Predicting global and speciﬁc neurological impairment with

Archives of Clinical Neuropsychology 21 (2006) 203–210

Predicting global and specific neurological impairment withsensory-motor functioning

Alessandra G. Volpe, Andrew S. Davis ∗, Raymond S. DeanDepartment 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 normalsubjects and neurologically impaired individuals. Scores from the subtests of the DWSMB for 250 normal and 250 neurologicallyimpaired individuals were randomly assigned to two equal groups to allow for cross-validation. The DWSMB was able to correctlyidentify 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 theneurologically impaired subjects. An additional discriminant analysis indicated that the DWSMB correctly identified the followingcases: 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 differentiatebetween 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 andmotor 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 sustainattention necessary to participate in more complex testing. As a result, the sensory-motor examination testifies tothe integrity of further neuropsychological test results (Reitan & Wolfson, 2003). However, despite their pivotal rolein neuropsychological assessment, few studies have been conducted on full sensory-motor batteries to assess theirability 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 standardizedadministration, scoring and interpretative procedures (i.e., Lang, Hill, & Dean, 2001) as well as for not linking theirfindings to a specific theory of underlying brain functioning (Golden et al., 1981). A review of neuropsychological andsensory-motor tests indicated that the majority of the sensory and motor tasks assess a limited range of functions, lackpsychometric sophistication, need standardization, have inadequate information about reliability, and are restrictedto 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: [email protected] (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

mailto:[email protected]

dx.doi.org/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 todifferentiate 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 researchdemonstrating 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 thosewith diagnosed pathology may present with no sensory-motor impairment at all (false negatives), further complicatingthe role of sensory-motor assessment (Dean & Woodcock, 1999). Finally, many neuropsychological studies of brainfunctions have elected to investigate the effects of brain dysfunction on higher levels of brain functioning (such ascognitive 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 ofcortical and subcortical motor and sensory skills. While some studies have established the reliability and validity of thisnew 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 individualsbased on the performance of basic sensory and motor functions has not been fully explored. Additionally, few studieshave been conducted to assess the ability of a sensory-motor battery to identify brain dysfunction without the aid ofcognitive or other neuropsychological information. The present study used a discriminant analysis to investigate theeffectiveness of the DWSMB in differentiating patients with neurological damage from normals without the aid ofother neuropsychological measures.

1. Method

1.1. Participants

Participants in this study included 250 patients who had originally been referred for neuropsychological evaluationto a large midwestern neurological practice and were selected because of diagnosed neurological disorders. The secondgroup consisted of 250 “normal” volunteers who had denied being diagnosed or treated for neurologic, psychiatric, ororthopedic 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 = 11.22years 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, 9months), 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% wereambidextrous (N = 9). The patients in the neurologic group were diagnosed as having a neurological disorder by aneurologist and classified according to the International Classification of Diseases, Ninth Revision (ICD-9) (AmericanMedical 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. Thesetests were drawn from long established neurological measures which were improved upon by providing standardizedprocedures for administration and scoring. The DWSMB is a standardized measure that combines sensory and motortests with qualitative features (such as qualifying and describing a patient’s gait) as well as quantitative scoring ofperformance driven tests (i.e., assessing a patient’s strength of grip). Eight of its tests assess sensory functions suchas 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 motorportion yields 15 scores. From a quantitative view, the DWSMB provides standardized procedures for test administrationand uses norms for the interpretation of individual performance. The DWSMB covers a broader range of sensory andmotor functions than either the HRNB or LNNB because it provides additional information necessary to differentiatebetween 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 ofperformance.

1.3. Description of procedures

Each participant was administered the tests of the DWSMB using the standardized administration proceduresdescribed in the Dean–Woodcock Sensory-Motor Battery’s manual (2003). All examiners had been previously trainedin the use of neuropsychological assessment instruments as well as standardized procedures. All participants weretreated in accordance with the Ethical Principles of Psychologists and Code of Conduct (American PsychologicalAssociation, 2004).

1.4. Data analysis

The Statistical Package for the Social Sciences Base 11.0 for Windows (2001) (SPSS) was utilized to analyze alldemographic information and provide descriptive statistics. For each group, 125 participants were randomly chosen bythe 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 betweentwo groups (neurologically impaired versus normals), and the extent to which it can be used to predict group membershipin future instances is often unclear. Therefore, a discriminant function was obtained on one group and then comparedto the other to establish the generalizability of the prediction. Discriminant analysis is applied to situations in which thedependent 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 analysisis 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 discriminantcoefficients to maximize differentiation between groups (Martella et al., 1999). The cross-validation sample was usedto compare the effectiveness of the discriminant function in terms of diagnosing between neurologically impaired andnormal subjects of the DWSMB.

2. Results

Three separate discriminant analyses were performed on the data. The first two discriminant analyses were conductedto examine the ability of the DWSMB to differentiate between normal subjects and neurologically impaired patients. Thefinal discriminant analysis was performed to investigate how accurately scores on the DWSMB would identify differenttypes of neurological impairment diagnoses. The 36 scores yielded by the 18 subtests of the DWSMB were entered aspredictors of subjects’ membership (normal versus neurologically impaired) into a stepwise discriminant analysis. Theresult 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 werecorrectly classified by the DWSMB, with a total of 92.8% of identified cases. Table 1 displays the means and standarddeviation for each of the predictor variables.

The relative magnitude of the standardized discriminant coefficients noted in Table 2 provided information regardingthe relative importance of each predictor variable in discriminating between groups. The stepwise discriminant analysisprocedure maximizes the prediction of the variables and the ability to separate the groups. The first variable chosen bythe statistical program is the one that maximizes separation among the groups.


Table 1First 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 25.95Left visual acuity 430.35 424.39 25.45 26.38Right visual confrontation 484.67 480.77 11.67 16.21Left visual confrontation 487.27 481.30 10.35 17.20Both visual confrontation 487.96 481.57 10.72 16.21Naming pictures of objects 534.72 445.37 9.21 110.89Right auditory perception 475.52 461.34 14.69 24.05Left auditory perception 482.83 463.29 14.01 26.21Both auditory perception 485.42 473.60 13.19 18.73Right palm writing 500.52 487.68 10.44 21.54Left palm writing 501.10 486.73 10.66 20.94Right object identification 493.51 486.26 8.79 14.54Left object identification 497.58 490.05 9.69 13.38Right finger identification 489.09 481.40 5.80 14.81Left finger identification 489.85 482.61 6.55 15.74Right hand sim. loc. 510.51 507.32 1.43 11.07Left hand sim. loc. 510.92 507.86 1.82 10.81Both hands sim. loc. 514.52 512.04 2.15 8.71R hands/cheeks sim. loc. 522.06 512.56 8.62 19.73L hands/cheeks sim. loc. 521.75 513.90 9.00 18.92Both hands/cheeks sim. loc. 515.03 506.90 8.11 16.82Gait and station 479.82 460.58 15.56 25.28Romberg testing 484.30 465.14 23.14 27.63Cross-construction 494.19 478.51 13.60 18.94Clock construction 494.91 484.27 10.96 18.90Right finger/nose coord. 490.28 468.16 15.36 31.82Left finger/nose coord. 490.46 466.48 12.37 30.96Right hand/thigh coord. 477.05 465.78 18.82 19.43Left hand/thigh coord. 477.50 465.83 21.34 18.56Mime movements 499.04 490.26 7.27 14.53Left–right movements 499.32 493.16 3.22 16.00Dominant finger tapping 503.41 498.15 7.18 7.40Non-dom. finger tapping 503.72 497.91 9.18 10.13Expressive speech 495.12 486.39 10.76 14.68Dominant strength of grip 526.96 521.33 13.33 23.58Non dom. strength of grip 524.94 519.88 14.59 22.64

sim.: simultaneous; coord.: coordination; dom.: dominant; loc.: localization.

Table 2DWSMB variables involved in the discriminant function predicting group membership for the neurologically impaired and normal subjects

Variable Standardized discriminant coefficients

Naming pictures of objects .978Left hand sim. localization .791Both hands sim. localization −.556Left finger to nose coordination .548Left hands and cheek sim. loc. −.445Mime movements .436Cross-construction .261Dominant palm writing .239Right near point visual acuity −.228Left auditory perception .183


Table 3Differential diagnoses classifications resultsa

Diagnosis CVA (%) MS (%) Seizure (%) TBI (%) Dementia (%) Parkinsons (%) Normals (%)

CVA 44.9 8.7 5.8 14.5 11.6 11.6 2.9MS .0 66.7 .0 16.7 .0 .0 16.7Seizures 16.0 4.0 40.0 24.0 .0 8.0 8.0TBI 10.2 12.5 10.2 42.0 4.5 10.2 10.2Dementia 7.8 3.9 2.0 3.9 62.7 15.7 3.9Parkinsons 9.1 9.1 9.1 .0 18.2 54.5 .0Normals .0 3.2 2.4 2.4 1.6 .4 90.0

a69.0% of original cases correctly classified.

The group centroids (the discriminant scores for each group when the variable means are entered into the discriminantequation) 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 whichthe discriminant function obtained in the initial analysis was successful in predicting group membership in anothersample population. When the discriminant function resulting from the first group was applied to the cross-validationgroup, the difference between the neurologically impaired and normal subjects was again statistically significant, with91.2% of the normal subjects and 83.2% of neurologically impaired patients correctly identified for a combined totalof 87.2% cases. Although in this analysis the results indicated a somewhat lower percentage of identification for thenormal population and equal percentages for the neurologically impaired group than the first analysis, these disparitieswere not statistically significantly different (p > .05).

The above discriminant analyses examined the 36 scores of the DWSMB as a total instrument. To inspect the degreeof contribution of individual tests within the resulting discriminative function, the 10 variables which had demonstrateda 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 subjectsand 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 impairmentand discriminate among neurologically impaired and normal subjects, suggesting the possibility that a shorter versionof 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 250normal subjects and 250 patients (N = 500) to investigate the ability of the DWSMB to predict individual neurologicaldiagnoses. The Box’ M statistic was not significant (F = 12.5, p = .001), indicating that the homogeneity of varianceassumption was met and a discriminant was allowable. Of the 250 neurologically impaired subjects, 88 were diagnosedwith TBI, 69 with CVA, 51 with dementia, 25 with seizure disorders, 11 with Parkinson’s disease, and 6 with multiplesclerosis. Six significant discriminant functions resulted from this analysis. The first function had a canonical correlationof .798 and accounted for 74.1% of the variance within the data. The second function accounted for 12.3%, the thirdfor 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-motorbattery 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 identifiedcorrectly within the discriminant analysis.

3. Discussion

The current investigation examined the differences between scores of neurologically impaired patients and thoseof normal subjects on the tests of the DWSMB. To examine the predictive utility of the DWSMB in distinguishingnormals and neurologically impaired subjects, a total of 36 scores for 125 normal and 125 neurologically impaired


subjects were entered into a stepwise discriminant analysis and scores for 125 more normal and 125 neurologicallyimpaired subjects were analyzed in a cross-validation. The results indicated similar percentages of correct classificationas 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 inclassifying a variety of cerebral dysfunctions (Reitan & Wolfson, 2003), and more specifically, that the DWSMB hasthe potential to serve as an effective instrument for predicting group membership among neurologically impaired andnormal individuals. Furthermore, using only 10 of the 36 predictive variables revealed a similar ability to predictneurologically impaired and normal subjects.

The third discriminant analysis investigated the ability of the DWSMB to predict among diagnoses. Although 90% ofthe normal subjects were identified, the correct classification for each of the diagnosis in this study ranged from 40% to67%. Considering that this outcome occurred by administering only the sensory-motor portion of a neuropsychologicalbattery, 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 Reitanand 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 identified63% of the time. When one takes into consideration the great deal of inter-individual variability on all measures ofneuropsychological functions among patients with dementia, as well as older normal subjects, these results indicatethat the evaluation of sensory-motor abilities can be helpful in confirming such a diagnosis. Parkinson’s disease caseswere limited to only 11 subjects, with the discriminant analysis identifying 54.5% of the cases. The patients withinthe CVA group were correctly identified 45% of the time, while traumatic brain injuries and seizure disorder patientswere correctly classified, respectively, 42% and 40% of the time. Once again, general impairment and specific deficitsdiffer greatly among patients who have sustained a stroke or a traumatic brain injury, and knowledge of the extent anddegree 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 betweenindividuals with and without neurological impairment. It should be expected that the DWSMB would have highpredictive validity, based not only on the rich history of validation that many of these classic neuropsychological testshave undergone, but because of the existence of age-based normative data that provides a wider spectrum from whichto examine individual differences than do most sensory-motor batteries that use a cutoff, or dichotomous approach toscoring. However, as with any new test, the continued analysis of reliability and validity information should progresswith 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 smallerrepresentation of African–American and Hispanic individuals participated in this study than appears in the normalpopulation. Although differences among neurologically impaired and normal individuals are usually not influencedby race, generalizability would be enhanced if the sample of participants represented a broader ethnic range. Anotherlimitation of the current study is that the groups of neurologically impaired individuals consist of a homogeneousrange of disease severity, and diagnosis was not subject to interrater reliability. For example, an individual who hadexperienced a profound CVA was included with individuals who had experienced a more minor CVA. A way to addressthis in future studies would be to include analyses of additional neuropsychological measures, such as cognitive ormemory measures. Finally, the ratio of subjects in the impaired group to the potential predictive variables offereda sample small for generalization. Discriminant analysis is greatly influenced by unequal sample size as well as bysmall sample size. Indeed, within the diagnostic discrimination analysis, the small number of cases available forall of the diagnoses, and especially in the Parkinson’s disease and multiple sclerosis cases, placed some constrainton the way the analysis was conducted and therefore introduced limitations in the generalizability of the results. TheDWSMB 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 additionalinformation 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 orabsence of neurological impairment, but this study is a stepping stone for further investigations. One area of futureresearch should focus on establishing how specific sensory-motor deficits as measured by the DWSMB relate todifferent academic difficulties later in life. Measurements of sensory-motor skills have already been found to be apredictor of later cognitive performance (Snow, Blondis, Accardo, & Cunningham, 1993). When looking at the source


of academic deficiencies in children, the combination of measures of sensory-motor functions and of intellectualand 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 ofacademically disabled students suggest that neurological damage may exhibit itself as a persistent delay in the sensoryareas even when motor abilities have improved (Snow et al., 1993). Further investigation of the relationship betweenthe 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 neurologicalimpairment 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 administrationof the entire DWNB. In addition, many of the patients whose performance was examined in this study were referredfor neuropsychological testing to confirm their neurological diagnoses. Future studies should include patients withpsychiatric disorders so as to examine the ability to predict patients with neurological and/or psychiatric diagnosesfrom 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, theDWSMB was able to identify different neurological disorders at an adequate rate, especially considering this study onlyassessed sensory-motor functioning. The advantages of this sensory-motor battery in comparison with other measuresof neurological damage are that it provides a broader neurological range, addressing not only a variety of pathognomonicmeasures but also the evaluation of subcortical functions. In addition, the DWSMB’s use of standardized administrationprocedures and consideration of behavioral information in its evaluation has resulted in the accurate discriminationbetween normal and neurologically damaged individuals at rates similar to those resulting from the administrationof full neuropsychological batteries. Furthermore, testing time is much shorter than most full neuropsychologicalassessments, and, from the results of this investigation, a shorter version of the DWSMB might even have the potentialto be as effective as the whole battery for certain diagnoses.

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Predicting global and speciﬁc neurological impairment with