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SignificanceA description of the structural and functional connections in the human brain is necessary for the understanding of both normal and abnormal brain functioning. Although it has become clear in recent years that stable patterns of functional connectivity can be observed during the resting state, to date, it remains unclear what the dominant patterns of information flow are in this functional connectome and how these relate to the integration of brain function. Our results are the first to describe the large-scale frequency-specific patterns of information flow in the human brain, showing that different subsystems form a loop through which information “reverberates” or “circulates.” These results could be extended to give insights into how such flow optimizes integrative cognitive processing. 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-to-posterior 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 frequency-dependent reentry loops that are dominated by flow from parieto-occipital cortex to integrative frontal areas in the higher-frequency bands, which is mirrored by a theta band anterior-to-posterior flow.

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.

Functional brain networks are shaped and constrained by the underlying structural network. However, functional networks are not merely a one-to-one reflection of the structural network. Several theories have been put forward to understand the relationship between structural and functional networks. However, it remains unclear how these theories can be unified. Two existing recent theories state that 1) functional networks can be explained by all possible walks in the structural network, which we will refer to as the series expansion approach, and 2) functional networks can be explained by a weighted combination of the eigenmodes of the structural network, the so-called eigenmode approach. To elucidate the unique or common explanatory power of these approaches to estimate functional networks from the structural network, we analysed the relationship between these two existing views. Using linear algebra, we first show that the eigenmode approach can be written in terms of the series expansion approach, i.e., walks on the structural network associated with different hop counts correspond to different weightings of the eigenvectors of this network. Second, we provide explicit expressions for the coefficients for both the eigenmode and series expansion approach. These theoretical results were verified by empirical data from Diffusion Tensor Imaging (DTI) and functional Magnetic Resonance Imaging (fMRI), demonstrating a strong correlation between the mappings based on both approaches. Third, we analytically and empirically demonstrate that the fit of the eigenmode approach to measured functional data is always at least as good as the fit of the series expansion approach, and that errors in the structural data lead to large errors of the estimated coefficients for the series expansion approach. Therefore, we argue that the eigenmode approach should be preferred over the series expansion approach. Results hold for eigenmodes of the weighted adjacency matrices as well as eigenmodes of the graph Laplacian. ​Taken together, these results provide an important step towards unification of existing theories regarding the structure-function relationships in brain networks. •Two prominent theories on mappings between structural and functional networks are:•Functional networks can be explained by all possible walks in the structural network.•Functional networks can be explained by the eigenmodes of the structural network.•We show that these two approaches are equivalent using empirical and simulated data.•We provide explicit expressions for model coefficients for both approaches.

Research has shown that dynamic functional connectivity (dFC) in Parkinson's disease (PD) is associated with better attention performance and with motor symptom severity. In the current study, we aimed to investigate dFC of both the default mode network (DMN) and the frontoparietal network (FPN) as neural correlates of cognitive functioning in patients with PD. Additionally, we investigated pain and motor problems as symptoms of PD in relation to dFC. Twenty-four PD patients and 27 healthy controls participated in this study. Memory and executive functioning were assessed with neuropsychological tests. Pain was assessed with the Numeric Rating Scale (NRS); motor symptom severity was assessed with the Unified Parkinson's Disease Rating Scale (UPDRS). All subjects underwent resting-state functional magnetic resonance imaging (fMRI), from which dFC was defined by calculating the variability of functional connectivity over a number of sliding windows within each scan. dFC of both the DMN and FPN with the rest of the brain was calculated. Patients performed worse on tests of visuospatial memory, verbal memory and working memory. No difference existed between groups regarding dFC of the DMN nor the FPN with the rest of the brain. A positive correlation existed between dFC of the DMN and visuospatial memory. Our results suggest that dynamics during the resting state are a neural correlate of visuospatial memory in PD patients. Furthermore, we suggest that brain dynamics of the DMN, as measured with dFC, could be a phenomenon specifically linked to cognitive functioning in PD, but not to other symptoms.

Introduction Patients with diffuse glioma often experience neurocognitive impairment already prior to surgery. Pertinent information on whether damage to a specific brain region due to tumor activity results in neurocognitive impairment or not, is relevant in clinical decision-making, and at the same time renders unique information on brain lesion location and functioning relationships. To examine the impact of tumor location on preoperative neurocognitive functioning (NCF), we performed MRI based lesion-symptom mapping. Methods Seventy-two patients (mean age 40 years) with a radiologically suspected glioma were recruited preoperatively. For each of the six cognitive domains tested, we used tumor localization maps and voxel-based lesion-symptom mapping analyses to identify cortical and subcortical regions associated with NCF impairment. Results Compared to healthy controls, preoperative NCF was significantly impaired in all cognitive domains. Most frequently affected were attention (30% of patients) and working memory (20% of patients). Deficits in attention were significantly associated with regions in the left frontal and parietal cortex, including the precentral and parietal-opercular cortex, and in left-sided subcortical fiber tracts, including the arcuate fasciculus and corticospinal tract. Surprisingly, no regions could be related to working memory capacity. For the other neurocognitive domains, impairments were mainly associated with regions in the left hemisphere. Conclusions Prior to treatment, patients with diffuse glioma in the left hemisphere run the highest risk to have NCF deficits. Identification of a left frontoparietal network involved in NCF not only may optimize surgical procedures but may also be integrated in counseling and cognitive rehabilitation for these patients.

Our previous study on cognitive functioning among 195 patients with low-grade glioma (LGG) a mean of 6 years after diagnosis suggested that the tumour itself, rather than the radiotherapy used to treat it, has the most deleterious effect on cognitive functioning; only high fraction dose radiotherapy (>2 Gy) resulted in significant added cognitive deterioration. The present study assesses the radiological and cognitive abnormalities in survivors of LGG at a mean of 12 years after first diagnosis. Patients who have had stable disease since the first assessment were invited for follow-up cognitive assessment (letter–digit substitution test, concept shifting test, Stroop colour–word test, visual verbal learning test, memory comparison test, and categoric word fluency). Compound scores in six cognitive domains (attention, executive functioning, verbal memory, working memory, psychomotor functioning, and information processing speed) were calculated to detect differences between patients who had radiotherapy and patients who did not have radiotherapy. White-matter hyperintensities and global cortical atrophy were rated on MRI scans. 65 patients completed neuropsychological follow-up at a mean of 12 years (range 6–28 years). 32 (49%) patients had received radiotherapy (three had fraction doses >2 Gy). The patients who had radiotherapy had more deficits that affected attentional functioning at the second follow-up, regardless of fraction dose, than those who did not have radiotherapy (−1·6 [SD 2·4] vs −0·1 [1·3], p=0·003; mean difference 1·4, 95% CI 0·5–2·4). The patients who had radiotherapy also did worse in measures of executive functioning (−2·0 [3·7] vs −0·5 [1·2], p=0·03; mean difference 1·5, 0·2–2·9) and information processing speed (−2·0 [3·7] vs −0·6 [1·5], p=0·05; mean difference 0·8, 0·009–1·6]) between the two assessments. Furthermore, attentional functioning deteriorated significantly between the first and second assessments in patients who had radiotherapy (p=0·25). In total, 17 (53%) patients who had radiotherapy developed cognitive disabilities deficits in at least five of 18 neuropsychological test parameters compared with four (27%) patients who were radiotherapy naive. White-matter hyperintensities and global cortical atrophy were associated with worse cognitive functioning in several domains. Long-term survivors of LGG who did not have radiotherapy had stable radiological and cognitive status. By contrast, patients with low-grade glioma who received radiotherapy showed a progressive decline in attentional functioning, even those who received fraction doses that are regarded as safe (≤2 Gy). These cognitive deficits are associated with radiological abnormalities. Our results suggest that the risk of long-term cognitive and radiological compromise that is associated with radiotherapy should be considered when treatment is planned. Kaptein Fonds; Schering Plough.

The success of epilepsy surgery in patients with refractory epilepsy depends upon correct identification of the epileptogenic zone (EZ) and an optimal choice of the resection area. In this study we developed individualized computational models based upon structural brain networks to explore the impact of different virtual resections on the propagation of seizures. The propagation of seizures was modelled as an epidemic process [susceptible-infected-recovered (SIR) model] on individual structural networks derived from presurgical diffusion tensor imaging in 19 patients. The candidate connections for the virtual resection were all connections from the clinically hypothesized EZ, from which the seizures were modelled to start, to other brain areas. As a computationally feasible surrogate for the SIR model, we also removed the connections that maximally reduced the eigenvector centrality (EC) (large values indicate network hubs) of the hypothesized EZ, with a large reduction meaning a large effect. The optimal combination of connections to be removed for a maximal effect were found using simulated annealing. For comparison, the same number of connections were removed randomly, or based on measures that quantify the importance of a node or connection within the network. We found that 90% of the effect (defined as reduction of EC of the hypothesized EZ) could already be obtained by removing substantially less than 90% of the connections. Thus, a smaller, optimized, virtual resection achieved almost the same effect as the actual surgery yet at a considerably smaller cost, sparing on average 27.49% (standard deviation: 4.65%) of the connections. Furthermore, the maximally effective connections linked the hypothesized EZ to hubs. Finally, the optimized resection was equally or more effective than removal based on structural network characteristics both regarding reducing the EC of the hypothesized EZ and seizure spreading. The approach of using reduced EC as a surrogate for simulating seizure propagation can suggest more restrictive resection strategies, whilst obtaining an almost optimal effect on reducing seizure propagation, by taking into account the unique topology of individual structural brain networks of patients.

Background Fatigue is the most frequent and burdensome symptom of patients with diffuse glioma. It is closely linked to decreased health-related quality of life and symptoms such as depression and sleep disturbances. Currently, there is no evidence-based treatment that targets severe fatigue in patients with brain tumours. Cognitive behavioural therapy is aimed at fatigue-maintaining beliefs and behaviour. This therapy has been proven effective in reducing severe fatigue in cancer survivors and patients with multiple sclerosis. A blended therapy program combines sessions with a therapist with therapist-guided web-based therapy modules. The aim of this randomized controlled trial is to determine the efficacy of blended cognitive behavioural therapy in treating severe fatigue in patients with diffuse glioma. Methods We will include a maximum of 100 patients with diffuse glioma with clinically and radiologically stable disease and severe fatigue (i.e. Checklist Individual Strength, subscale fatigue severity ≥ 35). Patients will be randomized to blended cognitive behavioural therapy or a waiting list condition. The 12-week intervention GRIP on fatigue consists of five patient-therapist sessions and five to eight individualized web-based therapy modules supported by email contact. The primary outcome measure is fatigue severity. Secondary outcome measures include sleep quality, health-related quality of life, depression, anxiety, functional impairment and subjective and objective cognitive functioning. Primary and secondary outcome measures will be assessed at baseline and after 14 and 24 weeks. Magnetoencephalography and MRI will be used to evaluate potential biomarkers for intervention success. This trial has a Bayesian design: we will conduct multiple interim analyses to test for efficacy or futility of the trial. This is the first trial within the GRIP trial platform : a platform developing four to five different interventions for the most common symptoms in patients with diffuse glioma. Discussion The results of the GRIP on fatigue trial will provide information about the efficacy of this intervention on fatigue in patients with diffuse glioma. Multiple other outcomes and possible predictors of treatment success will also be explored. Trial registration Netherlands Trial Register NL8711 . Registered on 14 June 2020.

There is an ongoing need for novel biomarkers in clinical neuroscience, as diagnosis of neurological and psychiatric disorders is hampered by the pronounced overlap of behavioral symptoms and other pathophysiological characteristics. The question that this Focus Feature puts center stage is whether network-based biomarkers may provide a viable tool for distinguishing between disordered populations or whether they may yield only limited differentiating power because of largely shared network characteristics across conditions.

Background Both 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. Results At 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. Conclusions These 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.