A DIAGNOSTIC APPROACH TO DIFFEREN TIATE BRAIN-DAMAGED FROM NON-BRAIN-DAMAGED ADOLESCENTS

GERALD FULLER AND DOUGLAS FRIEDRICH

Crntral Alachigan Unaversit?~

PROBLEM A serious problem in the evaluation of mentally retarded persons is that of

differentiating between the brain-damaged (BD) and non-brain-damaged (NBD) (9). One dimension that is used to assess the structural integrity of the retarded indi- vidual is visual-motor task performance. Some researchers ( 4 , !’) have been unable to differentiate between B D and NBD on the basis of visual-motor task performance. Other researchers (’* lo) have provided contradictory results with the same visual- motor test, such as the Bender-Gestalt. The Unnesota Percepto-Diagnostic test (RIPD) (2), an instrument used to evaluate visual-motor performance, will be reviewed in this article in relation to the utility of the test to differentiate BD from NBD.

Burnett and Fuller“) attempted to differentiate between BD and NBD chil- dren and adolescents by the use of rotation scores of the N P D . They compared 36 BD with 36 ISBD, pair matched on mental age. The rotation scores of the BD group of children and adolescents were significantly higher than those of the NBD group. The variability of the scores was so great, however, that it was impossible to differentiate between the individuals in the two groups or t o establish an adequate cut-off score. Fuller and Hawkins ( 3 ) noted that numerous separations and dis- tortions of M P D figures occurred among BD, but not NBD, adolescents. Separa- tions and distortions of the M P D figures thus were incorporated in the scoring system. Four groups were selected randomly from a population of retarded adoles- cents: BD and NBD groups for criterion and cross-validation studies. For the criterion study, 83 BD and 86 NBD retarded adolescents were included. And for the cross-validation study, 22 BD and 52 NBD retarded adolescents were included. When the means of the criterion rotation T-scores (intelligence and age controlled) were compared, a significant difference between the groups was found. As in the Burnett and Fuller study, however, the variability of scores within groups made i t impossible to differentiate consistently BD and NBD on the basis of the rotation T-scores alone. In an attempt to increase the utility of the MPD to diagnose brain dysfunction, Fuller and Hawkins scored each adoles- cent’s performance for separations of the circle and diamond figures (SpCD), distortions of the circle and diamond figures (DCD), and distortions of the dot patterns (DD). Fuller and Hawkins used a modification of the disjunctive method of predictionc8) in an actuarial table with SpCD, DCD, and DD as predictors with a cross-validation sample of 74 adolescent cases. With this predictive tech- nique, i t was found that 63 of the adolescents were identified correctly (8570 hit rate). For each of the groups, 19 of 22 (86%) BD and 44 of 52 (85% hit rate) NBD adolescents were identified correctly.

Hillow(6) failed to replicate the findings of Fuller and H:twkins. Her sample consisted of 20 BD and 20 NBD children and adolescents, matched on intelligence. When Fuller and Hawkins’ Actuarial Table predictive technique was used, Hillow found a hit rate of 45% (65% and 25y0 for the BD and NBD groups, repectively). Hillow used multiple discriminate analyses to establish an alternative predictive measure and included the following variables, with accompanying hit rate values : (1) analysis 1 - SpCD, DCD, degrees rotation, actuarial table diagnosis, and Bender-Gestalt performance errors (75Oj, hit rate for each group) ; and ( 2 ) analysis 2 - SpCD, DCD, and D D (67 ,70 and 68% for BD, NBD, and combined groups, respectively). Hillow concluded that the discriminate analysis approach more effectively discriminated BD from NBD Ss than the Fuller and Hawkins Actuarial Table diagnosis. In the present study, Hillow’s discriminate analysis method was subjected to cross-validation, and additional variable combinations were used to explore the best combination of B D and NBD predictors.

362 GERALD FULLER AND DOUGLAS FRIEDRICH

METHOD Subjects. A pool of MPD records of BD and NBD retarded adolescents had

been collected over the past 6 years. The number of confirmed BD MPD records totaled 106 and NBD records 135@). All BD adolescents indicated cerebral dys- function or structural damage, with the following diagnostic subgroups : 28 encepha- lophy, due to postnatal cerebral infection; 31 encephalophy, due to postnatal in- jury; 16 encephalophy, other, due t o unknown or uncertain cause; 25 postnatal anoxemia; and 6 hydrocephaly, congenital. The NBD group consisted of adoles- cents who had been diagnosed previously as such. These adolescents were selected when a family history of retardation was present and no organic signs in the history or neurological examinations were noted. All records for both groups had been checked to make sure that no gross visual field defects were present. Mean in- telligence levels of BD and NBD groups were 67.91 (SD = 11.81) and 69.15 (SD = 8-51), respectively. Mean ages of BD and NBD groups were 15.41 (SD = 2.76) and 14.95 (SD = 2.35), respectively.

RESULTS AND DISCUSSION With the variables of MPD rotation T-score, SpCD, DCD, DD, age and

intelligence, numerous multiple discriminate analyses were computed,’ and several interesting findings may be drawn from the analyses. First, the use of Hillow’s appropriate multiple discriminate constants and coefficients for the present Ss resulted in relatively poor predictive coefficients (SpCD, DCD, DD, combined hit rate = 63%). Second, multiple discriminate analyses for the variables (1) SpCD, (2) DCD, (3) DD, (4) intelligence, (5) T-score rotation, and (6) age resulted in relatively high predictive coefficients (combined BD and NBD predictive coef- ficients for analysis of the following set of variables: SpCD, DCD, DD: SpCD, DCD, DD, I&: SpCD, DCD, DD, T-score: and SpCD, DCD, DD, age: was -78 for each one). The multiple discriminate analysis that used all six variables yielded the highest hit rate. The formulae for the computations of group membership are as follows:

D’NBD = -83.89962 + 2.25172spc~ + 3 . 3 0 0 9 9 ~ ~ ~ + 6 . 7 0 4 3 1 ~ ~ + 1.025001~ + .90298~-score + 2.99198~ge D2BD = -82.85639 X 2.99297SpCD + 4 . 0 1 2 9 2 ~ ~ ~ + 7 . 2 5 5 8 7 ~ ~ 1.025001~ + .88649~-,,,, + 2 . 9 7 1 2 5 ~ ~ ~

The application of these formulae to the sample NBD and BD resulted In an 86% hit rate in the NBD group and a 71y0 hit rate in the BD group. The com- bined hit rate was 79%. The RIahalanobis distance function between groups resulted in a cut-off value of 115.48. In the application of the formulae a S who received a score higher than this value was assigned to the BD group.

First, there is a discrepancy between hit rates based on Hillow’s study (Actuarial Table diagnosis and multiple discriminate analyses) and (1) the present study (multiple discriminate analyses) and (2) Fuller and Hawkins’ study (Actuarial Table predic- tors). With the Actuarial Table, Hillow obtained a BD, NBD combined hit rate of 45y0. Hillow used multiple discriminate analysis on SpCD, DCD, and D D predictors and found a combined hit rate of 68%, and when Hillow’s appropriate constants and coefficients for the predictors were used with the Ss in the present study a combined hit rate of 63y0 was found. The present multiple discriminate analysis ( N = 243), which used the three predictors (SpCD, DCD, DD), resulted

Two interesting conclusions stem from the above reported findings.

‘Constants and coefficients for the mdtiple discriminate analyses reported are available from the authors.

A DIAGNOSTIC APPKOACH TO DIFFERENTIATE B-D FROM NON-B-D 363

in a combined hit rate of 7S7,.2 Also, the Actuarial Table predictive device used by Fuller and Hawkins resulted in a cross-validation combined hit rate of 86%. One possible explanation for the discrepancy between Hillow’s research and that of Fuller and Hawkins and the present authors is the lower intelligence levels of the groups in Hillow’s study (average mean = 53.1) relative to the Actuarial Table cross-validation investigation (average mean = 65.48) and the present research (average mean = 613.6). A second possible contributing factor to the above hit rate discrepancy, regardless of predictive device used, is the small sample size of Hillow’s study, relative to that of Fuller and Hawkins(3) and the present studies.

A second conclusion is the cautious recommendation of using either the multiple discriminate coefficients from this study or the Actuarial Table predictors ( 2 ) to differentiate BD and NBD individuals with the MPD. The note of caution relates to the hit rates obtained with multiple discriminate analyses. Although the com- bined hit rates were in the high .70s, hit rates for BD were lower than those for NBD (illustrated in the multiple discriminate analysis above). In the Fuller and Hawkins cross-validation study, hit rates for both groups were similar (middle 30s). The present summary of research with the Actuarial Table diagnosis and multiple discriminate analyses indicates high hit rates with both techniques. And, i t may be wise to use both predictive techniques when one makes diagnostic decisions for the retarded.

The present review of research used the RIPD scores and either the Actuarial Table or present study multiple discriminate functions and indicates that BD and NBD adolescents can be identified by their performance on a visual-motor task. The former group manifested a greater decrement in perceptual-motor ability. Thus the present findings suggest that the AIPD may be regarded as a sensitive instrument to assess physiological dysfunction among retardates in the perceptual-motor areas.

SUBINART Through the use of Fuller’s RZPD visual-motor test and two predictive tech-

niques (Actuarial Table diagnosis and multiple discriminate analysis), high hit rates (high .70 and .80s) were found to differentiate brain-damaged from non-brain- damaged adolescents. Findings support the notion that the M P D is a sensitive diagnostic tool to assess brain dysfunction.

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2The multiple discriminate analysis for SpCD, DCD, and 1)D predictors wa5 a5 follow\: cut-off value = 112.4, D2NBD = -.14191 + .28143SpCD + . 4 5 1 8 6 ~ c ~ + , 0 3 7 8 6 ~ ~ (89% hit rate); D2BD = -1.75 056 + 1.05785Spc~ + 1 . 2 2 9 ~ ~ ~ + . 6 9 0 7 8 ~ ~ (6670 hit rate), combined hit rate = 78%. As in all discriminate analyses reported above, score5 higher than the cut-off value predict BD.