Sunday, October 17, 2021

The Importance of REM sleep in Processing Traumatic Emotions and Using Music Therapy to Enhance REM Sleep



The concept of dreaming has been explored by humans over the test of time- in methods both abstract and scientific. For the large majority of human history, dreaming was seen as mystical and often associated with some form of divine intervention. However, newer research gives us more direction in the physiological purpose of dreaming, along with newfound methods of studying this phenomenon. In “Real-time dialogue between experimenters and dreamers during REM sleep,” Konkoly et al. explore different studies which suggest the dreamers in REM (rapid eye-movement) sleep retain many more cognitive abilities than previously assumed. Among these abilities, includes lucid dreamers being able to communicate with researchers in real time during REM sleep. While on a very general level, REM sleep is associated with mood regulation and memory consolidation, although the actual mechanisms of this require much more investigation. Konkoly’s research opens up many possibilities to future studies regarding REM sleep, through this method of communication with lucid dreamers during REM sleep. 

The effects of REM sleep are studied in “Restless REM Sleep Impedes Overnight Amygdala Adaptation,” where Wassing et al. found that poor REM sleep is associated with a decreased ability of the amygdala to process emotional memories during sleep. This somewhat unconventional study recorded participants singing karaoke while wearing muffled headphones, preventing them from hearing their own voices. Inevitably, their singing voices tended not to match the tune of the songs (“Silent Night” and the Dutch national anthem). When researchers played these recordings back to participants, greater activation in the amygdalae of these participants suggested feelings of shame after hearing these poor singing performances. For participants that experienced restful sleep after this first day of research, the amygdala response was lessened after hearing their singing the next day. However, for participants who experienced restless sleep (poor quality REM sleep monitored by electroencephalogram (EEG)), their amygdalae were just as sensitive, sometimes more, when hearing their singing during the next day of research. This study suggests that REM sleep plays an important role in processing memories overnight, especially these negative emotions of shame and embarrassment associated with the amygdala. Furthermore, those suffering from REM sleep disorders may be more susceptible to psychological disorders such as depression, anxiety, and post traumatic stress disorder (PTSD). 

There are many potential therapeutic applications within the intersection of these studies. From Wassing’s research, the importance of REM sleep is emphasized in regard to mood regulation, particularly after traumatic events and in those suffering from depression, anxiety, and PTSD. By employing the communication methods investigated by Konkoly, further strategies can be developed to enhance the quality of REM sleep, potentially reducing the severity of psychological disorders, such as depression. Current research implicates that music therapy during sleep can be beneficial to adults suffering from depression (Lund 2020). This can be combined with the work of Konkoly by providing music therapy to lucid dreamers over the course of several weeks and months, which should provide measurable differences in amygdala activity in response to traumatic events. The goal of this practice would be to increase the duration and quality of REM sleep. This could be an essential therapeutic measure for treating depression, anxiety, and PTSD. Other therapeutic practices should be researched using Konkoly’s methods of communicating with lucid dreamers to enhance REM sleep, ultimately decreasing the severity of amygdala responses to traumatic events (Wasser 2017). 


Konkoly, K., Appel, K., Chabani, E., Mironov, A. Y., Mangiaruga, A., Gott, J., Mallett, R., Caughran, B., Witkowski, S., Whitmore, N., Berent, J., Weber, F., Pipa, G., Türker, B., Maranci, J.-B., Sinin, A., Dorokhov, V., Arnulf, I., Oudiette, D., … Paller, K. (2021). Real-time dialogue between experimenters and dreamers during rem sleep. Current Biology, 31(7), R352–R353. https://doi.org/10.2139/ssrn.3606772 

Lund, H.N., Pedersen, I.N., Johnsen, S.P. et al. Music to improve sleep quality in adults with depression-related insomnia (MUSTAFI): study protocol for a randomized controlled trial. Trials21, 305 (2020). https://doi.org/10.1186/s13063-020-04247-9

Wassing, R., Lakbila-Kamal, O., Ramautar, J. R., Stoffers, D., Schalkwijk, F., & Van Someren, E. J. W. (2019). Restless REM sleep impedes overnight amygdala adaptation. Current Biology, 29(14), 2351–2358. https://doi.org/10.1016/j.cub.2019.06.034 

Zimmer, K. (2019, June 11). Karaoke-sleep study links disrupted REM with poor memory processing. The Scientist Magazine®. Retrieved October 17, 2021, from https://www.the-scientist.com/news-opinion/karaoke-sleep-study-links-disrupted-rem-with-poor-memory-processing-66139. 


 

Considerations for Lucid Dreaming Research

     The phenomenon of dreaming for many years has captivated us because of its illusiveness. From movies to novels, dreaming has been pervasive within our culture and gained popularity through its key feature of being fundamental to all humans with very little actually understood about it. For as much as dreaming is discussed in popular discourse, science has struggled to unravel the mystery of dreams. That is until recently, in an article published by Konkoly et al. (2021), where experimenters discuss how they successfully were able to communicate with participants lucid dreaming. This breakthrough opens the door for endless possibilities in dream research, but scientists should not start celebrating just yet. In order for Konkoly et al.’s method to work, the participants must lucid dream and that may pose an issue for continuing with this method as inducing lucid dreaming may not be as neutral to participants as experimenters thought.

    Nirit Soffer-Dudek in her article “Are Lucid Dreams Good for Us? Are We Asking the Right Question? A Call for Caution in Lucid Dream Research” discusses an overlooked part of research involving lucid dreaming, the potential negative side-effects that can occur from inducing lucid dreaming. Dream research has looked over the potential downsides of lucid dreaming because, as mentioned earlier, neuroscientists have struggled to get valid data from dream experiments leading to a hole in scientific literature. Since scientists naturally want to get as much research as possible to help people, it is understandable that negative effects would be overlooked. However, science cannot propose solutions until problems are addressed, and Soffer-Dudek lists out the potential drawbacks to inducing lucid dreaming in individuals and proposes directions for future research to ensure methods such as Konkoly et al. are safe.

    The most obvious drawback that comes with inducing lucid dreaming in a participant is the disrupted sleep quality that the participant may experience. Good quality sleep is vital for one’s mental and physical health. In certain cases, lucid dreaming is considered a hindrance to quality sleep and by extension to one’s overall health. There are reported instances where individuals have lucid dreams every time they sleep, disrupting normal sleep patterns that are conducive for a healthy lifestyle. For these individuals lucid dreaming impairs their lives and would be an example of the extreme drawback that lucid dreaming can have on sleep quality. Fear for studies, such as Konkoly et al.’s study where participants learn to induce lucid dreams, are that the participants too will be unable to stop the occurrence of lucid dreams. Thus, quality sleep would be disrupted, leading to a decline in the mental and physical health of the participants. Currently though lucid dreaming research has no consensus as to whether or not lucid dreaming does or does not hinder quality sleep, so research must be dedicated to solving this question to allow for safe experiments.

    The other important drawback to inducing lucid dreams that Soffer-Dudek points to is that individuals who lucid dream are more susceptible to losing a sense of reality. Lucid dreaming allows an individual to be aware of the fact they are sleeping, which for Konkoly et al. this is an advantage they are able to exploit to then get participants to answer questions via movements in a lucid state. For research this is astonishing, but some fear that this will cause individuals to lose their sense of reality. Having a grip on reality is important to mental health. Without knowing what is real, people tend to behave in strange ways that are harmful for themselves and others. Lucid dreaming, despite its potential benefits for mental health that researchers have found, other researchers point to evidence that inducing lucid dreams can be deleterious for mental health as it increases psychosis-proneness and dissociative symptoms. In experiments utilizing lucid dreaming participants could develop these mental health problems as an unforeseen side effect, so researchers should aim at diminishing these issues should they arise in their own experiments. Additional research in the area of lucid dreaming and mental health is crucial for labeling Konkoly et al.’s method of dream research as safe as well as effective.

    Konkoly et al. and studies utilizing similar methodology with lucid dreaming ought to pause and consider the effects of inducing lucid dreams in participants. While some research points to lucid dreaming as safe and even a method for treating mental health, other research points to negative side-effects lucid dreaming invokes. Soffer-Dudek wrote her review to remind researchers of that fact; lucid dreaming is complex and displays variant effects on individuals. For the future, research must be devoted to finding all the range of effects lucid dreaming can cause an individual. All this is to not say that lucid dreaming research such as Konkoly et al. should be stopped, it is to say it is imperative that researchers work not just in their own best interests, but also the best interest of their participants.  

References

Konkoly KR, Appel K, Chabani E, Mangiaruga A, Gott J, Mallett R, Caughran B, Witkowski S, Whitmore NW, Mazurek CY, Berent JB, Weber FD, Türker B, Leu-Semenescu S, Maranci JB, Pipa G, Arnulf I, Oudiette D, Dresler M, Paller KA. Real-time dialogue between experimenters and dreamers during REM sleep. Curr Biol. 2021 Apr 12;31(7):1417-1427.e6. doi: 10.1016/j.cub.2021.01.026.

Soffer-Dudek N (2020) Are Lucid Dreams Good for Us? Are We Asking the Right Question? A Call for Caution in Lucid Dream Research. Front. Neurosci. 13:1423. doi: 10.3389/fnins.2019.01423

Saturday, October 16, 2021

How Can Eye Movements Help Us Understand Lucid Dreaming?


How Can Eye Movements Help Us Understand Lucid Dreaming?

In the article, What Lucid Dreams Look Like, Dr. Susana Martinez-Conde, professor of ophthalmology, neurology, and physiology and pharmacology at SUNY Downstate Medical Center writes about how eye tracking further informs the study of lucid dreaming. A lucid dreamer herself, Dr. Martinez-Conde uses an example from two of her prior lucid dreaming episodes to explain the difference between eye movements that occur when viewing real objects versus when imagining objects, such as when dreaming. Dr. Martinez-Conde recalls being able to fly and joining a flock of birds in a lucid dream which felt very real to her while she experienced it. She explains that the smooth pursuit movements used to explore objects in motion and other details in one’s visual field are only observed when people are observing real objects. Dr. Martinez-Conde references work done at Stanford University and the University of Wisconsin-Madison which provided evidence that smooth pursuit movements are not observed in people when they are imagining the movement of objects. 


Researchers Stephen LaBerge, Benjamin Baird, and Philip G. Zimbardo used both EEG and electrooculography (EOG) to observe brain activity and eye gaze while sleeping participants were imagining objects during lucid dreams. These participants were frequent lucid dreamers that were instructed to use LRLR eye movement signals to let the researchers know that they were having a lucid dream and that they were now in control of their dream. At this point, the participants imagined a series of previously agreed-upon objects. The results of this study suggested that the eye movements while tracking objects while dreaming are more similar to object tracking that occurs while awake compared to tracking that occurs during imagination. More specifically, while imagining objects while lucid dreaming, more smooth pursuit movements were observed compared to the saccadic movements that occur while imagining objects. Although this is not an example of two-way communication, the sleeping participants are still providing important information to the researcher about what they are experiencing while asleep. Even if communication is only occurring one way, this still allows researchers the opportunity to answer a variety of questions about the cognitive processes that the participants are engaging in while asleep using EEG, EOG, and brain imaging techniques. 


Dr. Martinez-Conde’s article works in support of one of Karen Konkoly’s main arguments that sleeping individuals can still respond to researchers in a productive way while asleep and provide researchers with useful data. In this case, the researchers provide a basis for future studies in terms of potential physiological markers that may confirm or suggest that a participant is tracking an object in motion while dreaming. Additionally, this smooth pursuit movement cannot be faked, as noted by Dr. Martinez-Conde, and appears different from imaginary object tracking and real object tracking while awake. Therefore, this may be a way for researchers to check whether or not the participant has woken themselves up while moving their eyes to indicate the start of a lucid dream. This was a concern presented by Konkoly as she encouraged future research to consider methods that optimize this procedure to prevent participants from waking as they indicate the start of their lucid dream.



Konkoly, K., Appel, K., Chabani, E., Mironov, A. Y., Mangiaruga, A., Gott, J., Mallett, R., Caughran, B., Witkowski, S., Whitmore, N., Berent, J., Weber, F., Pipa, G., Türker, B., Maranci, J. B., Sinin, A., Dorokhov, V., Arnulf, I., Oudiette, D., . . . Paller, K. (2020). Real-Time Dialogue between Experimenters and Dreamers During rem Sleep. SSRN Electronic Journal. Published. https://doi.org/10.2139/ssrn.3606772

LaBerge, S., Baird, B., & Zimbardo, P. G. (2018). Smooth tracking of visual targets distinguishes lucid REM sleep dreaming and waking perception from imagination. Nature Communications, 9(1). https://doi.org/10.1038/s41467-018-05547-0

Martinez-Conde, S. (2018, September 7). What Lucid Dreams Look Like. Scientific American Blog Network.

https://blogs.scientificamerican.com/illusion-chasers/what-lucid-dreams-look-like/




Thursday, May 6, 2021

Emotion Reactivity and Regulation in Targeting Depression

From a 2017 WHO report, it was concluded that depression impacted 322 million people worldwide. For every one that underwent treatment for depression, 45-65% never reached remission. Even those who did reach remission had about a 50% chance of relapsing after 1-2 years. Mainstream interventions for treating depression have focused on repairing associated negative emotions, but do little in increasing positive emotions. Thus, interventions that target impairments in positive emotions could be critical in diminishing depression. Depression is related to impaired emotion reactivity and regulation, with emotion reactivity being an initial baseline response and emotion regulation being the process that influences an initial emotional response. However, there’s been difficulty disentangling emotion reactivity and regulation, and doing so is important in understanding how current treatments for depression should be refined. In the talk “Individual differences and neural correlates of emotion reactivity and regulation: potential intervention targets in depression” presented by Kahrilas (2021), the researcher presented three studies conducted to disentangle emotion reactivity and regulation to bring us closer to conceiving neuroscience-informed treatments for depression.

The first study was in regards to the concept of savoring the moment and linking affectivity and depression. Savoring capacity is the capacity for an individual to attend to, appreciate, and enhance the positive experience of one’s life. As such, one’s savoring capacity is the index of one’s ability to regulate their positive emotions. When you are savoring, you may anticipate future events before they occur (anticipating), or you can attend to and appreciate positive events as they are occurring in the present moment (savoring the moment), or you can reminisce upon positive events after they have occurred (reminiscing). Regardless of the temporal focus, each of these temporal domains of savoring is indicative of one’s ability to savor emotions in the present moment. In this study, 1,618 participants were measured with the Savoring Beliefs Inventory (SBI), the Mood and Anxiety Symptom Questionnaire (MASQ), the Patient Health Questionnaire (PHQ-9), and the Penn State Worry Questionnaire (PSWQ). Results showed a positive relationship between positive affectivity and each of the three temporal domains of savoring, and a negative relationship between positive affectivity and depression. A negative relationship between negative affectivity and each of the three temporal domains of savoring was also found, and a positive relationship between negative affectivity and depression was observed. Moreover, savoring the moment was the sole predictor of depression, suggesting that momentary savoring has a higher specificity to depression and might be a more effective intervention target.  

In the second study, the neural chronometry of positive and negative emotion reactivity and regulation was investigated. The main goal of this study was to disentangle the constructs of emotion reactivity and emotion regulation. Since the time course of reactivity and regulation likely overlap, electroencephalography (EEG) is an efficient psychophysiological measure of electrical cortical activity by event-related potentials (ERPs) with millisecond (ms) temporal resolution that can be used to evaluate the chronometry of reactivity and regulation processes. The present study utilized EEG methods to determine the neural time course of emotion reactivity and regulation to provide knowledge as to how these processes are implicated and how they can be altered in depression. A principal component approach (PCA) was used to measure ERPs, which is a dimension reduction technique that takes a large number of variables and reduces them to a smaller set of variables that constitute linear combinations of the original data set. The most common waveform from PCA is the Late Positive Potential (LPP), which is a positive slow wave EEG component observed as early as 300 ms following stimulus onset. LPP is an established index of evaluative congruency, valence, and arousal in response to visual stimuli, and is selectively enhanced in response to positive and negative visual stimuli relative to neutral stimuli. The study also examined early visual processes, seen with the N170 (a negative peak occurring at 170 ms) and EPN (negative peak occurring around 200-300 ms), since these components have been studied in previous literature on facial processing, showing that these components are enhanced in response to images of negative and positive faces. The present study used 120 standardized images from the open-affective standardized image set, which are images normed on valence (unpleasant and pleasant) and arousal (not aroused and aroused). Using the image sets, there were three distinct categories: 40 positive, 40 negative, 40 neutral. For the positive and negative image sets, there were three sets of instructions for participants: increase or decrease the emotional intensity they felt in response to the images, or passively view the images. For neutral images, participants were told to just passively view them. After viewing the images, participants rated how positive or negative they felt in the present moment on a 1-7 Likert Scale. They also reported the arousal of the emotion and the difficulty of the task. The chronometry of reactivity unfolded from 162 ms to 740 ms, with stable arousal and valence effect throughout the time course. In terms of regulation, negative regulatory processes unfolded earlier at 124 ms to 259 ms, whereas positive regulatory processes occurred later from 259 ms to 740 ms. These findings are important because different types of psychopathology might manifest as dysregulating positive or negative emotion regulation or reactivity, suggesting that we might need different intervention processes depending on the symptomatology. 

The third study presented summarized early neural activity as an indicator of the brightening effect. There are three theories of emotion reactivity in depression: positive attenuation (reduced reactivity in response to positive stimuli), negative potentiation (enhanced reactivity in response to negative stimuli), and emotion context insensitivity (ECI; reduced reactivity in response to positive and negative stimuli). Within the realm of lab-based research, ECI has emerged to be the most common finding; however, this is not the case in a different type of research called ecological momentary assessment. With this assessment, researchers have participants download an app on their phones and have them complete questionnaires throughout the day. A different pattern of emotion reactivity and depression emerges with this research, such that there is enhanced reactivity in response to positive stimuli in those with depression as opposed to those without depression. Much research of depression and emotion reactivity in psychopathology looks at depression as a heterogeneous cluster of symptoms, and findings from study 1 here show that positive affectivity shows specificity to depression, so Kahrilas was interested in how neural activity changes as a function of positive affectivity specifically, rather than depression as a whole. Previous research showed that those with depression tend to exhibit smaller amplitudes in response to both positive and negative images, which is consistent with the ECI view, where we see less reactivity in response to visual stimuli regardless of if they are positive or negative. Previous studies also show that those endorsing lower levels of positive affectivity tend to exhibit attenuated reactivity in response to negative and positive images. Moreover, previous literature employing structural equation modeling found a model corresponding to ECI that found associations between viewing pleasant and unpleasant images and depressive symptoms that were approaching significance. Following these findings, the present study hypothesized that reactivity in response to positive and negative stimuli would be positively associated with positive affectivity, such that those with depression (those with lower positive affectivity) would report smaller amplitudes in response to those images, and that an ECI model would best map onto the findings. Study 3 used the same EEG paradigm as Study 2. Study 3 also used the same sample as study 2, but included a new sample of participants from a different study in conjunction with the current impact lab that was recruited based on depressive symptoms. More specifically, participants recruited were those that endorsed moderate levels of depression, and Kahrilas harmonized them with the previous sample that was not recruited on this basis of depression. This introduces more variance of measures of central tendency, which results in a greater dimensional perspective of the concept of interest. A positive component peak at 371 ms at bilateral occipital electrode sites was found. Negative and positive images elicited augmented amplitude relative to neutral images. Another positive component occurring later in the time course was found, peaking at about 736 ms at a cluster of bilateral centroparietal electrode sites. A negative component peaking at 257 ms was also observed. For PCA in study 3, there was no peak at 162 ms that was observed in study 2, suggesting that there might be alterations in the early visual components with the introduction of depressive symptoms. For the 257 ms component results, the association between the residual variance of the positive viewing condition and positive affectivity was strongly correlated. However, there was no association found between negative viewing conditions and positive affectivity. Since the EPN is a negative-going component, this means that the positive association is in the opposite direction than the ECI model hypothesized. This is explained by the brightening effect model, which was found in the ecological momentary assessment literature, that says there's augmented emotional reactivity in response to positive images as a function of depression. The brightening effect model was the best model for the 257 ms component, which draws the association between positive images and positive affectivity, meaning that early visual ERP components might show specificity to those with low positive affectivity. For the 371 ms and 736 ms component results, nothing outperformed the measurement model here, meaning that these later ERP components may be independent of internalizing symptoms. Conclusions from study 3 were that only the positive viewing condition for the early visual 257 ms component was negatively related to positive affectivity, consistent with the brightening effect. Furthermore, later ERP components were not related to internalizing symptoms. Therefore, interventions that target early neural processes may strengthen positive affectivity and alleviate depression. 

Overall, Kahrilas’ Study 1 found that momentary savoring might diminish depressive symptoms for those with low positive affectivity and high negative affectivity. Study 2 disentangled positive emotion reactivity and regulation. Neural reactivity unfolded from 162 ms to 740 ms with stable arousal and valence effects. Negative regulatory processes unfolded at 124 ms to 259 ms, and positive regulatory processes occurred later from 259 ms to 740 ms. Study 3 found that early neural activity is related to positive affectivity. These studies collectively bring us closer to conceiving neurobiological treatments for depression. 

Similar to Kahrilas’ Study 2 that investigated the neural chronometry of positive and negative emotion reactivity and regulation to disentangle the constructs of emotion reactivity and emotion regulation, Ebneabbasi et al.’s (2021) “Emotion processing and regulation in major depressive disorder: A 7T resting-state fMRI study” went off the concept that debilitated emotion processing (EP) and emotion regulation (ER) are key factors in the pathophysiology of major depressive disorder (MDD), with biased processing and impaired regulation of affective stimuli. EP and emotion reactivity are equal. Disturbances of EP are seen with excessive attention toward negative events, and disturbances of ER correspond to insufficient suppression of negative affect and incompetent savoring of positive ones. Like Kahrilas’ previous issue with disentangling EP and ER, the present study utilized regional amplitude of low frequency fluctuations (ALFF) and whole-brain functional connectivity (FC) of EP- and ER-related areas compared between 32 healthy controls (HC) and 20 MDD patients to discern if EP- and ER-related areas are linked to regulatory behavior and whether this relation is impaired in MDD. Previous literature found that higher amygdala reactivity led to greater prefrontal activity, thus resulting in greater regulatory behavior. In MDD, previous analyses found hyperactivity of the amygdala and hypoactivity of the lateral prefrontal cortex with exposure to negative stimuli, suggesting an augmented emotional reactivity and decreased downregulation of debilitated amygdala reactivity, offsetting prefrontal recruitment, and meta-analytic disparities in MDD. Moreover, it was examined whether EP-related areas are predictors of ER-related areas and regulatory behavior in both experimental groups, and the brain-behavior associations between EP- and ER-related brain areas and depression severity were assessed. Results showed that affective areas were regionally and/or connectively impaired in MDD patients, and EP- and ER-related areas are disturbed in MDD patients. Overloading emotional reactivity in the amygdala has the potential to inversely affect cognitive control processes in prefrontal cortices, resulting in decreased regulatory actions. The amygdala plays a role in encoding relevant stimuli, provoking affective emotional responses. Higher amygdala activation was also found with exposure to negative stimuli in MDD patients, with prolonged processing of negative information, confirming Kahrilas’ finding that negative affectivity is positively associated with depression. Following the Kahrilas study that momentary savoring may diminish depressive symptoms, the current study found a decreased FC between the ventrolateral prefrontal cortex and intraparietal sulcus in MDD patients, suggesting an inability of MDD patients to rely on savoring capacity and attend to positive emotions in the present. Altogether, these findings provide new insights on the underlying neural correlates of affective dysfunctions experienced with depression, which was a future goal of the Kahrilas study. 



                References

Ebneabbasi, A., Mahdipour, M., Nejati, V., Li, M., Liebe, T., Colic, L., Leutritz, A. L., Vogel, M., Zarei, M., Walter, M., & Tahmasian, M. (2021). Emotion processing and regulation in major depressive disorder: A 7T resting-state fMRI study. Human brain mapping, 42(3), 797-810.


Foti, D., Hajcak, G., & Dien, J. (2009). Differentiating neural responses to emotional pictures: evidence from temporal-spatial PCA. Psychophysiology, 46(3), 521-530.


Hill, K. E., South, S. C., Egan, R. P., & Foti, D. (2019). Abnormal emotional reactivity in depression: Contrasting theoretical models using neurophysiological data. Biological psychology, 141, 35-43.


Silton, R. L., Kahrilas, I. K., Skymba, H. V., Smith, J., Bryant, F. B., & Heller, W. (2020). Regulating positive emotions: Implications for promoting well-being in individuals with depression. Emotion, 20(1), 93-97.


Wednesday, May 5, 2021

Potential Caveat For Early ASD Diagnosis In Infants

     Autism spectrum disorder (ASD) is a developmental disability that can produce a variety of different social and behavioral challenges upon those who are affected. Depending on the spectrum of Autism, people with ASD learn, think, and problem-solve differently than those who are not on the spectrum. ASD typically involves early brain overgrowth affecting several cortical and subcortical regions such as prefrontal and temporal cortices. These affected areas mediate the symptoms that a person with ASD experiences. Despite the increasing number of diagnoses, ASD is hard to diagnose because it is not detectable by medical tests but instead dependent on developmental and behavioral tracking. ASD can sometimes be detected at 18-months but many individuals do not receive a confirmed diagnosis until they are older. Some early signs of ASD include: avoiding eye contact, having little interest in other children or caretakers, limited display of language, or getting upset by minor changes in routine (“Screening and Diagnosis of Autism Spectrum Disorder”). 

In the article, "Face-sensitive brain responses in the first year of life", Guy and colleagues sought to examine the changes in neural response of infants to facial stimuli. Previous studies on adults have shown that the N170, located in the middle and posterior fusiform gyrus, is linked to the processing of facial stimuli. The study analyzed ERPs of infants that were face sensitive which involves P1, N290, P400, and Nc. Over a hundred infants were recruited and presented with stimuli consisting of pictures of faces and objects on various backgrounds. During the three experimental procedures, the ECG electrodes of the infants were used to determine the attention of the infants while the EEG electrodes to analyze the neural responses and calculate the amplitudes. The results depicted that the amplitudes of the ERPs increased in older infants 9+ months. The results of this study have lead Guy and colleagues to explore the possibility of using ERP analysis in diagnosing ASD in infants. 

In a separate study by Stoner and colleagues, "Patches of Disorganization in the Neocortex of Children with Autism", examined the neocortical architecture of children after the onset of autism by utilizing RNA in situ hybridization with a panel of layer- and cell-type–specific molecular markers to phenotype ASD. The study observed that children with ASD displayed focal disruptions of cortical laminar architecture in their cortices. Their data supported the idea of a potential dysregulation of layer formation and layer-specific neuronal differentiation at prenatal developmental stages of infants with ASD. In Guy’s study, the ERPs of older infants displaying proper development in facial stimuli response were larger in comparison to younger infants or infants that may have ASD. Stoner’s study could serve as supplemental information to support the results of Guy’s study as to why the ERPs were larger in normal developing infants rather than ASD infants. This could also be a potential caveat in future studies for early ASD diagnosis in order to create reliable medical tests. 


Citations

  • Conte, Stefania, et al. “Face-Sensitive Brain Responses in the First Year of Life.” NeuroImage, vol. 211, 2020, p. 116602., doi:10.1016/j.neuroimage.2020.116602. 

  • Stoner, Rich, et al. “Patches of Disorganization in the Neocortex of Children with Autism.” New England Journal of Medicine, vol. 370, no. 13, 2014, pp. 1209–1219., doi:10.1056/nejmoa1307491. 

  • “Signs and Symptoms of Autism Spectrum Disorders.” Centers for Disease Control and Prevention, Centers for Disease Control and Prevention, 29 Mar. 2021, www.cdc.gov/ncbddd/autism/signs.html. 

Neural Disorders and Potential HFS Treatment

  The medical field is always evolving, and researchers are constantly in search of new and improved treatment methods. For many decades, even to this day, physicians treat neural disorders such as Parkinson’s disease and epilepsy mostly using medication. However, a more recent focus on magnetic stimulation and high frequency stimulation (HFS), is showing promising results and proving to be very effective at successfully treating neural disorders. Thanks to researchers such as Ye et al., as well as Skach et al, who have worked diligently to examine new treatment methods such as microscopic magnetic stimulation as well as high frequency stimulation (HFS) respectively, physicians can hopefully start utilizing these faster and more effective treatment methods in the very near future.

In the research article “Axonal blockage with microscopic magnetic stimulation” Ye et al. worked to examine the feasibility of axonal blockage by the miniature coil. In order to test the effectiveness of this treatment method, the researchers used a combination of electrophysiological experiments and computational modeling. In order to accomplish this, they specifically designed a system that can deliver sufficient electric current of various frequency and intensity into a commercially available miniature coil. The researchers recorded axonal conductance in the buccal nerve II of the buccal ganglion in Aplysia californica while applying magnetic stimulation to the axon. As a result of this experiment, it was found that high frequency stimulation (HFS) with the miniature coil suppressed action potentials generated by antidromic stimulation. Antidromic stimulation of the BN2 activates B3, B6, B9, and B10 neurons in the buccal ganglion. Furthermore, HFS with miniature coil suppressed action potentials generated by specific soma activation. These results suggest that a population of axons, such as the 2nd largest units in BN2, have been inhibited by the miniature coil. The results of this study are very interesting and exciting. With further research this could lead to a groundbreaking treatment alternative, potentially more effective than standard medication.

Another study “Simulation Study of Intermittent Axonal Block and Desynchronization Effect Induced by High-Frequency Stimulation of Electrical Pulses” Skach et al shared a similar interest in using HFS in order to achieve axonal blockages. As a result, the researchers aimed to study the axonal responses during HFS. In order to do that, they developed a computational model of myelinated axons to simulate sequences of action potentials generated in single and multiple axons by stimulations, and applying the stimulations using a point source of current pulses with a frequency of 50–200 Hz, while taking into account the accumulation of potassium ions in the peri-axonal spaces. As a result of this experiment, it was found that there was an increase of potassium ions in the extracellular space, which generates intermittent depolarization blocks in the axons during the HFS treatment. This results in the axons firing at a much lower rate, and causes asynchronous firing of action potential on axon bundles. This could lead to the suppression of pathological synchronization of target nuclei by generating asynchronous activity in the neurons downstream. These results provide a very promising look into the therapeutic effects of DBS, which will hopefully result in the development of various effective treatments for neural disorders.

Looking at the results of these new treatment methods for neural disorders is very exciting. Hopefully future studies will further examine the effectiveness of such treatments and lead to a standardized HFS treatment for neural disorders. Studies such as these are vital for the field of neuroscience as they look into the effectiveness of our current techniques and ask what can be done to improve them. This type of outlook helps push the field forward, which in term helps medical professionals do a better job at diagnosing and treating patients with a variety of illnesses. With further research, these two methods have great potential to deepen our understanding of neural disorders and how they can be treated effectively using methods such as HFS and magnetic stimulation.

Sleep and Memory an Ongoing Investigation

 Sleep is an essential function in our lives. Sleep promotes cognitive performance, both mental and physical health, development, immune system functions, etc. Sleep also has shown to have vast benefits for memory consolidation, as well as promotes learning. During sleep, the body is able to repair itself and prepare itself for the following day. The effects sleep has on our learning/memory capabilities are still a subject of debate as determining the extensive working anatomy of memory and how sleep correlates to them.

While we associate things like cellular repair, physical development, etc with sleep, we still are left to understand how sleep promotes memory consolidation, as well as what mechanisms are at play during sleep. One such hypothesis is that sleep supports memory consolidation, perhaps when hippocampal neurons replay patterns of firing that were experienced during learning. In the article, How the brain consolidates memory during deep sleep”, researchers at the University of California were able to use a computational model looking at electrical activity in the brain during slow-wave sleep (deep sleep). The model displayed that patterns of slow oscillations in the cortex are influenced by hippocampal sharp-wave ripples, which determined synaptic changes in the cortex. Going off the theory of hippocampal neurons replaying patterns can be applied here as the model showed these synaptic changes affected patterns of slow oscillations, which replays a specific firing sequence of cortical neurons. Yina Wei explained how the inputs from the hippocampus determined the spatial and temporal pattern of the slow oscillations. She also explained that “by influencing the nature of these oscillations, this hippocampal input activates selective memories during deep sleep and causes a replay of specific memories.” These findings can be connected to other experiments and research into memory consolidation as well as further understanding mechanisms of sleep and memory.


This research conducted by Dr. Yina Wei and the team at UC Riverside can be used alongside the research of Laura Shanahan and Jay A. Gottfried. In their review article, “Scents and Reminiscence: Olfactory Influences on Memory Consolidation in the Sleeping Human Brain”, they referenced research regarding targeted memory reactivation (TMR), where research by Rasch et al. 2007 found that odor stimulation could be used to enhance consolidation of declarative memories. They had subjects learn the location of several card pairs. During this, they were exposed to a rose odor (phenylethyl alcohol), and then they would go to sleep. During sleep, they would be re-exposed to the odor, and then upon waking they were asked to recall the locations of the items. They found that during the slow-wave sleep showed to have the greatest response to the odor and enhancement of recall. As displayed by the research of the UC Riverside team they also saw that there was activation during deep sleep (slow-wave sleep). They also employed fMRI on hippocampal activation. The results showed that the rose odor activated the hippocampus to a greater extent during deep sleep than it did during wakefulness, which mirrors that computational model used by the UC Riverside group.


Both these studies identify non-REM sleep (slow-wave sleep) to be the most active and likely area of memory activation, but a recent study done at the University of Tsukuba provides insight into the possible role REM sleep plays on memory consolidation. The research article, “Memory consolidation during REM sleep”, found that adult-born neurons in the hippocampus may be responsible for memory consolidation during REM sleep. The researchers exposed mice to a context-specific fear memory task. They then recorded activity in the adult-born neurons across the stages of memory. They found that these neurons were most active during REM sleep after the memory task. They also found consolidation of contextual fear memories was impaired upon optogenetic silencing of young ABNs. This can be used alongside the research talked about in the Shanahan article about how odors can promote fear extinction during sleep. This is important as the other research points back to memory consolidation taking place in the slow-wave sleep while this research points to memory consolidation in REM sleep.


The three research articles all cover a different aspect of sleep and memory and have building blocks upon each other to further research into how sleep affects our memory system. The computational model supports many findings of increased activation during slow-wave sleep which is believed to be where memory-enhancing effects take place. While the odor experiment did not stimulate memory-enhancing effects during REM sleep, it is not fully conclusive that memory consolidation or activation can not take place during REM sleep. The REM sleep article offers insight into the role of adult-born neurons aiding in memory consolidation during REM, which could be further studied and applied to previous experiments. The hypothesis of hippocampal neurons replaying patterns of firing from learning also gains much support from the articles and experiments discussed. There is still much research to do on sleep and memory, but looking into ABNs effects with odor stimuli could provide interesting results to further our understanding, as well as identifying the specific tasks taking place in different stages of sleep.


References

Gazzaniga, M. S., Ivry, R. B., & Mangun, G. R. (2019). Cognitive neuroscience: the biology of the mind. W.W. Norton & Company.

Shanahan, Laura K., and Jay A. Gottfried. "Scents and Reminiscence: Olfactory Influences on Memory ..." Web. 6 May 2021.

University of California - Riverside. "How the brain consolidates memory during deep sleep: Using a computational model, study explains how hippocampus influences synaptic connections in cortex." ScienceDaily. ScienceDaily, 14 April 2016. <www.sciencedaily.com/releases/2016/04/160414214830.htm>.