ADHD is common, impairing, and controversial. Over the past 30 years science has had three fundamental realizations:
- ADHD and other mental disorders are related (half of children and adults with anxiety or depression had ADHD, most youth with conduct disorder had ADHD; over half of children with ADHD will go on to additional mental disorders including anxiety, depression, addiction, and psychosis). Therefore, we look at why and when ADHD leads to these serious secondary complications.
- ADHD is not just one disease, but a group of conditions with different biology and features. The problem can be observed with correlates at different “levels of analysis” such as cognitive development (memory, attention), emotion, physiology (heart rate, eye movement), brain (brain scans), and genes. Each of these is a piece of the whole story that may combine to improve prediction in different profiles. We therefore use an array of computer, physiological, brain, and gene measures to organize these profiles.
- ADHD and other mental disorders all emerge from an overlapping combination of multiple causes (accumulated small gene effects, accumulated early environmental causes). We therefore look at genes and environments together, separating which are causal and which are merely accidental. We use advanced computational methods to assemble algorithms to predict who will get ADHD, and who will recover or get worse.
The central clinical problems are that:
- ADHD is over and under-diagnosed because clinicians do not have an accurate way to tell who really has ADHD as a biological or neurological problem or who will respond to different treatments, or, among those who seem to have ADHD, who really has a condition that will get worse without care.
- Existing treatments do not solve the problem, they only temporarily mask the symptoms. Even with treatment, too many children with ADHD have poor life outcome. For this reason, we and others are actively researching better solutions. This is an exciting time with many new discoveries on the horizon.
Program areas by thematic focus
Three already-established program areas, involving five intersecting projects, organize and guide the work to provide us with the ability to answer fundamental questions and to provide a rich and deep portfolio of discovery opportunity.
To answer this we subdivide and organize the clinical problems better across time and different kinds of measurement that can be combined into new and better diagnostics. We create new clinical subtypes or profiles that are more accurate. To do this, we look at fundamentals like genes and brain structure and function. But we also look at low cost, easily-disseminated biomarkers that clinicians could use every day such as physiology (eye movements, heart rate acceleration), computerized tests of cognitive processes and of emotion processes, and simple ratings combined in new ways. We also follow children over years to learn to predict who will get better and who will get worse, and why, and when. This will help us identify the right time and way to intervene for different individuals. New computational algorithms are the goal.
Clinical phenotyping and physiological biomarkers project. This project uses detailed statistical models to better characterize the clinical phenomenon in terms of cognition, emotion, behavior, and clinical problems over time. Physiological and computer measures of attention serve as the biomarkers for future clinical use if successful. (Joel Nigg, Ph.D., Sarah Karalunas, Ph.D., Michael Mooney, Ph.D., Nathan Dieckmann, Ph.D., Xubo Song Ph.D.)
Brain development and biomarkers project. This project tracks changes in brain organization year by year in kids with ADHD to learn how brain development is related to the same clinical features and to identify subgroups with similar biology that can explain clinical features. Brain features serve as biomarkers for clinical use if successful. (Damien A. Fair, P.A.-C., Ph.D., Sarah Karalunas, Ph.D., Joel Nigg, Ph.D., Alice Graham, Ph.D.)
To understand how it all begins, we look at the early markers of future ADHD during pregnancy (we look at molecules in the mother’s blood, in the cord blood, in the placenta) and in early life (we look at biology, brain, and behavior in the infant and toddler). Here our primary hypothesis is that the many different causal influences converge on a small number of biological processes that we can learn to modify. Inflammation (exaggerated immune response) is our leading and most likely candidate. We have proven it affects early brain development and behavior.
Maternal-infant origins project. This project tracks biology and social context for families from the first or second trimester of conception on into the preschool years. We use blood biomarkers from the mom, the placenta, and the cord blood and behavioral markers in the infant to understand the development of self-control, attention, and emotion regulation and to predict future ADHD and other conditions. (Elinor Sullivan, Ph.D., Jennifer M. Loftis Ph.D., Hanna Gustafsson, Ph.D., Joel Nigg, Ph.D., Alice Graham, Ph.D.)
Genetics projects. We conduct genome wide tests to identify genetic algorithms that can predict clinical profile or clinical course, and to identify which features are related to genetic cause and which may be related to other causes. (Michael Mooney, Ph.D., Stephen Faraone, Ph.D.)
To find out, we test new treatments to prevent or cure. Here we are undertaking novel clinical trials that give hope of more dramatic change for individuals. We are not limited to new drugs—we believe some cures will involve intensive nutrition or simple anti-inflammatory treatments that are better targeted. We are particularly interested in modifiable environments. As we learn more about how mental control and mental effort work in the brain in ADHD, we can also develop new kinds of computerized training tools and more effective biofeedback tools. (Sarah Karalunas, Ph.D., Joel Nigg, Ph.D., Michael Mooney, Ph.D.)
Epigenetics project. This is a novel exploration to search for chemical signals in the genome that may map on to early environmental influences and enable us to identify causes as well as biomarkers of illness and course. It involves advanced analysis of blood, saliva, and placenta samples. (Michael Mooney, Ph.D., Jonathan Mill, Ph.D., Joel Nigg, Ph.D.)
Identical twin study. When identical twins develop different clinical profiles, we know the cause is very likely to be either a rare genetic mutation or else a modifiable environment, involving epigenetic change. We have a small cohort of identical twins and examine gene expression, epigenetics, genetic sequencing, and MRI brain imaging searching for clues. (Joel Nigg, Ph.D., Michael Mooney, Ph.D., Maximilian Muenke, M.D.)
Novel clinical trials. We already have enough information to undertake some novel clinical trials. For example, in collaboration with two other universities, We are conducting the first test in the nation to determine whether intensive high dose nutrient supplementation can help children with ADHD. We also are planning other trials. (Jeni Johnstone, Ph.D., Alice Graham, Ph.D.)
We have published over 150 papers, many of them well cited, on many aspects of ADHD and its underlying nature. Here is a sampler.
Lead Investigator: Joel Nigg, Ph.D.
Outside Co-Investigators and Consultants: Michelle Martel, Ph.D. (University of Kentucky)
Funding: NIMH R37 MH59105
Aims: The program is designed to better characterize the variety of clinical manifestations of ADHD over time and predict ADHD outcome using advanced analytical methods. At OHSU, we see ADHD as comprising several conditions currently hidden under one umbrella diagnosis, analogously to how mental retardation (now called intellectual disability) or cancer were once seen as single diseases. As we follow children into adulthood, we aim to characterize the clinical and physiological subgroups that predict varying clinical outcomes.
Aims: We aim to characterize ADHD and ADHD variation using new tools of MRI brain imaging. In particular, the past decade has seen dramatic advances in our ability to describe and visualize not just brain regions, but brain networks, with non-invasive brain imaging. These methods allow us to see the complex architecture of the developing brain, and apply this to a more nuanced understanding of how ADHD develops and how its clinical course can be predicted. We have been among the first to show characteristic alterations in a brain network called the "default mode network" (associated with introspection and daydreaming) in very young children (age 6-8 years) with ADHD, as well as to show widespread alterations in white matter connectivity in ADHD.
Lead Investigator: Michael Mooney, Ph.D.
Funding: This work is supported by NIH Grant MH086654 (PI: Joel Nigg) as well as by philanthropic gifts to OHSU's ADHD Center
Aims: This arm of the ADHD program has two principal aims. The first principal aim is to link whole genome data, which examines thousands of probes across the genome, in relation to consolidated gene sets that can enhance overall prediction of ADHD. In turn, these gene findings will then be related to MRI brain imaging findings from the Brain imaging arm of the program, to identify unique ADHD biotypes. The second principal aim is to identify unique epigenetic signatures that might serve as clinical biomarkers of ADHD. Here we receive critical consultative support from Dr. Mill. This work is done by examining marks known as DNA methylation that are observed on DNA samples. These are analyzed across the entire genome, again using thousands of probes, or else by targeted sequencing of key regions of the genome.
Funding: NIMH R01 MH117177. This work has received intramural funding from the OHSU Office of the Vice President, from the OHSU Moore Foundation, and from philanthropic funds.
Aims: The aim of this project is to better understand the role of nutrition in ADHD. Nutritional factors may work in ADHD in several ways. They may be involved in worsening symptoms of the disorder, as when children eat insufficient diet; or children with ADHD may metabolize food in ways that cause them to receive altered nutrition. Further, ADHD may interact in complex ways with over-nutrition (obesity). Third, maternal health and nutrition may alter risk for ADHD in offspring. We follow children over time and we also emphasize maternal-infant effects.
Lead investigator: Elinor Sullivan, Ph.D.
Funding: NIMH R01 MH117177
Aims: This project follows a cohort of 300 women from early in pregnancy and then identifies biomarkers that are related to offspring emotional and attentional regulation and dysregulation—hallmarks of future ADHD risk. We examine maternal health and nutrition, stress, and environmental toxicant effects in relation to offspring behavior. We have a large biobank that will be used to search for a wide range of possible biomarkers for clinical identification.
What we aim to accomplish
- Accurately predict which individuals with apparent ADHD are on a path to bad outcome and which ones are benign—thus markedly improve clinical identification.
- Identify and verify clinical subtypes that set the state for personalized medicine. Link these to brain signals using EEG and MRI.
- Identify inflammatory biomarkers as a “common pathway” for environmental risks in early life that disrupt brain development and lead to ADHD.
- Verify biotypes in the brain—different brain scan profiles that create an ADHD “look-alike” but may need different solutions.
- Predict ADHD risk from birth or shortly after birth so set the stage for early prevention trials in susceptible children.
- Complete clinical trials of a novel multi-nutrient treatment for irritable mood and inattention and begin to understand mechanisms so it can be steadily improved.
- Characterize different developmental paths of ADHD in relation to changes in the brain.
- Find biological evidence of environmental cause (that is, an epigenetic algorithm that predicts back to early exposure that triggered ADHD)
Role of inflammatory environments as causal path to ADHD
2012: Some synthetic additives in food (like dyes and preservatives) actually do influence ADHD symptoms
Nigg, J.T., Lewis, K., Edinger, T., & Falk, M. (2012). Meta-analysis of ADHD or ADHD symptoms, restriction diet, and synthetic food color additives. Journal of the American Academy of Child and Adolescent Psychiatry, 51, 86-97.e8.
2014: Omega 3 (fish oil, which is anti-inflammatory) supplementation causally helps ADHD symptoms though not enough to be a stand-alone treatment.
Hawkey, E. & Nigg, JT, (2014) Omega-3 fatty acid and ADHD: Blood level analysis and meta-analytic extension of supplementation trials. Clinical Psychology Review 34 (6): pp. 496-505 DOI information: 10.1016/j.cpr.2014.05.005
2016: Lead exposure is causally contributing to ADHD in humans.
Nigg, JT, Elmore AL, Natarajan, N, Friderici KH, & Nikolas, MA (2016). Variation in iron metabolism gene moderates the association between low-level blood lead exposure and attention-deficit/hyperactivity disorder. Psychological Science, 27, 257-269
Early Detection of ADHD and reversing it
2015: ADHD risk can be detected in infancy as a behavioral biomarker of emotional irritability.
Sullivan, E, Holten, K, Nousen, E., Nigg JT. (2015) Early identification of ADHD risk via infant temperament and emotion regulation. Journal of Child Psychology and Psychiatry. Sep;56(9):949-57.
2019: Emotional irritability in infancy, and inflammation of the mom during pregnancy, predicts ADHD at age 5 (under review).
Brain markers of ADHD and across time
2014-present: Developing and implementing increasingly effective ways to identify individual fingerprint in the brain (called a “connectotype”) that will identify biological groups in ADHD
Miranda-Dominguez O, Mills BD, Carpenter SD, Grant KA, Kroenke CD, Nigg, JT, Fair DA (2014) Connectotyping: model based fingerprinting of the functional connectome. PLoS ONE 9(11): e111048. doi:10.1371/journal.pone.0111048
Costa Dias TG, Iyer SP, Carpenter SD, Cary RP, Wilson VB, Mitchel SH, Nigg, JT, Fair DA (2015). Characterizing heterogeneity in children with and without ADHD based on reward system connectivity. Journal of Developmental Cognitive Neuroscience. Feb 11, 2015, p 155-174
2018: New evidence of brain network structure as key biomarker of ADHD
Mills BD, Miranda-Dominguez O, Mills KL, Earl E, Cordova M, Painter J, Karalunas SL, Nigg JT, Fair DA. (2018). ADHD and attentional control: Impaired segregation of task positive and task negative brain networks. Network Neuroscience. 2(2):200-217. PMID: 30215033
We are participating in a national multi-site study of 10,000 children from the general population (so we expect about 500 to have ADHD). Will use philanthropic funding to hire more analytic capacity to look at those data for new patterns we can verify across samples.
Targets for modifying the course of ADHD illness
2017: Linked improvement in working memory to improvement in ADHD—a new target for computerized brain training.
Karalunas SL, Gustafsson HC, Dieckmann NF, Tipsord J, Mitchell SH, Nigg JT (2017). Heterogeneity in development of aspects of working memory predicts longitudinal ADHD symptom change. Journal of Abnormal Psychology, 126, 774-792. PMID: 28782975
2018: Discovered that genetic influence on ADHD operates by disrupting working memory and cortical arousal. Working memory means the ability to hold something in mind and work on it mentally. It is one cognitive ability that is critical to planning, being able to organize, and to handle conflicting demands. It is impaired in ADHD and we found it is also the route of genetic activity. Cortical arousal is vital for being alert—noticing what is happening. This is fundamental to attention. Many other cognitive abilities were also tested and are not related to genetic influence on ADHD. This gives us more confidence in targets for new clinical treatments.
Nigg JT, Gustafsson HC, Karalunas SL, Ryabinin P, McWeeney S, Faraone SV, Mooney M, Fair DA, Wilmot B. (2018). Working memory and vigilance as multivariate endophenotypes related to common genetic risk for ADHD. Journal of the American Academy of Child and Adolescent Psychiatry, 57(3):175-182
2014-2019: Discovered and confirmed mathematical identification of a reliable subgroup of youth with ADHD with uncontrolled anger/irritability and related them to brain and genetic risk.
Karalunas, SL, Fair, D, Musser, ED, Aykes, K, Iyer, S., Nigg, JT. (2014). Subtyping ADHD using temperament dimensions: Toward a biologically based nosology. JAMA Psychiatry, 9, 763.
Karalunas SL, Gustafsson HC, Fair D, Musser ED, Nigg JT. (2019). Do we need an irritable subtype of ADHD? Replication and extension of a promising temperament profile approach to ADHD subtyping. Psychol Assess. 31(2):236-247. doi: 10.1037/pas0000664.
Nigg et al (under review). Role of irritable and emotional dysregulation in ADHD-genetic evidence.
- NIH: The National Institutes of Health. US federal funding is the largest source of scientific funding in the world directed at solving mental illness and the primary source of support for mental health research for us and the U.S. community.
- Food additives means dyes, preservatives, and any synthetic chemical added to food during processing.
- Inflammation/inflammatory/anti-inflammatory refers to the biology of the immune system, involving molecules such as cytokines and others that communicate throughout the body and the brain. Intended to help the body overcome health challenges, when overactive can damage brain and behavior.
- Emotional dysregulation means people who have temper tantrums, over-react, and are extremely reactive, cannot cope with minor stress, and as a result have major problems functioning. “Irritability” is a type of emotional problem that means problems controlling anger, but associated with underlying depression.
- Brain network refers to the way different parts of the brain are functionally organized into groups, analogous to the way social groups or teams are organized. Some networks in the brain handle memory, some attention, and some emotion. We map these using physical brain imaging.
- MRI: Magnetic Resonance Imaging. A method of brain imaging that uses a strong magnetic field to change the spin direction of molecules in the brain. This is used to infer blood flow (an index of neural activity) and water flow (an index of physical fiber connections) in the brain. A good method to map brain networks.
- EEG: Electroencephalograph. A method of measuring brain electrical activity using electrodes on the scalp, it allows us to measure second by second changes in brain reaction to something, even outside of mental awareness. A good method to measure the overall degree of functionality of the brain, especially the cortex, or higher brain levels that handle thinking and regulate emotion.
- Advanced analytics: This term and terms like it—machine learning, advanced mathematics, artificial intelligence, computational models—refer to very complex (“nonlinear”) models that identify patterns in the data not available through routine analysis. Similar to methods used by google or Facebook to identify patterns in their database.
- Epigenetic refers to chemical marks on the DNA that control gene expression and can be changed by either other genes or by experiences.