A quantitative model reveals a frequency ordering of prediction and prediction-error signals in the human brain

RDoC Framework Through the Lens of Predictive Processing: Focusing on Cognitive Systems Domain

In response to shortcomings of the current classification system in translating discoveries from basic science to clinical applications, NIMH offers a new framework for studying mental health disorders called Research Domain Criteria (RDoC). This framework holds a multidimensional outlook on psychopathologies focusing on functional domains of behavior and their implementing neural circuits. In parallel, the Predictive Processing (PP) framework stands as a leading theory of human brain function, offering a unified explanation for various types of information processing in the brain. While both frameworks share an interest in studying psychopathologies based on pathophysiology, their integration still needs to be explored. Here, we argued in favor of the explanatory power of PP to be a groundwork for the RDoC matrix in validating its constructs and creating testable hypotheses about mechanistic interactions between molecular biomarkers and clinical traits. Together, predictive processing may serve as a foundation for achieving the goals of the RDoC framework.

Differential modulations of theta and beta oscillations by audiovisual congruency in letter-speech sound integration

The integration of visual letters and speech sounds is a crucial part of learning to read. Previous studies investigating this integration have revealed a modulation by audiovisual (AV) congruency, commonly known as the congruency effect. To investigate the cortical oscillations of the congruency effects across different oscillatory frequency bands, we conducted a Japanese priming task in which a visual letter was followed by a speech sound. We analyzed the power and phase properties of oscillatory activities in the theta and beta bands between congruent and incongruent letter-speech sound (L-SS) pairs. Our results revealed stronger theta-band (5-7 Hz) power in the congruent condition and cross-modal phase resetting within the auditory cortex, accompanied by enhanced inter-trial phase coherence (ITPC) in the auditory-related areas in response to the congruent condition. The observed congruency effect of theta-band power may reflect increased neural activities in the left auditory region during L-SS integration. Additionally, theta ITPC findings suggest that visual letters amplify neuronal responses to the following corresponding auditory stimulus, which may reflect the differential cross-modal influences in the primary auditory cortex. In contrast, decreased beta-band (20-35 Hz) oscillatory power was observed in the right centroparietal regions for the congruent condition. The reduced beta power seems to be unrelated to the processing of AV integration, but may be interpreted as the brain response to predicting auditory sounds during language processing. Our data provide valuable insights by indicating that oscillations in different frequency bands contribute to the disparate aspects of L-SS integration.

Crossmodal hierarchical predictive coding for audiovisual sequences in the human brain

Predictive coding theory suggests the brain anticipates sensory information using prior knowledge. While this theory has been extensively researched within individual sensory modalities, evidence for predictive processing across sensory modalities is limited. Here, we examine how crossmodal knowledge is represented and learned in the brain, by identifying the hierarchical networks underlying crossmodal predictions when information of one sensory modality leads to a prediction in another modality. We record electroencephalogram (EEG) during a crossmodal audiovisual local-global oddball paradigm, in which the predictability of transitions between tones and images are manipulated at both the stimulus and sequence levels. To dissect the complex predictive signals in our EEG data, we employed a model-fitting approach to untangle neural interactions across modalities and hierarchies. The model-fitting result demonstrates that audiovisual integration occurs at both the levels of individual stimulus interactions and multi-stimulus sequences. Furthermore, we identify the spatio-spectro-temporal signatures of prediction-error signals across hierarchies and modalities, and reveal that auditory and visual prediction errors are rapidly redirected to the central-parietal electrodes during learning through alpha-band interactions. Our study suggests a crossmodal predictive coding mechanism where unimodal predictions are processed by distributed brain networks to form crossmodal knowledge.

Understanding music and aging through the lens of Bayesian inference

Bayesian inference has recently gained momentum in explaining music perception and aging. A fundamental mechanism underlying Bayesian inference is the notion of prediction. This framework could explain how predictions pertaining to musical (melodic, rhythmic, harmonic) structures engender action, emotion, and learning, expanding related concepts of music research, such as musical expectancies, groove, pleasure, and tension. Moreover, a Bayesian perspective of music perception may shed new insights on the beneficial effects of music in aging. Aging could be framed as an optimization process of Bayesian inference. As predictive inferences refine over time, the reliance on consolidated priors increases, while the updating of prior models through Bayesian inference attenuates. This may affect the ability of older adults to estimate uncertainties in their environment, limiting their cognitive and behavioral repertoire. With Bayesian inference as an overarching framework, this review synthesizes the literature on predictive inferences in music and aging, and details how music could be a promising tool in preventive and rehabilitative interventions for older adults through the lens of Bayesian inference.

Erroneous predictive coding across brain hierarchies in a non-human primate model of autism spectrum disorder

In autism spectrum disorder (ASD), atypical sensory experiences are often associated with irregularities in predictive coding, which proposes that the brain creates hierarchical sensory models via a bidirectional process of predictions and prediction errors. However, it remains unclear how these irregularities manifest across different functional hierarchies in the brain. To address this, we study a marmoset model of ASD induced by valproic acid (VPA) treatment. We record high-density electrocorticography (ECoG) during an auditory task with two layers of temporal control, and applied a quantitative model to quantify the integrity of predictive coding across two distinct hierarchies. Our results demonstrate a persistent pattern of sensory hypersensitivity and unstable predictions across two brain hierarchies in VPA-treated animals, and reveal the associated spatio-spectro-temporal neural signatures. Despite the regular occurrence of imprecise predictions in VPA-treated animals, we observe diverse configurations of underestimation or overestimation of sensory regularities within the hierarchies. Our results demonstrate the coexistence of the two primary Bayesian accounts of ASD: overly-precise sensory observations and weak prior beliefs, and offer a potential multi-layered biomarker for ASD, which could enhance our understanding of its diverse symptoms.

Dissecting Mismatch Negativity: Early and Late Subcomponents for Detecting Deviants in Local and Global Sequence Regularities

Mismatch negativity (MMN) is commonly recognized as a neural signal of prediction error evoked by deviants from the expected patterns of sensory input. Studies show that MMN diminishes when sequence patterns become more predictable over a longer timescale. This implies that MMN is composed of multiple subcomponents, each responding to different levels of temporal regularities. To probe the hypothesized subcomponents in MMN, we record human electroencephalography during an auditory local-global oddball paradigm where the tone-to-tone transition probability (local regularity) and the overall sequence probability (global regularity) are manipulated to control temporal predictabilities at two hierarchical levels. We find that the size of MMN is correlated with both probabilities and the spatiotemporal structure of MMN can be decomposed into two distinct subcomponents. Both subcomponents appear as negative waveforms, with one peaking early in the central-frontal area and the other late in a more frontal area. With a quantitative predictive coding model, we map the early and late subcomponents to the prediction errors that are tied to local and global regularities, respectively. Our study highlights the hierarchical complexity of MMN and offers an experimental and analytical platform for developing a multitiered neural marker applicable in clinical settings.

Processing of auditory novelty in human cortex during a semantic categorization task

Auditory semantic novelty - a new meaningful sound in the context of a predictable acoustical environment - can probe neural circuits involved in language processing. Aberrant novelty detection is a feature of many neuropsychiatric disorders. This large-scale human intracranial electrophysiology study examined the spatial distribution of gamma and alpha power and auditory evoked potentials (AEP) associated with responses to unexpected words during performance of semantic categorization tasks. Participants were neurosurgical patients undergoing monitoring for medically intractable epilepsy. Each task included repeatedly presented monosyllabic words from different talkers ("common") and ten words presented only once ("novel"). Targets were words belonging to a specific semantic category. Novelty effects were defined as differences between neural responses to novel and common words. Novelty increased task difficulty and was associated with augmented gamma, suppressed alpha power, and AEP differences broadly distributed across the cortex. Gamma novelty effect had the highest prevalence in planum temporale, posterior superior temporal gyrus (STG) and pars triangularis of the inferior frontal gyrus; alpha in anterolateral Heschl's gyrus (HG), anterior STG and middle anterior cingulate cortex; AEP in posteromedial HG, lower bank of the superior temporal sulcus, and planum polare. Gamma novelty effect had a higher prevalence in dorsal than ventral auditory-related areas. Novelty effects were more pronounced in the left hemisphere. Better novel target detection was associated with reduced gamma novelty effect within auditory cortex and enhanced gamma effect within prefrontal and sensorimotor cortex. Alpha and AEP novelty effects were generally more prevalent in better performing participants. Multiple areas, including auditory cortex on the superior temporal plane, featured AEP novelty effect within the time frame of P3a and N400 scalp-recorded novelty-related potentials. This work provides a detailed account of auditory novelty in a paradigm that directly examined brain regions associated with semantic processing. Future studies may aid in the development of objective measures to assess the integrity of semantic novelty processing in clinical populations.

Rhythmic temporal prediction enhances neural representations of movement intention for brain-computer interface

Objective.Detecting movement intention is a typical use of brain-computer interfaces (BCI). However, as an endogenous electroencephalography (EEG) feature, the neural representation of movement is insufficient for improving motor-based BCI. This study aimed to develop a new movement augmentation BCI encoding paradigm by incorporating the cognitive function of rhythmic temporal prediction, and test the feasibility of this new paradigm in optimizing detections of movement intention.Methods.A visual-motion synchronization task was designed with two movement intentions (left vs. right) and three rhythmic temporal prediction conditions (1000 ms vs. 1500 ms vs. no temporal prediction). Behavioural and EEG data of 24 healthy participants were recorded. Event-related potentials (ERPs), event-related spectral perturbation induced by left- and right-finger movements, the common spatial pattern (CSP) and support vector machine, Riemann tangent space algorithm and logistic regression were used and compared across the three temporal prediction conditions, aiming to test the impact of temporal prediction on movement detection.Results.Behavioural results showed significantly smaller deviation time for 1000 ms and 1500 ms conditions. ERP analyses revealed 1000 ms and 1500 ms conditions led to rhythmic oscillations with a time lag in contralateral and ipsilateral areas of movement. Compared with no temporal prediction, 1000 ms condition exhibited greater beta event-related desynchronization (ERD) lateralization in motor area (P< 0.001) and larger beta ERD in frontal area (P< 0.001). 1000 ms condition achieved an averaged left-right decoding accuracy of 89.71% using CSP and 97.30% using Riemann tangent space, both significantly higher than no temporal prediction. Moreover, movement and temporal information can be decoded simultaneously, achieving 88.51% four-classification accuracy.Significance.The results not only confirm the effectiveness of rhythmic temporal prediction in enhancing detection ability of motor-based BCI, but also highlight the dual encodings of movement and temporal information within a single BCI paradigm, which is promising to expand the range of intentions that can be decoded by the BCI.

Spatiotemporal dynamics across visual cortical laminae support a predictive coding framework for interpreting mismatch responses

Context modulates neocortical processing of sensory data. Unexpected visual stimuli elicit large responses in primary visual cortex (V1)-a phenomenon known as deviance detection (DD) at the neural level, or "mismatch negativity" (MMN) when measured with EEG. It remains unclear how visual DD/MMN signals emerge across cortical layers, in temporal relation to the onset of deviant stimuli, and with respect to brain oscillations. Here we employed a visual "oddball" sequence-a classic paradigm for studying aberrant DD/MMN in neuropsychiatric populations-and recorded local field potentials in V1 of awake mice with 16-channel multielectrode arrays. Multiunit activity and current source density profiles showed that although basic adaptation to redundant stimuli was present early (50 ms) in layer 4 responses, DD emerged later (150-230 ms) in supragranular layers (L2/3). This DD signal coincided with increased delta/theta (2-7 Hz) and high-gamma (70-80 Hz) oscillations in L2/3 and decreased beta oscillations (26-36 Hz) in L1. These results clarify the neocortical dynamics elicited during an oddball paradigm at a microcircuit level. They are consistent with a predictive coding framework, which posits that predictive suppression is present in cortical feed-back circuits, which synapse in L1, whereas "prediction errors" engage cortical feed-forward processing streams, which emanate from L2/3.