Cited by 2,553Functional Brain Connectivity StudiesComplex Network Analysis TechniquesAdvanced Neuroimaging Techniques and Applications

The DOI matches its Crossref record; 55 of the 55 references it cites were found in a registry or an index.

Efficiency and Cost of Economical Brain Functional Networks

Open access

Achard, Sophie, Bullmore, Ed·2007·PLoS Computational Biology

DOI 10.1371/journal.pcbi.0030017PMC PMC1794324

Open this paper in CoChat — free account Open PDF

Abstract

Brain anatomical networks are sparse, complex, and have economical small-world properties. We investigated the efficiency and cost of human brain functional networks measured using functional magnetic resonance imaging (fMRI) in a factorial design: two groups of healthy old (N = 11; mean age = 66.5 years) and healthy young (N = 15; mean age = 24.7 years) volunteers were each scanned twice in a no-task or "resting" state following placebo or a single dose of a dopamine receptor antagonist (sulpiride 400 mg). Functional connectivity between 90 cortical and subcortical regions was estimated by wavelet correlation analysis, in the frequency interval 0.06-0.11 Hz, and thresholded to construct undirected graphs. These brain functional networks were small-world and economical in the sense of providing high global and local efficiency of parallel information processing for low connection cost. Efficiency was reduced disproportionately to cost in older people, and the detrimental effects of age on efficiency were localised to frontal and temporal cortical and subcortical regions. Dopamine antagonism also impaired global and local efficiency of the network, but this effect was differentially localised and did not interact with the effect of age. Brain functional networks have economical small-world properties-supporting efficient parallel information transfer at relatively low cost-which are differently impaired by normal aging and pharmacological blockade of dopamine transmission.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

Integrity signals

Registry

Crossref · 10.1371/journal.pcbi.0030017

Cites

55 of 55 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

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?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

“Mini-mental state”
Folstein, M. F., Folstein, S. E., & McHugh, P. R. (1975) · Journal of Psychiatric Research · doi:10.1016/0022-3956(75)90026-6 · Cited by 88,282
Collective dynamics of ‘small-world’ networks
Watts, D. J., & Strogatz, S. H. (1998) · Nature · doi:10.1038/30918 · Cited by 43,060
[Japanese version of the Neuropsychiatric Inventory--a scoring system for neuropsychiatric disturbance in dementia patients].
Hirono, N., Mori, E., Ikejiri, Y., Imamura, T., Shimomura, T., Hashimoto, M., Yamashita, H., & Ikeda, M. (1997) · PubMed · Cited by 21,012
Special Care Units and Traditional Care in Dementia: Relationship with Behavior, Cognition, Functional Status and Quality of Life - A Review
Kok, J. S., Berg, I. J., & Scherder, E. J. A. (2013) · Dementia and Geriatric Cognitive Disorders Extra · doi:10.1159/000353441 · Cited by 20,589
Statistical mechanics of complex networks
Albert, R., & Barabási, A.-L. (2002) · Reviews of Modern Physics · doi:10.1103/revmodphys.74.47 · Cited by 20,471
Older Adults' Reasons for Using Technology while Aging in Place
Peek, S. T. M., Luijkx, K. G., Rijnaard, M. D., Nieboer, M. E., van der Voort, C. S., Aarts, S., van Hoof, J., Vrijhoef, H. J., & Wouters, E. (2015) · Gerontology · doi:10.1159/000430949 · Cited by 19,852
Sensitivity and False Alarm Rate of a Fall Sensor in Long-Term Fall Detection in the Elderly
Kangas, M., Korpelainen, R., Vikman, I., Nyberg, L., & Jämsä, T. J. (2014) · Gerontology · doi:10.1159/000362720 · Cited by 19,007
Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single subject brain
Tzourio-Mazoyer, N., Landeau, B., Papathanassiou, D., Crivello, F., Etard, O., Delcroix, N., Mazoyer, B., & Joliot, M. (2002) · NeuroImage · doi:10.1006/nimg.2001.0978 · Cited by 16,319
Functional connectivity in the motor cortex of resting human brain using echo‐planar mri
Biswal, B., Zerrin Yetkin, F., Haughton, V. M., & Hyde, J. S. (1995) · Magnetic Resonance in Medicine · doi:10.1002/mrm.1910340409 · Cited by 9,812
Exploring complex networks
Strogatz, S. H. (2001) · Nature · doi:10.1038/35065725 · Cited by 8,236

Flagged first, then by citations

Show all 55 references

Research works printed but not linked: 1

LezakMD 1995 Neuropsychological assessment New York Oxford University Press 1026

Sorted by citations

Items 801–900 of 1,003

Functional Specificity of TPS Brain Stimulation Effects in Patients with Alzheimer’s Disease: A Follow-up fMRI Analysis
Dörl, G., Matt, E., & Beisteiner, R. (2022) · Neurology and Therapy · doi:10.1007/s40120-022-00362-8 · Cited by 37
Graph theoretical analysis of resting magnetoencephalographic functional connectivity networks
Rutter, L., Nadar, S. R., Holroyd, T., Carver, F. W., Apud, J. A., Weinberger, D. R., & Coppola, R. (2013) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2013.00093 · Cited by 37
Connectome Reorganization Associated With Surgical Outcome in Temporal Lobe Epilepsy
Ji, G., Zhang, Z., Xu, Q., Wei, W., Wang, J., Wang, Z., Yang, F., Sun, K., Jiao, Q., Liao, W., et al. (2015) · Medicine · doi:10.1097/md.0000000000001737 · Cited by 39
An EEG study of brain connectivity dynamics at the resting state.
Dimitriadis, S. I., Laskaris, N., Tsirka, V., Vourkas, M., & Micheloyannis, S. (2012) · PubMed · Cited by 37
Transcranial direct current stimulation generates a transient increase of small-world in brain connectivity: an EEG graph theoretical analysis
Vecchio, F., Iorio, R. D., Miraglia, F., Granata, G., Romanello, R., Bramanti, P., & Rossini, P. M. (2018) · Experimental Brain Research · doi:10.1007/s00221-018-5200-z · Cited by 38
Altered structural and functional brain network overall organization predict human intertemporal decision‐making
Chen, Z., Hu, X., Chen, Q., & Feng, T. (2018) · Human Brain Mapping · doi:10.1002/hbm.24374 · Cited by 38
Concreteness and emotional valence of episodic future thinking (EFT) independently affect the dynamics of intertemporal decisions
Calluso, C., Tosoni, A., Cannito, L., & Committeri, G. (2019) · PLoS ONE · doi:10.1371/journal.pone.0217224 · Cited by 36
Network topology and machine learning analyses reveal microstructural white matter changes underlying Chinese medicine Dengzhan Shengmai treatment on patients with vascular cognitive impairment
Lu, H., Zhang, J., Liang, Y., Qiao, Y., Yang, C., He, X., Wang, W., Zhao, S., Wei, D., Li, H., et al. (2020) · Pharmacological Research · doi:10.1016/j.phrs.2020.104773 · Cited by 38
Structural brain network topology underpinning ADHD and response to methylphenidate treatment
Griffiths, K. R., Braund, T. A., Kohn, M. R., Clarke, S. D., Williams, L. M., & Korgaonkar, M. S. (2021) · Translational Psychiatry · doi:10.1038/s41398-021-01278-x · Cited by 41
Structural and Functional Connectivity Substrates of Cognitive Impairment in Multiple Sclerosis
Zhang, J., Cortese, R., Stefano, N. D., & Giorgio, A. (2021) · Frontiers in Neurology · doi:10.3389/fneur.2021.671894 · Cited by 38
Resting-state brain connectivity in healthy young and middle-aged adults at risk of progressive Alzheimer’s disease
Kucikova, L., Goerdten, J., Dounavi, M., Mak, E., Su, L., Waldman, A. D., Danso, S., Terrera, G. M., & Ritchie, C. W. (2021) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2021.07.024 · Cited by 42
Brain functional network abnormalities in parkinson’s disease with mild cognitive impairment
Suo, X., Lei, D., Li, N., Peng, J., Chen, C., Li, W., Qin, K., Kemp, G. J., Peng, R., & Gong, Q. (2021) · Cerebral Cortex · doi:10.1093/cercor/bhab520 · Cited by 37
Multilayer Network Science
Artime, O., Benigni, B., Bertagnolli, G., d’Andrea, V., Gallotti, R., Ghavasieh, A., Raimondo, S., & Domenico, M. D. (2022) · Cambridge University Press eBooks · doi:10.1017/9781009085809 · Cited by 38
Influence of Choice of Null Network on Small-World Parameters of Structural Correlation Networks
Hosseini, S. M. H., & Kesler, S. R. (2013) · PLoS ONE · doi:10.1371/journal.pone.0067354 · Cited by 39
Long-term reorganization of structural brain networks in a rabbit model of intrauterine growth restriction
Batallé, D., Muñoz‐Moreno, E., Arbat‐Plana, A., Illa, M., Figueras, F., Eixarch, E., & Gratacós, E. (2014) · NeuroImage · doi:10.1016/j.neuroimage.2014.05.065 · Cited by 38
Structural Network Topology Revealed by White Matter Tractography in Cannabis Users: A Graph Theoretical Analysis
Kim, D., Skosnik, P. D., Cheng, H., Pruce, B., Brumbaugh, M. S., Vollmer, J. M., Hetrick, W. P., O’Donnell, B. F., Sporns, O., Puce, A., et al. (2011) · Brain Connectivity · doi:10.1089/brain.2011.0053 · Cited by 38
Development of Large-Scale Functional Brain Networks in Children
Supekar, K., Musen, M., & Menon, V. (2009) · NeuroImage · doi:10.1016/s1053-8119(09)70984-x · Cited by 36
The effect of education on regional brain metabolism and its functional connectivity in an aged population utilizing positron emission tomography
Kim, J., Chey, J., Kim, S.-E., & Kim, H. (2015) · Neuroscience Research · doi:10.1016/j.neures.2014.12.009 · Cited by 36
Applied Strategies Towards EEG/MEG Biomarker Identification in Clinical and Cognitive Research
Sakkalis, V. (2011) · Biomarkers in Medicine · doi:10.2217/bmm.10.121 · Cited by 38
Highlighting the Structure-Function Relationship of the Brain with the Ising Model and Graph Theory
Das, T. K., Abeyasinghe, P. M., Crone, J. S., Sosnowski, A., Laureys, S., Owen, A. M., & Soddu, A. (2014) · BioMed Research International · doi:10.1155/2014/237898 · Cited by 38
Distinct disruptions of resting-state functional brain networks in familial and sporadic schizophrenia
Zhu, J., Zhuo, C., Liu, F., Qin, W., Xu, L., & Yu, C. (2016) · Scientific Reports · doi:10.1038/srep23577 · Cited by 35
Investigating cognitive neuroscience theories of human intelligence: A connectome‐based predictive modeling approach
Anderson, E. D., & Barbey, A. K. (2022) · Human Brain Mapping · doi:10.1002/hbm.26164 · Cited by 42
Data-Driven and Predefined ROI-Based Quantification of Long-Term Resting-State fMRI Reproducibility
Song, X., Panych, L. P., & Chen, N. (2015) · Brain Connectivity · doi:10.1089/brain.2015.0349 · Cited by 35
A significant risk factor for poststroke depression: the depression-related subnetwork.
Yang, S.-R., Hua, P., Shang, X.-Y., Cui, Z., Zhong, S.-Y., Gong, G.-L., & Humphreys, G. (2015) · Journal of Psychiatry & Neuroscience · doi:10.1503/jpn.140086 · Cited by 37
Disrupted Structural and Functional Networks and Their Correlation with Alertness in Right Temporal Lobe Epilepsy: A Graph Theory Study
Jiang, W., Li, J., Chen, X., Ye, W., & Zheng, J. (2017) · Frontiers in Neurology · doi:10.3389/fneur.2017.00179 · Cited by 37
The Posterior Insula Shows Disrupted Brain Functional Connectivity in Female Migraineurs Without Aura Based on Brainnetome Atlas
Zhang, J., Su, J., Wang, M., Zhao, Y., Zhang, Q., Qian, Y., Lu, H., Zhang, H., Li, G., Wu, Y.-L., et al. (2017) · Scientific Reports · doi:10.1038/s41598-017-17069-8 · Cited by 36
Networks of myelin covariance
Melie‐García, L., Slater, D., Ruef, A., Sanabria-Díaz, G., Preisig, M., Kherif, F., Draganski, B., & Lutti, A. (2017) · Human Brain Mapping · doi:10.1002/hbm.23929 · Cited by 36
Age-Dependent Modulations of Resting State Connectivity Following Motor Practice
Solesio‐Jofre, E., Beets, I. A. M., Woolley, D. G., Pauwels, L., Chalavi, S., Mantini, D., & Swinnen, S. P. (2018) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2018.00025 · Cited by 36
Hemisphere and Gender Differences in the Rich-Club Organization of Structural Networks
Wang, B., Zhan, Q., Yan, T., Imtiaz, S., Xiang, J., Niu, Y., Liu, M., Wang, G., Cao, R., & Li, D. (2019) · Cerebral Cortex · doi:10.1093/cercor/bhz027 · Cited by 37
Altered resting functional network topology assessed using graph theory in youth with attention-deficit/hyperactivity disorder
Wang, Y., Zuo, C., Xu, Q., Liao, S., Kanji, M., & Wang, D. (2019) · Progress in Neuro-Psychopharmacology and Biological Psychiatry · doi:10.1016/j.pnpbp.2019.109796 · Cited by 37
Functional near-infrared spectroscopy evidence for the development of topological asymmetry between hemispheric brain networks from childhood to adulthood
Cai, L., Dong, Q., Wang, M., & Niu, H. (2019) · Neurophotonics · doi:10.1117/1.nph.6.2.025005 · Cited by 37
Disruption of gray matter morphological networks in patients with paroxysmal kinesigenic dyskinesia
Li, X., Lei, D., Niu, R., Li, L., Suo, X., Li, W., Yang, C., Yang, T., Ren, J., Pinaya, W. H. L., et al. (2020) · Human Brain Mapping · doi:10.1002/hbm.25230 · Cited by 35
Concept generation techniques change patterns of brain activation during engineering design
Shealy, T., Gero, J. S., Hu, M., & Milovanovic, J. (2020) · Design Science · doi:10.1017/dsj.2020.30 · Cited by 39
Parcellation-induced variation of empirical and simulated brain connectomes at group and subject levels
Domhof, J. W. M., Jung, K., Eickhoff, S. B., & Popovych, O. V. (2021) · Network Neuroscience · doi:10.1162/netn_a_00202 · Cited by 37
The expanding horizons of network neuroscience: From description to prediction and control
Srivastava, P., Fotiadis, P., Parkes, L., & Bassett, D. S. (2022) · NeuroImage · doi:10.1016/j.neuroimage.2022.119250 · Cited by 38
Expertise modulates local regional homogeneity of spontaneous brain activity in the resting brain: An fMRI study using the model of skilled acupuncturists
Dong, M., Qin, W., Zhao, L., Yang, X., Yuan, K., Zeng, F., Sun, J., Yu, D., von Deneen, K. M., Liang, F., et al. (2013) · Human Brain Mapping · doi:10.1002/hbm.22235 · Cited by 36
Functional MRI at the crossroads
VANHORN, J. (2008) · International Journal of Psychophysiology · doi:10.1016/j.ijpsycho.2008.11.003 · Cited by 37
Diffusion spectral imaging modules correlate with EEG LORETA neuroimaging modules
Thatcher, R. W., North, D. M., & Biver, C. J. (2011) · Human Brain Mapping · doi:10.1002/hbm.21271 · Cited by 35
Activation of Functional Brain Networks in Children With Psychogenic Non-epileptic Seizures
Radmanesh, M., Jalili, M., & Kozlowska, K. (2020) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2020.00339 · Cited by 35
CR-GCN: Channel-Relationships-Based Graph Convolutional Network for EEG Emotion Recognition
Jia, J., Zhang, B., Lv, H., Xu, Z., Hu, S., & Li, H. (2022) · Brain Sciences · doi:10.3390/brainsci12080987 · Cited by 35
Change of Brain Functional Connectivity in Patients With Spinal Cord Injury: Graph Theory Based Approach
Min, Y.-S., Chang, Y., Park, J. W., Lee, J., Cha, J., Yang, J.-J., Kim, C.-H., Hwang, J.-M., Yoo, J.-N., & Jung, T. (2015) · Annals of Rehabilitation Medicine · doi:10.5535/arm.2015.39.3.374 · Cited by 36
Positive and negative affective processing exhibit dissociable functional hubs during the viewing of affective pictures
Zhang, W., Li, H., & Pan, X. (2014) · Human Brain Mapping · doi:10.1002/hbm.22636 · Cited by 38
Altered Hub Configurations within Default Mode Network following Acupuncture at ST36: A Multimodal Investigation Combining fMRI and MEG
You, Y., Bai, L., Dai, R., Cheng, H., Liu, Z., Wei, W., & Tian, J. (2013) · PLoS ONE · doi:10.1371/journal.pone.0064509 · Cited by 36
Abnormal functional brain network in epilepsy patients with focal cortical dysplasia
Jeong, W., Jin, S., Kim, M. S., Kim, J. S., & Chung, C. K. (2014) · Epilepsy Research · doi:10.1016/j.eplepsyres.2014.09.006 · Cited by 34
COMPLEX NETWORKS: NEW TRENDS FOR THE ANALYSIS OF BRAIN CONNECTIVITY
Chávez, M., Valencia, M., Latora, V., & Martinerie, J. M. (2010) · International Journal of Bifurcation and Chaos · doi:10.1142/s0218127410026757 · Cited by 43
Less Efficient Information Transfer in Cys-Allele Carriers of DISC1: A Brain Network Study Based on Diffusion MRI
Li, Y., Liu, B., Hou, B., Qin, W., Wang, D., Yu, C., & Jiang, T. (2012) · Cerebral Cortex · doi:10.1093/cercor/bhs167 · Cited by 35
Network functional connectivity and whole-brain functional connectomics to investigate cognitive decline in neurodegenerative conditions
Dipasquale, O. (2016) · Functional Neurology · doi:10.11138/fneur/2016.31.4.191 · Cited by 36
Naltrexone ameliorates functional network abnormalities in alcohol‐dependent individuals
Morris, L. S., Baek, K., Tait, R. J., Elliott, R., Ersche, K. D., Flechais, R., McGonigle, J., Murphy, A., Nestor, L. J., Orbán, C., et al. (2017) · Addiction Biology · doi:10.1111/adb.12503 · Cited by 35
Self-citation
Disrupted resting brain graph measures in individuals at high risk for alcoholism
Holla, B., Panda, R., Venkatasubramanian, G., Biswal, B. B., Bharath, R. D., & Benegal, V. (2017) · Psychiatry Research Neuroimaging · doi:10.1016/j.pscychresns.2017.05.002 · Cited by 34
Altered topological patterns of brain functional networks in Crohn’s disease
Liu, P., Li, R., Bao, C., Wei, Y., Fan, Y., Liu, Y., Wang, G., Wu, H., & Qin, W. (2018) · Brain Imaging and Behavior · doi:10.1007/s11682-017-9814-8 · Cited by 35
Disrupted grey matter network morphology in pediatric posttraumatic stress disorder
Niu, R., Lei, D., Chen, F., Chen, Y., Suo, X., Li, L., Lui, S., Huang, X., Sweeney, J. A., & Gong, Q. (2018) · NeuroImage Clinical · doi:10.1016/j.nicl.2018.03.030 · Cited by 35
Impact of Healthy Aging on Multifractal Hemodynamic Fluctuations in the Human Prefrontal Cortex
Mukli, P., Nagy, Z. Z., Rácz, F. S., Hermán, P., & Eke, A. (2018) · Frontiers in Physiology · doi:10.3389/fphys.2018.01072 · Cited by 35
Making Sense of Connectivity
Hahn, A., Lanzenberger, R. R., & Kasper, S. F. (2018) · The International Journal of Neuropsychopharmacology · doi:10.1093/ijnp/pyy100 · Cited by 40
Accelerated Aging of Functional Brain Networks Supporting Cognitive Function in Psychotic Disorders
Sheffield, J. M., Rogers, B. P., Blackford, J. U., Heckers, S., & Woodward, N. D. (2019) · Biological Psychiatry · doi:10.1016/j.biopsych.2018.12.016 · Cited by 34
The Impact of Emotional Music on Active ROI in Patients with Depression Based on Deep Learning: A Task-State fMRI Study
Gui, R., Chen, T., & Nie, H. (2019) · Computational Intelligence and Neuroscience · doi:10.1155/2019/5850830 · Cited by 35
Resting-state brain network topological properties and the correlation with neuropsychological assessment in adolescent narcolepsy
Fulong, X., Spruyt, K., Lu, C., Dianjiang, Z., Jun, Z., & Fang, H. (2020) · SLEEP · doi:10.1093/sleep/zsaa018 · Cited by 38
Genetic influence on ageing-related changes in resting-state brain functional networks in healthy adults: A systematic review
Foo, H., Mather, K. A., Jiang, J., Thalamuthu, A., Wen, W. X., & Sachdev, P. S. (2020) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2020.03.011 · Cited by 34
Within node connectivity changes, not simply edge changes, influence graph theory measures in functional connectivity studies of the brain
Luo, W., Greene, A. S., & Constable, R. T. (2021) · NeuroImage · doi:10.1016/j.neuroimage.2021.118332 · Cited by 34
Aberrant Functional Connectivity Architecture in Alzheimer’s Disease and Mild Cognitive Impairment: A Whole-Brain, Data-Driven Analysis
Zhou, B., Yao, H., Wang, P., Zhang, Z., Zhan, Y., Ma, J., Xu, K., Wang, L., An, N., Liu, Y., et al. (2015) · BioMed Research International · doi:10.1155/2015/495375 · Cited by 38
Structure of the cortical networks during successful memory encoding in TV commercials
Fallani, F. D. V., Astolfi, L., Cincotti, F., Mattia, D., Marciani, M. G., Gao, S., Salinari, S., Soranzo, R., Colosimo, A., & Babiloni, F. (2008) · Clinical Neurophysiology · doi:10.1016/j.clinph.2008.06.018 · Cited by 35
Autistic Traits in Neurotypical Adults: Correlates of Graph Theoretical Functional Network Topology and White Matter Anisotropy Patterns
András, J., Emri, M., Spisák, T., Szemán-Nagy, A., Béresová, M., Kis, S. A., Molnár, P., & Berényi, E. L. (2013) · PLoS ONE · doi:10.1371/journal.pone.0060982 · Cited by 36
Network-Level Structural Abnormalities of Cerebral Cortex in Type 1 Diabetes Mellitus
Lyoo, I. K., Yoon, S. J., Renshaw, P. F., Hwang, J., Bae, S., Musen, G., Kim, J. E., Bolo, N. R., Jeong, H. S., Simonson, D. C., et al. (2013) · PLoS ONE · doi:10.1371/journal.pone.0071304 · Cited by 35
Magnetoencephalography in Stroke Recovery and Rehabilitation
Paggiaro, A., Birbaumer, N., Cavinato, M., Turco, C., Formaggio, E., Felice, A. D., Masiero, S., & Piccione, F. (2016) · Frontiers in Neurology · doi:10.3389/fneur.2016.00035 · Cited by 38
Linking Resting-State Networks in the Prefrontal Cortex to Executive Function: A Functional Near Infrared Spectroscopy Study
Zhao, J. L., Liu, J., Jiang, X., Zhou, G., Chen, G., Ding, X. P., Fu, G., & Lee, K. (2016) · Frontiers in Neuroscience · doi:10.3389/fnins.2016.00452 · Cited by 39
Functional and structural connectome properties in the 5XFAD transgenic mouse model of Alzheimer’s disease
Kesler, S. R., Acton, P., Rao, V., & Ray, W. J. (2018) · Network Neuroscience · doi:10.1162/netn_a_00048 · Cited by 34
“Efficiency and path length are related measures (Achard & Bullmore, 2007).”
Background
Motor Impairment in Stroke Patients Is Associated With Network Properties During Consecutive Motor Imagery
Lee, M., Kim, Y.-H., & Lee, S.-W. (2022) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2022.3151742 · Cited by 34
Deciphering the functional specialization of whole-brain spatiomolecular gradients in the adult brain
Vogel, J. W., Alexander-Bloch, A. F., Wagstyl, K., Bertolero, M. A., Markello, R. D., Pines, A., Sydnor, V. J., Diaz-Papkovich, A., Hansen, J. Y., Evans, A. C., et al. (2024) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2219137121 · Cited by 34
Connectivity network measures predict volumetric atrophy in mild cognitive impairment
Nir, T. M., Jahanshad, N., Toga, A. W., Bernstein, M. A., Jack, C. R., Weiner, M. W., & Thompson, P. M. (2014) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2014.04.038 · Cited by 34
Predictors of coupling between structural and functional cortical networks in normal aging
Romero-García, R., Atienza, M., & Cantero, J. L. (2013) · Human Brain Mapping · doi:10.1002/hbm.22362 · Cited by 34
Quantitative evaluation of simulated functional brain networks in graph theoretical analysis
Lee, W. H., Bullmore, E. T., & Frangou, S. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2016.08.050 · Cited by 35
Self-citation
Abnormal brain white matter network in young smokers: a graph theory analysis study
Zhang, Y., Li, M., Wang, R., Bi, Y., Li, Y., Zhang, Y., Liu, J., Yu, D., & Yuan, K. (2017) · Brain Imaging and Behavior · doi:10.1007/s11682-017-9699-6 · Cited by 37
Disrupted small-world brain functional network topology in male patients with severe obstructive sleep apnea revealed by resting-state fMRI
Chen, L.-T., Fan, X., Li, H., Nie, S., Gong, H., Zhang, W., Zeng, X., Long, P., & Peng, D. (2017) · Neuropsychiatric Disease and Treatment · doi:10.2147/ndt.s135426 · Cited by 34
Thresholding functional connectomes by means of mixture modeling
Bielczyk, N., Walocha, F., Ebel, P., Haak, K. V., Llera, A., Buitelaar, J. K., Glennon, J. C., & Beckmann, C. F. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2018.01.003 · Cited by 36
Aberrant brain functional connectome in patients with obstructive sleep apnea
Chen, L.-T., Fan, X., Li, H., Ye, C., Yu, H., Xin, H., Gong, H., Peng, D., & Yan, L. (2018) · Neuropsychiatric Disease and Treatment · doi:10.2147/ndt.s161085 · Cited by 34
Brain structural connectivity network alterations in insomnia disorder reveal a central role of the right angular gyrus
Wei, Y., Bresser, T., Wassing, R., Stoffers, D., Someren, E. J. W. V., & Foster‐Dingley, J. C. (2019) · NeuroImage Clinical · doi:10.1016/j.nicl.2019.102019 · Cited by 36
Altered brain white matter connectome in children and adolescents with prenatal alcohol exposure
Long, X., Little, G. E., Treit, S., Beaulieu, C., Gong, G., & Lebel, C. A. (2020) · Brain Structure and Function · doi:10.1007/s00429-020-02064-z · Cited by 32
Selective Aberrant Functional–Structural Coupling of Multiscale Brain Networks in Subcortical Vascular Mild Cognitive Impairment
Ma, J., Liu, F., Yang, B., Xue, K., Wang, P., Zhou, J., Wang, Y., Niu, Y., & Zhang, J. (2020) · Neuroscience Bulletin · doi:10.1007/s12264-020-00580-w · Cited by 34
Social cognition and functional brain network in autism spectrum disorder: Insights from EEG graph-theoretic measures
Wadhera, T., & Kakkar, D. (2021) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2021.102556 · Cited by 35
Test-retest reliability of white matter structural brain networks: a multiband diffusion MRI study
Zhao, T., Duan, F., Liao, X., Dai, Z., Cao, M., Yong He, A., & Shu, N. (2015) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2015.00059 · Cited by 36
Multiple Pathways Analysis of Brain Functional Networks from EEG Signals: An Application to Real Data
Fallani, F. D. V., Rodrigues, F. A., Costa, L. D. F., Laura Astolfi, A., Febo Cincotti, A., Donatella Mattia, A., Salinari, S., & Babiloni, F. (2010) · Brain Topography · doi:10.1007/s10548-010-0152-z · Cited by 34
Aging and implicit learning: Explorations in contextual cuing.
Smyth, A. C., & Shanks, D. R. (2011) · Psychology and Aging · doi:10.1037/a0022014 · Cited by 35
Dedifferentiation Does Not Account for Hyperconnectivity after Traumatic Brain Injury
Bernier, R. A., Roy, A., Venkatesan, U. M., Grossner, E. C., Brenner, E. K., & Hillary, F. G. (2017) · Frontiers in Neurology · doi:10.3389/fneur.2017.00297 · Cited by 33
Asymmetry of Hemispheric Network Topology Reveals Dissociable Processes between Functional and Structural Brain Connectome in Community-Living Elders
Sun, Y., Li, J., Suckling, J., & Feng, L. (2017) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2017.00361 · Cited by 36
How the body remembers: Examining the default mode and sensorimotor networks during moral injury autobiographical memory retrieval in PTSD
Kearney, B. E., Terpou, B. A., Densmore, M., Shaw, S. B., Théberge, J., Jetly, R., McKinnon, M. C., & Lanius, R. A. (2023) · NeuroImage: Clinical · doi:10.1016/j.nicl.2023.103426 · Cited by 33
Evidence of neuroplasticity with brain–computer interface in a randomized trial for post-stroke rehabilitation: a graph-theoretic study of subnetwork analysis
Ma, Z., Wu, J., Hua, X., Zheng, M., Xing, X., Ma, J., Shan, C., & Xu, J. (2023) · Frontiers in Neurology · doi:10.3389/fneur.2023.1135466 · Cited by 38
Neuroimaging and plasticity in schizophrenia
Meyer-Lindenberg, A., & Tost, H. (2014) · Restorative Neurology and Neuroscience · doi:10.3233/rnn-139014 · Cited by 33
The Bounds Of Education In The Human Brain Connectome
Marques, P., Soares, J. M., Magalhães, R. J. D. S., Santos, N. C., & Sousa, N. (2015) · Scientific Reports · doi:10.1038/srep12812 · Cited by 34
Inferring Network Connectivity by Delayed Feedback Control
Yu, D., & Parlitz, U. (2011) · PLoS ONE · doi:10.1371/journal.pone.0024333 · Cited by 33
Organization of Anti-Phase Synchronization Pattern in Neural Networks: What are the Key Factors?
Li, D., & Zhou, C. (2011) · Frontiers in Systems Neuroscience · doi:10.3389/fnsys.2011.00100 · Cited by 34
Structural covariance of superficial white matter in mild Alzheimer's disease compared to normal aging
Carmeli, C., Fornari, E., Jalili, M., Meuli, R. A., & Knyazeva, M. G. (2014) · Brain and Behavior · doi:10.1002/brb3.252 · Cited by 32
Alterations of Functional and Structural Networks in Schizophrenia Patients with Auditory Verbal Hallucinations
Zhu, J., Wang, C., Liu, F., Qin, W., Li, J., & Zhuo, C. (2016) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2016.00114 · Cited by 34
Abnormal resting-state functional connectivity in the orbitofrontal cortex of heroin users and its relationship with anxiety: a pilot fNIRS study
Ieong, H. F.-H., & Yuan, Z. (2017) · Scientific Reports · doi:10.1038/srep46522 · Cited by 34
Working memory network alterations in high‐functioning adolescents with an autism spectrum disorder
Barendse, E. M., Schreuder, L. J., Thoonen, G. H. J., Hendriks, M. P. H., Kessels, R. P. C., Backes, W. H., Aldenkamp, A. P., & Jansen, J. F. A. (2017) · Psychiatry and Clinical Neurosciences · doi:10.1111/pcn.12602 · Cited by 36
Community and household-level socioeconomic disadvantage and functional organization of the salience and emotion network in children and adolescents
Gellci, K., Marusak, H. A., Peters, C. A., Elrahal, F., Iadipaolo, A. S., & Rabinak, C. A. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2018.09.077 · Cited by 32
Functional MRI in Parkinson's Disease Cognitive Impairment
Baggio, H. C., & Junqué, C. (2018) · International review of neurobiology · doi:10.1016/bs.irn.2018.09.010 · Cited by 34
Neural correlates of motor expertise: Extensive motor training and cortical changes
Calmels, C. (2019) · Brain Research · doi:10.1016/j.brainres.2019.146323 · Cited by 39
Altered Whole-Brain Functional Topological Organization and Cognitive Function in Type 2 Diabetes Mellitus Patients
Qin, C., Liang, Y., Tan, X. L., Leng, X., Lin, H., Zeng, H., Zhang, C., Yang, J., Li, Y., Zheng, Y., et al. (2019) · Frontiers in Neurology · doi:10.3389/fneur.2019.00599 · Cited by 33
Abnormalities in the thalamo-cortical network in patients with functional constipation
Liu, L., Hu, C., Hu, Y., Zhang, W., Zhang, Z., Ding, Y., Wang, Y., von Deneen, K. M., Sun, L., Wang, H., et al. (2020) · Brain Imaging and Behavior · doi:10.1007/s11682-020-00273-y · Cited by 34
Hemispheric Asymmetry of Functional Brain Networks under Different Emotions Using EEG Data
Cao, R., Shi, H., Wang, X., Huo, S., Hao, Y., Wang, B., Guo, H., & Xiang, J. (2020) · Entropy · doi:10.3390/e22090939 · Cited by 33
Accrual of functional redundancy along the lifespan and its effects on cognition
Sadiq, M. U., Langella, S., Giovanello, K. S., Mucha, P. J., & Dayan, E. (2021) · NeuroImage · doi:10.1016/j.neuroimage.2021.117737 · Cited by 35

Authors: Achard, S., & Bullmore, E.
Affiliations: University of Cambridge (GB)
ORCID: Achard, Sophie
Venue: PLoS Computational Biology
Publisher: Public Library of Science (PLoS)
Published: 2007-02-02
Volume: 3
Issue: 2
Pages: e17
ISSN: 1553-734X (ISSN-L)
1553-7358
DOI: 10.1371/journal.pcbi.0030017
PMC: PMC1794324
OpenAlex: W1974764791
Cited by: 2,553
References: 55
Citations by year: 2026: 103 · 2025: 147 · 2024: 150 · 2023: 142 · 2022: 162 · 2021: 201 · 2020: 169 · 2019: 162 · 2018: 185 · 2017: 175 · 2016: 157 · 2015: 144 · 2014: 130 · 2013: 143 · 2012: 115
FWCI: 26.22
Type: journal article
Language: en
Open access: gold
doi.org
Licence: cc-by
creativecommons.org/licenses/by/4.0/ (unspecified)
Locations: PLoS Computational Biology (publishedVersion, cc-by)
Europe PMC (PubMed Central) (publishedVersion, cc-by)
PubMed Central (publishedVersion, cc-by)
PubMed (publishedVersion)
DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Figshare (submittedVersion, cc-by)
Funders: Wellcome Trust
Medical Research Council
National Institute of Mental Health
Topics: Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Complex Network Analysis Techniques
Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced Neuroimaging Techniques and Applications
Keywords: functional brain networks, small-world networks, network efficiency, network cost, aging, sulpiride, dopamine receptor antagonism, functional connectivity, resting state, wavelet correlation, graph theory, frontal cortex, temporal cortex, subcortical structures
MeSH terms: Adult, Aged, Aging, Brain, Brain Mapping, Computer Simulation, Female, Humans, Magnetic Resonance Imaging, Male, Models, Neurological, Nerve Net, Neurons, Dopamine Antagonists

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

Efficiency and Cost of Economical Brain Functional Networks

Abstract

Overview

Integrity signals

Verification

Claims the paper makes

Claims other papers make about this one

Self-check

Citations

Cites

Cited by

Related

Metadata

Record

About these checks