The document summarizes methods for analyzing functional magnetic resonance imaging (fMRI) data. It discusses preprocessing steps like realignment and normalization required for the raw fMRI image data. It also covers resting state fMRI, confirmatory and exploratory data analysis techniques, important experimental parameters, analysis tools like FSL and SPM, feature extraction methods including independent component analysis and principal component analysis, region of interest analysis, and Fisher transformation of correlation matrices between regions of interest.

1. Iran Deep Learning Center
Paper presentation(Report):
A survey of
fMRI data analysis methods
Niloofar Sedighian Bidgoli
August 2020

2. What was covered:
 Functional magnetic resonance imaging (fMRI) data:
 is used to observe genuine or task induced brain activity networks
 The output of an fMRI scan is a series of raw images, meaning they contain errors
 Hence, some preprocessing on the data is required
 Realignment
 Coregistration
 Segmentation and Normalization
 Smoothing
 The concept of RESTING STATE FMRI
 The hypothesis is that task induced activation maps underestimate the size and number of
functionally connected areas

3. What was covered:
 DATA ANALYSIS TECHNIQUES
 Confirmatory: Problem > Data > Modeling > Analysis > Conclusion
 Exploratory: Problem > Data > Analysis > Modeling > Conclusion
 Bayesian: Problem > Data > Modeling > Prior Distribution > Analysis > Conclusion
 IMPORTANT PARAMETERS IN FMRI EXPERIMENTS
 Number of subjects
 Number of sessions per subject
 Repetition time
 Number of slices per subject
 FUNCTIONAL MRI DATA ANALYSIS TOOLS:
 FSL
 SPM

4. What was covered:
 FEATURE EXTRACTION METHODS: It is difficult to identify which fluctuations are related to
the brain activity
 blind signal separation methods such as ICA and PCA
 For fMRI data, spatial ICA (sICA)
 But before, temporal dimension of the data set may be optionally reduced using PCA
 Region of Interest (ROI) analysis
 Spectral clustering
 FISHER’S TRANSFORMATION
 In fMRI, the connectivity between ROIs is obtained using a Pearson’s correlation coefficient to get a
set of correlation matrices which then needs to be converted using Fisher transformation
 And last, machine learning phase