DRIVING: QUANTIFICATION AND DRIVING: QUANTIFICATION AND APPLICATIONS IN NEUROPSCHIATRYAPPLICATIONS IN NEUROPSCHIATRY

Godfrey Pearlson, M.D.Godfrey Pearlson, M.D.

Vince Calhoun PhD.Vince Calhoun PhD.

OVERVIEWOVERVIEW

This presentation consists of three parts:

– A general introduction to driving studies

– An fMRI study of simulated driving in sober and intoxicated subjects

– A validation of a driving simulator vs. on-road driving in an instrumented vehicle in sober and intoxicated subjects

PART 1: PART 1: WHY STUDY DRIVING? #1WHY STUDY DRIVING? #1

Driving is a behavior. Clinicians are frequently asked to judge the appropriateness of motor vehicle driving in patients with neuropsychiatric conditions (e.g. dementias, bipolar disorder).

Despite this, there is relatively little research on the development of quantitative measures for assessment of driving safety.

Vehicle driving consists of a complex series of quantifiable motor/cognitive behaviors, including divided attention, perception, planning visuo-motor integration, vigilance, tracking, working memory, psychomotor control and judgment.

These behaviors are affected by aging, some prescription medicines, neuropsychiatric illnesses and substance use.

WHY STUDY DRIVING? #2WHY STUDY DRIVING? #2

Altered driving behaviors have important public health consequences.

For example, in the U.S. more than 3 million persons were reported injured and over 40,000 persons died in motor vehicle crashes in 1996.

Traffic accidents are the greatest single cause of death in 5-32 year olds.

Most collisions are due to human performance problems. Many are due to intoxicated drivers.

AUTOMOBILE DRIVING IS A AUTOMOBILE DRIVING IS A MULTI-TASK COGNITIVE MULTI-TASK COGNITIVE

ACTIVITYACTIVITY

Continuous Tracking (e.g. keep in lane)Vigilance (Awareness of other vehicles,

pedestrians)Divided Visual Attention (pay attention to

simultaneous events in different places)Perceptual Judgment (how close to wall)Memory (e.g. what’s seen in mirror)

Driving

Working MemoryDivided Visual AttentionVisuo-motor Integration

Visual Reaction Time

Simple Visual Perception

“Top Down”Emergent Properties

“Bottom Up”Specific Components

ASPECTS OF DRIVER BEHAVIORASPECTS OF DRIVER BEHAVIOR

1. Performance -related - e.g. perception, attention

2. Motivational - e.g. sensation-seeking, aggression

3. State variables and - e.g. age, mood, fatigue and individual differences intoxication

Obviously, the three levels relate in complex ways,in such behaviors as speeding

PROBLEMS WITH REAL ON-ROAD DRIVINGPROBLEMS WITH REAL ON-ROAD DRIVING

• Difficulty of obtaining quantitative measures

• Potentially dangerous

• Must be constrained for safety - hence not veridical

• Cannot set up conditions of most interest (e.g. pedestrians, near misses with other vehicles, etc.)

ADVANTAGES OF SIMULATED DRIVINGADVANTAGES OF SIMULATED DRIVING

• Safety

• Repeatability of measures

• Interaction of driver and environment

• Ease of obtaining quantitative measures

• Can simulate any condition of interest

PROBLEMS WITH SIMULATED DRIVINGPROBLEMS WITH SIMULATED DRIVING

• Generalizability / Validity compared to on-road driving?

• Subjective realism poor except in very expensive setups

• Simulator sickness (vestibular) in absence of motion base - especially in women

• Too “game-like” – need contingencies (e.g. fines)

• Immersive / VR environments needed

Sociology “Road Rage”

Brain Diseases Schizophrenia

Parkinson’s

Huntington’s

Stroke

AIDS Dementia

Seizure disorders

Pharmacology Prescribed drugs

(e.g. BZ, Neuroleptics)

caffeine, alcohol, MJ

Part 2: Validation Study of Part 2: Validation Study of Computer Simulated Driving Computer Simulated Driving

with Alcoholwith Alcohol

BACKGROUNDBACKGROUND

Computerized driving simulators are one of the most common tools used in driving research

It is unclear whether simulated and on-road driving are truly comparable; this is especially true for low cost, fixed base driving simulator systems

In particular, no study has directly addressed the issue of driving simulator validity in studies of ethanol intoxication

Study OverviewStudy Overview

10 subjects completed a driving task both while sober and under influence of alcohol in two experimental setups (modes): a driving simulator and instrumented vehicle on a specialized road

Ethanol and realistic placebo drink were administered in randomized, single blind fashion

Directly comparable measures of driving performance were collected from the instrumented vehicle and driving simulator

Subject blood alcohol content (BAC) and subjective intoxication ratings were measured throughout experiment.

MaterialsMaterials

On-road driving facilities: Virginia Tech Smart Road, a 1.7 mile closed circuit two lane highway

Instrumented vehicle: ’97 Olds. Aurora (automatic transmission) with sensors, accelerometers, and computerized data collection and storage

Simulator: STISIM Drive 100 model, fixed base with steering wheel, foot pedals, and high quality computer monitor output.

Data collection: Vehicle and simulator share many output variables (velocity, turning rate, acceleration, etc.) sampled at 10Hz

Virginia Tech Smart RoadVirginia Tech Smart Road

STISIM Drive 100 SimulatorSTISIM Drive 100 Simulator

A subject seated at the A subject seated at the simulator simulator

The simulator outputThe simulator output

Design ConsiderationsDesign Considerations

STISIM course designed to faithfully replicate the geometry and features of the Smart Road

~7 minutes each on STISIM and Smart Road minimized time-on-task effects; 25mph speed limit on both road and simulator minimized kinesthetic feedback differences between the two

Ethanol dosing individualized to produced consistent BAC across subjects (0.07±0.015%)

Ethanol and placebo administration randomized, and the placebo masked with small amount of ethanol to minimize expectation effects

AnalysisAnalysis STISIM and instrumented vehicle shared many output

variables; we analyzed the intoxication effect within each mode separately and then directly compared the magnitude of the two effects.

Some measures of driving performance were not identical between modes, but were similar; we indirectly compared these output variables.

Some variability in subject BAC’s was present; we used BAC as a continuous rather then discrete predictor variable.

ResultsResults Table 1 shows two measures of longitudinal

vehicle control that are directly comparable between the simulator and on-road modes: time spend over the speed limit and the summed change in speed over the course of the entire experiment.

Tables 2 and 3 show measures of latitudinal vehicle control that are similar, but not directly comparable between modes: lateral range as reported in the simulator and the number of times subjects were verbally reminded (by a passenger side observer) to stay within their lane during the on road driving course

ResultsResultsChange in outcome t-Test >0

per unit BAC p

Time over SIM 596.6 ±1386.4 0.116speed limit CAR 452.9 ±596.6 0.026(sec) Difference 143.7 ±1558.5 0.789

Summed SIM 540.8 ±955.1 0.064change in CAR 183.3 ±478.0 0.142speed (m/s) Difference 357.5 ±1221.9 0.402

TABLE 1: Longitudinal vehicle controlTABLE 1: Longitudinal vehicle control

ResultsResults

Change in outcome t-Test > 0

Lateral per unit BAC p

Range (m) SIM 2.63 ± 2.86 0.02

Difference

Lane Placebo minus EtOH p

Reminders CAR 1.13 ±1.50 0.05

TABLES 2 and 3: Latitudinal vehicle control in both TABLES 2 and 3: Latitudinal vehicle control in both modesmodes

ConclusionsConclusions

Specific measures of latitudinal and longitudinal vehicle control (weaving and speeding) are similarly sensitive to ethanol intoxication effects in both the simulator and real road task.

There is good validity for time over speed limit, summed change in speed and lateral range variables on our fixed base simulator as compared to on-road driving in this paradigm

A comprehensive description of the study is in: McGinty et al. 2001; Assessment of intoxicated driving with a simulator: A validation study with on road driving

DWI-fMRI PERSONNELDWI-fMRI PERSONNEL

Vince CalhounVince McGintyTodd WatsonIllyas SheikhRegina ShihGeorge RebokGeorge BigelowSteven YantisDavid ScottDavid AltschulSusan CourtneyGodfrey Pearlson

SIMULATED DRIVING: SIMULATED DRIVING: Part 3:Part 3:

QUANTIFICATION, VALIDATION AND QUANTIFICATION, VALIDATION AND fMRI STUDIES OF NORMAL DRIVING & fMRI STUDIES OF NORMAL DRIVING &

DRIVING WHILE INTOXICATEDDRIVING WHILE INTOXICATED

How To Analyze fMRI StudiesHow To Analyze fMRI Studies

The Scanner EnvironmentThe Scanner Environment

Detection/Detection/EstimationEstimation

fMRI process chain

RegistrationRegistrationFunctional ImagesFunctional Images

ThresholThreshold/d/

OverlayOverlay

Phase FixPhase Fix

TimeTime 11 22 33 ……750 750 (secs)(secs)

112233

0s0s.66s.66s.33.33ss 11 22

y Xβ e

NormalizationNormalization

11 22

DATA DRIVEN APPROACH (ICA)DATA DRIVEN APPROACH (ICA)

Overview of Process:– Work with entire data set at once (not just one voxel)– The algorithm separates the data into spatially &/or

temporally independent components (1 map and 1 time course for each component)

Advantage: flexibility, does not assume particular time course (or HR) for data set, different sources represent different functional domains

Disadvantage: results must be monitored carefully to ensure the data is being properly characterized

Voxels

Tim

e

Data(X) = Components (C)*1ˆ W

Time courses

Spatially Independent Components

MixingMatrix

Independent Component AnalysisIndependent Component Analysis

Voxels

Tim

e

Data(X) = *G

“Activation maps”

Corresponding to columns of G

β

Time coursesDesignMatrix

General Linear ModelGeneral Linear Model

The GLM is by The GLM is by far the most far the most

common common approach to approach to

analyzing fMRI analyzing fMRI data. To use data. To use this approach, this approach, one needs a one needs a model for the model for the

fMRI time fMRI time coursecourse

In spatial ICA, In spatial ICA, there is no there is no

model for the model for the fMRI time fMRI time

course, this is course, this is estimated along estimated along

with the with the hemodynamic hemodynamic

source locationssource locations

General Linear Model

1. Model1. Model(1 or more(1 or moreRegressors)Regressors)

oror

RegressionRegressionResultsResults

2. Data2. Data

3. Fitting 3. Fitting the Model the Model to the Data to the Data at each at each voxelvoxel

ix j

y j

01

ˆ ˆM

i ii

y j x j e j

The ICA model assumes The ICA model assumes the fMRI data, the fMRI data, xx, is a , is a

linear mixture of linear mixture of statistically statistically

independent sources, independent sources, ss..

Independent Component AnalysisIndependent Component Analysis

**

**

++

Goal of ICA is to Goal of ICA is to separate the separate the

sourcessourcesGiven the mixed Given the mixed

datadata

Source 1Source 1

Source 2Source 2

A 1 2

Ts ss

fMRI data, fMRI data, xx

1ˆ i is A x

1 ,...,T

Ni x i x i x

1 ,...,T

Ni s i s i s

wherewhere

11

,...,N

N ii

p s s p s

1s v

Data Generation(synthesis)

(b) Data Reduction

Data Processing(analysis)

(c) ICA

1

N

1ˆ A .T

(a) Preprocessing, Normalization

Brain MR Scanner

Ns v 2s v

t1u v

tmu v

t2u v

B 2y i

Ky i

1y j

2y j

Ky j

1x j

Nx j

2x j1ˆ B

1s j

ˆNs j

2s j1ˆ AA

1y i

Model for Applying ICA to fMRIModel for Applying ICA to fMRI

Multi-Subject ModelMulti-Subject Model

METHODSMETHODS

20 Subjects/50 scans Scan Parameters

– Single-shot EPI– FOV = 24cm, 64x64– TR=1s, TE=40ms– 18 slices– Slice thickness = 5mm– Gap = .5mm

Procedure– Subjects were trained to asymptote

performance on driving simulator with a simulated driving game, ‘Need for Speed II’ (NFS II)

– fMRI Scan performed during driving paradigm

– Drug Administered (oral Marinol or ETOH or placebo)

– 2nd fMRI scan performed at maximal blood levels

Driving fMRI ParadigmDriving fMRI Paradigm

* Drive Watch

0 600

60

• The order of the watch/drive epochs was alternated across runs

• Subjects were instructed to:• Remain within 100-140

KPH (if successful received bonus)

• Stay in right lane• Avoid collisions

NFS IINFS II

•We show a QuickTime movie of a 23 year-old male subject, a non-user of recreational drugs. The movie shows a brief segment of simulated driving performance while intoxicated.

• The subject had practiced to asymptote on the driving simulation program Need for Speed II (NFS II) which was used as the in scanner active task.

• Movie shows a brief NFS II segment illustrating lane deviation (weaving), followed by a vehicle collision. At this time the subject’s self-rated impairment was 2 on a zero (least) to five (most) analog scale.

Run 1 Run 2 Run 3 Run 4

* * * *

P/D D/P D/P P/D

* * * *

P/D D/P D/P P/D

* * * * P/D D/P D/P P/D

* * * *

<- 3 Min -> <- 2 Hrs -> <- 3 Min ->

• One of 4 variants is shown above (AB/AB; BA/BA; AB/BA; BA/AB)• Each epoch is 1 minute• Key: * = asterisk viewing P = passive viewing of driving D = active driving

Paradigm for the entire experimental session

*

P

D

*

P

D D

P

*

10 Min

1 Min

Experimental paradigm is a hemi-castle design

*

METHODSMETHODS

20 Subjects/50 scans Scan Parameters

– Single-shot EPI– FOV = 24cm, 64x64– TR=1s, TE=40ms– 18 slices– Slice thickness = 5mm– Gap = .5mm

Preprocessing– Timing correction– Motion correction– Normalization– Smoothing (6mm)

ICA– Data were reduced from 600 to 30

time points using PCA– Data from all subjects were

concatenated and further reduced to 25 time points

– Data were then entered into an ICA estimation utilizing the infomax algorithm

Neural Substrates of Simulated DrivingNeural Substrates of Simulated Driving

VD Calhoun, JJ Pekar, VB McGinty, T Adali, TD Watson, & GD Pearlson. “Different Activation Dynamics in Multiple Neural Systems During Simulated Driving Revealed by ICA of fMRI Data.” Human Brain Mapping 16(3), 2002.

Higher Order Visual/Motor: Increases during driving; less during watching.Low Order Visual: Increases during driving; less during watching.Motor control: Increases only during driving.Vigilance: Decreases only during driving; amount proportional to speed.Error Monitoring and Inhibition:Decreases only during driving; rate proportional to speed.Visual Monitoring: Increases during epoch transitions.

Color Regions Hypothesized Function

Green Bilat. cuneus, precuneuslingual gyrus

Visual monitoring

Yellow Cerebellum, inferior occipital

Low-order visual

White Bilat. visual associationBilat. parietal

Visuomotor integrn. High-order visuomotor

Red Cerebellum and motor cortex

Motor control

Pink Orbitofrontal and anterior cingulate

Error monitoring,Inhibition

Blue Medial frontal, parietal, post. cingulate

Vigilance

Interpretation of ResultsInterpretation of Results

Driving While Driving While IntoxicatedIntoxicated

DRUG EFFECTSDRUG EFFECTS

Goal 1: To visualize the neural substrate for operating a driving simulator as assessed by fMRI

Goal 2: To visualize the effect that alcohol or oral Marinol (THC) has on the neural substrate for operating a driving simulator as assessed by fMRI

Goal 3: To study the effects of these drugs on driving performance as assessed by a driving simulator

WHY STUDY MARINOL (SYNTHETIC THC)?WHY STUDY MARINOL (SYNTHETIC THC)?

• Increased prescribing for cachexia (AIDS, cancer).

• Claim of no behavioral effects.

• Need to study the effects on driving, operating machinery.

• Are persons legally prescribed THC, DWI?

• Yields stable, long-lasting plasma THC levels – a useful “test-bed” for smoked MJ.

Alcohol and Driving Performance (N=10 x 2)Alcohol and Driving Performance (N=10 x 2)

Marinol and Driving Performance (N=10)Marinol and Driving Performance (N=10)

ICA Time Courses (ETOH)ICA Time Courses (ETOH)

The activation during driving of the fronto-parietal (blue) regions is most significantly affected during ETOH intoxication

The modulation of primary visual areas (yellow) between driving and watching is preserved (unlike for THC)

Driving and AlcoholDriving and Alcohol

The activation during driving of the fronto-parietal (blue) regions is the most significantly affected during ETOH intoxication

This difference is dose dependent and increases as ETOH dose increases

The largest difference occurs during the first portion of the driving epoch

ICA Time Courses (THC)ICA Time Courses (THC)

1. The modulation of primary visual areas (yellow) between driving and watching is reduced during THC intoxication

2. The activation during driving of the fronto-parietal regions (blue) is also disrupted during THC intoxication

3. The anterior cingulate/orbitofrontal regions (pink) are decreased in amplitude during THC intoxication

Results and ConclusionsResults and Conclusions Meaningful top-down approaches are feasible in

an fMRI environment for a complex behavior

Driving activates a distributed network of areas including cerebellum, prefrontal, frontal eye fields, primary and secondary visual areas.

Preliminary imaging results reveal that:– Intoxication appears to modulate the temporal patterns

in brain regions rather than turn on or off different brain regions

– Alcohol & Marinol affect these temporal patterns differently, as well as having different behavioral “footprints”

Preliminary ResultsPreliminary Results

Alcohol– Vigilance regions appear to be affected in a dose-dependent

manner (in particular, the early portion)– Visuomotor integrative regions do not appear to be affected by

alcohol

Marinol:– Vigilance regions are significantly disrupted during intoxication– Visuomotor integrative regions are differentially activated by

driving versus watching only when subjects are not intoxicated– Error monitoring/disinhibition regions are specifically

activated during driving only when subjects are not intoxicated