Literature DB >> 35028522

INCloud: integrated neuroimaging cloud for data collection, management, analysis and clinical translations.

Qingfeng Li1, Lijuan Jiang1, Kaini Qiao1, Yang Hu1, Bing Chen2, Xiaochen Zhang1, Yue Ding1, Zhi Yang1,3,4, Chunbo Li1,3,4,5.   

Abstract

BACKGROUND: Neuroimaging techniques provide rich and accurate measures of brain structure and function, and have become one of the most popular methods in mental health and neuroscience research. Rapidly growing neuroimaging research generates massive amounts of data, bringing new challenges in data collection, large-scale data management, efficient computing requirements and data mining and analyses. AIMS: To tackle the challenges and promote the application of neuroimaging technology in clinical practice, we developed an integrated neuroimaging cloud (INCloud). INCloud provides a full-stack solution for the entire process of large-scale neuroimaging data collection, management, analysis and clinical applications.
METHODS: INCloud consists of data acquisition systems, a data warehouse, automatic multimodal image quality check and processing systems, a brain feature library, a high-performance computing cluster and computer-aided diagnosis systems (CADS) for mental disorders. A unique design of INCloud is the brain feature library that converts the unit of data management from image to image features such as hippocampal volume. Connecting the CADS to the scientific database, INCloud allows the accumulation of scientific data to continuously improve the accuracy of objective diagnosis of mental disorders.
RESULTS: Users can manage and analyze neuroimaging data on INCloud, without the need to download them to the local device. INCloud users can query, manage, analyze and share image features based on customized criteria. Several examples of 'mega-analyses' based on the brain feature library are shown.
CONCLUSIONS: Compared with traditional neuroimaging acquisition and analysis workflow, INCloud features safe and convenient data management and sharing, reduced technical requirements for researchers, high-efficiency computing and data mining, and straightforward translations to clinical service. The design and implementation of the system are also applicable to imaging research platforms in other fields. © Author(s) (or their employer(s)) 2021. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.

Entities:  

Keywords:  cloud computing; computer-aided diagnosis system; computing system; data analysis system; data sharing; neuroimaging

Year:  2021        PMID: 35028522      PMCID: PMC8705204          DOI: 10.1136/gpsych-2021-100651

Source DB:  PubMed          Journal:  Gen Psychiatr        ISSN: 2517-729X


  30 in total

1.  Adjusting batch effects in microarray expression data using empirical Bayes methods.

Authors:  W Evan Johnson; Cheng Li; Ariel Rabinovic
Journal:  Biostatistics       Date:  2006-04-21       Impact factor: 5.899

2.  Multi-level bootstrap analysis of stable clusters in resting-state fMRI.

Authors:  Pierre Bellec; Pedro Rosa-Neto; Oliver C Lyttelton; Habib Benali; Alan C Evans
Journal:  Neuroimage       Date:  2010-03-10       Impact factor: 6.556

3.  The Image and Data Archive at the Laboratory of Neuro Imaging.

Authors:  Karen L Crawford; Scott C Neu; Arthur W Toga
Journal:  Neuroimage       Date:  2015-05-14       Impact factor: 6.556

4.  Amplitude of low-frequency fluctuations on Alzheimer's disease with depression: evidence from resting-state fMRI.

Authors:  Yuzhu Mu; Yumei Li; Qi Zhang; Zhongxiang Ding; Mei Wang; Xingguang Luo; Xiaoyun Guo; Maosheng Xu
Journal:  Gen Psychiatr       Date:  2020-07-09

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Authors:  Yan Chao-Gan; Zang Yu-Feng
Journal:  Front Syst Neurosci       Date:  2010-05-14

6.  Statistical harmonization corrects site effects in functional connectivity measurements from multi-site fMRI data.

Authors:  Meichen Yu; Kristin A Linn; Philip A Cook; Mary L Phillips; Melvin McInnis; Maurizio Fava; Madhukar H Trivedi; Myrna M Weissman; Russell T Shinohara; Yvette I Sheline
Journal:  Hum Brain Mapp       Date:  2018-07-01       Impact factor: 5.038

7.  COINS Data Exchange: An open platform for compiling, curating, and disseminating neuroimaging data.

Authors:  Drew Landis; William Courtney; Christopher Dieringer; Ross Kelly; Margaret King; Brittny Miller; Runtang Wang; Dylan Wood; Jessica A Turner; Vince D Calhoun
Journal:  Neuroimage       Date:  2015-05-24       Impact factor: 6.556

8.  CBRAIN: a web-based, distributed computing platform for collaborative neuroimaging research.

Authors:  Tarek Sherif; Pierre Rioux; Marc-Etienne Rousseau; Nicolas Kassis; Natacha Beck; Reza Adalat; Samir Das; Tristan Glatard; Alan C Evans
Journal:  Front Neuroinform       Date:  2014-05-21       Impact factor: 4.081

9.  Brain network informed subject community detection in early-onset schizophrenia.

Authors:  Zhi Yang; Yong Xu; Ting Xu; Colin W Hoy; Daniel A Handwerker; Gang Chen; Georg Northoff; Xi-Nian Zuo; Peter A Bandettini
Journal:  Sci Rep       Date:  2014-07-03       Impact factor: 4.379

10.  The autism brain imaging data exchange: towards a large-scale evaluation of the intrinsic brain architecture in autism.

Authors:  A Di Martino; C-G Yan; Q Li; E Denio; F X Castellanos; K Alaerts; J S Anderson; M Assaf; S Y Bookheimer; M Dapretto; B Deen; S Delmonte; I Dinstein; B Ertl-Wagner; D A Fair; L Gallagher; D P Kennedy; C L Keown; C Keysers; J E Lainhart; C Lord; B Luna; V Menon; N J Minshew; C S Monk; S Mueller; R-A Müller; M B Nebel; J T Nigg; K O'Hearn; K A Pelphrey; S J Peltier; J D Rudie; S Sunaert; M Thioux; J M Tyszka; L Q Uddin; J S Verhoeven; N Wenderoth; J L Wiggins; S H Mostofsky; M P Milham
Journal:  Mol Psychiatry       Date:  2013-06-18       Impact factor: 15.992

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