Biology subjects

Noreika, V.

Publications and source records attributed to Noreika, V..

18 recordsLinked to original sources

bioRxiv2025

Sleep disturbance is associated with increased repetitive sensorimotor behaviour in autism but not in learning/intellectual disabilities or ADHD: A comparative study

The interplay of sleep and sensorimotor processing is a fundamental neurobiological process crucial for well-being. Individuals with neurodevelopmental conditions like autism, ADHD, and learning disabilities frequently experience both significant sleep disturbance and repetitive sensorimotor behaviours. Although a link between these domains is established, it is unclear whether this relationship and its functional impact are consistent across different neurodevelopmental conditions. In a cross-sectional dataset of individuals aged 5-21 (n = 212), we investigated whether the interplay between sleep disturbance and repetitive sensorimotor behaviours differs between individuals diagnosed with autism (IQ>70), ADHD, a learning disability (IQ<70), as well as typically developed controls. We also examined whether interactions between sleep and repetitive sensorimotor behaviours predicts distinct functional outcomes between neurodevelopmental groups. We found that individuals with autism (IQ>70) were most strongly affected by both sleep disturbance and repetitive sensorimotor behaviours. Crucially, we show that while a robust bidirectional link between sleep and repetitive sensorimotor behaviours exists across the entire sample, sleep problems amplify repetitive sensorimotor behaviours more strongly only in autistic people than in typical controls. Contrary to previous work, we found that learning disabled children and adolescents engaged in more repetitive sensorimotor behaviour but did not experience more sleep disturbance. Although the interaction between sleep problems and repetitive sensorimotor behaviours did not determine functional outcomes in autistic people, the interplay of sleep problems and repetitive sensorimotor behaviour impaired social awareness in people with ADHD. These findings challenge a transdiagnostic view that treats the reinforcement between sleep and sensorimotor problems as a general marker of neurodevelopmental conditions. Understanding the distinct pathways between sleep and waking behaviours is a critical step toward developing personalized, mechanistic interventions targeting specific sleep and sensory impairment profiles in different neurodevelopmental conditions.

neuroscience↗

bioRxiv2025

A multimodal human-computer interaction dataset for neurocognitive user state evaluation

We introduce the Simulated Environment for Neurocognitive State Evaluation (SENSE-42), a multimodal dataset collected during user interactions with desktop computers. It is designed for studying spontaneous fluctuations in the neurocognitive state related to the tonic alertness of computer users, with recordings from 42 participants over 2-hour sessions. Within a simulated desktop environment, participants performed real-world routine tasks, including application switching, file management, typing, and web browsing. High-resolution data were recorded across physiological (electroencephalography, electrocardiography, respiration) and subjective modalities of alertness. At five-minute intervals, alertness state was reported using seven questions, addressing sleepiness (Karolinska Sleepiness Scale), mental and temporal demand, perceived performance, effort and frustration (NASA Task Load Index), as well as attentiveness. Behavioural data included keyboard, mouse and webcam inputs. Demographic information, experience metrics, habits, and preferences of computer usage were collected. In addition, individual differences in sleep quality were evaluated using the Pittsburgh Sleep Quality Index and the Epworth Sleepiness Scale. The SENSE-42 dataset can contribute to future research in user state monitoring, behavioural analysis and physiological computing.

neuroscience↗

bioRxiv2025

Distinct developmental changes in linear and nonlinear neural interactions across infancy and adulthood

The development of functional brain connectivity during early life depends on social experience and is best understood in the context of interactions with a caregiver. It is still an open question to what extent distinct modes of functional connectivity dominate in different stages of human brain development, and whether cognitive tasks can differentially modulate them. Using electroencephalography (EEG), we investigated the development of linear and nonlinear functional brain connectivity in infants and adults while they socially interacted. Using simultaneous EEG recordings in parent-infant dyads performing two different experiments (N = 160; 80 adults and 80 infants), we computed functional connectivity capturing distinct dynamics: a linear measure capturing phase synchronization (weighted phase lag index; WPLI), and a nonlinear measure capturing information sharing (weighted symbolic mutual information; WSMI). In both tasks, adults showed higher WSMI than infants, whereas infants showed higher WPLI than adults. Moreover, infant age predicted only task-related connectivity values computed with the nonlinear measure and not with its linear counterpart, suggesting that the information-theoretic measure was more sensitive to developmental changes in task-relevant neural processing. These findings suggest that over development, a shift in dominance from linear to nonlinear modes of brain communication may be essential for supporting emerging higher cognitive abilities, such as precursors to executive function (here, attention shifting) and social decision-making. Further, this work highlights the importance of using nonlinear measures in addition to traditional linear ones, which collectively permit a more robust capture of maturational changes.

neuroscience↗

bioRxiv2023

When the heart meets the mind Exploring the brain-heart interaction during time perception

It has been hypothesized that time estimation relies on bodily rhythms and interoceptive signals, such as heartbeats. Extending previous research demonstrating this connection, we provided further electrophysiological evidence that the brain registers heartbeats while tracking time intervals. We evaluated the heartbeat-evoked potential (HEP) and the contingent negative variation (CNV) during an auditory duration-reproduction task with intervals lasting 4, 8, and 12 seconds and a control reaction-time task employing the exact durations. The (interoceptive) Self-Awareness Questionnaire (SAQ) and the heartbeat-counting task (HCT) were administered. The SAQ, but not the HCT scores, correlated with the mean reproduced durations for 4s, 8s, and 12s intervals: the higher the SAQ score (a stronger awareness of bodily signals), the longer the duration reproductions and the more accurate the timing behavior. The HEP amplitude within 130-270 ms (HEP1) and 470-520 ms (HEP2) after the R peak was smaller for the 4s interval than for the 8s and 12s intervals. This is a timing-specific effect, as the HEP amplitude did not differ for intervals in the reaction-time task. A ramp-like increase in HEP2 amplitudes was significant for the duration-encoding phase of the timing task, but not for the control reaction-time task. The HEP2 increase within the reproduction phase of the timing task correlated significantly with the reproduced durations for the 8s and the 12s intervals. The larger the registered increase in the HEP2, the greater the under-reproduction of the estimated duration. The initial and late CNV components were significantly more negative during the encoding phase than during the reaction-time task. Given the link between these components with attention modulation and temporal memory, we interpret the CNV findings as indicating greater executive resources oriented towards time. We conclude that interoceptive awareness (SAQ) and state-like cortical responses to the heartbeat (HEP) predict duration reproductions, emphasizing the notion of the embodiment of time.

neuroscience↗

bioRxiv2023

Principles for Optimal Window Size Selection for Infant and Adult EEG Connectivity Analysis

Neural connectivity analysis is often performed on continuous data that has been discretized into temporal windows of a fixed length. However, the selection of an optimal window length is non-trivial, and depends on the properties of the connectivity metric being used as well as the effects of interest within the data (e.g. developmental or inter-brain effects). A systematic investigation of these factors, and objective criteria for window size selection are currently missing in the literature, particularly in regard to pediatric datasets. Here, we provide a principled examination of the effect of window size on optimization of signal to noise ratio for linear and non-linear EEG connectivity, as applied to infant, adult and dyadic (infant-adult) datasets. We employed a linear weighted phase lag index (wPLI), and a nonlinear weighted symbolic mutual information (wSMI) metric to assess brain connectivity for each dataset. Our results showed a clear polar dissociation between linear and non-linear metrics, as well as between infant and adult datasets in optimal window size. Further, optimal dyadic (infant-adult) window size settings defaulted to one or the partner rather than reflecting an intermediate compromise. Given the specificity of these results (i.e. there was no single window size that was optimal for all contrasts), we conclude that a formal analysis of optimal window size may be useful prior to conducting any new connectivity analysis. Here, we recommend guiding principles, performance metrics and decision criteria for optimal and unbiased window size selection.

neuroscience↗

bioRxiv2023

Spectrally and temporally resolved estimation of neural signal diversity

Quantifying the complexity of neural activity has provided fundamental insights into cognition, consciousness, and clinical conditions. However, the most widely used approach to estimate the complexity of neural dynamics, Lempel-Ziv complexity (LZ), has fundamental limitations that substantially restrict its domain of applicability. In this article we leverage the information-theoretic foundations of LZ to overcome these limitations by introducing a complexity estimator based on state-space models --which we dub Complexity via State-space Entropy Rate (CSER). While having a performance equivalent to LZ in discriminating states of consciousness, CSER boasts two crucial advantages: 1) CSER offers a principled decomposition into spectral components, which allows us to rigorously investigate the relationship between complexity and spectral power; and 2) CSER provides a temporal resolution two orders of magnitude better than LZ, which allows complexity analyses of e.g. event-locked neural signals. As a proof of principle, we use MEG, EEG and ECoG datasets of humans and monkeys to show that CSER identifies the gamma band as the main driver of complexity changes across states of consciousness; and reveals early entropy increases that precede the standard ERP in an auditory mismatch negativity paradigm by approximately 20ms. Overall, by overcoming the main limitations of LZ and substantially extending its range of applicability, CSER opens the door to novel investigations on the fine-grained spectral and temporal structure of the signal complexity associated with cognitive processes and conscious states.

neuroscience↗

bioRxiv2023

Effects of alertness on perceptual detection and discrimination

The level of alertness fluctuates throughout the day, exerting modulatory effects on human cognitive processes at any moment. However, our knowledge of how alertness level interacts with specific cognitive demands and perceptual rules of the task is still limited. Here we use perceptual decision-making paradigms to understand how alertness modulates the detection of a stimulus and the capacity to discriminate one stimulus from another. We analyzed data from four different experiments (113 participants in total): 1 - auditory masking detection; 2 - sensorimotor detection; 3 - auditory spatial discrimination; and 4 - auditory phoneme discrimination, and examined the performance of participants during the natural transition from awake (high alertness) to drowsy (low alertness). First, we fitted psychometric functions to the hit rates across different conditions of difficulty for EEG-defined high and low alertness metastable states, respectively. Second, we performed modelling of slope and threshold for the fitted curves as well as signal detection theory measures of perceptual sensitivity (d) and response bias (criterion). We found lower detection and discrimination sensitivity to stimuli as alertness level decreases, signalled by a shallower slope of the sigmoidal curve and a lower d, while the threshold increases slightly and equivalently across experiments during lower alertness. There was no change in the criterion to make the decision during the transition. These results suggest that reduced alertness generally decreases the quality of perceptual decision-making. Zooming in, we observed that the decrease in sensitivity measured by slope was stronger for discrimination than for detection decisions, indicating that lower alertness impairs the precision of decisions in discriminating alternatives more than identifying the presence of a stimulus around the threshold. Taken together, these results suggest that alertness has a common effect on perceptual decision-making and differentially modulates detection and discrimination decisions.

animal behavior and cognition↗

bioRxiv2021

Automatic classification of ICA components from infant EEG using MARA

Automated systems for identifying and removing non-neural ICA components are growing in popularity among adult EEG researchers. Infant EEG data differs in many ways from adult EEG data, but there exists almost no specific system for automated classification of source components from paediatric populations. Here, we adapt one of the most popular systems for adult ICA component classification for use with infant EEG data. Our adapted classifier significantly outperformed the original adult classifier on samples of naturalistic free play EEG data recorded from 10 to 12-month-old infants, achieving agreement rates with the manual classification of over 75% across two validation studies (n=44, n=25). Additionally, we examined both classifiers ability to remove stereotyped ocular artifact from a basic visual processing ERP dataset, compared to manual ICA data cleaning. Here the new classifier performed on level with expert manual cleaning and was again significantly better than the adult classifier at removing artifact whilst retaining a greater amount of genuine neural signal, operationalised through comparing ERP activations in time and space. Our new system (iMARA) offers developmental EEG researchers a flexible tool for automatic identification and removal of artifactual ICA components.

neuroscience↗

bioRxiv2019

Broadband Signal Rather than Frequency-Specific Rhythms Underlies Prediction Error in the Primate Auditory Cortex

Detection of statistical irregularities, measured as a prediction error response, is fundamental to the perceptual monitoring of the environment. We studied whether prediction error response is associated with neural oscillations or asynchronous broadband activity. Electrocorticography (ECoG) was carried out in three male monkeys, who passively listened to the auditory roving oddball stimuli. Local field potentials (LFP) recorded over the auditory cortex underwent spectral principal component analysis, which decoupled broadband and rhythmic components of the LFP signal. We found that the broadband component captured the prediction error response, whereas none of the rhythmic components were associated with statistical irregularities of sounds. The broadband component displayed more stochastic, asymmetrical multifractal properties than the rhythmic components, which revealed more self-similar dynamics. We thus conclude that the prediction error response is captured by neuronal populations generating asynchronous broadband activity, defined by irregular dynamical states which, unlike oscillatory rhythms, appear to enable the neural representation of auditory prediction error response. Significance StatementThis study aimed to examine the contribution of oscillatory and asynchronous components of auditory local field potentials in the generation of prediction error responses to sensory irregularities, as this has not been directly addressed in the previous studies. Here, we show that mismatch negativity - an auditory prediction error response - is driven by the asynchronous broadband component of potentials recorded in the auditory cortex. This finding highlights the importance of non-oscillatory neural processes in the predictive monitoring of the environment. At a more general level, the study demonstrates that stochastic neural processes, which are often disregarded as neural noise, do have a functional role in the processing of sensory information.

neuroscience↗

bioRxiv2019

In your phase! Neural phase synchronization underlies visual imagery of faces

Mental imagery is the process through which we retrieve and recombine information from our memory to elicit the subjective impression of "seeing with the minds eye". In the social domain, we imagine other individuals while recalling our encounters with them or modelling alternative social interactions in future. Many studies using imaging and neurophysiological techniques have shown several similarities in brain activity between visual imagery and visual perception, and have identified frontoparietal, occipital and temporal neural components of visual imagery. However, the neural connectivity between these regions during visual imagery of socially relevant stimuli have not been studied. Here we used electroencephalography to investigate neural connectivity and its dynamics between frontal, parietal, occipital and temporal electrodes during visual imagery of faces. We found that voluntary visual imagery of faces is associated with long-range phase synchronisation in the gamma frequency range between frontoparietal electrode pairs and between occipitoparietal electrode pairs. In contrast, no effect of imagery was observed in the connectivity between occipitotemporal electrode pairs. Gamma range synchronisation between occipitoparietal electrode pairs predicted subjective ratings of the contour definition of imagined faces. Furthermore, we found that visual imagery of faces is associated with an increase of short-range frontal synchronisation in the theta frequency range, which temporally preceded the long-range increase in the gamma synchronisation. We speculate that the local frontal synchrony in the theta frequency range might be associated with an effortful top-down mnemonic reactivation of faces. In contrast, the long-range connectivity in the gamma frequency range along the fronto-parieto-occipital axis might be related to the endogenous binding and subjective clarity of facial visual features.

neuroscience↗

bioRxiv2019

The Dream Catcher experiment: Blinded analyses disconfirm markers of dreaming consciousness in EEG spectral power

The Dream Catcher test defines the criteria for a genuine discovery of the neural constituents of phenomenal consciousness. Passing the test implies that some patterns of purely brain-based data directly correspond to the subjective features of phenomenal experience, which would help to bridge the explanatory gap between consciousness and brain. Here, we conducted the Dream Catcher test for the first time in a graded and simplified form, capturing its core idea. The experiment involved a Data Team, who measured participants brain activity during sleep and collected dream reports, and a blinded Analysis Team, who was challenged to predict better than chance, based solely on brain measurements, whether or not a participant had a dream experience. Using a serial-awakening paradigm, the Data Team prepared 54 one-minute polysomnograms of NREM sleep--27 of dreamful sleep (3 from each of the 9 participants) and 27 of dreamless sleep--redacting from them all associated participant and dream information. The Analysis Team attempted to classify each recording as either dreamless or dreamful using an unsupervised machine learning classifier, based on hypothesis-driven, extracted features of EEG spectral power and electrode location. The procedure was repeated over five iterations with a gradual removal of blindness. At no level of blindness did the Analysis Team perform significantly better than chance, suggesting that EEG spectral power does not carry any signatures of phenomenal consciousness. Furthermore, we demonstrate an outright failure to replicate key findings of recently reported correlates of dreaming consciousness.\n\nHighlightsThe first reported attempt of the Dream Catcher test.\nThe correlates of conscious experience may not lie in EEG spectral power.\nReported markers of NREM dreaming consciousness misperformed in a blinded setting.\nThose markers also could not be confirmed in an unblinded setting.

neuroscience↗

bioRxiv2019

Emotional valence modulates the topology of the parent-infant inter-brain network

Emotional communication between parents and children is crucial during early life, yet little is known about its neural underpinnings. Here, we adopt a dual-brain connectivity approach to assess how emotional valence modulates the parent-infant neural network. Fifteen mothers modelled positive and negative emotions toward pairs of objects during social interaction with their infants (aged 10.3 months) whilst their neural activity was concurrently measured using dual-EEG. Intra-brain and inter-brain network connectivity in the 6-9 Hz (infant Alpha) range was computed during maternal expression of positive and negative emotions using directed (partial directed coherence) and non-directed (phase-locking value) connectivity metrics. Graph theoretical metrics were used to quantify differences in network topology as a function of emotional valence. Inter-brain network indices (Density, Strength and Divisibility) consistently revealed that the integration of parents and childrens neural processes was significantly stronger during maternal demonstrations of positive than negative emotions. Further, directed inter-brain metrics indicated that mother-to-infant directional influences were stronger during the expression of positive than negative emotions. These results suggest that the parent-infant inter-brain network is modulated by the emotional quality and tone of dyadic social interactions, and that inter-brain graph metrics may be successfully applied to examine these changes in interpersonal network topology.

neuroscience↗

bioRxiv2019

Modulating dream experience: Noninvasive brrain stimulation over the sensorimotor cortex reduces dream movement

Recently, cortical correlates of specific dream contents have been reported, such as the activation of the sensorimotor cortex during dreamed hand clenching. Yet, the causal mechanisms underlying specific dream content remain largely elusive. Here, we investigated how alterations in the excitability of sensorimotor areas through transcranial direct current stimulation (tDCS) might alter dream content. Following bihemispheric tDCS or sham stimulation, participants who were awakened from REM sleep filled out a questionnaire on bodily sensations in dreams. tDCS, compared to sham stimulation, significantly decreased reports of dream movement, especially repetitive actions. Contrary to this, other types of bodily experiences, such as tactile or vestibular sensations, were not affected by tDCS, confirming the specificity of stimulation effects. In addition, tDCS reduced interhemispheric coherence in parietal areas and altered the phasic electromyography correlation between the two arms. These findings reveal that a complex reorganization of the motor network co-occurred with the reduction of dream movement, confirming spatial specificity of the stimulation site. We conclude that tDCS over the sensorimotor cortex causally interferes with dream movement during REM sleep.

neuroscience↗

bioRxiv2018

Parental neural responsivity to infants visual attention: how mature brains scaffold immature brains during social interaction.

Almost all attention and learning - in particular, most early learning - takes place in social settings. But little is known of how our brains support dynamic social interactions. We recorded dual-EEG from infants and parents during solo play and joint play. During solo play, fluctuations in infants Theta power significantly forward-predicted their subsequent attentional behaviours. But this forwards-predictiveness was lower during joint play than solo play, suggesting that infants endogenous neural control over attention is greater during solo play. Overall, however, infants were more attentive to the objects during joint play. To understand why, we examined how adult brain activity related to infant attention. We found that parents Theta power closely tracked and responded to changes in their infants attention. Further, instances in which parents showed greater neural responsivity were associated with longer sustained attention by infants. Our results offer new insights into how one partner influences another during social interaction.

neuroscience↗

bioRxiv2017

Wakefulness state modulates conscious access: Suppression of auditory detection in the transition to sleep

Mapping the reports of awareness and its neural underpinnings is instrumental to understand the limits of human perception. The capacity to become aware of objects in the world may be studied by suppressing faint target stimuli with strong masking stimuli, or - alternatively - by manipulating the level of wakefulness from full alertness to mild drowsiness. By combining these two approaches, we studied how perceptual awareness is modulated by decreasing wakefulness. We found dynamic changes in behavioural and neural signatures of conscious access in humans between awake and drowsy states. Behaviourally, we show a decrease in the steepness of the psychophysical function for conscious access in drowsy trials. Neural mapping showed delayed processing of target-mask interaction as the consciousness transition progressed, suggesting that the brain resolution of conscious access shifts from early sensory/perceptual to decision-making stages of processing. Once the goal to report the awareness of a target is set, the system behaviourally adapts to rapid changes in wakefulness, revealing the flexibility of the neural signatures of conscious access, and its suppression, to maintain performance. Significance statementMaintaining full alertness for long periods of time in attentionally demanding situations is challenging and may lead to a decrease in performance. We show the effect of wakefulness fluctuations on behaviour and brain dynamics that humans use to maintain performance. We reveal the neural strategies we have to cope with drowsiness by shifting the weights to more flexible brain processes and relaxing the precision of the decisions we take.

neuroscience↗

bioRxiv2017

Neurobehavioral dynamics of drowsiness

Transcranial magnetic stimulation (TMS) has been widely used in human cognitive neuroscience to examine the causal role of distinct cortical areas in perceptual, cognitive and motor functions. However, it is widely acknowledged that the effects of focal cortical stimulation on behaviour can vary substantially between participants and even from trial to trial within individuals. Here we asked whether spontaneous fluctuations in alertness can account for the variability in behavioural and neurophysiological responses to TMS. We combined single-pulse TMS with neural recording via electroencephalography (EEG) to quantify changes in motor and cortical reactivity with fluctuating levels of alertness defined objectively on the basis of ongoing brain activity. We observed rapid, non-linear changes in TMS-evoked neural responses - specifically, motor evoked potentials and TMS-evoked cortical potentials - as EEG activity indicated decreasing levels of alertness, even while participants remained awake and responsive in the behavioural task.\n\nIMPACT STATEMENTA substantial proportion of inter-trial variability in neurophysiological responses to TMS is due to spontaneous fluctuations in alertness, which should be controlled for during experimental and clinical applications of TMS.

neuroscience↗

bioRxiv2017

Altered neurocognitive processing of tactile stimuli in patients with Complex Regional Pain Syndrome (CRPS)

Chronic pain in CRPS has been linked to tactile misperceptions and deficits in somatotopic representation of the affected limb. Here, we identify altered cognitive processing of tactile stimuli in CRPS patients that we propose marks heterogeneity in tactile decision-making mechanisms. In a case-control design, we compared middle and late-latency somatosensory-evoked potentials (SEPs) in response to pseudo-randomised mechanical stimulation of the digits of both hands (including CRPS-affected and non-affected sides) between 13 CRPS patients and 13 matched healthy controls. During a task to discriminate the digit simulated, patients (compared to controls) had significantly lower accuracy and slowed response times but with high between-subject variability. At middle latencies (124-132 ms), tactile processing in patients relative to controls showed decrements in superior parietal lobe and precuneus (that were independent of task demands) but enhanced activity in superior frontal lobe (that were task-dependent). At late latencies, patients showed an augmented P300-like response under task demands that localised to supplementary motor area (SMA). Source activity in SMA correlated with slowed response times, while its scalp representation intriguingly correlated with better functioning of the affected limb, suggesting a compensatory mechanism. Future research should investigate the clinical utility of these putative markers of tactile decision-making mechanisms in CRPS.\n\nDisclosuresThe research was supported by funding from Cambridge Arthritis Research Endeavour (CARE). The study was sponsored by Cambridge University Hospitals NHS Foundation Trust and supported by its Rheumatology department. The authors report no conflicts of interest.\n\nPerspectiveWe present evidence of altered but highly variable cognitive processing (124 - 268ms latency) in response to mechanical tactile stimuli in patients with CRPS compared to healthy controls. Such mid-to-late latency responses could potentially provide convenient and robust biomarkers of abnormal perceptual decision-making mechanisms in CRPS to aid in clinical detection and treatment.

neuroscience↗

bioRxiv2017

Integration And Differentiation Of Neural Information Dissociate Between Conscious Percepts

At any given moment, we experience a perceptual scene as a single whole and yet we may distinguish a variety of objects within it. This phenomenon instantiates two properties of conscious perception: integration and differentiation. Integration to experience a collection of objects as a unitary percept, and differentiation to experience these objects as distinct from each other. Here we evaluated the neural information dynamics underlying integration and differentiation of perceptual contents during bistable perception. Participants listened to a sequence of tones (auditory bistable stimuli) experienced either as a single stream (perceptual integration) or as two parallel streams (perceptual differentiation) of sounds. We computed neurophysiological indices of information integration and information differentiation with electroencephalographic and intracranial recordings. When perceptual alternations were endogenously driven, the integrated percept was associated with an increase in neural information-integration and a decrease in neural differentiation across frontoparietal regions, whereas the opposite pattern was observed for the differentiated percept. However, when perception was exogenously driven by a change in the sound stream (no bistability) neural oscillatory power distinguished between percepts but information measures did not. We demonstrate that perceptual integration and differentiation can be mapped to theoretically-motivated neural information signatures, suggesting a direct relationship between phenomenology and neurophysiology.

neuroscience↗