Abstract
In this work, we study a novel approach of deep neural machine translation to find linkage between multimodal brain imaging data, such as structural MRI (sMRI) and functional MRI (fMRI). The idea is to consider two different imaging views of the same brain like two different languages conveying some common concepts or facts. An important aspect of the translation model is an attention network module that learns alignment between features from fMRI and sMRI. We use independent component analysis (ICA) based features for the translation model. Our study shows significant group differences between healthy controls and patients with schizophrenia in the learned alignments. Furthermore, this novel approach reveals a group differential relation between a cognitive score (attention and vigilance) and alignments that could not be found when individual modality of data were considered.
Original language | English (US) |
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Title of host publication | 2016 IEEE Southwest Symposium on Image Analysis and Interpretation, SSIAI 2016 - Proceedings |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 1-4 |
Number of pages | 4 |
Volume | 2016-April |
ISBN (Electronic) | 9781467399197 |
DOIs | |
State | Published - Apr 25 2016 |
Externally published | Yes |
Event | IEEE Southwest Symposium on Image Analysis and Interpretation, SSIAI 2016 - Santa Fe, United States Duration: Mar 6 2016 → Mar 8 2016 |
Other
Other | IEEE Southwest Symposium on Image Analysis and Interpretation, SSIAI 2016 |
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Country/Territory | United States |
City | Santa Fe |
Period | 3/6/16 → 3/8/16 |
ASJC Scopus subject areas
- Software
- Computer Vision and Pattern Recognition
- Computer Science Applications