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Psychophysiological Assessment Can Enhance ADHD Diagnosis and Training

Updated: Jan 18


Child with ADHD


The diagnostic process involves formulating a working model of the presenting problem. This process combines subjective and objective assessments to help determine the diagnosis and guide interventions. This process can sometimes rely on imprecise or misleading assumptions or information, leading to incorrect diagnosis and, subsequently, incorrect treatment or training approaches that are ineffectual at best and may also cause harm.


This post provides an overview of how applied psychophysiology assessment can contribute objective input to help clinicians refine their assessment process to produce more accurate and evidence-based diagnoses for their clients. Click on our narrator icon to listen to this post.

Jane






Sources of Assessment Information


The assessment process relies on two broad categories of information. One is the subjective impressions of the clinician, often informed by extensive education, training, and experience. These impressions are usually gathered during an intake process that includes interviews, histories, review of forms, questionnaires, and other documentation provided by the client/patient and possibly by a referral source, family member, work supervisor, teacher, or previous provider. The clinician’s direct observations of the client are integral to the assessment.


The second category is objective assessments such as height, weight, blood pressure, normed tests of emotion, cognition, and behavior, and possibly imaging studies (e.g., CT, MRI, fMRI) or other testing. In most cases, these objective measures are open to interpretation, and the clinician must understand the strengths and limitations of each tool to incorporate their findings as a valid and accurate contribution to the assessment.

fMRI


A clinician’s education, training, experience, social, economic, and perspective can also influence how these measures will be interpreted.


Also influencing the interpretation of these two categories of information is the client's background. Objective measures can help overcome bias and lead to designing the most appropriate intervention for each individual.



Psychophysiological Assessment


Psychophysiological assessment provides objective data that can contribute to a diagnosis. This is critical because problem behaviors involve networks of interacting physiological, emotional, cognitive, and behavioral processes. Psychophysiological assessment can incorporate capnometry, electroencephalography (EEG), heart rate, heart rate variability, hemoencephalography, respiration, skin conductance or resistance, skin temperature, and surface electromyography.


surface EMG

Providers can quantify clients' distinctive psychophysiological responses, which are part of their presenting problem. For example, clinicians and coaches can uptrain deficient low-frequency HRV (Shaffer & Ginsberg, 2017) or sensorimotor rhythm power (Sterman & Egner, 2006). These objective measures can aid diagnosis and serve as training benchmarks.



Attention Deficit Hyperactivity Disorder


Attention deficit hyperactivity disorder (ADHD) illustrates the benefit of integrating psychophysiological assessment into diagnosis. ADHD is a somewhat catch-all diagnosis that encompasses symptoms ranging from problems with attention, executive function, hyperactivity, impulse control, and more.


ADHD

Health problems can complicate ADHD diagnosis in elementary school-age children. Many symptoms of ADHD overlap with other psychiatric, developmental, and medical disorders. Anxiety disorders can lead to difficulties with concentration, and children with anxiety might appear inattentive. Mood disorders, like depression, can also have overlapping symptoms with ADHD, such as inattention or restlessness (Jensen et al., 2001). Children with learning disabilities may become frustrated or disengaged in classroom settings, appearing inattentive or disruptive (DuPaul et al., 2013). Medical conditions such as sleep disorders, vision or hearing problems, or even certain types of seizures can manifest symptoms that resemble ADHD (Corkum et al., 1998). A child's age, biological sex, culture, socioeconomic status, family crisis, and sleep disturbance can influence ADHD diagnosis.

Age

Younger children, especially those younger in their classroom age cohort, are more likely to be diagnosed with ADHD, because their behavior is being compared with older, more mature peers (Elder, 2010; Morrow et al., 2012).



Biological Sex

Boys are more frequently diagnosed with ADHD than girls. Boys often exhibit the more overt, disruptive symptoms of hyperactivity and impulsivity, whereas girls may display more inattentive symptoms, which might be overlooked. Since ADHD in girls often presents as inattention rather than hyperactivity, it may be misdiagnosed as anxiety or mood disorders, leading to underdiagnosis (Rucklidge, 2010).


Culture

In the U.S., African American and Latino children are often underdiagnosed with ADHD compared to White children, possibly due to cultural beliefs, stigma, or disparities in healthcare access (Coker et al., 2016). In some cultures, behaviors associated with ADHD might be perceived as normal. Conversely, in cultures with a high value on obedience, ADHD behaviors might be seen as particularly problematic (Bussing et al., 2003). Standard ADHD assessment tools might not be culturally sensitive, leading to potential misdiagnoses (Takeda et al., 2016).


Socioeconomic Status

In high-pressure, affluent communities, children might be overdiagnosed and overmedicated in the pursuit of academic achievement (Russell et al., 2015). Children from lower socioeconomic backgrounds might be underdiagnosed due to limited access to healthcare services and a lack of awareness or knowledge about ADHD. On the other hand, once these children interact with systems like schools or the juvenile justice system, they might be more readily diagnosed.



Family Crisis


Children with cold extremities and dysfunctional breathing may live in chaotic home environments where they or family members experience distress or trauma. Their hypervigilance for danger cues, including facial expressions and tone of voice, may exclude other sensory inputs. Physicians may misdiagnose their selective focus as the inattentive subtype of ADHD when they have actually adapted to life in a "war zone." Seventy-five percent of students diagnosed with PTSD receive other diagnoses that may include ADHD (Danese, 2020).


Sleep Deprivation


Children, adolescents, young adults, and adults are often chronically sleep-deprived.

sleep deprivation

Sleep deprivation and compensatory caffeine use can mimic hyperactive and inattentive ADHD subtypes, leading to misdiagnosis. Patients can present with problems with impulse control, attention, executive function, and emotional self-regulation. Self-medication with caffeine to combat short sleep duration effects can produce hyperactivity. About 92% of college students reported using caffeine to improve attention and overcome fatigue (Mahoney et al., 2019; Zahra et al., 2021).

Children

The Centers for Disease Control (2023) defines short sleep duration as <12 hours for children aged 4–12 months, <11 hours for children aged 1–2 years, <10 hours for children aged 3–5 years, <9 hours for children aged 6–12 years, and <8 hours for children aged 13–14 years. Short sleep duration ranged from 25.2% in Minnesota to 52.5% in Mississippi in 2018–2019.

children short sleep


High School Students

The Centers for Disease Control (2023) defines short sleep duration for high school students as less than 8 hours of sleep per day. The prevalence of short sleep duration for this age group increased between 2009 and 2019 and was higher for female than male students.


high school short sleep

Short sleep duration prevalence ranged from 70.1% in Montana to 84.3% in New Jersey.

high school short sleep


Adults

The Centers for Disease Control (2023) defines short sleep duration for adults as less than 7 hours of sleep per day. The prevalence of short sleep duration did not change from 2013 to 2020 and was higher in men than women.

adult short sleep


Age-adjusted short sleep duration prevalence ranged from 29.3% in Colorado to 42.8% in West Virginia in 2020.


adult short sleep

Improving ADHD Assessment

Psychophysiological assessment in ADHD diagnosis can provide objective data to support clinical decision-making. Psychophysiological measures assess physiological responses to certain stimuli or tasks, which can reveal information about brain activity, arousal levels, and attentional processes.


The Case for Psychophysiological Assessment

Unlike behavioral reports or clinical observations, psychophysiological measures provide quantitative, objective data that can support or challenge a tentative diagnosis (Barry et al., 2003).


Psychophysiological markers can help differentiate ADHD from other disorders with overlapping behavioral symptoms, thereby improving diagnostic accuracy (Snyder & Hall, 2006).


Some psychophysiological measures can help distinguish between the different subtypes of ADHD (i.e., inattentive, hyperactive/impulsive, combined; Hermens et al., 2005). Psychophysiological assessment can reveal the neurophysiological bases of ADHD, such as anomalies in cortical arousal, information processing, and inhibitory control (Loo & Makeig, 2012).


Psychophysiological measures can aid diagnosis and monitor treatment outcomes (Monastra, Lubar, & Linden, 2001).



EEG Monitoring Is Critical

Although EEG monitoring is not currently a standalone or primary diagnostic tool for ADHD according to American Academy of Pediatrics guidelines, it can provide valuable complementary information. An accurate diagnosis involves a multi-faceted approach that considers behavioral, clinical, educational, and, in some cases, neuropsychological and neurophysiological data. Combining EEG data with this comprehensive approach can add a layer of objectivity and depth to the diagnostic process.

EEG

EEG assessment can identify brain sites with clinically significant theta-to-beta ratios (Barry, Clarke, & Johnstone, 2003). Specific EEG patterns might differentiate ADHD from conditions like anxiety or learning disabilities (Snyder & Hall, 2006), although differential diagnosis requires multiple sources of information. Moreover, EEG patterns might vary among these subtypes, potentially assisting in subtype classification (Clarke et al., 2001).



The Benefits of Cognitive Tests

Depending on the goals of neurofeedback for a particular client, domains assessed may include one or more of the following: attention, memory, language, visual-spatial perception and construction, and executive functioning. Intellectual functioning, academic performance, sensorimotor ability, and effort/performance validity may also be assessed. A continuous performance test such as the Conners Continuous Performance Test (2014) may be used to assess sustained attention and distractibility. They can objectively measure attention, impulsivity, response time, and response time variability. Graphic © Pearson.

CPT


Where improved academic performance is the client’s goal, a practitioner can use an educational screening instrument such as the Wide Range Achievement Test. Graphic © Pearson.


WRAT5


Alternatively, a measure of intelligence (e.g., Wechsler Adult Intelligence Scale-IV; Wechsler, 2008) or collection of executive function or cognitive self-regulation measures may be of interest (e.g., Delis-Kaplan Executive Function System; Delis et al., 2001). Cognitive test batteries have several tests that assess representative domains of cognition and may include, for example, measures of aspects of attention, memory, and executive function (e.g., CNS Vital Signs; Gaultieri & Johnson, 2006). Graphic © Pearson.


WAIS-IV


Sleep Assessment

When clinicians suspect that sleep problems may masquerade as ADHD, they should conduct a sleep assessment, including a daytime sleepiness questionnaire. Examples include the Epworth Sleepiness Scale (ESS; Johns, 1991) and Stanford Sleepiness Scale (SSS). In addition, physicians can order a Multiple Sleep Latency Test (MSLT; Carskadon et al., 1986) in which a client attempts to nap in a dark room at several intervals throughout the day, typically every 2 hours. Physicians can order inexpensive home-based sleep monitoring to evaluate sleep duration, time in each sleep stage, sleep quality, apnea, heart rate, and cardiac arrhythmias.


home sleep study


Clinicians may order a clinic-based sleep study where findings of oxygen desaturation confirm conditions like apnea.

sleep study



Psychophysiological Monitoring

The difference between diagnosing clients with or without psychophysiological measures is analogous to evaluating them in normal daylight versus the dark. Psychophysiological measures allow you to "see" your clients more clearly and accurately, drawing conclusions using objective data instead of guesses and assumptions. Psychophysiological assessment can inform training approaches to correct the underlying mechanisms causing externally observable symptoms. They can also detect the physiological signatures of emerging anxiety responses during neurofeedback before they become overwhelming.


Skin resistance, peripheral skin temperature, and surface EMG (sEMG) monitoring can guide clinical interventions when clients experience challenging feelings, imagery, or memories.


skin resistance


In the screen capture below, relaxation was followed by a short period of increased anxiety when a difficult memory surfaced. The clinician quickly identified the response due to physiological monitoring. Skin resistance (blue) and peripheral skin temperature (red) decreased, and sEMG (gold) increased. A reassuring conversation with the client allowed the client to reframe their stress response and resume training.

anxiety

When a client experiences anxiety during a session, skin resistance and temperature can assess their recovery. In the screen capture below, the client's anxiety was associated with skin resistance (blue) below 100 Kohms and temperature (red) around 80 degrees F. The client increased the 6-9 Hz training frequency (light blue) voltage/power as training progressed. Skin resistance eventually increased to a final value over 400 Kohms (higher values mean less anxiety or arousal). Skin temperature eventually exceeded over 92 degrees F.

session monitoring

Conclusion


Psychophysiological assessment, including the EEG, cannot independently diagnose ADHD or rule out competing diagnoses. Instead, along with objective cognitive tests and sleep assessment, it can inform diagnosis and appropriate behavioral interventions. Furthermore, psychophysiological assessment can guide treatment sessions, detect distress signs before they disrupt training, and monitor client progress.


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Glossary


anxiety disorders: a group of psychological disorders characterized by feelings of anxiety and fear, which may manifest in physiological responses that can be tracked via biofeedback (e.g., decreased skin resistance). applied psychophysiology: the study and application of understanding psychophysiological processes for diagnosing and promoting self-regulation and treating various disorders. Attention Deficit Hyperactivity Disorder (ADHD): a neurodevelopmental disorder characterized by persistent patterns of inattention, hyperactivity, and impulsivity that interfere with functioning or development.


capnometry: measurement of the concentration of carbon dioxide in exhaled air.


continuous performance test (CPT): neuropsychological tests that measure sustained and selective attention and impulsivity, often used in ADHD assessments.


electroencephalography (EEG): measurement of brain electrical activity.


heart rate: the number of heartbeats per minute.


heart rate variability (HRV): the variation in time between successive heartbeats.


hemoencephalography (HEG): measurement of the relative blood flow in the brain using infrared light.


hypervigilance: a heightened state of sensory sensitivity and intense focus, often linked to anxiety disorders and PTSD.


learning disabilities: neurologically-based processing difficulties that can interfere with learning skills such as reading, writing, or math.


mood disorders: a group of psychophysiological disorders characterized by elevated or lowered mood, such as depression or bipolar disorder.


Post-Traumatic Stress Disorder (PTSD): a psychological disorder triggered by a traumatic event, often associated with heightened arousal, reactivity, and hypervigilance.


psychophysiological assessment: evaluating resting and phasic psychophysiological activity in response to standardized conditions.


skin resistance: also known as galvanic skin response (GSR), it measures the conductance of the skin, which varies with eccrine sweat gland activity.


skin temperature: the measurement of the skin's surface temperature using a thermistor.


surface electromyography (sEMG): measuring muscle action potential using surface electrodes.


theta-to-beta ratio: the ratio between 4-7 Hz theta and 13-21 Hz beta, measured most typically along the midline and generally in the anterior midline near the 10-20 system location Fz.



References

Barry, R. J., Clarke, A. R., & Johnstone, S. J. (2003). A review of electrophysiology in attention-deficit/hyperactivity disorder: I. Qualitative and quantitative electroencephalography. Clinical Neurophysiology: Official Journal of the International Federation of Clinical Neurophysiology, 114(2), 171–183. https://doi.org/10.1016/s1388-2457(02)00362-0

Bussing, R., Zima, B. T., Gary, F. A., & Garvan, C. W. (2003). Barriers to detection, help-seeking, and service use for children with ADHD symptoms. The Journal of Behavioral Health Services & Research, 30(2), 176–189. https://doi.org/10.1007/BF02289806


Carskadon, M. A., Dement, W. C., Mitler, M. M., Roth, T., Westbrook, P. R., & Keenan, S. (1986). Guidelines for the multiple sleep latency test (MSLT): A standard measure of sleepiness. Sleep, 9(4), 519–524. https://doi.org/10.1093/sleep/9.4.519


Centers for Disease Control (2023). Sleep and sleep disorders. Retrieved from https://www.cdc.gov/sleep/data-and-statistics/Adults.html Clarke, A. R., Barry, R. J., McCarthy, R., & Selikowitz, M. (2001). EEG-defined subtypes of children with attention-deficit/hyperactivity disorder. Clinical Neurophysiology: Official Journal of the International Federation of Clinical Neurophysiology, 112(11), 2098–2105. https://doi.org/10.1016/s1388-2457(01)00668-x Coker, T. R., Elliott, M. N., Toomey, S. L., Schwebel, D. C., Cuccaro, P., Tortolero Emery, S., Davies, S. L., Visser, S. N., & Schuster, M. A. (2016). Racial and ethnic disparities in ADHD diagnosis and treatment. Pediatrics, 138(3), e20160407. https://doi.org/10.1542/peds.2016-0407

Corkum, P., Tannock, R., & Moldofsky, H. (1998). Sleep disturbances in children with attention-deficit/hyperactivity disorder. Journal of the American Academy of Child and Adolescent Psychiatry, 37(6), 637–646. https://doi.org/10.1097/00004583-199806000-00014 Danese, A., McLaughlin, K. A., Samara, M., & Stover, C. S. (2020). Psychopathology in children exposed to trauma: detection and intervention needed to reduce downstream burden. BMJ (Clinical research ed.), 371, m3073. https://doi.org/10.1136/bmj.m3073 Delis, D. C., Kaplan, E. F., & Kramer, J. H. (2001). Delis-Kaplan executive function system. The Psychological Corporation. DuPaul, G. J., Gormley, M. J., & Laracy, S. D. (2013). Comorbidity of LD and ADHD: implications of DSM-5 for assessment and treatment. Journal of Learning Disabilities, 46(1), 43–51. https://doi.org/10.1177/0022219412464351 Elder T. E. (2010). The importance of relative standards in ADHD diagnoses: Evidence based on exact birth dates. Journal of Health Economics, 29(5), 641–656. https://doi.org/10.1016/j.jhealeco.2010.06.003 Gaultieri, T., & Johnson, L. G. (2006). Reliability and validity of a computerized neurocognitive test battery, CNS Vital Signs. Archives of Clinical Neuropsychology, 21, 623-643. https://doi.org/10.1016/j.acn.2006.05.007 Hermens, D. F., Soei, E. X., Clarke, S. D., Kohn, M. R., Gordon, E., & Williams, L. M. (2005). Resting EEG theta activity predicts cognitive performance in attention-deficit hyperactivity disorder. Pediatric Neurology, 32(4), 248–256. https://doi.org/10.1016/j.pediatrneurol.2004.11.009 Jensen, P. S., Hinshaw, S. P., Kraemer, H. C., Lenora, N., Newcorn, J. H., Abikoff, H. B., March, J. S., Arnold, L. E., Cantwell, D. P., Conners, C. K., Elliott, G. R., Greenhill, L. L., Hechtman, L., Hoza, B., Pelham, W. E., Severe, J. B., Swanson, J. M., Wells, K. C., Wigal, T., & Vitiello, B. (2001). ADHD comorbidity findings from the MTA study: Comparing comorbid subgroups. Journal of the American Academy of Child and Adolescent Psychiatry, 40(2), 147–158. https://doi.org/10.1097/00004583-200102000-00009 Johns M. W. (1991). A new method for measuring daytime sleepiness: The Epworth sleepiness scale. Sleep, 14(6), 540–545. https://doi.org/10.1093/sleep/14.6.540 Loo, S. K., & Makeig, S. (2012). Clinical utility of EEG in attention-deficit/hyperactivity disorder: A research update. Neurotherapeutics: The Journal of the American Society for Experimental NeuroTherapeutics, 9(3), 569–587. https://doi.org/10.1007/s13311-012-0131-z

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Rucklidge J. J. (2010). Gender differences in attention-deficit/hyperactivity disorder. The Psychiatric Clinics of North America, 33(2), 357–373. https://doi.org/10.1016/j.psc.2010.01.006

Russell, A. E., Ford, T., & Russell, G. (2015). Socioeconomic associations with ADHD: Findings from a mediation analysis. PloS One, 10(6), e0128248. https://doi.org/10.1371/journal.pone.0128248 Shaffer, F., & Ginsberg, J. P. (2017). An overview of heart rate variability metrics and norms. Frontiers in Public Health. https://doi.org/10.3389/fpubh.2017.00258 Snyder, S. M., & Hall, J. R. (2006). A meta-analysis of quantitative EEG power associated with attention-deficit hyperactivity disorder. Journal of Clinical Neurophysiology: Official Publication of the American Electroencephalographic Society, 23(5), 440–455. https://doi.org/10.1097/01.wnp.0000221363.12503.78 Sterman, M. B., & Egner, T. (2006). Foundation and practice of neurofeedback for the treatment of epilepsy. Applied Psychophysiology and Biofeedback, 31(1), 21-35. https://doi.org/10.1007/s10484-006-9002-x Takeda, T., Stotesbery, K., Power, T., Ambrosini, P. J., Berrettini, W., Hakonarson, H., & Elia, J. (2010). Parental ADHD status and its association with proband ADHD subtype and severity. The Journal of Pediatrics, 157(6), 995–1000.e1. https://doi.org/10.1016/j.jpeds.2010.05.053 Zahra, R., Maqsood, U., Latif, M. Z,, Athar, H., Shaikh, G. M., &Hassan, S. B. (2021). Caffeine consumption and perception of its effects amongst university students. Proceedings S.Z.M.C., 34(4), 46-51 https://doi.org/10.47489/p000s344z770mc

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