Alcohol addiction

Written by Quek Ten Cheer

Edited by Sasinthiran


Alcohol addiction is a serious societal issue that can lead to many domestic and social problems. One way to prevent this issue from becoming worse is to understand more about how alcohol, as a drug, causes these problems. In this article, we aim to inform readers on the biological effects of alcohol, especially on the brain, and how addiction is currently being treated medically.

Alcohol and crime

Alcohol has been implicated in between 57% and 85% of violent crimes1. In addition, many suicides are committed under the influence of alcohol2. The most likely mode of action for alcohol in stimulating aggression is its general disinhibiting effects on behaviour. Alcohol silences higher cortical areas responsible for impulse control, often leading to behaviour that is normally actively suppressed, including aggression.

What is alcohol?

Alcohol is a psychotropic drug, which means that it is a drug that affects our mental state. This places alcohol (commonly referred to as ethanol in scientific literature) in the same category as:

  • Cannabinoids (the active material in cannabis, or marijuana);
  • Nicotine (one of the active psychotropic agents in cigarettes and cigars);
  • Psychostimulants, much stronger drugs which include cocaine, MDMA (commonly known as ecstasy), and methamphetamine (you might know it as the blue ‘meth’ from Breaking Bad);
  • Opiates, morphine-like drugs such as heroin (diacetyl morphine).3,4

In addition, alcohol is a biphasic drug5; small amounts act as a stimulant by reducing inhibition and producing mild euphoria. Higher doses depress the central nervous system (CNS) that will initially promote relaxation but lead to the person experiencing effects such as ataxia (uncoordinated movement), sedation and general ‘drunkenness’.

Fun fact: Asian Flush

Some of us get the ‘Asian flush’, i.e. our faces become reddish from drinking (because the body is unable to quickly metabolize acetaldehyde, a by-product of alcohol metabolism). (Acetaldehyde is the main culprit behind headaches, nausea, and hangovers).

In fact, the reaction that breaks down acetaldehyde, by means of the enzyme aldehyde dehydrogenase, is faster in alcoholics than in non-alcoholics.6


Alcohol addiction

What makes a person addicted to alcohol? The 5th edition of the Diagnostic and Diagnostic and Statistical Manual of Mental Disorders (DSM-V), recognises excessive use of alcohol as a disorder in which patients are diagnosed with the substance use disorder when they display at least 2 of the following 11 symptoms within a 12 month period:

  • Consuming more substance than originally intended
  • Worrying about stopping or consistently failed efforts to control one’s use
  • Spending a large amount of time using the substance, or doing whatever is needed to obtain them
  • Use of the substance results in failure to “fulfil major role obligations”
  • “Craving” the substance
  • Continuing the use of a substance despite health problems caused or worsened by it
  • Continuing the use of a substance despite its having negative effects in relationships with others
  • Repeated use of a substance in a dangerous situation (e.g. when driving a car)
  • Giving up or reducing activities in a person’s life because of the substance use
  • Building up a tolerance to the alcohol or drug. Tolerance is defined by the DSM-V as “either needing to use noticeably larger amounts over time to get the desired effect or noticing less of an effect over time after repeated use of the same amount.”
  • Experiencing withdrawal symptoms after stopping use. Withdrawal symptoms typically include, according to the DSM-V: “anxiety, irritability, fatigue, nausea/vomiting, hand tremor or seizure in the case of alcohol.”

As can be seen from the DSM-V definition of alcohol use disorder, the interpretation of an individual’s use of alcohol as being excessive and leading to dysfunction is subjective and must be considered together with environmental and contextual factors.

However, alcohol addiction is also largely a problem that needs to be treated by addressing the environmental factors in addition to its targeted pharmacological treatment; prescribed medicines alone cannot fully treat the condition.

There is a plethora of literature on many illicit drugs of abuse, in particular cocaine. They demonstrate the pharmacological basis by which people become addicted to these drugs, in hopes of deriving better pharmacological treatment which targets, mainly, the addiction pathways in the brain.

Let’s talk about the brain

Firstly, if we want to know anything about alcohol addiction, we need to start with the very-important mesolimbic pathway, commonly called the ‘reward pathway’ for its role in addiction and associated disorders. You can recall this term and its position in the brain by the fact that ‘meso’ means ‘middle’ in Greek, and it is located in the middle of the brain. Thus ‘mesolimbic’ also means the midbrain, or ‘middle brain’.


The mesolimbic pathway, highlighted in the opaque blue in the above figure7, connects the ventral tegmental area (VTA) to the nucleus accumbens (NAC). It is also referred to as a dopaminergic pathway (abbreviated DAergic pathway) because it transmits the neurotransmitter dopamine throughout the two areas.8 Don’t underestimate its small size! It is the most significant neural pathway in the brain within which changes occur in all known forms of addiction. It is widely studied in the reward circuitry underlying drug abuse, depression, addiction, as well as conditioning and studies on human behaviour.

The brain is addicted to pleasure and positive effects; more specifically, it craves any activity which leads to the activation of dopaminergic pathways that trigger the brain’s reward response with the release of the neurotransmitter dopamine 9. Research shows that addictive substances such as alcohol are in fact addictive primarily because they activate such pathways leading to a reward response, leaving the brain craving for more 10. In other words, the more a particular behaviour, such as taking alcohol, triggers the reward centers of the brain, the more the brain seeks out such behaviour through learned operant conditioning by positive reinforcement. This then increases the occurrence of that behaviour in the future (thus, addiction).


A 2003 study done by Boileau et al.  have found that the consumption of alcohol stimulates the release of dopamine in the nucleus accumbens12. Dopamine (commonly abbreviated as DA in literature) was synthesised over 100 years ago (in 1910), but was recognised to be a neurotransmitter many decades later, in the 1950s. In the brain, dopamine is produced in hypothalamic neurons as well as neurons of the VTA and substantia nigra.13


After secretion of DA into the synapse, the intact molecule is reabsorbed into the neurons by a specific transporter, the dopamine transporter (DAT). They are then metabolised within cells by monoamine oxidase (MAO) or catecholamine O-methyl transferase (COMT); both enzymes convert dopamine into inactive products.

Compounds that inhibit DAT, such as cocaine (meaning the effect of dopamine is prolonged in the synapse as its concentration remains elevated), cause mood elevation and addiction.

Compounds that inhibit MAO (meaning DA does not get broken down after being re-uptaken) are effective antidepressants, which includes selective serotonin reuptake inhibitors (SSRIs), such as Prozac (you might know it as ‘fluoxetine’ or ‘fluoxetine HCl’ if you’ve ever had hypochondriac tendencies).

Neurotransmitters/neuropeptides that influence alcohol consumption

In alcohol addiction, there are several neurotransmitters and neuropeptides in the brain that influence alcohol consumption. These include Glutamate, GABA (gamma-aminobutyric acid), nACHR/glycine, DA/5-HT, Cannabinoids, Opioids, and CRF/NPY.11


Potassium channels and GABAA receptors in the VTA

Among the potential means by which alcohol might influence the firing rate of dopaminergic neurons in the brain, the best studied are the actions of ethanol on potassium channels and GABAA receptors in the VTA.

Alcohol functions as an agonist of GABAA receptors and its binding to these receptors leads to the inhibition of the post-synaptic neurons. Alcohol, by binding to GABAA receptors on VTA GABAergic interneurons, may disinhibit (activate) VTA dopaminergic neurons that project to the NAc (nucleus accumbens) which is involved in producing a feeling of pleasure and mood elevation (Nestler, 2005)14.

Interestingly, autopsies of alcoholics’ brains have revealed that they were in a hypodopaminergic state, which explains why alcoholics would continue to seek out more alcohol to achieve the sensation of pleasure and mood elevation they have learnt to associate with alcohol consumption.


Pharmacology of alcohol on the brain

Alcohol is generally viewed as being an unspecific pharmacological agent, but based on recent studies it has been shown to act by disrupting distinct receptor or effector proteins via direct or indirect interactions. There is a widespread plethora of literature on the abuse of psychostimulants, especially cocaine. (There is, however, scarce publications on abuse of alcohol.)

At concentrations in the 5-20mM range, which is the legal intoxication range for driving in many countries, alcohol directly interferes with the functions of several ion channels and receptors.11

Recent molecular pharmacology studies demonstrate that alcohol has a few primary targets, which include NMDA, GABAA, 5-HT3, nAChR, as well as L-type Ca2+ channels and GIRK, where concentrations as low as 1mM produce alterations in the functions of these receptors and ion channels. Some of these are outlined below:

NMDA receptors

NMDA receptors are commonly associated with excitatory glutamatergic activity and in the formation of Long Term Potentiation (LTP) which is essential for memory formation. Disruption of this receptor function by alcohol explains why many would find it difficult to remember the events of a night out when heavily intoxicated15– also known as ‘blackout’.

More inhibitory GABAA receptor activity

Moreover, alcohol has been found to stimulate inhibitory GABAA receptor activity by serving as an agonist in the hippocampus, an area of the brain associated with memory formation, contributing to this brief amnesic episode16.

Alcoholic activation of inhibitory GABAA activity of neurons projecting to higher areas of the cortex brings about reduced inhibitions and anxieties and facilitates better social interactions while reducing impulse control.

In fact, recovering alcohol addicts often experience life-threatening seizure episodes as a withdrawal symptom due to a rebound effect whereby inhibitory GABAA receptors become hypoactive in the absence of alcohol.


Alcohol also functions as an endorphin, mimicking the effects of opiate drugs and producing an endorphin ‘high’ associated with the use of such drugs.

More inhibitory neuromodulator activity

Alcohol has also been found to increase the activity of inhibitory neuromodulators such as adenosine which leads to sedative effects and a reduced state of awareness.

Treatment of alcohol addiction

It is estimated that 7.9% of people 12 years or older in the U.S. require help for alcoholism, more than twice the percentage of the population estimated to require treatment for the abuse of all illicit drugs collectively.

Alcohol use has been linked to diseases and ailments such as malnutrition (due to the ‘empty calories’ of alcohol) and in particular fetal alcohol syndrome (which causes developmental and physical abnormalities in the offspring of mothers who consume alcohol during pregnancy).

Alcohol abuse has been linked to domestic abuse, sexual assault, and can destroy families.

Treatment of alcohol addiction is largely based on psychological and psychiatric help, as compared to pharmacologically-based treatment.

Alcohol addiction requires a multi-pronged approach to treatment. Drugs alone show little effect insofar as treatment is concerned; environmental factors need to be addressed when treating alcohol addiction. Treatment involves the facilitation of abstinence and the prevention of relapse.

Pharmacologic treatment is often used to reduce withdrawal symptoms, but thus far has not been effective in preventing relapse. It is a theoretical possibility, however, that medications which block the reinforcing effects of drugs or drug-induced plasticity might reduce drug craving and the likelihood of relapse. Such medications can be effective if they can act without interfering with the body’s responsiveness to natural rewards (e.g. anhedonia, when the abuser suddenly finds a drastic disinterest in normal daily activities). Currently, no reward-reducing drug treatment has yet been established for clinical use.


  1. Cacioppo J., Freberg L., 2013. Discovering Psychology: The Science of Mind, Briefer Version. Wadsworth, Cengage Learning, Chapter 11, pp. 600.
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  4. Pierce, C.R., Kumaresan V., 2006. The mesolimbic dopamine system: The final common pathway for the reinforcing effect of drugs of abuse? Neuroscience and behavioural reviews, 30, Issue 2, pp. 215-238.
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  7. Mesolimbic pathway. (2015) [Image from]
  8. “Mesocorticolimbic Dopaminergic Neurons.” Neuropsychopharmacology: The Fifth Generation of Progress. Retrieved from
  9. Insel, T. R., 2003. Is Social Attachment an Addictive Disorder?. Physiology and Behavior, 79(3), pp. 351-357.
  10. Koob, G. F. & Moal, M. L., 1997. Drug Abuse: Hedonic Homeostatic Dysregulation. Science, Volume 278, pp. 52-58
  11. Vengeliene V.; Bilbao A.; Molander A.; Spanagel R. Neuropharmacology of alcohol addiction. British Journal of Pharmacology (2008) 154, 299-315.
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  13. W. Pfaff (ed.), Neuroscience in the 21st Century, DOI 10.1007/978-1-4614-1997-6_51, # Springer Science+Business Media, LLC 2013
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  16. Weiner, J. L.; Zhang, L.; Carlen, P. L. Potentiation of GABAA-mediated synaptic current by ethanol in hippocampal CA1 neurons: Possible role of protein kinase C. Journal of Pharmacology and Experimental Therapeutics 268:1388. 1395, 1994

A guide to effective learning – backed by neuroscience research

Written by Sasinthiran

Edited by Xin Chen, Yingchen and Keshiniy


I recently attended a seminar on ‘Applying Cognitive Science Principles to Promote Durable and Efficient Learning’ by Dr Sean Kang from the Dathmouth College Cognition and Learning Lab hosted by the NUS Department of Psychology. His talk focused on how optimizing test-taking and spaced learning could help improve memory for learned material and has inspired me to write this article, reviewing some of the findings he presented as well as other insights neuroscience research has provided that may be used to enhance learning and memory.

The Testing effect

For most students, tests and exams are just another hassle that stands between them and the holidays that everyone dreads and can’t wait to get over. Perhaps exams and tests elicit an unpleasant reaction in students because time and time again emphasis has been placed on performing well since the grades on such tests may determine placements in future classes, schools one is eligible to apply and even jobs one can apply to. Sadly, tests were actually initially introduced into the school curriculum to reinforce learning (Carrier & Pashler, 1992) and this true purpose has been eclipsed by the emphasis placed on scores and performing well as tests have evolved to be used as a tool to evaluate learning.

Research has shown that earlier testing will improve subsequent performance in a later test, a testing effect referred to as the benefit of retrieval practice (Carpenter, 2012; Rawson & Dunlosky, 2012). In fact, testing was found to render learned memories more robust to interference than mere reading of the material to be learnt (Tulving & Watkins, 1974; Szpunar, et al., 2008). Hence, it is a good practice for students to constantly test themselves while studying, as an adjunct to learning, to activate the same memory traces that were involved in encoding the memory the first time and allowing for re-consolidation. It was also found that a more demanding initial test (e.g. short answer questions which rely on recall memory instead of multiple choice questions which rely on recognition memory) led to better performances in a later test, regardless of whether it was short answer or multiple choice questions (K., et al., 2007). Most students would dread a tough mid-term exam and hopefully after reading this, they will learn to appreciate the fact that their lecturers are actually ‘helping’ them to perform better in the final exams by setting a tough mid-term paper!

The importance of feedback in learning

Studies have found that feedback with the correct answer following a test produced a significantly greater performance on a test administered a week later as compared to when no feedback was given (Pashler, et al., 2005), regardless of whether the initial answers were right or wrong (Butler, et al., 2007). Delayed feedback was found to produce beater retention, at least in the controlled setting of an experiment, than immediate feedback. Thus, students should make it a point to review their tests with their tutors.

Guessing answers during tests – does it affect learning?

Usually when you don’t know the answer to a question, the best option would be to guess at the answer, banking on the possibility of it being correct by chance, instead of leaving it blank and forfeiting the mark for the question. One might think that such guessing might interfere with later learning of the actual answer when feedback is given (retrograde interference). However, research has shown that such interference does not occur (Kang, et al., 2011). In fact, it was found that students learned the correct answer from feedback better when they were more confident of their earlier erroneous answers, as counter-intuitive as that might sound (Kulhavy, et al., 1976).

Context-dependent Memory

Research has shown that retrieval of learned information was better when keeping the context of retrieval (e.g. examination environment) similar to the context at encoding the memory (learning environment) (Godden & Baddeley, 1975; Smith, et al., 1978). Although students might not have the ability to modify the environment of the examination hall, they could study in a similar environment (e.g. a quiet library) to capitalize on this effect. Moreover, chewing on a particular flavor of sweet during learning and subsequent retrieval during exams might help with recall. Interestingly, research has also found that one’s general mood (Weingartner, et al., 1977), internal physiological state (Eich, et al., 1975; Miles & Hardman, 1998) or emotions (Lang, et al., 2001) and alcohol consumption (Lowe, 1982) could also affect context-dependent memory and subsequent recall.

Spaced vs Mass learning

Many of us might be guilty of cramming our lecture notes at the last minute just before the exams, thinking that keeping the content locked in our short term memory will allow us to recall the information during the test the next day. While it is true that such mass learning is superior to spaced learning in the short term retrieval of the memory, such memory traces are also more prone to decay and hence are, in the long term, forgotten more easily (Roediger & Karpicke, 2006). Thus, spaced learning is highly encouraged to maximize the lifespan of memory for learned items and has been shown to be more advantageous to mass learning in nearly all forms of learning (Dempster, 1996; Mammarella, et al., 2002; Cepada, et al., 2006).

However, in cases whereby you are just starting out on a topic, with little or no prior knowledge, mass learning is recommended to get that head start and spaced learning should be used to build up on that base knowledge in the time to come to maximize learning.

In the case of categorical learning, for example in learning to differentiate classes of organic compounds in organic chemistry, spaced learning with exposure to the different groups of compounds at the same time at each session is encouraged. By exposing one to the different classes of compounds at the same time (interleaving), the differences between them is emphasized and aids in better learning as opposed to exposing each compound one at the time (Kang & Pashler, 2012).

Notably, when the items being learned are similar and the differences between them are very subtle, for example when learning to distinguish different composers by being exposed to classical music composed by them, learning of the categories is better when music from the same composer is played in the same block so that the similarities can be abstracted better to form a concept of that composer’s style of composing (blocking). This blocked presentation in spaced learning is found to be superior to interleaving in this context when the items being learned are only subtly different.

Another finding is that spaced learning at increasing/ expanding intervals ( e.g. day 1, day 3, day 7, day 13) is slightly superior to spaced learning at equal intervals (e.g. day 1, day 3, day 5, day 7) as memory was more available and efficiently retrieved and available for longer periods (Landauer & Bjork, 1978; Kang, et al., 2014). Thus, students might find it useful to review study materials at increasing intervals.

Sleep and Memory

Besides the topics discussed above, sleep has also been demonstrated to have beneficial effects on learning and memory by re-organising existing memories to accommodate the learning of new information (Stickgold & Walker, 2007). Sleeping after learning has been found to aid in recall of memory (Gais, et al., 2006). Specifically, N-REM (non-rapid eye movement) phase of sleep was found to be essential for strengthening declarative (memory that can be verbalised such as semantic memory for knowledge and facts and episodic memory for life events) and procedural (skills-related) memories (Gais & Born, 2004). A study found that sleep deprivation for one night led to poor performance on cognitive tasks the following day and that this deficit could not be overcome even after 2 nights of adequate sleep (Stickgold, et al., 2000). Thus, pulling an all-nighter just before the exams might not be a good idea as you may be doing more harm than good.

Having read up more on this topic for the purpose of this brief review, I for one, am looking forward to my upcoming tests!


Butler, A. C., Karpicke, J. D. & Roediger, H. L., 2007. The Effect of Type and Timing of Feedback on Learning from Multiple-Choice Tests. Journal of Experimental Psychology: Applied, Volume 13, pp. 273-281.

Carpenter, S. K., 2012. Testing Enhances the Transfer of Learning. Current Directions in Psychological Science, Volume 21, pp. 279-283.

Carrier, M. & Pashler, H., 1992. The Influence of Retrieval in Retention. Memory and Cognition , Volume 20, pp. 633-642.

Cepada, N. J. et al., 2006. Distributed Practice in Verbal Recall Tasks: A Review and Quantitative Synthesis. Psychological Bulletin, Volume 132, pp. 354-380.

Dempster, F. N., 1996. Distributing and Managing the Conditions of Encoding and Practice. In: E. L. Bjork & R. A. Bjork, eds. Handbook of Perception and Cognition: Memory. San Diego, CA: Academic Press, pp. 317-344.

Eich, J. E., Weingartner, H., Stillman, R. & Gillin, J. C., 1975. State-Dependent Accessibility of Retrieval Cues in the Retention of a Categorized List. Journal of Verbal Learning and Verbal Behavior, Volume 14, pp. 408-417.

Gais, S. & Born, J., 2004. Declarative Memory Consolidation: Mechanisms Acting During Human Sleep. Learning & Memory, 11(6), pp. 679-685.

Gais, S., Lucas, B. & Born, J., 2006. Sleep After Learning Aids Memory Recall. Learning & Memory, Volume 13, pp. 259-262.

Godden, D. R. & Baddeley, A. D., 1975. Context-dependent Memory in Two Natural Environments: On Land and Underwater. British Journal of Psychology, 66(3), pp. 325-331.

K., K. S. H., McDermott, K. B. & Roedieger, H. L., 2007. Test Format and Corrective Feedback Modify the Effect of Testing on Long-Term Retention. European Journal of Cognitive Psychology, 19(4/5), pp. 528-558.

Kang, S. H. K., Lindsey, R. V., Mozer, M. C. & Pashler, H., 2014. Retrieval Practice Over the Long Term: Should Spacing Be Expanding or Equal-Interval?. Psychonomic Bulletin & Review, Volume 21, pp. 1544-1550.

Kang, S. H. K. & Pashler, H., 2012. Learning Painting Styles: Spacing is Advantageous when it Promotes Disciminitive Contrast. Applied Cognitive Psychology, Volume 26, pp. 97-103.

Kang, S. H. K. et al., 2011. Does Incorrect Guessing Impair Fact Learning?. Journal of Educational Psychology, 103(1), pp. 48-59.

Kulhavy, R. W., Yekovich, F. R. & Dyer, J. W., 1976. Feedback and Response Confidence. Journal of Educational Psychology, Volume 68, pp. 522-528.

Landauer, T. K. & Bjork, R. A., 1978. Optimum Rehearsal Patterns and Name Learning. In: M. M. Gruneberg, P. E. Morris & R. N. Sykes, eds. Practical Aspects of Memory. London: Academic Press, pp. 625-632.

Lang, A. J., Craske, M. G., Brown, M. & Ghaneian, A., 2001. Fear-Related State Dependent Memory. Cognition and Emotion, 15(5), pp. 695-703.

Lowe, G., 1982. Alcohol-Induced State-Dependent Learning: Differentiating Stimulus and Storage Hypotheses. Current Psychology, 2(1), pp. 215-222.

Mammarella, N., Russo, R. & Avons, S. E., 2002. Spacing Effects in Cued-Memory Tasks for Unfamiliar Faces and Nonwords. Memory and Cognition, 30(8), pp. 1238-1251.

Miles, C. & Hardman, E., 1998. State-Dependent Memory Produced by Aerobic Exercise. Ergonomics, 41(1), pp. 20-28.

Pashler, H., Cepeda, N. J., Wixted, J. T. & Rohrer, D., 2005. When Does Feedback Facilitate Learning of Words?. Journal of Experimental Psychology: Learning Memory, and Cognition, Volume 31, pp. 3-8.

Rawson, K. A. & Dunlosky, J., 2012. When is Practice Testing Most Effective For Improving the Durability and Efficiency of Student Learning?. Educational Psychology Review, Volume 24, pp. 419-435.

Roediger, H. L. & Karpicke, J. D., 2006. Test Enhanced Learning: Taking Memory Tests Improves Long-Term Retention. Psychological Science, Volume 17, pp. 249-255.

Smith, S. M., Glenberg, A. & Bjork, R. A., 1978. Environmental Context and Human Memory. Memory and Cognition, 6(4), pp. 342-353.

Stickgold, R., James, L. & Hobson, A., 2000. Visual Discrimination Learning Requires Sleep after Training. Nature Neuroscience, Volume 3, pp. 1237-1238.

Stickgold, R. & Walker, M. P., 2007. Sleep-Dependent Memory Consolidation and Reconsolidation. Sleep Medicine, 8(4), pp. 331-343.

Szpunar, K. K., McDermott, K. B. & Roediger, H. L., 2008. Testing During Study Insulates Against the Buildup of Proactive Interference. Journal of Experimentl Psychology: Learning, Memory and Cognition, Volume 34, pp. 1392-1399.

Tulving, E. & Watkins, M. J., 1974. On Negative Transfer: Effects of Testing One List on the Recall of Another. Journal of Verbal Learning and Verbal Behavior, Volume 13, pp. 181-193.

Weingartner, H., Miller, H. & Murphy, D. L., 1977. Mood-State Dependent Retrieval of Verbal Associations. Journal of Abnormal Psychology, 86(3), pp. 276-284.



Workshop on Neuroimaging

Written by Yingchen

Edited by Xin Chen, Sasinthiran and Keshiniy

Figure 1: Members of NUS Neuroscience Student Interest Group at Clinical Imaging Research Centre

On the 22nd of February 2016, members of NUS Neuroscience Student Interest group attended a workshop on Neuroimaging hosted by A*STAR-NUS Clinical Imaging Research Centre (CIRC).

The Workshop started with an introductory lecture by Dr John Totman, Head of Imaging Operations at CIRC. He recounted the history behind various imaging techniques such as Ultrasound, X-Ray, CT, PET and MRI, and gave a brief overview on the science behind these techniques, their limitations and common uses, for example, in the field of nuclear medicine.

One of the most common imaging techniques, ultrasound makes use of the Doppler Effect.  Ultrasound waves are emitted from the probe, which then detects the reflection of those waves off anatomical structures to construct an image of the structure; similar to echolocation used by dolphins and bats. Ultrasound is not commonly used in neuroimaging because sound waves cannot penetrate the skull.

Another commonly used technique, X-ray works on the principle that structures in the body attenuate x-rays that are projected on one side of the body such that the rays emerging from the other side expose a sheet of film to produce an image reflecting the body structures. However, the images produced are two-dimensional and thus may miss out certain structural details. As a result, multiple images at different sections of the body are required for a more accurate representation of the body structures.

A variant of X-ray imaging is Computerized Tomography (CT). It uses a rotating x-ray emitter and detector to provide a 3D reconstruction of anatomical structures. It is commonly used in neuroimaging to provide the structural information that aids in medical practices, such as detecting tumours and aneurysms. The grey and white matter of the brain can be distinguished clearly on CT images.

Another technique, Positron Emission Tomography (PET) requires an ingestible tracer consisting of glucose in conjugation with radioactive fluorine, which is prepared in the expensive cyclotron reactor by accelerating and colliding sub-atomic particles. Other positron-emitting radionuclides such as oxygen might also be used depending on how long the effect is required to last. When the radioactive substance decays, positrons are released which collide with the electrons released by the PET scanner, producing two gamma rays which scatter in exactly opposite directions. The PET scanner detects these two gamma rays and computes the average distance at which the rays originated to indicate the location of the anatomical structure. PET can be used for functional as well as anatomical imaging. The principle behind anatomical imaging is that cancer cells take up glucose rapidly, but fludeoxyglucose (FDG) cannot be easily metabolised and thus accumulate in these cells and can be detected.

SPECT, which stands for Single-photon Emission Computerized Tomography, is a combination of PET and CT. PET provides metabolic information while CT provides structural anatomical landmarks such as bones to give relevance to the metabolic activity reported by PET scans.

After Dr Totman’s lecture, Ms Caroline Wong, a Research Officer at CIRC, introduced us to Functional Magnetic Resonance Imaging (fMRI), which is the most commonly employed imaging tool for functional studies in neuroscience research. fMRI provides both structural and functional information of the brain. The basic unit of fMRI images is the voxel, which is a 3D pixel with modifiable size and length. Smaller the voxel, greater is the time required to acquire it, but more detailed the image would be, which is analogous to having thinner slices: many voxels make up a slice, and many slices make up the volume.

The science behind MRI is that protons have different spins in random directions, which align when placed in a magnetic field. When the MRI scanner emits radio frequency pulses, the protons are tilted out of alignment from each other. When the radio frequency pulses stop, the protons lose energy and return to their baseline aligned state, thereby releasing electromagnetic waves that are detected by the scanner.

In fMRI practice, active areas of the brain receive more oxygenated blood flow due to the dilation of local cerebral blood vessels. The protons in oxygenated haemoglobin and deoxygenated haemoglobin have different rates at which they return to their baseline aligned state. Moreover, oxygenated blood is diamagnetic, does not distort the surrounding magnetic field and thus there is no signal loss. In contrast, deoxygenated blood is paramagnetic, distorts the surrounding magnetic field and thus there is signal loss. These differences are used to highlight brain areas that are active during a task, since active areas are marked by a higher level of oxygenated blood flow. A more detailed relation is displayed by Figure 2, the hemodynamic response function profile.

Figure 2: Hemodynamic Response Function Profile (

In Figure 2, the initial dip is due to blood oxygen taken in by active brain areas from surrounding blood vessels. The rapid rise is due to overcompensation of blood flow due to dilation of blood vessels, which usually lasts for around 4-8 secs, but the exact time period depends on the brain area. The post-stimulus undershoot is due to elastic recoil of expanded blood vessels. Meanwhile, voxel colour changes with the time course of the curve.

Typical fMRI task designs include the block design, which consists of periods of rest between periods of activity; the slow event-related (ER) design, which has been phased out as it is inefficient; the rapid counterbalanced ER design, which is the fastest; and the mixed design, which consists of both block and ER. One difficulty with fMRI is that it requires minimal movement of non-task related areas of the body since movements may create “noises” that confound the results. Another difficulty is that brain functions are not localised to particular areas; meanwhile, the same area may be involved in many different functions. Notably, synchronisation between multiple brain areas that process the same cognitive properties have been seen to be out of synchrony in patients with neuropsychological disorders such as schizophrenia and bipolar disorder. Thus, careful interpretation of neuroimaging results is necessary.

After the presentations, we were invited to visit the fMRI laboratory, and witnessed several interesting phenomena such as the strong force of attraction between metal objects and the fMRI scanner, and the fact that conductors would fall due to gravity when suspended, albeit at a slower than expected rate in the fMRI scanner chamber due to electromagnetic induction. Towards the end of the workshop, Ms Caroline Wong also indicated that Dr Qiu Anqi from NUS Computational Functional Anatomy Lab was looking for research assistants. Details can be found in this link:

Within the two-hour session, the workshop provided us with an essential foundation for appreciating the various neuroimaging techniques used in neuroscience research and clinical practice. We would like to thank the Clinical Imaging Research Centre (CIRC) for accommodating our group and conducting this workshop.


“MDPI Open Access Publishing”, MDPI AG, 1996-2016. Accessed 7th March 2016. (

Seminar on Cellular Neuroscience

Written by Sasinthiran

Edited by Xin Chen, Yingchen and Keshiniy

Image: Overhead view of a voltage-dependent potassium ion channel shows four red-tipped “paddles” that open and close in response to positive and negative charges. (

On the 12th of February, members of NUS Neuroscience Student Interest group convened for a seminar on Cellular Neuroscience, as part of the fortnightly seminar series hosted by group members to explore different topics in neuroscience. The seminar was hosted by Enos Goo (Year 3, Life Sciences).

The seminar started off with a presentation on the basic principles of electrophysiology and the electrochemical basis of the membrane potential that is key for the generation of Action Potentials in neurons. Enos then touched on the remarkable diversity of individual neurons in terms of the vast variations in ion channels that confer unique properties when expressed in neurons. Differential expression and distribution of such ion channels allow for unique modulation of the membrane potentials of neurons and thus contribute to their diversity.

Next, Enos shared about specific types of ion currents such as the dendritic A-type current, and the dendritic H current which confer different electrophysiological properties to neurons through the action of specific ion channels. For example, it was noted that thalamic spindles observed during sleep are a result of an interaction between a calcium ion current and an inward pacemaker dendritic H current in neuronal populations at the thalamus. This property allows for the generation of rhythmic bursts of action potentials  that allows for a reduction in relaying of sensory input from sense organs to higher cortical areas for processing, since the thalamus functions as a relay centre for relaying such information (save for that of olfactory input).

It was noted that while science takes a reductionist approach in understanding the mechanisms of action in biological systems, a more global perspective yields greater insight into the more complex emergent properties of the components of such a biological system working together. An analogy was drawn to to our human society which is composed of numerous unique individuals, with not one of us being indispensable to the functioning of society as a whole. Thus, studying how a population of diverse neurons work together in neural circuits to give rise to complex emergent properties such as consciousness, the notion of morality etc., warrants significant interest.

It was also shared that back-propagated action potentials can be recorded in dendrites (albeit with a slight delay) as they too possess voltage-gated channels to support the propagation of action potentials.

The second part of the seminar involved discussing the notion of Intrinsic plasticity which involves the dynamic modulation of the electrophysiological properties of a neuron which affects their excitability and the computation of spatial and temporal summation of input the neuron receives ( as opposed to synaptic plasticity which is implicated in learning and memory) and its implications in disorders of the central nervous system. It was noted that in aging, an enhanced after-hyperpolarisation is seen at the end of action potentials and that this has been attributed to aberrant functioning of potassium ion channels which usually return the neuron to its resting membrane potential at the end of an action potential. The aberrant functioning of such channels with age could be due to the accumulation of mutations throughout life.

In epilepsy, it was noted that there was an aberrant down-regulation of potassium ion current and an enhanced persistent sodium ion current, leading to an increased intrinsic excitability of the neurons and hence the rapid burst of action potentials characteristic of an epileptic episode. In neurodegenerative diseases such as Alzheimer’s Disease (AD) and Parkinson’s Disease (PD), changes in the electrophysiological properties of the neurons have been observed prior to neuronal death (e.g. changes in ion channel expression and distribution). Moreover, degeneration of the Septal nuclei in AD leads to reduced cholinergic input to the hippocampus. This leads to an increased M current (efflux of potassium ions) and an enhanced after-hyperpolarisation of hippocampal neurons following action potentials, thus leading to the suppression of the neurons which usually fire in rapid bursts. It is noteworthy that firing in rapid bursts is an essential trait for memory coding and consolidation.

The seminar concluded with an interactive discussion on a few key topics. First, we reviewed the role of astrocytes in the ‘tripartite synapse’ and acknowledged the paradigm shift in considering the role of glial cells in the nervous system from one that views them as passively contributing to the matrix that hold neurons together in the brain to the view that they have active modulatory effects on neuronal activity (e.g. tripartite synapse’).

Next, we discussed the use of optogenetics (transfection of genes coding for light-sensitive rhodopsin activated ion channels into neurons) and considered potential applications of this technology in treating depression for example by activating serotonergic neurons instead of relying on drugs that have side-effects. However, the limitation of the technology is that light has to be directed towards the population of neurons via an optic cable (invasive). We also briefly discussed the possible physiological mechanism through which populations of neurons in migratory birds an other animals can use the earth’s magnetic field patterns to guide their migrations. We considered the possibility of using AC magnetic fields to heat (via eddy currents) a nanoparticle fused to an ion channel as a means of controlling the closure of the inactivation gate. This allows for a non-invasive way to control the activation of specific neuronal populations (a greater degree of spatial resolution).

Finally, we discussed the fact that a single nucleotide substitution on the voltage-gated sodium ion channel gene in pufferfishes allow them to be resistant to their own toxin (tetrodotoxin) which paralyses the nervous system of those who come into contact with it by inactivating the voltage-gated sodium ion channels. thus no action potential can be generated as the membrane cannot be depolarised by an influx of sodium ions. It is notable that this discovery was made by a research team based at NUS, headed by Professor Soong Tuck Wah.

The slides used for the presentation during the seminar can be found here (courtesy of Enos Goo): Cellular Neuroscience presentaion slides [943273]

The Neuroscience of Love

Writen by Sasinthiran

Edited by Xin Chen, Yingchen and Keshiniy


“How on earth are you ever going to explain in terms of chemistry and physics so important a biological phenomenon as first love?”

–Albert Einstein

While science has taught us much about the complexities of human behaviour, nothing seems to be as enigmatic as the concept of love. Love is such a powerful force that it has driven some to start great wars while inspiring others to produce marvelous works of art, poems, songs and novels. It is both rewarding and punishing, is often unpredictable and drives one to act in irrational, sometimes ridiculous ways. Einstein might be right in that this complex concept of love cannot be simply reduced to basic principles in science. However, in light of Valentine’s Day, let us review some of the insights neuroscience research has given us on how the brain handles matters of the heart.

Love, sex and other drugs

When falling in love, most people experience a rise in the levels of the stress hormone cortisol (a corticosteroid) which helps in overcoming the initial neophobia characteristic of the initial phases of starting a relationship with someone special (de Boer, et al., 2012). Many would also relate to experiencing changes in sleep patterns, a general loss of appetite and occasional mood swings when falling in love. These changes are an effect of depleting Serotonin (a neurotransmitter in the brain) levels which are inversely correlated with the levels of corticosteroids.

The brain has evolved several mechanisms to keep the passion burning after the initial period of falling in love. Most people will find it difficult to accept the idea that love is in fact an addiction. The brain is addicted to pleasure and positive affect, more specifically, it craves any activity that leads to the activation of dopaminergic pathways which trigger the brain’s reward response with the release of neurotransmitter dopamine (Insel, 2003). This pathway, known as the mesocorticolimbic pathway (see image below), originates in the ventral tegmental area (VTA) which project to the nucleus accumbens which in turn projects to the ventral pallidum and thalamus (midbrain structures as labelled below). Neurons from the thalamus then project to the prefrontal and cingulate cortex (PFC) (Everitt & Wolfe, 2002).

Image: The Mesocorticolimbic Pathway (

Research shows that addictive drugs such as cocaine are in fact addictive primarily because the activate such pathways leading to a reward response, leaving the brain craving for more (Koob & Moal, 1997). In other words, the more a particular behaviour such as taking cocaine triggers the reward centres of the brain, the more the brain seeks out such behaviour through learned operant conditioning by positive reinforcement. This then increases the occurrence of that behaviour in the future (addiction). Interestingly, in an fMRI study whereby participants were shown pictures of people they ‘liked’ versus people they ‘loved’, it was shown that the latter group elicited a greater activation of the brain’s reward pathways (Bartels & Zeki, 2000). Hence, being in love , just like drugs, triggers the same reward pathways of the brain and this leaves the brain craving for more due to positive reinforcement.

The effects of Oxytocin (OT) and arginine Vasopressin (AVP) have been extensively studied in bonding studies involving voles, comparing those that by their own nature, have monogamous relationships (prairie voles) with those that are polygamous (montane voles) (Young & Wang, 2004; Lim & Young, 2006). From such studies, it was found that blocking the release of these two hormones caused the prairie voles to be promiscuous while injection of the hormones into these voles caused them to be faithful to their partners, even when they were prevented from having sex. It would then be reasonable to expect that injecting the hormones into promiscous individuals could reduce this behaviour (many have actually suggested this as a ‘cure’ for human promiscuity!). However,  injection of the hormones into montane voles which are promiscuous by nature, did not render them monogamous and it was found that the reason for this was that montane voles did not have a sufficient number of receptors for these hormones in the reward centres of their brains (Edwards & Self, 2006). Hence, they were not responsive to the reinforcing effects of oxytocin and vasopressin. This possibly suggests that certain individuals lacking the receptors for these hormones (and hence not responsive to them) are actually predisposed to exhibit promiscuity!

Oxytocin and Vasopressin are both synthesized in the brain’s hypothalamic paraventricular and supraoptic nuclei and secreted by the posterior pituitary into circulation. Both neuropeptide hormones are known to released during breastfeeding, child birth and sexual stimulation and have a neuromodulatory effect on different regions of the brain (Insel, 2010). Some of these brain regions are involved in regulating social behaviour which is then modulated as a result. Oxytocin in particular, reduces fear and anxiety related to social situations by reducing amygdala activity (Neumann, et al., 2000; Kirsch, et al., 2005), enhances social memory (Insel, 2010) and activates reward circuits in the brain involving dopamine release (Lim & Young, 2006). In fact, Oxytocin receptors have been identified in the nucleus accumbens (Insel & Shapiro, 1992) and the V1a Vasopressin receptor has been identified in the ventral pallidum (Insel, et al., 1994), both of which are part of the mecocorticolimbic pathway described earlier. Ultimately these hormones help modulate social behaviours favouring the long term maintenance of monogamous relationships through operant conditioning by rewarding such pro-social behaviours via brain’s reward system (Insel, 2010).

Quite predictably, couples with higher blood plasma oxytocin levels were found to have more positive communication behaviours (Gouin, et al., 2010), greater perceived spousal support and a higher frequency of hugs and massages (Grewen, et al., 2005). Research has also found that an individual becomes a stronger learned stimuli for oxytocin release in their partners with each successive sexual encounter with that person (Witt, 1997), proving that sex itself can be addictive. This then helps to facilitate a deeper bond between the sexual partners and increase the likelihood that they will stay together, which from an evolutionary perspective, will be essential for raising a child since the act of copulation eventually leads to begetting an offspring.

Interestingly, it seems that love can even be affected at the genetic level. High levels of a polymorphic variant of the V1a Vasopressin receptor gene, with a variation in the RS3 344 section, has been found to correlate with lower partner bonding, higher incidences of marital crises within a year and an increased likelihood of cohabitating as compared to  being married to a partner in a self-report study that studied men in long-term relationships (Walum, et al., 2008).

The Social Brain

The Belongingness Hypothesis states that people have a pervasive need to form and maintain significant interpersonal relationships with others (Baumeister & Leary, 1995). In fact, research shows that the need for social interaction may be more profoundly felt than other basic needs such as hunger (Baumeister & Leary, 1995; Cacioppo, et al., 2000). Socialising with others was an essential skill for survival and reproduction in the hunter-gatherer days of human history (Baumeister & Leary, 1995). The brain has evolved over the years to become a highly social organ with specific neural networks that have been perfected over thousands of years of evolution to support this function.

One of the ways the brain has evolved to support social bond formation is by differentiating the cognitive processing of information pertaining to significant others as compared to general acquaintances. Memories and information relating to those with whom we share a significant personal relationship is processed in a unique person-by-person basis while cognitive processes for other acquaintances are stored, organised and processed on the basis of general attributional categories such as preferences, traits and duties instead of person categories (Pryor & Ostrom, 1981; Ostrom, et al., 1993). One might hypothesize that the purpose for such a distinction in cognitive processing might be to improve the speed and efficacy of the recall of information pertaining to significant people in our lives. In fact, it was reported that recall for information about both groups of people did not actually differ significantly. However, another possibility is that the specific neural networks catered to processing information about significant people may also project to emotion processing areas of the brain and thus allow for the addition of an extra layer (emotion) to the retrieval of stored information about them since relationships and attachment are closely tied to affect. This could also explain why recalling information about significant people often triggers positive emotions.

Love is blind and unconditional

The frontal cortex is the central executive centre of the brain that processes higher cognitive functions such as logical thinking, judgements, decision making and morality. In an fMRI study it was found that brain areas associated with negative emotional processing (the parietal cortex and temporal lobe) as well as other areas involved in assessing the emotions and intentions of other and other aspects of social judgements (the frontal cortex) were less active when viewing pictures of people they ‘loved’ versus pictures of their friends (Bartels & Zeki, 2000).

It then comes as no surprise that people in a relationship tend to demonstrate a self-serving bias when interpreting their partner’s outcomes in an experiment by giving them credit if they succeeded and not attributing blame to them when they fail (Fincham, et al., 1987). They also demonstrate this bias in giving their partners a  more favourable interpretation of their role in causing events (causal attribution) (Craig, 1991). This probably relates to why we tend to overlook the flaws of those we are smitten over as this is probably an adaptation of the brain to aid in maintaining an existing relationship.

Researchers have also found that activity in the amygdala which is associated with fearful situations, is reduced when viewing pictures of their partners (Zeki, 2007). The suppression of judgment and increase in trust as a result of diminished fear (amygdala activity suppression) leads to increased bonding between partners and may also account for the irrational behaviour of people in love.

Rejections are painful – literally

Some of us might have had the unfortunate experience of a break-up and we know that it can be an unpleasant experience. What is interesting though, is that the brain perceives the pain of social rejection the same way it would physical pain. This has been demonstrated in fMRI studies which have shown that the same brain areas, such as the anterior cingulate cortex (ACC), anterior insula (AI) and the right ventral prefrontal cortex (RVPFC), that are activated when processing the ‘affective’ or unpleasant component of physical pain, are also activated in response to social rejection (Eisenberger, et al., 2003). In fact, in one fMRI study it was found that presenting participants a picture of their ex-partners who rejected them not only activated the ACC but also triggered activity in the regions of the somatosensory cortex such as S2 which respond directly to physical sensations of pain (Kross, et al., 2011).

Image: Pain processing pathways in the brain (Bushnell et al., 2013)

It has been suggested that social system of the brain may have evolved to rely on the neural pathways for processing physical pain to indicate when social relationships are threatened, given that social connections are important for human survival (Panksepp, 1998). This further lends support to the role of operant conditioning in helping to maintain relationships. An individual learns the appropriate behaviours that will keep a relationship healthy through positive reinforcement in terms of triggering reward pathways of the brain that encourages future occurrence of such behaviour, as well as through positive punishment in terms of the pain associated with social rejection in response to behaviours that threaten relationships (Eisenberger, 2011).

The rules of attraction

An experiment involving the use of PET (positron emission tomography) to measure regional cerebral blood flow (rCBF) as a means of identifying brain areas that are more active (with more cerebral blood flow), identified that increased activity in the left insula correlated with reporting of the attractiveness of unfamiliar faces (Nakamura, et al., 1999). In what seems to be eerily similar to mind control, researchers have demonstrated that judgements of physical attractiveness can be manipulated by evoking different emotions in participants through music (May & Hamilton, 1980). It was found that evoking positive affect such that the brain’s reward pathways are activated through rock music increased perceptual judgements of attractiveness.

Anthropological research has shown that since the stone ages, people will select mates who stand out from the rest of the crowd when presented with a choice of mates of equal value (Frost, 2006). Researchers have found that the mere exposure effect, a principle in which regular exposure to a neutral of positive stimuli generally increases the liking for that stimuli (Zajonc, 1968) can be applicable to humans as well (Swap, 1977). This probably explains why some men finally win over the woman they court after some time. Either that or the women are just playing hard to get! Studies have shown that sharing a meal has a profound effect on human bonding as higher levels of oxytocin release has been measured in such settings (Wittig, et al., 2014). Hence, a nice romantic dinner on the first date might not be a bad idea after all.

In romantic situations, males use more uncommon, fancy words than they do in other situations (Rosenberg & Tunney, 2008). This could serve as a litmus test for knowledgeable women to identify if someone is trying to impress them! In one study, higher levels of fertility in women was found to be associated with lower levels of linguistic matching in their male partners in experimental setting (Coyle & Kaschak, 2012). Linguistic alignment is usually used to signal affiliation (Giles, et al., 1991) and leads to increased liking between participants in social interactions (Chartrand & Bargh, 1999; Cheng & Chartrand, 2003). However, it was noted that men paired with women in the fertile stages of their menstrual cycle, chose to use different syntactic structures in their speech and not mimic that of their partners (non-conforming behaviour) as one would expect. The men seemed to have picked up subtle subconscious cues about the female partner’s fertility and coud be presenting their non-conforming speech behaviour as a display of their fitness as a mate.

Having a sense of humour is a common feature that both men and women look for in their ideal mates, albeit with a significant difference. Men prefer women who appreciate their jokes, and not necessarily women who are funny themselves, while women prefer men who made them laugh (Bressler & Balshine, 2006). It was also observed from the study that women signal their level of attraction to their partners by the frequency of their laughter while men’s laughter was not correlated to their degree of attraction to a potential mate.

Studies have shown that men and women in a romantic relationship share fundamental differences in areas of the brain that are more active. Men, have been found to have increased activity in areas of the brain involved in integrating visual stimuli (Narumoto, et al., 2001) while women have greater activation in areas associated with memory, attention and emotion (Gray, et al., 2002; Maddock, et al., 2003; Velanova, et al., 2003). This is supported by the evolutionary view of gender priorities in looking for a mate as males look for healthy mothers to carry their child while females look for possible security and resources offered by a male mate in raising her child (Fisher, 2004).

Through better or worse

In one particular fMRI study, women who held their husband’s hands were found to have reduced activity in parts of their brain involved with processing the emotional and arousing aspects of pain when they were told to anticipate an electric shock, as compared to women who were alone (Coan, et al., 2006). Interestingly, it was found that the reduction in pain-associated activity correlated with the quality of their marriages such that happily married women had a greater degree of reduction in activity. Another observation from the study was that women who held the hands of men they did not know also showed a reduction of activity in pain processing, albeit a smaller reduction than the women who held their husband’s hands. This shows that love, or any relationship for that matter, may have a protective effect on the brain in terms of reducing the processing of unpleasant or noxious stimuli.


As reviewed in this article, neuroscience research may provide some insight, and at best, a fragmented view on neurological basis for some behaviours demonstrated by people in love. However, love is a complex emergent behaviour which we can never fully appreciate and artificially re-create even with the advances in knowledge.


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Seminar on Neurons, Glia and Neurophysiology

Written by Sasinthiran

Edited by Xin Chen, Yingchen and Keshiniy


On the 29th of January 2016, the NUS Neuroscience Student Interest Group conducted its first seminar titled ‘Neurons, Glia and Neurophysiology’, hosted by Sasinthiran (Year 3, Life Sciences).

Participants were provided reading material on the topic compiled from various sources two weeks prior to the seminar. At the start of the seminar, participants attempted to complete a quiz (18 questions) in groups of 3-4 members within 30 minutes.

_20160209_181639 [2344380].JPG

The quiz questions were then discussed in an interactive format whereby groups had to defend and justify their answers if they differed from that of other groups.

The discussion started off with a question on the permeability of the blood-brain barrier (BBB). It was first highlighted that the components of the BBB are the tight junctions between the capillary endothelial cells, the basal lamina of the capillary and the perivascular foot processes of astrocytes (a type of glial support cell). It was noted that water and water soluble agents were able to diffuse across via the tight junctions while charged species such as sodium ions were first transported by trans-membrane protein pumps/ channels into the endothelial cells of the blood capillaries and subsequently into the brain. Lipid soluble molecules are free to diffuse through the phospholipid bi-layer of the endothelial cell membranes while larger charged molecules such as glucose ( primary energy source of the brain) and amino acids are transported by by trans-membrane transport proteins. Other solutes may also be transported by receptor-mediated endocytosis when they bind to their specific receptors on the surface of the endothelial membrane, as well as adsorptive transcytosis. It was noted that due to tight junctions between the capillary endothelial cells, leukocytes a(white blood cells) and other immune cells may not be able to cross the BBB to a great extent and hence the brain relies on a local population of cells known as the microglia (not derived from neuroectodermal lineage but from immune cell lineage) which mediate local immune function (e.g. phagocytosis and secretion of inflammatory factors such as interleukins to attract other immune cells).

Next, the discussion focused on the profile of an action potential (AP). It was noted that the refractory period (decrease in membrane potential to a value more negative than the resting membrane potential) after the peak in voltage of an AP had two parts: the absolute refractory period (lasting 1 msec due to the closing of the inactivation gate of sodium ion channels) during which no  action potential can be generated followed by the relative refractory period during which a higher than usual stimulus is required to depolarize the membrane to threshold potential in order to trigger an action potential. It was also noted that the fall in membrane potential below that of resting potential because the voltage-gated potassium channels that opened at the peak of the AP were slow to close and hence allowed more potassium ions to diffuse out of the neuron, down its concentration gradient.

The discussion then looked at the factors affecting the speed of conduction along an axon and 4 factors were identified: higher temperature, wider diameter of the axon, increased myelination (insulation) and decreased length (between nodes of ranvier) all favor faster conduction. It was also noted that different classes of axons with different diameter and myelination properties exist in the body to serve their particular functions. for example, when someone hits his toes against a hard surface, the faster A-delta fibres that are myelinated conduct a sharp pain sensation while the slower unmyelinated C fibres conduct the slow, dull pain that follows.

Next, we reviewed the various gating mechanisms of ion channels: ligand-gated, mechanosensitive (e.g. stretch-activated), voltage-gated, photon-gated (responds to light) or even ungated. We briefly discussed the recent development of optogenetics in which genes coding for a light-sensitive receptor has been transferred and expressed in select populations of mice neurons to modulate their behavior when that region is exposed to light via an optic fiber cable.

Our discussion then moved on to define the terms that describe a cell membrane’s potential. Depolarisation represents a rise in membrane potential from a negative resting potential towards the potential of 0mV (same internal potential as outside of the cell). Repolarisation represents a drop in membrane potential from 0mV to a more negative potential. Hyperpolarisation refers to a drop in membrane potential to a value more negative than that of the resting potential.

Next, we had a brief review of the activity at the neuromusccular junction (NMJ) that results in the contraction of skeletal muscle fibres for movement. It was noted taht acetylcholine was the primary neurotransmitter released at the NMJ.

The discussion then moved on to briefly touch on the fact that groups of axonal tracts that run up and down the spinal cord are arranged in columns. It was noted that such tracts are called projections when observed in the brain.

We then proceeded to discuss the factors responsible for the establishment and maintenance of the action potential: the unequal distribution of ions inside and outside the neurons (sodium, chloride and calcium ions are concentrated outside the cell while the inside of the cell has a higher concentration of potassium ions and other large non-difussible anions that also contribute to the negative resting membrane potential), the action of the sodium-potassium ATPase pump which pumps out 3 sodium ions and pumps in 2 potassium ions against their concentration gradient (thereby resulting in the net loss of one positive ion) as well as the free diffusion (leakage) of potassium ions out of the cell through leaky potassium channels.

Next, the discussion focused on the role of glia in the nervous system. It was noted that there has been a paradigm shift from considering glia simply as cells that form the matrix that hold the neuron together to one that accepts their active role in the nervous system. For example, astrocytes have been found to form a ‘tripartite synapse’ with post- and pre-synaptic neurons and even modulate and synchronize their communication through the release of gliotransmitters (visualised trhough the observation of signature calcium waves in the astrocytes which lead to the fusion of vesicles containing gliotransmitters to the astrocyte membrane to release the gliotransmitters). Microglia have the potential to differentiate into astrocytes or neurons, modulate local immune response through phagocytosis and release of cytokines such as interleukins to attract other immune cells (inflammation) as well as guide young neurons during neuronal migration in early development. Schwann cells (in the PNS) and oligodendrocytes (in the CNS) form myelin sheaths around axons to nourish the cells and at the same time speed up the conduction of action potentials. It was noted that the key difference between neurons and glia is that glia do not generate action potentials. It was noted, however, that a population of the pacemaker cells in the sinoatrial (SA) and atrioventricular (AV) nodes of the heart are the only other cells outside of the nervous system that are capable of generating their own action potentials.

The next question revisited the profile of the action potential and it was noted that the rapid depolarisation of the membrane was due to the rapid influx of sodium ions through their voltage-gated channels (more open upon membrane potential reaching threshold potential), driven by both their electrical and chemical gradients into the neuron.

Next, we looked at the summation of input from pre-synaptic neurons in a post-synaptic neuron that could either lead to to an EPSP (excitatory post-synaptic potential) or an IPSP (inhibitory post-synaptic potential). It was noted that if the excitatory effect is greater than the inhibitory effect but less than the threshold of stimulation, the result is a subthreshold EPSP. Furtehrmore, if the excitatory effect is greater than the inhibitory effect and reaches or surpasses the threshold level of stimulation, the result is a threshold or suprathreshold EPSP and one or more nerve impulses. Alternatively, if the inhibitory effect is greater than the excitatory effect, the membrane hyperpolarizes, resulting in inhibition of the postsynaptic neuron and the inability of the neuron to generate a nerve impulse.

Subsequently, we went on to review the fact that stimuli intensity is encoded in the frequency of action potentials which are of equal magnitude (amplitude). The higher the stimuli intensity, the higher the frequency of the action potential.

We also identified that the falling of the membrane potential towards resting membrane potential was due to the opening of voltage-gated potassium channels at the peak of the action potential, leading to an efflux of potassium ions out of the cell, down its concentration gradient. As noted before, the slow closure of these channels results in the drop in membrane potential past that of the resting membrane potential (refractory period).

Next, we briefly discussed the experiment conducted by German physiologist Otto Loewi which proved that neurons communicated through chemical messengers that we now know to be neurotransmitters. He stimulated one frog’s heart, collected fluid around it, transferred it to another frog’s heart, and saw change in its heart rate.

We then moved on to briefly discuss a thought experiment and concluded that if we wanted to cause the pre-synaptic terminal of an axon to release its neurotransmitter without an action potential, we could inject calcium into its pre-synaptic terminal. This is because of the fact that the influx of calcium into the pre-synaptic terminal (usually when an action potential reaches the pre-synaptic terminal and depolarise the membrane, causing the opening of voltage-gated calcium ion channels) results in teh fusion of synaptic vesicles containing neurotransmitters to the pre-synaptic membrane.

Next, we discussed the differences between metabotropic and ionotropic receptors. Ionotropic receptors have a pore which opens/ closes to directly control the passage of ions while metabotropic receptors when activated, trigger an intra-cellular signalling cascade to cause an ion channel to open/ close in response (indirect control of movement of ions). Thus ionotropic receptors have fast acting effects and and its action is short-lived while metabotropic receptors have slower effects that last longer (due to signal amplification in the signalling cascade).

The seminar concluded with the discussion of the learned concepts in more challenging application questions. Firstly, we discussed the scenario whereby an action potentaial is triggered at same time from both directions of an axon (in both the orthodromic and antidromic directions). In this case, we concluded that when both action potentials meet at a point along the axon, they will not propagate any further. This is because the region just behind the each action potential is undergoing a 1 msec absolute refractory period in which no action potential can be generated since the voltage -gated sodium ion channels have their inactivation gates closed for that period.. Thus both the orthodromic and antidormic action potentials cannot proceed ahead in their direction of propagation.

There was also a short sharing on the role of GABA as an excitatory neurotransmitter in the neurons of fetuses (due to the high intracellular chloride ion concentration). The role of Glutamate as both an excitatory and inhibitory neurotransmitter in the retina was also discussed and it was noted that the effect of neurotransmitters is dependent on the type of receptor it binds to (metabotropic or ionotropic).

The quiz questions, answers and further information on the topics discussed can be found here: Neurons Glia and Neurophysiology.

Doctoral and Master Program in “Computational Neuroscience”

*Doctoral* and *Master Program* “Computational Neuroscience”
at the Bernstein Center for Computational Neuroscience Berlin
in Berlin, Germany

Application deadline: *March 15, 2016*
Begin of courses: October 2016

Doctoral Program

The Bernstein Center for Computational Neuroscience Berlin and the TU
Berlin invite applications for *6 fellowships* of the Research Training
Group “Sensory Computation in Neural Systems” (GRK 1589/2,

The *scientific program* of the research training group combines
techniques and concepts from machine learning, computational
neuroscience, and systems neurobiology in order to specifically address
sensory computation. Doctoral candidates will work on interdisciplinary
projects investigating the mechanisms of neural computation, address the
processes underlying perception on different scales and different levels
of abstraction, and develop new theories of computation hand in hand
with well-controlled experiments in order to put functional hypotheses
to the test.

The training group offers structured supervision complemented by a
teaching and training program. Each student will be supervised by two
investigators with complementary expertise and will be associated with
the Bernstein Center for Computational Neuroscience Berlin
( a leading research center dedicated to the
theoretical study of neural processing.

Candidates are expected to hold a Masters degree (or equivalent) in a
relevant subject (e.g., neuroscience, cognitive science, computer
science, physics, mathematics, etc.) and have the required advanced
mathematical background.

Candidates selected in the first application step will be invited for
lab visits and an interview, expected to take place in June 2015. The
*fellowships of 1468 €/month* – with additional children allowances if
applicable—will be granted for up to three years.

Master’s Program

The tuition-free Master program in Computational Neuroscience offers *15
places* per year, has a duration of 2 years and is fully taught in English.

The *curriculum* is subdivided into ten modules, whose content includes
theoretical neuroscience, programming, machine learning, cognitive
neuroscience, acquisition, modelling, and computational analysis of
neural data, with a strong focus on a complementary theoretical and
experimental training. Three lab rotations and a Master’s thesis are
accomplished in the second year. The aim of the program is to provide
the students with an interdisciplinary education and an early contact to
the neurocomputational research environment.

*Requirements* BSc or equivalent degree in a relevant subject (typically
in the natural sciences, in an engineering discipline, in cognitive
science, or in mathematics), certificate of English proficiency, proof
of sufficient mathematical knowledge (at least 24 ECTS credit points).


For more information:

… come and visit us at the BCCN Berlin:

… or browse:

… or e-mail: .

The NUS Neuroscience Student Interest Group


The NUS Neuroscience Student Interest Group is an academic student group which aims to bring together like-minded undergraduates across the faculties at the National University of Singapore (NUS), with a passion for neuroscience, in a collaborative learning environment.

The interest group hosted its Welcome Tea on 4th January to inaugurate the interest group and to orientate members to the upcoming schedule of activities. The slides from the Welcome Tea are available here: Welcome Tea

Our activities include bi-weekly Journal Club sessions during which we will engage in an interdisciplinary discussion on current research in the field, attending symposiums and seminars organized by the various departments at NUS and other research institutions, learning about the research projects of peers who are undertaking their FYP or UROPS in the field, and possibly even organizing events and activities for the university community and beyond.

This interest group will be a great opportunity for undergraduates to get an early exposure into the field and meet like-minded peers who could be potential collaborators, especially if they are considering a post-graduate career/ education in neuroscience. We hope to cultivate a community of collaborative learning to supplement our undergraduate education with exposure to the field, especially since neuroscience is not offered as an area of specialization/ major at NUS.

The possible topics that we will be covering for the upcoming bi-weekly Journal Club meetings can be found here: Journal Club Topics

The list of topics are based on chapters from a free, peer-reviewed textbook (Neuroscience in the 21st Century: From Basic to Clinical), which will serve as the primary resource for the Journal Club. Members will be split into groups of 3-5 based on their interest in the topics available.

In brief, groups will have to prepare a review presentation of the topic selected not exceeding 30 minutes using material found in the textbook  as well as other relevant primary literature. The rest of the members in the interest group are encouraged to read up on the relevant topics/ chapters and come up with their own ideas either from their own experience or literature review. After the presentation, the presenting group will facilitate an open-floor discussion in the remaining time (they may choose to structure it in any way they prefer) and invite opinions and ideas from the other members who will share relevant ideas and insights from their own disciplines. In this way, we hope that each session provides a multi-disciplinary view of the topics discussed, to all in attendance.

As part of our ad-hoc initiatives, this blog was set up with the aim of serving as a platform for the interest group to share about our activities, archive the knowledge that is shared during our discussions as well as to encourage science-related writing among our members. Our team of writers will periodically publish articles featuring events of the interest group, reviews of recent neuroscience research articles of interest as well as feature interviews with neuroscience researchers in Singapore.

Do follow our blog to keep updated on our events and to join us in this exploratory journey in discovering more about the brain!



President, NUS Neuroscience Student Interest Group