Everything Matters: The ReproNim Perspective on Reproducible Neuroimaging

Kennedy, David N.; Abraham, Sanu Ann; Bates, Julianna F.; Crowley, Albert; Ghosh, Samik; Gillespie, Tom; Goncalves, Mathias; Grethe, Jeffrey S.; Halchenko, Yaroslav O.; Hanke, Michael; Haselgrove, Christian; Hodge, Steven M.; Jarecka, Dorota; Kaczmarzyk, Jakub; Keator, David; Meyer, Kyle; Martone, Maryann E.; Padhy, Smruti; Poline, Jean‐Baptiste; Preuss, Nina; Sincomb, Troy; Travers, Matt

doi:10.3389/fninf.2019.00001

Cited by 74 publications

(54 citation statements)

References 34 publications

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“…The neuroimaging research community has taken the "replication crisis" very seriously, like through the ReproNim initiative (10), and the Organisation of Human Brain Mapping (OHBM) announced in 2016 a new replication award, and put reproducibility high up on their agenda with several new best practice and data sharing initiatives (see, e.g., http://www. ohbmbrainmappingblog.com).…”

Section: The Replication Crisis and Its Consequencesmentioning

confidence: 99%

Current Challenges in Translational and Clinical fMRI and Future Directions

Specht

2020

Front. Psychiatry

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Translational neuroscience is an important field that brings together clinical praxis with neuroscience methods. In this review article, the focus will be on functional neuroimaging (fMRI) and its applicability in clinical fMRI studies. In the light of the "replication crisis," three aspects will be critically discussed: First, the fMRI signal itself, second, current fMRI praxis, and, third, the next generation of analysis strategies. Current attempts such as restingstate fMRI, meta-analyses, and machine learning will be discussed with their advantages and potential pitfalls and disadvantages. One major concern is that the fMRI signal shows substantial within-and between-subject variability, which affects the reliability of both taskrelated, but in particularly resting-state fMRI studies. Furthermore, the lack of standardized acquisition and analysis methods hinders the further development of clinical relevant approaches. However, meta-analyses and machine-learning approaches may help to overcome current shortcomings in the methods by identifying new, and yet hidden relationships, and may help to build new models on disorder mechanisms. Furthermore, better control of parameters that may have an influence on the fMRI signal and that can easily be controlled for, like blood pressure, heart rate, diet, time of day, might improve reliability substantially.

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Section: The Replication Crisis and Its Consequencesmentioning

confidence: 99%

Current Challenges in Translational and Clinical fMRI and Future Directions

Specht

2020

Front. Psychiatry

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“…The T1-weighted structural data and rs-fMRI data were preprocessed using a Fusion of Neuroimaging Preprocessing (FuNP) pipeline that integrates AFNI, FSL, and ANTs software [39][40][41][42]. The T1-weighted structural data were preprocessed as follows: the distortion arising from the magnetic field inhomogeneity was corrected and non-brain tissues were removed.…”

Section: Data Preprocessingmentioning

confidence: 99%

Multivariate association between brain function and eating disorders using sparse canonical correlation analysis

et al. 2020

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Eating disorder is highly associated with obesity and it is related to brain dysfunction as well. Still, the functional substrates of the brain associated with behavioral traits of eating disorder are underexplored. Existing neuroimaging studies have explored the association between eating disorder and brain function without using all the information provided by the eating disorder related questionnaire but by adopting summary factors. Here, we aimed to investigate the multivariate association between brain function and eating disorder at fine-grained question-level information. Our study is a retrospective secondary analysis that re-analyzed resting-state functional magnetic resonance imaging of 284 participants from the enhanced Nathan Kline Institute-Rockland Sample database. Leveraging sparse canonical correlation analysis, we associated the functional connectivity of all brain regions and all questions in the eating disorder questionnaires. We found that executive-and inhibitory control-related frontoparietal networks showed positive associations with questions of restraint eating, while brain regions involved in the reward system showed negative associations. Notably, inhibitory control-related brain regions showed a positive association with the degree of obesity. Findings were well replicated in the independent validation dataset (n = 34). The results of this study might contribute to a better understanding of brain function with respect to eating disorder.

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“…Immunohistochemical techniques continue to serve important purposes for characterizing cell populations based on protein expression (which may only be a subset of those expressing the gene for the protein). To achieve efficient quantification of immunohistochemically labeled cells in sectioned material, we used the QUINT workflow ( Yates et al., 2019 ), which combines three open-access tools, QuickNII ( Puchades et al., 2019 ), ilastik ( Berg et al., 2019 ), and Nutil ( Groeneboom et al., 2020 ). This workflow achieves quantification of segmented objects in atlas-defined regions of interest, using customized brain atlas maps, section coordinates, and machine-learning-based segmentation of the labeled objects.…”

Section: Introductionmentioning

confidence: 99%

Densities and numbers of calbindin and parvalbumin positive neurons across the rat and mouse brain

et al. 2021

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Summary The calcium-binding proteins parvalbumin and calbindin are expressed in neuronal populations regulating brain networks involved in spatial navigation, memory processes, and social interactions. Information about the numbers of these neurons across brain regions is required to understand their functional roles but is scarcely available. Employing semi-automated image analysis, we performed brain-wide analysis of immunohistochemically stained parvalbumin and calbindin sections and show that these neurons distribute in complementary patterns across the mouse brain. Parvalbumin neurons dominate in areas related to sensorimotor processing and navigation, whereas calbindin neurons prevail in regions reflecting behavioral states. We also find that parvalbumin neurons distribute according to similar principles in the hippocampal region of the rat and mouse brain. We validated our results against manual counts and evaluated variability of results among researchers. Comparison of our results to previous reports showed that neuron numbers vary, whereas patterns of relative densities and numbers are consistent.

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Everything Matters: The ReproNim Perspective on Reproducible Neuroimaging

Cited by 74 publications

References 34 publications

Current Challenges in Translational and Clinical fMRI and Future Directions

Current Challenges in Translational and Clinical fMRI and Future Directions

Multivariate association between brain function and eating disorders using sparse canonical correlation analysis

Densities and numbers of calbindin and parvalbumin positive neurons across the rat and mouse brain

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