Spatially bivariate EEG-neurofeedback can manipulate interhemispheric inhibition

Abstract

Human behavior requires interregional crosstalk to employ the sensorimotor processes in the brain. Although external neuromodulation techniques have been used to manipulate interhemispheric sensorimotor activity, a central controversy concerns whether this activity can be volitionally controlled. Experimental tools lack the power to up- or down-regulate the state of the targeted hemisphere over a large dynamic range and, therefore, cannot evaluate the possible volitional control of the activity. We addressed this difficulty by using the recently developed method of spatially bivariate electroencephalography (EEG)-neurofeedback to systematically enable the participants to modulate their bilateral sensorimotor activities. Herein, we report that participants learn to up- and down-regulate the ipsilateral excitability to the imagined hand while maintaining constant the contralateral excitability; this modulates the magnitude of interhemispheric inhibition (IHI) assessed by the paired-pulse transcranial magnetic stimulation (TMS) paradigm. Further physiological analyses revealed that the manipulation capability of IHI magnitude reflected interhemispheric connectivity in EEG and TMS, which was accompanied by intrinsic bilateral cortical oscillatory activities. Our results show an interesting approach for neuromodulation, which might identify new treatment opportunities, for example, in patients suffering from a stroke.

On closed-loop brain stimulation systems for improving the quality of life of patients with neurological disorders

“…Transcranial magnetic stimulation (TMS) Problems with the motor cortex have been extensively studied by using TMS, with 18 articles dedicated to the issue: those by Ros et al ( 2010 ), Niazi et al ( 2012 ), Sitaram et al ( 2012 ), Mokienko et al ( 2013 ), Takemi et al ( 2013 , 2018 ), Hänselmann et al ( 2015 ), Kaplan et al ( 2016 ), Royter and Gharabaghi ( 2016 ), Schildt et al ( 2016 ), Hasegawa et al ( 2017 ), Mashat et al ( 2017 ), Daly et al ( 2018 ), Jochumsen et al ( 2018 ), Syrov et al ( 2020 ), Ding et al ( 2021 ), Grigorev et al ( 2021 ), and Mihelj et al ( 2021 ) for neugodegenerative disease. The second most commonly studied disease by using TMS was stroke, with five articles devoted to it: those by Gharabaghi et al ( 2014 ), Syrov et al ( 2019 ), Cantillo-Negrete et al ( 2021 ), Hayashi et al ( 2022 ), and Liang et al ( 2020 ). Four articles examine the sensorimotor cortex: those by Pichiorri et al ( 2011 ), Niazi et al ( 2014 ), Kraus et al ( 2016 ), and Naros et al ( 2020 ).…”

Interhemispheric connectivity during spontaneous switches in visual perceptual coherence as revealed by fMRI at multiple temporal resolutions

“…Here, we explore the interhemispheric functional connectivity of a region that responds to our stimuli, assessing its potential as a target for neuromodulation at different TRs. This has been successfully applied before in other neurofeedback paradigms (Pereira et al, 2019;Wang et al, 2020;Hayashi et al, 2022). Speci cally, we simulated the feedback of the perceptual switches based on the hMT + interhemispheric correlation value.…”

Effectiveness of motor and prefrontal cortical areas for brain-controlled functional electrical stimulation neuromodulation

“…It has also been suggested that functional connectivity at rest implies the structured pattern of brain networks associated with task-induced functional connectivity [39]. Therefore, we assessed functional connectivity during the 'Rest' epoch by computing the network intensity [11,40] based on the corrected imaginary part of coherence (ciCOH) measure [11,[40][41][42]. Specifically, ciCOH was initially estimated from preprocessed EEG data that were segmented every 1 s in the 'Rest' epoch with 50% overlap and multiplied by the Hanning window [11,41].…”

“…Specifically, ciCOH was initially estimated from preprocessed EEG data that were segmented every 1 s in the 'Rest' epoch with 50% overlap and multiplied by the Hanning window [11,41]. Subsequently, the ciCOH value was computed from the complex coherency function of the segmented data [11,[40][41][42]:…”

“…where Im (C ij ) denotes the imaginary part, and Re (C ij ) denotes the real part of the complex coherency function (C ij ). Subsequent network analysis using an asymptotic statistical procedure based on one thousand surrogate datasets was implemented to discard spurious connectivity, resulting in significant ciCOH values [11,40,43]. Finally, the network intensity was computed as the summation of significant ciCOH values between the target electrode (i.e.…”