2026-03-20
Daniela Esteves, Athanasios Vourvopoulos
IntroductionThe sense of embodiment (SoE), describing the experience of owning, controlling, and being located within a body, underpins virtual reality (VR) interaction, brain-computer interfaces (BCIs), and multisensory body-illusion research. Although SoE is typically assessed through subjective questionnaires, their variability and limited validity have motivated the search for objective neural markers. Electroencephalography (EEG) has become the most widely used technique given its portability and high temporal resolution; however, the existence of a consistent EEG correlate of embodiment remains unclear.MethodsThis systematic review summarizes 35 EEG studies (2010–June 2025) identified through structured database searches, examining SoE across immersive and non-immersive VR, augmented reality, and non-VR paradigms. We analyze EEG features including spectral power, event-related desynchronization/synchronization (ERD/ERS), connectivity, and temporal dynamics, and examine methodological variability in illusion induction and SoE assessment.ResultsAcross studies, the reduction of the alpha-band over central-parietal regions emerges as the most recurrent correlate of embodiment. Beta-band decreases and gamma-band increases appear in several studies but lack consistent replication, while findings in Delta and Theta bands remain sparse and contradictory. Considerable heterogeneity is found in VR paradigms, EEG setups, preprocessing, and psychometric tools, contributing to inconsistent results and limiting cross-study comparability.DiscussionCritically, no EEG feature demonstrates sufficient reproducibility to qualify as a universal biomarker of SoE, and no standardized protocol for EEG-based embodiment assessment currently exists. Overall, this review highlights both the promise and current limitations of EEG-based approaches to measuring embodiment. We conclude by identifying methodological gaps and outlining recommendations to support the development of reliable EEG markers for future applications in VR rehabilitation, MI-BCIs, cognitive neuroscience, and clinical interventions.
DOI: 10.3389/fnsys.2026.17564072026-03-17
Martin A. Raymond, Ian F. Chapman, Stephanie M. Staszko, Max L. Fletcher, John D. Boughter
Taste-responsive neurons in the gustatory cortex (GC) have been shown to encode multiple properties of stimuli, including whether they are palatable or not. Previous studies have suggested that a form of taste-involved learning, conditioned taste aversion (CTA), may alter the cortical representation of taste stimuli in a number of ways. We used miniscopes to image taste responses from a large population of neurons in the gustatory cortex of mice before and after CTA to NaCl, comparing taste responses in control and conditioned mice. Following conditioning, no significant effects on the number of responsive cells, or the magnitude of response to either NaCl or other taste stimuli were found. However, population-level analyses showed that in mice receiving a CTA, the representation of NaCl diverged from other appetitive stimuli in neural space and moved closer to that of aversive Quinine. We also tracked the extinction of the CTA in a subset of animals and showed that as NaCl became less aversive, the neural pattern reverted to match the behavior. These data suggest that the predominant function of the taste representation in GC is palatability; the neuronal response pattern to stimuli at the population level reflects the decision of the animal to consume or not consume the stimulus, regardless of quality or chemical identity.
DOI: 10.3389/fnsys.2026.17652042026-03-17
Eric M. Rouiller
A cohort of 13 adult macaques offered a unique opportunity to collect over several years manual dexterity data, from an initial learning phase in intact animals to a terminal phase of functional recovery after unilateral lesion of primary motor cortex (M1). Manual dexterity was assessed daily using the modified Brinkman Board task, yielding a total score given by the number of food pellets retrieved by one or the other hand from vertical and horizontal slots. A motor learning curve slope was established during the initial learning phase before reaching a stable performance with the dominant hand. Later, following contralateral M1 lesion, the manual dexterity score dropped to zero, before a progressive spontaneous functional recovery occurred, reaching a unique plateau of usually incomplete recovery. A recovery curve slope was calculated. In six of the 13 monkeys, a treatment aimed at enhancing the functional recovery of manual dexterity was applied, yielding a second plateau of recovery added to the first spontaneous recovery plateau. A recovery curve slope was also calculated for the second plateau. The hypothesis that steep initial motor learning is correlated with rapid and efficient functional recovery after M1 lesion was tested. In contradiction to this hypothesis, the data showed an inverse correlation with decreasing recovery curve slopes as a function of increasing learning curve slopes. This result suggests that the mechanisms underlying initial motor learning may be different from those mobilized for functional recovery after M1 lesion.
DOI: 10.3389/fnsys.2026.17547602026-03-10
Kuangfu Hsiao
The thalamic reticular nucleus orchestrates thalamocortical oscillations and sensory gating. Its early development features a unique confluence of depolarizing GABA signaling, immature chloride regulation, and transient electrical coupling via connexin-36 gap junctions. These developmental specializations, essential for synchronizing cortical maturation, also render thalamocortical networks vulnerable to hypoxic–ischemic insults such as perinatal asphyxia or pediatric cardiac arrest. Following cellular ATP depletion, rapid chloride imbalance eliminates fast synaptic inhibition, permitting abnormal network activity to propagate via gap-junction coupling that persists when chemical inhibition collapses. The resulting electrical hypersynchrony, exacerbated by depolarizing GABAergic currents and impaired chloride extrusion, promotes excitotoxicity and thalamocortical dysrhythmia. This review synthesizes recent evidence to establish a framework that accounts for the selective vulnerability of the immature brain. Understanding these mechanisms may inform strategies to preserve developmental integrity and promote circuit resilience after pediatric asphyxial events.
DOI: 10.3389/fnsys.2026.17535622026-03-09
Ryo Koshizawa, Zdeněk Ledvina, Jakub Pospíšil, Ondřej Peleška
IntroductionPredictive gaze behavior is essential in fast-paced esport environments; however, the visuomotor and neural mechanisms supporting predictive saccades in competitive first-person shooter (FPS) players remain insufficiently understood. This study investigated whether FPS-specialized players exhibit enhanced predictive saccade strategies compared to individuals without competitive FPS experience.MethodsSeventeen active gamers were assigned to either an FPS-specialized group (n = 6) or a non-FPS group (n = 11). Participants performed a target-arrival prediction task in which a parabolically moving target was occluded midway through its trajectory. They were instructed to fixate on the starting point, execute a predictive saccade toward the internally estimated arrival position, maintain fixation, and press a button at their judged arrival time. Position Error (PE) was derived from gaze and button-press data. Low-beta (12–16 Hz) electroencephalography (EEG) activity was extracted using the Hilbert transform and group differences were assessed using time-series statistics and cluster-based permutation testing.ResultsThe FPS-specialized group exhibited earlier emergence of predictive gaze shifts toward the anticipated arrival position and demonstrated substantially smaller spatial prediction errors, including reduced PE values. These behavioral advantages were accompanied by increased low-beta activity in right Brodmann area (BA) 7, left BA40, and left BA6—regions, associated with spatial prediction, visuomotor integration, and predictive motor planning.ConclusionThese findings suggest that competitive FPS experience cultivates a coordinated visuomotor prediction system that supports earlier initiation and improved accuracy of predictive saccade behavior.
DOI: 10.3389/fnsys.2026.17759732026-03-03
Hu-Cheng Yang, Si-Yu Gu, Hai-Hua Sun, Yuan-Ying Song, Feng-Mei Zhang, Zhen-Yu Dai, Ping-Lei Pan
BackgroundApathy is a prevalent and debilitating neuropsychiatric syndrome in Parkinson’s disease (PD). While numerous functional and structural brain studies have investigated the neural correlates of PD with apathy (PD-A), their findings have often been inconsistent. Network neuroscience suggests that such a syndrome may be best understood as disruptions of distributed brain networks.MethodsWe conducted a systematic review to identify whole-brain studies reporting functional or structural alterations in patients with PD-A compared to those without apathy (PD-NA), or studies correlating apathy severity. Significant peak coordinates (195 foci from 24 studies) were integrated using functional connectivity network mapping (FCNM), leveraging resting-state functional magnetic resonance imaging from 1,093 healthy Human Connectome Project (HCP) participants. We quantified spatial overlap between the PD-A-associated network and canonical brain networks.ResultsThe FCNM analysis revealed that the spatially diverse brain regions previously reported in the PD-A literature converged onto a common functional connectivity network. This network predominantly involved the bilateral inferior frontal gyrus, bilateral anterior insula, bilateral dorsolateral prefrontal cortex, bilateral caudate nucleus, and bilateral thalamus. The PD-A associated network showed the highest spatial overlap with the ventral attention network (VAN; 34.05%), subcortical network (28.47%), and frontoparietal network (FPN; 24.89%). Robustness analyses confirmed these findings.ConclusionBrain functional and structural correlates of apathy in PD converge on distributed networks involving the VAN, FPN, and subcortical circuits. Our network localization approach offers a unifying neurobiological framework for apathy in PD, potentially reconciling previous inconsistencies and informing the development of network-targeted interventions.
DOI: 10.3389/fnsys.2026.17244212026-01-23
J. J. Joshua Davis, Florian Schübeler, Ian J. Kirk, Robert Kozma
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.
DOI: 10.3389/fnsys.2025.17187332026-01-22
Gordana Dodig-Crnkovic
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.
DOI: 10.3389/fnsys.2026.17300972026-01-16
Almira Kustubayeva, Manzura Zholdassova, Altyngul Kamzanova, Zabira Madaliyeva, Aigul Suleimenova, Sultangali Nessipbayev, Gulnur Borbassova, Diana Arman, Erik Nelson, Gerald Matthews
ObjectiveBehavioral 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.MethodsParticipants 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.ResultsBoth 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.ConclusionNeurocognitive deficits in depression may relate to depletion of a general attentional resource.
DOI: 10.3389/fnsys.2025.16741242026-01-13
K. L. Kirkpatrick
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.
DOI: 10.3389/fnsys.2025.16954932026-01-13
Valentina Cesari, Enrico Cipriani, Giorgia Papini, Andrea Piarulli, Angelo Gemignani, Danilo Menicucci
DOI: 10.3389/fnsys.2025.1777499