A Review: The Biological, Neurochemical, and Functional Basis of ADHD
Attention-deficit/hyperactivity disorder (ADHD) is a common child neurodevelopmental disorder that can continue to influence their adult years and can cause significant distress in academic, occupational and personal situations. The DSM-5 criteria allow for the classification ADHD to broken into 3 subcategories primarily inattentive (ADHD-I), primarily hyperactive and impulsive (ADHD-H), or a combined presentation (ADHD-HI). Furthermore, even within each presentation there can be significant individual differences in symptoms. Due to the heterogeneous nature of ADHD, there is a large amount of contradicting information regarding specific cognitive functions that are affected in individuals diagnosed with ADHD. This purpose of this paper is to review some of the current research on the neurological findings associated with a diagnosis of ADHD.
In an fMRI study, ADHD children were found to have decreased performance in both response inhibition and interference suppression on a modified Eriksen flanker task involving congruent, non-congruent and no-go trials (Vaidya et al., 2005). In their 2016 study, Saomone and colleagues found that compared to healthy controls adults diagnosed with ADHD had significant deficits in executive function, divided attention, and sustained attention. The degree of these deficits correlated with the severity of their self-reported of symptoms.
Dang and Colleagues (2016) reported that greater the volume of the right caudate in comparison to the left caudate was associated with higher the measures of impulsiveness on the Test of Variable Attention (TOVA) and worse ADHD scores on self-reports. Indicating a possible instability of connectivity between the frontal-striatal networks may be one of the primary causes of attentional difficulties. (Dang et al., 2016)
In a study utilizing fractional anisotropy (FA) as a metric of the microstructure during diffusion-weighted imaging technique looked connectivity patterns between the caudate, putamen, dorsolateral prefrontal cortex (DLPFC), ventrolateral prefrontal cortex (VLPFC), and the orbitofrontal cortex (OFC) (Silk et al., 2016). They found that adolescent males had decreased volume in the caudate-VLPFC tract and the caudate-DLPFC tract and increased volume for the same tracts on the left side resulting in left lateralization of these tracts compared to healthy controls. This same pattern of left lateralization was found in of the Putamen-VLPFC tract in subjects with ADHD, furthermore, this increased lateralization was positively correlated with an increased level of symptoms of hyperactivity and inattention and the total DSM scores. Although the results were not significant, the caudate-OFC tract trended towards greater right lateralization in an individual with ADHD, furthermore, this increased right lateralization correlated significantly with symptoms of inattention, hyperactivity and total DSM score (Silk et al., 2016)
In another study, high definition MRI comparisons between ADHD participants and healthy control subjects found no difference in total intracranial volume or total grey matter, however, ADHD participants were found to have significantly decreased grey matter volume in the frontostriatal and limbic regions: Left prefrontal cortical areas (middle frontal cortex, inferior portions of the orbitofrontal cortex, and gyrus rectus), the bilateral putamen, amygdala, hippocampus, fusiform, insula, and cerebellum (Del Campo et al., 2013).
One study used the binding competition of a
D2/D3receptor radioligand (18F-fallypride) and endogenous dopamine to infer the amount of dopamine released in the striatal and midbrain regions (Del Campo et al., 2013). They found that there was no significant difference between the overall number of
D2/D3receptors between adult ADHD and control subjects. However, when considered independent from a diagnosis of ADHD reports of increased difficulties with inattention were negatively correlated with the binding potential (
BPND) in the total striatum (more specifically the caudate and sensorimotor striatum) but not the midbrain regions. In addition, reports of hyperactivity were associated with
BPNDin the left substantia nigra/ventral tegmental area but not the striatum (Del Campo et al., 2013). During the Rapid Visual Information Processing (RVP) task decreased performance on this task of sustained attention had significantly reduced
BDNPvalues in the left caudate (Del Campo et al., 2013).
A study of adults with a diagnosis of ADHD-C who had been off all medication for at least 6 months, compared dynamic tonic and phasic release of dopamine by using the radiolabeled dopamine receptor ligand 11C-raclopride during a PET scan (Badgaiyan, Sinha, Sajjad, & Wack, 2015). The tonic condition involved participants lay still for 45 minutes and during the phasic condition participants took part in a modified Eriksen’s flanker task with a congruent and non-congruent condition for 20 minutes. They found a reduced tonic (resting state) release of dopamine and increased phasic (during the task) release in the right caudate of the adults with ADHD (Badgaiyan et al., 2015). They also found altered enzyme kinetics between the two groups indicating differences in receptor/ligand binding. The authors hypothesized a possible reason for this finding would be an increased number of DAT transporters within the region of interest. This would result in a decrease of tonic dopamine levels in the synapse due to an increased rate of uptake. In support of this hypothesis, the authors had found evidence of a 30% increase of DAT binding in the same locations of the striatum as the differences in tonic and phasic dopamine release, indicating there is likely a reciprocal relationship between them. Badgaiyan and colleagues hypothesized that the low tonic levels of dopamine may result a compensatory increase in phasic dopamine, which also supported by the fact that one of the most effective ADHD medication, methylphenidate, is a DAT blocker and other medications that increase dopamine but have no effect on DAT activity (levodopa) also have no effects on the symptoms of ADHD (Badgaiyan et al., 2015).
A study using proton magnetic resonance spectroscopy, found that adults with a diagnosis of ADHD (ADHD-I and ADHD-C) had significantly lower concentration of glutamate/glutamine concentration and a reduction of other metabolites of glutamate including creatine and phosphocreatine (Cr) and N-acetylaspartate (NAA) in the basal ganglia (caudate/putamen) (Maltezos et al., 2014). They also found reduced levels of Cr in the DLPFC. These findings were unrelated to the individual status of medication or medication naive or any difference in grey/white matter ratios in these areas. For those that were medication naïve there was also a negative correlation (r = – 0.61) between their ratings of inattention and that of the glutamate/glutamine concentration found in the basal ganglia, however this was not evident in individuals taking medication. The authors suggested that this may indicate that one mechanism of a dopaminergic stimulant medication is to compensate for abnormalities in the glutamatergic system without altering the system itself. Maltezos and colleagues (2014) highlighted that the metabolic abnormalities were in a region (caudate/putamen) that has found to have a modulating influence on the prefrontal cortex during shifts attention, which is one area of function compromised in individuals with ADHD.
GABAergic neurons commonly considered the major system of inhibitory action on other neurotransmitter systems (Meyer & Quenzer, 2018). Research has found that a risk factor for the development of a neurodevelopmental and psychiatric disorder is certain genetic variations of the molecule Cadherin-3 (CDH13) which is part of the cadherin family of adhesion molecules (Rivero et al., 2015). This molecule has been implicated the guidance of new synaptic connections and in the correct functioning of mature synapses including in the hippocampus where it is found in significant amounts (Rivero et al., 2015). In their study, Rivero and colleagues stained presynaptic marks for vesicular glutamate transports (VGLUT) and vesicular GABA transporter (VGAT) and were able to determine that CHD13 was localized with the VGAT but not the VGLUT. Providing evidence that CDH13 is specifically associated with GABAergic interneurons within the hippocampus. The study then investigated the function of CDH13 and found that CDH13 knockout mice (
Cdh-/-) had increased inhibitory synaptic transmission to pyramidal neurons that was not associated with any change in the transmission of excitatory inputs to pyramidal neurons from other neurons. This finding indicates that the effect of some variations of the CDH13 molecule is to alter the balance of the excitatory-inhibitory activation within neural circuits. The authors proposed that this alteration in inhibitory/excitatory balance would cause deficits in information processing and signal gating, which are two important concepts of executive function and attentional processes, with the specific locations of the CHD13 molecule in throughout the brain determining the specific alterations in behaviour (Rivero et al., 2015). This was supported by the behaviour of CDH12 knockout mice in the study who showed evidence of reduced cognitive flexibility, difficulties learning as tasks became more complex and increased locomotor activity (Rivero et al., 2015).
In a study using resting-state functional MRI (rs-fMRI) and diffusion-weighted imaging (DTI) they found that adolescents diagnosed with ADHD had increased functional connectivity in prefrontal regions, right inferior frontal gyrus (IFG) and medial prefrontal cortex (mPFC), within the default mode network (DMN) (Bos et al., 2017). This recruitment of the IFG into the DMN may indicate a decreased ability to segregate the different neural networks and difficulty with modulating the transition between a task-positive and task-negative network (Bos et al., 2017). In the study, they found the recruitment of the IFG into the DMN decreased with age although did not disappear completely for individual diagnosed with ADHD. In addition, there was more widespread connectivity alteration throughout the brain including reduced connectivity of the right posterior cingulate gyrus (also within the DMN) (Bos et al., 2017). None of these alterations in connectivity were associated with any differences in white matter volume, thus the authors indicated finding may point to alterations in functional connectivity that precede alterations in the structural connectivity of the brain (Bos et al., 2017).
Vaidya and colleagues (2005) found that during a modified flaker task, ADHD children displayed decreased engagement of the frontal-striatal-temporal-parietal network compared to healthy controls. Furthermore, ADHD and control subjects displayed a different recruitment pattern of brain regions during the tasks (Vaidya et al., 2005). Specifically, control presented with activation in the anterior frontal cortex and caudate nucleus regions that failed to activate in the ADHD participants (Vaidya et al., 2005). Whereas, the ADHD participants instead showed activation in the posterior superior temporal cortex (Vaidya et al., 2005). For the trials related to interference suppression ADHD has decreased activation in the left inferior frontal premotor cortex and decreased frontal activation and weak activation in the right insula in relation to the inhibition trials (Vaidya et al., 2005). The authors indicated that differences frontal activity was similar to that found in the previous literature on response inhibition, working memory and interference control. The decreased activation in the caudate was suggested to be a result of a difference in the encoding of event probability and decreased response preparation in ADHD participants (Vaidya et al., 2005).
A longitudinal study of young adults with ADHD (all medication naïve except 1) and healthy controls it was found that of the individual with a diagnosis ADHD at age 16-17 no longer qualified for the same diagnosis at age 20-24 as determined by the DSM-IV (Roman-Urrestarazu et al., 2016). Cognitive tests found that all individual in the ADHD group had significant working memory deficits, even for individuals no longer qualifying for an ADHD diagnosis, this finding held even after controlling for poor performers. MRI scans during the same study found that these individuals had a bilateral decreased grey matter volume in the caudate. Furthermore, during tasks of working memory the individuals in the ADHD groups had a bilateral failure to activate the caudate as the cognitive load increased, with decreased activation correlating to decreased performance on the working memory task (Roman-Urrestarazu et al., 2016).
Methylphenidate was found to increase dopamine in the striatal and midbrain regions in a similar manner for both ADHD and control subjects. However, individuals who performed poorly on the task of sustained attention (with and without a diagnosis of ADHD) had a significantly reduced dopamine release in the midbrain and an increase in DA release in the left caudate normalizing activity than individual who performed better. The reduced activity in the midbrain points to the activation of dopamine autoreceptors in this area (Del Campo et al., 2013)
Resting state fMRI study in healthy participants found that a single dose of methylphenidate (40mg) increased functional connectivity between the NAcc, the medial dorsal nucleus (MDN) of the thalamus and area of the limbic system. It also decreased the functional connectivity between the NAcc, medial prefrontal cortex (mPFC), and the basal ganglia, temporal cortex. There were no changes in connectivity between the MDN and limbic circuit. (Ramaekers et al., 2013)
A 16-week double-blind placebo-controlled trial of methylphenidate with medication naïve boys (age 10-12) and male adults with pre and most measurements of cerebral blood flow (CBF) with increase CBF being used as an indirect measure of increasing dopamine levels. Found a significant increase in CBF in the thalamus of the children and a non-significant increase in the striatum and anterior cingulate cortex after a 1-week washout of the medication. This effect was not found in either the adults or the placebo groups. The authors suggested that this indicates that methylphenidate had a lasting effect on the dopamine system. In the comparison of medicated children and the placebo group they found that the striatal CBF values were significantly higher on the medication these children also reported significant improvement of their symptoms in self-reports (Schrantee et al., 2017). The authors suggested this finding is due to a long-lasting decrease in striatal dopamine transporters and a decrease of
D3receptors in the prefrontal cortex, increased of dopamine level and a reduction of neuron excitation and synaptic transmission in the prefrontal cortex.
A study looking at ADHD patient (age 23-40) with participants stratified into 3-groups: early stimulant treatment (EST) which was at least 4 months prior age 16, late stimulant treatment (LST) – at least 4 months of treatment after age 16, and stimulant treatment naïve (STN) (Schrantee et al., 2018). Looking at cerebral blood flow as an indirect method of measuring dopamine activity after a challenge with methylphenidate. However, they did find that individuals in the EST had decreased CBF in the anterior cingulate cortex (ACC) than individuals in the LST group. Individuals in the EST were found to have increased probability having a diagnosis of comorbid depression, and a decreased reported recreational drug use. They found no differences between the LST and the STN which is contradictory to the belief that methylphenidate treatment causes an increase in DAT transporters which should have appears as decreased CBF. During an MPH challenge the effect was similar across the groups with decreased CBF in the frontostriatal circuit and anterior cingulate cortex but not in the thalamus (Schrantee et al., 2018).
One reason for the inconsistent findings appears to be due to lack of control for age, subtype, and medication status of the individual used for the study of the neurological cause of ADHD. This is highlight by some studies that that separated the ADHD participants into different groups depending on their age, medication status, ADHD subtype before completing comparisons between them.
For example, the fact that Bos and colleagues (2017) found the recruitment of the IFG into the DMN decreased with age may indicate one reason for the alteration of typical symptoms and activation patterns of individuals as they age. This finding also lends support for the idea that ADHD symptoms may be related to immature neuronal development. However, because the pattern does not disappear completely it is highly probable there are other underlying factors than just increased the time necessary for the brain to develop fully.
Studies have shown that for a large sample size of children there an interaction between the age of participants with ADHD and the activity of specific brain regions (Bos et al., 2017). Other studies have shown that there are differences in patterns found in adults vs children (Schrantee et al., 2017). This variation if finds according to age makes sense considering ADHD is considered a neurodevelopmental disorder and as an individual matures there will be variations in the patterns found. In addition, there is evidence that even as the individual ages and no longer fits the diagnosis of ADHD, this doesn’t mean their connectivity is the same as individual who never had ADHD in the first place, as illustrated in a longitudinal study of individuals who still had alterations in their brain patterns that correlated with their performance on working memory tasks (Roman-Urrestarazu et al., 2016).
Furthermore, due to the plasticity of the brain it would make sense that there would be an attempt of the brain to create compensatory mechanisms to improve cognitive function to the greatest degree possible.
The effect of medication on the brain was evidence in the study of medicated vs non-medicated adults with ADHD (Schrantee et al., 2018). The study found a difference in the activation of adults with ADHD that was dependent on the time at which medication was started which indicates that stimulant has a long-term and permanent effect. This is especially for those who start the medication as a child or during adolescence during key neurodevelopmental periods (Schrantee et al., 2018). It has even been suggested that methylphenidate may normalize the trajectory of brain development and activation pattern to a certain extent (Bos et al., 2017).
Some major finding indicates that the correct activation of the frontostriatal circuit (including the caudate) being necessary for tasks that requires sustained attention and response inhibition. With previous research, indicating that the right caudate is involved in processes related to executive function, including inhibition and selective attention(Badgaiyan et al., 2015). Thus the finding that the right caudate in individuals with ADHD is significantly reduced in volume (Badgaiyan et al., 2015; Silk et al., 2016) and decreased activity (Badgaiyan et al., 2015).
Another area of interest is that alterations to the dopamine system, specifically the striatum and midbrain were associated with symptoms of ADHD (Badgaiyan et al., 2015; Del Campo et al., 2013) and the mechanisms of action for a common ADHD medication (Badgaiyan et al., 2015) However, other neurotransmitter system and mechanisms were also implicated on being related to symptoms of ADHD. Evidence of this is provided by the research of CDH13 and its influence on GABA neurons (Rivero et al., 2015). The finding that concentrations of glutamate/glutamine concentrations corresponded to similar brain regions as dopamine and to ADHD symptoms (Maltezos et al., 2014). One additional piece of interesting information is that an animal study points to the existence of multiple parallel mesocorticolimbic pathways including glutamate-only, glutamate-dopamine, dopamine-only, and GABA-only pathways (Yamaguchi, Wang, Li, Ng, & Morales, 2011).
From the combination of data is evident we are on the right track to determining the neural causes of ADHD and their relations to different symptoms. However, the overlapping and sometimes contradictory evidence regard ADHD also indicates we have a long way to go. To be able to develop a better understanding of how the different networks within the brain interact it is important that develop a systematic method of ensuring similar standards for the development of subject groups and other factors influencing the results of research. The preferred method would be a detailed longitudinal study of individuals from as an early of an age as possible to track different variations of symptoms of an individual with ADHD and healthy subjects. This would allow us to compare brain function at specific points in the lifespan in a healthy population and ADHD diagnosed individuals both on medication and off. This would also allow a better understanding of the development and progression of ADHD and possible comorbidities throughout the lifespan.
Badgaiyan, R. D., Sinha, S., Sajjad, M., & Wack, D. S. (2015). Attenuated tonic and enhanced phasic release of dopamine in attention deficit hyperactivity disorder. PLoS ONE, 10(9), 1–14. https://doi.org/10.1371/journal.pone.0137326
Bos, D. J., Oranje, B., Achterberg, M., Vlaskamp, C., Ambrosino, S., de Reus, M. A., … Durston, S. (2017). Structural and functional connectivity in children and adolescents with and without attention deficit/hyperactivity disorder. Journal of Child Psychology and Psychiatry and Allied Disciplines, 58(7), 810–818. https://doi.org/10.1111/jcpp.12712
Dang, L. C., Samanez-Larkin, G. R., Young, J. S., Cowan, R. L., Kessler, R. M. K., & Zald, D. H. (2016). Caudate asymmetry is related to attentional impulsivity and an objective measure of ADHD-like attentional problems in healthy adults. Brain Structure and Function, 221, 277–286. https://doi.org/10.1007/s00429-014-0906-6
Del Campo, N., Fryer, T. D., Hong, Y. T., Smith, R., Brichard, L., Acosta-Cabronero, J., … Müller, U. (2013). A positron emission tomography study of nigro-striatal dopaminergic mechanisms underlying attention: Implications for ADHD and its treatment. Brain, 136(11), 3252–3270. https://doi.org/10.1093/brain/awt263
Maltezos, S., Horder, J., Coghlan, S., Skirrow, C., O’Gorman, R., Lavender, T. J., … Murphy, D. G. (2014). Glutamate/glutamine and neuronal integrity in adults with ADHD: A proton MRS study. Translational Psychiatry, 4(3). https://doi.org/10.1038/tp.2014.11
Meyer, J. S., & Quenzer, L. F. (2018). Psychopharmacology: Drugs, the brain, and behaviour (2nd ed.). New York, NY.
Ramaekers, J. G., Evers, E. A., Theunissen, E. L., Kuypers, K. P. C., Goulas, A., & Stiers, P. (2013). Methylphenidate reduces functional connectivity of nucleus accumbens in brain reward circuit. Psychopharmacology, 229(2), 219–226. https://doi.org/10.1007/s00213-013-3105-x
Rivero, O., Selten, M. M., Sich, S., Popp, S., Bacmeister, L., Amendola, E., … Lesch, K. P. (2015). Cadherin-13, a risk gene for ADHD and comorbid disorders, impacts GABAergic function in hippocampus and cognition. Translational Psychiatry, 5(10), e655-11. https://doi.org/10.1038/tp.2015.152
Roman-Urrestarazu, A., Lindholm, P., Moilanen, I., Kiviniemi, V., Miettunen, J., Jääskeläinen, E., … Murray, G. K. (2016). Brain structural deficits and working memory fMRI dysfunction in young adults who were diagnosed with ADHD in adolescence. European Child & Adolescent Psychiatry, 25(5), 529–538. https://doi.org/10.1007/s00787-015-0755-8
Schrantee, A., Bouziane, C., Bron, E. E., Klein, S., Bottelier, M. A., Kooij, J. J. S., … Reneman, L. (2018). Long-term effects of stimulant exposure on cerebral blood flow response to methylphenidate and behavior in attention-deficit hyperactivity disorder. Brain Imaging and Behavior, 12(2), 402–410. https://doi.org/10.1007/s11682-017-9707-x
Schrantee, A., Tamminga, H. G. H., Bouziane, C., Marco, A., Bron, E. E., Mutsaerts, H. M. M., … Bascule, D. (2017). Age-dependent effects of methylphenidate on the human dopaminergic system in young vs adults patients with attention-deficit/hyperactivity disorder. JAMA Psychiatry, 73(9), 955–962. https://doi.org/10.1001/jamapsychiatry.2016.1572.Age-Dependent
Silk, T. J., Vilgis, V., Adamson, C., Chen, J., Smit, L., Vance, A., & Bellgrove, M. A. (2016). Abnormal asymmetry in frontostriatal white matter in children with attention deficit hyperactivity disorder. Brain Imaging and Behavior, 10(4), 1080–1089. https://doi.org/10.1007/s11682-015-9470-9
Vaidya, C. J., Bunge, S. A., Dudukovic, N. M., Zalecki, C. A., Elliott, G. R., & Gabrieli, J. D. E. (2005). Composition of Promotion Strategy. American Journal of Psychiatry, 162(9), 1605–1613. https://doi.org/https://doi.org/10.1176/appi.ajp.162.9.1605
Yamaguchi, T., Wang, H.-L., Li, X., Ng, T. H., & Morales, M. (2011). Mesocorticolimbic Glutamatergic Pathway. Journal