Test-retest assessment of independent component analysis-derived resting-state functional connectivity based on functional near-infrared spectroscopy

Recent studies of resting-state functional near-infrared spectroscopy (fNIRS) have emerged as a hot topic and revealed that resting-state functional connectivity (RSFC) is an inherent characteristic of the resting brain. However, it is currently unclear if fNIRS-based RSFC is test-retest reliable. In this study, we utilized independent component analysis (ICA) as an effective RSFC detection tool to address the reliability question. Sixteen subjects participated in two resting-state fNIRS recording sessions held 1week (6.88±1.09 days) apart. Then, RSFC in the sensorimotor regions was extracted using ICA. Test-retest reliability was assessed for intra- and inter-sessions, at both individual and group levels, and for different hemoglobin concentration signals. Our results clearly demonstrated that map-wise reliability was excellent at the group level (with Pearson's r coefficients up to 0.88) and generally fair at the individual level. Cluster-wise reliability was better at the group level (having reproducibility indices of up to 0.97 for the size and up to 0.80 for the location of the detected RSFC) and was weaker but still fair at the individual level (0.56 and 0.46 for intra- and inter-session reliabilities, respectively). Cluster-wise intra-class correlation coefficients (ICCs) also exhibited fair-to-good reliability (with single-measure ICC up to 0.56), while channel-wise single-measure ICCs indicated lower reliability. We conclude that fNIRS-based, ICA-derived RSFC is an essential and reliable biomarker at the individual and group levels if interpreted in map- and cluster-wise manners. Our results also suggested that channel-wise individual-level RSFC results should be interpreted with caution if no optode co-registration procedure had been conducted and indicated that "cluster" should be treated as a minimal analytical unit in further RSFC studies using fNIRS.     

脑卒中后完全性失语症患者功能连接模式的功能性近红外光谱成像研究

目的:采用功能性近红外光谱技术(functional near-infrared spectroscopy,f NIRS)观察脑卒中后完全性失语症患者功能连接模式的特征。方法:选取脑卒中后完全性失语症患者10例,脑卒中后非失语症患者10例为非失语症对照组,健康中老年人21例为健康对照组。采用f NIRS采集8min的静息态数据,并选择与语言相关的关键脑区,包括背外侧前额叶(dorsolateral prefrontal cortex,DLPFC)、Broca区、颞上回(superior temporal gyrus,STG)、颞中回(middle temporal gyrus,MTG)、Wernicke区、角回、辅助运动区(supplementary motor area,SMA)作为感兴趣区,分别与全脑做相关分析,获得各组的脑功能连接图。结果:与健康对照组比较,完全性失语症组全脑语言网络功能连通性和连接比下降,其中连接比下降有显著性意义(P0.05),同时在左侧DLPFC-右侧SMA、左侧DLPFC-右侧Wernicke、左侧MTG-右侧MTG、左侧SMA-右侧Wernicke、左侧DLPFC-左侧SMA、右侧DLPFC-左侧SMA、左侧SMA-右侧SMA间的功能连接差异有显著性意义(P0.05)。相较于非失语症组,完全性失语症组表现出左侧MTG-左侧角回间功能连接下降且差异有显著性意义(P0.05)。结论:脑卒中后完全性失语症患者全脑语言网络的功能连通性与连接比以及关键语言区之间的功能连接模式存在异常,相关脑区之间的功能连接减弱可能是引起脑卒中后语言功能减弱的原因之一。其中左侧MTG-左侧角回间的功能连接可能为关键功能连接,左侧MTG和角回有望成为针对脑卒中后完全性失语症患者的新的神经调控靶点。     

基于功能性近红外光谱技术的脑卒中后运动功能障碍患者脑网络功能连接研究

摘要 目的：采用功能性近红外光谱技术（fNIRS）对比观察运动功能障碍的脑卒中患者与健康人的静息态脑功能连接差异。 方法：应用fNIRS分析20例左侧、20例右侧脑卒中后运动功能障碍患者及20例健康对照组静息状态下各脑区之间功能连接强度,并在前额叶（prefrontal cortex,PFC）、第一躯体运动区（M1）、初级躯体感觉皮层区（primary somatosensory cortex,PSC）中比较组间差异。 结果：基于氧合血红蛋白情况下,左右脑卒中组的全脑功能连接平均强度均低于健康组;同源比较中左侧脑卒中组与左侧M1区（患侧）的功能连接强度与健康组有显著性差异（P＜0.05）,其余兴趣区（ROI）与健康组无明显差异。右脑卒中组的左侧PFC、左侧M1区（健侧）的功能连接强度与健康组有显著性差异（P＜0.05）,其余ROI与健康组无明显差异。异源比较中左脑卒中组和右脑卒中组与健康组相比左侧M1与右侧M1区间的功能连接强度差异有显著性意义（P<0.05）,提示与健康组相比,左右脑卒中组的患者的双侧大脑半球间即健侧和患侧半球间M1区间的功能连接强度有明显减弱。另外,相较于健康组,右脑卒中组在左右半球PFC间的功能连接强度也有显著性意义（P<0.05）;而左脑卒中组则没有显著性差异（P＞0.05）。其他ROI功能区间功能连接均无显著性差异（P＞0.05）。左右脑卒中组的PFC区、M1区与FMA评分以及Brunnstrom分期之间的相关性没有显著性差异（P＞0.05）。 结论：在采用功能性近红外脑功能成像的静息态脑功能连接研究中,左右脑卒中组的全脑功能连接平均强度均低于健康组;其中左脑卒中组的患侧M1区功能连接低于健康组,而在左右半球间的功能连接中左右脑卒中组的两侧半球M1区间的功能连接均减少,右脑卒中组的两侧半球PFC区间的功能连接减少。     

Resting state functional connectivity of the whole head with near-infrared spectroscopy

Resting state connectivity aims to identify spontaneous cerebral hemodynamic fluctuations that reflect neuronal activity at rest. In this study, we investigated the spatial-temporal correlation of hemoglobin concentration signals over the whole head during the resting state. By choosing a source-detector pair as a seed, we calculated the correlation value between its time course and the time course of all other source-detector combinations, and projected them onto a topographic map. In all subjects, we found robust spatial interactions in agreement with previous fMRI and NIRS findings. Strong correlations between the two opposite hemispheres were seen for both sensorimotor and visual cortices. Correlations in the prefrontal cortex were more heterogeneous and dependent on the hemodynamic contrast. HbT provided robust, well defined maps, suggesting that this contrast may be used to better localize functional connectivity. The effects of global systemic physiology were also investigated, particularly low frequency blood pressure oscillations which give rise to broad regions of high correlation and mislead interpretation of the results. These results confirm the feasibility of using functional connectivity with optical methods during the resting state, and validate its use to investigate cortical interactions across the whole head.     

Mapping resting-state functional connectivity using perfusion MRI

Resting-state, low-frequency (<0.08 Hz) fluctuations of blood oxygenation level-dependent (BOLD) magnetic resonance signal have been shown to exhibit high correlation among functionally connected regions. However, correlations of cerebral blood flow (CBF) fluctuations during the resting state have not been extensively studied. The main challenges of using arterial spin labeling perfusion magnetic resonance imaging to detect CBF fluctuations are low sensitivity, low temporal resolution, and contamination from BOLD. This work demonstrates CBF-based quantitative functional connectivity mapping by combining continuous arterial spin labeling (CASL) with a neck labeling coil and a multi-channel receiver coil to achieve high perfusion sensitivity. In order to reduce BOLD contamination, the CBF signal was extracted from the CASL signal time course by high frequency filtering. This processing strategy is compatible with sinc interpolation for reducing the timing mismatch between control and label images and has the flexibility of choosing an optimal filter cutoff frequency to minimize BOLD fluctuations. Most subjects studied showed high CBF correlation in bilateral sensorimotor areas with good suppression of BOLD contamination. Root-mean-square CBF fluctuation contributing to bilateral correlation was estimated to be 29+/-19% (N=13) of the baseline perfusion, while BOLD fluctuation was 0.26+/-0.14% of the mean intensity (at 3 T and 12.5 ms echo time).     

Exploring resting-state functional connectivity with total interdependence

Resting-state fMRI has become a powerful tool for studying network mechanisms of normal brain functioning and its impairments by neurological and psychiatric disorders. Analytically, independent component analysis and seed-based cross correlation are the main methods for assessing the connectivity of resting-state fMRI time series. A feature common to both methods is that they exploit the covariation structures of contemporaneously (zero-lag) measured data but ignore temporal relations that extend beyond the zero-lag. To examine whether data covariations across different lags can contribute to our understanding of functional brain networks, a measure that can uncover the overall temporal relationship between two resting-state BOLD signals is needed. In this paper we propose such a measure referred as total interdependence (TI). Comparing TI with zero-lag cross correlation (CC) we report three results. First, when combined with a random permutation procedure, TI can reveal the amount of temporal relationship between two resting-state BOLD time series that is not captured by CC. Second, comparing resting-state data with task-state data recorded in the same scanning session, we demonstrate that the resting-state functional networks constructed with TI match more precisely the networks activated by the task. Third, TI is shown to be more statistically sensitive than CC and provides better feature vectors for network clustering analysis.     

Anticorrelated resting-state functional connectivity in awake rat brain

Resting-state functional connectivity (RSFC) measured by functional magnetic resonance imaging has played an essential role in understanding neural circuitry and brain diseases. The vast majority of RSFC studies have been focused on positive RSFC, whereas our understanding about its conceptual counterpart - negative RSFC (i.e. anticorrelation) - remains elusive. To date, anticorrelated RSFC has yet been observed without the commonly used preprocessing step of global signal correction. However, this step can induce artifactual anticorrelation (Murphy et al., 2009), making it difficult to determine whether the observed anticorrelation in humans is a processing artifact (Fox et al., 2005). In this report we demonstrated robust anticorrelated RSFC in a well characterized frontolimbic circuit between the infralimbic cortex (IL) and amygdala in the awake rat. This anticorrelation was anatomically specific, highly reproducible and independent of preprocessing methods. Interestingly, this anticorrelated relationship was absent in anesthetized rats even with global signal correction, further supporting its functional significance. Establishing negative RSFC independent of data preprocessing methods will significantly enhance the applicability of RSFC in better understanding neural circuitries and brain networks. In addition, combining the neurobiological data of the IL-amygdala circuit in rodents, the finding of the present study will enable further investigation of the neurobiological basis underlying anticorrelation.     

Role of local network oscillations in resting-state functional connectivity

Spatio-temporally organized low-frequency fluctuations (<0.1 Hz), observed in BOLD fMRI signal during rest, suggest the existence of underlying network dynamics that emerge spontaneously from intrinsic brain processes. Furthermore, significant correlations between distinct anatomical regions-or functional connectivity (FC)-have led to the identification of several widely distributed resting-state networks (RSNs). This slow dynamics seems to be highly structured by anatomical connectivity but the mechanism behind it and its relationship with neural activity, particularly in the gamma frequency range, remains largely unknown. Indeed, direct measurements of neuronal activity have revealed similar large-scale correlations, particularly in slow power fluctuations of local field potential gamma frequency range oscillations. To address these questions, we investigated neural dynamics in a large-scale model of the human brain's neural activity. A key ingredient of the model was a structural brain network defined by empirically derived long-range brain connectivity together with the corresponding conduction delays. A neural population, assumed to spontaneously oscillate in the gamma frequency range, was placed at each network node. When these oscillatory units are integrated in the network, they behave as weakly coupled oscillators. The time-delayed interaction between nodes is described by the Kuramoto model of phase oscillators, a biologically-based model of coupled oscillatory systems. For a realistic setting of axonal conduction speed, we show that time-delayed network interaction leads to the emergence of slow neural activity fluctuations, whose patterns correlate significantly with the empirically measured FC. The best agreement of the simulated FC with the empirically measured FC is found for a set of parameters where subsets of nodes tend to synchronize although the network is not globally synchronized. Inside such clusters, the simulated BOLD signal between nodes is found to be correlated, instantiating the empirically observed RSNs. Between clusters, patterns of positive and negative correlations are observed, as described in experimental studies. These results are found to be robust with respect to a biologically plausible range of model parameters. In conclusion, our model suggests how resting-state neural activity can originate from the interplay between the local neural dynamics and the large-scale structure of the brain.     

Magnetoencephalographic evaluation of resting-state functional connectivity in Alzheimer's disease

Statistical interdependencies between magnetoencephalographic signals recorded over different brain regions may reflect the functional connectivity of the resting-state networks. We investigated topographic characteristics of disturbed resting-state networks in Alzheimer's disease patients in different frequency bands. Whole-head 151-channel MEG was recorded in 18 Alzheimer patients (mean age 72.1 years, SD 5.6; 11 males) and 18 healthy controls (mean age 69.1 years, SD 6.8; 7 males) during a no-task eyes-closed resting state. Pair-wise interdependencies of MEG signals were computed in six frequency bands (delta, theta, alpha1, alpha2, beta and gamma) with the synchronization likelihood (a nonlinear measure) and coherence and grouped into long distance (intra- and interhemispheric) and short distance interactions. In the alpha1 and beta band, Alzheimer patients showed a loss of long distance intrahemispheric interactions, with a focus on left fronto-temporal/parietal connections. Functional connectivity was increased in Alzheimer patients locally in the theta band (centro-parietal regions) and the beta and gamma band (occipito-parietal regions). In the Alzheimer group, positive correlations were found between alpha1, alpha2 and beta band synchronization likelihood and MMSE score. Resting-state functional connectivity in Alzheimer's disease is characterized by specific changes of long and short distance interactions in the theta, alpha1, beta and gamma bands. These changes may reflect loss of anatomical connections and/or reduced central cholinergic activity and could underlie part of the cognitive impairment.     

Modeling the outcome of structural disconnection on resting-state functional connectivity

A growing body of experimental evidence suggests that functional connectivity at rest is shaped by the underlying anatomical structure. Furthermore, the organizational properties of resting-state functional networks are thought to serve as the basis for an optimal cognitive integration. A disconnection at the structural level, as occurring in some brain diseases, would then lead to functional and presumably cognitive impairments. In this work, we propose a computational model to investigate the role of a structural disconnection (encompassing putative local/global and axonal/synaptic mechanisms) on the organizational properties of emergent functional networks. The brain's spontaneous neural activity and the corresponding hemodynamic response were simulated using a large-scale network model, consisting of local neural populations coupled through white matter fibers. For a certain coupling strength, simulations reproduced healthy resting-state functional connectivity with graph properties in the range of the ones reported experimentally. When the structural connectivity is decreased, either globally or locally, the resultant simulated functional connectivity exhibited a network reorganization characterized by an increase in hierarchy, efficiency and robustness, a decrease in small-worldness and clustering and a narrower degree distribution, in the same way as recently reported for schizophrenia patients. Theoretical results indicate that most disconnection-related neuropathologies should induce the same qualitative changes in resting-state brain activity.     

Antidepressant medications reduce subcortical-cortical resting-state functional connectivity in healthy volunteers

Studies have revealed abnormalities in resting-state functional connectivity in those with major depressive disorder specifically in areas such as the dorsal anterior cingulate, thalamus, amygdala, the pallidostriatum and subgenual cingulate. However, the effect of antidepressant medications on human brain function is less clear and the effect of these drugs on resting-state functional connectivity is unknown. Forty volunteers matched for age and gender with no previous psychiatric history received either citalopram (SSRI; selective serotonergic reuptake inhibitor), reboxetine (SNRI; selective noradrenergic reuptake inhibitor) or placebo for 7 days in a double-blind design. Using resting-state functional magnetic resonance imaging and seed based connectivity analysis we selected the right nucleus accumbens, the right amygdala, the subgenual cingulate and the dorsal medial prefrontal cortex as seed regions. Mood and subjective experience were also measured before and after drug administration using self-report scales. Despite no differences in mood across the three groups, we found reduced connectivity between the amygdala and the ventral medial prefrontal cortex in the citalopram group and the amygdala and the orbitofrontal cortex for the reboxetine group. We also found reduced striatal-orbitofrontal cortex connectivity in the reboxetine group. These data suggest that antidepressant medications can decrease resting-state functional connectivity independent of mood change and in areas known to mediate reward and emotional processing in the brain. We conclude that hypothesis-driven seed based analysis of resting-state fMRI supports the proposition that antidepressant medications might work by normalising the elevated resting-state functional connectivity seen in depressed patients.     

Bedside optical imaging of occipital resting-state functional connectivity in neonates

Resting-state networks derived from temporal correlations of spontaneous hemodynamic fluctuations have been extensively used to elucidate the functional organization of the brain in adults and infants. We have previously developed functional connectivity diffuse optical tomography methods in adults, and we now apply these techniques to study functional connectivity in newborn infants at the bedside. We present functional connectivity maps in the occipital cortices obtained from healthy term-born infants and premature infants, including one infant with an occipital stroke. Our results suggest that functional connectivity diffuse optical tomography has potential as a valuable clinical tool for the early detection of functional deficits and for providing prognostic information on future development.     

生理性进食之后静息态fMRI脑功能连接的变化

目的探讨生理性进食之后静息态脑功能连接的变化。材料与方法 42名健康被试按照人口统计学特征平衡分为三组。为了增加胃肠活动的差异,三组分别服用多潘立酮、奥替溴铵、安慰剂。所有被试均给予标准实验早餐,于空腹、早餐后、餐后2h及午餐前行静息态功能磁共振(functional MRI,fMRI)检查。数据分析采用扣带回后部-楔前叶作为种子点,计算全脑功能连接,并采用重复测量ANOVA分析在时间与药物因素上具有显著差异的脑区,并提取其功能连接Z值,进行事后检验。结果时间主效应:右侧眶额回/壳核/丘脑、右侧缘上回和右侧岛盖的功能连接在时间因素上有显著差异(P0.05)。药物主效应:右侧楔前叶、左侧颞中回、左侧舌回、右侧颞中回、右侧额上回、双侧眶额回、左侧额中回的功能连接在药物因素上有显著差异(P0.05)。结论静息态fMRI健康被试生理性进食前后,奖赏系统、右侧丘脑和躯体感觉区的功能连接会发生变化,并且呈现右侧偏侧化优势;胃动力药物对上述脑区的变化无影响。为常规fMRI实验中被试进食状态对实验结果的潜在影响提供了分析依据。     

Non-parametric model selection for subject-specific topological organization of resting-state functional connectivity

In recent years, graph theory has been successfully applied to study functional and anatomical connectivity networks in the human brain. Most of these networks have shown small-world topological characteristics: high efficiency in long distance communication between nodes, combined with highly interconnected local clusters of nodes. Moreover, functional studies performed at high resolutions have presented convincing evidence that resting-state functional connectivity networks exhibits (exponentially truncated) scale-free behavior. Such evidence, however, was mostly presented qualitatively, in terms of linear regressions of the degree distributions on log-log plots. Even when quantitative measures were given, these were usually limited to the r(2) correlation coefficient. However, the r(2) statistic is not an optimal estimator of explained variance, when dealing with (truncated) power-law models. Recent developments in statistics have introduced new non-parametric approaches, based on the Kolmogorov-Smirnov test, for the problem of model selection. In this work, we have built on this idea to statistically tackle the issue of model selection for the degree distribution of functional connectivity at rest. The analysis, performed at voxel level and in a subject-specific fashion, confirmed the superiority of a truncated power-law model, showing high consistency across subjects. Moreover, the most highly connected voxels were found to be consistently part of the default mode network. Our results provide statistically sound support to the evidence previously presented in literature for a truncated power-law model of resting-state functional connectivity.     

Amplitude of low frequency fluctuation within visual areas revealed by resting-state functional MRI

Most studies of resting-state functional magnetic resonance imaging (fMRI) have applied the temporal correlation in the time courses to investigate the functional connectivity between brain regions. Alternatively, the power of low frequency fluctuation (LFF) may also be used as a biomarker to assess spontaneous activity. The purpose of the current study is to evaluate whether the amplitude of the LFF (ALFF) relates to cerebral physiological states. Ten healthy subjects underwent four resting-state fMRI scanning sessions, two for eyes-open (EO) and two for eyes-closed (EC) conditions, with two sets of parameters (TR=400 ms and 2 s, respectively). After data preprocessing, ALFF was obtained by calculating the square root of the power spectrum in the frequency range of 0.01-0.08 Hz. Our results showed that the ALFF in EO was significantly higher than that in EC (P<0.05, corrected) in the bilateral visual cortices. Furthermore, the ALFF in EO was significantly reduced in the right paracentral lobule (PCL) than in EC (P<0.05, corrected). Region of interest (ROI) analysis showed that the ALFF differences between EO and EC were consistent for each subject. In contrast, no significant ALFF differences were found between EO and EC (P<0.381) in the posterior cingulate cortex. All these results agree well with previous studies comparing EO and EC states. Our finding of the distinct ALFF difference between EO and EC in the visual cortex implies that the ALFF may be a novel biomarker for physiological states of the brain.     

Frequency-dependent functional connectivity within resting-state networks: an atlas-based MEG beamformer solution

The brain consists of functional units with more-or-less specific information processing capabilities, yet cognitive functions require the co-ordinated activity of these spatially separated units. Magnetoencephalography (MEG) has the temporal resolution to capture these frequency-dependent interactions, although, due to volume conduction and field spread, spurious estimates may be obtained when functional connectivity is estimated on the basis of the extra-cranial recordings directly. Connectivity estimates on the basis of reconstructed sources may similarly be affected by biases introduced by the source reconstruction approach.Here we propose an analysis framework to reliably determine functional connectivity that is based around two main ideas: (i) functional connectivity is computed for a set of atlas-based ROIs in anatomical space that covers almost the entire brain, aiding the interpretation of MEG functional connectivity/network studies, as well as the comparison with other modalities; (ii) volume conduction and similar bias effects are removed by using a functional connectivity estimator that is insensitive to these effects, namely the Phase Lag Index (PLI).Our analysis approach was applied to eyes-closed resting-state MEG data for thirteen healthy participants. We first demonstrate that functional connectivity estimates based on phase coherence, even at the source-level, are biased due to the effects of volume conduction and field spread. In contrast, functional connectivity estimates based on PLI are not affected by these biases. We then looked at mean PLI, or weighted degree, over areas and subjects and found significant mean connectivity in three (alpha, beta, gamma) of the five (including theta and delta) classical frequency bands tested. These frequency-band dependent patterns of resting-state functional connectivity were distinctive; with the alpha and beta band connectivity confined to posterior and sensorimotor areas respectively, and with a generally more dispersed pattern for the gamma band. Generally, these patterns corresponded closely to patterns of relative source power, suggesting that the most active brain regions are also the ones that are most-densely connected.Our results reveal for the first time, using an analysis framework that enables the reliable characterisation of resting-state dynamics in the human brain, how resting-state networks of functionally connected regions vary in a frequency-dependent manner across the cortex.     

Optical imaging of resting-state functional connectivity in a novel arterial stiffness model

This study aims to assess the impact of unilateral increases in carotid stiffness on cortical functional connectivity measures in the resting state. Using a novel animal model of induced arterial stiffness combined with optical intrinsic signals and laser speckle imaging, resting state functional networks derived from hemodynamic signals are investigated for their modulation by isolated changes in stiffness of the right common carotid artery. By means of seed-based analysis, results showed a decreasing trend of homologous correlation in the motor and cingulate cortices. Furthermore, a graph analysis indicated a randomization of the cortex functional networks, suggesting a loss of connectivity, more specifically in the motor cortex lateral to the treated carotid, which however did not translate in differentiated metabolic activity.OCIS codes: (110.4234) Multispectral and hyperspectral imaging, (110.6150) Speckle imaging     

Frequency-specific functional connectivity in the brain during resting state revealed by NIRS

Analyses of spontaneous hemodynamic fluctuations observed on functional magnetic resonance imaging (fMRI) have revealed the existence of temporal correlations in signal changes between widely separated brain regions during the resting state, termed "resting state functional connectivity." Recent studies have demonstrated that these correlations are also present in the hemodynamic signals measured by near infrared spectroscopy (NIRS). However, it is still uncertain whether frequency-specific characteristics exist in these signals. In the present study, we used multichannel NIRS to investigate the frequency dependency of functional connectivity between diverse regions in the cerebral cortex by decomposing fluctuations of oxygenated hemoglobin (oxy-Hb) and deoxygenated hemoglobin (deoxy-Hb) signals into various frequency bands. First, within a wide frequency range (0.009-0.1Hz), we observed that both oxy-Hb and deoxy-Hb signals showed functional connectivity within local regions and between contralateral hemispheric regions of the cortex. Next, by decomposing measured fluctuations into narrower frequency components, we determined that only oxy-Hb signals showed frequency-specific functional connectivity between the frontal and occipital regions, emerging in a narrow frequency range (0.04-0.1Hz). To clarify the coherency of functional connectivity, we calculated the average coherence values between selected channel pairs. This approach demonstrated that functional connectivity based on the oxy-Hb signals between homologous cortical regions of the contralateral hemisphere (homologous connectivity) showed high coherence over a wide frequency range (0.009-0.1Hz), whereas connectivity between the prefrontal and occipital regions (fronto-posterior connectivity) showed high coherence only within a specific narrow frequency range (0.04-0.1Hz). Our findings suggest that homologous connectivity may reflect synchronization of neural activation over a wide frequency range through direct neuroanatomical connections, whereas fronto-posterior connectivity as revealed by high coherence only within a specific narrow frequency range corresponding to the time scale of typical hemodynamic response to a single event may reflect synchronization of transient neural activation among distant cortical regions. The present study demonstrated that NIRS provides a powerful tool to elucidate network properties of the cortex during resting state.     

Small-world and scale-free organization of voxel-based resting-state functional connectivity in the human brain

The brain is a complex dynamic system of functionally connected regions. Graph theory has been successfully used to describe the organization of such dynamic systems. Recent resting-state fMRI studies have suggested that inter-regional functional connectivity shows a small-world topology, indicating an organization of the brain in highly clustered sub-networks, combined with a high level of global connectivity. In addition, a few studies have investigated a possible scale-free topology of the human brain, but the results of these studies have been inconclusive. These studies have mainly focused on inter-regional connectivity, representing the brain as a network of brain regions, requiring an arbitrary definition of such regions. However, using a voxel-wise approach allows for the model-free examination of both inter-regional as well as intra-regional connectivity and might reveal new information on network organization. Especially, a voxel-based study could give information about a possible scale-free organization of functional connectivity in the human brain. Resting-state 3 Tesla fMRI recordings of 28 healthy subjects were acquired and individual connectivity graphs were formed out of all cortical and sub-cortical voxels with connections reflecting inter-voxel functional connectivity. Graph characteristics from these connectivity networks were computed. The clustering-coefficient of these networks turned out to be much higher than the clustering-coefficient of comparable random graphs, together with a short average path length, indicating a small-world organization. Furthermore, the connectivity distribution of the number of inter-voxel connections followed a power-law scaling with an exponent close to 2, suggesting a scale-free network topology. Our findings suggest a combined small-world and scale-free organization of the functionally connected human brain. The results are interpreted as evidence for a highly efficient organization of the functionally connected brain, in which voxels are mostly connected with their direct neighbors forming clustered sub-networks, which are held together by a small number of highly connected hub-voxels that ensure a high level of overall connectivity.     

Gender-related effects of prefrontal cortex connectivity: a resting-state functional optical tomography study

The prefrontal cortex (PFC) is thought to play an important role in “higher” brain functions such as personality and emotion that may associated with several gender-related mental disorders. In this study, the gender effects of functional connectivity, cortical lateralization and significantly differences in the PFC were investigated by using resting-state functional optical tomography (fOT) measurement. A total of forty subjects including twenty healthy male and twenty healthy female adults were recruited for this study. In the results, the hemoglobin responses are higher in the male group. Additionally, male group exhibited the stronger connectivity in the PFC regions. In the result of lateralization, leftward dominant was observed in the male group but bilateral dominance in the female group. Finally, the 11 channels of the inferior PFC regions (corresponding to the region of Brodmann area 45) are significant different with spectrum analysis. Our findings suggest that the resting-state fOT method can provide high potential to apply to clinical neuroscience for several gender-related mental disorders diagnosis.OCIS codes: (170.1610) Clinical applications, (170.2655) Functional monitoring and imaging, (170.3880) Medical and biological imaging