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Performance Evaluation of Big Data Processing Strategies for Neuroimaging [article]

Valérie Hayot-Sasson, Shawn T Brown, Tristan Glatard
2019 arXiv   pre-print
We conclude that Big Data processing strategies are worth developing for neuroimaging applications.  ...  However, the adoption of Big Data processing strategies by neuroimaging processing engines remains limited.  ...  Section V concludes on the relevance of Big Data processing strategies for neuroimaging applications. II.  ... 
arXiv:1812.06492v2 fatcat:fap4d26fq5gt5dn7tcs6loczuu

A performance comparison of Dask and Apache Spark for data-intensive neuroimaging pipelines [article]

Mathieu Dugré, Valérie Hayot-Sasson, Tristan Glatard
2019 arXiv   pre-print
We compare two popular Big Data engines with Python APIs, Apache Spark and Dask, for their runtime performance in processing neuroimaging pipelines.  ...  Our evaluation uses two synthetic pipelines processing the 81GB BigBrain image, and a real pipeline processing anatomical data from more than 1,000 subjects.  ...  We warmly thank Compute Canada and its regional center Westgrid for providing the cloud infrastructure used in these experiments, and the McGill Center for Integrative Neuroscience for giving us access  ... 
arXiv:1907.13030v3 fatcat:xmkwiasxyndtlizwrtnnjvxpie

Performance benefits of Intel(R) OptaneTM DC persistent memory for the parallel processing of large neuroimaging data [article]

Valerie Hayot-Sasson, Shawn T Brown, Tristan Glatard
2019 arXiv   pre-print
Whereas Big Data engines have significantly reduced application performance penalties with respect to data movement, their applied strategies (e.g. data locality, in-memory computing and lazy evaluation  ...  In this paper we evaluate the performance advantage brought by Intel(R) OptaneTM DC persistent memory for the processing of large neuroimaging datasets using the two available configurations modes: Memory  ...  Whereas Big Data engines have significantly reduced application performance penalties with respect to data movement, their applied strategies (e.g. data locality, in-memory computing and lazy evaluation  ... 
arXiv:1912.11794v1 fatcat:pdyl2rbya5bg7mg3mqs2naqbwu

Compressive Big Data Analytics: An Ensemble Meta-Algorithm for High-dimensional Multisource Datasets [article]

Simeone Marino, Yi Zhao, Nina Zhou, Yiwang Zhou, Arthur Toga, Lu Zhao, Yingsi Jian, Yehu Chen, Qiucheng Wu, Jessica Wild, Brandon Cummings, Ivo Dinov
2020 bioRxiv   pre-print
Open-science offers hope for tackling some of the challenges associated with Big Data and team-based scientific discovery.  ...  This study expands the functionality and utility of an ensemble semi-supervised machine learning technique called Compressive Big Data Analytics (CBDA).  ...  Acknowledgments Colleagues from the Statistics Online Computational Resource (SOCR), Center for Complexity and Self-management of Chronic Disease (CSCD), Big Data Discovery Science (BDDS), and the Michigan  ... 
doi:10.1101/2020.01.20.912485 fatcat:el4abuyyxvfavkm52klikx2xuy

Predictive Big Data Analytics: A Study of Parkinson's Disease Using Large, Complex, Heterogeneous, Incongruent, Multi-Source and Incomplete Observations

Ivo D. Dinov, Ben Heavner, Ming Tang, Gustavo Glusman, Kyle Chard, Mike Darcy, Ravi Madduri, Judy Pa, Cathie Spino, Carl Kesselman, Ian Foster, Eric W. Deutsch (+9 others)
2016 PLoS ONE  
techniques for data management, processing, visualization and interpretation.  ...  To explore PD risk using Big Data methodology, we jointly processed complex PPMI imaging, genetics, clinical and demographic data.  ...  Performed the experiments: IDD MT BH. Analyzed the data: IDD MT BH K. Chard RM. Contributed reagents/materials/analysis tools: IDD MT BH K. Chard RM GG K. Clark. Wrote the paper: IDD BH MT GG K.  ... 
doi:10.1371/journal.pone.0157077 pmid:27494614 pmcid:PMC4975403 fatcat:22f7ob34azftha4hc5zttibpv4

Connectomics and new approaches for analyzing human brain functional connectivity

R Cameron Craddock, Rosalia L Tungaraza, Michael P Milham
2015 GigaScience  
The number and complexity of functional interactions within the connectome and the large amounts of data required to study them position functional connectivity research as a "big data" problem.  ...  Maximizing the degree to which knowledge about human brain function can be extracted from the connectome will require developing a new generation of neuroimaging analysis algorithms and tools.  ...  Xin Di for their useful comments, which improved the manuscript.  ... 
doi:10.1186/s13742-015-0045-x pmid:25810900 pmcid:PMC4373299 fatcat:5rmlp74mbffudp7ipo4sgobxea

HAFNI-enabled largescale platform for neuroimaging informatics (HELPNI)

Milad Makkie, Shijie Zhao, Xi Jiang, Jinglei Lv, Yu Zhao, Bao Ge, Xiang Li, Junwei Han, Tianming Liu
2015 Brain Informatics  
Given the size of fMRI data are growing explosively recently due to the advancement of neuroimaging technologies, an effective and efficient fMRI informatics system which can process and analyze fMRI big  ...  Using efficient sampling module, the experimental results demonstrate that our HELPNI system has superior performance than other systems for large-scale fMRI data in terms of processing and storing the  ...  Acknowledgments We thank all investigators contributing data to the 1000 Functional Connectomes project, without whom this analysis could not have been performed. T.  ... 
doi:10.1007/s40708-015-0024-0 pmid:27747565 pmcid:PMC4737667 fatcat:q6bx2x34ifhfvcrpi5tc62jy6q

Federated Multi-Site Normative Modeling using Hierarchical Bayesian Regression [article]

Seyed Mostafa Kia, Hester Huijsdens, Saige Rutherford, Richard Dinga, Thomas Wolfers, Maarten Mennes, Ole Andreassen, Lars T. Westlye, Christian F Beckmann, Andre F Marquand
2021 bioRxiv   pre-print
The proposed method completes the life-cycle of normative modeling by providing the possibilities to learn, update, and adapt the model parameters on decentralized neuroimaging data.  ...  Clinical neuroimaging data availability has grown substantially in the last decade, providing the potential for studying heterogeneity in clinical cohorts on a previously unprecedented scale.  ...  Considering the real-world limitations in developing and deploying a reference normative model (multi-site data and data privacy/access issues), the modeling approach that is employed for estimating the  ... 
doi:10.1101/2021.05.28.446120 fatcat:iqzwoxwyjbcuheixe3zpubvdqa

Index [chapter]

2021 Advances in Learning and Behavioral Disabilities  
ambiguous standards for progress, 197-199 big claims, little evidence, 193-195 diverse educational needs, 196 eclectic approaches to instruction, 197 gradual process of scientific advancement,  ...  in special education, 2-4 neuroimaging techniques limitations for understanding, 85-86 preview of volume, 4-7 Machine learning better prediction of dyslexia risk using, 89 Magnetic resonance  ... 
doi:10.1108/s0735-004x20210000031014 fatcat:axtmns62jjektmuo2oaioeafja

Multi-Layer Picture of Neurodegenerative Diseases: Lessons from the Use of Big Data through Artificial Intelligence

Andrea Termine, Carlo Fabrizio, Claudia Strafella, Valerio Caputo, Laura Petrosini, Carlo Caltagirone, Emiliano Giardina, Raffaella Cascella
2021 Journal of Personalized Medicine  
In the big data era, artificial intelligence techniques have been applied to tackle traditional issues in the study of neurodegenerative diseases.  ...  In this context, the integration of multi-omics, neuroimaging, and electronic health records data can be exploited using deep learning methods to provide the most accurate representation of patients possible  ...  Acknowledgments: We thank Project MUSA CNR (FOE 2019) for supporting the making of this review article. Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/jpm11040280 pmid:33917161 pmcid:PMC8067806 fatcat:tbnwt53hj5a6dcboyen3pu5p7m

Distributed rank-1 dictionary learning: Towards fast and scalable solutions for fMRI big data analytics

Milad Makkie, Xiang Li, Tianming Liu, Shannon Quinn, Binbin Lin, Jieping Ye
2016 2016 IEEE International Conference on Big Data (Big Data)  
of fMRI data.  ...  such fMRI big data are extremely limited and largely under-discussed.  ...  The performance of the proposed framework on both individual and group-wise data from Human Connectome Project (HCP) Q1 release [10] shown that it is a suitable solution for fMRI big data analytics.  ... 
doi:10.1109/bigdata.2016.7841000 dblp:conf/bigdataconf/MakkieLLQLY16 fatcat:zk7qohvpi5ftjakakmfxtj7u2q

Hierarchical Bayesian Regression for Multi-Site Normative Modeling of Neuroimaging Data [article]

Seyed Mostafa Kia, Hester Huijsdens, Richard Dinga, Thomas Wolfers, Maarten Mennes, Ole A. Andreassen, Lars T. Westlye, Christian F. Beckmann, Andre F. Marquand
2020 arXiv   pre-print
Our experimental results confirm the superiority of HBR in deriving more accurate normative ranges on large multi-site neuroimaging data compared to widely used methods.  ...  , HBR closes the technical loop for applying normative modeling as a medical tool for the diagnosis and prognosis of mental disorders.  ...  Acknowledgements This work was supported by the Dutch Organisation for Scientific Research via Vernieuwingsimpuls VIDI fellowships to AM (016.156.415) and CB (864.12.003).  ... 
arXiv:2005.12055v1 fatcat:cpirihxvkrhmjd4jj4b4iappbe

Ten years of image analysis and machine learning competitions in dementia [article]

Esther E. Bron, Stefan Klein, Annika Reinke, Janne M. Papma, Lena Maier-Hein, Daniel C. Alexander, Neil P. Oxtoby
2022 arXiv   pre-print
Grand challenges would be an ideal venue for assessing the generalizability of algorithm performance to unseen data of other cohorts.  ...  Despite high state-of-the-art performances, most of the methods evaluated by the challenges are not clinically used.  ...  Winning algorithms generally had special attention for pre-processing of the data and combined a wide range of input features.  ... 
arXiv:2112.07922v2 fatcat:tfexsjejife3vav4ywx3nmifdy

A Connectome Computation System for discovery science of brain

Ting Xu, Zhi Yang, Lili Jiang, Xiu-Xia Xing, Xi-Nian Zuo
2015 Science Bulletin  
Beyond the ambitious aims of these projects, a fundamental challenge is the need for an efficient, robust, reliable and easy-to-use pipeline to mine such large neuroscience datasets.  ...  Here, we introduce a computational pipeline-namely the Connectome Computation System (CCS)-for discovery science of human brain connectomes at the macroscale with multimodal magnetic resonance imaging  ...  Conflict of interest The authors declare that they have no conflicts of interest.  ... 
doi:10.1007/s11434-014-0698-3 fatcat:7vvburbmdbha3cs57p7o45256u

Ten simple rules for predictive modeling of individual differences in neuroimaging

Dustin Scheinost, Stephanie Noble, Corey Horien, Abigail S. Greene, Evelyn MR. Lake, Mehraveh Salehi, Siyuan Gao, Xilin Shen, David O'Connor, Daniel S. Barron, Sarah W. Yip, Monica D. Rosenberg (+1 others)
2019 NeuroImage  
We hope these ten rules will increase the use of predictive models with neuroimaging data.  ...  Here, we offer practical advice and examples based on functional magnetic resonance imaging (fMRI) functional connectivity data for implementing these approaches.  ...  and Centers that support the NIH Blueprint for Neuroscience Research; and by the McDonnell Center for Systems Neuroscience at Washington University.  ... 
doi:10.1016/j.neuroimage.2019.02.057 pmid:30831310 pmcid:PMC6521850 fatcat:iekh6xmkwbbbjo7atbctftjnse
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