Methodologies

Linguistic methodologies

Neuroscientific methodologies

Electroencephalography

{Khanna et al., 2015} More than 80 years ago, Hans Berger coined the term EEG.

Event-related potentials

{DeLuca et al., 2020} A way of examining the time course of brain activity with the millisecond temporal resolution at which neurons operate is through the electroencephalogram (EEG), examining either evoked components or induced changes in oscillatory activity. With the evoked component approach, an averaging procedure is needed to extract the “event” related signal called the event related potential (ERP) which represents the neural activity precisely time- and phase-locked in response an experimental event. The peaks and troughs in an ERP waveform often follow a stereotypical temporal pattern of positive and negative voltage deflections, classified as ‘components’ (Luck, 2005) which are assumed to map onto various cognitive constructs.

ERPs

1. N2: detection of potentially conflicting information (e.g. is the stimulus an infrequently presented target or commonly occurring non-target) and inhibition of prepotent responses. The earlier the latency of the N2 and the larger its amplitude, the more readily the conflict detection process is thought to have occurred

2. P300: most prominent over parietal electrodes is a positive potential occurring 300–500 ms after the occurrence of a novel/rare/conflicting stimulus (e.g. the letter O occurring after a series of X’s). Previous research has suggested that the peak latency of the P300 reflects the time it took for the participant to automatically classify the stimulus as novel, and its amplitude reflecting the confidence of the classification.

3. ERN: over frontal central electrodes occurring 50–100 ms after an erroneous response, even without the explicit awareness of the participant. It has been speculated to reflect an online conflict monitoring system.

{Rappoport, 2019} To cite just one important piece of evidence, consider EEG event-related potential (ERP) components [Kotchoubey, 2006]. The activation of candidates in the superficial layers is what generates negative ERPs such as the N170 associated with face perception and the N400 associated with semantics (especially in language). Surprises yield increased negative ERP amplitude, because they induce the activation of more candidates. Competition and its resolution, which involve neurons in the deep layers, are associated with positive ERP components. For example, the language P600 ERP is larger when the ending of a sentence requires reinterpretation.

Oscillations

{Khanna et al., 2015} More than 80 years ago, Hans Berger coined the term EEG and for the first time recorded cortical oscillatory activity from the surface of the skull in humans (Berger, 1929). He described the potentiation and emergence of specific brain waves, today referred to as alpha oscillations (8–12 Hz), in posterior brain regions when human subjects were instructed to close their eyes. Since then, numerous studies have explored the association between various cortical frequency bands of delta (1–4 Hz), theta (4–7 Hz), alpha (8–12 Hz), beta (12–28 Hz), and gamma (>30 Hz) oscillations with different behavioral and disease states. Furthermore, due to the stochastic and multidimensional nature of EEG signals, a wide variety of analytical approaches have been proposed to quantify and discover different features of cortical oscillatory activity and the functional roles they serve. One common approach is to consider the EEG signal as a dynamical system that can be described in terms of its state and dynamics. The system state is the combination of all variables that describe the system at any given time t, and the system dynamics describe how the state changes over time.

{DeLuca et al., 2020} In contrast to the event-related averaging approach, the oscillatory changes in the EEG reflect activity that, while time-locked to an experimental event, is not phase-locked. EEG activity contains rhythmic activity at different characteristic frequencies called frequency bands, which are believed to be produced by specific areas of the brain and related to specific cognitive processes (Mazaheri, Slagter, Thut, & Foxe, 2018; Siegel, Donner, & Engel, 2012). One important rhythm, particularly relevant to control processes, is the theta rhythm (3–7), with a scalp topography over frontal-midline sites. The theta rhythm has primarily been observed to increase in amplitude during the implementation of executive processes (Nigbur, Ivanova, & Stürmer, 2011). Activity at the frequency range of 8–14 Hz, referred to as the alpha rhythm, has primarily been observed over sensory regions, such as the visual, sensory-motor, and auditory cortices (see recent review by Van Diepen, Foxe, & Mazaheri, 2019). The increase in the amplitude alpha activity over a brain area is believed to signal its inhibition or idling. The prevalent view on the functional role of the alpha rhythm during cognition is that it is involved in gating information processing away from task-irrelevant regions, to the task relevant (Van Diepen et al., 2019). This technique has been used less frequently within the bilingualism literature to measure brain activity (but see Rossi & Prystauka, 2020), but we will elaborate on the potential of EEG for future research later in this review.

Cerebral rhythms or frequency bands

1. delta (1–4 Hz),

2. theta (4–7 Hz), frontal-midline cortex, executive processes

3. alpha (8–12 Hz), sensory cortex

4. beta (12–28 Hz),

5. gamma (>30 Hz)

Functional magnetic resonance imaging

{DeLuca et al., 2020} In addition to brain structure, patterns in brain activation and functional connectivity, both at rest and in conjunction with specific stimuli or responses, can be inferred using functional magnetic resonance imaging (fMRI). fMRI measures where changes in oxygenated blood flow (the BOLD signal) are occurring in the brain, both at rest and during a task. fMRI is highly useful for assessing the location(s) of neural activity; however, its temporal resolution is limited, with the BOLD response to simple experimental stimuli occurring seconds after its onset. Using fMRI, studies have found monolinguals and bilinguals to exhibit diverging patterns of brain recruitment to handle given executive control demands (see for review Pliatsikas & Luk, 2016). Specifically, bilinguals have either been found to recruit alternative or additional regions and networks (Anderson, Chung-Fat-Yim et al., 2018a; Ansaldo et al., 2015; Luk et al., 2010) or vary in terms of the degree of activation in regions or networks (e.g. Abutalebi et al., 2012; Costumero, Rodríguez- Pujadas, Fuentes-Claramonte, & Ávila, 2015) to handle the same cognitive demands.

Structural magnetic resonance imaging

{DeLuca et al., 2020} Structural magnetic resonance imaging (sMRI) affords the opportunity to quantify patterns in regional grey matter- and white matter structure. Patterns of grey matter structure can be quantified in a variety of ways including grey matter volume or density (Ashburner & Friston, 2000), cortical thickness (Ad-Dab’bagh et al., 2005), and surface area as measured by vertex analyses (Patenaude, Smith, Kennedy, & Jenkinson, 2011). Bilingual experience has previously been found to be associated with changes in patterns of grey matter structure across various cortical and subcortical brain regions implicated in language and cognitive control processes (see for review Li et al., 2014; Pliatsikas, 2019a).

Diffusion tensor imaging

{DeLuca et al., 2020} Diffusion tensor imaging (DTI) captures the diffusivity of water molecules in tissue, which serves as a powerful way to quali- tatively and quantitatively investigate microstructural characteristics of the white-matter tracts in the brain (Le Bihan, 2003). Some of the more common measures stemming from this imaging technique relate to the degree of diffusivity in general (mean diffusivity; MD), diffusivity along a tract (axial diffusivity; AD), perpendicular to a tract (radial diffusivity; RD), and fractional anisotropy (FA) which is a scalar value computed from the ratio of AD and RD values. Each of these values is thought to relate to specific micro- structural properties of white matter including fiber density, axonal diameter, and degree of myelination (Mori & Zhang, 2006; Smith et al., 2006). Bilinguals have been found to diverge from monolinguals in terms of diffusivity patterns across a range of tracts (e.g. Anderson, Grundy, et al., 2018; Hämäläinen, Sairanen, Leminen, & Lehtonen, 2017; Luk, Bialystok, Craik, & Grady, 2011; Mohades et al., 2012; Pliatsikas, Moschopoulou, & Saddy, 2015; Rossi et al., 2017; Singh et al., 2017).

Summary

References

• DeLuca, V., Segaert, K., Mazaheri, A., Krott, A. 2020. Understanding bilingual brain function and structure changes? U bet! A unified bilingual experience trajectory model. Journal of Neurolinguistics 56: 100930

The next day

Come to class having read Auditory transduction and answered the questions.

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Last edited Aug 22, 2023