Healthy aging is accompanied by subtle difficulties in language production. While behavioral and neuroimaging studies suggest that older adults rely on acute semantic access to maintain language abilities, the underlying neurophysiological mechanisms remain poorly understood. In particular, it is still unclear how large-scale brain dynamics reorganize to support naturalistic sentence generation with age. In this study, we investigated the spatiotemporal brain-state dynamics during covert sentence generation (GE2REC protocol) in younger and older adults using magnetoencephalography (MEG). Source-reconstructed MEG signals were analyzed using a Hidden Markov Model which identified five recurrent brain states, encompassing language-semantic, language-control, sensorimotor, and visual domains. Latent modeling was then used to relate the spectral and temporal properties of these brain states to age and language performance. Spectrally, older adults appear to redistribute oscillatory activity from sensorimotor-related states toward semantic-related states across alpha, beta, and low-gamma frequency bands. Temporally, older adults exhibit a more segmented processing sequence between semantic and sensorimotor processing which interfaces with visuo-posterior processing. These changes robustly covaried with age and better verbal fluency (semantic and lexical). Taken together, these results suggest that the older adult brain undergoes a coordinated time-frequency reorganization to support sentence production. Older individuals likely establish an embodied semantic strategy that involves a more segmented processing sequence during sentence production via visuo-posterior information processing. We speculate that this may help shape a resource-efficient, predictive route for complex cognition in older adulthood.

Transcranial magnetic stimulation (TMS) is a non-invasive technique to stimulate the brain, while electroencephalography (EEG) is a non-invasive technique to record its electrical activity. Their combined use (TMS-EEG) has been established only relatively recently, after successful development of TMS-compatible EEG amplifiers. TMS-EEG offers the unparalleled opportunity to directly perturb the brain with TMS and simultaneously record its response with EEG. This allows inferences on causal input-output relationships, therefore going critically beyond purely observational techniques, such as resting-state EEG or functional MRI, in the study of brain dynamics. This consensus review updates the work of Tremblay and coworkers [Clin Neurophysiol 2019; 130: 802-844]. Since then, substantial advances have been made in understanding contamination of TMS-EEG signals by physiological and non-physiological artifacts, as well as in developing strategies to avoid or control them. In parallel, new insights have emerged regarding the physiological mechanisms underlying TMS-EEG responses and their diagnostic and prognostic utility in a broad range of psychiatric and neurological disorders. As such, TMS-EEG is rapidly shaping a dynamic new field in clinical neurophysiology and neuroscience. This review provides a critical and comprehensive synthesis of current knowledge, including practical guidance for implementing TMS-EEG in the clinical setting.

BackgroundStroke is the leading cause of long-term disability, making the search for successful rehabilitation treatment one of the most important public health issues. A better understanding of the neural mechanisms underlying impairment and recovery is critical for optimizing treatments. Objective: We studied the longitudinal changes in brain oscillatory modes, linked to GABAergic system activity, and determined their importance for residual upper-limb motor functions and recovery.MethodsTranscranial Magnetic Stimulation (TMS) was combined with scalp Electroencephalography (EEG) to analyze TMS-induced brain oscillations in a cohort of 66 stroke patients in the acute (N = 60), early (N = 48), and late subacute stages (N = 37).ResultsA data-driven parallel factor analysis (PARAFAC) approach to tensor decomposition extracted brain oscillatory modes, which significantly evolved longitudinally across stroke stages (permutation tests, pBonf < 0.05). Notably, the observed decrease of the α-mode, known to be linked with GABAergic system activity, was mainly driven by the recovering patients and was supportive of stroke recovery at the group level (Bayesian Kendall correlation, moderate to strong statistical evidence).ConclusionsOverall, longitudinal evaluation of brain modes provides novel insights into functional reorganization of brain networks after a stroke. Notably, we propose that the observed α-mode decrease could correspond to a beneficial disinhibition toward the late subacute stage that fosters plasticity and facilitates recovery. These results confirm the relevance of future individual and direct monitoring of post-stroke modulations in inhibitory system activity, with the ultimate goal of designing electrophysiological biomarkers and refining therapies based on personalized neuromodulation.

Objective.Transcranial magnetic stimulation (TMS) combined with electroencephalography (EEG) has become a valuable tool in clinical and cognitive neuroscience. However, TMS-EEG signals often suffer from severe artifacts, particularly in lateral cortical regions where TMS-evoked muscle artifacts are pronounced, making real-time recovery of TMS-evoked potentials (TEPs) challenging. We developed and validated a real-time, two-step independent component analysis (ICA)-based artifact cleaning method for TMS-EEG signals, facilitating the rapid extraction of clean neural signals for closed-loop neurostimulation applications.Approach.Our method involves an offline ICA training phase, where ICA weights and artifact topographies are identified using pre-experimental trials, followed by an online phase in which the precomputed weight matrices are applied in real-time to incoming data. We conducted simulations on two pre-published TMS-EEG datasets (N= 28, ROIs = 6) to validate the method by identifying the minimum number of trials required to estimate ICA weights. We also assessed the reproducibility of TEPs and the stability of ICA components, taking classical offline TEPs as the relative ground truth.Main Results.ICA analysis suggests that it can be applied reliably within each region without significant loss of convergence and stability, provided careful consideration is given to the size and composition of the data used for ICA training. Simulation results indicated that while central regions could achieve reliable TEPs similar to ground truth with as few as 20-30 trials to train ICA in the pre-experimental phase, frontal and occipital regions required 50-60 trials to reach a comparable level of reliability. Later TEP peaks (>100 ms) in all regions achieved high reproducibility when at least 35 training trials were used, whereas earlier peaks (<80 ms) showed moderate reproducibility with the same number of trials.Significance.These findings establish the feasibility and proof-of-concept for real-time ICA-based artifact removal for closed-loop TMS-EEG applications. The method enables rapid extraction of clean neural signals, allowing adaptation of stimulation parameters in real time, thereby facilitating individualized neurostimulation paradigms.

Lexical production performances have been associated with cognitive control demands increase with age to support efficient semantic access, thus suggesting an interplay between a domain-general and a language-specific component. Current neurocognitive models suggest the Default Mode Network (DMN) and Fronto-Parietal Network (FPN) connectivity may drive this interplay, impacting the trajectory of production performance with a pivotal shift around midlife. However, the corresponding time-varying architecture still needs clarification. Here, we leveraged MEG resting-state data from healthy adults aged 18-88 years from a CamCAN population-based sample. We found that DMN-FPN dynamics shift from anterior-ventral to posterior-dorsal states until midlife to mitigate word-finding challenges, concurrent with heightened alpha-band oscillations. Specifically, sensorimotor integration along this posterior path could facilitate cross-talk with lower-level circuitry as dynamic information flow with more anterior, higher-order cognitive states gets compromised. This suggests a bottom-up, exploitation-based form of cognitive control in the aging brain, highlighting the interplay between abstraction, control, and perceptive-motor systems in preserving lexical production.

Complex neural activity of the motor cortex is posited to serve as the foundation for a large repertoire of activation patterns crucial for executing movements. As transcranial magnetic stimulation (TMS) predominantly activates monosynaptic fast-conducting corticospinal projections, which are involved in dexterous movement control, the complexity of neural outputs elicited by TMS may reflect an underlying repertoire of activation patterns crucial for executing dexterous movements. We proposed to quantify dimensionality of multimuscle motor-evoked potentials (MEPs) through dimensionality reduction as an integrated measure to reflect the complexity of neural outputs elicited by TMS. For its validation, we conducted two experiments focusing on different stimulus parameters: stimulus intensity (experiment 1: n = 40) and size of motor mapping (experiment 2: n = 35). The present findings demonstrated that lower intensities resulted in higher complexity and vice versa but no effects of different sizes of motor mapping on complexity. Analyses incorporating disparities in MEP amplitude across different muscles supported that complexity was effectively captured through dimensionality reduction. Notably, complexity was minimally influenced by the selection of muscles and was associated with individual differences in motor cortex anatomy. We performed two fingers alternating tapping as an index of dexterous movements, and the results demonstrated its association with the complexity of neural outputs elicited by TMS: higher complexity corresponded to better dexterous performance. The proposed complexity measure might reflect neural processes aligned with the principle of motor abundance. The framework complements well-established MEP analyses and offers a novel perspective for investigating the motor system.NEW & NOTEWORTHY The study introduces a framework to assess the complexity of neural outputs elicited by transcranial magnetic stimulation (TMS). We demonstrated that complexity, obtained from multimuscle motor-evoked potentials (MEPs) through dimensionality reduction, varied depending on stimulus intensities, was minimally influenced by the selection of muscles, and was associated with individual differences in motor cortex anatomy. Importantly, individuals with higher complexity exhibited better dexterous performance. The framework offers a novel perspective for investigating the motor system.

Recent research suggests that autistic females may have superior socio-cognitive abilities compared to autistic males, potentially contributing to underdiagnosis in females. However, it remains unclear whether these differences arise from distinct neurophysiological functioning in autistic males and females. This study addresses this question by presenting 41 autistic and 48 non-autistic adults with a spatially filtered faces oddball paradigm. Analysis of event-related potentials from scalp electroencephalography reveal a neurophysiological profile in autistic females that fell between those of autistic males and non-autistic females, highlighting sex differences in autism from the initial stages of face processing. This finding underscores the urgent need to explore neurophysiological sex differences in autism and encourages efforts toward a better comprehension of compensation mechanism and a clearer definition of what is meant by camouflaging.

BACKGROUND:Cortical excitation/inhibition dynamics have been suggested as a key mechanism occurring after stroke. Their supportive or maladaptive role in the course of recovery is still not completely understood. Here, we used transcranial magnetic stimulation (TMS)-electroencephalography coupling to study cortical reactivity and intracortical GABAergic inhibition, as well as their relationship to residual motor function and recovery longitudinally in patients with stroke. METHODS:Electroencephalography responses evoked by TMS applied to the ipsilesional motor cortex were acquired in patients with stroke with upper limb motor deficit in the acute (1 week), early (3 weeks), and late subacute (3 months) stages. Readouts of cortical reactivity, intracortical inhibition, and complexity of the evoked dynamics were drawn from TMS-evoked potentials induced by single-pulse and paired-pulse TMS (short-interval intracortical inhibition). Residual motor function was quantified through a detailed motor evaluation. RESULTS:From 76 patients enrolled, 66 were included (68.2±13.2 years old, 18 females), with a Fugl-Meyer score of the upper extremity of 46.8±19. The comparison with TMS-evoked potentials of healthy older revealed that most affected patients exhibited larger and simpler brain reactivity patterns (Pcluster<0.05). Bayesian ANCOVA statistical evidence for a link between abnormally high motor cortical excitability and impairment level. A decrease in excitability in the following months was significantly correlated with better motor recovery in the whole cohort and the subgroup of recovering patients. Investigation of the intracortical GABAergic inhibitory system revealed the presence of beneficial disinhibition in the acute stage, followed by a normalization of inhibitory activity. This was supported by significant correlations between motor scores and the contrast of local mean field power and readouts of signal dynamics. CONCLUSIONS:The present results revealed an abnormal motor cortical reactivity in patients with stroke, which was driven by perturbations and longitudinal changes within the intracortical inhibition system. They support the view that disinhibition in the ipsilesional motor cortex during the first-week poststroke is beneficial and promotes neuronal plasticity and recovery.

Aging populations face significant cognitive challenges, particularly in working memory (WM). Transcranial alternating current stimulation (tACS) offer promising avenues for cognitive enhancement, especially when inspired by brain physiology. This study (NCT04986787) explores the effect of multifocal tACS on WM performance in healthy older adults, focusing on fronto-parietal network modulation. Individualized physiology-inspired tACS applied to the fronto-parietal network was investigated in two blinded cross-over experiments. The first experiment involved monofocal/bifocal theta-tACS to the fronto-parietal network, while in the second experiment cross-frequency theta-gamma interactions between these regions were explored. Participants have done online WM tasks under the stimulation conditions. Network connectivity was assessed via rs-fMRI and multichannel electroencephalography. Prefrontal monofocal theta tACS modestly improved WM accuracy over sham (d = 0.30). Fronto-parietal stimulation enhanced WM task processing speed, with the strongest effects for bifocal in-phase theta tACS (d = 0.41). Cross-frequency stimulations modestly boosted processing speed with or without impairing task accuracy depending on the stimulation protocol. This research adds to the understanding of physiology-inspired brain stimulation for cognitive enhancement in older subjects.

Objective.The literature investigating the effects of alpha oscillations on corticospinal excitability is divergent. We believe inconsistency in the findings may arise, among others, from the electroencephalography (EEG) processing for brain-state determination. Here, we provide further insights in the effects of the brain-state on cortical and corticospinal excitability and quantify the impact of different EEG processing.Approach.Corticospinal excitability was measured using motor evoked potential (MEP) peak-to-peak amplitudes elicited with transcranial magnetic stimulation (TMS); cortical responses were studied through TMS-evoked potentials' TEPs features. A TMS-EEG-electromyography (EMG) dataset of 18 young healthy subjects who received 180 single-pulse (SP) and 180 paired pulses (PP) to determine short-intracortical inhibition (SICI) was investigated. To study the effect of different EEG processing, we compared the brain-state estimation deriving from three published methods. The influence of presence of neural oscillations was also investigated. To evaluate the effect of the brain-state on MEP and TEP features variability, we defined the brain-state based on specific EEG phase and power combinations, only in trials where neural oscillations were present. The relationship between TEPs and MEPs was further evaluated.Main results.The presence of neural oscillations resulted in more consistent results regardless of the EEG processing approach. Nonetheless, the latter still critically affected the outcomes, making conclusive claims complex. With our approach, the MEP amplitude was positively modulated by the alpha power and phase, with stronger responses during the trough phase and high power. Power and phase also affected TEP features. Importantly, similar effects were observed in both TMS conditions.Significance.These findings support the view that the brain state of alpha oscillations is associated with the variability observed in cortical and corticospinal responses to TMS, with a tight correlation between the two. The results further highlight the importance of closed-loop stimulation approaches while underlining that care is needed in designing experiments and choosing the analytical approaches, which should be based on knowledge from offline studies to control for the heterogeneity originating from different EEG processing strategies.

Face processing relies on predictive processes driven by low spatial frequencies (LSF) that convey coarse information prior to fine information conveyed by high spatial frequencies. However, autistic individuals might have atypical predictive processes, contributing to facial processing difficulties. This may be more normalized in autistic females, who often exhibit better socio-communicational abilities than males. We hypothesized that autistic females would display a more typical coarse-to-fine processing for socio-emotional stimuli compared to autistic males. To test this hypothesis, we asked adult participants (44 autistic, 51 non-autistic) to detect fearful faces among neutral faces, filtered in two orders: from coarse-to-fine (CtF) and from fine-to-coarse (FtC). Results show lower d' values and longer reaction times for fearful detection in autism compared to non-autistic (NA) individuals, regardless of the filtering order. Both groups presented shorter P100 latency after CtF compared to FtC, and larger amplitude for N170 after FtC compared to CtF. However, autistic participants presented a reduced difference in source activity between CtF and FtC in the fusiform. There was also a more spatially spread activation pattern in autistic females compared to NA females. Finally, females had faster P100 and N170 latencies, as well as larger occipital activation for FtC sequences than males, irrespective of the group. Overall, the results do not suggest impaired predictive processes from LSF in autism despite behavioral differences in fear detection. However, they do indicate reduced brain modulation by spatial frequency in autism. In addition, the findings highlight sex differences that warrant consideration in understanding autistic females.

The growing popularity of virtual reality systems has led to a renewed interest in understanding the neurophysiological correlates of the illusion of self-motion (vection), a phenomenon that can be both intentionally induced or avoided in such systems, depending on the application. Recent research has highlighted the modulation of α power oscillations over the superior parietal cortex during vection, suggesting the occurrence of inhibitory mechanisms in the sensorimotor and vestibular functional networks to resolve the inherent visuo-vestibular conflict. The present study aims to further explore this relationship and investigate whether neuromodulating these waves could causally affect the quality of vection. In a crossover design, 22 healthy volunteers received high amplitude and focused α-tACS (transcranial alternating current stimulation) over the superior parietal cortex while experiencing visually induced vection triggered by optokinetic stimulation. The tACS was tuned to each participant's individual α peak frequency, with θ-tACS and sham stimulation serving as controls. Overall, participants experienced better quality vection during α-tACS compared with control θ-tACS and sham stimulations, as quantified by the intensity of vection. The observed neuromodulation supports a causal relationship between parietal α oscillations and visually induced self-motion illusions, with their entrainment triggering overinhibition of the conflict within the sensorimotor and vestibular functional networks. These results confirm the potential of noninvasive brain stimulation for modulating visuo-vestibular conflicts, which could help to enhance the sense of presence in virtual reality environments.

Lexical production remains relatively preserved across the lifespan, but cognitive control demands increase with age to support efficient semantic access. It suggests a domain-general and a language-specific component. Current neurocognitive models suggest the Default Mode Network (DMN) may drive the interplay between these components, impacting the trajectory of production performance with a pivotal shift around midlife. However, the corresponding time-varying architecture still needs clarification. Here, we leveraged MEG resting-state data from healthy adults aged 18-88 from a CamCAN population-based sample. We found that DMN temporal dynamics shift from anterior-ventral to posterior-dorsal states until midlife to mitigate word-finding challenges. Similarly, sensorimotor integration along this posterior path enhances cross-talk with lower-level circuitry as the dynamic information flow with more anterior, higher-order cognitive states gets compromised. It suggests a bottom-up, exploitation-based form of cognitive control in the aging brain, highlighting the interplay between abstraction, control, and perceptive-motor systems in preserving lexical production. ### Competing Interest Statement The authors have declared no competing interest.

Background: Around 25% of patients who have had a stroke suffer from severe upper-limb impairment and lack effective rehabilitation strategies. The AVANCER proof-of-concept clinical trial (NCT04448483) tackles this issue through an intensive and personalized-dosage cumulative inter-vention that combines multiple non-invasive neurotechnologies. Methods: The therapy consists of two sequential interventions, lasting until the patient shows no further motor improvement, for a minimum of 11 sessions each. The first phase involves a brain-computer interface governing an exoskeleton and multi-channel functional electrical stim-ulation enabling full upper-limb movements. The second phase adds anodal transcranial direct current stimulation of the motor cortex of the lesioned hemisphere. Clinical, electrophysiological, and neuroi-maging examinations are performed before, between, and after the two interventions (T0, T1, and T2). This case report presents the results from the first patient of the study. Findings: The primary outcome (i.e., 4-point improvement in the Fugl-Meyer assessment of the upper extremity) was met in the first patient, with an increase from 6 to 11 points between T0 and T2. This improvement was paralleled by changes in motor-network structure and function. Resting-state and transcranial magnetic stimulation -evoked electroencephalography revealed brain functional changes, and magnetic resonance imaging (MRI) measures detected structural and task-related functional changes. Conclusions: These first results are promising, pointing to feasibility, safety, and potential efficacy of this personalized approach acting syn-ergistically on the nervous and musculoskeletal systems. Integrating multi-modal data may provide valuable insights into underlying mech-anisms driving the improvements and providing predictive information regarding treatment response and outcomes.

IntroductionTranscranial magnetic stimulation (TMS) mapping has become a critical tool for exploratory studies of the human corticomotor (M1) organization. Here, we propose to gather existing cutting-edge TMS-EMG and TMS-EEG approaches into a combined multi-dimensional TMS mapping that considers local and whole-brain excitability changes as well as state and time-specific changes in cortical activity. We applied this multi-dimensional TMS mapping approach to patients with Parkinson’s disease (PD) with Deep brain stimulation (DBS) of the sub-thalamic nucleus (STN) ON and OFF. Our goal was to identifying one or several TMS mapping-derived markers that could provide unprecedent new insights onto the mechanisms of DBS in movement disorders.MethodsSix PD patients (1 female, mean age: 62.5 yo [59–65]) implanted with DBS-STN for 1 year, underwent a robotized sulcus-shaped TMS motor mapping to measure changes in muscle-specific corticomotor representations and a movement initiation task to probe state-dependent modulations of corticospinal excitability in the ON (using clinically relevant DBS parameters) and OFF DBS states. Cortical excitability and evoked dynamics of three cortical areas involved in the neural control of voluntary movements (M1, pre-supplementary motor area – preSMA and inferior frontal gyrus – IFG) were then mapped using TMS-EEG coupling in the ON and OFF state. Lastly, we investigated the timing and nature of the STN-to-M1 inputs using a paired pulse DBS-TMS-EEG protocol.ResultsIn our sample of patients, DBS appeared to induce fast within-area somatotopic re-arrangements of motor finger representations in M1, as revealed by mediolateral shifts of corticomuscle representations. STN-DBS improved reaction times while up-regulating corticospinal excitability, especially during endogenous motor preparation. Evoked dynamics revealed marked increases in inhibitory circuits in the IFG and M1 with DBS ON. Finally, inhibitory conditioning effects of STN single pulses on corticomotor activity were found at timings relevant for the activation of inhibitory GABAergic receptors (4 and 20 ms).ConclusionTaken together, these results suggest a predominant role of some markers in explaining beneficial DBS effects, such as a context-dependent modulation of corticospinal excitability and the recruitment of distinct inhibitory circuits, involving long-range projections from higher level motor centers and local GABAergic neuronal populations. These combined measures might help to identify discriminative features of DBS mechanisms towards deep clinical phenotyping of DBS effects in Parkinson’s Disease and in other pathological conditions.

Studies showed that motor expertise was found to induce improvement in language processing. Grounded and situated approaches attributed this effect to an underlying automatic simulation of the motor experience elicited by action words, similar to motor imagery (MI), and suggest shared representations of action conceptualization. Interestingly, recent results also suggest that the mental simulation of action by MI training induces motor-system modifications and improves motor performance. Consequently, we hypothesize that, since MI training can induce motor-system modifications, it could be used to reinforce the functional connections between motor and language system, and could thus lead to improved language performance. Here, we explore these potential interactions by reviewing recent fundamental and clinical literature in the action-language and MI domains. We suggested that exploiting the link between action language and MI could open new avenues for complementary language improvement programs. We summarize the current literature to evaluate the rationale behind this novel training and to explore the mechanisms underlying MI and its impact on language performance.

Stroke is the leading cause of motor disability in the adult population, making the understanding of the exact neural mechanisms underlying motor impairment and recovery one of the most important public health issues. Such knowledge is needed to tailor treatments to each individual patient with the ultimate goal to improve therapeutic outcomes. Using TMS-EEG coupling, we focused on the analysis of brain oscillations induced by transcranial magnetic stimulation (TMS) in a large cohort of 60 patients longitudinally, from the acute to the early chronic stage. A data-driven approach (PARAFAC tensor decomposition) allowed to unveil brain oscillatory modes that were specifically centered on the theta, alpha and beta frequency bands. Our results showed that the decomposition weight, i.e., the importance, associated to the alpha mode significantly decreased across the different stages after stroke. Furthermore, this change in alpha activity was correlated with the degree of motor recovery at the individual patient level. Overall, our results indicate that motor recovery follows changes in inhibitory activity represented by the alpha mode. We propose that the observed decrease of alpha activity represents beneficial disinhibition occurring between the subacute and early chronic stage which might foster structural and functional plasticity. Monitoring this phenomenon at the individual patient level could be critical information for programming therapies based on excitatory or inhibitory neuromodulation using non-invasive brain stimulation techniques. Research Category and Technology and Methods Translational Research: 10. Transcranial Magnetic Stimulation (TMS) Keywords: TMS-EEG, induced oscillations, stroke, desinhibition