Inter-study and inter-Individual Consistency and Variability of EEG/ERP Microstate Sequences in Referential Word Production

Laganaro, Marina

doi:10.1007/s10548-017-0580-0

Cited by 21 publications

(21 citation statements)

References 53 publications

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“…In particular, an additional component is observed in children's ERPs in the N170-like time-window (Campbell & Sharma, 2016;Yadav, Poudel, Limbu, Thakur, & Yadav, 2015). Here, the period of electrophysiological stability observed in this time-window (see topography of map D, Figure 4) has previously been reported for N1 (N170-like) adult ERPs (Fargier & Laganaro, 2016;Laganaro, 2017;Rossion & Caharel, 2011); we therefore interpreted the stability as a single N170-like component.…”

Section: Amplitude and Latency Decrease From Childhood To Adolescencesupporting

confidence: 66%

Dynamics of Word Production in the Transition from Adolescence to Adulthood

Atanasova

Fargier

Zesiger

et al. 2020

Neurobiology of Language

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Changes in word production occur across the lifespan. Previous studies have shown electrophysiological, temporal, and functional differences between children and adults accompanying behavioral changes in picture-naming tasks (Laganaro et al., 2015). Thus, a shift towards adult-like processes in referential word production occurs somewhere between the ages of 13 and 20. Our aim here was to investigate when and how the youngsters develop an adultlike behavior and brain activation in word production. To this aim, performance and eventrelated potentials (ERP) in a referential word production task were recorded and compared between two groups of adolescents (aged 14 to 16 and 17 to 18), children (aged 10 to 13) and young adults (aged 20 to 30). Both groups of adolescents displayed adult-like production latencies, which were longer for children only, while accuracy was lower in the younger group of adolescents and in children, compared to adults. ERP waveform analysis and topographic pattern analysis revealed significant inter-group differences in key time-windows on stimuluslocked ERPs, both early (150-220 ms) – associated with pre-linguistic processes, and late (280-330 ms) – associated with lexical processes. The results indicate that brain activation underlying referential word production is completely adult-like in 17 years-old adolescents, whereas an intermediate pattern is still observed in adolescents aged 14 to 16 years old, although their production speed, but not their accuracy, is already adult-like.

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Section: Amplitude and Latency Decrease From Childhood To Adolescencesupporting

confidence: 66%

Dynamics of Word Production in the Transition from Adolescence to Adulthood

Atanasova

Fargier

Zesiger

et al. 2020

Neurobiology of Language

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“…One potential avenue might be to search for temporal markers of early and late processes in online neural data, such as the electro-encephalogram, while speakers are preparing to name pictures. Laganaro (2017) argued that, during such tasks, there are distinct “microstates” with a global stable electrical field, and that such microstates might be linked to distinct cognitive processes taking place during the task. If it were possible to isolate a microstate corresponding to phonetic encoding, then the present account predicts a delay in the duration of this state in L2 speakers.…”

Section: Discussionmentioning

confidence: 99%

Delayed picture naming in the first and second language

et al. 2020

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Second language (L2) speakers produce speech more slowly than first language (L1) speakers. This may be due to a delay in lexical retrieval, but it is also possible that the delay is situated at later stages. This study used delayed picture naming to test whether late production stages (leading up to articulation) are slower in L2 than in L1. Dutch–English unbalanced bilinguals performed a regular and a delayed picture naming task in English and Dutch. Monolingual English controls performed these tasks in English. Speakers were slower when naming pictures in L2 during regular picture naming but not in delayed naming. Reaction time costs of using L2 did not vary with phonological complexity, but there was a larger L2 cost in accuracy with more complex words. We conclude that the very last stages prior to articulation are not significantly slower when bilinguals name pictures in their L2.

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“…We have called these sequences Cognitive States (CS), indicating that it might be a more appropriate representation of a "mind state", than an individual short-lived BS. Concepts similar to the CS has been used to interpret both task-related (Borst and Anderson, 2015;Laganaro, 2017) and resting-state (Michel and Koenig, 2018;Vidaurre et al, 2017) activity. The CS to be identified differed from the candidate CSs only in the mean duration of their component BSs.…”

Section: Discussionmentioning

confidence: 99%

The discrete logic of the Brain - Explicit modelling of Brain State durations in EEG and MEG

Trujillo-Barreto¹,

Araya

El-Deredy

2019

Preprint

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We consider the detection and characterisation of brain state transitions, based on ongoing Magneto and Electroencephalography (M/EEG). Here a brain state represents a specific brain dynamical regime or mode of operation, which produces a characteristic quasi-stable pattern of activity at topography, sources or network levels. These states and their transitions over time can reflect fundamental computational properties of the brain, shaping human behaviour and brain function. The Hidden Markov Model (HMM) has emerged as a useful model-based approach for uncovering the hidden dynamics of brain state transitions based on observed data. However, the Geometric distribution of state duration (dwell time) implicit in HMM places highest probability on very short durations, which makes it inappropriate for the accurate modelling of brain states in M/EEG. We propose using Hidden Semi Markov Models (HSMM), a generalisation of HMM that models the brain state duration distribution explicitly. We present a Bayesian formulation of HSMM and use the Variational Bayes framework to efficiently estimate the HSMM parameters, the number of brain states and select among alternative brain state duration distributions. We assess HSMM performance against HMM on simulated data and demonstrate that the accurate modelling of state duration is paramount for accurately and robustly modelling non-Markovian EEG brain state features. Finally, we used actual resting-state EEG data to demonstrate the approach in practice and conclude that it provides a flexible parameterised framework that permits closer interrogation of possible generative mechanisms.

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Inter-study and inter-Individual Consistency and Variability of EEG/ERP Microstate Sequences in Referential Word Production

Cited by 21 publications

References 53 publications

Dynamics of Word Production in the Transition from Adolescence to Adulthood

Dynamics of Word Production in the Transition from Adolescence to Adulthood

Delayed picture naming in the first and second language

The discrete logic of the Brain - Explicit modelling of Brain State durations in EEG and MEG

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