As traditional handwriting is progressively being replaced by digital devices, it is essential to investigate the implications for the human brain. Brain electrical activity was recorded in 36 university students as they were handwriting visually presented words using a digital pen and typewriting the words on a keyboard.
Connectivity analyses were performed on EEG data recorded with a 256-channel sensor array. When writing by hand, brain connectivity patterns were far more elaborate than when typewriting on a keyboard, as shown by widespread theta/ alpha connectivity coherence patterns between network hubs and nodes in parietal and central brain regions. Existing literature indicates that connectivity patterns in these brain areas and at such frequencies are crucial for memory formation and for encoding new information and, therefore, are beneficial for learning.
Our findings suggest that the spatiotemporal pattern from visual and proprioceptive information obtained through the precisely controlled hand movements when using a pen, contribute extensively to the brain’s connectivity patterns that promote learning. We urge that children, from an early age, must be exposed to handwriting activities in school to establish the neuronal connectivity patterns that provide the brain with optimal conditions for learning. Although it is vital to maintain handwriting practice at school, it is also important to keep up with continuously developing technological advances.
Therefore, both teachers and students should be aware of which practice has the best learning effect in what context, for example when taking lecture notes or when writing an essay.

Introduction
Digital devices are more and more replacing traditional handwriting (Longcamp et al., 2006; Kiefer et al., 2015), and as both writing and reading are becoming increasingly digitized in the classroom, we need to examine the implications of this practice (Mangen and Balsvik, 2016; Patterson and Patterson, 2017). Using a keyboard is now often recommended for young children as it is less demanding and frustrating (Cunningham and Stanovich, 1990; Fears and Lockman, 2018), allowing them to express themselves in written form earlier (Hultin and Westman, 2013).
Be that as it may, handwriting training has not only been found to improve spelling accuracy (Cunningham and Stanovich, 1990) and better memory and recall (Longcamp et al., 2006; Smoker et al., 2009; Mueller and Oppenheimer, 2014), but also to facilitate letter recognition and understanding (Longcamp et al., 2005, 2008; Li and James, 2016). Such benefits for learning have been reported irrespective of when writing by hand using a traditional pen or pencil or using a digital pen (Osugi et al., 2019). Also, brain research shows that it is not just any motor activity that facilitates learning, but that accurately coordinating the complex hand movements while carefully shaping each letter when using a pen, is crucial (Pei et al., 2021).
Apparently, the pen causes different underlying neurological processes that provide the brain with optimal conditions for learning and remembering (Askvik et al., 2020). Recent findings in neuroscience reveal that neural processes are not as localized and static as is commonly believed, but that the brain is organized in a highly dynamic functional manner (Lopes da Silva, 1991; Singer, 1993). Under normal circumstances, several brain
systems are continually working together (Buzsáki, 2006), showing an extremely flexible organization with structurally different neural tissue being involved in neural circuits that are only temporarily assembled so as to enable a given task (Edelman and Gally, 2013; Van der Weel et al., 2019). In such a view, neurons can change function entirely
when incorporated in different systems (Anderson, 2014). Bullmore and Sporns (2009) refer to this type of flexible organization of the brain as functional connectivity as against structural connectivity.
Electroencephalography is well suited to studying brain electrical activity as a function of handwriting and typewriting in the millisecond scale. It permits the investigation of changes in the status of the underlying active networks (Lopes da Silva, 1991) and can reveal the everchanging spatial patterns of activations that are specific to any given task (Pfurtscheller et al., 1996). In particular, studies of cortical oscillations detected with high-density EEG are now considered an indispensable aspect of contemporary systems neuroscience (Fröhlich, 2016). Brain oscillations can be considered as the interplay between the cortex and the thalamus and are generated by changes involved in the control of oscillations in neural networks (Pfurtscheller and Lopes da Silva, 1999). The complex interactions and the resulting particular frequencies are thought to reflect distinct cognitive processes (Klimesch et al., 1994; Berens and Horner, 2017). The temporal organization of neuronal firing is crucial as it is assumed to be fundamental when forming long-term memories in the hippocampus (Berens and Horner, 2017).
Frequency-specific changes in EEG recordings can be observed as event-related synchronization (ERS) or event-related desynchronization (ERD; Pfurtscheller and Aranibar, 1977; Pfurtscheller and Lopes da Silva, 1999). Spectral analyses are used to detect differences in a given frequency band (Pfurtscheller et al., 1994; Salmelin and Hari, 1994; Klimesch et al., 1996), by calculating the temporal dynamics of EEG oscillations and quantifying event-related amplifications and/or suppressions of rhythms. A recent EEG-study from our lab showed that drawing by hand causes more activity and involves larger areas in the brain as opposed to typing on a keyboard (Van der Meer and Van der Weel, 2017). We concluded that the involvement of fine and intricate hand movements in notetaking, in contrast with pressing keys on a keyboard that all require the same simple finger movement, may be more advantageous for learning (Van der Meer and Van der Weel, 2017). A follow-up study observed event-related synchronized activity in the theta range in both children and students in parietal and central brain regions, but only when writing by hand (Askvik et al., 2020).
As these studies have found evidence that writing by hand facilitates learning, the present study further investigated the neurobiological differences related to cursive writing and typewriting in the young adult brain. Specifically, we investigated how the various brain regions interconnect via neural networks when writing by hand as opposed to typing on a keyboard using frequency modulation and the latest in brain connectivity analysis (c.f., Solomon et al., 2017).

Methods
Participants
Forty university students in their early twenties took part in the study at the Developmental Neuroscience Laboratory, Norwegian University of Science and Technology (NTNU). HD EEG data from 36 students were of good enough quality and sufficiently artifact-free to be included in the analyses. The data from 12 adult participants were already used in analyses in the time-frequency domain (Askvik et al., 2020). The present study performed a brain connectivity analysis to investigate the underlying neural networks involved in tasks of handwriting and typewriting. Participants were mostly students and were recruited at the university campus. They received a $15 cinema ticket for taking part. To avoid crossover effects between the two hemispheres, only right-handed participants were included, as determined by the Edinburgh Handedness Inventory (Oldfield, 1971). Allowing the use of (the fingers of) both hands would cause many unforeseen effects on the brain, which would make it hard to interpret the results. Participants gave their informed written consent, and it was made clear that they could withdraw from the experiment at any time without consequences. The Regional Committee for Medical and Health Ethics (Central Norway) approved the study.
Experimental stimuli and EEG data acquisition
E-prime 2.0 was used to individually display 15 different Pictionary words on a Microsoft Surface Studio. The participants used a digital pen to write in cursive by hand directly on the touchscreen, and a keyboard to typewrite the presented words. The experiment comprised a total of 30 trials, where each word appeared in two different conditions, presented in a randomized order. For each trial, participants were instructed to either (a) write in cursive with their right hand the presented word with a digital pen directly on the screen, or (b) type the presented word using the right index finger on the keyboard. Before each trial, the instruction write or type appeared before one of the target words appeared, and the participants were given 25 s to either write by hand or type the word
multiple times, separated by a space. EEG data were recorded only during the first 5 s of each trial. To prevent artifacts produced by head and eye movements caused by shifting gaze between the screen and the keyboard, typed words did not appear on the screen while the participant was typewriting. The writings produced by the participants (see Figure 1 for example) were stored for offline analyses.

group ANOVA’s, these initial cluster values were passed through permutation and assigned new clusters so that the significance of the initial cluster could be determined. A Bonferroni correction was used for multiple comparisons. As in Askvik et al. (2020), cluster alpha, the significance level for building clusters in time and/or frequency, was
set at 0.01 and the number of permutations was set at 10.000. Low-and high cut-offs for frequency were kept at 2 Hz and 60 Hz respectively, and epochs were set from −250 to 4,500 ms.

y-axis shows the frequencies. The intensities are displayed as a color-coded plot. Figure 2 displays the results of grand average coherence results from just three selected connectivity areas of interest for clarity, for the two experimental conditions handwriting and typewriting (left panels), together with the difference in coherence between writing and typing and their permutation results (right panels). Connectivity areas of large significant difference between writing and typing included central and parietal brain regions in frequencies ranging from theta (2 Hz) and up to gamma (60 Hz). The signal magnitude reflects estimated connectivity strength between brain areas compared to
baseline (−250 to 0 ms) activity. Positive connectivity patterns are shown in (shades of) red. In the central and parietal areas, positive coherence patterns were more prominent in the lower frequencies (theta 3.5–7.5 Hz and alpha 7.5–12.5 Hz) for handwriting as opposed to typewriting. For handwriting, this activity appeared between 1,000 to 2000 ms and lasted throughout the trial.

signals at a specific frequency were described, and the corresponding functional brain network was visualized in Figure 3. Finally, network measures were extracted from the network and presented in Figure 4. Figure 3A displays the grand average connectivity matrix for writing compared to typing. The matrix offers a compact description of the pairwise connectivity between all separate regions of the brain.

but also in the central regions. As can be seen in Figure 4, significant clusters of differences in band power were found mainly in parietal and central brain regions.
handwriting compared to typewriting in this experiment. Proposed hubs (in red, ≥ 4 departures/arrivals) and nodes (in black, ≤ 3 departures/arrivals) interacting between brain regions PL, PM, PR and CL, CM, CR show widespread theta/alpha coherence patterns indicating stronger connectivity when writing as opposed to typing (Figure 5C).
participants were typing versus writing by hand. Focusing on brain connectivity that has shown to facilitate learning and memory (Pfurtscheller and Lopes da Silva, 1999), we investigated parietal and central areas in specific frequency bands. These brain areas have been associated with attentional mechanisms and cognitive processes in visual perception (Pfurtscheller et al., 1994; Vilhelmsen et al., 2019) and language (Brownsett and Wise, 2010; Benedek et al., 2014), and have strong links to sensorimotor cortex
(Velasques et al., 2007). We set out to investigate whether it is actually the act of forming the letters by hand itself that brings about larger connectivity in the brain, since perceptual, motor, and higher cognitive areas are more involved during handwriting as opposed to typewriting.

correspond to long-term memory performance, theta connectivity seems to be related to working memory and the ability to apprehend novel information (Klimesch et al., 1994, 1996, 2001; Klimesch, 1999; Raghavachari et al., 2001; Clouter et al., 2017). Thus, the enhanced brain connectivity for handwriting appears not to be related to differences in muscular involvement. It has also been proposed that hippocampal activity is reflected within the theta band (Klimesch et al., 1994), adding further support for the benefits of handwriting in terms of learning and memory formation. Lower frequencies are considered especially suited for facilitating communication over longer distances in the brain, and are often reported to “gate” the occurrence of faster oscillations, for example when theta oscillations in humans are proposed to gate gamma (>30 Hz) oscillations (Canolty et al., 2006; Halgren et al., 2018).
connectivity activity (see Figure 3) is positively correlated with a brain region’s gamma power, suggesting a potent low-frequency mechanism for communication between brain regions (Solomon et al., 2017). Exploring these interactions may disclose the relationship between a brain region’s functional connectivity and local processing. Our results reflect such a low-frequency mechanism for interregional communication. Present findings of theta synchrony for handwriting suggest that low-frequency connections support the integration of information during memory formation, and follow from earlier studies that have reported low-frequency entrainment to be essential to cognition (Solomon et al., 2017).

stimulated, resulting in the formation of more complex neural network connectivity. It appears that the movements related to typewriting do not activate these connectivity networks the same way that handwriting does.
finger for typing to prevent undesired crossover effects between the two hemispheres.

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