ReStNeuMap: a tool for automatic extraction of resting-state functional MRI networks in neurosurgical practice

7 T and beyond: toward a synergy between fMRI-based presurgical mapping at ultrahigh magnetic fields, AI, and robotic neurosurgery

Presurgical evaluation with functional magnetic resonance imaging (fMRI) can reduce postsurgical morbidity. Here, we discuss presurgical fMRI mapping at ultra-high magnetic fields (UHF), i.e., ≥ 7 T, in the light of the current growing interest in artificial intelligence (AI) and robot-assisted neurosurgery. The potential of submillimetre fMRI mapping can help better appreciate uncertainty on resection margins, though geometric distortions at UHF might lessen the accuracy of fMRI maps. A useful trade-off for UHF fMRI is to collect data with 1-mm isotropic resolution to ensure high sensitivity and subsequently a low risk of false negatives. Scanning at UHF might yield a revival interest in slow event-related fMRI, thereby offering a richer depiction of the dynamics of fMRI responses. The potential applications of AI concern denoising and artefact removal, generation of super-resolution fMRI maps, and accurate fusion or coregistration between anatomical and fMRI maps. The latter can benefit from the use of T1-weighted echo-planar imaging for better visualization of brain activations. Such AI-augmented fMRI maps would provide high-quality input data to robotic surgery systems, thereby improving the accuracy and reliability of robot-assisted neurosurgery. Ultimately, the advancement in fMRI at UHF would promote clinically useful synergies between fMRI, AI, and robotic neurosurgery.Relevance statement This review highlights the potential synergies between fMRI at UHF, AI, and robotic neurosurgery in improving the accuracy and reliability of fMRI-based presurgical mapping.Key points• Presurgical fMRI mapping at UHF improves spatial resolution and sensitivity.• Slow event-related designs offer a richer depiction of fMRI responses dynamics.• AI can support denoising, artefact removal, and generation of super-resolution fMRI maps.• AI-augmented fMRI maps can provide high-quality input data to robotic surgery systems.

Resting State Functional Networks in Gliomas: Validation With Direct Electric Stimulation of a New Tool for Planning Brain Resections

BACKGROUND AND OBJECTIVES

Precise mapping of functional networks in patients with brain tumor is essential for tailoring personalized treatment strategies. Resting-state functional MRI (rs-fMRI) offers an alternative to task-based fMRI, capable of capturing multiple networks within a single acquisition, without necessitating task engagement. This study demonstrates a strong concordance between preoperative rs-fMRI maps and the gold standard intraoperative direct electric stimulation (DES) mapping during awake surgery.

METHODS

We conducted an analysis involving 28 patients with glioma who underwent awake surgery with DES mapping. A total of 100 DES recordings were collected to map sensorimotor (SMN), language (LANG), visual (VIS), and speech articulation cognitive domains. Preoperative rs-fMRI maps were generated using an updated version of the ReStNeuMap software, specifically designed for rs-fMRI data preprocessing and automatic detection of 7 resting-state networks (SMN, LANG, VIS, speech articulation, default mode, frontoparietal, and visuospatial). To evaluate the agreement between these networks and those mapped with invasive cortical mapping, we computed patient-specific distances between them and intraoperative DES recordings.

RESULTS

Automatically detected preoperative functional networks exhibited excellent agreement with intraoperative DES recordings. When we spatially compared DES points with their corresponding networks, we found that SMN, VIS, and speech articulatory DES points fell within the corresponding network (median distance = 0 mm), whereas for LANG a median distance of 1.6 mm was reported.

CONCLUSION

Our findings show the remarkable consistency between key functional networks mapped noninvasively using presurgical rs-fMRI and invasive cortical mapping. This evidence highlights the utility of rs-fMRI for personalized presurgical planning, particularly in scenarios where awake surgery with DES is not feasible to protect eloquent areas during tumor resection. We have made the updated tool for automated functional network estimation publicly available, facilitating broader utilization of rs-fMRI mapping in various clinical contexts, including presurgical planning, functional reorganization over follow-up periods, and informing future treatments such as radiotherapy.

Comparative analysis of brain language templates with primary language areas detected from presurgical fMRI of brain tumor patients

INTRODUCTION

Functional brain templates are often used in the analysis of clinical functional MRI (fMRI) studies. However, these templates are mostly built based on anatomy or fMRI of healthy subjects, which have not been fully vetted in clinical cohorts. Our aim was to evaluate language templates by comparing with primary language areas (PLAs) detected from presurgical fMRI of brain tumor patients.

METHODS

Four language templates (A-D) based on anatomy, task-based fMRI, resting-state fMRI, and meta-analysis, respectively, were compared with PLAs detected by fMRI with word generation and sentence completion paradigms. For each template, the fraction of PLA activations enclosed by the template (positive inclusion fraction, [PIF]), the fraction of activations within the template but that did not belong to PLAs (false inclusion fraction, [FIF]), and their Dice similarity coefficient (DSC) with PLA activations were calculated.

RESULTS

For anterior PLAs, Template A had the greatest PIF (median, 0.95), whereas Template D had both the lowest FIF (median, 0.074), and the highest DSC (median, 0.30), which were all significant compared to other templates. For posterior PLAs, Templates B and D had similar PIF (median, 0.91 and 0.90, respectively) and DSC (both medians, 0.059), which were all significantly higher than that of Template C. Templates B and C had significantly lower FIF (median, 0.061 and 0.054, respectively) compared to Template D.

CONCLUSION

This study demonstrated significant differences between language templates in their inclusiveness of and spatial agreement with the PLAs detected in the presurgical fMRI of the patient cohort. These findings may help guide the selection of language templates tailored to their applications in clinical fMRI studies.

Systematic Review Between Resting-State fMRI and Task fMRI in Planning for Brain Tumour Surgery

As an alternative to task-based functional magnetic resonance imaging (T-fMRI), resting-state functional magnetic resonance imaging (Rs-fMRI) is suggested for preoperative mapping of patients with brain tumours, with an emphasis on treatment guidance and neurodegeneration prediction. A systematic review was conducted of 18 recent studies involving 1035 patients with brain tumours and Rs-fMRI protocols. This was accomplished by searching the electronic databases PubMed, Scopus, and Web of Science. For clinical benefit, we compared Rs-fMRI to standard T-fMRI and intraoperative direct cortical stimulation (DCS). The results of Rs-fMRI and T-fMRI were compared and their correlation with intraoperative DCS results was examined through a systematic review. Our exhaustive investigation demonstrated that Rs-fMRI is a dependable and sensitive preoperative mapping technique that detects neural networks in the brain with precision and identifies crucial functional regions in agreement with intraoperative DCS. Rs-fMRI comes in handy, especially in situations where T-fMRI proves to be difficult because of patient-specific factors. Additionally, our exhaustive investigation demonstrated that Rs-fMRI is a valuable tool in the preoperative screening and evaluation of brain tumours. Furthermore, its capability to assess brain function, forecast surgical results, and enhance decision-making may render it applicable in the clinical management of brain tumours.

Spontaneous unilateral spatial neglect recovery after brain tumour resection: A multimodal diffusion and rs-fMRI case report

Patients with unilateral spatial neglect (USN) are unable to explore or to report stimuli presented in the left personal and extra-personal space. USN is usually caused by lesion of the right parietal lobe: nowadays, it is also clear the key role of structural connections (the second and the third branch of the right Superior Longitudinal Fasciculus, respectively, SLF II and III) and functional networks (Dorsal and Ventral Attention Network, respectively, DAN and VAN) in USN. In this multimodal case report, we have merged those structural and functional information derived from a patient with a right parietal lobe tumour and USN before surgery. Functional, structural and neuropsychological data were also collected 6 months after surgery, when the USN was spontaneously recovered. Diffusion metrics and Functional Connectivity (FC) of the right SLF and DAN, before and after surgery, were compared with the same data of a patient with a tumour in a similar location, but without USN, and with a control sample. Results indicate an impairment in the right SLF III and a reduction of FC of the right DAN in patients with USN before surgery compared to controls; after surgery, when USN was recovered, patient's diffusion metrics and FC showed no differences compared to the controls. This single case and its multimodal approach reinforce the crucial role of the right SLF III and DAN in the development and recovery of egocentric and allocentric extra-personal USN, highlighting the need to preserve these structural and functional areas during brain surgery.

Multimodal Autism Spectrum Disorder Diagnosis Method Based on DeepGCN

Multimodal data play an important role in the diagnosis of brain diseases. This study constructs a whole-brain functional connectivity network based on functional MRI data, uses non-imaging data with demographic information to complement the classification task for diagnosing subjects, and proposes a multimodal and across-site WL-DeepGCN-based method for classification to diagnose autism spectrum disorder (ASD). This method is used to resolve the existing problem that deep learning ASD identification cannot efficiently utilize multimodal data. In the WL-DeepGCN, a weight-learning network is used to represent the similarity of non-imaging data in the latent space, introducing a new approach for constructing population graph edge weights, and we find that it is beneficial and robust to define pairwise associations in the latent space rather than the input space. We propose a graph convolutional neural network residual connectivity approach to reduce the information loss due to convolution operations by introducing residual units to avoid gradient disappearance and gradient explosion. Furthermore, an EdgeDrop strategy makes the node connections sparser by randomly dropping edges in the raw graph, and its introduction can alleviate the overfitting and oversmoothing problems in the DeepGCN training process. We compare the WL-DeepGCN model with competitive models based on the same topics and nested 10-fold cross-validation show that our method achieves 77.27% accuracy and 0.83 AUC for ASD identification, bringing substantial performance gains.

Detection and Mitigation of Neurovascular Uncoupling in Brain Gliomas

Functional magnetic resonance imaging (fMRI) with blood oxygen level-dependent (BOLD) technique is useful for preoperative mapping of brain functional networks in tumor patients, providing reliable in vivo detection of eloquent cortex to help reduce the risk of postsurgical morbidity. BOLD task-based fMRI (tb-fMRI) is the most often used noninvasive method that can reliably map cortical networks, including those associated with sensorimotor, language, and visual functions. BOLD resting-state fMRI (rs-fMRI) is emerging as a promising ancillary tool for visualization of diverse functional networks. Although fMRI is a powerful tool that can be used as an adjunct for brain tumor surgery planning, it has some constraints that should be taken into consideration for proper clinical interpretation. BOLD fMRI interpretation may be limited by neurovascular uncoupling (NVU) induced by brain tumors. Cerebrovascular reactivity (CVR) mapping obtained using breath-hold methods is an effective method for evaluating NVU potential.

Implementation of Automated Pipeline for Resting-State fMRI Analysis with PACS Integration

In recent years, the quantity and complexity of medical imaging acquisition and processing have increased tremendously. The explosion in volume and need for advanced imaging analysis have led to the creation of numerous software programs, which have begun to be incorporated into clinical practice for indications such as automated stroke assessment, brain tumor perfusion processing, and hippocampal volume analysis. Despite these advances, there remains a need for specialized, custom-built software for advanced algorithms and new areas of research that is not widely available or adequately integrated in these "out-of-the-box" solutions. The purpose of this paper is to describe the implementation of an image-processing pipeline that is versatile and simple to create, which allows for rapid prototyping of image analysis algorithms and subsequent testing in a clinical environment. This pipeline uses a combination of Orthanc server, custom MATLAB code, and publicly available FMRIB Software Library and RestNeuMap tools to automatically receive and analyze resting-state functional MRI data collected from a custom filter on the MR scanner output. The processed files are then sent directly to Picture Archiving and Communications System (PACS) without the need for user input. This initial experience can serve as a framework for those interested in simple implementation of an automated pipeline customized to clinical needs.

Surgical management of Glioma Grade 4: technical update from the neuro-oncology section of the Italian Society of Neurosurgery (SINch®): a systematic review

PURPOSE

The extent of resection (EOR) is an independent prognostic factor for overall survival (OS) in adult patients with Glioma Grade 4 (GG4). The aim of the neuro-oncology section of the Italian Society of Neurosurgery (SINch®) was to provide a general overview of the current trends and technical tools to reach this goal.

METHODS

A systematic review was performed. The results were divided and ordered, by an expert team of surgeons, to assess the Class of Evidence (CE) and Strength of Recommendation (SR) of perioperative drugs management, imaging, surgery, intraoperative imaging, estimation of EOR, surgery at tumor progression and surgery in elderly patients.

RESULTS

A total of 352 studies were identified, including 299 retrospective studies and 53 reviews/meta-analysis. The use of Dexamethasone and the avoidance of prophylaxis with anti-seizure medications reached a CE I and SR A. A preoperative imaging standard protocol was defined with CE II and SR B and usefulness of an early postoperative MRI, with CE II and SR B. The EOR was defined the strongest independent risk factor for both OS and tumor recurrence with CE II and SR B. For intraoperative imaging only the use of 5-ALA reached a CE II and SR B. The estimation of EOR was established to be fundamental in planning postoperative adjuvant treatments with CE II and SR B and the stereotactic image-guided brain biopsy to be the procedure of choice when an extensive surgical resection is not feasible (CE II and SR B).

CONCLUSIONS

A growing number of evidences evidence support the role of maximal safe resection as primary OS predictor in GG4 patients. The ongoing development of intraoperative techniques for a precise real-time identification of peritumoral functional pathways enables surgeons to maximize EOR minimizing the post-operative morbidity.

The role of the default mode network in longitudinal functional brain reorganization of brain gliomas

The study of patients after glioma resection offers a unique opportunity to investigate brain reorganization. It is currently unknown how the whole-brain connectomic profile evolves longitudinally after surgical resection of a glioma and how this may be associated with tumor characteristics and cognitive outcome. In this longitudinal study, we investigate the impact of tumor lateralization and grade on functional connectivity (FC) in highly connected networks, or hubs, and cognitive performance. Twenty-eight patients (17 high-grade, 11 low-grade gliomas) underwent longitudinal pre/post-surgery resting-state fMRI scans and neuropsychological assessments (73 total measures). FC matrices were constructed considering as functional hubs the default mode (DMN) and fronto-parietal networks. No-hubs included primary sensory functional networks and any other no-hubs nodes. Both tumor hemisphere and grade affected brain reorganization post-resection. In right-hemisphere tumor patients, regardless of grade and relative to left-hemisphere gliomas, FC increased longitudinally after the intervention, both in terms of FC within hubs (phubs = 0.0004) and FC between hubs and no-hubs (phubs-no-hubs = 0.005). Regardless of tumor side, only lower-grade gliomas showed longitudinal FC increases relative to high-grade tumors within a precise hub network, the DMN. The neurocognitive profile was longitudinally associated with spatial features of the connectome, mainly within the DMN. We provide evidence that clinical glioma features, such as lateralization and grade, affect post-surgical longitudinal functional reorganization and cognitive recovery. The data suggest a possible role of the DMN in supporting cognition, providing useful information for prognostic prediction and surgical planning.

Comparison between inferior frontal gyrus intrinsic connectivity network and verb-generation task fMRI network for presurgical language mapping in healthy controls and in glioma patients

Task-based functional MRI (tb-fMRI) represents an extremely valuable approach for the identification of language eloquent regions for presurgical mapping in patients with brain tumors. However, its routinely application is limited by patient-related factors, such as cognitive disability and difficulty in coping with long-time acquisitions, and by technical factors, such as lack of equipment availability for stimuli delivery. Resting-state fMRI (rs-fMRI) instead, allows the identification of distinct language networks in a 10-min acquisition without the need of performing active tasks and using specific equipment. Therefore, to test the feasibility of rs-fMRI as a preoperative mapping tool, we reconstructed a lexico-semantic intrinsic connectivity network (ICN) in healthy controls (HC) and in a case series of patients with gliomas and compared the organization of this language network with the one derived from tb-fMRI in the patient's group. We studied three patients with extra-frontal gliomas who underwent functional mapping with auditory verb-generation (AVG) task and rs-fMRI with a seed in the left inferior frontal gyrus (IFG). First, we identified the functional connected areas to the IFG in HC. We qualitatively compared these areas with those that showed functional activation in AVG task derived from Neurosynth meta-analysis. Last, in each patient we performed single-subject analyses both for rs- and tb-fMRI, and we evaluated the spatial overlap between the two approaches. In HC, the IFG-ICN network showed a predominant left fronto-temporal functional connectivity in regions overlapping with the AVG network derived from a meta-analysis. In two patients, rs- and tb-fMRI showed comparable patterns of activation in left fronto-temporal regions, with different levels of contralateral activations. The third patient could not accomplish the AVG task and thus it was not possible to make any comparison with the ICN. However, in this patient, task-free approach disclosed a consistent network of fronto-temporal regions as in HC, and additional parietal regions. Our preliminary findings support the value of rs-fMRI approach for presurgical mapping, particularly for identifying left fronto-temporal core language-related areas in glioma patients. In a preoperative setting, rs-fMRI approach could represent a powerful tool for the identification of eloquent language areas, especially in patients with language or cognitive impairments.

What Can Resting-State fMRI Data Analysis Explain about the Functional Brain Connectivity in Glioma Patients?

Resting-state functional MRI has been increasingly implemented in imaging protocols for the study of functional connectivity in glioma patients as a sequence able to capture the activity of brain networks and to investigate their properties without requiring the patients’ cooperation. The present review aims at describing the most recent results obtained through the analysis of resting-state fMRI data in different contexts of interest for brain gliomas: the identification and localization of functional networks, the characterization of altered functional connectivity, and the evaluation of functional plasticity in relation to the resection of the glioma. An analysis of the literature showed that significant and promising results could be achieved through this technique in all the aspects under investigation. Nevertheless, there is room for improvement, especially in terms of stability and generalizability of the outcomes. Further research should be conducted on homogeneous samples of glioma patients and at fixed time points to reduce the considerable variability in the results obtained across and within studies. Future works should also aim at establishing robust metrics for the assessment of the disruption of functional connectivity and its recovery at the single-subject level.

Resting-State Functional Magnetic Resonance Imaging for Surgical Neuro-Oncology Planning: Towards a Standardization in Clinical Settings

Resting-state functional magnetic resonance imaging (rest-f-MRI) is a neuroimaging technique that has demonstrated its potential in providing new insights into brain physiology. rest-f-MRI can provide useful information in pre-surgical mapping aimed to balancing long-term survival by maximizing the extent of resection of brain neoplasms, while preserving the patient’s functional connectivity. Rest-fMRI may replace or can be complementary to task-driven fMRI (t-fMRI), particularly in patients unable to cooperate with the task paradigm, such as children or sedated, paretic, aphasic patients. Although rest-fMRI is still under standardization, this technique has been demonstrated to be feasible and valuable in the routine clinical setting for neurosurgical planning, along with intraoperative electrocortical mapping. In the literature, there is growing evidence that rest-fMRI can provide valuable information for the depiction of glioma-related functional brain network impairment. Accordingly, rest-fMRI could allow a tailored glioma surgery improving the surgeon’s ability to increase the extent of resection (EOR), and simultaneously minimize the risk of damage of eloquent brain structures and neuronal networks responsible for the integrity of executive functions. In this article, we present a review of the literature and illustrate the feasibility of rest-fMRI in the clinical setting for presurgical mapping of eloquent networks in patients affected by brain tumors, before and after tumor resection.

Clinical applications of magnetic resonance imaging based functional and structural connectivity

Advances in computational neuroimaging techniques have expanded the armamentarium of imaging tools available for clinical applications in clinical neuroscience. Non-invasive, in vivo brain MRI structural and functional network mapping has been used to identify therapeutic targets, define eloquent brain regions to preserve, and gain insight into pathological processes and treatments as well as prognostic biomarkers. These tools have the real potential to inform patient-specific treatment strategies. Nevertheless, a realistic appraisal of clinical utility is needed that balances the growing excitement and interest in the field with important limitations associated with these techniques. Quality of the raw data, minutiae of the processing methodology, and the statistical models applied can all impact on the results and their interpretation. A lack of standardization in data acquisition and processing has also resulted in issues with reproducibility. This limitation has had a direct impact on the reliability of these tools and ultimately, confidence in their clinical use. Advances in MRI technology and computational power as well as automation and standardization of processing methods, including machine learning approaches, may help address some of these issues and make these tools more reliable in clinical use. In this review, we will highlight the current clinical uses of MRI connectomics in the diagnosis and treatment of neurological disorders; balancing emerging applications and technologies with limitations of connectivity analytic approaches to present an encompassing and appropriate perspective.

The inferior fronto-occipital fascicle: a century of controversies from anatomy theaters to operative neurosurgery

INTRODUCTION

Since its first description in the early 19th century, the inferior frontooccipital fascicle (IFOF) and its anatomo-functional features were neglected in the neuroscientific literature for the last century. In the last decade, the rapid development of in vivo imaging for the reconstruction of white matter (WM) connectivity (i.e., tractography) and the consequent interest in more traditional ex vivo methods (postmortem dissection) have allowed a renewed debate about course, termination territories, anatomical relationships, and functional roles of this fascicle.

EVIDENCE ACQUISITION

We reviewed the main current knowledge concerning the structural and functional anatomy of the IFOF and possible implications in neurosurgical practice.

EVIDENCE SYNTHESIS

The IFOF connects the occipital cortex, the temporo-basal areas, the superior parietal lobule, and the pre-cuneus to the frontal lobe, passing through the ventral third of subinsular WM of the external capsule. This wide distribution of cortical terminations provides multimodal integration between several functional networks, including language, non-verbal semantic processing, object identification, visuo-spatial processing and planning, reading, facial expression recognition, memory and conceptualization, emotional and neuropsychological behavior. This anatomo-functional organization has important implication also in neurosurgical practice, especially when approaching the frontal, insular, temporo-parieto-occipital regions and the ventricular system.

CONCLUSIONS

The IFOF is the most extensive associative bundle of the human connectome. Its multi-layer organization reflects important implications in many aspects of brain functional processing. Accurate awareness of IFOF functional anatomy and integration between multimodal datasets coming from different sources has crucial implications for both neuroscientific knowledge and quality of neurosurgical treatments.

Structural and Functional Imaging in Glioma Management

The goal of glioma surgery is maximal safe resection in order to provide optimal tumor control and survival benefit to the patient. There are multiple imaging modalities beyond traditional contrast-enhanced magnetic resonance imaging (MRI) that have been incorporated into the preoperative workup of patients presenting with gliomas. The aim of these imaging modalities is to identify cortical and subcortical areas of eloquence, and their relationship to the lesion. In this article, multiple modalities are described with an emphasis on the underlying technology, clinical utilization, advantages, and disadvantages of each. functional MRI and its role in identifying hemispheric dominance and areas of language and motor are discussed. The nuances of magnetoencephalography and transcranial magnetic stimulation in localization of eloquent cortex are examined, as well as the role of diffusion tensor imaging in defining normal white matter tracts in glioma surgery. Lastly, we highlight the role of stimulated Raman spectroscopy in intraoperative histopathological diagnosis of tissue to guide tumor resection. Tumors may shift the normal arrangement of functional anatomy in the brain; thus, utilization of multiple modalities may be helpful in operative planning and patient counseling for successful surgery.

Presurgical brain mapping of the language network in pediatric patients with epilepsy using resting-state fMRI

OBJECTIVE

Epilepsy affects neural processing and often causes intra- or interhemispheric language reorganization, rendering localization solely based on anatomical landmarks (e.g., Broca's area) unreliable. Preoperative brain mapping is necessary to weigh the risk of resection with the risk of postoperative deficit. However, the use of conventional mapping methods (e.g., somatosensory stimulation, task-based functional MRI [fMRI]) in pediatric patients is technically difficult due to low compliance and their unique neurophysiology. Resting-state fMRI (rs-fMRI), a "task-free" technique based on the neural activity of the brain at rest, has the potential to overcome these limitations. The authors hypothesized that language networks can be identified from rs-fMRI by applying functional connectivity analyses.

METHODS

Cases in which both task-based fMRI and rs-fMRI were acquired as part of the preoperative clinical protocol for epilepsy surgery were reviewed. Task-based fMRI consisted of 2 language tasks and 1 motor task. Resting-state fMRI data were acquired while the patients watched an animated movie and were analyzed using independent component analysis (i.e., data-driven method). The authors extracted language networks from rs-fMRI data by performing a similarity analysis with functionally defined language network templates via a template-matching procedure. The Dice coefficient was used to quantify the overlap.

RESULTS

Thirteen children underwent conventional task-based fMRI (e.g., verb generation, object naming), rs-fMRI, and structural imaging at 1.5T. The language components with the highest overlap with the language templates were identified for each patient. Language lateralization results from task-based fMRI and rs-fMRI mapping were comparable, with good concordance in most cases. Resting-state fMRI-derived language maps indicated that language was on the left in 4 patients (31%), on the right in 5 patients (38%), and bilateral in 4 patients (31%). In some cases, rs-fMRI indicated a more extensive language representation.

CONCLUSIONS

Resting-state fMRI-derived language network data were identified at the patient level using a template-matching method. More than half of the patients in this study presented with atypical language lateralization, emphasizing the need for mapping. Overall, these data suggest that this technique may be used to preoperatively identify language networks in pediatric patients. It may also optimize presurgical planning of electrode placement and thereby guide the surgeon's approach to the epileptogenic zone.

Planning Brain Tumor Resection Using a Probabilistic Atlas of Cortical and Subcortical Structures Critical for Functional Processing: A Proof of Concept

BACKGROUND

Functional preoperative planning for resection of intrinsic brain tumors in eloquent areas is still a challenge. Predicting subcortical functional framework is especially difficult. Direct electrical stimulation (DES) is the recommended technique for resection of these lesions. A reliable probabilistic atlas of the critical cortical epicenters and subcortical framework based on DES data was recently published.

OBJECTIVE

To propose a pipeline for the automated alignment of the corticosubcortical maps of this atlas with T1-weighted MRI.

METHODS

To test the alignment, we selected 10 patients who underwent resection of brain lesions by using DES. We aligned different cortical and subcortical functional maps to preoperative volumetric T1 MRIs (with/without gadolinium). For each patient we quantified the quality of the alignment, and we calculated the match between the location of the functional sites found at DES and the functional maps of the atlas.

RESULTS

We found an accurate brain extraction and alignment of the functional maps with both the T1 MRIs of each patient. The matching analysis between functional maps and functional responses collected during surgeries was 88% at cortical and, importantly, 100% at subcortical level, providing a further proof of the correct alignment.

CONCLUSION

We demonstrated quantitatively and qualitatively the reliability of this tool that may be used for presurgical planning, providing further functional information at the cortical level and a unique probabilistic prevision of distribution of the critical subcortical structures. Finally, this tool offers the chance for multimodal planning through integrating this functional information with other neuroradiological and neurophysiological techniques.

A Review of Passive Brain Mapping Techniques in Neurological Surgery

Brain mapping is a quintessential part of neurosurgical practice. Accordingly, much of our understanding of the brain's functional organization, and in particular the motor homunculus, is largely attributable to the clinical investigations of past neurosurgeons. Traditionally mapping was invasive and involved the application of electrical current to the exposed brain to observe focal disruption of function or to elicit overt actions. More recently, a wide variety of techniques have been developed that do not require electrical stimulation and often do not require any explicit participation by the subject. Collectively we refer to these as passive mapping modalities. Here we review the spectrum of passive mapping used by neurosurgeons for mapping and surgical planning that ranges from invasive intracranial recordings to noninvasive imaging as well as regimented task-based protocols to completely task-free paradigms that can be performed intraoperatively while under anesthesia.

Effects of supra-total resection in neurocognitive and oncological outcome of high-grade gliomas comparing asleep and awake surgery

PURPOSE

Awake surgery is an established technique for resection of low-grade gliomas, while its possible benefit for resection of high-grade gliomas (HGGs) needs further confirmations. This retrospective study aims to compare overall survival, extent of resection (EOR) and cognitive outcome in two groups of HGGs patients submitted to asleep or awake surgery.

METHODS

Thirty-three patients submitted to Gross Total Resection of contrast-enhancing area of HGGs were divided in two homogeneous groups: awake (AWg; N = 16) and asleep surgery (ASg; N = 17). All patients underwent to an extensive neuropsychological assessment before surgery (time_1), 1-week (time_2) and 4-months (time_3) after surgery. We performed analyses to assess differences in cognitive performances between groups, cognitive outcomes in each group and EOR. A comparison of overall survival (OS) between the two groups was conducted.

RESULTS

Statistical analyses showed no differences between groups at time_2 and time_3 in each cognitive domain, excluding selective attention that resulted higher in the AWg before surgery. Regarding cognitive outcomes, we found a reversible worsening of memory and constructional praxis, and a significant recovery at time_3, similar for both groups. Assessment of time_3 in respect to time_1 never showed differences (all ps > .074). Moreover we found a significant lower level of tumor infiltration after surgery for AWg (p < .05), with an influence on OS (p < .05). Indeed, patients of AWg showed a significant longer OS in comparison to those in the ASg (p < .01). This result was confirmed even considering only wildtype Glioblastoma (p < .05).

CONCLUSION

These results indicate that awake surgery, and in general a supra-total resection of enhancing area, can improve OS in HGGs patients, preserving neuro-cognitive profile and quality of life.

Mapping cognitive and emotional networks in neurosurgical patients using resting-state functional magnetic resonance imaging

Neurosurgery has been at the forefront of a paradigm shift from a localizationist perspective to a network-based approach to brain mapping. Over the last 2 decades, we have seen dramatic improvements in the way we can image the human brain and noninvasively estimate the location of critical functional networks. In certain patients with brain tumors and epilepsy, intraoperative electrical stimulation has revealed direct links between these networks and their function. The focus of these techniques has rightfully been identification and preservation of so-called "eloquent" brain functions (i.e., motor and language), but there is building momentum for more extensive mapping of cognitive and emotional networks. In addition, there is growing interest in mapping these functions in patients with a broad range of neurosurgical diseases. Resting-state functional MRI (rs-fMRI) is a noninvasive imaging modality that is able to measure spontaneous low-frequency blood oxygen level-dependent signal fluctuations at rest to infer neuronal activity. Rs-fMRI may be able to map cognitive and emotional networks for individual patients. In this review, the authors give an overview of the rs-fMRI technique and associated cognitive and emotional resting-state networks, discuss the potential applications of rs-fMRI, and propose future directions for the mapping of cognition and emotion in neurosurgical patients.

Presurgical resting-state functional MRI language mapping with seed selection guided by regional homogeneity

PURPOSE

Resting-state functional MRI (rs-FMRI) has shown potential for presurgical mapping of eloquent cortex when a patient's performance on task-based FMRI is compromised. The seed-based analysis is a practical approach for detecting rs-FMRI functional networks; however, seed localization remains challenging for presurgical language mapping. Therefore, we proposed a data-driven approach to guide seed localization for presurgical rs-FMRI language mapping.

METHODS

Twenty-six patients with brain tumors located in left perisylvian regions had undergone task-based FMRI and rs-FMRI before tumor resection. For the seed-based rs-FMRI language mapping, a seeding approach that integrates regional homogeneity and meta-analysis maps (RH+MA) was proposed to guide the seed localization. Canonical and task-based seeding approaches were used for comparison. The performance of the 3 seeding approaches was evaluated by calculating the Dice coefficients between each rs-FMRI language mapping result and the result from task-based FMRI.

RESULTS

With the RH+MA approach, selecting among the top 6 seed candidates resulted in the highest Dice coefficient for 81% of patients (21 of 26) and the top 9 seed candidates for 92% of patients (24 of 26). The RH+MA approach yielded rs-FMRI language mapping results that were in greater agreement with the results of task-based FMRI, with significantly higher Dice coefficients (P < .05) than that of canonical and task-based approaches within putative language regions.

CONCLUSION

The proposed RH+MA approach outperformed the canonical and task-based seed localization for rs-FMRI language mapping. The results suggest that RH+MA is a robust and feasible method for seed-based functional connectivity mapping in clinical practice.

A comparison of resting state functional magnetic resonance imaging to invasive electrocortical stimulation for sensorimotor mapping in pediatric patients

Localizing neurologic function within the brain remains a significant challenge in clinical neurosurgery. Invasive mapping with direct electrocortical stimulation currently is the clinical gold standard but is impractical in young or cognitively delayed patients who are unable to reliably perform tasks. Resting state functional magnetic resonance imaging non-invasively identifies resting state networks without the need for task performance, hence, is well suited to pediatric patients. We compared sensorimotor network localization by resting state fMRI to cortical stimulation sensory and motor mapping in 16 pediatric patients aged 3.1 to 18.6 years. All had medically refractory epilepsy that required invasive electrographic monitoring and stimulation mapping. The resting state fMRI data were analyzed using a previously trained machine learning classifier that has previously been evaluated in adults. We report comparable functional localization by resting state fMRI compared to stimulation mapping. These results provide strong evidence for the utility of resting state functional imaging in the localization of sensorimotor cortex across a wide range of pediatric patients.