1. Deep Learning of fMRI big data: a novel approach to subject-transfer decoding
Author: Sotetsu Koyamada, Yumi Shikauchi, et al. (Kyoto University)
Submitted to Neural Networks SI: NN learning in Big Data Februry 3,2015
Speaker: Tian kai
Date: /4/10

2. Content Briefing Introduction Data Description Model
Analysis for Trained Decoder
Results
Some Comments

3. Briefing Introduction
The problem?
Brain decoding
The difficulties?
Large variation in brain activities across individuals.
The possible application?
Brain machine interface(BMI), neuron rehabilitation, therapy of mental disorders
Decoder
Brain States
Brain Activities

4. Briefing Introduction
More Details
1.This problem can be thought as a classification problem.
2.It is difficult to obtain sufficient data from single person to build a reliable decoder.
3.The idea of subject-transfer.

5. fMRI Data Data acquisition: Human Connection Project(HCP)
499 healthy adults
TR=720 ms TE=33.1 ms flip angle 52° FOV=208*180 mm
72 slices resolution: 2.0*2.0 mm
Preprocessing: removal of spatial artifacts and distortions
Within-subject cross-modal registrations, reduction of the bias field, and alignment to standard space.
Feature dimension: 116

6. fMRI Data
Each participants was asked to perform seven tasks related to the following categories:
Emotion
Gambling
Language
Motor
Relational
Social
Working Memory

7. Model
DNN

8. Subject-transfer Decoding
Select 100 person from 499 individuals(D).
1) unrelated with each other
2)successfully completed all seven cognitive tasks twice.
Separate D into 10-fold
test
valid
train

9. Analysis for Trained Decoder
Sensitivity analysis
Sensitivity map：

10. PSA: to compute the direction v that f is most sensitive in the input space.

11. The solution to this problem is the maximal eigenvector of K.
This vector was defined as principal sensitivity map(PSM).

12. Results Some baseline methods: logistic regression
SVMs with linear kernel and RBF kernel

13. Results
They investigated how the decoder’s performance changes with the size of training dataset.

14. Results
Principle sensitivity analysis(PSA)
ROI

15. Results
PSA

16. Some Comments About this paper 1. What is big data? 2.Any innovation?
3. Deep learning for transfer learning.