Frontiers in Systems Neuroscience最新文献

This study explores the layered coherence within human cognition as measured through EEG. Signals were collected from two groups (meditators vs. non-meditators) across six conditions: Meditation, Scrambled Words, Ambiguous Images, Math Mind, Sentences, and Video Watching. We analyzed the EEG data using Shannon Entropy, Pearson's Skewness, Total Power, and Dominant Frequency indices, now taken together, to reveal distinct neurophysiological signatures and a different outcome of hypothesis testing based on one index at a time only. These patterns suggest that cognition is more than merely computational, since it seems to be expressive of deeper experiential states, raising profound questions about the nature of intelligence and whether the human psyche and its experience of meaning, in its different forms, can be meaningfully approached through objective methodologies. Our findings invite a re-examination of scientific inquiry itself, both as a pursuit of mechanistic regularities, and also, holistically, as a means of honoring the subtle interplay between structure and meaning. This is reminiscent of young Carl Friedrich Gauss revealing hidden structure beneath apparent complexity by summing up an arithmetic series with elegant simplicity. This way he reframed a problem through insight rather than brute calculation. If artificial intelligence is to mimic cognition, it must grapple with informational entropy and also with the values and consciousness that give rise to meaning. The entropic balance of EEG signals may offer a window into coherence, yet only a species that is mature enough to honor life, liberty, and the pursuit of deep meaning, should attempt to design artificial "minds." In this convergence of neuroscience and philosophical reflection, we glimpse a deeper imperative: to preserve the truth of what it means to be human in an age increasingly defined by machines.

Cognition, sentience, intelligence, awareness, and mind are often treated as distinct phenomena that emerge only at higher levels of biological organization, typically associated with nervous systems or human cognition. However, empirical research increasingly demonstrates learning, memory, adaptive behavior, and goal-directed regulation across a wide range of living systems, including single cells, tissues, and organisms without brains. This paper proposes a unifying framework in which cognition is understood as an organizational property of living systems, grounded in information embodied in their physical structures and in their ongoing interactions with the environment. Within this info-computational (ICON) perspective, living systems engage in behavior, learning, and anticipation by dynamically transforming embodied information through distributed, physically realized processes that support viability and self-maintenance. These processes are present from the onset of life and become progressively more integrated and temporally extended with increasing biological organization. The framework provides explanatory continuity across biological scales and clarifies how complex forms of cognition, awareness, and mind arise as elaborations of basic life-regulatory dynamics. It generates empirically grounded, testable implications for basal cognition, developmental biology, and embodied artificial systems, in the domains such as morphogenetic regulation, bioelectric control, and embodied physical architectures where its implications can be tested.

Objective: Behavioral and neurological studies suggest that major depressive disorder (MDD) is associated with pervasive deficits in executive control of attention. Research using Event-Related Potentials (ERPs) to investigate attentional impairments in depression has provided mixed results. The current study aimed to clarify abnormalities in ERPs associated with depression through use of the Attention Network Test (ANT) which assesses efficiency of three fundamental brain networks: executive control, alerting, and orienting.

Methods: Participants were 93 volunteers. We compared ERP amplitudes in healthy, subsyndromal depression, and MDD groups (31 participants per group) during performance of an extended-duration version of the ANT.

Results: Both N100 and P300 ERP amplitudes were generally lower in the MDD group across central-parietal and posterior sites, with medium-to-large effect sizes. There were also significant effects of depression on the ANT indices for executive control and alerting. Further analyses showed that some abnormalities in ERPs were seen in the subsyndromal group and that depression effects were stable across time, despite vigilance decrement.

Conclusion: Neurocognitive deficits in depression may relate to depletion of a general attentional resource.

Background: Microsaccades, a type of fixational eye movements occurring during visual fixation, are actively involved in the foveal vision and often linked to various attention and cognitive processes. Moreover, microsaccades are increasingly recognized as part of active adaptive mechanisms to continuously changing sensory environments. However, it remains unclear whether they also adjust to changes in luminance as part of this adaptive mechanism, and whether such luminance-regulated microsaccade responses are functionally significant.

Methods: Total forty participants were recruited in the saccade task with their eye position and pupil size measured by a video-based eye tracker. Participants were instructed to maintain fixation on a central spot and then execute a saccade to a peripheral target stimulus immediately upon detection. We systematically varied the background luminance while keeping foveal luminance constant, by which, allows isolation the effects of global luminance on microsaccade generation. We analyzed the effects of experimental condition (background luminance or stimulus contrast) on microsaccadic responses, microsaccadic suppression effects and the saccadic metrics.

Results: We found that darker background luminance systemically increased microsaccade rates (F(2,66) = 4.490, p = 0.015) and enhanced saccadic directional accuracy (F(2,44) = 8.314, p < 0.001). Microsaccades suppressions are significant in all experimental conditions, resulting in reduced saccadic directional accuracy and slower reaction times. Notably, the presence of peri-target microsaccade altered the dynamics of saccades, leading to higher peak velocity, larger amplitude, and greater endpoint deviation.

Conclusion: These findings demonstrate that microsaccade behavior changes as a function of global luminance level, suggesting its adaptive role as part of the oculomotor network. They also suggest a potential role for luminance-driven modulation of superior colliculus activity in oculomotor activities. Taken together, our results offer a new insight into visual-motor coordination under naturalistic conditions.

The theoretical foundation of neuroscience differs from that of artificial intelligence, and to bridge this gap with AI, we would need a new computing paradigm that describes both fields well. The gap came from mathematicians' invention of computability theory, which was deliberately narrower than cognition and yet became a cornerstone of computer science and cognitive science. It has resulted in circular logics for computational biology and biological computing: the computability model of human mathematical activities can limit the sort of technology we build, and in turn, the engineering constraints on our technologies can limit our understanding of brain systems. Here we study several important mathematical and biological activities that computability neglects, helping to bridge the gap between neurobiology and (aspirational) AGI. One such activity is mathematicians' producing proofs of theorems that lie outside artificial computers' logic. Another is neurons' functions that are more complex than transistors, informed by recent neurobiological findings. We end by surveying candidates and inspiration for a new synthesis of AGI with neurobiology, presenting the hypothesis that a new paradigm would have to thoroughly integrate cognition and motion.

Objectives: 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.

Methods: 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.

Results: 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).

Conclusion: 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.

Neurological disorders stem from an intermingled change to self-in-space. While many of these disorders present as spatial deficits-contralateral neglect syndrome, for example-they manifest from the same etiology: disruption to the brain's "default spatial representation" (DSR). DSR is a basic internally generated representation of space that delineates where the self is located in space-without attentional focus from an external drive. We review how pathologic disintegration of DSR is associated with anomalous activation and connectivity within distinct large-scale brain networks (e.g., the default mode network and a comprehensive attention-networked system), leading to a heterogeneous presentation of clinically assessed outcomes. The outcomes include psychogenic paralysis of limbs, left-side neglect, rectified sense of other locations, disorders of consciousness, symptoms related to autism spectrum disorder, Alzheimer's disease, schizophrenia, and depersonalization/derealization disorder. By consolidating evidence from neuroimaging, lesion-symptom mapping, and computational assessment, we aim to reconceptualize these disorders not as separate and independent maladies, but as manifestations of a deeper, shared etiology, supporting a network-based assessment strategy for diagnosis and treatment that seeks to restore self-in-space.

Studies of the microtubule-associated protein, tau suggest its promise as a potential target for epilepsy disease modification, but mechanisms underlying tau's effects on seizures are not well-defined. Acquired temporal lobe epilepsy (TLE) is the most prevalent form of focal epilepsy, yet the impact of tau expression on the process of TLE development is unexplored. We investigated tau's role in the epileptogenesis of acquired TLE using the intrahippocampal kainate (IHK) model in mice lacking tau expression (i.e., tau^-/- mice). We examined epileptiform activity during status epilepticus (SE) after IHK injection and assessed the subsequent development of spontaneous recurrent seizures (SRS) using video and video-electroencephalography (v-EEG). Results demonstrate that the lack of tau expression did not prevent evoked seizures or the development of TLE but reduced the number of convulsive seizures during SE and the severity of spontaneous seizures after TLE developed by suppressing epileptiform electrographic activity of convulsive seizures, which has not been shown in the context of an acquired TLE model. We assayed excitatory and inhibitory synaptic properties of dentate granule cells (DGCs) in the dorsal hippocampus using whole-cell patch clamp electrophysiology once TLE developed. Our results show that DGCs in tau^-/- mice receive significantly fewer spontaneous inhibitory synaptic current events than in wildtype controls and, after tau^-/- mice develop TLE, DGCs develop increased contralateral inhibitory input. The modified inhibitory synaptic neuroplasticity associated with acquired TLE development, which is consistent with altered EEG spectra during convulsive seizures, may contribute to modified spontaneous seizure expression. Deletion of tau expression therefore modifies seizure expression, potentially via mechanisms involving inhibitory synaptic circuits in the dentate gyrus but does not prevent epileptogenesis in a murine model of acquired TLE.

Introduction: Motor Imagery (MI) Electroencephalography (EEG)-based control in online Brain-Computer Interfaces requires decisions to be made within short temporal windows. However, the majority of published Deep Learning (DL) EEG decoders are developed and validated offline on public datasets using longer window lengths, leaving their real-time applicability unclear.

Methods: To address this gap, we evaluate 10 representative DL decoders, including convolutional neural networks (CNNs), filter-bank CNNs, temporal convolutional networks (TCNs), and attention- and Transformer-based hybrids-under a soft real-time protocol using 2-s windows. We quantify performance using accuracy, sensitivity, precision, miss-as-neutral rate (MANR), false-alarm rate (FAR), information-transfer rate (ITR), and workload. To relate decoder behavior to physiological markers, we examine lateralization indices, mu-band power at C3 vs. C4, and topographical contrasts between MI and neutral conditions.

Results: Results show shifts in performance ranking between offline and online BCI settings, along with a pronounced increase in inter-subject variability. Best online means were FBLight ConvNet 71.7% (±2.1) and EEG-TCNet 70.0% (±5.3), with attention/Transformer designs less stable. Errors were mainly Left-Right swaps while Neutral was comparatively stable. Lateralization indices/topomaps revealed subject-specific μ/β patterns consistent with class-wise precision/sensitivity.

Discussion: Compact spectro-temporal CNN backbones combined with lightweight temporal context (such as TCNs or dilated convolutions) deliver more stable performance under short-time windows, whereas deeper attention and Transformer architectures are more susceptible to variation across subjects and sessions. This study establishes a reproducible benchmark and provides actionable guidance for designing and calibrating online-first EEG decoders that remain robust under real-world, short-time constraints.

A theory of self-organization in the central nervous system is described, proposing that additive and dissipative synaptodendritic summation leads to synchronous oscillation as the equilibrium state, thereby underpinning a primary mechanism of prediction error minimization. As a consequence, synaptic connections become arranged in mirror-symmetric paired patterns, wherein exchanges of synaptic flux within each pattern form coupled spatial eigenmodes. The mirror-reflection axis between each pair functions as a Markov blanket that maintains excitatory-inhibitory equilibrium, while multiway exchanges among mirror pairs converge toward overall error minimization and mutual organization. The primary organization of this type is evident in the spinal cord. During cortical embryogenesis, connections develop in topographies interpretable as mirror reflections with broken symmetry, aligning along the radial and circumferential axes of cortical growth, as described by the Structural Model, and subsequently manifest at the millimetric scale throughout the cortex. The proposed framework integrates a diverse range of experimental data and provides an explanatory basis for how generative models with agency can emerge through both species evolution and individual learning.