scholarly journals Constructing the hierarchy of predictive auditory sequences in the marmoset brain

2021 ◽  
Author(s):  
Yuwei Jiang ◽  
Misako Komatsu ◽  
Yuyan Chen ◽  
Ruoying Xie ◽  
Kaiwei Zhang ◽  
...  
Keyword(s):  
Auditory Processing ◽  
Time Scale ◽  
Essential Feature ◽  
Predictive Coding ◽  
Auditory Pathway ◽  
Brain Regions ◽  
Predictive Processing ◽  
Short Time Scale ◽  
Prediction Errors ◽  
Surface Areas

Our brains constantly generate predictions of sensory input that are compared with actual inputs, propagate the prediction-errors through a hierarchy of brain regions, and subsequently update the internal predictions of the world. However, the essential feature of predictive coding, the notion of hierarchical depth and its neural mechanisms, remains largely unexplored. Here, we investigated the hierarchical depth of predictive auditory processing by combining functional magnetic resonance imaging (fMRI) and high-density whole-brain electrocorticography (ECoG) in marmoset monkeys during an auditory local-global paradigm in which the temporal regularities of the stimuli were designed at two hierarchical levels. The prediction-errors and prediction updates were examined as neural responses to auditory mismatches and omissions. Using fMRI, we identified a hierarchical gradient along the auditory pathway: midbrain and sensory regions represented local, short-time-scale predictive processing followed by associative auditory regions, whereas anterior temporal and prefrontal areas represented global, long-time-scale sequence processing. The complementary ECoG recordings confirmed the activations at cortical surface areas and further differentiated the signals of prediction-error and update, which were transmitted via putatively bottom-up γ and top-down β oscillations, respectively. Furthermore, omission responses caused by absence of input, reflecting solely the two levels of prediction signals that are unique to the hierarchical predictive coding framework, demonstrated the hierarchical predictions in the auditory, temporal, and prefrontal areas. Thus, our findings support the hierarchical predictive coding framework, and outline how neural circuits and spatiotemporal dynamics are used to represent and arrange a hierarchical structure of auditory sequences in the marmoset brain.

scholarly journals The auditory brain in action: Intention determines predictive processing in the auditory system—A review of current paradigms and findings

2021 ◽  
Author(s):  
Betina Korka ◽  
Andreas Widmann ◽  
Florian Waszak ◽  
Álvaro Darriba ◽  
Erich Schröger
Keyword(s):  
Auditory Processing ◽  
Predictive Coding ◽  
Predictive Processing ◽  
Future Research ◽  
Event Representation ◽  
Top Down ◽  
Ideomotor Theory ◽  
System A ◽  
Brain Responses ◽  
Action Intention

AbstractAccording to the ideomotor theory, action may serve to produce desired sensory outcomes. Perception has been widely described in terms of sensory predictions arising due to top-down input from higher order cortical areas. Here, we demonstrate that the action intention results in reliable top-down predictions that modulate the auditory brain responses. We bring together several lines of research, including sensory attenuation, active oddball, and action-related omission studies: Together, the results suggest that the intention-based predictions modulate several steps in the sound processing hierarchy, from preattentive to evaluation-related processes, also when controlling for additional prediction sources (i.e., sound regularity). We propose an integrative theoretical framework—the extended auditory event representation system (AERS), a model compatible with the ideomotor theory, theory of event coding, and predictive coding. Initially introduced to describe regularity-based auditory predictions, we argue that the extended AERS explains the effects of action intention on auditory processing while additionally allowing studying the differences and commonalities between intention- and regularity-based predictions—we thus believe that this framework could guide future research on action and perception.

scholarly journals Oversampled and undersolved: Depressive rumination from a predictive coding perspective

2021 ◽  
Author(s):  
Max Berg ◽  
Matthias Feldmann ◽  
Tobias Kube
Keyword(s):  
Problem Solving ◽  
Predictive Coding ◽  
Instrumental Learning ◽  
Mental Simulation ◽  
Clinical Findings ◽  
Predictive Processing ◽  
Prediction Errors ◽  
Depressive Rumination ◽  
Bayesian Sampling ◽  
Integrative View

Rumination is a widely recognized cognitive deviation in depression. An integrative view that combines clinical findings on rumination with theories of mental simulation and cognitive problem-solving could help explain the development and maintenance of rumination in a computationally and biologically plausible framework. In this review, we connect insights from neuroscience and computational psychiatry to elucidate rumination as repetitive but unsuccessful attempts at mental problem-solving. Appealing to a predictive processing account, we suggest that problem-solving is based on an algorithm that generates candidate behavior (policy primitives for problem solutions) using a Bayesian sampling approach, evaluates resulting policies for action, and then engages in instrumental learning to reduce prediction errors. We present evidence suggesting that this problem-solving algorithm is distorted in depression: Specifically, depressive rumination is regarded as excessive Bayesian sampling of candidates that is associated with high prediction errors without activation of the successive steps (policy evaluation, instrumental learning) of the algorithm. Thus, prediction errors cannot be decreased, and excessive resampling of the same problems occur. This then leads to reduced precision weighting attributed to external, “online” stimuli, low behavioral output and high opportunity costs due to the time-consuming nature of the sampling process itself. We review different computational reasons that make the proposed Bayesian sampling algorithm vulnerable to a ruminative „halting problem”. We also identify neurophysiological correlates of these deviations in pathological connectivity patterns of different brain networks. We conclude by suggesting future directions for research into behavioral and neurophysiological features of the model and point to clinical implications.

scholarly journals Disentangling predictive processing in the brain: a meta-analytic study in favour of a predictive network

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Linda Ficco ◽  
Lorenzo Mancuso ◽  
Jordi Manuello ◽  
Alessia Teneggi ◽  
Donato Liloia ◽  
...  
Keyword(s):  
Large Scale ◽  
Inferior Frontal Gyrus ◽  
Predictive Coding ◽  
Likelihood Estimation ◽  
Brain Regions ◽  
Predictive Processing ◽  
Spatial Convergence ◽  
Large Scale Networks ◽  
The Brain

AbstractAccording to the predictive coding (PC) theory, the brain is constantly engaged in predicting its upcoming states and refining these predictions through error signals. Despite extensive research investigating the neural bases of this theory, to date no previous study has systematically attempted to define the neural mechanisms of predictive coding across studies and sensory channels, focussing on functional connectivity. In this study, we employ a coordinate-based meta-analytical approach to address this issue. We first use the Activation Likelihood Estimation (ALE) algorithm to detect spatial convergence across studies, related to prediction error and encoding. Overall, our ALE results suggest the ultimate role of the left inferior frontal gyrus and left insula in both processes. Moreover, we employ a meta-analytic connectivity method (Seed-Voxel Correlations Consensus). This technique reveals a large, bilateral predictive network, which resembles large-scale networks involved in task-driven attention and execution. In sum, we find that: (i) predictive processing seems to occur more in certain brain regions than others, when considering different sensory modalities at a time; (ii) there is no evidence, at the network level, for a distinction between error and prediction processing.

scholarly journals Imaging in hearing using radiotracers

2017 ◽  
Vol 3 (2) ◽  
pp. 187-190
Author(s):  
Georg Berding ◽  
Thomas Lenarz
Keyword(s):  
Functional Imaging ◽  
Auditory Processing ◽  
Single Photon ◽  
Photon Emission ◽  
Auditory Pathway ◽  
Brain Regions ◽  
Emission Computed Tomography ◽  
Central Auditory Processing ◽  
Preclinical Research ◽  
Clinical Patient

AbstractRadiotracers offer unique options for brain imaging of functional and molecular processes related to hearing. Such imaging can be applied in a broad spectrum of situations from preclinical research to clinical patient care. Functional imaging to assess activation in brain regions and networks involved in auditory processing uses markers of blood flow or energy-metabolism in well-defined conditions with and without auditory stimulation. Molecular markers can be used in hearing research for example to study changes in inhibitory neurotransmission systems related to hearing loss. For imaging either positron emission tomography (PET) or single-photon emission computed tomography (SPECT) are employed. Data analysis can encompasses voxel-wise statistical analysis of activation and calculation of quantitative parameters like receptor binding-potentials based on bio-kinetic modeling. Functional imaging has been frequently used in the context of auditory implantation. Before implantation it aims to assess intactness of the central auditory pathway and prognosis. After implantation it is used to improve understanding of the outcome with respect to auditory function and finally speech understanding, e.g. by measuring correlates of central auditory processing and neuroplasticity.

scholarly journals Automatic Sensory Predictions: A Review of Predictive Mechanisms in the Brain and Their Link to Conscious Processing

2021 ◽  
Vol 15 ◽  
Author(s):  
Ruxandra I. Tivadar ◽  
Robert T. Knight ◽  
Athina Tzovara
Keyword(s):  
Visual Processing ◽  
Auditory Processing ◽  
Predictive Coding ◽  
Sensory Information ◽  
Neural Substrates ◽  
Predictive Processing ◽  
Computational Techniques ◽  
Auditory Modality ◽  
Future Events ◽  
Predictive Processes

The human brain has the astonishing capacity of integrating streams of sensory information from the environment and forming predictions about future events in an automatic way. Despite being initially developed for visual processing, the bulk of predictive coding research has subsequently focused on auditory processing, with the famous mismatch negativity signal as possibly the most studied signature of a surprise or prediction error (PE) signal. Auditory PEs are present during various consciousness states. Intriguingly, their presence and characteristics have been linked with residual levels of consciousness and return of awareness. In this review we first give an overview of the neural substrates of predictive processes in the auditory modality and their relation to consciousness. Then, we focus on different states of consciousness - wakefulness, sleep, anesthesia, coma, meditation, and hypnosis - and on what mysteries predictive processing has been able to disclose about brain functioning in such states. We review studies investigating how the neural signatures of auditory predictions are modulated by states of reduced or lacking consciousness. As a future outlook, we propose the combination of electrophysiological and computational techniques that will allow investigation of which facets of sensory predictive processes are maintained when consciousness fades away.

scholarly journals Disentangling Predictive Processing in the Brain: A Meta-analytic Study in Favour of a Predictive Network

2021 ◽  
Author(s):  
Linda Ficco ◽  
Lorenzo Mancuso ◽  
Jordi Manuello ◽  
Alessia Teneggi ◽  
Donato Liloia ◽  
...  
Keyword(s):  
Large Scale ◽  
Inferior Frontal Gyrus ◽  
Predictive Coding ◽  
Likelihood Estimation ◽  
Brain Regions ◽  
Predictive Processing ◽  
Spatial Convergence ◽  
Large Scale Networks ◽  
The Brain

Abstract According to the predictive coding (PC) theory, the brain is constantly engaged in predicting its upcoming states and refining these predictions through error signal. Despite extensive research has investigated the neural bases of this theory, to date no previous study has systematically attempted to define the neural mechanisms of predictive coding across studies and sensory channels, focussing on functional connectivity. In this study, we employ a coordinate-based meta-analytical approach to address this issue. We first use the Activation Likelihood Estimation (ALE) algorithm to detect spatial convergence across studies, related to prediction error and encoding. Overall, our ALE results suggest the ultimate role of the left inferior frontal gyrus and left insula in both processes. Moreover, we employ a task-based meta-analytic connectivity method (Seed-Voxel Correlations Consensus). This technique reveals a large, bilateral predictive network, which resembles large-scale networks involved in task-driven attention and execution. In sum, we find that: i) predictive processing seems to occur more in certain brain regions than others, when considering different sensory modalities at a time; ii) there is no evidence, at the network level, for a distinction between error and prediction processing.

scholarly journals BDNF Val66Met polymorphism as putative genetic substrate of music-induced plasticity in auditory prediction

2021 ◽  
Author(s):  
Silvia EP Bruzzone ◽  
Leonardo Bonetti ◽  
Tiina Paunio ◽  
Katri Kantojarvi ◽  
Marina Kliuchko ◽  
...  
Keyword(s):  
Auditory Cortex ◽  
Auditory Processing ◽  
Brain Activity ◽  
Musical Training ◽  
Predictive Processing ◽  
Brain Anatomy ◽  
Prediction Errors ◽  
Functional Changes ◽  
Human Participants ◽  
The Individual

Predictive processing of sounds depends on the constant updating of priors based on exposure to posteriors, which through repeated exposure mediates learning. The result of such corrections to the model is seen in musicians, whose lifelong training results in measurable plasticity of audio-motor brain anatomy and functionality. It has been suggested that the plasticity of auditory predictive processes depends on the interaction between the environment and the individual genetic substrate. However, empirical evidence to this is still missing. BDNF is a critical genetic factor affecting learning and plasticity, and its widely studied functional variant Val66Met single-nucleotide polymorphism offers a unique opportunity to investigate neuroplastic functional changes occurring upon a years-long training. We hypothesised that BDNF gene variations would be driving neuroplasticity of the auditory cortex in musically trained human participants. To this goal, musicians and non-musicians were recruited and divided in Val/Val and Met carriers and their brain activity measured with magnetoencephalography (MEG) while they listened to a regular auditory sequence containing different types of prediction errors. The auditory cortex responses to prediction errors was enhanced in Val/Val carriers who underwent intensive musical training, compared to Met and non-musicians. Our results point at a role of gene-regulated neurotrophic factors in the neural adaptations of auditory processing after long-term training.

scholarly journals Enhanced mismatch negativity responses in harmonic compared to inharmonic sound sequences

2021 ◽  
Author(s):  
David Ricardo Quiroga-Martinez ◽  
Krzysztof Basinski ◽  
Jonathan Nasielski ◽  
Barbara Tillmann ◽  
Elvira Brattico ◽  
...  
Keyword(s):  
Information Entropy ◽  
Mismatch Negativity ◽  
Predictive Coding ◽  
Predictive Processing ◽  
Oddball Paradigm ◽  
Auditory Information ◽  
Prediction Errors ◽  
Frequency Spectra ◽  
Auditory Information Processing ◽  
The Difference

Many natural sounds have frequency spectra composed of integer multiples of a fundamental frequency. This property, known as harmonicity, plays an important role in auditory information processing. However, the extent to which harmonicity influences the processing of sound features beyond pitch is still unclear. This is interesting because harmonic sounds have lower information entropy than inharmonic sounds. According to predictive processing accounts of perception, this property could produce more salient neural responses due to the brain weighting of sensory signals according to their uncertainty. In the present study, we used electroencephalography to investigate brain responses to harmonic and inharmonic sounds commonly occurring in music: piano tones and hi-hat cymbal sounds. In a multi-feature oddball paradigm, we measured mismatch negativity (MMN) and P3a responses to timbre, intensity, and location deviants in listeners with and without congenital amusia, an impairment of pitch processing. As hypothesized, we observed larger amplitudes and earlier latencies for harmonic compared to inharmonic sounds for both MMN and P3a responses. These harmonicity effects were modulated by sound feature. Moreover, the difference in P3a latency between harmonic and inharmonic sounds was larger for controls than amusics. We propose an explanation of these results based on predictive coding and discuss the relationship between harmonicity, information entropy, and precision weighting of prediction errors.

scholarly journals Cortico-Fugal Regulation of Predictive Coding

2021 ◽  
Author(s):  
Alexandria M.H. Lesicko ◽  
Christopher F. Angeloni ◽  
Jennifer M. Blackwell ◽  
Mariella De Biasi ◽  
Maria N. Geffen
Keyword(s):  
Auditory Cortex ◽  
Prediction Error ◽  
Predictive Coding ◽  
Brain Regions ◽  
Central Nucleus ◽  
Predictive Processing ◽  
Subcortical Structures ◽  
Cortical Input ◽  
Repetition Suppression ◽  
Error Suppression

ABSTRACTSensory systems must account for both contextual factors and prior experience to adaptively engage with the dynamic external environment. In the central auditory system, neurons modulate their responses to sounds based on statistical context. These response modulations can be understood through a hierarchical predictive coding lens: responses to repeated stimuli are progressively decreased, in a process known as repetition suppression, whereas unexpected stimuli produce a prediction error signal. Prediction error incrementally increases along the auditory hierarchy from the inferior colliculus (IC) to the auditory cortex (AC), suggesting that these regions may engage in hierarchical predictive coding. A potential substrate for top-down predictive cues is the massive set of descending projections from the auditory cortex to subcortical structures. To assess the role of these projections in predictive coding, we optogenetically suppressed the auditory cortico-collicular feedback in awake mice while recording responses from IC neurons to stimuli designed to test prediction error and repetition suppression. Suppression of the cortico-collicular pathway led to a decrease in prediction error in IC. Repetition suppression was unaffected by cortico-collicular inactivation, suggesting that this metric may reflect fatigue of bottom-up sensory inputs rather than predictive processing. We also discovered populations of IC neurons that exhibit repetition enhancement, an increase in firing with stimulus repetition, and error suppression, a stronger response to a tone in a predictable rather than unpredictable context. Cortico-collicular suppression led to a decrease in repetition enhancement in the central nucleus and a reduction in error suppression in shell regions of IC. These changes in predictive coding metrics arose from bidirectional modulations in the response to the standard and deviant contexts, such that neurons in IC responded more similarly to each context in the absence of cortical input. Our results demonstrate that the auditory cortex provides cues about the statistical context of sound to subcortical brain regions via direct feedback, regulating processing of both prediction and repetition.

scholarly journals Depth-resolved intersubject functional correlation of 7T BOLD signals reveals increased stimulus related information sharing across deep layers in premotor and parietal nodes for predictable actions

2021 ◽  
Author(s):  
Leonardo Cerliani ◽  
Ritu Bhandari ◽  
Lorenzo De Angelis ◽  
Wietske van der Zwaag ◽  
Pierre-Louis Bazin ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Action Observation ◽  
Predictive Coding ◽  
Brain Regions ◽  
Natural Order ◽  
Prediction Errors ◽  
Motor Programs ◽  
Feed Forward ◽  
Functional Correlation ◽  
Deep Layers

While the brain regions involved in action observation are relatively well documented in humans and primates, how these regions communicate to help understand and predict actions remains poorly understood. Traditional views emphasized a feed-forward architecture in which visual features are organized into increasingly complex representations that feed onto motor programs in parietal and then premotor cortices where the matching of observed actions upon the observer's own motor programs contributes to action understanding. Predictive coding models place less emphasis on feed-forward connections and propose that feed-back connections from premotor regions back to parietal and visual neurons represent predictions about upcoming actions that can supersede visual inputs when actions become predictable, with visual input then merely representing prediction errors. Here we leverage the notion that feed-back connections target deeper cortical layers than feed-forward connections to help adjudicate across these views. Specifically, we test whether observing sequences of hand actions in their natural order, which permits participants to predict upcoming actions, triggers more feed-back input to parietal regions than seeing the same actions in a scrambled sequence that hinders making predictions. Using submillimeter fMRI acquisition at 7T, we find that watching predictable sequences triggers more action-related activity (as measured using intersubject functional correlation) in deep layers of the parietal cortical area PFt than watching the exact same actions in scrambled and hence unpredictable sequence. In addition, functional connectivity analysis performed using intersubject functional connectivity confirms that this deep, action-related signals in PFt could originate from ventral premotor region BA44. This data showcases the utility of intersubject functional correlation in combination with 7T MRI to explore the architecture of social cognition under more naturalistic conditions, and provides evidence for models that emphasize the importance of feed-back connections in action prediction.

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