Sensitivity to face animacy and inversion in childhood: Evidence from EEG data

Bayet, Laurie; Saville, Alyson; Balas, Benjamin

doi:10.1016/j.neuropsychologia.2021.107838

Cited by 6 publications

(4 citation statements)

References 77 publications

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“…Segments containing artifacts related to vertical eye movements (eye blinks; ±140 μV) and horizontal eye movements (±55 μV), which were automatically identified by the built‐in NetStation artifact detection tool, were excluded. Following the manufacturer's recommendation and as is customary in other studies that used the Electrical Geodesics, Inc. (EGI) system, 67 − 71 a recording segment was marked as “bad” and rejected if it contained 10 or more bad channels (bad channel: ±200 μV for the entire segment). When possible, individual bad channels were replaced on a segment‐by‐segment basis using the built‐in NetStation bad‐channel replacement tool.…”

Section: Methodsmentioning

confidence: 99%

Comprehensive assessment of memory function, inhibitory control, neural activity, and cortisol levels in late pregnancy

Raz

2024

Annals of the New York Academy of Sciences

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A considerable proportion of women subjectively perceive a detriment to their cognitive capacity during pregnancy, with decreased memory functions being the most frequently self‐reported concerns. However, objective investigation of these perceived cognitive deficits has yielded inconsistent results. This study focused on memory functions during late pregnancy using multiple tasks designed to assess various memory indices, for example, working memory, learning rate, immediate recall, proactive and retroactive interference, delayed recall, retrieval efficiency, visuospatial constructional ability, recognition, and executive function. Additionally, sustained attention and inhibitory control were examined using a combined recognition stop‐signal task. Electrophysiological brain activity during this task was recorded using a 128‐channel electroencephalographic‐event‐related potential system. Salivary cortisol levels were assessed both prior to and following the experimental session. In contrast to the widely held belief, results demonstrated that women in late pregnancy did not exhibit a decline in their performance across the various memory tests. In terms of accuracy, there was not a single task in which poorer performance was found for pregnant women. The quality of memory performance was comparable, and in some cases even superior, among women in the pregnancy group. On the stop‐signal task, pregnant women exhibited significantly better performance, and their electrophysiological data revealed greater centrally distributed P300 amplitude to “stop” signs, which may signify an enhanced neural efficiency in the domains of inhibitory executive control. Endocrine results revealed that pregnant women exhibited significantly lower levels of salivary cortisol, suggesting an attenuation of hypothalamic−pituitary−adrenocortical axis activity, which may contribute to the optimization of fetal development and growth.

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Section: Methodsmentioning

confidence: 99%

Comprehensive assessment of memory function, inhibitory control, neural activity, and cortisol levels in late pregnancy

Raz

2024

Annals of the New York Academy of Sciences

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“…Similarly, MVPA also successfully discriminated NIRS responses to spoken and sign language in infants [19]. Using EEG, Bayet and colleagues [20] successfully employed MVPA on a selection of electrodes to discriminate the orientation of visual stimuli in children.…”

Section: Introductionmentioning

confidence: 99%

A Within-Subject Multimodal NIRS-EEG Classifier for Infant Data

Gemignani,

Gervain

2024

Sensors

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Functional Near Infrared Spectroscopy (fNIRS) and Electroencephalography (EEG) are commonly employed neuroimaging methods in developmental neuroscience. Since they offer complementary strengths and their simultaneous recording is relatively easy, combining them is highly desirable. However, to date, very few infant studies have been conducted with NIRS-EEG, partly because analyzing and interpreting multimodal data is challenging. In this work, we propose a framework to carry out a multivariate pattern analysis that uses an NIRS-EEG feature matrix, obtained by selecting EEG trials presented within larger NIRS blocks, and combining the corresponding features. Importantly, this classifier is intended to be sensitive enough to apply to individual-level, and not group-level data. We tested the classifier on NIRS-EEG data acquired from five newborn infants who were listening to human speech and monkey vocalizations. We evaluated how accurately the model classified stimuli when applied to EEG data alone, NIRS data alone, or combined NIRS-EEG data. For three out of five infants, the classifier achieved high and statistically significant accuracy when using features from the NIRS data alone, but even higher accuracy when using combined EEG and NIRS data, particularly from both hemoglobin components. For the other two infants, accuracies were lower overall, but for one of them the highest accuracy was still achieved when using combined EEG and NIRS data with both hemoglobin components. We discuss how classification based on joint NIRS-EEG data could be modified to fit the needs of different experimental paradigms and needs.

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“…Advances in the application of multivariate data-driven methods to infant-friendly neuroimaging tools such as EEG and fNIRS bears promise for developmental researchers to begin answering questions beyond what can be addressed with traditional neuroimaging analysis techniques ( Bayet et al, 2021 , Norman et al, 2006 , O’Brien et al, 2020 , Zinszer et al, 2017 ). While the existing methodology lays the groundwork for infant study, challenges inherent to the collection and analysis of infant neural data require specific solutions and a thorough investigation into best practices.…”

Section: Introductionmentioning

confidence: 99%