False positives and false negatives in functional near-infrared spectroscopy: issues, challenges, and the way forward

We highlight a significant problem that needs to be considered and addressed when performing functional near-infrared spectroscopy (fNIRS) studies, namely the possibility of inadvertently measuring fNIRS hemodynamic responses that are not due to neurovascular coupling. These can be misinterpreted as brain activity, i.e., "false positives" (errors caused by wrongly assigning a detected hemodynamic response to functional brain activity), or mask brain activity, i.e., "false negatives" (errors caused by wrongly assigning a not observed hemodynamic response in the presence of functional brain activity). Here, we summarize the possible physiological origins of these issues and suggest ways to avoid and remove them

Estimating and validating the interbeat intervals of the heart using near-infrared spectroscopy on the human forehead

In studies with near-infrared spectroscopy, the recorded signals contain information on the temporal interbeat intervals of the heart. If this cardiac information is needed exclusively and could directly be extracted, an additional electrocardiography device would be unnecessary. The aim was to estimate these intervals from signals measured with near-infrared spectroscopy with two novel approaches. In one approach, we model the heartbeat oscillations in these signals with a Fourier series where the coefficients and the fundamental frequency can continuously change over time. The time-dependent model parameters are estimated and used to calculate the interbeat intervals. The second approach uses empirical mode decomposition. The signal measured with near-infrared spectroscopy is empirically decomposed into a set of oscillatory components. The sum of a specific subset of them is an estimate of the pure heartbeat signal in which the diastolic peaks and consequential interbeat intervals are detected. We show in simultaneous electrocardiography and near-infrared spectroscopy measurements on 11 subjects (8 men and 3 woman with mean age 32.8 ± 8.1 yr), that the interbeat intervals (and the consequential pulse rate variability measures), estimated using the proposed approaches, are in high agreement with their correspondents from electrocardiography

Extension of mental preparation positively affects motor imagery as compared to motor execution: A functional near-infrared spectroscopy study

Motor imagery (MI) is widely used to study cognitive action control. Although, the neural simulation theory assumes that MI and motor execution (ME) share many common features, the extent of similarity and whether it spreads into the preparation phase is still under investigation. Here we asked, whether an extension of physiological mental preparation has a comparable effect on MI and ME. Data were recorded using wireless functional near-infrared spectroscopy (fNIRS) in a two-stage task design where subjects were cued with or without preparatory stimuli to either execute or imagine complex sequential thumb-finger tasks. The main finding is that the extended mental preparation has a significant positive effect on oxy-hemoglobin (∆[O(2)Hb]) in response to MI, which is proportionally larger as that found in response to ME. Furthermore, fNIRS was capable to discriminate within each task whether it was preceded by preparatory stimuli or not. Transition from mental preparation to actual performance (ME or MI) was reflected by a dip of the fNIRS signal presumably related to underlying cortical processes changing between preparation and task performance. Statistically significant main effects of 'Preparation' and 'Task' showed that ∆[O(2)Hb] during preparation was preparation-specific, i.e., positively affected by the presence of preparatory stimuli, whereas during task performance ∆[O(2)Hb] was both preparation- and task-specific, i.e., additionally affected by the task mode. These results are particularly appealing from a practical point of view for making use of MI in neuroscientific applications. Especially neurorehabilitation and neural interfaces may benefit from utilizing positive interactions between mental preparation and MI performance

Using multifractal analysis of ultra-weak photon emission from germinating wheat seedlings to differentiate between two grades of intoxication with potassium dichromate

Abstract. The aim of the present study was to test whether the multifractal properties of ultra-weak photon emission (UPE) from germinating wheat seedlings (Triticum aestivum) change when the seedlings are treated with different concentrations of the toxin potassium dichromate (PD). To this end, UPE was measured (50 seedlings in one Petri dish, duration: approx. 16.6–28 h) from samples of three groups: (i) control (group C, N = 9), (ii) treated with 25 ppm of PD (group G25, N = 32), and (iii) treated with 150 ppm of PD (group G150, N = 23). For the multifractal analysis, the following steps where performed: (i) each UPE time series was trimmed to a final length of 1000 min; (ii) each UPE time series was filtered, linear detrended and normalized; (iii) the multifractal spectrum (f(α)) was calculated for every UPE time series using the backward multifractal detrended moving average (MFDMA) method; (iv) each multifractal spectrum was characterized by calculating the mode (αmode) of the spectrum and the degree of multifractality (Δα); (v) for every UPE time series its mean, skewness and kurtosis were also calculated; finally (vi) all obtained parameters where analyzed to determine their ability to differentiate between the three groups. This was based on Fisher’s discriminant ratio (FDR), which was calculated for each parameter combination. Additionally, a non-parametric test was used to test whether the parameter values are significantly different or not. The analysis showed that when comparing all the three groups, FDR had the highest values for the multifractal parameters (αmode, Δα). Furthermore, the differences in these parameters between the groups were statistically significant (p < 0.05). The classical parameters (mean, skewness and kurtosis) had lower FDR values than the multifractal parameters in all cases and showed no significant difference between the groups (except for the skewness between group C and G150). In conclusion, multifractal analysis enables changes in UPE time series to be detected even when they are hidden for normal linear signal analysis methods. The analysis of changes in the multifractal properties might be a basis to design a classification system enabling the intoxication of cell cultures to be quantified based on UPE measurements

Modelling confounding effects from extracerebral contamination and systemic factors on functional near-infrared spectroscopy

Haemodynamics-based neuroimaging is widely used to study brain function. Regional blood flow changes characteristic of neurovascular coupling provide an important marker of neuronal activation. However, changes in systemic physiological parameters such as blood pressure and concentration of CO2 can also affect regional blood flow and may confound haemodynamics-based neuroimaging. Measurements with functional near-infrared spectroscopy (fNIRS) may additionally be confounded by blood flow and oxygenation changes in extracerebral tissue layers. Here we investigate these confounds using an extended version of an existing computational model of cerebral physiology, 'BrainSignals'. Our results show that confounding from systemic physiological factors is able to produce misleading haemodynamic responses in both positive and negative directions. By applying the model to data from previous fNIRS studies, we demonstrate that such potentially deceptive responses can indeed occur in at least some experimental scenarios. It is therefore important to record the major potential confounders in the course of fNIRS experiments. Our model may then allow the observed behaviour to be attributed among the potential causes and hence reduce identification errors