The physiological origin of task-evoked systemic artefacts in functional near infrared spectroscopy

By E. Kirilina, A. Jelzow, Angela Heine, M. Niessing, H. Wabnitz, R. Brühl, B. Ittermann, Arthur M. Jacobs, I. Tachtsidis. NeuroImage, 2012

499 citations recorded by the source corpus.

A major methodological challenge of functional near-infrared spectroscopy (fNIRS) is its high sensitivity to haemodynamic fluctuations in the scalp. Superficial fluctuations contribute on the one hand to the physiological noise of fNIRS, impairing the signal-to-noise ratio, and may on the other hand be erroneously attributed to cerebral changes, leading to false positives in fNIRS experiments. Here we explore the localisation, time course and physiological origin of task-evoked superficial signals in fNIRS and present a method to separate them from cortical signals. We used complementary fNIRS, fMRI, MR-angiography and peripheral physiological measurements (blood pressure, heart rate, skin conductance and skin blood flow) to study activation in the frontal lobe during a continuous performance task. The General Linear Model (GLM) was applied to analyse the fNIRS data, which included an additional predictor to account for systemic changes in the skin. We found that skin blood volume strongly depends on the cognitive state and that sources of task-evoked systemic signals in fNIRS are co-localized with veins draining the scalp. Task-evoked superficial artefacts were mainly observed in concentration changes of oxygenated haemoglobin and could be effectively separated from cerebral signals by GLM analysis. Based on temporal correlation of fNIRS and fMRI signals with peripheral physiological measurements we conclude that the physiological origin of the systemic artefact is a task-evoked sympathetic arterial vasoconstriction followed by a decrease in venous volume. Since changes in sympathetic outflow accompany almost any cognitive and emotional process, we expect scalp vessel artefacts to be present in a wide range of fNIRS settings used in neurocognitive research. Therefore a careful separation of fNIRS signals originating from activated brain and from scalp is a necessary precondition for unbiased fNIRS brain activation maps.

Subfields: Biomedical Engineering, Cardiology and Cardiovascular Medicine, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/bf8c02ca7c909aa3a0223a95e14288927cd6c4a1

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In Biomedical Engineering

In Cardiology and Cardiovascular Medicine

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Citations in the corpus

A NIRS-fMRI investigation of prefrontal cortex activity during a working memory task (2013, unknown): “A potential problem currently drawing attention is contamination of the measurement data due to extracranial hemodynamic changes such as skin blood flow (Germon et al., 1994, 1998; Kirilina et al., 2012; Kohno et al., 2007; Takahashi et al., 2011; Toronov et al., 2001).”
Time domain functional NIRS imaging for human brain mapping (2014, unknown): “TD fNIRS signals from cortical regions and changes in microcirculatory blood flow dynamics in the scalp as measured by laser Doppler flowmetry (LDF) were simultaneously recorded in a couple of recent studies (Aletti et al., 2012; Kirilina et al., 2012), strengthening”
Current Status and Issues Regarding Pre-processing of fNIRS Neuroimaging Data: An Investigation of Diverse Signal Filtering Methods Within a General Linear Model Framework (2019, background): “In the first case, the execution of particularly complex or stressful tasks can affect the psychophysiological state of the participant, resulting in changes in heart rate, breathing rate, blood pressure, carbon dioxide (CO2) concentration of the blood and autonomic regulatory activity happening bot”
A review on continuous wave functional near-infrared spectroscopy and imaging instrumentation and methodology (2014, methodology): “It was shown that MD fNIRI based changes in [O2Hb], [HHb], [tHb] and StO2 are only slightly influenced by changes in SBF/SBV induced by an extracarotid artery clamp (Al-Rawi et al., 2001), and the MD fNIRI methodology is superior to the traditional MBLL approach in excluding extracranial hemodynamic”
fNIRS-based brain-computer interfaces: a review (2015, methodology): “This method has been shown to work well, even though no significant classification-accuracy improvement over the heuristic methods has been demonstrated.”
Application of functional near-infrared spectroscopy in psychiatry (2014, background): “…the reliability of fNIRS data has been shown to be affected by anatomical parameters, such as scalp-to-cortex distance (SCD; Cui et al., 2011; Haeussinger et al., 2011) and peripheral hemodynamic parameters (i.e., skin perfusion; Kirilina et al., 2012; Takahashi et al., 2011; Tong et al., 2011).”
A Wearable Multi-Channel fNIRS System for Brain Imaging in Freely Moving Subjects (2013, methodology): “…in the raw data based on a simple stability criterion, the HbO 2 signal is much more sensitive to the physiological artifacts of the cardiac modulations caused by strenuous physical exercise (Boas et al., 2004; Huppert et al., 2009; Kirilina et al., 2012; Leff et al., 2011; Zhang et al., 2005).”