Biology subjects

Kringelbach, M. L.

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

At least 19 recordsLinked to original sources

bioRxiv2026

Structured recurrence of latent neural processes predicts memory recognition performance

Understanding cognition requires explaining not only which neural processes are engaged, but how their relationships evolve through time. We tested whether the recurrent organization of low-dimensional whole-brain dynamics relates to auditory recognition behaviour beyond activation magnitude. After a brief learning phase, 65 participants underwent magnetoencephalography (MEG) while recognizing three-, five-, and seven-sound auditory sequences, presented unchanged or with a single early or late tone alteration. Broadband Brain Network Estimation via Source Separation (BROAD-NESS) identified broadband latent networks from source-reconstructed activity and represented their joint activation as trajectories through a multidimensional state space; recurrence quantification analysis characterized their temporal organization. Memorized melodies elicited more recurrent states and longer, more deterministic and persistent recurrence structures, together with lower divergence, than changed melodies. Across participants, structured recurrence was consistently associated with higher accuracy and faster responses, whereas recurrence frequency showed no consistent behavioural association. After adjustment for condition, melody length, their interaction, and matched root-mean-square activation amplitude, recurrence measures increased marginal R2 by 0.273. Nested participant-wise cross-validation reduced held-out prediction error by 11.81% (95% CI: 4.01%-17.65%). These findings support a view of cognition in which performance depends critically on how low-dimensional neural processes jointly organize and evolve over time, beyond how strongly they are activated.

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bioRxiv2026

Diminished ascending drive in transdiagnostic anhedonia shifts the cortico-subcortical hierarchy away from bottom-up dominance

Anhedonia, the diminished capacity to experience or pursue pleasure, is a core feature of mood and psychotic disorders, yet the large-scale brain dynamics underlying it remain uncharacterized. Dominant accounts posit striatal hypofunction with compensatory prefrontal over-recruitment, but they rest on activation magnitudes and symmetric correlations, which cannot resolve the direction of influence. Here, in 210 adults from the Transdiagnostic Connectome Project, we defined symptom-derived low- and high-anhedonia subgroups and estimated each participant's directed effective connectivity using a whole-brain generative model fitted to resting-state fMRI. In high anhedonia, the cortex shifted upstream relative to the subcortex, flattening the cortico-subcortical hierarchy. Correspondingly, the net balance of direct cortico-subcortical flow shifted from bottom-up to balanced. This shift recurred within the frontostriatal reward circuit and replicated across independent halves of each participant's data. Flow asymmetry also increased with subject-level severity. Decomposing flow into ascending and descending arms showed that both weakened in high anhedonia, but ascending subcortex-to-cortex and striatum-to-prefrontal drive fell more than the descending drive. These findings identify a reorganization of cortico-subcortical hierarchy in anhedonia and suggest that the previously reported prefrontal over-recruitment reflects diminished ascending drive rather than stronger cortical output.

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bioRxiv2026

Spiral brain dynamics track the temporal unfolding of subjective intensity during the N,N-dimethyltryptamine experience

Psychedelic compounds such as N,N-dimethyltryptamine (DMT) induce rapid and profound changes in conscious experience, requiring neuroimaging measures that capture how brain dynamics evolve across space and time. Emerging evidence suggests that rotational (vortex-like) dynamics may contribute to the coordination of large-scale neural activity. Here, we introduce two vorticity-based metrics; spiral vorticity (SV) and spatial spiral vorticity dispersion (SSVD), to quantify the rotational organization and spatial heterogeneity of large-scale cortical phase dynamics. Using fMRI data acquired under placebo and DMT in a within-subject design, we assessed these measures globally, across resting-state networks, and regionally to examine their relationship to subjective intensity. DMT increased SV and SSVD and reduced temporal irreversibility, indicating stronger rotational organization, greater spatial heterogeneity, and altered directional structure of cortical dynamics. Effects were non-uniform across functional systems, with the largest changes in default mode, visual, frontoparietal, and dorsal attention networks. Both vorticity measures tracked the temporal evolution of subjective intensity globally, while network-level analyses revealed spatially heterogeneous brain-experience coupling. Spatiotemporally resolved analyses further showed that the strongest reorganization of spiral dynamics emerged during offset and recovery rather than at peak subjective intensity. These findings identify spiral dynamics as a time-resolved neural signature of the DMT experience. By quantifying the rotational structure and spatial heterogeneity of cortical phase flow, vorticity-based measures provide a framework for linking large-scale brain dynamics to evolving subjective experience.

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bioRxiv2026

Ageing reshapes the resting-state network landscape of the human brain

Ageing alters brain rhythms and large-scale functional organisation, but whether these changes reflect isolated effects or a coordinated reconfiguration remains unclear. We applied Frequency-resolved Network Estimation via Source Separation (FREQ-NESS) to source-reconstructed resting-state MEG from 164 healthy younger and older adults across two independent datasets. FREQ-NESS revealed that ageing reshaped the frequency-resolved organisation of whole-brain networks. Older adults showed a slower alpha-network peak and a flatter decay of network prominence from alpha into beta frequencies, together with changes in entropy across the spectrum. These spectral changes were accompanied by shifts in the spatial expression of dominant networks along the lateral, posterior-anterior, and inferior-superior spatial axes. Rather than following a uniform anatomical shift, the direction of age differences varied across frequencies. Together, these findings show that healthy ageing is associated with a reshaped configuration of endogenous brain networks, characterised not simply by a uniform loss of network organisation, but by selective reconfiguration of the prominence and spatiotemporal expression of whole-brain activity across frequencies.

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bioRxiv2026

Memory encoding begins with spontaneous neural reactivation

Long-term memory relies on neural reactivation mechanisms that support consolidation and retrieval. However, it remains unknown whether these mechanisms also operate during encoding, the initial stage of memory formation. Using a decoding classifier, we analyzed magnetoencephalography (MEG) data from 67 participants as they actively encoded or passively listened to brief melodies. We found that the classifier generalized to the rest period immediately following encoding, revealing spontaneous neural reactivation and stable patterns after stimulus offset. This extends prior evidence of neural replay, which is typically observed during sleep and rest, by demonstrating reactivation during encoding, within the task itself. Decoding accuracy during encoding was correlated with recognition performance in subsequent tests, reinforcing its role in memory, with the largest differences between low- and high-recognition performance groups emerging 10 days after encoding. Whole-brain network analysis identified two primary systems: an auditory network (primary auditory cortex, insula, superior temporal gyrus) showing few condition differences, and a memory/associative network (hippocampus, parahippocampal gyrus, cingulate cortex) driving the neural reactivation underlying successful encoding. Together, these results demonstrate that spontaneous neural reactivation supports memory encoding even before formal consolidation.

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bioRxiv2026

Brain dynamics of memory encoding for simple versus complex musical sequences

Memory encoding is the foundational process by which the brain transforms sensory input into lasting representations. While the neural mechanisms of auditory memory have been extensively studied, how musical complexity modulates the neural activity during memory encoding remains poorly understood. Here, we used magnetoencephalography (MEG) to investigate the encoding of simple (tonal) versus complex (atonal) musical melodies in 67 participants. Behaviorally, the latter melodies were consistently rated as more complex and associated with lower recognition accuracy across three testing sessions (same day, one day later, and ten days after the encoding task). At the neural level, source-localized analyses revealed distinct spatiotemporal dynamics: simple melodies elicited stronger activity in auditory cortices (left and right Heschl's gyrus) and cingulate regions (medial and anterior cingulate gyrus), while complex melodies recruited the left hippocampus more extensively across multiple tones. These findings demonstrate that musical complexity shapes neural encoding processes from the outset, with tonal sequences benefiting from efficient sensory processing and atonal sequences requiring greater memory-related recruitment. Our study provides novel insights into how the human brain encodes complex auditory information, providing a framework for understanding the neural basis of memory formation for temporally structured stimuli.

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bioRxiv2026

Near-critical brain dynamics track effortlessness during meditation

Effortful cognition is typically associated with controlled, task-constrained neural processing, whereas effortless awareness may require a more flexible, internally driven mode of brain organization. Critical brain dynamics provide a principled framework for characterizing this shift, as systems near criticality are thought to balance stability and flexibility, allowing efficient information processing without excessive control. Transcendental Meditation (TM), characterized by a shift from effortful mental engagement to effortless awareness, offers a natural model for testing this possibility. Here, we investigated whether critical brain dynamics track TM as a global meditative state, or instead reflect moment-to-moment fluctuations in subjective effortlessness. We combined high-density electroencephalography (EEG) with time-resolved phenomenological reports using Temporal Experience Tracing (TET). Experienced TM practitioners (N = 33) and matched controls (N = 33) completed resting-state recordings before and after a 30-minute TM or silent counting control task. Long-range temporal correlations (LRTCs) were quantified using detrended fluctuation analysis, while functional excitation/inhibition (fEI) balance was used to estimate directional deviations from criticality. State-based analyses showed that TM increased alpha and beta LRTCs relative to pre- and post-resting state within meditators, but revealed no robust between-group differences in either LRTCs or fEI balance. In contrast, neurophenomenological analyses showed that subjective effortlessness was robustly associated with increased theta, alpha, beta, and broadband LRTCs, with significantly stronger relationships in meditators than controls. Restricting analyses to low-effort periods further revealed higher beta LRTCs in meditators, a difference missed by conventional state comparisons. These findings identify scale-free neural dynamics as a candidate marker of "letting go" during meditation.

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bioRxiv2026

Flattened brain hierarchy and increased EEG complexity during unusual bodily experiences and out-of-body experiences

Experiences in which the sense of the body becomes distorted offer a unique window into how the brain constructs the bodily self. Despite their clinical relevance in conditions such as depersonalization and body-image disorders, large-scale brain dynamics underlying unusual bodily experiences (UBEs) remain poorly understood, and no prior study has examined them systematically across wakefulness and sleep. Here, we analyzed a high-density EEG dataset including 36 UBE episodes, such as floating sensations, distorted body boundaries, and out-of-body experiences, recorded across rapid eye-movement (REM) sleep, light sleep, sleep arousals, and wakefulness during meditation (N=20). We quantified the temporal irreversibility of neural dynamics, a marker of cortical hierarchical organization, and complemented these analyses with whole-brain signal complexity measures following a within-subject design. UBEs were consistently associated with a global reduction in neural irreversibility and cortical hierarchical differentiation relative to non-UBEs and wakefulness, along with a concurrent increase in whole-brain complexity to levels comparable to wakefulness. Together, these findings suggest that UBEs reflect a neural state combining high informational richness with a transient flattening of cortical hierarchy, a profile that resembles that observed in psychedelic states. Key pointsO_LIUBEs are associated with a global flattening of brain hierarchy measured by EEG. C_LIO_LIUBEs are associated with increased global EEG complexity, resembling the psychedelic state. C_LI

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bioRxiv2026

Bayesian heart-rate entropy identifies autonomic dynamics during seizure evolution: a reproducible pilot study

Epileptic seizures are accompanied by profound alterations in autonomic regulation, yet the physiological information encoded in cardiac dynamics remains incompletely understood. Bayesian heart-rate (HR) entropy has recently been proposed as a probabilistic measure of cardiac dynamics, but whether it captures clinically meaningful aspects of seizure physiology--such as behavioral awareness or seizure evolution--has not been established. We estimated Bayesian HR entropy from electrocardiographic recordings acquired during video-electroencephalographic monitoring using the BayesianAtHeart framework. Entropy-derived measures were integrated with quality-controlled clinical metadata to generate a frozen seizure-level analysis dataset, from which all subsequent analyses were performed. Associations between seizure-average Bayesian HR entropy and clinical variables were evaluated using linear mixed-effects models accounting for repeated seizures within patients, and time-resolved entropy trajectories were analyzed descriptively. Following ECG quality control, Bayesian HR entropy was successfully estimated for 51 of 67 seizures from 10 patients; the remaining 16 seizures were excluded because ECG quality was insufficient. Forty-eight seizures with complete awareness classification comprised the primary analysis cohort. Bayesian HR entropy was not associated with ictal awareness across seizure-average analyses, mixed-effects models, or time-resolved entropy trajectories. Instead, seizure duration emerged as the strongest clinical correlate of Bayesian HR entropy, with longer seizures exhibiting progressively lower Bayesian HR entropy. Time-resolved analyses indicated that this association reflected a gradual decline in entropy during seizure evolution rather than lower Bayesian HR entropy at seizure onset. Post hoc sensitivity analyses showed that this association was not attributable to selection bias or to the number of beat-to-beat intervals available to the entropy estimator, and that duration, rather than stable between-patient differences, was the dominant source of entropy variance. Entropy was not generally reduced during seizures relative to a pre-ictal baseline; instead, the variability of the entropy trajectory declined progressively with seizure duration, more strongly than its mean. These findings suggest that Bayesian HR entropy primarily reflects the evolving organization of autonomic regulation during seizures rather than behavioral awareness. Beyond identifying seizure duration as the strongest correlate of Bayesian HR entropy in this cohort, this study establishes a fully reproducible computational framework for Bayesian HR entropy analysis that provides a foundation for future prospective investigations of autonomic dynamics in epilepsy.

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bioRxiv2026

Hyper-Hierarchical Brain States Are Associated with Disorders of Consciousness

BackgroundConsciousness is increasingly understood as an emergent property of large-scale brain dynamics that depend upon flexible interactions among distributed cortical and subcortical systems. Although disorders of consciousness (DOC) have traditionally been associated with impaired integration and reduced network complexity, the role of hierarchical brain organization in supporting conscious awareness remains poorly understood. Here, we investigated how hierarchical organization relates to behavioral responsiveness in DOC by combining trophic-level analysis, trophic coherence, and whole-brain dynamical metrics. MethodsResting-state functional MRI data were analyzed from healthy controls (CNT), minimally conscious state (MCS) patients, and unresponsive wakefulness syndrome (UWS) patients drawn from a previously published DOC cohort. Static global and regional measures of functional hierarchy were computed from directed effective-connectivity networks. Dynamic trophic states were identified using time-resolved phase-coupling analyses and clustering of recurrent coordination patterns. State occupancy, dwell time, metastability, synchrony, and behavioral associations with Coma Recovery Scale-Revised (CRS-R) scores were evaluated. ResultsRegional trophic levels were positively associated with behavioral responsiveness, with higher frontal and thalamic trophic levels and lower insular trophic levels predicting higher Coma Recovery Scale-Revised (CRS-R) scores. Dynamic trophic-state analysis identified a pathological hyper-hierarchical state, defined by elevated frontal, thalamic, and insular trophic levels, that exhibited progressively greater occupancy and longer dwell times from healthy controls to minimally conscious state and unresponsive wakefulness syndrome patients. In contrast, occupancy and dwell time of this state distinguished diagnostic groups but were not significantly associated with behavioral responsiveness. Independent analyses demonstrated significant reductions in metastability and global synchrony across disorders of consciousness. Anatomical mapping localized elevated trophic levels within the pathological state predominantly to fronto-thalamo-limbic systems. ConclusionsDisorders of consciousness are characterized not simply by loss of hierarchical organization but by prolonged stabilization within recurrent hyper-hierarchical brain states. Conscious awareness appears to depend not only on hierarchical organization itself but also on the capacity to flexibly transition between distinct brain states. Severe disorders of consciousness are associated with persistent occupation of pathological hyper-hierarchical states, potentially restricting the dynamical repertoire available for conscious processing.

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bioRxiv2026

Meditation Depth Enhances the Functional Signal-to-Noise Ratio of the Brain

Across contemplative traditions, depper states of meditation are described as states of heightened clarity, vividness, and stillness of mind, yet what this clarity corresponds to in the brain has remained difficult to specify. The functional signal-to-noise ratio (f-SNR) framework frames mental clarity as a measurable property of neural signals: the degree to which brain activity tracks the causes of sensory signals rather than endogenous, irrelevant, fluctuations. It predicts that deepening meditation should raise f-SNR, expressing sensory events more faithfully in neural signals against ongoing background activity. We tested this prediction across different levels of meditative depth. Twenty-nine experienced Vipassana practitioners meditated while auditory tones were presented, periodically reporting their depth of meditation. f-SNR was quantified from event-related potentials (ERPs) in a fronto-central P3 window and from single-trial decodability of auditory tone-evoked activity against no-tone background EEG. High-depth states were associated with greater ERP signal-to-noise ratio, stronger single-trial signal consistency, and improved decodability of sound tones. These results suggest that meditative depth is expressed in the reproducibility and stimulus-background separability of sensory responses, consistent with deep meditation enhancing the brains functional signal-to-noise ratio by improving the clarity of sensory signals and reducing endogenous noise.

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bioRxiv2026

Divergent changes in perturbation-induced brain reconfiguration following depression treatment with psilocybin and escitalopram

A central challenge in neuroscience is understanding how the human brain is organised to support optimal functioning and adaptability. One approach to characterise complex brain dynamics is by artificially perturbing whole-brain models. Here, we asked whether whole-brain organisation under perturbation in major depressive disorder (MDD) changes after intervention with psilocybin and escitalopram. First, we built whole-brain models of pre- and post-treatment resting-state functional magnetic resonance imaging (fMRI) and obtained an initial generative effective connectivity (GEC) matrix for each individual. Then, we employed systematic and local artificial perturbations across intensities, re-optimised each model to create a response GEC (GECr), and assessed the extent of brain reorganisation by quantifying the brain network reconfiguration index (NRI). Our results showed that the global brain NRI increases with psilocybin and decreases with escitalopram. Across sessions and interventions, higher global NRI was related with localised perturbations in brain areas orchestrating the brains hierarchical dynamics. Traditional approaches complemented our investigation. Our findings suggest distinct neural changes following each treatment for MDD. The increase in brain reorganisation under perturbation following psilocybin is consistent with greater brain flexibility and changeability, whereas the decrease following escitalopram suggests more stabilised brain dynamics. Overall, perturbation-induced brain NRI may represent a useful approach for uncovering neural changes following different interventions for depression.

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bioRxiv2026

Distinct brain responses to psilocybin and escitalopram in depression captured by the Fluctuation-Dissipation Theorem

In recent decades, the psychedelic psilocybin has been studied as a potential treatment for major depressive disorder (MDD), offering an alternative to traditional antidepressants. However, the brain changes underlying the clinical effects of different interventions remain unclear. Here, we investigated the effects of psilocybin and a conventional antidepressant, escitalopram, from the double-blind randomised controlled trial (DB-RCT) -NCT03429075- on the brains hierarchical organisation. Using pre- and post-treatment resting-state functional magnetic resonance imaging (fMRI) we built whole-brain models and obtained a generative effective connectivity (GEC) matrix for each patient. Based on the GEC, we measured the level of non-equilibrium brain dynamics by quantifying the deviation from the fluctuation-dissipation theorem (FDT) and performed complementary analysis on brain segregation and asymmetry. Our results showed opposite reconfigurations of the hierarchical non-equilibrium brain dynamics following each treatment. Additionally, baseline measures effectively distinguished responders from non-responders within each treatment. These findings suggest that the deviation of the FDT may serve as a marker for differentiating the effects of psilocybin and escitalopram in MDD treatment, overall, contributing to the understanding of therapeutic mechanisms of depression.

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bioRxiv2026

Generalisable signatures of anaesthesia in the large-scale functional organisation of the marmoset brain

How the activity and connectivity of the brain support consciousness remains a central question in neuroscience. Recent progress driven by the use of functional MRI has seen growing recognition that large-scale distributed functional organisation of the human and non-human primate brain are systematically and consistently reshaped by anaesthetic-induced unconsciousness, across anaesthetics and across human and macaque. Here, we generalise these results to a different primate species that is gaining traction as model organism in neuroscience, the marmoset (Callithrix jacchus). We also generalise results to an additional anaesthetic, isoflurane, which we compare with propofol and sevoflurane. We report that under anaesthesia with propofol, sevoflurane, or isoflurane, distributed brain activity from functional MRI is increasingly constrained by the underlying structural connectivity across scales. Anaesthesia also induces a collapse of the principal gradient and intrinsic functional geometry of the marmoset brain, coinciding with a breakdown of hierarchical integration. Altogether, the present results indicate generalisable signatures of anaesthesia in the large-scale organisation of the primate brain.

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bioRxiv2026

One operator to rule them all: Unifying connectome harmonics, turbulence and complex harmonics in brain dynamics

Brain dynamics can be described in three different convenient mathematical languages, namely connectome harmonics, turbulence and complex harmonics (CHARM). Here we demonstrate that these theoretical frameworks can be rigorously unified, under the functional calculus, as one self-adjoint operator and its single spectral measure. The connectome Laplacian carries that measure; the harmonics are its spectral projections, the turbulence smoothing kernel is its resolvent, and the CHARM form is its unitary propagator. The bridge that makes this exact is a textbook fact: The exponential distance rule, which is the empirical kernel of the turbulence model, is the Greens function of a screened Laplacian, so the local order parameter is the phase field passed through the resolvent of the same operator whose eigenfunctions are the harmonics. A single shared control parameter, the spectral gap, simultaneously yields the cortical hierarchy, the turbulent information cascade and the structured interference the CHARM form measures. This unification makes a strong predictive claim. If the harmonic projections, the turbulence resolvent and the CHARM propagator really are three functions of one operator, then any structural perturbation that re-tunes the operator must move all three signatures in unison and must do so with a single coupling. We test this prediction with a pharmacological perturbation by lysergic acid diethylamide (LSD), which is known to change the emotional state, by empirically perturbing the operator with a 5-HT2A receptor density map and asking whether one scalar coupling can simultaneously predict the multi-scale turbulence shift observed, through the resolvent, and the macroscale harmonic energy redistribution, through first-order Rayleigh-Schrodinger perturbation theory. We found that the two independent functional domains respond in unison to one structural perturbation of one operator. The identity is exact as operator calculus and its purchase on the brain depends on a single load-bearing seam, the degree heterogeneity of the connectome, which we make explicit. We propose that this single-operator structure is the necessary mathematical scaffolding of our Entangled Loop theory.

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bioRxiv2026

Efficient ageing: Simulated lesion of the structural connectome reveals optimised decline in the healthy ageing brain

Healthy ageing is associated with widespread white-matter change and altered connectome organisation, yet the link between local microstructural decline and whole-brain network communication remains unclear. Here we combined tract-based spatial statistics (TBSS) with probabilistic tractography and graph analysis to quantify the connectome-level consequences of age-sensitive white-matter hotspots. In two independent diffusion MRI datasets of healthy young and older adults (n = 144 total; Dataset 1: 77 participants; Dataset 2: 67 participants), we first identified age-sensitive fractional anisotropy clusters and then used them as constraints in a cross-dataset simulated-lesion framework. This allowed us to estimate how strongly each structural connection depended on age-sensitive tissue and to test the resulting network effects against matched-mass null lesions. Across both datasets, ageing hotspot lesioning reduced global efficiency, but consistently less than expected under the null model, indicating a less-than-random disruption of network integration. Age-sensitive tissue was disproportionately embedded in the brains integrative backbone: proportional degree loss was strongest in high-degree nodes, rich-club connections showed the greatest hotspot dependence, and nodewise losses were concentrated in frontal, cingulate and subcortical association systems, whereas posterior sensory and temporo-limbic regions were relatively spared. These findings suggest that healthy ageing reflects a selective and constrained reconfiguration of structural connectivity rather than simply pointing to uniform decline.

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bioRxiv2026

Network reconfiguration preserves prediction error signallingin the aging brain

Cognitive aging is widely associated with a progressive weakening of predictive brain mechanisms. This view is supported by decades of electrophysiological studies reporting attenuated mismatch responses in older adults. Yet the literature remains inconsistent, suggesting that aging may not uniformly attenuate predictive processing. One possibility is that multiple predictive subsystems operate concurrently but have rarely been disentangled. Here we address this question by separating whole-brain networks underlying predictive processing in source-reconstructed magnetoencephalography (MEG) data from 77 younger and older adults performing the auditory local-global paradigm. Network decomposition revealed three temporally overlapping predictive subsystems with distinct functional profiles. Aging exerted selective effects across these networks. Sensory prediction error responses were enhanced within a network linking auditory cortices with medial cingulate regions, whereas responses associated with reorientation of attention and contextual pattern processing were attenuated in older adults. The level of multivariate recurrency across these networks was preserved with aging, while the processing of sensory violations induced more recurrency and less divergence relative to contextual violations in both groups. These findings challenge the prevailing view that predictive processing simply declines with age. Instead, aging redistributes predictive resources across distinct neural systems, amplifying sensory-based processes while weakening more cognitively demanding predictive mechanisms.

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bioRxiv2026

Functional connectome harmonics capture early brain organization and maturity in neonates

The functional organization of the human brain is established early, yet the ontogeny of its large-scale functional gradients remains unclear. Using resting-state fMRI data from 714 neonates in the Developing Human Connectome Project, we mapped neonatal brain gradients via functional connectome harmonics (FCH). We identified adult-like sensory-to-multimodal and cognitive gradient patterns present at birth. Applying three FCH-derived metrics--entropy, power, and energy--we found that power and energy were higher in term-born compared to preterm neonates, while entropy was elevated in preterms. These metrics predicted up to ~30% of postmenstrual age, indicating their biological relevance. Our findings reveal that the neonatal brain possesses a robust gradient architecture underpinning early functional organization, offering novel biomarkers for assessing brain maturity and the impact of prematurity on neurodevelopment.

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