After 5.5 years, the CoCA project has come to an end. In this large-scale European research project, an interdisciplinary group of researchers investigated comorbid conditions of ADHD. They particularly focussed on depression, anxiety, substance use disorder and obesity, as these conditions frequently co-occur with ADHD in adulthood.
What has this extensive study brought us? Experts dr. Catharina Hartman (University Medical Center Groningen, The Netherlands) and prof. dr. Andreas Reif (University Hospital Frankfurt, Germany) were invited by Jonathan Marx for an interview on the online radio program Go To Health Media. In this program they talk about several aspects of the CoCA project: How often do comorbid conditions co-occur with ADHD? What do the genetics of ADHD comorbidities tell us? What should clinicians do to prevent or reduce these comorbidities in ADHD?
As professor Andreas Reif summarizes at the end of the interview, the main things that we learned from the CoCA project are:
Comorbidity in ADHD is a very big problem. Adults with ADHD frequently have co-occuring conditions such as depression, anxiety, obesity and to a bit lesser extent substance use disorder.
The type and prevalence of comorbidities differ between men and women.
There is considerable genetic overlap between ADHD and comorbid conditions. We think that at least part of the overlap between comorbidities is caused by genetic effects (next to environmental effects that also play a role).
The dopamine system plays an important role in comorbidity, through influencing brain processes.
Disturbances in the circadian system (i.e. sleep cycle) are unlikely to play a causal role in these comorbidities, but they might be a consequence.
Clinicans should look out for comorbidities when they treat ADHD patients, and inform their patients about their increased risk to develop comorbidities so that they can take preventive measures (i.e. be careful with alcohol to avoid substance use disorder). Secondly, clinicians should actively look out for ADHD symptoms when treating conditions such as depression, anxiety, substance use disorder or obesity.
Watch the full interview with both experts by clicking on the image below:
Aid for ADHD individuals personal needs, right when it is needed
You might know the tenet of “just in time” from economics. It means bringing goods to a recipient at the right time, exactly when it is needed. But what if we could apply this also to treatments or interventions for mental health problems? Can we provide small interventions at exactly the time when a person needs it? And can this provide us with more insights into what triggers ADHD symptoms?
Just in time economics is possible and required because of dynamic processes in economical markets. Dynamic processes are also present in mental disorders. Attention-deficit/hyperactivity disorder (ADHD) is a condition that is dynamic by nature. Core symptoms of ADHD are hyperactivity, inattention and impulsivity, and many individuals also experience emotion dysregulation. In the past, research focused mainly on how patients with ADHD differ from healthy individuals or other disorders. But what about ADHD individuals’ context or other dynamics, that may trigger symptoms? For this we need to look much more closely at the dynamics of an individual’s life.
Ambulatory Assessment: collecting data in real time and in real life
The Ambulatory Assessment method makes use of smartphones, accelerometers, GPS-tracking and geolocation approaches to track how you feel, what you do, where you go, who you meet, what you eat, and how you’re body is doing (i.e. your heartrate) (1). This method has improved a lot over years and technical progress makes it more and more feasible to investigate associations between variables over time and how these variables interact in everyday life. This provides researchers with new insights into many different factors that can influence a person’s symptoms and mental health.
The importance of context
The Ambulatory Assessment method also enables to better differentiate between real and deceptive associations. Imagine, a person is asked for hyperactivity in the morning at 9:00 am, noon and evening and it turns out that the person is very hyperactive in the morning. Your conclusion may be that this individual is more hyperactive in the morning, but you don’t know why. If you know more about this person’s context, it may turn out that every day at 08:30 am the person drinks two cups of coffee which causes the measured hyperactivity at 9:00 am. This gives you much more insight into what triggers his or her symptoms.
Another example: imagine that a symptom always occurs in a special situation, at a special place or with a special person (e.g., after trying to catch the connecting train every morning at the same time). If you always ask for symptoms at the same time of day, you may miss this special occasion because it always occurs at another time. This way, you may miss out on important associations between symptoms and situations, places or persons. It is therefore very important to measure symptoms at random time points, or when they are triggered by certain events. This gives you much more informative data.
Cause or consequence?
However, the Ambulatory Assessment method is not yet perfect. The main limitation is that it’s difficult to determine what causes what (2). For example, do fluctuations in mood in patients with ADHD lead to impulsivity or hyperactivity? Or does mood change as a consequence of impulsivity? Another example: Do I feel better after exercising or do I move more because I feel good? Researchers recently found evidence for both directions (3,4).
Towards developing just in time treatment
Let’s think about the next step. A better understanding of causes and consequences and associations between symptoms and environmental triggers in an individual’s real world, creates the basis for just-in-time interventions (6). The idea is to react on dynamics in how symptoms are experienced or triggered, by timing the interventions exactly when it is needed. This could be realized by smartphones or wearables, which are already implemented in Ambulatory Assessment research. These devices are then not only used to collect data in real-time, but also to give feedback and provide interventions to reduce or prevent symptoms.
Exercise intervention through a smartphone app
The antecedent of just-in-time-adaptive-interventions are ecological momentary interventions (EMIs). One example of such an EMI or electronic diary intervention with a smartphone and an accelerometer for individuals with ADHD is the PROUD trial of the European funded project CoCA (5). In this trial, individuals with ADHD received a smartphone and a kind of sports watch (that measures your movement) that together measured their behavior, activity, daylight exposure, mood and symptoms during the day. The smartphone also provided an intervention, either in the form of sports exercises or in the form of bright light therapy. During the exercise intervention, participants are given instructions to perform exercises via a smartphone app by which they are guided through their training by weekly goals, motivational reminders, and training videos. Every evening, they get feedback on performed intervention parameters from that day in real time. This system was not yet so developed that it also changed the type or timing of the intervention to the data that was collected during the day, but that would be the next step to create a just-in-time intervention.
In conclusion, it is important to investigate the associations between ADHD individuals’ symptoms and their personal everyday lives. This helps researchers to understand the dynamic processes behind ADHD and to create tailor-made interventions that can easily be integrated in the everyday life of these individuals. A physician cannot support a patient throughout every step he/she takes, but there are already devices that can be supportive around the clock and technical innovations will surely pave the way to improve personal just-in-time interventions in the near future.
This blog was written by Elena Koch. She is a PhD student at Karlsruhe Institute for Technology in Germany.
A group of researchers from Spain, The Netherlands, Germany, Estonia, Denmark and USA have joined efforts to gain insight into the genetics of ADHD and its comorbidities. This ambitious objective was addressed by the Work Package 2 of a big project called CoCA: “Comorbid Conditions of Attention deficit/hyperactivity disorder (ADHD)”, funded by the European Union for the period 2016-2021.
In psychiatry, the co-occurrence of different conditions in the same individual (or comorbidity) is the rule rather than the exception. This is particularly true for ADHD, where conditions like major depressive disorder or substance use disorders frequently add to the primary diagnosis and lead to a worse trajectory across the lifespan.
There are different reasons that may explain the advent of the comorbidities: Sometimes the two conditions have independent origins but coincide in a single patient. Comorbidity can also appear as a consequence of a feature of a primary disorder that leads to a secondary disorder. For example, impulsivity, a trait that is common in ADHD, can be an entry point to substance use. Comorbidity can also be the result of shared genetic causes. The latter has been the focus of our investigations and it involves certain risk genes that act on different pathologies, a phenomenon called pleiotropy.
Our project started with an approach based on the exploration of candidate genes, particularly those involved in neurotransmission (i.e. the connectivity between neurons) and also in the regulation of the circadian rhythm. We used genetic data of more than 160,000 patients with any of eight psychiatric disorders, including ADHD, and identified a set of neurotransmission genes that are involved at the same time in ADHD and in autism spectrum disorder . In another study we identified the same gene set as involved in obesity measures .
Then we opened our analyses to genome-wide approaches, i.e. to the interrogation of every single gene in the genome. To do that we used different statistical methods, including the estimation of the overall shared genetics between pairs of disorders (genetic correlation, rg), the prediction of a condition based on the genetic risk factors for another condition (polygenic risk score analysis, PRS) and the establishment of the causal relationships between disorders (mendelian randomization). As a result, we encountered genetic connections between ADHD and several psychiatric disorders, like cannabis or cocaine use disorders [3, 4, 5], alcohol or smoking-related phenotypes [6, 7, 8], bipolar disorder , depression , disruptive behavior disorder , but also with personality or cognition traits, like neuroticism, risk taking, emotional lability, aggressive behavior or educational attainment [6 , 11, 12, 13], or with somatic conditions, such as obesity [11, 12].
All these results and others, reported in more than 40 (!) scientific publications, support our initial hypothesis that certain genetic factors cut across psychiatric disorders and explain, at least in part, the comorbidity that we observe between ADHD and many other conditions. This information can be very useful to anticipate possible clinical trajectories in ADHD patients, and hence prevent potential negative outcomes.
Dr. Bru Cormand is full professor of genetics and head of the department of Genetics, Microbiology & Statistics at the University of Barcelona. He leads workpackage 2 of the CoCA project (www.coca-project.eu) on the genetics of ADHD comorbidity.
Ever seen that meme with Homer Simpson lying awake in bed until 4 am and then falling asleep 8 minutes before the alarm rings? If it felt relatable, then you definitely know how relevant sleep problems can be! That situation shows problems with falling asleep (insomnia) as well as very late sleep timing (read more about this in my previous blog about circadian delay). Both are linked to an infinite number of health problems, especially mental illness. In fact, a typical teenager on TV can demonstrate how bad sleep affects you. Remember how moody, bad-tempered, inattentive at school they usually are or how much they drink and smoke? Well, bad sleep relates to very similar mental health problems: mood disorders, anxiety, aggression, attention deficit hyperactivity disorder (ADHD) and bad habits like smoking, drinking alcohol and taking drugs. The connection between bad sleep and ADHD, however, is one of the most studied.
What about sleep in people with ADHD?
We know that up to 80% of ADHD patients suffer from insomnia1,2 and most of them have a circadian delay3. Researchers commonly find that if a person has insomnia symptoms and later bed times, then this person also suffers from more severe ADHD4. Although it’s not clear why exactly this happens, some think that a natural circadian delay doesn’t let you fall asleep at socially acceptable times, so you regularly get insufficient sleep5,6. Interestingly, people without ADHD who sleep poorly also develop the same symptoms – inattention and hyperactivity7. You might even say that insomniacs develop temporary ADHD! This makes the connection between ADHD and sleep even more curious and important.
What did our research find?
My colleagues and I wanted to know if the same association with sleep happens in other mental illness and if it is different from the connection to ADHD. For this, we examined information from around 38,000 persons in The Netherlands with ages from 4 to 91. Each of them filled in a long online survey with questions about their sleep habits and mental health.
Later, we divided all these people into three groups based on their sleep behaviour. The first groups were people who prefer earlier sleep times and reported no insomnia symptoms. The other two groups comprised persons who preferred later sleep times (a sign of circadian delay). These groups differed in one thing: one group had very few symptoms of insomnia and the other a lot.
After that, we measured if some of these groups had more severe symptoms of mental illness, including ADHD. And yes, the groups with circadian delay – even the ones without insomnia – really did have significantly higher severity of all mental illness compared to early sleepers! Moreover, the individuals in the circadian delay group with insomnia had more mental health problems than those who slept well. In ADHD specifically, this link between circadian delay and insomnia was as large for symptoms of inattention as for hyperactivity/impulsivity. Children and adolescents had even stronger relation between poor sleep and mental health problems, just like that moody teenagers I mentioned before.
Why this matters
Insomnia and circadian delay, as we see from these results, is a common problem for different types of mental illness. Good sleep usually means better mental health, so people diagnosed with a mental illness might want to improve their sleep behaviour. The good news is that reducing mild insomnia might be easy: anyone can get blinders to keep their bedroom dark and drink less coffee. Circadian delay, though, is harder to change, because it is mainly ruled by your genes. This means that those born as late-night birds need to adapt their life to a more nocturnal rhythm to avoid worse mental state. Sadly, we all know it is often impossible. Younger people, for whom sleep is so important, still need to wake up unnaturally early for school. Adults go to sleep only late at night, even if they’d happily nap at 9 pm, because they were working all day and need to finish their house chores. Current expectations of a good worker and student fit morning people but fail to help and only cause more insomnia for those with a circadian delay. Unless we want to feed all adolescents melatonin tablets every day, our society needs to be more tolerant to our individual circadian preferences.
Dina Sarsembayeva is a neurologist and a research master’s student at the University of Groningen. She is using the data from the CoCa project to learn if the circadian preferences and sleep problems can be turned into profiles to predict specific psychiatric conditions.
1. Kessler, R. C. et al. Lifetime prevalence and age-of-onset distributions of mental disorders in the World Health Organization’ s. World Psychiatry 2007;6:168-176)6, 168–176 (2007).
2. Lugo, J. et al. Sleep in adults with autism spectrum disorder and attention deficit/hyperactivity disorder: A systematic review and meta-analysis. Eur. Neuropsychopharmacol. 1–24 (2020) doi:10.1016/j.euroneuro.2020.07.004.
3. Coogan, A. N. & McGowan, N. M. A systematic review of circadian function, chronotype and chronotherapy in attention deficit hyperactivity disorder. Atten. Defic. Hyperact. Disord.9, 129–147 (2017).
4. Lugo, J. et al. Sleep in adults with autism spectrum disorder and attention deficit/hyperactivity disorder: A systematic review and meta-analysis. Eur. Neuropsychopharmacol.38, 1–24 (2020).
5. Çetin, F. H. et al. Chronotypes and trauma reactions in children with ADHD in home confinement of COVID-19: full mediation effect of sleep problems. Chronobiol. Int.37, 1214–1222 (2020).
6. Eng, D. et al. Sleep problems mediate the relationship between chronotype and socioemotional problems during early development. Sleep Med.64, S104 (2019).
7. Lunsford-Avery, J. R., Krystal, A. D. & Kollins, S. H. Sleep disturbances in adolescents with ADHD: A systematic review and framework for future research. Clin. Psychol. Rev.50, 159–174 (2016).
Mindfulness is being investigated as a new treatment for children with ADHD. The question that rises often is whether the inattentive and/or hyperactive/impulsive behavior decrease after the training. However, the training can also have other effects on children and their parents, and these can differ greatly.
Attention can be trained
ADHD is a diagnosis characterized by symptoms of an attention deficit and/or hyperactivity/impulsivity that can lead to severe impairments in daily functioning. With mindfulness meditation you can train “your attention-muscle”: Over and over you need to focus your attention on what you are experiencing now. This happens with a friendly and curious attitude, without labeling the experiences as good or bad and without reacting to them immediately. From previous studies, we know that ADHD-symptoms – especially inattentiveness – can decrease because of mindfulness meditation, but these studies used small samples and did not make use of a comparison group.
Is an 8-week mindfulness training for children with ADHD and their parents a good addition to care as usual? We investigated this with the MindChamp project with more than one-hundred participating families the first big, rigorous study in this area. With some of these families we additionally did extensive interviews to make an overview of the hindering/helping factors of mindfulness training as well as the scope of different treatment effects. The results of these interviews with 20 parents, 17 children (9-16 years old), and the 3 mindfulness trainers have recently been published in a scientific article.
What did and did not help in the mindfulness training?
Participation was rated as positive. Parent and child were having quality-time together, helped each other and learned “a common language”. Both parents and children felt supported by other group members. Sharing experiences led to feeling recognized.
“There I was not different, but just the person I am and so were the other kids”– girl, 9 years old
It was both difficult and informative when group members were too disturbingly present. Many found the non-judging, friendly attitude of the trainers a relief, others would have liked more interference from them. The children could collect points by making assignments at home for which they received a reward from their parents. For some this was very helpful, for others stressful. In addition, sessions were experienced as a bit too long by children, it requested quite some time and energy investment. Nevertheless, most parents recommended the training.
Which treatment effects were experienced by children and parents?
The experiences differed. Some children and parents noticed that they reacted less heavily, or less impulsive, which led to a decrease in fights. Many were talking of more calmness and relaxations. These effects went beyond the context of the training itself and were for instance noticeable at school. Some families noticed little to no effect on ADHD symptoms and/or cognitive functioning of the child, but did experience effects in other domains. Many also experienced a better parent-child relationship, and better relations with others. The training brought awareness, insight, and acceptation (of self and others).
“I see better and better how he approaches things and what kind of help he needs”– father
When we only look at the (average) effect of the treatment for the entire group, we may miss very valuable information. It is important to not only investigate the effects on ADHD-symptoms, but also other effects ánd individual differences. The training for instance gave more insight, acceptation and improved relationships. We hope that our study leads to more insights into how mindfulness can be used for children with ADHD.
Psychiatric disorders, such as ADHD, are defined by categorical diagnostic borders: you either have it or you don’t. Research has shown that these borders do not accurately reflect what is happening on a biological level. In fact, these are complex traits that can be defined as quantitative characteristics that are present in people in different degrees. When you have or experience these traits in a very high degree, you may classify as having a psychiatric disorder. We also know that both genetic and environmental factors contribute to how much an individual is liable to ‘develop’ a psychiatric disorder, and that for each person, it is a different combination of such factors. This large variability between individuals is called heterogeneity.
The fact that ADHD is very often accompanied by other disorders (called comorbidities) also contributes to the notion that these conditions cannot be defined as a simple “yes/no” categorization. This refers to the notion of pleiotropy, meaning that one gene or biological mechanism can result in different outcomes. During my master’s thesis project, we investigated the genetic relationships between ADHD and insomnia, which is one of the most common conditions to co-occur with ADHD. We also looked into the role of depression, another common comorbidity, in the overlap between insomnia and ADHD.
Nowadays, there are very large datasets that we can use to explore such questions. In order to try to disentangle the genetic relationship between ADHD and insomnia, we calculated a genetic risk score for each individual. This method determines the estimated risk that an individual has to develop a certain trait based on their genetic make-up. We found that the genetic risk score for insomnia was linked to ADHD symptoms, and vice-versa: the genetic risk score for ADHD was linked to insomnia. We also observed a possible distinct genetic relationship between hyperactivity and inattention symptoms and insomnia: while we found that there was a shared genetic risk for insomnia and hyperactivity symptoms, we did not find this link with inattention symptoms.
Next, we tested the effect of depression in these relationships by the inclusion of depression-related variables as covariates in our analyses. We found that the association between genetic risk score for insomnia and ADHD symptoms was no longer considered significant, while the association between the genetic score for ADHD with insomnia was weaker. At last, we analysed the association of cumulative genetic risk for ADHD with insomnia while separating the individuals in two different groups by broad depression. The results suggest that genetic risk for ADHD is similarly associated with insomnia in individuals with and without depression. This indicates that the genetic relationship observed between ADHD and insomnia is not solely a consequence of the comorbidity between depression and the other two conditions.
The take-home message is that with these results we show that there are shared genetic influences between conditions that are traditionally defined as distinct or separate, so all three conditions might be all entangled in their underlying genetic factors. By advancing our understanding of how ADHD and its comorbidities are related, we can better refine the definition of ADHD. Also, from this research we learn more about the underlying mechanisms of ADHD (and associated conditions) from a biological (genetic) perspective. As the next step, we plan to include genetic data for separate ADHD symptom dimensions (hyperactivity and inattention), as well as depression in our analyses.
Victória Trindade Pons
I have recently concluded my Master’s in Biomedical Sciences at the Radboud University. This work was part of my final internship and was developed under the supervision of Dr. Nina Roth Mota in the Department of Human Genetics of the Radboudumc. This study is part of the CoCa project (Comorbid Conditions of ADHD), which has the aim to gain insight into the mechanisms underlying ADHD comorbidity and calculate the burden associated with such comorbidity for healthcare, economy, and society.
It is often said that people with ADHD are more creative than others. But is that true? What does science tell us?
To find an answer to this question, I collaborated with two experts in the field of creativity, Dr. Baas and Prof. Kroesbergen, and PhD-student Marije Stolte. Together, we reviewed scientifically published research on the topic of ADHD and creativity. We summarized behavioral studies looking at creativity performance data in groups of people with and without ADHD. We also reviewed studies looking at the effect of psychostimulant medication on creative performance as people with ADHD often report that their ADHD medication suppresses their creativity1,2.
How to measure creativity Creativity is a broad concept, but it’s often defined as “the generation of ideas or products that are original as well as useful”3 . The dual pathway to creativity model is a leading theory in the field and describes that there are many different processes involved in creativity. These can be divided into two types: cognitive flexibility and cognitive persistence4,5,6 . Cognitive flexibility is the ease with which people can switch to a different approach or consider a different perspective6 . The most prominent example of cognitive flexibility is divergent thinking. Divergent thinking is frequently assessed with the Alternative Uses Task where one has to name as many uses of a certain item as one can think of (see the below figure for an example).
The second broad type of creative thinking is cognitive persistence, defined as the degree of sustained and focused task-directed cognitive effort. A prime example of a persistent process is convergent thinking. This is often measured with the Remote Associations Task where participants have to generate a word that connects three stimulus words (e.g., black, bean, break; answer: coffee).
The Alternative Uses Task and the Remote Associations Task are both performance tasks, but in creativity research there are also measurements of creative achievements and abilities in daily life using questionnaires. These for instance ask about if you have ever won a price in a drawing competition or published a poem.
Outcomes of our review of the published articles Results from population-based studies found, in general, that people with a high number of ADHD symptoms, score high on divergent thinking. However, when comparing individuals with a clinical ADHD diagnosis to those without, they found no difference on divergent thinking. Studies that measured convergent thinking found no differences related to ADHD symptoms or diagnoses. But studies that assessed creative abilities or achievements in daily life, did find a positive association with ADHD. Lastly, we did not see an overall negative effect of psychostimulants on creativity in the studies that have been published so far. In addition to reviewing the outcomes of the studies, we also looked at the quality of the studies and found that almost all studies were underpowered to detect effects. Also a lot of different creativity instruments were used and a number of important potential confounding factors, such as presence of comorbid disorders, were left out of the research. Therefore, in the future we propose to collaborate to better take care of these quality issues. In conclusion, our review shows that certain aspects of creativity are indeed linked to ADHD. However, we have also identified gaps in the knowledge on this subject. For example, in future studies, we should try to find out what it is that causes the differences in divergent thinking between those with an ADHD diagnosis (low performance) compared with those without an ADHD diagnosis but with many ADHD symptoms (high performance).
Relevance For years we have been studying the deficits that are linked to ADHD, but there might also be advantages, such as increased creativity. With this review we indeed show a link between creativity and ADHD, but we also identified many more questions that we want to address in future studies. With a focused research agenda we will address the identified gaps in the literature to try to improve our understanding of the link between creativity and ADHD, generating a more complete picture of ADHD. The increase of knowledge about the positive aspects of ADHD may aid in treatment and coping with ADHD, reduce stigmatization, and increase the quality of life of patients. We hope that in the future we can translate the science output to more practical implications such as educational programs in the classroom.
Hoogman M, Stolte M, Baas M, Kroesbergen E. Creativity and ADHD: A review of behavioral studies, the effect of psychostimulants and neural underpinnings. Neuroscience Biobehavioral Reviews. 2020 Oct 6;119:66-85. doi: 10.1016/j.neubiorev.2020.09.029.
Martine Hoogman is funded by a personal Veni grant from NWO (Netherlands Scientific Organization) on the topic of creativity in ADHD.
Brinkman, W.B., Sherman, S.N., Zmitrovich, A.R., Visscher, M.O., Crosby, L.E., Phelan, K. J., Donovan, E.F., 2012. In their own words: adolescent views on ADHD and their evolving role managing medication. Acad. Pediatr. 12 (1), 53–61. https://doi.org/ 10.1016/j.acap.2011.10.003.
Kovshoff, H., Banaschewski, T., Buitelaar, J.K., Carucci, S., Coghill, D., Danckaerts, M., Sonuga-Barke, E.J.S., 2016. Reports of Perceived Adverse Events of Stimulant Medication on Cognition, Motivation, and Mood: Qualitative Investigation and the Generation of Items for the Medication and Cognition Rating Scale. J. Child Adolesc. Psychopharmacol. 26 (6), 537–5547. https://doi.org/10.1089/cap.2015.0218.
Amabile, T., Conti, R., Coon, H., Lazenby, J., Herron, M., 1996. Assessing the work environment for creativity. Acad. Manag. J. 39-5, 1154–1184.
Boot, N., Baas, M., van Gaal, S., Cools, R., De Dreu, C.K.W., 2017d. Creative cognition and dopaminergic modulation of fronto-striatal networks: integrative review and research agenda. Neurosci. Biobehav. Rev. 78, 13–23. https://doi.org/10.1016/j. neubiorev.2017.04.007.
Mekern, V., Hommel, B., Sjoerds, Z., 2019. Computational models of creativity: a review of single-process and multi-process recent approaches to demystify creative cognition. Curr. Opin. Behav. Sci. 27, 47–54. https://doi.org/10.1016/j. cobeha.2018.09.008.
Nijstad, B.A., De Dreu, C.K.W., Rietzschel, E.F., Baas, M., 2010. The dual pathway to creativity model: creative ideation as a function of flexibility and persistence. Eur. Rev. Soc. Psychol. 21, 34–77. https://doi.org/10.1080/10463281003765323
We want to acknowledge the noun project and Maxicons, Mikicon, Gregor Cresnar, Dannister, Arle for the pictures.
Substance use disorder (SUD) – also known as drug addiction – is one of the most problematic co-occurring conditions with ADHD. Children with ADHD are twice as likely to develop SUD later in life, compared to those without ADHD [1, 2]. Identifying children and adolescents who are at risk, even before they ever use substances, can be an important step in preventing SUD. But how do you know who is at risk? Artificial Intelligence (AI) technologies are promising tools to identify risk factors and predict the likelihood of an individual developing SUD later in life.
SUD and ADHD
SUD is a mental illness that involves impulsive use of substances such as alcohol, marijuana, nicotine and opioids. Substance use often starts with experimentation in adolescence, such as cigarette smoking and drinking. Studies have found that adolescents with ADHD often start these experiments at younger ages, more quickly become heavier users and develop more severe functional impairments . Indeed, younger ages of first use of a substance is associated with a higher risk of abusing the substance later in life.
While SUD causes significant harm, and personal and societal burdens, it is actually a preventable and treatable condition. Early identification of at-risk youth is especially important because it would allow for more targeted early interventions. Studies have shown that school- and community-based prevention programs can be highly effective at reducing substance use. Treating and managing ADHD, both with medication and behavioral therapy, has also been found beneficial in terms of reducing substance use [4, 5]. Being able to screen and identify the high-risk adolescents before their first attempt and being able to further “discourage” and “prevent” such onsets of substance use would probably be the most cost-effective prevention program for SUD.
But how do you know who is high-risk and who is not, even before they start using drugs? One method is to use Artificial Intelligence (AI) technologies in combination with very large databases.
Machine Learning Prediction Models for SUD
Machine learning is a special subset of AI technologies, that can learn hidden patterns and attributes in a completely data-driven fashion. Machine learning algorithms learn from the iterative exposures to a large amount of data, and subsequently make predictions based on what the model has learned from these training examples.
In this recently published EU-funded study in the Journal of Child Psychiatry and Psychology , my colleagues and I applied machine learning models to data collected from the Swedish national registers. These registries contain family and health data of millions of people, including information on clinical diagnoses such as ADHD and SUD. For our research, we focused on children with ADHD and trained various models to predict those who would eventually have a diagnosis of SUD, and those who would not. More than 19,000 children with ADHD were used in the study. The collected information that we used to train the models included psychiatric and somatic diagnoses, family history of these disorders, socioeconomic status, and birth complications.
The machine learning algorithm produced two useful models to predict SUD in children or adolescents with ADHD. The first one makes a prediction at age 17 for future SUD diagnosis between age 18-20. This is an important period when young adults, often leaving home for the first time, are more subjective to peer influence and start their first use of substance. The second model, a longitudinal model, makes a yearly prediction at every age from age 2 to 17 for SUD diagnosis in up to 10 years in the future. We found that both models were able to make significant predictions. This means that when we tested the models on part of the dataset (that was not used to train the model), the model was more often correct to predict SUD outcome than could be expected from chance. You can interpret this as that the model had learned certain parameters in the data that predict whether a person with ADHD will develop SUD or not.
Early Detection of SUD Risk
One important discovery was that the longitudinal model was able to make significant prediction at as early as age 2 for up to 10 years into the future. The earliest age of a valid SUD diagnosis in our dataset was at age 12. Using a method called “supervised learning”, we “taught” the model to identify those children at age 2 who would be diagnosed with SUD at age 12. Such supervised training was carried out for each age for any given outlook of 1, 2, 5 or 10 years. Being able successfully predict at-risk children years before their first attempt of substance use is a really promising result. Furthermore, the yearly prediction at every age provides a monitoring system tracking the risk for SUD, either increasing or decreasing risk over years.
What have we learned from our prediction models?
It is important to stress that the predictions of these models were not perfect. The algorithms are not always correct at identifying who will develop SUD and who does not. However, they do give insight into some important predictors that are more informative than others. For example, the early detection and diagnosis of ADHD and socioeconomic status. At the population level, the early diagnosis of ADHD was associated with a lower risk of developing SUD later in life, while adverse socioeconomic status was associated with a higher risk. However, we could not identify any predictors at the individual level that contributed their increased or decreased risks. The ability to identify such specific risk for each individual would be extremely useful when targeted interventions can be most effectively applied.
Despite that the prediction accuracies were modest and not ready for real-life deployment yet, these findings clearly have many, broad implications for policy-makers, parents, teachers and clinicians. Given that more and more data will become available to train these AI models, more accurate and generalizable predictions can be made at early ages regarding the disease risk, it is imperative to develop more effective and personalized measures for prevention and risk-reduction. We will therefore continue our work to expand the prediction accuracy of our models.
Overall, we provided a proof-of-concept that machine learning AI technologies – by leveraging the large volume of data, such as those of national registers and other electronic health records – can be used to predict disease risk, such as SUD, long before the disease onset.
Dr. Yanli Zhang-James is an associate professor at SUNY Upstate Medical University Department of Psychiatry and Behavioral Sciences. She is involved in the CoCA Project.
1. Ercan, E.S., et al., Childhood attention deficit/hyperactivity disorder and alcohol dependence: a 1-year follow-up. Alcohol Alcohol, 2003. 38(4): p. 352-6.
2. Wilens, T.E., et al., Does ADHD predict substance-use disorders? A 10-year follow-up study of young adults with ADHD. J Am Acad Child Adolesc Psychiatry, 2011. 50(6): p. 543-53.
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6. Zhang-James, Y., et al., Machine-Learning prediction of comorbid substance use disorders in ADHD youth using Swedish registry data. J Child Psychol Psychiatry, 2020.
What do you think of when I say “rewards”? Perhaps you think of the points you collect every time you shop or the badges you get when playing a videogame. Well, then you’re right! A reward can be anything. A good grade, going on a trip with friends, a smile, and even that dessert you crave in the middle of the night. Rewards are any stimuli with the potential to make us seek and consume them, and if we like, we will probably want to get them again .
Actually, you crave that dessert because you ate it once, and you liked it so much that your brain learned that eating that dessert again will make you feel good. This happens because of a neurotransmitter called “dopamine” released when you eat the dessert, giving you that little rush of pleasure. Now your brain knows what you like and will want more of that.
By now, you probably have realized that rewards are present in virtually everything we do in our daily lives. That is why seeking and consuming rewards are considered to be a fundamental characteristic of human behavior. These rewards that we keep consuming guarantee that we stay alive by eating and drinking water, for example. Rewards also have a huge influence on how we experience positive emotions, motivate ourselves to perform tasks, and learn new things .
What about the relationship between rewards and mental health problems?
Although rewards are natural stimuli that make us keep doing healthy and nurturing things, it can also become a problem. Rewards are not the problem itself, but some people can have an unhealthy behavior towards rewards. That’s where mental health problems come in. Did you know that most mental health conditions have alterations in how rewards are processed in the brain? It’s so common that these so-called reward processing alterations are now considered a “transdiagnostic feature,” meaning we can find them across different mental health conditions .
Reward processing is a term to refer to all aspects related to how we approach and consume rewards. For instance, how you respond after getting a reward (responsiveness), how motivated you are to go after a reward (drive/motivation), how impulsive you are when trying to get new and intense rewards (fun-seeking/impulsivity). So, as you can see, it’s not only about getting the rewards, but many different things play a role in a simple action we do.
Let’s think of an example: You are going to a party with your best friends. You are motivated to go out with your friends because you’re always happy when you are around them [this is the drive/motivation]. Once you are at the party, you meet your friends, talk, laugh and are happy you decided to join because you’re feeling that rush of pleasure [this is the responsiveness aspect]. At some parties, things can get a bit out of control, and some people might do risky things on the spur of the moment, like binge drinking. You refuse to binge drink because you thought of the risks, and you don’t want to be in trouble later [that’s the third aspect, the fun-seeking/impulsivity].
Now, let’s think of how that party would be for people with reward processing alterations. In the case of a very high drive, they would be super motivated to hang out with friends. On the other hand, if they have low responsiveness, they wouldn’t be able to have fun at the party even though all of their friends are there and the party is super fun. Lastly, in the case of high fun-seeking/impulsivity, they wouldn’t think of the risks and consequences and engage in binge drinking anyways.
As I mentioned before, these alterations play a role in different mental health conditions. They can affect one or more aspects of reward processing, and they can be either lower or higher than average. For example, people with ADHD can show higher risk-taking, meaning that they are more susceptible to take big risks without thinking about the consequences . This impulsive behavior might be a reflection of the altered fun-seeking aspect of reward processing. Another example is the lack of interest in social interactions in people with autism spectrum disorders . This lack of interest might reflect a reduced drive/motivation to go after social rewards.
These are just some examples of what reward processing alterations might look like in the context of mental health problems. There are still a lot of open questions. As part of my PhD research, I am trying to answer some of them. For example, which came first? Are reward processing alterations causing mental health problems, or are they just mere symptoms of these conditions? If you want to learn more about this topic, stay tuned as more blog posts will come!
Dener Cardoso Melo is a PhD candidate at the University Medical Center Groningen (UMCG). He is using data from the CoCA project together with other datasets to investigate the potential causal role of reward processing alterations in different mental health conditions.
Schultz, W. (2015). Neuronal reward and decision signals: From theories to data. Physiological Reviews, 95(3), 853-951. doi:10.1152/physrev.00023.2014
Wise, R. A. (2002). Brain reward circuitry: Insights from unsensed incentives. United States: Elsevier Inc. doi:10.1016/S0896-6273(02)00965-0
Zald, D. H., & Treadway, M. T. (2017). Reward processing, neuroeconomics, and psychopathology. Annual Review of Clinical Psychology, 13(1), 471-495. doi:10.1146/annurev-clinpsy-032816-044957
Luman, M., Tripp, G., & Scheres, A. (2010). Identifying the neurobiology of altered reinforcement sensitivity in ADHD: A review and research agenda. Neuroscience and Biobehavioral Reviews, 34(5), 744-754. doi:10.1016/j.neubiorev.2009.11.021
Stavropoulos, K. K., & Carver, L. J. (2018). Oscillatory rhythm of reward: Anticipation and processing of rewards in children with and without autism. Molecular Autism, 9(1), 4. doi:10.1186/s13229-018-0189-5
The majority of individuals with ADHD have one or more comorbid disorders. Comorbidity is a technical (and admittedly, not very cheerful) word for ‘co-occuring’, meaning that multiple disorders or conditions are present at the same time. Anxiety and depression are the most prevalent conditions that co-occur with ADHD.
Researchers and clinicians want to better understand this comorbidity in ADHD. Does having ADHD increase your risk of developing other conditions? Is there a biolgical mechanism that underlies both ADHD and other conditions? Or are symptoms of ADHD actually broader than the attentional, hyperactivity and impulsivity problems defined by the DSM/ICD, and therefore also linked to other conditions? Or all of the above?
Going with the third option (which by no means excludes the alternatives), clinicians have noticed that many individuals with ADHD experience symptoms that are not specific to ADHD, but are also often seen in other psychiatric conditions. You could call these symptoms ‘mainstream’, or ‘common’ mental health problems. Some examples that are often experienced by those with ADHD are emotional instability, sleep problems, low self-esteem, distractibility and concentration problems, and mental restlesnesss or excessive mind wandering.
Understanding these comorbidities better is important, because often one condition can hide the ‘true’ underlying condition. For instance, a person with ADHD who experiences many symptoms that are also characteristic of anxiety (i.e. low self-esteem, excessive mind-wandering, sleep problems, avoiding difficult situations). In such a case, the person could receive treatment for anxiety problems, while he or she is actually needing treatment for ADHD.
To distinguish between these conditions better, we need to find out more about these common symptoms. Being distracted can have many different causes and can happen in many different situations. For instance: are you distracted due to pervasive negative thoughts, because the task you’re doing is boring, or because you’re thinking of many related things and drift off to new ideas?
To learn more about the nature of these symptoms, researchers have given mobile apps or smartwatches to participants with ADHD. Several times a day, the watch buzzes and the app prompts a question that the person has to give answer to immediately. Questions can for instance be: How are you feeling right now? Have good/bad things happend to you in the last hour? How much has this affectd you? Were you concentrating on a task or where you distracted? Where you tinking about something (un)pleasant? etc. This method called ‘experience sampling’ can give very valuable information about someone’s symptoms. When combining the information from a lot of individuals, this can also identify differences between different disorders, that were not really known before.
If you want to learn more about this topic, you can watch this webinar by professor Philip Asherson from King’s College London. He explains the common mental health symptoms of ADHD in more detail, and gives examples from his research, also using experience sampling.
This blog is based on the webinar by Philip Asherson “ADHD in the mainstream” that was created as part of the CoCA project. The CoCA project investigates comorbid conditons of ADHD: http://www.coca-project.eu.