Regret Can Influence Decision-Making Behavior In Both Mice and Children

    Regret is an emotion that every person and animal has to go through and it is typically associated with sadness. Regret can be defined as a feeling of sorrow in response to losing something, disappointment, and doing something that you wish you did not. In addition, regret can be seen in influencing the behaviors of both animals and humans on a regular basis in order to make sure that both humans and animals in their daily lives make decisions that will be best suited to their needs.  

  

     Furthermore, as seen in a variety of studies, regret can be studied within the different study populations which can range from humans to animals through different learning tasks. One specific example of a study that studies regret is a research article done by Brian Sweis et al. called “Mice Learn to Avoid Regret”. In the article, the team performs an experiment that is broken up into stages with food-restricted mice in order to fully delve into the idea of whether animals can learn from regret and use that same feeling of regret to avoid making the same mistake in the future. In the study, the researchers trained the food-restricted mice over a series of days to go to each of the four feeding sites and decide whether they wanted to wait for the pellet or if they wanted to leave and go to the next feeding site and the mice were given a total of one hour to get the pellets. In addition, within each of the four feeding sites, each site had a unique spatial cue that alerted the mice to how long they would have to wait in the wait zone until they were given their pellet and each feeding site offered different flavored pellets such as banana, grape, chocolate and plain. After conducting their experiment, the researchers found that after mice felt regret due to accepting an offer that was too expensive for their limited time budget, the mice learned to take offers that were relatively inexpensive.

  

     In relation to the experiment conducted by Brian Sweis et al, another experiment that studied regret in children was a study done by Teresa McCormack et al. called “Experiencing Regret About A Choice Helps Children to Learn Delay Gratification”. In the study, the team conducted two experiments, where one of the experiment was to test whether the children felt regret and they decided to change their behavior from day 1 to day 2 and the second experiment was to test if intelligence and preferences for the treats were causing the children to change their behavior. In the first experiment, there were about 78 children who were 6 to 7 years old, and the researchers had two boxes in front of the children where one of the boxes contained two pieces of candy and the other box had four pieces of candy. The researchers told the children that they could either wait for the short delay which was about 30 seconds or they could wait for the long delay which was about 600 seconds but the researchers did not tell the children what the prizes were inside each box. At the end of the study, they found that children who decided to wait only for the short delay reported feeling sadder after seeing the bigger prize in the long delay box compared to the children who waited for the long delay reported feeling happier after seeing the small prize in the short delay box. Furthermore, when the researchers conducted the same study the following day, the children who decided to wait for the short delay box decided to wait for the long delay box due to experiencing regret on day one. In addition, the children who decided to wait for the long delay box on day one also decided to wait for the long delay on day two. Additionally, the results found in experiment two were the same as the results seen in experiment one where the children who experienced regret by choosing the short delay box on day one decided to change their decision and choose the long delay box on day two.  

    

    Overall, in both of these studies, it can be seen that regret plays a huge role in how one behaves and it can also influence animals as well as children to change their behavior in order to optimize their chances of getting either the pellet or the treats.

 References: 

McCormack, Teresa et al. (2019). Experiencing Regret About A Choice Helps Children Learn to Delay Gratification. Journal of Experimental Child Psychology. https://doi.org/10.1016/j.jecp.2018.11.005 

Sweis. M., Brian et al. (2018). Mice Learn to Avoid Regret. PLoS Biology 16 (6): e2005853. https://doi.org/10.1371/journal.pbio.2005853 

Cardiorespiratory: The Increase in Alzheimer’s Disease

 Cardiorespiratory: The Increase in Alzheimer’s Disease

 

Alzheimer’s is a type of dementia that affects memory, thinking and behavior. Symptoms start off small but eventually grow severe enough to interfere with daily tasks1. It accounts for approximately 60-80% of dementia cases1. It is known to have no current cure, but treatments for symptoms are available and so is research. Many changes begin in the brain before the first signs of memory loss. The brain is composed of more than 100 million nerve cells known as neurons in which each nerve cell connects to others composing communication networks. Each neuron has specialty ranging from thinking to remembering or even to our senses. To keep our brain functioning it requires oxygen which is crucial for many organs such as the heart and lungs.

With that being said, cardiorespiratory plays a large role in Alzheimer’s due to the increase in brain signal during an fMRI. An fMRI (functional magnetic resonance imaging) is a technique for measuring and mapping brain activity that is noninvasive and safe2. This approach was taken because the clinical diagnosis of Alzheimer’s is based on many extensive examinations neurologically and neuropsychological. Realistically, those whom have Alzheimer’s always wonder why and it is not part of the aging process. The diagnostic portion of Alzheimer’s is not fully accurate which then in turn causes unsatisfactory outcomes. With so much unsatisfied patients, in 2018, the National Institute of Aging and Alzheimer’s Association of the United States proposed a shift of the diagnostic paradigm away from clinical symptoms or post mortem findings, towards criteria based on some combination of biomarkers in living persons3. 

This experiment was performed to better understand how the brain works and mainly to understand the physiological brain pulsations are altered in Alzheimer’s disease then causing increased variability in the brain BOLD signal. During this study, the possibility is tested by measuring the variability of blood oxygen level-dependent signal in individuals from three data sets, 80 Alzheimer Disease patients with 90 controls2. Many objectives came about including; 1. Analyzing previous findings of BOLD brain signal variability in Alzheimer’s disease, 2. Developing noninvasive MR- biomarker for Alzheimer’s disease without contrast agents and lastly 3. Study the physiological origin of the signal alteration3. The data sets determined the rSDBOLD  for Alzheimer’s detection. The increase of BOLD signal variability was shown to be related to physiological mechanisms driving the network of vessels that clear waste from the Central Nervous system clearance than an indicator of VLF fluctuations of the BOLD signal arising from perturbed neuronal activity3. The BOLD signal indicated underlying vascular and respiratory factors in the brain which then shown elevated variability in the BOLD signal. 

The brain signal showed that Alzheimer’s patients increased rSDbold in clusters of the voxels distributed around the basal ganglia and the white matter around the lateral ventricles. With the findings it was then compared to the patterns of neurodegeneration with regional rSDBold changes3. Within brain signal variability it is then associated with declined Mini Mental State Examination (MMSE). The comparison brought to the attention that BOLD signal variability was associated with lower cognitive function scores during the exam. The comparison was then the voxel-wise group-level difference between the AD and control groups in rSD maps of full band MREGBOLD and in SD maps of cardiac (SDcard), respiratory (SDresp), and VLF (SDVLF) band data. This showed that rSD maps of the full band and SD maps of cardiac and respiratory parts of the signal overlapped spatially. Moreover, the SDcard showed widespread increases extending beyond the corresponding findings for full band rSDBOLD in the AD group. The SDresp maps indicated group differences in bilateral frontal and temporoparietal areas, left sensorimotor cortex, medial frontal and temporal gyri.3

While using three different Fmri data sets it has come to the conclusion that brain signal variability increase in Alzheimer’s disease. 

References

1. Alzheimer’s Assosiation, 2021 

2. https://cfmriweb.ucsd.edu/Research/whatisfmri.html

3. Tuovinen, T., Kananen, J., Rajna, Z. et al. The variability of functional MRI brain signal increases in Alzheimer's disease at cardiorespiratory frequencies. Sci Rep 10, 21559 (2020). https://doi.org/10.1038/s41598-020-77984-1

link: moreblogpost.com/cardiorespiratory-alzheimers-disease

Sunk-Cost Fallacy and Covid-19: Making Career decisions

The Covid-19 pandemic has led to substantial changes in the livelihoods of people around the world and here in the United States. The pandemic has significantly impacted the economy, and beyond the threat of unemployment workers and students are facing significant setbacks in their careers. However, the demographics of the American work force include several different generations often at corresponding milestones in their careers. While the pandemic has negative consequences for many people regardless of the time they have invested in their career, it may be easier for younger workers to make the career adjustments necessary to adapt, because what they may lack in job security, they gain in sunk-cost analysis. 

 In the paper ‘Sensitivity to “sunk costs’ in mice, rats, and humans” by Sweis et. al the researchers examine the way different species are sensitive to “sunk costs,” or how long they will continue an activity after it has been deemed unproductive because of their irrecoverable costs (Sweis et. al 2018). The researchers found similar sensitivities across the three species they examined, and also suggested that these sensitivities to specific temporal sunk costs arise from a vulnerability distinct from the deliberation process in these species (Sweis et. al).

 In a 2019 study by HL Miller Jr and V. Tait titled “Loss Aversion as a Potential Factor in the Sunk-Cost Fallacy” the researchers the unique and understudied connection between the sunk-cost fallacy and loss aversion. They found a negative relationship between loss aversion and the sink-cost fallacy across variables of money, time and effort (Tait 2019). 

 The Sunk-cost fallacy is a well-documented phenomenon, but has been significantly understudied in the way it operates in the decisions people make about their careers. While research has identified that the time spent in a specific job or career would be a large factor, as well as the cost of education and job training, there have not been any studies that show quantifiable data. The pandemic has opened this field and raised many more questions about how this works and how we can address it. 

 References:

 Tait V, Miller HL Jr. Loss Aversion as a Potential Factor in the Sunk-Cost Fallacy. International Journal of Psychological Research. 2019 Jul-Dec;12(2):8-16. DOI: 10.21500/20112084.3951. 

 Sweis BM, Abram SV, Schmidt BJ, Seeland KD, MacDonald AW 3rd, Thomas MJ, Redish AD. Sensitivity to "sunk costs" in mice, rats, and humans. Science. 2018 Jul 13;361(6398):178-181. doi: 10.1126/science.aar8644. PMID: 30002252; PMCID: PMC6377599.

Individual Differences in Functional Connectivity Using fMRI

The Human Connectome Project (HCP) launched back in July of 2009 in an effort to provide a comprehensive map of both functional and structural connectivity across the human brain. The project has made great strides since then, gathering publicly accessible fMRI data to leverage with other research. This fMRI data has been averaged across individuals and used to map out areas associated with certain cognitive tasks. In the article, “Brain scans from one person build reliable map of brain activity,” from Spectrum News, it is discussed how researchers are becoming aware of the potential inaccuracy of this method. It was found that in creating a brain model using a single individual’s fMRI data across multiple cognitive tasks was much more similar to other individuals using the same method compared to averaged brain maps of multiple individuals doing the same task. The data from 40 participants in the HCP were used in this comparison of brain mapping methods. It was hypothesized that these variations in functional connectivity causing inaccurate averaged models may be due to individuals using different strategies and possibly brain circuits for the same cognitive task (Katsnelson 2019)

As our understanding of fMRI scans applicability progresses, it is hopeful that we can make progress toward constructing personalized connectomes specific to individuals. This variation between individuals’ structural and functional connectivity may shine light onto the phenotypes of certain adult and pediatric psychiatric and neurological disorders. In the study, “Function in the Human Connectome: Task-FMRI and Individual Differences in Behavior,” researchers analyze task-fMRI scans to outline a set of functions central to understanding the interaction between human behavior and brain connectivity. Looking at motor, sensory, cognitive, and emotional processes using the methods above with tfMRI, they hope to find individual differences in neurobiological substrates (Barch et al.)

Caterina Gratton from Northwestern University has been working toward this task as well. Using data from her Midnight Scan Club leveraged with HCP data, she is hoping to characterize individual variation in brain networks and the relationship between individual differences and function. Specifically she is looking to understand possible structural or functional differences in individuals with control deficits such as schizophrenia and major depressive disorder. If research progresses in this way, we will be one step further in understanding and treating psychiatric and neurological disorders such as these. 

Barch, Deanna M., et al. “Function in the Human Connectome: Task-FMRI and Individual Differences in Behavior.” NeuroImage, vol. 80, 2013, pp. 169–189., doi:10.1016/j.neuroimage.2013.05.033. 

Katsnelson, Alla. “Brain Scans from One Person Build Reliable Map of Brain Activity.” Spectrum, 24 Oct. 2019, www.spectrumnews.org/news/brain-scans-from-one-person-build-reliable-map-of-brain-activity/. 

Mouse Models are Making Notable Contributions toward Neuroscientific Alzheimer's Research

Alzheimer’s Disease (AD) is a common form of dementia that currently affects 6.2 million Americans aged 65 and older. Data analysis predicts that this number may grow to 12.7 million by 2050. Alzheimer’s is a progressive disease that often begins with mild memory loss, but those with late-stage Alzheimer’s may lose the ability to hold a full conversation and respond to their environment. With this disease currently being the 6th leading cause of death in the United States, it is imperative that efforts to find a cure or new treatments continue to advance. Many neuroscientists have began using mouse models in their research studies, and many recent publications have shown that they have been useful in advancing Alzheimer’s-related research.

    The use of neutrophic factors such as brain-derived neurotrophic factor, BDNF, has been an area of interest for AD therapy becuase the factors play a role in neural survival and neuroplasticity, meaning they could possibly fight the neurodegenerative effects of AD. A major setback of this therapy is that researchers are struggling to find a way to effectively deliver these neurotrophic factors to the brain, but one study described in the article, “Conditional BDNF Delivery from Astrocytes Rescues Memory Deficits, Spine Density, and Synaptic Properties in the 5xFAD Mouse Model of Alzheimer Disease,” has identified a new potential regulator of neurotrophic therapy using a mouse model. Researchers of this study note that astrocytes in the nervous system express low levels of BDNF, and given that astrocyte activity increases as AD progresses, these cells could possibly be genetically modified to promote neurotrophin production. When testing this possibility, they found that when BDNF was overexpressed in AD transgenic mice, they showed a significant improvement in learning and memory deficits as a result of restoration of dendritic spine density. The study also emphasizes that genetically engineered astrocytes can be advantageous for AD treatment because their delivery of BDNF is conditionally and locally administered, meaning that this treatment can be customized specifically for AD treatment. 

Other neuroscientific research has utilized mouse models to identify specific genes that make individuals more susceptible to developing Alzheimer's Disease.  In the article, "Systems Genetics Identifies Hp1bp3 as a Novel Modulator of Cognitive Aging," researchers identify a gene heterochromatin protein 1 binding protein 3, Hp1bp3, as a regulator of cognitive aging, one of the leading risk factors for age-related AD. They found that after genetically knocking down this protein in mice, they presented with lower memory status in comparison to control mice. Their results were further emphasized after western blotting of post mortem hippocampal tissue of cognitively impaired humans. This study suggests that developing a treatment to restore Hp1bp3 levels may be an effective treatment for AD patients. 

These two studies mentioned represent only a fragment of the research efforts being made toward finding a cure and/or treatment for Alzheimer's Disease. As the research studying AD in mouse models continues, it is hopeful that the number of people affected by this disease will finally begin to decrease.  

References

de Pins, B., Cifuentes-Díaz, C., Thamila Farah, A., López-Molina, L., Montalban, E., Sancho-Balsells, A., López, A., Ginés, S., Delgado-García, J. M., Alberch, J., Gruart, A., Girault, J.-A., & Giralt, A. (2019). Conditional BDNF delivery from astrocytes rescues memory deficits, spine density and synaptic properties in the 5xFAD mouse model of Alzheimer disease. The Journal of Neuroscience, 2441–2458. https://doi.org/10.1523/jneurosci.2121-18.2019

Neuner, S. M., Garfinkel, B. P., Wilmott, L. A., Ignatowska-Jankowska, B. M., Citri, A., Orly, J., Lu, L., Overall, R. W., Mulligan, M. K., Kempermann, G., Williams, R. W., O’Connell, K. M. S., & Kaczorowski, C. C. (2016). Systems genetics identifies Hp1bp3 as a novel modulator of cognitive aging. Neurobiology of Aging, 46, 58–67. https://doi.org/10.1016/j.neurobiolaging.2016.06.008

What is Alzheimer’s? (2021). Alzheimer’s Disease and Dementia. https://www.alz.org/alzheimers-dementia/what-is-alzheimers

Significance in the Application of Cognitive States Towards Psychiatric Treatments

Cognitive states like moral cognition and regret are less widely recognized as heavy elements to consider in terms of psychiatric treatment. When we think of psychiatric diagnoses, it is more common to identify the surface-level, physical symptoms of the presenting illness. Discussions presented by Sweis and colleagues and Harwood-Gross acknowledge cognitive states and how they may also play a significant role in understanding clinical diagnoses. Specifically, root processing of the mind in decision making and individual moral cognition can be skewed in different contexts that are important fundamental aspects when considering psychiatric treatment and understanding for drug addiction and post-traumatic stress disorder (PTSD). These articles expand on the field of neuroscience in clinical applications to better understand lifestyle aspects and behaviors for the improvement of psychiatric treatments.

In “Mice learn to avoid regret”, Sweis and colleagues found evidence of decision-making strategies to avoid regret in mice. Regret is characterized as “the subjective experience of recognizing that one has made a mistake and that a better alternative could have been selected (Sweis, Thomas, Redish, 2018). They observed this behavior through a Restaurant Row experiment where mice are trained in a resource-scarce environment. They must learn to maximize their food reward under wait time conditions that may take away from the total amount of time they have in one session to get food for the day. Results revealed that regret in these mice was characterized by the change in behavior shown the following day, with improved and stabilized food reward outcome.

Sweis and colleagues uncovered a very interesting perspective that has never been uncovered before in mice. Furthermore, Sweis discussed these applications towards the understanding of decision-making processes in drug addiction studies with mice.

From Sweis’s talk at the Neuroscience Seminar, he explained how he trained mice similar to the Restaurant Row experiment but observed the behavior in mice with a history of drug addiction. From that study, he saw that mice with a history of drug use showed a harder time rationalizing their decision making compared to the control mice. Their addiction was characterized into impulsive decision making, which may have led to less beneficial outcomes and rewards compared to the controls. In understanding the affects of drug addiction on decision-making strategies that may lead to regret, these findings can contribute to novel, and improved psychiatric treatments in humans.

In relation to this study, I became curious about other possible applications from understanding other unique cognitive states, like regret, that can have similar significance in clinical settings. Likewise, Harwood-Gross’s Scientific American article mimics this relationship between cognitive states and psychiatric applications in “Treating “Moral” Injuries”, where disruption to moral cognition and beliefs should be recognized more in war veterans that cannot fully access traditional PTSD treatments.

Moral injury is described by Litz and colleagues as the “psychic fallout of ‘morally injurious events, such as perpetrating, failing to prevent, or bearing witness to acts that transgress [one’s own] deeply held moral beliefs and expectations” (Litz et. al., 2009). This is characterized with avoidance of shame triggers and rises from events like violence and killing of others and even innocent citizens that come with participation in war (Harwood-Gross, 2020).

Not only does Harwood-Gross introduce us to moral injuries and how it compares to PTSD, but the discussion emphasizes the understanding of treating moral injury in a clinical setting. This cognitive state is not understood to its entirety. Similar to mental health illnesses, the internal conflicts that moral injury presents itself in, creates real symptoms that need to be acknowledged. Because moral injury has distinguishing characteristics from individuals with PTSD, a lot of veterans cannot benefit from traditional treatments. More studies surrounding moral injury can greatly contribute to larger applications in a clinical setting.

Harwood-Gross and Sweis and colleagues take on a similar perspective between these cognitive states because something less clinically acknowledged into diagnoses, like regret, can be very significant and possibly fundamental to serious mental illnesses and their treatments, like addiction, that needs to be acknowledged and better understood for improved treatment options. What is uncovered by Harwood-Gross, is that the decisions around our morals can be a significant element to our mental health diagnosis, like PTSD. Research surrounding cognitive states like regret and morals can play a bigger role in psychiatric health than we think, and it is important to acknowledge all lifestyle aspects for the betterment of psychiatric treatments.

 

 

References

 

Harwood-Gross, A. (2020, March). Treating “Moral” Injuries. Scientific American. https://www.scientificamerican.com/article/treating-moral-injuries/

 

Litz et. al. (2009). Moral injury and moral repair in war veterans: A preliminary model and intervention strategy. Clinical Psychology Review 29(8). https://doi.org/10.1016/j.cpr.2009.07.003

 

Sweis, B. M., Thomas, M. J., Redish, A. D. (2018) Mice learn to avoid regret. PLoS Biol 16(6):

e2005853. https://doi.org/10.1371/journal.pbio.2005853

 

Neuroimaging Technique for Anxiety Disorders

        Alzheimer’s and Parkinson’s diseases have been identified as the most common neurodegenerative diseases in the U.S (NIH, 2019). Since such neurodegenerative diseases are thus far, incurable, it is crucial to understand possible underlying causes, treatments, or prevention techniques. In doing so, researchers have been able to utilize neuroimaging techniques like functional magnetic resonance imaging (fMRI) in measuring the cognitive and social processes reflected in an affected individual’s neural system. Thus, allowing for further advancements in understanding and treating such diseases as neuroimaging techniques have been able to provide more personalized treatment plans in prescribing medications or forms of therapies. This has been illustrated through several studies interested in how the human brain of an affected individual may change throughout their life span. 

        Caterina Gratton et al. were able to analyze the relationship between neuroimaging techniques and how the human brain systemically functions under neurodegenerative diseases through fMRI scans. This study examined how the cortical FC measures correlated to specific behaviors depending on the neural disease of interest in patients 21 years and older. Gratton et al. proposed an fMRI scan will allow for a more precise identification of the behaviors and neural activity correlated to the individual’s disease, which would then facilitate optimal treatment to be identified. Since the data collected from fMRI scans are not representative solely of the activation levels in particular regions of the brain but is a compilation of the “...complex experiential processes being implemented in different neural systems” (Paulus, 2008), fMRI scans are expected to account for a more holistic diagnosis of neurodegenerative diseases. However, with such complex information being given, it is crucial for researchers to be able to isolate only the relevant factors contributing to the diseases. Gratton et al. proposed individualized applications of fMRI scans would be able to identify the relevant activity as this would be noted as being stable across different trait-dependent contexts. Activity that is not constantly expressed in the scans would be associated with being unstable, such as the individual’s mood.

        Through a series of similar studies regarding neuroimaging for neurological diseases, Gratton and her team were able to conclude precise fMRI scans detect high levels of stable activity and regional sensitivity because of individuals’ differences (Gratton, 2019). Therefore, the neural system differences are not significantly related to the unstable activity or phenotypic variables in the tasks individuals performed. Defining Individual-Specific Functional Neuroanatomy for Precision Psychiatry illustrates how fMRI scans are beneficial in measuring the traits indicative of both neurodegenerative and neurological diseases such that diagnosis and treatments are better catered to the individual. Gratton also concludes fMRI scans to be beneficial for severely depressed individuals; with the data collected one may conclude fMRI scans facilitate more unique treatment depending on the individual case rather than a more general form of treatment for such diseases. 

        Similar to Gratton et al.’s results, Kathrin Holzschneider concluded fMRI scans being useful in monitoring and treating different anxiety disorders such as social anxiety, PTSD, and OCD, “...neuroimaging techniques offer the opportunity to monitor structural and functional neuronal changes as a result of psychotherapy that occur along with changes in patients’ perception and behavior...refine and optimize psychotherapeutic strategies” (Holzschneider, 2011). In gathering fMRI data, participants were exposed to stimuli that would induce their anxiety and revealed an increased level of activity in the fear network of the brain. Under most anxiety disorders, the amygdala is expected to be hyperactive and the frontal brain region is expected to be less active in regulation (Holzschneider, 2011). With the data collected from fMRI scans, researchers were able to provide treatment catered to the type of anxiety disorder. Those exhibiting neural patterns of social anxiety were exposed to mindfulness-based stress reduction therapy (MBSR) and prescribed specific serotonin reuptake inhibitors. In doing so, researchers observed “...oxytocin attenuated the heightened amygdala activation in response to fearful face. Hence it appears to modulate the exaggerated amygdala activity…”(Holzschnieder, 2011). Similarly, those with anxiety-inducing phobias were treated with exposure therapy and determined the fear network of the brain to be more regulated than without the therapy. By doing this, researchers are able to better decide what the optimal treatment may be; whether prescriptions are needed based on the individual differences in neural activity of the affected individual.

        This study illustrates fMRI scans being able to predict whether the affected individual responds to the form of treatment or intervention used across different anxiety disorders. Holzschneider references another study conducted by Bryan et al in which researchers “...demonstrated in PTSD patients that a smaller volume of the rostral anterior cingulate cortex predicted nonresponse to CBT. The authors assume that exposure-based CBT is...a process that requires anterior cingulate cortical structures. Thus, larger volumes of the anterior cingulate would lead to better control...better responding to CBT” (Holzschnieder, 2011). Through fMRI scans, researchers are able to identify pretreatment neural behaviors indicative of what the optimal treatment per case may be. 

        Neuroimaging techniques enable researchers and healthcare providers to closely analyze the neural system, identifying optimal treatment, and the individual’s treatment journey. Such techniques have demonstrated successful results in improving the affected individual’s brain activity whether it may be neurodegenerative, or anxiety and fear-based. In doing so, current treatment can advance to be catered specifically to the individual and create more effective options. This research brings rise to combining forms of treatment like cognitive behavioral therapy with certain medication for optimal results. Utilizing such techniques allows for a more thorough understanding of such neurological diseases and the possibility of preventing such diseases from worsening. 

References:

1. Gratton, C., Kraus, B. T., Greene, D. J., Gordon, E. M., Laumann, T. O., Nelson, S. M.,      Dosenbach, N., & Petersen, S. E. (2019). Defining Individual-Specific Functional    Neuroanatomy for Precision Psychiatry. Biological psychiatry, 88(1), 28–39.     https://doi.org/10.1016/j.biopsych.2019.10.026

2. Holzschneider, K., & Mulert, C. (2011). Neuroimaging in anxiety disorders. Dialogues in    clinical neuroscience, 13(4), 453–461.   https://doi.org/10.31887/DCNS.2011.13.4/kholzschneider

3. Neurodegenerative Diseases. (2019). Retrieved 4 March 2021, from   https://www.niehs.nih.gov/research/supported/health/neurodegenerative/index.cfm

4. Paulus M. P. (2008). The role of neuroimaging for the diagnosis and treatment of anxiety  disorders. Depression and anxiety, 25(4), 348–356. https://doi.org/10.1002/da.20499

The Evolution of fMRI Research in Neuropsychiatry

   In order to understand the insight that fMRI-based research has provided in the world of neuropsychiatry, it is important to understand how the use of fMRI has shifted over the years into a more insightful means of collecting data. Functional magnetic resonance imaging (fMRI) measures changes in blood flow in the brain that is correlated with brain activity. fMRI creates a map of the brain's activity by illuminating which parts of the brain are stimulated during the scan. Although fMRI can illuminate a great deal of information about neuroscience anatomy and neural networks, experimental research has pointed out many of its flaws. These flaws in the application of fMRI in neuropsychiatry have fueled the research of many with the desire to improve fMRI in a way that can be of great benefit in the clinical and diagnostic processes of psychiatry. 

    Introduced to the work of Caterina Gratton and her colleagues in our Neuroscience Seminar I was blown away by their work but wanted to know more about the research that preceded their work and lead them to their investigation. In a paper titled “Defining Individual Specific Functional Neuroanatomy for Precision Psychiatry”, they tackled how the fMRI process could be improved to provide further insight into neuropsychiatric research. The most common limitations their team of research was focused on was the mistranslation due to inter-subject heterogeneity and a low standard of reliability in individual fMRI techniques. Caterina along with her team was surely not the first to discover these limitations. This thought motivated my further interest in other research that uncovered similar shortcomings in the use of fMRI.

    While Gratton and colleagues focused on a solution to correct some of the limitations of the fMRI process through the use of pfMRI (precision fMRI), other research was focused on why the traditional approach to fMRI was not sufficient in clinical applications for neuropsychiatry. Discover magazine's online article titled “What can fMRI Tell Us About Mental Illness?” uses the research article “Addressing reverse inference in psychotic neuro imaging: Meta-analyses of task related brain activation in common mental disorders” to explore how much fMRI can truly tell us about psychiatric disorders. The article mentions that the overall conclusion of the research carried out by Emma Sprooten and team is that: 

“the abnormalities and network-regions we can observe with fMRI reflect general conditions that facilitate the emergence and persistence of symptoms but are insufficient for explaining symptomatic variability across disorders”.  

    Data generated through their study was collected through the combination of 537 studies with an enormous number of participants, over 20,000 with a focus on five mental illness, schizophrenia, bipolar disease, major depressive disorder, anxiety disorders, and (OCD) obsessive-compulsive disorder. What was fascinating was that upon cross-examination there seemed to be very few observable differences between the patients across each category of illness. Sprooten’s et al. research is further backed up by the claims made for the importance of pfMRI research. One of their biggest arguments in the work carried out by Gratton et al. was the inability of traditional fMRI to accurately and independently identify patient-specific data that could be used toward their diagnosis and treatment, the same issue that is identified by Sprooten and her team. 

    When trying to tackle the complex material presented in neuroscience literature it is important to understand the basis of the research being conducted and how the researchers were lead down the path to the generation of their hypothesis. Exploring the work of Sprooten et al. illuminates more evidently the motive behind the work of Gratton et al. and further supports their path of development and application of precision fMRI.

Alzheimer’s Disease in Marginalized Communities:

Racism’s Direct Relationship With Neurodegeneration 

 

Image Credit: NewScientist.com 

Alzheimer’s disease is a rapidly growing topic in medical research, with funding surges noted in the past couple of years.  What has perhaps remained the most elusive component of Alzheimer’s is the heritability of the disease, and the genetic variation by which the disease manifests itself.  A study conducted by Kaczorowski et. al. estimated the heritable component to comprise between 50% and 80% of the disease’s progression in humans (Kaczorowski et. al. 2019).  This study from 2019, delving into the genetics of Alzheimer’s, was wildly successful in producing novel AD mouse models to help study and, with further research, better identify the diverse genetic mechanisms that contribute to the disease.

Despite the major advancements made in research and clinical assessments of the disease, there remains a disconnect between Alzheimer’s and the racial implications of the disease.  In 2018, the CDC noted that Alzheimer’s rates differ across racial groups, with African Americans possessing the highest prevalence at 13.8% in the age cohort of 65 years and older.  Though several studies have similarly attested that the genetic mechanisms of Alzheimer’s also differ by race, not much progress has been made in determining exactly why this is.  The NCBI published a systematic review in 2019 with aims to increase participation and retention rates for communities who are often underrepresented in research to help expand knowledge bases of diseases, like Alzheimer’s, in marginalized communities (Gilmore-Bykovskyi et. al. 2019).  However, more answers may be found in why these populations, specifically Black communities, are underrepresented in research in the first place. 

It is no secret that the history of racial discrimination is extensive, with lasting effects that permeate the modern era.  But the conversation of systematic racism must be extended thoroughly to disciplines of research and medicine if adequate treatment options are to be actualized with equity in the population.  Additionally, researchers must recognize the history their discipline has in perpetuating racist ideology and gruesomely racist practices, such as in the infamous Tuskegee Experiment, and how such perceptions may be internalized by marginalized communities.  Alzheimer's is just one example of a disease with racial bias; perhaps, the several diseases exhibiting differential expressions across racial dimensions may be linked.  A study published by the NCBI in 2017 performed an analysis of telomere lengths in African American males in relationship to internalized racism and low socioeconomic status, concluding that the telomere lengths are undeniably shortened by the unique stresses of racist treatment and racialized distributions of wealth that disparately affect the Black community (Chae et. al. 2017).  Though shorter telomere lengths induce a variety of problems, two of the most notable are diseases implicated in chronic aging, namely Alzheimer’s and related dementias, and earlier mortality overall.  Alzheimer’s and racism cannot be separated from one another, and future AD research must implement integrated approaches in order to best treat populations who are suffering from the disease the most brutally and at the highest rates.

Such findings as those published by the NCBI may seem to carry an overwhelmingly afro-pessimist message, but addressing that there is an issue to begin with is a step towards progress.  Yet, the truth still remains: medicine and society operate in an intertwined relationship and, if proper precautions are not taken, they can reinforce the systemic racism that lurks in their foundations.  To ensure AD research does not fall into such a racialized feedback loop, further explorations must not only take a deeper look at the differences in expression of Alzheimer’s across racial demographics, but also aim to elaborate on how racism directly plays a role in Alzheimer’s and other degenerative diseases through telomere shortening.  Additionally, research must promptly begin investigating whether such changes to telomere length at the cellular level are genetically or epigenetically translatable to offspring.  Such findings will drastically impact how potential treatments, for Alzheimer’s or other racially disparate diseases, may impact marginalized communities.  But, perhaps the largest takeaway from AD research as it stands is that racism, though a social construct, has created very real and lasting medical problems at the biological level that are far more complex than the more commonly discussed issues with diversity in and accessibility to medical care.  Until racism, in blatant, systemic, or hidden forms, is retributed and eradicated entirely at a sociological level, research must be extra vigilant of how racism interacts both directly and indirectly with the phenomena of study at hand, again, Alzheimer’s being just one example among many diseases with racial implications.  

To provide a more afro-futuristic outlook of Alzheimer’s research, the work of Kaczorowski deserves another glance.  It is important to note that her team was able to create AD mouse models with the genetic diversity that more closely resembles human expression of the disease than ever before, which is a huge feat not only for AD research at large, but may also allow for a better understanding of divergences in cases of Alzheimer’s across racial demographics, with the potential for precision medicine in treatments down the line (Kaczorowski et. al. 2019).  Another study in 2019, as reported by NPR, performed a cross-cultural study in which lower levels of tau protein were linked to a variant APOE4 gene in African Americans; however, this genetic variant, when expressed in white individuals, can triple the risk for Alzheimer’s development (Hamilton 2019).  The feats of this research, therefore, is two fold.  First, it was able to successfully engage a racially diverse group of participants, which is often lacking in AD research.  Second, it was able to uncover a potential biological source of deviance in racial expressions of Alzheimer’s which, when cross-analyzed with the works of the NCBI, Kaczorowski, and research endeavors in the future, may be able to uncover the genetic mechanisms underlying Alzheimer’s in racially distinct groups.  Kaczorowski, in presenting her work in AD research, described the tau proteins and amyloid plaques, the hallmark biomarkers for Alzheimer’s, as a match, and the degree to which the fire spreads, the degree to which the disease is manifested, is robustly determined by interactions between genes.  Perhaps further research endeavors should embody the same metaphor.  Alzheimer’s is just the match, just the starting point for research, and other disciplines, such as the sociology of race, need to be addressed in order to extinguish the fire.

References: 

CDC. (2018). U.S. burden of Alzheimer’s disease, related dementias to double by 2060. Retrieved from https://www.cdc.gov/media/releases/2018/p0920-alzheimers-burden-double-2060.html

Chae, D. H., Nuru-Jeter, A. M., Adler, N. E., Brody, G. H., Lin, J., Blackburn, E. H., & Epel, E. S. (2014). Discrimination, racial bias, and telomere length in African-American men. American journal of preventive medicine, 46(2), 103–111. https://doi.org/10.1016/j.amepre.2013.10.020

Gilmore-Bykovskyi, A. L., Jin, Y., Gleason, C., Flowers-Benton, S., Block, L. M., Dilworth-Anderson, P., Barnes, L. L., Shah, M. N., & Zuelsdorff, M. (2019). Recruitment and retention of underrepresented populations in Alzheimer's disease research: A systematic review. Alzheimer's & dementia (New York, N. Y.), 5, 751–770. https://doi.org/10.1016/j.trci.2019.09.018

Hamilton, J. (2019, January 7). Alzheimer's Disease May Develop Differently In African-Americans, Study Suggests. In NPR. Retrieved from https://www.npr.org/sections/health-shots/2019/01/07/682036486/study-suggests-alzheimer-s-disease-may-work-differently-in-african-americans

Kaczorowski, K. M. (2021, February 16). Systems genetics of normal cognitive aging and Alzheimer’s disease. Paper presented at the meeting of Loyola University Chicago Neuroscience Department.

Neuner, S. M., Heuer, S. E., Huentelman, M. J., O'Connell, K. M., & Kaczorowski, C. C. (2019, February 6). Harnessing genetic complexity to enhance translatability of Alzheimer's disease mouse models: A path towards precision medicine. Neuron, 399-411. doi:https://doi.org/10.1016/j.neuron.2018.11.040