Handwriting but not typewriting leads to widespread brain connectivity: a high-density EEG study with implications for the classroom

Table of Contents

Overall Summary

Overview

This EEG study found that handwriting, compared to typing, was associated with significantly greater brain connectivity (coherence) in the theta (3.5-7.5Hz) and alpha (8-12.5Hz) frequency bands, particularly in the parietal and central brain regions. These regions and frequencies are implicated in learning and memory processes. However, the study's cross-sectional design precludes causal conclusions, and the lack of reported effect sizes limits the assessment of practical significance.

Key Points

Increased Brain Connectivity During Handwriting (written-content)
The study found statistically significant differences in brain connectivity (coherence) between handwriting and typing, particularly in the theta and alpha frequency bands within the parietal and central brain regions. These findings align with the study's hypothesis and suggest that handwriting may engage brain networks associated with learning and memory more effectively than typing.
Section: Results
Correlation vs. Causation (written-content)
The cross-sectional design limits causal inferences. The study cannot determine if handwriting *causes* increased connectivity, or if individuals with inherently different brain connectivity patterns gravitate towards different writing methods.
Section: General Discussion
Quantify Effect Sizes (written-content)
The lack of reported effect sizes makes it difficult to assess the practical significance of the observed differences in connectivity. While statistically significant, the magnitude of the effect might be small and not translate to meaningful differences in learning outcomes.
Section: Results
Generalizability to Other Age Groups (written-content)
The study's focus on young adults limits the generalizability of the findings to other age groups, particularly children, where the impact of handwriting on learning development is a key concern.
Section: General Discussion
Ecological Validity of Digital Pen (written-content)
The use of a digital pen, while allowing for precise movement recording, may not fully capture the sensorimotor experience of traditional pen-and-paper handwriting. This limits the ecological validity of the findings and their applicability to real-world classroom settings.
Section: General Discussion
Practical Implications for Education (written-content)
The study's findings, while correlational, suggest potential benefits of incorporating handwriting into educational practices. Educators could consider integrating handwriting activities, especially for tasks involving learning and memorization, while also acknowledging the practical role of typing in modern digital environments.
Section: General Discussion

Conclusion

This study suggests a correlation between handwriting and increased brain connectivity in specific regions and frequency bands associated with learning and memory, compared to typing. While the findings are intriguing and potentially relevant for educational practices, it's crucial to distinguish correlation from causation. The study's cross-sectional design doesn't allow us to conclude that handwriting *causes* increased connectivity, or that this increased connectivity directly translates to improved learning outcomes. Alternative explanations, such as pre-existing differences in brain connectivity between individuals who prefer handwriting vs. typing, or differences in cognitive effort or engagement with the tasks, cannot be ruled out. The use of a digital pen, while offering precise movement tracking, also limits the generalizability to traditional pen-and-paper handwriting. Despite these limitations, the study's rigorous methodology, including high-density EEG and robust statistical analysis, strengthens the observed association. Future longitudinal or experimental studies, incorporating diverse populations and learning assessments, are needed to investigate causal relationships and the long-term educational impact of handwriting.

Section Analysis

Abstract

Key Aspects

Strengths

Suggestions for Improvement

Introduction

Key Aspects

Strengths

Suggestions for Improvement

Methods

Key Aspects

Strengths

Suggestions for Improvement

Non-Text Elements

FIGURE 1 Task design, behavioral performance, and sequence of the connectivity...
Full Caption

FIGURE 1 Task design, behavioral performance, and sequence of the connectivity analyses. Visually presented words were either written by hand with a digital pen or typed on a keyboard while participants were wearing a 256-channel sensor array. EEG recordings were analyzed in terms of their functional connectivity, resulting in detailed network measures.

First Reference in Text
The writings produced by the participants (see Figure 1 for example) were stored for offline analyses.
Description
  • Overview of experimental setup: This figure illustrates the overall process of an experiment designed to understand how our brains work when we write by hand versus when we type on a keyboard. Participants in the study performed two main tasks: they either wrote words using a digital pen, which is like a regular pen but captures your writing on a computer, or they typed words on a standard keyboard. Importantly, the words used were shown to them visually, so they saw what they needed to write or type.
  • EEG recording during tasks: While participants were doing these writing or typing tasks, they wore a special cap with 256 sensors. This cap is part of an electroencephalography (EEG) setup. EEG is a method used to look at the electrical activity in the brain by detecting tiny electrical signals through these sensors placed on the scalp. The 256-channel sensor array refers to the dense network of sensors that provide a detailed map of brain activity.
  • Analysis of functional connectivity: The main focus of the analysis was on "functional connectivity." This is a way of looking at how different parts of the brain work together. Specifically, it examines how synchronized the activity is between various brain regions. If two regions show activity that rises and falls together, they are considered functionally connected. In this study, researchers were looking to see if and how functional connectivity differs when a person is handwriting versus typing.
  • Detailed network measures: The analysis resulted in "detailed network measures." These measures give a comprehensive view of the connections and interactions between different parts of the brain. Think of it like a map that shows not just cities (brain regions) but also all the roads (connections) between them, indicating how busy these roads are (strength of connection). These detailed maps can help researchers understand the complex ways in which the brain operates during handwriting as opposed to typing.
Scientific Validity
  • Relevance of Experimental Tasks: The experimental tasks, handwriting with a digital pen and typing on a keyboard, are appropriate for investigating differences in brain activity between these two modes of writing. However, the caption lacks detail on task parameters such as word presentation time and inter-stimulus intervals, which could affect the interpretation of EEG data.
  • EEG Methodology: Using a 256-channel EEG system is a scientifically sound method for measuring brain activity with high spatial resolution. The mention of functional connectivity analysis aligns with contemporary neuroscience practices for studying brain networks.
  • Lack of Specificity in Connectivity Analysis: While the caption mentions that EEG recordings were analyzed for functional connectivity, it does not specify the methods used for this analysis (e.g., coherence, phase-locking value). This lack of detail makes it difficult to assess the appropriateness of the analytical approach.
  • Omission of Behavioral Data Analysis: The caption refers to "behavioral performance," but it is unclear what behavioral data were collected and how they were analyzed. Integrating behavioral data with EEG data could provide a more comprehensive understanding of the cognitive processes involved in handwriting and typing.
Communication
  • Clarity of Figure's Purpose: The caption adequately conveys the overall purpose of the figure, which is to outline the experimental design and analytical approach. However, the succinctness, while beneficial for brevity, may necessitate further elaboration in the main text for full comprehension.
  • Visual Presentation of the Workflow: The figure effectively uses a flowchart to depict the sequence of experimental procedures and data analysis steps. The visual representation is intuitive, aiding in understanding the experimental workflow.
  • Ambiguity in Network Measures: The caption mentions "detailed network measures" but does not elaborate on what these measures entail. This lack of specificity could leave readers unfamiliar with network analysis uncertain about the nature of the results presented.
  • Use of Terminology: The caption uses technical terms such as "256-channel sensor array" and "functional connectivity," which are appropriate for a scientific audience. However, further explanation of these terms in the main text would enhance accessibility for readers less familiar with EEG methodology.

Results

Key Aspects

Strengths

Suggestions for Improvement

Non-Text Elements

FIGURE 2 Grand average coherence results. Displayed are only three selected...
Full Caption

FIGURE 2 Grand average coherence results. Displayed are only three selected connectivity areas of interest 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 handwriting and typewriting included brain regions CR-PM (central right-parietal midline, top two panels on the left) and CL-PM (central left-parietal midline, middle two panels on the left), as well as CM-CR (central midline-central right, bottom two panels on the left), in frequencies ranging from theta (2Hz) and up to gamma (60Hz). The x-axes display the time interval from baseline to 4,500ms of recordings of the trial. The signal magnitude reflects the estimated neural connectivity strength between the various brain areas during the experimental conditions compared to baseline activity (−250 to 0ms). Positive connectivity is shown as (shades of) red-colored contours in handwriting/typewriting plots (panels on the left) and difference plots between handwriting and typewriting/permutation results (panels on the right). Positive connectivity is significantly more prominent in lower frequencies (theta 3.5–7.5Hz and alpha 8–12.5) for handwriting (0<p<0.05, see also Figure 4).

First Reference in Text
A time-frequency display is shown for three important brain regions in Figure 2 where the power/amplitude for each time is normalized to the mean power/amplitude of the baseline epoch for that frequency.
Description
  • What is being measured: Coherence: This figure displays something called "coherence," which is a way to measure how different parts of the brain are working together or are in sync. Imagine two people clapping at the same rhythm; they are coherent. Similarly, if two brain areas have electrical activity that rises and falls together, they are said to have high coherence, suggesting they are working in coordination.
  • Experimental Conditions: Handwriting vs. Typewriting: The figure compares brain activity when people are handwriting versus when they are typewriting. It shows results from both activities in the left panels. This allows us to see if there are any differences in how the brain works between these two tasks.
  • Focus on Specific Brain Regions: The researchers focused on three specific areas of the brain known to be involved in tasks like these. They are abbreviated as CR-PM (central right-parietal midline), CL-PM (central left-parietal midline), and CM-CR (central midline-central right). These areas are important for controlling movements and processing sensory information.
  • Frequency Range: Theta to Gamma: Brain activity is often described in terms of "frequencies," which are like different types of brain waves. This study looks at frequencies ranging from theta (slow waves, 2Hz) up to gamma (fast waves, 60Hz). Different frequencies are thought to be involved in different types of brain functions. For example, theta waves are often associated with memory and learning.
  • Timeframe of Measurement: The x-axis (horizontal line) shows the time from the start of each trial (writing or typing task) up to 4.5 seconds. The measurements are compared to a "baseline" period, which is the brain activity right before the task started. This helps to see how the activity changes during the task compared to when the brain is at rest.
  • Signal Magnitude and Positive Connectivity: The "signal magnitude" represents how strong the coherence is between brain areas. Red color indicates "positive connectivity," meaning that the coherence is stronger during the task compared to the baseline. The redder it is, the stronger the connection.
  • Permutation Results: The right panels show "permutation results." Permutation is a statistical technique used to make sure that the differences observed between handwriting and typewriting are not just due to random chance. It involves shuffling the data many times to see if the observed differences are still significant.
  • Key Finding: Lower Frequencies for Handwriting: The main finding is that positive connectivity (stronger coherence) is more noticeable in lower frequencies, specifically theta (3.5-7.5Hz) and alpha (8-12.5Hz), when people are handwriting. This suggests that these slower brain waves are particularly important when we write by hand.
Scientific Validity
  • Use of Grand Average Coherence: The use of grand average coherence is a standard approach in EEG analysis to examine overall connectivity patterns across participants. However, averaging can obscure individual differences that might be relevant to understanding the neural mechanisms of handwriting and typewriting.
  • Selection of Connectivity Areas: The selection of three specific connectivity areas (CR-PM, CL-PM, CM-CR) is justified based on their relevance to motor control and sensory processing. However, the rationale for focusing on these areas to the exclusion of others could be more explicitly stated.
  • Frequency Range and Time Window: The chosen frequency range (theta to gamma) covers a broad spectrum of brain oscillations relevant to cognitive and motor processes. The time window (baseline to 4500ms) is appropriate for capturing task-related changes in connectivity.
  • Statistical Significance and Permutation Testing: The caption mentions a significance level of p < 0.05 and the use of permutation testing, which are appropriate methods for assessing the statistical significance of the observed differences in coherence. However, more details on the permutation procedure (e.g., number of permutations) would be beneficial.
  • Normalization to Baseline: Normalizing the power/amplitude to a baseline period is a crucial step in EEG analysis to control for individual variability and highlight task-specific changes. The chosen baseline period (-250 to 0ms) is standard.
Communication
  • Clarity of Caption: The caption provides a relatively detailed description of the figure's content. However, it is quite dense and assumes a high level of familiarity with EEG terminology and methodology.
  • Presentation of Results: The figure effectively displays coherence results for the two experimental conditions and their differences. The use of color-coded contours to represent connectivity strength is intuitive.
  • Explanation of Abbreviations: The caption defines the abbreviations for the brain regions (CR-PM, CL-PM, CM-CR), which is helpful for interpreting the figure. However, it could also benefit from briefly explaining what these regions do.
  • Interpretation of Findings: The caption highlights the main finding that positive connectivity is more prominent in lower frequencies for handwriting. However, it could provide more context for the implications of this finding, such as linking it to existing literature on theta and alpha oscillations.
  • Accessibility for Non-Experts: While the figure and caption are informative for researchers familiar with EEG analysis, they may be challenging for non-experts to understand fully. Providing a simplified summary of the main findings and their significance in the main text would improve accessibility.
FIGURE 3 Connectivity results of writing over typing. (A) Grand average...
Full Caption

FIGURE 3 Connectivity results of writing over typing. (A) Grand average connectivity matrix results show widespread theta/alpha coherence results (in red) between PL, PM, PR and CL, CM, CR brain regions when writing by hand, but not when typing. The y-axes display frequencies from 2 to 60Hz. The x-axes display the time interval from baseline to 4,500ms of recordings of the trial for all involved brain regions. The signal magnitude (coherence) reflects the estimated neural connectivity between the various brain regions during the writing condition compared to baseline activity (-250 to Oms). (B) Further illustration of connectivity patterns revealing a concentration of 16 significant connections for handwriting compared to typewriting. Connection lines in red indicate connectivity in the theta range whereas lines in blue indicate connectivity in the alpha range. Levels of significance in connectivity strength for handwriting, but not for typewriting are further indicated by solid (<0.0001), dashed (<0.005), and dotted (<0.05) connection lines.

First Reference in Text
Finally, network measures were extracted from the network and presented in Figure 4. Figure 3 displays the grand average connectivity matrix for writing compared to typing.
Description
  • What is being compared: Writing vs. Typing: This figure compares the connections in the brain when someone is writing by hand versus when they are typing. It's like comparing the traffic patterns in a city when people are using horses versus when they are using cars. The goal is to see how the brain's activity patterns differ between these two activities.
  • Panel A: Grand Average Connectivity Matrix: Panel A shows a "connectivity matrix," which is like a map showing the connections between different brain regions. The term "grand average" means that this map represents the average connectivity across all the people who participated in the study. It's like taking the average traffic patterns across many different cities.
  • Brain Regions and Coherence: The matrix highlights connections between specific brain regions labeled with abbreviations like PL, PM, PR, CL, CM, and CR. These labels refer to different parts of the brain, such as the parietal (P) and central (C) regions. "Coherence" is a measure of how synchronized the activity is between two brain regions. If two regions show activity that rises and falls together, they are said to have high coherence, suggesting they are working in coordination.
  • Theta/Alpha Coherence in Red: The red color in the matrix indicates coherence in the "theta/alpha" frequency range. Frequencies refer to different types of brain waves, which are like different rhythms of brain activity. Theta and alpha waves are slower brain waves that are often associated with things like memory, attention, and relaxation. So, the red areas show where these slower brain waves are synchronized between different brain regions.
  • X and Y Axes of the Matrix: The y-axis (vertical line) of the matrix shows the frequencies of brain waves, ranging from 2 to 60 Hz (Hertz, a unit of frequency). The x-axis (horizontal line) shows the time from the start of each trial up to 4.5 seconds. The measurements are compared to a "baseline" period, which is the brain activity right before the task started. This helps to see how the activity changes during the task compared to when the brain is at rest.
  • Panel B: Illustration of 16 Significant Connections: Panel B provides a simplified view of the connectivity patterns, focusing on 16 connections that were found to be significantly different when people were handwriting compared to typewriting. Think of it like highlighting the 16 most important roads on a map that are busier when people use horses compared to cars.
  • Color Coding: Red for Theta, Blue for Alpha: In Panel B, red lines indicate connections where theta waves are synchronized, while blue lines indicate connections where alpha waves are synchronized. This helps to distinguish between these two types of brain waves and their roles in handwriting.
  • Levels of Significance: Solid, Dashed, and Dotted Lines: The lines in Panel B are also coded by their "level of significance," which is a measure of how confident we can be that the observed differences are not just due to random chance. Solid lines represent the highest level of significance (less than 0.0001 probability of being due to chance), followed by dashed lines (less than 0.005) and dotted lines (less than 0.05). These significance levels indicate the connections that are most strongly associated with handwriting.
Scientific Validity
  • Grand Average Connectivity Matrix Approach: Using a grand average connectivity matrix is a standard method in neuroscience for visualizing overall connectivity patterns. However, averaging across participants can obscure individual variability, which might be relevant for understanding the neural mechanisms of handwriting.
  • Focus on Theta/Alpha Coherence: Focusing on theta and alpha coherence is justified given the known roles of these frequency bands in cognitive processes related to handwriting, such as motor control, attention, and memory. However, other frequency bands (e.g., beta, gamma) might also contribute to the observed differences between handwriting and typewriting.
  • Selection of 16 Significant Connections: The rationale for selecting 16 significant connections is based on statistical significance, which is appropriate. However, the specific criteria for determining significance (e.g., threshold, correction for multiple comparisons) should be explicitly stated.
  • Visualization of Connectivity Patterns: The visualization in Panel B effectively illustrates the connectivity patterns for handwriting. However, it would be beneficial to also show the corresponding patterns for typewriting to facilitate direct comparison.
  • Interpretation of Signal Magnitude: The interpretation of signal magnitude (coherence) as reflecting neural connectivity strength is accurate. The comparison to baseline activity is crucial for controlling for individual variability and highlighting task-specific changes.
Communication
  • Clarity of Caption for Panel A: The caption for Panel A provides a relatively clear description of the connectivity matrix, including the meaning of the axes and the color coding. However, it could be more explicit about the fact that the matrix shows differences between handwriting and typewriting.
  • Clarity of Caption for Panel B: The caption for Panel B clearly explains the color coding and the different line types used to represent connectivity strength and significance levels. However, it could benefit from explicitly stating that the connections shown are those that are significantly stronger for handwriting compared to typewriting.
  • Use of Technical Terms: The caption uses technical terms like "theta/alpha coherence," "grand average connectivity matrix," and "levels of significance," which are appropriate for a scientific audience. However, these terms might be challenging for readers unfamiliar with EEG analysis.
  • Visual Presentation: The visual presentation of the connectivity matrix and the simplified connectivity patterns is effective. The use of color and line types helps to convey complex information in a relatively intuitive way.
  • Overall Accessibility: While the figure and caption are informative for researchers familiar with EEG analysis, they may be challenging for non-experts to understand fully. Providing a simplified summary of the main findings and their implications in the main text would improve accessibility.
FIGURE 4 Symmetric connectivity matrix with t-values (A) and significance Table...
Full Caption

FIGURE 4 Symmetric connectivity matrix with t-values (A) and significance Table (B) with significant data clusters in the various sources of interest when handwriting is compared to typewriting in all participants. Thirty-two significant cluster differences marked in orange in (A) and fully described in (B) were found in the matrix and came out particularly significant in the parietal left (PL), parietal midline (PM), and parietal right (PR) areas.

First Reference in Text
Figure 4 displays the detailed effects (t-tests) of the permutation results.
Description
  • Comparison of Handwriting and Typewriting: This figure compares the brain's activity patterns when people are handwriting versus when they are typewriting. It's like comparing how the roads in a city are used when people are walking versus when they are driving. The goal is to see which brain areas are more active or more connected during each activity.
  • Panel A: Symmetric Connectivity Matrix with t-values: Panel A shows a "connectivity matrix," which is like a map of connections between different brain regions. It's called "symmetric" because the connections are shown in a mirrored way, like a reflection in a pond. The matrix displays "t-values," which are statistical measures that indicate the strength of the difference between two conditions (in this case, handwriting and typewriting). A higher t-value means a bigger difference.
  • Panel B: Significance Table: Panel B is a table that lists the "significant data clusters." A cluster is like a group of connected brain regions that show a similar pattern of activity. "Significant" means that the differences in activity between handwriting and typewriting are unlikely to be due to random chance. The table provides detailed information about each cluster, such as its location in the brain and the strength of the difference.
  • Orange মার্কিং: Significant Cluster Differences: In Panel A, the 32 significant cluster differences are marked in orange. This makes it easy to see which connections are most different between handwriting and typewriting. It's like highlighting the most important roads on a map that are busier when people are walking compared to driving.
  • Focus on Parietal Regions: The figure highlights that the most significant differences were found in the parietal left (PL), parietal midline (PM), and parietal right (PR) areas of the brain. The parietal regions are involved in processing sensory information and controlling movements, which are important for handwriting. This suggests that these brain areas are more engaged during handwriting compared to typewriting.
  • T-tests and Permutation Results: The reference text mentions that the figure displays the results of "t-tests" and "permutation tests." T-tests are a statistical method used to compare the means (averages) of two groups, in this case, the brain activity during handwriting and typewriting. Permutation tests are used to determine the statistical significance of the observed differences, meaning whether they are likely to be real or just due to random chance.
Scientific Validity
  • Use of t-values and Connectivity Matrix: Using t-values to represent differences in connectivity between handwriting and typewriting is a valid approach. The symmetric connectivity matrix is an appropriate way to visualize these differences.
  • Identification of Significant Clusters: The identification of 32 significant cluster differences is based on statistical testing, which is appropriate. However, the specific criteria for determining significance (e.g., threshold, correction for multiple comparisons) should be explicitly stated.
  • Focus on Parietal Regions: Focusing on the parietal regions is justified given their known roles in sensorimotor processing. However, other brain regions might also show significant differences between handwriting and typewriting.
  • Combination of t-tests and Permutation Tests: The combination of t-tests and permutation tests is a robust approach for assessing the statistical significance of the observed differences. However, more details on the permutation procedure (e.g., number of permutations) would be beneficial.
Communication
  • Clarity of Caption: The caption provides a relatively clear description of the figure's content, including the meaning of the matrix, the t-values, and the significance table. However, it could be more explicit about the purpose of the figure, which is to highlight the brain regions that show the most significant differences between handwriting and typewriting.
  • Visual Presentation: The visual presentation of the connectivity matrix and the significance table is effective. The use of orange to highlight significant differences in the matrix makes it easy to identify the most important connections.
  • Use of Technical Terms: The caption uses technical terms like "symmetric connectivity matrix," "t-values," and "significant data clusters," which are appropriate for a scientific audience. However, these terms might be challenging for readers unfamiliar with EEG analysis.
  • Accessibility for Non-Experts: While the figure and caption are informative for researchers familiar with EEG analysis, they may be challenging for non-experts to understand fully. Providing a simplified summary of the main findings and their implications in the main text would improve accessibility.
  • Explanation of Statistical Methods: The reference text briefly mentions t-tests and permutation tests, but the caption could provide a more detailed explanation of these methods and their purpose in the context of the study. This would help readers understand how the significant differences were determined.
FIGURE 5 The adjacency matrix for handwriting. (A) Hub, nodes, and edges of a...
Full Caption

FIGURE 5 The adjacency matrix for handwriting. (A) Hub, nodes, and edges of a simplified theoretical network. (B) Brain connectivity network of handwriting compared to typewriting in this experiment. (C) 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 connectivity patterns when writing by hand, but not when typing.

First Reference in Text
Figure 5 shows the adjacency matrix for handwriting in the form of a hub, nodes, and edges of a simplified theoretical network (Figure 5A).
Description
  • Analogy to Networks: This figure uses the concept of a network to illustrate how different parts of the brain are connected during handwriting. Think of a network like an airline route map, where cities are connected by flights. In the brain, different regions can be connected by neural pathways, and the activity between these regions can be synchronized, like flights being coordinated between cities.
  • Panel A: Simplified Theoretical Network: Panel A shows a simplified example of a network with three key components: hubs, nodes, and edges. In our airline analogy, a hub would be a major airport that has many flights coming in and going out (like a central connecting point). A node would be a smaller airport with fewer flights. Edges would be the flight routes connecting the airports. This panel provides a basic model for understanding how networks are structured.
  • Panel B: Brain Connectivity Network: Panel B shows the actual brain connectivity network observed during handwriting in the experiment. It compares the connections in the brain when someone is writing by hand versus when they are typing. This is like comparing the airline traffic patterns when people are using a new, faster type of airplane versus an older, slower type.
  • Panel C: Hubs and Nodes in Brain Regions: Panel C highlights the hubs and nodes within specific brain regions labeled as PL, PM, PR, CL, CM, and CR. These labels refer to different parts of the brain, such as the parietal (P) and central (C) regions. Red indicates a hub, which is a brain region with a high degree of connectivity (like a major airport). Black indicates a node, which is a region with a lower degree of connectivity (like a smaller airport). The panel shows that during handwriting, there is a widespread pattern of theta/alpha connectivity, which are slower brain waves associated with things like memory, attention, and relaxation. This suggests that these brain regions are more interconnected when we write by hand compared to when we type.
  • Adjacency Matrix: The term "adjacency matrix" refers to a way of representing a network in a table format. In this table, each row and column represents a node (or a brain region), and the cells at their intersections indicate whether there is an edge (connection) between them. If two nodes are connected, the cell might contain a number representing the strength of that connection. If they are not connected, the cell might be empty or contain a zero. It's a way to mathematically describe the network shown visually in the figure.
  • Departures/Arrivals: The terms "departures" and "arrivals" are used to quantify the connectivity of hubs and nodes. In our airline analogy, a departure would be a flight leaving an airport, and an arrival would be a flight landing at an airport. A hub is defined as having 4 or more departures/arrivals (a busy airport), while a node has 3 or fewer (a less busy airport). This helps to distinguish between brain regions that are highly connected and those that are less connected.
Scientific Validity
  • Use of Adjacency Matrix and Network Theory: Using an adjacency matrix to represent brain connectivity is a standard approach in network neuroscience. Applying concepts from network theory (hubs, nodes, edges) to analyze brain connectivity is appropriate and provides a useful framework for understanding the organization of brain activity during handwriting.
  • Comparison of Handwriting and Typewriting: Comparing the brain connectivity networks of handwriting and typewriting is relevant to the research question. However, the figure only shows the network for handwriting, making it difficult to directly compare the two conditions. Showing the network for typewriting would enhance the comparison.
  • Definition of Hubs and Nodes: The definition of hubs (≥ 4 departures/arrivals) and nodes (≤ 3 departures/arrivals) is somewhat arbitrary. While it provides a way to categorize brain regions based on their connectivity, the rationale for choosing these specific thresholds should be further explained or justified.
  • Focus on Theta/Alpha Connectivity: Focusing on theta/alpha connectivity is justified given the known roles of these frequency bands in cognitive processes related to handwriting. However, other frequency bands might also contribute to the observed differences between handwriting and typewriting.
  • Simplification of Network: The simplified theoretical network in Panel A is useful for illustrating basic network concepts. However, it is important to note that real brain networks are much more complex and may not always conform to this simplified model.
Communication
  • Clarity of Caption: The caption provides a relatively clear description of the figure's content, including the meaning of hubs, nodes, and edges. However, it could be more explicit about the purpose of the figure, which is to illustrate the differences in brain connectivity between handwriting and typewriting using a network approach.
  • Visual Presentation: The visual presentation of the simplified network and the brain connectivity network is effective. The use of color (red for hubs, black for nodes) helps to distinguish between different levels of connectivity.
  • Use of Technical Terms: The caption uses technical terms like "adjacency matrix," "hubs," "nodes," and "edges," which are appropriate for a scientific audience. However, these terms might be challenging for readers unfamiliar with network analysis. The explanation of the simplified network in Panel A helps to mitigate this issue.
  • Accessibility for Non-Experts: While the figure and caption are informative for researchers familiar with network neuroscience, they may be challenging for non-experts to understand fully. Providing a simplified summary of the main findings and their implications in the main text would improve accessibility.
  • Analogy to Real-World Networks: The use of the airline network analogy in the description section is helpful for understanding the concepts of hubs, nodes, and edges. Relating these concepts to familiar real-world networks can make the figure more accessible to a wider audience.

General discussion

Key Aspects

Strengths

Suggestions for Improvement

↑ Back to Top