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Graph theory-based dynamic detection method for central node of functional brain network

A dynamic detection and brain network technology, applied in the field of neuroscience brain network research, can solve problems such as the inconsistency of time results, the difficulty in capturing the dynamic changes of the central nodes, and the inability to guarantee the cognitive changes of the central nodes.

Pending Publication Date: 2020-09-01
HANGZHOU DIANZI UNIV
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Problems solved by technology

[0004] At present, the methods for central node detection are all designed for static functional brain networks, and it is difficult to capture the dynamic changes of central nodes over time, resulting in the detection results not having time consistency and unable to guarantee the changes of central nodes and functional brain networks. Consistent with the cognitive changes presented in

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  • Graph theory-based dynamic detection method for central node of functional brain network
  • Graph theory-based dynamic detection method for central node of functional brain network
  • Graph theory-based dynamic detection method for central node of functional brain network

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Embodiment Construction

[0051] The present invention will be further described in detail below in conjunction with the accompanying drawings and experiments.

[0052] The present invention utilizes the technique of sliding window to divide the blood oxygen signal into several sections on average with time as the dimension. In the sliding window of each period of time, the central node in the corresponding time window is detected, so as to obtain a change track of the central node moving with the sliding window (such as figure 1 shown). Finally, this change trajectory is used as a constraint on the multivariate detection method, so that more reliable and accurate central nodes can be dynamically detected.

[0053] Main steps realized by the embodiment of the present invention:

[0054] Step (1): Select the data of 63 normal people and 62 obsessive-compulsive disorder patients for experiment. Each subject has a T1-weighted magnetic resonance image (the specific parameters are TR = 8 ms, TE = 1.7 ms,...

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Abstract

The invention discloses a graph theory-based dynamic detection method for a central node of a functional brain network. The detection method is improved on the basis of a multivariable central node detection method, so that more reliable central node conforming to the cognitive activity of the neuroscience can be detected. The method comprises the following steps: firstly, averagely dividing a blood oxygen signal into a plurality of sections by taking time as a dimension by utilizing a sliding window technology; in the sliding window of each period of time, detecting a central node in the corresponding time window so as to obtain a change track of the central node along with the movement of the sliding window; finally, using the change track as a constraint to act on a multivariable detection method, so that a more reliable and accurate central node can be dynamically detected.

Description

technical field [0001] The invention relates to the field of neuroscience brain network research, in particular to a dynamic detection method for central nodes of functional brain networks based on graph theory. Background technique [0002] Resting-state magnetic resonance (fMRI) provides a non-invasive way to measure changes in cerebral blood oxygenation. In the resting state, the subject did not perform any explicit tasks, and the subject would spontaneously generate neural activity, and the fluctuation of neural activity was related to the change of the blood oxygen concentration signal. Therefore, the blood oxygen concentration signal is used to calculate the connection between brain regions to construct a functional brain network. [0003] The brain network can be divided into many modules, some modules are responsible for vision, some are responsible for hearing, etc. The modular structure allows us to more carefully distinguish the different roles and positions of t...

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Application Information

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IPC IPC(8): G06T7/00
CPCG06T7/0012G06T2207/20072G06T2207/30016G06T2207/10088
Inventor 颜成钢陈安琪朱嘉凯孙垚棋张继勇张勇东
Owner HANGZHOU DIANZI UNIV
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