Preoperative localization of the sensorimotor area using independent component analysis of resting-state fMRI

Sensorimotor network ALFF markers as prognostic indicators of cervical spondylotic myelopathy post-decompression surgery outcomes

PURPOSE

To investigate the prognostic value of amplitude of low-frequency fluctuations (ALFF) within the sensorimotor network (SMN) in patients with cervical spondylotic myelopathy (CSM) following decompression surgery.

METHODS

Eighty-three presurgical CSM patients (pre-CSM), 60 of the same group followed-up 3 months after decompression surgery (post-CSM) and 83 healthy controls (HC) matched for age, sex and level of education underwent resting-state functional magnetic resonance imaging scans by 3.0 T MR. Then, ALFF values measurements were compared and ALFF alterations were assessed among pre- or postsurgical CSM patients and HC, as well as correlations with clinical indexes by Pearson correlation.

RESULTS

Compared with HC, the ALFF value of left inferior parietal marginal angular gyrus was decreased and the bilateral medial frontal gyrus was increased within pre-CSM (GRF correction). Compared with HC, the ALFF values of the left precentral gyrus, superior marginal gyrus, inferior parietal marginal angular gyrus, parietal lobule and postcentral gyrus decreased, while the ALFF value of the left auxiliary motor area, right anterior cuneiform lobule and right parietal lobule increased within post-CSM. Compared with pre-CSM patients, post-CSM patients had lower ALFF value in bilateral precuneus and precentral gyrus, but increased ALFF value in left medial superior frontal gyrus (Frontal_Sup_Medial_L). The ALFF value of the bilateral precuneus was positively correlated with the mJOA improvement rate, and the ALFF value of Frontal_Sup_Medial_L was positively correlated with the upper and lower limb scores within post-CSM.

CONCLUSION

Functional impairment and plasticity of SMN exist in CSM patients before and after surgery. ALFF within the SMN serves as a potential biomarker for predicting recovery outcomes.

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

BACKGROUND AND OBJECTIVES

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

METHODS

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

RESULTS

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

CONCLUSION

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

Recommended Resting-State fMRI Acquisition and Preprocessing Steps for Preoperative Mapping of Language and Motor and Visual Areas in Adult and Pediatric Patients with Brain Tumors and Epilepsy

Resting-state (rs) fMRI has been shown to be useful for preoperative mapping of functional areas in patients with brain tumors and epilepsy. However, its lack of standardization limits its widespread use and hinders multicenter collaboration. The American Society of Functional Neuroradiology, American Society of Pediatric Neuroradiology, and the American Society of Neuroradiology Functional and Diffusion MR Imaging Study Group recommend specific rs-fMRI acquisition approaches and preprocessing steps that will further support rs-fMRI for future clinical use. A task force with expertise in fMRI from multiple institutions provided recommendations on the rs-fMRI steps needed for mapping of language, motor, and visual areas in adult and pediatric patients with brain tumor and epilepsy. These were based on an extensive literature review and expert consensus.Following rs-fMRI acquisition parameters are recommended: minimum 6-minute acquisition time; scan with eyes open with fixation; obtain rs-fMRI before both task-based fMRI and contrast administration; temporal resolution of ≤2 seconds; scanner field strength of 3T or higher. The following rs-fMRI preprocessing steps and parameters are recommended: motion correction (seed-based correlation analysis [SBC], independent component analysis [ICA]); despiking (SBC); volume censoring (SBC, ICA); nuisance regression of CSF and white matter signals (SBC); head motion regression (SBC, ICA); bandpass filtering (SBC, ICA); and spatial smoothing with a kernel size that is twice the effective voxel size (SBC, ICA).The consensus recommendations put forth for rs-fMRI acquisition and preprocessing steps will aid in standardization of practice and guide rs-fMRI program development across institutions. Standardized rs-fMRI protocols and processing pipelines are essential for multicenter trials and to implement rs-fMRI as part of standard clinical practice.

Mapping of the supplementary motor area using repetitive navigated transcranial magnetic stimulation

Background

The supplementary motor area (SMA) is important for motor and language function. Damage to the SMA may harm these functions, yet tools for a preoperative assessment of the area are still sparse.

Objective

The aim of this study was to validate a mapping protocol using repetitive navigated transcranial magnetic stimulation (rnTMS) and extend this protocol for both hemispheres and lower extremities.

Methods

To this purpose, the SMA of both hemispheres were mapped based on a finger tapping task for 30 healthy subjects (35.97 ± 15.11, range 21-67 years; 14 females) using rnTMS at 20 Hz (120% resting motor threshold (RMT)) while controlling for primary motor cortex activation. Points with induced errors were marked on the corresponding MRI. Next, on the identified SMA hotspot a bimanual finger tapping task and the Nine-Hole Peg Test (NHPT) were performed. Further, the lower extremity was mapped at 20 Hz (140%RMT) using a toe tapping task.

Results

Mean finger tapping scores decreased significantly during stimulation (25.70taps) compared to baseline (30.48; p < 0.01). Bimanual finger tapping led to a significant increase in taps during stimulation (28.43taps) compared to unimanual tapping (p < 0.01). Compared to baseline, completion time for the NHPT increased significantly during stimulation (baseline: 13.6 s, stimulation: 16.4 s; p < 0.01). No differences between hemispheres were observed.

Conclusion

The current study validated and extended a rnTMS based protocol for the mapping of the SMA regarding motor function of upper and lower extremity. This protocol could be beneficial to better understand functional SMA organisation and improve preoperative planning in patients with SMA lesions.

Detection and Mitigation of Neurovascular Uncoupling in Brain Gliomas

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

Task-Based and Resting-State Functional MRI in Observing Eloquent Cerebral Areas Personalized for Epilepsy and Surgical Oncology Patients: A Review of the Current Evidence

Functional magnetic resonance imaging (fMRI) is among the newest techniques of advanced neuroimaging that offer the opportunity for neuroradiologists, neurophysiologists, neuro-oncologists, and neurosurgeons to pre-operatively plan and manage different types of brain lesions. Furthermore, it plays a fundamental role in the personalized evaluation of patients with brain tumors or patients with an epileptic focus for preoperative planning. While the implementation of task-based fMRI has increased in recent years, the existing resources and evidence related to this technique are limited. We have, therefore, conducted a comprehensive review of the available resources to compile a detailed resource for physicians who specialize in managing patients with brain tumors and seizure disorders. This review contributes to the existing literature because it highlights the lack of studies on fMRI and its precise role and applicability in observing eloquent cerebral areas in surgical oncology and epilepsy patients, which we believe is underreported. Taking these considerations into account would help to better understand the role of this advanced neuroimaging technique and, ultimately, improve patient life expectancy and quality of life.

Resting state fMRI brain mapping in pediatric supratentorial brain tumors

Background

Functional mapping of eloquent brain areas is crucial for preoperative planning in patients with brain tumors. Resting state functional MRI (rs-fMRI) allows the localization of functional brain areas without the need for task performance, making it well-suited for the pediatric population. In this study the independent component analysis (ICA) rs-fMRI functional mapping results are reported in a group of 22 pediatric patients with supratentorial brain tumors. Additionally, the functional connectivity (FC) maps of the sensori-motor network (SMN) obtained using ICA and seed-based analysis (SBA) are compared.

Results

Different resting state networks (RSNs) were extracted using ICA with varying levels of sensitivity, notably, the SMN was identified in 100% of patients, followed by the Default mode network (DMN) (91%) and Language networks (80%). Additionally, FC maps of the SMN extracted by SBA were more extensive (mean volume = 25,288.36 mm3, standard deviation = 13,364.36 mm3) than those found on ICA (mean volume = 13,403.27 mm3, standard deviation = 9755.661 mm3). This was confirmed by statistical analysis using a Wilcoxon signed rank t test at p < 0.01.

Conclusions

Results clearly demonstrate the successful applicability of rs-fMRI for localizing different functional brain networks in the preoperative assessment of brain areas, and thus represent a further step in the integration of computational radiology research in a clinical setting.

Ranking of communities in multiplex spatiotemporal models of brain dynamics

As a relatively new field, network neuroscience has tended to focus on aggregate behaviours of the brain averaged over many successive experiments or over long recordings in order to construct robust brain models. These models are limited in their ability to explain dynamic state changes in the brain which occurs spontaneously as a result of normal brain function. Hidden Markov Models (HMMs) trained on neuroimaging time series data have since arisen as a method to produce dynamical models that are easy to train but can be difficult to fully parametrise or analyse. We propose an interpretation of these neural HMMs as multiplex brain state graph models we term Hidden Markov Graph Models. This interpretation allows for dynamic brain activity to be analysed using the full repertoire of network analysis techniques. Furthermore, we propose a general method for selecting HMM hyperparameters in the absence of external data, based on the principle of maximum entropy, and use this to select the number of layers in the multiplex model. We produce a new tool for determining important communities of brain regions using a spatiotemporal random walk-based procedure that takes advantage of the underlying Markov structure of the model. Our analysis of real multi-subject fMRI data provides new results that corroborate the modular processing hypothesis of the brain at rest as well as contributing new evidence of functional overlap between and within dynamic brain state communities. Our analysis pipeline provides a way to characterise dynamic network activity of the brain under novel behaviours or conditions.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s41109-022-00454-2.

The Correlation between Functional Connectivity of the Primary Somatosensory Cortex and Cervical Spinal Cord Microstructural Injury in Patients with Cervical Spondylotic Myelopathy

Objectives

To explore functional connectivity reorganization of the primary somatosensory cortex, the chronic microstructure damage of the cervical spinal cord, and their relationship in cervical spondylotic myelopathy (CSM) patients.

Methods

Thirty-three patients with CSM and 23 healthy controls (HCs) were recruited for rs-fMRI and cervical spinal cord diffusion tensor imaging (DTI) scans. Six subregions (including leg, back, chest, hand, finger and face) of bilateral primary somatosensory cortex (S1) were selected for seed-based whole-brain functional connectivity (FC). Then, we calculated the apparent diffusion coefficient (ADC) and fractional anisotropy (FA) values of the cervical spinal cord. Correlation analysis was conducted between FC values of brain regions and DTI parameters of cervical spinal cord (ADC, FA), and their relationship with each other and clinical parameters.

Results

Compared with the HC group, the CSM group showed decreased FC between areas of the left S1_hand, the left S1_leg, the right S1_chest, and the right S1_leg with brain regions. The mean FA values of the cervical spinal cord in CSM patients were positively correlated with JOA scores. Especially, the FA_pos values of bilateral posterior funiculus were positively correlated with JOA scores. The ADC and FA values of bilateral posterior funiculus in the cervical spinal cord were also positively correlated with the FC values.

Conclusions

There was synchronization between chronic cervical spinal cord microstructural injury and cerebral cortex sensory function compensatory recombination. DTI parameters of the posterior cervical spinal cord could objectively reflect the degree of cerebral cortex sensory function impairment to a certain extent.

Clinical applications of magnetic resonance imaging based functional and structural connectivity

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

The central executive network and executive function in healthy and persons with schizophrenia groups: a meta-analysis of structural and functional MRI

This meta-analysis evaluated the extent to which executive function can be understood with structural and functional magnetic resonance imaging. Studies included structural in schizophrenia (k = 8; n = 241) and healthy controls (k = 12; n = 1660), and functional in schizophrenia (k = 4; n = 104) and healthy controls (k = 12; n = 712). Results revealed a positive association in the brain behavior relationship when pooled across schizophrenia and control samples for structural (pr = 0.27) and functional (pr = 0.29) modalities. Subgroup analyses revealed no significant difference for functional neuroimaging (pr = .43, 95%CI = -.08-.77, p = .088) but with structural neuroimaging (pr = .37, 95%CI = -.08-.69, p = .015) the association to executive functions is lower in the control group. Subgroup analyses also revealed no significant differences in the strength of the brain-behavior relationship in the schizophrenia group (pr = .59, 95%CI = .58-.61, p = .881) or the control group (pr = 0.19, 95%CI = 0.18-0.19, p = 0.920), suggesting concordance.

Task-based and Resting State Functional MRI in Children

Functional MR imaging (MRI) is a valuable tool for presurgical planning and is well established in adult patients. The use of task-based fMRI is increasing in pediatric populations because it provides similar benefits for pre-surgical planning in children. This article reviews special adaptations that are required for successful applications of task-based fMRI in children, especially in the motor and language systems. The more recently introduced method of resting state fMRI is reviewed and its relative advantages and disadvantages discussed. Common pitfalls and other systems and networks that may be of interest in special circumstances also are reviewed.

Automated eloquent cortex localization in brain tumor patients using multi-task graph neural networks

Localizing the eloquent cortex is a crucial part of presurgical planning. While invasive mapping is the gold standard, there is increasing interest in using noninvasive fMRI to shorten and improve the process. However, many surgical patients cannot adequately perform task-based fMRI protocols. Resting-state fMRI has emerged as an alternative modality, but automated eloquent cortex localization remains an open challenge. In this paper, we develop a novel deep learning architecture to simultaneously identify language and primary motor cortex from rs-fMRI connectivity. Our approach uses the representational power of convolutional neural networks alongside the generalization power of multi-task learning to find a shared representation between the eloquent subnetworks. We validate our method on data from the publicly available Human Connectome Project and on a brain tumor dataset acquired at the Johns Hopkins Hospital. We compare our method against feature-based machine learning approaches and a fully-connected deep learning model that does not account for the shared network organization of the data. Our model achieves significantly better performance than competing baselines. We also assess the generalizability and robustness of our method. Our results clearly demonstrate the advantages of our graph convolution architecture combined with multi-task learning and highlight the promise of using rs-fMRI as a presurgical mapping tool.

Identification of the Somatomotor Network from Language Task-based fMRI Compared with Resting-State fMRI in Patients with Brain Lesions

Background Resting-state functional MRI (rs-fMRI) is a potential alternative to task-based functional MRI (tb-fMRI) for somatomotor network (SMN) identification. Brain networks can also be generated from tb-fMRI by using independent component analysis (ICA). Purpose To investigate whether the SMN can be identified by using ICA from a language task without a motor component, the sentence completion functional MRI (sc-fMRI) task, compared with rs-fMRI. Materials and Methods The sc-fMRI and rs-fMRI scans in patients who underwent presurgical brain mapping between 2012 and 2016 were analyzed, using the same imaging parameters (other than scanning time) on a 3.0-T MRI scanner. ICA was performed on rs-fMRI and sc-fMRI scans with use of a tool to separate data sets into their spatial and temporal components. Two neuroradiologists independently determined the presence of the dorsal SMN (dSMN) and ventral SMN (vSMN) on each study. Groups were compared by using t tests, and logistic regression was performed to identify predictors of the presence of SMNs. Results One hundred patients (mean age, 40.9 years ± 14.8 [standard deviation]; 61 men) were evaluated. The dSMN and vSMN were identified in 86% (86 of 100) and 76% (76 of 100) of rs-fMRI scans and 85% (85 of 100) and 69% (69 of 100) of sc-fMRI scans, respectively. The concordance between rs-fMRI and sc-fMRI for presence of dSMN and vSMN was 75% (75 of 100 patients) and 53% (53 of 100 patients), respectively. In 10 of 14 patients (71%) where rs-fMRI did not show the dSMN, sc-fMRI demonstrated it. This rate was 67% for the vSMN (16 of 24 patients). Conclusion In the majority of patients, independent component analysis of sentence completion task functional MRI scans reliably demonstrated the somatomotor network compared with resting-state functional MRI scans. Identifying target networks with a single sentence completion scan could reduce overall functional MRI scanning times by eliminating the need for separate motor tasks. © RSNA, 2021 Online supplemental material is available for this article. See also the editorial by Field and Birn in this issue.

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.

Machine learning may predict individual hand motor activation from resting-state fMRI in patients with brain tumors in perirolandic cortex

OBJECTIVE

The study aimed to evaluate the predictive validity of the neural network (NN) method for presurgical mapping of motor areas using resting-state functional MRI (rs-fMRI) data of patients with brain tumor located in the perirolandic cortex (PRC).

METHODS

A total of 109 patients with brain tumors occupying PRC underwent rs-fMRI and hand movement task-based fMRI (tb-fMRI) scans. Using a NN model trained on fMRI data of 47 healthy controls, individual task activation maps were predicted from their rs-fMRI data. NN-predicted maps were compared with task activation and independent component analysis (ICA)-derived maps. Spatial Pearson's correlation coefficients (CC) matrices and Dice coefficients (DC) between task activation and predicted activation using NN (DC_{NN_Act}) and ICA (DC_{ICA_Act}) were calculated and compared using non-parametric tests. The effects of tumor types and head motion on predicted maps were demonstrated.

RESULTS

The CC matrix of NN-predicted maps showed higher diagonal values compared with ICA-derived maps (p < 0.001). DC_{NN_Act} were higher than DC_{ICA_Act} (p < 0.001) for patients with or without motor deficits. Lower DCs were found in subjects with head motion greater than one voxel. DCs were higher on the nontumor side than on the tumor side (p < 0.001), especially in the glioma group compared with meningioma and metastatic groups.

CONCLUSIONS

This study indicated that the NN approach could predict individual motor activation using rs-fMRI data and could have promising clinical applications in brain tumor patients with anatomical and functional reorganizations.

KEY POINTS

• The neural network machine learning approach successfully predicted hand motor activation in patients with a tumor in the perirolandic cortex, despite space-occupying effects and possible functional reorganization. • Compared to the conventional independent component analysis, the neural network approach utilizing resting-state fMRI data yielded a higher correlation to the active task hand activation data. • The Dice coefficient of machine learning-predicted activation vs. task fMRI activation was different between tumor and nontumor side, also between tumor types, which might indicate different effects of possible neurovascular uncoupling on resting-state and task fMRI.

The utilisation of resting-state fMRI as a pre-operative mapping tool in patients with brain tumours in comparison to task-based fMRI and intraoperative mapping: A systematic review

PURPOSE

Resting-state functional Magnetic Resonance Imaging (rs-fMRI) is suggested to be a viable option for pre-operative mapping for patients with brain tumours. However, it remains an open issue whether the tool is useful in the clinical setting compared to task-based fMRI (T-fMRI) and intraoperative mapping. Thus, a systematic review was conducted to investigate the usefulness of this technique.

METHODS

A systematic literature search of rs-fMRI methods applied as a pre-operative mapping tool was conducted using the PubMed/MEDLINE and Cochrane Library electronic databases following PRISMA guidelines.

RESULTS

Results demonstrated that 50% (six out of twelve) of the studies comparing rs-fMRI and T-fMRI showed good concordance for both language and sensorimotor networks. In comparison to intraoperative mapping, 86% (six out of seven) studies found a good agreement to rs-fMRI. Finally, 87% (twenty out of twenty-three) studies agreed that rs-fMRI is a suitable and useful pre-operative mapping tool.

CONCLUSIONS

rs-fMRI is a promising technique for pre-operative mapping in assessing the functional brain areas. However, the agreement between rs-fMRI with other techniques, including T-fMRI and intraoperative maps, is not yet optimal. Studies to ascertain and improve the sophistication in pre-processing of rs-fMRI imaging data are needed.

The Problem of Neurovascular Uncoupling

Neurovascular uncoupling (NVU) is one of the most important confounds of blood oxygen level dependent (BOLD) functional magnetic resonance imaging (fMR imaging) in the setting of focal brain lesions such as brain tumors. This article reviews the assessment of NVU related to focal brain lesions with emphasis on the use of cerebrovascular reactivity mapping measurement methods and resting state BOLD fMR imaging metrics in the detection of NVU, as well as the use of amplitude of low-frequency fluctuation metrics to mitigate the effects of NVU on clinical fMR imaging activation.

Mapping of the Language Network With Deep Learning

Background: Pre-surgical functional localization of eloquent cortex with task-based functional MRI (T-fMRI) is part of the current standard of care prior to resection of brain tumors. Resting state fMRI (RS-fMRI) is an alternative method currently under investigation. Here, we compare group level language localization using T-fMRI vs. RS-fMRI analyzed with 3D deep convolutional neural networks (3DCNN).

Methods: We analyzed data obtained in 35 patients with brain tumors that had both language T-fMRI and RS-MRI scans during pre-surgical evaluation. The T-fMRI data were analyzed using conventional techniques. The language associated resting state network was mapped using a 3DCNN previously trained with data acquired in >2,700 normal subjects. Group level results obtained by both methods were evaluated using receiver operator characteristic analysis of probability maps of language associated regions, taking as ground truth meta-analytic maps of language T-fMRI responses generated on the Neurosynth platform.

Results: Both fMRI methods localized major components of the language system (areas of Broca and Wernicke). Word-stem completion T-fMRI strongly activated Broca's area but also several task-general areas not specific to language. RS-fMRI provided a more specific representation of the language system.

Conclusion: 3DCNN was able to accurately localize the language network. Additionally, 3DCNN performance was remarkably tolerant of a limited quantity of RS-fMRI data.

The Fluctuations of Blood Oxygen Level-Dependent Signals as a Method of Brain Tumor Characterization: A Preliminary Report

OBJECTIVE

In this study we present the nature and characteristic of the fluctuation of blood oxygen level-dependent (BOLD) signals measured from brain tumors.

METHODS

Supratentorial astrocytomas, which were neither operated nor previously managed with chemotherapy or radiotherapy, were segmented, and the time series of the BOLD signal fluctuations were extracted. The mean (across patients) power spectra were plotted for the different World Health Organization tumor grades. One-way analysis of variance (ANOVA) was performed to identify significant differences between the power spectra of different tumor grades. Results were considered significant at P < 0.05.

RESULTS

A total of 58 patients were included in the study. This group of patients included 1 patient with grade I glioma; 15 with grade II; 12 with grade III; and 30 with grade IV. The power spectra of the tumor time series were individually inspected, and all tumors exhibited high peaks at the lower frequency signals, but these were more pronounced in high-grade tumors. ANOVA showed a significant difference in power spectra between groups (P = 0.000). Post hoc analysis with Bonferroni correction showed a significant difference between grade II and grade III (P = 0.012) and grade IV (P = 0.000). There was no significant power spectra difference between grade III and IV tumors (P = 1).

CONCLUSIONS

The power spectra of BOLD signals from tumor tissue showed fluctuations in the low-frequency signals and were significantly correlated with tumor grade. These signals could have a misleading effect when analyzing resting state functional magnetic resonance imaging and could be also viewed as a potential method of tumor characterization.

Functional MRI in Children: Current Clinical Applications

Functional magnetic resonance imaging has become a critical research tool for evaluating brain function during active tasks and resting states. This has improved our understanding of developmental trajectories in children as well as the plasticity of neural networks in disease states. In the clinical setting, functional maps of eloquent cortex in patients with brain lesions and/or epilepsy provides crucial information for presurgical planning. Although children are inherently challenging to scan in this setting, preparing them appropriately and providing adequate resources can help achieve useful clinical data. This article will review the basic underlying physiologic aspects of functional magnetic resonance imaging, review clinically relevant research applications, describe known validation data compared to gold standard techniques and detail future directions of this technology.

Model-Based and Model-Free Analyses of the Neural Correlates of Tongue Movements

The tongue performs movements in all directions to subserve its diverse functions in chewing, swallowing, and speech production. Using task-based functional MRI in a group of 17 healthy young participants, we studied (1) potential differences in the cerebral control of frontal (protrusion), horizontal (side to side), and vertical (elevation) tongue movements and (2) inter-individual differences in tongue motor control. To investigate differences between different tongue movements, we performed voxel-wise multiple linear regressions. To investigate inter-individual differences, we applied a novel approach, spatio-temporal filtering of independent components. For this approach, individual functional data were decomposed into spatially independent components and corresponding time courses using independent component analysis. A temporal filter (correlation with the expected brain response) was used to identify independent components time-locked to the tongue motor tasks. A spatial filter (cross-correlation with established neurofunctional systems) was used to identify brain activity not time-locked to the tasks. Our results confirm the importance of an extended bilateral cortical and subcortical network for the control of tongue movements. Frontal (protrusion) tongue movements, highly overlearned movements related to speech production, showed less activity in the frontal and parietal lobes compared to horizontal (side to side) and vertical (elevation) movements and greater activity in the left frontal and temporal lobes compared to vertical movements (cluster-forming threshold of Z > 3.1, cluster significance threshold of p < 0.01, corrected for multiple comparisons). The investigation of inter-individual differences revealed a component representing the tongue primary sensorimotor cortex time-locked to the task in all participants. Using the spatial filter, we found the default mode network in 16 of 17 participants, the left fronto-parietal network in 16, the right fronto-parietal network in 8, and the executive control network in four participants (Pearson's r > 0.4 between neurofunctional systems and individual components). These results demonstrate that spatio-temporal filtering of independent components allows to identify individual brain activity related to a specific task and also structured spatiotemporal processes representing known neurofunctional systems on an individual basis. This novel approach may be useful for the assessment of individual patients and results may be related to individual clinical, behavioral, and genetic information.

Navigated transcranial magnetic stimulation of the supplementary motor cortex disrupts fine motor skills in healthy adults

Navigated transcranial magnetic stimulation (nTMS) over the supplementary motor area (SMA) may impact fine motor skills. This study evaluates different nTMS parameters in their capacity to affect fine motor performance on the way to develop an SMA mapping protocol. Twenty healthy volunteers performed a variety of fine motor tests during baseline and nTMS to the SMA using 5 Hz, 10 Hz, and theta-burst stimulation (TBS). Effects on performance were measured by test completion times (TCTs), standard deviation of inter-tap interval (SDIT), and visible coordination problems (VCPs). The predominant stimulation effect was slowing of TCTs, i.e. a slowdown of test performances during stimulation. Furthermore, participants exhibited VCPs like accidental use of contralateral limbs or inability to coordinate movements. More instances of significant differences between baseline and stimulation occurred during stimulation of the right hemisphere compared to left-hemispheric stimulation. In conclusion, nTMS to the SMA could enable new approaches in neuroscience and enable structured mapping approaches. Specifically, this study supports interhemispheric differences in motor control as right-hemispheric stimulation resulted in clearer impairments. The application of our nTMS-based setup to assess the function of the SMA should be applied in patients with changed anatomo-functional representations as the next step, e.g. among patients with eloquent brain tumors.

Real-time presurgical resting-state fMRI in patients with brain tumors: Quality control and comparison with task-fMRI and intraoperative mapping

Resting-state functional magnetic resonance imaging (rsfMRI) is a promising task-free functional imaging approach, which may complement or replace task-based fMRI (tfMRI) in patients who have difficulties performing required tasks. However, rsfMRI is highly sensitive to head movement and physiological noise, and validation relative to tfMRI and intraoperative electrocortical mapping is still necessary. In this study, we investigate (a) the feasibility of real-time rsfMRI for presurgical mapping of eloquent networks with monitoring of data quality in patients with brain tumors and (b) rsfMRI localization of eloquent cortex compared with tfMRI and intraoperative electrocortical stimulation (ECS) in retrospective analysis. Five brain tumor patients were studied with rsfMRI and tfMRI on a clinical 3T scanner using MultiBand(8)-echo planar imaging (EPI) with repetition time: 400 ms. Moving-averaged sliding-window correlation analysis with regression of motion parameters and signals from white matter and cerebrospinal fluid was used to map sensorimotor and language resting-state networks. Data quality monitoring enabled rapid optimization of scan protocols, early identification of task noncompliance, and head movement-related false-positive connectivity to determine scan continuation or repetition. Sensorimotor and language resting-state networks were identifiable within 1 min of scan time. The Euclidean distance between ECS and rsfMRI connectivity and task-activation in motor cortex, Broca's, and Wernicke's areas was 5-10 mm, with the exception of discordant rsfMRI and ECS localization of Wernicke's area in one patient due to possible cortical reorganization and/or altered neurovascular coupling. This study demonstrates the potential of real-time high-speed rsfMRI for presurgical mapping of eloquent cortex with real-time data quality control, and clinically acceptable concordance of rsfMRI with tfMRI and ECS localization.

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

OBJECTIVE

Resting-state functional MRI (rs-fMRI) represents a promising and cost-effective alternative to task-based fMRI for presurgical mapping. However, the lack of clinically streamlined and reliable rs-fMRI analysis tools has prevented wide adoption of this technique. In this work, the authors introduce an rs-fMRI processing pipeline (ReStNeuMap) for automatic single-patient rs-fMRI network analysis.

METHODS

The authors provide a description of the rs-fMRI network analysis steps implemented in ReStNeuMap and report their initial experience with this tool after performing presurgical mapping in 6 patients. They verified the spatial agreement between rs-fMRI networks derived by ReStNeuMap and localization of activation with intraoperative direct electrical stimulation (DES).

RESULTS

The authors automatically extracted rs-fMRI networks including eloquent cortex in spatial proximity with the resected lesion in all patients. The distance between DES points and corresponding rs-fMRI networks was less than 1 cm in 78% of cases for motor, 100% of cases for visual, 87.5% of cases for language, and 100% of cases for speech articulation mapping.

CONCLUSIONS

The authors' initial experience with ReStNeuMap showed good spatial agreement between presurgical rs-fMRI predictions and DES findings during awake surgery. The availability of the rs-fMRI analysis tools for clinicians aiming to perform noninvasive mapping of brain functional networks may extend its application beyond surgical practice.

Presurgical localization and spatial shift of resting state networks in patients with brain metastases

Brain metastases are the most prevalent cerebral tumors. Resting state networks (RSNs) are involved in multiple perceptual and cognitive functions. Therefore, precisely localizing multiple RSNs may be extremely valuable before surgical resection of metastases, to minimize neurocognitive impairments. Here we aimed to investigate the reliability of independent component analysis (ICA) for localizing multiple RSNs from resting-state functional MRI (rs-fMRI) data in individual patients, and further evaluate lesion-related spatial shifts of the RSNs. Twelve patients with brain metastases and 14 healthy controls were recruited. Using an improved automatic component identification method, we successfully identified seven common RSNs, including: the default mode network (DMN), executive control network (ECN), dorsal attention network (DAN), language network (LN), sensorimotor network (SMN), auditory network (AN) and visual network (VN), in both individual patients and controls. Moreover, the RSNs in the patients showed a visible spatial shift compared to those in the controls, and the spatial shift of some regions was related to the tumor location, which may reflect a complicated functional mechanism - functional disruptions and reorganizations - caused by metastases. Besides, higher cognitive networks (DMN, ECN, DAN and LN) showed significantly larger spatial shifts than perceptual networks (SMN, AN and VN), supporting a functional dichotomy between the two network groups even in pathologic alterations associated with metastases. Overall, our findings provide evidence that ICA is a promising approach for presurgical localization of multiple RSNs from rs-fMRI data in individual patients. More attention should be paid to the spatial shifts of the RSNs before surgical resection.

Functional Connectivity in Adult Brain Tumor Patients: A Systematic Review

Brain tumor (BT) patients often experience reduced cognitive abilities and disrupted adaptive functioning before and after treatment. An innovative approach to understanding the underlying brain networks associated with these outcomes has been to study the brain's functional connectivity (FC), the spatially distributed and temporally correlated activity throughout the brain, and how it can be affected by a tumor. The present review synthesized the extant BT FC literature that utilizes functional magnetic resonance imaging to study FC strength of commonly observed networks during rest and task. A systematic review of English articles using PubMed was conducted. Search terms included brain tumor OR glioma AND functional connectivity, independent component analysis, ICA, psychophysiological interaction, OR PPI. Studies in which participants were diagnosed with BTs as adults that evaluated specific networks of interest using independent component analysis or seed-based component analysis were included. Twenty-five studies met inclusion criteria. BT patients often presented with decreases in FC strength within well-established networks and increases in atypical FC patterns. Network differences were tumor adjacent and distal, and left hemisphere tumors generally had a greater impact on FC. FC alterations often correlated with behavioral or cognitive outcomes when assessed. Overall, BTs appear to lead to various alterations in FC across different functional networks, and the most common change is a decrease in expected FC strength. More longitudinal studies are needed to determine the time course of network alterations across treatment and recovery, the role of medical treatments in BT survivors' FC, and the potential of FC patterns as biomarkers of cognitive outcomes.

Resting-State Functional Connectivity of the Middle Frontal Gyrus Can Predict Language Lateralization in Patients with Brain Tumors

BACKGROUND AND PURPOSE

A recent study using task-based fMRI demonstrated that the middle frontal gyrus is comparable with Broca's area in its ability to determine language laterality using a measure of verbal fluency. This study investigated whether the middle frontal gyrus can be used as an indicator for language-hemispheric dominance in patients with brain tumors using task-free resting-state fMRI. We hypothesized that no significant difference in language lateralization would occur between the middle frontal gyrus and Broca area and that the middle frontal gyrus can serve as a simple and reliable means of measuring language laterality.

MATERIALS AND METHODS

Using resting-state fMRI, we compared the middle frontal gyrus with the Broca area in 51 patients with glial neoplasms for voxel activation, the language laterality index, and the effect of tumor grade on the laterality index. The laterality index derived by resting-state fMRI and task-based fMRI was compared in a subset of 40 patients.

RESULTS

Voxel activations in the left middle frontal gyrus and left Broca area were positively correlated (r = 0.47, P < .001). Positive correlations were seen between the laterality index of the Broca area and middle frontal gyrus regions (r = 0.56, P < .0005). Twenty-seven of 40 patients (67.5%) showed concordance of the laterality index based on the Broca area using resting-state fMRI and the laterality index based on a language task. Thirty of 40 patients (75%) showed concordance of the laterality index based on the middle frontal gyrus using resting-state fMRI and the laterality index based on a language task.

CONCLUSIONS

The middle frontal gyrus is comparable with the Broca area in its ability to determine hemispheric dominance for language using resting-state fMRI. Our results suggest the addition of resting-state fMRI of the middle frontal gyrus to the list of noninvasive modalities that could be used in patients with gliomas to evaluate hemispheric dominance of language before tumor resection. In patients who cannot participate in traditional task-based fMRI, resting-state fMRI offers a task-free alternate to presurgically map the eloquent cortex.

Seed-Based Resting-State Functional MRI for Presurgical Localization of the Motor Cortex: A Task-Based Functional MRI-Determined Seed Versus an Anatomy-Determined Seed

Objective

For localization of the motor cortex, seed-based resting-state functional MRI (rsfMRI) uses the contralateral motor cortex as a seed. However, research has shown that the location of the motor cortex could differ according to anatomical variations. The purpose of this study was to compare the results of rsfMRI using two seeds: a template seed (the anatomically expected location of the contralateral motor cortex) and a functional seed (the actual location of the contralateral motor cortex determined by task-based functional MRI [tbfMRI]).

Materials and Methods

Eight patients (4 with glioma, 3 with meningioma, and 1 with arteriovenous malformation) and 9 healthy volunteers participated. For the patients, tbfMRI was performed unilaterally to activate the healthy contralateral motor cortex. The affected ipsilateral motor cortices were mapped with rsfMRI using seed-based and independent component analysis (ICA). In the healthy volunteer group, both motor cortices were mapped with both-hands tbfMRI and rsfMRI. We compared the results between template and functional seeds, and between the seed-based analysis and ICA with visual and quantitative analysis.

Results

For the visual analysis, the functional seed showed significantly higher scores compared to the template seed in both the patients (p = 0.002) and healthy volunteers (p < 0.001). Although no significant difference was observed between the functional seed and ICA, the ICA results showed significantly higher scores than the template seed in both the patients (p = 0.01) and healthy volunteers (p = 0.005). In the quantitative analysis, the functional seed exhibited greater similarity to tbfMRI than the template seed and ICA.

Conclusion

Using the contralateral motor cortex determined by tbfMRI as a seed could enhance visual delineation of the motor cortex in seed-based rsfMRI.

Preoperative Mapping of the Supplementary Motor Area in Patients with Brain Tumor Using Resting-State fMRI with Seed-Based Analysis

BACKGROUND AND PURPOSE

The supplementary motor area can be a critical region in the preoperative planning of patients undergoing brain tumor resection because it plays a role in both language and motor function. While primary motor regions have been successfully identified using resting-state fMRI, there is variability in the literature regarding the identification of the supplementary motor area for preoperative planning. The purpose of our study was to compare resting-state fMRI to task-based fMRI for localization of the supplementary motor area in a large cohort of patients with brain tumors presenting for preoperative brain mapping.

MATERIALS AND METHODS

Sixty-six patients with brain tumors were evaluated with resting-state fMRI using seed-based analysis of hand and orofacial motor regions. Rates of supplementary motor area localization were compared with those in healthy controls and with localization results by task-based fMRI.

RESULTS

Localization of the supplementary motor area using hand motor seed regions was more effective than seeding using orofacial motor regions for both patients with brain tumor (95.5% versus 34.8%, P < .001) and controls (95.2% versus 45.2%, P < .001). Bilateral hand motor seeding was superior to unilateral hand motor seeding in patients with brain tumor for either side (95.5% versus 75.8%/75.8% for right/left, P < .001). No difference was found in the ability to identify the supplementary motor area between patients with brain tumors and controls.

CONCLUSIONS

In addition to task-based fMRI, seed-based analysis of resting-state fMRI represents an equally effective method for supplementary motor area localization in patients with brain tumors, with the best results obtained with bilateral hand motor region seeding.

Contextual interference enhances motor learning through increased resting brain connectivity during memory consolidation

Increasing contextual interference (CI) during practice benefits learning, making it a desirable difficulty. For example, interleaved practice (IP) of motor sequences is generally more difficult than repetitive practice (RP) during practice but leads to better learning. Here we investigated whether CI in practice modulated resting-state functional connectivity during consolidation. 26 healthy adults (11 men/15 women, age = 23.3 ± 1.3 years) practiced two sets of three sequences in an IP or RP condition over 2 days, followed by a retention test on Day 5 to evaluate learning. On each practice day, functional magnetic resonance imaging (fMRI) data were acquired during practice and also in a resting state immediately after practice. The resting-state fMRI data were processed using independent component analysis (ICA) followed by functional connectivity analysis, showing that IP on Day 1 led to greater resting connectivity than RP between the left premotor cortex and left dorsolateral prefrontal cortex (DLPFC), bilateral posterior cingulate cortices, and bilateral inferior parietal lobules. Moreover, greater resting connectivity after IP than RP on Day 1, between the left premotor cortex and the hippocampus, amygdala, putamen, and thalamus on the right, and the cerebellum, was associated with better learning following IP. Mediation analysis further showed that the association between enhanced resting premotor-hippocampal connectivity on Day 1 and better retention performance following IP was mediated by greater task-related functional activation during IP on Day 2. Our findings suggest that the benefit of CI to motor learning is likely through enhanced resting premotor connectivity during the early phase of consolidation.

Resting state signal latency predicts laterality in pediatric medically refractory temporal lobe epilepsy

PURPOSE

Temporal lobe epilepsy (TLE) affects resting state brain networks in adults. This study aims to correlate resting state functional MRI (rsMRI) signal latency in pediatric TLE patients with their laterality.

METHODS

From 2006 to 2016, 26 surgical TLE patients (12 left, 14 right) with a mean age of 10.7 years (range 0.9-18) were prospectively studied. Preoperative rsMRI was obtained in patients with concordant lateralizing structural MRI, EEG, and PET studies. Standard preprocessing techniques and seed-based rsMRI analyses were performed. Additionally, the latency in rsMRI signal between each 6 mm voxel sampled was examined, compared to the global mean signal, and projected onto standard atlas space for individuals and the cohort.

RESULTS

All but one of the 26 patients improved seizure frequency postoperatively with a mean follow-up of 2.9 years (range 0-7.7), with 21 patients seizure-free. When grouped for epileptogenic laterality, the latency map qualitatively demonstrated that the right TLE patients had a relatively early signal pattern, whereas the left TLE patients had a relatively late signal pattern compared to the global mean signal in the right temporal lobe. Quantitatively, the two groups had significantly different signal latency clusters in the bilateral temporal lobes (p < 0.001).

CONCLUSION

There are functional MR signal latency changes in medical refractory pediatric TLE patients. Qualitatively, signal latency in the right temporal lobe precedes the mean signal in right TLE patients and is delayed in left TLE patients. With larger confirmatory studies, preoperative rsMRI latency analysis may offer an inexpensive, noninvasive adjunct modality to lateralize pediatric TLE.

IClinfMRI Software for Integrating Functional MRI Techniques in Presurgical Mapping and Clinical Studies

Task-evoked and resting-state (rs) functional magnetic resonance imaging (fMRI) techniques have been applied to the clinical management of neurological diseases, exemplified by presurgical localization of eloquent cortex, to assist neurosurgeons in maximizing resection while preserving brain functions. In addition, recent studies have recommended incorporating cerebrovascular reactivity (CVR) imaging into clinical fMRI to evaluate the risk of lesion-induced neurovascular uncoupling (NVU). Although each of these imaging techniques possesses its own advantage for presurgical mapping, a specialized clinical software that integrates the three complementary techniques and promptly outputs the analyzed results to radiology and surgical navigation systems in a clinical format is still lacking. We developed the Integrated fMRI for Clinical Research (IClinfMRI) software to facilitate these needs. Beyond the independent processing of task-fMRI, rs-fMRI, and CVR mapping, IClinfMRI encompasses three unique functions: (1) supporting the interactive rs-fMRI mapping while visualizing task-fMRI results (or results from published meta-analysis) as a guidance map, (2) indicating/visualizing the NVU potential on analyzed fMRI maps, and (3) exporting these advanced mapping results in a Digital Imaging and Communications in Medicine (DICOM) format that are ready to export to a picture archiving and communication system (PACS) and a surgical navigation system. In summary, IClinfMRI has the merits of efficiently translating and integrating state-of-the-art imaging techniques for presurgical functional mapping and clinical fMRI studies.

PreSurgMapp: a MATLAB Toolbox for Presurgical Mapping of Eloquent Functional Areas Based on Task-Related and Resting-State Functional MRI

The main goal of brain tumor surgery is to maximize tumor resection while minimizing the risk of irreversible postoperative functional sequelae. Eloquent functional areas should be delineated preoperatively, particularly for patients with tumors near eloquent areas. Functional magnetic resonance imaging (fMRI) is a noninvasive technique that demonstrates great promise for presurgical planning. However, specialized data processing toolkits for presurgical planning remain lacking. Based on several functions in open-source software such as Statistical Parametric Mapping (SPM), Resting-State fMRI Data Analysis Toolkit (REST), Data Processing Assistant for Resting-State fMRI (DPARSF) and Multiple Independent Component Analysis (MICA), here, we introduce an open-source MATLAB toolbox named PreSurgMapp. This toolbox can reveal eloquent areas using comprehensive methods and various complementary fMRI modalities. For example, PreSurgMapp supports both model-based (general linear model, GLM, and seed correlation) and data-driven (independent component analysis, ICA) methods and processes both task-based and resting-state fMRI data. PreSurgMapp is designed for highly automatic and individualized functional mapping with a user-friendly graphical user interface (GUI) for time-saving pipeline processing. For example, sensorimotor and language-related components can be automatically identified without human input interference using an effective, accurate component identification algorithm using discriminability index. All the results generated can be further evaluated and compared by neuro-radiologists or neurosurgeons. This software has substantial value for clinical neuro-radiology and neuro-oncology, including application to patients with low- and high-grade brain tumors and those with epilepsy foci in the dominant language hemisphere who are planning to undergo a temporal lobectomy.

Tumor Tissue Detection using Blood-Oxygen-Level-Dependent Functional MRI based on Independent Component Analysis

Accurate delineation of gliomas from the surrounding normal brain areas helps maximize tumor resection and improves outcome. Blood-oxygen-level-dependent (BOLD) functional MRI (fMRI) has been routinely adopted for presurgical mapping of the surrounding functional areas. For completely utilizing such imaging data, here we show the feasibility of using presurgical fMRI for tumor delineation. In particular, we introduce a novel method dedicated to tumor detection based on independent component analysis (ICA) of resting-state fMRI (rs-fMRI) with automatic tumor component identification. Multi-center rs-fMRI data of 32 glioma patients from three centers, plus the additional proof-of-concept data of 28 patients from the fourth center with non-brain musculoskeletal tumors, are fed into individual ICA with different total number of components (TNCs). The best-fitted tumor-related components derived from the optimized TNCs setting are automatically determined based on a new template-matching algorithm. The success rates are 100%, 100% and 93.75% for glioma tissue detection for the three centers, respectively, and 85.19% for musculoskeletal tumor detection. We propose that the high success rate could come from the previously overlooked ability of BOLD rs-fMRI in characterizing the abnormal vascularization, vasomotion and perfusion caused by tumors. Our findings suggest an additional usage of the rs-fMRI for comprehensive presurgical assessment.

How much is enough-Can resting state fMRI provide a demarcation for neurosurgical resection in glioma?

This study represents a systematic review of the insights provided by resting state functional MRI (rs-fMRI) use in the glioma population. Following PRISMA guidelines, 45 studies were included in the review and were classified in glioma-related neuronal changes (n=28) and eloquent area localization (n=17). Despite the heterogeneous nature of the studies, there is considerable evidence of diffuse functional reorganization occurring in the setting of gliomas with local and interhemispheric functional connectivity alterations involving different functional networks. The studies showed evidence of decreased long distance functional connectivity and increased global local efficiency occurring in the setting of gliomas. The tumour grade seems to correlate with distinct functional connectivity changes. Overall, there is a potential clinical utility of rs-fMRI for identifying the functional brain network disruptions occurring in the setting of gliomas. Further studies utilizing standardized analytical methods are required to elucidate the mechanism through which gliomas induce global changes in brain connectivity.

Resting-state Functional Magnetic Resonance Imaging in Presurgical Functional Mapping: Sensorimotor Localization

This article compares resting-state functional magnetic resonance (fMR) imaging with task fMR imaging for presurgical functional mapping of the sensorimotor (SM) region. Before tumor resection, 38 patients were scanned using both methods. The SM area was anatomically defined using 2 different software tools. Overlap of anatomic regions of interest with task activation maps and resting-state networks was measured in the SM region. A paired t-test showed higher overlap between resting-state maps and anatomic references compared with task activation when using a maximal overlap criterion. Resting state-derived maps are more comprehensive than those derived from task fMR imaging.

Functional brain imaging in neuropsychology over the past 25 years

OBJECTIVE

Outline effects of functional neuroimaging on neuropsychology over the past 25 years.

METHOD

Functional neuroimaging methods and studies will be described that provide a historical context, offer examples of the utility of neuroimaging in specific domains, and discuss the limitations and future directions of neuroimaging in neuropsychology.

RESULTS

Tracking the history of publications on functional neuroimaging related to neuropsychology indicates early involvement of neuropsychologists in the development of these methodologies. Initial progress in neuropsychological application of functional neuroimaging has been hampered by costs and the exposure to ionizing radiation. With rapid evolution of functional methods-in particular functional MRI (fMRI)-neuroimaging has profoundly transformed our knowledge of the brain. Its current applications span the spectrum of normative development to clinical applications. The field is moving toward applying sophisticated statistical approaches that will help elucidate distinct neural activation networks associated with specific behavioral domains. The impact of functional neuroimaging on clinical neuropsychology is more circumscribed, but the prospects remain enticing.

CONCLUSIONS

The theoretical insights and empirical findings of functional neuroimaging have been led by many neuropsychologists and have transformed the field of behavioral neuroscience. Thus far they have had limited effects on the clinical practices of neuropsychologists. Perhaps it is time to add training in functional neuroimaging to the clinical neuropsychologist's toolkit and from there to the clinic or bedside. (PsycINFO Database Record

An automated method for identifying an independent component analysis-based language-related resting-state network in brain tumor subjects for surgical planning

As a noninvasive and “task-free” technique, resting-state functional magnetic resonance imaging (rs-fMRI) has been gradually applied to pre-surgical functional mapping. Independent component analysis (ICA)-based mapping has shown advantage, as no a priori information is required. We developed an automated method for identifying language network in brain tumor subjects using ICA on rs-fMRI. In addition to standard processing strategies, we applied a discriminability-index-based component identification algorithm to identify language networks in three different groups. The results from the training group were validated in an independent group of healthy human subjects. For the testing group, ICA and seed-based correlation were separately computed and the detected language networks were assessed by intra-operative stimulation mapping to verify reliability of application in the clinical setting. Individualized language network mapping could be automatically achieved for all subjects from the two healthy groups except one (19/20, success rate = 95.0%). In the testing group (brain tumor patients), the sensitivity of the language mapping result was 60.9%, which increased to 87.0% (superior to that of conventional seed-based correlation [47.8%]) after extending to a radius of 1 cm. We established an automatic and practical component identification method for rs-fMRI-based pre-surgical mapping and successfully applied it to brain tumor patients.

Real-Time Motor Cortex Mapping for the Safe Resection of Glioma: An Intraoperative Resting-State fMRI Study

BACKGROUND AND PURPOSE

Resting-state functional MR imaging has been used for motor mapping in presurgical planning but never used intraoperatively. This study aimed to investigate the feasibility of applying intraoperative resting-state functional MR imaging for the safe resection of gliomas using real-time motor cortex mapping during an operation.

MATERIALS AND METHODS

Using interventional MR imaging, we conducted preoperative and intraoperative resting-state intrinsic functional connectivity analyses of the motor cortex in 30 patients with brain tumors. Factors that may influence intraoperative imaging quality, including anesthesia type (general or awake anesthesia) and tumor cavity (filled with normal saline or not), were studied to investigate image quality. Additionally, direct cortical stimulation was used to validate the accuracy of intraoperative resting-state fMRI in mapping the motor cortex.

RESULTS

Preoperative and intraoperative resting-state fMRI scans were acquired for all patients. Fourteen patients who successfully completed both sufficient intraoperative resting-state fMRI and direct cortical stimulation were used for further analysis of sensitivity and specificity. Compared with those subjected to direct cortical stimulation, the sensitivity and specificity of intraoperative resting-state fMRI in localizing the motor area were 61.7% and 93.7%, respectively. The image quality of intraoperative resting-state fMRI was better when the tumor cavity was filled with normal saline (P = .049). However, no significant difference between the anesthesia types was observed (P = .102).

CONCLUSIONS

This study demonstrates the feasibility of using intraoperative resting-state fMRI for real-time localization of functional areas during a neurologic operation. The findings suggest that using intraoperative resting-state fMRI can avoid the risk of intraoperative seizures due to direct cortical stimulation and may provide neurosurgeons with valuable information to facilitate the safe resection of gliomas.

Functional MRI for Surgery of Gliomas

PURPOSE OF REVIEW

Advanced neuroimaging techniques such as functional MRI (fMRI) and diffusion MR tractography have been increasingly used at every stage of the surgical management of brain gliomas, as a means to improve tumor resection while preserving brain functions. This review provides an overview of the last advancements in the field of functional MRI techniques, with a particular focus on their current clinical use and reliability in the preoperative and intraoperative setting, as well as their future perspectives for personalized multimodal management of patients with gliomas.

RECENT FINDINGS

fMRI and diffusion MR tractography give relevant insights on the anatomo-functional organization of eloquent cortical areas and subcortical connections near or inside a tumor. Task-based fMRI and diffusion tensor imaging (DTI) tractography have proven to be valid and highly sensitive tools for localizing the distinct eloquent cortical and subcortical areas before surgery in glioma patients; they also show good accuracy when compared with intraoperative stimulation mapping data. Resting-state fMRI functional connectivity as well as new advanced HARDI (high angular resolution diffusion imaging) tractography methods are improving and reshaping the role of functional MRI for surgery of gliomas, with potential benefit for personalized treatment strategies. Noninvasive functional MRI techniques may offer the opportunity to perform a multimodal assessment in brain tumors, to be integrated with intraoperative mapping and clinical data for improving surgical management and oncological and functional outcome in patients affected by gliomas.

Altered Coupling between Motion-Related Activation and Resting-State Brain Activity in the Ipsilesional Sensorimotor Cortex after Cerebral Stroke

Functional connectivity maps using resting-state functional magnetic resonance imaging (rs-fMRI) can closely resemble task fMRI activation patterns, suggesting that resting-state brain activity may predict task-evoked activation or behavioral performance. However, this conclusion was mostly drawn upon a healthy population. It remains unclear whether the predictive ability of resting-state brain activity for task-evoked activation would change under different pathological conditions. This study investigated dynamic changes of coupling between patterns of resting-state functional connectivity (RSFC) and motion-related activation in different stages of cerebral stroke. Twenty stroke patients with hand motor function impairment were involved. rs-fMRI and hand motion-related fMRI data were acquired in the acute, subacute, and early chronic stages of cerebral stroke on a 3-T magnetic resonance (MR) scanner. Sixteen healthy participants were enrolled as controls. For each subject, an activation map of the affected hand was first created using general linear model analysis on task fMRI data, and then an RSFC map was determined by seeding at the peak region of hand motion activation during the intact hand task. We then measured the extent of coupling between the RSFC maps and motion-related activation maps. Dynamic changes of the coupling between the two fMRI maps were estimated using one-way repeated measures analysis of variance across the three stages. Moreover, imaging parameters were correlated with motor performances. Data analysis showed that there were different coupling patterns between motion-related activation and RSFC maps associating with the affected motor regions during the acute, subacute, and early chronic stages of stroke. Coupling strengths increased as the recovery from stroke progressed. Coupling strengths were correlated with hand motion performance in the acute stage, while coupling recovery was negatively correlated with the recovery outcome of hand motion performance in the early chronic stages. Couplings between RSFC and motion-related activation were dynamically changed with stroke progression, which suggested changes in the prediction of resting-state brain activity for task-evoked brain activity in different pathological states. The changes in coupling strength between these two types of brain activity implicate a reparative mechanism of brain injury and may represent a biomarker for predicting motor recovery in cerebral stroke.

Combining Functional Studies with Intraoperative MRI in Glioma Surgery

Maximal safe resection is the cornerstone of treatment for low-grade and high-grade gliomas. In addition to high-resolution anatomic MRI studies that highlight tumor architecture, it is important to determine the relationship of the tumor to the eloquent cortical and subcortical areas to avoid introducing or exacerbating a neurologic deficit. The goal of this review was to highlight imaging modalities that provide functional information and can be integrated with intraoperative MRI navigation to maximize the extent of resection while preserving a patient's neurologic function.

Presurgical Brain Mapping of the Ventral Somatomotor Network in Patients with Brain Tumors Using Resting-State fMRI

BACKGROUND AND PURPOSE

Resting-state fMRI readily identifies the dorsal but less consistently the ventral somatomotor network. Our aim was to assess the relative utility of resting-state fMRI in the identification of the ventral somatomotor network via comparison with task-based fMRI in patients with brain tumor.

MATERIALS AND METHODS

We identified 26 surgically naïve patients referred for presurgical fMRI brain mapping who had undergone both satisfactory ventral motor activation tasks and resting-state fMRI. Following standard preprocessing for task-based fMRI and resting-state fMRI, general linear model analysis of the ventral motor tasks and independent component analysis of resting-state fMRI were performed with the number of components set to 20, 30, 40, and 50. Visual overlap of task-based fMRI and resting-state fMRI at different component levels was assessed and categorized as full match, partial match, or no match. Rest-versus-task-fMRI concordance was calculated with Dice coefficients across varying fMRI thresholds before and after noise removal. Multithresholded Dice coefficient volume under the surface was calculated.

RESULTS

The ventral somatomotor network was identified in 81% of patients. At the subject level, better matches between resting-state fMRI and task-based fMRI were seen with an increasing order of components (53% of cases for 20 components versus 73% for 50 components). Noise-removed group-mean volume under the surface improved as component numbers increased from 20 to 50, though ANOVA demonstrated no statistically significant difference among the 4 groups.

CONCLUSIONS

In most patients, the ventral somatomotor network can be identified with an increase in the probability of a better match at a higher component number. There is variable concordance of the ventral somatomotor network at the single-subject level between resting-state and task-based fMRI.

Advanced MRI for Pediatric Brain Tumors with Emphasis on Clinical Benefits

Conventional anatomic brain MRI is often limited in evaluating pediatric brain tumors, the most common solid tumors and a leading cause of death in children. Advanced brain MRI techniques have great potential to improve diagnostic performance in children with brain tumors and overcome diagnostic pitfalls resulting from diverse tumor pathologies as well as nonspecific or overlapped imaging findings. Advanced MRI techniques used for evaluating pediatric brain tumors include diffusion-weighted imaging, diffusion tensor imaging, functional MRI, perfusion imaging, spectroscopy, susceptibility-weighted imaging, and chemical exchange saturation transfer imaging. Because pediatric brain tumors differ from adult counterparts in various aspects, MRI protocols should be designed to achieve maximal clinical benefits in pediatric brain tumors. In this study, we review advanced MRI techniques and interpretation algorithms for pediatric brain tumors.