Journal of Gerontology: PSYCHOLOGICAL SCIENCES
Copyright 1992 by The Gerontological Society of America
1992. Vol. 47, No. 5.P33I-P336
Cognitive Slowing in Alzheimer's Disease and Geriatric Depression Robert D. Nebes, Christopher B. Brady, and Charles F. Reynolds III Department of Psychiatry, Western Psychiatric Institute and Clinic, University of Pittsburgh School of Medicine.
D
IFFERENTIATING an early dementia from a major depression is one of the more difficult diagnostic problems in geriatrics. Both conditions are associated with a variety of cognitive deficits (Abas, Sahakian, & Levy, 1990), and therefore poor performance by a patient on neuropsychological testing may not be especially informative. However, an apparent similarity in the pattern of behavioral impairment in dementia and depression may mask major differences in the nature of the underlying deficit. For example, behavioral slowing, in the form of increased response time (RT) on cognitive tasks, is a prominent finding both in patients with Alzheimer's disease (AD) and in persons suffering from a major depression. However, there is some evidence that the underlying mechanism of the slowing in these two disorders is quite different. The increased RT in Alzheimer patients may be due to a reduction in the speed with which these individuals process information (i.e., a cognitive slowing). By contrast, the psychomotor slowing associated with a major depression may result from an increase in the time depressed patients take to initiate and execute motor acts (a motor retardation), while the rate at which they process information remains relatively normal. There are several lines of evidence supporting this hypothesis. A number of studies have attempted to determine whether the increased response time in AD represents a slowing of cognition, a motor slowing, or both. Vrtunski, Patterson, Mack, and Hill (1983), by recording the time course of the pressure a subject exerted on a response button, were able to separate total RT into premotor (the time between the stimulus and the initiation of pressure on the button) and motor time (the time between initial pressure and button activation). They found that both motor and premotor times were prolonged in AD patients, suggesting the presence of both a motor and a cognitive slowing. However, because the premotor time here included a variety of steps (including the time necessary to program the motor response), the premotor time may not reflect purely cognitive operations. Williams,
Jones, Briscoe, Thomas, and Cronin (1991) used a different technique to separate motor and cognitive time in a choice RT task. In addition to RT, they also recorded event-related potentials. They were particularly interested in the P300, a wave form associated with stimulus evaluation. They found that, in comparison to normals, the onset of the P300 was delayed in AD patients, indicating a slowing of cognitive processing. Because the AD patients' RT showed an even greater slowing, this suggested to these authors that motor processes were also retarded in AD patients. Finally, Gordon and Carson (1990) reached a similar conclusion based on fitting the RT distributions of AD patients to a model that allowed them to differentiate sensory motor time from decision time. Although a psychomotor slowing is often found in depressed patients (Nelson & Charney, 1981), it has been suggested that this is caused by psychological factors, such as reduced motivation (Hart & Kwentus, 1987), rather than by any slowing of information processing speed. Pirozzolo, Mahurin, Loring, Appel, and Maletta (1985) hypothesized that, among other factors, test anxiety might be responsible for the increased RT in depressed patients. Therefore, they compared subjects' RT using a standard RT procedure and using a procedure that encouraged subjects to relax. The relaxation technique improved the RT of depressed individuals, but not that of AD patients. Pirozzolo et al. concluded that the RT slowing found in depressed patients probably results from psychological factors rather than from any limitation in their cognitive functioning. The most impressive evidence that different mechanisms are responsible for the increased RT in depression and in AD comes from studies that have examined the speed with which subjects perform a single cognitive operation. The approach here is to have a subject repeat a particular mental operation multiple times. By examining how RT increases as a function of the number of repetitions of the mental operation, it is possible to determine the rate at which this operation is conducted. Perhaps the best example of this approach is P331
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Response slowing on psychological tasks is found both in Alzheimer's disease and depression. However, the underlying cause for this slowing may be different in the two disorders. This research examined whether the behavioral slowing found in Alzheimer patients results from a reduction in their rate of cognitive processing, whereas the slowing in depressed geriatric patients reflects a purely motor retardation. This hypothesis was tested using a task in which subjects had simply to determine the number of dots present in an array (i.e., enumeration). In all four subject groups (Alzheimer patients, depressed geriatric patients, healthy old controls, and healthy young controls), response time increased linearly with array size. The slope of this linear function (reflecting rate of enumeration) was the same in the normal and depressed patients, but was significantly greater in the Alzheimer patients, suggesting the presence of a cognitive slowing in Alzheimer's disease, but not in depression.
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patients, depressed geriatric patients, and normals conducted a simpler cognitive operation: enumeration (i.e, determining how many items are present in a visual array). The time it takes normal individuals to enumerate a small array of four or five items is a linear function of array size. As the number of items in the array increases, so too does RT, at a rate of approximately 40 msec for every additional item in the array (Chi & Klahr, 1975; Svenson & Sjoberg, 1983). Thus, like the memory-search procedure, the enumeration task yields a measure of the rate at which a simple cognitive operation is conducted, independent of motor time. In this enumeration task, subjects were tachistoscopically presented with an array of either one, two, three, or four randomly arranged dots and were to say how many dots were present. The array was visible for only 300 msec in order to avoid confounding the rate of enumeration with the rate of visual scanning. In order to control for a potential confound in the enumeration task, we also ran a simple reaction-time task. In the enumeration task, the subjects' vocal response varies depending on the number of dots in the array (i.e., they say "one," "two," etc.). Thus, differences in RT to the various array sizes could arise simply from differences in the time subjects take to pronounce the various number names. We therefore gave subjects a series of simple RT trials in which, before a trial began, the subject was told which number name to say (i.e., "one," "two," "three," or "four") when a neutral stimulus appeared in the tachistoscope. Any consistent variability in pronunciation time between the various number names would be evident in this control task. METHOD
Subjects. — Twenty-four subjects were tested in each of four groups: patients with AD, depressed-elderly patients, normal-young, and normal-elderly individuals (see Table 1 for subject characteristics). The Alzheimer patients (all outpatients) were recruited from the Alzheimer Disease Research Center and the Benedum Geriatric Clinic of the University of Pittsburgh. These patients met the criteria of McKhann et al. (1984) for probable AD based on the results of a complete medical, neurological, and psychiatric examination. None of the AD patients had a history of other major psychiatric or neurological disease. All AD patients had a modified Hachinksi score of 4 or less, making it unlikely that
Table 1. Means and (Standard Deviations) for Age, Years of Education, and Scores on the Mini-Mental State Exam (MMSE), Dementia Rating Scale (DRS), Geriatric Depression Scale (GDS), and Hamilton Rating Scale for Depression (HRSD) for the Subjects Used in the Data Analysis
Normal-young Normal-elderly Alzheimer patients Depressed-elderly
GDS
Age
Education
MMSE (n = 30)
DRS
No. of Subjects
(n = 144)
(n = 30)
23
23.9 (3.4) 76.4 (5.4) 76.9 (8.6) 66.5 (6.0)
13.7 (2.1) 13.3 (2.8) 13.2 (3.5) 12.0 (2.6)
29.3 (.6) 27.5 (1.6) 20.2 (3.1) 29.2 (1.1)
140.9 (2.4) 139.1 (3.4) 118.3 (11.7) 139.1 (4.6)
24 21 24
HRSD
2.8 (3.3)
3.0
—
(2.4)
6.9
—
(4.8) 18.9 (6.4)
20.9 (5.0)
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Steinberg's (1975) memory-search task. A subject is given a varying number of items to hold in memory (memory set). A single stimulus is then presented, and the subject has to decide whether or not this stimulus is a member of the memory set. Typically, RT increases linearly as a function of the number of items in the memory set (i.e., with every increase of one item in the memory set, RT rises by a relatively constant amount). This pattern of results has commonly been interpreted as evidence that subjects serially compare the stimulus with each item in the memory set. Because the motor response is the same regardless of the number of items in the memory set, the slope of the obtained linear RT function reflects the time subjects need to perform a single memory comparison. By contrast, the time involved in perceptual encoding of the stimulus, motor initiation, and execution shows up in the intercept of this linear function, which effectively represents the subjects' RT when no memory comparisons are required. Using this memory-search task, it has been found that normal aging produces a modest increase in the slope of this linear function, as well as an increase in the intercept (Anders, Fozard, & Lillyquist, 1972), suggesting a slowing of both motor and cognitive operations. Two studies (Hart & Kwentus, 1987; Hilbert, Niederehe, & Kahn, 1976) of depressed geriatric patients both found that, whereas the overall RT of the depressed patients was slower than that of the normal older adult, the rate at which the depressed patients searched their memory (i.e., the slope of the function relating memory-set size to RT) was no greater in depressed patients than it was in the normal older adult. Rather, it was the intercept that was increased in the depressed patients. This suggests that the slower RT of depressed geriatric patients is due to a motor retardation rather than a slowing in cognitive processing. Given these results, the obvious way to investigate the source of the RT slowing in AD would appear to be this same memory-search task. Unfortunately, this task has proved too difficult for AD patients, who make so many errors that it is impossible to interpret the pattern of their RTs (Hilbert et al., 1976). Therefore, at present, there is no direct evidence using this technique as to whether the RT slowing in AD is caused by: (a) a slowing of information processing, (b) a motor retardation, or (c) both. This study used the same general approach as the Sternberg task, but examined the speed with which Alzheimer
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Procedures. — In the enumeration task, the subject was shown an array of between one and four dots and was asked to say aloud the number of dots present. The dots were 1 cm in diameter (2° of visual angle) and fell randomly within a 10 x 10 cm area on the screen of a one-channel tachistoscope. On each trial, the experimenter said, "How many dots do you see?" and then .5 second later exposed the array for 300 msec. The subject's vocal response triggered a voice key, stopping a millisecond timer that started with presentation of the array. Before beginning the actual task, the subject was given two practice trials. This was followed by 64 trials: 16 each of one-, two-, three-, and four-dot arrays randomly intermixed. The whole task took less than 30 minutes. The subjects were also given a control task in which on each trial a 5 cm x 1 cm black bar was presented in the center of the tachistoscope screen. The bar appeared between .5 and 2 seconds after a vocal warning from the experimenter. This bar remained visible until the subject responded. The subject was given four blocks of six trials.
Before each block the experimenter told the subject what number name to say when the bar appeared (either "one," "two," "three," or "four"). The order in which the four different responses were tested was counterbalanced between subjects. The control task was always given before the enumeration task. RESULTS
Data from four subjects (one normal-young and three AD patients) were dropped from the analysis because their error rate on the enumeration task exceeded 15%. It should be noted that the pattern of RTs in these subjects was very similar to that of the remaining subjects. Data from the three excluded AD patients showed a highly linear (r2 = .99) function with a slope even steeper (slope = 356 msec) than that for the AD patients used in the data analysis. Mean latencies for correct responses served as the data in a repeated-measures analysis of variance (ANOVA). Because the AD patients showed substantial variability with respect to the normals, the assumption of homogeneity of variance was not met. However, following a logarithmic transformation, the experimental data passed Hartley's Fmax test and, therefore, all analyses used the transformed scores. It is important to note that this transformation did not change the pattern of the statistical results. For the purposes of readability, untransformed RT data are presented in Table 2. Dunn's a priori nonorthogonal contrasts and Dunn's a priori pairwise comparisons were used to assess the nature of the significant interactions. This study used a 4 X 4 design with Group (normalyoung, normal-elderly, depressed-elderly, and AD patients) as a between-subjects factor and Array Size as a withinsubjects factor. There was a significant main effect of Group: F(3,88) = 15.4, p < .001. Dunn's pairwise comparisons revealed that the AD patients were significantly slower than all the other groups (p < .01). The normal-elderly and depressed-elderly subjects did not differ from one another, although both groups were significantly slower than the young (p < .05). There was also a main effect of Array Size: F(3,264) = 232, p < .001. Dunn's pairwise comparisons
Table 2. Mean Response Time (Standard Deviation) in Milliseconds and Percent Errors for the Four Array Sizes, with the Parameters of the Linear Function Relating Array Size to Response Time Number of Dots 1 Normal-young
439 (43) .0% Normal-elderly 496 (48) .0% Alzheimer patients 557 (100) .9% Depressed-elderly 492 (63) .3%
2
3
4
r2
Slope
Intercept
477 (47) .3% 521 (57) .3% 565 (67) .9% 530 (78) 1.6%
520 (42) .5% 595 (57) 1.6% 635 (85) 3.9% 571 (84) 1.3%
521 (50) 1.4% 608 (61) 1.3% 759 (203) 6.8% 599 (83) .8%
.90
29
417
.93
41
452
.87
68
460
.99
36
458
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they suffered from multi-infarct dementia (Rosen, Terry, Fuld, Katzman, & Peck, 1980). The group of depressedelderly patients was composed of both inpatients and outpatients from the Western Psychiatric Institute and Clinic of the University of Pittsburgh. They carried a diagnosis of major depressive disorder (unipolar, nonpsychotic) based on Research Diagnostic Criteria (Spitzer, Endicott, & Robbins, 1978). The depressed patients had an average score on the first 17 items of the Hamilton Rating Scale for Depression (Hamilton, 1976) of 20.9 (SD = 5.0). The normal-young and normal-elderly subjects were recruited from the community and were matched to the AD group in sex, education, and, in the case of the normal-elderly, age. None of the subjects in this study, normals or patients, were taking psychoactive medications at the time of testing. The depressed patients were tested at the end of a 2-week, medication-free period. All of the subjects received two neuropsychological screening tests to assess their general level of cognitive functioning: the Mini-Mental State Exam (Folstein, Folstein, & McHugh, 1975) and the Dementia Rating Scale (Mattis, 1976). The AD patients' scores on these two screening tests were indicative of a mild to moderate dementia, whereas the scores of the normals and the depressed patients showed no evidence of a major cognitive impairment (see Table 1). All subjects were also given the Geriatric Depression Scale (Yesavage et al., 1983), a short self-rating scale developed specifically for use as a screening instrument in older persons. Possible scores on this scale range from 0 to 30, with scores between 11 and 19 suggesting the presence of a mild depression, and scores of 20 or more suggesting a moderate to severe depression. None of the normals scored above 9 on this scale, while four of the AD patients fell in the mildly depressed range, scoring between 12 and 18. Our depressed-elderly subjects were all in the moderate to severe range, and their scores on the Geriatric Depression Scale correlated significantly with their Hamilton scores (r = .52; p < .01). The health of the young and elderly control subjects was established on the basis of an extensive medical questionnaire that concentrated on symptoms of cardiovascular and neurological disease.
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800 'Alzheimer
750 -700 --
Normal Elderly
650 --
"
600 - • Depressed Elderly .
550 --
a:
was between the slope of the RT function and the Dementia Rating Scale (r = - . 4 9 , p < .05); such that the more demented the patient, the greater the slope. The slope and intercept for the depressed patients did not correlate significantly with any of the severity measures. From the error data in Table 2, it is clear that all four subject groups were very accurate on this task and that only in the AD patients was there any substantial variation in error rates between the different array sizes. Because both error rate and RT rose with increasing array size in the AD patients, there was no evidence for a speed-accuracy tradeoff. The data from the control task (see Table 3) again involved a 4 x 4 design, with Group as a between-subjects factor and Response (i.e., saying "one," "two," "three," or "four") as a within-subject factor. There was a main effect of Group: F(3,92) = 21.72,/? < .05. Dunn's pairwise comparisons indicated that the only significant difference was between the AD patients and the normal-young (p < .05). There was also a significant effect of Response, with Dunn's pairwise comparisons showing a significant difference only between responses "one" and "four." There was no Group by Response interaction: F(9,276) = 1.12. These results demonstrate that, whereas there may be some differences in the time needed to pronounce the various number names, they are certainly not sufficient to account for the linear pattern of results evident in the enumeration task, nor the difference in slope between the AD patients and the other subject groups. DISCUSSION
There are several major findings from this study. First, the time subjects took to say how many dots were present in an array rose linearly with array size. This replicates earlier work in normal-young (Chi & Klahr, 1975; Svenson & Sjoberg, 1983) and suggests that the subjects were performing a serial process. We can therefore estimate the rate at which they conducted this process from the slope of the function relating array size to RT. Second, although all four subject groups showed a linear function, the magnitude of the slope differed between the groups. The normal-young, normal-elderly, and depressed-elderly all had similar slopes ranging between 30 and 40 msec. By contrast, the slope for the AD patients was from 66 to 134% greater than that for the other three groups. This greater slope (see Figure 1) suggests that the longer RTs found in AD patients are at least partly due to a cognitive slowing or a reduction in the rate at which Table 3. Mean Response Times (Standard Deviations) for Responses in the Simple Response-time Control Task
500 --
Spoken Responses Young
"Four"
450 --
"One"
"Two"
"Three"
400 --
306
331
336
344
(54)
(62)
(65)
(69)
Normal-young Normal-elderly
350--
1
2
3
4
Number of Dots Figure 1. Response time (RT) in milliseconds as a function of the number of dots in the stimulus array.
Alzheimer patients Depressed-elderly
350
356
380
381
(69)
(57)
(70)
(60)
370
381
378
383
(138)
(106)
(132)
(102)
376
356
356
365
(132)
(80)
(71)
(83)
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revealed that all four array sizes were significantly different from one another (p < .01), with RTs increasing significantly with each increase in array size (one to two to three to four dots). Thus, as array size increased, the time subjects took to determine how many dots were present also increased. There was also a significant interaction between Group and Array Size: F(9,264) = 7.81, p < .00001. Dunn's nonorthogonal contrasts revealed that there was no difference in the pattern of RTs between the normal-elderly and depressed-elderly: F( 1,296) = 1.42, nor between the normal-young and the normal-elderly/depressed-elderly (pooled): F( 1,296) = 0.42. However, when the pooled data for the normal-young, normal-elderly, and depressed-elderly were compared with that for the AD patients, there was a significant difference: F(l,296) = 3.17, p < .01. Thus, the pattern of RTs as a function of array size differed between the AD patients and the other three groups. This is evident in Table 2 and Figure 1 as a greater slope for the AD patients than for any of the other groups. That is, with each additional item in the array, there was a greater increase in the AD patients' RT than there was in the RTs of the normals or depressed-elderly patients. Because it is possible that the linearity of the group data does not accurately reflect the pattern of results within individual subjects, we individually determined the function relating each subject's RT to array size. The individual function for most subjects (74% of the young, 83% of the normal-old, 76% of the AD patients, and 75% of the depressed-elderly) was highly linear (r > .9). In order to determine whether the performance of the AD and depressed-elderly patients varied as a function of the severity of their dementia or depression, we correlated the slope and intercept of each subject's individual function with that person's score on two measures of general cognitive impairment (Mini-Mental State Exam and Dementia Rating Scale) and two measures of depression severity (Hamilton Rating Scale for Depression and Geriatric Depression Scale). For the AD patients, the only significant correlation
COGNITIVE SLOWING IN ALZHEIMER'S DISEASE
al. and Hart and Kwentus studies that did find a substantial amount of psychomotor slowing. One possible explanation is that our depressed patients were specifically chosen to be free of cognitive impairment, whereas those of Hart and Kwentus definitely did have cognitive deficits (the status of the depressed patients in Hilbert et al. is not clear). Thus, major psychomotor slowing may only occur in those depressed patients who have an associated cognitive impairment. Another possible explanation is the previously mentioned intertrial variability produced by having vocal responses that differed in the ease with which they triggered the voice key. Although the lack of a substantial psychomotor slowing in our depressed patients limits our interpretation of their results, it should be remembered that Hart and Kwentus and Hilbert et al. did show major psychomotor slowing in their depressed patients, yet these depressed individuals still did not differ from normals in the slope of the function relating their RT to set size. One final theoretical issue relevant to these results is the distinction drawn by some investigators between cortical (e.g., AD and Picks disease) and subcortical dementias (e.g., Parkinson's disease, Huntington's disease, and supranuclear palsy). Cummings has argued (Cummings, 1986; Cummings & Benson, 1986) that those dementias having a predominantly subcortical pathology share a common set of behavioral symptoms not found in the cortical dementias. One of the symptoms that is supposed to distinguish a cortical from a subcortical dementia is an increased RT resulting from a slowing of information processing (bradyphrenia). He claims that such a slowing is found in subcortical dementias but not in cortical dementias. However, as Levy and Sahakian (1987) point out, there is no real evidence for this particular distinction. In fact, there is considerable evidence that, in comparison with the normalold, patients with AD are substantially slower both on simple and choice RT tasks (Gordon & Carson, 1990; Nestor, Parasuraman, & Haxby, 1991; Pirozzolo, Christensen, Ogle, Hansch, & Thompson, 1981), as well as on more complex cognitive tasks (e.g., Nebes, Boiler, & Holland, 1986). These results confirm this and suggest that the increased RT in Alzheimer patients reflects an actual reduction in the rate at which they process information (evident here in the slope). That is, AD produces a cognitive slowing or bradyphrenia. If this is correct, then cognitive slowing is not restricted to subcortical dementias such as Parkinson's disease (Wilson, Kaszniak, Klawans, & Garron, 1980), but is also present in the prototypical cortical dementia: AD. These findings certainly do not disprove the hypothesis that there are major behavioral differences between cortical and subcortical dementias. The pattern of neuropsychological deficits found in AD patients definitely differs from that found in Parkinson or Huntington patients in a number of ways (Brandt, Folstein, & Folstein, 1988; Heindel, Salmon, Shults, Walicke, & Butters, 1989). These results do, however, suggest that one of the major behavioral symptoms postulated to exist solely in subcortical dementias (i.e., a cognitive slowing) is, in fact, also a symptom of the dementia found in AD. What might be the neurological basis of the cognitive slowing associated with AD? It has been argued that the
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AD patients process information. This is consistent with the results of earlier studies (Gordon & Carson, 1990; Vrtunski et al., 1983; Williams et al., 1991) that used very different techniques to determine the source of the behavioral slowing in AD. These results are, however, inconsistent with these earlier studies in one major way. We found no evidence for a motor retardation in our AD patients. In this study, a motor slowing would have been evident in the intercept of the RT function. However, from Table 2 it is clear that there was practically no difference (8 msec) between the intercepts for the AD and normal-old subjects. There was also no significant difference between the normal-old and AD patients on the control task (Table 3) that measured primarily sensory-motor RT. Assuming that the motor slowing demonstrated in previous studies is real, why did we not find it in this study? One possible reason is the type of response used in the present task — a spoken word. The four possible response words had different initial sounds and varied in the ease with which they triggered the voice key. This introduced a substantial amount of variability into the pronunciation time that may have been sufficient to obscure any group differences. The pattern of RT results in the depressed-elderly was quite different from that in the AD patients. The slope of the function relating RT to array size was the same in the depressed patients as in the normal-old. This is consistent with findings using the memory-search paradigm (Hart & Kwentus, 1987; Hilbert et al., 1976) and suggests that depressed geriatric patients do not suffer from a cognitive slowing any greater than that associated with normal aging. Although there are some reports in the literature of a cognitive slowing in depressed patients, these tasks have generally required subjects to hold a central button depressed until one of several other buttons lights up, at which time they lift their finger and move it to the illuminated button. The time between the lighting of the button and the lifting of the finger has been considered decision time, a measure of the subject's speed of cognitive processing (e.g., Knott & Lapierre, 1987). However, as Cornell, Suarez, and Berent (1984) point out, this approach assumes that subjects do not begin to lift their finger until they have made their decision. This assumption may be unwarranted. Also, the decision time in this type of procedure is certainly confounded with motor initiation and execution time. Thus, these findings, in conjunction with the earlier memory-search results, suggest that the behavioral slowing often seen in depressed geriatric patients is not due to a reduction in the rate at which these subjects process information. However, there is a complicating factor. There was practically no difference between the intercepts of the depressedelderly and normal-elderly groups in this study (6 msec), especially in comparison with the 100 to 200 msec difference found in the memory-search studies (Hart & Kwentus, 1987; Hilbert et al., 1976). There was also no significant difference in overall RT or in simple RT (i.e., the control task) between the normal- and depressed-elderly. It is unlikely that this lack of a major psychomotor slowing in our depressed patients is caused by our patients being less depressed than those in other studies. The mean Hamilton score for our patients (20.1) falls in the same range as that in the Hilbert et
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ACKNOWLEDGMENTS
This study was supported by grants from the National Institute on Aging (AG-05133) and the National Institute of Mental Health (MH 30915, MH 00295, MH 37869, and MH 43832). The authors would like to thank Dr. Leslie Leahy for assistance with the statistical analysis. Address correspondence to Dr. Robert D. Nebes, Western Psychiatric Institute and Clinic, University of Pittsburgh, 3811 O'Hara Street, Pittsburgh, PA 15215-2593. REFERENCES
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Folstein, M. F., Folstein, S. E., & McHugh, P. R. (1975). Mini-Mental State: A practical method for grading the cognitive state of patients for the clinician. Journal of Psychiatric Research, 12, 189-198. Gordon, B., & Carson, K. (1990). The basis for choice reaction time slowing in Alzheimer's disease. Brain and Cognition, 13, 148-166. Hamilton, M. (1976). Development of a ratings scale for primary depressive illness. British Journal of Social and Clinical Psychology, 6, 278296. Hart, R. P., & Kwentus, J. A. (1987). Psychomotor slowing and subcortical-type dysfunction in depression. Journal of Neurology, Neurosurgery and Psychiatry, 50, 1263-1266. Heindel, W. C., Salmon, D. P., Shults, C. W., Walicke, P. A., & Butters, N. (1989). Neuropsychological evidence for multiple implicit memory systems: A comparison of Alzheimer's, Huntington's and Parkinson's disease patients. Journal of Neuroscience, 9, 582-587. Hilbert, T. N. M., Niederehe, G., & Kahn, R. L. (1976). Accuracy and speed of memory in depressed and organic aged. Educational Gerontology, 1, 131-146. Knott, V. J., & Lapierre, Y. D. (1987). Electrophysiological and behavioral correlates of psychomotor responsivity in depression. Biological Psychiatry, 22, 313-324. Levy, R., & Sahakian, B. J. (1987). Sub-cortical dementia. British Journal of Psychiatry, 150, 559-560. Mattis, S. (1976). Mental status examination for organic mental syndrome in the elderly patient. In L. Bellak & T. B. Karasu (Eds.), Geriatric psychiatry (pp. 77-121). New York: Grune & Stratton. McKhann, G., Drachman, D., Folstein, M., Katzman, R., Price, D., & Stadlan, E. (1984). Clinical diagnosis of Alzheimer's disease: Report of the NINCDS-ADRDA Work Group. Neurology, 34, 939-949. Nebes, R. D., Boiler, F., & Holland, A. (1986). Use of semantic context by patients with Alzheimer's disease. Psychology and Aging, /, 261-269. Nelson, J. C , &Charney, D. S. (1981). The symptoms of major depressive illness. American Journal of Psychiatry, 138, 1-13. Nestor, P. G., Parasuraman, R., & Haxby, J. V. (1991). Speed of information processing and attention in early Alzheimer's disease. Developmental Neuropsychology, 7, 243-256. Pirozzolo, F. J., Christensen, K. J., Ogle, K. M., Hansch, E. C , & Thompson, W. G. (1981). Simple and choice reaction time in dementia: Clinical implications. Neurobiology of Aging, 2, 113-117. Pirozzolo, F. J., Mahurin, R. K., Loring, D. W., Appel, S. H., & Maletta, G. J. (1985). Choice reaction time modifiability in dementia and depression. International Journal of Neuroscience, 26, 1-7. Rogers, D., Lees, A. J., Smith, E., Trimble, M., & Stern, G. M. (1987). Bradyphrenia in Parkinson's disease and psychomotor retardation in depressive illness. Brain, 110, 761-776. Rosen, W. G., Terry, R. D., Fuld, P. A., Katzman, R., & Peck, A. (1980). Pathological verification of ischemic score in differentiation of dementias. Annals of Neurology, 7, 486-488. Spitzer, R. L., Endicott, J., & Robins, E. (1978). Research diagnostic criteria: Rationale and reliability. Archives of General Psychiatry, 35, 773-782. Sternberg, S. (1975). Memory scanning: New findings and current controversies. Quarterly Journal of Experimental Psychology [A], 27, 1-32. Storandt, M. (1976). Speed and coding effects in relation to age and ability level. Developmental Psychology, 12, 177-178. Svenson, O., & Sjoberg, K. (1983). Speeds of subitizing and counting processes in different age groups. Journal of Genetic Psychology, 142, 203-211. Vrtunski, P. B., Patterson, M. B., Mack, J. L., & Hill, G. O. (1983). Microbehavioural analysis of the choice reaction time response in senile dementia. Brain, 106, 927-947. Williams, P. A., Jones, G. H., Briscoe, M., Thomas, R., & Cronin, P. (1991). P300 and reaction-time measures in senile dementia of the Alzheimer type. British Journal of Psychiatry, 159, 410-414. Wilson, R. S., Kaszniak, A. W., Klawans, H. L., &Garron, D. C. (1980). High speed memory scanning in Parkinsonism. Cortex, 16, 67-72. Yesavage, J. A., Brink, T. L., Rose, T. L., Lum, O., Huang, V., Adey, M., & Leirer, V. O. (1983). Development and validation of a geriatric depression screening scalae: a preliminary report. Journal of Psychiatric Research, 17, 37-49. Received September 10, 1991 Accepted January 28, 1992
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bradyphrenia (cognitive slowing) found in patients with a subcortical dementia, such as Parkinson's disease, is caused by the same basal ganglia lesions responsible for the patients' motor retardation (Rogers, Lees, Smith, Trimble, & Stern, 1987). This could be either a direct effect of the basal ganglia lesions themselves or an indirect effect caused by the reduction in dopaminergic innervation of the cortex that follows such lesions. Given that AD patients do not have prominent basal ganglia lesions and yet show a similar bradyphrenia, the neurological basis of cognitive slowing may very well be cortical. This would be consistent with Rogers et al., who found that bradyphrenia occurred primarily in those Parkinson patients who showed evidence of cortical atrophy on CT scan. Thus, it is possible that the cognitive slowing evident both in AD and in some subcortical dementias reflects a disruption of cortical function. However, considering the lack of direct evidence, this suggestion must be viewed as speculation. What clinical implications might these results have? With current techniques, it is often difficult to distinguish patients with a mild dementia from those suffering from depression. If our interpretation of these results is correct, then information about whether or not cognitive processing is slowed in a particular patient might have some clinical utility. Although this methodology is too complex for general clinical use, there is a simple paper-and-pencil task that can yield a measure of an individual's cognitive and motor speed (Storandt, 1976). To establish that this approach is actually useful, however, it will be necessary not only to demonstrate that these findings generalize to other cognitive operations, but also to show that a similar pattern of differential slowing in dementia and depression is evident with this simpler measure.
Copyright 1992 by The Gerontological Society of America
1992. Vol. 47, No. 5.P33I-P336
Cognitive Slowing in Alzheimer's Disease and Geriatric Depression Robert D. Nebes, Christopher B. Brady, and Charles F. Reynolds III Department of Psychiatry, Western Psychiatric Institute and Clinic, University of Pittsburgh School of Medicine.
D
IFFERENTIATING an early dementia from a major depression is one of the more difficult diagnostic problems in geriatrics. Both conditions are associated with a variety of cognitive deficits (Abas, Sahakian, & Levy, 1990), and therefore poor performance by a patient on neuropsychological testing may not be especially informative. However, an apparent similarity in the pattern of behavioral impairment in dementia and depression may mask major differences in the nature of the underlying deficit. For example, behavioral slowing, in the form of increased response time (RT) on cognitive tasks, is a prominent finding both in patients with Alzheimer's disease (AD) and in persons suffering from a major depression. However, there is some evidence that the underlying mechanism of the slowing in these two disorders is quite different. The increased RT in Alzheimer patients may be due to a reduction in the speed with which these individuals process information (i.e., a cognitive slowing). By contrast, the psychomotor slowing associated with a major depression may result from an increase in the time depressed patients take to initiate and execute motor acts (a motor retardation), while the rate at which they process information remains relatively normal. There are several lines of evidence supporting this hypothesis. A number of studies have attempted to determine whether the increased response time in AD represents a slowing of cognition, a motor slowing, or both. Vrtunski, Patterson, Mack, and Hill (1983), by recording the time course of the pressure a subject exerted on a response button, were able to separate total RT into premotor (the time between the stimulus and the initiation of pressure on the button) and motor time (the time between initial pressure and button activation). They found that both motor and premotor times were prolonged in AD patients, suggesting the presence of both a motor and a cognitive slowing. However, because the premotor time here included a variety of steps (including the time necessary to program the motor response), the premotor time may not reflect purely cognitive operations. Williams,
Jones, Briscoe, Thomas, and Cronin (1991) used a different technique to separate motor and cognitive time in a choice RT task. In addition to RT, they also recorded event-related potentials. They were particularly interested in the P300, a wave form associated with stimulus evaluation. They found that, in comparison to normals, the onset of the P300 was delayed in AD patients, indicating a slowing of cognitive processing. Because the AD patients' RT showed an even greater slowing, this suggested to these authors that motor processes were also retarded in AD patients. Finally, Gordon and Carson (1990) reached a similar conclusion based on fitting the RT distributions of AD patients to a model that allowed them to differentiate sensory motor time from decision time. Although a psychomotor slowing is often found in depressed patients (Nelson & Charney, 1981), it has been suggested that this is caused by psychological factors, such as reduced motivation (Hart & Kwentus, 1987), rather than by any slowing of information processing speed. Pirozzolo, Mahurin, Loring, Appel, and Maletta (1985) hypothesized that, among other factors, test anxiety might be responsible for the increased RT in depressed patients. Therefore, they compared subjects' RT using a standard RT procedure and using a procedure that encouraged subjects to relax. The relaxation technique improved the RT of depressed individuals, but not that of AD patients. Pirozzolo et al. concluded that the RT slowing found in depressed patients probably results from psychological factors rather than from any limitation in their cognitive functioning. The most impressive evidence that different mechanisms are responsible for the increased RT in depression and in AD comes from studies that have examined the speed with which subjects perform a single cognitive operation. The approach here is to have a subject repeat a particular mental operation multiple times. By examining how RT increases as a function of the number of repetitions of the mental operation, it is possible to determine the rate at which this operation is conducted. Perhaps the best example of this approach is P331
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Response slowing on psychological tasks is found both in Alzheimer's disease and depression. However, the underlying cause for this slowing may be different in the two disorders. This research examined whether the behavioral slowing found in Alzheimer patients results from a reduction in their rate of cognitive processing, whereas the slowing in depressed geriatric patients reflects a purely motor retardation. This hypothesis was tested using a task in which subjects had simply to determine the number of dots present in an array (i.e., enumeration). In all four subject groups (Alzheimer patients, depressed geriatric patients, healthy old controls, and healthy young controls), response time increased linearly with array size. The slope of this linear function (reflecting rate of enumeration) was the same in the normal and depressed patients, but was significantly greater in the Alzheimer patients, suggesting the presence of a cognitive slowing in Alzheimer's disease, but not in depression.
NEBESETAL.
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patients, depressed geriatric patients, and normals conducted a simpler cognitive operation: enumeration (i.e, determining how many items are present in a visual array). The time it takes normal individuals to enumerate a small array of four or five items is a linear function of array size. As the number of items in the array increases, so too does RT, at a rate of approximately 40 msec for every additional item in the array (Chi & Klahr, 1975; Svenson & Sjoberg, 1983). Thus, like the memory-search procedure, the enumeration task yields a measure of the rate at which a simple cognitive operation is conducted, independent of motor time. In this enumeration task, subjects were tachistoscopically presented with an array of either one, two, three, or four randomly arranged dots and were to say how many dots were present. The array was visible for only 300 msec in order to avoid confounding the rate of enumeration with the rate of visual scanning. In order to control for a potential confound in the enumeration task, we also ran a simple reaction-time task. In the enumeration task, the subjects' vocal response varies depending on the number of dots in the array (i.e., they say "one," "two," etc.). Thus, differences in RT to the various array sizes could arise simply from differences in the time subjects take to pronounce the various number names. We therefore gave subjects a series of simple RT trials in which, before a trial began, the subject was told which number name to say (i.e., "one," "two," "three," or "four") when a neutral stimulus appeared in the tachistoscope. Any consistent variability in pronunciation time between the various number names would be evident in this control task. METHOD
Subjects. — Twenty-four subjects were tested in each of four groups: patients with AD, depressed-elderly patients, normal-young, and normal-elderly individuals (see Table 1 for subject characteristics). The Alzheimer patients (all outpatients) were recruited from the Alzheimer Disease Research Center and the Benedum Geriatric Clinic of the University of Pittsburgh. These patients met the criteria of McKhann et al. (1984) for probable AD based on the results of a complete medical, neurological, and psychiatric examination. None of the AD patients had a history of other major psychiatric or neurological disease. All AD patients had a modified Hachinksi score of 4 or less, making it unlikely that
Table 1. Means and (Standard Deviations) for Age, Years of Education, and Scores on the Mini-Mental State Exam (MMSE), Dementia Rating Scale (DRS), Geriatric Depression Scale (GDS), and Hamilton Rating Scale for Depression (HRSD) for the Subjects Used in the Data Analysis
Normal-young Normal-elderly Alzheimer patients Depressed-elderly
GDS
Age
Education
MMSE (n = 30)
DRS
No. of Subjects
(n = 144)
(n = 30)
23
23.9 (3.4) 76.4 (5.4) 76.9 (8.6) 66.5 (6.0)
13.7 (2.1) 13.3 (2.8) 13.2 (3.5) 12.0 (2.6)
29.3 (.6) 27.5 (1.6) 20.2 (3.1) 29.2 (1.1)
140.9 (2.4) 139.1 (3.4) 118.3 (11.7) 139.1 (4.6)
24 21 24
HRSD
2.8 (3.3)
3.0
—
(2.4)
6.9
—
(4.8) 18.9 (6.4)
20.9 (5.0)
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Steinberg's (1975) memory-search task. A subject is given a varying number of items to hold in memory (memory set). A single stimulus is then presented, and the subject has to decide whether or not this stimulus is a member of the memory set. Typically, RT increases linearly as a function of the number of items in the memory set (i.e., with every increase of one item in the memory set, RT rises by a relatively constant amount). This pattern of results has commonly been interpreted as evidence that subjects serially compare the stimulus with each item in the memory set. Because the motor response is the same regardless of the number of items in the memory set, the slope of the obtained linear RT function reflects the time subjects need to perform a single memory comparison. By contrast, the time involved in perceptual encoding of the stimulus, motor initiation, and execution shows up in the intercept of this linear function, which effectively represents the subjects' RT when no memory comparisons are required. Using this memory-search task, it has been found that normal aging produces a modest increase in the slope of this linear function, as well as an increase in the intercept (Anders, Fozard, & Lillyquist, 1972), suggesting a slowing of both motor and cognitive operations. Two studies (Hart & Kwentus, 1987; Hilbert, Niederehe, & Kahn, 1976) of depressed geriatric patients both found that, whereas the overall RT of the depressed patients was slower than that of the normal older adult, the rate at which the depressed patients searched their memory (i.e., the slope of the function relating memory-set size to RT) was no greater in depressed patients than it was in the normal older adult. Rather, it was the intercept that was increased in the depressed patients. This suggests that the slower RT of depressed geriatric patients is due to a motor retardation rather than a slowing in cognitive processing. Given these results, the obvious way to investigate the source of the RT slowing in AD would appear to be this same memory-search task. Unfortunately, this task has proved too difficult for AD patients, who make so many errors that it is impossible to interpret the pattern of their RTs (Hilbert et al., 1976). Therefore, at present, there is no direct evidence using this technique as to whether the RT slowing in AD is caused by: (a) a slowing of information processing, (b) a motor retardation, or (c) both. This study used the same general approach as the Sternberg task, but examined the speed with which Alzheimer
P333
COGNITIVE SLOWING IN ALZHEIMER'S DISEASE
Procedures. — In the enumeration task, the subject was shown an array of between one and four dots and was asked to say aloud the number of dots present. The dots were 1 cm in diameter (2° of visual angle) and fell randomly within a 10 x 10 cm area on the screen of a one-channel tachistoscope. On each trial, the experimenter said, "How many dots do you see?" and then .5 second later exposed the array for 300 msec. The subject's vocal response triggered a voice key, stopping a millisecond timer that started with presentation of the array. Before beginning the actual task, the subject was given two practice trials. This was followed by 64 trials: 16 each of one-, two-, three-, and four-dot arrays randomly intermixed. The whole task took less than 30 minutes. The subjects were also given a control task in which on each trial a 5 cm x 1 cm black bar was presented in the center of the tachistoscope screen. The bar appeared between .5 and 2 seconds after a vocal warning from the experimenter. This bar remained visible until the subject responded. The subject was given four blocks of six trials.
Before each block the experimenter told the subject what number name to say when the bar appeared (either "one," "two," "three," or "four"). The order in which the four different responses were tested was counterbalanced between subjects. The control task was always given before the enumeration task. RESULTS
Data from four subjects (one normal-young and three AD patients) were dropped from the analysis because their error rate on the enumeration task exceeded 15%. It should be noted that the pattern of RTs in these subjects was very similar to that of the remaining subjects. Data from the three excluded AD patients showed a highly linear (r2 = .99) function with a slope even steeper (slope = 356 msec) than that for the AD patients used in the data analysis. Mean latencies for correct responses served as the data in a repeated-measures analysis of variance (ANOVA). Because the AD patients showed substantial variability with respect to the normals, the assumption of homogeneity of variance was not met. However, following a logarithmic transformation, the experimental data passed Hartley's Fmax test and, therefore, all analyses used the transformed scores. It is important to note that this transformation did not change the pattern of the statistical results. For the purposes of readability, untransformed RT data are presented in Table 2. Dunn's a priori nonorthogonal contrasts and Dunn's a priori pairwise comparisons were used to assess the nature of the significant interactions. This study used a 4 X 4 design with Group (normalyoung, normal-elderly, depressed-elderly, and AD patients) as a between-subjects factor and Array Size as a withinsubjects factor. There was a significant main effect of Group: F(3,88) = 15.4, p < .001. Dunn's pairwise comparisons revealed that the AD patients were significantly slower than all the other groups (p < .01). The normal-elderly and depressed-elderly subjects did not differ from one another, although both groups were significantly slower than the young (p < .05). There was also a main effect of Array Size: F(3,264) = 232, p < .001. Dunn's pairwise comparisons
Table 2. Mean Response Time (Standard Deviation) in Milliseconds and Percent Errors for the Four Array Sizes, with the Parameters of the Linear Function Relating Array Size to Response Time Number of Dots 1 Normal-young
439 (43) .0% Normal-elderly 496 (48) .0% Alzheimer patients 557 (100) .9% Depressed-elderly 492 (63) .3%
2
3
4
r2
Slope
Intercept
477 (47) .3% 521 (57) .3% 565 (67) .9% 530 (78) 1.6%
520 (42) .5% 595 (57) 1.6% 635 (85) 3.9% 571 (84) 1.3%
521 (50) 1.4% 608 (61) 1.3% 759 (203) 6.8% 599 (83) .8%
.90
29
417
.93
41
452
.87
68
460
.99
36
458
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they suffered from multi-infarct dementia (Rosen, Terry, Fuld, Katzman, & Peck, 1980). The group of depressedelderly patients was composed of both inpatients and outpatients from the Western Psychiatric Institute and Clinic of the University of Pittsburgh. They carried a diagnosis of major depressive disorder (unipolar, nonpsychotic) based on Research Diagnostic Criteria (Spitzer, Endicott, & Robbins, 1978). The depressed patients had an average score on the first 17 items of the Hamilton Rating Scale for Depression (Hamilton, 1976) of 20.9 (SD = 5.0). The normal-young and normal-elderly subjects were recruited from the community and were matched to the AD group in sex, education, and, in the case of the normal-elderly, age. None of the subjects in this study, normals or patients, were taking psychoactive medications at the time of testing. The depressed patients were tested at the end of a 2-week, medication-free period. All of the subjects received two neuropsychological screening tests to assess their general level of cognitive functioning: the Mini-Mental State Exam (Folstein, Folstein, & McHugh, 1975) and the Dementia Rating Scale (Mattis, 1976). The AD patients' scores on these two screening tests were indicative of a mild to moderate dementia, whereas the scores of the normals and the depressed patients showed no evidence of a major cognitive impairment (see Table 1). All subjects were also given the Geriatric Depression Scale (Yesavage et al., 1983), a short self-rating scale developed specifically for use as a screening instrument in older persons. Possible scores on this scale range from 0 to 30, with scores between 11 and 19 suggesting the presence of a mild depression, and scores of 20 or more suggesting a moderate to severe depression. None of the normals scored above 9 on this scale, while four of the AD patients fell in the mildly depressed range, scoring between 12 and 18. Our depressed-elderly subjects were all in the moderate to severe range, and their scores on the Geriatric Depression Scale correlated significantly with their Hamilton scores (r = .52; p < .01). The health of the young and elderly control subjects was established on the basis of an extensive medical questionnaire that concentrated on symptoms of cardiovascular and neurological disease.
NEBESETAL.
P334
800 'Alzheimer
750 -700 --
Normal Elderly
650 --
"
600 - • Depressed Elderly .
550 --
a:
was between the slope of the RT function and the Dementia Rating Scale (r = - . 4 9 , p < .05); such that the more demented the patient, the greater the slope. The slope and intercept for the depressed patients did not correlate significantly with any of the severity measures. From the error data in Table 2, it is clear that all four subject groups were very accurate on this task and that only in the AD patients was there any substantial variation in error rates between the different array sizes. Because both error rate and RT rose with increasing array size in the AD patients, there was no evidence for a speed-accuracy tradeoff. The data from the control task (see Table 3) again involved a 4 x 4 design, with Group as a between-subjects factor and Response (i.e., saying "one," "two," "three," or "four") as a within-subject factor. There was a main effect of Group: F(3,92) = 21.72,/? < .05. Dunn's pairwise comparisons indicated that the only significant difference was between the AD patients and the normal-young (p < .05). There was also a significant effect of Response, with Dunn's pairwise comparisons showing a significant difference only between responses "one" and "four." There was no Group by Response interaction: F(9,276) = 1.12. These results demonstrate that, whereas there may be some differences in the time needed to pronounce the various number names, they are certainly not sufficient to account for the linear pattern of results evident in the enumeration task, nor the difference in slope between the AD patients and the other subject groups. DISCUSSION
There are several major findings from this study. First, the time subjects took to say how many dots were present in an array rose linearly with array size. This replicates earlier work in normal-young (Chi & Klahr, 1975; Svenson & Sjoberg, 1983) and suggests that the subjects were performing a serial process. We can therefore estimate the rate at which they conducted this process from the slope of the function relating array size to RT. Second, although all four subject groups showed a linear function, the magnitude of the slope differed between the groups. The normal-young, normal-elderly, and depressed-elderly all had similar slopes ranging between 30 and 40 msec. By contrast, the slope for the AD patients was from 66 to 134% greater than that for the other three groups. This greater slope (see Figure 1) suggests that the longer RTs found in AD patients are at least partly due to a cognitive slowing or a reduction in the rate at which Table 3. Mean Response Times (Standard Deviations) for Responses in the Simple Response-time Control Task
500 --
Spoken Responses Young
"Four"
450 --
"One"
"Two"
"Three"
400 --
306
331
336
344
(54)
(62)
(65)
(69)
Normal-young Normal-elderly
350--
1
2
3
4
Number of Dots Figure 1. Response time (RT) in milliseconds as a function of the number of dots in the stimulus array.
Alzheimer patients Depressed-elderly
350
356
380
381
(69)
(57)
(70)
(60)
370
381
378
383
(138)
(106)
(132)
(102)
376
356
356
365
(132)
(80)
(71)
(83)
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revealed that all four array sizes were significantly different from one another (p < .01), with RTs increasing significantly with each increase in array size (one to two to three to four dots). Thus, as array size increased, the time subjects took to determine how many dots were present also increased. There was also a significant interaction between Group and Array Size: F(9,264) = 7.81, p < .00001. Dunn's nonorthogonal contrasts revealed that there was no difference in the pattern of RTs between the normal-elderly and depressed-elderly: F( 1,296) = 1.42, nor between the normal-young and the normal-elderly/depressed-elderly (pooled): F( 1,296) = 0.42. However, when the pooled data for the normal-young, normal-elderly, and depressed-elderly were compared with that for the AD patients, there was a significant difference: F(l,296) = 3.17, p < .01. Thus, the pattern of RTs as a function of array size differed between the AD patients and the other three groups. This is evident in Table 2 and Figure 1 as a greater slope for the AD patients than for any of the other groups. That is, with each additional item in the array, there was a greater increase in the AD patients' RT than there was in the RTs of the normals or depressed-elderly patients. Because it is possible that the linearity of the group data does not accurately reflect the pattern of results within individual subjects, we individually determined the function relating each subject's RT to array size. The individual function for most subjects (74% of the young, 83% of the normal-old, 76% of the AD patients, and 75% of the depressed-elderly) was highly linear (r > .9). In order to determine whether the performance of the AD and depressed-elderly patients varied as a function of the severity of their dementia or depression, we correlated the slope and intercept of each subject's individual function with that person's score on two measures of general cognitive impairment (Mini-Mental State Exam and Dementia Rating Scale) and two measures of depression severity (Hamilton Rating Scale for Depression and Geriatric Depression Scale). For the AD patients, the only significant correlation
COGNITIVE SLOWING IN ALZHEIMER'S DISEASE
al. and Hart and Kwentus studies that did find a substantial amount of psychomotor slowing. One possible explanation is that our depressed patients were specifically chosen to be free of cognitive impairment, whereas those of Hart and Kwentus definitely did have cognitive deficits (the status of the depressed patients in Hilbert et al. is not clear). Thus, major psychomotor slowing may only occur in those depressed patients who have an associated cognitive impairment. Another possible explanation is the previously mentioned intertrial variability produced by having vocal responses that differed in the ease with which they triggered the voice key. Although the lack of a substantial psychomotor slowing in our depressed patients limits our interpretation of their results, it should be remembered that Hart and Kwentus and Hilbert et al. did show major psychomotor slowing in their depressed patients, yet these depressed individuals still did not differ from normals in the slope of the function relating their RT to set size. One final theoretical issue relevant to these results is the distinction drawn by some investigators between cortical (e.g., AD and Picks disease) and subcortical dementias (e.g., Parkinson's disease, Huntington's disease, and supranuclear palsy). Cummings has argued (Cummings, 1986; Cummings & Benson, 1986) that those dementias having a predominantly subcortical pathology share a common set of behavioral symptoms not found in the cortical dementias. One of the symptoms that is supposed to distinguish a cortical from a subcortical dementia is an increased RT resulting from a slowing of information processing (bradyphrenia). He claims that such a slowing is found in subcortical dementias but not in cortical dementias. However, as Levy and Sahakian (1987) point out, there is no real evidence for this particular distinction. In fact, there is considerable evidence that, in comparison with the normalold, patients with AD are substantially slower both on simple and choice RT tasks (Gordon & Carson, 1990; Nestor, Parasuraman, & Haxby, 1991; Pirozzolo, Christensen, Ogle, Hansch, & Thompson, 1981), as well as on more complex cognitive tasks (e.g., Nebes, Boiler, & Holland, 1986). These results confirm this and suggest that the increased RT in Alzheimer patients reflects an actual reduction in the rate at which they process information (evident here in the slope). That is, AD produces a cognitive slowing or bradyphrenia. If this is correct, then cognitive slowing is not restricted to subcortical dementias such as Parkinson's disease (Wilson, Kaszniak, Klawans, & Garron, 1980), but is also present in the prototypical cortical dementia: AD. These findings certainly do not disprove the hypothesis that there are major behavioral differences between cortical and subcortical dementias. The pattern of neuropsychological deficits found in AD patients definitely differs from that found in Parkinson or Huntington patients in a number of ways (Brandt, Folstein, & Folstein, 1988; Heindel, Salmon, Shults, Walicke, & Butters, 1989). These results do, however, suggest that one of the major behavioral symptoms postulated to exist solely in subcortical dementias (i.e., a cognitive slowing) is, in fact, also a symptom of the dementia found in AD. What might be the neurological basis of the cognitive slowing associated with AD? It has been argued that the
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AD patients process information. This is consistent with the results of earlier studies (Gordon & Carson, 1990; Vrtunski et al., 1983; Williams et al., 1991) that used very different techniques to determine the source of the behavioral slowing in AD. These results are, however, inconsistent with these earlier studies in one major way. We found no evidence for a motor retardation in our AD patients. In this study, a motor slowing would have been evident in the intercept of the RT function. However, from Table 2 it is clear that there was practically no difference (8 msec) between the intercepts for the AD and normal-old subjects. There was also no significant difference between the normal-old and AD patients on the control task (Table 3) that measured primarily sensory-motor RT. Assuming that the motor slowing demonstrated in previous studies is real, why did we not find it in this study? One possible reason is the type of response used in the present task — a spoken word. The four possible response words had different initial sounds and varied in the ease with which they triggered the voice key. This introduced a substantial amount of variability into the pronunciation time that may have been sufficient to obscure any group differences. The pattern of RT results in the depressed-elderly was quite different from that in the AD patients. The slope of the function relating RT to array size was the same in the depressed patients as in the normal-old. This is consistent with findings using the memory-search paradigm (Hart & Kwentus, 1987; Hilbert et al., 1976) and suggests that depressed geriatric patients do not suffer from a cognitive slowing any greater than that associated with normal aging. Although there are some reports in the literature of a cognitive slowing in depressed patients, these tasks have generally required subjects to hold a central button depressed until one of several other buttons lights up, at which time they lift their finger and move it to the illuminated button. The time between the lighting of the button and the lifting of the finger has been considered decision time, a measure of the subject's speed of cognitive processing (e.g., Knott & Lapierre, 1987). However, as Cornell, Suarez, and Berent (1984) point out, this approach assumes that subjects do not begin to lift their finger until they have made their decision. This assumption may be unwarranted. Also, the decision time in this type of procedure is certainly confounded with motor initiation and execution time. Thus, these findings, in conjunction with the earlier memory-search results, suggest that the behavioral slowing often seen in depressed geriatric patients is not due to a reduction in the rate at which these subjects process information. However, there is a complicating factor. There was practically no difference between the intercepts of the depressedelderly and normal-elderly groups in this study (6 msec), especially in comparison with the 100 to 200 msec difference found in the memory-search studies (Hart & Kwentus, 1987; Hilbert et al., 1976). There was also no significant difference in overall RT or in simple RT (i.e., the control task) between the normal- and depressed-elderly. It is unlikely that this lack of a major psychomotor slowing in our depressed patients is caused by our patients being less depressed than those in other studies. The mean Hamilton score for our patients (20.1) falls in the same range as that in the Hilbert et
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ACKNOWLEDGMENTS
This study was supported by grants from the National Institute on Aging (AG-05133) and the National Institute of Mental Health (MH 30915, MH 00295, MH 37869, and MH 43832). The authors would like to thank Dr. Leslie Leahy for assistance with the statistical analysis. Address correspondence to Dr. Robert D. Nebes, Western Psychiatric Institute and Clinic, University of Pittsburgh, 3811 O'Hara Street, Pittsburgh, PA 15215-2593. REFERENCES
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bradyphrenia (cognitive slowing) found in patients with a subcortical dementia, such as Parkinson's disease, is caused by the same basal ganglia lesions responsible for the patients' motor retardation (Rogers, Lees, Smith, Trimble, & Stern, 1987). This could be either a direct effect of the basal ganglia lesions themselves or an indirect effect caused by the reduction in dopaminergic innervation of the cortex that follows such lesions. Given that AD patients do not have prominent basal ganglia lesions and yet show a similar bradyphrenia, the neurological basis of cognitive slowing may very well be cortical. This would be consistent with Rogers et al., who found that bradyphrenia occurred primarily in those Parkinson patients who showed evidence of cortical atrophy on CT scan. Thus, it is possible that the cognitive slowing evident both in AD and in some subcortical dementias reflects a disruption of cortical function. However, considering the lack of direct evidence, this suggestion must be viewed as speculation. What clinical implications might these results have? With current techniques, it is often difficult to distinguish patients with a mild dementia from those suffering from depression. If our interpretation of these results is correct, then information about whether or not cognitive processing is slowed in a particular patient might have some clinical utility. Although this methodology is too complex for general clinical use, there is a simple paper-and-pencil task that can yield a measure of an individual's cognitive and motor speed (Storandt, 1976). To establish that this approach is actually useful, however, it will be necessary not only to demonstrate that these findings generalize to other cognitive operations, but also to show that a similar pattern of differential slowing in dementia and depression is evident with this simpler measure.
