Bowdoin Science Journal

Psychology and Neuroscience

The Kleptomania Connection between Serotonin and Stealing

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Although many people steal in response to economic hardship, either perceived or actual, some individuals only steal to satisfy a powerful urge. These individuals may have an impulse control disorder known as kleptomania. People with kleptomania experience a sense of relief from stealing, so they steal to get rid of their anxiety (Talih, 2011). The prevalence of kleptomania in the U.S. is estimated to be 6 people per 1000, which is equivalent to more than 1.5 million kleptomaniacs in the U.S. population ​​(Aboujaoude et al., 2004).

What exactly causes this impulse to steal? Kleptomania has a range of biological, psychological, and sociological risk factors. One of the main biological factors has to do with neurotransmitters, such as serotonin (Sulthana, 2015). Serotonin plays an important role in our bodies, contributing to emotions and judgment, and low serotonin levels have been linked to impulsive and aggressive behaviors (Williams, 2002). The serotonin system is also thought to be involved in “increased cognitive impulsivity,” as has been observed in individuals with a higher number of kleptomania symptoms (Ascher & Levounis, 2014).

Throughout the nervous system, serotonin transporters (SERT) take up serotonin that is released from neurons (Rudnick, 2007). These transporters can also be found on blood platelets and take up serotonin from the blood plasma (Mercado & Kilic, 2010). We can study these particular transporters to better understand the levels of serotonin in one’s blood and how that relates to their level of impulsiveness.

A 2010 study looked into the relationship between the platelet serotonin transporter, impulsivity, and gender. They found that while women were, in general, more impulsive than men, there was only a positive correlation between the number of transporters and impulsivity in men. This means that higher amounts of platelet serotonin transporters and lower levels of serotonin are related to more impulsivity in men, but not in women. It was also found that higher amounts of SERT transporters were linked to more “aggressive” behaviors. The authors came to the conclusion that, even though women were found to display more impulsivity than men, serotonin plays a larger role in impulsivity with men than it does with women (Marazziti et al., 2010).

Understanding the relationship between serotonin and impulsivity with kleptomania has helped pioneer specific treatments, including Selective Serotonin Reuptake Inhibitors (SSRIs). Impulsivity is linked to low levels of serotonin, so SSRIs fix this by limiting the reuptake of serotonin through the blockage of serotonin transporters, leading to the buildup of serotonin in the synapse (Sulthana, 2015). There is no cure for kleptomania, but SSRIs help to control the impulse to steal. 

Overall, kleptomania is a secretive disorder, for which many people don’t seek help due to the legal system and the social stigma around theft. Thus, very little is known about what causes kleptomania, but trying to understand it through its link with neurotransmitters has uncovered potential causes and helped develop treatments. 

 

References

Ascher, M. S., & Levounis, P. (Eds.). (2014). The behavioral addictions. American Psychiatric Publishing.

Aboujaoude, E., Gamel, N., & Koran, L. M. (2004a). Overview of kleptomania and phenomenological description of 40 patients. Primary Care Companion to The Journal of Clinical Psychiatry, 6(6), 244–247. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC535651/ 

Marazziti, D., Baroni, S., Masala, I., Golia, F., Consoli, G., Massimetti, G., Picchetti, M., Dell’Osso, M. C., Giannaccini, G., Betti, L., Lucacchini, A., & Ciapparelli, A. (2010). Impulsivity, gender, and the platelet serotonin transporter in healthy subjects. Neuropsychiatric Disease and Treatment, 6, 9–15. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2951061/ 

Mercado, C. P., & Kilic, F. (2010). Molecular mechanisms of SERT in platelets: regulation of plasma serotonin levels. Molecular interventions, 10(4), 231–241. https://doi.org/10.1124/mi.10.4.6 

Rudnick, G. (2007). Sert, serotonin transporter. In S. J. Enna & D. B. Bylund (Eds.), XPharm: The Comprehensive Pharmacology Reference (pp. 1–6). Elsevier. https://doi.org/10.1016/B978-008055232-3.60442-8

Sulthana, N., Singh, M., & Vijaya, K. (2015). Kleptomania-the Compulsion to Steal. Am. J. Pharm. Tech. Res, 5(3). 

Talih, F. R. (2011b). Kleptomania and potential exacerbating factors. Innovations in Clinical Neuroscience, 8(10), 35–39. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3225132/ 

Williams, Julie. Pyromania, Kleptomania, and Other Impulse-Control Disorders. Enslow, 2002. 

Mimicking the Human Brain: The Role of Heterogeneity in Artificial Intelligence

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Picture this: you’re in the passenger seat of a car, weaving through an urban metropolis – say New York City. As expected, you see plenty of people: those who are rushed, lingering, tourists, locals, old, young, et cetera. But let’s zoom in: take just about any one of those individuals in the city, and you will find 86 billion nerve cells, or neurons, in their brain carrying them through daily life. For comparison, this means that the number of neurons in the human brain is about ten thousand times the number of residents in New York City.

But let’s zoom in even further: each one of those 86 billion neurons in the brain is ever-so-slightly different from one another. For example, while some neurons work extremely quickly in making decisions that guide basic processes in the brain, others work more slowly, basing their decisions off surrounding neurons’ activity. This difference in decision-making time among our neurons is called heterogeneity. Previously, researchers were unsure of heterogeneity’s importance in our lives, but its existence was certain. This is just one example of the almost incomprehensible detail of the brain that makes human thinking so complex, and even difficult to fully understand for modern researchers.

Now, let’s zoom in again, but this time not on the person’s brain. Instead, let’s zoom into the cell phone this individual might have in their pocket or their hand. While a cell phone does not function exactly the same as the human brain, aspects of the device are certainly modeled after human thinking. Virtual assistants, like Siri or Cortana, for instance, compose responses to general inquiries that resemble human interaction.

This type of highly advanced digital experience is the result of artificial intelligence. Since the 1940s, elements of artificial intelligence have been modeled after features of the human brain,  fashioned as a neural network composed of nodes, some serving as inputs and others as outputs. The nodes are comparable to brain cells, and they communicate with each other through a series of algorithms to produce outputs. However, in these technological brain models, every node is typically modeled in the same way in terms of the time they take to respond to a given situation (Science Daily 2021). This is quite unlike the human brain, where heterogeneity ensures that each neuron responds to stimuli at different speeds. But does this even matter? Do intricate qualities of the brain like heterogeneity really make a difference in our thinking, or in digital functioning if incorporated into artificial intelligence?

The short answer is yes, at least in the case of heterogeneity. Researchers have recently investigated how heterogeneity influences an artificial neural network’s performance on visual and auditory information classification tasks. In the study, each neural network had a different “time constant,” which is how long the cells take to respond to a situation given the responses of nearby cells. In essence, the researchers varied the heterogeneity of the artificial neural networks. The results were astonishing: once heterogeneity was introduced, the artificial neural networks completed the tasks more efficiently and accurately. The strongest result revealed a 15-20% improvement on auditory tasks as soon as heterogeneity was introduced to the artificial neural network (Perez-Nieves et al. 2021).

This result indicates that heterogeneity helps us think systematically, improve our task performance, and learn in changing conditions (Perez-Nieves et al. 2021). So perhaps it would be advantageous to incorporate heterogeneity into standard artificial intelligence models. With this change, technology’s way of “thinking” will come one step closer to functioning like a human brain, adopting a similar level of complexity and intricacy.

So, why does this matter? If parts of artificial intelligence are modeled closer and closer to how the human brain works, real-world benefits abound, and we’re talking on a level grander than virtual assistants. One prominent example is in head and neck cancer prognosis. Clinical predictors of head and neck cancer prognosis include factors like age, pathological findings, HPV status, and tobacco and alcohol consumption (Chinnery et al. 2020). With a multitude of factors at play, physicians spend excessive amounts of time analyzing head and neck cancer patients’ lifestyles in order to deduce an accurate prognosis. Alternatively, artificial intelligence could be used to model this complex web of factors for these cancer patients, and physicians’ time could be spent on other endeavors.

This type of clinical application is still far from implementation, but remains in sight for modern researchers. As the brain is further explored and understood, more and more of the elements that comprise advanced human thinking can be incorporated into technology. Now, put yourself in the shoes of our New York City passerby: how would you feel if the small cell phone in your pocket was just as intelligent and efficient as the 86 billion neurons in your head? How about if that cell phone solved problems like you do and thought like you think, in essence serving as a smaller version of your own brain? It is almost unfathomable! Yet, by harnessing heterogeneity, researchers have come one step closer toward realizing this goal.

 

References

Chinnery, T., Arifin, A., Tay, K. Y., Leung, A., Nichols, A. C., Palma, D. A., Mattonen, S. A., & Lang, P. (2020). Utilizing artificial intelligence for head and neck cancer outcomes prediction from imaging. Canadian Association of Radiologists Journal, 72(1), 73–85. https://doi.org/10.1177/0846537120942134.

Perez-Nieves, N., Leung, V. C. H., Dragotti, P. L., & Goodman, D. F. M. (2021). Neural heterogeneity promotes robust learning. Nature Communications, 12(1). https://doi.org/10.1038/s41467-021-26022-3. 

ScienceDaily. (2021, October 6). Brain cell differences could be key to learning in humans and ai. ScienceDaily. Retrieved February 27, 2022, from https://www.sciencedaily.com/releases/2021/10/211006112626.htm.  

When We Fall Asleep

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When our bodies shut down at night, our brains transport us into strange, convoluted alternate realities. Dreams range from the mundane to the fantastical, from classrooms to castles. Despite the sheer absurdity of many dreams, they always feel real. But where do dreams even come from, and why do we have them?

Definitions of a dream range from the generous “subjective experience during sleep” to the more specific “immersive spatiotemporal hallucination” (Siclari et al., 2020). Taken either way, dreams are characterized by increased blood flow to regions of the brain called the amygdala, hippocampus, and anterior cingulate cortex. The significant role that these regions play in regulating our emotions may explain the intense emotional aspect of many dreams (Schwartz & Maquet, 2002). 

There are five stages of sleep. The first four stages are collectively categorized as non-Rapid Eye Movement, or NREM, sleep. Accordingly, the fifth stage is referred to as the REM stage. During REM sleep, our eyeballs flit back and forth underneath our eyelids, our muscles are paralyzed to prevent self-injury from dream enactment, and our brain activity reflects that of wakefulness (Siclari et al., 2020). Although dreams are more common in REM sleep, recent research has shown that shorter and less bizarre dreams occur during NREM sleep as well (Nielsen, 2000).

It seems like something as peculiar as dreaming should have a distinct purpose. However, the exact function of dreams is still unknown. One theory speculates that dreams are simply a byproduct of other brain activity, such as memory consolidation, that occurs during sleep. Sigmund Freud, oft-considered the father of psychology, believed that dreams allowed for the disguised fulfillment of the sexual and aggressive desires of the id. According to Freud, the id is the component of our personality that lies below our consciousness and drives primitive, aggressive desires. Other theories suggest that dreaming is evolutionarily advantageous because it allows us to practice behaviors important to our survival in our sleep, preparing us for the same events in wakefulness. These behaviors include hunting, mating, responding to threats, and socializing (Siclari et al., 2020).

Some people seem to remember their dreams every night, while others claim to never dream at all. Dream recall averages at about one dream a week, but this varies widely. Practices such as keeping a dream journal and setting an alarm during a period of likely REM sleep improve recall.

 Recall is inherently easier with nightmares. By definition, nightmares cause awakening, while “bad dreams” contain similar emotionally troubling content but do not induce awakening (Robert & Zadra, 2014). There is evidence for a genetic predisposition to nightmares (Hublin et al., 1999).

Lucid dreams are a fascinating type of REM dreaming in which the individual is aware they are dreaming and may even be able to control the dream. Lucid dreams activate brain areas usually associated with insight and agency in wakefulness (Dresler et al., 2012). They also elicit the same eye movements and respiration patterns. For example, when asked to dive into a pool in their lucid dream, subjects briefly stopped breathing — as if they were underwater. The perception of time is also similar; counting from 0 to 10 in a lucid dream takes about as long as it does in real life. Lucid dreams provide particularly valuable insights into the mechanisms of dreaming because the dreamer can communicate with the researcher through pre-determined eye movements (Erlacher et al., 2014).

So what happens when we miss out on REM sleep and REM dreams? Unfortunately, modern society gives us plenty of chances to find out. Substances, especially alcohol and marijuana, decrease the time we spend in REM sleep. Medications such as benzodiazepines, antidepressants, and, ironically, sleeping pills also decrease REM sleep. Furthermore, exposure to artificial light before bed and the use of an alarm clock limit REM sleep. Collectively, the impact of these behaviors can hinder immune function, memory consolidation, and mood regulation (Naiman, 2017). 

Despite everything that scientists have discovered about dreams, there is still much about them that remains a mystery. Recently, researchers have been trying to interpret the content of dreams by using brain scans and machine learning to decode certain patterns of brain activity (Horikawa et al., 2013). For now, however, we can only take what we do know and marvel at the rest. Every night brings its own all-expenses-paid adventure into another reality.

References

Dresler, M., Wehrle, R., Spoormaker, V. I., Koch, S. P., Holsboer, F., Steiger, A., Obrig, H., Sämann, P. G., & Czisch, M. (2012). Neural Correlates of Dream Lucidity Obtained from Contrasting Lucid versus Non-Lucid REM Sleep: A Combined EEG/fMRI Case Study. Sleep, 35(7), 1017–1020. https://doi.org/10.5665/sleep.1974

Erlacher, D., Schädlich, M., Stumbrys, T., & Schredl, M. (2014). Time for actions in lucid dreams: Effects of task modality, length, and complexity. Frontiers in Psychology, 4, 1013. https://doi.org/10.3389/fpsyg.2013.01013

Horikawa, T., Tamaki, M., Miyawaki, Y., & Kamitani, Y. (2013). Neural Decoding of Visual Imagery During Sleep. Science, 340(6132), 639–642.

Hublin, C., Kaprio, J., Partinen, M., & Koskenvuo, M. (1999). Nightmares: Familial aggregation and association with psychiatric disorders in a nationwide twin cohort. American Journal of Medical Genetics, 88(4), 329–336. https://doi.org/10.1002/(SICI)1096-8628(19990820)88:4<329::AID-AJMG8>3.0.CO;2-E

Naiman, R. (2017). Dreamless: The silent epidemic of REM sleep loss. Annals of the New York Academy of Sciences, 1406(1), 77–85. https://doi.org/10.1111/nyas.13447

Nielsen, T. A. (2000). A review of mentation in REM and NREM sleep: “Covert” REM sleep as a possible reconciliation of two opposing models. Behavioral and Brain Sciences, 23(6), 851–866. https://doi.org/10.1017/S0140525X0000399X

Robert, G., & Zadra, A. (2014). Thematic and Content Analysis of Idiopathic Nightmares and Bad Dreams. Sleep, 37(2), 409–417. https://doi.org/10.5665/sleep.3426

Schwartz, S., & Maquet, P. (2002). Sleep imaging and the neuro-psychological assessment of dreams. Trends in Cognitive Sciences, 6(1), 23–30. https://doi.org/10.1016/S1364-6613(00)01818-0

Siclari, F., Valli, K., & Arnulf, I. (2020). Dreams and nightmares in healthy adults and in patients with sleep and neurological disorders. The Lancet Neurology, 19(10), 849–859. https://doi.org/10.1016/S1474-4422(20)30275-1

Toxin Therapy

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While the growth of mold on fruits and vegetables forgotten in the fridge is not an atypical occurrence, lethal spores slowly sprouting in improperly preserved or fermented foods lead to more than a smelly fridge. The Clostridium botulinum bacterium produces deadly botulinum toxins (BoNT) that destroy proteins critical for the release of acetylcholine, the neurotransmitter primarily responsible for muscular function, into the neuromuscular synapse. The simple bacterium may be microscopic, but its ability to inhibit signals in the muscular network are potent and can induce irreversible paralysis. 

Clostridium botulinum produces lethal toxins that disrupt muscular contraction.
Photo credits: Dr. Phil Luton/Science Photo Library/Corbis

Exocytosis, the release of neurotransmitters into the synapse via vesicle-membrane fusion, primarily requires the complete assembly of three proteins: SNAP-25, syntaxin, and synaptobrevin. The bridging of these proteins between the vesicle and the plasma membrane are crucial for neurotransmitter release. Once the vesicles bind to the plasma membrane, neurotransmitters are released into the synapse and the action potential signals from the presynaptic neurons are sent to the postsynaptic muscle fibers. When these signals are blocked, however, muscle contractions are inhibited — initiating paralysis. 


Several types of botulinum toxins target critical proteins for exocytosis and inhibit the release of acetylcholine.

The structure of BoNT allows it to penetrate neurons and cleave the proteins that transfer the signals for movement. The toxins have 2 subunits, a light and heavy chain, which work together to penetrate the neuron and wreak havoc. The heavy chain dictates which neurons are affected by the toxins by strongly binding to the external membrane. They facilitate the entry of the light chain into the cytoplasm of synaptic terminals, which then disrupts exocytosis by snipping the critical proteins for vesicle-membrane fusion. The structure of the light chain determines which proteins are cleaved. The toxins ultimately causes a paralytic effect by inhibiting membrane fusion of vesicles and acetylcholine release at neuromuscular junctions.

The extreme potency and lethality of botulinum toxins makes them potentially fatal bioweapons. Small amounts of BoNT can be deadly, where “a single gram of crystalline toxin, evenly dispersed and inhaled, can kill more than one million people.” The lethal dose for humans orally is estimated to be 30 ng and by inhalation 0.80 to 0.90 µg. An estimate of only 39.2 g of pure BoNT could eradicate humankind. While the inhibition of neurotransmitter release is irreversible, the paralytic effects are felt in full force by four to seven days after exposure. The long latency of effects can delay alarm and medical treatment. While some paralytic effects may be mediated by the growth of new nerve terminals and synaptic connections, these recovery processes can take up to months.

The lethality of these toxins have been harnessed for a range of purposes, from cosmetic procedures to treatments for movement disorders. BoNT is colloquially well-known as Botox, the drug commonly used to smooth facial wrinkles and enhance a youthful appearance. Beyond the surface, Botox has also been FDA-approved to treat chronic migraines, excessive sweating, and several other medical conditions. Other applications are under investigation, but the botulinum toxins have been found to reduce tremors, tics, muscle spasms, and other movement disorders that derive from debilitating neurological diseases.

The potential uses of these toxins may enhance the quality of life for many people. While the use of deadly botulinum toxins for medical treatments may seem unorthodox, these compounds have proven to be incredibly versatile in their application.