The research, led by Dr. Noam Goldway under the supervision of Prof. Talma Hendler and Dr. Haggai Sharon of Tel Aviv University’s Institute for Psychedelic Research at the Sagol School of Neuroscience, was published in the journal Neuropsychopharmacology. For the first time in humans, the team demonstrated that ketamine activates two distinct mechanisms in the brain: one responsible for pain relief and another responsible for the dissociative state the drug is famous for. The distinction is not merely a matter of subjective experience — it is visible in the brain’s activity patterns.

The question the researchers set out to answer is one of the most consequential in modern psychedelic medicine. In recent years, mind-altering substances have migrated from the fringes of culture to the center of serious clinical investigation. MDMA is being studied as a treatment for post-traumatic stress disorder; psilocybin, the active compound in magic mushrooms, is being tested primarily against depression; and ketamine has attracted considerable interest for its effectiveness in treating pain, depression and, more recently, PTSD. Yet a central unresolved debate shadows the entire field: is the alteration of consciousness produced by these drugs a necessary condition for their therapeutic benefit, or merely a side effect that happens to accompany it?

To address that question, the team turned to ketamine, a drug that relieves pain while simultaneously inducing dissociation — a temporary sense of detachment from one’s self, body or surroundings. For years, the working assumption had been that these two phenomena were causally linked: the more detached a patient felt, the less intense or threatening their pain would seem. If that were true, then the psychedelic experience itself would be an essential ingredient of the drug’s analgesic action, with major implications for how ketamine-based therapies should be designed.

The study enrolled 37 healthy volunteers, both women and men, each of whom participated in two separate sessions. In one session they received a ketamine infusion; in the other, a placebo. While under the influence of each infusion, participants underwent functional magnetic resonance imaging (fMRI) and were exposed to carefully controlled pain stimuli. Throughout the experiment, they reported both the intensity of their pain and the depth of their dissociative experience, allowing the researchers to track the two phenomena independently and in real time.

The behavioral results were strikingly clear. As expected, ketamine reduced pain and increased dissociation. But when the researchers analyzed the relationship between the two effects, no connection emerged. Participants who experienced strong dissociation did not necessarily enjoy greater pain relief, and those with the most substantial analgesia were not necessarily the most detached. As Dr. Goldway explained, the sense of detachment is simply not the mechanism through which ketamine reduces pain. The two effects, long assumed to be two faces of the same process, appear to run in parallel without one driving the other.

The brain imaging data reinforced the behavioral findings at the neural level. Pain relief was accompanied by reduced activity in brain regions classically involved in processing pain, including the insula and the anterior cingulate cortex — structures that map the intensity and emotional salience of noxious stimuli. Dissociation, by contrast, was related to changes in a different network altogether: the default mode network, a set of interconnected regions implicated in, among other things, the construction of the sense of self. In other words, the study did not merely document two uncorrelated subjective experiences; it revealed two different brain mechanisms, both triggered by the same drug but operating on distinct neural circuitry.

Prof. Hendler, who heads TAU’s Institute for Psychedelic Research, framed the findings within the broader scientific controversy. “In recent years, there has been a lively scientific and clinical debate over whether the subjective experience produced by psychoactive drugs is an integral part of their therapeutic effect,” she said. “Our study shows that, at least in the case of ketamine and pain relief, the answer is apparently no. We were able to show that the drug’s effect on pain is separate from its effect on consciousness — both behaviorally and in terms of brain activity.” She was careful, however, to draw a boundary around the conclusion: “It is important to note that this cannot be generalized to other psychedelic drugs, such as magic mushrooms. In this case, the burden of proof still rests with us, the scientists.”

Dr. Sharon, Deputy Head and Medical Director of the Institute and a specialist in pain medicine, emphasized what the results reveal about the brain itself. The study, he noted, provides further evidence of the complexity of the brain’s representation of the pain experience and of the many different ways the brain can regulate and alter it. Pain, in this view, is not a single signal to be switched off but a multidimensional construct assembled across multiple networks — and different drugs, or different mechanisms within the same drug, can intervene at different points in that assembly process.

The clinical implications could be far-reaching. If researchers can learn how to activate ketamine’s pain-relief mechanism without simultaneously inducing dissociation, it may become possible to develop future treatments that preserve the drug’s analgesic effectiveness while reducing the sense of detachment and other subjective side effects. Such decoupled therapies could broaden access to ketamine’s benefits, particularly for patients who cannot tolerate its psychological effects, and could reshape how clinicians think about the role of altered consciousness in psychedelic-assisted medicine more broadly. For now, the study stands as a landmark demonstration that, where ketamine and pain are concerned, relief and detachment are separable — and the road to better analgesics may not need to pass through an altered mind at all.

Subject of Research: Dissociation of ketamine's analgesic and consciousness-altering brain mechanisms in humans

Article Title: Ketamine relieves pain without “disconnecting” consciousness

Article References: Ketamine relieves pain without “disconnecting” consciousness. (n.d.). Original publication

Image Credits: AI Generated

DOI: Not provided

Keywords: ketamine, pain relief, dissociation, consciousness, fMRI, psychedelic research, insula, anterior cingulate cortex, default mode network, Neuropsychopharmacology, Tel Aviv University, analgesia

The paper, published as a Hypothesis and Theory article, introduces what its author calls the Emergent Flow Theory, or EFT. Its central claim is deceptively simple: consciousness is not an instantaneous state but an emergent process that requires specific, measurable windows of time for information to be consolidated. While dominant frameworks such as Integrated Information Theory, which quantifies informational complexity through the measure Φ, and the Free Energy Principle, which models brains as prediction-error minimizers, describe what gets integrated, they remain largely silent on the temporal bounds within which that integration must occur for experience to become phenomenologically accessible. EFT is designed to fill exactly that gap.

The empirical anchor for this claim comes from a striking line of research on non-neuronal cells. In work by Nikolay Kukushkin and colleagues, human kidney cells and astrocytes—cells with no synapses and no neural identity—were shown to discriminate temporal patterns of chemical stimulation. When stimuli were spaced out over minutes, the cells activated the CREB/ERK molecular cascade, a pathway long associated with long-term memory consolidation, and switched on memory-related genes. When the same total stimulation was delivered in a massed burst with no spacing, the cells did not consolidate. In other words, the classic spacing effect, familiar from behavioral psychology, operates at the level of individual cells: memory, in a molecular sense, takes time even where there are no neurons.

From this evidence, the paper derives its foundational construct: a minimum biological temporal window, denoted ΔT_b. This is defined as the shortest interval a single cell needs to consolidate repeated stimuli into a stable molecular state, characterized by sustained activation of plasticity-associated genes such as CREB and c-Fos and by measurable changes in how the cell responds to future inputs. The mechanism is biochemical: protein kinase cascades and transcriptional delays unfold over seconds to minutes, and stimuli must be separated enough to allow partial recovery of the intracellular signaling system so that successive activations can additively strengthen the signal that ultimately reaches the genome. Below a critical interval, information simply cannot be consolidated into a stable cellular state.

The author is careful to draw a boundary that prevents the theory from sliding into cellular mysticism. ΔT_b does not imply that kidney cells are conscious. It is proposed as a necessary but not sufficient condition for conscious processing—the bedrock upon which higher-order integrative architectures are built. Conscious systems, in this view, inherit temporal constraints from their constituent biological processes without inheriting phenomenology itself. The same logic underlies the paper’s use of the term basal cognition, which refers strictly to time-dependent state integration and adaptive regulation in non-neural systems, not to any form of subjective awareness.

The harder problem is scale. How do billions of cellular integration processes, each operating on its own minutes-long clock, contribute to the organism-level coherence that modulates conscious processing in the brain? The paper proposes bioelectricity as the translational medium. Beyond the fast, all-or-none action potentials of classical neuroscience, all living cells maintain slow, graded membrane voltage dynamics that can store information, couple through gap junctions into tissue-wide networks, and exhibit memory-like behavior as past stimuli alter ion-channel expression. Research on bioelectric patterning in regenerating planarians, tumor suppression through restored voltage states, and developmental organ patterning in frogs illustrates that stable voltage gradients can coordinate multicellular behavior without centralized control—findings the paper treats as illustrative rather than conclusive.

The bridge to the brain runs through the autonomic nervous system. Roughly 80 percent of vagal nerve fibers are afferent, carrying integrated visceral information to brainstem nuclei such as the nucleus tractus solitarius, which in turn regulate heart rate variability—the paper’s primary empirical proxy for what it calls basal coherence, symbolized as γ. The proposed pathway is explicitly labeled Tier-2 in the paper’s own epistemic classification: mechanistically plausible given established physiology of the inflammatory reflex, gut–brain signaling, and cardiac interoception, but requiring direct empirical validation. Cellular memory states established over minutes to hours would set the physiological baseline around which moment-to-moment autonomic signaling fluctuates, and long-term patterns of autonomic regulation could, through epigenetic mechanisms such as DNA methylation and chromatin remodeling documented in stress circuits, feed back to reshape cellular set-points in a bidirectional loop.

Within this architecture, the classical Libet paradigm—famous for showing that a Readiness Potential precedes reported conscious intention by several hundred milliseconds—acquires a new interpretation. Rather than reading those delays as evidence against free will, EFT reframes them as a map of necessary integration times. The interval between the onset of unconscious preparatory activity and the reported moment of intention may reflect the time required for basal cellular and autonomic information to reach sufficient functional integration complexity for phenomenological access. Consciousness, on this view, is not undermined by the delay; it is constituted by it.

The theory positions itself as complementary to, not competing with, existing frameworks. Against the global neuronal workspace theory, EFT adds a quantifiable autonomic parameter that modulates the probability of the cortical ignition event. Against Integrated Information Theory, it asks how the empirical proxy of integrated information—the Perturbational Complexity Index measured with transcranial magnetic stimulation and EEG—varies dynamically with autonomic state across the cardiac cycle. Against predictive processing, it proposes heart rate variability as a concrete physiological substrate for precision weighting. And it acknowledges its own limits: temporal integration is treated as a tractable entry point, not an exhaustive account, and the author notes that spatial structure in bioelectric gradients, vagal pathways, and cortical networks will need formal integration, potentially with temporo-spatial theories of consciousness.

What distinguishes the proposal from much consciousness theorizing is its explicit commitment to falsifiability. The paper lays out testable predictions: replication of cellular spacing effects across diverse human cell types; correlated modulation of cellular consolidation markers and heart rate variability in vagal-innervated tissues; and top-down experiments in which mindfulness training or transcutaneous vagus nerve stimulation should produce measurable changes in peripheral CREB expression or cortical complexity, respectively. A decisive test would require concurrent measurement of cellular consolidation dynamics, autonomic coherence, and perturbational cortical complexity—a resource-intensive but feasible multimodal protocol. Whether the unified pathway from cellular memory to phenomenological experience survives that scrutiny remains open, but the framework has converted one of philosophy’s hardest problems into a schedule of experiments that existing laboratory techniques can already begin to run.

Subject of Research: Temporal integration across biological scales as a proposed foundation of consciousness

Article Title: Consciousness emerges from temporal integration across biological scales: from cellular memory to phenomenological experience

Article References: Parra, A. L. (2026). Consciousness emerges from temporal integration across biological scales: from cellular memory to phenomenological experience. Frontiers in Integrative Neuroscience, 20, Article 1772467. https://doi.org/10.3389/fnint.2026.1772467

Image Credits: AI Generated

DOI: 10.3389/fnint.2026.1772467

Keywords: consciousness, temporal integration, cellular memory, Emergent Flow Theory, bioelectricity, vagus nerve, heart rate variability, Libet paradigm, Integrated Information Theory, Free Energy Principle, CREB, basal cognition

Darwin himself wrestled with the question. In an 1838 notebook entry, he wrote that as forms became complicated they opened fresh means of adding to their complexity, but that there was no necessary tendency in simple animals to become complicated. Edelman takes this nuance as his starting point: complexity increase is a tendency, not a universal law, and it need not apply across the board. Bacteria and archaea have coexisted with metazoans for hundreds of millions of years, and complexity has risen and fallen in individual lineages.

Edelman identifies at least three families of mechanisms that can drive complexity upward. The first is entirely passive: in any evolutionary system with variation and heredity, complexity tends to increase simply because it is bounded from below. A random walk in complexity space, starting near the minimum, can only drift upward in the long run. This insight underlies the Zero-Force Evolutionary Law proposed by McShea and Brandon, which holds that rising diversity and complexity are the null expectation in the total absence of selection.

The second mechanism is entropic. Complexity growth can proceed concomitantly with increasing entropy, the probable direction of a system’s progression through its configuration space. Edelman focuses on Boltzmann entropy, where the entropy of a macrostate is proportional to the logarithm of the number of microstates it contains. Transitions from macrostates with fewer microstates to those with more are more probable, so the succession of macrostates over time tends toward higher entropy. Crucially, this probabilistic dynamics does not require the system to be closed or to approach equilibrium.

The apparent paradox of ordered biological structures arising under a disordering principle dissolves once the word order is confined to its proper realm. As Wicken argued in 1979, biological structures are not ordered in the information-theoretic sense; they are organized. The Second Law acts to dissipate order, not organization, and organization tends to accumulate at the expense of the former. Brooks and Wiley formalized this idea, noting that when the phase space of possibilities expands faster than the system can fill it, increasing entropy can be accompanied by the emergence of organized structure.

The third family of mechanisms involves selection. Saunders and Ho showed in 1976 that at a local fitness peak, random removal of a component must make the system less fit, while random addition allows the possibility of an increase. This asymmetry acts as a ratchet, amplifying passive complexity increases. Adami and colleagues demonstrated in simulated evolution that selection forces genomes to behave as a natural Maxwell Demon, within a fixed environment, genomic complexity is forced to increase. Selection can also open new possibilities for adaptation, expanding the effective genetic phase space and echoing Darwin’s observation that complexity breeds complexity.

Edelman then extends these ideas to consciousness, focusing on basic phenomenal awareness, the simplest variety of consciousness, exemplified by simple perceptions such as the taste of a wine or the smell of a flower. According to his Dynamical Emergence Theory, this level of awareness consists in dynamically emergent diagnosis by a cognitive system of its own representational states, what he calls autodiagnosis. The stream of awareness is identified with the succession of emergent metastable macrostates in the system’s state space, and these macrostates are non-arbitrary because they are equivalence classes of future predictability.

Basic phenomenal awareness can become more complex over evolutionary time or through learning. Edelman illustrates the evolutionary scale with the progression from simple photosensitivity in planarian eyespots to the imaging camera eye of frogs, cortical shape perception in mammals, and color perception. Learning-induced complexification is exemplified by wine tasting expertise, where a taster progresses from distinguishing red from white wine to identifying a specific variety, vineyard, and vintage. On an abstract computational level, both types of complexification increase the topological complexity of the system’s trajectory space, and the acquisition of exemplars and categories makes the process entropic, as the system’s total entropy increases with learning.

As a test case, Edelman proposes reservoir computing, an architecture consisting of a reservoir of coupled neurons with nonlinear dynamics and a linear readout circuit. This architecture naturally maps onto the olfactory circuit of insects such as the locust and fruit fly, where recurrently interconnected projection neurons in the antennal lobe project to Kenyon cells in the mushroom body. Edelman outlines several experiments using this model to test whether the entropy and topological complexity of the system’s dynamics tend to increase over evolutionary time and through learning.

The review concludes that if one seeks a general tendency rather than a universal law, complexity increase emerges as a non-trivial principle of evolution, underwritten by several distinct mechanisms. This principle applies not just to the evolution of species morphologies but also to the evolution and learning of their capacity for phenomenal discernment, which constitutes the most basic level of consciousness. Edelman notes that the growth of complexity is expected to accelerate when greater complexity facilitates evolvability, and that biology co-evolving with culture provides a cumulative, self-reinforcing catalyst. The scope of such an approach could extend even to cosmological selection hypotheses, though that extension awaits future research.

Subject of Research: On the evolutionary dynamics of complexity and consciousness

Article Title: On the evolutionary dynamics of complexity and consciousness

Article References: Edelman, S. (2026). On the evolutionary dynamics of complexity and consciousness. Frontiers in Complex Systems, 3, Article 1672525. https://doi.org/10.3389/fcpxs.2025.1672525

Image Credits: AI Generated

DOI: 10.3389/fcpxs.2025.1672525

Keywords: evolution, complexity, consciousness, entropy, phenomenal awareness, Zero-Force Evolutionary Law, Dynamical Emergence Theory, reservoir computing, natural selection, thermodynamics, Shimon Edelman, Frontiers in Complex Systems

The hypothesis addresses one of the most stubborn gaps in consciousness science. Existing theories describe many functional correlates of conscious experience: Global Workspace Theory explains how information becomes globally broadcast once activation crosses a threshold, Integrated Information Theory formalizes how conscious systems generate more information as a whole than their parts do separately, and salience-network models identify the fronto-insular and anterior cingulate cortices as key nodes for detecting behaviorally relevant events. Electromagnetic field theories propose that conscious experience relates to specific patterns of brain-generated electromagnetic activity. Yet, as Raslan points out, none of these frameworks has identified a specific cellular mechanism embedded within a defined long-range cortical pathway that could physically carry integrated sensory, interoceptive, emotional, and cognitive information toward the anterior systems that grant conscious access.

Von Economo neurons are the linchpin of the proposal. First systematically described by Constantin von Economo and Georg Koskinas in the 1920s, these cells are strikingly different from ordinary pyramidal neurons. They possess an extremely elongated, stick-like or corkscrew-shaped soma, reside predominantly in layer Vb of the fronto-insular cortex and anterior cingulate cortex, and often cluster together. Von Economo himself initially wondered whether they were pathological artifacts before recognizing them as a specialized neuronal subtype. The new paper is careful to adopt a conservative definition: not every spindle-shaped neuron in the cortex qualifies as a true von Economo neuron, and reports of similar cells in other regions and species do not necessarily indicate identity with the original population. This precision matters because the entire hypothesis rests on the specific anatomical relationship between these cells and the two regions they inhabit.

Those two regions are not arbitrary. The fronto-insular cortex and anterior cingulate cortex are both heavily implicated in salience processing, interoception, emotional evaluation, autonomic regulation, cognitive control, attention, and conscious awareness. The insula itself occupies a paradoxical position in neuroanatomy: buried within the lateral sulcus and invisible without surgical retraction, it has been described in earlier clinical literature as an underestimated brain area in neuroscience, psychiatry, and neurology. Yet it sits at the anatomical intersection of external perception, internal bodily state, affective valuation, and cognitive control, receiving projections from posterior sensory and association cortices, limbic and paralimbic structures, thalamic relay nuclei, and prefrontal control systems. The anterior insula is more dysgranular and agranular, associated with salience and awareness, while the posterior insula is more granular and tied to sensory and interoceptive representation, suggesting a gradient along which information moves from bodily signals toward integrated subjective experience.

The biophysical core of the hypothesis hinges on a distinction that the author argues is often blurred. In electrostatics and classical neuroscience, a dipole refers to two opposite charges separated in space, or to the neural current dipoles used to model local field potentials, EEG, and MEG signals generated by aligned pyramidal neurons. These dipoles are descriptive: they explain how local neural activity produces measurable fields. A dipole antenna, by contrast, is a communication device—two aligned conductive elements separated by a feed point, capable of converting oscillating electrical activity into electromagnetic radiation and back again. Its defining concepts are resonance, wavelength, impedance, tuning, and radiation pattern. The Raslan Circuit relies on this second meaning. It proposes that the elongated, bipolar-like morphology of von Economo neurons provides a biological equivalent of antenna geometry, allowing these cells to organize electrical activity along their longitudinal axis and participate in directed electromagnetic communication between the fronto-insular cortex and the anterior cingulate cortex.

Crucially, the hypothesis does not reject conventional synaptic transmission. The author explicitly frames the proposed electromagnetic mode as complementary: axonal and synaptic signaling remains essential for neural communication, but the unique morphology and position of von Economo neurons may allow them to participate in an additional mode of information organization, tuning, transmission, and reception. The physical foundation for such field-based interaction comes from the established literature on ephaptic coupling—the influence of extracellular electric fields on neuronal membrane potentials independent of direct synaptic transmission. Decades of research, from early hippocampal studies in the 1980s to modern work demonstrating that endogenous electric fields can guide neocortical network activity, establish that neuronal electromagnetic fields can affect neuronal function. The Raslan Circuit extends this principle from local, near-field influence to a specialized anatomical system in which von Economo neurons actively organize, transmit, receive, and redistribute field-based information within a defined circuit.

The fronto-insular cortex and anterior cingulate cortex are envisioned as a paired, bidirectionally coupled antenna system rather than a simple transmitter-receiver pair. The fronto-insular cortex occupies the organizing pole of the circuit: positioned near the convergence of sensory, interoceptive, autonomic, emotional, and cognitive streams, its von Economo neurons are proposed to assemble and format multimodally integrated signals. The anterior cingulate cortex occupies the redistributive pole: embedded in networks for salience detection, attention, working memory, decision-making, and action selection, and densely connected with mediodorsal and anterior thalamic nuclei, its von Economo neurons are proposed to receive, evaluate, and route incoming signals toward downstream executive, limbic, motor, and autonomic systems. Because a dipole antenna element can by definition both transmit and receive, both populations are proposed to participate simultaneously in reciprocal, resonance-sensitive field coupling, with the anterior cingulate cortex producing a contextually reweighted version of the signal shaped by current goals, motivational state, and environmental contingencies.

Perhaps the most eye-catching—and most speculative—element of the proposal is its spectral prediction. Treating von Economo neurons as half-wave dipole antennas and applying classical electromagnetic relations, the author estimates that cells with effective dipole lengths of roughly 1,500 to 2,500 micrometers would resonate at frequencies around 60 to 100 terahertz, while larger spans of 5,000 to 6,000 micrometers would fall in the 25 to 30 terahertz range—placing the predicted resonances in the terahertz and far-infrared domains. These figures are explicitly heuristic rather than measured biological frequencies. Neural tissue is hydrated, conductive, lossy, and dispersive, so any real mechanism would likely involve near-field coupling, dielectric boundary effects, and tissue-specific field shaping rather than free-space radiation. The value of the terahertz hypothesis, the author argues, is falsifiability: if VEN-rich tissue has distinctive electromagnetic resonance properties, terahertz spectroscopy, near-field imaging, and finite-element modeling could detect measurable differences between VEN-rich and VEN-sparse cortical regions—even if the specific frequency predictions ultimately fail.

The hypothesis is positioned as a bridge rather than a rival to existing theories of consciousness. Within the embedded-processes view of working memory, which holds that conscious contents correspond to a limited-capacity focus of attention funneled through the intraparietal sulcus, the Raslan Circuit would serve as a deep paralimbic pathway that assembles interoceptively grounded, salience-weighted information and delivers it to anterior control systems. Within the recent awareness-buffer reformulation of the multicomponent working memory model, which reconceives the episodic buffer as a limited-capacity, multimodal, consciously accessible integration space, the circuit offers a candidate input-output pathway: the fronto-insular cortex assembles buffer-relevant content, and the anterior cingulate cortex receives and redistributes it. Global Workspace Theory describes the global consequence of ignition and broadcast; the Raslan Circuit addresses a specific upstream route by which multimodally integrated, body-weighted information reaches the anterior systems that participate in such ignition. Information Integration Theory explains why integrated causal structure matters; the circuit proposes one anatomically specific way such integration may be instantiated for a particular class of content.

The proposal concludes with a battery of testable predictions spanning multiple levels of analysis. High-resolution diffusion MRI and tractography should reveal robust white-matter pathways linking VEN-rich territories in the fronto-insular and anterior cingulate cortices, with connectivity profiles stronger than those between the fronto-insular cortex and VEN-poor cingulate regions. Laminar current-source-density analyses and high-density intracranial recordings should reveal oriented, depth-spanning field components associated with VEN populations during multimodal integration tasks. Clinically, patients with focal damage to VEN-rich regions, or conditions that reduce VEN density such as behavioral-variant frontotemporal dementia, should show disproportionate impairments in salience-based conscious access, emotional awareness, and interoceptive precision even when posterior sensory function is preserved, while states of abnormal hyperactivation of insula-cingulate circuits—certain forms of panic, pain catastrophizing, or epileptic auras—may reveal dysregulated contributions of the circuit to conscious experience. Computational modeling of VEN-based field interactions in a conductive, dispersive medium rounds out the empirical program. Whether the convergence of these tests ultimately supports or dismantles the antenna hypothesis, the author contends, the exercise would move the conversation about consciousness from abstract speculation toward experimentally grounded mechanisms.

Subject of Research: A hypothesis that von Economo neurons in the fronto-insular and anterior cingulate cortices function as biological dipole antennas in a proposed communication pathway for consciousness

Article Title: Raslan Circuit: von Economo neurons as biological dipole antenna elements in a proposed FI–ACC communication pathway for multimodal integration and conscious access

Article References: Raslan, M. K. Y. (2026). Raslan Circuit: von Economo neurons as biological dipole antenna elements in a proposed FI–ACC communication pathway for multimodal integration and conscious access. Frontiers in Systems Neuroscience, 20, Article 1779601. https://doi.org/10.3389/fnsys.2026.1779601

Image Credits: AI Generated

DOI: 10.3389/fnsys.2026.1779601

Keywords: von Economo neurons, fronto-insular cortex, anterior cingulate cortex, consciousness, dipole antenna, ephaptic coupling, terahertz resonance, electromagnetic field theory, salience network, working memory, Global Workspace Theory, Integrated Information Theory

One way to understand this gap is through the notion of system levels. A neuron, a neural circuit, a whole brain, an embodied organism, and a socially embedded person are not simply larger and larger versions of the same kind of thing. Each level has its own characteristic entities, regularities, and descriptive vocabulary. What counts as an explanation at the molecular level—say, the kinetics of a receptor protein—does not automatically translate into an explanation at the level of attention, memory, mood, or decision-making. The authors argue that failure to acknowledge these differences, and the complex interactions between levels, is a persistent weakness in the field. Domain-specific languages compound the problem: terms such as “representation,” “computation,” “regulation,” or “information” carry different meanings in molecular biology, systems neuroscience, psychology, and philosophy, and uncritical translation between them can create the illusion of integration where only juxtaposition exists.

The institutional analysis in the review adds an important dimension that is often absent from purely methodological discussions. Large-scale projects in neuroscience, sometimes described as “big science,” share features with big science in physics: major broadly accepted aims, promises of application, multiple participating institutions, standardized data formats, industrial-style workflows, and a focus on overarching goals rather than individual contributions. These features enable unprecedented scale and coordination, but they also concentrate decisions about research direction in the hands of funding bodies whose criteria include political and commercial considerations. The authors note that initiatives such as the “decade of the brain” and the “human brain project” raised expectations, particularly regarding clinical applications, that the results did not fully meet. This is not an argument against large-scale science; rather, it is a reminder that resource allocation decisions embed assumptions about what neuroscience is and what it can deliver, and those assumptions themselves merit critical scrutiny before commitments are made.

The emergence of large-scale digital infrastructures and curated data collections represents a genuinely novel development in this landscape. Well-organized corpora of neuroscientific data allow researchers to test hypotheses across datasets that no single laboratory could have produced, and they open possibilities for meta-analyses and computational modeling at scales previously unimaginable. Yet the authors point out that such resources, while powerful, do not by themselves resolve conceptual questions. A dataset assembled from many laboratories reflects the measurement choices, paradigms, and population characteristics of those laboratories, and aggregating it does not automatically yield multi-level understanding. Big data can reveal correlations and regularities, but the interpretation of those regularities—what they mean, at which level, and for whom—remains a theoretical task that no amount of data volume can perform on its own.

This is where the authors’ call for an interdisciplinary neurophilosophy becomes concrete rather than abstract. They are careful to specify that the task is not merely collaboration between philosophers and neuroscientists, in which philosophers comment on finished work from the outside. Instead, they advocate the development of a critical philosophical stance within the neurosciences themselves. This means that practicing neuroscientists should be equipped and encouraged to reflect on the nature of their explanations, the assumptions embedded in their models, and the interfaces between their level of analysis and others. Philosophy of mind and philosophy of science, on this view, are not ornamental additions but constitutive parts of a mature neuroscience. The demand for institutionalization is significant: without dedicated positions, curricula, journals, and funding lines, such reflection remains dependent on the incidental interests of individuals and cannot accumulate into a stable tradition.

The review’s turn to complex systems science provides the positive methodological program that complements this critical stance. Complex systems research studies how many interacting components give rise to collective phenomena that cannot be predicted from the properties of the components in isolation. Concepts such as self-organization, emergence, nonlinearity, feedback, and multi-scale dynamics offer a vocabulary for describing how molecular, cellular, network, organismic, and social processes can each contribute to mental phenomena without any single level being privileged as the “real” cause. The authors suggest that systemic approaches are particularly helpful for integrating interdisciplinary knowledge and for developing new strategies for modeling multi-level relations. In the mind sciences, this implies moving beyond linear causal chains toward models in which circular causality, reciprocal determination, and context-dependence are built in from the start rather than treated as complications to be controlled away.

A systems perspective also changes what counts as a disorder. In psychiatry, conditions such as depression, schizophrenia, or addiction involve disturbances that manifest simultaneously at biological, psychological, and social levels. A purely neurobiological account may identify correlates of symptom dimensions, but the lived experience of illness, the meaning of symptoms to the person, and the interpersonal and social conditions that trigger or maintain them are not captured by brain measures alone. The authors’ proposal of a systems psycho(patho)logy is an attempt to provide a theoretical framework in which these levels are related systematically rather than merely listed side by side. Such a framework would allow clinicians and researchers to ask how perturbations at one level propagate to others, how stabilizing processes maintain functioning, and why interventions at different levels—pharmacological, psychotherapeutic, or social—can each be effective without being reducible to one another.

The ecological perspective presented in the review extends this reasoning to the conception of the human being itself. The authors criticize what they call the cephalocentric paradigm, the tendency to locate all psychologically relevant activity in the brain. On their account, the brain should be understood as a regulative organ in a situated organism: it does not act alone but regulates the interactions of the whole body with its environment. In humans, moreover, the acting person is “extended” to tools, technologies, and social structures. This resonates with lines of research in cognitive science that treat cognition as distributed across brain, body, and environment, and with phenomenological traditions that emphasize the lived body as the medium of experience and action. The practical implication is considerable: understanding a mental disorder requires attention not only to neural dysregulation but also to the person’s bodily condition, material surroundings, technologies of daily life, and social relationships, because these are constitutive parts of the system whose functioning is disturbed.

The discussion of free will in the final section illustrates how these conceptual commitments play out in a concrete debate. Experimental studies that purport to show that conscious decisions are determined by unconscious brain processes ahead of awareness have often been interpreted through a deterministic lens. The authors argue that any position respecting the complexity and irreducibility of mental phenomena will escape inappropriate reductionist and deterministic assumptions while fully acknowledging the scientific evidence. This is not a retreat into dualism or an appeal to gaps in scientific knowledge. Rather, it reflects the systems-theoretic insight that a decision made by a situated, embodied person involves processes at multiple levels, including the person’s reasons, values, and social context, and that the relation between these levels is not one of simple one-way determination. Experimental findings about the timing of neural correlates of choice remain fully compatible with a conception of agency in which mental phenomena, as phenomena of persons, are not eliminated by their neural correlates.

Read as a whole, the anniversary review is best understood as a diagnosis of the relationship between neuroscience’s institutional dynamics and its conceptual development. Technical success attracts resources, resources enable big science, big science standardizes data and workflows, and standardization favors questions that fit existing measurement paradigms. Conceptual innovation, by contrast, requires reflective distance, interdisciplinary dialogue, and theoretical training—activities that are harder to fund and harder to standardize. The authors’ demand for an institutionalized, interdisciplinary, systems-oriented neurophilosophy is therefore not a complaint about neuroscience’s achievements but a proposal about the conditions under which the next decade of those achievements can become genuinely integrative. The measure of progress, on their account, will not be the volume of data accumulated but the field’s capacity to relate levels of description coherently, to respect the situated and extended character of human persons, and to hold its own explanatory claims to critical account.

Subject of Research: Progress and ongoing conceptual challenges “on the way to integrative human neuroscience”–ten years after

Article Title: Progress and ongoing conceptual challenges “on the way to integrative human neuroscience”–ten years after

Article References: Tretter, F., Löffler-Stastka, H., Braun, H. A., Schleim, S., Falkenburg, B., Hastedt, H., Northoff, G., Fuchs, T., Kotchoubey, B., Maderthaner, R., & Draguhn, A. (2026). Progress and ongoing conceptual challenges “on the way to integrative human neuroscience”–ten years after. Frontiers in Integrative Neuroscience, 20, Article 1792478. https://doi.org/10.3389/fnint.2026.1792478

Image Credits: AI Generated

DOI: 10.3389/fnint.2026.1792478

Keywords: neuroscience, neurophilosophy, free will, systems science, 4E cognition, consciousness, psychiatry, Progress, ongoing, conceptual, challenges, integrative

The research team, led by Samir Haj-Dahmane, with Renee Rouleau as first author, combined whole-cell patch-clamp recordings, light-based uncaging of serotonin, and precise receptor pharmacology in brain slices from adult rats. This technical arsenal allowed them to measure the electrical behavior of individual claustrum neurons with millivolt precision while systematically blocking or activating specific serotonin receptor subtypes. What they found was a level of cellular specialization that few would have predicted for a structure this small.

The first surprise came from the neurons themselves. Rather than behaving as a uniform population, claustrum neurons split into four electrophysiologically distinct groups. Regular-spiking type I neurons, which made up nearly half of the sampled cells, fire steadily with little fatigue and show a characteristic sag in their membrane voltage when hyperpolarized. Regular-spiking type II neurons fire at similar rates but display a fast afterdepolarization that can trigger bursts of doublets. Strongly adapting neurons, as their name suggests, rapidly dampen their own firing under sustained stimulation. A rare fourth group fires fast and hard, reaching frequencies around 60 hertz. When the researchers filled recorded cells with biocytin and reconstructed their shapes, the regular-spiking type I neurons turned out to have pyramid-like cell bodies with an apical dendrite, while the type II and strongly adapting neurons were triangular with multiple radiating dendrites, all bearing spines.

When serotonin was applied to these cells, the responses diverged along cell-type lines in a striking way. In regular-spiking type I and type II neurons, serotonin could push the membrane potential in either direction, producing depolarization in most recorded cells but hyperpolarization in a subset. In strongly adapting neurons, however, the response was unambiguous: every single one was hyperpolarized by serotonin. Using caged serotonin released by a flash of blue light, the team confirmed that these bidirectional effects could be evoked by brief, physiologically plausible surges of the transmitter, with depolarizations averaging about 4 millivolts and hyperpolarizations close behind at roughly 3.9 millivolts.

The molecular logic behind this push-pull system came into focus through receptor pharmacology. The depolarizing responses vanished when the researchers blocked 5-HT2A receptors with the antagonist MDL 100907, while the hyperpolarizing responses were abolished by WAY 100635, a 5-HT1A receptor antagonist. In other words, the same neurotransmitter carries two opposing messages to the same cells, and the net effect on any given claustrum neuron depends on the balance of receptor subtypes it happens to express. This arrangement mirrors what has been described in the prefrontal cortex, where 5-HT1A and 5-HT2A receptors likewise mediate opposing effects on membrane excitability, but the study is among the first to work out the details in the claustrum.

Serotonin’s influence did not stop at the neuronal membrane. The team next examined the glutamatergic synapses that deliver excitatory input to claustrum neurons, and here they found a second, independent layer of control. Bath application of serotonin cut the amplitude of evoked excitatory postsynaptic currents to roughly half of baseline. Three classic pieces of evidence pointed to a presynaptic site of action: the paired-pulse ratio increased, the coefficient of variation rose, and the frequency of miniature excitatory postsynaptic currents, which reflect single vesicles of glutamate released independently of action potentials, dropped significantly while their amplitudes stayed constant. Together, these results indicate that serotonin reduces the probability of glutamate release from the nerve terminals that feed the claustrum.

Given the claustrum’s famously high expression of 5-HT2A receptors, which are the principal molecular target of serotonergic psychedelics such as LSD and psilocybin, the team suspected these receptors were responsible for the synaptic inhibition. The evidence was decisive. Blocking 5-HT2A receptors with MDL 100907 eliminated the serotonin-induced suppression of synaptic currents, and blocking 5-HT2 receptors more broadly with mesulergine not only abolished the inhibition but unmasked a potentiation, suggesting that other serotonin receptor subtypes exert a weaker, opposing influence. Conversely, TCB-2, a potent and selective 5-HT2A agonist with psychedelic-like properties, reproduced the inhibition on its own, reducing evoked synaptic currents and miniature EPSC frequency in a manner fully reversed by the antagonist.

Perhaps the most intriguing finding concerns where these receptors sit and how they signal. Immunohistochemistry confirmed dense 5-HT2A receptor labeling around claustrum neuron cell bodies and along their processes. Yet when the researchers dialyzed recorded neurons with GDP-beta-S, a reagent that jams G-protein signaling inside the recorded cell, the synaptic inhibition persisted undiminished even though the postsynaptic receptor response was abolished. The canonical downstream pathway of 5-HT2A receptors runs through Gq/11 proteins and phospholipase C, but blocking PLC with U73122 failed to touch the inhibition, and blocking CB1 cannabinoid receptors with AM251, which would have disrupted a retrograde endocannabinoid mechanism, was equally ineffective. The conclusion is that presynaptic 5-HT2A receptors on the glutamate terminals themselves suppress release through a signaling route that departs from the textbook PLC cascade, and the authors acknowledge that identifying that route is a task for future work.

Why does this matter beyond the slice chamber? The claustrum has been implicated in attention, decision-making, multimodal sensory integration, and the coordination of cortical slow-wave activity during sleep. Recent in vivo studies have shown that claustrum neurons projecting to cortex fire most vigorously during the synchronized brain state of non-REM sleep and quiet down during waking and REM sleep, a pattern that mirrors, in inverse fashion, the firing of the dorsal raphe serotonin neurons that innervate the claustrum so densely. The new findings suggest a plausible cellular mechanism: when serotonin tone is low, as it is during deep sleep, the brakes come off both the intrinsic excitability of claustrum output neurons and the strength of their excitatory inputs, potentially allowing the claustrum to drive the synchronized slow waves that sweep the sleeping cortex. When serotonin neurons fire briskly during wakefulness, the same receptors would dampen claustrum activity and gate its excitatory drive.

The psychedelic connection adds another layer of fascination. Psilocybin has been shown to acutely alter the functional connectivity between the claustrum and the brain networks supporting perception, memory, and attention, and psychedelic effects on consciousness are thought to involve the claustrum at least in part. By demonstrating that 5-HT2A receptors simultaneously depolarize major claustrum output neurons and suppress their glutamatergic inputs through a noncanonical presynaptic mechanism, the Buffalo team has provided a cellular blueprint for how a single psychedelic-sensitive receptor could reshape information flow through one of the brain’s great integration centers. As the authors note, the multifaceted and sometimes contradictory effects of 5-HT2A receptors across brain regions likely reflect differences in their subcellular distribution and downstream signaling partners. In the claustrum, at least, serotonin turns out to be less a simple dial than a conductor, coaxing different sections of the neural ensemble into different responses at the same moment.

Subject of Research: Serotonergic modulation of neuronal excitability and glutamatergic synaptic transmission in the rat claustrum

Article Title: Serotonergic regulation of neuronal excitability and glutamatergic synaptic transmission in rat claustrum

Article References: Rouleau, R., Oubraim, S., Cirone, L., Shen, R.-Y., & Haj-Dahmane, S. (2026). Serotonergic regulation of neuronal excitability and glutamatergic synaptic transmission in rat claustrum. Frontiers in Cellular Neuroscience, 20, Article 1951375. https://doi.org/10.3389/fncel.2026.1951375

Image Credits: AI Generated

DOI: 10.3389/fncel.2026.1951375

Keywords: claustrum, serotonin, 5-HT2A receptor, 5-HT1A receptor, patch-clamp, glutamate release, dorsal raphe nucleus, psychedelics, sleep, consciousness, electrophysiology, presynaptic inhibition

The theory enters a crowded field. Global Neuronal Workspace Theory holds that conscious access corresponds to a non-linear ignition of fronto-parietal networks that broadcasts information widely across the brain. Integrated Information Theory characterizes consciousness in terms of irreducible integrated causal structure, quantified as phi, with the Perturbational Complexity Index serving as an empirically validated perturbational measure of cortical complexity. The Predictive Processing framework and Free Energy Principle describe the brain as a hierarchical inference engine minimizing prediction error, with neuromodulatory systems regulating the precision of ascending signals. Each framework has generated substantial empirical progress, yet López Parra identifies a shared blind spot: none of them formalizes autonomic and neuromodulatory state as explicit dynamic control parameters within models of conscious integration. That gap, he argues, is precisely where EFT belongs.

The core architecture of EFT organizes consciousness-supporting integration across three nested temporal regimes. The first, labeled ΔTi, spans roughly 50 to 200 milliseconds and is autonomic-interoceptive in character — a preparatory phase in which cardiovascular, respiratory, and gastrointestinal afferent signals are synthesized into a coherent representation of bodily state that shapes the gain conditions for subsequent cortical processing. The second regime, ΔTp, spans roughly 200 to 500 milliseconds and corresponds to conscious access itself: the non-linear, self-amplifying ignition cascade across fronto-parietal workspace neurons familiar from Global Neuronal Workspace Theory. The third, ΔTf, extends from about 500 milliseconds to 3 seconds and is phenomenological-narrative — the period during which globally accessed information is contextualized within episodic memory, a temporal self-model, and narrative comprehension, anchored in the Default Mode Network. These regimes are explicitly not rigid compartments but overlapping dominant processing epochs within a continuous dynamical system, with boundaries that vary across individuals, tasks, and physiological states.

The theory’s boldest move is its formalization of autonomic coherence, denoted gamma, as a dimensionless latent gain parameter modulating the probability and quality of transitions between these regimes. Operationally, gamma is estimated through a normalized heart rate variability measure: raw RMSSD — a standard beat-to-beat HRV metric — divided by an individual’s own resting baseline. The relationship is captured in a phenomenological dynamic equation for a latent integrated-complexity state Q: the rate of change of Q equals a driving term in which gamma scales the effective integration rate at every node-regime pair, minus a dissipation term reflecting the metabolic cost of sustained integration, plus additive stochastic noise. The equation is dimensionally closed and stated entirely in latent theoretical quantities, with the Perturbational Complexity Index treated as an observational constraint on Q rather than an identity — a distinction the author maintains in exact structural parallel to the gamma-versus-gamma-hat separation.

Why should cardiac variability matter for cortical integration? The proposed routing pathway runs from the nucleus tractus solitarius, the brainstem’s primary relay for vagal afference, through the parabrachial nucleus to the locus coeruleus, the brain’s principal noradrenergic nucleus. The author is careful to distinguish evidentiary layers here: the individual rodent-documented links are supported by tract-tracing work, while the proposal that they compose a functional gain-setting axis in humans is EFT’s own Tier-2 synthesis. From the locus coeruleus, the account draws on the adaptive gain model of Aston-Jones and Cohen, in which phasic noradrenergic bursts amplify responses to task-relevant signals while tonic-high activity degrades signal-to-noise discrimination. Computational and neuroimaging evidence independently supports the general principle: whole-brain information-processing dynamics have been shown to transition from storage-dominated to transfer-dominated states as neural gain increases, with a critical transition at intermediate gain values, and phasic neuromodulatory bursts indexed by pupil diameter coincide with abrupt large-scale network reconfigurations during perceptual switches.

EFT also connects its autonomic precondition to the cortical machinery of temporal segmentation. The framework is convergent with the oscillatory syntax perspective, which holds that the brain segments continuous time into discrete processing windows through hierarchically nested rhythms, coordinated by phase-amplitude coupling between slow theta oscillations and fast gamma activity. EFT proposes, as a Tier-2 mechanistic elaboration, that autonomic coherence modulates cortical gain through locus coeruleus-norepinephrine influences on excitatory-inhibitory balance and gamma-generating parvalbumin-positive interneuron networks. In the salience network — anchored by the anterior insula and dorsal anterior cingulate — the theory locates a state-dependent routing interface that may bias processing toward the executive network under optimal gain conditions, or toward subcortical threat-responsive circuits under hyperarousal. A putative top-down feedback loop, running from ventromedial prefrontal cortex through amygdala GABAergic intercalated cells to restore ventral vagal tone, is supported by meta-analytic evidence of consistent HRV associations with the amygdala and ventromedial prefrontal cortex, though the specific cellular mechanism remains untested.

What elevates EFT above armchair theorizing is its commitment to falsifiability. Five empirical predictions are derived with explicit adjudication criteria, including pre-registered equivalence testing that distinguishes genuine evidence against a prediction from merely underpowered results. Prediction P1 targets the covariation between the HRV-derived estimator and the Perturbational Complexity Index in within-subjects designs using transcranial magnetic stimulation and EEG. Prediction P2 tests whether transcutaneous vagus nerve stimulation enhances conscious access markers such as P3b amplitude and gamma synchrony. Prediction P3 examines whether acute stress disrupts the phenomenological-narrative regime, measurable through theta-gamma phase-amplitude coupling and narrative coherence. Prediction P4 is split into a falsifiable existence test — whether any systematic relationship between the observable estimator and the observable proxy exists at all — followed by a conditional model discrimination between a monotonic saturating relationship and a peaked inverted-U profile consistent with criticality accounts. Prediction P5 probes causal contributions of the salience network through disruption paradigms.

The author’s intellectual honesty is notable throughout. The framework is explicitly labeled Tier-2: grounded in and consistent with a substantial empirical literature, but requiring direct experimental validation of its integrated claims. The master equation, in its present first-order form, generates only a monotonic relationship between gain and integration; if the peaked inverted-U model were empirically favored, the equation itself — not merely its interpretation — would require extension. Methodological constraints are acknowledged candidly: HRV requires averaging windows of 30 seconds to 5 minutes, a discrepancy of at least three orders of magnitude with the 50-millisecond interoceptive window, which is why gamma is defined as a tonic baseline parameter rather than an instantaneous multiplier. No validated biomarker currently exists to dissociate the proposed brainstem gain-setting route from the independently established thalamo-insular interoceptive relay in human vagal stimulation studies.

Indirect clinical anchors lend the proposal plausibility. In 127 patients with disorders of consciousness, cardiac autonomic markers — including stimulus-related cardiac-cycle modulations elicited during an auditory oddball paradigm — improved data-driven classification between vegetative and minimally conscious states when combined with EEG complexity markers. EEG-derived criticality measures have been shown to predict perturbational complexity with mean error below 7 percent across propofol, xenon, and ketamine anesthesia states, though a preprint reports these features did not generalize to predicting maximal perturbational complexity in disorders of consciousness. None of this convergent evidence directly tests EFT’s specific claims; each anchor is offered at the level of plausibility and motivation, not confirmation.

If the predicted relationships between autonomic coherence, neuromodulatory gain, and the temporal architecture of cortical integration are confirmed, EFT will have provided substantive empirical support for a functional bridge between the body’s regulatory physiology and the dynamics of conscious access. The framework is offered as complementary to — not a replacement for — Global Neuronal Workspace Theory, Integrated Information Theory, and Predictive Processing, situating autonomic coherence as a proposed physiological precondition within which the integrative capacities described by those frameworks may be realized. Whether the heartbeat truly sets the gain on consciousness is now an empirically adjudicable question, and the tools to answer it — psychophysiology, TMS-EEG, non-invasive neuromodulation, and causal disruption paradigms — are already on the laboratory bench.

Subject of Research: A theoretical framework proposing that autonomic coherence, indexed by heart rate variability, functions as a gain parameter modulating the temporal architecture of conscious cortical integration.

Article Title: Emergent flow theory: autonomic coherence as a gain parameter for the temporal architecture of conscious integration

Article References: Parra, A. L. (2026). Emergent flow theory: autonomic coherence as a gain parameter for the temporal architecture of conscious integration. Frontiers in Integrative Neuroscience, 20, Article 1841714. https://doi.org/10.3389/fnint.2026.1841714

Image Credits: AI Generated

DOI: 10.3389/fnint.2026.1841714

Keywords: consciousness, autonomic coherence, heart rate variability, Emergent Flow Theory, locus coeruleus, Perturbational Complexity Index, salience network, temporal architecture, Global Neuronal Workspace Theory, Integrated Information Theory, predictive processing, vagus nerve stimulation

The central problem the work addresses is one of representation rather than mechanism. Modern neuroscience can measure neural activity with extraordinary precision, yet most quantitative analyses treat each observable — signal amplitude, spectral power, entropy, phase relationships, cross-frequency coupling, and recurrence structure — as an independent descriptor. No single measure captures the full evolution of large-scale brain dynamics. Klebba’s proposal is that these established observables can instead be interpreted as complementary properties of a single evolving neural state, represented mathematically by a complex-valued function in which instantaneous amplitude and phase unfold continuously over time.

The mathematical foundation is deliberately classical. Using the Hilbert transform, an oscillatory EEG signal can be converted into an analytic signal — a complex-valued representation whose magnitude gives instantaneous amplitude and whose argument gives instantaneous phase. This is a standard tool in electrophysiology, and the study is emphatic that nothing quantum is being claimed. The inspiration from Everett is strictly structural: his formalism showed how multiple evolving configurations could be represented within a single state space without requiring collapse into one physical state. The framework adopts that representational strategy, describing the neural state as a superposition of multiple simultaneously active neural configurations, each a transient pattern of coordinated activity whose relative contribution changes continuously.

Crucially, these configurations are not proposed as separate conscious entities or parallel minds. They are mathematical constructs describing the many interacting patterns of neural activity that coexist in one biological brain — sensory processing, memory retrieval, attentional control, motor planning, and predictive inference all running in parallel across distributed networks. The framework suggests, as a theoretical hypothesis rather than an empirical result, that a unified thought might correspond to the transient dominance of one configuration within this evolving landscape, while alternatives remain dynamically active outside immediate awareness. Whether that hypothesis holds is explicitly left for future investigation.

To ground the framework empirically, the study analyzed a publicly available EEG dataset collected through a collaboration between the Neurocomputing Laboratory at IIT Delhi and the Neurology & Sleep Center in New Delhi. Recordings from ten epilepsy patients, acquired at 200 Hz with standard international 10–20 electrode placement, were divided into 50 interictal, 50 preictal, and 50 ictal segments — 150 recordings in total, each 5.12 seconds long. The dataset was chosen because it contains clearly defined transitions between distinct physiological states, providing a natural testbed for asking whether multiple electrophysiological observables change in a coordinated manner.

The results were striking in their consistency. Root-mean-square amplitude increased from about 23.5 microvolts during interictal recordings to roughly 139 microvolts during seizures, one of the largest effects in the study with an effect size of ε² = 0.658. Spectral power redistributed dramatically toward lower frequencies: delta-band activity rose from 40 percent of total power to 75.4 percent during seizures, while theta, alpha, and gamma contributions fell sharply. All three entropy measures — spectral, permutation, and sample entropy — declined significantly, indicating progressively reduced signal complexity as seizures took hold, with sample entropy showing one of the largest effects at ε² = 0.586.

Temporal organization changed as well. Alpha-band phase concentration increased significantly during ictal recordings, and theta–gamma phase-amplitude coupling, quantified with the modulation index and validated against surrogate data, differed significantly between interictal and seizure-associated states. Recurrence quantification analysis, performed after reconstructing each signal’s state space through time-delay embedding, revealed that neural trajectories became more persistent and less mobile during seizures: mean diagonal line length grew from 4.05 to 7.70, determinism increased, and mean state-space displacement fell from 0.422 to 0.291. Every major analytical domain showed statistically significant differences across seizure states, assessed with Kruskal–Wallis tests, Holm-corrected post hoc comparisons, effect sizes, and bootstrap confidence intervals.

The author is careful to situate these findings relative to existing theories of consciousness. The results are broadly consistent with Integrated Information Theory’s emphasis on large-scale integration, but the framework does not attempt to compute Φ or propose an alternative measure of consciousness. It likewise differs from electromagnetic field theories such as CEMI and Susan Pockett’s formulation: the neural field here is a mathematical representation of measurable dynamics, not a claim that the brain’s physical electromagnetic field is the substrate of experience. The framework also engages the concept of metastability — the tendency of complex neural systems to occupy transiently organized configurations while retaining the capacity to reorganize — as the theoretical context in which the evolving state representation should be understood.

The study acknowledges its limitations candidly. The recordings were single-channel, so they cannot resolve the distributed cortical interactions that the proposed configurations would represent; the data come from pathological brain states that cannot be assumed to mirror healthy cognition; and neither the individual configurations nor the proposed neural coordination functional were directly estimated from the data. The empirical contribution is the demonstration that established observables change in a coordinated, multidimensional way during seizure progression — a quantitative foundation, not a validation, of the formalism.

What the work ultimately offers is a research program rather than a finished theory. Future investigations using high-density EEG, magnetoencephalography, intracranial recordings, and computational state-space reconstruction could test whether candidate neural configurations can be empirically identified, whether transitions among them correspond to changes in attention or cognition, and whether similar coordinated dynamics appear in sleep, anesthesia, meditation, and disorders of consciousness. The framework even suggests applications beyond neuroscience, potentially informing biologically inspired artificial intelligence systems that rely on dynamical representations of high-dimensional state spaces. Whether wave-function formalism proves genuinely useful for understanding the brain will depend not on its elegance but on whether it generates predictions that competing descriptions cannot — a question the author deliberately frames as open, empirical, and now, at least, formally posed.

Subject of Research: A wave-function-based mathematical framework for representing evolving neural dynamics measured with EEG during epileptic seizure states

Article Title: Wave-function formalism for evolving neural dynamics: a mathematical framework informed by EEG

Article References: Klebba, M. (2026). Wave-function formalism for evolving neural dynamics: a mathematical framework informed by EEG. Frontiers in Computational Neuroscience, 20, Article 1812909. https://doi.org/10.3389/fncom.2026.1812909

Image Credits: AI Generated

DOI: 10.3389/fncom.2026.1812909

Keywords: EEG, neural dynamics, wave-function formalism, epilepsy, computational neuroscience, Hilbert transform, entropy, phase-amplitude coupling, recurrence analysis, metastability, state space, consciousness

The editorial, authored by Adriana Michalak, Claudia Picard-Deland, Giulio Bernardi and Mohamed S. Ameen, frames four contributions that together map the field’s trajectory. They range from the clinical challenge of studying dreaming in stroke patients, through the philosophical puzzle of how to define altered states of consciousness, to the use of artificial intelligence for analyzing dream reports and the emerging practice of dream engineering, in which researchers attempt to steer the content of dreams themselves. Each contribution addresses a different layer of the same fundamental question: how can subjective experiences, which by their nature are private and difficult to verify, be studied with the rigor that modern neuroscience demands?

One of the most technically instructive studies in the collection tackles the problem of causality in dream research. Fischmann and colleagues set out to recruit patients with ischemic stroke of the posterior cerebral artery, a vascular territory that supplies the parieto-occipital regions of the brain. Previous work, including a landmark 2017 study by Siclari and colleagues published in Nature Neuroscience, had identified neural correlates of dreaming largely through correlational methods. If patients with parieto-occipital lesions were to lose the capacity to dream, that cessation would provide causal evidence for the role of these regions in generating dream experiences, moving the field beyond association toward mechanism. The study aimed to compare patients who had lost the ability to dream with those who retained it, investigating whether the loss of dreaming affects sleep quality and memory consolidation.

The recruitment effort, however, revealed the clinical complexity of this approach. Out of 255 patients screened, only 14, or 5.5 percent, met the inclusion criteria, and just six completed the study. Importantly, the participants who completed the study were generally younger, had milder strokes and better functional outcomes than those who dropped out. This recruitment pitfall alone provides a valuable insight, illustrating the multimorbidity and rarity of isolated posterior cerebral artery lesions that make this population so difficult to study in the context of dream research. The authors suggest that future investigations could instead use transcranial alternating current stimulation, or tACS, in healthy volunteers to create temporary functional lesions, overcoming some of the challenges inherent to pathological models. It is a striking example of how negative and null findings, when reported systematically, can yield methodological insights that shape the design of future experiments.

Beyond recruitment choices, another challenge lies in how conscious sleep experiences are defined in the first place. Conscious experiences are inherently subjective, which requires precise language and definitions to make findings comparable across studies. Weiler and colleagues examine the phenomenological boundaries between out-of-body experiences and lucid dreaming, asking whether these represent distinct categories or variations of the same underlying experience. The authors reviewed experiential features previously proposed to differentiate out-of-body experiences from lucid dreams, examining both the phenomenological characteristics and the interpretive frameworks used to categorize these states. Their proposal is that disembodiment, the subjective experience of being detached from the physical body, should serve as the primary distinguishing feature of out-of-body experiences, regardless of other phenomenological features that may not be unique to or consistently reported in them. This work aims to provide a more standardized classification strategy, a foundational step for a field in which terminological ambiguity has long hampered cross-study comparison.

Once experiences are characterized, the question becomes how to analyze large corpora of subjective experience reports. Natural language processing and artificial intelligence tools are of considerable interest here, as they allow reproducible and automated extraction of quantitative metrics from narrative dream reports. Manual content annotation is time-consuming, can be subject to cognitive bias and is prone to high inter-rater variability. AI-based approaches can accelerate and standardize linguistic-level analysis, thereby increasing scalability. Yet skeptics have argued that these tools might be poorly suited for modeling dream reports because of their unusual and bizarre content. Bertolini and colleagues tested this claim directly, using perplexity, a standard AI metric that estimates model uncertainty. By comparing the perplexity of large language models on dream reports from the DreamBank archive with that of Wikipedia articles, the authors showed that dream content can be as predictable as Wikipedia entries. Intriguingly, perplexity scores also reflected previously observed group differences in dream reports, such as those related to gender, visual impairment and clinical status. These findings challenge the view that dream reports are inherently more bizarre or unpredictable than standard texts and offer evidence in support of AI tools for dream analysis.

While computational approaches automate the analysis of dream content, dream engineering aims to manipulate and track the content of the experience itself, offering a systematic approach to investigating dreams despite their considerable variability. In addition to standardizing the definitions and methods used to study dreams, researchers are beginning to explore ways of standardizing dreams themselves. Haar Horowitz and colleagues used a targeted dream incubation technique involving verbal prompts and serial awakenings, both at sleep onset and after entry into REM sleep, to evoke dreams about a specific topic, in this case a tree. The experimental setup used the Dormio system, designed specifically to facilitate targeted dream incubation by detecting sleep onset based on physiological proxies and delivering the verbal prompt: Remember to think of a tree.

The results were striking. All 11 participants successfully incorporated the target tree theme into their sleep-onset dreams. Additionally, the authors provide preliminary evidence that targeted dream incubation may extend beyond the hypnagogic state and influence dream experiences in subsequent REM sleep. The technique is promising for investigating how dreams are generated and how they evolve across sleep stages. Perhaps most interestingly, the ability to steer dream content opens the possibility of experimentally testing hypotheses about the functions of dreams, for example whether inducing positive dream content improves next-day mood, or whether dreaming about a desired object changes waking desire for it. These are questions that have resisted direct experimental test for decades, and targeted dream incubation offers a tractable paradigm for answering them.

Several key directions for future research emerge from this research topic. The most foundational is terminological: establishing clearer definitions and categories of conscious sleep experiences will allow studies to consistently identify and compare the phenomena they investigate. Negative and null findings should be reported systematically, as the stroke study demonstrates, because recruitment challenges alone can yield valuable methodological insights. Finally, progress in dream engineering will require improving success rates, determining the extent and limits of control over dream content, and more directly assessing its possible applications and potential side effects.

Together, this multidisciplinary research topic outlines how the field is moving from simply analyzing dreams toward treating conscious sleep experiences as phenomena that can be systematically characterized, computationally modeled and potentially manipulated. As these technological approaches advance in capability, particularly in the ability to actively shape dream content, ethical frameworks in consciousness research must evolve alongside them, or ideally stay one step ahead, guarding dreams as dreams guard our sleep.

Subject of Research: Conscious experiences and dreaming during sleep, including dream analysis and dream incubation

Article Title: Editorial: Understanding conscious experiences during sleep

Article References: Michalak, A., Picard-Deland, C., Bernardi, G., & Ameen, M. S. (2026). Editorial: Understanding conscious experiences during sleep. Frontiers in Sleep, 5, Article 1985086. https://doi.org/10.3389/frsle.2026.1985086

Image Credits: AI Generated

DOI: 10.3389/frsle.2026.1985086

Keywords: dreaming, consciousness, sleep, lucid dreaming, dream engineering, artificial intelligence, natural language processing, out-of-body experience, phenomenology, stroke, targeted dream incubation, REM sleep

The study recruited 30 healthy, experienced breathwork practitioners who performed high-ventilation breathwork while lying inside a magnetic resonance imaging scanner. This design allowed the researchers to capture both physiological and subjective changes in real time, combining continuous measurement of cerebral blood flow with retrospective reports of the participants’ conscious experiences. Such a combination is rare in consciousness research, where altered states are often studied either through pharmacological manipulation with controlled substances or through meditation and sensory deprivation protocols that produce subtler shifts in awareness. Breathwork, by contrast, is legal, inexpensive, and free of the regulatory constraints that complicate psychedelic research, making it an attractive model system for scientists seeking to understand how consciousness can be systematically altered without drugs.

Participants reported experiencing strong altered states of consciousness during the scanning sessions. The dominant quality of these experiences was what the researchers describe as an oceanic state, a deep and pervasive feeling of connection to the universe and to other people. Many participants also reported altered perception, vivid mental imagery, a diminished or transformed sense of self, and a subjective sense of unity. When these reports were compared against established characterizations of psychedelic experiences, the researchers found that they resembled those produced by a moderate to high dose of LSD or psilocybin, although the breathwork-induced states involved less pronounced visual effects than are typically associated with classical psychedelics. This convergence between a breathing practice and a serotonergic drug state is one of the most intriguing aspects of the work, because the two interventions reach the brain through entirely different physiological pathways.

The imaging data revealed a substantial physiological effect. Global grey-matter cerebral blood flow fell by approximately 45 percent during the breathwork sessions, dropping on average from 16.24 to 9.81 milliliters per 100 grams of tissue per minute, a difference that was highly statistically significant across the 30 participants. This magnitude of reduction is remarkable for a voluntary behavioral intervention and reflects the intense hyperventilation that characterizes high-ventilation breathwork, which alters blood carbon dioxide levels and thereby constricts cerebral vessels. Yet the researchers emphasize that this dramatic reduction in blood supply does not by itself explain the intensity of the experiences participants reported. The brain, it appears, continues to function and to generate extraordinary states of consciousness even while its overall blood supply is sharply curtailed.

Instead, the key to understanding the altered states may lie in how the brain’s networks communicate with one another during the practice. The researchers found that participants who reported stronger and more profound altered states also showed greater changes in the default mode network, a set of interconnected brain regions involved in self-reflection, autobiographical thinking, and the construction of the sense of self. The default mode network has long been a focal point of psychedelic research, because drugs such as psilocybin and LSD are known to disrupt its normally synchronized activity, and the degree of this disruption correlates with experiences of ego dissolution. The new findings suggest that intensive breathing may converge on the same neural target through a fundamentally different mechanism, one driven by blood gas changes and vascular dynamics rather than by receptor pharmacology.

Lead researcher Dr Christian Schmitz of the Central Institute of Mental Health in Mannheim noted that although many practitioners of intensive breathwork report profound changes in perception and self-experience, the underlying brain mechanisms have remained poorly understood. According to Schmitz, the study indicates that these experiences are linked not only to marked changes in cerebral blood flow but also to alterations in how key brain networks communicate. This distinction matters because it reframes the question of how breathwork works. If reduced blood flow alone were responsible, one might expect any condition that lowers cerebral perfusion to produce similar states, which does not appear to be the case. The specificity of the network-level changes suggests that the pattern of reorganization, not the reduction in supply, carries the experiential signature.

Hana Adolphi, co-coordinator of the research, highlighted the practical implications of the work. Breathwork, she noted, is a legal, inexpensive, and non-pharmacological intervention that may provide a valuable tool for studying altered states of consciousness in laboratory settings. Understanding the mechanisms through which voluntary breathing patterns reshape brain communication could also inform future research into novel approaches for mental health treatment. This is a significant point given the current surge of interest in psychedelic-assisted therapies for depression, anxiety, and post-traumatic stress disorder. If breathwork can reliably induce states that overlap with those produced by psychedelics, and if those states can be harnessed therapeutically, an accessible and drug-free route to similar benefits may become available to a much broader population than psychedelic medicine can currently reach.

Independent commentators welcomed the study while cautioning that its conclusions remain preliminary. Dr Guy Fincham of Brighton and Sussex Medical School in the United Kingdom, who was not involved in the research, described the work as intriguing because it extends recent breathwork research suggesting that intensive breathing practices can yield altered states of consciousness that individuals describe in ways analogous to psychedelic compounds, and it does so under controlled brain-imaging conditions. He emphasized that such altered subjective experiences may be harnessed for the improvement of mental health, and that an accessible non-pharmacological route such as breathwork could prove invaluable for many people. At the same time, he pointed out that the study was uncontrolled and that its sample consisted entirely of experienced practitioners, so replication in breathwork-naive participants using a randomized controlled design will be needed to determine whether the findings translate more widely and produce genuine therapeutic benefit.

Professor David Nutt of Imperial College London, also commenting independently, framed the work in the context of the broader psychedelic research program. Breathwork, in his view, represents a non-pharmacological bridge to research into psychedelics, and studies that help clarify how it functions are a welcome addition to the field. The bridge metaphor captures an important scientific opportunity: because breathwork does not require controlled substances, special licensing, or supervised dosing of psychoactive drugs, it allows researchers to probe altered states of consciousness in settings that would otherwise be difficult to approve. Comparing the neural signatures of breathwork-induced states with those of psychedelic states could help isolate which aspects of network reorganization are essential to the therapeutic and experiential effects, and which are incidental to a particular method of induction.

The researchers themselves acknowledge the limitations of the current work. With 30 participants, all of them experienced practitioners, and no control condition, the study cannot yet establish whether the observed brain changes have clinical relevance or whether they would appear in people attempting intensive breathwork for the first time. Larger studies with controlled designs will be required to answer those questions. Nevertheless, the study marks a meaningful step forward in the science of consciousness. It demonstrates that a voluntary, drug-free behavioral practice can produce measurable reductions in cerebral blood flow alongside network-level reorganization that tracks with the intensity of subjective experience, and it points researchers toward a specific neural mechanism, altered communication within the default mode network, as the likely key to understanding how breathwork transforms the mind. For a field increasingly interested in non-pharmacological routes to altered states and mental health, the message is clear: the breath may be a more powerful instrument for probing and shaping consciousness than previously appreciated.

Subject of Research: Effects of intensive breathwork on cerebral blood flow, brain network communication, and altered states of consciousness

Article Title: Intensive breathing alters consciousness by changing brain communication

Article References: Intensive breathing alters consciousness by changing brain communication. (n.d.). Original publication

Image Credits: AI Generated

DOI: Not provided

Keywords: breathwork, consciousness, default mode network, cerebral blood flow, psychedelics, MRI, altered states, mental health, ECNP Congress, neuroscience, non-pharmacological intervention, self-experience