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    Predicting global and specific neurological impairment with ... Predicting global and specific neurological impairment with ... Document Transcript

    • Archives of Clinical Neuropsychology 21 (2006) 203–210 Predicting global and specific neurological impairment with sensory-motor functioning Alessandra G. Volpe, Andrew S. Davis ∗ , Raymond S. Dean Department of Educational Psychology, Teachers College Room 515, Ball State University, Muncie, IN 47306, United States Accepted 13 December 2005 Abstract The present study assessed the ability of the Dean–Woodcock Sensory-Motor Battery (DWSMB) to distinguish between normal subjects and neurologically impaired individuals. Scores from the subtests of the DWSMB for 250 normal and 250 neurologically impaired individuals were randomly assigned to two equal groups to allow for cross-validation. The DWSMB was able to correctly identify 92.8% of the cases, identifying 94.4% of the normal population and 91.2% of the neurologically impaired subjects. The cross-validation correctly identified 87.2% of the total cases, identifying 91.2% of the normal subjects and 83.2% of the neurologically impaired subjects. An additional discriminant analysis indicated that the DWSMB correctly identified the following cases: 44.9% cardio-vascular accidents, 66.7% multiple sclerosis, 40% seizures, 42% traumatic brain injuries, 62.7% dementia, and 54.5% Parkinson’s disease. The results add to the validity of the DWSMB by providing evidence of its ability to differentiate between neurologically impaired and normal individuals. © 2006 National Academy of Neuropsychology. Published by Elsevier Ltd. All rights reserved. Keywords: Neurological impairment; Sensory-motor functioning An integral component of most neurologic and neuropsychological assessments is the evaluation of sensory and motor functioning. The assessment of sensory-motor skills is significant because it provides fundamental informa- tion about the patient’s ability to comprehend instructions, produce meaningful responses, and satisfactorily sustain attention necessary to participate in more complex testing. As a result, the sensory-motor examination testifies to the integrity of further neuropsychological test results (Reitan & Wolfson, 2003). However, despite their pivotal role in neuropsychological assessment, few studies have been conducted on full sensory-motor batteries to assess their ability to identify brain dysfunctions in isolation (Reitan & Wolfson, 2003). Additionally, neuropsychological test- ing often employs sensory-motor batteries that are clinically based and have been criticized for lack of standardized administration, scoring and interpretative procedures (i.e., Lang, Hill, & Dean, 2001) as well as for not linking their findings to a specific theory of underlying brain functioning (Golden et al., 1981). A review of neuropsychological and sensory-motor tests indicated that the majority of the sensory and motor tasks assess a limited range of functions, lack psychometric sophistication, need standardization, have inadequate information about reliability, and are restricted to specific age groups such as early childhood, childhood through adolescence, and adulthood (Murphy, Conoley, & Impara, 1994). ∗ Corresponding author. Tel.: +1 765 285 8508; fax: +1 765 285 3653. E-mail address: davis@bsu.edu (A.S. Davis). 0887-6177/$ – see front matter © 2006 National Academy of Neuropsychology. Published by Elsevier Ltd. All rights reserved. doi:10.1016/j.acn.2005.12.005
    • 204 A.G. Volpe et al. / Archives of Clinical Neuropsychology 21 (2006) 203–210 Several successful studies have been conducted on the ability of comprehensive neuropsychological batteries to differentiate neurological impairment from normal functioning. For example, the Halstead–Reitan Neuropsychologi- cal Battery (HRNB, Reitan & Wolfson, 1993) and the Luria Nebraska Neuropsychological Battery (LNNB, Golden, Hammeke, & Purisch, 1978) have been respectively found to predict brain damage at rates of up to 90% (Golden, 1976) and 100% accuracy (Golden et al., 1978). However, systematic evaluation of sensory-motor measures to pre- dict neurological damage has been sparse. The lack of standardized administration and scoring (Reitan & Wolfson, 2003), differences in specific task selection (Adams & Victor, 1993; Glick, 1993), reliance on patients’ self-reporting, and the clinician’s experience in interpreting the results have been mentioned as reasons for the paucity of research demonstrating the predictive ability of sensory-motor tests (Dean & Woodcock, 1999; Malloy & Nadeau, 1986). Fur- thermore, research shows that normal subjects can display sensory-motor impairment (false positives) whereas those with diagnosed pathology may present with no sensory-motor impairment at all (false negatives), further complicating the role of sensory-motor assessment (Dean & Woodcock, 1999). Finally, many neuropsychological studies of brain functions have elected to investigate the effects of brain dysfunction on higher levels of brain functioning (such as cognitive abilities) rather than on lower level brain functions (sensory-perceptual and motor skills) (Reitan & Wolfson, 2003). The Dean–Woodcock Sensory-Motor Battery (DWSMB, Dean & Woodcock, 2003) is a comprehensive measure of cortical and subcortical motor and sensory skills. While some studies have established the reliability and validity of this new sensory-motor battery (i.e., Davis, Finch, Dean, & Woodcock, 2005; Woodward, Ridenour, Dean, & Woodcock, 2002), the effectiveness of the DWSMB to discriminate between neurologically impaired and normal individuals based on the performance of basic sensory and motor functions has not been fully explored. Additionally, few studies have been conducted to assess the ability of a sensory-motor battery to identify brain dysfunction without the aid of cognitive or other neuropsychological information. The present study used a discriminant analysis to investigate the effectiveness of the DWSMB in differentiating patients with neurological damage from normals without the aid of other neuropsychological measures. 1. Method 1.1. Participants Participants in this study included 250 patients who had originally been referred for neuropsychological evaluation to a large midwestern neurological practice and were selected because of diagnosed neurological disorders. The second group consisted of 250 “normal” volunteers who had denied being diagnosed or treated for neurologic, psychiatric, or orthopedic disorders. The clinical sample included 125 males and 125 females ranging in age between 4 and 93 years (mean = 51 years, 7 months) with a level of education ranging from pre-kindergarten to a graduate degree (mean = 11.22 years of education). Among the patients, 88% (N = 220) were right handed, 10.8% (N = 27) were left handed, and 1.2% ambidextrous (N = 3). The group of 250 participants who denied a history of neurological and psychiatric disorders (“normals”) was composed of 94 males and 156 females ranging in age from 3 to 95 years (mean = 48 years, 9 months), with a level of education ranging from pre-kindergarten to graduate degree (mean = 11.9 years of education). Among the normal participants, 90.4% were right handed (N = 226), 6% were left handed (N = 15), and 3.6% were ambidextrous (N = 9). The patients in the neurologic group were diagnosed as having a neurological disorder by a neurologist and classified according to the International Classification of Diseases, Ninth Revision (ICD-9) (American Medical Association, 2000). Diagnoses included cardio-vascular accidents (CVA), traumatic head injuries (TBI), seizure disorders, multiple sclerosis, dementia, and Parkinson’s disease. 1.2. Instrumentation The DWSMB is composed of 8 sensory tests and 10 motor tests that takes approximately 1 hour to administer. These tests were drawn from long established neurological measures which were improved upon by providing standardized procedures for administration and scoring. The DWSMB is a standardized measure that combines sensory and motor tests with qualitative features (such as qualifying and describing a patient’s gait) as well as quantitative scoring of performance driven tests (i.e., assessing a patient’s strength of grip). Eight of its tests assess sensory functions such as visual, auditory, and tactile perception and discrimination. The remaining 10 tests assess motor functioning such as
    • A.G. Volpe et al. / Archives of Clinical Neuropsychology 21 (2006) 203–210 205 upper extremity motor strength, movement, balance, and fine motor skills. Three of the motor tests (Gait and Station, Romberg Testing, and Coordination) are believed to assess sensory and motor strip functions at the subcortical level (Davis et al., 2005; Dean & Woodcock, 1999). The sensory portion of the DWSMB yields 21 scores, while the motor portion yields 15 scores. From a quantitative view, the DWSMB provides standardized procedures for test administration and uses norms for the interpretation of individual performance. The DWSMB covers a broader range of sensory and motor functions than either the HRNB or LNNB because it provides additional information necessary to differentiate between signs of subcortical dysfunction and those related to right hemispheric damage (Dean & Woodcock, 1999). Furthermore, left–right differences and pathological signs are incorporated with information regarding the level of performance. 1.3. Description of procedures Each participant was administered the tests of the DWSMB using the standardized administration procedures described in the Dean–Woodcock Sensory-Motor Battery’s manual (2003). All examiners had been previously trained in the use of neuropsychological assessment instruments as well as standardized procedures. All participants were treated in accordance with the Ethical Principles of Psychologists and Code of Conduct (American Psychological Association, 2004). 1.4. Data analysis The Statistical Package for the Social Sciences Base 11.0 for Windows (2001) (SPSS) was utilized to analyze all demographic information and provide descriptive statistics. For each group, 125 participants were randomly chosen by the statistical package (SPSS) for an analysis group and the other 125 in each group served as the cross-validation groups. This was necessary because discriminant analysis creates a regression equation that maximally discriminates between two groups (neurologically impaired versus normals), and the extent to which it can be used to predict group membership in future instances is often unclear. Therefore, a discriminant function was obtained on one group and then compared to the other to establish the generalizability of the prediction. Discriminant analysis is applied to situations in which the dependent variable is nominal in nature and is used predominately to predict group membership in two or more groups (in this case group membership: 1 = neurologically impaired and 2 = normal subject). Discriminant function analysis is used to identify the best set of independent variables,which maximizes the correct classification of participants (Martella, Nelson, & Merchand-Martella, 1999). The resulting discriminant function yields standardized discriminant coefficients to maximize differentiation between groups (Martella et al., 1999). The cross-validation sample was used to compare the effectiveness of the discriminant function in terms of diagnosing between neurologically impaired and normal subjects of the DWSMB. 2. Results Three separate discriminant analyses were performed on the data. The first two discriminant analyses were conducted to examine the ability of the DWSMB to differentiate between normal subjects and neurologically impaired patients. The final discriminant analysis was performed to investigate how accurately scores on the DWSMB would identify different types of neurological impairment diagnoses. The 36 scores yielded by the 18 subtests of the DWSMB were entered as predictors of subjects’ membership (normal versus neurologically impaired) into a stepwise discriminant analysis. The result of the discriminant analysis produced one significant function: Wilks’ lambda = .405, Chi-square transformation (10, N = 250) = 219.867, p < .001. The obtained discriminant function accounted for 100% of the explained variance. The results indicated that 94.4% of the normal population and 91.2% of the neurologically impaired subjects were correctly classified by the DWSMB, with a total of 92.8% of identified cases. Table 1 displays the means and standard deviation for each of the predictor variables. The relative magnitude of the standardized discriminant coefficients noted in Table 2 provided information regarding the relative importance of each predictor variable in discriminating between groups. The stepwise discriminant analysis procedure maximizes the prediction of the variables and the ability to separate the groups. The first variable chosen by the statistical program is the one that maximizes separation among the groups.
    • 206 A.G. Volpe et al. / Archives of Clinical Neuropsychology 21 (2006) 203–210 Table 1 First group discriminant analysis-descriptive statistic for each variable (in W-scores) Variable name Mean S.D. Normal Patients Normal Patients Right visual acuity 431.02 425.95 26.27 25.95 Left visual acuity 430.35 424.39 25.45 26.38 Right visual confrontation 484.67 480.77 11.67 16.21 Left visual confrontation 487.27 481.30 10.35 17.20 Both visual confrontation 487.96 481.57 10.72 16.21 Naming pictures of objects 534.72 445.37 9.21 110.89 Right auditory perception 475.52 461.34 14.69 24.05 Left auditory perception 482.83 463.29 14.01 26.21 Both auditory perception 485.42 473.60 13.19 18.73 Right palm writing 500.52 487.68 10.44 21.54 Left palm writing 501.10 486.73 10.66 20.94 Right object identification 493.51 486.26 8.79 14.54 Left object identification 497.58 490.05 9.69 13.38 Right finger identification 489.09 481.40 5.80 14.81 Left finger identification 489.85 482.61 6.55 15.74 Right hand sim. loc. 510.51 507.32 1.43 11.07 Left hand sim. loc. 510.92 507.86 1.82 10.81 Both hands sim. loc. 514.52 512.04 2.15 8.71 R hands/cheeks sim. loc. 522.06 512.56 8.62 19.73 L hands/cheeks sim. loc. 521.75 513.90 9.00 18.92 Both hands/cheeks sim. loc. 515.03 506.90 8.11 16.82 Gait and station 479.82 460.58 15.56 25.28 Romberg testing 484.30 465.14 23.14 27.63 Cross-construction 494.19 478.51 13.60 18.94 Clock construction 494.91 484.27 10.96 18.90 Right finger/nose coord. 490.28 468.16 15.36 31.82 Left finger/nose coord. 490.46 466.48 12.37 30.96 Right hand/thigh coord. 477.05 465.78 18.82 19.43 Left hand/thigh coord. 477.50 465.83 21.34 18.56 Mime movements 499.04 490.26 7.27 14.53 Left–right movements 499.32 493.16 3.22 16.00 Dominant finger tapping 503.41 498.15 7.18 7.40 Non-dom. finger tapping 503.72 497.91 9.18 10.13 Expressive speech 495.12 486.39 10.76 14.68 Dominant strength of grip 526.96 521.33 13.33 23.58 Non dom. strength of grip 524.94 519.88 14.59 22.64 sim.: simultaneous; coord.: coordination; dom.: dominant; loc.: localization. Table 2 DWSMB variables involved in the discriminant function predicting group membership for the neurologically impaired and normal subjects Variable Standardized discriminant coefficients Naming pictures of objects .978 Left hand sim. localization .791 Both hands sim. localization −.556 Left finger to nose coordination .548 Left hands and cheek sim. loc. −.445 Mime movements .436 Cross-construction .261 Dominant palm writing .239 Right near point visual acuity −.228 Left auditory perception .183
    • A.G. Volpe et al. / Archives of Clinical Neuropsychology 21 (2006) 203–210 207 Table 3 Differential diagnoses classifications resultsa Diagnosis CVA (%) MS (%) Seizure (%) TBI (%) Dementia (%) Parkinsons (%) Normals (%) CVA 44.9 8.7 5.8 14.5 11.6 11.6 2.9 MS .0 66.7 .0 16.7 .0 .0 16.7 Seizures 16.0 4.0 40.0 24.0 .0 8.0 8.0 TBI 10.2 12.5 10.2 42.0 4.5 10.2 10.2 Dementia 7.8 3.9 2.0 3.9 62.7 15.7 3.9 Parkinsons 9.1 9.1 9.1 .0 18.2 54.5 .0 Normals .0 3.2 2.4 2.4 1.6 .4 90.0 a 69.0% of original cases correctly classified. The group centroids (the discriminant scores for each group when the variable means are entered into the discriminant equation) were 1.208 for the normals and −1.208 for the neurologically impaired patients. Among the available subjects, 250 were included in the initial discriminant analysis, with a randomly selected 50% (N = 250) of the sample set aside for cross-validation. The purpose of cross-validation was to establish the extent to which the discriminant function obtained in the initial analysis was successful in predicting group membership in another sample population. When the discriminant function resulting from the first group was applied to the cross-validation group, the difference between the neurologically impaired and normal subjects was again statistically significant, with 91.2% of the normal subjects and 83.2% of neurologically impaired patients correctly identified for a combined total of 87.2% cases. Although in this analysis the results indicated a somewhat lower percentage of identification for the normal population and equal percentages for the neurologically impaired group than the first analysis, these disparities were not statistically significantly different (p > .05). The above discriminant analyses examined the 36 scores of the DWSMB as a total instrument. To inspect the degree of contribution of individual tests within the resulting discriminative function, the 10 variables which had demonstrated a statistically significant (p < .001) contribution to the discriminant function were entered into a discriminant analysis. The result of the analysis was significant, Wilks’ lambda = .446, Chi-square transformation (10, N = 500) = 398.025, p < .001, and indicated that the 10 variables alone predicted 93.0% of the total cases, with 94.8% of the normal subjects and 91.2% of the neurologically impaired cases correctly classified. The canonical correlation was .744 and the group centroids were 1.112 for the normal and −1.112 for the neuro- logically impaired subjects. Therefore, these 10 predictive variables can successfully predict neurological impairment and discriminate among neurologically impaired and normal subjects, suggesting the possibility that a shorter version of the battery could be used for quick screening in a variety of settings. A stepwise discriminant analysis was conducted with the initial 36 scores from the sensory-motor battery for 250 normal subjects and 250 patients (N = 500) to investigate the ability of the DWSMB to predict individual neurological diagnoses. The Box’ M statistic was not significant (F = 12.5, p = .001), indicating that the homogeneity of variance assumption was met and a discriminant was allowable. Of the 250 neurologically impaired subjects, 88 were diagnosed with TBI, 69 with CVA, 51 with dementia, 25 with seizure disorders, 11 with Parkinson’s disease, and 6 with multiple sclerosis. Six significant discriminant functions resulted from this analysis. The first function had a canonical correlation of .798 and accounted for 74.1% of the variance within the data. The second function accounted for 12.3%, the third for 7.1%, the fourth for 3.1%, and the fifth for 2.6% of variance. Canonical correlations were .474 for the second, .380 for the third, .262 for the fourth, and .131 for the fifth. The result of the analysis indicated that the sensory-motor battery correctly classified 69% of total cases (Table 3) and more specifically identified 45% of CVA (31 cases), 67% of multiple sclerosis diagnosis (4 cases), 40% of seizure disorders (10), 42% of TBI (37 cases), 63% dementia cases (32), and 55% of Parkinson’s disease patients (6 cases). A total of 90% or 225 of the normal cases were also identified correctly within the discriminant analysis. 3. Discussion The current investigation examined the differences between scores of neurologically impaired patients and those of normal subjects on the tests of the DWSMB. To examine the predictive utility of the DWSMB in distinguishing normals and neurologically impaired subjects, a total of 36 scores for 125 normal and 125 neurologically impaired
    • 208 A.G. Volpe et al. / Archives of Clinical Neuropsychology 21 (2006) 203–210 subjects were entered into a stepwise discriminant analysis and scores for 125 more normal and 125 neurologically impaired subjects were analyzed in a cross-validation. The results indicated similar percentages of correct classification as the initial discriminant analysis and cross-validation analysis (correct placement = 92.8%; cross-validation = 87.2%). These results supported studies that indicated that measures of sensory-motor functioning can be a powerful tool in classifying a variety of cerebral dysfunctions (Reitan & Wolfson, 2003), and more specifically, that the DWSMB has the potential to serve as an effective instrument for predicting group membership among neurologically impaired and normal individuals. Furthermore, using only 10 of the 36 predictive variables revealed a similar ability to predict neurologically impaired and normal subjects. The third discriminant analysis investigated the ability of the DWSMB to predict among diagnoses. Although 90% of the normal subjects were identified, the correct classification for each of the diagnosis in this study ranged from 40% to 67%. Considering that this outcome occurred by administering only the sensory-motor portion of a neuropsychological battery, the results are remarkable when considering that only 14% of group classification would be expected by chance. In terms of specific diagnoses, 67% of multiple sclerosis cases were identified with the DWSMB. A study by Reitan and Wolfson (2001) reported identification of 15 out of 16 multiple sclerosis patients out of a sample of 112 patients (93%). However, the entire HRNB was used for the study. In the present study, patients with dementia were identified 63% of the time. When one takes into consideration the great deal of inter-individual variability on all measures of neuropsychological functions among patients with dementia, as well as older normal subjects, these results indicate that the evaluation of sensory-motor abilities can be helpful in confirming such a diagnosis. Parkinson’s disease cases were limited to only 11 subjects, with the discriminant analysis identifying 54.5% of the cases. The patients within the CVA group were correctly identified 45% of the time, while traumatic brain injuries and seizure disorder patients were correctly classified, respectively, 42% and 40% of the time. Once again, general impairment and specific deficits differ greatly among patients who have sustained a stroke or a traumatic brain injury, and knowledge of the extent and degree of sensory-motor functioning adds much to the understanding of these patients’ deficits. The present results add evidence of validity by demonstrating the ability of the DWSMB to differentiate between individuals with and without neurological impairment. It should be expected that the DWSMB would have high predictive validity, based not only on the rich history of validation that many of these classic neuropsychological tests have undergone, but because of the existence of age-based normative data that provides a wider spectrum from which to examine individual differences than do most sensory-motor batteries that use a cutoff, or dichotomous approach to scoring. However, as with any new test, the continued analysis of reliability and validity information should progress with a series of different studies. Although the results of this study were statistically significant, it is important to point out a few minor limitations. Over 96% of the clinical group was Caucasian in comparison to a reported 75.1% by the 2002 Census and a smaller representation of African–American and Hispanic individuals participated in this study than appears in the normal population. Although differences among neurologically impaired and normal individuals are usually not influenced by race, generalizability would be enhanced if the sample of participants represented a broader ethnic range. Another limitation of the current study is that the groups of neurologically impaired individuals consist of a homogeneous range of disease severity, and diagnosis was not subject to interrater reliability. For example, an individual who had experienced a profound CVA was included with individuals who had experienced a more minor CVA. A way to address this in future studies would be to include analyses of additional neuropsychological measures, such as cognitive or memory measures. Finally, the ratio of subjects in the impaired group to the potential predictive variables offered a sample small for generalization. Discriminant analysis is greatly influenced by unequal sample size as well as by small sample size. Indeed, within the diagnostic discrimination analysis, the small number of cases available for all of the diagnoses, and especially in the Parkinson’s disease and multiple sclerosis cases, placed some constraint on the way the analysis was conducted and therefore introduced limitations in the generalizability of the results. The DWSMB was better able to discriminate CVA, traumatic brain injury, and seizure cases from normal subjects. However, the administration of a full neuropsychological battery would improve these predictions and provide the additional information necessary to fully determine the nature and degree of the impairment (Reitan & Wolfson 2001). The goals of neuropsychological assessment are much more complex than simply establishing the presence or absence of neurological impairment, but this study is a stepping stone for further investigations. One area of future research should focus on establishing how specific sensory-motor deficits as measured by the DWSMB relate to different academic difficulties later in life. Measurements of sensory-motor skills have already been found to be a predictor of later cognitive performance (Snow, Blondis, Accardo, & Cunningham, 1993). When looking at the source
    • A.G. Volpe et al. / Archives of Clinical Neuropsychology 21 (2006) 203–210 209 of academic deficiencies in children, the combination of measures of sensory-motor functions and of intellectual and cognitive functions has been found to differentiate between diagnoses of brain damage and behavioral disorders (Reitan & Wolfson, 2003). Furthermore, longitudinal studies on the development of sensory and motor abilities of academically disabled students suggest that neurological damage may exhibit itself as a persistent delay in the sensory areas even when motor abilities have improved (Snow et al., 1993). Further investigation of the relationship between the two could lead to more effective instructional and intervention strategies early in life (Reitan & Wolfson, 2003). In fact, below-normal sensory and motor performance has been found to be indicative not only of frank neurological impairment but also of more subtle learning disabilities (Gaddes & Edgell, 1994). Although attempted in this study, identification of specific diagnosis will be more generalizable with a larger data set, and possibly with the administration of the entire DWNB. In addition, many of the patients whose performance was examined in this study were referred for neuropsychological testing to confirm their neurological diagnoses. Future studies should include patients with psychiatric disorders so as to examine the ability to predict patients with neurological and/or psychiatric diagnoses from normal subjects. 4. Conclusion This investigation assessed the ability of a new sensory-motor battery, the DWSMB, to identify brain dysfunction. The DWSMB was found to be successful in differentiating neurologically impaired patients from normal individuals, confirming that sensory-motor functioning is of utility in the prediction of neurological integrity. Additionally, the DWSMB was able to identify different neurological disorders at an adequate rate, especially considering this study only assessed sensory-motor functioning. The advantages of this sensory-motor battery in comparison with other measures of neurological damage are that it provides a broader neurological range, addressing not only a variety of pathognomonic measures but also the evaluation of subcortical functions. In addition, the DWSMB’s use of standardized administration procedures and consideration of behavioral information in its evaluation has resulted in the accurate discrimination between normal and neurologically damaged individuals at rates similar to those resulting from the administration of full neuropsychological batteries. 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