Longitudinal assessment of network reorganizations and language recovery in postoperative patients with glioma

Noninvasive- and invasive mapping reveals similar language network centralities - A function-based connectome analysis

BACKGROUND

Former comparisons between direct cortical stimulation (DCS) and navigated transcranial magnetic stimulation (nTMS) only focused on cortical mapping. While both can be combined with diffusion tensor imaging, their differences in the visualization of subcortical and even network levels remain unclear. Network centrality is an essential parameter in network analysis to measure the importance of nodes identified by mapping. Those include Degree centrality, Eigenvector centrality, Closeness centrality, Betweenness centrality, and PageRank centrality. While DCS and nTMS have repeatedly been compared on the cortical level, the underlying network identified by both has not been investigated yet.

METHOD

27 patients with brain lesions necessitating preoperative nTMS and intraoperative DCS language mapping during awake craniotomy were enrolled. Function-based connectome analysis was performed based on the cortical nodes obtained through the two mapping methods, and language-related network centralities were compared.

RESULTS

Compared with DCS language mapping, the positive predictive value of cortical nTMS language mapping is 74.1%, with good consistency of tractography for the arcuate fascicle and superior longitudinal fascicle. Moreover, network centralities did not differ between the two mapping methods. However, ventral stream tracts can be better traced based on nTMS mappings, demonstrating its strengths in acquiring language-related networks. In addition, it showed lower centralities than other brain areas, with decentralization as an indicator of language function loss.

CONCLUSION

This study deepens the understanding of language-related functional anatomy and proves that non-invasive mapping-based network analysis is comparable to the language network identified via invasive cortical mapping.

A speech fluency brain network derived from gliomas

The brain network of speech fluency has not yet been investigated via a study with a large and homogenous sample. This study analysed multimodal imaging data from 115 patients with low-grade glioma to explore the brain network of speech fluency. We applied voxel-based lesion-symptom mapping to identify domain-specific regions and white matter pathways associated with speech fluency. Direct cortical stimulation validated the domain-specific regions intra-operatively. We then performed connectivity-behaviour analysis with the aim of identifying connections that significantly correlated with speech fluency. Voxel-based lesion-symptom mapping analysis showed that damage to domain-specific regions (the middle frontal gyrus, the precentral gyrus, the orbital part of inferior frontal gyrus and the insula) and white matter pathways (corticospinal fasciculus, internal capsule, arcuate fasciculus, uncinate fasciculus, frontal aslant tract) are associated with reduced speech fluency. Furthermore, we identified connections emanating from these domain-specific regions that exhibited significant correlations with speech fluency. These findings illuminate the interaction between domain-specific regions and 17 domain-general regions-encompassing the superior frontal gyrus, middle frontal gyrus, inferior frontal gyrus and rolandic operculum, superior temporal gyrus, temporal pole, inferior temporal pole, middle cingulate gyrus, supramarginal gyrus, fusiform gyrus, inferior parietal lobe, as well as subcortical structures such as thalamus-implicating their collective role in supporting fluent speech. Our detailed mapping of the speech fluency network offers a strategic foundation for clinicians to safeguard language function during the surgical intervention for brain tumours.

The domain-separation language network dynamics in resting state support its flexible functional segregation and integration during language and speech processing

Modern linguistic theories and network science propose that language and speech processing are organized into hierarchical, segregated large-scale subnetworks, with a core of dorsal (phonological) stream and ventral (semantic) stream. The two streams are asymmetrically recruited in receptive and expressive language or speech tasks, which showed flexible functional segregation and integration. We hypothesized that the functional segregation of the two streams was supported by the underlying network segregation. A dynamic conditional correlation approach was employed to construct framewise time-varying language networks and k-means clustering was employed to investigate the temporal-reoccurring patterns. We found that the framewise language network dynamics in resting state were robustly clustered into four states, which dynamically reconfigured following a domain-separation manner. Spatially, the hub distributions of the first three states highly resembled the neurobiology of speech perception and lexical-phonological processing, speech production, and semantic processing, respectively. The fourth state was characterized by the weakest functional connectivity and was regarded as a baseline state. Temporally, the first three states appeared exclusively in limited time bins (∼15%), and most of the time (> 55%), state 4 was dominant. Machine learning-based dFC-linguistics prediction analyses showed that dFCs of the four states significantly predicted individual linguistic performance. These findings suggest a domain-separation manner of language network dynamics in resting state, which forms a dynamic "meta-network" framework to support flexible functional segregation and integration during language and speech processing.

Diffusion Tensor Tractography Studies on Recovery Mechanisms of Aphasia in Stroke Patients: A Narrative Mini-Review

Aphasia is a common and serious clinical feature of stroke. Various neural tracts are known to be involved in language processing. Diffusion tensor tractography (DTT) appears to be an appropriate imaging technique for the elucidation of the recovery mechanisms of aphasia in the language-related neural tracts in stroke patients. In this article, twelve previous DTT-based studies on the recovery mechanisms of aphasia in stroke were reviewed. We classified the twelve studies into the following three categories according to the recovery mechanisms: recovery via the neural tracts in the dominant hemisphere (eight studies), via transcallosal fibers (two studies), and via the neural tracts in the non-dominant hemisphere (two studies). Although there are various neural tracts for language processing, eight of the ten studies focused only on the role of the arcuate fasciculus (AF) in the recovery process. Consequently, it appears from the studies that only one recovery mechanism of aphasia via the restoration of the integrity of the injured AF in the dominant hemisphere was clearly demonstrated. However, because various neural tracts are involved in language processing, there could be other mechanisms that have not yet been elucidated. Therefore, further original studies involving a larger number of patients with aphasia in stroke should be encouraged forthwith. Further studies involving various lesion locations and severity levels of injuries to the language-related neural tracts are also necessary because the recovery mechanisms of aphasia in stroke could be dependent on these factors.

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.