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Classical accounts of the pathophysiology of Parkinson's disease have emphasized degeneration of dopaminergic nigrostriatal neurons with consequent dysfunction of cortico-striatal-thalamic loops. In contrast, post-mortem studies indicate that pathological changes in Parkinson's disease (Lewy neurites and Lewy bodies) first appear primarily in the lower brainstem with subsequent progression to more rostral parts of the neuraxis. The nigrostriatal and histological perspectives are not incompatible, but they do emphasize different anatomical structures. To address the question of which brain structures are functionally most affected by Parkinson's disease, we performed a resting-state functional magnetic resonance imaging study focused on striatal functional connectivity. We contrasted 13 patients with advanced Parkinson's disease versus 19 age-matched control subjects, using methodology incorporating scrupulous attention to minimizing the effects of head motion during scanning. The principal finding in the Parkinson's disease group was markedly lower striatal correlations with thalamus, midbrain, pons and cerebellum. This result reinforces the importance of the brainstem in the pathophysiology of Parkinson's disease. Focally altered functional connectivity also was observed in sensori-motor and visual areas of the cerebral cortex, as well the supramarginal gyrus. Striatal functional connectivity with the brainstem was graded (posterior putamen > anterior putamen > caudate), in both patients with Parkinson's disease and control subjects, in a manner that corresponds to well-documented gradient of striatal dopaminergic function loss in Parkinson's disease. We hypothesize that this gradient provides a clue to the pathogenesis of Parkinson's disease. [ABSTRACT FROM AUTHOR]

Background: Resting-state fMRI is increasingly used to study the effects of gliomas on the functional organization of the brain. A variety of preprocessing techniques and functional connectivity analyses are represented in the literature. However, there so far has been no systematic comparison of how alternative methods impact observed results., New Method: We first surveyed current literature and identified alternative analytical approaches commonly used in the field. Following, we systematically compared alternative approaches to atlas registration, parcellation scheme, and choice of graph-theoretical measure as regards differentiating glioma patients (N = 59) from age-matched reference subjects (N = 163)., Results: Our results suggest that non-linear, as opposed to affine registration, improves structural match to an atlas, as well as measures of functional connectivity. Functionally- as opposed to anatomically-derived parcellation schemes maximized the contrast between glioma patients and reference subjects. We also demonstrate that graph-theoretic measures strongly depend on parcellation granularity, parcellation scheme, and graph density., Comparison With Existing Methods and Conclusions: Our current work primarily focuses on technical optimization of rs-fMRI analysis in glioma patients and, therefore, is fundamentally different from the bulk of papers discussing glioma-induced functional network changes. We report that the evaluation of glioma-induced alterations in the functional connectome strongly depends on analytical approaches including atlas registration, choice of parcellation scheme, and graph-theoretical measures., Competing Interests: Declaration of Competing Interest KYP, JSS, PHL, AZS, report the following conflict of interest. Licensing of Intellectual Property: Sora Neuroscience. AS reports the following conflict of interest. Stock ownership: TheraPanacea. DSM reports the following conflict of interest. Stock ownership: Sora Neuroscience, Flywheel Exchange LLC. ECL reports the following conflicts of interest. Stock ownership: Neurolutions, General Sensing, Osteovantage, Pear Therapeutics, Face to Face Biometrics, Immunovalent, Caeli Vascular, Acera, Sora Neuroscience, Inner Cosmos, Kinetrix, NeuroDev. Petal Surgical. Consultant: Monteris Medical, E15, Acera, Alcyone, Intellectual Ventures, Medtronic, Neurolutions, Osteovantage, Pear Therapeutics, Sante Ventures, Microbot. Licensing of Intellectual Property: Neurolutions, Osteovantage, Caeli Vascular, Sora Neuroscience. Washington University owns equity in Neurolutions. The other authors report they have no competing interests., (Copyright © 2023 The Authors. Published by Elsevier B.V. All rights reserved.)

Depression associated with traumatic brain injury (TBI) is believed to be clinically distinct from primary major depressive disorder (MDD) and may be less responsive to conventional treatments. Brain connectivity differences between the dorsal attention network (DAN), default mode network (DMN), and subgenual cingulate have been implicated in TBI and MDD. To characterize these distinctions, we applied precision functional mapping of brain network connectivity to resting-state functional magnetic resonance imaging data from five published patient cohorts, four discovery cohorts ( n = 93), and one replication cohort ( n = 180). We identified a distinct brain connectivity profile in TBI-associated depression that was independent of TBI, MDD, posttraumatic stress disorder (PTSD), depression severity, and cohort. TBI-associated depression was independently associated with decreased DAN-subgenual cingulate connectivity, increased DAN-DMN connectivity, and the combined effect of both. This effect was stronger when using precision functional mapping relative to group-level network maps. Our results support the possibility of a physiologically distinct "TBI affective syndrome," which may benefit from individualized neuromodulation approaches to target its distinct neural circuitry.

Background: Patients with glioblastoma (GBM) and high-grade glioma (HGG, World Health Organization [WHO] grade IV glioma) have a poor prognosis. Consequently, there is an unmet clinical need for accessible and noninvasively acquired predictive biomarkers of overall survival in patients. This study evaluated morphological changes in the brain separated from the tumor invasion site (ie, contralateral hemisphere). Specifically, we examined the prognostic value of widespread alterations of cortical thickness (CT) in GBM/HGG patients., Methods: We used FreeSurfer, applied with high-resolution T1-weighted MRI, to examine CT, evaluated prior to standard treatment with surgery and chemoradiation in patients (GBM/HGG, N = 162, mean age 61.3 years) and 127 healthy controls (HC; 61.9 years mean age). We then compared CT in patients to HC and studied patients' associated changes in CT as a potential biomarker of overall survival., Results: Compared to HC cases, patients had thinner gray matter in the contralesional hemisphere at the time of tumor diagnosis. patients had significant cortical thinning in parietal, temporal, and occipital lobes. Fourteen cortical parcels showed reduced CT, whereas in 5, it was thicker in patients' cases. Notably, CT in the contralesional hemisphere, various lobes, and parcels was predictive of overall survival. A machine learning classification algorithm showed that CT could differentiate short- and long-term survival patients with an accuracy of 83.3%., Conclusions: These findings identify previously unnoticed structural changes in the cortex located in the hemisphere contralateral to the primary tumor mass. Observed changes in CT may have prognostic value, which could influence care and treatment planning for individual patients., Competing Interests: E.C.L. has equity in Neurolutions, Inner Cosmos. E.C.L., D.S.M., and C.D.H. have equity in Sora Neuroscience. E.C.L, J.J.L., and J.S.S. have patent interests licensed to Sora Neuroscience. D.S.M. has equity in Radiologics, Inc. Washington University has equity in Neurolutions. B.L and E.C.L. have filed a provisional patent on the techniques described in this manuscript. All other authors declared that they had no conflicts of interest to their authorship or the publication of this article., (© The Author(s) 2023. Published by Oxford University Press, the Society for Neuro-Oncology and the European Association of Neuro-Oncology.)

At the group level, antidepressant efficacy of rTMS targets is inversely related to their normative connectivity with subgenual anterior cingulate cortex (sgACC). Individualized connectivity may yield better targets, particularly in patients with neuropsychiatric disorders who may have aberrant connectivity. However, sgACC connectivity shows poor test-retest reliability at the individual level. Individualized resting-state network mapping (RSNM) can reliably map inter-individual variability in brain network organization. Thus, we sought to identify individualized RSNM-based rTMS targets that reliably target the sgACC connectivity profile. We used RSNM to identify network-based rTMS targets in 10 healthy controls and 13 individuals with traumatic brain injury-associated depression (TBI-D). These "RSNM targets" were compared with consensus structural targets and targets based on individualized anti-correlation with a group-mean-derived sgACC region ("sgACC-derived targets"). The TBI-D cohort was also randomized to receive active (n = 9) or sham (n = 4) rTMS to RSNM targets with 20 daily sessions of sequential high-frequency left-sided stimulation and low-frequency right-sided stimulation. We found that the group-mean sgACC connectivity profile was reliably estimated by individualized correlation with default mode network (DMN) and anti-correlation with dorsal attention network (DAN). Individualized RSNM targets were thus identified based on DAN anti-correlation and DMN correlation. These RSNM targets showed greater test-retest reliability than sgACC-derived targets. Counterintuitively, anti-correlation with the group-mean sgACC connectivity profile was also stronger and more reliable for RSNM-derived targets than for sgACC-derived targets. Improvement in depression after RSNM-targeted rTMS was predicted by target anti-correlation with the portions of sgACC. Active treatment also led to increased connectivity within and between the stimulation sites, the sgACC, and the DMN. Overall, these results suggest that RSNM may enable reliable individualized rTMS targeting, although further research is needed to determine whether this personalized approach can improve clinical outcomes., (© 2023. The Author(s).)

Cerebrospinal fluid (CSF) is essential for the development and function of the central nervous system (CNS). However, the brain and its interstitium have largely been thought of as a single entity through which CSF circulates, and it is not known whether specific cell populations within the CNS preferentially interact with the CSF. Here, we develop a technique for CSF tracking, gold nanoparticle-enhanced X-ray microtomography, to achieve micrometer-scale resolution visualization of CSF circulation patterns during development. Using this method and subsequent histological analysis in rodents, we identify previously uncharacterized CSF pathways from the subarachnoid space (particularly the basal cisterns) that mediate CSF-parenchymal interactions involving 24 functional-anatomic cell groupings in the brain and spinal cord. CSF distribution to these areas is largely restricted to early development and is altered in posthemorrhagic hydrocephalus. Our study also presents particle size-dependent CSF circulation patterns through the CNS including interaction between neurons and small CSF tracers, but not large CSF tracers. These findings have implications for understanding the biological basis of normal brain development and the pathogenesis of a broad range of disease states, including hydrocephalus., (© 2023. The Author(s).)

Objective: We present a single-institution case series of patients who experienced pharyngoesophageal damage, specifically from extruded hardware occurring at an average of 7.5 years after anterior cervical diskectomy and fusion (ACDF)., Methods: A retrospective chart review was conducted of patients who had undergone ACDF with subsequent delayed pharyngoesophageal perforation or erosion from extruded hardware ≥1 year after surgery. A discussion of the literature surrounding this complication, including risk factors and management, is also presented., Results: Nine patients were identified (average age 58 years, 66.7% male) among a total of 4122 ACDF patients (incidence: 0.22%). Average time to injury was 7.5 years. Indications for initial ACDF were degenerative cervical disease (n = 7), ankylosing spondylitis (n = 1), and cervical fracture (n = 1). Eight patients had prior multilevel ACDF spanning 2 (n = 4), 3 (n = 1), or 4 levels (n = 2). Fusion levels for prior ACDF included C5-C7 (n = 3), C3-C7 (n = 2), C4-C7 (n = 1), C4-C6 (n = 1), C2-C5 (n = 1), and C6-C7 (n = 1). Pharyngoesophageal injuries included esophageal perforation (n = 3), pharyngeal perforation (n = 2), esophageal erosion (n = 3), and pharyngoesophageal erosion (n = 1). In most (n = 6) cases, the cause of pharyngoesophageal damage was due to ≥1 extruded screws. Dysphagia (n = 8) was the most common presenting symptom. For perforations (n = 5), 2 repairs used a rotational flap to reinforce a primary closure; the other 3 cases were repaired via primary closure., Conclusions: Pharyngoesophageal damage caused by extruded hardware may occur several years after ACDF. These delayed complications are difficult to predict. Proper screw placement may be the most important factor for minimizing the chances of this potentially devastating complication, particularly with multilevel constructs., (Copyright © 2022 Elsevier Inc. All rights reserved.)

Background: Gliomas exhibit widespread bilateral functional connectivity (FC) alterations that may be associated with tumor grade. Limited studies have examined the connection-level mechanisms responsible for these effects. Given the typically strong FC observed between mirroring/homotopic brain regions in healthy subjects, we hypothesized that homotopic connectivity (HC) is altered in low-grade and high-grade glioma patients and the extent of disruption is associated with tumor grade and predictive of overall survival (OS) in a cohort of de novo high-grade glioma (World Health Organization [WHO] grade 4) patients., Methods: We used a mirrored FC-derived cortical parcellation to extract blood-oxygen-level-dependent (BOLD) signals and to quantify FC differences between homotopic pairs in normal-appearing brain in a retrospective cohort of glioma patients and healthy controls., Results: Fifty-nine glioma patients (WHO grade 2, n = 9; grade 4 = 50; mean age, 57.5 years) and 30 healthy subjects (mean age, 65.9 years) were analyzed. High-grade glioma patients showed lower HC compared with low-grade glioma patients and healthy controls across several cortical locations and resting-state networks. Connectivity disruptions were also strongly correlated with hemodynamic lags between homotopic regions. Finally, in high-grade glioma patients with known survival times ( n = 42), HC in somatomotor and dorsal attention networks were significantly correlated with OS., Conclusions: These findings demonstrate an association between tumor grade and HC alterations that may underlie global FC changes and provide prognostic information., (© The Author(s) 2021. Published by Oxford University Press, the Society for Neuro-Oncology and the European Association of Neuro-Oncology.)

Objective: Resting-state functional MRI (rs-fMRI) has been used to evaluate brain network connectivity as a result of intracranial surgery but has not been used to compare different neurosurgical procedures. Laser interstitial thermal therapy (LITT) is an alternative to conventional craniotomy for the treatment of brain lesions such as tumors and epileptogenic foci. While LITT is thought of as minimally invasive, its effect on the functional organization of the brain is still under active investigation and its impact on network changes compared to conventional craniotomy has not yet been explored. We describe a novel computational method for quantifying and comparing the impact of two neurosurgical procedures on brain functional connectivity. Methods: We used a previously described seed-based correlation analysis to generate resting-state network (RSN) correlation matrices, and compared changes in correlation patterns within and across RSNs between LITT and conventional craniotomy for treatment of 24 patients with singular intracranial tumors at our institution between 2014 and 2017. Specifically, we analyzed the differences in patient-specific changes in the within-hemisphere correlation patterns of the contralesional hemisphere. Results: In a post-operative follow-up period up to 2 years within-hemisphere connectivity of the contralesional hemisphere after surgery was more highly correlated to the pre-operative state in LITT patients when compared to craniotomy patients ( P = 0.0287). Moreover, 4 out of 11 individual RSNs demonstrated significantly higher degrees of correlation between pre-operative and post-operative network connectivity in patients who underwent LITT (all P < 0.05). Conclusion: Rs-fMRI may be used as a quantitative metric to determine the impact of different neurosurgical procedures on brain functional connectivity. Global and individual network connectivity in the contralesional hemisphere may be more highly preserved after LITT when compared to craniotomy for the treatment of brain tumors., Competing Interests: EL is a consultant for Monteris Medical, E15, Acera, Alcyone, Intellectual Ventures, Medtronic Inc., Neurolutions, Osteovantage, Pear Therapeutics, Inc., Sante Ventures, and Microbot, owns equity in Neurolutions, General Sensing, Osteovantage, Pear Therapeutics, Face to Face Biometrics, Immunovalent, Caeli Vascular, Acera, Sora Neuroscience, Inner Cosmos, and Kinetrix, and is involved with licensing intellectual property in Neurolutions, Osteovantage, Caeli Vascular, Cerovations, and Intellectual Ventures. Washington University owns equity in Neurolutions. CH owns equity in Sora Neuroscience. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Yang, Hacker, Patel, Daniel and Leuthardt.)

Background: Glioblastoma (GBM; World Health Organization grade IV) assumes a variable appearance on MRI owing to heterogeneous proliferation and infiltration of its cells. As a result, the neurovascular units responsible for functional connectivity (FC) may exist within gross tumor boundaries, albeit with altered magnitude. Therefore, we hypothesize that the strength of FC within GBMs is predictive of overall survival., Methods: We used predefined FC regions of interest (ROIs) in de novo GBM patients to characterize the presence of within-tumor FC observable via resting-state functional MRI and its relationship to survival outcomes., Results: Fifty-seven GBM patients (mean age, 57.8 ± 13.9 y) were analyzed. Functionally connected voxels, not identifiable on conventional structural images, can be routinely found within the tumor mass and was not significantly correlated to tumor size. In patients with known survival times (n = 31), higher intranetwork FC strength within GBM tumors was associated with better overall survival even after accounting for clinical and demographic covariates., Conclusions: These findings suggest the possibility that functionally intact regions may persist within GBMs and that the extent to which FC is maintained may carry prognostic value and inform treatment planning., (© The Author(s) 2020. Published by Oxford University Press on behalf of the Society for Neuro-Oncology.)

Brain mapping is a quintessential part of neurosurgical practice. Accordingly, much of our understanding of the brain's functional organization, and in particular the motor homunculus, is largely attributable to the clinical investigations of past neurosurgeons. Traditionally mapping was invasive and involved the application of electrical current to the exposed brain to observe focal disruption of function or to elicit overt actions. More recently, a wide variety of techniques have been developed that do not require electrical stimulation and often do not require any explicit participation by the subject. Collectively we refer to these as passive mapping modalities. Here we review the spectrum of passive mapping used by neurosurgeons for mapping and surgical planning that ranges from invasive intracranial recordings to noninvasive imaging as well as regimented task-based protocols to completely task-free paradigms that can be performed intraoperatively while under anesthesia., (Copyright © 2020 by the Congress of Neurological Surgeons.)

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., (Copyright © 2020 Luckett, Lee, Park, Dierker, Daniel, Seitzman, Hacker, Ances, Leuthardt, Snyder and Shimony.)

Background: Use of functional MRI (fMRI) in pre-surgical planning is a non-invasive method for pre-operative functional mapping for patients with brain tumors, especially tumors located near eloquent cortex. Currently, this practice predominantly involves task-based fMRI (T-fMRI). Resting state fMRI (RS-fMRI) offers an alternative with several methodological advantages. Here, we compare group-level analyses of RS-fMRI vs. T-fMRI as methods for language localization., Purpose: To contrast RS-fMRI vs. T-fMRI as techniques for localization of language function., Methods: We analyzed data obtained in 35 patients who had both T-fMRI and RS-fMRI scans during the course of pre-surgical evaluation. The RS-fMRI data were analyzed using a previously trained resting-state network classifier. The T-fMRI data were analyzed using conventional techniques. Group-level results obtained by both methods were evaluated in terms of two outcome measures: (1) inter-subject variability of response magnitude and (2) sensitivity/specificity analysis of response topography, taking as ground truth previously reported maps of the language system based on intraoperative cortical mapping as well as meta-analytic maps of language task fMRI responses., Results: Both fMRI methods localized major components of the language system (areas of Broca and Wernicke) although not with equal inter-subject consistency. 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: We demonstrate several advantages of classifier-based mapping of language representation in the brain. Language T-fMRI activated task-general (i.e., not language-specific) functional systems in addition to areas of Broca and Wernicke. In contrast, classifier-based analysis of RS-fMRI data generated maps confined to language-specific regions of the brain., Competing Interests: The authors have declared that no competing interests exist.

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

Objective: The recent advent of individualized resting-state network mapping (RSNM) has revealed substantial interindividual variability in anatomical localization of brain networks identified by using resting-state functional MRI (rsfMRI). RSNM enables personalized targeting of focal neuromodulation techniques such as repetitive transcranial magnetic stimulation (rTMS). rTMS is believed to exert antidepressant efficacy by modulating connectivity between the stimulation site, the default mode network (DMN), and the subgenual anterior cingulate cortex (sgACC). Personalized rTMS may be particularly useful after repetitive traumatic brain injury (TBI), which is associated with neurodegenerative tauopathy in medial temporal limbic structures. These degenerative changes are believed to be related to treatment-resistant neurobehavioral disturbances observed in many retired athletes., Methods: The authors describe a case in which RSNM was successfully used to target rTMS to treat these neuropsychiatric disturbances in a retired NFL defensive lineman whose symptoms were not responsive to conventional treatments. RSNM was used to identify left-right dorsolateral prefrontal rTMS targets with maximal difference between dorsal attention network and DMN correlations. These targets were spatially distinct from those identified by prior methods. Twenty sessions of left-sided excitatory and right-sided inhibitory rTMS were administered at these targets., Results: Treatment led to improvement in Montgomery-Åsberg Depression Rating Scale (72%), cognitive testing, and headache scales scores. Compared with healthy individuals and subjects with TBI-associated depression, baseline rsfMRI revealed substantially elevated DMN connectivity with the medial temporal lobe (MTL). Serial rsfMRI scans revealed gradual improvement in MTL-DMN connectivity and stimulation site connectivity with sgACC., Conclusions: These results highlight the possibility of individualized neuromodulation and biomarker-based monitoring for neuropsychiatric sequelae of repetitive TBI.

Repetitive transcranial magnetic stimulation (rTMS) has demonstrated antidepressant efficacy but has limited evidence in depression associated with traumatic brain injury (TBI). Here, we investigate the use of rTMS targeted with individualized resting-state network mapping (RSNM) of dorsal attention network (DAN) and default mode network (DMN) in subjects with treatment-resistant depression associated with concussive or moderate TBI. The planned sample size was 50 with first interim analysis planned at 20, but only 15 were enrolled before the study was terminated for logistical reasons. Subjects were randomized to 20 sessions of bilateral rTMS (4000 left-sided excitatory pulses, 1000 right-sided inhibitory pulses) or sham. Treatment was targeted to the dorsolateral prefrontal cluster with maximal difference between DAN and DMN correlations based on resting-state functional magnetic resonance imaging with individualized RSNM. Mean improvement in the primary outcome, Montgomery-Asberg Depression Rating Scale (MADRS), was 56% ± 14% (n = 9) with active treatment and 27% ± 25% (n = 5) with sham (Cohen's d = 1.43). One subject randomized to sham withdrew before starting treatment. There were no seizures or other significant adverse events. MADRS improvement was inversely correlated with functional connectivity between the right-sided stimulation site and the subgenual anterior cingulate cortex (sgACC; r = -0.68, 95% confidence interval 0.03-0.925). Active treatment led to increased sgACC-DMN connectivity (d = 1.55) and increased sgACC anti-correlation with the left- and right-sided stimulation sites (d = -1.26 and -0.69, respectively). This pilot study provides evidence that RSNM-targeted rTMS is feasible in TBI patients with depression. Given the dearth of existing evidence-based treatments for depression in this patient population, these preliminarily encouraging results indicate that larger controlled trials are warranted.

Localizing neurologic function within the brain remains a significant challenge in clinical neurosurgery. Invasive mapping with direct electrocortical stimulation currently is the clinical gold standard but is impractical in young or cognitively delayed patients who are unable to reliably perform tasks. Resting state functional magnetic resonance imaging non-invasively identifies resting state networks without the need for task performance, hence, is well suited to pediatric patients. We compared sensorimotor network localization by resting state fMRI to cortical stimulation sensory and motor mapping in 16 pediatric patients aged 3.1 to 18.6 years. All had medically refractory epilepsy that required invasive electrographic monitoring and stimulation mapping. The resting state fMRI data were analyzed using a previously trained machine learning classifier that has previously been evaluated in adults. We report comparable functional localization by resting state fMRI compared to stimulation mapping. These results provide strong evidence for the utility of resting state functional imaging in the localization of sensorimotor cortex across a wide range of pediatric patients., (Copyright © 2019 The Author(s). Published by Elsevier Inc. All rights reserved.)

Resting state functional connectivity is defined in terms of temporal correlations between physiologic signals, most commonly studied using functional magnetic resonance imaging. Major features of functional connectivity correspond to structural (axonal) connectivity. However, this relation is not one-to-one. Interhemispheric functional connectivity in relation to the corpus callosum presents a case in point. Specifically, several reports have documented nearly intact interhemispheric functional connectivity in individuals in whom the corpus callosum (the major commissure between the hemispheres) never develops. To investigate this question, we assessed functional connectivity before and after surgical section of the corpus callosum in 22 patients with medically refractory epilepsy. Section of the corpus callosum markedly reduced interhemispheric functional connectivity. This effect was more profound in multimodal associative areas in the frontal and parietal lobe than primary regions of sensorimotor and visual function. Moreover, no evidence of recovery was observed in a limited sample in which multiyear, longitudinal follow-up was obtained. Comparison of partial vs. complete callosotomy revealed several effects implying the existence of polysynaptic functional connectivity between remote brain regions. Thus, our results demonstrate that callosal as well as extracallosal anatomical connections play a role in the maintenance of interhemispheric functional connectivity., Competing Interests: Conflict of interest statement: E.C.L. discloses financial relationships with the following companies: Intellectual Ventures, Monteris Medical, Acera Medical, Pear Therapeutics, General Sensing, Immunovalent, Face to Face Biometrics, Neurolutions, and Osteovantage., (Copyright © 2017 the Author(s). Published by PNAS.)

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

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., (Copyright © 2017 Elsevier Inc. All rights reserved.)

Resting state functional MRI (R-fMRI) studies have shown that slow (<0.1Hz), intrinsic fluctuations of the blood oxygen level dependent (BOLD) signal are temporally correlated within hierarchically organized functional systems known as resting state networks (RSNs) (Doucet et al., 2011). Most broadly, this hierarchy exhibits a dichotomy between two opposed systems (Fox et al., 2005). One system engages with the environment and includes the visual, auditory, and sensorimotor (SMN) networks as well as the dorsal attention network (DAN), which controls spatial attention. The other system includes the default mode network (DMN) and the fronto-parietal control system (FPC), RSNs that instantiate episodic memory and executive control, respectively. Here, we test the hypothesis, based on the spectral specificity of electrophysiologic responses to perceptual vs. memory tasks (Klimesch, 1999; Pfurtscheller and Lopes da Silva, 1999), that these two large-scale neural systems also manifest frequency specificity in the resting state. We measured the spatial correspondence between electrocorticographic (ECoG) band-limited power (BLP) and R-fMRI correlation patterns in awake, resting, human subjects. Our results show that, while gamma BLP correspondence was common throughout the brain, theta (4-8Hz) BLP correspondence was stronger in the DMN and FPC, whereas alpha (8-12Hz) correspondence was stronger in the SMN and DAN. Thus, the human brain, at rest, exhibits frequency specific electrophysiology, respecting both the spectral structure of task responses and the hierarchical organization of RSNs., (Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.)

While several studies have shown that focal lesions affect the communication between structurally normal regions of the brain, and that these changes may correlate with behavioural deficits, their impact on brain's information processing capacity is currently unknown. Here we test the hypothesis that focal lesions decrease the brain's information processing capacity, of which changes in functional connectivity may be a measurable correlate. To measure processing capacity, we turned to whole brain computational modelling to estimate the integration and segregation of information in brain networks. First, we measured functional connectivity between different brain areas with resting state functional magnetic resonance imaging in healthy subjects (n = 26), and subjects who had suffered a cortical stroke (n = 36). We then used a whole-brain network model that coupled average excitatory activities of local regions via anatomical connectivity. Model parameters were optimized in each healthy or stroke participant to maximize correlation between model and empirical functional connectivity, so that the model's effective connectivity was a veridical representation of healthy or lesioned brain networks. Subsequently, we calculated two model-based measures: 'integration', a graph theoretical measure obtained from functional connectivity, which measures the connectedness of brain networks, and 'information capacity', an information theoretical measure that cannot be obtained empirically, representative of the segregative ability of brain networks to encode distinct stimuli. We found that both measures were decreased in stroke patients, as compared to healthy controls, particularly at the level of resting-state networks. Furthermore, we found that these measures, especially information capacity, correlate with measures of behavioural impairment and the segregation of resting-state networks empirically measured. This study shows that focal lesions affect the brain's ability to represent stimuli and task states, and that information capacity measured through whole brain models is a theory-driven measure of processing capacity that could be used as a biomarker of injury for outcome prediction or target for rehabilitation intervention., (© The Author (2017). Published by Oxford University Press on behalf of the Guarantors of Brain. All rights reserved. For Permissions, please email: journals.permissions@oup.com.)

Objective: The brain's functional architecture of interconnected network-related oscillatory patterns in discrete cortical regions has been well established with functional magnetic resonance imaging (fMRI) studies or direct cortical electrophysiology from electrodes placed on the surface of the brain, or electrocorticography (ECoG). These resting state networks exhibit a robust functional architecture that persists through all stages of sleep and under anesthesia. While the stability of these networks provides a fundamental understanding of the organization of the brain, understanding how these regions can be perturbed is also critical in defining the brain's ability to adapt while learning and recovering from injury., Methods: Patients undergoing an awake craniotomy for resection of a tumor were studied as a unique model of an evolving injury to help define how the cortical physiology and the associated networks were altered by the presence of an invasive brain tumor., Results: This study demonstrates that there is a distinct pattern of alteration of cortical physiology in the setting of a malignant glioma. These changes lead to a physiologic sequestration and progressive synaptic homogeneity suggesting that a de-learning phenomenon occurs within the tumoral tissue compared to its surroundings., Significance: These findings provide insight into how the brain accommodates a region of "defunctionalized" cortex. Additionally, these findings may have important implications for emerging techniques in brain mapping using endogenous cortical physiology.

Background: Managing difficult-to-access lesions or surgically accessible lesions in fragile patients is a central problem in neurosurgery. MRI-guided interstitial laser ablation (ILA) is a minimally invasive option that may provide a safe means of treating these challenging patients., Objective: We aim to (1) evaluate safety, efficacy, and preliminary outcomes within a diverse and large series of ILA treatments; and (2) report technical details and operative trends that proved useful over time in the authors' experience and that may be of use to neurosurgeons who perform ILA., Methods: A retrospective evaluation of ILA patients was performed in terms of demographics, surgical techniques, and clinical outcomes., Results: A total of 133 intracranial lesions in 120 patients were treated with ILA, including glioblastomas (GBM), other gliomas, metastases, epilepsy foci, and radionecrosis. The rate of complications/unexpected readmission was 6.0%, and the mortality rate was 2.2%. With high-grade tumors, tumor volumes >3 cm in diameter trended toward a higher rate of complication (p = 0.056). Median progression-free survival (PFS) and overall survival (OS) for recurrent GBM were 7.4 and 11.6 months, respectively. As a frontline treatment for newly diagnosed GBM, median PFS and OS were 5.9 and 11.4 months, respectively. For metastases, median PFS was not yet reached, and OS was 17.2 months., Conclusion: Our series suggests that ILA is a safe and efficacious treatment for a variety of intracranial pathologies, can be tailored to treat difficult-to-access lesions, and may offer a novel alternative to open craniotomy in properly selected patients., (© 2018 The Author(s) Published by S. Karger AG, Basel.)

Declarative memory consolidation is hypothesized to require a two-stage, reciprocal cortical-hippocampal dialogue. According to this model, higher frequency signals convey information from the cortex to hippocampus during wakefulness, but in the reverse direction during slow-wave sleep (SWS). Conversely, lower-frequency activity propagates from the information "receiver" to the "sender" to coordinate the timing of information transfer. Reversal of sender/receiver roles across wake and SWS implies that higher- and lower-frequency signaling should reverse direction between the cortex and hippocampus. However, direct evidence of such a reversal has been lacking in humans. Here, we use human resting-state fMRI and electrocorticography to demonstrate that δ-band activity and infraslow activity propagate in opposite directions between the hippocampus and cerebral cortex. Moreover, both δ activity and infraslow activity reverse propagation directions between the hippocampus and cerebral cortex across wake and SWS. These findings provide direct evidence for state-dependent reversals in human cortical-hippocampal communication., Competing Interests: The authors declare no conflict of interest.

Understanding the amazingly complex human cerebral cortex requires a map (or parcellation) of its major subdivisions, known as cortical areas. Making an accurate areal map has been a century-old objective in neuroscience. Using multi-modal magnetic resonance images from the Human Connectome Project (HCP) and an objective semi-automated neuroanatomical approach, we delineated 180 areas per hemisphere bounded by sharp changes in cortical architecture, function, connectivity, and/or topography in a precisely aligned group average of 210 healthy young adults. We characterized 97 new areas and 83 areas previously reported using post-mortem microscopy or other specialized study-specific approaches. To enable automated delineation and identification of these areas in new HCP subjects and in future studies, we trained a machine-learning classifier to recognize the multi-modal 'fingerprint' of each cortical area. This classifier detected the presence of 96.6% of the cortical areas in new subjects, replicated the group parcellation, and could correctly locate areas in individuals with atypical parcellations. The freely available parcellation and classifier will enable substantially improved neuroanatomical precision for studies of the structural and functional organization of human cerebral cortex and its variation across individuals and in development, aging, and disease.

Deficits following stroke are classically attributed to focal damage, but recent evidence suggests a key role of distributed brain network disruption. We measured resting functional connectivity (FC), lesion topography, and behavior in multiple domains (attention, visual memory, verbal memory, language, motor, and visual) in a cohort of 132 stroke patients, and used machine-learning models to predict neurological impairment in individual subjects. We found that visual memory and verbal memory were better predicted by FC, whereas visual and motor impairments were better predicted by lesion topography. Attention and language deficits were well predicted by both. Next, we identified a general pattern of physiological network dysfunction consisting of decrease of interhemispheric integration and intrahemispheric segregation, which strongly related to behavioral impairment in multiple domains. Network-specific patterns of dysfunction predicted specific behavioral deficits, and loss of interhemispheric communication across a set of regions was associated with impairment across multiple behavioral domains. These results link key organizational features of brain networks to brain-behavior relationships in stroke.

The purpose of this manuscript is to provide an introduction to resting-state functional magnetic resonance imaging (RS-fMRI) and to review the current application of this new and powerful technique in the preoperative setting using our institute's extensive experience. RS-fMRI has provided important insights into brain physiology and is an increasingly important tool in the clinical setting. As opposed to task-based functional MRI wherein the subject performs a task while being scanned, RS-fMRI evaluates low-frequency fluctuations in the blood oxygen level dependent (BOLD) signal while the subject is at rest. Multiple resting state networks (RSNs) have been identified, including the somatosensory, language, and visual networks, which are of primary importance for presurgical planning. Over the past 4 years, we have performed over 300 RS-fMRI examinations in the clinical setting and these have been used to localize eloquent somatosensory and language cortices before brain tumor resection. RS-fMRI is particularly useful in this setting for patients who are unable to cooperate with the task-based paradigm, such as young children or those who are sedated, paretic, or aphasic.Although RS-fMRI is still investigational, our experience indicates that this method is ready for clinical application in the presurgical setting.

Previous studies suggest stable and robust control of a brain-computer interface (BCI) can be achieved using electrocorticography (ECoG). Translation of this technology from the laboratory to the real world requires additional methods that allow users operate their ECoG-based BCI autonomously. In such an environment, users must be able to perform all tasks currently performed by the experimenter, including manually switching the BCI system on/off. Although a simple task, it can be challenging for target users (e.g., individuals with tetraplegia) due to severe motor disability. In this study, we present an automated and practical strategy to switch a BCI system on or off based on the cognitive state of the user. Using a logistic regression, we built probabilistic models that utilized sub-dural ECoG signals from humans to estimate in pseudo real-time whether a person is awake or in a sleep-like state, and subsequently, whether to turn a BCI system on or off. Furthermore, we constrained these models to identify the optimal anatomical and spectral parameters for delineating states. Other methods exist to differentiate wake and sleep states using ECoG, but none account for practical requirements of BCI application, such as minimizing the size of an ECoG implant and predicting states in real time. Our results demonstrate that, across 4 individuals, wakeful and sleep-like states can be classified with over 80% accuracy (up to 92%) in pseudo real-time using high gamma (70-110 Hz) band limited power from only 5 electrodes (platinum discs with a diameter of 2.3 mm) located above the precentral and posterior superior temporal gyrus.

A long-held view is that stroke causes many distinct neurological syndromes due to damage of specialized cortical and subcortical centers. However, it is unknown if a syndrome-based description is helpful in characterizing behavioral deficits across a large number of patients. We studied a large prospective sample of first-time stroke patients with heterogeneous lesions at 1-2 weeks post-stroke. We measured behavior over multiple domains and lesion anatomy with structural MRI and a probabilistic atlas of white matter pathways. Multivariate methods estimated the percentage of behavioral variance explained by structural damage. A few clusters of behavioral deficits spanning multiple functions explained neurological impairment. Stroke topography was predominantly subcortical, and disconnection of white matter tracts critically contributed to behavioral deficits and their correlation. The locus of damage explained more variance for motor and language than memory or attention deficits. Our findings highlight the need for better models of white matter damage on cognition., (Copyright © 2015 Elsevier Inc. All rights reserved.)

Currently, functional magnetic resonance imaging (fMRI) facilitates a preoperative awareness of an association of an eloquent region with a tumor. This information gives the neurosurgeon helpful information that can aid in creating a surgical strategy. Typically, task-based fMRI has been employed to preoperatively localize speech and motor function. Task-based fMRI depends on the patient's ability to comply with the task paradigm, which often is impaired in the setting of a brain tumor. This problem is overcome by using resting-state fMRI (rs-fMRI) to localize function. rs-fMRI measures spontaneous fluctuations in the blood oxygen level-dependent (BOLD) signal, representing the brain's functional organization. In a neurosurgical context, it allows noninvasive simultaneous assessment of multiple large-scale distributed networks. Compared with task-related fMRI, rs-fMRI provides more comprehensive information on the functional architecture of the brain and is applicable in settings where task-related fMRI may provide inadequate information or could not be performed. Taken together, rs-fMRI substantially expands the preoperative mapping capability in efficiency, effectiveness, and scope. In this article, a brief introduction into rs-fMRI processing methods is followed by a detailed discussion on the role rs-fMRI plays in presurgical planning., (© 2016 S. Karger AG, Basel.)

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., (Copyright © 2014 Elsevier Inc. All rights reserved.)

Background: Functional imaging and lesion studies have associated willed behavior with the anterior cingulate cortex (ACC). Abulia is a syndrome characterized by apathy and deficiency of motivated behavior. Abulia is most frequently associated with ACC damage, but also occurs following damage to subcortical nuclei (striatum, globus pallidus, thalamic nuclei). We present resting state functional connectivity MRI (fcMRI) data from an individual who suffered a stroke leading to abulia. We hypothesized that, although structural imaging revealed no damage to the patient's ACC, fcMRI would uncover aberrant function in this region and in the relevant cortical networks., Methods: Resting state correlations in the patient's gray matter were compared to those of age-matched controls. Using a novel method to identify abnormal patterns of functional connectivity in single subjects, we identified areas and networks with aberrant connectivity., Results: Networks associated with memory (default mode network) and executive function (cingulo-opercular network) were abnormal. The patient's anterior cingulate was among the areas showing aberrant functional connectivity. In a rescan 3 years later, deficits remained stable and fcMRI findings were replicated., Conclusions: These findings suggest that the aberrant functional connectivity mapping approach described may be useful for linking stroke symptoms to disrupted network connectivity.

Objective: The role of resting state functional networks in epilepsy is incompletely understood. While some pathologic diagnoses have been shown to have maintained but altered resting state connectivity, others have implicated resting state connectivity in disease progression. However little is known about how these resting state networks influence the behavior of a focal neocortical seizure., Methods: Using data taken from invasively monitored patients with intractable focal neocortical epilepsy, we evaluated network connectivity (as determined by oscillatory covariance of the slow cortical potential (<0.5 Hz)) as it relates to neocortical seizure foci both in the interictal and ictal states., Results: Similar to what has been shown in the past for sleep and anesthesia, electophysiologic resting state networks that are defined by this slow cortical potential covariance maintain their topographic correlation structure throughout an ictal event. Moreover, in the context of focal epilepsy in which the seizure has a specific site of onset, seizure propagation is not chaotic or random. Rather, the seizure (reflected by an elevation of high frequency power) preferentially propagates along the network that contains the seizure onset zone., Significance: Taken together, these findings further undergird the fundamental role of resting state networks, provide novel insights into the network-influenced behavior of seizures, and potentially identify additional targets for surgical disconnection including informing the location for the completion of multiple subpial transections (MSPTs).

The discovery that spontaneous fluctuations in blood oxygen level-dependent (BOLD) signals contain information about the functional organization of the brain has caused a paradigm shift in neuroimaging. It is now well established that intrinsic brain activity is organized into spatially segregated resting-state networks (RSNs). Less is known regarding how spatially segregated networks are integrated by the propagation of intrinsic activity over time. To explore this question, we examined the latency structure of spontaneous fluctuations in the fMRI BOLD signal. Our data reveal that intrinsic activity propagates through and across networks on a timescale of ∼1 s. Variations in the latency structure of this activity resulting from sensory state manipulation (eyes open vs. closed), antecedent motor task (button press) performance, and time of day (morning vs. evening) suggest that BOLD signal lags reflect neuronal processes rather than hemodynamic delay. Our results emphasize the importance of the temporal structure of the brain's spontaneous activity., (Copyright © 2014 the American Physiological Society.)

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.

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., (Copyright © 2013 Elsevier Inc. All rights reserved.)

Recent advances in basic neuroscience research across a wide range of methodologies have contributed significantly to our understanding of human cortical electrophysiology and functional brain imaging. Translation of this research into clinical neurosurgery has opened doors for advanced mapping of functionality that previously was prohibitively difficult, if not impossible. Here we present the case of a unique individual with congenital blindness and medically refractory epilepsy who underwent neurosurgical treatment of her seizures. Pre-operative evaluation presented the challenge of accurately and robustly mapping the cerebral cortex for an individual with a high probability of significant cortical re-organization. Additionally, a blind individual has unique priorities in one's ability to read Braille by touch and sense the environment primarily by sound than the non-vision impaired person. For these reasons we employed additional measures to map sensory, motor, speech, language, and auditory perception by employing a number of cortical electrophysiologic mapping and functional magnetic resonance imaging methods. Our data show promising results in the application of these adjunctive methods in the pre-operative mapping of otherwise difficult to localize, and highly variable, functional cortical areas.

Background: The goal of the study was to demonstrate a hierarchical structure of resting state activity in the healthy brain using a data-driven clustering algorithm., Methodology/principal Findings: The fuzzy-c-means clustering algorithm was applied to resting state fMRI data in cortical and subcortical gray matter from two groups acquired separately, one of 17 healthy individuals and the second of 21 healthy individuals. Different numbers of clusters and different starting conditions were used. A cluster dispersion measure determined the optimal numbers of clusters. An inner product metric provided a measure of similarity between different clusters. The two cluster result found the task-negative and task-positive systems. The cluster dispersion measure was minimized with seven and eleven clusters. Each of the clusters in the seven and eleven cluster result was associated with either the task-negative or task-positive system. Applying the algorithm to find seven clusters recovered previously described resting state networks, including the default mode network, frontoparietal control network, ventral and dorsal attention networks, somatomotor, visual, and language networks. The language and ventral attention networks had significant subcortical involvement. This parcellation was consistently found in a large majority of algorithm runs under different conditions and was robust to different methods of initialization., Conclusions/significance: The clustering of resting state activity using different optimal numbers of clusters identified resting state networks comparable to previously obtained results. This work reinforces the observation that resting state networks are hierarchically organized.