Merge pull request #8 from Inria-Empenn/hugoblox-import-publications

Hugo Blox Builder - Import latest publications

content/publication/bao-integrating-2022/cite.bib

@@ -0,0 +1,17 @@
+@article{bao_integrating_2022,
+ abstract = {A robust medical image computing infrastructure must host massive multimodal archives, perform extensive analysis pipelines, and execute scalable job management. An emerging data format standard, the Brain Imaging Data Structure (BIDS), introduces complexities for interfacing with XNAT archives. Moreover, workflow integration is combinatorically problematic when matching large amount of processing to large datasets. Historically, workflow engines have been focused on refining workflows themselves instead of actual job generation. However, such an approach is incompatible with data centric architecture that hosts heterogeneous medical image computing. Distributed automation for XNAT toolkit (DAX) provides large-scale image storage and analysis pipelines with an optimized job management tool. Herein, we describe developments for DAX that allows for integration of XNAT and BIDS standards. We also improve DAX’s efficiencies of diverse containerized workflows in a high-performance computing (HPC) environment. Briefly, we integrate YAML configuration processor scripts to abstract workflow data inputs, data outputs, commands, and job attributes. Finally, we propose an online database–driven mechanism for DAX to efficiently identify the most recent updated sessions, thereby improving job building efficiency on large projects. We refer the proposed overall DAX development in this work as DAX-1 (DAX version 1). To validate the effectiveness of the new features, we verified (1) the efficiency of converting XNAT data to BIDS format and the correctness of the conversion using a collection of BIDS standard containerized neuroimaging workflows, (2) how YAML-based processor simplified configuration setup via a sequence of application pipelines, and (3) the productivity of DAX-1 on generating actual HPC processing jobs compared with earlier DAX baseline method. The empirical results show that (1) DAX-1 converting XNAT data to BIDS has similar speed as accessing XNAT data only; (2) YAML can integrate to the DAX-1 with shallow learning curve for users, and (3) DAX-1 reduced the job/assessor generation latency by finding recent modified sessions. Herein, we present approaches for efficiently integrating XNAT and modern image formats with a scalable workflow engine for the large-scale dataset access and processing.},
+ author = {Bao, Shunxing and Boyd, Brian D. and Kanakaraj, Praitayini and Ramadass, Karthik and Meyer, Francisco A. C. and Liu, Yuqian and Duett, William E. and Huo, Yuankai and Lyu, Ilwoo and Zald, David H. and Smith, Seth A. and Rogers, Baxter P. and Landman, Bennett A.},
+ doi = {10.1007/s10278-022-00679-8},
+ file = {Bao et al. - 2022 - Integrating the BIDS Neuroimaging Data Format and .pdf:/home/alpron/Zotero/storage/HIE7CU7H/Bao et al. - 2022 - Integrating the BIDS Neuroimaging Data Format and .pdf:application/pdf},
+ issn = {0897-1889, 1618-727X},
+ journal = {Journal of Digital Imaging},
+ language = {en},
+ month = {December},
+ number = {6},
+ pages = {1576--1589},
+ title = {Integrating the BIDS Neuroimaging Data Format and Workflow Optimization for Large-Scale Medical Image Analysis},
+ url = {https://link.springer.com/10.1007/s10278-022-00679-8},
+ urldate = {2024-02-09},
+ volume = {35},
+ year = {2022}
+}

content/publication/bao-integrating-2022/index.md

@@ -0,0 +1,56 @@
+---
+title: Integrating the BIDS Neuroimaging Data Format and Workflow Optimization for
+  Large-Scale Medical Image Analysis
+authors:
+- Shunxing Bao
+- Brian D. Boyd
+- Praitayini Kanakaraj
+- Karthik Ramadass
+- Francisco A. C. Meyer
+- Yuqian Liu
+- William E. Duett
+- Yuankai Huo
+- Ilwoo Lyu
+- David H. Zald
+- Seth A. Smith
+- Baxter P. Rogers
+- Bennett A. Landman
+date: '2022-12-01'
+publishDate: '2024-07-23T12:57:18.561209Z'
+publication_types:
+- article-journal
+publication: '*Journal of Digital Imaging*'
+doi: 10.1007/s10278-022-00679-8
+abstract: A robust medical image computing infrastructure must host massive multimodal
+  archives, perform extensive analysis pipelines, and execute scalable job management.
+  An emerging data format standard, the Brain Imaging Data Structure (BIDS), introduces
+  complexities for interfacing with XNAT archives. Moreover, workflow integration
+  is combinatorically problematic when matching large amount of processing to large
+  datasets. Historically, workflow engines have been focused on refining workflows
+  themselves instead of actual job generation. However, such an approach is incompatible
+  with data centric architecture that hosts heterogeneous medical image computing.
+  Distributed automation for XNAT toolkit (DAX) provides large-scale image storage
+  and analysis pipelines with an optimized job management tool. Herein, we describe
+  developments for DAX that allows for integration of XNAT and BIDS standards. We
+  also improve DAX’s efficiencies of diverse containerized workflows in a high-performance
+  computing (HPC) environment. Briefly, we integrate YAML configuration processor
+  scripts to abstract workflow data inputs, data outputs, commands, and job attributes.
+  Finally, we propose an online database–driven mechanism for DAX to efficiently identify
+  the most recent updated sessions, thereby improving job building efficiency on large
+  projects. We refer the proposed overall DAX development in this work as DAX-1 (DAX
+  version 1). To validate the effectiveness of the new features, we verified (1) the
+  efficiency of converting XNAT data to BIDS format and the correctness of the conversion
+  using a collection of BIDS standard containerized neuroimaging workflows, (2) how
+  YAML-based processor simplified configuration setup via a sequence of application
+  pipelines, and (3) the productivity of DAX-1 on generating actual HPC processing
+  jobs compared with earlier DAX baseline method. The empirical results show that
+  (1) DAX-1 converting XNAT data to BIDS has similar speed as accessing XNAT data
+  only; (2) YAML can integrate to the DAX-1 with shallow learning curve for users,
+  and (3) DAX-1 reduced the job/assessor generation latency by finding recent modified
+  sessions. Herein, we present approaches for efficiently integrating XNAT and modern
+  image formats with a scalable workflow engine for the large-scale dataset access
+  and processing.
+links:
+- name: URL
+  url: https://link.springer.com/10.1007/s10278-022-00679-8
+---

content/publication/bollmann-neurodesk-2023/cite.bib

@@ -0,0 +1,15 @@
+@misc{bollmann_neurodesk_2023,
+ abstract = {Abstract
+Neuroimaging data analysis often requires purpose-built software, which can be challenging to install and may produce different results across computing environments. Beyond being a roadblock to neuroscientists, these issues of accessibility and portability can hamper the reproducibility of neuroimaging data analysis pipelines. Here, we introduce the Neurodesk platform, which harnesses software containers to support a comprehensive and growing suite of neuroimaging software (https://www.neurodesk.org/). Neurodesk includes a browser-accessible virtual desktop environment and a command line interface, mediating access to containerized neuroimaging software libraries on various computing platforms, including personal and high-performance computers, cloud computing and Jupyter Notebooks. This community-oriented, open-source platform enables a paradigm shift for neuroimaging data analysis, allowing for accessible, flexible, fully reproducible, and portable data analysis pipelines.},
+ author = {Bollmann, Steffen and Renton, Angela and Dao, Thuy and Johnstone, Tom and Civier, Oren and Sullivan, Ryan and White, David and Lyons, Paris and Slade, Benjamin and Abbott, David and Amos, Toluwani and Bollmann, Saskia and Botting, Andy and Campbell, Megan and Chang, Jeryn and Close, Thomas and Eckstein, Korbinian and Egan, Gary and Evas, Stefanie and Flandin, Guillaume and Garner, Kelly and Garrido, Marta and Ghosh, Satrajit and Grignard, Martin and Hannan, Anthony and Huber, Laurentius (Renzo) and Kaczmarzyk, Jakub and Kasper, Lars and Kuhlmann, Levin and Lou, Kexin and Mantilla-Ramos, Yorguin-Jose and Mattingley, Jason and Morris, Jo and Narayanan, Akshaiy and Pestilli, Franco and Puce, Aina and Ribeiro, Fernanda and Rogasch, Nigel and Rorden, Chris and Schira, Mark and Shaw, Thomas and Sowman, Paul and Spitz, Gershon and Stewart, Ashley and Ye, Xincheng and Zhu, Judy and Hughes, Matthew and Narayanan, Aswin},
+ doi = {10.21203/rs.3.rs-2649734/v1},
+ file = {Bollmann et al. - 2023 - Neurodesk An accessible, flexible, and portable d.pdf:/home/alpron/Zotero/storage/AQR5KZMM/Bollmann et al. - 2023 - Neurodesk An accessible, flexible, and portable d.pdf:application/pdf},
+ month = {March},
+ publisher = {In Review},
+ shorttitle = {Neurodesk},
+ title = {Neurodesk: An accessible, flexible, and portable data analysis environment for reproducible neuroimaging},
+ type = {preprint},
+ url = {https://www.researchsquare.com/article/rs-2649734/v1},
+ urldate = {2024-01-04},
+ year = {2023}
+}

content/publication/bollmann-neurodesk-2023/index.md

@@ -0,0 +1,74 @@
+---
+title: 'Neurodesk: An accessible, flexible, and portable data analysis environment
+  for reproducible neuroimaging'
+authors:
+- Steffen Bollmann
+- Angela Renton
+- Thuy Dao
+- Tom Johnstone
+- Oren Civier
+- Ryan Sullivan
+- David White
+- Paris Lyons
+- Benjamin Slade
+- David Abbott
+- Toluwani Amos
+- Saskia Bollmann
+- Andy Botting
+- Megan Campbell
+- Jeryn Chang
+- Thomas Close
+- Korbinian Eckstein
+- Gary Egan
+- Stefanie Evas
+- Guillaume Flandin
+- Kelly Garner
+- Marta Garrido
+- Satrajit Ghosh
+- Martin Grignard
+- Anthony Hannan
+- Laurentius (Renzo) Huber
+- Jakub Kaczmarzyk
+- Lars Kasper
+- Levin Kuhlmann
+- Kexin Lou
+- Yorguin-Jose Mantilla-Ramos
+- Jason Mattingley
+- Jo Morris
+- Akshaiy Narayanan
+- Franco Pestilli
+- Aina Puce
+- Fernanda Ribeiro
+- Nigel Rogasch
+- Chris Rorden
+- Mark Schira
+- Thomas Shaw
+- Paul Sowman
+- Gershon Spitz
+- Ashley Stewart
+- Xincheng Ye
+- Judy Zhu
+- Matthew Hughes
+- Aswin Narayanan
+date: '2023-03-01'
+publishDate: '2024-07-23T12:57:18.497763Z'
+publication_types:
+- manuscript
+publication: '*In Review*'
+doi: 10.21203/rs.3.rs-2649734/v1
+abstract: Abstract Neuroimaging data analysis often requires purpose-built software,
+  which can be challenging to install and may produce different results across computing
+  environments. Beyond being a roadblock to neuroscientists, these issues of accessibility
+  and portability can hamper the reproducibility of neuroimaging data analysis pipelines.
+  Here, we introduce the Neurodesk platform, which harnesses software containers to
+  support a comprehensive and growing suite of neuroimaging software (https://www.neurodesk.org/).
+  Neurodesk includes a browser-accessible virtual desktop environment and a command
+  line interface, mediating access to containerized neuroimaging software libraries
+  on various computing platforms, including personal and high-performance computers,
+  cloud computing and Jupyter Notebooks. This community-oriented, open-source platform
+  enables a paradigm shift for neuroimaging data analysis, allowing for accessible,
+  flexible, fully reproducible, and portable data analysis pipelines.
+links:
+- name: URL
+  url: https://www.researchsquare.com/article/rs-2649734/v1
+---

content/publication/botvinik-nezer-fmri-2019/cite.bib

@@ -0,0 +1,19 @@
+@article{botvinik-nezer_fmri_2019,
+ abstract = {Abstract
+There is an ongoing debate about the replicability of neuroimaging research. It was suggested that one of the main reasons for the high rate of false positive results is the many degrees of freedom researchers have during data analysis. In the Neuroimaging Analysis Replication and Prediction Study (NARPS), we aim to provide the first scientific evidence on the variability of results across analysis teams in neuroscience. We collected fMRI data from 108 participants during two versions of the mixed gambles task, which is often used to study decision-making under risk. For each participant, the dataset includes an anatomical (T1 weighted) scan and fMRI as well as behavioral data from four runs of the task. The dataset is shared through OpenNeuro and is formatted according to the Brain Imaging Data Structure (BIDS) standard. Data pre-processed with fMRIprep and quality control reports are also publicly shared. This dataset can be used to study decision-making under risk and to test replicability and interpretability of previous results in the field.},
+ author = {Botvinik-Nezer, Rotem and Iwanir, Roni and Holzmeister, Felix and Huber, Jürgen and Johannesson, Magnus and Kirchler, Michael and Dreber, Anna and Camerer, Colin F. and Poldrack, Russell A. and Schonberg, Tom},
+ doi = {10.1038/s41597-019-0113-7},
+ file = {Botvinik-Nezer et al. - 2019 - fMRI data of mixed gambles from the Neuroimaging A.pdf:/home/alpron/Zotero/storage/7P9AT6CT/Botvinik-Nezer et al. - 2019 - fMRI data of mixed gambles from the Neuroimaging A.pdf:application/pdf},
+ issn = {2052-4463},
+ journal = {Scientific Data},
+ keywords = {NARPS},
+ language = {en},
+ month = {July},
+ number = {1},
+ pages = {106},
+ title = {fMRI data of mixed gambles from the Neuroimaging Analysis Replication and Prediction Study},
+ url = {https://www.nature.com/articles/s41597-019-0113-7},
+ urldate = {2024-01-22},
+ volume = {6},
+ year = {2019}
+}

content/publication/botvinik-nezer-fmri-2019/index.md

@@ -0,0 +1,39 @@
+---
+title: fMRI data of mixed gambles from the Neuroimaging Analysis Replication and Prediction
+  Study
+authors:
+- Rotem Botvinik-Nezer
+- Roni Iwanir
+- Felix Holzmeister
+- Jürgen Huber
+- Magnus Johannesson
+- Michael Kirchler
+- Anna Dreber
+- Colin F. Camerer
+- Russell A. Poldrack
+- Tom Schonberg
+date: '2019-07-01'
+publishDate: '2024-07-23T12:57:18.538832Z'
+publication_types:
+- article-journal
+publication: '*Scientific Data*'
+doi: 10.1038/s41597-019-0113-7
+abstract: Abstract There is an ongoing debate about the replicability of neuroimaging
+  research. It was suggested that one of the main reasons for the high rate of false
+  positive results is the many degrees of freedom researchers have during data analysis.
+  In the Neuroimaging Analysis Replication and Prediction Study (NARPS), we aim to
+  provide the first scientific evidence on the variability of results across analysis
+  teams in neuroscience. We collected fMRI data from 108 participants during two versions
+  of the mixed gambles task, which is often used to study decision-making under risk.
+  For each participant, the dataset includes an anatomical (T1 weighted) scan and
+  fMRI as well as behavioral data from four runs of the task. The dataset is shared
+  through OpenNeuro and is formatted according to the Brain Imaging Data Structure
+  (BIDS) standard. Data pre-processed with fMRIprep and quality control reports are
+  also publicly shared. This dataset can be used to study decision-making under risk
+  and to test replicability and interpretability of previous results in the field.
+tags:
+- NARPS
+links:
+- name: URL
+  url: https://www.nature.com/articles/s41597-019-0113-7
+---

content/publication/botvinik-nezer-reproducibility-2023/cite.bib

@@ -0,0 +1,19 @@
+@article{botvinik-nezer_reproducibility_2023,
+ abstract = {Recent years have marked a renaissance in efforts to increase research reproducibility in psychology, neuroscience, and related fields. Reproducibility is the cornerstone of a solid foundation of fundamental research—one that will support new theories built on valid findings and technological innovation that works. The increased focus on reproducibility has made the barriers to it increasingly apparent, along with the development of new tools and practices to overcome these barriers. Here, we review challenges, solutions, and emerging best practices with a particular emphasis on neuroimaging studies. We distinguish 3 main types of reproducibility, discussing each in turn. Analytical reproducibility is the ability to reproduce findings using the same data and methods. Replicability is the ability to find an effect in new datasets, using the same or similar methods. Finally, robustness to analytical variability refers to the ability to identify a finding consistently across variation in methods. The incorporation of these tools and practices will result in more reproducible, replicable, and robust psychological and brain research and a stronger scientific foundation across fields of inquiry.},
+ author = {Botvinik-Nezer, Rotem and Wager, Tor D.},
+ doi = {10.1016/j.bpsc.2022.12.006},
+ file = {Botvinik-Nezer and Wager - 2023 - Reproducibility in Neuroimaging Analysis Challeng.pdf:/home/alpron/Zotero/storage/HE2F52IP/Botvinik-Nezer and Wager - 2023 - Reproducibility in Neuroimaging Analysis Challeng.pdf:application/pdf},
+ issn = {24519022},
+ journal = {Biological Psychiatry: Cognitive Neuroscience and Neuroimaging},
+ keywords = {Important, Review},
+ language = {en},
+ month = {August},
+ number = {8},
+ pages = {780--788},
+ shorttitle = {Reproducibility in Neuroimaging Analysis},
+ title = {Reproducibility in Neuroimaging Analysis: Challenges and Solutions},
+ url = {https://linkinghub.elsevier.com/retrieve/pii/S245190222200341X},
+ urldate = {2024-01-19},
+ volume = {8},
+ year = {2023}
+}

content/publication/botvinik-nezer-reproducibility-2023/index.md

@@ -0,0 +1,33 @@
+---
+title: 'Reproducibility in Neuroimaging Analysis: Challenges and Solutions'
+authors:
+- Rotem Botvinik-Nezer
+- Tor D. Wager
+date: '2023-08-01'
+publishDate: '2024-07-23T12:57:18.472621Z'
+publication_types:
+- article-journal
+publication: '*Biological Psychiatry: Cognitive Neuroscience and Neuroimaging*'
+doi: 10.1016/j.bpsc.2022.12.006
+abstract: Recent years have marked a renaissance in efforts to increase research reproducibility
+  in psychology, neuroscience, and related fields. Reproducibility is the cornerstone
+  of a solid foundation of fundamental research—one that will support new theories
+  built on valid findings and technological innovation that works. The increased focus
+  on reproducibility has made the barriers to it increasingly apparent, along with
+  the development of new tools and practices to overcome these barriers. Here, we
+  review challenges, solutions, and emerging best practices with a particular emphasis
+  on neuroimaging studies. We distinguish 3 main types of reproducibility, discussing
+  each in turn. Analytical reproducibility is the ability to reproduce findings using
+  the same data and methods. Replicability is the ability to find an effect in new
+  datasets, using the same or similar methods. Finally, robustness to analytical variability
+  refers to the ability to identify a finding consistently across variation in methods.
+  The incorporation of these tools and practices will result in more reproducible,
+  replicable, and robust psychological and brain research and a stronger scientific
+  foundation across fields of inquiry.
+tags:
+- Important
+- Review
+links:
+- name: URL
+  url: https://linkinghub.elsevier.com/retrieve/pii/S245190222200341X
+---