The healing power of companionship

Shireene Kalbassi | 11 MAY 2018

When it comes to the recovery of wounds and other medical conditions, most people probably think of hospital beds, antibiotics, and maybe some stitches. What probably doesn’t come to mind is the role that companionship may play in speeding up the healing process.

And yet, studies in humans have shown a link between increased companionship and enhanced recovery prospects (Bae et al 2001, Boden-Albala et al 2005).

So why should it be that social interaction influences the recovery process? Well, in social species, social interaction leads to the release of the hormone known as oxytocin, AKA the “love hormone”. This hormone is released from the pituitary gland, located in the brain. Increased levels of oxytocin have been associated with lower levels of stress response hormones, such as cortisol and corticosterone, and high levels of these stress response hormones have been shown to lead to impaired healing (Padgett et al 1998, DeVries  et al 2002, Heinrichs et al 2003, Ebrecht et al 2004).

This link between social interaction, oxytocin, stress hormones and recovery has been explored in studies, such as the work of Detillion et al (2004). Here, the authors investigated how companionship impacts wound healing in stressed and non-stressed hamsters. The role of companionship was explored by comparing socially isolated hamsters to ‘socially housed’ hamsters, that share a home environment with another hamster. Stressed hamsters were physically restrained to induce a stress response, while non-stressed hamsters did not undergo physical restraint.

In order to understand how these factors relate, the authors therefore compared four different groups: hamsters that were socially isolated and stressed, hamsters that were socially housed and stressed, hamsters that were socially isolated and non-stressed, and hamsters that were socially housed and non-stressed.

The hamsters that were socially isolated and stressed showed decreased wound healing and increased cortisol levels, when compared to socially housed hamsters or non-stressed socially isolated hamsters. Furthermore, when a blocker of oxytocin was given to socially housed hamsters decreased wound healing was observed, while supplementing stressed hamsters with oxytocin lead to increased wound healing and lower levels of cortisol.

So it seems that when social animals interact oxytocin is released, which reduces the levels of stress hormones, leading to increased wound healing.

But what if there is more to the story than this? These studies, and others like it, demonstrate a relationship between companionship and wound healing, but how might factors relating to social interaction impact recovery?

Venna et al (2014) explored the recovery of mice that were given a brain occlusion, where part of the blood supply of the brain is shut off to try to replicate the damage seen in stroke. However, in this study the mice were either socially isolated, paired with another stroke mouse, or paired with a healthy partner. When assessing recovery, the authors looked at multiple parameters including death rates, recovery of movement, and new neuron growth. The authors observed that, as expected, socially isolated stroke mice showed the lowest rates of recovery. Interestingly, stroke mice that were housed with other stroke mice showed decreased recovery rates when compared to stroke mice that were housed with a healthy partner.

So why should the health status of the partner influence the healing process of the mice? The work of Venna et al did not assess if the amount of social contact between stroke mice that were housed with another stroke mouse was equal to that of stroke mice that were housed with a healthy partner, which may explain the discrepancy seen between the two groups. Exploration of this could lead to better understanding of whether the quantity of social interaction may be leading to decreased recovery rates in the stroke mice housed with other stroke mice groups, or if the decreased recovery may be due to other factors.

Regardless, it appears that social interaction may not be a simple box to tick when it comes to enhancing the recovery process but is instead dynamic in nature. And while nothing can replace proper medical care and attention, companionship may have a role in speeding up the recovery process.  

If you want to know more about the use of animals in research, please click here.

Edited By Sophie Waldron & Monika śledziowska


  • Bae, S.C., Hashimoto, H.I.D.E.K.I., Karlson, E.W., Liang, M.H. and Daltroy, L.H., 2001. Variable effects of social support by race, economic status, and disease activity in systemic lupus erythematosus. The Journal of Rheumatology, 28(6), pp.1245-125
  • Boden-Albala, B., Litwak, E., Elkind, M.S.V., Rundek, T. and Sacco, R.L., 2005. Social isolation and outcomes post stroke. Neurology, 64(11), pp.1888-1892
  • Padgett, D.A., Marucha, P.T. and Sheridan, J.F., 1998. Restraint stress slows cutaneous wound healing in mice. Brain, behavior, and immunity, 12(1), pp.64-73.
  • DeVries, A.C., 2002. Interaction among social environment, the hypothalamic–pituitary–adrenal axis, and behavior. Hormones and Behavior, 41(4), pp.405-413.
  • Heinrichs, M., Baumgartner, T., Kirschbaum, C. and Ehlert, U., 2003. Social support and oxytocin interact to suppress cortisol and subjective responses to psychosocial stress. Biological psychiatry, 54(12), pp.1389-1398.
  • Ebrecht, M., Hextall, J., Kirtley, L.G., Taylor, A., Dyson, M. and Weinman, J., 2004. Perceived stress and cortisol levels predict speed of wound healing in healthy male adults. Psychoneuroendocrinology, 29(6), pp.798-809.
  • Detillion, C.E., Craft, T.K., Glasper, E.R., Prendergast, B.J. and DeVries, A.C., 2004. Social facilitation of wound healing. Psychoneuroendocrinology, 29(8), pp.1004-1011.
  • Glasper, E.R. and DeVries, A.C., 2005. Social structure influences effects of pair-housing on wound healing. Brain, behavior, and immunity, 19(1), pp.61-68
  • Venna, V.R., Xu, Y., Doran, S.J., Patrizz, A. and McCullough, L.D., 2014. Social interaction plays a critical role in neurogenesis and recovery after stroke. Translational psychiatry, 4(1), p.e351

Can we solve problems in our sleep?

Sam Berry | 19 MAR 2018

Have you heard the song “Scrambled Eggs”? You know:

“Scrambled eggs. Oh my baby how I love your legs.”

No? Perhaps you would recognize the tune.

A young Paul McCartney woke up one morning with an amazing melody in his head. He sat at the piano by his bed and played it out, and he liked it so much he couldn’t quite believe it had come to him in a dream. The tune was there, but he just couldn’t find the right words to fit. For several months he tried, but he couldn’t get past “Scrambled Eggs” as a working title.

So how did the famous Beatle complete his masterpiece? He did some more sleeping. Another fateful day, he woke up and the song was there, fully formed with lyrics and the now famous title “Yesterday.”

“Yesterday, all my troubles seemed so far away.”

Recognise it now? A critically acclaimed worldwide hit had formed itself in his sleep. Boom. A chart smashing phenomenon.

—— —– —– —– —— ——

It may seem obvious, but not sleeping is extremely bad for you. Symptoms of sleep deprivation include a marked decline in the ability to concentrate, learn, and retain new information. It can affect your emotions, self-control, and cause visual and auditory hallucinations.

Whether not sleeping at all would actually kill you has not yet been established. The record time for someone staying awake is 11 days and 25 minutes during a science experiment in 1965. The subject was kept awake by two ‘friends’ as they observed him become a drooling delusional mess. Yet there are plenty of studies that demonstrate serious detrimental health effects of both short and long-term sleep deprivation.

Being mentally and physically alert will certainly help you to solve problems, but many scientists think something much more interesting is going on during sleep. Your brain is still learning whilst you are snoring.  

You are only coming through in waves…

Okay, so do we know how sleep can help us to learn? We’re getting there. Using brain imaging technology like fMRI scanners (giant magnets that use blood flow changes to see how different parts of the brain react to things) and EEG (funky hats with electrodes that measure how our neurons are firing in real time), we can have a look at what the brain is doing while we’re dozing off.

Our brains remain active while we sleep. Sleep can be split into different stages, and what happens during these stages is important for memory and learning. Broadly speaking, your sleep is split into non-REM (Stage 1, 2, and Slow Wave) and REM (Rapid Eye Movement) stages. These are traditionally separated depending on what the pattern of electrical output from the EEG is showing. I’ll briefly take you through what these different stages are and how our neuron activity changes as we go through them:

Stage One sleep is when we start to doze off and have our eyes closed. Have you ever noticed a grandparent falling asleep in their chair, but when you ask them to stop snoring they wake up insisting they were never asleep in the first place? That’s stage one sleep; you can be in it without even knowing.

Stage Two is still a light sleep, but when brain activity is viewed using EEG you can see an increase in spiking brain activity known as sleep spindles.

Slow Wave Sleep is so called because in this stage neurons across the brain activate together in unison, creating a slow, large coordinated electrical pattern. This makes the EEG output look like a wave. Slow wave sleep also contains some of Stage Two’s sleep spindles, and as well has something called sharp wave ripples. This is where a brain area called the Hippocampus (involved in memory and navigation) sends bursts of information to the Neocortex (involved in our senses, motor movement, language, and planning to name a few).

REM sleep is when our bodies are paralysed but our eyes dart around. Our blood pressure fluctuates and blood flow to the brain increases. While we dream throughout sleep, our dreams during REM become vivid and our brain activity looks similar to when we’re awake.

We cycle through these stages in 90 -120 minute intervals throughout the night, our sleep becoming deeper and more REM-based as the cycle progresses. Disruptions to the sleep cycle are associated with decreases in problem-solving ability as well as psychiatric and neurodegenerative disorders like Alzheimer’s.

Spikey learning

Problem solving requires memory: you need to use information you already have and apply it to the problem at hand. You also need to remember what you tried before so that you don’t keep making the same mistakes (like singing “Scrambled Eggs” over the same tune forever). The stages of sleep most relevant to helping us keep hold of our memories are the non-REM ones, and in particular Slow Wave Sleep.

Recent research reveals that sleep spindles, slow waves, and sharp wave ripples work together so when a slow wave is at its peak the brain cells are all excited, creating the perfect environment for the sleep spindles to activate. When the wave is crashing down, the sharp wave ripples from the Hippocampus are more likely to fire to the Neocortex. Recent research tells us this coupling of spindles and slow waves is associated with how well you retain memories overnight. Interestingly, in older adults spindles can fire prematurely before the wave reaches its peak, suggesting a possible reason why memory gets worse with age.

Researchers say this pattern of brain activity is a sign of the brain consolidating, or crystallising, what was learned or experienced whilst awake. This process strengthens the neural connections of the brain. Studies show that the pattern of neurons that get excited when we learn something are reactivated during sleep. This could mean that during sleep our brains replay experiences and strengthen newly formed connections.

Getting freaky

So what do our dreams mean? We’ve all had bizarre ones—how about that common dream where all your teeth fall out?

During REM sleep, our brain activity looks similar to when we’re awake. Scientist Deirdre Barrett suggested we think of REM sleep like merely a different kind of thinking. This type of thinking uses less input from the outside world or from the frontal parts of our brain in charge of logical thinking. REM is thought to be involved in consolidating our emotional memories, but it is also when we tend to have the vivid visual dreams that may defy logic. This combination enables REM “thinking” to be creative or even weird. REM sleep may allow us to form connections between ideas that are only distantly related.

Recently, a team in Germany suggested that Non-REM sleep helps put together what we know while REM breaks it up and puts it back together in new ways.

Thoughts before bed

So “sleeping on it” really can help solve problems. It strengthens the memories you make during the day and it helps learn and see things more clearly when you wake up. REM sleep may also allow thinking to be unconstrained by logic and divide and reshape ideas during REM. If reading this article made you sleepy, go ahead and take a nap. You might learn something.

Edited by Becca Loux. Becca is a guest editor for Brain Domain and an avid fan of science, technology, literature, art and sunshine–something she appreciates more than ever now living in Wales. She is studying data journalism and digital visualisation techniques and building a career in unbiased, direct journalism.


  • Barrett, D. (2017). Dreams and creative problem-solving: Dreams and creative problem-solving. Annals of the New York Academy of Sciences, 1406(1), 64–67.
  • Carskadon, M. A., & Dement, W. C. (2005). Normal human sleep: an overview. Principles and Practice of Sleep Medicine, 4, 13–23.
  • Chambers, A. M. (2017). The role of sleep in cognitive processing: focusing on memory consolidation: The role of sleep in cognitive processing. Wiley Interdisciplinary Reviews: Cognitive Science, 8(3), e1433.
  • Haus, E. L., & Smolensky, M. H. (2013). Shift work and cancer risk: Potential mechanistic roles of circadian disruption, light at night, and sleep deprivation. Sleep Medicine Reviews, 17(4), 273–284.
  • Helfrich, R. F., Mander, B. A., Jagust, W. J., Knight, R. T., & Walker, M. P. (2018). Old Brains Come Uncoupled in Sleep: Slow Wave-Spindle Synchrony, Brain Atrophy, and Forgetting. Neuron, 97(1), 221–230.e4.
  • Klinzing, J. G., Mölle, M., Weber, F., Supp, G., Hipp, J. F., Engel, A. K., & Born, J. (2016). Spindle activity phase-locked to sleep slow oscillations. NeuroImage, 134, 607–616.
  • Landmann, N., Kuhn, M., Maier, J.-G., Spiegelhalder, K., Baglioni, C., Frase, L., … Nissen, C. (2015). REM sleep and memory reorganization: Potential relevance for psychiatry and psychotherapy. Neurobiology of Learning and Memory, 122, 28–40.
  • Lewis, P. A., & Durrant, S. J. (2011). Overlapping memory replay during sleep builds cognitive schemata. Trends in Cognitive Sciences, 15(8), 343–351.
  • Ólafsdóttir, H. F., Bush, D., & Barry, C. (2018). The Role of Hippocampal Replay in Memory and Planning. Current Biology, 28(1), R37–R50.
  • Sio, U. N., Monaghan, P., & Ormerod, T. (2013). Sleep on it, but only if it is difficult: Effects of sleep on problem solving. Memory & Cognition, 41(2), 159–166.
  • Staresina, B. P., Bergmann, T. O., Bonnefond, M., van der Meij, R., Jensen, O., Deuker, L., … Fell, J. (2015). Hierarchical nesting of slow oscillations, spindles and ripples in the human hippocampus during sleep. Nature Neuroscience, 18(11), 1679–1686.

How to read a baby’s mind

Priya Silverstein | 3 OCT 2017

Priya, a guest writer for The Brain Domain, is a second-year PhD student at Lancaster University. She spends half her time playing with babies and the other half banging her head against her computer screen.

Okay, I’ll admit that was a bit of a clickbait-y title. But would you have started reading if I’d called it ‘Functional Near Infrared Spectroscopy and its use in studies on infant cognition’? I thought not. So, now that I’ve got your attention…

Before I tell you how to read a baby’s mind, first I have some explaining to do. There’s this cool method for studying brain activity but, as one of the lesser used technologies, it’s a bit underground. It’s called fNIRS (functional Near Infrared Spectroscopy). Think of fNIRS as fMRI’s cooler, edgier sister. Visually, the two couldn’t look more different – with an MRI scanner being a human-sized tube housing a massive magnet that you might have seen on popular hospital dramas, and NIRS simply looking like a strange hat.

MRI.png   fNIRS_cover
Left: MRI scanner, Right: NIRS cap
Picture credit left: Aston Brain Centre, right: Lancaster Babylab

What these two methods do have in common is that they both measure the BOLD (Blood Oxygen Level Dependent) response from the brain. Neurons can’t store excess oxygen, so when they are active, they need more of it to be delivered. Blood does this by ferrying oxygen to the active neurons faster than to their lazy friends. When this happens, you get a higher concentration of oxygenated to deoxygenated blood in the more active areas of the brain.

Now, to the difference between fMRI and fNIRS. fMRI infers brain activity due to oxygenated and deoxygenated blood having different magnetic properties. When the head is put inside a strong magnetic field (the MRI scanner) changes in blood oxygenation, due to changes in brain activity, alter the magnetic field in that area of the brain. fNIRS on the other hand, uses the fact that oxygenated and deoxygenated blood absorb a different amount of light, as deoxygenated blood is darker than oxygenated blood. Conveniently, near-infrared light goes straight through the skin and skull of a human head (don’t worry, this is not at all dangerous and a participant would not feel a thing). So, shining near-infrared light into the head at a source location, and measuring how much light you get back at a nearby detector, gives a measurement of how much light has been absorbed by the blood in that area of the brain. Therefore, you get a measure of a relative change in oxygenated and deoxygenated blood in that area. All of this without the need for a person to lie motionless in a massive cacophonous magnet, with greater portability, and for about a hundredth of the price of an MRI scanner (about £25,000 compared to £2,500,000).

The source and detector are placed on the scalp, so that the light received at the detector is reflected light following banana-shaped pathways

Picture credit: Tessari et al., 2017

“That sounds amazing! Sign me up!” I hear you say. However, I must put a little disclaimer out. There are reasons why fMRI is still the gold standard for functional brain imaging. As fNIRS relies on the measurement of light that gets back to the surface of the scalp after being in the brain, it can’t be used to measure activity from brain areas more than about 3 cm deep. This is being worked on by using cool ways of organising sources and detectors on the scalp. However, it is not thought that fNIRS will ever be able to produce a whole-brain map of brain activity. Also, as fNIRS is looking at the centimetre level, rather than millimetre, its spatial resolution and accuracy of location is limited in comparison to fMRI. Despite this, if the brain areas you’re interested in investigating are closer to the surface of the head, and not too teensy tiny, then fNIRS is a great technology to use.

So, what has this all got to do with babies? Well, fNIRS has one vice, one Achilles heel. Hair. Yes, this amazingly intelligent technology has such a primitive enemy. If your participants are blonde or bald, you’ll probably be fine. But anything deviating from this can block light from entering the head, and therefore weaken the light reaching the brain and eventually getting back to the detectors. However, do you know who has little to no hair? Babies. Plus, babies aren’t very good at lying still, particularly in a cacophonous magnet. This is why fNIRS is especially good for measuring brain activity in infants.

fNIRS is used to study a variety of topics related to infant development.  One of the most studied areas of infant psychology is language development. Minagawa-Kawai et al (2007) investigated how infants learn phonemes (the sound chunks that words are made up of). They used fNIRS to measure brain activation in Japanese 3 to 28-month-olds while they listened to different sounds. Infants listened to blocks of sounds that alternated between two phonemes (e.g. da and ba), and then other blocks that alternated between two different versions of the same phoneme (e.g. da and dha). In 3 to 11-month-olds, they found higher activation in a brain area responsible for handling language for both of these contrasts. So, this means that infants were treating ‘da’ and ‘ba’ and ‘dha’ as three different phonemes. However, 13 to 28-month-olds only had this higher activation when listening to the block of alternating ‘ba’ and ‘da’. This means that the older infants were treating ‘da’ and ‘dha’ as the same phoneme. This is consistent with behavioural studies showing that infants undergo ‘perceptual narrowing’, whereby over time they stop being able to discriminate between perceptual differences that are irrelevant for them. This has been related to why it’s much easier to be bilingual from birth if you have input from both languages, than it is to try to learn a second language later in life.

Another popular area of infant psychology is how infants perceive and understand objects. Wilcox et al (2012) used fNIRS to study the age at which infants began to understand shapes and colours of objects. They measured brain activation while infants saw objects move behind a screen and emerge at the other side. This study used a live presentation, made possible by the fact that fNIRS has no prerequisites for a testing environment except to turn the lights down a bit.


The shape change (left), colour change (middle), and no change (right) conditions of Wilcox et al. (2012). Each trial lasted 20 seconds, consisting of two 10 second cycles of the object moving from one side to the other (behind the occluder) and back again.

These objects were either the same when they appeared from behind the screen, or they had changed in shape or colour. They found heightened activation in the same area found in adult fMRI studies for only the shape change in 3 to 9-month olds, but for both shape and colour changes in the 11 to 12-month-olds. This confirms behavioural evidence that infants are surprised when the features of objects have changed, and that babies understand shape as an unchanging feature of an object before they understand colour in this way. This study shows how you can use findings from adult fMRI and infant behavioural studies to inform an infant fNIRS study, helping us learn how the brain’s complex visual and perceptual systems develop from infancy to adulthood.

There’s a lot more to learn if you wish to venture into the world of infant fNIRS research; it’s a fascinating area filled with untapped potential. fNIRS can help us to measure the brain activity of a hard-to-reach population (those pesky babies), enabling us to ask and answer questions about the development of language, vision, social understanding, and more! Questions being investigated in the Lancaster Babylab (where I am doing my PhD) include:

  • Do babies understand what pointing means?
  • Are bilingual babies better at discriminating between sounds?
  • Why do babies look at their parents when they are surprised?

And beyond this, the possibilities are endless!

If you are intrigued by fNIRS and want to learn more, I’d recommend review papers such as the one by Wilcox and Biondi (2015), and workshops such as the 3-day Birkbeck-UCL NIRS training course.

Edited by Jonathan Fagg and Rachael Stickland


  • Minagawa-Kawai, Y., Mori, K., Naoi, N., & Kojima, S. (2007). Neural Attunement          Processes in Infants during the Acquisition of a Language-Specific Phonemic Contrast. Journal Of Neuroscience, 27(2), 315-321.
  • Otsuka, Y., Nakato, E., Kanazawa, S., Yamaguchi, M., Watanabe, S., & Kakigi, R.   (2007). Neural activation to upright and inverted faces in infants measured by near infrared spectroscopy. Neuroimage, 34(1), 399-406
  • Tessari, M., Malagoni, A., Vannini, M., & Zamboni, P. (2015). A novel device for non-invasive cerebral perfusion assessment. Veins And Lymphatics, 4(1).
  • Wilcox, T., Stubbs, J., Hirshkowitz, A., & Boas, D. (2012). Functional activation of the infant cortex during object processing. Neuroimage, 62(3), 1833-1840.

Brain-controlled mice face robobugs

Monika Sledziowska | 6 JUL 2017

Have you ever wondered how realistic was the mind control technology presented in Kingsman: the secret service? I can’t claim to go to many Hollywood parties, but I may have an idea.

In a lesser-known series of experiments, in the 1960s a Yale University Professor, Jose Delgado, demonstrated that direct electrical stimulation of the brain is able to elicit various emotional states and reactions from a range of animals, including humans (Delgado, 1964). The stimulation and recording of electrical activity was also achieved using radio waves. Doesn’t it sound just like a recent film?

However cool (and perhaps disturbing) this line of enquiry was, it was soon abandoned. One reason for this may be that the technology of the time wasn’t quite advanced enough to establish which specific neural pathways were responsible for which state or reaction. However, in an interesting turn of events, a new technique has come along, which has the capacity to do precisely that.

The technique, by the name of optogenetics, doesn’t use radio waves but rather controls cells in the brain using light. Before you get too worried about the effects of light on your brain, I should explain that only cells that have been genetically modified to have light-sensitive proteins will be influenced by light of a specific frequency (Deisseroth et al., 2016). These light-sensitive proteins can be limited to a particular part of the brain and a particular cell type using genetic manipulation.

So, what can optogenetics tell us about the neural basis of specific behaviours? A recent paper by Han et al. (2017) focused on defining the neural pathways that are responsible for different aspects of hunting behaviour.

Step one: the authors injected a virus carrying the genes necessary to have light-sensitive proteins into the amygdala of mice. This is the part of the brain known to be involved in reward and fear processing (Han et al., 2017). The genetic background of the mice and the genes in the virus interacted in such a way that only inhibitory neurons (neurons that restrict or ‘inhibit’ activity), are affected. The researchers then implanted the mice with small optical fibres that could shine light of certain frequency onto the amygdala.

Step two: the mice’s amygdala was exposed to the light resulting in the inhibitory neurons being active in the amygdala. As a result, the mice showed increased jaw muscle activity (one could argue that jaws are quite important for capturing prey) and they were faster to chase and capture crickets (their natural prey).

What’s interesting is that they also bit and ate inedible objects such as wooden sticks and attacked artificial robotic bugs (you can purchase these, like anything else, from Amazon), which rarely happens in the real mouse world. And if you don’t believe me, you can see for yourself in the video below:

Using this technique, the researchers were also able to uncover the projection pathways from the amygdala to a part of the brainstem called the reticular formation (responsible for biting the prey), and to part of the midbrain called the periaqueductal grey (responsible for pursuing prey).

Even though we may still be quite far away from the mind-controlling techniques like those used by the villain in the Kingsman, current technology offers interesting opportunities for uncovering the workings of mouse and human brains.

If you want to know more about the use of animals in research, please click here.

Edited by Oly Bartley & Jonathan Fagg 


  • Deisseroth K, Feng G, Majewska AK, Miesenbock G, Ting A, Schnitzer MJ. Next-Generation Optical Technologies for Illuminating Genetically Targeted Brain Circuits. J Neurosci [Internet]. 2006;26(41):10380–6.
  • Delgado JMR. Free Behavior and Brain Stimulation. In: Neurobiology CCP and JRSBT-IR of, editor. Academic Press; 1964. p. 349–449.
  • Han W, Tellez LA, Rangel MJ, Motta SC, Zhang X, Perez IO, et al. Integrated Control of Predatory Hunting by the Central Nucleus of the Amygdala. Cell. 2017;168(1–2):311–324.e18.

The Neuroscience of Mindfulness: What Happens When We Meditate?

Joseph Holloway | 3 APR 2017

Joe is a guest writer for The Brain Domain, and is currently pursuing an MSc in Mindfulness-based Cognitive Therapies and Approaches, as well as an MA in 18th Century Literary Studies, at the University of Exeter.

‘Mindfulness’ is a word that has gathered momentum over the last decade. It has grown beyond associations of yoga and alternative therapies and moved into the realms of corporate culture, education, and mental health. Mindfulness has become such a prevalent aspect of our culture that there was even a Ladybird Books for Grown-Ups dedicated to it. When a phenomenon becomes this prominent and when it enters such fundamental spheres of our lives it is good to review its evidence base. What is Mindfulness meditation? How is it employed in a therapy context? What happens in the brain when we meditate? What evidence do we have that it is effective? This article attempts to answer these questions.

A Brief History of Mindfulness and Therapy

Firstly, what is Mindfulness? The term has an interesting history of development (Analayo, 2006, pp. 15-41) that is beyond the scope of this article, but a commonly accepted contemporary definition is: “moment-to-moment awareness” (Kabat-Zinn, 1990, p.2). Participants deliberately pay attention to thoughts, feelings, and sensations in the body, bringing their mind back to the task at hand when it wanders. This form of meditation is entrenched in many of the oldest religions and can be traced back to early canonical buddhist texts such as the Satipaṭṭhāna-sutta and the Mahāsatipatṭhāna Sutta. Contemporary Western understandings of Mindfulness meditation are a repackaging of the teachings of these texts in a secular context. They focus on the insights about the workings of the mind and the teachings on how to reduce the amount of distress that we cause ourselves.

A key example of such repackaging was Jon Kabat-Zinn’s Mindfulness Based Stress Reduction (MBSR) course originally developed at MIT in the 1970’s. This is an 8 week group course teaching participants how to engage with Mindfulness meditation and is open to all that feel (i) that they have too much stress in their lives, or (ii) that they are not relating to their stress healthily. In the 1990’s Mark Williams, John Teasdale and Zindel Segal combined Kabat-Zinn’s successful model with Beck’s Cognitive Behavior Therapy (CBT) to create a more specialised programme called Mindfulness-based Cognitive Therapy (MBCT). This programme is specifically designed to treat recurrent depression, and largely only open to those referred by their primary medical consultant. These two arms, the general MBSR and the specific MBCT, are the constituents of the Mindfulness-based interventions available on the NHS in the UK and through other providers around the world. They are widely used both as complementary and sole treatments for a variety of mental and physical health diagnoses including depression, generalised anxiety disorder, post-traumatic stress disorder, insomnia and eating disorders.

What evidence is there that Mindfulness is effective?

The effectiveness of Mindfulness-based interventions has been demonstrated through longitudinal studies, tracking the same people over time. An important early example found depressive participants in the MBCT programme to have half the amount of relapses one year after treatment compared to depressive participants that had treatment as usual (Teasdale et al, 2000). This finding was reinforced by the replication trial (Ma and Teasdale, 2004) concluding that there is ‘further evidence that MBCT is a cost-efficient and efficacious intervention to reduce relapse/recurrence in patients with recurrent major depressive disorder’ (ibid, p. 39). In these studies, the pool of participants in recovery from depression were randomly allocated into either the experimental or the control group. This was done by an external statistician and participants were matched for ‘age, gender, date of assessment, number of previous episodes of depression, and severity of last episode’ (ibid, p. 32). The results were important confirmation for the effectiveness of Mindfulness-based Interventions as therapy.

Whilst this was great news, it wasn’t until 2008 that Mindfulness-based interventions were compared to the gold standard for treatment of recurrent depression (Kuyken et al, 2008). This is maintenance antidepressive medication (m-ADM), requiring the participant to take antidepressive medication even when there are no indications of a relapse. Importantly, the 2008 study found that patients treated with MBCT were less likely to relapse than those treated with the gold standard after 15 months (47% compared to 60% of the m-ADM group). This was also replicated in a follow up study (Segle et al, 2010) where MBCT was compared against m-ADM and also against a placebo. Once participants were in remission they were given either MBCT, m-ADM or discontinued their active medication and given a placebo. Participants for all groups were randomly distributed by an external statistician, ensuring a close control on factors not being investigated. The MBCT and m-ADM group here showed the same levels of prevention from recurrence (73%), both much higher than the placebo group. Over a short term (15 months) Mindfulness-based interventions were thus shown to be better than m-ADM, and equally effective over an even longer period. In addition, it is arguably cheaper to administer Mindfulness-based interventions than m-ADM, there are no issues with drug tolerance, and unlike many antidepressants Mindfulness meditation can be utilised whilst pregnant or breastfeeding.

How does Mindfulness work?

When the brain is not responding to any particular task and is ‘at rest’, areas collectively known as the Default Mode Network (DMN) are activated (Berger, 1929), (Ingvar, 1974), (Andreasen et al, 1995).  This was found to be closely associated with mind wandering (Mason et al, 2007). It was also found it to be consistent with “internally focused tasks including autobiographical memory retrieval, envisioning the future, and conceiving the perspectives of others ” (Bruckner, 2008, p. 1). When our mind is wandering and not focused on a task we are normally either lost in personal memories or running through a scenario in our head, predicting, anticipating or worrying.

More frequent and more automatic activation of this network is associated with depressed individuals (Greicius et al, 2007); (Zhang et al, 2010) (Berman et al, 2011). Regularly wallowing in old memories or worrying about the future are perfect foundations for conditions that may lead to depression. These two functions, conducive to ‘living on autopilot’’, are the exact opposite to the definition of Mindfulness meditation given above: “moment-to-moment awareness.” Indeed, studies have shown that activation of the DMN can be regulated by Mindfulness meditation (Hasenkamp et al, 2012). Participants were observed meditating, and whenever they noticed their mind wandering they had to press a button. Immediately before this action the participants were unconsciously mind wandering. When the participants noticed that their mind had wandered, (indicated by the button press) the researchers regularly observed a deactivation of the DMN. The act of practising mindfulness-meditation was here regularly associated with a deactivation of the DMN.  A correlation between self-reported meditation experience and lower levels of DMN activation was also observed (Way et al, 2010).

Of course, the brain is never ‘doing nothing’ and a counter-network was regularly activated when participants weren’t mind-wandering: when they were paying attention to a task. This network in part consists of the anterior cingulate cortex (ACC), which is known to be instrumental in task monitoring (Carter et al, 1998). Activation of the ACC is closely associated with ‘executive control’ (Van Veen & Carter, 2002, p. 593) which detects incompatibilities or conflicts between a predicted outcome, and the observed reality. In this way the ACC functions as error-reporting or quality management. The ACC does not attempt to remedy the situation, but instead highlights it to other areas of the brain. This all happens before the subject is cognitively aware that there is a conflict.

Crucially, an association has been shown between meditation and activation of the ACC. A positive correlation between AAC thickness and meditation experience (Grant et al, 2010) and between mindfulness meditation and activation of the ACC (Zeidan et al, 2013), has been demonstrated. Mindfulness meditation is reliably shown to activate the ACC and improve the relative ease and likelihood of it being activated. Activation of the ACC prevents the mind from wandering, and prevents activation of the DMN. Mind wandering and activation of the DMN is related to depressive symptoms either developing or recurring. This is how Mindfulness-based interventions are thought to help those at a neurological level.


Mindfulness meditation has been around for 3500 years. It has been utilised in the West for nearly 40 years. We have had good evidence that it works for nearly 20 years but we are only just starting to explore how it works. The recent findings above help outline the process of change that the brain goes through whilst a regular Mindfulness-meditation practise is established, but they are by no means the full picture. We are also investigating how Mindfulness meditation facilitates people to more regularly respond instead of instinctively react. We are investigating how Mindfulness meditation enables decentering, and how it reduces the connectivity to the emotional areas of the brain. Research into the nuts and bolts of Mindfulness has never been so intense, and exciting results just like those depicted in this article are sure to arise soon.

Joe teaches a 10 week course devised by the Mindfulness in Schools Project (see details here). He teaches all levels and abilities, from College to University, and finds that it has had an overwhelmingly positive impact on level of well-being, achievement, and attendance of his students. If this is something that interests you, he can be contacted at is now taking bookings for autumn term 2017, and for 2018.

Edited by Jonathan Fagg and Rachael Stickland


  • Analyo (2003). Satipaṭṭhāna: The Direct Path to Realisation. Birmingham: Windhorse Publishing
  • Andreasen, N. et al. (1995). Remembering the past: two facets of episodic memory explored with positron emission tomography. Annals of the Journal of Psychiatry, 152, (1), pp 1576- 1585.
  • Berger, H. (1929). Über das elektrenkephalogramm des menschen. Archiv für Psychiatrie und Nervenkrankheiten, 87, (1), pp 527-570.
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  • Greicius, M. (2007). Resting-state functional connectivity in major depression: abnormally increased contributions from subgenual cingulate cortex and thalamus. Biological Psychiatry, 62, (5), pp 429-437.
  • Grant, J. et al. (2010). Cortical thickness and pain sensitivity in zen meditators. American Psychological Association, 10, (1) pp 43-53.
  • Hasenkamp, W. (2012). Mind wandering and attention during focused meditation: a fine-grained temporal analysis of fluctuating cognitive states. NeuroImage, 59, (1,) pp 750-760.
  • Holzel, B. et al. (2011). Mindfulness practise leads to increases in regional brain grey matter density. Psychiatry Research, 191, (1), pp 36-43.
  • Ingvar, D. (1974). Patterns of brain activity revealed by measurements of regional cerebral blood flow. Copenhagen: Alfred Benzon Symposium.
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  • Mason, M. et al (2007). Wandering mind: the default network and stimulus-independent thought. Science, 315, (19), pp 393-395.
  • Teasdale, J. et al. (2000). Prevention of relapse/recurrence in major depression by Mindfulness-Based Cognitive Therapy. Journal of Consulting and Clinical Psychology, 68 (4), pp 615-623.
  • Segle, Z. et al (2010). Antidepressant monotherapy vs sequential pharmacotherapy and Mindfulness-Based Cognitive Therapy, or placebo, for relapse prophylaxis in recurrent depression. Archives of General Psychiatry, 67, (12), pp 1256-1264.
  • Van Veen, V. & Carter, C. (2002). The timing of action-monitoring processes in the anterior cingulate cortex. Journal of Cognitive Neuroscience, 14, (4), pp 593-602.
  • Way, B. et al (2010). Dispositional mindfulness and depressive symptomatology. Correlations with limbic and self-referential neural activity during rest. Emotion, 10, (1), pp 12-24.
  • Zeidan, F. et al. (2013). Neural correlates of mindfulness meditation-related anxiety relief. Social Cognitive and Affective Neuroscience, 9, (6), pp 751-759.
  • Zhang, D. et al. (2010). Noninvasive functional and structural connectivity of the human thalamocortical system. Cerebral Cortex, 20, (1), pp 1187-1194.

The indirect applications of leisure technology

Kira Rienecker | 3 MAR 2017

As grant applications within science become increasingly competitive, the pressure grows to highlight the direct benefits of one’s research to human health and prosperity. These are the impact statements–is your research going to directly contribute to the “cure”?

Unfortunately, this attitude obscures a very important source of new knowledge and tools–simple curiosity and ‘play’. It is important to remember we don’t always know what we are doing when we dive into research. In fact, simple exploration of interesting concepts can have very important knock-on benefits!

For example, as we improve technology for leisure, developing more powerful smartphones and more realistic video games, we are also creating tools which can feed back into health and medicine applications. Smartphone apps are a very common example of this feedback. CloudUPDRS, an Android app designed by George Roussos and colleagues at Birbeck, University of London, takes advantage of the smartphone’s gyroscopic sensors to conduct frequent physical tests for Parkinson’s patients. Tying these physical tests and the associated self-assessment questionnaires to this constant companion device help researchers track symptoms and disease progression regularly and over an extended period of time. 

Developments in smartphones for leisure made this tool possible, but the feedback loop between leisure technology, health research, and medicine extends beyond our phones. Virtual reality, used for everything from gaming to drone flying, can be used to help train surgeons.

It is very important to keep investing in science and technology as a whole, even when the benefits to us aren’t immediately apparent. Encouraging play and building tools for play can help us creatively solve important problems. Restricting funding to “the most relevant” research angles may be an important investment strategy, but it may also risk restricting our creativity. Curiosity beyond ourselves helps us develop new knowledge –while our questions may not directly apply to a “cure”, they may incidentally equip us with tools we didn’t know we needed.

CloudUPDRS is explained in the New Scientist Article:

Feature Image Link

Perceptions of mental illness: Do biological explanations reduce stigma?

Rae Pass | 20 FEB 2017

If you haven’t already, read my related article ‘Perceptions of mental illness: The media and mental health’.

Over the last few years there has been a drive in mental health research to find biological explanations for mental illnesses, both to better understand the disorders themselves and to counteract the associated stigma. The hope is that if we can demonstrate that these conditions arise from faulty biology, people would be more understanding and compassionate, and the associated stigma would diminish. Logically, why would you blame someone for something they cannot control?

At first glance, this approach seems promising. A meta-analysis of studies, conducted over the last 20 years, into the beliefs and attitudes of the general population found that increased public understanding of biological explanations lead to greater acceptance of those seeking professional treatment (Schomerus, G. et al., 2012). When mental health disorders are framed as ‘brain diseases’, due to faulty genetics and biology, people tended to blame the sufferer less (Kvaale, Gottdiener & Haslam, 2013).

Unfortunately, these positive findings are in the minority, as surprisingly it appears that biological explanations do not reduce stigma, and may potentially increase it. Although the public appeared more accepting of the need for professional treatment overall stigma endured. The social rejection of sufferers was persistent and attitudes towards them remained negative, including stereotyping them as dangerous (Schomerus, G. et al., 2012). However, this study was conducted in western cultures and so the conclusions cannot be applied to all countries due to different societal norms. For example in some African tribes mental illness symptoms are misinterpreted as witchcraft. Additionally, the studies included in the analysis examined long-term impacts at a national level and not the short term impacts of anti-stigma campaigns.


Anti-stigma campaign poster from Time to Change.

In 2014, a study explored the impact of the chemical imbalance hypothesis on the sufferer’s self-stigma. This dominant, but controversial, hypothesis of depression states it is the result of an imbalance of neurotransmitters. Participants currently suffering, or who had previously suffered, a depressive episode were told their cause of the depression using a bogus test. Some were told their illness was caused by a chemical imbalance. Those given this biological explanation showed no reduction in blame (self-stigma), and an increased prognostic pessimism and worsened perceived self-efficacy (Kemp, Lickel & Deacon, 2014). This study demonstrates a surprising example where providing a biological explanation actually increased stigma, even if that stigma emanates from the victim themselves and not others. The study also found participants given the chemical imbalance theory viewed pharmaceutical intervention as more appropriate than therapy.

Biological explanations of mental illness seem to exacerbate the ‘us v them’ mentality, increasing distinction between ‘normal’ people and ‘abnormal’ sufferers (Lebowitz & Ahn, 2014). Additionally it increases avoidance of sufferers, who are portrayed as dangerous and not in control. A genetic cause may dehumanise sufferers by implying they are defective and distinct from others. It can also lead to stigmatisation of the entire family (Phelan, 2002) as family members are labelled as at risk or carriers, and potential partners may not want to pass on a genetic predisposition to their children. A Canadian survey in 2008 found that 55% people asked wouldn’t marry someone suffering from a mental illness. Even clinicians, the very people trying to help sufferers, appear to display decreased empathy for those suffering from a mental disorder when the patient’s disorder is described in biological terms (Lebowitz & Ahn, 2014).

Overall, a greater understanding of the biological causes of mental health conditions did lead people to blame the sufferer less for their condition, but reactions towards sufferers remained negative. Additionally, the sufferers themselves were more pessimistic about their recovery. It increased deterministic thinking which is extremely unhelpful, and untrue. Certain mutations guarantee you will develop a disease, as in Huntington’s disease, but this is rare. Other mutations do not always result in disease, but do significantly increase your risk: those who inherit two copies of the APOe4 allele are 10 fold more likely to develop Alzheimer’s disease, whilst those inheriting one copy have a 3 fold risk.

Genes do not act in isolation, and you will not develop schizophrenia because you have the ‘schizophrenia gene’: there is no such thing. Instead, it will be the interaction of different risk factors, both biological and environmental, that may result in you developing the disease. The interaction between different genes, and your environment, influences your responses to life events. A leading hypothesis in depression research focuses on the involvement of serotonin, the so called ‘happy chemical’. Serotonin is a chemical often believed to be at abnormal levels according to the chemical imbalance theory mentioned earlier. The gene SERT regulates how much serotonin is produced in your brain but its role is more complicated than simply not producing enough. A study published in 2015 found that a variation in the SERT gene moderated the development of depression in people abused as children (Nguyen et al., 2015). Only those with a specific version of SERT and had suffered abuse developed depression, whilst those with the same version but had not been subjected to abuse were reported to be the happiest participants.

This interaction highlights how a combination of factors collude to cause psychiatric diseases, and so the ideal method of treatment combines medication and therapy.  Medication alleviates symptoms and allows patients to benefit from psychotherapy, which facilitates learning of more healthy coping methods. Unfortunately, this is not always a viable option available to people, due to costs of services and difficulties accessing them. If patients are given a biological explanation for their illness they are more likely to view drugs as their best treatment option, and may not seek therapeutic help. This is despite the fact that pharmacological treatment can have a limited impact on their condition. No psychiatric drug works for all sufferers, potentially due to individual variation in disease diagnosis and symptoms, and thus response to treatment. Around 40% of depression is considered drug resistant and the negative symptoms of schizophrenia (e.g. social withdrawal, apathy) aren’t currently treatable with drugs. Indeed, medication is not a cure but a symptomatic treatment as patients relapse if they stop taking them, and the side effects are often debilitating.


A campaign poster from an American mental health association. Image source.

Another consideration, easily overlooked by well meaning scientists and clinicians, is not everyone with a condition considers themselves ‘diseased’ and may not want to be ‘cured’. These beliefs will vary between individuals and so it is important to take people’s own beliefs surrounding their conditions into account. Defining them by their disease is akin to defining a disabled person by their disability; defining them by what they cannot do. When it comes to mental health clinicians and researchers must avoid only thinking in pathological terms, and failing to consider the whole person. If not we risk perpetuating an unconscious us v them stigma, between those studying the disease and those living with it. Someone who has fully embraced her condition and sought to change how people think of it is Touretteshero. She is informative, delightfully hilarious and her website should definitely be checked out.

Clearly, emphasising the biological causes above all else is not the way to reduce stigma. Only focusing on these causes may actually increase stigma, and it ignores the fact that the environment is also crucial in mental health. That is not to say biology is not involved-it is! These conditions would not run in families if it was not. But, the environment you grow up and live in is also hugely influential.


Classic graph depicted % risk of developing schizophrenia first published in Gottesman, 1991. Image source.

A good example to end on is schizophrenia. This is often held up as a largely genetic based mental health condition. The classical illustration above depicts increasing likelihood for developing schizophrenia, as demonstrated by increased risk with increased genetic similarity. If you identical twin has schizophrenia your risk for also developing it is almost 50%.  Clearly, however, this genetic risk it is not 100%. Environmental factors will also hugely influence your risk, such as viral infection during the second trimester or suffering abuse as a child. In order to understand psychiatric diseases we need to consider the interaction of our environment and our biology. Only with better understanding of all aspects which interact and result in these diseases, rather than focusing on specific contributions, will we have a solid basis from which to combat mental health stigma.

Edited by Jonathan Fagg


  • Kemp, J., Lickel, J., & Deacon, B. (2014). Behav Res Ther, 56, 47-52.
  • Kvaale, E., Gottdiener, W., & Haslam, N. (2013). Soc Sci Med, 96, 95-103. 
  • Lebowitz, M., & Ahn, W. (2014). PNAS, 111 (50), 17786-17790.
  • Nguyen, T., et al. (2015). British Journal of Psychiatry, 1 (1), i104-109.
  • Phelan, J. (2002). Trends Neurosci, 25 (8), 430-1.
  • Schomerus, G., et al. (2012). Acta Psychiatrica Scandinavica, 125 (6), 440-452.

♥ Achy-breaky heart? Try touchy-feely brain! ♥

Laura Smith | 14 FEB 2017

As today is Valentine’s day, let’s get a bit touchy-feely. Whether you’re looking forward to a date with your significant other; planning to profess your feelings to a special someone; or hoping your soulmate will sweep you off your feet, you’d probably like to share a romantic caress. But what happens in the brain when we anticipate touching the one we desire? Using functional magnetic resonance imaging (fMRI), scientists in Italy set out to answer just that question. Isn’t that convenient!

fMRI uses the same principle as standard MRI: a large, very powerful electromagnet detects differences in the magnetic properties of different bodily tissues, and some fancy maths turns these signals into pictures. In fMRI, people in the scanner perform tasks, and scientists can locate brain areas where activity levels change in response to this.

In their fMRI study, published in the journal Frontiers in Behavioural Neuroscience, Ebisch, Ferri & Gallese (2014) wanted to find out if how much someone loved their partner was reflected in their brain activity when they anticipated caressing them. Participants in the MRI scanner were instructed to affectionately touch either a ball or their partner’s hand, which were both placed close to them.  They received a “touch” or “do not touch” instruction 3 seconds after they were told which item to touch (the hand or the ball). Therefore, they would anticipate performing the touch each time, which would involve a change in brain activity.  The task was performed many times but, 67% of the time, participants were asked not to perform the touch.

Participants also completed the Passionate Love Scale (PLS) (Hatfield & Sprecher, 1986) : a 15-item questionnaire measuring the intensity of their desire for their partner from “extremely cool” to “extremely passionate”, so that the researchers could see whether it was related to the changes in brain activity.  There was such a relationship in the right posterior insula: an area of cortex believed to act as a processing-hub for information about the body’s current physiological state (Augustine, 1996) .  Insula activity decreased during anticipation of touching but the more passionate the love, the less deactivation there was for anticipation of romantic but not non-romantic touching. So, when participants’ desire for their partners was higher, there was more neural response to anticipation of touching the partner versus the ball.  Additionally, insula activity increased when touches were actually performed, and significantly moreso for romantic versus non-romantic touches.


Location of right posterior insula. Retrieved from Ebisch et al. (2014)1

The insula interacts with brain areas involved in bodily sensation (Zweynert et al., 2011) , in particular the somatosensory cortex.  This area’s activity in response to touch was previously shown to be influenced by anticipation of a reward (Pleger et al., 2008). Taking this into account, the researchers suggest that the posterior insula, via its connection with somatosensory cortex, may influence how we actually experience touches. As such, because desire for the partner was associated with less insula deactivation during anticipation of touching them, it may be that wanting to touch someone actually makes the experience of doing so all the more pleasant.

So spare a thought for your clever insula today, and have a happy Valentine’s day.


  • Augustine, JR. (1996). Circuitry and functional aspects of the insular lobe in primates including humans. Brain Res. Rev., 22, 229-244.
  • Ebisch SJ, Ferri F & Gallese V. (2014). Touching moments: desire modulates the neural anticipation of active romantic caress.
  • Hatfield E. & Sprecher S. (1986). Measuring passionate love in intimate relations. Adolescence, 9, 383-410.
  • Pleger B, Blankenburg F, Ruff C, Driver J & Dolan R. (2008). Reward facilitates tactile judgments and modulates hemodynamic responses in human primary somatosensory cortex. Neurophysiol., 39, A9.
  • Zweynert S, Pade JP, Wustenberg T et al. (2011). Motivational salience modulates hippocampal repetition suppression and functional connectivity in humans. Hum. Neurosci, 5, 144.

Featured image by Alex Van

Understanding Social Behaviour in Research – Why we should remember RAT PARK

14 NOV 2016

One of the major aims of scientific investigation is to improve human lives, particularly our health. One of the best current tools for improving human health is the animal model, though there is a lot of ongoing work into alternative methods. An animal model is a representative living system that can be manipulated to reflect a particular condition or illness. Rodents are popularly used due to many factors including size, breeding rates, and the ease at which they can be altered in genetically meaningful ways. However, it is important to remember that rodents cannot be considered a tool in the same manner as a microscope as they are living organisms with their own set of behaviours. This may seem obvious or even silly to mention, but experiments that have not considered this important aspect of the animal model have affected our ability to improve human health.

    An excellent example of this is in the field of drug addiction studies which allowed animals to self administer recreational drugs which were common in the 1950’s and 60’s. These consisted of socially isolating each animal to prevent animals from damaging equipment and hindering each other’s surgical recovery. The results of such studies caused investigators to conclude that drug addiction was not only instant but impossible to stop. A research group from Simon Fraser University considered such experiments in the context of the human condition. Knowing that rats and humans are social animals, they considered the impact that social isolation may play in producing these results. After all, how would you respond to an endless supply of drugs and nothing else?

    To this end they built a free range environment they named ‘rat park’ and split the rats into two groups. One group lived in the free range rat park environment and the second group lived in classical single caged environments. Their results showed that not only were the rat park animals better at coping with addiction but they were also more resilient to becoming addicted in the first place. This is very important to understanding human addiction as it is clear that there is more to addiction than simply a supply of drugs.This example illustrates how animal models can be much more informative tools to medicine when their social behaviour is considered as an important factor.

For more information on Rat Park have a look at Prof Bruce Alexander’s website.

Or this comic by Stuart McMillan.

The header image was sourced from:

Who the hell is MEG, and how can she help us understand the brain?

Rachael Stickland | 8 NOV 2016

Let me tell you about a MEG who doesn’t get her fair share of the limelight. MEG uses her SQUIDs to catch your brain activity, after it has left your head. She’s quite a fast mover, and can do this at a millisecond rate! Strangely though, she’s kept locked in a room with really thick walls. Poor MEG.

Still confused? Of course you are.  I guess it is time for me to admit MEG isn’t a woman. Similar to an MRI scanner, MEG is a technique researchers use to learn about the brain.  MEG is short for Magnetoencephalography (magneto refers to magnetic fields, encephalon means the brain, and –graphy indicates the process of recording information). Nothing to do with the guy in the purple cape.

This is a MEG scanner:


Source of this image: Magnetoencephalography Wikipedia

I’d like to say this brain imaging technique was inspired by a woman getting a perm in the 80s, as that’s what it has always reminded me of.  I’m afraid that’s not the case.

So how does MEG measure these magnetic fields? Any electrical current will produce a magnetic field. Even the electrical currents in your brain. If a big group of neurons (brain cells) are facing the same direction and send electrical impulses to each other, they induce a weak magnetic field, with a certain direction and strength. These magnetic fields leave the brain, and can still be measured outside the skull.


Source of this image: Magnetoencephalography Wikipedia

Since the magnetic fields that leave the head are so weak (around a billion times weaker than the magnetic field of a typical fridge magnet!), a MEG scanner measures them using really sensitive instruments called SQUIDs (Superconducting Quantum Interference Devices). SQUIDs are quite high maintenance though; they only work at temperatures below -296°C! Bathing them in liquid helium keeps them this cold. As SQUIDs are so sensitive, they also pick up stronger magnetic fields from the environment, which can mask the ones we want to measure from the brain. Because of this, a MEG scanner has to be kept in a magnetically shielded room, with a door like this:


Source of this image: Magnetoencephalography Wikipedia

Why is it useful to measure these magnetic fields anyway? MEG allows us to measure brain activity in a non-invasive way; there is no discomfort for the person being scanned, and no side effects. MEG helps us to learn about how and where the brain responds to certain tasks, improving knowledge of the link between brain function and human behaviour. Brain function measured with MEG has been shown to be different in many neurological and psychiatric diseases.  MEG has a key role to play in helping localise regions of the brain that are faulty, and that might need to be surgically removed, for example in epilepsy.

I hope you’ve enjoyed being introduced to a new MEG. Watch this space for more articles on what she gets up to.