Tag Archives: MEG

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.

2a6556de3969141b8006024f2d14873e

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.

Picture1

(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!

 

Advertisements

Do children with stronger functional connectivity at rest have better working memory capacity?

You are driving to the house of a friend, and you are getting directions from a GPS. “Drive to the end of the road, turn right, drive straight ahead and turn down the third road on your left, and the house is at the end of the road.”  As you are driving you need to remember each instruction, and hold that information in mind whilst completing each step. This is an example of a task that requires working memory. Working memory is the ability to hold relevant information in mind for a short period of time, whilst processing or using other material to complete a task. Working memory is critically important for learning at school, and failures of working memory can be a key predictor of children showing poor academic performance.

One way in which we can start to think about why some children seem to do far better on working memory tasks than others is by exploring the underlying neurophysiological processes that support working memory. With a technique called magnetoencephalography (MEG) we are able to look at how and when different brain areas are communicating, by seeing whether activity between different brain regions co-occurs in time. Importantly, it is possible to observe the strength of communication – functional connectivity – between different brain areas at rest.

In a new paper, we found that a child’s spatial working memory capacity can be predicted on the basis of particular patterns of brain activity at rest. We measured the resting-state functional connectivity of 8 to 11-year-olds, and assessed whether the strength of functional connectivity could be predicted by the child’s capacity. To measure working memory, we used well-validated behavioural measures of spatial and verbal working memory, taken outside the MEG scanner. These measures are routinely used in educational settings and are closely related to children’s literacy and numeracy levels. To examine functional connectivity, we measured the extent to which the activity of different brain regions was coordinated whilst the children were at rest inside the scanner. We then selected specific networks of coordinated regions that we considered ought to relate to working memory, based on adult networks responsible for cognitive control, because this is thought to be a key component of working memory. We reasoned that better these control areas can become coordinated with other brain systems, the better the child ought to do at control-demanding working memory exercises.

And this was what we found. We identified that the strength of connectivity between areas of the frontal and parietal lobe, and a portion of the temporal lobe,  was related to children’s spatial working memory capacity. The stronger the coordination between these areas, the higher the child’s capacity. Importantly, this relationship could not be explained simply by differences in motivation or strategy during the spatial working memory tasks, as children were at rest when the MEG was measured. Interestingly, we did not find a relationship between verbal working memory scores and any functional networks at rest – this may have been because the brain networks involved in verbal working memory tasks are less discrete or easily detectible with MEG.

Why is this important? Working memory is a key predictor of educational outcomes, and being able to relate individual differences in working memory to the functional coordination of different brain regions helps us understand the mechanisms supporting successful (and less successful) performance. The specific network related to children’s spatial working memory was the bilateral superior parietal cortex and middle frontal gyri, areas often associated with spatial attentional control, and lower-level processing areas. This suggests that children with better spatial working memory may be those with stronger connections between these frontal control regions and lower-level sensory areas. We can use this knowledge to begin to ask questions such as whether training can strengthen this connectivity (yes, as we have seen in a previous blog post), and to identify and redress the ways in which it can impact on learning.

References

Barnes, J. K., Woolrich, M. W., Baker, K., Colclough, G. L., & Astle, D. E. (2015). Electrophysiological measures of resting state functional connectivity and their relationship with working memory capacity in childhood. Developmental Science, doi: 10.1111/desc.12297.