This year will celebrate 10 years of the UK Adult ADHD Network. During that time we have seen a rapid advance in our understanding of ADHD across the lifespan, the availability of diagnostic services and access to effective treatments. Advances seen in the UK are also seen in many other countries across the EU and worldwide. The meeting will highlight key advances in our understanding of course and outcome; genetic, environmental, and neuroscience of ADHD; and topics relevant to the diagnosis and treatment of ADHD from adolescence to early and late adulthood.
Aims of the Conference
This meeting aims to raise the level of knowledge and expertise among health care professionals about adults with ADHD and provide a better understanding of the persistence of the disorder, the development of comorbid mental health problems and the delivery of effective treatments. The program will be delivered by prominent opinion leaders, clinical experts and internationally recognised investigators.
The selection of speakers is important so that the audience can hear directly from the most experienced professionals working in this rapidly developing area of clinical psychiatry.
Speakers will include : David Nutt, Eric tayor, Anita Thapar, Alexandra Philipsen, Ian Wong, Samuele Cortese, Philip Shaw, Jessica Agnew-Blais and Pravina Rudra.
Welcome Reception – Art with Heart
There will be a welcome reception hosted by UKAAN on the evening of Thursday 12th September. This will be preceded by a Performance of ‘Declaration’ by Art with Heart. Developed in consultation with medical professionals, ADHD and mental health support groups, ‘Declaration’ examines when we want, need or are forced to declare our differences, and the faces we wear to fit in. Numbers are limited, so early booking is advised!
Professor Stephen Faraone – professor in Psychiatry at SUNY Upstate University and expert on ADHD – was interviewed by dr. Therese Markow for the podcast series ‘Critically Speaking’. In this podcast they discuss myths about ADHD and the scientific evidence that debunks these myths. Stephen Faraone explains why it is so important to diagnose and treat ADHD early. He also explains why ADHD is often undiagnosed in girls, and why sometimes adults are diagnosed with ADHD who have not sought treatment earlier in their life.
Critically Speaking is a podcasts series hosted by dr. Therese Markow who interviews experts to discuss in plain language complex issues that concern our health, society and planet.
Have you ever thought that ADHD and autism could perhaps be the same disorder? – Or have you thought that they are way too different, two different planets in the psychiatric universe? Researchers do not agree on this. We know that both ADHD and autism are neurodevelopmental conditions with onset in childhood and that they share some common genetic factors, however, they appear with quite different phenotypical characteristics. We also know that people with ADHD or autism have an increased risk of getting other psychiatric disorders, so-called comorbidities, and smaller studies have shown that individuals with ADHD or autism get different psychiatric disorders, and at a different degree.
How can we utilize this knowledge about different psychiatric comorbidities between ADHD and autism? How can we get closer to an answer to this question; are ADHD and autism similar or different conditions? By using large datasets; unique population-based registries in Norway, we wanted to compare the pattern of psychiatric comorbidities in adults diagnosed with ADHD, autism or both disorders. In addition, we wanted to compare the pattern of genetic correlations between ADHD and autism for the same psychiatric traits, and for this, we exploited summary statistics from relevant genome-wide association studies.
In the registries, we identified 39,000 adults with ADHD, 7,500 adults with autism and 1,500 with both ADHD and autism. We compared these three groups with the remaining population of 1.6 million Norwegian adult inhabitants without either ADHD or autism. The psychiatric disorders we studied were anxiety, bipolar, depression, personality disorder, schizophrenia spectrum (schizophrenia) and substance use disorders (SUD).
Interestingly, we found different patterns of psychiatric comorbidities between ADHD and autism, overall and when stratified by sex (Fig.1). These patterns were also reflected in the genetic correlations, however, only two of the six traits showed a significant difference between ADHD and autism (Fig.2).
Figure 2A: The pattern of prevalence ratios of psychiatric comorbidity in adults with ADHD or autism observed in this study (ADHD; n=38,636, autism; n=7,528). Psychiatric conditions are highly prevalent in both ADHD and ASD, with schizophrenia being most prevalent in ASD and antisocial personality disorders in ADHD. Figure from Solberg et al. 2019, CC-BY-NC-ND.
Figure 2B: Genetic correlations (rg) calculated from genome wide association studies. Genetic correlations are also high with both disorders, with especially high correlations between ADHD and alcohol dependence, smoking behavior and anti-social behavoiur. Major depressive disorder has high genetic correlations with both ADHD and autism. Figure from Solberg et al. 2019, CC-BY-NC-ND.
Figure 2. Left: The pattern of prevalence ratios of psychiatric comorbidity in adults with ADHD or autism observed in this study (ADHD; n=38,636, autism; n=7,528). Right: genetic correlations (rg) calculated from genome wide association studies. Psychiatric conditions are highly prevalent in both ADHD and ASD, with schizophrenia being most prevalent in ASD and antisocial personality disorders in ADHD. Genetic correlations are also high with both disorders, with especially high correlations between ADHD and alcohol dependence, smoking behavior and anti-social behavoiur. Major depressive disorder has high genetic correlations with both ADHD and autism. Figure from Solberg et al. 2019, CC-BY-NC-ND.
The most marked differences were found for schizophrenia and SUD. Schizophrenia was more common in adults with autism, and SUD more common in adults with ADHD. Associations with anxiety, bipolar and personality disorders were strongest in adults with both ADHD and autism, indicating that this group of adults suffers from more severe impairments than those with ADHD or autism only. The sex differences in risk of psychiatric comorbidities were also different among adults with ADHD and ASD.
In conclusion, our study provides robust and representative estimates of differences in psychiatric comorbidities between adults diagnosed with ADHD, autism or both ADHD and autism. With the results from analyses of genetic correlations, this finding contributes to our understanding of these disorders as being distinct neurodevelopmental disorders with partly shared common genetic factors.
Clinicians should be aware of the overall high level of comorbidity in adults with ADHD, autism or both ADHD and autism, and the distinct patterns of psychiatric comorbidities to detect these conditions and offer early treatment. It is also important to take into account the observed sex differences. The distinct comorbidity patterns may further provide information to etiologic research on biological mechanisms underlying the pathophysiology of these neurodevelopmental disorders.
This study was done at Stiftelsen Kristian Gerhard Jebsen Centre for Neuropsychiatric disorders, University of Bergen, Norway, and published OnlineOpen in Biological Psychiatry, April 2019, with the title:
Berit Skretting Solberg is a PhD-candidate at the Department of Biomedicine/Department of Global Health and Primary Care, University of Bergen, Norway. She is also a child- and adolescent psychiatrist/adult psychiatrist. She is affiliated with the CoCa-project, studying psychiatric comorbidities in adults with ADHD or autism, using unique population-based registries in Norway.
Is there an ‘average ADHD brain’? Our research group (from the Radboudumc in Nijmegen) shows that the average patient with ADHD does not exist biologically. These findings were recently published in the journal. Psychological Medicine.
Most biological psychiatry research heavily relies on so-called case-control comparisons. In this approach a group of patients with for instance ADHD is compared against a group of healthy individuals on a number of biological variables. If significant group effects are observed those are related to for instance the diagnosis ADHD. This often results in statements such as individuals with ADHD show differences in certain brain structures. While our results are in line with those earlier detected group effects, we clearly show that a simple comparison of these effects disguises individual differences between patients with the same mental disorder.
Modelling individual brains
In order to show this, we developed a technique called ‘normative modelling’ which allows us to map the brain of each individual patient against typical development. In this way we can see that individual differences in brain structure across individuals with ADHD are far greater than previously anticipated. In future, we hope that this approach provides important insights and sound evidence for an individualized approach to mental healthcare for ADHD and other mental disorders.
Individual differences in ADHD
When we studied the brain scans of individual patients, the differences between those were substantial. Only a few identical abnormalities in the brain occurred in more than two percent of patients. Marquand: “The brains of individuals with ADHD deviate so much from the average that the average has little to say about what might be occurring in the brain of an individual.”
Personalized diagnosis of ADHD
The research shows that almost every patient with ADHD has her or his own biological profile. The current method of making a diagnosis of psychiatric disorders based on symptoms is therefore not sufficient, the authors say: “Variation between patients is reflected in the brain, but despite this enormous variation all these people get the same diagnosis. Thus, we cannot achieve a better understanding of the biology behind ADHD by studying the average patient. We need to understand for each individual what the causes of a disorder may be. Insights based on research at group level say little about the individual patient.”
Re-conceptualize mental disorders
The researchers want to make a fingerprint of individual brains on the basis of differences in relation to the healthy range. Wolfers: “Psychiatrists and psychologists know very well that each patient is an individual with her or his own tale, history and biology. Nevertheless, we use diagnostic models that largely ignore these differences. Here, we raise this issue by showing that the average patient has limited informative value and by including biological, symptomatic and demographic information into our models. In future we hope that this kinds of models will help us to re-conceptualize mental disorders such as ADHD.”
Wolfers, T., Beckmann, C.F., Hoogman, M., Buitelaar, J.K., Franke, B., Marquand, A.F. (2019). Individual differences v. the average patient: mapping the heterogeneity in ADHD using normative models. Psychological Medicine, https://doi.org/10.1017/S0033291719000084 .
This blog was written by Thomas Wolfers and Andre Marquand from the Radboudumc and Donders Institute for Brain, Cognition and Behaviour in Nijmegen, The Netherlands. On 15 March 2019 Thomas Wolfers will defend his doctoral thesis entitled ‘Towards precision medicine in psychiatry’ at the Radboud university in Nijmegen. You can find his thesis at http://www.thomaswolfers.com
Our genes are very important for the development of mental disorders – including ADHD, where genetic factors capture up to 75% of the risk. Until now, the search for these genes had yet to deliver clear results. In the 1990s, many of us were searching for genes that increased the risk for ADHD because we know from twin studies that ADHD had a robust genetic component. Because I realized that solving this problem required many DNA samples from people with and without ADHD, I created the ADHD Molecular Genetics Network, funded by the US NIMH. We later joined forces with the Psychiatric Genomics Consortium (PTC) and the Danish iPSYCH group, which had access to many samples.
The result is a study of over 20,000 people with ADHD and 35,000 who do not suffer from it – finding twelve locations (loci) where people with a particular genetic variant have an increased risk of ADHD compared to those who do not have the variant. The results of the study have just been published in the scientific journal Nature Genetics, https://www.nature.com/articles/s41588-018-0269-7.
These genetic discoveries provide new insights into the biology behind developing ADHD. For example, some of the genes have significance for how brain cells communicate with each other, while others are important for cognitive functions such as language and learning.
We study used genomewide association study (GWAS) methodology because it allowed us to discover genetic loci anywhere on the genome. The method assays DNA variants throughout the genome and determines which variants are more common among ADHD vs. control participants. It also allowed for the discovery of loci having very small effects. That feature was essential because prior work suggested that, except for very rare cases, ADHD risk loci would individually have small effects.
The main findings are:
A) we found 12 loci on the genome that we can be certain harbor DNA risk variants for ADHD. None of these loci were traditional ‘candidate genes’ for ADHD, i.e., genes involved in regulating neurotransmission systems that are affected by ADHD medications. Instead, these genes seem to be involved in the development of brain circuits.
B) we found a significant polygenic etiology in our data, which means that there must be many loci (perhaps thousands) having variants that increase risk for ADHD. We will need to collect a much larger sample to find out which specific loci are involved;
We also compared the new results with those from a genetic study of continuous measures of ADHD symptoms in the general population. We found that the same genetic variants that give rise to an ADHD diagnosis also affect inattention and impulsivity in the general population. This supports prior clinical research suggesting that, like hypertension and hypercholesteremia, ADHD is a continuous trait in the population. These genetic data now show that the genetic susceptibility to ADHD is also a quantitative trait comprised of many, perhaps thousands, of DNA variants
The study also examined the genetic overlap with other disorders and traits in analyses that ask the questions: Do genetic risk variants for ADHD increase or decrease the likelihood a person will express other traits and disorders. These analyses found a strong negative genetic correlation between ADHD and education. This tell us that many of the genetic variants which increase the risk for ADHD also make it more likely that persons will perform poorly in educational settings. The study also found a positive correlation between ADHD and obesity, increased BMI and type-2 diabetes, which is to say that variants that increase the risk of ADHD also increase the risk of overweight and type-2 diabetes in the population.
This work has laid the foundation for future work that will clarify how genetic risks combine with environmental risks to cause ADHD. When the pieces of that puzzle come together, researchers will be able to improve the diagnosis and treatment of ADHD.
Stephen Faraone is distinguished Professor of Psychiatry and of Neuroscience and Physiology at SUNY Upstate Medical University and is working on the H2020-funded project CoCA.
A major international collaboration headed by researchers from the Danish iPSYCH project, the Broad Institute of Harvard and MIT, Massachusetts General Hospital, SUNY Upstate Medical University, and the Psychiatric Genomics Consortium has for the first time identified genetic variants which increase the risk of ADHD. The new findings provide a completely new insight into the biology behind ADHD.
Risk variants for ADHD
Our genes are very important for the development of ADHD, where genetic factors capture up to 75% of the risk. Until now, the search for locations in the genome with genetic variation that is involved in ADHD has not delivered clear results. A large genetic study performed by researchers from the Psychiatric Genomics Consortium have compared genetic variation across the entire genome for over 20,000 people with ADHD and 35,000 who do not suffer from it – finding twelve locations where people with a particular genetic variant have an increased risk of ADHD compared to those who do not have the variant.
The special about the new study is the large amount of data. The search for genetic risk variants for ADHD has spanned decades but without obtaining robust results. This time the study really expanded the number of study subjects substantially, increasing the power to obtain conclusive results.
The results of the study have just been published in the scientific journal Nature Genetics.
The new genetic discoveries provide new insights into the biology behind developing ADHD. For example, some of the genes have significance for how brain cells communicate with each other, while others are important for cognitive functions such as language and learning. Overall, the results show that the risk variants typically regulate how much a gene is expressed, and that the genes affected are primarily expressed in the brain.
The same genes affect impulsivity in healthy people In the study, the researchers have also compared the new results with those from a genetic study of continuous measures of ADHD behaviours in the general population. The researchers discovered that the same genetic variants that give rise to an ADHD diagnosis also affect inattention and impulsivity in the general population. This result tells us, that the risk variants are widespread in the population. The more risk variants a person has, the greater the tendency to have ADHD-like characteristics will be as well as the risk of developing ADHD.
The study also evaluated the genetic overlap with other diseases and traits, and a strong negative genetic correlation between ADHD and education was identified. This means that on average genetic variants which increase the risk of ADHD also influence performance in the education system negatively for people in the general population who carry these variants without having ADHD.
Conversely, the study found a positive correlation between ADHD and obesity, increased BMI and type-2 diabetes, which is to say that variants that increase the risk of ADHD also increase the risk of overweight and type-2 diabetes in the population.
The new findings mean that the scientists now – after many years of research – finally have robust genetic findings that can inform about the underlying biology and what role genetics plays in the diseases and traits that are often cooccurring with ADHD. In addition, the study is an important foundation for further research into ADHD. Studies can now be targeted, to focus on the genes and biological mechanisms identified in the new study in order to achieve a deeper understanding of how the genetic risk variants affect the development of ADHD with the aim of ultimately providing better help for people with ADHD.
Most people consider the tube maps of big cities e.g. London’s tube map complex and irritating.
On the other hand, people immediately spot patterns in most tube maps. Most often, you recognize a limited number of hubs e.g. the central station. If the central station is left out, traveling gets much harder. A tube map is a compromise between a completely random net between nodes (or call it stations) and a simple, extremely hierarchical one.
Mathematicians call this a small network and developed a whole theory for better characterization of the lines and nodes (vertices and edges, in the mathematicians’ slang).
So, what has this to do with ADHD? If brain connectivity plays a role for attention and better organization of your behavior, maybe the “tube map” of an ADHD patient is not efficiently organized. This could be analyzed by measuring the brain’s activity in several regions over time and then correlating these timecourses to look which regions are tightly linked (form connections) and which regions are not (no “subway connection available between these regions”). Than one could use this theory for characterizing the brain network. That’s exactly what Robert Cary and colleagues report in the Journal Cerebral Cortex (2016;1–10; doi: 10.1093/cercor/bhw209).
They investigated 22 patients with ADHD with and without their medication and compared it to the network pattern of 31 controls.
For this, they calculated for each point in the resting-state data of the brain (so-called “voxel”) a measure they termed “node dissociation index” (NDI). The basic idea is that for a part of the brain nodes, we can define a “module”. A module for a given node is its number of links to different nodes in relation to the sum of all links (a bit more complicated, but that’s the basic idea). The Modularity in a region is the sum of the modularity of all nodes in this region. The node dissociation index is the sum of all modules in relation to the number of connections (in the specific node i and across all nodes).
When we look at the nodes number 1,5 and six, we see two highly interconnected networks in green and blue. These form two different “modules”. While node 1 has three connections to 2,3, and 4, it does not connect to the blue module, therefore its NDI is zero. Node number six is highly connected within the blue module but not to the green module, its NDI is zero. Node number 5 has four connections and one connection to the green module, therefore its NDI is 0.25.
In the analysis by Cary et al. the NDI (summed for specific networks like “visual”, “default mode” or “salience” ) takes values between 0.1 (visual) or almost 0.7 (salience). These measures give us a clue how tightly these networks are linked to nodes within their own communities (low dissociation indices) or whether they “dissolve” their connections and have connections to nodes which are not grouped into their own node community.
What was the effect when patients were scanned after having had a short medication wash-out? And how does this compare to the healthy controls?
The controls had lower values of the dissociation index than the patients. In patients, the visual system was not affected (very plausible!), but a variety of networks showed a decrease of the dissociation index when patients were on medication. The largest differences in networks were found in the visual attention network, the salience network and the fronto-parietal network. These networks are involved in higher order cognitive functioning and mediate psychological functions which are implicated in ADHD. The interesting take home message is, that by giving stimulant medications to patients a confuse and badly organized “tube map” (or brain network) gets a more concise structure. Graph theory offers an interesting perspective on brain networks. Future work might look in detail at how clinical phenomena are connected to brain networks or how specific comorbidities (e.g. additional addiction disorders) influence brain networks.