Defining language networks from resting-state fMRI for surgical planning--a feasibility study

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.

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.

Autism Spectrum Disorder Related Functional Connectivity Changes in the Language Network in Children, Adolescents and Adults

Autism spectrum disorder (ASD) is a neurodevelopmental disability with global implication. Altered brain connectivity in the language network has frequently been reported in ASD patients using task-based functional magnetic resonance imaging (fMRI) compared to typically developing (TD) participants. Most of these studies have focused on a specific age group or mixed age groups with ASD. In the current study, we investigated age-related changes in functional connectivity related measure, degree centrality (DC), in the language network across three age groups with ASD (113 children, 113 adolescents and 103 adults) using resting-state fMRI data collected from the autism brain imaging data exchange repository. We identified regions with significant group-wise differences between ASD and TD groups for three age cohorts using DC based on graph theory. We found that both children and adolescents with ASD showed decreased DC in Broca's area compared to age-matched TD groups. Adults with ASD showed decreased DC in Wernicke's area compared to TD adults. We also observed increased DC in the left inferior parietal lobule (IPL) and left middle temporal gyrus (MTG) for children with ASD compared to TD children and for adults with ASD compared to TD adults, respectively. Overall, functional differences occurred in key language processing regions such as the left inferior frontal gyrus (IFG) and superior temporal gyrus (STG) related to language production and comprehension across three age cohorts. We explored correlations between DC values of our findings with autism diagnostic observation schedule (ADOS) scores related to severity of ASD symptoms in the ASD group. We found that DC values of the left IFG demonstrated negative correlations with ADOS scores in children and adolescents with ASD. The left STG showed significant negative correlations with ADOS scores in adults with ASD. These results might shed light on the language network regions that should be further explored for prognosis, diagnosis, and monitoring of ASD in three age groups.

Activation volume vs BOLD signal change as measures of fMRI activation - Its impact on GABA - fMRI activation correlation

PURPOSE

To explore the relative robustness of functional MRI (fMRI) activation volume and blood oxygen level-dependent (BOLD) signal change as fMRI metric, and to study the effect of relative robustness on the correlation between fMRI activation and cortical gamma amino butyric acid (GABA) in healthy controls and patients with multiple sclerosis (MS).

METHODS

fMRI data were acquired from healthy controls and patients with MS, with the subjects peforming self paced bilateral finger tapping in block design. GABA spectroscopy was performed with voxel placed on the area of maximum activation during fMRI. Activation volume and BOLD signal changes at primary motor cortex (M1), as well as GABA concentration were calculated for each patient.

RESULTS

Activation volume correlated with BOLD signal change in healthy controls, but no such correlation was observed in patients with MS. This difference was likely the result of higher intersubject noise variance in the patient population. GABA concentration correlated with M1 activation volume in patients but not in controls, and did not correlate with any fMRI metric in patients or controls.

CONCLUSION

Our data suggest that activation volume is a more robust measure than BOLD signal change in a group with high intersubject noise variance as in patients with MS. Additionally, this study demonstrated difference in correlation behavior between GABA concentration and the 2 fMRI metrics in patients with MS, suggesting that GABA - activation volume correlation is more appropriate measure in the patient group.

Functional network connectivity alterations in schizophrenia and depression

There is a high degree of overlap between the symptoms of major depressive disorder (MDD) and schizophrenia, but it remains unclear whether the similar symptoms are derived from convergent alterations in functional network connectivity. In this study, we performed a group independent component analysis on resting-state functional MRI data from 20 MDD patients, 24 schizophrenia patients, and 43 matched healthy controls. The functional network connectivity analysis revealed that, compared to healthy controls, the MDD and schizophrenia patients exhibited convergent decreased positive connectivity between the left and right fronto-parietal control network and decreased negative connectivity between the left control and medial visual networks. Furthermore, the MDD patients showed decreased negative connectivity between the left control and auditory networks, and the schizophrenia patients showed decreased positive connectivity between the bilateral control and language networks and decreased negative connectivity between the right control and dorsal attention networks. The convergent network connectivity alterations may underlie the common primary control and regulation disorders, and the divergent connectivity alterations may enable the distinction between the two disorders. All of the convergent and divergent network connectivity alterations were relevant to the control network, suggesting an important role of the network in the pathophysiology of MDD and schizophrenia.

Intrinsic functional organization of putative language networks in the brain following left cerebral hemispherectomy

In rare cases of severe and intractable epilepsy, cerebral hemispherectomy is performed to arrest seizure activity and improve quality of life. The remaining hemisphere is often capable of supporting many cognitive functions post-surgery, although the outcome depends on the underlying etiology, hemisphere removed, and age of resection. The mechanisms underlying this massive reorganization are at present unknown. Here we examined intrinsic functional connectivity of putative language brain networks in four children after left cerebral hemispherectomy using resting-state functional magnetic resonance imaging (rsfMRI). We compared these functional systems to intrinsic language networks in 15 neurotypical controls using region-of-interest (ROI)-based functional connectivity analyses. In three out of four hemispherectomy patients, the ROI placed in the right inferior gyrus revealed a functional network that strongly resembled the right-hemisphere intrinsic language network observed in controls. This network typically comprised inferior frontal gyrus, superior temporal sulcus, and premotor regions. Quantitative ROI-to-ROI analyses revealed that functional connectivity between major nodes of the language network was significantly altered in all 4 examined patients. Overall, our data demonstrate that the pattern of functional connectivity within language networks is at least partially preserved in the intact right hemisphere of patients who underwent left hemispherectomy.

Resting-State Seed-Based Analysis: An Alternative to Task-Based Language fMRI and Its Laterality Index

BACKGROUND AND PURPOSE

Language is a cardinal function that makes human unique. Preservation of language function poses a great challenge for surgeons during resection. The aim of the study was to assess the efficacy of resting-state fMRI in the lateralization of language function in healthy subjects to permit its further testing in patients who are unable to perform task-based fMRI.

MATERIALS AND METHODS

Eighteen healthy right-handed volunteers were prospectively evaluated with resting-state fMRI and task-based fMRI to assess language networks. The laterality indices of Broca and Wernicke areas were calculated by using task-based fMRI via a voxel-value approach. We adopted seed-based resting-state fMRI connectivity analysis together with parameters such as amplitude of low-frequency fluctuation and fractional amplitude of low-frequency fluctuation (fALFF). Resting-state fMRI connectivity maps for language networks were obtained from Broca and Wernicke areas in both hemispheres. We performed correlation analysis between the laterality index and the z scores of functional connectivity, amplitude of low-frequency fluctuation, and fALFF.

RESULTS

Pearson correlation analysis between signals obtained from the z score of fALFF and the laterality index yielded a correlation coefficient of 0.849 (P < .05). Regression analysis of the fALFF with the laterality index yielded an R² value of 0.721, indicating that 72.1% of the variance in the laterality index of task-based fMRI could be predicted from the fALFF of resting-state fMRI.

CONCLUSIONS

The present study demonstrates that fALFF can be used as an alternative to task-based fMRI for assessing language laterality. There was a strong positive correlation between the fALFF of the Broca area of resting-state fMRI with the laterality index of task-based fMRI. Furthermore, we demonstrated the efficacy of fALFF for predicting the laterality of task-based fMRI.

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.

Resting state fMRI: A review on methods in resting state connectivity analysis and resting state networks

The inquisitiveness about what happens in the brain has been there since the beginning of humankind. Functional magnetic resonance imaging is a prominent tool which helps in the non-invasive examination, localisation as well as lateralisation of brain functions such as language, memory, etc. In recent years, there is an apparent shift in the focus of neuroscience research to studies dealing with a brain at 'resting state'. Here the spotlight is on the intrinsic activity within the brain, in the absence of any sensory or cognitive stimulus. The analyses of functional brain connectivity in the state of rest have revealed different resting state networks, which depict specific functions and varied spatial topology. However, different statistical methods have been introduced to study resting state functional magnetic resonance imaging connectivity, yet producing consistent results. In this article, we introduce the concept of resting state functional magnetic resonance imaging in detail, then discuss three most widely used methods for analysis, describe a few of the resting state networks featuring the brain regions, associated cognitive functions and clinical applications of resting state functional magnetic resonance imaging. This review aims to highlight the utility and importance of studying resting state functional magnetic resonance imaging connectivity, underlining its complementary nature to the task-based functional magnetic resonance imaging.

Resting state functional magnetic resonance imaging processing techniques in stroke studies

In recent years, there has been considerable research interest in the study of brain connectivity using the resting state functional magnetic resonance imaging (rsfMRI). Studies have explored the brain networks and connection between different brain regions. These studies have revealed interesting new findings about the brain mapping as well as important new insights in the overall organization of functional communication in the brain network. In this paper, after a general discussion of brain networks and connectivity imaging, the brain connectivity and resting state networks are described with a focus on rsfMRI imaging in stroke studies. Then, techniques for preprocessing of the rsfMRI for stroke patients are reviewed, followed by brain connectivity processing techniques. Recent research on brain connectivity using rsfMRI is reviewed with an emphasis on stroke studies. The authors hope this paper generates further interest in this emerging area of computational neuroscience with potential applications in rehabilitation of stroke patients.

Disrupted functional connectivity affects resting state based language lateralization

Pre-operative assessment of language localization and lateralization is critical to preserving brain function after lesion or epileptogenic tissue resection. Task fMRI (t-fMRI) has been extensively and reliably used to this end, but resting state fMRI (rs-fMRI) is emerging as an alternative pre-operative brain mapping method that is independent of a patient's ability to comply with a task. We sought to evaluate if language lateralization obtained from rs-fMRI can replace standard assessment using t-fMRI. In a group of 43 patients scheduled for pre-operative fMRI brain mapping and 17 healthy controls, we found that existing methods of determining rs-fMRI lateralization by considering interhemispheric and intrahemispheric functional connectivity are inadequate compared to t-fMRI when applied to the language network. We determined that this was attributable to widespread but nuanced disturbances in the functional connectivity of the language network in patients. We found changes in interhemispheric and intrahemispheric functional connectivity that were dependent on lesion location, and particularly impacted patients with lesions in the left temporal lobe. We then tested whether a simpler measure of functional connectivity to the language network has a better relation to t-fMRI based language lateralization. Remarkably, we found that functional connectivity between the language network and the frontal pole, and superior frontal gyrus, as well as the supramarginal gyrus, significantly correlated to task based language lateralization indices in both patients and healthy controls. These findings are consistent with prior work with epilepsy patients, and provide a framework for evaluating language lateralization at rest.

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Existing methods of determining rs-fMRI lateralization are inadequate for language.

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Functional connectivity to language network correlates with task lateralization.

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Lesion location affects functional connectivity.

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Lesions exhibit some interhemispheric hyperconnectivity within language network.

Presurgical Mapping of the Language Network Using Resting-state Functional Connectivity

Resting-state functional magnetic resonance imaging (resting-state fMRI) is a tool for investigating the functional networks that arise during the resting state of the brain. Recent advances of the resting-state fMRI analysis suggest its feasibility for evaluating language function. The most common clinical application is for presurgical mapping of cortex for a brain tumor or for resective epilespy surgery. In this article, we review the techniques and presurgical applications of resting-state fMRI analysis for language evaluation, and discuss the use in the clinical setting, focusing on planning for neurosurgery.

Enhanced subject-specific resting-state network detection and extraction with fast fMRI

Resting-state networks have become an important tool for the study of brain function. An ultra-fast imaging technique that allows to measure brain function, called Magnetic Resonance Encephalography (MREG), achieves an order of magnitude higher temporal resolution than standard echo-planar imaging (EPI). This new sequence helps to correct physiological artifacts and improves the sensitivity of the fMRI analysis. In this study, EPI is compared with MREG in terms of capability to extract resting-state networks. Healthy controls underwent two consecutive resting-state scans, one with EPI and the other with MREG. Subject-level independent component analyses (ICA) were performed separately for each of the two datasets. Using Stanford FIND atlas parcels as network templates, the presence of ICA maps corresponding to each network was quantified in each subject. The number of detected individual networks was significantly higher in the MREG data set than for EPI. Moreover, using short time segments of MREG data, such as 50 seconds, one can still detect and track consistent networks. Fast fMRI thus results in an increased capability to extract distinct functional regions at the individual subject level for the same scan times, and also allow the extraction of consistent networks within shorter time intervals than when using EPI, which is notably relevant for the analysis of dynamic functional connectivity fluctuations. Hum Brain Mapp 38:817-830, 2017. © 2016 Wiley Periodicals, Inc.

Advanced MR imaging in hemispheric low-grade gliomas before surgery; the indications and limits in the pediatric age

INTRODUCTION

Advanced magnetic resonance imaging (MRI) techniques is an umbrella term that includes diffusion (DWI) and diffusion tensor (DTI), perfusion (PWI), spectroscopy (MRS), and functional (fMRI) imaging. These advanced modalities have improved the imaging of brain tumors and provided valuable additional information for treatment planning. Despite abundant literature on advanced MRI techniques in adult brain tumors, few reports exist for pediatric brain ones, potentially because of technical challenges.

REVIEW OF THE LITERATURE

The authors review techniques and clinical applications of DWI, PWI, MRS, and fMRI, in the setting of pediatric hemispheric low-grade gliomas.

PERSONAL EXPERIENCE

The authors propose their personal experience to highlight benefits and limits of advanced MR imaging in diagnosis, grading, and presurgical planning of pediatric hemispheric low-grade gliomas.

DISCUSSION

Advanced techniques should be used as complementary tools to conventional MRI, and in theory, the combined use of the three techniques should ensure achieving the best results in the diagnosis of hemispheric low-grade glioma and in presurgical planning to maximize tumor resection and preserve brain function.

FUTURE PERSPECTIVES

In the setting of pediatric neurooncology, these techniques can be used to distinguish low-grade from high-grade tumor. However, these methods have to be applied on a large scale to understand their real potential and clinical relapse, and further technical development is required to reduce the excessive scan times and other technical limitations.

Altered Resting-State Functional Connectivity in the Hand Motor Network in Glioma Patients

To examine the functional connectivity of the primary and supplementary motor areas (SMA) in glioma patients using resting-state functional MRI (rfMRI). To correlate rfMRI data with tumor characteristics and clinical information to characterize functional reorganization of resting-state networks (RSN) and the limitations of this method. This study was IRB approved and in compliance with Health Insurance Portability and Accountability Act. Informed consent was waived in this retrospective study. We analyzed rfMRI in 24 glioma patients and 12 age- and sex-matched controls. We compared global activation, interhemispheric connectivity, and functional connectivity in the hand motor RSNs using hemispheric voxel counts, pairwise Pearson correlation, and pairwise total spectral coherence. We explored the relationship between tumor grade, volume, location, and the patient's clinical status to functional connectivity. Global network activation and interhemispheric connectivity were reduced in gliomas (p < 0.05). Functional connectivity between the bilateral motor cortices and the SMA was reduced in gliomas (p < 0.01). High-grade gliomas had lower functional connectivity than low-grade gliomas (p < 0.05). Tumor volume and distance to ipsilateral motor cortex demonstrated no association with functional connectivity loss. Functional connectivity loss is associated with motor deficits in low-grade gliomas, but not in high-grade gliomas. Global reduction in resting-state connectivity in areas distal to tumor suggests that radiological tumor boundaries underestimate areas affected by glioma. Association between motor deficits and rfMRI suggests that rfMRI may accurately reflect functional changes in low-grade gliomas. Lack of association between rfMRI and clinical motor deficits implies decreased sensitivity of rfMRI in high-grade gliomas, possibly due to neurovascular uncoupling.

Resting-State Functional MR Imaging for Determining Language Laterality in Intractable Epilepsy

Purpose To measure the accuracy of resting-state functional magnetic resonance (MR) imaging in determining hemispheric language dominance in patients with medically intractable focal epilepsies against the results of an intracarotid amobarbital procedure (IAP). Materials and Methods This study was approved by the institutional review board, and all subjects gave signed informed consent. Data in 23 patients with medically intractable focal epilepsy were retrospectively analyzed. All 23 patients were candidates for epilepsy surgery and underwent both IAP and resting-state functional MR imaging as part of presurgical evaluation. Language dominance was determined from functional MR imaging data by calculating a laterality index (LI) after using independent component analysis. The accuracy of this method was assessed against that of IAP by using a variety of thresholds. Sensitivity and specificity were calculated by using leave-one-out cross validation. Spatial maps of language components were qualitatively compared among each hemispheric language dominance group. Results Measurement of hemispheric language dominance with resting-state functional MR imaging was highly concordant with IAP results, with up to 96% (22 of 23) accuracy, 96% (22 of 23) sensitivity, and 96% (22 of 23) specificity. Composite language component maps in patients with typical language laterality consistently included classic language areas such as the inferior frontal gyrus, the posterior superior temporal gyrus, and the inferior parietal lobule, while those of patients with atypical language laterality also included non-classical language areas such as the superior and middle frontal gyri, the insula, and the occipital cortex. Conclusion Resting-state functional MR imaging can be used to measure language laterality in patients with medically intractable focal epilepsy. (©) RSNA, 2016 Online supplemental material is available for this article.

A new fun and robust version of an fMRI localizer for the frontotemporal language system

A set of brain regions in the frontal, temporal, and parietal lobes supports high-level linguistic processing. These regions can be reliably identified in individual subjects using fMRI, by contrasting neural responses to meaningful and structured language stimuli vs. stimuli matched for low-level properties but lacking meaning and/or structure. We here present a novel version of a language 'localizer,' which should be suitable for diverse populations including children and/or clinical populations who may have difficulty with reading or cognitively demanding tasks. In particular, we contrast responses to auditorily presented excerpts from engaging interviews or stories, and acoustically degraded versions of these materials. This language localizer is appealing because it uses (a) naturalistic and engaging linguistic materials, (b) auditory presentation,

Language and thought are not the same thing: evidence from neuroimaging and neurological patients

Is thought possible without language? Individuals with global aphasia, who have almost no ability to understand or produce language, provide a powerful opportunity to find out. Surprisingly, despite their near-total loss of language, these individuals are nonetheless able to add and subtract, solve logic problems, think about another person's thoughts, appreciate music, and successfully navigate their environments. Further, neuroimaging studies show that healthy adults strongly engage the brain's language areas when they understand a sentence, but not when they perform other nonlinguistic tasks such as arithmetic, storing information in working memory, inhibiting prepotent responses, or listening to music. Together, these two complementary lines of evidence provide a clear answer: many aspects of thought engage distinct brain regions from, and do not depend on, language.

Multimodal study of default-mode network integrity in disorders of consciousness

OBJECTIVE

Understanding residual brain function in disorders of consciousness poses extraordinary challenges, and imaging examinations are needed to complement clinical assessment. The default-mode network (DMN) is known to be dysfunctional, although correlation with level of consciousness remains controversial. We investigated DMN activity with resting-state functional magnetic resonance imaging (rs-fMRI), alongside its structural and metabolic integrity, aiming to elucidate the corresponding associations with clinical assessment.

METHODS

We enrolled 119 consecutive patients: 72 in a vegetative state/unresponsive wakefulness state (VS/UWS), 36 in a minimally conscious state (MCS), and 11 with severe disability. All underwent structural MRI and rs-fMRI, and a subset also underwent ¹⁸ F-fluorodeoxyglucose positron emission tomography (FDG-PET). Data were analyzed with manual and automatic approaches, in relation to diagnosis and clinical score.

RESULTS

Excluding the quartile with largest head movement, DMN activity was decreased in VS/UWS compared to MCS, and correlated with clinical score. Independent-component and seed-based analyses provided similar results, although the latter and their combination were most informative. Structural MRI and FDG-PET were less sensitive to head movement and had better diagnostic accuracy than rs-fMRI only when all cases were included. rs-fMRI indicated relatively preserved DMN activity in a small subset of VS/UWS patients, 2 of whom evolved to MCS. The integrity of the left hemisphere appears to be predictive of a better clinical status.

INTERPRETATION

rs-fMRI of the DMN is sensitive to clinical severity. The effect is consistent across data analysis approaches, but heavily dependent on head movement. rs-fMRI could be informative in detecting residual DMN activity for those patients who remain relatively still during scanning and whose diagnosis is uncertain. Ann Neurol 2016;79:841-853.

Electrocorticographic mapping of expressive language function without requiring the patient to speak: A report of three cases

Objective

Patients requiring resective brain surgery often undergo functional brain mapping during perioperative planning to localize expressive language areas. Currently, all established protocols to perform such mapping require substantial time and patient participation during verb generation or similar tasks. These issues can make language mapping impractical in certain clinical circumstances (e.g., during awake craniotomies) or with certain populations (e.g., pediatric patients). Thus, it is important to develop new techniques that reduce mapping time and the requirement for active patient participation. Several neuroscientific studies reported that the mere auditory presentation of speech stimuli can engage not only receptive but also expressive language areas. Here, we tested the hypothesis that submission of electrocorticographic (ECoG) recordings during a short speech listening task to an appropriate analysis procedure can identify eloquent expressive language cortex without requiring the patient to speak.

Methods

Three patients undergoing temporary placement of subdural electrode grids passively listened to stories while we recorded their ECoG activity. We identified those sites whose activity in the broadband gamma range (70–170 Hz) changed immediately after presentation of the speech stimuli with respect to a prestimulus baseline.

Results

Our analyses revealed increased broadband gamma activity at distinct locations in the inferior frontal cortex, superior temporal gyrus, and/or perisylvian areas in all three patients and premotor and/or supplementary motor areas in two patients. The sites in the inferior frontal cortex that we identified with our procedure were either on or immediately adjacent to locations identified using electrical cortical stimulation (ECS) mapping.

Conclusions

The results of this study provide encouraging preliminary evidence that it may be possible that a brief and practical protocol can identify expressive language areas without requiring the patient to speak. This protocol could provide the clinician with a map of expressive language cortex within a few minutes. This may be useful as an adjunct to ECS interrogation or as an alternative to mapping using functional magnetic resonance imaging (fMRI). In conclusion, with further development and validation in more subjects, the approach presented here could help in identifying expressive language areas in situations where patients cannot speak in response to task instructions.

Resting-State Functional Magnetic Resonance Imaging for Language Preoperative Planning

Functional magnetic resonance imaging (fMRI) is a well-known non-invasive technique for the study of brain function. One of its most common clinical applications is preoperative language mapping, essential for the preservation of function in neurosurgical patients. Typically, fMRI is used to track task-related activity, but poor task performance and movement artifacts can be critical limitations in clinical settings. Recent advances in resting-state protocols open new possibilities for pre-surgical mapping of language potentially overcoming these limitations. To test the feasibility of using resting-state fMRI instead of conventional active task-based protocols, we compared results from fifteen patients with brain lesions while performing a verb-to-noun generation task and while at rest. Task-activity was measured using a general linear model analysis and independent component analysis (ICA). Resting-state networks were extracted using ICA and further classified in two ways: manually by an expert and by using an automated template matching procedure. The results revealed that the automated classification procedure correctly identified language networks as compared to the expert manual classification. We found a good overlay between task-related activity and resting-state language maps, particularly within the language regions of interest. Furthermore, resting-state language maps were as sensitive as task-related maps, and had higher specificity. Our findings suggest that resting-state protocols may be suitable to map language networks in a quick and clinically efficient way.

Evidence of Resting-state Activity in Propofol-anesthetized Patients with Intracranial Tumors

RATIONALE AND OBJECTIVES

Resting-state (RS) networks, revealed by functional magnetic resonance imaging (fMRI) studies in healthy volunteers, have never been evaluated in anesthetized patients with brain tumors. Our purpose was to examine the presence of residual brain activity on the auditory network during propofol-induced loss of consciousness in patients with brain tumors.

MATERIALS AND METHODS

Twenty subjects with intracranial masses were prospectively studied by means of intraoperative RS-fMRI acquisitions before any craniectomy. After performing single-subject independent component analysis, spatial maps and time courses were assigned to an auditory RS network template from the literature and compared via spatial regression coefficients.

RESULTS

All fMRI data were of sufficient quality for further postprocessing. In all but two patients, the RS functional activity of the auditory network could be successfully mapped. In almost all patients, contralateral activation of the auditory network was present. No significant difference was found between the mean distance of the RS activity clusters and the lesion periphery for tumors located in the temporal gyri vs. those in other brain regions. The spatial deviation between the activated cluster in our experiment and the template was significantly (P = 0.04) higher in patients with tumors located in the temporal gyri than in patients with tumors located in other regions.

CONCLUSIONS

Propofol-induced anesthesia in patients with intracranial lesions does not alter the blood-oxygenation level-depended signal, and independent component analysis of intraoperative RS-fMRI may allow assessment of the auditory network in a clinical setting.

Functional magnetic resonance imaging

Functional magnetic resonance imaging (fMRI) maps the spatiotemporal distribution of neural activity in the brain under varying cognitive conditions. Since its inception in 1991, blood oxygen level-dependent (BOLD) fMRI has rapidly become a vital methodology in basic and applied neuroscience research. In the clinical realm, it has become an established tool for presurgical functional brain mapping. This chapter has three principal aims. First, we review key physiologic, biophysical, and methodologic principles that underlie BOLD fMRI, regardless of its particular area of application. These principles inform a nuanced interpretation of the BOLD fMRI signal, along with its neurophysiologic significance and pitfalls. Second, we illustrate the clinical application of task-based fMRI to presurgical motor, language, and memory mapping in patients with lesions near eloquent brain areas. Integration of BOLD fMRI and diffusion tensor white-matter tractography provides a road map for presurgical planning and intraoperative navigation that helps to maximize the extent of lesion resection while minimizing the risk of postoperative neurologic deficits. Finally, we highlight several basic principles of resting-state fMRI and its emerging translational clinical applications. Resting-state fMRI represents an important paradigm shift, focusing attention on functional connectivity within intrinsic cognitive networks.

Presurgical brain mapping of the language network in patients with brain tumors using resting-state fMRI: Comparison with task fMRI

PURPOSE

To compare language networks derived from resting-state fMRI (rs-fMRI) with task-fMRI in patients with brain tumors and investigate variables that affect rs-fMRI vs task-fMRI concordance.

MATERIALS AND METHODS

Independent component analysis (ICA) of rs-fMRI was performed with 20, 30, 40, and 50 target components (ICA20 to ICA50) and language networks identified for patients presenting for presurgical fMRI mapping between 1/1/2009 and 7/1/2015. 49 patients were analyzed fulfilling criteria for presence of brain tumors, no prior brain surgery, and adequate task-fMRI performance. Rs-vs-task-fMRI concordance was measured using Dice coefficients across varying fMRI thresholds before and after noise removal. Multi-thresholded Dice coefficient volume under the surface (DiceVUS) and maximum Dice coefficient (MaxDice) were calculated. One-way Analysis of Variance (ANOVA) was performed to determine significance of DiceVUS and MaxDice between the four ICA order groups. Age, Sex, Handedness, Tumor Side, Tumor Size, WHO Grade, number of scrubbed volumes, image intensity root mean square (iRMS), and mean framewise displacement (FD) were used as predictors for VUS in a linear regression.

RESULTS

Artificial elevation of rs-fMRI vs task-fMRI concordance is seen at low thresholds due to noise. Noise-removed group-mean DiceVUS and MaxDice improved as ICA order increased, however ANOVA demonstrated no statistically significant difference between the four groups. Linear regression demonstrated an association between iRMS and DiceVUS for ICA30-50, and iRMS and MaxDice for ICA50.

CONCLUSION

Overall there is moderate group level rs-vs-task fMRI language network concordance, however substantial subject-level variability exists; iRMS may be used to determine reliability of rs-fMRI derived language networks.

Resting-state functional connectivity in epilepsy: growing relevance for clinical decision making

PURPOSE OF REVIEW

Seizures produce dysfunctional, maladaptive networks, making functional connectivity an ideal technique for identifying complex brain effects of epilepsy. We review the current status of resting-state functional connectivity (rsFC) research, highlighting its potential added value to epilepsy surgery programs.

RECENT FINDINGS

RsFC research has demonstrated that the brain impact of seizures goes beyond the epileptogenic zone, changing connectivity patterns in widespread cortical regions. There is evidence for abnormal connectivity, but the degree to which these represent adaptive or maladaptive plasticity responses is unclear. Empirical associations with cognitive performance and psychiatric symptoms have helped understand deleterious impacts of seizures outside the epileptogenic zone. Studies in the prediction of outcome suggest that there are identifiable presurgical patterns of functional connectivity associated with a greater likelihood of positive cognitive or seizure outcomes.

SUMMARY

The role of rsFC remains limited in most clinical settings, but shows great promise for identifying epileptic circuits and foci, predicting outcomes following surgery, and explaining cognitive deficits and psychiatric symptoms of epilepsy. RsFC has demonstrated that even focal epilepsies constitute a network and brain systems disorder. By providing a tool to both identify and characterize the brain network impact of epileptiform activity, rsFC can make a strong contribution to presurgical algorithms in epilepsy.

Combining task-evoked and spontaneous activity to improve pre-operative brain mapping with fMRI

Noninvasive localization of brain function is used to understand and treat neurological disease, exemplified by pre-operative fMRI mapping prior to neurosurgical intervention. The principal approach for generating these maps relies on brain responses evoked by a task and, despite known limitations, has dominated clinical practice for over 20years. Recently, pre-operative fMRI mapping based on correlations in spontaneous brain activity has been demonstrated, however this approach has its own limitations and has not seen widespread clinical use. Here we show that spontaneous and task-based mapping can be performed together using the same pre-operative fMRI data, provide complimentary information relevant for functional localization, and can be combined to improve identification of eloquent motor cortex. Accuracy, sensitivity, and specificity of our approach are quantified through comparison with electrical cortical stimulation mapping in eight patients with intractable epilepsy. Broad applicability and reproducibility of our approach are demonstrated through prospective replication in an independent dataset of six patients from a different center. In both cohorts and every individual patient, we see a significant improvement in signal to noise and mapping accuracy independent of threshold, quantified using receiver operating characteristic curves. Collectively, our results suggest that modifying the processing of fMRI data to incorporate both task-based and spontaneous activity significantly improves functional localization in pre-operative patients. Because this method requires no additional scan time or modification to conventional pre-operative data acquisition protocols it could have widespread utility.

Presurgical Assessment of the Sensorimotor Cortex Using Resting-State fMRI

BACKGROUND AND PURPOSE

The functional characterization of the motor cortex is an important issue in the presurgical evaluation of brain lesions. fMRI noninvasively identifies motor areas while patients are asked to move different body parts. This task-based approach has some drawbacks in clinical settings: long scanning times and exclusion of patients with severe functional or neurologic disabilities and children. Resting-state fMRI can avoid these difficulties because patients do not perform any goal-directed tasks.

MATERIALS AND METHODS

Nineteen patients with diverse brain pathologies were prospectively evaluated by using task-based and resting-state fMRI to localize sensorimotor function. Independent component analyses were performed to generate spatial independent components reflecting functional brain networks or noise. Three radiologists identified the motor components and 3 portions of the motor cortex corresponding to the hand, foot, and face representations. Selected motor independent components were compared with task-based fMRI activation maps resulting from movements of the corresponding body parts.

RESULTS

The motor cortex was successfully and consistently identified by using resting-state fMRI by the 3 radiologists for all patients. When they subdivided the motor cortex into 3 segments, the sensitivities of resting-state and task-based fMRI were comparable. Moreover, we report a good spatial correspondence with the task-based fMRI activity estimates.

CONCLUSIONS

Resting-state fMRI can reliably image sensorimotor function in a clinical preoperative routine. It is a promising opportunity for presurgical localization of sensorimotor function and has the potential to benefit a large number of patients affected by a wide range of pathologies.

3D interactive tractography-informed resting-state fMRI connectivity

In the past decade, the fusion between diffusion magnetic resonance imaging (dMRI) and functional magnetic resonance imaging (fMRI) has opened the way for exploring structure-function relationships in vivo. As it stands, the common approach usually consists of analysing fMRI and dMRI datasets separately or using one to inform the other, such as using fMRI activation sites to reconstruct dMRI streamlines that interconnect them. Moreover, given the large inter-individual variability of the healthy human brain, it is possible that valuable information is lost when a fixed set of dMRI/fMRI analysis parameters such as threshold values are assumed constant across subjects. By allowing one to modify such parameters while viewing the results in real-time, one can begin to fully explore the sensitivity of structure-function relations and how they differ across brain areas and individuals. This is especially important when interpreting how structure-function relationships are altered in patients with neurological disorders, such as the presence of a tumor. In this study, we present and validate a novel approach to achieve this: First, we present an interactive method to generate and visualize tractography-driven resting-state functional connectivity, which reduces the bias introduced by seed size, shape and position. Next, we demonstrate that structural and functional reconstruction parameters explain a significant portion of intra- and inter-subject variability. Finally, we demonstrate how our proposed approach can be used in a neurosurgical planning context. We believe this approach will promote the exploration of structure-function relationships in a subject-specific aspect and will open new opportunities for connectomics.

A New Paradigm for Individual Subject Language Mapping: Movie-Watching fMRI

BACKGROUND

Functional MRI (fMRI) based on language tasks has been used in presurgical language mapping in patients with lesions in or near putative language areas. However, if patients have difficulty performing the tasks due to neurological deficits, it leads to unreliable or noninterpretable results. In this study, we investigate the feasibility of using a movie-watching fMRI for language mapping.

METHODS

A 7-minute movie clip with contrasting speech and nonspeech segments was shown to 22 right-handed healthy subjects. Based on all subjects' language functional regions-of-interest, 6 language response areas were defined, within which a language response model (LRM) was derived by extracting the main temporal activation profile. Using a leave-one-out procedure, individuals' language areas were identified as the areas that expressed highly correlated temporal responses with the LRM derived from an independent group of subjects.

RESULTS

Compared with an antonym generation task-based fMRI, the movie-watching fMRI generated language maps with more localized activations in the left frontal language area, larger activations in the left temporoparietal language area, and significant activations in their right-hemisphere homologues. Results of 2 brain tumor patients' movie-watching fMRI using the LRM derived from the healthy subjects indicated its ability to map putative language areas; while their task-based fMRI maps were less robust and noisier.

CONCLUSIONS

These results suggest that it is feasible to use this novel "task-free" paradigm as a complementary tool for fMRI language mapping when patients cannot perform the tasks. Its deployment in more neurosurgical patients and validation against gold-standard techniques need further investigation.

Is fMRI "noise" really noise? Resting state nuisance regressors remove variance with network structure

Noise correction is a critical step towards accurate mapping of resting state BOLD fMRI connectivity. Noise sources related to head motion or physiology are typically modelled by nuisance regressors, and a generalised linear model is applied to regress out the associated signal variance. In this study, we use independent component analysis (ICA) to characterise the data variance typically discarded in this pre-processing stage in a cohort of 12 healthy volunteers. The signal variance removed by 24, 12, 6, or only 3 head motion parameters demonstrated network structure typically associated with functional connectivity, and certain networks were discernable in the variance extracted by as few as 2 physiologic regressors. Simulated nuisance regressors, unrelated to the true data noise, also removed variance with network structure, indicating that any group of regressors that randomly sample variance may remove highly structured “signal” as well as “noise.” Furthermore, to support this we demonstrate that random sampling of the original data variance continues to exhibit robust network structure, even when as few as 10% of the original volumes are considered. Finally, we examine the diminishing returns of increasing the number of nuisance regressors used in pre-processing, showing that excessive use of motion regressors may do little better than chance in removing variance within a functional network. It remains an open challenge to understand the balance between the benefits and confounds of noise correction using nuisance regressors.

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Data variance removed by nuisance regressors contains network structure.

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Simulated regressors unrelated to noise also extract data with network structure.

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Random sampling of original data (as few as 10% of volumes) reveals robust networks.

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After optimal number, motion regressors remove similar variance as simulated ones.

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Excessive nuisance regressors extract random signal variance with network structure.

Unmasking Language Lateralization in Human Brain Intrinsic Activity

Lateralization of function is a fundamental feature of the human brain as exemplified by the left hemisphere dominance of language. Despite the prominence of lateralization in the lesion, split-brain and task-based fMRI literature, surprisingly little asymmetry has been revealed in the increasingly popular functional imaging studies of spontaneous fluctuations in the fMRI BOLD signal (so-called resting-state fMRI). Here, we show the global signal, an often discarded component of the BOLD signal in resting-state studies, reveals a leftward asymmetry that maps onto regions preferential for semantic processing in left frontal and temporal cortex and the right cerebellum and a rightward asymmetry that maps onto putative attention-related regions in right frontal, temporoparietal, and parietal cortex. Hemispheric asymmetries in the global signal resulted from amplitude modulation of the spontaneous fluctuations. To confirm these findings obtained from normal, healthy, right-handed subjects in the resting-state, we had them perform 2 semantic processing tasks: synonym and numerical magnitude judgment and sentence comprehension. In addition to establishing a new technique for studying lateralization through functional imaging of the resting-state, our findings shed new light on the physiology of the global brain signal.

Resting-state functional magnetic resonance imaging: review of neurosurgical applications

Recent research in brain imaging has highlighted the role of different neural networks in the resting state (ie, no task) in which the brain displays spontaneous low-frequency neuronal oscillations. These can be indirectly measured with resting-state functional magnetic resonance imaging, and functional connectivity can be inferred as the spatiotemporal correlations of this signal. This technique has proliferated in recent years and has allowed the noninvasive investigation of large-scale, distributed functional networks. In this review, we give a brief overview of resting-state networks and examine the use of resting-state functional magnetic resonance imaging in neurosurgical contexts, specifically with respect to neurooncology, epilepsy surgery, and deep brain stimulation. We discuss the advantages and disadvantages compared with task-based functional magnetic resonance imaging, the limitations of resting-state functional magnetic resonance imaging, and the emerging directions of this relatively new technology.

Visual mapping using blood oxygen level dependent functional magnetic resonance imaging

Functional magnetic resonance imaging (fMRI) is used clinically to map the visual cortex before brain surgery or other invasive treatments to achieve an optimal balance between therapeutic effect and the avoidance of postoperative vision deficits. Clinically optimized stimuli, behavioral task, analysis, and displays permit identification of cortical subregions supporting high-acuity central vision that is critical for reading and other essential visual functions. Emerging techniques such as resting-state fMRI may facilitate the use of fMRI-based vision mapping in a broader range of patients.

Resting-state blood oxygen level-dependent functional magnetic resonance imaging for presurgical planning

Resting-state functional MR imaging (rsfMR imaging) measures spontaneous fluctuations in the blood oxygen level-dependent (BOLD) signal and can be used to elucidate the brain's functional organization. It is used to simultaneously assess multiple distributed resting-state networks. Unlike task-based functional MR imaging, rsfMR imaging does not require task performance. This article presents a brief introduction of rsfMR imaging processing methods followed by a detailed discussion on the use of rsfMR imaging in presurgical planning. Example cases are provided to highlight the strengths and limitations of the technique.

A novel data-driven approach to preoperative mapping of functional cortex using resting-state functional magnetic resonance imaging

Supplemental Digital Content is Available in the Text.

BACKGROUND:

Recent findings associated with resting-state cortical networks have provided insight into the brain's organizational structure. In addition to their neuroscientific implications, the networks identified by resting-state functional magnetic resonance imaging (rs-fMRI) may prove useful for clinical brain mapping.

OBJECTIVE:

To demonstrate that a data-driven approach to analyze resting-state networks (RSNs) is useful in identifying regions classically understood to be eloquent cortex as well as other functional networks.

METHODS:

This study included 6 patients undergoing surgical treatment for intractable epilepsy and 7 patients undergoing tumor resection. rs-fMRI data were obtained before surgery and 7 canonical RSNs were identified by an artificial neural network algorithm. Of these 7, the motor and language networks were then compared with electrocortical stimulation (ECS) as the gold standard in the epilepsy patients. The sensitivity and specificity for identifying these eloquent sites were calculated at varying thresholds, which yielded receiver-operating characteristic (ROC) curves and their associated area under the curve (AUC). RSNs were plotted in the tumor patients to observe RSN distortions in altered anatomy.

RESULTS:

The algorithm robustly identified all networks in all patients, including those with distorted anatomy. When all ECS-positive sites were considered for motor and language, rs-fMRI had AUCs of 0.80 and 0.64, respectively. When the ECS-positive sites were analyzed pairwise, rs-fMRI had AUCs of 0.89 and 0.76 for motor and language, respectively.

CONCLUSION:

A data-driven approach to rs-fMRI may be a new and efficient method for preoperative localization of numerous functional brain regions.

ABBREVIATIONS:

AUC, area under the curve

BA, Brodmann area

BOLD, blood oxygen level dependent

ECS, electrocortical stimulation

fMRI, functional magnetic resonance imaging

ICA, independent component analysis

MLP, multilayer perceptron

MP-RAGE, magnetization-prepared rapid gradient echo

ROC, receiver-operating characteristic

rs-fMRI, resting-state functional magnetic resonance imaging

RSN, resting-state network

Preoperative mapping of the sensorimotor cortex: comparative assessment of task-based and resting-state FMRI

Resting state fMRI (rs-fMRI) has recently been considered as a possible complement or alternative to task-based fMRI (tb-fMRI) for presurgical mapping. However, evidence of its usefulness remains scant, because existing studies have investigated relatively small samples and focused primarily on qualitative evaluation. The aim of this study is to investigate the clinical usefulness of rs-fMRI in the context of presurgical mapping of motor functions, and in particular to determine the degree of correspondence with tb-fMRI which, while not a gold-standard, is commonly used in preoperative setting. A group of 13 patients with lesions close to the sensorimotor cortex underwent rs-fMRI and tb-fMRI to localize the hand, foot and mouth motor areas. We assessed quantitatively the degree of correspondence between multiple rs-fMRI analyses (independent-component and seed-based analyses) and tb-fMRI, with reference to sensitivity and specificity of rs-fMRI with respect to tb-fMRI, and centre-of-mass distances. Agreement with electro-cortical stimulation (ECS) was also investigated, and a traditional map thresholding approach based on agreement between two experienced operators was compared to an automatic threshold determination method. Rs-fMRI can localize the sensorimotor cortex successfully, providing anatomical specificity for hand, foot and mouth motor subregions, in particular with seed-based analyses. Agreement with tb-fMRI was only partial and rs-fMRI tended to provide larger patterns of correlated activity. With respect to the ECS data available, rs-fMRI and tb-fMRI performed comparably, even though the shortest distance to stimulation points was observed for the latter. Notably, the results of both were on the whole robust to thresholding procedure. Localization performed by rs-fMRI is not equivalent to tb-fMRI, hence rs-fMRI cannot be considered as an outright replacement for tb-fMRI. Nevertheless, since there is significant agreement between the two techniques, rs-fMRI can be considered with caution as a potential alternative to tb-fMRI when patients are unable to perform the task.

A functional dissociation between language and multiple-demand systems revealed in patterns of BOLD signal fluctuations

What is the relationship between language and other high-level cognitive functions? Neuroimaging studies have begun to illuminate this question, revealing that some brain regions are quite selectively engaged during language processing, whereas other "multiple-demand" (MD) regions are broadly engaged by diverse cognitive tasks. Nonetheless, the functional dissociation between the language and MD systems remains controversial. Here, we tackle this question with a synergistic combination of functional MRI methods: we first define candidate language-specific and MD regions in each subject individually (using functional localizers) and then measure blood oxygen level-dependent signal fluctuations in these regions during two naturalistic conditions ("rest" and story-comprehension). In both conditions, signal fluctuations strongly correlate among language regions as well as among MD regions, but correlations across systems are weak or negative. Moreover, data-driven clustering analyses based on these inter-region correlations consistently recover two clusters corresponding to the language and MD systems. Thus although each system forms an internally integrated whole, the two systems dissociate sharply from each other. This independent recruitment of the language and MD systems during cognitive processing is consistent with the hypothesis that these two systems support distinct cognitive functions.

Attention shifts the language network reflecting paradigm presentation

OBJECTIVES

Functional magnetic resonance imaging (fMRI) is a reliable and non-invasive method with which to localize language function in pre-surgical planning. In clinical practice, visual stimulus presentation is often difficult or impossible, due to the patient's restricted language or attention abilities. Therefore, our aim was to investigate modality-specific differences in visual and auditory stimulus presentation.

METHODS

Ten healthy subjects participated in an fMRI study comprising two experiments with visual and auditory stimulus presentation. In both experiments, two language paradigms (one for language comprehension and one for language production) used in clinical practice were investigated. In addition to standard data analysis by the means of the general linear model (GLM), independent component analysis (ICA) was performed to achieve more detailed information on language processing networks.

RESULTS

GLM analysis revealed modality-specific brain activation for both language paradigms for the contrast visual > auditory in the area of the intraparietal sulcus and the hippocampus, two areas related to attention and working memory. Using group ICA, a language network was detected for both paradigms independent of stimulus presentation modality. The investigation of language lateralization revealed no significant variations. Visually presented stimuli further activated an attention-shift network, which could not be identified for the auditory presented language.

CONCLUSION

The results of this study indicate that the visually presented language stimuli additionally activate an attention-shift network. These findings will provide important information for pre-surgical planning in order to preserve reading abilities after brain surgery, significantly improving surgical outcomes. Our findings suggest that the presentation modality for language paradigms should be adapted on behalf of individual indication.

Specific impairment of functional connectivity between language regions in former early preterms

Very preterm (PT) birth (≤32 weeks of gestation) carries a high risk for an adverse neurodevelopmental outcome. In recent years, the importance of neurocognitive deficits in the language domain has been increasingly recognized, which can be well-characterized using neuropsychological testing and noninvasive imaging approaches. We compared former early PT born children and adolescents (PT, n = 29, 20M) and typically developing children (TD, n = 19, 7M), using conventional fMRI group analyses as well as functional connectivity analyses. We found only small regions with significantly different group activation (PT > TD) but significantly stronger connectivity between superior temporal lobe (STL) language regions in TD participants. There were also significant differences in local and global network efficiency (TD > PT). Surprisingly, there was a stronger connectivity of STL regions with non-STL regions both intrahemispherically and interhemispherically in PT participants, suggesting the coexistence of reduced and increased connectivity in the language network of former PTs. Very similar results were obtained when using task-based versus resting state functional connectivity approaches. Finally, lateralization of functional connectivity correlated with verbal comprehension abilities, suggesting that a more bilateral language comprehension representation is associated with better performance. Our results underline the importance of interhemispheric crosstalk for language comprehension.

Clinical applications of the functional connectome

Central to the development of clinical applications of functional connectomics for neurology and psychiatry is the discovery and validation of biomarkers. Resting state fMRI (R-fMRI) is emerging as a mainstream approach for imaging-based biomarker identification, detecting variations in the functional connectome that can be attributed to clinical variables (e.g., diagnostic status). Despite growing enthusiasm, many challenges remain. Here, we assess evidence of the readiness of R-fMRI based functional connectomics to lead to clinically meaningful biomarker identification through the lens of the criteria used to evaluate clinical tests (i.e., validity, reliability, sensitivity, specificity, and applicability). We focus on current R-fMRI-based prediction efforts, and survey R-fMRI used for neurosurgical planning. We identify gaps and needs for R-fMRI-based biomarker identification, highlighting the potential of emerging conceptual, analytical and cultural innovations (e.g., the Research Domain Criteria Project (RDoC), open science initiatives, and Big Data) to address them. Additionally, we note the need to expand future efforts beyond identification of biomarkers for disease status alone to include clinical variables related to risk, expected treatment response and prognosis.

Effect of disease and recovery on functional anatomy in brain tumor patients: insights from functional MRI and diffusion tensor imaging

Patients with brain tumors provide a unique opportunity to understand functional brain plasticity. Using advanced imaging techniques, such as functional MRI and diffusion tensor imaging, we have gained tremendous knowledge of brain tumor behavior, transformation, infiltration and destruction of nearby structures. Using these advanced techniques as an adjunct with more proven techniques, such as direct cortical stimulation, intraoperative navigation and advanced microsurgical techniques, we now are able to better formulate safer resection trajectories, perform larger resections at reduced risk and better counsel patients and their families about possible complications. Brain mapping in patients with brain tumors and other lesions has shown us that the old idea of fixed function of the adult cerebral cortex is not entirely true. Improving care for patients with brain lesions in the future will depend on better understanding of the functional organization and plasticity of the adult brain. Advanced noninvasive brain imaging will undoubtedly play a role in advancing this understanding.

Resting state network estimation in individual subjects

Resting state functional magnetic resonance imaging (fMRI) has been used to study brain networks associated with both normal and pathological cognitive functions. The objective of this work is to reliably compute resting state network (RSN) topography in single participants. We trained a supervised classifier (multi-layer perceptron; MLP) to associate blood oxygen level dependent (BOLD) correlation maps corresponding to pre-defined seeds with specific RSN identities. Hard classification of maps obtained from a priori seeds was highly reliable across new participants. Interestingly, continuous estimates of RSN membership retained substantial residual error. This result is consistent with the view that RSNs are hierarchically organized, and therefore not fully separable into spatially independent components. After training on a priori seed-based maps, we propagated voxel-wise correlation maps through the MLP to produce estimates of RSN membership throughout the brain. The MLP generated RSN topography estimates in individuals consistent with previous studies, even in brain regions not represented in the training data. This method could be used in future studies to relate RSN topography to other measures of functional brain organization (e.g., task-evoked responses, stimulation mapping, and deficits associated with lesions) in individuals. The multi-layer perceptron was directly compared to two alternative voxel classification procedures, specifically, dual regression and linear discriminant analysis; the perceptron generated more spatially specific RSN maps than either alternative.