Francesco de Pasquale

Dynamic functional connectivity: promise, issues, and interpretations.

The brain must dynamically integrate, coordinate, and respond to internal and external stimuli across multiple time scales. Non-invasive measurements of brain activity with fMRI have greatly advanced our understanding of the large-scale functional organization supporting these fundamental features of brain function. Conclusions from previous resting-state fMRI investigations were based upon static descriptions of functional connectivity (FC), and only recently studies have begun to capitalize on the wealth of information contained within the temporal features of spontaneous BOLD FC. Emerging evidence suggests that dynamic FC metrics may index changes in macroscopic neural activity patterns underlying critical aspects of cognition and behavior, though limitations with regard to analysis and interpretation remain. Here, we review recent findings, methodological considerations, neural and behavioral correlates, and future directions in the emerging field of dynamic FC investigations.

Temporal dynamics of spontaneous MEG activity in brain networks

Functional MRI (fMRI) studies have shown that low-frequency (<0.1 Hz) spontaneous fluctuations of the blood oxygenation level dependent (BOLD) signal during restful wakefulness are coherent within distributed large-scale cortical and subcortical networks (resting state networks, RSNs). The neuronal mechanisms underlying RSNs remain poorly understood. Here, we describe magnetoencephalographic correspondents of two well-characterized RSNs: the dorsal attention and the default mode networks. Seed-based correlation mapping was performed using time-dependent MEG power reconstructed at each voxel within the brain. The topography of RSNs computed on the basis of extended (5 min) epochs was similar to that observed with fMRI but confined to the same hemisphere as the seed region. Analyses taking into account the nonstationarity of MEG activity showed transient formation of more complete RSNs, including nodes in the contralateral hemisphere. Spectral analysis indicated that RSNs manifest in MEG as synchronous modulation of band-limited power primarily within the theta, alpha, and beta bands—that is, in frequencies slower than those associated with the local electrophysiological correlates of event-related BOLD responses.

A Cortical Core for Dynamic Integration of Functional Networks in the Resting Human Brain

We used magneto-encephalography to study the temporal dynamics of band-limited power correlation at rest within and across six brain networks previously defined by prior functional magnetic resonance imaging (fMRI) studies. Epochs of transiently high within-network band limited power (BLP) correlation were identified and correlation of BLP time-series across networks was assessed in these epochs. These analyses demonstrate that functional networks are not equivalent with respect to cross-network interactions. The default-mode network and the posterior cingulate cortex, in particular, exhibit the highest degree of transient BLP correlation with other networks especially in the 14-25 Hz (β band) frequency range. Our results indicate that the previously demonstrated neuroanatomical centrality of the PCC and DMN has a physiological counterpart in the temporal dynamics of network interaction at behaviorally relevant timescales. This interaction involved subsets of nodes from other networks during periods in which their internal correlation was low.

A signal-processing pipeline for magnetoencephalography resting-state networks.

To study functional connectivity using magnetoencephalographic (MEG) data, the high-quality source-level reconstruction of brain activity constitutes a critical element. MEG resting-state networks (RSNs) have been documented by means of a dedicated processing pipeline: MEG recordings are decomposed by independent component analysis (ICA) into artifact and brain components (ICs); next, the channel maps associated with the latter ones are projected into the source space and the resulting voxel-wise weights are used to linearly combine the IC time courses. An extensive description of the proposed pipeline is provided here, along with an assessment of its performances with respect to alternative approaches. The following investigations were carried out: (1) ICA decomposition algorithm. Synthetic data are used to assess the sensitivity of the ICA results to the decomposition algorithm, by testing FastICA, INFOMAX, and SOBI. FastICA with deflation approach, a standard solution, provides the best decomposition. (2) Recombination of brain ICs versus subtraction of artifactual ICs (at the channel level). Both the recombination of the brain ICs in the sensor space and the classical procedure of subtracting the artifactual ICs from the recordings provide a suitable reconstruction, with a lower distortion using the latter approach. (3) Recombination of brain ICs after localization versus localization of artifact-corrected recordings. The brain IC recombination after source localization, as implemented in the proposed pipeline, provides a lower source-level signal distortion. (4) Detection of RSNs. The accuracy in source-level reconstruction by the proposed pipeline is confirmed by an improved specificity in the retrieval of RSNs from experimental data.

Disruption of posteromedial large-scale neural communication predicts recovery from coma

Objective: We hypothesize that the major consciousness deficit observed in coma is due to the breakdown of long-range neuronal communication supported by precuneus and posterior cingulate cortex (PCC), and that prognosis depends on a specific connectivity pattern in these networks. Methods: We compared 27 prospectively recruited comatose patients who had severe brain injury (Glasgow Coma Scale score <8; 14 traumatic and 13 anoxic cases) with 14 age-matched healthy participants. Standardized clinical assessment and fMRI were performed on average 4 ± 2 days after withdrawal of sedation. Analysis of resting-state fMRI connectivity involved a hypothesis-driven, region of interest–based strategy. We assessed patient outcome after 3 months using the Coma Recovery Scale–Revised (CRS-R). Results: Patients who were comatose showed a significant disruption of functional connectivity of brain areas spontaneously synchronized with PCC, globally notwithstanding etiology. The functional connectivity strength between PCC and medial prefrontal cortex (mPFC) was significantly different between comatose patients who went on to recover and those who eventually scored an unfavorable outcome 3 months after brain injury (Kruskal-Wallis test, p < 0.001; linear regression between CRS-R and PCC-mPFC activity coupling at rest, Spearman ρ = 0.93, p < 0.003). Conclusion: In both etiology groups (traumatic and anoxic), changes in the connectivity of PCC-centered, spontaneously synchronized, large-scale networks account for the loss of external and internal self-centered awareness observed during coma. Sparing of functional connectivity between PCC and mPFC may predict patient outcome, and further studies are needed to substantiate this potential prognosis biomarker.

The connectivity of functional cores reveals different degrees of segregation and integration in the brain at rest

The principles of functional specialization and integration in the resting brain are implemented in a complex system of specialized networks that share some degree of interaction. Recent studies have identified wider functional modules compared to previously defined networks and reported a small-world architecture of brain activity in which central nodes balance the pressure to evolve segregated pathways with the integration of local systems. The accurate identification of such central nodes is crucial but might be challenging for several reasons, e.g. inter-subject variability and physiological/pathological network plasticity, and recent works reported partially inconsistent results concerning the properties of these cortical hubs. Here, we applied a whole-brain data-driven approach to extract cortical functional cores and examined their connectivity from a resting state fMRI experiment on healthy subjects. Two main statistically significant cores, centered on the posterior cingulate cortex and the supplementary motor area, were extracted and their functional connectivity maps, thresholded at three statistical levels, revealed the presence of two complex systems. One system is consistent with the default mode network (DMN) and gradually connects to visual regions, the other centered on motor regions and gradually connects to more sensory-specific portions of cortex. These two large scale networks eventually converged to regions belonging to the medial aspect of the DMN, potentially allowing inter-network interactions.

Being an agent or an observer: different spectral dynamics revealed by MEG.

Several neuroimaging studies reported that a common set of regions is recruited during action observation and execution and it has been proposed that the modulation of the μ rhythm, in terms of oscillations in the alpha and beta bands might represent the electrophysiological correlate of the underlying brain mechanisms. However, the specific functional role of these bands within the μ rhythm is still unclear. Here, we used magnetoencephalography (MEG) to analyze the spectral and temporal properties of the alpha and beta bands in healthy subjects during an action observation and execution task. We associated the modulation of the alpha and beta power to a broad action observation network comprising several parieto-frontal areas previously detected in fMRI studies. Of note, we observed a dissociation between alpha and beta bands with a slow-down of beta oscillations compared to alpha during action observation. We hypothesize that this segregation is linked to a different sequence of information processing and we interpret these modulations in terms of internal models (forward and inverse). In fact, these processes showed opposite temporal sequences of occurrence: anterior-posterior during action (both in alpha and beta bands) and roughly posterior-anterior during observation (in the alpha band). The observed differentiation between alpha and beta suggests that these two bands might pursue different functions in the action observation and execution processes.

Temporal and Spectral Signatures of the Default Mode Network

The existence of a structured pattern of neuronal activity in the brain at rest has been consistently reported in the neuroscience literature. Multiple techniques, such as fMRI, MEG and EEG, showed that spontaneous, slow fluctuations of cerebral activity are temporally coherent within distributed functional networks resembling those evoked by sensory, motor, and cognitive paradigms. Among these networks, the Default Mode network gained large interest because of its anatomical and functional architecture. In fact, this network seems to reflect the default brain activity at rest and it has been associated with internal mentation, autobiographical memory, thinking about one’s future, theory of mind, self-referential and affective decision making. What processing demands are shared in common across such a variety of tasks is presently unclear, and to disentangle such high level tasks into component processes is challenging. Here, we address some of these aspects by reviewing the current MEG studies on this network. In fact, while MEG data confirm the observed fMRI spatial topography, some new intriguing temporal and frequency properties of this network are revealed. Such findings enrich the original fMRI scenario on the DMN functional roles in terms of internal coupling and cross-network communication in the brain at rest. The Default Mode Network’s internal coupling seems to be characterized by slow frequencies in the alpha and beta range and the cross-network interaction reveals that the DMN plays a central role in the communication across many different resting state networks.

Influence of white matter fiber orientation on R2* revealed by MRI segmentation

To investigate white matter heterogeneity using a multichannel segmentation of a large sample of structural and diffusion magnetic resonance imaging (MRI) data.

The Default Mode Network Connectivity Predicts Cognitive Recovery in Severe Acquired Brain Injured Patients: A Longitudinal Study

To study the functional connectivity in patients with severe acquired brain injury is very challenging for their high level of disability because of a prolonged period of coma, extended lesions, and several cognitive and behavioral disorders. In this article, we investigated in these patients the default mode network and somatomotor connectivity changes at rest longitudinally, in the subacute and late phase after brain injury. The aim of the study is to characterize such connectivity patterns and relate the observed changes to clinical and neuropsychological outcomes of these patients after a period of intensive neurorehabilitation. Our findings show within the default mode network a disruption of connectivity of medial pre-frontal regions and a significant change of amplitude of internal connections. Notably, strongest changes in functional connectivity significantly correlated to consistent clinical and cognitive recovery. This evidence seems to indicate that the reorganization of the Default Mode Network may represent a valid biomarker for the cognitive recovery in patients with severe acquired brain injury.