The DOI matches its Crossref record; 39 of the 39 references it cites were found in a registry or an index.
Traumatic brain injury impairs small-world topology
Open accessAbstract
BACKGROUND AND PURPOSE: Brain metastases are a common finding on brain MRI. However, the factors that dictate their size and distribution are incompletely understood. Our aim was to discover a statistical model that can account for the size distribution of parenchymal metastases in the brain as measured on contrast-enhanced MR imaging. MATERIALS AND METHODS: Tumor volumes were calculated on the basis of measured tumor diameters from contrast-enhanced T1-weighted spoiled gradient-echo images in 68 patients with untreated parenchymal metastatic disease. Tumor volumes were then placed in rank-order distributions and compared with 11 different statistical curve types. The resultant R2 values to assess goodness of fit were calculated. The top 2 distributions were then compared using the likelihood ratio test, with resultant R values demonstrating the relative likelihood of these distributions accounting for the observed data. RESULTS: Thirty-nine of 68 cases best fit a power distribution (mean R2 = 0.938 ± 0.050), 20 cases best fit an exponential distribution (mean R2 = 0.957 ± 0.050), and the remaining cases were scattered among the remaining distributions. Likelihood ratio analysis revealed that 66 of 68 cases had a positive mean R value (1.596 ± 1.316), skewing toward a power law distribution. CONCLUSIONS: The size distributions of untreated brain metastases favor a power law distribution. This finding suggests that metastases do not exist in isolation, but rather as part of a complex system. Furthermore, these results suggest that there may be a relatively small number of underlying variables that substantially influence the behavior of these systems. The identification of these variables could have a profound effect on our understanding of these lesions and our ability to treat them.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“In a study of patients with mild TBI, networks had a longer average path length, reduced overall cost, and reduced network efficiency compared with controls.73”
From Graph theory analysis of complex brain networks: new concepts in brain mapping applied to neurosurgery (2016) · cites this paper as [73]
In the citing paper
“TBI characteristically produced injury focused on the posterior cingulate cortex, known to be a critical hub in the normally functioning brain.”
From Graph theory analysis of complex brain networks: new concepts in brain mapping applied to neurosurgery (2016) · cites this paper as [73]
Citations
Cites
Complex brain networks: graph theoretical analysis of structural and functional systems
Bullmore, E., & Sporns, O. (2009) · Nature Reviews Neuroscience · doi:10.1038/nrn2575 · Cited by 11,820
Complex network measures of brain connectivity: Uses and interpretations
Rubinov, M., & Sporns, O. (2010) · NeuroImage · doi:10.1016/j.neuroimage.2009.10.003 · Cited by 11,522
The Brain's Default Network: Anatomy, Function, and Relevance to Disease
Buckner, R. L., Andrews‐Hanna, J. R., & Schacter, D. L. (2008) · Annals of the New York Academy of Sciences · doi:10.1196/annals.1440.011 · Cited by 9,761
Mapping the structural core of human cerebral cortex
Hagmann, P., Cammoun, L., Gigandet, X., Meuli, R., Honey, C. J., Wedeen, V. J., & Sporns, O. (2008) · PLoS Biology · doi:10.1371/journal.pbio.0060159 · Cited by 4,173
Investigations into resting-state connectivity using independent component analysis
Beckmann, C. F., DeLuca, M., Devlin, J. T., & Smith, S. M. (2005) · Philosophical Transactions of the Royal Society B: Biological Sciences · doi:10.1098/rstb.2005.1634 · Cited by 3,398
Mesulam, M. (1998) · Brain · doi:10.1093/brain/121.6.1013 · Cited by 3,072
Functional-anatomic fractionation of the brain’s default network
Andrews-Hanna, J. R., Reidler, J. S., Sepulcre, J., Poulin, R., & Buckner, R. L. (2010) · Neuron · doi:10.1016/j.neuron.2010.02.005 · Cited by 2,767
Achard, S., Salvador, R., Whitcher, B., Suckling, J., & Bullmore, E. (2006) · The Journal of Neuroscience · doi:10.1523/jneurosci.3874-05.2006 · Cited by 2,393
Resting-State Functional Connectivity Reflects Structural Connectivity in the Default Mode Network
Greicius, M. D., Supekar, K., Menon, V., & Dougherty, R. F. (2009) · Cerebral Cortex · doi:10.1093/cercor/bhn059 · Cited by 2,175
Network modelling methods for FMRI
Smith, S. M., Miller, K. L., Salimi-Khorshidi, G., Webster, M., Beckmann, C. F., Nichols, T. E., Ramsey, J. D., & Woolrich, M. W. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2010.08.063 · Cited by 1,878
Cited by
Sorted by citations
Items 101–176 of 176
Porter, S., Torres, I. J., Panenka, W. J., Rajwani, Z., Fawcett, D., Hyder, A. A., & Virji‐Babul, N. (2017) · Heliyon · doi:10.1016/j.heliyon.2017.e00373 · Cited by 18
Aberrant Correlation Between the Default Mode and Salience Networks in Mild Traumatic Brain Injury
Liu, Y., Wu, W., Chen, X., Wu, M., Hu, G., Zhou, G., Wang, Z., & Chen, R. (2020) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2020.00068 · Cited by 15
Graph theoretical analysis of functional network for comprehension of sign language
Liu, L., Yan, X., Liu, J., Xia, M., Lu, C., Emmorey, K. D., Chu, M., & Ding, G. (2017) · Brain Research · doi:10.1016/j.brainres.2017.06.031 · Cited by 16
Neuronal Degeneration Impairs Rhythms Between Connected Microcircuits
Schumm, S. N., Gabrieli, D. J., & Meaney, D. F. (2020) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2020.00018 · Cited by 18
Yuan, W., Diekfuss, J. A., Foss, K. D. B., Dudley, J. A., Leach, J. L., Narad, M. E., DiCesare, C. A., Bonnette, S., Epstein, J. N., Logan, K., et al. (2021) · Journal of Neurotrauma · doi:10.1089/neu.2021.0141 · Cited by 15
Chronic Traumatic Encephalopathy
Iacono, D., Shively, S. B., Edlow, B. L., & Perl, D. P. (2017) · Physical Medicine and Rehabilitation Clinics of North America · doi:10.1016/j.pmr.2016.12.007 · Cited by 16
Gabrieli, D. J., Schumm, S. N., Vigilante, N. F., & Meaney, D. F. (2020) · Neural Computation · doi:10.1162/neco_a_01343 · Cited by 15
Ondek, K., Pevzner, A., Tercovich, K. G., Schedlbauer, A. M., Izadi, A., Ekstrom, A. D., Cowen, S. L., Shahlaie, K., & Gurkoff, G. G. (2020) · Frontiers in Neurology · doi:10.3389/fneur.2020.600171 · Cited by 14
Functional connectivity alterations in traumatic brain injury patients with late seizures
Rocca, M. L., Barisano, G., Garner, R., Ruf, S. F., Amoroso, N., Monti, M. M., Vespa, P. M., Bellotti, R., Deniz Erdogmus, B., Toga, A. W., et al. (2023) · Neurobiology of Disease · doi:10.1016/j.nbd.2023.106053 · Cited by 14
Yan, Y., Song, J., Xu, G., Yao, S., Cao, C., Li, C., Peng, G., & Du, H. (2017) · Journal of Clinical Neuroscience · doi:10.1016/j.jocn.2017.05.010 · Cited by 14
Resting-state network organisation in children with traumatic brain injury
Botchway-Commey, E. N., Kooper, C. C., Pouwels, P. J. W., Bruining, H., Engelen, M., Oosterlaan, J., & Königs, M. (2022) · Cortex · doi:10.1016/j.cortex.2022.05.014 · Cited by 14
Olsen, A., Babikian, T., Dennis, E. L., Ellis-Blied, M. U., Giza, C. C., Marion, S., Mink, R. B., Johnson, J. L., Babbitt, C. J., Thompson, P. M., et al. (2019) · Journal of Neurotrauma · doi:10.1089/neu.2019.6532 · Cited by 13
Gabrieli, D. J., Schumm, S. N., Vigilante, N. F., Parvesse, B., & Meaney, D. F. (2020) · PLoS ONE · doi:10.1371/journal.pone.0234749 · Cited by 13
Generalised Sandpile Dynamics on Artificial and Real-World Directed Networks
Zachariou, N., Expert, P., Takayasu, M., & Christensen, K. (2015) · PLoS ONE · doi:10.1371/journal.pone.0142685 · Cited by 12
Self-citationFunctional hub disruption emphasizes consciousness recovery in severe traumatic brain injury
Oujamaa, L., Delon‐Martin, C., Jaroszynski, C., Termenón, M., Silva, S., Payen, J., & Achard, S. (2023) · Brain Communications · doi:10.1093/braincomms/fcad319 · Cited by 12
Robalo, B. M. D. B., Vlegels, N., Meier, J. M., Leemans, A., Biessels, G. J., Reijmer, Y. D., on Behalf of the Utrecht VCI Study Group, van den Berg, E., Biesbroek, J. M., Brundel, M., et al. (2020) · Brain Connectivity · doi:10.1089/brain.2019.0686 · Cited by 10
Zhuang, Y., Zhang, Z., Tivarus, M. E., Qiu, X., Zhong, J., & Schifitto, G. (2020) · Human Brain Mapping · doi:10.1002/hbm.25207 · Cited by 10
Altered segregation between task-positive and task-negative regions in mild traumatic brain injury
Sours, C., Kinnison, J., Padmala, S., Gullapalli, R. P., & Pessoa, L. (2017) · Brain Imaging and Behavior · doi:10.1007/s11682-017-9724-9 · Cited by 13
Huang, S., Han, J., Zheng, H., Li, M., Huang, C., Kui, X., & Liu, J. (2023) · Neural Regeneration Research · doi:10.4103/1673-5374.387971 · Cited by 10
España‐Irla, G., Tinney, E. M., Ai, M., Nwakamma, M. C., & Morris, T. P. (2025) · Human Brain Mapping · doi:10.1002/hbm.70237 · Cited by 10
Lee, S., Polimeni, J. R., Price, C. M., Edlow, B. L., & McNab, J. A. (2018) · Brain Connectivity · doi:10.1089/brain.2017.0499 · Cited by 8
Biomarkers of memory variability in traumatic brain injury
Adamovich‐Zeitlin, R., Wanda, P. A., Solomon, E. A., Phan, T., Lega, B. C., Jobst, B. C., Gross, R. E., Ding, K., Diaz-Arrastia, R. R., & Kahana, M. J. (2020) · Brain Communications · doi:10.1093/braincomms/fcaa202 · Cited by 8
To, X. V., Soni, N., Medeiros, R., Alateeq, K., & Nasrallah, F. A. (2022) · Brain Research · doi:10.1016/j.brainres.2022.147955 · Cited by 8
Reorganized Hubs of Brain Functional Networks after Acute Mild Traumatic Brain Injury
Bai, L., Yin, B., Lei, S., Li, T., Wang, S., Pan, Y., Gan, S., Jia, X., Li, X., Xiong, F., et al. (2022) · Journal of Neurotrauma · doi:10.1089/neu.2021.0450 · Cited by 10
Changes in brain organization after TBI
Zhou, Y., & Lui, Y. W. (2013) · Neurology · doi:10.1212/wnl.0b013e318292a37d · Cited by 8
Graph alignment exploiting the spatial organization improves the similarity of brain networks
Calissano, A., Papadopoulo, T., Pennec, X., & Deslauriers‐Gauthier, S. (2024) · Human Brain Mapping · doi:10.1002/hbm.26554 · Cited by 7
Walton, S. R., Powell, J. R., Brett, B. L., Yin, W., Kerr, Z. Y., Liu, M., McCrea, M., Guskiewicz, K. M., & Giovanello, K. S. (2022) · PLoS ONE · doi:10.1371/journal.pone.0273918 · Cited by 9
Disruption of Rich-Club Connectivity in Cushing Disease
Xu, C.-X., Jiang, H., Zhao, Z., Sun, Y., Chen, X., Sun, B., Sun, Q., & Bian, L. (2021) · World Neurosurgery · doi:10.1016/j.wneu.2020.12.146 · Cited by 7
Cao, M., Halperin, J. M., & Li, X. (2021) · Brain Sciences · doi:10.3390/brainsci11101348 · Cited by 7
Mild traumatic brain injury history is associated with lower brain network resilience in soldiers
Powell, J. R., Hopfinger, J. B., Giovanello, K. S., Walton, S. R., DeLellis, S. M., Kane, S. F., Means, G. E., & Mihalik, J. P. (2023) · Brain Communications · doi:10.1093/braincomms/fcad201 · Cited by 6
Executive Dysfunction After Traumatic Brain Injury
García-Barrera, M. A., Agate, F. T., Wong, R. E., Smart, C. M., & Karr, J. E. (2019) · Springer eBooks · doi:10.1007/978-3-030-25077-5_5 · Cited by 5
Changes in resting state networks in high school football athletes across a single season
Ghiles, C. W., Clark, M. D., Kuzminski, S. J., Fraser, M. A., Petrella, J. R., & Guskiewicz, K. M. (2023) · British Journal of Radiology · doi:10.1259/bjr.20220359 · Cited by 5
Mertens, N., Cavanagh, J. F., Brandt, E., Fratzke, V., Story-Remer, J., Rieger, R. E., Wilson, J. K., Gill, D., Campbell, R., & Quinn, D. K. (2023) · Neurorehabilitation · doi:10.3233/nre-230014 · Cited by 5
Altered grey matter structural covariance in chronic moderate–severe traumatic brain injury
Symons, G. F., Gregg, M. C., Hicks, A. J., Rowe, C. C., Shultz, S. R., Ponsford, J. L., & Spitz, G. (2024) · Scientific Reports · doi:10.1038/s41598-023-50396-7 · Cited by 5
Impaired brain network architecture in Cushing’s disease based on graph theoretical analysis
Xu, C.-X., Jiang, H., Zheng, R., Sun, Y., Sun, Q., & Bian, L. (2020) · Aging · doi:10.18632/aging.102939 · Cited by 5
Wu, Z., Cao, M., Di, X., Wu, K., Gao, Y., & Li, X. (2021) · Brain Sciences · doi:10.3390/brainsci12010016 · Cited by 5
Identifying mild traumatic brain injury using measures of frequency-specified networks
Salsabilian, S., Bibineyshvili, Y., Margolis, D. J., & Najafizadeh, L. (2022) · Journal of Neural Engineering · doi:10.1088/1741-2552/ac954e · Cited by 5
Resting cerebral oxygen metabolism exhibits archetypal network features
Hubbard, N. A., Turner, M. P., Sitek, K. R., West, K. L., Kaczmarzyk, J., Himes, L. M., Thomas, B. P., Lu, H., & Rypma, B. (2021) · Human Brain Mapping · doi:10.1002/hbm.25352 · Cited by 4
Disrupted topological organization of functional brain networks in traumatic axonal injury
Li, J., Shu, Y., Chen, L., Wang, B., Chen, L., Zhan, J., Kuang, H., Xia, G.-J., Zhou, F., Gong, H., et al. (2023) · Brain Imaging and Behavior · doi:10.1007/s11682-023-00832-z · Cited by 4
Accounting for Changing Structure in Functional Network Analysis of TBI Patients
Dell’Italia, J., Johnson, M. A., Vespa, P. M., & Monti, M. M. (2020) · Frontiers in Systems Neuroscience · doi:10.3389/fnsys.2020.00042 · Cited by 4
Imaging the large‐scale and cellular response to focal traumatic brain injury in mouse neocortex
Bibineyshvili, Y., Vajtay, T. J., Salsabilian, S., Fliss, N., Suvarnakar, A., Fang, J. L., Teng, S. L., Alder, J., Najafizadeh, L., & Margolis, D. J. (2024) · Experimental Physiology · doi:10.1113/ep092219 · Cited by 3
Advances in Mild Traumatic Brain Injury Imaging Biomarkers
Shah, R. N., & Allen, J. W. (2017) · Current Radiology Reports · doi:10.1007/s40134-017-0210-3 · Cited by 4
Biological Relevance of Network Architecture
Gkigkitzis, I., Haranas, I. I., & Kotsireas, I. (2017) · Advances in experimental medicine and biology · doi:10.1007/978-3-319-56246-9_1 · Cited by 4
Hu, Y., Yue, J., Ge, Q., Feng, Z., Wang, J., & Zang, Y. (2022) · Frontiers in Neuroinformatics · doi:10.3389/fninf.2022.882126 · Cited by 3
Lexical network properties in aphasic discourse production
Chen, J.-Q., & Chang, H. (2025) · Clinical Linguistics & Phonetics · doi:10.1080/02699206.2025.2507049 · Cited by 4
Genetics of brain networks and connectivity
Dennis, E. L., Thompson, P. M., & Jahanshad, N. (2018) · Elsevier eBooks · doi:10.1016/b978-0-12-813838-0.00008-x · Cited by 3
Small Vessel Disease and the Resting Functional Architecture of the Brain
Stevens, R., Hannawi, Y., & Sair, H. I. (2014) · Journal of Cerebral Blood Flow and Metabolism · doi:10.1038/jcbfm.2014.69 · Cited by 4
Neurogenic organ dysfunction syndrome after acute brain injury
Zhang, H., Chen, W., Chao, Y., Su, N., Robba, C., Czosnyka, M., Smielewski, P. S., Czosnyka, Z. H., He, W. W., Hu, X., et al. (2025) · Military Medical Research · doi:10.1186/s40779-025-00662-8 · Cited by 2
Topological Properties of Functional Brain Connectivity in Obsessive-Compulsive Disorder
Saifutdinová, E., Kopřivová, J., Lhotská, L., & Macaš, M. (2016) · World Congress on Medical Physics and Biomedical Engineering, September 7 - 12, 2009, Munich, Germany · doi:10.1007/978-3-319-32703-7_32 · Cited by 2
Kuceyeski, A. F., Jamison, K. W., Owen, J. P., Raj, A., & Mukherjee, P. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/320515 · Cited by 3
Coenen, J., Strohm, M., & Reinsberger, C. (2024) · Scientific Reports · doi:10.1038/s41598-024-74474-6 · Cited by 1
Longitudinal Multimodal Neuroimaging After Traumatic Brain Injury
Radanovic, A., Jamison, K. W., Kang, Y., Tozlu, C., Shah, S. A., & Kuceyeski, A. F. (2026) · Human Brain Mapping · doi:10.1002/hbm.70534 · Cited by 1
Churchill, N. W. (2022) · Springer eBooks · doi:10.1007/978-3-030-93813-0_3 · Cited by 1
Flexibility of in vitro cortical circuits influences resilience from microtrauma
Adegoke, M. A., Teter, O. M., & Meaney, D. F. (2022) · Frontiers in Cellular Neuroscience · doi:10.3389/fncel.2022.991740 · Cited by 1
Ruíz, T., Brown, S. R., & Farivar, R. (2024) · Vision · doi:10.3390/vision8020033 · Cited by 1
Wen, X., Li, S., Wang, J., Bi, L., Sun, Y., Wang, X., Du, Y., Liang, Z., & Wei, C. (2025) · Anesthesia & Analgesia · doi:10.1213/ane.0000000000007614 · Cited by 1
Li, N., Zhang, W., Cheng, S., & Liu, Y. (2019) · International Conference on Communications, Information System and Computer Engineering (CISCE) · doi:10.1109/cisce.2019.00104 · Cited by 1
Tjerkaski, J., Thompson, W. H., Bellander, B., Thelin, E. P., & Fransson, P. (2022) · Neuroimage Reports · doi:10.1016/j.ynirp.2022.100079 · Cited by 1
Ravi, S., Criado‐Marrero, M., Barroso, D., Braga, I., Bolen, M. L., Rubinovich, U., Hery, G. P., Grudny, M. M., Koren, J., Prokop, S., et al. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.09.21.508917 · Cited by 1
Neuroimaging Rates for Closed Head Trauma in a Community Hospital
Rothman, S. M., & Alander, S. W. (2016) · Pediatric Emergency Care · doi:10.1097/pec.0000000000000683
Gonzalez‐Toledo, E., Fayed, N. M., Viguera, L., Sharma, K., Kalakoti, P., Samra, N. S., Nanda, A., & Sun, H. (2017) · Springer eBooks · doi:10.1007/978-3-319-53126-7_31
Intersecting geodesics and centrality in graphs
Carter, E. A., Ek, B., Gonzalez, D., Flórez, R., & Narayan, D. A. (2018) · Involve a Journal of Mathematics · doi:10.2140/involve.2019.12.31
Bigler, E. D., & Victoroff, J. (2019) · Cambridge University Press eBooks · doi:10.1017/9781139696432.008
Zgradba in delovanje možganskih omrežij
Berlot, R., & Repovš, G. (2019) · Slovenian Medical Journal · doi:10.6016/zdravvestn.2830
Altered Small-World, Functional Brain Networks in Children with Strabismus and AmblyopiaPreprint
Zhang, S., Gao, G., & Shao, Y. (2022) · Research Square · doi:10.21203/rs.3.rs-1263600/v1
Neurological Diseases as Networktopathies with Disconnection Phenomena
Hoffmann, M. W. (2019) · Cambridge University Press eBooks · doi:10.1017/9781108589246.009
Unraveling of Brain Networks in Neurological Conditions
Hoffmann, M. W. (2019) · Cambridge University Press eBooks · doi:10.1017/9781108589246.008
Functional Neuroradiology of Traumatic Brain Injury
Boffa, G., Raz, E., & Inglese, M. (2023) · Springer eBooks · doi:10.1007/978-3-031-10909-6_14
Bulama, I., Waziri, A., Sanda, K. A., Mshelia, P. A., Usman, B., Monguno, M. B., Sajo, M. U., Obansa, O. U., Hassan, A., Suleiman, N., et al. (2023) · Sahel Journal of Veterinary Sciences · doi:10.54058/saheljvs.v20i4.426
Tsikonofilos, K., Bruyns‐Haylett, M., May, H. G., Donat, C. K., & Kozlov, A. S. (2025) · Network Neuroscience · doi:10.1162/netn.a.21
Chepisheva, M., Shen, X., Lacadie, C., Luo, W., Appleton, J., Arora, J., Bhawnani, J., Mahajan, A., Omay, B., Gilmore, E. J., et al. (2025) · medRxiv · doi:10.1101/2025.05.20.25327881
Atsumi, N., Nakahira, Y., & Iwamoto, M. (2026) · Frontiers in Bioengineering and Biotechnology · doi:10.3389/fbioe.2026.1692718
Stulz, S. B. (2023) · theses.fr (ABES) · doi:10.70675/470b2d4bz0886z4876z846dzfefc3192a0fa
Mansour, M. A., Wahid, M., Molla, M. A. S. E., Helmy, M. H., Adel, T. M., Mostafa, H. N., & Elsanafiry, M. S. (2026) · Neuroimage Reports · doi:10.1016/j.ynirp.2026.100369 · Cited by 1
Villalon, A., Li, R., Currao, A., Lebas, A., Fonda, J. R., Fortier, C. B., Milberg, W., & Jorge, R. E. (2026) · Journal of Head Trauma Rehabilitation · doi:10.1097/htr.0000000000001212
Metadata
- Authors
- Pandit, A. S., Expert, P., Lambiotte, R., Bonnelle, V., Leech, R., Turkheimer, F. E., & Sharp, D. J.
- Affiliations
- University of Namur (BE)
- From The Computational, Cognitive and Clinical Neuroimaging Laboratory (A.S.P., V.B., R.L., D.J.S.), The Division of Experimental Medicine, Imperial College London, Hammersmith Hospital Campus, London; Centre for Neuroimaging Sciences (P.E., F.E.T.), Institute of Psychiatry, London, UK; and Department of Mathematics/Naxys (R.L.), University of Namur, Namur, Belgium.
- ORCID
- Venue
- Neurology
- Published
- 2013-05-14
- Volume
- 80
- Issue
- 20
- Pages
- 1826-1833
- ISSN
- 0028-3878 (ISSN-L)
- 1526-632X
- PMC
- PMC3908350
- OpenAlex
- W2172177542
- Cited by
- 176
- References
- 39
- Citations by year
- 2026: 4 · 2025: 6 · 2024: 5 · 2023: 11 · 2022: 16 · 2021: 12 · 2020: 18 · 2019: 16 · 2018: 10 · 2017: 20 · 2016: 17 · 2015: 17 · 2014: 16 · 2013: 8
- FWCI
- 10.60
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Neurology (publishedVersion)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Research Portal (King's College London) (submittedVersion)
- Repository of the University of Namur (submittedVersion)
- Spiral (Imperial College London) (submittedVersion)
- Funders
- National Institute for Health and Care Research (NIHR-RP-011-048)
- Engineering and Physical Sciences Research Council (EP/E049451/1)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Health Sciences › Medicine › Oncology › Brain Metastases and Treatment
- Health Sciences › Medicine › Clinical Neurology › Traumatic Brain Injury Research
- Keywords
- traumatic brain injury, small-world networks, brain network topology, brain metastases, power-law distribution, exponential distribution, contrast-enhanced MRI, tumor volume
- MeSH terms
- Adolescent, Adult, Brain Injuries, Brain Mapping, Female, Humans, Magnetic Resonance Imaging, Male, Middle Aged, Nerve Net, Neuropsychological Tests, Young Adult
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
