h 2 ﬃ 40% at age 18 years. However, the lower heritability estimates at age 13 years are likely due to the reduced sample size as a result of motion scrubbing and exclusion due to presence of dental braces incompatible with high magnetic ﬁelds or increased residual noise rather than represent “ true ” changes in additive genetic or common environ- mental variances. This effect is reﬂected in the two-factor common pathway model as “increasing” inﬂuences of the same additive genetic or common environmental factor over time, and is consequently found in the single factor common pathway model as varying estimates of heri- tability due to differences in factor loadings on the individual half-score measures. Therefore, the results from the two-factor model on dynamics of genetic and environmental in ﬂ uences are suggestive at best and generally demonstrate stable additive genetic or common environmental inﬂuences from a single source (Supplementary Table S6). Varying esti- mates for half-score of a single session (i.e. within the same age) are very unlikely to represent short-term ﬂuctuating genetic or environmental inﬂuences, but can most likely be attributed to ﬂuctuating noise (e.g. slight increase in head motion or restlessness during second half of scan). The longitudinal age effects that we found are subtle but wide-spread throughout the brain despite most resting-statenetworks already appearing “ adult-like ” by age 2 years (Gao et al., 2015; Gilmore et al., 2018). We found age-related decreases in functional connectivity for about half of the connections between cortical resting-statenetworks, which likely re ﬂ ect segregation between functionally distinct modules of the brain. A previous longitudinal study during early adolescence re- ported segregation between the frontoparietal (FPN) and default mode network (DMN) (Sherman et al., 2014). Although our results are not statistically signi ﬁ cant, it suggests a possible decrease between the FPN and DMN (β age ¼ 0.0076; p ¼ 0.061 [n.s.]; FDR-corrected p ¼ 0.089
Psychosis has been characterized as the abnormal functional integration of brain processes . In fMRI, functional integration is apparent when spatially inde- pendent brain regions are co-activated in either the pres- ence of a goal-directed task or while the brain is at rest [19, 20]. In the past, many functional imaging studies of individuals with 22q11DS have utilized task-based para- digms, which can be disadvantageous in clinical popula- tions of individuals who cannot complete the task . In contrast, rs-fMRI characterizes functional connectiv- ity (FC) of brain regions at rest. Both independent com- ponent analysis (ICA) and seed-based approaches have provided a canonical set of resting-statenetworks (RSNs) related to visual and sensorimotor processing, executive functioning, and a default mode network (DMN); the latter is involved in internal mentation and memory that increases in activity when the brain is not engaged in overt cognitive tasks [20, 22 – 26].
A frequent criticism of sensor-level connectivity/network analysis is that the results are biased by the effects of volume conduction/ ﬁ eld spread (Schoffelen and Gross, 2009). Our results show that this prob- lem is not completely solved by going to source-space (Fig. 3), as it reveals itself, in our case, in the form of correlation between beamfor- mer weights. As a solution, we used the PLI, which quanti ﬁ es func- tional interactions that are not caused by volume conduction or common sources. The main reason for going to the source-level, in combination with an atlas-based analysis approach, is therefore that it provides a general framework that allows for a direction anatomical interpretation of MEG data, as well as a direct comparison with (functional) connectivity and network studies based on anatomical MRI (Diffusion Tensor Imaging, Voxel-based Morphometry) and func- tional MRI (e.g. Gong et al., 2009; van den Heuvel and Hulshoff Pol, 2010). We envisage that the understanding of resting-statenetworks will be much enhanced by such a combination of different modalities. Similarly, our approach enables the integration and direct comparison of data recorded with different MEG systems. Although we have focussed here on the eyes-closed resting-state, our approach can also be applied to compare patterns of oscillatory activity and functional networks for different cognitive states.
RESULTS: The frontal network was the only network that showed reduced connectivity in patients relative to control subjects. The remaining 5 networks demonstrated both reduced and increased functional connectivity within resting-statenetworks in patients. There was a weak association between connectivity in frontal network and executive function (P ⫽ .029) and a signiﬁcant association between sensorimotor network and ﬁne motor function (P ⫽ .004). Control subjects had 79 pair-wise independent components that showed signiﬁcant temporal coherence across all resting-statenetworks except for default mode network–auditory network. Patients had 66 pairs of independent components that showed signiﬁcant temporal coherence across all resting-statenetworks. Group comparison showed reduced functional network connectivity between default mode network–attention, frontal-sensorimotor, and frontal-visual networks and increased functional network connectivity between frontal-attention, default mode network–sensorimotor, and frontal-visual net- works in patients relative to control subjects.
First, additional studies are needed to extend our evaluation be- yond the 8 RSNs in this study to validate the present findings. Second, the BN learning approach cannot model reciprocal con- nections and temporal causal relations between nodes. Although the BN has been demonstrated as one of the most promising methods in detecting network connections for resting fMRI data, the connection directionality should be cautiously interpreted. 15
Several studies have established high-density DOT imaging of neonatal brain function at the bedside, in the normal newborn nursery or complex neonatal intensive care unit [30,177,178]. Since it is quiet and less sensitive to motion artifacts than fMRI, these studies did not require using a sedating medication. The ﬁ rst high- density DOT study of neonatal restingstatenetworks measured FC within occipital regions of term and pre-term infants . A signi ﬁ cant ﬁ nding was that the bilateral correlation pattern seen within visual cortex of healthy infants was disrupted speci ﬁ cally in an infant with occipital stroke. This study was limited in its imaging ﬁ eld of view (FOV) but successfully showed the potential for clinical utility of high-density DOT. In a recent effort, the FOV was extended to enable mapping multiple restingstatenetworks in newborns . Quantitative and qualitative cross-modality comparisons of these results with subject-matched fMRI connec- tivity results, within a cohort of healthy full-term infants, showed a strong congruence emphasizing the supremacy of high-density DOT for neuroimaging at the bedside.
activity, as measured with BOLD fMRI, has been well established as a metric for identifying functionally connected networks (Fox and Raichle, 2007). Previous studies have demonstrated that the cortical topographies associated with a given restingstate network closely correlate with the cortical regions associated with a task-based activation (Smith et al., 2009). The feasibility of this approach has been preliminarily investigated in the setting of preoperative brain mapping (Kokkonen et al., 2009, Liu et al., 2009, Zhang et al., 2009). Despite the substantial scientific and more limited translational efforts performed thus far, there is still a substantial need for technical expertise to visualize these networks and therefore limited to more specialized centers. The purpose of this study was to investigate the possibility of using a data driven approach that can rapidly, effectively, and independently identify cortical networks. To achieve this goal, we selected a subset of the identified networks traditionally thought to be eloquent in nature and compared them with the clinical gold standard of electrocortical stimulation (ECS). To further define the clinical utility of this approach, we investigated the localization of eloquent cortex in patients with distorted anatomies due to mass lesions. Here we show that through the use of a novel artificial neural network approach, known as the MLP, that seven canonical networks can be identified with a single 30 minute scan. Moreover, there is strong concordance between language and somatomotor restingstatenetworks with ECS localization. Taken together, these findings provide evidence that restingstate fMRI and neural network classification of imaging data can potentially provide a novel tool for neurosurgical brain mapping in the future.
Resting-state fMRI studies demonstrated temporally synchronous fluctuations in brain activity among ensembles of brain regions, suggesting the existence of intrinsic functional networks. A spatial match between some of the resting-statenetworks and regional brain activation during cognitive tasks has been noted, suggesting that resting-statenetworks support particular cognitive abilities. However, the spatial match and predictive value of any resting-state network and regional brain activation during episodic memory is only poorly understood. In order to address this research gap, we obtained fMRI acquired both during rest and a face-name association task in 38 healthy elderly subjects. In separate independent component analyses, networks of correlated brain activity during rest or the episodic memory task were identified. For the independent components identified for task-based fMRI, the design matrix of successful encoding or retrieval trials was regressed against the time course of each of the component to identify significantly activated networks. Spatial regression was used to assess the match of resting-statenetworks against those related to successful memory encoding or retrieval. We found that resting-statenetworks covering the medial temporal, middle temporal, and frontal areas showed increased activity during successful encoding. Resting-statenetworks located within posterior brain regions showed increased activity during successful recognition. However, the level of resting-state network connectivity was not predictive of the task-related activity in these networks. These results suggest that a circumscribed number of functional networks detectable during rest become engaged during successful episodic memory. However, higher intrinsic connectivity at rest may not translate into higher network expression during episodic memory.
voxel time courses within a brain mask (Fig. 2, 1st panel). Correlations are calculated using metrics, such as the Pearson correlation coef ﬁ cients. After constructing FC maps from the individual subjects ’ data collected at rest and calculating group- level statistics, inferential tests are applied to examine the existence of functional connections between different areas. Also, the contents of FC maps are used as features and then they are used to train a supervised machine learning algorithm for classi ﬁ cation. Finally, the results, implications, and potential issues are explored and the signi ﬁ cant features can be used to ﬁ nd new biomarkers for brain disease diagnosis. The seed-based approach was the ﬁ rst method adopted by Biswal et al. to identify the restingstatenetworks  and is the most straight forward method for rs-FC data analysis. It is a model-based, robust and conceptually clear method, though, it relies strongly on some prior knowledge for the identi ﬁ cation of the seed regions. Thus, it does not determine the nature and number of independent networks supporting the resting-state of the brain function.
RESULTS: Patients had lower functional connectivity than healthy subjects in all 5 resting-statenetworks, mainly involving the basal ganglia, thalamus, anterior cingulate, dorsolateral prefrontal and temporo-occipital cortices, supramarginal gyrus, supplementary motor area, and cerebellum. Compared with healthy subjects, patients also displayed subcortical atrophy and DTI abnormalities. Decreased thalamic functional connectivity correlated with clinical scores, as assessed by the Hoehn and Yahr Scale and by the bulbar and mentation subitems of the Progressive Supranuclear Palsy Rating Scale. Decreased pallidum functional connectivity correlated with lower Mini- Mental State Examination scores; decreased functional connectivity in the dorsal midbrain tegmentum network correlated with lower scores in the Frontal Assessment Battery.
components. Such ambiguity renders it difﬁcult to study the superposed activity of RSNs including their lagging structure 31 . Therefore, we build upon a recent framework for sparsity- pursuing regularization, termed total activation (TA) 32 , to temporally deconvolve fMRI time series. TA makes use of the prior knowledge of the HRF that enables us to use the full- spectrum fMRI signal (that is, without bandpass ﬁltering). By applying TA, we obtain three types of information: (1) activity- related signals that are de-noised fMRI signals, (2) sustained, or block-type, activity-inducing signals that are deconvolved signals, (3) innovation signals that are the derivative of the activity- inducing signals and encode transient brain activity by spikes. We then perform temporal clustering on the whole-brain innovation signals extracted from resting-state fMRI data of 14 healthy volunteers and recover the corresponding spatial patterns, which we refer to as innovation-driven co-activation patterns (iCAPs). We demonstrate that, despite representing short transients in fMRI activity, these iCAPs are robust for both positive and negative transients, and reﬂect common resting-state patterns. In addition, iCAPs overlap not only spatially, but also temporally when back-projected to their sustained-activity-inducing signals. The total activity of all the iCAPs exceeds three times the duration of the resting-state scan, however, overlapping iCAPs do not co-occur in every possible combination. Clustering iCAPs according to observed combinations reveals a high-level organization of brain function during rest that is consistent with the iCAPs’ behaviour proﬁles 33 . We speciﬁcally study iCAPs that relate to well-known resting-statenetworks such as the default- mode network (DMN) and the attention network. Temporal dynamics of these iCAPs show that activity in the DMN is not always anti-correlated with attention network, as has been assumed on the basis of results from conventional analysis 34 . Instead, segregated subcomponents of the DMN in the posterior cingulate cortex (PCC) differentially interact with the attention network.
definition of a seed region based on anatomy or additional func- tional exploration, to reveal correlations between the average sig- nal time course of voxels within the ROI and the time courses of the other brain voxels. The approach we used here (ICA) does not require any prior knowledge of the temporal or spatial patterns of brain responses. ICA is a popular mathematic approach that max- imizes statistical independence among its components to identify distinct resting-statenetworks. A practical drawback that can be attributed to ICA is that the user must choose among dozens of functional components, which most probably reflect neurofunc- tional systems over noise. This selection is often made by visual inspection. Other strategies have been imagined to assist the IC selection. Kokkonen et al 12 used a template-matching method, for
3.13×3.13×3 mm voxels], using a General Electric (GE) 3.0 T Signa scanner 9.0, VH3 with quadrature birdcage transmit-receive radio frequency coil. During the 6 min restingstate fMRI acquisition period (180 scans) the subjects were asked to remain awake with their eyes open. A fixation cross was presented on the screen. Subjects were told to lie still and fixate on the cross throughout the scan. Minimal cognitive tasks such as staring at a cross typically do not disrupt restingstatenetworks 18 . Physiologic data was collected simultaneously with fMRI data because cardiorespiratory fluctuations are known to influence fMRI intrinsic connectivity within several brain networks 3, 7 . Respiratory volume data were collected by securing a GE magnetic resonance-compatible chest plethysmograph around each subject’s abdomen. Cardiac data were collected using an infrared pulse oximeter (GE) attached to the subject’s right middle finger. Participants’ motion was minimized using foam pads placed around the head along with a forehead strap. In addition high resolution structural images were acquired [TR = 10.5ms, TE = 3.4ms, TI= 200ms, FA = 25°, 24 cm FOV, 256×256 matrix, 0.94×0.94×1.5 mm voxels, yielding 106 slices] using a spoiled gradient echo (SPGR) inversion recovery sequence. Inspection of individual T1 MR-images revealed no gross morphological abnormalities for any patient or subject.
is, resting-statenetworks). However, given that these networks are present during tasks and that no brain is ever truly at ‘rest’, some investigators have oﬀ ered the more accurate term of intrinsic connectivity networks (ICNs)  rather than resting-statenetworks. Use of the ICN moniker is gaining popularity and we will use this term for the remainder of the review. For the same reasons, we prefer the term task-free fMRI (TF-fMRI) rather than rs-fMRI. Th e absence of a predetermined experimental paradigm in TF-fMRI pre cludes the use of traditional fMRI methods for modeling the hemodynamic response related to experimentally isolated changes in the BOLD signal. Th erefore, we will brieﬂ y review some of the most popular methods currently used to investigate ICNs in TF-fMRI and discuss potential confounds that these techniques are susceptible to before discussing the application of these techniques to studies related to AD.
Resting-state fMRI was first developed in human, and much more studies have been conducted in humans than all other species combined. At the local network level, converging evidence from different studies and with different analysis methods (seed based, ICA and clustering) have indicated that the human brain has several robust functional systems, including default mode network (DMN), visual networks, sensorimotor networks, frontal networks, salience networks and parietal-frontal networks (van den Heuvel 2010). Among those networks, DMN is perhaps the most well characterized network. Important anatomical structures of DMN include medial prefrontal cortex, posterior cingulated cortex and precuneus. Unlike other resting-statenetworks, DMN has been shown to have higher neural activity during rest, characterized by higher blood flow and oxygen consumption as measured by PET during rest and increased BOLD signal during rest (compared to task) measured by fMRI (Raichle et al., 2001). Therefore, DMN is a unique network with elevated and synchronized neural activity during rest, while other resting-statenetworks are only synchronized. Functions of this unique network is indicated in consciousness and internal states (Raichle and Snyder, 2007) and is related to a large number of cognitive and pathological conditions, such as development and ageing, disorders of consciousness and psychiatric diseases (Buckner et al., 2008).
Restingstatenetworks (RSNs) are of fundamental importance in human systems neuroscience with evidence suggesting that they are integral to healthy brain function and perturbed in pathology. Despite rapid progress in this area, the temporal dynamics governing the functional connectivities that underlie RSN structure remain poorly understood. Here, we present a framework to help further our understanding of RSN dynamics. We de- scribe a methodology which exploits the direct nature and high temporal resolution of magnetoencephalogra- phy (MEG). This technique, which builds on previous work, extends from solving fundamental confounds in MEG (source leakage) to multivariate modelling of transient connectivity. The resulting processing pipeline fa- cilitates direct (electrophysiological) measurement of dynamic functional networks. Our results show that, when functional connectivity is assessed in small time windows, the canonical sensorimotor network can be decomposed into a number of transiently synchronising sub-networks, recruitment of which depends on cur- rent mental state. These rapidly changing sub-networks are spatially focal with, for example, bilateral primary sensory and motor areas resolved into two separate sub-networks. The likely interpretation is that the larger canonical sensorimotor network most often seen in neuroimaging studies reﬂects only a temporal aggregate of these transient sub-networks. Our approach opens new frontiers to study RSN dynamics, showing that MEG is capable of revealing the spatial, temporal and spectral signature of the human connectome in health and disease.
To investigate the sustained effects of acupuncture into post-stimulus rest, we evaluated functional connectivity changes in “restingstatenetworks,” RSNs. Previous fMRI studies have found that in a task-free state (i.e. rest), multiple distributed brain areas demonstrate temporal correlation or intrinsic “functional connectivity” in low frequency ranges [11,22,32,46]. For example, studies have found correlation in resting fMRI signal from sensorimotor cortices of opposite hemispheres . This RSN has been referred to as the sensorimotor network, or SMN . Resting connectivity has also been described in the default mode network (DMN) [14,30,35], which involves brain regions putatively engaged in self-referential cognition that are “deactivated” during a variety of externally focused task conditions [for review see 38]. Pain is known to interact with the DMN. Both acute pain  and acupuncture  are known to induce deactivation in DMN regions, while chronic pain may be associated with less pronounced DMN deactivation in response to a visual attention task . Furthermore, perception of somatosensory stimuli near sensory threshold is facilitated by decreased DMN activity in a brief pre-event resting period .
dynamics of spontaneous neuronal activities due to the fractal behavior. The theoretical expectation that functional connectivity may be inuenced by fractal behavior leads us to take into account the correlation of fractal-free input signals as a novel concept of restingstate functional connectivity while the Pearson correlation of neuroimaging signals has been the most popular deni- tion of functional connectivity. This particular correlation, which is independent of fractal behavior, is called the nonfractal connectivity. Its mathematical de- scription is provided in section 3. The nonfractal connectivity is not exactly identical to the correlation of spontaneous neuronal population activities due to the nonlinearity of neurophysiological systems. However, it may give us better information on correlation structure of spontaneous neuronal populations than ordinary correlation of neuroimaging data since it eliminates the distortion of functional connectivity due to fractal behavior.
The aim of the present study was to quantify the spatial and spectral abnormalities in brain activity at a restingstate with MEG and to assess its relationship with clinical characteristics. To address this aim, we ana- lyzed MEG signals from low- to high-frequency ranges. To the best of our knowledge, the present study is the first to examine the neuromagnetic signatures of aber- rant resting-state brain activity in patients with acute migraine during headache attacks. With a better under- standing of the cerebral mechanisms of migraine, head- ache treatments targeting at cortical dysfunctions (for example, transcranial magnetic stimulation, which cur- rently shows great promise) could be refined and their clinical usefulness can be significantly improved.
In the present study, using resting-state EEG, we showed that migraine patients in the inter-ictal and ictal phases, but not in the pre- and post-ictal phases, exhibited lower EEG power and coherence than HCs. Comparing the phase groups in series pairs (inter-ictal, pre-ictal, ictal, post-ictal), we observed increases in EEG power and coherence from the inter-ictal to the pre-ictal phase, decreases from the pre-ictal to the ictal phase, and finally increases from the ictal to the post-ictal phase. The fronto-occipital network in inter-ictal patients showed enhanced EEG coherence as compared to HC or pre-ictal patients. Of note, our results showed higher effect sizes in the eyes-open EEG than eyes-closed EEG. The exact mechanisms are not clear. We do not know whether there is a link to the facts that visual cortical hyperexcitibility is more common in patients with mi- graine  and visual areas in eyes-open condition show greater activation than in eyes-closed condition .