Method and system for suicide risk assessment and intervention
11298542 · 2022-04-12
Assignee
Inventors
Cpc classification
G16H20/30
PHYSICS
A61N1/36096
HUMAN NECESSITIES
G16H20/70
PHYSICS
A61B5/165
HUMAN NECESSITIES
G16H50/20
PHYSICS
A61B6/501
HUMAN NECESSITIES
G16H20/10
PHYSICS
G16H50/30
PHYSICS
A61B5/4848
HUMAN NECESSITIES
International classification
G16H70/40
PHYSICS
A61B5/00
HUMAN NECESSITIES
G16H50/30
PHYSICS
G16H50/20
PHYSICS
Abstract
A brain mapping system and methods that allow to predict and monitor the risk of suicide and provide personalized therapy. The brain mapping system and methods detect if brain dysfunctions (injuries) are located in suicidal hubs that trigger increased suicidal ideation and high risk of suicide. The brain mapping technology is suited for different technologies and allows to monitor the effects of therapy, provide precise therapy to decrease the risk of suicide.
Claims
1. A brain mapping method for predicting and monitoring the risk of suicide comprising: a. using electroencephalographic (EEG) signals and a computing system (device) to estimated network measures and topographically display a network fragmentation map of the whole brain to reveal a network dysfunction located inside a suicidal hub; b. detecting of proximity and finding when the position of said network dysfunction is in the proximity of said suicidal hubs which triggers suicidal ideation and increases the risk of suicide; c. predicting and monitoring of high risk of suicide when the network dysfunctions is located in the proximity or inside of said suicidal hubs that are further comprising the left frontal region at electrode F7 or left temporal region at T7 site or in the right occipital region at electrode O2 or frontocentral region at electrode FCz or fronto-polar regions at electrode FP2 or left sensorimotor cortex at electrode FC3 and right centroparietal region at CP4 site; d. providing a precise individualized therapy using a non-invasive or invasive stimulation or drugs to target the location of said network dysfunctions located in the proximity or inside of said suicidal hubs to diminish or remove the network dysfunction and reduce the risk of suicide.
2. The method as set forth in claim 1, determines the locations of the network dysfunctions using the network fragmentation map through visual inspection or automatically by the computing system detecting the color scale of said network fragmentation map.
3. The method as set forth in claim 1, that topographically maps estimated network complexity measures wherein said network fragmentation brain map is plotted as said by the computing system.
4. The method as set forth in claim 1, wherein said the presence of network dysfunctions (injuries)-in two or more locations within or in a proximity of said suicidal hubs has a cumulative effect and can increase or decrease the risk of suicide.
5. The method as set forth in claim 1, wherein said the removal of dysfunctional (injured) networks located within of said suicidal hubs or in their proximity by further therapy is required to reduce suicidal ideation and the risk of suicide and antisocial behavior.
6. The method as set forth in claim 1, wherein said non-invasive stimulation is set to target dysfunctional (injured) brain regions with as said network dysfunctions while the other electrodes can be placed far from dysfunctional regions located on shoulder or right supraorbital ridge or spinal cord.
7. The method and system as set forth in claim 1, predicts and monitors the effects of different therapies (drug administration) that could potentially trigger suicide or antisocial behavior by expanding or shifting the location of network dysfunctions (injuries) in the proximity of said suicidal hubs once therapy has started and after therapy.
8. The method as set forth in claim 1, further defines the proximity as less than minimum half distance between adjacent EEG electrodes of a 32 channel EEG system using 10-20 EEG placement.
Description
BRIEF DESCRIPTION OF THE DRAWINGS
(1) The accompanying drawings illustrate non-limiting examples of the present invention.
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DETAILED DESCRIPTION OF THE INVENTION
(8) The next description includes details and examples to provide an understanding of the patent. This invention is presented in different forms and not all unnecessarily details have been shown or described to avoid obscuring the invention. The drawings and specifications are explanatory, rather than restrictive. In
(9) In one exemplary embodiment of the system brain maps that indicate regions with increased risk of suicide are built using recorded EEG data. Focal and diffuse injuries on dynamic network patterns were identified at baseline before drug therapy in patients with major depression using novel developed techniques. Network dysfunctions (injuries) are located in the brain regions with high, abnormal network fragmentation (Aur et al., 2018, Aur and Jog, 2019). The brain mapping procedure requires the subject to be connected to an encephalograph (EEG) that records electrical activity of the brain. The presence of network injuries in the regions labeled as “suicidal hubs” increases the risk of suicidal thoughts and suicidal behavior (Aur et al., 2019). The administration of antidepressants (escitalopram) and non-invasive stimulation can reduce network fragmentation in particular cortical, regions and has an impact on depression severity (Aur et al., 2019). In general, the administration of antidepressants reduces network fragmentation in the prefrontal cortical region, improves the mood and reduces suicidal thoughts. However, since each brain is different, in rare cases the administration of antidepressants may expand or shift the location of network dysfunctions (injuries) to “suicidal hubs” which will trigger increased suicidal ideation and the risk of suicide. These injured regions in patients with increased risk of suicide will generate differences in signature activation compared to control patients (Just et al., 2017).
(10) Electrical interactions and inherent structural information from the dynamics between different parts of the brain are analyzed using various complexity measures and mapped. Network fragmentation is estimated based on the inverse of dynamic cross-entropy (DCE) values using resting state EEG data recordings. Dynamic Cross-Entropy (DCE) is defined as a multidimensional complexity measure that quantifies the degree of regularity of EEG signals in different frequency bands (see, Aur and Vila-Rodriguez, 2017). The resulting system will be less vulnerable to noise artifacts and volume conduction compared to previous techniques.
(11) In an embodiment of the invention network dysfunctions (injuries) are the brain regions with high, abnormal increased network fragmentation. Normalized values of network fragmentation provide the regions with network injury that are brain mapped and used to monitor the effect of therapy or clinical worsening, the emergence of suicidal thoughts and behaviors. No reference to control group is needed to compute and display normalized network fragmentation maps (NFMs). In the examples provided herein, dynamic cross entropy is used without limiting the invention since other complexity measures could also be used to estimate network fragmentation of the brain network (e.g. Lyapunov exponent, algorithmic complexity measures such as Lempel-Ziv complexity, auto-mutual information, sample entropy, Tsallis entropy, approximate entropy, multiscale entropy (Vitanyi & Li, 1997; Mizuno et al. 2010). Also different clinician-administered questionnaire such Hamilton Depression Rating Scale (HAM-D), the Montgomery-Asberg Depression Rating Scale (MADRS) or Patient Health Questionnaire-9 (PHQ-9) can be used to assess the degree of depression severity. Continuous interactions between all brain regions are required for normal brain function (Aur et al, 2018; Aur et al., 2011; Aur and Jog, 2010, Aur and Tuszynski 2017). The brain maps of network fragmentation can be used to detect the location of network dysfunctions (injuries). In addition, linear mixed-effects models (Pinherio and Bates, 1996; Bates et al., 2014) are used to determine the relationship between the scalar variable represented by MADRS suicide items (SSI.sub.M), and explanatory variables represented by network fragmentation estimated at baseline before therapy. The linear regression model can be written:
SSI.sub.M=1+Σ.sub.ia.sub.ix.sub.i+Σ.sub.jb.sub.jx.sub.j+ε where a.sub.i>0,b.sub.j<0 (1)
(12) If random effects can be neglected (ε=0), then suicide items SSI.sub.M can be approximated by two separated terms that depend on positive a.sub.i, and negative coefficients b.sub.i. The significance of the F-test indicates if the linear regression model provides a better fit to the data than the model that contains no explanatory variables. If the F-test indicates a good model fit, the brain regions with network fragmentation x.sub.i that correspond to high positive regression coefficients a.sub.i define the location of “suicidal hubs”. An increase of suicidal ideation occurs once these brain regions become dysfunctional (injured). Contrarily, if the F-test indicates a good model fit, once injured the brain regions with network fragmentation x.sub.j and high negative b.sub.j coefficients will decrease suicidal ideation. The linear mixed-effects model was corrected for confounding factors such as age, sex. The presence of network dysfunctions (injuries) in two or more locations within suicidal hubs (high positive a.sub.i coefficients) has cumulative effect and increases the risk of suicide. The location of network dysfunctions in the brain regions with high negative b.sub.j coefficients will disrupt the presence of suicidal thoughts and diminish the risk of suicide.
(13) The topographic map of F-test statistic for linear regression shows in
(14) The presence of network injuries that expand to “suicidal hubs” located in the left frontal region at F7 site: F(1,16)=17.48, p=0.0007 that corresponds to Brodmann area 47 (BA47), left temporal region at site T7: F(1,16)=11.5, p=0.003 site that corresponds to BA42, right centroparietal region at electrode CP4: F(1,16)=5.17, p=0.037, right occipital region at site O2: F(1,16)=8.8, p=0.008 or right frontopolar region at site Fp2: F(1,16)=5.49, p=0.03 site and left sensorimotor cortex at electrode FC3: F(1,16)=5.80 p=0.028.
(15) Accordingly, this invention overcomes the limitations presented above and provides a method to decrease the severity of depression and lower the risk of suicide. Once the location of network dysfunction (injury) is known, either non-invasive stimulation or various drugs can be used to provide therapy by targeting the detected brain region. The example presented in
(16) Other techniques such as transcranial alternating current stimulation (tACS) (Kasten et al., 2017) transcranial pulsed current stimulation (tPCS) (Thibaut et al., 2017) near-infrared light-emitting therapy (Naeser et al., 2016), transcranial ultrasound stimulation (Hameroff et al., 2013) or transcranial magnetic stimulation (Aur, et al., 2016; Narayana et al., 2017) can be used to target the network injury in order to remove increased network fragmentation or in specific cases to increase network fragmentation if the detected region shows very low network fragmentation levels. Drug therapy and non-invasive stimulation will alter molecular structure within neurons, synapses, glial cells and reshape dysfunctional networks. In addition, it was proved that repeated non-invasive stimulation changed electrical activity and removed network injuries (Aur et al., 2019). The system predicts and monitors the risk of suicide by mapping the location of network dysfunctions (injuries) using complexity measures in particular network fragmentation.
(17) In another embodiment the effect of noninvasive stimulation is revealed in
(18) Normalized network fragmentation brain maps (NFMs) provide the regions with network dysfunctions (injuries) that can be used to monitor for clinical worsening and emergence of suicidal thoughts and behaviors (see,
(19) Having shift the location of network dysfunction (injury) to the right centroparietal at electrode CP4 increases suicidal ideation and the risk of suicidal behavior since CP4 site is a suicidal hub (see
EXAMPLES
Example 1: Topographic Network Fragmentation Map (NFM) is Used to Monitor the Effect of Drug Therapy
(20) The effect of therapy is monitored by displaying changes of network fragmentation that occur after the therapy to determine if network dysfunctions (injuries) are removed from or shifted to suicidal hubs. In
(21) The severity of depression decreases from MADRS=37 at baseline to MADRS=29 at week2 of therapy and the network dysfunction (injury) cannot be detected in the left frontal region or at F7 site 420. After over 8 weeks of escitalopram therapy network fragmentation decreases globally and a reduction of depression severity is determined by the clinician the patient is in remission, the sign of depression are gone (MADRS=6). In addition the risk of suicide is lifted, no network dysfunctions injury can be observed in suicidal hubs at F7 site 430 or at CP4 site. The removal of network dysfunctions (injuries) from suicidal hubs substantially reduces suicidal ideation SSI.sub.M that drops from 4 to 0. No reference to control group was needed to estimate network fragmentation, or plot NFM in this case.
Example 2: Topographic of Normalized Network Fragmentation Map (NFM) is Used to Monitor the Effect of Non-Invasive Stimulation
(22) In one exemplary embodiment of the system and methods disclosed herein, noninvasive stimulation can be included in addition to drug therapy, see the example presented in
(23) Three months later after non-invasive stimulation the injury cannot be detected in the left DLPFC 540 or at F7 site, 530. The depression symptoms and the risk of suicide are lifted since network dysfunctions (injuries) are removed from suicidal hub at F7 site.
Example 3: Topographic Representation of Normalized Network Fragmentation Map (NFM) Shows the Migration of Network Injuries to Suicidal Hubs During Drug Therapy
(24) In rare cases drug therapy can shift the location of network dysfunctions (injury) toward suicidal hubs and increase suicidal risk (Teicher et al., 1993; Olfson et al., 2006, Stone et al., 2009). This example show topographic plots of normalized network fragmentation of a selected non-remitter with the location of network dysfunctions in dark gray color at baseline. These network dysfunctions (injuries) are located in the right primary motor cortex at C4 electrode, 610. This injury can be observed after two weeks of therapy 620. The severity of depression also increases from MADRS=22 at baseline to MADRS=24 at week2 and remains at MADRS=22 after over 8 weeks of escitalopram therapy. After over 8 weeks of escitalopram therapy the network dysfunction (injury) expands from the primary motor region 630 to the right centroparietal region at CP4, 640. We already know that CP4 site is a suicidal hub and suicidal ideation SSI.sub.M increases from 1 to 2. This is the most interesting case since it shows that drug therapy can expand or shift network dysfunctions (injuries) to other brain regions. As a result of this spatial shift of network dysfunctions (injuries) other issues can potentially emerge and an increase of suicidal risk can be a side effect of therapy.
(25) Therefore, if drug therapy affects brain networks, monitoring the effects of therapy is always required. In young people the administration of antidepressants can increase the severity of depression and shift the location of network dysfunction (injuries) to suicidal hubs.
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