FEF Excitability in Attentional Bias: A TMS-EEG Study

Torriero, Sara; Mattavelli, Giulia; Gerfo, Emanuele Lo; Lauro, Leonor J. Romero; Actis-Grosso, Rossana; Ricciardelli, Paola

doi:10.3389/fnbeh.2018.00333

Cited by 11 publications

(8 citation statements)

References 118 publications

(135 reference statements)

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“…This indicates that semantic content within images is more consequential than color for producing model outputs and ERP waveforms, given that the scrambled images contained the same colors as unscrambled images in unrecognizable configurations. The research literature on N170 sensitivity to faces, other objects and non-objects is consistent with these observations 4,6,9,28 .…”

Section: Discussionsupporting

confidence: 80%

“…A consistent finding from event-related potential (ERP) studies concerning face perception is sensitivity of visually evoked N170 to images of human faces [5][6][7][8] . This ERP peak occurs earlier in response to human faces than other objects and typically has greater amplitude at right hemisphere occipitotemporal electrode sites associated with face processing specialization 9,10 . There remains considerable debate in the literature about whether the N170 component is specific to faces or rather represents a response to highly familiar stimuli -of which faces invariably are.…”

Section: Introductionmentioning

confidence: 93%

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Neural correlates of face perception modeled with a convolutional recurrent neural network

O’Reilly

Wehrman

Carey

et al. 2023

Preprint

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Event-related potential (ERP) sensitivity to faces is predominantly characterized by an N170 peak that has greater amplitude and shorter latency when elicited by human faces than images of other objects. We developed a computational model of visual ERP generation to study this phenomenon which consisted of a convolutional neural network (CNN) connected to a recurrent neural network (RNN). We used open-access data to develop the model, generated synthetic images for simulating experiments, then collected additional data to validate predictions of these simulations. For modeling, visual stimuli presented during ERP experiments were represented as sequences of images (time x pixels). These were provided as inputs to the model. The CNN transformed these inputs into sequences of vectors that were passed to the RNN. The ERP waveforms evoked by visual stimuli were provided to the RNN as labels for supervised learning. The whole model was trained end-to-end using data from the open-access dataset to reproduce ERP waveforms evoked by visual events. Cross-validation model outputs strongly correlated with open-access (r = 0.98) and validation study data (r = 0.78). Open-access and validation study data correlated similarly (r = 0.81). Some aspects of model behavior were consistent with neural recordings while others were not, suggesting promising albeit limited capacity for modeling the neurophysiology of face-sensitive ERP generation.

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Section: Discussionsupporting

confidence: 80%

Section: Introductionmentioning

confidence: 93%

Neural correlates of face perception modeled with a convolutional recurrent neural network

O’Reilly

Wehrman

Carey

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…A consistent finding from event-related potential (ERP) studies concerning face perception is sensitivity of visually evoked N170 to images of human faces [5][6][7][8]. This ERP peak occurs earlier in response to human faces than other objects and typically has greater amplitude at right hemisphere occipitotemporal electrode sites associated with face processing specialization [9,10]. There remains considerable debate in the literature about whether the N170 component is specific to faces or rather represents a response to highly familiar stimuli-of which faces invariably are [6][7][8][9][10].…”

Section: Introductionmentioning

confidence: 65%

“…This ERP peak occurs earlier in response to human faces than other objects and typically has greater amplitude at right hemisphere occipitotemporal electrode sites associated with face processing specialization [9,10]. There remains considerable debate in the literature about whether the N170 component is specific to faces or rather represents a response to highly familiar stimuli-of which faces invariably are [6][7][8][9][10]. One argument often cited in support of the specificity view is the observation that familiar but non-face objects tend to elicit an N170 later in time than faces do.…”

Section: Introductionmentioning

confidence: 95%

Neural correlates of face perception modeled with a convolutional recurrent neural network

et al. 2023

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Objective: Event-related potential (ERP) sensitivity to faces is predominantly characterized by an N170 peak that has greater amplitude and shorter latency when elicited by human faces than images of other objects. We aimed to develop a computational model of visual ERP generation to study this phenomenon which consisted of a three-dimensional convolutional neural network (CNN) connected to a recurrent neural network (RNN).Approach: The CNN provided image representation learning, complimenting sequence learning of the RNN for modeling visually-evoked potentials. We used open-access data from ERP CORE (40 subjects) to develop the model, generated synthetic images for simulating experiments with a generative adversarial network, then collected additional data (16 subjects) to validate predictions of these simulations. For modeling, visual stimuli presented during ERP experiments were represented as sequences of images (time x pixels). These were provided as inputs to the model. By filtering and pooling over spatial dimensions, the CNN transformed these inputs into sequences of vectors that were passed to the RNN. The ERP waveforms evoked by visual stimuli were provided to the RNN as labels for supervised learning. The whole model was trained end-to-end using data from the open-access dataset to reproduce ERP waveforms evoked by visual events. Main results: Cross-validation model outputs strongly correlated with open-access (r = 0.98) and validation study data (r = 0.78). Open-access and validation study data correlated similarly (r = 0.81). Some aspects of model behavior were consistent with neural recordings while others were not, suggesting promising albeit limited capacity for modeling the neurophysiology of face-sensitive ERP generation.Significance: The approach developed in this work is potentially of significant value for visual neuroscience research, where it may be adapted for multiple contexts to study computational relationships between visual stimuli and evoked neural activity.

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“…Different studies have indeed shown that a dorsal frontoparietal network is globally associated with attention orientation, even if the regions in the system are engaged differently over time and across the hemispheres concerning the type of attention (Chica et al, 2013). Within this framework, attentional performance critically depends on the interaction between PPC and the frontal eye fields, one holding the sensory representation, the other holding a motor representation (Mesulam, 1981; Torriero et al, 2019). In line with our results, Thiel et al (Thiel, Zilles, & Fink, 2004) showed that the specific reorienting attention activity in PCT increased activation in a bilateral frontoparietal network, including left and right intraparietal sulcus, right temporoparietal junction, and left and right middle frontal gyrus in healthy volunteers.…”

Section: Discussionmentioning

confidence: 99%

State-dependent effectiveness of cathodal transcranial direct current stimulation on cortical excitability

Vergallito

Varoli

Pisoni

et al. 2023

Preprint

Self Cite

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The extensive use of transcranial direct current stimulation (tDCS) in experimental and clinical settings does not correspond to an in-depth understanding of its underlying neurophysiological mechanisms. In previous studies, we employed an integrated system of Transcranial Magnetic Stimulation and Electroencephalography (TMS-EEG) to track the effect of tDCS on cortical excitability. At rest, anodal tDCS (a-tDCS) over the right Posterior Parietal Cortex (rPPC) elicits a widespread increase in cortical excitability. In contrast, cathodal tDCS (c-tDCS) fails to modulate cortical excitability, being indistinguishable from sham stimulation. Here we investigated whether an endogenous task-induced activation during stimulation might change this pattern, improving c-tDCS effectiveness in modulating cortical excitability. In Study 1, we tested whether performance in a Visuospatial Working Memory Task (VWMT) and a modified Posner Cueing Task (mPCT), involving rPPC, could be modulated by c-tDCS. Thirty-eight participants were involved in a two-session experiment receiving either c-tDCS or sham during tasks execution. In Study 2, we recruited sixteen novel participants who performed the same paradigm but underwent TMS-EEG recordings pre- and 10 minutes post-sham and c-tDCS. Behavioral results showed that c-tDCS significantly modulated mPCT performance compared to sham. At a neurophysiological level, c-tDCS significantly reduced cortical excitability in a frontoparietal network involved in task execution. Taken together, our results provide evidence of the state dependence of c-tDCS in modulating cortical excitability effectively. The conceptual and applicative implications are discussed.

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FEF Excitability in Attentional Bias: A TMS-EEG Study

Cited by 11 publications

References 118 publications

Neural correlates of face perception modeled with a convolutional recurrent neural network

Neural correlates of face perception modeled with a convolutional recurrent neural network

Neural correlates of face perception modeled with a convolutional recurrent neural network

State-dependent effectiveness of cathodal transcranial direct current stimulation on cortical excitability

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