Study Finds Traumatic Brain Injuries, Even Mild Ones, Increase Risk of ADHD

Joel L. Young, MD

The brain is a plastic organ that changes and reacts to its environment. In recent years, researchers have become increasingly interested in how brain injuries can affect development well into adulthood. More than 300,000 children are treated for traumatic brain injuries (TBI) each year. Two new studies point to a link between childhood TBI and later development of ADHD.

TBI and ADHD: Untangling the Connection
A cohort study that followed 187 children was published in the journal JAMA Pediatrics earlier this year. Each child had been hospitalized overnight for a traumatic brain injury between the ages of 3 and 7 years old. Researchers followed up with participants for several years following the study, and parents completed questionnaires about their children’s behavior and development at regular intervals.

The results suggest that the effects of TBI may extend well beyond the period immediately following the injury. Even as late as 6 years following the injury, children with a TBI history were more likely to have ADHD. Overall, 62% of children who sustained a TBI developed ADHD, compared to 15% of the non-TBI cohort.

New research published within the past few months arrives at similar conclusions. That study, published in the journal Biological Psychiatry, compared symptoms of ADHD in 418 children with a history of TBI to symptoms in 3,193 children with no prior TBI. They also assigned a genetic risk score to each child. They found that a higher genetic risk score correlated with higher risk of ADHD only in children with no history of TBI. This suggests that genetics may only play a role in ADHD risk in children with no TBI history.

Brain Changes Related to ADHD
Most children who experienced a severe TBI who later developed ADHD began showing symptoms of the disorder within 18 months. However, children with a mild or moderate TBI developed symptoms as late as more than six years following the initial injury.

Participants’ last follow-up visit coincided with many children’s entry into middle school. This is a time when many children must rely more on executive functioning skills. So it’s unclear whether this later development of ADHD coincides with continued brain changes, or is due to increased demands on a child’s brain.

While this research strongly suggests that a TBI may change brain regions associated with ADHD, it did not identify or test a causal connection between ADHD and TBI. More research is needed to fully flesh out this connection. However, other recent research strengthens the connection between ADHD and TBI, and suggests why one might lead to the other.

Does TBI Cause a Distinct Form of ADHD?
Stojanovski and colleagues’ 2018 study published in the journal Biological Psychiatry followed 3,611 youth, 418 of whom who had sustained a traumatic brain injury. Researchers also calculated each participant’s genetic risk score based on known genetic risk factors for ADHD.

Predictably, an increased risk of ADHD was found among participants who had a higher genetic risk score. What was surprising, however, is that there was no increased risk of ADHD among TBI survivors who had a higher genetic risk score. This suggests that ADHD following TBI develops differently, not due to genetic risk.

Brain imaging scans were conducted to look at brain structures associated with ADHD. Researchers found that brain volumes in structures, such as the basal ganglia, linked to ADHD was similar between the two groups. But an analysis that looked at connections between the two brain hemispheres found distinct differences in those with TBI-related ADHD and those with genetic ADHD.

This points to different neural underpinnings for the different manifestations of the disorder. TBI-related ADHD may even be a different disorder altogether.

TBI: A Permanent Injury?
For decades, most researchers thought that, should a person survive and thrive immediately following a TBI. We now know that ADHD is just one potential complication of TBI.

A 2017 study followed 285 patients who sustained TBIs. Researchers then followed a subset of 110 people who showed lingering concussion symptoms for three months or less. Only 27% fully recovered from their symptoms. Of those who did, 67% did so within the first year.

This suggests not only that symptoms following a TBI are common, but that the longer they last, the more likely the symptoms are to be permanent. The authors of that study emphasize the need for further research to better understand and interpret their findings.

The data is compelling, suggesting a clear relationship between brain damage and ADHD. We don’t yet know what this means, whether there may be interventions that can prevent ADHD, or to what extent TBI-related ADHD differs from traditional ADHD.

Clinicians must be mindful of the potential impacts of brain injuries, and should urge patients to seek prompt treatment for any new symptoms following a head injury.

References:
Bowser, A. D. (2018, April 16). Children, adolescents with TBI at risk of secondary ADHD. Retrieved from https://www.mdedge.com/pediatricnews/article/161152/mental-health/children-adolescents-tbi-risk-secondary-adhd

Long-term consequences of TBI. (n.d.). Retrieved from http://ohiovalley.org/informationeducation/long-termconsequences/

Stojanovski, S., Felsky, D., Viviano, J. D., Shahab, S., Bangali, R., Burton, C. L., . . . Wheeler, A. L. (2018). Polygenic risk and neural substrates of attention-deficit/hyperactivity disorder symptoms in youths with a history of mild traumatic brain injury. Biological Psychiatry. doi:10.1016/j.biopsych.2018.06.024

Study identifies distinct origin of ADHD in children with history of brain injury. (2018, August 14). Retrieved from https://www.sciencedaily.com/releases/2018/08/180814101302.htm

Traumatic brain injury & concussion. (2017, April 27). Retrieved from https://www.cdc.gov/traumaticbraininjury/get_the_facts.html
Hiploylee, C., Dufort, P. A., Davis, H. S., Wennberg, R. A., Tartaglia, M. C., Mikulis, D., . . . Tator, C. H. (2017). Longitudinal study of postconcussion syndrome: Not everyone recovers. Journal of Neurotrauma, 34(8), 1511-1523. doi:10.1089/neu.2016.4677

The Earlier, the Better: Diagnosing and Treating ADHD in Preschoolers

Vera Joffe, Ph.D. ABPP
www.verajoffe.com

The presence of mental health disorders in preschool children, such as anxiety, depression, bipolar disorder and ADHD has been documented more frequently in the past 10 years (Luby, 2017). However, despite recent evidence that early detection of mental health disorders may help in decreasing the severity and even the development of such conditions, child psychiatrists may not have an opportunity to screen young children for mental health disorders as parents usually do not use the service of these specialists when their children are very young.

Although pediatricians usually screen older children for symptoms of ADHD, parents usually report that pediatricians rarely assess, treat, and refer preschool children for symptoms of mental health disorders. There is evidence that the prevalence of preschool children with ADHD is 2 to 8% (Egger & Angold, 2006).

Longitudinal studies have indicated that when children are not diagnosed and correctly treated with ADHD, they may develop more impairments and comorbid disorders in adolescence and adulthood (Barkley, 2015). Pediatricians are the first professionals who are able to diagnose children with ADHD and comorbid conditions in early childhood.

Why is it important to diagnose children who are showing significant impairments and symptoms of ADHD at such an early age? Because early diagnosis and intervention may lead to more effective, successful and hopefully, shorter treatment. In addition, the brain’s ability to change in response to experiences is much higher in early childhood (Center on the Developing Child, Harvard University).

1. Preschoolers need to be fully assessed for many areas of functioning, such as emotional, social, cognitive, speech and language, and behavior. It is also important to conduct a behavioral assessment to learn about the environment the child lives in, and to develop a contingency program for a preschooler along with parent education/treatment.
2. Helping parents understand ADHD as an impairing condition and guiding them in developing behavioral strategies and contingency plans that actually may work well for children with symptoms of ADHD may prevent the development of more serious symptoms and impairments even before children enter formal education.
3. It is important to conduct a full assessment with a detailed developmental history, family history, and by using multi-informants and multi-methods to assess ADHD and comorbid conditions in preschool age children. It is recommended to obtain information through questionnaires, such as the Conners parent and teacher rating scales (Conners,2001), or the Child Behavior Checklist (Achenbach & Edelbrock, 1991). It is also important to conduct clinical observations of preschool children at school and in other settings (Luby, J.L., 2017).
4. Empirically-based treatments for ADHD in young children include behavioral and parenting treatment, such as Parent Child Interaction Therapy (Eyberg & Funderburk,
2011), Behavior parent training adapted to preschool population (BPT), and Community Parent Education (COPE).
5. There has been some research to study the contribution of medication in addition to parent-child and parenting interventions (and education), such as the Preschool ADHD Treatment Study (PATS), and the long-term PATS follow-up study. The PATS focused on the effect of one type of medication only (MPH). More recent studies have been completed for this age population. Most studies strongly suggest that one should consider behavioral treatments for preschoolers with ADHD as well as the protocols discussed above before including medication for preschoolers due to the strong side effects of medications for this age population.
6. “The apple does not fall far from the tree”: ADHD is highly genetic, and for this reason, it is important for pediatricians and others working with families with children with ADHD to assess whether parents also show symptoms of ADHD in order to help parents with the same diagnosis so that they can follow through with treatment recommendations for their own children.
7. Thus, it is necessary to provide education to the public in general and to other health care providers (especially pediatricians) about the advantages of diagnosing and treating children with ADHD early in life to help prevent the development of more severe and impairing comorbid conditions.

References:
Achenbach, T.M. & Edelbrock, C.S. (1991). Manual for the Child Behavior Checklist and Revised Child Behavior Profile. Burlington, VT: University Associates in Psychiatry.

Barkley, R. A. (2015). Attention-deficit hyperactivity disorder: A handbook for diagnosis and treatment (4th Edition). New York: Guilford Press.

Center for the Developing Child at Harvard University. https://developingchild.harvard.edu/

Conners, C.K. (2001). Conners’ Rating Scales- Revised: Instruments for use with children and adolescents. North Towanda, NY: Multi-Health Systems.

Egger, H.L. & Angold, A. (2006). Common behavioral and emotional disorders in preschool children: Presentation, nosology, and epidemiology. Journal of Child Psychology and Psychiatry, 47, 313-337.

Eyberg, S.M. & Funderburk, B. (2011). Parent-child interaction therapy protocol. Gainesville, FL: PCIT International.

Luby, J., editor (2017). Handbook of Preschool Mental Health: Development, Disorders, and Treatment. Second edition, New York: The Guilford Press.

The Central Mystery of ADHD

Thomas E. Brown, Ph.D. Keck School of Medicine of University of Southern California

Despite the many differences among children and adults with ADHD, there is one similarity shared by virtually all of them. Although they have considerable chronic difficulty in getting organized and getting started on many tasks, in focusing their attention, sustaining their efforts, and utilizing their short-term working memory, all of those diagnosed with ADHD tend to have at least a few specific activities or tasks for which they have no difficulty in exercising these very same functions quite well.

Many children with ADHD who struggle painfully to focus on their schoolwork and daily chores are able effortlessly to focus very well for playing a favorite sport or video games. Many college students with ADHD earn top grades in one or two courses for which they have strong interest due to the content or the skills and charisma of the professor, yet they fail out of college because they are unable to sustain their attention and effort for many other courses required for their curriculum. Many adults with ADHD are not promoted at work or repeatedly lose their jobs not because they do not do many aspects of their job quite well or very skillfully, but because they are consistently unable to awaken themselves to get to work on time or because they are excessively forgetful about attending to important assignments or fail to hand in required reports accurately done before established deadlines.
Many of all ages with ADHD demonstrate amazing ability to recall all the details of the storyline of a movie seen years earlier, or words and music of countless songs they once heard, or random details of long ago incidents they observed, yet they are often incapable of recalling what they have read or have heard just a few minutes ago. All those with ADHD tend to have a few tasks or situations where they demonstrate impressive or, at least, quite adequate competence in exercising various cognitive management skills that they are unable to exercise with consistency in most other activities of daily life, even though they see the importance of doing those tasks and very much want to perform them successfully.
Symptoms of ADHD are chronic, but in each person they appear with notable exceptions, usually in situations where the person has strong personal interest in that particular task or activity, or when they believe that something very unpleasant for them is likely to occur very quickly if they do not attend to this specific activity right here, right now. Clinical observations and empirical research have consistently demonstrated that ADHD symptoms are situationally variable, that there is much intra-individual variability in the symptoms of this disorder. This is the central mystery of ADHD.

A classic example of this puzzling paradox of ADHD is Larry, a sturdy, sandy-haired high school junior who was the goalie for his school’s ice hockey team. It happened that the day before his evaluation, Larry had helped his team win the state championship in hockey by blocking many shots on goal. He was an extraordinarily fine goalie and he was also a very bright student who scored in the very superior range on IQ tests. He wanted to get good grades because he was hoping eventually to go to medical school. Yet he was chronically in trouble with his teachers. Often they said to him, “Once in a while you make very perceptive comments in class that show how smart you are, but most of the time you’re out to lunch—looking out the window or staring at the ceiling. Occasionally you turn in a really good homework paper, but most of the time you don’t even know what the homework is supposed to be.” The teachers kept asking Larry, “If you can pay attention so well when you’re playing hockey, why can’t you pay attention when you are in class? If you can work so hard to practice and stay in shape for hockey, why can’t you show some consistent effort for your schoolwork?”

After hearing his parents tell me about these recurrent complaints from his teachers, Larry quietly responded, “I don’t know why this keeps happening. I’m just as frustrated and even more worried about this than you are…I know what I need to do and I really want to do it because I know how important it is for all the rest of my life…I know I should be able to do it; I just can’t! I just can’t make myself pay steady attention to my work for school anywhere near the way I pay attention for hockey.”

This inconsistency in motivation and performance is the most puzzling aspect of ADHD. It appears that the child or adult with ADHD who can show strong motivation and focus very well for some tasks should be able to do the same for most other tasks that they recognize as important. It appears that this is a simple problem of lacking “willpower.” If you can do it for this, why can’t you do the same for that and that which are even more important? Yet ADHD is not a matter of “willpower.” It is problem with the dynamics of the chemistry of the brain.
One of my patients once told me, “I’ve got a sexual example for you to show what it’s like to have ADHD. It’s like having erectile dysfunction of the mind. If the task you are faced with is something that turns you on, something that is really interesting for you, you’re “up for it” and you can perform. But if the task is not something that’s intrinsically interesting to you, if it doesn’t turn you on, you can’t get up for it and you can’t perform. It doesn’t matter how much you tell yourself, “I need to, I ought to. It’s just not a willpower kind of thing! (Brown, 2005)”

Recent research offers considerable evidence that ADHD is not a “willpower thing,” even though, in many ways, it appears to be a simple lack of willpower. The missing piece for most people trying to understand this is the fact that when a person is faced with a task that is really interesting to him or her, not because someone told them that it ought to be interesting, but just because it is interesting—either because to them at that moment it appears to offer appealing pleasure or seems to warn of some imminent unpleasantness that they want to avoid, that perception, conscious or unconscious, changes the chemistry of the brain instantly. But this process is not under voluntary control.
___________________________________________________________________
Brown, T.E. Outside the Box: Rethinking ADD/ADHD in Children and Adults—A Practical Guide
American Psychiatric Publishing, 2017.

Side Effects of Psychosocial Treatments

J. Russell Ramsay, Ph.D.
Associate Professor of Clinical Psychology
University of Pennsylvania, Perelman School of Medicine

(A blog from the Psychosocial Sub-Committee of APSARD)
Medications approved for the treatment of ADHD are required to list documented side effects. Some of these may be relatively mild and well-tolerated and are far outweighed by the benefits of the medication for symptom reduction and overall well-being. On the other hand, for some individuals these side effects may be significant or, in some cases, intolerable and require discontinuation of a medication.
The prospect of side effects in some common non-medical, psychosocial treatments for teen and adult ADHD, such as family behavior management training, cognitive-behavioral therapy, ADHD coaching, and mindfulness-based treatments does not immediately jump to mind as an issue when recommending them. However, a special edition of The ADHD Report was devoted to this very topic. The link below provides access to the website for this issue and the articles, as of the date of submission of this blog, are listed as open access.

https://guilfordjournals.com/toc/adhd/26/2

Machine Learning Predictive Models Will Not Replace Clinical Judgment Anytime Soon

Beth Krone, Ph.D.
Icahn School of Medicine at Mount Sinai

In the spirit of full disclosure, I am a technophile. My age-cohort was the first to have desktop computers as children. I first learned to program in binary. After a decade as an end-user, I still have the muscle memory of a programmer. The concept behind Machine Learning predictive models in mental health diagnostics – the idea that we can train computers to be ‘smart’ enough to recognize patterns in data and ‘learn’ to classify and predict outcomes from reading the data without any prior information or rule specification – does not intimidate me. I welcome our computer overlords. So far, though, computers have not been out-performing clinicians in separating ADHD from typically developing youth using brain-based biomarkers.

The ADHD 200 global competition freely gave a moderately large fMRI dataset to researchers and statisticians, who responded to the call and flexed their creativity in developing algorithms and models to distinguish between the dataset’s ADHD patients and healthy controls. Several teams have published on the data, using pieces of the dataset to test their theories and in search of the elusive definitive confirmatory biomarker of disease state that, so far, seems not to exist. In 2012, for example, Sato and his team created a classification model using brain region homogeneity as a measure of volume, Fractional amplitude of low frequency fluctuations (fALFF) as a measure of spontaneous brain activity at rest, and network maps of the default mode (positive values) and task-positive networks (negative values). The model returned a median predictive accuracy of 54% for discriminating ADHD from controls, providing no additive clinical value to the diagnostic process at that time.

Recently, Sen and his team (2018) published a general prediction model using the ADHD 200, then tested in the ABIDE dataset, which is also freely available. From MRI data, their team generated 3 dimensional representations of brain volumes, or ‘texture’, that discriminated between ADHD and typical development with 63% accuracy. Adding to the dataset information about 45 independent intrinsic connectivity networks (ICNs) derived from the resting state fMRI data (networks thought to underlie functions such as mind-wandering, and planning), raised the predictive accuracy of their model to 67% in the ADHD 200 dataset, and retested with 64% accuracy in the ABIDE dataset. An accuracy rate in the mid to high 60’s is still far below the expected performance of a well-trained human diagnostician, but not much different than the overall predictive validity of the Continuous Performance Test (CPT-II; Fazio, 2014). Given the differential between CPT and fMRI in terms of time, cost, and resources, the CPT is not likely to soon be replaced by fMRI for augmentation of clinical judgment as standard of care.

Other recently published works highlight the diversity of methods employed within machine learning and the range of quality control procedures in data acquisition and analysis, against the larger clinical backdrop of heterogeneity in ADHD and the value of clinical training in mental health care diagnostics. In 2017, for example, Lirong Tan and his team developed a Support Vector Machine (SVM) model to separate youth with ADHD from controls based on the volume of brain regions as measured by fMRI rather than using the more traditional approach of looking at the volume of brain regions measured as physical structures via MRI. The advantage of using the functional measure here was to capture how much a task caused activation in and around a particular structure of the brain. The team entered demographic data into their model, looking at socio-cultural contributors to the overall presentation of ADHD. They found that, brain-wide, functional volumes discriminated ADHD with equivalent accuracy (59.6% accuracy) to age and sex (58.5% accuracy), with neither being of strong clinical value. Tweaking the model by entering information about 10 brain regions of interest in ADHD pathology improved accuracy to 67%, correlating to subtle differences across the brain, rather than to a significant difference in any one particular region that could identify a group.

Xun-Heng Wang (2018) and his team also examined 10-ICNs, including an executive control network and a cerebellar network, for their predictive value. Their approach was to measure variability in the networks’ functional connectivity when not performing a task. Unfortunately, they included demographics in their model without examining the independent predictive quality of network variability. Since these were the same demographic features that Lirong found independently predicted with 58.5% accuracy, and we cannot determine the actual independent value of the connectivity analysis, we cannot be certain that their model truly achieves 75% accuracy with which the team presents us. These are claims that science will prove with replication, or not.

In the end, scientists will keep searching, fueled by the strong desire and public need to find ‘the’ biomarker or biomarkers that definitively separate ADHD from typical development. For the foreseeable future, though, clinical judgment is in no danger of being replaced by machine intelligence. Through more than a decade’s work as a clinician for a clinical and translational research group, I have frequently had to tell patients that, “No, I’m sorry, but we cannot use your fMRI/MRI to diagnose you. The science just is not there. No one can do that, yet.” Yet.

References:
Fazio, R., Dole, L. & King, J. (2014). CPT-II versus TOVA: Assessing the Diagnostic Power of Continuous Performance Tests. Archives of Clinical Neuropsychology 29(6):540
Sato, J.R., Hoexter, M.Q., Fujita, A., & Rohde, L.A. (2012). Evaluation of Pattern Recognition and Feature Extraction Methods in ADHD Prediction. Frontiers in Systems Neuroscience, 6
Sen, B., Borle, N., Greiner, R. & Brown, M.R.G. (2018). A General Prediction Model for the Detection of ADHD and Autism Using Structural and Functional Imaging. Plos One, 13(4), e0194856.
Tan, L., Guo, X., Ren, S., Epstein, J. & Lu, L.J. (2017) A Computational Model for the Automatic Diagnosis of Attention Deficit Hyperactivity Disorder Based on Functional Brain Volume. Frontiers in Computational Neuroscience, 11, 75. DOI 10.3389/fncom.2017.00075
Wang, X-H., Jiao, Y & Li, L. (2018) Identifying Individuals with Attention Deficit Hyperactivity Disorder Based on Temporal Variability of Dynamic Functional Connectivity. Nature Scientific Reports, 8/11789.