Biology subjects

Kringelbach, M. L.

Publications and source records attributed to Kringelbach, M. L..

At least 55 records · Page 3Linked to original sources

bioRxiv2025

Prediction of individual melodic contour processing in sensory association cortices from resting state functional connectivity

Recent studies suggest that it is possible to predict an individual brains spatial activation pattern in response to a paradigm from their functional connectivity at rest (rsFC). However, it is unclear whether this prediction works across the brain. We here aim to understand whether individual task activation can be best predicted in local regions that are highly specialised to the task at hand or whether there are domain-independent regions in the brain that carry most information about the individual. To answer this question, we used fMRI data from participants (nonmusicians, N=52) at rest and during an auditory oddball paradigm while watching a silent movie. We then predicted individual differences in brain responses to melodic deviants from their rsFC both across the whole brain (global parcellation in 22 regions) and within the auditory cortices (local parcellation in 22 regions). Predictability was consistently higher in specific brain areas. These areas are centred around sensory association cortices: In the local (auditory cortex) parcellation, the best predicted area is the right superior temporal gyrus (STG), an auditory association area. The right STG is a critical region in melodic contour processing, a capacity that is central to the paradigm at hand. Interestingly, the best predicted network in the global parcellation is the bilateral visual association cortex. Sensory association cortices may carry more information specific to an individual regardless of the particular paradigm. Our results indicate that individual differences can be predicted in paradigm-relevant areas or general areas with higher inter-individual variability.

neuroscience↗

bioRxiv2025

Perturbing whole-brain models of brain hierarchy: an application for depression following pharmacological treatment

Neural representation can extend beyond localised activity to encompass global patterns, where information is distributed across brain networks in a hierarchical manner. Recent research suggests that the hierarchy of causal influences shaping these patterns can serve as a signature of distinct brain states, with implications for neuropsychiatric disorders. Here, we first delve into how whole-brain models, guided by the Thermodynamics of Mind framework, can estimate the brain hierarchy of specific brain states, and how perturbations of such models can study the in-silico transitions to other states represented by static functional connectivity. We then show an application for major depressive disorder, where different brain hierarchical reconfigurations have been found following psilocybin and escitalopram treatments. We build whole-brain models of depressed patients before and after psilocybin and escitalopram interventions, and we carry a dynamic sensitivity analysis to explore the susceptibility of brain states and their drivability to healthier states. We show that susceptibility is on average reduced by escitalopram and increased by psilocybin, and that both treatments succeed in promoting healthier transitions. These results align with the post-treatment window of plasticity opened by serotonergic psychedelics, as well as with the similar clinical efficacy of both drugs observed in clinical trials. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=193 HEIGHT=200 SRC="FIGDIR/small/631011v1_ufig1.gif" ALT="Figure 1"> View larger version (56K): org.highwire.dtl.DTLVardef@7f1b46org.highwire.dtl.DTLVardef@d7a883org.highwire.dtl.DTLVardef@783e39org.highwire.dtl.DTLVardef@3d334d_HPS_FORMAT_FIGEXP M_FIG C_FIG We apply whole-brain models of brain hierarchy based on the Thermodynamics of Mind framework to investigate state transitions in depression. Dynamic sensitivity analysis explores how psilocybin and escitalopram affect susceptibility and drivability to healthier states. Results show that psilocybin increases susceptibility, while escitalopram reduces it, with both enabling optimal transitions. This pipeline demonstrates the promise of in-silico approaches to inform neurostimulation protocols, potentially enhancing or complementing antidepressant therapies.

neuroscience↗

bioRxiv2024

Collapse of directed functional hierarchy under classical serotonergic psychedelics

Despite divergent behavioral and phenomenological profiles, both psychedelic states and reduced states of consciousness have been associated with a flattening of the brains functional hierarchy. To address this apparent paradox, we developed a more specific definition of hierarchy based on the proximity of the brain to thermodynamic equilibrium and then applied it to investigate the changes to the functional hierarchy elicited by three classical serotonergic psychedelics - psilocybin, lysergic acid diethylamide, and dimethyltryptamine. We found that all three psychedelics consistently induced a global reduction in the functional hierarchy. In contrast to the flattening of the functional hierarchy observed during loss of consciousness, psychedelics displaced the brain towards equilibrium while simultaneously increasing the complexity of neural activity, indicating a unique mechanism linked to specific changes in the configuration and differentiation of resting-state networks. This work showcases how metrics based on statistical mechanics can be used for the specific characterization of different global brain states, contributing to the understanding of consciousness as a collective process emerging from complex neural interactions.

neuroscience↗

bioRxiv2024

Nonequilibrium dynamics elicited as the origin of perturbative complexity

Assessing the level of consciousness someone is in, is not a trivial question and physicians have to rely on behavioural evaluations instead of quantifiable metrics. Many studies have empirically investigated measures related to the complexity elicited after the brain is stimulated to quantify and assess the level of consciousness across different states. Here we hypothesized that the level of non-equilibrium dynamics of the unperturbed brain already contains the information needed to know how the system will react to an external stimulus. We created personalized whole-brain models fitted to resting state fMRI data recorded in participants in different states of reduced consciousness (such as deep sleep and disorders of consciousness) to infer the effective connections underlying their brain dynamics. We then measured the out-of-equilibrium nature of the unperturbed brain by evaluating the level of asymmetry of the inferred connectivity, the time irreversibility in each model and compared this with the elicited complexity generated after in silico perturbations. Crucially, we found that states of reduced consciousness had a lower level of asymmetry in their effective connectivities compared to control subjects, as well as a lower level of irreversibility in their simulated dynamics, and a lower complexity. We demonstrated that the asymmetry in the underlying connections drives the nonequilibrium state of the system and in turn the differences in complexity as a response to the external stimuli.

neuroscience↗

bioRxiv2024

Modelling of brain dynamics reveals reduced switching between brain states in insomnia disorder - a resting state fMRI study

Insomnia disorder is the most common sleep disorder, and neuroimaging research indicates that it is related to dysfunction in large-scale brain networks. Recently developed methods have enabled the investigation of the dynamic aspects of brain activity varying over time. In the present study, we used a novel data-driven approach to evaluate time-varying brain activity in adults with insomnia disorder compared to matched controls with no sleep problems. We acquired ten minutes resting state functional magnetic resonance images and T1-weighed images in all participants. We used Hidden Markov modelling for a data-driven definition of dynamic changes in whole-brain activity. The results showed that insomnia disorder is characterised by reduced switching rates between brain states. In line with the reduced switching, the HMM analyses suggested reduced prevalence of two whole-brain states - the default mode network and a fronto-parietal network - and an increase in just one brain state - a global activation state - in insomnia patients compared to controls. The findings suggest that insomnia disorder is characterised by less flexible transitions between brain states at wakeful rest, and thus highlight the importance of evaluating the spatiotemporal dynamics of brain activity to advance the understanding of the neural underpinnings of insomnia disorder.

neuroscience↗

bioRxiv2024

The neural mechanisms of concept formation over time in music

The interplay between abstraction, learning and memory is crucial for humans to interact with the environment. Abstraction in relation to concept formation is typically studied with Gestalt stimuli varying in their physical properties while maintaining abstract internal relations. The role of temporal integration in recognising abstract concepts has been, however, overlooked even in a sensory domain relying on temporal item succession such as audition. Here, we investigated the neural mechanisms underlying abstract concept generalisation over time using a musical adaptation of the old/new recognition paradigm. During magnetoencephalography (MEG) recordings, 108 participants categorised five-tone sequences as previously learned or novel or as musical or not. The sequences varied in pace, ranging from very fast (125 ms per sound) to very slow (5000 ms per sound). On the behavioural level, sequence duration exerted a substantial impact on the recognition and musicality judgments of the novel sequences. The differences in neural responses between memorised and novel sequences especially involved regions in the auditory cortex, medial cingulate gyrus and medial temporal lobe and were most pronounced at the highest musical pace, diminishing proportionally as the pace decreased. These findings contribute to our understanding of how the brain generalises and derives meaning from abstract concepts that unfold over time learned through the auditory system.

neuroscience↗

bioRxiv2024

BROADband brain Network Estimation via Source Separation (BROAD-NESS)

Auditory memory enables the recognition of sound sequences by integrating sensory input with memory traces and predictive mechanisms. While predictive coding has been proposed as a key framework for this process, the large-scale brain networks supporting it remain poorly understood. Using BROADband brain Network Estimation via Source Separation (BROAD-NESS), we provide three fundamental insights into the neural organisation of auditory memory and predictive coding. First, auditory cortices participate in two distinct, orthogonal whole-brain networks, expanding on the conventional view focused on forward and backward information flow between single brain regions. One network involves the medial cingulate gyrus, while the other integrates prefrontal and hippocampal regions, inferior temporal cortex, and insula. Second, we present evidence for a dual-stream mechanism in auditory memory recognition, which both parallels and diverges from the well-established dual-stream hypothesis in vision. Third, predictive coding in conscious auditory memory is supported by large-scale networks generating confirmed predictions and prediction errors. While previous studies examined predictive coding in isolated regions or pairwise connections, our findings reveal how whole-brain networks coordinate these processes, highlighting fine-grained spatial gradients and distinct temporal dynamics. These findings enhance our understanding of auditory perception, memory, and prediction, as well as their underlying basis in whole-brain dynamical networks.

neuroscience↗

bioRxiv2024

Competitive interactions shape brain dynamics and computation across species

Adaptive cognition relies on cooperation across anatomically distributed brain circuits. However, specialised neural systems are also in constant competition for limited processing resources. How does the brains network architecture enable it to balance these cooperative and competitive tendencies? Here we use computational whole-brain modelling to examine the dynamical and computational relevance of cooperative and competitive interactions in the mammalian connectome. Across human, macaque, and mouse we show that the architecture of the models that most faithfully reproduce brain activity, consistently combines modular cooperative interactions with diffuse, long-range competitive interactions. The model with competitive interactions consistently outperforms the cooperative-only model, with excellent fit to both spatial and dynamical properties of the living brain, which were not explicitly optimised but rather emerge spontaneously. Competitive interactions in the effective connectivity produce greater levels of synergistic information and local-global hierarchy, and lead to superior computational capacity when used for neuromorphic computing. Altogether, this work provides a mechanistic link between network architecture, dynamical properties, and computation in the mammalian brain.

neuroscience↗

bioRxiv2024

Working memory predicts long-term recognition of auditory sequences: Dissociation between confirmed predictions and prediction errors

Memory is a crucial cognitive process involving several subsystems: sensory memory (SM), short-term memory (STM), working memory (WM), and long-term memory (LTM). While each has been extensively studied, the interaction between WM and LTM, particularly in relation to predicting temporal sequences, remains largely unexplored. This study investigates the relationship between WM and LTM, and how these relate to aging and musical training. Using three datasets with a total of 244 healthy volunteers across various age groups, we examined the impact of WM on LTM recognition of novel and previously memorized musical sequences. Our results show that WM abilities are significantly related to recognition of novel sequences, with a more pronounced effect in older compared to younger adults. In contrast, WM did not similarly impact the recognition of memorized sequences, which implies that different cognitive processes are involved in handling prediction errors compared to confirmatory predictions, and that WM contributes to these processes differently. Additionally, our findings confirm that musical training enhances memory performance. Future research should extend our investigation to populations with cognitive impairments and explore the underlying neural substrates.

neuroscience↗

bioRxiv2024

Off-Equilibrium Fluctuation-Dissipation Theorem Paves the Way in Alzheimer's Disease Research

INTRODUCTIONAlzheimers disease (AD) is a neurodegenerative disorder characterized by progressive cognitive decline. Although traditional methods have provided insights into brain dynamics in AD, they have limitations in capturing non-equilibrium dynamics across disease stages. Recent studies suggest that dynamic functional connectivity in resting-state networks (RSNs) may serve as a biomarker for AD, but the role of deviations from dynamical equilibrium remains underexplored. OBJECTIVEThis study applies the off-equilibrium fluctuation-dissipation theorem (FDT)1 to analyze brain dynamics in AD, aiming to compare deviations from equilibrium in healthy controls, patients with mild cognitive impairment (MCI), and those with AD. The goal is to identify potential biomarkers for early AD detection and understand disease progressions mechanisms. METHODSWe employed a model-free approach based on FDT to analyze functional magnetic resonance imaging (fMRI) data, including healthy controls, MCI patients, and AD patients. Deviations from equilibrium in resting-state brain activity were quantified using fMRI scans. In addition, we performed model-based simulations incorporating Amyloid-Beta (A{beta}), tau burdens, and Generative Effective Connectivity (GEC) for each subject. RESULTSOur findings show that deviations from equilibrium increase during the MCI stage, indicating hyperexcitability, followed by a significant decline in later stages of AD, reflecting neuronal damage. Model-based simulations incorporating A{beta} and tau burdens closely replicated these dynamics, especially in AD patients, highlighting their role in disease progression. Healthy controls exhibited lower deviations, while AD patients showed the most significant disruptions in brain dynamics. DISCUSSIONThe study demonstrates that the off-equilibrium FDT framework can accurately characterize brain dynamics in AD, providing a potential biomarker for early detection. The increase in non-equilibrium deviations during the MCI stage followed by their decline in AD offers a mechanistic explanation for disease progression. Future research should explore how combining this framework with other dynamic brain measures could further refine diagnostic tools and therapeutic strategies for AD and other neurodegenerative diseases.

neuroscience↗

bioRxiv2024

FREQuency-resolved brain Network Estimation via Source Separation (FREQ-NESS)

The brain is a dynamic system whose network organization is often studied by focusing on specific frequency bands or anatomical regions, leading to fragmented insights, or by employing complex and elaborate methods that hinder straightforward interpretations. To address this issue, a new analytical pipeline named FREQuency-resolved Network Estimation via Source Separation (FREQ-NESS) is introduced. This is designed to estimate the activation and spatial configuration of simultaneous brain networks across frequencies by analyzing the frequency-resolved multivariate covariance between whole-brain voxel time series. FREQ-NESS is applied to source-reconstructed magnetoencephalography (MEG) data during resting state and isochronous auditory stimulation. Results reveal simultaneous, frequency-specific brain networks during resting state, such as the default mode, alpha-band, and motor-beta networks. During auditory stimulation, FREQ-NESS detects: (1) emergence of networks attuned to the stimulation frequency, (2) spatial reorganization of existing networks, such as alpha-band networks shifting from occipital to sensorimotor areas, (3) stability of networks unaffected by auditory stimuli. Furthermore, auditory stimulation significantly enhances cross-frequency coupling, with the phase of attuned auditory networks modulating the gamma band amplitude of medial temporal lobe networks. In conclusion, FREQ-NESS effectively maps the brains spatiotemporal dynamics, providing a comprehensive view of brain function by revealing simultaneous, frequency-resolved networks and their interaction.

neuroscience↗

bioRxiv2024

Reconfiguration of Functional Brain Hierarchy in Schizophrenia

The multidimensional nature of schizophrenia requires a comprehensive exploration of the functional and structural brain networks. While prior research has provided valuable insights into these aspects, our study goes a step further to investigate the reconfiguration of the hierarchy of brain dynamics, which can help understand how brain regions interact and coordinate in schizophrenia. We applied an innovative thermodynamic framework, which allows for a quantification of the degree of functional hierarchical organization by analysing resting state fMRI-data. Our findings reveal increased hierarchical organization at the whole-brain level and within specific resting-state networks in individuals with schizophrenia, which correlated with negative symptoms, positive formal thought disorder and apathy. Moreover, using a machine learning approach, we showed that hierarchy measures allow a robust diagnostic separation between healthy controls and schizophrenia patients. Thus, our findings provide new insights into the nature of functional connectivity anomalies in schizophrenia, suggesting that they could be caused by the breakdown of the functional orchestration of brain dynamics.

neuroscience↗

bioRxiv2024

Decoding the elite soccer players psychological profile

Soccer is arguably the most widely followed sport worldwide, and many dream of becoming soccer players. However, only a few manage to achieve this dream, which has cast a significant spotlight on elite soccer players who possess exceptional skills to rise above the rest. Originally, such attention was focused on their great physical abilities. However, recently, it a new perspective has emerged, suggesting that being an elite soccer player require a deep understanding of the game, rapid information processing and decision-making. This growing attention has led to several studies suggesting higher executive functions in soccer players compared to the general population. Unfortunately, these studies often had small and non-elite samples, focusing mainly on executive functions alone without employing advanced machine learning techniques. In this study, we used artificial neural networks to comprehensively investigate the personality traits and cognitive abilities of a sample of 328 participants, including 204 elite soccer players from the top teams in Brazil and Sweden. Our findings indicate that elite soccer players demonstrate heightened planning and memory capacities, enhanced executive functions, especially cognitive flexibility, elevated levels of conscientiousness, extraversion, and openness to experience, coupled with reduced neuroticism and agreeableness. This research provides insights into the psychology of elite soccer players, holding significance for talent identification, development strategies in soccer and offering insights into the psychological profiles associated with success. Significance statementThis study explores the psychological profiles of elite soccer players, revealing that success on the field goes beyond physical ability. By analysing a sample of 328 participants, including 204 elite soccer players from the top teams in Brazil and Sweden, we found that elite players have exceptional cognitive abilities, including improved planning, memory, and decision-making skills. They also possess personality traits like high conscientiousness and openness, along with reduced neuroticism. Using artificial intelligence, we identified unique psychological patterns that could help in talent identification and development. These insights can be used to better understand the mental attributes that contribute to success in soccer and other high-performance fields.

neuroscience↗

bioRxiv2024

The neurophysiology of healthy and pathological aging: A comprehensive systematic review

As the population of older adults grows, so does the prevalence of neurocognitive disorders such as mild cognitive impairment (MCI) and dementia. While biochemical, genetic, and neuroimaging biomarkers have accelerated early detection and diagnosis, neurophysiological measures are absent from daily medical use. Electroencephalography (EEG) and magnetoencephalography (MEG) are two non-invasive techniques that measure neurophysiological signals in the brain and convey information about signal strength at different frequency bands, event-related activity, signal complexity, and temporal correlation between spatially remote brain regions. Here we conducted a pre-registered, comprehensive systematic review of 942 studies using EEG, MEG, and combined MEG and EEG to characterise the neurophysiology of healthy aging, MCI, and dementia under resting-state and task conditions. To complement our search, we also reviewed 51 past reviews in the field. Relevant features of these papers were extracted to present a detailed overview of the current state of evidence. Overall, neurophysiological measures show great promise as diagnostic tools and could prove invaluable in predicting healthy and pathological aging trajectories. However, to reach this potential in clinical practice, it is crucial to adopt longitudinal designs, standardise methodologies, and identify biomarkers at the individual rather than group level.

neuroscience↗

bioRxiv2024

Ketamine-Induced Unresponsiveness Shows a Harmonic Shift from Global to Localised Functional Organisation.

Ketamine is classified as a dissociative anaesthetic that, in sub-anaesthetic doses, can produce an altered state of consciousness characterised by dissociative symptoms, visual and auditory hallucinations, and perceptual distortions. Given the anaesthetic-like and psychedelic-like nature of this compound, it is expected to have different effects on brain dynamics in anaesthetic doses than in low, sub-anaesthetic doses. We investigated this question using connectome harmonic decomposition (CHD), a recently developed method to decompose brain activity in terms of the network organisation of the underlying human structural connectome. Previous research using this method has revealed connectome harmonic signatures of consciousness and responsiveness, with increased influence of global network structure in disorders of consciousness and propofol-induced sedation, and increased influence of localised patterns under the influence of classic psychedelics and sub-anaesthetic doses of ketamine, as compared to normal wakefulness. When we applied the CHD analytical framework to resting-state fMRI data of volunteers during ketamine-induced unresponsiveness, we found increased prevalence of localised harmonics, reminiscent of altered states of consciousness. This is different from traditional GABAergic sedation, where instead the prevalence of global rather than localised harmonics seems to increase with higher doses. In addition, we found that ketamines harmonic signature shows higher alignment with those seen in LSD- or psilocybin-induced psychedelic states than those seen in unconscious individuals, whether due to propofol sedation or brain injury. Together, the results indicate that ketamine-induced unresponsiveness, which does not necessarily suppress conscious experience, seems to influence the prevalence of connectome harmonics in the opposite way compared to GABAergic hypnotics. We conclude that the CHD framework offers the possibility to track alterations in conscious awareness (e.g., dreams, sensations) rather than behavioural responsiveness - a discovery made possible by ketamines unique property of decoupling these two facets.

neuroscience↗

bioRxiv2024

Complex harmonics reveal low-dimensional manifolds of critical brain dynamics

The brain needs to perform time-critical computations to ensure survival. A potential solution lies in the non-local, distributed computation at the whole-brain level made possible by criticality and amplified by the rare long-range connections found in the brains unique anatomical structure. This non-locality can be captured by the mathematical structure of Schrodingers wave equation, which is at the heart of the novel CHARM (Complex Harmonics decomposition) framework that performs the necessary dimensional manifold reduction able to extract non-locality in critical spacetime brain dynamics. Using a large neuroimaging dataset of over 1,000 people, CHARM captured the critical, non-local/long-range nature of brain dynamics and the underlying mechanisms were established using a precise whole-brain model. Equally, CHARM revealed the significantly different critical dynamics of wakefulness and sleep. Overall, CHARM is a promising theoretical framework for capturing the low-dimensionality of the complex network dynamics observed in neuroscience and provides evidence that networks of brain regions rather than individual brain regions are the key computational engines of critical brain dynamics.

neuroscience↗

bioRxiv2024

Time-resolved coupling between connectome harmonics and subjective experience under the psychedelic DMT

Exploring the intricate relationship between brains structure and function, and how this affects subjective experience is a fundamental pursuit in neuroscience. Psychedelic substances offer a unique insight into the influences of specific neurotransmitter systems on perception, cognition and consciousness. Specifically, their impact on brain function propagates across the structural connectome -- a network of white matter pathways linking different regions. To comprehensively grasp the effects of psychedelic compounds on brain function, we used a theoretically rigorous framework known as connectome harmonic decomposition. This framework provides a robust method to characterize how brain function intricately depends on the organized network structure of the human connectome. We show that the connectome harmonic repertoire under DMT is reshaped in line with other reported psychedelic compounds - psilocybin, LSD and ketamine. Furthermore, we show that the repertoire entropy of connectome harmonics increases under DMT, as with those other psychedelics. Importantly, we demonstrate for the first time that measures of energy spectrum difference and repertoire entropy of connectome harmonics indexes the intensity of subjective experience of the participants in a time-resolved manner reflecting close coupling between connectome harmonics and subjective experience.

neuroscience↗

bioRxiv2024

Multilevel irreversibility reveals higher-order organisation of non-equilibrium interactions in human brain dynamics

Information processing in the human brain can be modelled as a complex dynamical system operating out of equilibrium with multiple regions interacting nonlinearly. Yet, despite extensive study of the global level of non-equilibrium in the brain, quantifying the irreversibility of interactions among brain regions at multiple levels remains an unresolved challenge. Here, we present the Directed Multiplex Visibility Graph Irreversibility framework, a method for analysing neural recordings using network analysis of time-series. Our approach constructs directed multi-layer graphs from multivariate time-series where information about irreversibility can be decoded from the marginal degree distributions across the layers, which each represents a variable. This framework is able to quantify the irreversibility of every interaction in the complex system. Applying the method to magnetoencephalography recordings during a long-term memory recognition task, we quantify the multivariate irreversibility of interactions between brain regions and identify the combinations of regions which showed higher levels of non-equilibrium in their interactions. For individual regions, we find higher irreversibility in cognitive versus sensorial brain regions whilst for pairs, strong relationships are uncovered between cognitive and sensorial pairs in the same hemisphere. For triplets and quadruplets, the most non-equilibrium interactions are between cognitive-sensorial pairs alongside medial regions. Finally, for quintuplets, our analysis finds higher irreversibility when the prefrontal cortex is included in the interaction. Combining these results, we show that multilevel irreversibility offers unique insights into the higher-order, hierarchical organisation of neural dynamics and presents a new perspective on the analysis of brain network dynamics.

biophysics↗