Reproducibility in Neuroscience - A best practice example
Polzeh, Jörg
Reproducibility in Neuroscience - A best practice example The last decade has seen intensive discussions on reproducibility in the analysis of neuroimaging experiments. This has lead to efforts in the neuroscience community in various directions, e.g., accessibility of 'minimally processed image and metadata' from large studies for scientific purposes public data archives open data standards open source software and analysis pipelines. This enables, e.g., the development of best practice analysis strategies combination of findings from different studies test of and comparisons with alternative modeling approaches I'll discuss how this fits, as a best practice example, into the current discussions on reproducibility of research and (open) research data.
 And so the natural way would have been to go and talk about what we do in statistics in neuroimaging. But I think having a look at current developments in the neurosciences and neuroimaging concerning reproducibility and here I mean reproducibility and replicability together, maybe of more interest for the general audience. So that's why. Let's see. Okay. Let's start with something provocative. This paper by Ioannidis 2005 was starting quite a discussion in the biomedical sciences community. And so it has at the title, Why Most Published Research Finding Affords. And what it pointed out was there are several reasons for this being. One is most research that are published are based on single studies. They are based on form statistical significance. So this started quite a discussion about P values. And it's based on publications discarding negative results. And not only publications, but already the authors or people who did the studies will start thinking about sending the results of a study to a publisher if the results weren't showing any effect. So this is something that's known in statistics as publication bias. And that's summarized here in table one from this paper. And it says if you have alpha type one error in your hypothesis testing, one minus beta the power of your tests assuming that all studies have the same power and R is the ratio of true and false hypothesis tested, then what you get out is this proportion of true relationships that are corresponding to your research findings. And depending on R, you will see that the proportion of events where you have a research finding guess and the true relationship is no, maybe larger than the opposite. Okay, let's continue. There's another issue coming in. And this issue is biased due to study design problems. And this may be that there are problems in a design, in the planning of the study. There may be problems concerning randomization of studies. There may be problems in the direction that there's too much flexibility in the study design. So there are decisions about hypothesis that are tested made after the study has been conducted or experiment has been conducted. And this adds to another effect that's considered here as you, the proportion of research findings due to study design problems that shouldn't be not addressed as findings because of problems in study planning. And then the situation even gets worse. So in the paper, your notice has five corollaries. And they state research findings are less likely to be true if the power of the, if the better, so describing error of the second kind in hypothesis testing gets smaller. If the fact gets, if the fact, no, if the size of the study gets smaller, so that goes into better. If the effect of the study is smaller, if the number of hypothesis tested gets larger, if the flexibility of the design definition of outcomes or analytical methods used gets larger, or if the financial or other interests of the experimenter or some pre-settings in the mind of the community are getting larger. So conclusions is where you have to do large studies or low bias meta-analysis combining information from different studies. There's a need for totality of evidence. So all studies independently of what they provide as a result should be published. And there should be something like an upfront registration of studies so that everybody knows how many studies were conducted and can see how many of these studies had a positive result. Okay. There was quite a time before really measures were taken. So in 2014, as a result of a meeting of exudeative of major journals in biomedical sciences of funding agencies and scientific societies, I think this meeting was held at NIH in the US, was conducted. And as a result, there was an editorial published in Science at the same time in Nature and I think also in some other journals calling for reproducibility, rigor, transparency and independent validation as cornerstones of the scientific methods and defining principle and guidelines for pre-clinical medical research. Okay. What they were demanding was reporting of experimental parameters, standards, replicates, statistics, randomization, blinding of studies, synthesized determination criteria for data exclusion. All this should be in the publications. They demanded the position of data in public repositories and to link these data to the publications and their metan obligation to consider publication of contradictory results in the same journal. Second interesting study, this time from psychology. This paper in Science compared or investigated about 100 studies in psychology for replicability. So these studies were conducted again and what you see is the original effect size and the replication effect size. And so there were studies that could be replicated here in blue, but there were a large number of studies that couldn't be replicated. Also a sign that there is a problem here in publication and conducting research. Okay. For neuroimaging, the first study that I found was a paper by Landmann in 2011 who conducted a multi-parametric neuroimaging reproducibility study at 3T. So they were recording 11 different modalities in a scan-rescan study. So scanning the same patient after a certain time again or same subject. That full reporting of experimental settings and acquisition parameters, open analysis pipelines. So the software is published through the neuroimaging informatics tools and resources clearing cows that's situated at NIH. The data are open available through repository and they explicitly encourage the use of these data for the development optimization and evaluation of algorithms. In neuroimaging, there is quite a long tradition of open software. So the main software environments and software tools were developed within the community on open science, open source licenses. So that's analysis of functional neuroimages, AFNI, started by Cox in 1996. Then there's the National Library of Medicine, Insights Segmentation and Registration Toolkit, ITK, the Advanced Normalization Tool, FreeServer, FSL and Statistic Parametric Mapping, just named the main tools or software collections that are in use here. In its 2014 meeting in Hamburg, the Organization of Human Brain Mapping was addressing the problem of reproducibility and data integrity in neuroscience research in a statement of its council. And at its meeting, a committee on best practices in data analysis and sharing of data in magnetic resonance imaging was established. This committee published a report in May 2016. So this was immediately before the 22nd HBM meeting. The committee was seated by Tom Nichols, a statistician at that time in Warwick, now in Oxford. And it's a report of about 30 pages plus 40 pages as a appendix says. And I think it's worth reading. A similar committee was established at the Vancouver meeting in 2017 for MEG and EEG. And there also is a report now in form of a pre-print. Okay, sharing the wells. In Neurimage, one of the leading journals in Neurimaging published two special issues in 2016 and 2017 on open data repositories. So let's just a long list here. And what I would like to do is to mention two of these repositories in a little bit more detail. So one is the Washington University Minnesota Human Connectome Project database. That's designed to share data from several huge publicly founded studies. The main study was the Human Connectome Project imaging study on a model 3D with 1,200 subjects, structural resting state, task-based ME, hardy data, behavioral data. There will also be genome-wide association data for this. These data are publicly available after registration for scientific purposes. And the use requires citation of certain papers depending on which type of data is accessed and used. And the second one is the lifespan pilot data where different age groups are followed over a certain amount of time. The second and even more interesting archive is the Open New Archive established by the Stanford Center for Reproducible New Science. It started as a repository for functional MR, the Open FMI repository, and now contains about 180 neuroimaging studies in standardized form, so-called BITS format, and also supplies standardized pipelines for processing of data for several modalities. The data from the Open New Archive can be directly downloaded via Amazon Web Services or directly from the archive website. So there's fast access to these archives. Data policies include access that access can be restricted for a period of 36 months, so that addresses the question of preference for owners to publish the sites based on the data before it can be publicly used. The owner of the data set needs to have necessary ethics permissions to share the data publicly, and the data has to be anonymized. Usually use of this data is on an open data license and requires citing of the papers where these data from these studies were first used. Okay, the BITS standard is a standard for the organization of MRI data in NIFTY format. It was adapted by several data sharing repositories, so one Open Neural is one of them, Human Connect Home Project is another one, and it's promoted by the Committee on Best Practices in Data Analysis and Sharing of the Human Brain Mapping Organization. It's organized in form of a data sub-formal structure that puts data for different subjects in the studies in different directories and then again separates the data according to different types of images, different modalities. There's metadata stored in JavaScript Object Notion or JSON files, which is certain type of text file and supplementary information is usually in TSFWO files. Okay, that's how the platform looks like, so we have access here at this point. This is how list of datasets looks like on an Open Neural. Special dataset in my Connect Home dataset. Here you have the file tree. Okay, this is about pipelines, FMI pipeline prepared by the Polter Club at Stanford. Okay, what I would like to also mention is the Neural Conductor Project. That's a project started by from the statistics community in Neural Imaging and its package development to prepare and encourage the use of R in Neural Imaging. It integrates with existing Open Neuroscience, Neural Imaging software and enables complete pipelines. Currently it's 91 packages. We also contribute here. And the last thing I would like to mention is a book project that we are just in the process of finishing. So we will send it next week to the publishers. That's about Neural Imaging in R. It provides in fully reproducible form an open analysis pipelines for different modalities or different experiments in R. It's completely based on open software. It's using open datasets from open repositories. It's completely written in latech using Knitter. It will be a dynamic document and that's what it covers. Okay, thank you for your attention.
 And so the natural way would have been to go and talk about what we do in statistics in  neuroimaging. But I think having a look at current developments in the neurosciences and  neuroimaging concerning reproducibility and here I mean reproducibility and replicability  together, maybe of more interest for the general audience. So that's why. Let's see.  Okay. Let's start with something provocative. This paper by Ioannidis 2005 was starting  quite a discussion in the biomedical sciences community. And so it has at the title, Why  Most Published Research Finding Affords. And what it pointed out was there are several  reasons for this being. One is most research that are published are based on single studies.  They are based on form statistical significance. So this started quite a discussion about  P values. And it's based on publications discarding negative results. And not only publications,  but already the authors or people who did the studies will start thinking about sending  the results of a study to a publisher if the results weren't showing any effect. So this  is something that's known in statistics as publication bias. And that's summarized here  in table one from this paper. And it says if you have alpha type one error in your hypothesis  testing, one minus beta the power of your tests assuming that all studies have the same  power and R is the ratio of true and false hypothesis tested, then what you get out is  this proportion of true relationships that are corresponding to your research findings.  And depending on R, you will see that the proportion of events where you have a research  finding guess and the true relationship is no, maybe larger than the opposite. Okay,  let's continue. There's another issue coming in. And this issue is biased due to study  design problems. And this may be that there are problems in a design, in the planning  of the study. There may be problems concerning randomization of studies. There may be problems  in the direction that there's too much flexibility in the study design. So there are decisions  about hypothesis that are tested made after the study has been conducted or experiment  has been conducted. And this adds to another effect that's considered here as you, the  proportion of research findings due to study design problems that shouldn't be not addressed  as findings because of problems in study planning. And then the situation even gets worse.  So in the paper, your notice has five corollaries. And they state research findings are less likely  to be true if the power of the, if the better, so describing error of the second kind in  hypothesis testing gets smaller. If the fact gets, if the fact, no, if the size of the  study gets smaller, so that goes into better. If the effect of the study is smaller, if  the number of hypothesis tested gets larger, if the flexibility of the design definition  of outcomes or analytical methods used gets larger, or if the financial or other interests  of the experimenter or some pre-settings in the mind of the community are getting larger.  So conclusions is where you have to do large studies or low bias meta-analysis combining  information from different studies. There's a need for totality of evidence. So all studies  independently of what they provide as a result should be published. And there should be something  like an upfront registration of studies so that everybody knows how many studies were  conducted and can see how many of these studies had a positive result. Okay. There was quite  a time before really measures were taken. So in 2014, as a result of a meeting of exudeative  of major journals in biomedical sciences of funding agencies and scientific societies,  I think this meeting was held at NIH in the US, was conducted. And as a result, there  was an editorial published in Science at the same time in Nature and I think also in some  other journals calling for reproducibility, rigor, transparency and independent validation  as cornerstones of the scientific methods and defining principle and guidelines for  pre-clinical medical research. Okay. What they were demanding was reporting of experimental  parameters, standards, replicates, statistics, randomization, blinding of studies, synthesized  determination criteria for data exclusion. All this should be in the publications. They  demanded the position of data in public repositories and to link these data to the publications  and their metan obligation to consider publication of contradictory results in the same journal.  Second interesting study, this time from psychology. This paper in Science compared or investigated  about 100 studies in psychology for replicability. So these studies were conducted again and  what you see is the original effect size and the replication effect size. And so there  were studies that could be replicated here in blue, but there were a large number of  studies that couldn't be replicated. Also a sign that there is a problem here in publication  and conducting research. Okay. For neuroimaging, the first study that I found was a paper by  Landmann in 2011 who conducted a multi-parametric neuroimaging reproducibility study at 3T.  So they were recording 11 different modalities in a scan-rescan study. So scanning the same  patient after a certain time again or same subject. That full reporting of experimental  settings and acquisition parameters, open analysis pipelines. So the software is published  through the neuroimaging informatics tools and resources clearing cows that's situated  at NIH. The data are open available through repository and they explicitly encourage the  use of these data for the development optimization and evaluation of algorithms. In neuroimaging,  there is quite a long tradition of open software. So the main software environments and software  tools were developed within the community on open science, open source licenses. So  that's analysis of functional neuroimages, AFNI, started by Cox in 1996. Then there's  the National Library of Medicine, Insights Segmentation and Registration Toolkit, ITK,  the Advanced Normalization Tool, FreeServer, FSL and Statistic Parametric Mapping, just  named the main tools or software collections that are in use here. In its 2014 meeting in  Hamburg, the Organization of Human Brain Mapping was addressing the problem of reproducibility  and data integrity in neuroscience research in a statement of its council. And at its  meeting, a committee on best practices in data analysis and sharing of data in magnetic  resonance imaging was established. This committee published a report in May 2016. So this was  immediately before the 22nd HBM meeting. The committee was seated by Tom Nichols, a statistician  at that time in Warwick, now in Oxford. And it's a report of about 30 pages plus 40 pages  as a appendix says. And I think it's worth reading. A similar committee was established  at the Vancouver meeting in 2017 for MEG and EEG. And there also is a report now in  form of a pre-print. Okay, sharing the wells. In Neurimage, one of the leading journals in  Neurimaging published two special issues in 2016 and 2017 on open data repositories. So  let's just a long list here. And what I would like to do is to mention two of these repositories  in a little bit more detail. So one is the Washington University Minnesota Human Connectome  Project database. That's designed to share data from several huge publicly founded studies.  The main study was the Human Connectome Project imaging study on a model 3D with 1,200 subjects,  structural resting state, task-based ME, hardy data, behavioral data. There will also be  genome-wide association data for this. These data are publicly available after registration  for scientific purposes. And the use requires citation of certain papers depending on which  type of data is accessed and used. And the second one is the lifespan pilot data where  different age groups are followed over a certain amount of time. The second and even more interesting  archive is the Open New Archive established by the Stanford Center for Reproducible New  Science. It started as a repository for functional MR, the Open FMI repository, and now contains  about 180 neuroimaging studies in standardized form, so-called BITS format, and also supplies  standardized pipelines for processing of data for several modalities. The data from the  Open New Archive can be directly downloaded via Amazon Web Services or directly from the  archive website. So there's fast access to these archives. Data policies include access  that access can be restricted for a period of 36 months, so that addresses the question  of preference for owners to publish the sites based on the data before it can be publicly  used. The owner of the data set needs to have necessary ethics permissions to share the  data publicly, and the data has to be anonymized. Usually use of this data is on an open data  license and requires citing of the papers where these data from these studies were first  used. Okay, the BITS standard is a standard for the organization of MRI data in NIFTY  format. It was adapted by several data sharing repositories, so one Open Neural is one of  them, Human Connect Home Project is another one, and it's promoted by the Committee on  Best Practices in Data Analysis and Sharing of the Human Brain Mapping Organization. It's  organized in form of a data sub-formal structure that puts data for different subjects in the  studies in different directories and then again separates the data according to different  types of images, different modalities. There's metadata stored in JavaScript Object Notion  or JSON files, which is certain type of text file and supplementary information is usually  in TSFWO files. Okay, that's how the platform looks like, so we have access here at this  point. This is how list of datasets looks like on an Open Neural. Special dataset in  my Connect Home dataset. Here you have the file tree. Okay, this is about pipelines,  FMI pipeline prepared by the Polter Club at Stanford. Okay, what I would like to also  mention is the Neural Conductor Project. That's a project started by from the statistics  community in Neural Imaging and its package development to prepare and encourage the use  of R in Neural Imaging. It integrates with existing Open Neuroscience, Neural Imaging  software and enables complete pipelines. Currently it's 91 packages. We also contribute here.  And the last thing I would like to mention is a book project that we are just in the  process of finishing. So we will send it next week to the publishers. That's about Neural  Imaging in R. It provides in fully reproducible form an open analysis pipelines for different  modalities or different experiments in R. It's completely based on open software. It's  using open datasets from open repositories. It's completely written in latech using Knitter.  It will be a dynamic document and that's what it covers. Okay, thank you for your attention.
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And so the natural way would have been to go and talk about what we do in statistics in neuroimaging. But I think having a look at current developments in the neurosciences and neuroimaging concerning reproducibility and here I mean reproducibility and replicability together, maybe of more interest for the general audience. So that's why. Let's see. Okay. Let's start with something provocative. This paper by Ioannidis 2005 was starting quite a discussion in the biomedical sciences community. And so it has at the title, Why Most Published Research Finding Affords. And what it pointed out was there are several reasons for this being. One is most research that are published are based on single studies. They are based on form statistical significance. So this started quite a discussion about P values. And it's based on publications discarding negative results. And not only publications, but already the authors or people who did the studies will start thinking about sending the results of a study to a publisher if the results weren't showing any effect. So this is something that's known in statistics as publication bias. And that's summarized here in table one from this paper. And it says if you have alpha type one error in your hypothesis testing, one minus beta the power of your tests assuming that all studies have the same power and R is the ratio of true and false hypothesis tested, then what you get out is this proportion of true relationships that are corresponding to your research findings. And depending on R, you will see that the proportion of events where you have a research finding guess and the true relationship is no, maybe larger than the opposite. Okay, let's continue. There's another issue coming in. And this issue is biased due to study design problems. And this may be that there are problems in a design, in the planning of the study. There may be problems concerning randomization of studies. There may be problems in the direction that there's too much flexibility in the study design. So there are decisions about hypothesis that are tested made after the study has been conducted or experiment has been conducted. And this adds to another effect that's considered here as you, the proportion of research findings due to study design problems that shouldn't be not addressed as findings because of problems in study planning. And then the situation even gets worse. So in the paper, your notice has five corollaries. And they state research findings are less likely to be true if the power of the, if the better, so describing error of the second kind in hypothesis testing gets smaller. If the fact gets, if the fact, no, if the size of the study gets smaller, so that goes into better. If the effect of the study is smaller, if the number of hypothesis tested gets larger, if the flexibility of the design definition of outcomes or analytical methods used gets larger, or if the financial or other interests of the experimenter or some pre-settings in the mind of the community are getting larger. So conclusions is where you have to do large studies or low bias meta-analysis combining information from different studies. There's a need for totality of evidence. So all studies independently of what they provide as a result should be published. And there should be something like an upfront registration of studies so that everybody knows how many studies were conducted and can see how many of these studies had a positive result. Okay. There was quite a time before really measures were taken. So in 2014, as a result of a meeting of exudeative of major journals in biomedical sciences of funding agencies and scientific societies, I think this meeting was held at NIH in the US, was conducted. And as a result, there was an editorial published in Science at the same time in Nature and I think also in some other journals calling for reproducibility, rigor, transparency and independent validation as cornerstones of the scientific methods and defining principle and guidelines for pre-clinical medical research. Okay. What they were demanding was reporting of experimental parameters, standards, replicates, statistics, randomization, blinding of studies, synthesized determination criteria for data exclusion. All this should be in the publications. They demanded the position of data in public repositories and to link these data to the publications and their metan obligation to consider publication of contradictory results in the same journal. Second interesting study, this time from psychology. This paper in Science compared or investigated about 100 studies in psychology for replicability. So these studies were conducted again and what you see is the original effect size and the replication effect size. And so there were studies that could be replicated here in blue, but there were a large number of studies that couldn't be replicated. Also a sign that there is a problem here in publication and conducting research. Okay. For neuroimaging, the first study that I found was a paper by Landmann in 2011 who conducted a multi-parametric neuroimaging reproducibility study at 3T. So they were recording 11 different modalities in a scan-rescan study. So scanning the same patient after a certain time again or same subject. That full reporting of experimental settings and acquisition parameters, open analysis pipelines. So the software is published through the neuroimaging informatics tools and resources clearing cows that's situated at NIH. The data are open available through repository and they explicitly encourage the use of these data for the development optimization and evaluation of algorithms. In neuroimaging, there is quite a long tradition of open software. So the main software environments and software tools were developed within the community on open science, open source licenses. So that's analysis of functional neuroimages, AFNI, started by Cox in 1996. Then there's the National Library of Medicine, Insights Segmentation and Registration Toolkit, ITK, the Advanced Normalization Tool, FreeServer, FSL and Statistic Parametric Mapping, just named the main tools or software collections that are in use here. In its 2014 meeting in Hamburg, the Organization of Human Brain Mapping was addressing the problem of reproducibility and data integrity in neuroscience research in a statement of its council. And at its meeting, a committee on best practices in data analysis and sharing of data in magnetic resonance imaging was established. This committee published a report in May 2016. So this was immediately before the 22nd HBM meeting. The committee was seated by Tom Nichols, a statistician at that time in Warwick, now in Oxford. And it's a report of about 30 pages plus 40 pages as a appendix says. And I think it's worth reading. A similar committee was established at the Vancouver meeting in 2017 for MEG and EEG. And there also is a report now in form of a pre-print. Okay, sharing the wells. In Neurimage, one of the leading journals in Neurimaging published two special issues in 2016 and 2017 on open data repositories. So let's just a long list here. And what I would like to do is to mention two of these repositories in a little bit more detail. So one is the Washington University Minnesota Human Connectome Project database. That's designed to share data from several huge publicly founded studies. The main study was the Human Connectome Project imaging study on a model 3D with 1,200 subjects, structural resting state, task-based ME, hardy data, behavioral data. There will also be genome-wide association data for this. These data are publicly available after registration for scientific purposes. And the use requires citation of certain papers depending on which type of data is accessed and used. And the second one is the lifespan pilot data where different age groups are followed over a certain amount of time. The second and even more interesting archive is the Open New Archive established by the Stanford Center for Reproducible New Science. It started as a repository for functional MR, the Open FMI repository, and now contains about 180 neuroimaging studies in standardized form, so-called BITS format, and also supplies standardized pipelines for processing of data for several modalities. The data from the Open New Archive can be directly downloaded via Amazon Web Services or directly from the archive website. So there's fast access to these archives. Data policies include access that access can be restricted for a period of 36 months, so that addresses the question of preference for owners to publish the sites based on the data before it can be publicly used. The owner of the data set needs to have necessary ethics permissions to share the data publicly, and the data has to be anonymized. Usually use of this data is on an open data license and requires citing of the papers where these data from these studies were first used. Okay, the BITS standard is a standard for the organization of MRI data in NIFTY format. It was adapted by several data sharing repositories, so one Open Neural is one of them, Human Connect Home Project is another one, and it's promoted by the Committee on Best Practices in Data Analysis and Sharing of the Human Brain Mapping Organization. It's organized in form of a data sub-formal structure that puts data for different subjects in the studies in different directories and then again separates the data according to different types of images, different modalities. There's metadata stored in JavaScript Object Notion or JSON files, which is certain type of text file and supplementary information is usually in TSFWO files. Okay, that's how the platform looks like, so we have access here at this point. This is how list of datasets looks like on an Open Neural. Special dataset in my Connect Home dataset. Here you have the file tree. Okay, this is about pipelines, FMI pipeline prepared by the Polter Club at Stanford. Okay, what I would like to also mention is the Neural Conductor Project. That's a project started by from the statistics community in Neural Imaging and its package development to prepare and encourage the use of R in Neural Imaging. It integrates with existing Open Neuroscience, Neural Imaging software and enables complete pipelines. Currently it's 91 packages. We also contribute here. And the last thing I would like to mention is a book project that we are just in the process of finishing. So we will send it next week to the publishers. That's about Neural Imaging in R. It provides in fully reproducible form an open analysis pipelines for different modalities or different experiments in R. It's completely based on open software. It's using open datasets from open repositories. It's completely written in latech using Knitter. It will be a dynamic document and that's what it covers. Okay, thank you for your attention.
current developments in the neurosciences and is something that's known in statistics as proportion of research findings due to study have to do large studies or low bias different studies. There's a need for totality of multi-parametric neuroimaging reproducibility data for the development optimization and science, open source licenses. So that's analysis data integrity in neuroscience research in a committee on best practices in data analysis and scientific purposes. And the use requires open data license and requires citing of the this point. This is how list of datasets looks from the statistics community in Neural Imaging integrates with existing Open Neuroscience, Neural Imaging software and enables complete form an open analysis pipelines for different