TheHiveDB Image Data Management and Analysis Framework

Overview

Journal Front Neuroinform

Specialty Neurology

Date 2014 Jan 17

PMID 24432000

Citations 58

Authors

J-Sebastian Muehlboeck

Eric Westman

Andrew Simmons

Affiliations

Soon will be listed here.

Abstract

The hive database system (theHiveDB) is a web-based brain imaging database, collaboration, and activity system which has been designed as an imaging workflow management system capable of handling cross-sectional and longitudinal multi-center studies. It can be used to organize and integrate existing data from heterogeneous projects as well as data from ongoing studies. It has been conceived to guide and assist the researcher throughout the entire research process, integrating all relevant types of data across modalities (e.g., brain imaging, clinical, and genetic data). TheHiveDB is a modern activity and resource management system capable of scheduling image processing on both private compute resources and the cloud. The activity component supports common image archival and management tasks as well as established pipeline processing (e.g., Freesurfer for extraction of scalar measures from magnetic resonance images). Furthermore, via theHiveDB activity system algorithm developers may grant access to virtual machines hosting versioned releases of their tools to collaborators and the imaging community. The application of theHiveDB is illustrated with a brief use case based on organizing, processing, and analyzing data from the publically available Alzheimer Disease Neuroimaging Initiative.

Citing Articles

Biological signatures in the Alzheimer's continuum discriminate between diagnosis-related and -unrelated associations to ATN categories.

Alanko V, Mravinacova S, Hall A, Hagman G, Mohanty R, Westman E Brain Commun. 2025; 7(2):fcaf078.

PMID: 40046342 PMC: 11881062. DOI: 10.1093/braincomms/fcaf078.

Biological brain age and resilience in cognitively unimpaired 70-year-old individuals.

Marseglia A, Dartora C, Samuelsson J, Poulakis K, Mohanty R, Shams S Alzheimers Dement. 2024; 21(2):e14435.

PMID: 39704304 PMC: 11848408. DOI: 10.1002/alz.14435.

Divergent neurodegenerative patterns: Comparison of [F] fluorodeoxyglucose-PET- and MRI-based Alzheimer's disease subtypes.

Wheatley S, Mohanty R, Poulakis K, Levin F, Muehlboeck J, Nordberg A Brain Commun. 2024; 6(6):fcae426.

PMID: 39703327 PMC: 11656166. DOI: 10.1093/braincomms/fcae426.

CSF levels of brain-derived proteins correlate with brain ventricular volume in cognitively healthy 70-year-olds.

Bergstrom S, Mravinacova S, Lindberg O, Zettergren A, Westman E, Wahlund L Clin Proteomics. 2024; 21(1):65.

PMID: 39668376 PMC: 11636040. DOI: 10.1186/s12014-024-09517-1.

Optimizing neuroscience data management by combining REDCap, BIDS and SQLite: a case study in Deep Brain Stimulation.

Stawiski M, Bucciarelli V, Vogel D, Hemm S Front Neuroinform. 2024; 18:1435971.

PMID: 39301120 PMC: 11410584. DOI: 10.3389/fninf.2024.1435971.

References

Marcus D, Olsen T, Ramaratnam M, Buckner R . The Extensible Neuroimaging Archive Toolkit: an informatics platform for managing, exploring, and sharing neuroimaging data. Neuroinformatics. 2007; 5(1):11-34. DOI: 10.1385/ni:5:1:11. View

Thambisetty M, Simmons A, Velayudhan L, Hye A, Campbell J, Zhang Y . Association of plasma clusterin concentration with severity, pathology, and progression in Alzheimer disease. Arch Gen Psychiatry. 2010; 67(7):739-48. PMC: 3111021. DOI: 10.1001/archgenpsychiatry.2010.78. View

Van Horn J, Toga A . Is it time to re-prioritize neuroimaging databases and digital repositories?. Neuroimage. 2009; 47(4):1720-34. PMC: 2754579. DOI: 10.1016/j.neuroimage.2009.03.086. View

Lovestone S, Francis P, Strandgaard K . Biomarkers for disease modification trials--the innovative medicines initiative and AddNeuroMed. J Nutr Health Aging. 2007; 11(4):359-61. View

Weiner M, Aisen P, Jack Jr C, Jagust W, Trojanowski J, Shaw L . The Alzheimer's disease neuroimaging initiative: progress report and future plans. Alzheimers Dement. 2010; 6(3):202-11.e7. PMC: 2927112. DOI: 10.1016/j.jalz.2010.03.007. View

Jack Jr C, Bernstein M, Fox N, Thompson P, Alexander G, Harvey D . The Alzheimer's Disease Neuroimaging Initiative (ADNI): MRI methods. J Magn Reson Imaging. 2008; 27(4):685-91. PMC: 2544629. DOI: 10.1002/jmri.21049. View

Dinov I, Lozev K, Petrosyan P, Liu Z, Eggert P, Pierce J . Neuroimaging study designs, computational analyses and data provenance using the LONI pipeline. PLoS One. 2010; 5(9). PMC: 2946935. DOI: 10.1371/journal.pone.0013070. View

Simmons A, Westman E, Muehlboeck S, Mecocci P, Vellas B, Tsolaki M . The AddNeuroMed framework for multi-centre MRI assessment of Alzheimer's disease: experience from the first 24 months. Int J Geriatr Psychiatry. 2010; 26(1):75-82. DOI: 10.1002/gps.2491. View

Fischl B, van der Kouwe A, Destrieux C, Halgren E, Segonne F, Salat D . Automatically parcellating the human cerebral cortex. Cereb Cortex. 2003; 14(1):11-22. DOI: 10.1093/cercor/bhg087. View

10.

Obeid J, McGraw C, Minor B, Conde J, Pawluk R, Lin M . Procurement of shared data instruments for Research Electronic Data Capture (REDCap). J Biomed Inform. 2012; 46(2):259-65. PMC: 3600393. DOI: 10.1016/j.jbi.2012.10.006. View

11.

Westman E, Wahlund L, Foy C, Poppe M, Cooper A, Murphy D . Magnetic resonance imaging and magnetic resonance spectroscopy for detection of early Alzheimer's disease. J Alzheimers Dis. 2011; 26 Suppl 3:307-19. DOI: 10.3233/JAD-2011-0028. View

12.

Das S, Zijdenbos A, Harlap J, Vins D, Evans A . LORIS: a web-based data management system for multi-center studies. Front Neuroinform. 2012; 5:37. PMC: 3262165. DOI: 10.3389/fninf.2011.00037. View

13.

Westman E, Muehlboeck J, Simmons A . Combining MRI and CSF measures for classification of Alzheimer's disease and prediction of mild cognitive impairment conversion. Neuroimage. 2012; 62(1):229-38. DOI: 10.1016/j.neuroimage.2012.04.056. View

14.

Schwartz Y, Barbot A, Thyreau B, Frouin V, Varoquaux G, Siram A . PyXNAT: XNAT in Python. Front Neuroinform. 2012; 6:12. PMC: 3354345. DOI: 10.3389/fninf.2012.00012. View

15.

Liu Y, Paajanen T, Westman E, Zhang Y, Wahlund L, Simmons A . APOE ε2 allele is associated with larger regional cortical thicknesses and volumes. Dement Geriatr Cogn Disord. 2010; 30(3):229-37. DOI: 10.1159/000320136. View

16.

Westman E, Aguilar C, Muehlboeck J, Simmons A . Regional magnetic resonance imaging measures for multivariate analysis in Alzheimer's disease and mild cognitive impairment. Brain Topogr. 2012; 26(1):9-23. PMC: 3536978. DOI: 10.1007/s10548-012-0246-x. View

17.

Westman E, Simmons A, Muehlboeck J, Mecocci P, Vellas B, Tsolaki M . AddNeuroMed and ADNI: similar patterns of Alzheimer's atrophy and automated MRI classification accuracy in Europe and North America. Neuroimage. 2011; 58(3):818-28. DOI: 10.1016/j.neuroimage.2011.06.065. View

18.

Fjell A, Westlye L, Amlien I, Espeseth T, Reinvang I, Raz N . High consistency of regional cortical thinning in aging across multiple samples. Cereb Cortex. 2009; 19(9):2001-12. PMC: 2733683. DOI: 10.1093/cercor/bhn232. View

19.

Liu Y, Paajanen T, Zhang Y, Westman E, Wahlund L, Simmons A . Analysis of regional MRI volumes and thicknesses as predictors of conversion from mild cognitive impairment to Alzheimer's disease. Neurobiol Aging. 2010; 31(8):1375-85. DOI: 10.1016/j.neurobiolaging.2010.01.022. View

20.

Westman E, Cavallin L, Muehlboeck J, Zhang Y, Mecocci P, Vellas B . Sensitivity and specificity of medial temporal lobe visual ratings and multivariate regional MRI classification in Alzheimer's disease. PLoS One. 2011; 6(7):e22506. PMC: 3141068. DOI: 10.1371/journal.pone.0022506. View