English
 
User Manual Privacy Policy Disclaimer Contact us
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT
  Echo state network prediction of intrinsic signals' slow fluctuations of rat and human cortical vasculature

Sobczak, F., & Yu, X. (2018). Echo state network prediction of intrinsic signals' slow fluctuations of rat and human cortical vasculature. Poster presented at 13th Annual Meeting of the European Society for Molecular Imaging (EMIM 2018), San Sebastian, Spain.

Item is

Basic

show hide
Item Permalink: http://hdl.handle.net/21.11116/0000-0001-7DFC-8 Version Permalink: http://hdl.handle.net/21.11116/0000-0001-831E-A
Genre: Poster

Files

show Files

Locators

show
hide
Locator:
Link (Abstract)
Description:
-

Creators

show
hide
 Creators:
Sobczak, F1, 2, Author              
Yu, X1, 2, Author              
Affiliations:
1Research Group Translational Neuroimaging and Neural Control, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_2528695              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497794              

Content

show
hide
Free keywords: -
 Abstract: Introduction Resting-state fMRI signal has been coupled to brain-wide neuronal signal fluctuations, presenting large-scale integration1,2,3. Spontaneous signals display <0.1 Hz oscillatory patterns at varied brain states4. Recently, a single-vessel fMRI method has been developed to characterize the rs-fMRI fluctuation of individual vessels5,6. Here, we hypothesize that the spectral feature of the slow oscillatory pattern can be learnt by an artificial neural network. We used Echo State Networks7 (ESN) to encode single-vessel BOLD fluctuations and predict the <0.1 Hz slow temporal dynamics 10 seconds ahead. Methods Data from 6 rats were acquired with a 14.1T magnet using the bSSFP8 method. 6 adult subjects were scanned using an EPI sequence in a 3T scanner. In both cases one slice TR was 1 s and the duration of each trial was 15 minutes. Using the A-V map5 and ICA9 single-vessel time courses were extracted only from veins exhibiting a strong slow fluctuation. After normalizing the data, the signals have been bandpass filtered in either 0.01-0.05 Hz (rat) or 0.01-0.1 Hz (human) frequency ranges to extract the slowly changing feature. ESN, an artificial neural network belonging to the class of reservoir computing methods, has been used to encode the slow oscillations using supervised learning (Fig 1A). Surrogate data10 served as controls verifying the degree of encoding performed by the chosen ESNs. Results/Discussion To encode the slow spontaneous dynamics, ESNs have been trained to predict the extracted features shifted by 10 seconds with regard to the normalized raw data. The networks were trained separately for human and rat data. ESN's hyperparameters have been optimized using random search11 and their performances have been evaluated by computing Pearson correlation coefficients between network-predictive outputs and input fresh data (Fig.1A,2B). In both human and rat cases the predictions of real data obtained significantly higher scores than those of controls (Fig.1CG,2B). The optimized ESN reservoir trained on 4 trials of one rat was used to predict slow fMRI signal fluctuations of other rats (Fig.1FH). Also, an ESN trained on human vessels has been employed to forecast time courses extracted from V1 ROIs of 250 subjects obtained from the Human Connectome Project12 data. The results demonstrate that it is possible to distinguish brain states based on the obtained prediction scores (Fig.2FG) Conclusions Using high-resolution imaging methods allowed us to extract data from individual venules and target a specific biological mechanism. We have shown that the vascular spontaneous slow oscillations are in many cases predictable and that vessels across subjects or specimen share common oscillatory features. By predicting V1 fluctuations of HCP subjects we demonstrated the connection between vascular and whole-brain dynamics. This paves the way for encoding intrinsic activity of the entire brain. The low computational demands of the method make it a good fit for real-time neurofeedback applications.

Details

show
hide
Language(s):
 Dates: 2018-03
 Publication Status: Published in print
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Method: -
 Identifiers: BibTex Citekey: SobczakY2018
 Degree: -

Event

show
hide
Title: 13th Annual Meeting of the European Society for Molecular Imaging (EMIM 2018)
Place of Event: San Sebastian, Spain
Start-/End Date: -

Legal Case

show

Project information

show

Source 1

show
hide
Title: 13th Annual Meeting of the European Society for Molecular Imaging (EMIM 2018)
Source Genre: Proceedings
 Creator(s):
Affiliations:
Publ. Info: -
Pages: - Volume / Issue: - Sequence Number: 019 Start / End Page: - Identifier: -