This work was partly supported by the Japan Society for the Promotion of Science (JSPS) Grant-in-Aid for Scientific Research (C) 18K08956 and a fund from the Central Research Institute of Fukuoka University (No. 201045).

This study was approved by the institutional review board (IRB) of the Fukuoka University, Japan (IRB No. 2017M017). Prior to participation, all patients provided written informed consent.
1. Preparation of the fNIRS experiment
NOTE: A multi-channel continuous-wave laser-based NIRS system for this experiment was used. The wavelengths of the near-infrared light were 780 nm, 805 nm, and 830 nm, and the sampling rate was set at 7.8 Hz. The time and spatial resolution (di...

<ol>
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The authors have no conflicts of interest relevant to this study to disclose.

In our group analytic methods for fNIRS, in addition to performing an imaging analytic method by qualitative t-statistic mappings, we compared pre- vs. post-intervention (robot-assisted exercise) using the comparative multichannel analysis. For qualitative analysis, we used the NIRS-SPM software as a mass-univariate approach based on the generalized linear model. The NIRS-SPM analysis shows qualitative results of each session by visualizing the activated area during the task. Moreover, the information of the non...

Neurorehabilitation plays an important role in the functional recovery following sensorimotor disturbance. To clarify the mechanisms of neuroplasticity-associated functional recovery, various neuroimaging technologies have been used, such as functional magnetic resonance imaging (fMRI), positron emission tomography (PET), electroencephalography (EEG), and functional near-infrared spectroscopy (fNIRS). Different imaging modalities have different advantages and disadvantages. Although the fMRI is the most typical device, it is affected by magnetic fields, has a high cost, high physical restriction, and limited sensorimotor tasks1,2,3,4. The fNIRS device stands out as a noninvasive optical neuroimaging and has a relatively lower spatial resolution, but it has a better temporal resolution than fMRI4. fNIRS is suitable when verifying treatment effects because it compares the pre- versus post-intervention effects, has dynamic motor tasks, is portable, and functions more in natural environments than fMRI1,2,4. NIRS has been reported to be more suitable in the fields of cerebrovascular disease, epileptic disorders, severe brain injury, Parkinson&#39;s disease, and cognitive impairment1,5. With regard to sensorimotor tasks, it is widely used in gait and standing balance6,7,8, upper limb function (hand grasping, finger tapping)8,9, complex motor skill training10,11, robotics12,13,14,15, and brain-computer interface16,17,18. The fNIRS is based on the principles of optical neuroimaging and neurovascular coupling, which measure cortical metabolic activity, increased blood flow, and consequently cortical activity as secondary signals19. fNIRS signals have been reported to have strong correlations with signals of blood oxygen level-dependent fMRI20. A continuous-wave fNIRS uses the modified Beer-Lambert law&#160;to determine the changes in oxygenated hemoglobin (HbO2) and deoxygenated hemoglobin (HHb) cortical concentration levels based on measured changes in broadband near-infrared light attenuation21,22. Because it was not possible to measure the differential path-length factor (DPF) using the continuous-wave NIRS system, we assumed that the DPF was constant and that hemoglobin signal changes were denoted in arbitrary units of millimole-millimeter (mM x mm)2,18.
The fNIRS experiments need to select the most adequate methods including the probe settings, the experiment designs, and the analysis methods. Regarding the probe setting, the international 10-20 method used in EEG measurement is the setting standard used by many researchers in neuroimaging. In recent years, coordinate settings based on the standard brain on the basis of Montreal Neurological Institute (MNI) coordinates have been used. The experiment uses a block design, generally used for sensorimotor tasks, and an event-related design. This is a method of comparing changes in hemoglobin concentration at rest and during tasks; HbO2 concentration levels increase and HHb concentration levels decrease with changes in cerebral blood flow associated with task-dependent cortical activity. Although there are various analysis methods, the NIRS-SPM free software enables an analysis similar to the statistical parametric mapping (SPM) of fMRI. The treatment of NIRS data uses a mass-univariate approach based on the general linear model (GLM). When performing task-dependent brain activity analysis, the fNIRS measurements can be affected by evoked or non-evoked neuronal activity and systemic physiological interferences (heart rate, blood pressure, breathing rate, and autonomic nervous system activity) in the cerebral and extracerebral compartment23. Therefore, pre-analysis processing, filtering, wavelet conversion, and principal component analysis are useful23. Regarding filtering and artifacts of the data processing using the NIRS-SPM, low-pass filtering9 and the wavelet minimum description length (Wavelet-MDL)24 detrending were used to overcome the motion or other sources of noise/artifact. For details of this analytic method, refer to the report of Ye et al.25. Although there are reports using only SPM, it is only a qualitative index by image analysis, and due to the low spatial resolution of NIRS, extreme caution is required for group analysis. Moreover, when the DPF is constant, numerical comparisons between channels and individuals should not be performed, but the difference in the changes in each channel can be verified. Based on the above conditions, in order to supplement the NIRS-SPM group analysis results, we used the original analysis method for multi-channel analysis after improving the accuracy of spatial registration. This multi-channel analysis compared the amplitude of the change in HbO2 and HHb levels between the rest and on-task periods at each channel before and immediately after treatment using hierarchical mixed models with fixed interventions (before or after), fixed periods (rest or on-task), and random individual effects.
In this way, there are several fNIRS measurement and analysis methods; however, no standard method has been established. In this paper, we introduce our methods, qualitative GLM-based statistical parametric mapping and the comparative multi-level hierarchical mixed model, to analyze data obtained from a multi-channel fNIRS experiment of pre- vs. post-intervention using a block design with sensorimotor tasks.

<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>3D-digitizer software</td>
			<td>TOPCON</td>
			<td>-</td>
			<td>NS-1000 software ver.1.50</td>
		</tr>
		<tr>
			<td>NIRS system</td>
			<td>Shimadzu</td>
			<td>-</td>
			<td>FOIRE-3000</td>
		</tr>
		<tr>
			<td>Robot</td>
			<td>CYBERDYNE</td>
			<td>-</td>
			<td>Single-joint type Hybrid Assitive Limb (HAL-SJ)</td>
		</tr>
	</tbody>
</table>

qualitative and comparative cortical activity data analyses from a functional near-infrared spectroscopy experiment applying block design

Neuroimaging studies play a pivotal role in the evaluation of pre- vs. post-interventional neurological conditions such as in rehabilitation and surgical treatment. Among the many neuroimaging technologies used to measure brain activity, functional near-infrared spectroscopy (fNIRS) enables the evaluation of dynamic cortical activities by measuring the local hemoglobin levels similar to functional magnetic resonance imaging (fMRI). Also, due to lesser physical restriction in fNIRS, multiple variants of sensorimotor tasks can be evaluated. Many laboratories have developed several methods for fNIRS data analysis; however, despite the fact that the general principles are the same, there is no universally standardized method. Here, we present the qualitative and comparative analytic methods of data obtained from a multi-channel fNIRS experiment using a block design. For qualitative analysis, we used a software for NIRS as a mass-univariate approach based on the generalized linear model. The NIRS-SPM analysis shows qualitative results for each session by visualizing the activated area during the task. In addition, the non-invasive three-dimensional digitizer can be used to estimate the fNIRS channel locations relative to the brain. To corroborate the NIRS-SPM findings, the amplitude of the changes in hemoglobin levels induced by the sensorimotor task can be statistically analyzed by comparing the data obtained from two different sessions (before and after intervention) of the same study subject using a multi-channel hierarchical mixed model. Our methods can be used to measure the pre- vs. post-intervention analysis in a variety of neurological disorders such as movement disorders, cerebrovascular diseases, and neuropsychiatric disorders.

Herein, we introduce the robot-assisted rehabilitation that our group is currently working on: the biofeedback effects on upper limb motor deficit in patients with acute stroke. We included 10 consenting stroke patients (mean age: 66.8 &#177; 12.0 years; two women and eight men) who were admitted to our hospital. At the subacute stroke stage, more than 2 weeks after the onset, we evaluated the motor-related cortical activity of these patients using an fNIRS system before and immediately a...

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Qualitative and Comparative Cortical Activity Data Analyses from a Functional Near-Infrared Spectroscopy Experiment Applying Block Design

We describe the analysis of continuous-wave functional near-infrared spectroscopy experiment using a block design with a sensorimotor task. To increase the reliability of the data analysis, we used the qualitative general linear model-based statistical parametric mapping and the comparative hierarchical mixed models for multi-channels.

Neuroimaging studies play a pivotal role in the evaluation of pre- vs. post-interventional neurological conditions such as in rehabilitation and surgical ...

The interpretation of F-N-I-R-S, or fNIRS data, can be difficult for novice users as the data usually cannot be quantified. Our protocol using two complimentary methods provides a better understanding of this data. The main advantage of this technique is the increase and the reliability of the data analysis using two qualitative, GLM-based statistical parametric mapping and comparative hierarchical mixed models for multi-channels.Before performing an fNIRS experiment, place the fNIRS device in a dark, noiseless area and place a whole head fNIRS recording cap onto the head of the subject, such that the position corresponding to the center of the international 10-20 system is located at the number 245 holder of the head cap. Attach the marking sticker to reference location points and take pictures of the subject's head with the probe locations, including the reference points from 15 perspectives. Arrange the 48-channel system with 32 optodes to a head cap bilaterally over the frontal and parietal areas as regions of interest and use the 3D digitizer software to determine the spatial registration.After scanning picture data for the entire head, determine the spatial coordinates of the subject by auto measurement and save the data as Origin and Others files. To perform an fNIRS analysis, have the subject get into a comfortable position and instruct the subject to close their eyes. Then inform the subject of the start and stop cues and have the subject perform the block design task while maintaining the same upright posture for each task.To use NIRS-SPM software to perform a qualitative general linear model analysis, start the NIRS-SPM program in the MATLAB software and select the NIRS system option from the pop-up menu. Select the Load button. To detect the spatial registration of the NIRS channel location, select the standalone and With 3D Digitizer checkboxes.In the From Real Coordinates to MNI Space, use the dialog box to select the coordinate reference point and coordinate probes/channels files. Click Registration and select the points to proceed to the spatial estimation. Click OK and Project MNI Coordinate to Rendered Brain.Select Dorsal View and click Save. In the Specify the 1st Level section, select the NIRS data filename and SPM directory. Check the hemoglobin checkbox and select specify design and sec.Select vector of onset and durations and enter a vector of onset multiplied by the duration of the experimental conditions as indicated. For detrending, select Wavelet-MDL. Use the precoloring method, low-pass filter, and select hrf.Correct for the serial correlation, select none. To estimate the temporal correlations, check Individual Analysis to analyze a single individual. To estimate the temporal correlations for a group of subjects, check Group Analysis, then compute the activation map based on the changes in hemoglobin level for the standardized brain.To perform a multi-channel comparative analysis based on a hierarchal mixed model, open an appropriate statistical analysis software program and convert the text document of oxygenated and deoxygenated hemoglobin concentration changes in the nearest data file processed with the low pass filter to the spreadsheet software comma separated values file. Use the commands to create a pre-versus post-intervention import data for each subject and run the pre-and post-intervention data command for each channel as indicated. Based on the data obtained from the output results, enter the pre-versus post-intervention differences in change, rest, and on-task values of each channel in the spreadsheet.Then enter the numerator and denominator degrees of freedom and the F and P values of the interact item of the fixed effect type three test to the spreadsheet. In this representative group analysis of 10 stroke patients, an increase in the cortical activity of the primary motor cortex was observed in the measured hemisphere immediately after the robot-assisted rehabilitation compared to that observed before the training. In this multi-channel group analysis comparing pre-versus post-intervention, an increased cortical activity was observed in the primary motor cortex after intervention, the same brain region as observed in the NIRS-SPM.Our methods can be used to conduct pre-and post-intervention analysis for a variety of neurological disorders, such as movement disorders, cerebrovascular diseases, and neuropsychiatric disorders. Instead of elbow movements, researchers can also apply other block design tasks, such as leg movements. In addition, our protocol is useful for revealing treatment effects in various settings.

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Research

JoVE Journal

Neuroscience

3.6K Views.  Fukuoka University. The interpretation of F-N-I-R-S, or fNIRS data, can be difficult for novice users as the data usually cannot be quantified. Our protocol using two complimentary methods provides a better understanding of this data. The main advantage of this technique is the increase and the reliability of the data analysis using two qualitative, GLM-based statistical parametric mapping and comparative hierarchical mixed models for multi-channels.Before performing an fNIRS experiment, place the fNIRS d...