Overview
The study utilizes investigational software, the SPARK Test, with an FDA-cleared electroencephalography (EEG) amplifier and EEG cap to collect and then analyze patient EEG data.
Description
The aim of this study is to develop an algorithm and then evaluate it to determine whether applying machine-learning techniques to resting-state electroencephalography (EEG) can characterize patient's cognitive status and detect the presence or absence of AD on the basis of the patient's EEG.
Eligibility
Inclusion Criteria:
- Age 55 to 85 at the time of consent
- Informant available and willing (remotely or in-person) to provide information about sleep patterns and cognitive functioning who spends >8 hours per week with primary subject
- Subject or Legally Authorized Representative (LAR) has the ability to provide informed consent and comply with the protocol.
Exclusion Criteria:
- Unable to remain still for up to 30 minutes during EEG data recording
- Subjects currently on and unable to wash out concomitant medications, including: 1) opiates; 2) benzodiazepines and nonbenzodiazepine hypnotics; 3) sedative antihistamines; 4) tricyclic anti-depressants; 5) skeletal muscle relaxants; 6) antiepileptics; 7) antipsychotics; 8) antimanic agents; 9) THC; 10) anticholinergics
- Previous history of stroke, severe head injury, craniotomy or any other potentially confounding neurologic illness causing known structural brain damage
- Medical or psychiatric illness that would interfere with study participation
- History of epilepsy or chronic seizure disorder
- Presence of non-dental metal in head
- Currently experiencing a skin disease on scalp that would affect electrode contacts
- TICS score indicative of cognitive impairment at screening
- Substance Use Disorder, including Alcohol