Unexpected hubness: a proof-of-concept study of the human connectome using pagerank centrality and implications for intracerebral neurosurgery

Yeung, Jacky T.; Taylor, Hugh M; Young, Isabella M.; Nicholas, Peter J.; Doyen, Stéphane; Sughrue, Michael E.

doi:10.1007/s11060-020-03659-6

Cited by 19 publications

(15 citation statements)

References 25 publications

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“…As previously described, 26 Quicktome (Omniscient Neurotechnology, Sydney, Australia) is a machine learning based software that creates a subject-specific version of the Human Connectome Project (HCP) 24 Multi-Model parcellation atlas. This is performed by using reparcellation based on structural connectivity instead of anatomic-based methods, which is more accurate in patients with a distorted anatomy, as those with brain tumors.…”

Section: Methodsmentioning

confidence: 99%

Using machine learning to evaluate large-scale brain networks in patients with brain tumors: Traditional and non-traditional eloquent areas

Morell

Eichberg

Shah

et al. 2022

Neuro-Oncology Advances

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Background Large-scale brain networks and higher cognitive functions are frequently altered in neuro-oncology patients, but comprehensive non-invasive brain mapping is difficult to achieve in the clinical setting. The objective of our study is to evaluate traditional and non-traditional eloquent areas in brain tumor patients using a machine learning platform. Methods We retrospectively included patients who underwent surgery for brain tumor resection at our Institution. Preoperative MRI with T1-weighted and DTI sequences were uploaded into the Quicktome platform. We categorized the integrity of nine large-scale brain networks: language, sensorimotor, visual, ventral attention, central executive, default mode, dorsal attention, salience and limbic. Network integrity was correlated with preoperative clinical data. Results One-hundred patients were included in the study. The most affected network was the central executive network (49%), followed by the default mode network (43%) and dorsal attention network (32%). Patients with preoperative deficits showed a significantly higher number of altered networks before the surgery (3.42 vs 2.19, p<.001), compared to patients without deficits. Furthermore, we found that patients without neurologic deficits had an average 2.19 networks affected and 1.51 networks at risk, with most of them being related to non-traditional eloquent areas (p<.001). Conclusion Our results show that large-scale brain networks are frequently affected in patients with brain tumors, even when presenting without evident neurologic deficits. In our study, the most commonly affected brain networks were related to non-traditional eloquent areas. Integrating non-invasive brain mapping machine-learning techniques into the clinical setting may help elucidate how to preserve higher-order cognitive functions associated with those networks.

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Section: Methodsmentioning

confidence: 99%

Using machine learning to evaluate large-scale brain networks in patients with brain tumors: Traditional and non-traditional eloquent areas

Morell

Eichberg

Shah

et al. 2022

Neuro-Oncology Advances

View full text Add to dashboard Cite

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“…Finally, cloud-based pipelines or platforms have been developed such as Flywheel (Wandell et al, 2015), O8T ( Yeung et al, 2021 ), Brain-Life ( Avesani et al, 2019 ), QuNex and QMENTA. These platforms allow to run multiple tools under a common interface in the cloud.…”

Section: Availability Of Toolsmentioning

confidence: 99%

What’s new and what’s next in diffusion MRI preprocessing

et al. 2022

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“…n number, yr year. Reproduced from [97] and bipolar disease, and automatic prediction of MRI image quality [98][99][100][101].When accessing such resources, care should be taken to check for updating datasets and processing pipelines and models, as well as ongoing quality control adjustments made by the host organisation; all of these factors can impact study outcomes [102][103][104][105][106].…”

Section: Neuroimaging Datasetsmentioning

confidence: 99%

The role of artificial intelligence in paediatric neuroradiology

et al. 2022

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Imaging plays a fundamental role in the managing childhood neurologic, neurosurgical and neuro-oncological disease. Employing multi-parametric MRI techniques, such as spectroscopy and diffusion- and perfusion-weighted imaging, to the radiophenotyping of neuroradiologic conditions is becoming increasingly prevalent, particularly with radiogenomic analyses correlating imaging characteristics with molecular biomarkers of disease. However, integration into routine clinical practice remains elusive. With modern multi-parametric MRI now providing additional data beyond anatomy, informing on histology, biology and physiology, such metric-rich information can present as information overload to the treating radiologist and, as such, information relevant to an individual case can become lost. Artificial intelligence techniques are capable of modelling the vast radiologic, biological and clinical datasets that accompany childhood neurologic disease, such that this information can become incorporated in upfront prognostic modelling systems, with artificial intelligence techniques providing a plausible approach to this solution. This review examines machine learning approaches than can be used to underpin such artificial intelligence applications, with exemplars for each machine learning approach from the world literature. Then, within the specific use case of paediatric neuro-oncology, we examine the potential future contribution for such artificial intelligence machine learning techniques to offer solutions for patient care in the form of decision support systems, potentially enabling personalised medicine within this domain of paediatric radiologic practice.

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Unexpected hubness: a proof-of-concept study of the human connectome using pagerank centrality and implications for intracerebral neurosurgery

Cited by 19 publications

References 25 publications

Using machine learning to evaluate large-scale brain networks in patients with brain tumors: Traditional and non-traditional eloquent areas

Using machine learning to evaluate large-scale brain networks in patients with brain tumors: Traditional and non-traditional eloquent areas

What’s new and what’s next in diffusion MRI preprocessing

The role of artificial intelligence in paediatric neuroradiology

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