FMRI school 2012
fMRI school 2012
Time: May 3-4 (Thu-Fri), 2012
Place: Aalto Otaniemi campus (Map of Otaniemi), Main Building, Otakaari 1
Lecture hall: M, 1st floor. The nearest entrance is staircase M, at the north-east end of the main building.
Organizers
Brain Research Unit, O.V. Lounasmaa Laboratory, Aalto University School of Science
Advanced Magnetic Imaging Centre, Aalto University School of Science
Finnish Graduate School of Neuroscience
Contact person
Simo Vanni, vanni [at] neuro.hut.fi
Register to
Marita Kattelus, marita.kattelus [at] aalto.fi, please register by April 26, 2012.
Aim
This course is directed to undergraduate and graduate students, postdocs, and more advanced research personnel who are interested in applying functional magnetic resonance imaging in their research. This year we have two lectures about multivariate methods, a rapidly growing field of functional MRI imaging. This is an introductory course, and thus no own experience in brain imaging is required.
Program
Thursday, 3rd May
10.00 Simo Vanni: What is BOLD signal?
10.45 Simo Vanni: Study design
11.30 Lunch
12.30 Linda Henriksson: Image data formats and preprocessing
- Preprocessing script for SPM8: conv_sli_rea_data_example.m
- DICOM to NIFTI image format conversion for SPM8 (for OLD AMI scanner): spm_vision_dicom_convert.m, spm_vision_dicom.m
- DICOM to NIFTI image format conversion for SPM8 (for NEW AMI scanner, thanks Simo): spm_visionS_dicom_convert.m, spm_visionS_dicom.m
13.15 Lauri Nurminen: General linear model applied to fMRI analysis I
14.00 Coffee break
14.15 Simo Särkkä, BECS: Physiological noise in fMRI – a signal processing perspective
15.00 Lauri Nummenmaa. Statistical inference
Friday, 4th May
10.00 Simo Vanni: General linear model applied to fMRI analysis II
10.45 Linda Henriksson: Visualization and quantification of fMRI results
11.30 Lunch
12.30 Toni Auranen: MRI @ AMI
13.15 Ville Renvall: Physics of functional MRI
14.00 Coffee break
14.15 Linda Henriksson: Multivariate pattern analysis of fMRI data
15.00 Miika Koskinen: Introduction to machine learning in fMRI analysis