Biology subjects

Canales-Johnson, A.

Publications and source records attributed to Canales-Johnson, A..

At least 19 recordsLinked to original sources

bioRxiv2026

Improved detection and spatiotemporal spectral analysis of neural traveling waves

Traveling waves (TWs) are a fundamental mode of neural dynamics, yet existing detection methods are limited by sensor geometry, spatial-frequency resolution, signal amplitude, and ambiguity between propagating and standing-wave patterns. Here we introduce the Traveling Wave Index (TWINDEX), a framework for three-dimensional spatiotemporal spectral analysis of TWs across temporal frequency, spatial frequency, and propagation direction. TWINDEX generalizes to irregular sensor layouts, and quantifies wave strength as the reduction in circular phase variance produced by a candidate planar wave. This normalization yields robust behavior at both low and high spatial frequencies and suppresses coherent in-phase activity. Directional moments further separate planar from standing waves. We derive analytical links between TWINDEX, parametric planar-wave fit, and distance-phase correlation, and introduce projected distance-phase correlation (ProDPC) for sensitive single-trial planar-wave detection. Applying these methods to large-scale marmoset ECoG and human EEG, we identify alpha/low-beta TWs localized in spatial and temporal frequency, with physiologically plausible propagation speeds. Across trials and epochs, waves occur in oppositely directed propagation modes, demonstrating that alpha/beta activity is associated with both feedforward- and feedback-directed large-scale dynamics.

neuroscience↗

bioRxiv2026

Information Flow and Traveling Waves show distinct cortical trajectories

Traveling waves (TWs) are a hallmark of cortical dynamics, proposed to organize neuronal activity across space and time. Yet their propagation can oppose the causal flow of information, challenging their functional interpretation. We demonstrate this using computational simulations and validate our predictions in electrophysiological recordings. We introduce the Directional Information Flow Field (DIFF), which adapts Granger causality to two-dimensional neural recordings to map directed interactions across cortical networks. In computational models, DIFF recovers the direction and sources of information flow, whereas delays and inhibition can cause TWs to propagate in opposing or orthogonal directions. We validate these findings in human and non-human primate electrophysiological recordings, revealing reversed or orthogonal flow axes, distinct spatial origins, and frequency-dependent differences in feedforward and feedback dynamics. These findings challenge the functional interpretation of TWs and establish DIFF as a complementary framework for revealing directed neural communication underlying cortical dynamics.

neuroscience↗

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.

neuroscience↗

bioRxiv2026

Thalamic and cortical signals synergistically represent auditory prediction errors

Prediction errors (PEs) are commonly described as cortical signals generated within sensory hierarchies, but whether the thalamus participates in their encoding and transmission remains unclear. We recorded Local Field Potentials (LFP) from the medial and lateral geniculate nuclei and electrocorticography (ECoG) from multiple cortical regions in three awake cats during two auditory prediction tasks. Mutual information (MI) analyses revealed PE encoding in both thalamic and cortical signals. Co-information (co-I) analyses showed off-diagonal temporal synergy between early and later thalamic response components, consistent with an early response inducing a neural state change that shaped the informational content of subsequent activity. Multivariate co-information (MVCo-I) further revealed that thalamic and cortical population activity carried complementary PE information unavailable from either thalamic or cortical areas alone. These synergistic interactions were reliable across animals for violations of structured auditory sequences and weaker for repetition-based deviants. These findings show that auditory PEs are not simply relayed or duplicated across the thalamocortical hierarchy. Instead, they emerge through state-dependent transformations within the thalamus and complementary interactions between thalamic and cortical populations, identifying the thalamus as an active node of context-dependent PE processing.

neuroscience↗

bioRxiv2026

Attention reshapes the information dynamics of thalamic and cortical prediction-error learning

Prediction errors (PEs) drive perceptual learning by updating internal models of the sensory environment, yet it remains unclear how attention reshapes their representation across distributed thalamocortical circuits. Using intracranial stereoelectroencephalography (sEEG) from 17 patients performing a roving auditory oddball task under attended and unattended conditions, we quantified PE encoding using mutual information and co-information to capture redundant and synergistic PE representations. Attention modulated PE encoding in both the thalamus and the temporal cortex, but with distinct informational dynamics. Thalamic encoding showed a stable reduction of PE information during distraction, consistent with state-dependent thalamocortical gating. In contrast, the temporal cortex expressed two opposing learning trajectories during attended listening that converged once attention was diverted, revealing distinct cortical learning regimes rather than a uniform attentional effect. Attention further reorganized the informational content of cortical PE representations by altering the balance between redundant and synergistic information. A biologically constrained neural network showed that attention-dependent changes in inhibition and long-range connectivity reproduced these dynamics through Hebbian learning. Together, these findings suggest that attention regulates predictive learning not simply by changing the strength of PE responses, but by reshaping how distributed thalamocortical circuits represent and integrate sensory evidence over time.

neuroscience↗

bioRxiv2025

Neural information sharing across the orienting attentional network predicts intelligence in children

Efficient brain functioning is often defined as the ability to achieve high performance with minimal cognitive resources. This study investigated the relationship between intelligence and attentional network efficiency in school-aged children, using electroencephalography (EEG) during the Attention Network Test (ANT). Participants were 38 children aged 11-14 years, recruited from schools in the Maule Region of Chile. Attentional network efficiency was assessed through event-related potentials (ERPs), midfrontal theta power as an index of conflict processing, and weighted Symbolic Mutual Information (wSMI) to quantify large-scale, nonlinear information sharing. Higher full-scale IQ scores were specifically associated with reduced wSMI within the orienting network, suggesting greater neural efficiency through less widespread information exchange between dorsal frontoparietal nodes. No significant associations were found between IQ and theta-band power during conflict processing. These findings provide novel evidence linking intelligence in childhood to network-level neural efficiency in attentional orienting, supporting the view that individual differences in cognitive ability reflect not only localized neural activity but also the efficiency of information integration within task-relevant networks.

neuroscience↗

bioRxiv2025

Distilling the neurophenomenological signatures of pure awareness during Transcendental Meditation

Pure awareness (PA) has been proposed as a form of minimal phenomenal experience, but its neurophenomenological signatures remain poorly characterized. Transcendental Meditation (TM) offers a particularly tractable empirical model of PA because its procedure is standardized, its induction is effortless, and it reliably elicits reports of awareness with minimal content. We combined electroencephalography (EEG) with Temporal Experience Tracing in 33 experienced TM practitioners and their matched controls (performing mental counting). TM practitioners reported significantly greater intensity and temporal variability of PA, independent of years of meditation practice. We then used multivariate classification of theoretically motivated EEG markers spanning temporal entropy, aperiodic activity, complexity, and linear and nonlinear functional connectivity. We observed a double dissociation. When TM was contrasted with counting, temporal entropy and aperiodic dynamics were the strongest discriminators, whereas phase-coherence functional connectivity contributed least. Conversely, when TM was contrasted with its own baseline, low-frequency functional connectivity dominated, whereas temporal entropy contributed minimally. Complementary topographical analyses indicated that these differences were not reducible to a few localized univariate effects, but were better understood as distributed multivariate neural patterns. Finally, TM showed little evidence of carryover into subsequent rest, whereas counting induced more residual change. Together, these findings provide a systematic electrophysiological characterization of PA and support neurophenomenology as a tractable framework for studying minimal phenomenal experience.

neuroscience↗

bioRxiv2025

Distinct developmental changes in linear and nonlinear neural interactions across infancy and adulthood

The development of functional brain connectivity during early life depends on social experience and is best understood in the context of interactions with a caregiver. It is still an open question to what extent distinct modes of functional connectivity dominate in different stages of human brain development, and whether cognitive tasks can differentially modulate them. Using electroencephalography (EEG), we investigated the development of linear and nonlinear functional brain connectivity in infants and adults while they socially interacted. Using simultaneous EEG recordings in parent-infant dyads performing two different experiments (N = 160; 80 adults and 80 infants), we computed functional connectivity capturing distinct dynamics: a linear measure capturing phase synchronization (weighted phase lag index; WPLI), and a nonlinear measure capturing information sharing (weighted symbolic mutual information; WSMI). In both tasks, adults showed higher WSMI than infants, whereas infants showed higher WPLI than adults. Moreover, infant age predicted only task-related connectivity values computed with the nonlinear measure and not with its linear counterpart, suggesting that the information-theoretic measure was more sensitive to developmental changes in task-relevant neural processing. These findings suggest that over development, a shift in dominance from linear to nonlinear modes of brain communication may be essential for supporting emerging higher cognitive abilities, such as precursors to executive function (here, attention shifting) and social decision-making. Further, this work highlights the importance of using nonlinear measures in addition to traditional linear ones, which collectively permit a more robust capture of maturational changes.

neuroscience↗

bioRxiv2025

Broadband synergy versus oscillatory redundancy in the visual cortex

The cortex generates diverse neural dynamics, ranging from broadband fluctuations to narrowband oscillations at specific frequencies. Here, we investigated whether broadband and oscillatory dynamics play different roles in the encoding and transmission of visual information. We used information-theoretical measures to dissociate neural signals sharing common information (i.e., redundancy) from signals encoding complementary information (i.e., synergy). We analyzed electrocorticography (ECoG) and local field potentials (LFP) in the visual cortex of human and non-human primates (macaque) to investigate the extent to which broadband signals (BB) and narrowband gamma (NBG) oscillations conveyed synergistic or redundant information about images. In both species, the information conveyed by BB signals was highly synergistic within and between visual areas. By contrast, the information carried by NBG was primarily redundant within and between the same visual areas. Finally, the information conveyed by BB signals emerged early after stimulus onset, while NBG sustained information at later time points. These results suggest a potential dual role of BB and NBG cortical dynamics in visual processing, with broadband dynamics supporting nonlinear pattern recognition and oscillations facilitating information maintenance across the cortex.

neuroscience↗

bioRxiv2024

Reduced and Redundant: Information Processing of Prediction Errors during Sleep

During sleep, the human brain transitions to a sentinel processing mode, enabling the continued processing of environmental stimuli despite the absence of consciousness. Here, we employed advanced information-theoretic analyses, including mutual information (MI) and co-information (co-I), alongside event-related potential (ERP) and temporal generalization analyses (TGA), to characterize auditory prediction error processing across wakefulness and sleep. We hypothesized that a shared neural code would be present across sleep stages, with deeper sleep being associated with reduced information content and increased information redundancy. To investigate this, twenty-nine young healthy participants were exposed to an auditory local-global oddball paradigm during wakefulness and during an 8-hour sleep opportunity monitored via polysomnography in a cross-sectional study. We focused on local mismatch responses to a deviating fifth tone following four standard tones. ERP analyses showed that prediction error processing continued throughout all sleep stages (N1-N3, REM). Mutual information analyses revealed a substantial reduction in the amount of encoded prediction error information particularly during N3 and REM, although ERP amplitudes increased with deeper NREM sleep. In addition, we observed delayed information encoding during sleep and co-information analyses showed that neural dynamics became increasingly redundant with increasing sleep depth. Temporal generalisation analyses revealed a largely shared neural code between N2 and N3 sleep, although it differed between wakefulness and sleep. Here, we showed how the neural code of the sentinel processing mode changes from wake to light to deep sleep and REM, characterised by delayed processing, more redundant and less rich neural information in the human cortex as consciousness wanes. This altered stimulus processing reveals how neural information changes with the changes of consciousness states as we traverse the night.

neuroscience↗

bioRxiv2024

Integrated phenomenology and brain connectivity demonstrate changes in nonlinear processing in jhana advanced meditation

We present a neurophenomenological case study investigating distinct neural connectivity regimes during an advanced concentrative absorption meditation called jhana (ACAM-J),characterized by highly-stable attention and mental absorption. Using EEG recordings and phenomenological ratings (29 sessions) from a meditator with +20,000 hours of practice, we evaluated connectivity metrics tracking distinct large-scale neural interactions: nonlinear (WSMI and Directed Information), capturing non-oscillatory dynamics; and linear (WPLI) connectivity metrics, capturing oscillatory synchrony. Results demonstrate ACAM-J are better distinguished by non-oscillatory compared to oscillatory dynamics across multiple frequency ranges. Furthermore, combining attention-related phenomenological ratings with WSMI improves Bayesian decoding of ACAM-J compared to neural metrics alone. Crucially, deeper ACAM-J indicate an equalization of feedback and feedforward processes, suggesting a balance of internally- and externally-driven information processing. The results from this intensively sampled case study are a promising initial step in revealing the distinct neural dynamics during ACAM-J, offering insights into refined conscious states and highlighting the value of nonlinear neurophenomenological approaches to studying attentional states.

neuroscience↗

bioRxiv2024

Breathwork-Induced Psychedelic Experiences Modulate Neural Dynamics

Breathwork is an understudied school of practices involving intentional respiratory modulation to induce an altered state of consciousness (ASC). We simultaneously investigate the phenomenological and neural dynamics of breathwork by combining Temporal Experience Tracing, a quantitative methodology that preserves the temporal dynamics of subjective experience, with low-density portable EEG devices. Fourteen novice participants completed a course of up to 28 breathwork sessions - of 20, 40 or 60 minutes - in 28 days, yielding a neurophenomenological dataset of 301 breathwork sessions. Using hypothesis-driven and data-driven approaches, we found that psychedelic-like subjective experiences were associated with increased neural Lempel-Ziv complexity during breathwork. Exploratory analyses showed that the aperiodic exponent of the power spectral density - but not oscillatory alpha power - yielded similar neurophenomenological associations. Non-linear neural features, like complexity and the aperiodic exponent, neurally map both a multidimensional data-driven composite of positive experiences, and hypothesis-driven aspects of psychedelic-like experience states such as high bliss.

neuroscience↗

bioRxiv2023

High arousal disrupts the neural signatures of conflict processing but not behavioural performance

Physical activation fluctuates throughout the day. Previous studies have shown that during periods of reduced activation, cognitive control remains resilient due to neural compensatory mechanisms. In this study, we in-vestigated the effects of high physical activation on both behavioural and neural markers of cognitive control. We hypothesized that while behavioural measures of cognitive control would remain intact during periods of high activation, there would be observable changes in neural correlates. In our electroencephalography study, we manipulated levels of physical activation through physical exercise. Although we observed no significant impact on behavioural measures of cognitive conflict, both univariate and multivariate time-frequency markers proved unreliable under conditions of high activation. Moreover, there was no modulation of whole-brain connectivity measures by physical activation. We suggest that this dissociation between behavioural and neural measures indicates that the human cognitive control system remains resilient even at high activation, possibly due to underlying neural compensatory mechanisms.

neuroscience↗

bioRxiv2023

Slow-wave brain connectivity predicts executive functioning and group belonging in socially vulnerable individuals

Important efforts have been made to describe the neural and cognitive features of healthy and clinical populations. However, the neural and cognitive features of socially vulnerable individuals remain largely unexplored, despite their proneness to developing neurocognitive disorders. Socially vulnerable individuals can be characterised as socially deprived, having a low socioeconomic status, suffering from chronic social stress, and exhibiting poor social adaptation. While it is known that such individuals are likely to perform worse than their peers on executive function tasks, studies on healthy but socially vulnerable groups are lacking. In the current study, we explore whether neural power and connectivity signatures can characterise executive function performance in healthy but socially vulnerable individuals, shedding light on the impairing effects that chronic stress and social disadvantages have on cognition. We measured resting-state electroencephalography and executive functioning in 38 socially vulnerable participants and 38 matched control participants. Our findings indicate that while neural power was uninformative, lower delta and theta phase synchrony are associated with worse executive function performance in all participants, whereas delta phase synchrony is higher in the socially vulnerable group compared to the control group. Finally, we found that delta phase synchrony and years of schooling are the best predictors for belonging to the socially vulnerable group. Overall, these findings suggest that exposure to chronic stress due to socioeconomic factors and a lack of education are associated with changes in slow-wave neural connectivity and executive functioning.

neuroscience↗

bioRxiv2023

Principles for Optimal Window Size Selection for Infant and Adult EEG Connectivity Analysis

Neural connectivity analysis is often performed on continuous data that has been discretized into temporal windows of a fixed length. However, the selection of an optimal window length is non-trivial, and depends on the properties of the connectivity metric being used as well as the effects of interest within the data (e.g. developmental or inter-brain effects). A systematic investigation of these factors, and objective criteria for window size selection are currently missing in the literature, particularly in regard to pediatric datasets. Here, we provide a principled examination of the effect of window size on optimization of signal to noise ratio for linear and non-linear EEG connectivity, as applied to infant, adult and dyadic (infant-adult) datasets. We employed a linear weighted phase lag index (wPLI), and a nonlinear weighted symbolic mutual information (wSMI) metric to assess brain connectivity for each dataset. Our results showed a clear polar dissociation between linear and non-linear metrics, as well as between infant and adult datasets in optimal window size. Further, optimal dyadic (infant-adult) window size settings defaulted to one or the partner rather than reflecting an intermediate compromise. Given the specificity of these results (i.e. there was no single window size that was optimal for all contrasts), we conclude that a formal analysis of optimal window size may be useful prior to conducting any new connectivity analysis. Here, we recommend guiding principles, performance metrics and decision criteria for optimal and unbiased window size selection.

neuroscience↗

bioRxiv2023

Staying in control: characterising the mechanisms underlying cognitive control in high and low arousal states

Throughout the day, humans show natural fluctuations in arousal that impact cognitive function. To study the behavioural dynamics of cognitive control during high and low arousal states, healthy participants performed an auditory conflict task during high-intensity physical exercise (N = 39) or drowsiness (N = 33). In line with the pre-registered hypotheses, conflict and conflict adaptation effects were preserved during both altered arousal states. Overall task performance was markedly poorer during low arousal, but not for high arousal. Modelling behavioural dynamics with drift-diffusion analysis revealed evidence accumulation and non-decision time decelerated, and decisional boundaries became wider during low arousal, whereas high arousal was unexpectedly associated with a decrease in the interference of task-irrelevant information processing. These findings show how arousal differentially modulates cognitive control at both sides of normal alertness, and further validates drowsiness and physical exercise as key experimental models to disentangle the interaction between physiological fluctuations on cognitive dynamics.

neuroscience↗

bioRxiv2023

Neural and behavioural dynamics of the individual and collective self(s)

Usually considered as internal representations of self-concepts, the individual-self and the collective-self have been primarily studied in social and personality psychology while the experimental and theoretical advances of the cognitive and neurophysiological mechanisms of these self-representations are poorly understood. Two competing hypotheses emerge to understand their structure: first, each self-representation corresponds to a specific and independent dimension of self-concept and is therefore conceptualized as separate cognitive components and different brain networks are predicted; and second, both "selfs", collective and individual, are part of the same structure and interdependent, sharing similar networks and showing a hierarchical organization from a core-self. Both perspectives have some support from current theories in social psychology but are still speculative and faintly supported by empirical evidence. To test this, we designed an experiment using sentences that would activate the individual or collective-self representations in 80 healthy right-handed participants. We use reaction times during a decision-making task, in combination with individualism/collectivism scales and characterize the neural dynamics throughout the experiment using event-related potentials and fronto-parietal informational connectivity networks. Participants reacted slower to the collective than individual self conditions and showed differences in neural activity and information Integration level that distinguished between each type of self. More importantly, the neural integration measure representing the core-self (subtraction between individual and collective wSMI) was only associated with the individualism scores but not collectivism, lending further support for the core-self perspective. We interpret that the collective self, in the broader sense, is a part of the self-concept and therefore probably assembled from the core-self.

animal behavior and cognition↗

bioRxiv2023

Distributed representations of prediction error signals across the cortical hierarchy are synergistic

An important question concerning inter-areal communication in the cortex is whether these interactions are synergistic, i.e. brain signals can either share common information (redundancy) or they can encode complementary information that is only available when both signals are considered together (synergy). Here, we dissociated cortical interactions sharing common information from those encoding complementary information during prediction error processing. To this end, we computed co-information, an information-theoretical measure that distinguishes redundant from synergistic information among brain signals. We analyzed auditory and frontal electrocorticography (ECoG) signals in five common awake marmosets performing two distinct auditory oddball tasks and investigated to what extent event-related potentials (ERP) and broadband (BB) dynamics encoded redundant and synergistic information during auditory prediction error processing. In both tasks, we observed multiple patterns of synergy across the entire cortical hierarchy with distinct dynamics. The information conveyed by ERPs and BB signals was highly synergistic even at lower stages of the hierarchy in the auditory cortex, as well as between auditory and frontal regions. Using a brain-constrained neural network, we simulated the spatio-temporal patterns of synergy and redundancy observed in the experimental results and further demonstrated that the emergence of synergy between auditory and frontal regions requires the presence of strong, long-distance, feedback and feedforward connections. These results indicate that the distributed representations of prediction error signals across the cortical hierarchy can be highly synergistic.

neuroscience↗