Can visual information encoded in cortical columns be decoded from magnetoencephalography data in humans?

Cichy, Radoslaw Martin; Ramírez, Fernando; Pantazis, Dimitrios

doi:10.1016/j.neuroimage.2015.07.011

Cited by 89 publications

(117 citation statements)

References 85 publications

(154 reference statements)

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“…1), with a sufficiently large number of trials for each face identity (104-112 trials per face identity, 9,464-10,192 trials per participant) to be able to evaluate the representation of individual face identities in each participant (26). We used MEG because it has excellent temporal resolution and sufficient spatial resolution for decoding of fine visual information from spatial patterns of neural activity (26,27). In each participant, we used an independent functional localizer task in MEG to identify face-selective regions in right lateral occipital cortex and right fusiform gyrus.…”

Section: Significancementioning

confidence: 99%

Spatiotemporal dynamics of similarity-based neural representations of facial identity

Vida

Nestor

Plaut

et al. 2016

Proc. Natl. Acad. Sci. U.S.A.

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Humans' remarkable ability to quickly and accurately discriminate among thousands of highly similar complex objects demands rapid and precise neural computations. To elucidate the process by which this is achieved, we used magnetoencephalography to measure spatiotemporal patterns of neural activity with high temporal resolution during visual discrimination among a large and carefully controlled set of faces. We also compared these neural data to lower level "image-based" and higher level "identitybased" model-based representations of our stimuli and to behavioral similarity judgments of our stimuli. Between ∼50 and 400 ms after stimulus onset, face-selective sources in right lateral occipital cortex and right fusiform gyrus and sources in a control region (left V1) yielded successful classification of facial identity. In all regions, early responses were more similar to the image-based representation than to the identity-based representation. In the face-selective regions only, responses were more similar to the identity-based representation at several time points after 200 ms. Behavioral responses were more similar to the identity-based representation than to the image-based representation, and their structure was predicted by responses in the face-selective regions. These results provide a temporally precise description of the transformation from low-to high-level representations of facial identity in human face-selective cortex and demonstrate that faceselective cortical regions represent multiple distinct types of information about face identity at different times over the first 500 ms after stimulus onset. These results have important implications for understanding the rapid emergence of fine-grained, highlevel representations of object identity, a computation essential to human visual expertise.face processing | magnetoencephalography | decoding | representational similarity analysis | face identity H umans can discriminate among thousands of highly similar and complex visual patterns, such as face identity, in less than half a second (1, 2). Efficient within-category discrimination of facial identity is important for real-world decisions (e.g., classifying a person as a friend or stranger) and social interactions. Progress has been made in elucidating the neural mechanisms underlying the discrimination of individual face identities in humans. Using fMRI, these studies demonstrate that individual face identities are represented by spatially distributed patterns of neural activity within occipitotemporal cortex (3-13). Because of the poor temporal resolution in fMRI studies (typically around 2 s), however, our understanding of the neural basis of discrimination among complex visual patterns in humans remains limited. For example, within a given region, different information relevant to discrimination may be represented at different times over the first few 100 ms after stimulus onset. However, current models of the neural basis of face recognition in humans do not typically allow for this possibility, becau...

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Section: Significancementioning

confidence: 99%

Spatiotemporal dynamics of similarity-based neural representations of facial identity

Vida

Nestor

Plaut

et al. 2016

Proc. Natl. Acad. Sci. U.S.A.

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“…Given the intermediate position of spatial layout perception in the visual processing hierarchy between image-specific processing of individual scenes and navigation-related processing, we hypothesized that a signal for spatial layout processing would emerge after signals related to low-level visual processing in early visual regions (~100 ms, (Schmolesky et al, 1998;Cichy et al, 2015a)), and before activity observed typically in navigationrelated regions such as the hippocampus (~400 ms (Mormann et al, 2008)). Further, to be considered as an independent step in visual scene processing, spatial layout must be processed tolerant to changes in low-level features, including typical variations in viewing conditions, and to changes in high-level features such as scene category.…”

Section: The Temporal Dynamics Of Spatial Layout Processingmentioning

confidence: 99%

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

Cichy

Khosla

Pantazis

et al. 2015

Preprint

Self Cite

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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 lowerlevel visual analysis of single scenes at ~100 ms, we found a marker of real-world scene size, i.e. spatial layout processing, at ~250 ms indexing neural representations robust to changes in unrelated scene properties and viewing conditions. For a quantitative model of how scene size representations may arise in the brain, 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 suggest that deep neural networks are a promising framework to investigate how spatial layout representations emerge in the human brain.

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“…MEG can resolve the timecourse of fast processes with a resolution in the order of milliseconds, while source localization and information‐based mapping through multivariate techniques can effectively increase its spatial resolution (Cichy et al, 2015; Kriegeskorte, Goebel, & Bandettini, 2006). MVPA offers increased sensitivity through its ability to extract information from responses at multiple locations in space and time, and it can thus resolve differences in overlapping patterns that averaging‐based statistical analyses fail to detect (Norman, Polyn, Detre, & Haxby, 2006).…”

Section: Introductionmentioning

confidence: 99%

Spatiotemporal dynamics in human visual cortex rapidly encode the emotional content of faces

Dima

Perry

Messaritaki

et al. 2018

Human Brain Mapping

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Recognizing emotion in faces is important in human interaction and survival, yet existing studies do not paint a consistent picture of the neural representation supporting this task. To address this, we collected magnetoencephalography (MEG) data while participants passively viewed happy, angry and neutral faces. Using time‐resolved decoding of sensor‐level data, we show that responses to angry faces can be discriminated from happy and neutral faces as early as 90 ms after stimulus onset and only 10 ms later than faces can be discriminated from scrambled stimuli, even in the absence of differences in evoked responses. Time‐resolved relevance patterns in source space track expression‐related information from the visual cortex (100 ms) to higher‐level temporal and frontal areas (200–500 ms). Together, our results point to a system optimised for rapid processing of emotional faces and preferentially tuned to threat, consistent with the important evolutionary role that such a system must have played in the development of human social interactions.

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Can visual information encoded in cortical columns be decoded from magnetoencephalography data in humans?

Abstract: It is a principal open question whether noninvasive imaging methods in humans candecode information encoded at a spatial scale as fine as the basic functional unit of cortex: cortical columns. We addressed this question in five magnetoencephalography

Cited by 89 publications

References 85 publications

Spatiotemporal dynamics of similarity-based neural representations of facial identity

Spatiotemporal dynamics of similarity-based neural representations of facial identity

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

Spatiotemporal dynamics in human visual cortex rapidly encode the emotional content of faces

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