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Task fMRI data analysis based on supervised stochastic coordinate coding
2017
Medical Image Analysis
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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: http://www.public.asu.edu/~jye02 ...
doi:10.1016/j.media.2016.12.003
pmid:28242473
pmcid:PMC5401655
fatcat:hfc2r72pdza4zkevtudif7znti
Learning Neural Representations of Human Cognition across Many fMRI Studies
[article]
2017
arXiv
pre-print
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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
2021
International Journal of Automation and Computing
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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
2019
Frontiers in Neuroscience
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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
2019
eLife
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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
2011
Information processing in medical imaging : proceedings of the ... conference
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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]
2019
arXiv
pre-print
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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
2006
Neural Networks
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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
2000
Neural Networks
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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]
2011
Lecture Notes in Computer Science
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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
2005
Current Opinion in Neurobiology
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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
2019
NeuroImage
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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
2021
NeuroImage
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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
2016
Journal of Neural Engineering
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[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
2019
Current Opinion in Anaesthesiology
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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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