ADHD - diagnostic methods

An ADHD diagnosis is conventionally determined by questionnaires, interviews and tests. In principle, several different instruments should be used. This means that a good diagnosis requires several questionnaires, several tests, and absolutely a personal interview by the diagnostician.
Inter-questionnaire and inter-test agreement is limited despite verification of the validity and reliability of the respective tests.
Symptoms of ADHD also occur in other disorders. A study of 10 disorders found that 60% of the symptoms occurred in at least half of all disorders and were assessed in the respective disorder-specific questionnaires and tests.¹

• 1. Questionnaires for ADHD diagnosis
• 2. Interviews for ADHD diagnostics
• 3. Tests for ADHD diagnostics
  • 3.1. Test procedures for ADHD diagnostics
    • 3.1.1. Neuropsychological test procedures
    • 3.1.2. Individual attention and reaction tests
      • 3.1.2.1. Stroop test
      • 3.1.2.2. Stroop Plus Test
      • 3.1.2.3. Perceptual Selectivity Test
      • 3.1.2.4. N-back test
      • 3.1.2.5. Stop Signal Task (SST)
      • 3.1.2.6. Children’s Color Trail Test (1/2)
      • 3.1.2.7. Continuous Performance Test (CPT)
      • 3.1.2.8. Digit Span Test
      • 3.1.2.9. Wisconsin Card Sorting Test
    • 3.1.3. Test matches
    • 3.1.4. Online self-tests
    • 3.1.5. Live interviews compared to video testing
  • 3.2. Problems with ADHD testing: measurement of ADHD symptoms does not occur consistently
    • 3.2.1. ADHD sufferers perform as well as non-affected individuals on tests when properly motivated
      • 3.2.1.1. No / fewer ADHD symptoms in tests with high motivation
        • 3.2.1.1.1. Attention tests
        • 3.2.1.1.2. Impulsivity (inhibition) can be improved by rewards
        • 3.2.1.1.3. Reaction times: Reward accelerates reaction times and decreases response variance in ADHD only
      • 3.2.1.2. ADHD symptoms more visible in tests with low demand
    • 3.2.2. ADHD symptoms and relationship to subject
    • 3.2.3. Smoking masks ADHD symptoms
    • 3.2.4. 116200 Different ADHD “subtypes”
    • 3.2.5. Ratings by parents and teachers differ significantly
    • 3.2.6. Conclusions
• 4. Objective measurement methods (biomarkers) in ADHD
  • 4.1. EEG / QEEG measurement
    • 4.1.1. Beta-theta ratio as a diagnostic tool
    • 4.1.2. Changed slope / offset of the 1/f noise in the EEG
    • 4.1.3. Measurement of evoked potentials
    • 4.1.4. Measurement of rsEEG
    • 4.1.5. Measurement of non-linear EEG values
    • 4.1.6. Phase space reconstruction of the EEG
    • 4.1.7. Gamma levels reduced in ADHD
    • 4.1.8. EPSPatNet86: 8-point EEG measurement on 85 wavelets
  • 4.2. Genetic testing
  • 4.3. Endocrine and physical biomarkers
    • 4.3.1. Measurement of cortisol levels
      • 4.3.1.1. Measurement of basal cortisol level
      • 4.3.1.2. Measurement of phasic cortisol response to psychological stressors
    • 4.3.2. Dexamethasone/ACTH/CRH test?
    • 4.3.3. Mineral analysis
    • 4.3.4. White matter axon damage
    • 4.3.5. 24-Hour motion profiles
    • 4.3.6. Facial Features
    • 4.3.7. MRI brain image analysis
• 5. Diagnostic guidelines
  • 5.1. Psychiatric history
  • 5.2. Differential diagnosis
    • 5.2.1. Exclusion of organic causes
    • 5.2.2. Exclusion of other psychological causes
  • 5.3. Interview with parents/trustworthy persons
  • 5.4. Standardized survey of ADHD symptoms
  • 5.5. Test psychological performance diagnostics

1. Questionnaires for ADHD diagnosis¶

The questionnaires are filled out by the affected persons themselves and reference persons (parents, teachers, friends).

Questionnaires are very subjective and contain the danger that the personal opinion of the person answering the question about ADHD itself influences the standard of answering. It happens that parents reject the diagnosis of ADHD in principle, even more so in the case of their own child. Likewise, subjective ideas of affected persons (perhaps that they “want” a diagnosis in the hope of finding a solution to their suffering, perhaps that they reject a diagnosis because they fundamentally dislike ADHD or in order to avoid stigmatization) or the change in assessment standards due to intensive prior involvement can distort the results (bias).

For example, in tests of elimination diets for ADHD, the results of parent surveys are always far more positive than the results of objective tests.² Since this occurs even in double-blind studies, there is a significant bias of parents to report the subjectively desired outcome (that ADHD could be treated by diet rather than by critically considered medication).
However, it is also conceivable that the parents were already enthusiastic about the small improvements that an elimination diet can produce or that the placebo effect can produce on the affected child and were not even aware of the much better effect that could be achieved by medication or therapy, or at least were not aware of it at the time of assessment.

Appropriate questionnaires for ADHD are:

• Conners Rating Scales³
• Conners 3-Parent Short Form, C 3-P(S)⁴
• Conners 3-Teacher Short Form, C 3-T(S)⁴
• Conners Early Childhood.⁵
• ASRS 1.1., WHO ADHD screening⁶
  • 6-Item Short Screening
  • 18 item long screening
• Youth Self-Report, YSR/11-18 (questionnaire for youth)⁷
• Child Behavior Checklist, CBCL/1.5-5 (Parent Behavior Questionnaire
  of preschool children between 1,5 and 5 years)⁸⁴
• Child Behavior Checklist, CBCL/6-18 (Parent Behavior Questionnaire
  of children and adolescents from 4 to 18 years)⁹⁴
• Child and Adolescent Behavior Inventory (CABI)
  CABI is a parent questionnaire with 75 questions.
  CABI was more accurate than CBCL with respect to ADHD and anxiety, CBCL was more accurate with respect to conduct disorder (CD) and oppositional defiant behavior (ODD).¹⁰
• Teacher Rating Form (TFR 6-18)⁹⁴
• Parent/Teacher Questionaries (Conner)¹¹
• Parent / Teacher Questionnaires¹²
• Quantitative Behaviour Test (QbTest)
  70% accuracy in adults 55 to 79 years of age. In combination with self-reported ADHD symptom severity, 91% accuracy.¹³¹⁴
• Assessment sheet for parents, teachers and educators (FBB-HKS)¹⁵
• “ADHD Screening for Adults (ADHD-E)” (including severity of expression compared to norm scores)
• Screening test with self-report scale V1.1 for adults with ADHD (ASRS-V1.1) (global assessment, no quality criteria on German-speaking countries)
• Before School Functioning Questionnaire (BSFQ)¹⁶
• Parent Rating of Evening and Morning Behavior Scale, Revised (PREMB-R)¹⁶
• ADHD/ODDEFB: ADHD/ODD parent questionnaire. Steinhausen (2002)
• AD-H-D test system: attention and hyperactivity deficit disorder questionnaire test. Sponsel (2002)
• CAPT: Continuous Attention Performance Test - German version. Nubel, Starzacher, Grohmann (2006)
• BADD: Brown Attention Deficit Disorders Scale, a 40-item self-completion questionnaire assessing cognitive ADHD symptoms

A meta-study of 9 ADHD questionnaires found that only 37% asked about identical symptoms/behaviors.¹ Most questionnaires for children were answered by parents, most questionnaires for adults were self-tests.

• Behavioral symptoms was asked from 28% to 81% of the questions of the questionnaires
• Cognitive symptoms were addressed by 9% to 44% of the questions
• Emotional symptoms concerned between 0 % and 24 % of the questions
• Physical symptoms were only asked about at all in 3 of the 9 instruments

In the case of questionnaires, a study showed considerable discrepancies in the assessments of parents, teachers and affected persons, with the exception of emotional dysregulation, which affected all existing symptoms, but even more so the frequency of their occurrence.¹⁷

Emotional dysregulation (also in ADHD) can be tested with the

• Reactivity, Intensity, Polarity and Stability questionnaire (RIPoSt-40)¹⁸

Individual studies found that ADHD-affected children performed worse on tests with slow event rates, while their scores on exciting, challenging tasks were comparable to those of unaffected individuals^{19 20}

It fits in with this that the results of tests with ADHD sufferers change when rewards are promised.²¹ This also suggests that it is not the ability to concentrate or inhibit per se that is impaired, but rather the insufficient activation by “normally interesting” stimuli that is the real key.

2. Interviews for ADHD diagnostics¶

Interviews are questionnaires that are filled out by the interviewing physician/therapist. These are usually very closely oriented to the DSM criteria.²² This narrows down the diagnosis unpleasantly, since DSM as well as ICD use only a very narrow catalog of symptoms.

• Diagnostic Interview Schedule for Children (DISC-IV)²³
  Acquisition period 6 months

• Diagnostic Interview for Children and Adolescents (DICA-R)²⁴

• Child and Adolescent Pschiatric Assessment (CAPA)²⁵
  Acquisition period 6 months

• Schedule for Affective Disorders and Schizophrenia for School-Age Children (K-SADS)²⁶

• Childrens Interview for Psychiatric Syndromes (ChIPS)²⁷

• Swanson, Nolan and Pelham-IV (SNAP IV)

  • 26 items across 18 ADHD and 9 ODD symptom criteria, aligned with DSM IV.
    Free of charge for clinical use.²⁸ Exists only in English language.²⁹

• Scale for the Assessment of Currently Present Symptoms of Attention Deficit/Hyperactivity Disorder and Oppositional Defiant Disorder According to DSM IV (ADHD-ODD Scale)³⁰

  • According to Kiddie-Sads-Present and Lifetime Version, K-SADS-PL

• Homburger ADHD Scales for Adults (HASE)
  HASE consists of four individual procedures³¹

  • Wender Utah Rating Scale - German short form (WURS-K)
    Objective: retrospective diagnosis of childhood ADHD-HI symptoms.
  • ADHD Self-Assessment Scale (ADHD-SB)
    Measurement of the 18 diagnostic criteria of DSM-IV and ICD-10.
  • ADHD Diagnostic Checklist (ADHD-DC)
    External assessment scale for experts based on DSM-IV and ICD-10 criteria
  • Wender Reimherr Interview (WRI)
    structured interview with 28 psychopathological characteristics that are particularly relevant for ADHD diagnosis in adults

3. Tests for ADHD diagnostics¶

Attention tests are significantly more objective than questionnaires.
But even with them there is a risk that the result can be distorted. Influences can arise from training, hyperfocusing of an ADHDer, test anxiety of a non-affected person or high giftedness. Especially the latter points should therefore always be clarified by differential diagnosis.

3.1. Test procedures for ADHD diagnostics¶

Source, with more detailed explanations: Schmidt, Petermann.³²

3.1.1. Neuropsychological test procedures¶

• Test battery for attention testing (TAP)
• Attention Load Test - Revision (d2)
• Frankfurt Attention Inventory (FAIR)
• Test battery for young professionals - concentration (START-K)
• Frankfurt Adaptive Concentration Performance Test (FAKT-II)
• BLAST (Bron/Lyon Attention Stability Test): Computerized test to identify task switching problems³³
• Cambridge Neuropsychological Test Automated Battery (CANTAB)
  The CANTAB was not found to be useful in diagnosing ADHD in one study.³⁴
• Conners’ Continuous Performance Test 3rd Edition (CPT3)
• Conners’ Continuous Auditory Test of Attention (CATA)
  CPT3 plus CATA had higher sensitivity (82.6%), higher specificity (76%), higher positive predictive value (88.8%), higher negative predictive value (65.5%), and higher overall correct classification rate (80.6%) than CPT3 or CATA alone.³⁵

3.1.2. Individual attention and reaction tests¶

One study found that Stroop Test, Stroop Plus Test, and Perceptual Selectivity Test did a good job of distinguishing ADHD in adults from non-affected individuals, with Stroop Test narrowly coming out on top. However, while Stroop Test and Stroop Plus Test differed according to the age of the subjects, Perceptual Selectivity Test showed little difference across age.³⁶ Other studies also showed that subjects’ performance on the Stroop Test was age-dependent, with sometimes younger and sometimes older subjects performing better. In the study cited here, adult ADHD sufferers, like non-affected individuals, performed less well on the Stroop Test at older ages.
The Stroop test is designed to examine selective attention in particular.³⁷

3.1.2.1. Stroop test¶

Study objective: selective attention in the dorsal anterior cingulate cortex (dACC) → striatum → thalamus → dACC network.³⁷

Color word color interference test.
4 color words (green, red, blue, yellow) are visually displayed in different colors. The color word as well as the 4 non-color answer buttons on the right side of the screen below each other are always in the same position. The color words are displayed for 2 seconds, in between for 0.75 seconds a point to be fixed. In every fourth display, word and color match, in the others they do not. The subject is asked to name the visually displayed color of the displayed word as quickly as possible.
If the color word and the color are different, this is more difficult. ADHD sufferers have a higher error rate (in %) and a higher reaction time (in ms).³⁶

3.1.2.2. Stroop Plus Test¶

Study objective: selective attention in the dorsal anterior cingulate cortex (dACC) → striatum → thalamus → dACC network.³⁷

In the Stroop Plus Test, the structure of the Stroop Test is extended by four colored boxes that are arranged around the word. Along with the word, an arrow appears between the word and the boxes, pointing to one of the colored boxes. As in the Stroop test, the visually displayed color of the word and the color known by the word do not match in 3 out of 4 runs. Only in every ninth run do the color, word, and displayed color box match. The subject is asked to name the visually displayed color of the displayed word as quickly as possible.³⁶

3.1.2.3. Perceptual Selectivity Test¶

Study objective: selective attention in the dorsal anterior cingulate cortex (dACC) → striatum → thalamus → dACC network.³⁷

In the center of the screen the Stroop test is presented. Around the word, four symbols (shape: circles or squares) are displayed in different colors (blue and yellow), 3 of which show one shape and one the other. In half of the runs, 3 symbols have the same color and one has the other color, in the other half, 2 each have one color and 2 have the other color.
In each of the shapes, a line is displayed that points (thought of as an hour hand) in at 12 o’clock, 13:30, 15 o’clock or 16:30. The subject should simply pay attention to the orientation of the line in the symbol that shows the individual shape (which here can be horizontal or vertical) and confirm it as quickly as possible with the assigned key.
This test is designed to measure what is known as perceptual selectivity, a subset of selective attention. This term refers to the discriminability of a stimulus, i.e., how effectively the participant can distinguish the target task when confronted with a single stimulus (shape change only) and with two stimuli (shape change and the presence of an irrelevant color).³⁶

3.1.2.4. N-back test¶

Study objective: sustained attention, working memory in the network dlPFC → striatum → thalamus → dlPFC.³⁷

N is a variable.
In the 0-back visual test, the subject is shown a number on a screen and is asked to press the key corresponding to the number. In the 1-back variant, the subject is asked to press the key of the number that was shown before the one just displayed. In the 2-back variant, the subject is asked to press the penultimate number before the number shown. The higher N is, the higher the load on working memory.

The N-back test can also be queried by playing an acoustic number announcement. There is also a variant in which various fields light up one after the other on a square field and the respondent has to tap the field previously activated by N displays.

3.1.2.5. Stop Signal Task (SST)¶

The stop signal task is used to measure impulsivity or inhibition.

The subject is to log results on visual signs that have two outcome alternatives. For example, a left or right key should be pressed as quickly as possible in response to arrow symbols pointing to the left or right. After a few unscored practice passes, the subject should not press a key when a simultaneous audio signal is heard, which sounds irregularly for about 25% of the passes. The stop signal sounds with a delay after the stimulus. This delay time can vary between 100 ms and 600 ms.³⁸

The following are measured

• Directional error count
• Proportion of successful stops
• Response time for Go - attempts
• Stop signal response time (SSRT)

The stop signals vary in different passes to calibrate the test results of the stop signal response time to 50% error to obtain the most comparable measured value. Here is a Video of the stop signal task on Youtube.

The Stop Signal Task is part of the CANTAB. The CANTAB was not found to be suitable for the diagnosis of ADHD in one study.³⁴

One study converted the stop-signal task to an application with mouse control instead of buttons, which showed several improvements in measurements:³⁹

• The SSRT shows a weak association with impulsivity, while the mouse movement measure shows a strong and significant association with impulsivity
• A machine learning model (weak AI) was able to accurately predict impulsivity ratings of “unknown” participants using mouse movement data from “known” participants
• Mouse movement characteristics such as maximum acceleration and maximum velocity are among the most important predictors of impulsivity
• The use of preset stop signal delays leads to a behavior that better indexes impulsivity than adjusting the delay value to previous results (staircase model)

One study found evidence that performance on stop-signal tasks in ADHD reflected impairments in early attentional processes rather than inefficiencies in the stopping process.⁴⁰

3.1.2.6. Children’s Color Trail Test (1/2)¶

The Children’s Color Trail Test measures attention, divided attention, and speed of mental processing and is used to diagnose ADHD, among other conditions.⁴¹⁴²

3.1.2.7. Continuous Performance Test (CPT)¶

A 14-minute continuous performance test (CPT), which requires a high reaction rate, shows as variables of improved performance with age:⁴³

• Response time (RT)
• RT standard error
• Ellipsis
• Commitment error
• Signal detection parameters (d’ and beta)

Gender-specific are in particular

• more impulsive mistakes in men
• lower variability in men
• faster RT in men

3.1.2.8. Digit Span Test¶

The Digit Span Test is a part of the Wechsler Adult Intelligence Scale- Revised (WAIS).
The memory capacity of the working memory for numbers is measured. The subjects see or hear a sequence of digits and are asked to reproduce it correctly (forwards or backwards). During the test, the length of the digit sequences is increased. The subject’s number span is the longest number of digits that he or she can correctly reproduce.
At https://tools.timodenk.com/digit-span-test a Digit Span test can be taken online.

3.1.2.9. Wisconsin Card Sorting Test¶

The Wisconsin Card Sorting Test is available in both electronic and manual forms.⁴⁴

3.1.3. Test matches¶

• Nesplora aquarium
  Virtual reality game / test, experimental. It is designed to predict ADHD symptoms in adults and adolescents based on current and retrospective self-reports.⁴⁵
• In another approach, artificial intelligence/machine learning was able to correctly diagnose ADHD with 81% probability in players of an online role-playing game (PlayerUnknown’s Battlegrounds).⁴⁶ Generalized anxiety disorder was detected with 84.9 %.

3.1.4. Online self-tests¶

Most online self-tests freely available on the Internet allow a rough initial assessment. However, preliminary exposure to such tests distorts the test results of subsequent tests (bias).
We have developed our own - quite comprehensive - online self-test, which shows an agreement of around 93% with existing ADHD diagnoses and around 96% with the CAARS-L. Nevertheless, like any online self-test, it is only an indication and can in no way replace a medical diagnosis.
⇒ ADHD online tests

3.1.5. Live interviews compared to video testing¶

One study found that live interviews and interviews via video showed no difference in outcomes.⁴⁷

3.2. Problems with ADHD testing: measurement of ADHD symptoms does not occur consistently¶

When testing ADHD sufferers, it must always be kept in mind that the symptoms being measured are not always and consistently present in ADHD sufferers. Rewards change the symptom pattern in ADHD.²¹ In particular, high motivation of the affected person to participate in the test (intrinsically or through high extrinsic incentives) can distort the test results. This can go so far that the test is negative, although ADHD is present.

3.2.1. ADHD sufferers perform as well as non-affected individuals on tests when properly motivated¶

In ADHD, motivation is altered. Extrinsic incentives must be higher than in non-affected persons to trigger the same motivation.

Against this background, computer games that aim to measure ADHD may face the problem that test results lose significance the more the computer game is fun. One study found nonsignificantly higher levels of flow and intrinsic motivation in a stop-signal game than in a non-game stop-signal test.⁴⁸

3.2.1.1. No / fewer ADHD symptoms in tests with high motivation¶

3.2.1.1.1. Attention tests¶

In ADHD, it is not the technical ability to pay attention or to direct attention that is impaired, but the execution of attention direction is subject to different criteria than in non-affected persons. In ADHD, attentional control is subject to a deviant control, which is much more dependent on the satisfaction of one’s own needs than in non-affected persons. However, this is not egoism, but an altered control of motivation, over which the affected person has no influence.

If personal needs are addressed by intrinsic interest or by rewards (which must be higher on average than for nonaffected individuals), the attention performance of ADHD sufferers improves more than that of nonaffected individuals.⁴⁹ A typical example is described by Ryffel-Rawak.⁵⁰
A meta-study confirms that in ADHD, high rewards greatly improve test scores.⁵¹

In ADHD, deactivation of the default mode network (DMN), as occurs with attention to the environment, is markedly reduced. Personal motivation elicited by high rewards equalized DMN deactivation in ADHD, making it equivalent to that of unaffected individuals. MPH showed the same effect.⁵²

3.2.1.1.2. Impulsivity (inhibition) can be improved by rewards¶

Impulsivity in ADHD (inhibitory ability) can also be influenced by rewards,⁵⁴ to outcomes equivalent to controls, whereas the same rewards improved inhibition less in children with brain injury.⁵⁵ Another study also found identical inhibitory ability in ADHD sufferers and nonafflicted individuals at high rewards.⁵⁶ Another study found no improvement.⁵⁷ It would be necessary to examine whether the rewards here may have been too low to arouse personal motivation.

3.2.1.1.3. Reaction times: Reward accelerates reaction times and decreases response variance in ADHD only¶

Test results on reaction times in ADHD are inconsistent. Many tests show decreased, and individual tests show increased, reaction times in ADHD sufferers. ⇒ Reaction time reduction in ADHD?

On the overall average, reaction time varies more in ADHD sufferers than in non-affected individuals.
⇒ Reaction time variance increased in ADHD

In one study, under reward, only ADHD sufferers and their unaffected siblings showed accelerated mean reaction times and decreased response variability, but not controls, whereas accuracy improved in all 3 groups.⁵⁸

3.2.1.2. ADHD symptoms more visible in tests with low demand¶

Activating, exciting tasks can cause test results of ADHD sufferers to match those of controls.⁵⁹ Thus, test results of ADHD sufferers show inconspicuous(r) results when a high test event rate (fast) is presented, while a low event rate (slow test) shows significant(r) differences.⁶⁰⁶¹

One study hypothesizes that a slower event rate on tests in ADHD sufferers will result in decreased intrinsic motivation due to their peculiarities in motivability.⁶²

The optimal arousal range, in which no under- or overstimulation occurs, is much narrower in ADHD than in non-affected individuals.
Even more important than the optimal arousal range, however, is the fact that ADHD sufferers can direct their attention just as well as non-affected persons if personal motivation is present (example: hyperfocus). Against the background of the stress benefit of altered attentional control in ADHD, this is understandable.
⇒ Stress benefits of distractibility, task switching problems, and attention problems

This limited capacity may exist not only in terms of under- and overload, but also in terms of time, in the sense that the ability to compensate for under- or overload is limited. One teacher described her perception of a child with ADHD-I to us by saying that she could watch him “fill up” in the first lesson and then develop the typical ADHD-I symptoms.

3.2.2. ADHD symptoms and relationship to subject¶

One study addresses how the relationship with the test administrator affects test scores in ADHD sufferers.⁶³

3.2.3. Smoking masks ADHD symptoms¶

Smoking as self-medication increases dopamine levels - albeit only ever in the short term. It further reduces stress symptoms and irritability. ADHD sufferers smoke about twice as often as non-affected people.
Smoking may therefore complicate the diagnosis of ADHD.⁶⁴

A study of emotional dysregulation in ADHD-affected smokers found no differences between those who smoked as usual and those who abstained from smoking for 24 hours.⁶⁵
It is well known that withdrawal from alcohol and other drugs requires a period of 2 to 4 weeks so that the receptors, which are downregulated by the high supply of neurotransmitters (dopamine, etc.), can be upregulated again. Among other things, this is responsible for the withdrawal symptoms. This is also true for nicotine. It is possible that only tests that require nicotine withdrawal of more than 2 to 4 weeks can show a difference.

3.2.4. 116200 Different ADHD “subtypes”¶

One study calculated that there were 116,200 different ADHD subtypes - if each symptom combination was considered its own subtype.⁶⁶

Against this background, the currently (still?) common test procedures, which (using the example of DSM 5) are based on 9 characteristics for hyperactivity/impulsivity and 9 characteristics for inattention, of which at least 6 characteristics must be fulfilled in at least one category, should be questioned. Affected individuals with 5 features in both categories would qualify as “not ADHD” by DSM 5. Not all physicians realize that DSM and ICD are merely guides and are not diagnostic measures - a misunderstanding that Francis Allen, the head of the DSM IV Commission, already strongly warned against.

Against this background, we consider the approach of diagnostics based on a query of a large number of symptoms, which Barkley and we have pursued, to be well worth considering. ⇒ The big ADxS.ORG - ADHD online test

3.2.5. Ratings by parents and teachers differ significantly¶

One study found that teachers’ and parents’ ratings of the effects of drug treatment for ADHD did not correlate. These strong differences demonstrate that different sources should be used when evaluating ADHD.⁶⁷

3.2.6. Conclusions¶

For reliable results, tests for ADHD would have to be conducted in different test environments that take into account the degree of underchallenge, appropriate arousal, and overchallenge. Furthermore, smoking behavior would have to be included in the weighting of test results, which has not been done sufficiently so far. Testing on the basis of all possible ADHD symptoms seems to us to be an option worth considering.

4. Objective measurement methods (biomarkers) in ADHD¶

To date, no diagnosis of ADHD has been established by measuring objective biological or neurological scores.

However, measurement procedures have already been researched far enough to validate and support diagnoses. The advantage of measurement procedures is that the result is not distorted by prior knowledge, training or special motivational situations (interest, hyperfocus).
However, it is known that ADHD sufferers “outsource” the functions of disturbed brain areas to other brain areas in order to compensate for the deficit. Similarly, symptoms are reduced by coping, for example by avoiding situations in which the distressing symptom may occur, or by reducing individual symptoms through intensive training. If only symptoms are asked about, the symptom masked by outsourcing to other brain areas or by coping may not be noticed. This is one of the reasons why tests (including our own online tests) alone can never be sufficient to diagnose or rule out ADHD with certainty.

It is hoped that with further refinement of measurement techniques and knowledge of outcome assessment, more objective diagnosis than by questionnaires and tests will become possible in the foreseeable future.

However, the fundamental problem with biomarker measurements is that they can only ever produce a snapshot. The results of this snapshot depend on whether the person concerned is under acute stress at the time of measurement or not. The usual ADHD diagnosis therefore correctly focuses on whether the (stress/ADHD) symptoms exist in the long term, i.e. also outside acute circumstances triggering particular stress.

To date, the relevant literature has not addressed the extent to which biomarkers can reliably distinguish long-term changes from merely acute stress responses.

4.1. EEG / QEEG measurement¶

Measurement of QEEG or EEG can support diagnoses. Further information on QEEG and therapy of ADHD using neurofeedback at ⇒ Neurofeedback as ADHD therapy In the chapter Treatment and Therapy.

4.1.1. 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 appeared promising, this diagnostic approach does not seem suitable for diagnosing ADHD so far.

The theta-beta ratio is reported 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.

From our discussions with a neurofeedback therapist, interesting findings emerged in this regard:

• Not only in ADHD, but also in several other mental disorders, such as depression, compulsions, trauma, or even migraine sufferers, beta increases in the 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 mind spinning and sleep problems.
  We conclude from this that the theta-beta ratio in the laboratory setting can at best enable an unambiguous diagnosis as a delineation of a group of ADHD sufferers in comparison to a group of (proven) healthy individuals. However, it is probably not suitable as a diagnostic tool in real life - for persons who do not know whether they suffer from ADHD or another disorder, or whether they 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 several 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 ADHD-I sufferers (without hyperactivity, dreamers) theta is typically too high and beta typically too low. Here, the theta-beta ratio should be rather too low - contrary to what is postulated 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 the exact opposite happens: theta is reduced and beta is increased (concentration training). Therapeutically effective in both cases is not only the objective change of the values, but the learned influenceability of the values, even if this changes the values in the direction of average values.

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

• Methylphenidate causes greater agitation within the QEEG values within a few minutes. This is unsurprising because methylphenidate is a stimulant. As expected, methylphenidate impeded the theta increase and beta decrease in ADHD-HI targeted by neurofeedback. This is not surprising because MPH is a stimulant. It justifies why MPH should be taken in such a way that the effect has worn off by bedtime.
• Sugar (2 candy bars within 5 minutes at 90 kg body weight) caused significant changes in QEEG in an ADHD-HI subject.
  • Within 10 minutes, Beta1 increased significantly.
    The threshold values during theta up / beta down training had to be reduced considerably. The 85% of target values reached before 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 well above SMR. (Beta1 should be below SMR, which is why SMR training (targeting it) is the first step of neurofeedback treatment for ADHD).
  • After 30 minutes, Beta1 had caught up somewhat with SMR. Hi-Beta was now significantly higher.

Furthermore, there is the problem that different EEG measurement software products seem to use different calculation methods to determine the beta-theta ratio and therefore produce significantly different results with the same data.⁷⁴ Therefore, the International Collaborative ADHD Neurofeedback (ICAN) randomized clinical trial used a fixed theta beta ration cutoff of ≥ 4.5 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 significant influence on the change in QEEG.
  ⇒ Binaural music for ADHD and sleep problems.

One study concluded that the theta-beta ratio of the EEG is a reflection of cognitive processing activity in both unaffected and ADHD-C affected individuals and can be measured by P 300 latency in the acoustic oddball test. In contrast, absolute alpha power did not correlate with P 300 latency or amplitude in ADHD-C affected subjects.⁷⁵

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

“1/f noise” in the EEG are arrhythmic signals in the cortex, which are neural noise typical for ADHD. Increased individual reaction time variance is a sign of increased neural noise. MPH improves this.⁷⁶

In 3 to 7-year-old children with ADHD, a steeper slope and a larger offset were found with respect 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 larger alpha power was found.⁷⁷

4.1.3. Measurement of evoked potentials¶

Another objectified diagnostic method is the measurement of evoked potentials. Here, characteristic EEG amplitude curves triggered by stimuli or actions are measured in various brain regions typically affected in 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 persons and data from affected persons with other disorders, which are collected in so-called QEEG databases.

The diagnostic accuracy of the measurement of evoked potentials is growing rapidly. While Strehl et al cite studies from 2005 with a (not yet satisfactory) diagnostic accuracy of 70 to 80 %, Müller et al 2011⁷⁸ already claim 89 % diagnostic accuracy: from a group of 212 adults with 106 ADHD sufferers, 89 % of the sufferers were correctly diagnosed using a 19-channel system through automatic evaluation of EVP. Models with machine learning achieve 94 %⁷⁹
However, these results were obtained in laboratory situations in which only ADHD was to be distinguished from non-affected persons. In real-life diagnostics, ADHD has to be distinguished from a multitude 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 88% diagnostic agreement⁸⁰

One study found no significant differences in evoked potentials between twins with and without ADHD on a continuous performance task.⁸¹

A meta-analysis of 52 studies involving 3,370 subjects on early (P100, N100, P200, N200, ERNNe) and late (P300, Pe, CNV) evoked potentials found that in ADHD⁸²

• 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 results and moderate effect sizes (d<0.6) at the same time.

Another study concluded that evoked potentials show very different correlations to symptoms depending on the age of the subjects (childhood or adolescence).⁸³

The diagnosis by means of evoked potentials is unfortunately time-consuming (4 hours of tests, which are subsequently evaluated) and consequently not cheap. At the Brain and Trauma Foundation Graubünden Switzerland in Chur, a diagnosis cost around 1250 francs (as of 2015).

4.1.4. Measurement of rsEEG¶

The measurement of 14 resting state EEG parameters was able to identify participating ADHD sufferers with an accuracy of around 85% in a process optimized by machine learning.⁸⁴ Since in the wild it is not only possible to diagnose against healthy individuals but also against other disorders, it is questionable whether the result will be accurate enough in practice to allow a meaningful application.

4.1.5. Measurement of non-linear EEG values¶

A measurement of non-linear EEG values showed a better diagnostic quality of ADHD than other EEG diagnostic methods, according to one study.⁸⁵

4.1.6. Phase space reconstruction of the EEG¶

One study found that a phase space reconstruction of the EEG can very well differentiate ADHD from non-affected individuals. ⁸⁶ However, for diagnostic application, a method must also be able to differentiate well from other disorders.

4.1.7. Gamma levels reduced in ADHD¶

One study reported elevated resting gamma levels in adults with ADHD in the range of 30 to 39 Hz (gamma1), but not in the range 39 - 48 Hz (gamma2). Resting gamma1 power increased with age and was significantly lower in ADHD than in controls in early adulthood.⁸⁷

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

A study of 85 EEG wavelet factors was able to correctly classify 87% of ADHD subjects using machine learning.⁸⁸ It remains to be seen what the recognition rate will be in an open group of subjects.

4.2. Genetic testing¶

To date, no usable genetic tests exist for the diagnosis of ADHD.
Meanwhile, first voices advocate (2020) to integrate certain genetic tests (here: array-CGH) into the standards of ADHD diagnostics.⁸⁹

There are several difficulties for genetic diagnosis in ADHD:

• ADHD is usually not caused by single gene variants, but by an interaction of a high number (several hundred to thousands) of gene variants (⇒ Gene candidates in ADHD)
• The (epigenetic) expression of genes (e.g., due to early childhood or chronic severe stress) may additionally alter gene action independent of gene variant.
• It can be assumed that different combinations of the genes and forms of expression considered coincide in different affected persons, but not all genes that contribute, for example, to the fact that the dopamine balance is disturbed.
• There is not yet a complete set of candidate genes, which is why there is not yet a genetic test for ADHD. We have collected over one hundred and fifty candidate genes.
  ⇒ Gene candidates in ADHD.
• Genetic tests are still too expensive to be able to test the required number of genes.
  It is possible that in the coming years genetic tests will be affordable enough to be used diagnostically - and subsequently to tailor individual-specific treatment.

One study examined miRNAs from extracellular vesicles in serum. A longitudinal analysis examining changes in miRNA expression over time between four groups with different diagnostic histories (persistent diagnosis, first onset, remitted, and typical development/control) found no statistically significant results. In the cross-sectional analysis, upregulation of miR-328-3p was found in only 1 of 2 ADHD groups. These miRNAs may regulate the expression of genes associated with these traits in genome-wide association studies⁹⁰

4.3. Endocrine and physical biomarkers¶

4.3.1. Measurement of cortisol levels¶

Cortisol level 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

4.3.1.1. Measurement of basal cortisol level¶

Basal cortisol levels are reduced in ADHD compared to non-affected individuals because of long-term ongoing stress. In the case of short-term stress, the basal cortisol level initially rises.

This difference would, in purely theoretical terms, be usable to distinguish acute stress response (elevated cortisol levels) from collapsed HPA axis regulation (decreased levels)-if not for the fact that individual variation between individuals within a group would be greater than the increase or decrease in cortisol levels to levels in a healthy state.

According to this hypothesis, however, an individual upward or downward change in cortisol levels could be detected if they were recorded regularly (annually) from the earliest age (in children: on the occasion of standard examinations).
However, without historical values of basal cortisol levels, such 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 occurring 20 to 40 minutes after waking.

A comparative graphical representation of the cortisol profiles of healthy, acutely stressed, chronically stressed, in partial burnout and in full burnout people can be found in Bieger.⁹¹

However, even here there is the difficulty that without individual comparative data an evaluation of the 24-hour picture of cortisol levels is hardly diagnostically usable.

A measurement of cortisol in hair (reflecting long-term levels) showed that low cortisol levels in preschool children predicted the expression of ADHD.⁹²
This could possibly be a result of a long-term stress response. The third stress development phase (resistance phase) typically shows a collapse of basal cortisol levels. The fourth phase (exhaustion phase) is then characterized by a breakdown of neurotransmitter systems. See more at ⇒ Resistance phase In the section ⇒ The stress response chain / stress phases of the article ⇒ The stress systems of humans - basics of stress.

However, changes in basal cortisol levels are not specific to ADHD and therefore have little utility for ADHD diagnosis.

4.3.1.2. Measurement of phasic cortisol response to psychological stressors¶

Based on the phasic cortisol response, the cortisol tolerant subtype of ADHD can be identified.

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

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

The Trier Stress Test (TSST) is typically used as the psychological stressor.
This is unlikely to be suitable for repeated (even annual) examination in view of the expected habituation.

4.3.2. Dexamethasone/ACTH/CRH test?¶

By means of a dexamethasone/ACTH/CRH test, it can be determined whether there is a permanent dysregulation at individual levels of the HPA axis.

If ADHD is characterized by a permanent dysregulation of the stress regulation systems, first and foremost the HPA axis, it should be possible to prove the individual dysregulation by appropriate testing of the HPA axis reactions (level changes of the stress hormones CRH, ACTH, cortisol to acute stressors). At the very least, corresponding tests should yield informative clues for the individual differentiation of membership in subtypes.

Dexamethasone is a glucocorticoid that selectively binds to glucocorticoid receptors and should thereby trigger downregulation of the HPA axis (after the stress response has occurred). If this downregulation fails to occur, this is a strong indication of a disturbance in the deactivation of the HPA axis.

See more at ⇒ Pharmacological endocrine function tests.

4.3.3. Mineral analysis¶

One study was able to create an impressively clear distinction between non-affected, ADHD, ASD, and affected comorbidities by analyzing zinc, lead, copper, cobalt, and vanadium from teeth.⁹³

4.3.4. White matter axon damage¶

A study of 50 untreated ADHD sufferers compared with their unaffected twins and 50 control subjects found higher axonal diffusivity in the ADHD sufferers’⁹⁴

• Perpendicular fasciculus
• Superior longitudinal fasciculus I
• Tractus corticospinalis lateralis
• 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 ADHD-affected and unaffected twins. Axial diffusivity is a marker of axonal undamage.

4.3.5. 24-Hour motion profiles¶

One study claims to have achieved diagnostic accuracy for ADHD-C of over 97% by evaluating neural network analyses of 24-hour movement profiles.⁹⁵

4.3.6. Facial Features¶

One study concluded that there may be a close relationship between ADHD and nose width, ear length, and upper face depth, which may be related to genetic signaling processes and a close relationship between brain and face formation processes in the embryonic period.⁹⁶

4.3.7. MRI brain image analysis¶

Optimized machine learning was able to discriminate very well between ADHD and controls and between persistent and remitting ADHD in young adults using MRI neuroimaging images. Relevant features of ADHD compared to controls were identified as:⁹⁷

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

Remission of inattention and hyperactivity/impulsivity correlated with

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

5. Diagnostic guidelines¶

Guidelines for ADHD diagnosis recommend the following diagnostic approach:⁹⁸

5.1. Psychiatric history¶

Survey of the individual problem situation including comorbid disorders, the developmental history of the person concerned and his family.

5.2. Differential diagnosis¶

5.2.1. Exclusion of organic causes¶

Differential diagnosis should be performed in parallel with ADHD diagnosis to ensure that symptoms do not result from other dominant organic causes.
⇒ Differential diagnostics in ADHD

5.2.2. Exclusion of other psychological causes¶

In mental differential diagnosis, particular care should be taken not to confuse the ADHD symptom of dysphoria with inactivity with depression. In our estimation, this is likely to be one of the most frequent misdiagnoses in ADHD and contributes to frequent mistreatment.
⇒ Depression and dysphoria in ADHD

5.3. Interview with parents/trustworthy persons¶

Retrospective and current symptomatology interviews and use of standardized assessment scales.

5.4. Standardized survey of ADHD symptoms¶

Use of standardized procedures for a detailed survey of relevant symptoms and their expression.

5.5. Test psychological performance diagnostics¶

Use of procedures to determine general cognitive performance levels.