Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
167 tokens/sec
GPT-4o
7 tokens/sec
Gemini 2.5 Pro Pro
42 tokens/sec
o3 Pro
4 tokens/sec
GPT-4.1 Pro
38 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

ACTION: Augmentation and Computation Toolbox for Brain Network Analysis with Functional MRI (2405.06178v1)

Published 10 May 2024 in eess.IV, cs.LG, and q-bio.NC

Abstract: Functional magnetic resonance imaging (fMRI) has been increasingly employed to investigate functional brain activity. Many fMRI-related software/toolboxes have been developed, providing specialized algorithms for fMRI analysis. However, existing toolboxes seldom consider fMRI data augmentation, which is quite useful, especially in studies with limited or imbalanced data. Moreover, current studies usually focus on analyzing fMRI using conventional machine learning models that rely on human-engineered fMRI features, without investigating deep learning models that can automatically learn data-driven fMRI representations. In this work, we develop an open-source toolbox, called Augmentation and Computation Toolbox for braIn netwOrk aNalysis (ACTION), offering comprehensive functions to streamline fMRI analysis. The ACTION is a Python-based and cross-platform toolbox with graphical user-friendly interfaces. It enables automatic fMRI augmentation, covering blood-oxygen-level-dependent (BOLD) signal augmentation and brain network augmentation. Many popular methods for brain network construction and network feature extraction are included. In particular, it supports constructing deep learning models, which leverage large-scale auxiliary unlabeled data (3,800+ resting-state fMRI scans) for model pretraining to enhance model performance for downstream tasks. To facilitate multi-site fMRI studies, it is also equipped with several popular federated learning strategies. Furthermore, it enables users to design and test custom algorithms through scripting, greatly improving its utility and extensibility. We demonstrate the effectiveness and user-friendliness of ACTION on real fMRI data and present the experimental results. The software, along with its source code and manual, can be accessed online.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (78)
  1. The architecture of complex weighted networks. Proceedings of the National Academy of Sciences 101, 3747–3752.
  2. Some unique properties of eigenvector centrality. Social Networks 29, 555–564.
  3. Random forests. Machine Learning 45, 5–32.
  4. The fast Fourier transform and its applications. Prentice-Hall, Inc.
  5. XGBoost: A scalable tree boosting system, in: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 785–794.
  6. Exploring simple Siamese representation learning, in: CVPR, pp. 15750–15758.
  7. Pearson correlation coefficient. Noise Reduction in Speech Processing , 1–4.
  8. The neuro bureau preprocessing initiative: Open sharing of preprocessed neuroimaging data and derivatives. Frontiers in Neuroinformatics 7, 27.
  9. Disrupted structural and functional brain networks in Alzheimer’s disease. Neurobiology of Aging 75, 71–82.
  10. Discovery of meaningful associations in genomic data using partial correlation coefficients. Bioinformatics 20, 3565–3574.
  11. Learning generalizable recurrent neural networks from small task-fMRI datasets, in: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer. pp. 329–337.
  12. Functional brain connectivity changes across the human life span: From fetal development to old age. Journal of Neuroscience Research 99, 236–262.
  13. Unsupervised cross-domain functional MRI adaptation for automated major depressive disorder identification. Medical Image Analysis 84, 102707.
  14. Discriminatory analysis: Nonparametric discrimination, consistency properties. volume 1. USAF School of Aviation Medicine.
  15. Spontaneous fluctuations in brain activity observed with functional magnetic resonance imaging. Nature Reviews Neuroscience 8, 700–711.
  16. Statistics (International Student Edition). Pisani, R. Purves, 4th edn. WW Norton & Company, New York .
  17. Centrality in social networks: Conceptual clarification. Social Network: Critical Concepts in Sociology. Londres: Routledge 1, 238–263.
  18. Spatio-temporal graph convolution for resting-state fMRI analysis, in: International Conference on Medical Image Computing and Computer Assisted Intervention, Springer. pp. 528–538.
  19. BASCO: A toolbox for task-related functional connectivity. Frontiers in Systems Neuroscience 9, 126.
  20. MANIA—A pattern classification toolbox for neuroimaging data. Neuroinformatics 12, 471–486.
  21. Inductive representation learning on large graphs. Advances in Neural Information Processing Systems 30.
  22. PyMVPA: A python toolbox for multivariate pattern analysis of fMRI data. Neuroinformatics 7, 37–53.
  23. Support vector machines. IEEE Intelligent Systems and Their Applications 13, 18–28.
  24. Relations between two sets of variates, in: Breakthroughs in statistics: Methodology and distribution. Springer, pp. 162–190.
  25. Multi-band brain network analysis for functional neuroimaging biomarker identification. IEEE Transactions on Medical Imaging 40, 3843–3855.
  26. A fast fixed-point algorithm for independent component analysis. Neural Computation 9, 1483–1492.
  27. A tutorial in connectome analysis: Topological and spatial features of brain networks. NeuroImage 57, 892–907.
  28. BrainNetCNN: Convolutional neural networks for brain networks; towards predicting neurodevelopment. NeuroImage 146, 1038–1049.
  29. Understanding graph isomorphism network for rs-fMRI functional connectivity analysis. Frontiers in Neuroscience , 630.
  30. Learning dynamic graph representation of brain connectome with spatio-temporal attention. Advances in Neural Information Processing Systems 34, 4314–4327.
  31. Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907 .
  32. Estimating mutual information. Physical Review E 69, 066138.
  33. GraphVar: A user-friendly toolbox for comprehensive graph analyses of functional brain connectivity. Journal of Neuroscience Methods 245, 107–115.
  34. MALINI (Machine Learning in NeuroImaging): A MATLAB toolbox for aiding clinical diagnostics using resting-state fMRI data. Data in Brief 29, 105213.
  35. Efficient behavior of small-world networks. Physical Review Letters 87, 198701.
  36. Data augmentation for time series classification using convolutional neural networks, in: ECML/PKDD Workshop on Advanced Analytics and Learning on Temporal Data.
  37. Model-contrastive federated learning, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 10713–10722.
  38. Federated optimization in heterogeneous networks. Proceedings of Machine Learning and Systems 2, 429–450.
  39. BrainGNN: Interpretable brain graph neural network for fMRI analysis. Medical Image Analysis 74, 102233.
  40. Think locally, act globally: Federated learning with local and global representations. arXiv preprint arXiv:2001.01523 .
  41. DynamicBC: A matlab toolbox for dynamic brain connectome analysis. Brain Connectivity 4, 780–790.
  42. Brain age prediction using fMRI network coupling in youths and associations with psychiatric symptoms. NeuroImage: Clinical 33, 102921.
  43. Altered modular organization of intrinsic brain functional networks in patients with Parkinson’s disease. Brain Imaging and Behavior 11, 430–443.
  44. Communication-efficient learning of deep networks from decentralized data, in: Artificial Intelligence and Statistics, PMLR. pp. 1273–1282.
  45. NeuroPycon: An open-source python toolbox for fast multi-modal and reproducible brain connectivity pipelines. NeuroImage 219, 117020.
  46. Assortative mixing in networks. Physical Review Letters 89, 208701.
  47. The structure and function of complex networks. SIAM Review 45, 167–256.
  48. Modularity and community structure in networks. Proceedings of the National Academy of Sciences 103, 8577–8582.
  49. Metrics of brain network architecture capture the impact of disease in children with epilepsy. NeuroImage: Clinical 13, 201–208.
  50. Data augmentation for fMRI-based functional connectivity and its application to cross-site ADHD classification. IEEE Transactions on Instrumentation and Measurement 72, 1–15.
  51. Guaranteed minimum-rank solutions of linear matrix equations via nuclear norm minimization. SIAM Review 52, 471–501.
  52. Complex network measures of brain connectivity: Uses and interpretations. NeuroImage 52, 1059–1069.
  53. PRoNTo: Pattern recognition for neuroimaging toolbox. Neuroinformatics 11, 319–337.
  54. REST: A toolkit for resting-state functional magnetic resonance imaging data processing. PloS One 6, e25031.
  55. Personalized federated learning with moreau envelopes. Advances in Neural Information Processing Systems 33, 21394–21405.
  56. MVPA-Light: A classification and regression toolbox for multi-dimensional data. Frontiers in Neuroscience 14, 491843.
  57. Attention is all you need. Advances in Neural Information Processing Systems 30.
  58. Graph Attention Networks. arXiv preprint arXiv:1710.10903 .
  59. GraphVar 2.0: A user-friendly toolbox for machine learning on functional connectivity measures. Journal of Neuroscience Methods 308, 21–33.
  60. Recognizing brain states using deep sparse recurrent neural network. IEEE Transactions on Medical Imaging 38, 1058–1068.
  61. GRETNA: A graph theoretical network analysis toolbox for imaging connectomics. Frontiers in Human Neuroscience 9, 386.
  62. Leveraging brain modularity prior for interpretable representation learning of fMRI. IEEE Transactions on Biomedical Engineering .
  63. Unsupervised contrastive graph learning for resting-state functional MRI analysis and brain disorder detection. Human Brain Mapping 44, 5672–5692.
  64. Collective dynamics of ‘small-world’ networks. Nature 393, 440–442.
  65. Time series data augmentation for deep learning: A survey, pp. 4653–4660. doi:10.24963/ijcai.2021/631.
  66. Conn: A functional connectivity toolbox for correlated and anticorrelated brain networks. Brain Connectivity 2, 125–141.
  67. Principal component analysis. Chemometrics and Intelligent Laboratory Systems 2, 37–52.
  68. Using spearman’s correlation coefficients for exploratory data analysis on big dataset. Concurrency and Computation: Practice and Experience 28, 3866–3878.
  69. BRANT: A versatile and extendable resting-state fMRI toolkit. Frontiers in Neuroinformatics 12, 52.
  70. L1/2subscript𝐿12{L}_{1/2}italic_L start_POSTSUBSCRIPT 1 / 2 end_POSTSUBSCRIPT regularization: A thresholding representation theory and a fast solver. IEEE Transactions on Neural Networks and Learning Systems 23, 1013–1027.
  71. DPARSF: A MATLAB toolbox for “pipeline” data analysis of resting-state fMRI. Frontiers in Systems Neuroscience 4, 1377.
  72. Diffusion models: A comprehensive survey of methods and applications. ACM Computing Surveys 56, 1–39.
  73. A survey on ensemble learning under the era of deep learning. Artificial Intelligence Review 56, 5545–5589.
  74. Graph contrastive learning with augmentations. Advances in Neural Information Processing Systems 33, 5812–5823.
  75. How to model an internetwork, in: Proceedings of IEEE INFOCOM’96. Conference on Computer Communications, IEEE. pp. 594–602.
  76. Topographical information-based high-order functional connectivity and its application in abnormality detection for mild cognitive impairment. Journal of Alzheimer’s Disease 54, 1095–1112.
  77. A-GCL: Adversarial graph contrastive learning for fMRI analysis to diagnose neurodevelopmental disorders. Medical Image Analysis 90, 102932.
  78. A toolbox for brain network construction and classification (BrainNetClass). Human Brain Mapping 41, 2808–2826.

Summary

We haven't generated a summary for this paper yet.

Ai Sparkles Streamline Icon: https://streamlinehq.com Summarize Now
X Twitter Logo Streamline Icon: https://streamlinehq.com

Tweets