Abstract
Graph theory is a branch of mathematics that allows for the characterization of complex networks, and has rapidly grown in popularity in network neuroscience in recent years. Researchers have begun to use graph theory to describe the brain networks of individuals with brain tumors to shed light on disrupted networks. This systematic review summarizes the current literature on graph theoretical analysis of magnetic resonance imaging data in the brain tumor population with particular attention paid to treatment effects and other clinical factors. Included papers were published through June 24th, 2020. Searches were conducted on Pubmed, PsycInfo, and Web of Science using the search terms (graph theory OR graph analysis) AND (brain tumor OR brain tumour OR brain neoplasm) AND (MRI OR EEG OR MEG). Studies were eligible for inclusion if they: evaluated participants with a primary brain tumor, used graph theoretical analyses on structural or functional MRI data, MEG, or EEG, were in English, and were an empirical research study. Seventeen papers met criteria for inclusion. Results suggest alterations in network properties are often found in people with brain tumors, although the directions of differences are inconsistent and few studies reported effect sizes. The most consistent finding suggests increased network segregation. Changes are most prominent with more intense treatment, in hub regions, and with factors such as faster tumor growth. The use of graph theory to study brain tumor patients is in its infancy, though some conclusions can be drawn. Future studies should focus on treatment factors, changes over time, and correlations with functional outcomes to better identify those in need of early intervention.
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16 August 2021
A Correction to this paper has been published: https://doi.org/10.1007/s11065-021-09520-5
References
Adebimpe, A., Aarabi, A., Bourel-Ponchel, E., Mahmoudzadeh, M., & Wallois, F. (2015). Functional Brain Dysfunction in Patients with Benign Childhood Epilepsy as Revealed by Graph Theory. PLoS ONE, 10(10), e0139228. https://doi.org/10.1371/journal.pone.0139228
Aerts, H., Fias, W., Caeyenberghs, K., & Marinazzo, D. (2016). Brain networks under attack: Robustness properties and the impact of lesions. Brain, 139(Pt 12), 3063–3083. https://doi.org/10.1093/brain/aww194
Aerts, H., Schirner, M., Dhollander, T., Jeurissen, B., Achten, E., Van Roost, D., & Marinazzo, D. (2020). Modeling brain dynamics after tumor resection using The virtual brain. NeuroImage, 213, 116738. https://doi.org/10.1016/j.neuroimage.2020.116738
Aerts, H., Schirner, M., Jeurissen, B., Van Roost, D., Achten, E., Ritter, P., & Marinazzo, D. (2018). Modeling brain dynamics in brain tumor patients using the virtual brain. eNeuro, 5(3). https://doi.org/10.1523/ENEURO.0083-18.2018
Ailion, A. S., Hortman, K., & King, T. Z. (2017). Childhood brain tumors: A systematic review of the structural neuroimaging literature. Neuropsychology Review.https://doi.org/10.1007/s11065-017-9352-6
Ailion, A. S., King, T. Z., Roberts, S. R., Tang, B., Turner, J. A., Conway, C. M., & Crosson, B. (2020) Double dissociation of auditory attention span and visual attention in long-term survivors of childhood cerebellar tumor: A deterministic tractography study of the cerebellar-frontal and the superior longitudinal fasciculus pathways. Journal of the International Neuropsychological Society, 1–15. https://doi.org/10.1017/S1355617720000417
Ailion, A. S., Roberts, S. R., Crosson, B., & King, T. Z. (2019). Neuroimaging of the component white matter connections and structures within the cerebellar-frontal pathway in posterior fossa tumor survivors. Neuroimage Clinical, 23, 101894. https://doi.org/10.1016/j.nicl.2019.101894
Ali, F. S., Hussain, M. R., Gutierrez, C., Demireva, P., Ballester, L. Y., Zhu, J. J., & Esquenazi, Y. (2018). Cognitive disability in adult patients with brain tumors. Cancer Treatment Reviews, 65, 33–40. https://doi.org/10.1016/j.ctrv.2018.02.007
Amboni, M., Tessitore, A., Esposito, F., Santangelo, G., Picillo, M., Vitale, C., & Barone, P. (2015). Resting-state functional connectivity associated with mild cognitive impairment in Parkinson’s disease. Journal of Neurology, 262(2), 425–434. https://doi.org/10.1007/s00415-014-7591-5
Aukema, E. J., Caan, M. W., Oudhuis, N., Majoie, C. B., Vos, F. M., Reneman, L., & Schouten-van Meeteren, A. Y. (2009). White matter fractional anisotropy correlates with speed of processing and motor speed in young childhood cancer survivors. International Journal of Radiation Oncology Biology Physics, 74(3), 837–843. https://doi.org/10.1016/j.ijrobp.2008.08.060
Bahrami, N., Seibert, T. M., Karunamuni, R., Bartsch, H., Krishnan, A., Farid, N., & McDonald, C. R. (2017). Altered network topology in patients with primary brain tumors after fractionated radiotherapy. Brain Connect, 7(5), 299–308. https://doi.org/10.1089/brain.2017.0494
Bartolomei, F., Bosma, I., Klein, M., Baayen, J. C., Reijneveld, J. C., Postma, T. J., & Stam, C. J. (2006). Disturbed functional connectivity in brain tumour patients: Evaluation by graph analysis of synchronization matrices. Clinical Neurophysiology, 117(9), 2039–2049. https://doi.org/10.1016/j.clinph.2006.05.018
Bassett, D. S., Bullmore, E. T., Meyer-Lindenberg, A., Apud, J. A., Weinberger, D. R., & Coppola, R. (2009). Cognitive fitness of cost-efficient brain functional networks. Proceedings of the National Academy of Sciences of the United States of America, 106(28), 11747–11752. https://doi.org/10.1073/pnas.0903641106.
Bernhardt, B. C., Bonilha, L., & Gross, D. W. (2015). Network analysis for a network disorder: The emerging role of graph theory in the study of epilepsy. Epilepsy & Behavior, 50, 162–170. https://doi.org/10.1016/j.yebeh.2015.06.005
Bettus, G., Bartolomei, F., Confort-Gouny, S., Guedj, E., Chauvel, P., Cozzone, P. J., & Guye, M. (2010). Role of resting state functional connectivity MRI in presurgical investigation of mesial temporal lobe epilepsy. Journal of Neurology, Neurosurgery & Psychiatry, 81(10), 1147. https://doi.org/10.1136/jnnp.2009.191460
Biswal, B. B., Mennes, M., Zuo, X. N., Gohel, S., Kelly, C., Smith, S. M., & Milham, M. P. (2010). Toward discovery science of human brain function. Proceedings of the National Academy of Sciences of the USA, 107(10), 4734–4739. https://doi.org/10.1073/pnas.0911855107
Bosma, I., Reijneveld, J. C., Klein, M., Douw, L., van Dijk, B. W., Heimans, J. J., & Stam, C. J. (2009). Disturbed functional brain networks and neurocognitive function in low-grade glioma patients: A graph theoretical analysis of resting-state MEG. Nonlinear Biomed Phys, 3(1), 9. https://doi.org/10.1186/1753-4631-3-9
Brinkman, T. M., Reddick, W. E., Luxton, J., Glass, J. O., Sabin, N. D., Srivastava, D. K., & Krull, K. R. (2012). Cerebral white matter integrity and executive function in adult survivors of childhood medulloblastoma. Neuroscience Oncology Supply, 14, iv25–36. https://doi.org/10.1093/neuonc/nos214
Bullmore, E., & Bassett, D. S. (2011). Brain graphs: Graphical models of the human brain connectome. Annual Review of Clinical Psychology, 7, 113–140. https://doi.org/10.1146/annurev-clinpsy-040510-143934
Bullmore, E., & Sporns, O. (2009). Complex brain networks: Graph theoretical analysis of structural and functional systems. Nature Reviews Neuroscience, 10(3), 186–198. https://doi.org/10.1038/nrn2575
Caeyenberghs, K., Verhelst, H., Clemente, A., & Wilson, P. H. (2017). Mapping the functional connectome in traumatic brain injury: What can graph metrics tell us? NeuroImage, 160, 113–123. https://doi.org/10.1016/j.neuroimage.2016.12.003
Chen, Z. J., He, Y., Rosa-Neto, P., Germann, J., & Evans, A. C. (2008). Revealing modular architecture of human brain structural networks by using cortical thickness from MRI. Cerebral Cortex, 18(10), 2374–2381. https://doi.org/10.1093/cercor/bhn003
Clark, S. V., Semmel, E. S., Aleksonis, H. A., Steinberg, S. N., & King, T. Z. (2021). Cerebellar-Subcortical-Cortical systems as modulators of cognitive functions. Neuropsychology Review. https://doi.org/10.1007/s11065-020-09465-1
Corn, B. W., Yousem, D. M., Scott, C. B., Rotman, M., Asbell, S. O., Nelson, D. F., & Curran, W. J. (1994). White-matter changes are correlated significantly with radiation-dose - observations from a randomized dose-escalation trial for malignant glioma (Radiation-Therapy-Oncology-Group-83-02). Cancer, 74(10), 2828–2835. https://doi.org/10.1002/1097-0142(19941115)74:10%3c2828::Aid-Cncr2820741014%3e3.0.Co;2-K
Crofts, J. J., Higham, D. J., Bosnell, R., Jbabdi, S., Matthews, P. M., Behrens, T. E., & Johansen-Berg, H. (2011). Network analysis detects changes in the contralesional hemisphere following stroke. NeuroImage, 54(1), 161–169. https://doi.org/10.1016/j.neuroimage.2010.08.032
Crossley, N. A., Mechelli, A., Scott, J., Carletti, F., Fox, P. T., McGuire, P., & Bullmore, E. T. (2014). The hubs of the human connectome are generally implicated in the anatomy of brain disorders. Brain, 137(Pt 8), 2382–2395. https://doi.org/10.1093/brain/awu132
De Baene, W., Rutten, G. J. M., & Sitskoorn, M. M. (2017). The Temporal Pattern of a Lesion Modulates the Functional Network Topology of Remote Brain Regions. Neural Plasticity, 2017, 3530723. https://doi.org/10.1155/2017/3530723
Dennis, E. L., Jahanshad, N., McMahon, K. L., de Zubicaray, G. I., Martin, N. G., Hickie, I. B., & Thompson, P. M. (2013). Development of brain structural connectivity between ages 12 and 30: A 4-Tesla diffusion imaging study in 439 adolescents and adults. NeuroImage, 64, 671–684. https://doi.org/10.1016/j.neuroimage.2012.09.004
Desai, A. A., Strother, M. K., Faraco, C. C., Morgan, V. L., Ladner, T. R., Dethrage, L. M., & Donahue, M. J. (2015). The contribution of common surgically implanted hardware to functional MR imaging artifacts. AJNR. American Journal of Neuroradiology, 36(11), 2068–2073. https://doi.org/10.3174/ajnr.A4419
Dwan, T. M., Ownsworth, T., Chambers, S., Walker, D. G., & Shum, D. H. (2015). Neuropsychological assessment of individuals with brain tumor: Comparison of approaches used in the classification of impairment. Frontiers in Oncology, 5, 56. https://doi.org/10.3389/fonc.2015.00056
Fox, M. E., & King, T. Z. (2016). Pituitary disorders as a predictor of apathy and executive dysfunction in adult survivors of childhood brain tumors. Pediatric Blood & Cancer, 63(11), 2019–2025. https://doi.org/10.1002/pbc.26144
Fox, M. E., & King, T. Z. (2018). Functional connectivity in adult brain tumor patients: A Systematic Review. Brain Connect, 8(7), 381–397. https://doi.org/10.1089/brain.2018.0623
Hagmann, P., Kurant, M., Gigandet, X., Thiran, P., Wedeen, V. J., Meuli, R., & Thiran, J. P. (2007). Mapping human whole-brain structural networks with diffusion MRI. PLoS ONE, 2(7), e597. https://doi.org/10.1371/journal.pone.0000597
Hallquist, M. N., & Hillary, F. G. (2019). Graph theory approaches to functional network organization in brain disorders: A critique for a brave new small-world. Network Neuroscience, 3(1), 1–26. https://doi.org/10.1162/netn_a_00054
Harrison, B. J., Pujol, J., Ortiz, H., Fornito, A., Pantelis, C., & Yucel, M. (2008). Modulation of brain resting-state networks by sad mood induction. PLoS ONE, 3(3), e1794. https://doi.org/10.1371/journal.pone.0001794
Hart, M. G., Price, S. J., & Suckling, J. (2016). Connectome analysis for pre-operative brain mapping in neurosurgery. British Journal of Neurosurgery, 30(5), 506–517. https://doi.org/10.1080/02688697.2016.1208809
Hendrix, P., Hans, E., Griessenauer, C. J., Simgen, A., Oertel, J., & Karbach, J. (2017). Neurocognitive status in patients with newly-diagnosed brain tumors in good neurological condition: The impact of tumor type, volume, and location. Clinical Neurology and Neurosurgery, 156, 55–62. https://doi.org/10.1016/j.clineuro.2017.03.009
Hillary, F. G., & Grafman, J. H. (2017). Injured brains and adaptive networks: The benefits and costs of hyperconnectivity. Trends in Cognitive Sciences, 21(5), 385–401. https://doi.org/10.1016/j.tics.2017.03.003
Hillary, F. G., Rajtmajer, S. M., Roman, C. A., Medaglia, J. D., Slocomb-Dluzen, J. E., Calhoun, V. D., & Wylie, G. R. (2014). The rich get richer: Brain injury elicits hyperconnectivity in core subnetworks. PLoS ONE, 9(8), e104021. https://doi.org/10.1371/journal.pone.0104021
Honey, C. J., Sporns, O., Cammoun, L., Gigandet, X., Thiran, J. P., Meuli, R., & Hagmann, P. (2009). Predicting human resting-state functional connectivity from structural connectivity. Proceedings of the National Academy of Sciences USA.
Huang, Q., Zhang, R., Hu, X., Ding, S., Qian, J., Lei, T., & Liu, H. (2014). Disturbed small-world networks and neurocognitive function in frontal lobe low-grade glioma patients. PLoS ONE, 9(4), e94095. https://doi.org/10.1371/journal.pone.0094095
Imms, P., Clemente, A., Cook, M., D’Souza, W., Wilson, P. H., Jones, D. K., & Caeyenberghs, K. (2019). The structural connectome in traumatic brain injury: A meta-analysis of graph metrics. Neuroscience and Biobehavioral Reviews. https://doi.org/10.1016/j.neubiorev.2019.01.002
Jayakar, R., King, T. Z., Morris, R., & Na, S. (2015). Hippocampal volume and auditory attention on a verbal memory task with adult survivors of pediatric brain tumor. Neuropsychology, 29(2), 303–319. https://doi.org/10.1037/neu0000183
Kautiainen, R. J., Dwivedi, B., MacDonald, T. J., & King, T. Z. (2020). GSTP1 polymorphisms sex-specific association with verbal intelligence in survivors of pediatric medulloblastoma tumors. Child Neuropsychology, 26(6), 739–753. https://doi.org/10.1080/09297049.2020.1726886
Kautiainen, R. J., Fox, M. E., & King, T. Z. (2021). The neurological predictor Scale Predicts Adaptive Functioning via Executive Dysfunction in Young Adult Survivors of Childhood Brain Tumor. Journal of the International Neuropsychological Society, 27(1), 1–11. https://doi.org/10.1017/S1355617720000624
Kesler, S. R., Noll, K., Cahill, D. P., Rao, G., & Wefel, J. S. (2017). The effect of IDH1 mutation on the structural connectome in malignant astrocytoma. Journal of Neuro-Oncology, 131(3), 565–574. https://doi.org/10.1007/s11060-016-2328-1
King, T. Z., Ailion, A. S., Fox, M. E., & Hufstetler, S. M., (2017). Neurodevelopmental model of long-term outcomes of adult survivors of childhood brain tumors. Child Neuropsychology, 1–21https://doi.org/10.1080/09297049.2017.1380178
King, T. Z., & Na, S. (2016). Cumulative neurological factors associated with long-term outcomes in adult survivors of childhood brain tumors. Child Neuropsychology, 22(6), 748–760. https://doi.org/10.1080/09297049.2015.1049591
King, T. Z., Na, S., & Mao, H. (2015a). Neural Underpinnings of Working Memory in Adult Survivors of Childhood Brain Tumors. Journal of the International Neuropsychological Society, 21(7), 494–505. https://doi.org/10.1017/S135561771500051X
King, T. Z., Wang, L., & Mao, H. (2015b). Disruption of white matter integrity in adult survivors of childhood brain tumors: Correlates with long-term intellectual outcomes. PLoS ONE, 10(7), e0131744. https://doi.org/10.1371/journal.pone.0131744
Law, N., Bouffet, E., Laughlin, S., Laperriere, N., Briere, M. E., Strother, D., & Mabbott, D. (2011). Cerebello-thalamo-cerebral connections in pediatric brain tumor patients: Impact on working memory. NeuroImage, 56(4), 2238–2248. https://doi.org/10.1016/j.neuroimage.2011.03.065
Li, Y., Liu, Y., Li, J., Qin, W., Li, K., Yu, C., & Jiang, T. (2009). Brain anatomical network and intelligence. PLoS Computational Biology, 5(5), e1000395. https://doi.org/10.1371/journal.pcbi.1000395
Liu, L., Zhang, H., Wu, J., Yu, Z., Chen, X., Rekik, I., & Shen, D. (2018). Overall survival time prediction for high-grade glioma patients based on large-scale brain functional networks. Brain Imaging and Behavior. https://doi.org/10.1007/s11682-018-9949-2
Loughan, A. R., Braun, S. E., & Lanoye, A. (2019). Executive dysfunction in neuro-oncology: Behavior rating inventory of executive function in adult primary brain tumor patients. Applied Neuropsychology. Adult 1–10. https://doi.org/10.1080/23279095.2018.1553175
Macartney, G., Harrison, M. B., VanDenKerkhof, E., Stacey, D., & McCarthy, P. (2014). Quality of life and symptoms in pediatric brain tumor survivors: A systematic review. Journal of Pediatric Oncology Nursing, 31(2), 65–77. https://doi.org/10.1177/1043454213520191
Marchand, W. R., Lee, J. N., Suchy, Y., Garn, C., Chelune, G., Johnson, S., & Wood, N. (2013). Functional architecture of the cortico-basal ganglia circuitry during motor task execution: Correlations of strength of functional connectivity with neuropsychological task performance among female subjects. Human Brain Mapping, 34(5), 1194–1207. https://doi.org/10.1002/hbm.21505
McCurdy, M. D., Rane, S., Daly, B. P., & Jacobson, L. A. (2016). Associations among treatment-related neurological risk factors and neuropsychological functioning in survivors of childhood brain tumor. Journal of Neuro-Oncology, 127(1), 137–144. https://doi.org/10.1007/s11060-015-2021-9
Micklewright, J. L., King, T. Z., Morris, R. D., & Krawiecki, N. (2008). Quantifying Pediatric Neuro-oncology Risk Factors: Development of the Neurological Predictor Scale. Journal of Child Neurology, 23(4), 455–458. https://doi.org/10.1177/0883073807309241
Moher, D., Liberati, A., Tetzlaff, J., Altman, D. G., & The PRISMA Group. (2009). Preferred Reporting Items for Systematic Reviews and Meta-Analyses: The PRISMA Statement. PLoS Med, 6(7). 10.1371/
Monje, M., & Dietrich, J. (2012). Cognitive side effects of cancer therapy demonstrate a functional role for adult neurogenesis. Behavioural Brain Research, 227(2), 376–379. https://doi.org/10.1016/j.bbr.2011.05.012
Na, S., Li, L., Crosson, B., Dotson, V., MacDonald, T. J., Mao, H., & King, T. Z. (2018). White matter network topology relates to cognitive flexibility and cumulative neurological risk in adult survivors of pediatric brain tumors. Neuroimage Clinical, 20, 485–497. https://doi.org/10.1016/j.nicl.2018.08.015
Nageswara Rao, A. A., & Packer, R. J. (2014). Advances in the management of low-grade gliomas. Current Oncology Reports, 16(8), 398. https://doi.org/10.1007/s11912-014-0398-9
Onoda, K., Ishihara, M., & Yamaguchi, S. (2012). Decreased functional connectivity by aging is associated with cognitive decline. Journal of Cognitive Neuroscience, 24(11), 2186–2198. https://doi.org/10.1162/jocn_a_00269%M22784277
Ostrom, Q. T., Gittleman, H., Liao, P., Vecchione-Koval, T., Wolinsky, Y., Kruchko, C., & Barnholtz-Sloan, J. S. (2017). CBTRUS Statistical Report: Primary brain and other central nervous system tumors diagnosed in the United States in 2010–2014. Neuroscience Oncology, 19(suppl_5), v1-v88. https://doi.org/10.1093/neuonc/nox158
Otten, M. L., Mikell, C. B., Youngerman, B. E., Liston, C., Sisti, M. B., Bruce, J. N., & McKhann, G. M. (2012). Motor deficits correlate with resting state motor network connectivity in patients with brain tumours. Brain, 135(Pt 4), 1017–1026. https://doi.org/10.1093/brain/aws041
Ozyurt, J., Muller, H. L., Warmuth-Metz, M., & Thiel, C. M. (2017). Hypothalamic tumors impact gray and white matter volumes in fronto-limbic brain areas. Cortex, 89, 98–110. https://doi.org/10.1016/j.cortex.2017.01.017
Palmer, S. L., Glass, J. O., Li, Y., Ogg, R., Qaddoumi, I., Armstrong, G. T., & Reddick, W. E. (2012). White matter integrity is associated with cognitive processing in patients treated for a posterior fossa brain tumor. Neuro-Oncology, 14(9), 1185–1193. https://doi.org/10.1093/neuonc/nos154
Pan-Weisz, T. M., Kryza-Lacombe, M., Burkeen, J., Hattangadi-Gluth, J., Malcarne, V. L., & McDonald, C. R. (2019). Patient-reported health-related quality of life outcomes in supportive-care interventions for adults with brain tumors: A systematic review. Psycho-Oncology, 28(1), 11–21. https://doi.org/10.1002/pon.4906
Panwala, T. F., Fox, M. E., DeVaughn, T. S., & King, T. Z. (2019a). The effects of radiation and sex differences on adaptive functioning in adult survivors of pediatric posterior fossa brain tumors. Journal of the International Neuropsychological Society, 1-11. https://doi.org/10.1017/s135561771900033x
Panwala, T. F., Fox, M. E., Tucker, T. D., & King, T. Z., (2019b). The effects of radiation and sex differences on adaptive functioning in adult survivors of pediatric posterior fossa brain tumors. Journal of the International Neuropsychological Society, 1–11. https://doi.org/10.1017/s135561771900033x
Park, C., Kim, S. Y., Kim, Y., & Kim, K. (2008). Comparison of the small-world topology between anatomical and functional connectivity in the human brain. Physica a: Statistical Mechanics and Its Applications, 387(23), 5958–5962. https://doi.org/10.1016/j.physa.2008.06.048
Park, J. E., Kim, H. S., Kim, S. J., Kim, J. H., & Shim, W. H. (2016). Alteration of long-distance functional connectivity and network topology in patients with supratentorial gliomas. Neuroradiology, 58(3), 311–320. https://doi.org/10.1007/s00234-015-1621-6
Pedersen, M., Omidvarnia, A. H., Walz, J. M., & Jackson, G. D. (2015). Increased segregation of brain networks in focal epilepsy: An fMRI graph theory finding. Neuroimage Clinical, 8, 536–542. https://doi.org/10.1016/j.nicl.2015.05.009
Ramaswamy, V., Remke, M., Adamski, J., Bartels, U., Tabori, U., Wang, X., & Bouffet, E. (2016). Medulloblastoma subgroup-specific outcomes in irradiated children: Who are the true high-risk patients? Neuro-Oncology, 18(2), 291–297. https://doi.org/10.1093/neuonc/nou357
Robinson, K. E., Fountain-Zaragoza, S., Dennis, M., Taylor, H. G., Bigler, E. D., Rubin, K., & Yeates, K. O. (2014). Executive functions and theory of mind as predictors of social adjustment in childhood traumatic brain injury. Journal of Neurotrauma, 31(22), 1835–1842. https://doi.org/10.1089/neu.2014.3422
Robinson, K. E., Pearson, M. M., Cannistraci, C. J., Anderson, A. W., Kuttesch, J. F., Jr., Wymer, K., & Compas, B. E. (2015). Functional neuroimaging of working memory in survivors of childhood brain tumors and healthy children: Associations with coping and psychosocial outcomes. Child Neuropsychology, 21(6), 779–802. https://doi.org/10.1080/09297049.2014.924492
Rubinov, M., & Sporns, O. (2010). Complex network measures of brain connectivity: Uses and interpretations. NeuroImage, 52(3), 1059–1069. https://doi.org/10.1016/j.neuroimage.2009.10.003
Rueckriegel, S. M., Bruhn, H., Thomale, U. W., & Hernaiz Driever, P. (2015). Cerebral white matter fractional anisotropy and tract volume as measured by MR imaging are associated with impaired cognitive and motor function in pediatric posterior fossa tumor survivors. Pediatric Blood & Cancer, 62(7), 1252–1258. https://doi.org/10.1002/pbc.25485
Scantlebury, N., Bouffet, E., Laughlin, S., Strother, D., McConnell, D., Hukin, J., & Mabbott, D. J. (2016). White matter and information processing speed following treatment with cranial-spinal radiation for pediatric brain tumor. Neuropsychology, 30(4), 425–438. https://doi.org/10.1037/neu0000258
Scoccianti, S., Detti, B., Cipressi, S., Iannalfi, A., Franzese, C., & Biti, G. (2012). Changes in neurocognitive functioning and quality of life in adult patients with brain tumors treated with radiotherapy. Journal of Neuro-Oncology, 108(2), 291–308. https://doi.org/10.1007/s11060-012-0821-8
Semmel, E. S., Quadri, T. R., & King, T. Z. (2020). Oral processing speed as a key mechanism in the relationship between neurological risk and adaptive functioning in survivors of pediatric brain tumors. Pediatric Blood Cancer, e28575. https://doi.org/10.1002/pbc.28575
Skudlarski, P., Jagannathan, K., Calhoun, V. D., Hampson, M., Skudlarska, B. A., & Pearlson, G. (2008). Measuring brain connectivity: Diffusion tensor imaging validates resting state temporal correlations. NeuroImage, 43(3), 554–561. https://doi.org/10.1016/j.neuroimage.2008.07.063
Smith, K. M., King, T. Z., Jayakar, R., & Morris, R. D. (2014). Reading skill in adult survivors of childhood brain tumor: A theory-based neurocognitive model. Neuropsychology, 28(3), 448–458. https://doi.org/10.1037/neu0000056
Stippich, C. (2015). Clinical functional MRI: presurgical functional neuroimaging: Springer.
Sun, T., Plutynski, A., Ward, S., & Rubin, J. B. (2015). An integrative view on sex differences in brain tumors. Cellular and Molecular Life Sciences, 72(17), 3323–3342. https://doi.org/10.1007/s00018-015-1930-2
Taphoorn, M. J. B., & Klein, M. (2004). Cognitive deficits in adult patients with brain tumours. The Lancet Neurology, 3(3), 159–168. https://doi.org/10.1016/s1474-4422(04)00680-5
Termenon, M., Achard, S., Jaillard, A., & Delon-Martin, C. (2016). The "Hub Disruption Index," a reliable index sensitive to the brain networks reorganization. A study of the contralesional hemisphere in stroke. Frontiers in Computational Neuroscience, 10, 84. https://doi.org/10.3389/fncom.2016.00084
van Dellen, E., Douw, L., Hillebrand, A., Ris-Hilgersom, I. H., Schoonheim, M. M., Baayen, J. C., & Reijneveld, J. C. (2012). MEG network differences between low- and high-grade glioma related to epilepsy and cognition. PLoS One, 7(11), e50122. https://doi.org/10.1371/journal.pone.0050122
van den Heuvel, M. P., & Sporns, O. (2013). Network hubs in the human brain. Trends in Cognitive Sciences, 17(12), 683–696. https://doi.org/10.1016/j.tics.2013.09.012
van den Heuvel, M. P., Stam, C. J., Kahn, R. S., & Hulshoff Pol, H. E. (2009). Efficiency of functional brain networks and intellectual performance. Journal of Neuroscience, 29(23), 7619–7624. https://doi.org/10.1523/JNEUROSCI.1443-09.2009
Wang, H., Douw, L., Hernandez, J. M., Reijneveld, J. C., Stam, C. J., & Van Mieghem, P. (2010). Effect of tumor resection on the characteristics of functional brain networks. Physical Review e: Statistical, Nonlinear, and Soft Matter Physics, 82(2 Pt 1), 021924. https://doi.org/10.1103/PhysRevE.82.021924
Warren, D. E., Power, J. D., Bruss, J., Denburg, N. L., Waldron, E. J., Sun, H., & Tranel, D. (2014). Network measures predict neuropsychological outcome after brain injury. Proc Natl Acad Sci U S A, 111(39), 14247–14252. https://doi.org/10.1073/pnas.1322173111
Wolfe, K. R., Walsh, K. S., Reynolds, N. C., Mitchell, F., Reddy, A. T., Paltin, I., & Madan-Swain, A. (2013). Executive functions and social skills in survivors of pediatric brain tumor. Child Neuropsychology, 19(4), 370–384. https://doi.org/10.1080/09297049.2012.669470
Xu, H., Ding, S., Hu, X., Yang, K., Xiao, C., Zou, Y., & Qian, Z. (2013). Reduced efficiency of functional brain network underlying intellectual decline in patients with low-grade glioma. Neuroscience Letters, 543, 27–31. https://doi.org/10.1016/j.neulet.2013.02.062
Yeh, C. H., Jones, D. K., Liang, X., Descoteaux, M., & Connelly, A. (2020). Mapping structural connectivity using diffusion MRI: challenges and opportunities. Journal of Magnetic Resonance Imaging. https://doi.org/10.1002/jmri.27188
Yu, Z., Tao, L., Qian, Z., Wu, J., Liu, H., Yu, Y., & Sun, J. (2016). Altered brain anatomical networks and disturbed connection density in brain tumor patients revealed by diffusion tensor tractography. International Journal of Computer Assisted Radiology and Surgery, 11(11), 2007–2019. https://doi.org/10.1007/s11548-015-1330-y
Zalesky, A., & Fornito, A. (2009). A DTI-derived measure of cortico-cortical connectivity. IEEE Transactions on Medical Imaging, 28(7), 1023–1036. https://doi.org/10.1109/TMI.2008.2012113
Zalesky, A., Fornito, A., & Bullmore, E. T. (2010). Network-based statistic: Identifying differences in brain networks. NeuroImage, 53(4), 1197–1207. https://doi.org/10.1016/j.neuroimage.2010.06.041
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Funding was provided by the Georgia State University’s Brains and Behavior Initiative, Graduate Student Fellowship (ESS) and the Alfred P. Sloan Foundation undergraduate student fellowship (TRQ).
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This work was funded by the Brains & Behavior Graduate Fellowship (ESS) and the Alfred P. Sloan Foundation (TRQ).
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The original online version of this article was revised: In this article the author name Eric S. Semmel was incorrectly written as Eric S. Semme. Additionally, in Table 2, the line below "n" does not extend under the other headings to the right (i.e., Age, Sex, Diagnoses, etc).
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Semmel, E.S., Quadri, T.R. & King, T.Z. Graph Theoretical Analysis of Brain Network Characteristics in Brain Tumor Patients: A Systematic Review. Neuropsychol Rev 32, 651–675 (2022). https://doi.org/10.1007/s11065-021-09512-5
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DOI: https://doi.org/10.1007/s11065-021-09512-5