Quantitative comparison of resting-state functional connectivity derived from fNIRS and fMRI: A simultaneous recording study

NeuroImage 60 (2012) 2008–2018

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Quantitative comparison of resting-state functional connectivity derived from fNIRS and fMRI: A simultaneous recording study Lian Duan, Yu-Jin Zhang, Chao-Zhe Zhu ⁎ State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, P. R. China

a r t i c l e

i n f o

Article history: Received 19 November 2011 Revised 2 February 2012 Accepted 7 February 2012 Available online 14 February 2012 Keywords: Resting-state functional connectivity fNIRS fMRI Validity Simultaneous recording Graph theory

a b s t r a c t The feasibility of functional near-infrared spectroscopy (fNIRS) to assess resting-state functional connectivity (RSFC) has already been demonstrated. However the validity of fNIRS-based RSFC has rarely been studied. In the present study, fNIRS and fMRI data were simultaneously acquired from 21 subjects during the resting state. After the spatial correspondence was established between the two imaging modalities by transforming the fMRI data into fNIRS measurements space, the index of Between-Modality-Similarity (BMS) of RSFC was evaluated across multiple spatial scales. First, the RSFC between the bilateral primary motor ROI was quite similar between fNIRS and fMRI for all the subjects (BMSROI = 0.95 ± 0.04 for HbO and BMSROI = 0.86 ± 0.13 for HbR). Second, group-level sensorimotor RSFC maps (0.79 for HbO and 0.74 for HbR) showed higher between-modality similarity than individual-level RSFC maps (0.48 ± 0.16 for HbO and 0.41 ± 0.15 for HbR). Finally, for the first time, we combined fNIRS and graph theory to investigate topological properties of resting-state brain networks. The clustering coefficient (Cp) and characteristic path length (Lp) which are the most important network topological parameters, both showed high between-modality similarities (BMSCp = 0.90 ± 0.03 for HbO and 0.90 ± 0.06 for HbR; BMSLp = 0.92 ± 0.04 for HbO and 0.91 ± 0.05 for HbR). In summary, the converged results across all the spatial scales demonstrated that fNIRS is capable of providing comparable RSFC measures to fMRI, and thus provide direct evidence for the validity of the optical brain connectivity and the optical brain network approaches to functional brain integration during resting state. © 2012 Elsevier Inc. All rights reserved.

Introduction Synchronization of spatially remote neurophysiological activity during the resting state, referred to as resting-state functional connectivity (RSFC), has received increasing attention, especially in the context of the functional magnetic resonance imaging (fMRI) community (Fingelkurts and Kahkonen, 2005; Fox and Raichle, 2007). RSFC has been found in various brain systems, such as motor (Biswal et al., 1995; Xiong et al., 1999), visual (Cordes et al., 2000; Lowe et al., 1998), auditory (Cordes et al., 2000), language (Cordes et al., 2000; Hampson et al., 2002), attention (Fox et al., 2006), and default-mode networks (Greicius et al., 2003), and is believed to reflect the ongoing interactions between neuronal populations during rest (He et al., 2008; Shmuel and Leopold, 2008). It has also been applied to study various neurological and psychiatric diseases (Fox and Greicius, 2010). In the last 2 years, functional near-infrared spectroscopy (fNIRS), an emerging non-invasive optical imaging technique for human brain mapping, has been introduced into RSFC studies by White et

⁎ Corresponding author. Fax: + 86 10 58806154. E-mail address: [email protected] (C.-Z. Zhu). 1053-8119/$ – see front matter © 2012 Elsevier Inc. All rights reserved. doi:10.1016/j.neuroimage.2012.02.014

al. (2009) and Lu et al. (2010). These pioneering studies and the following investigations (Homae et al., 2010; Mesquita et al., 2010; Zhang et al., 2010c) reported that RSFC was found using fNIRS in sensorimotor, visual, auditory, and language systems, as well as wholebrain functional networks. fNIRS has the capacity of measuring different types of concentration changes in hemoglobin parameters (oxygenated- (HbO) and deoxygenated- (HbR) hemoglobin), which provides a more comprehensive description of RSFC. Moreover, fNIRS is quiet, comfortable and insensitive to subject motion, which is well suited for special populations such as infants and patients. Finally, the portable and cost-effective features of fNIRS make it much easier to translate RSFC approaches from laboratory environments to clinical applications. Accordingly, fNIRS is becoming a promising imaging modality for RSFC assessment complementary to fMRI in spite of its limited spatial resolution (about 1–3 cm, see Boas et al., 2004). Reliability, reproducibility, and validity of this newly developed RSFC detecting technique is of great importance for its practical application in basic and clinical neuroscience. Recently, the test–retest reliability of fNIRS-based RSFC derived from both the seed-based correlation method (Zhang et al., 2011) and independent component analysis (ICA) (Zhang et al., 2010b) has been positively verified. The reproducibility of RSFC detection has also been demonstrated using

L. Duan et al. / NeuroImage 60 (2012) 2008–2018

different fNIRS machines (Niu et al., 2011). These studies have confirmed that fNIRS-based RSFC is reliable and reproducible for RSFC detection. Thus far, however, the validity of fNIRS-based RSFC has seldom been investigated. White and colleagues made a preliminary attempt for this purpose (White et al., 2009). They separately recorded the fNIRS and fMRI on one subject during the resting state and qualitatively demonstrated the congruence between these two imaging modalities for RSFC detection. However, significant effort is still required to clarify the issue. First, a direct comparison of RSFC between fNIRS and fMRI using simultaneous recording remains vacant. The simultaneous recording is critical to circumvent the possible influence of the variable ongoing brain activity on the comparisons from sequential recording (Kang et al., 2011; Majeed et al., 2009, 2011). Second, the two imaging modalities present significant differences in spatial resolution and measurable coverage, making it necessary to establish a spatial correspondence between these two kinds of imaging data to assist in the comparisons. Third, quantitative comparisons are demanded to clarify to what extent and in which aspects the two imaging modalities are consistent for RSFC detection. Finally, a larger sample size is necessary to draw a reliable conclusion to avoid error produced by the inter-subject variation. In the current study, fNIRS and fMRI were simultaneously recorded at the resting state in a relatively large subject group. After spatial correspondence between fNIRS and fMRI measurements was established by transforming fMRI data into fNIRS space, a direct and quantitative comparison of RSFC was conducted across three different spatial scales of functional brain integration during the resting state. Materials and methods Subjects and paradigm Twenty-one healthy young adult subjects (23 ± 2 years of age, 20 males and 1 female) recruited from Beijing Normal University participated in this study. Before the experiment, informed consent was obtained from all subjects. The study protocol was approved by the Institutional Review Board at State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University. All subjects underwent an 11-minute session of resting-state simultaneous recording of fNIRS and fMRI. Subjects were instructed to keep still with their eyes closed, relax their mind, and remain as motionless as possible during the scan. Data acquisition The fNIRS measurements were conducted using an ETG-4000 optical topography system (Hitachi Medical Company, Tokyo, Japan). The absorption of near-infrared light at two wavelengths (695 nm and 830 nm) was measured with a sampling rate of 10 Hz. Two 4 × 4 probe sets were used in this study with each probe set consisting of eight emitters and eight detectors, forming 24 measurement channels (48 channels in total). The emitters and detectors were positioned alternately in two-piece elastic holders, with an emitterdetector distance of 30 mm. To ensure that the probe set covered the bilateral sensorimotor area, holders were placed over the subject's head with the left channel 1 above T3 and the right channel 3 above T4 in accordance with the international 10–20 system (Jasper, 1958). All channels were marked on the scalp by Vitamin E capsules which are visible in structural MR imaging (Fig. 1A). A balloon-inflation model (Okamoto and Dan, 2005) was used to project the measurement channels onto the cortical surface. Then the automated anatomical labeling (AAL) template (Tzourio-Mazoyer et al., 2002) was used to further determine the anatomical localization of each fNIRS measurement channel (Fig. 1B). During data acquisition, the subjects were supine in the MRI scanner with their heads fixed by straps. The lighting in the scanner was

2009

turned off to reduce the interference of ambient light on the nearinfrared light. The ETG-4000 machine was located in the MRI control room and connected with the optodes using a set of 10 m long optical fibers (Hitachi Medical Company, Tokyo, Japan). ETG-4000 and fMRI were synchronously triggered to start/stop recording electronically when the functional scan started/ended. The structural and functional MRI data were acquired using a SIEMENS TRIO 3-Tesla scanner in the Imaging Center for Brain Research, Beijing Normal University. The functional images were obtained using an echo-planar imaging sequence with the following parameters: 33 axial slices, time repetition (TR) = 2000 ms, time echo (TE) = 30 ms, thickness/gap = 4/0.6 mm, flip angle (FA) = 90°, field-of-view (FOV) = 200 × 200 mm 2, acquisition matrix size = 64 × 64. The T1-weighted structural image was acquired using a magnetization-prepared rapid gradient echo (MPRAGE) sequence: 144 slices, TR = 2530 ms, TE = 3.39 ms, slice thickness = 1.33 mm, FA = 7°, FOV = 256 × 256 mm 2, and in-plane resolution = 256 × 256. Data pre-processing Functional MR images were slice timing corrected, realigned, and coregistered with structural images using SPM8 software (see http://www.fil.ion.ucl.ac.uk/spm). Each voxel's time course was detrended by removing the first- and second-order drifts and temporally band-pass filtered (0.01–0.08 Hz) to extract the low frequency fluctuations (Biswal et al., 1995). To control the confounding effect of the non-neurophysiological related processes, the average signals of the whole brain, white matter and cerebrospinal fluid, as well as the six motion parameter time courses, were removed via a linear regression procedure (Biswal et al., 2010). The fNIRS optical density data were first converted to concentration changes in HbO and HbR for each channel using the modified Beer–Lambert law (Cope and Delpy, 1988). Then the HbO and HbR data was detrended by removing the first- and second-order drifts. Typical noise components, such as cardiac and respiratory signals, motion-induced artifacts and other instrumental noise were further removed by ICA (Kohno et al., 2007; Zhang et al., 2010a). A band-pass filter (0.01–0.08 Hz) was used to extract the low frequency fluctuations. The first 20 seconds and the last 20 seconds of both types of data (fNIRS and BOLD) were discarded for steady-state control. During data preprocessing, it was found that three of the subjects exhibited large head motions in the fMRI scan (more than 2 mm displacement or 2 mm rotation), and another three subjects' optical data were too noisy (possibly due to poor contact between the optodes and the scalp). These six subjects were excluded from further analysis. Spatial correspondence between fNIRS and fMRI measurements The blood oxygenation level-dependent (BOLD) response measured by fMRI and the hemoglobin concentration changes measured by fNIRS both arise from the regional blood oxygenation changes. However, the two imaging modalities present significant differences in spatial resolution, measuring coverage and ability of spatial localization. These differences make it necessary to build a spatial correspondence between the two kinds of measurements before the direct comparison of the RSFC derived from the two modalities. In other words, for a given fNIRS measurement channel, it was necessary to determine which voxels were involved and how the BOLD signals there contributed to the channel measurement. Here, the spatially weighted averaging method (Sassaroli et al., 2006) was used to solve this problem:

n

BOLDtransformed ðt Þ ¼

∫V BOLDraw ðx; y; z; t ÞP n ðx; y; zÞdxdydz 1 T T ∫0 dt∫V BOLDraw ðx; y; z; tÞP n ðx; y; zÞdxdydz

;

ð1Þ

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Fig. 1. Configuration and localization of the fNIRS measurement. The structure images shown here is from Subject 9 as an example. (A) Left and right view of the fNIRS measurement configuration. All the measurement channels are labeled by Vitamin E capsules (bright spheres in the figure). (B) Anatomical locations of each measurement channel. Channels located near the central sulcus are labeled with red.

n where BOLDtransformed (t) is the BOLD time-course transformed into the fNIRS space corresponding to the Channel n, and BOLDraw(x, y, z, t) is the raw fMRI signal at time t and position (x, y, z). V denotes the set of voxels involved in the weighted averaging. T is the duration of the measurement. The banana-shaped photon-hitting density function for Channel n was given by

 
1=2 h i1=2  z2 exp −k x2 þ y2 þ z2 þ ðd−xÞ2 þ y2 þ z2 P n ðx; y; zÞ ¼
; 3=2 h i3=2 ðd−xÞ2 þ y2 þ z2 x2 þ y2 þ z2 
 h  1=2 i1=2  k x2 þ y2 þ z2 þ 1 k ðd−xÞ2 þ y2 þ z2 þ1 ð2Þ where d is the distance between the light source and detector, and k is the effective attenuation coefficient which demonstrates the depth of the “banana shape”. Here k = 0.23 was used according to Sassaroli et al. (2006). After the transformation, the raw fMRI signals were mapped into the fNIRS space resulting in a 48 fNIRS-format-like fMRI data set, each corresponding to one fNIRS measurement channel. Please note that only voxels in gray matter were used in the weighted summation because only fMRI signal in gray matter may theoretically be functionally associated to the spontaneous neural activity. In the following analysis, the BOLD signals refer to BOLDtransformed instead of BOLDraw. Comparisons between the fMRI-based RSFC and the fNIRS-based RSFC We systemically compared the fMRI-based RSFC (denoted as RSFCfMRI or RSFCBOLD) and the fNIRS-based RSFC (denoted as RSFCfNIRS; RSFCHbO/RSFCHbR is also used below to specifically indicate

RSFC derived from HbO/HbR signals). Three indices of BetweenModality-Similarity (BMS) were defined, across three spatial scales, to quantitatively assess the agreement between RSFCfMRI and RSFCfNIRS. ROI- based RSFC A number of hypothesis-driven fMRI studies have focused on RSFC between specific functional regions. In these studies, the functional significance of the RSFC between these prior-selected regions (region of interest, ROI) was usually investigated, and it was found that some specific inter-regional RSFC strengths may be related with some brain diseases (see Zhang and Raichle (2010d) for a review). In the present study, the ability of fNIRS to detect RSFC between bilateral primary motor cortex (M1) was investigated, as an example, and compared with that of fMRI. The left and right side ROI were defined as channels measuring the left and right side precentral gyrus respectively localized by using the individual structural MRI images (Cauda et al., 2011). The time courses of channels in each ROI were averaged and the Pearson correlation coefficient, r, between the two averaged time courses was calculated as the RSFC strength between the bilateral ROI. The index of BMSROI, measuring the consistency between the RSFC derived from the two modalities, was defined as BMSROI ¼1−jConnectivityfMRI ConnectivityfNIRS j:

ð3Þ

For each subject, ConnectivityfMRI and ConnectivityfNIRS were RSFC strengths from fMRI and fNIRS, respectively. The averaged BMSROI as well as its variance across subjects were then calculated. Moreover, a linear regression model was applied to determine the extent to which the inter-subject variability of RSFC strengths recorded by fMRI could be accounted for by fNIRS. In this way, the consistency between RSFCfMRI and RSFCfNIRS was further evaluated.

L. Duan et al. / NeuroImage 60 (2012) 2008–2018

RSFC spatial map The cerebral cortical areas are organized into multiple functional systems which can be identified using the RSFC approach (Zhang and Raichle, 2010d). In the present study, the sensorimotor system derived by using seed-based correlation method from both the resting-state fMRI and the resting-state fNIRS data were compared (Biswal et al., 1995; Lu et al., 2010; White et al., 2009). The seed region was defined as channels consistently belonging to the sensorimotor area in all subjects. The averaged time course from the seed region (Channel 9 and 16 in the left brain hemisphere) was used as the seed time course. Then the individual RSFC map of the sensorimotor system was obtained by calculating the Pearson correlation coefficient r between the seed time course and each channel's time course. The group-averaged RSFC maps of the sensorimotor system were also obtained by averaging all the individual RSFC maps. The BMSmap index was defined as the Pearson correlation coefficient r between the corresponding RSFC maps of the two modalities: N
P


 rifMRI −r fMRI r ifNIRS −r fNIRS i¼1 sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ; BMSmap ¼ sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi N  N  2 P 2 P i  rfMRI −r fMRI r ifNIRS −r fNIRS i¼1

ð4Þ

i¼1

i i and rfNIRS is the ith channel in the RSFC maps derived where rfMRI from fMRI and fNIRS respectively. r fMRI and r fNIRS is the mean value of the RSFC maps. N is the total number of channels in the map. Note that the seed channels were not involved in the calculation of BMSmap. The receiver operating characteristic (ROC) curve approach was further used to evaluate the sensitivity and specificity of the two modalities for sensorimotor RSFC detection. At individual level, channels anatomically localized in sensorimotor areas were defined as ‘true’, while the others were deemed ‘false’ for ROC calculation purposes (see Fig. 4D). For a given threshold, the channels with abovethreshold r value were determined as ‘positive’ while the others were ‘negative’. Similar to LaConte et al. (2000), the ‘top percentage’ approach was used to generate a series of thresholds, ranging from 0% to 100%, stepped by 8.3% (e.g., if the percentage threshold was 25%, then the 12 channels (48 × 25%) with the top 12 r values were determined as ‘positive’). The true positive rate and the false positive rate were then calculated for each threshold and plotted against each other to generate the ROC curve. In a similar way, the group-level ROC curves derived from the group-averaged RSFC maps were plotted (‘true’ defined as the channels which were localized in the sensorimotor areas on more than half of the subjects, see Fig. 4B). The area under the ROC curve (AUC) was used as the performance index to evaluate the detectability of the two modalities.

Topological properties of the RSFC network Recently, the graph theoretical approaches have been widely used to study the topological properties of resting-state fMRI brain networks and it has been found that the topological properties are closely related to normal and abnormal brain functions (Bullmore and Sporns, 2009; Wang et al., 2009, 2010a; Wang et al., 2010b). In the present study, topological parameters were computed and compared between RSFC networks derived from fMRI and fNIRS. Specifically, for each subject, both the fMRI- and fNIRS- RSFC matrices (with a size of 48 × 48) were generated by calculating the Pearson correlation coefficient (r) between the time courses from each of the possible channel pairs. Each RSFC matrix was then thresholded into a binary graph (i.e., network) with each channel as a network ‘vertex’ and the abovethreshold RSFC as a network ‘edge’. The threshold was determined with the ‘top percentage’ approach (Cole et al., 2010). For a given threshold t, if the r value between two channels is within the top t% r values of the whole matrix, then there is an ‘edge’ between the

2011

two corresponding ‘vertices’; otherwise there is no ‘edge’. Considering the possible effect of the threshold on the topological properties, the percentage threshold t was traversed from 10% to 50%, stepped by 1%. For each t, two basic network topological parameters, the clustering coefficient of the network (CP) and the characteristic path length (LP) (Salvador et al., 2005; Watts and Strogatz, 1998) were calculated for both fMRI- and fNIRS- RSFC networks. Parameter CP describes the local efficiency of information transfer of a network (Latora and Marchiori, 2001). CP is defined as the average of the clustering coefficients of all vertices (in which the clustering coefficient of a vertex is defined as the number of existing edges among the vertex's neighbors divided by all their possible edge numbers. ‘Neighbor’ of a vertex denotes those vertices that are directly connected to this vertex by an edge). Parameter LP quantifies the ability of a network to propagate parallel information or the global efficiency (in terms of 1/LP) (Latora and Marchiori, 2001). LP is defined as the average of the shortest path length over each possible pair of vertices (in which the path length is defined as the number of edges included in the path). Eqs. (5) and (6) formulate the calculations of CP and LP: CP ¼

N 1X Ei N i¼1 V i ðV i −1Þ=2

ð5Þ

N −1 X N X 1 minflpathði; jÞg; NðN−1Þ=2 i¼1 j¼iþ1

ð6Þ

and LP ¼

where N is the number of the vertices of a network, and Ei is the number of edges connecting neighbors of vertex i. Vi is the number of neighbors of vertex i. {lpath(i, j)} is the set of lengths of all possible paths connecting vertices i and j. To evaluate the consistency of the network properties from fMRI CP LP and fNIRS, BMSnetwork and BMSnetwork were defined as follows:

1 0

fMRI

fNIRS C ð t Þ−C ð t Þ

P P 1 Cp A; BMSnetwork ¼ ∑ @1− ½T  t∈T C P fMRI ðt Þ

ð7Þ

and

Lp BMSnetwork ¼

1 0

fMRI

ðt Þ−LP fNIRS ðt Þ

LP 1 A; ∑ @1− ½T  t∈T LP fMRI ðt Þ

ð8Þ

where T is the set of thresholds t and [T] is the number of the elements in T. The BMSnetwork values near to 1 represent high consistency between the network properties from fMRI and fNIRS. Results ROI- based RSFC Fig. 2 illustrates the RSFCfMRI and RSFCfNIRS between bilateral ROIs for each subject with black, red and blue for BOLD, HbO and HbR, respectively. In general, high similarity could be seen between BOLD and fNIRS (especially HbO) for all the subjects. Specifically, the mean value of BMSROI as well as its standard deviation was 0.95 ± 0.04 between BOLD and HbO, and 0.86 ± 0.13 between BOLD and HbR. Fig. 3 shows the scatter plot of the RSFCfMRI and RSFCfNIRS strengths with the linear regression results. Good correlation between the ROI-based RSFC from BOLD and HbO was found with the coefficients of determination of the regression (R 2) of 0.91, and fair correlation was also found between BOLD and HbR, with R 2 of 0.67.

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L. Duan et al. / NeuroImage 60 (2012) 2008–2018

These results suggested that the inter-subject variability of ROI-based RSFC recorded by fMRI could be well explained by fNIRS. RSFC spatial map

Fig. 2. ROI-based RSFC for all subjects. The black, red and blue bars represent RSFC strengths derived from BOLD, HbO and HbR, respectively. The mean BMSROI and the standard deviation is 0.95 ± 0.04 between BOLD and HbO, and is 0.86 ± 0.13 between BOLD and HbR.

The group-averaged RSFC maps of the two modalities are presented in Fig. 4A with three rows for BOLD, HbO and HbR, respectively. For convenience, Fig. 4B shows those channels which are localized in the sensorimotor areas on more than half of the subjects. In general, the group-averaged RSFC patterns derived from both modalities covered the left and right sensorimotor areas. The RSFCBOLD maps and RSFCHbO maps manifested a more similar pattern and were more spatially specific than the RSFCHbR. This result was further confirmed by the analysis of the index of BMSmap. The BMSmap between BOLD and HbO which reached 0.79 was a little higher than that between BOLD and HbR (0.74). In a similar way, an individual result is shown in Fig. 4C with a randomly selected subject as an example. Although the RSFC distributed within a relatively large extent for all the three RSFC maps, they covered most of the sensorimotor areas as shown in Fig. 4D. Moreover, fair BMSmap was also found between BOLD and HbO (0.53) and between BOLD and HbR (0.48). Finally, the BMSmap values for all 15 subjects are listed in Table 1. The BMSmap was 0.48 ± 0.16 (mean and standard deviation) for HbO, and 0.41 ± 0.15 for HbR. ROC curves derived from the group-averaged RSFC maps are shown in Fig. 5A for BOLD (black), HbO (red) and HbR (blue) respectively. The AUC of the HbO ROC curve (0.87) was much closer to that of BOLD (0.95) than that of HbR (0.79). Individual ROC results are shown in Fig. 5B–D for BOLD, HbO and HbR respectively with each black curve for one subject. On average, BOLD was more sensitive and specific than HbO and HbR in sensorimotor system detection, and HbO showed a performance closer to BOLD than HbR. The mean AUC across subjects of BOLD (0.79), HbO (0.69) and HbR (0.60) also presented a similar result. Please also note the difference between the AUC of the ROC curves derived from the group-averaged RSFC maps and the mean AUC of the ROC curves derived from individual RSFC maps. Topological properties of the RSFC Network Figs. 6 and 7 show CP and LP for all 15 subjects. CP and LP values were plotted against the increasing thresholds of reserved connections, from 10% to 50%, stepped by 1%, with black, red and blue representing BOLD, HbO and HbR, respectively. In general, CP and LP showed comparable values and similar going trends between the two modalities. More importantly, such consistencies held for almost all the subjects. Quantitative analysis on BMSnetwork also showed Cp high consistencies between the modalities. The BMSnetwork value was 0.90 ± 0.03 (mean and standard deviation) for BOLD and HbO, Lp and 0.90 ± 0.06 for BOLD and HbR. The BMSnetwork was 0.92 ± 0.04 for BOLD and HbO, and 0.91 ± 0.05 for BOLD and HbR. Discussions

Fig. 3. Linear regression results showing inter-subject variability of RSFC strengths recorded by fMRI could be well accounted for by (A) HbO and (B) HbR. Each small circle represents a subject. The coefficients of determination of the regression (R2) are 0.91 between BOLD and HbO, and 0.67 between BOLD and HbR.

Recently, increasing attention has been paid to applying fNIRS to investigate RSFC (Homae et al., 2010; Lu et al., 2010; Mesquita et al., 2010; White et al., 2009; Zhang et al., 2010c). It was necessary to test the reliability (Zhang et al., 2010b, 2011), reproducibility (Niu et al., 2011) as well as the validity of the fNIRS-based RSFC. In the pioneering work of White et al. (2009), the fNIRS-based RSFC was qualitatively compared with the fMRI-based RSFC for the first time in the motor and visual areas. Although their measurement was conducted non-concurrently on only one subject, the preliminary but important result implied a good similarity between the RSFC patterns derived from the two modalities. Therefore, the present study was designed to systemically and quantitatively compare the fNIRS-based RSFC

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2013

Fig. 4. RSFC maps. (A) The group-averaged RSFC maps derived from BOLD, HbO and HbR. The BMSmap are 0.79 between BOLD and HbO, and 0.74 between BOLD and HbR. The seed channels are highlighted with a yellow rectangle. Note that maps of different modalities have color ranges of their own to show the RSFC patterns more clearly. (B) The sensorimotor areas at group-level, with green indicating channels which measure the sensorimotor area on over half subjects. (C) RSFC maps of Subject 9 derived from BOLD, HbO and HbR. The BMSmap are 0.53 between BOLD and HbO, and 0.48 between BOLD and HbR. (D) Sensorimotor areas of Subject 9. Please also note illustrations (A) and (C), because the r values of the seed channels are near to 1. We simply set them to the maximum r value among the non-seed channels for clear display.

regional RSFC strengths among individuals. Previous studies have reported that inter-subject differences in RSFC strength could predict task-induced BOLD activity (Liu et al., 2011; Mennes et al., 2010). Many studies have also reported the differences in RSFC between patients with brain disorders and healthy subjects (see Fox and Greicius (2010) for a review). These studies implicated the significance of the inter-subject variability in RSFC. In the future, the simultaneous fMRIfNIRS recording techniques may be used to compare RSFCfMRI and RSFCfNIRS on different groups such as patients to further validate RSFCfNIRS. Second, comparison at the scale of the connectivity map also revealed good agreement between functional system RSFCfMRI and RSFCfNIRS. For the group-averaged RSFC maps, the BMSmap scores were 0.79 and 0.74 for HbO and HbR respectively. Specifically, as shown in Fig. 4A and B, the RSFC map from fMRI was highly specific to the bilateral sensorimotor areas. This also held for the RSFC maps from fNIRS, indicating good agreement between fMRI and fNIRS. Moreover, we have also compared the individual RSFC maps between the two modalities. Visually, as could be seen from the result of a randomly selected subject (Fig. 4C and D), the individual RSFC map from the fMRI was more specific than those from the fNIRS. Although this also holds true on other subjects, we have found that RSFC patterns

and the fMRI-based RSFC using simultaneous acquisition data on a larger subject group. A comprehensive analysis was conducted across three different spatial scales including the ROI, the connectivity map and the network. The results showed good consistencies between RSFCfMRI and RSFCfNIRS, and will be discussed in detail below. First, generally, the ROI-based RSFC analysis revealed a good agreement between the two imaging modalities with high BMSROI for BOLD-HbO (0.95 ± 0.04) and slightly lower BMSROI for BOLDHbR (0.86 ± 0.13). We then detailed the ROI-based RSFC derived from the two modalities for each subject in Fig. 2. As expected, the RSFC between bilateral sensorimotor areas was identified by fMRI for nearly all subjects with distinct and subject-specific strengths. Importantly, fNIRS held and revealed such subject-specific ROI-based RSFC, exhibiting almost as strong a validity as fMRI in detecting individual RSFC strength, especially for the HbO signal. It thus reasonably implies that fNIRS is able to measure the inter-regional RSFC within a widely varying range. Moreover, we used a linear regression model to further quantify the cross-subject consistency between fMRI and fNIRS for ROI-based RSFC. It was found that RSFCfNIRS could account for the inter-subject variability in RSFCfMRI to a large extent (with R 2 of 0.91 and 0.67, for HbO and HbR, respectively), which indicates that fNIRS has the ability to capture the discrepancy of the interTable 1 Individual-level BMSmap. Subject

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

Mean

Standard deviation

HbO vs. BOLD HbR vs. BOLD

.65 .53

.46 .48

.22 .36

.46 .63

.71 .53

.56 .28

.63 .62

.34 .26

.53 .48

.17 .16

.51 .32

.68 .59

.57 .31

.32 .30

.42 .27

.48 .41

.16 .15

2014

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Fig. 5. (A) The ROC curves of the group-averaged RSFC maps for BOLD, HbO and HbR, plotted in black, red and blue respectively. The AUC value for BOLD, HbO and HbR is 0.95, 0.87 and 0.79, correspondingly. (B–D) The individual ROC curves of 15 subjects for BOLD, HbO and HbR, with each black curve for one subject. The mean AUC is 0.79 for (B) BOLD, 0.69 for (C) HbO and 0.60 for (D) HbR.

of fNIRS still covered the sensorimotor areas well and presented higher connectivity strength within sensorimotor areas than between sensorimotor and non-sensorimotor areas. Further cross-modality correlation analysis on RSFC maps for all the subjects showed fair BMSmap scores with a moderate inter-subject variation (0.48 ± 0.16 for BOLD/HbO and 0.41 ± 0.15 for BOLD/HbR, see Table 1). These results indicated that fNIRS can provide satisfactory individual RSFC maps but may be more easily contaminated by noise than fMRI. More specifically, the AUC index of the ROC curve was conducted in order to quantify the performances of both fMRI and fNIRS in detecting the functional systems. The ROC curve from the group-averaged fMRI map showed notably high AUC score (0.95, see Fig. 5A), again demonstrating the excellent ability of fMRI in RSFC detection. On the other hand, as a potential modality carrying tremendous expectations to study RSFC, fNIRS also presented rather good performance with AUC scores of 0.87 and 0.79 for HbO and HbR respectively, which were derived from the group-averaged RSFC maps. For the ROC curves from the individual maps (Fig. 5B–D), both fMRI and fNIRS showed lower performance than the result from their groupaveraged map (with mean AUC scores across subjects of 0.79, 0.69 and 0.60 for BOLD, HbO and HbR, respectively) and showed notable inter-subject variance. fNIRS also presented lower but still comparable AUC scores with fMRI, which was in agreement with the map comparison results. These results suggested that compared with fMRI, the individual-level fNIRS-RSFC map should be interpreted with more caution.

Third, in recent years, the development of the multi-channel fNIRS imaging equipment has made it possible to combine fNIRS and the graph theoretical approaches to study brain function at a larger spatial scale, such as brain networks. In the current study, the topological properties of the brain network constructed from resting-state fNIRS data was assessed and compared with those from fMRI. Two basic topological property parameters (Cp and LP) were investigated. The CP BMSnetwork score was 0.90 ± 0.03 and 0.90 ± 0.06 for BOLD/HbO and LP BOLD/HbR respectively. High BMSnetwork were also presented with 0.92 ± 0.04 for BOLD/HbO and 0.91 ± 0.05 for BOLD/HbR. As shown in Figs. 6 and 7, Cp and LP presented high cross-modality agreement from both absolute values and varying trends throughout the threshold range on all the subjects. To reduce the sensitivity to threshold changes in assessing the similarity between modalities, Cp Lp BMSnetwork and BMSnetwork were defined as the averaging consistenCp cies across a wide range of thresholds. Such high BMSnetwork and Lp BMSnetwork indicated that by using resting-state fNIRS we may also assess and quantify the global and local efficiency of parallel information processing of the brain functional networks (Achard and Bullmore, 2007). With these two basic parameters, more complex features of the brain functional networks, such as the small-world property (Achard et al., 2006), can be derived. And thus the corresponding similarity between fMRI and fNIRS can also be expected and will be confirmed in detail in further studies. With the advantages of fNIRS in studying special populations such as patients and infants (Lloyd-Fox et al., 2010), this will help to disclose the possible

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2015

Fig. 6. CP curves for all 15 subjects. The x coordinate represents different thresholds of reserved connections, from 10% to 50%, stepped by 1%. The y coordinate represents the CP CP value. The CP curves derived from BOLD, HbO and HbR are plotted in black, red and blue, separately. The BMSnetwork score was 0.90 ± 0.03 (mean and standard deviation across subjects) for BOLD and HbO, and 0.90 ± 0.06 for BOLD and HbR.

underlying mechanisms of some neurological and psychiatric disorders (Micheloyannis et al., 2006; Wang et al., 2009, 2010a; Wang et al., 2010b) and investigate the brain's development. Additionally, in fMRI areas, both studies on specific functional networks (Wang et al., 2010a) and the whole brain functional network (Achard et al., 2006) are important. With limited channels supported by the ETG4000 system, the fNIRS measurement here was set to only cover the sensorimotor areas, which restricted us to study a 48-node specific network. In further studies, a larger cortical network could also be studied using the whole head covered fNIRS equipment. The high consistency of RSFC observed across all the spatial scales between fMRI and fNIRS probably originated from their common neurophysiological foundations. Previous studies on combined fMRIEEG (Goldman et al., 2002; Laufs et al., 2003a, 2003b, 2006; Moosmann et al., 2003) and fMRI-electrophysiological recording studies (Scholvinck et al., 2010; Shmuel and Leopold, 2008) have demonstrated that the BOLD fluctuations in the resting state are tightly coupled with the underlying spontaneous neuronal activity. Meanwhile, correlations between the brain's alpha rhythms and fNIRS-HbR signal have been found in a combined EEG-fNIRS study (Moosmann et al., 2003), implicating the underlying neurovascular coupling relationship between the resting-state fNIRS signal and the spontaneous neural activity. Accordingly, the common neurophysiological relevance between fMRI and fNIRS signals and the spontaneous neural activity forms the basis of the consistency between RSFC measured by the two modalities. Moreover, we have also compared the fNIRS-based RSFC derived by using the total-hemoglobin (HbT) signal with the fMRI-based RSFC across all the three spatial scales. In general, HbT showed similar RSFC characteristics with other hemoglobin parameters. Although RSFC derived from the two modalities showed high similarity, they were not completely the same (e.g. the spatial pattern).

This may be related to the following factors: First, as an optical imaging modality, fNIRS is much different from fMRI in imaging mechanism. fNIRS is a trans-cranial brain imaging modality. Before the near-infrared light can reach the cortex, it has to penetrate several superficial layers including skin, skull and CSF. This will introduces systemic physiological noises into the optic signals including cardiac pulsation, respiration, and blood pressure changes which are usually globally distributed (Huppert et al., 2009). Although ICA was used to remove these noises as much as possible, they were difficult to be completely eliminated from the signal. The residual confounding effect of the noises may cause spatiotemporal covariance and reduce the sensitivity and specificity of the fNIRS-based RSFC detection (White et al., 2009; Zhang et al., 2010a), and thus degenerate the consistency between RSFCfNIRS and RSFCfMRI. Second, in the present concurrent fMRI-fNIRS study, the quality of the fNIRS signal was lower than those in our previous fNIRS-alone studies. Contributing to this, in part, may be the lower signal-to-noise ratio (SNR) due to the higher signal attenuation in 10 meter-long optical fibers than in the 3 meter-long fibers usually adopted in fNIRS-alone studies. Additionally, the subjects were supine during the simultaneous recording experiment. This position may, according to our experience, make the contact between the optodes and the parietal scalp not as tight as with the sitting position, and it may induce artifacts to fNIRS signals. These factors would affect RSFCfNIRS result and thus its consistency with RSFCfMRI. Finally, the biophysical origins of the fMRI and fNIRS are not completely the same (Firbank et al., 1998; Gagnon et al., 2012; Liu et al., 1995; Toronov et al., 2007), which may also contribute to the discrepancy. Some methodological and technical issues should also be noted when comparing RSFCfMRI and RSFCfNIRS in a concurrent recording study. First, the simultaneous acquisition is very important here because previous studies suggested that the spontaneous neural activity

2016

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Fig. 7. LP curves for all 15 subjects. The x coordinate represents different thresholds of reserved connections, from 10% to 50%, stepped by 1%. The y coordinate represents the LP LP value. The LP curves derived from BOLD, HbO and HbR are plotted in black, red and blue, separately. The BMSnetwork score was 0.92 ± 0.04 (mean and standard deviation across subjects) for BOLD and HbO, and 0.91 ± 0.05 for BOLD and HbR.

has dynamic characteristics (Majeed et al., 2009). Further studies indicated that the RSFC patterns may also be time-varying (Kang et al., 2011; Majeed et al., 2011). These possible dynamic changes, as well as the variation of the subject state, may impact the homogeneity of the compared RSFC derived from the sequentially recorded data using different modalities, and lead to biased results. Thus, the comparison is much preferred in the context of the simultaneous recording of fMRI and fNIRS. After the simultaneous acquisition of the restingstate data, it is another important issue to establish the spatial correspondence between the two distinct imaging modalities. Some previous studies using diffuse optical tomography (DOT) systems built the correspondence in the fMRI space (Toronov et al., 2007; Zhang et al., 2005). In the present study, comparing the two modalities in the fMRI space would, however, include regions with reduced sensitivity due to our topography imaging system (ETG-4000). Thus we transformed fMRI signals into the fNIRS space with a banana-shaped photonhitting density weighting function. This weighting function can be derived from either a Monte Carlo simulation (Huppert et al., 2006) or a theoretical formula (Sassaroli et al., 2006). We have compared these two approaches and their results are quite similar. Thus the theoretical formula method was used for its convenience. It should be noted that although the fNIRS signals actually arise from the hemodynamic changes of the whole optic pathway covering the skin, skull, CSF, gray matter and the white matter, only voxels in the gray matter were involved in generating the transformed BOLD signals to increase the specificity of the transformed signals to neural activity. Using a simultaneous fMRI-fNIRS recording technique is an interesting but nascent way to investigate the resting state brain functions as well as the physiological basis of the spontaneous fluctuations.

Tong and Frederick (2010) and Tong et al. (2011) calculated correlations between the simultaneously recorded fMRI and fNIRS signals using different time shifts and found fNIRS signal was widely correlated with fMRI signals around the whole brain. Their results suggested that the low-frequency oscillation signals recorded by fMRI and fNIRS may have a global physiological origin. Cooper et al. (2011) also developed the utility of fNIRS in the regression of low-frequency physiological noise from fMRI. In the present study, however, we found a good consistency between RSFC derived from the two modalities through multiple aspects of RSFC properties. Our findings along with the plentiful evidence from resting-state fMRI studies (Goldman et al., 2002; Laufs et al., 2003a, 2003b, 2006; Moosmann et al., 2003; Scholvinck et al., 2010; Shmuel and Leopold, 2008) suggest that the low-frequency spontaneous fluctuations in resting state fNIRS signals may reflect spontaneous brain activity in spite of various physiological confounding origins. Accordingly, much more effort is still needed to quantify the separate contributions of the neural and non-neural activities to the resting-state signals. Our results also demonstrated significantly higher RSFC similarity between HbO and BOLD than that between HbR and BOLD, in scales of ROI (p b 2.7 × 10 -3, one-tail paired t-test for BMSROI) and individual connectivity map (p b 0.02, one-tail paired t-test for BMSmap). This finding is interesting because several previous studies have suggested that HbR shares a more similar signal source with BOLD (Kwong et al., 1992; Ogawa et al., 1992; Toronov et al., 2003). One of the possible reasons is the lower SNR of HbR than HbO (Hoshi, 2007; Tong and Frederick, 2010). In our previous studies, Lu et al. (2010) and Zhang et al. (2010a) reported stronger RSFC from HbO than that from HbR using the seed-based correlation and the ICA method respectively.

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Moreover, higher reliability of HbO than HbR for RSFC detection was also found by Zhang et al. (2010b). These results indicate that, compared with HbR, HbO is more reliable for fNIRS-based RSFC detection. In summary, our study demonstrated that fNIRS is capable of providing comparable measures of resting-state functional connectivity to fMRI. The findings may help to improve the comprehension and integration of results from the fMRI-based and fNIRS-based RSFC studies and promote the application of the optical brain connectivity and network approaches to basic and clinical neuroscience research. Acknowledgments The authors are extremely grateful to three anonymous reviewers for their significant and constructive critiques and suggestions which improve the article very much. The authors also thank Dr. Han Zhang for his comments and technical support. 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