Relationship between memory and sleep: is it truly a myth?

There are different types of memories they could be fact based (such as remembering a country’s capital), episodic (connected to specific events like your first kiss), while some are instructional (involves learning). All of the types of memories goes through the same process to become memories these processes are: Acquisition (first exposure to a new experience or knowledge), consolidation (stability of the memory in the brain) and recall (accessing the memory in the future). Acquisition and recall occurs during the state of wakefulness while studies have shown that consolidation occurs during sleep.

According to a study published in neuroscience news in 2014,”Brain cells that spark as we digest new information during waking hours replay during deep sleep, also known as slow-wave sleep, when brain waves slow down and rapid-eye movement, as well as dreaming, stops. Scientists have long believed that this nocturnal replay helps us form and recall new memories, yet the structural changes underpinning this process have remained poorly understood”. However, during this study the scientists were able to track and image the growth of dendritic spines along individual branches of dendrites before and after mice learned to balance on a spin rod.  The mice sprout new spines along dendritic branches, within six hours after training on the spinning rod, the researchers set out to understand how sleep would impact this physical growth. They trained two sets of mice: one trained on the spinning rod for an hour and then slept for 7 hours; the second trained for the same period of time on the rod but stayed awake for 7 hours. The scientists found that the sleep-deprived mice experienced significantly less dendritic spine growth than the well-rested mice. Furthermore, they found that the type of task learned determined which dendritic branches spines would grow.

This study thus explain why new knowledge are better retained after period of sleep/rest.

Interestingly, sleep has been implicated in the process of dementia and Alzheimer’s  disease development. According to a publication by Alzheimer’s disease association, it was found that beta amyloid (the protein that builds up in excess in victims of  Alzheimer’s disease), was found to be elevated in patients that reported poor sleep/sleep deprivation. Another study was conducted to determine if the beta-amyloid build up is the cause of poor sleep or if poor sleep precedes beta amyloid build up. The result of the study shows that 22 years after initial sleep length and quality assessment, investigators assessed the subjects’ cognitive function and found that lower cognitive scores were found in individuals who reported the following:
• Short (<7 hours) or long (>8 hours) hours of sleep at midlife compared to 7-8 hours per day.
• Poor sleep quality.
• Use of hypnotics for 60 or more days per year. Hence the conclusion was that sleep actually do play a role in development of dementia/ Alzheimer’s disease.

The good news is sleep quality can be improved by exercise and sleep duration can be controlled. So, the next time you are planning on reading through the night for a test or presentation; remember your memory can only be consolidated by sleep and that is important to prevent the build up of beta amyloid in your sharp brain.

REFERENCES

Sleep Deprivation and Memory Loss  http://www.webmd.com/sleep-disorders/sleep-deprivation-effects-on-memory#1

Abstract for “Sleep promotes branch-specific formation of dendritic spines after learning” by Guang Yang, Cora Sau Wan Lai, Joseph Cichon, Lei Ma, Wei Li, and Wen-Biao Gan in Science. Published online June 5 2014 doi:10.1126/science.1249098

Alzheimer’s disease: What’s sleep got to do with it?  https://www.alz.org/documents_custom/InBrief_Issue5.pdf

 

Sleep Disturbance, the HPA Axis, and Mental Illness

Our reading on sleep discussed a correlation between disordered sleep and mental illness. Problems with sleep are symptomatic of many different psychiatric conditions but could also be caused by or perpetuated by them as well. The chapter on sleep discussed a patient with Bipolar disorder who every 6-8 weeks cycled between depression and mania with great changes in his sleep during these times. He was encouraged to stay in bed in a dark room for fourteen hours per night, and later ten hours per night. After this intervention his mood and sleep stabilized and he maintained these improvements even a year later (Higgins, 2013). To me this shows that sleep regulation plays a much larger part in psychiatry than it is given credit for.

This successful intervention inspired me to look into current research connecting sleep and mental illness. The article Arousal Regulation in Affective Disorders found that disturbed sleep is a core symptom of depression and that many people with this disorder report excessive daytime sleepiness. This sleepiness is usually not the typical understanding of being tired, as in being more likely to fall asleep, instead it is “tiredness/fatigue in the sense of exhaustion with a tonically high inner tension and physiological arousal…Patients are convinced that they could improve their condition by extended bed rest, yet in many cases this only aggravates the underlying problem”  (Hegerl, 2016). This article also discussed that the mood of these patients was inversely correlated with the amount of sleep they had. While the subjects felt the need to sleep, the more they did, the worse their depressive symptoms became.  I found this extremely interesting as our textbook also mentioned that not only is depression worse in the morning, but sleep deprivation can act as a short term antidepressant (Higgins, 2013).

Conversely, other articles I found described the protective power of adequate sleep. Deep sleep inhibits HPA axis, the activation of which causes stress and disrupts the ability to sleep.  Insomniacs (measured here by <70% of time asleep during sleep testing) have higher cortisol levels than their counterparts who get enough sleep. Long term disturbed sleep leads to increased HPA axis activation, which in turn makes it more difficult to sleep, creating a positive feedback loop (Chrousos, 2016). When someone suffers from chronic stress, it may cause the deregulation of their HPA system, and over time can cause disturbed sleep and depression (Schmitt, 2016).

Stress has the potential to have severe impacts on sleep. For example, Post-Traumatic Stress Disorder (PTSD) is associated with sleep disturbances. The neurological changes observed in people with PTSD are very similar to healthy individuals who have experienced sleep deprivation. Sleep deprivation causes increased amygdala reactivity, weaker connections between the amygdala and the medial prefrontal cortex, and stronger connections between the amygdala and the sleep/wake centers. This suggests that decreased sleep leads to increased fear response and arousal. Patients with PTSD are also likely to have decreased hippocampal volume. This is potentially caused by the sleep disturbances themselves (Kelly, 2016).  The hippocampus is important in both stress and sleep as it regulates HPA axis activity (Jacobsen, 1991). When the hippocampus is compromised, the HPA axis can take over, contributing further to stress and  sleep disturbances.

Brain-Derived Neurotrophic Factor (BDNF) plays a part in brain neuroplasticity and is important in pathology of stress-related mood disorders. Increased stress decreases BDNF levels, leading to increased disorders depression. Decreased sleep increased stress susceptibility. In contrast to chronic stress and long term sleep deprivation, partial sleep deprivation is able to cause a rapid increase in BDNF levels, producing an antidepressant effect that is far more immediate than typical antidepressants (BDNF in sleep).

Sleep is highly important to mammalian functioning and is still surrounded with uncertainties. Sleep plays a major role in our mental health, even in people without mental illness. Looking at the research I was alarmed at how many areas create positive feedback loops where stress decreases sleep and that in turn leads to further stress along with other potential complications. This also shows an area that may be undervalued in the treatment process that could be utilized to significantly help patients.

Chrousos, Vgontzas, A. N., & I. Kritikou. (2016).  HPA Axis and Sleep. De Groot LJ.

Higgins, E. S., George, M. S. (2013). The Neuroscience of Clinical Psychiatry:  The Pathophysiology of Behavior and Mental Illness, 2nd edition.  Philadelphia, PA:  Lippincott Williams & Wilkins

Kelly, M.R., Killgore, W.D.S. & Haynes, P.L. (2016). Understanding Recent Insights in Sleep and Posttraumatic Stress Disorder from a Research Domain Criteria (RDoC) Framework.Curr Sleep Medicine Rep

Jacobson, L., & Sapolsky, R. (1991). The Role of the Hippocampus in Feedback Regulation of the Hypothalamic-Pituitary-Adrenocortical Axis*. Endocrine Reviews, 12(2), 118-134.

Hegerl, Sander, & Hensch. (2016) Chapter 12 – Arousal Regulation in Affective Disorders, Systems Neuroscience in Depression, Pages 341-370

Schmitt, K., Holsboer-Trachsler, E., & Eckert, A. (2016). BDNF in sleep, insomnia, and sleep deprivation. Annals of medicine, 48(1-2), 42-51.

Aliens, Ghosts, and Old Hags: The Terrifying Mysteries of Sleep Paralysis

”A sensed presence, vague gibberish spoken in one’s ear, shadowy creatures moving about the room, a strange immobility, a crushing pressure and painful sensations in various parts of the body — these are compatible not just with an assault by a primitive demon but also with probing by alien experimenters, …And the sensations of floating and flying account for the reports of levitation and transport to alien vessels.”

Dr. J. Allan Cheyne, Associate Professor of Psychology at the University of Waterloo for The New York Times

 

From the southwestern deserts of the US to remote Japanese forest villages and across the span of recorded human history, there have been numerous accounts of nocturnally-based paranormal phenomena. In fact, “the ‘mare’ in ‘nightmare’ originally refer[s] to a demonic woman who suffocated sleepers by lying on their chests (she was called ‘Old Hag’ in Newfoundland).” (Sacks, 2012).

Dr. Cheyne summarizes these accounts into what he believes is the experience of sleep paralysis.  Isolated sleep paralysis (SP) is a REM associated parasomnia characterized by “a transient, conscious state of involuntary immobility occurring immediately prior to falling asleep or upon wakening” (Cheyne, 2003). The occurrence of this distinct phenomenon unrelated to the presence of medical conditions like narcolepsy and seizure disorders defines isolated SP (Sharpless and Grom, 2016). SP is not only peculiar it’s also quite common.  Sharpless and Grom (2016) cite a recent study showing at least one SP episode as prevalent in 7.6% of the general population, 28.3% of students, and 31.9% of psychiatric patients from a sample of 36,000. Meanwhile a UK study suggests between 25% to 40% of the general population experienced SP, though referred to as awareness during sleep paralysis (ASP) in the article (Holden and French, 2002).

The frightfulness, and imagery-vivid recall, of SP results from hypnagogic and hypnopompic experiences (HHEs). Hypnagogic and hypnopompic derive from the Greek meaning sleep leading and sleep procession, respectively. Hypnagogic refers to the acute sense of a heinous presence and the sensations of floating and chest pressure. Hypnopompic denotes the same sensations but experienced post-sleep, just preceding complete awakening (Holden and French, 2002).  Cheyne, Rueffer, and Newby-Clark (1993) extend the definition of HHEs to include sensations of suffocating, choking, floating, out-of-body, and flying as well as combinations of auditory and visual hallucinations. “Visual hallucinations may involve lights, animals, strange figures, and demons. [AH] may include heavy footsteps, humming or buzzing noises, and sounds of heavy objects being moved” (Holden and French, 2002).  Other bemusing features include the likelihood of SP occurring while lying supine in preparation for or the initial emergence from the sleeping state. There is a feeling of progressive bodily heaviness. Often the eyes are the only mobile body part (Holden and French, 2002) along with the muscles of respiration and audition (Higgins and George, 2013).

Turning to the physiology of REM sleep helps to better understand what makes SP so noteworthy.  Rapid eye movement (REM) sleep occurs in the deepest stage of slumber. The brain’s electrical activity mimics wakefulness during REM episodes. In non-REM (NREM) sleep ocular motion is minimal and involuntary homeostatic processes, like metabolic and heart rates are decreased.  PET studies show an idling brain in NREM sleep and muscular movement is possible. It is in REM sleep that skeletal muscles become atonic, autonomic activation is heightened, and the brain is essentially active and hallucinating. Though the dreaming brain visits all stages of sleep, REM sleep elicits a more “illogical, bizarre, and even hallucinatory” variety compared to the thought-based, problem-solving dreams of NREM sleep (Higgins and George, 2013).

When everything is going right in REM sleep you are indeed paralyzed, mostly likely hallucinating, and have brain activity akin to being conscious. Holden and French (2002) analysis of the literature seeks to expound on just what makes SP anomalous.  Thus far, most ideas around the physiology of SP are speculative. However, a 3-category neurological model has been posited to gain further insight into this parasomnia. The first category labeled ‘Intruder’ characterizes the “sensed presence, extreme fear, and [AVH]” believed to stem from lengthy amygdala analysis of the possible fear source. This analysis is minutes long versus the normal seconds long processing. The increased processing time only serves to increase the sense of fear. Seeking to understand the fear source, “endogenous [cues] (middle ear activity) or external stimuli (e.g., shadows or external sounds)” are pooled and become increasingly susceptible to hallucinatory interpretations.

The second category, ‘Incubus’, marks the sensations of chest pressure, dyspnea and pain. Gaining a level of consciousness during REM sleep means that you have, at least, semi-awakened in a body out of your control. Respiratory rate (RR) is increased involuntarily. The semi-conscious person may instinctively attempt to control their RR (by trying to breathe deeply) but fail. The brain may register this failure as chest pressure and pain. The last category, ‘Unusual Bodily Experiences’, include floating, flying and/or out-of-body sensations.  A paralyzed body can not appropriately interpret feedback from vestibular activation. Vestibular activation gives us information about our bodies position and orientation in space. Misinterpretations in this area can lead to sensations of floating or flying.  Receiving visual information from the environment in addition to the aforementioned sensations can play into the perception of being out of one’s body.  Cheyne et al.’s neurological model as summarized by Holden and French is the closest researchers have come to explaining SP, or more specifically HHEs.

The experience of sleep paralysis is terrifying and not wholly uncommon. There is a possibility that we will have patients who have experienced SP episodes.  This can include both isolated and repeated occurrences with demonic/alien hallucinatory features and without. We should first rule out the possibility of a concurrent sleep or seizure disorder. Advising patients to try and avoid sleeping on their backs is also a helpful intervention. It’s also helpful to know that any disruptions to the sleep routine can increase the insistence of SP including jet lag and daytime naps.  Depression-based intrusions to a sleep schedule is another common impetus (McNally and Clancy, 2005). Continued work in psychotherapy can also help attenuate SP episodes. Harvard researchers have discovered a correlation between childhood sexual abuse and higher rates of SP than those denying a history (McNally and Clancy, 2005; 2005). Early bodily trauma experiences increase the likelihood of dissociative episodes. A study showed that those with higher scores of dissociation had a corresponding increased likelihood of “unusual sleep-related experiences (e.g., flying dreams, hypnopompic imagery, sensing the presence of someone)” (McNally and Clancy, 2005). Keeping this information in mind, psychiatric care providers can use effective minimally invasive interventions to treat seemingly far out disturbances like SP.

Sweet dreams!

References

Cheyne, J. A. (2003). Sleep paralysis and the structure of waking-nightmare hallucinations. Dreaming, 13(3), 163-179.

Cheyne, J. A., Rueffer, S. D., & Newby-Clark, I. R. (1999). Hyponagogic and hypopompic hallucinations during sleep paralysis: Neurological and cultural construction of the night-mare. Consciousness and Cognition, 8, 319-337.

Higgins, E. S., & George, M. S. (2013). Sleep and circadian rhythms. In The neuroscience of clinical psychiatry: The pathophysiology of behavior and mental illness (2nd ed., pp. 174-187). Philadelphia, PA: Wolters Kluwer.

Holden, K. J., & French, C. C. (2002). Alien abduction experiences: Some clues from neuropsychology and neuropsychiatry. Cognitive Neuropsychiatry, 7(3), 163-178.

Kristof, N. D. (1999, July 6). Alien abduction? Science calls it sleep paralysis. The New York Times. Retrieved from http://www.nytimes.com/1999/07/06/science/alien-abduction-science-calls-it-sleep-paralysis.html

McNally, R. J., & Clancy, S. A. (2005). Sleep paralysis in adults reporting repressed, recovered, or continuous memories of childhood sexual abuse. Journal of Anxiety Disorders, 19, 595-602.

McNally, R. J., & Clancy, S. A. (2005). Sleep paralysis, sexual abuse, and space alien abduction. Transcultural Psychiatry, 42(1), 113-122.

Sacks, O. (2012). Hallucinations. New York, NY: Random House, Inc.

 

Predicting Violence

Given the complexity of the human mind, how are we, as practitioners, to know if the patient in our office is likely to commit a violent crime? What if we are wrong? Once deemed violent, can someone ever be considered fully safe? How long must someone demonstrate mental health “stability” to be considered safe and how often should they be assessed? What of individuals who do not meet criteria for a particular disorder, but nevertheless cause a practitioner profound concern about the safety of others? Should everyone be assessed, whether or not there is an obvious cause for concern?

Exploring the answers to these questions is a large undertaking that must be met through many smaller steps. Through a series of questions, this post merely seeks to understand and handful of existing tools which aim to predict violence in individuals and reviews their reliability and validity where possible. This post does not attempt to be an exhaustive list of available predictive measures; rather, it serves as an entry point to continue the exploration of these topics in greater depth over time. From any perspective, implications for nursing and other professionals (health care, teachers, law enforcement, among others) are varied and profound. As arbiters of mental health diagnoses, having reliable and valid tools with which to practice and protect both the public and our selves is essential.

As a history of violence is the greatest predictor of future violence (Scott & Resnick, 2006), the Violence Risk 10 item scale (V-RISK-10) was studied in populations of both voluntary and involuntarily admitted patients with acute psychiatric disorders. Though they found the V-RISK-10 had good predictive validity in patients with a known history of violence (Roaldset, Hartvig, & Bjorkly, Mar 2011), a drawback to this study was the acknowledgement “violent persons with a personality disorder” are more likely to encounter law enforcement and incarceration. Therefore, there are a significant portion of violent individuals may be missed, should the focus of assessment dwell solely within mental health and psychiatric facilities.

In contrast, 1.794 Polish prisoners received the Psychopathic Personality Traits Scale (PPTS) (Boduszek, Debowska, Dhingra, & DeLisi, 2016). This 20-item, self-report scale attempts to identify violent tendencies despite age, cultural background, gender or criminal history. The scale seeks to quantify the prevalence of 4 factors: affective responsiveness, Cognitive responsiveness, interpersonal manipulation and egocentricity. Individual questions are then further categorized as belonging to one of two groups, either knowledge/skills or attitudes/belief. The study indicated that questions categorized as attitude/belief were much more indicative of violence in the respondent. A drawback to this study is the violence measured was historical, thus, to be utilized as a measure of predictive violence, the study must be replicated in persons who are not already detained for violent acts.

So where do these attitudes/beliefs come from? A study of juvenile offenders in Florida sought to evaluate the association between childhood trauma and future violence, using the Adverse Childhood Experiences (ACE) measure (Fox, Perez, Cass, Baglivio, & Epps, 2015). The study indicates the ACE measure is useful in identifying those who are at most risk to engage in violent activity (Fox et al., 2015). Early identification may enable providers to implement behavioral and mental health treatments aimed at violence prevention. Determining which interventions are effective and proving that intervention provided a reduction in violence is an obvious additional area of exploration.

What if we tackled these issues earlier? Can violent tendencies be identified in children? A 2015 study (Hong, Tillman, & Luby, 2015) attempted to distinguish between transient and normative conduct problems in children using the Kiddie Disruptive Behavior Disorder (KDBD) Scale. 2,232 children were followed from birth to 10 years old and evaluated through puppet interviews and questions for parents (mostly mothers), regarding their child’s behaviors (Hong et al., 2015). The results found that what constitutes “normal behavior” is a matter of degree. For instance, low-intensity defiance is not predictive of future violence, but high-intensity defiance is. Aggression to people or animals, however, were both associated with future violence regardless of intensity (Hong et al., 2015). The conclusion of this study suggests that use of the KDBD may be a useful tool in referring children to treatment and services.

Unfortunately, the costs of identifying children, adolescents and adults at risk for violence can be very steep, whether or not treatment is ever implemented. A 2013 study (Zagar et al., 2013) explored the question of whether computer testing would be equivalent to “paper and pencil” testing, as the use of computer tests is far less expensive and more readily available to those who might wish to administer such tests. Using five different measures, the tests were administered by paper/pencil or internet (Zagar et al., 2013). Findings concluded that the cost of internet testing was 70-80% less than paper tests, with the added benefit of immediately available results (Zagar et al., 2013). Immediate access to results may assist providers to make timely decisions about patient needs, referrals and safety.

It appears that there are numerous available scales with which to predict the likelihood of violence at many different ages and it is important to know that internet or computer-based testing may be cost-effective, immediate and accurate for many of these measures. These tools may help providers in areas with limited resources or access to services identify and refer individuals for psychiatric treatment. Of course, the incidence of violence with which we currently contend in this country, means there is no shortage of need for reliable, valid assessment measures and appropriate treatment. Nor will there be a shortage of opportunity to research the effectiveness of those scales and interventions. Nurses, ever on the front lines of patient care and intervention, be they in the schools, home, office or institutions, will undoubtedly be on the front lines of this effort, too.

References

Boduszek, D., Debowska, A., Dhingra, K., & DeLisi, M. (2016). Introduction and validation of psychopathic personality traits scale (PPTS) in a large prison sample. Journal of Criminal Justice, 46, 9-17. doi:10.1016/j.jcrimjus.2016.02.004

Dodge, K. A., Bierman, K. L., Coie, J. D., Greenberg, M. T., Lochman, J. E., McMahon, R. J., & Pinderhughes, E. E. (2015). Impact of early intervention on psychopathology, crime, and well-being at age 25. American Journal of Psychiatry, 172, 59-70. doi:10.1176/appi.ajp.2014.13060786

Fox, B. H., Perez, N., Cass, E., Baglivio, M. T., & Epps, N. (2015). Trauma changes everything: Examining the relationship between adverse childhood experiences and serious, violent and chronic juvenile offenders. Child Abuse and Neglect, 46, 163-173. doi:10.1016/j.chiabu.2015.01.011

Hong, J. S., Tillman, R., & Luby, J. L. (2015). Disruptive behavior in preschool children: Distinguishing normal misbehavior from markers of current and later childhood conduct disorder. Journal of Pediatrics, 166, 723-730. doi:10.1016/j.jpeds.2014.11.041

Im, D. S. (2016). Template to perpetrate: An update on violence in autism spectrum disorder. Harvard Review of Psychiatry, 24, 14-35. doi:10.1097/HRP.0000000000000087

Roaldset, J. O., Hartvig, P., & Bjorkly, S. (Mar 2011). V-RISK-10: Validation of a screen for risk of violence after discharge from acute psychiatry. European Psychiatry, 26, 85-91. doi:http://dx.doi.org/10.1016/j.eurpsy.2010.04.002

Scott, C. L., & Resnick, P. J. (2006). Violence risk assessment in persons with mental illness. Aggression and Violent Behavior, 11, 598-611. doi:10.1016/j.avb.2005.12.003

Zagar, R. J., Kovach, J. W., Basile, B. B., Hughes, J. R., Grove, W. M., Busch, K. G., . . . Zagar, A. K. (2013). Finding workers, offenders, or students most at-risk for violence: Actuarial tests save lives and resources. Psychological Reports, 113, 685-716. doi:10.2466/16.03.PR0.113x29z3

Memory and psychosis

Brief psychotic episodes or full blown schizophrenia are often associated with memory issues, which are often attributed to the disruption in cognitive processes that accompany these mental health issues. Higgins and George (2013) began to explore the psychopathology of this, making the connection between working memory (which was initially made the center of attention with the case of Phineas Gage) and the coordinated firing of pyramidal neurons in the prefrontal cortex. GABA, the major inhibitory neuron, is required to synchronize the pyramidal neurons, and the disruption of this mechanism was related to a decrease in working memory.

To try and better understand the connection between psychosis/schizophrenia and memory issues, let’s first turn to a paper by Ragland et al. (2015) out of UC Davis. This study tried to decipher which specific areas of the brain were affected during relational encoding (trying to remember something based on its relationship to something, such as an ingredient to a recipe) and retrieval and item specific encoding (focusing on unique features of an object) for patients with schizophrenia. Classically, relational encoding is associated with regions of the prefrontal cortex and retrieval is associated with the medial temporal lobe. Schizophrenia, in particular, has been seen to negatively affect relational memory, and this study used fMRIs to identify biomarkers related to this deficit. They found that individuals with schizophrenia who performed poorly on relational item retrieval and relational encoding tasks had less activation in the hippocampus and dorsolateral prefrontal cortex, respectively. In other words, the combination of the hippocampus and dorsolateral prefrontal cortex accounts for poor relational memories in individuals with schizophrenia. This not only gives an explanatory model for improving relational memory, but also provides a more specific target on how to improve relational memory in patients with schizophrenia. Other studies have shown a relationship between the disconnection of the right middle frontal gyrus and right superior parietal lobule in at risk mental status and first episode psychosis patients with working memory deficits (Schmidt et al, 2013), while others have pointed to the medial frontal and medial posterior parietal cortex in patients in at-risk mental states and first episode psychosis with impaired spatial working memory (Broome et al., 2010). Twin studies have also shown poorer spatial working memory performance in both twins affected by and not affected by schizophrenia (twins discordant for schizophrenia) (Pirkola et al., 2005).

Other studies, such as the paper by Badcock et al. (2005), makes the connection between executive functioning and memory as it specifically relates to patients with schizophrenia. They found that manipulation of stored information was related to executive functioning, and that changes in the frontal-parietal circuitry in patients with schizophrenia were responsible for these memory related impairments.

This connection between working memory and schizophrenia has also been used from an assessment standpoint. Bendfeldt et al. (2015) looked to see if the working memory areas of their brain could be used to identify patients as being in a at-risk mental state or ARMS for schizophrenia. Analysis of working memory task induced fMRIs showed that they were able to differentiate ARMs from healthy controls with 76.2% accuracy, with the medial frontal, paracingulate, cingulate, inferior frontal and superior frontal gyri, inferior and superior parietal lobules, and precuneus, with varying activation depending on the verbal working memory activity. It is notable that this study was not able to differentiate between fMRIs of individuals with first episode psychosis and at-risk mental state, which was attributed to the inability to differentiate between the two with the given methods of measurement.

From a genetic perspective, the catechol-O-methyltransferase (COMT) and dopamine transporter (DAT1) have been looked at in relation to memory and schizophrenia, where changes and vulnerabilities of these have been linked to poorer outcomes (Shifman et al., 2004). They both modulate dopamine inactivation in the prefrontal cortex, which is related to the effectiveness of the neuronal signal in working memory. Some studies have found COMT specifically affects more complex cognitive processes related to information maintenance and modulation in working memory (Brudger et al., 2005).

In speaking to treatment, antipsychotics have often been used to improve cognitive abilities in patients with schizophrenia and psychosis, such as with working memory (Schmidt et al., 2013). The use of other medications, such as anti-depressants, was explored by Steen et al. (2015), specifically looking at escitalopram, citalopram, and venlafaxine plus O-desmethylvenlaflaxine.  It has been shown in other studies that antidepressants can increase neurogenesis in (Dranvnovsky and Hen, 2006) and be neuroprotective (Sheline et al., 2003) to the hippocampus, and Steen et al. (2015) point to Han et al. (2011) that suggests his combination may have improve cognition. They found that patients’ schizophrenia with serum levels of venlafaxine plus O-desmethylvenlaflaxine had better verbal memory and long term delayed recall, although this same effect was not found for citalopram or escitalopram. This was possibly attributed to the combined use of serotonin and norepinephrine in venlafaxine.

The psychopathological, genetic, assessment, and treatment related to memory and psychosis and schizophrenia still has more answers than questions, but promising research has provided us with solid starting points that have implications not only for further research, but possible intervention and treatment that can hopefully inform practical, clinical practice.

Badcock J.C., Michael, P.T., Rock, D. (2005). Spatial working memory and planning ability: Contrasts between schizophrenia and bipolar I disorder. Cortex, 41(6): 753–763.

Bendfeldt, K., Smieskova, R., Koutsouleris, N., et al. (2015). Classifying individuals at high-risk for psychosis based on functional brain activity during working memory processing. Neuroimag Clin., 9: 555-563. doi:  10.1016/j.nicl.2015.09.015

Broome M.R., Fusar-Poli P., Matthiasson P., et al.  (2010). Neural correlates of visuospatial working memory in the ‘at-risk mental state’. Psychol. Med., 40(12): 1987–1999.

Brudger, G.E., Keilp, J.G., Xu, H., et al. (2005). Catechol-o-methyltransferase (COMT) genotypes and working memory: associations with differing cognitive operations. Biol Psychiatry, 58(11): 901–907.

Dranovsky, A., and Hen, R.(2006). Hippocampal neurogenesis: regulation by stress and antidepressants. Biol. Psychiatry, 59: 1136-1143

Han X., Tong J., Zhang J., (2011). Imipramine treatment improves cognitive outcome associated with enhanced hippocampal neurogenesis after traumatic brain injury in mice. J. Neurotrauma, 28: 995-1007

Higgins, E. S., George, M. S. The Neuroscience of Clinical Psychiatry:  The Pathophysiology of Behavior and Mental Illness, 2nd edition.  Philadelphia, PA:  Lippincott Williams & Wilkins; 2013.

Pirkola, T., Tuulio-Henriksson, A., Glahn, D., et al. (2005). Spatial working memory function in twins with schizophrenia and bipolar disorder. Biol. Psychiatry, 58(12): 930–936.

Ragland, D.J., Ranganath, C., Harms, M.P., et al (2015). Functional and Neuroanatomic Specificity of Episodic Memory Dysfunction in Schizophrenia: A Functional Magnetic Resonance Imaging Study of the Relational and Item-Specific Encoding Task. JAMA Psychiatry, 72(9): 909-916. doi:10.1001/jamapsychiatry.2015.0276

Schmidt, A., Smieskova, R., Aston, J. (2013). Brain connectivity abnormalities predating the onset of psychosis: correlation with the effect of medication. J.A.M.A. Psychiatry, 70(9): 903–912.

Sheline Y.I., Gado M.H., and Kraemer H.C. (2003). Untreated depression and hippocampal volume loss. Am. J. Psychiatry, 160: 1516-1518

Shifman, S., Bronstein, M., Sternfeld, M., et al. (2004). COMT: A common susceptibility gene in bipolar disorder and schizophrenia. Am J Med Genet B Neuro Psychiatr Genet, 128B(1): 61–64.

 

Intelligence

Intelligence is a commonly discussed characteristic, and many may consider it a straight forward concept.  It has been described as “problem-solving skills. . . logical reasoning, and/or adaptation to the environment” (Seigel 1989), in other words, an innate ability to find solutions.  When it comes to biology, the intricate details of intelligence are still rather misty.  Research suggests that intelligence is related to a combination of density and connectedness of the neurons of the brain.  It is not a simple matter of size or number of synapses, but a complicated web of multiple variables (Higgens & George 2007).

Theoretically, intelligence is an intrinsic quality that does not change over time.  There is evidence that suggests IQ is fairly constant throughout life.  In fact, mental ability in old age has been shown to be strongly influenced by childhood intelligence (Deary et al. 2004).  However, intelligence has also been shown to have a positive relationship with education (Lynn & Vanhanon 2002).  Such evidence begs the question, if intelligence is intrinsic and constant over time, shouldn’t it remain constant regardless of education?  If education truly does result in an increase in intelligence does our definition and understanding of intelligence need to change?  Or is the problem related to the method used to measure intelligence?

Intelligence, has been correlated with numerous variables, from health to material wellbeing.  It has been suggested that there is evidence indicating that intelligence is correlated to longevity.  One study found that childhood psychometric intelligence was associated with improved survival past the age of 76.  It is difficult to conclude this to be a direct relationship.  Lower IQ is also associated with lower socioeconomic background (Hernstein & Murray 1994), lower socioeconomic background is similarly correlated with greater disease and health concerns.  So it would make sense that such individuals with lower IQ and socioeconomic background have shorter life spans.  To clearly understand the relationship between intelligence and longevity, one would need to control for socioeconomic status (Deary et al. 2004).

Numerous studies have been conducted investigating the relationship between intelligence and prosperity.  One study found a positive relationship between a nation’s average IQ and GDP (Lynn & Vanhanon 2002).  Some investigations have found a positive relationship between IQ and financial wellbeing (Wachtel 1976), or that there are few individuals with high IQ who live in poverty (Hernstein & Murray 1994).  Other studies have found IQ to be a poor predictor of and individual’s monetary situation.  One study found a strong relationship between IQ and annual income, but that relationship did not translate to wealth or net worth.  In fact, high IQ also had a correlation with behaviors that could lead to financial distress (Zagorsky 2007).

Research has also investigated the relationship between intelligence to certain mental illnesses.  High IQ is a risk factor for bipolar disorder.  However, IQ has been shown to have a protective effect against schizophrenia (Matheson & Langdon 2008)

One difficulty encountered in the endeavor to understand intelligence is finding an accurate method for measuring intelligence.  IQ tests, probably the most common measure used to quantify intelligence, provide a number that purportedly reflects intelligence.  However, some argue that tests are not a truly objective measure.   Some tests rely heavily on written language.  As a result, a test may not accurately reflect the intelligence of say an individual with a learning disability or who did not have educational opportunities.  A low score could reflect such an individual’s difficulty with written language, nor their innate ability to solve problems (Seigel 1989).  IQ measure could even be impacted by external factors of the day of examination such as health or stress level (Zagorsky 2007).

Test outcomes can be skewed in other ways as well.  Researchers Croizet and Dutrévis found that students from disadvantaged socioeconomic backgrounds performed poorer on standardized tests than other students, but only when they were told that such test would be used as a diagnostic for intellectual ability.  When students from disadvantaged backgrounds did not know that the tests would be used as a diagnostic measure for intellectual ability, their scores were as good as the scores of their higher socioeconomic level counterparts.  Evidently scores can be impacted by perceived intent (Croizet & Dutrévis 2004).  This suggests that tests of intelligence may not accurately reflect the problem solving capacity of individuals from disadvantaged backgrounds.

It seems that there are certainly some holes in our current understanding of intelligence, particularly with regards to its relationship to socioeconomic status and education.  If intelligence is truly an intrinsic characteristic, one would think that it would not be related to such external factors.  On the other hand, if intelligence truly does have a substantial relationship with things like education and socioeconomic status, one would conclude that it is not an intrinsic characteristic, but rather an acquirable trait.  In reality, intelligence is most likely a combination of both intrinsic capacities for problem solving and education.  With the recent increases in our understanding of the plasticity of the brain, it seems reasonable to conclude that intelligence is not a static quality.  It also indicates that interventions can be taken to increase the IQ level of individuals from less advantaged backgrounds.

 

References

Croizet, J., & Dutrévis, M. (2004). Socioeconomic status and intelligence: Why test scores do not equal merit. Journal of Poverty, 8, 91-107. doi:10.1300/J134v08n03_05

Deary, I.J., Whiteman, M.C., Starr, J.M., Whalley, L.J., & Fox, H.C. (2004). The impact of childhood intelligence on later life: following up the Scottish mental surveys of 1932 and 1947. Journal of Personality and Social Psychology, 86, 130–147. doi: 10.1037/0022-3514.86.1.130

Hernstein, R., & Murray, C. (1994). The Bell curve: Intelligence and class structure in American life. New York, NY: Free Press.

Higgens, E.S., & George M.S. (2007). The neuroscience of clinical psychology. Philadelphia, PA: Lippincott Williams & Wilkins.

Lynn, R., & Vanhanen, T. (2002). IQ and the wealth of nations. Westport, CT: Praeger Publishers.

Matheson, S., & Langdon, R. (2008). Schizotypal traits impact upon executive working memory and aspects of IQ. Psychiatry Research, 159, 207-214. doi:http://dx.doi.org/10.1016/j.psychres.2007.04.006

Sewell, W. H., & Shah, V. P. (1967). Socioeconomic status, intelligence, and the attainment of higher education. Sociology of Education, 40, 1-23. doi:10.2307/2112184

Siegel, L. S. (1989). IQ is irrelevant to the definition of learning disabilities. Journal of Learning Disabilities, 22, 469-78, 486.

Wachtel, P. (1976). The effect on earnings of school and college investment expenditures. The Review of Economics and Statictics, 58, 326-331.

Zagorsky, J. L. (2007). Do you have to be smart to be rich? the impact of IQ on wealth, income and financial distress. Intelligence, 35, 489-501. doi:http://dx.doi.org/10.1016/j.intell.2007.02.003

Neurocognition in Prodromal Psychosis

JAMA Psychiatry recently published new results from the North American Prodrome Longitudinal Study (NAPLS), a multi-site longitudinal study of the early warning signs of schizophrenia and other psychotic disorders. NAPLS tested and followed 689 clinical high risk (CHR) subjects (398 male and 281 female) and 264 healthy control (HCs) subjects (137 male and 127 female). In this latest analysis, the authors found that neurocognitive dysfunction is related to later conversion to full-blown psychotic disorders in clinical high-risk subjects (CHRs). Patients were initially identified as high-risk by factors such as unusual thoughts, family history, and functional deficits. In particular, CHRs who later converted to psychosis (CHR-Cs) showed high premorbid verbal abilities but deficits in attention, working memory, and declarative memory when compared to either healthy controls (HCs) or CHRs who did not convert to psychosis (CHR-NCs).

napls

The long-term goal of this study is to improve early detection and treatment of psychosis. As far as early detection goes, the results they report appear to be confirmation that the combination of cognitive signs they identified, especially declarative memory, are a sensitive marker for conversion from prodromal to active psychosis. In terms of treatment, there did not seem to be a large difference in cognitive deficits between medicated and unmedicated CHR-Cs. Medicated subjects showed somewhat greater attention and working memory abilities but were overall comparable to unmedicated subjects. The authors of the study emphasize that more research should be done to determine what early interventions might be more protective of cognition specifically.

One intervention that might be promising is cognitive training. One small pilot study from 2009 found that prodromal patients benefited from 10 sessions of computer-based cognitive training, showing improvements in long-term memory and attention. Patients who already had schizophrenia, on the other hand, showed no benefit from the same intervention. According to the authors of the study,

The existence of such a higher potential in comparison to patients with schizophrenia is supported by the findings that persons at ultra-high-risk mental state show smaller gray matter volumes than controls but still larger gray matter volumes than patients who already underwent transition to psychosis. […] In terms of the improved performance of the persons at risk mental state in this study, it is conceivable that cognitive training may facilitate neuroplastic phenomena and may thus have a neuroprotective functioning. Thus, the application of cognitive training has to be provided as early as possible in the prodromal phases of schizophrenia.

They added that additional research with larger sample sizes would be needed to confirm these results.

A 2014 review of cognitive training and mental illness in the American Journal of Psychiatry suggests that training seems to be of modest benefit overall, but that the best results so far have come from studies that combine cognitive training with other forms of rehabilitation, such as medication, and strategy training. Initial results from neuroimaging studies show that  positive results correspond with structural and functional changes in the prefrontal regions of the brain. The review also suggests that factors like therapeutic alliance, age, motivation, and clinician expertise could play a large role in successful cognitive training, and suggests that future research should focus on teasing out the different mechanisms involved.

 

Sources:

Keshavan, M. S., Vinogradov, S., Rumsey, J., Sherrill, J., & Wagner, A. (2014). Cognitive training in mental disorders: update and future directions. American Journal of Psychiatry.

Rauchensteiner, S. S. (2011-02). Test-performance after cognitive training in persons at risk mental state of schizophrenia and patients with schizophrenia.. Psychiatry research, 185(3), 334-339.doi:10.1016/j.psychres.2009.09.003

Seidman LJ, Shapiro DI, Stone WS, Woodberry KA, Ronzio A, Cornblatt BA, Addington J, Bearden CE, Cadenhead KS, Cannon TD, Mathalon DH, McGlashan TH, Perkins DO, Tsuang MT, Walker EF, Woods SW. Association of Neurocognition With Transition to PsychosisBaseline Functioning in the Second Phase of the North American Prodrome Longitudinal Study. JAMA Psychiatry. Published online November 02, 2016. doi:10.1001/jamapsychiatry.2016.2479