BRAINZOOM: High resolution reconstruction from multi-modal brain signals

Xiao Fu, Kejun Huang, Otilia Stretcu, Hyun Ah Song, Evangelos Papalexakis, Partha Talukdar, Tom Mitchell, Nicholas Sidiropoulos, Christos Faloutsos, Barnabas Poczos

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Scopus citations


How close can we zoom in to observe brain activity? Our understanding is limited by the resolution of imaging modalities that exhibit good spatial but poor temporal resolution, or vice-versa. In this paper, we propose BRAINZOOM, an efficient imaging algorithm that cross-leverages multi-modal brain signals. BRAINZOOM (a) constructs high resolution brain images from multi-modal signals, (b) is scalable, and (c) is flexible in that it can easily incorporate various priors on the brain activities, such as sparsity, low rank, or smoothness. We carefully formulate the problem to tackle nonlinearity in the measurements (via variable splitting) and auto-scale between different modal signals, and judiciously design an inexact alternating optimization-based algorithmic framework to handle the problem with provable convergence guarantees. Our experiments using a popular realistic brain signal simulator to generate fMRI and MEG demonstrate that high spatio-temporal resolution brain imaging is possible from these two modalities. The experiments also suggest that smoothness seems to be the best prior, among several we tried.

Original languageEnglish (US)
Title of host publicationProceedings of the 17th SIAM International Conference on Data Mining, SDM 2017
EditorsNitesh Chawla, Wei Wang
PublisherSociety for Industrial and Applied Mathematics Publications
Number of pages8
ISBN (Electronic)9781611974874
StatePublished - 2017
Event17th SIAM International Conference on Data Mining, SDM 2017 - Houston, United States
Duration: Apr 27 2017Apr 29 2017

Publication series

NameProceedings of the 17th SIAM International Conference on Data Mining, SDM 2017


Other17th SIAM International Conference on Data Mining, SDM 2017
Country/TerritoryUnited States

Bibliographical note

Funding Information:
This material is based upon work supported by the National Science Foundation under Grants No. IIS-1247489, IIS-1247632.

Funding Information:
This work is also partially supported by an IBM Faculty Award and a Google Focused Research Award. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation, or other funding parties.

Publisher Copyright:
Copyright © by SIAM.


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