Experimental Aging Research

ISSN: 0361-073X (Print) 1096-4657 (Online) Journal homepage: http://www.tandfonline.com/loi/uear20

Modeling age using cognitive, psychosocial and physiological variables: The boston normative aging study Kenneth J. Jones , Marilyn S. Albert , Frank H. Duffy , Mary R. Hyde , Margaret Naeser & Carolyn Aldwin To cite this article: Kenneth J. Jones , Marilyn S. Albert , Frank H. Duffy , Mary R. Hyde , Margaret Naeser & Carolyn Aldwin (1991) Modeling age using cognitive, psychosocial and physiological variables: The boston normative aging study, Experimental Aging Research, 17:4, 227-242, DOI: 10.1080/03610739108253900 To link to this article: http://dx.doi.org/10.1080/03610739108253900

Published online: 27 Sep 2007.

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Experimental Aging Research. Volume 17, Number 4, 1991,

QUANTITATIVE TOPICS IN RESEARCH ON AGING J.J. McArdle and S.A. Cohen, Eds.

ISSN 0734-0664

@I991 Beech Hill Enterprises Inc.

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Modeling Age Using Cognitive, Psychosocial and Physiological Variables: The Boston Normative Aging Study KENNETHJ. JONES Brandeis University

MARILYN S . ALBERT AND FRANK H. DUFFY Harvard Medical School

MARGARET NAESER

MARYR. HYDE Massachusetts General Hospital

Boston University Medical School

CAROLYN ALDWIN Boston VA Outpatient Clinic

A structural equation model is computed for 36 variables from eight domains of data using 100 healthy male subjects whose age varies between 30 and 80 years. Chronological age is required to be an exogenous variable while cognitive function variables are required to be an ultimate endogenous or outcome set. The model suggests that the direct effect of age on cognition is substantially reduced when social, life style, physiological, and brain state variables are allowed to become intervening variables. The study also finds that there is an association between cognitive function and psychosocial measures relating to general psychiatric symptomatology and social support systems.

N

umerous cognitive differences exist between optimally healthy older and younger individuals (Albert, 1988). The cause of these differences is unclear. Some investigators have suggested that they are a result of age-related changes in the brain, while others have argued that they are primarily the outcome of psychosocial differences between cohorts of younger and older individuals. One way of assessing the causal role of each of these domains is to evaluate them in a cross-sectional fashion and examine their relationship via linear structural equations which embody a set of hypothesized causal links. We present such an analysis of variables spanning several logical domains. Data Domains

The domains selected for incorporation in the model included neuropsychological test scores, neurophysiological data derived from measurements of brain struc-

ture and function, and demographic data. These data were collected in connection with several large ongoing multi-disciplinarystudies. In the present paper, data from 100 subjects, all male, are presented. Since this is a relatively small sample, the results presented below can only be considered as preliminary and suggestive for additional investigations involving larger samples of individuals and variables. An additional sample of about one hundred females soon will be available which will permit us to investigate other hypotheses suggested by the present analysis. The Sample

The subjects in the study ranged in age from 30 to 80 with an average of 20 subjects per decade group (i.e., 30-39,4049, 50-59,60-69,70-80). In order to be selected for the study subjects had to meet strict health criteria. Thus, the participants did not represent “average” indi-

The preparation of this manuscript was supported in part from grant Pol-AGO4953 from the National Institute on Aging. Send reprint requests to: Dr. Kenneth Jones, Florence Heller Graduate School for Advanced Studies in Social Welfare, Brandeis University, Waltham, MA 02154.

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viduals of a particular age, but rather optimally healthy persons across the age range. The selection criteria excluded individuals with hypertension, coronary artery disease, lung disease, kidney disease, alcoholism, psychiatric illness, learning disabilities, severe head trauma, or epilepsy. Assessment of these medical conditions was greatly facilitated by the fact that all of the subjects were members of the Normative Aging Study conducted by the Boston Veterans Administration Outpatient Clinic. As such they were all male and had received periodic medical check-ups for the previous twenty years. The participants included both white and blue collar workers and, in general, represented a socioeconomic cross-section of the population. Their education ranged from 10 to 22 years with a mean of approximately 15 years for all age groups. Neuropsychological Testing

Subjects were administered a large battery of neuropsychological tests chosen to span all major aspects of cognitive function. Tests of attention, language, memory, visuospatial ability, conceptualization, and general intelligence were included. Attention was assessed by auditory and visual continuous performance tasks (Mirsky, 1978). Language functions wre evaluated by the Boston Naming Test (Kaplan, Goodglass, & Weintraub, 1982) and the controlled Word Association Test (Benton & Hamsher, 1976). The Wechsler Memory Scale (WMS) (Wechsler, 1945) was used to assess memory for paragraphs, line drawings and paired associates. In addition, tests of delayed recall and recognition were administered. Visuospatial ability was examined by copying and matching using stimuli from the Visual Reproductions subtest of the WMS, the Block Design subtest of the Wechsler Adult Intelligence Scale (WAIS) (Wechsler, 1955), clock drawing to command, and clock copying. Gorham’s Proverb Interpretation Test (Gorham, 1956), the Visual-Verbal Test (Feldman & Drasgow, 1959), and competing motor programs evaluated abstraction and set maintenance. The Digit Symbol Substitution subtest of the WAIS, and the Vocabulary subtest of the WAIS were also administered. Assessment of Brain Structure

Computerized Tomography (CT)scans were done without contrast on an Ohio Nuclear Delta 2010 CT scanner. The scans were taken approximately 15% to the canthomeatal line. The pixel size was 1 x 1 mm, and the image was reconstructed on a 256 x 256 matrix. The CT density number scale was - lo00 for air ( f2 Hounsfield units [HU] scale) and 0 for water. Three CT slices from each scan were selected for evaluation: (1) a slice at the widest portion of the third ventricle, (2) a slice at the maximum width of the bodies of the lateral ventricles, and (3) a supraventricular slice at the level of the centrum semiovale that was selected on the basis of slice size (Lee, 8000 to 1O,o00 pixels). These

JONES ET AL.

slices were chosen because they represent the brain at three different levels: midventricular, high ventricular, and supraventricular . Computer analyses were performed on each slice twice: once a program which provides CT density numbers of the brain tissue, and once by a program which yields measures of cerebrospinal fluid volume. For details of these analyses see Stafford et al. (1988). Assessment of Brain Electrical Activity

The brain electrical activity data for the subjects were obtained and analyzed by a technique known as Brain Electrical Activity Mapping (BEAM). Electroencephalographic (EEG) data were gathered during ten different behavioral states. These included the following: resting with eyes open (EOP), resting with eyes closed (ECL), listening to speech (SPE), listening to music (MUS), memorizing (KFI) and being tested (KFT) on the recall of geometric shapes, reading (RTI) and recalling the text of paragraphs (RTT), and forming (PAI)and being tested on sound-symbol associations (PAT). Seven EEG bands were analyzed for each activation paradigm, from 0.5 to 32 Hz. These were delta, 0.50 to 3.75 Hz; theta, 4.00 to 7.75 Hz; alpha, 8.00 to 11.75 Hz; beta-I, 12.00 to 15.75 Hz; beta-2, 16.00 to 19.75 Hz; beta-3,20.00 to 23.75 Hz; and beta4,24.00 to 27.75 Hz. The activating paradigms (i.e., those paradigms other than eyes open and eyes closed) are described in previous reports (Duffy et al., 1980). The evoked potential data were derived during three states: flash visual evoked potential (VEP), tone-pip auditory evoked potential (AEP), and the P300, or event related potential paradigm using high and low frequency auditory stimuli. The electrophysiological data analysis procedure utilizing significance probability mapping is described in detail in Duffy, Bartels, and Burchfiel(l981). Biomedical Variables

This domain contains data from the physical examination undergone by the subjects. Eight variables were selected that were thought to reflect general health status and potential risk factors for disease. The variables are total cholesterol, hematocrit, alubumin, sed rate, systolic blood pressure, the number of drinks consumed per year, and smoking status. Psychosocial Variables

The psychosocial domain provides data concerning personality traits, psychiatric symptoms, social networks, and stressful life events. The personality data are derived from the Eysenck Personality Inventory which produces two scales, extroversion and neuroticism (Eysenck & Eysenck, 1963). The present study used an 18-item version of the Eysenck Personality Inventory developed by Floderus (1974). The extroversion scale assesses how outgoing people are in social situations. The neuroticism

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scale measures the degree to which an individual's emotions are easily aroused. The social network of each participant was evaluated by a questionnaire that addressed both the quantitativeand qualitative nature of their social network (Aldwin, Bosser, & Levinson, 1987). The quantitative items included questions such as the number of relatives within easy driving distance, and the frequency of interaction with friends. The qualitative items pertained to the perceived availability of friends or relatives as confidants or sources of support in time of crisis. The items were based on standard questions used to assess social support in large scale community surveys. Psychiatric symptomatology was measured by the SCL-90 (Derogatis, Rickels, & Rock, 1976). It is a widely used selfreport symptom inventory that has been applied in both clinical and community settings. It addresses a number of psychiatric parameters of interest including: somatic anxiety, enervation, depressed mood, hostility, anxious mood, panic and phobia, sleep disturbance, and interpersonal sensitivity. The measure used in the present study was the Global Severity Index, a summary measure of all symptoms reported. The Elders Life Stress Inventory provided information concerning recent stressful life events. It yields two summary scores: the number of stressful events and a summed stress rating (Aldwin, in press).

Demographic Variables This domain is composed of four variables. These provide data regarding education, occupation, religion, and marital status. The education variable is measured as years of education. Occupation is coded according to the Dictionary of Occupational Types and provides a rank ordering according to socio-economic status (Hauser & Featherman, 1977). Religion was coded as Protestant or not. Marital status was coded as presently married and not presently married.

Summary The available data fall into several logical domains. Listed below are the names we have associated with each domain and the number of variables. 1. 2. 3. 4. 5. 6. 7. 8.

Age Demographic Biomedical Psychosocial Brain EEG Brain EP Brain Structure Cognitive Total

1 variable 4 variables 8 variables 4 variables 5 factors from 5 factors from 4 factors from 5 factors from 36

to reduce the number of variables in order to build a credible model. Our procedure was to compute for each domain containing a large number of variables a principal component reduction. We used as a root selection criterion the rule that a summary variable (factor) must contain at least 4% of the total variance of the domain. These factors became representatives of the domains. Below we discuss each principal component analysis before moving to a general discussion of the model building process.

Cognitive Factors The 43 neuropsychological variables on which data were available were used to generate the intercorrelations between pairs of variables. The resultant matrix was then subjected to a principal component reduction. This yielded five factors which in total accounted for 53% of the domain variance. Table 1 lists our names for each of the factors along with the relative percent of captured variance attributable to each. Although these factors capture only 53% of the total variance, they appear to represent the major portion of the meaningful variance of the domain. After careful scrutiny of factors six through twelve, we found no credible interpretations possible even after rotation. Factors 6 to 12 seemed to be highly specific to single variables in the battery. Appendix A1 presents the indicators of this set of latent variables together with their loadings. They represent the correlations of each observed variable with the particular latent variable. Only values above the absolute value 0.45 are included. These are a result of a Varimax rotation of the principal components.

Brain Structure Factors The brain structure variables comprise measurements derived from CT scans and relate to headsize, fluid volume, and tissue density for several brain slices. In all 18 variables were analyzed using the principal components procedure. Four factors were captured whose variances accounted for at least 4% of the total variance. Overall the four factors captured 77% of the total battery variance. Table 2 lists the factors, the relative variance of each, and the factor names. The major factor can be identified as CT density. This variable pertains across all slices, so we can say that it TABLE 1

45 18 19 47

variables variables variables variables

Factor Relative Description Yo Variance 1

2 Factor Analyses of Selected Domains

Many of the domains discussed above are represented by a large number of variables as noted. It was necessary

3 4 5

15 11 11 11 5

Description Verbal memory and skill Non-verbal memory and skill Speed At tent ion Cognitive flexibility

JONES ET AL.

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TABLE 4

TABLE 2

Factor DescriDtion

Relative Variance

CT tissue density Ventricular volume Headsize Sulcal volume

36

1 2 3 4

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Description

070

17 12 11

is the density of the tissue irrespective of location which accounts for a substantial amount of variation in the data. The factors also reveal that fluid volume of the ventricles and fluid volume at the level of the sulci are independent measures of brain character. We also have headsize as an independent factor. These four factors are the most parsimonious representation of all 18 variables and reflect over 75% of the variance contained in the battery. As far as this analysis is concerned there is little distinction to be made among slices. Appendix A2 presents the indicators of this set of latent variables together with their loadings. They represent the correlations of each variable with the particular latent variable. Only values above the absolute value 0.50 are included. These are a result of a Varimax rotation of the principal components.

This domain is composed of electrophysiologicmeasurements. There are actually two logical sub-domains included, and each is handled separately. The first is the set of 45 electroencephalographic(EEG) measurements of several regions of the scalp in several frequency bands. The second set consists of 18 evoked potential (EP) measurements. Once again we applied principal components analysis to the matrices of inter-correlations. Using the 4% criterion for retaining a root, we found five factors which captured 75% of the total battery variance for the set of 45 EEG measurements. Table 3 gives the relative variance in each factor along with our label of that factor. In Appendix A3 are listed the variables which define each factor. The components are primarily related to EEG frequency. This suggests that the matrix of intercorrelations TABLE 3

1 2

Relative Variance

070

18

3

15 15

4

15

5

12

1

17

2

3

17 12

4

10

5

9

Description Late auditory evoked potential Late visual evoked potential Intermediate visual evoked potential Early auditory evoked potential Early visual evoked potential

can be explained fairly well (75% of the variance) by five factors. The data reduction related to the EP variables resulted in five factors above 4% of the total variance. These factors captured 65% of the total battery variance. They are described in Table 4 and are primarily early or late auditory or visual evoked potentials. The variables defining each factor are given in Appendix A4. These factors indicate that the evoked potential variables are indicative of early or late auditory or visual evoked potentials. The LISREL Model

Brain Electrical Activity Factors

Factor Description

Factor Relative Description Yo Variance

Description Theta activity (global) Beta activity (right>left) Passive state synchronized Delta activity Active state synchronized Delta activity Delta (posterior)

It is only by investigating the relationships among the data domains simultaneously and causally that we can make judgements concerning the role of age. It is well known that causal statements about the role of any particular variable with respect to another are conditional on an identified system. The latter implies that there are no unincluded, non-orthogonal variables. All causal variables must be measured and included in order for the causal role of any one to be evaluated without bias. It is clear that one can prove causality only be exhaustion. It is clear that all conclusions from a causal model are conditional on these assumptions. Since no large scale model for aging among normal males exists, we have proceeded heuristically. The analysis plan utilized two phases. The first phase was the structural development phase, which consisted of defining relationships among the latent variable derived from the above domains. The second phase was the measurement phase which consisted of determining the relationships between the observed vriables in each domain and the latent variable set for that domain. This method of developing the measurement model necessarily entails a number of compromises which may affect the ultimate identification of the model. In such situations one is never sure if some crucial variance has been excluded due to the unlinking of measurement model estimation from structural model estimation. This is one of several compromises which had to be made because of sample size, and other considerations. However, our hope is two-fold; one is that the resultant model will be suggestive enough to encourage greater efforts to gather a broader array of

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FIGURE 1. Schematic causal diagram for data domains.

data on other larger samples, and two, that we will demonstrate the value and feasibility of structural equation modeling in the macro-analysis of cognitive behavior. We hypothesized one exogenous domain (age) and seven endogenous domains (demographic, biomedical, psychosocial, brain EEG, brain EP, brain structure, and cognitive). According to the model, the ultimate endogenous, or dependent domain was the cognitive one. Figure 1 represents the domain relationships schematically. The model was embodied in structural equations derived from linear structural modeling, or LISREL (Joreskog & Sorbom, 1979). In order to make the computer analyses more tractable, we proceeded by modeling each of the five cognitive factors separately. The estimation method used was generalized least squares (GLS)as programed in LISREL VI (Joreskog & Sorbom, 1984). This method allows one to compute the ratios of the first to second derivatives for the fitting function, i.e., the modification indices. By using these along with the t-values for each parameter, we iteratively added and subtracted parameters until an adequate fit was obtained. Lastly, the information regarding structure was combined to suggest an appropriate set of parameters to be fit for a model containing all five neuropsychological factors as ultimate endogenous variables. During this process, it was discovered that four variables contained little variance, were highly colinear with other model variables, or were isolates. These were religion, marital status, occupation, and stressful life

events. These were dropped leaving us with 32 variables in all final models. The results presented here contain this subset. Table 5 gives coefficients of fit adequacy for each of these models. Estimated are the null model, an age only model, and the “best” model. These are computed for each neuropsychological factor separately, and one for the combined model. In the course of fitting the combined model we allowed the residuals for neuroticism and extraversion, and the residuals for neuroticism and GSI to become correlated. All other residuals were orthogonal. The null model is one in which no parameters are estimated. For that we present a chi-square, an adjusted fit coefficient, and a root mean square residual for a worst fitting model. Also we present these coefficients for models in which age is the only explanatory variable. Lastly, we present the best fitting model’s characteristics. Since we are fitting parameters to a standardized covariance, or correlation matrix, the chi-square value must be regarded only as a relative index of merit. The adjusted goodness of fit (AGF) index generally has a range of zero to one though in very poor models it may become negative (Hayduk, 1987). The closer the value approaches one, the better the fit. The root means square residual (RMS) is a residual, or unexplained, correlation given the model. The smaller this value, the better is the fit of the model. From Table 5 , it may be seen that the null model fits extremely poorly. Models with age as the only explanatory

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JONES ET AL.

variable fit substantiallybetter. Finally, the “best” models fit better than do age-only models, but no models fit perfectly as the chi-squares remain larger than their degrees of freedom. The model presented as “best” is not adequate to reduce all residuals to pure randomness, since the chi-square value remains significant; however, it is less than twice its degrees of freedom. This is a commonly quoted ratio for the acceptance of the notion of an adequate fit, especially for large models. Table 6 displays the squared multiple correlations for each of the neuropsychological variables. As may be seen from the table, the fits for each variable are substantial. Due to the manner in which the model was fitted (opportunistically rather than by an a priori hypothesis), one might expect some degree of overfitting. The adjusted Rsquare attempts to compensate for this by reducing the value of R-square as a function of the number of fitted parameters. In this computation we used 27 variables as the potential number fitted even though no equation used

that many, hence we have a conservative estimate. By comparing the R-square for each variable with the adjusted R-square one can assess the degree of overfitting. As may be seen, the differences between the R-squares and their adjusted values are modest. Figures 2 and 3 present the summary results of the best structural model which we have so far attained. Figure 2 gives the direct paths pointing to the five ultimate endogenous variables, the dependent neuropsychological factors. Figure 3 gives the paths for the remaining intervening endogenous variables and the single exogenous variable, age. Figures 2 and 3 relate to each other as overlays. Tables 7 and 8 list the path weights for the standardized solution. Only paths whose t-values were significant at or below the 5% level were allowed to remain in the model and all those appearing in Tables 7 and 8 passed this test. Table 8 gives the paths for the endogenous intervening and exogenous variables, while Table 7 gives

TABLE 5

Measures of Fit for Alternative Models Variable/Model Verbal Null Age Best Ratio X2/df Non Verbal Null Age Best Ratio X2/df Speed Null Age Best Ratio X2/df Attention Null

Age Best Ratio X2/df Cognitive Flexibility Null Age Best Ratio X2/df Combined Null Age Best Ratio X2/df

X2

df

AGF

RMS

1951 732 490

406 378 361

- .46

.27 .23 .19

.4 1 .59

1.36 3009 715 517

- .99

406 378 368

.43 .57

.29 .23 .19

.59 .42 .59

.27 .23 -19

- .46

.27 .23 .19

1.40 2128 720 485

406 378 358 1.35

957 686 489

406 378 364

.45 .59 1.34

91 8 672 47 9

- .43

406 378 365

.46 .60

.27 .22 .19

1.31 4715 994 574

- .99

528 496 459

.31 .57 1.25

.29 .27 .18

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MODELING AGE

TABLE 6 Squared Multiple Correlations from the Models Variables Model

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Null Age only Best Adjusted R-square

Verbal

Non-Verbal

Speed

Attention

Cog. Flexibility

0.00

0.00

0.00

0.00

0.00

0.15 0.97 0.96

0.09

0.03

0.02

0.01

0.91 0.89

0.79 0.7 1

0.64 0.50

0.54 0.37

those pointing towards the neuropsychologicalvariables. All unlisted paths had modification indices less than 5.0, and/or t-values less than 1.0 when introduced, or were contrary to the causal ordering. Only one path had a modification index above 5 and was contrary to the causal model; this is the path from non-verbal to headsize which had a modification index of 11. From a technical view the model for non-verbal has two compromising properties. One is that there are still significantlylarge residuals which are reflected in the chi-square which exceeds its degrees of freedom, as mentioned above. The other is a path coefficient which exceeds 1.0. This is from age to

non-verbal memory and skill. Such values are not, as is widely believed, forbidden. They merely reflect so-called suppressor effects in the system. This value, which is the only such one in the model, means that a one standard deviation change in age is causal of a larger than one standard deviation change in non-verbal memory and skill in the negative direction, other things being equal. As a result of our modeling efforts it became clear that the variables related to passive delta, ventricular volume, age, and non-verbal memory and skill are linked in a suppressor relationship to one another. The exact implications or interpretation of this remains to be seen.

1

1

Verbal Memory still

1

on-verbal Memor

Sfill

I

I

1

r l Attention

El Cognitive Flexibiltj

FIGURE 2. Direct paths to the neuropsychological variables.

234

JONES ET AL.

-I Smoking

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A

c

E Albumin

m I

-

Sysdc

1 1

ognilive Flexibili.

BP

FIGURE 3. Direct paths linking the causal variables.

Table 9 shows the effects of age-only on the five neuropsychological variables contrasted with the effect of age on the neuropsychologicalvariables in the full model. For the most part the effect of age is eliminated or reduced. The major exception is the enhanced role of age on nonverbal memory skill.

Discussion The causal model presented here indicates that the direct effect of age on cognition is reduced by the introduction of variables pertaining to physiological neuropsychological, and psychosocial function. This suggests that these intervening variables are causally related to changes in cognitive function with age. Some of the causal relationships indicated by the present results have been previously suggested by correlational studies. In particular, high correlations between neuropsychological tests scores and measures of brain structure and brain function have been recently described (Duffy et al., 1984; Stafford, Albert, Naeser, Garvey, & Sandor, 1988). This is, however, the first substantial indication that there is a causal relationship between changes in the brain and age-related changes in cognitive function in healthy individuals. Other causal relationships suggested by the present results are being reported for the first time. The asso-

ciation between cognitive function and psychosocial measures relating to general psychiatric symptomatology and social support has not, to our knowledge, been previously found in individuals who are functioning at a relatively high level. Although a relationship between measures of stress and cognitive test scores has been found in earlier studies, these involved the careful manipulation of stress levels during cognitive testing. The ratings of stress in the present study related to the general state of the individual apart from the actual neuropsychological test session. Other causal relationships found here have been the subject of controversy. For example, some studies have found a relationship between blood pressure and/or alcohol intake and the cognitive abilities of non-hypertensive, non-alcoholic individuals, while many studies of this nature have been negative. The present findings suggest that these relationships need further exploration. One of the most intriguing outcomes of the present study is the difficulty in modeling the non-verbal cognitive variable. The path coefficient for this factor is greater than 1.O. This suggests a strong suppressor effect present in the model or, more likely, that important intervening variables are missing from the model. The results indicate that the non-verbal factor is linked strongly to age, electrical brain state and ventricular volume. While missing variables might change these relationships, and their

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TABLE 7 Path Coefficients for Direct Paths to the Neuropsychological Variables Variables

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Causes

Verbal

Theta Beta Passive Delta Active Delta Delta (post) Late Auditory Late Visual Intermediate Visual Early Auditory Early Visual CT Density Ventricular Volume Sulci Volume General Symptoms Stress Rating Social Supports Extroversion Neuroticism Total Cholesterol Sed Rate Hematocrit Albumin Systolic Blood Pressure Smoking Drinking Education Headsize Age Total Causal Paths

Non-Verbal

Speed

Attention

Cog. Flexibility

- .41 - .79 .58

.32

- .24 - .52 .86

- .20

- .25

.29 .69 .46

- .79

.29

- .55 - .25 - .22

- .20 .28 .38

.35

.32

- .29

.38 .22

.17

.23

- 1.70

9

5

6

- .32 - .37

5

5

nature is unclear, the present findings suggest that the non-verbal factor differs substantially in its causal role from those of the other cognitive factors. This fact has not been previously recognized. In summary, the present study suggests that the structural equation modeling of the age effect on cognitive function can serve both to describe the inter-relationships of a large multivariate data set and to investigate hypotheses from other studies. By allowing coefficients to be opportunistically estimated new hypotheses may be generated for testing in independent samples.

A study of familial stress. In Stress and coping in later life families. New York: Hemisphere Press. Aldwin, C., Bosse, R., & Levinson, M. (1987). Stress, social support, and health in older adults. Paper presented at the annual meeting of the Gerontological Society of America, November, 1987. Bendon, A.L., & Hamsher, K. (1976). Multilingual Aphasia Examination. Iowa City: University of Iowa Press. Derogatis, L.R., Rickels, K., & Rock, A.F. (1976). The SCL-90 and the MMPI: A step in the validation of a new self-report scale. British Journal of Psychiatry,

References

Duffy, F.H., Albert, M.S., McAnulty, G., et al. (1984). Age-related differences in brain electrical activity of healthy subjects. Annals of Neurology, 16, 430-438. Duffy, F.H., Bartels, P.H., & Burchfiel, J.L. (1981). Significance probability mapping: An aid in the topographic analysis of brain electrical activity. Electro-

Albert, M. (1988). Cognitive function. In M. Albert & M. Moss (Eds.), Geriatric neuropsychology (pp. 3356). New York: Guilford. Aldwin, C. (in press). The Elders Life Stress Inventory:

128, 280-289.

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TABLE 8 The Causal Paths for the Endogenous Variables* Theta Sed Rate .28 Tissue Density - .47

- -60 Late Auditory Drinking - .34 Smoking .37

-

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.62

CT Density

-

Gen. Symptoms

-

.14

Total Cholesterol

-

Smoking Extroversion .39 Drinking .24 - -45

Beta Education - .33

Active Delta Sed Rate - .48

- .62

- .32

Intermed. Visual Albumin .40

Early Auditory Stress Rating .38 Systolic BP .47 Education - .31

Early Visual Sulci Volume - .44 Gen. Symptoms .59

Extroversion

Neuroticism Total Cholesterol .56

-

-

.69

Late Visual Hematocrit - 3 9

- .63

-

.62

Ventricular Volume

-

Delta (post) Systolic BP .25

-

Passive Delta

-

-

.28

-

-

Sulci Volume Extroversion .44 .33

.81

Stress Rating

-

Social Supports

-

-

Hematocrit

Albumin

-

-

Drinking

Education

Headsize

-

-

Sed Rate

-

-

-

-

-

-

Systolic BP Hematocrit .40 .37

-

.23

~~

*Boldface signifies the “effect” variable. Its “causes” are listed below it.

encephalographyin Clinical Neurology, 51, 455-462. Duffy, F.H., Denckla, M., Bartels, P.H., et al. (1980). Dyslexia: Automated diagnosis to computerized classification of brain electrical activity. Annals of Neurology, 7, 421-428. Duncan, O.D. (1985). Introduction to structural equation models. New York: Academic Press. Eysenck, H.J., & Eysenck, S. (1963). Eysenck Personality Inventory. London: University of London Press. Feldman, M.J., & Drasgow, J.A. (1959). A visual-verbal test for schizophrenia. Psychiatric Quarterly Supplement, 25, 55-64.

Floderus, B. (1974). Psychological factors in relation to coronary heart disease and associated risk factors. Stockholm: Norsdisk Hygienisk Tidskrift Supplementum. Gorham, D.R. (1956). A proverbs test for clinical and experimental use. Psychological Reports, I, 1-12. Hauser, R.M., & Featherman, D.L. (1977). Theprocess of stratification trends and analysis. New York: Academic Press. Hayduk, L.A. (1987). Structural equation modeling with LZSREL. Baltimore: Johns Hopkins University Press. Joreskog, K.G., & Sorbom, D. (1979). Advances in fac-

TABLE 9 Age Only Model

Verbal Non-verbal Speed Attention Cognitive Flexibility

Full Model

Gamma

t-Value

Gamma

t-Value

- 0.38 - 0.30

- 4.0

- 0.23 - 1.70

- 3.2

0.18 -0.13

1.8 1.3

- 0.12

- 1.2

- 3.0

0.00 0.00 0.00

-9.1 0.0 0.0 0.0

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tor analysis and structural equation models. Cambridge, MA: Abt Books. Joreskog, K.G., & Sorbom, D. (1984). LISREL VI user's guide. Mooresville, IN: Scientific Software. Kaplan, E., Goodglass, H., & Weintraub, S. (1982). The Boston Naming Test. Philadelphia: Lea and Febiger. Mirsky, A. (1978). Attention: A neuropsychological perspective. In J. Chall & A. Mirsky (Eds.), Education and the brain. Chicago: University of Chicago Press.

Stafford, J., Albert, M.S., Naeser, M.A., Garvey, J., & Sandor, T. (1988). Age-related differences in computed tomographic scan measurements. Archives of Neurology, 19, 87-95. Wechsler, D.A. (1946). A standardized memory scale for clinical use. Journal of PsychologyD45, 409-419. Wechsler, D.A. (1955). The measurement of adult intelligence. Baltimore: Williams and Wilkins.

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Appendix A1

Cognitive Domain Measurement Model Selected Loadings (A) from the VARIMAX Rotation of Principal Components Factor I

Name Verbal memory and general skill

%Variance Loading 15.0%

.83 .75 .75 .73 .70 .65 .60 .59 .58

.54 .54 .52 .50

I1

Non-verbal memory and general skill 11.3070

I11

Speed

10.9%

.46 .73 .65 .69 - .58 .56 .51 .49 .47 .49 .63 .72 .50

IV

V

Attention

Cognitive flexibility

TOTAL

10.9%

5.3%

53.5%

.49 -49 -.74 .69

Varname DS2STRIC IS2STRIC ISISTRIC DSlSTRIC WMSSCORE IPATOTAL DWSDIS3 FASTOTAL PVBMTWOS VOCRAW PVBMTOT DSSUMTOT DPATOTAL BTNSPON SYPAIR SYSYMB DFITOTAL DWSTRIAL DF4TOTAL CMTOTAL IFRECOGN IFlTOTAL DDPATOTAL B2TIME B4TIME BSROTOT BSTIME B3TIME B4ROTOT AAUDTOT

.51 IFZTOTAL .50 AUNTOTAL .59 ASTTOTAL .56 CF2TOTAL .52 VVTOTAL .51 CTRLTOT

Description Delayed story2 IMM recall story2 IMM recall storyl Delayed recall storyl Wechsler memory score Immediate paired associates total DWS distraction 3min FAS score Proverb free interpretation Vocabulary raw score Proverbs interpretation concrete Digits forward and back Delayed paired associates total Boston Naming Test Digit symbol pairs recall Digit symbol symbol recalled Del. Fig. 1 total DWS Imm. learn trials to criterion Del. Fig. 4 total Clock drawing to command Immediate recognition IMM Fig. 1 total Delayed paired associates total Block design2 time Block design4 time Block design5 rotation Block design5 time Block design3 time Block design4 rotation Auditory continuous performance test IMM Fig. 2 total VCP unstructured number correct Visual continuous performance test Copy Fig 2 total Visual verbal test Mental control total

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Appendix A2

Brain Structure (CT) Domain Measurement Model Selected Loadings (A) from the VARIMAX Rotation of Principal Components

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Factor

Name

%Variance Loading

I

Tissue Density

36.4%

I1

Fluid Volume

17.4%

I11

Headsize

12.0%

IV

Fluid Volume

11.2%

TOTAL

77.0%

.92 .89 .87 .84 .82 .78 .93 .88 .83 .79 .96 .90 .83 .79

Varname SMHTS BWHTS C7HTS BWXCT SMXCT C7XCT SMPVOL SMPOS BWPOS BWPVOL BWHDSZ SMHDSZ C7P05 C7PVOL

Description Slice SM HTS Slice BW HTS Slice C7 HTS Slice BW XCT Slice SM XCT Slice C7 XCT Slice SM partial volume Slice SM PO5 Slice BW PO5 Slice BW partial volume Headsize at slice BW Headsize at slice SM Fluid volume at slice C7 Fluid volume at slize C7

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Appendix A3

Brain EEG Domain Measurement Model Selected Loadings (A) from the VARIMAX Rotation of Principal Components

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Factor

Name

070

Variance Loading Varname Frequency

I

Global Theta

18.2%

I1

Beta activity, Right > left

15.1%

.80 .80 .77 .74 .74 .73 .65 .63 .62 .75 .72 .70 .69 .62 .62 .57 .57

I11

Passive-state synchronization

14.9%

.52 .83 .78 .72 - .68 - .64

- .59 IV

Active-state synchronization

14.5%

V

Delta (posterior)

12.4%

TOTAL

75.1%

.54 .56 .74 .63 .66 - .62 - .58 .56 .62 - .53 .83 .82 .82 .79 .74 .67

FEA36 FEA43 FEA18 FEA3 FEA22 FEA26 FEA9 FEA30 FEA13 FEA32 FEA5 FEA38 FEAll FEA15 FEA2O FEA4 1 FEA28 FEA24 FEA 12 FEA2 FEA8 FEA14 FEAlO FEA4 FEA7 FEA35 FEA2 1 FEA 17 FEA25 FEA23 FEA24 FEA35 FEA42 FEA44 FEA34 FEA33 FEA6 FEA4O FEA 1 FEA 16

Location

4-8Hz RPT, LPT,RP,RC ” LP,MP,RD,P ” RMT ,RC,RPT ” RC ,RMT ” LMT,RPT ” LPT, RPT ,RP ” RPT ” LPT,RPT ” RMT,RPT 20-24Hz RPT ” RPT ” RPT,LPT ” RPT,RMT ” RPT,LPT,RMT ” RPT ” RPT ” RPT,LPT ” RPT,LMT 0-4Hz RPT,LPT,RMT ” BT ” RPT 16-20Hz LMT,RPT ” RMT,RPT ” BT ” RPT 0-4Hz LPT,RPT O-4HZ LPT,RPT ” RPT ” LPT,RPT 16-20Hz LPT,RPT 20-24Hz LMT,RPT 0-4Hz LPT,RPT ” LPT,RC,RMT 16-20Hz LPT,RC,RPT 0.4Hz LP ” RP ” RP ” LP,RPT ” LC,RC ” LC ,RC

Legend: R = right; L = left; P = parietal; T = temporal; F = frontal; C = central; M =medial; 0 =occipital.

State Side RTI RTT KFI EOP KFT PA1 EOP PAT MUS PAT EOP RII SPE MUS KFI RTT PA1 KFT MUS EOP SPE MUS SPE EOP SPE RTI KFT KFI PA1 KFT KFT RTI RTT RTT EOP ECL KFI RTI RTI RTT

R B RE R R R RE R RE RE RE B RE R RE R B L R R RE R RE R R R R RE R R L R R R B B R B B B

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Appendix A4

Brain Evoked Potential Domain Measurement Model Selected Loadings (A) from the VARIMAX Rotation of Principal Components Factor I

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I1

Name Late Auditory

Late Visual

%Variance Loading Varname 17.2%

16.6%

I11

Intermediate Visual

12.2%

IV

Early Auditory

10.2%

V

Early Visual

TOTAL

8.4%

FEA61 FEA60 FEA58 FEA59 FEA62 FEA52 FEA53 FEA54 FEA55 FEA47 FEA5O FEA48 FEA56 FEA57 FEA63 FEA46 FEA47 .50 FEA5l

.90 .87 -.75 .60 .48 .85 .84 -.73 .63 .51 .87 .82 .73 .71 .65 .81 - -59

Description 264 + msec AER 264 + msec AER 152+ msec AER 152+ msec AER 472+msec P300 184+ msec VER 184+ msec VER 284+msec VER 284 + msec VER 92 + msec VER 120+ msec VER 104+ msec VER 76 + msec AER 76 + msec AER 472 + msec P300 92+msec VER 92 + msec VER 120 + msec VER

LC,V,RAQ LPF

R L

V,RP LMT LC ,V

B B

BPQ

B

RC,RMT LF,MF, RF V,RC

RE B R

LPT,LO,RO

L

64.6%

Appendix AS

Linear Structural Relations Modeling In the mid-1970’s, Karl Joreskog and his associates developed a statistical technique, known as LISREL (Linear Structural Relationships) to address some of the problems of structural change and measurement error. The technique unites factor analysis with structural equation modeling. It uses multiple indicators to detect underlying latent concepts and estimates linear equations to determine the strength of causal relationships. LISREL is a very flexible, but complex program for modeling the effects of structural change. It is a model fitting process, testing how well an hypothesized matrix qf relationships reproduces the observed matrix of indicators. Because no structural model can ever be “proved” (Duncan, 1975), each model must be compared to other plausible alternatives to determine the most reasonable configuration of relationships. The LISREL program works from either a correlation or covariance matrix of observed variables. Most LISREL analyses use the covariance matrix because it allows the variance of variables to increase or decrease over time. Correlation matrices artificially constrain all variables to a common variance and thereby lose the mean and variance, which may or may not be useful information. The LISREL program uses a maximum likelihood estimation technique to determine the parameters of the model. LISREL infers from the matrix of observed variables how well each latent variable reflects its hypothesized construct and how well it reflects something else - namely, measurement error. The test for successfully differentiating between structural change and measurement error is based on how well the

hypothesized model reproduces the original matrix of variables. A chi-square statistic is used to determine the goodness-of-fit in the model (Joreskog & Sorbom, 1978). There are two basic components to the LISREL program: a measurement model and a structural model. Although both components can be estimated simultaneously, some recommend that initial attempts at model fitting proceed in a two-step fashion. First, the measurement model is estimated to determine the goodness-of-fit between the observed and latent variables and to assess the reliability of the measures. Then the structural model is estimated to establish the causal effects. The technical aspects of manipulating these modeling components are described below. Before elaborating on the measurement and structural modeling components of the LISREL program, a brief discussion on model identification is provided.

Identification of the Model To identify a model means that a unique value has been obtained for each estimated parameter in the model. If more than one set of values can solve the series of structural equations, then the model is said to be underidentified. The problem of identification in the context of the solution of simultaneous equations is a familiar one. Sets of equations which have more unknowns than independent relationships (equations) are not solvable, and are underidentified. Sets of equations where we have more equations, or relationships, which must be satisfied than we have unknowns are said to be overidentified. In the latter situation, there may be several solutions for subsets of the equations, which are mutually inconsistent. Where there are exactly the same number of independent equations as unknowns,

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we have an exactly identified set. In the fully recursive path model, we insure exact identification by placing restrictions on the model. If we should set certain paths to zero, in an otherwise recursive model, we may introduce problems of overidentification. If we should introduce feedback loops into the system or relax the requirement of independent residuals, we may (but not necessarily) make the system underidentified. Underidentified systems can never be solved uniquely, but many systems, though complex, may still be identified. All full recursive systems are exactly identified; though not all identified systems must be recursive. As Joreskog and Sorbom (1978) note, general rules for identification are extremely difficult to give. In LISREL, a necessary condition for identification is that: t

Modeling age using cognitive, psychosocial and physiological variables: the Boston Normative Aging Study.

A structural equation model is computed for 36 variables from eight domains of data using 100 healthy male subjects whose age varies between 30 and 80...
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