bids-standard/pybids: 0.9.3

Yarkoni, Tal; Markiewicz, Christopher J.; Vega, Alejandro de la; Gorgolewski, Krzysztof J.; Halchenko, Yaroslav O.; Salo, Taylor; McNamara, Quinten; DeStasio, Krista; Poline, Jean‐Baptiste; Hayot‐Sasson, Valérie; Nielson, Dylan; Carlin, Johan D.; Kiar, Gregory; Whitaker, Kirstie; Wagner, Adina; DuPré, Elizabeth; Appelhoff, Stefan; Ivanov, Alexander; Wennberg, Johannes; Tirrell, Lee S.; Estéban, Oscar; Jas, Mainak; Hanke, Michael; Poldrack, Russell A.; Holdgraf, Chris; Staden, Isla; Rokem, Ariel; Thirion, Bertrand; Boulay, Chadwick; Kleinschmidt, Dave F.; Dickie, Erin W.; Lee, John A.; Castello, Matteo Visconti di Oleggio; Notter, Michael; Roca, Pauline; Blair, Ross

doi:10.5281/zenodo.3363985

Cited by 3 publications

(1 citation statement)

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“…We focused on BIDS-compliant datasets which included the exact stimuli presented with precise timing information. Datasets were queried and parsed using pybids ( https://github.com/bids-standard/pybids ; Yarkoni et al, 2019b ; Yarkoni et al, 2019a ) and ingested into a SQL database for further subsequent analysis. Several datasets spanned various original studies or distinct simuli (e.g.…”

Section: Methodsmentioning

confidence: 99%

Neuroscout, a unified platform for generalizable and reproducible fMRI research

Vega

Rocca

Blair

et al. 2022

eLife

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Functional magnetic resonance imaging (fMRI) has revolutionized cognitive neuroscience, but methodological barriers limit the generalizability of findings from the lab to the real world. Here, we present Neuroscout, an end-to-end platform for analysis of naturalistic fMRI data designed to facilitate the adoption of robust and generalizable research practices. Neuroscout leverages state-of-the-art machine learning models to automatically annotate stimuli from dozens of fMRI studies using naturalistic stimuli-such as movies and narratives-allowing researchers to easily test neuroscientific hypotheses across multiple ecologically-valid datasets. In addition, Neuroscout builds on a robust ecosystem of open tools and standards to provide an easy-to-use analysis builder and a fully automated execution engine that reduce the burden of reproducible research. Through a series of meta-analytic case studies, we validate the automatic feature extraction approach and demonstrate its potential to support more robust fMRI research. Owing to its ease of use and a high degree of automation, Neuroscout makes it possible to overcome modeling challenges commonly arising in naturalistic analysis and to easily scale analyses within and across datasets, democratizing generalizable fMRI research.

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Section: Methodsmentioning

confidence: 99%

Neuroscout, a unified platform for generalizable and reproducible fMRI research

Vega

Rocca

Blair

et al. 2022

eLife

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A kinematic dataset of locomotion with gait and sit-to-stand movements of young adults

Hanisch,

Pogrzeba,

Muschter

et al. 2024

Sci Data

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Kinematic data is a valuable source of movement information that provides insights into the health status, mental state, and motor skills of individuals. Additionally, kinematic data can serve as biometric data, enabling the identification of personal characteristics such as height, weight, and sex. In CeTI-Locomotion, four types of walking tasks and the 5 times sit-to-stand test (5RSTST) were recorded from 50 young adults wearing motion capture (mocap) suits equipped with Inertia-Measurement-Units (IMU). Our dataset is unique in that it allows the study of both intra- and inter-participant variability with high quality kinematic motion data for different motion tasks. Along with the raw kinematic data, we provide the source code for phase segmentation and the processed data, which has been segmented into a total of 4672 individual motion repetitions. To validate the data, we conducted visual inspection as well as machine-learning based identity and action recognition tests, achieving 97% and 84% accuracy, respectively. The data can serve as a normative reference of gait and sit-to-stand movements in healthy young adults and as training data for biometric recognition.

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CLIMATE BRAIN - Questionnaires, Tasks and the Neuroimaging Dataset

Zaremba,

Kossowski,

Wypych

et al. 2024

Preprint

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Climate change presents a fundamental threat to human populations and ecosystems across the globe. Neuroscience researchers have recently started developing ways to advance research on this topic. However, validated questionnaires, experimental stimuli, and fMRI tasks are still needed. Here we describe the CLIMATE BRAIN dataset, a multimodal collection of questionnaire, behavioural, and neuroimaging data related to climate change, acquired from 160 healthy individuals. In particular, it includes data from (1) various questionnaire measures, including the Inventory of Climate Emotions (ICE); (2) a neuroimaging task for measuring emotional reactions to standardized Emotional Climate Change Stories (ECCS); and (3) a neuroimaging task based on Carbon Emission Task (CET) to measure climate action-taking. For technical validation, we provide image quality metrics and show the evidence for the effectiveness of tasks consistent with prior studies. To our knowledge, the proposed dataset is currently the only publicly available resource specifically designed to investigate human brain responses to climate change.

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bids-standard/pybids: 0.9.3

Cited by 3 publications

References 0 publications

Neuroscout, a unified platform for generalizable and reproducible fMRI research

Neuroscout, a unified platform for generalizable and reproducible fMRI research

A kinematic dataset of locomotion with gait and sit-to-stand movements of young adults

CLIMATE BRAIN - Questionnaires, Tasks and the Neuroimaging Dataset

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