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

Abstract

<jats:sec><jats:title>OBJECTIVE</jats:title><jats:p>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.</jats:p></jats:sec><jats:sec><jats:title>METHODS</jats:title><jats:p>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.</jats:p></jats:sec><jats:sec><jats:title>RESULTS</jats:title><jats:p>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.</jats:p></jats:sec><jats:sec><jats:title>CONCLUSIONS</jats:title><jats:p>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.</jats:p></jats:sec>

Machine learning applications in epilepsy

“…72 In a pediatric population, Roland et al highlighted the use of a neural network-based algorithm in identifying canonical resting-state networks on fMRI as part of presurgical planning, allowing for identification of clinically relevant networks even under general anesthesia. 73 Alongside applications in FCD detection and TLE lateralization, these findings begin to highlight the broader applicability of machine learning techniques in surgical planning for extratemporal epilepsies or those with ostensibly unrevealing imaging findings.…”

Network‐targeted approach and postoperative resting‐state functional magnetic resonance imaging are associated with seizure outcome

“…8 Current automated classification schemes perform relatively well in categorizing expected major RSNs. 46,48,49 Thus, similar to all currently employed seizure localization techniques used for presurgical evaluation, 50 this strategy also relies on interpreter determination of pathogenicity and clinical correlation, and is therefore limited by the same levels and types of subjective bias. 46,48,49 Thus, similar to all currently employed seizure localization techniques used for presurgical evaluation, 50 this strategy also relies on interpreter determination of pathogenicity and clinical correlation, and is therefore limited by the same levels and types of subjective bias.…”

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

“…4) It can assist with functional mapping in diseases not otherwise requiring awake surgery, such as neuroendocrine diseases and hydrocephalus. Leuthardt et al 35 and Roland et al 51 are pioneering the integration of rs-fMRI into clinical practice using well-established imaging data pipelines. These centers have been able to integrate rs-fMRI into clinical practice by converting group mapping averages to individual patient-specific brain maps by training a multilayer machine learning algorithm to assign RSNs based on individual patient data.…”