BrainQuake: An Open-Source Python Toolbox for the Stereoelectroencephalography Spatiotemporal Analysis

Intracranial stereoelectroencephalography (SEEG) is broadly used in the presurgical evaluation of intractable epilepsy, due to its high temporal resolution in neural activity recording and high spatial resolution within suspected epileptogenic zones. Neurosurgeons or technicians face the challenge of conducting a workflow of post-processing operations with the multimodal data (e.g., MRI, CT, and EEG) after the implantation surgery, such as brain surface reconstruction, electrode contact localization, and SEEG data analysis. Several software or toolboxes have been developed to take one or more steps in the workflow but without an end-to-end solution. In this study, we introduced BrainQuake, an open-source Python software for the SEEG spatiotemporal analysis, integrating modules and pipelines in surface reconstruction, electrode localization, seizure onset zone (SOZ) prediction based on ictal and interictal SEEG analysis, and final visualizations, each of which is highly automated with a user-friendly graphical user interface (GUI). BrainQuake also supports remote communications with a public server, which is facilitated with automated and standardized preprocessing pipelines, high-performance computing power, and data curation management to provide a time-saving and compatible platform for neurosurgeons and researchers.

Potential Locations for Non-Invasive Brain Stimulation in Treating Schizophrenia: A Resting-State Functional Connectivity Analysis

Introduction: Non-invasive brain stimulation (NIBS) techniques have been widely used for the purpose of improving clinical symptoms of schizophrenia. However, the ambiguous stimulation targets may limit the efficacy of NIBS for schizophrenia. Exploring effective stimulation targets may improve the clinical efficacy of NIBS in schizophrenia.Methods: We first conducted a neurosynth-based meta-analysis of 715 functional magnetic resonance imaging studies to identify schizophrenia-related brain regions as regions of interest. Then, we performed the resting-state functional connectivity analysis in 32 patients with first-episode schizophrenia to find brain surface regions correlated with the regions of interest in three pipelines. Finally, the 10–20 system coordinates corresponding to the brain surface regions were considered as potential targets for NIBS.Results: We identified several potential targets of NIBS, including the bilateral dorsal lateral prefrontal cortex, supplementary motor area, bilateral inferior parietal lobule, temporal pole, medial prefrontal cortex, precuneus, superior and middle temporal gyrus, and superior and middle occipital gyrus. Notably, the 10-20 system location of the bilateral dorsal lateral prefrontal cortex was posterior to F3 (F4), not F3 (F4).Conclusion: Conclusively, our findings suggested that the stimulation locations corresponding to these potential targets might help clinicians optimize the application of NIBS therapy in individuals with schizophrenia.

MeTiS: a modular pipeline for extracting 3D-printable brain-surface models from conventional and ultra-high field MRI

Aim: To develop a modular software pipeline for robustly extracting 3D brain-surface models from MRIs for visualization or printing. No other end-to-end pipeline specialized for neuroimaging does this directly with an interchangeable combination of methods. Materials & methods: A software application was developed to dynamically generate Nipype workflows using interfaces from the Analysis of Functional NeuroImages, Advanced Normalization Tools, FreeSurfer, BrainSuite, Nighres and the FMRIB Software Library suites. The application was deployed for public use via the LONI pipeline environment. Results: In a small, head-to-head comparison test, a pipeline using FreeSurfer for both the skull stripping and cortical-mesh extraction stages earned the highest subjective quality scores. Conclusion: We have deployed a publicly available and modular software tool for extracting 3D models from brain MRIs to use in medical education.

Pathological Investigation of Meningioma Capsule with respect to Tumor Cell Invasion

OBJECTIVE No previous study has pathologically investigated whether the meningioma capsule presents with tumor cells. We investigated which types of tumor capsules include tumor cells to help decide the kind of capsules which can be left intraoperatively without recurrence risk. METHODS We investigated 22 specimens of 14 newly diagnosed meningiomas between February 2011 and June 2021. Capsules were classified into three types: tumor capsule (TC), capsule-like thickened arachnoid membrane (CAM), and extended membrane (EM). Capsule properties were scored as hardness (soft = 1, medium = 2, hard = 3) and transparency (high = 1, medium = 2, low = 3). Hardness, transparency, and score sum was compared between capsules with/without tumor invasion in CAM and EM types. RESULTS The mean follow-up duration was 28.1 months, and there was only one recurrence in a remote location from the residual capsule. Nine capsules were classified as TC, seven as CAM, and six as EM. 88.9% of TCs, 42.9% of CAMs, and 50% of EMs were invaded by tumor cells. Hardness, transparency, and score sum in CAM with tumor invasion was lower than in CAM without, but not significant (p = 0.114, p = 0.114, p = 0.057). CONCLUSION Thickened TC or soft and highly transparent CAM imply a high risk of tumor cell invasion, thus such cases should be followed up long and carefully. The hard and low transparent residual CAMs may have low risk of tumor invasion, thus these kinds of residual capsules might not increase the recurrence risk. Thus, leaving such capsules tightly adhered to the eloquent cortex is theoretically justified to avoid damaging the brain surface.

Decoding of cortex-wide brain activity from local recordings of neural potentials

Objective. Electrical recordings of neural activity from brain surface have been widely employed in basic neuroscience research and clinical practice for investigations of neural circuit functions, brain-computer interfaces, and treatments for neurological disorders. Traditionally, these surface potentials have been believed to mainly reflect local neural activity. It is not known how informative the locally recorded surface potentials are for the neural activities across multiple cortical regions. Approach. To investigate that, we perform simultaneous local electrical recording and wide-field calcium imaging in awake head-fixed mice. Using a recurrent neural network model, we try to decode the calcium fluorescence activity of multiple cortical regions from local electrical recordings. Main results. The mean activity of different cortical regions could be decoded from locally recorded surface potentials. Also, each frequency band of surface potentials differentially encodes activities from multiple cortical regions so that including all the frequency bands in the decoding model gives the highest decoding performance. Despite the close spacing between recording channels, surface potentials from different channels provide complementary information about the large-scale cortical activity and the decoding performance continues to improve as more channels are included. Finally, we demonstrate the successful decoding of whole dorsal cortex activity at pixel-level using locally recorded surface potentials. Significance. These results show that the locally recorded surface potentials indeed contain rich information of the large-scale neural activities, which could be further demixed to recover the neural activity across individual cortical regions. In the future, our cross-modality inference approach could be adapted to virtually reconstruct cortex-wide brain activity, greatly expanding the spatial reach of surface electrical recordings without increasing invasiveness. Furthermore, it could be used to facilitate imaging neural activity across the whole cortex in freely moving animals, without requirement of head-fixed microscopy configurations.

Development of a New Image-Guided Neuronavigation System: Mixed-Reality Projection Mapping Is Accurate and Feasible

BACKGROUND Image-guided systems improve the safety, functional outcome, and overall survival of neurosurgery but require extensive equipment. OBJECTIVE To develop an image-guided surgery system that combines the brain surface photographic texture (BSP-T) captured during surgery with 3-dimensional computer graphics (3DCG) using projection mapping. METHODS Patients who underwent initial surgery with brain tumors were prospectively enrolled. The texture of the 3DCG (3DCG-T) was obtained from 3DCG under similar conditions as those when capturing the brain surface photographs. The position and orientation at the time of 3DCG-T acquisition were used as the reference. The correct position and orientation of the BSP-T were obtained by aligning the BSP-T with the 3DCG-T using normalized mutual information. The BSP-T was combined with and displayed on the 3DCG using projection mapping. This mixed-reality projection mapping (MRPM) was used prospectively in 15 patients (mean age 46.6 yr, 6 males). The difference between the centerlines of surface blood vessels on the BSP-T and 3DCG constituted the target registration error (TRE) and was measured in 16 fields of the craniotomy area. We also measured the time required for image processing. RESULTS The TRE was measured at 158 locations in the 15 patients, with an average of 1.19 ± 0.14 mm (mean ± standard error). The average image processing time was 16.58 min. CONCLUSION Our MRPM method does not require extensive equipment while presenting information of patients’ anatomy together with medical images in the same coordinate system. It has the potential to improve patient safety.

Basis profile curve identification to understand electrical stimulation effects in human brain networks

Brain networks can be explored by delivering brief pulses of electrical current in one area while measuring voltage responses in other areas. We propose a convergent paradigm to study brain dynamics, focusing on a single brain site to observe the average effect of stimulating each of many other brain sites. Viewed in this manner, visually-apparent motifs in the temporal response shape emerge from adjacent stimulation sites. This work constructs and illustrates a data-driven approach to determine characteristic spatiotemporal structure in these response shapes, summarized by a set of unique “basis profile curves” (BPCs). Each BPC may be mapped back to underlying anatomy in a natural way, quantifying projection strength from each stimulation site using simple metrics. Our technique is demonstrated for an array of implanted brain surface electrodes in a human patient. This framework enables straightforward interpretation of single-pulse brain stimulation data, and can be applied generically to explore the diverse milieu of interactions that comprise the connectome.