Anxious Phobia Disorder Patients’ Brain & Behavior

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Table of Contents

Introduction to the Problem
Description and Background Issues
Rationale and Purpose of the Study
Study Hypothesis
Method
Results
Discussion
References

Introduction to the Problem

The development of approaches to the estimation and control of brain functions in disorders control has attracted broad interest from evidence-based researchers. The improvement of the methods of spectral and multifractal analyses of the electroencephalogram (EEG) has enabled scientists and psychologists to sort the chaotic and fractal dynamics of the brain associated with anxious phobia disorders. According to Dick, Svyatogor, Ishinova, and Nozdrachev (2012), there is also growing scholarly interest in the degree of both multifractality and mono-fractality of EEG.

Panic and anxiety disorders have become increasingly frequent, with estimates in 2018 indicating that the reported incidences had risen by 3.1% every month for the past 12 months. The estimated social anxiety lifetime prevalence in the United States is 12%, and the percentage is projected to keep increasing (Feng, Cao, Li, Wu, and Mobbs, 2018). Furthermore, Johnson et al. (2019) note that nearly 75% of the US population is exposed to a risk of severe trauma caused by degeneration of the stress levels, decreasing genetic resilience to traumatic situations and occurrences.

Based on the facts presented above, this paper will discuss the functional state of the brain of patients having anxiety and stress disorders. It aims at analyzing the issue using the facial stimuli, which, in its turn, will help to determine consistency in the level of perceived attractiveness. It is possible to suggest that anxiety, induced during the study, will affect the hippocampus and the amygdala. The method of beauty has been selected because it may illustrate how the brain functions differently after the stimuli, which may affect individuals’ preferences and the situations they encounter in their daily lives.

Description and Background Issues

Given that the function of anxiety remains the detection of threat, several studies have aimed to create an experimental cognitive psychology model that will inform future clinical research and practice. Burkhardt et al. (2019) found in a past survey that phobic disorder patients are highly responsive to script-driven imagery and prone to elicitation from natural stimuli. Similarly, França et al. (2018) accentuate that EEG correlational values are affected by neurodynamics, including psycho-emotional stress and disorder variations.

However, despite initial breakthrough studies in the past decade, the application of fractal features of EEG to treatment and control nervous phobia disorders is still limited. França et al. (2018) observe that complete characterization of the brain function dynamics and the quantification of the variants of fractal geometry would remain a challenge even as standardized structures are being formulated and implemented. The reason for this trend is because brain dynamics, including electrical activity, diffuse processes, and chemical reactions, remain non-linear and operate under the most complex natural phenomena. Therefore, scaling fractal geometry and invariant dynamics will take time, and a significant research gap still exists in this area.

Psychogenic pain management and psycho-relaxation alternative manipulations to anxiety disorders have remained the main focus areas in the initial experimental analysis for most studies. Other experiments have been conducted among non-humans and have helped to affirm the evidence for genetic variations leading to brain disorders. For instance, Johnson et al. (2019) used rat samples to experiment on serotonin transporter (SERT) and how it reduces transcriptional efficiency associated with anxiety traits among those animals. The study found that rats showed increased baseline anxiety-like behaviors commonly associated with people in cases of heightened panic situations.

The normalization of brain responses to persistent fear is now widely done by 5HT1A antagonist infusions (França et al., 2018). However, contrary to the power spectra, the distinctions of EEGs on a quantitative basis in the examination of the brain continue to influence the singularity spectra. It is crucial to understand the behavioral and neurological dynamics of patients with nervous phobic disorders to maximize the success of disorder diagnosis and treatment. The dynamics may be measured through the analysis of the structural connectivity of basal-limbic areas, such as the amygdala (Duval, Javanbakht, & Liberzon, 2015).

Rationale and Purpose of the Study

The purpose of the study is to discuss the functional state of the brain of patients with phobia, panic, and anxiety disorders. The rationale of the study is to obtain reliable information about the factors associated with the behavior and psychological state of phobia disorder patients. Some of the elements of interest include the body’s sense of pain, the emotional state of patients, exponent correlations of brain functions, and dominant areas of EEG segments. This approach is significant for the research, as one of the types of behavioral measures is the collection of body responses, which will be performed before and after stimuli.

Study Hypothesis

The following study hypothesis will guide the presented research:

H0 – The changes in the shown EEG signal variance of brain function for phobic disorder patients will be significant. This hypothesis suggests that the changes in EEG results after stimulation will be present in individuals having phobic disorders.
H1 – Phobic disorder patients will be highly responsive to script-driven imagery. This hypothesis corresponds to the theory presented by Burkhardt et al. (2019) that states that individuals with such mental health conditions are prone to elicitation from not only natural stimuli but also script-driven imagery.
H2 – The fractal elements of behavior (timing of rhythmic movement, motor performances, postural sway) will change significantly with respect to the signal variance of the brain for phobic disorders patients. This hypothesis implies that exposure to stimuli will result in changes in individuals’ fractal behavior.
H3- Phobic disorder patient will exhibit a high degree of multifractality when exposed to stimuli. It means that individuals having phobic disorders will show changes in brain dynamics.
H4- The rhythm of the EEG power spectrum before, during, and after stimulus among patients with nervous phobic disorders will change significantly, showing a variation of brain performance. It suggests that individuals experiencing phobias and anxiety are prone to changes in the brain after exposure to stressful situations.
H5- Individuals not having phobic disorders will not show significant differences in the levels of perceived attraction before and after stimuli. This hypothesis suggests that exposure to stressful situations will not affect the functions of their participants’ brains.
H6- Individuals having phobic disorders will show changes in the amygdala and the hippocampus.

Method

The study will examine 30 patients with phobic disorders alongside a group of 30 healthy persons. It will utilize descriptive and quantitative data collected from this group to establish consistent relations between the variables. The independent variables in the study are randomized facial stimuli, brain electrical activity, and EEG factors. The EEG factors will include frequency bands, such as delta, alpha, gamma, beta, and theta, analyzed for several regions of the brain. The parts will include the amygdala, the prefrontal cortex, the insula, and the hippocampus. Stimuli will be expected to affect these areas of the brain, as they are the ones responsible for emotion modulation and processing (Duval et al., 2015).

The only independent variable (IV) that will be manipulated is the facial stimuli, which will be changed from time to time to determine how consistently the patients can rate the same pictures for attractiveness. The choice for this independent variable is determined by the results of studies showing that the responses of facial stimuli may differ in phobic and non-phobic patients (Kang, Kim, Kim, & Lee, 2019). The study will use an adaptive algorithm to keep the normative ratings from the patients within the same range of experimental estimates. Participants will be put in situations that make them feel uncomfortable to test them in a state of anxiety.

The behavioral dependent variable (DV) is the attractiveness rating of the faces in the pictures given to participants and the conformity scores of individuals due to peer influence. The method of attractiveness has been selected because patients having phobic disorders respond well to script-driven imagery; moreover, this method will help to analyze whether the functional state of the brain may change after stimulation (Dick et al., 2012; Burkhardt et al., 2019).

The study will be done in two phases. The attractiveness rating will be determined by asking the participants to give normative values to the degree of attractiveness on a 10-scale range starting from 1-unattractive to 10-very attractive. An average behavioral update will be recorded against the name of the participant and compared with the mean rating of a healthy person. The normative ratings given by healthy people will be used as baseline values for comparison.

The neurological DV is the brain activity values obtained as electrooculograms from a NeuroScan system. The data will be taken while the participants are doing the attractiveness rating. The research will take place at a local psychiatric and correction center. The EEG value will be estimated by what Dick et al. (2012) refer to as a Svyatogotor’s classification. Alongside this method, the study will use other multifractal approaches to check for the validity and reliability of the data collected.

Results

It is highly likely that phobic disorder patients will demonstrate a high degree of response to anxiety-inducing stimuli, and their attractiveness ratings of faces will change significantly between the two phases of manipulating anxiety-inducing activities. Burkhardt et al. (2019) found in a past study that phobic disorder patients are highly responsive to script-driven imagery and prone to elicitation from natural stimuli. However, there may not be a significant change in the attractiveness rating due to the safe conditioning factors among these patients. Further, the brain stem hyperactivation among anxious phobic disorder patients will create marginal differences for EEG power spectrums. EEG correlational values are affected by neurodynamics, including psycho-emotional stress and disorder variations (França et al., 2018).

The degree of multifractality among phobic disorder patients is high, and the functional state of the brain will fluctuate widely when the participants are exposed to stimuli. Alternatively, the emotion regulation deficits among the participants due to disrupted neurological functions will likely reduce the variation, and there may not be any significant change (Becker et al., 2001). For the last study variable, the fractal elements of behavior will change significantly with respect to the signal variance of brain function for phobic disorder patients. In addition to influencing the brain’s functional components and cognitive structures, phobic stimuli affect the personality identity variables (Rudaz, Ledermann, Margraf, Becker, & Craske, 2017).

Conversely, the fractal EEG variations, such as the ones in frontal, occipital, and other lobes, may not be significant because of dissociated consistencies. The analysis of variance (ANOVA) will be used to determine the relationship between phobic stimuli, neurological DVs, and behavioral DVs. The ANOVA technique will also be used in the study to assess how the neurological and behavioral measures differ between healthy people and phobic disorder patients. An ad-hoc test will be needed to meet the statistical controls of the independent variable in the study.

Discussion

The variance of attractiveness rating can be induced by such factors as feared outcomes and social acceptance needs among patients with the phobic disorder. According to Rudaz et al. (2017), phobic individuals have a high avoidance score. For instance, people living with anxiety disorders reveal higher conformity scores compared to those who are healthy because of the underlying behavioral factors influenced by biased social formations. The subtle safety behavior inherent among those patients contributes to the exacerbation and maintenance of anxiety. Even among animals like rats, Johnson et al. (2019) state, the innate anxiety-associated behaviors considerably change and evoke spontaneous actions, which can be considered to affect behaviors.

The neural patterns of anxiety disorder operating alongside prosocial motivations and pursuit of social acceptance leads to the creation of positive links of neuropsychological mechanisms. Dick et al. (2012) underscore that several psychorelaxation trials are required to facilitate the fixation of the functional state of the brain for people with anxiety disorders. This study seeks to establish the behavioral and neuropsychological variants associated with anxiety disorder patients, and the study rationale can adequately test the suppositions.

This study will change the approaches to cognitive-behavioral therapies given to phobic disorder patients. It will also help define and regulate excessive emotional responses in people living with anxiety disorders by informing approaches to deliberate modulations or mind reactivity. The study is, however, limited by the emotional and behavioral regulation deficits that can influence alternative hypotheses in the study.

Generally, the research must foster the reliability of multifractal estimation techniques by refining conceptual frameworks to meet the expected measures of accuracy. Future studies must focus on establishing deeper insights into the selective attentional processes and biases in the minds of people with anxious phobic disorders. Further, in the future, it would be essential to use the experimental paradigms to predict diagnosis and therapy outcomes more effectively.

References

Becker, E. S., Rinck, M., Margraf, J., & Roth, W. T. (2001). The emotional Stroop effect in anxiety disorders: General emotionality or disorder specificity. Anxiety Disorders, 15(1), 147-159.

Burkhardt, A., Buff, C., Brinkmann, L., Feldker, K., Gathmann, B., Hofmann, D., & Straube, T. (2019). Brain activation during disorder-related script-driven imagery in panic disorder: a pilot study. Scientific Reports, 9(2415), 1-35.

Dick, O. E., Svyatogor, A., Ishinova, V. A., & Nozdrachev, A. D. (2012). Fractal characteristics of the functional state of the brain in patients with anxious phobic disorders. Human Physiology, 38(3), 249-254.

Duval, E. R., Javanbakht, A., & Liberzon, I. (2015). Neural circuits in anxiety and stress disorders: a focused review. Therapeutics and Clinical Risk Management, 11, 115-126.

Feng, C., Cao, J., Li, Y., Wu, H., & Mobbs, D. (2018). The pursuit of social acceptance: Aberrant conformity in social anxiety disorder. Social Cognitive and Affective Neuroscience, 13(8), 809-817.

França, L. G., Miranda, J. G., Leite, M., Sharma, N. K., Walker, M. C., Lemieux, L., & Wang, Y. (2018). Fractal and Multifractal properties of electrographic recordings of human brain activity: Toward its use as a signal feature for machine learning in clinical applications. Frontiers in Psychology, 9(1767), 1-30.

Johnson, P. L., Molosh, A. I., Federici, L. M., Bernabe, C., Gerty, D. H., Fitz, S. D., & Shekhar, A. (2019). Assessment of fear and anxiety associated with reduced serotonin transporter (SERT) levels. Translational Psychiatry, 9(33), 1-35.

Kang, W., Kim, G., Kim, H., & Lee, S. H. (2019). The influence of anxiety on the recognition of facial emotion depends on the emotion category and race of the target faces. Experimental Neurobiology, 28(2), 261-269.

Rudaz, M., Ledermann, T., Margraf, J., Becker, E. S., & Craske, M. G. (2017). The moderating role of avoidance behavior on anxiety over time: Is there a difference between social anxiety disorder and specific phobia? PloS One, 12(7), 1-17.

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