CN112022136A - Near-infrared brain function and gait parameter based assessment method and system - Google Patents
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Abstract
The invention discloses a method and a system for evaluating near-infrared brain function and gait parameters, which are used for evaluating the correlation between the brain function and the gait parameters. The system comprises: the near-infrared brain function information acquisition module is used for acquiring a brain blood oxygen signal; the gait parameter acquisition module is used for acquiring gait data; the evaluation analysis module is used for analyzing the difference between the near-infrared brain function and the gait parameter of the cognitive disorder population and the normal population according to the cerebral blood oxygen signal acquired by the near-infrared brain function information acquisition module and the gait data acquired by the gait parameter acquisition module; and the correlation analysis module is used for calculating the correlation between the near-infrared brain functions and the gait parameters of the two groups of people and analyzing to obtain the gait parameters reflecting the change of the brain functions.
Description
Technical Field
The invention belongs to the field of rehabilitation aids, and particularly relates to an evaluation method and system based on near-infrared brain function and gait parameters.
Background
Mild Cognitive Impairment (MCI) is a transitional state between normal aging and early dementia. The annual conversion rate to AD for MCI patients is 8% -15%, MCI being a suitable stage of prophylactic intervention. Almost one third of MCI patients remain clinically stable even with restoration of normal cognitive function after initial diagnosis, highlighting the risk of MCI patients being considered a homogenous population. Therefore, accurate and timely assessment of MCI is of great significance in preventing disease progression. However, an accurate and objective early evaluation technology for MCI is still lacking at home and abroad.
The assessment scale is the currently common MCI assessment method. Common assessment scales are the simple intellectual state test (MMSE), the montreal cognitive assessment scale (MoCA), the Loewenstein cognitive function assessment scale (LOTCA), the clinical dementia assessment scale (CDR), the long-valley dementia scale (HDS), the martis dementia score scale (MDRS) and the neurobehavioral cognitive state test (NCSE) [4-6 ]. The scale is a comprehensive screening test function aiming at cognitive ability, can roughly detect whether a patient has cognitive dysfunction, and has strong subjectivity and time and labor consumption.
The development of neuroimaging techniques has greatly improved our understanding of brain mechanisms that underlie cognitive abilities over the last two decades. The development of functional neuroimaging techniques and studies are of great importance to our understanding of brain function in patients with MCI. Diffusion weighted and diffusion tensor imaging (DWI and DTI), Magnetic Resonance Spectroscopy (MRS) and perfusion imaging are used for cognitive function studies. A variety of brain metabolites essential to brain function and present in specific neurons, cell membranes, glial metabolism, and energy metabolism in brain tissue can be dynamically monitored using hydrogen proton magnetic resonance spectroscopy (H-MRS) imaging technology.
The functional near infrared spectroscopy (fNIRS) is a non-invasive brain function imaging technique, which can reflect the activity of cerebral cortex on line during rehabilitation training. The low sensitivity of fNIRS to body movements and the portability of the device make it suitable for monitoring the hemodynamic conditions of the cortex during motor tasks or tasks involving walking. The design task simultaneously evaluates brain activity and gait in order to see how the MCI patient's brain functions when gait changes are involved during walking.
Hemodynamic response was measured using fNIRS and cognitive interference was assessed using a multisource interference task. Spatiotemporal gait parameters under cognitive and non-cognitive load were assessed using the instrument footpath. The result is consistent with an assertion that the control force drop is related to fall risk. The population differences observed in the prefrontal cortex activation and gait parameters may ultimately precede the differences observed in cognitive performance. As described above, current cognitive impairment lacks objective assessment methods and gait correlation studies. The new fNIRS technology shows wide application prospects in cognitive impairment assessment. The cognitive disorder quantitative evaluation technology with good specificity and sensitivity is developed, objective evaluation indexes are provided for cognitive rehabilitation training effect evaluation, the problem of screening the cognitive disorders of community people is solved, and meanwhile, a certain theoretical basis is provided for a training mode of cognitive intervention.
Disclosure of Invention
The invention aims to provide an evaluation method and system based on near-infrared brain function and gait parameters. The evaluation system can fuse near-infrared cerebral blood oxygen signal parameters and gait parameters, establish a mathematical model of a cerebral function network evaluation model and the relation between the cerebral function network evaluation model and the gait parameters, find out the gait parameters capable of reflecting the change of cerebral functions, and establish an early evaluation method of the cognitive cerebral function of the old people so as to realize early intervention and early prevention.
In order to achieve the purpose, the invention adopts the following technical scheme:
one aspect of the present invention provides an evaluation system based on near-infrared brain function and gait parameters, comprising:
the near-infrared brain function information acquisition module is used for acquiring a brain blood oxygen signal;
the gait parameter acquisition module is used for acquiring gait data;
the evaluation analysis module is used for analyzing the differences of the near-infrared brain functions and the gait parameters of the cognitive disorder population and the normal population according to the cerebral blood oxygen signals collected by the near-infrared brain function information collection module and the gait data collected by the gait parameter collection module;
and the correlation analysis module is used for calculating the correlation between the near-infrared brain functions and the gait parameters of the two groups of people and analyzing to obtain the gait parameters reflecting the change of the brain functions.
In an advantageous embodiment, the near-infrared brain function and gait parameter based evaluation system further comprises a preprocessing module, which is used for preprocessing the raw data collected by the near-infrared brain function information collecting module in real time, and removing the noise source in the raw data. Preferably, the pre-processing module comprises a moving average module, a motion artifact removal module and a butterworth filter module.
In an advantageous embodiment, the evaluation analysis module obtains wavelet phase coherence from the preprocessed data of the cerebral blood oxygen signals through wavelet transformation and performs phase extraction, wherein the value of the wavelet phase coherence is used as an index of functional connection strength between cerebral regions, and the phase extraction obtains coupling strength and coupling direction through dynamic Bayesian inference and is used as an index of cerebral effect connection, so as to evaluate the difference of the cerebral connection of two groups of people; and the evaluation and analysis module is also used for processing the gait data acquired by the gait parameter acquisition module to obtain gait parameter indexes including gait symmetry and gait speed consumption and evaluating the difference of the gait parameters of the two groups of people.
In an advantageous embodiment, the evaluation and analysis module performs normal distribution check and homogeneity of variance check on the inter-brain functional connection strength index, the brain effect connection index and the gait parameter index respectively, and performs two sets of data comparison on data with normal distribution and homogeneous variance through a parameter method, and performs two sets of data comparison on data with non-normal distribution or inhomogeneous variance through a non-parameter method.
According to an advantageous embodiment, the correlation analysis module analyzes the strength and direction of the linear relationship between the brain regional functional connection strength index (including wavelet phase coherence value, coupling strength) and the gait parameters and the strength and direction of the linear relationship between the brain effect connection index and the gait parameters of the two groups of people through the correlation coefficients; or the correlation analysis module establishes brain function network evaluation models of the two groups of people according to the functional connection strength index and the brain effect connection index between the brain regions, and establishes a mathematical model of the relation between the brain function network evaluation models of the two groups of people and the gait parameters by adopting multiple linear regression so as to find out the gait parameters capable of reflecting the change of the brain function.
According to an advantageous embodiment, when the correlation analysis module performs the correlation analysis by the correlation coefficient, when it is determined that the node indicating the brain coupling strength change or the node indicating the brain interval functional connection strength change provided by the evaluation analysis module has a correlation with the node indicating the gait parameter change, respectively, the correlation analysis module determines that the brain function is changed at the node.
According to an advantageous embodiment, the correlation analysis module uses multiple linear regression to establish a mathematical model of the relationship between the brain function network evaluation models of the two groups of people and the gait parameters, and when the node indicating the change of the brain coupling strength provided by the evaluation analysis module or the node indicating the change of the brain inter-regional functional connection strength and the node indicating the change of the gait parameters have correlations respectively displayed in the mathematical model, the change of the brain function at the node is judged.
The second aspect of the present invention also provides an evaluation method based on near-infrared brain function and gait parameters, which comprises the following steps:
the evaluation analysis module evaluates and analyzes the brain functions of the cognitive disorder population and the normal population and the difference of the gait parameters calculated according to the gait data according to the cerebral blood oxygen signals and the gait data of the cognitive disorder population and the normal population, and transmits the evaluation analysis result to the correlation analysis module; and
and the correlation analysis module performs correlation calculation on the brain function connection index and the gait parameter obtained by the evaluation and analysis, and analyzes to obtain the gait parameter reflecting the change of the brain function.
Preferably, the evaluation and analysis module calculates the gait parameters according to the collected gait data as follows: selecting a video starting frame and a video ending frame in the VICON; identifying and checking the mark points, and if necessary, supplementing some incompletely identified mark points; selecting and operating a dynamic template, and storing after completion; and importing the output processed data into MATLAB software gait parameter scripts to complete the calculation of the gait speed, step length change and consumption parameters of the double-task speed.
Furthermore, the evaluation and analysis module respectively performs normal distribution verification and variance homogeneity verification on the functional connection strength index between the brain regions, the index of brain effect connection and the gait parameters of the two groups of people, performs two groups of data comparison on the data with normal distribution and variance homogeneity through a parameter method, provides a non-parameter method for the data with non-normal distribution or variance homogeneity to perform two groups of data comparison, and obtains results for evaluating the difference of the gait parameters of the two groups of people.
In an advantageous embodiment, the correlation analysis module analyzes the strength and direction of the linear relationship between the inter-brain function connection strength index and the gait parameter index and the strength and direction of the linear relationship between the brain effect connection index and the gait parameter index of the two groups of people through the correlation coefficient.
According to another advantageous embodiment, the correlation analysis module may further establish a brain function network evaluation model of the two groups of people according to the brain region functional connection strength index and the brain effect connection index, and establish a mathematical model of a relationship between the brain function network evaluation models of the two groups of people and the gait parameters by using multiple linear regression to find the gait parameters that can reflect changes in brain functions.
In one embodiment, the calculation of the functional connectivity indicator of cerebral blood oxygen signals is performed as follows: firstly, respectively carrying out wavelet transformation on signals of each channel; secondly, calculating the phase synchronism among the channels one by one; and finally, checking the effectiveness of the wavelet phase coherence value of the original signal by using the wavelet phase coherence of the substitute signal, thereby evaluating the difference of the brain connections of the two groups of people.
According to one example, the correlation analysis module uses multiple linear regression to study the number dependence relationship between the brain function dependent variable and the plurality of gait parameter independent variables to find out the gait parameters capable of reflecting the brain function change; or the correlation analysis module adopts multiple linear regression to establish mathematical models of the relation between the brain function network evaluation model and the gait parameters respectively, and finds out the gait parameters capable of reflecting the change of the brain function.
In addition, another aspect of the present invention provides an evaluation system based on near-infrared brain function and gait parameters, which includes: the system comprises a near-infrared brain function information acquisition module, a gait parameter acquisition module, an evaluation analysis module and a brain function and gait parameter correlation analysis module; the system monitors the lower limb gait motion parameters, fuses the near-infrared cerebral blood oxygen signal parameters, and realizes the quantitative evaluation of the cerebral-gait parameters.
In an advantageous embodiment, the brain acquisition module is a near-infrared brain function information acquisition module, in particular a near-infrared spectroscopy device, which utilizes non-invasive brain function imaging technology to online reflect the activity of the cerebral cortex as it relates to gait changes during walking.
In an advantageous embodiment, the data preprocessing module comprises a moving average module, a motion artifact removal and butterworth filter module, and can preprocess the raw data collected by the near infrared spectroscopy device in real time, remove various noise sources, and more accurately calculate the hemodynamic response induced by functional activity.
In an advantageous embodiment, data acquired by the near-infrared brain function information is subjected to data preprocessing, Wavelet Phase Coherence (WPCO) and Phase extraction are obtained through Wavelet transformation, and a WPCO value is used as an index of brain inter-regional function connection strength; the phase extraction obtains the coupling strength and the coupling direction through dynamic Bayesian inference, and the coupling strength and the coupling direction are used as indexes of brain effect connection.
In an advantageous embodiment, the selecting of the video start frame and the end frame is performed in VICON; secondly, identifying and checking the mark points, and if necessary, supplementing some incompletely identified mark points; selecting and operating a dynamic template, and storing after completion; and finally, importing the output processed data into MATLAB software gait parameter scripts to complete parameter calculation of gait speed, step length change, dual task speed consumption and the like.
In an advantageous embodiment, the near-infrared brain function and gait parameter based evaluation system establishes brain function connection and effect connection through wavelet transformation and Bayesian inference, and gait symmetry and gait speed consumption of double tasks are obtained through a gait parameter formula. The pearson correlation coefficient is used to analyze the strength and direction of the linear relationship between brain connectivity and gait parameters. And establishing mathematical models of the relation between the brain function network evaluation model and the gait parameters respectively by adopting a multiple linear regression analysis method. Regression analysis is to study how gait parameters change with brain function network assessment models. And (3) respectively establishing mathematical models for the two groups of people, and finding out how the gait parameters of the two groups of people are different along with the change of the brain function network evaluation model. Therefore, gait parameters which can reflect the change of brain functions can be found out in the model.
In another aspect, the present invention provides a method for evaluating brain function and gait parameters based on near infrared, comprising the following steps:
1) the brain blood oxygen and gait synchronous acquisition experiment platform is constructed: a portable fNIRS and three-dimensional gait software (VICON) for realizing the synchronous monitoring of the cerebral blood oxygen and the gait under the marking of the synchronizer;
2) respectively sticking 8 gait mark points (16 mark points in total on the left and right anterior superior iliac spines, the left and right posterior superior iliac spines, knee joints, thighs, shanks, ankles, toes and heels) on two sides of thighs of a subject;
3) based on gait-cognition neural basis, 14 acquisition channels of fNIRS are arranged to cover the forehead, the motor area and the occipital lobe area respectively;
4) design of experimental schemes under single and double tasks: five different tests were performed for each test (resting, single task, countdown-walking task, named animal-walking task, count-walking task in gait lab);
5) transmitting the brain oxygen signals and gait data acquired in the steps 3) and 4) to an evaluation analysis module;
6) and the evaluation analysis module performs brain function connection index calculation, gait parameter analysis calculation and correlation calculation of the near-infrared brain function and the gait parameters according to the received results. The FC of two groups of people at the joint of a certain brain area has significant difference; two groups of people have significant difference in certain gait parameters; FC in the brain region junction of both groups of people is positively and negatively correlated with this gait parameter. It can be shown that this gait parameter reflects a gait parameter of altered brain function.
In an advantageous embodiment, in step 1), the physiological signal acquisition module is a portable near-infrared spectroscopy device;
in an advantageous embodiment, the evaluation and analysis module in said step 5) comprises a brain function data preprocessing module comprising a moving average module, a motion artifact removal and butterworth filter module; the sliding average module is used for preprocessing abnormal signals on a time sequence by utilizing an average method; the spline interpolation module performs spline interpolation on the signal segment with the motion artifact and performs subtraction operation on a spline interpolation function; the butterworth filter is a filter that removes long-range baseline wander and preserves to the maximum extent the amplitude and phase information of the signal.
The technical scheme provided by the invention has the beneficial effects that:
compared with electroencephalogram, the electroencephalogram brain-based data acquisition system has high spatial resolution and lower requirements on the aspects of the posture of a subject and the like, and can monitor and record the activity signals of the cerebral cortex.
Compared with functional magnetic resonance, the invention has the advantages of high time resolution, low cost and portable instrument, and can display and record the cerebral cortex activity signal in real time.
The invention can provide multi-band physiological signals, including multi-band functional connection and multi-band effect connection.
The present invention provides a single or dual task to be tested to simultaneously assess brain activity and gait in order to understand how the MCI patient's brain functions when gait changes are involved during walking.
Preferably, gait parameters such as gait symmetry, consumption of gait speed of double tasks and the like are obtained by processing the gait data acquired by the method through VICON and MATLAB. The time phase symmetry index and the symmetry index value based on the Fitts law are between 0 and 1, and the closer to 1 indicates the better gait symmetry. The index value of normal people is in the range of 0.6-1. The FC of two groups of people at the joint of a certain brain area has significant difference; two groups of people have significant difference in certain gait parameters; FC in the brain region junction of both groups of people is positively and negatively correlated with this gait parameter. It can be shown that this gait parameter reflects a gait parameter with altered brain function.
Preferably, the invention uses multiple linear regression analysis method to establish the mathematical model of the relation between the brain function network evaluation model and the gait parameters. Regression analysis is to study how gait parameters change with the brain function network evaluation model. And (3) respectively establishing mathematical models for the two groups of people, and finding out how the gait parameters of the two groups of people are different along with the change of the brain function network evaluation model. Therefore, the step state parameters which can reflect the change of the brain function can be found in the model.
The foregoing summary is provided for the purpose of description only and is not intended to be limiting in any way. In addition to the illustrative aspects, embodiments, and features described above, further aspects, embodiments, and features of the present invention will be readily apparent by reference to the drawings and following detailed description.
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The present invention is described below in order to make the features and aspects of the present invention clearer in accordance with the attached drawings. It is appreciated that these drawings depict only some embodiments in accordance with the disclosure and are therefore not to be considered limiting of its scope.
FIG. 1 is a block diagram illustrating a near-infrared brain function and gait parameter based assessment system according to the invention;
FIG. 2 is a diagram illustrating the operational steps of an assessment analysis module of the assessment system shown in FIG. 1;
FIG. 3 is a diagram of the probe placement in a near-infrared cerebral blood oxygen detection system;
FIG. 4 is a schematic illustration of the functional connectivity of the participants' brain during the course of the experimental task;
FIG. 5 is a schematic representation of the brain effect connections of participants during the course of an experimental task;
fig. 6 shows gait data processed in VICON.
Detailed Description
The evaluation method and system based on the near-infrared brain function and gait parameters of the invention are described in detail below with reference to the accompanying drawings. It should be understood by those skilled in the art that the following described embodiments are only illustrative of the present invention and are not intended to limit the same in any way.
Generally speaking, the evaluation method and system based on near infrared brain function and gait parameters provided by the invention can use non-invasive near infrared equipment to acquire cerebral cortex blood oxygen signals of a subject in single and double task processes, and simultaneously use a VICON system to acquire gait data. After data preprocessing is carried out on the acquired cerebral blood oxygen signals through methods of sliding average, motion artifact removal, Butterworth and the like, phase synchronism is calculated by adopting a wavelet phase coherence method, coupling strength and coupling direction are calculated by adopting a Bayesian inference method, and a cerebral function network evaluation model of MCI (brain functional interface) crowds based on functional connection and effect connection is constructed. Gait data are processed in VICON and MATLAB software to obtain gait symmetry and consumption of dual task speed and other parameter calculation. The evaluation analysis module is used for evaluating the difference of gait parameters of functional connection and effect connection of two groups of people, and the Pearson correlation analysis is used for researching the relation between wavelet phase coherence numerical values, coupling strength and the gait parameters. And (3) researching the quantity dependence relationship between the brain function dependent variable and the plurality of gait parameter independent variables by adopting multiple linear regression, and establishing mathematical models of the relationship between the brain function network evaluation model and the gait parameters respectively. And finding out gait parameters capable of reflecting the change of the brain function. An early evaluation method of the senile cognitive disorder is established to realize early intervention and early prevention.
The method and system for near-infrared brain function and gait parameter-based assessment are described in detail below, wherein fig. 1 is a block diagram illustrating a near-infrared brain function and gait parameter-based assessment system according to the present invention, and fig. 2 is an operation step diagram illustrating an assessment analysis module of the assessment system illustrated in fig. 1.
The near-infrared brain function and gait parameter-based evaluation system comprises: the near-infrared brain function information acquisition module is used for acquiring a brain blood oxygen signal; the gait parameter acquisition module is used for acquiring gait data; the evaluation analysis module is used for analyzing the difference between the near-infrared brain function and the gait parameter of the cognitive disorder population and the normal population according to the cerebral blood oxygen signal acquired by the near-infrared brain function information acquisition module and the gait data acquired by the gait parameter acquisition module; and the correlation analysis module is used for calculating the correlation between the near-infrared brain functions and the gait parameters of the two groups of people and analyzing to obtain the gait parameters reflecting the change of the brain functions.
The near-infrared brain function information acquisition module is a portable near-infrared spectrum device, the device is a non-invasive brain function imaging technology, concentration changes of oxygenated hemoglobin and deoxygenated hemoglobin in brain tissues after neuron activation can be measured nondestructively by using the portable near-infrared spectrum device, and the activity of cerebral cortex related to gait changes during walking can be reflected on line.
The near-infrared brain function and gait parameter-based evaluation system further comprises a preprocessing module, and the brain blood oxygen original data collected by the near-infrared spectrum equipment is transmitted to the preprocessing module for real-time preprocessing so as to remove various noise sources, so that the hemodynamic response caused by functional activities can be calculated more accurately. The preprocessing module comprises a moving average module, a motion artifact removal module and a Butterworth filter module, wherein motion drift in the near infrared experiment is removed by using a moving standard deviation and spline interpolation method. Preprocessing of brain oximetry signals is performed as follows:
(1) moving average
The moving average is a method for preprocessing abnormal signals on a time series by using an averaging method, so as to improve the signal-to-noise ratio. The average of the 2N +1 points is used instead of outliers in the original signal. The experiment replaces the abnormal point in the original signal with 5 points before the abnormal point. The expression is as follows:
in this expression, x (n) is a time series of the original signal, and y (n) is a time series after the moving average. In this study, an amplitude threshold value of 5 was set, an anomaly point was selected, and points above this threshold value were considered to be anomaly points.
(2) Motion artifact removal
When NIRS signals of a certain channel are found to have noise such as motion drift, the motion drift is removed semi-automatically by adopting a motion standard deviation and spline interpolation method. Motion artifacts due to head shaking and the like are detected by calculating the moving standard deviation of the NIRS signal, and then their spline function difference functions are subtracted to remove these motion artifacts. The method comprises the following steps: (1) the mobile standard deviation is calculated. (2) And detecting a signal segment of the motion artifact, and finding a starting point and an end point. (3) The time series is segmented according to the period of the motion artifact. (4) Spline interpolation is performed on the signal segments with motion artifacts. (5) And carrying out subtraction operation on the spline interpolation function. (6) The entire event sequence is recombined.
(3) Butterworth filter
The butterworth filter is a filter that removes long-range baseline wander and preserves to the maximum extent the amplitude and phase information of the signal. The expression for an nth order butterworth filter is:
in the expression, the order n, fc is the cutoff frequency, and fp is the passband edge frequency. In this context, the value of n is chosen to be 6.
The evaluation analysis module obtains wavelet phase coherence from the preprocessed data of the cerebral blood oxygen signals through wavelet transformation and extracts phases, the value of the wavelet phase coherence is used as an index of functional connection strength between cerebral regions, and the phase extraction is used for obtaining coupling strength and coupling direction through dynamic Bayesian inference and used as an index of cerebral effect connection, so that the difference of the cerebral connection of two groups of people is evaluated.
Wavelet Phase Coherence (WPCO) is a method of analyzing brain functional connectivity to illustrate the phase relationship of brain functional regulation. A larger WPCO value indicates a higher phase synchronism and a stronger functional connection. In this context, the WPCO method is used to calculate the phase synchronization values and to use them for analyzing the functional connectivity of each brain region. The functional connection calculation of the cerebral blood oxygen signals adopts the following method: (1) respectively performing wavelet transformation on the signals of each channel; (2) calculating the phase synchronism among the channels one by one; (3) the wavelet phase coherence of the substitute signal is used to check the validity of the wavelet phase coherence value of the original signal. For a more sufficient analysis of wavelet phase coherence to be performed in the frequency band 0.0095-2 Hz, the NIRS signal is divided into 4 frequency bands: i, 0.6-2.0 Hz; II, 0.145-0.6 Hz; III, 0.052-0.145 Hz; IV, 0.021-0.052 Hz. Each frequency bin corresponds to a different physiological source: frequency band I represents heart rate activity, frequency band II represents respiratory activity, frequency band III represents myogenic activity, and frequency band IV represents neurogenic activity.
Calculating wavelet phase coherence comprises the following steps: firstly, two time series x are obtained by wavelet transformation twice1(tn) And x2(tn). Their corresponding instantaneous phases are respectively
Again, cos Δ φ (f, t)n) And sin Δ φ (f, t)n) Averaged in the time domain. Finally, phase coherence is defined as:
the wavelet phase coherence has a value between 0 and 1. The larger the WPCO value, the higher the phase synchronism and the stronger the functional connection strength. WPCO can be used to assess phase differences in brain activity in different brain regions independent of amplitude. In this context, the phase synchronization value is calculated by the WPCO method and used to analyze the functional connectivity of each brain region.
Figure 4 shows a schematic diagram of brain function connections characterized by a multi-band WPCO, showing an example of brain function connections for four bands I-IV. Functional connectivity of brain blood oxygen signals was calculated as follows: firstly, respectively carrying out wavelet transformation on signals of each channel; secondly, calculating the phase synchronism among the channels one by one; finally, the wavelet phase coherence of the substitute signal is used for checking the effectiveness of the wavelet phase coherence value of the original signal, so as to evaluate the difference of the brain connections of the two groups of people.
Effector junctions (ECs) are another type of junction in studies of functional integration of the brain. EC effectively describes the interaction between cortical regions and reflects the effect of one cortical region on the other, and thus the efficiency and direction of information transfer between two functional compartments. For brain studies, ECs can describe the magnitude and causal relationships of brain information transfer network interactions dynamically, quantitatively. Here, Dynamic Bayesian Inference (DBI) is used for the evaluation, which is characterized by the coupling strength and coupling direction between blood oxygen signals in different brain regions. FIG. 5 is a schematic diagram showing the connection of brain effects of participants during the experimental training task.
The coupling strength is a quantitative representation of the coupling amplitude, showing the effect between two signals and the effect of one signal on the other. The coupling strength is calculated using the euclidean norm corresponding to the fourier component of the signal source and the signal coupling:
where e 12 represents a first coupling strength with the second and e 21 represents a second coupling strength with the first. The direction of coupling is used to reveal the causal (driving) relationship between the two phase elements, and can be obtained by normalizing the coupling strength:
the sign of the parameter D describes the main coupling direction and represents the relationship of the two vibrators being driven and driven in a coupled relationship.
The evaluation and analysis module is also used for processing the gait data acquired by the gait parameter acquisition module to obtain gait parameters including gait symmetry and dual-task gait speed consumption, and evaluating the difference of the gait parameters of the two groups of people. A gait parameter acquisition module in the evaluation analysis module calculates the gait parameters as follows: firstly, selecting a video starting frame and a video ending frame in a VICON; secondly, identifying and checking the mark points, and if necessary, supplementing some incompletely identified mark points; then, selecting and operating a dynamic template, and storing after completion; and finally, importing the output processed data into an MATLAB software step state parameter script to run to complete the calculation of the parameters of the gait speed, the step length change and the consumption of the double-task speed.
The evaluation and analysis module respectively performs normal distribution verification and homogeneity of variance verification on the functional connection strength index among brain regions, the brain effect connection index and the gait parameter index, performs two groups of data comparison on data with normal distribution and homogeneous variance through a parameter method, and performs two groups of data comparison on data with non-normal distribution or inhomogeneous variance through a non-parameter method, and the obtained result is used for evaluating the difference of the gait parameters of the two groups of people, as shown in fig. 2.
One prominent feature of a person's normal gait is the symmetry of motion. The effect of special or abnormal conditions (e.g., crossing obstacles and walking obstacles) on the overall characteristics of the gait disrupts the symmetry of walking. Therefore, the symmetry of the gait is an important aspect of the comprehensive assessment of gait movement and walking function. Fig. 6 is a graph of gait data processed in VICON.
1. Phase symmetry index (IDps): the symmetry of the gait of the left and right legs is evaluated by the phase of the gait cycle.
Wherein T is0Is the average value of the gait cycle of a healthy person; t is the gait cycle of the object to be measured; ssAnd SmRespectively the minimum value and the maximum value of the single-leg supporting period; wsAnd WmRespectively the minimum and maximum of the period of oscillation of the single leg. The asynchronous periods of the left and right legs are divided into left and right relative symmetry indices (IDpsL, IDpsR). The index value is [0,1]]Closer to 1 indicates better gait symmetry.
2. The comprehensive symmetry index based on the Fitts law is as follows: rectangular coordinate system F stride length symmetry index (IDsp)
In the formula, ST1And STrStep lengths of the left leg and the right leg are respectively set; ST (ST)l0And STr0Respectively are stride length precision error values of a left leg and a right leg under normal gait of a healthy person; t is t1And trThe time for the left and right legs to stride is the time for the swing period.
3. Step length symmetry index under polar coordinates (IDsp theta and IDsp rho)
In the formula, theta and rho are absolute values of the difference values of polar angles and polar diameters of the toes relative to the hip joint at the motion starting point and the motion ending point; thetaSTsAnd ρSTsIs the smaller value in the stride length; thetaSTmAnd ρSTmThe larger value in the step size; t is tsAnd tmAre respectively log2(2θST/θST0) The step execution time of the smaller side and the larger side of the/t value; thetaST0And ρST0Is the precision error value of the step length under the normal gait of a normal person. The value of the symmetry index based on the Fitts law is [0,1]]Closer to 1 indicates better gait symmetry.
4. Coefficient of asymmetry
In the formula XLAnd XRRespectively the supporting time of the single supporting phase of the left foot and the right foot under ideal conditions
ASI＝0。
5. Consumption of dual task speed: expressing the effect of cognitive challenge on gait expression by calculating the gait cost of dual tasks, and is shown in a formula
The correlation analysis not only can describe the relation condition of the near infrared brain function and the gait parameter, but also can be used for prediction.
And the correlation analysis module analyzes the strength and direction of the linear relation between the functional connection strength index between the brain regions and the gait parameter indexes of the two groups of people and the strength and direction of the linear relation between the brain effect connection index and the gait parameter indexes through correlation coefficients. The numerical range of the correlation coefficient is between-1 and +1, and the correlation coefficient is usually expressed in the form of decimal number, generally two digits after the decimal number, so as to describe the correlation degree more accurately. The degree of correlation between the two variables is represented by the absolute value of the correlation coefficient r, and the closer the absolute value is to 1, the higher the degree of correlation between the two variables is; the closer its absolute value is to 0, the lower the degree of correlation between the two variables. If its absolute value is equal to 1, it means that the two variables are perfectly linearly related. If its absolute value is 0, it means that the two variables are completely uncorrelated.
For example, task 02 and task 03 are completed in the I-th band for patients in the MCI group: if the LOL-LPFC coupling strength of task 03 is significantly reduced compared to task 02 (F2.933, p 0.003), and the gait symmetry indices (IDpsL and IDpsR) of task 03 are significantly reduced compared to task 02 (F7.421, p 0.036; F7.35, p 0.035), and the MCI group gait symmetry indices (IDpsL and IDpsR) are significantly positively correlated with the EC of LOL-LPFC in the I-th band (R0.263, p 0.008; R0.265, p 0.008), IDpsL and IDpsR can reflect the index of significant change in the EC of the I-th band of MCI population LOL-LPFC.
The linear correlation coefficient is also called simple correlation coefficient, pearson product-moment correlation coefficient, and sometimes called product-difference correlation coefficient. The calculation formula of the correlation coefficient of the Pearson product moment is as follows:
the SPSS software will automatically calculate the r statistic. The statistical test of the correlation coefficient of the Pearson product moment is to calculate t statistic, and the calculation formula is as follows:
the t statistic obeys a t distribution with n-2 degrees of freedom.
When the correlation analysis module performs correlation analysis through the correlation coefficient, when it is judged that the node indicating the brain coupling strength change provided by the evaluation analysis module or the node indicating the brain inter-region functional connection strength change and the node indicating the gait parameter change have correlation respectively, it is judged that the brain function is changed here.
The correlation analysis module can also establish brain function network evaluation models of the two groups of people according to the functional connection strength index and the brain effect connection index between the brain regions, and establish a mathematical model of the relation between the brain function network evaluation models of the two groups of people and the gait parameters by adopting multiple linear regression so as to find out the gait parameters capable of reflecting the change of the brain functions. Specifically, the correlation analysis module analyzes the wavelet phase coherence value, the coupling strength, the relation with the gait parameters and the brain function, researches the quantity dependence relation between the brain function dependent variable and a plurality of gait parameter independent variables by adopting multiple linear regression, and establishes mathematical models of the brain function network evaluation model and the gait parameter relation respectively so as to find out the gait parameters capable of reflecting the change of the brain function.
The model of establishing the brain-gait symmetry index by using a multiple linear regression method finds that LPFC _ RPFC of the brain EC has statistical significance (p is 0.003) to the gait symmetry index (IDspP) in the I-th frequency band.
When the node indicating the brain coupling strength change provided by the evaluation analysis module or the node indicating the brain regional function connection strength change and the node indicating the gait parameter change are respectively correlated in the mathematical model, the brain function change is judged.
The evaluation method based on near-infrared brain function and gait parameters is described below. The method comprises the following steps:
1) constructing a brain blood oxygen and gait synchronous acquisition experiment platform: a portable near infrared (fNIRS) and three-dimensional gait software (VICON) for realizing the synchronous monitoring of the cerebral blood oxygen and the gait under the marking of the synchronizer;
2) a near-infrared light source emission probe and a light receiving probe are reasonably arranged in a brain function area to be detected, and a near-infrared brain function information acquisition module is used for acquiring brain blood oxygen signals of cognitive disorder people and normal people. Specifically, based on gait-cognitive neural basis, 14 acquisition channels of fNIRS were arranged, covering the frontal lobe, motor zone, occipital lobe zone, respectively. Fig. 3 is a diagram showing the positions of channels corresponding to near infrared spectrum equipment arranged according to an international 10-10 electrode arrangement system, wherein S represents the position of a light source emission probe of the near infrared spectrum cerebral blood oxygen detection equipment, and D represents the position of a light receiving probe of the near infrared spectrum cerebral blood oxygen detection equipment. When monitoring cerebral blood oxygen, the positions of the near-infrared channels are reasonably arranged in a brain area to be detected according to the position shown in figure 3;
3) respectively sticking 8 gait mark points (16 mark points in total, namely, a left anterior superior iliac spine, a left posterior superior iliac spine, a right posterior superior iliac spine, a knee joint, a thigh, a shank, an ankle joint, a toe and a heel) on two sides of a thigh of a subject so as to collect gait data of a cognitive disorder crowd and a normal crowd;
4) design of experimental schemes under single and double tasks: each subject was subjected to five different tests (resting, single task, countdown-walking task, named animal-walking task, count-walking task in a gait laboratory); the single task is walking on a specified footpath; dual tasks refer to maintaining postural control while performing two or more cognitive and motor tasks. The dual task mentioned here refers to making cognitive task while walking, and includes three dual tasks;
5) transmitting the brain oxygen signals and gait data acquired in the steps 2) and 3) to an evaluation analysis module to calculate and analyze the brain connection and gait parameter difference of the cognitive disorder population and the normal population; and
6) and performing brain function connection index calculation, gait parameter analysis calculation and near-infrared brain function and gait parameter correlation calculation on the received signals by using a correlation analysis module, and analyzing to obtain gait parameters reflecting brain function changes.
And processing the gait data in the step 6) by VICON software and MATLAB to obtain gait parameters such as gait symmetry, dual-task gait speed consumption and the like. Performing normal distribution check on the gait parameters of two groups of people (MCI group and healthy control group) through a Chariro-Wilk test, and performing homogeneity check on the variance through a Leven's test; two sets of data comparisons were performed using the parametric method (t-test) for normally distributed and uniform variance data. And (3) carrying out nonparametric method Mann-Whitney U-test (Mann-Whitney U-test) on the data with non-normal distribution or uneven variance to compare two groups of data, and using the obtained result to evaluate the difference of the gait parameters of the two groups of people. Similarly, the brain blood oxygen signal also evaluates the difference of the brain connection of the two groups of people through the module. Those skilled in the art will appreciate that the drawings to which the invention relates are merely schematic representations of preferred embodiments. While the invention has been described with reference to specific embodiments thereof, it will be understood by those skilled in the art that various changes, substitutions, and alterations can be made herein without departing from the spirit and scope of the invention as defined by the appended claims.
Claims (10)
1. An assessment system based on near-infrared brain function and gait parameters, comprising:
the near-infrared brain function information acquisition module is used for acquiring a brain blood oxygen signal;
the gait parameter acquisition module is used for acquiring gait data;
the evaluation analysis module is used for analyzing the difference between the near-infrared brain function and the gait parameter of the cognitive disorder population and the normal population according to the cerebral blood oxygen signal acquired by the near-infrared brain function information acquisition module and the gait data acquired by the gait parameter acquisition module;
and the correlation analysis module is used for calculating the correlation between the near-infrared brain functions and the gait parameters of the two groups of people and analyzing to obtain the gait parameters reflecting the change of the brain functions.
2. The near-infrared brain function and gait parameter-based evaluation system according to claim 1, wherein the evaluation analysis module performs wavelet transformation on the preprocessed brain blood oxygen signal data to obtain wavelet phase coherence and performs phase extraction, wherein the value of the wavelet phase coherence is used as an index of functional connection strength between brain regions, and the phase extraction is used for obtaining coupling strength and coupling direction as indexes of brain effect connection through dynamic Bayesian inference, so as to evaluate the difference of brain connection between two groups of people; and the evaluation and analysis module is also used for processing the gait data acquired by the gait parameter acquisition module to obtain gait parameter indexes including gait symmetry and gait speed consumption and evaluating the difference of the gait parameters of the two groups of people.
3. The near-infrared brain function and gait parameter-based evaluation system according to claim 2, wherein the evaluation analysis module performs a normal distribution check and a homogeneity of variance check on the brain interregional function connection strength index, the brain effect connection index and the gait parameter index, respectively, and performs two sets of data comparison by a parametric method for data with normal distribution and homogeneous variance, and performs two sets of data comparison by a non-parametric method for data with non-normal distribution or inhomogeneous variance.
4. The near-infrared brain function and gait parameter-based assessment system according to claim 2, wherein the correlation analysis module analyzes the strength and direction of the linear relationship between the brain regional functional connection strength indicator and the gait parameter indicator and the strength and direction of the linear relationship between the brain effect connection indicator and the gait parameter indicator of the two groups of people by the correlation coefficient; or the correlation analysis module establishes brain function network evaluation models of the two groups of people according to the functional connection strength index and the brain effect connection index between the brain regions, and establishes a mathematical model of the relation between the brain function network evaluation models of the two groups of people and the gait parameters by adopting multiple linear regression so as to find out the gait parameters capable of reflecting the change of the brain function.
5. The near-infrared brain function and gait parameter-based evaluation system according to claim 4, wherein the correlation analysis module, when performing correlation analysis by using the correlation coefficient, determines that the brain function is changed when it determines that there is a correlation between the node indicating the change in the brain coupling strength or the node indicating the change in the brain inter-regional functional connection strength provided by the evaluation analysis module and the node indicating the change in the gait parameter.
6. The near-infrared brain function and gait parameter-based assessment system according to claim 4, wherein the correlation analysis module uses multiple linear regression to establish a mathematical model of the relationship between the brain function network assessment model and the gait parameters of the two groups of people, and when the mathematical model shows that there is correlation between the node indicating the brain coupling strength change provided by the assessment analysis module or the node indicating the brain regional functional connection strength change and the node indicating the gait parameter change, the brain function change at this point is determined.
7. An assessment method based on near-infrared brain function and gait parameters comprises the following steps:
the evaluation analysis module evaluates and analyzes the brain functions of the cognitive disorder population and the normal population and the difference of gait parameters calculated according to the gait data according to the cerebral blood oxygen signals and the gait data of the cognitive disorder population and the normal population, and transmits the evaluation analysis result to the correlation analysis module; and
and the correlation analysis module performs correlation calculation on the brain function connection index and the gait parameter obtained by the evaluation and analysis, and analyzes to obtain the gait parameter reflecting the change of the brain function.
8. The near-infrared brain function and gait parameter-based assessment method according to claim 7, wherein the assessment analysis module performs the calculation of the gait parameters based on the collected gait data as follows: selecting a video starting frame and a video ending frame; identifying and checking the mark points, and if necessary, supplementing some incompletely identified mark points; selecting and operating a dynamic template; and importing the output processed data into a software gait parameter script to complete the calculation of the gait speed, step length change and the consumption parameter of the dual task speed.
9. The near-infrared brain function and gait parameter-based assessment method according to claim 8, wherein the assessment analysis module performs normal distribution verification and variance homogeneity verification on the brain interregional functional connection strength index, the brain effect connection index and the gait parameter of the two groups of people respectively, and performs two groups of data comparison on the data with normal distribution and variance homogeneity through a parameter method, and performs two groups of data comparison on the data with non-normal distribution or variance heterogeneity by a non-parameter method, and the obtained results are used for assessing the difference of the gait parameters of the two groups of people.
10. The near-infrared brain function and gait parameter-based assessment method according to claim 7, wherein the correlation analysis module analyzes the strength and direction of the linear relationship between the brain regional functional connection strength indicator and the gait parameter indicator and the strength and direction of the linear relationship between the brain effect connection indicator and the gait parameter indicator of the two groups of people by the correlation coefficient; or the correlation analysis module establishes brain function network evaluation models of the two groups of people according to the functional connection strength index and the brain effect connection index between the brain regions, and establishes a mathematical model of the relation between the brain function network evaluation models of the two groups of people and the gait parameters by adopting multiple linear regression so as to find out the gait parameters capable of reflecting the change of the brain function.
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