EP3320480A1 - Verfahren zur konstruktion einer datenstruktur, die eine dynamische reorganisation einer vielzahl von gehirnnetzen repräsentiert, zugehörige vorrichtung und programm - Google Patents

Verfahren zur konstruktion einer datenstruktur, die eine dynamische reorganisation einer vielzahl von gehirnnetzen repräsentiert, zugehörige vorrichtung und programm

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Publication number
EP3320480A1
EP3320480A1 EP16723662.9A EP16723662A EP3320480A1 EP 3320480 A1 EP3320480 A1 EP 3320480A1 EP 16723662 A EP16723662 A EP 16723662A EP 3320480 A1 EP3320480 A1 EP 3320480A1
Authority
EP
European Patent Office
Prior art keywords
connectivity
networks
network
vector
sources
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Ceased
Application number
EP16723662.9A
Other languages
English (en)
French (fr)
Inventor
Fabrice Wendling
Mahmoud Hassan
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Universite de Rennes 1
Institut National de la Sante et de la Recherche Medicale INSERM
Original Assignee
Universite de Rennes 1
Institut National de la Sante et de la Recherche Medicale INSERM
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Universite de Rennes 1, Institut National de la Sante et de la Recherche Medicale INSERM filed Critical Universite de Rennes 1
Publication of EP3320480A1 publication Critical patent/EP3320480A1/de
Ceased legal-status Critical Current

Links

Classifications

    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
    • A61B5/316—Modalities, i.e. specific diagnostic methods
    • A61B5/369—Electroencephalography [EEG]
    • A61B5/377—Electroencephalography [EEG] using evoked responses
    • A61B5/378—Visual stimuli
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
    • A61B5/316—Modalities, i.e. specific diagnostic methods
    • A61B5/369—Electroencephalography [EEG]
    • A61B5/372—Analysis of electroencephalograms
    • A61B5/374—Detecting the frequency distribution of signals, e.g. detecting delta, theta, alpha, beta or gamma waves
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/10—Complex mathematical operations
    • G06F17/16—Matrix or vector computation, e.g. matrix-matrix or matrix-vector multiplication, matrix factorization
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F3/00—Input arrangements for transferring data to be processed into a form capable of being handled by the computer; Output arrangements for transferring data from processing unit to output unit, e.g. interface arrangements
    • G06F3/01—Input arrangements or combined input and output arrangements for interaction between user and computer
    • G06F3/011—Arrangements for interaction with the human body, e.g. for user immersion in virtual reality
    • G06F3/015—Input arrangements based on nervous system activity detection, e.g. brain waves [EEG] detection, electromyograms [EMG] detection, electrodermal response detection
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/0002—Inspection of images, e.g. flaw detection
    • G06T7/0012—Biomedical image inspection
    • G06T7/0014—Biomedical image inspection using an image reference approach
    • G06T7/0016—Biomedical image inspection using an image reference approach involving temporal comparison
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2218/00—Aspects of pattern recognition specially adapted for signal processing
    • G06F2218/22—Source localisation; Inverse modelling

Definitions

  • Functional medical imaging is subdivided into several types depending on the imaging technology used. These include:
  • fMRI functional magnetic resonance imaging
  • PS phase synchronization method
  • said step of grouping the connectivity networks according to a similarity parameter comprises at least one iteration of the following steps:
  • brain activity is recorded using a high spatial resolution 256-electrode EEG system (EGI, "Electrical Geodesy”). Inc. ").
  • EEG Electronic Geodesy
  • the main feature of this system is the large coverage of the subject's head by surface electrodes to improve the analysis of intra-cerebral activity from non-invasive measurements obtained on the scalp, compared to standard systems with 32 to 128 electrodes.
  • the EEG signals are acquired at a sampling frequency of 1 kHz and the bandpass filter is defined between 3 and 45 Hz.
  • the size of a vector is 256 thus comprising 256. signal values.
  • Vector data is spatially localized (this is where the electrode is positioned) and has a signal value.
  • the method described above is a group average approach, which means that this method was based on the calculation of the spatial correlation between the networks on the averaged PLV (t) proximal matrices obtained from all the subjects. Nevertheless, the algorithm does not ignore inter-subject variability.
  • the same type of calculation (mainly step 2) can be performed between the single subject PLV (t) and the clusters that are obtained by the clustering algorithm applied to PLV (t) p years this case auss j ⁇ cna q ue time point is marked according to the graph with which it is best correlated, giving a measure of the "network presence.” This procedure is particularly useful for analyzing, extracting and identifying spatio-temporal behaviors that are common among subjects.

Landscapes

  • Engineering & Computer Science (AREA)
  • Health & Medical Sciences (AREA)
  • Physics & Mathematics (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Theoretical Computer Science (AREA)
  • General Physics & Mathematics (AREA)
  • General Health & Medical Sciences (AREA)
  • Biomedical Technology (AREA)
  • Medical Informatics (AREA)
  • Mathematical Physics (AREA)
  • General Engineering & Computer Science (AREA)
  • Biophysics (AREA)
  • Psychiatry (AREA)
  • Veterinary Medicine (AREA)
  • Public Health (AREA)
  • Animal Behavior & Ethology (AREA)
  • Surgery (AREA)
  • Molecular Biology (AREA)
  • Heart & Thoracic Surgery (AREA)
  • Pathology (AREA)
  • Psychology (AREA)
  • Mathematical Analysis (AREA)
  • Mathematical Optimization (AREA)
  • Computational Mathematics (AREA)
  • Data Mining & Analysis (AREA)
  • Pure & Applied Mathematics (AREA)
  • Dermatology (AREA)
  • Algebra (AREA)
  • Software Systems (AREA)
  • Neurology (AREA)
  • Databases & Information Systems (AREA)
  • Human Computer Interaction (AREA)
  • Computing Systems (AREA)
  • Neurosurgery (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Quality & Reliability (AREA)
  • Radiology & Medical Imaging (AREA)
  • Nuclear Medicine, Radiotherapy & Molecular Imaging (AREA)
  • Magnetic Resonance Imaging Apparatus (AREA)
  • Measurement And Recording Of Electrical Phenomena And Electrical Characteristics Of The Living Body (AREA)
EP16723662.9A 2015-07-10 2016-03-31 Verfahren zur konstruktion einer datenstruktur, die eine dynamische reorganisation einer vielzahl von gehirnnetzen repräsentiert, zugehörige vorrichtung und programm Ceased EP3320480A1 (de)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
FR1556586 2015-07-10
PCT/EP2016/057140 WO2017008926A1 (fr) 2015-07-10 2016-03-31 Procede de construction d'une structure de donnees representative d'une reorganisation dynamique d'une pluralite de reseaux cerebraux, dispositif et programme correspondant

Publications (1)

Publication Number Publication Date
EP3320480A1 true EP3320480A1 (de) 2018-05-16

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EP16723662.9A Ceased EP3320480A1 (de) 2015-07-10 2016-03-31 Verfahren zur konstruktion einer datenstruktur, die eine dynamische reorganisation einer vielzahl von gehirnnetzen repräsentiert, zugehörige vorrichtung und programm

Country Status (3)

Country Link
US (1) US10588535B2 (de)
EP (1) EP3320480A1 (de)
WO (1) WO2017008926A1 (de)

Families Citing this family (15)

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Publication number Priority date Publication date Assignee Title
WO2018052987A1 (en) 2016-09-13 2018-03-22 Ohio State Innovation Foundation Systems and methods for modeling neural architecture
US10588561B1 (en) * 2017-08-24 2020-03-17 University Of South Florida Noninvasive system and method for mapping epileptic networks and surgical planning
CN109589113B (zh) * 2018-10-26 2021-04-20 天津大学 一种多电极阵列神经元放电序列的时空网络构建方法
CN110353666A (zh) * 2019-06-13 2019-10-22 浙江师范大学 一种基于脑电波的构建动态脑功能网络方法
CN111227828A (zh) * 2020-02-14 2020-06-05 广东司法警官职业学院 一种大脑功能网络的建立方法
CN112401905B (zh) * 2020-11-11 2021-07-30 东南大学 一种基于源定位和脑网络的自然动作脑电识别方法
CN112716477B (zh) * 2020-12-28 2024-03-08 聊城大学 一种模块化大脑功能连接网络的估计方法
CN112641450B (zh) * 2020-12-28 2023-05-23 中国人民解放军战略支援部队信息工程大学 面向动态视频目标检测的时变脑网络重构方法
CN113974650B (zh) * 2021-06-29 2024-06-14 华南师范大学 一种脑电网络功能分析方法、装置，电子设备及存储介质
CN114463607B (zh) * 2022-04-08 2022-07-26 北京航空航天大学杭州创新研究院 基于h无穷滤波方式构建因效脑网络的方法和装置
US12588950B2 (en) * 2022-06-02 2026-03-31 Clearpoint Neuro, Inc. Trajectory planning for minimally invasive therapy delivery using local mesh geometry
CN115317002B (zh) * 2022-07-01 2024-12-24 灵犀云医学科技(北京)有限公司 用于确定脑电图的微状态的方法和设备
CN115115896B (zh) * 2022-07-28 2025-08-01 华南师大(清远)科技创新研究院有限公司 一种脑功能磁共振图像分类方法、装置、终端及存储介质
CN117132815B (zh) * 2023-08-28 2025-09-30 东南大学 一种基于时频多层脑网络的自然手部动作脑电识别方法
CN118094277B (zh) * 2024-04-25 2024-07-02 之江实验室 基于动态功能脑网络的大脑视听融合机制探索方法和装置

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US9883812B2 (en) * 2010-06-28 2018-02-06 The Regents Of The University Of California Enhanced multi-core beamformer algorithm for sensor array signal processing by combining data from magnetoencephalography
US9044596B2 (en) * 2011-05-24 2015-06-02 Vanderbilt University Method and apparatus of pulsed infrared light for central nervous system neurons

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
HASSAN MAHMOUD ET AL: "Dynamic reorganization of functional brain networks during picture naming", CORTEX, ELSEVIER MASSON, AMSTERDAM, NL, vol. 73, 28 September 2015 (2015-09-28), pages 276 - 288, XP029349437, ISSN: 0010-9452, DOI: 10.1016/J.CORTEX.2015.08.019 *

Also Published As

Publication number Publication date
US20180199848A1 (en) 2018-07-19
WO2017008926A1 (fr) 2017-01-19
US10588535B2 (en) 2020-03-17

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