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Grayden, D. B.

Publications and source records attributed to Grayden, D. B..

At least 19 recordsLinked to original sources
bioRxiv2026

When Tagging Frequency Matters to Attention: Effects on SSVEPs, ERPs, and Cognitive Processing

Selective attention enables the prioritization of task-relevant information while managing distractors, and steady-state visual evoked potentials (SSVEPs) are widely used to track this process by tagging different visual objects at distinct flicker frequencies. However, whether the choice of tagging frequency itself influences other neural and cognitive measures remains unclear. Here, 27 participants performed detection and 1-back working memory tasks while a central target and peripheral distractors flickered at either 8.6 Hz or 12 Hz. The working memory task produced slower responses, more errors, and greater perceived difficulty than detection. Tagging frequency strongly shaped neural responses, with 8.6 Hz eliciting higher SSVEP signal-to-noise ratios than 12 Hz regardless of stimulus location. Nevertheless, stronger SSVEP responses for centrally attended stimuli were associated with fewer working memory errors and larger early visual ERP responses, while SSVEPs for attended and distractor stimuli were negatively correlated. In addition, the working memory task produced a larger P1-N1 peak-to-peak difference, and tagging frequency altered the timing and amplitude of early ERP effects. Together, these findings show that tagging frequency is not a neutral methodological parameter, but one that shapes both neural indices of attention and their relationship to cognitive performance.

neuroscience↗
bioRxiv2026

A Neural Mass Modelling Framework for Evaluating EEG Source Localisation of Seizure Activity

Electroencephalography and magnetoencephalography (EEG/MEG) provide non-invasive measurements of large-scale neural activity but do not directly reveal the underlying cortical sources, motivating the use of source localisation algorithms. However, objective evaluation of these methods remains challenging due to the absence of an experimentally verifiable ground truth. This study presents a simulation framework for generating biologically plausible ictal dynamics and their corresponding EEG signals to enable systematic benchmarking of source imaging approaches. Cortical seizure initiation and propagation were simulated using network-coupled neural mass (Epileptor) models, and combined with realistic forward models of the human head to produce macroscopic, electrophysiological data with known ground truth under varying conditions. Using this dataset, we evaluated established source localisation methods across idealised and realistic scenarios. Existing approaches achieved reasonable spatial accuracy under high-density, noise-free conditions; however, performance degraded substantially with reduced sensor coverage and added noise. This degradation was driven primarily by failures to recover source polarity, even when spatial localisation remained relatively accurate. These results suggest that current methods may be sufficient for identifying epileptogenic regions or tracking regional recruitment, but highlight polarity reconstruction as a key limitation for studies of seizure dynamics and network organisation. The proposed framework provides a reproducible and biologically grounded testbed for the development and evaluation of electrophysiological source localisation techniques.

neuroscience↗
bioRxiv2025

Structural Eigenmodes of the Brain to Improve the Source Localisation of EEG: Application to Epileptiform Activity

A fundamental view of neuroscience is that, in addition to neuronal activity, the structure of the brain constrains and explains brain function. An alluring formalism in computational neuroscience has been the generation of structural eigenmodes of neural activity from a matrix representing the anatomy of the brain. Traditionally, brain connectomics has been the gold standard for the coupling between structure and function. However, it has recently been suggested that simpler brain geometry can provide more explanatory power in fMRI. An adjacent modality is the source localisation problem of EEG, which aims to identify the underlying generators of EEG recordings. The underdetermined nature of the problem requires sufficient constraints to produce realistic and unique solutions of source activity. In this work, we presented a simple framework for incorporating different forms of structural brain eigenmodes to constrain the source localisation problem in epilepsy. We found that geometric eigenmodes were able to reconstruct the spread of a seizure through the brain slightly better than connectome eigenmodes, and both types of structural modes significantly outperformed commonly used approaches.

neuroscience↗
bioRxiv2025

Forecasting Seizure Duration from Neural Connectivity Patterns in a Neural Mass Model

Understanding seizure duration is crucial for predicting seizure evolution and developing targeted therapeutic strategies. This study investigates how changes in neural connectivity patterns estimated in a Neural Mass Model (NMM) relate to seizure duration and whether these parameters can be used to forecast seizure duration. We applied a biologically plausible NMM to intracranial EEG recordings from a Tetanus Toxin rat model of epilepsy. Model parameters, representing synaptic connectivity strengths between excitatory and inhibitory neuronal populations across different cortical layers, were estimated using the Unscented Kalman Filter. A Random Forest classifier was trained to predict seizure duration (short vs. long) based on these connectivity parameters. We assessed the classification performance using the receiver operating characteristic (ROC) curve and area under the curve (AUC). Our findings reveal that stronger excitatory-to-excitatory connectivity before seizure onset was associated with longer seizure durations. In contrast, inhibitory-to-inhibitory and excitatory-to-inhibitory connectivity strengths decreased before long seizures. The Random Forest classifier achieved an AUC of 0.91 when using both preictal (1 minute before seizure onset) and early ictal (first 5 seconds of the seizures) parameters, demonstrating excellent predictive power. Classification using only preictal parameters resulted in an AUC of 0.70. Our study highlights the critical role of preictal neural connectivity in determining seizure duration. The findings suggest that network excitability and inhibitory control before seizure onset influence how long seizures persist. These insights provide a foundation for personalized, time-based therapeutic interventions and seizure management strategies, ultimately improving the quality of life of patients with epilepsy.

pathology↗
bioRxiv2024

Cycles in Seizure Duration and Their Underlying Dynamics in the Tetanus Toxin Rat Model

Seizure duration, a characteristic of epilepsy that is understudied in relation in its relationship with rhythmic cycles, provides critical insights into the severity and temporal dynamics of seizures. This study investigates the rhythmic patterns of seizure duration in the Tetanus Toxin (TT) rat model of epilepsy, which is a well-established platform enabling long-term, stable recordings and observation of seizure emergence and remission. Our analysis shows significant cyclical patterns in seizure durations, with periods ranging from 4 to 8 days across rats. The synchronization index and circular-linear correlations revealed phase-locked relationships between seizure durations and cycles, suggesting non-random, predictable temporal dynamics. Further analyses examined the relationship between seizure durations, inter-seizure intervals, and dominant EEG power. The relationship between inter-seizure intervals and seizure duration was modest, suggesting little to no temporal dependency. In contrast, seizure duration showed stronger associations with EEG power in dominant frequency bands, potentially reflecting fluctuations in neural excitability. The findings highlight that seizure durations exhibit predictable rhythms, which could transform seizure prediction and enable time-based intervention strategies, ultimately improving epilepsy management and patient outcomes. These insights lay the groundwork for personalized, rhythm-aware therapeutic approaches.

neuroscience↗
bioRxiv2024

Reduced Synaptic Heterogeneity in a Tetanus ToxinModel of Epilepsy: Insights from ComputationalModeling

A neural mass model was used to assess connectivity strength across diverse populations by fitting the model to background EEG data obtained from a Tetanus Toxin rat model of epilepsy. Our findings reveal a notable decline in the variability of estimated parameters when using EEG data recorded from rats in the Tetanus Toxin group compared with the control group. A detailed comparison of standard deviations in estimated parameters between day 1 and day 20 recordings, coinciding with a heightened number of seizures, underscores the impact of Tetanus Toxin on diminishing synaptic strength variability across recordings. This study supports electrophysiological studies suggesting that epileptogenesis induces a reduction in biophysical heterogeneity, potentially leading to an increase in network synchrony associated with epilepsy. Furthermore, our computational model establishes a foundation for future explorations of the implications of this diminished variability.

neuroscience↗
bioRxiv2024

A microendovascular system can record precise neural signals from cortical and deep vessels with minimal invasiveness

Minimally invasive intravascular electroencephalography (ivEEG) signals are a promising tool for developing clinically feasible brain-computer interfaces (BCIs) that restore communication and motor functions in paralyzed patients. However, current ivEEG techniques can only record signals from the superior sagittal sinus (SSS), making it challenging to record motor responses related to hand and mouth movements from brain regions distant from the SSS, despite their critical role in BCIs. Here, using micro intravascular electrodes, ivEEGs recorded from the cortical or deep veins of eight pigs could measure cortical activities with greater signal power and better spatial resolution than those recording in the SSS, thus allowing mapping of the sensorimotor and visual functional areas. Additionally, electrical stimulation in the cortical vein between the micro intravascular electrodes induced muscle contractions contralateral to the stimulated area in five anesthetized pigs. These results demonstrate that ivEEG using micro intravascular electrodes is a promising tool for developing BCIs.

neuroscience↗
bioRxiv2024

Balancing Prior Knowledge and Sensory Data in a Predictive Coding Model: Insights into Coherent Motion Detection in Schizophrenia

This study introduces a biologically plausible computational model based on the predictive coding algorithm, providing insights into motion detection processes and potential deficiencies in schizophrenia. The model decomposes motion structures into individual and shared sources, highlighting a critical role of surround suppression in detecting global motion. This biologically plausible model sheds light on how the brain extracts the structure of motion and comprehends shared or coherent motion within the visual field. The results obtained from random dot stimuli underscore the delicate balance between sensory data and prior knowledge in coherent motion detection. Model testing across varying noise levels reveals longer convergence times with higher noise, consistent with psychophysical experiments showing that response duration (e.g., reaction time or decision-making time) also increases with noise levels. The model suggests that an excessive emphasis on prior knowledge extends the convergence time in motion detection. Conversely, for faster convergence, the model requires a certain level of prior knowledge to prevent excessive disturbance due to noise. These findings contribute to potential explanations for motion detection deficiencies observed in schizophrenia.

neuroscience↗
bioRxiv2024

Neural Dynamics and Seizure Correlations: Insights from Neural Mass Models in a Tetanus Toxin Rat Model of Epilepsy

This study focuses on the use of a neural mass model to investigate potential relationships between functional connectivity and seizure frequency in epilepsy. We fitted a three-layer neural mass model of a cortical column to intracranial EEG (iEEG) data from a Tetanus Toxin rat model of epilepsy, which also included responses to periodic electrical stimulation. Our results show that some of the connectivity weights between different neural populations correlate significantly with the number of seizures each day, offering valuable insights into the dynamics of neural circuits during epileptogenesis. We also simulated single-pulse electrical stimulation of the neuronal populations to observe their responses after the connectivity weights were optimized to fit background (non-seizure) EEG data. The recovery time, defined as the time from stimulation until the membrane potential returns to baseline, was measured as a representation of the critical slowing down phenomenon observed in nonlinear systems operating near a bifurcation boundary. The results revealed that recovery times in the responses of the computational model fitted to the EEG data were longer during 5 min periods preceding seizures compared to 1 hr before seizures in four out of six rats. Analysis of the iEEG recorded in response to electrical stimulation revealed results similar to the computational model in four out of six rats. This study supports the potential use of this computational model as a model-based biomarker for seizure prediction when direct electrical stimulation to the brain is not feasible.

neuroscience↗
bioRxiv2023

Neural Activity Shaping in Visual Prostheses with Deep Learning

ObjectiveThe visual perception provided by retinal prostheses is poor and limited to images constructed of phosphenes generated by the electrodes. One limiting factor has been the conventional strategy used to encode the target image into a stimulation pattern. Under this strategy, if the electrode density is high, the current spread of neighbouring unipolar stimuli overlaps, leading to blurred images. Simultaneous multipolar stimulation guided by the measured neural responses can attenuate excessive spread of excitation and allows for a more precise electrical input to the retina. However, it is far from trivial to predict what multipolar stimulus pattern will elicit the desired retinal response for a given target image. Here, we propose to solve this problem using an Artificial Neural Network (ANN) that could be trained with data acquired from the implant itself. ApproachOur method consists of two ANNs trained sequentially. The Measurement Predictor Network (MPN) is trained on data from the implant and is used to predict how the retina responds to multipolar stimulation by learning the forward model. The Stimulus Generator Network (STG) is trained on a large dataset of natural images and uses the trained MPN to determine efficient multipolar stimulus patterns by learning the inverse model. We validate our method in silico using a realistic model of retinal response to multipolar stimulation. Main ResultsWe show that the simulated retinal activations elicited with our ANN-based approach are considerably sharper when compared with the conventional method used in existing devices. The SGN finds multipolar stimulation patterns that are tuned to a specific retina, thus providing patient-specific stimuli. Also, due to its small computational cost, the SGN can output stimulation patterns at a very high rate. SignificanceOur novel protocol opens the door to personalized multipolar retinal stimulation, which may improve the visual experience and quality of life of retinal prosthesis users.

bioengineering↗
bioRxiv2023

Response to photic stimulation as a measure of cortical excitability in epilepsy patients

AO_SCPLOWBSTRACTC_SCPLOWStudying states and state transitions in the brain is challenging due to nonlinear, complex dynamics. In this research, we analyse the brains response to non-invasive perturbations. Perturbation techniques offer a powerful method for studying complex dynamics, though their translation to human brain data is under-explored. This method involves applying small inputs, in this case via photic stimulation, to a system and measuring its response. Sensitivity to perturbations can forewarn a state transition. Therefore, biomarkers of the brains perturbation response or cortical excitability could be used to indicate seizure transitions. However, perturbing the brain often involves invasive intracranial surgeries or expensive equipment such as transcranial magnetic stimulation (TMS) which is only accessible to a minority of patient groups, or animal model studies. Photic stimulation is a widely used diagnostic technique in epilepsy that can be used as a non-invasive perturbation paradigm to probe brain dynamics during routine electroencephalography (EEG) studies in humans. This involves changing the frequency of strobing light, sometimes triggering a photo-paroxysmal response (PPR), which is an electrographic event that can be studied as a state transition to a seizure state. We investigate alterations in the response to these perturbations in patients with genetic generalised epilepsy (GGE), with (n = 10) and without (n = 10) PPR, and patients with psychogenic non-epileptic seizures (PNES; n = 10), compared to resting controls (n = 10). Metrics of EEG time-series data were evaluated as biomarkers of the perturbation response including variance, autocorrelation, and phase-based synchrony measures. We observed considerable differences in all group biomarker distributions during stimulation compared to controls. In particular, variance and autocorrelation demonstrated greater changes in epochs close to PPR transitions compared to earlier stimulation epochs. Comparison of PPR and spontaneous seizure morphology found them indistinguishable, suggesting PPR is a valid proxy for seizure dynamics. Also, as expected, posterior channels demonstrated the greatest change in synchrony measures, possibly reflecting underlying PPR pathophysiologic mechanisms. We clearly demonstrate observable changes at a group level in cortical excitability in epilepsy patients as a response to perturbation in EEG data. Our work re-frames photic stimulation as a non-invasive perturbation paradigm capable of inducing measurable changes to brain dynamics.

neuroscience↗
bioRxiv2022

Inhibitory stabilized network behaviour in a balanced neural mass model of a cortical column

Strong inhibitory recurrent connections can reduce the tendency for a neural network to become unstable. This is known as inhibitory stabilization; networks that are unstable in the absence of strong inhibitory feedback because of their unstable excitatory recurrent connections are known as Inhibition Stabilized Networks (ISNs). One of the characteristics of ISNs is their "paradoxical response", where perturbing the inhibitory neurons with additional excitatory input results in a decrease in their activity after a temporal delay instead of increasing their activity. Here, we develop a model of populations of neurons across different layers of cortex. Within each layer, there is one population of inhibitory neurons and one population of excitatory neurons. The connectivity weights across different populations in the model are derived from a synaptic physiology database provided by the Allen Institute. The model shows a gradient of excitation-inhibition balance across different layers in the cortex, where superficial layers are more inhibitory dominated compared to deeper layers. To investigate the presence of ISNs across different layers, we measured the membrane potentials of neural populations in the model after perturbing inhibitory populations. The results show that layer 2/3 in the model does not operate in the ISN regime but layers 4 and 5 do operate in the ISN regime. These results accord with neurophysiological findings that explored the presence of ISNs across different layers in the cortex. The results show that there may be a systematic macroscopic gradient of inhibitory stabilization across different layers in the cortex that depends on the level of excitation-inhibition balance, and that the strength of the paradoxical response increases as the model moves closer to bifurcation points. Author summaryStrong feedback inhibition prevents neural networks from becoming unstable. Inhibition Stabilized Networks (ISNs) have strong inhibitory connections combined with high levels of unstable excitatory recurrent connections. In the absence of strong inhibitory feedback, ISNs become unstable. ISNs demonstrate a paradoxical effect: perturbing inhibitory neurons in an ISN by increasing their excitatory input results in a decrease in their activity after a temporal delay instead of increasing their activity. Here, we developed a neural mass model of a cortical column based on neurophysiological data. The model shows a gradual change in inhibitory stabilization across different layers in the cortex where layer 2/3 is less inhibitory stabilized and shows no paradoxical effect in contrast to layer 4 and layer 5, which operate in the ISN regime and show paradoxical responses to perturbation.

neuroscience↗
bioRxiv2022

Quantifying Visual Acuity for Pre-Clinical Testing of Visual Prostheses.

Visual prostheses currently restore only limited vision. More research and pre-clinical work are required to improve the devices and stimulation strategies that are used to induce neural activity that results in visual perception. Evaluation of candidate strategies and devices requires an objective way to convert measured and modelled patterns of neural activity into a quantitative measure of visual acuity. This study presents an approach that compares evoked patterns of neural activation with target and reference patterns. A d-prime measure of discriminability determines whether the evoked neural activation pattern is sufficient to discriminate between the target and reference patterns and thus provide a quantified level of visual perception in the clinical Snellen and MAR scales. The measure was accurate in providing an estimate of the perceivable feature sizes in scaled standardized "C" and "E" optotypes. The approach was used to assess the visual acuity provided by two alternative stimulation strategies applied to simulated retinal implants with different phosphene sizes and electrode pitch configurations. It was found that when there is substantial overlap in neural activity generated by different electrodes, an estimate of acuity based only upon electrode pitch is incorrect; our proposed method gives an accurate result in these circumstances. Quantification of visual acuity using this approach in pre-clinical development will allow for more rapid and accurate prototyping of improved devices and neural stimulation strategies.

bioengineering↗
bioRxiv2020

Learning receptive field properties of complex cells in V1

There are two distinct classes of cells in the primary visual cortex (V1): simple cells and complex cells. One defining feature of complex cells is their spatial phase invariance; they respond strongly to oriented grating stimuli with a preferred orientation but with a wide range of spatial phases. A classical model of complete spatial phase invariance in complex cells is the energy model, in which the responses are the sum of the squared outputs of two linear spatially phase-shifted filters. However, recent experimental studies have shown that complex cells have a diverse range of spatial phase invariance and only a subset can be characterized by the energy model. While several models have been proposed to explain how complex cells could learn to be selective to orientation but invariant to spatial phase, most existing models overlook many biologically important details. We propose a biologically plausible model for complex cells that learns to pool inputs from simple cells based on the presentation of natural scene stimuli. The model is a three-layer network with rate-based neurons that describes the activities of LGN cells (layer 1), V1 simple cells (layer 2), and V1 complex cells (layer 3). The first two layers implement a recently proposed simple cell model that is biologically plausible and accounts for many experimental phenomena. The neural dynamics of the complex cells is modeled as the integration of simple cells inputs along with response normalization. Connections between LGN and simple cells are learned using Hebbian and anti-Hebbian plasticity. Connections between simple and complex cells are learned using a modified version of the Bienenstock, Cooper, and Munro (BCM) rule. Our results demonstrate that the learning rule can describe a diversity of complex cells, similar to those observed experimentally. Author summaryMany cortical functions originate from the learning ability of the brain. How the properties of cortical cells are learned is vital for understanding how the brain works. There are many models that explain how V1 simple cells can be learned. However, how V1 complex cells are learned still remains unclear. In this paper, we propose a model of learning in complex cells based on the Bienenstock, Cooper, and Munro (BCM) rule. We demonstrate that properties of receptive fields of complex cells can be learned using this biologically plausible learning rule. Quantitative comparisons between the model and experimental data are performed. Results show that model complex cells can account for the diversity of complex cells found in experimental studies. In summary, this study provides a plausible explanation for how complex cells can be learned using biologically plausible plasticity mechanisms. Our findings help us to better understand biological vision processing and provide us with insights into the general signal processing principles that the visual cortex employs to process visual information.

neuroscience↗
bioRxiv2020

Spatiotemporal patterns of high-frequency activity (80-170 Hz) in long-term intracranial EEG

ObjectiveTo assess the variability in the rates and locations of high-frequency activity (HFA) and epileptiform spikes after electrode implantation, and to examine the long-term patterns of HFA using ambulatory intracranial EEG (iEEG) recordings. MethodsContinuous iEEG recordings obtained over an average of 1.4 years from 15 patients with drug-resistant focal epilepsy were used in this study. HFA was defined as high-frequency events with amplitudes clearly larger than the background, which was automatically detected using a custom algorithm. High-frequency oscillations (HFOs) were also visually annotated by three neurologists in randomly sampled segments of the total data. The automatically detected HFA was compared with the visually marked HFOs. The variations of HFA rates were compared with spikes and seizures on patient-specific and electrode-specific bases. ResultsHFA was a more general event that encompassed HFOs manually annotated by different reviewers. HFA and spike rates had high amounts of intra- and inter-patient variability. The rates and locations of HFA and spikes took up to weeks to stabilize after electrode implantation in some patients. Both HFA and spike rates showed strong circadian rhythms in all patients and some also showed multiday cycles. Furthermore, the circadian patterns of HFA and spike rates had patient-specific correlations with seizures, which tended to vary across electrodes. ConclusionsAnalysis of HFA and epileptiform spikes should account for post-implantation variability. Like seizures, HFA and epileptiform spikes show circadian rhythms. However, the circadian profiles can vary spatially within patients and their correlations to seizures are patient-specific.

neuroscience↗
bioRxiv2019

Critical slowing as a biomarker for seizure susceptibility

The human brain has the capacity to rapidly change state, and in epilepsy these state changes can be catastrophic, resulting in loss of consciousness, injury and even death. Theoretical interpretations considering the brain as a dynamical system would suggest that prior to a seizure recorded brain signals may exhibit critical slowing, a warning signal preceding many critical transitions in dynamical systems. Using long-term intracranial electroencephalography (iEEG) recordings from fourteen patients with focal epilepsy, we found key signatures of critical slowing prior to seizures. Signals related to a critically slowing process fluctuated over temporally long scales (hours to days), longer than would be detectable in standard clinical evaluation settings. Seizure risk was associated with a combination of these signals together with epileptiform discharges. These results provide strong validation of theoretical models and demonstrate that critical slowing is a reliable indicator that could be used in seizure forecasting algorithms.

neuroscience↗
bioRxiv2019

GABA-mediated tonic inhibition differentially modulates gain in functional subtypes of cortical interneurons

The binding of GABA to extra-synaptic GABAA receptors generates tonic inhibition that acts as a powerful modulator of cortical network activity. Despite GABA being present at low levels throughout the extracellular space of the brain, previous work has shown that GABA may differentially modulate the excitability of neuron subtypes through variation in chloride gradient. Here, we introduce a distinct mechanism through which extracellular GABA can differentially modulate the excitability of neuron subtypes through variation in neuronal electrophysiological properties. Using biophysically-detailed computational models, we found that tonic inhibition enhanced the responsiveness (or gain) of models with electrophysiological features typically observed in somatostatin (Sst) interneurons and reduced gain in models with features typical for parvalbumin (Pv) interneurons. These predictions were experimentally verified using patch-clamp recordings. Further analysis revealed that differential gain modulation is also dependent upon the extent of outward rectification of the GABAA receptor-mediated tonic current. Our detailed neuron models demonstrate two subcellular consequences of tonic inhibition. First, tonic inhibition enhances somatic action potential repolarisation by increasing current flow into the dendritic compartment. This enhanced repolarisation then reduces voltage-dependent potassium currents at the soma during the afterhyperpolarisation. Finally, we show that reductions of potassium current selectively increase gain within neurons exhibiting action potential dynamics typical for Sst interneurons. Potassium currents in Pv-type interneurons are not sensitive to this mechanism as they deactivate rapidly and are unavailable for further modulation. These findings introduce a neuromodulatory paradigm in which GABA can induce a state of differential interneuron excitability through differences in intrinsic electrophysiological properties.

neuroscience↗