Repetitive sensorimotor mu-alpha phase-targeted afferent stimulation produces no phase-dependent plasticity related changes in somatosensory evoked potentials or sensory thresholds.

Phase-dependent plasticity has been proposed as a neurobiological mechanism by which oscillatory phase-amplitude cross-frequency coupling mediates memory process in the brain. Mimicking this mechanism, real-time EEG oscillatory phase-triggered transcranial magnetic stimulation (TMS) has successfully induced LTP-like changes in corticospinal excitability in the human motor cortex. Here we asked whether EEG phase-triggered afferent stimulation alone, if repetitively applied to the peaks, troughs, or random phases of the sensorimotor mu-alpha rhythm, would be sufficient to modulate the strength of thalamocortical synapses as assessed by changes in somatosensory evoked potential (SEP) N20 and P25 amplitudes and sensory thresholds (ST). Specifically, we applied 100 Hz triplets of peripheral electrical stimulation (PES) to the thumb, middle, and little finger of the right hand in pseudorandomized trials, with the afferent input from each finger repetitively and consistently arriving either during the cortical mu-alpha trough or peak or at random phases. No significant changes in SEP amplitudes or ST were observed across the phase-dependent PES intervention. We discuss potential limitations of the study and argue that suboptimal stimulation parameter choices rather than a general lack of phase-dependent plasticity in thalamocortical synapses are responsible for this null finding. Future studies should further explore the possibility of phase-dependent sensory stimulation.

No robust online effects of transcranial direct current stimulation on corticospinal excitability.

Transcranial direct current stimulation (tDCS) has been used for over twenty years to modulate cortical (particularly motor corticospinal) excitability both during (online) and outlasting (offline) the stimulation, with the former effects associated to the latter. However, tDCS effects are highly variable, partially because stimulation intensity is commonly not adjusted individually (in contrast to transcranial magnetic stimulation, TMS). In Experiment 1, we therefore explored an empirical approach of personalizing tDCS intensity for the primary motor cortex (M1) based on dose-response curves (DRCs), individually relating tDCS Intensity (in steps from 0.3 to 2.0 mA) and Polarity (anodal, cathodal) to the online modulation of concurrent TMS motor evoked potentials (MEP), assessing DRC reliability across two separate days. No robust DRCs could be observed, neither at the individual nor at the group level, with the only robust effect being a (paradoxical) MEP facilitation during cathodal tDCS at 2.0 mA, but no modulation at traditional intensities of or near 1 mA. In Experiment 2, we therefore attempted to replicate the classical bidirectional online MEP modulation during 1 mA tDCS that had been reported by several of the early seminal tDCS papers. We either closely recreated stimulation parameters and temporal protocol of these original studies (Experiment 2A) or slightly modernized them according to current standards (Experiment 2B). In neither experiment did we observed any significant online MEP modulation. We conclude that an empirical titration of individually effective tDCS intensities may not be feasible as online tDCS effects do not appear to be sufficiently robust.

The Brain Electrophysiological recording & STimulation (BEST) toolbox.

Non-invasive brain stimulation (NIBS) experiments involve many recurring procedures that are not sufficiently standardized in the community. Given the diversity in experimental design and experience of the investigators, automated but yet flexible data collection and analysis tools are needed to increase objectivity, reliability, and reproducibility of NIBS experiments. The Brain Electrophysiological recording and STimulation (BEST) Toolbox is a MATLAB-based, open-source software with graphical user interface that allows users to design, run, and share freely configurable multi-protocol, multi-session NIBS studies, including transcranial magnetic, electric, and ultrasound stimulation (TMS, tES, TUS). Interfacing with a variety of recording and stimulation devices, the BEST toolbox analyzes EMG and EEG data, and configures stimulation parameters on-the-fly to facilitate closed-loop protocols and real-time applications. Its functionality is continuously expanded and includes e.g., TMS motor hotspot search, threshold estimation, motor evoked potential (MEP) and TMS-evoked EEG potential (TEP) measurements, dose-response curves, paired-pulse and dual-coil TMS, rTMS interventions.

An Effective Neurofeedback Intervention to Improve Social Interactions in Children with Autism Spectrum Disorder.

Neurofeedback training (NFT) approaches were investigated to improve behavior, cognition and emotion regulation in children with autism spectrum disorder (ASD). Thirteen children with ASD completed pre-/post-assessments and 16 NFT-sessions. The NFT was based on a game that encouraged social interactions and provided feedback based on imitation and emotional responsiveness. Bidirectional training of EEG mu suppression and enhancement (8-12 Hz over somatosensory cortex) was compared to the standard method of enhancing mu. Children learned to control mu rhythm with both methods and showed improvements in (1) electrophysiology: increased mu suppression, (2) emotional responsiveness: improved emotion recognition and spontaneous imitation, and (3) behavior: significantly better behavior in every-day life. Thus, these NFT paradigms improve aspects of behavior necessary for successful social interactions.

Neurofeedback training produces normalization in behavioural and electrophysiological measures of high-functioning autism.

Autism spectrum disorder (ASD) is a neurodevelopmental condition exhibiting impairments in behaviour, social and communication skills. These deficits may arise from aberrant functional connections that impact synchronization and effective neural communication. Neurofeedback training (NFT), based on operant conditioning of the electroencephalogram (EEG), has shown promise in addressing abnormalities in functional and structural connectivity. We tested the efficacy of NFT in reducing symptoms in children with ASD by targeting training to the mirror neuron system (MNS) via modulation of EEG mu rhythms. The human MNS has provided a neurobiological substrate for understanding concepts in social cognition relevant to behavioural and cognitive deficits observed in ASD. Furthermore, mu rhythms resemble MNS phenomenology supporting the argument that they are linked to perception and action. Thirty hours of NFT on ASD and typically developing (TD) children were assessed. Both groups completed an eyes-open/-closed EEG session as well as a mu suppression index assessment before and after training. Parents filled out pre- and post-behavioural questionnaires. The results showed improvements in ASD subjects but not in TDs. This suggests that induction of neuroplastic changes via NFT can normalize dysfunctional mirroring networks in children with autism, but the benefits are different for TD brains.