阅读说明：本技术 一种经颅直流电刺激的干预方法 (Intervention method of transcranial direct current stimulation ) 是由 姚乃琳 于 2020-07-27 设计创作，主要内容包括：本发明公开了一种经颅直流电刺激的干预方法。它具体包括如下步骤：用磁共振脑成像获得个体大脑结构和功能影像,收集弥散张量影像和4D功能影像数据,并用脑网络分析方法将数据解构为大脑结构网络和大脑功能网络；通过脑网络分析方法分析得出个体大脑不同区域的连接紧密程度及活跃程度,据此得出个体症状对应的大脑网络异常；通过脑网络AI算法基于大脑结构网络和大脑功能网络两个方面的影像数据分析脑影像多人大数据,对tDCS经典干预方式有积极反应的大脑网络分布形态特征被抓取,并用于识别新个体的大脑积极反应可能性,从而预先判断经典tDCS干预范式是否对该个体有效。本发明的有益效果是：避免医疗资源浪费；增加临床反应比例,提升治疗效果。(The invention discloses an intervention method of transcranial direct current stimulation. The method specifically comprises the following steps: acquiring individual brain structure and function images by magnetic resonance brain imaging, collecting diffusion tensor images and 4D function image data, and deconstructing the data into a brain structure network and a brain function network by using a brain network analysis method; analyzing the connection tightness and the activity degree of different areas of the individual brain by a brain network analysis method, and accordingly obtaining the brain network abnormality corresponding to individual symptoms; the brain image multi-person big data is analyzed through a brain network AI algorithm based on image data of a brain structure network and a brain function network, brain network distribution morphological characteristics which have positive response to a tDCS classical intervention mode are captured and used for identifying the brain positive response possibility of a new individual, and therefore whether a classical tDCS intervention paradigm is effective for the individual is judged in advance. The invention has the beneficial effects that: medical resource waste is avoided; increase the clinical reaction proportion and improve the treatment effect.)

1. An intervention method of transcranial direct current stimulation is characterized by comprising the following steps:

(1) acquiring individual brain structure and function images by magnetic resonance brain imaging, collecting diffusion tensor images and 4D function image data, and deconstructing the data into a brain structure network and a brain function network by using a brain network analysis method;

(2) the connection tightness and the activity of different areas of the individual brain are obtained through analysis of a brain network analysis method, and are subjected to associated analysis with subjective experience symptoms of the individual and the intervention effect of transcranial direct current stimulation, so that brain network abnormality and intervention effectiveness corresponding to the individual symptoms are obtained;

(3) the brain image multi-person big data is analyzed through a brain network AI algorithm based on image data of a brain structure network and a brain function network, brain network distribution morphological characteristics which have positive response to a tDCS classical intervention mode are captured and used for identifying the brain positive response possibility of a new individual, and therefore whether a classical tDCS intervention paradigm is effective for the individual is judged in advance.

2. The method as claimed in claim 1, wherein the brain network structure is a nerve fiber network structure, the imaging method is water molecule diffusion tensor imaging, the connection strength of nerve fibers among different areas of the brain and the white matter nerve network structure of the whole brain are drawn, partial anisotropy indexes, namely the free motion degree of water molecules in brain tissues in all directions, are calculated, and the indexes are used for judging whether the tissues are neurons or nerve fibers and are used for constructing a structure network.

3. The method of claim 1, wherein the brain function network structure is a 4-dimensional gray image of the brain that varies with time by recording the blood oxygen saturation level inside the cerebral cortex, and then extracting the numerical value sequence of the gray values of different brain regions, i.e., voxel points, that varies with time, and calculating the correlation between voxels, thereby obtaining the integration of the prefrontal lobe attention network and the left and right hemispheres of the saliency network, the integration being expressed according to the characteristic values of the small world network, wherein the small world network is a special network structure in which most nodes are not connected to each other, but most nodes are reached through a few steps, and the brain network structure conforms to the characteristics of the small world network, so that the small world network characteristic values of the brain can be calculated therefrom.

4. The method according to claim 3, wherein the characteristic values of the network refer to a cluster coefficient and a characteristic path, wherein the cluster coefficient is a characteristic value describing a small world network, and is a coefficient describing a degree of clustering between vertices in a network, and specifically, a degree of interconnection between adjacent points of a point; the characteristic path is also a characteristic value for describing the small world network, and refers to an average value of the shortest path length between two points in a network; the characteristic values of the network are extracted and are subjected to relevant analysis with the treatment effects of different models of tDCS, a brain network characteristic value combination with the maximum correlation degree with the treatment effect of the tDCS is obtained, then the brain network characteristic value combination is used for testing the prediction effect in the new individual, and finally the obtained brain network characteristic with the optimal prediction effect is continuously used for making an accurate scheme of the new individual.

Technical Field

The invention relates to the technical field related to transcranial electrical stimulation, in particular to an intervention method of transcranial direct current stimulation.

Background

Transcranial electrical stimulation (tES) delivers low-intensity current (1-2 mA) into the brain through conductive patches on the surface of the skull to achieve the purpose of increasing or decreasing excitability of neurons to regulate brain region activities. Transcranial direct current stimulation (tDCS) is a classic usage paradigm of transcranial electrical stimulation, and has been proven to be effective in improving emotional problems such as depression and anxiety, and improving cognitive functions (attention, language ability, memory, etc.) in both research fields and clinical applications. For example, in 2006 the first double-blind randomized controlled trial found that stimulation of the left dorsolateral prefrontal lobe (DLPFC) with transcranial direct current stimulation (tDCS) could alleviate symptoms in 60% -70% of patients with depression. Another study in 2008 found that the improvement in depression symptoms could be maintained for approximately 30 days by tDCS.

The existing transcranial direct current stimulation (tDCS) technology, although clinically effective, does not provide positive therapeutic response for every patient that is intervened. In fact, 30% -50% of patients with anxiety or depression receive a particular paradigm of tDCS dry prognosis for 5-15 days without significant therapeutic effect. This is because the brain function map structure of each person is different, and the neural networks corresponding to different emotional problems are different, which results in that the same treatment paradigm is not applicable to all patients with the same abnormal clinical emotional manifestations.

The accurate medical treatment is a diagnosis and treatment concept actively promoted worldwide in the medical field in the last 5 years, and the theoretical basis thereof is that the same physiological/psychological symptoms correspond to each individual, the internal physiological mechanism may be very different, and the same internal physiological pathological changes may have completely different symptom expression forms on different individuals. Thus, the same treatment plan is often only effective in one part of the population and completely ineffective in another part of the population. The individual difference is known through detailed evaluation to accurate medical treatment before deciding treatment scheme to deciding accurate individual diagnosis and treatment scheme that corresponds in advance, accomplishing targeted treatment, thereby greatly promote treatment.

In the traditional tDCS intervention, medical resources and personal time are wasted inevitably due to lack of accurate evaluation of individuals and formulation of corresponding personalized schemes. As a result of the accumulation of recent 30 years of brain image studies, it has been found that emotional problems such as depression and anxiety, and cognitive disorders such as concentration loss correspond to different brain network abnormalities, and that improvement of emotional problems and cognitive problems generally corresponds to changes in the brain network. Thus, pre-detection of abnormal features of the brain network helps to determine whether a particular treatment regimen is likely to have a positive therapeutic effect on the individual.

Disclosure of Invention

The invention provides an intervention method of transcranial direct current stimulation for improving treatment effect, aiming at overcoming the defects in the prior art.

In order to achieve the purpose, the invention adopts the following technical scheme:

an intervention method of transcranial direct current stimulation specifically comprises the following steps:

(1) acquiring individual brain structure and function images by magnetic resonance brain imaging, collecting diffusion tensor images and 4D function image data, and deconstructing the data into a brain structure network and a brain function network by using a brain network analysis method;

(2) the connection tightness and the activity of different areas of the individual brain are obtained through analysis of a brain network analysis method, and are subjected to associated analysis with subjective experience symptoms of the individual and the intervention effect of transcranial direct current stimulation, so that brain network abnormality and intervention effectiveness corresponding to the individual symptoms are obtained;

(3) the brain image multi-person big data is analyzed through a brain network AI algorithm based on image data of a brain structure network and a brain function network, brain network distribution morphological characteristics which have positive response to a tDCS classical intervention mode are captured and used for identifying the brain positive response possibility of a new individual, and therefore whether a classical tDCS intervention paradigm is effective for the individual is judged in advance.

The method combines non-invasive brain network detection (magnetic resonance scanning and brain network analysis) to pre-evaluate the treatment response rate of the tDCS to the individual, thereby achieving the purpose of purposefully applying the tDCS to intervene the emotional problem, in brief, specifically eliminating the individual possibly having no response to the tDCS intervention, and greatly avoiding the waste of medical resources; different tDCS intervention schemes are adapted to the brain network of a specific individual, so that the clinical response proportion can be obviously increased, and the treatment effect is improved.

Preferably, the brain network structure refers to a nerve fiber network structure, the imaging method is water molecule diffusion tensor imaging, the connection strength of nerve fibers among different areas of the brain and the white matter nerve network structure of the whole brain are drawn, partial anisotropic indexes, namely the free motion degree of water molecules in brain tissues in all directions, are calculated, and the indexes are used for judging whether the tissues are neurons or nerve fibers and are used for constructing a structural network.

Preferably, the brain function network structure is a brain 4-dimensional gray image which is changed along with time and is drawn by recording the blood oxygen saturation in the cerebral cortex, then numerical value sequences of gray values of different brain areas, namely voxel points, which are changed along with time are extracted, the correlation degree among voxels is calculated, and therefore the integration degree of the prefrontal brain attention network and the left and right hemispheres of the salient network is obtained, the integration degree is represented according to all characteristic values of the small world network, wherein the small world network is a special network structure, most nodes in the network are not connected with each other, but most nodes can be reached through a few steps, and the brain network structure accords with the characteristics of the small world network, so the small world network characteristic value of the brain can be calculated.

Preferably, the characteristic value of the network refers to a clustering coefficient and a characteristic path, wherein the clustering coefficient is a characteristic value describing a small-world network and is a coefficient for describing a degree of clustering between vertexes in a network, and specifically, a degree of interconnection between adjacent points of a point; the characteristic path is also a characteristic value for describing the small world network, and refers to an average value of the shortest path length between two points in a network; the characteristic values of the network are extracted and are subjected to relevant analysis with the treatment effects of different models of tDCS, a brain network characteristic value combination with the maximum correlation degree with the treatment effect of the tDCS is obtained, then the brain network characteristic value combination is used for testing the prediction effect in the new individual, and finally the obtained brain network characteristic with the optimal prediction effect is continuously used for making an accurate scheme of the new individual.

The invention has the beneficial effects that: individuals who possibly do not respond to tDCS intervention are purposefully excluded, and waste of medical resources is greatly avoided; different tDCS intervention schemes are adapted to the brain network of a specific individual, so that the clinical response proportion can be obviously increased, and the treatment effect is improved.

Detailed Description

The invention is further described with reference to specific embodiments.

An intervention method of transcranial direct current stimulation specifically comprises the following steps:

(1) acquiring individual brain structure and function images by magnetic resonance brain imaging, collecting diffusion tensor images and 4D function image data, and deconstructing the data into a brain structure network and a brain function network by using a brain network analysis method;

(2) the method comprises the steps of obtaining connection tightness and activity of different areas of the individual brain through analysis of a brain network analysis method, carrying out correlated analysis on subjective experience symptoms of the individual and intervention effects of transcranial direct current stimulation, obtaining brain network abnormity and intervention effectiveness corresponding to the individual symptoms according to the analysis, and obtaining measurement indexes including balance degree of the frontal lobe left and right hemisphere networks of the brain, integration degree of each local area network of the brain and the like;

(3) analyzing brain image multi-person big data based on image data of two aspects of a brain structure network and a brain function network through a brain network AI algorithm, capturing brain network distribution morphological characteristics which have positive response to a tDCS classical intervention mode, and identifying the brain positive response possibility of a new individual, thereby judging whether a classical tDCS intervention paradigm is effective for the individual in advance; similarly, the method is further used to identify individuals for which other tDCS intervention paradigms may be effective.

The technical scheme combines the brain network AI algorithm and the transcranial electrical stimulation method, can make and implement a more accurate tDCS intervention scheme than the existing method, and effectively avoids the waste of medical resources and personal time of patients. And (4) judging the tDCS treatment scheme most suitable for the new individual based on the brain network AI algorithm of the brain structure network and the functional network characteristics.

The brain network structure refers to a nerve fiber network structure, the imaging method is water molecule diffusion tensor imaging, the connection strength of nerve fibers among different areas of the brain and the white matter nerve network structure of the whole brain are drawn, partial anisotropy indexes (FA), namely the free motion degree of water molecules in brain tissues in all directions, are calculated, and the indexes are used for judging whether the tissues are neurons or nerve fibers and are used for constructing a structure network.

The brain function network structure is characterized in that a 4-dimensional brain gray image changing along with time is drawn by recording blood oxygen saturation inside a cerebral cortex, then numerical value sequences of gray values of different brain areas, namely voxel points, changing along with time are extracted, correlation degree among voxels is calculated, and accordingly integration degree of a prefrontal lobe attention network and a left hemisphere and a right hemisphere of a salient network is obtained, the integration degree is represented according to all characteristic values of a small-world network, wherein the small-world network is a special network structure, most nodes in the network are not connected with each other, but can be reached through a few steps, and the brain network structure accords with characteristics of the small-world network, so that the characteristic value of the small-world network of the brain can be calculated.

The characteristic value of the network refers to a clustering coefficient and a characteristic path, wherein the clustering coefficient is a characteristic value describing a small-world network and is used for describing the degree of clustering of the vertexes in the network, specifically, the degree of interconnection between adjacent points of one point; the characteristic path is also a characteristic value describing a small-world network, and refers to an average value of shortest path lengths (or distances) between two points in a network; the characteristic values of the network are extracted and are subjected to relevant analysis with the treatment effects of different paradigms of the tDCS, a brain network characteristic value combination with the maximum correlation degree with the treatment effect of the tDCS is obtained and is used for testing the prediction effect in the new individual, the finally obtained brain network characteristic with the optimal prediction effect is the balance degree of the frontal lobe network of the left hemisphere and the right hemisphere of the brain, but not limited to, and the network balance characteristic value is continuously used for making an accurate scheme of the new individual.

The method combines non-invasive brain network detection (magnetic resonance scanning and brain network analysis) to pre-evaluate the treatment response rate of the tDCS to the individual, thereby achieving the purpose of purposefully applying the tDCS to intervene the emotional problem, in brief, specifically eliminating the individual possibly having no response to the tDCS intervention, and greatly avoiding the waste of medical resources; different tDCS intervention schemes are adapted to the brain network of a specific individual, so that the clinical response proportion can be obviously increased, and the treatment effect is improved.