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Method for evaluating disease recovery of patient suffering from depression based on diffusion tensor imaging

A technology of diffusion tensor imaging and depression, applied in health index calculation, medical automated diagnosis, medical informatics, etc., to avoid errors and avoid over-treatment

Active Publication Date: 2018-03-09
SOUTHEAST UNIV
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Problems solved by technology

[0003] Purpose of the invention: In order to solve the problem of subjective evaluation of disease recovery of depression patients in the prior art, the present invention proposes a method for evaluating disease recovery of depression patients based on diffusion tensor imaging

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  • Method for evaluating disease recovery of patient suffering from depression based on diffusion tensor imaging
  • Method for evaluating disease recovery of patient suffering from depression based on diffusion tensor imaging
  • Method for evaluating disease recovery of patient suffering from depression based on diffusion tensor imaging

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Embodiment Construction

[0042]In this example, 55 untreated depression patients, 55 treated depression patients, and 55 healthy controls matched in gender, age, and education level were selected, and the diffusion tensor imaging signals of the three groups of subjects were collected. , constructed a diffusion tensor imaging-based assessment model for disease recovery in patients with depression, see figure 1 , including the following steps:

[0043] (1) Using the diffusion tensor imaging technology to construct the brain structural network SN (Structural Network) of the modeling sample: for the data of the diffusion tensor imaging of each sample in the modeling sample, use the automatic anatomical labeling template (ALL structural template), Divide the human brain into 90 brain regions, and each brain region is a node of the brain structure network, trace the cellulose between each two brain regions, and divide the anisotropic fraction of the water molecules in the cellulose (FA) is used as the weig...

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Abstract

The invention discloses a method for evaluating disease recovery of a patient suffering from depression based on diffusion tensor imaging. The method comprises the following steps: establishing a brain structure network of a modeling sample; confirming a Rich club structure; establishing a Feeder-local sub-network; establishing a characteristic matrix; and through support vector regression, by taking an established characteristic matrix as a characteristic set, establishing an evaluation model through polynomial kernel functions. By adopting the method, the recovery process of a disease is evaluated according to objective iconography data, pure data driving is achieved, the disease recovery degree of a patient is judged completely on the basis of research on topology structure similarity models of Feeder-local sub-networks after layering of the Rich-club structure, doctors or patients do not need to join in the evaluation, and errors caused by objective factors are avoided.

Description

technical field [0001] The invention relates to a disease recovery assessment method, in particular to a diffusion tensor imaging-based assessment method for depression patients' disease recovery. Background technique [0002] At present, after a period of treatment for depression patients, the clinical diagnosis of functional recovery is mainly through the relief of symptoms by clinicians, combined with scales such as the Hamilton Depression Rating Scale (HAMD) and the General Assessment Scale (GAS). assessment and patient self-assessment. This method is highly subjective and has extremely high requirements for the clinician's inquiry experience. In addition, the manifestation of symptoms often lags behind the changes in brain circuits. These factors make it difficult to objectively and timely reflect the true degree of recovery of patients. After acute treatment in patients with depression, the brain structure and functional activity level can be restored to a certain e...

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Application Information

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IPC IPC(8): G16H50/30G16H50/20
Inventor 卢青姚志剑王心怡
Owner SOUTHEAST UNIV
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