Complementary physiological and behavioral data streams enhance analysis of fNIRS … · 2016. 11....
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Baseline Adaptation 1 Adaptation 2 Washout Complementary physiological and behavioral data streams enhance analysis of fNIRS data during a real-world driving task Andrew Gundran 1 , Aaron Piccirilli 1 , Zachary Stuart 2 , Joseph M. Baker 1 , Jennifer L. Bruno 1 , Lene Harbott 2 , Hadi Hosseini 1 , J. Christian Gerdes 2 , Allan L. Reiss 1,3 1 Center for Interdisciplinary Brain Sciences Research, 2 Mechanical Engineering, 3 Radiology and Pediatrics, Stanford University, Stanford, CA Functional near infrared spectroscopy (fNIRS) is an ultra-portable neuroimaging technique that allows imaging of brain activity in real world, dynamic scenarios by measuring relative changes in oxygenated and deoxygenated hemoglobin (HbO, HbR). Automobile driving is one such scenario that involves several higher-order cognitive functions including visuospatial working memory, divided attention, and decision making. Because of its methodological benefits, fNIRS is an optimal platform for investigating cortical function during real world driving. However, with the ability to study the brain in realistic settings comes the loss of experimental control often present in lab- based experiments. We demonstrate the utility of combining data from multiple sources including head acceleration, cardiac rate, pupil dilation, and vehicle dynamics to enhance fNIRS and adaptation data analysis on the single subject level. Introduction Figure 3. Evaluating channel quality by comparing ECG and NIRS-derived heart rates. Channels with correlations below 0.2 were excluded from analysis. Below, examples of an (A) included and (B) excluded channel. A total of 21 participants completed a naturalistic driving adaptation task in which they made a series of lane-change maneuvers (Figure 1A). Following six lane changes with normal steering congruence, each participant experienced 12 lane changes in which the steering congruence was reversed, which caused the vehicle to turn in the opposite direction of the steering wheel movement. For the final six lane changes the steering congruence was normalized (Figure 1B). During the task, our suite of data streams captured all task-related changes in bilateral frontoparietal cortical activation (NIRSport Tandem, NIRx, Figures 2A/2E), behavioral/vehicle dynamics (X1, Figure 2B), pupillometry/eye tracking (SMI ETG 2.0, Figure 2C), and heart rate and head acceleration (CIBSR biometrics kit, Figure 2D). Figure 6. Exploring the differences between normal trials (vertical blue lines) and outlier trials (vertical red lines, trials where participants could not successfully complete the lane change) using (A) heart rate and driving performance (B: vehicle position, hand wheel rate). Figure 7. Changes in (A) heart rate and (B) pupil diameter averaged per trial by condition. Figure 1. Overview of experimental design. Figure 2. Overview of data streams collected during experiment and cortical projections of fNIRS channels. Figure 5. Evaluating channel quality by correlating accelerometer time series with HbO and HbR time series separately (A). When HbO and HbR are moderately correlated (with the same sign) to the accelerometer time series, we can quantitatively flag channels contaminated by motion artifacts (B) to avoid false positives reflected in beta coefficients (C). D A B C E Assessing NIRS channel quality: ECG correlations Exploring outlier trials: Heart rate & driving performance Methods Assessing NIRS channel quality: Accelerometer correlations Exploring adaptation: Heart rate & pupil diameter Figure 4. Mean beta coefficients for right prefrontal cortex (A) before and (B) after removing channels flagged by correlation to ECG-derived heart rate. Asterisks indicate significant (p < 0.05) two sample t-test comparisons. Baseline (congruent) Trials 1-6 Adaptation 1 (incongruent) Trials 7-12 Adaptation 2 (incongruent) Trials 13-18 Washout (congruent) Trials 19-24 Acknowledgements: We’d like to thank Semir Shafi and Sarah Jiang for helping to develop data collection methods and logistics for the experiment. This work was funded in part by the Toyota Class Action Settlement Safety Research and Education Program. The conclusions being expressed are the authors’ only, and have not been sponsored, approved, or endorsed by Toyota or Plaintiffs’ Class Counsel. Contact: [email protected] • Physiological and environmental data streams (e.g., electrocardiography, head acceleration) can inform NIRS data quality assessments, in particular, when motion artifacts are highly correlated with the task. • Understanding of adaptation to a novel steering condition can be enhanced by complementary data streams that inform and add to investigating brain function not achievable solely through fNIRS imaging during naturalistic experiments. • Adaptation is seen in cortical activation by an initial increase in beta coefficients followed by a subsequent decrease; this pattern is also seen in heart rate and pupil diameter. A B * * * A B A B C A B A B HbO HbR HbO HbR Conclusions
Transcript of Complementary physiological and behavioral data streams enhance analysis of fNIRS … · 2016. 11....
Baseline Adaptation 1 Adaptation 2 Washout
Complementary physiological and behavioral data streams enhance analysis of fNIRS data during a real-world driving taskAndrew Gundran1, Aaron Piccirilli1, Zachary Stuart2, Joseph M. Baker1, Jennifer L. Bruno1, Lene Harbott2, Hadi Hosseini1, J. Christian Gerdes2, Allan L. Reiss1,3
1Center for Interdisciplinary Brain Sciences Research, 2Mechanical Engineering, 3Radiology and Pediatrics, Stanford University, Stanford, CA
Functional near infrared spectroscopy (fNIRS) is an ultra-portable neuroimaging technique thatallows imaging of brain activity in real world, dynamic scenarios by measuring relative changes inoxygenated and deoxygenated hemoglobin (HbO, HbR). Automobile driving is one such scenariothat involves several higher-order cognitive functions including visuospatial working memory,divided attention, and decision making. Because of its methodological benefits, fNIRS is an optimalplatform for investigating cortical function during real world driving. However, with the ability tostudy the brain in realistic settings comes the loss of experimental control often present in lab-based experiments. We demonstrate the utility of combining data from multiple sources includinghead acceleration, cardiac rate, pupil dilation, and vehicle dynamics to enhance fNIRS andadaptation data analysis on the single subject level.
Introduction
Figure 3. Evaluating channel quality by comparing ECG and NIRS-derived heart rates. Channels withcorrelations below 0.2 were excluded from analysis. Below, examples of an (A) included and (B) excluded channel.
A total of 21 participants completed a naturalistic driving adaptation task in which they made aseries of lane-change maneuvers (Figure 1A). Following six lane changes with normal steeringcongruence, each participant experienced 12 lane changes in which the steering congruence wasreversed, which caused the vehicle to turn in the opposite direction of the steering wheelmovement. For the final six lane changes the steering congruence was normalized (Figure 1B).During the task, our suite of data streams captured all task-related changes in bilateralfrontoparietal cortical activation (NIRSport Tandem, NIRx, Figures 2A/2E), behavioral/vehicledynamics (X1, Figure 2B), pupillometry/eye tracking (SMI ETG 2.0, Figure 2C), and heart rate andhead acceleration (CIBSR biometrics kit, Figure 2D).
Figure 6. Exploring the differences between normal trials (vertical blue lines) and outlier trials (verticalred lines, trials where participants could not successfully complete the lane change) using (A) heart rateand driving performance (B: vehicle position, hand wheel rate).
Figure 7. Changes in (A) heart rate and (B) pupil diameter averaged per trial by condition.
Figure 1. Overview of experimental design.
Figure 2. Overview of data streams collected during experiment and cortical projections of fNIRS channels.
Figure 5. Evaluating channel quality by correlating accelerometer time series with HbO and HbR time seriesseparately (A). When HbO and HbR are moderately correlated (with the same sign) to the accelerometer timeseries, we can quantitatively flag channels contaminated by motion artifacts (B) to avoid false positives reflectedin beta coefficients (C).
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Assessing NIRS channel quality: ECG correlations Exploring outlier trials: Heart rate & driving performance
Methods
Assessing NIRS channel quality: Accelerometer correlations
Exploring adaptation: Heart rate & pupil diameter
Figure 4. Mean beta coefficients for right prefrontal cortex (A) before and (B) after removing channels flagged by correlation to ECG-derived heart rate. Asterisks indicate significant (p < 0.05) two sample t-test comparisons.
Baseline (congruent) Trials 1-6
Adaptation 1 (incongruent) Trials 7-12
Adaptation 2 (incongruent) Trials 13-18
Washout (congruent) Trials 19-24
Acknowledgements: We’d like to thank Semir Shafi and Sarah Jiang for helping to develop data collection methods and logistics for the experiment. Thiswork was funded in part by the Toyota Class Action Settlement Safety Research and Education Program. The conclusions being expressed are the authors’only, and have not been sponsored, approved, or endorsed by Toyota or Plaintiffs’ Class Counsel.
Contact: [email protected]
• Physiological and environmental data streams (e.g., electrocardiography, headacceleration) can inform NIRS data quality assessments, in particular, when motionartifacts are highly correlated with the task.
• Understanding of adaptation to a novel steering condition can be enhanced by complementary data streams that inform and add to investigating brain function not achievable solely through fNIRS imaging during naturalistic experiments.
• Adaptation is seen in cortical activation by an initial increase in beta coefficients followed by a subsequent decrease; this pattern is also seen in heart rate and pupil diameter.
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