Computer Assisted Ambulatory EEG Monitoring for Early Onset Alzheimer's Disease

Overseen By Sabrina Bunn

Age: 18 - 65

Sex: Any

Trial Phase: Academic

Sponsor: Mayo Clinic

Disqualifiers: Non-AD dementia, ADAD gene mutation, others

No Placebo Group All trial participants will receive the active study treatment (no placebo)

Approved in 3 Jurisdictions This treatment is already approved in other countries

Compensation: Varies

Number of Visits: 1

20 Participants Needed

What You Need to Know Before You Apply

What is the purpose of this trial?

Researchers are trying to determine the frequency of seizures and epilepsy in patients with Early-onset Alzheimer's disease (EOAD) using a 48-hour computer assisted ambulatory electroencephalogram.

Do I need to stop taking my current medications to join the trial?

The trial protocol does not specify whether you need to stop taking your current medications. Please consult with the trial coordinators for more information.

What safety data exists for Computer Assisted Ambulatory EEG Monitoring?

The provided research does not directly address safety data for Computer Assisted Ambulatory EEG Monitoring. However, it highlights that ambulatory EEG (AEEG) is a well-established technique used for long-term monitoring in various settings, including at home, and is considered a cost-effective alternative to inpatient monitoring. The studies emphasize the importance of technical standards to ensure quality, but specific safety data is not mentioned. ^{1 2 3 4 5}

Is the treatment in the trial 'Computer Assisted Ambulatory EEG Monitoring for Early Onset Alzheimer's Disease' a promising treatment?

Yes, the treatment is promising because EEG monitoring can help predict the progression of Alzheimer's disease, identify patients who may need more attention, and differentiate between Alzheimer's patients and healthy individuals. This can lead to better management and care for those with the disease. ^{6 7 8 9 10}

What data supports the idea that Computer Assisted Ambulatory EEG Monitoring for Early Onset Alzheimer's Disease is an effective treatment?

The available research shows that Computer Assisted Ambulatory EEG Monitoring can provide valuable information about the progression of Alzheimer's disease. For example, one study found that patients with abnormal EEG patterns at the early stage of the disease experienced a more severe decline in cognitive functions compared to those with normal EEG patterns. Another study indicated that EEG markers could help identify Alzheimer's patients who might need more clinical attention due to their 'frailty'. While these studies highlight the usefulness of EEG in predicting disease progression and patient outcomes, they do not directly show that EEG monitoring is an effective treatment for Alzheimer's disease itself. Instead, they suggest that EEG can be a helpful tool in managing and understanding the condition. ^{6 8 11 12 13}

Who Is on the Research Team?

Neill Graff-Radford, MD

Principal Investigator

Mayo Clinic

Are You a Good Fit for This Trial?

This trial is for people aged 40-64 with Early-onset Alzheimer's Disease (EOAD) who have a partner to report on their daily activities. Participants must not be pregnant, should speak English, and show amyloid presence in PET scans. They need to meet criteria for mild cognitive impairment or probable AD dementia with a global CDR score of ≤ 1.0.

Inclusion Criteria

You are between 40 and 64 years old.

Must have a study partner (informant) who spends a minimum average of 10 hours per week with the participant (e.g., family member, significant other, friend, and caregiver) who is generally aware of the participants' daily activities and can provide information about the participant's cognitive and functional performance. If the participant does not have a study partner who spends 10 face-to-face hours per week, other arrangements for identifying a viable study partner will be granted on a case-by-case basis by the Site PI.

Patients with early-onset Alzheimer's disease from the LEADS trial.

Exclusion Criteria

You have symptoms that match the main features of non-Alzheimer's dementia.

If two or more close family members have early-onset Alzheimer's disease that seems to run in the family, you may not be able to participate unless specific gene mutations have been ruled out.

Any significant systemic illness or unstable medical condition, which could lead to difficulty complying with the protocol (at the discretion of the Site PI)

Timeline for a Trial Participant

Screening

Participants are screened for eligibility to participate in the trial

2-4 weeks

EEG Monitoring

Participants undergo a 48-hour computer assisted ambulatory electroencephalogram to determine the frequency of seizures and epilepsy

1 week

1 visit (in-person)

Follow-up

Participants are monitored for safety and effectiveness after EEG monitoring

4 weeks

What Are the Treatments Tested in This Trial?

Interventions

• Computer assisted ambulatory electroencephalogram

Trial Overview The study uses a 48-hour computer-assisted ambulatory EEG to check how often seizures and epilepsy occur in EOAD patients. It aims to improve understanding of these events' frequency using non-invasive brain monitoring technology.

How Is the Trial Designed?

Group I: Early-onset Alzheimer's disease (EOAD) subjects Experimental Treatment 1 Intervention

Subjects with mild cognitive impairment due to EOAD will undergo a 48 hour computer assisted ambulatory electroencephalogram

Who Is Running the Clinical Trial?

Mayo Clinic

Lead Sponsor

Trials

3,427

Recruited

3,221,000+

Published Research Related to This Trial

In a study of 24 Alzheimer patients over 3 years, EEG findings showed that while most patients experienced slowing of brain activity, a subgroup of 12 patients maintained stable EEGs and mild dementia, indicating a potentially benign course of the disease.

EEG slowing at diagnosis was linked to worse outcomes after 3 years, with absolute theta amplitude being the most effective predictor of patient prognosis, highlighting the importance of EEG in assessing the progression of Alzheimer's disease.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Serial EEG in Alzheimer's disease: 3 year follow-up and clinical outcome. Soininen, H., Partanen, J., Laulumaa, V., et al.[2019]

The study identified an EEG marker that can effectively classify Alzheimer's disease (AD) patients with 77.2% sensitivity and 65% specificity, based on a sample of 127 AD individuals and 121 cognitively intact elderly controls.

AD patients with a positive EEG marker (EEG+) exhibited lower cognitive function and more significant brain abnormalities compared to those with a negative marker (EEG-), suggesting that EEG+ patients may require closer clinical monitoring and could be used to create more uniform groups in clinical trials.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Neurophysiological assessment of Alzheimer's disease individuals by a single electroencephalographic marker. Lizio, R., Del Percio, C., Marzano, N., et al.[2016]

In a study of 24 Alzheimer's disease patients over 3 years, those with abnormal EEG (DEEG) experienced significant declines in specific cognitive functions like praxic abilities and naming, while those with normal EEG (NEEG) did not show such deterioration.

Patients with DEEG also had a higher likelihood of developing extrapyramidal symptoms and a greater risk of needing institutional care, suggesting that an abnormal EEG may indicate a more severe progression of cognitive decline in Alzheimer's disease.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Different patterns of cognitive decline related to normal or deteriorating EEG in a 3-year follow-up study of patients with Alzheimer's disease. Helkala, EL., Laulumaa, V., Soininen, H., et al.[2019]

In a study of 101 patients with early Alzheimer disease, lower levels of beta and alpha brain wave activity measured by EEG were found to be significant independent predictors of mortality, indicating that these measures could help assess patient prognosis.

Higher theta activity was also associated with increased mortality risk, suggesting that specific EEG patterns may provide valuable insights into the survival outcomes of individuals with early Alzheimer disease.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Quantitative spectral electroencephalography in predicting survival in patients with early Alzheimer disease. Claus, JJ., Ongerboer de Visser, BW., Walstra, GJ., et al.[2019]

In a study of 15 Alzheimer's patients followed for up to 4 years, a correlation was found between the power of alpha brain waves (8-12 Hz) measured by EEG and the rate of cognitive decline as assessed by the Mini-Mental State Examination (MMSE).

This suggests that measuring absolute alpha power through EEG could be a useful tool for predicting how quickly cognitive function may decline in individuals with Alzheimer's disease.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Rate of cognitive decline in Alzheimer's disease is associated with EEG alpha power. Kuskowski, MA., Mortimer, JA., Morley, GK., et al.[2019]

Ambulatory electroencephalography (AEEG) demonstrated a high diagnostic yield of 72% in a study of 101 patients, providing valuable information for managing conditions like epilepsy without the need for hospital admission.

AEEG is particularly effective in characterizing non-epileptic events and clarifying unclear epilepsy diagnoses, making it a cost-effective alternative to traditional video-EEG monitoring.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Ambulatory EEG: a cost-effective alternative to inpatient video-EEG in adult patients. Dash, D., Hernandez-Ronquillo, L., Moien-Afshari, F., et al.[2018]

Ambulatory EEG (AEEG) devices provide a portable and effective way to record brain activity in a patient's natural environment, similar to traditional EEG methods but designed for wearability.

This guideline establishes essential minimum technical standards for AEEG performance, addressing the need for quality assurance in clinical and research settings, as current standards for routine EEG do not fully apply to AEEG.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Minimum Technical Requirements for Performing Ambulatory EEG. Tatum, WO., Halford, JJ., Olejniczak, P., et al.[2022]

Ambulatory EEG monitoring (A/EEG) is effective for diagnosing and characterizing epilepsy, especially in patients with drug-resistant epilepsy, by allowing prolonged recording of seizures in a natural setting.

A/EEG is also beneficial for evaluating sleep disorders, as it can monitor sleep patterns over 24 hours, complementing other sleep studies like multiple sleep latency tests.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

[Ambulatory monitoring of the EEG (A/EEG). Guidelines, methodology and indications]. Espinosa, ML., García, R., Padrino, C., et al.[2015]

In a study of 264 ambulatory EEG records, significant findings included that 42% of patients evaluated for epileptic episodes showed focal epileptic activity, indicating that ambulatory EEG can effectively identify seizure-related abnormalities.

Ambulatory EEG monitoring proved useful in assessing patients at risk of relapse during anti-seizure treatment withdrawal, with 28.6% showing focal interictal epileptic activity, suggesting its role in guiding treatment decisions.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

[The role of ambulatory electroencephalogram monitoring: experience and results in 264 records]. González de la Aleja, J., Saiz Díaz, RA., Martín García, H., et al.[2018]

The ambulatory electroencephalogram (AEEG) was found to be useful in 64.8% of cases among 199 pediatric referrals, indicating its overall effectiveness in assessing neurological conditions in children.

The AEEG was particularly effective for investigating suspected encephalopathy with status epilepticus during sleep (ESES), providing useful information in 97.5% of those cases, while the main limitation was the failure to capture events in 55.6% of referrals aimed at event capture.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

The clinical utility of ambulatory EEG in childhood. Nagyova, R., Horsburgh, G., Robertson, A., et al.[2019]

In a study of 79 subjects with probable early-stage Alzheimer's dementia and 17 normal-aged individuals, simple computerized EEG parameters like mean dominant frequency (MF) and alpha to theta ratio effectively distinguished between the two groups, outperforming traditional visual analysis.

The study identified specific cutoff values of 8.6 for MF and 1.3 for the alpha/theta ratio, which can be used to differentiate normal aging from early Alzheimer's dementia.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

[Quantified EEG in the diagnosis of Alzheimer's type dementia]. Gueguen, B., Derouesné, C., Bourdel, MC., et al.[2019]

In a study of 24 patients with mild to moderate Alzheimer's disease, 50% showed stable EEG readings over one year, indicating that not all patients experience the same progression of the disease.

The other half of the patients exhibited significant deterioration in EEG measures, highlighting the heterogeneity of Alzheimer's disease and suggesting that some patients may have a more stable condition despite overall clinical progression.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Longitudinal EEG spectral analysis in early stage of Alzheimer's disease. Soininen, H., Partanen, J., Laulumaa, V., et al.[2022]

A pilot study using a single lead dry electrode EEG device found that Alzheimer's disease (AD) patients showed distinctive EEG patterns, such as elevated D4 power during resting states, which can help differentiate them from control subjects.

During cognitive and sensory tasks, AD patients exhibited unique EEG features, including larger minimum D3 values and increased kurtosis of D5, suggesting that active engagement may reveal more predictive characteristics of the disease.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Identification of resting and active state EEG features of Alzheimer's disease using discrete wavelet transform. Ghorbanian, P., Devilbiss, DM., Verma, A., et al.[2013]

Citations

1. Ireland pubmed.ncbi.nlm.nih.gov

Serial EEG in Alzheimer's disease: 3 year follow-up and clinical outcome. [2019]

2. Netherlands pubmed.ncbi.nlm.nih.gov

Neurophysiological assessment of Alzheimer's disease individuals by a single electroencephalographic marker. [2016]

3. United States pubmed.ncbi.nlm.nih.gov

Different patterns of cognitive decline related to normal or deteriorating EEG in a 3-year follow-up study of patients with Alzheimer's disease. [2019]

4. United States pubmed.ncbi.nlm.nih.gov

Quantitative spectral electroencephalography in predicting survival in patients with early Alzheimer disease. [2019]

5. United States pubmed.ncbi.nlm.nih.gov

Rate of cognitive decline in Alzheimer's disease is associated with EEG alpha power. [2019]

6. United States pubmed.ncbi.nlm.nih.gov

Ambulatory EEG: a cost-effective alternative to inpatient video-EEG in adult patients. [2018]

7. United States pubmed.ncbi.nlm.nih.gov

Minimum Technical Requirements for Performing Ambulatory EEG. [2022]

8. Spain pubmed.ncbi.nlm.nih.gov

[Ambulatory monitoring of the EEG (A/EEG). Guidelines, methodology and indications]. [2015]

9. Spain pubmed.ncbi.nlm.nih.gov

[The role of ambulatory electroencephalogram monitoring: experience and results in 264 records]. [2018]

10. England pubmed.ncbi.nlm.nih.gov

The clinical utility of ambulatory EEG in childhood. [2019]

11. France pubmed.ncbi.nlm.nih.gov

[Quantified EEG in the diagnosis of Alzheimer's type dementia]. [2019]

12. Ireland pubmed.ncbi.nlm.nih.gov

Longitudinal EEG spectral analysis in early stage of Alzheimer's disease. [2022]

13. United States pubmed.ncbi.nlm.nih.gov

Identification of resting and active state EEG features of Alzheimer's disease using discrete wavelet transform. [2013]