Tracking whole-brain connectivity dynamics in the resting state

A high resolution 7-Tesla resting-state fMRI test-retest dataset with cognitive and physiological measures

Here we present a test-retest dataset of functional magnetic resonance imaging (fMRI) data acquired at rest. 22 participants were scanned during two sessions spaced one week apart. Each session includes two 1.5 mm isotropic whole-brain scans and one 0.75 mm isotropic scan of the prefrontal cortex, giving a total of six time-points. Additionally, the dataset includes measures of mood, sustained attention, blood pressure, respiration, pulse, and the content of self-generated thoughts (mind wandering). This data enables the investigation of sources of both intra- and inter-session variability not only limited to physiological changes, but also including alterations in cognitive and affective states, at high spatial resolution. The dataset is accompanied by a detailed experimental protocol and source code of all stimuli used.

Transition dynamics of EEG-based network microstates during mental arithmetic and resting wakefulness reflects task-related modulations and developmental changes

We studied how maturation influences the organization of functional brain networks engaged during mental calculations and in resting state. Surface EEG measurements from 20 children (8-12 years) and 25 students (21-26 years) were analyzed. Interregional synchronization of brain activity was quantified by means of Phase Lag Index and for various frequency bands. Based on these pairwise estimates of functional connectivity, we formed graphs which were then characterized in terms of local structure [local efficiency (LE)] and overall integration (global efficiency). The overall data analytic scheme was applied twice, in a static and time-varying mode. Our results showed a characteristic trend: functional segregation dominates the network organization of younger brains. Moreover, in childhood, the overall functional network possesses more prominent small-world network characteristics than in early acorrect in xmldulthood in accordance with the Neural Efficiency Hypothesis. The above trends were intensified by the time-varying approach and identified for the whole set of tested frequency bands (from δ to low γ). By mapping the time-indexed connectivity patterns to multivariate timeseries of nodal LE measurements, we carried out an elaborate study of the functional segregation dynamics and demonstrated that the underlying network undergoes transitions between a restricted number of stable states, that can be thought of as "network-level microstates". The rate of these transitions provided a robust marker of developmental and task-induced alterations, that was found to be insensitive to reference montage and independent component analysis denoising.

Large-scale probabilistic functional modes from resting state fMRI

It is well established that it is possible to observe spontaneous, highly structured, fluctuations in human brain activity from functional magnetic resonance imaging (fMRI) when the subject is ‘at rest’. However, characterising this activity in an interpretable manner is still a very open problem.

In this paper, we introduce a method for identifying modes of coherent activity from resting state fMRI (rfMRI) data. Our model characterises a mode as the outer product of a spatial map and a time course, constrained by the nature of both the between-subject variation and the effect of the haemodynamic response function. This is presented as a probabilistic generative model within a variational framework that allows Bayesian inference, even on voxelwise rfMRI data. Furthermore, using this approach it becomes possible to infer distinct extended modes that are correlated with each other in space and time, a property which we believe is neuroscientifically desirable.

We assess the performance of our model on both simulated data and high quality rfMRI data from the Human Connectome Project, and contrast its properties with those of both spatial and temporal independent component analysis (ICA). We show that our method is able to stably infer sets of modes with complex spatio-temporal interactions and spatial differences between subjects.

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We introduce a probabilistic model for modes in resting state fMRI.

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Our hierarchical model captures subject variability and haemodynamic effects.

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We illustrate its performance on simulated data and rfMRI data from 200 subjects.

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We demonstrate the ability of our method to infer spatio-temporally interacting modes.

Simultaneous blind separation and clustering of coactivated EEG/MEG sources for analyzing spontaneous brain activity

Analysis of the dynamics (non-stationarity) of functional connectivity patterns has recently received a lot of attention in the neuroimaging community. Most analysis has been using functional magnetic resonance imaging (fMRI), partly due to the inherent technical complexity of the electro- or magnetoencephalography (EEG/MEG) signals, but EEG/MEG holds great promise in analyzing fast changes in connectivity. Here, we propose a method for dynamic connectivity analysis of EEG/MEG, combining blind source separation with dynamic connectivity analysis in a single probabilistic model. Blind source separation is extremely useful for interpretation of the connectivity changes, and also enables rejection of artifacts. Dynamic connectivity analysis is performed by clustering the coactivation patterns of separated sources by modeling their variances. Experiments on resting-state EEG show that the obtained clusters correlate with physiologically meaningful quantities.

Identification of brain networks using time-varying spatial constraints of neural activity reconstruction

Electroencephalographic (EEG) data give a direct non-invasive measurement of neural brain activity. Nevertheless, the common assumption about EEG stationarity (time-invariant process) is a strong limitation for understanding real behavior of underlying neural networks. Here, we propose an approach for finding networks of brain regions connected by functional associations (functional connectivity) that vary along the time. To this end, we compute a set of a priori spatial dictionaries that represent brain areas with similar temporal stochastic dynamics, and then, we model relationship between areas as a time-varying process. We test our approach in both simulated and real EEG data where results show that inherent interpretability provided by the time-varying process can be useful to describe underlying neural networks.

Predicting learning plateau of working memory from whole-brain intrinsic network connectivity patterns

Individual learning performance of cognitive function is related to functional connections within ‘task-activated' regions where activities increase during the corresponding cognitive tasks. On the other hand, since any brain region is connected with other regions and brain-wide networks, learning is characterized by modulations in connectivity between networks with different functions. Therefore, we hypothesized that learning performance is determined by functional connections among intrinsic networks that include both task-activated and less-activated networks. Subjects underwent resting-state functional MRI and a short period of training (80–90 min) in a working memory task on separate days. We calculated functional connectivity patterns of whole-brain intrinsic networks and examined whether a sparse linear regression model predicts a performance plateau from the individual patterns. The model resulted in highly accurate predictions (R² = 0.73, p = 0.003). Positive connections within task-activated networks, including the left fronto-parietal network, accounted for nearly half (48%) of the contribution ratio to the prediction. Moreover, consistent with our hypothesis, connections of the task-activated networks with less-activated networks showed a comparable contribution (44%). Our findings suggest that learning performance is potentially constrained by system-level interactions within task-activated networks as well as those between task-activated and less-activated networks.

Signature of consciousness in the dynamics of resting-state brain activity

At rest, the brain is traversed by spontaneous functional connectivity patterns. Two hypotheses have been proposed for their origins: they may reflect a continuous stream of ongoing cognitive processes as well as random fluctuations shaped by a fixed anatomical connectivity matrix. Here we show that both sources contribute to the shaping of resting-state networks, yet with distinct contributions during consciousness and anesthesia. We measured dynamical functional connectivity with functional MRI during the resting state in awake and anesthetized monkeys. Under anesthesia, the more frequent functional connectivity patterns inherit the structure of anatomical connectivity, exhibit fewer small-world properties, and lack negative correlations. Conversely, wakefulness is characterized by the sequential exploration of a richer repertoire of functional configurations, often dissimilar to anatomical structure, and comprising positive and negative correlations among brain regions. These results reconcile theories of consciousness with observations of long-range correlation in the anesthetized brain and show that a rich functional dynamics might constitute a signature of consciousness, with potential clinical implications for the detection of awareness in anesthesia and brain-lesioned patients.

Dynamic brain functional connectivity modulated by resting-state networks

Studies of large-scale brain functional connectivity using the resting-state functional magnetic resonance imaging have advanced our understanding of human brain functions. Although the evidence of dynamic functional connectivity is accumulating, the variations of functional connectivity over time have not been well characterized. In the present study, we aimed to associate the variations of functional connectivity with the intrinsic activities of resting-state networks during a single resting-state scan by comparing functional connectivity differences between when a network had higher and lower intrinsic activities. The activities of the salience network, default mode network (DMN), and motor network were associated with changes of resting-state functional connectivity. Higher activity of the salience network was accompanied by greater functional connectivity between the fronto-parietal regions and the DMN regions, and between the regions within the DMN. Higher DMN activity was associated with less connectivity between the regions within the DMN, and greater connectivity between the regions within the fronto-parietal network. Higher motor network activity was correlated with greater connectivity between the regions within the motor network, and smaller connectivity between the DMN regions and fronto-parietal regions, and between the DMN regions and the motor regions. In addition, the whole brain network modularity was positively correlated with the motor network activity, suggesting that the brain is more segregated as sub-systems when the motor network is intrinsically activated. Together, these results demonstrate the association between the resting-state connectivity variations and the intrinsic activities of specific networks, which can provide insights on the dynamic changes in large-scale brain connectivity and network configurations.

Circuit to construct mapping: a mathematical tool for assisting the diagnosis and treatment in major depressive disorder

Major depressive disorder (MDD) is a serious condition with a lifetime prevalence exceeding 16% worldwide. MDD is a heterogeneous disorder that involves multiple behavioral symptoms on the one hand and multiple neuronal circuits on the other hand. In this review, we integrate the literature on cognitive and physiological biomarkers of MDD with the insights derived from mathematical models of brain networks, especially models that can be used for fMRI datasets. We refer to the recent NIH research domain criteria initiative, in which a concept of "constructs" as functional units of mental disorders is introduced. Constructs are biomarkers present at multiple levels of brain functioning - cognition, genetics, brain anatomy, and neurophysiology. In this review, we propose a new approach which we called circuit to construct mapping (CCM), which aims to characterize causal relations between the underlying network dynamics (as the cause) and the constructs referring to the clinical symptoms of MDD (as the effect). CCM involves extracting diagnostic categories from behavioral data, linking circuits that are causal to these categories with use of clinical neuroimaging data, and modeling the dynamics of the emerging circuits with attractor dynamics in order to provide new, neuroimaging-related biomarkers for MDD. The CCM approach optimizes the clinical diagnosis and patient stratification. It also addresses the recent demand for linking circuits to behavior, and provides a new insight into clinical treatment by investigating the dynamics of neuronal circuits underneath cognitive dimensions of MDD. CCM can serve as a new regime toward personalized medicine, assisting the diagnosis and treatment of MDD.

Ketamine induces a robust whole-brain connectivity pattern that can be differentially modulated by drugs of different mechanism and clinical profile

Ketamine, an N-methyl-D-aspartate receptor (NMDAR) antagonist, has been studied in relation to the glutamate hypothesis of schizophrenia and increases dissociation, positive and negative symptom ratings. Ketamine effects brain function through changes in brain activity; these activity patterns can be modulated by pre-treatment of compounds known to attenuate the effects of ketamine on glutamate release. Ketamine also has marked effects on brain connectivity; we predicted that these changes would also be modulated by compounds known to attenuate glutamate release. Here, we perform task-free pharmacological magnetic resonance imaging (phMRI) to investigate the functional connectivity effects of ketamine in the brain and the potential modulation of these effects by pre-treatment of the compounds lamotrigine and risperidone, compounds hypothesised to differentially modulate glutamate release. Connectivity patterns were assessed by combining windowing, graph theory and multivariate Gaussian process classification. We demonstrate that ketamine has a robust effect on the functional connectivity of the human brain compared to saline (87.5 % accuracy). Ketamine produced a shift from a cortically centred, to a subcortically centred pattern of connections. This effect is strongly modulated by pre-treatment with risperidone (81.25 %) but not lamotrigine (43.75 %). Based on the differential effect of these compounds on ketamine response, we suggest the observed connectivity effects are primarily due to NMDAR blockade rather than downstream glutamatergic effects. The connectivity changes contrast with amplitude of response for which no differential effect between pre-treatments was detected, highlighting the necessity of these techniques in forming an informed view of the mechanistic effects of pharmacological compounds in the human brain.

Multimodal based classification of schizophrenia patients

Schizophrenia is currently diagnosed by physicians through clinical assessment and their evaluation of patient's self-reported experiences over the longitudinal course of the illness. There is great interest in identifying biologically based markers at the onset of illness, rather than relying on the evolution of symptoms across time. Functional network connectivity shows promise in providing individual subject predictive power. The majority of previous studies considered the analysis of functional connectivity during resting-state using only fMRI. However, exclusive reliance on fMRI to generate such networks, may limit inference on dysfunctional connectivity, which is hypothesized to underlie patient symptoms. In this work, we propose a framework for classification of schizophrenia patients and healthy control subjects based on using both fMRI and band limited envelope correlation metrics in MEG to interrogate functional network components in the resting state. Our results show that the combination of these two methods provide valuable information that captures fundamental characteristics of brain network connectivity in schizophrenia. Such information is useful for prediction of schizophrenia patients. Classification accuracy performance was improved significantly (up to ≈ 7%) relative to only the fMRI method and (up to ≈ 21%) relative to only the MEG method.

Assessing dynamic brain graphs of time-varying connectivity in fMRI data: application to healthy controls and patients with schizophrenia

Graph theory-based analysis has been widely employed in brain imaging studies, and altered topological properties of brain connectivity have emerged as important features of mental diseases such as schizophrenia. However, most previous studies have focused on graph metrics of stationary brain graphs, ignoring that brain connectivity exhibits fluctuations over time. Here we develop a new framework for accessing dynamic graph properties of time-varying functional brain connectivity in resting-state fMRI data and apply it to healthy controls (HCs) and patients with schizophrenia (SZs). Specifically, nodes of brain graphs are defined by intrinsic connectivity networks (ICNs) identified by group independent component analysis (ICA). Dynamic graph metrics of the time-varying brain connectivity estimated by the correlation of sliding time-windowed ICA time courses of ICNs are calculated. First- and second-level connectivity states are detected based on the correlation of nodal connectivity strength between time-varying brain graphs. Our results indicate that SZs show decreased variance in the dynamic graph metrics. Consistent with prior stationary functional brain connectivity works, graph measures of identified first-level connectivity states show lower values in SZs. In addition, more first-level connectivity states are disassociated with the second-level connectivity state which resembles the stationary connectivity pattern computed by the entire scan. Collectively, the findings provide new evidence about altered dynamic brain graphs in schizophrenia, which may underscore the abnormal brain performance in this mental illness.

Spin-glass model predicts metastable brain states that diminish in anesthesia

Patterns of resting state connectivity change dynamically and may represent modes of cognitive information processing. The diversity of connectivity patterns (global brain states) reflects the information capacity of the brain and determines the state of consciousness. In this work, computer simulation was used to explore the repertoire of global brain states as a function of cortical activation level. We implemented a modified spin glass model to describe UP/DOWN state transitions of neuronal populations at a mesoscopic scale based on resting state BOLD fMRI data. Resting state fMRI was recorded in 20 participants and mapped to 10,000 cortical regions (sites) defined on a group-aligned cortical surface map. Each site represented the population activity of a ~20 mm(2) area of the cortex. Cross-correlation matrices of the mapped BOLD time courses of the set of sites were calculated and averaged across subjects. In the model, each cortical site was allowed to interact with the 16 other sites that had the highest pair-wise correlation values. All sites stochastically transitioned between UP and DOWN states under the net influence of their 16 pairs. The probability of local state transitions was controlled by a single parameter T corresponding to the level of global cortical activation. To estimate the number of distinct global states, first we ran 10,000 simulations at T = 0. Simulations were started from random configurations that converged to one of several distinct patterns. Using hierarchical clustering, at 99% similarity, close to 300 distinct states were found. At intermediate T, metastable state configurations were formed suggesting critical behavior with a sharp increase in the number of metastable states at an optimal T. Both reduced activation (anesthesia, sleep) and increased activation (hyper-activation) moved the system away from equilibrium, presumably incompatible with conscious mentation. During equilibrium, the diversity of large-scale brain states was maximum, compatible with maximum information capacity-a presumed condition of consciousness.

Exploring spatiotemporal network transitions in task functional MRI

A critical question for cognitive neuroscience regards how transitions between cognitive states emerge from the dynamic activity of functional brain networks. Here we combine a simple data reorganization with spatial independent component analysis (ICA), enabling a spatiotemporal ICA (stICA) which captures the consistent evolution of networks during the onset and offset of a task. The technique was applied to functional magnetic resonance imaging (MRI) (FMRI) datasets involving alternating between rest and task, and to simple synthetic data. Starting and finishing time-points of periods of interest (anchors) were defined at task block onsets and offsets. For each subject, the 10 volumes following each anchor were extracted and concatenated spatially, producing a single 3D sample. Samples for all anchors and subjects were concatenated along the fourth dimension. This 4D dataset was decomposed using ICA into spatiotemporal components. One component exhibited the transition with task onset from a default mode network (DMN) becoming less active to a frontoparietal control network becoming more active. We observed other changes with relevance to understanding network dynamics, for example, the DMN showed a changing spatial distribution, shifting to an anterior/superior pattern of deactivation during task from a posterior/inferior pattern during rest. By anchoring analyses to periods associated with the onsets and offsets of task, our approach reveals novel aspects of the dynamics of network activity accompanying these transitions. Importantly, these findings were observed without specifying a priori either the spatial networks or the task time courses.

Mutually temporally independent connectivity patterns: a new framework to study the dynamics of brain connectivity at rest with application to explain group difference based on gender

Functional connectivity analysis of the human brain is an active area in fMRI research. It focuses on identifying meaningful brain networks that have coherent activity either during a task or in the resting state. These networks are generally identified either as collections of voxels whose time series correlate strongly with a pre-selected region or voxel, or using data-driven methodologies such as independent component analysis (ICA) that compute sets of maximally spatially independent voxel weightings (component spatial maps (SMs)), each associated with a single time course (TC). Studies have shown that regardless of the way these networks are defined, the activity coherence among them has a dynamic nature which is hard to estimate with global coherence analysis such as correlation or mutual information. Sliding window analyses in which functional network connectivity (FNC) is estimated separately at each time window is one of the more widely employed approaches to studying the dynamic nature of functional network connectivity (dFNC). Observed FNC patterns are summarized and replaced with a smaller set of prototype connectivity patterns ("states" or "components"), and then a dynamical analysis is applied to the resulting sequences of prototype states. In this work we are looking for a small set of connectivity patterns whose weighted contributions to the dynamically changing dFNCs are independent of each other in time. We discuss our motivation for this work and how it differs from existing approaches. Also, in a group analysis based on gender we show that males significantly differ from females by occupying significantly more combinations of these connectivity patterns over the course of the scan.

Effects of Beta-Amyloid on Resting State Functional Connectivity Within and Between Networks Reflect Known Patterns of Regional Vulnerability

Beta-amyloid (Aβ) deposition is one of the hallmarks of Alzheimer's disease (AD). However, it is also present in some cognitively normal elderly adults and may represent a preclinical disease state. While AD patients exhibit disrupted functional connectivity (FC) both within and between resting-state networks, studies of preclinical cases have focused primarily on the default mode network (DMN). The extent to which Aβ-related effects occur outside of the DMN and between networks remains unclear. In the present study, we examine how within- and between-network FC are related to both global and regional Aβ deposition as measured by [(11)C]PIB-PET in 92 cognitively normal older people. We found that within-network FC changes occurred in multiple networks, including the DMN. Changes of between-network FC were also apparent, suggesting that regions maintaining connections to multiple networks may be particularly susceptible to Aβ-induced alterations. Cortical regions showing altered FC clustered in parietal and temporal cortex, areas known to be susceptible to AD pathology. These results likely represent a mix of local network disruption, compensatory reorganization, and impaired control network function. They indicate the presence of Aβ-related dysfunction of neural systems in cognitively normal people well before these areas become hypometabolic with the onset of cognitive decline.

Dynamic connectivity states estimated from resting fMRI Identify differences among Schizophrenia, bipolar disorder, and healthy control subjects

Schizophrenia (SZ) and bipolar disorder (BP) share significant overlap in clinical symptoms, brain characteristics, and risk genes, and both are associated with dysconnectivity among large-scale brain networks. Resting state functional magnetic resonance imaging (rsfMRI) data facilitates studying macroscopic connectivity among distant brain regions. Standard approaches to identifying such connectivity include seed-based correlation and data-driven clustering methods such as independent component analysis (ICA) but typically focus on average connectivity. In this study, we utilize ICA on rsfMRI data to obtain intrinsic connectivity networks (ICNs) in cohorts of healthy controls (HCs) and age matched SZ and BP patients. Subsequently, we investigated difference in functional network connectivity, defined as pairwise correlations among the timecourses of ICNs, between HCs and patients. We quantified differences in both static (average) and dynamic (windowed) connectivity during the entire scan duration. Disease-specific differences were identified in connectivity within different dynamic states. Notably, results suggest that patients make fewer transitions to some states (states 1, 2, and 4) compared to HCs, with most such differences confined to a single state. SZ patients showed more differences from healthy subjects than did bipolars, including both hyper and hypo connectivity in one common connectivity state (dynamic state 3). Also group differences between SZ and bipolar patients were identified in patterns (states) of connectivity involving the frontal (dynamic state 1) and frontal-parietal regions (dynamic state 3). Our results provide new information about these illnesses and strongly suggest that state-based analyses are critical to avoid averaging together important factors that can help distinguish these clinical groups.

The 4th Schizophrenia International Research Society Conference, 5-9 April 2014, Florence, Italy: a summary of topics and trends

The 4th Schizophrenia International Research Society Conference was held in Florence, Italy, April 5-9, 2014 and this year had as its emphasis, "Fostering Collaboration in Schizophrenia Research". Student travel awardees served as rapporteurs for each oral session, summarized the important contributions of each session and then each report was integrated into a final summary of data discussed at the entire conference by topic. It is hoped that by combining data from different presentations, patterns of interest will emerge and thus lead to new progress for the future. In addition, the following report provides an overview of the conference for those who were present, but could not participate in all sessions, and those who did not have the opportunity to attend, but who would be interested in an update on current investigations ongoing in the field of schizophrenia research.

Cortical connective field estimates from resting state fMRI activity

One way to study connectivity in visual cortical areas is by examining spontaneous neural activity. In the absence of visual input, such activity remains shaped by the underlying neural architecture and, presumably, may still reflect visuotopic organization. Here, we applied population connective field (CF) modeling to estimate the spatial profile of functional connectivity in the early visual cortex during resting state functional magnetic resonance imaging (RS-fMRI). This model-based analysis estimates the spatial integration between blood-oxygen level dependent (BOLD) signals in distinct cortical visual field maps using fMRI. Just as population receptive field (pRF) mapping predicts the collective neural activity in a voxel as a function of response selectivity to stimulus position in visual space, CF modeling predicts the activity of voxels in one visual area as a function of the aggregate activity in voxels in another visual area. In combination with pRF mapping, CF locations on the cortical surface can be interpreted in visual space, thus enabling reconstruction of visuotopic maps from resting state data. We demonstrate that V1 ➤ V2 and V1 ➤ V3 CF maps estimated from resting state fMRI data show visuotopic organization. Therefore, we conclude that—despite some variability in CF estimates between RS scans—neural properties such as CF maps and CF size can be derived from resting state data.

The functional connectivity landscape of the human brain

Functional brain networks emerge and dissipate over a primarily static anatomical foundation. The dynamic basis of these networks is inter-regional communication involving local and distal regions. It is assumed that inter-regional distances play a pivotal role in modulating network dynamics. Using three different neuroimaging modalities, 6 datasets were evaluated to determine whether experimental manipulations asymmetrically affect functional relationships based on the distance between brain regions in human participants. Contrary to previous assumptions, here we show that short- and long-range connections are equally likely to strengthen or weaken in response to task demands. Additionally, connections between homotopic areas are the most stable and less likely to change compared to any other type of connection. Our results point to a functional connectivity landscape characterized by fluid transitions between local specialization and global integration. This ability to mediate functional properties irrespective of spatial distance may engender a diverse repertoire of cognitive processes when faced with a dynamic environment.

The chronnectome: time-varying connectivity networks as the next frontier in fMRI data discovery

Recent years have witnessed a rapid growth of interest in moving functional magnetic resonance imaging (fMRI) beyond simple scan-length averages and into approaches that capture time-varying properties of connectivity. In this Perspective we use the term "chronnectome" to describe metrics that allow a dynamic view of coupling. In the chronnectome, coupling refers to possibly time-varying levels of correlated or mutually informed activity between brain regions whose spatial properties may also be temporally evolving. We primarily focus on multivariate approaches developed in our group and review a number of approaches with an emphasis on matrix decompositions such as principle component analysis and independent component analysis. We also discuss the potential these approaches offer to improve characterization and understanding of brain function. There are a number of methodological directions that need to be developed further, but chronnectome approaches already show great promise for the study of both the healthy and the diseased brain.

Characterizing and Differentiating Brain State Dynamics via Hidden Markov Models

Functional connectivity measured from resting state fMRI (R-fMRI) data has been widely used to examine the brain's functional activities and has been recently used to characterize and differentiate brain conditions. However, the dynamical transition patterns of the brain's functional states have been less explored. In this work, we propose a novel computational framework to quantitatively characterize the brain state dynamics via hidden Markov models (HMMs) learned from the observations of temporally dynamic functional connectomics, denoted as functional connectome states. The framework has been applied to the R-fMRI dataset including 44 post-traumatic stress disorder (PTSD) patients and 51 normal control (NC) subjects. Experimental results show that both PTSD and NC brains were undergoing remarkable changes in resting state and mainly transiting amongst a few brain states. Interestingly, further prediction with the best-matched HMM demonstrates that PTSD would enter into, but could not disengage from, a negative mood state. Importantly, 84% of PTSD patients and 86% of NC subjects are successfully classified via multiple HMMs using majority voting.

Dynamic resting state functional connectivity in awake and anesthetized rodents

Since its introduction, resting-state functional magnetic resonance imaging (rsfMRI) has been a powerful tool for investigating functional neural networks in both normal and pathological conditions. When measuring resting-state functional connectivity (RSFC), most rsfMRI approaches do not consider its temporal variations and thus only provide the averaged RSFC over the scan time. Recently, there has been a surge of interest to investigate the dynamic characteristics of RSFC in humans, and promising results have been yielded. However, our knowledge regarding the dynamic RSFC in animals remains sparse. In the present study we utilized the single-volume co-activation method to systematically study the dynamic properties of RSFC within the networks of infralimbic cortex (IL) and primary somatosensory cortex (S1) in both awake and anesthetized rats. Our data showed that both IL and S1 networks could be decomposed into several spatially reproducible but temporally changing co-activation patterns (CAPs), suggesting that dynamic RSFC was indeed a characteristic feature in rodents. In addition, we demonstrated that anesthesia profoundly impacted the dynamic RSFC of neural circuits subserving cognitive and emotional functions but had less effects on sensorimotor systems. Finally, we examined the temporal characteristics of each CAP, and found that individual CAPs exhibited consistent temporal evolution patterns. Together, these results suggest that dynamic RSFC might be a general phenomenon in vertebrate animals. In addition, this study has paved the way for further understanding the alterations of dynamic RSFC in animal models of brain disorders.

Study on the Relationships between Intrinsic Functional Connectivity of the Default Mode Network and Transient Epileptic Activity

Rationale: Simultaneous recording of electroencephalogram and functional MRI (EEG–fMRI) is a powerful tool for localizing epileptic networks via the detection of hemodynamic changes correlated with interictal epileptic discharges (IEDs). fMRI can be used to study the long-lasting effect of epileptic activity by assessing stationary functional connectivity during the resting-state period [especially, the connectivity of the default mode network (DMN)]. Temporal lobe epilepsy (TLE) and idiopathic generalized epilepsy (IGE) are associated with low responsiveness and disruption of DMN activity. A dynamic functional connectivity approach might enable us to determine the effect of IEDs on DMN connectivity and to better understand the correlation between DMN connectivity changes and altered consciousness.

Method: We studied dynamic changes in DMN intrinsic connectivity and their relation to IEDs. Six IGE patients (with generalized spike and slow-waves) and 6 TLE patients (with unilateral left temporal spikes) were included. Functional connectivity before, during, and after IEDs was estimated using a sliding window approach and compared with the baseline period.

Results: No dependence on window size was observed. The baseline DMN connectivity was decreased in the left hemisphere (ipsilateral to the epileptic focus) in TLEs and was less strong but remained bilateral in IGEs. We observed an overall increase in DMN intrinsic connectivity prior to the onset of IEDs in both IGEs and TLEs. After IEDs in TLEs, we found that DMN connectivity increased before it returned to baseline values. Most of the DMN regions with increased connectivity before and after IEDs were lateralized to the left hemisphere in TLE (i.e., ipsilateral to the epileptic focus).

Conclusion: Results suggest that DMN connectivity may facilitate IED generation and may be affected at the time of the IED. However, these results need to be confirmed in a larger independent cohort.

Differential lateralization of hippocampal connectivity reflects features of recent context and ongoing demands: an examination of immediate post-task activity

Neuroimaging studies have shown that task demands affect connectivity patterns in the human brain not only during task performance but also during subsequent rest periods. Our goal was to determine whether ongoing connectivity patterns during rest contain information about both the current rest state, as well as the recently terminated task. Our experimental design consisted of two types of active tasks that were followed by two types of low-demand rest states. Using this design, we examined whether hippocampal functional connectivity during wakeful rest reflects both features of a recently terminated task and those of the current resting-state condition. We identified four types of networks: (i) one whose connectivity with the hippocampus was determined only by features of a recently terminated task, (ii) one whose connectivity was determined only by features of the current resting-state, (iii) one whose connectivity reflected aspects of both the recently terminated task and ongoing resting-state features, and (iv) one whose connectivity with the hippocampus was strong, but not affected by any external factor. The left and right hippocampi played distinct roles in these networks. These findings suggest that ongoing hippocampal connectivity networks mediate information integration across multiple temporal scales, with hippocampal laterality moderating these connectivity patterns.

Spatial and temporal functional connectivity changes between resting and attentive states

Remote brain regions show correlated spontaneous activity at rest within well described intrinsic connectivity networks (ICNs). Meta-analytic coactivation studies have uncovered networks similar to resting ICNs, suggesting that in task states connectivity modulations may occur principally within ICNs. However, it has also been suggested that specific "hub" regions dynamically link networks under different task conditions. Here, we used functional magnetic resonance imaging at rest and a continuous visual attention task in 16 participants to investigate whether a shift from rest to attention was reflected by within-network connectivity modulation, or changes in network topography. Our analyses revealed evidence for both modulation of connectivity within the default-mode (DMN) and dorsal attention networks (DAN) between conditions, and identified a set of regions including the temporoparietal junction (TPJ) and posterior middle frontal gyrus (MFG) that switched between the DMN and DAN depending on the task. We further investigated the temporal nonstationarity of flexible (TPJ and MFG) regions during both attention and rest. This showed that moment-to-moment differences in connectivity at rest mirrored the variation in connectivity between tasks. Task-dependent changes in functional connectivity of flexible regions may, therefore, be understood as shifts in the proportion of time specific connections are engaged, rather than a switch between networks per se. This ability of specific regions to dynamically link ICNs under different task conditions may play an important role in behavioral flexibility.

Reduced resting-state brain activity in the default mode network in children with (central) auditory processing disorders

BACKGROUND

In recent years, there has been a growing interest in Central Auditory Processing Disorder (C)APD. However, the neural correlates of (C)APD are poorly understood. Previous neuroimaging experiments have shown changes in the intrinsic activity of the brain in various cognitive deficits and brain disorders. The present study investigated the spontaneous brain activity in (C)APD subjects with resting-state fMRI (rs-fMRI).

METHODS

Thirteen children diagnosed with (C)APD and fifteen age and gender-matched controls participated in a rs-fMRI study during which they were asked to relax keeping their eyes open. Two different techniques of the rs-fMRI data analysis were used: Regional Homogeneity (ReHo) and Independent Component Analysis (ICA), which approach is rare.

RESULTS

Both methods of data analysis showed comparable results in the pattern of DMN activity within groups. Additionally, ReHo analysis revealed increased co-activation of the superior frontal gyrus, the posterior cingulate cortex/the precuneus in controls, compared to the (C)APD group. ICA yielded inconsistent results across groups.

CONCLUSIONS

Our ReHo results suggest that (C)APD children seem to present reduced regional homogeneity in brain regions considered a part of the default mode network (DMN). These findings might contribute to a better understanding of neural mechanisms of (C)APD.

Behavioral relevance of the dynamics of the functional brain connectome

While many previous studies assumed the functional connectivity (FC) between brain regions to be stationary, recent studies have demonstrated that FC dynamically varies across time. However, two challenges have limited the interpretability of dynamic FC information. First, a principled framework for selecting the temporal extent of the window used to examine the dynamics is lacking and this has resulted in ad-hoc selections of window lengths and subsequent divergent results. Second, it is unclear whether there is any behavioral relevance to the dynamics of the functional connectome in addition to that obtained from conventional static FC (SFC). In this work, we address these challenges by first proposing a principled framework for selecting the extent of the temporal windows in a dynamic and data-driven fashion based on statistical tests of the stationarity of time series. Further, we propose a method involving three levels of clustering-across space, time, and subjects-which allow for group-level inferences of the dynamics. Next, using a large resting-state functional magnetic resonance imaging and behavioral dataset from the Human Connectome Project, we demonstrate that metrics derived from dynamic FC can explain more than twice the variance in 75 behaviors across different domains (alertness, cognition, emotion, and personality traits) as compared with SFC in healthy individuals. Further, we found that individuals with brain networks exhibiting greater dynamics performed more favorably in behavioral tasks. This indicates that the ease with which brain regions engage or disengage may provide potential biomarkers for disorders involving altered neural circuitry.

A sticky weighted regression model for time-varying resting-state brain connectivity estimation

Despite recent progress on brain connectivity modeling using neuroimaging data such as fMRI, most current approaches assume that brain connectivity networks have time-invariant topology/coefficients. This is clearly problematic as the brain is inherently nonstationary. Here, we present a time-varying model to investigate the temporal dynamics of brain connectivity networks. The proposed method allows for abrupt changes in network structure via a fused least absolute shrinkage and selection operator (LASSO) scheme, as well as recovery of time-varying networks with smoothly changing coefficients via a weighted regression technique. Simulations demonstrate that the proposed method yields improved accuracy on estimating time-dependent connectivity patterns when compared to a static sparse regression model or a weighted time-varying regression model. When applied to real resting-state fMRI datasets from Parkinson's disease (PD) and control subjects, significantly different temporal and spatial patterns were found to be associated with PD. Specifically, PD subjects demonstrated reduced network variability over time, which may be related to impaired cognitive flexibility previously reported in PD. The temporal dynamic properties of brain connectivity in PD subjects may provide insights into brain dynamics associated with PD and may serve as a potential biomarker in future studies.

On spurious and real fluctuations of dynamic functional connectivity during rest

Functional brain networks reconfigure spontaneously during rest. Such network dynamics can be studied by dynamic functional connectivity (dynFC); i.e., sliding-window correlations between regional brain activity. Key parameters-such as window length and cut-off frequencies for filtering-are not yet systematically studied. In this letter we provide the fundamental theory from signal processing to address these parameter choices when estimating and interpreting dynFC. We guide the reader through several illustrative cases, both simple analytical models and experimental fMRI BOLD data. First, we show how spurious fluctuations in dynFC can arise due to the estimation method when the window length is shorter than the largest wavelength present in both signals, even for deterministic signals with a fixed relationship. Second, we study how real fluctuations of dynFC can be explained using a frequency-based view, which is particularly instructive for signals with multiple frequency components such as fMRI BOLD, demonstrating that fluctuations in sliding-window correlation emerge by interaction between frequency components similar to the phenomenon of beat frequencies. We conclude with practical guidelines for the choice and impact of the window length.

Deep learning for neuroimaging: a validation study

Deep learning methods have recently made notable advances in the tasks of classification and representation learning. These tasks are important for brain imaging and neuroscience discovery, making the methods attractive for porting to a neuroimager's toolbox. Success of these methods is, in part, explained by the flexibility of deep learning models. However, this flexibility makes the process of porting to new areas a difficult parameter optimization problem. In this work we demonstrate our results (and feasible parameter ranges) in application of deep learning methods to structural and functional brain imaging data. These methods include deep belief networks and their building block the restricted Boltzmann machine. We also describe a novel constraint-based approach to visualizing high dimensional data. We use it to analyze the effect of parameter choices on data transformations. Our results show that deep learning methods are able to learn physiologically important representations and detect latent relations in neuroimaging data.

Disentangling dynamic networks: Separated and joint expressions of functional connectivity patterns in time

Resting-state functional connectivity (FC) is highly variable across the duration of a scan. Groups of coevolving connections, or reproducible patterns of dynamic FC (dFC), have been revealed in fluctuating FC by applying unsupervised learning techniques. Based on results from k-means clustering and sliding-window correlations, it has recently been hypothesized that dFC may cycle through several discrete FC states. Alternatively, it has been proposed to represent dFC as a linear combination of multiple FC patterns using principal component analysis. As it is unclear whether sparse or nonsparse combinations of FC patterns are most appropriate, and as this affects their interpretation and use as markers of cognitive processing, the goal of our study was to evaluate the impact of sparsity by performing an empirical evaluation of simulated, task-based, and resting-state dFC. To this aim, we applied matrix factorizations subject to variable constraints in the temporal domain and studied both the reproducibility of ensuing representations of dFC and the expression of FC patterns over time. During subject-driven tasks, dFC was well described by alternating FC states in accordance with the nature of the data. The estimated FC patterns showed a rich structure with combinations of known functional networks enabling accurate identification of three different tasks. During rest, dFC was better described by multiple FC patterns that overlap. The executive control networks, which are critical for working memory, appeared grouped alternately with externally or internally oriented networks. These results suggest that combinations of FC patterns can provide a meaningful way to disentangle resting-state dFC.

Evolution of functional connectivity of brain networks and their dynamic interaction in temporal lobe epilepsy

This study presents a cross-sectional investigation of functional networks in temporal lobe epilepsy (TLE) as they evolve over years of disease. Networks of interest were identified based on a priori hypotheses: the network of seizure propagation ipsilateral to the seizure focus, the same regions contralateral to seizure focus, the cross hemisphere network of the same regions, and a cingulate midline network. Resting functional magnetic resonance imaging data were acquired for 20 min in 12 unilateral TLE patients, and 12 age- and gender-matched healthy controls. Functional changes within and between the four networks as they evolve over years of disease were quantified by standard measures of static functional connectivity and novel measures of dynamic functional connectivity. The results suggest an initial disruption of cross-hemispheric networks and an increase in static functional connectivity in the ipsilateral temporal network accompanying the onset of TLE seizures. As seizures progress over years, the static functional connectivity across the ipsilateral network diminishes, while dynamic functional connectivity measures show the functional independence of this ipsilateral network from the network of midline regions of the cingulate declines. This implies a gradual breakdown of the seizure onset and early propagation network involving the ipsilateral hippocampus and temporal lobe as it becomes more synchronous with the network of regions responsible for secondary generalization of the seizures, a process that may facilitate the spread of seizures across the brain. Ultimately, the significance of this evolution may be realized in relating it to symptoms and treatment outcomes of TLE.

Impact of autocorrelation on functional connectivity

Although the impact of serial correlation (autocorrelation) in residuals of general linear models for fMRI time-series has been studied extensively, the effect of autocorrelation on functional connectivity studies has been largely neglected until recently. Some recent studies based on results from economics have questioned the conventional estimation of functional connectivity and argue that not correcting for autocorrelation in fMRI time-series results in "spurious" correlation coefficients. In this paper, first we assess the effect of autocorrelation on Pearson correlation coefficient through theoretical approximation and simulation. Then we present this effect on real fMRI data. To our knowledge this is the first work comprehensively investigating the effect of autocorrelation on functional connectivity estimates. Our results show that although FC values are altered, even following correction for autocorrelation, results of hypothesis testing on FC values remain very similar to those before correction. In real data we show this is true for main effects and also for group difference testing between healthy controls and schizophrenia patients. We further discuss model order selection in the context of autoregressive processes, effects of frequency filtering and propose a preprocessing pipeline for connectivity studies.

How local excitation-inhibition ratio impacts the whole brain dynamics

The spontaneous activity of the brain shows different features at different scales. On one hand, neuroimaging studies show that long-range correlations are highly structured in spatiotemporal patterns, known as resting-state networks, on the other hand, neurophysiological reports show that short-range correlations between neighboring neurons are low, despite a large amount of shared presynaptic inputs. Different dynamical mechanisms of local decorrelation have been proposed, among which is feedback inhibition. Here, we investigated the effect of locally regulating the feedback inhibition on the global dynamics of a large-scale brain model, in which the long-range connections are given by diffusion imaging data of human subjects. We used simulations and analytical methods to show that locally constraining the feedback inhibition to compensate for the excess of long-range excitatory connectivity, to preserve the asynchronous state, crucially changes the characteristics of the emergent resting and evoked activity. First, it significantly improves the model's prediction of the empirical human functional connectivity. Second, relaxing this constraint leads to an unrealistic network evoked activity, with systematic coactivation of cortical areas which are components of the default-mode network, whereas regulation of feedback inhibition prevents this. Finally, information theoretic analysis shows that regulation of the local feedback inhibition increases both the entropy and the Fisher information of the network evoked responses. Hence, it enhances the information capacity and the discrimination accuracy of the global network. In conclusion, the local excitation-inhibition ratio impacts the structure of the spontaneous activity and the information transmission at the large-scale brain level.

Dynamic functional connectivity analysis reveals transient states of dysconnectivity in schizophrenia

Schizophrenia is a psychotic disorder characterized by functional dysconnectivity or abnormal integration between distant brain regions. Recent functional imaging studies have implicated large-scale thalamo-cortical connectivity as being disrupted in patients. However, observed connectivity differences in schizophrenia have been inconsistent between studies, with reports of hyperconnectivity and hypoconnectivity between the same brain regions. Using resting state eyes-closed functional imaging and independent component analysis on a multi-site data that included 151 schizophrenia patients and 163 age- and gender matched healthy controls, we decomposed the functional brain data into 100 components and identified 47 as functionally relevant intrinsic connectivity networks. We subsequently evaluated group differences in functional network connectivity, both in a static sense, computed as the pairwise Pearson correlations between the full network time courses (5.4 minutes in length), and a dynamic sense, computed using sliding windows (44 s in length) and k-means clustering to characterize five discrete functional connectivity states. Static connectivity analysis revealed that compared to healthy controls, patients show significantly stronger connectivity, i.e., hyperconnectivity, between the thalamus and sensory networks (auditory, motor and visual), as well as reduced connectivity (hypoconnectivity) between sensory networks from all modalities. Dynamic analysis suggests that (1), on average, schizophrenia patients spend much less time than healthy controls in states typified by strong, large-scale connectivity, and (2), that abnormal connectivity patterns are more pronounced during these connectivity states. In particular, states exhibiting cortical–subcortical antagonism (anti-correlations) and strong positive connectivity between sensory networks are those that show the group differences of thalamic hyperconnectivity and sensory hypoconnectivity. Group differences are weak or absent during other connectivity states. Dynamic analysis also revealed hypoconnectivity between the putamen and sensory networks during the same states of thalamic hyperconnectivity; notably, this finding cannot be observed in the static connectivity analysis. Finally, in post-hoc analyses we observed that the relationships between sub-cortical low frequency power and connectivity with sensory networks is altered in patients, suggesting different functional interactions between sub-cortical nuclei and sensorimotor cortex during specific connectivity states. While important differences between patients with schizophrenia and healthy controls have been identified, one should interpret the results with caution given the history of medication in patients. Taken together, our results support and expand current knowledge regarding dysconnectivity in schizophrenia, and strongly advocate the use of dynamic analyses to better account for and understand functional connectivity differences.

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Studied both static and dynamic connectivity changes in schizophrenia during rest

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Small but significant connectivity differences might be obscured in static analysis.

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Patients show significant differences in dwell times in multiple states.

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Disrupted thalamo-cortical connectivity in schizophrenia in a state-specific manner

Interplay between functional connectivity and scale-free dynamics in intrinsic fMRI networks

Studies employing functional connectivity-type analyses have established that spontaneous fluctuations in functional magnetic resonance imaging (fMRI) signals are organized within large-scale brain networks. Meanwhile, fMRI signals have been shown to exhibit 1/f-type power spectra - a hallmark of scale-free dynamics. We studied the interplay between functional connectivity and scale-free dynamics in fMRI signals, utilizing the fractal connectivity framework - a multivariate extension of the univariate fractional Gaussian noise model, which relies on a wavelet formulation for robust parameter estimation. We applied this framework to fMRI data acquired from healthy young adults at rest and while performing a visual detection task. First, we found that scale-invariance existed beyond univariate dynamics, being present also in bivariate cross-temporal dynamics. Second, we observed that frequencies within the scale-free range do not contribute evenly to inter-regional connectivity, with a systematically stronger contribution of the lowest frequencies, both at rest and during task. Third, in addition to a decrease of the Hurst exponent and inter-regional correlations, task performance modified cross-temporal dynamics, inducing a larger contribution of the highest frequencies within the scale-free range to global correlation. Lastly, we found that across individuals, a weaker task modulation of the frequency contribution to inter-regional connectivity was associated with better task performance manifesting as shorter and less variable reaction times. These findings bring together two related fields that have hitherto been studied separately - resting-state networks and scale-free dynamics, and show that scale-free dynamics of human brain activity manifest in cross-regional interactions as well.

Isoflurane induces dose-dependent alterations in the cortical connectivity profiles and dynamic properties of the brain's functional architecture

Despite their widespread use, the effect of anesthetic agents on the brain's functional architecture remains poorly understood. This is particularly true of alterations that occur beyond the point of induced unconsciousness. Here, we examined the distributed intrinsic connectivity of macaques across six isoflurane levels using resting-state functional MRI (fMRI) following the loss of consciousness. The results from multiple analysis strategies showed stable functional connectivity (FC) patterns between 1.00% and 1.50% suggesting this as a suitable range for anesthetized nonhuman primate resting-state investigations. Dose-dependent effects were evident at moderate to high dosages showing substantial alteration of the functional topology and a decrease or complete loss of interhemispheric cortical FC strength including that of contralateral homologues. The assessment of dynamic FC patterns revealed that the functional repertoire of brain states is related to anesthesia depth and most strikingly, that the number of state transitions linearly decreases with increased isoflurane dosage. Taken together, the results indicate dose-specific spatial and temporal alterations of FC that occur beyond the typically defined endpoint of consciousness. Future work will be necessary to determine how these findings generalize across anesthetic types and extend to the transition between consciousness and unconsciousness.

Posterior cingulate cortex-related co-activation patterns: a resting state FMRI study in propofol-induced loss of consciousness

Background

Recent studies have been shown that functional connectivity of cerebral areas is not a static phenomenon, but exhibits spontaneous fluctuations over time. There is evidence that fluctuating connectivity is an intrinsic phenomenon of brain dynamics that persists during anesthesia. Lately, point process analysis applied on functional data has revealed that much of the information regarding brain connectivity is contained in a fraction of critical time points of a resting state dataset. In the present study we want to extend this methodology for the investigation of resting state fMRI spatial pattern changes during propofol-induced modulation of consciousness, with the aim of extracting new insights on brain networks consciousness-dependent fluctuations.

Methods

Resting-state fMRI volumes on 18 healthy subjects were acquired in four clinical states during propofol injection: wakefulness, sedation, unconsciousness, and recovery. The dataset was reduced to a spatio-temporal point process by selecting time points in the Posterior Cingulate Cortex (PCC) at which the signal is higher than a given threshold (i.e., BOLD intensity above 1 standard deviation). Spatial clustering on the PCC time frames extracted was then performed (number of clusters = 8), to obtain 8 different PCC co-activation patterns (CAPs) for each level of consciousness.

Results

The current analysis shows that the core of the PCC-CAPs throughout consciousness modulation seems to be preserved. Nonetheless, this methodology enables to differentiate region-specific propofol-induced reductions in PCC-CAPs, some of them already present in the functional connectivity literature (e.g., disconnections of the prefrontal cortex, thalamus, auditory cortex), some others new (e.g., reduced co-activation in motor cortex and visual area).

Conclusion

In conclusion, our results indicate that the employed methodology can help in improving and refining the characterization of local functional changes in the brain associated to propofol-induced modulation of consciousness.

Time-resolved resting-state brain networks

Neuronal dynamics display a complex spatiotemporal structure involving the precise, context-dependent coordination of activation patterns across a large number of spatially distributed regions. Functional magnetic resonance imaging (fMRI) has played a central role in demonstrating the nontrivial spatial and topological structure of these interactions, but thus far has been limited in its capacity to study their temporal evolution. Here, using high-resolution resting-state fMRI data obtained from the Human Connectome Project, we mapped time-resolved functional connectivity across the entire brain at a subsecond resolution with the aim of understanding how nonstationary fluctuations in pairwise interactions between regions relate to large-scale topological properties of the human brain. We report evidence for a consistent set of functional connections that show pronounced fluctuations in their strength over time. The most dynamic connections are intermodular, linking elements from topologically separable subsystems, and localize to known hubs of default mode and fronto-parietal systems. We found that spatially distributed regions spontaneously increased, for brief intervals, the efficiency with which they can transfer information, producing temporary, globally efficient network states. Our findings suggest that brain dynamics give rise to variations in complex network properties over time, possibly achieving a balance between efficient information-processing and metabolic expenditure.

Evaluating dynamic bivariate correlations in resting-state fMRI: a comparison study and a new approach

To date, most functional Magnetic Resonance Imaging (fMRI) studies have assumed that the functional connectivity (FC) between time series from distinct brain regions is constant across time. However, recently, there has been an increased interest in quantifying possible dynamic changes in FC during fMRI experiments, as it is thought that this may provide insight into the fundamental workings of brain networks. In this work we focus on the specific problem of estimating the dynamic behavior of pair-wise correlations between time courses extracted from two different regions of the brain. We critique the commonly used sliding-window technique, and discuss some alternative methods used to model volatility in the finance literature that could also prove to be useful in the neuroimaging setting. In particular, we focus on the Dynamic Conditional Correlation (DCC) model, which provides a model-based approach towards estimating dynamic correlations. We investigate the properties of several techniques in a series of simulation studies and find that DCC achieves the best overall balance between sensitivity and specificity in detecting dynamic changes in correlations. We also investigate its scalability beyond the bivariate case to demonstrate its utility for studying dynamic correlations between more than two brain regions. Finally, we illustrate its performance in an application to test-retest resting state fMRI data.

Dynamic functional connectivity of the default mode network tracks daydreaming

Humans spend much of their time engaged in stimulus-independent thoughts, colloquially known as "daydreaming" or "mind-wandering." A fundamental question concerns how awake, spontaneous brain activity represents the ongoing cognition of daydreaming versus unconscious processes characterized as "intrinsic." Since daydreaming involves brief cognitive events that spontaneously fluctuate, we tested the hypothesis that the dynamics of brain network functional connectivity (FC) are linked with daydreaming. We determined the general tendency to daydream in healthy adults based on a daydreaming frequency scale (DDF). Subjects then underwent both resting state functional magnetic resonance imaging (rs-fMRI) and fMRI during sensory stimulation with intermittent thought probes to determine the occurrences of mind-wandering events. Brain regions within the default mode network (DMN), purported to be involved in daydreaming, were assessed for 1) static FC across the entire fMRI scans, and 2) dynamic FC based on FC variability (FCV) across 30s progressively sliding windows of 2s increments within each scan. We found that during both resting and sensory stimulation states, individual differences in DDF were negatively correlated with static FC between the posterior cingulate cortex and a ventral DMN subsystem involved in future-oriented thought. Dynamic FC analysis revealed that DDF was positively correlated with FCV within the same DMN subsystem in the resting state but not during stimulation. However, dynamic but not static FC, in this subsystem, was positively correlated with an individual's degree of self-reported mind-wandering during sensory stimulation. These findings identify temporal aspects of spontaneous DMN activity that reflect conscious and unconscious processes.

Understanding Structural-Functional Relationships in the Human Brain

Relating the brain’s structural connectivity (SC) to its functional connectivity (FC) is a fundamental goal in neuroscience because it is capable of aiding our understanding of how the relatively fixed SC architecture underlies human cognition and diverse behaviors. With the aid of current noninvasive imaging technologies (e.g., structural MRI, diffusion MRI, and functional MRI) and graph theory methods, researchers have modeled the human brain as a complex network of interacting neuronal elements and characterized the underlying structural and functional connectivity patterns that support diverse cognitive functions. Specifically, research has demonstrated a tight SC-FC coupling, not only in interregional connectivity strength but also in network topologic organizations, such as community, rich-club, and motifs. Moreover, this SC-FC coupling exhibits significant changes in normal development and neuropsychiatric disorders, such as schizophrenia and epilepsy. This review summarizes recent progress regarding the SC-FC relationship of the human brain and emphasizes the important role of large-scale brain networks in the understanding of structural-functional associations. Future research directions related to this topic are also proposed.

Structure and function of complex brain networks

An increasing number of theoretical and empirical studies approach the function of the human brain from a network perspective. The analysis of brain networks is made feasible by the development of new imaging acquisition methods as well as new tools from graph theory and dynamical systems. This review surveys some of these methodological advances and summarizes recent findings on the architecture of structural and functional brain networks. Studies of the structural connectome reveal several modules or network communities that are interlinked by hub regions mediating communication processes between modules. Recent network analyses have shown that network hubs form a densely linked collective called a "rich club," centrally positioned for attracting and dispersing signal traffic. In parallel, recordings of resting and task-evoked neural activity have revealed distinct resting-state networks that contribute to functions in distinct cognitive domains. Network methods are increasingly applied in a clinical context, and their promise for elucidating neural substrates of brain and mental disorders is discussed.

Same task, different strategies: how brain networks can be influenced by memory strategy

Previous functional neuroimaging studies demonstrated that different neural networks underlie different types of cognitive processing by engaging participants in particular tasks, such as verbal or spatial working memory (WM) tasks. However, we report here that even when a WM task is defined as verbal or spatial, different types of memory strategies may be used to complete it, with concomitant variations in brain activity. We developed a questionnaire to characterize the type of strategy used by individual members in a group of 28 young healthy participants (18-25 years) during a spatial WM task. A cluster analysis was performed to differentiate groups. We acquired functional magnetoencephalography and structural diffusion tensor imaging measures to characterize the brain networks associated with the use of different strategies. We found two types of strategies were used during the spatial WM task, a visuospatial and a verbal strategy, and brain regions and time courses of activation differed between participants who used each. Task performance also varied by type of strategy used with verbal strategies showing an advantage. In addition, performance on neuropsychological tests (indices from Wechsler Adult Intelligence Scale-IV, Rey Complex Figure Test) correlated significantly with fractional anisotropy measures for the visuospatial strategy group in white matter tracts implicated in other WM and attention studies. We conclude that differences in memory strategy can have a pronounced effect on the locations and timing of brain activation and that these differences need further investigation as a possible confounding factor for studies using group averaging as a means for summarizing results.

The spatial structure of resting state connectivity stability on the scale of minutes

Resting state functional MRI (rsfMRI) connectivity patterns are not temporally stable, but fluctuate in time at scales shorter than most common rest scan durations (5–10 min). Consequently, connectivity patterns for two different portions of the same scan can differ drastically. To better characterize this temporal variability and understand how it is spatially distributed across the brain, we scanned subjects continuously for 60 min, at a temporal resolution of 1 s, while they rested inside the scanner. We then computed connectivity matrices between functionally-defined regions of interest for non-overlapping 1 min windows, and classified connections according to their strength, polarity, and variability. We found that the most stable connections correspond primarily to inter-hemispheric connections between left/right homologous ROIs. However, only 32% of all within-network connections were classified as most stable. This shows that resting state networks have some long-term stability, but confirms the flexible configuration of these networks, particularly those related to higher order cognitive functions. The most variable connections correspond primarily to inter-hemispheric, across-network connections between non-homologous regions in occipital and frontal cortex. Finally we found a series of connections with negative average correlation, but further analyses revealed that such average negative correlations may be related to the removal of CSF signals during pre-processing. Using the same dataset, we also evaluated how similarity of within-subject whole-brain connectivity matrices changes as a function of window duration (used here as a proxy for scan duration). Our results suggest scanning for a minimum of 10 min to optimize within-subject reproducibility of connectivity patterns across the entire brain, rather than a few predefined networks.