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

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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]

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

ADHD and sugar: new avenues for an old question?

There is something about sugar consumption and hyperactivity that seems very intuitive. Perhaps, because everyone has experienced kids going crazy at a birthday party after having consumed industrial amounts of sugar through cakes, sweets, and lemonade. According to questionnaire studies, this opinion is also shared by parents of children with attention deficit hyperactivity disorder (ADHD). For some parents, dietry restrictions that cut-out all refined carbohydrates are preferred over gold standard stimulant treatment (Sciutto, 2015). However, the evidence that sugar has any effect on children’s behaviour and cognitive performance is not well supported. A number of studies were conducted in the 1980s and 1990s that investigated the link between sugar consumption and behaviour in children. The general design of these studies involved having children consume food items that contained either sugar or an artificial sweetener (aspartame or saccharine) without the child or the experimenter being aware which group the individual child was assigned to (double blind experiment). Next, the outcome of the food intake was measured as performance on a particular cognitive task (mostly standard assessments of general cognitive ability). A meta-analysis of these studies found no effect on test performance or observed behaviour (Wolraich, Wilson, & White, 1995). These results suggest that there is no effect of sugar consumption on general measures of behaviour and performance in typical children. But what about children with an ADHD diagnosis?

Some studies that looked at associations between environmental influences and ADHD in a large number of people found links between a diet high in carbohydrates and saturated fats and ADHD scores in independent samples in the UK, Western Australia, and Korea (Howard et al., 2011; Woo et al., 2014). However, it is difficult to draw firm conclusions from these population-based studies as both dietary patterns and ADHD behaviours may be influenced by a common underlying factor, e.g. differences in family lifestyle or socio-economic status. Conversely, some studies investigated the effect of specific diets as a treatment for ADHD. A recent review reported no effects of sugar vs apartame/saccharine on ADHD symptoms in 3 out of 4 studies (Heilskov Rytter et al., 2015). Interestingly, the one study that found differences reported differences on a measure of attention, while a behavioural assessment of aggressive behaviour was not influenced by sugar ingestion (Wender & Solanto, 1991). The other studies were based on playroom observations or assessments of general learning and memory (Gross, 1984; Milich & Pelham, 1986; Wolraich, Milich, Stumbo, & Schultz, 1985). The possibility that sugar consumption only affects performance on certain cognitive tasks but not general behaviour remains to be further investigated. In addition, research in the last decades has established that children diagnosed with ADHD present a very heterogeneous sample. This lead to the inclusion of subtypes in the last revision Diagnostic and Statistical Manual of Mental Disorders (DSM-IV). It is possible that different subtypes of the disorder are differently affected by sugar consumption and that different studies find different results due to differences in the inclusion of subtypes within the study sample. The scientific interest in links between sugar and ADHD has ebbed away in the new millennium, while parents and educators continue to belief in a link. It seems that future work should address the questions that have not been satisfyingly answered in the previous work.

Some support for an association between ADHD and sugar comes from studies on the physiological level. Children are typically less able to regulate blood sugar levels compared to adults and this seems to be especially the case for children with ADHD (Lindblad, Eickhoff, Forslund, Isaksson, & Gustafsson, 2015). The brain is the most energy-hungry organ consuming 25% of the energy while only accounting for 2% of the body’s mass. Differences in energy metabolism are also known to influence behaviour, e.g. in the case of hypoglycemia (Millichap & Yee, 2012). Sugar intake was also found to increased EEG beta activity over frontal regions in children with “food-induced” hyperactivity (Uhlig, Merkenschlager, Brandmaier, & Egger, 1997), which related to measures of problematic behaviour. Based on these findings and others, a recent extensive review suggested that differences in energy metabolism are at the heart of ADHD symptomatology (Killeen, Russell, & Sergeant, 2013). According to the neuroenergetics theory, ADHD is characterised by a less efficient regulation of energy supply to neurons, which results in a reduction of around 15% in energy capacity. Further, based on this model inconsistencies between studies are also expected when the demands of the task vary. However, it is currently not clear from the model how differences in neuronal energy metabolism relate to dietary patterns. It is possible that attraction to high energy foods is an attempt of the system to counter-balance inefficient energy supply, but further empirical and theoretical work will be needed to fully understand the link between dietary preferences and brain metabolism.

In summary, the discussed studies do not support that there is a strong link between sugar consumption and ADHD. However, evidence from populations studies and physiological investigations indicate that energy regulation is affected in children with ADHD. These studies highlight that ADHD needs to be understood as a complex systemic disorder that affects many different levels of observation from cell biology to behaviour, which will hopefully be addressed in future research.

This line of investigation also poses questions that are extremely important for parents and educators, but it also raises concerns about current research practices. If food intake has an immediate effect on behavioural performance that differs systematically between ADHD and comparison groups, any study that aims to investigate neuro-cognitive differences should be controlling this factor.

Photo credit: Moyan Brenn (https://www.flickr.com/photos/aigle_dore/)

References:

Gross, M. D. (1984). Effect of sucrose on hyperkinetic children. Pediatrics, 74(5), 876–878.

Heilskov Rytter, M. J., Andersen, L. B. B., Houmann, T., Bilenberg, N., Hvolby, A., Mølgaard, C., et al. (2015). Diet in the treatment of ADHD in children – a systematic review of the literature. Nordic Journal of Psychiatry, 69(1), 1–18. doi:10.3109/08039488.2014.921933

Howard, A. L., Robinson, M., Smith, G. J., Ambrosini, G. L., Piek, J. P., & Oddy, W. H. (2011). ADHD is associated with a “Western” dietary pattern in adolescents. Journal of Attention Disorders, 15(5), 403–411. doi:10.1177/1087054710365990

Killeen, P. R., Russell, V. A., & Sergeant, J. A. (2013). A behavioral neuroenergetics theory of ADHD. Neuroscience & Biobehavioral Reviews, 37(4), 625–657. doi:10.1016/j.neubiorev.2013.02.011

Lindblad, F., Eickhoff, M., Forslund, A. H., Isaksson, J., & Gustafsson, J. (2015). Fasting blood glucose and HbA1c in children with ADHD. Psychiatry Research. doi:10.1016/j.psychres.2015.01.028

Milich, R., & Pelham, W. E. (1986). Effects of sugar ingestion on the classroom and playground behavior of attention deficit disordered boys. Journal of Consulting and Clinical Psychology, 54(5), 714–718.

Millichap, J. G., & Yee, M. M. (2012). The diet factor in attention-deficit/hyperactivity disorder. Pediatrics, 129(2), 330–337. doi:10.1542/peds.2011-2199

Sciutto, M. J. (2015). ADHD knowledge, misconceptions, and treatment acceptability. Journal of Attention Disorders, 19(2), 91–98. doi:10.1177/1087054713493316

Uhlig, T., Merkenschlager, A., Brandmaier, R., & Egger, J. (1997). Topographic mapping of brain electrical activity in children with food-induced attention deficit hyperkinetic disorder. European Journal of Pediatrics, 156(7), 557–561. doi:10.1007/s004310050662

Wender, E. H., & Solanto, M. V. (1991). Effects of sugar on aggressive and inattentive behavior in children with attention deficit disorder with hyperactivity and normal children. Pediatrics, 88(5), 960–966.

Wolraich, M. L., Wilson, D. B., & White, J. W. (1995). The Effect of Sugar on Behavior or Cognition in Children: A Meta-analysis, 274(20), 1617–1621. doi:10.1001/jama.1995.03530200053037

Wolraich, M., Milich, R., Stumbo, P., & Schultz, F. (1985). Effects of sucrose ingestion on the behavior of hyperactive boys. The Journal of Pediatrics, 106(4), 675–682.

Woo, H. D., Kim, D. W., Hong, Y.-S., Kim, Y.-M., Seo, J.-H., Choe, B. M., et al. (2014). Dietary patterns in children with attention deficit/hyperactivity disorder (ADHD). Nutrients, 6(4), 1539–1553. doi:10.3390/nu6041539

Getting figures publication ready

Part I: R with ggplot2
The ggplot2 packages for R has some fantastic features for very powerful, flexible, and aesthetic data visualisation (If you are not familiar with the packages, you can have a look at some of the capabilities here: [http://docs.ggplot2.org/current/]). It is also relatively easy to export figures in a way that matches any journals figure specifications.

I will use the sample dataset ‘iris’ that is included with R for the following demonstration. This dataset contains data on petal length of three iris species among other measures.


# Loading the data
data(iris)

# Producing a basic plot
ggplot(data=iris,aes(x=factor(Species), y=Petal.Length)) +
geom_boxplot() +
xlab('Species') +
ylab('petal length [cm]')

Example_plot1

For publication, we would probably like to make the main features of the plot a bit bolder, control the font size of the axis labels, and use a white background:


figure <- ggplot(data=iris,aes(x=factor(Species), y=Petal.Length)) +
geom_boxplot(width=0.5,lwd=1,fill='grey') +
xlab('Species') +
ylab('petal length [cm]') +
theme_bw() +
theme(axis.text=element_text(size=12),
axis.title=element_text(size=13))

Example_plot2

We might also want to include annotations that indicate results of statistical analyses. Here is a one-way ANOVA to compare petal length between species followed by post-hoc t-tests to determine differences between species pairs:


# one-way ANOVA:
summary(aov(data=iris,Petal.Length ~ Species))

# t-test single contrasts:
t.test(iris$Petal.Length[iris$Species=='setosa'],iris$Petal.Length[iris$Species=='versicolor'],paired=FALSE)
t.test(iris$Petal.Length[iris$Species=='setosa'],iris$Petal.Length[iris$Species=='virginica'],paired=FALSE)
t.test(iris$Petal.Length[iris$Species=='versicolor'],iris$Petal.Length[iris$Species=='virginica'],paired=FALSE)

This analysis indicates that there are significant differences between all species in petal length. Next, we will add information about the group differences to the boxplot for the convenience of the reader:


figure +
 geom_segment(aes(x=1, y=7, xend=2, yend=7), size=0.1) +
 geom_segment(aes(x=2, y=7.2, xend=3, yend=7.2), size=0.1) +
 geom_segment(aes(x=1, y=7.4, xend=3, yend=7.4), size=0.1) +
 annotate("text",x=1.5, y=7,label="*",size=8) +
 annotate("text",x=2.5, y=7.2,label="*",size=8) +
 annotate("text",x=2, y=7.5, label="*",size=8)

Example_plot3

The final step is to export the figure with properties that match the specifications of the publisher. As an example, I will export a figure with 4cm height and 3cm width at 300 dpi resolution in PNG format:


ggsave(figure,file=‘/Users/joebathelt/Example.png’,   width=3,height=4,dpi=300,limitsize=TRUE)

Example_plot4

Coder’s Little Time Saver

We all know the problem; it’s getting late in the office, all your colleagues left hours ago, and your eyes are watering from staring at the analysis output of a script that should have finished running ages ago. Yet for some inexplicable reason, it’s still not done. Wouldn’t it be great if you could just nip out to get some fresh air and be informed when the script is finally done? Well actually, you can! Here’s a handy little tip explaining how to embed email alerts in MATLAB and Python scripts:

MATLAB
MATLAB comes with a handy function that supports sending emails within scripts. But before we can actually get to the email sending, we need to configure some server information. Here is an example for a gmail account:


mail = 'Me@gmail.com'; %Your GMail email address
password = 'secret'; %Your GMail password
setpref('Internet','SMTP_Server','smtp.gmail.com');

setpref('Internet','E_mail',mail);
setpref('Internet','SMTP_Username',mail);
setpref('Internet','SMTP_Password',password);
props = java.lang.System.getProperties;
props.setProperty('mail.smtp.auth','true');
props.setProperty('mail.smtp.socketFactory.class', 'javax.net.ssl.SSLSocketFactory');
props.setProperty('mail.smtp.socketFactory.port','465');

Now, we are ready to send an email:

sendmail(‘someone@coldmail.com’,’Hello there!’);

The second argument in the sendmail function corresponds to the subject line of the email. If you are keen to let MATLAB send a more elaborate email, you can also include a text body:


sendmail('someone@coldmail.com','Hello there!','Have you seen that great post on the Forging Connections Blog?');

It is even possible to send attachments:

sendmail('recipient@someserver.com','Hello there!','Have you seen that great post on the Forging Connections Blog?',{'/Users/Fred/image.jpeg’});

By including these few lines of code in your time-consuming MATLAB script, you can now get notified when it is time to go back to the office for the results.

Python
Python offers a simple solution to send emails from within scripts via the smtplib module. Here is a function that provides the configuration for gmail:
def send_email():
import smtplib


gmail_user = "Fred@gmail.com"
gmail_pwd = "Password"
FROM = 'Fred@gmail.com'
TO = ['Luke@gmail.com']
SUBJECT = "Meeting at 3pm"
TEXT = "Are you coming to the meeting?"

# Prepare actual message
message = """\From: %s\nTo: %s\nSubject: %s\n\n%s
""" % (FROM, ", ".join(TO), SUBJECT, TEXT)
try:
server = smtplib.SMTP("smtp.gmail.com", 587)
server.ehlo()
server.starttls()
server.login(gmail_user, gmail_pwd)
server.sendmail(FROM, TO, message)
server.close()
print 'successfully sent the mail'
except:
print "failed to send mail"

For more information see http://www.tutorialspoint.com/python/python_sending_email.htm and http://stackoverflow.com/questions/10147455/trying-to-send-email-gmail-as-mail-provider-using-python

ADHD and Low Working Memory: A comparison

I was recently directed to a paper titled ‘Children with low working memory and children with ADHD: same or different?’ From the title alone this paper piqued my curiosity. As scientists we often focus on collecting large amounts of data, and in some cases forget the importance of forming new theories and ideas. For me, this paper casts the profiles of an ADHD diagnosis and a deficit in working memory, a common childhood issue, in a new light. What does it mean (if anything) to have a clinical diagnosis? And what is the best way of characterising children with problems of attention and memory?

Whilst I will summarise the main findings below, for those that want to read the paper for themselves it can be accessed here:

http://journal.frontiersin.org/Journal/10.3389/fnhum.2014.00976/full

This paper provides a novel comparison of cognitive skills, executive function, educational attainment and behaviour between those with a low WM and those with a diagnosis of ADHD. It utilised three groups of children (8-11yrs): one group identified by routine screening as having a low WM, one group with diagnoses of ADHD and one group of typically developing children.

Age was controlled for across the groups and for each group the following measures were taken: Working memory (Automated working memory assessment); Executive function (Delis-Kaplan Executive Function System, The K-test of the Continuous Performance Test and Walk/Don’t Walk from the Test of Everyday Attention for Children); Academic ability (Wechsler reading, spelling, reading comprehension, mathematical reasoning and number operations and the four WASI subtests); And classroom behaviour problems (Connors teacher rating and the Behaviour rating inventory of executive function). Those children taking medication for their ADHD stopped taking it at least 24hrs prior to the session to ensure the effects of the drugs were eliminated by the time of testing.

The theory that makes up the basis for the research, includes the fact that both ADHD and low WM in childhood are significantly associated with poor educational attainment. It has also been established in the literature that there are associations between poor WM and inattentive behaviour and poor WM and executive function deficits. These elements combined suggest a high degree of overlap between the profiles of those with a deficit in WM and those with an ADHD diagnosis. In ADHD, we see executive function problems (deficits in response inhibition, attentional switching, planning and sustained attention) and also excessively high levels of motor activity and impulsive behaviour.

Some have argued that the executive function deficits in ADHD are a result of an underlying low WM, whilst others have proposed that ADHD involves two distinct neurodevelopmental systems:

1) a ‘cool/cognitive system’ that affects executive functions and WM

and

2) a ‘hot/affective system’ that leads to ‘aversion to delay that manifests as impulsive behaviour’.

It is this impulsive behaviour that manifests as excessive motor activity and problems in impulse control that are core characteristics of ADHD and are not associated with low WM. Thus, this study hypothesised that the two groups share a common deficit in the ‘cool executive function system’ reflected in the fact that both profiles exhibit lack of attentional control, but only those with the ADHD diagnosis have impairments in the ‘hot executive function system’ that results in hyperactivity and impulsivity.

The ADHD and low WM group, as expected, performed more poorly than the typically developing children on the tests of working memory (necessarily so for the latter group, because they were selected on this basis). Performance was significantly decreased for the low WM group in the two WM subtests that they were initially screened on. This suggests a selection artefact as ‘the two groups did not differ on the verbal WM task that was not used at screening’. In the executive function switching task no significant difference was seen between the low WM and ADHD group when considering accuracy, however, the low WM group showed a significantly slower performance than the ADHD and typical group. The sustained attention executive function task showed that both the ADHD and low WM children were significantly less accurate than the typically developing group and that they both made a significantly greater number of omissions than the typically developing group. The ADHD group also further made a significantly greater number of commission errors than the other two groups. No other significant differences were seen between the low WM group and the ADHD group on any other test of executive function. It is also worth noting that although there are a number of different models proposed for the functional structure of WM, the findings of this paper remain consistent across models on the basis that the ‘storage-only capacities of short-term memory (STM) and the capacity-limited attentional control functions of WM can be distinguished’.

They concluded that the ADHD and low WM groups have very similar profiles of WM ability and executive function impairment. They did however find two important differences: the low WM group were ‘slower to respond on several tasks’ and the ADHD group were ‘more hyperactive and exhibited more difficulties in controlling impulsivity in sustained attention’.  The processing speed impairments in the low WM group were an unexpected finding. They state that ‘it does not appear to be a part of a broader problem in fluid intelligence, as controlling statistically for performance IQ had little impact’. They present the idea that the impairment in response time may be a result of sluggish cognitive tempo (SCT). Sluggish cognitive tempo is a ‘set of symptoms strongly associated with the predominantly inattentive form of ADHD that includes high levels of daydreaming, slow response times, poor mental alertness and hypoactivity.’ It could perhaps be the case that those with a low WM may correspond to those with SCT, a predominantly inattentive subtype of ADHD. At present SCT is not a commonly utilised diagnosis in the UK. The more hyperactive behaviour in the ADHD children however was expected. ‘They violated rules more frequently during a planning task, and made more commission errors on the Continuous Performance Test of sustained attention’. This elevated hyperactivity was also shown in the Connor’s teacher rating as increased impulsivity and oppositional behaviour.

Interestingly they found the two groups to have equivalently high levels of inattentive behaviour. Whilst it has been shown before that children with low WM often exhibit inattentiveness, this paper is the first to show that this inattentive behaviour is of a very similar degree to that of  children with ADHD. In terms of the deficits in working memory, both groups exhibited deficits in the same regions and of a similar magnitude. They had impairment in ‘visuospatial short term memory, verbal working memory and visuo-spatial working memory’; but ‘their verbal short term memory scores fell within the typical age range’. The novel finding here is that there is such a high degree of correspondence between the profile and severity of the WM impairments in the two groups.

Previous research has also suggested a close relationship between ‘controlled cognitive attention and working memory’. This paper takes this understanding one step further suggesting that it is now looking more likely that there is a ‘link between poor working memory and overt inattentive and distractible behaviours’ as seen through the inattentiveness in the behaviour of those children with a low WM. In addition to being comparable in inattentiveness and working memory impairment, the two groups both exhibited ‘high rates of problem behaviours across a wide range of executive function behaviours’. Previous studies have also indicated that problem behaviours associated with executive function are seen in children with a low WM. This paper supports and extends this idea by showing that both groups ‘performed poorly on direct measures of switching, inhibition, sorting, planning, sustained attention and response suppression’.

When considering again, the cool (cognitively based) and the hot (affective) model of executive function, the pattern found in the results from this study suggest that there is some shared general executive deficit between the ADHD and low WM groups. The cool deficits manifest as inattention and low WM in both groups whilst the hot deficits, only present in the ADHD group, manifests as the hyperactivity. The additional delay in response times in the low WM group is the only component that does not fit with this theory, but the authors have identified that it ‘may be symptomatic of a subgroup of children with the predominantly inattentive form of ADHD who are characterised by SCT’.

In conclusion, this paper shows some very interesting preliminary findings into two conditions that are generally considered distinct. Both groups showed equal levels of underachievement when IQ differences were taken into account (‘they are indistinguishable in terms of their poor learning progress in mathematics and reading’). This is of great significance as those with a diagnosis of ADHD will almost certainly have learning support provided to them whilst those with a low WM will not. The educational needs of the group of children with a low WM are being overlooked as they do not show the disruptive hyperactivity of ADHD, even though in many respects the groups are indistinguishable.

Holmes J, Hilton KA, Place M, Alloway TP, Elliott JG and Gathercole SE (2014) Children with low working memory and children with ADHD: same or different? Front. Hum. Neurosci. 8:976. doi: 10.3389/fnhum.2014.00976

Tools of the Mind: An Effective Intervention for High Poverty Schools?

Early childhood interventions are believed to be a key step towards ending the cycle of poverty. This belief is based upon the large evidence base that demonstrates that good childhood development (measured in various ways) is highly predictive of a large variety of positive outcomes later in life.  If we give children the emotional, social and cognitive support they need in their early years it is hoped that this will make a lasting improvement that persists for the rest of their lives and halt the transmission of negative social problems from one generation to the next.

However, high quality intervention studies are rare, making it difficult to know which type of approach will work (if any).  The lack of research is unsurprising due to the fact that this kind of study is very hard to do well. They usually require a huge investment in time, money and effort. Furthermore, they involve a number of complex factors that might affect both the validity and applicability of the results, such as individual teaching style and child demographics.

This is what makes the recent study into the effectiveness of Tools of the Mind [1], an early intervention programme, by Drs Blair and Raver from New York University [2] a particularly interesting research piece. They argue that children receiving the Tools of the Mind program showed a significant improvement in learning in comparison to children in typical kindergarten classrooms. Importantly, the authors claim that some of these benefits continue into the first grade, after the program had finished, and that many of these effects were stronger in high-poverty schools.

In this post, I will be putting Tools of the Mind to the test, loosely using Dorothy Bishop’s framework for identifying red flags in interventions (It’s a good read. For those interested see [3]). So, what is Tools of the Mind? Is it credible? How solid is the scientific evidence behind it? Does it really work and, if so, are the effects worth the effort and cost involved?

What is Tools of the Mind?

Tools of the Mind is an educational program, developed over 18 years and now used in prekindergartens and kindergartens across the USA and Canada. I will be focussing on the kindergarten program here (that’s ‘reception’ for the British). It is based on the Vygotskian approach: the idea that it is important to teach children to master ‘mental tools’ such as attention and emotion-regulation skills that promote intentional and self-regulated learning. This is expected to develop their executive functions, social and emotional competence at a greater rate. In practice, it involves 60 or more Vygotskian-based activities including activities that require children to create their own learning plans, reflect on their learning and work in pairs with a strong focus on intentional make-believe play tied to stories and literature. Tools of the Mind was named an exemplary education program by the International Bureau of Education at UNESCO in 2001. Maybe this sounds marvellous, but does it actually work?

Who is behind the program and what are their credentials?

The program has largely been developed by Drs Bodrova and Leong. Encouragingly, both appear to be experts in the field. Dr Elena Bodrova was, until recently, Principal Researcher at Mid-continent Research for Education and Learning, a non-profit, non-partisan education research and development corporation, and Dr Deborah Leong is the Professor Emerita of Psychology, Metropolitan State College of Denver. They’ve both written a number of papers, articles and books, and, to the best of my knowledge, have each authored 10 papers in peer review journals in topics related to the Tools of the Mind program. I can’t find any red flags here!

Is there credible science behind the program?

The program claims to improve academic achievement and socio-emotional skills through improving executive functions, and in particular, the ability to self- regulate. Executive functions encompass many different processes involved in the management of cognition and actions: the ability to pay attention to and remember relevant details, to plan, to solve problems strategically and to regulate emotions and behaviour successfully. If you think of your brain as an orchestra, executive functions would be the conductor,  organizing many different instruments to play together as a coherent whole, bringing some in and fading others outand changing the pace and intensity of the music. Indeed, they seem to be involved in pretty much every higher-order cognitive process, calling into question whether the idea of executive functions is too vague and general a concept to be of any use and they are sometimes seen as a controversial topic amongst cognitive scientists.

Putting this issue aside for now, performance on tasks purporting to tap executive functions are excellent predictors of educational progress. More and more research is pointing to the development of these as being one of the most important domains in early childhood for positive short and long-term outcomes. The argument goes something like this: in the same way that a bad conductor is likely to produce dreadful music, no matter how excellent the individual musicians are, poor development of executive function is a very strong predictor of poor social and academic outcomes even if the rest of the cognitive system is well developed. Great claims are made about the extent to which these skills can be boosted by intervention. When you combine this with the fact that growing up in a deprived environment is frequently found to have a profound negative effect on executive functions, it seems a credible and good target for a program like Tools of The Mind.

The approach used to develop executive functions is based on the relatively well known and researched Vygotskian Approach. Unfortunately, I don’t have time and space to review this here but the Tools of the Mind website provides a lot of information on the science behind this.

Is there evidence that the intervention is effective from controlled trials?

Blair and Raver provide the first cluster randomized controlled study into the effectiveness of the Tools of the Mind curriculum in comparison to current practices in kindergartens. They looked at its effects on a range of different cognitive and academic skills. During the two year study, an impressive 795 children took part from 79 different classrooms and 29 schools. The schools were randomly assigned to either the control group who simply continued business as usual or the treatment group who received training to implement the Tools for the Mind programme in their schools. Children were tested in the first term of kindergarten, with follow up tests at a mean of 5 months and 1 year later. It’s questionable as to whether this can be considered an active control group, given that there may have been a potential bias for teachers receiving the new and novel Tools training to expect better results in comparison to those that continued as before, but overall I think this is quite a good study design.

They found that children in the Tools of the Mind classrooms were significantly better at keeping information in their working memory (effect size, ES = 0.14), maintaining attention in the face of distractions (ES = 0.12) and processing information (ES = 0.08) at the first follow up. This was accompanied by a greater rate in academic improvements in mathematics (ES = 0.13), vocabulary (ES not given) and reading (ES = 0.07), in comparison to the control classrooms (although this was only conventionally significant for mathematics). The faster rate of improvement in reading (Figure 1, ES = 0.14) and vocabulary (ES = 0.1) extended into the first grade, becoming significant, after the program had finished, suggesting that it has long term effects.

Most of the effects seen were stronger in high-poverty schools where more than 75% of the pupils are eligible for free or reduced-price lunch. In particular, the effect of Tools of the Mind in comparison to the control group was significant in tests that measured the child’s ability to maintain attention despite emotionally arousing distractors (ES = 0.82), fluid IQ (the ability to solve problems in novel situations, ES = 0.46) and vocabulary (ES = 0.43) in high poverty schools.

Whilst this is the only study of the kindergarten program, it should be noted that both the National Institute for early education research (NIEER) and the Peabody research institute (PRI) have been investigating the pre-kindergarten program. Despite the fact that both studies have included only high-poverty classrooms (around 80%+ children receiving free or reduced price lunch) their results appear to have some substantial disagreements. In one study, NIEER compared 88 children in Tools classrooms with 122 children receiving the district’s balanced literacy curriculum. They found that by the end of the first year, children in the Tools classroom had significantly better classroom experiences, far fewer behavioural problems (an indicator of self-regulation) and some improvement in language performance, although not significant after correcting for multiple comparisons, in comparison to children in the control group. They found no improvement in reading or mathematics. In a subsequent study of the same program but with a slightly different sample (85 children in Tools, 62 in the control classrooms) they found that children in the Tools classrooms performed significantly better in tests of executive functions (stoop battery and flanker tasks) in comparison to children in the control classrooms and that this difference was greater, the more taxing the task.

In contrast, PRI have been conducting a much larger study comparing 498 pupils receiving the Tools curriculum with 379 pupils receiving a range of curricula that would normally be found in pre-school classrooms. They found that the Tools curriculum had no significant effect on direct assessments of achievement or executive function or any teacher ratings of language, executive function, or social behaviour by the end of pre-kindergarten in comparison to the control classrooms. Remarkably, when the children were assessed a year later at the end of kindergarten, they found that the comparison children that had received the normal curricula actually had significantly greater gains in achievement and executive function composite scores and on many of their subtests in comparison to those that received Tools of the Mind!

Are these effects worth the effort, time and cost involved?

Being subjective, this isn’t an easy question to answer. Note that the Tools curriculum can’t simply be taken off the shelf and implemented as other curricula might be: it requires about two years of teacher training including in-classroom coaching and a shift in the teacher’s role in the classroom. However, Blair and Raver appear to believe that: ‘teachers received typical levels of training and implemented the curriculum with materials that are well within the budget of the average kindergarten classroom. Information about the actual cost is difficult to find but in general sources place it at £5000 – £7000 per classroom, which seems reasonable [4, 5] and the materials required appear to be simple, inexpensive and readily available.

However, I think the key to answering this question is to consider the effect sizes produced by the program, the number of standard deviations between the mean score of the control and target groups. In general, the effect sizes found for the kindergarten version of Tools of the Mind in Blair and Raver’s study are relatively low: they all hover around the 0.1 mark, 5 months into the program. This is around half the average effect size for childhood interventions in 2006 found by Duncan et al. [6]. In addition, whilst the effects in high poverty schools look much more promising, it should be noted that the confidence intervals are also much larger for these effects.

To get a feel for what this means, we can get a rough estimate for how this effect size translates into months of development. Bloom et al. [7] found that children progress with an effect size of 1.52 in reading and 1.14 in maths over the first year of schooling. Averaging this and dividing by 12 gives as a rough expected progress estimate of 0.1 per month. This means that many of the effects of Tools of the Mind put children only one month ahead of children not receiving the program.  However, when considering vocabulary in high poverty schools, for example, with an effect size of 0.46, this offers nearly 5 months advantage. Of course, this analysis is something of an oversimplification, but it provides a little context as to whether these effect sizes are meaningful.

How do these effect sizes compare to differences between children coming from disadvantaged and advantaged children?  Whilst it is difficult to put numbers on these differences as many researchers use different measures of what counts  as ‘advantaged’ and ‘disadvantaged’, in order to get an idea of this, I’ll compare the ES to a few studies that found differences within the usual range. I’ll steer clear of the issues in measuring something as vague of ‘executive functions’ and concentrate on one aspect of them, working memory. Noble et al [8] found a difference of 0.31 SD between high and low SES children in the first year of school. Given that Tools of the Mind was found to improve working memory by 0.14, this would be the equivalent of closing half the income related gap. Vocabulary also shows promising results. A study comparing children receiving free school meals (FSM) and those that didn’t at the start of school found a gap of 0.62 SD between the groups [9]. The 0.46 ES for vocabulary in high poverty schools would close this gap by three quarters. Unfortunately, they don’t report what this figure was in high poverty schools one year later, which would be a particularly interesting result. Reading and maths are less promising however. The same study found gaps of 0.69 for reading and 0.68 for mathematics between children receiving FSM and those that didn’t. The ES of 0.13 in maths and 0.14 in reading won’t have much effect on closing this gap, which is a shame, because arguably these are the more valuable educational skills.

In conclusion, Tools of the Mind appears to be a relatively well-grounded intervention program. A random controlled study showed that Tools of the Mind improved children’s executive functions and academic skills in comparison to normal kindergarten classrooms. Despite not being one of the most effective interventions available, the costs, effort and time required seem reasonable and the results suggest that some of the benefits of the program are long term. It holds particular potential for high poverty schools, where the effects of the program appear to go some way to closing income related achievement gaps. However, it is questionable whether the control group used can be considered a full active control and it would perhaps be better to see these results replicated in a study that used a similarly new and novel curriculum or simply used just part of the Tools curriculum as the control group. In addition, results from studies of the pre-kindergarten program by two highly distinguished research bodies are inconclusive with a large study indicating that children receiving Tools of The Mind were at a disadvantage in comparison to normal curricula at the start of school. With this in mind, and the fact that we only have one study investigating the kindergarten program, I would suggest that more research needs to be done to establish the overall effectiveness of this curriculum (and ideally to identify which aspects of the Tools have the greatest effect) before we can advocate it as an effective intervention for kindergarten classes in high poverty schools.

[1] http://www.toolsofthemind.org/

[2] The full paper by Blair and Raver can be found here: http://dx.plos.org/10.1371/journal.pone.0112393

[3] http://deevybee.blogspot.co.uk/2012/02/neuroscientific-interventions-for.html

[4] http://www.washingtonpost.com/local/education/dc-school-reform-targets-early-lessons/2011/11/04/gIQAGZ2VCN_story.html

[5] http://economicdiscipleship.com/2010/12/23/profile-tools-of-the-mind/

[6] https://socialinnovation.usc.edu/files/2014/03/Duncan-Two-Policies-to-Boost-School-Readiness.pdf

[7] http://www.mdrc.org/sites/default/files/full_473.pdf

[8] http://onlinelibrary.wiley.com/doi/10.1111/j.1467-7687.2005.00394.x/pdf

[9] http://www.scotland.gov.uk/Publications/2005/02/20634/51605

State of the Nation Report: Linking Research to Policy

We have recently started a new project focused around children that are considered to be “at risk” of poor educational attainment due to their socioeconomic status. Indexes of socioeconomic status include complex measures of interrelated components, with one key component being family income. As a result, socioeconomic status gives an index of poverty that incorporates other forms of deprivation. There is, at present, an increasing effort to provide better links between research on socioeconomic status, its impact on cognition, and government policy. However, when considering educational attainment, there is still little being done to support children from a lower socioeconomic background. The recently released State of the Nation Report [1] indicates the need for further research into this area. It calls for increased interest and action in order to help close the performance gap between children from different socioeconomic backgrounds and reduce the effects of poverty on children.

Whilst the UK is beginning to recover from the economic crisis, social recovery is lagging behind. For example, a family is described as being in relative poverty when the level of family income is less than 60% of the median UK family income. The State of the Nation report from the Social Mobility and Child Poverty Commission states that, before consideration for housing costs, 17% of children in the UK are living in in a state of relative poverty. After housing costs, 27% are living in relative poverty – that is 3.7 million children. Additionally, the report gives values for absolute child poverty, which are also still worryingly high despite government interventions. The House of Commons Scottish Affairs Committee defined absolute poverty as “the lack of sufficient resources with which to keep body and soul together”. [2]

The State of the Nation Report calls for the UK government to re-think their existing approach to child poverty if they wish to achieve the targets set out in the “2020 challenge” to “reduce child poverty by half and prevent Britain becoming a permanently divided society.” The knock-on implications of poverty on educational attainment are well noted in the scientific literature. In 2013, 38.5% of children receiving free school meals, which is a commonly used indicator for poverty, achieved an A*- C grade in Maths and English, compared to 65.3% of children not receiving free school meals. In addition, research published by the Social Mobility and Child Poverty Commission has shown that children from deprived backgrounds that are considered to be high achieving aged 7 fall behind children from the most affluent backgrounds that were considered to be low achieving at the age of 7. This crossing over in attainment levels is seen by 14-16 years of age (see Figure 1) [3]. This pattern of crossing over indicates that the environmental components of socioeconomic status have an impact on a child’s potential for academic attainment above and beyond genetic influences. Since 2005, the attainment gap in education between children from a low socioeconomic background and children from a high socioeconomic background has only closed by 1.6%. The implication is that those children with a low level of educational attainment are 4 times more likely to remain in poverty.

Figure 1. Trajectories from key stage 1 to key stage 4 by early achievement (defined using key stage 1 writing) for the most deprived and least deprived quintiles of socioeconomic status (state school only)SES attainment crossover_KS1 writing

It is well known that working protects against poverty. Children in working households are only a third as likely to suffer from poverty as those in workless households. However, the report also highlights that working is simply not enough to prevent childhood poverty. In 2012/2013 62% of children deemed to be living in poverty lived in a household where at least one person worked. And perhaps most concerning is that if trends continue, “2010-2020 is set to be the first decade with a rise in absolute poverty since records began in the early 1960s.”

With all this considered, further research is needed to provide a clearer picture of how socioeconomic status influences cognition and education. A key focus of this should involve identification of the factors within socioeconomic status and cognition that mark children as being more susceptible to the risk of poor educational attainment. In turn, greater resources need to be devoted to identifying the most effective interventions. These studies are difficult and complicated to conduct, but as The State of the Nation Report identifies, improving the educational prospects of children growing up in poverty provides one of the best mechanisms for creating a more equal society.

In conclusion, although the issues surrounding the socioeconomic impact on education have in part been researched and evaluated, there is yet to be a thorough exploration of all the factors involved.  Further investigation is needed to identify which cognitive, neural and environmental measures provide the most prominent markers of risk and resilience for children growing up in poverty. Research along these lines is sorely needed if government policy is to target those most in need of support.

[1] https://www.gov.uk/government/publications/state-of-the-nation-2014-report

[2] http://www.bbc.co.uk/news/uk-politics-29686628

[3]https://www.gov.uk/government/uploads/system/uploads/attachment_data/file/324501/High_attainers_progress_report_final.pdf