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

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.

scholarly journals Motion opponency examined throughout visual cortex with multivariate pattern analysis of fMRI data

2020 ◽  
Author(s):  
Andrew E. Silva ◽  
Benjamin Thompson ◽  
Zili Liu
Keyword(s):  
Visual Cortex ◽  
Visual Processing ◽  
Pattern Analysis ◽  
Flicker Noise ◽  
Univariate Analysis ◽  
Motion Processing ◽  
List Type ◽  
Multivariate Pattern Analysis ◽  
Motion Energy ◽  
Multivariate Pattern

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.

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

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.

Role of the occipito-temporal theta rhythm in hand visual identification

2020 ◽  
Vol 123 (1) ◽  
pp. 167-177 ◽  
Author(s):  
Quentin Moreau ◽  
Eleonora Parrotta ◽  
Vanessa Era ◽  
Maria Luisa Martelli ◽  
Matteo Candidi
Keyword(s):  
Visual Processing ◽  
Functional Role ◽  
Pattern Analysis ◽  
Multivariate Pattern Analysis ◽  
Theta Band ◽  
Time Frequency ◽  
Visual Identification ◽  
Multivariate Pattern ◽  
Healthy Participants

Neuroimaging and EEG studies have shown that passive observation of the full body and of specific body parts is associated with 1) activity of an occipito-temporal region named the extrastriate body area (EBA), 2) amplitude modulations of a specific posterior event-related potential (ERP) component (N1/N190), and 3) a theta-band (4–7 Hz) synchronization recorded from occipito-temporal electrodes compatible with the location of EBA. To characterize the functional role of the occipito-temporal theta-band increase during the processing of body-part stimuli, we recorded EEG from healthy participants while they were engaged in an identification task (match-to-sample) of images of hands and nonbody control images (leaves). In addition to confirming that occipito-temporal electrodes show a larger N1 for hand images compared with control stimuli, cluster-based analysis revealed an occipito-temporal cluster showing an increased theta power when hands are presented (compared with leaves) and show that this theta increase is higher for identified hands compared with nonidentified ones while not being significantly different between not identified nonhand stimuli. Finally, single trial multivariate pattern analysis revealed that time-frequency modulation in the theta band is a better marker for classifying the identification of hand images than the ERP modulation. The present results support the notion that theta activity over the occipito-temporal cortex is an informative marker of hand visual processing and may reflect the activity of a network coding for stimulus identity. NEW & NOTEWORTHY Hands provide crucial information regarding the identity of others, which is a key information for social processes. We recorded EEG activity of healthy participants during the visual identification of hand images. The combination of univariate and multivariate pattern analysis in time- and time-frequency domain highlights the functional role of theta (4–7 Hz) activity over visual areas during hand identification and emphasizes the robustness of this neuromarker in occipito-temporal visual processing dynamics.

scholarly journals Time-resolved multivariate pattern analysis of infant EEG data

2021 ◽  
Author(s):  
Kira Ashton ◽  
Benjamin Zinszer ◽  
Radoslaw Cichy ◽  
Charles Nelson ◽  
Richard Aslin ◽  
...  
Keyword(s):  
Time Course ◽  
Pattern Analysis ◽  
Multivariate Pattern Analysis ◽  
Time Resolved ◽  
Promising Tool ◽  
Eeg Data ◽  
Neuroimaging Data ◽  
Linear Svm ◽  
Multivariate Pattern ◽  
The Impact

Time-resolved multivariate pattern analysis (MVPA), a popular technique for analyzing magneto- and electro-encephalography (M/EEG) neuroimaging data, quantifies the extent and time-course by which neural representations support the discrimination of relevant stimuli dimensions. As EEG is widely used for infant neuroimaging, time-resolved MVPA of infant EEG data is a particularly promising tool for infant cognitive neuroscience. MVPA methods have recently been applied to common infant imaging methods such as EEG and fNIRS. In this tutorial, we provide and describe code to implement time-resolved, within-subject MVPA with infant EEG data. A pipeline for time-resolved MVPA based on linear SVM classification is described and implemented with accompanying code in both Matlab and Python. Results from a test dataset indicated that in both infants and adults this method reliably produced above chance classification accuracy. Extensions of the core pipeline are presented including both geometric- and accuracy-based representational similarity analysis, implemented in Python. Common choices of implementation are presented and discussed. As the amount of artifact-free EEG data contributed by each participant is lower in studies of infants than in studies of children and adults, we also explore and discuss the impact of varying participant-level inclusion thresholds on resulting MVPA findings in these datasets.

scholarly journals Temporal multivariate pattern analysis (tMVPA): A single trial approach exploring the temporal dynamics of the BOLD signal

2018 ◽  
Vol 308 ◽  
pp. 74-87 ◽  
Author(s):  
Luca Vizioli ◽  
Alexander Bratch ◽  
Junpeng Lao ◽  
Kamil Ugurbil ◽  
Lars Muckli ◽  
...  
Keyword(s):  
Pattern Analysis ◽  
Temporal Dynamics ◽  
Bold Signal ◽  
Multivariate Pattern Analysis ◽  
Single Trial ◽  
Multivariate Pattern

scholarly journals Temporal Multivariate Pattern Analysis (tMVPA): a single trial approach exploring the temporal dynamics of the BOLD signal

2018 ◽  
Author(s):  
Luca Vizioli ◽  
Alexander Bratch ◽  
Junpeng Lao ◽  
Kamil Ugurbil ◽  
Lars Muckli ◽  
...  
Keyword(s):  
Pattern Analysis ◽  
Temporal Dynamics ◽  
Temporal Information ◽  
Single Subject ◽  
Bold Signal ◽  
Multivariate Pattern Analysis ◽  
Single Trial ◽  
Neural Representations ◽  
Multivariate Pattern ◽  
Over Time

AbstractBackgroundfMRI provides spatial resolution that is unmatched by any non-invasive neuroimaging technique. Its temporal dynamics however are typically neglected due to the sluggishness of the hemodynamic based fMRI signal.New MethodsWe present temporal multivariate pattern analysis (tMVPA), a method for investigating the temporal evolution of neural representations in fMRI data, computed using pairs of single-trial BOLD time-courses, leveraging both spatial and temporal components of the fMRI signal. We implemented an expanding sliding window approach that allows identifying the time-window of an effect.ResultsWe demonstrate that tMVPA can successfully detect condition-specific multivariate modulations over time, in the absence of univariate differences. Using Monte Carlo simulations and synthetic data, we quantified family-wise error rate (FWER) and statistical power. Both at the group and at the single subject level, FWER was either at or significantly below 5%. For the group level, we reached the desired power with 18 subjects and 12 trials; for the single subject scenario, 14 trials were required to achieve comparable power.Comparison with existing methodstMVPA adds a temporal multivariate dimension to the tools available for fMRI analysis, enabling investigations of the evolution of neural representations over time. Moreover, tMVPA permits performing single subject inferential statistics by considering single-trial distribution.ConclusionThe growing interest in fMRI temporal dynamics, motivated by recent evidence suggesting that the BOLD signal carries temporal information at a finer scale than previously thought, advocates the need for analytical tools, such as the tMVPA approach proposed here, tailored to investigating BOLD temporal information.

scholarly journals Neural correlates of conscious visual perception lie outside the visual system: evidence from the double-drift illusion

2019 ◽  
Author(s):  
Sirui Liu ◽  
Qing Yu ◽  
Peter U. Tse ◽  
Patrick Cavanagh
Keyword(s):  
Visual System ◽  
Visual Processing ◽  
Pattern Analysis ◽  
Activity Patterns ◽  
Neural Correlates ◽  
Multivariate Pattern Analysis ◽  
Visual Areas ◽  
Visual Cortices ◽  
Multivariate Pattern ◽  
The Brain

SummaryWhen perception differs from the physical stimulus, as it does for visual illusions and binocular rivalry, the opportunity arises to localize where perception emerges in the visual processing hierarchy. Representations prior to that stage differ from the eventual conscious percept even though they provide input to it. Here we investigate where and how a remarkable misperception of position emerges in the brain. This “double-drift” illusion causes a dramatic mismatch between retinal and perceived location, producing a perceived path that can differ from its physical path by 45° or more [1]. The deviations in the perceived trajectory can accumulate over at least a second [1] whereas other motion-induced position shifts accumulate over only 80 to 100 ms before saturating [2]. Using fMRI and multivariate pattern analysis, we find that the illusory path does not share activity patterns with a matched physical path in any early visual areas. In contrast, a whole-brain searchlight analysis reveals a shared representation in more anterior regions of the brain. These higher-order areas would have the longer time constants required to accumulate the small moment-to-moment position offsets that presumably originate in early visual cortices, and then transform these sensory inputs into a final conscious percept. The dissociation between perception and the activity in early sensory cortex suggests that perceived position does not emerge in what is traditionally regarded as the visual system but emerges instead at a much higher level.

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