List of articles that have used BRAPH:

  1. DeSouza, Danielle D., et al. “Altered structural brain network topology in chronic migraine.” Brain Structure and Function 225.1 (2020): 161-172.
  2. Meier, Timothy B., et al. “Resting-state fMRI metrics in acute sport-related concussion and their association with clinical recovery: a study from the NCAA-DOD care consortium.” Journal of neurotrauma 37.1 (2020): 152-162.
  3. Mhiri, Islem, et al. “Brain graph super-resolution for boosting neurological disorder diagnosis using unsupervised multi-topology connectional brain template learning.” Medical Image Analysis 65 (2020): 101768.
  4. Stanković, Ljubiša, et al. “Vertex-frequency graph signal processing: A comprehensive review.” Digital Signal Processing (2020): 102802.
  5. Johnson, Jeffrey P., et al. “Pre-treatment graph measures of a functional semantic network are associated with naming therapy outcomes in chronic aphasia.” Brain and Language 207 (2020): 104809.
  6. Imai, Masamichi, et al. “Metabolic Network Topology of Alzheimer’s Disease and Dementia with Lewy Bodies Generated Using Fluorodeoxyglucose Positron Emission Tomography.” Journal of Alzheimer’s Disease Preprint (2020): 1-11.
  7. Nawaz, Rab, Humaira Nisar, and Yap Vooi Voon. “Changes in Spectral Power and Functional Connectivity of Response-Conflict Task After Neurofeedback Training.” IEEE Access 8 (2020): 139444-139459.
  8. Han, Xiao, et al. “Acupuncture Modulates Disrupted Whole-Brain Network after Ischemic Stroke: Evidence Based on Graph Theory Analysis.” Neural Plasticity 2020 (2020).
  9. Gupta, Yubraj, et al. “Classification and Graphical Analysis of Alzheimer’s Disease and Its Prodromal Stage Using Multimodal Features From Structural, Diffusion, and Functional Neuroimaging Data and the APOE Genotype.” Frontiers in aging neuroscience 12 (2020): 238.
  10. Fazio, Patrik, et al. “High-resolution PET imaging reveals subtle impairment of the serotonin transporter in an early non-depressed Parkinson’s disease cohort.” European Journal of Nuclear Medicine and Molecular Imaging (2020): 1-10.
  11. Wen, Qiuting, et al. “Tau-Related White-Matter Alterations Along Spatially Selective Pathways.” NeuroImage (2020): 117560.
  12. Yin, Wutao, Sakib Mostafa, and Fang-xiang Wu. “Diagnosis of Autism Spectrum Disorder Based on Functional Brain Networks with Deep Learning.” Journal of Computational Biology (2020).
  13. Kim, Jiyoung, et al. “Can we predict drug response by functional connectivity in patients with juvenile myoclonic epilepsy?.” Clinical Neurology and Neurosurgery 198 (2020): 106119.
  14. Collier, Eleanor, and Meghan L. Meyer. “Memory of Others’ Disclosures Is Consolidated during Rest and Associated with Providing Support: Neural and Linguistic Evidence.” Journal of Cognitive Neuroscience (2020): 1-16.
  15. Gonzalez-Burgos, Lissett, José Barroso, and Daniel Ferreira. “Cognitive reserve and network efficiency as compensatory mechanisms of the effect of aging on phonemic fluency.” Aging 12 (2020).
  16. Jafari, Zahra, Bryan E. Kolb, and Majid H. Mohajerani. “Neural Oscillations and Brain Stimulation in Alzheimer’s Disease.” Progress in Neurobiology (2020): 101878.
  17. Mårtensson, Gustav. “Quantifying neurodegeneration from medical images with machine learning and graph theory.” (2020).
  18. Singh, Gajendra Pratap, Ekta Raphael Anthony, and Mamtesh Singh. “A Graph-Theoretic Analysis on Functional EEG Network in Igraph R.” Decision Analytics Applications in Industry. Springer, Singapore, 2020. 541-555.
  19. Amiri, Saba, et al. “Graph theory application with functional connectivity to distinguish left from right temporal lobe epilepsy.” Epilepsy Research 167 (2020): 106449.
  20. Pereira, Joana B. “Detecting early changes in Alzheimer’s disease with graph theory.” Brain Communications (2020).
  21. Ahmadi, Hessam, Emad Fatemizadeh, and Ali Motie-Nasrabadi. “fMRI functional connectivity analysis via kernel graph in Alzheimer’s disease.” Signal, Image and Video Processing (2020): 1-9.
  22. Yegnanarayanan, V. “Understanding Alzheimer’s Disease through Graph Theory.” Journal of Applied Mathematics and Physics 8.10 (2020): 2182.
  23. Arbabi, M., et al. “22 Whole-brain functional connectivity based on the graph theory analysiisn Psychogenic Non-Epileptic Seizures (PNES).” (2020): e17-e17.
  24. Park, Bong Soo, et al. “Alterations of gray matter volumes and connectivity in patients with tuberous sclerosis complex.” Journal of Clinical Neuroscience 72 (2020): 360-364.
  25. Espinoza-Valdez, Aurora, et al. “EEG Data Modeling for Brain Connectivity Estimation in 3D Graphs.” International Conference on Software Process Improvement. Springer, Cham, 2020.
  26. Lin, Xiaofeng, et al. “Altered Topological Patterns of Gray Matter Networks in Tinnitus: A Graph-Theoretical-Based Study.” Frontiers in Neuroscience 14 (2020).
  27. Hinault, T., et al. “Age-related differences in network structure and dynamic synchrony of cognitive control.” bioRxiv (2020).
  28. Lai, Kuan-Lin, and David M. Niddam. “Brain Metabolism and Structure in Chronic Migraine.” Current Pain and Headache Reports 24.11 (2020): 1-9.
  29. Shim, Hye-Kyung, et al. “Alterations in the metabolic networks of temporal lobe epilepsy patients: A graph theoretical analysis using FDG-PET.” NeuroImage: Clinical 27 (2020): 102349.
  30. Prajapati, Rutvi, and Isaac Arnold Emerson. “Construction and analysis of brain networks from different neuroimaging techniques.” International Journal of Neuroscience (2020): 1-32.
  31. Freire, Rodrigo Argenton, and Evandro Ziggiatti Monteiro. “Measuring the development and communication of open design communities: The case of the OpenAg Initiative.” First Monday (2020).
  32. Freire, Rodrigo Argenton. “Investigating open design: current practices and implications for architecture and urban design= Open design: práticas atuais e implicações para a arquitetura e desenho urbano.” (2020).
  33. Karwowski, Waldemar, Farzad Vasheghani Farahani, and Nichole Lighthall. “Application of graph theory for identifying connectivity patterns in human brain networks: a systematic review.” frontiers in Neuroscience 13 (2019): 585.
  34. Wolters, Amée F., et al. “Resting-state fMRI in Parkinson’s disease patients with cognitive impairment: A meta-analysis.” Parkinsonism & Related Disorders 62 (2019): 16-27.
  35. Ferreira, Daniel, et al. “Subtypes of Alzheimer’s disease display distinct network abnormalities extending beyond their pattern of brain atrophy.” Frontiers in neurology 10 (2019): 524.
  36. Kaushal, Mayank, et al. “Resting‐state functional connectivity after concussion is associated with clinical recovery.” Human brain mapping 40.4 (2019): 1211-1220.
  37. Pietzuch, Manuela, et al. “The influence of genetic factors and cognitive reserve on structural and functional resting-state brain networks in aging and Alzheimer’s disease.” Frontiers in Aging Neuroscience 11 (2019): 30.
  38. Stankovic, Ljubisa, et al. “Graph Signal Processing–Part I: Graphs, Graph Spectra, and Spectral Clustering.” arXiv preprint arXiv:1907.03467 (2019).
  39. Bidelman, Gavin M., et al. “Age-related hearing loss increases full-brain connectivity while reversing directed signaling within the dorsal–ventral pathway for speech.” Brain Structure and Function 224.8 (2019): 2661-2676.
  40. Carnevale, Lorenzo, and Giuseppe Lembo. “Innovative MRI techniques in neuroimaging approaches for cerebrovascular diseases and vascular cognitive impairment.” International journal of molecular sciences 20.11 (2019): 2656.
  41. Mostafa, Sakib, Lingkai Tang, and Fang-Xiang Wu. “Diagnosis of autism spectrum disorder based on eigenvalues of brain networks.” IEEE Access 7 (2019): 128474-128486.
  42. Ha, Sam Yeol, and Kang Min Park. “Alterations of structural connectivity in episodic cluster headache: A graph theoretical analysis.” Journal of Clinical Neuroscience 62 (2019): 60-65.
  43. Kiran, Swathi, Erin L. Meier, and Jeffrey P. Johnson. “Neuroplasticity in aphasia: A proposed framework of language recovery.” Journal of Speech, Language, and Hearing Research 62.11 (2019): 3973-3985.
  44. Lee, Ho‐Joon, and Kang Min Park. “Structural and functional connectivity in newly diagnosed juvenile myoclonic epilepsy.” Acta Neurologica Scandinavica 139.5 (2019): 469-475.
  45. Solana, Elisabeth, et al. “Modified connectivity of vulnerable brain nodes in multiple sclerosis, their impact on cognition and their discriminative value.” Scientific reports 9.1 (2019): 1-8.
  46. Mojica-Benavides, Martin, et al. “Intercellular communication induces glycolytic synchronization waves between individually oscillating cells.” arXiv preprint arXiv:1909.05187 (2019).
  47. Park, Kang Min, et al. “Alterations of the brain network in idiopathic rapid eye movement sleep behavior disorder: structural connectivity analysis.” Sleep and Breathing 23.2 (2019): 587-593.
  48. Mojica Benavides, Martin. “Metabolic communication between individual yeast cells.” (2019).
  49. Mostafa, Sakib, Wutao Yin, and Fang-Xiang Wu. “Autoencoder Based Methods for Diagnosis of Autism Spectrum Disorder.” International Conference on Computational Advances in Bio and Medical Sciences. Springer, Cham, 2019.
  50. Shin, Kyong Jin, Ho-Joon Lee, and Kang Min Park. “Alterations of individual thalamic nuclei volumes in patients with migraine.” The Journal of Headache and Pain 20.1 (2019): 112.
  51. Amin, M., Farshad Safaei, and N. S. Ghaderian. “Extracting a Discriminative Structural Sub-Network for ASD Screening using the Evolutionary Algorithm.” arXiv preprint arXiv:1911.05484 (2019).
  52. Meyer, Meghan L., and Eleanor Collier. “Memory of others’ disclosures is consolidated during rest and associated with providing support: neural and linguistic evidence.” (2019).
  53. Bu, Xixi, et al. “Evaluation of Metabolic Network for Alzheimer’s Disease.” 2019 6th International Conference on Systems and Informatics (ICSAI). IEEE, 2019.
  54. Mostafa, Sakib. Machine Learning for the Diagnosis of Autism Spectrum Disorder. Diss. University of Saskatchewan, 2019.
  55. Caremel, Cedric. “Doctoral Dissertation Academic Year 2019.”
  56. Amiri, Saba, et al. “Resting-State Functional Connectivity in Popular Targets for Deep Brain Stimulation in the Treatment of Major Depression: An Application of a Graph Theory.” 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE, 2019.
  57. Smit, Nikolaos. Contributions of Graph Theory and Algorithms to Animal Behaviour and Neuroscience. Diss. National and Kapodistrian University of Athens, 2019.
  58. Verger, Antoine, et al. “Brain PET substrate of impulse control disorders in Parkinson’s disease: a metabolic connectivity study.” Human brain mapping 39.8 (2018): 3178-3186.
  59. Pereira, Joana B., et al. “Abnormal structural brain connectome in individuals with preclinical Alzheimer’s disease.” Cerebral cortex 28.10 (2018): 3638-3649.
  60. Mårtensson, Gustav, et al. “Stability of graph theoretical measures in structural brain networks in Alzheimer’s disease.” Scientific reports 8.1 (2018): 1-15.
  61. Pereira, Joana B., et al. “Amyloid network topology characterizes the progression of Alzheimer’s disease during the predementia stages.” Cerebral Cortex 28.1 (2018): 340-349.
  62. Voevodskaya, Olga, et al. “Altered structural network organization in cognitively normal individuals with amyloid pathology.” Neurobiology of aging 64 (2018): 15-24.
  63. Gaudio, Santino, et al. “Altered cerebellar–insular–parietal–cingular subnetwork in adolescents in the earliest stages of anorexia nervosa: a network–based statistic analysis.” Translational psychiatry 8.1 (2018): 1-10.
  64. Paban, Veronique, et al. “Resting brain functional networks and trait coping.” Brain connectivity 8.8 (2018): 475-486.
  65. Tolan, Ertan, and Zerrin Isik. “Graph theory based classification of brain connectivity network for autism spectrum disorder.” International Conference on Bioinformatics and Biomedical Engineering. Springer, Cham, 2018.
  66. Waller, Lea, et al. “GraphVar 2.0: A user-friendly toolbox for machine learning on functional connectivity measures.” Journal of Neuroscience Methods 308 (2018): 21-33.
  67. Mahmud, Md Sultan, et al. “What brain connectivity patterns from EEG tell us about hearing loss: A graph theoretic approach.” 2018 10th International Conference on Electrical and Computer Engineering (ICECE). IEEE, 2018.
  68. Imai, Emiko, and Yoshitada Katagiri. “Cognitive Control and Brain Network Dynamics during Word Generation Tasks Predicted Using a Novel Event-Related Deep Brain Activity Method.” Journal of Behavioral and Brain Science 8.2 (2018): 93-115.
  69. Brovkin, A., et al. “GraphVar 2.0: A user-friendly toolbox for machine learning on functional connectivity measures.” pre-print (Arxiv) (2018).
  70. Silvestri, Erica. “Simultaneous PET/MRI for Connectivity Mapping: Quantitative Methods in Clinical Setting.” (2018).
  71. Mostafa, Sakib, Wutao Yin, and Fang-Xiang Wu. “A Neural Network Based Method for the Diagnosis of Autism Spectrum Disorder.”
  72. Bielikh, Oleksii. Generation of Random Graphs for Graph Theory Analysis Applied to the Study of Brain Connectivity. MS thesis. 2017.
  73. Sanchez-Catasus, Carlos A., et al. “Use of Nuclear Medicine Molecular Neuroimaging to Model Brain Molecular Connectivity.” PET and SPECT in Neurology. Springer, Cham 181-207.
  74. Kakaei, Ehsan. Braph: a toolbox developed for brain graph analysis of various imaging modalities. Diss. Bilkent University, 2017.