fMRI measures brain activity by detecting changes in blood oxygenation and flow related to neural activity. Machine learning can be applied to fMRI data to identify the cognitive state of a human subject based on their brain activity patterns over time. The process involves preprocessing the high-dimensional fMRI imaging data, then using classifiers like Gaussian Naive Bayes, k-Nearest Neighbor, or Support Vector Machines to learn patterns that distinguish different cognitive states from the multi-voxel activity patterns.

1. MACHINE LEARNING
APPLICATION OF MACHINE LEARNING TO FMRI DATA ANALYSIS

2. 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.

3. 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

4. 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.

5. 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

6. BOLD signal change depends on
magnetic field, which can be
measured by MRI

7. 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

8. 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.

9. 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.

10. 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.

11. 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.

12. 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