Patient Conditions Related to qEEG Evaluation

Learning Objectives

After completing this section, you will be able to:

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EEG Phenotypes

Johnstone, Gunkelman, and Lunt (2005) have proposed several EEG phenotypes that the raw EEG and qEEG can assess. Table 2 from Johnstone et al. (2005, p. 101) is reproduced below. As can be seen, these phenotypes have been clinically correlated with medication and neurofeedback protocol recommendations. Arns, Gunkelman, Breteler, and Spronk (2008) have reported that such phenotypes can be reliably assessed.

Koberda (2017) also reviews conditions for which qEEG is useful in a general neurology practice. These conditions include traumatic brain injury, cerebrovascular accident, pain and headache, depression and anxiety, cognitive dysfunction, Alzheimer's disease, autism spectrum disorder, ADHD, seizures, and epilepsy.

Kropotov (2016) reviews neuromarkers, including qEEG findings, that are helpful for the early identification, prevention, and personalized treatment of several mental disorders, such as ADHD and schizophrenia.

Diagnosis and Management

The qEEG has proved valuable in the diagnosis and management of ADHD, dementia, neurological disorders, Parkinson's disease, and refractory patients. It is a valuable tool in the differential diagnosis of dementia, monitoring neurological disorders, and assessing cognitive impairment in Parkinson's disease.

The effectiveness of the qEEG largely depends on the expertise of the clinicians and the quality of the laboratory performing the analysis. Therefore, a qEEG referral is appropriate in cases where detailed brainwave analysis can provide additional diagnostic or monitoring insights, particularly in complex neurological and psychiatric conditions.

Attention-Deficit/Hyperactivity Disorder

Evidence from multiple studies supports the appropriateness of qEEG as an auxiliary tool in the diagnosis and management of ADHD. It provides objective data that can complement traditional diagnostic methods, help identify ADHD subtypes, and guide treatment planning.

Research has identified distinct qEEG subtypes within the ADHD population, indicating that children with ADHD do not constitute a neurophysiologically homogeneous group. These subtypes are characterized by different patterns of brain wave activity, such as elevated delta power or increased alpha power, which can aid in more accurate diagnosis and understanding of ADHD (Byeon et al., 2020).

A systematic review highlighted that qEEG parameters could help characterize ADHD subtypes, contributing to personalized and more effective treatments (Galiana-Simal et al., 2020).

qEEG data have been shown to correlate with symptom severity in ADHD patients. For instance, theta-band power was positively correlated with inattention scores and reaction times, while gamma-band power was correlated with auditory continuous performance test results (Roh et al., 2015).

Differential Diagnosis of Dementia

The qEEG is useful for differentiating between various forms of dementia, such as Alzheimer's disease, Lewy body dementia, Parkinson's disease dementia, frontotemporal dementia, and vascular dementia. Specific qEEG patterns, like EEG slowing and coherence changes, help in distinguishing these conditions (Livint Popa et al., 2020).

Diagnosis and Monitoring of Neurological Disorders

The qEEG is valuable in diagnosing and monitoring neurological disorders, including epilepsy, vascular diseases, dementia, and encephalopathy. It provides detailed brain wave data that can complement traditional diagnostic methods and assessments (Kopańska et al., 2022).

Assessment of Cognitive Impairment in Parkinson's Disease

qEEG measures, particularly those reflecting EEG slowing such as decreased dominant frequency and increased theta power, correlate with cognitive impairment in Parkinson's disease and can predict future cognitive deterioration (Geraedts et al., 2018).

Refractory Patients

Swatzyna et al. (2016) proposed that isolated epileptiform discharges (IEDs) may be a transdiagnostic biomarker for refractory cases without seizures. Of 76 refractory patients presenting with IEDs and treated with anticonvulsants, 65, or 86%, improved; 6, or 8%, were unchanged; and 5, or 7%, deteriorated.

A systematic review by Swatzyna et al. (2022) concluded that IEDs were highly prevalent in children and adolescents without seizures diagnosed with ADHD, at more than 25%, and with autism spectrum disorder, at more than 59%. The prevalence was low, at 3%, in depression.

General Clinical Utility

The qEEG is beneficial for detecting organic brain dysfunctions, categorizing clinical conditions, and localizing epileptic sources. However, its effectiveness depends on the competency of the clinicians and the quality of the laboratory performing the analysis (Duffy et al., 1994).

Cutting-Edge Topics in qEEG Research

Assignment

Now that you have completed this unit, select one condition covered here and describe what a qEEG assessment would contribute to its evaluation that other methods would not. Then describe what the qEEG could not establish on its own, and name the other sources of information you would need.

Glossary

References