Modern Research in EEG Diagnostics
As more humans are living longer because of biotechnological, nutritional, medical, and gerontological advancements, we are beginning to see a higher prevalence of aging-associated neurodegenerative diseases in society. Some of these include Alzheimer’s disease, Huntington’s disease, Parkinson’s disease, and dementia. Recent research has concluded that electroencephalogram (EEG) markers may be the future of cost-efficient, noninvasive strategies. This is because they have proven to be sensitive enough to detect cortical abnormalities in the brain and diagnose neurodegenerative diseases years before symptoms appear (Al-Qazzaz, et al., 2014). EEG data is characterized by neural oscillations, or brainwaves. These are the rhythmic patterns analyzed by neurologists, which can be indicators of cortical thickness abnormalities in humans. Greater EEG amplitudes point to increasing neuronal density and synchrony of synaptic potentials of cells but decreasing cortical thickness (the distance between the inner and outer surfaces of the cortex) (Figure 1). Thus, higher amplitudes in EEG oscillations are indicative of cortical thinning in the brain, which occurs progressively with age, but is accelerated in patients with neurodegenerative disorders like epilepsy and hippocampal sclerosis (Provencher, et al., 2016). Knowing this, neurologists are beginning to invest in the use of EEGs to assess cortical thickness and provide diagnoses for patients at the onset of their neurological symptoms.
Figure 1: Varying cortical thickness in the brain (Christian, 2009).
A study conducted by Provencher, et al. in 2016 discovered a correlation between specific EEG patterns during REM (rapid eye movement) sleep and NREM (non-rapid eye movement) sleep, and cortical thinning in Alzheimer’s disease patients. Breakthroughs like this are evidence that structural changes to the brain caused by neurodegenerative disorders can be detected by analysis of EEG patterns during sleep or other activities. In 2021, D’Atri, et al. discovered that changes to the cortical thickness of the brain associated with neurodegenerative diseases are most apparent in the entorhinal cortex and fusiform gyrus sectors of the temporal lobe (Figure 2). They concluded that damage to these areas is detectable by EEG analysis long before disease symptoms, like amnesia, aphasia, apraxia, and agnosia, present themselves (Kramer & Duffy, 1996).
Figure 2: See entorhinal cortex and fusiform gyrus (Hagmann, et al., 2008).
Further methods of EEG analysis involve determining the type of brainwave produced. Clinical EEG waves can be classified into five different categories, alpha (α), beta (β), theta (θ), delta (δ), and gamma (γ) waves (Figure 3). α and β waves are typically prevalent and strong in EEGs of healthy adults, while they are reduced in patients with dementia and coherence impairments, and instead, the power of θ and δ waves is increased (Al-Qazzaz, et al., 2014). With this understanding, neurologists may be able to perform routine EEG screening on patients above a certain age during neurological exams, to see if their brainwaves are indicative of the onset of a neurodegenerative disorder. Although there has been a significant amount of research regarding the use of EEGs for neurodegenerative disease screening, the implementation of this practice is not routine yet, and thus, research and efficacy analysis is still emerging in this area.
Figure 3: EEG brainwaves, (a) Delta wave. (b) Theta wave. (c) Alpha wave. (d). Beta wave (Al-Qazzaz, et al., 2014).
The Future of Neurodegenerative EEG Diagnostics
Advancements in the efficacy of neurotechnology has been crucial to the recent accelerations we are seeing in neurodegenerative disease diagnosis. Studies have demonstrated that to progress in the development of a cure for diseases such as Alzheimer’s, a diagnosis must be determined long before symptoms arise. The most promising technology anticipated to achieve early diagnosis in these neurological deficits is the multi-channelled EEG system. When it comes to the implementation of EEG techniques in the field of neurotechnology and the diagnosis of neurodegenerative disorders, more electrodes typically result in greater spatial resolution. (NeuroSky, 2015).
As discussed earlier, the practical use of EEGs in the diagnosis of neurodegenerative diseases is not something which is currently in practice. In order to progress in the study of cortical thinning as an indicator of the onset of brain diseases, we must understand and evaluate the extent to which we will consider cortical thinning in a patient to be concerning. Additionally, as normal cortical thickness varies from person to person, it would be beneficial to assess individuals throughout stages in their lives, to determine the rate at which their own cortex thins, rather than the width of their cortex in comparison to data. Studying the rate of thinning and asymptomatic neurological deficits on an EEG would allow researchers to decipher between normal cerebral thinning and concerning changes. Once EEGs are implemented into regular practice, individuals at or above the age of 40 should be referred to a neurologist to have their cortical thickness assessed every few years during an exam. The distance between the inner and outer cortex one year of the patient’s life would be compared relatively to the following years in their life. Encouraging individuals to get these tests may lead to the prediction of future neurodegenerative issues in patients, which may then encourage them to take the necessary precautions, like exercising and staying intellectually active, to potentially delay the onset of disease. As well, if these diseases are regularly diagnosed far before symptoms appear, drugs can be developed in the future which target the neurons and tissues responsible for changes in the brain. The end goal would be for these therapies to inhibit the progression of neurodegeneration well before brain function is compromised.
To further enhance breakthroughs in this field, we can combine the knowledge taken from Provencher, et al.’s study, with Al-Qazzaz, et al.’s findings, and analyze the type of brainwaves (α, β, θ, or δ) produced on an EEG during REM and NREM sleep. By analyzing the class of brainwave patterns in one’s sleep, we may be able to connect this data to the extent of cortical thinning occurring in one’s brain. In other words, could very high frequencies of θ and δ waves in an EEG reading mean concerning cortical thinning, while lower frequencies of these waves could mean normal aging patterns? If specific neurodegenerative disease-linked EEG readings have been identified during REM and NREM sleep, we should be able to use the data found to answer this question. The best approach to this could be enrolling symptomatic, neurologically diseased individuals, and neurologically healthy individuals in a study. Variables such as the parent's age and other would need to be controlled. As symptoms of Alzheimer’s disease, for example, typically appear in people’s mid-60s, a fixed age for this study may be 70 years of age. All individuals would be assessed using multi-channelled EEG systems during natural sleep. Data would be collected during REM and NREM sleep, and the most prevalent type of frequency band (α, β, θ, or δ) during that period would be analyzed. Upon this, researchers would be able to conclude whether high frequencies of θ and δ waves are correlated with neurodegeneration in the neurologically diseased group, and if low frequencies of these waves present in the neurologically healthy group. The differences in average θ and δ wave frequencies would also be compared between the two groups to determine the wave pattern which distinguishes healthy from sick. Once EEGs become regularly used in medical neuroscience, the findings and data from this study could be used to analyze and identify disease onset in seemingly healthy individuals by assessing the frequency of their brainwaves. Studying these topics provides a gateway into the early diagnosis of neurological diseases using EEGs, as well as how this incredible neurotechnology can be used to detect abnormalities in the brain prior to cognitive decline.
Al-Qazzaz, N. K., et al. (2014). Role of EEG as Biomarker in the Early Detection and Classification of Dementia. The Scientific World Journal, Article 906038. https://doi.org/10.1155/2014/906038
Provencher, D., et al. (2016). Cortical Thinning in Healthy Aging Correlates with Larger Motor-Evoked EEG Desynchronization. Frontiers in Aging Neuroscience; 8: 63. doi: 10.3389/fnagi.2016.00063
Christian, G. (2009). Gyrification and cortical measures. Structural Brain Mapping Group. http://www.neuro.uni-jena.de/wordpress/research/gyrification-and-cortical-measures/
D’Atri, A., et al. (2021). Relationship between Cortical Thickness and EEG Alterations during Sleep in the Alzheimer’s Disease. Brain Sci., 11,1174. doi: 10.3390/brainsci11091174
Kramer, J. H., Duffy, J. M. (1996). Aphrasia, Apraxia, and Agnosia in the Diagnosis of Dementia. Dementia and Geriatric Cognitive Disorders; 7:23-26. https://doi.org/10.1159/000106848
Hagmann, P., et al. (2008). Mapping the Structural Core of the Human Cerebral Cortex. PLoS Biol 6(7): e159. doi:10.1371/journal.pbio.006015
NeuroSky. (2015). Multi-Channel EEG (BCI) Devices. http://neurosky.com/2015/07/multi-channel-eeg-bci-devices/