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Extracting Brain Regions from Rest fMRI with Total-Variation Constrained Dictionary Learning [chapter]

Alexandre Abraham, Elvis Dohmatob, Bertrand Thirion, Dimitris Samaras, Gael Varoquaux
<span title="">2013</span> <i title="Springer Berlin Heidelberg"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/2w3awgokqne6te4nvlofavy5a4" style="color: black;">Lecture Notes in Computer Science</a> </i> &nbsp;
In this paper, we extend a network-discovery approach, namely dictionary learning, to readily extract brain regions.  ...  Our approach automatically extracts regions from rest fMRI that better explain the data and are more stable across subjects than reference decomposition or clustering methods.  ...  Acknowledgments We acknowledge funding from the NiConnect project and NIDA R21 DA034954, SUBSample project from the DIGITEO Institute, France.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/978-3-642-40763-5_75">doi:10.1007/978-3-642-40763-5_75</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/wnq23x7eo5e7znzam2a3rsq56y">fatcat:wnq23x7eo5e7znzam2a3rsq56y</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20151101195758/https://hal.inria.fr/hal-00853242/file/paper598.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/bd/e7/bde7b7e6e2eec349f949d97b36d027d760b47c0b.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/978-3-642-40763-5_75"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> springer.com </button> </a>

Benchmarking functional connectome-based predictive models for resting-state fMRI

Kamalaker Dadi, Mehdi Rahim, Alexandre Abraham, Darya Chyzhyk, Michael Milham, Bertrand Thirion, Gaël Varoquaux
<span title="2019-03-02">2019</span> <i title="Elsevier BV"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/sa477uo7lveh7hchpikpixop5u" style="color: black;">NeuroImage</a> </i> &nbsp;
The typical prediction procedure from rest-fMRI consists of three main steps: defining brain regions, representing the interactions, and supervised learning.  ...  For each step we benchmark typical choices: 8 different ways of defining regions -either pre-defined or generated from the rest-fMRI data- 3 measures to build functional connectomes from the extracted  ...  Extracting brain regions from rest fMRI with totalvariation constrained dictionary learning, in: MICCAI, p. 607. Abraham, A., Dohmatob, E., Thirion, B., Samaras, D., Varoquaux, G., 2014a.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1016/j.neuroimage.2019.02.062">doi:10.1016/j.neuroimage.2019.02.062</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/30836146">pmid:30836146</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/gyc6jxopp5gihn3alwgrf7zcge">fatcat:gyc6jxopp5gihn3alwgrf7zcge</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20190620230507/https://hal.inria.fr/hal-01824205v2/document" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/6a/f5/6af50c0e8b502e5e934de8c4e99bae9961641aee.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1016/j.neuroimage.2019.02.062"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> elsevier.com </button> </a>

Decoding the Encoding of Functional Brain Networks: an fMRI Classification Comparison of Non-negative Matrix Factorization (NMF), Independent Component Analysis (ICA), and Sparse Coding Algorithms [article]

Jianwen Xie, Pamela K. Douglas, Ying Nian Wu, Arthur L. Brody, Ariana E. Anderson
<span title="2016-07-01">2016</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
cue, or at rest, in 304 fMRI scans from 51 subjects.  ...  from a restricted number of possible brain networks.  ...  cue, or at rest, in 304 fMRI scans from 51 subjects.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1607.00435v1">arXiv:1607.00435v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/caxeab3hqfarxeaheqflmuy26y">fatcat:caxeab3hqfarxeaheqflmuy26y</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200825201644/https://arxiv.org/pdf/1607.00435v1.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/83/65/8365ff244ec219fb394759ad383ed7068a9e6be8.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1607.00435v1" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

Decoding the encoding of functional brain networks: An fMRI classification comparison of non-negative matrix factorization (NMF), independent component analysis (ICA), and sparse coding algorithms

Jianwen Xie, Pamela K. Douglas, Ying Nian Wu, Arthur L. Brody, Ariana E. Anderson
<span title="2017-03-18">2017</span> <i title="Elsevier BV"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/huhco7lwxvct3fbbxk44mmpflu" style="color: black;">Journal of Neuroscience Methods</a> </i> &nbsp;
from a restricted number of possible brain networks.  ...  These encodings are then decoded using machine learning, by using the time series weights to predict within scan whether a subject is viewing a video, listening to an audio cue, or at rest, in 304 fMRI  ...  K-SVD is a sparse data-representation algorithm to learn an over-complete dictionary, such that any single observation is constructed from a subset of the total number of dictionary elements.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1016/j.jneumeth.2017.03.008">doi:10.1016/j.jneumeth.2017.03.008</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/28322859">pmid:28322859</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC5507942/">pmcid:PMC5507942</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/ymwe4txaijae5iujxa2iffdawu">fatcat:ymwe4txaijae5iujxa2iffdawu</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20190502132253/https://cloudfront.escholarship.org/dist/prd/content/qt2n032848/qt2n032848.pdf?t=ph2oh1" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/3b/98/3b98b2b5472c6dd916157761f68c03797a8f8d5b.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1016/j.jneumeth.2017.03.008"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> elsevier.com </button> </a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5507942" title="pubmed link"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> pubmed.gov </button> </a>

Machine learning in resting-state fMRI analysis [article]

Meenakshi Khosla, Keith Jamison, Gia H. Ngo, Amy Kuceyeski, Mert R. Sabuncu
<span title="2018-12-30">2018</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
We identify three major divisions of unsupervised learning methods with regard to their applications to rs-fMRI, based on whether they discover principal modes of variation across space, time or population  ...  The goal is to provide a high-level overview of the burgeoning field of rs-fMRI from the perspective of machine learning applications.  ...  [55] adopt a dictionary learning framework for segmenting functional regions from resting-state fMRI time series.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1812.11477v1">arXiv:1812.11477v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/nd6j5jbspzh2rmxyyufdyesxom">fatcat:nd6j5jbspzh2rmxyyufdyesxom</a> </span>
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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
<span title="">2017</span> <i title="Elsevier BV"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/kpkfymbkufcnzjfc5ydyokby4y" style="color: black;">Medical Image Analysis</a> </i> &nbsp;
certain brain networks are learned with known information such as pre-defined temporal patterns and spatial network patterns, and at the same time other networks are learned automatically from data.  ...  brain networks are learned with known information such as pre-defined temporal features and spatial network patterns, and at the same time other concurrent networks are learned automatically from data.  ...  Briefly, fMRI signals extracted from a brain mask can be organized into a signal matrix S (Fig. 1a) .  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1016/j.media.2016.12.003">doi:10.1016/j.media.2016.12.003</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/28242473">pmid:28242473</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC5401655/">pmcid:PMC5401655</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/hfc2r72pdza4zkevtudif7znti">fatcat:hfc2r72pdza4zkevtudif7znti</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200506212736/http://europepmc.org/backend/ptpmcrender.fcgi?accid=PMC5401655&amp;blobtype=pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/9e/10/9e10bd26aa63d65bb1ce040f13ac56d8ef0a6574.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1016/j.media.2016.12.003"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> elsevier.com </button> </a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5401655" title="pubmed link"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> pubmed.gov </button> </a>

Fine-grain atlases of functional modes for fMRI analysis

Kamalaker Dadi, Gaël Varoquaux, Antonia Machlouzarides-Shalit, Krzysztof J. Gorgolewski, Demian Wassermann, Bertrand Thirion, Arthur Mensch
<span title="2020-07-13">2020</span> <i title="Elsevier BV"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/sa477uo7lveh7hchpikpixop5u" style="color: black;">NeuroImage</a> </i> &nbsp;
These dictionaries of functional modes (DiFuMo) are trained on millions of fMRI functional brain volumes of total size 2.4TB, spanned over 27 studies and many research groups.  ...  of fMRI across sessions and individuals, extraction of resting-state functional-connectomes biomarkers for 2,500 individuals, data compression and meta-analysis over more than 15,000 statistical maps.  ...  The dictionary D is to be learned from data.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1016/j.neuroimage.2020.117126">doi:10.1016/j.neuroimage.2020.117126</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/32673748">pmid:32673748</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/ffkcrn2q6felrjv5xf2dfvnu7i">fatcat:ffkcrn2q6felrjv5xf2dfvnu7i</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200729024339/https://pdf.sciencedirectassets.com/272508/1-s2.0-S1053811920X00139/1-s2.0-S1053811920306121/main.pdf?X-Amz-Security-Token=IQoJb3JpZ2luX2VjEEIaCXVzLWVhc3QtMSJHMEUCIQCqflUnsqLQ%2FX9lFQyN9ljBClZfMPR%2B1s2rq83kbKbl%2FgIgMqJJWkVL72Dke4mzWhl%2BCeOisP3yXFpGq9dbmYIuSTQqvQMI%2B%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2FARADGgwwNTkwMDM1NDY4NjUiDF1lCX1R1M1T5TXEWyqRAyGMdQMXGdtyQ7TjCBiNHJ6bs6j0XgSWz%2FwPaxBR9QyO%2FnpMd54y4aomdc%2BzDGCrNrTm8%2BoUvjs6SFe0E%2FyWBF87jma3n4CeU2Iw%2BXxOTyeZt4Cg3ZdtVtk2zcql8K%2FCfHNpVSUobBAWXVbftvfrnpcmRmoM51quXsvYVLoUZXcQEfXw2kcCK6tnoq7ed8XKhqLIRr71UoumTzmJQHUgDi%2Ff%2BnUANzSWepSlghPtJa0Htjtv%2BAnkB8jCgl0sRvE24U%2FpohbuXbMcz1PBa8%2BvbXMv0rzVwZp19gAjzJImkJ2g9aWQSoV1WF8L45s4b4pO5mzRwEOeCtJcQQ5ryYz2gzGOtU8swA%2FehCBgu%2FETWewauVCZeYVCn6JfHeCXE9sQEzAU8M1QqpS03iVYVf%2FxmpKSGYxIsgB4jJznCuU5DHQDhYfF9kVWyLPILt9qmQucDutI1aVwu4pHpiTmaITkzaVWM%2BEKOLbELz21vxoEERfUueL6Rj28SvUMDZCaS4cPOoJR0wEk2tQ%2Ba7o97r6z9h7mMIykg%2FkFOusB93N38VRVex7UfUtZO2ywpu181OC2%2Fnv1FhD6anv4s3fVLHuDVZNdSz0MdoFm1JD%2BV72TAK26CRnj6uEUQgX9NAOYlKIqGsJvc662lc%2FHtP%2F51pnv%2FMlTe2WfF2EHM15aCH4WnOwUbjxxkIupF5nrPTstnFszxTHJ2sTKI1%2FjipUpUBxADABaB4o5TyFmUw6FVSi%2BLMblDG%2B7x7uCv1eoN2CG5KBLViHQDaAlnKf1OMqpi3hhqNF2XGN2NYXt32hzpLiSuC4UCZKWhhZDz52f9xVWjx%2B6FbZW0Gjxpw%2BI887E9oB57LWSykQpLw%3D%3D&amp;X-Amz-Algorithm=AWS4-HMAC-SHA256&amp;X-Amz-Date=20200729T024332Z&amp;X-Amz-SignedHeaders=host&amp;X-Amz-Expires=300&amp;X-Amz-Credential=ASIAQ3PHCVTY4OTDBUHO%2F20200729%2Fus-east-1%2Fs3%2Faws4_request&amp;X-Amz-Signature=07121489546903351ac4dde45d8cf7b4a769c7b74d3d064909ed96abaf39f2e1&amp;hash=5e18990763b009ce10e069c35caf89b17289513be39047aaad9793dec3012bb9&amp;host=68042c943591013ac2b2430a89b270f6af2c76d8dfd086a07176afe7c76c2c61&amp;pii=S1053811920306121&amp;tid=spdf-ac7be552-8942-408d-b69a-30e8419a63e4&amp;sid=61e481e36b6c004a3f58dda519e25d59cb3fgxrqa&amp;type=client" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1016/j.neuroimage.2020.117126"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> elsevier.com </button> </a>

Fine-grain atlases of functional modes for fMRI analysis [article]

Kamalaker Dadi, Antonia Machlouzarides-Shalit, Krzysztof J. Gorgolewski, Demian Wassermann
<span title="2020-03-05">2020</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
These dictionaries of functional modes (DiFuMo) are trained on millions of fMRI functional brain volumes of total size 2.4TB, spanned over 27 studies and many research groups.  ...  of fMRI across sessions and individuals, extraction of resting-state functional-connectomes biomarkers for 2,500 individuals, data compression and meta-analysis over more than 15,000 statistical maps.  ...  The dictionary D is to be learned from data.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2003.05405v1">arXiv:2003.05405v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/3ed452xtnnfylbvcveixb3hdqa">fatcat:3ed452xtnnfylbvcveixb3hdqa</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200320181548/https://arxiv.org/pdf/2003.05405v1.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2003.05405v1" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

Formal Models of the Network Co-occurrence Underlying Mental Operations

Danilo Bzdok, Gaël Varoquaux, Olivier Grisel, Michael Eickenberg, Cyril Poupon, Bertrand Thirion, Danielle S Bassett
<span title="2016-06-16">2016</span> <i title="Public Library of Science (PLoS)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/ch57atmlprauhhbqdf7x4ytejm" style="color: black;">PLoS Computational Biology</a> </i> &nbsp;
Hence, the dichotomic view predicts that extracting network components from resting-state data will yield a dictionary of network definitions insufficient to delineate task-specific network compositions  ...  Hence, the manifold view predicts that network components extracted from either rest or task data will perform similarly in capturing task-specific neural activity.  ...  The term "network" henceforth refers to spatiotemporal modes of variation extracted from time series of fMRI activity whose weighted linear combination sum up to whole-brain activity patterns [6, 47,  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1371/journal.pcbi.1004994">doi:10.1371/journal.pcbi.1004994</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/27310288">pmid:27310288</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC4911040/">pmcid:PMC4911040</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/2cj2eehfh5emvj5ggkihbaegry">fatcat:2cj2eehfh5emvj5ggkihbaegry</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20171015153252/http://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1004994&amp;type=printable" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/3d/5f/3d5f94e799e3cf74036d41dc8ea53c9f8e234ebe.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1371/journal.pcbi.1004994"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> plos.org </button> </a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4911040" title="pubmed link"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> pubmed.gov </button> </a>

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

Arthur Mensch , Danilo Bzdok, Bertrand Thirion, Gaël Varoquaux
<span title="2017-11-11">2017</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
We cast this challenge in a machine-learning approach to predict conditions from statistical brain maps across different studies.  ...  For this, we leverage multi-task learning and multi-scale dimension reduction to learn low-dimensional representations of brain images that carry cognitive information and can be robustly associated with  ...  To extract sparse dictionaries, we perform dictionary learning on resting state time-series from the 900 subject of the HCP900 dataset.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1710.11438v2">arXiv:1710.11438v2</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/lkeagri2lfellfwexzpqvudwye">fatcat:lkeagri2lfellfwexzpqvudwye</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200904143032/https://arxiv.org/pdf/1710.11438v2.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/84/22/8422fd9e5129689f0c4b9cf411989ea9e8fe774b.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1710.11438v2" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

A Matrix Autoencoder Framework to Align the Functional and Structural Connectivity Manifolds as Guided by Behavioral Phenotypes [article]

Niharika Shimona D'Souza, Mary Beth Nebel, Deana Crocetti, Nicholas Wymbs, Joshua Robinson, Stewart Mostofsky, Archana Venkataraman
<span title="2021-07-09">2021</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
We propose a novel matrix autoencoder to map functional connectomes from resting state fMRI (rs-fMRI) to structural connectomes from Diffusion Tensor Imaging (DTI), as guided by subject-level phenotypic  ...  We demonstrate that the model reliably recovers structural connectivity patterns across individuals, while robustly extracting predictive and interpretable brain biomarkers in a cross-validated setting  ...  Likewise, resting-state fMRI (rs-fMRI) captures inter-regional co-activation, which can be used to infer functional connectivity [25, 13] .  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2105.14409v2">arXiv:2105.14409v2</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/ra3xixhzjfdohg7tql624ql64a">fatcat:ra3xixhzjfdohg7tql624ql64a</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20210715052918/https://arxiv.org/pdf/2105.14409v2.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/a1/9e/a19ea0068cb8a47ea1c1bbf0f92fc59fb985c58f.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2105.14409v2" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

Unsupervised Learning of Functional Network Dynamics in Resting State fMRI [chapter]

Harini Eavani, Theodore D. Satterthwaite, Raquel E. Gur, Ruben C. Gur, Christos Davatzikos
<span title="">2013</span> <i title="Springer Berlin Heidelberg"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/2w3awgokqne6te4nvlofavy5a4" style="color: black;">Lecture Notes in Computer Science</a> </i> &nbsp;
Our model generates these covariance matrices from a common but unknown set of sparse basis networks, which capture the range of functional activity co-variations of regions of interest (ROIs).  ...  Distinct hidden states arise due to a variation in the strengths of these basis networks. Thus, our generative model combines a HMM framework with sparse basis learning of positive definite matrices.  ...  Fig. 7 . 7 Four HMM States obtained from resting state fMRI data.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/978-3-642-38868-2_36">doi:10.1007/978-3-642-38868-2_36</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/24683988">pmid:24683988</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC3974209/">pmcid:PMC3974209</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/pnai4b6v4jajpjx5smxrlny6km">fatcat:pnai4b6v4jajpjx5smxrlny6km</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200206231205/http://europepmc.org/backend/ptpmcrender.fcgi?accid=PMC3974209&amp;blobtype=pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/84/dd/84ddeae2965dd12e68e33b9ef33d18e64cfbae20.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/978-3-642-38868-2_36"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> springer.com </button> </a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3974209" title="pubmed link"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> pubmed.gov </button> </a>

Assessing effects of prenatal alcohol exposure using group-wise sparse representation of fMRI data

Jinglei Lv, Xi Jiang, Xiang Li, Dajiang Zhu, Shijie Zhao, Tuo Zhang, Xintao Hu, Junwei Han, Lei Guo, Zhihao Li, Claire Coles, Xiaoping Hu (+1 others)
<span title="">2015</span> <i title="Elsevier BV"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/pqgqyradj5fpvb5x22wo3ecaui" style="color: black;">Psychiatry Research : Neuroimaging</a> </i> &nbsp;
Specifically, a common time series signal dictionary is learned from the aggregated fMRI signals of all three groups of subjects, and then the weight coefficient matrices (named statistical coefficient  ...  map (SCM)) associated with each common dictionary were statistically assessed for each group separately.  ...  Second, the columns d 1 ,d 2 , ……d m are constrained with Eq.(5). This is implemented with an iterative normalization of dictionary atoms during learning.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1016/j.pscychresns.2015.07.012">doi:10.1016/j.pscychresns.2015.07.012</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/26195294">pmid:26195294</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC4536108/">pmcid:PMC4536108</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/223mdelgvnbhddu2r3bw4qhosq">fatcat:223mdelgvnbhddu2r3bw4qhosq</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200209224620/http://europepmc.org/backend/ptpmcrender.fcgi?accid=PMC4536108&amp;blobtype=pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/ee/25/ee250b8390b0eb7569e981e0a14dae4313349933.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1016/j.pscychresns.2015.07.012"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> elsevier.com </button> </a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4536108" title="pubmed link"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> pubmed.gov </button> </a>

Connectomic Profiles for Individualized Resting State Networks and Regions of Interest

Kaiming Li, Jason Langley, Zhihao Li, Xiaoping P. Hu
<span title="">2015</span> <i title="Mary Ann Liebert Inc"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/v4t2k6us4zdn7imahrf7iujoiq" style="color: black;">Brain Connectivity</a> </i> &nbsp;
It first employed a dual-sparsity dictionary learning algorithm to extract group connectomic profiles of ROIs and RSNs from noisy and high-dimensional fMRI data, with special attention to the well-known  ...  Functional connectivity analysis of human brain resting state functional magnetic resonance imaging (rsfMRI) data and resultant functional networks, or RSNs, have drawn increasing interest in both research  ...  First, groupwise quantitative connectomic profiles for functional ROIs were extracted from large multi-subject datasets using a dictionary learning algorithm.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1089/brain.2014.0229">doi:10.1089/brain.2014.0229</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/25090040">pmid:25090040</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC4361391/">pmcid:PMC4361391</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/5smk53rz2nci3miu4j7qgkogbq">fatcat:5smk53rz2nci3miu4j7qgkogbq</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200430075521/http://europepmc.org/backend/ptpmcrender.fcgi?accid=PMC4361391&amp;blobtype=pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/0f/6b/0f6b762c557268446c59bc820e6f5bdcf734e7d5.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1089/brain.2014.0229"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> Publisher / doi.org </button> </a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4361391" title="pubmed link"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> pubmed.gov </button> </a>

Deep sr-DDL: Deep Structurally Regularized Dynamic Dictionary Learning to Integrate Multimodal and Dynamic Functional Connectomics data for Multidimensional Clinical Characterizations [article]

Niharika Shimona D'Souza, Mary Beth Nebel, Deana Crocetti, Nicholas Wymbs, Joshua Robinson, Stewart H. Mostofsky, Archana Venkataraman
<span title="2020-08-27">2020</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
We propose a novel integrated framework that jointly models complementary information from resting-state functional MRI (rs-fMRI) connectivity and diffusion tensor imaging (DTI) tractography to extract  ...  The generative component is a structurally-regularized Dynamic Dictionary Learning (sr-DDL) model that decomposes the dynamic rs-fMRI correlation matrices into a collection of shared basis networks and  ...  More specifically, resting state fMRI (rs-fMRI) is acquired in the absence of a task paradigm, thus allowing us to probe the spontaneous co-activation patterns in the brain.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2008.12410v1">arXiv:2008.12410v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/54f6z2o2krevtkiq53eboldnce">fatcat:54f6z2o2krevtkiq53eboldnce</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200912060312/https://arxiv.org/pdf/2008.12410v1.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2008.12410v1" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>
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