Alterations of white matter integrity associated with cognitive deficits in patients with glioma

Metabolic signatures derived from whole-brain MR-spectroscopy identify early tumor progression in high-grade gliomas using machine learning

PURPOSE

Recurrence for high-grade gliomas is inevitable despite maximal safe resection and adjuvant chemoradiation, and current imaging techniques fall short in predicting future progression. However, we introduce a novel whole-brain magnetic resonance spectroscopy (WB-MRS) protocol that delves into the intricacies of tumor microenvironments, offering a comprehensive understanding of glioma progression to inform expectant surgical and adjuvant intervention.

METHODS

We investigated five locoregional tumor metabolites in a post-treatment population and applied machine learning (ML) techniques to analyze key relationships within seven regions of interest: contralateral normal-appearing white matter (NAWM), fluid-attenuated inversion recovery (FLAIR), contrast-enhancing tumor at time of WB-MRS (Tumor), areas of future recurrence (AFR), whole-brain healthy (WBH), non-progressive FLAIR (NPF), and progressive FLAIR (PF). Five supervised ML classification models and a neural network were developed, optimized, trained, tested, and validated. Lastly, a web application was developed to host our novel calculator, the Miami Glioma Prediction Map (MGPM), for open-source interaction.

RESULTS

Sixteen patients with histopathological confirmation of high-grade glioma prior to WB-MRS were included in this study, totaling 118,922 whole-brain voxels. ML models successfully differentiated normal-appearing white matter from tumor and future progression. Notably, the highest performing ML model predicted glioma progression within fluid-attenuated inversion recovery (FLAIR) signal in the post-treatment setting (mean AUC = 0.86), with Cho/Cr as the most important feature.

CONCLUSIONS

This study marks a significant milestone as the first of its kind to unveil radiographic occult glioma progression in post-treatment gliomas within 8 months of discovery. These findings underscore the utility of ML-based WB-MRS growth predictions, presenting a promising avenue for the guidance of early treatment decision-making. This research represents a crucial advancement in predicting the timing and location of glioblastoma recurrence, which can inform treatment decisions to improve patient outcomes.

Predicting Cognitive Functioning for Patients with a High-Grade Glioma: Evaluating Different Representations of Tumor Location in a Common Space

Cognitive functioning is increasingly considered when making treatment decisions for patients with a brain tumor in view of a personalized onco-functional balance. Ideally, one can predict cognitive functioning of individual patients to make treatment decisions considering this balance. To make accurate predictions, an informative representation of tumor location is pivotal, yet comparisons of representations are lacking. Therefore, this study compares brain atlases and principal component analysis (PCA) to represent voxel-wise tumor location. Pre-operative cognitive functioning was predicted for 246 patients with a high-grade glioma across eight cognitive tests while using different representations of voxel-wise tumor location as predictors. Voxel-wise tumor location was represented using 13 different frequently-used population average atlases, 13 randomly generated atlases, and 13 representations based on PCA. ElasticNet predictions were compared between representations and against a model solely using tumor volume. Preoperative cognitive functioning could only partly be predicted from tumor location. Performances of different representations were largely similar. Population average atlases did not result in better predictions compared to random atlases. PCA-based representation did not clearly outperform other representations, although summary metrics indicated that PCA-based representations performed somewhat better in our sample. Representations with more regions or components resulted in less accurate predictions. Population average atlases possibly cannot distinguish between functionally distinct areas when applied to patients with a glioma. This stresses the need to develop and validate methods for individual parcellations in the presence of lesions. Future studies may test if the observed small advantage of PCA-based representations generalizes to other data.

Neuroimaging Genomics a Predictor of Major Depressive Disorder (MDD)

Depression is a complex psychiatric disorder influenced by various genetic and environmental factors. Strong evidence has established the contribution of genetic factors in depression through twin studies and the heritability rate for depression has been reported to be 37%. Genetic studies have identified genetic variations associated with an increased risk of developing depression. Imaging genetics is an integrated approach where imaging measures are combined with genetic information to explore how specific genetic variants contribute to brain abnormalities. Neuroimaging studies allow us to examine both structural and functional abnormalities in individuals with depression. This review has been designed to study the correlation of the significant genetic variants with different regions of neural activity, connectivity, and structural alteration in the brain as detected by imaging techniques to understand the scope of biomarkers in depression. This might help in developing novel therapeutic interventions targeting specific genetic pathways or brain circuits and the underlying pathophysiology of depression based on this integrated approach can be established at length.

Cognitive functioning in untreated glioma patients: The limited predictive value of clinical variables

BACKGROUND

Previous research identified many clinical variables that are significantly related to cognitive functioning before surgery. It is not clear whether such variables enable accurate prediction for individual patients' cognitive functioning because statistical significance does not guarantee predictive value. Previous studies did not test how well cognitive functioning can be predicted for (yet) untested patients. Furthermore, previous research is limited in that only linear or rank-based methods with small numbers of variables were used.

METHODS

We used various machine learning models to predict preoperative cognitive functioning for 340 patients with glioma across 18 outcome measures. Predictions were made using a comprehensive set of clinical variables as identified from the literature. Model performances and optimized hyperparameters were interpreted. Moreover, Shapley additive explanations were calculated to determine variable importance and explore interaction effects.

RESULTS

Best-performing models generally demonstrated above-random performance. Performance, however, was unreliable for 14 out of 18 outcome measures with predictions worse than baseline models for a substantial number of train-test splits. Best-performing models were relatively simple and used most variables for prediction while not relying strongly on any variable.

CONCLUSIONS

Preoperative cognitive functioning could not be reliably predicted across cognitive tests using the comprehensive set of clinical variables included in the current study. Our results show that a holistic view of an individual patient likely is necessary to explain differences in cognitive functioning. Moreover, they emphasize the need to collect larger cross-center and multimodal data sets.

Understanding language and cognition after brain surgery - Tumour grade, fine-grained assessment tools and, most of all, individualized approach

Cognitive performance influences the quality of life and survival of people with glioma. Thus, a detailed neuropsychological and language evaluation is essential. In this work, we tested if an analysis of errors in naming can indicate semantic and/or phonological impairments in 87 awake brain surgery patients. Secondly, we explored how language and cognition change after brain tumour resection. Finally, we checked if low-tumour grade had a protective effect on cognition. Our results indicated that naming errors can be useful to monitor semantic and phonological processing, as their number correlated with scores on tasks developed by our team for testing these domains. Secondly, we showed that - although an analysis at a whole group level indicates a decline in language functions - significantly more individual patients improve or remain stable when compared to the ones who declined. Finally, we observed that having LGG, when compared with HGG, favours patients' outcome after surgery, most probably due to brain plasticity mechanisms. We provide new evidence of the importance of applying a broader neuropsychological assessment and an analysis of naming errors in patients with glioma. Our approach may potentially ensure better detection of cognitive deficits and contribute to better postoperative outcomes. Our study also shows that an individualized approach in post-surgical follow-ups can reveal reassuring results showing that significantly more patients remain stable or improve and can be a promising avenue for similar reports. Finally, the study captures that plasticity mechanisms may act as protective in LGG versus HGG after surgery.

Structural connectome-based predictive modeling of cognitive deficits in treated glioma patients

Background

In glioma patients, tumor growth and subsequent treatments are associated with various types of brain lesions. We hypothesized that cognitive functioning in these patients critically depends on the maintained structural connectivity of multiple brain networks.

Methods

The study included 121 glioma patients (median age, 52 years; median Eastern Cooperative Oncology Group performance score 1; CNS-WHO Grade 3 or 4) after multimodal therapy. Cognitive performance was assessed by 10 tests in 5 cognitive domains at a median of 14 months after treatment initiation. Hybrid amino acid PET/MRI using the tracer O-(2-[18F]fluoroethyl)-L-tyrosine, a network-based cortical parcellation, and advanced tractography were used to generate whole-brain fiber count-weighted connectivity matrices. The matrices were applied to a cross-validated machine-learning model to identify predictive fiber connections (edges), critical cortical regions (nodes), and the networks underlying cognitive performance.

Results

Compared to healthy controls (n = 121), patients' cognitive scores were significantly lower in 9 cognitive tests. The models predicted the scores of 7/10 tests (median correlation coefficient, 0.47; range, 0.39-0.57) from 0.6% to 5.4% of the matrix entries; 84% of the predictive edges were between nodes of different networks. Critically involved cortical regions (≥10 adjacent edges) included predominantly left-sided nodes of the visual, somatomotor, dorsal/ventral attention, and default mode networks. Highly critical nodes (≥15 edges) included the default mode network's left temporal and bilateral posterior cingulate cortex.

Conclusions

These results suggest that the cognitive performance of pretreated glioma patients is strongly related to structural connectivity between multiple brain networks and depends on the integrity of known network hubs also involved in other neurological disorders.

The involvement of brain regions associated with lower KPS and shorter survival time predicts a poor prognosis in glioma

Background

Isocitrate dehydrogenase-wildtype glioblastoma (IDH-wildtype GBM) and IDH-mutant astrocytoma have distinct biological behaviors and clinical outcomes. The location of brain tumors is closely associated not only with clinical symptoms and prognosis but also with key molecular alterations such as IDH. Therefore, we hypothesize that the key brain regions influencing the prognosis of glioblastoma and astrocytoma are likely to differ. This study aims to (1) identify specific regions that are associated with the Karnofsky Performance Scale (KPS) or overall survival (OS) in IDH-wildtype GBM and IDH-mutant astrocytoma and (2) test whether the involvement of these regions could act as a prognostic indicator.

Methods

A total of 111 patients with IDH-wildtype GBM and 78 patients with IDH-mutant astrocytoma from the Cancer Imaging Archive database were included in the study. Voxel-based lesion-symptom mapping (VLSM) was used to identify key brain areas for lower KPS and shorter OS. Next, we analyzed the structural and cognitive dysfunction associated with these regions. The survival analysis was carried out using Kaplan-Meier survival curves. Another 72 GBM patients and 48 astrocytoma patients from Harbin Medical University Cancer Hospital were used as a validation cohort.

Results

Tumors located in the insular cortex, parahippocampal gyrus, and middle and superior temporal gyrus of the left hemisphere tended to lead to lower KPS and shorter OS in IDH-wildtype GBM. The regions that were significantly correlated with lower KPS in IDH-mutant astrocytoma included the subcallosal cortex and cingulate gyrus. These regions were associated with diverse structural and cognitive impairments. The involvement of these regions was an independent predictor for shorter survival in both GBM and astrocytoma.

Conclusion

This study identified the specific regions that were significantly associated with OS or KPS in glioma. The results may help neurosurgeons evaluate patient survival before surgery and understand the pathogenic mechanisms of glioma in depth.

White matter tracts contribute selectively to cognitive functioning in patients with glioma

Objective

The functional organization of white matter (WM) tracts is not well characterized, especially in patients with intrinsic brain tumors where complex patterns of tissue injury, compression, and neuroplasticity may be present. This study uses diffusion tensor imaging (DTI) to investigate the relationships between WM tract disruption and cognitive deficits in glioma patients.

Methods

Seventy-nine patients with glioma underwent preoperative DTI and neuropsychological testing. Thirteen WM tracts were reconstructed bilaterally. Fractional anisotropy and streamline number were obtained for each tract as indices of connectivity. Univariate regression models were used to model the association between WM tract connectivity and neuropsychological outcomes.

Results

Glioma patients exhibited variable injury to WM tracts and variable cognitive deficits on validated neuropsychological tests. We identified 16 age-adjusted associations between WM tract integrity and neuropsychological function. The left inferior frontal-occipital fasciculus (IFOF) predicted list learning and dominant-hand fine motor dexterity. The right IFOF predicted non-dominant-hand fine motor dexterity and visuospatial index scores. The left inferior longitudinal fasciculus (ILF) predicted immediate memory list learning and index scores. The right ILF predicted non-dominant-hand fine motor dexterity and backward digit span scores. The left superior longitudinal fasciculus (SLF) I predicted processing speed. The left SLF III predicted list learning, immediate memory index scores, phonemic fluency, and verbal abstract reasoning. The left cingulum predicted processing speed. The right anterior AF predicted verbal abstract reasoning.

Conclusion

WM tract disruption predicts cognitive dysfunction in glioma patients. By improving knowledge of WM tract organization, this analysis may guide maximum surgical resection and functional preservation in glioma patients.

Improved prediction of glioma-related aphasia by diffusion MRI metrics, machine learning, and automated fiber bundle segmentation

White matter impairments caused by gliomas can lead to functional disorders. In this study, we predicted aphasia in patients with gliomas infiltrating the language network using machine learning methods. We included 78 patients with left-hemispheric perisylvian gliomas. Aphasia was graded preoperatively using the Aachen aphasia test (AAT). Subsequently, we created bundle segmentations based on automatically generated tract orientation mappings using TractSeg. To prepare the input for the support vector machine (SVM), we first preselected aphasia-related fiber bundles based on the associations between relative tract volumes and AAT subtests. In addition, diffusion magnetic resonance imaging (dMRI)-based metrics [axial diffusivity (AD), apparent diffusion coefficient (ADC), fractional anisotropy (FA), and radial diffusivity (RD)] were extracted within the fiber bundles' masks with their mean, standard deviation, kurtosis, and skewness values. Our model consisted of random forest-based feature selection followed by an SVM. The best model performance achieved 81% accuracy (specificity = 85%, sensitivity = 73%, and AUC = 85%) using dMRI-based features, demographics, tumor WHO grade, tumor location, and relative tract volumes. The most effective features resulted from the arcuate fasciculus (AF), middle longitudinal fasciculus (MLF), and inferior fronto-occipital fasciculus (IFOF). The most effective dMRI-based metrics were FA, ADC, and AD. We achieved a prediction of aphasia using dMRI-based features and demonstrated that AF, IFOF, and MLF were the most important fiber bundles for predicting aphasia in this cohort.

Fiber Density and Structural Brain Connectome in Glioblastoma Are Correlated With Glioma Cell Infiltration

BACKGROUND

Glioblastoma (GBM) preferred to infiltrate into white matter (WM) beyond the recognizable tumor margin.

OBJECTIVE

To investigate whether fiber density (FD) and structural brain connectome can provide meaningful information about WM destruction and glioma cell infiltration.

METHODS

GBM cases were collected based on inclusion criteria, and baseline information and preoperative MRI results were obtained. GBM lesions were automatically segmented into necrosis, contrast-enhanced tumor, and edema areas. We obtained the FD map to compute the FD and lnFD values in each subarea and reconstructed the structural brain connectome to obtain the topological metrics in each subarea. We also divided the edema area into a nonenhanced tumor (NET) area and a normal WM area based on the contralesional lnFD value in the edema area, and computed the NET ratio.

RESULTS

Twenty-five GBM cases were included in this retrospective study. The FD/lnFD value and topological metrics (aCp, aLp, aEg, aEloc, and ar) were significantly correlated with GBM subareas, which represented the extent of WM destruction and glioma cell infiltration. The FD/lnFD values and topological parameters were correlated with the NET ratio. In particular, the lnFD value in the edema area was correlated with the NET ratio (coefficient, 0.92). Therefore, a larger lnFD value indicates more severe glioma infiltration in the edema area and suggests an extended resection for better clinical outcomes.

CONCLUSION

The FD and structural brain connectome in this study provide a new insight into glioma infiltration and a different consideration of their clinical application in neuro-oncology.

The anatomy of neuroepithelial tumours

Many neurological conditions conceal specific anatomical patterns. Their study contributes to the understanding of disease biology and to tailored diagnostics and therapy. Neuroepithelial tumours exhibit distinct anatomical phenotypes and spatiotemporal dynamics that differ from those of other brain tumours. Brain metastases display a preference for the cortico-subcortical boundaries of watershed areas and have a predominantly spherical growth. Primary CNS lymphomas localize to the white matter and generally invade along fibre tracts. In neuroepithelial tumours, topographic probability mapping and unsupervised topological clustering have identified an inherent radial anatomy and adherence to ventriculopial configurations of specific hierarchical orders. Spatiotemporal probability and multivariate survival analyses have identified a temporal and prognostic sequence underlying the anatomical phenotypes of neuroepithelial tumours. Gradual neuroepithelial de-differentiation and declining prognosis follow (i) an expansion into higher order radial units; (ii) a subventricular spread; and (iii) the presence of mesenchymal patterns (expansion along white matter tracts, leptomeningeal or perivascular invasion, CSF spread). While different pathophysiological hypotheses have been proposed, the cellular and molecular mechanisms dictating this anatomical behaviour remain largely unknown. Here we adopt an ontogenetic approach towards the understanding of neuroepithelial tumour anatomy. Contemporary perception of histo- and morphogenetic processes during neurodevelopment permit us to conceptualize the architecture of the brain into hierarchically organized radial units. The anatomical phenotypes in neuroepithelial tumours and their temporal and prognostic sequences share remarkable similarities with the ontogenetic organization of the brain and the anatomical specifications that occur during neurodevelopment. This macroscopic coherence is reinforced by cellular and molecular observations that the initiation of various neuroepithelial tumours, their intratumoural hierarchy and tumour progression are associated with the aberrant reactivation of surprisingly normal ontogenetic programs. Generalizable topological phenotypes could provide the basis for an anatomical refinement of the current classification of neuroepithelial tumours. In addition, we have proposed a staging system for adult-type diffuse gliomas that is based on the prognostically critical steps along the sequence of anatomical tumour progression. Considering the parallels in anatomical behaviour between different neuroepithelial tumours, analogous staging systems may be implemented for other neuroepithelial tumour types and subtypes. Both the anatomical stage of a neuroepithelial tumour and the spatial configuration of its hosting radial unit harbour the potential to stratify treatment decisions at diagnosis and during follow-up. More data on specific neuroepithelial tumour types and subtypes are needed to increase the anatomical granularity in their classification and to determine the clinical impact of stage-adapted and anatomically tailored therapy and surveillance.

Oncological and functional neurosurgery: Perspectives for the decade regarding diffuse gliomas

For decades, diffuse glioma (DG) studies mostly focused on oncological considerations, whereas functional outcomes received less attention. Currently, because overall survival has increased in DG, especially in low-grade glioma (overall survival > 15 years), quality of life including neurocognitive and behavioral aspects should be assessed and preserved more systematically, particularly regarding surgery. Indeed, early maximal tumor removal results in greater survival in both high-grade and low-grade gliomas, leading to propose "supra-marginal" resection, with excision of the peritumoral zone in diffuse neoplasms. To minimize functional risks while maximizing the extent of resection, traditional "tumor-mass resection" is replaced by "connectome-guided resection" conducted under awake mapping, taking into account inter-individual brain anatomo-functional variability. A better understanding of the dynamic interplay between DG progression and reactional neuroplastic mechanisms is critical to adapt a personalized multistage therapeutic strategy, with integration of functional neurooncological (re)operation(s) in a multimodal management scheme including repeated medical therapies. Because the therapeutic armamentarium remains limited, the aims of this paradigmatic shift are to predict one/several step(s) ahead glioma behavior, its modifications, and compensatory neural networks reconfiguration over time in order to optimize the onco-functional benefit of each treatment - either in isolation or in combination with others - in human beings bearing a chronic tumoral disease while enjoying an active familial and socio-professional life as close as possible to their expectations. Thus, new ecological endpoints such as return to work should be incorporated into future DG trials. "Preventive neurooncology" might also be envisioned, by proposing a screening policy to discover and treat incidental glioma earlier.

Deep Learning Classifies Low- and High-Grade Glioma Patients with High Accuracy, Sensitivity, and Specificity Based on Their Brain White Matter Networks Derived from Diffusion Tensor Imaging

Classifying low-grade glioma (LGG) patients from high-grade glioma (HGG) is one of the most challenging tasks in planning treatment strategies for brain tumor patients. Previous studies derived several handcrafted features based on the tumor's texture and volume from magnetic resonance images (MRI) to classify LGG and HGG patients. The accuracy of classification was moderate. We aimed to classify LGG from HGG with high accuracy using the brain white matter (WM) network connectivity matrix constructed using diffusion tensor tractography. We obtained diffusion tensor images (DTI) of 44 LGG and 48 HGG patients using routine clinical imaging. Fiber tractography and brain parcellation were performed for each patient to obtain the fractional anisotropy, axial diffusivity, radial diffusivity, and mean diffusivity weighted connectivity matrices. We used a deep convolutional neural network (DNN) for classification and the gradient class activation map (GRAD-CAM) technique to identify the neural connectivity features focused on by the DNN. DNN could classify both LGG and HGG with 98% accuracy. The sensitivity and specificity values were above 0.98. GRAD-CAM analysis revealed a distinct WM network pattern between LGG and HGG patients in the frontal, temporal, and parietal lobes. Our results demonstrate that glioma affects the WM network in LGG and HGG patients differently.

A Personalized Longitudinal Strategy in Low-Grade Glioma Patients: Predicting Oncological and Neural Interindividual Variability and Its Changes over Years to Think One Step Ahead

Diffuse low-grade glioma (LGG) is a rare cerebral cancer, mostly involving young adults with an active life at diagnosis. If left untreated, LGG widely invades the brain and becomes malignant, generating neurological worsening and ultimately death. Early and repeat treatments for this incurable tumor, including maximal connectome-based surgical resection(s) in awake patients, enable postponement of malignant transformation while preserving quality of life owing to constant neural network reconfiguration. Due to considerable interindividual variability in terms of LGG course and consecutive cerebral reorganization, a multistage longitudinal strategy should be tailored accordingly in each patient. It is crucial to predict how the glioma will progress (changes in growth rate and pattern of migration, genetic mutation, etc.) and how the brain will adapt (changes in patterns of spatiotemporal redistribution, possible functional consequences such as epilepsy or cognitive decline, etc.). The goal is to anticipate therapeutic management, remaining one step ahead in order to select the optimal (re-)treatment(s) (some of them possibly kept in reserve), at the appropriate time(s) in the evolution of this chronic disease, before malignization and clinical worsening. Here, predictive tumoral and non-tumoral factors, and their ever-changing interactions, are reviewed to guide individual decisions in advance based on patient-specific markers, for the treatment of LGG.

Involvement of White Matter Language Tracts in Glioma: Clinical Implications, Operative Management, and Functional Recovery After Injury

To achieve optimal survival and quality of life outcomes in patients with glioma, the extent of tumor resection must be maximized without causing injury to eloquent structures. Preservation of language function is of particular importance to patients and requires careful mapping to reveal the locations of cortical language hubs and their structural and functional connections. Within this language network, accurate mapping of eloquent white matter tracts is critical, given the high risk of permanent neurological impairment if they are injured during surgery. In this review, we start by describing the clinical implications of gliomas involving white matter language tracts. Next, we highlight the advantages and limitations of methods commonly used to identify these tracts during surgery including structural imaging techniques, functional imaging, non-invasive stimulation, and finally, awake craniotomy. We provide a rationale for combining these complementary techniques as part of a multimodal mapping paradigm to optimize postoperative language outcomes. Next, we review local and long-range adaptations that take place as the language network undergoes remodeling after tumor growth and surgical resection. We discuss the probable cellular mechanisms underlying this plasticity with emphasis on the white matter, which until recently was thought to have a limited role in adults. Finally, we provide an overview of emerging developments in targeting the glioma-neuronal network interface to achieve better disease control and promote recovery after injury.

Constant Multi-Tasking With Time Constraint to Preserve Across-Network Dynamics Throughout Awake Surgery for Low-Grade Glioma: A Necessary Step to Enable Patients Resuming an Active Life

Awake surgery for brain gliomas improves resection while minimizing morbidity. Although intraoperative mapping was originally used to preserve motor and language functions, the considerable increase of life expectancy, especially in low-grade glioma, resulted in the need to enhance patients’ long-term quality of life. If the main goal of awake surgery is to resume normal familial and socio-professional activities, preventing hemiparesis and aphasia is not sufficient: cognitive and emotional functions must be considered. To monitor higher-order functions, e.g., executive control, semantics or mentalizing, further tasks were implemented into the operating theater. Beyond this more accurate investigation of function-specific neural networks, a better exploration of the inter-system communication is required. Advances in brain connectomics led to a meta-network perspective of neural processing, which emphasizes the pivotal role of the dynamic interplay between functional circuits to allow complex and flexible, goal-directed behaviors. Constant multi-tasking with time constraint in awake patients may be proposed during intraoperative mapping, since it provides a mirror of the (dys)synchronization within and across neural networks and it improves the sensitivity of behavioral monitoring by increasing cognitive demand throughout the resection. Electrical mapping may hamper the patient to perform several tasks simultaneously whereas he/she is still capable to achieve each task in isolation. Unveiling the meta-network organization during awake mapping by using a more ecological multi-demand testing, more representative of the real-life conditions, constitutes a reliable way to tailor the surgical onco-functional balance based upon the expectations of each patient, enabling him/her to resume an active life with long-lasting projects.

Machine learning-based prediction of motor status in glioma patients using diffusion MRI metrics along the corticospinal tract

Along tract statistics enables white matter characterization using various diffusion MRI metrics. These diffusion models reveal detailed insights into white matter microstructural changes with development, pathology and function. Here, we aim at assessing the clinical utility of diffusion MRI metrics along the corticospinal tract, investigating whether motor glioma patients can be classified with respect to their motor status. We retrospectively included 116 brain tumour patients suffering from either left or right supratentorial, unilateral World Health Organization Grades II, III and IV gliomas with a mean age of 53.51 ± 16.32 years. Around 37% of patients presented with preoperative motor function deficits according to the Medical Research Council scale. At group level comparison, the highest non-overlapping diffusion MRI differences were detected in the superior portion of the tracts’ profiles. Fractional anisotropy and fibre density decrease, apparent diffusion coefficient axial diffusivity and radial diffusivity increase. To predict motor deficits, we developed a method based on a support vector machine using histogram-based features of diffusion MRI tract profiles (e.g. mean, standard deviation, kurtosis and skewness), following a recursive feature elimination method. Our model achieved high performance (74% sensitivity, 75% specificity, 74% overall accuracy and 77% area under the curve). We found that apparent diffusion coefficient, fractional anisotropy and radial diffusivity contributed more than other features to the model. Incorporating the patient demographics and clinical features such as age, tumour World Health Organization grade, tumour location, gender and resting motor threshold did not affect the model’s performance, revealing that these features were not as effective as microstructural measures. These results shed light on the potential patterns of tumour-related microstructural white matter changes in the prediction of functional deficits.

Characterization of Cognitive Function in Survivors of Diffuse Gliomas Using Morphometric Correlation Networks

This pilot study investigates structural alterations and their relationships with cognitive function in survivors of diffuse gliomas. Twenty-four survivors of diffuse gliomas (mean age 44.5 ± 11.5), from whom high-resolution T1-weighted images, neuropsychological tests, and self-report questionnaires were obtained, were analyzed. Patients were grouped by degree of cognitive impairment, and interregional correlations of cortical thickness were computed to generate morphometric correlation networks (MCNs). The results show that the cortical thickness of the right insula (R² = 0.3025, p = 0.0054) was negatively associated with time since the last treatment, and the cortical thickness of the left superior temporal gyrus (R² = 0.2839, p = 0.0107) was positively associated with cognitive performance. Multiple cortical regions in the default mode, salience, and language networks were identified as predominant nodes in the MCNs of survivors of diffuse gliomas. Compared to cognitively impaired patients, cognitively non-impaired patients tended to have higher network stability in network nodes removal analysis, especially when the fraction of removed nodes (among 66 nodes in total) exceeded 55%. These findings suggest that structural networks are altered in survivors of diffuse gliomas and that their cortical structures may also be adapting to support cognitive function during survivorship.

Tumor-associated alterations in white matter connectivity have prognostic significance in MGMT-unmethylated glioblastoma

PURPOSE

We investigated the prognostic significance of tumor-associated white matter (TA-WM) tracts in glioblastoma (GBM) using magnetic resonance-diffusion tensor imaging (MR-DTI). We hypothesized that (1) TA-WM tracts harbor microscopic disease not targeted through surgery or radiotherapy (RT), and (2) the greater the extent of TA-WM involvement, the worse the survival outcomes.

METHODS

We studied a retrospective cohort of 76 GBM patients. TA-WM tracts were identified by MR-DTI fractional anisotropy (FA) maps. For each patient, 22 TA-WM tracts were analyzed and each tract was graded 1-3 based on FA. A TA-WM score (TA-WMS) was computed based on number of involved tracts and corresponding FA grade of involvement. Kaplan-Meier statistics were utilized to determine survival outcomes, log-rank test was used to compare survival between groups, and Cox regression was utilized to determine prognostic variables.

RESULTS

For the MGMT-unmethylated cohort, there was a decrease in OS for increasing TA-WMS (median OS 16.5 months for TA-WMS 0-4; 13.6 months for TA-WMS 5-8; 7.3 months for TA-WMS > 9; p = 0.0002). This trend was not observed in the MGMT-methylated cohort. For MGMT-unmethylated patients with TA-WMS > 6 and involvement of tracts passing through brainstem or contralateral hemisphere, median OS was 8.3 months versus median OS 14.1 months with TA-WMS > 6 but not involving aforementioned critical tracts (p = 0.003 log-rank test). For MGMT-unmethylated patients, TA-WMS was predictive of overall survival in multivariate analysis (HR = 1.14, 95% CI 1.03-1.27, p = 0.012) while age, gender, and largest tumor dimension were non-significant.

CONCLUSION

Increased TA-WMS and involvement of critical tracts are associated with decreased overall survival in MGMT-unmethylated GBM.

White Matter Tracts and Diffuse Lower-Grade Gliomas: The Pivotal Role of Myelin Plasticity in the Tumor Pathogenesis, Infiltration Patterns, Functional Consequences and Therapeutic Management

For many decades, interactions between diffuse lower-grade glioma (LGG) and brain connectome were neglected. However, the neoplasm progression is intimately linked to its environment, especially the white matter (WM) tracts and their myelin status. First, while the etiopathogenesis of LGG is unclear, this tumor seems to appear during the adolescence, and it is mostly located within anterior and associative cerebral areas. Because these structures correspond to those which were myelinated later in the brain maturation process, WM myelination could play a role in the development of LGG. Second, WM fibers and the myelin characteristics also participate in LGG diffusion, since glioma cells migrate along the subcortical pathways, especially when exhibiting a demyelinated phenotype, which may result in a large invasion of the parenchyma. Third, such a migratory pattern can induce functional (neurological, cognitive and behavioral) disturbances, because myelinated WM tracts represent the main limitation of neuroplastic potential. These parameters are critical for tailoring an individualized therapeutic strategy, both (i) regarding the timing of active treatment(s) which must be proposed earlier, before a too wide glioma infiltration along the WM bundles, (ii) and regarding the anatomic extent of surgical resection and irradiation, which should take account of the subcortical connectivity. Therefore, the new science of connectomics must be integrated in LGG management, based upon an improved understanding of the interplay across glioma dissemination within WM and reactional neural networks reconfiguration, in order to optimize long-term oncological and functional outcomes. To this end, mechanisms of activity-dependent myelin plasticity should be better investigated.

Altered static and dynamic voxel-mirrored homotopic connectivity in patients with frontal glioma

Contralateral regions play critical role in functional compensation in glioma patients. Voxel-mirrored homotopic connectivity (VMHC) characterizes the intrinsic functional connectivity (FC) of the brain, considered to have a regional functional basis. We aimed to investigate the alterations of brain regional function and VMHC in patients with frontal glioma, and further investigated the correlation between these alterations and cognition. We enrolled patients with frontal glioma and matched healthy controls (HC). We chose degree centrality (DC), regional homogeneity (ReHo), and VMHC to investigate the alterations of regional function and intrinsic FC in patients. Furthermore, partial correlation analyses were conducted to explore the relationship between imaging functional indicators and cognitions. Compared with HC, patients showed decreased static VMHC within right and left middle frontal gyrus (MFG.R, MFG.L), left superior frontal gyrus (SFG.L), right precuneus (PCUN.R), and left precuneus (PCUN.L), decreased static DC within left cingulate gyrus (CG.L), right superior frontal gyrus (SFG.R), and right postcentral gyrus (POCG.R), decreased static ReHo within CG.L, decreased dynamic ReHo within right inferior parietal lobule (IPL.R), but increased dynamic VMHC (dVMHC) within PCUN.R and PCUN.L. Furthermore, values of decreased VMHC within MFG.R, decreased DC within CG.L, decreased ReHo within CG.L, and increased dVMHC within PCUN.R were significantly positively correlated with cognitive functions. We preliminarily confirmed glioma causes regional dysfunction and disturbs long-distance FC, and long-distance FC showed strong instability in patients with frontal glioma. Meanwhile, the correlation analyses indicated directions for cognitive protection in patients with frontal glioma.

The death of localizationism: The concepts of functional connectome and neuroplasticity deciphered by awake mapping, and their implications for best care of brain-damaged patients

Although clinical neurology was mainly erected on the dogma of localizationism, numerous reports have described functional recovery after lesions involving presumed non-compensable areas in an inflexible view of brain processing. Here, the purpose is to review new insights into the functional connectome and the mechanisms underpinning neural plasticity, gained from intraoperative direct electrostimulation mapping and real-time behavioral monitoring in awake patients, combined with perioperative neuropsychological and neuroimaging data. Such longitudinal anatomo-functional correlations resulted in the reappraisal of classical models of cognition, especially by highlighting the dynamic interplay within and between neural circuits, leading to the concept of meta-network (network of networks), as well as by emphasizing that subcortical connectivity is the main limitation of neuroplastic potential. Beyond their contribution to basic neurosciences, these findings might also be helpful for an optimization of care for brain-damaged patients, such as in resective oncological or epilepsy neurosurgery in structures traditionally deemed inoperable (e.g., in Broca's area) as well as for elaborating new programs of functional rehabilitation, eventually combined with transcranial brain stimulation, aiming to change the connectivity patterns in order to enhance cognitive competences following cerebral injury.

Dynamic Interplay between Lower-Grade Glioma Instability and Brain Metaplasticity: Proposal of an Original Model to Guide the Therapeutic Strategy

The behavior of lower-grade glioma (LGG) is changing over time, spontaneously, and in reaction to treatments. First, due to genomic instability and clonal expansion, although LGG progresses slowly during the early period of the disease, its growth velocity will accelerate when this tumor will transform to a higher grade of malignancy. Furthermore, its pattern of progression may change following therapy, e.g., by switching from a proliferative towards a more diffuse profile, in particular after surgical resection. In parallel to this plasticity of the neoplasm, the brain itself is constantly adapting to the tumor and possible treatment(s) thanks to reconfiguration within and between neural networks. Furthermore, the pattern of reallocation can also change, especially by switching from a perilesional to a contrahemispheric functional reorganization. Such a reorientation of mechanisms of cerebral reshaping, related to metaplasticity, consists of optimizing the efficiency of neural delocalization in order to allow functional compensation by adapting over time the profile of circuits redistribution to the behavioral modifications of the glioma. This interplay between LGG mutations and reactional connectomal instability leads to perpetual modulations in the glioma-neural equilibrium, both at ultrastructural and macroscopic levels, explaining the possible preservation of quality of life despite tumor progression. Here, an original model of these dynamic interactions across LGG plasticity and the brain metanetwork is proposed to guide a tailored step-by-step individualized therapeutic strategy over years. Integration of these new parameters, not yet considered in the current guidelines, might improve management of LGG patients.

Common genetic variation influencing human white matter microstructure

Brain regions communicate with each other through tracts of myelinated axons, commonly referred to as white matter. We identified common genetic variants influencing white matter microstructure using diffusion magnetic resonance imaging of 43,802 individuals. Genome-wide association analysis identified 109 associated loci, 30 of which were detected by tract-specific functional principal components analysis. A number of loci colocalized with brain diseases, such as glioma and stroke. Genetic correlations were observed between white matter microstructure and 57 complex traits and diseases. Common variants associated with white matter microstructure altered the function of regulatory elements in glial cells, particularly oligodendrocytes. This large-scale tract-specific study advances the understanding of the genetic architecture of white matter and its genetic links to a wide spectrum of clinical outcomes.

Brain connectomics applied to oncological neuroscience: from a traditional surgical strategy focusing on glioma topography to a meta-network approach

The classical way for surgical selection and planning in cerebral glioma mainly focused on tumor topography. The emerging science of connectomics, which aims of mapping brain connectivity, resulted in a paradigmatic shift from a modular account of cerebral organization to a meta-network perspective. Adaptive behavior is actually mediated by constant changes in interactions within and across large-scale delocalized neural systems underlying conation, cognition, and emotion. Here, to optimize the onco-functional balance of glioma surgery, the purpose is to switch toward a connectome-based resection taking account of both relationships between the tumor and critical distributed circuits (especially subcortical pathways) as well as the perpetual instability of the meta-network. Such dynamic in the neural spatiotemporal integration permits functional reallocation leading to neurological recovery after massive resection in structures traditionally thought as "inoperable." This better understanding of connectome increases benefit/risk ratio of surgery (i) by selecting resection in areas deemed "eloquent" according to a localizationist dogma; (ii), conversely, by refining intraoperative awake cognitive mapping and monitoring in so-called non-eloquent areas; (iii) by improving preoperative information, enabling an optimal selection of intrasurgical tasks tailored to the patient's wishes; (iv) by developing an "oncological disconnection surgery"; (v) by defining a personalized multistep surgical strategy adapted to individual brain reshaping potential; and (vi) ultimately by preserving environmentally and socially appropriate behavior, including return to work, while increasing the extent of (possibly repeated) resection(s). Such a holistic vision of neural processing can enhance reliability of connectomal surgery in oncological neuroscience and may also be applied to restorative neurosurgery.

Updated perspectives on awake neurosurgery with cognitive and emotional assessment for patients with low-grade gliomas

Introduction: Thanks to early extensive surgical resection combined with medical oncological therapies, life expectancy dramatically increased in low-grade glioma (LGG), with an overall survival currently over 15 years. Therefore, patients should be able to maintain valuable family and socio-professional activities.Areas covered: For many decades, cognitive and emotional aspects were neglected by surgical and medical neurooncologists. The goal of surgery was to avoid hemiplegia and/or aphasia, with no considerations regarding behavior. However, because LGG patients live longer, they must be cognitively and affectively able to make long-term projects. Preservation of higher-order functions should be considered systematically in LGG surgery by means of awake cognitive/emotional mapping and monitoring.Expert opinion: The aim is to incorporate recent advances in neurosciences, which proposed revisited models of cerebral processing relying on a meta-network perspective, into the pre-, intra- and postoperative procedure. In this connectomal approach, brain functions result from complex interactions within and between neural networks. This improved understanding of a constant instability of the neural system allows a better cognitive/emotional assessment before and after each treatment over years, in order to preserve personality and adaptive behavior for each LGG patient, based on his/her own definition of quality of life. It is time to create oncological neurosciences.

Risk Assessment by Pre-surgical Tractography in Left Hemisphere Low-Grade Gliomas

Background: Tracking the white matter principal tracts is routinely typically included during the pre-surgery planning examinations and has revealed to limit functional resection of low-grade gliomas (LGGs) in eloquent areas.

Objective: We examined the integrity of the Superior Longitudinal Fasciculus (SLF) and Inferior Fronto-Occipital Fasciculus (IFOF), both known to be part of the language-related network in patients with LGGs involving the temporo-insular cortex. In a comparative approach, we contrasted the main quantitative fiber tracking values in the tumoral (T) and healthy (H) hemispheres to test whether or not this ratio could discriminate amongst patients with different post-operative outcomes.

Methods: Twenty-six patients with LGGs were included. We obtained quantitative fiber tracking values in the tumoral and healthy hemispheres and calculated the ratio (H_IFOF–T_IFOF)/H_IFOF and the ratio (H_SLF–T_SLF)/H_SLF on the number of streamlines. We analyzed how these values varied between patients with and without post-operative neurological outcomes and between patients with different post-operative Engel classes.

Results: The ratio for both IFOF and SLF significantly differed between patient with and without post-operative neurological language deficits. No associations were found between white matter structural changes and post-operative seizure outcomes.

Conclusions: Calculating the ratio on the number of streamlines and fractional anisotropy between the tumoral and the healthy hemispheres resulted to be a useful approach, which can prove to be useful during the pre-operative planning examination, as it gives a glimpse on the potential clinical outcomes in patients with LGGs involving the left temporo-insular cortex.

Perspectives on (A)symmetry of Arcuate Fasciculus. A Short Review About Anatomy, Tractography and TMS for Arcuate Fasciculus Reconstruction in Planning Surgery for Gliomas in Language Areas

Gliomas are brain tumors that are treated with surgical resection. Prognosis is influenced by the extent of resection and postoperative neurological status. As consequence, given the extreme interindividual and interhemispheric variability of subcortical white matter (WM) surgical planning requires to be patient's tailored. According to the “connectionist model,” there is a huge variability among both cortical areas and subcortical WM in all human beings, and it is known that brain is able to reorganize itself and to adapt to WM lesions. Brain magnetic resonance imaging diffusion tensor imaging (DTI) tractography allows visualization of WM bundles. Nowadays DTI tractography is widely available in the clinical setting for presurgical planning. Arcuate fasciculus (AF) is a long WM bundle that connects the Broca's and Wernicke's regions with a complex anatomical architecture and important role in language functions. Thus, its preservation is important for the postoperative outcome, and DTI tractography is usually performed for planning surgery within the language-dominant hemisphere. High variability among individuals and an asymmetrical pattern has been reported for this WM bundle. However, the functional relevance of AF in the contralateral non-dominant hemisphere in case of tumoral or surgical lesion of the language-dominant AF is unclear. This review focuses on AF anatomy with special attention to its asymmetry in both normal and pathological conditions and how it may be explored with preoperative tools for planning surgery on gliomas in language areas. Based on the findings available in literature, we finally speculate about the potential role of preoperative evaluation of the WM contralateral to the surgical site.

Functional Mapping before and after Low-Grade Glioma Surgery: A New Way to Decipher Various Spatiotemporal Patterns of Individual Neuroplastic Potential in Brain Tumor Patients

Intraoperative direct electrostimulation mapping (DEM) is currently the gold-standard for glioma surgery, since functional-based resection allows an optimization of the onco-functional balance (increased resection with preserved quality of life). Besides intrasurgical awake mapping of conation, cognition, and behavior, preoperative mapping by means of functional neuroimaging (FNI) and transcranial magnetic stimulation (TMS) has increasingly been utilized for surgical selection and planning. However, because these techniques suffer from several limitations, particularly for direct functional mapping of subcortical white matter pathways, DEM remains crucial to map neural connectivity. On the other hand, non-invasive FNI and TMS can be repeated before and after surgical resection(s), enabling longitudinal investigation of brain reorganization, especially in slow-growing tumors like low-grade gliomas. Indeed, these neoplasms generate neuroplastic phenomena in patients with usually no or only slight neurological deficits at diagnosis, despite gliomas involving the so-called "eloquent" structures. Here, data gained from perioperative FNI/TMS mapping methods are reviewed, in order to decipher mechanisms underpinning functional cerebral reshaping induced by the tumor and its possible relapse, (re)operation(s), and postoperative rehabilitation. Heterogeneous spatiotemporal patterns of rearrangement across patients and in a single patient over time have been evidenced, with structural changes as well as modifications of intra-hemispheric (in the ipsi-lesional and/or contra-lesional hemisphere) and inter-hemispheric functional connectivity. Such various fingerprints of neural reconfiguration were correlated to different levels of cognitive compensation. Serial multimodal studies exploring neuroplasticity might lead to new management strategies based upon multistage therapeutic approaches adapted to the individual profile of functional reallocation.

Why brain radiation therapy should take account of the individual structural and functional connectivity: Toward an irradiation "à la carte"

Although radiation therapy (RT) is a main treatment of brain tumors, delayed cerebral toxicity may lead to cognitive deteriorations with adverse effects on quality of life. Despite technological advances in RT, the concept of brain connectome has not yet been incorporated in the strategy of irradiation. Because white matter tracts represent the main limitation of neuroplasticity, tumor surgery is increasingly performed with awake cortical-subcortical mapping. Here, the purpose is to reinforce the link between cognitive neurosciences and neurooncology, which is critical for neurosurgeons but also for medical oncologists, especially brain radiation oncologists. The goal is to optimize RT planning by sparing individual critical neural networks. A redefinition of "organs at risk" should be proposed, beyond the few structures (such as brainstem, optic pathway, pituitary gland, hippocampi) which are classically preserved for brain radiation, by considering the structural and functional connectivity in order to evolve toward a RT "à la carte".

Alterations of white matter integrity associated with cognitive deficits in patients with glioma

OBJECTIVE

This study aimed to investigate the characteristic of brain structural connections in glioma patients and further evaluate the relationship between changes in the white matter tracts and cognitive decline.

METHODS

This retrospective study included a total of 35 subjects with glioma and 14 demographically matched healthy controls, who underwent diffusion tensor imaging scans and formal neuropsychological assessment tests. Fractional anisotropy (FA) values of white matter tracts were derived from atlas-based analysis to compare group differences. Furthermore, subgroup-level analysis was performed to differentiate the effects of tumor location on white matter tracts. Partial correlation analysis was used to examine the associations between neurocognitive assessments and the integrity of tracts. Region of interest-based network analysis was performed to validate the alteration of structural brain network in subjects with glioma.

RESULTS

Compared with controls, subjects with glioma exhibited reduced FA values in the right uncinate fasciculus. Besides, subjects with glioma exhibited worse performance in several cognitive assessments. Partial correlation analysis indicated that the FA value in the right superior longitudinal fasciculus temporal part was significantly positively correlated with scores of visual-spatial abilities in subjects with glioma in the right temporal lobe (r = .932, p = .002). Region of interest-based network analysis revealed that subjects with glioma exhibited reduced FA, fiber length (FL), and fiber number (FN) between specific brain regions compared with controls.

CONCLUSION

The present study demonstrated the reduced integrity of white matter tracts and altered structural connectivity in brain networks in patients with glioma. Notably, white matter tracts in the right hemisphere might be vulnerable to the effects of a frontal or temporal lesion and might be associated with deficient cognitive function.