Pre- and post-surgery brain tumor multimodal magnetic resonance imaging data optimized for large scale computational modelling.

We present a dataset of magnetic resonance imaging (MRI) data (T1, diffusion, BOLD) acquired in 25 brain tumor patients before the tumor resection surgery, and six months after the surgery, together with the tumor masks, and in 11 controls (recruited among the patients' caregivers). The dataset also contains behavioral and emotional scores obtained with standardized questionnaires. To simulate personalized computational models of the brain, we also provide structural connectivity matrices, necessary to perform whole-brain modelling with tools such as The Virtual Brain. In addition, we provide blood-oxygen-level-dependent imaging time series averaged across regions of interest for comparison with simulation results. An average resting state hemodynamic response function for each region of interest, as well as shape maps for each voxel, are also contributed.

Quality of Hospital Electronic Health Record (EHR) Data Based on the International Consortium for Health Outcomes Measurement (ICHOM) in Heart Failure: Pilot Data Quality Assessment Study.

BACKGROUND: There is increasing recognition that health care providers need to focus attention, and be judged against, the impact they have on the health outcomes experienced by patients. The measurement of health outcomes as a routine part of clinical documentation is probably the only scalable way of collecting outcomes evidence, since secondary data collection is expensive and error-prone. However, there is uncertainty about whether routinely collected clinical data within electronic health record (EHR) systems includes the data most relevant to measuring and comparing outcomes and if those items are collected to a good enough data quality to be relied upon for outcomes assessment, since several studies have pointed out significant issues regarding EHR data availability and quality. OBJECTIVE: In this paper, we first describe a practical approach to data quality assessment of health outcomes, based on a literature review of existing frameworks for quality assessment of health data and multistakeholder consultation. Adopting this approach, we performed a pilot study on a subset of 21 International Consortium for Health Outcomes Measurement (ICHOM) outcomes data items from patients with congestive heart failure. METHODS: All available registries compatible with the diagnosis of heart failure within an EHR data repository of a general hospital (142,345 visits and 12,503 patients) were extracted and mapped to the ICHOM format. We focused our pilot assessment on 5 commonly used data quality dimensions: completeness, correctness, consistency, uniqueness, and temporal stability. RESULTS: We found high scores (>95%) for the consistency, completeness, and uniqueness dimensions. Temporal stability analyses showed some changes over time in the reported use of medication to treat heart failure, as well as in the recording of past medical conditions. Finally, the investigation of data correctness suggested several issues concerning the characterization of missing data values. Many of these issues appear to be introduced while mapping the IMASIS-2 relational database contents to the ICHOM format, as the latter requires a level of detail that is not explicitly available in the coded data of an EHR. CONCLUSIONS: Overall, results of this pilot study revealed good data quality for the subset of heart failure outcomes collected at the Hospital del Mar. Nevertheless, some important data errors were identified that were caused by fundamentally different data collection practices in routine clinical care versus research, for which the ICHOM standard set was originally developed. To truly examine to what extent hospitals today are able to routinely collect the evidence of their success in achieving good health outcomes, future research would benefit from performing more extensive data quality assessments, including all data items from the ICHOM standards set and across multiple hospitals.

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.

Hub disruption in patients with chronic neck pain: a graph analytical approach.

Chronic pain is known to alter the brain's network dynamics. These dynamics are often demonstrated by identifying alterations in the brain network topology. A common approach used for this purpose is graph theory. To date, little is known on how these potentially altered networks in chronic pain relate to the symptoms reported by these patients. Here, we applied a graph theoretical approach to identify network changes in patients suffering from chronic neck pain, a group that is often neglected in chronic pain research. Participants with chronic traumatic and nontraumatic neck pain were compared to healthy pain-free controls. They showed higher levels of self-reported symptoms of sensitization, higher levels of disability, and impaired sensorimotor control. Furthermore, the brain suffering from chronic neck pain showed altered network properties in the posterior cingulate cortex, amygdala, and pallidum compared with the healthy pain-free brain. These regions have been identified as brain hubs (ie, regions that are responsible for orchestrating communication between other brain regions) and are therefore known to be more vulnerable in brain disorders including chronic pain. We were furthermore able to uncover associations between these altered brain network properties and the symptoms reported by patients. Our findings indicate that chronic neck pain patients reflect brain network alterations and that targeting the brain in patients might be of utmost importance.

The interplay between emotion regulation, emotional well-being, and cognitive functioning in brain tumor patients and their caregivers: An exploratory study.

OBJECTIVE: Brain tumor patients may suffer from a range of health-impairing problems reducing their quality of life. To identify potential targets for interventions, we examined the influence of different emotion regulation strategies on affective and cognitive functioning as indices of quality of life in patients and their caregivers in the early phase of treatment. METHODS: To this end, we conducted an exploratory longitudinal study on a small cohort, measuring emotion regulation, emotional well-being, and cognitive functioning on the day before each patient's tumor resection (28 patients and 11 caregivers) and several months after neurosurgery (22 patients and 10 caregivers). RESULTS: Results showed that emotion regulation strategies are relatively stable from preoperative to postoperative assessment. Nevertheless, several associations between emotion regulation strategies and quality of life indices were evident after tumor resection. In particular, our results were largely in line with previous research findings in healthy and other patient populations, corroborating the adaptive character of cognitive reappraisal, whereas suppression and expression of emotions were related to reduced cognitive and affective functioning, respectively. CONCLUSIONS: Based on these results, we suggest that further intervention or qualitative studies explore whether therapeutic interventions directed toward mastery of cognitive reappraisal techniques and appropriate expression of emotions could lead to improved long-term adjustment among brain tumor patients and their caregivers.

Associations between brain morphology and motor performance in chronic neck pain: A whole-brain surface-based morphometry approach.

Changes in brain morphology are hypothesized to be an underlying process that drive the widespread pain and motor impairment in patients with chronic neck pain. However, no earlier research assessed whole-brain cortical morphology in these patients. This case-control study assesses group-differences in whole-brain morphology between female healthy controls (HC; n = 34), and female patients with chronic idiopathic neck pain (CINP; n = 37) and whiplash-associated disorders (CWAD; n = 39). Additionally, the associations between whole-brain morphology and motor performance including balance, strength, and neuromuscular control were assessed. Cortical volume, thickness, and surface area were derived from high resolution T1-weighted images. T2*-weighted images were obtained to exclude traumatic brain injury. Vertex-wise general-linear-model-analysis revealed cortical thickening in the left precuneus and increased volume in the left superior parietal gyrus of patients with CINP compared to HC, and cortical thickening of the left superior parietal gyrus compared to HC and CWAD. Patients with CWAD showed a smaller cortical volume in the right precentral and superior temporal gyrus compared to HC. ANCOVA-analysis revealed worse neuromuscular control in CWAD compared to HC and CINP, and in CINP compared to HC. Patients with CWAD showed decreased levels of strength and sway area compared to CINP and HC. Partial correlation analysis revealed significant associations between the volume of the precentral gyrus, and neuromuscular control and strength together with an association between the volume of the superior temporal gyrus and strength. Our results emphasize the role of altered gray matter alterations in women with chronic neck pain, and its association with pain and motor impairment.

Predicting functional networks from region connectivity profiles in task-based versus resting-state fMRI data.

Intrinsic Connectivity Networks, patterns of correlated activity emerging from "resting-state" BOLD time series, are increasingly being associated with cognitive, clinical, and behavioral aspects, and compared with patterns of activity elicited by specific tasks. We study the reconfiguration of brain networks between task and resting-state conditions by a machine learning approach, to highlight the Intrinsic Connectivity Networks (ICNs) which are more affected by the change of network configurations in task vs. rest. To this end, we use a large cohort of publicly available data in both resting and task-based fMRI paradigms. By applying a battery of different supervised classifiers relying only on task-based measurements, we show that the highest accuracy to predict ICNs is reached with a simple neural network of one hidden layer. In addition, when testing the fitted model on resting state measurements, such architecture yields a performance close to 90% for areas connected to the task performed, which mainly involve the visual and sensorimotor cortex, whilst a relevant decrease of the performance is observed in the other ICNs. On one hand, our results confirm the correspondence of ICNs in both paradigms (task and resting) thus opening a window for future clinical applications to subjects whose participation in a required task cannot be guaranteed. On the other hand it is shown that brain areas not involved in the task display different connectivity patterns in the two paradigms.

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.

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.