Tag: adhd

CRYSTALS, CLAIRVOYANCE, AND CUDDLES 

On By Fiona Barnett

A real-world implication of biomarker evidence

© Fiona Barnett 

            On 3 October 2024, the Australian federal government introduced legislation banning participants in the National Disability Insurance Scheme (NDIS) from accessing electroencephalogram neurofeedback (EEG-NFB) as a funded support. The Minister for Social Services grouped EEG-NFB with a list of “snake oil” goods and services, including clairvoyance, crystal healing, and something called cuddle therapy – and rejected all of these practices as having no “scientific or evidentiary basis.” 1

            This development is a setback for Australia’s EEG-NFB practitioner community, which has devoted decades to garnering recognition and funding for a neuroscience-based treatment modality. Australia’s political stance denies 70 years of published case studies and scientific research documenting EEG-NFB’s ability to treat a range of disorders, especially attention-deficit/hyperactivity disorder (ADHD).

            The Australian government’s denigration of EEG-NFB starkly contrasts with the US Food and Drug Administration’s (FDA) approval of the first EEG-based diagnostic biomarker in 2013.2 The theta-to-beta ratio (TBR) is used to assist clinical diagnosis of ADHD in children and to inform EEG-NFB treatment (NFB is also FDA-approved).

            The opposing stances by two Western countries raise the question of why. How conclusive was the scientific evidence base that met the threshold of US standards over a decade ago? Why did the existing evidence base recently fall short of domestic approval? This paper will consider these questions. Spurred by current political events, this paper aims to examine the potential of an EEG biomarker for ADHD, the TBR, and its ability to: inform diagnosis; identify specific ADHD subgroups; predict, inform, and evaluate NFB treatment response; and help avoid adverse events.

            ADHD diagnosis shortcomings

            An estimated 800,000 Australians share an ADHD diagnosis.3ADHD diagnosis in Australia is based on the Diagnostic and Statistical Manual of Mental Disorders (DSM5).4 According to DSM5 diagnostic criteria, ADHD’s core symptoms are age-inappropriate hyperactivity, impulsivity, and inattention. Use of the DSM poses the following problems:

  • DSM5 diagnosis primarily relies on developmental history, subjective observation, parent/teacher checklists, and psychometric measures mainly designed and normed on males.5,6 This approach risks bias and inaccuracy, potentially contributing to misdiagnosis and inappropriate treatment.7-9
  • The DSM5 content was determined by pharmaceutical company shareholders with a vested interest in the business of prescribing drugs to ADHD patients as young as 3 years of age.10,11
  • The DSM5 is steeped in cultural and gender bias, ignorant of ADHD aetiology, and it relies on subjective clinical opinion about vague symptom clusters instead of objective examination of the brain.12
  • Differential diagnosis is difficult because ADHD symptoms overlap with those of other DSM disorders, including oppositional defiance disorder (ODD), autism spectrum disorder, borderline personality disorder, anxiety, plus intellectual giftedness.13,14

            Also, the DSM5 recognises only three main ADHD presentations: predominantly inattentive, predominantly hyperactive-impulsive, and combined.15 However, researchers have identified other ADHD subtypes. For instance, based on thousands of single-photon emission computed tomography (SPECT) scans, USA psychiatrist Daniel Amen reportedly identified seven distinct ADHD subtypes, differentiated these from other DSM diagnoses with overlapping symptomology, and discovered that each subtype responded to different treatments, with one subtype having an adverse reaction to stimulant medication.16,17 The latter finding is particularly pertinent to the topic of this paper.

            The DSM’s deficits might help explain: (a) why ADHD children are commonly misdiagnosed (for example, studies have found ADHD clinical diagnosis to be inaccurate in 34 to 38% of cases18,19), (b) why males are overrepresented in ADHD samples (for example, 1/5 of USA boys are diagnosed ADHD),20 and (c) why most children diagnosed with ADHD are prescribed psychopharmaceuticals.21

            ADHD biomarker advantages

            It appears that psychiatric diagnosis may benefit from neuroscientific methods. It seems advantageous for clinical diagnosis and treatment to be informed by objective biomarkers.

            A biomarker is physical evidence of a condition of interest. Biomarkers are used to measure the onset and progression of disorders and/or treatment effects.22 Scarr et al. asserted that biomarkers must be simple, standardised, inexpensive, individualised, and capable of discriminating between disorder subtypes.23 ADHD biomarkers could help classify clients, identify ADHD subgroups, prevent ADHD over-diagnosis, determine treatment, predict clinical outcomes, plus stop blanket stimulant prescription and subsequent drug abuse.24

            The World Federation of ADHD defined the ideal ADHD biomarker as being: greater than 80% accurate at detecting ADHD; greater than 80% accurate at distinguishing between ADHD and other disorders with similar symptomology; reliable; reproducible; inexpensive; non-invasive; easily administered; and confirmed by two or more independent studies conducted by qualified researchers who publish their results in peer-reviewed journals.25 This list of parameters eliminates neuroimaging techniques like magnetic resonance imaging (MRI), positron emission tomography (PET), and SPECT due to cost, complexity, plus the last two involve injecting potentially carcinogenic radioactive substances in child subjects.26,27 This leaves EEG-based modalities, including event-related potentials (ERP), quantitative EEG (QEEG), and the TBR as ADHD biomarker candidates.28,29 This paper will now consider whether the TBR fulfils the ADHD Federation’s biomarker requirements.

            TBR mechanism

            The TBR is an EEG biomarker, a measure of electrical currents in the brain that vary with function. Originally defined at the Cz electrode placement site, the TBR is the ratio of slow (θ, typically 4-8 Hz) to fast (β, typically 15-20 Hz) brainwaves.30 The largest TBR is located at Cz or Fz electrode placement sites. Theoretically, the TBR is higher in ADHD subjects in the resting state.31 Normal adult TBR is 2:1, exceeding this marks the ADHD threshold. Owing to θ differences in children (aged 5 to 13 years), their TBR threshold for ADHD is >θ=3xβ.

            EEG-NFB training is a form of biofeedback that uses EEG technology to feed brainwave information back to the brain in real time, through sight and sound, which the brain uses to change itself.32 Repeated activation strengthens synaptic connections between neurons and forms new neural pathways.33 The initial research on EEG-NFB as a treatment for ADHD (then called hyperkinesis) was published in 1976.34 Lubar’s original EEG-NFB training protocol for ADHD targeted the left-brain motor strip with θ inhibition. The effects of this protocol were comparable to stimulants. Because the main result was a reduction in θ to β amplitude, Lubar adopted the TBR as an ADHD biomarker. His theta-beta NFB protocol targeted the sensorimotor rhythm (SMR) site (Cz), where the TBR was highest.35 Variations of Lubar’s treatment protocol were developed, including that which placed the feedback electrode at Cz and inhibited θ while increasing β. Elevated θ is considered a marker of drowsiness and sleep, while βbrainwaves are related to attention, or externally-focused sustained concentration. The basic goal of TBR-NFB training, therefore, was to increase attention by decreasing θ and increasing β. 

            TBR in diagnosis and treatment

            Early TBR studies yielded results portraying the TBR as a reliable ADHD biomarker clinically useful for diagnosing ADHD and tailoring NFB treatment protocols.36-38 For example, Monastra et al.39 reported that ADHD subjects with high pre-test TBR showed decreased TBR plus reduced clinical symptoms following TBR-NFB training. This suggests the TBR is useful for predicting treatment response. The effect size (2.22) for the NFB protocol was enormous. This result was attributed to subjects being selected and matched to treatment based on their TBRs. A meta-analysis by Arns et al. of studies using the TBR-NFB protocol showed a large effect size for reducing symptoms of inattention and impulsivity, but not as impressive a result for hyperactivity. This and other studies reported TBR-NFB to be as effective as stimulants in improving inattention and impulsivity in ADHD subjects.40 Snyder et al.41 combined clinical ADHD evaluation with the TBR biomarker to improve the diagnostic accuracy of ADHD children by up to 88%. Such research findings impressed the US FDA to approve the TBR as a paediatric ADHD biomarker.

            TBR criticisms

            Subsequent TBR studies yielded inconsistent results. Ogrim et al. did not repeat the TBR or θ differences between ADHD versus normal subjects.42 A large study by Loo et al. found the TBR did not discriminate between ADHD subtypes.43 Liechti et al. found no reliable θ or θ-β increases in ADHD subjects.44 Hernandez et al. found no significant differences between ADHD subtypes using the TBR.45 Only recently, Bluschke et al. found limited effects for the TBR-NFB protocol.46 These contradictory findings may reflect various problems. Back in 2009, Arns et al. identified several issues, including poor research design and a lack of evidence that EEG-NFB exclusively accounts for the clinical benefits.47 These same problems have not been rectified 15 years later.

            Many TBR-related studies excluded hyperactive subjects from their samples.48,49 For instance, Snyder et al. excluded subjects who could not sit still for at least 30 seconds of EEG recording.50 Such exclusion biases the data in favour of an anticipated outcome. It strengthens the growing argument that the TBR is only applicable to ADHD characterised by inattentiveness. It supports criticism that the TBR captures just one of many ADHD subtypes, each possibly possessing a different aetiology, a subject that will now be explored in detail.

            ADHD subtype discrimination

            The clinical utility of the TBR is questioned due to the limited information it provides. The TBR only indicates whether someone is on the boundary of having a particular ADHD-like profile. It cannot discriminate between ADHD subtypes.51,52 Multiple studies have found high-β in ADHD subjects, suggesting ADHD is not always characterised by slow EEG activity, and that there are other ADHD subtypes which the TBR does not capture.53,54 Clarke et al. identified three different EEG profiles: (1) excessive slow brainwave activity plus fast brainwave deficits, (2) increased theta amplitude plus decreased beta activity, and (3) excessive beta activity. 55

                  Researchers disagree that resting-state TBR is a reliable EEG biomarker for ADHD because it is seemingly insensitive to the hyperactive type. Arns et al. found that only one subtype of ADHD children showed an excessive TBR in the resting state.56 Heinrich et al. tested TBR during attention and found excessive TBR only for children with the inattentive ADHD type.57 Van Doren et al. found TBR-NFB treatment effects were driven by decreased θ activity in a group of mainly inattentive ADHD children, and concluded TBR-NFB training is mainly suited to one ADHD subtype. 58

            ADHD subtypes with alternative aetiologies may necessitate alternatives to stimulant prescription as the first-line treatment. Researchers and clinicians have suggested the following as possible ADHD aetiologies:

            1. Developmental trauma. ADHD children are more likely than controls to have developmental trauma histories, suggesting trauma may cause an ADHD subtype.59 To illustrate, the entire USA trauma therapy community of 150,000 practitioners lobbied the DSM5 board to include developmental trauma disorder (DTD) in the 5th edition, on the grounds that DTD is typically misdiagnosed as ODD, conduct disorder, and ADHD.60 Adding weight to the hypothesis that trauma may cause a type of ADHD, Saccaro et al. concluded that ADHD is a stress response and hence why it is comorbid with high anxiety.61 Misdiagnosing trauma as ADHD has serious treatment implications because trauma disorders do not respond to the cognitive-behavioural learning models routinely used to treat ADHD.62-64

            2. Lead toxicity. There exists an association between ADHD and lead poisoning, a disease that causes motor coordination problems.65,66 For instance, a cluster of Wollongong children exposed to lead fallout from a local copper smelter displayed extremely high-β in temporal, frontal lobe, and midline sensory motor areas, plus complex ADHD presentations including problems with regulation, behaviour, focus, attention, and comprehension.67

            3. Developmental coordination disorder (DCD) has the highest comorbidity with ADHD.68,69 Inattentive-type ADHD children often present with DCD.70 This is interesting, considering the TBR is measured on the somatosensory strip (Cz, C3, C4), which coordinates sensory input with motor output, and concerns gross and fine motor movements. Researchers found increased activity in the brain area responsible for visual processing in children with hyperactive ADHD.71 Since motor imagery and actual motor movement share common neural substrates,72 perhaps the ADHD subjects overly imagine themselves executing a movement but are disabled by DCD from achieving this. This is the clinical observation of art therapists who report that ADHD children with perinatal trauma histories routinely present with delayed motor coordination, which manifests as an inability to simply grasp clay.73 It is common for a course of clay field therapy to quickly close this sensory-motor-level developmental gap, resulting in parent and teacher reports of markedly changed behaviour at school and at home.

            4. Sleep disorders. Research shows that insomnia causes core ADHD symptoms, including inattention and behaviour dysregulation.74,75 ADHD symptoms have improved after sleep disorders were treated. This link between lack of sleep and ADHD may explain why both SMR-NFB and TBR-NFB proved effective at reducing ADHD symptoms. Sleep disorders may therefore cause an ADHD subtype. 

            Research by Arns and colleagues suggests the TBR is a biomarker of drowsiness, and that this explains why inattentive subjects have excessive θ, plus why the inattentive ADHD subtype responds to stimulants.76,77 Arns’ sleep-deprivation hypothesis may support the notion that TBR is more a measure of ADHD prognosis rather than diagnosis.78Kropotov concurred that the TBR is a biomarker of prognosis instead of diagnosis. 79

                  TBR denotes executive control?

            The subjects Arns trained at midline sites (Fz, Fpz or Cz) learned to self-regulate.80 Since these sites can affect impulsivity, attention, and emotional inhibition, Arns’ results suggest decreased θ plus increased β may indicate a desynchronised brain state. Perhaps TBR-NFB’s mechanism, therefore, is to synchronise and integrate the brain and thereby return self-control to the subject. This is what Van Doren et al. alluded to, concluding that the TBR-NFB is best considered a kind of performance training that improves attention in any brain, whether normal or abnormal.81 It makes sense that NFB training at Cz could generally assist regulation, because this site simultaneously affects the cingulate, which is associated with emotions, attention, and working memory. Van Son et al. similarly concluded that the TBR is likely a stable biomarker of executive control.82 

            Standardisation issues

            A major limitation of EEG-based research is the lack of standardisation, not only within this domain but also across the scientific field in general. There is no strict consensus on terminology or definitions amongst scientific researchers, who use different language and labels to describe the same thing. Science is so compartmentalised that different disciplines ignore each other’s findings and will announce the discovery of something published decades ago in different terms. Consequently, contemporary researchers give the impression that NFB is “still very young”83 and therefore an under-researched modality lacking reliability and validity, even though NFB research began in the 1960s.84

            Within EEG-based research, only electrode placement is internationally standardised via the 10-20 system.85However, site specificity still involves guesswork and results from multiple areas interacting in unpredictable ways. The numerical boundaries defining the different frequency bands (α, β, θ, δ, γ) are arbitrary. Some NFB researchers mix sensorimotor rhythm (SMR) with low-β and call it β. NFB clinicians typically call high-β just that and insist 12-15 Hz should only be called SMR when measuring the sensory motor strip. Others define β1, β2, and β3 as SMR, low-β, and high-β. Some say SMR is 12-15 Hz. Some call β1 SMR, β2 (15-22 Hz) low-β, and define high-β as 22 to 38 Hz. Paediatricians often max β readings at 30 Hz, while others reach 44 Hz. Some say high-β reaches a maximum of 38 Hz before transitioning to γ, which extends to 42 Hz. Yet some neurologists claim γ reaches 100 Hz. Such inconsistency is confusing and renders comparison between EEG-based studies impossible. It also provides ammunition for politicians to dismiss the validity of research findings and remove disability supports.

            Other possible confounds include subjects’ individual and/or developmental effects. For instance, the TBR pattern changes with age. Saad et al. found that θ remains elevated, whereas β does not remain low with maturity.86EEG parameters exhibit enormous individual variation, as demonstrated by the fact that high-IQ subjects exhibit faster α oscillations, widespread γ bursts, and greater right-hemispheric activity during processing compared to the norm.87,88Such individual variation complicates standardisation of EEG-NFB diagnostic and treatment protocols.

            Adding to the difficulty of identifying the electrophysiological correlates of ADHD, the brain is far too sophisticated and dynamic to approach from a black-and-white research perspective. One slight alteration in EEG-NFB can unexpectedly affect a different area that the clinician is not directly targeting.89 Consider a simple protocol where θor high-β is inhibited, plus 12-15 Hz SMR or β is rewarded. In theory, the researcher expects to see suppression of θ or high-β plus increased low-β or SMR. Yet the brain sometimes does nothing, or does its own thing in response, and suddenly nothing makes sense to the EEG reader anymore. So, θ or high-β might increase while SMR or low-β stays the same, and yet the patient still feels benefits. Thus, when EEG-NFB researchers think mechanically in terms of standard responses and expect simple increases or decreases, this approach may confound the research. A core issue here is the translational gap between scientist-researchers and experienced clinicians who understand the nuances of EEG-NFB in the context of ADHD.90

            NFB’s mechanism of action appears elusive. While pioneer researcher Steerman attributed its mechanism to operant conditioning,91 clinical NFB pioneers, the Othmers totally disagreed, believing it to be the result of implicit processing.92 The brain is so complicated, it is perhaps quantum, meaning it processes data inter-dimensionally.93Therefore, it may take quantum computers (that is, artificial intelligence) to comprehensively measure brain activity. Furthermore, with approximately 3,000 new research papers published daily, no human can keep abreast of the exponential growth in research publications and synthesise all study findings to identify reliable biomarkers. Yet AI possibly could.

            Future directions

            Little has changed since TBR was first used to identify ADHD groups and target NFB protocols for ADHD 50-plus years ago.94 Currently, the body of scientific literature offers insufficient clarity concerning how many ADHD subtypes exist, the underlying aetiology of each subtype, the most effective EEG-NFB protocol for each subtype, or the optimal number of sessions for each ADHD subtype. Perhaps the TBR is now an outdated measure, and it is time to consider alternatives. The future options are to (a) improve research methods or (b) find a new neuroimaging approach to help fill the current knowledge void.

            ADHD research typically assigns subjects to groups based on teacher rating scales, whereas NFB research typically applies a universal protocol to all subjects. Randomly assigning subjects to a flexible NFB protocol that adapts based on individual responses might yield new insights. This was the innovative approach taken by van der Kolk and colleagues.95,96 They started training at a certain frequency range, and then dropped this down 1 Hz every session until the subject complained of feeling under-aroused. This was then incrementally raised by ½ Hz until the optimal was reached. A few subjects had to swap protocols. Further, this research was designed in cooperation with leading EEG-NFB clinicians who can analyse QEEGs and detect subtypes and variations. This approach resulted in research publications that clinicians have found applicable, thereby bridging the translational gap.

            Finding an alternative biomarker to inform EEG-NFB training protocols for ADHD that addresses existing limitations seems difficult. Take QEEG, for example. ADHD children find it difficult to sit still and focus with their eyes closed for a QEEG assessment. Consequently, many resultant artefacts must be cut, leaving only a few seconds of data for analysis. If the QEEG amplifier has only 4 channels, the assessment must be completed in 4-channel lots, which lengthens the assessment time. Bigger clinics that own 21-channel units can complete the assessment in a single sitting. Another issue is that the QEEG requires greater experience to calculate and interpret, with emerging NFB clinicians often sending their data overseas for expert analysis. Further, although QEEG shows the whole brain, it does not prescribe the optimal reward range, where to start NFB training, or predict how subjects will respond to an intervention.

            In 2022, the US FDA approved a Korean invention called the iSyncWave.97,98 This device may signify a paradigm shift in functional neuroimaging, biomarker identification, plus EEG-NFB treatment protocols and analysis. The iSyncWave is a lightweight QEEG scanner helmet that measures brain changes, monitors shifts in NFB training protocols, and produces 3D images in real time. This device does not need electrode gel and is adjustable to fit different head sizes. The EEG scanner uses AI to quickly analyse QEEG results against large data sets and cross-reference them with measures of heart rate variability and vagal tone, which are deep physiological indicators that can be compared with EEG markers to validate findings. It also provides sex normative comparisons, thereby removing gender bias. This invention matches the brain’s complexity, which is not static but always changing. While the iSyncWave is currently expensive for the average clinician to purchase, it is cheaper and more portable than MRI and PET scanners, and it will surely become more affordable over time with mainstream use.

Conclusion

            In conclusion, although theoretically appealing, the TBR is an empirically inconsistent ADHD biomarker. This fact limits its role in informing NFB training protocols. Owing to variability across studies, age-related changes, and a lack of specificity, the TBR falls short of meeting the World Federation of ADHD’s biomarker criteria.

            Despite its limitations, the TBR might still have clinical utility as an indicator of inattentive ADHD subjects suited to EEG-NFB training protocols that increase theta oscillations, and/or who may respond to stimulant medication due to drowsiness. It seems that those ADHD subjects who do not reach the TBR threshold belong to a different subtype with a completely different aetiology and may not benefit from – or possibly be harmed by – EEG-TBR training or prescription drugs.

            Despite decades of investment in TBR research, relatively little progress has been made in terms of establishing its clinical utility. This may be attributed largely to poor research design and a lack of standardisation. It may be time to funnel resources into developing AI-driven alternatives for functional brain assessment. Perhaps the quality of the resulting scientific evidence will help Australian politicians distinguish EEG-based interventions from crystal healing, clairvoyance, and cuddle therapy.

References

1. The Hon Bill Shorten MP. Transcripts. Minister Shorten doorstop interview at Parliament House, Canberra. 1 October 2024. https://ministers.dss.gov.au/transcripts/16216

2 FDA Permits Marketing of First Brain-Wave Test to Assess ADHD. Vol 23. Wiley Periodicals, Inc; 2013:5-.

3 May T, Birch E, Chaves K, et al. The Australian evidence-based clinical practice guideline for attention deficit hyperactivity disorder. Aust N Z J Psychiatry. 2023;57(8):1101-1116. doi:10.1177/00048674231166329

4 Diagnostic and Statistical Manual of Mental Disorders: DSM-5. 5th ed. American Psychiatric Association. 2013.

5. Saad JF, Kohn MR, Clarke S, Lagopoulos J, Hermens DF. Is the Theta/Beta EEG Marker for ADHD Inherently Flawed? J Atten Disord. 2018;22(9):815–26.

6. Putris J, Aydin A, Balkanas M, et al. A Preliminary Investigation into Quantitative Assessment of ADHD Treatment Efficacy on Hyperactivity Levels via Actigraphy. In: IEEE EMBS Int Conf Biomed Health Inform. IEEE; 2023:1-4. doi:10.1109/BHI58575.2023.10313419

7. Wakefield JC. Diagnostic issues and controversies in DSM-5: Return of the false positives problem. Annu Rev Clin Psychol. 2016;12(1):105-132. doi:10.1146/annurev-clinpsy-032814-112800

8. Newson JJ, Pastukh V, Thiagarajan TC. Poor separation of clinical symptom profiles by DSM-5 disorder criteria. Front Psychiatry. 2021;12:775762-775762. doi:10.3389/fpsyt.2021.775762

9. Gardner C, Kleinman A. Medicine and the mind – The consequences of psychiatry’s identity crisis. N Engl J Med. 2019;381(18):1697-1699. doi:10.1056/NEJMp1910603

10. Cosgrove L, Krimsky S. A comparison of DSM-IV and DSM-5 panel members’ financial associations with industry: A pernicious problem persists. PLoS Med. 2012;9(3):e1001190-e1001190. doi:10.1371/journal.pmed.1001190

11. Prescribing study reveals undertreatment of ADHD, depression, and anxiety among youth. The Brown University Child and Adolescent Psychopharmacology Update. 2018;20(4):1-4. doi:10.1002/cpu.30286

12. Clark LA, Cuthbert B, Lewis-Fernández R, Narrow WE, Reed GM. Three approaches to understanding and classifying mental disorders: ICD-11, DSM-5, and the National Institute of Mental Health’s Research Domain Criteria (RDoC). Psychol Sci Public Interest. 2017;18(2):72-145. doi:10.1177/1529100617727266

13. Bishop JC, Rinn AN. The potential of misdiagnosis of high IQ youth by practicing mental health professionals: A mixed methods study. High Ability Studies. 2020;31(2):213-243. doi:10.1080/13598139.2019.1661223

14. Ercan ES, Unsel-Bolat G, Tufan AE, et al. Effect of Impairment on the Prevalence and Comorbidities of Attention Deficit Hyperactivity Disorder in a National Survey: Nation-Wide Prevalence and Comorbidities of ADHD. J Atten Disord. 2022;26(5):674-684. doi:10.1177/10870547211017985

15. Epstein JN, Loren RE. Changes in the Definition of ADHD in DSM-5: Subtle but Important. Neuropsychiatry. 2013 Oct 1;3(5):455-458. doi: 10.2217/npy.13.59. PMID: 24644516; PMCID: PMC3955126.

16. Stafford C, Jaramillo C, Golden C, Amen D, Willeumier K. A-130 Measuring Baseline Regional Cerebral Blood Flow with Single-Photon Emission Computed Tomography (SPECT) in ADHD. Arch Clin Neuropsychol. 2022;37(6):1284-1284. doi:10.1093/arclin/acac060.130

17. Perez SG, Slicer K, Harrigan D, Arias B, Golden CJ, Willeumier K, et al. A-260 Regional Cerebral Blood Flow Differences in ADHD Subtypes Among Children. Arch Clin Neuropsychol. 2022;37(6):1404–1404.

18. Snyder SM, Rugino TA, Hornig M, Stein MA. Integration of an EEG biomarker with a clinician’s ADHD evaluation. Brain Behav. 2015;5(4):1-n/a. doi:10.1002/brb3.330

19. Chilakamarri JK, Filkowski MM, Ghaemi SN. Misdiagnosis of bipolar disorder in children and adolescents: A comparison with ADHD and major depressive disorder. Ann Clin Psychiatry. 2011;23(1):25–9.

20. Wakefield JC. Diagnostic issues and controversies in DSM-5: Return of the false positives problem. Annu Rev Clin Psychol. 2016;12(1):105-132. doi:10.1146/annurev-clinpsy-032814-112800

2[1]. Ibid.

22. Kropotov JD. Functional Neuromarkers for Psychiatry: Applications for Diagnosis and Treatment. 1st ed. San Diego: Elsevier Science & Technology; 2016. 500 p.

23. Scarr E, Millan MJ, Bahn S, et al. Biomarkers for Psychiatry: The Journey from Fantasy to Fact, a Report of the 2013 CINP Think Tank. Int J Neuropsychopharmacol. 2015;18(10):pyv042-pyv042. doi:10.1093/ijnp/pyv042

24. Saad JF, Kohn MR, Clarke S, Lagopoulos J, Hermens DF. Is the Theta/Beta EEG Marker for ADHD Inherently Flawed? J Atten Disord. 2018;22(9):815–26.

25.Thome J, Ehlis AC, Fallgatter AJ, et al. Biomarkers for attention-deficit/hyperactivity disorder (ADHD). A consensus report of the WFSBP task force on biological markers and the World Federation of ADHD. World J Biol Psychiatry. 2012;13(5):379-400. doi:10.3109/15622975.2012.690535

26.Cui J, Wang Y, Zhou L, Fei Y, Zhou J, Shen D. 3D Point-based Multi-Modal Context Clusters GAN for Low-Dose PET Image Denoising. IEEE Trans Circuits Syst Video Technol. Published online 2024:1-1. doi:10.1109/TCSVT.2024.3398686

27. National Cancer Institute. https://www.cancer.gov/about-cancer/causes-prevention/risk/radiation/nuclear-accidents-fact-sheet#:~:text=At%20high%20doses%2C%20ionizing%20radiation,cataracts%2C%20as%20well%20as%20cancer. Accessed 15 September 2024.

28. Kropotov JD. Functional Neuromarkers for Psychiatry: Applications for Diagnosis and Treatment. 1st ed. San Diego: Elsevier Science & Technology; 2016. 500 p.

29. Häger LA, Åsberg Johnels J, Kropotov JD, et al. Biomarker support for ADHD diagnosis based on Event Related Potentials and scores from an attention test. Psychiatry Res. 2021;300:113879-113879. doi:10.1016/j.psychres.2021.113879

30. Kropotov JD. Functional Neuromarkers for Psychiatry: Applications for Diagnosis and Treatment. 1st ed. San Diego: Elsevier Science & Technology; 2016. 500 p.

31. Mann CA, Lubar JF, Zimmerman AW, Miller CA, Muenchen RA. Quantitative analysis of EEG in boys with attention-deficit-hyperactivity disorder: Controlled study with clinical implications. Pediatr Neurol. 1992;8(1):30–6.

32. Marzbani H, Marateb HR, Mansourian M. Methodological note: Neurofeedback: A comprehensive review on system design, methodology and clinical applications. Basic Clin Neurosci. 2016;7(2):143-158. doi:10.15412/j.bcn.03070208

33. Gunkelman JD, Johnstone J. Neurofeedback and the brain. J Adult Dev. 2005;12(2-3):93-98. doi:10.1007/s10804-005-7024-x

34. Lubar JF, Shouse MN. EEG and behavioral changes in a hyperkinetic child concurrent with training of the sensorimotor rhythm (SMR): a preliminary report. Biofeedback Self Regul. 1976;1(3):293-306. doi:10.1007/BF01001170 

35. Othmer, S. History of Neurofeedback. In Kirk HW. Restoring the Brain: Neurofeedback as an Integrative Approach to Health. Second edition. Oxford: Routledge; 2020. 22 p. https://doi.org/10.4324/9780429275760

36. Arns M, Conners CK, Kraemer HC. A Decade of EEG Theta/Beta Ratio Research in ADHD: A Meta-Analysis. J Atten Disord. 2013;17(5):374-383. doi:10.1177/1087054712460087

37. Snyder SM, Quintana H, Sexson SB, Knott P, Haque AFM, Reynolds DA. Blinded, multi-center validation of EEG and rating scales in identifying ADHD within a clinical sample. Psychiatry Res. 2008;159(3):346-358. doi:10.1016/j.psychres.2007.05.006

38. Quintana H, Snyder SM, Purnell W, Aponte C, Sita J. Comparison of a standard psychiatric evaluation to rating scales and EEG in the differential diagnosis of attention-deficit/hyperactivity disorder. Psychiatry Res. 2007;152(2):211-222. doi:10.1016/j.psychres.2006.04.015

39. Monastra VJ, Monastra DM, George S. The effects of stimulant therapy, EEG biofeedback, and parenting style on the primary symptoms of attention-deficit/hyperactivity disorder. Appl Psychophysiol Biofeedback. 2002;27(4):231-249. doi:10.1023/A:1021018700609

40. Arns M, de Ridder S, Strehl U, Breteler M, Coenen A. Efficacy of Neurofeedback Treatment in ADHD: The Effects on Inattention, Impulsivity and Hyperactivity: A Meta-Analysis. Clin EEG Neurosci. 2009;40(3):180-189. doi:10.1177/155005940904000311

41. Snyder SM, Rugino TA, Hornig M, Stein MA. Integration of an EEG biomarker with a clinician’s ADHD evaluation. Brain Behav. 2015;5(4):1-n/a.

42. Ogrim G, Kropotov J, Hestad K. The quantitative EEG theta/beta ratio in attention deficit/hyperactivity disorder and normal controls: Sensitivity, specificity, and behavioral correlates. Psychiatry Res. 2012;198(3):482-488. doi:10.1016/j.psychres.2011.12.041

43. Loo SK, Cho A, Hale TS, McGough J, McCracken J, Smalley SL. Characterization of the Theta to Beta Ratio in ADHD: Identifying Potential Sources of Heterogeneity. J Atten Disord. 2013;17(5):384-392. doi:10.1177/1087054712468050

44. Liechti MD, Maurizio S, Heinrich H, Jäncke L, Meier L, Steinhausen HC, et al. First clinical trial of tomographic neurofeedback in attention-deficit/hyperactivity disorder: Evaluation of voluntary cortical control. Clin Neurophysiol. 2012;123(10):1989–2005.

45. Duarte Hernández E, González Marqués J, Alvarado JM. Effect of the Theta-Beta Neurofeedback Protocol as a Function of Subtype in Children Diagnosed with Attention Deficit Hyperactivity Disorder. The Spanish journal of psychology. 2016;19. doi:10.1017/sjp.2016.31

46. Bluschke A, Eggert E, Friedrich J, Jamous R, Prochnow A, Pscherer C, et al. The effects of different theta and beta neurofeedback training protocols on cognitive control in ADHD. J Cogn Enhanc 2022;6:463–77. https://doi.org/10.1007/ s41465-022-00255-6.

47. Arns M, de Ridder S, Strehl U, Breteler M, Coenen A. Efficacy of Neurofeedback Treatment in ADHD: The Effects on Inattention, Impulsivity and Hyperactivity: A Meta-Analysis. Clinical EEG and neuroscience. 2009;40(3):180-189. doi:10.1177/155005940904000311

48. Bussalb A, Collin S, Barthélemy Q, et al. Is there a cluster of high theta-beta ratio patients in attention deficit hyperactivity disorder? Clin Neurophysiol. 2019;130(8):1387-1396. doi:10.1016/j.clinph.2019.02.021

49. Ahmadi M, Kazemi K, Kuc K, et al. Cortical source analysis of resting state EEG data in children with attention deficit hyperactivity disorder. Clin Neurophysiol. 2020;131(9):2115-2130. doi:10.1016/j.clinph.2020.05.028

50. Snyder SM, Rugino TA, Hornig M, Stein MA. Integration of an EEG biomarker with a clinician’s ADHD evaluation. Brain Behav. 2015;5(4):1-n/a.

51. Saad JF, Kohn MR, Clarke S, Lagopoulos J, Hermens DF. Is the Theta/Beta EEG Marker for ADHD Inherently Flawed? J Atten Disord. 2018;22(9):815–26.

52. Heinrich H, Busch K, Studer P, Erbe K, Moll GH, Kratz O. EEG spectral analysis of attention in ADHD: implications for neurofeedback training? Front Hum Neurosci. 2014;8. doi:10.3389/tnhurn.2014.00611

53. Chabot RJ, Merkin H, Wood LM, Davenport TL, Serfontein G. Sensitivity and Specificity of QEEG in Children with Attention Deficit or Specific Developmental Learning Disorders. Clin EEG Neurosci. 1996;27(1):26-34. doi:10.1177/155005949602700105

54. Kuperman S, Johnson B, Arndt S, Lindgren S, Wolraich M. Quantitative EEG Differences in a Nonclinical Sample of Children with ADHD and Undifferentiated ADD. J Am Acad Child Adolesc Psychiatry 1996;35(8):1009-1017. doi:10.1097/00004583-199608000-00011

55. Clarke AR, Barry RJ, McCarthy R, Selikowitz M. EEG-defined subtypes of children with attention-deficit/hyperactivity disorder. Clinical neurophysiology. 2001;112(11):2098-2105. doi:10.1016/S1388-2457(01)00668-X

56. Arns M, Feddema I, Kenemans JL. Differential effects of theta/beta and SMR neurofeedback in ADHD on sleep onset latency. Front Hum Neurosci. 2014;8:1019-1019. doi:10.3389/fnhum.2014.01019

57. Heinrich H, Busch K, Studer P, Erbe K, Moll GH, Kratz O. EEG spectral analysis of attention in ADHD: implications for neurofeedback training? Front Hum Neurosci. 2014;8. doi:10.3389/tnhurn.2014.00611

58. Van Doren J, Heinrich H, Bezold M, et al. Theta/beta neurofeedback in children with ADHD: Feasibility of a short-term setting and plasticity effects. Int J Psychophysiol. 2017;112:80-88. doi:10.1016/j.ijpsycho.2016.11.004

59. Schilpzand EJ, Sciberras E, Alisic E, et al. Trauma exposure in children with and without ADHD: prevalence and functional impairment in a community-based study of 6–8-year-old Australian children. Eur Child Adolesc Psychiatry. 2018;27(6):811-819. doi:10.1007/s00787-017-1067-y

60. van der Kolk, B. Bessel van der Kolk on Understanding Trauma . 2019. Psychotherapy.net and Kanopy.

61. Saccaro LF, Schilliger Z, Perroud N, Piguet C. Inflammation, Anxiety, and Stress in Attention-Deficit/Hyperactivity Disorder. Biomedicines. 2021;9(10):1313-. doi:10.3390/biomedicines9101313

62. van der Kolk B, Ford JD, Spinazzola J. Comorbidity of developmental trauma disorder (DTD) and post-traumatic stress disorder: findings from the DTD field trial. Eur J Psychotraumatol. 2019;10(1):1562841-1562841. doi:10.1080/20008198.2018.1562841

63. Spinazzola J, der Kolk B, Ford JD. When nowhere is safe: Interpersonal trauma and attachment adversity as antecedents of posttraumatic stress disorder and developmental trauma disorder. J Trauma Stress. 2018;31(5):631-642. doi:10.1002/jts.22320

64. van der Kolk BA. Developmental trauma disorder. Psychiatr Ann. 2005;35(5):401-. doi:10.3928/00485713-20050501-06

65. Donzelli G, Carducci A, Llopis-Gonzalez A, et al. The association between lead and attention-deficit/hyperactivity disorder: A systematic review. Int J Environ Res Public Health. 2019;16(3):382-. doi:10.3390/ijerph16030382

66. Sanders T, Liu Y, Buchner V, Tchounwou PB. Neurotoxic Effects and Biomarkers of Lead Exposure: A Review. Rev Environ Health. 2009;24(1):15–46.

67. Jonathan Banks. Personal communication. October 2024.

68. Villa M, Barriopedro MI, Ruiz LM. Motor competence difficulties and attention deficit and hyperactivity disorder (ADHD) among secondary students. Cuadernos de psicología del deporte. 2020;20(2):47-62. doi:10.6018/CPD.360491.

69. Goulardins JB, Marques JCB, De Oliveira JA. Attention Deficit Hyperactivity Disorder and Motor Impairment: A Critical Review. Percept Mot Skills. 2017;124(2):425-440. doi:10.1177/0031512517690607

70. Montes-Montes R, Delgado-Lobete L, Rodríguez-Seoane S. Developmental coordination disorder, motor performance, and daily participation in children with attention deficit and hyperactivity disorder. Children. 2021;8(3):187-. doi:10.3390/children8030187

71. Perez SG, Slicer K, Harrigan D, Arias B, Golden CJ, Willeumier K, et al. A-260 Regional Cerebral Blood Flow Differences in ADHD Subtypes Among Children. Archives of clinical neuropsychology. 2022;37(6):1404–1404.

72. Mustile M, Kourtis D, Edwards MG, Donaldson DI, Ietswaart M. Neural correlates of motor imagery and execution in real-world dynamic behavior: evidence for similarities and differences. Front Hum Neurosci. 2024;18:1412307-. doi:10.3389/fnhum.2024.1412307

73. Elbrecht C. Certificate in Clay Field Therapy with Children. 2023. [Training video].

74. Arns M, Kenemans JL. Neurofeedback in ADHD and insomnia: vigilance stabilization through sleep spindles and circadian networks. Neuroscience and biobehavioral reviews. 2014;44:183-194. doi:10.1016/j.neubiorev.2012.10.006

75. Arns M, Feddema I, Kenemans JL. Differential effects of theta/beta and SMR neurofeedback in ADHD on sleep onset latency. Front Hum Neurosci. 2014;8:1019-1019. doi:10.3389/fnhum.2014.01019

76. Ibid

77. Olbrich S, van Dinteren R, Arns M. Personalized Medicine: Review and Perspectives of Promising Baseline EEG Biomarkers in Major Depressive Disorder and Attention Deficit Hyperactivity Disorder. Neuropsychobiology. 2015;72(3-4):229-240. doi:10.1159/000437435

78. Ibid

79. Kropotov JD. Functional Neuromarkers for Psychiatry: Applications for Diagnosis and Treatment. 1st ed. San Diego: Elsevier Science & Technology; 2016. 500 p.

80. Arns M, Feddema I, Kenemans JL. Differential effects of theta/beta and SMR neurofeedback in ADHD on sleep onset latency. Front Hum Neurosci. 2014;8:1019-1019. doi:10.3389/fnhum.2014.01019

81. Van Doren J, Heinrich H, Bezold M, et al. Theta/beta neurofeedback in children with ADHD: Feasibility of a short-term setting and plasticity effects. Int J Psychophysiol. 2017;112:80-88. doi:10.1016/j.ijpsycho.2016.11.004

82. van Son D, van der Does W, Band GPH, Putman P. EEG Theta/Beta Ratio Neurofeedback Training in Healthy Females. Appl Psychophysiol Biofeedback. 2020 Sep;45(3):195-210. doi: 10.1007/s10484-020-09472-1. PMID: 32458282; PMCID: PMC7391399.

83. Mohagheghi A, Amiri S, Moghaddasi Bonab N, et al. A Randomized Trial of Comparing the Efficacy of Two Neurofeedback Protocols for Treatment of Clinical and Cognitive Symptoms of ADHD: Theta Suppression/Beta Enhancement and Theta Suppression/Alpha Enhancement. Biomed Res Int. 2017;2017:3513281-3513287. doi:10.1155/2017/3513281

84. Kamiya J. Some call me the father of clinical neurofeedback. In: Neurofeedback. United States: Elsevier Science & Technology; 2019. p. 1–6.

85. Kropotov JD. Functional Neuromarkers for Psychiatry: Applications for Diagnosis and Treatment. 1st ed. San Diego: Elsevier Science & Technology; 2016. 500 p.

86. Saad JF, Kohn MR, Clarke S, Lagopoulos J, Hermens DF. Is the Theta/Beta EEG Marker for ADHD Inherently Flawed? J Atten Disord. 2018;22(9):815–26.

87. Grandy TH, Werkle-Bergner M, Chicherio C, Lövdén M, Schmiedek F, Lindenberger U. Individual alpha peak frequency is related to latent factors of general cognitive abilities. NeuroImage (Orlando, Fla). 2013;79:10-18. doi:10.1016/j.neuroimage.2013.04.059

88. Zhang L, Gan JQ, Wang H. Localization of neural efficiency of the mathematically gifted brain through a feature subset selection method. Cogn Neurodyn. 2015;9(5):495-508. doi:10.1007/s11571-015-9345-1

89. Neuhäußer AM, Bluschke A, Roessner V, Beste C. Distinct effects of different neurofeedback protocols on the neural mechanisms of response inhibition in ADHD. Clinical neurophysiology. 2023;153:111-122. doi:10.1016/j.clinph.2023.06.014

90. Williams LM, Tsang TW, Clarke S, Kohn M. An “integrative neuroscience” perspective on ADHD: linking cognition, emotion, brain and genetic measures with implications for clinical support. Expert Rev Neurother. 2010;10(10):1607-1621. doi:10.1586/ern.10.140

91. Sterman MB. Basic Concepts and Clinical Findings in the Treatment of Seizure Disorders with EEG Operant Conditioning. Clin Electroencephalogr. 2000;31(1):45-55. doi:10.1177/155005940003100111

92. Othmer S, Othmer SF, Kaiser DA, Putman J. Endogenous neuromodulation at infralow frequencies. Semin Pediatr Neurol. 2013;20(4):246-257. doi:10.1016/j.spen.2013.10.006

93. Swan M, dos Santos RP, Witte F. Quantum Neurobiology. Quantum reports. 2022;4(1):107-126. doi:10.3390/quantum4010008

94. Lubar JF, Shouse MN. EEG and behavioral changes in a hyperkinetic child concurrent with training of the sensorimotor rhythm (SMR) – A preliminary report. Biofeedback Self Regul. 1976;1(3):293-306. doi:10.1007/BF01001170

95. Van Der Kolk BA, Hodgdon H, Gapen M, et al. A randomized controlled study of neurofeedback for chronic PTSD. PloS one. 2016;11(12):e0166752-e0166752. doi:10.1371/journal.pone.0166752

96. Rogel A, Loomis AM, Hamlin E, Hodgdon H, Spinazzola J, van der Kolk B. The Impact of Neurofeedback Training on Children With Developmental Trauma: A Randomized Controlled Study. Psychol Trauma. 2020;12(8):918-929. doi:10.1037/tra0000648

97. sythe-Stephens K. FDA OKs iSyncWave EEG Scanner. Anthem Media Group; 2022.

98. https://www.imedisync.com/en/products/isyncwave/. Accessed October 2024.