Extracting default mode network based on graph neural network for resting state fMRI study

Functional magnetic resonance imaging (fMRI)-based study of functional connections in the brain has been highlighted by numerous human and animal studies recently, which have provided significant information to explain a wide range of pathological conditions and behavioral characteristics. In this paper, we propose the use of a graph neural network, a deep learning technique called graphSAGE, to investigate resting state fMRI (rs-fMRI) and extract the default mode network (DMN). Comparing typical methods such as seed-based correlation, independent component analysis, and dictionary learning, real data experiment results showed that the graphSAGE is more robust, reliable, and defines a clearer region of interests. In addition, graphSAGE requires fewer and more relaxed assumptions, and considers the single subject analysis and group subjects analysis simultaneously.

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.

Resting-state network mapping in neurosurgical practice: a review

Resting-state functional MRI (rs-fMRI) is a well-established method for studying intrinsic connectivity and mapping the topography of functional networks in the human brain. In the clinical setting, rs-fMRI has been used to define functional topography, typically language and motor systems, in the context of preoperative planning for neurosurgery. Intraoperative mapping of critical speech and motor areas with electrocortical stimulation (ECS) remains standard practice, but preoperative noninvasive mapping has the potential to reduce operative time and provide functional localization when awake mapping is not feasible. Task-based fMRI has historically been used for this purpose, but it can be limited by the young age of the patient, cognitive impairment, poor cooperation, and need for sedation. Resting-state fMRI allows reliable analysis of all functional networks with a single study and is inherently independent of factors affecting task performance. In this review, the authors provide a summary of the theory and methods for resting-state network mapping. They provide case examples illustrating clinical implementation and discuss limitations of rs-fMRI and review available data regarding performance in comparison to ECS. Finally, they discuss novel opportunities for future clinical applications and prospects for rs-fMRI beyond mapping of regions to avoid during surgery but, instead, as a tool to guide novel network-based therapies.

Language lateralization with resting-state and task-based functional MRI in pediatric epilepsy

In Brief: The study compared two types of functional MRI (fMRI) to see which side of the brain is most responsible for language: traditional task-based fMRI, which requires a high level of patient interaction, and resting-state fMRI, which is typically performed with the patient under light sedation and has no interaction requirement. The authors found that the test correlation was 93%, indicating resting state fMRI has potential to locate language in those unable to participate in task-based fMRI.

Resting-state functional magnetic resonance imaging for surgical planning in pediatric patients: a preliminary experience

OBJECTIVE Cerebral mapping for surgical planning and operative guidance is a challenging task in neurosurgery. Pediatric patients are often poor candidates for many modern mapping techniques because of inability to cooperate due to their immature age, cognitive deficits, or other factors. Resting-state functional MRI (rs-fMRI) is uniquely suited to benefit pediatric patients because it is inherently noninvasive and does not require task performance or significant cooperation. Recent advances in the field have made mapping cerebral networks possible on an individual basis for use in clinical decision making. The authors present their initial experience translating rs-fMRI into clinical practice for surgical planning in pediatric patients. METHODS The authors retrospectively reviewed cases in which the rs-fMRI analysis technique was used prior to craniotomy in pediatric patients undergoing surgery in their institution. Resting-state analysis was performed using a previously trained machine-learning algorithm for identification of resting-state networks on an individual basis. Network maps were uploaded to the clinical imaging and surgical navigation systems. Patient demographic and clinical characteristics, including need for sedation during imaging and use of task-based fMRI, were also recorded. RESULTS Twenty patients underwent rs-fMRI prior to craniotomy between December 2013 and June 2016. Their ages ranged from 1.9 to 18.4 years, and 12 were male. Five of the 20 patients also underwent task-based fMRI and one underwent awake craniotomy. Six patients required sedation to tolerate MRI acquisition, including resting-state sequences. Exemplar cases are presented including anatomical and resting-state functional imaging. CONCLUSIONS Resting-state fMRI is a rapidly advancing field of study allowing for whole brain analysis by a noninvasive modality. It is applicable to a wide range of patients and effective even under general anesthesia. The nature of resting-state analysis precludes any need for task cooperation. These features make rs-fMRI an ideal technology for cerebral mapping in pediatric neurosurgical patients. This review of the use of rs-fMRI mapping in an initial pediatric case series demonstrates the feasibility of utilizing this technique in pediatric neurosurgical patients. The preliminary experience presented here is a first step in translating this technique to a broader clinical practice.

Cortical plasticity after brachial plexus injury and repair: a resting-state functional MRI study

OBJECTIVE The authors aimed to understand the alterations of brain resting-state networks (RSNs) in patients with pan-brachial plexus injury (BPI) before and after surgery, which might provide insight into cortical plasticity after peripheral nerve injury and regeneration. METHODS Thirty-five patients with left pan-BPI before surgery, 30 patients after surgery, and 25 healthy controls underwent resting-state functional MRI (rs-fMRI). The 30 postoperative patients were subdivided into 2 groups: 14 patients with improvement in muscle power and 16 patients with no improvement in muscle power after surgery. RSNs were extracted using independent component analysis to evaluate connectivity at a significance level of p < 0.05 (familywise error corrected). RESULTS The patients with BPI had lower connectivity in their sensorimotor network (SMN) and salience network (SN) and greater connectivity in their default mode network (DMN) before surgery than the controls. Connectivity of the left supplementary motor cortex in the SMN and medial frontal gyrus and in the anterior cingulate cortex in the SN increased in patients whose muscle power had improved after surgery, whereas no significant changes were noted in the unimproved patients. There was a trend toward reduction in DMN connectivity in all the patients after surgery compared with that in the preoperative patients; however, this result was not statistically significant. CONCLUSIONS The results of this study highlight the fact that peripheral nerve injury, its management, and successful treatment cause dynamic changes within the brain's RSNs, which includes not only the obvious SMN but also the higher cognitive networks such as the SN and DMN, which indicates brain plasticity and compensatory mechanisms at work.