Changes in Electroencephalography and Sleep Architecture as Biomarkers for AD

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Changes in Electroencephalography and Sleep Architecture as Potential Electrical Biomarkers for Alzheimer’s Disease

Alzheimer’s disease (AD), a progressive neurodegenerative disorder characterized by dementia and psychiatric symptoms, is primarily driven by the accumulation of amyloid-beta (Aβ) plaques and phosphorylated tau protein. With China’s aging population, the prevalence of AD among adults aged ≥60 years is projected to rise from 5.4% in 2015 to 6.7% by 2050. Early diagnosis remains a critical challenge, as current methods rely on clinical observation, neuroimaging, or invasive cerebrospinal fluid (CSF) biomarker analysis. These approaches are costly and often only applicable in later disease stages, by which time irreversible neuronal loss in the cortex and hippocampus has already occurred. Consequently, there is an urgent need for low-cost, noninvasive tools to detect AD early. Recent studies highlight electroencephalography (EEG) and sleep architecture analysis as promising candidates for this purpose.

Bidirectional Relationship Between Sleep Disruption and AD Pathology

Sleep disturbances and AD share a bidirectional relationship. On one hand, AD pathology disrupts sleep regulatory centers, leading to abnormal sleep-wake cycles, reduced slow-wave sleep (SWS), frequent nighttime awakenings, and excessive daytime sleepiness. Notably, Aβ accumulation correlates with reduced slow-wave activity (SWA) in non-rapid eye movement (NREM) sleep at frequencies <1 Hz, while tau pathology is linked to SWA loss in the 1–2 Hz range. Conversely, poor sleep exacerbates AD progression by impairing glymphatic clearance of Aβ and tau. During sleep, the glymphatic system actively removes these toxic proteins, but its efficiency declines with age. Sleep disorders such as obstructive sleep apnea (OSA) further disrupt this process, elevating levels of Aβ42 in blood and phosphorylated tau in CSF. Longitudinal studies reveal that apnea and hypopnea events correlate with accelerated CSF Aβ42 decline, suggesting that sleep disturbances not only accompany AD but also contribute to its pathogenesis.

Sleep architecture changes are detectable even before overt AD pathology. For instance, reduced REM sleep duration and prolonged REM latency are strong predictors of dementia risk—each 1% decrease in REM sleep raises dementia risk by 9%. Midlife insomnia and terminal insomnia in later life are also associated with higher Aβ and tau burdens. Specific sleep biomarkers, such as SWA-sleep spindle coupling, have been shown to predict tau accumulation in the medial temporal lobe. These findings underscore sleep metrics as potential early indicators of AD risk.

EEG Spectral Power Shifts in AD

EEG provides insights into neuronal activity disrupted by AD. Key spectral changes include:

  • Increased δ (1–4 Hz) and θ (4–8 Hz) power: Elevated δ and θ activity correlates with lower CSF Aβ42 levels, reflecting Aβ-related synaptic dysfunction.
  • Decreased α (8–12 Hz) and β (12–30 Hz) power: Reduced α and β power, particularly in posterior regions, is linked to higher CSF phosphorylated tau and total tau levels. AD patients exhibit a shift toward lower dominant frequencies, with peak α power declining as the disease progresses.
  • Altered α sub-bands: Prodromal AD patients show reduced α1 (6.9–8.9 Hz) and α2 (8.9–10.9 Hz) power, while the α3/α2 ratio (α3: 10.9–12.9 Hz) increases. This ratio correlates with cortical atrophy, hypoperfusion, and memory impairment, making it a promising early diagnostic marker.

Event-Related Potentials and Evoked Oscillations

AD disrupts event-related potentials (ERPs), which measure brain responses to sensory or cognitive stimuli. Patients exhibit:

  • Reduced P300 amplitude: This late positive ERP component, peaking around 300 ms, is diminished in AD and correlates with disease severity.
  • Attenuated visual evoked potentials: AD patients show smaller P1 (100–130 ms) and N1 (150–200 ms) amplitudes during visual tasks, reflecting impaired early sensory processing.
  • Altered gamma oscillations: Decreased gamma-band (25–30 Hz) evoked oscillations over the left hemisphere and delayed event-related gamma responses suggest impaired high-frequency neural synchronization.

Network Connectivity and Information Processing

AD disrupts functional and effective connectivity across brain networks. Resting-state EEG studies reveal:

  • Reduced global connectivity in α and β bands: Lower CSF Aβ42 and higher tau levels are associated with weakened long-range synchronization, indicative of impaired information integration.
  • Enhanced local connectivity: Compensatory hyperactivation of local circuits, particularly in sensorimotor regions, may explain preserved segregation despite global network breakdown.
  • Phase synchronization deficits: AD patients exhibit reduced lagged phase synchronization in δ and θ bands between cortical regions, as well as disrupted β-band synchronization within the middle temporal gyrus. These changes reflect inefficient communication between brain regions critical for memory and cognition.

Sleep and EEG as Complementary Biomarkers

Combining sleep architecture analysis with EEG enhances AD detection. For example:

  • SWA and spindle coupling: SWA during NREM sleep correlates with Aβ and tau pathology. Reduced SWA-spindle coupling predicts medial temporal tau burden, while standalone SWA declines reflect Aβ accumulation.
  • Respiratory sleep disorders: OSA severity correlates with Aβ/tau biomarkers, and longitudinal data link apnea events to faster Aβ42 decline.
  • REM sleep alterations: Lower REM sleep percentage and prolonged REM latency precede clinical AD onset, offering a window for early intervention.

Clinical Implications and Future Directions

EEG and sleep monitoring hold transformative potential for AD management:

  1. Early detection: Spectral shifts in α sub-bands and sleep architecture changes may identify at-risk individuals before significant neurodegeneration occurs.
  2. Disease monitoring: Longitudinal EEG can track progression via declining α power, increasing δ/θ activity, and network connectivity loss.
  3. Intervention targets: Improving sleep quality may slow AD progression by enhancing glymphatic clearance. Treatments targeting SWA or REM sleep could modulate Aβ/tau dynamics.

Future research must address key gaps:

  • Determine whether EEG/sleep biomarkers are AD-specific or shared with other dementias.
  • Validate findings across diverse populations and disease stages.
  • Develop standardized protocols for integrating EEG, sleep, and neuroimaging data.

In conclusion, EEG and sleep architecture analysis offer a noninvasive, cost-effective approach to AD diagnosis and monitoring. By capturing early neuronal dysfunction and sleep-pathology interactions, these tools could revolutionize AD care, enabling timely interventions to alter disease trajectories.

doi.org/10.1097/CM9.0000000000001394

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