scholarly journals Stability and flexibility in multisensory sampling: insights from perceptual illusions

2019 ◽  
Vol 121 (5) ◽  
pp. 1588-1590 ◽  
Author(s):  
Luca Casartelli
Keyword(s):  
Temporal Dynamics ◽  
Multisensory Processing ◽  
General Idea ◽  
Perceptual Illusions ◽  
The Brain ◽  
Multisensory Processes

Neural, oscillatory, and computational counterparts of multisensory processing remain a crucial challenge for neuroscientists. Converging evidence underlines a certain efficiency in balancing stability and flexibility of sensory sampling, supporting the general idea that multiple parallel and hierarchically organized processing stages in the brain contribute to our understanding of the (sensory/perceptual) world. Intriguingly, how temporal dynamics impact and modulate multisensory processes in our brain can be investigated benefiting from studies on perceptual illusions.

scholarly journals The brain timewise: how timing shapes and supports brain function

2015 ◽  
Vol 370 (1668) ◽  
pp. 20140170 ◽  
Author(s):  
Riitta Hari ◽  
Lauri Parkkonen
Keyword(s):  
Human Brain ◽  
Brain Imaging ◽  
Brain Function ◽  
Temporal Dynamics ◽  
Generative Models ◽  
Network Activity ◽  
Brain Diseases ◽  
Specific Information ◽  
Non Invasive ◽  
The Brain

We discuss the importance of timing in brain function: how temporal dynamics of the world has left its traces in the brain during evolution and how we can monitor the dynamics of the human brain with non-invasive measurements. Accurate timing is important for the interplay of neurons, neuronal circuitries, brain areas and human individuals. In the human brain, multiple temporal integration windows are hierarchically organized, with temporal scales ranging from microseconds to tens and hundreds of milliseconds for perceptual, motor and cognitive functions, and up to minutes, hours and even months for hormonal and mood changes. Accurate timing is impaired in several brain diseases. From the current repertoire of non-invasive brain imaging methods, only magnetoencephalography (MEG) and scalp electroencephalography (EEG) provide millisecond time-resolution; our focus in this paper is on MEG. Since the introduction of high-density whole-scalp MEG/EEG coverage in the 1990s, the instrumentation has not changed drastically; yet, novel data analyses are advancing the field rapidly by shifting the focus from the mere pinpointing of activity hotspots to seeking stimulus- or task-specific information and to characterizing functional networks. During the next decades, we can expect increased spatial resolution and accuracy of the time-resolved brain imaging and better understanding of brain function, especially its temporal constraints, with the development of novel instrumentation and finer-grained, physiologically inspired generative models of local and network activity. Merging both spatial and temporal information with increasing accuracy and carrying out recordings in naturalistic conditions, including social interaction, will bring much new information about human brain function.

scholarly journals Using the past to estimate sensory uncertainty

2019 ◽  
Author(s):  
Ulrik Beierholm ◽  
Tim Rohe ◽  
Ambra Ferrari ◽  
Oliver Stegle ◽  
Uta Noppeney
Keyword(s):  
Temporal Dynamics ◽  
Visual Signals ◽  
Perceptual Inference ◽  
Reliability Estimates ◽  
Uncertainty Estimates ◽  
Classical Models ◽  
Past Experiences ◽  
Combine Information ◽  
Sensory Uncertainty ◽  
The Brain

AbstractTo form the most reliable percept of the environment, the brain needs to represent sensory uncertainty. Current theories of perceptual inference assume that the brain computes sensory uncertainty instantaneously and independently for each stimulus.In a series of psychophysics experiments human observers localized auditory signals that were presented in synchrony with spatially disparate visual signals. Critically, the visual noise changed dynamically over time with or without intermittent jumps. Our results show that observers integrate audiovisual inputs weighted by sensory reliability estimates that combine information from past and current signals as predicted by an optimal Bayesian learner or approximate strategies of exponential discountingOur results challenge classical models of perceptual inference where sensory uncertainty estimates depend only on the current stimulus. They demonstrate that the brain capitalizes on the temporal dynamics of the external world and estimates sensory uncertainty by combining past experiences with new incoming sensory signals.

scholarly journals From static to temporal network theory: Applications to functional brain connectivity

2017 ◽  
Vol 1 (2) ◽  
pp. 69-99 ◽  
Author(s):  
William Hedley Thompson ◽  
Per Brantefors ◽  
Peter Fransson
Keyword(s):  
Network Theory ◽  
Temporal Dynamics ◽  
Brain Connectivity ◽  
Resting State Fmri ◽  
Network Activity ◽  
Temporal Network ◽  
Functional Brain ◽  
Brain Connectome ◽  
The Brain ◽  
Network Neuroscience

Network neuroscience has become an established paradigm to tackle questions related to the functional and structural connectome of the brain. Recently, interest has been growing in examining the temporal dynamics of the brain’s network activity. Although different approaches to capturing fluctuations in brain connectivity have been proposed, there have been few attempts to quantify these fluctuations using temporal network theory. This theory is an extension of network theory that has been successfully applied to the modeling of dynamic processes in economics, social sciences, and engineering article but it has not been adopted to a great extent within network neuroscience. The objective of this article is twofold: (i) to present a detailed description of the central tenets of temporal network theory and describe its measures, and; (ii) to apply these measures to a resting-state fMRI dataset to illustrate their utility. Furthermore, we discuss the interpretation of temporal network theory in the context of the dynamic functional brain connectome. All the temporal network measures and plotting functions described in this article are freely available as the Python package Teneto.

Peri-personal space as an interface for self-environment interaction

2021 ◽  
pp. 17-46
Author(s):  
Jean-Paul Noel ◽  
Tommaso Bertoni ◽  
Andrea Serino
Keyword(s):  
Motor System ◽  
Computational Models ◽  
Multisensory Processing ◽  
Critical Role ◽  
Personal Space ◽  
Environment Interaction ◽  
Specific System ◽  
Physical Interactions ◽  
Psychophysical Studies ◽  
The Brain

The brain has developed a specific system to encode the space closely surrounding our body, our peri-personal space (PPS). This space is the theatre where all physical interactions with objects in the environment occur, and thus is postulated to play a critical role in both approaching and defensive behaviour. Here, we first describe the classic neurophysiological findings that have led researchers to conceive of PPS as a multisensory-motor interface. This historical perspective is given to clarify what properties are strictly related to PPS encoding, and what characteristics bear out or are related to PPS. Then, in an effort to uncover gaps in knowledge that often go unnoticed, we critically examine the association between PPS and i) multisensory processing, and ii) the motor system—its strongest allies. We do not mean to say that PPS isn’t multisensory-motor, simply to pinpoint current research shortcomings. Subsequently, we detail more recent psychophysical studies, highlighting the extreme plasticity of PPS, and its putative role in bodily self-consciousness and social cognition. Lastly, we briefly discuss computational models of PPS. Throughout the chapter, we particularly attempt to emphasize open areas of investigation. By critically evaluating past findings, many of them our own, we hope to provide a forward-looking perspective on the study of PPS.

Capturing the Forest but Missing the Trees: Microstates Inadequate for Characterizing Shorter-Scale EEG Dynamics

2019 ◽  
Vol 31 (11) ◽  
pp. 2177-2211 ◽  
Author(s):  
Saurabh Bhaskar Shaw ◽  
Kiret Dhindsa ◽  
James P. Reilly ◽  
Suzanna Becker
Keyword(s):  
Systems Analysis ◽  
Temporal Dynamics ◽  
Field Potential ◽  
Stable States ◽  
Dynamical Systems Analysis ◽  
Eeg Data ◽  
Show Evidence ◽  
Winner Takes All ◽  
Microstate Analysis ◽  
The Brain

The brain is known to be active even when not performing any overt cognitive tasks, and often it engages in involuntary mind wandering. This resting state has been extensively characterized in terms of fMRI-derived brain networks. However, an alternate method has recently gained popularity: EEG microstate analysis. Proponents of microstates postulate that the brain discontinuously switches between four quasi-stable states defined by specific EEG scalp topologies at peaks in the global field potential (GFP). These microstates are thought to be “atoms of thought,” involved with visual, auditory, salience, and attention processing. However, this method makes some major assumptions by excluding EEG data outside the GFP peaks and then clustering the EEG scalp topologies at the GFP peaks, assuming that only one microstate is active at any given time. This study explores the evidence surrounding these assumptions by studying the temporal dynamics of microstates and its clustering space using tools from dynamical systems analysis, fractal, and chaos theory to highlight the shortcomings in microstate analysis. The results show evidence of complex and chaotic EEG dynamics outside the GFP peaks, which is being missed by microstate analysis. Furthermore, the winner-takes-all approach of only one microstate being active at a time is found to be inadequate since the dynamic EEG scalp topology does not always resemble that of the assigned microstate, and there is competition among the different microstate classes. Finally, clustering space analysis shows that the four microstates do not cluster into four distinct and separable clusters. Taken collectively, these results show that the discontinuous description of EEG microstates is inadequate when looking at nonstationary short-scale EEG dynamics.

Saliency-based Rhythmic Coordination of Perceptual Predictions

2020 ◽  
Vol 32 (2) ◽  
pp. 201-211 ◽  
Author(s):  
Qiaoli Huang ◽  
Huan Luo
Keyword(s):  
Temporal Dynamics ◽  
Theta Band ◽  
Behavioral Profile ◽  
Fine Grained ◽  
Attentional Process ◽  
Behavioral Measurement ◽  
Processing Resources ◽  
Rhythmic Coordination ◽  
The Brain ◽  
Different Levels

Objects, shown explicitly or held in mind internally, compete for limited processing resources. Recent studies have demonstrated that attention samples locations and objects rhythmically. Interestingly, periodic sampling not only operates over objects in the same scene but also occurs for multiple perceptual predictions that are held in attention for incoming inputs. However, how the brain coordinates perceptual predictions that are endowed with different levels of bottom–up saliency information remains unclear. To address the issue, we used a fine-grained behavioral measurement to investigate the temporal dynamics of processing of high- and low-salient visual stimuli, which have equal possibility to occur within experimental blocks. We demonstrate that perceptual predictions associated with different levels of saliency are organized via a theta-band rhythmic course and are optimally processed in different phases within each theta-band cycle. Meanwhile, when the high- and low-salient stimuli are presented in separate blocks and thus not competing with each other, the periodic behavioral profile is no longer present. In summary, our findings suggest that attention samples and coordinates multiple perceptual predictions through a theta-band rhythm according to their relative saliency. Our results, in combination with previous studies, advocate the rhythmic nature of attentional process.

scholarly journals STAB: a spatio-temporal cell atlas of the human brain

2020 ◽  
Vol 49 (D1) ◽  
pp. D1029-D1037
Author(s):  
Liting Song ◽  
Shaojun Pan ◽  
Zichao Zhang ◽  
Longhao Jia ◽  
Wei-Hua Chen ◽  
...  
Keyword(s):  
Human Brain ◽  
Single Cell ◽  
Temporal Dynamics ◽  
Cell Types ◽  
Brain Regions ◽  
Molecular Characteristics ◽  
Great Effort ◽  
Brain Functions ◽  
Spatio Temporal ◽  
The Brain

Abstract The human brain is the most complex organ consisting of billions of neuronal and non-neuronal cells that are organized into distinct anatomical and functional regions. Elucidating the cellular and transcriptome architecture underlying the brain is crucial for understanding brain functions and brain disorders. Thanks to the single-cell RNA sequencing technologies, it is becoming possible to dissect the cellular compositions of the brain. Although great effort has been made to explore the transcriptome architecture of the human brain, a comprehensive database with dynamic cellular compositions and molecular characteristics of the human brain during the lifespan is still not available. Here, we present STAB (a Spatio-Temporal cell Atlas of the human Brain), a database consists of single-cell transcriptomes across multiple brain regions and developmental periods. Right now, STAB contains single-cell gene expression profiling of 42 cell subtypes across 20 brain regions and 11 developmental periods. With STAB, the landscape of cell types and their regional heterogeneity and temporal dynamics across the human brain can be clearly seen, which can help to understand both the development of the normal human brain and the etiology of neuropsychiatric disorders. STAB is available at http://stab.comp-sysbio.org.

scholarly journals On the brains of two sub-fossil malagasy lemuroids

1898 ◽  
Vol 62 (379-387) ◽  
pp. 46-50 ◽  
Keyword(s):  
General Idea ◽  
Zoological Society ◽  
The Brain

The casts here described and figured have been moulded from the brain-cavities of the skulls of two sub-fossil Lemuroids from Madagascar, the descriptions of which I have already published. For comparison with the brains of living Lemuroids the figures published by P. Gervais are the best adapted for the present purpose, since they, too, are drawn from moulds of the brain cavity, and give on one plate a good general idea of the variations of the Lemur brain. 1. Globilemur Flacourti , Maj. The larger of the two casts was taken from the skull briefly described by me at the meeting of the Zoological Society of London, June 20, 1893.

scholarly journals Brain network dynamics are hierarchically organized in time

2017 ◽  
Vol 114 (48) ◽  
pp. 12827-12832 ◽  
Author(s):  
Diego Vidaurre ◽  
Stephen M. Smith ◽  
Mark W. Woolrich
Keyword(s):  
Large Scale ◽  
Temporal Dynamics ◽  
Brain Network ◽  
Specific Measure ◽  
Brain Areas ◽  
Neuronal Populations ◽  
Subject Specific ◽  
Large Scale Networks ◽  
Network Patterns ◽  
The Brain

The brain recruits neuronal populations in a temporally coordinated manner in task and at rest. However, the extent to which large-scale networks exhibit their own organized temporal dynamics is unclear. We use an approach designed to find repeating network patterns in whole-brain resting fMRI data, where networks are defined as graphs of interacting brain areas. We find that the transitions between networks are nonrandom, with certain networks more likely to occur after others. Further, this nonrandom sequencing is itself hierarchically organized, revealing two distinct sets of networks, or metastates, that the brain has a tendency to cycle within. One metastate is associated with sensory and motor regions, and the other involves areas related to higher order cognition. Moreover, we find that the proportion of time that a subject spends in each brain network and metastate is a consistent subject-specific measure, is heritable, and shows a significant relationship with cognitive traits.

Temporal Dynamics of Emotional Processing in the Brain

2015 ◽  
Vol 7 (4) ◽  
pp. 323-329 ◽  
Author(s):  
Christian E. Waugh ◽  
Elaine Z. Shing ◽  
Brad M. Avery
Keyword(s):  
Emotional Processing ◽  
Temporal Dynamics ◽  
The Brain
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