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An ADHD diagnosis is traditionally determined using questionnaires, interviews and tests. In principle, several different instruments should be used. This means that several questionnaires, several tests and a personal interview by the diagnostician are essential for a good diagnosis.
The agreement between questionnaires and tests is limited despite the validity and reliability of the respective tests being checked.
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
ADHD is typically diagnosed by the number of relevant symptoms. This increases the diagnostic accuracy of ADHD.2 This model was exemplified by Barkley:
Non-affected people often have 1 to 2 of 18 symptoms on average (around 5%)3
ADHD sufferers often have 12 of these 18 symptoms on average (around 66%)3
The online screening of the ADxS.org symptom test is based on this model. The test queries 45 symptoms, but is not medically validated and is not used for medical diagnosis.
Questionnaires are filled out by the affected person themselves and caregivers (parents, teachers, friends).
Questionnaires are very subjective and entail the risk that the respondent’s personal opinion of ADHD itself influences the scale of response. It can happen that parents generally reject the diagnosis of ADHD, especially in their own child. Likewise, subjective ideas of those affected (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 reject ADHD or to avoid stigmatization) or the change in assessment standards due to intensive prior employment can distort the results (bias).
For example, in tests on elimination diets for ADHD, the results of parent surveys are always far more positive than the results of objective tests.4 As this occurs even in double-blind studies, there is a considerable bias on the part of parents to report the subjectively desired result (that ADHD can be treated with a diet instead of critically considered medication).
However, it is also conceivable that the parents were already enthusiastic about the small improvements that an elimination diet can bring or that a placebo effect can have on the affected child and were not even aware of the much better effect that could be achieved through medication or therapy, or at least were not aware of it at the time of the assessment.
Child Behavior Checklist, CBCL/1.5-5 (parent questionnaire on behavior)
of preschool children between 1.5 and 5 years)97
Child Behavior Checklist, CBCL/6-18 (Parent Behavior Questionnaire)
of children and young people from 4 to 18 years)107
Child and Adolescent Behavior Inventory (CABI)
CABI is a parent questionnaire with 75 questions.
CABI is more accurate than CBCL in relation to ADHD and anxiety, CBCL is more accurate in relation to conduct disorder (CD) and oppositional defiant disorder (ODD).11
Quantitative Behavior Test (QbTest)
70% accuracy in adults aged 55 to 79 years. In combination with self-reported ADHD symptom severity, 91% accuracy.1415
Assessment form for parents, teachers and educators (FBB-HKS)16
The ASRS rating scales have a very limited diagnostic value20
A meta-analysis of 9 ADHD questionnaires found that only 37% of the questionnaires asked about identical symptoms/behaviors.1 Most of the questionnaires for children were answered by parents, most of the questionnaires for adults were self-tests.
Youth Self-Report, YSR/11-18 (questionnaire for young people)21
Behavioral symptoms were asked in 28% to 81% of the questions in the questionnaires
Cognitive symptoms were addressed by 9% to 44% of the questions
Emotional symptoms accounted for between 0% and 24% of the questions
Physical symptoms were only asked about in 3 of the 9 instruments
In questionnaires, a study showed considerable deviations in the assessments of parents, teachers and those affected, with the exception of emotional dysregulation, all existing symptoms, but even more so the frequency of their occurrence.22
Emotional dysregulation (also in ADHD) can be tested with the
Reactivity, Intensity, Polarity and Stability questionnaire (RIPoSt-40)23
Individual studies found that children with ADHD performed worse on tests with a slow event rate, while their results on exciting, challenging tasks were comparable to those of non-affected children2425
It is consistent with this that the results of tests with ADHD sufferers change when rewards are promised.26 This also indicates that it is not the ability to concentrate or inhibit that is impaired per se, but rather that insufficient activation by “normally interesting” stimuli is the actual key.
Interviews are questionnaires that are completed by the doctor/therapist conducting the interview. These are usually based very closely on the DSM criteria.27 This narrows down the diagnosis in an unpleasant way, as the DSM and ICD only use a very narrow catalog of symptoms.
2.1. Interviews / instruments with preschool children¶
Behavior Rating Inventory of Executive Function in Preschool (BRIEF-P)28
2.2. Interviews / instruments with schoolchildren / adolescents¶
Diagnostic Interview Schedule for Children (DISC-IV)29
Recording period 6 months
Diagnostic Interview for Children and Adolescents (DICA-R)30
Child and Adolescent Psychiatric Assessment (CAPA)31
Recording period 6 months
Schedule for Affective Disorders and Schizophrenia for School-Age Children (K-SADS)32
Childrens Interview for Psychiatric Syndromes (ChIPS)33
Swanson, Nolan and Pelham-IV (SNAP IV)
26 items across 18 ADHD and 9 ODD symptom criteria, based on DSM IV.
Free of charge for clinical use.34 Only available in English.35
Scale for recording current symptoms of attention deficit/hyperactivity disorder and oppositional defiant disorder according to DSM IV (ADHD-ODD scale)36
According to Kiddie-Sads-Present and Lifetime Version, K-SADS-PL
Homburg ADHD Scales for Adults (HASE)
HASE consists of five individual procedures3839
Wender Utah Rating Scale - German short form (WURS-K)
Objective: retrospective diagnosis of childhood ADHD-HI symptoms.40 Strictly speaking, this refers to the age of 8 to 10 years. After the DSM criteria have been raised to the age of up to 12 years, the questionnaire must be adapted accordingly.
Method: Self-assessment
Time required: 10 - 15 min.
ADHD self-assessment scale (ADHS-SB)
Objective: To measure the 18 diagnostic criteria of DSM-IV and ICD-10.
Method: Self-assessment
Time required: 10 - 15 min.
ADHD Diagnostic Checklist (ADHD-DC)
External assessment scale for experts based on DSM-IV and ICD-10 criteria
Method: External assessment
Time required: 10 - 15 min.
Wender Reimherr Interview (WRI)
structured interview with 28 psychopathological characteristics that are particularly important for the diagnosis of ADHD in adults
Method: Interview
Time required: 25 - 35 min.
Wender-Reimherr self-assessment for adult ADHD (WR-SB)
new self-assessment scale for ADHD in adults
Method: Self-assessment
Time required: 30 - 45 min.
Test system “Integrated diagnosis of ADHD in adulthood” (IDA-R)41
IDA-R summarizes relevant self-assessment and external assessment instruments to enable a time-efficient and reliable diagnosis based on the current DSM standard. It is available as a print and online version. The set consists of a training video and 3 tests:
ASRS of the WHO
Validated short form of the Wender-Utah Rating Scale (WURS-K) for the retrospective assessment of ADHD symptoms in childhood and
A diagnostic interview based on the latest DSM criteria to assess the current symptoms.
Attention tests are much more objective than questionnaires.
On the one hand, however, there is a risk that the results may be distorted. Influences can arise from training, hyperfocus of an ADHD sufferer, test anxiety of a non-affected person or giftedness. The latter points in particular should therefore always be clarified through differential diagnosis.
On the other hand, their diagnostic value is very limited.
Barkley warns in no uncertain terms against the use of neuropsychological tests to diagnose ADHD. Neuropsychological tests - even if they test executive functions - can be successfully completed by more than half of those affected and therefore lead to false negative diagnoses. Rating scales, on the other hand, are much better suited to diagnosing ADHD.42
Test battery for career starters - concentration (START-K)
Frankfurt Adaptive Concentration Test (FAKT-II)
BLAST (Bron/Lyon Attention Stability Test): Computer-aided test to determine task switching problems44
Cambridge Neuropsychological Test Automated Battery (CANTAB)
In a study, the CANTAB proved to be unsuitable for diagnosing ADHD.45
Conners’ Continuous Performance Test 3rd Edition (CPT3)
Conners’ Continuous Auditory Test of Attention (CATA)
CPT3 plus CATA had a higher sensitivity (82.6%), a higher specificity (76%), a higher positive predictive value (88.8%), a higher negative predictive value (65.5%) and an overall higher correct classification rate (80.6%) than CPT3 or CATA alone.46
One study found that the Stroop Test, Stroop Plus Test and Perceptual Selectivity Test were good at distinguishing ADHD in adults from those who were not affected, with the Stroop Test performing best by a narrow margin. However, while the Stroop Test and Stroop Plus Test differed depending on the age of the test subjects, the Perceptual Selectivity Test showed hardly any difference across age.47 Other studies also showed that the performance of the test subjects in the Stroop Test was age-dependent, with sometimes younger and sometimes older test subjects performing better. In the study mentioned here, adult ADHD sufferers and non-affected people were less able to perform well in the Stroop Test with increasing age.
The Stroop test is designed to examine selective attention in particular.48
Aim of the study: selective attention in the network of the dorsal anterior cingulate cortex (dACC) → striatum → thalamus → dACC.48
Color word-color interference test.
4 color words (green, red, blue, yellow) are visually displayed in different colors. The color word and the 4 uncolored answer buttons on the right side of the screen below each other are always in the same place. The color words are displayed for 2 seconds, with a dot to be fixed for 0.75 seconds in between. The word and color match in every fourth display, but not in the others. The respondent is asked to name the visual color of the displayed word as quickly as possible.
If the color word and color are different, this is more difficult. People with ADHD have a higher error rate (in %) and a higher reaction time (in ms).47
Aim of the study: selective attention in the network of the dorsal anterior cingulate cortex (dACC) → striatum → thalamus → dACC.48
In the Stroop Plus Test, the structure of the Stroop Test is extended by four colored boxes arranged around the word. Together with the word, an arrow appears between the word and the boxes, pointing to one of the colored boxes. The visually displayed color of the word and the color known from the word do not match in 3 out of 4 trials, as in the Stroop test. Only in every ninth trial do the color, word and displayed color box match. The respondent is asked to name the visual color of the displayed word as quickly as possible.47
Aim of the study: selective attention in the network of the dorsal anterior cingulate cortex (dACC) → striatum → thalamus → dACC.48
The Stroop test is presented in the middle of the screen. Four symbols (shape: circles or squares) in different colors (blue and yellow) are displayed around the word, 3 of which show one shape and one the other. In half of the runs, 3 symbols have the same color and one the other, in the other half 2 have one color and 2 the other.
In each of the shapes, a line is displayed that points to 12 o’clock, 1:30 p.m., 3 p.m. or 4:30 p.m. (intended as an hour hand). The test person should only pay attention to the orientation of the line in the symbol that shows the individual shape (which can be horizontal or vertical here) and confirm this as quickly as possible with the assigned button.
This test is designed to measure 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).47
Aim of the study: sustained attention, working memory in the dlPFC → striatum → thalamus → dlPFC network.48
N is a variable.
In the visual 0-back test, the respondent is shown a number on a screen and asked to press the key that corresponds to the number. In the 1-back variant, the respondent is asked to press the key of the number that was shown before the number just displayed. In the 2-back variant, the respondent is asked for the penultimate number before the number shown. The higher N is, the greater the load on the working memory.
The N-back test can also be performed by playing an acoustic number announcement. There is also a variant in which different fields light up one after the other on a square field and the respondent has to tap the N field previously activated.
The stop signal task is used to measure impulsivity or inhibition.
The respondent should record the results of visual symbols that have two alternative outcomes. For example, a left or right button should be pressed as quickly as possible on arrow symbols pointing to the left or right. After a few unevaluated practice runs, the test person should not press a button when an audio signal sounds at the same time, which sounds irregularly for approx. 25% of the runs. The stop signal sounds with a delay after the stimulus. This delay time can vary between 100 ms and 600 ms.49
The following are measured
Number of directional errors
Proportion of successful stops
Response time for Go attempts
Stop signal response time (SSRT)
The stop signals vary in different passes in order to measure the test results of the stop signal response time to 50 % error in order to achieve a measured value that is as comparable as possible. Here is a Video of the stop signal task on Youtube.
The Stop Signal Task is part of the CANTAB. In a study, the CANTAB proved to be unsuitable for diagnosing ADHD.45
One study converted the stop signal task into an application with mouse control instead of buttons, which showed various improvements in the measurements:50
The SSRT shows a weak association with impulsivity, while the measurement of mouse movement shows a strong and significant association with impulsivity
A machine learning model (weak AI) was able to accurately predict the impulsivity rating of “unknown” participants using mouse movement data from “known” participants
Mouse movement characteristics such as maximum acceleration and maximum speed are among the most important predictors of impulsivity
The use of preset stop signal delays leads to a behavior that indicates impulsiveness better than an adjustment of the delay value to previous results (staircase model)
One study found evidence that the performance of stop signal tasks in ADHD reflects impairments in early attention processes rather than inefficiency in the stopping process.51
The Children’s Color Trail Test measures attention, divided attention and speed of mental processing and is used, among other things, to diagnose ADHD.5253
The Digit Span Test is part of the Wechsler Adult Intelligence Scale- Revised (WAIS).
The test measures the storage capacity of the working memory for numbers. The test subjects see or hear a sequence of numbers and are asked to reproduce them correctly (forwards or backwards). During the test, the length of the number sequences is increased. The respondent’s number span is the longest number of digits that they can reproduce correctly.
At https://tools.timodenk.com/digit-span-test a Digit Span test can be taken online.
The QbTest combines a continuous performance task (CPT) with a movement tracking system and is intended to support the diagnosis of ADHD. A weak to moderate diagnostic quality was found in children and adolescents.56
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.57
In another approach, artificial intelligence/machine learning was able to correctly diagnose ADHD in players of an online role-playing game (PlayerUnknown’s Battlegrounds) with an 81% probability.58 A generalized anxiety disorder was detected 84.9% of the time.
One study investigated the use of virtual reality (VR) to provide a more realistic and complex, yet standardized, testing environment. In VSR, a virtual continuous performance task (CPT) was combined with simultaneous visual, auditory and audiovisual distractions. At the same time, head movements (actigraphy), gaze behavior (eye tracking), subjective experience, electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) were recorded. Significant differences were found in non-medicated ADHD sufferers with regard to59
CPT performance
Head actigraphy
Distraction gaze behavior
the subjective experience
although no group differences were found in theta-beta ratio (EEG) or dorsolateral prefrontal oxyhemoglobin (fNIRS).
Most of the online self-tests freely available on the internet provide a rough initial assessment. However, pre-testing with such tests distorts the test results of subsequent tests (bias).
We have developed our own - quite extensive - 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
Since 2023, AI knowledge systems such as Chat-GPT have been on everyone’s lips. One study looked at the use of a special AI system to diagnose ADHD and found a “balanced accuracy” (BAC) for the diagnostic performance of the model for ADHD of 0.82. The sensitivity was 0.64 and the specificity 0.99. The accuracy was higher than for all other disorders tested with it.61 Compared to the varying reliability of mental health diagnoses, this is an excellent value.62
Further AI systems for ADHD diagnosis are currently under development63
3.2. Problems with ADHD tests: measurement of ADHD symptoms does not occur consistently¶
When testing ADHD sufferers, it must always be taken into account that the symptoms of ADHD sufferers to be measured are not always and consistently present. Rewards change the symptom pattern in ADHD.26 In particular, a high level of motivation on the part of the person affected to take part in the test (intrinsic or through high extrinsic incentives) can distort the test results. This can go so far that the test is negative even though ADHD is present.
3.2.1. ADHD sufferers perform as well as non-affected people in tests with appropriate motivation¶
Motivation is altered in ADHD. Extrinsic incentives must be higher than for those without the disorder in order to trigger the same motivation.
This is consistent with our hypothesis that ADHD mediates its symptoms in the same way as certain severe chronic stress (dopamine and noradrenaline deficiency), with one benefit of chronic stress being a shift in motivation towards personal own needs. This leads to a shift towards intrinsic motivation and a weakening of motivational capacity through extrinsic incentives and at the same time explains why, in the case of chronic stress such as ADHD, many symptoms are reduced or even lost when personal interest is high.
Against this background, computer games that want to measure ADHD could face the problem that the more fun the computer game is, the less meaningful the test results become. One study found unsignificantly higher levels of flow and intrinsic motivation in a stop-signal game than in a non-gameified stop-signal test.64
3.2.1.1. No / fewer ADHD symptoms in tests with high motivation¶
In ADHD, it is not the technical ability to pay attention or direct attention that is impaired, but the way attention is directed is subject to different criteria than in people who are not affected. Attention control in ADHD is subject to a different control system, which is much more dependent on the satisfaction of one’s own needs than in non-affected people. However, this is not selfishness, but an altered control of motivation over which those affected have no influence.
Anyone who knows enough ADHD sufferers knows how much they wish they could just be like everyone else - how much easier their lives would be. What looks from the outside like “You can do it if you want to” is actually “I can’t direct my will as I should”.
If personal needs are addressed through intrinsic interest or rewards (which must be higher on average than for those not affected), the attention performance of ADHD sufferers improves more than that of those not affected.65 Ryffel-Rawak describes a typical example.66
A meta-study confirms that high rewards greatly improve test results in ADHD.67
In ADHD, the deactivation of the Default Mode Network (DMN), which occurs with attention to the environment, is significantly reduced. Personal motivation induced by high rewards equalized DMN deactivation in ADHD, so that it corresponded to that of non-affected individuals. MPH had the same effect.68
The fact that an insufficiently stimulating environment (underactivation) can also cause inattention in healthy people, e.g. in gifted children in schools not suited to their abilities,69 is probably not due to an altered control profile of attention, but rather to an underchallenge that causes boredom.
3.2.1.1.2. Impulsivity (inhibition) can be improved through rewards¶
Impulsivity in ADHD (inhibition ability) can also be influenced by rewards,70 up to results that correspond to the controls, while the same rewards improved inhibition to a lesser extent in children with brain injuries.71 Another study also found identical inhibition ability in ADHD sufferers and non-sufferers with high rewards.72 Another study found no improvement.73 It would be necessary to check whether the rewards here were possibly too low to arouse personal motivation.
3.2.1.1.3. Reaction times: Reward accelerates reaction times and reduces reaction variance only in ADHD¶
The test results on reaction times in ADHD are inconsistent. Many tests show a reduced reaction time in ADHD sufferers, while individual tests show an increased reaction time. ⇒ Reduced reaction time in ADHD?
In one study, only the ADHD sufferers and their unaffected siblings showed accelerated mean reaction times and lower response variability under reward, but not the controls, while accuracy improved in all 3 groups.74
3.2.1.2. ADHD symptoms more visible in low demand tests¶
Activating, exciting tasks can cause test results of ADHD sufferers to correspond to those of controls.75 For example, test results of ADHD sufferers show inconspicuous results when a high test event rate (fast) is presented, while a low event rate (slow test) shows clear differences.7677
One study hypothesizes that a slower event rate in tests with ADHD sufferers causes reduced intrinsic motivation due to their special motivational characteristics.78
The optimal arousal range, in which no under- or overstimulation occurs, is much narrower with ADHD than with non-affected people.
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 people if they are personally motivated (example: hyperfocus). This is understandable in light of the stress benefits of altered attention control in ADHD. ⇒ Stress benefits of distractibility, task switching problems and attention problems
In addition to under- and overload, this limited capacity can also exist 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 see how he or she would “fill up” in the first lesson and then develop the typical ADHD-I symptoms.
Special features of sleep tests
Another special feature was found in sleep tests. Many sleep tests found reduced REM sleep in ADHD sufferers. However, these tests only lasted one night. However, in sleep tests in which there was an acclimatization night and in which only the second night was evaluated, even more REM sleep was found in ADHD sufferers than in non-affected persons.4
When the first and second nights were analyzed together, the total amount of REM sleep was the same as for non-affected persons. These results could be interpreted to mean that particular excitement (the first night in an unfamiliar environment) is a stronger stressor for ADHD sufferers than for non-affected persons.
Whether more intensive REM sleep is possibly related to the high sensitivity symptom of more intense, more colorful dreams is an open question. According to our understanding, high sensitivity underlies all ADHD.
3.2.2. ADHD symptoms and relationship to test subject¶
One study looks at how the relationship with the test administrator influences the test results of ADHD sufferers.79
Smoking as self-medication increases dopamine levels - albeit only in the short term. It also reduces stress symptoms and irritability. People with ADHD smoke about twice as often as those without the disorder.
Smoking can therefore make the diagnosis of ADHD more difficult.80
A study of emotional dysregulation in smokers with ADHD found no differences between those who smoked as usual and those who abstained from smoking for 24 hours.81
It is well known that withdrawal from alcohol and other drugs requires a period of 2 to 4 weeks so that the receptors downregulated by the high supply of neurotransmitters (dopamine, etc.) can upregulate again. This is responsible for the withdrawal symptoms, among other things. This also applies to nicotine. It is possible that only tests that require nicotine withdrawal of more than 2 to 4 weeks will be able to show a difference.
One study calculated that there are 116,200 different ADHD subtypes - if each combination of symptoms is considered a separate subtype.82
Against this background, the currently (still?) common test procedures, which (using DSM 5 as an example) 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 persons with 5 characteristics in both categories would be qualified by DSM 5 as “no ADHD”. Not all doctors realize that the DSM and ICD are merely guidelines and not diagnostic standards - a misunderstanding that Francis Allen, the head of the DSM IV Commission, warned against.
Against this background, we believe that the diagnostic approach pursued by Barkley and ourselves based on a query of a large number of symptoms is definitely worth considering. ⇒ The big ADxS.ORG - ADHD online test
3.2.5. Evaluations by parents and teachers differ significantly¶
One study found that teachers’ and parents’ evaluations of the effects of ADHD medication did not correlate. These strong differences show that different sources should be used when evaluating ADHD.83
For reliable results, tests for ADHD should properly be carried out in different test environments that take into account the degree of underchallenge, appropriate arousal and overload. Furthermore, smoking behavior should be included in the weighting of the test results, which has not been done sufficiently to date. Testing on the basis of all possible ADHD symptoms seems to us to be an option worth considering.
4. Objective measurement methods (biomarkers) for ADHD¶
To date, no diagnosis of ADHD has been established by measuring objective biological or neurological values.
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 ADHD sufferers “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 depend on whether or not the affected person is experiencing acute stress at the time of measurement. 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 stress reactions.
The measurement of QEEG or EEG can support diagnoses. Further information on QEEG and ADHD therapy using neurofeedback can be found at ⇒ Neurofeedback as ADHD therapy In the chapter Treatment and therapy.
One study reported an ADHD detection rate of an EEG transformer model with an average accuracy of 95.85% and an average AUC value of 0.9926.84 An EEG classification model with machine learning reported a classification accuracy of 98.28% to 98.86%.85 Using phase-based analysis, one study identified two biomarkers that differentiate ADHD in children from healthy children with an accuracy of 99.174%:86
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, ADHD sufferers do not need to be distinguished from healthy people, but ADHD needs to be distinguished from other disorders of unknown composition. This is a much more complex task.
Attempts have been made to use the theta-beta ratio as a diagnostic tool for ADHD.
Although individual studies have appeared promising in this regard, this diagnostic approach does not yet seem suitable for diagnosing ADHD.
The theta-beta ratio is said to be significantly altered in ADHD.
Using quantitative encephalographic measurements (QEEG), it was found that ADHD sufferers with mental stress have too little activation in the beta wave range (13-30 Hz), which is important for concentrated alertness, while theta waves (4-8 Hz), which are associated with daydreams, slipping attention, transition to sleep and creativity, are too strong.
The increased theta-beta ratio was so clear in a study of ADHD sufferers that 98% of ADHD sufferers (of the purely inattentive type, ADHD-I, as well as the mixed type) could be distinguished from healthy individuals on the basis of the theta-beta ratio in children. The degree of deviation in the theta-beta ratio correlated with the intensity of the ADHD symptoms.87
However, this result could not be replicated in all follow-up studies. Ogirim et al 2012 were only able to correctly identify around 60% of n = 101 test subjects, including 63 ADHD sufferers, using the theta-beta ratio, whereas omission errors in a sustained attention task resulted in 85% correct diagnoses.88 Another study found in adults with ADHD that the theta-beta ratio could not diagnose ADHD, whereas absolute and relative EEG power with eyes open differentiated ADHD from controls. The best ADHD predictors were named:89
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.
A more recent meta-analysis of 17 studies found an effect size of the total sample for absolute theta of p = 0.58 and for relative theta of p = 0.92. The beta-theta ratio or the increased theta value is therefore suitable as a diagnostic instrument in a certain development phase and could be further developed as a diagnostic instrument.90
The oversized theta-beta ratio is reduced in adulthood,9126 so that a diagnosis with this, if at all, could probably only be considered for children.
A meta-study found no conclusive link between the theta-beta ratio and ADHD. Unfortunately, however, no distinction was made between subtypes.92 However, an increased theta-beta ratio is said to predict responding to treatment with stimulants or neurofeedback.92
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 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 between a group of ADHD sufferers and a group of (proven) healthy people. However, it is probably not suitable as a diagnostic tool in real life - for people who do not know whether they suffer from ADHD or another disorder, or are healthy - because deviations in the theta and beta range occur in a variety 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 ADHD-I sufferers (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 sought 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.
This is a single test with a single subject and therefore cannot be generalized. The subject was aware of the expected reaction. The aggravation of his ADHD symptoms after sugar consumption had been mirrored by a number of people and was consistent with his own observations.
As we understand it, the observation fits the description of the effect of an oligoantigenic diet for a person who is sensitive to sugar. More on oligoantigenic diet and other food influences on ADHD at ⇒ Nutrition and diet for ADHD
During a conversation about this result, we were told by another ADHD-HI sufferer that he has noticed a significant increase in his ADHD symptoms, especially procrastination, after consuming sugar (chocolate).
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.93 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.
One study concluded that the theta-beta ratio of the EEG is a reflection of cognitive processing activity in both non-affected and ADHD-C sufferers 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 ADHD-C sufferers.94
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 that are typical of ADHD as neural noise. Increased individual reaction time variance is a sign of increased neural noise. MPH improves this.95
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.96
Another objectified diagnostic procedure is the measurement of evoked potentials. This involves measuring characteristic EEG amplitude curves triggered by stimuli or actions 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 persons and data from affected persons with other disorders, which are collected in so-called QEEG databases.
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 201197 already claim 89 % diagnostic accuracy: from a group of 212 adults with 106 ADHD sufferers, 89 % of those affected were correctly diagnosed using a 19-channel system through automatic evaluation of EVP. Models with machine learning achieve 94 %98
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%99
One study found no significant differences in evoked potentials between twins with and without ADHD in a continuous performance task.100
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 ADHD101
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).102
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 costs around CHF 1,250 (as of 2015).
The measurement of 14 resting state EEG parameters was able to identify the participating ADHD sufferers with an accuracy of around 85% in a process optimized by machine learning.103 As it is not only possible to diagnose healthy individuals in the wild, but also other disorders, it is questionable whether the results will be accurate enough to be useful in practice.
One study found that phase space reconstruction of the EEG can distinguish ADHD from non-affected people very well. 105 For diagnostic use, however, a method must also be able to differentiate well between other disorders.
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.106
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 sufferers using machine learning.107 It remains to be seen what the recognition rate will be for an open group of test subjects.
One study used VMD-HT and other secondary EEG characteristics to distinguish between 61 ADHD sufferers and 60 healthy controls:108
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 ADHD sufferers and healthy controls, but also sufferers of other disorders are tested.
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).109
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.110 So far, this is a purely experimental approach.
Another study found no differences with fNIRS in ADHD59
To date, there are no usable genetic tests for the diagnosis of ADHD.
However, initial voices are now (2020) advocating the integration of certain genetic tests (here: array CGH) into the standards of ADHD diagnostics.111
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 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 patients, 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.
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, upregulation of miR-328-3p was found in only one of 2 ADHD groups. These miRNAs may regulate the expression of genes associated with these traits in genome-wide association studies112
The basal cortisol level is lower in people with ADHD compared to those who are 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.113
However, here too there is the difficulty that without individual comparative data, an assessment of the 24-hour picture of cortisol levels is hardly diagnostically useful.
A measurement of cortisol in hair (reflecting long-term levels) showed that low cortisol levels in preschool children predicted the development of ADHD.114
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 of little use in the diagnosis of ADHD.
4.4.1.2. Measurement of the phasic cortisol response to psychological stressors¶
The phasic cortisol response can be used to determine the cortisol tolerant 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 ADHD-I sufferers, SSRIs should only 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.
A dexamethasone/ACTH/CRH test can be used to determine 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, 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 disruption of the HPA axis shutdown.
By analyzing zinc, lead, copper, cobalt and vanadium from teeth, one study was able to make an impressively clear distinction between non-affected persons, ADHD, ASD and those affected by comorbidities.115
A study of 50 untreated ADHD sufferers compared to their unaffected twins and 50 control subjects found higher axonal diffusivity in the ADHD sufferers in the116
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 in the unaffected twins were between those affected and unaffected by ADHD. Axial diffusivity is a marker for the undamaged condition of axons.
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.117118
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.119
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:121
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
A functional analysis of urine values for the diagnosis of ADHD has not yet been reported. One study also found no connection between urine values and ADHD in dogs.122
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 Neurological chapter on ADHD.
One study achieved a sensitivity of 77.3% and a specificity of 75.3% in the diagnosis of ADHD compared to a control group using a machine learning method based on pupil measurements.124
4.4.9.3. Eye tracker: Duration of target fixation¶
The investigation of distractibility by recording and analyzing eye movements during a task-irrelevant distraction showed a significant correlation between the duration of target fixation and the attention problems reported by the parents (p < 0.001).125 Other studies also report diagnostic benefits of measuring eye movements during a continuous performance task 126127
Assessment of the individual problem situation, taking into account comorbid disorders, the developmental history of the affected person and their family.
Parallel to the ADHD diagnosis, a differential diagnosis should be carried out to ensure that symptoms do not result from other dominant organic causes. ⇒ Differential diagnosis for ADHD
The head marks / behavioral grades / individual assessments in primary school reports are often a great help in reconstructing the behavior of those affected in childhood.
Some of those affected no longer have these certificates. We have received alarming reports that some doctors have refused to diagnose such cases. Of course, this is in no way acceptable.
Existing primary school reports are certainly an aid. Alternatively, parental reports, reports from school friends or high school journals, which often contain very accurate characterizations of the person by classmates, can also make a (correspondingly smaller) contribution. However, the lack of primary school reports should never be the (sole) reason for refusing a diagnosis, as no one is obliged to keep these reports. ADHD sufferers often have a poor long-term memory and therefore few tangible memories of their childhood.
It should also be noted that women often develop psychological problems at a later age than boys due to differences in sex hormone development.
In our opinion, the current DSM and ICD criteria still overrepresent a classic boy’s ADHD with hyperactivity.
More on this at Gender differences in ADHD.