Cost-effectiveness of preoperative motor mapping with navigated transcranial magnetic brain stimulation in patients with high-grade glioma.

OBJECTIVE Navigated transcranial magnetic stimulation (nTMS) is used to identify the motor cortex prior to surgery. Yet, there has, until now, been no published evidence on the economic impact of nTMS. This study aims to analyze the cost-effectiveness of nTMS, evaluating the incremental costs of nTMS motor mapping per additional quality-adjusted life year (QALY). By doing so, this study also provides a model allowing for future analysis of general cost-effectiveness of new neuro-oncological treatment options. METHODS The authors used a microsimulation model based on their cohort population sampled for 1000 patients over the time horizon of 2 years. A health care provider perspective was used to assemble direct costs of total treatment. Transition probabilities and health utilities were based on published literature. Effects were stated in QALYs and established for health state subgroups. RESULTS In all scenarios, preoperative mapping was considered cost-effective with a willingness-to-pay threshold < 3*per capita GDP (gross domestic product). The incremental cost-effectiveness ratio (ICER) of nTMS versus no nTMS was 45,086 Euros/QALY. Sensitivity analyses showed robust results with a high impact of total treatment costs and utility of progression-free survival. Comparing the incremental costs caused by nTMS implementation only, the ICER decreased to 1967 Euros/QALY. CONCLUSIONS Motor mapping prior to surgery provides a cost-effective tool to improve the clinical outcome and overall survival of high-grade glioma patients in a resource-limited setting. Moreover, the model used in this study can be used in the future to analyze new treatment options in neuro-oncology in terms of their general cost-effectiveness.

Exploring the alpha desynchronization hypothesis in resting state networks with intracranial electroencephalography and wiring cost estimates.

This paper addresses a fundamental question, are eyes closed and eyes open resting states equivalent baseline conditions, or do they have consistently different electrophysiological signatures? We compare the functional connectivity patterns in an eyes closed resting state with an eyes open resting state to investigate the alpha desynchronization hypothesis. The change in functional connectivity from eyes closed to eyes open, is here, for the first time, studied with intracranial recordings. We perform network connectivity analysis in iEEG and we find that phase-based connectivity is sensitive to the transition from eyes closed to eyes open only in interhemispheral and frontal electrodes. Power based connectivity, on the other hand, consistently discriminates between the two conditions in temporal and interhemispheral electrodes. Additionally, we provide a calculation for the wiring cost, defined in terms of the connectivity between electrodes weighted by distance. We find that the wiring cost variation from eyes closed to eyes open is sensitive to the eyes closed and eyes open conditions. We extend the standard network-based approach using the filtration method from algebraic topology which does not rely on the threshold selection problem. Both the wiring cost measure defined here and this novel methodology provide a new avenue for understanding the electrophysiology of resting state.

Performance Assessment of a Custom, Portable, and Low-Cost Brain-Computer Interface Platform.

OBJECTIVE: Conventional brain-computer interfaces (BCIs) are often expensive, complex to operate, and lack portability, which confines their use to laboratory settings. Portable, inexpensive BCIs can mitigate these problems, but it remains unclear whether their low-cost design compromises their performance. Therefore, we developed a portable, low-cost BCI and compared its performance to that of a conventional BCI. METHODS: The BCI was assembled by integrating a custom electroencephalogram (EEG) amplifier with an open-source microcontroller and a touchscreen. The function of the amplifier was first validated against a commercial bioamplifier, followed by a head-to-head comparison between the custom BCI (using four EEG channels) and a conventional 32-channel BCI. Specifically, five able-bodied subjects were cued to alternate between hand opening/closing and remaining motionless while the BCI decoded their movement state in real time and provided visual feedback through a light emitting diode. Subjects repeated the above task for a total of 10 trials, and were unaware of which system was being used. The performance in each trial was defined as the temporal correlation between the cues and the decoded states. RESULTS: The EEG data simultaneously acquired with the custom and commercial amplifiers were visually similar and highly correlated ( ρ = 0.79). The decoding performances of the custom and conventional BCIs averaged across trials and subjects were 0.70 ± 0.12 and 0.68 ± 0.10, respectively, and were not significantly different. CONCLUSION: The performance of our portable, low-cost BCI is comparable to that of the conventional BCIs. SIGNIFICANCE: Platforms, such as the one developed here, are suitable for BCI applications outside of a laboratory.

A New Strategy for Fast MRI-Based Quantification of the Myelin Water Fraction: Application to Brain Imaging in Infants.

The volume fraction of water related to myelin (fmy) is a promising MRI index for in vivo assessment of brain myelination, that can be derived from multi-component analysis of T1 and T2 relaxometry signals. However, existing quantification methods require rather long acquisition and/or post-processing times, making implementation difficult both in research studies on healthy unsedated children and in clinical examinations. The goal of this work was to propose a novel strategy for fmy quantification within acceptable acquisition and post-processing times. Our approach is based on a 3-compartment model (myelin-related water, intra/extra-cellular water and unrestricted water), and uses calibrated values of inherent relaxation times (T1c and T2c) for each compartment c. Calibration was first performed on adult relaxometry datasets (N = 3) acquired with large numbers of inversion times (TI) and echo times (TE), using an original combination of a region contraction approach and a non-negative least-square (NNLS) algorithm. This strategy was compared with voxel-wise fitting, and showed robust estimation of T1c and T2c. The accuracy of fmy calculations depending on multiple factors was investigated using simulated data. In the testing stage, our strategy enabled fast fmy mapping, based on relaxometry datasets acquired with reduced TI and TE numbers (acquisition <6 min), and analyzed with NNLS algorithm (post-processing <5min). In adults (N = 13, mean age 22.4±1.6 years), fmy maps showed variability across white matter regions, in agreement with previous studies. In healthy infants (N = 18, aged 3 to 34 weeks), asynchronous changes in fmy values were demonstrated across bundles, confirming the well-known progression of myelination.

The economics of functional magnetic resonance imaging: clinical and research.

It is difficult to justify maintaining a clinical functional magnetic resonance imaging (fMRI) program based solely on revenue generation. The use of fMRI is, therefore, based mostly in patient care considerations, leading to better outcomes. The high costs of the top-of-the-line equipment, hardware, and software needed for state-of-the-art fMRI and the time commitment by multiple professionals are not adequately reimbursed at a representative rate by current payor schemes for the Current Procedure Terminology codes assigned.

In search of biomarkers in psychiatry: EEG-based measures of brain function.

Current clinical parameters used for diagnosis and phenotypic definitions of psychopathology are both highly variable and subjective. Intensive research efforts for specific and sensitive biological markers, or biomarkers, for psychopathology as objective alternatives to the current paradigm are ongoing. While biomarker research in psychiatry has focused largely on functional neuroimaging methods for identifying the neural functions that associate with psychopathology, scalp electroencephalography (EEG) has been viewed, historically, as offering little specific brain source information, as scalp appearance is only loosely correlated to its brain source dynamics. However, ongoing advances in signal processing of EEG data can now deliver functional EEG brain-imaging with distinctly improved spatial, as well as fine temporal, resolution. One computational approach proving particularly useful for EEG cortical brain imaging is independent component analysis (ICA). ICA decomposition can be used to identify distinct cortical source activities that are sensitive and specific to the pathophysiology of psychiatric disorders. Given its practical research advantages, relatively low cost, and ease of use, EEG-imaging is now both feasible and attractive, in particular for studies involving the large samples required by genetically informative designs to characterize causal pathways to psychopathology. The completely non-invasive nature of EEG data acquisition, coupled with ongoing advances in dry, wireless, and wearable EEG technology, makes EEG-imaging increasingly attractive and appropriate for psychiatric research, including the study of developmentally young samples. Applied to large genetically and developmentally informative samples, EEG imaging can advance the search for robust diagnostic biomarkers and phenotypes in psychiatry.

Cost-utility of maximal safe resection of WHO grade II gliomas within eloquent areas.

BACKGROUND: Despite the growing use of intraoperative electrical stimulation (IES) mapping for resection of WHO grade II gliomas (GIIG) located within eloquent areas, some authors claim that this is a complex, time-consuming and expensive approach, and not well tolerated by patients, so they rely on other mapping techniques. Here we analyze the health related quality of life, direct and indirect costs of surgeries with and without intraoperative electrical stimulation (IES) mapping for resection of GIIG within eloquent areas. METHODS: A cohort of 11 subjects with GIIG within eloquent areas who had IES while awake (group A) was matched by tumor side and location to a cohort of 11 subjects who had general anesthesia without IES (group B). Direct and indirect costs (measured as loss of labor productivity) and utility (measured in quality adjusted life years, QALYs), were compared between groups. RESULTS: Total mean direct costs per patient were $38,662.70 (range $19,950.70 to $61,626.40) in group A, and $32,116.10 (range $22,764.50 to $46,222.50) in group B (p = 0.279). Total mean indirect costs per patient were $10,640.10 (range $3,010.10 to $86,940.70) in group A, and $48,804.70 (range $3,340.10 to $98,400.60) in group B (p = 0.035). Mean costs per QALY were $12,222.30 (range $3,801.10 to $47,422.90) in group A, and $31,927.10 (range $6,642.90 to $64,196.50) in group B (p = 0.023). CONCLUSIONS: Asleep-awake-asleep craniotomies with IES are associated with an increase in direct costs. However, these initial expenses are ultimately offset by medium and long-term costs averted from a decrease in morbidity and preservation of the patient's professional life. The present study emphasizes the importance to switch to an aggressive and safer surgical strategy in GIIG within eloquent areas.

Brain network analysis: separating cost from topology using cost-integration.

A statistically principled way of conducting brain network analysis is still lacking. Comparison of different populations of brain networks is hard because topology is inherently dependent on wiring cost, where cost is defined as the number of edges in an unweighted graph. In this paper, we evaluate the benefits and limitations associated with using cost-integrated topological metrics. Our focus is on comparing populations of weighted undirected graphs that differ in mean association weight, using global efficiency. Our key result shows that integrating over cost is equivalent to controlling for any monotonic transformation of the weight set of a weighted graph. That is, when integrating over cost, we eliminate the differences in topology that may be due to a monotonic transformation of the weight set. Our result holds for any unweighted topological measure, and for any choice of distribution over cost levels. Cost-integration is therefore helpful in disentangling differences in cost from differences in topology. By contrast, we show that the use of the weighted version of a topological metric is generally not a valid approach to this problem. Indeed, we prove that, under weak conditions, the use of the weighted version of global efficiency is equivalent to simply comparing weighted costs. Thus, we recommend the reporting of (i) differences in weighted costs and (ii) differences in cost-integrated topological measures with respect to different distributions over the cost domain. We demonstrate the application of these techniques in a re-analysis of an fMRI working memory task. We also provide a Monte Carlo method for approximating cost-integrated topological measures. Finally, we discuss the limitations of integrating topology over cost, which may pose problems when some weights are zero, when multiplicities exist in the ranks of the weights, and when one expects subtle cost-dependent topological differences, which could be masked by cost-integration.