Decoding the difference between explicit and implicit body expression representation in high level visual, prefrontal and inferior parietal cortex

Marrazzo, Giuseppe; Vaessen, Maarten J.; Gelder, Béatrice de

doi:10.1016/j.neuroimage.2021.118545

Cited by 10 publications

(5 citation statements)

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“…In agreement with our findings, previous studies have suggested that the LOCi region may serve both as an entry of higher-level visual streams as well as a node for integrating the dorsal and ventral stream feedback (Grill-Spector & Malach, 2004; Larsson & Heeger, 2006; Sayres & Grill-Spector, 2008). Recent studies have also found involvement of the LOCi in body kinematics (Marrazzo et al, 2023) and motion feature processing (Robert et al, 2023). Thus, this node may be related to an early integration of the low-/mid-level visual information.…”

Section: Discussionmentioning

confidence: 99%

“…The actual contribution of these body selective areas is not well understood (de Gelder & Poyo Solanas, 2021; de Gelder et al, 2010; Vogels, 2022). A number of studies found that these body selective areas play a role in processing emotional expressions (de Gelder et al, 2004; Grèzes et al, 2007; Peelen et al, 2007; Pichon et al, 2008), biological movement (Jastorff & Orban, 2009), specific postural and kinematic features of the body (Marrazzo et al, 2023; Marrazzo et al, 2021; Poyo Solanas et al, 2020), action recognition (Goldberg et al, 2014; Shmuelof & Zohary, 2005), motor planning (Zimmermann et al, 2012), as well as social perception (Kret et al, 2011a; Moreau et al, 2023). Taken together, these findings suggest that body selectivity may be understood not simply as a matter of category selectivity but as resulting from the activity of a sparsely distributed ensemble of brain areas (de Gelder & Poyo Solanas, 2021; Weiner & Grill-Spector, 2011).…”

Section: Introductionmentioning

confidence: 99%

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Connectivity and functional diversity of different temporo-occipital nodes for action perception

Li,

Poyo Solanas,

Marrazzo

et al. 2024

Preprint

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The temporo-occipital cortex (TOC) plays a key role in body and action perception, but current understanding of its functions is still limited. TOC body regions are heterogeneous and their role in action perception is poorly understood. This study adopted data-driven approaches to region selectivity and investigated the connectivity of TOC nodes and the functional network sensitivity for different whole body action videos. In two human 7T fMRI experiments using independent component analysis, four adjacent body selective nodes were detected within the TOC network with distinct connectivity profiles and functional roles. Action type independent connectivity was observed for the posterior-ventral node to the visual cortex, the posterior-dorsal node to the precuneus and the anterior nodes to the frontal cortex. Action specific connectivity modulations were found in middle frontal gyrus for the aggressive condition with increased connectivity to the anterior node and decreased connectivity to the posterior-dorsal node. But for the defensive condition, node-nonspecific enhancement was found for the TOC-cingulate connectivity. By addressing the issue of multiple nodes in the temporo-occipital network we show a functional dissociation of different body selective centres related to the action type and a potential hierarchy within the TOC body network.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Connectivity and functional diversity of different temporo-occipital nodes for action perception

Li,

Poyo Solanas,

Marrazzo

et al. 2024

Preprint

Self Cite

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“…on a Gabor wavelet decomposition of the input images [Naselaris et al, 2015]. It has additionally been shown that, when moving across the ventral stream, more complex neural computational models better explain the imaging data [Güçlü and van Gerven, 2014, Storrs et al, 2021, Cichy et al, 2016] and that postural features are encoded in regions that have been characterized as selective to body stimuli [Marrazzo et al, 2023]. In the auditory domain, encoding models have been successfully used to characterize the spectral content of natural sounds [Moerel et al, 2012, Moerel et al, 2013], compare models of acoustic processing [Santoro et al, 2014, Norman-Haignere and McDermott, 2018], evaluate models of pitch perception [De Angelis et al, 2018], test deep networks trained for specific tasks [Kell et al, 2018], map the sensitivity to spectral information and sound location in subcortical areas [De Martino et al, 2013] and evaluate the sensitivity of cortical responses to acoustic, articulatory and semantic features of speech [de Heer et al, 2017].…”

Section: Introductionmentioning

confidence: 99%

Unbiased estimation of the coefficient of determination in linear models: an application to fMRI encoding model comparison

Castellanos,

De Martino,

Valente

2024

Preprint

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Neuroscientific investigation has greatly benefited from the combination of functional Magnetic Resonance Imaging (fMRI) with linearized encoding, which allows to validate and compare computational models of neural activity based on neuroimaging data. In linearized encoding, a multidimensional feature space, usually obtained from a computational model applied to the stimuli, is related to the measured brain activity. This is often done by mapping such space to a dataset (training data, or in-sample), and validating the mapping on a separate dataset (test data, or out-of-sample), to avoid overfitting. When comparing models, the one with the highest explained variance on the test data, as indicated by the coefficient of determination (R2 ), is the one that better reflects the neural computations performed by the brain. An implicit assumption underlying this procedure is that the out-of-sample R2 is an unbiased estimator of the explanatory power of a computational model in the population of stimuli, and can therefore be safely used to compare models. In this work, we show that this is not the case, as the out-of-sample R2 has a negative bias, related to the amount of overfitting in the training data. This phenomenon has dramatic implications for model comparison when models of different dimensionalities are compared. To this aim, we develop an analytical framework that allows us to evaluate and correct biases in both in- and out-of-sample R2, with and without L2 regularization. Our proposed approach yields unbiased estimators of the population R2, thus enabling a valid model comparison. We validate it through illustrative simulations and with an application to a large public fMRI dataset.

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“…Critchley et al ( 28 ) concluded that explicit tasks activated the temporal lobe cortex, and implicit tasks activated the amygdala region. Marrazzo et al ( 29 ) proved that prefrontal areas, inferior parietal lobule, and high-level visual cortex played key roles in discriminating implicit/explicit tasks. Pierce et al ( 30 ) took cerebellum as a region of interest, and discovered that posterior cerebellar hemispheres activated in response to explicit tasks, and bilateral lobules VI/Crus I/II, right Crus II/lobule VIII, anterior lobule VI, and lobules I-IV/V activated during implicit tasks.…”

Section: Introductionmentioning

confidence: 99%

The conscious processing of emotion in depression disorder: a meta-analysis of neuroimaging studies

Gou

Guo

et al. 2023

Front. Psychiatry

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BackgroundDepression is generally accompanied by a disturbed conscious processing of emotion, which manifests as a negative bias to facial/voice emotion information and a decreased accuracy in emotion recognition tasks. Several studies have proved that abnormal brain activation was responsible for the deficit function of conscious emotion recognition in depression. However, the altered brain activation related to the conscious processing of emotion in depression was incongruent among studies. Therefore, we conducted an activation likelihood estimation (ALE) analysis to better understand the underlying neurophysiological mechanism of conscious processing of emotion in depression.MethodElectronic databases were searched using the search terms “depression,” “emotion recognition,” and “neuroimaging” from inceptions to April 10th, 2023. We retrieved trials which explored the neuro-responses of depressive patients to explicit emotion recognition tasks. Two investigators independently performed literature selection, data extraction, and risk of bias assessment. The spatial consistency of brain activation in conscious facial expressions recognition was calculated using ALE. The robustness of the results was examined by Jackknife sensitivity analysis.ResultsWe retrieved 11,365 articles in total, 28 of which were included. In the overall analysis, we found increased activity in the middle temporal gyrus, superior temporal gyrus, parahippocampal gyrus, and cuneus, and decreased activity in the superior temporal gyrus, inferior parietal lobule, insula, and superior frontal gyrus. In response to positive stimuli, depressive patients showed hyperactivity in the medial frontal gyrus, middle temporal gyrus, and insula (uncorrected p < 0.001). When receiving negative stimuli, a higher activation was found in the precentral gyrus, middle frontal gyrus, precuneus, and superior temporal gyrus (uncorrected p < 0.001).ConclusionAmong depressive patients, a broad spectrum of brain areas was involved in a deficit of conscious emotion processing. The activation of brain regions was different in response to positive or negative stimuli. Due to potential clinical heterogeneity, the findings should be treated with caution.Systematic review registrationhttps://inplasy.com/inplasy-2022-11-0057/, identifier: 2022110057.

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Decoding the difference between explicit and implicit body expression representation in high level visual, prefrontal and inferior parietal cortex

Cited by 10 publications

References 104 publications

Connectivity and functional diversity of different temporo-occipital nodes for action perception

Connectivity and functional diversity of different temporo-occipital nodes for action perception

Unbiased estimation of the coefficient of determination in linear models: an application to fMRI encoding model comparison

The conscious processing of emotion in depression disorder: a meta-analysis of neuroimaging studies

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