Biology subjects

Bor, D.

Publications and source records attributed to Bor, D..

15 recordsLinked to original sources

bioRxiv2026

Temporal and Spectral Neural Complexity Reveal Graded Auditory Awareness

Recent advances have shown that the complexity of neural signals tracks global states of consciousness, such as wakefulness versus sleep. However, it is still unclear to what extent neural complexity reflects fine-grained changes in conscious content within the same global state. Here, we investigate how the complexity of brain signals is affected by increased perceptual clarity of a stimulus. To this end, we estimated neural signal complexity using Complexity via State-space Entropy Rate (CSER) to EEG recordings from an auditory discrimination task. In this paradigm, auditory stimuli were presented at varying signal-to-noise ratios (SNRs), with higher SNRs corresponding to greater subjective audibility and perceptual clarity, enabling us to relate neural complexity to graded perceptual awareness within a constant global state of consciousness. Our results showed that, while broadband CSER remains constant across SNRs, its spectral decomposition displays frequency-specific effects, with higher SNRs associated with a decreased complexity in and {beta} bands, increased complexity in{delta} , and no significant changes in{gamma} . Additionally, a temporal investigation of CSER exhibited a significant increase in complexity with stimulus clarity, with deviations from baseline peaking approximately 30 ms before the ERP. Extending this analysis to pairs of brain regions, mutual information rate uncovered a sudden post-stimulus breakdown in long-range information transmission relative to baseline. Taken together, these results reveal that while aggregated complexity measures track global states of consciousness, time- and frequency-resolved information-theoretic measures can capture variations in perceptual awareness, demonstrating their sensitivity as estimators of the level of conscious experience.

neuroscience↗

bioRxiv2026

Synergistic and redundant information dynamics are modulated by Alzheimer's disease and cognitive impairment

BackgroundThe early diagnosis of Alzheimers disease (AD), a cause of progressive cognitive decline, remains challenging. Recent information-theoretic advances allow brain dynamics to be quantified in terms of how regions share and combine information. Integrated Information Decomposition ({Phi}ID) separates redundant (the same content present in multiple regions) from synergistic information (new content that emerges only when regions are considered together). Such information-dynamic measures may provide biomarkers relevant to AD risk and progression. MethodsHere we applied integrated information decomposition ({Phi}ID) to resting-state fMRI from the Alzheimers Disease Neuroimaging Initiative (ADNI), to test whether {Phi}ID measures are diagnostically sensitive and track cognition along the AD spectrum. For each region, we computed total synergy and redundancy and compared values across cognitively normal (CN), mild cognitive impairment (MCI), and AD groups. ResultsCompared to CN, AD patients showed a striking synergy reduction across the entire brain, in concert with widespread redundancy increases, particularly in the executive and default mode networks. Transitions from CN to AD included an intermediate MCI decrease in redundancy, possibly reflecting early disease compensation strategies. This AD informational shift from complex higher level information processing to more robust inefficient forms likely reflects a cognitive shift to simpler, less integrative cognitive processes. Indeed, when re-analysing the data according to a standard cognitive clinical test (the Montreal Cognitive Assessment), we found a synergy-redundancy shift in high versus low performers broadly very similar to the CN to AD shift. ConclusionAD shows a clear information-processing signature: reduced global synergy and increased redundancy, especially in the executive control network. These striking results provide powerful insights into the widespread information processing reconfiguration that occurs in AD, with clear changes already emerging at the earlier MCI stage. Further, these results provide a novel route to support early diagnosis and stratification.

neuroscience↗

bioRxiv2025

Informational Complexity as a Neural Marker of Cognitive Reserve

In Alzheimers disease (AD), a mismatch between neurological damage and cognitive functioning often is attributed to individual differences in cognitive reserve. Understanding the neural mechanism of cognitive reserve could help assessing the therapeutic effectiveness of interventions in AD. To address this, here, 38 elderly participants performed a sustained attention task during high-density EEG while awake and during drowsiness. Operationally, the degree to which performance was impaired under drowsiness signalled the extent of cognitive reserve, with less impairment indicating a higher level of cognitive reserve. Investigating performance variations during the active management of neural challenges offers a novel approach to studying cognitive reserve, capturing dynamics that mirror everyday cognitive demand. We related cognitive reserve to various measures, including informational complexity using the Lempel-Ziv (LZSUM) algorithm. We found a significant interaction effect between arousal and performance, where LZSUM values increased in high performers when drowsy but decreased in low performers. This effect was most pronounced in the frontal and central areas. Our findings suggest LZSUM to be indicative of a compensatory mechanism and thus show potential for LZSUM as a neural marker in assessing cognitive reserve. However, we found no consistent relationship between performance and structural brain measures, and proxies of cognitive reserve. Critically, our findings present a counterexample to the prevailing view that informational complexity purely reflects conscious level. Further research, such as a study with the same paradigm in patients with mild cognitive impairment (MCI) and AD, may lead to additional insights of whether we are truly measuring cognitive reserve.

neuroscience↗

bioRxiv2023

The entropic heart: Tracking the psychedelic state via heart rate dynamics

A growing body of work shows that autonomic signals provide a privileged evidence-stream to capture various aspects of subjective and neural states. This work investigates the potential for autonomic markers to track the effects of psychedelics -- potent psychoactive drugs with important scientific and clinical value. For this purpose, we introduce a novel Bayesian framework to estimate the entropy of heart rate dynamics under psychedelics. We also calculate Bayesian estimates of mean heart rate and heart rate variability, and investigate how these measures relate to subjective reports and neural effects. Results on datasets covering four drugs -- lysergic acid diethylamide (LSD), dimethyltryptamine (DMT), psilocybin, and sub-anaesthetic doses of the dissociative agent ketamine -- show consistent increases in mean heart rate, high-frequency heart rate variability, and heart rate entropy during the psychedelic experience. Moreover, these effects have predictive power over various dimensions of the psychedelic experience. Changes in heart rate entropy were found to be correlated with increases in brain entropy, while other autonomic markers were not. Overall, our results show that a cost-efficient autonomic measure has the potential to reveal surprising detail about subjective and brain states, opening up a range of new research avenues to explore in both basic and clinical neuroscience.

physiology↗

bioRxiv2023

Neural Markers Of Reduced Arousal And Consciousness In Mild Cognitive Impairment

ObjectivesPeople with Alzheimers Disease (AD) experience changes in their level and content of consciousness, but there is little research on biomarkers of consciousness in pre-clinical AD and MCI. This study investigated whether levels of consciousness are decreased in people with MCI. MethodsA multi-site MEG dataset, BIOFIND, comprising 83 people with MCI and 83 age matched controls, was analysed. Arousal (and drowsiness) was assessed by computing the theta-alpha ratio (TAR). The Lempel-Ziv algorithm (LZ) was used to quantify the information content of brain activity, with higher LZ values indicating greater complexity and potentially a higher level of consciousness. ResultsLZ was lower in the MCI group vs controls, indicating a reduced level of consciousness in MCI. TAR was higher in the MCI group vs controls, indicating a reduced level of arousal (i.e. increased drowsiness) in MCI. LZ was also found to be correlated with MMSE scores, suggesting a direct link between cognitive impairment and level of consciousness in people with MCI. ConclusionsA decline in consciousness and arousal can be seen in MCI. As cognitive impairment worsens, measured by MMSE scores, levels of consciousness and arousal decrease. These findings highlight how monitoring consciousness using biomarkers could help understand and manage impairments found at the preclinical stages of AD. Further research is needed to explore markers of consciousness between people who progress from MCI to dementia and those who do not, and in people with moderate and severe AD, to promote person-centred care.

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↗

bioRxiv2022

Reduced emergent character of neural dynamics in patients with a disrupted connectome

High-level brain functions are widely believed to emerge from the orchestrated activity of multiple neural systems. However, lacking a formal definition and practical quantification of emergence for experimental data, neuroscientists have been unable to empirically test this long-standing conjecture. Here we investigate this fundamental question by leveraging a recently proposed framework known as "Integrated Information Decomposition," which establishes a principled information-theoretic approach to operationalise and quantify emergence in dynamical systems -- including the human brain. By analysing functional MRI data, our results show that the emergent and hierarchical character of neural dynamics is significantly diminished in chronically unresponsive patients suffering from severe brain injury. At a functional level, we demonstrate that emergence capacity is positively correlated with the extent of hierarchical organisation in brain activity. Furthermore, by combining computational approaches from network control theory and whole-brain biophysical modelling, we show that the reduced capacity for emergent and hierarchical dynamics in severely brain-injured patients can be mechanistically explained by disruptions in the patients structural connectome. Overall, our results suggest that chronic unresponsiveness resulting from severe brain injury may be due to structural impairment of the fundamental neural infrastructures required for brain dynamics to support emergence.

neuroscience↗

bioRxiv2022

Psychedelics and schizophrenia: Distinct alterations to Bayesian inference

Schizophrenia and states induced by certain psychotomimetic drugs may share some physiological and phenomenological properties, but they differ in fundamental ways: one is a crippling chronic mental disease, while the others are temporary, pharmacologically-induced states presently being explored as treatments for mental illnesses. Building towards a deeper understanding of these different alterations of normal consciousness, here we compare the changes in neural dynamics induced by LSD and ketamine (in healthy volunteers) against those associated with schizophrenia, as observed in resting-state M/EEG recordings. While both conditions exhibit increased neural signal diversity, our findings reveal that this is accompanied by an increased transfer entropy from the front to the back of the brain in schizophrenia, versus an overall reduction under the two drugs. Furthermore, we show that these effects can be reproduced via different alterations of standard Bayesian inference applied on a computational model based on the predictive processing framework. In particular, the effects observed under the drugs are modelled as a reduction of the precision of the priors, while the effects of schizophrenia correspond to an increased precision of sensory information. These findings shed new light on the similarities and differences between schizophrenia and two psychotomimetic drug states, and have potential implications for the study of consciousness and future mental health treatments.

neuroscience↗

bioRxiv2021

Fluctuations in Neural Complexity During Wakefulness Relate To Conscious Level and Cognition

There has been considerable recent progress in measuring conscious level using neural complexity measures. For instance, such measures can reliably distinguish healthy awake from asleep subjects and vegetative state patients. However, this line of research has never explored the dynamics of conscious level during normal wakefulness. Being able to capture meaningful differences in conscious level during wakefulness may provide a vital new insight into the nature of consciousness, by demonstrating what biological, behavioural and cognitive factors relate to such differences. Here we take advantage of a large MEG and fMRI dataset of healthy adults, to examine within-subject conscious level fluctuations during resting state and tasks, by using a range of complexity measures. We first establish the validity of this approach in both neuroimaging domains by relating neural complexity measures to pre-existing techniques for capturing transitions of consciousness from full wakefulness into drowsiness and the earliest stages of sleep, finding decreased complexity as participants become increasingly drowsy. We further demonstrate that neural complexity measures in both MEG and fMRI change both within and between tasks, and relate to performance on an executive task, with higher complexity associated with better performance and faster reaction times. This approach provides a powerful new route to further explore the cognitive and neural underpinnings of consciousness.

neuroscience↗

bioRxiv2021

Ketamine and sleep modulate neural complexity dynamics in cats

There is increasing evidence that level of consciousness can be captured by neural informational complexity: for instance, complexity, as measured by the Lempel Ziv (LZ) compression algorithm, decreases during anesthesia and non-rapid eye movement (NREM) sleep in humans and rats, when compared to LZ in awake and REM sleep. In contrast, LZ is higher in humans under the effect of psychedelics, including subanesthetic doses of ketamine. However, it is both unclear how this result would be modulated by varying ketamine doses, and whether it would extend to other species. Here we studied LZ with and without auditory stimulation during wakefulness and different sleep stages in 5 cats implanted with intracranial electrodes, as well as under subanesthetic doses of ketamine (5, 10, and 15 mg/kg i.m.). In line with previous results, LZ was lowest in NREM sleep, but similar in REM and wakefulness. Furthermore, we found an inverted U-shaped curve following different levels of ketamine doses in a subset of electrodes, primarily in prefrontal cortex. However, it is worth noting that the variability in the ketamine dose-response curve across cats and cortices was larger than that in the sleep-stage data, highlighting the differential local dynamics created by two different ways of modulating conscious state. These results replicate previous findings, both in humans and other species, demonstrating that neural complexity is highly sensitive to capture state changes between wake and sleep stages while adding a local cortical description. Finally, this study describes the differential effects of ketamine doses, replicating a rise in complexity for low doses, and further fall as doses approach anesthetic levels in a differential manner depending on the cortex. In briefPrevious studies have shown that Lempel Ziv complexity (LZ) decreases during anesthesia and non-rapid eyes movement (NREM) sleep in humans and rats whereas it increases in REM sleep and under the effect of psychedelics. In this work we show that in the cat, LZ is lowest in NREM sleep, but similar in REM and wakefulness. We also found a ketamine inverted U-shape dose-response curve only in the auditory and prefrontal cortex, with a much larger variability in the ketamine across cats and cortices when compared to the sleep cycle. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=160 SRC="FIGDIR/small/449513v2_ufig1.gif" ALT="Figure 1"> View larger version (62K): org.highwire.dtl.DTLVardef@1bab0d0org.highwire.dtl.DTLVardef@1ae33dborg.highwire.dtl.DTLVardef@1cea0f6org.highwire.dtl.DTLVardef@a9b1e0_HPS_FORMAT_FIGEXP M_FIG C_FIG

neuroscience↗

bioRxiv2021

Paths to Oblivion: Common Neural Mechanisms of Anaesthesia and Disorders of Consciousness

The human brain generates a rich repertoire of spatiotemporal dynamics during normal wakefulness, supporting a wide variety of conscious experiences and cognitive functions. However, neural dynamics are reconfigured, in comparable ways, when consciousness is lost either due to anaesthesia or disorders of consciousness (DOC). Here, leveraging a neurobiologically realistic whole-brain computational model informed by functional MRI, diffusion MRI, and PET, we sought to identify the neurobiological mechanisms that explain the common reconfiguration of neural dynamics observed both for transient pharmacological intervention and chronic neuroanatomical injury. Our results show that, by incorporating local inhibitory action through a PET-based GABA receptor density map, our model can reproduce the brain dynamics of subjects undergoing propofol anaesthesia, and that this effect depends specifically on the spatial distribution of GABA receptors across cortical regions. Additionally, using a structural connectome obtained from DOC patients, we demonstrate how the dynamics that characterise loss of consciousness can emerge from changes in neuroanatomical connectivity. Crucially, we find that each of these two interventions generalises across datasets: a model with increased GABA-mediated inhibition can reproduce the dynamics of DOC patients brains, and a model with a DOC connectome is also compatible with brain dynamics observed during propofol anaesthesia. These results demonstrate how increased inhibition and connectome randomisation represent different neurobiological paths towards the characteristic dynamics of the unconscious brain. Overall, the present findings begin to disentangle the neurobiological mechanisms by which highly dissimilar perturbations of the brains neurodynamics can lead to unconsciousness.

neuroscience↗

bioRxiv2020

A Synergistic Workspace for Human Consciousness Revealed by Integrated Information Decomposition

A central goal of neuroscience is to understand how the brain orchestrates information from multiple input streams into a unified conscious experience. Here, we address two fundamental questions: how is the human information-processing architecture functionally organised, and how does its organisation support consciousness? We combine network science and a rigorous information-theoretic notion of synergy to delineate a "synergistic global workspace", comprising gateway regions that gather synergistic information from specialised modules across the brain. This information is then integrated within the workspace and widely distributed via broadcaster regions. Through functional MRI analysis, we show that gateway regions of the synergistic workspace correspond to the brains default mode network, whereas broadcasters coincide with the executive control network. Demonstrating the empirical relevance of our proposed architecture for neural information processing, we show that loss of consciousness due to general anaesthesia or disorders of consciousness corresponds to a diminished ability of the synergistic workspace to integrate information, which is restored upon recovery. Thus, loss of consciousness coincides with a breakdown of information integration within the synergistic workspace of the human brain. This work contributes to conceptual and empirical reconciliation between two prominent scientific theories of consciousness, the Global Neuronal Workspace and Integrated Information Theory. Taken together, this work provides a new perspective on the role of prominent resting-state networks within the human information-processing architecture, while also advancing our understanding of how the human brain supports consciousness through the synergistic integration of information.

neuroscience↗

bioRxiv2020

Effects of external stimulation on psychedelic state neurodynamics

Recent findings have shown that psychedelics reliably enhance brain entropy (understood as neural signal diversity), and this effect has been associated with both acute and long-term psychological outcomes such as personality changes. These findings are particularly intriguing given that a decrease of brain entropy is a robust indicator of loss of consciousness (e.g. from wakefulness to sleep). However, little is known about how context impacts the entropy-enhancing effect of psychedelics, which carries important implications for how it can be exploited in, for example, psychedelic psychotherapy. This article investigates how brain entropy is modulated by stimulus manipulation during a psychedelic experience, by studying participants under the effects of LSD or placebo, either with gross state changes (eyes closed vs. open) or different stimulus (no stimulus vs. music vs. video). Results show that while brain entropy increases with LSD in all the experimental conditions, it exhibits largest changes when subjects have their eyes closed. Furthermore, brain entropy changes are consistently associated with subjective ratings of the psychedelic experience, but this relationship is disrupted when participants are viewing video -- potentially due to a "competition" between external stimuli and endogenous LSD-induced imagery. Taken together, our findings provide strong quantitative evidence for the role of context in modulating neural dynamics during a psychedelic experience, underlining the importance of performing psychedelic psychotherapy in a suitable environment. Additionally, our findings put into question simplistic interpretations of brain entropy as a direct neural correlate of conscious level. Significance StatementThe effects of psychedelic substances on conscious experience can be substantially affected by contextual factors, which play a critical role in the outcomes of psychedelic therapy. This study shows how context can modulate not only psychological, but also neurophysiological phenomena during a psychedelic experience. Our findings reveal distinctive effects of having eyes closed after taking LSD, including a more pronounced change on the neural dynamics, and a closer correspondence between brain activity and subjective ratings. Furthermore, our results suggest a competition between external stimuli and internal psychedelic-induced imagery, which supports the practice of carrying out psychedelic therapy with patients having their eyes closed.

neuroscience↗

bioRxiv2020

A synergistic core for human brain evolution and cognition

A fundamental question in neuroscience is how brain organisation gives rise to humans unique cognitive abilities. Although complex cognition is widely assumed to rely on frontal and parietal brain regions, the underlying mechanisms remain elusive: current approaches are unable to disentangle different forms of information processing in the brain. Here, we introduce a powerful framework to identify synergistic and redundant contributions to neural information processing and cognition. Leveraging multimodal data including functional MRI, PET, cytoarchitectonics and genetics, we reveal that synergistic interactions are the fundamental drivers of complex human cognition. Whereas redundant information dominates sensorimotor areas, synergistic activity is closely associated with the brains prefrontal-parietal and default networks; furthermore, meta-analytic results demonstrate a close relationship between high-level cognitive tasks and synergistic information. From an evolutionary perspective, the human brain exhibits higher prevalence of synergistic information than non-human primates. At the macroscale, we demonstrate that high-synergy regions underwent the highest degree of evolutionary cortical expansion. At the microscale, human-accelerated genes promote synergistic interactions by enhancing synaptic transmission. These convergent results provide critical insights that synergistic neural interactions underlie the evolution and functioning of humans sophisticated cognitive abilities, and demonstrate the power of our widely applicable information decomposition framework.

neuroscience↗

bioRxiv2020

Extensive Phenomenological Overlap between Induced and Naturally-Occurring Synaesthetic Experiences

Grapheme-colour synaesthesia (GCS) is defined by additional perceptual experiences, which are automatically and consistently triggered by specific inducing stimuli. The associative nature of GCS has motivated attempts to induce synaesthesia by means of associative learning. Two recent studies have shown that extensive associative training can generate not only behavioural (consistency and automaticity) and neurophysiological markers of GCS, but also synaesthesia-like phenomenology [1,2]. However, these studies provided only superficial descriptions regarding the training-related changes in subjective experience: they did not directly assess how closely induced synaesthetic experiences mirror those found in natural GCS. Here we report an extended qualitative analysis of the transcripts of the semi-structured interviews obtained following the completion of the associative training protocol used by [2]. In addition, we performed a comparable analysis of responses to an interview with a new population of natural occurring grapheme-colour synaesthetes (NOS), allowing us to directly compare the phenomenological dimensions of induced and naturally occurring synaesthetic experience. Our results provide an extensive addition to the description of the phenomenology of NOS experience, revealing a high degree of heterogeneity both within and across all experiential categories. Capitalising on this unique level of detail, we identified a number of shared experiential categories between NOS and induced synaesthesia-like (ISL) groups, including: stability of experience, location of colour experience, shape of co-occurring colour experience, relative strength of colour experience and automaticity of colour experience. Only the automaticity of colour experience differed significantly between the two groups: NOS experience was reported as being mostly automatic, whereas induced ISL were mostly described as being wilful. We observed three additional experiential categories relating to the automaticity of synaesthetic experience within the NOS group: contextually varied experience, semi-automatic experience and reflective association, which suggests that, as with other experiential categories, the automaticity of synaesthetic experience is also highly heterogeneous. Our results provide new evidence that that intensive training of letter-colour associations can alter conscious perceptual experiences in non-synaesthetes, and that such alterations produce synaesthesia-like phenomenology which substantially resembles similarities to natural grapheme-colour synaesthesia.

neuroscience↗