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Introduction to fMRI technique using Machine Learning
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MACHINE LEARNINGAPPLICATION OF MACHINE LEARNING TO FMRI DATA ANALYSIS
WHAT IS FMRI?
BRAIN IMAGING METHOD for obtaining 3D images related to activity in the brain.
fMRI measures the ratio of oxygenated haemoglobin to deoxygenated haemoglobin in the blood, at various locations in the brain.
Performs brain activation studies by measuring BRAIN-OXYGEN-LEVEL DEPENDENT (BOLD) signal.
DIFFERENCE BETWEEN FMRI AND MRI
MRI views anatomical structure
Studies water molecule’s hydrogen nuclei
Views in high resolution the difference between the tissue types with respect to space
FMRI views metabolic function
fMRI calculates the level of oxygen
Views the tissue difference with respect to time
TERMINOLOGIES
COGNITION refers to information processing or the processes that affect our mental contents. Example – the process of thinking, the process of remembering.
COGNITIVE SCIENCE is the scientific study of mind and its processing. It examines what cognition is and how it works
BLOOD-OXYGEN-LEVEL DEPENDENT (BOLD) is a method used in functional magnetic resonance to observe different areas of the brain which are found to be active at any given time.
WHAT IS FUNCTIONAL IMAGING?
Measures brain activity by measuring BOLD signals
BOLD signals and neural activation are dependent and the signal distorts the magnetic field.
Working of fMRI Brain requires a steady supply of oxygen for metabolism.
The oxygen is provided by haemoglobin in the blood.
Neural activity consumes oxygen, so on activation there is a momentary decrease in blood oxygenation
The blood flow increases to bring more oxygen to the activated area
The blood flow peaks after around 6 seconds
BOLD signal change depends on magnetic field, which can be measured by MRI
FMRI DATA COLLECTION
Consists of time-series of 3D functional image of subject’s brain
Time interval between each image is called the time of repetition (TR), usually between 2-3 sec.
Each 3D image consist of 20-30 slices of 2D image
One 2D slice contains 64 X 64 voxels
Physically each voxel correspond to 2mm X 2mm X 2mm
FMRI DATA PRE-PROCESSING
SLICE-TIME CORRECTION: Each voxel is acquired at different time point in one TR. This causes discrepancy between actual hemodynamic response of a region of interest. The correction technique is temporal interpolation.
HEAD MOTION CORRECTION: Employs motion correction algorithm
NORMALIZATION: Involves multiple subjects. A standard brain atlas is developed. Functional data from different subjects is properly mapped to the atlas.
THE AIM
fMRI has emerged as a powerful technique to locate activity of human brain while engaged in a particular task or cognitive state.
We consider the inverse problem of detecting the cognitive state of a human subject based on the fMRI data.
Our aim is to identify the cognitive state of human subject that is persistent with time , given the fMRI activity within that interval.
Popular classification techniques include Gaussian Naive Bayes, k-Nearest Neighbour and Support Vector Machines.
THE PROCESS
An fMRI scanner measures the value of the fMRI signal at all the points in a three dimensional grid, or image every few seconds (4-6 seconds).
The number of voxels that constitute the whole brain is very large (1,20,000 – 1,80,000) thereby resulting in very high dimensional data.
Since the signals measure tiny fluctuations in the magnetic field, known as the Blood Oxygen Level Dependent (BOLD) response, the signal-to-noise ratio (SNR) for fMRI data is very low.
Hence fMRI data is very noisy.
CLASSIFIER TECHNIQUE
The classifier is a function of the form:
f : fMRI-sequence(t1, t2) CognitiveState
where fMRI-sequence(t1, t2) is the sequence of fMRI images collected during the contiguous time interval [t1, t2], and where CognitiveState is the set of cognitive states to be discriminated.
MULTI-VOXEL PATTERN ANALYSIS
Traditional fMRI response finds voxels that show statistically significant response. They average the voxels
MVPA uses pattern-classification algorithm applied to multiple voxels to decode the pattern of brain activity.
It offers reduced noise and increased sensitivity