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 kaiDate: 2015/4/10

Content

• Briefing Introduction• Data Description• Model• Analysis for Trained Decoder• Results• Some Comments

Brain Activities

Brain StatesDecoder

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

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.

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

fMRI Data

• Each participants was asked to perform seven tasks related to the following categories:

• Emotion• Gambling• Language• Motor• Relational• Social• Working Memory

Model

• DNN

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

Analysis for Trained Decoder

• Sensitivity analysis

Sensitivity map：

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

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

Results

• Some baseline methods:– logistic regression– SVMs with linear kernel and RBF kernel

Results

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

Results

• Principle sensitivity analysis(PSA)ROI

Results

• PSA

Some Comments

About this paper• 1. What is big data?

• 2.Any innovation?

• 3. Deep learning for transfer learning.