Integrative Imaging Informatics for Cancer Research: Workflow Automation for Neuro-Oncology (I3CR-WANO).

PURPOSE: Efforts to use growing volumes of clinical imaging data to generate tumor evaluations continue to require significant manual data wrangling, owing to data heterogeneity. Here, we propose an artificial intelligence-based solution for the aggregation and processing of multisequence neuro-oncology MRI data to extract quantitative tumor measurements. MATERIALS AND METHODS: Our end-to-end framework (1) classifies MRI sequences using an ensemble classifier, (2) preprocesses the data in a reproducible manner, (3) delineates tumor tissue subtypes using convolutional neural networks, and (4) extracts diverse radiomic features. Moreover, it is robust to missing sequences and adopts an expert-in-the-loop approach in which the segmentation results may be manually refined by radiologists. After the implementation of the framework in Docker containers, it was applied to two retrospective glioma data sets collected from the Washington University School of Medicine (WUSM; n = 384) and The University of Texas MD Anderson Cancer Center (MDA; n = 30), comprising preoperative MRI scans from patients with pathologically confirmed gliomas. RESULTS: The scan-type classifier yielded an accuracy of >99%, correctly identifying sequences from 380 of 384 and 30 of 30 sessions from the WUSM and MDA data sets, respectively. Segmentation performance was quantified using the Dice Similarity Coefficient between the predicted and expert-refined tumor masks. The mean Dice scores were 0.882 (±0.244) and 0.977 (±0.04) for whole-tumor segmentation for WUSM and MDA, respectively. CONCLUSION: This streamlined framework automatically curated, processed, and segmented raw MRI data of patients with varying grades of gliomas, enabling the curation of large-scale neuro-oncology data sets and demonstrating high potential for integration as an assistive tool in clinical practice.

Automated MRI segmentation for individualized modeling of current flow in the human head.

OBJECTIVE: High-definition transcranial direct current stimulation (HD-tDCS) and high-density electroencephalography require accurate models of current flow for precise targeting and current source reconstruction. At a minimum, such modeling must capture the idiosyncratic anatomy of the brain, cerebrospinal fluid (CSF) and skull for each individual subject. Currently, the process to build such high-resolution individualized models from structural magnetic resonance images requires labor-intensive manual segmentation, even when utilizing available automated segmentation tools. Also, accurate placement of many high-density electrodes on an individual scalp is a tedious procedure. The goal was to develop fully automated techniques to reduce the manual effort in such a modeling process. APPROACH: A fully automated segmentation technique based on Statical Parametric Mapping 8, including an improved tissue probability map and an automated correction routine for segmentation errors, was developed, along with an automated electrode placement tool for high-density arrays. The performance of these automated routines was evaluated against results from manual segmentation on four healthy subjects and seven stroke patients. The criteria include segmentation accuracy, the difference of current flow distributions in resulting HD-tDCS models and the optimized current flow intensities on cortical targets. MAIN RESULTS: The segmentation tool can segment out not just the brain but also provide accurate results for CSF, skull and other soft tissues with a field of view extending to the neck. Compared to manual results, automated segmentation deviates by only 7% and 18% for normal and stroke subjects, respectively. The predicted electric fields in the brain deviate by 12% and 29% respectively, which is well within the variability observed for various modeling choices. Finally, optimized current flow intensities on cortical targets do not differ significantly. SIGNIFICANCE: Fully automated individualized modeling may now be feasible for large-sample EEG research studies and tDCS clinical trials.

An automated method for high-definition transcranial direct current stimulation modeling.

Targeted transcranial stimulation with electric currents requires accurate models of the current flow from scalp electrodes to the human brain. Idiosyncratic anatomy of individual brains and heads leads to significant variability in such current flows across subjects, thus, necessitating accurate individualized head models. Here we report on an automated processing chain that computes current distributions in the head starting from a structural magnetic resonance image (MRI). The main purpose of automating this process is to reduce the substantial effort currently required for manual segmentation, electrode placement, and solving of finite element models. In doing so, several weeks of manual labor were reduced to no more than 4 hours of computation time and minimal user interaction, while current-flow results for the automated method deviated by less than 27.9% from the manual method. Key facilitating factors are the addition of three tissue types (skull, scalp and air) to a state-of-the-art automated segmentation process, morphological processing to correct small but important segmentation errors, and automated placement of small electrodes based on easily reproducible standard electrode configurations. We anticipate that such an automated processing will become an indispensable tool to individualize transcranial direct current stimulation (tDCS) therapy.

Optimized multi-electrode stimulation increases focality and intensity at target.

Transcranial direct current stimulation (tDCS) provides a non-invasive tool to elicit neuromodulation by delivering current through electrodes placed on the scalp. The present clinical paradigm uses two relatively large electrodes to inject current through the head resulting in electric fields that are broadly distributed over large regions of the brain. In this paper, we present a method that uses multiple small electrodes (i.e. 1.2 cm diameter) and systematically optimize the applied currents to achieve effective and targeted stimulation while ensuring safety of stimulation. We found a fundamental trade-off between achievable intensity (at the target) and focality, and algorithms to optimize both measures are presented. When compared with large pad-electrodes (approximated here by a set of small electrodes covering 25 cm(2)), the proposed approach achieves electric fields which exhibit simultaneously greater focality (80% improvement) and higher target intensity (98% improvement) at cortical targets using the same total current applied. These improvements illustrate the previously unrecognized and non-trivial dependence of the optimal electrode configuration on the desired electric field orientation and the maximum total current (due to safety). Similarly, by exploiting idiosyncratic details of brain anatomy, the optimization approach significantly improves upon prior un-optimized approaches using small electrodes. The analysis also reveals the optimal use of conventional bipolar montages: maximally intense tangential fields are attained with the two electrodes placed at a considerable distance from the target along the direction of the desired field; when radial fields are desired, the maximum-intensity configuration consists of an electrode placed directly over the target with a distant return electrode. To summarize, if a target location and stimulation orientation can be defined by the clinician, then the proposed technique is superior in terms of both focality and intensity as compared to previous solutions and is thus expected to translate into improved patient safety and increased clinical efficacy.

Spectrum separation resolves partial-volume effect of MRSI as demonstrated on brain tumor scans.

Magnetic resonance spectroscopic imaging (MRSI) is currently used clinically in conjunction with anatomical MRI to assess the presence and extent of brain tumors and to evaluate treatment response. Unfortunately, the clinical utility of MRSI is limited by significant variability of in vivo spectra. Spectral profiles show increased variability because of partial coverage of large voxel volumes, infiltration of normal brain tissue by tumors, innate tumor heterogeneity, and measurement noise. We address these problems directly by quantifying the abundance (i.e. volume fraction) within a voxel for each tissue type instead of the conventional estimation of metabolite concentrations from spectral resonance peaks. This 'spectrum separation' method uses the non-negative matrix factorization algorithm, which simultaneously decomposes the observed spectra of multiple voxels into abundance distributions and constituent spectra. The accuracy of the estimated abundances is validated on phantom data. The presented results on 20 clinical cases of brain tumor show reduced cross-subject variability. This is reflected in improved discrimination between high-grade and low-grade gliomas, which demonstrates the physiological relevance of the extracted spectra. These results show that the proposed spectral analysis method can improve the effectiveness of MRSI as a diagnostic tool.

Effects of glucose and glutamine concentration in the formulation of the artificial cerebrospinal fluid (ACSF).

The composition of the ACSF is fundamental in controlling the extracellular environment of the brain slice preparation. 'Typical' formulations lack amino acids and contain a higher concentration of glucose (10 mM) than in the cerebrospinal fluid (0.47-4.4 mM). We examined the effects of different concentrations of glutamine, the most abundant amino acid in the CSF, and glucose on rat hippocampal slice physiology. Bipolar paired-pulse stimulation was applied to the Schaffer collaterals and population spikes were monitored in the CA1 pyramidal layer for approximately 1 hour. Addition of glutamine (0.5 mM) to slices superfused with 10 mM of glucose did not enhance population spike amplitude. Higher concentration of glutamine (2-5 mM) resulted in spreading-depression. Decreasing glucose concentration from 10 mM to 5 mM, in the absence of glutamine, attenuated population spikes. Decreasing glucose to 2 mM, in the absence of glutamine, suppressed evoked population spikes. Superfusing brain slices with ACSF containing 'physiological' concentrations of both glucose (2 mM) and glutamine (0.5 mM) similarly suppressed population spikes. In separate experiments, during high-K+ induced epileptiform activity, glutamine (0.5 mM) did not affect the burst duration, frequency or waveform. These results suggest that the concentration of glucose in ACSF should conservatively be 10 mM in order to maximize paired-pulse population responses while the presence of physiological concentration of glutamine (0.5 mM) has minimal effects on paired-pulse responses and high-K+ induced epileptiform activity. These results are discussed in the context of fundamental differences between in vitro brain slice superfusion and in vivo brain perfusion.

Effects of high-frequency stimulation on epileptiform activity in vitro: ON/OFF control paradigm.

PURPOSE: To determine the effects of high-frequency electrical stimulation on electrographic seizure activity during and after stimulation (ON-effect and OFF-effect). METHODS: The modulation and suppression of epileptiform activity during (ON-effect) and after (OFF-effect) high-frequency electrical stimulation was investigated using the high-K(+) and picrotoxin hippocampal slice epilepsy models. Uniform sinusoidal fields (50 Hz) were applied with various intensity levels for 1 min across brain slices. Extracellular and intracellular activity were monitored during and after stimulation. RESULTS: The ON-effects of high-frequency stimulation were highly variable across individual slices and models; ON-effects included modulation of activity, pacing, partial suppression, or activity resembling spreading-depression. On average, epileptic activity, measured as power in the extracellular fields, increased significantly during stimulation. Following the termination of electrical stimulation, a robust poststimulation suppression period was observed. This OFF suppression was observed even at relatively moderate stimulation intensities. The duration of OFF suppression increased with stimulation intensity, independent of ON-effects. Antagonism of GABA(A)function did not directly effect OFF suppression duration. CONCLUSIONS: The present results suggest that "rational" seizure control protocols using intermittent high-frequency electrical stimulation should control for both ON and OFF effects.

Spike timing amplifies the effect of electric fields on neurons: implications for endogenous field effects.

Despite compelling phenomenological evidence that small electric fields (<5 mV/mm) can affect brain function, a quantitative and experimentally verified theory is currently lacking. Here we demonstrate a novel mechanism by which the nonlinear properties of single neurons "amplify" the effect of small electric fields: when concurrent to suprathreshold synaptic input, small electric fields can have significant effects on spike timing. For low-frequency fields, our theory predicts a linear dependency of spike timing changes on field strength. For high-frequency fields (relative to the synaptic input), the theory predicts coherent firing, with mean firing phase and coherence each increasing monotonically with field strength. Importantly, in both cases, the effects of fields on spike timing are amplified with decreasing synaptic input slope and increased cell susceptibility (millivolt membrane polarization per field amplitude). We confirmed these predictions experimentally using CA1 hippocampal neurons in vitro exposed to static (direct current) and oscillating (alternating current) uniform electric fields. In addition, we develop a robust method to quantify cell susceptibility using spike timing. Our results provide a precise mechanism for a functional role of endogenous field oscillations (e.g., gamma) in brain function and introduce a framework for considering the effects of environmental fields and design of low-intensity therapeutic neurostimulation technologies.