on the large scale of studying dynamics with meg: lessons learned from the human connectome project

On the large scale of studying dynamics with MEG: Lessons learned from the Human Connectome Project

Robert Oostenveldtogether with the HCP MEG team

Donders Institute, Radboud University, Nijmegen, NLKarolinska Institute, Stockholm, SE

r.oostenveld@donders.ru.nl

AcknowledgementsLinda Larson-PriorAvi SnyderStefania Della PennaVittorio PizzellaGian-Luca RomaniLaura MazzettiFrancesco de PasqualeFred PriorRichard Bucholz

Francesco di PompeoGiorgos MichalareasJan-Mathijs Schoffelen

Tracy NolanMatthew KelseyAbbas Babajani-FeremiJeff StoutTera Kiser

David van Essen and ~100 researchers in the WU-Minn HCP consortium

All subjects that participated

Human Connectome Project

2010: start of project2012: start of acquisition2015: complete MEG release2015: first large outreach workshop (100 people)2016: second workshop (>100 people)2017: complete MRI release2017: third workshop

Human Connectome Project - Goals IStudy a large population:

1,200 healthy adults300 twin pairs and their non-twin siblings

Use cutting-edge neuroimaging methods3T Skyra MRI, customized gradient7T MRI (UMinn, 200 subjects)MEG/EEG (SLU, 100 subjects)dMRI/tractographyResting-fMRI & rMEGTask-fMRI & tMEG

Extensive behavioral testing Blood samples for genotyping

Nelson et al. (Neuron, 2010)

Human Connectome Project - Goals IIMake raw+processed data freely availableProvide a user-friendly informatics platform

Data mining, discovery scienceRelate brain connectivity to individual

capabilitiesLink to heritability, genetics

Provide a baseline for future studies of brain disorders

Autism, schizophrenia, ADHD, etc.Neurosurgical planning and interpretation

Paradigms with MEG – subset of fMRI tasks

3x 5 mins Resting state0 and 2-back Working Memory taskStory/Math taskMotor task (with EMG)

Overview of MEG analysis

Adding dynamics to the Human Connectome Project with MEG. Neuroimage (2013).

PREPROCESSING

CONNECTIVITY

TEMPORAL AND SPECTRAL ESTIMATES

SOURCE ESTIMATION

Overview of MEG analysis

PREPROCESSING

RESTING STATE DATA TASK DATA

CONNECTIVITY

Adding dynamics to the Human Connectome Project with MEG. Neuroimage (2013).

ANAT

OMY

PREPROCESSING

RESTING STATE DATA

TASK DATA

CONNECTIVITY

ConnectomeDBfunctional data in

shared anatomical representation tabular data ?new CIFTI data format building on NIFTI and GIFTI

HCP data organisation (>20TB, now 900 subjects)

subjectid/unprocessed/MEGsubjectid/unprocessed/3T/…subjectid/unprocessed/7T/…

subjectid/MEG/anatomysubjectid/MEG/Restinsubjectid/MEG/Motortsubjectid/MEG/WrkMemsubjectid/MEG/StoryM

subjectid/T1w/…subjectid/Diffusion/…subjectid/MNINonlinear/…

RnoisePnoiseRestin1Restin2Restin3WrkMem1…

datacheckbaddataicaclassrmegpreprocicamnebfblpenvicaimagcoh…

resultsquality controlfigures

consistent naming scheme and organization of data

Processing – over and over again

MEG data acquired from 2012-2014~10 pilot subjects, ~120 study subjects

Pipeline development from 2012-2015

WashU HCP centre compute cluster & storageCompiled MATLAB code with explicit release

versions (0.1, 0.2, … 1.0, 2.0, 2.1, 3.0)Each result file complemented with automatic

“log” file with version detailsVerification of version details prior to release

proper data management, instead of ad-hoc processing

MEG analysis strategies

Currently released results represent the state-of-the-art (at the time of data processing) from the MEG perspective

Comparisons with other HCP results should shed light on the validity of the assumptions in the MEG analyses

Application of new analysis techniques will certainly improve the connectivity patterns

Why sharing of both data and software?

Do analysis on our (MEG/fMRI/DTI) connectomes

Different choices for parameters in pipelinesDifferent source estimation methods

models are based on imperfect/unknown assumptions Different connectivity estimation methods

sensitivity to different features and better robustnessMore elaborate channel level analyses

certain electrophysiological features do not require spatial interpretation

Reanalysis in other software packagesSee also poster Mo-008, HCP with MNE-Python, Engeman et al.

Beyond HCP

Existing Neuroimaging (fMRI) databases

Primary and minimally processed dataGeneral: fMRIDC [RIP], 1000FC, openfMRITargeted: ADNI, NKI-Rockland; ConnectomeDB, CCF

Extensively processed (published) resultsCoordinate-based: Brainmap, SumsDB [RIP], NeurosythStatistical maps: Neurovault

MEG databases

Wakeman and Hensonraw data from 16 subjects (subset from 19)

Open MEG Archive (OMEGA)raw data from 127 subjects

Human Connectome Projectraw and processed data from 95 subjects

(subset from 1200)Others

…

Data sharing is not easy

Resting state data is easier to share than functional task data

Examples available that demonstrate how to share corresponding anatomical data

Brain Imaging Data Structure: draft now available for MEG (BIDS-MEG, see poster Mo-P011)

http://bids.neuroimaging.io

Data sharing is not easy

Dealing with very rich human dataMEG electrophysiologyMR imagingselect populationgeneticsbehavioural testsblood tests

Solutionde-identify all datade-face the imaging datashare coregistered dataprovide age in 5-year binsshare sensitive data only under special agreement

Data sharing is not easy

Data use agreements may have to applyNot all data may be available (e.g. coreg)

“Free as in free speech, not as in free beer” (Richard Stalman)

Wikipedia: The English adjective free is commonly used in one of two meanings: "for zero price" (gratis) and "with little or no restriction" (libre). This ambiguity of free can cause issues where the distinction is important, as it often is in dealing with laws concerning the use of information, such as copyright and patents.

Analysis pipeline sharing is not easy

Dependencies on external components, compute platforms, commercial software, …

Requires structuring and documenting codeAllowing others to use your code also allows them

to test novel ideas or extend your code

HCP focus on reproducibility – all code shared!

Lessons learned from HCP

There are no short term incentives in making MEG research more reproducible

The community needs to take long-term responsibility in pushing the field forward, solidifying methods and translating them to basic and clinical applications

Sharing (of results, data and pipelines) beyond the primary publications is a vital step forward

Resting-state MEG connectivity studies are among the easiest to take this step forward

Lessons learned from HCP

Sharing of expertise is indispensible

You may have data, we have expertise and we are willing to share!

r.oostenveld@donders.ru.nlor contact the HCP team

On the large scale of studying dynamics with MEG: Lessons learned from the Human Connectome Project

Robert Oostenveldtogether with the HCP MEG team

Donders Institute, Radboud University, Nijmegen, NLKarolinska Institute, Stockholm, SE

r.oostenveld@donders.ru.nl

http://www.humanconnectome.org