4. Biomarkers in ADHD - theoretical approaches for objective measurement methods?

Author: Ulrich Brennecke
Review: Dipl.-Psych. Waldemar Zdero (08/2024)

To date, there is no biomarker in sight that could be used to diagnose ADHD by measuring objective biological or neurological values.¹

However, measurement methods have already been researched enough to validate and support diagnoses. The advantage of measurement methods is that the result is not distorted by prior knowledge, training or particular motivations (interest, hyperfocus).
However, it is known that people with ADHD “outsource” the functions of disturbed brain areas to other areas of the brain in order to compensate for the deficit. Symptoms are also reduced through coping, e.g. by avoiding situations in which the stressful symptom can occur, or individual symptoms are reduced through intensive training. If only symptoms are asked about, the symptom masked by relocation to other areas of the brain or by coping may not be noticed. This is one of the reasons why tests (including our own online tests) can never be sufficient on their own to reliably diagnose or rule out ADHD.
It is to be hoped that with further refinement of the measurement methods and knowledge about the evaluation of results, a more objective diagnosis than questionnaires and tests will be possible in the foreseeable future.

However, the fundamental problem with measuring biomarkers is that they can only ever provide a snapshot. The results of this snapshot also depend on whether the person with ADHD is experiencing acute stress at the time of measurement or not. The usual ADHD diagnosis is therefore correctly based on whether the (stress/ADHD) symptoms exist in the long term, i.e. also outside of acute circumstances that trigger particular stress.
To date, the relevant literature has not yet addressed the extent to which biomarkers can reliably distinguish long-term changes from merely acute or chronic stress reactions.
As ADHD is a syndrome, meaning that the symptoms can be triggered by hundreds of different disorders, we assume that there will be no single biomarker for ADHD that would be sufficiently diagnostically valid, but that a multidimensional approach can achieve good diagnostic quality. This is ultimately also the case with diagnostics based on symptoms, where the focus is also not on individual symptoms but on a certain number of symptoms from a symptom group. Studies show that a combination of several biomarkers improves diagnostic accuracy.⁵

• 1. EEG / QEEG measurement
  • 1.1. Beta-theta ratio as a diagnostic tool
  • 1.2. EEG-Alpha Power frontal
  • 1.3. Changed slope / offset of the 1/f noise in the EEG
  • 1.4. Measurement of evoked potentials
  • 1.5. Measurement of rsEEG
  • 1.6. Measurement of non-linear EEG values
  • 1.7. Phase space reconstruction of the EEG
  • 1.8. Gamma levels reduced in ADHD
  • 1.9. EPSPatNet86: 8-point EEG measurement on 85 wavelets
  • 1.10. EEG analysis using VMD-HT
  • 1.11. Brain complexity
  • 1.12. Dynamics between alpha, beta and gamma bursts
  • 1.13. Aperiodic exponent in EEG reduced
• 2. Functional near-infrared spectroscopy (fNIRS)
• 3. Genetic testing
  • 4.3.1. MiRNA
• 4. Endocrine and physical biomarkers
  • 4.1. Measurement of cortisol levels
    • 4.1.1. Measurement of the basal cortisol level
    • 4.1.2. Measurement of the phasic cortisol response to psychological stressors
  • 4.2. Dexamethasone/ACTH/CRH test?
  • 4.3. Mineral analysis
  • 4.4. Axonal damage to the white matter
  • 4.5. 24-Hour movement profiles
  • 4.6. Facial features
    • 4.6.1. Facial morphology in ADHD
    • 4.6.2. Facial morphology in ASD
  • 4.7. Analysis of MRI brain images
  • 4.8. Analysis of urine values
  • 4.9. Analysis of eye values
    • 4.9.1. Electroretinography (ERG)
    • 4.9.2. Pupillometry
    • 4.9.3. Eye tracker: duration of target fixation (?), duration of gaze on irrelevant areas
  • 4.10. Activity measurement / actigraphy
  • 4.11. Inflammatory biomarkers
    • 4.11.1. Platelet-lymphocyte ratio (PLR)
    • 4.11.2. Neutrophil to lymphocyte ratio (NLR)
    • 4.11.3. Monocyte/lymphocyte ratio (MLR)
    • 4.11.4. Mean platelet volume (MPV)
    • 4.11.5. Platelet distribution width (PDW)
    • 4.11.5. Leukocyte count (MBC)
    • 4.11.6. Anti-myelin proteins
  • 4.12. Intrinsically disordered regions of active proteins (IDR)

1. EEG / QEEG measurement¶

The measurement of QEEG or EEG can support diagnoses. Further information on QEEG and neurofeedback therapy for ADHD can be found at ⇒ Neurofeedback as ADHD therapy In the chapter Treatment and therapy.

A multifactorial 19-channel EEG analysis called “ADHD-AID” reported an ADHD diagnostic accuracy of:⁶

• Accuracy: 0.991
• Sensitivity: 0.989
• Specificity: 0.992
• F1 score: 0.989
• Mathew correlation coefficient: 0.982.
• AUC: 0.9958

Older studies reported an ADHD detection rate of an EEG transformer model with an average accuracy of 95.85% and an average AUC value of 0.9926.⁷ An EEG classification model with machine learning reported a classification accuracy of 98.28% to 98.86%.⁸ Using phase-based analysis, one study preidentified two biomarkers that differentiate ADHD in children from healthy children with an accuracy of 99.174%:⁹

• the subgraph centrality of the phase shift index of brain connectivity within the beta and delta frequency bands
• the node betweenness centrality of the inter-site phase cluster connectivity within the delta and theta bands

In practice, however, people with ADHD do not have to be distinguished from healthy people, but ADHD has to be distinguished from other disorders of unknown composition. This is a much more complex task than all the methods presented so far have been able to solve.

A distinction is made between the following types of biomarkers:¹⁰

Diagnostic biomarkers: predict the presence of a disease; support diagnosis
Predictive biomarkers: predict the most likely response to a certain type of treatment
Prognostic biomarkers: indicate disease progression over time with or without treatment
Mechanistic biomarkers: indicate the underlying pathophysiological and/or psychological process
Surrogate outcome biomarkers: serve as surrogate endpoints for a relevant clinical outcome to be observed later
Stratification bid markers: divide heterogeneous diseases into more homogeneous groups
Genetic markers: identify changes in DNA or RNA (, such as mutations.
Molecular markers: proteins, hormones or other molecules in the blood or other body fluids.
Physiological markers: body temperature, blood pressure, respiratory rate.
Imaging markers: Visible changes that can be detected by X-ray or MRI.

1.1. Beta-theta ratio as a diagnostic tool¶

Attempts have been made to use the theta-beta ratio as a diagnostic tool for ADHD.
Although individual studies on this have appeared promising, this diagnostic approach does not yet seem suitable for diagnosing ADHD.

The theta-beta ratio is said to be significantly altered in ADHD.

• Increased power in delta, theta and low-alpha over central parietal regions
• Increased power in low beta in frontal regions
• Increased power in mid-beta over parietal regions.

Our discussions with a neurofeedback therapist revealed some interesting findings:

• Not only in ADHD, but also in a number of other mental disorders, such as depression, Compulsions, trauma or even migraine sufferers, beta increases in a state of relaxation, while it decreases in healthy people when they relax.
  This - characteristic - increase in beta levels during relaxation could possibly explain the symptoms of racing thoughts and sleep problems.
  We conclude from this that the theta-beta ratio in the laboratory environment can at best enable a clear diagnosis to be made as a differentiation of a group of people with ADHD compared to a group of (proven) healthy people. However, it is probably not suitable as a diagnostic tool in real life - for people for whom it is not known whether they suffer from ADHD or another Disorder, or are healthy - because deviations in the theta and beta range occur in a large number of mental disorders and the alternative to the presence of ADHD, unlike in the test environment, is not necessarily “healthy”, but includes the possibility of a number of other mental disorders or tendencies.
• ADHD-HI and ADHD-I have characteristically different theta/beta profiles.
  • In ADHD-HI and ADHD-C (with hyperactivity), theta is typically too low and beta is typically too high
  • In people with ADHD-I (without hyperactivity, dreamers), theta is typically too high and beta is typically too low. Here, the theta-beta ratio should therefore be too low - contrary to the postulation in the studies cited above.
  • In both groups, theta/beta training (relaxation training) proves to be helpful - in ADHD-HI, theta is trained up and beta is trained down at the same time, while in ADHD-I it is the other way round, reducing theta and increasing beta (concentration training). In both cases, it is not just the objective change in the values that is therapeutically effective, but the learned ability to influence the values, even if this results in the values being changed towards average values.

Joint tests with a neurofeedback therapist revealed a significant influence of medication and food (sugar) on the QEEG values.

• Methylphenidate causes greater agitation within the QEEG values within a few minutes. As expected, methylphenidate impeded the theta increase and beta decrease in ADHD-HI targeted by neurofeedback. This is not surprising as MPH is a stimulant. It explains why MPH should be taken in such a way that the effect has worn off by bedtime.
• Sugar (2 chocolate bars within 5 minutes at 90 kg body weight) caused significant changes in the QEEG of an ADHD-HI subject.
  • Within 10 minutes, Beta1 increases considerably.
    The threshold values for theta up / beta down training had to be reduced considerably. The 85 % of the target values achieved before the sugar intake had dropped to 50 %. This means that the ability to relax had decreased drastically.
  • After 20 minutes, all values (theta, alpha, beta1, beta 2, hi-beta) were significantly reduced. Relatively speaking, however, beta 1 was now clearly above SMR. (Beta1 should be below SMR, which is why SMR training (which targets it) is the first step in neurofeedback treatment for ADHD).
  • After 30 minutes, Beta1 had caught up slightly with SMR. However, Hi-Beta was now significantly higher.

There is also the problem that different EEG measurement software products apparently use different calculation methods to determine the beta-theta ratio and therefore produce significantly different results with the same data.¹⁸ Therefore, the International Collaborative ADHD Neurofeedback (ICAN) randomized clinical trial used a fixed theta-beta ration cutoff of ≥ 4.5, which had to be measured with the Thought Technology Monastra-Lubar Assessment Suite, 1.5 SD above the norms collected with this system.

• Binaural music also has a significant influence on the change in the QEEG.
  ⇒ Binaural music for ADHD and sleep problems.

One study came to the conclusion that the theta-beta ratio of the EEG in both non-affected and persons with ADHD-C is a reflection of cognitive processing activity and can be measured using the P 300 latency in the acoustic oddball test. In contrast, absolute alpha power did not correlate with P 300 latency or amplitude in persons with ADHD-C.¹⁹

1.2. EEG-Alpha Power frontal¶

In 5-month-old children who were later diagnosed with ADHD, there was reduced mean frontal EEG activity during an attention task. Since only the group values differed, diagnostic use was not possible.
²⁰

1.3. Changed slope / offset of the 1/f noise in the EEG¶

“1/f noise” in the EEG are arrhythmic signals in the cortex that are typical of ADHD as neural noise. Increased individual reaction time variance is a sign of increased neural noise. MPH improves this.²¹

In 3- to 7-year-old children with ADHD, a steeper slope and a larger offset were found with regard to the “1/f noise” in the EEG. These data correlated with altered theta/beta ratios. Both values were normalized by medication. In addition, a greater alpha power was found.²²

1.4. Measurement of evoked potentials¶

Event-related potentials are waveforms in the electroencephalogram (EEG) that are related (correlated) to an observable event (sensory stimulus, movement).
Evoked potentials are event-related potentials in which a test person always receives the same stimulus without further instruction, so that it is purely perceptual.

The measurement of evoked potentials is an objectified diagnostic procedure. Characteristic EEG amplitude curves triggered by stimuli are measured in various regions of the brain typically affected by ADHD. A typical average amplitude curve is determined from a large number of test runs. These EEG courses can be compared with data from non-affected people and data from people with other disorders, which are collected in so-called QEEG databases.
Unfortunately, diagnosis using evoked potentials is time-consuming (4 hours of tests, which are subsequently evaluated) and therefore not cheap. At the Brain and Trauma Foundation Graubünden Switzerland in Chur, a diagnosis based on evoked potentials costs around CHF 1,250 (as of 2015).

The diagnostic accuracy of measuring evoked potentials is growing rapidly. While Strehl et al cite studies from 2005 with a (still unsatisfactory) diagnostic accuracy of 70 to 80 %, Müller et al 2011²³ already claim 89 % diagnostic accuracy: from a group of 212 adults with 106 persons with ADHD, 89 % of those affected were correctly diagnosed using a 19-channel system through automatic evaluation of EVP. Models with machine learning achieve 94 %²⁴
However, these results were determined in laboratory situations in which only ADHD could be distinguished from non-affected individuals. In real-life diagnostics, ADHD can be distinguished from a variety of other disorders. It remains to be seen when this will be possible in practice using such models.

By comparison, trained diagnosticians and experienced clinicians achieved a diagnostic agreement of 88%²⁵

One study found no significant differences in evoked potentials between twins with and without ADHD in a continuous performance task.²⁶

A meta-analysis of 52 studies with 3,370 subjects on early (P100, N100, P200, N200, ERNNe) and late (P300, Pe, CNV) evoked potentials found in ADHD²⁷

• Shorter Go-P100 latencies
• Smaller Cue-P300 amplitudes
• Longer Go-P300 latencies
• Smaller NoGo P300 amplitudes
• Longer NoGo P300 latencies
• Smaller CNV amplitudes
• Smaller Pe amplitudes.

with considerable heterogeneity of the results and moderate effect sizes (d<0.6).

Another study came to the conclusion that evoked potentials show very different correlations with symptoms depending on the age of the test subjects (childhood or adolescence).²⁸

The mismatch negativity (MMN) is a negative component of the event-related response in an EEG signal that is triggered by a perceptible change in a repetitive aspect of auditory stimulation (e.g. stimulus pitch, stimulus duration).²⁹
Children with ADHD showed reduced mismatch negativity (MMN) amplitudes in response to two blocks of duration- and ISI-based deviants:³⁰

• MMN amplitude measurements:
  • Fz
    • ADHD: -1.2097 ± 0.2938 (continuous block); -0.8553 ± 0.4423 (ISI block)
    • Controls: -1.8325 ± 0.3689 (continuous block) and -2.0855 ± 0.3802 (ISI block)
  • Cz
    • ADHD: -1.2515 ± 0.3261 (continuous block) and -0.9367 ± 0.3432
    • Controls: -2.1319 ± 0.4445 (continuous block) and -2.7561 ± 0.4883
• MMN latencies
  Children with ADHD showed longer MMN latencies in both experimental blocks, indicating atypical responses.
  • Fz:
  • ADHD: 239.68 ± 5.059 (continuous block) or 226.88 ± 4.885 (ISI block)
  • Controls: 228.56 ± 6.584 (continuous block) or 213.56 ± 4.153 (ISI block)
  • Cz
    • ADHD: 234.40 ± 5.741 (continuous block) and 231.44 ± 5.464 (ISI block)
    • Controls: 227.52 ± 6.710 (continuous block) and 218.00 ± 5.261 (ISI block)

1.5. Measurement of rsEEG¶

The measurement of 14 resting state EEG parameters was able to identify the participating people with ADHD with an accuracy of around 85% in a process optimized by machine learning.³¹ As it is not only possible to diagnose healthy individuals in the wild, but also other disorders, it is questionable whether the result will be accurate enough to enable meaningful application in practice.

1.6. Measurement of non-linear EEG values¶

According to a study, a measurement of non-linear EEG values showed a better diagnostic quality of ADHD than other EEG diagnostic methods.³²

1.7. Phase space reconstruction of the EEG¶

One study found that phase space reconstruction of the EEG is very good at distinguishing ADHD from non-affected individuals. ³³ For diagnostic use, however, a method must also be able to differentiate well between other disorders.

1.8. Gamma levels reduced in ADHD¶

One study reported elevated resting gamma levels in adults with ADHD in the 30 to 39 Hz range (gamma1), but not in the 39 - 48 Hz range (gamma2). Resting gamma1 power increased with age and was significantly lower in ADHD than in control subjects in early adulthood.³⁴

1.9. EPSPatNet86: 8-point EEG measurement on 85 wavelets¶

A study of 85 EEG wavelet factors was able to correctly classify 87% of people with ADHD using machine learning.³⁵ It remains to be seen what the recognition rate will be for an open group of test subjects.

1.10. EEG analysis using VMD-HT¶

One study used VMD-HT and other secondary EEG characteristics to distinguish between 61 people with ADHD and 60 healthy controls:³⁶

• an accuracy of 99.81 %
• a sensitivity of 99.78 %
• a specificity of 99.84 %
• an F-1 measure of 99.83 %
• a precision of 99.87 %
• a false detection rate of 0.13 %.
• Detection rate for ADHD of 99.87 %
• Detection rate for healthy controls of 99.73 %

These impressive figures will have to be proven in the real world when not only persons with ADHD and healthy controls, but also people with other disorders are tested.

1.11. Brain complexity¶

An analysis of brain complexity in ADHD, including using EEG, found that brain complexity is increased in children with ADHD (with the increase being weaker than in schizophrenia (positive traits) and significantly weaker than in depression) and decreased in adults with ADHD (with the decrease being weaker than in autism and significantly weaker than in dementia).³⁷

1.12. Dynamics between alpha, beta and gamma bursts¶

The dynamics between alpha/beta and gamma bursts in the dlPFC and posterior parietal cortex during a visual Sternberg task on spatial working memory showed a complementary role to working memory processes in adolescents with ADHD. Alpha/beta bursts decreased during encoding of the stimuli and increased during the delay, whereas gamma bursts were increased during encoding and decreased during the delay. Deviations in bursting patterns were associated with working memory errors and clinical symptoms.³⁸

1.13. Aperiodic exponent in EEG reduced¶

The aperiodic resting electroencephalographic (EEG) activity is dynamic and reflects the changing balance between excitation and inhibition (E:I) under changing environmental conditions. Resting EEG on n = 285 children aged 2 to 14 years under high and low visual stimulus exposure showed³⁹

• a linear increase in the aperiodic exponent from early to middle childhood
  • –> low E:I value, followed by declining development in late childhood.
• The aperiodic exponent was
  • greater in younger children with high visual stimulation than with low visual stimulation
  • this effect was reversed with increasing age
• ADHD correlated with a reduced aperiodic exponent
• IQ correlated with dynamic aperiodic activity, i.e. shifts in the E:I equilibrium

Conclusions: The aperiodic exponent and aperiodic dynamics are indicators of age-related maturation of the cerebral cortex and the E:I ratio and are clearly related to ADHD symptoms and cognitive abilities.

Significance: We provide new evidence that dynamic aperiodic activity is a possible marker for the efficiency of the cerebral cortex in childhood.

Translated with DeepL.com (free version)

2. Functional near-infrared spectroscopy (fNIRS)¶

A prospective study of 30 boys using fNIRS of the forehead while performing mental arithmetic tasks was able to identify almost 100 % of the 15 boys with ADHD.⁴⁰ So far, this is a purely experimental approach.
Another study found no differences by fNIRS in ADHD⁴¹

3. Genetic testing¶

To date, there are no usable genetic tests for the diagnosis of ADHD.
However, initial voices are now (2020) in favor of integrating certain genetic tests (here: array CGH) into the standards of ADHD diagnostics.⁴²

There are several difficulties for genetic diagnostics in ADHD:

• ADHD is usually not caused by individual gene variants, but by the interaction of a large number (several hundred to thousands) of gene variants (⇒ gene candidates in ADHD) Gene candidates for ADHD)
• The (epigenetic) expression of genes (e.g. through early childhood or chronic severe stress) can also change the gene effect independently of the gene variant.
• It can be assumed that different combinations of the genes and forms of expression under consideration coincide in different persons with ADHD, but not all genes that contribute to the dopamine balance being disturbed, for example.
• There is not yet a complete set of candidate genes, which is why there is no genetic test for ADHD. We have collected over one hundred and fifty gene candidates.
  ⇒ Gene candidates for ADHD.
• Genetic tests are still too expensive to be able to test the required number of genes.
  It is possible that genetic tests will become so affordable in the coming years that they can also be used for diagnostic purposes - and subsequently to adapt individualized treatment.

4.3.1. MiRNA¶

One study investigated miRNAs of extracellular vesicles in serum. A longitudinal analysis examining changes in miRNA expression over time between four groups with different diagnostic trajectories (persistent diagnosis, first onset, remitted and typical development/control) found no statistically significant results. In the cross-sectional analysis, miR-328-3p was found to be upregulated in only one of 2 ADHD groups. These miRNAs may regulate the expression of genes associated with these traits in genome-wide association studies⁴³

One review found in children with ADHD⁴⁴

• highly regulated
  • miRNA-let-7d
  • miR-132-3p
  • miR-5692b
• down-regulated
  • miR34c-3p
  • miR-138-1
  • miR-107
  • miR-142-3p
  • miR-378
  • miR-30e-5p
  • miR-140-3
  • miR-1265p

Treatment success in ADHD by MPH (responders) at 12 months correlated with increased expression of miR-140-3p, miR-27a-3p, miR-486-5p and miR-151-5p.⁴⁵

4. Endocrine and physical biomarkers¶

4.1. Measurement of cortisol levels¶

The cortisol value has two meanings:

• The basal = tonic = long-term value represents the basic level, which changes rhythmically throughout the day (circadian rhythm)
• The phasic = short-term value represents a response reaction to an acute stressor

4.1.1. Measurement of the basal cortisol level¶

The basal cortisol level is lower in people with ADHD than in those not affected because the stress load is long-term. In the case of short-term stress, the basal cortisol level initially rises.

This difference could theoretically be used to differentiate between an acute stress reaction (increased cortisol levels) and a collapsed HPA axis regulation (reduced levels) - if the individual fluctuations between people within a group were not greater than the increase or decrease in cortisol levels compared to healthy levels.

According to this hypothesis, however, an individual change in cortisol levels upwards or downwards could be determined if these were recorded regularly (annually) from the earliest age (in children: on the occasion of standard examinations).
Without historical values of the basal cortisol level, however, such a measurement is useless.

In addition, a measurement of the morning cortisol awakening response (CAR) could provide information about the state of the HPA axis. The CAR is a persistent increase in cortisol levels that occurs 20 to 40 minutes after waking up.

A comparative graphical representation of the cortisol profiles of healthy, acutely stressed, chronically stressed, partially burned out and fully burned out people can be found in Bieger.⁴⁶

However, here too there is the difficulty that without individual comparative data, an assessment of the 24-hour picture of cortisol values is barely diagnostically usable.

A measurement of cortisol in hair (which reflects long-term levels) showed that low cortisol levels in preschool children predicted the expression of ADHD.⁴⁷
This could possibly be the result of a long-term stress reaction. The third phase of stress development (resistance phase) is typically characterized by a collapse of basal cortisol levels. The fourth phase (exhaustion phase) is then characterized by a breakdown of the neurotransmitter systems. More on this at ⇒ Resistance phase In the section ⇒ The stress response chain / stress phases of the article ⇒ The human stress system - the basics of stress.

However, changes in basal cortisol levels are not specific to ADHD and are therefore barely useful for ADHD diagnosis.

4.1.2. Measurement of the phasic cortisol response to psychological stressors¶

The phasic cortisol response can be used to determine the cortisolergic subtype of ADHD.

ADHD-I is typically associated with an elevated cortisol response compared to unaffected individuals, ADHD-HI with a flattened cortisol response compared to unaffected individuals.

The differences are relevant for treatment. In people with ADHD-I, SSRIs should be used with caution. In ADHD-HI, low-dose SSRIs (2 to 4 mg, i.e. 1/5 to 1/10 of the level used in depression) could potentially improve impulsivity, allowing lower dosing with stimulants.

The Trier Stress Test (TSST) is typically used as a psychological stressor.
This is probably not suitable for a (even annually) repeated examination in view of the expected habituation.

4.2. Dexamethasone/ACTH/CRH test?¶

A dexamethasone/ACTH/CRH test can be used to determine whether there is permanent dysregulation at individual levels of the HPA axis.

If ADHD is characterized by a permanent dysregulation of the stress regulation systems, above all the HPA axis, it should be possible to prove the individual dysregulation by testing the HPA axis reactions (changes in the levels of the stress hormones CRH, ACTH, cortisol to acute stressors). At the very least, such tests should provide informative indications for the individual differentiation of subtypes.

Dexamethasone is a glucocorticoid that selectively binds to the glucocorticoid receptors and should thereby trigger the downregulation of the HPA axis (after a stress reaction has occurred). If this downregulation does not occur, this is a strong indication of a disorder in the shutdown of the HPA axis.

More on this at ⇒ Pharmacological endocrine function tests.

4.3. Mineral analysis¶

By analyzing zinc, lead, copper, cobalt and vanadium from teeth, one study was able to create an impressively clear distinction between non-affected people, people with ADHD, people with ASD and people with comorbidities.⁴⁸

4.4. Axonal damage to the white matter¶

A study of 50 untreated persons with ADHD compared to their unaffected twins and 50 control subjects found higher axonal diffusivity in the ADHD sufferers in the⁴⁹

• Perpendicular fasciculus
• Superior longitudinal fasciculus I
• Lateral corticospinal tract
• Corpus callosum

The values correlated significantly (except in the perpendicular fasciculus) with ADHD symptoms such as inattention and working memory problems and were intermediate between those of people with ADHD and non-affected twins. Axial diffusivity is a marker for the undamaged condition of axons.

4.5. 24-Hour movement profiles¶

One study claims to have achieved a diagnostic accuracy for ADHD-C of over 97% by evaluating neural network analyses of 24-hour movement profiles.⁵⁰⁵¹

4.6. Facial features¶

4.6.1. Facial morphology in ADHD¶

One study concluded that there may be a close relationship between ADHD and nasal width, ear length and upper face depth, which could be related to genetic signaling processes and a close relationship between the brain and face formation process in the embryonic period.⁵²

4.6.2. Facial morphology in ASD¶

A study reports on subtle phenotypic facial morphologies in ASD.⁵³

4.7. Analysis of MRI brain images¶

Optimized machine learning was able to distinguish very well between ADHD and controls and between persistent and remitting ADHD in young adults using MRI neuroimaging. Relevant features of ADHD compared to controls were identified as:⁵⁴

• Nodal efficiency in the right inferior frontal gyrus
• Functional connectivity between brain regions right medial frontal and inferior parietal
• Volume of the right amygdala

The remission of inattention and hyperactivity/impulsivity correlated with

• Higher nodal efficiency right medial frontal
• Reduced functional connectivity between right medial frontal and inferior parietal brain areas

4.8. Analysis of urine values¶

A sufficiently accurate analysis of urine values to diagnose ADHD has not yet been reported. One study also found no connection between urine values and ADHD in dogs.⁵⁵

A meta-analysis of k = 210 studies found statistically significant correlations for the following urine values:⁵⁶

• Noradrenaline
  • ADHD
  • ADHD drug reaction
  • Severity of ADHD symptoms
• MPHG (3-methoxy-4-hydroxyphenylethylene glycol)
  • ADHD
  • ADHD drug reaction
  • Severity of ADHD symptoms
• MAO (monoamine oxidase)
  • ADHD
  • ADHD drug reaction
  • Severity of ADHD symptoms
• Zinc
  • ADHD
  • Severity of ADHD symptoms
• Cortisol
  • ADHD
  • ADHD drug reaction
  • Severity of ADHD symptoms
• PEA
  • ADHD drug reaction
• Ferritin
  • Severity of ADHD symptoms

Trends (not statistically significant) were shown for⁵⁶

• Neuropeptide Y
• Manganese
• DHEA (dehydroepiandrosterone)

4.9. Analysis of eye values¶

4.9.1. Electroretinography (ERG)¶

One study found evidence of gender-specific biomarkers in electroretinography in ADHD.⁵⁷

4.9.2. Pupillometry¶

Pupil diameters at rest are a biomarker for tonic noradrenal infiring. Pupil changes during tasks and in response to stimuli are a biomarker for phasic noradrenal infiring. Find out more at* Tonic and phasic noradrenaline* in the article Noradrenaline in the chapter Neurological aspects of ADHD.
One study achieved a sensitivity of 77.3% and a specificity of 75.3% in the diagnosis of ADHD compared to a healthy control group using a machine learning method from pupil measurements.⁵⁸

Several studies found no evidence for the sometimes reported correlation between pupil diameter and fluid intelligence.⁵⁹⁶⁰

4.9.3. Eye tracker: duration of target fixation (?), duration of gaze on irrelevant areas¶

The study of distractibility by recording and analyzing eye movements during a task-irrelevant distraction in children and adolescents aged 6 to 17 years showed a significant (negative) correlation between a (reduced) duration of target fixation and (increased) attention problems reported by parents (p < 0.001).⁶¹ However, 5-month-old children who were later diagnosed with ADHD showed a positive correlation of prolonged gaze fixations during an attention task (21.2 seconds) compared to the 5-month-old children who later did not show ADHD (12.7 seconds). As only the group values were so clear, diagnostic use was not possible.²⁰

Two studies report extended gaze times to irrelevant areas during a continuous performance task, which could have diagnostic value as additional information ⁶²⁶³

4.10. Activity measurement / actigraphy¶

A very exciting study reports a highly significant measurement of inattention and hyperactivity in a school class (n = 39) using actigraphs (accelerometers) over 3 weeks.⁶⁴
The measurement of activity synchrony found a negative correlation between synchrony of movements and inattention, but not hyperactivity (in the teacher’s report).
The measurement of activity volume correlated with hyperactivity (in the parent report), but not with inattention.

Other small studies dealt with the diagnosis of hyperactivity using a smart watch with a special app⁶⁵, actigraph measurements for ADHD diagnostics⁶⁶⁶⁷ , differential diagnostics for Bipolar Disorder⁶⁸ and the detection of aggression in ADHD⁶⁹

The measurement of pixel changes in a 4-minute video recording to detect ADHD compared to non-affected individuals showed an accuracy of 90.24%, a sensitivity of 88.85%, a specificity of 91.75% and an area under the curve of 93.87%.⁷⁰

4.11. Inflammatory biomarkers¶

4.11.1. Platelet-lymphocyte ratio (PLR)¶

The platelet/lymphocyte ratio (PLR) is a biomarker for inflammation.⁷¹⁷²

ADHD shows an increased PLR.^73717274 ADHD medication, age and gender did not influence PLR. The measure of PLR showed no correlation with the severity of ADHD symptoms.⁷¹
A study of n = 1,455 children, 91 of whom had ADHD found an increased platelet-to-lymphocyte ratio in ADHD, particularly in males, children of mothers aged 20-29 years and children with asthma.⁷⁵

4.11.2. Neutrophil to lymphocyte ratio (NLR)¶

The neutrophil-lymphocyte ratio is a biomarker for inflammation.⁷¹⁷²
ADHD shows an increased NLR.^7371727476
The amount of lymphocytes correlated with the severity of ADHD symptoms.⁷⁶
ADHD medication, age and gender did not affect NLR. The measure of NLR showed no correlation with the severity of ADHD symptoms.⁷¹

4.11.3. Monocyte/lymphocyte ratio (MLR)¶

The monocyte/lymphocyte ratio (MLR) is a biomarker for inflammation.⁷²

A meta-analysis found no change in MPR in ADHD.⁷³

One study found a significantly increased MLR in children with ADHD (n = 152).⁷²
The lymphocyte count in ADHD was significantly reduced.⁷²

4.11.4. Mean platelet volume (MPV)¶

The mean platelet volume (MPV) is a biomarker for inflammation.⁷²

The MPV was significantly increased in children with ADHD (n = 152)⁷², as well as in children and adolescents with ADHD (n = 552)⁷⁴.

4.11.5. Platelet distribution width (PDW)¶

PDW was elevated in ADHD.⁷⁴

4.11.5. Leukocyte count (MBC)¶

The white blood cell (WBC) count was elevated in ADHD.⁷⁴

4.11.6. Anti-myelin proteins¶

One study found significantly increased levels of in drug-naïve children with ADHD aged 8 to 14 years:⁷⁷

• MAG (myelin-associated glycoprotein)
• CDNF (cerebral dopamine neurotrophin)
• hs-CRP (highly sensitive C-reactive protein)
• Reelin
• Cerebellin-1

Unchanged were:

• Anti-MBP (anti-myelin basic protein)
• Anti-MOG Anti-myelin oligodendrocyte glycoprotein)

4.12. Intrinsically disordered regions of active proteins (IDR)¶

Intrinsically disordered regions are biologically active protein regions that have high conformational variability but no stable three-dimensional structures. Intrinsically disordered proteins (IDP), which contain IDRs of different lengths or are completely disordered, are of central importance in many biological processes, such as⁷⁸

• Signal paths
• Transcription
• Translation
• Cell cycle

The precise control of the numerous intrinsically disordered proteins (IDPs) in cells is important for proper signal transmission. Mutations or changes in IDPs can lead to various diseases,⁷⁸

The proportion of IDPs in ADHD (73.4 %) and ASD (75.4 %) was increased (normal value 66.2 %), unlike in anxiety (significantly reduced here) or bipolar, depression, schizophrenia or mental retardation (little different here).⁷⁸ There were also significantly more proteins with disordered binding sites. In addition, ADHD (and even more so ASD) showed a significantly increased proportion of LLPS proteins (liquid-phase-separated proteins).

IDPs for ADHD affect⁷⁸

• the activities of various ion transmembrane transport proteins
• the regulation of the membrane potential
• the regulation of the ionic transmembrane transporter
• the regulation of synaptic signal transmission

IDP for ASS concern⁷⁸

• molecular functions, especially those closely related to the regulation of gene expression, such as
  • Chromatin binding
  • DNA binding
  • Binding of DNA-binding transcription factors
• biological processes
  • Learning and memory
  • chemical synaptic regulation
  • Behavior
  • Head development
  • Cognition

The proteins prominently named by Zhang et al. with a high involvement in mental disorders have all already attracted attention in connection with ADHD.

• TP53 is degraded by the protein encoded by the ADHD candidate gene TAF1
• CREB1 is encoded by a candidate ADHD gene
• CREBBP is known to be a monogenetic cause of hyperactivity
• KRAS: The protein encoded by the ADHD candidate gene RASSF4 can act as a KRAS protein