Category: Brain

Introduction

Magnetic Resonance Imaging (MRI) has emerged as one of the most powerful diagnostic tools in the radiology clinic. The chief strengths of MRI are its ability to provide cross sectional images of anatomical regions in any arbitrary plane and its excellent soft tissue contrast (Sunders 4).

MRI has the ability to provide functional as well as anatomical information. The nuclear energy states of certain atoms interact with incidence radio frequency photons in the presence of a statistic magnetic field. The radio frequency emission by tissue that follows the absorption of photons can be exploited to generate images (Sunders 4). MRI has become a vital diagnostic tool during the screening of the pathophysiology of brain related illnesses. This paper discusses the application of MRI in brain imaging.

Advantages of MRI in Brain Imaging

MRI has much strength as a brain imaging technique. Unlike Computerized Tomography scan (CT), MRI requires no ionization, and it has no known physical hazards in human beings (Andreasean 56).

The pictures of the brain produced by MRI are reminiscent of the postmortem brain slices seen in neuroanatomy laboratories and neuroanatomy atlases. This technique provides excellent resolution between gray matter and white matter, permitting visualization of tiny structures, such as cranial nerves, nuclei of the basal ganglia, or limbic structures such as the hippocampus (Andreasean 56).

Magnetic resonance imaging can be obtained in three planes: sagittal, coronal, and transverse. As a consequence, MRI provides a much greater potential for three dimensional reconstruction of the brain. Not only does magnetic resonance permit visualization in new planes, such as in coronal sections, but it is also free of bony artifacts (Andreasean 56).

Bone, in fact, cannot be visualized well with MRI. However, with MRI it is possible to look into the posterior fossa, a region that is obscured by bone artifacts on other diagnostic regimes such CT scan (Andreasean 56). Further, with this technique, fine shades of tissue abnormality can be identified very well.

Basic Principles

Magnetic resonance employs principles which govern electricity and magnetism. This technique exploits the inherent magnetic field produced by the nuclei of some atoms (Andreasean 56). By far, the most common of these is hydrogen, which is composed of a single proton. Because hydrogen is widely distributed in the body, it forms the basis of MRI.

In addition, hydrogen produces a strong signal. MRI induces a magnetic field to the nuclei in one plane, thereby creating a non random magnetization that is strong enough to measure. Protons have their own specific spin and wobble, which can be excited by a radio signal broadcast at their specific frequency known as Larmor frequency, by a radio field transmitter.

The gradual decay in this resonance is then measured by a radio frequency receiver. The radio frequency signal can then be converted by computer to shades of gray, white, and black corresponding to the strength of the signal, and used to make images or pictures.

Components of MRI Signal

The MRI signal is produced by three different components which include proton density, T1 relaxation time, and T2 relaxation time (Andreasean 56). Proton density reflects the number of protons present in a particular tissue (Andreasean 56).

On the other hand, T1 relaxation time is an exponential growth constant that reflects the return of magnetization of protons to their equilibrium state in the Z axis (Andreasean 56). In addition, T2 relaxation time is an exponential decay constant that reflects the loss of signal strength as dephasing of spin occurs after excitation (Andreasean 56). These components are often defined by Bloch equation.

Interpretation of the MRI Signal

The signals emitted are turned into images by assigning various shades of gray, white, and black to tiny blocks of tissue according to their difference in signal intensity. Generally, if the signal is strong, the image will be brighter. Several factors influence signal intensity. They include proton density, decreased T1, and increase T2 (Andreasean 56).

Tissues with a short T1 relax and return to equilibrium more quickly and therefore give off a brighter signal. Tissues with a long T2 stay in phase longer and give out a brighter signal. Therefore, one must know whether the image is T1 or T2 weighted in order to interpret it. Most pathological processes lengthen T1 and T2 (Andreasean 56). This is attributed to an increase in water content in those tissues (Andreasean 56).

Clinical Applications

A variety of conditions of the brain can be detected using MRI. Examples of brain conditions which can be detected by MRI include “bleeding, cysts, tumors, developmental and structural abnormalities, inflammatory conditions, swellings, or problems associated with blood vessels” (Andreasean 56).

In addition, “MRI can be useful in evaluating problems such as persistent headaches, dizziness, weakness, and blurry vision or seizures, and it can help detect certain chronic diseases of the nervous system, such as multiple sclerosis” (Rosenbloom 364).

MRI is vey helpful in the diagnosis of brain tumors. It gives detailed information on cellular structure, vascular supply, and tumor anatomy (Rosenbloom 366). In addition, MRI provides essential details which reveal the location of the tumor, the type of the tumor, and the size of the tumor.

MRI thus necessitates the effective diagnosis, monitoring, and treatment of brain tumors. The imaging of brain tumors using MRI can be produced in several ways. It can be achieved through “Diffusion weighted MRI, perfusion weighted MRI, diffusion tensor MRI, intraoperative MRI, and awake cranial anatomy with MRI” (Rosenbloom 367).

Rosenbloom argues that this technique is vital in the identification of defects in the brain, which are as a result of alcoholism. In addition, MRI is essential in the identification of changes that occur due to soberness and recurrence. MRI has shown that alcoholism causes “shrinkage in the frontal cortex, 1 underlying white matter, and cerebellum and expansion of the ventricles” (Rosenbloom 368).

Rosenbloom also notes that “these changes are reversible with abstinence, although some appear to be enduring” (368). Imaging studies have shown that the brain has the potential to compensate for cognitive inadequacy. “The myriad concomitants of alcoholism, the antecedents, and the consumption patterns each may influence the observed brain changes associated with alcoholism, which tend to be deleterious with increasing age”( Rosenbloom 368).

The complex features of alcoholism limit the comprehension of the mechanism of alcoholism induced neuropathology. However, “in vivo longitudinal MRI brain studies can be used to understand the development and scope of alcohol dependence” (Rosenbloom 368).

In addition, Edith, Adron and Adolf, argue that multidisciplinary studies have played a key role in the examination of brain function, structure and attending factors.

These studies have been instrumental in evaluating alcohol related damage to the brain. Most importantly, these studies have necessitated the identification of substrates which initiate alcohol related neuropathology. A majority of these studies have concentrated on the neuropsychological sequelae of alcoholism.

This has led to the evaluation of a pattern of sparing and impairment, which has been instrumental in understanding functional deficits. “These studies have elucidated the component processes of memory, problem solving, and cognitive control, as well as visuospatial, and motor processes and their interactions with cognitive control processes” (Edith, Adron and Adolf 127).

The main advantage of MRI in brain imaging science, according to Edith, Adron and Adolf is that “it has necessitated the analysis of the course brain of structural changes during periods of drinking, and abstinence, and relapse” (127).

“MRI studies in patients with phenylketonuria revealed white matter alternations that correlated to most recent blood phenylalanine concentrations as well as brain phenylalanine measured by magnetic resonance spectroscopy”( Rosenbloom 368).

Furthermore, Rutherford et al. conducted a study “to establish a more objective method for confirming tissue injury in term neonates who have early seizures that are believed to be hypoxic ischaemic in origin” (1004). The researchers found out that MRI is essential in the early diagnosis of tissue injury in term neonates who have early seizures.

Conclusion

This paper has noted that Magnetic Resonance Imaging (MRI) has emerged as one of the most powerful diagnostic tools in the radiology clinic. The chief strengths of MRI are its ability to provide cross sectional images of anatomical regions in any arbitrary plane and its excellent soft tissue contrast (Sunders 4). MRI has the ability to provide functional as well as anatomical information. Magnetic resonance employs principles which govern electricity and magnetism.

This technique exploits the inherent magnetic field produced by the nuclei of some atoms (Andreasean 56). By far, the most common of these is hydrogen, which is composed of a single proton. The radio frequency signal is usually converted by computer to shades of gray, white, and black corresponding to the strength of the signal and used to make images or pictures. The MRI signal is produced by three different components which include proton density, T1 relaxation time, and T2 relaxation time (Andreasean 56).

Generally, if the signal is strong, the image will be brighter. Several factors influence signal intensity. They include proton density, decreased TI, and increase T2. Most pathological processes lengthen T1 and T2 (Andreasean 56). This is attributed to an increase in water content in those tissues (Andreasean 56). A variety of conditions of the brain can be detected using MRI.

Examples of brain conditions which can be detected by MRI include “bleeding, cysts, tumors, developmental and structural abnormalities, swellings, inflammatory conditions, or problems associated with blood vessels; MRI can be employed in the evaluation of abnormalities in the brain that occur as a result of alcoholism as well as changes that occur with sobriety and relapse” (Andreasean 56).

MRI has shown that alcoholism causes “shrinkage in the frontal cortex, 1 underlying white matter, and cerebellum and expansion of the ventricles” (Rosenbloom 368).

In addition, “MRI can be useful in evaluating problems such as persistent headaches, dizziness, weakness, and blurry vision or seizures, and it can help detect certain chronic diseases of the nervous system, such as multiple sclerosis” (Rosenbloom 364). The main advantage of MRI in brain imaging science, according to Edith, Adron and Adolf is that “it has necessitated the analysis of the course brain of structural changes during periods of drinking, and abstinence and relapse” (127).

Works Cited

Andreasean, Nancy. Brain Imaging: Applications in Psychiatry. New York: American Psychiatric Pub, 1989. Web.

Edith, Sullivan, Adron Harris, and Adolf Pfefferbaum. “Alcohol’s Effects on Brain and Behavior.” Alcohol Research & Health, 33.2 (2010): 127-143. Web.

Moller, Harald, et al. “Brain Imaging and Proton Magnetic Resonance Spectroscopy in Patients With Phenylketonuria.” Pediatrics 112 (2003): 1580-1583. Web.

Rosenbloom, Margaret. “Magnetic Resonance Imaging of the Living Brain.” Alcohol Research & Health 31.4 (2008): 362-376. Web.

Rutherford, Mark, et al. “Diffusion-Weighted Magnetic Resonance Imaging in Term Perinatal Brain Injury: A Comparison With Site of Lesion and Time From Birth.” Pediatrics 114.4 (2004): 1004-1014. Web.

Sunders, Rajan. MRI: A Conceptual Overview. Berlin, Heidelberg: Springer, 1998. Web.

Presentation

Traumatic brain injury (TBI) is a non-congenital and non-generative condition, which may result from a wide range of injuries occurring when the brain is affected by an external mechanical force (a jolt or a blow to the head or an object penetrating the skull). It may result in temporary or permanent dysfunction of the brain (impairing physical, cognitive, and psychological functions of the patient), bleeding, tom tissues, bruising, or other complications, some of which might lead to death (Algattas & Huang, 2013). Due to the number of causes and inclusion criteria, the definition of TBI is often inconsistent and problematic.

Post-concussive syndrome (PCS) may appear as a result of a mild traumatic brain injury or concussion and has a number of lingering symptoms such as fatigue, dizziness, headache, etc. It may be referred to as a mild type of TBI, which makes the conditions similar in symptoms. However, the key difference is that it is not life-threatening even despite the fact that its consequences can be rather long-lasting and serious (Boyd, 2014). Both conditions imply a change in the normal brain function; yet, the degree of this change is different.

Pathophysiology

There are two key mechanisms of TBI: focal (laceration, contusion, hemorrhage) and diffuse (brain swelling) damage to the brain. The type determines the outcome of the injury for the patient. The primary impact of TBI is produced at the moment of contact with an external mechanic force while the secondary impact covers subsequent, delayed pathological processes including but not limited to intracranial hypertension and cerebral ischemia. At the first stage of its development, the condition may be characterized by evident tissue damage as well as distorted metabolism and CBF regulation, which makes it similar to ischemia. Increased membrane permeability, anaerobic glycolysis, and formation of edema make lactic acid accumulate in the body (Algattas & Huang, 2013). Due to this, anaerobic metabolism dysfunction occurs as it is no longer able to regulate and maintain energy in the cells. As a result, the energy-dependent membrane fails, and the stores of ATP deplete. At the second stage, the terminal membrane depolarizes, and an excessive amount of excitatory neurotransmitters is released; catabolic processes are launched owing to Ca2þ- and Naþ-influx. The amount of intracellular free radicals and fatty acids increases as a result of the activation of lipid proteases, peroxidases, and phospholipases. All these processes affect the nucleosomal DNA and cause membrane degradation and apoptosis (Diaz-Arrastia et al., 2014).

The situation is less clear with post-concussive syndrome. It is considered by some scholars to be different from TBI is that it is unclear whether it has an organic or psychological basis. Still, the majority of studies accept its organic nature. It appears after an excessive amount of aspartate and glutamate is released, which results in a calcium influx leading to neuronal toxicity and cell death (frequently referred to as excitotoxic reaction). In this respect, the conditions are similar (Boyd, 2014). However, some researchers argue that memory impairment, cognitive and other disturbances may be purely psychological.

Assessment

To diagnose TBI, a thorough assessment of the patient’s physical and neurological damage is required as well as his/her course of recovery. This necessitates implementing a multidisciplinary approach to exclude potential errors of judgment. If an individual reveals some swallowing, cognitive, or communication problems, he/she must be assessed comprehensively. The assessment includes the investigation of the individuals’ medical and socioeconomic status, education, job, background, motor, cognitive, auditory, and emotional status, vision, the integrity of speech, motor planning, swallowing, speaking, reading, and writing abilities, memory, etc.

As far as PCS is concerned, the mechanism of assessment is generally the same (as it often appears sequelae of TBM). However, the difference is there is no reliable quantitative method that would allow identifying patients who are likely to develop the condition after being inflicted a brain injury (Boyd, 2014). That makes it rather challenging to assess risks and identify risk groups.

Diagnosis

In cases of moderate or severe TBIs, it is rather easy to diagnose the condition since the damage is evident. However, if the patient has other serious injuries, it is possible that a closed TBI can go unnoticed. Therefore, a proper neurological examination is crucial for giving necessary evidence. It is performed via MRI, CAT scan, PET scan, and SPECT (Carney et al., 2017). The diagnosis should be supported by neuropsychologists, occupational, physical, and speech therapists.

PCS is different since it is harder to diagnose until the patient starts having problems with routine tasks, which he/she used to perform successfully. No scan or testing can prove the presence of PCS. This accounts for the fact that the condition is typically diagnosed by its symptoms (which are basically the same as those of TBI): dizziness, headache, irritability, fatigue, insomnia, anxiety, blurry vision, loss of memory or concentration, light hypersensitivity, ringing in the ears, etc. (Boyd, 2014).

Treatment

Depending on the severity of the condition, there can be several types of treatment for TBI. The initial treatment is aimed to stabilize the patient shortly after the brain was damaged. Its goal is to assess vital body functions and divert possible life-threatening changes. Then, rehabilitative care center treatment follows, which is aimed at restoring the individual’s daily life functions. Minimization of secondary injury is achieved through the acute treatment program. Finally, surgical treatment may be needed to deal with swelling and a lack of oxygen (Carney et al., 2017)

Unlike TBI, the treatment of PCS is not so complex. No medications, rehabilitation therapies, or surgical interventions are required. In the majority of cases, time is the best treatment even if the patient has certain cognitive problems as they usually pass away on the own with the course of time (Boyd, 2014).

References

Algattas, H., & Huang, J. H. (2013). Traumatic brain injury pathophysiology and treatments: Early, intermediate, and late phases post-injury. International Journal of Molecular Sciences, 15(1), 309-341.

Boyd, W. D. (2014). Post-Concussion Syndrome. Bloomington, IN: Xlibris Corporation.

Carney, N., Totten, A. M., O’Reilly, C., Ullman, J. S., Hawryluk, G. W., Bell, M. J.,… Rubiano, A. M. (2017). Guidelines for the management of severe traumatic brain injury. Neurosurgery, 80(1), 6-15.

Diaz-Arrastia, R., Wang, K. K., Papa, L., Sorani, M. D., Yue, J. K., Puccio, A. M.,… Maas, A. I. (2014). Acute biomarkers of traumatic brain injury: Relationship between plasma levels of ubiquitin C-terminal hydrolase-L1 and glial fibrillary acidic protein. Journal of Neurotrauma, 31(1), 19-25.

A study by Parikh, Lasky, Kennedy, McDavid, and Tyson (2013) explored various clinical antecedents of brain volume abnormalities in extremely low birth weight infants (ELBW). Preterm infants are associated with an exceptionally high rate of neurodevelopmental impairments. By using volumetric and diffusion tensor magnetic resonance imaging (MRI), the scientists examined a range of perinatal antecedent factors in a group of ELBW infants in order to better understand modifiable risk factors related to the abnormalities. Upon measuring brain volumes with the help of automatic tissue segmentation methods and correlating the data with total brain volumes and white matter hyperintensities adjusted for age, Parikh et al. (2013) discovered that ELBW infants’ regional brain volumes were noticeably lower than those of term newborns.

Relative percentage difference for brain volumes was from -11 percent to -35.9 percent (Parikh et al., 2013). The following risk factors were clinically associated with the abnormalities: seizures, apnea/caffeine therapy, total prenatal nutrition (TPN) duration, and pulmonary hemorrhage (Parikh et al., 2013). Relative difference range for these factors was from -1.4 percent to -15 percent (Parikh et al., 2013). In addition, it was discovered that cerebral atrophy plays a major role in the reduction of total and regional brain volumes (Parikh et al., 2013). The findings of the study were consistent with the extant literature on brain volume abnormalities and helped to broaden understanding of clinical precursors of white matter hyperintensities. However, the researchers did not adjust their data for head size, which prevented them from discovering additional risk factors such as secondary atrophy (Parikh et al., 2013). Other limitations of the study were a low incidence of the antecedents and differences in magnetic field strengths for two groups of infants, which resulted in volumetric distortions.

Reference

Parikh, N., Lasky, R., Kennedy, K., McDavid, G., & Tyson, J. (2013). Perinatal factors and regional brain volume abnormalities at term in a cohort of extremely low birth weight infants. PLoS ONE, 8(5), 1-19.

Our brain has a group of cells called the neurons that are the basic units of the brain involved in sending signals to the body for motor function and through the five senses, to receive signals and give back the appropriate response. Then the brain processes this information through conscious thought and unconsciously through nerve systems that control all the basic bodily functions, such as heart rate, temperature control and balance. In general, the all these functions can be proper if the blood supplies the required oxygen and nutrients to the entire region. Any blockage to these supplies can cause the basic units of the brain, the neurons to die.

The brain gets the supply of blood from the carotid arteries located in the front of the neck and the vertebral arteries that run in the back of neck through small canals in the bony spine of the neck. When any part of the brain loses its blood supply, it becomes oxygen deficient and can cause damage and the death of the brain cells. Additionally, the corresponding body functions also get hampered. This is called a stroke or a cerebro-vascular accident (CVA). However, in some cases the brain is able to regain its blood supply quickly, and the symptoms may resolve and this is known as a transient ischemic attack (TIA) (MedicineNet).

Transient ischemic attack is also called a brain attack. It is in general said that if a person has a TIA, the specific neurons in a specific region of the brain are dying. Since neurons typically do not undergo mitosis, when neurons ‘die’ they are not replaced with ‘new’ neurons so whatever physiological functions are controlled or regulated by those neurons (movement, speech, cognition) are greatly reduced or sometimes lost.

The symptoms of TIA disappear within an hour or in rare cases it may persist for up to 24 hours. Some of the most common symptoms include: numbness or weakness in the face, arm, or leg, especially on one side of the body; confusion or difficulty in speech or understanding speech; trouble seeing in one or both eyes; and difficulty with walking, dizziness, or loss of balance and coordination (National Institute of Neurological Disorders and Stroke).

TIA is also called a “warning stroke” or “mini-stroke” that produces stroke-like symptoms but with no lasting damage. However, it is important to recognize and treat TIAs as these can reduce the risk of a major stroke which can happen any time within a few months or a year.

The main difference between a stroke and TIA is that the blood flow stays blocked, and the brain has permanent damage in a stroke. It is found that blood clots can be the result of hardening of the arteries (atherosclerosis), heart attack, or abnormal heart rhythms and as a result the brain cells are affected within seconds of the blockage. Sometimes a TIA is caused by a sharp drop in blood pressure that reduces blood flow to the brain. This is called a “low-flow” TIA (American Heart Association).

In general, during a TIAs the blockages occur in the major arteries to the brain, such as the carotid arterie that are involved in supplying oxygenated blood to brain cells. These arteries are clogged with fatty deposits, called plaques that partially block the artery, and can lead to the formation of a blood clot. Further, this clot or the thrombus can completely block the artery, which slows or blocks blood flow to the area of brain fed by that artery. When the blood supply is blocked due to some of the above mentioned reasons, the brain cells die partially or completely based on the duration of loss of blood supply and result in further complications.

There are also cases when TIAs can be caused by blood clots that form in the heart and travel to the brain. And this is called emboli. Additionally, there are also cases when there is the closure of small blood vessels deep inside the brain (National Institute of Neurological Disorders and Stroke). In conclusion, it can be said that a person who experience TIA and its symptoms need to rush to the doctor and get treated. It can be a precursor of a major stroke that lead to damage of different parts of the body. Therefore, good treatment can prevent major damage.

Work Cited

American Heart Association Transient Ischemic Attack (TIA) [2008]. Web.

MedicineNet Transient Ischemic Attack (TIA, Mini-Stroke) [2008]. Web.

National Institute of Neurological Disorders and Stroke NINDS Transient Ischemic Attack Information Page [2008]. Web.

Introduction

Traumatic brain injury (TBI) is a key health problem, especially amongst male teenagers and youthful adults ages 15 to 24, and among old people of both sexes 75 years and older. Children aged 5 and younger are also at a threat for TBI. Survivors of TBI are frequently left with major cognitive, behavioral, and communicative disabilities, and some patients build up long-term medical complications, such as epilepsy.

TBI, also known as acquired brain injury or head injury, occurs when an unexpected trauma causes damage to the brain. The damage can be focal – confined to one area of the brain – or diffuse – involving more than one area of the brain. TBI can result from a closed head injury or a penetrating head injury. A closed injury occurs when the head abruptly and violently collides with an object but the object does not break through the skull. A penetrating injury occurs when an object pierces the skull and enters brain tissue.

The study of individuals with traumatic brain injury reveals the cognitive models of human behaviors by showing the relationship among consciousness, awareness, and behavior which are all interrelated with the mode in which the brain works. It also reveals that the brain is divided into two sides or hemispheres. For motor functions, sight, and hearing, the left side of the brain controls the right side of the body, and the right side of the brain controls the left side of the body (“contralateral”). Further, it shows that the two hemispheres of the brain do not function identically. For most right-handed persons, the left side of the brain controls language functions and the processing of verbal information. Generally, the right side of the brain processes visual and spatial information.

How is the brain constructed

The hemispheres of the brain are further divided into four lobes. They are the frontal lobe, parietal lobe, occipital lobe, and temporal lobe. Each one of these areas of the brain is accountable for a different function. The effect an injury to the brain will have on an individual depends in large part on where the injury occurs. The two areas most susceptible to injury by the forces involved when the brain is subjected to rapid acceleration/deceleration are the frontal and temporal lobes.

The frontal Lobe normally controls the higher cognitive functions of our brains which separate us from other life on the planet and give us our human character. The frontal lobe is thus accountable for consciousness and awareness that are related to goal-directed behavior and for cognitive flexibility. These include foresight, judgment, initiation, organization, planning, and execution. Patients with frontal lobe injury often display incapability to manage their emotions characterized by severe mood swings (“emotional liability”). They may also experience a loss of inhibition and difficulty maintaining concentration and attention. Significant injury to the frontal lobe will often result in profound personality changes.

The temporal lobe creates the consciousness of verbal and nonverbal auditory information and is responsible for our awareness of time. The hippocampus is located within the temporal lobe. The hippocampus plays a major role in the function of memory. Thus, damage to the temporal lobes can severely affect an individual’s ability to remember new information or to recall existing information. Damage in this area may also affect the victim’s ability to discriminate speech sounds and understand what they hear. Difficulty in remembering lately learned information is a frequent problem in people who have had traumatic brain injuries. The ability to recall remote information (long-term memory) is seldom affected, even in cases of moderate injury.

The parietal lobe enables individuals to figure out spatial information and differentiate shapes, sizes, and textures. Other functions comprise right/left differentiation, mathematical abilities, and the ability to express or comprehend emotion. Injury in this area interferes with reading, math, attention to the contralateral hemispace and results in a flattened affect. The perception and understanding of emotion in others may be compromised in some individuals with injuries in this area. The occipital lobe is located in the lower rear portion of the skull and controls the visual awareness of the individual. Damage to this area may lead to a lack of self-consciousness and awareness of visual information hence impairs the individual ability to understand and interpret visual information.

The study of individuals with TBI also has enabled the discovery of sophisticated machines that can assess the degree of the brain injury which further assists in diagnosing the program of the patient to be easy. For instance, cognitive assessment by visual electrons (cave), Wessex head injury matrix, smart, etc.

Introduction

The analysis of parts of the brain has been an object of investigation for many centuries. Modern neuroscientists continue to investigate the various functions of the brain, awareness, and the sensory system (Tatham and Morton 242). They examine the links within these areas to find out how they can impact one another in the formation of various processes in people’s organisms. For instance, our speech production process is a result of neural interaction and involves two components: the ways of producing speech in terms of mental tasks and the related relevant brain tasks, which together form speech behavior (Tatham and Morton 242).

Speech production necessitates a combination of various types of information, such as motor, auditory, and somatosensory. These information forms are reflected in the frontal, temporal, and parietal lobes of the cerebral cortex (Guenther and Vladusich 408). In the integration with subcortical systems such as the brain stem, basal ganglia, and cerebellum, the cortical areas, along with their operative links, establish the motor control system (Guenther and Vladusich 408-409). The current literature review is dedicated to the mechanisms for speech production and their implications in the field of neuroscience.

Speech Production Mechanisms: Present State and Future Implications

In their research, Guenther, and Vladusich investigate DIVA — a “computational model” that creates a quantitative framework, which makes it possible to analyze the functions of different brain areas that take part in the production and acquisition of speech (408). The article pays special attention to the speech sound map of DIVA (409). According to Guenther and Vladusich, this map is engaged at the initial stage of sound production (409). When the neurons of the speech sound map are initiated, the “motor commands” appear in the primary motor cortex (Guenther and Vladusich 409). These commands are enacted through two control subsystems: feedforward and feedback (Guenther and Vladusich 409). The authors note that the speech sound map performs three crucial functions: promoting the discrete sounds obtainment, delineating the probable signals of sensory feedback, and helping to extract the motor programs necessary for the formation of speech sounds (Guenther and Vladusich 418).

Simonyan et al. dedicate their article to progressive experiments in the sphere of speech motor regulation (11440). Simonyan et al. analyze the cohesion of the motor cortex in their study (11440). The authors emphasize scholars’ rising interest in motor control (11440), remarking that electrocorticography (ECoG) studies are a productive way to enhance scholars’ comprehension of the specific organization of the ventral sensorimotor cortex (vSMC) for the speech motor government (11444). In addition, Simonyan et al. note that modern functional magnetic resonance imaging studies (fMRI) and diffusion-weighted tractography are productive in establishing the extensive design of neural network pertaining to the speech sensorimotor command (11444). The authors discuss the following innovative technologies in “invasive human brain mapping” as a changeable cortical perturbation, concurrent field mapping, and the discovery of the electrical stimulation tract (11442). Simonyan et al. remark that these advances in research make it possible to investigate the arrangement of vSMC and the neural mechanisms taking part in speech production control (11442).

Various Types of Feedback from Speech Movements (Somatosensory and Auditory)

Lametti et al. investigate somatosensory (SF) and auditory (AF) feedback occurring in the course of speech production (9351). The authors note that these two types of feedback are closely related (9351). Lametti et al. consider SF the core method of controlling the precision of speech production by the cortical speech areas (9351). To perform the experiment, Lametti et al. resorted to auditory and somatosensory perturbations, kinematic and acoustical analysis, and quantifying adaptation (9352-9353). The scholars conclude that SF and AF can change individually or in combination when a person repeats an uncomplicated speech utterance (9356). Lametti et al. also draw attention to the negative interaction in the compensation measurement for the two perturbations (9356). The negative correlation, according to Lametti et al., appears as an outcome of a favored reliance expressed by the participants for either SF or AF in the course of speech production (9356).

In his research, Perkell also analyzes auditory feedback as a means of acquisition and maintenance of auditory objectives in the progress of feedforward and feedback control processes (382). The author argues that speakers who have sharper sensory discrimination acquire more definite goal areas and, as a result, generate the sound of speech in more divergent ways (382). Perkell pays special attention to the role that phonemic goals play in speech production (385). According to the author, the phonemic goal areas are delineated by the general characteristics of a person’s perception and production structures (385). Perkell emphasizes the importance of cooperation between the somatosensory and auditory spheres (386). He notes that auditory objectives prevail at the early learning stage, where they form the feedforward commands (386). The somatosensory goals, according to Perkell, become an element of the control mechanism when the feedforward motor commands are achieved (386).

Speech Disorders and Ways of Managing Them

Apart from analyzing the investigation into new ways the brain functions, researchers pay attention to speech difficulties that may occur due to brain damage. In her article, Adank summarizes the results of two activation likelihood estimation (ALE) studies aimed at finding out the reasons for difficulties in speech production and comprehension (42). The author notes the spheres most frequently activated when there are complications in intelligible speech: the bilateral anterior insulae, the right and the left posterior middle temporal gyrus (MTG), and anterior supplementary motor area (pre-SMA) (49).

The article by Basilakos et al. is dedicated to two language disorders: acquired apraxia of speech (AOS) and aphasia (1561). The authors aim to investigate whether the errors in the production of speech in the two disorders are connected with particular types of brain damage (1561). Basilakis et al.’s research allowed the authors to establish the sectors in somatosensory and cortical motor spheres that are capable of predicting the AOS failures (1564). The authors conclude that the employment of structural neuroimaging may enhance the differential diagnosis of the failures in speech production caused by AOS and aphasia respectively (1565).

Conclusion

Neuroscience is making critical achievements in the investigation of speech production mechanisms in the human brain. A variety of scholarly articles and experiments have been dedicated to the analysis of the functions of different parts of the brain in the speech process. Researchers have been studying various models of speech regulation and acquisition. Another research trend is devoted to feedforward and feedback types of speech movements. Also, a considerable amount of scholarly work is dedicated to the analysis of speech disorders and ways of dealing with them.

Because of comprehensive research regarding the role of the brain in the production of speech, present and future scholars may focus on the particular areas and identify the gaps in the studies to put forward new research aims.

Works Cited

Adank, Patti. “The Neural Bases of Difficult Speech Comprehension and Speech Production: Two Activation Likelihood Estimation (ALE) Meta-Analyses.” Brain and Language, vol. 122, no. 1, 2012, pp. 42-54.

Basilakos, Alexandra, et al. “Patterns of Poststroke Brain Damage That Predict Speech Production Errors in Apraxia of Speech and Aphasia Dissociate.” Stroke, vol. 46, no. 6, 2015, pp. 1561-1566.

Guenther, Frank H., and Tony Vladusich. “A Neural Theory of Speech Acquisition and Production.” Journal of Neurolinguistics, vol. 25, no. 5, 2012, pp. 408-422.

Lametti, Daniel R., et al. “Sensory Preference in Speech Production Revealed by Simultaneous Alteration of Auditory and Somatosensory Feedback.” The Journal of Neuroscience, vol. 32, no. 27, 2012, pp. 9351-9358.

Perkell, Joseph S. “Movement Goals and Feedback and Feedforward Control Mechanisms in Speech Production.” Journal of Neurolinguistics, vol. 25, no. 5, 2012, pp. 382-407.

Simonyan, Kristina, et al. “New Developments in Understanding the Complexity of Human Speech Production.” The Journal of Neuroscience, vol. 36, no. 45, 2016, pp. 11440-11448.

Tatham. Mark, and Katherine Morton. Speech Production and Perception. Palgrave MacMillan, 2006.

Brain death was defined in 1968 as a condition characterized by three major features. A brain-dead individual is not capable of responding and receiving stimuli from his surrounding environment. In addition, a brain-dead person does not have the capacity to perform spontaneous actions, including breathing and beating of the heart (Boissy et al., 2008). Lastly, a brain-dead person does not show any reflex actions. The cessation of the pumping of the heart can be technically determined through the employment of an electroencephalogram (EEG) which detects any electrical activity that originates from the brain of an individual. Thirty years after the establishment of the definition of brain death, additional descriptors were incorporated into the concept. The concepts of brain death and cerebral definitions of death influence the decision-making process in medical and social-ethical areas because the main concept that still remains unclear now is not only the definition of death but also the definition of life. If death is defined as the loss of breathing and the loss of the heartbeat, which are both based on the proper functioning of the brain, then it can then be subjectively derived that life is the presence of breathing and the existence of a heartbeat.

The controversy over brain death was triggered by the inception of innovative medical equipment such as the life support system that is commonly attached to a patient that is in a comatose condition. The life support system provides a means for a comatose patient to continue breathing through the use of a respirator. In addition, the development of defibrillators serves as a tool in introducing electrical impulses to the chest of an individual who is experiencing either a loss of heartbeat or an improper rhythm of the heart. The controversy over brain death started when the classical definition of brain death involves the loss of capacity in breathing and maintaining a heartbeat. However, with the development of new medical equipment that could revive and maintain the essential processes of the human body, the definition of brain death became vague. The concept of brain death is also further complicated by the questions raised by healthcare providers wherein these companies would like to define the limits of their coverage, especially when the person that is covered by healthcare is already considered brain-dead.

Blank (2001) has explained that the current concepts and definitions of death can be integrated using technological innovations in brain research and imaging technologies. The author explained in his paper the difference in the definition of death several decades ago, which actually only involves the stoppage of breathing through the lungs and beating of an individual’s heart. However, due to the advances in medical equipment and technologies, it is now possible to keep individual breathing through the use of a ventilator. Hence the classical definition of death has now evolved to the cessation of the functioning of the brain amidst the prolongation of heart and lung function due to the employment of medical equipment that has the capability of replacing the functions of the heart and lungs. The related issues of human life, as well as the benefits and risks of disconnecting an individual from a life support system, should also be discussed in terms of its impact on the values and conscience of society. The implications of brain death are also discussed in connection to the continuation of coverage of health insurance amidst indications that a patient in a vegetative state is actually brain dead. The employment of methods for euthanasia in terms of cremation and the administration of lethal injections for the preparation of a patient for death may generate conflicts and moral issues with respect to the psychological load that the immediate family members are carrying. The development of new medical equipment has strongly influenced the evolution of the definition of death because the employment of this medical equipment has provided ways in substituting specific mechanisms of the body that are essential in establishing the life of an individual.

Monaghan (2002) has comprehensively explained how to handle a patient with a non-functioning brain. Brain death is perceived in different ways in different countries. For example, in Japan, the United States, and Germany, the definition of brain death is swayed to another dimension because the concept of organ donation and transplant is added to the complex issue. Hence it is much more difficult to deal with death in this modern age because not only is brain death implicated in the scenario, but also whether it is already possible to collect specific organs of a brain-dead patient in order to use this in organ transplant procedures. The employment of life support systems has thus initiated more issues to be debated and discussed, instead of just achieving one goal, and that is to sustain the life of the patient. It is also interesting to know that different countries accepted the concept of brain death during different decades of the 20^th century, with Japan debating over the issue for almost 30 years and Sweden contemplating on the topic for almost 20 years. Thus culture plays a major role in the concept of brain death; hence the values of Asian societies may not always be the same as that of Western societies. The principles and mechanisms of organ transplantation are also different from one country to another; hence it is important for all of us to be aware of these differences.

The proposed cerebral definition of death is radically different because the concept of brain death is now defined based on two important functions of the brain. Each critical brain function has been described to be situated in separate areas of the brain; hence the proposed cerebral definition of death requires that both critical regions of the brain should be confirmed to be nonfunctional before an individual can be validly claimed as brain-dead. The two critical and essential regions of the brain are the cerebral cortex and the brain stem. The cerebral cortex is responsible for maintaining the consciousness of an individual. This brain region is also accountable for the capacity of an individual to perform any mental functions such as thinking and reading. The brain stem, on the other hand, is responsible for providing an individual the dexterity and adroitness in performing motor activities and movement. Research has claimed that the brain stem remains functional even when the cerebral cortex has been determined to be nonfunctional. This observation can also be observed a few minutes after the death of an individual, wherein some individuals tend to slightly jerk by themselves even when they have also lost their heartbeat and have already stopped breathing. It should be remembered that breathing and heart activities are maintained by the cerebral cortex, which is only one of the two critical regions of the brain. The classical definition of brain death states that the nonfunctioning of at least one of the two regions of the brain is enough to ascertain the death of an individual. However, controversy has now arisen because of the emergence of new medical equipment that can replace the lungs and the heart of an individual, thus substituting for the cerebral cortex. There has been a debate with regards to this new medical setting because even if the individual continues to breathe and keep a pulse, the individual remains unconscious and unresponsive, and these features of normal mental functioning can not be substituted by any medical equipment and at the same time, can not be reversed to the original normal condition.

References

Blank RH (2001). Technology and death policy: Redefining death. Mortality, 6(2):191-202.

Boissy AR, Ford PJ, Edgell RC, Furlan AJ (2008). Ethics consultations in stroke and neurological disease: A 7-year retrospective review. Neurocritical Care, 9(3):394-9.

McMahan J (1998). Brain death, cortical death and persistent vegetative state. In: Kuhse H and Singer P (eds.), A companion to bioethics (pp. 250–260). Oxford: Blackwell.

Monaghan P (2002). The unsettled question of brain death. Chronicles of Higher Education, 48(24):A14-A18.

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.

A fixed, firm and false belief that people, in particular the close relatives or the spouse of a person, have been replaced by doubles or imposters is termed as a Capgras delusion (Capgras, 19232 cited in Edelstyn,1999 ). This term was first coined in the early 19^th century when Capgras and Reboul reported the case of a patient named Mme M. who presented with psychotic symptoms and a unique kind of delusion of substitution that imposters had replaced her family members (Capgras, 19232 cited in Sinkman, 2008). Ever since then, several cases presenting with similar features have been reported and extensive research in this arena has been conducted, in order to explore the underlying mechanisms and pathology of this rare and interesting presentation. Lately, Capgras delusion has been grouped under the umbrella term of Delusional Misidentification Syndromes (DMS) along with 11 other similar disorders (Joseph, 1986 cited in Edelstyn, 1999). Although Capgras delusions are considered to be a rare disorder, studies have shown that they are observed in up to 4% of patients with psychosis (Frazer and Roberts, 1994 & Kirov et al., 1994 cited in Edelstyn, 1999) and approximately 30% of patients suffering from Alzheimer’s diseases have also been shown to demonstrate this symptom (Ballard et al., 1995 cited in Edelstyn, 1999).

Symptoms associated with Capgras Delusions

Studies have shown that Capgras delusions do not occur in isolation. Rather, they are accompanied by a variety of other features which constitute the Capgras syndrome. A review of the case reports of patients with this disorder has shown that the most consistent presenting feature this disorder is the monothematic delusion of substitution of ones close relatives by a look alike or an imposter. Other abnormalities of thought which can coexist with Capgras delusions include multiple person misidentifications (Oyebode and Sargeant, 1996, Edelstyn et al., 1998a), presence of misidentification of inanimate objects (Young et al., 1994, Silva and Leong, 1995a & Edelstyn et al., 1998a), delusions of multiplicity of self, delusions of persecutions (Sinkman, 2008) and perception of morphological changes in the body (Sinkman, 2008). There have also been reports of associated hostility and aggression towards the imposters in some cases (O’Reilly, 1987)

Various presentations of Capgras delusions have been reported in literature. Lucchelli and Spinnler, reported a case of a gentleman who presented with capgras delusions in association with dementia, obsessive-compulsive symptoms and motor disturbances. During the course of the disease the patient also demonstrated the presence of the “phantom boarder” phenomenon and the “mirror sign phenomenon. In the quest to identify the etiology and pathophysiological basis of Capgras delusions, the patient was subjected to several diagnostic tests and assessment modalities. Neuroradiological investigations (CT and MRI) revealed nonspecific findings of mild enlargement of the ventricles and the brain sulci while the EEG showed bilaterally slowed cortical activity in the anterior regions. General cognitive assessment was also performed which showed global cognitive impairment which deteriorated over time. In order to assess the face and person recognition, the face assessment tools developed by Faglioni et al. and other commonly used assessment tools were employed. A modified version of Ekman and Friesen’s test was used to assess perceptual deficits with unknown faces and impairment in emotion recognition but the results were unremarkable. The patient was also tested for recognition of famous faces and autobiographical faces. The pertinent findings of these tests were that although the ability to identify faces and names were spared, patient’s familiarity judgment was significantly impaired (Lucchelli and Spinnler, 2007).

Similarly, Sinkman (2008) reported three cases of Capgras Syndrome amongst known cases of severe Schizophrenia, who in addition to Capgras delusions, demonstrated a variety of misidentity delusions including Fregoli delusions in one instance and profound identity diffusion and loss of ego boundaries. However, this theory has the limitation that it does not explain the multiplicity of delusions observed in several cases of Capgras delusions. Other similar cases of Capgras delusions have been reported by Young et al. (1993) while Hayman has reported two cases of Capgras delusions associated with pre-existing cerebral dysfunction (Hayman, 1977).

Moreover, patients with Capgras delusions have also been tested for the autonomic skin conductance response in the presence of familiar faces. Amongst normal peoples, there is an increase in this autonomic response when a familiar face is encountered. In contrast, various studies have shown that patients with Capgras delusions demonstrate a lack of differential skin conductance response in the presence of close relatives. In addition, their overall skin conduction responses are also diminished (Ellis 1997 & Hirstien, 1997).

The role of diagnostic investigations in Capgras delusions is limited. Cerceo et al. suggested that although brain imaging may play a role in revealing underlying abnormalities, the cost of imaging outweighs the benefits since results of brain imaging do not contribute significantly towards the treatment. No evidence has been found which supports use of CT to diagnose Capgras syndrome while some evidence supports use of brain MRI, however, but more research is required (Cerceo, 2006)

Areas of the brain associated with Capgras Delusions

Originally, this disorder was thought to be exclusively psychiatric in origin, since Capgras delusions have been found to be associated with certain psychiatric conditions including Schizophrenia especially the paranoid type, schizoaffective disorders and less commonly, affective disorders (Edelstyn, 1999). Over time, it has been increasingly observed that this delusion can also result from organic brain lesions that have resulted either due to trauma, hypoxia, toxins, neoplasms (Lucchelli and Spinnler, 2007).

Capgras delusions are also commonly observed in association with degenerative diseases such as Alzheimer ’s disease and dementia. Josephs (2007) in their study of 38 patients with Capgras delusions reported that 81% of the patients had concomitant neurodegenerative disease, the most common one being Lewy body disease. There was also coexistence of Visual hallucinations. They also reported that in the absence of neurodegenerative disease, the age of onset of Capgras delusion is lower and it is most commonly associated with psychiatric illnesses, substance abuse and cerebrovascular diseases (Josephs, 2007).

Cerebral dysfunction leading to Capgras delusions can involve different regions of the brain. The role of right hemispheric involvement in misidentification syndromes was first proposed by Wienstien et al. and later supported by many studies (Breen, 2001). Although some contradictory views exist, which have propose limbic system dysfunction and basal ganglia lesions as the primary factors involved in the development of capgras delusions while cortical involvement plays a secondary role in modifying the characteristics, content and form of the delusions(Cummings, 1997). A review of the identified cases of Capgras from 1968- 1999, revealed that most patients with this disorder demonstrate global atrophy and lateralization of the lesions to the right hemisphere is more common as compared to the left (Breen, 2001). The areas of the brain most commonly seen involved are the frontal and the temporal lobes (Edelstyn, 1999). Keeping in view the transient nature of the disorder as observed in certain cases, it has been postulated that instead of there being structural involvement of the brain tissues, dopaminergic or serotonergic overactivity and reduced activity if platelet monoamine oxidase, contribute towards the development of this condition (Daniel et al., 1987, Canagasabey and Katona, 1991 & Lehmann, 1988 cited in Edelstyn, 1999)

Models of Face Recognition and Capgras Delusions

The ability to recognize different faces, acknowledge, remember and recall them is a complex phenomenon. The first model which was thought to be involved in facial recognition was the modal model, which proposed that the transfer of information related to the identity of a particular face is takes place via a single route which is parallel to those for dealing with the expression of the face and picture codes of the image. (Hay and Young, 1982). Later on, Bauers introduced a two route model of face recognition according to which facial recognition occurs via two distinct and independent routes. Facial recognition can be classified as either covert (which occurs via the ventral route, which involves the longitudinal fasciculus between visual cortex and limbic system) or overt (which involves the use of the dorsal route which passes from visual cortex via the superior temporal sulcus, inferior parietal lobe and cingulate gyrus and finally gets relayed to the parts of the limbic system, especially the amygdala) (Bauer, 1984). Based on this two route model of face recognition, Ellis and Young hypothesized that patients experiencing Capgras delusions have intact covert facial recognition but a compromised dorsal route which leads to the impairment of affect associated with recognition of familiar faces and subsequent loss of familiarity (Ellis, 1990). This loss of proven by demonstration of decreased skin conduction response in the presence of familiar faces as shown by different studies (Ellis 1997 & Hirstien, 1997).

The cognitive model of face recognition introduced by Bruce and Young (1986) proposed that a single sequential pathway is involved in face recognition. When a person encounters a new face, its details are encoded using viewer centered descriptions in the Face Recognitiom Units (FRU) of the persons memory. There are separate descriptions about the person which are integrated in the memory regarding the person’s expression, speech, and demographic information (e.g.: sex, age, and race). When the same face is encountered again, the information stored in the FRU is retrieved and leads to activation of the Person Identity Node (PIN), which resembles a database containing stored semantic and biographical information for previously encountered persons. The PINs can be accessed by other input modalities including voice and gait. When all this information is retrieved, then the person’s name is recalled via a different pathway. This constitutes the cognitive model of face recognition (Young, 1986)

More recently, however, Breen e al. have postulated a modified dual route model of face recognition according to which delusions occur when the Face Recognition Units (FRUs) are intact but there is a disturbance in either in the connection between FRUs and the affective response or in the generation of an affective response itself (Breen, 2001).Similarly Luchelli and Spinnler have hypothesized that Capgras delusions constitute a cross modal disorder of familiar people processing in which the Personal Identity Nodes (PINs), involved in storing knowledge about familiar faces, are functioning normally and thus an encounter with a familiar face leads to addressing of the proper the Exemplar Semantics archives and normal Gestalt guessing. There is, however, a disturbance at the level of analytical checking which leads to the origination of a conflict and subsequent delusions (Lucchelli and Spinnler, 2007). Other proposed theories derived from observation of patients with frontal lobe damage include, the inability to form successive modification and integration to pre-existing memories if a person, on repetitive encounters with the same person which leads to formation of new memories of the same person every time that person is encountered (Hirstien, 1997) and a possible conflict between previous memories and actual experience (Alexander, 1979).

Various psychodynamic models have also been proposed to explain these delusions including the theories of Oedipal and Electra complex and the concept of personality disintegration leading to a regression to elementary modes of functioning (Edelstyn, 1999). Another psychodynamic model postulates that Capgras delusion are mechanisms adopted by patients in order deal with ambivalent feelings and unresolved conflicts towards a close relative. Since unacceptable feelings such as anger, hatred, etc are inadequately repressed and since they cannot be directed at the close relative, the patient justifies those feelings by targeting them towards the perceived imposter (Sinkman, 2008).

Sinkman (20008) has proposed a different model for the multitude of delusions seen in schizophrenics with Capgras syndrome. According to this model, the underlying disorder contributing to all delusions is the breakdown of the patient’s ability to evoke and employ the preformed mental representations of both self and other people which leads to mental fragmentation and disorganization. This is overcome and rationalized in the form of delusions (Sinkman, 2008). It has also been postulated that the loss of ego boundaries observed in schizophrenia is due to frontal lobe damage.

Treatment for Capgras Delusions

As discussed above Capgras delusions can occur with a variety of psychiatric and neurological disorders. The treatment of Capgras delusions therefore cannot occur in isolation. It has to be integrated with the treatment of the underlying disorder. Also, till date no definitive treatment for Capgras delusions has been discovered, the treatment options available help to ameliorate the symptoms but do not completely treat the condition. Two modalities can be used for the treatment of Capgras delusions viz. psychological treatment and pharmacological treatment. As with most other delusions, psychological treatment is the mainstay of therapy for Capgras Delusions. It has to be kept in mind that therapy has to be tailored on individual basis keeping in mind the patient’s delusions and the spectrum of thought disorders that a patient might be present concomitantly.

The role of Cognitive Behavioral Therapy in treating delusions in schizophrenic patients is well established. Keeping in view the cognitive and psychoanalytical models proposed in the pathophysiology of Capgras delusions, Cognitive Behavioral Therapy (CBT) has been proposed to be an effective treatment for Capgras delusions. Over time, various theories have been proposed to explain the causative factors for delusions. Maher (1992) defined delusions as ‘hypotheses generated by normal reasoning processes to explain abnormal sensory input’ (Maher, 1992 cited in Brakoulias, 2008:149). On the other hand, Coltheart et al. have proposed a two model theory for delusions which states that an abberent experience leads to an abnormal belief evaluation. For this abnormal belef to progress to a delusion, concomitant impairment in perception or affect must be present (Coltheart, 2005 cited in Brakoulias, 2008:149). Extensive research has shown three main reasoning anomalies in delusional patients viz. ‘anomalies on probabilistic reasoning, theory of mind (ToM) tasks, and attributional biases’ as summarized by Brakoulias (Brakoulias, 2008:149). Keeping this background in view, CBT is proposed to be effective in the treatment of Capgras delusion. It is thought to work by modifying the patients perceptions regarding different condition and thus reduces delusional conviction. However, the limitation of CBT is that it does not modify the general reasoning styles of the person which are thought to be the main anomaly in delusional patients (Brakoulias, 2008:149). An important factor while considering psychotherapy, in particular CBT, for delusions is persistence in establishing a therapeutic empathy. The physician/therapist should refrain from both validating the person’s delusions, even inadvertently, and overtly confronting the system. In addition, cognitive techniques that include reality testing and reframing can be used (Capgras Delusion/Syndrome, 2003).

In the pharmacological treatment, no single drug has been shown to be particularly effective in treating Capgras delusions. Trials of an antipsychotics or SSRI at starting doses can be given (Cerceo, 2006). Drugs which are indicated in the treatment of Capgras delusions include Pimozide, Risperidone and Clozapine (Capgras Delusion/Syndrome, 2003). Moreover, no difference in response to atypical antipsychotics has been shown by comparative studies done between patients with schizophrenia and comcomitant Capgras symptoms and those with schizophrenia alone. However, treatment of Capggras delusions leads to concomitant improvement in the symptoms of schizophrenia (Cerceo, 2006).

References

Alexander MP, Stuss D, Benson DF (1979) Capgras syndrome: a reduplicative phenomenon. Neurology 29:334–339

Brakoulias, V., Langdon, R., Sloss, G., Coltheart, M., Meares, R. & Harris, A. (2008). Delusions and reasoning: A study involving cognitive behavioural therapy. Cognitive Neuropsychiatry, 13, 148-165.Ballard, C. G., Saad, K., Patel, A., Gahir, M., Solis, M., Coope, B. and Wilcock, G. (1995) The prevalence and phenomenology of psychotic symptoms in dementia su€erers. Int. J. Geriatr. Psychiat. 10, 477±485.

Bauer, R.M. (1984) Autonomic recognition of names and faces in prosopagnosia: a neuropsychological application of the guilty knowledge test. Neuropsychologia 22, 457–469

Breen N, Caine D, Coltheart M (2000) Models of face recognition and delusional misidentification: a critical review. Cogn Neuropsychol 17:55–71

Breen N, Caine D, Coltheart M (2001) Mirrored-self misidentification: two cases of focal onset dementia. Neurocase 7:239–254

Hayman Martin A. and Abrams R, Capgras’ Syndrome and Cerebral Dysfunction (1977)Bril. J. Psychiatry, 130:68-71

Canagasabey, B. and Katona, C. L. E. (1991) Capgras syndrome in association with lithium toxicity. Brit. J. Psychiat. 159, 879±881.

Capgras, J. and Reboul-Lachaux, J. (1923) Illusions des sosies dans un de lire systeÂmatise chronique. Bull. Soc.Clin. MeÂd. Ment. ii, 6±16.

Capgras Delusion/Syndrome. (2003). Web.

Cerceo E., Dunn J. and Newmark T. (2006) When your brother becomes a ‘stranger’.The Journal of Family Practice Vol. 5, No. 6

Coltheart, M. (2005). Delusional belief. Australian Journal of Psychology, 57, 72_76.

Cummings JL. Neuropsychiatric manifestations of right hemisphere lesions (1997). Brain and Language, 52:22-37.

Daniel, D. G., Swallows, A. andWol€, F. (1987) Capgras delusion and seizures in association with therapeutic dosage. Southern Med. J. 80, 1577±1579.

Davies M, Coltheart M, Langdon R, Breen N (2002) Monothematic delusions: towards a two-factor account. Philos Psychiatr Psychol 8:133–158.

Edelstyn, N. M. J., Oyebode, F., Booker, E. and Humphreys, G. W. (1998a) Facial processing and the delusional misidentifications syndromes. Cog. Neuro- psychiat.

Edelstyn N.M.J. and Oyebode F. (1999) A review of the phenomenology and cognitive neuropsychological origins of the Capgras Syndrome. International Journal of Geriatric Psychiatry 14, 48±59

Ellis HD, Young AW (1990) Accounting for delusional misidentifications. Br J Psychiatry 157:239–248

Ellis, H.D., Young, A.W., Quayle, A.H., & de Pauw, K.W. (1997). Reduced autonomic responses to faces in Capgras delusion. Proceedings of the Royal Society, London(B), 264, 1085–1092.

Ellis HD, Young AW, Quayle AH, dePauw KW (1997) Reduced autonomic responses to faces in Capgras delusion. Proc R Soc Lond 264:1085–1092

Ellis HD, Lewis MB (2001) Capgras delusion: a window on face recognition. Trends Cogn Sci 5:149–156

Young A., Reid I.,Wright S. and Hella Well (1993) Face- Processing Impairments and the Capgras Delusion. British Journal of Psychiatry, 162, 695-698

Frazer, S. J. and Roberts, J. M. (1994) Three cases of Capgras’ syndrome. Brit. J. Psychiat. 164, 557±559.

Hay, D.C. and Young, A.W. (1982) The human face. In Normality and Pathology in Cognitive Functions (Ellis, A.W. ed.), pp. 173–202, Academic Press.

Hirstein W, Ramachandran VS (1997) Capgras syndrome: a novel probe for understanding the neural representation of the identity and familiarity of persons. Proc R Soc Lond 264:437–444

Joseph, A. B. (1986) Focal nervous system abnormalities in patients with misidenti®cation syndromes. Biblio. Psychiat. 164, 68±79.

Josephs A. (2007) Gapgras syndrome and its relationship to neurodegenerative disease. Archives of Neurology. 64(12), 1762-1766.

Kirov, G., Jones, P. and Lewis, S. W. (1994) Prevalence of delusional misidenti®cation syndromes. Psychopath. 27, 148±149.

Langdon R, Coltheart M (2000) The cognitive neuropsychology of delusions. Mind Lang 15:184–218

Lucchelli F. and Spinnler H. (2007) The case of lost Wilma: a clinical report of Capgras delusion. Neurological Sciences 28:188–195

Maher, B. A. (1992). Delusions: Contemporary aetiological hypotheses. Psychiatric Annals, 22, 260-268.

Oyebode, F. and Sargeant, R. (1996) Delusional mis- identi®cation syndromes: A descriptive study. Psycho- path. 29, 209±214.

Silva, J. A., Tekell, J. L., Leong, G. B. and Bowden, C. L. (1995a) Delusional misidenti®cation of the self associ- ated with nondominant cerebral palsy. J. Clin. Psychiat. 56(4), 171.

Sinkman A. (2008) The syndrome of Capgras. Psychiatry 21(4): 371-378.

Staton RD, Brumback RA, Wilson H (1982) Reduplicative paramnesia: a disconnection syndrome of memory. Cortex 18:23–36

Typical psychosis and brief reactive psychoses. In Comprehensive Textbook of Psychiatry (H. I. Kaplan, A. M. Freedman and B. J. Sadock, Eds). William & Wilkins, Baltimore.

Weinstien EA., Kahn RI., Sugarman R. (1952) Phenomenon of Reduplcaiton. Archives of Neurology and Psychiatry, 67:808-814.

Young, A. W., Hellawell, D., Wright, S. and Ellis, H. D. (1994) Reduplication of visual stimuli. Behav. Neurol. 7, 135±142.

Introduction

Holistic care delivery models encourage practitioners to focus on their patients’ mental health and wellbeing. Social workers can introduce brain exercises to different beneficiaries depending on several factors, such as age, gender, and existing medical conditions. This paper describes several ways for encouraging older adults to experiment with new activities that are outside their comfort zones.

Brain Exercises for Older Adults

Since brain activities should be unfamiliar and outside a person’s comfort zone, practitioners can consider various strategies to encourage the elderly to experiment with new exercises. The first one is collaborating with them whenever targeting unfamiliar events. For example, clinicians and family members can be involved when playing untried computer games and puzzles (Jackson et al., 2015). The second strategy is ensuring that such beneficiaries are relocated to new environments or locations. This kind of change will encourage them to engage in complex brain activities. All participants should remain supportive throughout the period. Caregivers can consider the power of guidance and effective communication when encouraging older adults to try activities that are outside their comfort zones.

Another evidence-based approach is identifying specific exercises that are unfamiliar but enjoyable. A good example is the introduction of painting activities or paper collages (Barnes, 2015). Finally, those involved can establish desirable environments that promote teamwork. When two or more elderly citizens cooperate in a given brain action, chances are high that they will record positive results.

Conclusion

The above discussion has explained why there is a need for family members and caregivers to allow older adults to work in groups and relocate to new environments when engaging in brain exercises. Such activities should be enjoyable and informed by each individual’s expectations. Effective support and communication will make the entire process successful.

References

Barnes, J. N. (2015). Exercise, cognitive function, and aging. Advances in Physiology Education, 39(2), 55-62. Web.

Jackson, P. A., Pialoux, V., Corbett, D., Drogos, L., Erickson, K. I., Eskes, G. A., & Poulin, M. J. (2015). Promoting brain health through exercise and diet in older adults: A physiological perspective. The Journal of Physiology, 594(16), 4485-4498. Web.