When choosing the number of clusters, k, for use in the k-means algorithm there is an inherent tradeoff between compu- tational complexity and the homogeneity of clusters. For increas- ing values of k, the number of permutation tests that must be run increases as the square of the number of clusters while the spa- tial extent of each cluster decreases. This leads to increases in the complexity of the calculation making it more difficult to achieve significance, however smaller clusters would yield greater simi- larity between dipoles contained in the cluster and provide more granular detail of functional regions. To ascertain an appropri- ate k-value, we calculated a dendrogram in which we iteratively generated clusters using successively smaller k-values upon the obtained centrality scores ( Cali ´nski, 2005 ). Briefly, at each itera- tion the number of clusters is reduced by one and correspondingly two clusters are aggregated into a combined cluster with a lower similarity score (as a result of inhomogeneity of the precursor clusters). We can characterize this level of dissimilarity by com- puting 1-similarity between the merged clusters. The choice of k = 40 corresponded to cutoff in which we chose the number of clusters small enough to be computationally tractable while still providing neuroscientifically relevant network structures such that clusters were small enough to make interpretation possible. This value was used for both the real and simulated data. The den- drogram is shown is Figure 2, where increasing y-axis results in greater dissimilarity between clusters being joined at lower lev- els. The red dashed line in the figure indicates our choice for the number of clusters.
Legion is a MATLAB software framework for computing large-scale functionalconnectivity networks in a time efficient manner. Phaselockingfunctionalconnectivity is a computationally intensive algorithm whose computation time scales rapidly upon application to neuroimaging datasets. By utilizing clusters of computers, instead of repeated serial computations on a single computer, Legion is capable of distributing those computations and solving pieces simultaneously. This improved processing speed can lead to the application of functionalconnectivity to wholebrainfunctionalconnectivity networks. Large-scale data analysis provides a tool for neuroscientists to investigate the complex network dynamics of neural populations. The Legion software library provides a generic software framework for batch computing using MATLAB. It provides the explicit capability for handling pairwise computations that are required for functionalconnectivity studies. It’s distributed computing architecture greatly improves processing times making previously intractable or computationally prohibitive analyses accessible to a much broader range of applications.
As noted already in the introduction, it is worth keeping in mind the history of the Big Five: They derive from factor analyses of words, of the vocabularies that we use to describe people. As such, they fundamentally reflect our folk psychology, and our social inferences (“theory of mind”) about other people. This factor structure was then used to design a self-report instrument, in which participants are asked about themselves (the NEO or variations thereof). Unlike some other self-report indices (such as the Minnesota Multiphasic Personality Inventory), the NEO-FFI does not assess test-taking approach (e.g., consistency across items or tendency toward a particular response set), and thus, offers no insight regarding validity of any individual’s responses. This is a notable limitation, as there is substantial evidence that NEO-FFI scores may be intentionally manipulated by the sub- ject’s response set (Furnham, 1997; Topping & O’Gorman, 1997). Even in the absence of intentional “faking,” NEO outcomes are likely to be influenced by an individual’s insight, impression management, and reference group effects. However, these lim- itations may be addressed by applying the same analysis to multiple personality measures with varying degrees of face- validity and objectivity, as well as measures that include indices of response bias. This might include ratings provided by a familiar informant, implicit-association tests (e.g. Schnabel, Asendorpf, & Greenwald, 2008), and spontaneous behavior (e.g. Mehl, Gosling, & Pennebaker, 2006). Future development of behavioral measures of personality that provide better convergent validity and dis- criminative specificity will be an important component of per- sonality neuroscience.
We used data from a public repository, the 1200 subjects release of the Human Connectome Project (HCP) (Van Essen et al., 2013). The HCP provides MRI data and extensive behavioral assessment from almost 1200 subjects. Acquisition parameters and “minimal” preprocessing of the resting-state fMRI data is described in the original publication (Glasser et al., 2013). Briefly, each subject underwent two sessions of resting-state fMRI on separate days, each session with two separate 15 minute acquisitions generating 1200 volumes (customized Siemens Skyra 3 Tesla MRI scanner, TR = 720 ms, TE = 33 ms, flip angle= 52°, voxel size = 2 mm isotropic, 72 slices, matrix = 104 x 90, FOV = 208 mm x 180 mm, multiband acceleration factor = 8). The two runs acquired on the same day differed in the phase encoding direction, left-right and right-left (which leads to differential signal intensity especially in ventral temporal and frontal structures). The HCP data was downloaded in its minimally preprocessed form, i.e. after motion correction, B 0 distortion correction, coregistration to T 1 -weighted images and normalization to MNI space (the T1w image is registered to MNI space with a FLIRT 12 DOF affine and then a FNIRT nonlinear registration, producing the final nonlinear volume transformation from the subject's native volume space to MNI space).
The present study applied graph-theory based complex network analysis and network-based statistic to investigate BPD-related alter- ations of topological organizations and connectivity in resting-statefunctionalbrain networks. In the 0.03 – 0.06 Hz functionalbrain net- works, BPD patients showed increased local cliquishness characterized by increased size of largest connected component, clustering coef ﬁ cient, local ef ﬁ ciency, and small-worldness, particularly at the limbic areas. Patients also showed decreased nodal centrality at several hub nodes, but increased nodal centrality at several non-hub nodes in the network. Furthermore, an interconnected subnetwork in the 0.03 – 0.06 Hz fre- quency band showed signi ﬁ cantly lower connectivity strength in BPD patients, the mean connectivity of which was negatively correlated with the increased topology measures. In addition, the signi ﬁ cant net- work measures were correlated with several clinical symptom scores for BPD diagnosis, and showed high predictive power in patient vs. con- trol classi ﬁ cation using a machine learning classi ﬁ er. The ﬁ ndings of this work may help in gaining new knowledge into the neural under- pinnings of BPD. However, due to limitation of small sample sizes, the re- ported results should be viewed as exploratory and need to be validated on large samples in future works. Future efforts will be directed towards studying functionalbrain networks constructed with different node sets and connectivitymeasures, exploring the dynamic network structure across time, and testing the results on a larger sample size. Future work will also be directed towards comparing the topological properties of functionalbrain networks in different psychiatric disorders, including BPD, obsessive compulsive disorder, and major depressive disorder.
An electroencephalogram (EEG) is a test that measures and records the electrical activity of your brain. Special sensors (electrodes) are attached to your head and hooked by wires to a computer. The computer records your brain's electrical activity on the screen or on paper as wavy lines. Certain conditions, such as seizures, can be seen by the changes in the normal pattern of the brain's electrical activity.
apart than 8 to 10 cm necessary to circumvent influences of volume conduction (Nunez & Srinivasan, 2006; Srinivasan et al., 2007). This suggests that the sparsely placed electrodes avoid a large influence of volume conduction and as a result it is unlikely that volume conduction has introduced much to the variation in functionalconnectivity in our recordings. Within the last decade, neurophysiologists have pro- gressed in terms of accuracy of measuring true synchro- nization between spatially and functionally distinct brain areas. SL and its graph theoretical derivatives CC and L have established themselves in several clinical studies as a useful tool for describing functionalconnectivity and capturing brain activity underlying behavioral traits and neurological disorders. These include differences in brain organization in schizophrenia (Micheloyannis et al., 2006) and Alzheimer’s disease (de Haan et al., 2009). In normal development, both connectivity and graph parameters show marked changes from childhood to adulthood (Smit et al., 2012). Moreover, SL correlates with brain white matter volume (Smit et al., 2012). Many of these (pathological and non-pathological) traits are highly heritable. And since the brain-derived con- nectivity measures have shown to be heritable as well, we may conclude that SL and its graph theoretical derivatives are potential endophenotypes for these traits and neurolog- ical disorders.
Here, we present an exploratory examination of developmental changes in intrinsic connectivity patterns of children from age 5 to age 6 by using a network measure, which allows an unbiased comparison at a voxel-wise level. The range was chosen since at this age the struc- tural and functional development of the brain is in full progress ( Gogtay et al., 2004; Knoll et al., 2012; Skeide et al., 2014 ) while at the same time performance in language functions increases steadily ( Guasti, 2002; Sakai, 2005; Skeide et al., 2014 ). Combining resting- statefunctionalconnectivity (RSFC) with behavioral data on the devel- opment of sentence comprehension carries the potential to open new perspectives on the relation between brain maturation and the ontoge- ny of language in children. In order to explore the developmental changes in intrinsic connectivity patterns, longitudinal resting-state fMRI data were acquired from a cohort of typically developing children aged 5 years and one year later, and subjected to degree centrality anal- ysis. As a measure of graph theory, degree centrality is among the most fundamental and most common centrality measures, and has been widely used to identify hubs in the human brain (e.g., Buckner et al., 2009; Cole et al., 2010; Tomasi and Volkow, 2011 ). Degree centrality has been found to be physiologically meaningful ( Liang et al., 2013; Tomasi et al., 2015, 2013 ) and has been applied to investigate the changes in network connectivity associated with healthy aging ( Hampson et al., 2012 ) and cognitive functions ( van den Heuvel et al., 2009 ). Hubs, as highly connected central nodes in a network, are thought to play pivotal roles in the coordination of information ﬂow ( Sporns et al., 2007 ) and may also help to minimize wiring and metab- olism costs by providing a limited number of long-distance connections that integrate local networks ( Bassett and Bullmore, 2006 ). The
The present group differences that reached statistical significance levels were confined to the broad alpha band. Oscillatory activity in alpha frequencies have been associated with cognitive and memory performance, with links to aspects such as attention and semantic memory performance ( Klimesch, 1999 ). Another predominant line of work has related alpha to functional inhibition of task-irrelevant brain network activity ( Jensen and Mazaheri, 2010 ). Accordingly, such task-irrelevant inhibition would facilitate the allocation of resources to task-relevant regions that are necessary for optimal task performance. Several resting-state studies reported abnormalities in the alpha band in reading-impaired children ( Babiloni et al., 2012; Dimitriadis et al., 2013; Schiavone et al., 2014; Papagiannopoulou and Lagopoulos, 2016 ). One of those studies reported a significant correlation between a measure of global network efficiency and reading performance in typically reading children ( Dimitriadis et al., 2013 ). Another study in adults found decreased and more diffused inter-hemisphere alpha coherence at centro-parietal sites in dyslexics relative to controls during a visuo-spatial attention task ( Dhar et al., 2010 ). Our current findings, then, suggesting differences in the organization of alpha oscillatory activity in dyslexics further supports the relevance of these oscillations to cognitive and attentional
Traditional resting-state network concept is based on calculating linear dependence of spontaneous low frequency fluctuations of the BOLD signals of different brain areas, which assumes temporally stable zero-lag synchrony across regions. However, growing amount of experimental findings suggest that functionalconnectivity exhibits dynamic changes and a complex time-lag structure, which cannot be captured by the static zero-lag correlation analysis. Here we propose a new approach applying Dynamic Time Warping (DTW) distance to evaluate functionalconnectivity strength that accounts for non-stationarity and phase-lags between the observed signals. Using simulated fMRI data we found that DTW captures dynamic interactions and it is less sensitive to linearly combined global noise in the data as compared to traditional correlation analysis. We tested our method usingresting-state fMRI data from repeated measurements of an individual subject and showed that DTW analysis results in more stable connectivity patterns by reducing the within-subject variability and increasing robustness for preprocessing strategies. Classification results on a public dataset revealed a superior sensitivity of the DTW analysis to group differences by showing that DTW based classifiers outperform the zero-lag correlation and maximal lag cross-correlation based classifiers significantly. Our findings suggest that analysing resting-statefunctionalconnectivityusing DTW provides an efficient new way for characterizing functional networks.
Thus, although the scores on various indices appear to correlate with the extent of executive disability in CADA- SIL patients, it is currently considered that human execu- tive functions are mediated by the dynamic interplay of large-scale brain networks . However, it remains largely unknown whether changes in resting-statebrainfunctional network connectivity and regional homogeneities (ReHos) can provide novel insights into the executive dysfunction mechanisms in play in CADASIL patients. Resting-statefunctional network connectivity evaluated via independent component analysis (ICA) has been used to define several, intrinsic functional networks . In addition, ReHos can be used to identify local features of spontaneous brain activ- ity when the Kendall coefficient of concordance (KCC) is employed during synchrony evaluation of BOLD time series. The intra-network connectivity within brain net- works and ReHos have been shown to correlate with be- havioral measures in both healthy subjects and those with neuropsychiatric conditions . Based on the above men- tioned abnormalities in executive behaviors and brain structure [6–15], we hypothesized that both brain func- tional network connectivity and ReHos would be altered in CADASIL patients. To this end, we used resting-state fMRI to explore changes in brainfunctional networks as assessed by ICA and local ReHos.
Graph methods provide a distinct alternative to seed-based and ICA methods [48,60 – 63]. This approach views RSNs as a collection of nodes connected by edges (Fig. 2, 3rd panel). Here, the relation between the nodes and edges can be established as G ¼ ð V : E Þ where V is a gathering of nodes connected by edges E, which describes the interaction between nodes. In this approach, ROIs are represented by nodes and the correlations among the ROIs are demonstrated as the level of connectivity (weights) using the edges. The characteristics of the graph can be evaluated to quantify the distribution of functional hubs (highly functionally connected nodes) in the human brain [38,63]. Examples of measures of interest include: (i) average path length; (ii) clustering coef ﬁ cient; (iii) nodal degree; (iv) centrality measures; and (v) level of modularity . Using the graph theory technique, several studies have demonstrated that the brain exhibits a small world topology. Small world topology was ﬁ rst described in social networks. It allows each node to have a relatively low number of connections while still being connected to all other nodes within a short distance (that is, short distances between any two nodes). The small world is achieved through the existence of hubs, which are critical nodes with large number of connections, allowing a high level of local connectivity (neighboring nodes) . While seed- based analysis focuses only on the strength of correlation between one ROI to another, graph theory measures the topological properties of an ROI within the wholebrain or the network related to a particular function. It has exhibited good correspon- dence with well-known anatomical white matter tracts (structural highways of the brain) and resting-state networks .
Graph methods provide a distinct alternative to seed-based and ICA methods [49,62 – 65]. This approach views RSNs as a collection of nodes connected by edges (Fig. 2, 3rd panel). Here, the relation between the nodes and edges can be established as G ¼ ð V : E Þ where V is a gathering of nodes connected by edges E, which describes the interaction between nodes. In this approach, ROIs are represented by nodes and the correlations among the ROIs are demonstrated as the level of connectivity (weights) using the edges. The characteristics of the graph can be evaluated to quantify the distribution of functional hubs (highly functionally connected nodes) in the human brain [39,65]. Examples of measures of interest include: (i) average path length; (ii) clustering coef ﬁ cient; (iii) nodal degree; (iv) centrality measures; and (v) level of modularity . Using the graph theory technique, several studies have demonstrated that the brain exhibits a small world topology. Small world topology was ﬁ rst described in social networks. It allows each node to have a relatively low number of connections while still being connected to all other nodes within a short distance (that is, short distances between any two nodes). The small world is achieved through the existence of hubs, which are critical nodes with large number of connections, allowing a high level of local connectivity (neighboring nodes) . While seed- based analysis focuses only on the strength of correlation between one ROI to another, graph theory measures the topological properties of an ROI within the wholebrain or the network related to a particular function. It has exhibited good correspon- dence with well-known anatomical white matter tracts (structural highways of the brain) and resting-state networks .
Ten continuous minutes of MEG resting-state data were ac- quired per participant on an Elekta Neuromag 306-channel scanner (Elekta AB, Stockholm, Sweden) at the Oxford Centre for Human Brain Activity. The participant was seated comfortably in the scanner and instructed to remain still and awake, but without performing any speciﬁc task. Immediately prior to acquisition, a Polhemus 3D tracking system (Polhe- mus, Colchester, Vermont) recorded 4 ﬁxed coil positions relative to nasion and preauricular ﬁducial landmarks, along- side distributed points (;100) covering the scalp and in later cases the nose (;50 points) surface. Coils were intermittently energized to obtain a continuous record of head position with the MEG helmet. The recording was made in the “eyes open” state with participants’ visual ﬁxation directed to a black cross printed on white paper 90 cm away. Blinks and saccades were monitored continuously using a combination of surface electrooculography and infrared eye tracker. ECG was mon- itored at the wrists. Participants underwent a structural MRI for coregistration purposes, typically on the same day as MEG (or within 1 month), using a Siemens Trio 3T (Sie- mens, Munich, Germany) (3-dimensional, whole-brain, T1- weighted using a magnetization-prepared rapid-acquisition gradient echo sequence; repetition time/echo time = 2,040 milliseconds/4.7 milliseconds; ﬂip angle = 8°; 1-mm isotropic resolution; 6-minute acquisition time).
The goal of the present study is to improve our under- standing of smoking-related sex differences, specifically differences in inherent brainconnectivity that may increase or protect against vulnerability to smoking behaviors and relapse. To this end, we expand upon pre- vious research [20, 34] by examining menstrual cycle phase differences in rsFC of the mOFC cluster that differed between FPs and LPs during smoking cue ex- posure. Based on our previous findings and research suggesting that the follicular phase (low progesterone to estradiol ratio) is associated with increased activation of reward-related circuitry , we hypothesized that FPs would exhibit increased functionalconnectivity between the mOFC and other reward-related brain regions. In order to explore potential luteal phase effects, we fo- cused on rsFC of the dorsal anterior cingulate cortex (dACC), a brain region shown to be involved in cogni- tive control, craving reappraisal, and modulation of reactivity to cues [34, 36–38]. Given that the luteal phase is associated with higher levels of progesterone and pro- tection against vulnerabilities to smoking behaviors and relapse, we hypothesized that LPs would show greater rsFC between the dACC and reward-related regions, as the dACC could be exerting more cognitive control over reward-related responses.
In general, the machine learning based diagnosis methods using fMRI can be sum- marized as two categories, i.e. the voxel based methods and the brain network [9–11] based methods. The voxel based methods need to construct the correlation model for each pair of voxels. For example, Demirci et al. used independent component analysis (ICA) to obtain independent component (IC) spatial maps from the voxels of fMRI data, and exploited projection pursuit algorithm for classification . A three-phase feature selection method was proposed for diagnosis of schizophrenia using fMRI . Cao et al. exploited sparse representation classification for voxel based schizophrenia diag- nosis and biomarker selection . It is usually hard to obtain enough schizophrenia participants to construct robust voxel network model, and the high computational cost also constrains its application. Besides above voxel based methods, brain network based methods also have been applied into neurodegenerative diseases diagnosis including schizophrenia and Alzheimer’s disease . Most of these methods first constructed the brainfunctional network by measuring the correlation between each pair of brain regions, and then they selected the significant regions with different metrics, e.g. clus- tering coefficient  and Bayesian information criterion (BIC) . Compared to the voxel based methods, the brain network based methods has higher classification effi- ciency and can provide more intuitive biomarkers for diagnosis.
Assessing the functionalconnectivity (FC) of the brain has proven valuable in enhancing our understanding of brain function. Recent developments in the field demonstrated that FC fluctuates even in the restingstate, which has not been taken into account by the widely applied static approaches introduced earlier. In a recent study usingfunctional near-infrared spectroscopy (fNIRS) global dynamic functionalconnectivity (DFC) has also been found to fluctuate according to scale-free i.e., fractal dynamics evidencing the true multifractal (MF) nature of DFC in the human prefrontal cortex. Expanding on these findings, we performed electroencephalography (EEG) measurements in 14 regions over the whole cortex of 24 healthy, young adult subjects in eyes open (EO) and eyes closed (EC) states. We applied dynamic graph theoretical analysis to capture DFC by computing the pairwise time-dependent synchronization between brain regions and subsequently calculating the following dynamic graph topological measures: Density, Clustering Coefficient, and Efficiency. We characterized the dynamic nature of these global network metrics as well as local individual connections in the networks using focus-based multifractal time series analysis in all traditional EEG frequency bands. Global network topological measures were found fluctuating–albeit at different extent–according to true multifractal nature in all frequency bands. Moreover, the monofractal Hurst exponent was found higher during EC than EO in the alpha and beta bands. Individual connections showed a characteristic topology in their fractal properties, with higher autocorrelation owing to short-distance connections–especially those in the frontal and pre-frontal cortex–while long-distance connections linking the occipital to the frontal and pre-frontal areas expressed lower values. The same topology was found with connection-wise multifractality in all but delta band connections, where the very opposite pattern appeared. This resulted in a positive correlation between global autocorrelation and connection-wise multifractality in the higher frequency bands, while a strong anticorrelation in the delta band. The proposed analytical tools allow for capturing the fine details of functionalconnectivity dynamics that are evidently present in DFC, with the presented results implying that multifractality is indeed an inherent property of both global and local DFC.
by using standard Montreal neurological institute (MNI) template provided by SPM2 (resam- pling voxel size: 3 × 3 × 3mm 3 ). Subsequent to smoothing (with FWHM = 8mm), the BOLD signal was filtered in order to reduce low-frequency drift and high-frequency noise with band- pass = 0.01*0.08Hz. Furthermore, the following variables were regressed out: (i) 6 parameters for head motion, (ii) global mean signal, (iii) white signal, and (iv) CSF signal. The automated anatomical labeling (AAL) template of Tzourio-Mazoyer et al.  was used to segment regis- tered fMRI time series into 116 regions, 90 for cortex and 26 for cerebellum. The list of 90 cortical regions is given in S1 Table. Though the AAL parcellation is anatomical in nature, the regions thus identified are mapped to brain function. The AAL parcellation is commonly used in func- tional neuroimaging studies (e.g. [28, 29]). For each region, fMRI time series of all voxels lying in that region were averaged to obtain representative fMRI time series or BOLD signal of that region. Therefore, for each subject, there are 116 BOLD signals where x i (t) represents BOLD
Alzheimer's disease, schizophrenia; Greicius, 2008). Indeed, differences between groups in resting-statefunctionalconnectivity are increasingly identified as potential biomarkers of disease or disorder, for example in epilepsy (Quraan, McCormick, Cohn, Valiante, & McAndrews, 2013) or schizophrenia (Arbabshirani et al., 2013). However, if functionalconnectivity and neural network measures are to be used as biomarkers in this way, it is important to determine whether these metrics are stable for each person over time in the absence of disease or disorder. In fMRI, the spatial pattern of RSNs has been demonstrated to show consistency and overlap across healthy individuals (Damoiseaux et al., 2006). Other fMRI work has also shown that RSNs exhibit spatial reproducibility over repeated scans (Braun et al., 2012; Meindl et al., 2010). There has also been some work investigating network and connectivity repeatability using MEG. One MEG study demonstrated, using a seed-based network analysis, that primary sensory RSNs exhibit topographical variability both within and between subjects, and this variability relates to within-network connectivity levels (Wens et al., 2014). Overall, however, individual variability in spatial pattern was found to be minimised by sufficient group averaging. Some studies have investigated repeatability of graph theory metrics in studying RSNs with MEG (Deuker et al., 2009; Jin, Seol, Kim, & Chung, 2011), but neither specifically consider repeatability of the underlying estimates of connectivity.
Low-Frequency Optogenetic Stimulation of dDG Excitatory Neurons in dHP Enhances Brain-Wide Resting-StateFunctionalConnectivity. Next, we examined the effects of low-frequency activity propagating along the dorsal hippocampal–cortical pathway (Figs. 1 and 3) on interhemispheric or bilateral rsfMRI connectivity (Fig. 4A). Before the rsfMRI acquisition, we measured LFPs in dDG/dHP and V1 to ensure that sustained 1-Hz stimulation (400 s) did not evoke different LFP response characteristics observed during shorter stimulation durations (20 s) (Fig. S6 vs. Fig. 3B). LFPs showed steady evoked responses, which demonstrate the stability of 1-Hz evoked responses in both stimulated and activated re- gions. The strength of interhemispheric rsfMRI connectivity in- creased progressively in dHP, V1, primary auditory cortex (A1), and primary somatosensory cortex (S1) during (during) and after (post) 1-Hz dDG stimulation, which showed an increase in the spatial extent of connectivity maps (Fig. 4B, Left). The interhemi- spheric rsfMRI connectivity strengthened significantly during stim- ulation in dHP, V1, A1, and S1 (Fig. 4B, Middle; n = 18; dHP: 37.3 ± 7.4%, P < 0.01; V1: 55.6 ± 11.5%, P < 0.01; A1: 44.2 ± 6.9%, P < 0.05; and S1: 43.3 ± 9.0%, P < 0.01; one-way ANOVA followed by Bonferroni’s post hoc test). We also observed a sig- nificant enhancement of interhemispheric rsfMRI connectivity post stimulation (Fig. 4B, Middle; n = 18; dHP: 46.2 ± 8.6%, P < 0.001; V1: 72.9 ± 13.8%, P < 0.001; A1: 53.9 ± 7.6%, P < 0.01; and S1: 58.9 ± 10.9%, P < 0.001; one-way ANOVA fol- lowed by Bonferroni’s post hoc test). We then computed the connectivity spectrum of rsfMRI signals that were bilaterally correlated. We observed an increase in infraslow (<0.1 Hz) rsfMRI BOLD activity in dHP, V1, A1, and S1 during and post stimulation (Fig. 4B, Right). We further examined the intra- hemispheric or local rsfMRI connectivity, which was increased during (Fig. 4C, Left; n = 18; ipsilateral dHP: 59.6 ± 8.6%, P < 0.001; contralateral dHP: 55.6 ± 8.2%, P < 0.01; ipsilateral S1: 38.3 ± 9.4%, P < 0.05; and contralateral S1: 45.6 ± 8.7%, P < 0.01; one-way ANOVA followed by Bonferroni’s post hoc test) and post stimulation (Fig. 4C; n = 18; ipsilateral dHP: 58.4 ± 6.9%, P < 0.01; contralateral dHP: 57.4 ± 6.8%, P < 0.01; ip- silateral V1: 43.8 ± 5.4%, P < 0.01; contralateral V1: 45.7 ± 11.3%, P < 0.01; contralateral A1: 33.7 ± 7.3%, P < 0.05; ip- silateral S1: 51.2 ± 7.7%, P < 0.01; and contralateral S1: 60.5 ± 13.0%, P < 0.01; one-way ANOVA followed by Bonferroni’s post hoc test).