Journal of Neuroscience Methods

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.

Wide-field imaging (WFI) is a mesoscopic approach for monitoring cortex-wide activity with high temporal resolution and a broad field of view. Owing to its simple optical configuration and compatibility with chronic preparations, WFI has become an important tool in systems neuroscience and disease-model research. In this chapter, we describe practical protocols for chronic transcranial WFI in mice using two complementary optical signals: genetically encoded calcium indicators (GCaMP) and endogenous flavoprotein autofluorescence. Calcium imaging provides a robust readout of neuronal population activity, whereas flavoprotein imaging reflects mitochondrial redox dynamics and cellular metabolic demand. We detail procedures for animal preparation, skull clearing, headplate implantation, macroscope assembly, synchronized sensory stimulation, triggered image acquisition, and MATLAB-based data analysis. The analysis workflow includes {Delta}F/F normalization, reference-based signal correction, and artifact reduction, followed by trial averaging, atlas registration, and region-of-interest analysis. Because imaging is performed through the intact skull, the protocol enables repeated longitudinal measurements in the same animal over extended periods. This approach is reproducible, cost-effective, and adaptable to studies of cortical physiology and neurological disorders.

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.

Adeno-associated viral (AAV) vectors are foundational tools for dissecting brain structure-function relationships, but AAV serotype tropism varies across brain regions and species, requiring empirical validation to inform experimental design. This need is especially important in non-model organisms, where molecular neuroscience tools remain underdeveloped and access to research subjects is often limited. The Arctic ground squirrel (AGS, Urocitellus parryii) is a valuable model for studying extreme physiology, including metabolic suppression during hibernation and resistance to cerebral ischemia/reperfusion, yet no studies have evaluated AAV performance in the AGS brain. Here, we investigated the ability of AAV serotypes 1, 8, 9, and DJ to transduce the AGS hypothalamus using the human synapsin (hSyn) promoter and directly compared cellular transduction rates in a region implicated in thermoregulation and hibernation. To maximize data collection from a limited experimental population, we used a within-animal, contralateral stereotaxic injection design. Recombinant AAV vectors expressing enhanced green fluorescent protein or mCherry were delivered bilaterally, and reporter expression was analyzed four weeks later. All tested serotypes produced clear and reproducible reporter expression, establishing AAV as a viable molecular tool in the AGS hypothalamus. AAV1 produced significantly greater cellular transduction rates than AAV-DJ (17.2% {+/-} 3.5% vs 8.4% {+/-} 2.9%, paired t-test, p = 0.032). AAV8 and AAV9 showed transduction rates of 22.8% {+/-} 0.6% and 20.1% {+/-} 1.5%, respectively; however, with only two biological replicates per serotype, formal statistical comparison was not performed. These findings provide the first direct characterization of AAV-mediated gene delivery in the AGS brain and establish a foundation for future molecular interrogation of hypothalamic circuits in this extreme mammalian hibernator.

PurposeWe present a large-scale (N=120) comparative study of gel-based and dry electroencephalography systems for cognitive load analysis in tasks involving information visualization stimuli. Although dry systems are increasingly adopted owing to their portability and fast setup, their sensitivity to cognitive-related measurements (as compared to gel-based systems) remains debated. This limits the understanding of whether dry systems provide sufficient sensitivity for cognitive load assessment under controlled task conditions. MethodsWe analyzed a diverse set of signal quality metrics, such as signal-to-noise ratio and channel retention, combined with spectral features across frequency bands to evaluate the ability for each device to capture workload-related neural markers during information visualization tasks. ResultsAlthough the gel-based device showed consistently better quality results than the dry one, the effect sizes suggest a small practical significance of the differences between systems. These results demonstrate that dry systems can provide adequate physiological sensitivity for cognitive load assessments. ConclusionOur findings highlight the trade-off between usability (setup, calibration, etc.) and data fidelity, providing practical guidance for choosing electroencephalography systems for cognitive workload monitoring and applied neuroengineering research. Overall, the results suggest that dry systems can support coarse-grained cognitive load assessment, while gel-based systems remain advantageous when greater sensitivity is required.

CaMKII promoter is widely used to label and manipulate hippocampal pyramidal neurons via transgenic mouse lines or viral approaches. While it targets most excitatory neurons, a small subset remains unlabeled and often overlooked. We present an AAV-based strategy combined with CaMKII-driven Cre expression to access and study this remaining population. Furthermore, we provide a detailed protocol for in-house AAV production, targeted stereotaxic delivery, and functional validation of targeted neurons through slice electrophysiology and behavior. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=194 HEIGHT=200 SRC="FIGDIR/small/723440v1_ufig1.gif" ALT="Figure 1"> View larger version (50K): org.highwire.dtl.DTLVardef@3a31ccorg.highwire.dtl.DTLVardef@9b7e90org.highwire.dtl.DTLVardef@92297borg.highwire.dtl.DTLVardef@1e159eb_HPS_FORMAT_FIGEXP M_FIG C_FIG

Sensorimotor beta activity (13-30 Hz) is a key neuronal signature in the human sensorimotor system, and its features can be effectively measured using functional brain imaging methods such as magnetoencephalography (MEG). In addition to its importance in healthy brain processing, beta activity has been shown to be altered in several neurological diseases, underscoring its potential as a biomarker. To serve as biomarkers, features must be reliably defined, stable across measurements and, ideally, amenable to automated analysis, yet current approaches to beta characterization require subjective decisions and manual work. We here describe a hidden Markov model (HMM) based approach to automatically segment beta events from source level MEG beta band activity into discrete high- and low-beta states. We demonstrate the differences between the proposed HMM based approach and a commonly used amplitude-envelope based approach to analyse high- and low-beta modulation. We show that the methods complement each other both when applied to resting data and task related passive movement data. Furthermore, we assess the test-retest reliability of the proposed pipeline within individuals using intraclass correlation coefficients (ICC), and test if HMM constructed at one measurement site can be applied to data acquired at another site, thereby evaluating its multisite transferability. We show that the proposed approach produces stable results within subjects and across sites for many of the features. The ICC values were excellent for high-beta state (86-100% of brain areas), while low-beta state test-retest reliability was more modest. Most of the features showed statistically significant differences between sites only in a few brain areas, indicating very good multisite stability. The proposed approach can serve as an automated, reproducible analysis pipeline for, e.g., clinical applications, and appears suitable for multi-site datasets.

High-density electrophysiological recording using Neuropixels probes enables single-unit resolution of human neural activity. However, integrating these systems into clinical environments remains challenging. Reported human recordings have been limited to a few centres in the United States utilising variable regulatory, sterilisation and operative techniques. Here, we present human Neuropixels recordings under a nationally managed ethical and regulatory framework in the United Kingdom. We provide a reproducible roadmap to overcome regulatory and equipment constraints. Guided by the IDEAL Stage 2a (Development) framework, we established a frameless intraoperative workflow utilising manufacturer-sterilised probes and a commercially available, clinical-grade setup for Neuropixels insertion including micromanipulator and endoscope holder. We prospectively evaluated this workflow across six participants (mean age 62.5 years) undergoing elective ventriculoperitoneal shunt surgery. Iterative failure-mitigation cycles successfully resolved key technical barriers, including neuronavigation interference and hardware instability. Assessed across three predefined endpoints (clinical safety, procedural timing, and neural data yield), the workflow achieved zero research-related adverse events and maintained a strict 30-minute procedural extension. Progressive technical refinements increased single-unit yield from 25 units during early development to 146 manually curated units. This approach provides a scalable, clinically integrated workflow to safely perform high-density electrophysiology in routine neurosurgical environments.

ObjectiveSimultaneous recording of brain activity, behaviour, and virtual environments is essential for understanding large-scale neural dynamics during behaviour. However, existing systems often rely on software-based synchronization or post hoc alignment, introducing latency, jitter, and drift that obscure fast brain-behavior interactions. ApproachHere, we present a deterministically synchronized widefield calcium imaging platform that unifies neural imaging, high-speed behavioural monitoring, and closed-loop virtual reality (VR) under a shared hardware-defined clock. This system enables millisecond-precision temporal alignment across modalities, including dual-wavelength hemodynamic correction, pupil and orofacial tracking, locomotion sensing, and VR rendering. Main resultsThe platform achieves stable hardware-level synchronization across neural imaging, behavioural recordings, and VR rendering without reliance on software timestamps. It supports widefield imaging rates up to 100 Hz and integrates seamlessly with both ViRMEn and Blender VR engines, exhibiting a mean locomotion-to-VR update latency of [~]1.5 ms. Multimodal recordings during VR navigation demonstrate robust temporal alignment between cortical activity, facial dynamics, pupil signals, and locomotion. SignificanceThis system provides a deterministic multimodal framework for studying brain-behaviour relationships during active behaviour. By enabling millisecond-precision synchronization across neural imaging, behaviour, and virtual environments, this platform enables causal investigation of brain-behaviour interactions at millisecond precision and provides a foundation for next-generation closed-loop neuroengineering experiments.

Zebrafish (Danio rerio) are an important vertebrate model for vision and neuroscience research. In the larval stages, the aquatic species begins to elicit the optomotor response (OMR) to stabilize themselves in water -- a behaviour that may be exploited in the laboratory to measure visual acuity. However, up to now, the measurement of the OMR in juvenile and adult zebrafish has been limited due to their behavioural complexity. Here, we optimize a protocol to assay zebrafish aged between 4 and 9 weeks-post-fertilization, by displaying sinusoidal gratings parallel to the zebrafish eye to elicit a robust OMR. We assessed the visual spatial-frequency tuning function of an environmentally induced myopia model to confirm the sensitivity and robustness of the protocol. Additionally, we show the OMR is sensitive to the contrast and temporal resolution of the sinusoidal gratings. Furthermore, we found that the time between stimulus presentations impact the spatial-frequency tuning function likely as time is required for zebrafish to return to baseline swimming after eliciting the OMR. Finally, we found that the OMR after ten versus twenty seconds of stimulus onset appears comparable; indicating that robust OMR responses in zebrafish can be elicited through relatively short stimulus presentations. Through the experiments conducted, we present an optimized protocol specific to zebrafish. The protocol may be used to follow the progression or treatment efficacy of progressive neurological disorders including specific visual disorders and higher brain functions with visual endophenotypes. Ultimately, this protocol allows for high-throughput robust measures of visual and neural function in zebrafish.

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.

BackgroundThe optic nerve serves as a vital conduit for visual signaling, and its degeneration in optic neuropathy results in irreversible vision loss. It is also a widely used model for studying central nervous system (CNS) injury and repair. Although adeno-associated virus (AAV) and lentivirus are extensively applied in CNS research, their transduction efficiency and cell-type specificity within the optic nerve remain poorly characterized. This study aimed to identify the most effective viral vector, serotype, and promoter for direct gene delivery to the adult rat optic nerve. MethodsSprague-Dawley rats (7-10 weeks) received intra-optic nerve injections of lentiviral or AAV vectors encoding GFP under different promoters (CAG, CMV, or GFAP). Two to three weeks post-injection, optic nerves were collected for immunohistochemistry with markers of oligodendrocytes (Olig2), astrocytes (GFAP, Sox9), and microglia (IBA1). Transduction efficiency and cell-type specificity were assessed using confocal microscopy. ResultsAAV2, AAV5, and lentivirus showed minimal transduction, with only sparse GFP-positive cells observed near injection sites. In contrast, AAV-PHP.eB carrying the CAG promoter yielded robust and widespread GFP expression near the injection site. Quantitative analysis revealed that approximately 90% of transduced cells were Olig2-positive oligodendrocytes, indicating strong tropism for this glial population. ConclusionAAV-PHP.eB driven by the CAG promoter enables efficient gene delivery to the optic nerve, with a predominant tropism for oligodendrocytes. This targeted intra-optic nerve injection approach offers a reliable platform for manipulating oligodendrocytes and investigating mechanisms of CNS development, injury, and repair relevant to both optic neuropathies and other CNS diseases.

Three-dimensional (3D) whole-organ imaging and analysis at cellular resolution (termed 3D histology) provide profound insights into the organization and interactions of cells throughout organs. However, the quantitative analysis of these massive datasets remains a significant bottleneck due to the lack of integrated, user-friendly tools. Here, we present 3DBrainOne, an end-to-end ImageJ plugin that streamlines the essential 3D histological analysis of the mouse brain--from raw image preprocessing to region-wise quantification--within a single platform. 3DBrainOne features a robust whole-brain cell-counting module that uses a Difference-of-Gaussians (DoG) blob detection algorithm followed by a ResNet18-based deep learning classifier, enabling high-fidelity automatic whole-brain cell counting with a graphical user interface (GUI) for visual inspection and manual curation of analysis results. 3DBrainOne also supports multi-channel colocalization analysis. Furthermore, this platform includes modules for atlas alignment and brain-region-wise volumetric quantification, enabling brain region-resolved cell counting and structural analyses. As an ImageJ plugin, 3DBrainOne is compatible with a range of operating systems and hardware. In summary, 3DBrainOne is an integrated, versatile, and easy-to-use platform that will facilitate 3D histological analyses in experimental neuroscience.

Meditation has been associated with improvements in attention, emotional regulation, and mental well-being, motivating increasing interest in objective methods for assessing meditative states. In this study, we investigate whether EEG-based machine learning can reliably distinguish between multiple meditation styles and mind-wandering states. EEG data were recorded from experienced meditators performing three meditation styles, Shamatha, Vipassana, and Metta, together with an eyes-closed mind-wandering condition. EEG signals were preprocessed to remove artifacts, and features were extracted from frequency, time-frequency, and time domains. Classification was evaluated using both intra-subject and inter-subject strategies with multiple machine learning classifiers. Results demonstrate high intra-subject classification accuracy across meditation-versus-mind-wandering and meditation-style comparisons, indicating strongly discriminative subject-specific neural signatures. In contrast, inter-subject performance decreased substantially, particularly for distinguishing meditation styles, suggesting considerable inter-individual variability in meditation-related EEG patterns. Furthermore, temporal analysis revealed that classification performance increase over time, indicating that the neural distinctions between meditation states become increasingly pronounced over time. Additionally, t-SNE visualization showed clear within-subject clustering but increased overlap across subjects, explaining the reduced inter-subject generalization. Overall, these findings highlight the potential of EEG-based machine learning for personalized assessment and monitoring of meditative states while emphasizing the challenges of developing subject-independent meditation classification systems.

Statistical parametric mapping (SPM) software was implemented in the early 1990s so that neuroscientists could test spatially extended hypotheses using functional imaging data, usually in 3D space and allowing for a mass univariate approach to hypothesis testing that is agnostic to where any significant effects may lie. Here, we apply the same approach to gaze duration data, i.e. visual fixations, collected using a virtual reality headset, which extends across a large 2D area of visual space, measuring 32{degrees} either side of central fixation and 24{degrees} above and below this point. In order to evaluate this novel method, we measured the locus of average gaze in a group of 17 patients with hemispatial inattention to the left, a neurological condition caused by damage to the right parieto-frontal brain networks, that induces a systematic bias in lateralised visual attention. This causes people to experience difficulty in paying attention to one side of space, both in their extrapersonal world and relative to their own bodies. We used a free visual exploration paradigm (viewing multiple naturalistic scenes for 7 seconds), which is sensitive to spatial biases encountered in this condition. 23 age-matched and neurologically healthy controls also took part. The visual stimuli were original and mirror flipped versions (Left to Right ie L-R) to correct for any lateralised informational biases inherent in the images. When compared with age-matched controls, the patients exhibited an average spatial shift of attention of 18{degrees} to the right of the midline. We demonstrated this approach using patients with hemispatial inattention, but it can be applied to any fixation-based or dwell time data. This is an advance on current methods that generated visual heatmaps or attentional maps, as our technique allows formal testing of spatially extended hypotheses on gaze duration data using a standard, frequentist statistical approach.

PurposeAlthough electroretinography (ERG) is vital for evaluating retinal function, conventional corneal electrodes slide or detach in animals. This study aimed to investigate the effectiveness of a novel approach to ERG recording using a metal eyelid speculum for both active and reference electrodes in conjunction with a skin electrode-based ERG device. MethodsWe tested a stainless-steel eyelid speculum as both active and reference electrodes with a skin-electrode ERG system (HE-2000vet) in six healthy Japanese White rabbits. Dark-adapted rod and maximal responses and light-adapted cone and 30 Hz flicker ERGs were recorded in three weekly sessions. ResultsReproducible waveforms with identifiable a- and b-waves were obtained in every eye; rod b-waves reached 50-90 {micro}V and cone b-waves 40-55 {micro}V. Intraclass correlation coefficients revealed substantial interocular agreement and moderate-to-substantial inter-session reproducibility for b-wave amplitude and implicit time, whereas a-wave metrics were less reliable owing to lower amplitudes. The advantages of speculum electrode over corneal electrodes are that it requires no fur shaving, maintains stable contact regardless of globe orientation, and allows real-time observation. ConclusionsThis study demonstrated that an eyelid-speculum electrode is a practical, non-invasive alternative for veterinary and experimental ERG recordings, producing signal quality sufficient for longitudinal and interocular analyses while avoiding cosmetic and technical drawbacks of conventional methods.

Understanding intracranial pressure (ICP) dynamics is essential for interpreting clinical infusion tests used in the diagnosis of cerebrospinal fluid circulation disorders. However, the complex coupling between vascular pulsations, cerebrospinal fluid flow, and intracranial compliance makes quantitative interpretation of these tests challenging. Here, I present a patient specific simulation framework based on an extended electrical analog model that reproduces intracranial pressure dynamics observed during clinical infusion tests. The model integrates physiological inputs including arterial blood pressure, heart rate, respiratory rhythm, and resistance to cerebrospinal fluid outflow derived from clinical data. Built upon the classical Ursino framework, the model incorporates several modifications enabling realistic representation of physiological pulsations and infusion test conditions. The resulting system functions as a hybrid electrical-numerical simulation model representing a simplified digital electrical twin of intracranial hydrodynamics. The model was validated using data from 21 clinical infusion tests performed in patients with suspected normal pressure hydrocephalus. Simulated intracranial pressure recordings were compared with clinical measurements using regression and residual analysis. The simulations demonstrated strong agreement with measured data, with a mean correlation coefficient of r = 0.95 (95% CI 0.94 - 0.96), mean residual values within -1.71 to +1.68 mmHg, and a mean root mean square error (RMSE) of 2.07 mmHg. These results demonstrate that the proposed model accurately reproduces the dynamic behavior of intracranial pressure observed during clinical infusion tests. The framework provides a physiologically grounded computational tool for studying patient specific intracranial dynamics and may support improved interpretation of infusion test results in clinical practice.

BackgroundThe relevance of spontaneous activity has been unlocked thanks to recent large scale recordings that revealed, via Shared Variance Component Analysis (SVCA), the high-dimensional nature of the ongoing activity. A fundamental problem is how the dimension modifies when more neurons are included in the analysis. Contradictory results have been reported on this subject based on SVCA and Principal Component Analysis (PCA). New MethodWe investigate pro et contra of SVCA and PCA for the identification of reliable responses encoding underlying state variables. We focus on common features of the spectra of the reliable variances (RVs) and on their dimensionality. The analysis is demonstrated on previously published Ca2+ data from the visual and the dorsal cortex in head fixed mice during spontaneous behavior. ResultsRVs grow proportionally to the number N of neurons and show a power-law decay k- with the k-th SVC dimension over a range bounded by a maximal dimension kc, initially diverging as N 1/ and then saturating at sufficiently large N. The reliable dimensionality, estimated with different methodologies, also shows a clear saturation to an asymptotic value for large N. Furthermore, its value decreases when becomes larger, as demonstrated by employing experimental data as well as theoretical predictions. ConclusionWe have shown that SVCA is an extremely effective tool to extract reliable features from the neural signals, and that the exponent represents a biomarker able to reveal the level of correlation of the neurons as well as the dimensionality of the reliable space. HighlightsO_LIAdvantages and drawbacks of Shared Variance Component Analysis to extract reliable signals from neural data C_LIO_LIComparison of different methods to estimate reliable neural dimensionality associated to spontaneous activity C_LIO_LIAnalytical expressions of embedding dimensionality for power-law decaying reliable variances C_LIO_LIBounded growth of the dimensionality with the number of neurons C_LI

This paper presents experimental evidence that alpha-band EEG signals can be reliably detected from an in-ear electrode during physical activity, enabling fatigue monitoring in dynamic, real-world conditions such as sports. We collected an EEG dataset using a custom-designed, compact wearable system measuring only 20 mm in diameter, integrated inside the earphone. It supports five channels, four head electrodes (T3, C3, C4, T4) and one in-ear electrode, allowing simultaneous multi-site recordings. Recordings were made while a participant engaged in a controlled cycling protocol designed to induce physical fatigue. We demonstrated a direct relationship between alpha power and entropy in EEG data recorded from both the head and ear, during both activity and rest. To our knowledge, this is the first study to demonstrate in-ear alpha power tracking during active physical movement for sports-related fatigue monitoring. These findings open new possibilities for compact, wearable EEG systems in athletic and high-performance settings, where traditional EEG setups are impractical

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.