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Periodic sensory inputs entrain oscillatory brain activity, reflecting a neural mechanism that might be fundamental to temporal prediction and perception. Most environmental rhythms and patterns in human behavior, such as walking, dancing, and speech do not, however, display strict isochrony but are instead quasi-periodic. Research has shown that neural tracking of speech is driven by modulations of the amplitude envelope, especially via sharp acoustic edges, which serve as prominent temporal landmarks. In the same vein, research on rhythm processing in music supports the notion that perceptual timing precision varies systematically with the sharpness of acoustic onset edges, conceptualized in the beat bin hypothesis. Increased envelope sharpness induces increased precision in localizing a sound in time. Despite this tight relationship between envelope shape and temporal processing, it is currently unknown how the brain uses predictive information about envelope features to optimize temporal perception. With the current EEG study, we show that the predicted sharpness of the amplitude envelope is encoded by pre-target neural activity in the beta band (15–25 Hz), and has an impact on the temporal perception of target sounds. We used probabilistic sound cues in a timing judgment task to inform participants about the sharpness of the amplitude envelope of an upcoming target sound embedded in a beat sequence. The predictive information about the envelope shape modulated task performance and pre-target beta power. Interestingly, these conditional beta-power modulations correlated positively with behavioral performance in the timing judgment task and with perceptual temporal precision in a click-alignment task. This study provides new insight into the neural processes underlying prediction of the sharpness of the amplitude envelope during beat perception, which modulate the temporal perception of sounds. This finding could reflect a process that is involved in temporal prediction, exerting top-down control on neural entrainment via the prediction of acoustic edges in the auditory stream.

Brain activity differs vastly between sleep, cognitive tasks, and action. Information theory is an appropriate concept to analytically quantify these brain states. Based on neurophysiological recordings, this concept can handle complex data sets, is free of any requirements about the data structure, and can infer the present underlying brain mechanisms. Specifically, by utilizing algorithmic information theory, it is possible to estimate the absolute information contained in brain responses. While current approaches that apply this theory to neurophysiological recordings can discriminate between different brain states, they are limited in directly quantifying the degree of similarity or encoded information between brain responses. Here, we propose a method grounded in algorithmic information theory that affords direct statements about responses' similarity by estimating the encoded information through a compression-based scheme. We validated this method by applying it to both synthetic and real neurophysiological data and compared its efficiency to the mutual information measure. This proposed procedure is especially suited for task paradigms contrasting different event types because it can precisely quantify the similarity of neuronal responses.

The brain responds to violations of expected rhythms, due to extraction- and prediction of the temporal structure in auditory input. Yet, it is unknown how probability of rhythm violations affects the overall rhythm predictability. Another unresolved question is whether predictive processes are independent of attention processes. In this study, EEG was recorded while subjects listened to rhythmic sequences. Predictability was manipulated by changing the stimulus-onset-asynchrony (SOA deviants) for given tones in the rhythm. When SOA deviants were inserted rarely, predictability remained high, whereas predictability was lower with more frequent SOA deviants. Dichotic tone-presentation allowed for independent manipulation of attention, as specific tones of the rhythm were presented to separate ears. Attention was manipulated by instructing subjects to attend to tones in one ear only, while keeping the rhythmic structure of tones constant. The analyses of event-related potentials revealed an attenuated N1 for tones when rhythm predictability was high, while the N1 was enhanced by attention to tones. Bayesian statistics revealed no interaction between predictability and attention. A right-lateralization of attention effects, but not predictability effects, suggested potentially different cortical processes. This is the first study to show that probability of rhythm violation influences rhythm predictability, independent of attention.

Orbitofrontal cortex (OFC) is implicated in multiple cognitive processes, including inhibitory control, context memory, recency judgment, and choice behavior. Despite an emerging understanding of the role of OFC in memory and executive control, its necessity for core working memory (WM) operations remains undefined. Here, we assessed the impact of OFC damage on interference effects in WM using a Recent Probes task based on the Sternberg item-recognition task (1966). Subjects were asked to memorize a set of letters and then indicate whether a probe letter was presented in a particular set. Four conditions were created according to the forthcoming response ("yes"/"no") and the recency of the probe (presented in the previous trial set or not). We compared behavioral and electroencephalography (EEG) responses between healthy subjects (n = 14) and patients with bilateral OFC damage (n = 14). Both groups had the same recency pattern of slower reaction time (RT) when the probe was presented in the previous trial but not in the current one, reflecting the proactive interference (PI). The within-group electrophysiological results showed no condition difference during letter encoding and maintenance. In contrast, event-related potentials (ERPs) to probes showed distinct within-group condition effects, and condition by group effects. The response and recency effects for controls occurred within the same time window (300-500 ms after probe onset) and were observed in two distinct spatial groups including right centro-posterior and left frontal electrodes. Both clusters showed ERP differences elicited by the response effect, and one cluster was also sensitive to the recency manipulation. Condition differences for the OFC group involved two different clusters, encompassing only left hemisphere electrodes and occurring during two consecutive time windows (345-463 ms and 565-710 ms). Both clusters were sensitive to the response effect, but no recency effect was found despite the behavioral recency effect. Although the groups had different electrophysiological responses, the maintenance of letters in WM, the evaluation of the context of the probe, and the decision to accept or reject a probed letter were preserved in OFC patients. The results suggest that neural reorganization may contribute to intact recency judgment and response after OFC damage.

Flexibly adapting to our dynamic surroundings requires anticipating upcoming events and focusing our attention accordingly. Rhythmic patterns of sensory input offer valuable cues for these temporal expectations and facilitate perceptual processing. However, a gap in understanding persists regarding how rhythms outside of periodic structures influence perception. Our study aimed to delineate the distinct roles of predictability and periodicity in rhythm-based expectations. Participants completed a pitch-identification task preceded by different rhythm types: periodic predictable, aperiodic predictable, and aperiodic unpredictable. By manipulating the timing of the target sound, we observed how auditory sensitivity was modulated by the target position in the different rhythm conditions. The results revealed a clear behavioral benefit of predictable rhythms, regardless of their periodicity. Interestingly, we also observed an additional effect of periodicity. While both periodic and aperiodic predictable rhythms improved overall sensitivity, only the periodic rhythm seemed to induce an entrained sensitivity pattern, wherein sensitivity peaked in synchrony with the expected continuation of the rhythm. The recorded event-related brain potentials further supported these findings. The target-evoked P3b, possibly a neural marker of attention allocation, mirrored the sensitivity patterns. This supports our hypothesis that perceptual sensitivity is modulated by temporal attention guided by rhythm-based expectations. Furthermore, the effect of rhythm predictability seems to operate through climbing neural activity (similar to the CNV), reflecting preparation for the target. The effect of periodicity is likely related to more precise temporal expectations and could possibly involve neural entrainment. Our findings suggest that predictability and periodicity influence perception via distinct mechanisms.

The ability to use predictive information to guide perception and action relies heavily on the prefrontal cortex (PFC), yet the involvement of its subregions in predictive processes remains unclear. Recent perspectives propose that the orbitofrontal cortex (OFC) generates predictions about perceptual events, actions, and their outcomes while the lateral prefrontal cortex (LPFC) is involved in prospective functions, which support predictive processes, such as selective attention, working memory, response preparation or inhibition. To further delineate the roles of these PFC areas in predictive processing, we investigated whether lesions would impair the ability to build predictions of future events and detect deviations from expected regularities. We used an auditory deviance detection task, in which the structural regularities of played tones were controlled at two hierarchical levels by rules defined at a local (i.e., between tones within sequences) and global (i.e., between sequences) level. We have recently shown that OFC lesions affect detecting prediction violations at two hierarchical levels of rule abstraction, i.e., altered MMN and P3a to local and simultaneous local + global prediction violations (https://doi.org/10.7554/eLife.86386). Now, we focus on the task's predictive aspect and present the latest results showing the involvement of PFC subregions in anticipation of deviances informed by implicit predictive information. Behavioral data shows that deviance expectancy induced faster deviance detection in healthy adults (n=22), suggesting that participants track a state space representation of the task and anticipate upcoming deviant sequences. The analysis of EEG data from patients with focal lesions to the OFC (n = 12) or LPFC (n = 10), and SEEG from the same areas in patients with epilepsy (n = 7), revealed interesting differences. Healthy adults (n = 15) showed modulations of the Contingent Negative Variation (CNV) – a marker of anticipatory activity - tracking the expectancy of deviant tone sequences. However, patients with OFC lesions lacked CNV sensitivity to the predictive context, while patients with LPFC lesions showed moderate sensitivity compared to healthy adults. These results were further supported by intracranial recordings, which revealed expectancy modulation of the high-frequency broadband signal from electrodes in OFC and LPFC, with an earlier latency of activity modulation for the OFC and a later one for the LPFC. Altogether, the complementary approach from behavioral, intracerebral EEG, scalp EEG, and causal lesion data provides compelling evidence for the distinct engagement of the two prefrontal areas in predicting future events and signaling deviations.

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Current findings of orbitofrontal cortex (OFC) function suggest that this region might have a role in the generation of prediction error signals associated with top-down expectation of upcoming stimuli. We investigated the impact of lesions to the OFC on the Contingent Negative Variation (CNV), an electrophysiological marker of cognitive expectation and time perception. Twelve OFC patients and fifteen healthy controls performed an auditory local-global paradigm while brain electrical activity was recorded. The structural regularities of the tones were controlled at two hierarchical levels by rules defined at a local (i.e., between tones within sequences) level with a short timescale and at a global (i.e., between sequences) level with a longer timescale. At the global level, deviant tone sequences were interspersed among standard tone sequences in a pseudorandom order, rendering some deviant sequences more anticipated than others. We found that healthy controls exhibited CNV build-up before the occurrence of deviant sequences. The CNV drift rate was modulated by the expectancy of deviant sequences (i.e., the higher the expectancy, the higher the CNV drift rate), reflecting their ability to anticipate when a deviant tone sequence would occur. However, patients with OFC lesions did not show CNV drift modulations by the expectancy of the deviant tone sequences, indicating impaired anticipation of these upcoming events. These findings suggest involvement of the OFC in generating auditory expectations based on the contextual and temporal structure of the task.

In this study, we tested the causal involvement of the OFC in noticing breaches of predictions (i.e., PEs) at different hierarchical levels of task structural complexity. With this aim, we examined the event-related potentials (ERPs) of patients with focal OFC lesions and healthy adults while performing an auditory local-global oddball paradigm. Altogether, we found that after OFC damage, low-level PEs (i.e., processing of stimuli that are unpredicted at the local level) and combined low- and high-level PEs (i.e., processing of stimuli that are unpredicted at both the local and global level) were impacted. However, the processing of standard tones was not affected. We conclude that the OFC may contribute to a top-down process that modulates the deviance detection system in the primary auditory cortices, and may be involved in connecting PEs at lower hierarchical areas with predictions at higher areas. The study sheds new light on the poorly explored deficits of hierarchical auditory prediction in patients with damaged OFC.

The accurate localization of electrodes in relationship to the brain’s anatomy is the foundation of the spatial resolution of intracranial EEG recordings. However, in “difficult cases“ the localization needs to be done manually since automatic methods fails, e.g high density arrays up to 3 mm inter-electrode distance, overlapping electrodes, low resolution CT images, or connection cables ovelaying grids. Here, we present a new automatic method that models a flexible array of electrodes and fits it to the artifacts observed in post implantation CT images. We evaluated data from 18 adult patients with drug resistant epilepsy implanted with depth electrodes and/or subdural grids (18 patients, 3261 electrodes). The automatic method was contrasted against manual localization. The main processing steps (Fig. 1 A) were: Thresholding and selection of a cloud of CT voxels containing the electrode artifacts Assembling a model of the grid (depth) array of electrodes Fitting the model to a smooth surface (line) approximation of CT artifacts Fitting the model to the cloud of voxels by minimizing the energy function E = -Ec + Et + Ed Ec was the gaussian weighted spatial correlation between the electrodes and the cloud of voxels. Et penalized the translation of electrodes, and Ed the deformation of a spring grid connecting the electrodes. Automatic localization resulted to be more precise than manual selection, observed as a significant reduction of the inter-electrode distance variance (Fig. 1 B). We provide a robust method for intracranial electrode localization that is applicable to “difficult cases” were previous automatic methods fail (Fig. 1 C). The method was implemented in the open-source iElectrodes toolbox and is available to the research community (Blenkmann et al., 2017).

The neural network underlying human auditory deviance detection is not fully understood. To address this, we recorded intracranial EEG from 22 adult patients with drug resistant epilepsy undergoing presurgical monitoring who had depth electrodes implanted in all brain lobes (1193 channels in total). Patients passively heard a stream of bilaterally presented tones while reading. We used the Optimum-1 paradigm, that consisted of 300 standard tones interleaved with 300 randomly presented deviant tones per block. Patients completed between 3 to 10 blocks. Deviant tones differed from standards in: 1) intensity (louder or softer), 2) frequency (higher or lower), 3) sound source location (right or left), 4) a shorter duration, or 5) a silent gap in the middle of the tone (Näätänen et al., 2004). Electrode coordinates were obtained from coregistered MRI and CT images using iElectrodes toolbox (Blenkmann et al., 2017). Channels were bipolar referenced and high frequency band activity (HFA) analytic amplitude signal was obtained using the Hilbert transform (75-145 Hz). Compared to the baseline period, significant HFA responses to tones in general were observed in 31% of the channels. We used an ANOVA to quantify the HFA response variance across trials that could be explained by the different factors of the stimuli: Intensity, Laterality, Frequency, Duration and Gap. We estimated the amount of explained variance by using ?2 (Siegel et al., 2015). Eighteen % of the channels showed a significant increase of the condition-specific explained variance. Some channels showed condition-specific activations to one particular deviant, while others showed activations to a combination of two or more deviants. The channels showing responses to tones in general and condition-specific effects were mostly observed bilaterally in temporal cortex. Frontal, anterior cingulate, and parietal cortices were also involved to a lesser extent. Our results, in line with the predictive coding framework, reveal that a distributed brain network is involved in auditory processing and deviance detection.

Sensory consequences of actions are predicted by the brain via an internal forward model to prepare sensory cortical areas, referred to as motor prediction. In a similar vein, the predictive coding framework suggests that perception is based on internal models making predictions about sensory events, based on statistical probabilities of the stimuli. In the current study we investigated action-based sensory predictions. We used a self-paced, two-choice random generation task, infrequently inducing deviant outcomes of voluntary action. Participants repeatedly pressed a right and a left button normatively associated with a 70 ms long 1 kHz and 2 kHz tone, respectively. Occasional deviants occurred, inverting the learned button-tone association. Participants were instructed that their button presses should be random, at a regular but self-paced tempo of one press per 1-2 s, and that they should press both buttons with equal probability. They were informed that the tones are task-irrelevant. We used intracranial EEG (iEEG) data recorded from 10 adult patients with electrodes localized in frontal and temporal lobes. The patients had drug resistant epilepsy and were undergoing presurgical monitoring via implanted stereotactic electrodes. Electrode coordinates and anatomical labels were obtained from coregistered MRI and CT images using iElectrodes toolbox. Initial results indicate that violations of action intentions modulated high frequency band activity (HFA, 75-145 Hz) in distributed brain regions including temporal and prefrontal cortices.