Early Assessment Tool for Alzheimer’s Detection using the Neurosky EEG Headset Naomi Yu, Sara Lok, Shreya Motaganahalli, Sreeja Polkampally, Dino Vidaich, Adithya Krishnan, Sophia Keschner, Ranjani Srinivasan, and Devansh Gandhi Supervised by Oskar Pineno and Cristianna Colella Background Literature: Prevalence of Alzheimer’s Disease - Alzheimer’s disease (AD) is a neurodegenerative disease that has been associated with impairments in attentional operations (visual attention, selective vs divided). (Li, et al, 2005) - The diagnosis of Alzheimer’s disease has been based on clinical criteria, this criteria requires postmortem confirmation of special neuropathological changes. Attention Deficits in AD - In a study done, it was found that AD patients had significant deficits in visual attention which was shown by slower detection speeds on a conjoined feature task (Behrmann, Foster, & Stuss, 1999) - P300 component: large waveform extracted from an ongoing EEG during multi-stimuli tasks; associated with cognitive functions such as visuospatial and sustained attention. Neurosky Brain Computer Interface - Measures electroencephalographic (EEG) signals; provides raw EEG data as well as frequency of power bands; algorithm removes ambient noise and muscle movement (Cantero et. al., 2017) - Most widely used to monitor attention: selective and divided.
Purpose: To use attention-monitoring hardware to aid with the early diagnosis of Alzheimer’s. Which is one of the most common causes of dementia. Measuring these attention levels may prove to be crucial when aiding with the early diagnosis of Alzheimer’s.
Hypothesis: If attention monitoring hardware. Like an EEG, is utilized as a diagnostic tool, then this device be used in the pre-diagnosis of Alzheimer's because it can track electrical impulses localized in the frontal lobe, correlating with the onset of this neurodegenerative disease.
Methodology: Attention Stimulus Task 1. Placement of Neurosky Mindwave at the Fp1 position; data synchronization to MindfulXperience IOS app (Narayana, Prasad, & Warmerdam, 2018; Pineno, (whenever the app was created)) 2. Participant completion of gradual-onset continual performance task (gradCPT) via free online HTML browser “TestMyBrain.org” (Fortenbaugh et. al, 2015) 3. Mean reaction time and discrimination ability calculated according to Beradi et.al. in response to stimuli (2008) EEG Signal Detection 1. EEG signals recorded 200ms pre-stimulus and 800 ms post-stimulus during gradCPT task (Nijis, Muris, Euser, & Franken, 2009) 2. P300 component was defined as the mean amplitude within the 300-450 ms window (Nijis et. al., 2009) Data Analysis Attention Stimulus Task 1. Indexing of mean reaction time and discrimination as a function of age. 2. Amplitude from 2.2 µV to 18.5 µV (±9.2)34 and latency values from 320 ms (±20.2)22 to 484 ms during gradCPT considered within normal range for healthy Fig 1: MindfulXperience IOS app elderly (Pavarini et.a., 2018) (Narayana, Prasad, & Warmerdam, 2018; 3. Failure to meet both criteria Pineno) warrants concern (Fortenbaugh et. al, 2015) 4.
Limitations: - Detecting during early stage or late (only can detect obvious signs of Alzheimer’s which usually means that the individual is in the far later stages making it harder to treat) - Limited to the app (will require extra parts and probably a better app in order to measure things more precisely) - EEG isn’t so specific (only can detect a very general area of the brain) - Alzheimer’s can only be diagnosed with complete certainty after death - The best diagnosis comes from a doctor
Future Applications: - Using attention-monitoring hardware in clinics for their patients - Attention-monitoring hardware is less intense than brain imaging procedure to detect Alzheimer's - Easier detection of early onset Alzheimer's - Cost-efficient in the long run - Useful in the development of pharmacotherapies targeted at prevention of Alzheimer's - Similar screening tests can potentially be used for other brain conditions