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Rafael Romero-Garcia, John Suckling, Mallory Owen, Moataz Assem, Rohitashwa Sinha, Pedro Coelho, Emma Woodberry, Stephen J. Price, Amos Burke, Thomas Santarius, Yaara Erez, and Michael G. Hart

OBJECTIVE

The aim of this study was to test brain tumor interactions with brain networks, thereby identifying protective features and risk factors for memory recovery after resection.

METHODS

Seventeen patients with diffuse nonenhancing glioma (ages 22–56 years) underwent longitudinal MRI before and after surgery, and during a 12-month recovery period (47 MRI scans in total after exclusion). After each scanning session, a battery of memory tests was performed using a tablet-based screening tool, including free verbal memory, overall verbal memory, episodic memory, orientation, forward digit span, and backward digit span. Using structural MRI and neurite orientation dispersion and density imaging (NODDI) derived from diffusion-weighted images, the authors estimated lesion overlap and neurite density, respectively, with brain networks derived from normative data in healthy participants (somatomotor, dorsal attention, ventral attention, frontoparietal, and default mode network [DMN]). Linear mixed-effect models (LMMs) that regressed out the effect of age, gender, tumor grade, type of treatment, total lesion volume, and total neurite density were used to test the potential longitudinal associations between imaging markers and memory recovery.

RESULTS

Memory recovery was not significantly associated with either the tumor location based on traditional lobe classification or the type of treatment received by patients (i.e., surgery alone or surgery with adjuvant chemoradiotherapy). Nonlocal effects of tumors were evident on neurite density, which was reduced not only within the tumor but also beyond the tumor boundary. In contrast, high preoperative neurite density outside the tumor but within the DMN was associated with better memory recovery (LMM, p value after false discovery rate correction [Pfdr] < 10−3). Furthermore, postoperative and follow-up neurite density within the DMN and frontoparietal network were also associated with memory recovery (LMM, Pfdr = 0.014 and Pfdr = 0.001, respectively). Preoperative tumor and postoperative lesion overlap with the DMN showed a significant negative association with memory recovery (LMM, Pfdr = 0.002 and Pfdr < 10−4, respectively).

CONCLUSIONS

Imaging biomarkers of cognitive recovery and decline can be identified using NODDI and resting-state networks. Brain tumors and their corresponding treatment affecting brain networks that are fundamental for memory functioning such as the DMN can have a major impact on patients’ memory recovery.

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Chao Li, Shuo Wang, Jiun-Lin Yan, Turid Torheim, Natalie R. Boonzaier, Rohitashwa Sinha, Tomasz Matys, Florian Markowetz, and Stephen J. Price

OBJECTIVE

The objective of this study was to characterize the abnormalities revealed by diffusion tensor imaging (DTI) using MR spectroscopy (MRS) and perfusion imaging, and to evaluate the prognostic value of a proposed quantitative measure of tumor invasiveness by combining contrast-enhancing (CE) and DTI abnormalities in patients with glioblastoma.

METHODS

Eighty-four patients with glioblastoma were recruited preoperatively. DTI was decomposed into isotropic (p) and anisotropic (q) components. The relative cerebral blood volume (rCBV) was calculated from the dynamic susceptibility contrast imaging. Values of N-acetylaspartate, myoinositol, choline (Cho), lactate (Lac), and glutamate + glutamine (Glx) were measured from multivoxel MRS and normalized as ratios to creatine (Cr). Tumor regions of interest (ROIs) were manually segmented from the CE T1-weighted (CE-ROI) and DTI-q (q-ROI) maps. Perfusion and metabolic characteristics of these ROIs were measured and compared. The relative invasiveness coefficient (RIC) was calculated as a ratio of the characteristic radii of CE-ROI and q-ROI. The prognostic significance of RIC was tested using Kaplan-Meier and multivariate Cox regression analyses.

RESULTS

The Cho/Cr, Lac/Cr, and Glx/Cr in q-ROI were significantly higher than CE-ROI (p = 0.004, p = 0.005, and p = 0.007, respectively). CE-ROI had significantly higher rCBV values than q-ROI (p < 0.001). A higher RIC was associated with worse survival in a multivariate overall survival (OS) model (hazard ratio [HR] 1.40, 95% confidence interval [CI] 1.06–1.85, p = 0.016) and progression-free survival (PFS) model (HR 1.55, 95% CI 1.16–2.07, p = 0.003). An RIC cutoff value of 0.89 significantly predicted shorter OS (median 384 vs 605 days, p = 0.002) and PFS (median 244 vs 406 days, p = 0.001).

CONCLUSIONS

DTI-q abnormalities displayed higher tumor load and hypoxic signatures compared with CE abnormalities, whereas CE regions potentially represented the tumor proliferation edge. Integrating the extents of invasion visualized by DTI-q and CE images into clinical practice may lead to improved treatment efficacy.

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Jiun-Lin Yan, Anouk van der Hoorn, Timothy J. Larkin, Natalie R. Boonzaier, Tomasz Matys, and Stephen J. Price

OBJECTIVE

Diffusion tensor imaging (DTI) has been shown to detect tumor invasion in glioblastoma patients and has been applied in surgical planning. However, the clinical value of the extent of resection based on DTI is unclear. Therefore, the correlation between the extent of resection of DTI abnormalities and patients' outcome was retrospectively reviewed.

METHODS

A review was conducted of 31 patients with newly diagnosed supratentorial glioblastoma who underwent standard 5-aminolevulinic acid–aided surgery with the aim of maximal resection of the enhancing tumor component. All patients underwent presurgical MRI, including volumetric postcontrast T1-weighted imaging, DTI, and FLAIR. Postsurgical anatomical MR images were obtained within 72 hours of resection. The diffusion tensor was split into an isotropic (p) and anisotropic (q) component. The extent of resection was measured for the abnormal area on the p, q, FLAIR, and postcontrast T1-weighted images. Data were analyzed in relation to patients' outcome using univariate and multivariate Cox regression models controlling for possible confounding factors including age, O6-methylguanine-DNA-methyltrans-ferase methylation status, and isocitrate dehydrogenase–1 mutation.

RESULTS

Complete resection of the enhanced tumor shown on the postcontrast T1-weighted images was achieved in 24 of 31 patients (77%). The mean extent of resection of the abnormal p, q, and FLAIR areas was 57%, 83%, and 59%, respectively. Increased resection of the abnormal p and q areas correlated positively with progression-free survival (p = 0.009 and p = 0.006, respectively). Additionally, a larger, residual, abnormal q volume predicted significantly shorter time to progression (p = 0.008). More extensive resection of the abnormal q and contrast-enhanced area improved overall survival (p = 0.041 and 0.050, respectively).

CONCLUSIONS

Longer progression-free survival and overall survival were seen in glioblastoma patients in whom more DTI-documented abnormality was resected, which was previously shown to represent infiltrative tumor. This highlights the potential usefulness and the importance of an extended resection based on DTI-derived maps.

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Michael G. Hart, Stephen J. Price, and John Suckling

OBJECTIVE

Resection of focal brain lesions involves maximizing the resection while preserving brain function. Mapping brain function has entered a new era focusing on distributed connectivity networks at “rest,” that is, in the absence of a specific task or stimulus, requiring minimal participant engagement. Central to this frame shift has been the development of methods for the rapid assessment of whole-brain connectivity with functional MRI (fMRI) involving blood oxygenation level–dependent imaging. The authors appraised the feasibility of fMRI-based mapping of a repertoire of functional connectivity networks in neurosurgical patients with focal lesions and the potential benefits of resting-state connectivity mapping for surgical planning.

METHODS

Resting-state fMRI sequences with a 3-T scanner and multiecho echo-planar imaging coupled to independent component analysis were acquired preoperatively from 5 study participants who had a right temporoparietooccipital glioblastoma. Seed-based functional connectivity analysis was performed with InstaCorr. Network identification focused on 7 major functional connectivity networks described in the literature and a putative language network centered on Broca's area.

RESULTS

All 8 functional connectivity networks were identified in each participant. Tumor-related topological changes to the default mode network were observed in all participants. In addition, each participant had at least 1 other abnormal network, and each network was abnormal in at least 1 participant. Individual patterns of network irregularities were identified with a qualitative approach and included local displacement due to mass effect, loss of a functional network component, and recruitment of new regions.

CONCLUSIONS

Resting-state fMRI can reliably and rapidly detect common functional connectivity networks in patients with glioblastoma and also has sufficient sensitivity for identifying patterns of network alterations. Mapping of functional connectivity networks offers the possibility to expand investigations to less commonly explored neuropsychological processes, such as executive control, attention, and salience. Changes in these networks may allow insights into mechanisms underlying the functional consequences of tumor growth, surgical intervention, and patient rehabilitation.

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Michael G. Hart, Rolf J. F. Ypma, Rafael Romero-Garcia, Stephen J. Price, and John Suckling

Neuroanatomy has entered a new era, culminating in the search for the connectome, otherwise known as the brain’s wiring diagram. While this approach has led to landmark discoveries in neuroscience, potential neurosurgical applications and collaborations have been lagging. In this article, the authors describe the ideas and concepts behind the connectome and its analysis with graph theory. Following this they then describe how to form a connectome using resting state functional MRI data as an example. Next they highlight selected insights into healthy brain function that have been derived from connectome analysis and illustrate how studies into normal development, cognitive function, and the effects of synthetic lesioning can be relevant to neurosurgery. Finally, they provide a précis of early applications of the connectome and related techniques to traumatic brain injury, functional neurosurgery, and neurooncology.