Modulatory dynamics of periodic and aperiodic activity in respiration-brain coupling.

Bodily rhythms such as respiration are increasingly acknowledged to modulate neural oscillations underlying human action, perception, and cognition. Conversely, the link between respiration and aperiodic brain activity - a non-oscillatory reflection of excitation-inhibition (E:I) balance - has remained unstudied. Aiming to disentangle potential respiration-related dynamics of periodic and aperiodic activity, we applied recently developed algorithms of time-resolved parameter estimation to resting-state MEG and EEG data from two labs (total N = 78 participants). We provide evidence that fluctuations of aperiodic brain activity (1/f slope) are phase-locked to the respiratory cycle, which suggests that spontaneous state shifts of excitation-inhibition balance are at least partly influenced by peripheral bodily signals. Moreover, differential temporal dynamics in their coupling to non-oscillatory and oscillatory activity raise the possibility of a functional distinction in the way each component is related to respiration. Our findings highlight the role of respiration as a physiological influence on brain signalling.

Fluctuations in alpha and beta power provide neural states favourable for contextually relevant anticipatory processes.

Cued sensory input occasionally fails to immediately ensue its respective trigger. Given that our environments are rich in sensory cues, we often end up processing other contextually relevant information in the meantime. The experimental design of the present study allowed us to investigate how such temporal delays and visual interferences may impact anticipatory processes. Thirty-four participants were trained to remember an individualised set of eight paired-up faces. These paired-up faces were presented pseudorandomly in sequences of unpaired face images. To keep participants engaged throughout the electroencephalography study, they were instructed to classify each face image, according to its sex, as fast as possible without compromising accuracy. We observed dissimilar modulations in alpha and beta power between the 6-s timeframe encompassing the onsets of predictive and expected images (temporal delay block) and the 6-s timeframe encompassing the predictive, interference and expected images (visual interference block). Furthermore, an expectation-facilitated reduction of the face-sensitive N170 component was only observed if an anticipated face image directly followed its corresponding predictive counterpart. This effect was no longer evident when the expected face was preceded by a distracting face image. Regardless of the block type, behavioural measures confirmed that anticipated faces were classified significantly faster and with fewer erroneous responses than faces not foretold by a predictive face. Collectively, these results demonstrate that whilst the brain continuously adjusts internal hierarchical generative models to account for temporal delays in stimulus onset and visual interferences, the higher levels, and subsequent predictions, fundamental for expectation-facilitated behaviours remain intact.

Predictive coordination of breathing during intra-personal speaking and listening.

It has long been known that human breathing is altered during listening and speaking compared to rest: during speaking, inhalation depth is adjusted to the air volume required for the upcoming utterance. During listening, inhalation is temporally aligned to inhalation of the speaker. While evidence for the former is relatively strong, it is virtually absent for the latter. We address both phenomena using recordings of speech envelope and respiration in 30 participants during 14 min of speaking and listening to one's own speech. First, we show that inhalation depth is positively correlated with the total power of the speech envelope in the following utterance. Second, we provide evidence that inhalation during listening to one's own speech is significantly more likely at time points of inhalation during speaking. These findings are compatible with models that postulate alignment of internal forward models of interlocutors with the aim to facilitate communication.

Spatiotemporal dynamics characterise spectral connectivity profiles of continuous speaking and listening.

Speech production and perception are fundamental processes of human cognition that both rely on intricate processing mechanisms that are still poorly understood. Here, we study these processes by using magnetoencephalography (MEG) to comprehensively map connectivity of regional brain activity within the brain and to the speech envelope during continuous speaking and listening. Our results reveal not only a partly shared neural substrate for both processes but also a dissociation in space, delay, and frequency. Neural activity in motor and frontal areas is coupled to succeeding speech in delta band (1 to 3 Hz), whereas coupling in the theta range follows speech in temporal areas during speaking. Neural connectivity results showed a separation of bottom-up and top-down signalling in distinct frequency bands during speaking. Here, we show that frequency-specific connectivity channels for bottom-up and top-down signalling support continuous speaking and listening. These findings further shed light on the complex interplay between different brain regions involved in speech production and perception.

Breathing in waves: Understanding respiratory-brain coupling as a gradient of predictive oscillations.

Breathing plays a crucial role in shaping perceptual and cognitive processes by regulating the strength and synchronisation of neural oscillations. Numerous studies have demonstrated that respiratory rhythms govern a wide range of behavioural effects across cognitive, affective, and perceptual domains. Additionally, respiratory-modulated brain oscillations have been observed in various mammalian models and across diverse frequency spectra. However, a comprehensive framework to elucidate these disparate phenomena remains elusive. In this review, we synthesise existing findings to propose a neural gradient of respiratory-modulated brain oscillations and examine recent computational models of neural oscillations to map this gradient onto a hierarchical cascade of precision-weighted prediction errors. By deciphering the computational mechanisms underlying respiratory control of these processes, we can potentially uncover new pathways for understanding the link between respiratory-brain coupling and psychiatric disorders.

Speech onsets and sustained speech contribute differentially to delta and theta speech tracking in auditory cortex.

When we attentively listen to an individual's speech, our brain activity dynamically aligns to the incoming acoustic input at multiple timescales. Although this systematic alignment between ongoing brain activity and speech in auditory brain areas is well established, the acoustic events that drive this phase-locking are not fully understood. Here, we use magnetoencephalographic recordings of 24 human participants (12 females) while they were listening to a 1 h story. We show that whereas speech-brain coupling is associated with sustained acoustic fluctuations in the speech envelope in the theta-frequency range (4-7 Hz), speech tracking in the low-frequency delta (below 1 Hz) was strongest around onsets of speech, like the beginning of a sentence. Crucially, delta tracking in bilateral auditory areas was not sustained after onsets, proposing a delta tracking during continuous speech perception that is driven by speech onsets. We conclude that both onsets and sustained components of speech contribute differentially to speech tracking in delta- and theta-frequency bands, orchestrating sampling of continuous speech. Thus, our results suggest a temporal dissociation of acoustically driven oscillatory activity in auditory areas during speech tracking, providing valuable implications for orchestration of speech tracking at multiple time scales.

Multivariate analysis of speech envelope tracking reveals coupling beyond auditory cortex.

The systematic alignment of low-frequency brain oscillations with the acoustic speech envelope signal is well established and has been proposed to be crucial for actively perceiving speech. Previous studies investigating speech-brain coupling in source space are restricted to univariate pairwise approaches between brain and speech signals, and therefore speech tracking information in frequency-specific communication channels might be lacking. To address this, we propose a novel multivariate framework for estimating speech-brain coupling where neural variability from source-derived activity is taken into account along with the rate of envelope's amplitude change (derivative). We applied it in magnetoencephalographic (MEG) recordings while human participants (male and female) listened to one hour of continuous naturalistic speech, showing that a multivariate approach outperforms the corresponding univariate method in low- and high frequencies across frontal, motor, and temporal areas. Systematic comparisons revealed that the gain in low frequencies (0.6 - 0.8 Hz) was related to the envelope's rate of change whereas in higher frequencies (from 0.8 to 10 Hz) it was mostly related to the increased neural variability from source-derived cortical areas. Furthermore, following a non-negative matrix factorization approach we found distinct speech-brain components across time and cortical space related to speech processing. We confirm that speech envelope tracking operates mainly in two timescales (δ and θ frequency bands) and we extend those findings showing shorter coupling delays in auditory-related components and longer delays in higher-association frontal and motor components, indicating temporal differences of speech tracking and providing implications for hierarchical stimulus-driven speech processing.

The Role of Soluble Urokinase Plasminogen Activator Receptor (suPAR) in the Context of Aneurysmal Subarachnoid Hemorrhage (aSAH)-A Prospective Observational Study.

Objective: Outcome after aneurysmal subarachnoid hemorrhage (aSAH) is highly variable and largely determined by early brain injury and delayed cerebral ischemia (DCI). Soluble urokinase plasminogen activator receptor (suPAR) represents a promising inflammatory marker which has previously been associated with outcome in traumatic brain injury and stroke patients. However, its relevance in the context of inflammatory changes after aSAH is unclear. Here, we aimed to characterize the role of circulating suPAR in both serum and cerebrospinal fluid (CSF) as a novel biomarker for aSAH patients. Methods: A total of 36 aSAH patients, 10 control patients with unruptured abdominal aneurysm and 32 healthy volunteers were included for analysis. suPAR was analyzed on the day of admission in all patients. In aSAH patients, suPAR was also determined on the day of DCI and the respective time frame in asymptomatic patients. One- and two-sample t-tests were used for simple difference comparisons within and between groups. Regression analysis was used to assess the influence of suPAR levels on outcome in terms of modified Rankin score. Results: Significantly elevated suPAR serum levels (suPAR-SL) on admission were found for aSAH patients compared to healthy controls, but not compared to vascular control patients. Disease severity as documented according to Hunt and Hess grade and modified Fisher grade was associated with higher suPAR CSF levels (suPAR-CSFL). In aSAH patients, suPAR-SL increased daily by 4%, while suPAR-CSFL showed a significantly faster daily increase by an average of 22.5% per day. Each increase of the suPAR-SL by 1 ng/ml more than tripled the odds of developing DCI (OR = 3.06). While admission suPAR-CSFL was not predictive of DCI, we observed a significant correlation with modified Rankin's degree of disability at discharge. Conclusion: Elevated suPAR serum level on admission as a biomarker for early inflammation after aSAH is associated with an increased risk of DCI. Elevated suPAR-CSFL levels correlate with a higher degree of disability at discharge. These distinct relations and the observation of a continuous increase over time affirm the role of inflammation in aSAH and require further study.

Coupling of pupil- and neuronal population dynamics reveals diverse influences of arousal on cortical processing.

Fluctuations in arousal, controlled by subcortical neuromodulatory systems, continuously shape cortical state, with profound consequences for information processing. Yet, how arousal signals influence cortical population activity in detail has so far only been characterized for a few selected brain regions. Traditional accounts conceptualize arousal as a homogeneous modulator of neural population activity across the cerebral cortex. Recent insights, however, point to a higher specificity of arousal effects on different components of neural activity and across cortical regions. Here, we provide a comprehensive account of the relationships between fluctuations in arousal and neuronal population activity across the human brain. Exploiting the established link between pupil size and central arousal systems, we performed concurrent magnetoencephalographic (MEG) and pupillographic recordings in a large number of participants, pooled across three laboratories. We found a cascade of effects relative to the peak timing of spontaneous pupil dilations: Decreases in low-frequency (2-8 Hz) activity in temporal and lateral frontal cortex, followed by increased high-frequency (>64 Hz) activity in mid-frontal regions, followed by monotonic and inverted U relationships with intermediate frequency-range activity (8-32 Hz) in occipito-parietal regions. Pupil-linked arousal also coincided with widespread changes in the structure of the aperiodic component of cortical population activity, indicative of changes in the excitation-inhibition balance in underlying microcircuits. Our results provide a novel basis for studying the arousal modulation of cognitive computations in cortical circuits.

Respiration aligns perception with neural excitability.

Recent studies from the field of interoception have highlighted the link between bodily and neural rhythms during action, perception, and cognition. The mechanisms underlying functional body-brain coupling, however, are poorly understood, as are the ways in which they modulate behavior. We acquired respiration and human magnetoencephalography data from a near-threshold spatial detection task to investigate the trivariate relationship between respiration, neural excitability, and performance. Respiration was found to significantly modulate perceptual sensitivity as well as posterior alpha power (8-13 Hz), a well-established proxy of cortical excitability. In turn, alpha suppression prior to detected versus undetected targets underscored the behavioral benefits of heightened excitability. Notably, respiration-locked excitability changes were maximized at a respiration phase lag of around -30° and thus temporally preceded performance changes. In line with interoceptive inference accounts, these results suggest that respiration actively aligns sampling of sensory information with transient cycles of heightened excitability to facilitate performance.

Respiration modulates oscillatory neural network activity at rest.

Despite recent advances in understanding how respiration affects neural signalling to influence perception, cognition, and behaviour, it is yet unclear to what extent breathing modulates brain oscillations at rest. We acquired respiration and resting state magnetoencephalography (MEG) data from human participants to investigate if, where, and how respiration cyclically modulates oscillatory amplitudes (2 to 150 Hz). Using measures of phase-amplitude coupling, we show respiration-modulated brain oscillations (RMBOs) across all major frequency bands. Sources of these modulations spanned a widespread network of cortical and subcortical brain areas with distinct spectrotemporal modulation profiles. Globally, delta and gamma band modulations varied with distance to the head centre, with stronger modulations at distal (versus central) cortical sites. Overall, we provide the first comprehensive mapping of RMBOs across the entire brain, highlighting respiration-brain coupling as a fundamental mechanism to shape neural processing within canonical resting state and respiratory control networks (RCNs).

Comparison of undirected frequency-domain connectivity measures for cerebro-peripheral analysis.

Analyses of cerebro-peripheral connectivity aim to quantify ongoing coupling between brain activity (measured by MEG/EEG) and peripheral signals such as muscle activity, continuous speech, or physiological rhythms (such as pupil dilation or respiration). Due to the distinct rhythmicity of these signals, undirected connectivity is typically assessed in the frequency domain. This leaves the investigator with two critical choices, namely a) the appropriate measure for spectral estimation (i.e., the transformation into the frequency domain) and b) the actual connectivity measure. As there is no consensus regarding best practice, a wide variety of methods has been applied. Here we systematically compare combinations of six standard spectral estimation methods (comprising fast Fourier and continuous wavelet transformation, bandpass filtering, and short-time Fourier transformation) and six connectivity measures (phase-locking value, Gaussian-Copula mutual information, Rayleigh test, weighted pairwise phase consistency, magnitude squared coherence, and entropy). We provide performance measures of each combination for simulated data (with precise control over true connectivity), a single-subject set of real MEG data, and a full group analysis of real MEG data. Our results show that, overall, WPPC and GCMI tend to outperform other connectivity measures, while entropy was the only measure sensitive to bimodal deviations from a uniform phase distribution. For group analysis, choosing the appropriate spectral estimation method appears to be more critical than the connectivity measure. We discuss practical implications (sampling rate, SNR, computation time, and data length) and aim to provide recommendations tailored to particular research questions.

Surmising synchrony of sound and sight: Factors explaining variance of audiovisual integration in hurdling, tap dancing and drumming.

Auditory and visual percepts are integrated even when they are not perfectly temporally aligned with each other, especially when the visual signal precedes the auditory signal. This window of temporal integration for asynchronous audiovisual stimuli is relatively well examined in the case of speech, while other natural action-induced sounds have been widely neglected. Here, we studied the detection of audiovisual asynchrony in three different whole-body actions with natural action-induced sounds-hurdling, tap dancing and drumming. In Study 1, we examined whether audiovisual asynchrony detection, assessed by a simultaneity judgment task, differs as a function of sound production intentionality. Based on previous findings, we expected that auditory and visual signals should be integrated over a wider temporal window for actions creating sounds intentionally (tap dancing), compared to actions creating sounds incidentally (hurdling). While percentages of perceived synchrony differed in the expected way, we identified two further factors, namely high event density and low rhythmicity, to induce higher synchrony ratings as well. Therefore, we systematically varied event density and rhythmicity in Study 2, this time using drumming stimuli to exert full control over these variables, and the same simultaneity judgment tasks. Results suggest that high event density leads to a bias to integrate rather than segregate auditory and visual signals, even at relatively large asynchronies. Rhythmicity had a similar, albeit weaker effect, when event density was low. Our findings demonstrate that shorter asynchronies and visual-first asynchronies lead to higher synchrony ratings of whole-body action, pointing to clear parallels with audiovisual integration in speech perception. Overconfidence in the naturally expected, that is, synchrony of sound and sight, was stronger for intentional (vs. incidental) sound production and for movements with high (vs. low) rhythmicity, presumably because both encourage predictive processes. In contrast, high event density appears to increase synchronicity judgments simply because it makes the detection of audiovisual asynchrony more difficult. More studies using real-life audiovisual stimuli with varying event densities and rhythmicities are needed to fully uncover the general mechanisms of audiovisual integration.

Early alpha/beta oscillations reflect the formation of face-related expectations in the brain.

Although statistical regularities in the environment often go explicitly unnoticed, traces of implicit learning are evident in our neural activity. Recent perspectives have offered evidence that both pre-stimulus oscillations and peri-stimulus event-related potentials are reliable biomarkers of implicit expectations arising from statistical learning. What remains ambiguous, however, is the origination and development of these implicit expectations. To address this lack of knowledge and determine the temporal constraints of expectation formation, pre-stimulus increases in alpha/beta power were investigated alongside a reduction in the N170 and a suppression in peri-/post-stimulus gamma power. Electroencephalography was acquired from naive participants who engaged in a gender classification task. Participants were uninformed, that eight face images were sorted into four reoccurring pairs which were pseudorandomly hidden amongst randomly occurring face images. We found a reduced N170 for statistically expected images at left parietal and temporo-parietal electrodes. Furthermore, enhanced gamma power following the presentation of random images emphasized the bottom-up processing of these arbitrary occurrences. In contrast, enhanced alpha/beta power was evident pre-stimulus for expected relative to random faces. A particularly interesting finding was the early onset of alpha/beta power enhancement which peaked immediately after the depiction of the predictive face. Hence, our findings propose an approximate timeframe throughout which consistent traces of enhanced alpha/beta power illustrate the early prioritisation of top-down processes to facilitate the development of implicitly cued face-related expectations.

Depth and phase of respiration modulate cortico-muscular communication.

Recent studies in animals have convincingly demonstrated that respiration cyclically modulates oscillatory neural activity across diverse brain areas. To what extent this generalises to humans in a way that is relevant for behaviour is yet unclear. We used magnetoencephalography (MEG) to assess the potential influence of respiration depth and respiration phase on the human motor system. We obtained simultaneous recordings of brain activity, muscle activity, and respiration while participants performed a steady contraction task. We used corticomuscular coherence as a measure of efficient long-range cortico-peripheral communication. We found coherence within the beta range over sensorimotor cortex to be reduced during voluntary deep compared to involuntary normal breathing. Moreover, beta coherence was found to be cyclically modulated by respiration phase in both conditions. Overall, these results demonstrate how respiratory rhythms influence the synchrony of brain oscillations, conceivably regulating computational efficiency through neural excitability. Intriguing questions remain with regard to the shape of these modulatory processes and how they influence perception, cognition, and behaviour.

Incidental or Intentional? Different Brain Responses to One's Own Action Sounds in Hurdling vs. Tap Dancing.

Most human actions produce concomitant sounds. Action sounds can be either part of the action goal (GAS, goal-related action sounds), as for instance in tap dancing, or a mere by-product of the action (BAS, by-product action sounds), as for instance in hurdling. It is currently unclear whether these two types of action sounds-incidental or intentional-differ in their neural representation and whether the impact on the performance evaluation of an action diverges between the two. We here examined whether during the observation of tap dancing compared to hurdling, auditory information is a more important factor for positive action quality ratings. Moreover, we tested whether observation of tap dancing vs. hurdling led to stronger attenuation in primary auditory cortex, and a stronger mismatch signal when sounds do not match our expectations. We recorded individual point-light videos of newly trained participants performing tap dancing and hurdling. In the subsequent functional magnetic resonance imaging (fMRI) session, participants were presented with the videos that displayed their own actions, including corresponding action sounds, and were asked to rate the quality of their performance. Videos were either in their original form or scrambled regarding the visual modality, the auditory modality, or both. As hypothesized, behavioral results showed significantly lower rating scores in the GAS condition compared to the BAS condition when the auditory modality was scrambled. Functional MRI contrasts between BAS and GAS actions revealed higher activation of primary auditory cortex in the BAS condition, speaking in favor of stronger attenuation in GAS, as well as stronger activation of posterior superior temporal gyri and the supplementary motor area in GAS. Results suggest that the processing of self-generated action sounds depends on whether we have the intention to produce a sound with our action or not, and action sounds may be more prone to be used as sensory feedback when they are part of the explicit action goal. Our findings contribute to a better understanding of the function of action sounds for learning and controlling sound-producing actions.

Exploitation of local and global information in predictive processing.

While prediction errors have been established to instigate learning through model adaptation, recent studies have stressed the role of model-compliant events in predictive processing. Specifically, probabilistic information at critical points in time (so-called checkpoints) has been suggested to be sampled in order to evaluate the internal model, particularly in uncertain contexts. This way, initial model-based expectations are iteratively reaffirmed under uncertainty, even in the absence of prediction errors. Using electroencephalography (EEG), the present study aimed to investigate the interplay of such global uncertainty information and local adjustment cues prompting on-line adjustments of expectations. Within a stream of single digits, participants were to detect ordered sequences (i.e., 3-4-5-6-7) that had a regular length of five digits and were occasionally extended to seven digits. Over time, these extensions were either rare (low irreducible uncertainty) or frequent (high uncertainty) and could be unexpected or indicated by incidental colour cues. Accounting for cue information, an N400 component was revealed as the correlate of locally unexpected (vs expected) outcomes, reflecting effortful integration of incongruous information. As for model-compliant information, multivariate pattern decoding within the P3b time frame demonstrated effective exploitation of local (adjustment cues vs non-informative analogues) and global information (high vs low uncertainty regular endings) sampled from probabilistic events. Finally, superior fit of a global model (disregarding local adjustments) compared to a local model (including local adjustments) in a representational similarity analysis underscored the precedence of global reference frames in hierarchical predictive processing. Overall, results suggest that just like error-induced model adaptation, model evaluation is not limited to either local or global information. Following the hierarchical organisation of predictive processing, model evaluation too can occur at several levels of the processing hierarchy.

Being right matters: Model-compliant events in predictive processing.

While prediction errors (PE) have been established to drive learning through adaptation of internal models, the role of model-compliant events in predictive processing is less clear. Checkpoints (CP) were recently introduced as points in time where expected sensory input resolved ambiguity regarding the validity of the internal model. Conceivably, these events serve as on-line reference points for model evaluation, particularly in uncertain contexts. Evidence from fMRI has shown functional similarities of CP and PE to be independent of event-related surprise, raising the important question of how these event classes relate to one another. Consequently, the aim of the present study was to characterise the functional relationship of checkpoints and prediction errors in a serial pattern detection task using electroencephalography (EEG). Specifically, we first hypothesised a joint P3b component of both event classes to index recourse to the internal model (compared to non-informative standards, STD). Second, we assumed the mismatch signal of PE to be reflected in an N400 component when compared to CP. Event-related findings supported these hypotheses. We suggest that while model adaptation is instigated by prediction errors, checkpoints are similarly used for model evaluation. Intriguingly, behavioural subgroup analyses showed that the exploitation of potentially informative reference points may depend on initial cue learning: Strict reliance on cue-based predictions may result in less attentive processing of these reference points, thus impeding upregulation of response gain that would prompt flexible model adaptation. Overall, present results highlight the role of checkpoints as model-compliant, informative reference points and stimulate important research questions about their processing as function of learning und uncertainty.

Graph measures in task-based fMRI: Functional integration during read-out of visual and auditory information.

This study investigated how attending to auditory and visual information systematically changes graph theoretical measures of integration and functional connectivity between three network modules: auditory, visual, and a joint task core. Functional MRI BOLD activity was recorded while healthy volunteers attended to colour and/or pitch information presented within an audiovisual stimulus sequence. Network nodes and modules were based on peak voxels of BOLD contrasts, including colour and pitch sensitive brain regions as well as the dorsal attention network. Network edges represented correlations between nodes' activity and were computed separately for each condition. Connection strength was increased between the task and the visual module when participants attended to colour, and between the task and the auditory module when they attended to pitch. Moreover, several nodal graph measures showed consistent changes to attentional modulation in form of stronger integration of sensory regions in response to attention. Together, these findings corroborate dynamical adjustments of both modality-specific and modality-independent functional brain networks in response to task demands and their representation in graph theoretical measures.

Strategic adaptation to non-reward prediction error qualities and irreducible uncertainty in fMRI.

Prediction errors are deemed necessary for the updating of internal models of the environment, prompting us to stop or assert current action plans and helping us to adapt to environmental features. The aim of the present study was twofold: First, we sought to determine the neural underpinnings of qualitatively different abstract prediction errors in a serial pattern detection task. Distinct frontoparietal components were found for sequential terminations (inferior frontal gyrus - IFG) and extensions (superior frontal sulcus, posterior cingulate cortex, and angular gyrus), respectively. These findings provide a novel approach of distinguishing non-reward prediction error signals with regard to behavioural consequences they entail. Second, we investigated predictive processing as a function of statistical context (irreducible uncertainty). We hypothesised that the prospective scope of model-based expectancies is adapted to the stability of respective contexts in that unstable environments call for more frequent comparisons of expectancies with sensory input, resulting in stepwise predictions. Changes in environmental stability were reflected in activation of the angular gyrus and IFG for the highly uncertain context at potential points of prediction violation (checkpoints). Notably, this effect was not due to local fluctuations in stimulus improbability (surprise). Although further behavioural support is needed, data point towards a context-dependent adaptation of predictive strategies. Conceivably, enhanced neural responses at sequential checkpoints could reflect stepwise rather than full-length prediction. This strategic adjustment presumably relies on the iterant evaluation of model information retrieved from working memory, as suggested by strengthened functional connectivity of the parahippocampal area during epochs of high uncertainty.