Neuropsychology Review

, Volume 5, Issue 1, pp 1–68 | Cite as

The accuracy of automated and clinical detection of brain damage and lateralization in neuropsychology

  • Elbert W. Russell


The validity of both computer programs and clinical judgment in neuropsychology for determining the existence and lateralization of brain damage is reviewed. Computerized interpretation in neuropsychology, after a propitious beginning, was largely abandoned due to severe criticism, essentially based on only three studies. Only one of these studies compared clinical judgment with computer programs. A thorough examination of the literature located many more studies assessing the accuracy of computer programs, clinical judgment, and discriminant analysis. When reviewed, these studies found that the computer programs, especially the Neuropsychological Key, were quite accurate though not as accurate as clinical judgment. Computer programs and especially the Lateralization Index are potentially as accurate as expert clinical judgment. The rationale related to computer programs is also discussed. This includes the implications of impairment, criterion adequacy, and methods used in designing the neuropsychological Key and the Lateralization Index. Since computer programs are completely reliable across studies, they can be used to examine the differences between sample populations and criterion accuracy. Factors contributing to reduced accuracy in both clinical judgment and computer programs are also explicated.

Key Words

neuropsychology computer clinical judgment validity Halstead Reitan Battery 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Adams, K. M. (1975). Automated clinical interpretation of the neuropsychological test battery: An ability based approach. Dissertation Abstracts International, 35, 6085B (University Microfilms No. 75-13, 289).Google Scholar
  2. Adams, K. M. (1986). Concepts and methods in the design of automata for the neuropsychological test interpretation. In Filskov, S. B., and Boll, T. J. (eds.),Handbook of Clinical Neuropsychology, John Wiley & Sons, New York, pp. 561–575.Google Scholar
  3. Adams, K. M., and Brown, G. G. (1986). The role of the computer in neuropsychological assessment. In Grant, I. and Adams, K. M. (eds.),Neuropsychological Assessment of Neuropsychiatric Disorders, Oxford University Press, New York, pp. 87–99.Google Scholar
  4. Adams, K. M., and Heaton, R. K. (1985). Automated interpretation of neuropsychological test data.Journal of Consulting and Clinical Psychology 53: 790–802.PubMedGoogle Scholar
  5. Adams, K. M., and Heaton, R. K. (1987). Computerized neuropsychological assessment: Issues and applications. In J. N. Butcher, J. N. (ed.),Computerized Psychological Assessment, New York, Basic Books, pp. 355–365.Google Scholar
  6. Adams, K. M., Kvale, V. I., and Keegan, J. F. (1984). Relative accuracy of three automated systems for neuropsychological interpretation.Journal of Clinical Neuropsychology, 6: 413–431.PubMedGoogle Scholar
  7. Anthony, W. Z., Heaton, R. K., and Lehman, R. A. W. (1980). An attempt to cross-validate two actuarial systems for neuropsychological test interpretation.Journal of Consulting and Clinical Psychology, 48: 317–326.PubMedGoogle Scholar
  8. Bak, J. S., and Greene, R. L. (1980). Changes in neuropsychological functioning in an aging population.Journal of Consulting and Clinical Psychology 48: 395–399.PubMedGoogle Scholar
  9. Barron, J. H., and Russell, E. W. (1992). Fluidity theory and the neurospsychological impairment in alcoholism.Archives of Clinical Neuropsychology 7: 175–188.PubMedGoogle Scholar
  10. Bernard, L. C. (1988).Eclectic Neuro Score (ENS) [computer program], Version 2. Loyola Marymount University, Loyola Boulevard & 80th Street, Los Angeles, CA.Google Scholar
  11. Bornstein, R. A. (1985). Normative data on selected neuropsychological measures from a nonclinical sample.Journal of Clinical Psychology 41: 651–659.Google Scholar
  12. Boyar, J. I., and Tsushima, W. T. (1975). Cross-validation of the Halstead-Reitan Neuropsychological Battery: Application in Hawaii.Hawaii Medical Journal 34: 94–96.PubMedGoogle Scholar
  13. Butcher, J. N. (Ed.). (1987).Computerized Psychological Assessment, Basic Books, New York.Google Scholar
  14. Butters, N., Delis, D. C. and Lucas, J. A. (1995). Clinical assessment of memory disorders in amnesia and dementia.Annual Review of Psychology 46: 493–523.PubMedGoogle Scholar
  15. Dobbins, C., and Russell, E. W. (1990). Left temporal lobe damage pattern on the Wechsler Adult Intelligence Scale.Journal of Clinical Psychology 46: 863–868.PubMedGoogle Scholar
  16. Dodrill, C. B. (1988).What constitutes normal performance in clinical neuropsychology? Paper presented at the 97th Annual Convention of the American Psychological Association, Atlanta, GA.Google Scholar
  17. Ellis R. J., and Oscar-Berman, M. (1989). Alcoholism, aging and functional cerebral asymmetries.Psychological Bulletin 106: 128–147.PubMedGoogle Scholar
  18. EsData. (1984).Halstead-Reitan Neuropsychological Battery Software [Computer program]. Youngstown OH: EsData, Integrated Professional Systems, Inc., No. 1207.Google Scholar
  19. Faust, D. (1984).The Limits of Scientific Reasoning, University of Minnesota Press, Minneapolis, Minnesota.Google Scholar
  20. Faust, D., Guilmette, T. J., Hart, K., Arkes, H. R., Fishburne, F. J., and Davey, L. (1988a). Neuropsychologists' training, experience, and judgment accuracy.Archives of Clinical Neuropsychology 3: 145–163.PubMedGoogle Scholar
  21. Faust, D., Ziskin, J., and Hiers, J. B. (1988b).Brain Damage Claims: Coping with Neuropsychological Evidence (Vol. 1), Law and Psychology Press, Los Angeles, pp. 186–188.Google Scholar
  22. Fields, F. R. J. (1987). Brain dysfunction: Relative discrimination accuracy of Halstead-Reitan and Luria-Nebraska Neuropsychological test batteries.Neuropsychology 1: 9–12.Google Scholar
  23. Filskov, S. B., and Goldstein, S. G. (1974). Diagnostic validity of the Halstead-Reitan Neuropsychological Battery.Journal of Consulting and Clinical Psychology 42: 382–388.PubMedGoogle Scholar
  24. Finkelstein, J. N. (1977). BRAIN: A computer program for interpretation of the Halstead-Reitan Neuropsychological Test Battery.Dissertation Abstracts International. 37, B (University Microfilms No. 77-8, 8864).Google Scholar
  25. Fromm-Auch, D., and Yeudall, L. T. (1983). Normative data for the Halstead-Reitan neuropsychological tests.Journal of Clinical Neuropsychology 5: 221–238.PubMedGoogle Scholar
  26. Gade, A., and Mortensen, E. L. (1984, December).The influence of age, education, and intelligence on neuropsychological test performance. Paper presented at the 3rd Nordic Conference in Behavioral Toxicology, Arhus, Denmark.Google Scholar
  27. Gade, A., Mortensen, E. L., Udesen, H., and Jonsson, A. (1985, June).Predictors of cognitive performance: Age, education, and intelligence. Paper presented at the 8th INS European Conference, Copenhagen, Denmark.Google Scholar
  28. Golden, C. (1977). Validity of the Halstead-Reitan Neuropsychological Battery in a mixed psychiatric and brain-Injured population.Journal of Consulting and Clinical Psychology 43: 1043–1051.Google Scholar
  29. Golden, J. G. (1987). Computers in neuropsychology. In Butcher, J. N. (ed.),Computerized psychological Assessment, Basic Books, New York, pp. 344–354.Google Scholar
  30. Halstead, W. C. (1947).Brain and Intelligence, University of Chicago Press, Chicago.Google Scholar
  31. Goldstein, G. (1986). The neuropsychology of schizophrenia. In I. Grant, I. and Adams, K. M. (eds.),Neuropsychological Assessment of Neuropsychiatric Disorders, Oxford University Press, New York, pp. 147–171.Google Scholar
  32. Goldstein, G. (1987). Etiological considerations regarding the neuropsychological consequences of alcoholism. In Parsons, O. A., Butters, N., and Nathan, P. E. (eds.),Neuropsychology of Alcoholism, Gilford Press, New York.Google Scholar
  33. Goldstein, G., and Shelly, C. H. (1972). Statistical and normative studies of the Halstead Neuropsychological Test Battery relevant to a neuropsychiatric setting.Perceptual and Motor Skills 34: 603–620.PubMedGoogle Scholar
  34. Goldstein, G., and Shelly, C. (1982a). A further attempt to cross-validate the Russell, Neuringer, and Goldstein Neuropsychological Keys.Journal of Consulting and Clinical Psychology 50: 721–726.PubMedGoogle Scholar
  35. Goldstein, G., and Shelly, C. (1982b). A multivariate neuropsychological approach to brain lesion localization in alcoholism.Addictive Behaviors 7: 165–175.PubMedGoogle Scholar
  36. Goldstein, S. G., Deysach, R. E., and Kleinknecht, R. A. (1973). Effect of experience and amount of information on identification of cerebral impairment.Journal of Consulting and Clinical Psychology 41: 30–34.PubMedGoogle Scholar
  37. Guilford, J. P. (1965).Fundamental Statistics in Psychology and Education (4th ed.), McGraw-Hill, New York.Google Scholar
  38. Halstead, W. C. (1947).Brain and Intelligence. University of Chicago Press, Chicago.Google Scholar
  39. Heaton, R. K., and Adams, K.M. (1987). Potential versus current reality of automation in neuropsychology: Reply to Kleinmuntz.Journal of Consulting and Clinical Psychology 55: 268–269.Google Scholar
  40. Heaton, R. K., Grant, I., Anthony, W. Z., and Lehman, A. W. (1981). A comparison of clinical and automated interpretation of the Halstead-Reitan Battery.Journal of Clinical Neuropsychology 3: 121–141.PubMedGoogle Scholar
  41. Heaton, R. K., Grant, I., and Matthews, C. G. (1991).Comprehensive Norms for an Expanded Halstead-Reitan Battery, Psychological Assessment Resources, Odessa, FL.Google Scholar
  42. Heaton, R. K., Grant, I., and Matthews, C. G. (1986). Differences in neuropsychological test performance associated with age, education and sex. In Grant, I., and Adams, K. M. (eds.),Neuropsychological Assessment of Neuropsychiatric Disorders, Oxford, New York, pp. 100–120.Google Scholar
  43. Honaker, L. M., and Fowler, R. D. (1990). Computer-assisted pychological assessment. In Goldstein, G. and Hersen, M. (eds.),Handbook of Psychological Assessment, Pergamon, New York, pp. 521–546.Google Scholar
  44. Huberty, C. J. (1984). Issues in the use and interpretation of discriminant analysis.Psychologicla Bulletin 95: 156–171.Google Scholar
  45. Incagnoli, T. (1986). Current directions and future trends in clinical neuropsychology. In Incagnoli, T. Goldstein, G., and Golden, C. G. (eds.),Clinical Application of Neuropsychological Test Batteries, pp. 1–44. Plenum, New York, pp. 1–44.Google Scholar
  46. Kane, R. L., and Kay, G. G. (1992). Computerized assessment in neuropsychology: A review of tests and test batteries.Neuropsychology Review 3: 1–117.PubMedGoogle Scholar
  47. Kane, R. L., Sweet, J. J., Golden, C. J., Parsons, O. A., and Moses, J. A. (1981). Comparative diagnostic accuracy of the Halstead-Reitan and Standardized Luria-Nebraska Neuropsychological batteries in a mixed psychiatric and brain-damaged population.Journal of Consulting and Clinical Psychology 49: 484–485.PubMedGoogle Scholar
  48. Kleinmuntz, B. (1987). Automated interpretation of neuropsychological test data: Comments on Adams and Heaton.Journal of Consulting and Clinical Psychology 55: 266–267.Google Scholar
  49. Knights, R. (1973). Problems of criteria in diagnosis: A profile similarity approach.Annuals of the New York Academy of Science 205: 124–131.Google Scholar
  50. Lezak, M. D. (1983).Neuropsychological Assessment (2nd ed.), Oxford, New York.Google Scholar
  51. Long, C. J., and Wagner, M. (1986). Computer applications in neuropsychology. In Wedding, D., Horton, A. M., and Webster, J. (eds.),The Neuropsychology Handbook, Springer, New York, pp. 548–569.Google Scholar
  52. Lynch, W. (1988). Computers in neuropsychological assessment.Journal of Head Trauma and Rehabilitation 3: 92–94.Google Scholar
  53. Martin, A., Brouwers, P., Lalonde, F., Cox, C., Teleska, P., Fedio, P., Foster, N. L., and Chase, T. N. (1986). Towards a behavioral typology of Alzheimer's patients.Journal of Consulting and Experimental Neuropsychology 8: 594–610.Google Scholar
  54. Matarazzo, J. D. (1990). Psychological assessment versus psychological testing, Validation from Binet to the school, clinic, and courtroom.American Psychologist 45: 99–1017.Google Scholar
  55. McCaffrey, R. J. and Lynch, J. K. (1993). A methodological review of “method skeptic” reports.Neuropsychology Review 3: 235–248.Google Scholar
  56. McSweeny, A. J., and Swiercinsky, D. P. (1985). Computer based neuropsychological test administration: Convergent and discriminant validity of the SAINT [abstract].Journal of Clinical and Experimental Neuropsychology, 7: 629.Google Scholar
  57. Meehl, P. E. (1954).Clinical versus Statistical Prediction: A Theoretical Analysis and a Review of the Evidence, University of Minnesota Press, Minneapolis.Google Scholar
  58. Mutchnick, M., Ross, L. K., and Long, C. J. (1991). Decision strategies for cerebral dysfunction IV: Determination of cerebral dysfunction.Archives of Clinical Neuropsychology 6: 259–270.PubMedGoogle Scholar
  59. Netter, F. H., Jones, H. R., and Dingle, R. V. (1985).The Ciba Collection of Medical Illustrations, Ciba, West Caldwell, NJ.Google Scholar
  60. Pauker, J. D. (1977).Adult norms for the Halstead-Reitan neuropsychological test battery: Preliminary data. Paper presented at the Annual Meeting of the International Neuropsychological Society, Santa Fe, NM.Google Scholar
  61. Prigatano, G. P., and Parsons, O. A. (1976). Relationship of age and education to Halstead Test Performance in different patient populations.Journal of Consulting and Clinical Psychology 44: 527–533.PubMedGoogle Scholar
  62. Reitan, R. M. (1955a). An investigation of the validity of Halstead's measures of biological intelligence.Archives of Neurology and Psychiatry 73: 28–35.Google Scholar
  63. Reitan, R. M. (1955b). The distribution according to age of a psychologic measure dependent upon organic brain functions.Journal of Gerontology 10: 338–340.PubMedGoogle Scholar
  64. Reitan, R. M. (1964). Psychological deficits resulting from cerebral lesions in men. In Warren, J. M., and Akert, K. (eds.),The Frontal Granular Cortex and Behavior, McGraw-Hill, New York, pp. 295–312.Google Scholar
  65. Reitan, R. M. (1991).The Neuropsychological Deficit Scale for adults computer program, Users Manual, Neuropsychology Press, Tucson, AZ.Google Scholar
  66. Reitan, R. M., and Wolfson, D. (1985).The Halstead-Reitan Neuropsychological Test Battery; Theory and Clinical Interpretation. Neuropsychology Press, Tucson, AZ.Google Scholar
  67. Reitan, R. M., and Wolfson, D. (1986). The Halstead-Reitan Neuropsychological Test Battery. In Wedding, D., Horton, A. M., Webster, J. (Eds.),The Neuropsychology Handbook. Springer, New York, pp. 134–160.Google Scholar
  68. Reitan, R. M., and Wolfson, D. (1988).Traumatic Brain Injury: Vol. 2. Recovery and Rehabilitation. Neuropsychology Press, Tucson, AZ.Google Scholar
  69. Retzlaff, P. D., and Gibertini, M. (1994) Neuropsychometric issues and problems. In Vanderploeg, R. D. (ed.),A Guide to Neuropsychological Practice, Erlbaum, Hillsdale, NJ, pp. 185–210.Google Scholar
  70. Robbins, D. E. (1989). The Halstead-Reitan Neuropsychological Battery. In Franzen, M. D. (ed.),Reliability and Validity in Neuropsychological Assessment, Plenum, New York, pp. 91–107.Google Scholar
  71. Rojas, D. C., and Bennett, T. L. (1995). Single versus composit score discriminative validity with the Halstead-Reitan Battery and the Stroop Test in mild brain injury.Archives of Clinical Neuropsychology 10: 101–110.PubMedGoogle Scholar
  72. Ross, L., Thrasher, M., and Long, C. J. (1990). Decision strategies in neuropsychology I: Determination of lateralized cerebral dysfunction.Archives of Clinical Neuropsychology 5: 273–285.PubMedGoogle Scholar
  73. Russell, E. W. (1976). The Bender-Gestalt and the Halstead-Reitan Battery: A case study.Journal of Clinical Psychology 32: 355–361.PubMedGoogle Scholar
  74. Russell, E. W. (1968). Neuropsychological keys for assessing the localization and process status of cerebral damage. Ph.D. dissertation, University of Kansas.Dissertation Abstracts International, 68, 17, 448 (University Microfilms No. 68-17448).Google Scholar
  75. Russell, E. W. (1979). Three patterns of brain damage on the WAIS.Journal of Clinical Psychology 37: 246–253.Google Scholar
  76. Russell, E. W. (1980). Tactile sensation: An all-or-none effect of cerebral damage.Journal of Clinical Psychology 36: 858–864.PubMedGoogle Scholar
  77. Russell, E. W. (1984a). Psychometric parameters of the Average Impairment Rating scale.Journal of Consulting and Clinical Psychology 52, 717–718.PubMedGoogle Scholar
  78. Russell, E. W. (1984b). Theory and developments of pattern analysis methods related to the Halstead-Reitan battery. In Logue, P. E., and Shear, J. M. (eds.),Clinical Neuropsychology: A Multidiscinlinary Approach, Charles C. Thomas. Springfield, IL, pp. 50–98.Google Scholar
  79. Russell, E. W. (1994). The cognitive-metric fixed battery approach to neuropsychological assessment. In Vanderploeg, R. D., (ed.),A Guide to Neuropsychological Practice, Erlbaum, Hillsdale, NJ, pp. 211–258.Google Scholar
  80. Russell, E. W., Neuringer, C., and Goldstein, G. (1970).Assessment of brain damage: A Neuropsychological Approach, John Wiley & Sons, New York.Google Scholar
  81. Russell, E. W., and Russell, S. L. K. (1993). Left temporal lobe brain damage pattern on the WAIS, Addendum.Journal of Clinical Psychology 49: 241–244.PubMedGoogle Scholar
  82. Russell, E. W., and Starkey, R. I. (1993).Halstead, Russell Neuropsychological Evaluation System [Manual and Computer program]. Western Psychological Services, Los Angeles.Google Scholar
  83. Russell, E. W., Starkey, R. I., Fernandez, C. D., and Starkey, T. W. (1988).Halstead, Rennick. Russell Battery [Manual and Computer program]. Scientific Psychology, Miami, FL.Google Scholar
  84. Schreiber, D. J., Goldman, H., Kleinman, K. M., Goldfader, P. R., and Snow, M. Y. (1976). The relationship between independent neuropsychological and neurological detection and localization of cerebral impairment.Journal of Nervous and Mental Disease 162: 360–365.PubMedGoogle Scholar
  85. Sherer, M., and Adams, R. L. (1993). Cross-Validation of Reitan and Wolfson's Neuropsychological Deficit Scales.Archives of Clinical Neuropsychology 8: 429–435.PubMedGoogle Scholar
  86. Snow, W. G. (1981). A comparison of frequency of abnormal results in neuropsychological vs. neurodiagnostic procedures.Journal of Clinical Psychology 37: 22–28.PubMedGoogle Scholar
  87. Spreen, O., and Benton, A. L. (1965). Comparative studies of some psychological tests for cerebral damage.Journal of Nervous and Mental Disease 140: 323–333.PubMedGoogle Scholar
  88. Stuss, D. T., and Trites, R. L. (1977). Classification of neurological status using multiple discriminant function analysis of neuropsychological test scores.Journal of Consulting and Clinical Psychology 45: 145.PubMedGoogle Scholar
  89. Swiercinsky, D. P. (1978, September).Computerized SAINT: System for analysis and interpretation of neuropsychological tests. Paper presented at the meeting of the American Psychological Association, Toronto.Google Scholar
  90. Swiercinsky, D. P., and Leigh, G. (1979). Comparison of neuropsychological data in the diagnosis of brain impairment with computerized tomography and other neurological procedures.Journal of Clinical Psychology 35: 242–246.PubMedGoogle Scholar
  91. Swiercinsky, D. P., and Warnock, J. K. (1977). Comparison of the neuropsychological key and discriminant analysis approaches in predicting cerebral damage and localization.Journal of Consulting and Clinical Psychology 45: 808–814.PubMedGoogle Scholar
  92. Tsushima, W. T., and Wedding, D. (1979). A comparison of the Halstead-Reitan Neuropsychological Battery and computerized tomography in the identification of brain disorder.Journal of Nervous and Mental Disease 167: 704–707.PubMedGoogle Scholar
  93. Vega, A., and Parsons, O. A. (1967). Cross-validation of the Halstead-Reitan tests for brain damage.Journal of Consulting Psychology 31: 619–625.PubMedGoogle Scholar
  94. Wedding, D. (1983a). Clinical and statistical prediction.Clinical Neuropsychology 5: 49–55.Google Scholar
  95. Wedding, D. (1983b). Comparison of statistical and actuarial models for predicting lateralization of brain damage.Clinical Neuropsychology 4: 15–20.Google Scholar
  96. Wedding, D., and Faust, D. (1989). Clinical Judgment and decision making in neuropsychology.Archives of Clinical Neuropsychology, 4: 233–265.PubMedGoogle Scholar
  97. Wheeler, L., and Reitan, R. M. (1963). Discriminant functions applied to the problem of predicting cerebral damage from behavioral tests: A cross-validation study.Perceptual and Motor Skills 16: 681–701.PubMedGoogle Scholar
  98. Wheeler, L., Burke, C. J., and Reitan, R. M. (1963). An application of discriminant functions to the problem of predicting brain damage using behavioral tests.Perceptual and Motor Skills 16: 417–440.PubMedGoogle Scholar
  99. Willis, W. G. (1984). Reanalysis of an actuarial approach to neuropsychological diagnosis in consideration of base rates.Journal of Consulting and Clinical Psychology 52, 567–569.PubMedGoogle Scholar
  100. Wolfson, D., and Reitan, R. M. (1995). Cross-validation of the General Neuropsychological Deficit Scale (GNDS).Archives of Clinical Neuropsychology 10: 125–131.PubMedGoogle Scholar

Copyright information

© Plenum Publishing Corporation 1995

Authors and Affiliations

  • Elbert W. Russell
    • 1
  1. 1.Veterans Administration Medical CenterMiami

Personalised recommendations