Decoding the orientation of contrast edges from MEG evoked and induced responses

Pantazis, Dimitrios; Fang, Mingtong; Qin, Sheng; Mohsenzadeh, Yalda; Li, Quanzheng; Cichy, Radoslaw Martin

doi:10.1016/j.neuroimage.2017.07.022

Cited by 46 publications

(50 citation statements)

References 70 publications

(118 reference statements)

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“…A useful tool for comparing data from different modalities has been representational similarity analysis 18,37,39,40,[44][45][46][47][48][49] . This technique can quantitatively relate brain-activity measurements, such as fMRI, with computational models, such as DCNNs, by abstracting the data into representational space using matrices of pairwise similarity (a representational dissimilarity matrix-RDM).…”

Section: Resultsmentioning

confidence: 99%

Emergence of Visual Center-Periphery Spatial Organization in Deep Convolutional Neural Networks

Mohsenzadeh

Mullin

Lahner³

et al. 2020

Sci Rep

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Research at the intersection of computer vision and neuroscience has revealed hierarchical correspondence between layers of deep convolutional neural networks (Dcnns) and cascade of regionsalong human ventral visual cortex. Recently, studies have uncovered emergence of human interpretable concepts within DCNNs layers trained to identify visual objects and scenes. Here, we asked whether an artificial neural network (with convolutional structure) trained for visual categorization would demonstrate spatial correspondences with human brain regions showing central/peripheral biases. Using representational similarity analysis, we compared activations of convolutional layers of a DCNN trained for object and scene categorization with neural representations in human brain visual regions.Results reveal a brain-like topographical organization in the layers of the DCNN, such that activations of layer-units with central-bias were associated with brain regions with foveal tendencies (e.g. fusiform gyrus), and activations of layer-units with selectivity for image backgrounds were associated with cortical regions showing peripheral preference (e.g. parahippocampal cortex). the emergence of a categorical topographical correspondence between Dcnns and brain regions suggests these models are a good approximation of the perceptual representation generated by biological neural networks.Cortical regions along the ventral visual stream of the human brain (extending from occipital to temporal lobe) have been shown to preferentially activate to specific image categories 1 . For instance, while the fusiform gyrus shows specialization for faces 2 , the parahippocampal cortex (PHC) is more selective to spatial layout, places 3,4 and large-size objects 5,6 . In characterizing the functional properties of these regions, Levy and colleagues (2001) discovered distinct topographical response patterns, such that face selective regions of the fusiform gyrus showed a strong preference for central visual field, while the building selective regions of PHC exhibited a peripheral selectivity bias to images of scene and large spaces 7 . Thus, while these regions show categorical selectivity to scenes or faces, their response patterns are strongest when their preferred category is presented in a topographically favorable location in the visual field. More specifically, the face selective voxels in the fusiform gyrus have a stronger response when faces are presented centrally; whereas scene-selective voxels show stronger activity to space features in the periphery 7-13 .These topographical preferences raise questions regarding the origin of this functional organizing principles: does the way we look at faces and scenes in our natural visual world account for this bias? We most often fixate on faces bringing face-related information into our central, high acuity fovea to extract subtle visual features like facial expressions [14][15][16] . Places, on the other hand, are used for navigation, extending all around the visual field, thus we more readily percei...

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Section: Resultsmentioning

confidence: 99%

Emergence of Visual Center-Periphery Spatial Organization in Deep Convolutional Neural Networks

Mohsenzadeh

Mullin

Lahner³

et al. 2020

Sci Rep

Self Cite

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“…Finally, to assess whether the two experimental conditions were differentiated by neural patterns stable over time, we performed a temporal generalization multivariate analysis. The algorithm was the same as the one described above, with the difference that in this case we used each time-point of the training set to predict not only the same time-point in the testing set, but all time-points [58,59,60].…”

Section: Multivariate Pattern Analysismentioning

confidence: 99%

Spatiotemporal whole-brain dynamics of auditory patterns recognition

Bonetti

Brattico

Carlomagno

et al. 2020

Preprint

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Music is a universal non-verbal human language, built on logical structures and articulated in balanced hierarchies between sounds, offering excellent opportunities to explore how the brain creates meaning for complex spatiotemporal auditory patterns. Using the high temporal resolution of magnetoencephalography in 70 participants, we investigated their unfolding brain dynamics during the recognition of Johann Sebastian Bach's original musical patterns compared to new variations thereof. Remarkably, the recognition of Bach's original music ignited a widespread music processing brain network comprising primary auditory cortex, superior temporal gyrus, insula, frontal operculum, cingulate gyrus, orbitofrontal cortex, basal ganglia and hippocampus. Furthermore, both activity and connectivity increased over time, following the evolution and unfolding of Bach's original patterns. This study shed new light on brain activity and connectivity dynamics underlying auditory patterns recognition, highlighting the crucial role of fast neural phase synchronization underlying meaningful, complex cognitive processes.

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“…We tested our hypothesis based on predictive coding following the proposal of Lewis and Bastiaansen (). Previous studies have shown that synchronization between cortical areas may indicate communication between those areas, with the phase locking values as a measure of synchrony (Doesburg, Kitajo, & Ward, ; Fries, , ; Panagiotaropoulos, Deco, Kapoor, & Logothetis, ; Pantazis et al, ; Tallon‐Baudry & Bertrand, ; Weeks et al, ). Phase locking values were estimated between cortical areas involved in semantic processing.…”

Section: Introductionmentioning

confidence: 99%

Oscillatory dynamics of cortical functional connections in semantic prediction

Mamashli

Khan

Obleser

et al. 2018

Human Brain Mapping

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An event related potential, known as the N400, has been particularly useful in investigating language processing as it serves as a neural index for semantic prediction. There are numerous studies on the functional segregation of N400 neural sources; however, the oscillatory dynamics of functional connections among the relevant sources has remained elusive. In this study we acquired magnetoencephalography data during a classic N400 paradigm, where the semantic predictability of a fixed target noun was manipulated in simple German sentences. We conducted inter‐regional functional connectivity (FC) and time–frequency analysis on known regions of the semantic network, encompassing bilateral temporal, and prefrontal cortices. Increased FC was found in less predicted (LP) nouns compared with highly predicted (HP) nouns in three connections: (a) right inferior frontal gyrus (IFG) and right middle temporal gyrus (MTG) from 0 to 300 ms mainly within the alpha band, (b) left lateral orbitofrontal (LOF) and right IFG around 400 ms within the beta band, and (c) left superior temporal gyrus (STG) and left LOF from 300 to 700 ms in the beta and low gamma bands. Furthermore, gamma spectral power (31–70 Hz) was stronger in HP nouns than in LP nouns in left anterior temporal cortices in earlier time windows (0–200 ms). Our findings support recent theories in language comprehension, suggesting fronto‐temporal top–down connections are mainly mediated through beta oscillations while gamma band frequencies are involved in matching between prediction and input.

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Decoding the orientation of contrast edges from MEG evoked and induced responses

Cited by 46 publications

References 70 publications

Emergence of Visual Center-Periphery Spatial Organization in Deep Convolutional Neural Networks

Emergence of Visual Center-Periphery Spatial Organization in Deep Convolutional Neural Networks

Spatiotemporal whole-brain dynamics of auditory patterns recognition

Oscillatory dynamics of cortical functional connections in semantic prediction

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