MEG Hackathon 2022: Difference between revisions
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*'''Bidsify your own data and run through mne-bids-pipeline''' |
*'''Bidsify your own data and run through mne-bids-pipeline''' |
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*'''Naturalistic Viewing Data from Movie -- Need a lead for this project''' |
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**Data available on approximately 60 subjects |
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==Data== |
==Data== |
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Revision as of 10:47, 2 May 2022
!Under Construction! More information forthcoming
Location
9-5pm in FAES Classroom #7 (Bldg. 10 Rm. B1C206)
Access via stairs:
From the Masur Auditorium, go north, past the Main Elevators. Look for a bookstore/coffee shop on your right and a sign from the ceiling saying “FAES Academic Center.” Take a right down the hallway, through the double-door; then take the steps down to the lower level. Classroom 7 is on your left.
Wheelchair Access via Main elevator:
Take the Main Elevators outside Masur to the B1 level. Follow the “FAES Academic Center” signs down the hallway towards the Post Office. Across from the Post Office, enter the FAES Academic Center on the left. Once inside the FAES academic Center, Classroom 7 is on your left.
Topics
Possible topics:
- Hidden Markov Models
- Based on:
- Task-Evoked Dynamic Network Analysis Through Hidden Markov Modeling: Andrew J. Quinn, Diego Vidaurre, Romesh Abeysuriya, Robert Becker,Anna C. Nobre1, and Mark W. Woolrich
- Transient spectral events in resting state MEG predict individual task responses - R. Becker, D. Vidaurre, A.J. Quinn, R.G. Abeysuriya, O. Parker Jones, S. Jbabdi, M.W. Woolrich
- Spontaneous network activity <35 Hz accounts for variability in stimulus-induced gamma responses - Jan Hirschmann, Sylvain Baillet, Mark Woolrich, Alfons Schnitzler, Diego Vidaurre, Esther Florin
- Finish porting python HMM models (from github repo) to work with MNE
- Based on:
- DTI FA vs MEG signals - Joint ICA
- Based on:
- Using joint ICA to link function and structure using MEG and DTI in schizophrenia J.M.Stephen, B.A.Coffman, R.E.Jung, J.R.Bustillo, C.J.Aine, V.D.Calhoun
- HV protocol data (DTI + MEG) will be preprocessed and on biowulf
- ICA code can be found in scikit-learn
- Based on:
- Bidsify your own data and run through mne-bids-pipeline
- Naturalistic Viewing Data from Movie -- Need a lead for this project
- Data available on approximately 60 subjects
Data
Currently, but not exclusively
- NIMH HV Data (in BIDS) - on biowulf - .../path