Comparing individual and group-level simulated neurophysiological brain connectivity using the Jansen and Rit neural mass model.

Computational models are often used to assess how functional connectivity (FC) patterns emerge from neuronal population dynamics and anatomical brain connections. It remains unclear whether the commonly used group-averaged data can predict individual FC patterns. The Jansen and Rit neural mass model was employed, where masses were coupled using individual structural connectivity (SC). Simulated FC was correlated to individual magnetoencephalography-derived empirical FC. FC was estimated using phase-based (phase lag index (PLI), phase locking value (PLV)), and amplitude-based (amplitude envelope correlation (AEC)) metrics to analyze their goodness of fit for individual predictions. Individual FC predictions were compared against group-averaged FC predictions, and we tested whether SC of a different participant could equally well predict participants' FC patterns. The AEC provided a better match between individually simulated and empirical FC than phase-based metrics. Correlations between simulated and empirical FC were higher using individual SC compared to group-averaged SC. Using SC from other participants resulted in similar correlations between simulated and empirical FC compared to using participants' own SC. This work underlines the added value of FC simulations using individual instead of group-averaged SC for this particular computational model and could aid in a better understanding of mechanisms underlying individual functional network trajectories.

Symptom networks in glioma patients: understanding the multidimensionality of symptoms and quality of life.

PURPOSE: To comprehend the complex relationship between symptoms and health-related quality of life (HRQoL) in patients with diffuse glioma, we applied symptom network analysis to identify patterns of associations between depression, cognition, brain tumor-related symptoms, and HRQoL. Additionally, we aimed to compare global strength between symptom networks to understand if symptoms are more tightly connected in different subgroups of patients. METHODS: We included 256 patients and stratified the sample based on disease status (preoperative vs. postoperative), tumor grade (grade II vs. III/IV), and fatigue status (non-fatigued vs. fatigued). For each subgroup of patients, we constructed a symptom network. In these six networks, each node represented a validated subscale of a questionnaire and an edge represented a partial correlation between two nodes. We statistically compared global strength between networks. RESULTS: Across the six networks, nodes were highly correlated: fatigue severity, depression, and social functioning in particular. We found no differences in GS between the networks based on disease characteristics. However, global strength was lower in the non-fatigued network compared to the fatigued network (5.51 vs. 7.49, p < 0.001). CONCLUSIONS: Symptoms and HRQoL are highly interrelated in patients with glioma. Interestingly, nodes in the network of fatigued patients were more tightly connected compared to non-fatigued patients. IMPLICATIONS FOR CANCER SURVIVORS: We introduce symptom networks as a method to understand the multidimensionality of symptoms in glioma. We find a clear association between multiple symptoms and HRQoL, which underlines the need for integrative symptom management targeting fatigue in particular.

Non-invasively measured brain activity and radiological progression in diffuse glioma.

Non-invasively measured brain activity is related to progression-free survival in glioma patients, suggesting its potential as a marker of glioma progression. We therefore assessed the relationship between brain activity and increasing tumor volumes on routine clinical magnetic resonance imaging (MRI) in glioma patients. Postoperative magnetoencephalography (MEG) was recorded in 45 diffuse glioma patients. Brain activity was estimated using three measures (absolute broadband power, offset and slope) calculated at three spatial levels: global average, averaged across the peritumoral areas, and averaged across the homologues of these peritumoral areas in the contralateral hemisphere. Tumors were segmented on MRI. Changes in tumor volume between the two scans surrounding the MEG were calculated and correlated with brain activity. Brain activity was compared between patient groups classified into having increasing or stable tumor volume. Results show that brain activity was significantly increased in the tumor hemisphere in general, and in peritumoral regions specifically. However, none of the measures and spatial levels of brain activity correlated with changes in tumor volume, nor did they differ between patients with increasing versus stable tumor volumes. Longitudinal studies in more homogeneous subgroups of glioma patients are necessary to further explore the clinical potential of non-invasively measured brain activity.

Dorsal attention network centrality increases during recovery from acute stress exposure.

Stress is a major risk factor for the development of almost all psychiatric disorders. In addition to the acute stress response, an efficient recovery in the aftermath of stress is important for optimal resilience. Increased stress vulnerability across psychiatric disorders may therefore be related to altered trajectories during the recovery phase following stress. Such recovery trajectories can be quantified by changes in functional brain networks. This study therefore evaluated longitudinal functional network changes related to stress in healthy individuals (N = 80), individuals at risk for psychiatric disorders (healthy siblings of schizophrenia patients) (N = 39), and euthymic bipolar I disorder (BD) patients (N = 36). Network changes were evaluated before and at 20 and 90 min after onset of an experimental acute stress task (Trier Social Stress Test) or a control condition. Whole-brain functional networks were analyzed using eigenvector centrality as a proxy for network importance, centrality change over time was related to the acute stress response and recovery for each group. In healthy individuals, centrality of the dorsal attention network (DAN; p = 0.007) changed over time in relation to stress. More specifically, DAN centrality increased during the recovery phase after acute stress exposure (p = 0.020), while no DAN centrality change was observed during the initial stress response (p = 0.626). Such increasing DAN centrality during stress recovery was also found in healthy siblings (p = 0.016), but not in BD patients (p = 0.554). This study highlights that temporally complex and precise changes in network configuration are vital to understand the response to and recovery from stress.

Introducing Multiple Screener: An unsupervised digital screening tool for cognitive deficits in MS.

BACKGROUND: Cognitive deficits affect up to 70% of all patients with Multiple Sclerosis (MS) and have a significant impact on quality of life. Cognitive assessments need to be performed by a neuropsychologist and are often time-consuming, hampering timely identification and adequate monitoring of cognitive decline in MS. OBJECTIVE: To develop a time-efficient, unsupervised, digital tool to screen for cognitive deficits in MS. METHODS: A digital (adjusted) version of the Brief International Cognitive Assessment for MS, including the Symbol Digit Modalities Test (SDMT, information processing speed), the California Verbal Learning Test (CVLT-II, verbal memory) and the Spatial Recall Test (SPART, visuospatial memory) was developed: Multiple Screener (intellectual property of Sanofi Genzyme). Firstly, the clarity and feasibility of the tool was confirmed by 16 patients with MS (mean age 50.9 years (SD 9.4, range 37-68). Next, in 60 healthy controls (HCs, mean age 44.5 years (SD 14.0, range 18-67), intraclass correlation coefficients (ICC) were calculated to describe how strongly the digital version resembled the paper and pencil-based assessment. Finally, 236 HCs (mean age 42.8 years (SD 12.8, range 18-69) were included to obtain norm scores for each test. RESULTS: ICCs between digital and paper and pencil-based assessment were excellent to good (SDMT (ICC 0.79, confidence interval (CI) 0.67-0.87); CVLT-II (ICC 0.77, CI 0.64-0.85); SPART (ICC 0.61, CI 0.42-0.75)). For each test, a regression-based correction for the effect of age was applied on the raw scores before converting them to norm Z-scores. Additionally, the SDMT scores needed correction for education and the CVLT-II for education and sex (subgroups were created). CONCLUSIONS: Performance on an adjusted, digital version of the BICAMS correlates highly with the standard paper-and-pencil based test scores in HCs. Multiple Screener is an unsupervised, digital tool, with available norm scores, ultimately allowing for easy monitoring of cognitive decline in patients with MS.

Information processing speed in multiple sclerosis: Relevance of default mode network dynamics.

Objective: To explore the added value of dynamic functional connectivity (dFC) of the default mode network (DMN) during resting-state (RS), during an information processing speed (IPS) task, and the within-subject difference between these conditions, on top of conventional brain measures in explaining IPS in people with multiple sclerosis (pwMS). Methods: In 29 pwMS and 18 healthy controls, IPS was assessed with the Letter Digit Substitution Test and Stroop Card I and combined into an IPS-composite score. White matter (WM), grey matter (GM) and lesion volume were measured using 3 T MRI. WM integrity was assessed with diffusion tensor imaging. During RS and task-state fMRI (i.e. symbol digit modalities task, IPS), stationary functional connectivity (sFC; average connectivity over the entire time series) and dFC (variation in connectivity using a sliding window approach) of the DMN was calculated, as well as the difference between both conditions (i.e. task-state minus RS; ΔsFC-DMN and ΔdFC-DMN). Regression analysis was performed to determine the most important predictors for IPS. Results: Compared to controls, pwMS performed worse on IPS-composite (p = 0.022), had lower GM volume (p < 0.05) and WM integrity (p < 0.001), but no alterations in sFC and dFC at the group level. In pwMS, 52% of variance in IPS-composite could be predicted by cortical volume (ß = 0.49, p = 0.01) and ΔdFC-DMN (ß = 0.52, p < 0.01). After adding dFC of the DMN to the model, the explained variance in IPS increased with 26% (p < 0.01). Conclusion: On top of conventional brain measures, dFC from RS to task-state explains additional variance in IPS. This highlights the potential importance of the DMN to adapt upon cognitive demands to maintain intact IPS in pwMS.

Central neurotoxicity of standard treatment in patients with newly-diagnosed high-grade glioma: a prospective longitudinal study.

Following tumor resection, the majority of high-grade glioma (HGG) patients are treated with a combined modality regimen of radiotherapy and temozolomide. As a result of the tumor itself or as treatment-related neurotoxic side-effects, these patients may experience cognitive deficits. Additionally, radiological abnormalities expressed as white matter hyperintensities (WMH) and cerebral atrophy (CA) can develop. In this study, these functional and morphological parameters are evaluated, and their relation is investigated. After surgery, HGG patients underwent chemo-irradiation for six weeks, followed by six cycles of temozolomide. Assessments were performed before chemo-irradiation, post-concomitantly, after the third and sixth adjuvant cycle, and 3 and 7 months after treatment. Degree of WMH and CA was scored on MRI. Patients' neuropsychological performance was compared to healthy matched controls, yielding six cognitive domain z-scores. Development or progression of pre-existing WMH and CA during follow-up was observed in 36 and 45 % of the patients (n = 39) respectively. Cognitive functioning remained stable or improved in 70 % of the patients and deteriorated in 30 % of the patients (n = 33). Of the cognitive decliners, 80 % had tumor progression within 4 months thereafter. No clear association between cognitive functioning and WMH or CA was found. Central neurotoxic effects of combined modality treatment in HGG patients expressed by radiological abnormalities are encountered in approximately 40 % of patients. However, functional impact as indexed by cognitive functioning was found to be limited. Furthermore, development or progression of pre-existing WMH and CA does not consistently result in functional impairment as measured by cognitive tests.

Cognition in MS correlates with resting-state oscillatory brain activity: An explorative MEG source-space study.

Clinical and cognitive dysfunction in Multiple Sclerosis (MS) is insufficiently explained by structural damage as identified by traditional magnetic resonance imaging (MRI) of the brain, indicating the need for reliable functional measures in MS. We investigated whether altered resting-state oscillatory power could be related to clinical and cognitive dysfunction in MS. MEG recordings were acquired using a 151-channel whole-head MEG system from 21 relapsing remitting MS patients and 17 healthy age-, gender-, and education-matched controls, using an eyes-closed no-task condition. Relative spectral power was estimated for 78 regions of interest, using an atlas-based beamforming approach, for classical frequency bands; delta, theta, alpha1, alpha2, beta and gamma. These cortical power estimates were compared between groups by means of permutation analysis and correlated with clinical disability (Expanded Disability Status Scale: EDSS), cognitive performance and MRI measures of atrophy and lesion load. Patients showed increased power in the alpha1 band and decreased power in the alpha2 band, compared to controls, mainly in occipital, parietal and temporal areas, confirmed by a lower alpha peak-frequency. Increased power in the alpha1 band was associated with worse overall cognition and especially with information processing speed. Our quantitative relative power analysis of MEG recordings showed abnormalities in oscillatory brain dynamics in MS patients in the alpha band. By applying source-space analyses, this study provides a detailed topographical view of abnormal brain activity in MS patients, especially localized to occipital areas. Interestingly, poor cognitive performance was related to high resting-state alpha1 power indicating that changes in oscillatory activity might be of value as an objective measure of disease burden in MS patients.

Local polymorphic delta activity in cortical lesions causes global decreases in functional connectivity.

Increasing evidence from neuroimaging and modeling studies suggests that local lesions can give rise to global network changes in the human brain. These changes are often attributed to the disconnection of the lesioned areas. However, damaged brain areas may still be active, although the activity is altered. Here, we hypothesize that empirically observed global decreases in functional connectivity in patients with brain lesions can be explained by specific alterations of local neural activity that are the result of damaged tissue. We simulated local polymorphic delta activity (PDA), which typically characterizes EEG/MEG recordings of patients with cerebral lesions, in a realistic model of human brain activity. 78 neural masses were coupled according to the human structural brain network. Lesions were created by altering the parameters of individual neural masses in order to create PDA (i.e. simulating acute focal brain damage); combining this PDA with weakening of structural connections (i.e. simulating brain tumors), and fully deleting structural connections (i.e. simulating a full resection). Not only structural disconnection but also PDA in itself caused a global decrease in functional connectivity, similar to the observed alterations in MEG recordings of patients with PDA due to brain lesions. Interestingly, connectivity between regions that were not lesioned directly also changed. The impact of PDA depended on the network characteristics of the lesioned region in the structural connectome. This study shows for the first time that locally disturbed neural activity, i.e. PDA, may explain altered functional connectivity between remote areas, even when structural connections are unaffected. We suggest that focal brain lesions and the corresponding altered neural activity should be considered in the framework of the full functionally interacting brain network, implying that the impact of lesions reaches far beyond focal damage.

Local MEG networks: the missing link between protein expression and epilepsy in glioma patients?

Connectivity and network analysis in neuroscience has been applied to multiple spatial scales, but the links between these different scales have rarely been investigated. In tumor-related epilepsy, altered network topology is related to behavior, but the molecular basis of these observations is unknown. We elucidate the associations between microscopic features of brain tumors, local network topology, and functional patient status. We hypothesize that expression of proteins related to tumor-related epilepsy is directly correlated with network characteristics of the tumor area. Glioma patients underwent magnetoencephalography, and functional network topology of the tumor area was used to predict tissue protein expression patterns of tumor tissue collected during neurosurgery. Protein expression and network topology were interdependent; in particular between-module connectivity was selectively associated with two epilepsy-related proteins. Total number of seizures was related to both the role of the tumor area in the functional network and to protein expression. Importantly, classification of protein expression was predicted by between-module connectivity with up to 100% accuracy. Thus, network topology may serve as an intermediate level between molecular features of tumor tissue and symptomatology in brain tumor patients, and can potentially be used as a non-invasive marker for microscopic tissue characteristics.

Connectivity in MEG resting-state networks increases after resective surgery for low-grade glioma and correlates with improved cognitive performance.

PURPOSE: Low-grade glioma (LGG) patients often have cognitive deficits. Several disease- and treatment related factors affect cognitive processing. Cognitive outcome of resective surgery is unpredictable, both for improvement and deterioration, especially for complex domains such as attention and executive functioning. MEG analysis of resting-state networks (RSNs) is a good candidate for presurgical prediction of cognitive outcome. In this study, we explore the relation between alterations in connectivity of RSNs and changes in cognitive processing after resective surgery, as a stepping stone to ultimately predict postsurgical cognitive outcome. METHODS: Ten patients with LGG were included, who had no adjuvant therapy. MEG recording and neuropsychological assessment were obtained before and after resective surgery. MEG data were recorded during a no-task eyes-closed condition, and projected to the anatomical space of the AAL atlas. Alterations in functional connectivity, as characterized by the phase lag index (PLI), within the default mode network (DMN), executive control network (ECN), and left- and right-sided frontoparietal networks (FPN) were compared to cognitive changes. RESULTS: Lower alpha band DMN connectivity was increased after surgery, and this increase was related to improved verbal memory functioning. Similarly, right FPN connectivity was increased after resection in the upper alpha band, which correlated with improved attention, working memory and executive functioning. DISCUSSION: Increased alpha band RSN functional connectivity in MEG recordings correlates with improved cognitive outcome after resective surgery. The mechanisms resulting in functional connectivity alterations after resection remain to be elucidated. Importantly, our findings indicate that connectivity of MEG RSNs may be used for presurgical prediction of cognitive outcome in future studies.

Cognition is related to resting-state small-world network topology: an magnetoencephalographic study.

Brain networks and cognition have recently begun to attract attention: studies suggest that more efficiently wired resting-state brain networks are indeed correlated with better cognitive performance. "Small-world" brain networks combine local segregation with global integration, hereby subserving information processing. Furthermore, recent studies implicate that gender effects may be present in both network dynamics and its correlations with cognition. This study reports on the relation between resting-state functional brain topology with overall and domain-specific cognitive performance in healthy participants and possible gender differences herein. Healthy participants underwent neuropsychological tests, of which individual scores were converted to z-scores. Network analysis was performed on resting-state, eyes-closed magnetoencephalography (MEG) data, after determining functional connectivity between each pair of sensors. The clustering coefficient (local specialization), average path length (overall integration and efficiency) and "small-world index" (i.e. ratio between clustering and path length) were calculated in six frequency bands. 14 male and 14 female participants were included. Better total cognitive performance was related to increased local connectivity in the theta band, higher clustering coefficient (in delta and theta bands) and higher small-worldness (in theta and lower gamma bands). Women showed less clustering and shorter path length in the delta band. There were no significant correlations between network topology and cognitive functioning in females. In contrast, higher cognitive scores in men were associated with increased theta band clustering and small-worldness. These results provide further evidence for the value of functional brain network topology for cognitive functioning and suggest that gender is an important factor in this respect.

Effect of tumor resection on the characteristics of functional brain networks.

Brain functioning such as cognitive performance depends on the functional interactions between brain areas, namely, the functional brain networks. The functional brain networks of a group of patients with brain tumors are measured before and after tumor resection. In this work, we perform a weighted network analysis to understand the effect of neurosurgery on the characteristics of functional brain networks. Statistically significant changes in network features have been discovered in the beta (13-30 Hz) band after neurosurgery: the link weight correlation around nodes and within triangles increases which implies improvement in local efficiency of information transfer and robustness; the clustering of high link weights in a subgraph becomes stronger, which enhances the global transport capability; and the decrease in the synchronization or virus spreading threshold, revealed by the increase in the largest eigenvalue of the adjacency matrix, which suggests again the improvement of information dissemination.

Indications for network regularization during absence seizures: weighted and unweighted graph theoretical analyses.

Previous studies with intracranial recordings suggested that a more random spatial structure of functional brain networks could be related to seizure generation. Here, we studied whether similar network changes in weighted and unweighted networks can be found in generalized absence seizures recorded with surface EEG. We retrospectively selected EEG recordings of eleven children with absence seizures. The functional neural networks were characterized by calculating both coherence and synchronization likelihood (SL) between 21 EEG signals that were either broad band filtered (1-48 Hz) or filtered in different frequency bands. From both weighted and unweighted networks the clustering coefficient (C) and path length (L) were computed and compared to 500 random networks. We compared the ictal with the pre-ictal network structure. During absence seizures there was an increase of synchronization in all frequency bands, seen most clearly in the SL-based networks, and the functional network topology changed towards a more ordered pattern, with an increase of C/C-s and L/L-s. This study supports the hypothesis of functional neural network changes during absence seizures. The network became more regularized in weighted and unweighted analyses, when compared to the more randomized pre-ictal network configuration.

The influence of low-grade glioma on resting state oscillatory brain activity: a magnetoencephalography study.

PURPOSE: In the present MEG-study, power spectral analysis of oscillatory brain activity was used to compare resting state brain activity in both low-grade glioma (LGG) patients and healthy controls. We hypothesized that LGG patients show local as well as diffuse slowing of resting state brain activity compared to healthy controls and that particularly global slowing correlates with neurocognitive dysfunction. PATIENT AND METHODS: Resting state MEG recordings were obtained from 17 LGG patients and 17 age-, sex-, and education-matched healthy controls. Relative spectral power was calculated in the delta, theta, upper and lower alpha, beta, and gamma frequency band. A battery of standardized neurocognitive tests measuring 6 neurocognitive domains was administered. RESULTS: LGG patients showed a slowing of the resting state brain activity when compared to healthy controls. Decrease in relative power was mainly found in the gamma frequency band in the bilateral frontocentral MEG regions, whereas an increase in relative power was found in the theta frequency band in the left parietal region. An increase of the relative power in the theta and lower alpha band correlated with impaired executive functioning, information processing, and working memory. CONCLUSION: LGG patients are characterized by global slowing of their resting state brain activity and this slowing phenomenon correlates with the observed neurocognitive deficits.