Possible neural oscillatory mechanisms underlying learning

during performance of speeded skills. AIMS Neuroscience, 3 (1), 40–55. doi:10.3934/Neuroscience.2016.1.40

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doi:10.1038/nn.3045

Possible neural oscillatory

mechanisms underlying learning

Olga Kepinska

and Niels O. Schiller

a

Leiden University Centre for Linguistics, Leiden, the Netherlands;

b

Leiden Institute for Brain and Cognition, Leiden, the Netherlands

ABSTRACT

In response to Voelker et al. (this issue), we argue for a wide array of neural oscillatory mechanisms underlying learning and practice. While the authors propose frontal theta power as the basis for learning-induced neuro- plasticity, we believe that the temporal dynamics of other frequency bands, together with their synchroni- zation properties can offer a fuller account of the neu- rophysiological changes occurring in the brain during cognitive tasks.

ARTICLE HISTORY

Received 9 May 2016; Published online 22 July 2016 KEYWORDS

Learning; neural oscillation; neuroplasticity

Voelker et al. (this issue) offer a fascinating view on the molecular basis of learning, a line of study that clearly merits investigation.

While we agree with the premise that changes in white matter can underlie behavioral manifestations of learning, we believe that learning and practice might be based on a much wider array of different neural mechanisms.

We would like to mention a possible extension of the working hypothesis that considers the frontal theta rhythm as the basis for neuroplasticity mechan- isms coupled with learning, as the authors suggest.

We believe that apart from local oscillatory mechanisms, such as frontal theta power, global mechanisms, such as long-range coherence, should be taken into account. For example, information about functional cooperation between cortical regions during learning can be obtained by analyzing the synchronization properties of the electroencepha- logram (EEG) signal within certain frequency bands.

In the language-learning domain, for instance, increased long-range gamma band phase coherence has been shown to accompany successful rule learn- ing (De Diego-Balaguer, Fuentemilla, & Rodriguez- Fornells, 2011). These results are in line with the view that an increase in coherence in the gamma band ‘could fulfil the criteria required for the forma- tion of Hebbian cell assemblies, binding together parts of the brain that must communicate with one another in order for associative learning to take place’

(Miltner, Braun, Arnold, Witte, & Taub, 1999, p. 434).

Also, our data (Kepinska, Pereda, Caspers, &

Schiller, in prep.) provide evidence for the role of gamma band coherence in the process of learning.

Employing a bivariate, frequency-specific index of phase synchronization termed Phase Locking Value (PLV, Mormann, Lehnertz, David, & Elger, 2000), we evaluated the contribution of four fre- quency bands (alpha, beta, gamma, and theta) to online learning of novel grammar. We observed a negative correlation between global PLV values of the slow frequency bands (theta and alpha) and scores on the learning task, and a positive correla- tion between the high frequency bands (gamma and beta) and the scores. However, only the gamma band global PLV values proved predictive of the performance according to a stepwise linear regression analysis.

In the context of investigations into neuroplasti- city mechanisms, it therefore seems vital to us not to limit the observations of the EEG signal to theta band power, but to expand the approach and include the temporal dynamics of other frequency bands, together with their synchronization properties as well. Such an approach can offer a fuller account of the neurophysiological changes occurring in the brain during cognitive tasks.

CONTACT Olga Kepinska o.kepinska@hum.leidenuniv.nl Leiden University Centre for Linguistics, Leiden, the Netherlands

128 COMMENTARIES

© 2016 Informa UK Limited, trading as Taylor & Francis Group

http://dx.doi.org/10.1080/17588928.2016.1205578

Disclosure statement

No potential conflict of interest was reported by the authors.

Funding

This work was supported by the Leiden University Centre for Linguistics and the NWO Graduate Programme.

References

De Diego-Balaguer, R., Fuentemilla, L., & Rodriguez-Fornells, A.

(2011). Brain dynamics sustaining rapid rule extraction from speech. Journal of Cognitive Neuroscience, 23, 3105–3120.

Kepinska, O., Pereda, E., Caspers, J., & Schiller, N. O. (in prep.).

Neural oscillatory mechanisms during novel grammar learning.

Miltner, W. H., Braun, C., Arnold, M., Witte, H., & Taub, E. (1999).

Coherence of gamma-band EEG activity as a basis for asso- ciative learning. Nature, 397, 434–436.

Mormann, F., Lehnertz, K., David, P., & Elger, C. E. (2000).

Mean phase coherence as a measure for phase synchro- nization and its application to the EEG of epilepsy- patients. Physica D: Nonlinear Phenomena, 144, 358–369.

Voelker, P., Piscopo, D., Weible, A. P., Lynch, G., Rothbart, M. K., Posner, M. I., & Niell, C. M. (2016). How changes in white matter might underlie improved reaction time due to practice. Cognitive Neuroscience, 1–7. doi:10.1080/

17588928.2016.1173664

Complex models of white and gray matter integration following training

J. Michael Williams

Department of Psychology, Drexel University, Philadelphia, PA, USA

ABSTRACT

For many tasks, an increase in competence is associated with faster response time. Voelker et al (this issue) explore the possible role of white matter reorganization as a mechanism underlying this relationship. With such a strong focus on this possible interpretation and the limits of current neuroimaging methods, the authors constrained their options to the point of only consider- ing simplified models of how training might result in faster responses.

ARTICLE HISTORY

Received 10 May 2016; Accepted 22 June 2016; Published online 27 July 2016

Among the enduring mysteries of modern neu- roscience are the mechanisms by which the brain

changes with experience. There are four major domains of such change: evolutionary change, growth and maturation across development, changes associated with recovery from brain ill- ness, and plasticity associated with training.

Although Voelker et al. touch on development and recovery from illness, they are primarily con- cerned with the changes associated with training.

The authors focus on the possible role of white matter changes mediating learning and training, especially the decrease in response time associated with better performance.

The present commentary will focus on two major aspects of their arguments. The first is the simplification of theories of cerebral reorganization that are likely derived from a bias in reasoning about structural changes associated with currently limited neuroima- ging methods. The second is the issue of time and its role in explanations of reorganization associated with training.

The field of neural reorganization associated with training is strongly influenced by the current neuroi- maging methods. Functional magnetic resonance imaging is a functional method confined to the gray matter. Diffusion Tensor Imaging (DTI) is a struc- tural method confined to the white matter. Voxel- based morphometry (VBM) methods are structural methods used to assess gray and white matter.

There is no functional method used to examine white matter (Lebby, 2013).

The imaging methods reinforce an arbitrary divi- sion between gray and white matter. The title of the Voelker et al. article and many of the argu- ments presented within it imply that gray and white matter behave independently in mediating functions such as training. They are obviously interconnected, as every axon making up the white matter is part of a neuron whose cell body makes up the bulk of the gray matter. This sug- gests that a change in fractional anisotropy (FA) in the white matter, detected using DTI, probably reflects a change in the network of neurons med- iating the training effects. This may appear obvious to the authors as it does to the reader except that their emphasis on white matter conduction speed as an explanation of reduced response time reveals the bias and an overly simplified theory. The model of change following training and the relationship of response time to skill acquisition is likely much

CONTACT J. Michael Williams jw37@drexel.edu

COGNITIVE NEUROSCIENCE 129

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