Category Archives: Blog

OHBM 2016 – Impressions and Perspectives

I had the immense privilege to attend the annual meeting of the Organization for Human Brain Mapping (OHBM) in Geneva last week. OHBM is a fantastic venue to see the latest and greatest developments in the field of human neuroimaging and this year made no exception. The program was jam-packed with keynote lectures, symposia, poster presentations, educational courses, and many informal discussions. It is almost impossible to describe the full breadth of the meeting, but I will try to summarize the ideas and developments that were most interesting to me.

Three broad themes emerged for me: big data, methodological rigor, and new approaches to science. Big data was pervasive throughout the meeting with a large number of posters making use of huge databases and special interest talks focussing on the practicalities and promises of data sharing and meta-analysis. It seems like the field is reacting to the widely discussed reproducibility crises (http://www.apa.org/monitor/2015/10/share-reproducibility.aspx) and prominent review articles about the faults of the low sample sizes that so far have been common practice in neuroimaging (http://www.nature.com/nrn/journal/v14/n5/full/nrn3475.html). These efforts seem well suited to firmly establish many features of brain structure and function, especially around typical brain development. In the coming years, this is likely to influence publishing standards, education, and funding priorities on a wide scale. I hope that this will not lead to the infant being ejected with the proverbial lavational liquid. There is still a need to study small samples of rare populations that give a better insight into biological mechanisms, e.g. rare genetic disorders. Further, highly specific questions about cognitive and brain mechanisms that require custom assessments will probably continue to be assessed in smaller scale studies, before being rolled out for large samples.

A related issue that probably also arose from the replication discussions is methodological rigor. Symposia at OHBM2016 discussed many issues that had been raised in the literature, like the effect of head motion on structural and functional imaging, comparison of post-mortem anatomy with diffusion imaging, and procedures to move beyond statistical association. Efforts to move towards higher transparency of analysis strategies were also prominently discussed. This includes sharing of more complete statistical maps (more info here: http://nidm.nidash.org/specs/nidm-overview.html– soon to be available in SPM and FSL), tools for easier reporting and visualisation of analysis pipelines, and access to well described standard datasets. I can imagine a future in which analyses are published in an interactive format that allows for access to the data and the possibility to tweak parameters to assess the robustness of the results.

These exciting developments also pose some challenges. The trend towards large datasets also requires a new kind of analytic and theoretical approach. This leads to a clash between traditional scientific approach and big data science. Let me expand: The keynote lectures presented impressive work that was carried out in the traditional hypothesis-test-refine-hypothesis fashion. For instance, keynote speaker Nora Volkow, National Institute of Drug Abuse, presented a comprehensive account of dopamine receptors in human addiction based on a series of elegant, but conceptually simple PET experiments. In contrast to the traditional approach of collecting a few measurements to test a specific hypothesis, big data covers a lot of different measurements with a very broad aim. This creates the problem of high-dimensional data that need to be reduced to reach meaningful conclusions. Machine learning approaches emerged as a relatively new addition to the human neuroscience toolkit to tackle pervasive problems associated with this. There is great promise in these methods, but standards for reliability still need to be established and new theoretical developments are needed to integrate these findings with current knowledge. Hopefully, there will be closer communication between method developers and scientists applying these tools to human neuroscience as these methods mature.

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A single-gene disorder affects brain organisation

Many children struggle with learning particular skills despite good access to learning opportunities. For instance, some children need more time and considerably more effort to learn language compared to their peers. In some cases, this is associated with other problems, e.g. in certain types of childhood epilepsy, in others, these difficulties occur in isolation, e.g. specific language impairment. It is vital to understand the processes that lead to these difficulties so that problems can be identified early and treated with the most effective interventions. Yet, understanding these developmental difficulties still poses a scientific challenge. We know that genetic predisposition plays a role based on heritability studies and intermediate difficulties in family members. However, learning difficulties are associated with a large number of genes that individually have only very small effects. A possible reason for this could be that developmental disorders that are defined on the basis of behaviour reflect a mixture of underlying biology. This heterogeneity makes it very difficult to establish any causative mechanism.

One way of getting around this conundrum is to study known genetic disorders that share some similarity with more common developmental syndromes. To this end, we investigated a case group of individuals with mutations in a particular gene. We established that this mutation is associated with disproportionate deficits in attention, language, and oro-motor control (Baker et al., 2015). In the current study, we explored the effect of this genetic mutation on the organisation of the brain network to understand how a genetic difference may lead to differences in thinking and behaviour. Brain regions with typically high expression of this gene showed the highest connectivity, which may indicate that it is important for the development of structural connections. Further, cases with mutations in this gene displayed reduced efficiency of information transfer in the brain network. These findings suggest that brain organisation may provide an important intermediate level of description that could help to reveal how genetic differences give rise to learning difficulties. We hope to extend this work to compare brain organisation between genetic groups and developmental disorders directly in the future.

 

The article about the study is now available as a preprint:  http://dx.doi.org/10.1101/057687

Interested readers can also retrieve the analysis scripts for this study here:  https://github.com/joebathelt/ZDHHC9_connectome

 

 

Reference:

Baker, K., Astle, D. E., Scerif, G., Barnes, J., Smith, J., Moffat, G., et al. (2015). Epilepsy, cognitive deficits and neuroanatomy in males with ZDHHC9 mutations. Annals of Clinical and Translational Neurology, n/a–n/a. http://doi.org/10.1002/acn3.196

The resting brain… that never rests

“Spend 5-10 minutes lying down, make yourself comfortable, and keep your eyes open. Be still. Don’t think of anything specific”

These are the typical instructions gives to participants in a ‘resting state’. This is the study of brain activity with neuroimaging while the subject is literally told to do nothing.  This approach is very popular in our field… but why is it worth putting such effort into understanding a brain that isn’t doing anything? But in reality, the brain is never doing nothing. And studying the ongoing spontaneous activity that it produces can provide key insights to how the brain is organised.

Traditionally, a technique called functional Magnetic Resonance Imaging (fMRI) is used to study the resting brain. This uses changes in metabolism to chart brain activity. It turns out that the patterns of activity across the brain are not random, but are highly consistent across many studies. Some brain areas – in some cases anatomically distant from one another – have very similar patterns of activity to each other. These are referred to as resting state networks (RSNs).

A problem with this imaging method is that it is slow. It measures changes in metabolism in the order of seconds, even though electrical brain activity really occurs on a millisecond scale.  A landmark paper by Baker et al. (2014) instead used an electrophysiological technique, MEG, which can capture this incredibly rapid brain activity. They combined this technique with a statistical model, called a Hidden Markov Model (HMM).

They showed that contrary to previous thinking, these networks are not stable and consistent over time. Even when they brain is at rest they change in a rapid and dynamic way – the resting brain is never actually resting.

For more details of how they did – read on:

 

What is a Hidden Markov Model (HMM)?

A model, such HMM, is a representation of reality, built around several predictions based on elementary observations and a set of rules which aim to find the best solution of the problem. Let’s think about Santa Claus: he has to carry a present to all the nice kids. The problem is that he doesn’t know how to fill the sack with the toys, which has limited capacity. The input in this case will be the toys which have a certain weight and volume. Santa could try lots of solutions, until he finds this optimal configuration of toys. In essence, he is using a ‘stochastic model’ that tries multiple solutions. Santa knows the inputs, and can see how varying this results in a more optimal solution.

An HMM is also a stochastic model.  But this time the input is hidden, that is, we cannot observe it. These are the brain states that produce these network patterns. Instead, the output – the brain recordings – is visible. To better understand how the model works, imagine a prisoner is locked in a windowless cell for a long time. They want to know about the weather outside.  The only source of information is if the guard in front of his cell is carrying an umbrella (🌂) or not (x🌂x). In this case the states are the weather: sunny (☀), cloudy (☁), or rainy (☔), and they are hidden. The observation is the presence or absence of an umbrella. Imagine now that after a few days the prisoner has recorded a sequence of observations so he can turn to science and use HMMs to predict what the weather is like. The prisoner needs to know just 3 things to set up the model on the basis of their observation:

  • Prior probabilities: the initial probabilities of any particular type of weather e.g. if the prisoner lives in a country where it is just as likely to be sunny, cloudy or rainy, then the prior probabilities are equiprobable.
  • Emission probabilities: the probability of the guard bringing an umbrella or not given a weather condition.
  • Transition probabilities: the probabilities that a state is influenced by the past states, e.g. if it’s raining today, what is the probability of it being sunny, cloudy or rainy tomorrow.

What is the probability that the next day will be ☔ given that the guard is carrying an umbrella 🌂? After many days of observations, let’s say 50, what is the probability that day 51 will be ☀? In this case the calculation is really hard. The prisoner needs to integrate the various sources of information in order to establish the most likely weather condition on the following day – there is actually an algorithm for doing this, it is called a ‘Viterbi algorithm’.Picture1

How HMM is used in resting state paradigm

Using HMMs, Baker et al. (2014) identified 8 different brain states that match the networks typically found using fMRI. More importantly, they revealed that the transitions between the different RSNs are much faster than previously suggested. Because they used MEG and not fMRI, it was possible to calculate when a state is active or not, that is, the temporal characteristics of the states.

The authors additionally mapped where the state was active. They used the temporal information of the states to identify only the neural activity that is unique in each state. Therefore, they combined this information with the neuronal activity localization to build the networks maps.  This procedure identifies the brain areas associated with each state.

This study provides evidence that within-network functional connectivity is sustained by temporal dynamics that fluctuate from 200-400 ms. These dynamics are generated by brain states that match the classic RSNs, but which are constituted in a much more dynamic way than previously thought. The fact that each state remains active for only 100-200 ms, suggests that these brain states are underpinned by fast and transient mechanisms. This is important, because it has previously been unclear how these so called ‘resting’ networks are related to rapid psychological processes. This new approach provides an important step in bridging this gap. At last we have a method capable of exploring these networks on a time-scale that allows us to explore how they meaningfully support cognition.

 

Reference:

Baker, A. P., Brookes, M. J., Rezek, I. A., Smith, S. M., Behrens, T., Probert Smith, P. J., & Woolrich, M. (2014). Fast transient networks in spontaneous human brain activity. eLife, 3, e01867–18. http://doi.org/10.7554/eLife.01867

How being overweight may cause children difficulties

Obesity is a growing problem in developed countries. In the UK, a recent WHO studied indicated that one in four adults is obese (WHO obesity in the UK) and up to one in five children (WHO childhood obesity in the UK). Health minister Jeremy Hunt referred to the increasing number of children with severe weight problem as a national emergency (Interview with Jeremy Hunt on childhood obesity). Worryingly, obesity in children may not only be associated with increased risk for cardiovascular conditions, but may also hinder children’s academic progress. For instance, a cohort study of 5,000 children in Australia found that obesity was related to lower school performance for boys. That relationship persisted even when the researchers took other factors like family wealth into account (Black, Johnston, & Peeters, 2015, Taras & Potts Datema, 2005). Similarly, a study of 600 high-school students in the UK found that higher body-mass index (BMI), that is body weight relative to height, was negatively associated with school performance (Arora et al., 2013).

 

Obesity in childhood and adolescence is associated with lower cognitive performance

Lower school performance may be caused by differences in cognitive skills. Several studies investigated differences in cognitive performance in children and adolescents with obesity. Most of these studies focussed on executive functions. EF is used as an umbrella term for a set of inter-related abilities, including goal planning, attention, working memory, inhibition, and cognitive flexibility (Anderson, 2002; Diamond, 2013). Wirt and colleagues found a negative association between body weight, inhibitory control, and cognitive flexibility in a community sample of nearly 500 children (Cserjési, Molnár, Luminet, & Lénárd, 2007; Wirt, Schreiber, Kesztyüs, & Steinacker, 2015), even when controlling for family and lifestyle factors. Higher BMI was also associated with worse performance on executive function assessments in a sample of children with attention deficit hyperactivity disorder (ADHD) (Graziano et al., 2012). Adolescents show a similar association between obesity and cognitive performance. A study by Lokken and colleagues found impairments in attention and executive function in adolescents with obesity (Lokken, Boeka, Austin, Gunstad, & Harmon, 2009).

Together, these studies suggest that obesity in children and adolescents is associated with poorer performance on executive function tests (Liang, Matheson, Kaye, & Boutelle, 2014). These findings indicate that children and adolescents with higher body weight may find it more difficult to control their behaviour.

 

Obesity in childhood and adolescence is associated with structural and functional brain differences

Cognitive differences associated with childhood and adolescent obesity are also linked to structural and functional differences in the brain. Yau and colleagues compared 30 adolescents with obesity to a control group of 30 adolescents with normal weight matched for age, gender, and socio-economic status. The study found lower academic achievement, lower working memory, attention, and mental flexibility in the obese group that was associated with reduced cortical thickness in the orbitofrontal and anterior cingulate cortex (Bauer et al., 2015; Ou, Andres, Pivik, Cleves, & Badger, 2015; Yau, Kang, Javier, & Convit, 2014). These brain areas are generally associated with behavioural control. Further, the authors reported reductions in the microstructural integrity of several major white matter tracts, which may indicate that obesity is associated with differences in the efficiency of communication between brain areas (Stanek et al., 2011; Yau et al., 2014). Schwartz and colleagues took a closer look the relationship between body fat and white matter composition in a sample of 970 adolescents (Schwartz et al., 2014). The findings of the study suggested that white matter differences associated with obesity are linked to differences in the fatty acid composition of brain white matter. Further research is needed to interpret these results. But it could indicate that differences in diet associated with obesity could impact on cognitive performance by influencing the insulation of the brain’s wiring.

Brain function may also be affected by obesity. A series of studies by Kamijo and colleagues investigated functional differences in children with obesity using event-related potentials (ERP) (Kamijo, Khan, et al., 2012a; Kamijo, Pontifex, et al., 2012b; Kamijo et al., 2014). To obtain ERPs, the electro-encephalogram (EEG) is recorded while participants perform a cognitive task. The signal is then averaged to derive the electrophysiological response that is directly linked to a particular cognitive event. Children with obesity were found to perform worse on tasks that required inhibition of prepotent responses. The lower performance was associated with lower amplitude of an ERP response related to error monitoring and a less frontal distribution of an attention-related ERP. The ERP results may indicate a less efficient conflict monitoring system and differences in the neural organisation of the attention system in children with obesity.

 

Limitations: the chicken, the egg, and the confounding effect of the rooster

Like in many other areas of human cognitive neuroscience, studies of children and adolescents with obesity are based on correlations. This leads to some limitations of the conclusions that can be drawn from such work. For one, it is not possible to draw any firm conclusion about the causal relationship between the variables. In other words, it is not clear if obesity leads to differences in cognition or if cognitive differences predispose individuals to become obese. Secondly, the relationship between two variables may be influences by a third variable that has not been assessed. Some of these variables include other environmental influences that may both be associated with obesity as well as cognitive differences. These are likely include measures like family wealth and education (O’Dea & Wilson, 2006) among other influences that have not yet been investigated. Other physiological factors that are associated with obesity may also confound the relationship between obesity and cognition. For instance, differences in cardiovascular health or insulin metabolism in children with obesity may influence brain function and cognitive performance. However, some of the studies took these factors into account by matching control groups on cardiovascular health (Kamijo et al., 2014; Kamijo, Pontifex, et al., 2012b) or studying obese groups with a typical insulin response (Stanek et al., 2011). These studies found that differences in cognitive performance in the obese group were still observed when controlling for the influence of these factors.

Another factor that is rarely assessed it sleep apnea. A study by Tau found that differences in school achievement in children and adolescents with obesity were associated with obstructive sleep apnea. While this result does not invalidate other findings about cognitive performance deficits in children and adolescents with obesity, it highlights a potential mechanism by which obesity may impact on the cognitive performance.

 

Conclusion

The current literature suggests that obesity in childhood and adolescence is associated with cognitive differences in executive function and differences in the organisation of brain system related to executive function and cognitive control. Future research will be needed to identify the mechanism by which body fat content, brain physiology, and cognitive performance may be linked to address the unprecedented scale of weight problems in children and adolescents and their consequences.

 

References

Anderson, P. (2002). Assessment and development of executive function (EF) during childhood. Child Neuropsychology, 8(2), 71–82. http://doi.org/10.1076/chin.8.2.71.8724

Arora, T., Hosseini Araghi, M., Bishop, J., Yao, G. L., Thomas, G. N., & Taheri, S. (2013). The complexity of obesity in UK adolescents: relationships with quantity and type of technology, sleep duration and quality, academic performance and aspiration. Pediatric Obesity, 8(5), 358–366. http://doi.org/10.1111/j.2047-6310.2012.00119.x

Bauer, C. C. C., Moreno, B., González-Santos, L., Concha, L., Barquera, S., & Barrios, F. A. (2015). Child overweight and obesity are associated with reduced executive cognitive performance and brain alterations: a magnetic resonance imaging study in Mexican children. Pediatric Obesity, 10(3), 196–204. http://doi.org/10.1111/ijpo.241

Black, N., Johnston, D. W., & Peeters, A. (2015). Childhood Obesity and Cognitive Achievement (Vol. 24, pp. 1082–1100). Presented at the Health Economics (United Kingdom). http://doi.org/10.1002/hec.3211

Cserjési, R., Molnár, D., Luminet, O., & Lénárd, L. (2007). Is there any relationship between obesity and mental flexibility in children? Appetite, 49(3), 675–678. http://doi.org/10.1016/j.appet.2007.04.001

Diamond, A. (2013). Executive functions. Annual Review of Psychology, 64(1), 135–168. http://doi.org/10.1146/annurev-psych-113011-143750

Graziano, P. A., Bagner, D. M., Waxmonsky, J. G., Reid, A., McNamara, J. P., & Geffken, G. R. (2012). Co-occurring weight problems among children with attention deficit/hyperactivity disorder: The role of executive functioning. International Journal of Obesity, 36(4), 567–572. http://doi.org/10.1038/ijo.2011.245

Kamijo, K., Khan, N. A., Pontifex, M. B., Scudder, M. R., Drollette, E. S., Raine, L. B., et al. (2012a). The relation of adiposity to cognitive control and scholastic achievement in preadolescent children. Obesity, 20(12), 2406–2411. http://doi.org/10.1038/oby.2012.112

Kamijo, K., Pontifex, M. B., Khan, N. A., Raine, L. B., Scudder, M. R., Drollette, E. S., et al. (2012b). The association of childhood obesity to neuroelectric indices of inhibition. Psychophysiology, 49(10), 1361–1371. http://doi.org/10.1111/j.1469-8986.2012.01459.x

Kamijo, K., Pontifex, M. B., Khan, N. A., Raine, L. B., Scudder, M. R., Drollette, E. S., et al. (2014). The negative association of childhood obesity to cognitive control of action monitoring. Cerebral Cortex, 24(3), 654–662. http://doi.org/10.1093/cercor/bhs349

Liang, J., Matheson, B. E., Kaye, W. H., & Boutelle, K. N. (2014). Neurocognitive correlates of obesity and obesity-related behaviors in children and adolescents. International Journal of Obesity, 38(4), 494–506. http://doi.org/10.1038/ijo.2013.142

Lokken, K. L., Boeka, A. G., Austin, H. M., Gunstad, J., & Harmon, C. M. (2009). Evidence of executive dysfunction in extremely obese adolescents: a pilot study. Surgery for Obesity and Related Diseases : Official Journal of the American Society for Bariatric Surgery, 5(5), 547–552. http://doi.org/10.1016/j.soard.2009.05.008

O’Dea, J. A., & Wilson, R. (2006). Socio-cognitive and nutritional factors associated with body mass index in children and adolescents: Possibilities for childhood obesity prevention. Health Education Research, 21(6), 796–805. http://doi.org/10.1093/her/cyl125

Ou, X., Andres, A., Pivik, R. T., Cleves, M. A., & Badger, T. M. (2015). Brain gray and white matter differences in healthy normal weight and obese children. Journal of Magnetic Resonance Imaging, 42(5), 1205–1213. http://doi.org/10.1002/jmri.24912

Schwartz, D. H., Dickie, E., Pangelinan, M. M., Leonard, G., Perron, M., Pike, G. B., et al. (2014). Adiposity is associated with structural properties of the adolescent brain. NeuroImage, 103, 192–201. http://doi.org/10.1016/j.neuroimage.2014.09.030

Stanek, K. M., Grieve, S. M., Brickman, A. M., Korgaonkar, M. S., Paul, R. H., Cohen, R. A., & Gunstad, J. J. (2011). Obesity is associated with reduced white matter integrity in otherwise healthy adults. Obesity, 19(3), 500–504. http://doi.org/10.1038/oby.2010.312

Taras, H., & Potts Datema, W. (2005). Obesity and Student Performance at School. Journal of School Health, 75(8), 291–295. http://doi.org/10.1111/j.1746-1561.2005.00040.x

Wirt, T., Schreiber, A., Kesztyüs, D., & Steinacker, J. M. (2015). Early Life cognitive abilities and body weight: Cross-sectional study of the association of inhibitory control, cognitive flexibility, and sustained attention with BMI percentiles in primary school children. Journal of Obesity, 2015(3), 1–10. http://doi.org/10.1155/2015/534651

Yau, P. L., Kang, E. H., Javier, D. C., & Convit, A. (2014). Preliminary evidence of cognitive and brain abnormalities in uncomplicated adolescent obesity. Obesity, 22(8), 1865–1871. http://doi.org/10.1002/oby.20801

 

Picture credit: Charlie and the Chocolate Factory, Warner Bros Pictures, 2005

The brain that determines itself – a story of ingrained native language

“Pardon, comment dit-on en Francais?” – I tried hard to learn French to keep up with the bilingual son of close friends, but failed miserably. DuoLingo and sub-titled TV gave me the edge early on, but even at the tender age of 2.5 years, he is already overtaking me. Of course, in my profession, this makes me wonder how the brain manages this remarkable feat of mastering several languages so rapidly at his age – and offer some explanatory solace for my failings.

A popular belief is that children’s brains are more plastic, which explains their greater ease of language learning. However, studies in developmental cognitive neuroscience suggest that reality is a bit more complicated than that. Research shows that the brain expects particular stimuli during important periods of plasticity. During these periods, the brain soaks up particular bits of information like a sponge. However, the brain also has to keep this plasticity check. Otherwise, it would be like building a tower with wet concrete. The plasticity of the wet concrete is useful at first to shape the structure. But one has to wait until is hardens to further build on it to avoid ending up with a pile of sludge. Similarly, once acquired, representations in the brain have to set and might be difficult to alter later on.

A recent study by Pierce and colleagues provides an excellent example of this type of research. They recruited three groups of school-age children to their study: a group of monolingual French children, a group of bilingual children who spoke Chinese and learned French before their third birthday, and a group of Chinese children who were adopted by French-speaking parents before their third birthday. Importantly, the last group consisted of children who were exposed to Chinese early on, but did not speak Chinese anymore after adoption. In the experiment, the children performed a test of verbal working memory. For this task, children were presented with a sequence of French pseudowords*. When prompted, the child had to recall the last pseudoword that he or she had just seen (0-back), the one before that (1-back), or the one two pseudowords before (2-back) while an MRI machine measured the oxygenation of blood in their brain.

Response times and accuracy were the same in all groups, but the brain responses showed interesting differences. Monolingual French children showed increased blood oxygenation in regions that are typically associated with verbal working memory tasks like this. In contrast, the bilingual Chinese group showed higher activation in areas involved in frontal and parietal areas involved in cognitive control. Crucially, the third group of children who were exposed to Chinese early, but only spoke French, displayed activations that were more similar to the bilingual group than the monolingual group. So, despite relatively early acquisition of the second language and only speaking the second language, the early exposure resulted in a similar brain response as in bilingual children.

Previous studies also found higher activation in executive areas in bilingual adults. It was thought that this arises because both languages are automatically activated. The bilingual constantly suppresses the language that is not needed in the current situation. This explanation was supported by higher performance of bilinguals on general tasks that require inhibition. However, these new developmental findings could mean that the recruitment of executive areas is not driven by bilingualism per se, but perhaps by differences in early development that persists even when only one language is used later in life.

Importantly, the study does not suggest that there is any advantage or disadvantage in being exposed to a different language early in life. After all, the performance of all children in terms of accuracy and reaction time was statistically indistinguishable. However, the results provide an interesting insight into cognitive development beyond the early-is-better mantra so frequently repeated in pop science publications.

 

Footnote:

* Pseudowords are pronouncable sequences of letters that resemble words that could be found in a dictionary of a language, but do not have any meaning. An example of a pseudoword in English is ‘shum’ or ‘dake’

Reference:

Pierce, L., Chen, J.-K., Delcenserie, A., Genesee, F. & Klein, D. (2015): “Past experience shapes ongoing neural patterns for language”. Nature Communications 6 http://www.nature.com/ncomms/2015/151201/ncomms10073/full/ncomms10073.html

Image credit:

Pieter Brueghel the Elder (1526/1530–1569): The Tower of Babel [retrieved from commons.wikimedia.org]

1.4 percent: Behind the numbers of ADHD prevalence

A question closely tied to the issue of prescribing ADHD medication is the actual growth of the disorder. Media reports have cited substantial increases in Ritalin use in the UK, and from looking at prescribing trends in the US one possibility is that the diagnosis of ADHD increases in parallel with growing Ritalin prescriptions. It is particularly important to understand how the increased prescribing of ADHD medication in the UK relates to the actual prevalence of the disorder. However, a key issue in the ADHD debate is whether the diagnosis represents a core developmental disorder. The growth in Ritalin prescriptions and the disproportionately high rates of ADHD in the US in particular have often been used to suggest that ADHD diagnosis is socially motivated.

When we look more closely at the numbers around ADHD, however, three important observations emerge.  First, we can see that although there has been a consistent growth in ADHD medication prescriptions in the UK, there is no evidence speaking to whether this also corresponds to an increase in ADHD diagnoses. Second, the common perception of the US being disproportionately affected by ADHD is surprisingly unsupported. Rather, we can see that different methods and approaches to ADHD diagnosis are what best explain its relative prevalence.  Third, thinking about ADHD as a label for a highly heterogeneous group can perhaps more readily account for the differential rates of ADHD based on the diagnostic criteria used.

Has the prevalence of ADHD in the UK increased in line with Ritalin prescriptions?

As we saw in our previous blog post, the number of Ritalin prescriptions dispensed in England increased by 156% from 2004 to 2014. However, this does not mean that the prevalence of ADHD has increased by a similar proportion. Why? Factors that can influence the number of prescriptions for ADHD medication written include GPs being more likely to prescribe such medication due to more positive peer-reviewed articles about its effects, changes in prescription protocols (such as more frequent repeat prescriptions being provided), and more awareness amongst parents and the public about the possible benefit of ADHD medication meaning a higher proportion of people ask for medication1. Further, although NICE guidelines published in 2013 recommended treatment for ADHD with medication as part of a comprehensive treatment programme (including psychological, behavioural and educational support), reductions in funding available for such support may contribute to a higher prescription of medication by GPs as an alternative. All of these factors can lead to a higher number of prescriptions being written, and this means the number of prescriptions does not necessarily align with the number of individuals diagnosed with ADHD.

So we need to take a different approach to estimating the prevalence of ADHD diagnoses, by looking at the proportion of actual ADHD diagnoses in the population. However, there are currently no available public health records about ADHD prevalence in the UK. Instead, one useful source of data come from population-based studies, which take a large sample of children (often around 8000-14,000) and estimate the prevalence of ADHD through the percentage of children with parent reports of symptoms and/or ADHD diagnoses. These studies have the benefit of estimating the prevalence of ADHD through a non-referred sample, avoiding the biases arising from estimates reached by only counting the number of children brought to medical attention.

One such study conducted by Green and colleagues in 20042 estimated the UK prevalence of ADHD in 5-15 year-olds at approximately 1.5%. (A similar report from 2003 estimated the prevalence in this age group at 2.2%3). A more recent report by Russell et al. (2014)4 based on the Millenium Cohort Study estimated ADHD prevalence in the UK at 1.4%. This estimate came from data collected in 2008-2009 when children were 6-8 years old, and parents reported whether ADHD had been identified by a doctor or other health professional.

At face value, these figures are a striking contrast to the prescription data: the parent-report data suggest no increase in ADHD diagnoses (from 2004-2009, at least), in contrast to the yearly increase in Ritalin prescriptions over the same time period. However, it is important to consider that the more recent Millennium Cohort Study figures may slightly underestimate the prevalence of children affected by ADHD: this is because the Millennium Cohort sample children were younger, and more children are likely to be diagnosed when they are older.

So whilst it is possible that ADHD diagnoses in the UK have increased, we do not have the data to accurately evaluate this change. As we saw in our previous blog post, data from the US would suggest that growing use of ADHD medication does align with increasing diagnoses, but it is difficult to evaluate this relationship for the UK because we do not have statistics from comparable samples of children at different time points. It is particularly important to verify if the actual number of individuals diagnosed with ADHD has increased to understand whether such a growth in prescriptions stems from more diagnoses, a growing trend to provide medication in lieu of other treatment options, or a combination of the two.

But the current wave of Millennium Cohort Study data were recently collected when children were 11 years old, and using these data researchers can assess if more children in the sample have now been diagnosed with ADHD as they have gotten older – so we might have an answer soon.

Is the United States disproportionately affected by cases of ADHD, and does this tell us anything about causality?

The growth of ADHD medication use and the concurrent prevalence of the disorder is closely related to the debate around whether it reflects an increase in a genuine developmental disorder, or whether such growth is part of a cultural trend towards diagnosis. The geographical vagaries of ADHD in the US particularly have often been invoked as an argument against its legitimacy as a disorder, with some suggestion that the US peak in ADHD rates indicates its diagnosis can be culturally motivated. Sir Ken Robinson, an author, speaker and education adviser, gave a popular TED talk in 2010 in which he showed a map of ADHD prescription rates across the USA, indicating that they increased as one moved east across the country – or as put by Sir Ken, “people start losing interest in Oklahoma, they can hardly think straight in Arkansas, and by the time they get to Washington they’ve lost it completely.”

This brings us to our next question: is it really the case that ADHD is over diagnosed in the US relative to the rest of the world, as media discussion often suggests? The geographical distribution of ADHD is important for two reasons. First, consistent differences in the geographical spread of ADHD may offer clues as to its aetiology: possible environmental causation in areas of high rates, and preventative factors in areas of low rates. Second, the worldwide distribution of ADHD informs the debate on whether it stems from cultural factors which make it an ‘American’ disorder – whatever this may mean – in which case we would expect it to be more prevalent in the US than anywhere else.

When we look at just the US and UK, it appears that US does have higher diagnosis rate of 6.3% (as reported by Russell et al., 2014). This is the closest estimate using the same sampling methods as the UK figure of 1.4% from the Millennium Cohort Study. However, as pointed out in the Millennium Cohort report by Russell et al., there is an explanation for this: a difference in the measurement of ADHD between the US and UK. When the data were collected the DSM-IV was used in the US, but researchers in the UK from whom the data were collected mainly used the ICD-10. The ICD-10 is more stringent, in which a child must show symptoms in all three dimensions of inattention, hyperactivity, and impulsivity to be diagnosed with ADHD, with no comorbid disorders. The DSM-IV is less stringent in the classification of ADHD, whereby children can be diagnosed with symptoms in one dimension only (such as inattention) alongside comorbid disorders. This difference in relative preference of diagnostic criteria can at least partly explain why the DSM-IV-using US has an epidemic compared to the relative calm of the ICD-10-preferring UK5.

When we look at the geographical spread of ADHD across the rest of the world, the figures are particularly striking. A meta-analysis by Polanczyk et al. in 20076 combined 102 studies, comprising data from 171,756 children, to estimate the average prevalence rates reported from samples worldwide in the previous 25 years. This figure was 5.3%. Second, they evaluated whether the US prevalence rates really did exceed everywhere else in the world, relative to Africa, Asia, Europe (excluding the UK), the Middle East, Oceania, and South America. The US rate of 6.2% did not significantly differ from the European rate (4.6%), or the highest rate in South America (11.8%). And importantly, there was no overall association between geographic location and ADHD prevalence rates when the methodology of the studies was controlled for. The figure below shows the prevalence estimates for each region (the bars represent the variability in this estimate).Polanczyk et al. (2007) Worldwide Figure

Finally, Polanczyk and colleagues verified that the different ADHD prevalence estimates between the US and Europe in particular were indeed explained by the different diagnostic criteria of the DSM and ICD. They did this by coding for all the methodological features in each study, and found that US diagnoses were more frequently made on DSM criteria, and European diagnoses more so on ICD-10 criteria. Another meta-analysis by Willcutt (2012)7 also found that differences in ADHD prevalence across countries were not significant after diagnostic approaches were controlled for. So these data suggest that ADHD is not disproportionately prevalent in the US; rather, its relative prevalence seems to depend more on the diagnostic tool used.

But on an individual level, any parent of a child with ADHD, or any individual with ADHD, is unlikely to attest that their difficulties with learning, concentration and communication are because they are living in Bolivia rather than Finland. The critical ongoing issue is therefore what constitutes our diagnosis of ADHD. In practice, a child is likely to be identified with ADHD because of how their various cognitive difficulties are expressed, and how this expression is interpreted by clinicians. Part of this interpretation may be due to less quantifiable environmental, social and cultural factors: in particular, Conrad and Bergey (2014)8 recognise that the historical approach to ADHD and medication use in a country can greatly influence whether clinicians classify children’s difficulties under the ADHD diagnostic label. But the different diagnostic criteria of the DSM and ICD do play a role. As noted by Russell et al. (2014), children with hyperactive behaviour and social difficulties may be more likely to be diagnosed with autism spectrum disorder in the UK (due to ICD-10 criteria ruling out a combined diagnosis), and ADHD in the US (due to the DSM-IV allowing ADHD to be diagnosed alongside other developmental disorders). It therefore seems that ADHD is not disproportionately prevalent in the US when we take diagnosis methods into account, but that fact that different tools can yield different prevalence rates makes it important to verify what the ADHD diagnostic label is mapping onto.

A dimensional approach to ADHD

At first glance, quantifying the prevalence of ADHD would suggest counting all children and adults struggling with attention, hyperactivity, and impulsivity and suggesting those individuals reflect the relative rate of ADHD in a particular population. In practise, however, there is no natural threshold between affected and unaffected individuals with ADHD. ADHD can frequently co-occur with other developmental disorders, such as autism spectrum disorder (ASD); in the Millennium Cohort dataset, 24% of the children with ADHD were also diagnosed with ASD. This individual variation supports a dimensional approach to classifying developmental disorders, which considers the extent to which various cognitive difficulties are present rather than a clear-cut diagnostic label.

This is critical in deciding how we give children help in the classroom. For example, we have seen previously that children with poor working memory and children with ADHD have highly similar working memory and executive function deficits, indicating that these shared difficulties are not limited to children with ADHD. Further, although an estimated 25-40% of children with ADHD also have dyslexia9, the treatment approaches for ADHD do not usually address reading difficulties, even though these can be a main constraint on a child’s progress at school. The diagnostic label of ADHD thus represents a highly heterogeneous group of children, and what best helps one child with ADHD may not be what best helps another.

But one might argue that neuroimaging data suggesting reduced prefrontal cortex activity in ADHD is indicative of a clear group of children who suffer from a specific neurobiological disorder. However, even such neurobiological ‘markers’ of ADHD are present to a varying degree in individuals without ADHD. Shaw et al. (2007)10 reported that the age of reaching peak cortical thickness – a measure of brain maturation, before cortical thinning proceeds throughout late childhood and adolescence – was delayed in children with ADHD relative to typically developing children. This finding suggested that ADHD may reflect a delay in typical brain development, rather than a qualitative difference between children with and without an ADHD diagnosis. And Shaw et al. (2011)11 found that this rate of cortical thinning was related to the degree of hyperactive and impulsive symptoms in typically developing children without an ADHD diagnosis. Children with higher ratings of hyperactive/impulsive symptoms had a slower rate of cortical thinning, similar to what was observed in children with ADHD. These data align with the idea that ADHD may be a disorder that is better understood dimensionally, with those who are affected being at the end of a continuum of inattention and/or hyperactivity symptoms, rather than there being a distinct cut-off between ADHD and ‘typical’ development.

How does this inform the question on whether ADHD exists as a developmental disorder? From the evidence above it seems that the label of ADHD does not identify a separate category of children who are inattentive and hyperactive. Rather, what we think of as ADHD is a complex cluster of cognitive and behavioural difficulties which fall on a scale for all children, from unaffected to severe. It is how these difficulties are expressed by children and interpreted by different diagnostic approaches, in different cultural contexts, which determine the cut-off at which a child is diagnosed. Whilst difficult to unpick, these factors may best capture how ADHD is identified and why prescriptions have steadily grown; however, we critically need further data to accurately evaluate ADHD growth in the UK. And on an individual level, the symptoms of ADHD have a substantial impact on the day-to-day lives of those who are affected. It is important we ensure that diagnosis appropriately reflects individual differences between children, and think about how to best to commission education and health services to help affected children and adults.

References

  1. Prescribing in primary care: Understanding what shapes GPs’ prescribing choices and how might these be changed, RAND Technical Report. Available at http://www.nao.org.uk/wp-content/uploads/2007/05/TR443_3C.pdf
  2. Green, H., McGinnity, A., Meltzer, H., & Ford, T. G., R. (2004). Mental health of children and young people in Great Britain, 2004. Report commissioned by the Office for National Statistics.
  3. Reported in the NICE Guidelines pp. 26, available here: https://www.nice.org.uk/guidance/cg72/resources/cg72-attention-deficit-hyperactivity-disorder-adhd-full-guideline-2
  4. Russell, G., Rodgers, L. R., Ukoumunne, O. C., & Ford, T. (2014). Prevalence of parent-reported ASD and ADHD in the UK: findings from the Millennium Cohort Study. J Autism Dev Disord, 44(1), 31-40. doi: 10.1007/s10803-013-1849-0
  5. For interested readers, there is also an excellent timeline here depicting ADHD rates in the US as diagnostic tools have changed from 1970 to the present day.
  6. Polanczyk, G., de Lima, M. S., Horta, B. L., & Biederman. J. (2007). The Worldwide Prevalance of ADHD: A Systematic Review and Metaregression Analysis. American Journal of Psychiatry, 164(6). 942-948.
  7. Willcutt, E. G. (2012). The prevalence of DSM-IV attention-deficit/hyperactivity disorder: a meta-analytic review. Neurotherapeutics, 9(3), 490-499. doi: 10.1007/s13311-012-0135-8
  8. Conrad, P., & Bergey, M. R. (2014). The impending globalization of ADHD: notes on the expansion and growth of a medicalized disorder. Soc Sci Med, 122, 31-43. doi: 10.1016/j.socscimed.2014.10.019
  9. Reported in Eden, G. F., & Vaidya, C. J. (2008). ADHD and developmental dyslexia: two pathways leading to impaired learning. Ann N Y Acad Sci, 1145, 316-327. doi: 10.1196/annals.1416.022
  10. Shaw, P., Eckstrand, K., Sharp, W., Blumenthal, J., Lerch, J. P., Greenstein, D., . . . Rapoport, J. L. (2007). Attention-deficit/hyperactivity disorder is characterized by a delay in cortical maturation. Proc Natl Acad Sci U S A, 104(49), 19649-19654. doi: 10.1073/pnas.0707741104
  11. Shaw, P., Gilliam, M., Liverpool, M., Weddle, C., Malek, M., Sharp, W., . . . Giedd, J. (2011). Cortical development in typically developing children with symptoms of hyperactivity and impulsivity: support for a dimensional view of attention deficit hyperactivity disorder. Am J Psychiatry, 168(2), 143-151. doi: 10.1176/appi.ajp.2010.10030385

Ritalin and ADHD: What is the story so far?

The use of Ritalin to treat ADHD has become a highly contentious issue over the past decade. Part of this contention stems from a marked growth in use, with a 156% increase in the number of Ritalin prescriptions dispensed in England over the last ten years. Another source of contention comes from fragmented information about what the precise effects of Ritalin are (and ADHD medications in general) and what governs whether it is prescribed. We can unpick at least some of this debate by asking two questions: what is the evidence for what Ritalin does, and the extent to which it can help children with ADHD? And why could the number of Ritalin prescriptions have steadily increased over the past decade?

Methylphenidate medication in ADHD: What does Ritalin do, and can it help children with ADHD?

ADHD is a developmental disorder which, despite having a single diagnostic label, presents substantial individual differences between children who are affected. ADHD is characterised by deficits of inattention, impulsiveness, and hyperactive behaviour. These difficulties are often understood to be tied to reduced functionality of the prefrontal cortex (PFC) in individuals with ADHD. The PFC is important for attentional control and executive functions – a broad term for cognitive functions governing organised and controlled behaviour – and this reduced PFC functionality is suggested to result in the core ADHD symptoms of attentional and behaviour control deficits. However, ADHD also frequently co-occurs with other developmental disorders, such as dyslexia and autism. This means that children who are diagnosed with ADHD will range from those severely affected by difficulties in concentrating and sitting still, to those who struggle to pay attention and have comorbid difficulties in areas such as reading and communication. From the outset it is therefore important to recognise that there is huge individual variation in the children (and adults) whom fall under the ADHD diagnostic umbrella.

Ritalin, also known as methylphenidate, is a stimulant medicine which is commonly used to treat ADHD symptoms of inattention and hyperactivity. To understand the effects of Ritalin on cognition, research studies compare performance on tasks when participants have taken Ritalin to when they have taken a placebo. A meta-analysis of these studies by Coghill et al. (2014)1 combined papers investigating the effects of Ritalin in children and adolescents with ADHD. They found that the medication improved short term memory accuracy, working memory capacity, response speeds, and response inhibition relative to a placebo. Because the meta-analysis combined results across at least ten published studies for each aspect of cognition, these results are more representative than findings from a single study. A similar review by Epstein et al. (2014)2 assessed the effects of Ritalin medication in adults with ADHD, and observed that it improved inattentiveness, hyperactivity and impulsive behaviour. These findings overall suggest that Ritalin has positive effects on cognition and controlled behaviours compared to a placebo, in both children and adults with ADHD.

A suggested neurophysiological mechanism for Ritalin’s effect on cognition and hyperactivity is via increased functionality of the prefrontal cortex (Spencer et al., 2015)3. Ritalin prevents the reuptake of the neurotransmitters dopamine and norepinephrine in the PFC. Because these neurotransmitters allow nerve cells to communicate, their increased levels in the PFC strengthen its signalling and therefore improve its functionality. This increased activity of the PFC is measured in studies using fMRI, and is suggested to be tied to Ritalin’s effects of improving PFC-dependent cognitive skills such as attentional control, working memory, impulse control, and organised behaviour (Berridge & Devilbiss, 2011)4.

Importantly, however, these effects do not mean that the Ritalin treats the causes of ADHD. A key piece of evidence comes from the finding that Ritalin has the same ‘cognition-enhancing’ effects on individuals without ADHD. A review by Linssen et al. (2014)5 found that when adults without ADHD were given Ritalin their performance improved on tests of working memory and processing speed, and to a lesser extent attention and vigilance. Whilst these data do not allow us to assess if the magnitude of these methylphenidate effects were statistically identical to those in individuals with ADHD, they are an important indicator that Ritalin can improve these cognitive markers irrespective of an ADHD diagnosis (as current American college trends may also attest). These findings suggest that whilst Ritalin can be beneficial, it does not operate on the root aetiology of ADHD.

But interestingly, despite the cognitive benefits measured in short-term studies, Ritalin does not seem to improve children’s academic achievement in the long term. A large-scale longitudinal study reported by Molina et al. (2009)6 assessed the long-term outcomes of 579 children with ADHD who were randomly allocated to one of four treatment groups: methylphenidate medication, behaviour therapy, combined medication and behaviour therapy, or community care. At the end of the fourteen-month treatment period, all groups had improved relative to their pre-treatment baseline on a range of outcomes including parent and teacher-rated ADHD symptoms, teacher-rated social skills, and reading and maths achievement. Interestingly, the greatest improvements were for the medication group and combined medication plus behaviour therapy group. At the first follow-up ten months after treatment, the initial advantage of the medication and combined groups was still present (although the magnitude of this effect was about half what it had been immediately after treatment).

However, when the children were followed up several years later, this added benefit of medication had levelled out. At the three-year follow-up the four treatment groups remained above their pre-treatment baseline, but there was no difference between the treatment options; that is, the relative advantage of the medication groups was no longer present. After eight years the groups continued to perform above a pre-treatment baseline, but there remained no differential treatment benefit of medication. These findings suggest that medication can have a benefit during a relatively short-term treatment period, but beyond this initial treatment there is no evidence for a lasting medication advantage for the majority of children.

It is particularly interesting that there was no evidence for medication specifically improving long-term academic attainment in light of its short-term improvement of attention, short-term memory, and working memory. Because we know these cognitive skills are significant predictors of educational outcomes, we might have expected their improvement with Ritalin to also boost educational outcomes. Of course, the reason for no longer-term effects of medication may simply be that it can improve cognitive control during treatment, but these effects of the drug do not continue to have additive benefits after treatment is discontinued. There is also relatively little existing research on the effects of taking Ritalin for a longer time period, which limits our understanding of its effects from longer-term use.

However, another interesting explanation for the lack of such an effect may come from the heterogeneity of children diagnosed with ADHD. In their report Molina et al. (2009) identified three subgroups of children in the three-year follow-up, each with different ADHD trajectories as a result of treatment. One group showed an increasing steady benefit of medication use over three years; a second group showed a very large initial benefit for medication use which then plateaued, and was maintained three years after treatment; and a final group returned to pre-treatment levels with no ongoing benefit of treatment. These different profiles of children continued to be associated with their performance on the outcome measures of cognitive, social, and academic attainment at the eight-year follow-up. Importantly, these findings suggest that children’s longer-term outcomes may be determined more by their individual profile of symptoms than the specific type of treatment they receive.

So overall, what do these data us about the decision to use Ritalin for individuals with ADHD? Whilst research studies conducted over the short-term indicate that Ritalin can improve aspects of controlled cognition such as working memory and attention relative to a placebo, there is no evidence that medication continues to have an added benefit to non-medication treatments (such as behavioural therapy) after about two years following the start of treatment. The varying effects of medication may come from the heterogeneity of children diagnosed with ADHD. For many children, comorbid difficulties with reading or communication, for example, could be a key limiter on their performance at school, which Ritalin may do little to redress. Although Ritalin can undoubtedly be beneficial for children who substantially suffer with attention or behavioural control difficulties, the fact it is not universally helpful is an indicator of how much children diagnosed with ADHD can differ in their profile of strengths and weaknesses. Importantly, the effects of medication may therefore depend more on the symptoms of an individual child than the presence of an ADHD diagnosis.

156 percent: What are the reasons behind growing Ritalin use?

A herein lies the controversy: despite the above findings, and an anecdotal lack of consensus about its effects, the number of Ritalin prescriptions dispensed in England increased by 156% between 2004 and 2014. These data, published by the Health and Social Care Information Centre in 2015 and shown on the figure below, show Ritalin prescriptions at 359,068 in 2004 and 922,206 in 20147. (Interestingly, whilst the HSCIC data show that prescriptions for Ritalin are of a larger quantity than the other ADHD medicines on the market, the average use of other ADHD medicines also increased by a modest 136%).

PrescriptionsRitalinUK_FigureBut could it just be the case that all prescriptions have increased this much in general over the last decade? No; the average number of prescriptions dispensed across England rose by 55.2% over the same ten-year period. This discrepancy prompted the National Institute for Health and Care Excellence (NICE) to release guidelines in 2013 that initial treatment of moderate ADHD should not include methylphenidate medication, prompting substantial debate as to what constituted ‘moderate’. So whilst it is agreed that the prescription of ADHD medication has grown substantially, there is no consensus on what is driving this change.

What reasons could there be behind a growing number of Ritalin prescriptions? One obvious reason is an increase in the number of children (and adults) diagnosed with ADHD. In the United States at least, this argument for a relationship between prescription rates and ADHD diagnoses seems to hold. The Centre for Disease Control and Prevention collected comprehensive data on the percent of 4-17 year-olds with parent-reported ADHD diagnoses in each state, and the percent of children taking ADHD medication in each state, in 2007 and 20118. These data mean we can look at whether there is a relationship between each state’s change in ADHD diagnoses in this period, and their simultaneous change in medication use. The figure below shows the relationship between the change in the percentage of children with ADHD diagnoses and percent of children taking ADHD stimulant medication in each state, and there is indeed a significant positive correlation between the two. This positive correlation means that states with a larger increase in ADHD diagnoses also had a larger increase in the number of children prescribed stimulant medication. Whilst we cannot infer causality from these data (that is, we cannot assume that more ADHD diagnoses cause more medication prescriptions) they do suggest that the growth in diagnoses and prescriptions are related.ADHD medication and diagnosis change US states, 2007-2011

The main question these data raise – apart from what is going on in Texas – is why ADHD diagnoses have increased. In a recent paper investigating reasons behind the global expansion of ADHD diagnoses and medication use, Conrad and Bergey (2014)9 suggested that increased promotion of ADHD drugs by the pharmaceutical industry, the increased adoption of DSM (the Diagnostic and Statistical Manual of Mental Disorders, an American publication) diagnostic criteria, and more accessible information about ADHD online could all contribute to its steady growth. ADHD information being more visible and accessible online can prompt more individuals to seek and thus obtain a diagnosis, and promotions by pharmaceutical companies can result in medication treatment being more frequently offered to individuals diagnosed with ADHD. Further, the diagnostic criteria used are particularly important: Conrad and Bergey suggested that a shift towards the DSM criteria in the 1990s contributed to increasing rates of ADHD, as the DSM uses a lower threshold for diagnosis and allows the co-occurrence of ADHD with other developmental disorders. Whilst these are broad brush strokes for reasons for which Ritalin use has grown, they nonetheless suggest possible mechanisms for the increase in prescriptions.

However, it is important for us to recognise that measuring the number of prescriptions dispensed may not be the best indicator of how many individuals are actually taking the medication. A good example of this comes from looking at the headline finding from a 2008 study which reported huge variation in the number of Ritalin prescriptions dispensed across England, with as much as a ­23-fold difference between the lowest and highest-prescribing areas of the UK (Stoke-on-Trent and Wirral, respectively). However, the prescription rate for each region was measured by the number of prescriptions dispensed as a proportion of the population in that region. These figures meant that differences in prescribing practises across regional primary care trusts, such as the standard frequency of repeat prescriptions, could contribute to a lot of this variation. The overall population of a region also influenced its ‘rate’ of prescribing; this might explain why the relatively small Isle of Wight had the second-highest rate at 107 prescriptions per 1000 children. Although just one example, this points to the need to look at Ritalin use in terms of the number of individuals prescribed Ritalin and ask how this aligns with the number of individuals actually diagnosed with ADHD.

So the story about Ritalin seems to have two parts: first, it can have beneficial effects on cognition, but these benefits may critically depend on children’s individual profile of symptoms. Second, it is possible that Ritalin use has increased in line with growing ADHD diagnoses, but we need to carefully evaluate the evidence we have to assess this. Possible vehicles for growth include more individuals seeking a diagnosis due to the information now available about ADHD, promotion by pharmaceutical companies for the use of medication, and a shift in diagnostic criteria meaning a higher proportion of people are identified as having ADHD. In the UK, however, we know relatively little about how increasing Ritalin use relates to the number of individuals with ADHD taking medication, and the wider growth of ADHD as a disorder. This is the question we will turn to in our next post.

References

  1. Coghill, D. R., Seth, S., Pedroso, S., Usala, T., Currie, J., & Gagliano, A. (2014). Effects of methylphenidate on cognitive functions in children and adolescents with attention-deficit/hyperactivity disorder: evidence from a systematic review and a meta-analysis. Biol Psychiatry, 76(8), 603-615. doi: 10.1016/j.biopsych.2013.10.005
  2. Epstein, T., Patsopoulos, N. A., & Weiser, M. (2014). Immediate-release methylphenidate for attention deficit hyperactivity disorder (ADHD) in adults. The Cochrane Review, (9). Retrieved from http://www.cochrane.org/CD005041/BEHAV_ritalin-for-adult-attention-deficit-hyperactivity-disorder-adhd
  3. Spencer, R. C., Devilbiss, D. M., & Berridge, C. W. (2015). The cognition-enhancing effects of psychostimulants involve direct action in the prefrontal cortex. Biol Psychiatry, 77(11), 940-950. doi: 10.1016/j.biopsych.2014.09.013
  4. Berridge, C. W., & Devilbiss, D. M. (2011). Psychostimulants as cognitive enhancers: the prefrontal cortex, catecholamines, and attention-deficit/hyperactivity disorder. Biol Psychiatry, 69(12), e101-111. doi: 10.1016/j.biopsych.2010.06.023
  5. Linssen, A. M., Sambeth, A., Vuurman, E. F., & Riedel, W. J. (2014). Cognitive effects of methylphenidate in healthy volunteers: a review of single dose studies. International Journal of Neuropsychopharmacology, 17(6), 961-977. doi: 10.1017/S1461145713001594
  6. Molina, B. S., Hinshaw, S. P., Swanson, J. M., Arnold, L. E., Vitiello, B., Jensen, P. S., . . . Group, M. T. A. C. (2009). The MTA at 8 years: prospective follow-up of children treated for combined-type ADHD in a multisite study. J Am Acad Child Adolesc Psychiatry, 48(5), 484-500. doi: 10.1097/CHI.0b013e31819c23d0
  7. http://www.hscic.gov.uk/catalogue/PUB17644/pres-disp-com-eng-2004-14-rep.pdf, pp. 139-140. Data available in ‘Appendix 2 Tables’ Excel Spreadsheet here: http://www.hscic.gov.uk/catalogue/PUB17644
  8. The researchers measured medication prevalence by the number of individual children who were prescribed ADHD medication, rather than the number of prescriptions being written.
  9. Conrad, P., & Bergey, M. R. (2014). The impending globalization of ADHD: notes on the expansion and growth of a medicalized disorder. Soc Sci Med, 122, 31-43. doi: 10.1016/j.socscimed.2014.10.019

The developmental cognitive neuroscience of the Great British Bake-off

The Great British Bake-off has quickly become a fixture in the cultural life of the UK. For a few weeks the country is obsessed with the fluffiness of the ultimate Victoria sponge, the moistness of the ideal carrot cake, and the layering of the perfect Black Forest Gateau. For the poor souls who are not familiar with the format, a selection of amateur bakers is charged with making a certain type of baked good. Usually the contestants have some artistic licence to add interesting flavour combinations to the standard recipe. The creations are then judged on taste, presentation, texture and other aspects.

But how do we find the perfect cake and tell it apart from a mediocre one? The first line of judgment is pretty obvious. Cakes that are misshapen, under-baked, or horrible in taste are pretty easy to spot. However, the difference between good bakes is more subtle. Furthermore, there is a great variety of cakes and full marks in one category do not guarantee full marks in another. For instance, a sponge cake is supposed to be very light and fluffy, not too moist and with little taste on its own. In contrast, the carrot cake is much denser, contains more moisture, and is richly flavoured with spices. These categories are almost exclusive – one would not want a dense sponge cake or a flavourless carrot cake. It seems that bakers and cake connoisseurs posses a representation of cake categories that they can use to judge creations against something like the Platonic ideal of a certain cake type.

The formation of (cake) categories

Surely, even Mary Berry wasn’t born with a representation of the most angelic Angel cake and must have learned these categories along the way. Probably, the representation differentiated from simple categories like “sweet stuff” to a more refined representations liked “baked sweet stuff with a light texture and golden brown colour”. An important aspect of the formation of these representations is exposure to many different examples that enable the extraction of communalities that allow for category formation. This process is known as statistical learning. A classic example of statistical learning comes from studies about language acquisition in infants by Jenny Saffran and colleagues. One of the first steps in language learning for infants is to segment the continuous stream of speech into chunks and then to identify words from these chunks. For example, the infant would have to segment the speech stream ‘prettybaby’ into the constituent parts ‘pret-ty-ba-by’ and then identify their association with words, i.e. ‘pret-ty ba-by’ rather than ‘pre ty-ba by’. A possible way for the infant to achieve this feat is to learn which syllables frequently go together. In order to investigate this possibility, Saffran and colleagues exposed infants to different combinations of three syllables to create pseudowords that do not actually exist in the infants’ native language. After a short exposure, infants reacted differently to re-ordered combinations of the pseudowords compared to entirely new syllable combinations indicating that the infants had learned the statistical association between the syllables. Similarly, by exposure to many different examples of Sponge cake, we learn that they occupy a particular corner in the cake representational space in which features like golden colour, light texture, and buttery taste co-occur.

Neural correlates of category formation

Now, what are the underlying mechanisms in the brain that help us to achieve such a remarkable ability to extract communalities and form categories from the vast complexity of objects that we encounter? Unsurprisingly multiple brain areas need to interact to accomplish this task. On one hand, there are primary sensory regions that extract the lower level features of the stimulus in each modality, e.g. the basic shape and colour of the cake, the lemony smell etc. Many of these features are shared between different types of cake and are not specific to a cake category. However, these features can be connected to previous encounters in memory. This is mediated by a medial temporal system, particularly circuits of the hippocampus. Particular combinations of features may evoke a memory that links to a particular type of cake, e.g. the memory of the marvellous madeleines that Grandma Proust used to make.

However, category learning extends beyond retrieval of explicit memories. Freedman and colleagues identified a neural correlate of this categorisation in neurons in the prefrontal cortex. They trained monkeys to categories pictures of cats and dogs that were morphed to lie along a continuum. Neurons in the prefrontal cortex corresponded to their categorisation, e.g. “on” for picture categorised as a cat and “off” for a picture categorised as a dog, even though the stimuli themselves were continuous. These sharp categorisations may arise through feedback loops between the prefrontal cortex and the mesolimbic system involved in reward and reward prediction. Through the integration of these systems, learning about the association between the a particular stimulus and a reward is generalised to other similar stimuli.

As these examples show, the art of baking links to the deepest mysteries of how mind and brain make sense of a complex world based on limited experience and from a very early age. So, enjoy your next bake – it might expand the palette of your categorical representation!

References

Saffran, J. R., Aslin, R. N., & Newport, E. L. (1996). Statistical Learning by 8-Month-Old Infants. Science (New York, N.Y.), 274(5294), 1926–1928. http://doi.org/10.1126/science.274.5294.1926

Seger, C. A., & Miller, E. K. (2010). Category learning in the brain. Annual Review of Neuroscience, 33(1), 203–219. http://doi.org/10.1146/annurev.neuro.051508.135546

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.

Why should we take a dimensional approach to studying developmental disorders?

Developmental disorders like attention deficit disorder, attention deficit hyperactivity disorder (ADD/ADHD), autism spectrum disorder (ASD), language, learning and movement disorders are relatively common, more common then we might think. Furthermore, these disorders have a considerable impact upon the daily lives of those who struggle with them. Because some of these disorders are more apparent in some contexts than others, and their severity is highly variable, they may go unrecognised for some time. Indeed, in many cases these disorders are only formally recognised when a child has already progressed through years of formal schooling. This means that they may already have had a largely negative experience of learning, may have lost motivation, and may already have fallen far behind their peers.

When trying to study these disorders, researchers normally use a case-control approach. It’s an observational study in which two groups differing in outcome are identified and compared on the basis of some presumed causal attribute. Researchers use this method to identify factors that may contribute to a medical condition with the help of comparing subjects who have that condition (“cases”) with patients who do not have the condition but are otherwise similar (“controls”).

Case-control studies are relatively cheap and they are a frequently used type of epidemiological study that can be carried out by small teams or individual researchers in single facilities in a way that more complex experimental studies often cannot be. This design is often used in the study of rare diseases or as an exploratory study where little is known about the connection between the risk factor and the disease. In several cases they have bigger statistical power than cohort studies. This approach has largely been translated from the clinical sphere to study developmental disorders.

Well-designed observational studies, like case-control designs, can provide valuable evidence. It is however worth noting that they are quasi-experimental in nature and thus do not bring the same level of evidence as randomized controlled experiments. That said, case-control designs can sit well alongside complementary randomized controlled experiments. There are however other problematic features of case-control designs, which are particularly highlighted when studying developmental disorders. Selecting an outcome of choice, indeed the basis for choosing one particular group, may produce unintended biases that can have a strong effect in overall findings.

One such example is the exclusion of children with any comorbid symptoms – this is routine practice when using a case-control design to study developmental disorders. The most important disadvantage in case-control studies relates to the problem of acquiring reliable information about an individual’s status over time, and then using this as a basis for choosing some children whilst excluding others. The children actually included may be atypical of those with a particular disorder. This exemplifies why this design does not always translate well into the study of developmental disorders; in developmental disorder comorbidity is more the rule than the exception. This approach can also give a false impression of the nature of these disorders, which can be graded rather than discrete.

However, it also is possible to use a dimensional approach instead of the case-control approach. A dimensional approach puts focus on the kind of problem a person is experiencing and on the extent to which that aspect of cognition is impaired. It doesn’t place people into diagnostic categories but along dimensions. Diagnosis then becomes not a process of deciding the presence or absence of a symptom or disorder, but the degree to which particular characteristic is present. This is entirely the approach taken by the Centre for Attention Learning & Memory (CALM; http://calm.mrc-cbu.cam.ac.uk/). Children are referred not on the basis of any discrete disorder or diagnosis, but because they are experiencing problems in the areas of attention, learning and memory. The researchers are taking a dimensional approach to exploring the nature of these impairments.

Instead of making judgements of “present or not?” the dimensional approach asks the question “how much?”. It ranks disorder on a continuum based upon multiple domains of cognition, assessed using standardised materials. A dimension is viewed as a cluster of related psychological/behavioural characteristics that occur together. This approach generates profiles, rather than discrete diagnostic categories. Of course, one could argue that this approach is far ‘messier’ than a simple case-control approach. However, one might also argue that this unique profiling is far more informative about the nature and extent of the impairments themselves, and provides a far clearer picture about the pattern of deficits actually present in a population of children with problems of learning.

Whilst working at CALM I have been trying to understand how children attend, listen and remember and how these skills impact on learning. These include difficulties in language, literacy and maths. By improving our understanding of the cognitive and brain processes involved in learning, we hope to develop ways of identifying and overcoming problems that might appear during childhood. We also hope to provide an information hub for researchers and professionals in children’s services, and to run regular workshops.

A child visiting the CALM clinic is profiled by grading the severity of symptoms from a number of dimensions using standardised tests. For example these dimensions include working memory, attentional control, short-term memory, phonological skills, the ability to inhibit and control responses, to initiate, plan, organise and set goals, inattention, hyperactivity, aggression, conduct problems, emotional symptoms, peer relations, prosocial behaviour, as well as aspects of communication (speech, syntax, semantics, coherence, initiation, use of context and non-verbal communication). This information can be fed back to the referrer and can then be used to help guide the support that the child receives. In parallel to this we are building a large and rich dataset. A dimensional approach is better able to capture the complexities that a categorical approach may miss. Of course, this approach is not without its challenges also. How does one define a dimension? What statistical approaches ought we to use? And what kind of scores would warrant some form of intervention?

Despite these challenges, we think that the dimensional approach will provide a way of capturing the rich complexities of these data. Whilst other disciplines may strongly favour an approach with rigid categorical boundaries, this approach is not always appropriate for studying developmental disorders. Whilst strict case-control studies can be valuable, reliance on these designs alone can provide a biased and unrealistic view of the children with problems of learning.

References

Gelder M, Harrison P, Cowen P. Classification and diagnosis. In: Shorter oxford textbook of psychiatry. 5 th ed. Oxford: Oxford University Press; 2006. p. 21-34.

Helzer, J. E., Kraemer, H. C., & Krueger, R. F. (2006). The feasibility and need for dimensional psychiatric diagnoses. Psychological Medicine(36), 1671–1680. http://psych.colorado.edu/~willcutt/pdfs/Helzer_2006.pdf

Kendell RE. Five criteria for an improved taxonomy of mental. In: Helzer JE, Hudziak J, editors. Defining psychopathology in the 21 st century: DSM-V and beyond. Washington DC: American Psychiatric Publishing; 2002. p. 3-18.

Lewallen, S., & Courtright, P. (1998). Epidemiology in Practice: Case-Control Studies. Community Eye Health(11(28)), 57–58. http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1706071/

http://www.healthknowledge.org.uk/elearning/epidemiology/practitioners/introduction-study-design-ccs