Reorganization and Plasticity of the Language Network in Patients with Cerebral Gliomas

Function-guided differences of arcuate fascicle and inferior fronto-occipital fascicle tractography as diagnostic indicators for surgical risk stratification

BACKGROUND

Several patients with language-eloquent gliomas face language deterioration postoperatively. Persistent aphasia is frequently associated with damage to subcortical language pathways. Underlying mechanisms still need to be better understood, complicating preoperative risk assessment. This study compared qualitative and quantitative functionally relevant subcortical differences pre- and directly postoperatively in glioma patients with and without aphasia.

METHODS

Language-relevant cortical sites were defined using navigated transcranial magnetic stimulation (nTMS) language mapping in 74 patients between 07/2016 and 07/2019. Post-hoc nTMS-based diffusion tensor imaging tractography was used to compare a tract's pre- and postoperative visualization, volume and fractional anisotropy (FA), and the preoperative distance between tract and lesion and postoperative overlap with the resection cavity between the following groups: no aphasia (NoA), tumor- or previous resection induced aphasia persistent pre- and postoperatively (TIA_P), and surgery-induced transient or permanent aphasia (SIA_T or SIA_P).

RESULTS

Patients with NoA, TIA_P, SIA_T, and SIA_P showed distinct fasciculus arcuatus (AF) and inferior-fronto-occipital fasciculus (IFOF) properties. The AF was more frequently reconstructable, and the FA of IFOF was higher in NoA than TIA_P cases (all p ≤ 0.03). Simultaneously, SIA_T cases showed higher IFOF fractional anisotropy than TIA_P cases (p < 0.001) and the most considerable AF volume loss overall. While not statistically significant, the four SIA_P cases showed complete loss of ventral language streams postoperatively, the highest resection-cavity-AF-overlap, and the shortest AF to tumor distance.

CONCLUSION

Functionally relevant qualitative and quantitative differences in AF and IFOF provide a pre- and postoperative pathophysiological and clinically relevant diagnostic indicator that supports surgical risk stratification.

Structural connectome combining DTI features predicts postoperative language decline and its recovery in glioma patients

OBJECTIVES

A decline in language function is a common complication after glioma surgery, affecting patients' quality of life and survival. This study predicts the postoperative decline in language function and whether it can be recovered based on the preoperative white matter structural network.

MATERIALS AND METHODS

Eighty-one right-handed patients with glioma involving the left hemisphere were retrospectively included. Their language function was assessed using the Western Aphasia Battery before and 1 week and 3 months after surgery. Structural connectome combining DTI features was selected to predict postoperative language decline and recovery. Nested cross-validation was used to optimize the models, evaluate the prediction performance of the models, and identify the most predictive features.

RESULTS

Five, seven, and seven features were finally selected as the predictive features in each model and used to establish predictive models for postoperative language decline (1 week after surgery), long-term language decline (3 months after surgery), and language recovery, respectively. The overall accuracy of the three models in nested cross-validation and overall area under the receiver operating characteristic curve were 0.840, 0.790, and 0.867, and 0.841, 0.778, and 0.901, respectively.

CONCLUSION

We used machine learning algorithms to establish models to predict whether the language function of glioma patients will decline after surgery and whether postoperative language deficit can recover, which may help improve the development of treatment strategies. The difference in features in the non-language decline or the language recovery group may reflect the structural basis for the protection and compensation of language function in gliomas.

CLINICAL RELEVANCE STATEMENT

Models can predict the postoperative language decline and whether it can recover in glioma patients, possibly improving the development of treatment strategies. The difference in selected features may reflect the structural basis for the protection and compensation of language function.

KEY POINTS

• Structural connectome combining diffusion tensor imaging features predicted glioma patients' language decline after surgery. • Structural connectome combining diffusion tensor imaging features predicted language recovery of glioma patients with postoperative language disorder. • Diffusion tensor imaging and connectome features related to language function changes imply plastic brain regions and connections.

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.

Structural networking of the developing brain: from maturation to neurosurgical implications

Modern neuroscience agrees that neurological processing emerges from the multimodal interaction among multiple cortical and subcortical neuronal hubs, connected at short and long distance by white matter, to form a largely integrated and dynamic network, called the brain "connectome." The final architecture of these circuits results from a complex, continuous, and highly protracted development process of several axonal pathways that constitute the anatomical substrate of neuronal interactions. Awareness of the network organization of the central nervous system is crucial not only to understand the basis of children's neurological development, but also it may be of special interest to improve the quality of neurosurgical treatments of many pediatric diseases. Although there are a flourishing number of neuroimaging studies of the connectome, a comprehensive vision linking this research to neurosurgical practice is still lacking in the current pediatric literature. The goal of this review is to contribute to bridging this gap. In the first part, we summarize the main current knowledge concerning brain network maturation and its involvement in different aspects of normal neurocognitive development as well as in the pathophysiology of specific diseases. The final section is devoted to identifying possible implications of this knowledge in the neurosurgical field, especially in epilepsy and tumor surgery, and to discuss promising perspectives for future investigations.

The effect of different brain lesions on the reorganization of language functions within the dominant hemisphere assessed with task-based BOLD-fMRI

BACKGROUND AND PURPOSE

Language reorganization has been described in brain lesions with respect to their location and timing, but little is known with respect to their etiology. We used fMRI to investigate the effects of different types of left hemisphere lesions (GL = gliomas, TLE = temporal lobe epilepsy and CA = cavernous angioma) on the topographic intra-hemispheric language plasticity, also considering their location.

METHODS

Forty-seven right-handed patients with 3 different left hemisphere lesions (16 GL, 15 TLE and 16 CA) and 17 healthy controls underwent BOLD fMRI with a verb-generation task. Euclidean distance was used to measure activation peak shifts among groups with respect to reference Tailarach coordinates of Inferior Frontal Gyrus, Superior Temporal Sulcus and Temporo-Parietal Junction. Mixed-model ANOVAs were used to test for differences in activation peak shifts.

RESULTS

Significant activation peak shifts were found in GL patients with respect both to HC and other groups (TLA and CA). In addition, in the same group of patients a significant effect of tumor location (anterior or posterior) was detected.

CONCLUSIONS

We demonstrated that intra-hemispheric language plasticity is influenced by the type of lesion affecting the left hemisphere and that fMRI is especially valuable in the preoperative assessment of such reorganization in glioma patients.