Tracking the Spatiotemporal Neural Dynamics of Real-world Object Size and Animacy in the Human Brain

2018 ◽  
Vol 30 (11) ◽  
pp. 1559-1576 ◽  
Author(s):  
Seyed-Mahdi Khaligh-Razavi ◽  
Radoslaw Martin Cichy ◽  
Dimitrios Pantazis ◽  
Aude Oliva
Keyword(s):  
Human Brain ◽  
Real World ◽  
Temporal Dynamics ◽  
Brain Activity ◽  
Temporal Cortex ◽  
Neural Dynamics ◽  
Control Analysis ◽  
Object Size ◽  
Parahippocampal Cortex ◽  
Early Visual Cortex

Animacy and real-world size are properties that describe any object and thus bring basic order into our perception of the visual world. Here, we investigated how the human brain processes real-world size and animacy. For this, we applied representational similarity to fMRI and MEG data to yield a view of brain activity with high spatial and temporal resolutions, respectively. Analysis of fMRI data revealed that a distributed and partly overlapping set of cortical regions extending from occipital to ventral and medial temporal cortex represented animacy and real-world size. Within this set, parahippocampal cortex stood out as the region representing animacy and size stronger than most other regions. Further analysis of the detailed representational format revealed differences among regions involved in processing animacy. Analysis of MEG data revealed overlapping temporal dynamics of animacy and real-world size processing starting at around 150 msec and provided the first neuromagnetic signature of real-world object size processing. Finally, to investigate the neural dynamics of size and animacy processing simultaneously in space and time, we combined MEG and fMRI with a novel extension of MEG–fMRI fusion by representational similarity. This analysis revealed partly overlapping and distributed spatiotemporal dynamics, with parahippocampal cortex singled out as a region that represented size and animacy persistently when other regions did not. Furthermore, the analysis highlighted the role of early visual cortex in representing real-world size. A control analysis revealed that the neural dynamics of processing animacy and size were distinct from the neural dynamics of processing low-level visual features. Together, our results provide a detailed spatiotemporal view of animacy and size processing in the human brain.

scholarly journals Early Visual Cortex Dynamics during Top–Down Modulated Shifts of Feature-Selective Attention

2016 ◽  
Vol 28 (4) ◽  
pp. 643-655 ◽  
Author(s):  
Matthias M. Müller ◽  
Mireille Trautmann ◽  
Christian Keitel
Keyword(s):  
Visual Cortex ◽  
Selective Attention ◽  
Time Course ◽  
Temporal Dynamics ◽  
Behavioral Data ◽  
Coherent Motion ◽  
Neural Dynamics ◽  
Early Visual Cortex ◽  
Time Courses ◽  
Feature Dimension

Shifting attention from one color to another color or from color to another feature dimension such as shape or orientation is imperative when searching for a certain object in a cluttered scene. Most attention models that emphasize feature-based selection implicitly assume that all shifts in feature-selective attention underlie identical temporal dynamics. Here, we recorded time courses of behavioral data and steady-state visual evoked potentials (SSVEPs), an objective electrophysiological measure of neural dynamics in early visual cortex to investigate temporal dynamics when participants shifted attention from color or orientation toward color or orientation, respectively. SSVEPs were elicited by four random dot kinematograms that flickered at different frequencies. Each random dot kinematogram was composed of dashes that uniquely combined two features from the dimensions color (red or blue) and orientation (slash or backslash). Participants were cued to attend to one feature (such as color or orientation) and respond to coherent motion targets of the to-be-attended feature. We found that shifts toward color occurred earlier after the shifting cue compared with shifts toward orientation, regardless of the original feature (i.e., color or orientation). This was paralleled in SSVEP amplitude modulations as well as in the time course of behavioral data. Overall, our results suggest different neural dynamics during shifts of attention from color and orientation and the respective shifting destinations, namely, either toward color or toward orientation.

scholarly journals Brain activity reveals multiple motor-learning mechanisms in a real-world task

2020 ◽  
Author(s):  
Shlomi Haar ◽  
A. Aldo Faisal
Keyword(s):  
Motor Learning ◽  
Real World ◽  
Brain Activity ◽  
Skill Learning ◽  
Neural Dynamics ◽  
Complex Task ◽  
Motor Skill Learning ◽  
Learning Mechanisms ◽  
Learning Mechanism ◽  
Eeg Recordings

AbstractMany recent studies found signatures of motor learning in neural Beta oscillations (13– 30Hz), and specifically in the post-movement Beta rebound (PMBR). All these studies were in controlled laboratory-tasks in which the task designed to induce the studied learning mechanism. Interestingly, these studies reported opposing dynamics of the PMBR magnitude over learning for the error-based and reward-based tasks (increase versus decrease, respectively). Here we explored the PMBR dynamics during real-world motor-skill-learning in a billiards task using mobile-brain-imaging. Our EEG recordings highlight the opposing dynamics of PMBR magnitudes (increase versus decrease) between different subjects performing the same task. The groups of subjects, defined by their neural dynamics, also showed behavioural differences expected for different learning mechanisms. Our results suggest that when faced with the complexity of the real-world different subjects might use different learning mechanisms for the same complex task. We speculate that all subjects combine multi-modal mechanisms of learning, but different subjects have different predominant learning mechanisms.

Decoding human brain activity during real-world experiences

2007 ◽  
Vol 11 (8) ◽  
pp. 356-365 ◽  
Author(s):  
Hugo J. Spiers ◽  
Eleanor A. Maguire
Keyword(s):  
Human Brain ◽  
Real World ◽  
Brain Activity

scholarly journals Combining human MEG and fMRI data reveals the spatio-temporal dynamics of animacy and real-world object size

2017 ◽  
Vol 17 (10) ◽  
pp. 574
Author(s):  
Seyed-Mahdi Khaligh-Razavi ◽  
Radoslaw Cichy ◽  
Dimitrios Pantazis ◽  
Aude Oliva
Keyword(s):  
Real World ◽  
Temporal Dynamics ◽  
Fmri Data ◽  
Object Size ◽  
Spatio Temporal

scholarly journals A spatiotemporal complexity architecture of human brain activity

2021 ◽  
Author(s):  
Stephan Krohn ◽  
Nina von Schwanenflug ◽  
Leonhard Waschke ◽  
Amy Romanello ◽  
Martin Gell ◽  
...  
Keyword(s):  
Human Brain ◽  
Neural Activity ◽  
Large Scale ◽  
Temporal Dynamics ◽  
Brain Activity ◽  
Brain Regions ◽  
Brain Organization ◽  
Functional Connections ◽  
Signal Complexity ◽  
The Brain

The human brain operates in large-scale functional networks, collectively subsumed as the functional connectome1-13. Recent work has begun to unravel the organization of the connectome, including the temporal dynamics of brain states14-20, the trade-off between segregation and integration9,15,21-23, and a functional hierarchy from lower-order unimodal to higher-order transmodal processing systems24-27. However, it remains unknown how these network properties are embedded in the brain and if they emerge from a common neural foundation. Here we apply time-resolved estimation of brain signal complexity to uncover a unifying principle of brain organization, linking the connectome to neural variability6,28-31. Using functional magnetic resonance imaging (fMRI), we show that neural activity is marked by spontaneous "complexity drops" that reflect episodes of increased pattern regularity in the brain, and that functional connections among brain regions are an expression of their simultaneous engagement in such episodes. Moreover, these complexity drops ubiquitously propagate along cortical hierarchies, suggesting that the brain intrinsically reiterates its own functional architecture. Globally, neural activity clusters into temporal complexity states that dynamically shape the coupling strength and configuration of the connectome, implementing a continuous re-negotiation between cost-efficient segregation and communication-enhancing integration9,15,21,23. Furthermore, complexity states resolve the recently discovered association between anatomical and functional network hierarchies comprehensively25-27,32. Finally, brain signal complexity is highly sensitive to age and reflects inter-individual differences in cognition and motor function. In sum, we identify a spatiotemporal complexity architecture of neural activity — a functional "complexome" that gives rise to the network organization of the human brain.

scholarly journals Neural dynamics of real-world object vision that guide behaviour

2017 ◽  
Author(s):  
Radoslaw M. Cichy ◽  
Nikolaus Kriegeskorte ◽  
Kamila M. Jozwik ◽  
Jasper J.F. van den Bosch ◽  
Ian Charest
Keyword(s):  
Human Brain ◽  
Real World ◽  
Brain Activity ◽  
Stimulus Onset ◽  
Visual Features ◽  
Object Representations ◽  
Neuronal Dynamics ◽  
Visual Stream ◽  
Semantic Categories ◽  
Ventral Visual Stream

1AbstractVision involves complex neuronal dynamics that link the sensory stream to behaviour. To capture the richness and complexity of the visual world and the behaviour it entails, we used an ecologically valid task with a rich set of real-world object images. We investigated how human brain activity, resolved in space with functional MRI and in time with magnetoencephalography, links the sensory stream to behavioural responses. We found that behaviour-related brain activity emerged rapidly in the ventral visual pathway within 200ms of stimulus onset. The link between stimuli, brain activity, and behaviour could not be accounted for by either category membership or visual features (as provided by an artificial deep neural network model). Our results identify behaviourally-relevant brain activity during object vision, and suggest that object representations guiding behaviour are complex and can neither be explained by visual features or semantic categories alone. Our findings support the view that visual representations in the ventral visual stream need to be understood in terms of their relevance to behaviour, and highlight the importance of complex behavioural assessment for human brain mapping.

scholarly journals Spotting the path that leads nowhere: Modulation of human theta and alpha oscillations induced by trajectory changes during navigation

2018 ◽  
Author(s):  
Amir-Homayoun Javadi ◽  
Eva Zita Patai ◽  
Aaron Margois ◽  
Heng-Ru M. Tan ◽  
Darshan Kumaran ◽  
...  
Keyword(s):  
Temporal Dynamics ◽  
Brain Activity ◽  
Cognitive Map ◽  
Lava Flows ◽  
Neural Dynamics ◽  
Alpha Power ◽  
Neural Basis ◽  
Alpha Oscillations ◽  
Fine Grained ◽  
Desert Island

AbstractThe capacity to take efficient detours and exploit novel shortcuts during navigation is thought to be supported by a cognitive map of the environment. Despite advances in understanding the neural basis of the cognitive map, little is known about the neural dynamics associated with detours and shortcuts. Here, we recorded magnetoencephalography from humans as they navigated a virtual desert island riven by shifting lava flows. The task probed their ability to take efficient detours and shortcuts to remembered goals. We report modulation in event-related fields and theta power as participants identified real shortcuts and differentiated these from false shortcuts that led along suboptimal paths. Additionally, we found that a decrease in alpha power preceded ‘back-tracking’ where participants spontaneously turned back along a previous path. These findings help advance our understanding of the fine-grained temporal dynamics of human brain activity during navigation and support the development of models of brain networks that support navigation.

scholarly journals Fast temporal dynamics and causal relevance of face processing in the human temporal cortex

2018 ◽  
Author(s):  
Jessica Schrouff ◽  
Omri Raccah ◽  
Sori Baek ◽  
Vinitha Rangarajan ◽  
Sina Salehi ◽  
...  
Keyword(s):  
Human Brain ◽  
Face Processing ◽  
Common Ground ◽  
Temporal Dynamics ◽  
Time Window ◽  
Temporal Cortex ◽  
Surrogate Marker ◽  
Marine Animal ◽  
Distributed Models ◽  
Spatio Temporal

ABSTRACTRecordings with a large number of intracranial electrodes in eight neurosurgical subjects offered a unique opportunity to examine the fast temporal dynamics of face processing simultaneously across a relatively large extent of the human temporal cortex (TC). Measuring the power of slow oscillatory bands of activity (θ, α, β, and γ) as well as High-Frequency Broadband (HFB, 70-177 Hz) signal, we found that the HFB showed the strongest univariate and multivariate changes in response to face compared to non-face stimuli. Using the HFB signal as a surrogate marker for local cortical engagement, we identified recording sites with selective responses to faces that were anatomically consistent across subjects and responded with graded strength to human, mammal, bird, and marine animal faces. Importantly, the most face selective sites were located more posteriorly and responded earlier than those with less selective responses to faces. Using machine learning based methods, we demonstrated that a sparse model focusing on information from the human face selective sites performed as well as, or better than, anatomically distributed models of face processing when discriminating faces from non-faces stimuli. Lastly, we identified the posterior fusiform (pFUS) site as causally the most relevant node for inducing distortion of face perception by direct electrical stimulation. Our findings support the notion of face information being processed first in the most selective sites - that are anatomically discrete and localizable within individual brains and anatomically consistent across subjects – which is then distributed in time to less selective anterior temporal sites within a time window that is too fast to be detected by current neuroimaging methods. The new information about the fast spatio-temporal dynamics of face processing across multiple sites of the human brain provides a new common ground for unifying the seemingly contradictory modular and distributed models of face processing in the human brain.

scholarly journals Self-similarity and multifractality in human brain activity: a wavelet-based analysis of scale-free brain dynamics☆

2018 ◽  
Author(s):  
Daria La Rocca ◽  
Nicolas Zilber ◽  
Patrice Abry ◽  
Virginie van Wassenhove ◽  
Philippe Ciuciu
Keyword(s):  
Human Brain ◽  
Resting State ◽  
Temporal Dynamics ◽  
Brain Activity ◽  
Temporal Structure ◽  
Estimation Methods ◽  
Brain Dynamics ◽  
List Type ◽  
Self Similarity ◽  
Scale Free

AbstractBackgroundThe temporal structure of macroscopic brain activity displays both oscillatory and scale-free dynamics. While the functional relevance of neural oscillations has been largely investigated, both the nature and the role of scale-free dynamics in brain processing have been disputed.New MethodHere, we offer a novel method to rigorously enrich the characterization of scale-free brain activity using a robust wavelet-based assessment of self-similarity and multifractality. For this, we analyzed human brain activity recorded with magnetoencephalography (MEG) while participants were at rest or performing a task.ResultsFirst, we report consistent infraslow (from 0.1 to 1.5 Hz) scalefree dynamics (i.e., self-similarity and multifractality) in resting-state and task data. Second, we observed a fronto-occipital gradient of self-similarity reminiscent of the known hierarchy of temporal scales from sensory to higherorder cortices; the anatomical gradient was more pronounced in task than in rest. Third, we observed a significant increase of multifractality during task as compared to rest. Additionally, the decrease in self-similarity and the increase in multifractality from rest to task were negatively correlated in regions involved in the task, suggesting a shift from structured global temporal dynamics in resting-state to locally bursty and non Gaussian scalefree structures during task.Comparison with Existing Method(s)We showed that the wavelet leader based multifractal approach extends power spectrum estimation methods in the way of characterizing finely scale-free brain dynamics.ConclusionsAltogether, our approach provides novel fine-grained characterizations of scale-free dynamics in human brain activity.HighlightsWe estimated scale-free human brain dynamics using wavelet-leader formalism.High-to-low self-similarity defined a fronto-occipital gradient.The gradient was enhanced in task compared to resting-state.Scale-free brain dynamics showed multifractal properties.Self-similarity decreased whereas multifractality increased from rest to task.

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