Modern tools for sharing and synthesizing neuroimaging results

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Krzysztof Gorgolewski

Organization for Human Brain Mapping 2017

Modern tools for sharing and
synthesizing neuroimaging
results
Chris Gorgolewski
Center for Reproducible Neuroscience
Stanford University

SHARING STATISTICAL MAPS
Every voxel counts

Neuroimaging data sharing
hierarchy
Poldrack and Gorgolewski, 2014

Just coordinates?
• Databases such as Neurosynth or
BrainMap rely on peak coordinates
reported in papers (only strong effects)

Are we throwing money away?

NeuroVault.org
simple data sharing
• Minimize the cost!
• We just want your statistical maps with
minimum description (DOI)
– If you want you can put more metadata, but
you don’t have to
• We streamline login process (Google,
Facebook)

Gorgolewski, et al., 2015

Benefits - visualisation

Benefits - decoding

Benefits – similarity search

Benefits – similarity search

Gene decoding
Gorgolewski KJ, Fox AS, Chang L et
al. Tight fitting genes: finding relations
between statistical maps and gene
expression patterns. F1000Posters
2014,5:1607 (poster)

Gene decoding

Surface data

Power analysis

Benefits - other
• Private collections
• Multiple contributors to one collection
• Sharable persistent URLs
• Viewer embeddable on your labs website
or your private blog
• Improved exposure of your research
• Improved reusability of your results

Validation and gains in sensitivity

NeuroVault for developers
• RESTful API (field tested by Neurosynth)
• Source code available on GitHub

What is NIDM-Results?
Poster Number: 1851

Direct upload from SPM12

NeuroVault.org data reuse
Sochat et al. 2015

Data-Driven Extraction of a Nested Model of Human Brain Function
Taylor Bolt, Jason S. Nomi, B. T. Thomas Yeo, Lucina Q. Uddin
Journal of Neuroscience 20 June 2017, 0323-17;

Similar databases
• ANIMA (metaanalyses)
– anima.modelgui.org [Reid et al. 2016]
• USC Multimodal Connectivity Database
– umcd.humanconnectomeproject.org
[Brown et al. 2012]
• BALSA (HCP style)
– balsa.wustl.edu [Van Essen et al. 2016]

Neuroimaging data sharing
hierarchy
Poldrack and Gorgolewski, 2014

Acknowledgements
Russell A. Poldrack
Jean-Baptiste Poline
Yannick Schwarz
Tal Yarkoni
Michael Milham
Daniel Margulies
Yannick Schwartz
Gael Varoquox
Joseph Wexler
Gabriel Rivera
Camile Maumet
Vanessa Sochat
Thomas Nichols
MPI CBS Resting state
group
Poldrack Lab
INCF Data Sharing Task
Force

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STS-UNIT 4 CLIMATE CHANGE POWERPOINT PRESENTATION

STS-UNIT 4 CLIMATE CHANGE POWERPOINT PRESENTATION

STS-UNIT 4 CLIMATE CHANGE POWERPOINT PRESENTATION

Cotton crops are vulnerable to a variety of sucking pests, which can severely impact plant health, yield, and fiber quality. These pests primarily feed on plant sap, extracting nutrients directly from the plant's vascular system. Here's a breakdown of some of the most significant sucking pests in cotton cultivation: Aphids: Cotton aphids or melon aphids can cause direct damage by sucking sap and indirect damage by secreting honeydew, which encourages sooty mold growth. This can interfere with photosynthesis and weaken the plant. Aphids can also transmit viral diseases. Whiteflies: Two species, the silverleaf whitefly and the bandedwinged whitefly, are particularly troublesome. They not only suck sap from the underside of leaves, causing yellowing and leaf drop, but their honeydew excretion promotes sooty mold and they can transmit several plant viruses. Thrips: While thrips can chew on plants, their primary damage to cotton is through sucking. They attack the cotton plant during its seedling stage, which can stunt growth and reduce vigor. Thrips are also capable of transmitting the Cotton Bud disease. Spider Mites: These are not insects but arachnids. Spider mites, such as the two-spotted spider mite, suck cell contents from the leaves, leading to speckled discoloration and potentially significant leaf loss if infestations are severe. Leafhoppers: Including various species, leafhoppers can cause direct damage through feeding, which results in leaf curling and stunted growth. They can also be vectors for plant diseases. Mealybugs: These pests are less common but can be problematic, especially in clustered planting conditions. They suck sap and secrete honeydew, which leads to sooty mold. Mealybugs can also spread viruses. Stink Bugs: Although primarily known for their chewing mouthparts, certain stink bugs can cause damage similar to sucking pests by injecting saliva into the plant and sucking out nutrients, leading to boll damage and stained lint. Management Strategies: Cultural Controls: This includes practices such as crop rotation, using resistant varieties, and managing planting and harvesting times to avoid peak pest populations. Biological Controls: Beneficial insects like lady beetles, lacewings, and predatory mites can naturally control sucking pest populations. Parasitic wasps also play a role in controlling aphid and whitefly populations. Chemical Controls: Insecticides may be used but should be chosen carefully to minimize resistance development and preserve beneficial insects. Systemic insecticides can be particularly effective against sucking pests. Integrated Pest Management (IPM): Combining multiple control strategies based on monitoring and thresholds to apply the most effective and environmentally sensitive approach. Effective management of sucking pests in cotton requires a thorough understanding of the pest species present, their life cycles, and the ecological balance of the field environment.

Pests of cotton_Sucking_Pests_Dr.UPR.pdf

Pests of cotton_Sucking_Pests_Dr.UPR.pdf

Pests of cotton_Sucking_Pests_Dr.UPR.pdf

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Unit5-Cloud.pptx for lpu course cse121 o

Unit5-Cloud.pptx for lpu course cse121 o

Unit5-Cloud.pptx for lpu course cse121 o

ManavSingh202607

COST ESTIMATION FOR A RESEARCH PROJECT.pptx

COST ESTIMATION FOR A RESEARCH PROJECT.pptx

COST ESTIMATION FOR A RESEARCH PROJECT.pptx

FarihaAbdulRasheed

Context. WASP-76 b has been a recurrent subject of study since the detection of a signature in high-resolution transit spectroscopy data indicating an asymmetry between the two limbs of the planet. The existence of this asymmetric signature has been confirmed by multiple studies, but its physical origin is still under debate. In addition, it contrasts with the absence of asymmetry reported in the infrared (IR) phase curve. Aims. We provide a more comprehensive dataset of WASP-76 b with the goal of drawing a complete view of the physical processes at work in this atmosphere. In particular, we attempt to reconcile visible high-resolution transit spectroscopy data and IR broadband phase curves. Methods. We gathered 3 phase curves, 20 occultations, and 6 transits for WASP-76 b in the visible with the CHEOPS space telescope. We also report the analysis of three unpublished sectors observed by the TESS space telescope (also in the visible), which represents 34 phase curves. Results. WASP-76 b displays an occultation of 260±11 and 152±10 ppm in TESS and CHEOPS bandpasses respectively. Depending on the composition assumed for the atmosphere and the data reduction used for the IR data, we derived geometric albedo estimates that range from 0.05 ± 0.023 to 0.146 ± 0.013 and from <0.13 to 0.189 ± 0.017 in the CHEOPS and TESS bandpasses, respectively. As expected from the IR phase curves, a low-order model of the phase curves does not yield any detectable asymmetry in the visible either. However, an empirical model allowing for sharper phase curve variations offers a hint of a flux excess before the occultation, with an amplitude of ∼40 ppm, an orbital offset of ∼−30◦ , and a width of ∼20◦ . We also constrained the orbital eccentricity of WASP-76 b to a value lower than 0.0067, with a 99.7% confidence level. This result contradicts earlier proposed scenarios aimed at explaining the asymmetry observed in high-resolution transit spectroscopy. Conclusions. In light of these findings, we hypothesise that WASP-76 b could have night-side clouds that extend predominantly towards its eastern limb. At this limb, the clouds would be associated with spherical droplets or spherically shaped aerosols of an unknown species, which would be responsible for a glory effect in the visible phase curves.

Asymmetry in the atmosphere of the ultra-hot Jupiter WASP-76 b

Asymmetry in the atmosphere of the ultra-hot Jupiter WASP-76 b

Asymmetry in the atmosphere of the ultra-hot Jupiter WASP-76 b

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Feature-aligned N-BEATS with Sinkhorn divergence (ICLR '24)

Feature-aligned N-BEATS with Sinkhorn divergence (ICLR '24)

Feature-aligned N-BEATS with Sinkhorn divergence (ICLR '24)

Bacterial Identification and Classifications

Bacterial Identification and Classifications

Bacterial Identification and Classifications

The computation of anti-derivatives is just an in-tellectual challenge, we know how to take deriv-atives, but … can we invert the process? We call this Computing the indefinite integral . In the last presentation we have seen a few indefinite integrals (we called them bricks), but they did not include the anti-derivative of many functions! We are going to try and do better !

COMPUTING ANTI-DERIVATIVES(Integration by SUBSTITUTION)

COMPUTING ANTI-DERIVATIVES(Integration by SUBSTITUTION)

COMPUTING ANTI-DERIVATIVES(Integration by SUBSTITUTION)

Understanding circumstellar disks is of prime importance in astrophysics, however, their birth process remains poorly constrained due to observational and numerical challenges. Recent numerical works have shown that the small-scale physics, often wrapped into a sub-grid model, play a crucial role in disk formation and evolution. This calls for a combined approach in which both the protostar and circumstellar disk are studied in concert. Aims. We aim to elucidate the small scale physics and constrain sub-grid parameters commonly chosen in the literature by resolving the star-disk interaction. Methods. We carry out a set of very high resolution 3D radiative-hydrodynamics simulations that self-consistently describe the collapse of a turbulent dense molecular cloud core to stellar densities. We study the birth of the protostar, the circumstellar disk, and its early evolution (< 6 yr after protostellar formation). Results. Following the second gravitational collapse, the nascent protostar quickly reaches breakup velocity and sheds its surface material, thus forming a hot (∼ 103 K), dense, and highly flared circumstellar disk. The protostar is embedded within the disk, such that material can flow without crossing any shock fronts. The circumstellar disk mass quickly exceeds that of the protostar, and its kinematics are dominated by self-gravity. Accretion onto the disk is highly anisotropic, and accretion onto the protostar mainly occurs through material that slides on the disk surface. The polar mass flux is negligible in comparison. The radiative behavior also displays a strong anisotropy, as the polar accretion shock is shown to be supercritical whereas its equatorial counterpart is subcritical. We also f ind a remarkable convergence of our results with respect to initial conditions. Conclusions. These results reveal the structure and kinematics in the smallest spatial scales relevant to protostellar and circumstellar disk evolution. They can be used to describe accretion onto regions commonly described by sub-grid models in simulations studying larger scale physics.

Formation of low mass protostars and their circumstellar disks

Formation of low mass protostars and their circumstellar disks

Formation of low mass protostars and their circumstellar disks

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GBSN - Microbiology (Unit 2)

GBSN - Microbiology (Unit 2)

GBSN - Microbiology (Unit 2)

Último (20)

pumpkin fruit fly, water melon fruit fly, cucumber fruit fly

pumpkin fruit fly, water melon fruit fly, cucumber fruit fly

pumpkin fruit fly, water melon fruit fly, cucumber fruit fly

module for grade 9 for distance learning

module for grade 9 for distance learning

module for grade 9 for distance learning

Factory Acceptance Test( FAT).pptx .

Factory Acceptance Test( FAT).pptx .

Factory Acceptance Test( FAT).pptx .

FAIRSpectra - Enabling the FAIRification of Spectroscopy and Spectrometry

FAIRSpectra - Enabling the FAIRification of Spectroscopy and Spectrometry

FAIRSpectra - Enabling the FAIRification of Spectroscopy and Spectrometry

Clean In Place(CIP).pptx .

Clean In Place(CIP).pptx .

Clean In Place(CIP).pptx .

Seismic Method Estimate velocity from seismic data.pptx

Seismic Method Estimate velocity from seismic data.pptx

Seismic Method Estimate velocity from seismic data.pptx

STS-UNIT 4 CLIMATE CHANGE POWERPOINT PRESENTATION

STS-UNIT 4 CLIMATE CHANGE POWERPOINT PRESENTATION

STS-UNIT 4 CLIMATE CHANGE POWERPOINT PRESENTATION

Pests of cotton_Sucking_Pests_Dr.UPR.pdf

Pests of cotton_Sucking_Pests_Dr.UPR.pdf

Pests of cotton_Sucking_Pests_Dr.UPR.pdf

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Unit5-Cloud.pptx for lpu course cse121 o

Unit5-Cloud.pptx for lpu course cse121 o

Unit5-Cloud.pptx for lpu course cse121 o

COST ESTIMATION FOR A RESEARCH PROJECT.pptx

COST ESTIMATION FOR A RESEARCH PROJECT.pptx

COST ESTIMATION FOR A RESEARCH PROJECT.pptx

Asymmetry in the atmosphere of the ultra-hot Jupiter WASP-76 b

Asymmetry in the atmosphere of the ultra-hot Jupiter WASP-76 b

Asymmetry in the atmosphere of the ultra-hot Jupiter WASP-76 b

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Feature-aligned N-BEATS with Sinkhorn divergence (ICLR '24)

Feature-aligned N-BEATS with Sinkhorn divergence (ICLR '24)

Feature-aligned N-BEATS with Sinkhorn divergence (ICLR '24)

Bacterial Identification and Classifications

Bacterial Identification and Classifications

Bacterial Identification and Classifications

COMPUTING ANTI-DERIVATIVES(Integration by SUBSTITUTION)

COMPUTING ANTI-DERIVATIVES(Integration by SUBSTITUTION)

COMPUTING ANTI-DERIVATIVES(Integration by SUBSTITUTION)

Formation of low mass protostars and their circumstellar disks

Formation of low mass protostars and their circumstellar disks

Formation of low mass protostars and their circumstellar disks

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GBSN - Microbiology (Unit 2)

GBSN - Microbiology (Unit 2)

GBSN - Microbiology (Unit 2)

Modern tools for sharing and synthesizing neuroimaging results

1. Modern tools for sharing and synthesizing neuroimaging results Chris Gorgolewski Center for Reproducible Neuroscience Stanford University

2. SHARING STATISTICAL MAPS Every voxel counts

3. Neuroimaging data sharing hierarchy Poldrack and Gorgolewski, 2014

4. Just coordinates? • Databases such as Neurosynth or BrainMap rely on peak coordinates reported in papers (only strong effects)

5. Are we throwing money away?

6.

7. NeuroVault.org simple data sharing • Minimize the cost! • We just want your statistical maps with minimum description (DOI) – If you want you can put more metadata, but you don’t have to • We streamline login process (Google, Facebook)

8. Gorgolewski, et al., 2015

9. Benefits - visualisation

10. Benefits - decoding

11. Benefits – similarity search

12. Benefits – similarity search

13. Gene decoding Gorgolewski KJ, Fox AS, Chang L et al. Tight fitting genes: finding relations between statistical maps and gene expression patterns. F1000Posters 2014,5:1607 (poster)

14. Gene decoding

15. Surface data

16. Power analysis

17.

18. Benefits - other • Private collections • Multiple contributors to one collection • Sharable persistent URLs • Viewer embeddable on your labs website or your private blog • Improved exposure of your research • Improved reusability of your results

19. Validation and gains in sensitivity

20. NeuroVault for developers • RESTful API (field tested by Neurosynth) • Source code available on GitHub

21. What is NIDM-Results? Poster Number: 1851

22. Direct upload from SPM12

23. NeuroVault.org data reuse Sochat et al. 2015

24. Data-Driven Extraction of a Nested Model of Human Brain Function Taylor Bolt, Jason S. Nomi, B. T. Thomas Yeo, Lucina Q. Uddin Journal of Neuroscience 20 June 2017, 0323-17;

25. Similar databases • ANIMA (metaanalyses) – anima.modelgui.org [Reid et al. 2016] • USC Multimodal Connectivity Database – umcd.humanconnectomeproject.org [Brown et al. 2012] • BALSA (HCP style) – balsa.wustl.edu [Van Essen et al. 2016]

26. Neuroimaging data sharing hierarchy Poldrack and Gorgolewski, 2014

27. Acknowledgements Russell A. Poldrack Jean-Baptiste Poline Yannick Schwarz Tal Yarkoni Michael Milham Daniel Margulies Yannick Schwartz Gael Varoquox Joseph Wexler Gabriel Rivera Camile Maumet Vanessa Sochat Thomas Nichols MPI CBS Resting state group Poldrack Lab INCF Data Sharing Task Force

Notas del editor

Think how much money and effort goes into one study 100,000 USD to produce one paper: 6-12 pages of authors interpretation of acquired data …without the data itself By not reporting subthreshold effects we are wasting (taxpayers) money!
Calles for sharing statistical maps, data sharing has been problematic in the past
Add Camiles poster number.