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Touchscreen virtuosos: how smartphone use influences the brain maps of our fingertips

Twenty years ago, very few among us had ever used touchscreens; now, there is one in every pocket. Their ease of use provides a seamless interface with mobile phones, tablets and computers, and we spend literally hours every day interacting with those devices through touchscreens. How does all that tapping and swiping influence the brain? Quite a bit, according to scientists at the University of Fribourg and the Swiss Federal Institute of Technology of Zurich, both in Switzerland, who recently reported their findings in Current Biology. Using EEG and somatosensory evoked potentials (an EEG measure of the brain’s response to tactile stimulation), they found that users of smartphones had increased responses to stimulation of their thumb. This increase was directly proportional to both the average intensity of smartphone use and its day-to-day fluctuations. The researchers conclude that sensory processing in our brains is continuously updated by our use of modern technology.

Touchscreen virtuosos

Previous research had shown that musicians had increased cerebral responses to finger touch, as did blind people who read Braille. Here, Anne-Dominique Gindrat and her colleagues recruited 38 university students, 27 of whom had a smartphone, the remaining 11 still using old-technology mobile phones (those with numbered buttons on them, if you remember). They first confirmed that the owners of touchscreen smartphones used their devices for a much longer time each day, and that they predominantly manipulated them with their right thumb. Then, while recording their EEG, the researchers delivered tactile stimulation to the tip of each of the first three fingers. They found a considerable increase in the magnitude of the brain responses, most importantly for the thumb, but also for the index and middle fingers. Based on the location and timing of the responses, they could ascertain that the changes involved the representation of the fingers in the primary somatosensory cortex.

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Updating cortical maps

Spectacularly, the changes in the brain’s response to touch correlated with each participant’s recent history of smartphone use. In order to estimate that use over a 10-day period, Gindrat and colleagues cleverly turned to battery logs: they had the participants install an app that would record battery usage every ten minutes when the phone was in use. Hourly phone usage as well as the time elapsed since the period of most intense use during the last 10 days were extracted from those logs and used as regressors on the brain responses. The researchers found that the more the volunteers had used their smartphone in the days before the EEG recording session, the more intense their brain responses to tactile stimulation of the thumb. Similarly, the closer the period of most intense touchscreen use from the recording session, the more intense the changes in brain responses. Results for the index finger were along the same lines, although less pronounced. By contrast, the researchers found that the total time of smartphone ownership (a measure that they termed “age of inception”) did not meaningfully impact brain tactile responses. These findings strongly suggest that the brain continuously updates its sensory representations of the environment to reflect day-to-day variations in sensory inputs.

No loss of lateral inhibition

Turning to the potential mechanisms for this striking plasticity, Gindrat and colleagues explored whether it could be due to a loss of lateral inhibition. Simply put, brain responses to simultaneous stimulation of the thumb and index fingers are normally smaller than what would be expected by summing the brain responses to either finger in isolation. This phenomenon is presumably explained by lateral inhibitory interactions between the cortical representations of neighboring fingers. Here, the researchers found that responses to combined thumb and index finger stimulation were indeed smaller than expected in smartphone users; in fact, that reduction was even more pronounced than in non-smartphone users.

Overall, the results of this study suggest that, in smartphone users, the representations of the thumb and index fingers in the somatosensory cortex are both enhanced and better individualized. This likely reflects the importance of somatosensory inputs and feedback for the fine motor behaviors that are required to efficiently control smartphones and other computers using a touchscreen.

A caveat regarding waveform-based EEG analysis

No scientific study is perfect, and the major weakness of the present work, in my opinion, is the fact that all the analyses were based on the EEG measurements themselves. Furthermore, some of the statistical tests were performed directly on the individual EEG waveforms, which are not entirely adequate indexes of the underlying neural activity. Modern EEG analysis incorporates information from every EEG electrode into whole-head maps which better reflect cerebral activity (much more on this technical but important subject in this review article). These maps can then be used to estimate the active sources in the brain that generated them (EEG source imaging). Such an approach could have better revealed the cerebral sites where plasticity took place, as well as the neural correlates of the intensity of recent smartphone use.

Nevertheless, this study is a beautiful experimental confirmation of the notion that the brain dynamically and continuously adapts to changes in the environment and in sensorimotor experience.

Discussion
  1. I disagree strongly with your criticism of this work with regards to use of the EEG waveform instead of “whole-head maps”. Such maps are calculated by making a large number of never-justified assumptions about the electric current density within the brain in an attempt to solve the EEG inverse problem which is known to be horridly ill-posed. Relying on such maps would cast more doubt rather than less upon these results. I commend the authors for having chosen the defensible path of following the changes in the EEG waveform that are associated with motor activity in question.

  2. Thank you for your comment. This is rather technical but very interesting to me. There are two issues at stake here: (1) are EEG waveforms vs. scalp voltage maps better indexes of brain function, and (2) can one use scalp EEG data to estimate which brain structures are active.

    Regarding (1): EEG waveforms bear an ambiguous relationship with the underlying brain activity for at least one reason: their morphology depends on the position of the reference electrode. Yet many scientists (and most likely many laypersons as well) incorrectly interpret evoked potential waveforms obtained from a given scalp area as reflecting the activity of the patch of brain lying directly underneath (by the way, Gindrat and colleagues do NOT make that mistake here!). By contrast, the topography of scalp voltage maps is reference-independent. Think of it as altitude contour maps: the relative altitude difference between two points on the map does not change even if the reference point changes (a rise in sea level does not affect the difference of altitude between the mountain’s peak and base camp in the valley). Many of the analytical approaches traditionally applied to EEG “peaks” (latency and amplitude) can be transposed to the analysis of EEG maps. Now, it is entirely possible to analyse EEG maps in themselves as indexes of the underlying cerebral activity, without making any extra assumption on the location of the maps’ generators. However, EEG maps are one step closer to the cerebral generators of the EEG in that maps whose topography is demonstrably different must have been generated by different configurations of activity within the brain. Until here the ill-posed inverse problem is out of the picture.

    Regarding (2): it is true that the inverse problem, the relationship between EEG measurements at the scalp and the underlying cerebral activity, is mathematically ill-posed. A given EEG map could in theory be generated by an infinity of configurations of generators. However, scientists can estimate which of those infinite solutions make the most sense, given what we know about brain function and the biophysics of EEG. Note that inverse solutions always remain estimates, never measurements; therefore, they must be interpreted with the appropriate caution. Nevertheless, given reasonable a priori constraints, EEG and MEG have been used successfully to localize brain activity in numerous instances, both in cognitive neuroscience and in clinical applications such as epilepsy, with demonstrable reliability.

    For more information, one can start with Michel CM and Murray MM. Towards the utilization of EEG as a brain imaging tool. Neuroimage. June 2012;61(2):371–385. doi:10.1016/j.neuroimage.2011.12.039

  3. “However, scientists can estimate which of those infinite solutions make the most sense, given what we know about brain function and the biophysics of EEG.”

    The vast majority of the information needed to uniquely solve the EEG/MEG inverse problem will come from the constraints applied to the problem. The measured fields contribute relatively little. Authors such as A. Fokas and D. Sheltraw have made this clear. So any error in the applied constraints has huge impact on the maps. So what is the error within the maps? You can’t know that without knowing the error in the constraints. And when those constraints are based on nebulous constraints such as minimum norms (the current density is not known to obey such a minimization) or what “makes sense” estimation of error is not possible. Hence you have maps giving intensities of something with no known error in those intensities. Not very useful to real science is it?

    The authors did the right thing by not getting involved with the inverse problem. They didn’t need to.

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