Clinical Neurophysiology

IntroductionTo leverage high-frequency oscillations (HFOs) as a biomarker with significant potential, this study compared a large set of detectors on a unified dataset, aiming to evaluate their clinical applicability under realistic conditions. MethodsEleven automatic detectors were applied to a retrospective dataset of intracranial and scalp EEGs from 27 consecutive pediatric patients. Inter-detector agreement was assessed using Spearmans Rho, and the area under the curve (AUC) for seizure onset zone (SOZ) prediction served as a consistent reference standard to enable reliable comparisons across recording modalities. Analyses were conducted separately for HFO and Spike-HFO detections. ResultsThe average age of our cohort was 12.4 years (SD 4.0; range 5-18). AUC values in scalp EEG ranged from 0.61 to 0.67 for HFOs and from 0.53 to 0.63 for Spike-HFO. AUC values in intracranial EEG ranged from 0.48 to 0.66 for HFOs and 0.54 to 0.69 in Spike-HFO. Although only three of the 11 detectors were specifically developed or adapted for scalp EEG, the detectors generally achieved higher AUC values and stronger agreement in scalp EEG ConclusionsWe present the first study comparing intracranial and scalp detectors by testing them beyond the modalities for which they were originally designed. Although the clinical utility of detections was comparable across EEG modalities, it remained lower than reported in original studies assessing the diagnostic value of HFOs. Caution is warranted when applying a publicly available detector to a new dataset, and detector robustness remains a critical issue. Key points- A comprehensive head-to-head comparison of 11 detectors demonstrated significant variability in detector agreement and clinical utility - Clinical utility was not necessarily linked to the EEG recording type the detector was originally designed for - Despite widely accepted use of automatic detections, detector robustness remains a critical issue

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

BackgroundElectroencephalography (EEG) can be combined with transcranial magnetic stimulation (TMS) to perform brain-state-dependent stimulation. EEG-TMS studies have shown that corticospinal excitability, as measured via motor evoked potentials (MEPs), is modulated by pre-stimulus periodic EEG features, such as sensorimotor mu-rhythm phase and power. However, the influence of aperiodic brain activity on corticospinal excitability is largely unexplored. ObjectivesWe evaluated the relationship between aperiodic and periodic mu-power, aperiodic exponent, and mu-phase on MEP amplitudes using EEG-TMS. MethodsWe applied 800 single TMS pulses to the left primary motor cortex in 78 healthy adults. We calculated aperiodic/periodic mu-power, aperiodic exponent, and mu-phase for each trial from the pre-stimulus C3-Hjorth transformed EEG. MEP amplitudes were extracted from the right first dorsal interosseous muscle. A linear mixed-effects model assessed relationships between MEP amplitudes and EEG features, with interactions between mu-phase and all other EEG features. ResultsAperiodic and periodic mu-power, aperiodic exponent, and mu-phase significantly modulated MEP amplitudes. Higher aperiodic/periodic mu-power was associated with larger MEP amplitudes, while higher aperiodic exponent was associated with smaller MEP amplitudes. We found a significant interaction effect of aperiodic exponent and mu-phase on MEP amplitude. Aperiodic exponent was negatively associated with MEPs for trough, rising, falling phases, but positively associated with MEPs for peak phase. ConclusionsAperiodic and periodic features of brain activity are reflective of dissociable corticospinal excitability states. Future brain-state-dependent TMS interventions may include aperiodic EEG features, such as aperiodic mu-power and exponent, in addition to the well-established periodic features.

BackgroundTranscranial Magnetic Stimulation combined with electroencephalography (TMS-EEG) offers unique insights into cortical excitability and connectivity, yet current analyses are primarily limited to group-level inferences with little validation of individual reliability and feature redundancy. ObjectiveTo construct a comprehensive, open-access, and reliable normative dataset of TMS-EEG features that enables individual-level comparison MethodsWe aggregated TMS-EEG data recorded over the primary motor cortex (M1) from 164 healthy adults (30.8 {+/-} 9.8 years; 88 female) across nine studies using harmonized acquisition and preprocessing pipelines. Reliability analysis was conducted on a test-retest subset (N=57) for 968 extracted features, evaluating systematic bias, absolute error, and relative reliability (Intraclass Correlation Coefficient categorized by the lower bound of the 95% confidence interval). Additionally, feature clustering was performed to quantify redundancy and correlations across the high-dimensional feature space. We then established normative distributions and developed an online benchmarking platform. ResultsReliability analyses (N=57) of the high-dimensional feature set revealed that 525 out of 968 features (54.3%) met at least moderate reliability standards (ICC lower bound > 0.5). Cluster analysis indicated substantial redundancy among metrics, with three distinct clusters having a moderate-to-high internal correlation (|r| = 0.64, 0.48, 0.39, respectively). Finally, normative data from the database identified abnormal results in a test patient, supporting the feasibility of individual-level classification in an open-science framework. ConclusionsHarmonization of data acquisition and analysis pipelines led to the development of a reliable normative M1 TMS-EEG reference. This publicly available resource provides a validated tool for future individual-level classifications and an open platform for ongoing community contributions.

Genetic deletion or pharmacological blockade of P2X7 receptors (Rs) counteract status epilepticus (SE) in animal models of epilepsy. It is, however, unclear whether P2X7Rs are localized at astrocytes or neurons, and the reason for astrocytic atrophy arising in consequence of SE is also ambiguous. We conducted a combined morphological/electrophysiological study in order to investigate these issues. It has been shown that kainic acid (KA)-induced SE in mice led to the atrophy of hippocampal astrocytes and at the same time to the decrease of ezrin immunoreactivity and its co-expression with mCherry, whose synthesis has been initiated by the injection of a virus complex. mCherry expression in astrocytes enabled us to study changes in cell somata and processes brought about by KA-injection. Ezrin is a plasmalemmal-cytoskeleton linker; its grade of expression indicates changes in the existence/function of small peripheral astrocytic processes. Pretreatment of mice with the blood-brain barrier-permeable P2X7R antagonist JNJ-47965567 prevented the SE-induced damage of astrocytes. KA caused a potentiation of dibenzoyl-ATP (Bz-ATP) currents in astrocytes but not neurons of the hippocampus. This effect was also abolished by pre-treatment of mice with JNJ-47965567 before applying KA, although no similar changes occurred in hippocampal CA1 neurons. The measurement of spontaneous postsynaptic currents (sPSCs) and spontaneous excitatory postsynaptic currents (sEPSCs) indicated a presynaptic facilitation of neurotransmitter release by Bz-ATP. In conclusion, we suggest that astrocytic P2X7Rs are the primary target of ATP release from damaged CNS cells in the hippocampus which simultaneously causes damage to astrocytic somata and processes.

Short-interval intracortical inhibition (SICI) is the most widely used neurophysiological index of GABAergic inhibition in the human cortex. However, it is an indirect measure, inferring synaptic inhibition from suppression of peripherally recorded motor-evoked potentials (MEPs) elicited by transcranial magnetic stimulation (TMS). In the standard protocol, a subthreshold conditioning pulse suppresses the MEP evoked by a suprathreshold test pulse delivered 1-5 ms later. Interpretation is further complicated by temporal overlap with short-interval intracortical facilitation (SICF), reflecting excitatory interactions at interstimulus intervals of [~]1.5 and 2.7 ms. To overcome these limitations, we recorded immediate TMS-evoked EEG potentials (iTEPs; 1-10 ms post-stimulus) as a more direct measure of motor cortical activity in 16 healthy volunteers (20-35 years; 7 male). The conventional SICI protocol suppressed only later components of the iTEP, likely corresponding to late corticospinal volleys previously identified in epidural spinal recordings after suprathreshold TMS, while the earliest iTEP component was unaffected. Importantly, later iTEPs were suppressed to a similar extent whether conditioning-test intervals coincided with SICF peaks or troughs, and the magnitude of iTEP suppression correlated with concurrently recorded paired-pulse MEP suppression. SICI also reduced an early TEP component (N15; 10-20 ms), but paired-pulse N15 suppression showed a different dependence on stimulus intensity and did not correlate with MEP suppression. These findings demonstrate that SICI measured via MEPs does not reflect a global index of cortical GABAergic motor cortical inhibition but instead reflects inhibition within specific cortical circuits that can be investigated directly with iTEPs.

Dorsal (PMd) and ventral (PMv) premotor cortices can modulate contralateral primary motor cortex (M1) excitability, but their distinct interhemispheric influence via transcranial magnetic stimulation (TMS) remains unclear. Single-pulse TMS over PMd, PMv and M1 assessed transcallosal inhibition via the ipsilateral silent period (iSP). Dual-site TMS examined short-(10 ms inter-stimulus interval [ISI]), long-(50 ms ISI) and non-callosal-(0 ms ISI) interhemispheric inhibition (IHI). An iSP was elicited from PMd, PMv, and M1, with distinctly evoked iSP parameters. The iSP magnitude was greatest from M1, followed by PMd and then PMv, while iSP duration was greatest for M1 and showed no differences between PMd and PMv. Dual-site TMS revealed that PMd and M1 inhibited contralateral M1 excitability across all ISIs, while PMv showed inhibition at 0-and 50-ms ISIs. PMd and M1 demonstrated greater short-IHI compared to PMv, all demonstrating similar long-IHI, and PMd demonstrating greater non-callosal-IHI than M1. PMv displayed distinct IHI across ISIs, PMd showed differences across most ISIs and M1 demonstrated the fewest differences across ISIs. Longer iSP duration related to greater long-IHI magnitude elicited from PMd and PMv. Our findings demonstrate differential IHI from PMd and PMv on contralateral M1, which may inform neuromodulation strategies in rehabilitation contexts.

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.

Background: High-dose high-intensity upper limb neurorehabilitation can lead to meaningful clinical gains even in chronic stroke, yet substantial variability in recovery remains unexplained. Identifying neurophysiological markers linked to neuroplasticity and recovery could provide mechanistic insights and guide personalised rehabilitation. Objective: To characterise stroke-related alterations in {beta}-activity during movement and neural activity at rest and explore associations between brain activity and changes in upper limb clinical outcomes in chronic stroke survivors undergoing three-week high-dose rehabilitation. Methods: Electroencephalography (EEG) was recorded during the three-week rehabilitation programme in 40 chronic stroke survivors participating in the Queen Square Upper Limb (QSUL) Programme, as well as in 26 healthy controls. Recordings were taken during passive movement of the affected and unaffected index fingers (~70 movements per hand) and at rest (~7 min). Clinical assessments included the Fugl-Meyer Upper Limb Assessment (FM-UE), reflecting impairment-level deficits, and the Chedoke Arm and Hand Activity Inventory (CAHAI-13), capturing real-world upper limb activity, to examine their differential relationships with movement-related {beta}-activity. Results: Stroke survivors showed significant improvements in FM-UE and CAHAI scores following the rehabilitation programme (Mean {Delta}: FM-UE = 7.5, CAHAI = 7.4), exceeding minimum clinically important differences. Compared to controls, stroke survivors exhibited less strong {beta}-event-related desynchronization/synchronization ({beta}-ERD/ERS) during passive movement of the affected and unaffected index finger, with effects lateralised to the lesioned hemisphere. No significant differences at rest were observed between stroke participants and healthy controls. Only improvements in CAHAI, but not FM-UE, were associated with stronger {beta}-ERD (more negative) and stronger {beta}-ERS (more positive) responses during passive movement. Conclusions: Stronger movement-related {beta}-activity is associated with improvements in upper limb activity following high-dose high-intensity neurorehabilitation, suggesting {beta}-activity as a potential marker of neuroplasticity.

BackgroundDistinguishing epilepsy from functional/dissociative seizures (FDS) is an ongoing diagnostic challenge. Misdiagnosis delays appropriate treatment and puts patients at significant risk. Quantitative analyses of clinical EEG offer a potential avenue for developing decision-support tools in the diagnosis of seizure disorders. Recent work using univariate features demonstrated that reliably identifying diagnostic traits in the presence of confounding factors remains challenging. However, diagnostic information might be available in multivariate features such as network-based measures. Using a well-controlled dataset, we run the first diagnostic accuracy study assessing the potential of multivariate resting-state EEG markers to directly discriminate between a diagnosis of epilepsy and one of FDS at the time when a diagnosis is suspected and prior to treatment initiation. MethodsThe dataset, previously examined in a published study, includes 148 age- and sex-matched individuals with suspected seizure disorders who were later diagnosed with non-lesional epilepsy (n=75) or FDS (n=73). Eyes-closed, resting-state EEG data used for the analyses were normal on visual inspection, and acquired while participants were medication-free. Functional network measures in the 6-9 Hz range were extracted and machine learning implemented to assess their predictive potential; different model configurations (including varying model types, dimensionality reduction methods, and approaches to enhance feature stability) were tested to identify the most promising approach for future translational implementations. ResultsNetwork measures derived from resting-state EEG discriminate between conditions at levels significantly above chance (maximum balanced accuracy: 67.5%). Their sensitivity to epilepsy (81.8%) is consistently higher than their sensitivity to FDS (53.3%). A systematic assessment of model choices indicates that improving the temporal stability of network features through epoch-wise averaging improves classification accuracy (62.6% to 67.5%). Multiple nonlinear model types succeed on the classification problem, with the three-best performing assigning a consistent diagnostic label to 77.5% of the individuals; however, model choice remains a strong determinant of overall classification accuracy. Dimensionality reduction did not provide a significant advantage in our models. ConclusionWe establish evidence for the clinical validity of selected network-based markers to discriminate between a diagnosis of non-lesional epilepsy and FDS prior to treatment initiation, highlighting the measures potential to support post-test probability estimation in the clinic. Our models, configured to optimise balanced accuracy, classified people with epilepsy more accurately than people with FDS, indicating that these measures are specific to epilepsy and should not be interpreted as markers of a positive diagnosis of FDS.

Objective: Electrical brain stimulations (EBS) are central to epileptic network identification and functional mapping during stereo-electroencephalography (SEEG), yet stimulation frequencies remain empirical, and standardized across patients and brain regions, producing false negatives and false positives, and potentially compromising surgical outcome. We investigated theta-range EBS (7 Hz) in the temporal lobe, a prominent physiological frequency band in this region, and compared it with conventional 1-Hz and 50-Hz protocols. Methods: We analyzed 1,408 temporal EBS in 25 patients with drug-resistant epilepsy. Epileptic responses (afterdischarges, seizures) and clinical signs were assessed across the epileptic network and temporal structures (amygdala, hippocampus, neocortex, parahippocampal gyrus, white matter), and analyzed according to stimulation parameters (frequency, intensity, duration, total charge). Results: At matched intensity and duration, 7-Hz EBS were associated with a higher occurrence of afterdischarges and clinical signs than 1-Hz EBS in several temporal structures (e.g., parahippocampal epileptogenic zone: p=0.014). Effects on usual seizure induction were less consistent. Comparisons with 50 Hz showed no systematic significant differences, with responses observed at one or both frequencies depending on structure and outcome. When controlling for total charge, frequency-related differences were attenuated. Some effects were sporadically observed at both intermediate frequency and charge quantity. No adverse events occured. Significance: Theta-range stimulation modulates electrophysiological and clinical responses during SEEG mapping and may provide complementary information to conventional frequencies. These findings support exploring a broader range of stimulation frequencies, rather than relying solely on standard protocols.

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.

Parkinsons Disease (PD) is a complex neurodegenerative disorder that manifests through systemic, large-scale physiological reorganizations. While research often focuses on region-specific neural changes, there is a growing need for multidomain approaches to capture the complexity of the disease and its clinical heterogeneity. This study proposes an analytical pipeline to evaluate Brain-Heart Interplay (BHI) as a novel systemic biomarker for neurodegeneration and healthy ageing. In this study we assessed BHI across three open-source datasets (EEG and ECG signals). We compared Healthy Young, Healthy Elderly, and PD patients in resting state to investigate the effects of ageing and cognitive performance. Additionally, we studied BHI trends in PD patients in the moment of freezing of gait (FOG). Methodologically, brain network organization was quantified using coherence-based EEG connectivity and graph theory, while heart activity was analyzed through Poincare plot-derived measures of cardiac autonomic activity. The coupling between these two systems was measured using the Maximal Information Coefficient to capture linear and non-linear dependencies between global cortical organization and cardiac autonomic outflow. The results demonstrate that BHI is a sensitive biomarker for detecting early multisystem dysfunction in both neurodegeneration and ageing. Furthermore, the identification of specific BHI trends during FOG onset suggests new opportunities for understanding the physiological mechanisms driving motor complications in PD. Our proposed pipeline provides a guiding tool for large-scale physiological assessment in clinical research. HighlightsO_LIWe propose a pipeline based on EEG-ECG to assess ageing and neurodegeneration C_LIO_LIBrain-heart networks detect systemic changes in ageing and early PD C_LIO_LIResting brain-heart networks relate to cognitive performance in early PD C_LIO_LISpecific brain-heart interaction clusters emerge during freezing of gait C_LIO_LIBrain-heart networks offer a promising tool to understand PDs symptomatology C_LI

BackgroundAccumulating results suggest that reticulospinal tract (RST) excitability increases after stroke. While animal studies suggest this hyperexcitability may compensate for corticospinal tract (CST) damage, its role in motor function in people with stroke (PwS) remains debated. This study aimed to: (1) replicate findings of RST hyperexcitability in PwS using the StartReact paradigm, measuring acceleration of motor response to a startling auditory stimulus; (2) examine the relationship between RST hyperexcitability and motor impairments after stroke; and (3) explore whether RST hyperexcitability provides functional benefits in severely impaired PwS. MethodsForty-six PwS completed the StartReact paradigm and motor assessments (Fugl-Meyer, ARAT, grip strength, Modified Ashworth Scale). PwS were categorized into high StartReact effect and typical StartReact effect subgroups based on comparisons with a healthy control group (n=37). Severe impairment was defined as ARAT [≤]10. ResultsPwS exhibited significantly greater StartReact effects than controls. The high StartReact effect subgroup showed worse motor function, weaker grip strength, and higher spasticity. Among severely impaired PwS, high StartReact effect was not associated with improved grip strength. ConclusionsThese findings confirm the existence of RST hyperexcitability after stroke and suggest it is associated with poorer motor outcomes, likely due to reduced cortical input to the brainstem. The absence of functional benefit in severely impaired individuals supports the interpretation that RST hyperexcitability is a maladaptive rather than a compensatory reaction to brain damage. These findings provide insight into the neurophysiological mechanisms underlying motor impairments after stroke and do no imply direct clinical or therapeutic applications.

The amplitude of motor-evoked potentials (MEPs) elicited using transcranial magnetic stimulation (TMS) has been shown to decrease in the short interval prior to response initiation. The cause of this premovement MEP suppression is currently unclear and has been attributed to various processes such as preparation-related inhibition preventing the premature release of planned action or increasing signal-to-noise ratio to facilitate rapid response initiation. The present study explored whether the decrease in MEP amplitude is affected by the task requirements, using reaction time (RT) paradigms that differ in the timeline of preparation and initiation of a motor response. Participants completed simple RT (SRT), choice RT (CRT), and go/no-go (GNG) tasks, while TMS was applied at various times between the warning signal and go-signal. It was hypothesized that if MEP suppression relates to preparation level, the greatest suppression would be observed during the SRT and GNG tasks, as these paradigms encourage advance preparation and response inhibition. Conversely, if the reduction in corticospinal excitability is associated with facilitating response initiation processes, then suppression would be expected for all tasks, including the CRT paradigm in which preparation does not occur until presentation of the go-signal. Results showed MEP amplitudes decreased for all tasks as the go-signal approached; however, both the SRT and GNG had significantly greater MEP suppression 50 ms prior to, and coincident with the go-signal. These results indicate that the nature and origin of the suppression is likely multifactorial and relates to both preparatory and initiation-related processes, with the timeline and magnitude of suppression dependent on the nature of the task being executed. Impact StatementTranscranial magnetic stimulation was used to elicit motor-evoked potentials to examine the timeline of corticospinal activation during the instructed delay period for choice, simple and go/no-go reaction time tasks. For all tasks, corticospinal excitability was initially elevated compared to baseline, followed by a similar magnitude of early suppression. However, just prior to the go-signal, those tasks that allowed advance preparation showed additional suppression, providing novel information linking pre-movement corticospinal suppression to preparatory and inhibition processes.

Voluntary contraction in one limb can facilitate motor output in a distant limb, a phenomenon commonly referred to as the remote effect. However, the neural mechanisms underlying this remote interlimb facilitation remain unclear. This study investigated cortical and spinal contributions to the remote effect in able-bodied participants. Transcranial magnetic stimulation (TMS) was applied over the hand area of the primary motor cortex using posterior-anterior (PA) and anterior-posterior (AP) current directions, which are sensitive to different cortical inputs. Cortical excitability was assessed using single- and paired-pulse paradigms to measure short-interval intracortical inhibition (SICI), short-interval intracortical facilitation (SICF), and short-latency afferent inhibition (SAI). Spinal motoneuron excitability was assessed from F-waves elicited by peripheral nerve stimulation. During voluntary lower-limb contractions, single-pulse TMS elicited larger motor evoked potentials in hand muscles across current directions, indicating a broad increase in net corticospinal output. However, only AP-sensitive paired-pulse measures showed reduced SICI and enhanced SICF during contraction, whereas PA-sensitive SICI and SICF were not significantly altered, suggesting that cortical modulation during the remote effect is expressed more clearly in AP-sensitive measures. SAI with PA stimulation was less consistently expressed during contraction, suggesting that afferent-related inhibitory modulation may also be influenced during the remote effect. In parallel, F-wave amplitude and persistence increased, consistent with enhanced spinal motoneuron excitability. Together, these results provide converging evidence that the remote effect in humans involves broad corticospinal and spinal facilitation, accompanied by current direction-dependent modulation of cortical excitability measures. KEY POINTS SUMMARYO_LIVoluntary contraction in one limb can facilitate motor output in a distant limb, but the mechanisms underlying this remote interlimb facilitation remain unclear. C_LIO_LIWe tested whether remote lower-limb contraction modulates corticospinal output, intracortical excitability, and spinal motoneuron excitability in a resting hand muscle. C_LIO_LISingle-pulse transcranial magnetic stimulation showed that motor evoked potentials in the hand were facilitated during remote lower-limb contraction across multiple current directions, indicating a broad increase in net corticospinal output. C_LIO_LIPaired-pulse measures were modulated preferentially with anterior-posterior stimulation, with reduced short-interval intracortical inhibition and increased short-interval intracortical facilitation, suggesting current direction-dependent modulation of cortical excitability measures. C_LIO_LIF-wave amplitude and persistence were also enhanced during remote lower-limb contraction, indicating increased spinal motoneuron excitability. These findings provide converging evidence that the remote effect involves both cortical and spinal contributions. C_LI

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

Structured AbstractO_ST_ABSBackgroundC_ST_ABSAtrial fibrillation (AF) is maintained by complex dynamics, clinically characterised by bursting periods of organization and disorganization in intracardiac electrograms. We have previously postulated that cardiac conduction behaves like a critical system, where phase shift from organised rhythm to AF is a phase transition at the critical point. We thus hypothesized that using multifractal analysis of AF electrograms could potentially quantify non-stationary fluctuations, revealing novel mechanistic insights into the cardiac critical system and examine potential clinically relevant markers of AF dynamics, phenotype and treatment response. ObjectivesTo determine whether multifractal analysis of AF electrograms can (i) Distinguish paroxysmal (PAF0 and non-paroxysmal AF (NPAF), (ii) predict response to pharmacologic modulation, and (iii) identify imminent spontaneous termination, thereby acting as marker of proximity to criticality along complex system phase spectrum. MethodsWe analysed >1.4 million seconds of high-density bipolar electrograms from 106 patients (paroxysmal n{approx}52, non-paroxysmal n{approx}54) undergoing left atrial mapping with a 24-bipole HD-Grid catheter at standardized sites (RENEWAL AF-ANZCTR ACTRN12619001172190)). Multifractal analysis using the Wavelet Transform Modulus Maxima Method (WTMM) was applied to a burst-energy observable to derive log-normal multifractal parameters c (support dimension), c (spectrum location), and c2 (fluctuations). Hierarchical mixed-effects models accounted for channels nested within locations within patients. A flecainide sub-study (n=15) provided paired pre-/post-infusion recordings, and 27 spontaneous termination events in 15 patients were analysed using 60-s pre-termination windows. Spatial texture of c2 was quantified by variogram-derived correlation length and sill. ResultsAF electrograms exhibited robust multifractality confirming multifractal fluctuations as an intrinsic property of AF. Non-paroxysmal AF showed significantly reduced fluctuations versus paroxysmal AF (c2: {beta}=-0.01, p=0.001), indicating a paradoxical loss of fluctuations with disease progression. Flecainide selectively increased fluctuations in paroxysmal AF ({Delta}c2 = +0.04, p<0.01; {Delta}c = +0.06, p<0.01) but had no significant effect on fluctuations (c2) in non-paroxysmal AF, revealing phenotype-dependent drug response. Immediately prior to spontaneous AF termination, fluctuations increased significantly compared with sustained AF (c2: 0.198 vs 0.181, p=0.024). Spatial variogram analysis revealed heterogenous patterns in paroxysmal AF, whereas non-paroxysmal AF displayed a homogenised, flattened fluctuations landscape. ConclusionsAtrial fibrillation exhibits robust multifractal dynamics rather than random electrical activity. Reduced fluctuations characterizes non-paroxysmal AF, whereas higher fluctuations is observed in paroxysmal AF, during flecainide modulation, and immediately prior to spontaneous termination. These findings suggest that multifractal fluctuations (c2) reflects the dynamical state of AF and may serve as a quantitative biomarker of disease progression, pharmacologic responsiveness, and proximity to termination. CONDENSED ABSTRACTTAtrial fibrillation (AF) exhibits multifractal electrogram fluctuations that vary with disease stage, pharmacologic responsiveness, and proximity to spontaneous termination. In this study, multifractal fluctuations (c2) was higher in paroxysmal than non-paroxysmal AF, increased selectively with flecainide in paroxysmal AF, and rose immediately before spontaneous termination. These findings identify c2 as a quantitative marker of AF progression, and imminent reorganization. Clinically, multifractal analysis may enhance intra-procedural assessment of AF phenotype, guide drug selection, and improve recognition of transitions toward sinus rhythm, and connects AF with concepts of criticality and phase transitions.

Parkinsons disease patients may experience a different therapeutic effect after replacement of the Medtronic Activa(R) deep brain stimulation neurostimulator with the newer Percept model, which features multiple independent current sources and constant-current control. We analyzed patient-reported therapeutic effect changes after Activa(R)-to-Percept replacements (AP, n=52) across six Dutch DBS-centers, comparing appropriate (AP+, n=36) and inappropriate/no (AP-, n=16) use of the manufacturers replacement workflow. Previous Activa(R)-to-Activa(R) replacements (AA, n=69) were used as reference. Worsened therapeutic effect was reported in 75.0% of AP-, 44.4% of AP+, and 21.7% of AA replacements (p<0.001). In the AP group, most patients with worsened effect were previously programmed with constant-voltage. Concluding, the risk of worsened therapeutic effect following AP replacements is higher compared to AA replacements, in particular when the replacement workflow is not properly used or in complex electrode configurations. We advise to use the workflow, inform the patient and plan closer follow-up appointments.