Networks of anatomical covariance

Collective dynamics of ‘small-world’ networks

Watts, D. J., & Strogatz, S. H. (1998) · Nature · doi:10.1038/30918 · Cited by 43,060

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

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

Cortical Surface-Based Analysis

Dale, A. M., Fischl, B., & Sereno, M. I. (1999) · NeuroImage · doi:10.1006/nimg.1998.0395 · Cited by 10,910

Voxel-Based Morphometry—The Methods

Ashburner, J., & Friston, K. J. (2000) · NeuroImage · doi:10.1006/nimg.2000.0582 · Cited by 8,676

Exploring complex networks

Strogatz, S. H. (2001) · Nature · doi:10.1038/35065725 · Cited by 8,236

Error and attack tolerance of complex networks

Albert, R., Jeong, H., & Barabási, A.-L. (2000) · Nature · doi:10.1038/35019019 · Cited by 7,534

Tract-based spatial statistics: Voxelwise analysis of multi-subject diffusion data

Smith, S. M., Jenkinson, M., Johansen‐Berg, H., Rueckert, D., Nichols, T. E., Mackay, C. E., Watkins, K. E., Ciccarelli, O., Cader, M. Z., Matthews, P. M., et al. (2006) · NeuroImage · doi:10.1016/j.neuroimage.2006.02.024 · Cited by 6,598

Error and attack tolerance of complex networksPreprint

Albert, R., Jeong, H., & Barabási, A.-Ĺ. (2000) · Cited by 6,554

A multimodal characterization of low-dimensional thalamocortical structural connectivity patterns

John, A., Hettwer, M. D., Schaare, H. L., Saberi, A., Bayrak, Ş., Wan, B., Royer, J., Bernhardt, B. C., & Valk, S. L. (2025) · Communications Biology · doi:10.1038/s42003-025-07528-8 · Cited by 8

Structural Cerebellar and Lateral Frontoparietal Networks are altered in CUD: An SBM Analysis

Lacomba‐Arnau, E., Martínez‐Molina, A., & Barrós‐Loscertales, A. (2025) · Addiction Biology · doi:10.1111/adb.70021 · Cited by 5

Cortical Thickness and Functional Networks Modules by Cortical Lobes

Vuksanović, V. (2019) · Neuroscience · doi:10.1016/j.neuroscience.2019.10.038 · Cited by 6

Extended multimodal whole-brain anatomical covariance analysis: detection of disrupted correlation networks related to amyloid deposition

Ye, C., Albert, M., Brown, T., Bilgel, M., Hsu, J., Ma, T., Caffo, B. C., Miller, M. I., Mori, S., & Oishi, K. (2019) · Heliyon · doi:10.1016/j.heliyon.2019.e02074 · Cited by 5

Percentage amplitude of fluctuation and structural covariance changes of subjective cognitive decline in patients: A multimodal imaging study

Xu, K., Wei, Y., Zhang, S., Zhao, L., Geng, B., Mai, W., Li, P., Liang, L., Chen, D., Zeng, X., et al. (2022) · Frontiers in Neuroscience · doi:10.3389/fnins.2022.888174 · Cited by 5

Structural covariance predictors of clinical improvement at 2-year follow-up in first-episode psychosis

Saiz-Masvidal, C., Contreras, F., Soriano‐Mas, C., Mezquida, G., Díaz‐Caneja, C. M., Vieta, E., Amoretti, S., Lobo, A., González-Pinto, A. M., Janssen, J., et al. (2022) · Progress in Neuro-Psychopharmacology and Biological Psychiatry · doi:10.1016/j.pnpbp.2022.110645 · Cited by 4

Structural covariances of prefrontal subregions selectively associate with dopamine-related gene coexpression and schizophrenia

Liu, H., Li, W., Liu, N., Tang, J., Sun, L., Xu, J., Ji, Y., Xie, Y., Ding, H., Ye, Z., et al. (2023) · Cerebral Cortex · doi:10.1093/cercor/bhad096 · Cited by 5

Histogram-weighted Networks for Feature Extraction, Connectivity and Advanced Analysis in Neuroscience

Raamana, P. R., & Strother, S. C. (2017) · The Journal of Open Source Software · doi:10.21105/joss.00380 · Cited by 5

Does size matter? The relationship between predictive power of single-subject morphometric networks to spatial scale and edge weightPreprint

Raamana, P. R., Strother, S. C., Initiative, A. D. N., & Initiative, A. D. N. (2017) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/170381 · Cited by 6

Brain Structural Covariance Network in Asperger Syndrome Differs From Those in Autism Spectrum Disorder and Healthy Controls

Faridi, F., Seyedebrahimi, A., & Khosrowabadi, R. (2021) · Basic and Clinical Neuroscience Journal · doi:10.32598/bcn.2021.2262.1 · Cited by 4

Age- and sex-related differences in baboon (Papio anubis) gray matter covariation

Mulholland, M. M., Meguerditchian, A., & Hopkins, W. D. (2023) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2023.01.005 · Cited by 5

Abnormal structural covariance networks in young adults with recent cannabis use

Li, J., & Xu, H. (2024) · Addictive Behaviors · doi:10.1016/j.addbeh.2024.108029 · Cited by 4

Individual-based morphological brain network changes in children with Rolandic epilepsy

Yin, Y., Qiu, X., Nie, L., Wang, F., Luo, X., Zhao, C., Yu, H., Luo, D., Wang, J., & Liu, H. (2024) · Clinical Neurophysiology · doi:10.1016/j.clinph.2024.06.013 · Cited by 4

Organization of distributed cortical connections underlying the processing of auditory information in dogs assessed by diffusion MRI

Cordeau, M., Barton, S. A., & Hecht, E. E. (2025) · Imaging Neuroscience · doi:10.1162/imag.a.16 · Cited by 5

Orbitofrontal Thickness and Network Associations as Transdiagnostic Signature of Amotivation Along the Bipolar-Schizophrenia Spectrum

Franz, M., Kebets, V., Berg, X., Georgiadis, F., Milano, B. A., Burrer, A., Brakowski, J., Kaiser, S., Seifritz, E., Homan, P., et al. (2025) · Schizophrenia Bulletin · doi:10.1093/schbul/sbaf078 · Cited by 6

Morphometric features of drug-resistant essential tremor and recovery after stereotactic radiosurgical thalamotomy

Bolton, T. A. W., Ville, D. V. D., Régis, J. M., Witjas, T., Girard, N. J., Levivier, M., & Tuleasca, C. (2022) · Network Neuroscience · doi:10.1162/netn_a_00253 · Cited by 4

Parkinson’s disease may disrupt overlapping subthalamic nucleus and pallidal motor networks

Santos, A. N., Kherif, F., Melie‐García, L., Lutti, A., Chiappini, A., Rauschenbach, L., Dinger, T. F., Rieß, C., Rahal, A. E., Oppong, M. D., et al. (2023) · NeuroImage Clinical · doi:10.1016/j.nicl.2023.103432 · Cited by 4

The relationship between negative life events and cortical structural connectivity in adolescents

Sibilia, F., Jost-Mousseau, C., Banaschewski, T., Barker, G. J., Büchel, C., Desrivières, S., Flor, H., Grigis, A., Garavan, H., Gowland, P. A., et al. (2024) · IBRO Neuroscience Reports · doi:10.1016/j.ibneur.2024.01.012 · Cited by 5

Dynamic network model with continuous valued nodes for longitudinal brain morphometry

Chen, R., Zheng, Y., Nixon, E. E., & Herskovits, E. H. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.05.018 · Cited by 4

Motor learning induces myelin-related white matter changes revealed by MRI-based in vivo histology

Aye, N., Kaufmann, J., Heinze, H., Düzel, E., Ziegler, G., Taubert, M., & Lehmann, N. (2026) · Communications Biology · doi:10.1038/s42003-026-09712-w · Cited by 3

Mapping progressive damage epicenters in epilepsy with generalized tonic-clonic seizures by causal structural covariance network density (CaSCNd)

Hsieh, H., Xu, Q., Zhang, Q., Yang, F., Xu, Y., Liu, G., Liu, R., Yu, Q., Zhang, Z., Gu, X., et al. (2024) · Brain Research · doi:10.1016/j.brainres.2024.148766 · Cited by 3

Comparing intra- and inter-individual correlational brain connectivity from functional and structural neuroimaging data

Di, X., Biswal, B. B., & Initiative, A. D. N. (2025) · Brain Structure and Function · doi:10.1007/s00429-025-02972-y · Cited by 3

Exploring Heterogeneity in Vestibular Migraine Using Individualized Differential Structural Covariance Network Analysis

Chen, W., Zhao, H., Dai, L., Xiong, X., Feng, Q., Ke, J., & Hu, C. (2025) · CNS Neuroscience & Therapeutics · doi:10.1111/cns.70599 · Cited by 3

Insights into déjà vu: Associations between the frequency of experience and amplitudes of low‐frequency oscillations in resting‐state functional magnetic resonance imaging

Zatloukalová, E., Mikl, M., Shaw, D. J., Mareček, R., Sakálošová, L., Kuřátková, M., Mitterova, K., Sklenarova, B., & Brázdil, M. (2021) · European Journal of Neuroscience · doi:10.1111/ejn.15570 · Cited by 3

Neuroanatomical correlates of screening for aphasia in NeuroDegeneration (SAND) battery in non-fluent/agrammatic variant of primary progressive aphasia

Premi, E., Cotelli, M., Gobbi, E., Pagnoni, I., Binetti, G. G., Gadola, Y., Libri, I., Mattioli, I., Pengo, M., Iraji, A., et al. (2022) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2022.942095 · Cited by 3

Predicting Brain Age Using Transferable Covariance Neural Networks

Sihag, S., Mateos, G., McMillan, C. T., & Ribeiro, A. (2023) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49357.2023.10095085 · Cited by 3

Connectome-based predictive modeling of smoking severity using individualized structural covariance network in smokers

Wang, W., Kang, Y., Niu, X., Zhang, Z., Li, S., Gao, X., Zhang, M., Cheng, J., & Zhang, Y. (2023) · Frontiers in Neuroscience · doi:10.3389/fnins.2023.1227422 · Cited by 3

Gray matter morphological abnormities are constrained by normal structural covariance network in OCD

Wen, B., Xu, Y., Fang, K., Guo, H., Liu, H., Liu, L., Wei, Y., Zhang, Y., Cheng, J., & Han, S. (2023) · Progress in Neuro-Psychopharmacology and Biological Psychiatry · doi:10.1016/j.pnpbp.2023.110884 · Cited by 4

Resolving heterogeneity in schizophrenia, bipolar I disorder, and attention-deficit/hyperactivity disorder through individualized structural covariance network analysis

Niu, L., Fang, K., Han, S., Xu, C., & Sun, X. (2023) · Cerebral Cortex · doi:10.1093/cercor/bhad391 · Cited by 3

Changes in microstructural similarity of hippocampal subfield circuits in pathological cognitive aging

Fang, M., Huang, H., Yang, J., Zhang, S., Wu, Y., & Huang, C. (2023) · Brain Structure and Function · doi:10.1007/s00429-023-02721-z · Cited by 3

Delineating a Pathway for the Discovery of Functional Connectome Biomarkers of Autism

Park, S., Thomson, P., Kiar, G., Castellanos, F. X., Milham, M. P., Bernhardt, B. C., & Martino, A. D. (2024) · Advances in neurobiology · doi:10.1007/978-3-031-69491-2_18 · Cited by 3

Cortical Thickness Mapping

Lerch, J. P. (2015) · Elsevier eBooks · doi:10.1016/b978-0-12-397025-1.00305-5 · Cited by 3

Disturbed hippocampal intra-network in first-episode of drug-naïve major depressive disorder

Watanabe, K., Okamoto, N., Ueda, I., Tesen, H., Fujii, R., Ikenouchi, A., Yoshimura, R., & Kakeda, S. (2022) · Brain Communications · doi:10.1093/braincomms/fcac323 · Cited by 3

Structural covariability hubs in old age

Forsberg, L. E., Sigurdsson, S. O., Launer, L. J., Gudnason, V. G., & Ullén, F. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2019.01.032 · Cited by 4

Brain morphological network and its applications in human brain development

Zhao, R., Zhao, Z., Wang, J., & Wu, D. (2022) · Chinese Science Bulletin (Chinese Version) · doi:10.1360/tb-2022-0621 · Cited by 3

Cortical Morphological Brain Networks: Phenotypic Associations and Microbiological SubstratesPreprint

Li, Z., Li, J., Wang, N., Lv, Y., Zou, Q., & Wang, J. (2022) · SSRN Electronic Journal · doi:10.2139/ssrn.4094329 · Cited by 3

Altered connectivity among the triple brain networks in patients with mild cognitive impairment: a source-based morphometry study with a large elderly population

Yotsuya, C., Watanabe, K., Kasai, S., Umemura, Y., Shintaku, T., Ishimoto, Y., Sasaki, M., Nagaya, H., Tatsuo, S., Mikami, T., et al. (2025) · Japanese Journal of Radiology · doi:10.1007/s11604-025-01791-9 · Cited by 2

Disentangling Neurodegeneration With Brain Age Gap Prediction Models: A graph signal processing perspective

Sihag, S., Mateos, G., & Ribeiro, A. (2025) · IEEE Signal Processing Magazine · doi:10.1109/msp.2025.3596731 · Cited by 2

Limbic Structural Alterations and Structural Covariance Across Prodromal Phenotypes and Early Parkinson's Disease

Zhao, Y., Wang, L., Suo, Y., Liu, M., Lin, Y., Fang, L., & Ding, Z. (2026) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.70544 · Cited by 2

Novel histogram-weighted cortical thickness networks and a multi-scale analysis of predictive power in Alzheimer's disease

Raamana, P. R., & Strother, S. C. (2016) · International Workshop on Pattern Recognition in NeuroImaging · doi:10.1109/prni.2016.7552334 · Cited by 2

Pediatric Moderate-Severe Traumatic Brain Injury and Gray Matter Structural Covariance Networks: A Preliminary Longitudinal Investigation

Tuerk, C., Dégeilh, F., Catroppa, C., Anderson, V. A., & Beauchamp, M. H. (2021) · Developmental Neuroscience · doi:10.1159/000518752 · Cited by 2

Brain Network Topology in Deficit and Non-Deficit Schizophrenia: Application of Graph Theory to Local and Global Indices

Vecchio, D., Piras, F., Ciullo, V., Piras, F., Natalizi, F., Ducci, G., Ambrogi, S., Spalletta, G., & Banaj, N. (2023) · Journal of Personalized Medicine · doi:10.3390/jpm13050799 · Cited by 3

Prediction of clinical progression of subjective cognitive decline through alterations in morphology and structural covariance networks

Hu, Z., Zhang, X. C., Hou, X., Wang, L., Chen, H., Huang, L., Yang, D., Mo, Y., Xu, Y., & Bai, F. (2024) · Brain and Behavior · doi:10.1002/brb3.3408 · Cited by 2

Age-related changes of node degree in the multiple-demand network predict fluid intelligence

Yu, L., Zhang, Q., Li, X., Zhang, M., Chen, X., Lu, M., & Ouyang, Z. (2024) · IBRO Neuroscience Reports · doi:10.1016/j.ibneur.2024.06.005 · Cited by 3

covSTATIS: A multi-table technique for network neuroscience

Baracchini, G., Yu, J., Rieck, J. R., Beaton, D., Guillemot, V., Grady, C. L., Abdi, H., & Spreng, R. N. (2024) · Aperture Neuro · doi:10.52294/001c.124518 · Cited by 2

Reorganization of cortical individualized differential structural covariance network is associated with regional morphometric changes in chronic subcortical stroke

Zhang, H., Guo, J., Liu, J., Wang, C., Ding, H., Han, T., Chen, J., Yu, C., & Qin, W. (2025) · NeuroImage Clinical · doi:10.1016/j.nicl.2025.103735 · Cited by 4

Amygdala-Based Structural Covariance and Functional Connectivity Alterations in Patients with Generalized Anxiety Disorder

Chen, Y., Cui, Q., Tang, Q., Sheng, W., Yang, Y., & Chen, H. (2020) · International Symposium on Image Computing and Digital Medicine (ISICDM) · doi:10.1145/3451421.3451462 · Cited by 2

Brain Morphometry: Schizophrenia

Chiapponi, C., Rossi, P. D., Piras, F., Gili, T., & Spalletta, G. (2018) · Neuromethods · doi:10.1007/978-1-4939-7647-8_19 · Cited by 2

Cortical Surface-Based Construction of Individual Structural Network with Application to Early Brain Development Study

Meng, Y., Li, G., Lin, W., Gilmore, J. H., & Shen, D. G. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-24574-4_67 · Cited by 2

Hubs of long-distance co-alteration in brain pathologyPreprint

Cauda, F., Mancuso, L., Nani, A., Manuello, J., Liloia, D., Gelmini, G., Ficco, L. A., Premi, E., Duca, S., & Costa, T. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/846642 · Cited by 2

Towards understanding interindividual differences in cortical morphological brain networksPreprint

Li, Z., Li, J., Wang, N., & Wang, J. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.12.21.423884 · Cited by 2

An edge-centric model for harmonizing multi-relational network datasetsPreprint

Faskowitz, J., Tanner, J., Mišić, B., & Betzel, R. F. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.01.07.425450 · Cited by 2

Disproportionate neuroanatomical effects of DCC haploinsufficiency in adolescence compared with adulthood: links to dopamine, connectivity, covariance, and gene expression brain maps in mice

Hoops, D., Yee, Y., Hammill, C. F., Wong, S., Manitt, C., Bedell, B. J., Cahill, L. S., Lerch, J. P., Flores, C., & Sled, J. G. (2024) · Journal of Psychiatry and Neuroscience · doi:10.1503/jpn.230106 · Cited by 1

From resting-state functional hippocampal centrality to functional outcome: An extended neurocognitive model of psychosis

Totzek, J. F., Shah, J. L., Young, A. L., MALLA, A. K., Joober, R., Raucher‐Chéné, D., Lepage, M., & Lavigne, K. M. (2025) · Psychiatry Research · doi:10.1016/j.psychres.2025.116538 · Cited by 1

Structural brain alterations in patients with anxious depression: evidence from the REST-meta-MDD project

Hu, S., Zhu, L., & Zhang, X. (2025) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2025.1589040 · Cited by 1

Individualized mapping of aberrant cortical thickness via stochastic cortical self-reconstruction

Wachinger, C., Hedderich, D. M., Thalhammer, M., & Bongratz, F. (2025) · Medical Image Analysis · doi:10.1016/j.media.2025.103788 · Cited by 1

Shared and Unique Structural Covariance Connectivity in Comorbidity of Obsessive‐Compulsive Disorder and Major Depressive Disorder

Du, H., Gao, F., Han, Y., Xiao, C., Li, Q., Zhang, D., Wang, Z., Yu, Q., Cai, S., Fan, J., et al. (2025) · Depression and Anxiety · doi:10.1155/da/1087782 · Cited by 1

Structural covariance network of inhibition and shifting in major depressive disorder

Liu, X., Li, Y., Li, Q., Hu, Y., Fu, C. H. Y., & Gong, Q. (2025) · Cerebral Cortex · doi:10.1093/cercor/bhaf312 · Cited by 2

Structural covariance network in the striatum is modulated by non-suicidal self-injury in adolescents with major depressive disorder

Luo, Z., Chen, Y., Xu, Y., Qiu, X., Li, W., Hu, Z., Lu, H., Wang, C., Lan, X., Mai, S., et al. (2025) · BMC Psychiatry · doi:10.1186/s12888-025-07662-x · Cited by 1

Structural alterations related to emotion dysregulation, anxiety and self-harm in adolescents borderline personality disorder. A source-based morphometry study

Yi, X., Wang, X., Zhang, J., Wu, F., Shen, L., Wang, L., Xiao, Q., & Grecucci, A. (2025) · Journal of Affective Disorders · doi:10.1016/j.jad.2025.121035 · Cited by 2

Neuromodulatory effects of dual-site repetitive transcranial magnetic stimulation targeting frontal and temporal cortices in subjective tinnitus: A functional magneti resonance imaging study

Wen, F., Yang, X., Jing, G., Yu, L. L., Mou, C., Ran, J., & Zhang, Y. (2026) · Neurochemistry International · doi:10.1016/j.neuint.2026.106110 · Cited by 1

Cortical morphology of insomnia with objective short sleep duration phenotype

Yang, K., Ran, B., He, D., Guo, Z., & Jiang, B. (2026) · Sleep And Breathing · doi:10.1007/s11325-025-03495-w · Cited by 1

Cortical Surface Morphometry

Evans, A. C. (2015) · Elsevier eBooks · doi:10.1016/b978-0-12-397025-1.00210-4 · Cited by 1

Abnormalities of structural covariance of insular subregions in drug-naïve OCD patients

Zhang, X., Xie, M., Li, W., Xu, Z., Wang, Z., Jiang, W., Wu, Y., & Liu, N. (2023) · Cerebral Cortex · doi:10.1093/cercor/bhad469 · Cited by 1

Brain Structural Covariances in the Ageing Brain in the UK Biobank

Dong, C., Thalamuthu, A., Jiang, J., Mather, K. A., Sachdev, P. S., & Wen, W. X. (2022) · Brain Structure and Function · doi:10.1007/s00429-024-02794-4 · Cited by 1

Increased structural covariance of cortical measures in individuals with an at-risk mental state

Sasabayashi, D., Tsugawa, S., Nakajima, S., Takahashi, T., Takayanagi, Y., Koike, S., Katagiri, N., Katsura, M., Furuichi, A., Mizukami, Y., et al. (2024) · Progress in Neuro-Psychopharmacology and Biological Psychiatry · doi:10.1016/j.pnpbp.2024.111197 · Cited by 1

Latent dimensions of brain asymmetry

Zhang, C., Pu, Y., & Kong, X. (2025) · Handbook of clinical neurology · doi:10.1016/b978-0-443-15646-5.00027-0 · Cited by 1

Chronic insomnia disorder: perspectives from structural neuroimaging

Joo, E. Y. (2018) · Precision and Future Medicine · doi:10.23838/pfm.2018.00156 · Cited by 1

The developmental connectome

Ouyang, M., Fields, C. R., Zhu, T., & Huang, H. (2023) · Elsevier eBooks · doi:10.1016/b978-0-323-85280-7.00018-x · Cited by 1

False discovery rate control under reduced precision computation for analysis of neuroimaging dataPreprint

Nguyen, H. D., Yee, Y., McLachlan, G. J., & Lerch, J. P. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1805.04394 · Cited by 1

Rapid processing and quantitative evaluation of multicontrast EPImix scans for adaptive multimodal imagingPreprint

Váša, F., Hobday, H., Stanyard, R. A., Daws, R. E., Giampietro, V., O’Daly, O., Lythgoe, D. J., Seidlitz, J., Skare, S. T., Williams, S., et al. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.02.12.430956 · Cited by 1

Orbitofrontal thickness and network associations as transdiagnostic signature of negative symptoms along the bipolar-schizophrenia spectrumPreprint

Franz, M., Kebets, V., Berg, X., Georgiadis, F., Burrer, A., Brakowski, J., Kaiser, S., Seifritz, E., Homan, P., Walton, E., et al. (2024) · medRxiv · doi:10.1101/2024.07.29.24311172 · Cited by 1

Desenvolvimento de um software para geração de redes complexas formadas a partir de estimativas de conectividade cerebral: um estudo da espessura cortical no cérebro de indivíduos saudáveis e pacientes com epilepsia.

Cunha, H. H. (2014) · doi:10.11606/d.59.2014.tde-14032014-104256

EP 59. Multi-modal imaging of neural correlates of motor speed performance in the trail making test

Camilleri, J., Reid, A., Müller, V. I., Grefkes, C., Amunts, K., & Eickhoff, S. B. (2016) · Clinical Neurophysiology · doi:10.1016/j.clinph.2016.05.112

IC‐P‐044: Systematic Comparison of Cortical Thickness Based Networks

Raamana, P. R., & Strother, S. C. (2016) · Alzheimer s & Dementia · doi:10.1016/j.jalz.2016.06.054

A Critical Comparison of Pipelines for Structural Brain Network Analysis

Franke, R., & Ivanova, G. (2019) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2019.8857032

P4‐187: Systematic Comparison of Cortical Thickness–Based Networks

Raamana, P. R., & Strother, S. C. (2016) · Alzheimer s & Dementia · doi:10.1016/j.jalz.2016.06.2279

Early Childhood Brain Development and Schizophrenia: An Imaging Perspective

Gilmore, J. H. (2020) · Springer eBooks · doi:10.1007/978-3-030-35206-6_15

Hippocampal Network Abnormality in Major Depressive Disorder [Presidential Award Proceedings]

Watanabe, K., Kakeda, S., SUGIMOTO, K., Katsuki, A., Yoshimura, R., & Korogi, Y. (2020) · Japanese Journal of Magnetic Resonance in Medicine · doi:10.2463/jjmrm.2019-1699

Peer Review #2 of "The brain-derived neurotrophic factor Val66Met polymorphism increases segregation of structural correlation networks in healthy adult brains (v0.1)"

(2020) · PeerJ · doi:10.7287/peerj.9632v0.1/reviews/2

Peer Review #2 of "The brain-derived neurotrophic factor Val66Met polymorphism increases segregation of structural correlation networks in healthy adult brains (v0.2)"

(2020) · PeerJ · doi:10.7287/peerj.9632v0.2/reviews/2

On the neurobiology of apathy and depression in cerebral small vessel disease

Tay, J. (2019) · Apollo (University of Cambridge) · doi:10.17863/cam.56769

Clinically-Feasible Brain Morphometric Similarity Network Construction Approaches with Restricted Magnetic Resonance Imaging Acquisitions and their Relationship with CognitionPreprint

Griffiths-King, D., & WOOD, A. G. (2019) · PsyArXiv (OSF Preprints) · doi:10.31234/osf.io/vks3u

Constructing Reliable Network Of Biomarker Covariance By Joint Data Harmonization And Graph Learning

Kim, M., & Guorong Wu, A. (2021) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi48211.2021.9433967

Grey-matter networks in aging

Koini, M. (2021) · Elsevier eBooks · doi:10.1016/b978-0-12-817990-1.00016-0

The Brain’s Structural Differences in Peripheral Neuropathic Pain: A Comparison Study Between Postherpetic Neuralgia and Lower Back PainPreprint

Qiu, J., Du, M., Yang, J., Lin, Z.-M., Qin, N., Sun, X., Li, L., Zou, R., Wei, J., Wu, B., et al. (2021) · Research Square · doi:10.21203/rs.3.rs-737579/v1

Peer Review #1 of "The brain-derived neurotrophic factor Val66Met polymorphism increases segregation of structural correlation networks in healthy adult brains (v0.2)"

(2020) · PeerJ · doi:10.7287/peerj.9632v0.2/reviews/1

The functional brain network that supports human spatial navigation

Kong, X., ZHANG, F., & Pu, Y. (2022) · Advances in Psychological Science · doi:10.3724/sp.j.1042.2023.00330

Computational Anatomy Going Beyond Brain Morphometry

Draganski, B., Paunova, R., Latypova, A., & Kherif, F. (2023) · Neuromethods · doi:10.1007/978-1-0716-3230-7_8

Identifying network hubs from brain anatomical similarity networks

Mastrou, A., & Vuksanović, V. (2023) · Alzheimer s & Dementia · doi:10.1002/alz.063568

Network Neuroscience Methods for Investigating Brain Plasticity

Finc, K. (2023) · Oxford University Press eBooks · doi:10.1093/oxfordhb/9780197677131.013.13

Enhancing perinatal brain maturity estimation using surface deep learning and cross-modal relationship inference technologyPreprint

Yang, Z., He, R., Sheng, Y., Zhu, D., Wang, Y., Zhang, Y., Hu, B., & Zheng, W. (2024) · Research Square · doi:10.21203/rs.3.rs-5636908/v1

Connectivity and connectomics

Zalesky, A., Sotiropoulos, S. N., Jbabdi, S., & Fornito, A. (2025) · Elsevier eBooks · doi:10.1016/b978-0-12-818894-1.00007-0

[Illness duration-related developmental trajectory of progressive cerebral gray matter changes in schizophrenia].

Chang, X., Yang, Z., Tang, Y., Sun, X., Luo, C., & Yao, D. (2025) · PubMed Central · doi:10.7507/1001-5515.202401053

Altered structural covariance networks in nonsuicidal self-injury: Implications for socio-affective dysfunctions

Lee, S., & Hur, J. (2025) · NeuroImage · doi:10.1016/j.neuroimage.2025.121339

Additional file 1 of Tau-related grey matter network breakdown across the Alzheimer’s disease continuum

Pelkmans, W., Ossenkoppele, R., Dicks, E., Strandberg, O., Barkhof, F., Tijms, B. M., Pereira, J. B., & Hansson, O. (2021) · Figshare · doi:10.6084/m9.figshare.15178725

Brain morphological changes across behaviour spectrums in attention‐deficit/hyperactivity disorder

Zhong, T., Wang, F. H., Qiu, J., & Lu, W. (2025) · General Psychiatry · doi:10.1136/gpsych-2025-102340 · Cited by 1

Cerebral Cortical Reorganization After Intracerebral Hemorrhage in ChildrenPreprint

Choi, H. L., Mahuvakar, S., Schollenberger, J., Shuere, R., Sadikov, A., Cai, L. T., Mrakotsky, C., Nesbit, K. C., Hess, L. G., Kim, H., et al. (2025) · medRxiv · doi:10.64898/2025.12.19.25341480

Risperidone reduces individualized morphometric similarity deviation in schizophrenia and associates with cortical transcriptomic patterns

Liu, L., Yang, M., Chen, J., Yi, C., Kong, D., Zhao, G., Chen, H., & Zhang, X. (2026) · Schizophrenia · doi:10.1038/s41537-025-00724-9

Structural and Network Adaptations in Visuospatial Cortical Regions of Elite Soccer Players: A Morphometric and Structural Covariance Network Study

Chen, J., & Wang, X. (2026) · Brain Topography · doi:10.1007/s10548-026-01173-y

The Convergence of Maturational Change and Structural Covariance in Human Cortical Networks

Alexander‐Bloch, A. F., Raznahan, A., Bullmore, E. T., & Giedd, J. N. (2013) · Journal of Neuroscience · doi:10.1523/jneurosci.3554-12.2013 · Cited by 471

Structural covariance analysis for neurodegenerative and neuroinflammatory brain disorders

Mongay‐Ochoa, N., González‐Escamilla, G., Fleischer, V., Pareto, D., Rovira, À., Sastre‐Garriga, J., & Groppa, S. (2025) · Brain · doi:10.1093/brain/awaf151 · Cited by 32

Structural and Functional Connectivity Underlying Gray Matter Covariance: Impact of Developmental Insult

Paquola, C., Bennett, M. R., & Lagopoulos, J. (2018) · Brain Connectivity · doi:10.1089/brain.2018.0584 · Cited by 12

Network-level structural covariance in the developing brain

Zielinski, B. A., Gennatas, E. D., Zhou, J., & Seeley, W. W. (2010) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1003109107 · Cited by 405

MRI-based structural covariance network in early human brain development

Duan, D., & Dong, W. (2023) · Frontiers in Neuroscience · doi:10.3389/fnins.2023.1302069 · Cited by 4