• 专利附录
  • 专利说明
  • 权利要求
  • 类似技术
  • 同族专利
  • 引用文献
  • 法律状态
  • 优先权
    • 专利摘要:
    Method and neurophysiological apparatus for identification of brain nuclei comprising a stage of data acquisition, a stage of elimination of high frequency artifacts; a stage of determination of action potentials; a stage of grouping of action potentials; a stage of analysis of the properties of the average action potential; and a stage of analysis of a discharge pattern of the electrode or electrodes; wherein the characterization of a deep brain nucleus is executed by analyzing the average action potential and the properties of the electrode or electrode discharge pattern. Thanks to the invention, it is identified specifically and safely of different deep brain nuclei, by the excitability and morphology properties of their action potentials (PA) by extracellular registration. (Machine-translation by Google Translate, not legally binding)
    公开号:ES2733746A2
    申请号:ES201830370
    申请日:2018-04-13
    公开日:2019-12-02
    发明作者:Gómez Jesús Pastor；Zelaya Lorena Carolina Vega
    申请人:Fundacion para la Investigacion Biomedica del Hospital Universitario de la Princesa；
    IPC主号:
    专利说明:

    [0001]
    [0002]
    [0003]
    [0004] The object of the invention consists of a neurophysiological method and apparatus for the specific and safe identification of different deep brain nuclei, due to the excitability and morphology properties of their action potentials (PA) by extracellular registration.
    [0005]
    [0006] Technical sector
    [0007]
    [0008] The field of application of the present invention is that of deep brain stimulation (ECP) for the treatment of movement diseases - such as, for example, Parkinson's disease, tremor or dystonia -, epilepsy, pain or pathologies Psychiatric such as obsessive-compulsive disorder, schizophrenia, aggressiveness, among others.
    [0009]
    [0010] State of the art
    [0011]
    [0012] The correct identification of the different brain nuclei is essential to ensure the efficacy of deep brain stimulation (ECP) and minimize side effects. However, the anatomical identification criteria by magnetic resonance imaging (MRI) are insufficient in thalamic surgery and the use of functional tests (motor response or electrical stimulation) is nonspecific, and cannot be used in anesthetized patients. Registration with microelectrodes (RME) is a technique commonly used, but it has great deficiencies in thalamus surgery. However, it is known from animal and computational studies that different neurons have different endowments of ionic channels, which modifies their excitability properties (frequency and discharge pattern) and the characteristics of their action potentials (PA). Using this hypothesis, the inventors have shown that the PAs of different thalamic nuclei have properties of duration, amplitude, excitability and composition of different phases.
    [0013]
    [0014] In this sense, US2017106193 is known where a system for modulating the parameters of implantable neuro stimulators is disclosed, both in the system peripheral nervous as in deep brain stimulation. The system compares a reference evoked action potential (previously stored in the system) with the action potential recorded at the time. The stimulation parameters are adjusted to ensure that the stimulated tissue potential has the same characteristics of the reference action potential. However, this patent differs from the present invention in that it is a system created particularly for methods of modulation of stimulation parameters, after having implanted the device, while the present invention manages to identify different brain nuclei during the implantation procedure.
    [0015]
    [0016] It is also known the patent US2017164852 where a communication system is used that uses an implantable electronic circuit for the detection of action potentials, which sends it to another remote station for the analysis of the morphology of the action potential and thus be able to determine at what Neurons belong to these action potentials. They are doing it to be used in research (since it is a chronic record) and in patients with prostheses in limbs, to know which neurons respond to movement. However, this patent differs from the present invention in that they are identifying the characteristics of the action potentials to identify individual neurons, especially in the cortex and spinal cord, so that it is not the same objective and, therefore, It is not an equivalent method either.
    [0017]
    [0018] Currently, there are two conventional methods for these brain nucleus analyzes:
    [0019] a) the location of the nucleus by MRI (anatomical or functional) and
    [0020] b) the conventional RME.
    [0021]
    [0022] However, neither is satisfactory. The first because the MRI does not allow the identification of the different sub-nuclei that form the thalamus and, in addition, there are important inter-individual variations between patients with respect to the atlas used for localization. The second method currently only makes use of the most basic identification properties of a nucleus such as the presence or absence of PA (whose morphology, however, is ignored), the overall discharge frequency and the clinical or cellular response to certain stimuli, such as limb movements, tactile stimulation or the presence of sensations induced by electrical stimuli.
    [0023] The need for the patient to report these latest sensations, requires conscious collaboration during surgery. This is, in the best of cases, an important nuisance to it since it involves surgeries that last several hours, in which the patient must remain immobilized by a craniostat fixation system to perform the stereotaxia and, In some other cases, it prevents the performance of this in patients with cognitive problems or mental retardation.
    [0024]
    [0025] Description of the invention
    [0026]
    [0027] An object of the present invention is a method that solves the problems indicated in the state of the art. More specifically, it is an object of the present invention to provide a method that positively identifies the nucleus in which the recording is performed, so that it is possible to select with certainty and safety where the final ECP electrode is to be placed, even in patients anesthetized This object is achieved by the method of claim 1. Particular embodiments of the method object of the present invention are described in the dependent claims.
    [0028]
    [0029] More specifically, the neurophysiological method of identification of brain nuclei comprising a stage of data acquisition, where for each depth recorded by a data acquisition team connected to at least one electrode, such that for the data acquired by the electrode or electrodes runs a stage of elimination of high frequency artifacts; a stage of determination of action potentials; a stage of grouping of action potentials; a stage of analysis of the properties of the average action potential; and a stage of analysis of a discharge pattern of the electrode or electrodes; and that is characterized in that the characterization of a deep brain nucleus is executed by analyzing the average action potential and the properties of the electrode or electrode discharge pattern.
    [0030]
    [0031] In a second aspect of the invention, the neurophysiological apparatus for the identification of brain nuclei comprising logically computerized means -implemented in an electronic computer is defined.
    [0032] of the method object of the present invention.
    [0033]
    [0034] The advantages of the method of the present invention over known methods are two:
    [0035] i) Firstly, it allows a positive identification of the nucleus in which we are making the registration, so that it can be selected with certainty and security where the final ECP electrode is to be placed. As we have indicated, this is the necessary and sufficient condition for such stimulation to be safe, efficient and economical.
    [0036] ii) Secondly, since the neuronal excitability properties are highly constant, the method object of the present invention will allow the identification of the nuclei even in anesthetized patients. This fact will make surgery much more comfortable for patients, the surgical team and will broaden the spectrum of patients who today cannot be offered, due to their age, for presenting cognitive or psychiatric problems or for excessive anxiety.
    [0037]
    [0038] These two advantages allow us the following actions: i) the clinical efficacy of the ECP is improved, ii) the need for stimulation with high currents is reduced, so less replacement of implantable batteries will be necessary, iii) the appearance is decreased of side effects, derived from the inaccurate location of the electrode and, in addition, iv) this technique allows this surgery to be performed in sleeping patients.
    [0039]
    [0040] Therefore, it can be said that the present methodology solves an important problem, such as the identification of a specific nucleus within a set of more than 40 anatomically intermingled nuclei. This means that there is the possibility of improving the location of a specific nucleus where the implantation of the final ECP electrode is intended. This methodology, although described for actions in the thalamus, is perfectly extensible to any other nucleus susceptible to ECP, such as the subthalamus, the pale globe, and the hypothalamus, among others.
    [0041]
    [0042] Throughout the description and the claims, the word "comprises" and its variants are not intended to exclude other technical characteristics, components or steps. For those skilled in the art, other objects, advantages and features of the invention will be derived partly from the invention and partly from the practice of the invention. The following examples and drawings are provided by way of illustration and are not intended to restrict the present invention. In addition, the invention covers all possible combinations of particular and preferred embodiments indicated herein.
    [0043]
    [0044] Brief description of the drawings
    [0045]
    [0046] Next, a series of drawings that help to better understand the invention and that expressly relate to an embodiment are described very briefly. of said invention, which is illustrated as a non-limiting example thereof.
    [0047]
    [0048] A graph showing the dynamics of the spectral area for the frequency range between 1000-2000 Hz is shown in Figure 1.
    [0049]
    [0050] Figure 2 shows a scatter plot that shows the grouping of a series of action potentials (PA) in different clusters as a function of distance, where only three axes are shown.
    [0051]
    [0052] Figure 3 shows two graphs with the most common types of PA. Above: negative (PA) tips. Below: positive (PA) tips. Each dashed line is a PA generated by the same cell (so it is selected in the same cluster). The continuous thick line represents the average of all dashed lines.
    [0053]
    [0054] Figure 4 shows two graphs with examples of the first phase positive (above) and negative (below) for two potentials of action N. The characteristic times that will define each of the phases and their amplitudes are indicated.
    [0055]
    [0056] Figure 5 shows two graphs with the determination of the phases of the repolarization period of the average action potential (above). The points correspond to the times at which a change in trend is observed for the first derivative (below). The dashed vertical lines show the correspondence between the characteristic points in both functions.
    [0057]
    [0058] Figure 6 shows three graphs that collect the graphic information about the download pattern. Above: download times of the PAs of the different units determined. Each point represents a PA and each unit is represented at a different vertical level. Center: graphic that shows the overall download pattern for the entire record, without distinguishing by units. Each AP is represented by a vertical line. Below: graph showing the evolution of the frequency in periods of 0.5 s.
    [0059]
    [0060] Figure 7 shows the probability density function (fdp) of the interval between consecutive action potentials.
    [0061]
    [0062] Figure 8 shows the three-dimensional configuration space that shows the position occupied by four electrodes as a function of the physical discharge properties of the global record, represented by the pause rate (TP), the pause index (IP) and the modified burst index (MRI).
    [0063]
    [0064] Explanation of a detailed embodiment of the invention
    [0065]
    [0066] The method object of the present invention comprises a first stage of data acquisition, where in a particular embodiment the records are exported in ASCII format from the acquisition equipment, although, obviously, other formats, such as EDF +, can be used. For each recorded depth, the file contains information between one and five electrodes. The sampling frequency (freq) is variable for each device. The data is grouped, therefore, in a matrix (Mk e M (M) (/ re ( Xt) xfe) that has k electrodes and freq xt sampled points, where t is the recording time measured in seconds. , M ( R) pxq represents the set of real matrices of p-rows and columns.For each of the electrodes, the following steps are carried out:
    [0067]
    [0068] 1) Elimination of high frequency artifacts
    [0069]
    [0070] In this first stage, and for each of the electrodes, consecutive and non-overlapping measurement windows of 213 points (8192, equivalent to 341.3 ms) are determined for each of which its power spectrum is determined using the Fast Fourier transformation (FFT). The area below the spectrum curve between 1000 2000 Hz is determined. In those regions where the area is 3.5 times greater than the standard deviation (SD), it is considered that there is high frequency non-neural noise contamination and, by Both are removed. Figure 1 shows the spectral area dynamics for the frequency range of 1000-2000 Hz. The threshold, which is variable, is defined by the dotted line. A region (1a) is observed whose power exceeds the threshold and which corresponds to a high frequency artifact.
    [0071]
    [0072] 2) Determination of action potentials ( PA)
    [0073]
    [0074] A threshold voltage is defined for the identification of positive and negative peaks. This threshold - in a particular non-limiting embodiment, ± 2.5 times the standard deviation identifies all those peaks. However, not all voltage peaks will correspond to an action potential.
    [0075]
    [0076] An action potential is defined by two conditions: (i) the presence of two peaks of opposite polarity voltage, Vmax and Vmin ( VmaxIVmin < 0) separated by a characteristic time ( A t). Depending on the polarity of the action potential, these voltages can be positive (P, when they go down) or negative (N, when they go up); and (ii) a definite separation between the two, that is, Amin <A t <Amax , the minimum interval Amin and maximum being the maximum Amax .
    [0077]
    [0078] For each well-defined BP, the time where its maximum peak measured in absolute terms ( IVmaxl ), which is tPA , is determined. Thus, for potential N, a Vtiempo vector is obtained
    [0079] time = í tPAl <tPA2 <■■■ ^ Aw)
    [0080]
    [0081] Over each time tPA a period of 0.75 ms earlier (18 points) and 1.75 later (60 points) is taken. With the sampling frequency used, this assumes 79 points (adding the tPA time) so that the period analyzed for each AP is 3.2917 ms. With the equipment that implements the method object of the present invention freq = 24 kHz, so the sampling period will be T = 41.7 ps. This is the minimum mistake made when measuring times.
    [0082]
    [0083] These voltage data ( Vi j; i = 1.2, ... N; j = 1.2, ... 79 ) for each i electrode are grouped in a matrix containing each PA ( MPA and M (M) 79xw ) in the form of a column, the rows being the sampled times ( j )
    [0084]
    [0085]
    [0086]
    [0087] However, in order to improve the accuracy of the temporal dimension, before interpolation -ie grouping of PA action potentials- an interpolation is performed on each of the PAs. In this way, the number of points for each AP is increased to 313, which means that the new sampling period will be T = 10.4 ps, improving the precision in the measurement of times by four times. Therefore, the previous matrix becomes another matrix MPA and M (M) 3l3xW .
    [0088]
    [0089] 3) Grouping of Action Potentials ( PA)
    [0090]
    [0091] The following measures are taken for each action potential:
    [0092] 3.1. - Maximum amplitude (V max )
    [0093] 3.2. - Computation of the first derivative (^), whose expression is for an approximation discreet
    [0094] AF V ( n + A) - V ( n)
    [0095] ~ Kt = Ai
    [0096] Being A t the sampling interval (ie A t = fre q _1). On this new function we find the extreme values, dVmax and dVmin.
    [0097] 3.3.- Determination of the duration of the positive and negative phases. Although sometimes there is an early phase to the maximum phase, this small phase has not been taken into account for clustering. These durations have been called durF1 and durF2.
    [0098]
    [0099] Therefore, each AP is characterized by a set of descriptors that form a vector of dimension 1 x 6.
    [0100] PA = ( Vmax, Vmin, durF l, durF2, dVmax, dVmin)
    [0101]
    [0102] This set of descriptors will be used to group the potentials according to their common characteristics. To do this, we use the Minkowsky distance method
    [0103]
    [0104]
    [0105]
    [0106] This allows grouping those cells that have less distance between them, forming groups or clusters. Figure 2 shows a scatter plot that shows the grouping of a series of action potentials in different clusters as a function of distance, where only three axes are shown.
    [0107]
    [0108] 4) Analysis of the properties of the average action potential
    [0109]
    [0110] Each cluster or group forms a set of similar characteristics, so all PAs are averaged to find the average action potential (PAp). A series of characteristic times, phases and amplitudes are determined on this average potential. But it must be borne in mind that APs can be of two fundamental types: positive, when their extreme value is down and negative, when their extreme value is up. This terminology of positives and negatives is common in the field of neurophysiology and in any extracellular registry. Figure 3 shows two examples of both types. Above: negative (PA) tips. Below: positive (PA) tips. Each dashed line is a PA generated by the same cell (so it is selected in the same cluster). The solid continuous line represents the average of all the dashed lines: the PAp and the dashed lines represent the values ± 2.5 SD.
    [0111] Another important aspect is that each type of cell (P or N) formed by the two main phases, can have in its initial part another component that can be positive or negative. This component is significantly smaller than the other two and does not appear in all cases. Figure 4 shows two examples (with a negative PAp) of the first positive and negative component and the characteristic times to be determined in each case.
    [0112]
    [0113] The determination of these characteristic times is carried out automatically.
    [0114] For this, the first 20 points of the PAp are used (which means 0.208 ms). On them its mean and SEM are calculated, to define the thresholds ut = V ± 3.5 SEM; i = 1,2, being i = in faith r io r and 2 = its superior. These thresholds will be used to automatically define significant times using the following sub-stages:
    [0115]
    [0116] 4.1.
    [0117] 4.2. - Definition of t2: the point where maximum such that t1 <t2 <t3.
    [0118] 4.3.
    [0119] 4.4. - Definition of t4: corresponds to the point of greatest amplitude.
    [0120] 4.5. - Definition of t5: it is the point where PAp (t) cuts to zero.
    [0121] 4.6.
    [0122] 4.7.
    [0123]
    [0124] These characteristic times and voltages allow us to determine, for each PAp, the following parameters:
    [0125] PAp = {Vl, durt, V2, dur2, V3, dur3}
    [0126] Being V i the maximum values of the potential for the phases i = 1,2, 3. The same is true for the durations. It may happen that V 1 and dun do not exist.
    [0127] Finally, the phases of the repolarization of the main component of the PAp are determined.
    [0128]
    [0129] For this, the number of phases that form the negative phase of the first derivative between points t4 and t6 are determined, as observed, for example, in Figure 5.
    [0130]
    [0131] 5) Download pattern properties
    [0132]
    [0133] The characterization of a deep core is not going to be characterized by the properties of the PAp, but also by the properties of the discharge pattern. For this, different measures are made:
    [0134]
    [0135] 5.1.- Discharge frequency ( F, which can be done for each of the units) and for the complete pattern. The temporal structure (that is, where the action potentials corresponding to the same unit appear) is encoded in the vtiempo¡j vectors ; j = 1,2, ... 5. It is defined as follows:
    [0136]
    [0137]
    [0138]
    [0139] In addition to the overall frequency value, it is very important to know how this frequency varies throughout the entire record. Therefore, the overall recording time is divided into non-overlapping windows of 0.5-1 s and the frequency for said interval is computed using the upper formula, but applied only to each window.
    [0140]
    [0141] These analyzes are shown both numerically, and graphically, as usual in clinical practice, as depicted in Figure 6.
    [0142]
    [0143] 5.2.- Histogram of interval between consecutive points. All intervals between two consecutive APs are determined and the absolute frequency of the intervals in periods of 10 ms (bin) is calculated. This histogram can be shown as a probability density function (fdp), dividing the count of each interval bin by the total sum of k intervals
    [0144]
    [0145] fd p = Nb in, ¡
    [0146] U NbinJ
    [0147] The probability density function of the interval between consecutive action potentials is shown in Figure 7.
    [0148]
    [0149] 5.3.- Physical properties of the discharge pattern. The following are used variables:
    [0150] 5.3.1.- Modified outbreak index (modified Burst Index, mBI). It is defined as the number of intervals between potentials of <10 ms, divided by the number of intervals between potentials of> 10 ms. It gives an idea of the number of outbreaks of downloads with respect to individual downloads.
    [0151]
    [0152] mBI _ N <10 ms
    [0153] N> 10 ms
    [0154] 5.3.2.- Pause Index (Pause Index, PI). It is defined as the number of intervals between potentials of> 50 ms divided by the number of intervals between potentials <50 ms.
    [0155] pi _ IIC> 50 ms
    [0156] IIC <50 ms
    [0157] 5.3.3.- Reason for pause (Pause Ratio, PR). It is defined as the total duration of pauses (intervals between potentials> 50 ms) divided by the total duration of non-breaks (intervals between potentials <50 ms). The information you provide is totally different from the pause index.
    [0158] yN i ic í
    [0159] PR _ ¿-¡i = 1I IJ <50 ms
    [0160] and ¿ M ij = iI i I ic 3 í > 50 ms
    [0161] Finally, in order to achieve a unique characterization of each recorded electrode, these indices are used to construct a three-dimensional vector ( vnucieo), so that
    [0162] ^ core {'mBI, PI, PR}
    [0163]
    [0164] As can be seen in Figure 8, these values are capable of isolating the different registers well (corresponding to different subthalamic nuclei, in general).
    权利要求:
    Claims (8)
    [1]
    1 A neurophysiological method of identification of brain nuclei comprising a stage of data acquisition, where for each depth recorded by a data acquisition team connected to at least one electrode, such that for the data acquired by the electrode or The electrodes runs a stage of elimination of high frequency artifacts; a stage of determination of action potentials; a stage of grouping of action potentials; a stage of analysis of the properties of the average action potential; and a stage of analysis of a discharge pattern of the electrode or electrodes; characterized in that the characterization of a deep brain nucleus is executed by analyzing the average action potential and the properties of the electrode or electrode discharge pattern.
    [2]
    2. - The method of claim 1 wherein in the data acquisition stage, the sampling frequency is variable, such that the data is grouped in a matrix that has k electrodes and freq x t sampled points, where t is the record time measured in seconds.
    [3]
    3. - The method according to any one of claims 1 to 2 wherein a plurality of consecutive and non-overlapping windows are determined in the stage of elimination of high frequency artifacts, for each of which its power spectrum is determined and the area under the curve of said spectrum is determined; and where in those regions where the area is, at least 3.5 times greater than the standard deviation, it is considered that there is high frequency non-neural noise contamination and such data is eliminated in the analysis of subsequent stages.
    [4]
    4. - The method according to any of claims 1 to 3 wherein in the step of determining the action potentials determined by: (i) the presence of two voltage peaks of opposite polarity separated by a characteristic time; and (ii) a definite separation between the two.
    [5]
    5. - The method according to any one of claims 1 to 4 wherein at the stage of grouping the action potentials, for each action potential determined in the stage of determination of action potentials the following measures are performed:
    the maximum amplitude;
    the computation of the first derivative (^), whose expression is for a discrete approximation
    AF V ( n + A) - V ( n)
    ~ Kt = Ai
    where A t is the sampling interval and where the extreme values, dVmax and dVmin are found;
    and the determination of the duration of the positive and negative phases durF1 and durF2; and where each action potential is characterized by a set of descriptors that form a vector of dimension 1 x 6
    PA = ( Vmax, Vmin, durF l, durF2, dVmax, dVmin) in such a way that this set of descriptors is used to group the action potentials according to their common characteristics so that those cells that have less distance between them are grouped , forming groups or clusters.
    [6]
    6. The method according to any one of claims 1 to 5 wherein in the stage of analysis of the properties of the average action potential (PAp), for each group or cluster of action potentials the average action potential is calculated by determining a plurality of characteristic times, phases and amplitudes, according to the following stages:
    a stage of definition of t1, which is the point where:
    the function PAp (t) cuts a lower threshold u1 if the first phase is positive or an upper threshold u2 if this first phase is negative;
    and where if the first phase does not exist, t1 = t3;
    a stage of definition of t2 which is the point where maximum such that t1 <t2 <t3; a stage of definition of t3 which is the point where the phase with the greatest amplitude begins (defines the P or N points), where:
    if the first phase is positive, t3 is the point where PAp (t) cuts to zero; if the first phase is negative, t3 is the point where the first derivative has a local minimum value;
    a stage of definition of t4 that corresponds to the point of greatest amplitude; a stage of definition of t5 which is the point where PAp (t) cuts to zero; a stage of definition of t6 which is the extreme voltage point opposite that of t4 and corresponds to the most negative voltage for negative PAp and the most positive for positive ones;
    a stage of definition of t7 which is the point where PAp (t) returns again to the value zero and which is operatively determined when it crosses u1 or u2 (for points P;
    and where these characteristic times and voltages determine the following parameters for each PAp:
    PAp = {V1 ( dur1, V2, dur2, V3, dur3}
    where Vi is the maximum potential values for phases i = 1,2,3; and dun the duration values for phases i = 1,2,3;
    and where, in addition, the phases of the repolarization of the main component of the PAp are determined by determining the number of phases that form the negative phase of the first derivative between points t4 and t6.
    [7]
    7. - The method according to any of claims 1 to 6 wherein the electrode discharge pattern is determined by the following measures:
    measurement of the frequency of discharge;
    determine all the intervals between two consecutive action potentials and calculate the absolute frequency, establishing a probability density function;
    Calculate for each download pattern:
    a modified outbreak index that is defined as the number of intervals between potentials of <10 ms, divided by the number of intervals between potentials of> 10 ms;
    a pause index that is defined as the number of intervals between potentials of> 50 ms divided by the number of intervals between potentials <50 ms.
    a pause ratio that is defined as the total duration of pauses with intervals between potentials> 50 ms, divided by the total duration of non-pauses with intervals between potentials <50 ms.
    [8]
    8. - A neurophysiological apparatus for the identification of brain nuclei comprising at least one electrode and means for executing the method according to any one of claims 1 to 7.
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    同族专利:
    公开号 | 公开日
    ES2733746R1|2020-03-12|
    引用文献:
    公开号 | 申请日 | 公开日 | 申请人 | 专利标题
    US8150795B2|2004-12-22|2012-04-03|Wisconsin Alumni Research Foundation|Methods and devices for analysis of clustered data, in particular action potentials |
    GB0613500D0|2006-07-07|2006-08-16|Lectus Therapeutics Ltd|Apparatus and Methods|
    US20140277282A1|2013-03-15|2014-09-18|Boston Scientific Neuromodulation Corporation|Use of compound action potentials to automatically adjust neurostimulation therapy in response to postural changes of patient|
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    ES201830370A|ES2733746R1|2018-04-13|2018-04-13|METHOD AND NEUROPHYSIOLOGICAL APPARATUS OF IDENTIFICATION OF THE CEREBRAL NUCLEUS|ES201830370A| ES2733746R1|2018-04-13|2018-04-13|METHOD AND NEUROPHYSIOLOGICAL APPARATUS OF IDENTIFICATION OF THE CEREBRAL NUCLEUS|
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