1. FMRI experimental design and data processing
Xi Yu
Basic introduction of experimental designs and data processing for functional imaging studies.

2. Learning goals:
Understand and criticize the methodological section of imaging paper;
2. Guidelines for future fmri study planning;
3. Mechanisms underlying data preprocessing and modeling
Majors aspects of imaging studies
And the third point is that when you are required to analyze imaging data in future. You are probably ganna have a manual or protocol which teaches you how to use one specific analytic tool
So in the following two hours, I will try my best to explain each relevant concept, and avoid mathematical equation as much as possible.

3. Background information
Seiji Ogawa
The increase in neural activity in a brain area will result in an initial increase in oxygen consumption. After a delay of about 2 sec, a large increase in localized cerebral blood flow is triggered, which over-compensates the oxygen consumption. Therefore, localized increases in blood flow increase blood oxygenation and consequently reduce deoxyhaemoglobin. In other words, when a neuron spikes, oxyhemoglobin will reduce. But very quickly, a lot more oxyhemoglobin will flood in.
And in 1990, a Japanese researcher demonstrated that this change in blood oxygenation could be measured by imaging techniques, due to the different magnetic susceptibility of oxyheamoglobin, and deoxyheamoglobin. And put all the physics aside, the result is that the more the blood is oxygenated, the stronger the signal would be. And it is why we also imaging data as BOLD signal
Therefore, what we want to find in a fmri study is region that would show the peak associated with the stimulus/task given at the corresponding time.
Apparently, there are two limitations of Fmri studies. One is that it is measuring the oxygen rate in blood, therefore is a indirect measurement of neural activity. The other is that it measures event change at second level, which is very long compared to the neuronal fire, which only lasts for a few mille seconds. Therefore, it has poor temporal resolution. These are the things you should keep in mind when interpreting your results. And the one of main purposes of the experimental design is to maximize the peak effect.
HRF:
HRF
blood-oxygenation-level-dependent (BOLD) signal

4. Imaging noise 1. Scanner noise 2. Data noise Resting-state
Spatial resolution
The timecourse of the voxel with strongest effect

5. Imaging terminology TR: Repetition time; Scan=Volume=image;
Run=Session;

6. Block design (FSM) +

7. Model Estimation: General Linear Model (GLM)
Forward Model
(X = design matrix) Y X
Inverse Model
Residual Error
Collinearity issue with multiple condition:
Shared variance between regressors, so the contribution of each event type to the changes in timecourses in the brain.
The statistical significance is a ratio between explained variance (β) and unexplained variance (e).

8. Block design Advantages: Easiest way to design the experiment;
Flanker Task
Advantages:
Easiest way to design the experiment;
Effect is vary strong, more likely to be detected;
More robust to unexpected HRF shapes;
Congruent
Disadvantages: Can not differentiate trials within a block
Incompatible with experiment where the trials of the same condition require the same responses
Incongruent
2. Erroroneous responses
High accuracy, comparable between conditions
3.Habituation
Block of medial length, and repeat several times

9. Baseline and Cognitive subtraction
FSM VM
Aim
Whole process? Or one single component?
What this single component is?
Visual and auditory processing
Visual and auditory processing
Phonological decoding
Contrast:
the critical assumption of pure insertion
[Task with Y] – [control task without Y] = Y
Mental judgment
Mental judgment
Fixation vs. Baseline (control)
Motor response
Motor response
FSM- Fixation
FSM- Baseline

10. Event-related design (ER design)
Slow(Spaced) ER design
Rapid ER design
ISI: inter-stimulus-interval
Jittered ISI: to optimize the sequence of your design for better estimation for your GLM (Optseq)

11. Optseq TR – time between volume acquisition (temporal resolution).
Ntp – number of time points (TRs, frames, volumes, …)
Nc – number of event types (conditions)
Npc – number of events/repetitions of each event type (can vary across event types)
Tpc – duration of each event type (can vary across event types)
Schedule – event onset time and identity
Event Response Model – FIR Post-Stimulus Delay Window (needed for optimization)

12. Rapid ER design Disadvantages: Not so efficient as the block design;
More difficult to design (Optseq), implement and model.
Advantages:
More flexible with the experimental design (e.g., Flanker task);
2. Highly resistant to habituation, set, and expectation;
3. Easy to run Post-hoc analysis (e.g., redefine the conditions)

13. Imaging acquisition:
For each run, 56 functional whole-brain images were acquired with a 32-slice EPI interleaved acquisition on a SIEMENS 3T Trio MRscanner including the following specifications: TR 2700 ms; TE 35 ms; flip angle 90°;field-of-view 256 mm; voxelsize, 3×3×4 mm; slice thickness, 4 mm
Temporal order 1 17 2 18
× 56
32 slices
·····
Matrix Size
e.g., 86 x 86
In-plane resolution
256 mm / 86
= 3 mm 31 15 32 16

14. Imaging Processing: Slice timing Timeline Realignment Smooth
Only for ER design
Timeline
Subject 1
Realignment
Smooth
Normalization
Normalization
Realignment
First level modeling
Subject 2
Second level modeling

15. Normalization method:
1. Normalization directly to template functional images (MNI or Talaraich, for customized templates);
2. Corregister functional images with their structural images; normalize structural images to the template, and apply the transformation matrix to functional images;
3. Surface-based normalization--Freesurfer

16. Statistical inferences:
Subject Level (1st Level)
ΒFSM βVM βFixation
Group Level (2nd Level)
One sample T- Test
ConFSM-Fsub01
ConFSM-Fsub02
ConFSM-Fsub03 .
ConFSM-FsubXX .
FSM-Fixation
[ ]
vs.
Correct for
multiple comparisons!
ConFSM-VMsub01
ConFSM-VMsub02
ConFSM-VMsub03 .
ConFSM-VMsubXX .
FSM-VM
[ ]
vs.

17. Thanks!!