├── README.md └── parkinsons.md /README.md: -------------------------------------------------------------------------------- 1 | # Brain-Image-Analysis 2 | Paper list and resources on machine learning for brain image (e. g. fMRI and sMRI) analysis. 3 | 4 | Contributed by Jinlong Hu, Yuezhen Kuang, and Lijie Cao, from School of Computer Science and Engineering, South China University of Technology, Guangzhou, China. 5 | 6 | (Our research collection on Artificial Intelligence, specifically focusing on eXplainable Artificial Intelligence (XAI) for brain image analysis is available on [this link](https://github.com/largeapp/AI-for-Brain-Image-Analysis)) 7 | 8 | ##### Table of Contents 9 | 10 | 1. [Survey](#survey) 11 | 2. [Resting-state fMRI (voxel)](#Resting-state-fMRI-on-voxel-level) 12 | 3. [Resting-state fMRI (region)](#Resting-state-fMRI-on-region-level) : [Special issue](#Special-issue) 13 | 4. [Task fMRI](#task-fmri) 14 | 5. [sMRI and others](#sMRI-and-other-data) 15 | 6. Special diseases: [Parkinson](#Parkinson), [Autism](#Autism), [Depression](#depression) 16 | 7. [Dataset](#dataset) 17 | 8. Other algorithms: [Multiview learning](#Multiview-learning) 18 | 19 | ## Survey 20 | 21 | #### On machine learning 22 | 1. **Machine learning studies on major brain diseases: 5-year trends of 2014–2018** 23 | - [paper](https://link.springer.com/article/10.1007/s11604-018-0794-4), 2018 24 | 25 | 1. **Machine Learning for Predicting Cognitive Diseases: Methods, Data Sources and Risk Factors** 26 | - [paper](https://link.springer.com/article/10.1007/s10916-018-1071-x), 2018 27 | 28 | 1. **Adaptive Sparse Learning for Neurodegenerative Disease Classification** 29 | - [paper](https://ieeexplore.ieee.org/abstract/document/8241617), 2017 30 | 31 | 1. **Classification on Brain Functional Magnetic Resonance Imaging: Dimensionality, Sample Size, Subject Variability and Noise** 32 | - [paper](https://www.cs.purdue.edu/homes/jhonorio/fmrisynth_bookchapter14.pdf) 33 | 34 | 1. **Classification and Prediction of Brain Disorders Using Functional Connectivity: Promising but Challenging** 35 | - [paper](https://www.frontiersin.org/articles/10.3389/fnins.2018.00525/full), 2018 36 | 37 | #### On brain connectivity dynamics 38 | 1. **Brain Connectivity Dynamics** issue, NeuroImage, October 2018 39 | - [link](https://www.sciencedirect.com/journal/neuroimage/vol/180/part/PB) 40 | 41 | 1. **Dynamic Graph Metrics: Tutorial, Toolbox, and Tale** 42 | - Ann E. Sizemore and Danielle S. Bassett, 2017 43 | - [code](https://github.com/asizemore/Dynamic-Graph-Metrics) 44 | 45 | 1. **The dynamic functional connectome: State-of-the-art and perspectives** 46 | - Maria Giulia Pretia, etc., NeuroImage, 2017 47 | 48 | 1. **BRAPH: A graph theory software for the analysis of brain connectivity** 49 | - Mite Mijalkov, etc. , PLOS ONE, 2017. 50 | - [paper](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0178798) 51 | - [code](http://www.braph.org/) 52 | 53 | #### On deep learning 54 | 1. **Deep Learning in Medical Image Analysis** 55 | - *Dinggang Shen, et al.* 2017. 56 | 57 | 1. **Applications of Deep Learning to MRI Images: A Survey** 58 | - *Jin Liu, et al.* 2018. 59 | - [paper](https://www.researchgate.net/profile/Jin_Liu20/publication/323491805_Applications_of_deep_learning_to_MRI_images_A_survey/links/5aa0be5caca272d448b2175f/Applications-of-deep-learning-to-MRI-images-A-survey.pdf) 60 | 61 | 1. **A Comprehensive Survey on Graph Neural Networks** 62 | - *Zonghan Wu, et al.* 2019. 63 | - [paper](https://arxiv.org/pdf/1901.00596.pdf) 64 | - [with Chinese](https://mp.weixin.qq.com/s/0rs8Wur7Iv6jSpFz5C-KNg) 65 | - More graph neural networks (GNN) papers, see [GNN-paper-list](https://github.com/largeapp/GNNPapers) 66 | 67 | ## Resting-state fMRI on voxel level 68 | #### Deep learning for voxel 69 | 1. **Deep Learning in Medical Imaging: fMRI Big Data Analysis via Convolutional Neural Networks** 70 | - *Amirhessam Tahmassebi，et al.* 2018. 71 | 72 | 1. **deep learning of resting state networks from independent component analysis** 73 | - *Yiyu Chou，et al.* 2018. 74 | 75 | 1. **Learning Neural Markers of Schizophrenia Disorder Using Recurrent Neural Networks** 76 | - *Jumana Dakka, et al.* 2017. 77 | 78 | 1. **Using deep learning to investigate the neuroimaging correlates of psychiatric and neurological disorders: Methods and applications** 79 | - *Sandra Vieira, et al.* 2017. 80 | 81 | 1. **using resting state functional mri to build a personalized autism diagnosis system** 82 | - *Omar Dekhil, et al.* ISBI 2018. 83 | 84 | 1. **Whole Brain fMRI Pattern Analysis Based on Tensor Neural Network** 85 | - *XIAOWEN XU, et al.* 2018. 86 | 87 | 1. **2-channel convolutional 3d deep neural network (2cc3d) for fmri analysis: asd classification and feature learning** 88 | - *Xiaoxiao Li, et al.* ISBI 2018. 89 | 90 | 1. **Brain Biomarker Interpretation in ASD Using Deep Learning and fMRI** 91 | - *Xiaoxiao Li, et al.* 2018. 92 | 93 | 94 | 1. **The Unsupervised Hierarchical Convolutional Sparse Auto-Encoder for Neuroimaging Data Classification** 95 | - *Xiaobing Han, et al.* 2015. 96 | 97 | 1. **Learning Neural Markers of Schizophrenia Disorder Using Recurrent Neural Networks** 98 | - *Jumana Dakka, et al.* 2017. 99 | 100 | 1. **Classification of Alzheimer’s Disease Using fMRI Data and Deep Learning Convolutional Neural Networks** 101 | - *Saman Sarraf, Ghassem Tofighi* 2016. 102 | 103 | 1. **Deep learning for neuroimaging: a validation study** 104 | - *Sergey M. Plis*, 2014. 105 | 106 | 1. **The Unsupervised Hierarchical Convolutional Sparse Auto-Encoder for Neuroimaging Data Classification** 107 | - *Xiaobing Han, et al.* 2015. 108 | - auto encoding，ADHD-200，ADNI data 109 | 110 | 1. **Group-wise Sparse Representation Of Resting-state fMRI Data For Better Understanding Of Schizophrenia** 111 | - *Lin Yuan, et al.* 2017. 112 | 113 | 114 | 1. **Neuroscience meets Deep Learning** 115 | - *Dhruv Nathawani, et al.* 116 | - CNN, CMU 2008 data 117 | 118 | 1. **Brain Age Prediction Based On Resting-state Functional Connectivity Patterns Using Convolutional Neural Networks** 119 | - *Hongming Li* 120 | - 3D, t-SNE analysis 121 | 122 | 1. **Voxelwise 3D Convolutional and Recurrent Neural Networks for Epilepsy and Depression Diagnostics from Structural and Functional MRI Data** 123 | - *Marina Pominova, et al.* 2018. 124 | 125 | 1. **Automatic Recognition of fMRI-derived Functional Networks using 3D Convolutional Neural Networks** 126 | - *Yu Zhao, et al.* 2017. 127 | 128 | 129 | 1. **3D CNN Based Automatic Diagnosis of Attention Deficit Hyperactivity Disorder Using Functional and Structural MRI** 130 | - *LIANG ZOU, et al.* 2017. 131 | 132 | 1. **3D fully convolutional networks for subcortical segmentation in MRI: A large-scale study** 133 | - *Jose Dolz, et al.* 2016. 134 | - segmentation，multiple data sets. 135 | 136 | 1. **Multi-Scale 3D Convolutional Neural Networks for Lesion Segmentation in Brain MRI** 137 | - *Konstantinos Kamnitsas, et al.* 138 | 139 | 1. **3-D Functional Brain Network Classification using Convolutional Neural Networks** 140 | - *Dehua Ren, et al.* 2017. 141 | 142 | 143 | 1. **Modeling 4D fMRI Data via Spatio-Temporal Convolutional Neural Networks (ST-CNN)** 144 | - *Yu Zhao, et al.* 2018. 145 | 146 | 1. **3D Deep Learning for Multi-modal Imaging-Guided Survival Time Prediction of Brain Tumor Patients** 147 | - *Dong Nie, et al.* 2016. 148 | - multi-modal 149 | 150 | 1. **DeepAD: Alzheimer’s Disease Classification via Deep Convolutional Neural Networks using MRI and fMRI** 151 | - *Saman Sarraf, et al.* 2016. 152 | - multi-modal：MRI, fMRI 153 | 154 | 155 | 156 | 1. **Multi-tasks Deep Learning Model for classifying MRI images of AD/MCI Patients** 157 | - *S.Sambath Kumar, et al.* 2017. 158 | 159 | 1. **Retrospective head motion estimation in structural brain MRI with 3D CNNs** 160 | - *Juan Eugenio Iglesias, et al.* 161 | - head moving, ABIDE data set. 162 | 163 | 1. **Transfer learning improves resting-state functional connectivity pattern analysis using convolutional neural networks** 164 | - *Pál Vakli, et al.* 2018. 165 | - fMRI, transfer learing 166 | 167 | 168 | 1. **Towards Alzheimer’s Disease Classification through Transfer Learning** 169 | - *Marcia Hon, et al.* BIBM 2017. 170 | - transfer learning 171 | 172 | 173 | 1. **Deep neural network with weight sparsity control and pre-training extracts hierarchical features and enhances classification performance: Evidence from whole-brain resting-state functional connectivity patterns of schizophrenia** 174 | - *Junghoe Kim, et al.* 2016. 175 | 176 | 1. **Reproducibility of importance extraction methods in neural network based fMRI classification** 177 | - *Athanasios Gotsopoulos, et al.* NeuroImage 2018. 178 | - Important voxels 179 | 180 | 1. **Spatiotemporal feature extraction and classification of Alzheimer’s disease using deep learning 3D-CNN for fMRI data** 181 | - *Harshit Parmar*,2020 182 | - [paper](https://www.spiedigitallibrary.org/journals/journal-of-medical-imaging/volume-7/issue-05/056001/Spatiotemporal-feature-extraction-and-classification-of-Alzheimers-disease-using-deep/10.1117/1.JMI.7.5.056001.full?SSO=1) 183 | 184 | #### Non-deep-learning for voxel 185 | 186 | 1. **Multi-way Multi-level Kernel Modeling for Neuroimaging Classification** 187 | - *Lifang He, et al.* CVPR 2017. 188 | 189 | 1. **Spatio-Temporal Tensor Analysis for Whole-Brain fMRI Classi cation** 190 | - *Guixiang Ma, et al.* 191 | 192 | 1. **Feature Selection with a Genetic Algorithm for Classification of Brain Imaging Data** 193 | - *Annamária Szenkovits, et al.* 2017. 194 | - feature selection 195 | 196 | 1. **Building a Science of Individual Differences from fMRI** 197 | - *Julien Dubois* 2016. 198 | - from group to individual 199 | 200 | 1. **Feature fusion via hierarchical supervised local CCA for diagnosis of autism spectrum disorder** 201 | - *Feng Zhao, et al.* 2016. 202 | - feature fusion, multi-modal data 203 | 204 | 205 | 206 | ## Resting-state fMRI on region level 207 | #### Deep learning for region 208 | 1. **GAT-LI: a graph attention network based learning and interpreting method for functional brain network classification** 209 | - *Jinlong Hu, et al*, 2021 210 | 211 | 1. **Interpretable Learning Approaches in Resting-State Functional Connectivity Analysis: The Case of Autism Spectrum Disorder** 212 | - *Jinlong Hu, et al*, 2020 213 | - [code](https://github.com/largeapp/ifc) 214 | 215 | 1. **Resting State fMRI Functional Connectivity-Based Classification Using a Convolutional Neural Network Architecture** 216 | - *Regina Júlia Meszlényi, et al.* 2017. 217 | - 499 brain regions, CNN 218 | 219 | 1. **Identifying Connectivity Patterns for Brain Diseases via Multi-side-view Guided Deep Architectures** 220 | - *Jingyuan Zhang, et al.* 2016. 221 | 222 | 1. **Do Deep Neural Networks Outperform Kernel Regression for Functional Connectivity Prediction of Behavior?** 223 | - *Tong He, et al.* 2018. 224 | - [paper](https://www.biorxiv.org/content/biorxiv/early/2018/11/19/473603.full.pdf) 225 | - simple version：**Is deep learning better than kernel regression for functional connectivity prediction of fluid intelligence?** [paper](http://holmeslab.yale.edu/wp-content/uploads/2018-He.pdf) 226 | 227 | 1. **Metric learning with spectral graph convolutions on brain connectivity networks** 228 | -*Sofia IraKtena, et al.* 2018 229 | - [paper](https://www.sciencedirect.com/science/article/pii/S1053811917310765) 230 | 231 | 1. **Multi Layered-Parallel Graph Convolutional Network (ML-PGCN) for Disease Prediction** 232 | - *Anees Kazi, Shadi Albarqouni, Karsten Kortuem, Nassir Navab* 2018. 233 | - [paper](https://arxiv.org/abs/1804.10776) 234 | 235 | 1. **Classifying resting and task state brain connectivity matrices using graph convolutional networks** 236 | - *Michael Craig, et al.* 237 | - [paper](https://www.researchgate.net/profile/Michael_Craig15/publication/320347164_Classifying_resting_and_task_state_brain_connectivity_matrices_using_graph_convolutional_networks/links/5a38db04458515919e7278ab/Classifying-resting-and-task-state-brain-connectivity-matrices-using-graph-convolutional-networks.pdf) 238 | 239 | 1. **Multi-View Graph Convolutional Network and Its Applications on Neuroimage Analysis for Parkinson's Disease** 240 | - *xi zhang, et al.* 2018. 241 | - [paper](https://arxiv.org/abs/1805.08801) 242 | - data：PPMI, DTI 243 | 244 | #### Non-deep-learning for region 245 | 1. **Resting-State Functional Connectivity in Autism Spectrum Disorders: A Review** 246 | - *Jocelyn V. Hull, et al.* 2017. 247 | 248 | 1. **A Novel Approach to Identifying a Neuroimaging Biomarker for Patients With Serious Mental Illness** 249 | - *Alok Madan, et al.* 250 | 251 | 1. **Classification of Resting State fMRI Datasets Using Dynamic Network Clusters** 252 | - *Hyo Yul Byun, et al.* 2014 253 | - dynamic brain network, clustering 254 | 255 | 256 | 257 | #### Special issue 258 | Contributed by Lijie. 259 | 1. **Spectral Graph Convolutions for Population-Based Disease Prediction** 2017. 260 | - subjects as nodes 261 | 262 | 1. **Distance Metric Learning using Graph Convolutional Networks: Application to Functional Brain Networks** 2017. 263 | - brain networks as input，mutric learning with GCN Siamese network 264 | 265 | 1. **Disease Prediction using Graph Convolutional Networks: Application to Autism Spectrum Disorder and Alzheimer’s Disease** 266 | 1. **Multi Layered-Parallel Graph Convolutional Network (ML-PGCN) for Disease Prediction** 267 | 1. **SELF-ATTENTION EQUIPPED GRAPH CONVOLUTIONS FOR DISEASE PREDICTION** 268 | - Paper 3, 4, and 5 refer to Paper 1. 269 | 270 | 1. **Metric Learning with Spectral Graph Convolutions on Brain Connectivity Networks** 271 | - refer to Paper 2. 272 | 273 | 1. **Similarity Learning with Higher-Order Proximity for Brain Network Analysis** 274 | - refer to Paper 6, and introduce higher-order information 275 | 276 | 1. **Multi-View Graph Convolutional Network and Its Applicationson Neuroimage Analysis for Parkinson’sDisease** 277 | - refer to Paper 6, with multi view 278 | 279 | 1. **Graph Saliency Maps through Spectral Convolutional Networks: Application to Sex Classification with Brain Connectivity** 280 | - refer to Paper 6, with explanation 281 | 282 | 1. **Integrative Analysis of Patient Health Records and Neuroimages via Memory-based Graph Convolutional Network** 283 | - refer to Paper 6, multi-modal 284 | 285 | 286 | 287 | ## task fMRI 288 | #### Deep learning for voxel 289 | 1. **A Multichannel 2D Convolutional Neural Network Model for Task-Evoked fMRI Data Classification** 290 | - *Jinlong Hu, et al.*, 2019. 291 | - [code](https://github.com/largeapp/M2DCNN) 292 | 293 | 1. **Deep learning of fMRI big data: a novel approach to subject-transfer decoding** 294 | - *Sotetsu Koyamada, et al.* 2015. 295 | 296 | 1. **Brains on Beats** 297 | - *Umut Guclu, et al.* 298 | - DNN, reaction to the music。 299 | 300 | 1. **deep learning for brain decoding** 301 | - *Orhan Firat, et al.* 2014. 302 | - auto encoder 303 | 304 | 1. **Learning Representation for fMRI Data Analysis using Autoencoder** 305 | - *Suwatchai Kamonsantiroj, et al.* 2016. 306 | - auto encoder, CMU 2008 data 307 | 308 | 1. **modeling task fMRI data via deep convolutional autoencoder** 309 | - *Heng Huang, et al.* 2017. 310 | - convolution autoencoder 311 | 312 | 1. **Learning Deep Temporal Representations for fMRI Brain Decoding** 313 | - *Orhan Firat, et al.* 2015. 314 | 315 | 1. **Task-specific feature extraction and classification of fMRI volumes using a deep neural network initialized with a deep belief network: Evaluation using sensorimotor tasks** 316 | - *Hojin Jang, et al.* 2017. 317 | 318 | 319 | #### Non-deep-learning for region 320 | 1. **Improving accuracy and power with transfer learning using a meta-analytic database** 321 | - *Yannick Schwartz, et al.* 2012. 322 | - transfer learning 323 | 324 | 325 | ## sMRI and other data 326 | 327 | 1. **Alzheimer’s Disease Diagnostics By Adaptation Of 3d Convolutional Network** 328 | - *Ehsan Hosseini-Asl, et al.* 2016. 329 | - sMRI 330 | 331 | 1. **Alzheimer’s disease diagnostics by a 3D deeply supervised adaptable convolutional network** 332 | - *Ehsan Hosseini Asl, et al.* 2018. 333 | - sMRI ADNI, transfer learing and adapting 334 | 335 | 1. **Alzheimer's Disease Classification Based on Combination of Multi-model Convolutional Networks** 336 | - *Fan Li, et al.* 2017. 337 | - multi 3D auto-encoding convolutinal networks 338 | - sMRI (ADNI) 339 | 340 | 1. **3D CNN-based classification using sMRI and MD-DTI images for Alzheimer disease studies** 341 | - *Alexander Khvostikov, et al.* 2018. 342 | - sMRI,DTI 343 | 344 | 1. **Deep MRI brain extraction: A 3D convolutional neural network for skull stripping** 345 | - *Jens Kleesiek, et al.* 2016. 346 | - sMRI 347 | 348 | 1. **Predicting Alzheimer’s disease: a neuroimaging study with 3D convolutional neural networks** 349 | - *Adrien Payan and Giovanni Montana* 2015. 350 | - sMRI 351 | 352 | 1. **Using structural MRI to identify bipolar disorders – 13 site machine learning study in 3020 individuals from the ENIGMA Bipolar Disorders Working Group** 353 | - [link](https://www.nature.com/articles/s41380-018-0228-9) 354 | - sMRI 355 | 356 | 1. **Sex Differences in the Adult Human Brain: Evidence from 5216 UK Biobank Participants** 357 | - [link](https://academic.oup.com/cercor/article/28/8/2959/4996558) 358 | - [Chinese analysis](http://k.sina.com.cn/article_5994750011_16550a03b00100fdqn.html?cre=tianyi&mod=pcpager_fintoutiao&loc=5&r=9&doct=0&rfunc=86&tj=none&tr=9) 359 | - sMRI 360 | 361 | 362 | ### others 363 | 1. **Automatic Detection Of Cerebral Microbleeds Via Deep Learning Based 3d Feature Representation** 364 | - *Hao Chen, et al.* 2015. 365 | - SWI 366 | 367 | 1. **learning representations from eeg with deep recurrent-convolutional neural networks** 368 | - *Pouya Bashivan, et al.* ICLR 2016. 369 | - EEG 370 | 371 | 1. **Classification of Clinical Significance of MRI Prostate Findings Using 3D Convolutional Neural Networks** 372 | - *Alireza Mehrtash, et al.* 373 | - Multi-parametric magnetic resonance imaging (mpMRI), DWI and DCE-MRI modalities 374 | 375 | 1. **Marginal Space Deep Learning: Efficient Architecture for Detection in Volumetric Image Data** 376 | - *Florin C. Ghesu, et al.* 377 | - data: supersound，non brain imaging，2D to nD 378 | 379 | ## Parkinson 380 | 1. **Discriminating cognitive status in Parkinson’s disease through functional connectomics and machine learning** 381 | - *Alexandra Abós, et al.* 2017. 382 | 383 | 1. **Graph Theoretical Metrics and Machine Learning for Diagnosis of Parkinson's Disease Using rs-fMRI** 384 | - *Amirali Kazeminejad, et al.* 2017. 385 | 386 | 1. **Joint feature-sample selection and robust diagnosis of Parkinson’s disease from MRI data** 387 | - *Ehsan Adeli, et al.* 2016 388 | 389 | 1. **Aberrant regional homogeneity in Parkinson’s disease: A voxel-wise meta-analysis of resting-state functional magnetic resonance imaging studies** 390 | - *PingLei Pan, et al.* 2016. 391 | 392 | 1. **Abnormal Spontaneous Brain Activity in Early Parkinson’s Disease With Mild Cognitive Impairment: A Resting-State fMRI Study** 393 | - *Zhijiang Wang, et al.* 2018. 394 | 395 | 1. **Can neuroimaging predict dementia in Parkinson’s disease?** 396 | - *Juliette H. Lanskey, et al.* 2018. 397 | 398 | 1. **Classification of Resting-State fMRI for Olfactory Dysfunction in Parkinson’s Disease using Evolutionary Algorithms** 399 | - *Amir Dehsarvi, et al.* 2018. 400 | 401 | 402 | 1. **Decreased interhemispheric homotopic connectivity in Parkinson's disease patients with freezing of gait: A resting state fMRI study** 403 | - *Junyi Li, et al.* 2018. 404 | 405 | 1. **Model-based and Model-free Machine Learning Techniques for Diagnostic Prediction and Classifcation of Clinical Outcomes in Parkinson’s Disease** 406 | - *Chao Gao, et al.* 2018. 407 | 408 | 1. **On the Integrity of Functional Brain Networks in Schizophrenia, Parkinson’s Disease, and Advanced Age: Evidence from Connectivity-Based Single-Subject Classification** 409 | - *Rachel N. Pl€aschke, et al.* 2017. 410 | 411 | 412 | 1. **Resting State fMRI: A Valuable Tool for Studying Cognitive Dysfunction in PD** 413 | - *Kai Li, et al.* 2018. 414 | 415 | 1. **The Parkinson’s progression markers initiative (PPMI) – establishing a PD biomarker cohort** 416 | - *Kenneth Marek, et al.* 2018. 417 | - PPMI data. 418 | 419 | 1. **Multi-View Graph Convolutional Network and Its Applications on Neuroimage Analysis for Parkinson’s Disease** 420 | - [paper](https://arxiv.org/pdf/1805.08801.pdf), 2018 421 | - DTI 422 | - multi-view, DTI with multi edge building methods, multiple graphs. 423 | 424 | 1. **A Fully-Automatic Framework for Parkinson’s Disease Diagnosis by Multi-Modality Images** 425 | - [paper](https://arxiv.org/ftp/arxiv/papers/1902/1902.09934.pdf) 426 | - sMRI, PET, multi-modal 427 | 428 | 1. **Multi-task Sparse Low-Rank Learning for Multi-classification of Parkinson’s Disease** 429 | - [paper](https://link.springer.com/chapter/10.1007/978-3-030-00889-5_41) 430 | - PPMI 431 | 432 | 1. **Parkinson's Disease Diagnosis via Joint Learning from Multiple Modalities and Relations** 433 | - [paper](https://ieeexplore.ieee.org/abstract/document/8453792) 434 | - PPMI, multi-modal 435 | 436 | 1. [more about Parkinson](https://github.com/largeapp/Brain-Image-Analysis/blob/master/parkinsons.md) 437 | 438 | ## Autism 439 | 1. **The Autism Brain Imaging Data Exchange: Towards Large-Scale Evaluation of the Intrinsic Brain Architecture in Autism** 440 | - [paper](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4162310/) 441 | - ABIDE 1 442 | 443 | 1. **Enhancing studies of the connectome in autism using the autism brain imaging data exchange II** 444 | - [paper](https://www.nature.com/articles/sdata201710) 445 | - ABIDE 2 446 | 447 | 1. **Predicting autism spectrum disorder using domain-adaptive cross-site evaluation** 448 | - *Bhaumik R, Pradhan A, Das S, et al.*, Neuroinformatics, 2018. 449 | - [paper](https://link.springer.com/article/10.1007/s12021-018-9366-0) 450 | - dataset: ABIDE 451 | 452 | 1. **Promises, Pitfalls, and Basic Guidelines for Applying Machine Learning Classifiers to Psychiatric Imaging Data, with Autism as an Example** 453 | - *Pegah Kassraian-Fard, et al.*, 2016. 454 | - [paper](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5133050/) 455 | - dataset: ABIDE 456 | 457 | 1. **Identification of autism spectrum disorder using deep learning and the ABIDE dataset** 458 | - *Heinsfeld A S, Franco A R, Craddock R C, et al.* , 2018 459 | - dataset: ABIDE 460 | - [paper](https://www.sciencedirect.com/science/article/pii/S2213158217302073) 461 | - algorithm：deep learning, DNN 462 | 463 | 1. **Age and Gender Effects on Intrinsic Connectivity in Autism Using Functional Integration and Segregation** 464 | - *Teague Rhine Henry, Gabriel S. Dichter, and Kathleen Gates*, 2017 465 | - dataset: ABIDE 466 | 467 | 1. **Enhancing the representation of functional connectivity networks by fusing multi‐view information for autism spectrum disorder diagnosis** 468 | - *Huifang Huang Xingdan Liu Yan Jin Seong‐Whan Lee Chong‐Yaw Wee Dinggang Shen*, 2018 469 | - *Human brain mapping*, February 15, 2019 470 | - dataset: ABIDE 471 | 472 | 1. **Towards Accurate Personalized Autism Diagnosis Using Different Imaging Modalities: sMRI, fMRI, and DTI** 473 | - *ElNakieb Y, Ali M T, Dekhil O, et al.* 2018 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT). 474 | - multi-modal：sMRI, fMRI, DTI 475 | 476 | 477 | 478 | ## Depression 479 | 1. **Studying depression using imaging and machine learning methods** 480 | - *Meenal J. Patel, et al.* 2015. 481 | 482 | 1. **Dynamic Resting-State Functional Connectivity in Major Depression** 483 | - *Roselinde H Kaiser, et al.* 2016. 484 | 485 | 1. **Detecting Neuroimaging Biomarkers for Depression: A Meta-analysis of Multivariate Pattern Recognition Studies** 486 | - *Joseph Kambeitz, et al.* 2016. 487 | - meta analysis 488 | 489 | 1. **Depression Disorder Classification of fMRI Data Using Sparse Low-Rank Functional Brain Network and Graph-Based Features** 490 | - *Xin Wang, et al.* 2016. 491 | 492 | 1. **Biomarker approaches in major depressive disorder evaluated in the context of current hypotheses** 493 | - *Mike C Jentsch, et al.* 2015. 494 | 495 | 1. **Accuracy of automated classification of major depressive disorder as a function of symptom severity** 496 | - *Rajamannar Ramasubbu, et al.* 2016 497 | 498 | 1. **Resting-state connectivity biomarkers define neurophysiological subtypes of depression** 499 | - *Andrew T Drysdale, et al.* 2017. 500 | - subtypes. 501 | 502 | 1. **Diagnostic classification of unipolar depression based on restingstate functional connectivity MRI: effects of generalization to a diverse sample** 503 | - *Benedikt Sundermann, et al.* 2017. 504 | 505 | 1. **Multivariate Classification of Blood Oxygen Level–Dependent fMRI Data with Diagnostic Intention: A Clinical Perspective** 506 | - *B. Sundermann, et al.* 2014. 507 | 508 | 1. **Identification of depression subtypes and relevant brain regions using a data-driven approach** 509 | - *Tomoki Tokuda, et al.* 2018. scientific reports 510 | - [link](https://www.nature.com/articles/s41598-018-32521-z) 511 | - subtypes 512 | - [media reports](https://www.medicalnewstoday.com/articles/323559.php) 513 | 514 | 515 | 516 | ## dataset 517 | 1. Human Connectome Project (HCP) 518 | - [HCP](https://www.humanconnectome.org/) 519 | 520 | 1. Openfmri & openneuro 521 | - [openneuro](https://openneuro.org/) 522 | 523 | 1. Parkinson's Progression Markers Initiative (PPMI) 524 | - [PPMI](https://www.ppmi-info.org/) 525 | 526 | 1. Autism Brain Imaging Data Exchange (ABIDE) 527 | - [ABIDE](http://fcon_1000.projects.nitrc.org/indi/abide/) 528 | 529 | ## Multiview learning 530 | #### Survey 531 | 1. **A Survey on Multi-view Learning** 532 | - *Chang Xu, Dacheng Tao, Chao Xu* 2013 533 | - [Paper](https://arxiv.org/pdf/1304.5634.pdf) 534 | 535 | 1. **Multi-view learning overview: Recent progress and new challenges** 536 | - *Jing Zhao,Xijiong Xie, Xin Xu, Shiliang Sun* 2017 537 | - [Paper](https://www.sciencedirect.com/science/article/pii/S1566253516302032) 538 | 539 | #### Tutorial 540 | 1. **Multiview Feature Learning Tutorial** *@ CVPR 2012* 541 | - [Tutorial link](http://www.cs.toronto.edu/~rfm/multiview-feature-learning-cvpr/) 542 | 543 | 1. **Multiview Feature Learning** *@ IPAM 2012* 544 | - [Tutorial link](http://helper.ipam.ucla.edu/publications/gss2012/gss2012_10790.pdf) 545 | 546 | 547 | #### MVL with Deep Learning 548 | 1. **On deep multi-view representation learning** 549 | - *Wang, Weiran, et al.* 2015. 550 | 1. **Multi-view deep network for cross-view classification** 551 | - *Kan, Meina, Shiguang Shan, and Xilin Chen* 2016. 552 | 553 | 1. **Multi-view perceptron: a deep model for learning face identity and view representations** 554 | - *Zhu, Zhenyao, et al.* 2014. 555 | 556 | 1. **A multi-view deep learning approach for cross domain user modeling in recommendation systems** 557 | - *Elkahky, Ali Mamdouh, Yang Song, and Xiaodong He* 2015. 558 | 559 | 1. **A novel channel-aware attention framework for multi-channel EEG seizure detection via multi-view deep learning** 560 | - *Yuan, Ye, et al.* 2018. 561 | 562 | 1. **Volumetric and multi-view cnns for object classification on 3d data** 563 | - *Qi, Charles R., et al.* 2016. 564 | 565 | #### Multimodal Deep Learning 566 | 1. **Multimodal deep learning** 567 | - *Ngiam, Jiquan, et al.* ICML 2011. 568 | - [paper](http://ai.stanford.edu/~ang/papers/icml11-MultimodalDeepLearning.pdf) 569 | 570 | 1. **Multimodal learning with deep boltzmann machines** 571 | - *Srivastava, Nitish, and Ruslan R. Salakhutdinov* NIPS 2012 572 | - [paper](http://120.52.51.18/www.cs.toronto.edu/~rsalakhu/papers/Multimodal_DBM.pdf) 573 | 574 | 1. **Deep multimodal learning: A survey on recent advances and trends** 575 | - *Ramachandram, Dhanesh, and Graham W. Taylor* 2017. 576 | 577 | 578 | #### Brain Image 579 | 1. **Deep Learning Approaches to Unimodal and Multimodal Analysis of Brain Imaging Data With Applications to Mental Illness** 580 | - *Calhoun, Vince, and Sergey Plis* 2018. 581 | 582 | 1. **Multimodal neuroimaging feature learning with multimodal stacked deep polynomial networks for diagnosis of Alzheimer's disease** 583 | - *Shi, Jun, et al.* 2018. 584 | 585 | 1. **Exploring diagnosis and imaging biomarkers of Parkinson's disease via iterative canonical correlation analysis based feature selection** 586 | - *Liu L , Wang Q , Adeli E , et al.* 2018. 587 | - Discussed in lab meeting (LJ Cao). 588 | 589 | -------------------------------------------------------------------------------- /parkinsons.md: -------------------------------------------------------------------------------- 1 | Open Data and other resources for Parkinson's disease data analysis. 2 | 3 | Contributed by Jinlong Hu. 4 | 5 | ## Parkinson‘s disease 6 | 1. https://en.wikipedia.org/wiki/Neurodegeneration 7 | 1. https://en.wikipedia.org/wiki/Parkinson%27s_disease 8 | 9 | 10 | ## Python Packages for Brain Imaging 11 | 1. nilearn: Neural Networks for neuro-imaging + pre-processed fMRI datasets 12 | - [nilearn](https://nilearn.github.io/introduction.html) 13 | 14 | 1. niBabel: data processing for MRI 15 | - [nibabel](http://nipy.org/nibabel/gettingstarted.html) 16 | 17 | ## Brain imaging data 18 | 1. Parkinson's Progression Markers Initiative (PPMI) 19 | - [PPMI](https://www.ppmi-info.org/) 20 | - Type: fMRI, sMRI, DTI... 21 | 22 | 1. Olfactory dysfunction and functional connectivity changes in cognitively normal Parkinson’s disease 23 | - [openfmri-parkinson](https://www.openfmri.org/dataset/ds000245/) 24 | - Type: fMRI 25 | 26 | 1. paper: White matter alterations in Parkinson's disease with normal cognition precede grey matter atrophy 27 | - [data](https://datadryad.org/resource/doi:10.5061/dryad.b4q8k) 28 | - Type: sMRI 29 | 30 | 1. diffusion-weighted images (DWI) 31 | - [data](https://www.nitrc.org/projects/parktdi/) 32 | - [code](https://github.com/CyclotronResearchCentre/parktdi_scripts) 33 | - Type: DWI 34 | 35 | 36 | ## Computerized assessments data (Gait, video, speech...) 37 | 38 | #### UCI ML repository datasets on PD: 39 | 1. Parkinson's Disease Classification Data Set (Speech) 40 | - [data](https://archive.ics.uci.edu/ml/datasets/Parkinson%27s+Disease+Classification) 41 | - data abstract: The data used in this study were gathered from 188 patients with PD (107 men and 81 women) with ages ranging from 33 to 87. 42 | - 2018. A comparative analysis of speech signal processing algorithms for Parkinson's disease classification and the use of the tunable Q-factor wavelet transform. Applied Soft Computing, [Paper](https://www.sciencedirect.com/science/article/pii/S1568494618305799?via%3Dihub) 43 | 44 | 1. Parkinson Speech Dataset with Multiple Types of Sound Recordings Data Set 45 | - [data](https://archive.ics.uci.edu/ml/datasets/Parkinson+Speech+Dataset+with++Multiple+Types+of+Sound+Recordings) 46 | - data abstract: The training data belongs to 20 Parkinson's Disease (PD) patients and 20 healthy subjects. From all subjects, multiple types of sound recordings (26) are taken. 47 | - Collection and Analysis of a Parkinson Speech Dataset with Multiple Types of Sound Recordings', IEEE Journal of Biomedical and Health Informatics, vol. 17(4), pp. 828-834, 2013. 48 | 49 | 1. Parkinson Disease Spiral Drawings Using Digitized Graphics Tablet Data Set 50 | - [data](https://archive.ics.uci.edu/ml/datasets/Parkinson+Disease+Spiral+Drawings+Using+Digitized+Graphics+Tablet) 51 | - data abstract: Handwriting database consists of 62 PWP(People with Parkinson) and 15 healthy individuals 52 | - 'Improved spiral test using digitized graphics tablet for monitoring Parkinson's disease.' The 2nd International Conference on e-Health and Telemedicine (ICEHTM-2014), pp. 171-175, 2014. 53 | 54 | #### Physiomed datasets - Keystrokes, Tremors and Gait: 55 | 1. 帕金森病的步态数据 56 | - [data-paper](https://physionet.org/pn3/gaitpdb/) 57 | 58 | 1. 计算机键盘动作作为早期帕金森病的指标 59 | - [data-paper](https://physionet.org/physiobank/database/nqmitcsxpd/) 60 | - Computer keyboard interaction as an indicator of early Parkinson's disease. Scientific Reports: 2016 61 | - SVM, AUC=0.81 62 | 63 | 1. 神经退行性疾病患者的步态数据 64 | - [data-paper](https://physionet.org/physiobank/database/gaitndd/) 65 | 66 | 1. 教学用步态数据，不适合基础研究和发表论文 67 | - [data](https://physionet.org/physiobank/database/gaitdb/) 68 | 69 | 70 | 1. 食指的静止性震颤振幅和频率 71 | - [data-paper](https://physionet.org/physiobank/database/tremordb/) 72 | - Effect of deep brain stimulation on amplitude and frequency characteristics of rest tremor in Parkinson's disease. Thalamus & Related Systems,2001 73 | 74 | 1. more at [Gait and Balance Databases](https://physionet.org/physiobank/database/) 75 | 76 | 77 | #### Others 78 | 79 | 1. Parkinson's Pose Estimation Dataset 80 | - [data-code](https://github.com/limi44/Parkinson-s-Pose-Estimation-Dataset) 81 | - 2D 82 | 83 | 84 | 85 | ## Diagnosis & Treatments 86 | 1. https://en.wikipedia.org/wiki/Management_of_Parkinson%27s_disease 87 | 1. https://en.wikipedia.org/wiki/Antiparkinson_medication 88 | 1. https://en.wikipedia.org/wiki/Dietary_management_of_Parkinson%27s_disease 89 | 90 | ## Other doc 91 | 1. https://jamanetwork.com/collections/5826/parkinson-disease 92 | 1. https://www.futurity.org/algorithm-medical-records-parkinsons-1544852/ 93 | 1. https://www.ninds.nih.gov/Disorders/All-Disorders/Parkinsons-Disease-Information-Page 94 | 1. https://www.epda.eu.com/latest/resources/pd-doc/ 95 | 1. http://www.mayfieldclinic.com/PE-PD.htm 96 | 1. https://www.michaeljfox.org/ 97 | 1. https://parkinsonsnewstoday.com/ 98 | 1. https://www.webmd.com/parkinsons-disease/parkinsons-disease-overview#1 99 | --------------------------------------------------------------------------------