CN112259241A - Depression brain function connection change detection system and method based on dynamic causal model - Google Patents
Depression brain function connection change detection system and method based on dynamic causal model Download PDFInfo
- Publication number
- CN112259241A CN112259241A CN202011148064.8A CN202011148064A CN112259241A CN 112259241 A CN112259241 A CN 112259241A CN 202011148064 A CN202011148064 A CN 202011148064A CN 112259241 A CN112259241 A CN 112259241A
- Authority
- CN
- China
- Prior art keywords
- module
- data
- input end
- output end
- dynamic
- 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.)
- Granted
Links
- 230000001364 causal effect Effects 0.000 title claims abstract description 39
- 238000000034 method Methods 0.000 title claims abstract description 27
- 238000001514 detection method Methods 0.000 title claims abstract description 26
- 230000003925 brain function Effects 0.000 title claims abstract description 23
- 230000008859 change Effects 0.000 title claims abstract description 16
- 238000012545 processing Methods 0.000 claims abstract description 52
- 238000004458 analytical method Methods 0.000 claims abstract description 31
- 210000004556 brain Anatomy 0.000 claims abstract description 30
- 230000005540 biological transmission Effects 0.000 claims abstract description 27
- 238000003745 diagnosis Methods 0.000 claims abstract description 20
- 238000002599 functional magnetic resonance imaging Methods 0.000 claims description 17
- 210000003478 temporal lobe Anatomy 0.000 claims description 12
- 238000012047 cause and effect analysis Methods 0.000 claims description 10
- 238000001816 cooling Methods 0.000 claims description 10
- 238000012544 monitoring process Methods 0.000 claims description 8
- 230000008569 process Effects 0.000 claims description 7
- 230000004913 activation Effects 0.000 claims description 6
- 238000006243 chemical reaction Methods 0.000 claims description 6
- 238000012937 correction Methods 0.000 claims description 6
- 230000000694 effects Effects 0.000 claims description 6
- 230000037361 pathway Effects 0.000 claims description 6
- 230000002123 temporal effect Effects 0.000 claims description 6
- 238000012360 testing method Methods 0.000 claims description 6
- 230000006870 function Effects 0.000 claims description 4
- 101100009524 Anas platyrhynchos HSD17B12 gene Proteins 0.000 claims description 3
- 101100256975 Saccharomyces cerevisiae (strain ATCC 204508 / S288c) SIP2 gene Proteins 0.000 claims description 3
- 101100203850 Saccharomyces cerevisiae (strain ATCC 204508 / S288c) SPT2 gene Proteins 0.000 claims description 3
- 101100291461 Schizosaccharomyces pombe (strain 972 / ATCC 24843) spm2 gene Proteins 0.000 claims description 3
- QVGXLLKOCUKJST-UHFFFAOYSA-N atomic oxygen Chemical compound [O] QVGXLLKOCUKJST-UHFFFAOYSA-N 0.000 claims description 3
- 238000010009 beating Methods 0.000 claims description 3
- 239000008280 blood Substances 0.000 claims description 3
- 210000004369 blood Anatomy 0.000 claims description 3
- 238000013461 design Methods 0.000 claims description 3
- 230000005611 electricity Effects 0.000 claims description 3
- 238000005516 engineering process Methods 0.000 claims description 3
- 230000001037 epileptic effect Effects 0.000 claims description 3
- 238000011156 evaluation Methods 0.000 claims description 3
- 230000004424 eye movement Effects 0.000 claims description 3
- 238000001914 filtration Methods 0.000 claims description 3
- 238000009499 grossing Methods 0.000 claims description 3
- 238000003384 imaging method Methods 0.000 claims description 3
- 238000002955 isolation Methods 0.000 claims description 3
- 238000010606 normalization Methods 0.000 claims description 3
- 229910052760 oxygen Inorganic materials 0.000 claims description 3
- 239000001301 oxygen Substances 0.000 claims description 3
- 238000007781 pre-processing Methods 0.000 claims description 3
- 238000000513 principal component analysis Methods 0.000 claims description 3
- 230000010349 pulsation Effects 0.000 claims description 3
- 238000005316 response function Methods 0.000 claims description 3
- 208000020401 Depressive disease Diseases 0.000 description 3
- 208000010877 cognitive disease Diseases 0.000 description 3
- 238000010586 diagram Methods 0.000 description 3
- 230000000994 depressogenic effect Effects 0.000 description 2
- 230000008482 dysregulation Effects 0.000 description 2
- 210000001320 hippocampus Anatomy 0.000 description 2
- 230000036651 mood Effects 0.000 description 2
- 238000011160 research Methods 0.000 description 2
- 208000024891 symptom Diseases 0.000 description 2
- 230000009466 transformation Effects 0.000 description 2
- 208000019901 Anxiety disease Diseases 0.000 description 1
- 206010012239 Delusion Diseases 0.000 description 1
- 206010012374 Depressed mood Diseases 0.000 description 1
- 208000004547 Hallucinations Diseases 0.000 description 1
- 208000019022 Mood disease Diseases 0.000 description 1
- 208000028017 Psychotic disease Diseases 0.000 description 1
- 208000010513 Stupor Diseases 0.000 description 1
- 206010042464 Suicide attempt Diseases 0.000 description 1
- 238000013019 agitation Methods 0.000 description 1
- 230000036506 anxiety Effects 0.000 description 1
- 230000006399 behavior Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000008901 benefit Effects 0.000 description 1
- 230000007177 brain activity Effects 0.000 description 1
- 210000001638 cerebellum Anatomy 0.000 description 1
- 210000003710 cerebral cortex Anatomy 0.000 description 1
- 230000001684 chronic effect Effects 0.000 description 1
- 238000013145 classification model Methods 0.000 description 1
- 238000013480 data collection Methods 0.000 description 1
- 231100000868 delusion Toxicity 0.000 description 1
- 230000001627 detrimental effect Effects 0.000 description 1
- 201000010099 disease Diseases 0.000 description 1
- 208000037265 diseases, disorders, signs and symptoms Diseases 0.000 description 1
- 229940079593 drug Drugs 0.000 description 1
- 239000003814 drug Substances 0.000 description 1
- 230000004064 dysfunction Effects 0.000 description 1
- 238000013399 early diagnosis Methods 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 238000010801 machine learning Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000007230 neural mechanism Effects 0.000 description 1
- 230000002085 persistent effect Effects 0.000 description 1
- 230000002265 prevention Effects 0.000 description 1
- 230000000306 recurrent effect Effects 0.000 description 1
- 230000003068 static effect Effects 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
- G16H50/50—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for simulation or modelling of medical disorders
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H20/00—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance
- G16H20/70—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to mental therapies, e.g. psychological therapy or autogenous training
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
- G16H50/20—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
Abstract
The invention discloses a depression brain function connection change detection system and method based on a dynamic causal model. In the invention, the output end of the brain scanning module is connected with the input end of the data transmission module, the output end of the data transmission module is connected with the input end of the dynamic causal analysis module, the output end of the dynamic causal analysis module is connected with the input end of the main processing module, the output end of the main processing module is connected with the input end of the data transmission module, the output end of the data transmission module is connected with the input end of the diagnosis module, the output end of the diagnosis module is connected with the input end of the data output module, and the dynamic causal analysis module is adopted to analyze data; the dynamic causal model is adopted to discuss the influence of depression on the connection change of functional sites in the brain more deeply, and compared with the traditional analysis method, the analysis speed is improved, and the labor burden of medical workers is saved.
Description
Technical Field
The invention belongs to the technical field of depression treatment, and particularly relates to a depression brain function connection change detection system and method based on a dynamic causal model.
Background
Depression, also known as depressive disorder, is characterized clinically by a marked and persistent depression in the mood, the main type of mood disorder. The low mood is not matched with the situation in clinic, the depression of the mood can be from sultriness to sadness, and the self-declining depression and even the pessimism are taken away, and suicide attempts or behaviors can be caused; even the occurrence of stupor; in some cases, there is significant anxiety and motor agitation; in severe cases, psychotic symptoms such as hallucinations and delusions may occur. Each episode lasts at least 2 weeks, more than long, or even years, and most cases have a tendency to have recurrent episodes, most of which can be alleviated, and some of which can have residual symptoms or become chronic. In 2020, depression is predicted to jump over global second-leading-disability diseases, 79% of patients combine cognitive disorders with different degrees, but the medical treatment and prevention recognition rate of depression and cognitive disorders caused by depression in China is low, a combined machine learning method is researched, a classification model of cognitive disorders of first-onset depression patients is constructed from two angles of long-range (high-order functional connection) and local (dynamic local consistency), two groups of brain image markers are found, the diagnosis accuracy is respectively improved to 82.47% and 72.5%, the diagnosis accuracy is improved by about 15% and 12% compared with that of the traditional low-order and static methods, and the high-order and dynamic brain function networks are disclosed to be possibly used as a new method for assisting early diagnosis of depression. The results indicate that local dysregulation of the lateral hippocampus on the left side of the depressed patient is an embodiment of the dysfunction of the execution of the depressed patient, and the local dysregulation of the left side hippocampus can predict the course of the depression. In addition, the research on the causal relationship of the mutual influence among all brain regions of patients without taking medicines for depression proves that the depression patients have the change of higher-order brain function network causal connection, and the research further stresses the important role of the wide higher-order function connection change between the brain and the cerebellum in the internal neural mechanism of the depression.
But the efficiency of common detection methods; the failure to observe the brain function connectivity changes caused by depression affects the causal relationship between connectivity and the anteroposterior effects between depression and brain function, which is detrimental to the subsequent treatment of patients.
Disclosure of Invention
The invention aims to: in order to solve the problems, a depression brain function connection change detection system and method based on a dynamic causal model are provided.
The technical scheme adopted by the invention is as follows: the depression brain function connection change detection system and method based on the dynamic causal model comprises a brain scanning module, a data transmission module, a dynamic causal analysis module, a general processing module, a data transmission module, a diagnosis module, a data output module, a data sending module, a power supply module, a data receiving terminal, a power failure judgment module, a data recording module, a single chip microcomputer module, an EEG data acquisition module, an fMRI data processing module, a DCM modeling module, a Bayesian model selection module, a temperature sensor and a cooling fan module, wherein the output end of the brain scanning module is connected with the input end of the data transmission module, the output end of the data transmission module is connected with the input end of the dynamic causal analysis module, the output end of the dynamic causal analysis module is connected with the input end of the general processing module, and the output end of the general processing module is connected with the input end of the data transmission module, the output end of the data transmission module is connected with the input end of the diagnosis module, the output end of the diagnosis module is connected with the input end of the data output module, the output end of the data output module is connected with the input end of the data sending module, and the output end of the data sending module is connected with the input end of the data receiving terminal;
the power output end of the power supply module is connected with the power input end of the main processing module, the power-off judging module is fixedly arranged outside the main processing module, the output end of the power failure judging module is connected with the input end of the data recording module, the output end of the data recording module is connected with the input end of the data output module, an EEG data acquisition module is arranged in the dynamic causal analysis module, the output end of the EEG data acquisition module is connected with the input end of the fMRI data processing module, the output end of the fMRI data processing module is connected with the input end of the DCM modeling module, the output end of the DCM modeling module is connected with the input end of the Bayesian model selecting module, the temperature sensor and the cooling fan module block are fixedly mounted outside the power supply module, and the control end of the single chip microcomputer module is connected with the power input end of the cooling fan module block.
In a preferred embodiment, the EEG data collected inside the EEG data collection module is processed offline by Brain Vision analyzer1.05 software, artifact removal is performed according to 200V/ms gradient trigger transformation rate, and then electrocardiographic pulsation and eye movement interference are respectively filtered; brain Vision Analyzer has been applied to the removal of imaging artifacts and beating artifacts of Brain electricity at home and abroad; the EEG signal after filtering in this paper carries on the analysis of respective reading through 2 experienced electroencephalogram doctors, discern the typical spike wave or spike-slow wave that both sides confirmed sends the time, EEG time precision is an integer of seconds, for example 2 spikes within 1 second also as a valid sending.
In a preferred embodiment, the fMRI data processing module performs processing analysis by using SPM2 software, the preprocessing includes time correction, cephalometric correction, normalization and smoothing, the task time points are typical epileptic wave emission points detected by EEG, the mode definition is performed according to an event-related mode, and a blood oxygen response function with a peak value of 5 seconds is selected to observe the positive activation condition of the brain of each patient; and then carrying out random effect analysis based on single-sample T-test on each T-test statistical chart of the group to obtain a group analysis result, and superposing the positive activation result in a T1 standard template for displaying.
In a preferred embodiment, the DCM modeling module uses the VOI tool in the SPM2 to obtain a time series of selected regions in the valid data, and the spherical radius of the region of interest is set to 4 mm; then loading the most main time sequence obtained by the VOI through principal component analysis into a DCM module of an SPM5 software package for model evaluation; each patient designs two DCM models, model 1 loads IEDs acquired by EEG to the medial temporal lobe area as disturbance, and loads the IEDs to the conduction pathway from the medial temporal lobe to the lateral temporal lobe cortex at the same time, so as to study the influence of the IEDs on the connection; model 2 loaded IEDs into the area of the lateral temporal cortex and into the conduction pathway of the lateral temporal cortex to the medial temporal lobe, both models being designed for bi-directional connectivity.
In a preferred embodiment, a power-off detection module with the model number of C2000DH08 is arranged inside the power-off determination module, the power-off detection module of C2000DH08 is a stable and reliable eight-channel alternating current on-off and alternating current on-off state detection module, provides a collection and conversion function of 8 paths of 220V alternating current input to 8 paths of collector open circuit output, can detect the on-off condition of each path of 220V alternating current in real time, and outputs a corresponding collector open circuit signal, thereby realizing the conversion from alternating current to a switching value signal, facilitating the monitoring access of dry contact detection equipment, and being widely applied to the monitoring of the alternating current power supply on-off state and alternating current on-off monitoring of important equipment in a machine room; the power-off detection module of the C2000DH08 can detect the on-off condition of each path of 220V alternating current in real time and output a corresponding collector open-circuit signal; the 220V mains supply input state can be indicated by 8 paths of LEDs; the power failure detection module of the C2000DH08 adopts a photoelectric isolation technology to prevent lightning surge from being introduced into the detector and equipment.
In a preferred embodiment, a starting switch module is arranged inside the cooling fan module block, the starting switch module is connected with the control end of the single chip microcomputer module, and a non-contact temperature sensor is fixedly mounted inside the temperature sensor.
In a preferred embodiment, the system and method for detecting brain function connection change based on dynamic causal model depression comprises the following steps:
s1: the power supply module provides electric energy for the whole system, the system starts to operate, when the dynamic cause and effect analysis module is started, the brain scanning module scans the human body ground brain, and the scanned data are transmitted to the interior of the central processing unit module main processing module through the dynamic cause and effect analysis module;
s2: the power-off judging module detects the interior of the main processing module in the process of supplying electric energy by the power supply module, if the interior of the main processing module is powered off, the data recording module records power-off information, and then the information is transmitted to the interior of the data transmitting module and the data receiving terminal through the data output module to remind a worker of processing the power-off information in time;
s3: an EEG data acquisition module in the dynamic causal analysis module acquires data, an fMRI data processing module analyzes and processes the data, then the information is transmitted to the interior of a DCM modeling module, the DCM modeling module performs modeling according to the information, and finally a Bayesian model selection module creates a proper model and outputs the data;
s4: the diagnosis module diagnoses the input data so as to diagnose the scanning result, and sends the diagnosis data to the interior of the data receiving terminal through the data sending module.
In summary, due to the adoption of the technical scheme, the invention has the beneficial effects that:
1. in the invention, a dynamic causal analysis module is adopted to analyze data; the method adopts a dynamic causal model to observe the brain function connection change caused by depression, analyzes the effect connectivity between the two and the causal relationship of the front and back influences, and further discusses the influence of depression on the brain function connection change.
2. In the invention, the fMRI data processing module can accurately and reliably position the cortical areas of specific brain activities, the spatial resolution reaches 2mm, and the object can be repeatedly scanned in various modes, so that the accuracy of the system scanning device is improved.
Drawings
FIG. 1 is a block diagram of the system of the present invention;
FIG. 2 is a block diagram of a dynamic causal analysis model system of the present invention;
fig. 3 is a system block diagram of a power supply module according to the present invention.
The labels in the figure are: the system comprises a brain scanning module 1, a data transmission module 2, a dynamic causal analysis module 3, a total processing module 4, a data transmission module 5, a diagnosis module 6, a data output module 7, a data sending module 8, a power supply module 9, a data receiving terminal 10, a power failure judgment module 11, a data recording module 12, a singlechip module 13, an EEG data acquisition module 14, an fMRI data processing module 15, a DCM modeling module 16, a Bayesian model selection module 17 and a Bayesian model selection module 181.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
Referring to fig. 1-3, the depression brain function connection change detection system and method based on the dynamic causal model comprises a brain scanning module 1, a data transmission module 2, a dynamic causal analysis module 3, a total processing module 4, a data transmission module 5, a diagnosis module 6, a data output module 7, a data transmission module 8, a power supply module 9, a data receiving terminal 10, a power failure determination module 11, a data recording module 12, a single chip microcomputer module 13, an EEG data acquisition module 14, an fMRI data processing module 15, a DCM modeling module 16, a bayesian model selection module 17, a temperature sensor 18, a cooling fan module block 19, wherein an output end of the brain scanning module 1 is connected with an input end of the data transmission module 2, an output end of the data transmission module 2 is connected with an input end of the dynamic causal analysis module 3, an output end of the dynamic causal analysis module 3 is connected with an input end of the total processing module, the output end of the general processing module 4 is connected with the input end of a data transmission module 5, the output end of the data transmission module 5 is connected with the input end of a diagnosis module 6, the output end of the diagnosis module 6 is connected with the input end of a data output module 7, the output end of the data output module 7 is connected with the input end of a data sending module 8, and the output end of the data sending module 8 is connected with the input end of a data receiving terminal 10;
the power output end of the power supply module 9 is connected with the power input end of the main processing module 4, the power failure judging module 11 is fixedly installed outside the main processing module 4, the power failure detecting module with the model number of C2000DH08 is arranged inside the power failure judging module 11, the power failure detecting module of C2000DH08 is a stable and reliable eight-channel alternating current on-off and alternating current on-off state detecting module, the collecting and converting function of 8-channel 220V alternating current input to 8-channel collector open circuit output is provided, the on-off condition of each channel of 220V alternating current can be detected in real time, and corresponding collector open circuit signals are output, so that the conversion from the alternating current to switching value signals is realized, the monitoring access of dry contact detecting equipment is facilitated, and the AC power supply on-off state and the alternating current on-off monitoring of; the power-off detection module of the C2000DH08 can detect the on-off condition of each path of 220V alternating current in real time and output a corresponding collector open-circuit signal; the 220V mains supply input state can be indicated by 8 paths of LEDs; the power failure detection module of the C2000DH08 adopts a photoelectric isolation technology to prevent lightning surge from being introduced into a detector and equipment; the output end of the power failure judging module 11 is connected with the input end of a data recording module 12, the output end of the data recording module 12 is connected with the input end of a data output module 7, an EEG data acquisition module 14 is arranged in the dynamic cause and effect analysis module 3, EEG data acquired in the EEG data acquisition module 14 is subjected to offline processing through Brain Vision Analyzer1.05 software, artifact removal is carried out according to 200 Ziv/ms gradient trigger transformation rate, and then electrocardio pulsation and eye movement interference are respectively filtered; brain Vision Analyzer has been applied to the removal of imaging artifacts and beating artifacts of Brain electricity at home and abroad; the EEG signal after filtering in this text carries on the analysis of respective reading through 2 experienced electroencephalogram doctors, discern the typical spike wave or spike slow-wave that both sides confirm is issued the time, EEG time precision is an integer of seconds, if 2 spike slow-waves within 1 second also issue as one time effectively; the output end of the EEG data acquisition module 14 is connected with the input end of an fMRI data processing module 15, the fMRI data processing module 15 adopts SPM2 software to carry out processing analysis, the preprocessing comprises time correction, cephalotaxis correction, normalization and smoothing, typical epileptic wave release points detected by EEG are taken as task time points, mode definition is carried out according to an event related mode, a blood oxygen response function with the peak value of 5 seconds is selected, and the positive activation condition of the brain of each patient is observed; then, carrying out random effect analysis based on single-sample T-test on each T-test statistical graph of the group to obtain a group analysis result, and overlapping the positive activation result in a T1 standard template for displaying; the output end of the fMRI data processing module 15 is connected with the input end of the DCM modeling module 16, the DCM modeling module 16 obtains a time sequence of a selected region in the valid data by using a VOI tool in the SPM2, and the spherical radius of the region of interest is set to 4 mm; then loading the most main time sequence obtained by the VOI through principal component analysis into a DCM module of an SPM5 software package for model evaluation; each patient designs two DCM models, model 1 loads IEDs acquired by EEG to the medial temporal lobe area as disturbance, and loads the IEDs to the conduction pathway from the medial temporal lobe to the lateral temporal lobe cortex at the same time, so as to study the influence of the IEDs on the connection; model 2 loaded IEDs into the area of the lateral temporal cortex and into the conduction pathway from the lateral temporal cortex to the medial temporal lobe, both models designed for bi-directional connectivity; the output end of the DCM modeling module 16 is connected with the input end of a Bayesian model selection module 17, the external fixed mounting of the power supply module 9 is provided with a temperature sensor 18 and a cooling fan module block 19, the control end of the single chip microcomputer module 13 is connected with the power input end of the cooling fan module block 19, the internal of the cooling fan module block 19 is provided with a starting switch module, the starting switch module is connected with the control end of the single chip microcomputer module 13, and the internal fixed mounting of the temperature sensor 18 is provided with a non-contact temperature sensor.
The depression brain function connection change detection system and method based on the dynamic causal model comprises the following steps:
s1: the power supply module 9 provides electric energy for the whole system, the system starts to operate, when the dynamic cause and effect analysis module 3 is started, the brain scanning module 1 scans the human body brain, and the scanned data is transmitted to the interior of the central processing unit module main processing module 4 through the dynamic cause and effect analysis module 3;
s2: in the process of supplying electric energy by the power supply module 9, the power failure judgment module 11 detects the interior of the main processing module 4, if the interior of the main processing module 4 is powered off, the data recording module 12 records power failure information, and then the information is transmitted to the interior of the data sending module 8 and the data receiving terminal 10 through the data output module 7 to remind a worker to process the power failure information in time;
s3: an EEG data acquisition module 14 in the dynamic causal analysis module 3 acquires data, an fMRI data processing module 15 analyzes and processes the data, then information is transmitted to the interior of a DCM modeling module 16, the DCM modeling module 16 performs modeling according to the information, and finally a Bayesian model selection module 17 creates a proper model and outputs the data;
s4: the diagnosis module 6 diagnoses the input data to diagnose the scanning result, and transmits the diagnosis data to the inside of the data receiving terminal 10 through the data transmission module 8.
The present invention is not limited to the above preferred embodiments, and any modifications, equivalent substitutions and improvements made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (7)
1. Based on dynamic cause and effect model depression brain function connection change detection system and method, including brain scanning module (1), data transmission module (2), dynamic cause and effect analysis module (3), total processing module (4), data transmission module (5), diagnosis module (6), data output module (7), data transmission module (8), power module (9), data receiving terminal (10), outage judgement module (11), data record module (12), single chip microcomputer module (13), EEG data acquisition module (14), fMRI data processing module (15), DCM modeling module (16), Bayesian model selection module (17), temperature sensor (18), radiator fan module piece (19), its characterized in that: the output end of the brain scanning module (1) is connected with the input end of the data transmission module (2), the output end of the data transmission module (2) is connected with the input end of the dynamic cause and effect analysis module (3), the output end of the dynamic cause and effect analysis module (3) is connected with the input end of the total processing module (4), the output end of the general processing module (4) is connected with the input end of the data transmission module (5), the output end of the data transmission module (5) is connected with the input end of the diagnosis module (6), the output end of the diagnosis module (6) is connected with the input end of the data output module (7), the output end of the data output module (7) is connected with the input end of the data sending module (8), the output end of the data sending module (8) is connected with the input end of the data receiving terminal (10);
the power output end of the power supply module (9) is connected with the power input end of the total processing module (4), the outside of the total processing module (4) is fixedly provided with a power-off judging module (11), the output end of the power-off judging module (11) is connected with the input end of the data recording module (12), the output end of the data recording module (12) is connected with the input end of the data output module (7), the interior of the dynamic causal analysis module (3) is provided with an EEG data acquisition module (14), the output end of the EEG data acquisition module (14) is connected with the input end of the fMRI data processing module (15), the output end of the fMRI data processing module (15) is connected with the input end of the DCM modeling module (16), and the output end of the DCM modeling module (16) is connected with the input end of the Bayesian model selecting module (17), the temperature sensor (18) and the cooling fan module block (19) are fixedly mounted outside the power supply module (9), and the control end of the single chip microcomputer module (13) is connected with the power supply input end of the cooling fan module block (19).
2. The system and method for detecting altered brain function based on dynamic causal model of depression as claimed in claim 1, wherein: EEG data collected inside the EEG data collecting module (14) is processed off-line through Brain Vision Analyzer1.05 software, artifact removal is carried out according to 200-volt/ms gradient trigger conversion rate, and then electrocardio pulsation and eye movement interference are respectively filtered; brain Vision Analyzer has been applied to the removal of imaging artifacts and beating artifacts of Brain electricity at home and abroad; the EEG signal after filtering in this text is carried on the respective interpretation analysis through 2 experienced electroencephalogram doctors, discern the typical spike or spike-slow wave that both sides confirm and issue the time, EEG time precision is an integer of seconds, for example 2 spike (spike-slow) waves within 1 second also issue as one time effectively.
3. The system and method for detecting altered brain function based on dynamic causal model of depression as claimed in claim 1, wherein: the fMRI data processing module (15) adopts SPM2 software to perform processing analysis, the preprocessing comprises time correction, cephalotaxis correction, normalization and smoothing, typical epileptic wave release points detected by EEG are taken as task time points, mode definition is performed according to an event correlation mode, and a blood oxygen response function with a peak value of 5 seconds is selected to observe the positive activation condition of the brain of each patient; and then carrying out random effect analysis based on single-sample T-test on each T-test statistical chart of the group to obtain a group analysis result, and superposing the positive activation result in a T1 standard template for displaying.
4. The system and method for detecting altered brain function based on dynamic causal model of depression as claimed in claim 1, wherein: the interior of the DCM modeling module (16) utilizes a VOI tool in the SPM2 to obtain a time sequence of a selected region in effective data, and the spherical radius of a region of interest is set to be 4 mm; then loading the most main time sequence obtained by the VOI through principal component analysis into a DCM module of an SPM5 software package for model evaluation; each patient designs two DCM models, model 1 loads IEDs acquired by EEG to the medial temporal lobe area as disturbance, and loads the IEDs to the conduction pathway from the medial temporal lobe to the lateral temporal lobe cortex at the same time, so as to study the influence of the IEDs on the connection; model 2 loaded IEDs into the area of the lateral temporal cortex and into the conduction pathway of the lateral temporal cortex to the medial temporal lobe, both models being designed for bi-directional connectivity.
5. The system and method for detecting altered brain function based on dynamic causal model of depression as claimed in claim 1, wherein: the power failure detection module with the model number of C2000DH08 is arranged in the power failure judgment module (11), the power failure detection module with the model number of C2000DH08 is a stable and reliable eight-channel alternating current on-off and alternating current on-off state detection module, provides the acquisition and conversion functions of 8 paths of 220V alternating current input to 8 paths of collector open circuit output, can detect the on-off condition of each path of 220V alternating current in real time, and outputs corresponding collector open circuit signals, so that the conversion from alternating current to switching value signals is realized, the monitoring access of dry contact detection equipment is facilitated, and the power failure detection module is widely applied to the monitoring of the on-off state and the alternating current on-off state of important equipment for monitoring a; the power-off detection module of the C2000DH08 can detect the on-off condition of each path of 220V alternating current in real time and output a corresponding collector open-circuit signal; the 220V mains supply input state can be indicated by 8 paths of LEDs; the power failure detection module of the C2000DH08 adopts a photoelectric isolation technology to prevent lightning surge from being introduced into the detector and equipment.
6. The system and method for detecting altered brain function based on dynamic causal model of depression as claimed in claim 1, wherein: the inside of radiator fan module piece (19) is provided with start switch module, and start switch module with the control end of single chip module (13) is connected, the inside fixed mounting of temperature sensor (18) has non-contact temperature sensor.
7. The system and method for detecting altered brain function based on dynamic causal model of depression as claimed in claim 1, wherein: the depression brain function connection change detection system and method based on the dynamic causal model comprises the following steps:
s1: the power supply module (9) provides electric energy for the whole system, the system starts to operate, when the dynamic cause and effect analysis module (3) is started, the brain scanning module (1) scans the human body ground brain, and the scanned data are transmitted to the interior of the block total processing module (4) through the dynamic cause and effect analysis module (3);
s2: in the process of supplying electric energy, the power-off judging module (11) can detect the interior of the main processing module (4) by the power supply module (9), if the interior of the main processing module (4) is powered off, the data recording module (12) can record power-off information at the moment, and then the information is transmitted to the interior of the data sending module (8) and the data receiving terminal (10) through the data output module (7) to remind a worker of processing the information in time;
s3: an EEG data acquisition module (14) in the dynamic causal analysis module (3) acquires data, an fMRI data processing module (15) analyzes and processes the data, then information is transmitted to the interior of a DCM modeling module (16), the DCM modeling module (16) performs modeling according to the information, and finally a Bayesian model selection module (17) creates a proper model and outputs the data;
s4: the diagnosis module (6) diagnoses the input data so as to diagnose the scanning result, and transmits the diagnosis data to the interior of the data receiving terminal (10) through the data transmitting module (8).
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011148064.8A CN112259241B (en) | 2020-10-23 | 2020-10-23 | Dynamic causal model-based depression brain function connection change detection system and method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011148064.8A CN112259241B (en) | 2020-10-23 | 2020-10-23 | Dynamic causal model-based depression brain function connection change detection system and method |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN112259241A true CN112259241A (en) | 2021-01-22 |
| CN112259241B CN112259241B (en) | 2024-02-13 |
Family
ID=74261082
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202011148064.8A Active CN112259241B (en) | 2020-10-23 | 2020-10-23 | Dynamic causal model-based depression brain function connection change detection system and method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112259241B (en) |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102293647A (en) * | 2011-06-08 | 2011-12-28 | 北京师范大学 | Feedback system combining electroencephalogram and functional magnetic resonance signals |
| US20170120043A1 (en) * | 2005-01-21 | 2017-05-04 | Michael Sasha John | Programming Adjustment for Brain Network Treatment |
| CN106709244A (en) * | 2016-12-12 | 2017-05-24 | 西北工业大学 | Brain function network modeling method for resting state synchronization EEG-fMRI |
| CN109589089A (en) * | 2017-09-30 | 2019-04-09 | 复旦大学 | Analysis method and application thereof based on multi-modal image data |
| CN109935321A (en) * | 2019-04-11 | 2019-06-25 | 东南大学 | Patients with depression based on function nmr image data switchs to the risk forecast model of bipolar disorder |
| CN110522463A (en) * | 2019-08-28 | 2019-12-03 | 常州大学 | A kind of depression assistant diagnosis system based on brain function linking parsing |
-
2020
- 2020-10-23 CN CN202011148064.8A patent/CN112259241B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20170120043A1 (en) * | 2005-01-21 | 2017-05-04 | Michael Sasha John | Programming Adjustment for Brain Network Treatment |
| CN102293647A (en) * | 2011-06-08 | 2011-12-28 | 北京师范大学 | Feedback system combining electroencephalogram and functional magnetic resonance signals |
| CN106709244A (en) * | 2016-12-12 | 2017-05-24 | 西北工业大学 | Brain function network modeling method for resting state synchronization EEG-fMRI |
| CN109589089A (en) * | 2017-09-30 | 2019-04-09 | 复旦大学 | Analysis method and application thereof based on multi-modal image data |
| CN109935321A (en) * | 2019-04-11 | 2019-06-25 | 东南大学 | Patients with depression based on function nmr image data switchs to the risk forecast model of bipolar disorder |
| CN110522463A (en) * | 2019-08-28 | 2019-12-03 | 常州大学 | A kind of depression assistant diagnosis system based on brain function linking parsing |
Also Published As
| Publication number | Publication date |
|---|---|
| CN112259241B (en) | 2024-02-13 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| EP1802230B1 (en) | Monitoring physiological activity using partial state space reconstruction | |
| CN105212919B (en) | physiological signal analysis system | |
| US20130300575A1 (en) | Cooperative processing with mobile monitoring device and computer system | |
| Akrivopoulos et al. | Design and evaluation of a person-centric heart monitoring system over fog computing infrastructure | |
| KR102631311B1 (en) | Apparatus and method for cardiac signal processing, monitoring system comprising the same | |
| CN112022136A (en) | Near-infrared brain function and gait parameter based assessment method and system | |
| CN105919583B (en) | A kind of foetus teaching apparatus fetus-voice meter all-in-one machine | |
| Sobya et al. | Wireless ECG monitoring system using IoT based signal conditioning module for real time signal acquisition | |
| Kim et al. | Arrhythmia detection model using modified DenseNet for comprehensible Grad-CAM visualization | |
| CN112259241A (en) | Depression brain function connection change detection system and method based on dynamic causal model | |
| CN104622467A (en) | Method for detecting electroencephalogram signal complexity abnormity of Alzheimer disease | |
| Kim et al. | Analysis of energy consumption for wearable ECG devices | |
| Lakkamraju et al. | Improvements in accurate detection of cardiac abnormalities and prognostic health diagnosis using artificial intelligence in medical systems | |
| CN103892797A (en) | Signal processing method and device for sleep structure analysis | |
| CN106308791B (en) | The auxiliary data remote transmission method and device of electrocardiosignal record system | |
| Jun et al. | Wearable ECG recognition and monitor | |
| CN111643075A (en) | EEG real-time detection and analysis platform based on micro-state analysis method | |
| Donnelly et al. | Lead selection: old and new methods for locating the most electrocardiogram information | |
| Bayasi et al. | A 65-nm low power ECG feature extraction system | |
| Zhang et al. | Online removal of eye blink artifact from scalp EEG using canonical correlation analysis based method | |
| Shaikh et al. | A fully automatic model for premature ventricular heartbeat arrhythmia classification using the internet of medical things | |
| KR20150026083A (en) | An apparatus for analyzing brain wave image | |
| CN106821368B (en) | Small-sized electrocardiogram acquisition equipment, cardiac diagnosis lead signal method for transformation and detection method | |
| Saadi et al. | Low-cost ecg monitoring system with classification using deep learning | |
| Zhang et al. | The dominant T wave cluster and One-Class SVM based analysis of multilead ECG for classification of myocardial infarction |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |