Through a Scanner Darkly: Functional Neuroimaging as Evidence of a Criminal Defendant's Past Mental States

Article excerpt

As with phrenology and the polygraph, society is again confronted with a device that the media claims is capable of reading our minds. Functional magnetic resonance imaging ("fMRI"), along with other types of functional brain imaging technologies, is currently being introduced at various stages of a criminal trial as evidence of a defendant's past mental state. This Article demonstrates that functional brain images should not currently be admitted as evidence into courts for this purpose. Using the analytical framework provided by Federal Rule of Evidence 403 as a threshold to a Daubert/Frye analysis, we demonstrate that, when fMRI methodology is properly understood brain images are only minimally probative of a defendant's past mental states and are almost certainly more unfairly prejudicial than probative on balance. Careful and detailed explanation of the underlying science separates this Article from others, which have tended to paint fMRI with a gloss of credibility and certainty for all courtroom-relevant applications. Instead we argue that this technology may present a particularly strong form of unfair prejudice in addition to its potential to mislead jurors and waste the court's resources. Finally, since fMRI methodology may one day improve such that its probative value is no longer eclipsed by its extreme potential for unfair prejudice, we offer a nonexhaustive checklist that judges and counsel can use to authenticate functional brain images and assess the weight these images are to be accorded by fact finders.

INTRODUCTION
  I. FUNCTIONAL NEUROIMAGING FOR MENS REA CLAIMS
     A. What Is Functional Neuroimaging?
     B. Mens Rea Claims
     C. Present and Anticipated Future Use of Functional Brain
        Imaging in Courts
     D. The Impact of Neuroscience on the Law: Grounding in Evidence
 II. SCIENTIFIC BACKGROUND: IMAGING BRAIN ACTIVITY AND MENTAL STATES
     A. The Science of Functional Neuroimaging
        1. Overview of older methods
        2. Principles of fMRI
        3. Knowns and unknowns about the BOLD response
        4. The semantics of "activation"
     B. The "Function" of Functional Imaging: Task Dependency and
        Behavior
     C. Variables in Data Collection, Processing, and Analysis
        1. Hardware and software: the scanner
        2. Processing the raw data
        3. Individual differences and reliance on the group data
           a. Something with which to compare." defining "normal"
           b. Individual differences are important but are often
              ignored
        4. Variance: the statistical threshold can be manipulated
           to affect the results
        5. Variance: the statistical analysis employed can affect the
           results
III. LEGAL ANALYSIS
     A. Admissibility Is Specific to the Evidentiary Purpose
     B. Classifying Functional Brain Images
     C. Relevance
        1. Logical inference and relationships between brain data and
            mental states
     D. Authentication
        1. The pictorial and silent witness theory of admissibility may
           accommodate the authentication of fMRI images
        2. Images must accurately capture the individual's brain under
           the same conditions that existed at the time of the crime
        3. The procedure for creating the image should be described in
           detail to remove any possibility of tampering, error, or
           distortion
        4. Underlying statistical computer programs must demonstrate
           reliance on irrefutable scientific principles
        5. Authentication should be specific to fMRI and distinct
           from other image types
     E. Why Daubert, Frye, and FRE 702 Should be Secondary
        Considerations After Rule 403
     F. Probative Value
        1. fMRI has limited probative value unless the question of
           proper base rates is resolved
        2. fMRI has limited probative value as it relies on averaged
           group data and ignores individual differences
        3. …