Topological measures of connectomics for low grades Glioma

Recent advancements in neuroimaging have allowed the use of network analysis to study the brain in a system-based approach. In fact, several neurological disorders have been investigated from a network perspective. These include Alzheimer’s disease, autism spectrum disorder, stroke, and traumatic brain injury. So far, few studies have been conducted on glioma by using connectome techniques. A connectomebased approach might be useful in quantifying the status of patients, in supporting surgical procedures, and ultimately shedding light on the underlying mechanisms and the recovery process. In this manuscript, by using graph theoretical methods of segregation and integration, topological structural connectivity is studied comparing patients with low grade glioma to healthy control. These measures suggest that it is possible to quantify the status of patients pre- and post-surgical intervention to evaluate the condition

Brain networks under attack : robustness properties and the impact of lesions

A growing number of studies approach the brain as a complex network, the so-called ‘connectome’. Adopting this framework, we examine what types or extent of damage the brain can withstand—referred to as network ‘robustness’—and conversely, which kind of distortions can be expected after brain lesions. To this end, we review computational lesion studies and empirical studies investigating network alterations in brain tumour, stroke and traumatic brain injury patients. Common to these three types of focal injury is that there is no unequivocal relationship between the anatomical lesion site and its topological characteristics within the brain network. Furthermore, large-scale network effects of these focal lesions are compared to those of a widely studied multifocal neurodegenerative disorder, Alzheimer’s disease, in which central parts of the connectome are preferentially affected. Results indicate that human brain networks are remarkably resilient to different types of lesions, compared to other types of complex networks such as random or scale-free networks. However, lesion effects have been found to depend critically on the topological position of the lesion. In particular, damage to network hub regions—and especially those connecting different subnetworks—was found to cause the largest disturbances in network organization. Regardless of lesion location, evidence from empirical and computational lesion studies shows that lesions cause significant alterations in global network topology. The direction of these changes though remains to be elucidated. Encouragingly, both empirical and modelling studies have indicated that after focal damage, the connectome carries the potential to recover at least to some extent, with normalization of graph metrics being related to improved behavioural and cognitive functioning. To conclude, we highlight possible clinical implications of these findings, point out several methodological limitations that pertain to the study of brain diseases adopting a network approach, and provide suggestions for future research

BOLD Coupling between Lesioned and Healthy Brain Is Associated with Glioma Patients’ Recovery

This article belongs to the Special Issue Perioperative Imaging and Mapping Methods in Glioma Patients.[Simple Summary] Glioma, a type of brain tumour, affects not only the function of immediately adjacent brain tissue but also that in more distant areas, potentially impacting cognitive function after its surgical removal. Here, 17 patients with glioma had brain scans and tests of cognitive function during treatment and recovery. We investigated the effects of glioma on the brain, and what happens during recovery, using the brain’s “global signal” detected with magnetic resonance imaging (MRI). We found that the signal from gliomas was synchronised with the global signal in all patients and that this synchronisation was associated with the recovery of cognition after surgery. Specifically, patients with a greater reduction in glioma–global signal synchronisation following surgery were more likely to have a larger number of newly acquired cognitive difficulties. Together, these results suggest that the interaction between gliomas and the brain can predict how patients recover their cognitive abilities, which is important for their quality of life.[Abstract] Predicting functional outcomes after surgery and early adjuvant treatment is difficult due to the complex, extended, interlocking brain networks that underpin cognition. The aim of this study was to test glioma functional interactions with the rest of the brain, thereby identifying the risk factors of cognitive recovery or deterioration. Seventeen patients with diffuse non-enhancing glioma (aged 22–56 years) were longitudinally MRI scanned and cognitively assessed before and after surgery and during a 12-month recovery period (55 MRI scans in total after exclusions). We initially found, and then replicated in an independent dataset, that the spatial correlation pattern between regional and global BOLD signals (also known as global signal topography) was associated with tumour occurrence. We then estimated the coupling between the BOLD signal from within the tumour and the signal extracted from different brain tissues. We observed that the normative global signal topography is reorganised in glioma patients during the recovery period. Moreover, we found that the BOLD signal within the tumour and lesioned brain was coupled with the global signal and that this coupling was associated with cognitive recovery. Nevertheless, patients did not show any apparent disruption of functional connectivity within canonical functional networks. Understanding how tumour infiltration and coupling are related to patients’ recovery represents a major step forward in prognostic development.This research was supported by the Guarantors of Brain, Cancer Research UK Cambridge Centre, The Brain Tumour Charity and the EMERGIA Junta de Andalucia program. Y.E. is funded by a Royal Society Dorothy Hodgkin Research Fellowship (DHF130100). JMG is funded by the Ministerio de Ciencia e Innovación (España)/FEDER under the RTI2018-098913-B100 project, by the Consejería de Economía, Innovación, Ciencia y Empleo (Junta de Andalucía) and FEDER under CV20-45250, A-TIC-080-UGR18, B-TIC-586-UGR20 and P20-00525 projects. MA was funded by a Cambridge Trust—Yousef Jameel Scholarship. This research was also supported by the NIHR Cambridge Biomedical Research Centre (BRC-1215-20014). SJP (NIHR Career Development Fellowship, CDF-2018-11-ST2-003) is funded by the National Institute for Health Research (NIHR) for this research project

BOLD Coupling between Lesioned and Healthy Brain Is Associated with Glioma Patients’ Recovery

Predicting functional outcomes after surgery and early adjuvant treatment is difficult due to the complex, extended, interlocking brain networks that underpin cognition. The aim of this study was to test glioma functional interactions with the rest of the brain, thereby identifying the risk factors of cognitive recovery or deterioration. Seventeen patients with diffuse non-enhancing glioma (aged 22–56 years) were longitudinally MRI scanned and cognitively assessed before and after surgery and during a 12-month recovery period (55 MRI scans in total after exclusions). We initially found, and then replicated in an independent dataset, that the spatial correlation pattern between regional and global BOLD signals (also known as global signal topography) was associated with tumour occurrence. We then estimated the coupling between the BOLD signal from within the tumour and the signal extracted from different brain tissues. We observed that the normative global signal topography is reorganised in glioma patients during the recovery period. Moreover, we found that the BOLD signal within the tumour and lesioned brain was coupled with the global signal and that this coupling was associated with cognitive recovery. Nevertheless, patients did not show any apparent disruption of functional connectivity within canonical functional networks. Understanding how tumour infiltration and coupling are related to patients’ recovery represents a major step forward in prognostic development.Consejeria de Economia, Innovacion, Ciencia y Empleo.Junta de Andalucia CV20-45250; A-TIC-080-UGR18; B-TIC-586-UGR20; P20-0052

Bold coupling between lesioned and healthy brain is associated with glioma patients’ recovery

Predicting functional outcomes after surgery and early adjuvant treatment is difficult due to the complex, extended, interlocking brain networks that underpin cognition. The aim of this study was to test glioma functional interactions with the rest of the brain, thereby identifying the risk factors of cognitive recovery or deterioration. Seventeen patients with diffuse non-enhancing glioma (aged 22–56 years) were longitudinally MRI scanned and cognitively assessed before and after surgery and during a 12-month recovery period (55 MRI scans in total after exclusions). We initially found, and then replicated in an independent dataset, that the spatial correlation pattern between regional and global BOLD signals (also known as global signal topography) was associated with tumour occurrence. We then estimated the coupling between the BOLD signal from within the tumour and the signal extracted from different brain tissues. We observed that the normative global signal topography is reorganised in glioma patients during the recovery period. Moreover, we found that the BOLD signal within the tumour and lesioned brain was coupled with the global signal and that this coupling was associated with cognitive recovery. Nevertheless, patients did not show any apparent disruption of functional connectivity within canonical functional networks. Understanding how tumour infiltration and coupling are related to patients’ recovery represents a major step forward in prognostic development.</p

Transcriptomic and connectomic correlates of differential spatial patterning among gliomas.

Unravelling the complex events driving grade-specific spatial distribution of brain tumour occurrence requires rich datasets from both healthy individuals and patients. Here, we combined open-access data from The Cancer Genome Atlas, the UKBiobank and the Allen Brain Human Atlas to disentangle how the different spatial occurrences of Glioblastoma Multiforme (GBM) and Low-Grade Gliomas (LGG) are linked to brain network features and the normative transcriptional profiles of brain regions. From MRI of brain tumour patients we first constructed a grade-related frequency map of the regional occurrence of LGG and the more aggressive GBM. Using associated mRNA transcription data, we derived a set of differential gene expressions from GBM and LGG tissues of the same patients. By combining the resulting values with normative gene expressions from postmortem brain tissue, we constructed a grade-related expression map indicating which brain regions express genes dysregulated in aggressive gliomas. Additionally, we derived an expression map of genes previously associated with tumour subtypes in a GWAS study (tumour-related genes). There were significant associations between grade-related frequency, grade-related expression, and tumour-related expression maps, as well as functional brain network features (specifically, nodal strength and participation coefficient) that are implicated in neurological and psychiatric disorders. These findings identify brain network dynamics and transcriptomic signatures as key factors in regional vulnerability for GBM and LGG occurrence, placing primary brain tumours within a well-established framework of neurological and psychiatric cortical alterations

Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries

This two-volume set LNCS 12962 and 12963 constitutes the thoroughly refereed proceedings of the 7th International MICCAI Brainlesion Workshop, BrainLes 2021, as well as the RSNA-ASNR-MICCAI Brain Tumor Segmentation (BraTS) Challenge, the Federated Tumor Segmentation (FeTS) Challenge, the Cross-Modality Domain Adaptation (CrossMoDA) Challenge, and the challenge on Quantification of Uncertainties in Biomedical Image Quantification (QUBIQ). These were held jointly at the 23rd Medical Image Computing for Computer Assisted Intervention Conference, MICCAI 2020, in September 2021. The 91 revised papers presented in these volumes were selected form 151 submissions. Due to COVID-19 pandemic the conference was held virtually. This is an open access book

Improving our understanding of speech and language outcome in neurosurgery patients

Malignant gliomas remain incurable and result in more years of life lost than any other tumours. Surgical resection is strongly recommended but carries a risk of causing functional impairment. This thesis aims to demonstrate how state-of-the-art functional magnetic resonance imaging (fMRI) language paradigms can contribute to neurosurgical planning. The first three experiments use a multitask fMRI language paradigm to functionally segregate left posterior temporal and left posterior frontal regions involved in the perception and production of speech. Experiment 1 demonstrated three functionally distinct responses in the left posterior superior temporal sulcus (STS), left temporo-parietal junction and anterior ascending terminal branch of the left STS. Experiment 2 validates these findings in an independent group of participants, increasing confidence that they are robust. Experiment 3 dissociates the response of three different parts of the left premotor cortex during speech production. Experiment 4 shows that left posterior temporal regions are more consistently activated, in neurotypical controls, when a picture naming task presents pairs of objects rather than single objects. Further work could therefore test whether paired object naming is a more sensitive task for pre- and intra-operative language mapping. Finally, Experiment 5 found that successful reading before and after surgery, in two patients with gliomas affecting the left temporo-parietal junction, enhanced activation in bilateral perirhinal regions that were associated with semantic identification of visually presented objects in neurotypical controls. Future studies can now test whether patients who undergo resection of the left temporo-parietal junction have better reading, post-surgery, when bilateral perirhinal activation is enhanced prior to surgery. Taken together, this work expands our knowledge of the functional anatomy of language, proposes a new way of utilising fMRI data from neurotypical controls to tailor pre- and intra-operative language mapping strategies and provides an insight into how the reading system reorganises itself after brain damage

Improving the Tractography Pipeline: on Evaluation, Segmentation, and Visualization

Recent advances in tractography allow for connectomes to be constructed in vivo. These have applications for example in brain tumor surgery and understanding of brain development and diseases. The large size of the data produced by these methods lead to a variety problems, including how to evaluate tractography outputs, development of faster processing algorithms for tractography and clustering, and the development of advanced visualization methods for verification and exploration. This thesis presents several advances in these fields. First, an evaluation is presented for the robustness to noise of multiple commonly used tractography algorithms. It employs a Monte–Carlo simulation of measurement noise on a constructed ground truth dataset. As a result of this evaluation, evidence for obustness of global tractography is found, and algorithmic sources of uncertainty are identified. The second contribution is a fast clustering algorithm for tractography data based on k–means and vector fields for representing the flow of each cluster. It is demonstrated that this algorithm can handle large tractography datasets due to its linear time and memory complexity, and that it can effectively integrate interrupted fibers that would be rejected as outliers by other algorithms. Furthermore, a visualization for the exploration of structural connectomes is presented. It uses illustrative rendering techniques for efficient presentation of connecting fiber bundles in context in anatomical space. Visual hints are employed to improve the perception of spatial relations. Finally, a visualization method with application to exploration and verification of probabilistic tractography is presented, which improves on the previously presented Fiber Stippling technique. It is demonstrated that the method is able to show multiple overlapping tracts in context, and correctly present crossing fiber configurations