Cited by 122Functional Brain Connectivity StudiesDementia and Cognitive Impairment ResearchAdvanced Neuroimaging Techniques and Applications

The DOI matches its Crossref record; 83 of the 83 references it cites were found in a registry or an index.

Disrupted Functional Brain Connectivity and Its Association to Structural Connectivity in Amnestic Mild Cognitive Impairment and Alzheimer’s Disease

Open access

Sun, Yu, Yin, Qihua, Fang, Rong, Yan, Xiaoxiao, Wang, Ying, Bezerianos, Anastasios, Tang, Huidong, Miao, Fei, Sun, Junfeng·2014·PLoS ONE

DOI 10.1371/journal.pone.0096505PMC PMC4013022

Open this paper in CoChat — free account Open PDF

Abstract

Although anomalies in the topological architecture of whole-brain connectivity have been found to be associated with Alzheimer's disease (AD), our understanding about the progression of AD in a functional connectivity (FC) perspective is still rudimentary and few study has explored the function-structure relations in brain networks of AD patients. By using resting-state functional MRI (fMRI), this study firstly investigated organizational alternations in FC networks in 12 AD patients, 15 amnestic mild cognitive impairment (aMCI) patients, and 14 age-matched healthy aging subjects and found that all three groups exhibit economical small-world network properties. Nonetheless, we found a decline of the optimal architecture in the progression of AD, represented by a more localized modular organization with less efficient local information transfer. Our results also show that aMCI forms a boundary between normal aging and AD and represents a functional continuum between healthy aging and the earliest signs of dementia. Moreover, we revealed a dissociated relationship between the overall FC and structural connectivity (SC) in AD patients. In this study, diffusion tensor imaging tractography was used to map the structural network of the same individuals. The decreased FC-SC coupling may be indicative of more stringent and less dynamic brain function in AD patients. Our findings provided insightful implications for understanding the pathophysiological mechanisms of brain dysfunctions in aMCI and AD patients and demonstrated that functional disorders can be characterized by multimodal neuroimaging-based metrics.

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.pone.0096505

Cites

83 of 83 references verified0 citation errors · 0 not checkable

Statistics

Figure 1 The flowchart of the analysis steps. (A) The construction of resting-state functional connectivity (FC) network for graph theoretic analysis. Note that, two parcellation scales (low: 90 ROIs, and high: 1024 ROIs) were employed to estimate the graph metrics. For better presentation purpose, only low parcellation scale was utilized here. (B) The construction of structural connectivity (SC) network for assessing the FC-SC relationship. In the current study, the level of coupling between FC and SC was examined by calculating the level of correlation between the weights of existing SC and their functional counterparts.CC BY · PubMed Central

Figure 2 Small-world network metrics of functional brain networks at two different parcellation scales. The graphs show the changes in γ (solid line) and λ (dashed line) in the functional brain networks of AD (red), aMCI (green), and NC (blue) groups at (A) low parcellation scale and (B) high parcellation scale with respect to sparsity. For all the three groups, γ was significantly greater than 1 and λ was relatively close to 1, indicating prominent small-world properties for all groups.CC BY · PubMed Central

Figure 3 The distribution of brain regions with both significant (p<0.05, uncorrected) groups effects in BC and significant (p<0.05) correlation between BC and MMSE scores. Each bar represents the average BC values for AD (red), aMCI (green), and NC (blue). Error bars represent standard deviations (S.D.). The regions were overlaid on the brain surface at the Medium view. Post hoc pairwise comparison was performed through Fisher’s least significant difference (LSD) with * indicates p<0.05 and ** indicates p<0.01. Regions showing positive correlation are coded in purple and region showing negative correlation is coded in light grey. For the abbreviations of cortical regions, see Table S1. L = left; R = right. The figure was visualized with BrainNet Viewer software (http://www.nitrc.org/projects/bnv/).CC BY · PubMed Central

Figure 4 Modularity Q of the functional brain networks for AD, aMCI and NC groups with respect to sparsity. The black dashed line indicates the threshold for the nonrandom community structure. Note that only the modularity of AD group is less than 0.3 for S = 25%.CC BY · PubMed Central

Figure 5 The modular structure of group averaged functional brain network at S = 10%. (A) Color coded modular structure plotted in the anatomical space. The brain network is presented with the frontal cortex on the left of the panel and the occipital region on the right of the panel. Inter-modular links are colored in gray for better illustration. Detailed information about the module regions and regional node roles for AD, aMCI and NC groups are presented in (B). The module order was determined according the descending order of the region numbers in AD group. The topological representations were drawn by the Pajek (http://pajek.imfm.si/doku.php). For the abbreviations of cortical regions, see Table S1. L = left; R = right.CC BY · PubMed Central

Figure 6 The overall FC-SC relationships. (A) The probability densities of FC strengths between structurally connected and unconnected regions pairs. When direct SC presents, higher FC strength appears than the case direct SC absents, indicating a robust relationship between FC and SC. (B) Scatter plot of SC strength against FC strength for the regions with direct SC. Further analysis showed significant coefficient between FC and SC strengths for all groups. Data are from group averaged functional connectivity matrix of AD, aMCI, and NC groups, at the high resolution (1024×1024).CC BY · PubMed Central

Figure 7 The functional connectivity (FC) – structural connectivity (SC) coupling for AD patients, aMCI patients, and healthy controls. AD patients exhibited a significant decrease in FC-SC coupling compared to aMCI patients and healthy controls. Significant statistical differences are indicated with * for p<0.05 and ** for p<0.01.CC BY · PubMed Central

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
Clinical diagnosis of Alzheimer's disease: Report of the NINCDS‐ADRDA Work Group* under the auspices of Department of Health and Human Services Task Force on Alzheimer's Disease
McKhann, G., Drachman, D., Folstein, M., Katzman, R., Price, D., & Stadlan, E. M. (1984) · Neurology · doi:10.1212/wnl.34.7.939 · Cited by 27,915
[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
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
Studies of interference in serial verbal reactions
Stroop, J. R. (1935) · Journal of Experimental Psychology · doi:10.1037/h0054651 · Cited by 18,622
Assessment of Older People: Self-Maintaining and Instrumental Activities of Daily Living
Lawton, M. P., & Brody, E. M. (1969) · The Gerontologist · doi:10.1093/geront/9.3_part_1.179 · Cited by 18,540
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

Show all 83 references

Research works printed but not linked: 1

Q Guo, 2009, A comparison study of mild cognitive impairment with 3 memory tests among Chinese individuals, Alzheimer Dis Assoc Disord, 23, 253, 10.1097/WAD.0b013e3181999e92 · DOI

Sorted by citations

Early-Onset Alzheimer Disease
Mendez, M. F. (2017) · Neurologic Clinics · doi:10.1016/j.ncl.2017.01.005 · Cited by 389
Structure–function coupling in macroscale human brain networks
Fotiadis, P., Parkes, L., Davis, K. A., Satterthwaite, T. D., Shinohara, R. T., & Bassett, D. S. (2024) · Nature reviews. Neuroscience · doi:10.1038/s41583-024-00846-6 · Cited by 280
Resting-state connectivity in neurodegenerative disorders: Is there potential for an imaging biomarker?
Hohenfeld, C., Werner, C. J., & Reetz, K. (2018) · NeuroImage: Clinical · doi:10.1016/j.nicl.2018.03.013 · Cited by 232
Changes of Functional Brain Networks in Major Depressive Disorder: A Graph Theoretical Analysis of Resting-State fMRI
Ye, M., Yang, T., Qing, P., Lei, X., Qiu, J., & Liu, G. (2015) · PLoS ONE · doi:10.1371/journal.pone.0133775 · Cited by 155
Differentiation of Schizophrenia by Combining the Spatial EEG Brain Network Patterns of Rest and Task P300
Li, F., Wang, J., Liao, Y., Yi, C., Jiang, Y., Si, Y., Peng, W., Yao, D., Zhang, Y., Dong, W., et al. (2019) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2019.2900725 · Cited by 125
Changes in functional and structural brain connectome along the Alzheimer’s disease continuum
Filippi, M., Basaia, S., Canu, E., Imperiale, F., Magnani, G., Falautano, M., Comı, G., Falini, A., & Agosta, F. (2018) · Molecular Psychiatry · doi:10.1038/s41380-018-0067-8 · Cited by 120
EEG Cortical Connectivity Analysis of Working Memory Reveals Topological Reorganization in Theta and Alpha Bands
Dai, Z., de Souza, J., Lim, J., Ho, P. M., Chen, Y., Li, J., Thakor, N. V., Bezerianos, A., & Sun, Y. (2017) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2017.00237 · Cited by 115
Self-citation
A systematic review on the quantitative relationship between structural and functional network connectivity strength in mammalian brains
Straathof, M., Sinke, M. R. T., Dijkhuizen, R. M., & Otte, W. M. (2018) · Journal of Cerebral Blood Flow & Metabolism · doi:10.1177/0271678x18809547 · Cited by 109
Differentiation of Alzheimer's disease based on local and global parameters in personalized Virtual Brain models
Zimmermann, J., Perry, A., Breakspear, M. J., Schirner, M., Sachdev, P. S., Wen, W., Kochan, N. A., Mapstone, M., Ritter, P., McIntosh, A. R., et al. (2018) · NeuroImage Clinical · doi:10.1016/j.nicl.2018.04.017 · Cited by 102
Functional degeneration in dorsal and ventral attention systems in amnestic mild cognitive impairment and Alzheimer’s disease: An fMRI study
Zhang, Z., Hong, Z., Liang, K., Wang, H., Kong, S., Hu, J., Wu, F., & Sun, G. (2014) · Neuroscience Letters · doi:10.1016/j.neulet.2014.11.050 · Cited by 96
Progressive Gender Differences of Structural Brain Networks in Healthy Adults: A Longitudinal, Diffusion Tensor Imaging Study
Sun, Y., Lee, R., Yu, C., Collinson, S. L., Thakor, N. V., Bezerianos, A., & Sim, K. (2015) · PLoS ONE · doi:10.1371/journal.pone.0118857 · Cited by 96
Self-citation
Modulatory effects of acupuncture on brain networks in mild cognitive impairment patients
Chen, S., Tan, T., Wang, D., Huang, J.-K., Zhou, X., Xu, Y., Liang, J.-P., Yin, L., Xie, H.-L., Jia, X., et al. (2017) · Neural Regeneration Research · doi:10.4103/1673-5374.200808 · Cited by 91
Graph theoretic analysis of structural connectivity across the spectrum of Alzheimer's disease: The importance of graph creation methods
Phillips, D. J., McGlaughlin, A., Ruth, D. M., Jager, L. R., & Soldan, A. (2015) · NeuroImage Clinical · doi:10.1016/j.nicl.2015.01.007 · Cited by 92
Functional near-infrared spectroscopy reveals decreased resting oxygenation levels and task-related oxygenation changes in mild cognitive impairment and dementia: A systematic review
Yeung, M. K.-C., & Chan, A. S.-Y. (2020) · Journal of Psychiatric Research · doi:10.1016/j.jpsychires.2020.02.017 · Cited by 87
Divergent brain functional network alterations in dementia with Lewy bodies and Alzheimer's disease
Peraza, L. R., Taylor, J., & Kaiser, M. (2015) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2015.05.015 · Cited by 82
Abnormal Anatomical Rich-Club Organization and Structural–Functional Coupling in Mild Cognitive Impairment and Alzheimer's Disease
Cao, R., Wang, X., Gao, Y., Li, T., Zhang, H., Hussain, W., Xie, Y., Wang, J., Wang, B., & Xiang, J. (2020) · Frontiers in Neurology · doi:10.3389/fneur.2020.00053 · Cited by 81
Brain network disintegration as a final common pathway for delirium: a systematic review and qualitative meta-analysis
van Montfort, S. J. T., van Dellen, E., Stam, C. J., Ahmad, A., Mentink, L. J., Kraan, C. W., Zalesky, A., & Slooter, A. J. C. (2019) · NeuroImage Clinical · doi:10.1016/j.nicl.2019.101809 · Cited by 86
The Abnormality of Topological Asymmetry between Hemispheric Brain White Matter Networks in Alzheimer’s Disease and Mild Cognitive Impairment
Yang, C., Zhong, S., Zhou, X., Wei, L., Wang, L., & Nie, S. (2017) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2017.00261 · Cited by 77
NODDI and Tensor-Based Microstructural Indices as Predictors of Functional Connectivity
Deligianni, F., Carmichael, D. W., Zhang, H. G., Clark, C. A., & Clayden, J. D. (2016) · PLoS ONE · doi:10.1371/journal.pone.0153404 · Cited by 73
Early brain connectivity alterations and cognitive impairment in a rat model of Alzheimer’s disease
Muñoz‐Moreno, E., Tudela, R., López-Gil, X., & Sòria, G. (2018) · Alzheimer s Research & Therapy · doi:10.1186/s13195-018-0346-2 · Cited by 65
The Importance of Complement-Mediated Immune Signaling in Alzheimer’s Disease Pathogenesis
Batista, A. F., Khan, K., Papavergi, M.-T., & Lemere, C. A. (2024) · International Journal of Molecular Sciences · doi:10.3390/ijms25020817 · Cited by 72
Mindfulness practice alters brain connectivity in community‐living elders with mild cognitive impairment
Fam, J., Sun, Y., Qi, P., Lau, R. C., Feng, L., Kua, E. H., & Mahendran, R. (2019) · Psychiatry and Clinical Neurosciences · doi:10.1111/pcn.12972 · Cited by 67
Self-citation
The Reorganization of Resting-State Brain Networks Associated With Motor Imagery Training in Chronic Stroke Patients
Wang, H., Xu, G., Wang, X., Sun, C., Zhu, B. D., Fan, M., Jia, J., Guo, X., & Sun, L. (2019) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2019.2940980 · Cited by 63
Dynamic Functional Segregation and Integration in Human Brain Network During Complex Tasks
Ren, S., Li, J., Taya, F., de Souza, J., Thakor, N. V., & Bezerianos, A. (2016) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2016.2597961 · Cited by 56
Self-citation
What can DTI tell about early cognitive impairment?—Differentiation between MCI subtypes and healthy controls by diffusion tensor imaging
Gyebnár, G., Szabó, Á., Sirály, E., Fodor, Z., Sákovics, A., Salacz, P., Hidasi, Z., Csibri, É., Rudas, G., Kozák, L. R., et al. (2018) · Psychiatry Research: Neuroimaging · doi:10.1016/j.pscychresns.2017.10.007 · Cited by 49
Disrupted Patterns of Rich-Club and Diverse-Club Organizations in Subjective Cognitive Decline and Amnestic Mild Cognitive Impairment
Xue, C., Sun, H., Hu, G., Qi, W., Yue, Y., Rao, J., Yang, W., Xiao, C., Chen, J., & Initiative, T. A. D. N. (2020) · Frontiers in Neuroscience · doi:10.3389/fnins.2020.575652 · Cited by 47
Structural–functional coupling changes in temporal lobe epilepsy
Chiang, S., Stern, J. M., Engel, J., & Haneef, Z. (2015) · Brain Research · doi:10.1016/j.brainres.2015.04.052 · Cited by 48
Functional-structural degeneration in dorsal and ventral attention systems for Alzheimer’s disease, amnestic mild cognitive impairment
Qian, S., Zhang, Z., Li, B., & Sun, G. (2014) · Brain Imaging and Behavior · doi:10.1007/s11682-014-9336-6 · Cited by 48
The effects of a mid-task break on the brain connectome in healthy participants: A resting-state functional MRI study
Sun, Y., Lim, J., Dai, Z., Wong, K., Taya, F., Chen, Y., Li, J., Thakor, N. V., & Bezerianos, A. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.02.084 · Cited by 49
Self-citation
Exploring Patterns of Alteration in Alzheimer's Disease Brain Networks: A Combined Structural and Functional Connectomics Analysis
Palesi, F., Castellazzi, G., Casiraghi, L., Sinforiani, E., Vitali, P., Wheeler‐Kingshott, C. A. M. G., & D'Angelo, E. U. (2016) · Frontiers in Neuroscience · doi:10.3389/fnins.2016.00380 · Cited by 48
Alzheimer Classification Using a Minimum Spanning Tree of High-Order Functional Network on fMRI Dataset
Guo, H., Liu, L., Chen, J., Xu, Y. J., & Xiang, J. (2017) · Frontiers in Neuroscience · doi:10.3389/fnins.2017.00639 · Cited by 46
Differences in Aβ brain networks in Alzheimer's disease and healthy controls
Duan, H., Jiang, J., Xu, J. L., Zhou, H., Huang, Z., Yu, Z., & Yan, Z. (2016) · Brain Research · doi:10.1016/j.brainres.2016.11.019 · Cited by 42
Altered functional connectivity density in high myopia
Zhai, L., Li, Q., Tianyue, W., Dong, H., Peng, Y., Guo, M., Qin, W., & Yu, C. (2016) · Behavioural Brain Research · doi:10.1016/j.bbr.2016.01.046 · Cited by 38
Multiparametric graph theoretical analysis reveals altered structural and functional network topology in Alzheimer's disease
Lin, S.-Y., Lin, C., Hsieh, T.-J., Lin, C.-F., Chen, S.-H., Chao, Y., Chen, Y., Hsu, C.-C., & Kuo, L. (2019) · NeuroImage Clinical · doi:10.1016/j.nicl.2019.101680 · Cited by 37
Synchronization dependent on spatial structures of a mesoscopic whole-brain network
Choi, H., & Mihalaş, Ş. (2019) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1006978 · Cited by 36
Crosstalk between Depression and Dementia with Resting-State fMRI Studies and Its Relationship with Cognitive Functioning
Kim, J., & Kim, Y. (2021) · Biomedicines · doi:10.3390/biomedicines9010082 · Cited by 36
Capturing the Forest but Missing the Trees: Microstates Inadequate for Characterizing Shorter-Scale EEG Dynamics
Shaw, S. B., Dhindsa, K., Reilly, J. P., & Becker, S. (2019) · Neural Computation · doi:10.1162/neco_a_01229 · Cited by 51
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
“However, Sun et al. (2014) observed lower structure-function coupling in connectomes of patients with AD.”
Background
Predicting the transition from normal aging to Alzheimer's disease: A statistical mechanistic evaluation of FDG-PET data
Pagani, M., Giuliani, A., Öberg, J., Chincarini, A., Morbelli, S. D., Brugnolo, A., Arnaldi, D., Picco, A., Bauckneht, M., Buschiazzo, A., et al. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2016.07.043 · Cited by 33
A Network-Based Perspective in Alzheimer's Disease: Current State and an Integrative Framework
Dragomir, A., Vrahatis, A. G., & Bezerianos, A. (2018) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2018.2863202 · Cited by 34
Self-citation
Machine learning based on the multimodal connectome can predict the preclinical stage of Alzheimer’s disease: a preliminary study
Chen, H., Li, W., Sheng, X., Ye, Q., Zhao, H., Xu, Y., Bai, F., & Initiative, A. D. N. (2021) · European Radiology · doi:10.1007/s00330-021-08080-9 · Cited by 33
The efficacy of supplementation with the novel medical food, Souvenaid, in patients with Alzheimer's disease: A systematic review and meta-analysis of randomized clinical trials
Onakpoya, I., & Heneghan, C. J. (2015) · Nutritional Neuroscience · doi:10.1080/1028415x.2015.1110899 · Cited by 33
Fronto‐Parietal Subnetworks Flexibility Compensates For Cognitive Decline Due To Mental Fatigue
Taya, F., Dimitriadis, S. I., Dragomir, A., Lim, J., Sun, Y., Wong, K. F., Thakor, N. V., & Bezerianos, A. (2018) · Human Brain Mapping · doi:10.1002/hbm.24192 · Cited by 37
Self-citation
Non-monotonic reorganization of brain networks with Alzheimer's disease progression
Kim, H., Yoo, K., Na, D. L., Seo, S. W., Jeong, J., & Jeong, Y. (2015) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2015.00111 · Cited by 32
Investigation on the Alteration of Brain Functional Network and Its Role in the Identification of Mild Cognitive Impairment
Zhang, L., Ni, H., Yu, Z., Wang, J., Qin, J., Hou, F., Yang, A. C., & Initiative, A. D. N. (2020) · Frontiers in Neuroscience · doi:10.3389/fnins.2020.558434 · Cited by 28
Comparison method for community detection on brain networks from neuroimaging data
Taya, F., de Souza, J., Thakor, N. V., & Bezerianos, A. (2016) · Applied Network Science · doi:10.1007/s41109-016-0007-y · Cited by 29
Self-citation
The structural–functional-connectivity coupling of the aging brain
Zhang, H., Cao, P., Mak, H. K., & Hui, E. S. K. (2024) · GeroScience · doi:10.1007/s11357-024-01106-2 · Cited by 29
An EEG study of human trust in autonomous vehicles based on graphic theoretical analysis
Xu, T., Dragomir, A., Liu, X., Yin, H., Wan, F., Bezerianos, A., & Wang, H. (2022) · Frontiers in Neuroinformatics · doi:10.3389/fninf.2022.907942 · Cited by 26
Self-citation
Applying graph theory to find key leverage points in the transition toward urban renewable energy systems
Rozhkov, A. (2024) · Applied Energy · doi:10.1016/j.apenergy.2024.122854 · Cited by 25
Meta-Analysis of Memory-Focused Training and Multidomain Interventions in Mild Cognitive Impairment
Sherman, D. S., Durbin, K. A., & Ross, D. M. (2020) · Journal of Alzheimer s Disease · doi:10.3233/jad-200261 · Cited by 24
Abnormal Functional Brain Networks in Mild Cognitive Impairment and Alzheimer’s Disease: A Minimum Spanning Tree Analysis
Wang, B., Miao, L., Niu, Y., Cao, R., Li, D., Yan, P., Guo, H., Yan, T., Wu, J., Xiang, J., et al. (2018) · Journal of Alzheimer s Disease · doi:10.3233/jad-180603 · Cited by 24
Increased intrinsic default-mode network activity as a compensatory mechanism in aMCI: a resting-state functional connectivity MRI study
Liang, J., Li, Y., Liu, H., Zhang, S., Wang, M., Chu, Y., Ye, J., Xi, Q., & Zhao, X. (2020) · Aging · doi:10.18632/aging.102986 · Cited by 23
The impacts of pesticide and nicotine exposures on functional brain networks in Latino immigrant workers
Bahrami, M., Laurienti, P. J., Quandt, S. A., Talton, J. W., Pope, C., Summers, P., Burdette, J. H., Chen, H., Liu, J., Howard, T. D., et al. (2017) · NeuroToxicology · doi:10.1016/j.neuro.2017.06.001 · Cited by 20
Abnormal global functional network connectivity and its relationship to medial temporal atrophy in patients with amnestic mild cognitive impairment
Sheng, C., Xia, M., Yu, H., Huang, Y. M., Lu, Y., Liu, F., Yong He, A., & Han, Y. (2017) · PLoS ONE · doi:10.1371/journal.pone.0179823 · Cited by 20
Altered intra- and inter-hemispheric functional dysconnectivity in schizophrenia
Zhang, Y., Dai, Z., Chen, Y. S., Sim, K., Sun, Y., & Yu, R. (2018) · Brain Imaging and Behavior · doi:10.1007/s11682-018-9935-8 · Cited by 21
Self-citation
Functional correlates of preserved naming performance in amnestic Mild Cognitive Impairment.
Catricalà, E., Rosa, P. D. D., Parisi, L., Zippo, A., Borsa, V., Iadanza, A., Castiglioni, I., Falini, A., & Cappa, S. (2015) · Neuropsychologia · doi:10.1016/j.neuropsychologia.2015.01.009 · Cited by 21
Mitigating effects and mechanisms of Tai Chi on mild cognitive impairment in the elderly
Wang, X., Si, K., Gu, W., & Wang, X. (2023) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2022.1028822 · Cited by 19
Deep sr-DDL: Deep structurally regularized dynamic dictionary learning to integrate multimodal and dynamic functional connectomics data for multidimensional clinical characterizations
D’Souza, N. S., Nebel, M. B., Crocetti, D., Robinson, J., Wymbs, N. F., Mostofsky, S. H., & Venkataraman, A. (2021) · NeuroImage · doi:10.1016/j.neuroimage.2021.118388 · Cited by 18
Different Scales of Cortical Organization are Selectively Targeted in the Progression to Alzheimer’s Disease
Romero-García, R., Atienza, M., & Cantero, J. L. (2015) · International Journal of Neural Systems · doi:10.1142/s0129065716500039 · Cited by 17
Coupling analysis between functional and structural brain networks in Alzheimer's disease
Xu, X., Song, X., Han, L., & Yao, X. (2022) · Mathematical Biosciences & Engineering · doi:10.3934/mbe.2022416 · Cited by 18
Altered brain networks and connections in chronic heart failure patients complicated with cognitive impairment
Wang, M., Xu, B., Hou, X., Shi, Q., Zhao, H., Gui, Q., Wu, G., Dong, X., Xu, Q., Shen, M., et al. (2023) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2023.1153496 · Cited by 15
Distance disintegration delineates the brain connectivity failure of Alzheimer's disease
Costumero, V., Uquillas, F. D., Diez, I. P., Andorrà, M., Basaia, S., Bueichekú, E., Ortiz-Terán, L., Belloch, V., Escudero, J. V., Ávila, C., et al. (2019) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2019.12.005 · Cited by 16
Episodic memory in mild cognitive impairment inversely correlates with the global modularity of the cerebral blood flow network
Sánchez-Catasús, C. A., Willemsen, A. T. M., Boellaard, R., Juárez‐Orozco, L. E., Noa, J. A. S., Ruiz, Á. Á., Deyn, P. P. D., Dierckx, R., Iturria‐Medina, Y., & Melie‐García, L. (2018) · Psychiatry Research Neuroimaging · doi:10.1016/j.pscychresns.2018.11.003 · Cited by 15
Disrupted Time-Dependent and Functional Connectivity Brain Network in Alzheimer's Disease: A Resting-State fMRI Study Based on Visibility Graph
Gao, Z., Feng, Y., Ma, C., Ma, K., Cai, Q., & Initiative, A. F. T. A. D. N. (2020) · Current Alzheimer Research · doi:10.2174/1567205017666200213100607 · Cited by 15
The Effect of Multiple Factors on Working Memory Capacities: Aging, Task Difficulty, and Training
Xu, T., Huang, J., Pei, Z., Chen, J., Li, J., Bezerianos, A., Thakor, N. V., & Wang, H. (2022) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2022.3232849 · Cited by 15
Self-citation
Distributed Subnetworks of Depression Defined by Direct Intracranial Neurophysiology
Scangos, K. W., Khambhati, A. N., Daly, P. M., Owen, L. W., Manning, J. R., Ambrose, J. B., Austin, E., Dawes, H. E., Krystal, A. D., & Chang, E. F. (2021) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2021.746499 · Cited by 15
Topological Changes in the Brain Network Induced by the Training on a Piloting Task: An EEG-Based Functional Connectome Approach
Taya, F., Sun, Y., Babiloni, F., Thakor, N. V., & Bezerianos, A. (2016) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2016.2581809 · Cited by 14
Self-citation
A Comparative Study of Structural and Metabolic Brain Networks in Patients With Mild Cognitive Impairment
Wei, C., Gong, S., Zou, Q., Zhang, W., Kang, X., Lu, X., Chen, Y., Yang, Y., Wang, W., Jia, L., et al. (2021) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2021.774607 · Cited by 15
Breastfeeding improves dynamic reorganization of functional connectivity in preterm infants: a temporal brain network study
Niu, W., Xu, X., Zhang, H., Zhang, Y., Ni, S., Liu, T., Li, Q., Lai, C., & Sun, Y. (2020) · Medical & Biological Engineering & Computing · doi:10.1007/s11517-020-02244-3 · Cited by 14
Self-citation
Detection of structural-functional coupling abnormalities using multimodal brain networks in Alzheimer’s disease: A comparison of three computational models
Lu, Y., Wang, L.-Y., Murai, T., Wu, J., Liang, D., & Zhang, Z. (2025) · NeuroImage Clinical · doi:10.1016/j.nicl.2025.103764 · Cited by 14
Exploring dynamic functional connectivity alterations in the preclinical stage of Alzheimer’s disease: an exploratory study from SILCODE
Yang, F., Jiang, X., Yue, F., Wang, L., Boecker, H., Han, Y., & Jiang, J. (2022) · Journal of Neural Engineering · doi:10.1088/1741-2552/ac542d · Cited by 12
Sex differences in human brain networks in normal and psychiatric populations from the perspective of small-world properties
Zhou, Y., & Long, Y. (2024) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2024.1456714 · Cited by 13
The whole-brain structural and functional connectome in Alzheimer’s disease spectrum: A multimodal Bayesian meta-analysis of graph theoretical characteristics
Liu, W., Zuo, C., Li, C., Lan, H., Luo, C., Li, X., Kemp, G. J., Lui, S., Suo, X., & Gong, Q. (2025) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2025.106174 · Cited by 13
Study of amyloid-β peptide functional brain networks in AD, MCI and HC
Jiang, J., Duan, H., Huang, Z., Yu, Z., & Initiative, T. A. D. N. (2015) · Bio-Medical Materials and Engineering · doi:10.3233/bme-151525 · Cited by 12
Reduced striato‐cortical and inhibitory transcallosal connectivity in the motor circuit of Huntington's disease patients
Garcia‐Gorro, C., de Diego‐Balaguer, R., Martínez‐Horta, S., Pérez‐Pérez, J., Kulisevsky, J., Rodríguez‐Dechicha, N., Vaquer, I., Subirà, S., Calopa, M., Muñoz, E., et al. (2017) · Human Brain Mapping · doi:10.1002/hbm.23813 · Cited by 12
Relationship between Amyloid-β Deposition and the Coupling between Structural and Functional Brain Networks in Patients with Mild Cognitive Impairment and Alzheimer’s Disease
Zhang, H., Hui, E. S. K., Cao, P., & Mak, H. K. (2021) · Brain Sciences · doi:10.3390/brainsci11111535 · Cited by 12
High-altitude exposure leads to increased modularity of brain functional network with the increased occupation of attention resources in early processing of visual working memory
Zhou, J., Wang, N.-N., Huang, X., Su, R., Li, H., Ma, H., Liu, M., & Zhang, D.-L. (2024) · Cognitive Neurodynamics · doi:10.1007/s11571-024-10091-3 · Cited by 11
Abnormal Default-Mode Network Homogeneity in Patients With Mild Cognitive Impairment in Chinese Communities
Cao, Y., Yang, H., Zhou, Z., Cheng, Z., & Zhao, X. (2021) · Frontiers in Neurology · doi:10.3389/fneur.2020.569806 · Cited by 11
Visuospatial representation in patients with mild cognitive impairment: Implication for rehabilitation
Derbie, A. Y., Dejenie, M. A., & Zegeye, T. G. (2022) · Medicine · doi:10.1097/md.0000000000031462 · Cited by 14
Consciousness, Functional Networks and Delirium Screening
Eeles, E. M., Burianová, H., Pandy, S., & Pinsker, D. M. (2016) · Current Aging Science · doi:10.2174/1874609809666161004142027 · Cited by 11
Reorganized brain functional network topology in stable and progressive mild cognitive impairment
Xue, C., Zheng, D., Ruan, Y., Cao, X., Zhang, X., Qi, W., Yuan, Q., Liang, X., & Huang, Q. (2024) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2024.1467054 · Cited by 10
Identifying aMCI with functional connectivity network characteristics based on subtle AAL atlas
Zhuo, Z., Mo, X., Ma, X., Han, Y., & Li, J. (2018) · Brain Research · doi:10.1016/j.brainres.2018.04.042 · Cited by 10
Wavelet multiresolution complex network for decoding brain fatigued behavior from P300 signals
Gao, Z., Wang, Z., Yang, Y., Li, S., Dang, W., & Mao, X. (2018) · Physica A Statistical Mechanics and its Applications · doi:10.1016/j.physa.2018.04.075 · Cited by 10
The complement cascade in Alzheimer’s disease: modern implications of an ancient immune protagonist
Papavergi, M.-T., Bathini, P., Singh, B. B., & Lemere, C. A. (2025) · Molecular Neurodegeneration · doi:10.1186/s13024-025-00916-y · Cited by 12
Long-Term Connectome Analysis Reveals Reshaping of Visual, Spatial Networks in a Model With Vascular Dementia Features
Hall, G. R., Boehm‐Sturm, P., Dirnagl, U., Finke, C., Foddis, M., Harms, C., Koch, S. P., Kuchling, J., Madan, C. R., Mueller, S., et al. (2022) · Stroke · doi:10.1161/strokeaha.121.036997 · Cited by 9
Resting-State fMRI Whole Brain Network Function Plasticity Analysis in Attention Deficit Hyperactivity Disorder
Tang, Y., Zheng, S., & Tian, Y. (2022) · Journal of Neural Transplantation and Plasticity · doi:10.1155/2022/4714763 · Cited by 6
PearNet: A Pearson Correlation-based Graph Attention Network for Sleep Stage Recognition
Lu, J., Tian, Y., Wang, S., Sheng, Q. Z., & Zheng, X. (2022) · IEEE International Conference on Data Science and Advanced Analytics (DSAA) · doi:10.1109/dsaa54385.2022.10032354 · Cited by 8
Aberrant Multimodal Connectivity Pattern Involved in Default Mode Network and Limbic Network in Amyotrophic Lateral Sclerosis
Chen, H., Hu, Z., Ke, Z., Xu, Y., Bai, F., & Liu, Z. (2023) · Brain Sciences · doi:10.3390/brainsci13050803 · Cited by 8
Cholinergic modulation supports dynamic switching of resting state networks through selective DMN suppression
Šanda, P., Hlinka, J., van den Berg, M., Škoch, A., Bazhenov, M., Keliris, G. A., & Krishnan, G. P. (2024) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1012099 · Cited by 9
Multimodal neuroimaging study reveals dissociable processes between structural and functional networks in patients with subacute intracerebral hemorrhage
Zhang, X., Yu, X., Bao, Q., Yang, L., Sun, Y., & Qi, P. (2019) · Medical & Biological Engineering & Computing · doi:10.1007/s11517-019-01953-8 · Cited by 8
Self-citation
Exploring Functional Brain Networks in Alzheimer’s Disease Using Resting State EEG Signals
Oikonomou, V. P., Georgiadis, K., Lazarou, I., Nikolopoulos, S., & Kompatsiaris, I. Y. (2025) · Journal of dementia and Alzheimer's disease · doi:10.3390/jdad2020012 · Cited by 8
Self-organization and evolution of structure and function in cultured neuronal networks
Ballesteros-Esteban, L. M., Leyva, I., Almendral, J. A., & Sendiña–Nadal, I. (2023) · Chaos Solitons & Fractals · doi:10.1016/j.chaos.2023.113764 · Cited by 6
GraphSensor: A Graph Attention Network for Time-Series Sensor
Ge, J., Xu, G., Lu, J., Xu, X., & Meng, X. (2024) · Electronics · doi:10.3390/electronics13122290 · Cited by 6
Training-induced changes in information transfer efficiency of the brain network: A functional connectome approach
Taya, F., Sun, Y., Borghini, G., Aricò, P., Babiloni, F., Bezerianos, A., & Thakor, N. V. (2015) · International IEEE/EMBS Conference on Neural Engineering · doi:10.1109/ner.2015.7146802 · Cited by 6
Self-citation
Information transfer efficiency during rest and task a functional connectome approach
Taya, F., Sun, Y., Thakor, N. V., & Bezerianos, A. (2014) · IEEE Biomedical Circuits and Systems Conference (BioCAS) · doi:10.1109/biocas.2014.6981655 · Cited by 6
Self-citation
Characterization of network hierarchy reflects cell state specificity in genome organizationPreprint
Wang, J., Xue, Y., He, Y., Quan, H., Zhang, J., & Gao, Y. Q. (2023) · Genome Research · doi:10.1101/gr.277206.122 · Cited by 6
Longitudinal changes in brain connectivity correlate with neuropsychological testing in brain tumor resection patients
Ellis, D. G., Warren, D. E., Garlinghouse, M., & Aizenberg, M. R. (2025) · Frontiers in Neuroscience · doi:10.3389/fnins.2025.1532433 · Cited by 5
Neurological Biomarkers and Neuroinformatics
Solodkin, A., Zimmermann, J., McIntosh, A. R., Stefanovski, L., & Ritter, P. (2018) · Elsevier eBooks · doi:10.1016/b978-0-12-804078-2.00001-5 · Cited by 6
Distributed Subnetworks of Depression Defined by Direct Intracranial NeurophysiologyPreprint
Scangos, K. W., Khambhati, A. N., Daly, P., Owen, L., Manning, J., Ambrose, J., Austin, E., Dawes, H. E., Krystal, A., & Chang, E. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.02.14.943118 · Cited by 5
Optimizing Connectivity-Driven Brain Parcellation Using Ensemble Clustering
Kurmukov, A. G., Mussabayeva, A., Denisova, Y., Moyer, D., Jahanshad, N., Thompson, P. M., & Gutman, B. A. (2020) · Brain Connectivity · doi:10.1089/brain.2019.0722 · Cited by 4

Authors: Sun, Y., Yin, Q., Fang, R., Yan, X., Wang, Y., Bezerianos, A., Tang, H., Miao, F., & Sun, J.
Affiliations: National University of Singapore (SG)
Shanghai Jiao Tong University (CN)
Corresponding author: Tang, Huidong
Miao, Fei
Sun, Junfeng
ORCID: Sun, Yu
Fang, Rong
Yan, Xiaoxiao
Wang, Ying
Bezerianos, Anastasios
Tang, Huidong
Miao, Fei
Sun, Junfeng
Venue: PLoS ONE
Publisher: Public Library of Science (PLoS)
Published: 2014-05-07
Volume: 9
Issue: 5
Pages: e96505
ISSN: 1932-6203 (ISSN-L)
DOI: 10.1371/journal.pone.0096505
PMC: PMC4013022
OpenAlex: W1998486151
Cited by: 122
References: 83
Citations by year: 2026: 4 · 2025: 11 · 2024: 11 · 2023: 6 · 2022: 8 · 2021: 9 · 2020: 12 · 2019: 10 · 2018: 14 · 2017: 11 · 2016: 11 · 2015: 12 · 2014: 3
FWCI: 6.88
Type: journal article
Language: en
Open access: gold
doi.org
Licence: cc-by
creativecommons.org/licenses/by/4.0/ (unspecified)
Locations: PLoS ONE (publishedVersion, cc-by)
Europe PMC (PubMed Central) (publishedVersion, other-oa)
PubMed Central (publishedVersion, cc-by)
PubMed (publishedVersion)
National University of Singapore (submittedVersion)
RePEc: Research Papers in Economics (submittedVersion)
DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Figshare (submittedVersion, cc-by)
Funders: Shanghai Jiao Tong University (YG2012MS09)
National Key Research and Development Program of China (2011CB013304)
Topics: Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced Neuroimaging Techniques and Applications
Keywords: Alzheimer's disease, amnestic mild cognitive impairment, functional connectivity, structural connectivity, small-world networks, brain network topology, modular organization, healthy aging, dementia continuum, diffusion tensor tractography
MeSH terms: Aged, Aged, 80 and over, Aging, Alzheimer Disease, Amnesia, Brain, Brain Mapping, Female, Humans, Magnetic Resonance Imaging, Male, Middle Aged, Nerve Net, Neuropsychological Tests, Disease Progression, Diffusion Tensor Imaging, Neuroimaging, Cognitive Dysfunction

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

Disrupted Functional Brain Connectivity and Its Association to Structural Connectivity in Amnestic Mild Cognitive Impairment and Alzheimer’s Disease

Abstract

Overview

Integrity signals

Figures

Verification

Claims the paper makes

Claims other papers make about this one

Self-check

Citations

Cites

Cited by

Related

Metadata

Record

About these checks