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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} +} diff --git a/content/publication/bao-integrating-2022/index.md b/content/publication/bao-integrating-2022/index.md new file mode 100644 index 0000000..fa0f17a --- /dev/null +++ b/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 +--- diff --git a/content/publication/bollmann-neurodesk-2023/cite.bib b/content/publication/bollmann-neurodesk-2023/cite.bib new file mode 100644 index 0000000..4ea8678 --- /dev/null +++ b/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} +} diff --git a/content/publication/bollmann-neurodesk-2023/index.md b/content/publication/bollmann-neurodesk-2023/index.md new file mode 100644 index 0000000..7868e8a --- /dev/null +++ b/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 +--- diff --git a/content/publication/botvinik-nezer-fmri-2019/cite.bib b/content/publication/botvinik-nezer-fmri-2019/cite.bib new file mode 100644 index 0000000..76f52c6 --- /dev/null +++ b/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} +} diff --git a/content/publication/botvinik-nezer-fmri-2019/index.md b/content/publication/botvinik-nezer-fmri-2019/index.md new file mode 100644 index 0000000..155cc8c --- /dev/null +++ b/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 +--- diff --git a/content/publication/botvinik-nezer-reproducibility-2023/cite.bib b/content/publication/botvinik-nezer-reproducibility-2023/cite.bib new file mode 100644 index 0000000..a7c6b35 --- /dev/null +++ b/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} +} diff --git a/content/publication/botvinik-nezer-reproducibility-2023/index.md b/content/publication/botvinik-nezer-reproducibility-2023/index.md new file mode 100644 index 0000000..0fd88cb --- /dev/null +++ b/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 +--- diff --git a/content/publication/chekroud-illusory-2024/cite.bib b/content/publication/chekroud-illusory-2024/cite.bib new file mode 100644 index 0000000..1e3a8f0 --- /dev/null +++ b/content/publication/chekroud-illusory-2024/cite.bib @@ -0,0 +1,28 @@ +@article{chekroud_illusory_2024, + abstract = {It is widely hoped that statistical models can improve decision-making related to medical treatments. Because of the cost and scarcity of medical outcomes data, this hope is typically based on investigators observing a model’s success in one or two datasets or clinical contexts. We scrutinized this optimism by examining how well a machine learning model performed across several independent clinical trials of antipsychotic medication for schizophrenia. Models predicted patient outcomes with high accuracy within the trial in which the model was developed but performed no better than chance when applied out-of-sample. Pooling data across trials to predict outcomes in the trial left out did not improve predictions. These results suggest that models predicting treatment outcomes in schizophrenia are highly context-dependent and may have limited generalizability. +, +Editor’s summary + +A central promise of artificial intelligence (AI) in healthcare is that large datasets can be mined to predict and identify the best course of care for future patients. Unfortunately, we do not know how these models would perform on new patients because they are rarely tested prospectively on truly independent patient samples. Chekroud +et al +. showed that machine learning models routinely achieve perfect performance in one dataset even when that dataset is a large international multisite clinical trial (see the Perspective by Petzschner). However, when that exact model was tested in truly independent clinical trials, performance fell to chance levels. Even when building what should be a more robust model by aggregating across a group of similar multisite trials, subsequent predictive performance remained poor. —Peter Stern + +, +Clinical prediction models that work in one trial do not work in future trials of the same condition and same treatments.}, + author = {Chekroud, Adam M. and Hawrilenko, Matt and Loho, Hieronimus and Bondar, Julia and Gueorguieva, Ralitza and Hasan, Alkomiet and Kambeitz, Joseph and Corlett, Philip R. and Koutsouleris, Nikolaos and Krumholz, Harlan M. and Krystal, John H. and Paulus, Martin}, + doi = {10.1126/science.adg8538}, + file = {Chekroud et al. - 2024 - Illusory generalizability of clinical prediction m.pdf:/home/alpron/Zotero/storage/WRRD7K3B/Chekroud et al. - 2024 - Illusory generalizability of clinical prediction m.pdf:application/pdf}, + issn = {0036-8075, 1095-9203}, + journal = {Science}, + keywords = {Important}, + language = {en}, + month = {January}, + note = {Number: 6679}, + number = {6679}, + pages = {164--167}, + title = {Illusory generalizability of clinical prediction models}, + url = {https://www.science.org/doi/10.1126/science.adg8538}, + urldate = {2024-01-15}, + volume = {383}, + year = {2024} +} diff --git a/content/publication/chekroud-illusory-2024/index.md b/content/publication/chekroud-illusory-2024/index.md new file mode 100644 index 0000000..9539517 --- /dev/null +++ b/content/publication/chekroud-illusory-2024/index.md @@ -0,0 +1,50 @@ +--- +title: Illusory generalizability of clinical prediction models +authors: +- Adam M. Chekroud +- Matt Hawrilenko +- Hieronimus Loho +- Julia Bondar +- Ralitza Gueorguieva +- Alkomiet Hasan +- Joseph Kambeitz +- Philip R. Corlett +- Nikolaos Koutsouleris +- Harlan M. Krumholz +- John H. Krystal +- Martin Paulus +date: '2024-01-01' +publishDate: '2024-07-23T12:57:18.512569Z' +publication_types: +- article-journal +publication: '*Science*' +doi: 10.1126/science.adg8538 +abstract: It is widely hoped that statistical models can improve decision-making related + to medical treatments. Because of the cost and scarcity of medical outcomes data, + this hope is typically based on investigators observing a model’s success in one + or two datasets or clinical contexts. We scrutinized this optimism by examining + how well a machine learning model performed across several independent clinical + trials of antipsychotic medication for schizophrenia. Models predicted patient outcomes + with high accuracy within the trial in which the model was developed but performed + no better than chance when applied out-of-sample. Pooling data across trials to + predict outcomes in the trial left out did not improve predictions. These results + suggest that models predicting treatment outcomes in schizophrenia are highly context-dependent + and may have limited generalizability. , Editor’s summary A central promise of + artificial intelligence (AI) in healthcare is that large datasets can be mined to + predict and identify the best course of care for future patients. Unfortunately, + we do not know how these models would perform on new patients because they are rarely + tested prospectively on truly independent patient samples. Chekroud et al . showed + that machine learning models routinely achieve perfect performance in one dataset + even when that dataset is a large international multisite clinical trial (see the + Perspective by Petzschner). However, when that exact model was tested in truly independent + clinical trials, performance fell to chance levels. Even when building what should + be a more robust model by aggregating across a group of similar multisite trials, + subsequent predictive performance remained poor. —Peter Stern , Clinical prediction + models that work in one trial do not work in future trials of the same condition + and same treatments. +tags: +- Important +links: +- name: URL + url: https://www.science.org/doi/10.1126/science.adg8538 +--- diff --git a/content/publication/chen-reproducing-2022/cite.bib b/content/publication/chen-reproducing-2022/cite.bib new file mode 100644 index 0000000..5fb32ec --- /dev/null +++ b/content/publication/chen-reproducing-2022/cite.bib @@ -0,0 +1,18 @@ +@article{chen_reproducing_2022, + abstract = {The “replication crisis” in neuroscientific research has led to calls for improving reproducibility. In traditional neuroscience analyses, irreproducibility may occur as a result of issues across various stages of the methodological process. For example, different operating systems, different software packages, and even different versions of the same package can lead to variable results. Nipype, an open-source Python project, integrates different neuroimaging software packages uniformly to improve the reproducibility of neuroimaging analyses. Nipype has the advantage over traditional software packages (e.g., FSL, ANFI, SPM, etc.) by (1) providing comprehensive software development frameworks and usage information, (2) improving computational efficiency, (3) facilitating reproducibility through sufficient details, and (4) easing the steep learning curve. Despite the rich tutorials it has provided, the Nipype community lacks a standard three-level GLM tutorial for FSL. Using the classical Flanker task dataset, we first precisely reproduce a three-level GLM analysis with FSL +via +Nipype. Next, we point out some undocumented discrepancies between Nipype and FSL functions that led to substantial differences in results. Finally, we provide revised Nipype code in re-executable notebooks that assure result invariability between FSL and Nipype. Our analyses, notebooks, and operating software specifications (e.g., docker build files) are available on the Open Science Framework platform.}, + author = {Chen, Yibei and Hopp, Frederic R. and Malik, Musa and Wang, Paula T. and Woodman, Kylie and Youk, Sungbin and Weber, René}, + doi = {10.3389/fnimg.2022.953215}, + file = {Chen et al. - 2022 - Reproducing FSL's fMRI data analysis via Nipype R.pdf:/home/alpron/Zotero/storage/SA5LQY4U/Chen et al. - 2022 - Reproducing FSL's fMRI data analysis via Nipype R.pdf:application/pdf}, + issn = {2813-1193}, + journal = {Frontiers in Neuroimaging}, + month = {July}, + pages = {953215}, + shorttitle = {Reproducing FSL's fMRI data analysis via Nipype}, + title = {Reproducing FSL's fMRI data analysis via Nipype: Relevance, challenges, and solutions}, + url = {https://www.frontiersin.org/articles/10.3389/fnimg.2022.953215/full}, + urldate = {2024-02-28}, + volume = {1}, + year = {2022} +} diff --git a/content/publication/chen-reproducing-2022/index.md b/content/publication/chen-reproducing-2022/index.md new file mode 100644 index 0000000..ab4d592 --- /dev/null +++ b/content/publication/chen-reproducing-2022/index.md @@ -0,0 +1,40 @@ +--- +title: "Reproducing FSL's fMRI data analysis via Nipype: Relevance, challenges, and + solutions" +authors: +- Yibei Chen +- Frederic R. Hopp +- Musa Malik +- Paula T. Wang +- Kylie Woodman +- Sungbin Youk +- René Weber +date: '2022-07-01' +publishDate: '2024-07-23T12:57:18.583343Z' +publication_types: +- article-journal +publication: '*Frontiers in Neuroimaging*' +doi: 10.3389/fnimg.2022.953215 +abstract: The “replication crisis” in neuroscientific research has led to calls for + improving reproducibility. In traditional neuroscience analyses, irreproducibility + may occur as a result of issues across various stages of the methodological process. + For example, different operating systems, different software packages, and even + different versions of the same package can lead to variable results. Nipype, an + open-source Python project, integrates different neuroimaging software packages + uniformly to improve the reproducibility of neuroimaging analyses. Nipype has the + advantage over traditional software packages (e.g., FSL, ANFI, SPM, etc.) by (1) + providing comprehensive software development frameworks and usage information, (2) + improving computational efficiency, (3) facilitating reproducibility through sufficient + details, and (4) easing the steep learning curve. Despite the rich tutorials it + has provided, the Nipype community lacks a standard three-level GLM tutorial for + FSL. Using the classical Flanker task dataset, we first precisely reproduce a three-level + GLM analysis with FSL via Nipype. Next, we point out some undocumented discrepancies + between Nipype and FSL functions that led to substantial differences in results. + Finally, we provide revised Nipype code in re-executable notebooks that assure result + invariability between FSL and Nipype. Our analyses, notebooks, and operating software + specifications (e.g., docker build files) are available on the Open Science Framework + platform. +links: +- name: URL + url: https://www.frontiersin.org/articles/10.3389/fnimg.2022.953215/full +--- diff --git a/content/publication/colliot-reproducibility-2024/cite.bib b/content/publication/colliot-reproducibility-2024/cite.bib new file mode 100644 index 0000000..4706e5f --- /dev/null +++ b/content/publication/colliot-reproducibility-2024/cite.bib @@ -0,0 +1,9 @@ +@article{colliot_reproducibility_2024, + abstract = {Medical image computing (MIC) is devoted to computational methods for analysis of medical imaging data and their assessment through experiments. It is thus an experimental science. Reproducibility is a cornerstone of progress in all experimental sciences. As in many other fields, there are major concerns that reproducibility is unsatisfactory in MIC. However, reproducibility is not a single concept but a spectrum, which is often misunderstood by researchers. Moreover, even though some measures have been put in place to promote reproducibility in the MIC community, it is unclear if they have been effective so far.}, + author = {Colliot, Olivier and Thibeau-Sutre, Elina and Brianceau, Camille and Burgos, Ninon}, + file = {Colliot et al. - Reproducibility in medical image computing what i.pdf:/home/alpron/Zotero/storage/XCGVIGEI/Colliot et al. - Reproducibility in medical image computing what i.pdf:application/pdf}, + journal = {Open Review 3fIXW9mFfn}, + language = {en}, + title = {Reproducibility in medical image computing: what is it and how is it assessed?}, + year = {2024} +} diff --git a/content/publication/colliot-reproducibility-2024/index.md b/content/publication/colliot-reproducibility-2024/index.md new file mode 100644 index 0000000..3257be7 --- /dev/null +++ b/content/publication/colliot-reproducibility-2024/index.md @@ -0,0 +1,21 @@ +--- +title: 'Reproducibility in medical image computing: what is it and how is it assessed?' +authors: +- Olivier Colliot +- Elina Thibeau-Sutre +- Camille Brianceau +- Ninon Burgos +date: '2024-01-01' +publishDate: '2024-07-23T12:57:18.506190Z' +publication_types: +- article-journal +publication: '*Open Review 3fIXW9mFfn*' +abstract: Medical image computing (MIC) is devoted to computational methods for analysis + of medical imaging data and their assessment through experiments. It is thus an + experimental science. Reproducibility is a cornerstone of progress in all experimental + sciences. As in many other fields, there are major concerns that reproducibility + is unsatisfactory in MIC. However, reproducibility is not a single concept but a + spectrum, which is often misunderstood by researchers. Moreover, even though some + measures have been put in place to promote reproducibility in the MIC community, + it is unclear if they have been effective so far. +--- diff --git a/content/publication/dafflon-guided-2022/cite.bib b/content/publication/dafflon-guided-2022/cite.bib new file mode 100644 index 0000000..d02a5f7 --- /dev/null +++ b/content/publication/dafflon-guided-2022/cite.bib @@ -0,0 +1,18 @@ +@article{dafflon_guided_2022, + abstract = {Abstract +For most neuroimaging questions the range of possible analytic choices makes it unclear how to evaluate conclusions from any single analytic method. One possible way to address this issue is to evaluate all possible analyses using a multiverse approach, however, this can be computationally challenging and sequential analyses on the same data can compromise predictive power. Here, we establish how active learning on a low-dimensional space capturing the inter-relationships between pipelines can efficiently approximate the full spectrum of analyses. This approach balances the benefits of a multiverse analysis without incurring the cost on computational and predictive power. We illustrate this approach with two functional MRI datasets (predicting brain age and autism diagnosis) demonstrating how a multiverse of analyses can be efficiently navigated and mapped out using active learning. Furthermore, our presented approach not only identifies the subset of analysis techniques that are best able to predict age or classify individuals with autism spectrum disorder and healthy controls, but it also allows the relationships between analyses to be quantified.}, + author = {Dafflon, Jessica and F. Da Costa, Pedro and Váša, František and Monti, Ricardo Pio and Bzdok, Danilo and Hellyer, Peter J. and Turkheimer, Federico and Smallwood, Jonathan and Jones, Emily and Leech, Robert}, + doi = {10.1038/s41467-022-31347-8}, + file = {Dafflon et al. - 2022 - A guided multiverse study of neuroimaging analyses.pdf:/home/alpron/Zotero/storage/8S87S654/Dafflon et al. - 2022 - A guided multiverse study of neuroimaging analyses.pdf:application/pdf}, + issn = {2041-1723}, + journal = {Nature Communications}, + language = {en}, + month = {June}, + number = {1}, + pages = {3758}, + title = {A guided multiverse study of neuroimaging analyses}, + url = {https://www.nature.com/articles/s41467-022-31347-8}, + urldate = {2024-03-06}, + volume = {13}, + year = {2022} +} diff --git a/content/publication/dafflon-guided-2022/index.md b/content/publication/dafflon-guided-2022/index.md new file mode 100644 index 0000000..a64a065 --- /dev/null +++ b/content/publication/dafflon-guided-2022/index.md @@ -0,0 +1,38 @@ +--- +title: A guided multiverse study of neuroimaging analyses +authors: +- Jessica Dafflon +- Pedro F. Da Costa +- František Váša +- Ricardo Pio Monti +- Danilo Bzdok +- Peter J. Hellyer +- Federico Turkheimer +- Jonathan Smallwood +- Emily Jones +- Robert Leech +date: '2022-06-01' +publishDate: '2024-07-23T12:57:18.618079Z' +publication_types: +- article-journal +publication: '*Nature Communications*' +doi: 10.1038/s41467-022-31347-8 +abstract: Abstract For most neuroimaging questions the range of possible analytic + choices makes it unclear how to evaluate conclusions from any single analytic method. + One possible way to address this issue is to evaluate all possible analyses using + a multiverse approach, however, this can be computationally challenging and sequential + analyses on the same data can compromise predictive power. Here, we establish how + active learning on a low-dimensional space capturing the inter-relationships between + pipelines can efficiently approximate the full spectrum of analyses. This approach + balances the benefits of a multiverse analysis without incurring the cost on computational + and predictive power. We illustrate this approach with two functional MRI datasets + (predicting brain age and autism diagnosis) demonstrating how a multiverse of analyses + can be efficiently navigated and mapped out using active learning. Furthermore, + our presented approach not only identifies the subset of analysis techniques that + are best able to predict age or classify individuals with autism spectrum disorder + and healthy controls, but it also allows the relationships between analyses to be + quantified. +links: +- name: URL + url: https://www.nature.com/articles/s41467-022-31347-8 +--- diff --git a/content/publication/demidenko-impact-2024/cite.bib b/content/publication/demidenko-impact-2024/cite.bib new file mode 100644 index 0000000..f2fb7e2 --- /dev/null +++ b/content/publication/demidenko-impact-2024/cite.bib @@ -0,0 +1,28 @@ +@misc{demidenko_impact_2024, + abstract = {Abstract + +Empirical studies reporting low test-retest reliability of individual blood oxygen-level dependent (BOLD) signal estimates in functional magnetic resonance imaging (fMRI) data have resurrected interest among cognitive neuroscientists in methods that may improve reliability in fMRI. Over the last decade, several individual studies have reported that modeling decisions, such as smoothing, motion correction and contrast selection, may improve estimates of test-retest reliability of BOLD signal estimates. However, it remains an empirical question whether certain analytic decisions +consistently +improve individual and group level reliability estimates in an fMRI task across multiple large, independent samples. This study used three independent samples ( +N +s: 60, 81, 119) that collected the same task (Monetary Incentive Delay task) across two runs and two sessions to evaluate the effects of analytic decisions on the individual (intraclass correlation coefficient [ICC(3,1)]) and group (Jaccard/Spearman +rho +) reliability estimates of BOLD activity of task fMRI data. The analytic decisions in this study vary across four categories: smoothing kernel (five options), motion correction (four options), task parameterizing (three options) and task contrasts (four options), totaling 240 different pipeline permutations. Across all 240 pipelines, the median ICC estimates are consistently low, with a maximum median ICC estimate of .43 - .55 across the three samples. The analytic decisions with the greatest impact on the median ICC and group similarity estimates are the +Implicit Baseline +contrast, Cue Model parameterization and a larger smoothing kernel. Using an +Implicit Baseline +in a contrast condition meaningfully increased group similarity and ICC estimates as compared to using the +Neutral +cue. This effect was largest for the Cue Model parameterization; however, improvements in reliability came at the cost of interpretability. This study illustrates that estimates of reliability in the MID task are consistently low and variable at small samples, and a higher test-retest reliability may not always improve interpretability of the estimated BOLD signal.}, + author = {Demidenko, Michael I. and Mumford, Jeanette A. and Poldrack, Russell A.}, + doi = {10.1101/2024.03.19.585755}, + file = {Demidenko et al. - 2024 - Impact of analytic decisions on test-retest reliab.pdf:/home/alpron/Zotero/storage/JDL4YPK4/Demidenko et al. - 2024 - Impact of analytic decisions on test-retest reliab.pdf:application/pdf}, + keywords = {to read, to_add_github}, + language = {en}, + month = {March}, + shorttitle = {Impact of analytic decisions on test-retest reliability of individual and group estimates in functional magnetic resonance imaging}, + title = {Impact of analytic decisions on test-retest reliability of individual and group estimates in functional magnetic resonance imaging: a multiverse analysis using the monetary incentive delay task}, + url = {http://biorxiv.org/lookup/doi/10.1101/2024.03.19.585755}, + urldate = {2024-07-22}, + year = {2024} +} diff --git a/content/publication/demidenko-impact-2024/index.md b/content/publication/demidenko-impact-2024/index.md new file mode 100644 index 0000000..d226c2a --- /dev/null +++ b/content/publication/demidenko-impact-2024/index.md @@ -0,0 +1,47 @@ +--- +title: 'Impact of analytic decisions on test-retest reliability of individual and + group estimates in functional magnetic resonance imaging: a multiverse analysis + using the monetary incentive delay task' +authors: +- Michael I. Demidenko +- Jeanette A. Mumford +- Russell A. Poldrack +date: '2024-03-01' +publishDate: '2024-07-23T12:57:18.697602Z' +publication_types: +- manuscript +doi: 10.1101/2024.03.19.585755 +abstract: 'Abstract Empirical studies reporting low test-retest reliability of individual + blood oxygen-level dependent (BOLD) signal estimates in functional magnetic resonance + imaging (fMRI) data have resurrected interest among cognitive neuroscientists in + methods that may improve reliability in fMRI. Over the last decade, several individual + studies have reported that modeling decisions, such as smoothing, motion correction + and contrast selection, may improve estimates of test-retest reliability of BOLD + signal estimates. However, it remains an empirical question whether certain analytic + decisions consistently improve individual and group level reliability estimates + in an fMRI task across multiple large, independent samples. This study used three + independent samples ( N s: 60, 81, 119) that collected the same task (Monetary Incentive + Delay task) across two runs and two sessions to evaluate the effects of analytic + decisions on the individual (intraclass correlation coefficient [ICC(3,1)]) and + group (Jaccard/Spearman rho ) reliability estimates of BOLD activity of task fMRI + data. The analytic decisions in this study vary across four categories: smoothing + kernel (five options), motion correction (four options), task parameterizing (three + options) and task contrasts (four options), totaling 240 different pipeline permutations. + Across all 240 pipelines, the median ICC estimates are consistently low, with a + maximum median ICC estimate of .43 - .55 across the three samples. The analytic + decisions with the greatest impact on the median ICC and group similarity estimates + are the Implicit Baseline contrast, Cue Model parameterization and a larger smoothing + kernel. Using an Implicit Baseline in a contrast condition meaningfully increased + group similarity and ICC estimates as compared to using the Neutral cue. This effect + was largest for the Cue Model parameterization; however, improvements in reliability + came at the cost of interpretability. This study illustrates that estimates of reliability + in the MID task are consistently low and variable at small samples, and a higher + test-retest reliability may not always improve interpretability of the estimated + BOLD signal.' +tags: +- to read +- to_add_github +links: +- name: URL + url: http://biorxiv.org/lookup/doi/10.1101/2024.03.19.585755 +--- diff --git a/content/publication/elisadecastroguerra-vers-2024/cite.bib b/content/publication/elisadecastroguerra-vers-2024/cite.bib new file mode 100644 index 0000000..b322086 --- /dev/null +++ b/content/publication/elisadecastroguerra-vers-2024/cite.bib @@ -0,0 +1,10 @@ +@book{elisadecastroguerra_vers_2024, + abstract = {Livre d’introduction à la recherche reproductible rédigé lors d’un booksprint.}, + author = {Elisa de Castro Guerra, Sabrina Granger, Boris Hejblum, Arnaud Legrand, Pascal Pernot, Nicolas Rougier Facilitatrice :, Loïc Desquilbet}, + file = {Snapshot:/home/alpron/Zotero/storage/7VJEDL93/bookrr.html:text/html}, + month = {January}, + title = {Vers une recherche reproductible}, + url = {https://bookdown.org/alegrand/bookdown/}, + urldate = {2024-01-16}, + year = {2024} +} diff --git a/content/publication/elisadecastroguerra-vers-2024/index.md b/content/publication/elisadecastroguerra-vers-2024/index.md new file mode 100644 index 0000000..a4359ce --- /dev/null +++ b/content/publication/elisadecastroguerra-vers-2024/index.md @@ -0,0 +1,14 @@ +--- +title: Vers une recherche reproductible +authors: +- Sabrina Granger, Boris Hejblum, Arnaud Legrand, Pascal Pernot, Nicolas Rougier Facilitatrice + :, Loïc Desquilbet Elisa de Castro Guerra +date: '2024-01-01' +publishDate: '2024-07-23T12:57:18.520397Z' +publication_types: +- book +abstract: Livre d’introduction à la recherche reproductible rédigé lors d’un booksprint. +links: +- name: URL + url: https://bookdown.org/alegrand/bookdown/ +--- diff --git a/content/publication/giehl-sharing-2024/cite.bib b/content/publication/giehl-sharing-2024/cite.bib new file mode 100644 index 0000000..45e271b --- /dev/null +++ b/content/publication/giehl-sharing-2024/cite.bib @@ -0,0 +1,18 @@ +@article{giehl_sharing_2024, + author = {Giehl, Kathrin and Mutsaerts, Henk-Jan and Aarts, Kristien and Barkhof, Frederik and Caspers, Svenja and Chetelat, Gaël and Colin, Marie-Elisabeth and Düzel, Emrah and Frisoni, Giovanni B and Ikram, M Arfan and Jovicich, Jorge and Morbelli, Silvia and Oertel, Wolfgang and Paret, Christian and Perani, Daniela and Ritter, Petra and Segura, Bàrbara and Wisse, Laura E M and De Witte, Elke and Cappa, Stefano F and Van Eimeren, Thilo}, + doi = {10.1016/S2589-7500(24)00069-4}, + file = {Giehl et al. - 2024 - Sharing brain imaging data in the Open Science era.pdf:/home/alpron/Zotero/storage/KLC63VH2/Giehl et al. - 2024 - Sharing brain imaging data in the Open Science era.pdf:application/pdf}, + issn = {25897500}, + journal = {The Lancet Digital Health}, + keywords = {Review}, + language = {en}, + month = {July}, + number = {7}, + pages = {e526--e535}, + shorttitle = {Sharing brain imaging data in the Open Science era}, + title = {Sharing brain imaging data in the Open Science era: how and why?}, + url = {https://linkinghub.elsevier.com/retrieve/pii/S2589750024000694}, + urldate = {2024-07-15}, + volume = {6}, + year = {2024} +} diff --git a/content/publication/giehl-sharing-2024/index.md b/content/publication/giehl-sharing-2024/index.md new file mode 100644 index 0000000..3500a28 --- /dev/null +++ b/content/publication/giehl-sharing-2024/index.md @@ -0,0 +1,36 @@ +--- +title: 'Sharing brain imaging data in the Open Science era: how and why?' +authors: +- Kathrin Giehl +- Henk-Jan Mutsaerts +- Kristien Aarts +- Frederik Barkhof +- Svenja Caspers +- Gaël Chetelat +- Marie-Elisabeth Colin +- Emrah Düzel +- Giovanni B Frisoni +- M Arfan Ikram +- Jorge Jovicich +- Silvia Morbelli +- Wolfgang Oertel +- Christian Paret +- Daniela Perani +- Petra Ritter +- Bàrbara Segura +- Laura E M Wisse +- Elke De Witte +- Stefano F Cappa +- Thilo Van Eimeren +date: '2024-07-01' +publishDate: '2024-07-23T12:57:18.662341Z' +publication_types: +- article-journal +publication: '*The Lancet Digital Health*' +doi: 10.1016/S2589-7500(24)00069-4 +tags: +- Review +links: +- name: URL + url: https://linkinghub.elsevier.com/retrieve/pii/S2589750024000694 +--- diff --git a/content/publication/goble-fair-2020/cite.bib b/content/publication/goble-fair-2020/cite.bib new file mode 100644 index 0000000..d5430b6 --- /dev/null +++ b/content/publication/goble-fair-2020/cite.bib @@ -0,0 +1,17 @@ +@article{goble_fair_2020, + abstract = {Computational workflows describe the complex multi-step methods that are used for data collection, data preparation, analytics, predictive modelling, and simulation that lead to new data products. They can inherently contribute to the FAIR data principles: by processing data according to established metadata; by creating metadata themselves during the processing of data; and by tracking and recording data provenance. These properties aid data quality assessment and contribute to secondary data usage. Moreover, workflows are digital objects in their own right. This paper argues that FAIR principles for workflows need to address their specific nature in terms of their composition of executable software steps, their provenance, and their development.}, + author = {Goble, Carole and Cohen-Boulakia, Sarah and Soiland-Reyes, Stian and Garijo, Daniel and Gil, Yolanda and Crusoe, Michael R. and Peters, Kristian and Schober, Daniel}, + doi = {10.1162/dint_a_00033}, + file = {Goble et al. - 2020 - FAIR Computational Workflows.pdf:/home/alpron/Zotero/storage/B723BQ83/Goble et al. - 2020 - FAIR Computational Workflows.pdf:application/pdf}, + issn = {2641-435X}, + journal = {Data Intelligence}, + language = {en}, + month = {January}, + number = {1-2}, + pages = {108--121}, + title = {FAIR Computational Workflows}, + url = {https://direct.mit.edu/dint/article/2/1-2/108-121/10003}, + urldate = {2024-02-28}, + volume = {2}, + year = {2020} +} diff --git a/content/publication/goble-fair-2020/index.md b/content/publication/goble-fair-2020/index.md new file mode 100644 index 0000000..21ab5b4 --- /dev/null +++ b/content/publication/goble-fair-2020/index.md @@ -0,0 +1,31 @@ +--- +title: FAIR Computational Workflows +authors: +- Carole Goble +- Sarah Cohen-Boulakia +- Stian Soiland-Reyes +- Daniel Garijo +- Yolanda Gil +- Michael R. Crusoe +- Kristian Peters +- Daniel Schober +date: '2020-01-01' +publishDate: '2024-07-23T12:57:18.576413Z' +publication_types: +- article-journal +publication: '*Data Intelligence*' +doi: 10.1162/dint_a_00033 +abstract: 'Computational workflows describe the complex multi-step methods that are + used for data collection, data preparation, analytics, predictive modelling, and + simulation that lead to new data products. They can inherently contribute to the + FAIR data principles: by processing data according to established metadata; by creating + metadata themselves during the processing of data; and by tracking and recording + data provenance. These properties aid data quality assessment and contribute to + secondary data usage. Moreover, workflows are digital objects in their own right. + This paper argues that FAIR principles for workflows need to address their specific + nature in terms of their composition of executable software steps, their provenance, + and their development.' +links: +- name: URL + url: https://direct.mit.edu/dint/article/2/1-2/108-121/10003 +--- diff --git a/content/publication/halchenko-datalad-2021/cite.bib b/content/publication/halchenko-datalad-2021/cite.bib new file mode 100644 index 0000000..00fad5c --- /dev/null +++ b/content/publication/halchenko-datalad-2021/cite.bib @@ -0,0 +1,18 @@ +@article{halchenko_datalad_2021, + author = {Halchenko, Yaroslav and Meyer, Kyle and Poldrack, Benjamin and Solanky, Debanjum and Wagner, Adina and Gors, Jason and MacFarlane, Dave and Pustina, Dorian and Sochat, Vanessa and Ghosh, Satrajit and Mönch, Christian and Markiewicz, Christopher and Waite, Laura and Shlyakhter, Ilya and De La Vega, Alejandro and Hayashi, Soichi and Häusler, Christian and Poline, Jean-Baptiste and Kadelka, Tobias and Skytén, Kusti and Jarecka, Dorota and Kennedy, David and Strauss, Ted and Cieslak, Matt and Vavra, Peter and Ioanas, Horea-Ioan and Schneider, Robin and Pflüger, Mika and Haxby, James and Eickhoff, Simon and Hanke, Michael}, + doi = {10.21105/joss.03262}, + file = {Halchenko et al. - 2021 - DataLad distributed system for joint management o.pdf:/home/alpron/Zotero/storage/JWK96IST/Halchenko et al. - 2021 - DataLad distributed system for joint management o.pdf:application/pdf}, + issn = {2475-9066}, + journal = {Journal of Open Source Software}, + keywords = {datalad}, + month = {July}, + note = {Number: 63}, + number = {63}, + pages = {3262}, + shorttitle = {DataLad}, + title = {DataLad: distributed system for joint management of code, data, and their relationship}, + url = {https://joss.theoj.org/papers/10.21105/joss.03262}, + urldate = {2024-01-03}, + volume = {6}, + year = {2021} +} diff --git a/content/publication/halchenko-datalad-2021/index.md b/content/publication/halchenko-datalad-2021/index.md new file mode 100644 index 0000000..1af03b0 --- /dev/null +++ b/content/publication/halchenko-datalad-2021/index.md @@ -0,0 +1,47 @@ +--- +title: 'DataLad: distributed system for joint management of code, data, and their + relationship' +authors: +- Yaroslav Halchenko +- Kyle Meyer +- Benjamin Poldrack +- Debanjum Solanky +- Adina Wagner +- Jason Gors +- Dave MacFarlane +- Dorian Pustina +- Vanessa Sochat +- Satrajit Ghosh +- Christian Mönch +- Christopher Markiewicz +- Laura Waite +- Ilya Shlyakhter +- Alejandro De La Vega +- Soichi Hayashi +- Christian Häusler +- Jean-Baptiste Poline +- Tobias Kadelka +- Kusti Skytén +- Dorota Jarecka +- David Kennedy +- Ted Strauss +- Matt Cieslak +- Peter Vavra +- Horea-Ioan Ioanas +- Robin Schneider +- Mika Pflüger +- James Haxby +- Simon Eickhoff +- Michael Hanke +date: '2021-07-01' +publishDate: '2024-07-23T12:57:18.490462Z' +publication_types: +- article-journal +publication: '*Journal of Open Source Software*' +doi: 10.21105/joss.03262 +tags: +- datalad +links: +- name: URL + url: https://joss.theoj.org/papers/10.21105/joss.03262 +--- diff --git a/content/publication/halchenko-nipyheudiconv-2023/cite.bib b/content/publication/halchenko-nipyheudiconv-2023/cite.bib new file mode 100644 index 0000000..42c2c84 --- /dev/null +++ b/content/publication/halchenko-nipyheudiconv-2023/cite.bib @@ -0,0 +1,14 @@ +@misc{halchenko_nipyheudiconv_2023, + abstract = {💥 Breaking Change gh-actions: Bump actions/checkout from 3 to 4 #703 (@dependabot[bot]) 🚀 Enhancement Fix inconsistent behavior of existing session when using -d compared to --files option: raise an AssertionError instead of just a warning #682 (@neurorepro) 🐛 Bug Fix Various tiny enhancements flake etc demanded #702 (@yarikoptic) Boost claimed BIDS version to 1.8.0 from 1.4.1 #699 (@yarikoptic) Point to Courtois-neuromod heuristic #702 (@yarikoptic) 🏠 Internal Add codespell to lint tox env #706 (@yarikoptic) test-compare-two-versions.sh: also ignore differences in HeudiconvVersion field in jsons since we have it there now #685 (@yarikoptic) 📝 Documentation Add description of placeholders which could be used in the produced templates #681 (@yarikoptic) Authors: 3 @dependabot[bot] Michael (@neurorepro) Yaroslav Halchenko (@yarikoptic)}, + author = {Halchenko, Yaroslav and Goncalves, Mathias and Velasco, Pablo and Di Oleggio Castello, Matteo Visconti and Satrajit Ghosh and Salo, Taylor and Wodder, John T. and Hanke, Michael and Sadil, Patrick and Christian, Horea and Michael and Dae and Tilley, Steven and Kent, James and To, Isaac and Brett, Matthew and Amlien, Inge and Gorgolewski, Chris and Markiewicz, Chris and Lukas, Darren Christopher and Callenberg, Keith and Aksoo and Kahn, Ari and Macdonald, Austin and Poldrack, Benjamin and Melo, Bruno and Braun, Henry and Lee, John and Pellman, John and Michael}, + copyright = {Open Access}, + doi = {10.5281/ZENODO.1012598}, + keywords = {datalad, BIDS}, + month = {September}, + publisher = {[object Object]}, + shorttitle = {nipy/heudiconv}, + title = {nipy/heudiconv: v1.0.0}, + url = {https://zenodo.org/record/1012598}, + urldate = {2024-03-19}, + year = {2023} +} diff --git a/content/publication/halchenko-nipyheudiconv-2023/index.md b/content/publication/halchenko-nipyheudiconv-2023/index.md new file mode 100644 index 0000000..f953dc7 --- /dev/null +++ b/content/publication/halchenko-nipyheudiconv-2023/index.md @@ -0,0 +1,56 @@ +--- +title: 'nipy/heudiconv: v1.0.0' +authors: +- Yaroslav Halchenko +- Mathias Goncalves +- Pablo Velasco +- Matteo Visconti Di Oleggio Castello +- Satrajit Ghosh +- Taylor Salo +- John T. Wodder +- Michael Hanke +- Patrick Sadil +- Horea Christian +- ' Michael' +- ' Dae' +- Steven Tilley +- James Kent +- Isaac To +- Matthew Brett +- Inge Amlien +- Chris Gorgolewski +- Chris Markiewicz +- Darren Christopher Lukas +- Keith Callenberg +- ' Aksoo' +- Ari Kahn +- Austin Macdonald +- Benjamin Poldrack +- Bruno Melo +- Henry Braun +- John Lee +- John Pellman +- ' Michael' +date: '2023-09-01' +publishDate: '2024-07-23T12:57:18.603055Z' +publication_types: +- manuscript +publication: '*[object Object]*' +doi: 10.5281/ZENODO.1012598 +abstract: '💥 Breaking Change gh-actions: Bump actions/checkout from 3 to 4 #703 (@dependabot[bot]) + 🚀 Enhancement Fix inconsistent behavior of existing session when using -d compared + to --files option: raise an AssertionError instead of just a warning #682 (@neurorepro) + 🐛 Bug Fix Various tiny enhancements flake etc demanded #702 (@yarikoptic) Boost + claimed BIDS version to 1.8.0 from 1.4.1 #699 (@yarikoptic) Point to Courtois-neuromod + heuristic #702 (@yarikoptic) 🏠 Internal Add codespell to lint tox env #706 (@yarikoptic) + test-compare-two-versions.sh: also ignore differences in HeudiconvVersion field + in jsons since we have it there now #685 (@yarikoptic) 📝 Documentation Add description + of placeholders which could be used in the produced templates #681 (@yarikoptic) + Authors: 3 @dependabot[bot] Michael (@neurorepro) Yaroslav Halchenko (@yarikoptic)' +tags: +- datalad +- BIDS +links: +- name: URL + url: https://zenodo.org/record/1012598 +--- diff --git a/content/publication/jadavji-editorial-2023/cite.bib b/content/publication/jadavji-editorial-2023/cite.bib new file mode 100644 index 0000000..37da7e9 --- /dev/null +++ b/content/publication/jadavji-editorial-2023/cite.bib @@ -0,0 +1,16 @@ +@article{jadavji_editorial_2023, + author = {Jadavji, Nafisa M. and Haelterman, Nele A. and Sud, Reeteka and Antonietti, Alberto}, + doi = {10.3389/fnint.2023.1271818}, + file = {Jadavji et al. - 2023 - Editorial Reproducibility in neuroscience.pdf:/home/alpron/Zotero/storage/G7DDIIRI/Jadavji et al. - 2023 - Editorial Reproducibility in neuroscience.pdf:application/pdf}, + issn = {1662-5145}, + journal = {Frontiers in Integrative Neuroscience}, + language = {en}, + month = {August}, + pages = {1271818}, + shorttitle = {Editorial}, + title = {Editorial: Reproducibility in neuroscience}, + url = {https://www.frontiersin.org/articles/10.3389/fnint.2023.1271818/full}, + urldate = {2024-07-15}, + volume = {17}, + year = {2023} +} diff --git a/content/publication/jadavji-editorial-2023/index.md b/content/publication/jadavji-editorial-2023/index.md new file mode 100644 index 0000000..42afe04 --- /dev/null +++ b/content/publication/jadavji-editorial-2023/index.md @@ -0,0 +1,17 @@ +--- +title: 'Editorial: Reproducibility in neuroscience' +authors: +- Nafisa M. Jadavji +- Nele A. Haelterman +- Reeteka Sud +- Alberto Antonietti +date: '2023-08-01' +publishDate: '2024-07-23T12:57:18.676307Z' +publication_types: +- article-journal +publication: '*Frontiers in Integrative Neuroscience*' +doi: 10.3389/fnint.2023.1271818 +links: +- name: URL + url: https://www.frontiersin.org/articles/10.3389/fnint.2023.1271818/full +--- diff --git a/content/publication/luppi-systematic-2024/cite.bib b/content/publication/luppi-systematic-2024/cite.bib new file mode 100644 index 0000000..2b53b3c --- /dev/null +++ b/content/publication/luppi-systematic-2024/cite.bib @@ -0,0 +1,18 @@ +@article{luppi_systematic_2024, + abstract = {Abstract +Functional interactions between brain regions can be viewed as a network, enabling neuroscientists to investigate brain function through network science. Here, we systematically evaluate 768 data-processing pipelines for network reconstruction from resting-state functional MRI, evaluating the effect of brain parcellation, connectivity definition, and global signal regression. Our criteria seek pipelines that minimise motion confounds and spurious test-retest discrepancies of network topology, while being sensitive to both inter-subject differences and experimental effects of interest. We reveal vast and systematic variability across pipelines’ suitability for functional connectomics. Inappropriate choice of data-processing pipeline can produce results that are not only misleading, but systematically so, with the majority of pipelines failing at least one criterion. However, a set of optimal pipelines consistently satisfy all criteria across different datasets, spanning minutes, weeks, and months. We provide a full breakdown of each pipeline’s performance across criteria and datasets, to inform future best practices in functional connectomics.}, + author = {Luppi, Andrea I. and Gellersen, Helena M. and Liu, Zhen-Qi and Peattie, Alexander R. D. and Manktelow, Anne E. and Adapa, Ram and Owen, Adrian M. and Naci, Lorina and Menon, David K. and Dimitriadis, Stavros I. and Stamatakis, Emmanuel A.}, + doi = {10.1038/s41467-024-48781-5}, + file = {Luppi et al. - 2024 - Systematic evaluation of fMRI data-processing pipe.pdf:/home/alpron/Zotero/storage/HB2VTQ7Y/Luppi et al. - 2024 - Systematic evaluation of fMRI data-processing pipe.pdf:application/pdf}, + issn = {2041-1723}, + journal = {Nature Communications}, + language = {en}, + month = {June}, + number = {1}, + pages = {4745}, + title = {Systematic evaluation of fMRI data-processing pipelines for consistent functional connectomics}, + url = {https://www.nature.com/articles/s41467-024-48781-5}, + urldate = {2024-06-07}, + volume = {15}, + year = {2024} +} diff --git a/content/publication/luppi-systematic-2024/index.md b/content/publication/luppi-systematic-2024/index.md new file mode 100644 index 0000000..0c02274 --- /dev/null +++ b/content/publication/luppi-systematic-2024/index.md @@ -0,0 +1,39 @@ +--- +title: Systematic evaluation of fMRI data-processing pipelines for consistent functional + connectomics +authors: +- Andrea I. Luppi +- Helena M. Gellersen +- Zhen-Qi Liu +- Alexander R. D. Peattie +- Anne E. Manktelow +- Ram Adapa +- Adrian M. Owen +- Lorina Naci +- David K. Menon +- Stavros I. Dimitriadis +- Emmanuel A. Stamatakis +date: '2024-06-01' +publishDate: '2024-07-23T12:57:18.640790Z' +publication_types: +- article-journal +publication: '*Nature Communications*' +doi: 10.1038/s41467-024-48781-5 +abstract: Abstract Functional interactions between brain regions can be viewed as + a network, enabling neuroscientists to investigate brain function through network + science. Here, we systematically evaluate 768 data-processing pipelines for network + reconstruction from resting-state functional MRI, evaluating the effect of brain + parcellation, connectivity definition, and global signal regression. Our criteria + seek pipelines that minimise motion confounds and spurious test-retest discrepancies + of network topology, while being sensitive to both inter-subject differences and + experimental effects of interest. We reveal vast and systematic variability across + pipelines’ suitability for functional connectomics. Inappropriate choice of data-processing + pipeline can produce results that are not only misleading, but systematically so, + with the majority of pipelines failing at least one criterion. However, a set of + optimal pipelines consistently satisfy all criteria across different datasets, spanning + minutes, weeks, and months. We provide a full breakdown of each pipeline’s performance + across criteria and datasets, to inform future best practices in functional connectomics. +links: +- name: URL + url: https://www.nature.com/articles/s41467-024-48781-5 +--- diff --git a/content/publication/martone-past-2024/cite.bib b/content/publication/martone-past-2024/cite.bib new file mode 100644 index 0000000..949b02f --- /dev/null +++ b/content/publication/martone-past-2024/cite.bib @@ -0,0 +1,18 @@ +@article{martone_past_2024, + abstract = {Neuroscience has made significant strides over the past decade in moving from a largely closed science characterized by anemic data sharing, to a largely open science where the amount of publicly available neuroscience data has increased dramatically. While this increase is driven in significant part by large prospective data sharing studies, we are starting to see increased sharing in the long tail of neuroscience data, driven no doubt by journal requirements and funder mandates. Concomitant with this shift to open is the increasing support of the FAIR data principles by neuroscience practices and infrastructure. FAIR is particularly critical for neuroscience with its multiplicity of data types, scales and model systems and the infrastructure that serves them. As envisioned from the early days of neuroinformatics, neuroscience is currently served by a globally distributed ecosystem of neuroscience-centric data repositories, largely specialized around data types. To make neuroscience data findable, accessible, interoperable, and reusable requires the coordination across different stakeholders, including the researchers who produce the data, data repositories who make it available, the aggregators and indexers who field search engines across the data, and community organizations who help to coordinate efforts and develop the community standards critical to FAIR. The International Neuroinformatics Coordinating Facility has led efforts to move neuroscience toward FAIR, fielding several resources to help researchers and repositories achieve FAIR. In this perspective, I provide an overview of the components and practices required to achieve FAIR in neuroscience and provide thoughts on the past, present and future of FAIR infrastructure for neuroscience, from the laboratory to the search engine.}, + author = {Martone, Maryann E.}, + doi = {10.3389/fninf.2023.1276407}, + file = {Martone - 2024 - The past, present and future of neuroscience data .pdf:/home/alpron/Zotero/storage/JSZZGSIN/Martone - 2024 - The past, present and future of neuroscience data .pdf:application/pdf}, + issn = {1662-5196}, + journal = {Frontiers in Neuroinformatics}, + keywords = {Important, Review, oscars}, + language = {en}, + month = {January}, + pages = {1276407}, + shorttitle = {The past, present and future of neuroscience data sharing}, + title = {The past, present and future of neuroscience data sharing: a perspective on the state of practices and infrastructure for FAIR}, + url = {https://www.frontiersin.org/articles/10.3389/fninf.2023.1276407/full}, + urldate = {2024-04-22}, + volume = {17}, + year = {2024} +} diff --git a/content/publication/martone-past-2024/index.md b/content/publication/martone-past-2024/index.md new file mode 100644 index 0000000..2fd7f8c --- /dev/null +++ b/content/publication/martone-past-2024/index.md @@ -0,0 +1,41 @@ +--- +title: 'The past, present and future of neuroscience data sharing: a perspective on + the state of practices and infrastructure for FAIR' +authors: +- Maryann E. Martone +date: '2024-01-01' +publishDate: '2024-07-23T12:57:18.633187Z' +publication_types: +- article-journal +publication: '*Frontiers in Neuroinformatics*' +doi: 10.3389/fninf.2023.1276407 +abstract: Neuroscience has made significant strides over the past decade in moving + from a largely closed science characterized by anemic data sharing, to a largely + open science where the amount of publicly available neuroscience data has increased + dramatically. While this increase is driven in significant part by large prospective + data sharing studies, we are starting to see increased sharing in the long tail + of neuroscience data, driven no doubt by journal requirements and funder mandates. + Concomitant with this shift to open is the increasing support of the FAIR data principles + by neuroscience practices and infrastructure. FAIR is particularly critical for + neuroscience with its multiplicity of data types, scales and model systems and the + infrastructure that serves them. As envisioned from the early days of neuroinformatics, + neuroscience is currently served by a globally distributed ecosystem of neuroscience-centric + data repositories, largely specialized around data types. To make neuroscience data + findable, accessible, interoperable, and reusable requires the coordination across + different stakeholders, including the researchers who produce the data, data repositories + who make it available, the aggregators and indexers who field search engines across + the data, and community organizations who help to coordinate efforts and develop + the community standards critical to FAIR. The International Neuroinformatics Coordinating + Facility has led efforts to move neuroscience toward FAIR, fielding several resources + to help researchers and repositories achieve FAIR. In this perspective, I provide + an overview of the components and practices required to achieve FAIR in neuroscience + and provide thoughts on the past, present and future of FAIR infrastructure for + neuroscience, from the laboratory to the search engine. +tags: +- Important +- Review +- oscars +links: +- name: URL + url: https://www.frontiersin.org/articles/10.3389/fninf.2023.1276407/full +--- diff --git a/content/publication/michael-demidenko-demidenmpyrelimri-2024/cite.bib b/content/publication/michael-demidenko-demidenmpyrelimri-2024/cite.bib new file mode 100644 index 0000000..e16070d --- /dev/null +++ b/content/publication/michael-demidenko-demidenmpyrelimri-2024/cite.bib @@ -0,0 +1,25 @@ +@misc{michael_demidenko_demidenmpyrelimri_2024, + abstract = {Several changes and additions are made: + + + +A more comprehensive docstring is used to enhance information on PyReliMRI readthedocs + +The ICC function tests were expanded to confirm ICC, between subject variance and within subject variance estimates from sumqc_icc() are compared to estimates from liner mixed effect model outputs from stat models. Specifically, see test comparing pyrelimri.icc vs statsmodels + +The returned list of estimates for ICC computations have been revised. Previously, the MSBS and MSWS were returned. However, this was not always informative to the ICC being computed, nor to understanding how variances are impacted. For example, when the ICC(3,1) was returned, the MSBS and MSWS were not directly used in the computation. Furthermore, they were not true to the denominator and the numerator for each formula. Thus, for voxelwise, edgewise, rois, etc., where sumsq_icc() is used, now a dictionary is returned with: 1) ICC estimate, 2) upper 95CI, 3) lower 95CI, 4) between subject variance, 5) within subject variance, 6) between measure variance (in case of ICC(2,1), otherwise None/empty values) + +Included a TR-by-TR timeseries extraction for masks/coordinates and locked to onset times/behavioral events. This provides the extraction of the mean signal change across a timeseries for a given ROI. This is based on Nilearn's niftimaskers where standardizing via the call 'psc' + +Included an edgewise_icc computation on correlation matrices. This is strictly for comparing a list of lists of variables includes subjects' correlation matrices across runs/sessions or the paths to these correlation matrices. Note, if using pandas dataframe standards, it is assumed that the header = None and row index = None}, + author = {Michael Demidenko and Russ Poldrack and Chris Markiewicz and Elizabeth DuPre}, + copyright = {Creative Commons Attribution 4.0 International}, + doi = {10.5281/ZENODO.12512085}, + month = {June}, + publisher = {Zenodo}, + shorttitle = {demidenm/PyReliMRI}, + title = {demidenm/PyReliMRI: v2.1.0}, + url = {https://zenodo.org/doi/10.5281/zenodo.12512085}, + urldate = {2024-06-24}, + year = {2024} +} diff --git a/content/publication/michael-demidenko-demidenmpyrelimri-2024/index.md b/content/publication/michael-demidenko-demidenmpyrelimri-2024/index.md new file mode 100644 index 0000000..8639ce6 --- /dev/null +++ b/content/publication/michael-demidenko-demidenmpyrelimri-2024/index.md @@ -0,0 +1,39 @@ +--- +title: 'demidenm/PyReliMRI: v2.1.0' +authors: +- Michael Demidenko +- Russ Poldrack +- Chris Markiewicz +- Elizabeth DuPre +date: '2024-06-01' +publishDate: '2024-07-23T12:57:18.648058Z' +publication_types: +- manuscript +publication: '*Zenodo*' +doi: 10.5281/ZENODO.12512085 +abstract: "Several changes and additions are made: A more comprehensive docstring + is used to enhance information on PyReliMRI readthedocs The ICC function tests + were expanded to confirm ICC, between subject variance and within subject variance + estimates from sumqc_icc() are compared to estimates from liner mixed effect model + outputs from stat models. Specifically, see test comparing pyrelimri.icc vs statsmodels\ + \ The returned list of estimates for ICC computations have been revised. Previously, + the MSBS and MSWS were returned. However, this was not always informative to the + ICC being computed, nor to understanding how variances are impacted. For example, + when the ICC(3,1) was returned, the MSBS and MSWS were not directly used in the + computation. Furthermore, they were not true to the denominator and the numerator + for each formula. Thus, for voxelwise, edgewise, rois, etc., where sumsq_icc() is + used, now a dictionary is returned with: 1) ICC estimate, 2) upper 95CI, 3) lower + 95CI, 4) between subject variance, 5) within subject variance, 6) between measure + variance (in case of ICC(2,1), otherwise None/empty values) Included a TR-by-TR + timeseries extraction for masks/coordinates and locked to onset times/behavioral + events. This provides the extraction of the mean signal change across a timeseries + for a given ROI. This is based on Nilearn's niftimaskers where standardizing via + the call 'psc' Included an edgewise_icc computation on correlation matrices. This + is strictly for comparing a list of lists of variables includes subjects' correlation + matrices across runs/sessions or the paths to these correlation matrices. Note, + if using pandas dataframe standards, it is assumed that the header = None and row + index = None" +links: +- name: URL + url: https://zenodo.org/doi/10.5281/zenodo.12512085 +--- diff --git a/content/publication/nichols-best-2017/cite.bib b/content/publication/nichols-best-2017/cite.bib new file mode 100644 index 0000000..2108b6d --- /dev/null +++ b/content/publication/nichols-best-2017/cite.bib @@ -0,0 +1,10 @@ +@article{nichols_best_2017, + author = {Nichols, Thomas E and Das, Samir and Eickhoff, Simon B and Evans, Alan C and Glatard, Tristan and Hanke, Michael and Kriegeskorte, Nikolaus and Milham, Michael P and Poldrack, Russell A and Poline, Jean-Baptiste and Proal, Erika and Thirion, Bertrand}, + file = {Nichols et al. - 2017 - Best practices in data analysis and sharing in neu.pdf:/home/alpron/Zotero/storage/H88I739N/Nichols et al. - 2017 - Best practices in data analysis and sharing in neu.pdf:application/pdf}, + journal = {CO M M E N TA RY}, + language = {en}, + number = {3}, + title = {Best practices in data analysis and sharing in neuroimaging using MRI}, + volume = {20}, + year = {2017} +} diff --git a/content/publication/nichols-best-2017/index.md b/content/publication/nichols-best-2017/index.md new file mode 100644 index 0000000..6097b83 --- /dev/null +++ b/content/publication/nichols-best-2017/index.md @@ -0,0 +1,21 @@ +--- +title: Best practices in data analysis and sharing in neuroimaging using MRI +authors: +- Thomas E Nichols +- Samir Das +- Simon B Eickhoff +- Alan C Evans +- Tristan Glatard +- Michael Hanke +- Nikolaus Kriegeskorte +- Michael P Milham +- Russell A Poldrack +- Jean-Baptiste Poline +- Erika Proal +- Bertrand Thirion +date: '2017-01-01' +publishDate: '2024-07-23T12:57:18.596931Z' +publication_types: +- article-journal +publication: '*CO M M E N TA RY*' +--- diff --git a/content/publication/niso-open-2022/cite.bib b/content/publication/niso-open-2022/cite.bib new file mode 100644 index 0000000..d20f973 --- /dev/null +++ b/content/publication/niso-open-2022/cite.bib @@ -0,0 +1,16 @@ +@article{niso_open_2022, + author = {Niso, Guiomar and Botvinik-Nezer, Rotem and Appelhoff, Stefan and De La Vega, Alejandro and Esteban, Oscar and Etzel, Joset A. and Finc, Karolina and Ganz, Melanie and Gau, Rémi and Halchenko, Yaroslav O. and Herholz, Peer and Karakuzu, Agah and Keator, David B. and Markiewicz, Christopher J. and Maumet, Camille and Pernet, Cyril R. and Pestilli, Franco and Queder, Nazek and Schmitt, Tina and Sójka, Weronika and Wagner, Adina S. and Whitaker, Kirstie J. and Rieger, Jochem W.}, + doi = {10.1016/j.neuroimage.2022.119623}, + file = {Niso et al. - 2022 - Open and reproducible neuroimaging From study inc.pdf:/home/alpron/Zotero/storage/6IM7E6YQ/Niso et al. - 2022 - Open and reproducible neuroimaging From study inc.pdf:application/pdf}, + issn = {10538119}, + journal = {NeuroImage}, + language = {en}, + month = {November}, + pages = {119623}, + shorttitle = {Open and reproducible neuroimaging}, + title = {Open and reproducible neuroimaging: From study inception to publication}, + url = {https://linkinghub.elsevier.com/retrieve/pii/S1053811922007388}, + urldate = {2024-02-09}, + volume = {263}, + year = {2022} +} diff --git a/content/publication/niso-open-2022/index.md b/content/publication/niso-open-2022/index.md new file mode 100644 index 0000000..f539f32 --- /dev/null +++ b/content/publication/niso-open-2022/index.md @@ -0,0 +1,36 @@ +--- +title: 'Open and reproducible neuroimaging: From study inception to publication' +authors: +- Guiomar Niso +- Rotem Botvinik-Nezer +- Stefan Appelhoff +- Alejandro De La Vega +- Oscar Esteban +- Joset A. Etzel +- Karolina Finc +- Melanie Ganz +- Rémi Gau +- Yaroslav O. Halchenko +- Peer Herholz +- Agah Karakuzu +- David B. Keator +- Christopher J. Markiewicz +- Camille Maumet +- Cyril R. Pernet +- Franco Pestilli +- Nazek Queder +- Tina Schmitt +- Weronika Sójka +- Adina S. Wagner +- Kirstie J. Whitaker +- Jochem W. Rieger +date: '2022-11-01' +publishDate: '2024-07-23T12:57:18.554336Z' +publication_types: +- article-journal +publication: '*NeuroImage*' +doi: 10.1016/j.neuroimage.2022.119623 +links: +- name: URL + url: https://linkinghub.elsevier.com/retrieve/pii/S1053811922007388 +--- diff --git a/content/publication/noauthor-bids-standardbep-028-bidsprov-2024/cite.bib b/content/publication/noauthor-bids-standardbep-028-bidsprov-2024/cite.bib new file mode 100644 index 0000000..8105b7d --- /dev/null +++ b/content/publication/noauthor-bids-standardbep-028-bidsprov-2024/cite.bib @@ -0,0 +1,12 @@ +@misc{noauthor_bids-standardbep028_bidsprov_2024, + abstract = {Organizing and coordinating BIDS extension proposal 28 : BIDS Provenance}, + copyright = {CC-BY-4.0}, + keywords = {BIDS, provenance}, + month = {January}, + note = {original-date: 2020-03-05T14:14:58Z}, + publisher = {Brain Imaging Data Structure}, + title = {bids-standard/BEP028_BIDSprov}, + url = {https://github.com/bids-standard/BEP028_BIDSprov}, + urldate = {2024-02-29}, + year = {2024} +} diff --git a/content/publication/noauthor-bids-standardbep-028-bidsprov-2024/index.md b/content/publication/noauthor-bids-standardbep-028-bidsprov-2024/index.md new file mode 100644 index 0000000..d6016c3 --- /dev/null +++ b/content/publication/noauthor-bids-standardbep-028-bidsprov-2024/index.md @@ -0,0 +1,15 @@ +--- +title: bids-standard/BEP028_BIDSprov +date: '2024-01-01' +publishDate: '2024-07-23T12:57:18.590759Z' +publication_types: +- manuscript +publication: '*Brain Imaging Data Structure*' +abstract: 'Organizing and coordinating BIDS extension proposal 28 : BIDS Provenance' +tags: +- BIDS +- provenance +links: +- name: URL + url: https://github.com/bids-standard/BEP028_BIDSprov +--- diff --git a/content/publication/noauthor-containers-2023/cite.bib b/content/publication/noauthor-containers-2023/cite.bib new file mode 100644 index 0000000..daca724 --- /dev/null +++ b/content/publication/noauthor-containers-2023/cite.bib @@ -0,0 +1,13 @@ +@article{noauthor_containers_2023, + abstract = {This PrimeView highlights the range of applications benefiting from the use of containers for reproducibility of computational data analysis.}, + doi = {10.1038/s43586-023-00244-9}, + issn = {2662-8449}, + journal = {Nature Reviews Methods Primers}, + month = {July}, + number = {1}, + pages = {51}, + title = {Containers for computational reproducibility}, + url = {https://doi.org/10.1038/s43586-023-00244-9}, + volume = {3}, + year = {2023} +} diff --git a/content/publication/noauthor-containers-2023/index.md b/content/publication/noauthor-containers-2023/index.md new file mode 100644 index 0000000..2f998fa --- /dev/null +++ b/content/publication/noauthor-containers-2023/index.md @@ -0,0 +1,14 @@ +--- +title: Containers for computational reproducibility +date: '2023-07-01' +publishDate: '2024-07-23T12:57:18.526633Z' +publication_types: +- article-journal +publication: '*Nature Reviews Methods Primers*' +doi: 10.1038/s43586-023-00244-9 +abstract: This PrimeView highlights the range of applications benefiting from the + use of containers for reproducibility of computational data analysis. +links: +- name: URL + url: https://doi.org/10.1038/s43586-023-00244-9 +--- diff --git a/content/publication/rosenblatt-data-2024-1/cite.bib b/content/publication/rosenblatt-data-2024-1/cite.bib new file mode 100644 index 0000000..88ba982 --- /dev/null +++ b/content/publication/rosenblatt-data-2024-1/cite.bib @@ -0,0 +1,18 @@ +@article{rosenblatt_data_2024-1, + abstract = {Abstract +Predictive modeling is a central technique in neuroimaging to identify brain-behavior relationships and test their generalizability to unseen data. However, data leakage undermines the validity of predictive models by breaching the separation between training and test data. Leakage is always an incorrect practice but still pervasive in machine learning. Understanding its effects on neuroimaging predictive models can inform how leakage affects existing literature. Here, we investigate the effects of five forms of leakage–involving feature selection, covariate correction, and dependence between subjects–on functional and structural connectome-based machine learning models across four datasets and three phenotypes. Leakage via feature selection and repeated subjects drastically inflates prediction performance, whereas other forms of leakage have minor effects. Furthermore, small datasets exacerbate the effects of leakage. Overall, our results illustrate the variable effects of leakage and underscore the importance of avoiding data leakage to improve the validity and reproducibility of predictive modeling.}, + author = {Rosenblatt, Matthew and Tejavibulya, Link and Jiang, Rongtao and Noble, Stephanie and Scheinost, Dustin}, + doi = {10.1038/s41467-024-46150-w}, + file = {Rosenblatt et al. - 2024 - Data leakage inflates prediction performance in co.pdf:/home/alpron/Zotero/storage/P3NIEWBK/Rosenblatt et al. - 2024 - Data leakage inflates prediction performance in co.pdf:application/pdf}, + issn = {2041-1723}, + journal = {Nature Communications}, + language = {en}, + month = {February}, + number = {1}, + pages = {1829}, + title = {Data leakage inflates prediction performance in connectome-based machine learning models}, + url = {https://www.nature.com/articles/s41467-024-46150-w}, + urldate = {2024-07-15}, + volume = {15}, + year = {2024} +} diff --git a/content/publication/rosenblatt-data-2024-1/index.md b/content/publication/rosenblatt-data-2024-1/index.md new file mode 100644 index 0000000..3f367f1 --- /dev/null +++ b/content/publication/rosenblatt-data-2024-1/index.md @@ -0,0 +1,33 @@ +--- +title: Data leakage inflates prediction performance in connectome-based machine learning + models +authors: +- Matthew Rosenblatt +- Link Tejavibulya +- Rongtao Jiang +- Stephanie Noble +- Dustin Scheinost +date: '2024-02-01' +publishDate: '2024-07-23T12:57:18.669316Z' +publication_types: +- article-journal +publication: '*Nature Communications*' +doi: 10.1038/s41467-024-46150-w +abstract: Abstract Predictive modeling is a central technique in neuroimaging to identify + brain-behavior relationships and test their generalizability to unseen data. However, + data leakage undermines the validity of predictive models by breaching the separation + between training and test data. Leakage is always an incorrect practice but still + pervasive in machine learning. Understanding its effects on neuroimaging predictive + models can inform how leakage affects existing literature. Here, we investigate + the effects of five forms of leakage–involving feature selection, covariate correction, + and dependence between subjects–on functional and structural connectome-based machine + learning models across four datasets and three phenotypes. Leakage via feature selection + and repeated subjects drastically inflates prediction performance, whereas other + forms of leakage have minor effects. Furthermore, small datasets exacerbate the + effects of leakage. Overall, our results illustrate the variable effects of leakage + and underscore the importance of avoiding data leakage to improve the validity and + reproducibility of predictive modeling. +links: +- name: URL + url: https://www.nature.com/articles/s41467-024-46150-w +--- diff --git a/content/publication/rosenblatt-data-2024/cite.bib b/content/publication/rosenblatt-data-2024/cite.bib new file mode 100644 index 0000000..9c95e28 --- /dev/null +++ b/content/publication/rosenblatt-data-2024/cite.bib @@ -0,0 +1,18 @@ +@article{rosenblatt_data_2024, + abstract = {Abstract +Predictive modeling is a central technique in neuroimaging to identify brain-behavior relationships and test their generalizability to unseen data. However, data leakage undermines the validity of predictive models by breaching the separation between training and test data. Leakage is always an incorrect practice but still pervasive in machine learning. Understanding its effects on neuroimaging predictive models can inform how leakage affects existing literature. Here, we investigate the effects of five forms of leakage–involving feature selection, covariate correction, and dependence between subjects–on functional and structural connectome-based machine learning models across four datasets and three phenotypes. Leakage via feature selection and repeated subjects drastically inflates prediction performance, whereas other forms of leakage have minor effects. Furthermore, small datasets exacerbate the effects of leakage. Overall, our results illustrate the variable effects of leakage and underscore the importance of avoiding data leakage to improve the validity and reproducibility of predictive modeling.}, + author = {Rosenblatt, Matthew and Tejavibulya, Link and Jiang, Rongtao and Noble, Stephanie and Scheinost, Dustin}, + doi = {10.1038/s41467-024-46150-w}, + file = {Rosenblatt et al. - 2024 - Data leakage inflates prediction performance in co.pdf:/home/alpron/Zotero/storage/78MZZXHW/Rosenblatt et al. - 2024 - Data leakage inflates prediction performance in co.pdf:application/pdf}, + issn = {2041-1723}, + journal = {Nature Communications}, + language = {en}, + month = {February}, + number = {1}, + pages = {1829}, + title = {Data leakage inflates prediction performance in connectome-based machine learning models}, + url = {https://www.nature.com/articles/s41467-024-46150-w}, + urldate = {2024-03-19}, + volume = {15}, + year = {2024} +} diff --git a/content/publication/rosenblatt-data-2024/index.md b/content/publication/rosenblatt-data-2024/index.md new file mode 100644 index 0000000..e3e8746 --- /dev/null +++ b/content/publication/rosenblatt-data-2024/index.md @@ -0,0 +1,33 @@ +--- +title: Data leakage inflates prediction performance in connectome-based machine learning + models +authors: +- Matthew Rosenblatt +- Link Tejavibulya +- Rongtao Jiang +- Stephanie Noble +- Dustin Scheinost +date: '2024-02-01' +publishDate: '2024-07-23T12:57:18.611025Z' +publication_types: +- article-journal +publication: '*Nature Communications*' +doi: 10.1038/s41467-024-46150-w +abstract: Abstract Predictive modeling is a central technique in neuroimaging to identify + brain-behavior relationships and test their generalizability to unseen data. However, + data leakage undermines the validity of predictive models by breaching the separation + between training and test data. Leakage is always an incorrect practice but still + pervasive in machine learning. Understanding its effects on neuroimaging predictive + models can inform how leakage affects existing literature. Here, we investigate + the effects of five forms of leakage–involving feature selection, covariate correction, + and dependence between subjects–on functional and structural connectome-based machine + learning models across four datasets and three phenotypes. Leakage via feature selection + and repeated subjects drastically inflates prediction performance, whereas other + forms of leakage have minor effects. Furthermore, small datasets exacerbate the + effects of leakage. Overall, our results illustrate the variable effects of leakage + and underscore the importance of avoiding data leakage to improve the validity and + reproducibility of predictive modeling. +links: +- name: URL + url: https://www.nature.com/articles/s41467-024-46150-w +--- diff --git a/content/publication/segal-embracing-2024/cite.bib b/content/publication/segal-embracing-2024/cite.bib new file mode 100644 index 0000000..d4109b0 --- /dev/null +++ b/content/publication/segal-embracing-2024/cite.bib @@ -0,0 +1,13 @@ +@misc{segal_embracing_2024, + abstract = {Despite decades of research, we lack objective diagnostic or prognostic biomarkers of mental health problems. A key reason for this limited progress is a reliance on the traditional case-control paradigm, which assumes that each disorder has a single cause that can be uncovered by comparing average phenotypic values of cases and control samples. Here, we discuss the problematic assumptions on which this paradigm is based and highlight recent efforts that seek to characterize, rather than minimize, the inherent clinical and biological variability that characterizes psychiatric populations. We argue that embracing such variability will be necessary to understand pathophysiological mechanisms and to develop more targeted and effective treatments.}, + author = {Segal, Ashlea and Tiego, Jeggan and Holmes, Avram J and Andre and Fornito, Alex}, + copyright = {https://creativecommons.org/licenses/by/4.0/legalcode}, + doi = {10.31219/osf.io/5mz46}, + file = {Segal et al. - 2024 - Embracing variability in the search for biological.pdf:/home/alpron/Zotero/storage/PC58H3RX/Segal et al. - 2024 - Embracing variability in the search for biological.pdf:application/pdf}, + keywords = {to read, generalizibility}, + month = {June}, + title = {Embracing variability in the search for biological mechanisms of psychiatric illness}, + url = {https://osf.io/5mz46}, + urldate = {2024-07-05}, + year = {2024} +} diff --git a/content/publication/segal-embracing-2024/index.md b/content/publication/segal-embracing-2024/index.md new file mode 100644 index 0000000..ff63484 --- /dev/null +++ b/content/publication/segal-embracing-2024/index.md @@ -0,0 +1,31 @@ +--- +title: Embracing variability in the search for biological mechanisms of psychiatric + illness +authors: +- Ashlea Segal +- Jeggan Tiego +- Avram J Holmes +- ' Andre' +- Alex Fornito +date: '2024-06-01' +publishDate: '2024-07-23T12:57:18.655512Z' +publication_types: +- manuscript +doi: 10.31219/osf.io/5mz46 +abstract: Despite decades of research, we lack objective diagnostic or prognostic + biomarkers of mental health problems. A key reason for this limited progress is + a reliance on the traditional case-control paradigm, which assumes that each disorder + has a single cause that can be uncovered by comparing average phenotypic values + of cases and control samples. Here, we discuss the problematic assumptions on which + this paradigm is based and highlight recent efforts that seek to characterize, rather + than minimize, the inherent clinical and biological variability that characterizes + psychiatric populations. We argue that embracing such variability will be necessary + to understand pathophysiological mechanisms and to develop more targeted and effective + treatments. +tags: +- to read +- generalizibility +links: +- name: URL + url: https://osf.io/5mz46 +--- diff --git a/content/publication/soskic-garden-2024/cite.bib b/content/publication/soskic-garden-2024/cite.bib new file mode 100644 index 0000000..67034ce --- /dev/null +++ b/content/publication/soskic-garden-2024/cite.bib @@ -0,0 +1,19 @@ +@article{soskic_garden_2024, + abstract = {Abstract +This study tackles the Garden of Forking Paths, as a challenge for replicability and reproducibility of ERP studies. Here, we applied a multiverse analysis to a sample ERP N400 dataset, donated by an independent research team. We analyzed this dataset using 14 pipelines selected to showcase the full range of methodological variability found in the N400 literature using systematic review approach. The selected pipelines were compared in depth by looking into statistical test outcomes, descriptive statistics, effect size, data quality, and statistical power. In this way we provide a worked example of how analytic flexibility can impact results in research fields with high dimensionality such as ERP, when analyzed using standard null‐hypothesis significance testing. Out of the methodological decisions that were varied, high‐pass filter cut‐off, artifact removal method, baseline duration, reference, measurement latency and locations, and amplitude measure (peak vs. mean) were all shown to affect at least some of the study outcome measures. Low‐pass filtering was the only step which did not notably influence any of these measures. This study shows that even some of the seemingly minor procedural deviations can influence the conclusions of an ERP study. We demonstrate the power of multiverse analysis in both identifying the most reliable effects in a given study, and for providing insights into consequences of methodological decisions. +, +This study shows that most ERP preprocessing and analysis steps can affect study outcomes, and that even some of the seemingly minor procedural deviations can influence conclusions of an ERP study, and it demonstrates the power of the multiverse analysis approach in both identifying the most reliable effects and providing concrete insights into consequences of a given methodological decision.}, + author = {Šoškić, Anđela and Styles, Suzy J. and Kappenman, Emily S. and Ković, Vanja}, + doi = {10.1111/psyp.14628}, + issn = {0048-5772, 1469-8986}, + journal = {Psychophysiology}, + keywords = {to read, to_add_github}, + language = {en}, + month = {July}, + pages = {e14628}, + shorttitle = {Garden of forking paths in \textlessspan style="font-variant}, + title = {Garden of forking paths in \textlessspan style="font-variant:small-caps;"\textgreaterERP\textless/span\textgreater research – Effects of varying pre‐processing and analysis steps in an \textlessspan style="font-variant:small-caps;"\textgreaterN400\textless/span\textgreater experiment}, + url = {https://onlinelibrary.wiley.com/doi/10.1111/psyp.14628}, + urldate = {2024-07-22}, + year = {2024} +} diff --git a/content/publication/soskic-garden-2024/index.md b/content/publication/soskic-garden-2024/index.md new file mode 100644 index 0000000..02d2f64 --- /dev/null +++ b/content/publication/soskic-garden-2024/index.md @@ -0,0 +1,45 @@ +--- +title: Garden of forking paths in textlessspan style=\"font-variant:small-caps;\"textgreaterERPtextless/spantextgreater + research – Effects of varying pre‐processing and analysis steps in an textlessspan + style=\"font-variant:small-caps;\"textgreaterN400textless/spantextgreater experiment +authors: +- Anđela Šoškić +- Suzy J. Styles +- Emily S. Kappenman +- Vanja Ković +date: '2024-07-01' +publishDate: '2024-07-23T12:57:18.682485Z' +publication_types: +- article-journal +publication: '*Psychophysiology*' +doi: 10.1111/psyp.14628 +abstract: Abstract This study tackles the Garden of Forking Paths, as a challenge + for replicability and reproducibility of ERP studies. Here, we applied a multiverse + analysis to a sample ERP N400 dataset, donated by an independent research team. + We analyzed this dataset using 14 pipelines selected to showcase the full range + of methodological variability found in the N400 literature using systematic review + approach. The selected pipelines were compared in depth by looking into statistical + test outcomes, descriptive statistics, effect size, data quality, and statistical + power. In this way we provide a worked example of how analytic flexibility can impact + results in research fields with high dimensionality such as ERP, when analyzed using + standard null‐hypothesis significance testing. Out of the methodological decisions + that were varied, high‐pass filter cut‐off, artifact removal method, baseline duration, + reference, measurement latency and locations, and amplitude measure (peak vs. mean) + were all shown to affect at least some of the study outcome measures. Low‐pass filtering + was the only step which did not notably influence any of these measures. This study + shows that even some of the seemingly minor procedural deviations can influence + the conclusions of an ERP study. We demonstrate the power of multiverse analysis + in both identifying the most reliable effects in a given study, and for providing + insights into consequences of methodological decisions. , This study shows that + most ERP preprocessing and analysis steps can affect study outcomes, and that even + some of the seemingly minor procedural deviations can influence conclusions of an + ERP study, and it demonstrates the power of the multiverse analysis approach in + both identifying the most reliable effects and providing concrete insights into + consequences of a given methodological decision. +tags: +- to read +- to_add_github +links: +- name: URL + url: https://onlinelibrary.wiley.com/doi/10.1111/psyp.14628 +--- diff --git a/content/publication/vallet-toward-2022/cite.bib b/content/publication/vallet-toward-2022/cite.bib new file mode 100644 index 0000000..e512a2c --- /dev/null +++ b/content/publication/vallet-toward-2022/cite.bib @@ -0,0 +1,18 @@ +@article{vallet_toward_2022, + abstract = {Abstract +Reproducibility crisis urge scientists to promote transparency which allows peers to draw same conclusions after performing identical steps from hypothesis to results. Growing resources are developed to open the access to methods, data and source codes. Still, the computational environment, an interface between data and source code running analyses, is not addressed. Environments are usually described with software and library names associated with version labels or provided as an opaque container image. This is not enough to describe the complexity of the dependencies on which they rely to operate on. We describe this issue and illustrate how open tools like Guix can be used by any scientist to share their environment and allow peers to reproduce it. Some steps of research might not be fully reproducible, but at least, transparency for computation is technically addressable. These tools should be considered by scientists willing to promote transparency and open science.}, + author = {Vallet, Nicolas and Michonneau, David and Tournier, Simon}, + doi = {10.1038/s41597-022-01720-9}, + file = {Vallet et al. - 2022 - Toward practical transparent verifiable and long-t.pdf:/home/alpron/Zotero/storage/KT6J79AS/Vallet et al. - 2022 - Toward practical transparent verifiable and long-t.pdf:application/pdf}, + issn = {2052-4463}, + journal = {Scientific Data}, + language = {en}, + month = {October}, + number = {1}, + pages = {597}, + title = {Toward practical transparent verifiable and long-term reproducible research using Guix}, + url = {https://www.nature.com/articles/s41597-022-01720-9}, + urldate = {2024-02-27}, + volume = {9}, + year = {2022} +} diff --git a/content/publication/vallet-toward-2022/index.md b/content/publication/vallet-toward-2022/index.md new file mode 100644 index 0000000..2328034 --- /dev/null +++ b/content/publication/vallet-toward-2022/index.md @@ -0,0 +1,29 @@ +--- +title: Toward practical transparent verifiable and long-term reproducible research + using Guix +authors: +- Nicolas Vallet +- David Michonneau +- Simon Tournier +date: '2022-10-01' +publishDate: '2024-07-23T12:57:18.569402Z' +publication_types: +- article-journal +publication: '*Scientific Data*' +doi: 10.1038/s41597-022-01720-9 +abstract: Abstract Reproducibility crisis urge scientists to promote transparency + which allows peers to draw same conclusions after performing identical steps from + hypothesis to results. Growing resources are developed to open the access to methods, + data and source codes. Still, the computational environment, an interface between + data and source code running analyses, is not addressed. Environments are usually + described with software and library names associated with version labels or provided + as an opaque container image. This is not enough to describe the complexity of the + dependencies on which they rely to operate on. We describe this issue and illustrate + how open tools like Guix can be used by any scientist to share their environment + and allow peers to reproduce it. Some steps of research might not be fully reproducible, + but at least, transparency for computation is technically addressable. These tools + should be considered by scientists willing to promote transparency and open science. +links: +- name: URL + url: https://www.nature.com/articles/s41597-022-01720-9 +--- diff --git a/content/publication/vila-impact-2024/cite.bib b/content/publication/vila-impact-2024/cite.bib new file mode 100644 index 0000000..85f4cbe --- /dev/null +++ b/content/publication/vila-impact-2024/cite.bib @@ -0,0 +1,13 @@ +@unpublished{vila_impact_2024, + abstract = {The reproducibility of neuroimaging analyses across computational environments has gained significant attention over the last few years. While software containerization solutions such as Docker and Singularity have been deployed to mask the effects of softwareinduced variability, variations in hardware architectures still impact neuroimaging results in an unclear way. We study the effect of hardware variability on linear registration results produced by the FSL FLIRT application, a widely-used software component in neuroimaging data analyses. Using the Grid'5000 infrastructure, we study the effect of nine different CPU models using two software packaging systems (Docker and Guix), and we compare the resulting hardware variability to numerical variability measured with random rounding. Results show that hardware, software, and numerical variability lead to perturbations of similar magnitudes — albeit uncorrelated — suggesting that these three types of variability act as independent sources of numerical noise with similar +magnitude. Therefore, random rounding is as a practical solution to measure the effect of numerical noise induced by hardware variability in this application. The effect of hardware perturbations on linear registration remains moderate, with average translation errors of 0.1 mm (maximum: 0.5 mm) and average rotation errors of 0.02 deg (maximum: 0.2 deg). Such variations might impact downstream analyses when linear registration is used as initialization step for other operations.}, + author = {Vila, Gaël and Medernach, Emmanuel and Gonzalez, Inés and Bonnet, Axel and Chatelain, Yohan and Sdika, Michaël and Glatard, Tristan and Camarasu-Pop, Sorina}, + file = {HAL PDF Full Text:/home/alpron/Zotero/storage/HQPFHLFC/Vila et al. - 2024 - The Impact of Hardware Variability on Applications.pdf:application/pdf}, + keywords = {CPU micro-architecture, Neuroimaging, Random rounding, Reproducibility, Software packaging}, + month = {February}, + shorttitle = {The Impact of Hardware Variability on Applications Packaged with Docker and Guix}, + title = {The Impact of Hardware Variability on Applications Packaged with Docker and Guix: a Case Study in Neuroimaging}, + url = {https://hal.science/hal-04480308}, + urldate = {2024-03-21}, + year = {2024} +} diff --git a/content/publication/vila-impact-2024/index.md b/content/publication/vila-impact-2024/index.md new file mode 100644 index 0000000..c35b930 --- /dev/null +++ b/content/publication/vila-impact-2024/index.md @@ -0,0 +1,45 @@ +--- +title: 'The Impact of Hardware Variability on Applications Packaged with Docker and + Guix: a Case Study in Neuroimaging' +authors: +- Gaël Vila +- Emmanuel Medernach +- Inés Gonzalez +- Axel Bonnet +- Yohan Chatelain +- Michaël Sdika +- Tristan Glatard +- Sorina Camarasu-Pop +date: '2024-02-01' +publishDate: '2024-07-23T12:57:18.625381Z' +publication_types: +- manuscript +abstract: "The reproducibility of neuroimaging analyses across computational environments + has gained significant attention over the last few years. While software containerization + solutions such as Docker and Singularity have been deployed to mask the effects + of softwareinduced variability, variations in hardware architectures still impact + neuroimaging results in an unclear way. We study the effect of hardware variability + on linear registration results produced by the FSL FLIRT application, a widely-used + software component in neuroimaging data analyses. Using the Grid'5000 infrastructure, + we study the effect of nine different CPU models using two software packaging systems + (Docker and Guix), and we compare the resulting hardware variability to numerical + variability measured with random rounding. Results show that hardware, software, + and numerical variability lead to perturbations of similar magnitudes — albeit uncorrelated + — suggesting that these three types of variability act as independent sources of + numerical noise with similar magnitude. Therefore, random rounding is as a practical + solution to measure the effect of numerical noise induced by hardware variability + in this application. The effect of hardware perturbations on linear registration + remains moderate, with average translation errors of 0.1 mm (maximum: 0.5 mm) and + average rotation errors of 0.02 deg (maximum: 0.2 deg). Such variations might impact + downstream analyses when linear registration is used as initialization step for + other operations." +tags: +- CPU micro-architecture +- Neuroimaging +- Random rounding +- Reproducibility +- Software packaging +links: +- name: URL + url: https://hal.science/hal-04480308 +--- diff --git a/content/publication/vogt-reproducibility-2023/cite.bib b/content/publication/vogt-reproducibility-2023/cite.bib new file mode 100644 index 0000000..8366e3e --- /dev/null +++ b/content/publication/vogt-reproducibility-2023/cite.bib @@ -0,0 +1,15 @@ +@article{vogt_reproducibility_2023, + author = {Vogt, Nina}, + doi = {10.1038/s41592-022-01737-3}, + issn = {1548-7091, 1548-7105}, + journal = {Nature Methods}, + language = {en}, + month = {January}, + number = {1}, + pages = {34--34}, + title = {Reproducibility in MRI}, + url = {https://www.nature.com/articles/s41592-022-01737-3}, + urldate = {2024-01-19}, + volume = {20}, + year = {2023} +} diff --git a/content/publication/vogt-reproducibility-2023/index.md b/content/publication/vogt-reproducibility-2023/index.md new file mode 100644 index 0000000..359cbc2 --- /dev/null +++ b/content/publication/vogt-reproducibility-2023/index.md @@ -0,0 +1,14 @@ +--- +title: Reproducibility in MRI +authors: +- Nina Vogt +date: '2023-01-01' +publishDate: '2024-07-23T12:57:18.532799Z' +publication_types: +- article-journal +publication: '*Nature Methods*' +doi: 10.1038/s41592-022-01737-3 +links: +- name: URL + url: https://www.nature.com/articles/s41592-022-01737-3 +--- diff --git a/content/publication/wagner-fairly-2022/cite.bib b/content/publication/wagner-fairly-2022/cite.bib new file mode 100644 index 0000000..d170b67 --- /dev/null +++ b/content/publication/wagner-fairly-2022/cite.bib @@ -0,0 +1,21 @@ +@article{wagner_fairly_2022, + abstract = {Abstract +Large-scale datasets present unique opportunities to perform scientific investigations with unprecedented breadth. However, they also pose considerable challenges for the findability, accessibility, interoperability, and reusability (FAIR) of research outcomes due to infrastructure limitations, data usage constraints, or software license restrictions. Here we introduce a DataLad-based, domain-agnostic framework suitable for reproducible data processing in compliance with open science mandates. The framework attempts to minimize platform idiosyncrasies and performance-related complexities. It affords the capture of machine-actionable computational provenance records that can be used to retrace and verify the origins of research outcomes, as well as be re-executed independent of the original computing infrastructure. We demonstrate the framework’s performance using two showcases: one highlighting data sharing and transparency (using the studyforrest.org dataset) and another highlighting scalability (using the largest public brain imaging dataset available: the UK Biobank dataset).}, + author = {Wagner, Adina S. and Waite, Laura K. and Wierzba, Małgorzata and Hoffstaedter, Felix and Waite, Alexander Q. and Poldrack, Benjamin and Eickhoff, Simon B. and Hanke, Michael}, + doi = {10.1038/s41597-022-01163-2}, + file = {Wagner et al. - 2022 - FAIRly big A framework for computationally reprod.pdf:/home/alpron/Zotero/storage/JQW82RZ9/Wagner et al. - 2022 - FAIRly big A framework for computationally reprod.pdf:application/pdf}, + issn = {2052-4463}, + journal = {Scientific Data}, + keywords = {datalad, Important}, + language = {en}, + month = {March}, + note = {Number: 1}, + number = {1}, + pages = {80}, + shorttitle = {FAIRly big}, + title = {FAIRly big: A framework for computationally reproducible processing of large-scale data}, + url = {https://www.nature.com/articles/s41597-022-01163-2}, + urldate = {2024-01-03}, + volume = {9}, + year = {2022} +} diff --git a/content/publication/wagner-fairly-2022/index.md b/content/publication/wagner-fairly-2022/index.md new file mode 100644 index 0000000..aa75632 --- /dev/null +++ b/content/publication/wagner-fairly-2022/index.md @@ -0,0 +1,39 @@ +--- +title: 'FAIRly big: A framework for computationally reproducible processing of large-scale + data' +authors: +- Adina S. Wagner +- Laura K. Waite +- Małgorzata Wierzba +- Felix Hoffstaedter +- Alexander Q. Waite +- Benjamin Poldrack +- Simon B. Eickhoff +- Michael Hanke +date: '2022-03-01' +publishDate: '2024-07-23T12:57:18.482981Z' +publication_types: +- article-journal +publication: '*Scientific Data*' +doi: 10.1038/s41597-022-01163-2 +abstract: 'Abstract Large-scale datasets present unique opportunities to perform scientific + investigations with unprecedented breadth. However, they also pose considerable + challenges for the findability, accessibility, interoperability, and reusability + (FAIR) of research outcomes due to infrastructure limitations, data usage constraints, + or software license restrictions. Here we introduce a DataLad-based, domain-agnostic + framework suitable for reproducible data processing in compliance with open science + mandates. The framework attempts to minimize platform idiosyncrasies and performance-related + complexities. It affords the capture of machine-actionable computational provenance + records that can be used to retrace and verify the origins of research outcomes, + as well as be re-executed independent of the original computing infrastructure. + We demonstrate the framework’s performance using two showcases: one highlighting + data sharing and transparency (using the studyforrest.org dataset) and another highlighting + scalability (using the largest public brain imaging dataset available: the UK Biobank + dataset).' +tags: +- datalad +- Important +links: +- name: URL + url: https://www.nature.com/articles/s41597-022-01163-2 +--- diff --git a/content/publication/weigard-flexible-2024/cite.bib b/content/publication/weigard-flexible-2024/cite.bib new file mode 100644 index 0000000..18a063d --- /dev/null +++ b/content/publication/weigard-flexible-2024/cite.bib @@ -0,0 +1,18 @@ +@article{weigard_flexible_2024, + abstract = {Abstract +Efficiency of evidence accumulation (EEA), an individual’s ability to selectively gather goal-relevant information to make adaptive choices, is thought to be a key neurocomputational mechanism associated with cognitive functioning and transdiagnostic risk for psychopathology. However, the neural basis of individual differences in EEA is poorly understood, especially regarding the role of largescale brain network dynamics. We leverage data from 5198 participants from the Human Connectome Project and Adolescent Brain Cognitive Development Study to demonstrate a strong association between EEA and flexible adaptation to cognitive demand in the “task-positive” frontoparietal and dorsal attention networks. Notably, individuals with higher EEA displayed divergent task-positive network activation across n-back task conditions: higher activation under high cognitive demand (2-back) and lower activation under low demand (0-back). These findings suggest that brain networks’ flexible adaptation to cognitive demands is a key neural underpinning of EEA.}, + author = {Weigard, Alexander and Angstadt, Mike and Taxali, Aman and Heathcote, Andrew and Heitzeg, Mary M. and Sripada, Chandra}, + doi = {10.1038/s42003-024-06506-w}, + issn = {2399-3642}, + journal = {Communications Biology}, + keywords = {to read}, + language = {en}, + month = {July}, + number = {1}, + pages = {801}, + title = {Flexible adaptation of task-positive brain networks predicts efficiency of evidence accumulation}, + url = {https://www.nature.com/articles/s42003-024-06506-w}, + urldate = {2024-07-22}, + volume = {7}, + year = {2024} +} diff --git a/content/publication/weigard-flexible-2024/index.md b/content/publication/weigard-flexible-2024/index.md new file mode 100644 index 0000000..2759ada --- /dev/null +++ b/content/publication/weigard-flexible-2024/index.md @@ -0,0 +1,35 @@ +--- +title: Flexible adaptation of task-positive brain networks predicts efficiency of + evidence accumulation +authors: +- Alexander Weigard +- Mike Angstadt +- Aman Taxali +- Andrew Heathcote +- Mary M. Heitzeg +- Chandra Sripada +date: '2024-07-01' +publishDate: '2024-07-23T12:57:18.690434Z' +publication_types: +- article-journal +publication: '*Communications Biology*' +doi: 10.1038/s42003-024-06506-w +abstract: 'Abstract Efficiency of evidence accumulation (EEA), an individual’s ability + to selectively gather goal-relevant information to make adaptive choices, is thought + to be a key neurocomputational mechanism associated with cognitive functioning and + transdiagnostic risk for psychopathology. However, the neural basis of individual + differences in EEA is poorly understood, especially regarding the role of largescale + brain network dynamics. We leverage data from 5198 participants from the Human Connectome + Project and Adolescent Brain Cognitive Development Study to demonstrate a strong + association between EEA and flexible adaptation to cognitive demand in the “task-positive” + frontoparietal and dorsal attention networks. Notably, individuals with higher EEA + displayed divergent task-positive network activation across n-back task conditions: + higher activation under high cognitive demand (2-back) and lower activation under + low demand (0-back). These findings suggest that brain networks’ flexible adaptation + to cognitive demands is a key neural underpinning of EEA.' +tags: +- to read +links: +- name: URL + url: https://www.nature.com/articles/s42003-024-06506-w +--- diff --git a/content/publication/zhao-reproducible-2024/cite.bib b/content/publication/zhao-reproducible-2024/cite.bib new file mode 100644 index 0000000..d8080e2 --- /dev/null +++ b/content/publication/zhao-reproducible-2024/cite.bib @@ -0,0 +1,18 @@ +@article{zhao_reproducible_2024, + abstract = {Abstract +Neuroimaging research faces a crisis of reproducibility. With massive sample sizes and greater data complexity, this problem becomes more acute. Software that operates on imaging data defined using the Brain Imaging Data Structure (BIDS)—the BIDS App—has provided a substantial advance. However, even using BIDS Apps, a full audit trail of data processing is a necessary prerequisite for fully reproducible research. Obtaining a faithful record of the audit trail is challenging—especially for large datasets. Recently, the FAIRly big framework was introduced as a way to facilitate reproducible processing of large-scale data by leveraging DataLad—a version control system for data management. However, the current implementation of this framework was more of a proof of concept, and could not be immediately reused by other investigators for different use cases. Here, we introduce the BIDS App Bootstrap (BABS), a user-friendly and generalizable Python package for reproducible image processing at scale. BABS facilitates the reproducible application of BIDS Apps to large-scale datasets. Leveraging DataLad and the FAIRly big framework, BABS tracks the full audit trail of data processing in a scalable way by automatically preparing all scripts necessary for data processing and version tracking on high performance computing (HPC) systems. Currently, BABS supports jobs submissions and audits on Sun Grid Engine (SGE) and Slurm HPCs with a parsimonious set of programs. To demonstrate its scalability, we applied BABS to data from the Healthy Brain Network (HBN; n = 2,565). Taken together, BABS allows reproducible and scalable image processing and is broadly extensible via an open-source development model.}, + author = {Zhao, Chenying and Jarecka, Dorota and Covitz, Sydney and Chen, Yibei and Eickhoff, Simon B. and Fair, Damien A. and Franco, Alexandre R. and Halchenko, Yaroslav O. and Hendrickson, Timothy J. and Hoffstaedter, Felix and Houghton, Audrey and Kiar, Gregory and Macdonald, Austin and Mehta, Kahini and Milham, Michael P. and Salo, Taylor and Hanke, Michael and Ghosh, Satrajit S. and Cieslak, Matthew and Satterthwaite, Theodore D.}, + doi = {10.1162/imag_a_00074}, + file = {Zhao et al. - 2024 - A reproducible and generalizable software workflow.pdf:/home/alpron/Zotero/storage/SLA62NAB/Zhao et al. - 2024 - A reproducible and generalizable software workflow.pdf:application/pdf}, + issn = {2837-6056}, + journal = {Imaging Neuroscience}, + keywords = {Important}, + language = {en}, + month = {January}, + pages = {1--19}, + title = {A reproducible and generalizable software workflow for analysis of large-scale neuroimaging data collections using BIDS Apps}, + url = {https://direct.mit.edu/imag/article/doi/10.1162/imag_a_00074/119046/A-reproducible-and-generalizable-software-workflow}, + urldate = {2024-02-09}, + volume = {2}, + year = {2024} +} diff --git a/content/publication/zhao-reproducible-2024/index.md b/content/publication/zhao-reproducible-2024/index.md new file mode 100644 index 0000000..6004b20 --- /dev/null +++ b/content/publication/zhao-reproducible-2024/index.md @@ -0,0 +1,58 @@ +--- +title: A reproducible and generalizable software workflow for analysis of large-scale + neuroimaging data collections using BIDS Apps +authors: +- Chenying Zhao +- Dorota Jarecka +- Sydney Covitz +- Yibei Chen +- Simon B. Eickhoff +- Damien A. Fair +- Alexandre R. Franco +- Yaroslav O. Halchenko +- Timothy J. Hendrickson +- Felix Hoffstaedter +- Audrey Houghton +- Gregory Kiar +- Austin Macdonald +- Kahini Mehta +- Michael P. Milham +- Taylor Salo +- Michael Hanke +- Satrajit S. Ghosh +- Matthew Cieslak +- Theodore D. Satterthwaite +date: '2024-01-01' +publishDate: '2024-07-23T12:57:18.546135Z' +publication_types: +- article-journal +publication: '*Imaging Neuroscience*' +doi: 10.1162/imag_a_00074 +abstract: Abstract Neuroimaging research faces a crisis of reproducibility. With massive + sample sizes and greater data complexity, this problem becomes more acute. Software + that operates on imaging data defined using the Brain Imaging Data Structure (BIDS)—the + BIDS App—has provided a substantial advance. However, even using BIDS Apps, a full + audit trail of data processing is a necessary prerequisite for fully reproducible + research. Obtaining a faithful record of the audit trail is challenging—especially + for large datasets. Recently, the FAIRly big framework was introduced as a way to + facilitate reproducible processing of large-scale data by leveraging DataLad—a version + control system for data management. However, the current implementation of this + framework was more of a proof of concept, and could not be immediately reused by + other investigators for different use cases. Here, we introduce the BIDS App Bootstrap + (BABS), a user-friendly and generalizable Python package for reproducible image + processing at scale. BABS facilitates the reproducible application of BIDS Apps + to large-scale datasets. Leveraging DataLad and the FAIRly big framework, BABS tracks + the full audit trail of data processing in a scalable way by automatically preparing + all scripts necessary for data processing and version tracking on high performance + computing (HPC) systems. Currently, BABS supports jobs submissions and audits on + Sun Grid Engine (SGE) and Slurm HPCs with a parsimonious set of programs. To demonstrate + its scalability, we applied BABS to data from the Healthy Brain Network (HBN; n + = 2,565). Taken together, BABS allows reproducible and scalable image processing + and is broadly extensible via an open-source development model. +tags: +- Important +links: +- name: URL + url: + https://direct.mit.edu/imag/article/doi/10.1162/imag_a_00074/119046/A-reproducible-and-generalizable-software-workflow +---