Brain networks under attack : robustness properties and the impact of lesions

A growing number of studies approach the brain as a complex network, the so-called ‘connectome’. Adopting this framework, we examine what types or extent of damage the brain can withstand—referred to as network ‘robustness’—and conversely, which kind of distortions can be expected after brain lesions. To this end, we review computational lesion studies and empirical studies investigating network alterations in brain tumour, stroke and traumatic brain injury patients. Common to these three types of focal injury is that there is no unequivocal relationship between the anatomical lesion site and its topological characteristics within the brain network. Furthermore, large-scale network effects of these focal lesions are compared to those of a widely studied multifocal neurodegenerative disorder, Alzheimer’s disease, in which central parts of the connectome are preferentially affected. Results indicate that human brain networks are remarkably resilient to different types of lesions, compared to other types of complex networks such as random or scale-free networks. However, lesion effects have been found to depend critically on the topological position of the lesion. In particular, damage to network hub regions—and especially those connecting different subnetworks—was found to cause the largest disturbances in network organization. Regardless of lesion location, evidence from empirical and computational lesion studies shows that lesions cause significant alterations in global network topology. The direction of these changes though remains to be elucidated. Encouragingly, both empirical and modelling studies have indicated that after focal damage, the connectome carries the potential to recover at least to some extent, with normalization of graph metrics being related to improved behavioural and cognitive functioning. To conclude, we highlight possible clinical implications of these findings, point out several methodological limitations that pertain to the study of brain diseases adopting a network approach, and provide suggestions for future research

Modeling brain dynamics in brain tumor patients using the virtual brain

Presurgical planning for brain tumor resection aims at delineating eloquent tissue in the vicinity of the lesion to spare during surgery. To this end, noninvasive neuroimaging techniques such as functional MRI and diffusion-weighted imaging fiber tracking are currently employed. However, taking into account this information is often still insufficient, as the complex nonlinear dynamics of the brain impede straightforward prediction of functional outcome after surgical intervention. Large-scale brain network modeling carries the potential to bridge this gap by integrating neuroimaging data with biophysically based models to predict collective brain dynamics. As a first step in this direction, an appropriate computational model has to be selected, after which suitable model parameter values have to be determined. To this end, we simulated large-scale brain dynamics in 25 human brain tumor patients and 11 human control participants using The Virtual Brain, an open-source neuroinformatics platform. Local and global model parameters of the Reduced Wong-Wang model were individually optimized and compared between brain tumor patients and control subjects. In addition, the relationship between model parameters and structural network topology and cognitive performance was assessed. Results showed (1) significantly improved prediction accuracy of individual functional connectivity when using individually optimized model parameters; (2) local model parameters that can differentiate between regions directly affected by a tumor, regions distant from a tumor, and regions in a healthy brain; and (3) interesting associations between individually optimized model parameters and structural network topology and cognitive performance

Motor and language resting state functional magnetic resonance imaging in brain tumor patients

Background and purpose: Resting state functional magnetic resonance (RS-fMRI) correlation with pre-surgical functional status in patients with brain tumors is scarcely documented in the present literature. Aim of the present study was to investigate the validity of RS-fMRI as potential preoperative functional mapping tool in tumor brain surgery by exploring the association of motor and language RS-fMRI networks with subjects’ preoperative performance on motor and language clinical assessment respectively in patients with brain tumor. Materials and methods: 85 patients presented with brain tumor entities and 27 healthy controls were prospectively recruited for the present study. Clinical sample was subdivided into two groups according to mass localization: patients with tumors in proximity to motor cortex (n=59) underwent clinical examination for gross (paresis- muscle weakness) and fine (finger tapping) motor deficits. Patients harboring tumors in proximity to the left inferior frontal gyrus (n=35) were clinically assessed for apparent (expressive aphasia) and subtle language function (phonological verbal fluency) disturbances. All patients and healthy subjects underwent RS-fMRI with motor and language resting networks being derived by Independent Component Analysis (ICA). Results: In the motor group, patients with paresis demonstrated significantly (p=<0.01) reduced resting state BOLD-signal intensity in ipsilesional motor cortex in comparison to the respective one in contralesional-intact motor cortex. Significantly (p<0.01) decreased BOLD-signal intensity was additionally noticed in ipsilesional motor cortex of patients with paresis in comparison to patients with normal muscle strength. Furthermore, in patients with intact muscle strength, a strong positive correlation (r=0.70, p<0.01) between ipsilesional pre-central gyrus BOLD-signal and performance on finger tapping task was demonstrated. Compared to the healthy group, clinical motor group showed reduced resting state network activity, with patients’ ipsilesional pre- central gyrus BOLD-signal intensity to be significantly (p<0.01) lower than normals’ left and right pre-central gyri BOLD-signal intensities. Concerning language group, patients presented with expressive aphasia exhibited significantly (p=<0.01) reduced RS-fMRI BOLD-signal intensity in left inferior frontal gyrus (Broadmann area 44) when compared with patients without aphasia. In non-aphasic patients, a strong positive correlation (r=0.70, P<0.01) between left inferior frontal gyrus’ BOLD-signal intensity and phonological fluency scoring was demonstrated. Similarly with the motor group, language group also showed significantly (p=<0.01) reduced left inferior gyrus RS- fMRI BOLD-signal when compared to healthy controls. Finally, RS-fMRI BOLD signal was not observed to have an association with demographic parameters (age, gender) for both clinical and healthy groups and with tumor histopathological grading for both motor and language clinical groups. Conclusions: Our findings show a significant affection of motor and language RS-fMRI networks’ BOLD-signal intensity by the presence of a tumor and a correlation with clinical performance of patients providing thus evidence for the functional validity of RS-fMRI in brain tumor patients; our results indicate therefore, that RS-fMRI may be a valuable complementary tool for preoperative mapping of eloquent areas, at least in patients who cannot cooperate satisfactory in a traditional task-based motor and language fMRI

Personalised, image-guided, noninvasive brain stimulation in gliomas : Rationale, challenges and opportunities

Malignant brain tumours are among the most aggressive human cancers, and despite intensive efforts made over the last decades, patients' survival has scarcely improved. Recently, high-grade gliomas (HGG) have been found to be electrically integrated with healthy brain tissue, a communication that facilitates tumour mitosis and invasion. This link to neuronal activity has provided new insights into HGG pathophysiology and opened prospects for therapeutic interventions based on electrical modulation of neural and synaptic activity in the proximity of tumour cells, which could potentially slow tumour growth. Noninvasive brain stimulation (NiBS), a group of techniques used in research and clinical settings to safely modulate brain activity and plasticity via electromagnetic or electrical stimulation, represents an appealing class of interventions to characterise and target the electrical properties of tumour-neuron interactions. Beyond neuronal activity, NiBS may also modulate function of a range of substrates and dynamics that locally interacts with HGG (e.g., vascular architecture, perfusion and blood-brain barrier permeability). Here we discuss emerging applications of NiBS in patients with brain tumours, covering potential mechanisms of action at both cellular, regional, network and whole-brain levels, also offering a conceptual roadmap for future research to prolong survival or promote wellbeing via personalised NiBS interventions

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

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

Correlation between brain functional connectivity and neurocognitive function in patients with left frontal glioma

The association between neurocognitive function (NCF) impairment and brain cortical functional connectivity in glioma patients remains unclear. The correlations between brain oscillatory activity or functional connectivity and NCF measured by the Wechsler Adult Intelligence Scale full-scale intelligence quotient scores (WAIS FSIQ), the Wechsler Memory Scale-revised general memory scores (WMS-R GM), and the Western aphasia battery aphasia quotient scores (WAB AQ) were evaluated in 18 patients with left frontal glioma using resting-state electroencephalography (EEG). Current source density (CSD) and lagged phase synchronization (LPS) were analyzed using exact low-resolution electromagnetic tomography (eLORETA). Although 2 and 2 patients scored in the borderline range of WAIS FSIQ and WMS-R GM, respectively, the mean WAIS FSIQ, WMS-R GM, and WAB AQ values of all patients were within normal limits, and none had aphasia. In the correlation analysis, lower WMS-R GM was associated with a higher LPS value between the right anterior prefrontal cortex and the left superior parietal lobule in the beta1 band (13-20 Hz, R = - 0.802, P = 0.012). These findings suggest that LPS evaluated by scalp EEG is associated with memory function in patients with left frontal glioma and mild NCF disorders

An automated method for identifying an independent component analysis-based language-related resting-state network in brain tumor subjects for surgical planning

As a noninvasive and "task-free" technique, resting-state functional magnetic resonance imaging (rs-fMRI) has been gradually applied to pre-surgical functional mapping. Independent component analysis (ICA)-based mapping has shown advantage, as no a priori information is required. We developed an automated method for identifying language network in brain tumor subjects using ICA on rs-fMRI. In addition to standard processing strategies, we applied a discriminability-index-based component identification algorithm to identify language networks in three different groups. The results from the training group were validated in an independent group of healthy human subjects. For the testing group, ICA and seed-based correlation were separately computed and the detected language networks were assessed by intra-operative stimulation mapping to verify reliability of application in the clinical setting. Individualized language network mapping could be automatically achieved for all subjects from the two healthy groups except one (19/20, success rate = 95.0%). In the testing group (brain tumor patients), the sensitivity of the language mapping result was 60.9%, which increased to 87.0% (superior to that of conventional seed-based correlation [47.8%]) after extending to a radius of 1 cm. We established an automatic and practical component identification method for rs-fMRI-based pre-surgical mapping and successfully applied it to brain tumor patients

Modern Developments in Transcranial Magnetic Stimulation (TMS) – Applications and Perspectives in Clinical Neuroscience

Transcranial magnetic stimulation (TMS) is being increasingly used in neuroscience and clinics. Modern advances include but are not limited to the combination of TMS with precise neuronavigation as well as the integration of TMS into a multimodal environment, e.g., by guiding the TMS application using complementary techniques such as functional magnetic resonance imaging (fMRI), electroencephalography (EEG), diffusion tensor imaging (DTI), or magnetoencephalography (MEG). Furthermore, the impact of stimulation can be identified and characterized by such multimodal approaches, helping to shed light on the basic neurophysiology and TMS effects in the human brain. Against this background, the aim of this Special Issue was to explore advancements in the field of TMS considering both investigations in healthy subjects as well as patients

Resting-State Electroencephalography Functional Connectivity Networks Relate to Pre- and Postoperative Language Functioning in Low-Grade Glioma and Meningioma Patients

Introduction: Preservation of language functioning in patients undergoing brain tumor surgery is essential because language impairments negatively impact the quality of life. Brain tumor patients have alterations in functional connectivity (FC), the extent to which brain areas functionally interact. We studied FC networks in relation to language functioning in glioma and meningioma patients. Method: Patients with a low-grade glioma (N = 15) or meningioma (N = 10) infiltrating into/pressing on the language-dominant hemisphere underwent extensive language testing before and 1 year after surgery. Resting-state EEG was registered preoperatively, postoperatively (glioma patients only), and once in healthy individuals. After analyzing FC in theta and alpha frequency bands, weighted networks and Minimum Spanning Trees were quantified by various network measures. Results: Pre-operative FC network characteristics did not differ between glioma patients and healthy individuals. However, hub presence and higher local and global FC are associated with poorer language functioning before surgery in glioma patients and predict worse language performance at 1 year after surgery. For meningioma patients, a greater small worldness was related to worse language performance and hub presence; better average clustering and global integration were predictive of worse outcome on language function 1 year after surgery. The average eccentricity, diameter and tree hierarchy seem to be the network metrics with the more pronounced relation to language performance. Discussion: In this exploratory study, we demonstrated that preoperative FC networks are informative for pre- and postoperative language functioning in glioma patients and to a lesser extent in meningioma patients