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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.

Patent No.:

US 12555232 B1

PDFFull Text
Date of Patent:
Feb. 17, 2026

Filed:

May. 23, 2023

Supervised classifier for optimizing target for neuromodulation, implant localization, and ablation

Applicant:

Washington University, St. Louis, MO (US);

Inventors:

Eric Leuthardt, St. Louis, MO (US);

Carl Hacker, St. Louis, MO (US);

Shan Siddiqi, St. Louis, MO (US);

Tim Laumann, St. Louis, MO (US);

Andy Daniel, St. Louis, MO (US);

Assignee:

Washington University, St. Louis, MO (US);

Attorney:

Armstrong Teasdale LLP

Primary Examiner:

Shahdeep Mohammed

Int. Cl.
CPC ...
G06T 7/00 (2017.01); A61B 5/00 (2006.01); A61B 5/055 (2006.01); A61B 34/10 (2016.01); G01R 33/48 (2006.01); G06F 18/21 (2023.01); G06F 18/2413 (2023.01); G06F 18/2415 (2023.01); G06N 3/00 (2023.01); G06V 10/764 (2022.01); G16H 30/40 (2018.01); G16H 50/20 (2018.01); G16H 20/40 (2018.01);
U.S. Cl.
CPC ...
G06T 7/0012 (2013.01); A61B 5/0042 (2013.01); A61B 5/055 (2013.01); A61B 5/4064 (2013.01); A61B 34/10 (2016.02); G06F 18/2178 (2023.01); G06F 18/24133 (2023.01); G06F 18/2415 (2023.01); G06N 3/00 (2013.01); G06V 10/764 (2022.01); G16H 30/40 (2018.01); G16H 50/20 (2018.01); A61B 5/4848 (2013.01); A61B 5/7267 (2013.01); A61B 2034/107 (2016.02); A61B 2576/026 (2013.01); G01R 33/4806 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/30016 (2013.01); G06V 2201/031 (2022.01); G16H 20/40 (2018.01);
Abstract

A method for selecting a seed region for use in a seed-based cortical functional mapping method includes providing at least one RSN map of at least one subject, each of the at least one RSN maps comprising a plurality of functional voxels within a brain of each of the at least one subjects, each functional voxel of the plurality of functional voxels associated with a probability of membership in an RSN. A subset of the functional voxels characterizing a contiguous region is selected as the seed region, each functional voxel of the seed region having a probability of membership in the RSN above a threshold value.

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