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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
On average, people with ADHD often have 12 of these 18 symptoms (around 66%)3
The online screening of the ADxS.org symptom test is based on this model. It queries 45 symptoms, but is not medically validated and is not used for medical diagnosis.
Neuropsychological tests4 or individual biomarkers are too imprecise to diagnose ADHD. We suspect that, similar to the rating scales for symptoms of ADHD, which are based not only on a single symptom but on the cluster of symptoms common in ADHD, a test cluster of a group of neuropsychological tests or measurement of a group of biomarkers typical of ADHD could result in adequate test accuracy. Since ADHD is a syndrome, i.e. a multitude of different causes that manifest themselves in common symptoms, biomarkers should only be able to measure either the mechanisms that mediate the common symptoms (although here too, with the dopamine and noradrenaline systems alone, there are several similar, because functionally redundant, systems) or individual causes of the syndrome, but barely all of them. Against this background, it is surprising that the improvement of the detection rate through individual biomarkers continues to be pursued instead of researching the appropriate combination of different biomarkers that, in their entirety, enable ADHD diagnostics. There are individual approaches that pursue this idea.56
Questionnaires are filled out by people with ADHD themselves and their caregivers (parents, teachers, friends).
Questionnaires are very subjective and involve the risk that the respondent’s personal opinion about ADHD itself influences the response scale. It can happen that parents reject the diagnosis of ADHD in principle, especially in their own child. Likewise, subjective ideas of people with ADHD (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 of elimination diets for ADHD, the results of parent surveys are always far more positive than the results of objective tests.7 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 rather than with 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 child with ADHD 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)1210
Child Behavior Checklist, CBCL/6-18 (Parent Behavior Questionnaire)
of children and young people from 4 to 18 years)1310
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 behavior (ODD).14
Quantitative Behavior Test (QbTest)
70% accuracy in adults aged 55 to 79 years. In combination with self-reported ADHD symptom severity, 91% accuracy.1718
Assessment form for parents, teachers and educators (FBB-HKS)19
The ASRS rating scales have a very limited diagnostic value23
A meta-study of 9 ADHD questionnaires found that only 37% asked 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)24
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 differences in the assessments of parents, teachers and people with ADHD, with the exception of emotional dysregulation, in all existing symptoms, but even more so in the frequency of their occurrence.25
Emotional dysregulation (also in ADHD) can be tested with the
Reactivity, Intensity, Polarity and Stability questionnaire (RIPoSt-40)26
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 people with ADHD2728
It is consistent with this that the results of tests with people with ADHD change when rewards are promised.29 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.30 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)31
2.2. Interviews / instruments with schoolchildren / adolescents¶
Diagnostic Interview Schedule for Children (DISC-IV)32
Recording period 6 months
Diagnostic Interview for Children and Adolescents (DICA-R)33
Child and Adolescent Psychiatric Assessment (CAPA)34
Recording period 6 months
Schedule for Affective Disorders and Schizophrenia for School-Age Children (K-SADS)35
Childrens Interview for Psychiatric Syndromes (ChIPS)36
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.37 Only available in English.38
Scale for recording current symptoms of attention deficit/hyperactivity disorder and oppositional defiant disorder according to DSM IV (ADHD-ODD scale)39
According to Kiddie-Sads-Present and Lifetime Version, K-SADS-PL
However, the DIVA 5 evaluation differentiates according to age:
Children between 5 and 12 years: ADHD (+), if:
at least 6 symptoms either
Of inattention (6 in total) or
Hyperactivity/impulsivity (6 in total) or
Of both inattention and hyperactivity/impulsivity (12 in total) are present.
Adolescents between the ages of 12 and 17: ADHD (+) if
At least 6 symptoms either
Of inattention (6 in total) or
Hyperactivity/impulsivity (6 in total) or
Of both inattention and hyperactivity/impulsivity (12 in total).
And in addition at least 3 symptoms (of these or others) occurred before the age of 12 years
Adults: ADHD (+), if
At least 3 symptoms of inattention or hyperactivity/impulsivity before the age of 12 years
And additionally at least 5 symptoms of either inattention (5 in total) or hyperactivity/impulsivity (5 in total) or both (10 in total)n
Homburg ADHD Scales for Adults (HASE)
HASE consists of five individual procedures4243
Wender Utah Rating Scale - German short form (WURS-K)
Objective: retrospective diagnosis of childhood ADHD-HI symptoms.44 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 (ADHD-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.
Integrated Diagnosis of ADHD in Adults“ (IDA-R) test system45
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 children and
A diagnostic interview based on the latest DSM criteria to assess the current symptoms.
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 showed 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
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 result from training, hyperfocus of a person with ADHD, test anxiety of a person without ADHD or giftedness. The latter points in particular should therefore always be clarified by means of a 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 people with ADHD and therefore lead to false non-diagnoses. Rating scales, on the other hand, are much better suited to diagnosing ADHD.4 Using the NIH Toolbox, Little Man Task, Matrix Reasoning Task and Rey Delayed Recall, one study found a high variance of 31% for ADHD compared to 2.7% for ASD.47
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 problems49
Cambridge Neuropsychological Test Automated Battery (CANTAB)
In a study, the CANTAB proved to be unsuitable for the diagnosis of ADHD.50
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.51
One study found that the Stroop Test, Stroop Plus Test and Perceptual Selectivity Test were good at distinguishing ADHD in adults from non-affected individuals, 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 barely showed any difference across age.52 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, both adult persons with ADHD and non-affected people with ADHD performed less well on the Stroop Test as they got older.
The Stroop test is designed to examine selective attention in particular.53
Aim of the study: selective attention in the network of the dorsal anterior cingulate cortex (dACC) → striatum → thalamus → dACC.53
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).52
Aim of the study: selective attention in the network of the dorsal anterior cingulate cortex (dACC) → striatum → thalamus → dACC.53
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 visually displayed color of the word shown as quickly as possible.52
Aim of the study: selective attention in the network of the dorsal anterior cingulate cortex (dACC) → striatum → thalamus → dACC.53
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).52
Objective: Sustained attention, working memory in the network dlPFC → striatum → thalamus → dlPFC.53
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.54
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 the diagnosis of ADHD.50
One study converted the stop signal task into an application with mouse control instead of buttons, which showed various improvements in the measurements:55
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 attentional processes rather than inefficiency in the stopping process.56
The Children’s Color Trail Test measures attention, divided attention and speed of mental processing and is used, among other things, to diagnose ADHD.5758
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.61
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.62
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.63 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 persons with ADHD with regard to64
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).
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 for diagnosing 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.65 Compared to the varying reliability of mental health diagnoses, this is an excellent value.66
A combination of 4 machine learning algorithms achieved an AUC of 79% by analyzing registry data alone67
Further AI systems for diagnosing ADHD are currently under development.68
When testing people with ADHD, it must always be taken into account that the symptoms to be measured are not always and consistently present in people with ADHD. Rewards change the symptom pattern in ADHD.29 In particular, a high motivation of the person with ADHD to participate 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. People with ADHD perform as well in tests as people without ADHD with the right motivation¶
Motivation is altered in people with 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.69
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 those 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 the person with ADHD has no influence.
Anyone who knows enough people with ADHD 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 people not affected), the attention performance of people with ADHD improves more than that of those not affected.70 Ryffel-Rawak describes a typical example.71
A meta-study confirms that high rewards greatly improve test scores in ADHD.72
In ADHD, deactivation of the default mode network (DMN), as 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.73
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,74 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,75 up to results equivalent to controls, while the same rewards improved inhibition less in children with brain injuries.76 Another study also found identical inhibition ability of persons with ADHD and non-affected people with high rewards.77 Another study found no improvement.78 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 people with ADHD, while individual tests show an increased reaction time. ⇒ Reduced reaction time with ADHD?
In one study, when rewarded, only the persons with ADHD and their unaffected siblings showed accelerated mean reaction times and lower response variability, but not the controls, while accuracy improved in all 3 groups.79
3.2.1.2. ADHD symptoms more visible in low-demand tests¶
Activating, exciting tasks can cause test results of people with ADHD to correspond to those of controls.80 For example, test results of people with ADHD show inconspicuous results when a high test event rate (fast test) is presented, while a low event rate (slow test) shows significant differences.8182
One study hypothesizes that a slower event rate on tests in people with ADHD causes reduced intrinsic motivation due to their specific motivational characteristics.83
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 persons with ADHD can direct their attention just as well as people without ADHD 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 the child “ran full” in the first lesson and then developed 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 people with ADHD. However, these tests only lasted one night. However, in sleep tests in which there was one night of acclimatization and in which only the second night was evaluated, even more REM sleep was found in persons with ADHD than in people without ADHD.7
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 people with ADHD than for those not affected.
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 addresses how the relationship with the test administrator influences the test results of people with ADHD.84
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 people without ADHD.
Smoking can therefore make the diagnosis of ADHD more difficult.85
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.86
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.87
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. People with ADHD with 5 features 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.
On this basis, we consider Barkley’s and our approach of a diagnosis based on a query of a large number of symptoms to be well 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 medication for ADHD did not correlate. These strong differences demonstrate that different sources should be used when evaluating ADHD.88
For reliable results, tests for ADHD would have to be carried out correctly in different test environments that take into account the degree of underchallenge, appropriate arousal and overchallenge. 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, there is no biomarker in sight that could be used to diagnose ADHD by measuring objective biological or neurological values.89
However, measurement methods have already been researched enough to validate and support diagnoses. The advantage of measurement methods is that the result is not distorted by prior knowledge, training or particular motivations (interest, hyperfocus).
However, it is known that people with ADHD “outsource” the functions of disturbed brain areas to other areas of the brain in order to compensate for the deficit. Symptoms are also reduced through coping, e.g. by avoiding situations in which the stressful symptom can occur, or individual symptoms are reduced through intensive training. If only symptoms are asked about, the symptom masked by relocation to other areas of the brain or by coping may not be noticed. This is one of the reasons why tests (including our own online tests) can never be sufficient on their own to reliably diagnose or rule out ADHD.
It is to be hoped that with further refinement of the measurement methods and knowledge about the evaluation of results, a more objective diagnosis than questionnaires and tests will be possible in the foreseeable future.
However, the fundamental problem with measuring biomarkers is that they can only ever provide a snapshot. The results of this snapshot depend on whether the person with ADHD is experiencing acute stress at the time of measurement or not. The usual ADHD diagnosis is therefore correctly based on whether the (stress/ADHD) symptoms exist in the long term, i.e. also outside of acute circumstances that trigger particular stress.
To date, the relevant literature has not yet addressed the extent to which biomarkers can reliably distinguish long-term changes from merely acute stress reactions.
The measurement of QEEG or EEG can support diagnoses. Further information on QEEG and neurofeedback therapy for ADHD can be found at ⇒ Neurofeedback as ADHD therapy In the chapter Treatment and therapy.
A multifactorial 19-channel EEG analysis called “ADHD-AID” reported an ADHD diagnostic accuracy of:90
Accuracy: 0.991
Sensitivity: 0.989
Specificity: 0.992
F1 score: 0.989
Mathew correlation coefficient: 0.982.
AUC: 0.9958
Older studies reported an ADHD detection rate of an EEG transformer model with an average accuracy of 95.85% and an average AUC value of 0.9926.91 An EEG classification model with machine learning reported a classification accuracy of 98.28% to 98.86%.92 Using phase-based analysis, one study preidentified two biomarkers that differentiate ADHD in children from healthy children with 99.174% accuracy:93
the subgraph centrality of the phase shift index of brain connectivity within the beta and delta frequency bands
the node betweenness centrality of the inter-site phase cluster connectivity within the delta and theta bands
In practice, however, people with ADHD do not have to be distinguished from healthy people, but ADHD has to be distinguished from other disorders of unknown composition. This is a much more complex task than all the methods presented so far have been able to solve.
Attempts have been made to use the theta-beta ratio as a diagnostic tool for ADHD.
Although individual studies on this have appeared promising, this diagnostic approach does not yet seem suitable for diagnosing ADHD.
The theta-beta ratio is said to be significantly altered in ADHD.
Using quantitative encephalographic measurements (QEEG), it was found that in people with ADHD there is too little activation in the area of beta waves (13-30 Hz), which are important for concentrated alertness, while theta waves (4-8 Hz), which are associated with daydreams, slipping away attention, transition to sleep, creativity, are too strong.
The increased theta-beta ratio was so clear in a study of people with ADHD that 98% of people with ADHD (of the purely inattentive type, ADHD-I, as well as the mixed type) could be distinguished from healthy people 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.94
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 people with ADHD, using the theta-beta ratio, whereas omission errors in a sustained attention task resulted in 85% correct diagnoses.95 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 as:96
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 recent meta-analysis of 17 studies found a total sample Effect size of p = 0.58 for absolute theta and p = 0.92 for relative theta. 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.97
The oversized theta-beta ratio is reduced in adulthood,9829 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.99 However, an increased theta-beta ratio is said to predict responding to treatment with stimulants or neurofeedback.99
Our discussions with a neurofeedback therapist revealed some interesting findings:
Not only in ADHD, but also in a number of other mental disorders, such as depression, Compulsions, trauma or even migraine sufferers, beta increases in a state of relaxation, while it decreases in healthy people when they relax.
This - characteristic - increase in beta levels during relaxation could possibly explain the symptoms of circling thoughts and sleep problems.
We conclude from this that the theta-beta ratio in the laboratory environment can at best enable a clear diagnosis to be made as a differentiation of a group of people with ADHD in comparison to a group of (proven) healthy people. However, it is probably not suitable as a diagnostic tool in real life - for people for whom it is not known whether they suffer from ADHD or another Disorder, or are healthy - because deviations in the theta and beta range occur in a large number of mental disorders and the alternative to the presence of ADHD, unlike in the test environment, is not necessarily “healthy”, but includes the possibility of a number of other mental disorders or tendencies.
ADHD-HI and ADHD-I have characteristically different theta/beta profiles.
In ADHD-HI and ADHD-C (with hyperactivity), theta is typically too low and beta is typically too high
In people with ADHD-I (without hyperactivity, dreamers), theta is typically too high and beta is typically too low. Here, the theta-beta ratio should therefore be too low - contrary to the postulation in the studies cited above.
In both groups, theta/beta training (relaxation training) proves to be helpful - in ADHD-HI, theta is trained up and beta is trained down at the same time, while in ADHD-I it is the other way round, reducing theta and increasing beta (concentration training). In both cases, it is not just the objective change in the values that is therapeutically effective, but the learned ability to influence the values, even if this results in the values being changed towards average values.
Joint tests with a neurofeedback therapist revealed a significant influence of medication and food (sugar) on the QEEG values.
Methylphenidate causes greater agitation within the QEEG values within a few minutes. As expected, methylphenidate impeded the theta increase and beta decrease in ADHD-HI targeted by neurofeedback. This is not surprising as MPH is a stimulant. It explains why MPH should be taken in such a way that the effect has worn off by bedtime.
Sugar (2 chocolate bars within 5 minutes at 90 kg body weight) caused significant changes in the QEEG of an ADHD-HI subject.
Within 10 minutes, Beta1 increases considerably.
The threshold values for theta up / beta down training had to be reduced considerably. The 85 % of the target values achieved before the sugar intake had dropped to 50 %. This means that the ability to relax had decreased drastically.
After 20 minutes, all values (theta, alpha, beta1, beta 2, hi-beta) were significantly reduced. Relatively speaking, however, beta 1 was now clearly above SMR. (Beta1 should be below SMR, which is why SMR training (which targets it) is the first step in neurofeedback treatment for ADHD).
After 30 minutes, Beta1 had caught up slightly with SMR. However, Hi-Beta was now significantly higher.
This is a single test with a single subject and therefore cannot be generalized. The subject was aware of the expected reaction. The exacerbation of his ADHD symptoms after sugar consumption had been mirrored by several people and coincided 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 person with ADHD-HI 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.100 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 came to the conclusion that the theta-beta ratio of the EEG in both non-affected and persons with ADHD-C is a reflection of cognitive processing activity and can be measured using the P 300 latency in the acoustic oddball test. In contrast, absolute alpha power did not correlate with P300 latency or amplitude in persons with ADHD-C.101
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.102
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.103
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 people and data from people 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 2011104 already claim 89 % diagnostic accuracy: from a group of 212 adults with 106 persons with ADHD, 89 % of those affected were correctly diagnosed using a 19-channel system through automatic evaluation of EVP. Models with machine learning achieve 94 %105
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%106
One study found no significant differences in evoked potentials between twins with and without ADHD in a continuous performance task.107
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 ADHD108
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).109
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 people with ADHD with an accuracy of around 85% in a process optimized by machine learning.110 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 is very good at distinguishing ADHD from non-affected individuals. 112 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.113
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 people with ADHD using machine learning.114 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 people with ADHD and 60 healthy controls:115
an accuracy of 99.81 %
a sensitivity of 99.78 %
a specificity of 99.84 %
an F-1 measure of 99.83 %
a precision of 99.87 %
a false detection rate of 0.13 %.
Detection rate for ADHD of 99.87%
Detection rate for healthy controls of 99.73 %
These impressive figures will have to be proven in the real world when not only persons with ADHD and healthy controls, but also people with other disorders are tested.
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).116
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.117 So far, this is a purely experimental approach.
Another study found no differences by fNIRS in ADHD64
To date, there are no usable genetic tests for the diagnosis of ADHD.
However, initial voices are now (2020) in favor of integrating certain genetic tests (here: array CGH) into the standards of ADHD diagnostics.118
There are several difficulties for genetic diagnostics in ADHD:
ADHD is usually not caused by individual gene variants, but by the interaction of a large number (several hundred to thousands) of gene variants (⇒ gene candidates in ADHD) Gene candidates for ADHD)
The (epigenetic) expression of genes (e.g. through early childhood or chronic severe stress) can also change the gene effect independently of the gene variant.
It can be assumed that different combinations of the genes and forms of expression under consideration coincide in different persons with ADHD, but not all genes that contribute to the dopamine balance being disturbed, for example.
There is not yet a complete set of candidate genes, which is why there is no genetic test for ADHD. We have collected over one hundred and fifty gene candidates. ⇒ Gene candidates for ADHD.
Genetic tests are still too expensive to be able to test the required number of genes.
It is possible that genetic tests will become so affordable in the coming years that they can also be used for diagnostic purposes - and subsequently to adapt individualized treatment.
One study investigated miRNAs of extracellular vesicles in serum. A longitudinal analysis examining changes in miRNA expression over time between four groups with different diagnostic trajectories (persistent diagnosis, first onset, remitted and typical development/control) found no statistically significant results. In the cross-sectional analysis, miR-328-3p was found to be upregulated in only 1 of 2 ADHD groups. These miRNAs may regulate the expression of genes associated with these traits in genome-wide association studies119
The basal cortisol level is lower in people with ADHD than in those not affected because the stress load is long-term. In the case of short-term stress, the basal cortisol level initially rises.
This difference could theoretically be used to differentiate between an acute stress reaction (increased cortisol levels) and a collapsed HPA axis regulation (reduced levels) - if the individual fluctuations between people within a group were not greater than the increase or decrease in cortisol levels compared to healthy levels.
According to this hypothesis, however, an individual change in cortisol levels upwards or downwards could be determined if these were recorded regularly (annually) from the earliest age (in children: on the occasion of standard examinations).
Without historical values of the basal cortisol level, however, such a measurement is useless.
In addition, a measurement of the morning cortisol awakening response (CAR) could provide information about the state of the HPA axis. The CAR is a persistent increase in cortisol levels that occurs 20 to 40 minutes after waking up.
A comparative graphical representation of the cortisol profiles of healthy, acutely stressed, chronically stressed, partially burned out and fully burned out people can be found in Bieger.120
However, here too there is the difficulty that without individual comparative data, an assessment of the 24-hour picture of cortisol values is barely diagnostically usable.
A measurement of cortisol in hair (which reflects long-term levels) showed that low cortisol levels in preschool children predicted the expression of ADHD.121
This could possibly be the result of a long-term stress reaction. The third stress development phase (resistance phase) is typically characterized by a collapse of basal cortisol levels. The fourth phase (exhaustion phase) is then characterized by a breakdown of the neurotransmitter systems. More on this at ⇒ Resistance phase In the section ⇒ The stress response chain / stress phases of the article ⇒ The human stress system - the basics of stress.
However, changes in basal cortisol levels are not specific to ADHD and are therefore barely useful for ADHD diagnosis.
4.4.1.2. Measurement of the phasic cortisol response to psychological stressors¶
The phasic cortisol response can be used to determine the cortisolergic subtype of ADHD.
ADHD-I is typically associated with an elevated cortisol response compared to unaffected individuals, ADHD-HI with a flattened cortisol response compared to unaffected individuals.
The differences are relevant for treatment. In people with ADHD-I, SSRIs should be used with caution. In ADHD-HI, low-dose SSRIs (2 to 4 mg, i.e. 1/5 to 1/10 of the level used in depression) could potentially improve impulsivity, allowing lower dosing with stimulants.
The Trier Stress Test (TSST) is typically used as a psychological stressor.
This is probably not suitable for a (even annually) repeated examination in view of the expected habituation.
A dexamethasone/ACTH/CRH test can be used to determine whether there is permanent dysregulation at individual levels of the HPA axis.
If ADHD is characterized by a permanent dysregulation of the stress regulation systems, above all the HPA axis, it should be possible to prove the individual dysregulation by testing the HPA axis reactions (changes in the levels of the stress hormones CRH, ACTH, cortisol to acute stressors). At the very least, such tests should provide informative indications for the individual differentiation of subtypes.
Dexamethasone is a glucocorticoid that selectively binds to the glucocorticoid receptors and should thereby trigger the downregulation of the HPA axis (after a stress reaction has occurred). If this downregulation does not occur, this is a strong indication of a disorder in the shutdown of the HPA axis.
By analyzing zinc, lead, copper, cobalt and vanadium from teeth, one study was able to create an impressively clear distinction between non-affected people, people with ADHD, people with ASD and people with comorbidities.122
A study of 50 untreated persons with ADHD compared to their unaffected twins and 50 control subjects found higher axonal diffusivity in the ADHD sufferers in the123
Perpendicular fasciculus
Superior longitudinal fasciculus I
Lateral corticospinal tract
Corpus callosum
The values correlated significantly (except in the perpendicular fasciculus) with ADHD symptoms such as inattention and working memory problems and were intermediate between those of people with ADHD and non-affected twins. Axial diffusivity is a marker for the undamaged condition of axons.
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.124125
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.126
Optimized machine learning was able to distinguish very well between ADHD and controls and between persistent and remitting ADHD in young adults using MRI neuroimaging. Relevant features of ADHD compared to controls were identified as:128
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.129
Pupil diameters at rest are a biomarker for tonic noradrenal infiring, pupil changes during tasks and in response to stimuli are a biomarker for phasic noradrenal infiring. Find out more at* Tonic and phasic noradrenaline* in the article Noradrenaline in the chapter Neurological aspects of ADHD.
One study achieved a sensitivity of 77.3% and a specificity of 75.3% in the diagnosis of ADHD compared to a control group using a machine learning method from pupil measurements.131
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).132 Other studies also report diagnostic benefits of measuring eye movements during a continuous performance task 133134
Assessment of the individual problem situation, taking into account comorbid disorders, the developmental history of the person with ADHD and their family.
A differential diagnosis should be carried out in parallel to the ADHD diagnosis to ensure that symptoms do not result from other dominant organic causes.
More on this at ⇒ Differential diagnosis of ADHD
Interviews on retrospective and current symptoms and the use of standardized assessment scales can help to record current symptoms and symptoms in childhood.
People with ADHD often have a poor long-term memory and therefore have few tangible memories of their childhood.
The head marks / behavioral marks / individual assessments in primary school reports are often helpful in reconstructing the behavior of people with ADHD in childhood.
Some people with ADHD 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:
DSM and ICD require first symptoms by the age of 12, which is outside primary school age. Barkley also assumes a first onset up to the age of 18.
It is therefore not appropriate to rule out a diagnosis solely on the basis of inconspicuous primary school reports.
A high level of intelligence or a high compensatory willingness to perform can make people with ADHD appear inconspicuous in elementary school.
Girls are also more adapted and often do not stand out as people with ADHD. Women often develop psychological problems at a later age than boys due to their different sex hormone development. More on this under Gender differences in ADHD.
In our opinion, the current DSM and ICD criteria still overrepresent a classic boy’s ADHD with hyperactivity.
In addition, some teachers formulated their assessments in a rather friendly way. At the time, no teacher was aware that their assessment would one day be used as a diagnostic measure.
As an alternative to primary school reports, reports from parents, relatives, school friends or graduation newspapers, which often contain very accurate characterizations of the persons by classmates, can also make a contribution.
The absence of primary school reports must never be the (sole) reason for refusing a diagnosis, as no one is obliged to keep these reports. Accordingly, primary school reports should be viewed with caution. In both positive and negative cases, they are an indication that contributes to the overall picture, but are by no means the only decisive “proof”.
