Cortical activity allotted to the tactile receptors on fingertips conforms to skilful use of the hand []. For instance, in string instrument players, the somatosensory cortical activity in response to touch on the little fingertip is larger than that in control subjects []. Such plasticity of the fingertip sensory representation is not limited to extraordinary skills and occurs in monkeys trained to repetitively grasp and release a handle as well []. Touchscreen phones also require repetitive finger movements, but whether and how the cortex conforms to this is unknown. By using electroencephalography (EEG), we measured the cortical potentials in response to mechanical touch on the thumb, index, and middle fingertips of touchscreen phone users and nonusers (owning only old-technology mobile phones). Although the thumb interacted predominantly with the screen, the potentials associated with the three fingertips were enhanced in touchscreen users compared to nonusers. Within the touchscreen users, the cortical potentials from the thumb and index fingertips were directly proportional to the intensity of use quantified with built-in battery logs. Remarkably, the thumb tip was sensitive to the day-to-day fluctuations in phone use: the shorter the time elapsed from an episode of intense phone use, the larger the cortical potential associated with it. Our results suggest that repetitive movements on the smooth touchscreen reshaped sensory processing from the hand and that the thumb representation was updated daily depending on its use. We propose that cortical sensory processing in the contemporary brain is continuously shaped by the use of personal digital technology.

A primate genesis model of focal dystonia and repetitive strain injury: I. Learning-induced dedifferentiation of the representation of the hand in the primary somatosensory cortex in adult monkeys.

When neighboring fingertips are simultaneously stimulated, the magnitude of the ERP is smaller than the arithmetic sum of signals from the corresponding individual stimulations []. This difference is theoretically explained by cortical lateral inhibitory interactions between the neighboring fingers. The increased cortical activity associated with individual fingertips in touchscreen users may have come at the cost of such inhibitory interactions. Essentially, unmasking the inhibition between the neighboring fingertips may have contributed to the larger potentials in touchscreen users []. To address this issue, we measured the difference between the predicted and real ERPs in response to simultaneous stimulation of the thumb and index fingertips ( Figure 4 A). Touchscreen users were compared to nonusers using two-sample t tests across all electrodes and time points (50 ms prestimulation to 120 ms poststimulation) and were corrected for multiple comparisons using 2D spatiotemporal clustering. Interestingly, the proxy measure of inhibition was significantly enhanced in touchscreen users compared to the nonusers between 40 and 57 ms ( Figures 4 B and 4C).

(C) Scalp maps of voltage differences between the predicted and real response magnitudes in both groups and the corresponding T value map.

(A) An example measure from one volunteer depicting “inhibitory” interactions between the thumb and index fingertips. Note that the predicted (linear sum) signal magnitude (in gray) is larger than the real response evoked by simultaneous stimulations (in black).

A primate genesis model of focal dystonia and repetitive strain injury: I. Learning-induced dedifferentiation of the representation of the hand in the primary somatosensory cortex in adult monkeys.

In sum, the cortical potentials associated with the thumb and index fingertips reflected the touchscreen phone use history recorded by using the 10-day battery logs. The cortical activity evoked by touch to the thumb tip was directly proportional to the amount of phone use over the past 10 days and inversely proportional to the time elapsed from a period of intense use. The potential evoked by touch to the index fingertip was also related to the amount of use, albeit to a lesser extent and not related to the latter variable.

For the index fingertip, the linear relationships at the maximum positive and negative ERP electrodes were more restricted than for the thumb tip ( Figures 3 A and 3B ). Essentially, a significant relationship was found between the “phone use per hour” variable and ERP, but only for the maximum positive electrode between 32 and 43 ms. Simply put, the more the phone was used over the 10 days preceding the EEG recording, the larger the signal on the rising edge of the positive ERP. According to the scalp maps, the positive ERP electrodes showed positive ERCs ( Figure 3 E). The rest of the variables did not show any significant relationship to brain activity ( Figures 3 D and 3F). Nevertheless, up to 54% of the variations were explained by the linear model ( Figure 3 C). For the middle fingertip, no significant ERCs were found, although the linear model explained up to 55% of the variation (see Figure S2 ).

The same conventions are used as in Figure 2 . See also Figures S1 and S2

(D–F) Scalp maps of individual β values and the corresponding F statistics. Note that only “phone use per hour” was significantly linked to the index finger ERPs.

(C) Scalp map of the mean ERPs and the corresponding goodness-of-fit estimate of the full regression model (R 2 ).

(A) At the positive peak ERP electrode, the area in the dotted line box depicts the significant β values or ERCs (“phone use per hour”).

The same variables as illustrated in Figure 2 for the thumb ERPs were used for regression analysis to model the index finger ERPs.

For the thumb tip, at the electrode with maximum mean positive ERP (grand mean of touchscreen user group), the corresponding “phone use per hour” ERC was also positive, and this linear relationship was significant between 33 and 44 ms and 53 and 61 ms ( Figure 2 D). Essentially, the higher the amount of phone use in the preceding 10 days, the larger the signal at the rising edge, peak, and falling edge of the positive ERP. At the electrode with the maximum mean negative ERP amplitude, the “duration from peak” ERC was significantly positive between 56 and 68 ms ( Figure 2 E). In other words, the longer the time elapsed from a period of intense use, the lesser the signal at the falling edge of the negative ERP. Scalp maps of the ERCs and the corresponding statistics captured the widespread impact of phone use ( Figures 2 F–2I). Overall, according to the Rvalue of the linear model, up to 60% of the interindividual variation in cortical activity could be explained by the chosen variables ( Figure 2 F). Focusing on individual ERC scalp maps, for the “phone use per hour,” the electrodes that detected positive ERP showed positive ERCs, and the negative ERP electrodes showed negative ERCs ( Figure 2 H). The pattern was distinct for “duration from peak”—here, only the negative ERP electrodes were related to the variable and the relationship was reversed, i.e., the negative ERP electrodes showed positive ERCs ( Figure 2 I). Although the spatiotemporal pattern of “age of inception” ERCs appeared to be converse to the “phone use per hour” ERCs, no significant relationship was found between this variable and brain activity ( Figure 2 G).

To evaluate whether the cortical alterations scaled corresponding to touchscreen use, we identified three different attributes related to phone use: first, the self-reported age at which volunteers started using their touchscreen phone (“age of inception,” Figure 2 A). This attribute was inspired by previous reports on elite musicians and athletes in which the somatosensory representation of the corresponding body part was linked to the age at which practice began []. Second, we quantified the history of phone use over a 10-day period by using built-in battery logs. Essentially, as the battery was drained with each phone use, the logs provided a proxy measure of finger-touchscreen interactions with a 10 min resolution, and the data were smoothed using a 50 min moving window []. The area under this curve was divided by the length of the recording period to derive the “phone use per hour”’ ( Figure 2 B). Third, using the same smoothed battery signals, we estimated the time elapsed from a period of intense use—defined as the peak of battery drain—to the time of electroencephalogram (EEG) measurement (“duration from peak,” Figure 2 C; see also Figure S1 for scatter-plot matrix using the three variables). Based on preliminary simple linear regression between this measure and brain activity, we used the natural log of hours elapsed from the peak. Multiple regression analysis was conducted using these three phone use variables (Z′ normalized) for all time points (50 ms prestimulation to 120 ms poststimulation) and across all electrodes, resulting in event-related coefficients (ERCs) for each variable []. The regression statistics were corrected for multiple comparisons using 2D spatiotemporal clustering.

(G–I) Scalp maps of the estimated β values and the corresponding F statistics for the three variables. Note that both “phone use per hour” and “duration from peak” variables were significantly related to the ERPs across several electrodes.

(F) Scalp maps of the mean ERPs and the corresponding goodness-of-fit estimate of the full regression model (R 2 ) at three consecutive time points poststimulation.

(D and E) The regression analysis of the right thumb tip ERPs resulted in a time series of β values or event-related coefficients (ERCs), and the β values at the positive peak ERP electrode (red dot; D) and the negative peak ERP electrode (red dot; E) are shown. Twenty-four individual positive and negative ERP traces are plotted with thin gray lines, and thick black lines depict the corresponding means. The areas in the dotted line boxes depict significant β values and are color coded according to the variable. The small arrow above the traces points at the stimulation onset (i.e., 0 ms).

(A–C) To investigate how touchscreen use shaped cortical sensory processing, we identified three independent variables for multiregression analysis. We determined from the self-reports the age at which volunteers began using touchscreen phones (“age of inception,” A). From the battery logs, we extracted the area under the curve to determine how much the phone was used in a 10-day period (“phone use per hour,” B) and the “duration from peak” of use to EEG measurement expressed as natural log of hours (C). All the variables were Z′ normalized.

The increased cortical activity in touchscreen users compared to nonusers could be due to a more intense usage of the hand, in the sense that the former group used the right thumb more than the latter group did. Alternatively, it could be due to the development of touchscreen-specific motor routines or “skills” as the movements associated with push buttons (in nonusers, who used only old-technology mobile phones) versus taps or swipes on a smooth screen (in touchscreen phone users) were distinct.

In short, touchscreen users relied mostly on their thumb to interact with the screen, but the cortical potentials associated with the first three fingertips were enhanced in comparison to nonusers. However, the spatiotemporal impact of phone use was the least prominent for the middle fingertip.

We investigated whether the somatosensory cortical electrical activity evoked from the fingertips differed between touchscreen phone users and nonusers. Sixty-two surface electrodes distributed over the entire scalp were used to detect cortical potentials evoked by touch on the thumb, index, and middle fingertips of the right hand. Each tactile stimulus consisted of a light mechanical contact that lasted for 2 ms, and event-related potentials (ERPs) were based on 1,250 stimulations on each fingertip. For all three fingertips tested both in touchscreen users and nonusers, the touch resulted in a dipole field around the contralateral (to stimulation) somatosensory cortex with signal onset at 32 ms and peak at 55 ms (on grand mean traces). The positive ERPs were detected in the contralateral parietal electrodes, and the negative signals were detected more medially in the contra- and ipsilateral frontal electrodes ( Figures 1 C–1H). Based on the latency and signal topology, we could assert that these signals originated from the primary somatosensory cortex []. We analyzed the signal differences between the touchscreen users and nonusers across all time points (50 ms prestimulation to 120 ms poststimulation) and for each electrode by using two-sample t tests corrected for multiple comparisons using 2D spatiotemporal clustering []. Interestingly, for all of the tested fingertips, the amplitude of the positive ERP was larger in touchscreen users compared to nonusers ( Figures 1 C–1H). Temporally, the positive signals differed between 39 and 68 ms for the thumbtip, between 38 and 60 ms for the index fingertip, and between 48 and 66 ms for the middle fingertip ( Figures 1 C, 1E, and 1G). Spatially, the statistical maps revealed that the differences were clustered on the contralateral parietal scalp for all the three fingertips ( Figures 1 D, 1F, and 1H). However, the spatial extent of these differences was the smallest for the middle finger ( Figure 1 H).

We analyzed 37 right-handed volunteers, 26 of whom used touchscreen phones and 11 of whom used old-technology mobile phones. Questionnaires provided few key insights into how the more modern phones were used. First, touchscreen users primarily used their right thumb on the screen as opposed to other fingers ( Figure 1 A), and none of them used a stylus. The thumb preference was expected given that hand-held phones were designed as such []. Second, in agreement with a US national survey on smartphone use, 80% of the touchscreen users in our study mainly used their phone for receiving and sending text messages or e-mails, as opposed to passively listening to music, watching videos, or making calls []. Finally, according to the self-reports, touchscreen users spent noticeably more time with their phones than did the nonusers ( Figure 1 B).

(D) The corresponding scalp maps of the ERPs at 55 ms comparing the touchscreen users and nonusers. The multiple comparison corrected T value map revealed the electrodes with significant differences at 55 ms.

(C) Group means of the ERPs ± SEM (lighter shade) from the electrode with maximal positivity (red dot) in response to the right thumb tip stimulation in touchscreen users and nonusers. The gray area depicts significant differences between both groups—p < 0.05 and T > 1. The small arrow above the traces points at the stimulation onset (i.e., 0 ms).

(A) Our study sample consisted of touchscreen phone users (red) and users of old-technology phones without touchscreens (blue), and most of the touchscreen users relied on their right thumb to interact with the screen (dark red).

Discussion

Plasticity of cortical tactile processing has been of intense interest, but how it is applied through our daily lives remains poorly understood. Here, we found that the common use of touchscreen phones was associated with cortical reorganization. Touchscreen users showed larger amplitude of cortical potentials in response to tactile stimulation of the fingertips compared to nonusers. Furthermore, the amplitude was directly proportional to the recent phone use history quantified using battery logs built into the touchscreen phones. Intriguingly, transient cortical plasticity was induced within the monitoring period such that the thumb cortical potential was larger when volunteers’ brain activity was measured soon after an episode of intense phone use than when measured later.

16 Allison T.

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Jones S.J. Potentials evoked in human and monkey cerebral cortex by stimulation of the median nerve. A review of scalp and intracranial recordings. 17 Murakami S.

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Berg P. Source analysis of median nerve and finger stimulated somatosensory evoked potentials: multichannel simultaneous recording of electric and magnetic fields combined with 3D-MR tomography. The scalp recordings revealed positive and negative fields in response to fingertip stimulations, and yet the effects of touchscreen use were not always symmetric on either side of the putative dipole projection. First, for all fingertips, the positive ERP, but not the negative ERP, was significantly enhanced in touchscreen users compared to nonusers. Second, only the negative thumb tip ERP, not the positive one, was linked to the duration from the peak of use. It is important to note that the signal magnitudes were also asymmetric, i.e., the magnitude of the negative potential was 60% of the positive signal. Three factors were previously raised to explain this positive-negative magnitude asymmetry []. First, the volume conduction of the currents may be asymmetrically distorted due to the variations in the skull and head tissue. Nevertheless, this can be eliminated as an explanation of the touchscreen use-associated asymmetry, as these physical factors were unlikely to be systematically modified by phone use. Similarly, the curvature of the cortical surface could be eliminated as an explanation. The final and the most promising candidate is linked to the notion that EEG signals reflect a “spatial average” of several current dipoles []. In theory, the scalp signals reflect a combination of tangential and radial dipoles. The former ones generate both positive and negative fields on the scalp, and the latter ones introduce a positive or negative component depending on their orientation. Indeed, according to a combined EEG and magnetoencephalography (MEG) study, the primary somatosensory cortex (area 3b) generates both tangential and radial dipoles in response to electrical stimulation of the fingers []. Speculatively, the asymmetries described in our study may reflect touchscreen use-dependent alterations of the tangential as opposed to the radial sources. However, the exact neuronal origin of such a tangential source remains beyond the scope of our speculations, and isolating it would require a more improved theoretical understanding of how individual neurons in the somatosensory cortex contribute to the EEG signal at the scalp.

1 Elbert T.

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Taub E. Increased cortical representation of the fingers of the left hand in string players. At first glance, the increased cortical activity in touchscreen phone users compared to nonusers appears to be similar to what occurs in string instrument players []. But a more detailed examination reveals two notable differences. First, the age at which musical practice began was strongly and linearly related to the cortical activity evoked from the little finger. However, this link between the age of inception and the cortical activity was not significant for touchscreen users. Furthermore, a daily diary of musical practice was maintained for a week, analogous to the 10-day battery logs used here. Whereas the musicians did not show any linear relationship to the recent activity, the touchscreen users did. Perhaps musicians enjoyed a more stable sensory representation than touchscreen users, shaped by disciplined practice through the early years. Notably, the minimum age of inception for musical practice was 5 years old, whereas for the touchscreen use it was 15 years old.

20 Dai, P., and Ho, S.S. (2014). A smartphone user activity prediction framework utilizing partial repetitive and landmark behaviors). IEEE 15th International Conference on Mobile Data Management (MDM) 1, 205–210. 21 Rahmati A.

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Cohen L.G. Rapid plasticity of human cortical movement representation induced by practice. Based on the 10-day battery log versus brain activity correlations alone, it was not clear whether cortical processing was shaped by phone use over the past 10 days. Essentially, did the 10-day log reflect use over the past 10 days only, or was this log representative of use over a much longer period? For instance, the phone use levels may have remained stable over months and gradually shaped the cortical processing, but due to the stable usage the cortical signals may have still correlated well with the recent log. Based on previous studies, it appears that touchscreen use is at best “partially stable” []. Among university students (studied here), several factors and their interactions may have contributed to unstable usage: as touchscreen phones are used toward educational activities, usage may have increased when approaching semester deadlines []. Intuitively, the usage levels varied with semester breaks as well. Intriguingly, moving from high school (where phones were generally disallowed) to university was also expected to alter how phones were used. Therefore, the 10-day log may have reflected past use on the scale of a few weeks but not years. Nevertheless, within the 10-day period of our study, phone use was uneven in each individual. Interestingly, cortical activity was significantly related to the day-to-day fluctuations, and this strongly suggested that the cortex was reshaped within this 10-day period. Essentially, volunteers who peaked their use a few days prior to the brain measurement had a larger thumb cortical potential than volunteers who had a longer gap between the peak of use and the brain measurement. Interestingly, previous laboratory experiments showed that 30 min of repeating simple taps with the thumb transiently reinforced motor cortical outputs []. Taken together, we speculate that both somatosensory and motor cortices conform to temporary increases in motor behavior by temporary reallocation of neuronal resources.

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Schreiner C.E. Changes in the distributed temporal response properties of SI cortical neurons reflect improvements in performance on a temporally based tactile discrimination task. Although the rapidly transient cortical alterations were limited to the thumb, the cortical potentials from all the first three fingertips were enhanced in touchscreen users compared to nonusers. This suggests that the longer-term cortical alterations were not restricted to the skin surface most frequently used on the touchscreen (i.e., thumb fingertip). Kinematically, the index and middle fingers were involved in gripping and stabilizing the phone as the thumb hovered to touch the screen (data not shown) []. Therefore, the tactile receptors of the index and middle fingers tips were also activated during the phone use. Additionally, a less intuitive source of activations during phone use may have come from the tactile receptors on the hand, which are activated during grasping actions even without direct contact []. Therefore, repetitive contact-based and contact-free coactivations of several receptors across the hand surface may have driven “hebbian-like” plasticity to increase the cortical potentials associated with all the fingertips []. However, this form of widespread plasticity was not triggered by the very short-term fluctuations in use, restricting the rapidly transient cortical alterations to the thumb tip only.

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Feldman S.R. Frictional lichenified dermatosis from prolonged use of a computer mouse: Case report and review of the literature of computer-related dermatoses. The mechanisms underlying cortical reorganization in touchscreen users remain unclear. The potential explanations are as follows: first, use-dependent increase in cortical activity has been previously associated with a recession of intracortical inhibition [], but this was not found here with simultaneous stimulation of thumb and index fingertips. Still, as only the fingertips were tested, this mechanism cannot be entirely ruled out by our data. Second, touchscreen phone use may have strengthened the synapses in the somatosensory cortex, resulting in larger cortical potentials. This idea is supported by experiments involving brief periods of low-intensity direct-current stimulation of the cerebral cortex—which strengthens cortical synapses and increases the amplitude of somatosensory cortical potentials []. Third, the cortical alterations may be accompanied with subcortical alterations in touchscreen users. After amputation or spinal cord injury, nonhuman primates showed profound cortical changes, which were partly explained by the plasticity of the brainstem and thalamic circuits []. Finally, we cannot entirely rule out peripheral modifications such as a decrease in threshold of the mechanical receptors driven by phone use, but such a use-dependent alteration of peripheral structures remains unreported in the neuroscientific literature. Furthermore, extensive research on experienced blind Braille readers provides strong evidence for central, but not peripheral, changes in people subjected to repeated tactile contacts with a fingertip []. Nevertheless, according to dermatological research, “friction-induced dermatoses” may be observed in computer and mouse users, but only in case of severe usage (4–10 hr of daily use for 5–10 years), putatively resulting in the reduction of tactile inputs due to a build-up of extra layer of tissue over the damaged skin []. Still, we cautiously speculate that a combination of central changes, rather than changes in the periphery, is more likely to be the underlying cause of the altered cortical potentials linked to touchscreen use.