THINGS-data, a multimodal collection of large-scale datasets for investigating object representations in human brain and behavior
- Max Planck Institute for Human Cognitive and Brain Sciences, Germany
- National Institute of Mental Health, United States
Abstract
Understanding object representations requires a broad, comprehensive sampling of the objects in our visual world with dense measurements of brain activity and behavior. Here we present THINGS-data, a multimodal collection of large-scale neuroimaging and behavioral datasets in humans, comprising densely-sampled functional MRI and magnetoencephalographic recordings, as well as 4.70 million similarity judgments in response to thousands of photographic images for up to 1,854 object concepts. THINGS-data is unique in its breadth of richly-annotated objects, allowing for testing countless hypotheses at scale while assessing the reproducibility of previous findings. Beyond the unique insights promised by each individual dataset, the multimodality of THINGS-data allows combining datasets for a much broader view into object processing than previously possible. Our analyses demonstrate the high quality of the datasets and provide five examples of hypothesis-driven and data-driven applications. THINGS-data constitutes the core public release of the THINGS initiative (https://things-initiative.org) for bridging the gap between disciplines and the advancement of cognitive neuroscience.
Data availability
All parts of the THINGS-data collection are freely available on scientific data repositories. We provide the raw MRI (https://openneuro.org/datasets/ds004192) and raw MEG (https://openneuro.org/datasets/ds004212) datasets in BIDS format98 on OpenNeuro109. In addition to these raw datasets, we provide the raw and preprocessed MEG data as well as the raw and derivative MRI data on Figshare110 (https://doi.org/10.25452/figshare.plus.c.6161151). The MEG data derivatives include preprocessed and epoched data that are compatible with MNE-python and CoSMoMVPA in MATLAB. The MRI data derivatives include single trial response estimates, category-selective and retinotopic regions of interest, cortical flatmaps, independent component based noise regressors, voxel-wise noise ceilings, and estimates of subject specific retinotopic parameters. In addition, we included the preprocessed and epoched eyetracking data that were recorded during the MEG experiment in the OpenNeuro repository. The behavioral triplet odd-one-out dataset can be accessed on OSF (https://osf.io/f5rn6/, https://doi.org/10.17605/OSF.IO/F5RN6).
-
THINGS-fMRIOpenNeuro doi:10.18112/openneuro.ds004192.v1.0.5.
-
THINGS-MEGOpenNeuro doi:10.18112/openneuro.ds004212.v2.0.0.
-
THINGS-odd-one-outOpen Science Foundation doi:10.17605/OSF.IO/F5RN6.
Article and author information
Author details
Maryam Vaziri-Pashkam
Funding
National Institutes of Health (ZIA-MH-002909)
- Martin N Hebart
- Lina Teichmann
- Adam H Rockter
- Alexis Kidder
- Anna Corriveau
- Maryam Vaziri-Pashkam
- Chris I Baker
National Institutes of Health (ZIC-MH002968)
- Charles Y Zheng
Max-Planck-Gesellschaft (Max Planck Research Group M.TN.A.NEPF0009)
- Martin N Hebart
- Oliver Contier
European Research Council (Starting Grant StG-2021-101039712)
- Martin N Hebart
Hessisches Ministerium für Wissenschaft und Kunst (LOEWE Start Professorship)
- Martin N Hebart
Max Planck School of Cognition
- Oliver Contier
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Ethics
Human subjects: All research participants for the fMRI and MEG studies provided informed consent in participation and data sharing, and they received financial compensation for taking part in the respective studies. The research was approved by the NIH Institutional Review Board as part of the study protocol 93-M-0170 (NCT00001360).All research participants taking part in the online behavioral study provided informed consent for the participation in the study. The online study was conducted in accordance with all relevant ethical regulations and approved by the NIH Office of Human Research Subject Protection (OHSRP).
Copyright
This is an open-access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication.
Metrics
-
- 9,089
- views
-
- 1,208
- downloads
-
- 35
- citations
Views, downloads and citations are aggregated across all versions of this paper published by eLife.
Download links
A two-part list of links to download the article, or parts of the article, in various formats.
Downloads (link to download the article as PDF)
Open citations (links to open the citations from this article in various online reference manager services)
Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)
THINGS-data, a multimodal collection of large-scale datasets for investigating object representations in human brain and behavior
eLife 12:e82580.
https://doi.org/10.7554/eLife.82580
Further reading
-
- Developmental Biology
- Neuroscience
We established a volumetric trans-scale imaging system with an ultra-large field-of-view (FOV) that enables simultaneous observation of millions of cellular dynamics in centimeter-wide three-dimensional (3D) tissues and embryos. Using a custom-made giant lens system with a magnification of ×2 and a numerical aperture (NA) of 0.25, and a CMOS camera with more than 100 megapixels, we built a trans-scale scope AMATERAS-2, and realized fluorescence imaging with a transverse spatial resolution of approximately 1.1 µm across an FOV of approximately 1.5×1.0 cm2. The 3D resolving capability was realized through a combination of optical and computational sectioning techniques tailored for our low-power imaging system. We applied the imaging technique to 1.2 cm-wide section of mouse brain, and successfully observed various regions of the brain with sub-cellular resolution in a single FOV. We also performed time-lapse imaging of a 1-cm-wide vascular network during quail embryo development for over 24 hr, visualizing the movement of over 4.0×105 vascular endothelial cells and quantitatively analyzing their dynamics. Our results demonstrate the potential of this technique in accelerating production of comprehensive reference maps of all cells in organisms and tissues, which contributes to understanding developmental processes, brain functions, and pathogenesis of disease, as well as high-throughput quality check of tissues used for transplantation medicine.
-
- Neuroscience
Dendritic branching and synaptic organization shape single-neuron and network computations. How they emerge simultaneously during brain development as neurons become integrated into functional networks is still not mechanistically understood. Here, we propose a mechanistic model in which dendrite growth and the organization of synapses arise from the interaction of activity-independent cues from potential synaptic partners and local activity-dependent synaptic plasticity. Consistent with experiments, three phases of dendritic growth – overshoot, pruning, and stabilization – emerge naturally in the model. The model generates stellate-like dendritic morphologies that capture several morphological features of biological neurons under normal and perturbed learning rules, reflecting biological variability. Model-generated dendrites have approximately optimal wiring length consistent with experimental measurements. In addition to establishing dendritic morphologies, activity-dependent plasticity rules organize synapses into spatial clusters according to the correlated activity they experience. We demonstrate that a trade-off between activity-dependent and -independent factors influences dendritic growth and synaptic location throughout development, suggesting that early developmental variability can affect mature morphology and synaptic function. Therefore, a single mechanistic model can capture dendritic growth and account for the synaptic organization of correlated inputs during development. Our work suggests concrete mechanistic components underlying the emergence of dendritic morphologies and synaptic formation and removal in function and dysfunction, and provides experimentally testable predictions for the role of individual components.
