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Task fMRI data analysis based on supervised stochastic coordinate coding

Jinglei Lv, Binbin Lin, Qingyang Li, Wei Zhang, Yu Zhao, Xi Jiang, Lei Guo, Junwei Han, Xintao Hu, Christine Guo, Jieping Ye, Tianming Liu
2017 Medical Image Analysis  
To bridge this gap, we here propose a novel supervised sparse representation and dictionary learning framework based on stochastic coordinate coding (SCC) algorithm for task fMRI data analysis, in which  ...  In this paper, we propose a novel supervised sparse representation and dictionary learning framework, named supervised stochastic coordinate coding (SCC), for task fMRI data analysis, in which certain  ...  To achieve this goal, we here propose an innovative supervised sparse dictionary learning framework for task fMRI data analysis based on stochastic coordinate coding (SCC:  ... 
doi:10.1016/ pmid:28242473 pmcid:PMC5401655 fatcat:hfc2r72pdza4zkevtudif7znti

Learning Neural Representations of Human Cognition across Many fMRI Studies [article]

Arthur Mensch , Danilo Bzdok, Bertrand Thirion, Gaël Varoquaux
2017 arXiv   pre-print
Finding a unified perspective for all available data calls for scalable and automated solutions to an old challenge: how to aggregate heterogeneous information on brain function into a universal cognitive  ...  a substantial performance boost to the analysis of small datasets, and can be introspected to identify universal template cognitive concepts.  ...  A first set of approaches relies on coordinate-based meta-analysis to define robust neural correlates of cognitive processes: those are extracted from the descriptions of experimentsbased on categories  ... 
arXiv:1710.11438v2 fatcat:lkeagri2lfellfwexzpqvudwye

fMRI-based Decoding of Visual Information from Human Brain Activity: A Brief Review

Shuo Huang, Wei Shao, Mei-Ling Wang, Dao-Qiang Zhang
2021 International Journal of Automation and Computing  
In addition, online resources and open research problems on brain pattern analysis are also provided for the convenience of future research.  ...  However, the unprecedented scale and complexity of the fMRI data have presented critical computational bottlenecks requiring new scientific analytic tools.  ...  based on classifiers to fMRI datasets.  ... 
doi:10.1007/s11633-020-1263-y fatcat:kwls2cvw4zgd5dti5d54uy6pgi

A Functional Data Method for Causal Dynamic Network Modeling of Task-Related fMRI

Xuefei Cao, Björn Sandstede, Xi Luo
2019 Frontiers in Neuroscience  
Using the basis function expansion approach in functional data analysis, we develop an optimization-based criterion that combines data-fitting errors and ODE fitting errors.  ...  One major statistical challenge here is to infer, in a data-driven fashion, the underlying differential equation models from fMRI data.  ...  We preprocess data using a suggested preprocessing pipeline based on FSL, a standard fMRI analysis software.  ... 
doi:10.3389/fnins.2019.00127 pmid:30872989 pmcid:PMC6402339 fatcat:eigipqb32ngs3pbxloa5km5obm

The hippocampus supports deliberation during value based decisions

Akram Bakkour, Daniela J Palombo, Ariel Zylberberg, Yul HR Kang, Allison Reid, Mieke Verfaellie, Michael N Shadlen, Daphna Shohamy
2019 eLife  
Second, we found that patients with hippocampal damage exhibited more stochastic choices and longer reaction times than controls, possibly due to their failure to construct value based on internal evidence  ...  First, using fMRI in healthy participants, we found that BOLD activity in the hippocampus increased as a function of deliberation time.  ...  The authors would like to thank Daniel Kimmel, Danique Jeurissen, and David Barack for feedback on an earlier draft of the manuscript and helpful discussions.  ... 
doi:10.7554/elife.46080 pmid:31268419 pmcid:PMC6693920 fatcat:pzkc4g4plvgk3ng7id2ze4wupe

State-space models of mental processes from fMRI

Firdaus Janoos, Shantanu Singh, Raghu Machiraju, William M Wells, Istvan A Mórocz
2011 Information processing in medical imaging : proceedings of the ... conference  
The advantages of such a model in determining the mental-state of the subject over pattern classifiers are demonstrated using an fMRI study of mental arithmetic.  ...  Moreover, they learn a mapping from the data to experimental conditions and therefore do not explain the intrinsic patterns in the data.  ...  Multivariate unsupervised approaches such as clustering [7] or matrix-based decompositions (e.g. ICA or PCA) are also popular [11] , especially for resting-state and non task-related data.  ... 
pmid:21761688 pmcid:PMC4011193 fatcat:57akny7yf5hwzgjswzifijm6va

A deep learning based tool for automatic brain extraction from functional magnetic resonance images in rodents [article]

Sidney Pontes-Filho, Annelene Gulden Dahl, Stefano Nichele, Gustavo Borges Moreno e Mello
2019 arXiv   pre-print
analysis pipeline.  ...  Because the fMRI field has grown mostly due to human studies, many new tools were developed to handle human data.  ...  We hope this tool helps neuroscientists to reduce time in preprocessing steps of their analysis of fMRI data in non-human models.  ... 
arXiv:1912.01359v2 fatcat:o2u3hr734jgc7cpgjpfevps7hq

Heterarchical reinforcement-learning model for integration of multiple cortico-striatal loops: fMRI examination in stimulus-action-reward association learning

Masahiko Haruno, Mitsuo Kawato
2006 Neural Networks  
Our fMRI results revealed that different cortico-striatal loops are operating, as suggested by the proposed model.  ...  The model makes several fMRI-testable predictions of brain activity during stimulus-action-reward association learning.  ...  Correlation analysis of fMRI data We carried out an event-related regression analysis of the fMRI data with SADRP and RPE.  ... 
doi:10.1016/j.neunet.2006.06.007 pmid:16987637 fatcat:i4fwn6jok5djjjeevtzxk2phxy

Synthetic brain imaging: grasping, mirror neurons and imitation

M.A Arbib, A Billard, M Iacoboni, E Oztop
2000 Neural Networks  
For analysis directly at the level of such data, a schema-based model may be most appropriate.  ...  Models tied to human brain imaging data often focus on a few "boxes" based on brain regions associated with exceptionally high blood flow, rather than analyzing the cooperative computation of multiple  ...  We also need to add a stochastic analysis to account for the variation in PET activity seen in the same subject on different trials.  ... 
doi:10.1016/s0893-6080(00)00070-8 pmid:11156205 fatcat:woxvazcl3ngyzalyqesgf4armu

State–Space Models of Mental Processes from fMRI [chapter]

Firdaus Janoos, Shantanu Singh, Raghu Machiraju, William M. Wells, Istvan Á. Mórocz
2011 Lecture Notes in Computer Science  
The advantages of such a model in determining the mental-state of the subject over pattern classifiers are demonstrated using an fMRI study of mental arithmetic.  ...  Moreover, they learn a mapping from the data to experimental conditions and therefore do not explain the intrinsic patterns in the data.  ...  fMRI run of one subject using the model for that subject (i.e. trained on the same data).  ... 
doi:10.1007/978-3-642-22092-0_48 fatcat:wtwb4o6esjhntnum7loc6q55lq

Computational motor control in humans and robots

Stefan Schaal, Nicolas Schweighofer
2005 Current Opinion in Neurobiology  
Computational models can provide useful guidance in the design of behavioral and neurophysiological experiments and in the interpretation of complex, high dimensional biological data.  ...  Acknowledgements This research was supported in part for S Schaal by the National Science Foundation grants ECS-0325383, IIS-0312802, IIS-0082995, ECS-0326095, ANI-0224419, a NASA grant AC#98-516, an AFOSR grant on  ...  Second, there have been several model-based experiments, in which complex models function as a guide to the experiment design and subsequent data analysis (e.g. [78, 79] ).  ... 
doi:10.1016/j.conb.2005.10.009 pmid:16271466 fatcat:vtfytqhoqnctzdrq4czcclnkk4

Ensemble learning with 3D convolutional neural networks for functional connectome-based prediction

Meenakshi Khosla, Keith Jamison, Amy Kuceyeski, Mert R. Sabuncu
2019 NeuroImage  
In this study, we critically evaluate the effect of brain parcellations on machine learning models applied to rs-fMRI data.  ...  We thus propose an ensemble learning strategy to combine the predictions from models trained on connectivity data extracted using different (e.g., stochastic) parcellations.  ...  schemes consistently perform better than individual atlas-based models and are thus a safer choice for supervised machine learning on connectomes.  ... 
doi:10.1016/j.neuroimage.2019.06.012 pmid:31220576 pmcid:PMC6777738 fatcat:iy4fxyvtmzb3ximsyqz4thm72q

Representation Learning of Resting State fMRI with Variational Autoencoder

Jung-Hoon Kim, Yizhen Zhang, Kuan Han, Zheyu Wen, Minkyu Choi, Zhongming Liu
2021 NeuroImage  
Our results demonstrate that VAE is a valuable addition to existing tools, particularly suited for unsupervised representation learning of resting state fMRI activity.  ...  Resting state functional magnetic resonance imaging (rsfMRI) data exhibits complex but structured patterns. However, the underlying origins are unclear and entangled in rsfMRI data.  ...  Data and code availability statement  ... 
doi:10.1016/j.neuroimage.2021.118423 pmid:34303794 fatcat:ctec5vqvazgsdda5jz42nhitva

Automated selection of brain regions for real-time fMRI brain–computer interfaces

Michael Lührs, Bettina Sorger, Rainer Goebel, Fabrizio Esposito
2016 Journal of Neural Engineering  
[15] and one based on real-time independent component analysis (ICA) [16] , and (re-)analyze data presented in [9] and an additional data set, in a simulated real-time framework.  ...  A standard convolution-based GLM analysis was performed on the localizer datasets using three box-car functions for the three tasks.  ... 
doi:10.1088/1741-2560/14/1/016004 pmid:27900950 fatcat:upx3edgiajaldp3mrvbhz3v4nm

Primer on machine learning

Parisa Rashidi, David A. Edwards, Patrick J. Tighe
2019 Current Opinion in Anaesthesiology  
Aside from increasing use in the analysis of electronic health record data, machine and deep learning algorithms are now key tools in the analyses of neuroimaging and facial expression recognition data  ...  In the coming years, machine learning is likely to become a key component of evidence-based medicine, yet will require additional skills and perspectives for its successful and ethical use in research  ...  The supervised task itself can be a classification task, where the target label is a nominal label (e.g. 0 = mortality, 1 = survival).  ... 
doi:10.1097/aco.0000000000000779 pmid:31408024 pmcid:PMC6785021 fatcat:7j7vvvfoezgzfotrvyks5irxne
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