2.1. Subjects
Twenty healthy uninjured adults (n = 20 subjects with n = 10 male and n = 10 female) were recruited for this study. One male and two female subjects were left-handed (n = 3 left-handed). The remaining subjects were all right-handed (n = 17 right-handed). Only subjects without a history of psychological, psychiatric, and neurological disorders were included. Hand dominance was determined based on the self-report of the subject. All subjects were admitted to the study following written informed consent for both study participation and publication of identifying information/images in an open-access publication, as approved by the Institutional Review Board of the University of Nebraska Medical Center. All methods were performed in accordance with the relevant guidelines and regulations.
2.2. Hand Movement Tasks
Subjects were asked to sit in a chair that was pushed up to a table with their back resting on the backrest, and feet flat on the ground. Both hands of the subject were resting and elevated so that the hands were off the table and able to open and close freely. Subjects were asked to perform four hand movement tasks. Hand movement tasks were conducted with the left and right hand separately resulting in four conditions: 1) Left Hand Open, 2) Right Hand Open, 3) Left Hand Close, and 4) Right Hand Close. For each hand movement task, the subjects were given instructions of “ready, set, go” to begin the task and the instruction of “rest” when the rest period would begin. During the resting periods, the subjects were instructed to remain still and relaxed with the hands in the beginning position of the task. During each Hand Open task, the subjects were instructed to begin with the relevant hand closed to make a loose, comfortable fist while the non-involved hand was to remain still in a relaxed position. When the task was to begin, the subjects were instructed to open the involved hand quickly and relax the hand back to closed position. During each Hand Close task, the subjects were instructed to begin with the involved hand opened to make a flat palm with fingers extended with minimal effort while the non-involved hand remained still in a relaxed position. When the task was to begin, the subjects were instructed to close the hand quickly and relax the hand back to the open position. These tasks were conducted in a block format (five trials each consisting of a 30 sec rest period followed by a 10 sec task period) following a metronome beat of 30 BPM. To minimize motor planning involvement, the subjects were instructed to perform the task after they heard the beat. They were specifically instructed to avoid anticipating the tempo.
2.3. Functional Near-Infrared Spectroscopy (fNIRS)
FNIRS data were collected using a continuous wave fNIRS system (NIRSport 2, NIRx Medical Technologies, LLC, Berlin, Germany) during each task performed. Data were sampled at 8 Hz operating at 760 and 850 nm wavelengths. Eight sources and seven long separation detectors (~ 3 cm distance from the source) along with eight short separation detectors (~ 8 mm distance from each source) were placed in a cap fitted to each participant’s head circumference. The cap was positioned on the head following the 10–20 international system and the probes were placed according to a standardized montage available through the NIRx support portal (NIRSite Montage Motor with Short Channels 8x7) (Klem, Lüders et al. 1999) (Fig. 1A). The fNIRS channels covered the area around the C3 and C4 landmarks which have been shown to detect motor activity that drives hand and arm movement (Nishiyori, Bisconti et al. 2016).
To better understand how the laterality varied over the parietal lobes, laterality indices were calculated for bilateral regions of interest (ROIs). These ROIs were derived from the MNI coordinates of each source and detector. These coordinates were provided using AtlasViewer, specifically using the “Probe to Cortex” feature which projects approximate locations on the cortex from locations of the probes. The MNI coordinates were then compared to literature providing three-dimensional anatomical boundaries for these regions in more spatially precise modalities (Mayka, Corcos et al. 2006). Additionally, all channels from each hemisphere were grouped to form a ROI to represent each hemisphere, and one channel assumed to be over the hand representation of the primary motor cortex was chosen to create an M1 channel region of interest group. A single channel representation of M1 (left hemisphere = S1, D3; right hemisphere = S5, D7) allowed for a reduction in the spatial limitations of the fNIRS probes during hand movements. Thus, five regions of interest were derived for this study: 1) Primary motor cortex (M1), 2) premotor cortex (PMC), 3) primary sensory cortex (S1), 4) hemisphere, and 5) one channel over the hand area in M1 (M1 channel) (Fig. 1B).
2.4. fNIRS Block Average Analysis
Data were analyzed using the open-sourced Homer3 (v1.26) Toolbox (BUNPC, Huppert, Diamond et al. 2009). The raw fNIRS signals were first converted into changes in optical density data by taking the logarithm of the signal (function: hmrR_Itensity2OD). A PCA filter (function: hmrR_PCAFilter; input parameters: nSV [0.80]) and a bandpass filter (function: hmrR_BandpassFilt; input parameters: hpf [0.01] and lpf [0.20]) were applied to the optical density data to remove motion artifacts and physiological noise, respectively. This method omits the use of short separation channels for limiting of motion artifacts to avoid over-simplifying the data. The oxygenated-hemoglobin (HbO) and deoxygenated-hemoglobin (HbR) concentrations were then obtained using the modified Beer-Lambert law with a partial pathlength factor of 1 (function: hmrR_OD2Conc; input parameters: ppf = [1.0 1.0]). The hemodynamic response function (HRF) was then estimated by a block averaging approach of each task/stimulus event (function: mhrR_BlockAvg; input parameters: trange [-2.0 15.0]. Beta values (i.e., HbO) were calculated during the Block Averaging analysis. The processed oxygenated hemodynamic (HbO) data were exported from Homer3 and analyzed separately. Regions of interest were determined by grouping each channel based on the MNI coordinates determined in AtlasViewer (Aasted, Yucel et al. 2015). The beta values from a time range of [5 10] sec were extracted from each channel and averaged for each region of interest.
2.5. fNIRS GLM Analysis:
Data were analyzed using the open-sourced Homer3 (v1.26) Toolbox (BUNPC, Huppert, Diamond et al. 2009). The raw fNIRS signals were first converted into changes in optical density data by taking the logarithm of the signal (function: hmrR_Itensity2OD). The oxygenated-hemoglobin (HbO) and deoxygenated-hemoglobin (HbR) concentrations were then obtained using the modified Beer-Lambert law with a partial pathlength factor of 1 (function: hmrR_OD2Conc; input parameters: ppf = [1.0 1.0]). The hemodynamic response function (HRF) was then estimated by a general linear model (GLM) approach that uses iterative weighted least squares (function: mhrR_GLM; input parameters: trange [-2.0 15.0], glmSolveMethod = 2, idxBasis = 1, paramsBasis = [0.5 0.5], rhoSD_ssThresh = 15.0, flagNuisanceRMethod = 1, driftOrder = 0) (Barker, Aarabi et al. 2013). The response was modeled using consecutive Gaussian temporal basis function with a time delay of 6 sec, a standard deviation of 0.5 sec, and with their means separated by 0.5 sec over the regression time range of -2 to 15 seconds. Within this calculation, short separation channels were used as regressors to limit motion artifacts. These time ranges were chosen to include the full hemodynamic response to the task. Beta values (i.e., changes in HbO) were calculated during the GLM analysis and represent the weighted responses of the individual channels during the task compared to baseline levels. The processed changes in oxygenated hemodynamic (ΔHbO) data were exported from Homer3 and analyzed separately. Regions of interest were determined by grouping each channel based on the MNI coordinates determined in AtlasViewer. The beta values from a time range of [5 10] sec were extracted from each channel and averaged for each region of interest.
2.6. Negative Beta Value Processing
After deciding the fNIRS analysis to conduct, the management of negative values for usage in laterality indices must be determined. When utilizing the block averaging analysis, the beta values become a direct measure of the oxygenated hemodynamic response, which was labeled as HbO. When utilizing the GLM analysis, the beta values become a measure of the change of HbO from the canonical model, or the Gaussian model used in this study, which was labeled as a change in HbO. Thus, in this study, “HbO” means the block averaging analysis was conducted, and the “change in HbO” means the GLM analysis was conducted; however, the term “beta” value was used to describe both analysis techniques as a means of simplicity.
Inclusion of All Beta Values
When using the block averaging analysis, the beta (or HbO) values are more frequently positive values than negative values. However, when using the GLM analysis, the beta (or change in HbO) values tend to produce positive and negative values. Thus, one option to consider is the inclusion of all beta values, which includes both positive and negative integers. Using negative values would accordingly allow for the inclusion of all activity within a given region of interest if the included channels had both positive and negative values.
Inclusion of Positive Beta Values Only
Another option to consider is the inclusion of positive integers only. In this strategy, the negative beta values were changed to zero under the inclusion criteria of positive beta values only. Negative beta values can indicate lower activation (block averaging and GLM) or suppression (GLM analysis). Only including positive beta values would include higher activation (block averaging and GLM) or a significant increase in activation (GLM).
2.7. Laterality Index Formulas
Finally, a laterality index (LI) formula needs to be determined. Three laterality index (LI) formulas were chosen for this study to reveal hemispheric dominance (i.e., brain laterality), as several variations of the formula have been reported (Seghier 2008). Formula 1 included all beta (positive and negative) values and took the absolute value of all beta values. In this case, the averaged overall amount of activity within each ROI (both activation and suppression) would then be expressed in the laterality index. Formula 2 only included positive beta values, which were assumed to be activation (i.e., an increase in neuronal firing), thus excluding negative values which were assumed to be suppression (i.e., a decrease in neuronal firing). In this case, including the absolute value in the denominator of the formula would prevent possible zero value (i.e., division error) as well as increase normalization of the beta values. Formula 3 included all beta (positive and negative) values and did not take the absolute value of all beta values. In this case, all activity would be included, normalization was restricted to the beta values, and no steps were taken to prevent a division error.
Left vs. Right | Ipsilateral vs. Contralateral |
Formula 1: \(LI= \frac{{ABS(HbO}_{L})-ABS({HbO}_{R})}{{ABS(HbO}_{L})+{ABS(HbO}_{R})}\)\(LI= \frac{{ABS(HbO}_{ipsi})-ABS({HbO}_{contra})}{{ABS(HbO}_{ipsi})+{ABS(HbO}_{contra})}\)
Formula 2: \(LI= \frac{{HbO}_{L}-{HbO}_{R}}{{ABS(HbO}_{L})+ABS({HbO}_{R})}\)\(LI= \frac{{HbO}_{ipsi}-{HbO}_{contra}}{{ABS(HbO}_{ipsi})+ABS({HbO}_{contra})}\)
Formula 3: \(LI= \frac{{HbO}_{L}-{HbO}_{R}}{{HbO}_{L}+{HbO}_{R}}\)\(LI= \frac{{HbO}_{ipsi}-{HbO}_{contra}}{{HbO}_{ipsi}+{HbO}_{contra}}\)
In these equations, HbOL represents the average oxygenated hemodynamic response of channels in the left hemisphere, HbOR represents the average oxygenated hemodynamic response of channels in the right hemisphere, HbOipsi represents the average oxygenated hemodynamic response of channels in the ipsilateral hemisphere to the hand performing the task, HbOcontra represents the average oxygenated hemodynamic response of channels in the contralateral hemisphere to the hand performing the task. The three formulas under the criterion of Left vs. Right did not account for handedness (i.e., the hemisphere or ROI relative to the hand performing the task) and will not change based on the hand performing the task (Nishiyori, Bisconti et al. 2016). The three formulas under the criterion of Ipsilateral vs. Contralateral did account for handedness and thus the formulas will change based on the hand performing the task (Kim, Lee et al. 2022).
The LI normalizes cortical activation differences between channels, thereby revealing which hemisphere experience a larger change during the task. Negative LI values indicate right hemisphere dominant activity, while positive LI values indicate left hemisphere dominant activity. Thus, an LI value of “-1” indicates complete right hemisphere dominance, an LI value of “+1” indicates complete left hemisphere dominance, and an LI value between “+0.2” and “-0.2” indicates bilateral dominance (Seghier 2008).
2.8. Statistical Analysis
Paired t-tests were conducted between the average beta values of each region of interest in the left hemisphere and region of interest in the right hemisphere. This statistical approach has also been used by other researchers to determine brain lateralization (Khaksari, Smith et al. 2021). If the difference between two regions of interest is statistically significant (p < 0.05), then the larger beta value indicates hemispheric dominance (i.e., if the left beta value is higher, then brain laterality indicates left hemispheric dominance). However, if the comparison between two regions of interest is not statistically different, then laterality is deemed to have a bilateral dominance.