Individualized time windows enhance TMS-EEG signal characterization and improve assessment of cortical function in schizophrenia.

Transcranial magnetic stimulation and electroencephalography (TMS-EEG) recordings are crucial to directly assess cortical excitability and inhibition in a non-invasive and task-free manner. TMS-EEG signals are characterized by TMS-evoked potentials (TEPs), which are employed to evaluate cortical function. Nonetheless, different time windows (TW) have been used to compute them over the years. Moreover, these TWs tend to be the same for all participants omitting the intersubject variability. Therefore, the objective of this study is to assess the effect of using different TWs to compute the TEPs, moving from a common fixed TW to more adaptive individualized TWs. Twenty-nine healthy (HC) controls and twenty schizophrenia patients (SCZ) underwent single-pulse (SP) TMS-EEG protocol. Firstly, only the HC were considered to evaluate the TEPs for three different TWs in terms of amplitude and topographical distribution. Secondly, the SCZ patients were included to determine which TW is better to characterize the brain alterations of SCZ. The results indicate that a more individualized TW provides a better characterization of the SP TMS-EEG signals, although all of them show the same tendency. Regarding the comparison between groups, the individualized TW is the one that provides a better differentiation between populations. They also provide further support to the possible imbalance of cortical excitability/inhibition in the SCZ population due to its reduced activity in the N45 TEP and greater amplitude values in the N100. Results also suggest that the SCZ brain has a baseline hyperactive state since the TEPs of the SCZ appear earlier than those of the HC.

Densely sampled stimulus-response map of human cortex with single pulse TMS-EEG and its relation to whole brain neuroimaging measures.

Large-scale networks underpin brain functions. How such networks respond to focal stimulation can help decipher complex brain processes and optimize brain stimulation treatments. To map such stimulation-response patterns across the brain non-invasively, we recorded concurrent EEG responses from single-pulse transcranial magnetic stimulation (i.e., TMS-EEG) from over 100 cortical regions with two orthogonal coil orientations from one densely-sampled individual. We also acquired Human Connectome Project (HCP)-styled diffusion imaging scans (six), resting-state functional Magnetic Resonance Imaging (fMRI) scans (120 mins), resting-state EEG scans (108 mins), and structural MR scans (T1- and T2-weighted). Using the TMS-EEG data, we applied network science-based community detection to reveal insights about the brain's causal-functional organization from both a stimulation and recording perspective. We also computed structural and functional maps and the electric field of each TMS stimulation condition. Altogether, we hope the release of this densely sampled (n=1) dataset will be a uniquely valuable resource for both basic and clinical neuroscience research.

Validation of the number of pulses required for TMS-EEG in the prefrontal cortex considering test feasibility.

BACKGROUND: Transcranial magnetic stimulation (TMS) combined with electroencephalography (EEG), TMS-EEG, is a useful neuroscientific tool for the assessment of neurophysiology in the human cerebral cortex. Theoretically, TMS-EEG data is expected to have a better data quality as the number of stimulation pulses increases. However, since TMS-EEG testing is a modality that is examined on human subjects, the burden on the subject and tolerability of the test must also be carefully considered. METHOD: In this study, we aimed to determine the number of stimulation pulses that satisfy the reliability and validity of data quality in single-pulse TMS (spTMS) for the dorsolateral prefrontal cortex (DLPFC). TMS-EEG data for (1) 40-pulse, (2) 80-pulse, (3) 160-pulse, and (4) 240-pulse conditions were extracted from spTMS experimental data for the left DLPFC of 20 healthy subjects, and the similarities between TMS-evoked potentials (TEP) and oscillations across the conditions were evaluated. RESULTS: As a result, (2) 80-pulse and (3) 160-pulse conditions showed highly equivalent to the benchmark condition of (4) 240-pulse condition. However, (1) 40-pulse condition showed only weak to moderate equivalence to the (4) 240-pulse condition. Thus, in the DLPFC TMS-EEG experiment, 80 pulses of stimulations was found to be a reasonable enough number of pulses to extract reliable TEPs, compared to 160 or 240 pulses. CONCLUSIONS: This is the first substantial study to examine the appropriate number of stimulus pulses that are reasonable and feasible for TMS-EEG testing of the DLPFC.

Local neuronal sleep after stroke: The role of cortical bistability in brain reorganization.

BACKGROUND: Acute cerebral ischemia triggers a number of cellular mechanisms not only leading to excitotoxic cell death but also to enhanced neuroplasticity, facilitating neuronal reorganization and functional recovery. OBJECTIVE: Transferring these cellular mechanisms to neurophysiological correlates adaptable to patients is crucial to promote recovery post-stroke. The combination of TMS and EEG constitutes a promising readout of neuronal network activity in stroke patients. METHODS: We used the combination of TMS and EEG to investigate the development of local signal processing and global network alterations in 40 stroke patients with motor deficits alongside neural reorganization from the acute to the chronic phase. RESULTS: We show that the TMS-EEG response reflects information about reorganization and signal alterations associated with persistent motor deficits throughout the entire post-stroke period. In the early post-stroke phase and in a subgroup of patients with severe motor deficits, TMS applied to the lesioned motor cortex evoked a sleep-like slow wave response associated with a cortical off-period, a manifestation of cortical bistability, as well as a rapid disruption of the TMS-induced formation of causal network effects. Mechanistically, these phenomena were linked to lesions affecting ascending activating brainstem fibers. Of note, slow waves invariably vanished in the chronic phase, but were highly indicative of a poor functional outcome. CONCLUSION: In summary, we found evidence that transient effects of sleep-like slow waves and cortical bistability within ipsilesional M1 resulting in excessive inhibition may interfere with functional reorganization, leading to a less favorable functional outcome post-stroke, pointing to a new therapeutic target to improve recovery of function.

Prefrontal oscillatory slowing in early-course schizophrenia is associated with worse cognitive performance and negative symptoms: a TMS-EEG 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 EC-SCZ and 28 healthy control (HC) subjects. Goal-directed working memory performance was assessed using the "AX" Continuous Performance Task (AX-CPT). 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 (RSP) as the average power in each frequency band divided by the broadband power. RESULTS: Compared to HC, EC-SCZ had reduced DLPFC natural frequency (p=0.0000002, Cohen's d=-2.32) and higher DLPFC beta-range RSP (p=0.0003, Cohen's d=0.77). In EC-SCZ, the DLPFC natural frequency was inversely associated with negative symptoms. Across all participants, the beta-band RSP negatively correlated with the AX-CPT performance. CONCLUSIONS: A 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 non-invasive neurostimulation can ameliorate prefrontal oscillatory deficits and related clinical functions in EC-SCZ.

N100 as a response prediction biomarker for accelerated 1 Hz right DLPFC-rTMS in major depression.

BACKGROUND AND OBJECTIVE: Repetitive transcranial magnetic stimulation (rTMS) is a safe and effective treatment for major depressive disorder (MDD); however, this treatment currently lacks reliable biomarkers of treatment response. TMS-evoked potentials (TEPs), measured using TMS-electroencephalography (TMS-EEG), have been suggested as potential biomarker candidates, with the N100 peak being one of the most promising. This study investigated the association between baseline N100 amplitude and 1 Hz right dorsolateral prefrontal cortex (R-DLPFC) accelerated rTMS (arTMS) treatment in MDD. METHODS: Baseline TMS-EEG sessions were performed for 23 MDD patients. All patients then underwent 40 sessions of 1 Hz R-DLPFC (F4) arTMS over 5 days and a follow-up TMS-EEG session one week after the end of theses arTMS sessions. RESULTS: Baseline N100 amplitude at F4 showed a strong positive association (p < .001) with treatment outcome. The association between the change in N100 amplitude (baseline to follow-up) and treatment outcome did not remain significant after Bonferroni correction (p = .06, corrected; p = .03, uncorrected). Furthermore, treatment responders had a significantly larger mean baseline F4 TEP amplitude during the N100 time frame compared to non-responders (p < .001). Topographically, after Bonferroni correction, F4 is the only electrode at which its baseline N100 amplitude showed a significant positive association (p < .001) with treatment outcome. LIMITATIONS: Lack of control group and auditory masking. CONCLUSION: Baseline N100 amplitude showed a strong association with treatment outcome and thus demonstrated great potential to be utilized as a cost-effective and widely adoptable biomarker of rTMS treatment in MDD.

Insight into motor fatigue mechanisms in natalizumab treated multiple sclerosis patients with wearing off.

Motor fatigue in Multiple Sclerosis (MS) is due to reduced motor cortex (M1) output and altered sensorimotor network (SMN) modulation. Natalizumab, a disease-modifying therapy, reduces neuroinflammation and improves fatigue. However, some patients treated with natalizumab experience fatigue recurrence ('wearing-off') before subsequent infusions. Wearing-off provides a valuable window into MS-related motor fatigue mechanisms in a controlled, clinically stable, setting. This study investigates whether wearing-off is associated with worsening motor fatigue and its neurophysiological mechanisms and assesses natalizumab's effect on MS-related fatigue. Forty-five relapsing-remitting MS patients with wearing-off symptoms were evaluated pre- and post-natalizumab infusion. Assessments included evaluating disability levels, depressive symptoms, and the impact of fatigue symptoms on cognitive, physical, and psychosocial functioning. The motor fatigue index was computed through the number of blocks completed during a fatiguing task and peripheral, central, and supraspinal fatigue (M1 output) were evaluated by measuring the superimposed twitches evoked by peripheral nerve and transcranial magnetic stimulation of M1. Transcranial magnetic stimulation-electroencephalography assessed M1 effective connectivity by measuring TMS-evoked potentials (TEPs) within the SMN before- and after the task. We found that wearing-off was associated with increased motor fatigue index, increased central and supraspinal fatigue, and diminished task-related modulation of TEPs compared to post-natalizumab infusion. Wearing-off was also associated with worsened fatigue impact and depression symptom scores. We conclude that the wearing-off phenomenon is associated with worsening motor fatigue due to altered M1 output and modulation of the SMN. Motor fatigue in MS may reflect reversible, inflammation-related changes in the SMN that natalizumab can modulate. Our findings apply primarily to MS patients receiving natalizumab, emphasizing the need for further research on other treatments with wearing-off.

Assessing the impact of repetitive transcranial magnetic stimulation on effective connectivity in autism spectrum disorder: An initial exploration using TMS-EEG analysis.

Initial indications propose that repetitive transcranial magnetic stimulation (rTMS) could mitigate clinical manifestations in patients with autism spectrum disorder (ASD). Nevertheless, the precise mechanisms responsible for these therapeutic and behavioral outcomes remain elusive. We examined alterations in effective connectivity induced by rTMS using concurrent transcranial magnetic stimulation and electroencephalography (TMS-EEG) in children with ASD. TMS-EEG data were acquired from 12 children diagnosed with ASD both before and following rTMS treatment. The rTMS intervention regimen included delivering 5-s trains at a frequency of 15 Hz, with 10-min intervals between trains, targeting the left parietal lobe. This was conducted on each consecutive weekday over 3 weeks, totaling 15 sessions. The dynamic EEG network analysis revealed that following the rTMS intervention, long-range feedback connections within the brains of ASD patients were strengthened (e.g., frontal to parietal regions, frontal to occipital regions, and frontal to posterior temporal regions), and short-range connections were weakened (e.g., between the bilateral occipital regions, and between the occipital and posterior temporal regions). In alignment with alterations in network connectivity, there was a corresponding amelioration in fundamental ASD symptoms, as assessed through clinical scales post-treatment. According to our findings, people with ASD may have increased long-range frontal-posterior feedback connection on application of rTMS to the parietal lobe.

Real-time optimization to enhance noninvasive cortical excitability assessment in the human dorsolateral prefrontal cortex.

Objective: We currently lack a robust noninvasive method to measure prefrontal excitability in humans. Concurrent TMS and EEG in the prefrontal cortex is usually confounded by artifacts. Here we asked if real-time optimization could reduce artifacts and enhance a TMS-EEG measure of left prefrontal excitability. Methods: This closed-loop optimization procedure adjusts left dlPFC TMS coil location, angle, and intensity in real-time based on the EEG response to TMS. Our outcome measure was the left prefrontal early (20-60 ms) and local TMS-evoked potential (EL-TEP). Results: In 18 healthy participants, this optimization of coil angle and brain target significantly reduced artifacts by 63% and, when combined with an increase in intensity, increased EL-TEP magnitude by 75% compared to a non-optimized approach. Conclusions: Real-time optimization of TMS parameters during dlPFC stimulation can enhance the EL-TEP. Significance: Enhancing our ability to measure prefrontal excitability is important for monitoring pathological states and treatment response.

Mapping cortical excitability in the human dorsolateral prefrontal cortex.

BACKGROUND: Transcranial magnetic stimulation (TMS) to the dorsolateral prefrontal cortex (dlPFC) is an effective treatment for depression, but the neural effects after TMS remains unclear. TMS paired with electroencephalography (TMS-EEG) can causally probe these neural effects. Nonetheless, variability in single pulse TMS-evoked potentials (TEPs) across dlPFC subregions, and potential artifact induced by muscle activation, necessitate detailed mapping for accurate treatment monitoring. OBJECTIVE: To characterize early TEPs anatomically and temporally (20-50 ms) close to the TMS pulse (EL-TEPs), as well as associated muscle artifacts (<20 ms), across the dlPFC. We hypothesized that TMS location and angle influence EL-TEPs, and specifically that conditions with larger muscle artifact may exhibit lower observed EL-TEPs due to over-rejection during preprocessing. Additionally, we sought to determine an optimal group-level TMS target and angle, while investigating the potential benefits of a personalized approach. METHODS: In 16 healthy participants, we applied single-pulse TMS to six targets within the dlPFC at two coil angles and measured EEG responses. RESULTS: Stimulation location significantly influenced observed EL-TEPs, with posterior and medial targets yielding larger EL-TEPs. Regions with high EL-TEP amplitude had less muscle artifact, and vice versa. The best group-level target yielded 102% larger EL-TEP responses compared to other dlPFC targets. Optimal dlPFC target differed across subjects, suggesting that a personalized targeting approach might boost the EL-TEP by an additional 36%. SIGNIFICANCE: EL-TEPs can be probed without significant muscle-related confounds in posterior-medial regions of the dlPFC. The identification of an optimal group-level target and the potential for further refinement through personalized targeting hold significant implications for optimizing depression treatment protocols.

Breakdown of effective information flow in disorders of consciousness: Insights from TMS-EEG.

BACKGROUND: The complexity of the neurophysiological mechanisms underlying human consciousness is widely acknowledged, with information processing and flow originating in cortex conceived as a core mechanism of consciousness emergence. Combination of transcranial magnetic stimulation and electroencephalography (TMS-EEG) is considered as a promising technique to understand the effective information flow associated with consciousness. OBJECTIVES: To investigate information flow with TMS-EEG and its relationship to different consciousness states. METHODS: We applied an effective information flow analysis by combining time-varying multivariate adaptive autoregressive model and adaptive directed transfer function on TMS-EEG data of frontal, motor and parietal cortex in patients with disorder of consciousness (DOC), including 14 vegetative state/unresponsive wakefulness syndrome (VS/UWS) patients, 21 minimally conscious state (MCS) patients, and 22 healthy subjects. RESULTS: TMS in DOC patients, particularly VS/UWS, induced a significantly weaker effective information flow compared to healthy subjects. The bidirectional directed information flow was lost in DOC patients with TMS of frontal, motor and parietal cortex. The interactive ROI rate of the information flow network induced by TMS of frontal and parietal cortex was significantly lower in VS/UWS than in MCS. The interactive ROI rate correlated with DOC clinical scales. CONCLUSIONS: TMS-EEG revealed a physiologically relevant correlation between TMS-induced information flow and levels of consciousness. This suggests that breakdown of effective cortical information flow serves as a viable marker of human consciousness. SIGNIFICANCE: Findings offer a unique perspective on the relevance of information flow in DOC, thus providing a novel way of understanding the physiological basis of human consciousness.

Changes in Cortical Activation by Transcranial Magnetic Stimulation Due to Coil Rotation Are Not Attributable to Cranial Muscle Activation.

Transcranial magnetic stimulation coupled with electroencephalography (TMS-EEG) allows for the study of brain dynamics in health and disease. Cranial muscle activation can decrease the interpretability of TMS-EEG signals by masking genuine EEG responses and increasing the reliance on preprocessing methods but can be at least partly prevented by coil rotation coupled with the online monitoring of signals; however, the extent to which changing coil rotation may affect TMS-EEG signals is not fully understood. Our objective was to compare TMS-EEG data obtained with an optimal coil rotation to induce motor evoked potentials (M1standard) while rotating the coil to minimize cranial muscle activation (M1emg). TMS-evoked potentials (TEPs), TMS-related spectral perturbation (TRSP), and intertrial phase clustering (ITPC) were calculated in both conditions using two different preprocessing pipelines based on independent component analysis (ICA) or signal-space projection with source-informed reconstruction (SSP-SIR). Comparisons were performed with cluster-based correction. The concordance correlation coefficient was computed to measure the similarity between M1standard and M1emg TMS-EEG signals. TEPs, TRSP, and ITPC were significantly larger in M1standard than in M1emg conditions; a lower CCC than expected was also found. These results were similar across the preprocessing pipelines. While rotating the coil may be advantageous to reduce cranial muscle activation, it may result in changes in TMS-EEG signals; therefore, this solution should be tailored to the specific experimental context.

Monitoring Changes in TMS-Evoked EEG and EMG Activity During 1†Hz rTMS of the Healthy Motor Cortex.

Repetitive transcranial magnetic stimulation (rTMS) is a non-invasive brain stimulation technique capable of inducing neuroplasticity as measured by changes in peripheral muscle electromyography (EMG) or electroencephalography (EEG) from pre-to-post stimulation. However, temporal courses of neuromodulation during ongoing rTMS are unclear. Monitoring cortical dynamics via TMS-evoked responses using EMG (motor-evoked potentials; MEPs) and EEG (transcranial-evoked potentials; TEPs) during rTMS might provide further essential insights into its mode of action - temporal course of potential modulations. The objective of this study was to first evaluate the validity of online rTMS-EEG and rTMS-EMG analyses, and second to scrutinize the temporal changes of TEPs and MEPs during rTMS. As rTMS is subject to high inter-individual effect variability, we aimed for single-subject analyses of EEG changes during rTMS. Ten healthy human participants were stimulated with 1,000 pulses of 1 Hz rTMS over the motor cortex, while EEG and EMG were recorded continuously. Validity of MEPs and TEPs measured during rTMS was assessed in sensor and source space. Electrophysiological changes during rTMS were evaluated with model fitting approaches on a group- and single-subject level. TEPs and MEPs appearance during rTMS was consistent with past findings of single pulse experiments. Heterogeneous temporal progressions, fluctuations or saturation effects of brain activity were observed during rTMS depending on the TEP component. Overall, global brain activity increased over the course of stimulation. Single-subject analysis revealed inter-individual temporal courses of global brain activity. The present findings are in favor of dose-response considerations and attempts in personalization of rTMS protocols.

The effect of EEG microstate on the characteristics of TMS-EEG.

OBJECTIVE: Differences in neural states at the time of transcranial magnetic stimulation (TMS) can lead to variations in the effectiveness of TMS stimulation. Strategies that aim to lock neural activity states and improve the precision of stimulation timing in TMS optimization should gradually receive attention. One feasible approach is to utilize microstate locking for TMS stimulation, and understanding the impact of microstates at the time of stimulation on TMS response forms the foundation of this approach. APPROACH: TMS-EEG data were extracted from 21 healthy subjects through experiments. Based on the different microstates at the time of stimulation, the trials were classified into four datasets. TMS-evoked potential (TEP), topographical distribution, and natural frequency, were computed for each dataset to explore the differences in TMS-EEG characteristics across different microstates. MAIN RESULTS: The N100 component of microstate C group (-2.376 µV) was significantly higher (p = 0.003) than of microstate D group (-1.739 µV), and the P180 component of microstate D group (2.482 µV) was significantly higher (p = 0.024) than of microstate B group (1.766 µV) and slightly higher (p = 0.058) than of microstate C group (1.863 µV) by calculating the ROI. The topographical distribution of TEP components during microstate C and microstate D still retained the template characteristics of the microstate at the time of stimulation, and the natural frequencies did not differ among the four classical microstates. SIGNIFICANCE: This study showed the potential for future closed-loop TMS based on microstates and would guiding the development of microstate-based closed-loop TMS techniques.

Efficacy of transcranial photobiomodulation in the treatment for major depressive disorder: A TMS-EEG and pilot study.

BACKGROUND: Major depressive disorder (MDD) was a prevalent mental condition that may be accompanied by decreased excitability of left frontal pole (FP) and abnormal brain connections. An 820 nm tPBM can induce an increase in stimulated cortical excitability. The purpose of our study was to establish how clinical symptoms and time-varying brain network connectivity of MDD were affected by transcranial photobiomodulation (tPBM). METHODS: A total of 11 patients with MDD received 820 nm tPBM targeting the left FP for 14 consecutive days. The severity of symptoms was evaluated by neuropsychological assessments at baseline, after treatment, 4-week and 8-week follow-up; 8-min transcranial magnetic stimulation combined electroencephalography (TMS-EEG) was performed for five healthy controls and five patients with MDD before and after treatment, and time-varying EEG network was analyzed using the adaptive-directed transfer function. RESULTS: All of scales scores in the 11 patients decreased significantly after 14-day tPBM (p < .01) and remained at 8-week follow-up. The time-varying brain network analysis suggested that the brain regions with enhanced connection information outflow in MDD became gradually more similar to healthy controls after treatment. CONCLUSIONS: This study showed that tPBM of the left FP could improve symptoms of patients with MDD and normalize the abnormal network connections.

Decreased short-latency afferent inhibition in individuals with mild cognitive impairment: A TMS-EEG study.

TMS combined with EEG (TMS-EEG) is a tool to characterize the neurophysiological dynamics of the cortex. Among the TMS paradigms, short-latency afferent inhibition (SAI) allows the investigation of inhibitory effects mediated by the cholinergic system. The aim of this study was to compare cholinergic function in the DLPFC between individuals with mild cognitive impairment (MCI) and healthy controls (HC) using TMS-EEG with the SAI paradigm. In this study, 30 MCI and 30 HC subjects were included. The SAI paradigm consisted of 80 single pulse TMS and 80 SAI stimulations applied to the left DLPFC. N100 components, global mean field power (GMFP) and total power were calculated. As a result, individuals with MCI showed reduced inhibitory effects on N100 components and GMFP at approximately 100 ms post-stimulation and on ß-band activity at 200 ms post-stimulation compared to HC. Individuals with MCI showed reduced SAI, suggesting impaired cholinergic function in the DLPFC compared to the HC group. We conclude that these findings underscore the clinical applicability of the TMS-EEG method as a powerful tool for assessing cholinergic function in individuals with MCI.

Neuroplasticity of the left dorsolateral prefrontal cortex in patients with treatment-resistant depression as indexed with paired associative stimulation: a TMS-EEG study.

Major depressive disorder affects over 300 million people globally, with approximately 30% experiencing treatment-resistant depression (TRD). Given that impaired neuroplasticity underlies depression, the present study focused on neuroplasticity in the dorsolateral prefrontal cortex (DLPFC). Here, we aimed to investigate the differences in neuroplasticity between 60 individuals with TRD and 30 age- and sex-matched healthy controls (HCs). To induce neuroplasticity, participants underwent a paired associative stimulation (PAS) paradigm involving peripheral median nerve stimulation and transcranial magnetic stimulation (TMS) targeting the left DLPFC. Neuroplasticity was assessed by using measurements combining TMS with EEG before and after PAS. Both groups exhibited significant increases in the early component of TMS-evoked potentials (TEP) after PAS (P < 0.05, paired t-tests with the bootstrapping method). However, the HC group demonstrated a greater increase in TEPs than the TRD group (P = 0.045, paired t-tests). Additionally, event-related spectral perturbation analysis highlighted that the gamma power significantly increased after PAS in the HC group, whereas it was decreased in the TRD group (P < 0.05, paired t-tests with the bootstrapping method). This gamma power modulation revealed a significant group difference (P = 0.006, paired t-tests), indicating an inverse relationship for gamma power modulation. Our findings underscore the impaired neuroplasticity of the DLPFC in individuals with TRD.

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.

Development of Artificial Intelligence for Determining Major Depressive Disorder Based on Resting-State EEG and Single-Pulse Transcranial Magnetic Stimulation-Evoked EEG Indices.

Depression is the disorder with the greatest socioeconomic burdens. Its diagnosis is still based on an operational diagnosis derived from symptoms, and no objective diagnostic indicators exist. Thus, the present study aimed to develop an artificial intelligence (AI) model to aid in the diagnosis of depression from electroencephalography (EEG) data by applying machine learning to resting-state EEG and transcranial magnetic stimulation (TMS)-evoked EEG acquired from patients with depression and healthy controls. Resting-state EEG and single-pulse TMS-EEG were acquired from 60 patients and 60 healthy controls. Power spectrum analysis, phase synchronization analysis, and phase-amplitude coupling analysis were conducted on EEG data to extract feature candidates to apply different types of machine learning algorithms. Furthermore, to address the limitation of the sample size, dimensionality reduction was performed in a manner to increase the quality of information by featuring robust neurophysiological metrics that showed significant differences between the two groups. Then, nine different machine learning models were applied to the data. For the EEG data, we created models combining four modalities, including (1) resting-state EEG, (2) pre-stimulus TMS-EEG, (3) post-stimulus TMS-EEG, and (4) differences between pre- and post-stimulus TMS-EEG, and evaluated their performance. We found that the best estimation performance (a mean area under the curve of 0.922) was obtained using receiver operating characteristic curve analysis when linear discriminant analysis (LDA) was applied to the combination of the four feature sets. This study showed that by using TMS-EEG neurophysiological indices as features, it is possible to develop a depression decision-support AI algorithm that exhibits high discrimination accuracy.

Unconsciousness or unresponsiveness in akinetic mutism? Insights from a multimodal longitudinal exploration.

The clinical assessment of patients with disorders of consciousness (DoC) relies on the observation of behavioural responses to standardised sensory stimulation. However, several medical comorbidities may directly impair the production of reproducible and appropriate responses, thus reducing the sensitivity of behaviour-based diagnoses. One such comorbidity is akinetic mutism (AM), a rare neurological syndrome characterised by the inability to initiate volitional motor responses, sometimes associated with clinical presentations that overlap with those of DoC. In this paper, we describe the case of a patient with large bilateral mesial frontal lesions, showing prolonged behavioural unresponsiveness and severe disorganisation of electroencephalographic (EEG) background, compatible with a vegetative state/unresponsive wakefulness syndrome (VS/UWS). By applying an unprecedented multimodal battery of advanced imaging and electrophysiology-based techniques (AIE) encompassing spontaneous EEG, evoked potentials, event-related potentials, transcranial magnetic stimulation combined with EEG and structural and functional MRI, we provide the following: (i) a demonstration of the preservation of consciousness despite unresponsiveness in the context of AM, (ii) a plausible neurophysiological explanation for behavioural unresponsiveness and its subsequent recovery during rehabilitation stay and (iii) novel insights into the relationships between DoC, AM and parkinsonism. The present case offers proof-of-principle evidence supporting the clinical utility of a multimodal hierarchical workflow that combines AIEs to detect covert signs of consciousness in unresponsive patients.