scholarly journals Dynamics of scene representations in the human brain revealed by magnetoencephalography and deep neural networks

2015 ◽  
Author(s):  
Radoslaw Martin Cichy ◽  
Aditya Khosla ◽  
Dimitrios Pantazis ◽  
Aude Oliva
Keyword(s):  
Human Brain ◽  
Time Course ◽  
Visual Analysis ◽  
Quantitative Model ◽  
Spatial Layout ◽  
Scene Recognition ◽  
Early Signal ◽  
Multistep Process ◽  
Electrophysiological Signal ◽  
Multivariate Pattern

Human scene recognition is a rapid multistep process evolving over time from single scene image to spatial layout processing. We used multivariate pattern analyses on magnetoencephalography (MEG) data to unravel the time course of this cortical process. Following an early signal for lower-level visual analysis of single scenes at ~100ms, we found a marker of real-world scene size, i.e. spatial layout processing, at ~250ms indexing neural representations robust to changes in unrelated scene properties and viewing conditions. For a quantitative explanation that captures the complexity of scene recognition, we compared MEG data to a deep neural network model trained on scene classification. Representations of scene size emerged intrinsically in the model, and resolved emerging neural scene size representation. Together our data provide a first description of an electrophysiological signal for layout processing in humans, and a novel quantitative model of how spatial layout representations may emerge in the human brain.

scholarly journals Dissociation of early and late face-related processes in Autism Spectrum Disorder and Williams syndrome

2021 ◽  
Author(s):  
Alice Gomez ◽  
Guillaume Lio ◽  
Manuela Costa ◽  
Angela Sirigu ◽  
Caroline Demily
Keyword(s):  
Williams Syndrome ◽  
Autism Spectrum ◽  
Neural Signals ◽  
Face To Face ◽  
Face Features ◽  
Early Signal ◽  
Reverse Pattern ◽  
The Face ◽  
Evoked Activity ◽  
Multivariate Pattern

Abstract Williams syndrome (WS) and Autism Spectrum Disorders (ASD) are psychiatric conditions associated with atypical but opposite face-to-face interactions patterns: WS patients overly stare at others, ASD individuals escape eye contact. Whether these behaviors result from dissociable visual processes within the occipito-temporal pathways is unknown. Using high-density electroencephalography, multivariate pattern classification and group blind source separation, we searched for face-related neural signals that could best discriminate WS (N = 14), ASD (N = 14) and neurotypical populations (N = 14). We found two peaks in neurotypical participants: the first at 170ms, an early signal known to be implicated in low-level face features, the second at 260ms, a late component implicated in decoding salient face social cues. The late 260ms signal varied as a function of the distance of the eyes in the face stimulus with respect to the viewers’ fovea, meaning that it was strongest when the eyes were projected on the fovea and weakest when projected in the retinal periphery. Remarkably, both components were found distinctly impaired and preserved in WS and ASD. In WS, we could weakly decode the 170ms signal probably due to their relatively poor ability to process faces’ morphology while the late 260ms component shown to be eye sensitive was highly significant. The reverse pattern was observed in ASD participants who showed neurotypical like early 170ms evoked activity but impaired late evoked 260ms signal. Our study reveals a dissociation between WS and ASD patients and point at different neural origins for their social impairments.

scholarly journals The neural representational space of social memory

2017 ◽  
Author(s):  
Sarah L. Dziura ◽  
James C. Thompson
Keyword(s):  
Social Networks ◽  
Human Brain ◽  
Incidental Learning ◽  
Functional Neuroimaging ◽  
Brain Activity ◽  
Temporoparietal Junction ◽  
Representational Space ◽  
Analysis Technique ◽  
The Social ◽  
Multivariate Pattern

AbstractSocial functioning involves learning about the social networks in which we live and interact; knowing not just our friends, but also who is friends with our friends. Here we utilized a novel incidental learning paradigm and representational similarity analysis (RSA), a functional MRI multivariate pattern analysis technique, to examine the relationship between learning social networks and the brain's response to the faces within the networks. We found that accuracy of learning face pair relationships through observation is correlated with neural similarity patterns to those pairs in the left temporoparietal junction (TPJ), the left fusiform gyrus, and the subcallosal ventromedial prefrontal cortex (vmPFC), all areas previously implicated in social cognition. This model was also significant in portions of the cerebellum and thalamus. These results show that the similarity of neural patterns represent how accurately we understand the closeness of any two faces within a network, regardless of their true relationship. Our findings indicate that these areas of the brain not only process knowledge and understanding of others, but also support learning relations between individuals in groups.Significance StatementKnowledge of the relationships between people is an important skill that helps us interact in a highly social world. While much is known about how the human brain represents the identity, goals, and intentions of others, less is known about how we represent knowledge about social relationships between others. In this study, we used functional neuroimaging to demonstrate that patterns in human brain activity represent memory for recently learned social connections.

Time course of loudness in tone patterns is automatically represented by the human brain

1995 ◽  
Vol 202 (1-2) ◽  
pp. 117-120 ◽  
Author(s):  
Erich Schröger ◽  
Mari Tervaniemi ◽  
Risto Näätänen
Keyword(s):  
Human Brain ◽  
Time Course

scholarly journals Infectomic Analysis of Gene Expression Profiles of Human Brain Microvascular Endothelial Cells Infected withCryptococcus neoformans

2008 ◽  
Vol 2008 ◽  
pp. 1-7 ◽  
Author(s):  
Ambrose Jong ◽  
Chun-Hua Wu ◽  
Wensheng Zhou ◽  
Han-Min Chen ◽  
Sheng-He Huang
Keyword(s):  
Gene Expression ◽  
Endothelial Cells ◽  
Human Brain ◽  
Time Course ◽  
Expression Profiles ◽  
Host Cells ◽  
Microvascular Endothelial Cells ◽  
Brain Microvascular Endothelial Cells ◽  
Microvascular Endothelial ◽  
Time Point

In order to dissect the pathogenesis ofCryptococcus neoformansmeningoencephalitis, a genomic survey of the changes in gene expression of human brain microvascular endothelial cells infected byC.neoformanswas carried out in a time-course study. Principal component analysis (PCA) revealed sigificant fluctuations in the expression levels of different groups of genes during the pathogen-host interaction. Self-organizing map (SOM) analysis revealed that most genes were up- or downregulated 2 folds or more at least at one time point during the pathogen-host engagement. The microarray data were validated by Western blot analysis of a group of genes, includingβ-actin, Bcl-x, CD47, Bax, Bad, and Bcl-2. Hierarchical cluster profile showed that 61 out of 66 listed interferon genes were changed at least at one time point. Similarly, the active responses in expression of MHC genes were detected at all stages of the interaction. Taken together, our infectomic approaches suggest that the host cells significantly change the gene profiles and also actively participate in immunoregulations of the central nervous system (CNS) duringC.neoformansinfection.

scholarly journals On the Time Course of Synchronization Patterns of Neuronal Discharges in the Human Brain during Cognitive Tasks

PLoS ONE ◽  
2013 ◽  
Vol 8 (5) ◽  
pp. e63293 ◽  
Author(s):  
Milan Brázdil ◽  
Jiří Janeček ◽  
Petr Klimeš ◽  
Radek Mareček ◽  
Robert Roman ◽  
...  
Keyword(s):  
Human Brain ◽  
Time Course ◽  
Cognitive Tasks

scholarly journals Semi-automatic Extraction of Functional Dynamic Networks Describing Patient's Epileptic Seizures

2020 ◽  
Vol 11 ◽  
Author(s):  
Gaëtan Frusque ◽  
Pierre Borgnat ◽  
Paulo Gonçalves ◽  
Julien Jung
Keyword(s):  
Time Course ◽  
Visual Analysis ◽  
Dynamic Networks ◽  
Epileptic Seizures ◽  
Brain Regions ◽  
Epileptic Activity ◽  
Patient Specific ◽  
Time Varying ◽  
Visual Interpretation ◽  
Network Decomposition

Intracranial electroencephalography (EEG) studies using stereotactic EEG (SEEG) have shown that during seizures, epileptic activity spreads across several anatomical regions from the seizure onset zone toward remote brain areas. A full and objective characterization of this patient-specific time-varying network is crucial for optimal surgical treatment. Functional connectivity (FC) analysis of SEEG signals recorded during seizures enables to describe the statistical relations between all pairs of recorded signals. However, extracting meaningful information from those large datasets is time consuming and requires high expertise. In the present study, we first propose a novel method named Brain-wide Time-varying Network Decomposition (BTND) to characterize the dynamic epileptogenic networks activated during seizures in individual patients recorded with SEEG electrodes. The method provides a number of pathological FC subgraphs with their temporal course of activation. The method can be applied to several seizures of the patient to extract reproducible subgraphs. Second, we compare the activated subgraphs obtained by the BTND method with visual interpretation of SEEG signals recorded in 27 seizures from nine different patients. As a whole, we found that activated subgraphs corresponded to brain regions involved during the course of the seizures and their time course was highly consistent with classical visual interpretation. We believe that the proposed method can complement the visual analysis of SEEG signals recorded during seizures by highlighting and characterizing the most significant parts of epileptic networks with their activation dynamics.

scholarly journals Hierarchical Classification of Event-Related Potentials for the Recognition of Gender Differences in the Attention Task

Entropy ◽  
2021 ◽  
Vol 23 (11) ◽  
pp. 1547
Author(s):  
Karina Maciejewska ◽  
Wojciech Froelich
Keyword(s):  
Gender Differences ◽  
Human Brain ◽  
Statistical Power ◽  
Temporal Dynamics ◽  
Hierarchical Classification ◽  
Event Related Potentials ◽  
Human Cognition ◽  
Attention Task ◽  
Related Potentials ◽  
Electrophysiological Signal

Research on the functioning of human cognition has been a crucial problem studied for years. Electroencephalography (EEG) classification methods may serve as a precious tool for understanding the temporal dynamics of human brain activity, and the purpose of such an approach is to increase the statistical power of the differences between conditions that are too weak to be detected using standard EEG methods. Following that line of research, in this paper, we focus on recognizing gender differences in the functioning of the human brain in the attention task. For that purpose, we gathered, analyzed, and finally classified event-related potentials (ERPs). We propose a hierarchical approach, in which the electrophysiological signal preprocessing is combined with the classification method, enriched with a segmentation step, which creates a full line of electrophysiological signal classification during an attention task. This approach allowed us to detect differences between men and women in the P3 waveform, an ERP component related to attention, which were not observed using standard ERP analysis. The results provide evidence for the high effectiveness of the proposed method, which outperformed a traditional statistical analysis approach. This is a step towards understanding neuronal differences between men’s and women’s brains during cognition, aiming to reduce the misdiagnosis and adverse side effects in underrepresented women groups in health and biomedical research.

scholarly journals Electrophysiological Correlates of Refreshing: Event-related Potentials Associated with Directing Reflective Attention to Face, Scene, or Word Representations

2015 ◽  
Vol 27 (9) ◽  
pp. 1823-1839 ◽  
Author(s):  
Matthew R. Johnson ◽  
Gregory McCarthy ◽  
Kathleen A. Muller ◽  
Samuel N. Brudner ◽  
Marcia K. Johnson
Keyword(s):  
Temporal Resolution ◽  
Time Course ◽  
Event Related Potentials ◽  
Multivariate Pattern Analysis ◽  
Top Down ◽  
Two Phase ◽  
Electrophysiological Response ◽  
Related Potentials ◽  
Multivariate Pattern ◽  
Directing Attention

Refreshing is the component cognitive process of directing reflective attention to one of several active mental representations. Previous studies using fMRI suggested that refresh tasks involve a component process of initiating refreshing as well as the top–down modulation of representational regions central to refreshing. However, those studies were limited by fMRI's low temporal resolution. In this study, we used EEG to examine the time course of refreshing on the scale of milliseconds rather than seconds. ERP analyses showed that a typical refresh task does have a distinct electrophysiological response as compared to a control condition and includes at least two main temporal components: an earlier (∼400 msec) positive peak reminiscent of a P3 response and a later (∼800–1400 msec) sustained positivity over several sites reminiscent of the late directing attention positivity. Overall, the evoked potentials for refreshing representations from three different visual categories (faces, scenes, words) were similar, but multivariate pattern analysis showed that some category information was nonetheless present in the EEG signal. When related to previous fMRI studies, these results are consistent with a two-phase model, with the first phase dominated by frontal control signals involved in initiating refreshing and the second by the top–down modulation of posterior perceptual cortical areas that constitutes refreshing a representation. This study also lays the foundation for future studies of the neural correlates of reflective attention at a finer temporal resolution than is possible using fMRI.

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