Q1: Will the MNB run on an Apple computer (Mac)?
The MNB currently only runs on the Windows platform. A macOS version is in the works, but for now if you want to run it on a Mac, you will need to install a windows emulator (e.g. Boot Camp, Parallels, Fusion).
Q2. How do I get the Spanish protocols?
There is a Spanish version of the MNS. The Spanish version protocols can be downloaded from the resources section of the website.
Q3. What published articles are there on the Validity and Reliability and Sensitivity and Specificity of the MNB?
The Meyers Neuropsychological Battery (MNB) has been ongoing for more than 20 years. The MNB was developed as a battery of tests that were both sensitive and specific to the presence of brain injury and other cognitive impairments. These tests are able to differentiate between different neuropsychological injuries and diseases. The tests selected for the MNB were chosen by a series of discriminant functions that identified tests that were sensitive and specific to cognitive injury and disease. The discriminant functions were able to identify tests that could differentiate such conditions as Normal vs Traumatic Brain Injury (TBI), or ADD vs TBI, or Depression vs TBI, or ADD vs Anxiety and so forth. Tests that were able to differentiate between these conditions were then included in the MNB.
The MNB has a statistical methods (Rohling Interpretive Method, and other statistics) used to analyze and compare the patient data with various comparison groups. The MNB is composed of well-known neuropsychology tests. Some tests in the MNB are parts of other test batteries, (Block Design, Similarities, Digit Span, Arithmetic, Information, Coding, Picture Completion from the Wechsler Scale (WAIS) [R, III, IV], and Category (Victoria version or Full version), Finger Tapping, and Trails A and B from the Halstead Reitan Battery (HRB). The seven WAIS-IV subtests used in the MNB can also be used to calculate the Ward (Ward, 1990) seven subtest version of the WAIS-IV (Meyers, Zellinger, Kockler, Wagner, Miller, 2013); but the IQ number is not used in any of the MNB calculations.
The MNB consists of using statistics such as means, standard deviations, t-tests, effect sizes, confidence intervals, correlations, configurations, Kullback-Leibler and so forth. The MNB also includes the use of comparison groups as well as discriminant functions and a neural network (machine learning). All these methods are designed to provide an objective matching of the patient data with the comparison groups such that any examiner bias is reduced or eliminated. By having comparison groups one can easily compare a particular patient’s data with known patient groups to see if that patient’s data matches the comparison groups.
Although the MNB is designed to use seven (Block Design, Similarities, Digit Span, Arithmetic, Information, Coding, Picture Completion) parts of the WAIS-IV (Meyers, Zellinger, Kockler, Wagner, Miller, 2013), the resulting estimations of IQ are not used in any of the computations, comparison groups or statistical methods. The individual tests from the WAIS-IV are included (along with other non-WAIS tests) in the domains within the MNB. The MNB assigns test scores to neuropsychological domains that are similar to those proposed by Larrabee (2000, 2017). Furthermore, the MNB includes neuropsychological measures that are commonly administered by clinicians (Miller, Fichtenberg, & Millis, 2010; Meyers & Rohling, 2004). The normative data in the MNB has also been compared to other normative samples and found to be as good or better than other normative systems (Rohling, Miller, Axelrod, Wall, Lee, Kinikini, 2015).
The purpose of the MNB is to provide an accurate, reliable and valid method of identifying cognitive impairment. The MNB has more than 50 publications demonstrating and corroborating the tests, methods and uses of the MNB. The MNB has been found to meet Daubert standards (Rohling, Meyers, Williams, Kalat, Williams, Keene, 2015). The normative data in the MNB has been independently evaluated and found to be superior to other large normative data bases. The MNB has been checked for bias in forensic use (Meyers, Reinsch-Boothby, Miller, Rohling, and Axelrod, 2011). This study also checked for any bias in the methodology used in the MNB. The methodology used is of such quality that the American Medical Association (AMA) has selected it as the recommended method for rating cognitive impairment and percentage of impairment from cognitive injury or disease (Barth and Meyers, 2017).
Another feature of the MNB is the built-in method for Premorbid Estimate (PE) functioning (Meyers, Miller, Rohling, Kalat, 2019). The MNB contains a method using demographics, word reading (adapted from the NAART) and an estimate based on history. The PE uses the Revised NAART (Meyers, et al., 2019). These three parts are averaged to produce the Premorbid Estimate (PE) which estimates the expected patient performance on neuropsychological tests (Rohling, Langhinrichsen-Rohling, Meyers, 2020). This is different from the Test of Premorbid Functioning (TOPF; Holdnack, Schoenberg, & Lange, 2013), which predicts IQ performance. The TOPF is a word reading test that was adapted from the Wechsler Test of Adult Reading (WTAR; Wechsler, 2001). The word list of the TOPF contains three words from the NAART. The PE predicts expected neuropsychological functioning rather than IQ. Neuropsychological functioning includes tasks that are not in IQ tests, such as memory (other than the Wechsler Memory Scale), motor, and sensory tests. Tests like the TOPF that predict IQ will often overpredict the PE as the PE and TOPF are predicting different expectations.
The MNB also has built in validity checks which are important as the validity of the data being interpreted must be established. There are multiple validity checks in the MNB at the individual test level, domain level, and at the global level, (Meyers, Galinsky, and Volbrecht, 1999; Meyers, Morrison, & Miller, 2001; Larrabee, Rohling, & Meyers, 2019; Hill, Rohling, Boettcher, Meyers, 2013; Meyers, 2007; Meyers, & Volbrecht, 2003; Larrabee, Rohling, & Meyers, 2019). In all the studies, the MNB validity measures have been shown to appropriately identify invalid data sets. In the MNB, the different validity measures used in any single case depend on the demographics, diagnosis and functional ability of the individual being assessed. The MNB adapts to each case such that only the appropriate validity measures are used, and measures that would not be appropriate due to demographics or diagnosis, are excluded.
The MNB has been validated many times in various ways. One of the first validity studies published was Volbrecht, Meyers, and Kaster-Bundgaard, (2000) who demonstrated that the MNB was able to discriminate TBI from cerebral vascular accidents, mental health and dementia. Meyers & Rohling, (2004) found that the MNB was able to correctly identify normal controls, depressed, chronic pain patients and patients with mild TBI, with a 96.1% correct classification rate. The Meyers and Rohling study also showed that the MNB has very good test-retest reliability with r=.86, at a one year later test-retest. These results show that the MNB has good stability over time and is reliable. The MNB was also compared with a set of HRB data and the MNB was not only found to be sensitive to the presence of brain injury but also to the degree of impairment.
The MNB also has methods to objectively match patterns of test performance. A study by Meyers and Miller, (2021) showed that the statistical methods used in the MNB were very accurate in matching patterns of Neuropsychological data between a patient dat set and comparison groups. The results of this study showed better than 90% correct in pattern matching.
The MNB was also validated against CT and MRI data (Meyers & Rohling, 2009), showing that the tests in the MNB assess whole brain areas. These results also indicated that neuropsychological tests assess larger areas of the brain rather than single areas.
The MNB method has also been found to be able to identify TBI, normal, anxiety, depression and chronic pain patients (Meyers & Diep, 2000; Bianchini, Etherton, Greve, Heinly & Meyers, 2008; Meyers, 2017). In other words, the MNB is able to identify those who have cognitive difficulties associated with pain. This, along with the other studies mentioned, show that the MNB is able to discriminate different clinical groups.
The MNB was also subjected to validation using machine learning. Meyers, Miller, and Tuita, (2013) found that the MNB was able to consistently and accurately identify individuals who had non-valid data, TBI, PTSD and other mental health diagnoses. The machine learning algorithm was accurate at more than 90% in identifying individuals from these groups. The machine learning algorithm (neural network) was so accurate that the algorithm method used by the MNB was used in training medical decision-making models (Miller, Rupp, Lee, & Meyers 2014). The MNB model of using neural networks is also being used to train others in the use of neural networks (Miller, Rupp, Lee, & Meyers, 2014).
References
Barth, R.J. and Meyers, J.E. (2017). Rating Cognitive Impairment, Part 2: Objective and Evidence-Based Integration of Neuropsychological Test data. AMA Guides Newsletter. Updates, authoritative guidance, Practical Information, and rationales for proper use of AMA guides. AMA Guidelines.
Bianchini, K.J., Etherton, J. L., Greve, K. W., Heinly, M. T., & Meyers, J. E. (2008). Classification accuracy of MMPI-2 validity scales in the detection of pain-related malingering: a known-groups approach. Assessment, 15, 435-449.
Bush, S. S., and Planning Committee (2005). Independent and court ordered forensic neuropsychological examinations: official statement of the National Academy of Neuropsychology. Archives of Clinical Neuropsychology, 20 (8), 997-1007.
Hill, B.D., Rohling, M.L., Boettcher, A.C., Meyers, J.E. (2013). Cognitive intra-individual variability has a positive association with traumatic brain injury severity and suboptimal effort. Archives of Clinical Neuropsychology, 28, (7), 640-8.
Holdnack, J.A., Schoenberg, M.R., Lange, R.T., Iverson, G.,(2013) Chapter 5 – Predicting Premorbid Ability for WAIS–IV, WMS–IV and WASI–II,Editor(s): James A. Holdnack, Lisa Whipple Drozdick, Lawrence G. Weiss, Grant L. Iverson, WAIS-IV, WMS-IV, and ACS, Academic Press, 217-278, ISBN 9780123869340,
Larrabee, G.J. (2000). FORUM Association between IQ and neuropsychological test performance: Commentary on Tremont, Hoffman, Scott, and Adams (1998). The Clinical Neuropsychologist, 14(1), 139-145.
Larrabee, G.J. (2017). Selection of tests and batteries for forensic neuropsychological evaluations. In S.S. Bush, G.J. Demakis, & M.L. Rohling (Eds.), APA handbook of forensic neuropsychology (pp. 57-66). Washington, DC: APA Publications.
Larrabee, G.L., Rohling, M.L., & Meyers, J.E., (2019): Use of multiple performance and symptom validity measures: Determining the optimal per test cutoff for determination of invalidity, analysis of skew, and inter-test correlations in valid and invalid performance groups, The Clinical Neuropsychologist, DOI: 10.1080/13854046.2019.1614227
Meyers, J.E., and Miller, R.M., (2021). Objective methods for matching neuropsychological patterns: Formulas and Comparisons. Applied Neuropsychology: Adult. http://dx.doi.org/10.1080/23279095.2021.1929986
Meyers, J.E. & Diep, A. (2000). Assessment of malingering in chronic pain patients using neuropsychological tests. Applied Neuropsychology, 7, 133-139.
Meyers, J.E. & Volbrecht, M.E. (2003). A Validation of multiple malingering detection methods in a large clinical sample, Archives of Clinical Neuropsychology, 18 (3), 261-276.
Meyers, J.E., & Rohling, M.L. (2004). Validation of the Meyers Short Battery on mild TBI patients. Archives of Clinical Neuropsychology, 19, 637-651.
Meyers, J.E., (2007). Malingering Mild Traumatic Brain Injury: Behavioral Approaches Used by Both Malingering Actors and Probable Malingerers. In Assessment of Feigned Cognitive Impairment: A Neuropsychological Perspective. Kyle Boone, Ed. Guilford Press.
Meyers, J.E., Galinsky, A. & Volbrecht, M. (1999). Malingering and mild brain injury: how low is too low. Applied Neuropsychology, 6, 208-216.
Meyers, J.E., Morrison, A.L. & Miller, J.C. (2001). How low is too low, revisited: Sentence Repetition and AVLT-Recognition in the detection of malingering. Applied Neuropsychology, 8 (4), 234-241.
Meyers, J.E., Reinsch-Boothby, L., Miller, R., Rohling, M., & Axelrod, B., (2011). Does the source of a forensic referral affect neuropsychological test performance on a standardized battery of tests? The Clinical Neuropsychologist, 25, 477-487.
Meyers, J.E., Rohling, M. L., (2009). CT and MRI correlations with neuropsychological tests. Applied Neuropsychology, 16 (4), 237-253.
Meyers, J.E. (2017). Chronic Pain and Neuropsychological Assessment (Chapter 11). APA Handbook of Forensic Neuropsychology. SS Bush (Editor in Chief), ML Rohling, GJ Demakis. APA Handbooks in Psychology Series/APA Reference Books Collection. American Psychological Association. https://books.google.com/books?id=5SldnQAACAAJ.
Meyers, J.E., Miller, R.M., Rohling, M.L., Kalat, S.S. (2019). Premorbid Estimates of Neuropsychological Functioning for Diverse Groups. Applied Neuropsychology: Adult, https://doi.org/10.1080/23279095.2018.1550412.
Meyers, J.E., Miller, R.M., Tuita, A. R. R. (2013). Using Pattern Analysis Matching to differentiate TBI and PTSD in a military sample. Applied Neuropsychology: Adult, 21, 1, 60-68.
Meyers, J.E., Zellinger, M.M., Kockler, T., Wagner, M., Miller, R.M. (2013): A Validated seven-subtest short form for the WAIS-IV, Applied Neuropsychology, 20,249-256.
Miller, J. B., Fichtenberg, N. L., & Millis, S. R. (2010). Diagnostic efficiency of an ability-focused battery. The Clinical Neuropsychologist, 24, 678-688.
Miller, R. M., Rupp, Z. W., Lee, A. J., & Meyers, J. E. (2014). Tutorial P: Using neural network analysis to assist in classifying neuropsychological data. In Winter-Miner, L. A., Bolding, P. S., Hilbe, J. M., Goldstein, M., Hill, T., Nisbet, R., Walton, N, & Miner, G. D. (Eds.), Practical predictive analytics and decisioning systems for medicine (pp. 745-756).
National Academy of Neuropsychology, Policy and Planning Committee (2003). Test security: an update, official statement of the National Academy of Neuropsychology. National Academy of Neuropsychology, approved by the NAN Board of Directors 10/13/2003.
Rohling, M.L., Meyers, J. E., Millis, S. R. (2003). Neuropsychological impairment following traumatic brain injury: a dose-response analysis. The Clinical Neuropsychologist, 17, 289-302.
Rohling, M.L., Langhinrichsen-Rohling, J., and Meyers, J.E., (2020) Effects of Premorbid Ability, Neuropsychological Impairment and Invalid Test Performance on the Frequency of Low Scores. Volume Editors: Kyle B. Boone, Assessment of Feigned Cognitive Impairment: A Neuropsychological Perspective – Second Edition. Guilford Publications, Inc.
Rohling, M.L., Meyers, J.E., Williams, G., Kalat, S.S., Williams, S.K., Keene, J., (2015). Application of the Daubert Standards to the Meyers Neuropsychological Battery Using the Rohling Interpretive Method. Psychol. Inj. and Law, DOI 10.1007/s12207-015-9227-1
Rohling, M.L., Miller, R.M., Axelrod, B.N., Wall, J.R., Lee, A. J.H., Kinikini, D.T., (2015). Is Co-Norming Required. Archives of Clinical Neuropsychology, 30,7, 611–633, https://doi.org/10.1093/arclin/acv039.
Volbrecht, M., Meyers, J. E. & Kaster-Bundgaard, J. (2000). Neuropsychological outcome of head injury using a short battery. Archives of Clinical Neuropsychology, 15, 251-265.
Ward, L. C. (1990). Prediction of Verbal, Performance and Full-Scale IQs from seven subtests of the WAIS-R. Journal of Clinical Psychology, 46, 436–440.
Wechsler, D. (2001). Wechsler Test of Adult Reading: WTAR. San Antonio, TX: Psychological Corporation.
Wechsler, D. (2008). Wechsler Adult Intelligence Scale (4th ed.). San Antonio, TX: The Psychological Corporation.
Wechsler, D. (2009). Wechsler Memory Scale (4th ed.). San Antonio, TX: The Psychological Corporation.
Q4. Where is the MNB used?
The MNB has over 1700 computer registrations. The MNB is used in the US, Canada, and some sites in the Caribbean, UK, Australia and New Zealand.
