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Task-dependent functional organizations of the visual ventral stream.

Han-Gue Jo | Junji Ito | Barbara Schulte Holthausen | Conrad Baumann | Sonja Grün | Ute Habel | Thilo Kellermann
Scientific reports | 2019

The visual hierarchy of the ventral stream has been widely studied. However, it remains unclear how the hierarchical system organizes its functional coupling during top-down cognitive process. The present fMRI study investigated task-dependent functional connectivity along the ventral stream, while twenty-eight participants performed object recognition tasks that required different types of visual processing: i) searching or ii) memorizing visual objects embedded in natural scene images or iii) free viewing of the same images. Utilizing a seed-based approach that explicitly compared task-specific BOLD time-series, we identified task-dependent functional connectivity of the visual ventral stream, demonstrating different correlation structures. Searching for a target object manifested both correlated and anti-correlated structures, separating the visual areas V1 and V4 from the posterior part of the inferior temporal cortex (PIT). In contrast, the ventral stream structure remained correlated during memorizing objects, but increased the correlation between the right V4 and PIT. On the other hand, V1 and V4 showed task-dependent activation, whereas PIT was deactivated. These results highlight the context-dependent nature of the visual ventral stream and shed light on how the visual hierarchy is selectively organized to bias object recognition toward features of interest.

Pubmed ID: 31249350

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Functional Connectivity Toolbox (tool)

RRID:SCR_006394

MATLAB toolbox for performing functional connectivity analyses includes many of the most commonly-used approaches researchers have utilized to date for the identification of condition-dependent functional interactions between fMRI time-series obtained from two or more brain regions. The approaches are either bivariate or multivariate methods defined in time or frequency domains that emphasize distinct features of relationships among the time-series.

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