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Local Discriminant Hyperalignment for multi-subject fMRI data alignment [article]

Muhammad Yousefnezhad, Daoqiang Zhang
<span title="2016-11-25">2016</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
However, analyzing multi-subject fMRI data requires accurate functional alignments between neuronal activities of different subjects, which can rapidly increase the performance and robustness of the final  ...  alignment for MVP analysis.  ...  Acknowledgment We thank the anonymous reviewers for comments.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1611.08366v1">arXiv:1611.08366v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/44tvjzlhnnaglnlnhmatxbmfim">fatcat:44tvjzlhnnaglnlnhmatxbmfim</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200824033608/https://arxiv.org/pdf/1611.08366v1.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/6e/65/6e6537b3fdfa3cd9de10966e4f2337d37e6e5b4d.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1611.08366v1" 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>

Local Discriminant Hyperalignment for multi-subject fMRI data alignment [article]

Muhammad Yousefnezhad, Daoqiang Zhang
<span title="2016-12-07">2016</span> <i title="Cold Spring Harbor Laboratory"> bioRxiv </i> &nbsp; <span class="release-stage" >pre-print</span>
However, analyzing multi-subject fMRI data requires accurate functional alignments between neuronal activities of different subjects, which can rapidly increase the performance and robustness of the final  ...  alignment for MVP analysis.  ...  Acknowledgment We thank the anonymous reviewers for comments.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1101/092247">doi:10.1101/092247</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/iw3mfk2osbchdnjzqlsapnaqom">fatcat:iw3mfk2osbchdnjzqlsapnaqom</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20190502121127/https://www.biorxiv.org/content/biorxiv/early/2016/12/07/092247.full.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/98/99/9899290f0a1ee885d8ad04fa5bc088fc0c983bf2.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1101/092247"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> biorxiv.org </button> </a>

Gradient Hyperalignment for multi-subject fMRI data alignment [article]

Tonglin Xu, Muhammad Yousefnezhad, Daoqiang Zhang
<span title="2018-07-07">2018</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
This paper proposes Gradient Hyperalignment (Gradient-HA) as a gradient-based functional alignment method that is suitable for multi-subject fMRI datasets with large amounts of samples and voxels.  ...  Multi-subject fMRI data analysis is an interesting and challenging problem in human brain decoding studies.  ...  Conclusion This paper proposes a gradient-based functional alignment algorithm in order to apply hyperalignment to multi-subject fMRI big data.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1807.02612v1">arXiv:1807.02612v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/di67gtcvzbbe3jbbqkphgcw2wu">fatcat:di67gtcvzbbe3jbbqkphgcw2wu</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200930111050/https://arxiv.org/ftp/arxiv/papers/1807/1807.02612.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/82/b8/82b8f5280334991f93b6199b3a018c026daa4484.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1807.02612v1" 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>

Supervised Hyperalignment for multi-subject fMRI data alignment

Muhammad Yousefnezhad, Alessandro Selvitella, Liangxiu Han, Daoqiang Zhang
<span title="">2020</span> <i title="Institute of Electrical and Electronics Engineers (IEEE)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/ebf5qcn4yrdcnjbbdfccmlscme" style="color: black;">IEEE Transactions on Cognitive and Developmental Systems</a> </i> &nbsp;
Hyperalignment has been widely employed in Multivariate Pattern (MVP) analysis to discover the cognitive states in the human brains based on multi-subject functional Magnetic Resonance Imaging (fMRI) datasets  ...  Experiments on multi-subject datasets demonstrate that SHA method achieves up to 19% better performance for multi-class problems over the state-of-the-art HA algorithms.  ...  [6] introduced a two-phase joint Singular Value Decomposition Hyperalignment (SVDHA) algorithm, where Singular Value Decomposition (SVD) is used for reducing data dimensions.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/tcds.2020.2965981">doi:10.1109/tcds.2020.2965981</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/f2w35kom5bahjpfgnmwrsn6nkm">fatcat:f2w35kom5bahjpfgnmwrsn6nkm</a> </span>
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Graph-Based Decoding Model for Functional Alignment of Unaligned fMRI Data

Weida Li, Mingxia Liu, Fang Chen, Daoqiang Zhang
<span title="2020-04-03">2020</span> <i title="Association for the Advancement of Artificial Intelligence (AAAI)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/wtjcymhabjantmdtuptkk62mlq" style="color: black;">PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE</a> </i> &nbsp;
Aggregating multi-subject functional magnetic resonance imaging (fMRI) data is indispensable for generating valid and general inferences from patterns distributed across human brains.  ...  The disparities in anatomical structures and functional topographies of human brains warrant aligning fMRI data across subjects.  ...  To address this issue, there have been several works: Chen et al. developed a Singular Vector Decomposition Hyperalignment (SVDHA), which firstly carries out a joint-SVD by grouping all subjects' fMRI  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1609/aaai.v34i03.5650">doi:10.1609/aaai.v34i03.5650</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/jbw3yf4uvncq7piiqslsatv4xy">fatcat:jbw3yf4uvncq7piiqslsatv4xy</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20201104105550/https://aaai.org/ojs/index.php/AAAI/article/download/5650/5506" 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/3a/41/3a41f43af3d5a3e5d7a66803a9f5ef5645229ce4.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1609/aaai.v34i03.5650"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> Publisher / doi.org </button> </a>

Graph-Based Decoding Model for Functional Alignment of Unaligned fMRI Data [article]

Weida Li, Mingxia Liu, Fang Chen, Daoqiang Zhang
<span title="2019-11-19">2019</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Aggregating multi-subject functional magnetic resonance imaging (fMRI) data is indispensable for generating valid and general inferences from patterns distributed across human brains.  ...  The disparities in anatomical structures and functional topographies of human brains warrant aligning fMRI data across subjects.  ...  61861130366, 61703301), the National Key R&D Program of China (Nos. 2018YFC2001600, 2018YFC2001602), the Taishan Scholar Program of Shandong Province in China, and the Shandong Natural Science Foundation for  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1905.05468v8">arXiv:1905.05468v8</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/4p2klqaojfeuzculchlbcynxyu">fatcat:4p2klqaojfeuzculchlbcynxyu</a> </span>
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fMRI-based Decoding of Visual Information from Human Brain Activity: A Brief Review

Shuo Huang, Wei Shao, Mei-Ling Wang, Dao-Qiang Zhang
<span title="2021-01-16">2021</span> <i title="Springer Science and Business Media LLC"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/nvylz6fxhjaqtckvt4zfwob2qi" style="color: black;">International Journal of Automation and Computing</a> </i> &nbsp;
In this paper, we mainly provide a comprehensive and up-to-date review of machine learning methods for analyzing neural activities with the following three aspects, i.e., brain image functional alignment  ...  However, the unprecedented scale and complexity of the fMRI data have presented critical computational bottlenecks requiring new scientific analytic tools.  ...  However, due to the heterogeneous patterns in multi-subject datasets, the fMRI data collected from different subjects must be aligned into a common space in multi-subject cognitive analysis to overcome  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/s11633-020-1263-y">doi:10.1007/s11633-020-1263-y</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/kwls2cvw4zgd5dti5d54uy6pgi">fatcat:kwls2cvw4zgd5dti5d54uy6pgi</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20210429062843/https://link.springer.com/content/pdf/10.1007/s11633-020-1263-y.pdf?error=cookies_not_supported&amp;code=40c85316-a7db-4346-85fa-baa37ea52fd0" 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/37/48/3748588dae7700313880204fec41acad67b37188.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/s11633-020-1263-y"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> springer.com </button> </a>

Hyperalignment: Modeling shared information encoded in idiosyncratic cortical topographies

James V Haxby, J Swaroop Guntupalli, Samuel A Nastase, Ma Feilong
<span title="2020-06-02">2020</span> <i title="eLife Sciences Publications, Ltd"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/en4qj5ijrbf5djxx7p5zzpjyoq" style="color: black;">eLife</a> </i> &nbsp;
Hyperalignment captures shared information by projecting pattern vectors for neural responses and connectivities into a common, high-dimensional information space, rather than by aligning topographies  ...  In this Perspective, we present the conceptual framework that motivates hyperalignment, its computational underpinnings for joint modeling of a common information space and idiosyncratic cortical topographies  ...  Higher bsMVPC than within-subject MVPC of hyperaligned data demonstrated the added power of using large multi-subject data sets for training a pattern classifier, which hyperalignment makes possible.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.7554/elife.56601">doi:10.7554/elife.56601</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/32484439">pmid:32484439</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/dbikkgynz5fx3hpxyjdmal5bn4">fatcat:dbikkgynz5fx3hpxyjdmal5bn4</a> </span>
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A Searchlight Factor Model Approach for Locating Shared Information in Multi-Subject fMRI Analysis [article]

Hejia Zhang, Po-Hsuan Chen, Janice Chen, Xia Zhu, Javier S. Turek, Theodore L. Willke, Uri Hasson, Peter J. Ramadge
<span title="2016-09-29">2016</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
There is a growing interest in joint multi-subject fMRI analysis. The challenge of such analysis comes from inherent anatomical and functional variability across subjects.  ...  This assumes a shared and time synchronized stimulus across subjects.  ...  The raw fMRI data is thus temporally aligned, but is neither anatomically nor functionally aligned across subjects.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1609.09432v1">arXiv:1609.09432v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/ocqoep76gjgtbdmi2k7q6iy5zi">fatcat:ocqoep76gjgtbdmi2k7q6iy5zi</a> </span>
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Are you thinking what I'm thinking? Synchronization of resting fMRI time-series across subjects

Anand A. Joshi, Minqi Chong, Jian Li, Soyoung Choi, Richard M. Leahy
<span title="2018-02-08">2018</span> <i title="Elsevier BV"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/sa477uo7lveh7hchpikpixop5u" style="color: black;">NeuroImage</a> </i> &nbsp;
parcellation across a population, timing recovery in task fMRI data, comparison of task and resting state data, and an application to complex naturalistic stimuli for annotation prediction.  ...  This transform is unique, invertible, efficient to compute, and preserves the connectivity structure of the original data for all subjects.  ...  Another recent technique for inter-subject comparison is hyperalignment (Haxby et al., 2011) which aligns multi-subject brain data in a high-dimensional functional space.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1016/j.neuroimage.2018.01.058">doi:10.1016/j.neuroimage.2018.01.058</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/29428580">pmid:29428580</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC6338442/">pmcid:PMC6338442</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/jcuxhfahfbebpmjyqkck7ngeem">fatcat:jcuxhfahfbebpmjyqkck7ngeem</a> </span>
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BrainIAK: The Brain Imaging Analysis Kit

Manoj Kumar, Michael J. Anderson, James W. Antony, Christopher Baldassano, Paula P. Brooks, Ming Bo Cai, Po-Hsuan Cameron Chen, Cameron T. Ellis, Gregory Henselman-Petrusek, David Huberdeau, J. Benjamin Hutchinson, Y. Peeta Li (+26 others)
<span title="2022-01-21">2022</span> <i title="Organization for Human Brain Mapping"> Aperture Neuro </i> &nbsp;
Functional magnetic resonance imaging (fMRI) offers a rich source of data for studying the neural basis of cognition.  ...  For each of the aforementioned techniques, we describe the data analysis problem that the technique is meant to solve and how it solves that problem; we also include an example Jupyter notebook for each  ...  Thus maximum likelihood estimation for this model matches (1) . In our fMRI datasets, and most multi-subject fMRI datasets available today, d m.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.52294/31bb5b68-2184-411b-8c00-a1dacb61e1da">doi:10.52294/31bb5b68-2184-411b-8c00-a1dacb61e1da</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/n3ulc3rd6vf5rixvsogbauebjq">fatcat:n3ulc3rd6vf5rixvsogbauebjq</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20190728185921/http://papers.nips.cc:80/paper/5855-a-reduced-dimension-fmri-shared-response-model.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/5c/ea/5cead56f3dd1b38bb2ab2f090a2b6aaa212cce1c.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.52294/31bb5b68-2184-411b-8c00-a1dacb61e1da"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> Publisher / doi.org </button> </a>

Common functional localizers to enhance NHP & cross-species neuroscience imaging research

Brian E Russ, Christopher I Petkov, Sze Chai Kwok, Qi Zhu, Pascal Belin, Wim Vanduffel, Suliann Ben Hamed
<span title="2021-05-25">2021</span> <i title="Elsevier BV"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/sa477uo7lveh7hchpikpixop5u" style="color: black;">NeuroImage</a> </i> &nbsp;
Functional localizers are invaluable as they can help define regions of interest, provide cross-study comparisons, and most importantly, allow for the aggregation and meta-analyses of data across studies  ...  As has been shown with the aggregation of resting-state imaging data in the original PRIME-DE submissions, we believe that the field is ready to apply the same initiative for task-based functional localizers  ...  A second approach known as Shared Response Model, which learns a joint singular value decomposition (joint-SVD) and can project subjects into a lower dimensional common space ( Chen et al., 2014 ( Chen  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1016/j.neuroimage.2021.118203">doi:10.1016/j.neuroimage.2021.118203</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/34048898">pmid:34048898</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/mllpdlbosrhmdk74myowr66ka4">fatcat:mllpdlbosrhmdk74myowr66ka4</a> </span>
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Towards a learning fingerprint: new methods and paradigms for complex motor skill learning in fMRI [article]

Eric Lacosse, Universitaet Tuebingen, Scheffler, Klaus (Prof. Dr.)
<span title="2021-04-13">2021</span>
Second, a complex motor learning task performed during an fMRI measurement was designed to relate learning effects observed in both types [...]  ...  First, basic fMRI methodological considerations were made. Machine learning methods that claimed to predict individual tfMRI task maps from rsfMRI activity were improved.  ...  about fMRI, Georg Martius for having me under his wing of thoughtful guidance and support at Autonomous Learning, Marc Himmelbach for giving me the confidence to hold it together and nail the interesting  ... 
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