Published online by Cambridge University Press: 06 January 2021
Brain imaging is one of the most remarkable technological advances towards understanding the relationship of behavior to brain anatomy and physiology. Brain images provide insight to understanding behavior. Additionally, the images themselves carry great impact, particularly when used to show differences in either the anatomy or the biological functioning of two different brains. For these reasons, brain images have increasingly been used in both criminal and civil trials.
After describing some general features of brain imaging, we will focus on functional magnetic imaging (fMRI), as many believe this technology has the most potential for advancing our understanding of how parts of the brain function, including perhaps linking specific functions with cognition and behavior. Brain imaging as a field is vast and therefore our discussion will be limited. First, we will assess the advantages and limitations of fMRI, including research efforts towards standardizing equipment thereby assuring reliability and reproducibility.
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4 As with EEGs and computerized electroencephalography (CEEG). Schmid et al., supra note 1, at 335.
5 As with MRIs, fMRIs, and MEGs. See wikipedia, supra note 2.
6 As in the cases of positron emission tomography (PET) and single-photon emission computed tomography (SPECT). Brodie, Jonathan D., Imaging for the Clinical Psychiatrist: Facts, Fantasies and Other Musings, 153 Am. J. Psychiatry 145, 145 (1996)Google ScholarPubMed; Leenders, K. et al., Positron Emission Tomography of the Brain: New Possibilities for the Investigation of Human Cerebral Pathophysiology, 23 Progress In Neurobiology 1, 1 (1984).CrossRefGoogle ScholarPubMed
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8 Leenders, supra note 6, at 15-16.
9 See id. at 1, 11.
10 A major conceptual problem with all imaging is that the differences in the tissue are usually small numerically. Reeves, supra note 3, at 90-92. The color coding “can be arbitrary” and set up by individual technicians so that small differences in brain activity among images “may present the illusion of huge differences” because the color can go from blue-green to yellow or red. Id. The significance of this becomes conspicuous in its influence in the courtroom. Id. at 89.
11 Note that anatomical imaging devices, such as computerized axial tomography (CT) and nuclear magnetic resonance (NMR) are important clinically for detecting well-localized brain abnormalities, such as hemorrhages, strokes, brain tumors, and vascular abnormalities.
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16 Telephone Interview with Dean Wong, Professor, Johns Hopkins Univ. Sch. of Med., in Balt., Md. (Nov. 24, 2006). Dr. Wong's position regarding PET is that it is not diagnostic for anything but for the differential diagnosis of Alzheimer and perhaps other forms of memory loss. Dr. Wong also emphasized the value of PET for drug development (through the use of radiotracers etc.) Regarding the study of behavior with PET, he also emphasized PET is useful for establishing group differences, not for determining individual differences. It is therefore most useful in a research setting for establishing statistical correlations.
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19 Volkow & Tancredi, supra note 17, at 578.
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30 Id.
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33 Id.
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35 Id. The accumulation of toxic wastes is related to some extent to glycogen metabolism, but perhaps that is not the whole explanation. This calls into question the nexus or pristine nature of the connection between Cerebral Blood Flow and energy metabolism.
36 See Logothetis, supra note 28, at 3967-69.
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44 Formisano & Goebel, supra note 41, at 175.
45 Id. at 174.
46 See Scott A. Huettel et al., Functional Magnetic Resonance Imaging (2004).
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54 Matthews and Jezzard, supra note 50, at 6-12; see also Brown & Eyler, supra note 12, at 55.
55 Brown & Eyler, supra note 12, at 55. Alternatives to the use of BOLD as a marker for neural activation exist for more direct Cerebral Blood Flow measurements using MRI. One method involves the use of arterial spin labeling (ASL) perfusion imaging. See Wang, Yihong et al., Simultaneous MRI Acquisition of Blood Volume, Blood Flow and Blood Oxygenation Information During Brain Activation, 52 Magnetic Resonance Med. 1407, 1407-1417 (2004).Google Scholar This technique is believed to provide greater sensitivity than BOLD, especially for tasks involving low-frequency. Id. It has also been shown to have lower inter-subject variability. Wang, Jiongjiong et al., Arterial Spin Labeling Perfusion fMRI with Very Low Task Frequency, 49 Magnetic Resonance Med. 796, 796-802 (2003)CrossRefGoogle ScholarPubMed; see also Tjandra, supra note 50, at 394.
56 Matthews et al., supra note 22, at 733.
57 Id.
58 Id.
59 Id.
60 Id.
61 Brown & Eyler, supra note 12, at 52.
62 Id.
63 Id.
64 For a discussion of the use of PET in the studies of monoamine oxidase (MAO), an important enzyme for regulating neurotransmitters such as dopamine, norepinephrine and serotonin, see Fowler, Joanna S. et al., Translational Neuroimaging: Positron Emission Tomography Studies of Monoamine Oxidase, 7 Molecular Imaging & Biology 377 (2005).CrossRefGoogle ScholarPubMed
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68 Id.
69 Id.
70 Id.
71 Id. at 80-81.
72 Id. at 81.
73 Id. at 81-82.
74 Signal Dropout occurs secondary to the differential magnetizability of the physiological compartments of the head. Boundaries involving air and tissue are particularly important as locations for magnetic field gradients that may offset precessing and diphase spin. The result is diminishing of the MR signal causing “dropout.” For a detailed discussion of this problem, see Brown & Eyler, supra note 12, at 57.
75 Distortions are also brought about by the effects of technical issues like gradient coil nonlinearities. The shape of images is affected by being selected over a curved rather than rectilinear surface. Added to this are the distortions brought about by so-called Nyquist ghost artifact seen with echo planar imaging. This apparently is due to differences between the echoes obtained from read-out gradients that are positive and negative. Again, methods exist to correct the ghosting effect. For a detailed discussion, see Jessard & Clare, supra note 67, at 84-85; see also Jovicich, Jorge et al., Biomedical Information Research Network: Characterization and Correction of Image Distortions in Multi-Site Structural MRI, 19 NeuroImage (Supp. Issue) 1666, 1666-7 (2003).Google Scholar
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77 Brown & Eyler, supra note 12, at 56.
78 Id.
79 Id.
80 Id.
81 Id. (citing R.B. Buxton, Introduction to Functional Magnetic Resonance Imaging: Principles and Techniques (Cambridge Univ. Press 2002)).
82 Tjandra et al., supra note 50, at 393.
83 See id. at 393-395 for a more detailed discussion of the impact of BOLD on signal location.
84 Id. at 393-394.
85 Id. at 393.
86 Id.
87 See, e.g., Murphy, Kevin et al., A Validation of Event-Related fMRI Comparisons Between Users of Cocaine, Nicotine, or Cannabis and Control Subjects, 163 Am. J. Psychiatry 1245 (2006).CrossRefGoogle ScholarPubMed
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89 See Murphy et al., supra note 87, at 1246.
90 See Wikipedia, Functional Magnetic Resonance Imaging, http://en.wikipedia.org/wiki/functional_magnetic_resonance_imaging (last visited Mar. 2, 2007).
91 Certainly whole-brain images provide a broader base for looking at the neural mechanisms underlying social, emotional and cognitive information processing, though the difficulty rests in the temporal relationships among parts of the brain impacted by a thought or an emotion. See Norris, Catherine J. et al., The Interaction of Social and Emotional Processes in the Brain, 16 J. Cognitive Neuroscience 1818 (2004).CrossRefGoogle Scholar
92 E-mail Dr. Bruce Rosen, Faculty Member, Harvard Medical School and Massachusetts General Hospital (October 4, 2006) (on file with author) (regarding the dearth of good research on issues of reliability and reproducibility of fMRI). Rosen emphasized that the BIRN project is making strides in this direction, but that there is a need for much more research on these sets of issues. Id.
93 For a discussion of inter-session variability, which according to the researchers is in fact not high, see Smith, Stephen M. et al., Variability in fMRI: A Re-Examination of Inter-Session Differences, 24 Hum. Brain Mapping 248 (2005).CrossRefGoogle ScholarPubMed
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96 See id. at 1656.
97 See id. at 1657, 1664.
98 See Jorge Jovicich et al., Test-Retest Reliability Assessment for Longitudinal MRI Studies: An Intensity-Based Comparison of T1-Weighted Protocols and Field Strengths, 11th Annual Meeting of the Organization for Human Brain Mapping (2005), available at http://legacy-web.nbirn.net/Publications_rd/Presentations/OHBM_2005_Pfz_intens_final.pdf; Brian Quinn et al., Test-Retest Reliabity Assessment for Longitudinal MRI Studies: A comparison of the Effects of Different T1-Weighted Protocols, Scanner Platforms, and Field Strengths on Semi-Automated Hippocampal Volume Measures, 11th Annual Meeting of the Organization for Human Brain Mapping (2005), available at http://legacy-web.nbirn.net/Publications_rd/Presentations/OHBM_2005_Pfz_morph_final.pdf; Jorge Jovicich et al., Reliability in Multi-site Structural MRI Studies: Effects of Gradient Non-Linearity Correction on Volume and Displacement of Brain Subcortical Structures, 10th Annual Meeting of the Organization for Human Brain Mapping (2004), available at http://www.nbirn.net/publications/abstracts/pdf/Jovicich_HBM_2004.pdf.
99 Jovicich, Jorge et al., Reliability in Multi-Site Structural MRI Studies: Effects of Gradient Non-Linearity Correction on Phantom and Human Data, 30 NeuroImage 436, 436 (2006).CrossRefGoogle ScholarPubMed
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102 Id.
103 Id.
104 Id.
105 Id.
106 Id. at 785.
107 Machielsen et al., supra note 49, at 156.
108 Studies 1 and 2 involved the same scanning session followed by a third session several days later. Id. at 157.
109 Id. at 159.
110 Id. at 163.
111 Id. at 164.
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114 Id.
115 Id. at 969-970.
116 Id. at 969.
117 Id.
118 Fernandez et al., supra note 112, at 970.
119 Id. at 970.
120 Id.
121 Id. at 974.
122 Id.
123 Id. at 973.
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126 See id. at 431.
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129 Id. at 1004-05.
130 See Detre, supra note 27, at 812-813.
131 Id. at 812.
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134 Id. at 451-53.
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137 Id. at 1312.
138 Id. at 1313-14.
139 Id. at 1312.
140 Id.
141 Id. at 1313-1315 (finding, of the forty-six functions studied, 100% correlated between the two methods within twenty millimeters and an 86% correlation within 10 millimeters).
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144 Id. at 2286.
145 Id. at 2288.
146 Id. at 2289.
147 Id.
148 Id. at 2293.
149 Id. at 2297.
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153 Id.
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157 Id.
158 Id.
159 ASL uses magnetically-labeled arterial blood water as a flow tracer. Detre, supra note 27, at 812; see also Feng, Ching-Mei et al., CBF Changes During Brain Activation: fMRI vs. PET, 22 NeuroImage 443, 443-44 (2004).CrossRefGoogle ScholarPubMed
160 This hypothetical is based on United States v. Eduardo Sandoval-Mendoza, a case decided by the Ninth Circuit on December 27, 2006. 472 F.3d 645 (9th Cir. 2006). In this case, a fMRI was not done for lie detection, but a structural MRI did reveal the presence of a large pituitary tumor. Id. at 653. The district court in this case found the medical expert opinion unreliable as it did not scientifically establish that the brain tumor caused the mental incompetency alleged. Id. at 652-55.
161 Reeves, supra note 3, at 90. Authors of this piece focus primarily on PET and SPECT, but there are similarities in the determination of abnormality with fMRI. See id.
162 See Brodie, supra note 6, at 145-46. Brodie stresses that images and their statistical representations of activations reflect complex assumptions and mathematical manipulations of data, all of which compromise the validity of the conclusions that are drawn by the researchers. Id.
163 Joseph Dumit, Picturing Personhood: Brain Scans and Biomedical Identity 8-9, 16-18 (2004).
164 Id. at 16-18.
165 Id. at 8-9.
166 Id. at 8.
167 Reeves, supra note 3, at 90.
168 See Dorothy Nelkin & Laurence Tancredi, Dangerous Diagnostics: The Social Power of Biological Information 37-50 (1994).
169 See id.
170 To begin with, functional neuroimages are generally not stable. They may change over time, reflective of improvement of blood flow or even the positive benefits of neuroplasticity.
171 See infra notes 172-173 and accompanying text.
172 The connectivity through complex neural networks can be studied with Diffusion Tensor Imaging. Mori, Susumu & Zhang, Jiangyang, Principles of Diffusion Tensor Imaging and Its Applications to Basic Neuroscience Research, 51 Neuron 527, 527 (2006).CrossRefGoogle ScholarPubMed
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175 Id.
176 Id.
177 Robbins, Lee N., Epidemiology: Reflections on Testing the Validity of Psychiatric Interviews, 42 Archives Gen. Psychiatry 918, 918-20 (1985).CrossRefGoogle Scholar
178 Reeves, supra note 3, at 93-94.
179 Id. at 94.
180 See id.
181 See id. at 90.
182 See id. at 90-91.
183 See People v. Smith, 107 P. 3d 229, 234 (Cal. 2005) (where Dr. Buchsbaum testified that the PET scan showed brain damage to defendant); People v. Kraft, 5 P.3d 68, 98 (Cal. 2000) (where Dr. Buchsbaum testified that the PET scan showed a previous head injury consistent with obsessive compulsive disorder as part of a defensive strategy to show that the defendant would not cause problems in prison and that the death penalty would not be necessary); People v. Holt, 937 P.2d 213, 231 (Cal. 1997) (where Dr. Buchsbaum testified that PET Scan showed abnormalities of defendant's frontal and temporal lobes and claimed this revealed “emotional system damage”); see also People v. Protsman, 105 Cal. Rptr. 2d 819, 823-24 (Cal. Ct. App. Mar. 13, 2001) (upholding trial court's exclusion of PET scan evidence of brain damage).
184 Interview with Dr. Michael Hutchinson, Department of Neurology, New York University School of Medicine (July 20, 2006). Dr. Hutchinson discussed MRI and fMRI in injury cases, in which he testified as an expert. Id. He sees a growing role of fMRI in both civil and criminal cases. Id.; see People v. Combs, 101 P. 3d. 1007 (Cal. 2004) (upholding trial court finding of first degree murder and the death penalty where the defense hired an expert who did an EEG and MRI and claimed that the scan demonstrated brain lesions consistent with organic brain syndrome). Thus far, there are too many inconsistencies in the use of fMRI, certainly for relating structure to behavior. C.f. Mayberg, Helen S., Functional Brain Scans as Evidence in Criminal Court: An Argument for Caution, 33 J. Nuclear Med. 18n, 25n (1992)Google ScholarPubMed (arguing that the sensitivity and specificity of patterns from PET and SPECT are still too ambiguous for these technologies to be useful in the courtroom).
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188 Id. at 731.
189 See Davatzikos, C. et al., Classifying Spatial Patterns of Brain Activity with Machine Learning Methods: Application to Lie Detection, 28 NeuroImage 663, 663 (2005)CrossRefGoogle ScholarPubMed (99% accurate discrimination was obtained for 22 of the subject but “predictive accuracy … was 88%”); Langleben, Daniel D. et al., Telling Truth from Lie in Individual Subjects with Fast Event-Related fMRI, 26 Human Brain Mapping 262, 262 (2005).CrossRefGoogle ScholarPubMed
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191 Friedman et al., supra note 94, at 1656 (discussing an attempt to standardize fMRI scanning).
192 See Entertainment Software Ass’n v. Granholm, 404 F. Supp. 2d 978, 982 (2005) (where fMRI research was used to allege that medial violence exposure may be associated with alterations in brain functioning).
193 See Roper v. Simmons, 543 U.S. 551 (2005); Brief for the Am. Psychological Ass’n and the Mo. Psychological Ass’n as Amici Curiae Supporting Respondent at 11, Roper v. Simmons, 543 U.S. 551 (2005) (No. 03-633), 2004 WL 1636447.
194 Id.
195 Giedd, Jay, Brain Development, IX: Human Brain Growth, 156 Am. J. Psychiatry 4 (1999).CrossRefGoogle ScholarPubMed See also Giedd, Jay N. et al., Brain Development During Childhood and Adolescence: A Longitudinal MRI Study, 2 Nature Neuroscience 861, 861-862 (1999).CrossRefGoogle ScholarPubMed
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197 See supra notes 42-47, 131, 152-159 and accompanying text.
198 See supra notes 15-64 and accompanying text; see also Chorvat, Terrence et al., Law and Neuroeconomics, 13 Sup. Ct. Econ. Rev. 35, 45-46 (2005).Google Scholar
199 See Linda H. Lamb, New MRI has Enormous Potential: Rare Machine's Brain-Imaging Tests Can Find Clues on Stroke, State (Columbia, S.C.), April 6, 2006 at B1 (discussing the use of 3-tesla MRI).
200 Karen Sandrick, 3–Tesla MRI Bests 1.5–Tesla in Body and Brain, Advanced MR (Special Supplement to Diagnosticimaging), Dec. 2001, available at http://www.diagnosticimaging.com/advancedMR/3tmri.jhtml.
201 Id.
202 Id.
203 Id.
204 Id.
205 Id.
206 See id.
207 Stephan E. Maier et al., Diffusion MRI Explores New Indications, Advanced MR (Special Supplement to Diagnosticimaging), Dec. 2001, http://www.diagnosticimaging.com/advancedMR/diffusion.jhtml.
208 Sandrick, supra note 200.
209 See supra notes 185-188 and accompanying text.
210 See generally Keckler, supra note 185 (discussing various lie-detection methods); see also Bush, John C., Warping the Rules: How Some Courts Misapply Generic Evidentiary Rues to Exclude Polygraph Evidence, 59 Vand. L. Rev. 539 (2006)Google Scholar (discussing polygraph limitations).
211 See Laurence Tancredi, Neuroscience and the Law, in Neuroscience and the Law 193 (Brent Garland ed., 2004).
212 See id.
213 National Library of Medicine (U.S.), MedlinePlus Medical Enclyclopedia, MRI, http://www.nlm.nih.gov/medlineplus/print/ency/article/003335.htm (last visited May 22, 2007).
214 There needs to be more research into the variables that may alter the image over time. As pointed out earlier, these images are not free of environmental and other influences. In part this is good because it means we have a way of studying the impact of external influences on the brain as reflected in the images. On the other hand, the lack of stability of the image may cause us to doubt the validity of diagnoses based in part on these images.