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

Outlook Magazine, Autumn 2014

https://digitalcommons.wustl.edu/outlook/1193/thumbnail.jp

Modeling brain dynamics after tumor resection using The Virtual Brain

Brain tumor patients scheduled for tumor resection often face significant uncertainty, as the outcome of neurosurgery is difficult to predict at the individual patient level. Recently, simulation of the activity of neural populations connected according to the white matter fibers, producing personalized brain network models, has been introduced as a promising tool for this purpose. The Virtual Brain provides a robust open source framework to implement these models. However, brain network models first have to be validated, before they can be used to predict brain dynamics. In prior work, we optimized individual brain network model parameters to maximize the fit with empirical brain activity. In this study, we extend this line of research by examining the stability of fitted parameters before and after tumor resection, and compare it with baseline parameter variability using data from healthy control subjects. Based on these findings, we perform the first "virtual neurosurgery", mimicking patient's actual surgery by removing white matter fibers in the resection mask and simulating again neural activity on this new connectome. We find that brain network model parameters are relatively stable over time in brain tumor patients who underwent tumor resection, compared with baseline variability in healthy control subjects. Concerning the virtual neurosurgery analyses, use of the pre-surgery model implemented on the virtually resected structural connectome resulted in improved similarity with post-surgical empirical functional connectivity in some patients, but negligible improvement in others. These findings reveal interesting avenues for increasing interactions between computational neuroscience and neuro-oncology, as well as important limitations that warrant further investigation

Network analysis shows decreased ipsilesional structural connectivity in glioma patients

Tumors and their location distinctly alter both local and global brain connectivity within the ipsilesional hemisphere of glioma patients. Gliomas that infiltrate networks and systems, such as the motor system, often lead to substantial functional impairment in multiple systems. Network-based statistics (NBS) allow to assess local network differences and graph theoretical analyses enable investigation of global and local network properties. Here, we used network measures to characterize glioma-related decreases in structural connectivity by comparing the ipsi- with the contralesional hemispheres of patients and correlated findings with neurological assessment. We found that lesion location resulted in differential impairment of both short and long connectivity patterns. Network analysis showed reduced global and local efficiency in the ipsilesional hemisphere compared to the contralesional hemispheric networks, which reflect the impairment of information transfer across different regions of a network.Peer reviewe

Subcortical brain mapping of executive functions glioma patients:Towards standardized cognitive monitoring during awake brain surgery

Bij patiënten met een glioom, een type hersentumor, is de eerste keuze van behandeling in de meeste gevallen een operatie om zoveel mogelijk kwaadaardig tumorweefsel weg te halen en daarbij de beschadiging van gezond hersenweefsel te voorkomen. In sommige gevallen kan de neurochirurg ervoor kiezen om de hersenoperatie onder wakkere omstandigheden uit te voeren, om de kans op schade aan gebieden die belangrijk zijn voor bepaalde hersenfuncties te beperken. Directe Elektrische Stimulatie (DES) is de techniek die momenteel wordt gezien als de gouden standaard om tijdens wakkere hersenoperaties belangrijke hersenfuncties in kaart te brengen en te bewaken. Deze techniek wordt gebruikt om de hoeveelheid tumorweefsel dat weggehaald zal worden af te wegen tegen het behouden van functioneel belangrijke gebieden. DES is ontwikkeld om tijdens een operatie vast te kunnen stellen of specifieke hersengebieden of hersenverbindingen, een belangrijke rol spelen bij hersenfuncties zoals taal en motoriek. Het bewaken van taal en motoriek levert een bewezen betere uitkomst door het voorkomen van verlammingen of taalstoornissen. De laatste jaren zijn verschillende neurochirurgische centra deze techniek ook gaan gebruiken om andere functies te monitoren, zoals executieve functies. Executieve functies zijn de regelfuncties van de hersenen die nodig zijn voor het realiseren van doelgericht en aangepast gedrag, zoals werkgeheugen, inhibitie en cognitieve flexibiliteit. Deze functies zijn van essentieel belang om alledaagse taken uit te voeren, zoals boodschappen doen, koken, autorijden, studeren en je beroep uitoefenen. De reden om executieve functies te monitoren is dat stoornissen in deze functies veel voorkomen bij patiënten met een glioom, zowel voor als na de operatie. Het is echter nog onbekend of specifieke hersenverbindingen, ook wel subcorticale banen genoemd, onmisbaar zijn voor executieve functies, waardoor de wetenschappelijke onderbouwing en klinische relevantie om deze functies te monitoren tijdens wakkere hersenoperaties voorlopig ontbreekt. Het hoofddoel van dit proefschrift was om te onderzoeken of specifieke hersenverbindingen betrokken zijn bij executieve functies en gemonitord kunnen worden met een gestandaardiseerde set van taken tijdens wakkere chirurgie bij patiënten met een glioom. Om meer inzicht te verkrijgen in de functionele rol van subcorticale banen, en ter voorbereiding op de ontwikkeling van gestandaardiseerd monitoring protocol, hebben we structurele en functionele laesie-symptoom studies verricht en de resultaten van deze studies gecombineerd met bevindingen uit de literatuur. De bevindingen toonden onder andere dat specifieke hersenbanen, zoals de Frontal Aslant Tract en de rechter en linker Superior Longitudinal Fasciculus III betrokken zijn bij executieve functies en voorzag ons van argumenten om ze tijdens wakkere hersenoperaties te monitoren. Samen met de klinische ervaring van de afdeling neurochirurgie van het ETZ Tilburg en dat van drie andere neurochirurgische centra (Lariboisière Parijs, EMC Rotterdam, UMC Utrecht), hebben we een protocol ontwikkeld om deze hersenbanen te monitoren. Dit protocol omvat een set van executieve taken die op dezelfde manier in de verschillende centra worden afgenomen. Met dit protocol werd overeenstemming bereikt voor klinisch haalbare en wetenschappelijk onderbouwde intra-operatieve cognitieve monitoring. Hiermee werd een pad gebaand voor toekomstig onderzoek dat zich richt op de mate waarin de monitoring van deze functies leidt tot een vermindering van cognitieve problemen na een hersentumoroperatie en daarmee tot een betere kwaliteit van het sociale en professionele leven

Neuroimaging of structural pathology and connectomics in traumatic brain injury: Toward personalized outcome prediction.

Recent contributions to the body of knowledge on traumatic brain injury (TBI) favor the view that multimodal neuroimaging using structural and functional magnetic resonance imaging (MRI and fMRI, respectively) as well as diffusion tensor imaging (DTI) has excellent potential to identify novel biomarkers and predictors of TBI outcome. This is particularly the case when such methods are appropriately combined with volumetric/morphometric analysis of brain structures and with the exploration of TBI-related changes in brain network properties at the level of the connectome. In this context, our present review summarizes recent developments on the roles of these two techniques in the search for novel structural neuroimaging biomarkers that have TBI outcome prognostication value. The themes being explored cover notable trends in this area of research, including (1) the role of advanced MRI processing methods in the analysis of structural pathology, (2) the use of brain connectomics and network analysis to identify outcome biomarkers, and (3) the application of multivariate statistics to predict outcome using neuroimaging metrics. The goal of the review is to draw the community's attention to these recent advances on TBI outcome prediction methods and to encourage the development of new methodologies whereby structural neuroimaging can be used to identify biomarkers of TBI outcome

Network perspectives on epilepsy using EEG/MEG source connectivity

The evolution of EEG/MEG source connectivity is both, a promising, and controversial advance in the characterization of epileptic brain activity. In this narrative review we elucidate the potential of this technology to provide an intuitive view of the epileptic network at its origin, the different brain regions involved in the epilepsy, without the limitation of electrodes at the scalp level. Several studies have confirmed the added value of using source connectivity to localize the seizure onset zone and irritative zone or to quantify the propagation of epileptic activity over time. It has been shown in pilot studies that source connectivity has the potential to obtain prognostic correlates, to assist in the diagnosis of the epilepsy type even in the absence of visually noticeable epileptic activity in the EEG/MEG, and to predict treatment outcome. Nevertheless, prospective validation studies in large and heterogeneous patient cohorts are still lacking and are needed to bring these techniques into clinical use. Moreover, the methodological approach is challenging, with several poorly examined parameters that most likely impact the resulting network patterns. These fundamental challenges affect all potential applications of EEG/MEG source connectivity analysis, be it in a resting, spiking, or ictal state, and also its application to cognitive activation of the eloquent area in presurgical evaluation. However, such method can allow unique insights into physiological and pathological brain functions and have great potential in (clinical) neuroscience

Clinical applications of magnetic resonance imaging based functional and structural connectivity

Advances in computational neuroimaging techniques have expanded the armamentarium of imaging tools available for clinical applications in clinical neuroscience. Non-invasive, in vivo brain MRI structural and functional network mapping has been used to identify therapeutic targets, define eloquent brain regions to preserve, and gain insight into pathological processes and treatments as well as prognostic biomarkers. These tools have the real potential to inform patient-specific treatment strategies. Nevertheless, a realistic appraisal of clinical utility is needed that balances the growing excitement and interest in the field with important limitations associated with these techniques. Quality of the raw data, minutiae of the processing methodology, and the statistical models applied can all impact on the results and their interpretation. A lack of standardization in data acquisition and processing has also resulted in issues with reproducibility. This limitation has had a direct impact on the reliability of these tools and ultimately, confidence in their clinical use. Advances in MRI technology and computational power as well as automation and standardization of processing methods, including machine learning approaches, may help address some of these issues and make these tools more reliable in clinical use. In this review, we will highlight the current clinical uses of MRI connectomics in the diagnosis and treatment of neurological disorders; balancing emerging applications and technologies with limitations of connectivity analytic approaches to present an encompassing and appropriate perspective

Resting-State Functional Connectome in Patients with Brain Tumors Before and After Surgical Resection

Purpose: High-grade glioma surgery has evolved around the principal belief that a safe maximal tumor resection improves symptoms, quality of life, and survival. Mapping brain function has been recently improved by resting-state functional magnetic resonance imaging (rest-fMRI), a novel imaging technique that explores networks connectivity at “rest.” Methods: This prospective study analyzed 10 patients with high-grade glioma in whom rest-fMRI connectivity was assessed both in single-subject and in group analysis before and after surgery. Seed-based functional connectivity analysis was performed with CONN toolbox. Network identification focused on 8 major functional connectivity networks. A voxel-wise region of interest (ROI) to ROI correlation map to assess functional connectivity throughout the whole brain was computed from a priori seeds ROI in specific resting-state networks before and after surgical resection in each patient. Results: Reliable topography of all 8 resting-state networks was successfully identified in each participant before surgical resection. Single-subject functional connectivity analysis showed functional disconnection for dorsal attention and salience networks, whereas the language network demonstrated functional connection either in the case of left temporal glioblastoma. Functional connectivity in group analysis showed wide variations of functional connectivity in the default mode, salience, and sensorimotor networks. However, salience and language networks, salience and default mode networks, and salience and sensorimotor networks showed a significant correlation (P uncorrected <0.0025; P false discovery rate <0.077) in comparison before and after surgery confirming non-disconnection of these networks. Conclusions: Resting-state fMRI can reliably detect common functional connectivity networks in patients with glioma and has the potential to anticipate network alterations after surgical resection

Inference of Language Functional Network in Healthy, Cancerous and Bilingual Brains by fMRI and Network Modeling

We study the underlying mechanism by which language processing occurs in the human brain using inference methods on functional magnetic resonance imaging data. The data analyzed stems from several cohorts of subjects; a monolingual group, a bilingual group, a healthy control group and one diseased case. We applied a complex statistical inference pipeline to determine the network structure of brain components involved with language. This healthy network reveals a fully connected triangular relationship between the pre-Supplementary Motor Area (pre-SMA), the Broca\u27s Area (BA), and the ventral Pre-Motor Area (PreMA) in the left hemisphere. This triangle\u27\u27 shows consistently in all the healthy subjects (100%) we analyzed regardless of their mono- or multi-lingual status. In addition, we found that Wernicke\u27s Area (WA) on the left hemisphere connects with BA and PreMA to form a V\u27\u27 shape connectivity across 75% of the monolinguals, 50% of the bilinguals speaking a second language and 100% of the bilinguals speaking their native language. By comparing the quantified link weights, we found that the strongest link is between BA and PreMA, followed by pre-SMA and PreMA, and then pre-SMA and BA. This is consistent for all healthy subjects (p \u3c 0.05). Furthermore, we conducted a k-core analysis testing the resiliency of subnetworks in the three groups. Our results show that nodes in the three triangle areas belong mostly to the maximum shell, whereas WA populates mostly in the lower shell, consistently across the data. In a separate study, we describe frontal language reorganization in a 57-year-old right-handed patient with a low-grade left frontotemporal insular glioma. Pre-operative fMRI revealed robust activation in left WA and in the right BA. Intra-operative cortical stimulation of the left inferior frontal gyrus and adjacent cortices elicited no speech deficits, and gross total resection including the expected location of BA resulted in no speech impairment. Our network model found that the right homologue of the BA in this patient functionally connected to the same areas as the left BA in a typical healthy control. As opposed to the functional connection of the left BA in a healthy brain, the right BA did not connect directly with the left WA, but connected indirectly, mediated by the pre-SMA and preMA. In addition, the trans-located BA and WA moves from the lower k shell to the maximum shell during the recovery of the surgery. This case illustrates that pre-surgical fMRI can be used to identify atypical hemispheric language reorganization in the presence of a brain tumor and that network theory can help understand the underlying structure behind functional reorganization