Assessing Tilt Illusions in Human Visual Cortex Using fMRI and Multivariate Pattern Analysis

AbstractThis study explores how the human brain solves the challenge of flicker noise in motion processing. Despite providing no useful directional motion information, flicker is common in the visual environment and exhibits omnidirectional motion energy which is processed by low-level motion detectors. Models of motion processing propose a mechanism called motion opponency that reduces the processing of flicker noise. Motion opponency involves the pooling of local motion signals to calculate an overall motion direction. A neural correlate of motion opponency has been observed in human area MT+/V5 using fMRI, whereby stimuli with perfectly balanced motion energy constructed from dots moving in counter-phase elicit a weaker BOLD response than non-balanced (in-phase) motion stimuli. Building on this previous work, we used multivariate pattern analysis to examine whether the patterns of brain activation elicited by motion opponent stimuli resemble the activation elicited by flicker noise across the human visual cortex. Robust multivariate signatures of opponency were observed in V5 and in V3A. Our results support the notion that V5 is centrally involved in motion opponency and in the reduction of flicker noise during visual processing. Furthermore, these results demonstrate the utility of powerful multivariate analysis methods in revealing the role of additional visual areas, such as V3A, in opponency and in motion processing more generally.HighlightsOpponency is demonstrated in multivariate and univariate analysis of V5 data.Multivariate fMRI also implicates V3A in motion opponency.Multivariate analyses are useful for examining opponency throughout visual cortex.

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82. Assessing Fidelity of Object Representations in Visual Cortex in Schizophrenia and Bipolar Disorder Using Multivariate Pattern Analysis

Schizophrenia Bulletin ◽

10.1093/schbul/sbx021.121 ◽

2017 ◽

Vol 43 (suppl_1) ◽

pp. S46-S46

Author(s):

Eric Reavis ◽

Junghee Lee ◽

Jonathan Wynn ◽

Stephen Engel ◽

Mark Cohen ◽

...

Keyword(s):

Bipolar Disorder ◽

Visual Cortex ◽

Pattern Analysis ◽

Object Representations ◽

Multivariate Pattern Analysis ◽

Multivariate Pattern

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Concurrent contextual and time-distant mnemonic information co-exist as feedback in human visual cortex.

10.1101/2021.06.16.448735 ◽

2021 ◽

Author(s):

Javier Ortiz-Tudela ◽

Johanna Bergmann ◽

Matthew Bennett ◽

Isabelle Ehrlich ◽

Lars Muckli ◽

...

Keyword(s):

Visual Cortex ◽

Visual Experience ◽

Pattern Analysis ◽

Multivariate Pattern Analysis ◽

Early Visual Cortex ◽

Efficient Processing ◽

Sensory Evidence ◽

Past Experiences ◽

Multivariate Pattern ◽

The Brain

Efficient processing of visual environment necessitates the integration of incoming sensory evidence with concurrent contextual inputs and mnemonic content from our past experiences. To delineate how this integration takes place in the brain, we studied modulations of feedback neural patterns in non-stimulated areas of the early visual cortex in humans (i.e., V1 and V2). Using functional magnetic resonance imaging and multivariate pattern analysis, we show that both, concurrent contextual and time-distant mnemonic information, coexist in V1/V2 as feedback signals. The extent to which mnemonic information is reinstated in V1/V2 depends on whether the information is retrieved episodically or semantically. These results demonstrate that our stream of visual experience contains not just information from the visual surrounding, but also memory-based predictions internally generated in the brain.

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Multivariate pattern analysis of MEG and EEG: a comparison of representational structure in time and space

10.1101/095620 ◽

2016 ◽

Author(s):

Radoslaw Martin Cichy ◽

Dimitrios Pantazis

Keyword(s):

Visual Cortex ◽

Visual Processing ◽

Pattern Analysis ◽

Temporal Dynamics ◽

Multivariate Pattern Analysis ◽

Eeg Data ◽

Early Visual Cortex ◽

Spatio Temporal ◽

High Level ◽

Multivariate Pattern

1AbstractMultivariate pattern analysis of magnetoencephalography (MEG) and electroencephalography (EEG) data can reveal the rapid neural dynamics underlying cognition. However, MEG and EEG have systematic differences in sampling neural activity. This poses the question to which degree such measurement differences consistently bias the results of multivariate analysis applied to MEG and EEG activation patterns. To investigate, we conducted a concurrent MEG/EEG study while participants viewed images of everyday objects. We applied multivariate classification analyses to MEG and EEG data, and compared the resulting time courses to each other, and to fMRI data for an independent evaluation in space. We found that both MEG and EEG revealed the millisecond spatio-temporal dynamics of visual processing with largely equivalent results. Beyond yielding convergent results, we found that MEG and EEG also captured partly unique aspects of visual representations. Those unique components emerged earlier in time for MEG than for EEG. Identifying the sources of those unique components with fMRI, we found the locus for both MEG and EEG in high-level visual cortex, and in addition for MEG in early visual cortex. Together, our results show that multivariate analyses of MEG and EEG data offer a convergent and complimentary view on neural processing, and motivate the wider adoption of these methods in both MEG and EEG research.

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