A Data Driven Approach to Tackling Big Data ConnectomicsGreg Kiar

McGill University Healthy Brains for Healthy Lives Fellow,McGill Centre for Integrative Neuroscience, Montreal Neurological Institute,Ph.D. student McGill University,M.S.E. Johns Hopkins University,B.Eng Carleton University

2018-02-05

Outline

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• Context

• The common approach

• An approach based on accessibility, robustness, and scalability

Publicly available MRI datasets• ADNI• ABCD• ABIDE• ADHD-200• Age-ility• AIBL• BRAINS• CamCAN• CMI-HBN• COBRE• CoRR/FCP-INDI• DLBS

• fBIRN• GSP• HCP• IXI• Kirby21• MASSIVE• MindBoggle-101• MIRIAD• MPI-LMBB• MSC• NACC• NCANDA

• NKIRS• OASIS-CS• OASIS-Long• OpenfMRI• PING• PNC• PTBP• SALD• SchizConnect• StudyForrest• UK-Biobank

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Source:https://github.com/cMadan/openMorph

Publicly supported BIDS apps• AFNI• ANTS Cortical Thickness• Baracus• Brainiak-srm• BROCCOLI• CPAC• DPARSF• Fibre Density and Cross-section• fMRIprep• Freesurfer• FSL Tools• HCP Pipelines• Hyper Alignment

• MAGeTbrain• MindBoggle• MRIQC• MRtrix3 Connectome• ndmg• NIAK• OPPNI• SRM• SPM• Tracula• QAP

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Source:http://bids-apps.neuroimaging.io/apps/

Common approach1. Pose a hypothesis2. Collect + curate dataset3. Manually perform QC on dataset4. Pick processing pipeline and parameters5. Process random subset with pipeline in 4.6. Manually perform QC on derivatives7. Redo from 4. if not happy with 6.8. Process all data with pipeline in 4.9. Answer statistical question10. Publish claim11. Get tenure

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Common Honest approach1. Poste ahoc hypothesis2. Collect + curate dataset3. Undergrads Manually perform QC on dataset4. Pick processing pipeline and parameters5. Process random subset first subject with pipeline in 4.6. Grad students Manually perform QC on derivatives7. Don’t redo from 4. if not happy with 6.8. Process all data with pipeline in 4.9. Answer statistical question (see updated 1.)10. Publish claim11. Get tenure

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Problems with this approach

1. Manual QC is subjective; we need LOTS to be reliable

2. Pipelines and parameters aren’t ”optimized” objectively

3. Datasets aren’t homogeneous

4. Incentive to publish/graduate rather than redo experiments

5. Computer infrastructures are expensive, and not always equal

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Proposed solution

1. Optimize pipeline for stability and robustness; remove bias

2. Automate QC where possible

3. Evaluate on public data with known “truths” (i.e. TestReTest)

4. Automate pipeline deployment

5. Automate data discovery*

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Proposed solution

1. Optimize pipeline for stability and robustness; remove bias

2. Automate QC where possible

3. Evaluate on public data with known “truths” (i.e. TestReTest)

4. Automate pipeline deployment

5. Automate data discovery*

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ndmg: one-click connectomes

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Registration to MNI152

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DWI corr. DWI

T1wint. DWI

Template

xfmreg. DWI QAExternal Dep.

SubjectIntermediateOutput

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Compute Tensor Field

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bvals

grad. table

reg. DWI

Mask

Tensors QA

bvecs

External Dep.SubjectIntermediateOutput

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Estimate Streamlines

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FA thresh.

Mask

Fibers QA

Tensors

External Dep.SubjectIntermediateOutput

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Graph Generation

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Parcellation

Connectome QA

Fibers

External Dep.SubjectIntermediateOutput

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Optimizing for discriminability

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g: connectomei: class label (i.e. subject)j: observation label (i.e. session)

“My brain looks more like my brain than my brain looks like your brain”or

“My brain looks more like brains of the same {dataset, sex, age, handedness, etc.} than brains of another {“, “, “,”}”

Reliable structural connectivity

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easy to use/install$ # Installable on Python2.7 if you have FSL installed…$ pip install ndmg$$ # Run on your dataset$ ndmg_bids /data /outs session $ ndmg_bids /data /outs group$$ # Or install and run through Docker$ docker run –ti –v /data –v /outs bids/ndmg /data /outs session$ docker run –ti –v /data –v /outs bids/ndmg /data /outs group

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Proposed solution

1. Optimize pipeline for stability and robustness; remove bias

2. Automate QC where possible

3. Evaluate on public data with known “truths” (i.e. TestReTest)

4. Automate pipeline deployment

5. Automate data discovery*

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Boutiques

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Boutiques

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{

"name": "echo",

"tool-version": "1.0",

"description": "A simple script to test output files",

"command-line": "echo [PARAM] > output.txt",

"schema-version": "0.5",

"inputs": [{ "id": "param",

"name": "Parameter",

"value-key": "[PARAM]",

"type": "Number" }],

"output-files": [{ "id": "output_file",

"name": "Output file",

"path-template": "output.txt" }]

}

Clowdr

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Boutiques on pip, Clowdr soon

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$ # Installable on Python2 or 3…$ pip install boutiques$$ # Describe, validate, launch your tool, or more!$ bosh validate descriptor.json$ bosh exec simulate descriptor.json –r$ bosh exec launch descriptor.json invocation.json$$ # Soon… (currently the API is a bit uglier than this)$ clowdr deploy descriptor.json invocation.json s3://dataset

Proposed solution

1. Optimize pipeline for stability and robustness; remove bias

2. Automate QC where possible

3. Evaluate on public data with known “truths” (i.e. TestReTest)

4. Automate pipeline deployment

5. Automate data discovery*

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Apine

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So, why are we doing this again?• If tools and platforms are made to be reproducible and robust…

SCIENTISTS CAN FOCUS ON SCIENCE!

• Free validation and summary of the quality of work being done• Enables scaling to datasets beyond reach of manual curation

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The dream• Go to a website• Pick a dataset• Pick an analysis• Design a hypothesis• Launch it• Go outside & run around• Come back to your answer• Share the results, form new hypotheses, and collect new data

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Acknowledgements• McGill Centre for Integrative Neuroscience (Alan Evans, et al.)• Big-Data for Neuroinformatics Lab (Tristan Glatard, et al.)• Jean-Baptiste Poline, Pierre Bellec, Christine Tardif• Montreal Neurological Institute/The Neuro• Healthy Brains for Healthy Lives• Lab-mates, Family, Friends, Universe

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All code demonstrated in this presentation is publicly available on GitHub.

Thanks!Find me @

gkiar

g_kiar

greg.kiar@mcgill.ca

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