Automated web based behavioral test for early detection of alzheimer s disease
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Automated Web-Based Behavioral Test for Early Detection of Alzheimer’s Disease. Eugene Agichtein* , Elizabeth Buffalo , Dmitry Lagun , Allan Levey, Cecelia Manzanares, JongHo Shin, Stuart Zola . Emory University. Intelligent Information Access Lab.

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Automated web based behavioral test for early detection of alzheimer s disease

Automated Web-Based Behavioral Test for Early Detection of Alzheimer’s Disease

Eugene Agichtein*, Elizabeth Buffalo, Dmitry Lagun, Allan Levey, Cecelia Manzanares, JongHo Shin, Stuart Zola

  • Emory University

Intelligent Information Access Lab

Emory ir lab research directions
Emory IR Lab: Alzheimer’s DiseaseResearch Directions

  • Modelingcollaborativecontent creation for information organization and indexing.

  • Miningsearchbehavior data to improve information finding.

  • Medical applications of Search, NLP, behavior modeling.

Mild cognitive impairment mci and alzheimer s disease
Mild Cognitive Impairment (MCI) and Alzheimer’s Disease Alzheimer’s Disease

  • Alzheimer’s disease (AD) affects more than 5M Americans, expected to grow in the coming decade

  • Memory impairment (aMCI) indicates onset of AD (affects hippocampus first)

  • Visual Paired Comparison (VPC) task: promising for early diagnosis of both MCI and AD before it is detectableby other means

Vpc familiarization phase
VPC: Familiarization Phase Alzheimer’s Disease

Vpc delay phase
VPC: Delay Phase Alzheimer’s Disease


Vpc test phase
VPC: Test Phase Alzheimer’s Disease

Vpc task eye tracking equipment
VPC Task: Eye Tracking Equipment Alzheimer’s Disease

Automated web based behavioral test for early detection of alzheimer s disease

VPC: on Low Performance Indicates Increased Risk for Alzheimer’s Disease

Eugene Agichtein, Emory University

1. Detects onset earlier than

ever before possible

2. Sets stage for


Behavioral performance on the vpc test is a predictor of cognitive decline
Behavioral Performance on the VPC test is a on Predictorof Cognitive Decline

Eugene Agichtein, Emory University

[Zola et al., AAIC 2012]

Scores on the VPC task accurately predicted, up to three years prior to a change in clinical diagnosis, MCI patients who would progress to AD, and Normal subjects who would progress to MCI

Vpc gaze movement analysis
VPC: Gaze Movement Analysis on

Lagun et al., Journal of Neuroscience Methods, 2011

Visual examination behavior in the VPC test phase. In this representative example, the familiar image is on the left (A), and the novel image is on the right (B), for a normal control subject. The detected gaze positions are indicated by blue circles, with the connecting lines indicating the ordering of the gaze positions.

Technical contribution eye movement analysis
Technical Contribution: Eye Movement Analysis on

Lagun et al., Journal of Neuroscience Methods, 2011

Significant performance improvements
Significant Performance Improvements on

Lagun et al., Journal of Neuroscience Methods, 2011

Our big idea web based vpc task vpw with e buffalo d lagun s zola
Our Big Idea: on Web-based VPC task (VPW)with E. Buffalo, D. Lagun, S. Zola

  • Web-based version of VPC without an eye tracker

  • Can be administered anywhere in the worldon any modern computer.

  • Can adapt classification algorithms to automatically interpret the viewing data collected with VPW

Vpc w basic prototype demo
VPC-W: basic prototype demo on


Familiarization (identical images)


Test (novel image on left)

Experiment overview
Experiment Overview on

  • Step 1: Optimize VPC-W on (presumably) Normal Control (NC) subjects

  • Step 2: Analyze VPC-W subject behavior with both gaze tracking and viewport tracking simultaneously

  • Step 3: Validate VPC-W prediction on discriminating Impaired (MCI/AD) vs. NC

Vpc w novelty preference preserved
VPC-W: on Novelty Preference Preserved

Self-reported elderly NC subjects tested with VPC-W over the internet exhibit similar novelty preference to that of VPC.

Single-factor ANOVA reveals no significant difference between VPC and VPC-W subjects

Vpc vs vpc w similar areas of interest
VPC vs. VPC-W: Similar Areas of Interest on

Areas of attention: heat map for VPW (viewport-based) is concentrated in similar areas to VPC (unrestricted eye-tracking) .



Quantifying viewing similarity: Coarse measure: divide into 9 regions (3x3), rank by VPC and VPW viewing time. The Spearman rank correlation varies between 0.56 and 0.72 for different stimuli.

VPC ranking


Actual gaze vs viewport position
Actual Gaze vs. Viewport Position on

Attention w.r.t. ViewPort

Eye cursor time lag analysis
Eye-Cursor Time Lag Analysis on

XY: minimum at -75.00 ms 199.8578X:minimum at -90.00 ms 161.8480Y:minimum at -35.00 ms 116.3665

Viewport movement eye movement
Viewport Movement ~ Eye Movement on

Normal elderly subject (NP=88%, novel image is on left).

Impaired elderly subject (NP=49%, novel image is on left).

Exploiting viewport movement data
Exploiting Viewport Movement Data on

Novelty Preference


fixation duration distribution

Vpc w results detecting mci
VPC-W Results on : Detecting MCI

21 Subjects (11 NC, 10 aMCI), recruited @Emory ADRC:

Accuracy on the pilot data comparabletobest reported values for manually administered cognitive assessment test (MC-FAQ, reported accuracy, specificity, and sensitivity of 0.83, 0.9, and 0.89 respectively) (Steenland et al., 2009).

Accuracy, Sensitivity, Specificity, and AUC (area under the ROC curve) for automatically classifying patients tested with VPC-W using 5-fold, 10-fold, and leave-one-out cross validation.

Current work
Current Work on

  • Analysis:

    • Applying deep learning and “motif” analysis for more accurate analysis of trajectory

    • Incorporating visual saliency signals

  • Data collection:

    • Longitudinal tracking of subjects

    • “Test/Retest”: effects of repeated testing

    • Sensitivity analysis: for possible use in drug trials

    • Wide range of “normative” data using Mturk worker pool

Future directions and collaboration possibilities
Future Directions and on Collaboration Possibilities

  • Can we apply similar or the same techniques for cost-effective and accessible detection of:

    • Autism (previous work on difference in gaze patterns)

    • ADHD

    • Stroke/Brain trauma

    • Other possibilities?

  • What can we learn about the searcher from their natural search and browsing behavior?

    • Image search and examination preferences (anorexia)

    • Correlate behavior with biomarkers (Health 101 cohort)

Vpc w summary
VPC-W Summary on

  • VPC-W, administered over the internet, elicits viewing behavior in normal elderly subjects similar to eye tracking-based VPC task in the clinic.

  • Preliminary results show automatic identification of amnesticMCI subjects with accuracy comparable to best manually administered tests.

  • VPC-W and associated classification algorithms could facilitate cost-effective and widely accessible screening for memory loss with a simple log on to a computer.

  • Other potential applications: online detection and monitoring of ADD, ADHD, Autism and other neurological disorders.

  • This project has the potential to dramatically enhance the current practice of Alzheimer’s clinical and translational research.

Eye tracking for interpreting search behavior
Eye Tracking for on InterpretingSearch Behavior

  • Eye tracking gives information about searcher interests:

    • Eye position

    • Pupil diameter

    • Saccades and fixations




We will put an eye tracker on every table e agichtein 2010
We Will Put an Eye Tracker on Every Table! on - E. Agichtein, 2010

  • Problem: eye tracking equipment is expensive and not widely available.

  • Solution: infer searcher gaze position from searcher interactions.

Inferring gaze from mouse movements
Inferring on Gaze from Mouse Movements

Guo & Agichtein, CHI WIP 2010


Actual Eye-Mouse Coordination

No Coordination (35%)

Bookmarking (30%)

Eye follows mouse (35%)

Post click page examination patterns
Post-click Page Examination Patterns on

  • Two basic patterns: “Reading” and “Scanning”

    • “Reading”: consuming or verifying when (seemingly) relevant information is found

    • “Scanning”: not yet found the relevant information, still in the process of visually searching

Cursor heapmaps reading vs scanning task verizon helpline number
Cursor on Heapmaps (Reading vs. Scanning)[Task: “verizon helpline number”]

Relevant (dwell time: 30s)

Not Relevant (dwell time: 30s)

Move cursor horizontally

 “reading”

Passively move cursor

 “scanning”

Typical viewing behavior complex patterns task number of dead pixels to replace a mac
Typical Viewing Behavior (Complex Patterns) on [Task: “number of dead pixels to replace a Mac”]

Relevant (dwell time: 70s)

Not Relevant (dwell time: 80s)

Keep the cursor still and scroll

 “scanning” dominant

Actively move the cursor with pauses  “reading” dominant