Detailed Information

Cited 0 time in webofscience Cited 0 time in scopus
Metadata Downloads

Analyzing Neuroimaging Data Through Recurrent Deep Learning Models

Authors
Thomas, Armin W.Heekeren, Hauke R.Mueller, Klaus-RobertSamek, Wojciech
Issue Date
10-12월-2019
Publisher
FRONTIERS MEDIA SA
Keywords
decoding; neuroimaging; fMRI; whole-brain; deep learning; recurrent; interpretability
Citation
FRONTIERS IN NEUROSCIENCE, v.13
Indexed
SCIE
SCOPUS
Journal Title
FRONTIERS IN NEUROSCIENCE
Volume
13
URI
https://scholar.korea.ac.kr/handle/2021.sw.korea/60932
DOI
10.3389/fnins.2019.01321
ISSN
1662-4548
Abstract
The application of deep learning (DL) models to neuroimaging data poses several challenges, due to the high dimensionality, low sample size, and complex temporo-spatial dependency structure of these data. Even further, DL models often act as black boxes, impeding insight into the association of cognitive state and brain activity. To approach these challenges, we introduce the DeepLight framework, which utilizes long short-term memory (LSTM) based DL models to analyze whole-brain functional Magnetic Resonance Imaging (fMRI) data. To decode a cognitive state (e.g., seeing the image of a house), DeepLight separates an fMRI volume into a sequence of axial brain slices, which is then sequentially processed by an LSTM. To maintain interpretability, DeepLight adapts the layer-wise relevance propagation (LRP) technique. Thereby, decomposing its decoding decision into the contributions of the single input voxels to this decision. Importantly, the decomposition is performed on the level of single fMRI volumes, enabling DeepLight to study the associations between cognitive state and brain activity on several levels of data granularity, from the level of the group down to the level of single time points. To demonstrate the versatility of DeepLight, we apply it to a large fMRI dataset of the Human Connectome Project. We show that DeepLight outperforms conventional approaches of uni- and multivariate fMRI analysis in decoding the cognitive states and in identifying the physiologically appropriate brain regions associated with these states. We further demonstrate DeepLight's ability to study the fine-grained temporo-spatial variability of brain activity over sequences of single fMRI samples.
Files in This Item
There are no files associated with this item.
Appears in
Collections
Graduate School > Department of Artificial Intelligence > 1. Journal Articles

qrcode

Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.

Altmetrics

Total Views & Downloads

BROWSE