The brain is a complex, multiscale dynamical system composed of many interacting regions. Knowledge of the spatiotemporal organization of these interactions is critical for establishing a solid understanding of the brain’s functional architecture and the relationship between neural dynamics and cognition in health and disease. The possibility of studying these dynamics through careful analysis of neuroimaging data has catalyzed substantial interest in methods that estimate time-resolved fluctuations in functional connectivity (often referred to as “dynamic” or time-varying functional connectivity; TVFC). At the same time, debates have emerged regarding the application of TVFC analyses to resting fMRI data, and about the statistical validity, physiological origins, and cognitive and behavioral relevance of resting TVFC. These and other unresolved issues complicate interpretation of resting TVFC findings and limit the insights that can be gained from this promising new research area. This article brings together scientists with a variety of perspectives on resting TVFC to review the current literature in light of these issues. We introduce core concepts, define key terms, summarize controversies and open questions, and present a forward-looking perspective on how resting TVFC analyses can be rigorously and productively applied to investigate a wide range of questions in cognitive and systems neuroscience.

Normal brain function requires interactions between spatially separated, and functionally specialized, macroscopic regions, yet the directionality of these interactions in large-scale functional networks is unknown. Magnetoencephalography was used to determine the directionality of these interactions, where directionality was inferred from time series of beamformer-reconstructed estimates of neuronal activation, using a recently proposed measure of phase transfer entropy. We observed well-organized posterior-to-anterior patterns of information flow in the higher-frequency bands (alpha1, alpha2, and beta band), dominated by regions in the visual cortex and posterior default mode network. Opposite patterns of anterior-toposterior flow were found in the theta band, involving mainly regions in the frontal lobe that were sending information to a more distributed network. Many strong information senders in the theta band were also frequent receivers in the alpha2 band, and vice versa. Our results provide evidence that large-scale resting-state patterns of information flow in the human brain form frequencydependent reentry loops that are dominated by flow from parietooccipital cortex to integrative frontal areas in the higher-frequency bands, which is mirrored by a theta band anterior-to-posterior flow.information flow | phase transfer entropy | resting-state networks | magnetoencephalography | atlas-based beamforming T he brain is an extremely complex system (1-3) containing, at the macroscopic scale, interconnected functional units (4) with more-or-less specific information processing capabilities (5). However, cognitive functions require the coordinated activity of these spatially separated units, where the oscillatory nature of neuronal activity may provide a possible mechanism (6-9). A complete description of these interactions, in terms of both strength and directionality, is therefore necessary for the understanding of both normal and abnormal brain functioning.Functional interactions may be inferred from statistical dependencies between the time series of neuronal activity at different sites, so-called functional connectivity (10). Indeed, interactions in large-scale functional networks have been observed using Electroencephalography, Magnetoencephalography (EEG/MEG) and functional Magnetic Resonance Imaging (fMRI) (e.g., refs. 11-14). However, as yet, little is known about the directionality of these interactions in large-scale functional networks during the resting state. Estimating directionality from fMRI is challenging due to its limited temporal resolution and indirect relation to neuronal activity (15, 16). In contrast, EEG studies in healthy controls have revealed a front-to-back pattern of directed connectivity, particularly in the alpha band (17-22), consistent with modeling studies that have shown that such patterns may arise due to differences in the number of anatomical connections (the degree) of anterior and posterior regions (22, 23). However, modeled patterns of information flow depend on the a...

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Objectives: Gray matter (GM) atrophy is common in multiple sclerosis (MS), as is cognitive dysfunction. Understanding the exact relationship between atrophy and cognition requires further investigation. The aim of this study was to investigate the relationship between subcortical GM atrophy and cognition in early relapsing onset MS.Methods: Structural MRI and neuropsychological evaluations were performed in 120 patients (80 women) and 50 controls (30 women), part of an early inception cohort, 6 years postdiagnosis. Deep GM volumes were segmented automatically. Cognition was assessed in 7 domains.Stepwise linear regression was used to predict average cognition in the patient group.Results: Most deep GM volumes were reduced in patients, with larger effects on average in men (Ϫ11%) than in women (Ϫ6.3%). Only the bilateral hippocampus, amygdala, and right nucleus accumbens in men, and right hippocampus and nucleus accumbens, bilateral amygdala, and putamen in women, showed no atrophy compared to controls. All cognitive domains except visuospatial memory were affected in men; none were significantly affected in women. In the MS group, average cognition was best predicted by thalamic volume, sex, and education (adjusted R 2 ϭ 0.31), while lesion volume was not a significant predictor in the model. Conclusions:Six years postdiagnosis, almost all subcortical structures were affected by MS, especially in men. Cognition was most severely affected in male patients. Thalamic volume, sex, and education best predicted average cognition. These results underline the relevance of specific subcortical structures to cognition, as well as the relevance of (sex-specific) atrophy in MS. Neurology ® 2012;79:1754-1761 GLOSSARY BRB-N ϭ Brief Repeatable Battery for Neurological disease; DGM ϭ deep gray matter; DMT ϭ disease-modifying therapy; EDSS ϭ Expanded Disability Status Scale; GLM ϭ general linear model; GM ϭ gray matter; MS ϭ multiple sclerosis; NBV ϭ normalized whole brain volume; NCGMV ϭ normalized cortical gray matter volume; NDGMV ϭ normalized deep gray matter volume; NGMV ϭ normalized total gray matter volume; NWMV ϭ normalized total white matter volume; RRMS ϭ relapsingremitting multiple sclerosis; WM ϭ white matter.Atrophy of the gray matter (GM) and white matter (WM) is frequently found in multiple sclerosis (MS), 1,2 and can be reliably measured with MRI.3 Although already present in early stages, GM atrophy becomes much more dominant in the progressive phase of the disease. 4 As the clinical relevance and the specific histopathologic substrate of GM atrophy in MS is not well known, it is the subject of many recent studies. 5,6 While the relationship between brain atrophy and clinical measures has been investigated extensively in the past, 7 how atrophy relates to cognition is only recently becoming clearer. -11As cognitive dysfunction is common and present in all stages of the disease, 12-15 exploring the relationship between atrophy and cognition could provide valuable new information.As most studies that focus...

ObjectiveTo reveal possible differences in whole brain topology of epileptic glioma patients, being low-grade glioma (LGG) and high-grade glioma (HGG) patients. We studied functional networks in these patients and compared them to those in epilepsy patients with non-glial lesions (NGL) and healthy controls. Finally, we related network characteristics to seizure frequency and cognitive performance within patient groups.MethodsWe constructed functional networks from pre-surgical resting-state magnetoencephalography (MEG) recordings of 13 LGG patients, 12 HGG patients, 10 NGL patients, and 36 healthy controls. Normalized clustering coefficient and average shortest path length as well as modular structure and network synchronizability were computed for each group. Cognitive performance was assessed in a subset of 11 LGG and 10 HGG patients.ResultsLGG patients showed decreased network synchronizability and decreased global integration compared to healthy controls in the theta frequency range (4–8 Hz), similar to NGL patients. HGG patients’ networks did not significantly differ from those in controls. Network characteristics correlated with clinical presentation regarding seizure frequency in LGG patients, and with poorer cognitive performance in both LGG and HGG glioma patients.ConclusionLesion histology partly determines differences in functional networks in glioma patients suffering from epilepsy. We suggest that differences between LGG and HGG patients’ networks are explained by differences in plasticity, guided by the particular lesional growth pattern. Interestingly, decreased synchronizability and decreased global integration in the theta band seem to make LGG and NGL patients more prone to the occurrence of seizures and cognitive decline.

Background: To understand neurophysiological mechanisms underlying cognitive dysfunction in low-grade glioma (LGG) patients by evaluating the spatial structure of 'resting-state' brain networks with graph theory.

BackgroundAlthough epilepsy affects almost 1% of the world population, diagnosis of this debilitating disease is still difficult. The EEG is an important tool for epilepsy diagnosis and classification, but the sensitivity of interictal epileptiform discharges (IEDs) on the first EEG is only 30–50%. Here we investigate whether using ‘functional connectivity’ can improve the diagnostic sensitivity of the first interictal EEG in the diagnosis of epilepsy.Methodology/Principal FindingsPatients were selected from a database with 390 standard EEGs of patients after a first suspected seizure. Patients who were later diagnosed with epilepsy (i.e. ≥two seizures) were compared to matched non-epilepsy patients (with a minimum follow-up of one year). The synchronization likelihood (SL) was used as an index of functional connectivity of the EEG, and average SL per patient was calculated in seven frequency bands. In total, 114 patients were selected. Fifty-seven patients were diagnosed with epilepsy (20 had IEDs on their EEG) and 57 matched patients had other diagnoses. Epilepsy patients had significantly higher SL in the theta band than non-epilepsy patients. Furthermore, theta band SL proved to be a significant predictor of a diagnosis of epilepsy. When only those epilepsy patients without IEDs were considered (n = 74), theta band SL could predict diagnosis with specificity of 76% and sensitivity of 62%.Conclusion/SignificanceTheta band functional connectivity may be a useful diagnostic tool in diagnosing epilepsy, especially in those patients who do not show IEDs on their first EEG. Our results indicate that epilepsy diagnosis could be improved by using functional connectivity.

BackgroundBoth epilepsy patients and brain tumor patients show altered functional connectivity and less optimal brain network topology when compared to healthy controls, particularly in the theta band. Furthermore, the duration and characteristics of epilepsy may also influence functional interactions in brain networks. However, the specific features of connectivity and networks in tumor-related epilepsy have not been investigated yet. We hypothesize that epilepsy characteristics are related to (theta band) connectivity and network architecture in operated glioma patients suffering from epileptic seizures. Included patients participated in a clinical study investigating the effect of levetiracetam monotherapy on seizure frequency in glioma patients, and were assessed at two time points: directly after neurosurgery (t1), and six months later (t2). At these time points, magnetoencephalography (MEG) was recorded and information regarding clinical status and epilepsy history was collected. Functional connectivity was calculated in six frequency bands, as were a number of network measures such as normalized clustering coefficient and path length.ResultsAt the two time points, MEG registrations were performed in respectively 17 and 12 patients. No changes in connectivity or network topology occurred over time. Increased theta band connectivity at t1 and t2 was related to a higher total number of seizures. Furthermore, higher number of seizures was related to a less optimal, more random brain network topology. Other factors were not significantly related to functional connectivity or network topology.ConclusionsThese results indicate that (pathologically) increased theta band connectivity is related to a higher number of epileptic seizures in brain tumor patients, suggesting that theta band connectivity changes are a hallmark of tumor-related epilepsy. Furthermore, a more random brain network topology is related to greater vulnerability to seizures. Thus, functional connectivity and brain network architecture may prove to be important parameters of tumor-related epilepsy.