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:
Jul. 12, 2022

Filed:

Nov. 22, 2016
Applicant:

Advanced Telecommunications Research Institute International, Kyoto, JP;

Inventors:

Giuseppe Lisi, Soraku-gun, JP;

Jun Morimoto, Soraku-gun, JP;

Mitsuo Kawato, Soraku-gun, JP;

Noriaki Yahata, Soraku-gun, JP;

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
A61B 5/00 (2006.01); G01R 33/48 (2006.01); G06T 7/00 (2017.01);
U.S. Cl.
CPC ...
A61B 5/4064 (2013.01); G01R 33/4806 (2013.01); G06T 7/0012 (2013.01); G06T 2207/10088 (2013.01); G06T 2207/30016 (2013.01);
Abstract

[Object] An object is to provide a brain activity analyzing method for realizing a biomarker using brain function imaging for neurological/mental disorders. [Solution] From data of resting-state functional connectivity MRI obtained by measuring groups of healthy subjects and patients, a correlation matrix of degrees of activities among prescribed brain regions is derived. By a sparse canonical correlation analysis (SCCA) of attributes of subjects and the correlation matrix, elements of correlation matrix that connect to canonical variables corresponding only to the diagnosis label are extracted. By sparse logistic regression (SLR) during Leave-One-Out Cross Validation of a first sum-set of elements of correlation matrix obtained as a result of feature extraction by a sparse regularized canonical correlation analysis, a second sum-set of elements of the correlation matrix is extracted. By discrimination analysis using sparse logistic regression on the second sum-set, a discriminator is generated.


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