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A computational model of the influence of myelin excess for patients with Post-traumatic stress disorder

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The brain is the central organ of stress and controls the adaptation to stressors, while it perceives what is potentially threatening and determines the behavioral and physiological responses. Post-traumatic stress disorder (PTSD) is a mental health disease in which an individual has been exposed to a traumatic event that involves actual or imminent death or serious injury, or threatens the physical integrity of the self or others. The effects on the brain caused by stress for people with PTSD are the main subject of this paper. A literature research was conducted to see how stress affects the brain and how regions of the brain are distorted by an excess of myelin, which is formed by oligodendrocytes, in people with PTSD. Network-Oriented Modeling perspective is proposed as an alternative way to address complexity. This perspective takes the concept of network and the interactions within a network as a basis for conceptualization and structuring of any complex processes. It appears myelin, and the oligodendrocytes which produce the myelin can have altering effects in the brain of patients with PTSD. The fear response is increased significantly and the forming and retrieval of memories is also disrupted. As the effect of myelin is decreased in the model, the effects are also decreased. The main purpose of this paper is providing insight into what the effects of myelin excess might be for patients with PTSD, and simulating these effects to make these insights easily accessible.
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A Computational Model of Myelin Excess for Patients
with Post-Traumatic Stress Disorder
Jelmer Langbroek1 [https://orcid.org/0000-0002-9154-7292], Jan Treur1[https: //orcid .org/0 000-0003-2466-9158],
S. Sahand Mohammadi Ziabari1[https: //orcid .org/0 000-0003-3803-6714]
1 Social AI group, Vrije Universiteit Amsterdam, De Boelelaan 1105, The Netherlands
Jelmerlangbroek@gmail.com j.treur@vu.nl
sahandmohammadiziabari@gmail.com
Abstract. The brain is the central organ of stress and controls the
adaptation to
stressors, while it perceives what is potentially threatening and determines the behav-
ioral and physiological responses. Post-traumatic stress disorder (PTSD) is a
mental
health disease in which an individual has been exposed to a traumatic event that in-
volves actual or imminent
death or serious injury, or threatens the physical integrity
of
the self or others. The effects on the brain caused by stress
for people with
PTSD are the main subject of this paper.
A literature research was conducted to
see how stress af
fects the brain and how regions of the brain are distorted by an
excess of myelin, which is formed by oligodendrocytes,
in people with PTSD
. Net-
work-Oriented
Modeling perspective is proposed as an alternative way to
address
complexity. This perspective takes the concept of network and the interactions within
a network as a basis for
conceptualization and structuring of any complex processes.
It
appears myelin, and the oligodendrocytes which pro
duce the myelin can have alter-
ing effects in the brain of
patients with PTSD. The fear response is increased signif-
icantly and the forming and retrieval of memories is also
disrupted. As the effect
of myelin is decreased in the model,
the effects are also decreased. The main purpose
of this paper is providing insight into what the effects of myelin excess might be for
patients with PTSD, and simulating these effects to make these insights easily acces-
sible.
Keywords: Network-Oriented Modeling, PTSD, Stress, Myelin.
1 Introduction
The brain is the central organ of stress and controls the adaptation to stressors, while it
perceives what is potentially threatening and determines the behavioral and physiolog-
ical responses. In addition, the brain is a target of stress. Stressful experiences change
the architecture, gene expression and function through internal neurobiological mech-
anisms in which circulating hormones play a role [8].
The effects of stress on an individual’s health is a widely researched area, in which
it is mostly found that stress causes a negative impact on health. In most recent research
the subject is not if stress affects health, but is focused more on causes of stress, for
whom and what type of stressors cause the effect of stress on health [1].
Stress is often seen as a negative phenomenon, but stress has played an important role
in our survival as a species. Animals and even plants have a stress response, however,
the stress response can cause harm if not controlled properly [11]. Different individuals
2
experience stress in different ways. Symptoms vary from an anxious or depressed
mood, anger, irritability to the digestive system or skin complains.
Stress plays an important role in our lives as it both negatively and positively effects
our physical and mental health. Prolonged stress can cause mental health problems such
as burnout, depression and post-traumatic stress disorder [5]. According to [12] and
stress disrupts normal brain functioning by interrupting connectivity between different
brain regions.
The effects on the brain caused by stress for people with PTSD are the main subject
of this paper. Some network-oriented models for PTSD have been published [14-21].
Literature research will be conducted to see how stress effects the brain and how regions
of the brain are distorted in people with PTSD. The interruptions in connections in the
brain will be displayed in a model using network-oriented modeling as proposed by
[13]. Network-Oriented Modeling perspective is proposed as an alternative way to ad-
dress complexity. This perspective takes the concept of network and the interactions
within a network as a basis for conceptualization and structuring of any complex pro-
cesses. Network- Oriented Modeling is not considered here as modeling of networks,
but modeling any (complex) processes by networks.
2 Underlying Biological and Neurological Principles
Stress can also come from the inside. For example, when someone has fear and is un-
certain. For example, if you do not have enough money to pay the rent [4]. Stressors
can be real or perceived. Moreover, stressors are not only dependent on the subject, but
also have a clear dynamic in time (recurring, short-term or long-term) and can vary in
intensity (or at least in the perception of the individual). That is, stressors can be mild
and relatively harmless or result from major events and can have immediate and/or
long-lasting effects on the well-being of the person. Therefore, it is fundamentally in-
evitable that most people will feel stressed from time to time [7].
Stress will almost always find a way to show itself as a physical condition such as
fatigue, headaches or muscular tension. Stress is not always seen as a cause for physical
problems at first. As people are unaware of the effects stress can have on their body
and mind. They then tend to have the symptoms treated rather than the cause [3].
According to [9] there are three types of stress. eustress, neustress, and distress. Eu-
stress is a positive kind of stress which involves positive emotions. For example, meet-
ing your favorite artist. Mostly situations in which eustress occurs are considered en-
joyable and are not a threat. Neustress is either good or bad. For example, news of an
earthquake somewhere in a remote area. Distress is a negative variant of stress. Distress,
in turn, is divided into two types: acute stress and chronic stress. Acute stress occurs
suddenly, is quite intense but lasts for a short period of time. Chronic stress can last for
a prolonged period of time but is mostly less intense.
We can label a stressful experience as good, tolerable or toxic, depending on the degree
to which a person has control over a particular stressor and has supportive systems and
resources to deal with it. Meeting the demands imposed by stressful experiences can
lead to growth, adaptation, and beneficial forms of learning that promote resiliency and
good health. In contrast, other stressful experiences can promote proliferation of recur-
sive neural, physiological, behavioral, cognitive and emotional changes that increase
3
the vulnerability to poor health and premature death from various chronic medical con-
ditions [6].
According to [8] the brain adapts to stress and can sometimes be even named as
resilient towards stress. [7] highlight evidence that the brain is the central mediator and
target of stress resiliency and vulnerability processes. They emphasize that the brain
determines what is threatening, and therefore what is stressful to the individual. The
brain regulates the physiological, behavioral, cognitive, and emotional responses for an
individual in order to cope with a certain stressor. The brain changes in its plasticity
both adaptively and maladaptively as a result of coping with stressful experiences.
Research in both humans and animal models has begun to identify morphological cor-
relates of functional changes. These include altered cell fate in cortical and subcortical
structures, dendritic and synaptic reorganization and glial remodeling. The emerging
view is that stress causes a dislocation syndrome, which disrupts transmission and in-
tegration of information critical to orchestrating appropriate physiological and behav-
ioral responses [12].
In Research done by [10] in the field of fundamental science and functional neuroim-
aging has helped identify three brain regions that may be involved in the pathophysiol-
ogy of PTSD: the amygdala, medial prefrontal cortex and hippocampus. The amygdala
is involved in the assessment of threat-related stimuli and biologically relevant ambi-
guity and is necessary for the process of anxiety conditioning. Given that people with
PTSD are hypervigilant with regard to a potential threat in the environment and that
they show a relatively increased acquisition of conditioned fear in the laboratory, many
researchers have the hypothesis that the amygdala is hyperreactive in people with
PTSD. Functional neuroimaging studies have provided evidence for amygdala hyper
reactivity in PTSD.
Research by [2] has shown a difference in the gray versus white matter ratio between
healthy people and patients with PTSD. Gray matter in the brain is composed mainly
of neurons and glia-cells. Neurons store and process information and glia-cells support
the neurons. White matter is mostly composed of axons, which form a network of fibers
to connect the neurons. White matter is called white because of the white fatty sheath
of myelin coating that insulates the nerves and accelerates the transmission of the sig-
nals between the cells. The research by [2] focused on the cells in the brain that can
produce myelin to see if a connection could be found between stress and the proportion
of white brain matter to grey. Research was done on the hippocampus area of the brain
within adult rats. The general belief as to stem cells is that they would only become
neurons or astrocyte cells which are a type of glial cells. But the neural stem cells
seemed to behave differently. Under stress, the cells became oligodendrocytes which
are another type of glial cells which produce myelin. These cells also help to form the
synapses, the communication tools with which nerve cells can exchange information.
As can be inferred from these findings, chronic stress can cause more myelin-producing
cells and fewer neurons. Communication in the brain can be disrupted by this imbalance
and could lead to problems. This also might mean that people with stress disorders,
such as PTSD, have alterations in their brain connectivity. This can lead to a stronger
connection between the hippocampus and the amygdala (the area that processes the
fight-or-flight reaction).
4
The most effective way to treat PTSD is with trauma-focused psychotherapy. This
type of treatment focuses on the memory of the traumatic event or its meaning (United
States Department of Veterans Affairs, 2017). There are also some types of medication,
but most of this only help in reducing symptoms. Anti-depressants for example help
the patient feel less depressed, but the cause of these feelings is not removed. In this
article excess of myelin is proposed as a negative result caused by PTSD. There seems
to be no medication yet to reduce the amount of myelin. There is also no medication
which can cause fewer oligodendrocytes to form, the type of cells that produce myelin.
There are some medications which can do the opposite, generate more myelin-produc-
ing cells. It seems the only way to reduce the excess myelin is to undergo psychother-
apy.
3 The Adaptive Temporal-Causal Network Model
First, the Network-Oriented Modelling approach used to model this process is briefly
explained. As discussed in detail in [13, Ch 2] this approach is based on temporal-causal
network models which can be represented at two levels: by a conceptual representation
and by a numerical representation. A conceptual representation of a temporal-causal
network model in the first place involves representing in a declarative manner states
and connections between them that represent (causal) impacts of states on each other,
as assumed to hold for the application domain addressed. In reality, not all causal rela-
tions are equally strong, so some notion of the strength of a connection is used. Fur-
thermore, when more than one causal relation affects a state, some way to aggregate
multiple causal impacts on a state is used. Moreover, a notion of speed of change of a
state is used for timing of the processes. These three notions form the defining part of
a conceptual representation of a temporal-causal network model:
Strength of a connection
w
X,Y Each connection from a state X to a state Y has a
connection weight value
w
X,Y representing the strength of the connection, often be-
tween 0 and 1, but sometimes also below 0 (negative effect) or above 1.
Combining multiple impacts on a state cY(..) For each state (a reference to) a
combination function cY(..) is chosen to combine the causal impacts of other states
on state Y.
The Speed of change of a state
h
Y For each state Y a speed factor
h
Y is used to
represent how fast a state is changing upon causal impact.
Combination functions can have different forms, as there are many different approaches
possible to address the issue of combining multiple impacts. Therefore, the Network-
Oriented Modelling approach based on temporal-causal networks incorporates for each
state, as a kind of label or parameter, a way to specify how multiple causal impacts on
this state are aggregated by some combination function. For this aggregation a number
of standard combination functions are available as options and a number of desirable
properties of such combination functions have been identified; see [13, Ch 2, Sections
2.6 and 2.7]. In Fig. 1 the conceptual representation of the temporal-causal network
model is depicted. A brief explanation of the states used is shown in Table 1.
5
Table 1. Explanation of the states in the model
Figure 1 is a visual representation of the basic model. The
myelin has an effect on the
connection between the control
state and both psfear and fsfear, as stated before the myelin
excess can cause a disruption in connectivity between the
hippocampus and the prefrontal
cortex. The prefrontal cortex moderates/controls the fear responses. States
psfear
and
fsfear
thus cannot be decreased properly. Also because of the increase between the hippocampus
and the amygdala, caused
by the excess myelin, the fear responses connection increases
over
time.
Fig. 1. Visual representation of the fear model
X1
srss
Sensory representation state of extreme fear
X2
psfear
Preparation state for extreme fear
X3
fsfear
State of fear
X4
cs
Control state for regulating the fear response (located in the
prefrontal cortex)
X5
myelin
State which represents the fatty sheet around axons and
helps to increase the speed of impulses along fibers. It also
insulates, so the electrical current cannot leave the axon
X6
sri
Sensory input: This can be any kind of sensory input, such
as visual, audio, smell.
X7
sm
Sensory memory: The sensory memory holds input after it
is perceived for less than a second. Thus, this information
has to be recalled immediately or else it decays.
X8
stm
Short-term memory: The short-term memory can hold input
from seconds to a minute. This allows for rehearsal, which
can lead to information to be stored from short-term to
long-term memory.
X9
ltm
Long-term memory: The long-term memory can store large
quantities of information and hold these for a long period of
time. It is quite unmeasurable, as some information decays
after a period of time.
X10
csm
Control state memory: The connection between long-term
and short-term memory is mostly controlled by the hippo-
campus which in this case is the control state for memory.
6
In this model the connections have a certain weight.
These can both be negative or positive.
These amounts can
have a big effect (when the amount is near 1 or -1 for negative
effects)
or a smaller effect (when the amount is near 0). In
this case for example it is clear that
psfear and FSfear have a small negative effect on cs. The values for these connections
are based on the literature study. The small
negative effect occurs because the control
state will not be
able to have a control effect on psfear and fsfear.
Fig. 2. Visual representation of the memory model
These states are implemented in a visual model (Figure
2). As an individual perceives
a certain sensory input (sri),
which can be visual, auditory or any other kind that uses the
human senses, this is stored in the Sensory memory (sm).
The sensory memory can only
store information for a very
short period of time (less than a second), after this, it either
decays or stays present in the short-term memory (stm).
In the state sm the information
can be stored a few seconds to a
minute, which allows for rehearsal which in turn can lead
to
storage in the long-term memory (ltm). Information can
also be transferred from stm
to ltm without rehearsal.
The ltm can store information up to a lifetime, but can
also
suffer from decay.
Fig. 3. Visual representation of the combined model
7
When an individual tries to access
information stored in the LTM, the information will
be retrieved from the ltm to the stm allowing the individual
to ’work’ with the infor-
mation. The state stm is also referred to as
working memory, which in turn is split up
into three different parts. For this research the simpler stm is used to
ensure the model is
not too comprehensive.
With the retrieval and storage of information from stm to ltm and ltm to stm for-
mation and elimination of axons and synapses is critical. If any disruptions occur, the
forming of new memories and the retrieval of former memories can be corrupted. There
is evidence that myelin in
hibits synapse formation and reduces plasticity in the central
nervous system, that oligodendrocytes inhibit axon growth
cones, and that oligodendrocytes
precursor cells are repulsive for growing axons. Thus, one consequence of excessive
mye-
lin, particularly in the hippocampus, can very well be
a reduced ability to learn and
remember. Which is shown
in Figure 2 by the negative arrows towards the connections
between stm and ltm. The ltm also has a connection to
itself which will lead to a more
persistent connection.
This visual model is converted into table 2. Again, the
effects can both be negative or
positive. These amounts
can have a big effect (when the amount is near 1 or -1 for
negative effects) or a smaller effect (when the amount is near
0). The amounts are based
on the literature on how well the
brain stores and retrieves memories. Also, all connec-
tions
are implemented in the table where Hebbian learning, state-
connection amplification
and the positive or negative effect
of myelin come into play.
The conceptual representation was transformed into a numerical representation as
follows [13, Ch 2]:
at each time point t each state Y in the model has a real number value in the
interval [0, 1], denoted by Y(t)
at each time point t each state X connected to state Y has an impact on Y defined
as impactX,Y(t) = wX,Y X(t) where wX,Y is the weight of the connection from X to Y
The aggregated impact of multiple states Xi on Y at t is determined using a
combination function cY(..):
aggimpactY(t) = cY(impactX1,Y(t), …, impactXk,Y(t))
= cY(wX1,YX1(t), …, wXk,YXk(t))
where Xi are the states with connections to state Y
The effect of aggimpactY(t) on Y is exerted over time gradually, depending on
speed factor hY:
Y(t+Dt) = Y(t) + hY [aggimpactY(t) - Y(t)] Dt
or dY(t)/dt = hY [aggimpactY(t) - Y(t)]
Thus, the following difference and differential equation for Y are obtained:
Y(t+Dt) = Y(t) + hY [cY(wX1,YX1(t), …, wXk,YXk(t)) - Y(t)] Dt
dY(t)/dt = hY [cY(wX1,YX1(t), …, wXk,YXk(t)) - Y(t)]
For states the following combination functions cY(…) were used, the identity function
id(.) for states with impact from only one other state, and for states with multiple im-
pacts the scaled sum function ssuml() with scaling factor l, and the advanced lo-
gistic sum function alogistics,t() with steepness s and threshold t.
id(V) = V
ssuml(V1, …, Vk) = (V1, …, Vk)/l
alogistics,t(V1, …,Vk) = [(1/(1+eσ(V1+ … + Vk -t))) 1/(1+eσt)] (1+eσt)
8
Here first the general Hebbian Learning is explained. In a general example model con-
sidered it is assumed that the strength w of such a connection between states X1 and X2
is adapted using the following Hebbian Learning rule, taking into account a maximal
connection strength 1, a learning rate
and a persistence factor µ
, and activa-
tion levels X1(t) and X2(t) (between 0 and 1) of the two states involved. The first ex-
pression is in differential equation format, the second one in difference equation format:
dw(t)/dt =
[X1(t) X2(t) (1- w(t) - (1-µ)w(t)]
w(t +
) = w(t) +
[X1(t) X2(t)(1 - w(t)) - (1-µ)w(t)]
4 Example Simulation
An example simulation of this process is shown in Fig. 2 and 3. The connection
weight parameters are shown in Tables 2 and 3.
Table 2. Connection weights for the example simulation for the fear model
The memory model will be a representation of how the myelin
producing oligoden-
drocytes effect the encoding and retrieval
of memories from the short-term memory
to the long-term
memory and vice versa.
Table 3. Connection weights for the example simulation of memory model
Figure 4 is a representation of the effect of myelin excess
on the fear response. This
scenario shows how two connec
tion becomes stronger and two others become weaker.
The
control state(X4) is getting less grip on the situation
. As long as the fear response
cannot be controlled, the
symptoms of PTSD will not be reduced.
Connection weights
X1
X2
X3
X4
X5
srss
psfear
fsfear
cs
myelin
X1
srss
1
0.7
X2
psfear
0.5
0.6
X3
fsfear
0.4
1
X4
cs
-0.2
-0.2
X5
myelin
Connection weights
X6
X7
X8
X9
X10
sri
sm
stm
ltm
csm
X6
sri
1
1
X7
sm
0.8
X8
stm
0.9
0.4
X9
ltm
0.85
0.3
0.5
X10
csm
0.6
0.7
9
Fig. 4. Simulation result for the effect of myelin excess
on the fear response
Figure 5 shows the connections between ltm and stm
and vice versa. Both connections
decrease over time, which
indicates that the storage and retrieval of memories within
a pa-
tient with PTSD and myelin excess is getting worse.
Since myelin inhibits synapse for-
mation and reduces plasticity in the central nervous system, oligodendrocytes inhibit
axon
growth cones, and oligodendrocytes precursor cells are
repulsive for growing axons. And
the formation and elimination of axons and synapses are critical for learning and
memory. Patients with PTSD tend to have problems with
their memory. Forming new
memories is often disrupted,
and the retrieval of memories can also be a problem.
Fig. 5. Simulation results for the memory connections
For the memory model, the myelin effect is also lowered
accordingly. Again, this re-
sults in a much less dramatic
negative effect on the retrieval and forming of memories.
Figures 6 and 7 show the simulation of this lowered myelin effect.
Here it is shown that
both the memory retrieval is and the
formation of memories is moving towards a more
steady
value.
In Figure 6 the influence of myelin excess on the fear response and the forming and
retrieving of memories is simulated. From 1 and -1 the effect is now 0.3 and -0.3 for a
positive and negative effect receptively. The result is shown in Figure 6. It seems that
the initial effects are still present, but in a far less dramatic way. This may indicate, the
lower the myelin excess is the less negative effect occurs on the control of the fear
response and the fear response itself.
10
Fig. 6. Simulation results for decreased myelin effects on fear
For the memory model the myelin effect is also lowered accordingly. Again, this re-
sults in a much less dramatic negative effect on the retrieval and forming of memories.
Figure 7 shows the simulation of this lowered myelin effect. Here it is shown that both
the memory retrieval is and the formation of memories are moving towards more
steady value.
Fig. 7. Simulation results for decreased myelin effects on memory
An overview of the result of the simulation of both models combined is shown in Figure
8. The decreased effect of myelin in the combined model is shown in Figure 9.
Fig. 8. Simulation results for the combined model
11
Fig. 9. Simulation results for decreased myelin effect in the combined model
5 Conclusion
The network-oriented modeling technique is used in this thesis as a way to simulate
certain scenarios for patients with PTSD. This resulted in the formation of a computa-
tional model. It appears myelin, and the oligodendrocytes which produce the myelin
can have altering effects in the brain of patients with PTSD. When under stress, patients
with PTSD are found to form an excess of oligodendrocytes. By simulating what the
effects might be it was found that myelin excess can result in increased fear response,
reduced control of this response and a decrease in the storing and retrieval of memories.
Since myelin inhibits synapse formation and reduces plasticity in the central nervous
system, oligodendrocytes inhibit axon growth cones, and oligodendrocytes precursor
cells are repulsive for growing axons. The formation and elimination of axons and syn-
apses are critical for learning and memory.
For further research, the assumptions made in this research have to be tested. More
research on the myelin excess in patients with PTSD has to be done. Not only fear and
memory are parts that are negatively affected in patients with PTSD but there might
also be more which is influenced by the growth of myelin-producing oligodendrocytes.
When the effects of the myelin are proven, the medical field tries to manipulate the
oligodendrocytes, and maybe form some type of drug that can suppress the effects.
Also, it might be interesting to see if psychotherapy, which is proven to decrease PTSD
symptoms, also decreases the amount of myelin present in the brain of a patient with
PTSD.
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18. Mohammadi Ziabari, S.S., Treur, J.: An adaptive cognitive temporal-causal network model
of a mindfulness therapy based on music. Proceedings of the 10th International Conference
on Intelligent Human-Computer Interaction, IHCI’18, Springer, India,2018.
19. Mohammadi Ziabari, S.S., Treur, J.: Cognitive Modelling of Mindfulness Therapy by Au-
togenic Training. Proceedings of the 5th International Conference on Information System
Design and Intelligent Applications, INDIA'18. Advances in Intelligent Systems and Com-
puting. Springer Publishers, Berlin (2018).
20. Lelieveld, I., Storre, G., Mohammadi Ziabari.: A Temporal Cognitive Model of The Influ-
ence of Methylphenidate (Ritalin) on Test Anxiety. Proceedings of the 4th International Con-
gress on Information and Communication Technology (ICICT2019), 25-26 Feb, Springer,
London, UK (2019).
21. Mohammadi Ziabari, S.S., Treur, J.: An adaptive cognitive temporal-causal network model
of a mindfulness therapy based on humor. NeuroIS Retreat, 4-6 June, Vienna, Austria, 2019.
13
22. Mohammadi Ziabari, S.S.: Integrative cognitive and affective modeling of deep Brain stim-
ulation. Proceedings of the 32nd International conference on industrial, engineering and other
applications of applied intelligent systems (IEA/AIE 2019), Graz, Austria, 2019.
23. Andrianov, A., Guerriero, E., Mohammadi Ziabari, S.S.: Cognitive Modeling of Mindful-
ness Therapy: Effects of Yoga on Overcoming Stress. Proceedings of the 16th International
conference on Distributed Computing and Artificial Intelligence (DCAI 2019), 26-28 June,
Avila, Spain, 2019.
24. E. de Haan, R., Blankert, M., Mohammadi Ziabari, S.S.: Integrative Biological, Cognitive
and Affective Modeling of Caffeine use on Stress. Proceedings of the 16th International con-
ference on Distributed Computing and Artificial Intelligence (DCAI 2019), 26-28 June,
Avila, Spain, 2019.
25. Mohammadi Ziabari, S.S.: An adaptive Temporal-Causal Network Model for Stress Extinc-
tion Using Fluoxetine. Proceedings of the 15th International conference on Artificial Intelli-
gence Applications and Innovations (AIAI 2019), 24-26 May, Crete, Greece, 2019.
26. Mohammadi Ziabari, S.S., Gerritsen, C.: An Adaptive Temporal-Causal Network Model
Using Electroconvulsive Therapy (ECT) for PTSD Patients, 12th International conference
on brain informatics (BI 2019), submitted.
27. Mohammadi Ziabari, S.S., Treur, J.: An Adaptive Cognitive Temporal-Causal Model for
Extreme Emotion Extinction Using Psilocybin, International conference on hybrid artificial
intelligent systems (HAIS), 4-6 September, Leon, Spain, 2019.
28. Mohammadi Ziabari, S.S., Treur, J.: An Adaptive Cognitive Temporal-Causal Model for
Extreme Emotion Extinction Using Psilocybin, International conference on hybrid artificial
intelligent systems (HAIS), 4-6 September, Leon, Spain, 2019.
29. Mohammadi Ziabari, S.S.: A cognitive temporal-causal network model of hormone therapy,
Proceedings of the 11th international conference on computational collective intelligence,
ICCCI’19, Lecture Notes in Computer Science, Springer Publishers, 4-6 September,
Hendaye, France, 2019.
... 50,62,63 The excess myelin may be attributed to increased oligodendrogenesis, which can lead to increased fear response. 64,65 Given the correlation between increased myelination and fear response, the decreased myelination in Fkbp5 −/− mice might contribute to stress resilience. ...
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... A cognitive development of a designed model and incorporating cognitive computational models helps individuals, who experience high level of stress, to better understand their stress level and the roles of different therapies to decrease their stress level during a period of following therapy and later on a potential recommendation of changing or continuing therapy based on the performance of the selected therapy. There are some previous works on cognitive behavior of individuals with a high level of stress [60][61][62][63][64][65][66][67][68]. ...
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Stress is often seen as a negative factor which affects every individ-ual's life quality and decision making. To help avoid or deal with extreme emotions caused by an external stressor, a number of practices have been introduced. In the scope of this paper, we take three kinds of therapy into account: mindful-ness, humor, and music therapy. This paper aims to see how various practices help people to cope with stress, using mathematical modelling. We present practical implementations in the form of client-server software, incorporating the computational model which describes therapy effects for overcoming stress based on quantitative neuropsychological research. The underlying network model simulates the elicitation of an extremely stressful emotion due to a strong stress-inducing event as an external stimulus, followed by a therapy practice simulation leading to a reduction of the stress level. Each simulation is based on user input and preferences, integrating a parameter tuning process; it fits a simulation for a particular user. The client-server architecture software which has been designed and developed completely fulfills this objective. It includes server part with embedded MATLAB interaction and API for client communication.
... Many studies concentrated on fluctuations of sex hormones in women such as estrogen and their impacts on brain and behavioral changes [17]. There are some previous works b temporal-causal modeling via network-oriented modeling on the cognitive behavior of the patient in a stressful condition [25][26][27][28][29][30][31][32][33][34][35][36][37][38][39]. The paper is organized as follows. ...
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In this paper, the effect of using sex hormones such as estrogen and producing this hormone in the body using cognitive, biological temporal-causal network model is presented. Reviewing neuroscience pieces of literature about hormone therapy shows the effect of estrogen on some brain components such as basolateral Amygdala, ventromedial Prefrontal Cortex, Hippocampus and resulting in decreasing the extreme emotion. This finding shows that for postnatal depression transdermal estrogen is an effective treatment. Moreover, the presented model integrates the cognitive, biological and effective principle of neuroscience.
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In this paper, an adaptive temporal causal network model based on electroconvulsive therapy to reduce the stress level of post-traumatic stress disorder (PTSD) is presented. The stress reduction is triggered by a cognitive electroconvulsive therapy that uses continuous usage of this therapy. The goal of this therapy is to decrease the strength between certain parts of the brain which are responsible for causing stress. This computational model aims to illustrate the effect of the therapy on different components of the brain. The cognitive model begins with a state of strong and continuous stress within a post-traumatic disorder patient and after following electroconvulsive therapy the stress level starts to decrease over time. The results show that, in the end, the patient will have a declined stress level compared to not using electroconvulsive therapy.
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