Understanding the association between fatigue and neurocognitive functioning in patients with glioma: A cross-sectional multinational study

Background

Fatigue and neurocognitive impairment are highly prevalent in patients with glioma, significantly impacting health-related quality of life. Despite the presumed association between these two factors, evidence remains sparse. Therefore, we aimed to investigate this relationship using multinational data.

Methods

We analyzed data on self-reported fatigue and neurocognitive outcomes from postoperative patients with glioma from the University of California San Francisco (n = 100, UCSF) and Amsterdam University Medical Center (n = 127, Amsterdam UMC). We used multiple linear regression models to assess associations between fatigue and seven (sub)domains of neurocognitive functioning and latent profile analysis to identify distinct patterns of fatigue and neurocognitive functioning.

Results

UCSF patients were older (median age 49 vs. 43 years, P = .002), had a higher proportion of grade 4 tumors (32% vs. 18%, P = .03), and had more neurocognitive deficits (P = .01). While the number of clinically fatigued patients was similar between sites (64% vs. 58%, P = .12), fatigue and the number of impaired neurocognitive domains were not correlated (P = .16-.72). At UCSF, neurocognitive domains were not related to fatigue, and at Amsterdam UMC attention and semantic fluency explained only 4-7% of variance in fatigue. Across institutions, we identified four distinct patterns of neurocognitive functioning, which were not consistently associated with fatigue.

Conclusions

Although individual patients might experience both fatigue and neurocognitive impairment, the relationship between the two is weak. Consequently, both fatigue and neurocognitive functioning should be independently assessed and treated with targeted therapies.

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.

Unraveling robust brain-behavior links of depressive complaints through granular network models for understanding heterogeneity

BACKGROUND

Depressive symptoms are highly prevalent, present in heterogeneous symptom patterns, and share diverse neurobiological underpinnings. Understanding the links between psychopathological symptoms and biological factors is critical in elucidating its etiology and persistence. We aimed to evaluate the utility of using symptom-brain network models to parse the heterogeneity of depressive complaints in a large adolescent sample.

METHODS

We used data from the third wave of the IMAGEN study, a multi-center panel cohort study involving 1317 adolescents (52.49 % female, mean ± SD age = 18.5 ± 0.7). Two network models were estimated: one including an overall depressive symptom severity sum score based on the Adolescent Depression Rating Scale (ADRS), and one incorporating individual ADRS item scores. Both networks included measures of cortical thickness in several regions (insula, cingulate, mOFC, fusiform gyrus) and hippocampal volume derived from neuroimaging.

RESULTS

The network based on individual item scores revealed associations between cortical thickness measures and specific depressive complaints, obscured when using an aggregate depression severity score. Notably, the insula's cortical thickness showed negative associations with cognitive dysfunction (partial cor. = -0.15); the cingulate's cortical thickness showed negative associations with feelings of worthlessness (partial cor. = -0.10), and mOFC was negatively associated with anhedonia (partial cor. = -0.05).

LIMITATIONS

This cross-sectional study relied on the self-reported assessment of depression complaints and used a non-clinical sample with predominantly healthy participants (19 % with depression or sub-threshold depression).

CONCLUSIONS

This study showcases the utility of network models in parsing heterogeneity in depressive complaints, linking individual complaints to specific neural substrates. We outline the next steps to integrate neurobiological and cognitive markers to unravel MDD's phenotypic heterogeneity.

Higher-order functional connectivity analysis of resting-state functional magnetic resonance imaging data using multivariate cumulants

Blood-level oxygenation-dependent (BOLD) functional magnetic resonance imaging (fMRI) is the most common modality to study functional connectivity in the human brain. Most research to date has focused on connectivity between pairs of brain regions. However, attention has recently turned towards connectivity involving more than two regions, that is, higher-order connectivity. It is not yet clear how higher-order connectivity can best be quantified. The measures that are currently in use cannot distinguish between pairwise (i.e., second-order) and higher-order connectivity. We show that genuine higher-order connectivity can be quantified by using multivariate cumulants. We explore the use of multivariate cumulants for quantifying higher-order connectivity and the performance of block bootstrapping for statistical inference. In particular, we formulate a generative model for fMRI signals exhibiting higher-order connectivity and use it to assess bias, standard errors, and detection probabilities. Application to resting-state fMRI data from the Human Connectome Project demonstrates that spontaneous fMRI signals are organized into higher-order networks that are distinct from second-order resting-state networks. Application to a clinical cohort of patients with multiple sclerosis further demonstrates that cumulants can be used to classify disease groups and explain behavioral variability. Hence, we present a novel framework to reliably estimate genuine higher-order connectivity in fMRI data which can be used for constructing hyperedges, and finally, which can readily be applied to fMRI data from populations with neuropsychiatric disease or cognitive neuroscientific experiments.

The relationship between pathological brain activity and functional network connectivity in glioma patients

PURPOSE

Glioma is associated with pathologically high (peri)tumoral brain activity, which relates to faster progression. Functional connectivity is disturbed locally and throughout the entire brain, associating with symptomatology. We, therefore, investigated how local activity and network measures relate to better understand how the intricate relationship between the tumor and the rest of the brain may impact disease and symptom progression.

METHODS

We obtained magnetoencephalography in 84 de novo glioma patients and 61 matched healthy controls. The offset of the power spectrum, a proxy of neuronal activity, was calculated for 210 cortical regions. We calculated patients' regional deviations in delta, theta and lower alpha network connectivity as compared to controls, using two network measures: clustering coefficient (local connectivity) and eigenvector centrality (integrative connectivity). We then tested group differences in activity and connectivity between (peri)tumoral, contralateral homologue regions, and the rest of the brain. We also correlated regional offset to connectivity.

RESULTS

As expected, patients' (peri)tumoral activity was pathologically high, and patients showed higher clustering and lower centrality than controls. At the group-level, regionally high activity related to high clustering in controls and patients alike. However, within-patient analyses revealed negative associations between regional deviations in brain activity and clustering, such that pathologically high activity coincided with low network clustering, while regions with 'normal' activity levels showed high network clustering.

CONCLUSION

Our results indicate that pathological activity and connectivity co-localize in a complex manner in glioma. This insight is relevant to our understanding of disease progression and cognitive symptomatology.

Multiscale network neuroscience in neuro-oncology: How tumors, brain networks, and behavior connect across scales

Network neuroscience refers to the investigation of brain networks across different spatial and temporal scales, and has become a leading framework to understand the biology and functioning of the brain. In neuro-oncology, the study of brain networks has revealed many insights into the structure and function of cells, circuits, and the entire brain, and their association with both functional status (e.g., cognition) and survival. This review connects network findings from different scales of investigation, with the combined aim of informing neuro-oncological healthcare professionals on this exciting new field and also delineating the promising avenues for future translational and clinical research that may allow for application of network methods in neuro-oncological care.

Unravelling Robust Brain-Behavior Links of Depressive Symptoms Through Granular Network Models: Understanding Heterogeneity and Clinical Implications

Background

Depressive symptoms are highly prevalent, present in heterogeneous symptom patterns, and share diverse neurobiological underpinnings. Understanding the links between psychopathological symptoms and biological factors is critical in elucidating its etiology and persistence. We aimed to evaluate the utility of using symptom-brain networks to parse the heterogeneity of depressive symptomatology in a large adolescent sample.

Methods

We used data from the third wave of the IMAGEN study, a multi-center panel cohort study involving 1,317 adolescents (52.49% female, mean±SD age=18.5±0.72). Two network models were estimated: one including an overall depressive symptom severity sum score based on the Adolescent Depression Rating Scale (ADRS), and one incorporating individual ADRS symptom/item scores. Both networks included measures of cortical thickness in several regions (insula, cingulate, mOFC, fusiform gyrus) and hippocampal volume derived from neuroimaging.

Results

The network based on individual symptom scores revealed associations between cortical thickness measures and specific symptoms, obscured when using an aggregate depression severity score. Notably, the insula's cortical thickness showed negative associations with cognitive dysfunction (partial cor.=-0.15); the cingulate's cortical thickness showed negative associations with feelings of worthlessness (partial cor. = -0.10), and mOFC was negatively associated with anhedonia (partial cor. = -0.05).

Limitations

This cross-sectional study included participants who were relatively healthy and relied on the self-reported assessment of depression symptoms.

Conclusions

This study showcases the utility of network models in parsing heterogeneity in depressive symptoms, linking individual symptoms to specific neural substrates. We outline the next steps to integrate neurobiological and cognitive markers to unravel MDD's phenotypic heterogeneity.

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.

The longitudinal relation between executive functioning and multilayer network topology in glioma patients

Many patients with glioma, primary brain tumors, suffer from poorly understood executive functioning deficits before and/or after tumor resection. We aimed to test whether frontoparietal network centrality of multilayer networks, allowing for integration across multiple frequencies, relates to and predicts executive functioning in glioma. Patients with glioma (n = 37) underwent resting-state magnetoencephalography and neuropsychological tests assessing word fluency, inhibition, and set shifting before (T1) and one year after tumor resection (T2). We constructed binary multilayer networks comprising six layers, with each layer representing frequency-specific functional connectivity between source-localized time series of 78 cortical regions. Average frontoparietal network multilayer eigenvector centrality, a measure for network integration, was calculated at both time points. Regression analyses were used to investigate associations with executive functioning. At T1, lower multilayer integration (p = 0.017) and epilepsy (p = 0.006) associated with poorer set shifting (adj. R2 = 0.269). Decreasing multilayer integration (p = 0.022) and not undergoing chemotherapy at T2 (p = 0.004) related to deteriorating set shifting over time (adj. R2 = 0.283). No significant associations were found for word fluency or inhibition, nor did T1 multilayer integration predict changes in executive functioning. As expected, our results establish multilayer integration of the frontoparietal network as a cross-sectional and longitudinal correlate of executive functioning in glioma patients. However, multilayer integration did not predict postoperative changes in executive functioning, which together with the fact that this correlate is also found in health and other diseases, limits its specific clinical relevance in glioma.

Connecting Neurobiological Features with Interregional Dysconnectivity in Social-Cognitive Impairments of Schizophrenia

Schizophrenia (SZ) is a devastating psychiatric disorder affecting about 1% of the world's population. Social-cognitive impairments in SZ prevent positive social interactions and lead to progressive social withdrawal. The neurobiological underpinnings of social-cognitive symptoms remain poorly understood, which hinders the development of novel treatments. At the whole-brain level, an abnormal activation of social brain regions and interregional dysconnectivity within social-cognitive brain networks have been identified as major contributors to these symptoms. At the cellular and subcellular levels, an interplay between oxidative stress, neuroinflammation and N-methyl-D-aspartate receptor hypofunction is thought to underly SZ pathology. However, it is not clear how these molecular processes are linked with interregional dysconnectivity in the genesis of social-cognitive symptoms. Here, we aim to bridge the gap between macroscale (connectivity analyses) and microscale (molecular and cellular mechanistic) knowledge by proposing impaired myelination and the disinhibition of local microcircuits as possible causative biological pathways leading to dysconnectivity and abnormal activity of the social brain. Furthermore, we recommend electroencephalography as a promising translational technique that can foster pre-clinical drug development and discuss attractive drug targets for the treatment of social-cognitive symptoms in SZ.

Cortical thickness and neurocognitive performance in former high-level female soccer and non-contact sport athletes

BACKGROUND

Long-term effects of playing soccer (football) on the brain structure and function of the brain are vividly debated. While some studies showed differences in neurocognitive performance and structural brain changes in retired male players, data on female players are scarce. The present study compares cortical thickness and neurocognitive performance in former high-level female soccer (SOC) and non-contact sport athletes (CON).

METHODS

3 T T1-weighted 3D MPRAGE MRI was performed, and vertex-wise cortical thickness was analyzed using FreeSurfer (v. 6.0.0). Neurocognitive performance in seven domains of SOC and CON was assessed. A multivariate linear model was used to analyze interactions with respect to heading frequency and a history of concussion.

RESULTS

SOC (n = 15, mean age 38.3 ± 5.1 years) and CON (n = 16, mean age 36.6 ± 5.8 years) had a similar cortical thickness and performed similarly in the neurocognitive tests except for verbal memory and psychomotor speed, where SOC performed significantly worse than CON. Moderate headers had a significantly larger cortical thickness than rare headers in the right inferior parietal region. Visual memory and cortical thickness were positively correlated in the group of frequent headers and negatively correlated in CON, but not in the other header groups.

PERSPECTIVE

In contrast to previous reports in male soccer players, female players did not reveal cortical thinning in comparison with control athletes, whereas neurocognitive profiles of female soccer players might not significantly differ from male athletes. Small sample sizes, subjective header assessment, and the case-control study design require a cautious interpretation.

Cancer meets neuroscience: the association between glioma occurrence and intrinsic brain features

This scientific commentary refers to ‘Transcriptomic and connectomic correlates of differential spatial patterning among gliomas’ by Romero-Garcia et al. (https://doi.org/10.1093/brain/awac378)

Cannabinoids to Improve Health-Related Quality of Life in Patients with Neurological or Oncological Disease: A Meta-Analysis

Background: Cannabinoids have been suggested to alleviate frequently experienced symptoms of reduced mental well-being such as anxiety and depression. Mental well-being is an important subdomain of health-related quality of life (HRQoL). Reducing symptoms and maintaining HRQoL are particularly important in malignant primary brain tumor patients, as treatment options are often noncurative and prognosis remains poor. These patients frequently report unprescribed cannabinoid use, presumably for symptom relieve. As studies on brain tumor patients specifically are lacking, we performed a meta-analysis of the current evidence on cannabinoid efficacy on HRQoL and mental well-being in oncological and neurological patients. Methods: We performed a systematic PubMed, PsychINFO, Embase, and Web of Science search according to PRISMA guidelines on August 2 and 3, 2021. We included randomized controlled trials (RCTs) that assessed the effects of tetrahydrocannabinol (THC) or cannabidiol (CBD) on general HRQoL and mental well-being. Pooled effect sizes were calculated using Hedges g. Risk of bias of included studies was assessed using Cochrane's Risk of Bias tool. Results: We included 17 studies: 4 in oncology and 13 in central nervous system (CNS) disease. Meta-analysis showed no effect of cannabinoids on general HRQoL (g=-0.02 confidence interval [95% CI -0.11 to 0.06]; p=0.57) or mental well-being (g=-0.02 [95% CI -0.16 to 0.13]; p=0.81). Conclusions: RCTs in patients with cancer or CNS disease showed no effect of cannabinoids on HRQoL or mental well-being. However, studies were clinically heterogeneous and since many glioma patients currently frequently use cannabinoids, future studies are necessary to evaluate its value in this specific population.

Sensorimotor network dynamics predict decline in upper and lower limb function in people with multiple sclerosis

BACKGROUND

Upper and lower limb disabilities are hypothesized to have partially independent underlying (network) disturbances in multiple sclerosis (MS).

OBJECTIVE

This study investigated functional network predictors and longitudinal network changes related to upper and lower limb progression in MS.

METHODS

Two-hundred fourteen MS patients and 58 controls underwent functional magnetic resonance imaging (fMRI), dexterity (9-Hole Peg Test) and mobility (Timed 25-Foot Walk) measurements (baseline and 5 years). Patients were stratified into progressors (>20% decline) or non-progressors. Functional network efficiency was calculated using static (over entire scan) and dynamic (fluctuations during scan) approaches. Baseline measurements were used to predict progression; significant predictors were explored over time.

RESULTS

In both limbs, progression was related to supplementary motor area and caudate efficiency (dynamic and static, respectively). Upper limb progression showed additional specific predictors; cortical grey matter volume, putamen static efficiency and posterior associative sensory (PAS) cortex, putamen, primary somatosensory cortex and thalamus dynamic efficiency. Additional lower limb predictors included motor network grey matter volume, caudate (dynamic) and PAS (static). Only the caudate showed a decline in efficiency over time in one group (non-progressors).

CONCLUSION

Disability progression can be predicted using sensorimotor network measures. Upper and lower limb progression showed unique predictors, possibly indicating different network disturbances underlying these types of progression in MS.

Multimodal multilayer network centrality relates to executive functioning

Executive functioning (EF) is a higher order cognitive process that is thought to depend on a network organization facilitating integration across subnetworks, in the context of which the central role of the fronto-parietal network (FPN) has been described across imaging and neurophysiological modalities. However, the potentially complementary unimodal information on the relevance of the FPN for EF has not yet been integrated. We employ a multilayer framework to allow for integration of different modalities into one 'network of networks.' We used diffusion MRI, resting-state functional MRI, MEG, and neuropsychological data obtained from 33 healthy adults to construct modality-specific single-layer networks as well as a single multilayer network per participant. We computed single-layer and multilayer eigenvector centrality of the FPN as a measure of integration in this network and examined their associations with EF. We found that higher multilayer FPN centrality, but not single-layer FPN centrality, was related to better EF. We did not find a statistically significant change in explained variance in EF when using the multilayer approach as compared to the single-layer measures. Overall, our results show the importance of FPN integration for EF and underline the promise of the multilayer framework toward better understanding cognitive functioning.

NCOG-17. THE CROSS-SECTIONAL AND LONGITUDINAL ASSOCIATION BETWEEN EXECUTIVE FUNCTIONING AND MULTILAYER CENTRALITY OF THE FRONTOPARIETAL NETWORK IN GLIOMA PATIENTS

BACKGROUND

Many patients with glioma suffer from poorly understood executive functioning deficits before and/or after tumor resection. In the past decade, it has become increasingly clear that there is a complex interplay between glioma and both local and global functional activity and connectivity. Moreover, multilayer network integration of the frontoparietal network across multiple modalities has recently been found to explain individual differences in executive functioning in healthy subjects.

OBJECTIVE

To test whether multilayer frontoparietal network integration relates to individual differences in executive functioning before and after glioma resection.

METHODS

Patients with glioma (n = 37) underwent neuropsychological tests assessing word fluency, inhibition, and set shifting as components of executive functioning, and resting-state magnetoencephalography at T1 (at diagnosis, before resection) and T2 (1 year after resection). We constructed binary multilayer networks comprising six layers, with each layer representing frequency-specific functional connectivity between source-localized time series of 78 cortical regions. Average frontoparietal network multilayer eigenvector centrality, a measure for network integration, was calculated at both time points. Regression analyses were used to investigate associations with executive functioning.

RESULTS

At T1, lower multilayer integration (p = .017) and having epilepsy (p = .006) associated with poorer set shifting. Decreasing multilayer integration (p = .022) between T1 and T2 and not undergoing chemotherapy at T2 (p = .004) related to deteriorating set shifting. No significant associations were found for word fluency or inhibition.

CONCLUSION

As hypothesized, our results establish multilayer integration of the frontoparietal network as a cross-sectional and longitudinal correlate of executive functioning in glioma patients, further building upon the idea that glioma impacts the whole brain network and general cognitive functioning. However, multilayer integration did not significantly predict postoperative changes in executive functioning, limiting the direct clinical relevance of this measure.

CNSC-10. THE COMPLEX RELATIONSHIP BETWEEN NEURONAL ACTIVITY AND FUNCTIONAL NETWORK PROPERTIES IN GLIOMA PATIENTS

BACKGROUND

Glioma is associated with pathologically high peritumoral neuronal activity, which associates with faster tumor progression. Concurrently, glioma patients have local and widespread disturbances of the functional brain network as measured with magnetoencephalography (MEG), such as higher network clustering (the extent to which regions connected to a particular area are also connected to each other) and locally altered integrative connectivity (for instance assessed with a centrality measure). How local neuronal activity and nodal network properties relate to each other has yet to be investigated.

METHODS

We obtained eyes-closed resting-state MEG in 95 de novo glioma patients and 57 matched healthy controls (HCs). The offset of the power spectrum was calculated for 210 cortical atlas regions as a proxy for neuronal activity. Regional clustering coefficient (CC) and eigenvector centrality (EC) were calculated in the delta, theta and alpha bands. Offset and network values were then averaged across peritumoral regions, contralateral homologue regions and all non-peritumoral regions. Linear mixed models were used to relate nodal CC and EC to local offset in patients and HCs.

RESULTS

The peritumoral area was significantly more active than the non-peritumoral homologue in patients, and showed pathologically high activity in comparison to HCs. However, patients’ functional network was disturbed globally, showing higher clustering and lower centrality than HCs. Whereas high activity related to high centrality in HCs and patients alike, high activity seemed to relate to low clustering in non-peritumoral regions in patients, but not HCs.

CONCLUSION

We find that the relationship between neuronal activity and functional network properties is disturbed in a complex manner in glioma patients. Our results further underline the importance of investigating how local activity may impact global network topology in order to understand how neuron-glioma interactions shape brain functioning.

Effects of a single-dose methylphenidate challenge on resting-state functional connectivity in stimulant-treatment naive children and adults with ADHD

Prior studies suggest that methylphenidate, the primary pharmacological treatment for attention-deficit/hyperactivity disorder (ADHD), alters functional brain connectivity. As the neurotransmitter systems targeted by methylphenidate undergo significant alterations throughout development, the effects of methylphenidate on functional connectivity may also be modulated by age. Therefore, we assessed the effects of a single methylphenidate challenge on brain network connectivity in stimulant-treatment naïve children and adults with ADHD. We obtained resting-state functional MRI from 50 boys (10-12 years of age) and 49 men (23-40 years of age) with ADHD (DSM IV, all subtypes), before and after an oral challenge with 0.5 mg/kg methylphenidate; and from 11 boys and 12 men as typically developing controls. Connectivity strength (CS), eigenvector centrality (EC), and betweenness centrality (BC) were calculated for the striatum, thalamus, dorsal anterior cingulate cortex (dACC), and prefrontal cortex (PFC). In line with our hypotheses, we found that methylphenidate decreased measures of connectivity and centrality in the striatum and thalamus in children with ADHD, but increased the same metrics in adults with ADHD. Surprisingly, we found no major effects of methylphenidate in the dACC and PFC in either children or adults. Interestingly, pre-methylphenidate, participants with ADHD showed aberrant connectivity and centrality compared to controls predominantly in frontal regions. Our findings demonstrate that methylphenidate's effects on connectivity of subcortical regions are age-dependent in stimulant-treatment naïve participants with ADHD, likely due to ongoing maturation of dopamine and noradrenaline systems. These findings highlight the importance for future studies to take a developmental perspective when studying the effects of methylphenidate treatment.

Regional healthy brain activity, glioma occurrence and symptomatology

It is unclear why exactly gliomas show preferential occurrence in certain brain areas. Increased spiking activity around gliomas leads to faster tumour growth in animal models, while higher non-invasively measured brain activity is related to shorter survival in patients. However, it is unknown how regional intrinsic brain activity, as measured in healthy controls, relates to glioma occurrence. We first investigated whether gliomas occur more frequently in regions with intrinsically higher brain activity. Second, we explored whether intrinsic cortical activity at individual patients’ tumour locations relates to tumour and patient characteristics.

Across three cross-sectional cohorts, 413 patients were included. Individual tumour masks were created. Intrinsic regional brain activity was assessed through resting-state magnetoencephalography acquired in healthy controls and source-localized to 210 cortical brain regions. Brain activity was operationalized as: (i) broadband power; and (ii) offset of the aperiodic component of the power spectrum, which both reflect neuronal spiking of the underlying neuronal population. We additionally assessed (iii) the slope of the aperiodic component of the power spectrum, which is thought to reflect the neuronal excitation/inhibition ratio. First, correlation coefficients were calculated between group-level regional glioma occurrence, as obtained by concatenating tumour masks across patients, and group-averaged regional intrinsic brain activity. Second, intrinsic brain activity at specific tumour locations was calculated by overlaying patients’ individual tumour masks with regional intrinsic brain activity of the controls and was associated with tumour and patient characteristics.

As proposed, glioma preferentially occurred in brain regions characterized by higher intrinsic brain activity in controls as reflected by higher offset. Second, intrinsic brain activity at patients’ individual tumour locations differed according to glioma subtype and performance status: the most malignant isocitrate dehydrogenase-wild-type glioblastoma patients had the lowest excitation/inhibition ratio at their individual tumour locations as compared to isocitrate dehydrogenase-mutant, 1p/19q-codeleted glioma patients, while a lower excitation/inhibition ratio related to poorer Karnofsky Performance Status, particularly in codeleted glioma patients.

In conclusion, gliomas more frequently occur in cortical brain regions with intrinsically higher activity levels, suggesting that more active regions are more vulnerable to glioma development. Moreover, indices of healthy, intrinsic excitation/inhibition ratio at patients’ individual tumour locations may capture both tumour biology and patients’ performance status. These findings contribute to our understanding of the complex and bidirectional relationship between normal brain functioning and glioma growth, which is at the core of the relatively new field of ‘cancer neuroscience’.

OS09.4.A Cognitive functioning of patients with diffuse glioma during stable disease

Background

Glioma patients often experience cognitive problems, which are associated with decreased functional independence and health-related quality of life. However, the prevalence and nature of cognitive impairment in these patients is relatively underreported, particularly during stable disease. In this study we determine the prevalence of cognitive deficits and explore distinct cognitive profiles of patients with diffuse glioma at least two months after tumor treatment.

Material and Methods

A total of 112 patients (mean age: 43 years) without clinical or radiological progression with a grade II-IV glioma were included in this observational cohort study. Cognitive functioning was assessed in five cognitive domains (attention, information processing speed, verbal memory, working memory, and executive functioning). Cognitive impairment was defined as a z-score of 1.5 SD below that of healthy controls. Hierarchical cluster analysis was used to examine cognitive profiles.

Results

In total, 46 patients (41%) had cognitive impairment. More than one domain was impaired in 25 of these patients (54%). The domains were affected in decreasing order of frequency: working memory in 31%, information processing speed in 22%, attention in 19%, verbal memory in 12%, and executive functioning in 11% of the patients. A clustered heatmap revealed three prominent cognitive clusters with an increasing number of cognitive domains impaired. The first cluster displayed patients with only working memory deficits, patients in the second cluster also had deficits in information processing speed, and the last cluster showed patients with impairments across all domains.

Conclusion

Cognitive impairment is highly prevalent in patients with diffuse glioma during stable disease. Working memory and information processing speed are most frequently affected. Importantly, our analyses show evidence for three subgroups in cognitively impaired glioma patients. Working memory seems an important driver in cognitive impairment given the involvement in all subgroups.

P01.10.B The relation between executive functioning and MEG multilayer network centrality in glioma patients

Background

Many patients with glioma suffer from deficits in executive functioning (EF), regardless of tumor location and size. While some patients experience improving EF after tumor resection, others deteriorate over time. This variability in cognitive trajectories is still poorly understood, but neuroscientific approaches that view the brain as a network may bear correlative and predictive value in this respect. Particularly integrative network connectivity of the fronto-parietal network are crucial for EF, but studies so far have only investigated isolated neural frequency bands of interest. Here, we synergize fronto-parietal network connectivity across frequency bands and test its explanatory power for EF at diagnosis and 1 year after tumor resection.

Material and Methods

Patients with diffuse glioma (n=37) underwent neuropsychological assessments, including three tests for EF, and resting-state magnetoencephalography (MEG) at both time points. Patients’ EF performance was standardized to z-scores using validated norm scores, adjusting for age, sex and education. MEG was source-reconstructed using patients’ MRI in combination with a beamformer approach and time series were filtered into six classical frequency bands. Band-specific functional connectivity between 78 cortical regions was estimated, after which an interconnected multilayer, multi-frequency network was created per patient. Multilayer centrality was then calculated as a measure of integrative network connectivity per region, and averaged over all fronto-parietal network regions to yield a single value reflecting integrative, multi-frequency network connectivity per patient at each time point.

Results

At diagnosis, eight patients had z-scores &lt;-1.5, indicating clinically relevant cognitive deficits. Preoperatively, poorer performance on the Concept Shifting Task was associated with lower multilayer centrality (p=.017), while improving performance on this task from diagnosis to 1 year after resection was associated with an increase in multilayer centrality (p=.022). Multilayer centrality at diagnosis did not significantly predict EF at follow-up.

Conclusion

Our results establish a significant relationship between poorer and decreasing EF with lower and decreasing multilayer, multi-frequency integrative connectivity of the fronto-parietal network in glioma patients. Studies with larger and more homogeneous samples may further explore the relevance of this multilayer approach in understanding and particularly predicting postoperative decline in EF in these patients, as our sample was characterized by large heterogeneity in cognitively relevant patient and tumor characteristics.

P01.03.B A quantitative comparison of cognitive performance and patient-reported symptoms in preoperative lower-grade glioma patients from two Dutch Hospitals

Background

Protocols for assessment of (neuro)psychological outcomes in lower-grade glioma patients vary between hospitals. This potentially complicates generalization of these outcomes. We compared standardized scores on tests of two frequently impaired cognitive domains (attention and executive functioning (EF)), and two relevant patient-reported outcomes (PROs; depression and fatigue) of two neuro-oncological hospitals that use different measurement instruments.

Material and Methods

Data were used from preoperative assessments of patients with (IDH-mut) WHO grade II/III glioma tested between 2007 and 2021 at Amsterdam UMC (AMS) or at Elisabeth-Tweesteden Hospital Tilburg (ETZ). AMS patients were referred for (neuro)psychological assessment based on physician and patient preference (paper and pencil tests), whereas all ETZ patients routinely undergo screening (computerized tests). To compare scores of the different attention and EF tests we converted patients’ performances to z-scores based on normative data. For cognitive performance, we compared scores of different cognitive flexibility tests (CST vs SAT), processing speed tests (SDC vs LDMT), and Stroop tests (Stroop I and Stroop III). PROs included the CES-D vs HADS-D and the CIS-fatigue vs MVI-general fatigue (AMS vs ETZ, resp.). Differences were tested using Fisher's, χ 2, and Mann-Whitney U tests.

Results

Assessments were done median 4 weeks (AMS, n=97, range 19-0 weeks) and 1 day (ETZ, n=106; range 14-0 days) preoperatively. Age, sex, tumor location and histology were comparable between cohorts (p&gt;0.05), but the AMS cohort showed significantly more grade III tumors (36% vs 16%) and more awake surgeries (84% vs 46%). Z-scores measuring attention and EF (n=94 and n=95, AMS vs ETZ) were not significantly different (CST vs SAT, percentage with a disorder (z &lt;-1.5SD) 15% vs 13%; SDC vs LDMT 13% vs 14%; Stroop I 11% vs 18%; Stroop III 13% vs 16% at AMS and ETZ, resp.). Percentages of patients with possible depression (CES-D≥16, n=88 and HADS-D≥8, n=106) did not differ significantly between hospitals (28% vs 26%), nor did percentages of patients with severe fatigue (CIS-fatigue≥35, n=88 and MVI-general fatigue (z &lt;-1.5SD), n=38, 42% vs 24% at AMS and ETZ, resp.).

Conclusion

Standardized scores of glioma patients on cognitive domains (attention and EF) and PROs (depression and fatigue) did not differ between two centers with slightly different samples using different testing protocols. This cautiously suggests that study findings on cognitive functioning and symptoms could be generalized. For research purposes, conjoint use of pooled populations for outcome evaluation could be explored with different samples from other centers using different instruments.

Long-term wellbeing and neurocognitive functioning of diffuse low-grade glioma patients and their caregivers: A longitudinal study spanning two decades

BACKGROUND

While patients with diffuse low-grade glioma (LGG) often survive for years, there is a risk of tumor progression which may impact patients' long-term health-related quality of life (HRQOL) and neurocognitive functioning (NCF). We present a follow-up of LGG patients and their informal caregivers (T3) who took part in our previous HRQOL investigations (T1, M = 7 and T2 M = 13 years after diagnosis).

METHODS

Participants completed HRQOL (short form-36 health survey [SF-36]; EORTC-BN20), fatigue (Checklist Individual Strength [CIS]), and depression (Center for Epidemiological Studies-Depression [CES-D]) questionnaires and underwent NCF assessments. T3 scores were compared with matched controls. Changes over time (T1-T2-T3) on group and participant level were assessed. Where available, histology of the initial tumor was revised and immunohistochemical staining for IDH1 R132H mutant protein was performed.

RESULTS

Thirty patients and nineteen caregivers participated. Of N = 11 with tissue available, 3 patients had confirmed diffuse LGG. At T3, patients (M = 26 years after diagnosis) had HRQOL and NCF similar to, or better than controls, yet 23.3% and 53.3% scored above the cut-off for depression (≥16 CES-D) and fatigue (≥35 CIS), respectively. Caregivers' HRQOL was similar to controls but reported high rates of fatigue (63.2%). Over time, patients' mental health improved (P < .05). Minimal detectable change in HRQOL over time was observed in individual patients (30% improvement; 23.3% decline; 20% both improvement and decline) with 23.3% remaining stable. NCF remained stable or improved in 82.8% of patients.

CONCLUSIONS

While HRQOL and NCF do not appear greatly impacted during long-term survivorship in LGG, depressive symptoms and fatigue are persistent.

Reducing severe fatigue in patients with diffuse glioma: a study protocol for an RCT on the effect of blended cognitive behavioural therapy

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.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13063-022-06485-5.

Temporal Dynamics of Resting-state Functional Networks and Cognitive Functioning following Systemic Treatment for Breast Cancer

Many women with breast cancer suffer from a decline in memory and executive function, particularly after treatment with chemotherapy. Recent neuroimaging studies suggest that changes in network dynamics are fundamental in decline in these cognitive functions. This has, however, not yet been investigated in breast cancer patients. Using resting state functional magnetic resonance imaging, we prospectively investigated whether changes in dynamic functional connectivity were associated with changes in memory and executive function. We examined 34 breast cancer patients that received chemotherapy, 32 patients that did not receive chemotherapy, and 35 no-cancer controls. All participants were assessed prior to treatment and six months after completion of chemotherapy, or at similar intervals for the other groups. To assess memory and executive function, we used the Hopkins Verbal Learning Test – Immediate Recall and the Trail Making Test B, respectively. Using a sliding window approach, we then evaluated dynamic functional connectivity of resting state networks supporting memory and executive function, i.e. the default mode network and frontoparietal network, respectively. Next, we directly investigated the association between cognitive performance and dynamic functional connectivity. We found no group differences in cognitive performance or connectivity measures. The association between dynamic functional connectivity of the default mode network and memory differed significantly across groups. This was not the case for the frontoparietal network and executive function. This suggests that cancer and chemotherapy alter the role of dynamic functional connectivity in memory function. Further implications of these findings are discussed.

Editorial: Focus feature on biomarkers in network neuroscience

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.

A more unstable resting-state functional network in cognitively declining multiple sclerosis

Cognitive impairment is common in people with multiple sclerosis and strongly affects their daily functioning. Reports have linked disturbed cognitive functioning in multiple sclerosis to changes in the organization of the functional network. In a healthy brain, communication between brain regions and which network a region belongs to is continuously and dynamically adapted to enable adequate cognitive function. However, this dynamic network adaptation has not been investigated in multiple sclerosis, and longitudinal network data remain particularly rare. Therefore, the aim of this study was to longitudinally identify patterns of dynamic network reconfigurations that are related to the worsening of cognitive decline in multiple sclerosis. Resting-state functional MRI and cognitive scores (expanded Brief Repeatable Battery of Neuropsychological tests) were acquired in 230 patients with multiple sclerosis and 59 matched healthy controls, at baseline (mean disease duration: 15 years) and at 5-year follow-up. A sliding-window approach was used for functional MRI analyses, where brain regions were dynamically assigned to one of seven literature-based subnetworks. Dynamic reconfigurations of subnetworks were characterized using measures of promiscuity (number of subnetworks switched to), flexibility (number of switches), cohesion (mutual switches) and disjointedness (independent switches). Cross-sectional differences between cognitive groups and longitudinal changes were assessed, as well as relations with structural damage and performance on specific cognitive domains. At baseline, 23% of patients were cognitively impaired (≥2/7 domains Z < −2) and 18% were mildly impaired (≥2/7 domains Z < −1.5). Longitudinally, 28% of patients declined over time (0.25 yearly change on ≥2/7 domains based on reliable change index). Cognitively impaired patients displayed more dynamic network reconfigurations across the whole brain compared with cognitively preserved patients and controls, i.e. showing higher promiscuity (P = 0.047), flexibility (P = 0.008) and cohesion (P = 0.008). Over time, cognitively declining patients showed a further increase in cohesion (P = 0.004), which was not seen in stable patients (P = 0.544). More cohesion was related to more severe structural damage (average r = 0.166, P = 0.015) and worse verbal memory (r = −0.156, P = 0.022), information processing speed (r = −0.202, P = 0.003) and working memory (r = −0.163, P = 0.017). Cognitively impaired multiple sclerosis patients exhibited a more unstable network reconfiguration compared to preserved patients, i.e. brain regions switched between subnetworks more often, which was related to structural damage. This shift to more unstable network reconfigurations was also demonstrated longitudinally in patients that showed cognitive decline only. These results indicate the potential relevance of a progressive destabilization of network topology for understanding cognitive decline in multiple sclerosis.

Toward unraveling the correlates of fatigue in glioma

BACKGROUND

Even though fatigue is one of the most prevalent and burdensome symptoms in patients with glioma, its etiology and determinants are still poorly understood. We aimed to identify which demographic, tumor- and treatment-related characteristics and patient-reported outcome measures (PROMs) are associated with or are predictors of fatigue in glioma.

METHODS

In this retrospective observational study, we included glioma patients with preoperative and postoperative assessments including PROMs on fatigue, depression, cognitive functioning, and health-related quality of life (HRQoL). Linear mixed models were used to identify which clinical factors and PROMs were associated with fatigue and linear multiple regression was used to detect predictors of postoperative fatigue.

RESULTS

In this study, 222 patients were included (78% grade II-III glioma, 22% grade IV). These patients had performed 333 assessments (193 preoperative and 116 one year postoperatively). Of all assessments, 39% was indicative of severe fatigue. Several HRQoL domains, depression, and right-sided tumors were significantly associated with fatigue (marginal R 2 = 0.63). Contrary to common expectations, tumor type, treatment-related factors, and timing of the assessment, were not associated with fatigue. In a subgroup of 70 patients with follow-up assessments, preoperative fatigue, and physical functioning were predictors of postoperative fatigue (adjusted R 2 = 0.31).

CONCLUSION

Fatigue is a complex symptom, which should not solely be attributed to the tumor or its treatment, but is instead related to different aspects of mood and HRQoL. These insights are important in understanding fatigue and could guide symptom management, especially in patients with lower-grade tumors.

Structure-function coupling as a correlate and potential biomarker of cognitive impairment in multiple sclerosis

Multiple sclerosis (MS) features extensive connectivity changes, but how structural and functional connectivity relate, and whether this relation could be a useful biomarker for cognitive impairment in MS is unclear.

This study included 79 MS patients and 40 healthy controls (HCs). Patients were classified as cognitively impaired (CI) or cognitively preserved (CP). Structural connectivity was determined using diffusion MRI and functional connectivity using resting-state magnetoencephalography (MEG) data (theta, alpha1 and alpha2 bands). Structure-function coupling was assessed by correlating modalities, and further explored in frequency bands that significantly correlated with whole-brain structural connectivity. Functional correlates of short- and long-range structural connections (based on tract length) were then specifically assessed. ROC analyses were performed on coupling values to identify biomarker potential.

Only the theta band showed significant correlations between whole-brain structural and functional connectivity (rho = −0.26, p = 0.023, only in MS). Long-range structure-function coupling was higher in CI patients compared to HCs (p = 0.005). Short-range coupling showed no group differences. Structure-function coupling was not a significant classifier of cognitive impairment for any tract length (short-range AUC = 0.498, p = 0.976, long-range AUC = 0.611, p = 0.095).

Long-range structure-function coupling was higher in CI-MS compared to HC, but more research is needed to further explore this measure as biomarkers in MS.

QOLP-05. HEALTH-RELATED QUALITY OF LIFE IN LOW-GRADE GLIOMA SURVIVORS 26 YEARS AFTER DIAGNOSIS

BACKGROUND

Patients with low-grade gliomas (LGGs) often survive for many years, yet little is known about patients’ health-related quality of life (HRQOL) in long-term survivorship. We previously investigated HRQOL in LGG patients diagnosed on average 6 years prior to assessment (T1, N=195) with a follow-up in stable patients on average 12 years after diagnosis (T2, N=65). We present a final follow-up of LGG survivors (T3), now decades after diagnosis.

METHODS

Patients completed questionnaires to assess HRQOL (Short Form-36 Health Survey; EORTC-BN20), fatigue (Checklist Individual Strength (CIS)), and depressive symptoms (Center for Epidemiological Studies-Depression Scale (CES-D)). Changes over time (T1-T2-T3) on group level and participant level were assessed.

RESULTS

Of the 65 patients approached, 18 (27.7%) were deceased, 3 (4.6%) experienced tumour progression to WHO III, 7 (10.8%) declined, and 3 (4.6%) could not be contacted. Thirty-four patients (52.3%) participated (missing HRQOL data N=2; N=32 included in analysis). Survivors were M=52.0 (sd=11.3) years old and diagnosed M=26.2 (sd=3.7, range:19-35) years prior. On group level, a statistically significant (but not clinically relevant) improvement in mental health (p=0.049), and a clinically relevant (but not statistically significant) decline in emotional role functioning was found. Minimal detectable change in HRQOL scale scores over time was observed in individual participants (28.1% only improvement; 25.0% only decline; 21.9% both improvement and decline) with 25.0% remaining completely stable. At T3, 25.0% and 53.1% of survivors scored above the cut-off for high risk of depression (≥16 CES-D) and severe fatigue (≥35 CIS), respectively.

CONCLUSION

In LGG survivors, assessed decades after diagnosis and treatment, HRQOL does not appear to be greatly impacted during survivorship. However, depressive symptoms and fatigue remain relatively common. Findings can help inform patients, their families, and clinicians and can serve as a benchmark for treatment trials evaluating interventions that can have very long-term effects.

Cellular Substrates of Functional Network Integration and Memory in Temporal Lobe Epilepsy

Temporal lobe epilepsy (TLE) patients are at risk of memory deficits, which have been linked to functional network disturbances, particularly of integration of the default mode network (DMN). However, the cellular substrates of functional network integration are unknown. We leverage a unique cross-scale dataset of drug-resistant TLE patients (n = 31), who underwent pseudo resting-state functional magnetic resonance imaging (fMRI), resting-state magnetoencephalography (MEG) and/or neuropsychological testing before neurosurgery. fMRI and MEG underwent atlas-based connectivity analyses. Functional network centrality of the lateral middle temporal gyrus, part of the DMN, was used as a measure of local network integration. Subsequently, non-pathological cortical tissue from this region was used for single cell morphological and electrophysiological patch-clamp analysis, assessing integration in terms of total dendritic length and action potential rise speed. As could be hypothesized, greater network centrality related to better memory performance. Moreover, greater network centrality correlated with more integrative properties at the cellular level across patients. We conclude that individual differences in cognitively relevant functional network integration of a DMN region are mirrored by differences in cellular integrative properties of this region in TLE patients. These findings connect previously separate scales of investigation, increasing translational insight into focal pathology and large-scale network disturbances in TLE.

Optimization of epilepsy surgery through virtual resections on individual structural brain networks

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.

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.

P12.02 Symptoms of patients with diffuse glioma during stable disease as a potential target for treatment

BACKGROUND

Patients with diffuse glioma experience many tumor- and treatment-related symptoms, including fatigue, cognitive deficits, and anxiety. It remains unclear how the frequency and burden of these symptoms change over time and specifically which symptoms patients experience during the stable phase of the disease. We assessed the frequency and burden of common symptoms in glioma patients during stable disease and whether patients were willing to undergo treatment for those symptoms. We suggest a top five of potentially treatable symptoms.

METHODS

Patients with a diffuse glioma during stable disease completed a questionnaire on symptoms they experienced during the past two weeks, such as irritability, fatigue, stress, pain, depressive thoughts and relationship problems. They rated the frequency and burden of seventeen symptoms on a seven-point numeric scale ranging from ‘never’ to ‘continuously’ and from ‘no burden’ to ‘unbearable’, respectively. Also, they stated whether they were willing to undergo treatment for each symptom. Data was collected retrospectively. The median values of the frequency and burden dimension were calculated for each symptom. We composed a top five of most frequent and burdensome symptoms. We used Kendall’s Tau to correlate frequency, burden and willingness to undergo treatment per symptom.

RESULTS

Fifty-two patients were included, 77% had a KPS score of 90–100, 23% a KPS score of 70–80 and 40% had a glioblastoma. The top five most frequent and burdensome symptoms were fatigue, memory problems, reduced physical fitness, concentration problems and drowsiness. Fatigue had the highest median value on the frequency (median 4.5, IQR 2.5) and burden (median 4.0, IQR 3.00) dimension and 21% of the patients were willing to undergo treatment for fatigue. Overall 35% of the patients were willing to undergo treatment for at least one symptom. Frequency and burden of symptoms correlated well (median Kendall’s Tau of 0.84 with a range of correlations of 0.73–0.95). Frequency and willingness to undergo treatment correlated less (median Kendall’s Tau of 0.35, range 0.24–0.57), as well as burden and willingness to undergo treatment (median Kendall’s Tau, range 0.28–0.61).

CONCLUSION

The top five symptoms most often reported by patients with diffuse glioma during stable disease were: fatigue, memory problems, reduced physical fitness, concentration problems and drowsiness. These symptoms may serve as a roadmap for future research on treatment strategies to alleviate these symptoms.

SUPPORT/DISCLOSURE

This project is funded by The Anita Veldman Foundation (CCA-2019-2-21).

OS09.4.A Health-related quality of life in low-grade glioma survivors 26 years after diagnosis

BACKGROUND

Many patients with low-grade gliomas (LGGs) continue to survive for many years, yet little is known about patients’ health-related quality of life (HRQOL) in long-term survivorship. We previously investigated HRQOL in LGG patients diagnosed on average 6 years prior to assessment (T1, N=195) with a follow-up in stable patients on average 12 years after diagnosis (T2, N=65). We present a final follow-up of LGG survivors (T3), now decades after diagnosis.

MATERIAL AND METHODS

We invited patients who participated in our previous assessment (N=65), regardless of disease status. Patients completed questionnaires to assess HRQOL, fatigue, and depressive symptoms: Short Form-36 Health Survey (SF-36), European Organisation for Research and Treatment of Cancer Quality of Life Questionnaire-Brain Tumour Module (EORTC BN20), Checklist Individual Strength (CIS), and the Center for Epidemiological Studies Depression Scale (CES-D). Changes over time (T1-T2-T3) on group level and participant level were assessed.

RESULTS

Of the 65 patients, 18 (27.7%) were deceased, 3 (4.6%) experienced tumour progression to WHO III, 7 (10.8%) declined, and 3 (4.6%) could not be contacted. Thirty-four patients (52.3%) participated. Of these, 2 had missing HRQOL data, with 32 patients included in analysis. Survivors were M=52.0 (sd=11.3) years old and diagnosed M=26.2 (sd=3.7, range 19–35) years prior. On group level, a statistically significant (but not clinically relevant) improvement in mental health (p=0.049), and a clinically relevant (but not statistically significant) decline in emotional role functioning was found. No other group-level changes over time in HRQOL were found. Minimal detectable change in HRQOL scale scores over time was observed in individual participants (28.1% only improvement; 25.0% only decline; 21.9% both improvement and decline) with 25.0% remaining completely stable. At T3, 25.0% of survivors scored above the cut-off for high risk of clinical depression (≥16 CES-D), and 53.1% of survivors classed as severely fatigued (≥35 CIS).

CONCLUSION

In this cohort of LGG survivors, assessed decades after diagnosis and treatment, HRQOL does not appear to be greatly impacted during survivorship. However, depressive symptoms and fatigue remain relatively common. Findings can help inform patients, their families, and clinicians and can serve as a benchmark for treatment trials evaluating interventions that can have very long-term effects.

P11.01 Symptom networks in glioma: a novel approach to study multidimensional symptomatology in glioma patients

BACKGROUND

Glioma patients experience a high symptom burden contributing to poorer quality of life. Symptoms include depression, cognitive impairment, and fatigue and vary throughout the disease. These symptoms are rarely studied from a comprehensive perspective, while their interdependence may be relevant for their development, perpetuation, and ultimately successful treatment. The emerging field of symptom network analysis uncovers the multidimensional symptom space. Nodes are the symptoms, and edges are operationalized as the full conditional association, or partial correlation, between two symptom severity scores across patients. Highly connected nodes are considered central and may be particularly relevant targets for treatment as disruption of these central nodes impact the entire network. We visualized the overall glioma symptom network, compared multidimensional results to known literature, and statistically compared networks between relevant patient subgroups.

MATERIAL AND METHODS

A dataset comprised of 355 observations of 180 glioma patients at different disease phases was analysed. Cognitive testing and questionnaires regarding health-related and glioma-specific quality of life, fatigue, depression, and cognition resulted in the definition of 30 symptom nodes. Symptom clusters were visually explored in the resulting networks, as were node strength, betweenness, and closeness centrality measures for each node. Networks were statistically compared between preoperative patients and patients during stable disease, as well as patients with low versus high-grade gliomas. Networks of patients with normal versus severe levels of fatigue were also compared as cancer-/glioma-related fatigue has a strong impact on quality of life and can correlate with other common symptoms such as pain, depression, and/or sleep disturbance.

RESULTS

Symptom clusters existed between: 1) bodily pain, headache and physical functioning; 2) concentration and motivation; and 3) fatigue and drowsiness. Fatigue and mental health were the most central nodes in the networks. Furthermore, the overall connectivity between symptoms was significantly higher in the severely fatigued patients than in patients with normal fatigue. No network differences were found between low versus high-grade, and preoperative versus stable disease networks.

CONCLUSION

Fatigue is a central node in glioma patients’ burden of disease, and symptoms are more tightly intercorrelated in patients experiencing severe fatigue. From our data, we hypothesize that fatigue co-exists with or perpetuates other symptoms. Thus, although these results are preliminary, the network approach may innovate hypothesis generation in symptom management.

P12.07 The potential of cannabinoids to improve quality of life in glioma patients: A meta-analysis in patients with neurological and oncological disease

BACKGROUND

Gliomas are primary brain tumors with a dismal prognosis. Reducing symptoms and maintaining quality of life (QoL) are main treatment aims in glioma patients. Mental well-being is an important subdomain of QoL. Cannabinoids have been suggested to alleviate frequently experienced symptoms of reduced mental well-being such as anxiety or depression. Glioma patients frequently report unprescribed cannabinoid use for these reasons. We performed a meta-analysis of the current evidence on cannabinoid efficacy on QoL and mental well-being to identify its added value in treatment of glioma patients.

MATERIAL AND METHODS

We performed a systematic PubMed, Embase and Web of Science search according to the PRISMA guidelines on September 22nd and 23rd, 2020. The effects of any dose of tetrahydrocannabinol (THC) or cannabidiol (CBD) on both general QoL and mental well-being were evaluated. The intervention had to be given for at least a week to establish a steady-state concentration. Effect size was calculated using Hedges g. Risk of bias of included studies was assessed using Cochrane’s Risk of Bias tool 2.0.

RESULTS

We retrieved no publications on cannabinoids use and QoL in glioma and, therefore, we expanded the search to cannabinoid use in other cancer types and chronic central nervous system (CNS) diseases. Sixteen studies were identified: four in cancer and twelve in CNS disease. Meta-analysis showed no effect of cannabinoids on general QoL (twelve studies in 1,740 patients; g = -0.02, 95% CI -0.11 to 0.07, p = 0.65) and mental well-being (twelve studies in 1,587 patients; g = -0.00, 95% CI -0.15 to 0.14, p = 0.96). Risk of bias was low in five studies, raised some concern in one study and was high in ten studies, mainly due to possible unblinding of patients after psychoactive adverse effects.

CONCLUSION

No studies on the effects of cannabinoids on QoL in glioma patients have been reported. A pooled analysis of studies in oncological patients and patients with CNS disease showed no effect of cannabinoids on QoL or mental well-being. However, studies were clinically heterogeneous and only one small study investigated monotherapy CBD with undecided results. As many glioma patients currently use cannabinoids, and monotherapy CBD has not been sufficiently investigated, future studies are necessary to evaluate its value in this specific population.

SUPPORT/DISCLOSURE

This meta-analysis has been funded by The Anita Veldman Foundation (CCA-2019-2-21).

Connecting brain and behavior in clinical neuroscience: A network approach

In recent years, there has been an increase in applications of network science in many different fields. In clinical neuroscience and psychopathology, the developments and applications of network science have occurred mostly simultaneously, but without much collaboration between the two fields. The promise of integrating these network applications lies in a united framework to tackle one of the fundamental questions of our time: how to understand the link between brain and behavior. In the current overview, we bridge this gap by introducing conventions in both fields, highlighting similarities, and creating a common language that enables the exploitation of synergies. We provide research examples in autism research, as it accurately represents research lines in both network neuroscience and psychological networks. We integrate brain and behavior not only semantically, but also practically, by showcasing three methodological avenues that allow to combine networks of brain and behavioral data. As such, the current paper offers a stepping stone to further develop multi-modal networks and to integrate brain and behavior.

Understanding Global Brain Network Alterations in Glioma Patients

Introduction: Glioma patients show increased global brain network clustering related to poorer cognition and epilepsy. However, it is unclear whether this increase is spatially widespread, localized in the (peri)tumor region only, or decreases with distance from the tumor. Materials and Methods: Weighted global and local brain network clustering was determined in 71 glioma patients and 53 controls by using magnetoencephalography. Tumor clustering was determined by averaging local clustering of regions overlapping with the tumor, and vice versa for non-tumor regions. Euclidean distance was determined from the tumor centroid to the centroids of other regions. Results: Patients showed higher global clustering compared with controls. Clustering of tumor and non-tumor regions did not differ, and local clustering was not associated with distance from the tumor. Post hoc analyses revealed that in the patient group, tumors were located more often in regions with higher clustering in controls, but it seemed that tumors of patients with high global clustering were located more often in regions with lower clustering in controls. Conclusions: Glioma patients show non-local network disturbances. Tumors of patients with high global clustering may have a preferred localization, namely regions with lower clustering in controls, suggesting that tumor localization relates to the extent of network disruption. Impact statement This work uses the innovative framework of network neuroscience to investigate functional connectivity patterns associated with brain tumors. Glioma (primary brain tumor) patients experience cognitive deficits and epileptic seizures, which have been related to brain network alterations. This study shows that glioma patients have a spatially widespread increase in global network clustering, which cannot be attributed to local effects of the tumor. Moreover, tumors occur more often in brain regions with higher network clustering in controls. This study emphasizes the global character of network alterations in glioma patients and suggests that preferred tumor locations are characterized by particular network profiles.

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.

The cerebellum and its network: Disrupted static and dynamic functional connectivity patterns and cognitive impairment in multiple sclerosis

Background:

The impact of cerebellar damage and (dys)function on cognition remains understudied in multiple sclerosis.

Objective:

To assess the cognitive relevance of cerebellar structural damage and functional connectivity (FC) in relapsing-remitting multiple sclerosis (RRMS) and secondary progressive multiple sclerosis (SPMS).

Methods:

This study included 149 patients with early RRMS, 81 late RRMS, 48 SPMS and 82 controls. Cerebellar cortical imaging included fractional anisotropy, grey matter volume and resting-state functional magnetic resonance imaging (MRI). Cerebellar FC was assessed with literature-based resting-state networks, using static connectivity (that is, conventional correlations), and dynamic connectivity (that is, fluctuations in FC strength). Measures were compared between groups and related to disability and cognition.

Results:

Cognitive impairment (CI) and cerebellar damage were worst in SPMS. Only SPMS showed cerebellar connectivity changes, compared to early RRMS and controls. Lower static FC was seen in fronto-parietal and default-mode networks. Higher dynamic FC was seen in dorsal and ventral attention, default-mode and deep grey matter networks. Cerebellar atrophy and higher dynamic FC together explained 32% of disability and 24% of cognitive variance. Higher dynamic FC was related to working and verbal memory and to information processing speed.

Conclusion:

Cerebellar damage and cerebellar connectivity changes were most prominent in SPMS and related to worse CI.

Fatigue and resting-state functional brain networks in breast cancer patients treated with chemotherapy

PURPOSE

This longitudinal study aimed to disentangle the impact of chemotherapy on fatigue and hypothetically associated functional brain network alterations.

METHODS

In total, 34 breast cancer patients treated with chemotherapy (BCC +), 32 patients not treated with chemotherapy (BCC -), and 35 non-cancer controls (NC) were included. Fatigue was assessed using the EORTC QLQ-C30 fatigue subscale at two time points: baseline (T1) and six months after completion of chemotherapy or matched intervals (T2). Participants also underwent resting-state functional magnetic resonance imaging (rsfMRI). An atlas spanning 90 cortical and subcortical brain regions was used to extract time series, after which Pearson correlation coefficients were calculated to construct a brain network per participant per timepoint. Network measures of local segregation and global integration were compared between groups and timepoints and correlated with fatigue.

RESULTS

As expected, fatigue increased over time in the BCC + group (p = 0.025) leading to higher fatigue compared to NC at T2 (p = 0.023). Meanwhile, fatigue decreased from T1 to T2 in the BCC - group (p = 0.013). The BCC + group had significantly lower local efficiency than NC at T2 (p = 0.033), while a negative correlation was seen between fatigue and local efficiency across timepoints and all participants (T1 rho = - 0.274, p = 0.006; T2 rho = - 0.207, p = 0.039).

CONCLUSION

Although greater fatigue and lower local functional network segregation co-occur in breast cancer patients after chemotherapy, the relationship between the two generalized across participant subgroups, suggesting that local efficiency is a general neural correlate of fatigue.

Functional brain network organization measured with magnetoencephalography predicts cognitive decline in multiple sclerosis

BACKGROUND

Cognitive decline remains difficult to predict as structural brain damage cannot fully explain the extensive heterogeneity found between MS patients.

OBJECTIVE

To investigate whether functional brain network organization measured with magnetoencephalography (MEG) predicts cognitive decline in MS patients after 5 years and to explore its value beyond structural pathology.

METHODS

Resting-state MEG recordings, structural MRI, and neuropsychological assessments were analyzed of 146 MS patients, and 100 patients had a 5-year follow-up neuropsychological assessment. Network properties of the minimum spanning tree (i.e. backbone of the functional brain network) indicating network integration and overload were related to baseline and longitudinal cognition, correcting for structural damage.

RESULTS

A more integrated beta band network (i.e. smaller diameter) and a less integrated delta band network (i.e. lower leaf fraction) predicted cognitive decline after 5 years (Radj2=15%), independent of structural damage. Cross-sectional analyses showed that a less integrated network (e.g. lower tree hierarchy) related to worse cognition, independent of frequency band.

CONCLUSIONS

The level of functional brain network integration was an independent predictive marker of cognitive decline, in addition to the severity of structural damage. This work thereby indicates the promise of MEG-derived network measures in predicting disease progression in MS.

Long-range connections are more severely damaged and relevant for cognition in multiple sclerosis

An efficient network such as the human brain features a combination of global integration of information, driven by long-range connections, and local processing involving short-range connections. Whether these connections are equally damaged in multiple sclerosis is unknown, as is their relevance for cognitive impairment and brain function. Therefore, we cross-sectionally investigated the association between damage to short- and long-range connections with structural network efficiency, the functional connectome and cognition. From the Amsterdam multiple sclerosis cohort, 133 patients (age = 54.2 ± 9.6) with long-standing multiple sclerosis and 48 healthy controls (age = 50.8 ± 7.0) with neuropsychological testing and MRI were included. Structural connectivity was estimated from diffusion tensor images using probabilistic tractography (MRtrix 3.0) between pairs of brain regions. Structural connections were divided into short- (length < quartile 1) and long-range (length > quartile 3) connections, based on the mean distribution of tract lengths in healthy controls. To determine the severity of damage within these connections, (i) fractional anisotropy as a measure for integrity; (ii) total number of fibres; and (iii) percentage of tract affected by lesions were computed for each connecting tract and averaged for short- and long-range connections separately. To investigate the impact of damage in these connections for structural network efficiency, global efficiency was computed. Additionally, resting-state functional connectivity was computed between each pair of brain regions, after artefact removal with FMRIB’s ICA-based X-noiseifier. The functional connectivity similarity index was computed by correlating individual functional connectivity matrices with an average healthy control connectivity matrix. Our results showed that the structural network had a reduced efficiency and integrity in multiple sclerosis relative to healthy controls (both P < 0.05). The long-range connections showed the largest reduction in fractional anisotropy (z = −1.03, P < 0.001) and total number of fibres (z = −0.44, P < 0.01), whereas in the short-range connections only fractional anisotropy was affected (z = −0.34, P = 0.03). Long-range connections also demonstrated a higher percentage of tract affected by lesions than short-range connections, independent of tract length (P < 0.001). Damage to long-range connections was more strongly related to structural network efficiency and cognition (fractional anisotropy: r = 0.329 and r = 0.447. number of fibres r = 0.321 and r = 0.278. and percentage of lesions: r = −0.219; r = −0.426, respectively) than damage to short-range connections. Only damage to long-distance connections correlated with a more abnormal functional network (fractional anisotropy: r = 0.226). Our findings indicate that long-range connections are more severely affected by multiple sclerosis-specific damage than short-range connections. Moreover compared to short-range connections, damage to long-range connections better explains network efficiency and cognition.

Resting-state and task-based centrality of dorsolateral prefrontal cortex predict resilience to 1 Hz repetitive transcranial magnetic stimulation

Repetitive transcranial magnetic stimulation (rTMS) is used to investigate normal brain function in healthy participants and as a treatment for brain disorders. Various subject factors can influence individual response to rTMS, including brain network properties. A previous study by our group showed that “virtually lesioning” the left dorsolateral prefrontal cortex (dlPFC; important for cognitive flexibility) using 1 Hz rTMS reduced performance on a set‐shifting task. We aimed to determine whether this behavioural response was related to topological features of pre‐TMS resting‐state and task‐based functional networks. 1 Hz (inhibitory) rTMS was applied to the left dlPFC in 16 healthy participants, and to the vertex in 17 participants as a control condition. Participants performed a set‐shifting task during fMRI at baseline and directly after a single rTMS session 1–2 weeks later. Functional network topology measures were calculated from resting‐state and task‐based fMRI scans using graph theoretical analysis. The dlPFC‐stimulated group, but not the vertex group, showed reduced setshifting performance after rTMS, associated with lower task‐based betweenness centrality (BC) of the dlPFC at baseline (p = .030) and a smaller reduction in task‐based BC after rTMS (p = .024). Reduced repeat trial accuracy after rTMS was associated with higher baseline resting state node strength of the dlPFC (p = .017). Our results suggest that behavioural response to 1 Hz rTMS to the dlPFC is dependent on baseline functional network features. Individuals with more globally integrated stimulated regions show greater resilience to rTMS effects, while individuals with more locally well‐connected regions show greater vulnerability.

Relationship between β-amyloid and structural network topology in decedents without dementia

OBJECTIVE

To investigate the association between β-amyloid (Aβ) load and postmortem structural network topology in decedents without dementia.

METHODS

Fourteen decedents (mean age at death 72.6 ± 7.2 years) without known clinical diagnosis of neurodegenerative disease and meeting pathology criteria only for no or low Alzheimer disease (AD) pathologic change were selected from the Normal Aging Brain Collection Amsterdam database. In situ brain MRI included 3D T1-weighted images for anatomical registration and diffusion tensor imaging for probabilistic tractography with subsequent structural network construction. Network topologic measures of centrality (degree), integration (global efficiency), and segregation (clustering and local efficiency) were calculated. Tissue sections from 12 cortical regions were sampled and immunostained for Aβ and hyperphosphorylated tau (p-tau), and histopathologic burden was determined. Linear mixed effect models were used to assess the relationship between Aβ and p-tau load and network topologic measures.

RESULTS

Aβ was present in 79% of cases and predominantly consisted of diffuse plaques; p-tau was sparsely present. Linear mixed effect models showed independent negative associations between Aβ load and global efficiency (β = -0.83 × 10^-3, p = 0.014), degree (β = -0.47, p = 0.034), and clustering (β = -0.55 × 10^-2, p = 0.043). A positive association was present between Aβ load and local efficiency (β = 3.16 × 10^-3, p = 0.035). Regionally, these results were significant in the posterior cingulate cortex (PCC) for degree (β = -2.22, p < 0.001) and local efficiency (β = 1.01 × 10^-2, p = 0.014) and precuneus for clustering (β = -0.91 × 10^-2, p = 0.017). There was no relationship between p-tau and network topology.

CONCLUSION

This study in deceased adults with AD-related pathologic change provides evidence for a relationship among early Aβ accumulation, predominantly of the diffuse type, and structural network topology, specifically of the PCC and precuneus.

Brain Network Integration in Patients with Migraine: A Magnetoencephalography Study

Migraine is a common disorder with high social and medical impact. Patients with migraine have a much higher chance of experiencing headache attacks compared with the general population. Recent neuroimaging studies have confirmed that pathophysiology in the brain is not only limited to the moment of the attack but is also present in between attacks, the interictal phase. In this study, we hypothesized that the topology of functional brain networks is also different in the interictal state, compared with people who are not affected by migraine. We also expected that the level of network disturbances scales with the number of years people have suffered from migraine. Functional connectivity between 78 cortical brain regions was estimated for source-level magnetoencephalography data by calculating the phase lag index, in five frequency bands (delta-beta), and compared between healthy controls (n = 24) and patients who had been suffering from migraine for longer than 6 years (n = 12) or shorter than 6 years (n = 12). Moreover, the topology of the functional networks was characterized using the minimum spanning tree. The migraine groups did not differ from each other in functional connectivity. However, the network topology was different compared with healthy controls. The results were frequency specific, and higher average nodal betweenness centrality was specifically evident in higher frequency bands in patients with longer disease duration, while an opposite trend was present for lower frequencies. This study shows that patients with migraine have a different network topology in the resting state compared with healthy controls, whereby specific brain areas have altered topological roles in a frequency-specific manner. Some alterations appear specifically in patients with long-term migraine, which might show the long-term effects of the disease.