Tag Archives: research

The weather and the brain – using new methods to understand developmental disorders

 

The latest article was written by our brilliant lab member Danyal Akarca. It describes some of his MPhil research which aims to explore transient brain networks in individuals with a particular type of genetic mutation. Dan finished his degree in Pre-Clinical Medicine before joining our lab and has since been fascinated by the intersection of genetic disorders and the dynamics of brain networks.

The brain is a complex dynamic system. It can be very difficult to understand how specific differences within that system can be associated with the cognitive and behavioural difficulties that some children experience. This is because even if we group children together on the basis that they all have a particular developmental disorder, that group of children will likely have a heterogeneous aetiology. That is, even though they all fall into the same category, there may be a wide variety of different underlying brain causes. This makes these disorders notoriously difficult to study.

Developmental disorders that have a known genetic cause can be very useful for understanding these brain-cognition relationships, because by definition they all have the same causal mechanism (i.e. the same gene is responsible for the difficulties that each child experiences). We have been studying a language disorder caused by a mutation to a gene called ZDHHC9. These children have broader cognitive difficulties, and more specific difficulties with speech production, alongside a form of childhood epilepsy called rolandic epilepsy.

In our lab, we have explored how brain structure is organised differently in individuals with this mutation, relative to typically developing controls. Since then our attention has turned to applying new analysis methods to explore differences in dynamic brain function. We have done this by directly recording magnetic fields generated by the activity of neurons, through a device known as a magnetoencephalography (MEG) scanner. The scanner uses magnetic fields generated by the brain to infer electrical activity.

The typical way that MEG data is interpreted, is by comparing how electrical activity within the brain changes in response to a stimulus. These changes can take many forms, including how well synchronised different brain areas are, or the how size of the magnetic response differs across individuals. However, in our current work, we are trying to explore how the brain configures itself within different networks, in a dynamic fashion. This is especially interesting to us, because we think that the ZDHHC9 gene has an impact on the excitability of neurons in particular parts of the brain, specifically in those areas that are associated with language. These changes in network dynamics might be linked to the kinds of cognitive difficulties that these individuals have.

We used an analysis method called “Group Level Exploratory Analysis of Networks” – or GLEAN for short – and has recently been developed at the Oxford centre for Human Brain Activity. The concept behind GLEAN is that the brain changes between different patterns of activation in a fashion that is probabilistic. This is much like the concept of the weather – just as the weather can change from day to day in some probabilistic way, so too may the brain change in its activation.

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This analysis method not only allows us to observe what regions of the brain are active when the participants are in the MEG scanner. It also allows us to see the probabilistic way in which they can change between each other. For example, just as it is more likely to transition from rain one day to cloudiness the next day, relative to say rain to blistering sun, we find that brain activation patterns can be described in a very similar way over sub-second timescales. We can characterise those dynamic transitions in lots of different ways, such as how long you stay in a specific brain state or how long does it take to return to a state once you’ve transitioned away. (A more theoretical account of this can be found in another recent blog post in our Methods section – “The resting brain… that never rests”.) We have found that a number networks differ between individuals with the mutation and our control subjects.

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(These are two brain networks that show the most differences in activation – largely in the parietal and frontotemporal regions of the brain.)

Interestingly, these networks strongly overlap with areas of the brain that are known to express the gene (we found this out by using data from the Allen Atlas). This is the first time that we know of that researchers have been able to link a particular gene, to differences dynamic electrical brain networks, to a particular pattern of cognitive difficulties. And we are really excited!

 

Brain Training: Placebo effects, publication bias, small sample sizes… and what we do next?

Over the past decade the young field of cognitive training – sometimes referred to as ‘brain training’ – has expanded rapidly. In our lab we have been extremely interested in brain training (Astle et al. 2015; Barnes et al. 2016). It has the potential to tell us a lot about the brain and how it can dynamically respond to changes in our experience.

The basic approach is to give someone lots of practice on a set of cognitive exercises (e.g. memory games), see whether they get better at other things too, and in some cases see whether there are significant brain changes following the training. The appeal is obvious: the potential to slow age-related cognitive decline (e.g. Anguera et al. 2013), remediate cognitive deficits following brain injury (e.g. Westerberg et al. 2007), boost learning (e.g. Nevo and Breznitz 2014) and reduce symptoms associated with neurodevelopmental disorders (e.g. Klingberg et al. 2005). But these strong claims require compelling evidence and the findings in this area have been notoriously inconsistent.

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(Commercial brain training programmes are available to both academics and the general public)

I have been working on a review paper for a special issue, and having trawled through the various papers, I think that some consensus is emerging. Higher-order cognitive processes like attention and memory can be trained. These gains will transfer to similarly structured but untrained tasks, and are mirrored by enhanced activity and connectivity within the brain systems responsible for these cognitive functions. However, the scope of these gains is currently very narrow. To give an extreme example, learning to remember very long lists of letters does not necessarily transfer to learning long lists of words, even though those two tasks are so similar – the training can be very content specific (Harrison et al. (2013); see also Ericcson et al. (1980)). But other studies seem to buck that trend, and show substantial wide transfer effects – i.e. people get better not just at what they trained on, but even very different tasks. Why this inconsistency? Well I think there are a few important differences in how the studies are designed, here are two of the most important:

  1. Control groups: Some studies don’t have control groups at all, and many that do don’t have active control groups (i.e. the controls don’t actually do anything, so it is pretty obvious that they are controls). This means that these studies can’t properly control for the placebo effect (https://en.wikipedia.org/wiki/Placebo). If a study doesn’t have an active control group then it is more likely to show a wide transfer effect.
  2. Sample size: The smaller the study (i.e. the fewer the participants) the more likely it is to show wider transfer effects. If studies include lots of participants then it is far more likely to accurately estimate the true size of the transfer effect, which is very small.

When you consider these two factors and only look at the best designed studies, the effect size for wider transfer effects is about d=0.25 – if you are not familiar with this statistic, this is small (Melby-Lervag et al., in press). Furthermore, when considering the effect sizes in this field it is important to remember that this literature almost certainly suffers from a publication bias – it is difficult to publish null effects, and easier to publish positive results. Meaning that there are probably quite a few studies showing no training effects sat in researchers’ drawers, unpublished. As a result, even this small effect size is likely an overestimate of the genuine underlying effect. The true effect is probably even closer to zero.

So claims that training on some cognitive games can produce improvements that spread to symptoms associated with particular disorders – like ADHD – are particularly incredible. Just looking at the best designed studies, the effect size is small, again about d=0.25 (Sonuga-Barke et al., 2013). The publication bias caveat applies here too – even this small effect size is likely an overestimate of the true effect. Some studies do show substantially larger effects, but these are usually not double blind. That is, the person rating those symptoms knows whether or not the individual (usually a child) received the training. This will result in a substantial placebo effect, and this likely explains these supposed enhanced benefits.

Where do we go from here? As a field we need to ensure that future studies have active control groups, double blinding and that we include enough participants to show the effects we are looking for. I think we also need theory. A typical approach is to deliver a training programme, alongside a long list of assessments, and then explore which assessments show transfer. There is little work that explicitly generates and then tests a theory, but I think this is necessary for future progress. Where research is theoretically grounded it is far easier for a field to make meaningful progress, because it gives a collective focus, creates a shared set of critical questions, and provides a framework that can be tested, falsified and revised.

Author information:

Dr. Duncan Astle, Medical Research Council Cognition and Brain Science Unit, Cambridge.

https://www.mrc-cbu.cam.ac.uk/people/duncan.astle/

Reference:

Anguera JA, Boccanfuso J, Rintoul JL, Al-Hashimi O, Faraji F, Janowich J, Kong E, Larraburo Y, Rolle C, Johnston E, Gazzaley A (2013) Video game training enhances cognitive control in older adults. Nature 501:97-101.

Astle DE, Barnes JJ, Baker K, Colclough GL, Woolrich MW (2015) Cognitive training enhances intrinsic brain connectivity in childhood. J Neurosci 35:6277-6283.

Barnes JJ, Nobre AC, Woolrich MW, Baker K, Astle DE (2016) Training Working Memory in Childhood Enhances Coupling between Frontoparietal Control Network and Task-Related Regions. J Neurosci 36:9001-9011.

Ericcson KA, Chase WG, Faloon S (1980) Acquisition of a memory skill. Science 208:1181-1182.

Harrison TL, Shipstead Z, Hicks KL, Hambrick DZ, Redick TS, Engle RW (2013) Working memory training may increase working memory capacity but not fluid intelligence. Psychological science 24:2409-2419.

Klingberg T, Fernell E, Olesen PJ, Johnson M, Gustafsson P, Dahlstrom K, Gillberg CG, Forssberg H, Westerberg H (2005) Computerized training of working memory in children with ADHD–a randomized, controlled trial. Journal of the American Academy of Child and Adolescent Psychiatry 44:177-186.

Melby-Lervag M, Redick TS, Hulme C (in press) Working memory training does not improve performance on measures of intelligence or other measures of “Far Transfer”: Evidence from a meta-analytic review. Perspectives on Psychological Science.

Nevo E, Breznitz Z (2014) Effects of working memory and reading acceleration training on improving working memory abilities and reading skills among third graders. Child neuropsychology : a journal on normal and abnormal development in childhood and adolescence 20:752-765.

Sonuga-Barke EJ, Brandeis D, Cortese S, Daley D, Ferrin M, Holtmann M, Stevenson J, Danckaerts M, van der Oord S, Dopfner M, Dittmann RW, Simonoff E, Zuddas A, Banaschewski T, Buitelaar J, Coghill D, Hollis C, Konofal E, Lecendreux M, Wong IC, Sergeant J (2013) Nonpharmacological interventions for ADHD: systematic review and meta-analyses of randomized controlled trials of dietary and psychological treatments. The American journal of psychiatry 170:275-289.

Westerberg H, Jacobaeus H, Hirvikoski T, Clevberger P, Ostensson ML, Bartfai A, Klingberg T (2007) Computerized working memory training after stroke–a pilot study. Brain injury 21:21-29.