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

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.

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

PLoS ONE ◽

10.1371/journal.pone.0063293 ◽

2013 ◽

Vol 8 (5) ◽

pp. e63293 ◽

Cited By ~ 13

Author(s):

Milan Brázdil ◽

Jiří Janeček ◽

Petr Klimeš ◽

Radek Mareček ◽

Robert Roman ◽

...

Keyword(s):

Human Brain ◽

Time Course ◽

Cognitive Tasks

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Asymmetric Compression of Representational Space for Object Animacy Categorization under Degraded Viewing Conditions

Journal of Cognitive Neuroscience ◽

10.1162/jocn_a_01177 ◽

2017 ◽

Vol 29 (12) ◽

pp. 1995-2010 ◽

Cited By ~ 9

Author(s):

Tijl Grootswagers ◽

J. Brendan Ritchie ◽

Susan G. Wardle ◽

Andrew Heathcote ◽

Thomas A. Carlson

Keyword(s):

Human Brain ◽

Time Course ◽

Reaction Times ◽

Choice Response ◽

Neural Activation ◽

Decision Boundary ◽

Activation Patterns ◽

Categorization Task ◽

Brain And Behavior ◽

And Behavior

Animacy is a robust organizing principle among object category representations in the human brain. Using multivariate pattern analysis methods, it has been shown that distance to the decision boundary of a classifier trained to discriminate neural activation patterns for animate and inanimate objects correlates with observer RTs for the same animacy categorization task [Ritchie, J. B., Tovar, D. A., & Carlson, T. A. Emerging object representations in the visual system predict reaction times for categorization. PLoS Computational Biology, 11, e1004316, 2015; Carlson, T. A., Ritchie, J. B., Kriegeskorte, N., Durvasula, S., & Ma, J. Reaction time for object categorization is predicted by representational distance. Journal of Cognitive Neuroscience, 26, 132–142, 2014]. Using MEG decoding, we tested if the same relationship holds when a stimulus manipulation (degradation) increases task difficulty, which we predicted would systematically decrease the distance of activation patterns from the decision boundary and increase RTs. In addition, we tested whether distance to the classifier boundary correlates with drift rates in the linear ballistic accumulator [Brown, S. D., & Heathcote, A. The simplest complete model of choice response time: Linear ballistic accumulation. Cognitive Psychology, 57, 153–178, 2008]. We found that distance to the classifier boundary correlated with RT, accuracy, and drift rates in an animacy categorization task. Split by animacy, the correlations between brain and behavior were sustained longer over the time course for animate than for inanimate stimuli. Interestingly, when examining the distance to the classifier boundary during the peak correlation between brain and behavior, we found that only degraded versions of animate, but not inanimate, objects had systematically shifted toward the classifier decision boundary as predicted. Our results support an asymmetry in the representation of animate and inanimate object categories in the human brain.

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P.1.e.010 Understanding postpartum blues: a time course of MAO-A levels in the living human brain during the postpartum period

European Neuropsychopharmacology ◽

10.1016/s0924-977x(09)70454-7 ◽

2009 ◽

Vol 19 ◽

pp. S305-S306

Author(s):

J. Sacher ◽

A.A. Wilson ◽

P. Rusjan ◽

S. Hassan ◽

L. Jacobs ◽

...

Keyword(s):

Human Brain ◽

Postpartum Period ◽

Time Course ◽

Postpartum Blues ◽

Mao A

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The time course of shape discrimination in the human brain

NeuroImage ◽

10.1016/j.neuroimage.2012.10.044 ◽

2013 ◽

Vol 67 ◽

pp. 77-88 ◽

Cited By ~ 19

Author(s):

Justin M. Ales ◽

L. Gregory Appelbaum ◽

Benoit R. Cottereau ◽

Anthony M. Norcia

Keyword(s):

Human Brain ◽

Time Course ◽

Shape Discrimination

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

10.1101/032623 ◽

2015 ◽

Cited By ~ 6

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.

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Asymmetric compression of representational space for object animacy categorization under degraded viewing conditions

10.1101/166660 ◽

2017 ◽

Author(s):

Tijl Grootswagers ◽

J. Brendan Ritchie ◽

Susan G. Wardle ◽

Andrew Heathcote ◽

Thomas A. Carlson

Keyword(s):

Human Brain ◽

Time Course ◽

Reaction Times ◽

Neural Activation ◽

Decision Boundary ◽

Activation Patterns ◽

Categorization Task ◽

Brain And Behavior ◽

Time Accuracy ◽

And Behavior

AbstractAnimacy is a robust organizing principle amongst object category representations in the human brain. Using multivariate pattern analysis methods (MVPA), it has been shown that distance to the decision boundary of a classifier trained to discriminate neural activation patterns for animate and inanimate objects correlates with observer reaction times for the same animacy categorization task (Carlson, Ritchie, Kriegeskorte, Durvasula, & Ma, 2014; Ritchie, Tovar, & Carlson, 2015). Using MEG decoding, we tested if the same relationship holds when a stimulus manipulation (degradation) increases task difficulty, which we predicted would systematically decrease the distance of activation patterns from the decision boundary, and increase reaction times. In addition, we tested whether distance to the classifier boundary correlates with drift rates in the Linear Ballistic Accumulator (Brown & Heathcote, 2008). We found that distance to the classifier boundary correlated with reaction time, accuracy, and drift rates in an animacy categorization task. Split by animacy, the correlations between brain and behavior were sustained for longer over the time course for animate than for inanimate stimuli. Interestingly, when examining the distance to the classifier boundary during the peak correlation between brain and behavior, we found that only degraded versions of animate, but not inanimate, objects had systematically shifted towards the classifier decision boundary as predicted. Our results support an asymmetry in the representation of animate and inanimate object categories in the human brain.

Download Full-text