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Mental workload during n-back task captured by TransCranial Doppler (TCD) sonography and functional Near-Infrared Spectroscopy (fNIRS) monitoring

Peter Mukli Andriy Yabluchanskiy Tamas Csipo

Published: April 21, 2021. Version: 1.0


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Mukli, P., Yabluchanskiy, A., & Csipo, T. (2021). Mental workload during n-back task captured by TransCranial Doppler (TCD) sonography and functional Near-Infrared Spectroscopy (fNIRS) monitoring (version 1.0). PhysioNet. https://doi.org/10.13026/zfb2-1g43.

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Goldberger, A., Amaral, L., Glass, L., Hausdorff, J., Ivanov, P. C., Mark, R., ... & Stanley, H. E. (2000). PhysioBank, PhysioToolkit, and PhysioNet: Components of a new research resource for complex physiologic signals. Circulation [Online]. 101 (23), pp. e215–e220.

Abstract

The authors used this dataset to assess the neurovascular coupling responses evoked by a cognitive paradigm with the aid of TransCranial Doppler (TCD) sonography and functional Near-InfraRed Spectroscopy (fNIRS). Fourteen healthy young individuals were recruited for this study conducted in 2018 at the University of Oklahoma, Translational Geroscience Laboratory (age: 31±5.94 years, BMI: 24.9±2.95). The following physiological signals were measured non-invasively during the examination: relative concentration of oxy- and deoxyhemoglobin (HbO and HbR, respectively), and cerebral Blood Flow Velocity (BFV) in the middle cerebral arteries for the entire duration of the testing (~11 minutes). In this study, fNIRS signals from the frontal cortex were acquired by a NIRScout platform (NIRx Medical Technologies LLC, NY, USA) at a sampling frequency of 3.9 Hz, HbO and HbR time series signals can be obtained by applying the differential modified Beer Lambert Law on the preprocessed records of near-infrared intensities. BFV was recorded using TCD sonography (Digi-Lite, Rimed, Raanana, Israel) by placing 2 Mhz ultrasound probes over the left and right temporal acoustic windows. The paradigm used for cognitive testing was an n-back task where the participant was administered a series of alphabetical letters on the computer screen and had to provide a response whenever a stimulus is presented that is the same as the one presented n trials previously. The examination protocol consisted of the following 134 second sessions: 0-back, 1-back, 0-back, and 2-back. This dataset was collected for the following study: "Increased Cognitive Workload Evokes Greater Neurovascular Coupling Responses in Healthy Young Adults".


Background

Brain function critically depends on the regulation of cerebral blood flow which provides additional nutrients to match the metabolic demand of active neurons. Neuronal activity elicits a hemodynamic response via a NeuroVascular Coupling (NVC) mechanism, whose impairment is associated with the aging process and increases the risk of cognitive impairment [1]. Localized activation of the specific brain regions required to perform the cognitive task, coupled with good performance, represents highly efficient recruitment. Insufficient delivery of nutrients to these neuronal populations may lead to impaired cognitive performance. These recent developments highlight the importance of further investigations to better understand the resource allocation of the brain under physiological conditions.

NVC can be assessed non-invasively with the aid of TransCranial Doppler (TCD) sonography and functional Near-Infrared Spectroscopy (fNIRS) which are both used to evaluate the vascular component of NVC. TCD can only monitor accelerating blood flow in major cerebral arteries (such as Middle Cerebral Artery, MCA) in response to mental workload [2] but not changes in their diameter. Hence it offers low spatial resolution as TCD can not localize activation to a specific area of the brain. Optical properties of the cerebral cortex shows task-specific changes that can be captured by fNIRS that is capable of monitoring relative tissue concentration of oxy- and deoxyhemoglobin (HbO and HbR, respectively). fNIRS is a cost-effective, portable, and non-invasive imaging method for cerebral microcirculatory function that offers sufficient spatial resolution for capturing hemodynamic response in the DorsoLateral PreFrontal Cortex (DLPFC) during administration of working memory paradigms [3]. Results from fNIRS studies correlate well with those observed with functional Magnetic Resonance Imaging (fMRI) methodologies [4, 5]. Left DLPFC shows increased activation in cases of more difficult tasks [6]. However, the bilateral vascular responses detected by TCD during this type of cognitive stimulation can not be solely attributed to localized activation of the PreFrontal Cortex (PFC) [5].

The aforementioned studies utilized a working memory paradigm as cognitive stimulation to evoke task-related NVC [2, 4, 6]. The n-back approach [7] can be used to examine working memory in which the cognitive domain is responsible for temporarily holding information available for manipulating, processing, critical thinking, solving problems, and interacting with others.

This dataset comprises of cerebrovascular physiological records collected during an n-back working memory paradigm that was utilized as cognitive stimulation to evoke task-related NVC. The cognitive stimulation paradigm consisted of several difficulty levels and fNIRS was utilized to measure NVC in the PFC (not limited to DLPFC). TCD was used to measure hemodynamic changes in the upstream MCA during the same cognitive task in young, healthy individuals. This dataset enables further expansion of the knowledge on cognitive task-evoked NVC and allows future research to examine the capability and sensitivity of fNIRS and TCD to discriminate NVC evoked by tasks with different levels of cognitive workload.


Methods

The dataset was collected as part of a study to assess the neurovascular coupling responses evoked by a cognitive paradigm with the aid of TransCranial Doppler (TCD) sonography and functional Near-InfraRed Spectroscopy (fNIRS).

Participants

Fourteen healthy adults (10 males and 4 females) participated in this study after giving informed consent. Subjects were asked to refrain from the consumption of caffeinated beverages at least 6 hours prior to the assessments. A more detailed, deidentified description of the study population is provided in the "Data Description" and "Files" sections of this project. Measurements were carried out in the Translational Geroscience Laboratory of Oklahoma University (Center for Healthy Brain Aging) in a quiet and dimly lit room. The study protocol was approved by the Institutional Review Board (IRB) of the University of Oklahoma Health Sciences Center and was conducted in compliance with the Helsinki Declaration.

Cognitive stimulation

In this study, we used an n-back paradigm-based cognitive test to evoke NVC responses, as further described in the "Background" section [5]. During the test, participants were seated comfortably in front of a 22-inch monitor. The monitor displayed instructions, fixation rectangles in the interstimulus period, and rapidly flashing letters during stimulus. The total stimulus set consisted of 60 letters for each session that were presented within the fixation rectangle in a random order for 250 ms. Our n-back protocol was implemented in ePrime 3 (Psychology Software Tools, Sharpsburg, PA) using custom scripts and was administered to participants in the following sequence:

  1. 0-back session (identify W): A click of a mouse button was requested whenever the letter ‘W’ was shown within the fixation rectangle.
  2. 1-back: The task was to identify if two consecutive letters are the same (e. g. x-y-B-B).
  3. 0-back session (identify W): A click of a mouse button was requested whenever the letter ‘W’ was shown within the fixation rectangle.
  4. 2-back: The task was to identify if a letter was the same as the one presented 2 letters before (e. g. x-C-y-C).

The duration of interstimulus interval varied between 1850 ms and 2050 ms and was randomized yielding an average length of 134.13 ± 1.37 s for each session and a total measurement time of 11 minutes. The following outcome measures were determined from n-back task to characterize cognitive performance: success rate (calculated as the percentage of correct responses (%correct)) and reaction time (for each subject, n-back task, and each visit).

Transcranial doppler sonography

MCA flow velocity (MCAv) was monitored using TCD sonography (Digi-Lite, Rimed, Raanana, Israel) by placing 2 Mhz ultrasound probes over the temporal acoustic windows. A probe fixation device ensured stable position of the probes (LMY-2, Rimed, Raanana, Israel) that allowed MCAv monitoring at a constant angle for the whole measurement. Left and right MCAv signals were identified according to standard criteria based on the depth and velocity of the signal [7, 8]. Channels with inadequate temporal acoustic windows or flow velocities were excluded from the analysis. The analog signal was sampled at 400 Hz (DI-4108-U, Dataq instruments, Akron, OH, USA) yielding a discrete time series of MCAv.

For TCD analysis, mean MCAv was determined in a 100 second time window extracted from each n-back task. The first 10 seconds of each session was not taken into account since this recorded segment was likely contaminated by motion and physiological artefacts (breathing). The following equation was used to calculate the normalized increase in MCAv:

M C A v [ n b a c k ] M C A v [ 0 b a c k ] M C A v [ 0 b a c k ] 100 % \frac{MCAv_{[n-back]} - MCAv_{[0-back]}}{MCAv_{[0-back]}} * 100\%

Functional near-infrared spectroscopy

Functional NIRS measurements were performed using a NIRScout platform (NIRx Medical Technologies LLC, NY, USA). The system was equipped with 16 sources (F3, AF7, AF3, Fz, Fpz, AF4, F4, AF8, FC6, C4, FC2, CP2, FC1, CP1, C3, and FC5) emitting light at two different wavelengths (760 and 850 nm) and 16 photodetectors (F5, F1, Fp1, AFz, F2, Fp2, F6, AFF6h, C6, CC4, CP4, C2, C1, FC3, CP3, and C5) defining 48 channels. The position of the optodes yielded an average source-detector separation of 3 cm probing the PFC, DLPFC, and medial motor cortex (M1). A detailed definition of the channels is shown below:

Channel Name
1 F3-F5
2 F3-F1
3 F3-FC3
4 AF7-F5
5 AF7-Fp1
6 AF3-F5
7 AF3-F1
8 AF3-Fp1
9 AF3-AFz
10 Fz-F1
11 Fz-AFz
12 Fz-F2
13 Fpz-Fp1
14 Fpz-AFz
15 Fpz-Fp2
16 Af4-Afz
17 AF4-F2
18 AF4-Fp2
19 AF4-F6
20 F4-F2
21 F4-F6
22 F4-CC4
23 AF8-Fp2
24 AF8-F6
25 FC6-F6
26 FC6-C6
27 FC6-CC4
28 C4-C6
29 C4-CC4
30 C4-CP4
31 C4-C2
32 FC2-F2
33 FC2-CC4
34 FC2-C2
35 CP2-CP4
36 CP2-C2
37 FC1-F1
38 FC1-C1
39 FC1-FC3
40 CP1-C1
41 CP1-CP3
42 C3-C1
43 C3-FC3
44 C3-CP3
45 C3-C5
46 FC5-F5
47 FC5-FC3
48 FC5-C5

For the fNIRS analysis, the raw data was processed using:

  • A pipeline based on a General Linear Model (GLM) approach implemented in Brain AnalyzIR toolbox [10] for the assessment of neurovascular coupling responses.
  • nirsLAB (NIRx Medical Technologies, NY, USA) which yielded the timecourse of HbO and HbR concentration changes.

Block averages of HbO and HbR were then calculated for each channel during each stimulus. Channel means were also averaged for the region of interest for the two conditions associated with different levels of mental workload (1-back and 2-back conditions).


Data Description

  • Raw intensity data from NIRS measurements  as .wl1 and .wl2 files (ASCII text file, tabulated data): wl1 files refer to near-infrared light intensities measured at 760 nm; wl2 files refer  to near-infrared light intensities measured at 850 nm. Meta-information about the measurement: marker positions, channel layout, and other details are described in txt files and shared as ASCII text format (following file extensions: .evt, .hdr, .tpl, .inf, and .set).
  • MAT files contain information about the montage, probe, and channel layout and can be viewed in MATLAB. These files are recommended for NIRS analysis with AnalyzIR toolbox of MATLAB [6]. All of these information are also provided in this description and ASCII text files as mentioned above.
  • Raw blood flow velocity (measured in both MCA) values from calibrated TCD sonography measurement are stored in Comma-Separated Values (CSV) separately for each subject (amplitude of signals - "Volt"). Column D - left MCA; Column E - right MCA. Actual velocities were calculated as 241 * Amplitude / 4.096 (according to Rimed manual). A single CSV file is also provided containing the mean blood flow velocity (cm/s) and pulsatility index (for each side and for their average) derived from TCD analysis.
  • Reaction time and success rate (%) were key outcome measures of cognitive performance and are stored in a single CSV file for all subjects.

Usage Notes

The recommended software for data analysis is MATLAB (version 2016a or newer) with Wavelet Toolbox and AnalyzIR toolbox. In addition, freeware programs mentioned in the "Methods" section are recommended for viewing and processing the shared data, however all data files are machine-readable and do not require specific programs for reading them.

This dataset was collected for the following study: “Increased Cognitive Workload Evokes Greater Neurovascular Coupling Responses in Healthy Young Adults”.

We recommend readers to analyze further features of NIRS data to explore what parameters of cerebral hemodynamics are most sensitive to maintained cognitive stimulation and what task-state parameters derived from NIRS signals show the strongest correlation with cognitive performance.

Limitations

  • Small sample size: n = 14 (before quality control of raw data)
  • The shared NIRS data should be preprocessed to eliminate artefacts and to increase the representation of signal components of interest.
  • The shared data does not allow investigations into the effect of cognitive stimulation on other components of the neurovascular unit, such as neuronal activity.

Acknowledgements

This work was supported by grants from the American Heart Association, the Oklahoma Center for the Advancement of Science and Technology, the National Institute on Aging (R01-AG047879; R01-AG038747; R01-AG055395), the National Institute of Neurological Disorders and Stroke (NINDS; R01-NS056218, R01-NS100782, R01-NS085002), the Oklahoma Shared Clinical and Translational Resources (OSCTR) program funded by the National Institute of General Medical Sciences (GM104938), the Presbyterian Health Foundation, the NIA-supported Geroscience Training Program in Oklahoma (T32AG052363), the Oklahoma Nathan Shock Center (P30AG050911), and the Cellular and Molecular GeroScience CoBRE (1P20GM125528).


Conflicts of Interest

The authors declare no conflict of interest.


References

  1. Girouard H, Iadecola C. Neurovascular coupling in the normal brain and in hypertension, stroke, and Alzheimer disease. J Appl Physiol. 2006;100(1):328-35.
  2. Sorond FA, Kiely DK, Galica A, Moscufo N, Serrador JM, Iloputaife I, et al. Neurovascular coupling is impaired in slow walkers: the MOBILIZE Boston Study. Annals of neurology. 2011;70(2):213-20.
  3. Vassena E, Gerrits R, Demanet J, Verguts T, Siugzdaite R. Anticipation of a mentally effortful task recruits Dorsolateral Prefrontal Cortex: An fNIRS validation study. Neuropsychologia. 2019;123:106-15.
  4. Jaeggi SM, Seewer R, Nirkko AC, Eckstein D, Schroth G, Groner R, et al. Does excessive memory load attenuate activation in the prefrontal cortex? Load-dependent processing in single and dual tasks: functional magnetic resonance imaging study. Neuroimage. 2003;19(2):210-25.
  5. Huppert TJ, Hoge RD, Diamond SG, Franceschini MA, Boas DA. A temporal comparison of BOLD, ASL, and NIRS hemodynamic responses to motor stimuli in adult humans. Neuroimage. 2006;29(2):368-82.
  6. Ayaz H, Shewokis PA, Bunce S, Izzetoglu K, Willems B, Onaral B. Optical brain monitoring for operator training and mental workload assessment. Neuroimage. 2012;59(1):36-47.
  7. Kirchner WK. Age differences in short-term retention of rapidly changing information. J Exp Psychol. 1958;55(4):352-8.
  8. Aaslid R, Markwalder TM, Nornes H. Noninvasive transcranial Doppler ultrasound recording of flow velocity in basal cerebral arteries. J Neurosurg. 1982;57(6):769-74.
  9. Nicoletto HA, Burkman MH. Transcranial Doppler series part III: interpretation. Am J Electroneurodiagnostic Technol. 2009;49(3):244-59.
  10. Santosa H, Zhai XT, Fishburn F, Huppert T. The NIRS Brain AnalyzIR Toolbox. Algorithms. 2018;11(5).

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NIRS-2019-06-19_003.evt (download) 86 B 2019-08-15
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NIRS-2019-06-19_003.inf (download) 174 B 2021-03-14
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NIRS-2019-06-19_003_config.txt (download) 432 B 2019-08-15
NIRS-2019-06-19_003_probeInfo.mat (download) 3.6 MB 2019-08-15