The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
May. 11, 2021

Filed:

Oct. 25, 2017
Applicant:

Siemens Medical Solutions Usa, Inc., Malvern, PA (US);

Inventors:

Francisco Pereira, Jersey City, NJ (US);

Ahmet Tuysuzoglu, Franklin Park, NJ (US);

Bin Lou, West Windsor, NJ (US);

Tommaso Mansi, Plainsboro, NJ (US);

Dorin Comaniciu, Princeton Junction, NJ (US);

Assignee:
Attorney:
Primary Examiner:
Assistant Examiner:
Int. Cl.
CPC ...
G01R 33/48 (2006.01); A61B 5/055 (2006.01); A61B 5/00 (2006.01); G06F 3/01 (2006.01); G06T 7/00 (2017.01); G16H 50/70 (2018.01); G06K 9/62 (2006.01);
U.S. Cl.
CPC ...
G01R 33/4806 (2013.01); A61B 5/0042 (2013.01); A61B 5/055 (2013.01); A61B 5/7267 (2013.01); A61B 5/0022 (2013.01); A61B 5/4064 (2013.01); A61B 5/7246 (2013.01); A61B 5/7264 (2013.01); A61B 2576/026 (2013.01); G06F 3/015 (2013.01); G06K 9/6247 (2013.01); G06K 9/6269 (2013.01); G06K 2209/05 (2013.01); G06T 7/0012 (2013.01); G06T 2207/20081 (2013.01); G16H 50/70 (2018.01);
Abstract

A computer-implemented method for decoding brain imaging data of individual subjects by using additional imaging data from other subjects includes receiving a plurality of functional Magnetic Resonance Imaging (fMRI) datasets corresponding to a plurality of subjects. Each fMRI dataset corresponds to a distinct subject and comprises brain activation patterns resulting from presentation of a plurality of stimuli to the distinct subject. A group dimensionality reduction (GDR) technique is applied to the example fMRI datasets to yield a low-dimensional space of response variables shared by the plurality of subjects. A model is trained to predict a set of target variables based on the low-dimensional space of response variables shared by all subjects, wherein the set of target variables comprise one or more characteristics of the plurality of stimuli.


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