Combining Transcranial Magnetic Stimulation with Electroencephalography to Study Human Cortical Excitability and Effective Connectivity

Prefrontal Oscillatory Slowing in Early-Course Schizophrenia Is Associated With Worse Cognitive Performance and Negative Symptoms: A Transcranial Magnetic Stimulation-Electroencephalography Study

BACKGROUND

Abnormalities in dorsolateral prefrontal cortex (DLPFC) oscillations are neurophysiological signatures of schizophrenia thought to underlie its cognitive deficits. Transcranial magnetic stimulation with electroencephalography (TMS-EEG) provides a measure of cortical oscillations unaffected by sensory relay functionality and/or patients' level of engagement, which are important confounding factors in schizophrenia. Previous TMS-EEG work showed reduced fast, gamma-range oscillations and a slowing of the main DLPFC oscillatory frequency, or natural frequency, in chronic schizophrenia. However, it is unclear whether this DLPFC natural frequency slowing is present in early-course schizophrenia (EC-SCZ) and is associated with symptom severity and cognitive dysfunction.

METHODS

We applied TMS-EEG to the left DLPFC in 30 individuals with EC-SCZ and 28 healthy control participants. Goal-directed working memory performance was assessed using the AX-Continuous Performance Task. The EEG frequency with the highest cumulative power at the stimulation site, or natural frequency, was extracted. We also calculated the local relative spectral power as the average power in each frequency band divided by the broadband power.

RESULTS

Compared with the healthy control group, the EC-SCZ group had reduced DLPFC natural frequency (p = .0000002, Cohen's d = -2.32) and higher DLPFC beta-range relative spectral power (p = .0003, Cohen's d = 0.77). In the EC-SCZ group, the DLPFC natural frequency was inversely associated with negative symptoms. Across all participants, the beta band relative spectral power negatively correlated with AX-Continuous Performance Task performance.

CONCLUSIONS

DLPFC oscillatory slowing is an early pathophysiological biomarker of schizophrenia that is associated with its symptom severity and cognitive impairments. Future work should assess whether noninvasive neurostimulation, including repetitive TMS, can ameliorate prefrontal oscillatory deficits and related clinical functions in patients with EC-SCZ.

Classical, spaced, or accelerated transcranial magnetic stimulation of motor cortex for treating neuropathic pain: A 3-arm parallel non-inferiority study

BACKGROUND

Repetitive transcranial magnetic stimulation (rTMS) of the primary motor cortex (M1) at high frequency (HF) is an effective treatment of neuropathic pain. The classical HF-rTMS protocol (CHF-rTMS) includes a daily session for one week as an induction phase of treatment followed by more spaced sessions. Another type of protocol without an induction phase and based solely on spaced sessions of HF-rTMS (SHF-rTMS) has also been shown to produce neuropathic pain relief. However, CHF-rTMS and SHF-rTMS of M1 have never been compared regarding their analgesic potential. Another type of rTMS paradigm, called accelerated intermittent theta burst stimulation (ACC-iTBS), has recently been proposed for the treatment of depression, the other clinical condition for which HF-rTMS is proposed as an effective therapeutic strategy. ACC-iTBS combines a high number of pulses delivered in short sessions grouped into a few days of stimulation. This type of protocol has never been applied to M1 for the treatment of pain.

METHODS/DESIGN

The objective of this single-centre randomized study is to compare the efficacy of three different rTMS protocols for the treatment of chronic neuropathic pain: CHF-rTMS, SHF-rTMS, and ACC-iTBS. The CHF-rTMS will consists of 10 stimulation sessions, including 5 daily sessions of 10Hz-rTMS (3,000 pulses per session) over one week, then one session per week for 5 weeks, for a total of 30,000 pulses delivered in 10 stimulation days. The SHF-rTMS protocol will only include 4 sessions of 20Hz-rTMS (1,600 pulses per session), one every 15 days, for a total of 6,400 pulses delivered in 4 stimulation days. The ACC-iTBS protocol will comprise 5 sessions of iTBS (600 pulses per session) completed in half a day for 2 consecutive days, repeated 5 weeks later, for a total of 30,000 pulses delivered in 4 stimulation days. Thus, CHF-rTMS and ACC-iTBS protocols will share a higher total number of TMS pulses (30,000 pulses) compared to SHF-rTMS protocol (6,400 pulses), while CHF-rTMS protocol will include a higher number of stimulation days (10 days) compared to ACC-iTBS and SHF-rTMS protocols (4 days). In all protocols, the M1 target will be defined in the same way and stimulated at the same intensity using a navigated rTMS (nTMS) procedure. The evaluation will be based on clinical outcomes with various scales and questionnaires assessed every week, from two weeks before the 7-week period of therapeutic stimulation until 4 weeks after. Additionally, three sets of neurophysiological outcomes (resting-state electroencephalography (EEG), nTMS-EEG recordings, and short intracortical inhibition measurement with threshold tracking method) will be assessed the week before and after the 7-week period of therapeutic stimulation.

DISCUSSION

This study will make it possible to compare the analgesic efficacy of the CHF-rTMS and SHF-rTMS protocols and to appraise that of the ACC-iTBS protocol for the first time. This study will also make it possible to determine the respective influence of the total number of pulses and days of stimulation delivered to M1 on the extent of pain relief. Thus, if their analgesic efficacy is not inferior to that of CHF-rTMS, SHF-rTMS and especially the new ACC-iTBS protocol could be an optimal compromise of a more easy-to-perform rTMS protocol for the treatment of patients with chronic neuropathic pain.

Mapping the routes of perception: Hemispheric asymmetries in signal propagation dynamics

The visual system has long been considered equivalent across hemispheres. However, an increasing amount of data shows that functional differences may exist in this regard. We therefore tried to characterize the emergence of visual perception and the spatiotemporal dynamics resulting from the stimulation of visual cortices in order to detect possible interhemispheric asymmetries. Eighteen participants were tested. Each of them received 360 transcranial magnetic stimulation (TMS) pulses at phosphene threshold intensity over left and right early visual areas while electroencephalography was being recorded. After each single pulse, participants had to report the presence or absence of a phosphene. Local mean field power analysis of TMS-evoked potentials showed an effect of both site (left vs. right TMS) of stimulation and hemisphere (ipsilateral vs. contralateral to the TMS): while right TMS determined early stronger activations, left TMS determined later stronger activity in contralateral electrodes. The interhemispheric signal propagation index revealed differences in how TMS-evoked activity spreads: left TMS-induced activity diffused contralaterally more than right stimulation. With regard to phosphenes perception, distinct electrophysiological patterns were found to reflect similar perceptual experiences: left TMS-evoked phosphenes are associated with early occipito-parietal and frontal activity followed by late central activity; right TMS-evoked phosphenes determine only late, fronto-central, and parietal activations. Our results show that left and right occipital TMS elicits differential electrophysiological patterns in the brain, both per se and as a function of phosphene perception. These distinct activation patterns may suggest a different role of the two hemispheres in processing visual information and giving rise to perception.

"Tricking the Brain" Using Immersive Virtual Reality: Modifying the Self-Perception Over Embodied Avatar Influences Motor Cortical Excitability and Action Initiation

To offer engaging neurorehabilitation training to neurologic patients, motor tasks are often visualized in virtual reality (VR). Recently introduced head-mounted displays (HMDs) allow to realistically mimic the body of the user from a first-person perspective (i.e., avatar) in a highly immersive VR environment. In this immersive environment, users may embody avatars with different body characteristics. Importantly, body characteristics impact how people perform actions. Therefore, alternating body perceptions using immersive VR may be a powerful tool to promote motor activity in neurologic patients. However, the ability of the brain to adapt motor commands based on a perceived modified reality has not yet been fully explored. To fill this gap, we "tricked the brain" using immersive VR and investigated if multisensory feedback modulating the physical properties of an embodied avatar influences motor brain networks and control. Ten healthy participants were immersed in a virtual environment using an HMD, where they saw an avatar from first-person perspective. We slowly transformed the surface of the avatar (i.e., the "skin material") from human to stone. We enforced this visual change by repetitively touching the real arm of the participant and the arm of the avatar with a (virtual) hammer, while progressively replacing the sound of the hammer against skin with stone hitting sound via loudspeaker. We applied single-pulse transcranial magnetic simulation (TMS) to evaluate changes in motor cortical excitability associated with the illusion. Further, to investigate if the "stone illusion" affected motor control, participants performed a reaching task with the human and stone avatar. Questionnaires assessed the subjectively reported strength of embodiment and illusion. Our results show that participants experienced the "stone arm illusion." Particularly, they rated their arm as heavier, colder, stiffer, and more insensitive when immersed with the stone than human avatar, without the illusion affecting their experienced feeling of body ownership. Further, the reported illusion strength was associated with enhanced motor cortical excitability and faster movement initiations, indicating that participants may have physically mirrored and compensated for the embodied body characteristics of the stone avatar. Together, immersive VR has the potential to influence motor brain networks by subtly modifying the perception of reality, opening new perspectives for the motor recovery of patients.

Cell type-specific excitability probed by optogenetic stimulation depends on the phase of the alpha oscillation

BACKGROUND

Alpha oscillations have been proposed to provide phasic inhibition in the brain. Yet, pinging alpha oscillations with transcranial magnetic stimulation (TMS) to examine phase-dependent network excitability has resulted in conflicting findings. At the cellular level, such gating by the alpha oscillation remains poorly understood.

OBJECTIVE

We examine how the excitability of pyramidal cells and presumed fast-spiking inhibitory interneurons depends on the phase of the alpha oscillation.

METHODS

Optogenetic stimulation pulses were administered at random phases of the alpha oscillation in the posterior parietal cortex (PPC) of two adult ferrets that expressed channelrhodopsin in pyramidal cells. Post-stimulation firing probability was calculated as a function of the stimulation phase of the alpha oscillation for both verum and sham stimulation.

RESULTS

The excitability of pyramidal cells depended on the alpha phase, in anticorrelation with their intrinsic phase preference; pyramidal cells were more responsive to optogenetic stimulation at the alpha phase with intrinsically low firing rates. In contrast, presumed fast-spiking inhibitory interneurons did not show such a phase dependency despite their stronger intrinsic phase preference.

CONCLUSIONS

Alpha oscillations gate input to PPC in a phase-dependent manner such that low intrinsic activity was associated with higher responsiveness to input. This finding supports a model of cortical oscillation, in which internal processing and communication are limited to the depolarized half-cycle, whereas the other half-cycle serves as a signal detector for unexpected input. The functional role of different parts of the alpha cycle may vary across the cortex depending on local neuronal firing properties.

Classification of Cognitive Impairment and Healthy Controls Based on Transcranial Magnetic Stimulation Evoked Potentials

Backgrounds: Nowadays, risks of Cognitive Impairment (CI) [highly suspected Alzheimer's disease (AD) in this study] threaten the quality of life for more older adults as the population ages. The emergence of Transcranial Magnetic Stimulation-Electroencephalogram (TMS-EEG) enables noninvasive neurophysiological investi-gation of the human cortex, which might be potentially used for CI detection. Objectives: The aim of this study is to explore whether the spatiotemporal features of TMS Evoked Potentials (TEPs) could classify CI from healthy controls (HC). Methods: Twenty-one patients with CI and 22 HC underwent a single-pulse TMS-EEG stimulus in which the pulses were delivered to the left dorsolateral prefrontal cortex (left DLPFC). After preprocessing, seven regions of interest (ROIs) and two most reliable TEPs' components: N100 and P200 were selected. Next, seven simple and interpretable linear features of TEPs were extracted for each region, three common machine learning algorithms including Support Vector Machine (SVM), Random Forest (RF), and K-Nearest Neighbor (KNN) were used to detect CI. Meanwhile, data augmentation and voting strategy were used for a more robust model. Finally, the performance differences of features in classifiers and their contributions were investigated. Results: 1. In the time domain, the features of N100 had the best performance in the SVM classifier, with an accuracy of 88.37%. 2. In the aspect of spatiality, the features of the right frontal region and left parietal region had the best performance in the SVM classifier, with an accuracy of 83.72%. 3. The Local Mean Field Power (LMFP), Average Value (AVG), Latency and Amplitude contributed most in classification. Conclusions: The TEPs induced by TMS over the left DLPFC has significant differences spatially and temporally between CI and HC. Machine learning based on the spatiotemporal features of TEPs have the ability to separate the CI and HC which suggest that TEPs has potential as non-invasive biomarkers for CI diagnosis.

Increased cortical excitability but stable effective connectivity index during attentional lapses

Modern lifestyle curtails sleep and increases nighttime work and leisure activities. This has a deleterious impact on vigilance and attention, exacerbating chances of committing attentional lapses, with potential dramatic outcomes. Here, we investigated the brain signature of attentional lapses and assessed whether cortical excitability and brain response propagation were modified during lapses and whether these modifications changed with aging. We compared electroencephalogram (EEG) responses to transcranial magnetic stimulation (TMS) during lapse and no-lapse periods while performing a continuous attentional/vigilance task at night, after usual bedtime. Data were collected in healthy younger (N = 12; 18-30 years) and older individuals (N = 12; 50-70 years) of both sexes. The amplitude and slope of the first component of the TMS-evoked potential were larger during lapses. In contrast, TMS response scattering over the cortical surface, as well as EEG response complexity, did not significantly vary between lapse and no-lapse periods. Importantly, despite qualitative differences, age did not significantly affect any of the TMS-EEG measures. These results demonstrate that attentional lapses are associated with a transient increase of cortical excitability. This initial change is not associated with detectable changes in subsequent effective connectivity-as indexed by response propagation-and are not markedly different between younger and older adults. These findings could contribute to develop models aimed to predicting and preventing lapses in real-life situations.

Circadian regulation of human cortical excitability

Prolonged wakefulness alters cortical excitability, which is essential for proper brain function and cognition. However, besides prior wakefulness, brain function and cognition are also affected by circadian rhythmicity. Whether the regulation of cognition involves a circadian impact on cortical excitability is unknown. Here, we assessed cortical excitability from scalp electroencephalography (EEG) responses to transcranial magnetic stimulation in 22 participants during 29 h of wakefulness under constant conditions. Data reveal robust circadian dynamics of cortical excitability that are strongest in those individuals with highest endocrine markers of circadian amplitude. In addition, the time course of cortical excitability correlates with changes in EEG synchronization and cognitive performance. These results demonstrate that the crucial factor for cortical excitability, and basic brain function in general, is the balance between circadian rhythmicity and sleep need, rather than sleep homoeostasis alone. These findings have implications for clinical applications such as non-invasive brain stimulation in neurorehabilitation.

Cognitive performance is impaired after prolonged wakefulness, yet the contribution of circadian rhythms for proper brain function remains unclear. Here the authors show that cortical excitability measured using TMS exhibits robust circadian dynamics which is correlated with cognitive performance.

Eyes Open on Sleep and Wake: In Vivo to In Silico Neural Networks

Functional and effective connectivity of cortical areas are essential for normal brain function under different behavioral states. Appropriate cortical activity during sleep and wakefulness is ensured by the balanced activity of excitatory and inhibitory circuits. Ultimately, fast, millisecond cortical rhythmic oscillations shape cortical function in time and space. On a much longer time scale, brain function also depends on prior sleep-wake history and circadian processes. However, much remains to be established on how the brain operates at the neuronal level in humans during sleep and wakefulness. A key limitation of human neuroscience is the difficulty in isolating neuronal excitation/inhibition drive in vivo. Therefore, computational models are noninvasive approaches of choice to indirectly access hidden neuronal states. In this review, we present a physiologically driven in silico approach, Dynamic Causal Modelling (DCM), as a means to comprehend brain function under different experimental paradigms. Importantly, DCM has allowed for the understanding of how brain dynamics underscore brain plasticity, cognition, and different states of consciousness. In a broader perspective, noninvasive computational approaches, such as DCM, may help to puzzle out the spatial and temporal dynamics of human brain function at different behavioural states.

Quantifying cortical EEG responses to TMS in (un)consciousness

We normally assess another individual's level of consciousness based on her or his ability to interact with the surrounding environment and communicate. Usually, if we observe purposeful behavior, appropriate responses to sensory inputs, and, above all, appropriate answers to questions, we can be reasonably sure that the person is conscious. However, we know that consciousness can be entirely within the brain, even in the absence of any interaction with the external world; this happens almost every night, while we dream. Yet, to this day, we lack an objective, dependable measure of the level of consciousness that is independent of processing sensory inputs and producing appropriate motor outputs. Theoretically, consciousness is thought to require the joint presence of functional integration and functional differentiation, otherwise defined as brain complexity. Here we review a series of recent studies in which Transcranial Magnetic Stimulation combined with electroencephalography (TMS/EEG) has been employed to quantify brain complexity in wakefulness and during physiological (sleep), pharmacological (anesthesia) and pathological (brain injury) loss of consciousness. These studies invariably show that the complexity of the cortical response to TMS collapses when consciousness is lost during deep sleep, anesthesia and vegetative state following severe brain injury, while it recovers when consciousness resurges in wakefulness, during dreaming, in the minimally conscious state or locked-in syndrome. The present paper will also focus on how this approach may contribute to unveiling the pathophysiology of disorders of consciousness affecting brain-injured patients. Finally, we will underline some crucial methodological aspects concerning TMS/EEG measurements of brain complexity.