Disconnection of the pathological connectome for multifocal epilepsy surgery.

OBJECTIVERecent neuroimaging studies suggest that intractable epilepsy involves pathological functional networks as well as strong epileptogenic foci. Combining cortico-cortical evoked potential (CCEP) recording and tractography is a useful strategy for mapping functional connectivity in normal and pathological networks. In this study, the authors sought to demonstrate the efficacy of preoperative combined CCEP recording, high gamma activity (HGA) mapping, and tractography for surgical planning, and of intraoperative CCEP measures for confirmation of selective pathological network disconnection.METHODSThe authors treated 4 cases of intractable epilepsy. Diffusion tensor imaging-based tractography data were acquired before the first surgery for subdural grid implantation. HGA and CCEP investigations were done after the first surgery, before the second surgery was performed to resect epileptogenic foci, with continuous CCEP monitoring during resection.RESULTSAll 4 patients in this report had measurable pathological CCEPs. The mean negative peak-1 latency of normal CCEPs related to language functions was 22.2 ± 3.5 msec, whereas pathological CCEP latencies varied between 18.1 and 22.4 msec. Pathological CCEPs diminished after complete disconnection in all cases. At last follow-up, all of the patients were in long-term postoperative seizure-free status, although 1 patient still suffered from visual aura every other month.CONCLUSIONSCombined CCEP measurement, HGA mapping, and tractography greatly facilitated targeted disconnection of pathological networks in this study. Although CCEP recording requires technical expertise, it allows for assessment of pathological network involvement in intractable epilepsy and may improve seizure outcome.

Clinical Impact and Implication of Real-Time Oscillation Analysis for Language Mapping.

BACKGROUND: We developed a functional brain analysis system that enabled us to perform real-time task-related electrocorticography (ECoG) and evaluated its potential in clinical practice. We hypothesized that high gamma activity (HGA) mapping would provide better spatial and temporal resolution with high signal-to-noise ratios. METHODS: Seven awake craniotomy patients were evaluated. ECoG was recorded during language tasks using subdural grids, and HGA (60-170 Hz) maps were obtained in real time. The patients also underwent electrocortical stimulation (ECS) mapping to validate the suspected functional locations on HGA mapping. The results were compared and calculated to assess the sensitivity and specificity of HGA mapping. For reference, bedside HGA-ECS mapping was performed in 5 epilepsy patients. RESULTS: HGA mapping demonstrated functional brain areas in real time and was comparable with ECS mapping. Sensitivity and specificity for the language area were 90.1% ± 11.2% and 90.0% ± 4.2%, respectively. Most HGA-positive areas were consistent with ECS-positive regions in both groups, and there were no statistical between-group differences. CONCLUSIONS: Although this study included a small number of subjects, it showed real-time HGA mapping with the same setting and tasks under different conditions. This study demonstrates the clinical feasibility of real-time HGA mapping. Real-time HGA mapping enabled simple and rapid detection of language functional areas in awake craniotomy. The mapping results were highly accurate, although the mapping environment was noisy. Further studies of HGA mapping may provide the potential to elaborate complex brain functions and networks.

Effectiveness of Reference Signal-Based Methods for Removal of EEG Artifacts Due to Subtle Movements During fMRI Scanning.

OBJECTIVE: Subtle motion of an epileptic patient examined with co-registered EEG and functional MRI (EEG-fMRI) may often lead to spurious fMRI activation patterns when true epileptic spikes are contaminated with motion artefacts. In recent years, methods relying on reference signals for correcting these subtle movements in the EEG have emerged. In this study, the performance of two reference-based devices are compared to the template-based method with regard to their ability to remove movement-related artifacts in EEG measured during scanning. METHODS: Measurements were performed with a novel double layer cap consisting of 29 EEG and 29 reference electrodes, and with a current loop cap consisting of 60 electrodes and three current loop wires attached to the cap. EEG was acquired inside the scanner during resting state, as well as when the subject was performing a cued movement task. For the double layer cap recordings, newly developed artifact removal algorithms are introduced and both reference signal-based methods are compared to a template-based correction method. RESULTS: The BCG artifacts occurring at resting state could be removed successfully by both the reference signal-based methods as well as by the template-based method. However, the reference signal-based methods were also capable of removing EEG artifacts induced by subtle movements, whereas the template-based method failed to remove these artifacts. CONCLUSION: Reference signal-based methods enable to correct for artifacts due to subtle movements, which are not removed by commonly used template-based removal algorithms. SIGNIFICANCE: Sensitivity of EEG-fMRI analysis in patients with focal epilepsy is improved by avoiding erroneous detections of subtle movements as epileptic spikes in the EEG.

Passive language mapping combining real-time oscillation analysis with cortico-cortical evoked potentials for awake craniotomy.

OBJECTIVE Electrocortical stimulation (ECS) is the gold standard for functional brain mapping; however, precise functional mapping is still difficult in patients with language deficits. High gamma activity (HGA) between 80 and 140 Hz on electrocorticography is assumed to reflect localized cortical processing, whereas the cortico-cortical evoked potential (CCEP) can reflect bidirectional responses evoked by monophasic pulse stimuli to the language cortices when there is no patient cooperation. The authors propose the use of "passive" mapping by combining HGA mapping and CCEP recording without active tasks during conscious resections of brain tumors. METHODS Five patients, each with an intraaxial tumor in their dominant hemisphere, underwent conscious resection of their lesion with passive mapping. The authors performed functional localization for the receptive language area, using real-time HGA mapping, by listening passively to linguistic sounds. Furthermore, single electrical pulses were delivered to the identified receptive temporal language area to detect CCEPs in the frontal lobe. All mapping results were validated by ECS, and the sensitivity and specificity were evaluated. RESULTS Linguistic HGA mapping quickly identified the language area in the temporal lobe. Electrical stimulation by linguistic HGA mapping to the identified temporal receptive language area evoked CCEPs on the frontal lobe. The combination of linguistic HGA and frontal CCEPs needed no patient cooperation or effort. In this small case series, the sensitivity and specificity were 93.8% and 89%, respectively. CONCLUSIONS The described technique allows for simple and quick functional brain mapping with higher sensitivity and specificity than ECS mapping. The authors believe that this could improve the reliability of functional brain mapping and facilitate rational and objective operations. Passive mapping also sheds light on the underlying physiological mechanisms of language in the human brain.

CortiQ-based Real-Time Functional Mapping for Epilepsy Surgery.

PURPOSE: To evaluate the use of the cortiQ-based mapping system (g.tec medication engineering GmbH, Austria) for real-time functional mapping (RTFM) and to compare it to results from electrical cortical stimulation mapping (ESM) and functional magnetic resonance imaging (fMRI). METHODS: Electrocorticographic activity was recorded in 3 male patients with intractable epilepsy by using cortiQ mapping system and analyzed in real time. Activation related to motor, sensory, and receptive language tasks was determined by evaluating the power of the high gamma frequency band (60-170 Hz). The sensitivity and specificity of RTFM were tested against ESM and fMRI results. RESULTS: "Next-neighbor" approach demonstrated [sensitivity/specificity %] (1) RTFM against ESM: 100.00/79.70 for hand motor; 100.00/73.87 for hand sensory; -/87 for language (it was not identified by the ESM); (2) RTFM against fMRI: 100.00/84.4 for hand motor; 66.70/85.35 for hand sensory; and 87.85/77.70 for language. CONCLUSIONS: The results of the quantitative "next-neighbor" RTFM evaluation were concordant to those from ESM and fMRI. The RTFM correlates well with localization of hand motor function provided by ESM and fMRI, which may offer added localization in the operating room and guidance for extraoperative ESM mapping. Real-time functional mapping correlates with fMRI language activation when ESM findings are negative. It has fewer limitations than ESM and greater flexibility in activation paradigms and measuring responses.

Novel techniques of real-time blood flow and functional mapping: technical note.

There are two main approaches to intraoperative monitoring in neurosurgery. One approach is related to fluorescent phenomena and the other is related to oscillatory neuronal activity. We developed novel techniques to visualize blood flow (BF) conditions in real time, based on indocyanine green videography (ICG-VG) and the electrophysiological phenomenon of high gamma activity (HGA). We investigated the use of ICG-VG in four patients with moyamoya disease and two with arteriovenous malformation (AVM), and we investigated the use of real-time HGA mapping in four patients with brain tumors who underwent lesion resection with awake craniotomy. Real-time data processing of ICG-VG was based on perfusion imaging, which generated parameters including arrival time (AT), mean transit time (MTT), and BF of brain surface vessels. During awake craniotomy, we analyzed the frequency components of brain oscillation and performed real-time HGA mapping to identify functional areas. Processed results were projected on a wireless monitor linked to the operating microscope. After revascularization for moyamoya disease, AT and BF were significantly shortened and increased, respectively, suggesting hyperperfusion. Real-time fusion images on the wireless monitor provided anatomical, BF, and functional information simultaneously, and allowed the resection of AVMs under the microscope. Real-time HGA mapping during awake craniotomy rapidly indicated the eloquent areas of motor and language function and significantly shortened the operation time. These novel techniques, which we introduced might improve the reliability of intraoperative monitoring and enable the development of rational and objective surgical strategies.

Rapid and minimum invasive functional brain mapping by real-time visualization of high gamma activity during awake craniotomy.

BACKGROUND: Electrocortical stimulation (ECS) is the gold standard for functional brain mapping during an awake craniotomy. The critical issue is to set aside enough time to identify eloquent cortices by ECS. High gamma activity (HGA) ranging between 80 and 120 Hz on electrocorticogram is assumed to reflect localized cortical processing. In this report, we used real-time HGA mapping and functional neuronavigation integrated with functional magnetic resonance imaging (fMRI) for rapid and reliable identification of motor and language functions. METHODS: Four patients with intra-axial tumors in their dominant hemisphere underwent preoperative fMRI and lesion resection with an awake craniotomy. All patients showed significant fMRI activation evoked by motor and language tasks. During the craniotomy, we recorded electrocorticogram activity by placing subdural grids directly on the exposed brain surface. RESULTS: Each patient performed motor and language tasks and demonstrated real-time HGA dynamics in hand motor areas and parts of the inferior frontal gyrus. Sensitivity and specificity of HGA mapping were 100% compared with ECS mapping in the frontal lobe, which suggested HGA mapping precisely indicated eloquent cortices. We found different HGA dynamics of language tasks in frontal and temporal regions. Specificities of the motor and language-fMRI did not reach 85%. The results of HGA mapping was mostly consistent with those of ECS mapping, although fMRI tended to overestimate functional areas. CONCLUSIONS: This novel technique enables rapid and accurate identification of motor and frontal language areas. Furthermore, real-time HGA mapping sheds light on underlying physiological mechanisms related to human brain functions.

A Cerebellar Neuroprosthetic System: Computational Architecture and in vivo Test.

Emulating the input-output functions performed by a brain structure opens the possibility for developing neuroprosthetic systems that replace damaged neuronal circuits. Here, we demonstrate the feasibility of this approach by replacing the cerebellar circuit responsible for the acquisition and extinction of motor memories. Specifically, we show that a rat can undergo acquisition, retention, and extinction of the eye-blink reflex even though the biological circuit responsible for this task has been chemically inactivated via anesthesia. This is achieved by first developing a computational model of the cerebellar microcircuit involved in the acquisition of conditioned reflexes and training it with synthetic data generated based on physiological recordings. Secondly, the cerebellar model is interfaced with the brain of an anesthetized rat, connecting the model's inputs and outputs to afferent and efferent cerebellar structures. As a result, we show that the anesthetized rat, equipped with our neuroprosthetic system, can be classically conditioned to the acquisition of an eye-blink response. However, non-stationarities in the recorded biological signals limit the performance of the cerebellar model. Thus, we introduce an updated cerebellar model and validate it with physiological recordings showing that learning becomes stable and reliable. The resulting system represents an important step toward replacing lost functions of the central nervous system via neuroprosthetics, obtained by integrating a synthetic circuit with the afferent and efferent pathways of a damaged brain region. These results also embody an early example of science-based medicine, where on the one hand the neuroprosthetic system directly validates a theory of cerebellar learning that informed the design of the system, and on the other one it takes a step toward the development of neuro-prostheses that could recover lost learning functions in animals and, in the longer term, humans.

An electrocorticographic BCI using code-based VEP for control in video applications: a single-subject study.

A brain-computer-interface (BCI) allows the user to control a device or software with brain activity. Many BCIs rely on visual stimuli with constant stimulation cycles that elicit steady-state visual evoked potentials (SSVEP) in the electroencephalogram (EEG). This EEG response can be generated with a LED or a computer screen flashing at a constant frequency, and similar EEG activity can be elicited with pseudo-random stimulation sequences on a screen (code-based BCI). Using electrocorticography (ECoG) instead of EEG promises higher spatial and temporal resolution and leads to more dominant evoked potentials due to visual stimulation. This work is focused on BCIs based on visual evoked potentials (VEP) and its capability as a continuous control interface for augmentation of video applications. One 35 year old female subject with implanted subdural grids participated in the study. The task was to select one out of four visual targets, while each was flickering with a code sequence. After a calibration run including 200 code sequences, a linear classifier was used during an evaluation run to identify the selected visual target based on the generated code-based VEPs over 20 trials. Multiple ECoG buffer lengths were tested and the subject reached a mean online classification accuracy of 99.21% for a window length of 3.15 s. Finally, the subject performed an unsupervised free run in combination with visual feedback of the current selection. Additionally, an algorithm was implemented that allowed to suppress false positive selections and this allowed the subject to start and stop the BCI at any time. The code-based BCI system attained very high online accuracy, which makes this approach very promising for control applications where a continuous control signal is needed.

CortiQ - clinical software for electrocorticographic real-time functional mapping of the eloquent cortex.

Planning for epilepsy surgery depends substantially on the localization of brain cortical areas responsible for sensory, motor, or cognitive functions, clinically also known as eloquent cortex. In this paper, we present the novel software package 'cortiQ' that allows clinicians to localize eloquent cortex, thus providing a safe margin for surgical resection with a low incidence of neurological deficits. This software can be easily used in addition to traditional mapping procedures such as the electrical cortical stimulation (ECS) mapping. The software analyses task-related changes in gamma activity recorded from implanted subdural electrocorticography electrodes using extensions to previously published methods. In this manuscript, we describe the system's architecture and workflow required to obtain a map of the eloquent cortex. We validate the system by comparing our mapping results with those acquired using ECS mapping in two subjects. Our results indicate that cortiQ reliably identifies eloquent cortex much faster (several minutes compared to an hour or more) than ECS mapping. Next-neighbour analyses show that there are no false positives and an average of 1.24% false negatives.

Poor performance in SSVEP BCIs: are worse subjects just slower?

Brain-computer interface (BCI) systems translate brain activity into messages or commands. BCI studies that record from a dozen or more subjects typically report substantial variations in performance, as measured by accuracy. Usually, some subjects attain excellent (even perfect) accuracy, while at least one subject performs so poorly that effective communication would not be possible with that BCI. This study aims to further explore the differences between the best and worst performers by studying the changes in estimated accuracy within each trial in an offline simulation of an SSVEP BCI. Results showed that the worst performers not only attained lower accuracies, but needed more time after cue onset before their accuracies improved substantially. This outcome suggests that poor performance may be partly (though not completely) explained by the latency between cue onset and improved accuracy.