Author: Sotetsu Koyamada, Yumi Shikauchi, et al. (Kyoto University)

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Presentation transcript:

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: 2015/4/10

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

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

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.