychology, University of Padova, Padova, Italy, Padova, Italy, 5DIMES, University of Genoa, Genova, Italy
First Author:
Co-Author(s):
Ambra Bisio
DIMES, Section of Human Physiology, University of Genoa, Genova, Liguria, Italy
Genova, Italy
DIMES, Section of Human Physiology, University of Genoa, Genova, Liguria, Italy
Genova, Italy
Sabrina Brigadoi
Department of Developmental Psychology, University of Padova, Padova, Italy
Padova, Italy
Department of Developmental Psychology, University of Padova, Padova, Italy
Padova, Italy
Simone Cutini
Department of Developmental Psychology, University of Padova, Padova, Italy
Padova, Italy
Department of Developmental Psychology, University of Padova, Padova, Italy
Padova, Italy
Introduction:
The ipsilateral silent period (iSP) is employed to investigate the interhemispheric control of voluntary cortical motor output [1]. It involves a brief interruption of voluntary electromyography (EMG) in a hand muscle through focal transcranial magnetic stimulation (TMS) of the ipsilateral primary motor cortex (M1). Given observed differences in iSP outputs across various studies, variations in dynamics may account for iSP disparities within and between groups. Moreover, it has been observed that the volitional activation of M1, induced by movement of the contralateral hand, leads to an augmentation of interhemispheric motor inhibition [2]. This phenomenon may also be further elucidated by examining iSP cortical correlates. Despite being a frequently measured index of interhemispheric inhibition, the neural dynamics underlying the iSP effect remain unknown. Some insights were discovered using the combination of TMS and EEG, the larger the TMS-evoked potential, occurring approximately 15 ms after the stimulation, the higher the iSP area [3]. However, although EEG provides a global view of brain activity with a precision of milliseconds, it does not allow for the exact localization of the sources of the waves. In this context, functional Near Infra-Red Spectroscopy (fNIRS) could be employed to explore the cortical correlates of iSP. Recent studies emphasize the potential of the combined use of TMS and fNIRS [4]. Consequently, the aim of this study is to develop an experimental protocol able to simultaneously investigate the iSP and the associated neural dynamics using both TMS and fNIRS.
Methods:
To investigate the left iSP and its neural correlates, TMS was positioned on the left hemisphere, while fNIRS optodes were arranged on the right hemisphere. To test the protocol, five healthy participants were recruited. The fNIRS array was arranged to obtain 49 standard channels (3cm) and 8 short-separation channels (8mm) (Fig.1). The TMS intensity stimulation was set to the 120% of the passive motor threshold for each subject [2]. Surface EMG activity was recorded both from the right and left first dorsal interosseous muscles to quantify the motor activity. The experiment consisted of three different conditions in which the participant was instructed to maximally contract either the left hand or both hands. The first condition involved TMS stimulation and contraction of the left hand, the second involved TMS stimulation and contraction of both hands, and the third, without TMS stimulation, required contraction of the left hand. In all conditions, fNIRS measured the neural activity of the right hemisphere. For each condition, 25 trials were collected, resulting in a total of 75 trials (task duration: ~1s; rest duration: ~20s). After the acquisition, the fNIRS signal was pre-processed: channels with low signal-to-noise ratio were discarded; intensity data were converted to attenuation changes and motion artifacts were corrected by applying spline [5] and wavelet [6] motion correction techniques; a band-pass filter (0.01-3Hz) was applied, and the mean hemodynamic response for each task block, and channel was recovered using a general linear model approach.
Results:
We propose for the first time a method to assess iSP combining TMS and fNIRS: TMS stimulates a hemisphere while fNIRS records the neural activity of the contralateral one. Standard pipeline used in fNIRS field to preprocess the signal [7] can remove TMS artifacts leading to a reliable result. Some channels related to premotor, motor, and parietal areas seem to be modulated by conditions (Fig. 2). Behavioral data confirms the literature by showing a greater iSP area during the bilateral hand contraction compared to only left-hand contraction.
Conclusions:
fNIRS is a powerful tool to investigate cortical correlates simultaneously with TMS stimulation. The possibility of studying TMS and fNIRS together can unveil neural mechanisms associated with interhemispheric circuits of motor control.
Brain Stimulation:
TMS
Modeling and Analysis Methods:
Methods Development 1
Novel Imaging Acquisition Methods:
NIRS 2
Keywords:
Acquisition
ADULTS
Cortex
Design and Analysis
Near Infra-Red Spectroscopy (NIRS)
Transcranial Magnetic Stimulation (TMS)
Other - ipsilateral silent period
1|2Indicates the priority used for review
Provide references using author date format
2. Giovannelli, F. (2009). Modulation of interhemispheric inhibition by volitional motor activity: an ipsilateral silent period study. The Journal of Physiology, 587(22), 5393-5410.
3. Bortoletto, M. (2021). Asymmetric transcallosal conduction delay leads to finer bimanual coordination. Brain Stimulation, 14(2), 379-388.
4. Curtin, A. (2019). A systematic review of integrated functional near-infrared spectroscopy (fNIRS) and transcranial magnetic stimulation (TMS) studies. Frontiers in Neuroscience, 13, 84.
5. Scholkmann, F. (2010). How to detect and reduce movement artifacts in near-infrared imaging using moving standard deviation and spline interpolation. Physiological Measurement, 31(5), 649.
6. Molavi, B. (2012). Wavelet-based motion artifact removal for functional near-infrared spectroscopy. Physiological Measurement, 33(2), 259.
7. Brigadoi, S. (2014). Motion artifacts in functional near-infrared spectroscopy: a comparison of motion correction techniques applied to real cognitive data. Neuroimage 85, 181–191.