DRIVING: QUANTIFICATION AND APPLICATIONS IN NEUROPSCHIATRY

1. DRIVING: QUANTIFICATION AND APPLICATIONS IN NEUROPSCHIATRY Godfrey Pearlson, M.D. Vince Calhoun PhD.

2. OVERVIEW • 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

3. PART 1: WHY 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.

4. WHY 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.

5. AUTOMOBILE DRIVING IS A MULTI-TASK COGNITIVE ACTIVITY • 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)

6. Driving Working Memory Divided Visual Attention Visuo-motor Integration Visual Reaction Time Simple Visual Perception “Bottom Up” Specific Components “Top Down” Emergent Properties

7. ASPECTS 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

8. PROBLEMS 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.)

9. ADVANTAGES OF SIMULATED DRIVING • Safety • Repeatability of measures • Interaction of driver and environment • Ease of obtaining quantitative measures • Can simulate any condition of interest

10. PROBLEMS 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

16. 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

17. Part 2: Validation Study of Computer Simulated Driving with Alcohol

18. BACKGROUND • 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

19. Study 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.

20. Materials • 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

21. Virginia Tech Smart Road

22. STISIM Drive 100 Simulator The simulator output A subject seated at the simulator

23. Design 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

24. Analysis • STISIM and instrumented vehicle shared many output variables; we analyzed the intoxication effect within each mode separately and then directlycompared 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.

25. Results • 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

26. Results TABLE 1: Longitudinal vehicle control

27. Results TABLES 2 and 3: Latitudinal vehicle control in both modes

28. Conclusions • 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

29. DWI-fMRI PERSONNEL Vince Calhoun Vince McGinty Todd Watson Illyas Sheikh Regina Shih George Rebok George Bigelow Steven Yantis David Scott David Altschul Susan Courtney Godfrey Pearlson

30. SIMULATED DRIVING: Part 3: QUANTIFICATION, VALIDATION AND fMRI STUDIES OF NORMAL DRIVING & DRIVING WHILE INTOXICATED

31. How To Analyze fMRI Studies

32. The Scanner Environment

33. fMRI process chain Phase Fix Registration Functional Images Time 1 2 3 … 750 (secs) 2 3 1 .33s 0s .66s 1 2 Normalization Threshold/Overlay Detection/Estimation 1 2

34. 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

35. General Linear Model “Activation maps” Corresponding to columns of G Voxels  Time  Data(X) * = Design Matrix Time courses The GLM is by far the most common approach to analyzing fMRI data. To use this approach, one needs a model for the fMRI time course Independent Component Analysis Spatially Independent Components In spatial ICA, there is no model for the fMRI time course, this is estimated along with the hemodynamic source locations Voxels  Data(X) Components (C) Time  = * Mixing Matrix Time courses

36. General Linear Model 1. Model (1 or more Regressors) or 2. Data 3. Fitting the Model to the Data at each voxel Regression Results

37. Independent Component Analysis Source 1 * + * Source 2 Goal of ICA is to separate the sources Given the mixed data where The ICA model assumes the fMRI data, x, is a linear mixture of statistically independent sources, s. fMRI data, x

38. Model for Applying ICA to fMRI Data Generation (synthesis) Data Processing (analysis) 1 MR Scanner (a) Preprocessing, Normalization (b) Data Reduction (c) ICA Brain

39. Multi-Subject Model

40. 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 METHODS

41. Driving fMRI Paradigm • The order of the watch/drive epochs was alternated across runs NFS II * Watch Drive • Subjects were instructed to: • Remain within 100-140 KPH (if successful received bonus) • Stay in right lane • Avoid collisions 60 0 600

42. 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.

44. <- 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

45. D D D P P P * * * * 1 Min 10 Min Experimental paradigm is a hemi-castle design

46. 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 METHODS

47. Neural Substrates of Simulated Driving 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. 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.

49. Interpretation of Results

50. Driving While Intoxicated