Connectomic profile and clinical phenotype in newly diagnosed glioma patients

Gliomas are primary brain tumors, originating from the glial cells in the brain. In contrast to the more traditional view of glioma as a localized disease, it is becoming clear that global brain functioning is impacted, even with respect to functional communication between brain regions remote from the tumor itself. However, a thorough investigation of glioma-related functional connectomic profiles is lacking. Therefore, we constructed functional brain networks using functional MR scans of 71 glioma patients and 19 matched healthy controls using the automated anatomical labelling (AAL) atlas and interregional Pearson correlation coefficients. The frequency distributions across connectivity values were calculated to depict overall connectomic profiles and quantitative features of these distributions (full-width half maximum (FWHM), peak position, peak height) were calculated. Next, we investigated the spatial distribution of the connectomic profile. We defined hub locations based on the literature and determined connectivity (1) between hubs, (2) between hubs and non-hubs, and (3) between non-hubs. Results show that patients had broader and flatter connectivity distributions compared to controls. Spatially, glioma patients particularly showed increased connectivity between non-hubs and hubs. Furthermore, connectivity distributions and hub-non-hub connectivity differed within the patient group according to tumor grade, while relating to Karnofsky performance status and progression-free survival. In conclusion, newly diagnosed glioma patients have globally altered functional connectomic profiles, which mainly affect hub connectivity and relate to clinical phenotypes. These findings underscore the promise of using connectomics as a future biomarker in this patient population.

[1]  Linda Douw,et al.  MEG Network Differences between Low- and High-Grade Glioma Related to Epilepsy and Cognition , 2012, PloS one.

[2]  Jonathan D. Power,et al.  Evidence for Hubs in Human Functional Brain Networks , 2013, Neuron.

[3]  Susan Y. Bookheimer,et al.  Altered functional connectivity of the default mode network in diffuse gliomas measured with pseudo-resting state fMRI , 2013, Journal of Neuro-Oncology.

[4]  Harry Eugene Stanley,et al.  Catastrophic cascade of failures in interdependent networks , 2009, Nature.

[5]  Jonathan D. Power,et al.  Multi-task connectivity reveals flexible hubs for adaptive task control , 2013, Nature Neuroscience.

[6]  R. Mirimanoff,et al.  Radiotherapy plus concomitant and adjuvant temozolomide for glioblastoma. , 2005, The New England journal of medicine.

[7]  Stephen M Smith,et al.  Fast robust automated brain extraction , 2002, Human brain mapping.

[8]  Cedric E. Ginestet,et al.  Cognitive relevance of the community structure of the human brain functional coactivation network , 2013, Proceedings of the National Academy of Sciences.

[9]  L. Deangelis,et al.  Long-term outcome of low-grade oligodendroglioma and mixed glioma , 2000, Neurology.

[10]  F. de Pasquale,et al.  Transient effects of tumor location on the functional architecture at rest in glioblastoma patients: three longitudinal case studies , 2016, Radiation Oncology.

[11]  M. V. D. Heuvel,et al.  Exploring the brain network: A review on resting-state fMRI functional connectivity , 2010, European Neuropsychopharmacology.

[12]  Wim Fias,et al.  Brain networks under attack: robustness properties and the impact of lesions. , 2016, Brain : a journal of neurology.

[13]  G. Reifenberger,et al.  The WHO Classification of Tumors of the Nervous System , 2002, Journal of neuropathology and experimental neurology.

[14]  Keith A. Johnson,et al.  Cortical Hubs Revealed by Intrinsic Functional Connectivity: Mapping, Assessment of Stability, and Relation to Alzheimer's Disease , 2009, The Journal of Neuroscience.

[15]  Stephen M. Smith,et al.  Investigations into resting-state connectivity using independent component analysis , 2005, Philosophical Transactions of the Royal Society B: Biological Sciences.

[16]  Onder Hazaroglu,et al.  A human brain atlas derived via n-cut parcellation of resting-state and task-based fMRI data. , 2016, Magnetic resonance imaging.

[17]  Zhiyu Qian,et al.  Reduced efficiency of functional brain network underlying intellectual decline in patients with low-grade glioma , 2013, Neuroscience Letters.

[18]  T. Lumley,et al.  The importance of the normality assumption in large public health data sets. , 2002, Annual review of public health.

[19]  Erno J. Hermans,et al.  Enhanced sensitivity with fast three‐dimensional blood‐oxygen‐level‐dependent functional MRI: comparison of SENSE–PRESTO and 2D‐EPI at 3 T , 2008, NMR in biomedicine.

[20]  K. Whittingstall,et al.  Exploratory study of the effect of brain tumors on the default mode network , 2016, Journal of Neuro-Oncology.

[21]  Yonghong Shi,et al.  Alteration of the Intra- and Cross- Hemisphere Posterior Default Mode Network in Frontal Lobe Glioma Patients , 2016, Scientific Reports.

[22]  D. Louis,et al.  Specific genetic predictors of chemotherapeutic response and survival in patients with anaplastic oligodendrogliomas. , 1998, Journal of the National Cancer Institute.

[23]  Gian Luca Romani,et al.  Modifications of Default-Mode Network Connectivity in Patients with Cerebral Glioma , 2012, PloS one.

[24]  B T Thomas Yeo,et al.  Reconfigurable task-dependent functional coupling modes cluster around a core functional architecture , 2014, Philosophical Transactions of the Royal Society B: Biological Sciences.

[25]  Linda Douw,et al.  Disturbed functional brain networks and neurocognitive function in low-grade glioma patients: a graph theoretical analysis of resting-state MEG , 2009, Nonlinear biomedical physics.

[26]  R. Simon,et al.  Prognostic Factors for Survival , 2005 .

[27]  Randy L. Buckner,et al.  Unrest at rest: Default activity and spontaneous network correlations , 2007, NeuroImage.

[28]  A. Karim,et al.  Prognostic factors for survival in adult patients with cerebral low-grade glioma. , 2002, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[29]  C. Stam Modern network science of neurological disorders , 2014, Nature Reviews Neuroscience.

[30]  A. Gatherer,et al.  Sarcoma of the Larynx , 1958, The Journal of Laryngology & Otology.

[31]  B. Scheithauer,et al.  The 2007 WHO classification of tumours of the central nervous system , 2007, Acta Neuropathologica.

[32]  Linda Douw,et al.  Neural network alterations underlie cognitive deficits in brain tumor patients , 2014, Current opinion in oncology.

[33]  G L Shulman,et al.  INAUGURAL ARTICLE by a Recently Elected Academy Member:A default mode of brain function , 2001 .

[34]  Matthijs Vink,et al.  Cardiorespiratory effects on default‐mode network activity as measured with fMRI , 2009, Human brain mapping.

[35]  C. J. Stam,et al.  The influence of low-grade glioma on resting state oscillatory brain activity: a magnetoencephalography study , 2008, Journal of Neuro-Oncology.

[36]  F. Barkhof,et al.  Functional adaptive changes within the hippocampal memory system of patients with multiple sclerosis , 2012, Human brain mapping.

[37]  E. Bullmore,et al.  The hubs of the human connectome are generally implicated in the anatomy of brain disorders , 2014, Brain : a journal of neurology.

[38]  Stephen M. Smith,et al.  Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm , 2001, IEEE Transactions on Medical Imaging.

[39]  M. Schoonheim,et al.  Sleep benefits subsequent hippocampal functioning , 2009, Nature Neuroscience.

[40]  K. Skullerud,et al.  Survival, prognostic factors, and therapeutic efficacy in low-grade glioma: a retrospective study in 379 patients. , 1997, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[41]  David T. Jones,et al.  Cascading network failure across the Alzheimer’s disease spectrum , 2015, Brain : a journal of neurology.

[42]  Michael Brady,et al.  Improved Optimization for the Robust and Accurate Linear Registration and Motion Correction of Brain Images , 2002, NeuroImage.

[43]  Jan C Buckner,et al.  Factors influencing survival in high-grade gliomas. , 2003, Seminars in oncology.

[44]  Timothy O. Laumann,et al.  Functional Network Organization of the Human Brain , 2011, Neuron.

[45]  Michael W. Cole,et al.  The role of default network deactivation in cognition and disease , 2012, Trends in Cognitive Sciences.

[46]  D. Karnofsky,et al.  The use of the nitrogen mustards in the palliative treatment of carcinoma. With particular reference to bronchogenic carcinoma , 1948 .

[47]  Archana Venkataraman,et al.  Intrinsic functional connectivity as a tool for human connectomics: theory, properties, and optimization. , 2010, Journal of neurophysiology.

[48]  A. Hillebrand,et al.  Connectivity in MEG resting-state networks increases after resective surgery for low-grade glioma and correlates with improved cognitive performance☆ , 2012, NeuroImage: Clinical.

[49]  Peter A. Bandettini,et al.  Separating respiratory-variation-related fluctuations from neuronal-activity-related fluctuations in fMRI , 2006, NeuroImage.

[50]  Olaf Sporns,et al.  Complex network measures of brain connectivity: Uses and interpretations , 2010, NeuroImage.

[51]  Cornelis J. Stam,et al.  Activity Dependent Degeneration Explains Hub Vulnerability in Alzheimer's Disease , 2012, PLoS Comput. Biol..

[52]  C. Stam,et al.  Disturbed functional connectivity in brain tumour patients: Evaluation by graph analysis of synchronization matrices , 2006, Clinical Neurophysiology.

[53]  Linda Douw,et al.  Local polymorphic delta activity in cortical lesions causes global decreases in functional connectivity , 2013, NeuroImage.

[54]  Linda Douw,et al.  Synchronized brain activity and neurocognitive function in patients with low-grade glioma: a magnetoencephalography study. , 2008, Neuro-oncology.

[55]  Martin Klein,et al.  How do brain tumors alter functional connectivity? A magnetoencephalography study , 2006, Annals of neurology.

[56]  Mark W. Woolrich,et al.  Advances in functional and structural MR image analysis and implementation as FSL , 2004, NeuroImage.

[57]  Cornelis J. Stam,et al.  Cognitive and Clinical Dysfunction, Altered MEG Resting-State Networks and Thalamic Atrophy in Multiple Sclerosis , 2013, PloS one.

[58]  Linda Douw,et al.  Epilepsy is related to theta band brain connectivity and network topology in brain tumor patients , 2010, BMC Neuroscience.

[59]  N. Tzourio-Mazoyer,et al.  Automated Anatomical Labeling of Activations in SPM Using a Macroscopic Anatomical Parcellation of the MNI MRI Single-Subject Brain , 2002, NeuroImage.

[60]  M K Habib,et al.  Dynamics of neuronal firing correlation: modulation of "effective connectivity". , 1989, Journal of neurophysiology.

[61]  G. Reifenberger,et al.  The 2016 World Health Organization Classification of Tumors of the Central Nervous System: a summary , 2016, Acta Neuropathologica.

[62]  F. Barkhof,et al.  Memory impairment in multiple sclerosis: Relevance of hippocampal activation and hippocampal connectivity , 2015, Multiple sclerosis.