Attention enhances category representations across the brain with strengthened residual correlations to ventral temporal cortex

How does attention enhance neural representations of goal-relevant stimuli while suppressing representations of ignored stimuli across regions of the brain? While prior studies have shown that attention enhances visual responses, we lack a cohesive understanding of how selective attention modulates visual representations across the brain. Here, we used functional magnetic resonance imaging (fMRI) while participants performed a selective attention task on superimposed stimuli from multiple categories and used a data-driven approach to test how attention affects both decodability of category information and residual correlations (after regressing out stimulus-driven variance) with category-selective regions of ventral temporal cortex (VTC). Our data reveal three main findings. First, when two objects are simultaneously viewed, the category of the attended object can be decoded more readily than the category of the ignored object, with the greatest attentional enhancements observed in occipital and temporal lobes. Second, after accounting for the response to the stimulus, the correlation in the residual brain activity between a cortical region and a category-selective region of VTC was elevated when that region's preferred category was attended vs. ignored, and more so in the right occipital, parietal, and frontal cortices. Third, we found that the stronger the residual correlations between a given region of cortex and VTC, the better visual category information could be decoded from that region. These findings suggest that heightened residual correlations by selective attention may reflect the sharing of information between sensory regions and higher-order cortical regions to provide attentional enhancement of goal-relevant information.

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Principles governing the topological organization of object selectivities in ventral temporal cortex

10.1101/2021.09.15.460220 ◽

2021 ◽

Author(s):

Yiyuan Zhang ◽

Ke Zhou ◽

Pinglei Bao ◽

Jia Liu

Keyword(s):

Visual Processing ◽

Temporal Cortex ◽

High Dimensional ◽

Complex Object ◽

Object Space ◽

Minimization Principle ◽

Topological Organization ◽

Ventral Temporal Cortex ◽

Dimensional Object ◽

The Brain

To achieve the computational goal of rapidly recognizing miscellaneous objects in the environment despite large variations in their appearance, our mind represents objects in a high-dimensional object space to provide separable category information and enable the extraction of different kinds of information necessary for various levels of the visual processing. To implement this abstract and complex object space, the ventral temporal cortex (VTC) develops different object-selective regions with a certain topological organization as the physical substrate. However, the principle that governs the topological organization of object selectivities in the VTC remains unclear. Here, equipped with the wiring cost minimization principle constrained by the wiring length of neurons in the human temporal lobe, we constructed a hybrid self-organizing map (SOM) model as an artificial VTC (VTC-SOM) to explain how the abstract and complex object space is faithfully implemented in the brain. In two in silico experiments with the empirical brain imaging and single-unit data, our VTC-SOM predicted the topological structure of fine-scale functional regions (face-, object-, body-, and place-selective regions) and the boundary (i.e., middle Fusiform Sulcus) in large-scale abstract functional maps (animate vs. inanimate, real-word large-size vs. small-size, central vs. peripheral), with no significant loss in functionality (e.g., categorical selectivity, a hierarchy of view-invariant representations). These findings illustrated that the simple principle utilized in our model, rather than multiple hypotheses such as temporal associations, conceptual knowledge, and computational demands together, was apparently sufficient to determine the topological organization of object-selectivities in the VTC. In this way, the high-dimensional object space is implemented in a two-dimensional cortical surface of the brain faithfully.

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Finding decodable information that is read out in behaviour

10.1101/248583 ◽

2018 ◽

Cited By ~ 3

Author(s):

Tijl Grootswagers ◽

Radoslaw M. Cichy ◽

Thomas A. Carlson

Keyword(s):

Temporal Cortex ◽

Brain Activation ◽

Object Perception ◽

Neural Activation ◽

Experimental Conditions ◽

Activation Patterns ◽

Visual Objects ◽

Neuroimaging Data ◽

Ventral Temporal Cortex ◽

The Brain

AbstractMultivariate decoding methods applied to neuroimaging data have become the standard in cognitive neuroscience for unravelling statistical dependencies between brain activation patterns and experimental conditions. The current challenge is to demonstrate that information decoded as such by the experimenter is in fact used by the brain itself to guide behaviour. Here we demonstrate a promising approach to do so in the context of neural activation during object perception and categorisation behaviour. We first localised decodable information about visual objects in the human brain using a spatially-unbiased multivariate decoding analysis. We then related brain activation patterns to behaviour using a machine-learning based extension of signal detection theory. We show that while there is decodable information about visual category throughout the visual brain, only a subset of those representations predicted categorisation behaviour, located mainly in anterior ventral temporal cortex. Our results have important implications for the interpretation of neuroimaging studies, highlight the importance of relating decoding results to behaviour, and suggest a suitable methodology towards this aim.

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Brain Networks Sensitive to Object Novelty, Value, and Their Combination

Cerebral Cortex Communications ◽

10.1093/texcom/tgaa034 ◽

2020 ◽

Vol 1 (1) ◽

Author(s):

Ali Ghazizadeh ◽

Mohammad Amin Fakharian ◽

Arash Amini ◽

Whitney Griggs ◽

David A Leopold ◽

...

Keyword(s):

Brain Networks ◽

Temporal Cortex ◽

Common Currency ◽

Brain Responses ◽

Activation Dynamics ◽

Fractal Patterns ◽

The Common ◽

Ventral Temporal Cortex ◽

Value Coding ◽

The Brain

Abstract Novel and valuable objects are motivationally attractive for animals including primates. However, little is known about how novelty and value processing is organized across the brain. We used fMRI in macaques to map brain responses to visual fractal patterns varying in either novelty or value dimensions and compared the results with the structure of functionally connected brain networks determined at rest. The results show that different brain networks possess unique combinations of novelty and value coding. One network identified in the ventral temporal cortex preferentially encoded object novelty, whereas another in the parietal cortex encoded the learned value. A third network, broadly composed of temporal and prefrontal areas (TP network), along with functionally connected portions of the striatum, amygdala, and claustrum, encoded both dimensions with similar activation dynamics. Our results support the emergence of a common currency signal in the TP network that may underlie the common attitudes toward novel and valuable objects.

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Object novelty and value coding segregate across functionally connected brain networks

10.1101/2019.12.25.887976 ◽

2019 ◽

Author(s):

Ali Ghazizadeh ◽

MohammadAmin Fakharian ◽

Arash Amini ◽

Whitney Griggs ◽

David A. Leopold ◽

...

Keyword(s):

Brain Activity ◽

Brain Networks ◽

Temporal Cortex ◽

Common Currency ◽

Activation Dynamics ◽

Fractal Patterns ◽

The Common ◽

Ventral Temporal Cortex ◽

Value Coding ◽

The Brain

AbstractNovel and valuable objects are motivationally attractive for animals including primates. However, little is known about how novelty and value processing is organized across the brain. We used fMRI in macaques to map brain activity to fractal patterns varying in either novelty or value dimensions in the context of functionally connected brain networks determined at rest. Results show unique combinations of novelty and value coding across the brain networks. Networks in the ventral temporal cortex and in the parietal cortex showed preferential coding of novelty and value dimensions, respectively, while a wider network composed of temporal and prefrontal areas (TP network), along with functionally connected portions of the striatum, amygdala, and claustrum, responded to both dimensions with similar activation dynamics. Our results support emergence of a common currency signal in the TP network that may underlie the common attitudes toward novel and valuable objects.

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Dynamic Domain Specificity In Human Ventral Temporal Cortex

10.1101/2020.11.11.378877 ◽

2020 ◽

Author(s):

Brett B. Bankson ◽

Matthew J. Boring ◽

R. Mark Richardson ◽

Avniel Singh Ghuman

Keyword(s):

Visual Processing ◽

Temporal Cortex ◽

Processing System ◽

Neural Representation ◽

Domain Specificity ◽

Individual Level ◽

Ventral Temporal Cortex ◽

Intracranial Recordings ◽

Dynamic Domain ◽

The Brain

ABSTRACTAn enduring neuroscientific debate concerns the extent to which neural representation is restricted to networks of patches specialized for particular domains of perceptual input (Kaniwsher et al., 1997; Livingstone et al., 2019), or distributed outside of these patches to broad areas of cortex as well (Haxby et al., 2001; Op de Beeck, 2008). A critical level for this debate is the localization of the neural representation of the identity of individual images, (Spiridon & Kanwisher, 2002) such as individual-level face or written word recognition. To address this debate, intracranial recordings from 489 electrodes throughout ventral temporal cortex across 17 human subjects were used to assess the spatiotemporal dynamics of individual word and face processing within and outside cortical patches strongly selective for these categories of visual information. Individual faces and words were first represented primarily only in strongly selective patches and then represented in both strongly and weakly selective areas approximately 170 milliseconds later. Strongly and weakly selective areas contributed non-redundant information to the representation of individual images. These results can reconcile previous results endorsing disparate poles of the domain specificity debate by highlighting the temporally segregated contributions of different functionally defined cortical areas to individual level representations. Taken together, this work supports a dynamic model of neural representation characterized by successive domain-specific and distributed processing stages.SIGNIFICANCE STATEMENTThe visual processing system performs dynamic computations to differentiate visually similar forms, such as identifying individual words and faces. Previous models have localized these computations to 1) circumscribed, specialized portions of the brain, or 2) more distributed aspects of the brain. The current work combines machine learning analyses with human intracranial recordings to determine the neurodynamics of individual face and word processing in and outside of brain regions selective for these visual categories. The results suggest that individuation involves computations that occur first in primarily highly selective parts of the visual processing system, then later recruits highly and non-highly selective regions. These results mediate between extant models of neural specialization by suggesting a dynamic domain specificity model of visual processing.

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