Challenges and techniques for presurgical brain mapping with functional MRI

Utilizing connectome fingerprinting functional MRI models for motor activity prediction in presurgical planning: A feasibility study

Presurgical planning prior to brain tumor resection is critical for the preservation of neurologic function post-operatively. Neurosurgeons increasingly use advanced brain mapping techniques pre- and intra-operatively to delineate brain regions which are "eloquent" and should be spared during resection. Functional MRI (fMRI) has emerged as a commonly used non-invasive modality for individual patient mapping of critical cortical regions such as motor, language, and visual cortices. To map motor function, patients are scanned using fMRI while they perform various motor tasks to identify brain networks critical for motor performance, but it may be difficult for some patients to perform tasks in the scanner due to pre-existing deficits. Connectome fingerprinting (CF) is a machine-learning approach that learns associations between resting-state functional networks of a brain region and the activations in the region for specific tasks; once a CF model is constructed, individualized predictions of task activation can be generated from resting-state data. Here we utilized CF to train models on high-quality data from 208 subjects in the Human Connectome Project (HCP) and used this to predict task activations in our cohort of healthy control subjects (n = 15) and presurgical patients (n = 16) using resting-state fMRI (rs-fMRI) data. The prediction quality was validated with task fMRI data in the healthy controls and patients. We found that the task predictions for motor areas are on par with actual task activations in most healthy subjects (model accuracy around 90%-100% of task stability) and some patients suggesting the CF models can be reliably substituted where task data is either not possible to collect or hard for subjects to perform. We were also able to make robust predictions in cases in which there were no task-related activations elicited. The findings demonstrate the utility of the CF approach for predicting activations in out-of-sample subjects, across sites and scanners, and in patient populations. This work supports the feasibility of the application of CF models to presurgical planning, while also revealing challenges to be addressed in future developments. PRACTITIONER POINTS: Precision motor network prediction using connectome fingerprinting. Carefully trained models' performance limited by stability of task-fMRI data. Successful cross-scanner predictions and motor network mapping in patients with tumor.

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.

Review of the Brain's Behaviour after Injury and Disease for Its Application in an Agent-Based Model (ABM)

The brain is the most complex organ in the human body and, as such, its study entails great challenges (methodological, theoretical, etc.). Nonetheless, there is a remarkable amount of studies about the consequences of pathological conditions on its development and functioning. This bibliographic review aims to cover mostly findings related to changes in the physical distribution of neurons and their connections-the connectome-both structural and functional, as well as their modelling approaches. It does not intend to offer an extensive description of all conditions affecting the brain; rather, it presents the most common ones. Thus, here, we highlight the need for accurate brain modelling that can subsequently be used to understand brain function and be applied to diagnose, track, and simulate treatments for the most prevalent pathologies affecting the brain.

What helps patients to prepare for and cope during awake craniotomy? A prospective qualitative study

There is growing interest in awake craniotomies, but some clinicians are concerned that such procedures are poorly tolerated by patients. Therefore, we conducted a study to assess this phenomenon. In this prospective qualitative study, 68 patients who qualified for awake craniotomy were asked to complete the Hospital Anxiety and Depression Scale (HADS)-two days before the surgery and visual analogue scales (VAS) for pain and stress, two days before the surgery and again about two days after. In addition, after their surgery, they took part in a structured interview about what helped them prepare for and cope with the surgery. Most patients tolerated the awake surgery well, scoring low on stress and pain scales. They reported a lower level of stress during the surgery (when questioned afterwards) than before it. Intensity of stress before the surgery correlated negatively with age, positively with HADS anxiety score and positively with stress subsequently experienced during surgery. The level of stress during surgery was associated with stress experienced before the surgery, pain and HADS anxiety and depression scores. Severity of pain during the surgery was positively correlated with stress during surgery and HADS depression and anxiety scores before the surgery. There was no correlation between stress, pain, anxiety and depression and the location of the lesion. Patients have a high tolerance for awake craniotomy. Various factors have an impact on how well patients cope with the operation. Extensive preoperative preparation should be considered a key part of the procedure.

An Agent-Based Model to Reproduce the Boolean Logic Behaviour of Neuronal Self-Organised Communities through Pulse Delay Modulation and Generation of Logic Gates

The human brain is arguably the most complex "machine" to ever exist. Its detailed functioning is yet to be fully understood, let alone modelled. Neurological processes have logical signal-processing and biophysical aspects, and both affect the brain's structure, functioning and adaptation. Mathematical approaches based on both information and graph theory have been extensively used in an attempt to approximate its biological functioning, along with Artificial Intelligence frameworks inspired by its logical functioning. In this article, an approach to model some aspects of the brain learning and signal processing is presented, mimicking the metastability and backpropagation found in the real brain while also accounting for neuroplasticity. Several simulations are carried out with this model to demonstrate how dynamic neuroplasticity, neural inhibition and neuron migration can reshape the brain's logical connectivity to synchronise signal processing and obtain certain target latencies. This work showcases the importance of dynamic logical and biophysical remodelling in brain plasticity. Combining mathematical (agents, graph theory, topology and backpropagation) and biomedical ingredients (metastability, neuroplasticity and migration), these preliminary results prove complex brain phenomena can be reproduced-under pertinent simplifications-via affordable computations, which can be construed as a starting point for more ambitiously accurate simulations.

Comparing the efficacy of awake and sedated MEG to TMS in mapping hand sensorimotor cortex in a clinical cohort

Non-invasive methods such as Transcranial Magnetic Stimulation (TMS) and magnetoencephalography (MEG) aid in the pre-surgical evaluation of patients with epilepsy or brain tumor to identify sensorimotor cortices. MEG requires sedation in children or patients with developmental delay. However, TMS can be applied to awake patients of all ages with any cognitive abilities. In this study, we compared the efficacy of TMS with MEG (in awake and sedated states) in identifying the hand sensorimotor areas in patients with epilepsy or brain tumors. We identified 153 patients who underwent awake- (n = 98) or sedated-MEG (n = 55), along with awake TMS for hand sensorimotor mapping as part of their pre-surgical evaluation. TMS involved stimulating the precentral gyrus and recording electromyography responses, while MEG identified the somatosensory cortex during median nerve stimulation. Awake-MEG had a success rate of 92.35 % and TMS had 99.49 % (p-value = 0.5517). However, in the sedated-MEG cohort, TMS success rate of 95.61 % was significantly higher compared to MEG's 58.77 % (p-value = 0.0001). Factors affecting mapping success were analyzed. Logistic regression across the entire cohort identified patient sedation as the lone significant predictor, contrary to age, lesion, metal, and number of antiseizure medications (ASMs). A subsequent analysis replaced sedation with anesthetic drug dosage, revealing no significant predictors impacting somatosensory mapping success under sedation. This study yields insights into the utility of TMS and MEG in mapping hand sensorimotor cortices and underscores the importance of considering factors that influence eloquent cortex mapping limitations during sedation.

Innovations in imaging modalities: a comparative review of MRI, long-axial field-of-view PET, and full-ring CZT-SPECT in detecting bone metastases

The accurate diagnosis of bone metastasis, a condition in which cancer cells have spread to the bone, is essential for optimal patient care and outcome. This review provides a detailed overview of the current medical imaging techniques used to detect and diagnose this critical condition focusing on three cardinal imaging modalities: positron emission tomography (PET), single photon emission computed tomography (SPECT) and magnetic resonance imaging (MRI). Each of these techniques has unique advantages: PET/CT combines functional imaging with anatomical imaging, allowing precise localization of metabolic abnormalities; the SPECT/CT offers a wider range of radiopharmaceuticals for visualizing specific receptors and metabolic pathways; MRI stands out for its unparalleled ability to produce high-resolution images of bone marrow structures. However, as this paper shows, each modality has its own limitations. The comprehensive analysis does not stop at the technical aspects, but ventures into the wider implications of these techniques in a clinical setting. By understanding the synergies and shortcomings of these modalities, healthcare professionals can make diagnostic and therapeutic decisions. Furthermore, at a time when medical technology is evolving at a breakneck pace, this review casts a speculative eye towards future advances in the field of bone metastasis imaging, bridging the current state with future possibilities. Such insights are essential for both clinicians and researchers navigating the complex landscape of bone metastasis diagnosis.

Functional neuroimaging as a catalyst for integrated neuroscience

Functional magnetic resonance imaging (fMRI) enables non-invasive access to the awake, behaving human brain. By tracking whole-brain signals across a diverse range of cognitive and behavioural states or mapping differences associated with specific traits or clinical conditions, fMRI has advanced our understanding of brain function and its links to both normal and atypical behaviour. Despite this headway, progress in human cognitive neuroscience that uses fMRI has been relatively isolated from rapid advances in other subdomains of neuroscience, which themselves are also somewhat siloed from one another. In this Perspective, we argue that fMRI is well-placed to integrate the diverse subfields of systems, cognitive, computational and clinical neuroscience. We first summarize the strengths and weaknesses of fMRI as an imaging tool, then highlight examples of studies that have successfully used fMRI in each subdomain of neuroscience. We then provide a roadmap for the future advances that will be needed to realize this integrative vision. In this way, we hope to demonstrate how fMRI can help usher in a new era of interdisciplinary coherence in neuroscience.

Data-efficient resting-state functional magnetic resonance imaging brain mapping with deep learning

OBJECTIVE

Resting-state functional MRI (RS-fMRI) enables the mapping of function within the brain and is emerging as an efficient tool for the presurgical evaluation of eloquent cortex. Models capable of reliable and precise mapping of resting-state networks (RSNs) with a reduced scanning time would lead to improved patient comfort while reducing the cost per scan. The aims of the present study were to develop a deep 3D convolutional neural network (3DCNN) capable of voxel-wise mapping of language (LAN) and motor (MOT) RSNs with minimal quantities of RS-fMRI data.

METHODS

Imaging data were gathered from multiple ongoing studies at Washington University School of Medicine and other thoroughly characterized, publicly available data sets. All study participants (n = 2252 healthy adults) were cognitively screened and completed structural neuroimaging and RS-fMRI. Random permutations of RS-fMRI regions of interest were used to train a 3DCNN. After training, model inferences were compared using varying amounts of RS-fMRI data from the control data set as well as 5 patients with glioblastoma multiforme.

RESULTS

The trained model achieved 96% out-of-sample validation accuracy on data encompassing a large age range collected on multiple scanner types and varying sequence parameters. Testing on out-of-sample control data showed 97.9% similarity between results generated using either 50 or 200 RS-fMRI time points, corresponding to approximately 2.5 and 10 minutes, respectively (96.9% LAN, 96.3% MOT true-positive rate). In evaluating data from patients with brain tumors, the 3DCNN was able to accurately map LAN and MOT networks despite structural and functional alterations.

CONCLUSIONS

Functional maps produced by the 3DCNN can inform surgical planning in patients with brain tumors in a time-efficient manner. The authors present a highly efficient method for presurgical functional mapping and thus improved functional preservation in patients with brain tumors.

Current State of Functional MRI in the Presurgical Planning of Brain Tumors

Surgical resection of brain tumors is challenging because of the delicate balance between maximizing tumor removal and preserving vital brain functions. Functional MRI (fMRI) offers noninvasive preoperative mapping of widely distributed brain areas and is increasingly used in presurgical functional mapping. However, its impact on survival and functional outcomes is still not well-supported by evidence. Task-based fMRI (tb-fMRI) maps blood oxygen level-dependent (BOLD) signal changes during specific tasks, while resting-state fMRI (rs-fMRI) examines spontaneous brain activity. rs-fMRI may be useful for patients who cannot perform tasks, but its reliability is affected by tumor-induced changes, challenges in data processing, and noise. Validation studies comparing fMRI with direct cortical stimulation (DCS) show variable concordance, particularly for cognitive functions such as language; however, concordance for tb-fMRI is generally greater than that for rs-fMRI. Preoperative fMRI, in combination with MRI tractography and intraoperative DCS, may result in improved survival and extent of resection and reduced functional deficits. fMRI has the potential to guide surgical planning and help identify targets for intraoperative mapping, but there is currently limited prospective evidence of its impact on patient outcomes. This review describes the current state of fMRI for preoperative assessment in patients undergoing brain tumor resection. Keywords: MR-Functional Imaging, CNS, Brain/Brain Stem, Anatomy, Oncology, Functional MRI, Functional Anatomy, Task-based, Resting State, Surgical Planning, Brain Tumor © RSNA, 2023.

Neuropsychological Evaluation and Functional Magnetic Resonance Imaging Tasks in the Preoperative Assessment of Patients with Brain Tumors: A Systematic Review

BACKGROUND

Current surgical treatment of gliomas relies on a function-preserving, maximally safe resection approach. Functional Magnetic Resonance Imaging (fMRI) is a widely employed technology for this purpose. A preoperative neuropsychological evaluation should accompany this exam. However, only a few studies have reported both neuropsychological tests and fMRI tasks for preoperative planning-the current study aimed to systematically review the scientific literature on the topic.

METHODS

PRISMA guidelines were followed. We included studies that reported both neuropsychological tests and fMRI. Exclusion criteria were: no brain tumors, underage patients, no preoperative assessment, resting-state fMRI only, or healthy sample population/preclinical studies.

RESULTS

We identified 123 papers, but only 15 articles were included. Eight articles focused on language; three evaluated cognitive performance; single papers studied sensorimotor cortex, prefrontal functions, insular cortex, and cerebellar activation. Two qualitative studies focused on visuomotor function and language. According to some authors, there was a strong correlation between performance in presurgical neuropsychological tests and fMRI. Several papers suggested that selecting well-adjusted and individualized neuropsychological tasks may enable the development of personalized and more efficient protocols. The fMRI findings may also help identify plasticity phenomena to avoid unintentional damage during neurosurgery.

CONCLUSIONS

Most studies have focused on language, the most commonly evaluated cognitive function. The correlation between neuropsychological and fMRI results suggests that altered functions during the neuropsychological assessment may help identify patients who could benefit from an fMRI and, possibly, functions that should be tested. Neuropsychological evaluation and fMRI have complementary roles in the preoperative assessment.

Evaluation of inverse treatment planning for gamma knife radiosurgery using fMRI brain activation maps as organs at risk

BACKGROUND

Stereotactic radiosurgery (SRS) can be an effective primary or adjuvant treatment option for intracranial tumors. However, it carries risks of various radiation toxicities, which can lead to functional deficits for the patients. Current inverse planning algorithms for SRS provide an efficient way for sparing organs at risk (OARs) by setting maximum radiation dose constraints in the treatment planning process.

PURPOSE

We propose using activation maps from functional MRI (fMRI) to map the eloquent regions of the brain and define functional OARs (fOARs) for Gamma Knife SRS treatment planning.

METHODS

We implemented a pipeline for analyzing patient fMRI data, generating fOARs from the resulting activation maps, and loading them onto the GammaPlan treatment planning software. We used the Lightning inverse planner to generate multiple treatment plans from open MRI data of five subjects, and evaluated the effects of incorporating the proposed fOARs.

RESULTS

The Lightning optimizer designs treatment plans with high conformity to the specified parameters. Setting maximum dose constraints on fOARs successfully limits the radiation dose incident on them, but can have a negative impact on treatment plan quality metrics. By masking out fOAR voxels surrounding the tumor target it is possible to achieve high quality treatment plans while controlling the radiation dose on fOARs.

CONCLUSIONS

The proposed method can effectively reduce the radiation dose incident on the eloquent brain areas during Gamma Knife SRS of brain tumors.

Independent component analysis: a reliable alternative to general linear model for task-based fMRI

Background

Functional magnetic resonance imaging (fMRI) is a valuable tool for the presurgical evaluation of patients undergoing neurosurgeries. Although many pre-processing steps have been modified according to advances in recent years, statistical analysis has remained largely the same since the first days of fMRI. In this study, we examined the ability of Independent Component Analysis (ICA) to separate the activation of a language task in fMRI, and we compared it with the results of the General Lineal Model (GLM).

Methods

Sixty patients undergoing evaluation for brain surgery due to various brain lesions and/or epilepsy and 20 control subjects completed an fMRI language mapping protocol that included three tasks, resulting in 259 fMRI scans. Depending on brain lesion characteristics, patients were allocated to (1) static/chronic not-expanding lesions (Group 1) and (2) progressive/expanding lesions (Group 2). GLM and ICA statistical maps were evaluated by fMRI experts to assess the performance of each technique.

Results

In the control group, ICA and GLM maps were similar without any superiority of either technique. In Group 1 and Group 2, ICA performed statistically better than GLM, with a p-value of < 0.01801 and < 0.0237, respectively. This indicated that ICA performs as well as GLM when the subjects are able to cooperate well (less movement, good task performance), but ICA could outperform GLM in the patient groups. When both techniques were combined, 240 out of 259 scans produced reliable results, showing that the sensitivity of task-based fMRI can be increased when both techniques are integrated with the clinical setup.

Conclusion

ICA may be slightly more advantageous, compared to GLM, in patients with brain lesions, across the range of pathologies included in our population and independent of symptoms chronicity. Our findings suggest that GLM analysis may be more susceptible to brain activity perturbations induced by a variety of lesions or scanner-induced artifacts due to motion or other factors. In our research, we demonstrated that ICA is able to provide fMRI results that can be used in surgery, taking into account patient and task-wise aspects that differ from those when fMRI is used in research.

Interindividual variability and consistency of language mapping paradigms for presurgical use

Most functional MRI studies of language processing have focussed on group-level inference, but for clinical use, the aim is to predict outcomes at an individual patient level. This requires being able to identify atypical activation and understand how differences relate to language outcomes. A language mapping paradigm that selectively activates left hemisphere language regions in healthy individuals allows atypical activation in a patient to be more easily identified. We investigated the interindividual variability and consistency of language activation in 12 healthy participants using three tasks-verb generation, responsive naming, and sentence comprehension-for future presurgical use. Responsive naming produced the most consistent left-lateralised activation across participants in frontal and temporal regions that postsurgical voxel-based lesion-symptom mapping studies suggest are most critical for language outcomes. Studies with a long-term clinical aim of predicting language outcomes in neurosurgical patients and stroke patients should first establish paradigm validity at an individual level in healthy participants.

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 Advanced Imaging in Neurosurgical Diagnosis

Neurosurgery as a specialty has developed at a rapid pace as a result of the continual advancements in neuroimaging modalities. With more sophisticated imaging options available to the modern neurosurgeon, diagnoses become more accurate and at a faster rate, allowing for greater surgical planning and precision. Herein, the authors review the current heavily used imaging modalities within neurosurgery, weighing their strengths and weaknesses, and provide a look into new advances and imaging options within the field. Of the many imaging modalities currently available to the practicing neurosurgeon, magnetic resonance imaging (MRI), computed tomography (CT), positron emission tomography (PET), and ultrasonography (US) are used most heavily within the field for appropriate diagnosis of neuropathologies in question. For each, their strengths are weighed regarding appropriate capabilities in accurate diagnosis of cranial or spinal lesions. Reasoning for choosing one over the other for various pathologies is also reviewed. Current limitations of each is also assessed, providing insight for possible improvement for each. New advancements in imaging options are subsequently reviewed for best uses within neurosurgery, including the new utilization of FIESTA sequencing, glymphatic mapping, black-blood MRI, and functional MRI. The specialty of neurosurgery will continue to heavily rely on improvements within imaging options available for improved diagnosis and greater surgical outcomes for the patients treated. The synthesis of techniques provided herein may provide meaningful guidance for neurosurgeons in effectively diagnosing neurological pathologies while also helping guide future efforts in neuroimaging developments.

Cortical mapping in multilinguals undergoing awake brain surgery for brain tumors: Illustrative cases and systematic review

BACKGROUND

Awake brain mapping in multilingual patients with brain tumors presents unique challenges to the neurosurgeon. Knowledge of potential eloquent sites is vital to preserve language function.

METHODS

We present two cases of pars opercularis glioma and perform a systematic review in accordance with PRISMA guidelines.

RESULTS

Our review yielded 7 studies, with a total of 25 multilingual brain tumor patients who underwent awake brain mapping. The age ranged from 25 to 62 years. Majority were female (56.5%). Most (52%) were trilingual, while 20% were quadrilingual and 28% were pentalingual. All tumors were left-sided, mostly in the frontal lobe. These were predominantly gliomas. Extent of resection was gross total in 61%. The brain mapping findings were heterogeneous. Some authors reported a greater number of cortical sites for the first language compared to others. Others found that the first and second languages shared cortical sites whereas the third and subsequent languages were located in distant sites. The peri-Sylvian area was also found to be involved in language that was learned at an earlier age. Subsequent languages thus involved more distant sites. A larger number of cortical areas were also activated for languages that were learned later in life. In terms of language disturbance and recovery, there were mixed results.

CONCLUSION

Cortical mapping in multilingual brain tumor patients showed heterogeneity in terms of the location and number of language areas in the face of pathology. These findings may influence neurosurgical and oncological management of tumors in the speech area but emphasize the need to tailor surgical approaches and intraoperative testing to the patient.

Graph Theory Measures and Their Application to Neurosurgical Eloquence

Improving patient safety and preserving eloquent brain are crucial in neurosurgery. Since there is significant clinical variability in post-operative lesions suffered by patients who undergo surgery in the same areas deemed compensable, there is an unknown degree of inter-individual variability in brain 'eloquence'. Advances in connectomic mapping efforts through diffusion tractography allow for utilization of non-invasive imaging and statistical modeling to graphically represent the brain. Extending the definition of brain eloquence to graph theory measures of hubness and centrality may help to improve our understanding of individual variability in brain eloquence and lesion responses. While functional deficits cannot be immediately determined intra-operatively, there has been potential shown by emerging technologies in mapping of hub nodes as an add-on to existing surgical navigation modalities to improve individual surgical outcomes. This review aims to outline and review current research surrounding novel graph theoretical concepts of hubness, centrality, and eloquence and specifically its relevance to brain mapping for pre-operative planning and intra-operative navigation in neurosurgery.

Advancing brain network models to reconcile functional neuroimaging and clinical research

Functional magnetic resonance imaging (fMRI) captures information on brain function beyond the anatomical alterations that are traditionally visually examined by neuroradiologists. However, the fMRI signals are complex in addition to being noisy, so fMRI still faces limitations for clinical applications. Here we review methods that have been proposed as potential solutions so far, namely statistical, biophysical and decoding models, with their strengths and weaknesses. We especially evaluate the ability of these models to directly predict clinical variables from their parameters (predictability) and to extract clinically relevant information regarding biological mechanisms and relevant features for classification and prediction (interpretability). We then provide guidelines for useful applications and pitfalls of such fMRI-based models in a clinical research context, looking beyond the current state of the art. In particular, we argue that the clinical relevance of fMRI calls for a new generation of models for fMRI data, which combine the strengths of both biophysical and decoding models. This leads to reliable and biologically meaningful model parameters, which thus fulfills the need for simultaneous interpretability and predictability. In our view, this synergy is fundamental for the discovery of new pharmacological and interventional targets, as well as the use of models as biomarkers in neurology and psychiatry.

Resting-state functional MRI for motor cortex mapping in childhood-onset focal epilepsy

BACKGROUND AND PURPOSE

Task-based functional MRI (fMRI) mapping of the motor function prior to epilepsy surgery has limitations in children with epilepsy. We present a data-driven method to automatically delineate the motor cortex using task-free, resting-state fMRI (rsfMRI) data.

METHODS

We used whole-brain rsfMRI for independent component analysis (ICA). A template matching process with Discriminability Index-based Component Identification score was used for each participant to select and combine motor ICA components in their native brain space, resulting in a whole-brain ICA Motor Map (wIMM). We validated wIMM by comparing individual results with bilateral finger-tapping motor task fMRI activation, and evaluated its reproducibility in controls.

RESULTS

Data from 64 patients and 12 controls were used to generate group wIMM maps. The hit rate between wIMM and motor task activation ranged from 60% to 79% across all participants. Sensitivity of wIMM for capturing the task activation peak was 87.5% among 32 patients and 100% in 12 controls with available motor task results. We also showed high similarity in repeated runs in controls.

CONCLUSIONS

Our results show the sensitivity and reproducibility of an automated motor mapping method based on ICA analysis of rsfMRI in children with epilepsy. The ICA maps may provide different, but useful, information than task fMRI. Future studies will expand our method to mapping other brain functions, and may lead to a surgical planning tool for patients who cannot perform task fMRI and help predict their postsurgical function.

Non-Neural Factors Influencing BOLD Response Magnitudes within Individual Subjects

To what extent is the size of the BOLD response influenced by factors other than neural activity? In a reanalysis of three neuroimaging datasets (male and female human participants), we find large systematic inhomogeneities in the BOLD response magnitude in primary visual cortex (V1): stimulus-evoked BOLD responses, expressed in units of percent signal change, are up to 50% larger along the representation of the horizontal meridian than the vertical meridian. To assess whether this surprising effect can be interpreted as differences in local neural activity, we quantified several factors that potentially contribute to the size of the BOLD response. We find relationships between BOLD response magnitude and cortical thickness, curvature, depth, and macrovasculature. These relationships are consistently found across subjects and datasets and suggest that variation in BOLD response magnitudes across cortical locations reflects, in part, differences in anatomy and vascularization. To compensate for these factors, we implement a regression-based correction method and show that, after correction, BOLD responses become more homogeneous across V1. The correction reduces the horizontal/vertical difference by about half, indicating that some of the difference is likely not because of neural activity differences. We conclude that interpretation of variation in BOLD response magnitude across cortical locations should consider the influence of the potential confounding factors of thickness, curvature, depth, and vascularization.SIGNIFICANCE STATEMENT The magnitude of the BOLD signal is often used as a surrogate of neural activity, but the exact factors that contribute to its strength have not been studied on a voxel-wise level. Here, we examined several anatomical and measurement-related factors to assess their relationship with BOLD signal magnitude. We find that BOLD magnitude correlates with cortical anatomy, depth, and macrovasculature. To remove the contribution of these factors, we propose a simple, data-driven correction method that can be used in any fMRI experiment. After accounting for the confounding factors, BOLD magnitude becomes more spatially homogeneous. Our correction method improves the ability to make more accurate inferences about local neural activity from fMRI data.

The challenge of BWAs: Unknown unknowns in feature space and variance

The recent paper by Marek et al.¹ has shown that, to capture brain-wide associations using fMRI and MRI measures, thousands of individuals are required. These results can be potentially misunderstood to imply that MRI or fMRI lack sensitivity or specificity. This commentary discusses the demonstrated sensitivity of fMRI and focuses on methodology that may allow improvements in BWA studies. While individual variation may be an ultimate constraint, refinements in acquisition, population selection, and processing may bring about higher correlations.

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.

Effects of Different Scan Duration on Brain Effective Connectivity among Default Mode Network Nodes

Background: Resting-state functional magnetic resonance imaging (rs-fMRI) can evaluate brain functional connectivity without requiring subjects to perform a specific task. This rs-fMRI is very useful in patients with cognitive decline or unable to respond to tasks. However, long scan durations have been suggested to measure connectivity between brain areas to produce more reliable results, which are not clinically optimal. Therefore, this study aims to evaluate a shorter scan duration and compare the scan duration of 10 and 15 min using the rs-fMRI approach. Methods: Twenty-one healthy male and female participants (seventeen right-handed and four left-handed), with ages ranging between 21 and 60 years, were recruited. All participants underwent both 10 and 15 min of rs-fMRI scans. The present study evaluated the default mode network (DMN) areas for both scan durations. The areas involved were the posterior cingulate cortex (PCC), medial prefrontal cortex (mPFC), left inferior parietal cortex (LIPC), and right inferior parietal cortex (RIPC). Fifteen causal models were constructed and inverted using spectral dynamic causal modelling (spDCM). The models were compared using Bayesian Model Selection (BMS) for group studies. Result: The BMS results indicated that the fully connected model was the winning model among 15 competing models for both 10 and 15 min scan durations. However, there was no significant difference in effective connectivity among the regions of interest between the 10 and 15 min scans. Conclusion: Scan duration in the range of 10 to 15 min is sufficient to evaluate the effective connectivity within the DMN region. In frail subjects, a shorter scan duration is more favourable.

Advances in Neuroimaging and Monitoring to Defend Cerebral Perfusion in Noncardiac Surgery

Noncardiac surgery conveys a substantial risk of secondary organ dysfunction and injury. Neurocognitive dysfunction and covert stroke are emerging as major forms of perioperative organ dysfunction, but a better understanding of perioperative neurobiology is required to identify effective treatment strategies. The likelihood and severity of perioperative brain injury may be increased by intraoperative hemodynamic dysfunction, tissue hypoperfusion, and a failure to recognize complications early in their development. Advances in neuroimaging and monitoring techniques, including optical, sonographic, and magnetic resonance, have progressed beyond structural imaging and now enable noninvasive assessment of cerebral perfusion, vascular reserve, metabolism, and neurologic function at the bedside. Translation of these imaging methods into the perioperative setting has highlighted several potential avenues to optimize tissue perfusion and deliver neuroprotection. This review introduces the methods, metrics, and evidence underlying emerging optical and magnetic resonance neuroimaging methods and discusses their potential experimental and clinical utility in the setting of noncardiac surgery.

Alternations and Applications of the Structural and Functional Connectome in Gliomas: A Mini-Review

In the central nervous system, gliomas are the most common, but complex primary tumors. Genome-based molecular and clinical studies have revealed different classifications and subtypes of gliomas. Neuroradiological approaches have non-invasively provided a macroscopic view for surgical resection and therapeutic effects. The connectome is a structural map of a physical object, the brain, which raises issues of spatial scale and definition, and it is calculated through diffusion magnetic resonance imaging (MRI) and functional MRI. In this study, we reviewed the basic principles and attributes of the structural and functional connectome, followed by the alternations of connectomes and their influences on glioma. To extend the applications of connectome, we demonstrated that a series of multi-center projects still need to be conducted to systemically investigate the connectome and the structural–functional coupling of glioma. Additionally, the brain–computer interface based on accurate connectome could provide more precise structural and functional data, which are significant for surgery and postoperative recovery. Besides, integrating the data from different sources, including connectome and other omics information, and their processing with artificial intelligence, together with validated biological and clinical findings will be significant for the development of a personalized surgical strategy.

A New Functional Magnetic Resonance Imaging Localizer for Preoperative Language Mapping Using a Sentence Completion Task: Validity, Choice of Baseline Condition, and Test–Retest Reliability

To avoid post-neurosurgical language deficits, intraoperative mapping of the language function in the brain can be complemented with preoperative mapping with functional magnetic resonance imaging (fMRI). The validity of an fMRI “language localizer” paradigm crucially depends on the choice of an optimal language task and baseline condition. This study presents a new fMRI “language localizer” in Russian using overt sentence completion, a task that comprehensively engages the language function by involving both production and comprehension at the word and sentence level. The paradigm was validated in 18 neurologically healthy volunteers who participated in two scanning sessions, for estimating test–retest reliability. For the first time, two baseline conditions for the sentence completion task were compared. At the group level, the paradigm significantly activated both anterior and posterior language-related regions. Individual-level analysis showed that activation was elicited most consistently in the inferior frontal regions, followed by posterior temporal regions and the angular gyrus. Test–retest reliability of activation location, as measured by Dice coefficients, was moderate and thus comparable to previous studies. Test–retest reliability was higher in the frontal than temporo-parietal region and with the most liberal statistical thresholding compared to two more conservative thresholding methods. Lateralization indices were expectedly left-hemispheric, with greater lateralization in the frontal than temporo-parietal region, and showed moderate test-retest reliability. Finally, the pseudoword baseline elicited more extensive and more reliable activation, although the syllable baseline appears more feasible for future clinical use. Overall, the study demonstrated the validity and reliability of the sentence completion task for mapping the language function in the brain. The paradigm needs further validation in a clinical sample of neurosurgical patients. Additionally, the study contributes to general evidence on test–retest reliability of fMRI.

Awake brain surgery for language mapping in pediatric patients: a single-center experience

OBJECTIVE

The goal of this study was to evaluate the feasibility, benefit, and safety of awake brain surgery (ABS) and intraoperative language mapping in children and adolescents with structural epilepsies. Whereas ABS is an established method to monitor language function in adults intraoperatively, reports of ABS in children are scarce.

METHODS

A retrospective chart review of pediatric patients ≤ 18 years of age who underwent ABS and cortical language mapping for supratentorial tumors and nontumoral epileptogenic lesions between 2008 and 2019 was conducted. The authors evaluated the global intellectual and specific language performance by using detailed neuropsychological testing, the patient's intraoperative compliance, results of intraoperative language mapping assisted by electrocorticography (ECoG), and postsurgical language development and seizure outcomes. Descriptive statistics were used for this study, with a statistical significance of p < 0.05.

RESULTS

Eleven children (7 boys) with a median age of 13 years (range 10-18 years) underwent ABS for a lesion in close vicinity to cortical language areas as defined by structural and functional MRI (left hemisphere in 9 children, right hemisphere in 2). Patients were neurologically intact but experiencing seizures; these were refractory to therapy in 9 patients. Compliance during the awake phase was high in 10 patients and low in 1 patient. Cortical mapping identified eloquent language areas in 6/10 (60%) patients and was concordant in 3/8 (37.5%), discordant in 3/8 (37.5%), and unclear in 2/8 (25%) patients compared to preoperative functional MRI. Stimulation-induced seizures occurred in 2 patients and could be interrupted easily. ECoG revealed that afterdischarge potentials (ADP) were involved in 5/9 (56%) patients with speech disturbances during stimulation. None of these patients harbored postoperative language dysfunction. Gross-total resection was achieved in 10/11 (91%) patients, and all were seizure free after a median follow-up of 4.3 years. Neuropsychological testing using the Wechsler Intelligence Scale for Children and the verbal learning and memory test showed an overall nonsignificant trend toward an immediate postoperative deterioration followed by an improvement to above preoperative levels after 1 year.

CONCLUSIONS

ABS is a valuable technique in selected pediatric patients with lesions in language areas. An interdisciplinary approach, careful patient selection, extensive preoperative training of patients, and interpretation of intraoperative ADP are pivotal to a successful surgery.

Evaluation of a Simple Clinical Language Paradigm With Respect to Sensory Independency, Functional Asymmetry, and Effective Connectivity

The present study replicates a known visual language paradigm, and extends it to a paradigm that is independent from the sensory modality of the stimuli and, hence, could be administered either visually or aurally, such that both patients with limited sight or hearing could be examined. The stimuli were simple sentences, but required the subject not only to understand the content of the sentence but also to formulate a response that had a semantic relation to the content of the presented sentence. Thereby, this paradigm does not only test perception of the stimuli, but also to some extend sentence and semantic processing, and covert speech production within one task. When the sensory base-line condition was subtracted, both the auditory and visual version of the paradigm demonstrated a broadly overlapping and asymmetric network, comprising distinct areas of the left posterior temporal lobe, left inferior frontal areas, left precentral gyrus, and supplementary motor area. The consistency of activations and their asymmetry was evaluated with a conjunction analysis, probability maps, and intraclass correlation coefficients (ICC). This underlying network was further analyzed with dynamic causal modeling (DCM) to explore whether not only the same brain areas were involved, but also the network structure and information flow were the same between the sensory modalities. In conclusion, the paradigm reliably activated the most central parts of the speech and language network with a great consistency across subjects, and independently of whether the stimuli were administered aurally or visually. However, there was individual variability in the degree of functional asymmetry between the two sensory conditions.

Eigenvector PageRank difference as a measure to reveal topological characteristics of the brain connectome for neurosurgery

PURPOSE

Applying graph theory to the human brain has the potential to help prognosticate the impacts of intracerebral surgery. Eigenvector (EC) and PageRank (PR) centrality are two related, but uniquely different measures of nodal centrality which may be utilized together to reveal varying neuroanatomical characteristics of the brain connectome.

METHODS

We obtained diffusion neuroimaging data from a healthy cohort (UCLA consortium for neuropsychiatric phenomics) and applied a personalized parcellation scheme to them. We ranked parcels based on weighted EC and PR, and then calculated the difference (EP difference) and correlation between the two metrics. We also compared the difference between the two metrics to the clustering coefficient.

RESULTS

While EC and PR were consistent for top and bottom ranking parcels, they differed for mid-ranking parcels. Parcels with a high EC centrality but low PR tended to be in the medial temporal and temporooccipital regions, whereas PR conferred greater importance to multi-modal association areas in the frontal, parietal and insular cortices. The EP difference showed a weak correlation with clustering coefficient, though there was significant individual variation.

CONCLUSIONS

The relationship between PageRank and eigenvector centrality can identify distinct topological characteristics of the brain connectome such as the presence of unimodal or multimodal association cortices. These results highlight how different graph theory metrics can be used alone or in combination to reveal unique neuroanatomical features for further clinical study.

Characterization of cortical hemodynamic changes following sensory, visual, and speech activation by intraoperative optical imaging utilizing phase-based evaluation methods

Alterations within cerebral hemodynamics are the intrinsic signal source for a wide variety of neuroimaging techniques. Stimulation of specific functions leads due to neurovascular coupling, to changes in regional cerebral blood flow, oxygenation and volume. In this study, we investigated the temporal characteristics of cortical hemodynamic responses following electrical, tactile, visual, and speech activation for different stimulation paradigms using Intraoperative Optical Imaging (IOI). Image datasets from a total of 22 patients that underwent surgical resection of brain tumors were evaluated. The measured reflectance changes at different light wavelength bands, representing alterations in regional cortical blood volume (CBV), and deoxyhemoglobin (HbR) concentration, were assessed by using Fourier-based evaluation methods. We found a decrease of CBV connected to an increase of HbR within the contralateral primary sensory cortex (SI) in patients that were prolonged (30 s/15 s) electrically stimulated. Additionally, we found differences in amplitude as well as localization of activated areas for different stimulation patterns. Contrary to electrical stimulation, prolonged tactile as well as prolonged visual stimulation are provoking increases in CBV within the corresponding activated areas (SI, visual cortex). The processing of the acquired data from awake patients performing speech tasks reveals areas with increased, as well as areas with decreased CBV. The results lead us to the conclusion, that the CBV decreases in connection with HbR increases in SI are associated to processing of nociceptive stimuli and that stimulation type, as well as paradigm have a nonnegligible impact on the temporal characteristics of the following hemodynamic response.

Cognitive Biomarkers in the Clinic: Lessons From Presurgical fMRI

SUMMARY

Cognitive biomarkers are vital and uniquely challenging clinical tools. There has been marked growth in neuroimaging-based cognitive biomarkers across the past 40 years with more in development (e.g., clinical cognitive EEG). The challenges involved in developing cognitive biomarkers and key milestones in their development are reviewed here using clinical functional MRI's evolution as a case study. It is argued that indexing cognition is uniquely challenging because it requires patients to consistently use specific cognitive processes, and it is difficult or impossible to independently verify this occurred. This limitation can be successfully managed through careful analysis of standardized protocols for acquisition and interpretation, and ensuring the clinical application of biomarkers integrates disciplines with complementary expertise. Factors beneficial to the adoption of a novel cognitive biomarker include a clinical need and inadequate alternatives. Key milestones in the development of functional MRI included (1) demonstration that its performance was equivalent to its predecessor; (2) demonstration it predicted a clinically meaningful outcome; and (3) the establishment of infrastructure for both its execution and billing. Review of functional MRI and its predecessors suggest a cycle whereby successful cognitive biomarkers are validated, experience widespread adoption and customization/fragmentation, go through a period of review, and finally are refined and standardized. Those applying future cognitive biomarkers in the clinic can avoid some of the failures of clinical functional MRI by defining the skills and disciplines the method requires and routinely evaluating patient outcomes.

Functional Approaches to the Surgery of Brain Gliomas

In the surgery of gliomas, recent years have witnessed unprecedented theoretical and technical development, which extensively increased indication to surgery. On one hand, it has been solidly demonstrated the impact of gross total resection on life expectancy. On the other hand, the paradigm shift from classical cortical localization of brain function towards connectomics caused by the resurgence of awake surgery and the advent of tractography has permitted safer surgeries focused on subcortical white matter tracts preservation and allowed for surgical resections within regions, such as Broca's area or the primary motor cortex, which were previously deemed inoperable. Furthermore, new asleep electrophysiological techniques have been developed whenever awake surgery is not an option, such as operating in situations of poor compliance (including paediatric patients) or pre-existing neurological deficits. One such strategy is the use of intraoperative neurophysiological monitoring (IONM), enabling the identification and preservation of functionally defined, but anatomically ambiguous, cortico-subcortical structures through mapping and monitoring techniques. These advances tie in with novel challenges, specifically risk prediction and the impact of neuroplasticity, the indication for tumour resection beyond visible borders, or supratotal resection, and most of all, a reappraisal of the importance of the right hemisphere from early psychosurgery to mapping and preservation of social behaviour, executive control, and decision making.Here we review current advances and future perspectives in a functional approach to glioma surgery.

Awake Craniotomy Under 3-Tesla Intraoperative Magnetic Resonance Imaging: A Retrospective Descriptive Report and Canadian Institutional Experience

BACKGROUND

The role of high-field 3-Tesla intraoperative magnetic resonance imaging (I-MRI) during awake craniotomy (AC) has not been extensively studied. We report the feasibility and safety of AC during 3-Tesla I-MRI.

METHODS

This retrospective descriptive report compared 3 groups: AC with minimal sedation and I-MRI; I-MRI-guided craniotomy under general anesthesia (GA), and; AC without I-MRI. Perioperative factors, surgical, anesthetic and radiologic complications, and postoperative morbidity and mortality were recorded.

RESULTS

Overall, 85 patients are included in this report. Five of 23 patients (22%) who underwent AC with I-MRI had anesthetic complications (nausea/vomiting and conversion to GA) compared with 3 of 40 (8%) who underwent I-MRI-guided craniotomy under GA (nausea/vomiting during extubation, and arrhythmia). Intraoperative surgical complications (seizures and speech deficits) occurred in 5 patients (22%) who underwent AC and I-MRI, excessive intraoperative bleeding occurred in 2 patients (5%) who had I-MRI-guided craniotomy under GA, and 4 of 22 (18%) patients who underwent AC without I-MRI experienced neurological complications (seizures, motor deficits, and transient loss of consciousness). Eight patients (20%) who had I-MRI with GA had postoperative complications, largely neurological. The duration of surgery and anesthesia were shortest in the group of patients receiving AC without I-MRI. Seventy-three percent of the patients in this group had residual tumor postoperatively compared with 44% and 38% in those having I-MRI with AC or GA, respectively. Patients who underwent I-MRI-guided craniotomy with GA had the highest morbidity (8%) at hospital discharge.

CONCLUSIONS

Our institutional experience suggests that AC under 3-Tesla I-MRI could be an option for glioma resection, although firm conclusions cannot be drawn given the limited and heterogenous nature of our data. Future multicenter trials comparing anesthetic and imaging modalities for glioma resection are recommended.

Machine learning in neuroimaging: from research to clinical practice

Neuroimaging is critical in clinical care and research, enabling us to investigate the brain in health and disease. There is a complex link between the brain's morphological structure, physiological architecture, and the corresponding imaging characteristics. The shape, function, and relationships between various brain areas change during development and throughout life, disease, and recovery. Like few other areas, neuroimaging benefits from advanced analysis techniques to fully exploit imaging data for studying the brain and its function. Recently, machine learning has started to contribute (a) to anatomical measurements, detection, segmentation, and quantification of lesions and disease patterns, (b) to the rapid identification of acute conditions such as stroke, or (c) to the tracking of imaging changes over time. As our ability to image and analyze the brain advances, so does our understanding of its intricate relationships and their role in therapeutic decision-making. Here, we review the current state of the art in using machine learning techniques to exploit neuroimaging data for clinical care and research, providing an overview of clinical applications and their contribution to fundamental computational neuroscience.

Elucidating the structural-functional connectome of language in glioma-induced aphasia using nTMS and DTI

Glioma‐induced aphasia (GIA) is frequently observed in patients with newly diagnosed gliomas. Previous studies showed an impact of gliomas not only on local brain regions but also on the functionality and structure of brain networks. The current study used navigated transcranial magnetic stimulation (nTMS) to localize language‐related regions and to explore language function at the network level in combination with connectome analysis. Thirty glioma patients without aphasia (NA) and 30 patients with GIA were prospectively enrolled. Tumors were located in the vicinity of arcuate fasciculus‐related cortical and subcortical regions. The visualized ratio (VR) of each tract was calculated based on their respective fractional anisotropy (FA) and maximal FA. Using a thresholding method of each tract at 25% VR and 50% VR, DTI‐based tractography was performed to construct structural brain networks for graph‐based connectome analysis, containing functional data acquired by nTMS. The average degree of left hemispheric networks (M_left) was higher in the NA group than in the GIA group for both VR thresholds. Differences of global and local efficiency between 25% and 50% VR thresholds were significantly lower in the NA group than in the GIA group. Aphasia levels correlated with connectome properties in M_left and networks based on positive nTMS mapping regions (M_pos). A more substantial relation to language performance was found in M_pos and M_left compared to the network of negative mapping regions (M_neg). Gliomas causing deterioration of language are related to various cerebral networks. In NA patients, mainly M_neg was impacted, while M_pos was impacted in GIA patients.

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.

A Data-Based Approach for Selecting Pre- and Intra-Operative Language Mapping Tasks

Background: Pre- and intra-operative language mapping in neurosurgery patients frequently involves an object naming task. The choice of the optimal object naming paradigm remains challenging due to lack of normative data and standardization in mapping practices. The aim of this study was to identify object naming paradigms that robustly and consistently activate classical language regions and could therefore be used to improve the sensitivity of language mapping in brain tumor and epilepsy patients. Methods: Functional magnetic resonance imaging (fMRI) data from two independent groups of healthy controls (total = 79) were used to generate threshold-weighted voxel-based consistency maps. This novel approach allowed us to compare inter-subject consistency of activation for naming single objects in the visual and auditory modality and naming two objects in a phrase or a sentence. Results: We found that the consistency of activation in language regions was greater for naming two objects per picture than one object per picture, even when controlling for the number of names produced in 5 s. Conclusion: More consistent activation in language areas for naming two objects compared to one object suggests that two-object naming tasks may be more suitable for delimiting language eloquent regions with pre- and intra-operative language testing. More broadly, we propose that the functional specificity of brain mapping paradigms for a whole range of different linguistic and non-linguistic functions could be enhanced by referring to databased models of inter-subject consistency and variability in typical and atypical brain responses.

Current and Future Challenges of Functional MRI and Diffusion Tractography in the Surgical Setting: From Eloquent Brain Mapping to Neural Plasticity

Decades ago, Spetzler (1986) and Sawaya (1998) provided a rough brain segmentation of the eloquent areas of the brain, aimed to help surgical decisions in cases of vascular malformations and tumors, respectively. Currently in clinical use, their criteria are in need of revision. Defining functions (eg, sensorimotor, language and visual) that should be preserved during surgery seems a straightforward task. In practice, locating the specific areas that could cause a permanent vs transient deficit is not an easy task. This is particularly true for the associative cortex and cognitive domains such as language. The old model, with Broca's and Wernicke's areas at the forefront, has been superseded by a dual-stream model of parallel language processing; named ventral and dorsal pathways. This complicated network of cortical hubs and subcortical white matter pathways needing preservation during surgery is a work in progress. Preserving not only cortical regions but most importantly preserving the connections, or white matter fiber bundles, of core regions in the brain is the new paradigm. For instance, the arcuate fascicululs and inferior fronto-occipital fasciculus are key components of the dorsal and ventral language pathways, respectively; and their damage result in permanent language deficits. Interestedly, the damage of the temporal portions of these bundles -where there is a crossroad with other multiple bundles-, appears to be more important (permanent) than the damage of the frontal portions - where plasticity and contralateral activation could help. Although intraoperative direct cortical and subcortical stimulation have contributed largely, advanced MR techniques such as functional MRI (fMRI) and diffusion tractography (DT), are at the epi-center of our current understanding. Nevertheless, these techniques posse important challenges: such as neurovascular uncoupling or venous bias on fMRI; and appropriate anatomical validation or accurate representation of crossing fibers on DT. These limitations should be well understood and taken into account in clinical practice. Unifying multidisciplinary research and clinical efforts is desirable, so these techniques could contribute more efficiently not only to locate eloquent areas but to improve outcomes and our understanding of neural plasticity. Finally, although there are constant anatomical and functional regions at the individual level, there is a known variability at the inter-individual level. This concept should strengthen the importance of a personalized approach when evaluating these regions on fMRI and DT. It should strengthen the importance of personalized treatments as well, aimed to meet tailored needs and expectations.

Effects of Thresholding on Voxel-Wise Correspondence of Breath-Hold and Resting-State Maps of Cerebrovascular Reactivity

Functional magnetic resonance imaging for presurgical brain mapping enables neurosurgeons to identify viable tissue near a site of operable pathology which might be at risk of surgery-induced damage. However, focal brain pathology (e.g., tumors) may selectively disrupt neurovascular coupling while leaving the underlying neurons functionally intact. Such neurovascular uncoupling can result in false negatives on brain activation maps thereby compromising their use for surgical planning. One way to detect potential neurovascular uncoupling is to map cerebrovascular reactivity using either an active breath-hold challenge or a passive resting-state scan. The equivalence of these two methods has yet to be fully established, especially at a voxel level of resolution. To quantitatively compare breath-hold and resting-state maps of cerebrovascular reactivity, we first identified threshold settings that optimized coverage of gray matter while minimizing false responses in white matter. When so optimized, the resting-state metric had moderately better gray matter coverage and specificity. We then assessed the spatial correspondence between the two metrics within cortical gray matter, again, across a wide range of thresholds. Optimal spatial correspondence was strongly dependent on threshold settings which if improperly set tended to produce statistically biased maps. When optimized, the two CVR maps did have moderately good correspondence with each other (mean accuracy of 73.6%). Our results show that while the breath-hold and resting-state maps may appear qualitatively similar they are not quantitatively identical at a voxel level of resolution.

BOLD Asynchrony Elucidates Tumor Burden in IDH-Mutated Gliomas

BACKGROUND

Gliomas comprise the most common type of primary brain tumor, are highly invasive, and often fatal. IDH-mutated gliomas are particularly challenging to image and there is currently no clinically accepted method for identifying the extent of tumor burden in these neoplasms. This uncertainty poses a challenge to clinicians who must balance the need to treat the tumor while sparing healthy brain from iatrogenic damage. The purpose of this study was to investigate the feasibility of using resting-state blood oxygen level dependent (BOLD) functional magnetic resonance imaging (fMRI) to detect glioma-related asynchrony in vascular dynamics for distinguishing tumor from healthy brain.

METHODS

Twenty-four stereotactically localized biopsies were obtained during open surgical resection from ten treatment-naïve patients with IDH-mutated gliomas who received standard of care preoperative imaging as well as echo-planar resting-state BOLD fMRI. Signal intensity for BOLD asynchrony and standard of care imaging was compared to cell counts of total cellularity (H&E), tumor density (IDH1 & Sox2), cellular proliferation (Ki67), and neuronal density (NeuN), for each corresponding sample.

RESULTS

BOLD asynchrony was directly related to total cellularity (H&E, p = 4 x 10 -5), tumor density (IDH1, p = 4 x 10 -5; Sox2, p = 3 x 10 -5), cellular proliferation (Ki67, p = 0.002), and as well as inversely related to neuronal density (NeuN, p = 1 x 10 -4).

CONCLUSIONS

Asynchrony in vascular dynamics, as measured by resting-state BOLD fMRI, correlates with tumor burden and provides a radiographic delineation of tumor boundaries in IDH-mutated gliomas.

Role of Functional Magnetic Resonance Imaging in the Presurgical Mapping of Brain Tumors

When planning for brain tumor resection, a balance between maximizing resection and minimizing injury to eloquent brain parenchyma is paramount. The advent of blood oxygenation level-dependent functional magnetic resonance (fMR) imaging has allowed researchers and clinicians to reliably measure physiologic fluctuations in brain oxygenation related to neuronal activity with good spatial resolution. fMR imaging can offer a unique insight into preoperative planning for brain tumors by identifying eloquent areas of the brain affected or spared by the neoplasm. This article discusses the fMR imaging techniques and their applications in neurosurgical planning.

The Future of Neuroscience: Flexible and Wireless Implantable Neural Electronics

Neurological diseases are a prevalent cause of global mortality and are of growing concern when considering an ageing global population. Traditional treatments are accompanied by serious side effects including repeated treatment sessions, invasive surgeries, or infections. For example, in the case of deep brain stimulation, large, stiff, and battery powered neural probes recruit thousands of neurons with each pulse, and can invoke a vigorous immune response. This paper presents challenges in engineering and neuroscience in developing miniaturized and biointegrated alternatives, in the form of microelectrode probes. Progress in design and topology of neural implants has shifted the goal post toward highly specific recording and stimulation, targeting small groups of neurons and reducing the foreign body response with biomimetic design principles. Implantable device design recommendations, fabrication techniques, and clinical evaluation of the impact flexible, integrated probes will have on the treatment of neurological disorders are provided in this report. The choice of biocompatible material dictates fabrication techniques as novel methods reduce the complexity of manufacture. Wireless power, the final hurdle to truly implantable neural interfaces, is discussed. These aspects are the driving force behind continued research: significant breakthroughs in any one of these areas will revolutionize the treatment of neurological disorders.