23 research outputs found
Parallel workflow tools to facilitate human brain MRI post-processing
Multi-modal magnetic resonance imaging (MRI) techniques are widely applied in human brain studies. To obtain specific brain measures of interest from MRI datasets, a number of complex image post-processing steps are typically required. Parallel workflow tools have recently been developed, concatenating individual processing steps and enabling fully automated processing of raw MRI data to obtain the final results. These workflow tools are also designed to make optimal use of available computational resources and to support the parallel processing of different subjects or of independent processing steps for a single subject. Automated, parallel MRI post-processing tools can greatly facilitate relevant brain investigations and are being increasingly applied. In this review, we briefly summarize these parallel workflow tools and discuss relevant issues
The MNI data-sharing and processing ecosystem
AbstractNeuroimaging has been facing a data deluge characterized by the exponential growth of both raw and processed data. As a result, mining the massive quantities of digital data collected in these studies offers unprecedented opportunities and has become paramount for today's research. As the neuroimaging community enters the world of “Big Data”, there has been a concerted push for enhanced sharing initiatives, whether within a multisite study, across studies, or federated and shared publicly. This article will focus on the database and processing ecosystem developed at the Montreal Neurological Institute (MNI) to support multicenter data acquisition both nationally and internationally, create database repositories, facilitate data-sharing initiatives, and leverage existing software toolkits for large-scale data processing
A numerical variability approach to results stability tests and its application to neuroimaging
Ensuring the long-term reproducibility of data analyses requires results
stability tests to verify that analysis results remain within acceptable
variation bounds despite inevitable software updates and hardware evolutions.
This paper introduces a numerical variability approach for results stability
tests, which determines acceptable variation bounds using random rounding of
floating-point calculations. By applying the resulting stability test to
\fmriprep, a widely-used neuroimaging tool, we show that the test is sensitive
enough to detect subtle updates in image processing methods while remaining
specific enough to accept numerical variations within a reference version of
the application. This result contributes to enhancing the reliability and
reproducibility of data analyses by providing a robust and flexible method for
stability testing
Sharing brain mapping statistical results with the neuroimaging data model
Only a tiny fraction of the data and metadata produced by an fMRI study is finally conveyed to the community. This lack of transparency not only hinders the reproducibility of neuroimaging results but also impairs future meta-analyses. In this work we introduce NIDM-Results, a format specification providing a machine-readable description of neuroimaging statistical results along with key image data summarising the experiment. NIDM-Results provides a unified representation of mass univariate analyses including a level of detail consistent with available best practices. This standardized representation allows authors to relay methods and results in a platform-independent regularized format that is not tied to a particular neuroimaging software package. Tools are available to export NIDM-Result graphs and associated files from the widely used SPM and FSL software packages, and the NeuroVault repository can import NIDM-Results archives. The specification is publically available at: http://nidm.nidash.org/specs/nidm-results.html
Everything Matters: The ReproNim Perspective on Reproducible Neuroimaging
There has been a recent major upsurge in the concerns about reproducibility in many areas of science. Within the neuroimaging domain, one approach is to promote reproducibility is to target the re-executability of the publication. The information supporting such re-executability can enable the detailed examination of how an initial finding generalizes across changes in the processing approach, and sampled population, in a controlled scientific fashion. ReproNim: A Center for Reproducible Neuroimaging Computation is a recently funded initiative that seeks to facilitate the “last mile” implementations of core re-executability tools in order to reduce the accessibility barrier and increase adoption of standards and best practices at the neuroimaging research laboratory level. In this report, we summarize the overall approach and tools we have developed in this domain
Efficacy of MRI data harmonization in the age of machine learning. A multicenter study across 36 datasets
Pooling publicly-available MRI data from multiple sites allows to assemble
extensive groups of subjects, increase statistical power, and promote data
reuse with machine learning techniques. The harmonization of multicenter data
is necessary to reduce the confounding effect associated with non-biological
sources of variability in the data. However, when applied to the entire dataset
before machine learning, the harmonization leads to data leakage, because
information outside the training set may affect model building, and potentially
falsely overestimate performance. We propose a 1) measurement of the efficacy
of data harmonization; 2) harmonizer transformer, i.e., an implementation of
the ComBat harmonization allowing its encapsulation among the preprocessing
steps of a machine learning pipeline, avoiding data leakage. We tested these
tools using brain T1-weighted MRI data from 1740 healthy subjects acquired at
36 sites. After harmonization, the site effect was removed or reduced, and we
measured the data leakage effect in predicting individual age from MRI data,
highlighting that introducing the harmonizer transformer into a machine
learning pipeline allows for avoiding data leakage
Behavior, sensitivity, and power of activation likelihood estimation characterized by massive empirical simulation
Given the increasing number of neuroimaging publications, the automated knowledge extraction on brain-behavior associations by quantitative meta-analyses has become a highly important and rapidly growing field of research. Among several methods to perform coordinate-based neuroimaging meta-analyses, Activation Likelihood Estimation (ALE) has been widely adopted. In this paper, we addressed two pressing questions related to ALE meta-analysis: i) Which thresholding method is most appropriate to perform statistical inference? ii) Which sample size, i.e., number of experiments, is needed to perform robust meta-analyses? We provided quantitative answers to these questions by simulating more than 120,000 meta-analysis datasets using empirical parameters (i.e., number of subjects, number of reported foci, distribution of activation foci) derived from the BrainMap database. This allowed to characterize the behavior of ALE analyses, to derive first power estimates for neuroimaging meta-analyses, and to thus formulate recommendations for future ALE studies. We could show as a first consequence that cluster-level family-wise error (FWE) correction represents the most appropriate method for statistical inference, while voxel-level FWE correction is valid but more conservative. In contrast, uncorrected inference and false-discovery rate correction should be avoided. As a second consequence, researchers should aim to include at least 20 experiments into an ALE meta-analysis to achieve sufficient power for moderate effects. We would like to note, though, that these calculations and recommendations are specific to ALE and may not be extrapolated to other approaches for (neuroimaging) meta-analysis