Clinical Neurophysiology

BackgroundTranscranial magnetic stimulation (TMS) over the primary motor cortex (M1) elicits motor-evoked potentials (MEPs), a neurophysiological marker of corticospinal excitability. Ongoing brain activity at the time of stimulation, such as the phase and power of the sensorimotor mu rhythm (8-13 Hz), has a significant impact on MEP amplitudes. However, it remains unclear whether these endogenous excitability states also influence the consistency of MEP amplitudes across repeated trials. ObjectivesWe investigated whether instantaneous mu dynamics modulate not only the magnitude but also the consistency of corticospinal responses to TMS. MethodsTwenty-nine healthy participants received 1200 single TMS pulses over the left M1 during simultaneous EEG recording. Trials were stratified based on pre-stimulus mu power, phase, and interhemispheric M1-M1 functional connectivity. Brain-state-resolved MEP variability was quantified using the coefficient of variation (CV) within subsets of trials defined by similar pre-stimulus mu dynamics. ResultsTrial subsets characterized by high mu power or high M1-M1 functional connectivity were associated with reduced MEP variability, indicating more consistent corticospinal output. In contrast, the mu phase did not significantly influence response consistency. Brain-state-resolved MEP variability showed greater stability across sessions compared to MEP variability estimated from random trial subsampling. ConclusionsPre-stimulus mu dynamics shape not only magnitude but also consistency of corticospinal responses to TMS. We show that corticospinal response consistency reflects a structured, brain-state-dependent property of the sensorimotor network. These findings contribute to our mechanistic understanding of brain-state-dependent neuromodulation and may be leveraged to reduce variability and improve efficacy to TMS. HighlightsO_LIOngoing sensorimotor mu dynamics shape both magnitude and consistency of MEPs. C_LIO_LITrial subsets characterized by high mu power were associated with reduced MEP variability. C_LIO_LIMu phase modulated MEP amplitude but did not influence MEP consistency. C_LIO_LIBrain-state-resolved estimates of MEP variability were more reliable across sessions. C_LIO_LIFuture TMS protocols may reduce effect variability by targeting stable excitability states. C_LI

Cognitive decline is a major non-motor feature of Parkinsons disease (PD), but reliable and accessible biomarkers remain limited. Resting-state electroencephalography (EEG) is a promising candidate because it is low-cost, portable, and well suited to repeated assessment. Recent work has increasingly focused on source-space functional connectivity (FC) for the prediction of cognition. However, the influence of source-modelling based on an individualized MRI-based head model relative to that based on standard template model is unknown. To compare these two source-space EEG FC methods, we analysed EEG data from the New Zealand Parkinsons Progression Programme, including 136 people with PD and 51 age-similar controls. Source reconstructed resting-state EEG was parcellated with the HCP-MMP1 atlas, and used to derive amplitude envelope correlation (AEC) and debiased weighted phase lag index (dwPLI) across six canonical frequency bands. The twenty-four FC modalities were evaluated using six machine-learning regression algorithms within a nested cross-validation framework. Theta-, alpha-, and beta-band FC showed the most consistent prediction of global cognition, with the strongest performance observed for theta- and alpha-band AEC and dwPLI features (maximum R{superscript 2} = 0.170, r = 0.439). Standard and individualized head models showed comparable predictive performance across nearly all modalities. Feature-importance patterns for Cole-Anticevic networks were also highly similar between the two head-model options. These findings show that source-space resting-state EEG FC can predict cognitive performance in PD. The comparability of the two head models suggests that the more user-friendly and less resource intense standard head model template is satisfactory. This supports feasible, scalable, and clinically accessible EEG-based biomarkers of cognition in PD.

Objective. Pathological beta oscillations are a hallmark of Parkinson's Disease (PD) and are linked with symptom severity and therapeutic efficacy of deep brain stimulation (DBS). Although some studies suggest that beta oscillations may propagate from the frontal cortex to the subthalamic nucleus (STN), direct evidence based on cortical and subcortical neural recordings remains limited. This study investigates synchrony and directionality of beta-band interactions between the frontal cortex and STN in PD. Approach. Simultaneous electrocorticography and STN local field potential recordings were obtained from three PD patients undergoing awake DBS lead placement surgery. Cortical-STN beta phase synchrony was quantified using phase locking value, and directed functional connectivity was analyzed using time-resolved bivariate Granger causality. Main results. Phase locking value mapping revealed a spatially non-uniform distribution of beta phase synchrony, with the strongest coupling localized most prominently within the precentral and superior frontal gyri. Granger causality analysis demonstrated a predominance of cortical-to-subthalamic beta-band interactions across all subjects with intermittent bidirectional coupling. Significance. These findings provide evidence that pathological beta oscillations in Parkinson's may preferentially propagate from the frontal cortex to the basal ganglia, consistent with known motor pathways. These findings are consistent with a cortical contribution to pathological beta oscillations and highlight potential methods for obtaining cortical targets for phase-dependent neuromodulation.

IntroductionStimulation-evoked potentials (SEPs), recorded both during and after deep brain stimulation (DBS) surgery, have shown promise for guiding DBS targeting and programming. However, filtering protocols applied to stimulation trains produce an artifact we call a filter-induced oscillation (FIO) which closely mimics physiological SEPs. Hence, we outline the mechanistic origins of this distortion and describe a means of differentiating it from valid SEP activity. MethodsWe recorded in 18 patients undergoing DBS surgery targeting the subthalamic nucleus or globus pallidus internus. We stimulated target nuclei with cathode-first (CF) and anode-first (AF) pulses to record native SEPs, and in white matter tracts (null condition). Recordings were subsequently filtered to illustrate FIO. Next, we filtered harmonic frequencies of an artificial stimulation train to demonstrate FIO origins. Finally, FIO was deliberately generated in white matter recordings with a notch filter, and its behaviour contrasted with SEPs during AF and CF stimulation. ResultsFiltering stimulation trains produced FIOs that depended on filter order and corner frequency. We also showed that FIO emerges from filter-induced attenuations of harmonic frequencies which compose stimulation trains, producing oscillations of like frequency around pulses. Finally, FIOs reverse in polarity depending on AF or CF stimulation, whereas SEPs do not. ConclusionsGiven the potential for widespread adoption of SEPs in DBS targeting and programming, safe analytical protocols are imperative to avoid the induction of processing-related artifacts which can be misinterpreted as biological signals. Here we establish the necessary theory for identifying FIOs and tuning analytical pipelines to avoid their generation.

AIM: STXBP1-related disorder (STXBP1-RD) is a severe developmental and epileptic encephalopathy characterized by early-onset seizures and persistent cognitive and motor impairments. With disease-modifying trials emerging, a disorder-specific severity scale is needed. To address this, we adapted a validated clinician-reported measure from CDKL5 Deficiency Disorder to develop the STXBP1 Clinical Severity Assessment (S-CSA) and evaluated its psychometric properties. METHOD: The S-CSA was adapted from the CDKL5 Clinical Severity Assessment through expert consensus sessions with STXBP1 clinicians. Revisions addressed gaps in motor and vision domains, adding tremor and vision items. The measure was administered to 123 individuals with STXBP1-RD. Psychometric evaluation included confirmatory factor analysis, internal consistency, composite reliability, average variance extracted, and distinctiveness, compared with recommended thresholds. RESULTS: Analyses supported a three-domain structure (motor, communication, vision) with factor loadings >0.5 and strong internal consistency (Cronbachs alpha >0.7; composite reliability >0.88). Model fit and variance metrics met recommended standards, and domains demonstrated distinctiveness. No ceiling or floor effects were observed. Minimal skew was seen in motor (0.34) and communication (0.16) domains; positive skew in vision (2.2) was seen, identifying patients with and without cortical visual impairment. INTERPRETATION: The S-CSA demonstrates strong validity and reliability in STXBP1-RD and may show utility in clinical trials for STXBP1-RD and potentially other severe DEEs. Key Words: STXBP1-Related Disorder, Developmental and Epileptic Encephalopathies, Clinical Outcome Assessments

BackgroundElectrophysiological recordings from chronically implanted Deep Brain Stimulation (DBS) electrodes can greatly advance understanding of disease and treatment mechanisms of motor and psychiatric disorders. The Medtronic Percept system allows for chronic recordings of local field potentials (LFP) from DBS target regions. However, these systems lack an inbuilt synchronization option to align LFP recordings to other recording modalities and consequently events in computerized tasks. ObjectiveWe propose and evaluate a synchronization method based on Transcutaneous Electrical Stimulation (TES) with low amplitudes to precisely align recorded LFP signals from the DBS electrodes to EEG recordings. MethodsThe TES-based synchronization approach was implemented and tested in 11 participants implanted with the Medtronic Percept for treatment of Parkinsons disease. ResultsThe proposed method provides high reliability, precise alignment and usability across all Medtronic Percept recording modes. Notably, the method enables recordings during adaptive DBS and with stimulation turned off. In this recording mode, LFP signals can be acquired from all recording contact pairs simultaneously, with a high signal-to-noise ratio. We provide detailed setup plans and share Python and Matlab scripts for signal alignment to enable easy application of our approach. ConclusionBy enabling reliable, well-aligned LFP recordings from all DBS contacts, our method provides a robust tool for studying neural dynamics and refining therapeutic interventions in diverse neurological conditions.

Objective: Interictal spikes have been proposed as a biomarker for both localizing seizure onset zones (SOZ) and tracking changes in seizure risk with neurostimulation in patients with drug-resistant epilepsy. Electrical stimulation can modulate spike rates acutely, and it has been proposed that measuring this modulation can help localize the SOZ. However, it is unclear whether stimulation-induced spike rate changes reflect epilepsy-specific pathology in the stimulated network or simply intrinsic regional excitability, which limits our understanding of their utility in epilepsy surgery planning. Methods: We analyzed low-frequency stimulation (LFS; 1 Hz) applied during a clinical seizure-induction protocol systematically targeting multiple brain regions in 43 patients with drug-resistant epilepsy undergoing intracranial EEG monitoring. A validated, automated spike detector was used to quantify pre-, during-, and post-stimulation spike rates. We tested whether the stimulation-evoked spike rate response (i) tracks the expected change in seizure risk from a seizure induction protocol, (ii) varies with anatomical stimulation site and epilepsy localization, (iii) localizes the SOZ beyond baseline spike rate, and (iv) is accompanied by changes in spike morphology. Results: Nearby LFS acutely increased spike rates in high-spiking channels (inter-stimulation median 2.25 vs. during-stimulation 4.25 spikes/min; p < 0.001), with effects attenuating with distance and resolving within approximately 30 seconds of stimulation offset. Mesial temporal lobe stimulation produced the largest increase in nearby spike rates relative to temporal neocortex and other cortex (Kruskal-Wallis p = 0.003), but this effect did not differ between patients with and without mesial temporal lobe epilepsy. A random forest classifier incorporating stimulation-evoked modulation features achieved an AUC of 0.787, comparable to a resting-state spike model (AUC 0.747; DeLong p = 0.81), indicating that stimulation-evoked spike changes do not add localizing information beyond resting-state spike rates. Stimulation produced a small but significant shift in spike morphology toward broader, higher-amplitude discharges (PERMANOVA p < 0.001), consistent with recruitment of a broader neuronal population. Significance: LFS-evoked increases in interictal spike rates reflect intrinsic regional excitability, greatest in the mesial temporal lobe, rather than epilepsy-specific pathology, and do not improve SOZ localization over resting-state spike rates. These results argue against using the change in spikes with stimulation to localize the SOZ. On the other hand, the transient spike rate increase induced by a pro-epileptic protocol supports the acute change in spike rate as a biomarker of the effect of stimulation on seizure risk, with potential to guide parameter selection for epilepsy neuromodulation.

Mild malformation of cortical development with oligodendroglial hyperplasia and epilepsy (MOGHE) is a recently recognized cause of drug-resistant focal epilepsy. It is often MRI-negative or shows imaging features mimicking focal cortical dysplasias, which makes recognition difficult and limits presurgical counseling. We aimed to identify an intracranial EEG (iEEG) biomarker that distinguishes MOGHE from other developmental brain lesions encountered in epilepsy surgery. In a retrospective multicenter test cohort of 38 patients (18 MOGHE, 20 non-MOGHE), we analyzed long-term stereo-EEG and subdural recordings. Only MOGHE patients showed highly stereotyped clusters of very brief low-voltage fast activity (LVFA) events, organized into status-like 3 to 12-minute episodes that often lacked clear clinical symptoms. LVFA clusters were present in 16/18 MOGHE and 0/22 non-MOGHE patients. We then tested diagnostic performance in an independent, blinded single-center validation cohort of 22 patients (11 MOGHE, 11 non-MOGHE), in which visual identification of LVFA clusters correctly classified 10/11 MOGHE and 10/11 non-MOGHE cases (Cohens kappa=0.82). Penalized logistic regression further confirmed MOGHE histology as the strongest predictor of LVFA clusters, independent of age and lobe localization. Because LVFA clusters can be recognized visually on routine intracranial EEG recordings without specialized software, this biomarker is readily applicable in clinical practice and may improve presurgical identification of MOGHE. Future prospective studies should determine whether its recognition influences surgical planning, improves outcome prediction, or facilitates selection of patients for mechanism-based therapies.

Rationale: Reliable electroencephalography (EEG) biomarkers of cortical excitability could improve diagnosis and longitudinal monitoring in epilepsy, yet it remains unclear which metrics best balance sensitivity across individuals with intra-individual stability over time. Methods: We analyzed scalp EEG recordings from the open-access Temple University Hospital EEG Epilepsy Corpus, comprising 1,404 recordings from 96 individuals with neurologist-confirmed epilepsy and 85 healthy controls across multiple sessions. Eight global measures were computed: aperiodic exponent and offset, sample entropy, detrended fluctuation analysis exponent and derived index, spatial gamma-band phase consistency, and absolute and relative alpha power. Group differences were assessed by permutation tests with false discovery rate correction at recording, session, and subject levels. Associations with antiseizure medication burden, temporal stability, and cross-metric correlation structure were evaluated as secondary analyses. Results: Aperiodic parameters showed the most robust case-control separation, remaining significant after subject-level averaging (exponent: median difference = 0.20, q = 0.010; offset: median difference = 0.25, q = 0.011). Entropy and alpha power distinguished groups at the recording and session levels, while gamma-band phase consistency was significant at the session level only; none of these survived subject-level averaging, suggesting greater state-dependency. Higher medication burden was associated with reductions in alpha power and detrended fluctuation analysis, and adjusting for it substantially attenuated group differences, though residual effects in the aperiodic exponent persisted. Cross-metric correlation structure was preserved between groups but modestly reorganized by medication burden. Conclusions: Aperiodic spectral parameters are the most robust EEG markers of epilepsy, reflecting stable trait-like network properties. Complexity and synchrony measures capture complementary, state-sensitive dimensions. Medication burden substantially influences multiple metrics, underscoring the need to account for pharmacological effects when interpreting EEG biomarkers in epilepsy.

Normal emotional experience depends on dynamic modulation of neural excitability across limbic and prefrontal circuits, yet the spectral markers that reflect these shifts in humans remain incompletely understood. In this study, we combined a validated video-based emotion induction paradigm with stereotactic electroencephalography (SEEG) in 31 patients with drug-resistant epilepsy to investigate how positive and negative affective states modulate oscillatory and aperiodic (asynchronous) neural activity. Using spectral parameterization to dissociate oscillatory power from the aperiodic 1/f component, we found that emotional valence robustly altered the aperiodic slope in a regionally specific manner: negative valence flattened the slope in thalamus, posterior insula, and posterior cingulate cortex, whereas positive valence produced flattening in dorsolateral prefrontal cortex. Simultaneous oscillatory changes included increased high-frequency activity and decreased alpha/beta power during negative affect, and reduced alpha power during positive affect, which were elucidated after adjusting for broadband aperiodic spectral shifts. These effects persisted after controlling for audiovisual stimulus or physiological features and were not evident in simultaneously recorded scalp EEG, underscoring their localization to intracranial sites. Together, these results provide the first direct evidence that active induction of emotional states modulates the aperiodic slope of human intracranial field potentials, reflecting valence-dependent shifts in local circuit excitability. The findings highlight the 1/f slope as a sensitive neural marker of affective brain states and for mood dysregulation.

Objective: To determine whether absolute ictal energy on intracranial EEG identifies brain regions whose epileptogenic involvement is attenuated under existing baseline-normalized, dynamic-systems, and event-based frameworks. Approach: Intracranial EEG from 56 patients (five centers; 21 SEEG, 35 ECoG) was analyzed using the Teager-Kaiser Energy Operator computed as z-scored and raw envelopes; energy-dominant network regions (EDNRs) were defined as electrodes whose raw-energy rank exceeded their z-score rank by at least 2 positions. Hilbert decomposition characterized instantaneous amplitude and frequency. Main results: EDNRs were identified in 51 of 56 patients (91%; mean 3.4). Hilbert decomposition revealed elevated baseline amplitude in EDNRs relative to both non-involved regions (p < 0.001) and potential seizure onset zones (PSOZs, the top-ranked electrodes under both metrics; p = 0.029), with EDNRs participating in seizure-frequency dynamics comparable to PSOZs (mean ictal frequency shift +3.7 versus +4.1 Hz). EDNR detectability correlated directly with electrode count (Spearman r = 0.899, p < 0.001) without plateau. Significance: Absolute ictal energy identifies an epileptogenic network component with elevated baseline amplitude attenuated under baseline-normalized metrics. The dual-metric framework defines a complementary energy-based axis and establishes the second layer of a two-layer approach with seizure onset and propagation mapping as the first layer. EDNR detectability scales with electrode count, directly relevant to SEEG implantation strategy and to network-level inferences from heterogeneously covered cohorts.

Abstract Background Prediction of long-term neurodevelopmental outcomes remains challenging after perinatal asphyxia. Here, we studied whether computational metrics of brain function derived from neonatal EEG are associated with long-term neurodevelopment in infants with perinatal asphyxia. Methods Total of 36 term-born infants with perinatal asphyxia with or without hypoxic-ischemic encephalopathy were studied with neonatal multichannel electroencephalography (EEG). We computed local EEG amplitudes and phase-amplitude coupling (PAC), as well as large-scale functional cortical networks estimated using amplitude-amplitude correlations (AAC) and phase-phase correlations (PPC). These EEG-derived markers were tested for associations with neurodevelopmental outcomes at two years, assessed using the Griffiths Scales of Child Development, 3rd edition (GMDS-III). Results EEG amplitudes showed positive associations with GMDS-III Foundations of Learning and General Development scores across most electrodes during quiet sleep, with the strongest effects observed at frontal and central regions (r = 0.44-0.66). PAC showed negative associations with the same scores mainly over parietal and temporal regions (r = -0.45 to -0.55). Cortical AAC networks demonstrated the most robust and widespread negative associations in all frequency bands during quiet sleep (r = -0.47 to -0.54), with 70-72% of connections significant in high delta frequency. In turn, PPC networks showed frequency-selective and more spatially constrained negative associations during quiet sleep (r = -0.48 to -0.53), involving 5-12% of the network. Conclusions Both local and network-based metrics in the newborn brain show significant association with neurodevelopmental outcome at 2 years after perinatal asphyxia.

Background and Purpose: Drug resistant epilepsy (DRE) affects approximately 15 million people worldwide, and surgery remains the only curative option. A key challenge in predicting outcomes is the lack of standardized, quantitative tools to help distinguish seizure driver regions from responder regions during stereoelectroencephalography (sEEG) recordings. We validated the CN Suite, a computational platform that uses causal network mapping and machine learning to assign criticality scores to sEEG contacts, testing whether higher scores correspond to surgically treated tissue in patients with favorable outcomes. Methods: We analyzed deidentified clinical data from 60 patients (aged 2 years and older) with focal or multifocal DRE who underwent sEEG monitoring and proceed to surgery at four U.S. Level 4 epilepsy centers. The algorithm was trained on an independent cohort (N=37) and locked prior to validation. The primary outcome was the standardized effect size (Cohens d) of the patient level surgical zone enrichment ratio between more favorable (Engel I or II) and less favorable (Engel III or IV) outcome groups. Contact level sensitivity, specificity, PPV, and NPV were evaluated at a prelocked threshold. Results: The findings support our hypothesis: the algorithm results showed significantly higher criticality values for surgically treated tissue in favorable outcome patients (d=0.74, 95% CI: 0.39 to 1.06, p=0.003). Three potentially clinically actionable findings emerged. First, high-criticality contacts formed spatially compact clusters (~9 mm nearest-neighbor distance vs. 17mm expected by chance), consistent with focal targets amenable to minimally invasive ablation. Second, sensitivity was highest in small focal procedures (80% at 10 or fewer treated contacts) and decreased with resection size. Third, in patients whose surgery failed, high-critical tissue remained outside the resection boundary, suggesting incomplete treatment coverage of the epileptogenic zone rather than mislocalization. Prediction specificity was 84% at the contact level. For adult and pediatric cases (n=28), 88% of contacts that were identified as seizure free were in fact seizure free. Conclusions: Causal network mapping of sEEG identifies compact epileptogenic targets that correspond to surgically treated tissue in patients with more favorable outcomes. CN-Suite performed best in focal procedures and may be best suited for LITT and other minimally invasive approaches. In addition, low-criticality regions were infrequently associated with seizure-generating tissue, particularly in the pediatric cohort although our sample size was small. When surgery failed, residual high-critical tissue outside the resection boundary offered both a mechanistic explanation for less favorable surgical outcome as well as a potential target for reoperation.

Objective Memory impairment is a frequent comorbidity of focal epilepsy, incompletely explained by seizure frequency or structural pathology. Ictal and postictal hippocampal dysfunction disrupt memory processes, but their cumulative impact remains poorly quantified. This study introduces cumulative hippocampal seizure-related burden metrics and examines their association with long-term memory consolidation. Methods Twenty consecutive patients undergoing stereo-EEG in Marseille (2016-2018) were prospectively included. Continuous stereo-EEG recordings between two memory assessments (30 minutes and one week post-encoding) were analysed. Hippocampal ictal involvement and durations were assessed using epileptogenicity markers and visual stereo-EEG analysis. The postictal period was quantified using permutation entropy. Cumulative hippocampal seizure-related burden metrics (ictal, postictal and combined: c-HipSZB) were computed across hippocampus-involving ictal events. Verbal and visual memory were assessed using standardized recall and recognition tasks. Associations were examined using univariate and multivariate analyses. Results Higher dominant-hemisphere hippocampal burden was associated with poorer one-week verbal memory (performance and retention), independently of most covariates. Higher c-HipSZB was associated with lower total recall performance (RT; free + cued) and RT retention ({beta} = -25.04 and -23.88; R2 = 0.57 and 0.53; p < 0.05) and accounted for the greatest variance in both outcomes (adjusted R2= 0.59 and 0.53; {beta} = -25.45 and -24.27; p < 0.01), particularly when adjusting for epilepsy duration. No robust associations were observed between non-dominant-hemisphere hippocampal seizure-related burden metrics and visual memory. Effects predominantly involved recall. Interpretation Cumulative ictal-postictal hippocampal dysfunction is a major determinant of impaired long-term verbal memory consolidation in focal epilepsy.

Background: Deep brain stimulation (DBS) of the ventral intermediate nucleus and posterior subthalamic area (VIM/PSA) in Essential Tremor (ET) and the subthalamic nucleus (STN) in Parkinson's disease (PD) are established treatment for tremor. To achieve maximum tremor control, increasing stimulation frequency beyond 130 Hz is part of clinical practice, but lacks scientific evidence. Objective: To compare tremor suppression under total electrical energy delivered (TEED)-equivalent stimulation at 130 Hz versus 185 Hz in STN-DBS for PD and VIM/PSA-DBS for ET. Methods: In this prospective, double-blind study, acute DBS effects were assessed in 18 people with ET (n = 29 hemispheres), and 25 people with PD (n = 30 hemispheres). Tremor-suppressive effects, evaluated by accelerometry, were compared with TEED-equivalent stimulation at 130 Hz and 185 Hz using linear mixed-effects models, explorative pairwise comparisons, and equivalency testing. Results: Linear mixed-effects models revealed no significant effect of stimulation frequency on tremor improvement in both cohorts. Pairwise comparisons showed no consistent differences in total tremor improvement with TEED-equivalent 185 Hz vs 130 Hz DBS. Post-hoc equivalence testing confirmed equivalence of stimulation frequencies under TEED-equivalent conditions within a +/- 20% margin of relative tremor improvement. Conclusion: This study provides Level II evidence that a higher stimulation frequency of 185 Hz does not offer additional benefit in deep brain stimulation for tremor and supports 130 Hz as the standard stimulation frequency for tremor suppression in ET and PD.

Background and Objectives In ADHD, a heterogeneous neurodevelopmental condition, behavioral and motor manifestations may reflect multiple inefficient or perturbed inhibitory systems. To evaluate Transcranial Magnetic Stimulation (TMS) evoked cortical silent period (CSP) duration, an indicator of GABA(B) receptor-mediated inhibition in motor cortex, as a potential biomarker of Attention-Deficit/Hyperactivity Disorder (ADHD) in children. Method We retrospectively analyzed TMS data, obtained using both round and figure-of-8 coils, from three cross-sectional studies conducted in 8- to 12-year-old children with ADHD (n=79; 10.7 +/- 1.5 years old) and age-and-sex-matched typically developing controls (n=96; 10.5 +/- 1.4 years old). Results Median CSP was 32% shorter in ADHD (p=0.02). Regression analysis demonstrated a relationship between shorter CSP and both lower active motor thresholds (p < 0.0001) and more severe hyperactivity symptom rating (p = 0.026). Test-retest CSP measures in 83 children showed moderate reliability (intraclass correlation 0.77 [ADHD], 0.75 [controls]). Conclusion TMS-evoked CSP may be a useful biomarker in future investigations of ADHD subtypes, domains of impaired function, or treatment outcomes.

BackgroundNeuronavigation based on the standard MNI template (MNI-protocol) offers a cost-effective alternative to the gold-standard individualized T1-weighted MRI approach (T1-protocol). However, it remains unclear whether the reduced anatomical precision of the MNI-protocol compromises its functional efficacy, creating a critical need to verify protocol interchangeability. ObjectiveWe aimed to determine whether the MNI-protocol yields targeting efficacy comparable to the T1-protocol by specifically testing their functional and biophysical equivalence. MethodsWe employed a novel tri-level within-subject framework. The behavioral level assessed functional efficacy via the size congruity effect (SCE) during TMS to the right intraparietal sulcus (IPS). Anatomical accuracy (coil-to-cortex distances) and electromagnetic efficacy (E-field simulations) were evaluated across three distinct regions (right IPS, left dorsolateral prefrontal cortex, and left primary motor cortex) to assess regional generalizability. ResultsThe MNI-protocol demonstrated functional similarity to the T1-protocol, yielding behavioral outcomes that were statistically indistinguishable. This functional equivalence was corroborated by electromagnetic analyses, which revealed nearly identical induced E-field magnitudes and spatial distributions across all three target regions. Although the T1-protocol achieved significantly shorter coil-to-cortex distances, this anatomical advantage did not confer any measurable functional benefit. ConclusionThe MNI-protocol produced behavioral and electromagnetic outcomes equivalent to the T1-protocol. These findings validate the MNI-protocol as a scientifically sound and scalable alternative to individualized MRI-guided targeting, supporting its broader application in diverse research and clinical settings. HighlightsO_LIFunctional equivalence of MNI-vs. T1-guided TMS was systematically tested. C_LIO_LIA novel tri-level framework compared behavioral, anatomical, and E-field metrics. C_LIO_LIMNI- and T1-guided targeting yielded comparable behavioral and E-field outcomes. C_LIO_LIAnatomical proximity does not ensure better behavior or stronger E-field strength. C_LIO_LIMNI-guided targeting offers a robust, practical alternative to individual MRI. C_LI

Transcranial magnetic stimulation combined with electroencephalography (TMS-EEG) enables direct measurement of cortical reactivity via TMS-evoked potentials (TEPs). Interpretation of early TEP components however, is highly sensitive to stimulation and hardware-related artifacts. We identified and characterised a persistent, non-neural step-drift artifact unexpectedly present in recent TMS-EEG recordings from our group. We show that the artifact is distinct from previously described TMS pulse and discharge/decay artifacts and likely reflects a hardware interaction phenomenon. We demonstrated that amplifier settings, but not TMS pulse shape, substantially influenced artifact expression, with DC-coupled recordings with no online high-pass filter reducing step amplitude compared with AC-coupled recordings with a high-pass filter. Simulations additionally revealed that filtering over the step-drift artifact introduced pronounced ringing and edge artifacts, highlighting the need to address this artifact prior to data processing. We propose a processing pipeline incorporating robust polynomial detrending and a modified Butterworth filter with autoregressive extrapolation that minimised TEP distortion in both simulated and real data containing the step-drift artifact. Together, these findings provide practical recommendations for both preventing and correcting step-drift artifacts and underscore the need for formal definition and routine recognition of this artifact to improve reproducibility and data quality in TMS-EEG research.

Tourette syndrome (TS) is a neurodevelopmental disorder of childhood onset characterised by vocal and motor tics and is associated with cortical-striatal-thalamic-cortical circuit [CSTC] dysfunction. TS often follows a developmental time course in which tics become increasingly more controlled during adolescence. However, many individuals continue to have debilitating tics into adulthood. This indicates that there may be important differences between adults with TS for whom the clinical phenotype is more stable, and children and adolescents with the disorder who may be undergoing developmental neuroplastic changes linked to the reduction of their tics. Previous studies have used transcranial magnetic stimulation (TMS) to investigate changes in cortical motor excitability in individuals with TS, including measurement of resting motor threshold (RMT). However, the findings from these studies have been mixed, have varied between adult and child samples, and have often been based on small sample sizes. Here we report a multi-centre, mega-analytic, study in which RMT data collected from children and adults with TS at multiple research centres was pooled for analysis. Results confirmed that mean RMT was significantly increased in individuals with TS compared to neurotypical controls. However, this result can be explained by the more important findings that: (a) RMT for adults with TS did not differ from that of neurotypical adults; and (b) the rate that RMT decreases with age during childhood and adolescence is reduced in individuals with TS compared to controls. Thus, while neurotypical individuals reach an adult RMT level by ~12-13 years of age, individuals with TS are substantially delayed in doing so, and do not reach an adult RMT level until much later, at ~24 years of age. We conclude therefore that differences in measures of cortical excitability between children and adolescents with TS and chronologically age-matched neurotypical controls may likely reflect a developmental delay in the maturation of functional brain networks in individuals with TS, which may normalise with age.

The current model of the action inhibition network includes the prefrontal cortex and the subthalamic nucleus (STN) connected via the prefrontal hyperdirect pathway. Proactive inhibition refers to preparatory mechanisms that facilitate action inhibition (i.e. enables a person to act with restraint), while reactive inhibition is a sudden stopping triggered by an external stimulus. Most research has focused on the reactive paradigm, with more limited investigation of proactive inhibition. We studied electrophysiologic activity in multiple cortical and STN regions in 17 patients with Parkinsons disease using high-resolution intracranial electrodes. Subjects performed a Go/NoGo task and a simpler Go task. Proactive inhibition was assessed by contrasting Go trials in the context of two tasks. In the rostral middle frontal gyrus, we found increased beta oscillations during movement preparation in Go trials of the Go/NoGo task compared to the Go task. A similar but weaker, preparatory beta modulation was observed in dorsal STN, while central STN was associated with significant modulation in theta power prior to movement. We interpret this activity as a reflection of the role of these regions in proactively restraining anticipated responses. Conversely, inferior frontal gyrus and ventral STN were primarily engaged during rapid post-cue action control. Specifically, withholding of action after the NoGo signal was accompanied by increased theta activity in these regions. Beta modulation within the STN mirrored those of sensorimotor cortex during successful inhibition and movement execution. In both regions, beta activity decreased during movement and was higher when movement was withheld. We conclude that communication within the hypothesized motor control network is frequency dependent, with key nodes promoting specific functions.