Recent interdisciplinary research has raised interest in whether non-classical physical principles may impose fundamental constraints on how neural information can be observed, extracted, or decoded. While conventional neuroscience models neural signaling primarily through classical electrochemical processes, a growing body of theoretical literature has speculated that quantum-mechanical concepts—such as coherence, entanglement, or quantum-inspired information processing—may offer alternative perspectives on the limits of neural observability. This article provides a critical and integrative review of theoretical proposals situated at the intersection of neuroscience, quantum biology, and information theory, without assuming the physical realizability of quantum information processing in biological neural systems. We examine conceptual motivations, key physical constraints (including decoherence, thermal noise, and system complexity), and unresolved theoretical challenges that arise when extending classical neural decoding frameworks toward non-classical regimes. Rather than proposing an experimentally validated mechanism for brain decoding, this work focuses on identifying conceptual boundaries, potential misinterpretations, and open questions that must be addressed before non-classical approaches to neural signal decoding can be meaningfully evaluated. Ethical considerations, methodological limitations, and future research directions are discussed to clarify the conditions under which such speculative frameworks may contribute to neuroscience, while avoiding overextension beyond current empirical evidence.

Persistent post-concussive symptoms are often attributed to diffuse cortical dysfunction, yet this perspective may overlook key systems-level mechanisms. We propose a conceptual framework in which dysfunction arises from the dynamic interplay among five functional nodes: the sensory interface, reflex-brain stem hub, cerebellar module, basal ganglia–thalamic modulator, and cerebral cortex. Grounded in clinical observation and systems-level dynamic modeling, this framework treats the brain as a time-evolving control network that processes inputs, integrates them across hierarchical nodes, and generates adaptive or maladaptive outputs. Subcortical reflex circuits serve as critical nodes in the sensorimotor network, coordinating posture, orientation, and autonomic tone, and are modulated by cortical and thalamic systems. Injury to any of these nodes - or to the connections between them - can disrupt reflex control, distort afferent-efferent signaling, and compromise thalamocortical integration. The cerebellum calibrates predictive timing and coordination, while the basal ganglia-thalamic complex regulates gain and context-dependent gating. The cerebral cortex integrates intention, perception, and prediction to shape voluntary behavior and modulate reflex sensitivity. Reafferent feedback continuously updates the system, creating a dynamic loop of adaptation or maladaptation. Though this model has been applied clinically to guide early intervention, it remains a theoretical framework, untested by formal mathematical modeling or rigorous experimental validation. We offer it as a systems-level model that reframes post-concussive dysfunction as a network-level disorder, with reflex disintegration as a central, actionable mechanism.

IntroductionThe mechanisms by which conscious breathing influences brain-body signaling remain largely unexplored. Understanding how controlled breathing modulates neural and autonomic activity can offer insights into self-regulation and adaptive physiological control. This study investigates how conscious breathing affects cortical-autonomic communication by analyzing bidirectional interactions between EEG band power time series (BPts), heart rate variability (HRV), and breathing signals.MethodsData were collected from fifteen healthy subjects during three experimental conditions: a spontaneous breathing state (Rest) and two controlled breathing tasks (CBT 1 and CBT 2). EEG recordings were analyzed to compute BPts across the δ, θ, α, β, and γ frequency bands, while HRV and breathing signals were derived from ECG data. Cross-spectrum analysis and Granger causality tests were performed between HRV and BPts. To further investigate directional interactions, Granger-causal relationships were explored between components of the BPts extracted using empirical mode decomposition and the HRV and breathing signals.ResultsBidirectional Granger-causal relationships were found between neural and autonomic systems, emphasizing the dynamic interaction between the brain and body. Specific BPts components mediated neural-autonomic communication, with one component consistently aligning with the frequency of conscious breathing (~0.05 Hz) during the CBTs. Cross-spectral peaks at this frequency and its harmonics highlight the role of respiratory entrainment in optimizing neuro-autonomic synchronization. Frequency-specific mechanisms observed in both fast and slow components reflect the complex regulation of autonomic functions through cortical modulation. The most prominent causal effects were observed in the γ band, suggesting its pivotal role in dynamic autonomic regulation, potentially acting as a communication pathway between the brain and body.DiscussionOur results demonstrate that conscious breathing enhances bidirectional cortical-autonomic modulation through frequency-specific dynamic neural mechanisms. These findings support a closed-loop model of physiological regulation driven by neural-respiratory entrainment and suggest that respiration can serve as a top-down mechanism for autonomic control. By clarifying how conscious breathing shapes brain-body dynamics, this work lays the foundation for research on neural self-regulation and supports the development of non-pharmacological interventions for improving mental and physiological health.

Psilocybin, a compound found in Psilocybe mushrooms, is emerging as a promising treatment for neurodegenerative and psychiatric disorders, including major depressive disorder. Its potential therapeutic effects stem from promoting neuroprotection, neurogenesis, and neuroplasticity, key factors in brain health. Psilocybin could help combat mild neurodegeneration by increasing synaptic density and supporting neuronal growth. With low risk for addiction and adverse effects, it presents a safe option for long-term use, setting it apart from traditional treatments. Despite their relatively simpler neuronal networks, studies using animal models, such as Drosophila and fish, have provided essential insights on the efficacy and mechanism of action of psilocybin. These models provide foundational information that guides more focused investigations, facilitating high-throughput screening, enabling researchers to quickly explore the compound’s effects on neural development, behavior, and underlying genetic pathways. While mammalian models are indispensable for comprehensive studies on psilocybin’s pharmacokinetics and its nuanced interactions within the complex nervous systems, small non-mammalian models remain valuable for identifying promising targets and mechanisms at early research stages. Together, these animal systems offer a complementary approach to drive rapid hypothesis generation to refine our understanding of psilocybin as a candidate for not only halting but potentially reversing neurodegenerative processes. This integrative strategy highlights the transformative potential of psilocybin in addressing neurodegenerative disorders, leveraging both small and mammalian models to achieve translational research success.

BackgroundShift work sleep disorder (SWSD) in nurses is highly prevalent and is increasingly recognized for its profound impact on human health. However, the brain functional network topology, which provides a comprehensive map of the brain’s information processing architecture, remains partially understood in nurses with SWSD.Methods45 nurses with SWSD and 45 healthy controls (HCs) underwent a resting-state functional magnetic resonance imaging (rs-fMRI) scan. Graph theoretical analysis was used to investigate alterations in brain functional network topology. Functional network connectivity was further examined in nurses with SWSD relative to HCs. Correlations between network metrics and clinical sleep scores were also examined.ResultsCompared to HCs, the SWSD group exhibited significantly lower global network metrics. Additionally, at the regional level, the SWSD group showed reduced nodal efficiency in specific regions, particularly within the visual processing areas and the caudate nucleus. Functional network connectivity analysis revealed a predominant pattern of weakened connectivity within the limbic network (LN), visual network (VN), default mode network (DMN), subcortical network (SN) and between the LN and SN in the SWSD group, although some inter-network connections were strengthened, predominantly the VN-ventral attention network (VAN), frontoparietal network (FPN)-VN, somatomotor network-VAN, and VN-DMN. Furthermore, poorer sleep quality correlated with reduced local efficiency in the visual cortex and insomnia severity was associated with weakened frontal connectivity.ConclusionsThis study reveals significant alterations in brain functional network topology and predominantly weakened functional connectivity across multiple brain networks, despite some strengthened inter-network links. These neuroimaging changes correlated with clinical measures of sleep disturbance. Our findings highlight compromised brain network organization in SWSD, offering insights into its neural mechanisms and potential biomarkers.

It has been shown that the choice of preprocessing pipelines to remove contamination from functional magnetic resonance images can significantly impact the results, particularly in resting-state functional connectivity (rsFC) studies. This underscores the critical importance of replication studies with different preprocessing methodologies. In this study, we attempted to reproduce the rsFC results presented in an original study by Bauer et al. in 2017 on a group of sighted control (SC) and early blind (EB) subjects. By using the original dataset, we utilized another widely used software package to investigate how applying different implementations of the original pipeline (RMin model) or a more rigorous and extensive preprocessing stream (RExt model) can alter the whole-brain rsFC results. Our replication study was not able to fully reproduce the findings of the original paper. Overall, RExt shifted the distribution of rsFC values and reduced functional network density more drastically compared with RMin and the original pipeline. Remarkably, the largest rsFC effects appeared to primarily belong to certain connection pairs, irrespective of the pipeline used, likely demonstrating immunity of the larger effects and the true results against suboptimal processing. This may highlight the significance of results verification across different computational streams in pursuit of the true findings.

Although neuroticism is a major risk factor for adverse health outcomes, its neural basis is obscured by inconsistent findings from studies of regional gray matter volume (GMV) correlates. This study sought to identify convergent functional brain networks underlying these heterogeneous GMV correlates using functional connectivity network mapping (FCNM), and to explore their neurochemical basis. We systematically identified 10 voxel-based morphometry (VBM) studies (N = 1,595) reporting neuroticism-associated GMV coordinates. Using resting-state fMRI data from 1,093 healthy Human Connectome Project participants, FCNM was applied to map functional connectivity patterns associated with these coordinates. Overlap with canonical networks was assessed. The Juspace toolbox explored spatial relationships between identified networks and major neurotransmitter receptor distributions. Despite spatial heterogeneity, neuroticism-related GMV changes consistently mapped onto three principal functional networks: the default mode network (DMN), frontoparietal network (FPN), and ventral attention network (VAN). These mappings were robust across varied analytical parameters. Moreover, the implicated networks demonstrated significant spatial correlation with the distributions of 5-hydroxytryptamine receptor 2A (5-HT2A), cannabinoid receptor type 1 (CB1), and metabotropic glutamate receptor 5 (mGluR5). Despite regional variability, GMV correlates of neuroticism converge on common large-scale brain networks involved in self-referential processing, cognitive control, and salience processing. Their significant spatial coupling with 5-HT2A, CB1, and mGluR5 receptor distributions suggests serotonergic, endocannabinoid, and glutamatergic modulatory mechanisms contributing to network-level alterations. This cross-modal and network-based approach provides a unified framework for understanding the biological substrates of neuroticism, reconciling prior inconsistencies, and identifying key targets for prevention or biomarker development.

Evoked potentials can be used as an intraoperative monitoring measure in neurological surgery. Auditory evoked potentials (AEPs), or specifically brainstem auditory evoked responses (BAERs), are known for being minimally affected by anesthetics, while visually evoked potentials (VEPs) are presumed to be unreliable and easily affected by anesthetics. While many anesthesia trials or intraoperative recordings have provided evidence in support of these hypotheses, the comparisons were always made between AEPs and VEPs recorded sequentially, rather than recorded at the same time. Although the logistics of improving data comparability of AEPs and VEPs may be a challenge in clinical settings, it is much more approachable in animal models to measure AEPs and VEPs as simultaneously as possible. Five cats under dexmedetomidine sedation received five, 10-min blocks of isoflurane with varying concentrations while click-evoked AEPs and flash-evoked VEPs were recorded from subdermal electrodes. We found that, in terms of their waveforms, (1) short-latency AEPs (BAERs) were the least affected while middle-latency AEPs were dramatically altered by isoflurane, and (2) short-latency VEPs was less persistent than that of short-latency AEPs, while both middle- and long-latency VEPs were largely suppressed by isoflurane and, in some cases, completely diminished. In addition, the signal strength in all but the middle-latency AEPs was significantly suppressed by isoflurane. We identified multiple AEP or VEP peak components demonstrating suppressed amplitudes and/or changed latencies by isoflurane. Overall, we confirmed that both cat AEPs and VEPs are affected during isoflurane anesthesia, as in humans.

Patients with acute unilateral peripheral vestibular hypofunction (AUVP) show postural, ocular motor, and perceptive signs on the diseased side. The subjective visual vertical (SVV) test measures the perceived bias in earth-vertical orientation with a laser line in darkness. This study was aimed at (1) examining whether SVV bias could depend on preset line orientation and angles, and (2) investigating whether vestibular rehabilitation (VR) can improve SVV normalization. To our knowledge, SVV symmetry/asymmetry and impact of VR on SVV normalization have never been documented in the literature. We investigated the SVV bias in a retrospective study (Study 1: n = 42 AUVP patients) comparing the data recorded for line orientation to the ipsilateral and contralateral sides at preset angles of 15° and 30°. We investigated the effects of VR on SVV normalization in a prospective study (Study 2: n = 20 AUPV patients) in which patients were tilted in the roll plane using a support tilted to the hypofunction side with the same amplitude as the SVV bias. This VR protocol was performed twice a week for 4 weeks. Supplementary data on body weight distribution and medio-lateral position of the center of foot pressure (CoP) were obtained using posturography recordings. Study 1 showed asymmetrical values of the SVV bias. On average, the SVV errors were significantly higher for ipsilateral compared to contralateral line orientation (6.98° ± 3.7° vs. 4.95° ± 3.6°; p < 0.0001), and for 30° compared to 15° preset angle (6.76° ± 4.2° vs. 5.66° ± 3.3°; p < 0.0001). Study 2 showed a fast SVV normalization with VR. Non-pathological SVV bias (below ±2°) was found after only 3 to 5 VR sessions while pathological SVV values were still observed at the same time after symptoms onset in patients without VR (1.25° ± 1.46° vs. 4.32° ± 2.81°, respectively; p < 0.0001). A close temporal correlation was observed in the time course of body weight distribution, mediolateral CoP position, and SVV bias over time, suggesting beneficial effects of the VR protocol at both the perceptive and postural levels. We recommend routine assessment of the ipsilateral and contralateral SVV bias separately for a better evaluation of otolith organs imbalance that can trigger chronic instability and dizziness. The SVV bias and the postural impairment caused by the imbalanced otolith inputs after unilateral vestibular loss can be rapidly normalized by tilting the patients in the roll plane, an additional means in the physiotherapist's toolbox. The protocol likely reweights the visual and somatosensory cues involved in the perception of verticality.

The cerebellum plays a major role in balance, motor control and sensorimotor integration, but also in cognition, language, and emotional regulation. Several neuropsychiatric disorders such as attention deficit-hyperactivity disorder (ADHD), autism spectrum disorder (ASD), as well as neurological diseases such as spinocerebellar ataxia type 3 (SCA3) are associated with differences in cerebellar function. Morphological abnormalities in different cerebellar subregions produce distinct behavioral symptoms related to the functional disruption of specific cerebro-cerebellar circuits. The specific contribution of the cerebellum to typical development may therefore involve the optimization of the structure and function of cerebro-cerebellar circuits underlying skill acquisition in multiple domains. Here, we review cerebellar structural and functional differences between healthy and patients with ADHD, ASD, and SCA3, and explore how disruption of cerebellar networks affects the neurocognitive functions in these conditions. We discuss how cerebellar computations contribute to performance on cognitive and motor tasks and how cerebellar signals are interfaced with signals from other brain regions during normal and dysfunctional behavior. We conclude that the cerebellum plays a role in many cognitive functions. Still, more clinical studies with the support of neuroimaging are needed to clarify the cerebellum’s role in normal and dysfunctional behavior and cognitive functioning.