Biology subjects

El-Deredy, W.

Publications and source records attributed to El-Deredy, W..

At least 19 recordsLinked to original sources

bioRxiv2026

Characterizing EEG Spectro-Temporal Variability Signatures in Alzheimer's and Parkinson's Disease

We present an EEG-based approach to characterize disease-related spectro-temporal signatures in Alzheimers disease (AD) and Parkinsons disease (PD). To this end, key spectral features were first identified using explainable machine learning, and their temporal dynamics were then examined to characterize variability patterns and statistical properties. EEG recordings were segmented into non-overlapping 4-s epochs, from which spectral features based on relative band power and spectral entropy were extracted. Random Forest classifiers were trained to discriminate individual subjects with AD and PD from healthy controls (HC) using a Leave-One-Subject-Out Cross-Validation (LOSOCV) strategy. The most discriminative spectral features and the directionality of their contributions were identified through a SHAP-based explainable analysis. Subsequently, the temporal dynamics of the key features were analyzed to characterize disease fingerprints in terms of variability at both inter-subject and intra-subject levels and their distributional profiles. Our results confirmed spectral slowing in both disorders and revealed disorder-specific differences in the dominant spectral markers: the theta/alpha ratio was the most influential feature for AD, whereas mean relative theta power was the primary feature for PD discrimination. We show that increased variability in key spectral features is a distinguishing signature of AD and PD, with disease groups exhibiting greater inter-subject heterogeneity and higher intra-subject temporal variability than HC. Moreover, the key features showed heavy-tailed behavior, for which a lognormal model provided a plausible fit across groups. We conclude that this EEG-based characterization provides a meaningful avenue for tracking deviations from healthy neural activity.

neuroscience↗

bioRxiv2024

Differentiating hallucination proneness dimensions through resting state alpha dynamics

BackgroundHallucination-like experiences (HLEs) are untriggered sensory perceptions linked to externalizing bias - the misattribution of self-generated sensory experiences to an external source. The vulnerability to HLEs, i.e., hallucination proneness (HP), is typically assessed by the Launay-Slade Hallucination Scale (LSHS). A recent LSHS factor analysis revealed four distinct HP dimensions: Multisensory HLEs, Auditory daydreaming, Vivid thoughts and inner speech, and Personified HLEs. The current study assesses whether these HP dimensions map onto distinct patterns of resting state brain dynamics in the alpha frequency band due to its modulatory role in attention and perception. MethodsWe used a Hidden semi-Markov Model to segment continuous RS alpha activity into nine recurrent brain states and extracted the total number of transitions (TT) and the number of visits per state (SV). We assessed how the HP dimensions relate to these metrics, calculated across the entire 3-minute recording and within shorter sliding windows to capture finer temporal changes. ResultsAll HP dimensions and increased RS time correlated with increased TT. Increased Personified HLEs scores linked to different time-dependent changes of SV in two states (SV to state 5 decreased over time, while visits to state 9 increased), highlighting distinct alpha dynamics in high- and low-hallucination prone individuals. ConclusionsIncreased TT could indicate frequent attentional switches between internal and external states. Different SV patterns related to higher Personified HLEs scores suggest unstable source monitoring, potentially inducing an externalizing bias. These findings provide novel predictors of HP dimensions, revealing distinct neural profiles associated with different vulnerability profiles.

neuroscience↗

bioRxiv2024

Synchronization of the prefrontal cortex with the hippocampus and posterior parietal cortex is navigation strategy-dependent during spatial learning

1.During goal-directed spatial learning, subjects progressively change their navigation strategies to increase their navigation efficiency, an operation supported by the medial prefrontal cortex (mPFC). However, how the mPFC may integrate relevant information in a wider memory networks involving the hippocampus (HPC) and the posterior parietal cortex (PPC) is poorly understood. We recorded local-field potential and neuronal firing simultaneously from the mPFC, HPC and PPC in mice subjected to spatial memory acquisition in the Barnes maze. During navigation trials, animals demonstrated two consecutive behavioral stages: searching and exploration. Throughout training, mice gradually switched from less efficient (non-spatial) to more efficient (spatial) goal-oriented strategies exclusively during the searching stage. 4-Hz and theta (6-12 Hz) oscillations were detected during spatial navigation in the three recorded areas associated with episodes of immobility and locomotion, respectively. The entrainment of prefrontal gamma oscillations (60-100 Hz) by hippocampal and parietal 4-Hz and theta oscillations, as well as the incidence of prefrontal gamma, was higher when mice implemented spatial strategies during the searching stage. Interestingly, 4-Hz and theta from HPC and PPC also synchronized the spike-timing of prefrontal neurons, which was maximum during spatial strategies in the searching stage. Finally, neurons recorded in the mPFC increased their task stage firing selectivity when they used spatial strategy. Altogether, these results provide evidence for the neural mechanisms underlying the prefrontal large-scale coordination with distributed neural networks during spatial learning.

neuroscience↗

bioRxiv2024

Striatal Dopamine Modulates Temporal Surprise P3a

Dopamine is vital in forming mental models of "what" and "when" sensory events occur that essentially guide goal-directed behaviour. However, it remains largely unknown how variations in temporal predictability are incorporated into such mental models. A better understanding of the underlying mechanisms is important, considering dopaminergic depletion in diseases such as Parkinsons Disease and Schizophrenia, where abnormal temporal processing is observed. Some electroencephalographic (EEG) studies indicate that noradrenergic mechanisms, as reflected in the P3b event-related potential, are modulated by temporal predictability, whereas others indicate that dopaminergic mechanisms as reflected in the P3a, underlie surprise. In this study, resting-state and task-dependent EEG was recorded from 24 healthy participants who were administered a selective D2 agonist or antagonist before they performed a pure tone auditory "oddball" task. Two oddball sequences included either partially predictable, with inter-stimulus intervals (ISIs) of 400ms/1200ms; or fully temporally predictable tones, with a consistent ISI of 600ms. Tones following 400ms ISIs were perceived as surprising, or "early", as shown in an enhanced P3a response; tones following a 1200ms ISIs showed a much reduced P3a response ("late"). The agonist accentuated the "late" effect, demonstrating that drugs targeting D2 receptors modulate temporal prediction. These findings differentiate the role of the dopaminergic system in temporal processing and model-based auditory predictions.

neuroscience↗

bioRxiv2024

Emergence of multiple spontaneous coherent subnetworks from a single configuration of human connectome-coupled oscillators model

Large-scale brain models with biophysical or biophysically inspired parameters generate brain-like dynamics with multi-state metastability. Multi-state metastability reflects the capacity of the brain to transition between different network configurations and cognitive states in response to changing environments or tasks, thus relating to cognitive flexibility. To study this phenomenon, we used a Kuramoto network of oscillators corresponding to a human brain atlas of 90 nodes, each with an intrinsic frequency of 40 Hz. The networks nodes were interconnected based on the structural connectivity strengths and delays found in the human brain. We identified global coupling and delay scale parameters corresponding to maximum spectral entropy, a proxy for maximal multi-state metastability. At this point, we show that multiple coherent (functional) sub-networks spontaneously emerge across multiple oscillatory modes, and persist in time for periods between 140 and 4389 ms. Most nodes in the network exhibit broad frequency spectra away from their intrinsic frequency, and switch between modes, in a manner similar to that reported in empirical resting-state neuroimaging data. We suggest that the obtained dynamics at maximum metastability is a suitable model of the awake brain. Further, we show that global coupling and delay scale parameters away from maximum metastability yield dynamical features similar to other brain states such as sleep and anesthesia. Therefore, spectral entropy also correlates with wakefulness and the synchrony of functional networks.

neuroscience↗

bioRxiv2023

A High-Resolution LED Display for Steady State Visual Stimulation: Customized, Affordable, and Open Source

Visually evoked steady-state potentials (SSVEPs) are neural responses elicited by visual stimuli oscillating at specific frequencies. In this study, we introduce a novel LED display system designed specifically for steady-state visual stimulation, offering precise control over visual stimulus parameters, including frequency resolution, luminance, and the ability to control the phase at the end of the stimulation. The LED display provides a personalized, modular, and affordable option for experimental setups. Based on the Teensy 3.2 board, the display utilizes Direct Digital Synthesis and Pulse Width Modulation techniques to control the LEDs. Its performance is validated through four experiments: the first two measure LED light intensities directly, while the last two assess the displays impact on EEG recordings. The results demonstrate that the display can deliver a stimulus suitable for generating SSVEPs with the desired frequency and phase resolution. We provide comprehensive documentation, including all necessary codes and electrical diagrams, as an open-source resource. This facilitates the replication and adaptation of the system for specific experimental requirements, enhancing its potential for widespread use in the field of neuroscience.

bioinformatics↗

bioRxiv2023

Misperceiving momentum: computational mechanisms of biased striatal reward prediction errors in bipolar disorder

BackgroundDysregulated reward processing and mood instability are core features of bipolar disorder that have largely been considered separately, and with contradictory findings. We sought to test a mechanistic account that proposes that, in bipolar disorder, there is an excessive tendency to enter recursive cycles in which reward perception is biased by signals that the environment might be changing for the better or worse. MethodsParticipants completed a probabilistic reward task with functional Magnetic Resonance Imaging. Using an influential computational model, we ascertained whether participants with bipolar disorder (n = 21) show greater striatal tracking of momentum-biased reward prediction errors (RPEs) than healthy controls (n = 21). We conducted psychophysiological interaction analyses to quantify the degree to which each group modulated functional connectivity between the ventral striatum and left anterior insula in response to fluctuations in momentum. ResultsIn the bipolar disorder group, but not controls, the momentum-biased RPE model accounted for significant additional variance in ventral striatal activity beyond a standard mode of veridical RPEs. Compared to controls, the bipolar disorder group exhibited lower ventral striatal-left insular functional connectivity modulated by momentum-biased RPE. Moreover, this reduction in connectivity was more pronounced as a function of current manic symptoms. ConclusionsConsistent with an existing theory, we found evidence that bipolar disorder is associated with a tendency for momentum to excessively bias striatal tracking of RPE signals. We identify impaired striatal-insular connectivity as a possible locus for this propensity. We argue that computational psychiatric approaches that examine momentary shifts in reward and mood dynamics have strong potential for new mechanistic insights and targets for intervention.

neuroscience↗

bioRxiv2023

EEG resting state alpha dynamics predict individual proneness to auditory hallucinations

IntroductionAuditory verbal hallucinations (AVH) are a transdiagnostic phenomenon but also occur in the general population. The disposition to experience AVH is considered a continuous expression from non-clinical to clinical hallucination proneness (HP). Currently, little is known about the neurophysiology of the non-clinical HP part of the continuum. AVH might result from a heightened sensitivity to sensory inputs and a decreased ability to differentiate between externally and internally generated input. Resting state (RS) alpha band activity is associated with perceptual sensitivity, attentional shifts, and cognitive control. Accordingly, spontaneous alpha fluctuations might present as a HP correlate. To investigate the time-varying dynamics of alpha band activity, we deployed a novel method for brain state allocation. MethodsWe recorded RS electroencephalography (EEG) data from 33 individuals with varying levels of HP but without clinically relevant hallucinations and used a Hidden Semi-Markov Model (HsMM) to identify five recurrent alpha states with unique temporal dynamics and topographies. The states mean duration and occupancy were analyzed as a function of HP. The sources of each state were reconstructed to identify the most active brain areas and their correspondence with known resting state networks. ResultsOccupancy and mean duration of a state corresponding to sensorimotor, auditory, and default-mode network (DMN) areas significantly predicted auditory and auditory-verbal HP, but not general HP. The temporal dynamics of all other states did not relate to HP. ConclusionAlpha brain state sources align with prior results on the role of the alpha in the DMN. The temporal dynamics of alpha might reflect individual differences for attentional biases to internally generated sensory events and altered auditory perceptual sensitivity. Thus, changes in the temporal brain state dynamics of RS alpha oscillations could present as a neural marker of increased vulnerability to auditory hallucinatory experiences.

neuroscience↗

bioRxiv2023

Modeling EEG Resting-State Brain Dynamics: A proof of concept for clinical studies

Functional brain imaging has shown that the awake brain, independent of a task, spontaneously switches between a small set of functional networks. How useful this dynamical view of brain activity is for clinical studies, e.g., as early markers of subsequent structural and/or functional change or for assessing successful training or intervention effects, remains unclear. Core to addressing this question is to assess the robustness and reproducibility of the analysis methods that model, characterize, or infer the features of brain dynamics, and the accuracy by which these features represent and classify specific cognitive or altered cognitive states. This is particularly key given inter- and intra-individual variability and measurement noise. Here we used resting-state EEG from persons with Parkinsons Disease (PD) and healthy matched controls to systematically assess the reliability, robustness, and sensitivity of Hidden semi-Markov models (HsMM). These models are an example of model-based probabilistic methods for Brain-State allocations that are estimated from observed data. The method estimates model parameters, if the M/EEG recording or observations, over the scale of minutes, are emissions from hidden states that persist over short durations, before switching or transitioning to other states. We introduce an analysis pipeline that leads to sets of reproducible features of neurophysiological dynamics at the individual level. These features can be used as discriminatory variables to classify individuals and to evaluate the effect of non-pharmacological training schemes like in the current example a music-gait exercise program for Parkinsons Disease. Given the method stochasticity and the data variability, we emphasize the importance of repeating the analysis to reliably identify brain states and their dynamical trajectories that subsequently can be related to individualized variables.

neuroscience↗

bioRxiv2022

Efficient estimation of time-dependent functional connectivity using Structural Connectivity constraints

Multivariate autoregressive models [MAR] allows estimating effective brain connectivity by considering both power and phase fluctuations of the signals involved. A MAR models brain activity in one region as a linear combination of past activations in all other regions. A Hidden Markov model, HMM, whose states emisions are drawn from state-specific MARs, can then be used to model fast switching of effective brain connectivity over time. However, the large number of MAR parameters, impede the accurate and efficient estimation of such models from neuroimaging timeseries with limited length. We propose a new model for inferring time-dependent effective brain connectivity by using a sparse MAR parameterisation to model the states emisions of a Hidden Semi-Markov Model, HsMM-MAR-AC. The sparse MAR model parameters are restricted by Anatomical Connectivity information in two ways: direct effective connectivity between two regions is only considered if the corresponding structural link/connection exists; and the time-lag associated with each direct connection is computed based on the average fibre length between the two regions, such that only one lag per connection is estimated. We simulated ground truth time-dependent brain connectivity states by generating time-series of 4 to 10 minutes sampled at 5ms, from switching Resting State Networks with a reference structural connectivity, and evaluated the accuracy of the new model in recovering the simulated Brain connectivity States against different levels of connectivity thresholding above and below the reference. We show that even when restricting the MAR to half of the reference connections, the model was able to recover the number of brain states, the associated connectivity features and their dynamics, with as little data as 4 mins. More relaxed structural connectivity thresholds required longer data to estimate the model accurately, and became computationally unfeasible without anatomical restrictions. HsMM-MAR-AC offers an efficient algorithm for estimating time-depend Effective Connectivity (tdEC) from neuroimaging data, that exploits the advantages of MAR without identifiability problems, excessive demand on data collection, or unnecessary computational complexity.

bioinformatics↗

bioRxiv2021

Modelling neural entrainment and its persistence: influence of frequency of stimulation and phase at the stimulus offset

The entrainment (synchronization) of brain oscillations to the frequency of sensory stimuli is a key mechanism that shapes perceptual and cognitive processes, such that atypical neural entrainment leads to neuro-psychological deficits. ObjectiveWe investigated the dynamic of neural entrainment. Particular attention was paid to the oscillatory behavior that succeed the end of the stimulation, since the persistence (reverberation) of neural entrainment may condition future sensory representations based on predictions about stimulus rhythmicity. ApproachA modified Jansen-Rit neural mass model of coupled cortical columns generated a time series whose frequency spectrum resembled that of the electroencephalogram. We evaluated spectro-temporal features of entrainment, during and after rhythmic stimulation of different frequencies, as a function of the resonance frequency of the neural population and the coupling strength between cortical columns. We tested if the duration of the entrainment persistence depended on the state of the neural network at the time the stimulus ends. Main ResultsThe entrainment of the column that received the stimulation was maximum when the frequency of the entrainer was within a narrow range around the resonance frequency of the column. When this occurred, entrainment persisted for several cycles after the stimulus terminated, and the propagation of the entrainment to other columns was facilitated. Propagation depended on the resonance frequency of the second column, and the coupling strength between columns. The duration of the persistence of the entrainment depended on the phase of the neural oscillation at the time the entrainer terminated, such that falling phases (from{pi} /2 to 3{pi}/2 in a sine function) led to longer persistence than rising phases (from 0 to{pi} /2 and 3{pi}/2 to 2{pi}). SignificanceThe study bridges between models of neural oscillations and empirical electrophysiology, and provides insights to the use of rhythmic sensory stimulation for neuroenhancement.

bioengineering↗

bioRxiv2021

From synaptic activity to human in vivo quantification of neurotransmitter dynamics: a neural modelling approach

Understanding the role of neurotransmitters glutamate and GABA during normal and abnormal brain function and under external stimulation in humans are critical neuroscientific and clinical goals. The recent development of functional 1H-Magnetic resonance spectroscopy (fMRS) has allowed us to study neuro-transmitter activity in vivo for the first time. However, the physiological basis of the observed fMRS signal remains unclear. It has been proposed that fMRS detects shifts in metabolite concentrations as they move from presynaptic vesicles, where they are largely invisible, to extracellular and cytosolic pools, where they are visible. Here we bridge the gap between neural dynamics and fMRS by developing a mean-field model to link the neurotransmitter dynamics at the microscopic-level to the macroscopic-level imaging measurements. GABA and glutamate are described as cycling between three metabolic pools: in the vesicles; active in the cleft; or undergoing recycling in the astrocytic or neuronal cytosol. We interrogate the model by applying a current to manipulate the mean membrane potential and firing rate of the neural populations. We find that by disregarding the contribution from the vesicular pool, our model predicts activity-dependent changes in the MRS signal, which are consistent with reported empirical findings. Further, we show that current magnitude and direction has a selective effect on the GABA/glutamate-MRS signal: inhibitory stimulation leads to reduction of both metabolites, whereas excitatory stimulation leads to increased glutamate and decreased GABA. In doing so, we link neural dynamics and fMRS and provide a mechanistic account for the activity-dependent change in the observed MRS signal. Key Points SummaryO_LIThe recent development of functional 1H-Magnetic resonance spectroscopy (fMRS) has allowed us to study neurotransmitter activity in vivo for the first time in humans. However, the physiological basis of the observed fMRS signal is unclear. C_LIO_LIIt has been proposed that fMRS detects shifts in metabolite concentrations as they move from presynaptic vesicles, where they are largely invisible to MRS, to extracellular and cytosolic pools, where they are visible to MRS. C_LIO_LIWe test this hypothesis using a mean field model which links the neural dynamics of neurotransmitters at the microscopic-level to the macroscopic-level imaging measurements obtained in experimental studies. C_LIO_LIBy disregarding activity in the vesicular pool, our model can generate activity-dependent changes in the MRS signal in response to stimulation which are consistent with experimental findings in the literature. C_LIO_LIWe provide a mechanistic account for the activity-dependent change in observed neurotransmitter concentrations using MRS. C_LI

neuroscience↗

bioRxiv2020

Cortical ignition dynamics is tightly linked to the core organisation of the human connectome

The capability of cortical regions to flexibly sustain an "ignited" state of activity has been discussed in relation to conscious perception or hierarchical information processing. Here, we investigate how the intrinsic propensity of different regions to get ignited is determined by the specific topological organisation of the structural connectome. More specifically, we simulated the resting-state dynamics of mean-field whole-brain models and assessed how dynamic multi-stability and ignition differ between a reference model embedding a realistic human connectome, and alternative models based on a variety of randomised connectome ensembles. We found that the strength of global excitation needed to first trigger ignition in a subset of regions is substantially smaller for the model embedding the empirical human connectome. Furthermore, when increasing the strength of excitation, the propagation of ignition outside of this initial core -which is able to self-sustain its high activity- is way more gradual than for any of the randomised connectomes, allowing for graded control of the number of ignited regions. We explain both these assets in terms of the exceptional weighed core-shell organisation of the empirical connectome, speculating that this topology of human structural connectivity may be attuned to support an enhanced ignition dynamic. Author summaryThe activity of the cortex in mammals constantly fluctuates in relation to cognitive tasks, but also during rest. The ability of brain regions to display ignition, a fast transition from low to high activity is central for the emergence of conscious perception and decision making. Here, using a biophysically inspired model of cortical activity, we show how the structural organization of human cortex supports and constrains the rise of this ignited dynamics in spontaneous cortical activity. We found that the weighted core-shell organization of the human connectome allows for a uniquely graded ignition. This graded ignition implies a smooth control of the ignition in cortical areas tuned by the global excitability. The smooth control cannot be replicated by surrogate connectomes, even though they conserve key local or global network properties. Indeed, the first trigger of ignition in the human cortex has the lowest global excitability and corresponds with the strongest interconnected areas, the ignition core. Finally, we suggest developmental and evolutionary constraints of the mesoscale organization that support this enhanced ignition dynamics in cortical activity.

neuroscience↗

bioRxiv2019

The discrete logic of the Brain - Explicit modelling of Brain State durations in EEG and MEG

We consider the detection and characterisation of brain state transitions, based on ongoing Magneto and Electroencephalography (M/EEG). Here a brain state represents a specific brain dynamical regime or mode of operation, which produces a characteristic quasi-stable pattern of activity at topography, sources or network levels. These states and their transitions over time can reflect fundamental computational properties of the brain, shaping human behaviour and brain function. The Hidden Markov Model (HMM) has emerged as a useful model-based approach for uncovering the hidden dynamics of brain state transitions based on observed data. However, the Geometric distribution of state duration (dwell time) implicit in HMM places highest probability on very short durations, which makes it inappropriate for the accurate modelling of brain states in M/EEG. We propose using Hidden Semi Markov Models (HSMM), a generalisation of HMM that models the brain state duration distribution explicitly. We present a Bayesian formulation of HSMM and use the Variational Bayes framework to efficiently estimate the HSMM parameters, the number of brain states and select among alternative brain state duration distributions. We assess HSMM performance against HMM on simulated data and demonstrate that the accurate modelling of state duration is paramount for accurately and robustly modelling non-Markovian EEG brain state features. Finally, we used actual resting-state EEG data to demonstrate the approach in practice and conclude that it provides a flexible parameterised framework that permits closer interrogation of possible generative mechanisms.

neuroscience↗

bioRxiv2018

Boundary effects of expectation in human pain perception

Perception of sensory stimulation is influenced by numerous psychological variables. One example is placebo analgesia, where expecting low pain causes a painful stimulus to feel less painful. Yet, because pain evolved to signal threats to survival, it should be maladaptive for highly-erroneous expectations to yield unrealistic pain experiences. Therefore, we hypothesised that a cue followed by a highly discrepant stimulus intensity, which generates a large prediction error, will have a weaker influence on the perception of that stimulus. To test this hypothesis we collected two independent pain-cueing datasets. The second dataset and the analysis plan were preregistered (osf.io/5r6z7). Regression modelling revealed that reported pain intensities were best explained by a quartic polynomial model of the prediction error. The results indicated that the influence of cues on perceived pain decreased when stimulus intensity was very different from expectations, suggesting that prediction error size has an immediate functional role in pain perception.

neuroscience↗

bioRxiv2018

Transcranial alternating current stimulation at 10 Hz modulates response bias in the Somatic Signal Detection Task

BackgroundOngoing, pre-stimulus oscillatory activity in the 8-13 Hz alpha range has been shown to correlate with both true and false reports of peri-threshold somatosensory stimuli. However, to directly test the role of such oscillatory activity in behaviour, it is necessary to manipulate it. Transcranial alternating current stimulation (tACS) offers a method of directly manipulating oscillatory brain activity using a sinusoidal current passed to the scalp.\n\nObjectiveWe tested whether alpha tACS would change somatosensory sensitivity or response bias in a signal detection task in order to test whether alpha oscillations have a causal role in behaviour.\n\nMethodsActive 10 Hz tACS or sham stimulation was applied using electrodes placed bilaterally at positions CP3 and CP4 of the 10-20 electrode placement system. Participants performed the Somatic Signal Detection Task (SSDT), in which they must detect brief somatosensory targets delivered at their detection threshold. These targets are sometimes accompanied by a light flash, which could also occur alone.\n\nResultsActive tACS did not modulate sensitivity to targets but did modulate response criterion. Specifically, we found that active stimulation generally increased touch reporting rates, but particularly increased responding on light trials. Stimulation did not interact with the presence of touch, and thus increased both hits and false alarms.\n\nConclusionstACS stimulation increased reports of touch in a manner consistent with our observational reports, changing response bias, and consistent with a role for alpha activity in somatosensory detection.

neuroscience↗

bioRxiv2017

Sensitivity to Pain Expectations: A Bayesian Model of Individual Differences

The thoughts and feelings people have about pain (referred to as pain expectations) are known to alter the perception of pain. However little is known about the cognitive processes that underpin pain expectations, or what drives the differing effect that pain expectations have between individuals. This paper details the testing of a model of pain perception which formalises the response to pain in terms of a Bayesian prior-to-posterior updating process. Using data acquired from a short and deception-free predictive cue task, it was found that this Bayesian model predicted ratings of pain better than other, simpler models. At the group level, the results confirmed two core predictions of predictive coding; that expectation alters perception and that increased uncertainty in the expectation reduces its impact on perception. The addition to the model of parameters relating to trait differences in pain expectation, improved its fit, suggesting that such traits play a significant role in perception beyond those expectations triggered by the pain cue. When model parameters were allowed to vary by participant, the models fit improved further. This final model produced a characterisation of each individuals sensitivity to pain expectations. This model is relevant for the understanding of the cognitive basis of pain expectations and could potentially act as a useful tool for guiding patient stratification and clinical experimentation.

neuroscience↗

bioRxiv2017

Identification of Memory Reactivation during Sleep by EEG Classification

Memory reactivation during sleep is critical for consolidation, but also extremely difficult to measure as it is subtle, distributed and temporally unpredictable. This article reports a novel method for detecting such reactivation in standard sleep recordings. During learning, participants produced a complex sequence of finger presses, with each finger cued by a distinct audio-visual stimulus. Auditory cues were then re-played during subsequent sleep to trigger neural reactivation through a method known as targeted memory reactivation (TMR). Next, we used electroencephalography data from the learning session to train a machine learning classifier, and then applied this classifier to sleep data to determine how successfully each tone had elicited memory reactivation. Above chance classification was significantly higher in slow wave sleep than in stage 2, suggesting differential efficacy of TMR in these two sleep stages. Interestingly, classification success reduced across numerous repetitions of the tone cue, suggesting either a gradually reducing responsiveness to such cues or a plasticity-related change in the neural signature as a result of cueing. We believe this method will be invaluable for future investigations of memory consolidation.

neuroscience↗