DOI: 10.5061/dryad.z08kprrkg
title: Data from: Sensing, feeling, and regulating: Investigating the association of focal brain damage with voluntary respiratory and motor control

description: Breathing is a complex, vital function that can be modulated to influence
 physical and mental well-being. However, the role of cortical and
 subcortical brain regions in voluntary control of human respiration is
 underexplored. Here we investigated the influence of damage to human
 frontal, temporal, or limbic regions on the sensation and regulation of
 breathing patterns. Participants performed a respiratory regulation task
 across regular and irregular frequencies ranging from 6 to 60 breaths per
 minute (bpm), with a counterbalanced hand motor control task.
 Interoceptive and affective states induced by each condition were assessed
 via questionnaire and autonomic signals were indexed via skin conductance.
 Participants with focal lesions to the bilateral frontal lobe, right
 insula/basal ganglia, and left medial temporal lobe demonstrated
 significantly lower performance than individually matched healthy
 comparisons during the breathing and motor tasks. They also reported
 significantly higher anxiety during the 60-bpm regular and irregular
 breathing trials than healthy participants. Correlations between task
 performance, anxiety, and difficulty varied across experimental conditions
 and groups. This study demonstrates that damage to frontal, temporal, or
 limbic regions is associated with abnormal voluntary respiratory and motor
 regulation and tachypnea-related anxiety, highlighting the role of the
 forebrain in affective and motor responses during breathing.
description: <strong>Measurements and Recording
 Devices</strong> Demographic data (age, gender, handedness,
 ethnicity, and height) were gathered through a questionnaire. Body weight
 was physically measured to calculate body mass index (BMI).
 Physiological data were gathered via a MP100
 acquisition unit (Biopac Inc., Santa Barbara, CA). Breathing activity was
 monitored using an elastic thoracic breathing belt and amplifier (RSP100C)
 at the mid-sternal level. Cardiac activity was measured using two
 electrodes paired with an amplifier (ECG100C) designed for
 electrocardiogram (ECG) monitoring and recorded at 200 Hz with a high pass
 filter to stabilize the ECG baseline. The positive electrode was
 positioned on the left lower anterior abdomen and the negative electrode
 on the right anterior chest at the mid-clavicular line (lead II
 configuration). Galvanic skin response electrodes, placed on the thenar
 and hypothenar eminences of the hand contralateral to the dial hand, were
 collected using a single-channel amplifier (EDA100C) at 200 Hz.
 A custom-built dial was employed to measure hand
 movement regulation, consisting of a rotating potentiometer with a
 continuous rating scale ranging from 0.000 to 5.000 Volts at a sampling
 rate of 200 Hz, as utilized in our previous studies (Khalsa et al., 2009b;
 2020). Participants were asked to rotate the dial using their dominant
 hand following a visual cue, while breathing normally during each
 trial. <strong>Statistical
 Analysis</strong> Descriptive statistics for
 demographic data were compared for the lesion and matched-healthy
 comparison group. T-tests were used for metric variables (age, BMI), with
 normality checked using the Shapiro-Wilk test and histogram
 distributions. To evaluate respiratory and motor
 regulation performance, maximum cross-correlation values were calculated
 between the instructed and measured signals (respiratory trace for
 breathing trials and dial trace for motor trials) using MatLab R2021a
 software (Mathworks Inc., Natick, MA) and transformed using Fisher's
 Z-transformation to address skewed sampling distributions of correlation
 coefficients (Silver & Dunlap, 1987). Cross-correlations were
 calculated using the built-in xcorr function. Linear mixed effects models
 (LME) were used to compare performance across the respiration and motor
 regulation tasks via separate models for the regular and irregular tasks
 using the lme4 package in R (Bates et al., 2015). For
 LMEs, random intercepts were included for each subject, as represented by
 the term (1|ID) in the model formulations; see next paragraph. This
 allowed us to include individual baseline values for each subject to
 account for between-participant variability. Additionally, we controlled
 for age in our models to account for its potential influence on the
 outcomes. However, random slopes for individual subjects were not included
 in these models, as it was assumed that the variability in slopes across
 subjects was not essential for the dependent variables under
 investigation. We used the respective interaction terms
 between group membership (healthy vs. lesion) and the other predictor
 variables (breathing and motor performance, anxiety, HRV) as fixed effects
 for all tests, while controlling for age. The full term is outlined here
 as a model: value ~ group*condition + age + (1|ID). For
 the significance tests of the fixed effects in our Linear Mixed-Effects
 (LME) models, Wald chi-square tests were conducted. These tests aimed to
 determine the significance of the fixed effects and were carried out using
 the summary-function in conjunction with the <em>lmerTest</em>
 package in R (Kuznetsova, Brockhoff & Christensen, 2022). For the
 post-hoc analysis, the <em>emmeans</em> R-package (Lenth,
 2018) was employed. The analysis excluded participants who were missing
 respiratory or motor regulation data for more than two trials per task. In
 the multilevel correlation analysis, Spearman's correlation
 coefficients were calculated using R. For skin
 conductance response (SCR) analysis,<strong> </strong>we
 utilized the open-source software Ledalab (version 3.x; Benedek &
 Kaernbach, 2010) for Matlab. Data were first preprocessed using a 1st
 order low-pass Butterworth filter with a cutoff frequency of 5 Hz.
 Adaptive smoothing was performed using Gaussian windows with a width of 16
 samples. CDA.ISCR (Cumulative Driver Amplitude Integrated with Skin
 Conductance Response) was the dependent variable calculated for in-depth
 analysis of the SCR signals. CDA.ISCR represents the area covered by the
 temporal integration of the phasic driver of the SCR with respect to the
 duration of the SCR. This is achieved by multiplying the SCR by the size
 of the response window. CDA.ISCR is expressed in microsiemens-seconds
 (μS*s) and accounts for both the intensity and duration of the SCR over
 time. This allows a detailed study of the temporal evolution and intensity
 of responses to different stimuli.
description: # Data from: Sensing, feeling, and regulating: Investigating the
 association of focal brain damage with voluntary respiratory and motor
 control
 [https://doi.org/10.5061/dryad.z08kprrkg](https://doi.org/10.5061/dryad.z08kprrkg) The scripts used for the individual sub-analyses in R and the associated data frames are described in the following ## Description of the data and file structure **Analysis of Interoceptive Performance:** With the R script "Analysis_InteroceptivePerformance" we investigate the study participants' ability to regulate their breathing and motor activity under regular and irregular conditions. Two main task types are considered: 1\. voluntary breathing 2\. motor dial (as a control task). The related data for this study are recorded in the following Excel files: * *InteroceptivePerformance_RegularMotorConditionsSeparated*: Motor performance data under regular conditions. * *InteroceptivePerformance_RegularBreathingConditionsSeparated*: Breathing performance data under regular conditions. * *InteroceptivePerformance_RegularBreathingAndMotorTask*: Combined data for breathing and motor performance under regular conditions. * *InteroceptivePerformance_IrregularBreathAndMotorTask*: Combined data for breathing and motor function under irregular conditions. In addition, the script contains statistical analyses on the demographic variables age and BMI of the study participants. The relevant data for this are contained in the data frame "SampleStatistics". Linear mixed effects models were applied for the performance comparisons in the breathing and motor regulation tasks. For this purpose, separate models were created for regular and irregular tasks using the *lme4* package in R. To calculate statistical significance and perform Bonferroni-corrected post-hoc tests for group comparisons, the *lmerTest* package was used. The post-hoc analysis was done with the *emmeans* R package. For visualisation, packages *ggplot2* and *apa* were used. **Analysis of Anxiety Rating:** Using the R script "Analysis_AnxietyRating", we perform a detailed evaluation of the anxiety reported by the participants during the different task conditions. The following Excel files containing the corresponding data frames are available for analysis: * *Anxiety_Breathing_Regular*: data on reported anxiety during regular breathing tasks. * *Anxiety_Breathing_Irregular*: Data on reported anxiety during irregular breathing tasks. * *Anxiety_Motor_Regular*: Data on reported anxiety during regular motor tasks. * *Anxiety_Motor_Irregular*: Data on reported anxiety during irregular motor tasks. The R script analyses the reported anxiety during regular and irregular task types in the following way: 1\. Preprocessing: The dataset is loaded and categorised. 2\. Descriptive statistics: Average anxiety scores and standard deviations are calculated for the "Lesion" and "Healthy" groups. 3\. Linear Mixed Effects Model (LME): A model is created to analyse influences of group membership and condition on anxiety score. 4\. Visualisations: A boxplot shows the distribution of anxiety; QQ plots test model assumptions. 5\. Post-hoc tests: Differences between groups and conditions are assessed using Bonferroni-corrected tests. 6\. Graphical representation: A line graph depicts group differences across different breathing conditions. Packages used shown at the beginning of the script. **Analysis of Difficulty Rating:** The R-script "Analysis_DifficultyRating" is used to analyse the perceived difficulty after performing breathing and motor regulation tasks. The script loads the corresponding data sets: * *Difficulty_RegularBreathAndMotorTask* * *Difficulty_IrregularBreathAndMotorTask* The R script analyses the reported difficulty during regular and irregular task types in the following way: 1\. Preprocessing: The dataset is loaded and categorised. 2\. Descriptive statistics: Average difficulty scores and standard deviations are calculated for the "Lesion" and "Healthy" groups. 3\. Linear Mixed Effects Model:Build an LME model that accounts for group membership and task type. 4\. Visual data inspection: Plotting the data using boxplots, checking the assumption of a normal distribution for random intercepts and residuals. 5\. Post-hoc tests: Bonferroni-corrected post-hoc tests to identify specific differences between groups and task types. 6\. Graphical representation: Visualisation of group differences in perceived task difficulty for regular and irregular breathing and motor regulation. Packages used shown at the beginning of the script. **Analysis of Skin Conductance Rate (SCR):** The R-script "Analysis_SkinConductance" is used to analyse the SCR following regular and irregular breathing and motor tasks. The script processes the following data sets: * *SCR_Breathing_Regular* * *SCR_Motor_Regular* The R script analyses the SCR data in these steps: 1\. Preprocessing: Data is loaded, categorized, and subsets for "Lesion" and "Healthy" groups are created. 2\. Descriptive Statistics: Calculates mean and standard deviation of the AUC values for both subsets. 3\. LME: Constructs an LME model with fixed effects for 'Group' and 'Condition', and random effect for subject ID. 4\. Visual Data Inspection: Generates boxplots for AUC values distribution and checks the LME model's assumptions. 5\. Post-hoc Analysis: Performs Bonferroni-corrected post-hoc tests to dissect significant interactions or main effects. 6\. Graphical Representation: Visualizes SCR differences using enhanced visualizations, roviding clarity on the differences between conditions and groups. Necessary R packages should be installed and loaded as indicated at the beginning of the script. **Analysis of Multilevel Correlation:** The R-script "Correlation_Matrix" is designed to scrutinize the multilevel correlation for both breathing and motor tasks under various conditions. The script processes the following data sets: * *Multilevel_Correlation_Breathing* * *Multilevel_Correlation_Motor* The R script analyses the multilevel correlation data in these steps: 1\. Data Loading & Categorization: Data from the specified tasks is loaded and divided based on unique 'Group' and 'Condition' combinations. 2\. Descriptive Analysis: For each subset, correlations between performance (Z-Value), anxiety, difficulty, and SCR are calculated. 3\. Statistical Significance: Using the `cor.test()` function, p-values are derived for the correlation between each variable pair. 4\. P-value Adjustment: To control for the False Discovery Rate, p-values are adjusted using the Benjamini-Hochberg correction method. 5\. Result Formatting: The correlation matrix is enhanced with asterisks (*, **, ***) to denote the significance level of each correlation. The actual adjusted p-values are also appended for clarity. 6\. Output & Visualization: The enhanced correlation matrix is printed to the console, providing clear insights into the relationships between variables for each group and condition. **Analysis of Heart Rate Variability (HRV)** The R-script "Analysis_HeartRateVariability_RegularTasks" analyzes the HRV with a focus on both Time Domain and Frequency Domain parameters during regular breathing and motor tasks. The script processes the following data sets: * *HRV_TimeDomain_BreathingRegular* * *HRV_TimeDomain_MotorRegular* * *HRV_FrequencyDomain_BreathingRegular* * *HRV_FrequencyDomain_MotorRegular* Analysis of Heart Rate Variability (HRV) for Irregular Tasks: The subsequent R-script "Analysis_HeartRateVariability_IrregularTasks" analyzes Time Domain and Frequency Domain parameters, but with an emphasis on irregular breathing and motor tasks. The script delves into these data sets: * *HRV_TimeDomain_BreathingIrregular* * *HRV_TimeDomain_MotorIrregular* * *HRV_FrequencyDomain_BreathingIrregular* * *HRV_FrequencyDomain_MotorIrregular* The R script analyzes HRV data in these steps: Time Domain Analysis For breathing and motor tasks: Data loaded from Excel Estimated parameters: Average Heart Rate RMSSD SDNN pNN50 Groups: "Lesion" and "Healthy For each parameter: Descriptive statistics (mean and standard deviation) for each group. Verification of normal distribution Test of variance homogeneity Group comparison using Welch test Additionally for Motor Regular Tasks: Data imputation with mice *Frequency Domain Analysis* Load data: Example: HRV_motor_reg_fd &lt;- HRV_FrequencyDomain_MotorRegular Data imputation: Using mice to impute missing data. Convert group and state as factors: Group and bpm are defined as factors Descriptive statistics for LF and HF: Create subsets for "lesion" and "healthy" groups Calculate mean and standard deviation for pLF and pHF Group comparison for LF and HF: Check assumption of normal distribution with shapiro.test Check homogeneity of variances with leveneTest Determine group differences with Welch test Density plots for HRV frequency domain parameters: Create density plots for pLF and pHF between groups Plot mean of each group Arrange and label graphs Necessary R packages should be installed and loaded as indicated at the beginning of the script. ### **Datasets** Here is the description for the Excel data sheets included in this publication. These files represent the datasets collected during the study, with variable descriptions listed for each dataset. Empty cells within the datasets indicate missing data and are left blank intentionally. **Datasets and Variable Descriptions:** * **HRV_FrequencyDomain_BreathingRegular.xlsx**\ Variables: ID, Subject, Group , Condition, pLF (prevalent low-frequency oscillation of heart rate in Hz), pHF (prevalent high-frequency oscillation of heart rate in Hz) * **HRV_FrequencyDomain_MotorRegular.xlsx**\ Variables: ID, Subject, Group, Condition, pLF (prevalent low-frequency oscillation of heart rate in Hz), pHF (prevalent high-frequency oscillation of heart rate in Hz) * **HRV_TimeDomain_BreathingRegular.xlsx**\ Variables: ID, Subject, Group, bpm (instructed beats per minute in trial), HR (Heart Rate, beats per minute), SDNN (Standard Deviation of NN intervals, in ms), RMSSD (Root Mean Square of Successive Differences, in ms), pNN50 (Percentage of consecutive NN intervals that differ by more than 50 ms, in %) * **HRV_TimeDomain_MotorRegular.xlsx**\ Variables: ID, Subject, Group, bpm (instructed beats per minute in trial), HR (Heart Rate, beats per minute), SDNN (Standard Deviation of NN intervals, in ms), RMSSD (Root Mean Square of Successive Differences, in ms), pNN50 (Percentage of consecutive NN intervals that differ by more than 50 ms, in %) * **HRV_TimeDomain_MotorIrregular.xlsx**\ Variables: Group, ID, HR (Heart Rate, beats per minute), SDNN (Standard Deviation of NN intervals, in ms), RMSSD (Root Mean Square of Successive Differences, in ms), pNN50 (Percentage of consecutive NN intervals that differ by more than 50 ms, in %) * **InteroceptivePerformance_RegularMotorConditionsSeparated.xlsx**\ Variables: ID, Group, Subject_code, Condition, Value (Cross-correlation Value, decimal), Age * **Difficulty_RegularBreathAndMotorTask.xlsx**\ Variables: ID, Subj, Value (Perceived Difficulty Level, Likert 1-5, with 5 = high difficulty, ordinal), Condition, Task, Group * **InteroceptivePerformance_RegularBreathAndMotorTask.xlsx**\ Variables: ID, Group, Subject_code, Value (Cross-correlation Value, decimal), Task, Age * **InteroceptivePerformance_RegularBreathingConditionsSeparated.xlsx**\ Variables: ID, Group, Subject_code, Condition, Value (Cross-correlation Value, decimal), Age * **SampleStatistics.xlsx**\ Variables: Group, Age, BMI * **Anxiety_Breathing_Irregular.xlsx**\ Variables: ID, Subject, Value (Anxiety Level, Likert 1-5, with 5 = high anxiety, ordinal), Condition, Group * **Anxiety_Breathing_Regular.xls**\ Variables: ID, Subject, Value (Anxiety Level, Likert 1-5, with 5 = high anxiety, ordinal), Condition, Group * **Anxiety_Motor_Irregular.xlsx**\ Variables: ID, Subject, Value (Anxiety Level, Likert 1-5, with 5 = high anxiety, ordinal), Condition, Group * **Anxiety_Motor_Regular.xls**\ Variables: ID, Subject, Value (Anxiety Level, Likert 1-5, with 5 = high anxiety, ordinal),  Condition, Group * **Difficulty_IrregularBreathAndMotorTask.xlsx**\ Variables: ID, Subj, Condition, Value (Perceived Difficulty Level, Likert 1-5, with 5 = high difficulty, ordinal), Task, Group * **InteroceptivePerformance_IrregularBreathAndMotorTask.xlsx**\ Variables: ID, Group, Subject_code, Value (Cross-correlation Value, decimal), Task, Age * **SCR_Breathing_Regular.xlsx**\ Variables: ID, Subject, Group, AUC (Area under curve), Condition * **SCR_Motor_Regular.xlsx**\ Variables: ID, Subject, Group, AUC (Area under curve), Condition * **Multilevel_Correlation_Motor.xlsx**\ Variables: ID, Subject, Condition, Group, Z_Value, Anxiety, Difficulty, SCR (Skin conductance rate; MicroSiemens (µS)) * **Multilevel_Correlation_Breathing.xlsx**\ Variables: ID, Subject, Condition, Group, Z_Value, Anxiety, Difficulty, SCR (Skin conductance rate; MicroSiemens (µS)) * **HRV_FrequencyDomain_BreathingIrregular.xlsx**\ Variables: Group, Subject, bpm (instructed beats per minute in trial), pLF (prevalent low-frequency oscillation of heart rate in Hz), pHF (prevalent high-frequency oscillation of heart rate in Hz) * **HRV_FrequencyDomain_MotorIrregular.xlsx**\ Variables: Subject, Group, Condition (instructed beats per minute in trial), pLF (prevalent low-frequency oscillation of heart rate in Hz), pHF (prevalent high-frequency oscillation of heart rate in Hz) * **HRV_TimeDomain_BreathingIrregular.xlsx**\ Variables: Group, ID, bpm (instructed beats per minute in trial), HR (Heart Rate, beats per minute), SDNN (Standard Deviation of NN intervals, in ms), RMSSD (Root Mean Square of Successive Differences, in ms), pNN50 (Percentage of consecutive NN intervals that differ by more than 50 ms, in %)