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January2005 Vol.42 Issue:      1 Table of Contents
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Neuropsychological Performance in Normal Adults In Relation to Clinical Variables and Regional Brain Perfusion

Mahmoud Allam1, Fadya Elwan2, Manal S. Fahmy1, Ebtesam Fahmy1, Hosna Mostafa3, Nervana El-Faioumy1, Noha Abo-Krysha1, Jehan Ramzy1
Departments of Neurology1, Psychology2, Nuclear Medicine3, Cairo University

ABSTRACT

Objective: Mainly to characterize changes in neuropsychological performance and regional brain perfusion at single photon emission computed tomography (SPECT) associated with normal aging, gender, education and cerebrovascular risk factors and to consider SPECT findings as predictors of performance on psychometric tests. Methodology: Neuropsychological testing and perfusion SPECT images using 99mTc-hexamethyl propyleneamine oxime (HMPAO) were obtained from 48 normal subjects (16 men, 32 women) aged 21-84 yrs. Results: Advancing age, after adjusting for gender, vascular risk factors and education,  was associated with poorer performance on tests for fluid analytical abilities, executive functions, speed of information processing, attention, visuospatial and constructional abilities but not with crystallized abilities. Women did better on incidental (verbal short-term) memory tests compared to men. Education was a strong predictor of increased scores on nearly all psychometric tests except for incidental memory; whereas vascular risk factors were associated with only executive functions. Multiple regression analyses demonstrated an age-related regional decline in right frontal, bilateral parietal, and occipital lobes. Women had higher perfusion than men in all regions of interest except bilateral temporal lobes. Vascular risk factors accounted for a significant proportion of variance in average cerebral perfusion of left temporal, right thalamus and bilateral basal ganglia regions, all of which were not influenced by advancing age. Increased average perfusion of right frontal region was a significant predictor of  better performance on tests of fluid analytic abilities (abstract/spatial reasoning), visuospatial and constructional abilities, whereas increased perfusion of left frontal region was a significant predictor of  better performance on tests of executive functions mainly speed of information processing and perceptual mental state as well as crystallized abilities (similarities). Increased perfusion of right occipital region was a significant predictor of  better performance on tests for intentional (sensory) memory; while  increased perfusion of right parietal and right thalamic regions was a significant predictor of  better performance on tests for incidental (verbal short-term) memory. On the other hand decreased perfusion of left occipital region was a significant predictor of improving performance on tests of executive function. In addition, decreased perfusion of left parietal region was a significant predictor of better performance on tests of crystallized abilities (intentional memory and similarities) and block design. Relative decrease of cerebral perfusion in other regions in association with better performance of psychometric tests was attributed to either a compensatory mechanism or a lack of inhibition or task-induced deactivation that occurs at younger years and leads to reduced resting metabolic activity with advancing age. Conclusion: Performance on psychometric tests is associated with activation of a distributed network of brain regions which varies with advancing age. The recognition of functional imaging correlates to changes in cognitive function is important in enabling the distinction to be made between age- and dementia-related abnormalities, especially that functional abnormalities may be detected earlier.

(Egypt J. Neurol. Psychiat. Neurosurg., 2005, 42(1): 115-135).

 




INTRODUCTION

 

Historically neuropsychologists have been most concerned with understanding brain-behavior relationships. That is, trying to understand what brain regions are responsible for which cognitive abilities and behaviors. Accordingly, a major focus of neuropsychological research and clinical practice was to develop methods that directly assessed the functional integrity of specific brain regions. Traditionally, clinical neuropsychologists have sought to identify brain functions by observing the defects that resulted when specific brain areas were damaged1.

Marchal et al.2 were concerned with the differentiation between healthy and normal aging which included age-related subclinical disease as slowly progressive cognitive or cerebrovascular impairment, hypertension, diabetes and hypercholesterolemia. They described healthy aging as lacking risk factors, in addition to high educational level and normal IQs.

Areas of cognitive functioning that tend to change with time or in response to medical interventions that bring about subtle cognitive effects include: memory, attention, and general cognitive speed. These are listed among the so called fluid abilities. Many other cognitive domains are far less likely to change, for example, reading, general knowledge, and language abilities. These are called crystallised abilities, and by definition remain relatively constant over the normal adult’s life span. Fluid abilities are tested for to track individual changes in cognition where a decline also marks incipient dementia. As these abilities can be assessed without time consuming or effort consuming procedures they are used widely3-5.

The most anterior part of the frontal lobe is involved in complex cognitive processes like reasoning and judgment. Collectively, these processes may be called biological intelligence. A component of biological intelligence is executive function. According to Denckla6 executive function regulates and directs cognitive processes. Decision making, problem solving, learning, reasoning and strategic thinking are all part of executive functioning7.

A breakdown in executive functioning affects goal directed behavior and the individual may not only have difficulty getting started on an activity, but once started may have difficulty changing or stopping the activity8-9. Goal directed behavior includes functional activities such as cooking, dressing, and housework. Accordingly, age-related decline in executive control functions is one reasonable explanation for the decline in functional status i.e. the ability to care for one's self, that accompanies old age10.

Researchers have used the concept of executive functioning in recent years to account for cognitive decline in older adults. Disruption of central control processes can result in impaired behavior even if component processes such as memory and attention are intact. However, it has been suggested that people who differ on tests of executive functioning differ in nearly the same manner on tests commonly used to assess inductive reasoning and fluid intelligence11.

Neuroimaging such as positron emission tomography (PET), functional magnetic resonance imaging (fMRI) and single photon emission computed tomography (SPECT) have been extensively used to explore the functional neuroanatomy of cognitive functions12-13.

Brain SPECT is a nuclear medicine imaging study that uses isotopes bound to neuro-specific pharmaceuticals to evaluate regional cerebral blood flow (rCBF) and indirectly metabolic activity. SPECT with the tracer 99Tcm-hexamethylpropyleneamine oxime (HMPAO) does not provide absolute quantification of rCBF but provides a moment-in-time image of relative rCBF, where each patient acts as his or her own control. In order to fully evaluate brain function, two studies are done: a baseline study done at rest and a concentration study done while a patient performs a concentration task14. SPECT perfusion patterns differ from rCBF-PET images  probably due to differences in the mechanism of accumulation of used agent in the brain and so for clinical diagnoses, these patterns must be taken into consideration15.

Problems that arise from the great variability in brain anatomy between individuals have been studied. These problems cause uncertainty in localization, which limits the effective resolution of functional imaging, especially for brain areas involved in higher cognitive function16.

The main objective of this study was: to characterize changes in neuropsychological performance and regional brain perfusion at single photon emission computed tomography (SPECT) associated with normal aging, gender, education and cerebrovascular risk factors and to consider SPECT findings as predictors of performance on psychometric tests.

 

SUBJECTS AND METHODS

 

Subjects

Fourty-eight normal right-handed subjects [16 men (33.3%); 32 women (66.7%)] of age range 21-84 (mean= 54.29 S.D= 18.723) were included in the study (Table 1). They were selected from hospital staff as well as patients' relatives attending Neurology department at Kasr El-Aini Teaching hospital.

Total years of formal education (hereinafter referred to as education) were used as an index of education. Years of education ranged from 6-20 yrs (mean= 10.02; S.D= 3.564) i.e. high education level (Table 1).

 

Inclusion criteria (Table 1):

1.         An IQ>70 according to Wechsler adult intelligence scale–revised (WAIS-R)17. 

2.         Mini-mental state examination (MMSE) ≥ 2418: Crum et al.19 postulated a scoring system for MMSE according to the person's age and educational level.

3.         A score of ten points or less on the beck depression scale (BDS)20. Because depression could lead to cognitive impairment, we used the BDS to adjust for the effect of depressive symptoms on cognitive function. A score above ten points on the BDS indicates that the presence of depression   is likely.

4.         Normal performance of activities of daily living.

5.         Normal clinical examination, with no history of neurological, psychiatric or medical illnesses (renal, hepatic or endocrinal)

6.         Normal brain computerized tomography (CT) or magnetic resonance image (MRI) findings.

 

Methods

All subjects were submitted to:

I.      Complete Clinical Assessment: medical, neurological and cardiovascular.

II.     Laboratory Workup: fasting and two-hours post-prandial blood glucose level; serum lipogram: cholesterol, triglycerides, high-density and low-density lipoproteins; and serum uric acid level.

III.    Neuropsychological Assessment:  A comprehensive battery of neuropsychological tests was designed, based on a review of relevant published reports. Tests were grouped into cognitive domains, as follows:

A)  General and global intellectual functions: WAIS-R17 providing verbal IQ (vocabulary and similarities subtests) and performance IQ (block design for visuospatial and constructional abilities and picture completion for visual perception).

B)  Executive function tests: the two tests that were used were heavily influenced by attention, concentration, resistance to distraction, and cognitive flexibility (or set-shifting).

1.   Visual Search Test: (21) which is an important tool in research on visual attention. The test gives an indication of the speed of information processing and perceptual mental strategies. It consists of two parts: a) visual search for letter and b) visual search for numbers. The score is an average of both parts and is recorded in seconds.

2.   Trail Making Test (TMT parts A, B): standardized test for evaluating the speed  of  information  processing and the visuo-motor perception (recorded in seconds). It is derived from Halstead - Reitan Battery of Neuropsychological Tests (HRBNT)22.

C)  Fluid analytic ability tets: three subtests of the Stanford-Binet intelligence scale were used grouping visual-spatial processing and fluid reasoning together into an abstract/visual-spatial reasoning factor23: pattern analysis; copying; paper folding and cutting. There average score was recorded.

D) Verbal Memory:

1.     Intentional Memory (Sensory Memory)  Test:

It is a test designed to assess verbal-associate memory. The candidate is instructed to tap on the table by his hand whenever he (or she)  hears a specific word in a certain passage. It tests for distractability24.

2.     Incidental Memory (Short-term memory) Test :

There is no warning about upcoming memory test. The candidate is asked five given questions concerning the events taking place in a short story told by the examiner25.

P.S. Both tests for memory together with verbal subtests of WAIS-R were considered together as crystallized abilities.

IV.           Neuroimaging:

A)  Computed tomography (CT) and/or Magnetic Resonance Imaging (MRI): to exclude any structural brain lesion.

B)  Single Photon Emission Computed Tomography (SPECT)      SPECT scans of the brain were performed at the Department of Nuclear Medicine, Cairo University.

Radiopharmaceutical: was the tracer 99Tcm - hexamethyl - propyleneamine oxime (HMPAO) that provides images allowing semi-quantitative estimation of regional cerebral blood flow (rCBF). Patients were made to lie in the supine position with canthometal line parallel to head detectors or the gamma camera.

SPECT images:

SPECT images were acquired 60 minutes post-injection of the radiopharmaceutical by a dual head ADAC gamma camera equipped with high-resolution collimators interfaced to a dedicated unix computer . Data were collected in a 64 x 64 matrix without zooming through a 360 degree rotation , 6 angle interval for 30 seconds per arc interval.

Approximately 6.5 million counts acquired images were reconstructed and filtered using a hamming filter (order 5, cut off frequency 6) and back projection. The trans-axial sections were re-oriented parallel to the base of the brain to obtain sagittal and coronal reconstruction. The SPECT images were analyzed semi-quantitatively. Average counts / pixel expressions regional blood flow were calculated for each region of interest (ROI) and measured as a ratio to mean total cerebellar counts per pixel. Thus, the value obtained by computer analysis of the cerebellum was considered as 100 %.

V.     Statistical Analysis: This is a cross-sectional study where statistical analysis was performed using software package SPSS for Windows (version 7.5.1).

The following was done:

1)   Multiple regression analysis: the purpose of this analysis was:

a)   To estimate the coefficients of the linear equation, involving one or more independent variables,  that best predict the value of the dependent variable, holding the values of all other variables in the regression equation constant. Independent variables included clinical characteristics: age – gender (coded as male=1, female=2) – years of education - cerebrovascular risk factors (coded as 1= present, 0=absent) while dependent (outcome variable) was psychometric tests in one model and average cerebral perfusion in another. Independent variables were entered in a single step to be allowed to compete for shared variance in the dependent variable, so that no single variable is given greater priority than another.

b)   To reduce a large number of variables (average cerebral perfusion in six ROI on both sides) to a “best” subset of variables of a manageable size to verify their association with  the dependent variable being  psychometric tests in this case. All independent variables were entered in a single step and then removed one at a time based on removal criteria i.e. backward variable elimination.  For each model we conclude: Multiple R squared (R2): is a measure of how much of the variance of the dependant variable is accounted for by the predictor; (b): beta weights, standardized coefficient, and P: a two-tailed significance level of t (t value for B).

2)   Spearman's rho bivariate correlation 'r': to measure the association between categorical variables (non-parametric).

3)   Descriptive statistics: mean and standard deviation (S.D), minimum and maximum values and proportions.

 

RESULTS

 

Clinical Characteristics of Normal subjects

The clinical characteristics of included subjects are presented in table (1). Sixteen subjects (33.3%) were males and 32 were females (66.7%).

Vascular risk factors were reported in 45.8% of whole group (Table 2). Nine subjects had only one risk factor (18.8%); 6 had 2 risk factors (12.5%); 4 had 3 (8.3%) and 4 risk factors were found in only two subjects (4.2%).

A significant positive correlation was found between presence of vascular risk factors and both age (r= 0.456, p= 0.001) and gender (r= 0.296, p= 0.041) where presence of risk factors was more in advancing age and in female gender.

 

Clinical variables as predictors of performance on psychometric tests:

Using multiple regression analysis, the effect of clinical characteristics (age – gender- education – vascular risk factors) on scores of psychometric tests was  assessed  (Tables 3-5) (Figure 1) where they significantly accounted for :

§          48%, 52.5% and 51.1% of variation in performance on tests for fluid analytic abilities, TMT A and TMT B respectively, with strong predictors being: (i) age (P<0.001) i.e. advancing age was associated with a decline in performance, (ii) education (P<0.001; 0.004) i.e. increased years of education was associated with improved performance and (iii) presence of vascular risk factors (P=0.001; 0.008; 0.049). (Table 3).

§          28.7%, 15.7% and 39.4%  of variation in  performance on visual search, picture completion and block design tests respectively,  with (i) age and (ii) education as main predictors  i.e. advancing age and decreased years of education  are associated with impaired performance  (P<0.001). (Table 3, 5).

§          12.9%, 27.4% and 25.3% of variation in performance on intentional memory tests, vocabulary and similarities respectively (crystallized abilities), with education being main predictor i.e. better performance with higher education (P=0.002; <0.001). (Table 4).

§          12.7% of variation in performance on incidental memory tests where female gender was a strong predictor (P=0.004). (Table 4).

 

Clinical variables as predictors of average cerebral perfusion according to SPECT:

Analysis of the influence of clinical characteristics (age – gender- education – vascular risk factors) on average cerebral perfusion in ROI (Tables 6, 7) (Figure 2) deduced that these variables significantly accounted for:

§          20.3%, 23.2 % , 33.6 %, 22.1 % and 27 % of variation in average cerebral perfusion of  right frontal, right and left parietal, right and left occipital regions with two strong  predictors being (i) age (P= 0.032; 0.008; 0.002; 0.039; 0.003) i.e. advancing age was associated with decreased perfusion, and (ii) gender  (P= 0.002; <0.001; 0.002) i.e. significantly higher perfusion in females.

§          22 %, 17.4 % and 13.7 %  of variation of average cerebral perfusion of  right thalamus, right and left basal ganglia respectively,  with two strong  predictors being (i) gender (P=0.004; 0.003; 0.009) i.e. significantly higher perfusion in females, and (ii) risk factors (P=0.013; 0.002; 0.011) i.e. their presence is associated with decreased perfusion.

§          20.6 % and 14.6 % of variation of average cerebral perfusion of  left frontal and left thalamus regions respectively, with gender  as a strong predictor  ( P=0.007; 0.013) i.e. significantly higher perfusion in females.

§          10.5 % of variation of average cerebral perfusion of left temporal  region, with presence of vascular risk factors  as a strong predictor  ( P = 0.042) i.e. their presence  is associated with decreased perfusion.

 

Average cerebral perfusion as predictor of performance on  psychometric tests:

Tables (8-10) present significant subsets of variables (average cerebral perfusion in ROI) to verify their association with performance on psychometric tests where average cerebral perfusion of:

§          Right frontal, right temporal and right basal ganglia as well as left parieto-occipital regions significantly accounted for 56.5%  of variation in performance on tests of fluid analytic abilities, significant predictors being that of right frontal (where increased perfusion is associated with higher scores) (P<0.001); right temporal and basal ganglia regions (where increased perfusion is associated with lower scores) (P=0.010;  0.004).

§          Left fronto – tempro - occipital regions accounted for 37.7% of variation in performance on TMT A  with main predictors being that of left frontal (where increased perfusion is associated with better performance) (P=0.03) and left occipital regions (where increased perfusion is associated with declining performance) (P=0.026).

§          Left fronto-parietal regions and left thalamus accounted for 43.2% of variation in performance on TMT B  with main predictor being that of left frontal region (where increased perfusion is associated with improved performance) (P=0.045).

§          Left fronto-occipital regions accounted for 23% of variation in performance on visual search tests with a trend wise significant association of increased perfusion of left frontal region with better performance (P=0.08) and  increased perfusion of left occipital region with poorer performance (P=0.087).

§          Right occipital and left parietal and thalamic regions accounted for 28.2% of variation in performance on intentional memory tests, these regions being significant predictors where increased perfusion of right occipital region was associated with improved performance (P=0.026) while that of left parietal and left thalamus with declining performance (P=0.024, 0.047).

§          Right parietal, right occipital, right thalamic and left temporal regions accounted for 30.7% of variation in incidental memory scores, where all these regions were significant predictors as increased perfusion of right parietal and right thalamic regions was associated with higher scores (P=0.022, P=0.002) whereas increased perfusion of right occipital and left temporal regions were associated with impaired incidental memory (P=0.005, P=0.044).

§          Right temporal, left frontal and left parietal regions accounted for 29.3% of variation in performance on similarities where main predictors were left frontal region as increased perfusion is associated with higher scores (P=0.002) and left parietal region where increased perfusion is associated with lower scores on similarities (P=0.008).

§          Right frontal, right basal ganglia and left parietal regions accounted for 33.6% of variation in performance on block design main predictors being right frontal region where increased perfusion is associated with higher scores (P=0.003) and left parietal, where increased perfusion is associated with lower scores (P=0.008).

 

Summary: Increased perfusion of right frontal region was a significant predictor of  better performance on tests of fluid analytic abilities and block design (visuospatial and constructional abilities), whereas increased perfusion of left frontal region was a significant predictor of  better performance on tests of executive functions (TMT A & B; visual search) and test for crystallized abilities (similarities).

Increased perfusion of right occipital region was a significant predictor of  better performance on tests for intentional memory; while  increased perfusion of right parietal and right thalamic regions was a significant predictor of  better performance on tests for incidental memory.

On the other hand decreased perfusion of left occipital region was a significant predictor of improved  performance on tests of executive function, whereas decreased perfusion of left parietal region was a significant predictor of  better  performance on tests of crystallized abilities (intentional memory and similarities) and block design.


 

 

Table 1. Clinical Characteristics of subjects.

 

Variable

MEAN

S.D.

RANGE

AGE (yrs)

54.292

18.72

21-84

EDUCATION (yrs)

10.02

3.564

6-20

IQ

94.792

14.28

71-140

MMSE

26.23

1.84

24-30

BDS

5.71

2.327

2-10

IQ: intelligence quotient; MMSE: mini-mental state examination; BDS: beck depression scale.

 

Table 2. Frequency of vascular risk  factors in descending order of frequency.

 

Vascular Risk Factors

Count

%

Diabetes

14

29.2

Hypertension

12

25

Cardiac

5

10.4

Dyslipidemia

5

10.4

Hyperuricemia

4

8.3

Smoking

3

6.3

Total

22

45.8

Table 3. Multiple regression analysis of clinical characteristics as predictor variables of performance on tests for fluid analytic abilities  & executive functions.

 

Outcome

Variable

Predictor

Variable

(b)

P-value

R2

Fluid analytic abilities

Age

-0.600

0.000**

0.480**

Gender

-0.101

0.226

Education

0.489

0.000**

Risk factors

0.342

0.001**

TMT A

Age

0.333

0.001**

0.525**

Gender

0.141

0.080

Education

-0.294

0.000**

Risk factors

0.271

0.008**

TMT B

Age

0.444

0.000**

0.511**

Gender

0.128

0.116

Education

-0.236

0.004**

Risk factors

0.201

0.049*

Visual search

Age

0.393

0.001**

0.287**

Gender

0.103

0.295

Education

-0.323

0.001**

Risk factors

-0.061

0.617

* significant P<0.05   ** Highly significant P<0.01 # trend wise significant. R2: R squared; (b): standardized regression coefficient; P: two-tailed significance level of t (t of B).

 

 

 

Fig. (1): Scatterplot indicating negative association between age and scores of tests for fluid analytic abilities  (b)= -0.600

Table 4. Multiple regression analysis of clinical characteristics as predictors of performance on tests for crystallized abilities.

 

Outcome

Variable

Predictor

Variable

(b)

P-value

R2

Intentional memory

Age

0.115

0.379

0.129*

Gender

0.147

0.190

Education

-0.258

0.022*

Risk factors

-0.004

0.976

Incidental memory

Age

-0.088

0.488

0.127*

Gender

0.318

0.004**

Education

-0.016

0.883

Risk factors

-0.175

0.199

Vocabulary

Age

0.126

0.276

0.274**

Gender

-0.054

0.586

Education

0.501

0.000**

Risk factors

-0.054

0.662

Similarities

Age

-0.122

0.298

0.253**

Gender

0.158

0.115

Education

0.406

0.000**

Risk factors

-0.125

0.319

* significant P<0.05   ** Highly significant P<0.01 # trend wise significant. R2: R squared; (b): standardized regression coefficient; P: two-tailed significance level of t (t of B).

 

 

 

Table 5. Multiple regression analysis of clinical characteristics as predictors of performance on tests for visual perception and constructional abilities.

 

Outcome

Variable

Predictor

Variable

(b)

P-value

R2

Picture Completion

Age

-0.230

0.067#

0.157**

Gender

0.088

0.409

Education

0.230

0.028*

Risk factors

-0.069

0.606

Block design

Age

-0.414

0.000**

0.394**

Gender

-0.103

0.254

Education

0.482

0.000**

Risk factors

0.160

0.158

* significant P<0.05   ** Highly significant P<0.01 # trend wise significant. R2: R squared; (b): standardized regression coefficient; P: two-tailed significance level of t (t of B).

 

 

Table 6. Multiple regression analysis of clinical characteristics as predictors of average cerebral perfusion in ROI  in relation to cerebellum.

 

Outcome

Variable

Predictor

Variable

(b)

P-value

R2

Right frontal

Age

-0.262

0.032*

0.203**

Gender

0.330

0.002

**

Education

0.035

0.729

Risk factors

-0.086

0.506

Right temporal

Age

-0.057

0.665

0.044

Gender

0.052

0.645

Education

-0.077

0.488

Risk factors

-0.186

0.192

Right parietal

Age

-0.320

0.008**

0.232**

Gender

0.343

0.001**

Education

0.013

0.897

Risk factors

-0.019

0.878

Right occipital

Age

-0.250

0.039*

0.221**

Gender

0.378

0.000**

Education

0.058

0.562

Risk factors

-0.053

0.678

Right thalamus

Age

-0.158

0.189

0.220**

Gender

0.298

0.004**

Education

-0.077

0.437

Risk factors

-0.324

0.013*

Right basal

Age

-0.043

0.726

0.174**

Gender

0.318

0.003**

Education

-0.181

0.079#

Risk factors

-0.308

0.002**

* significant P<0.05   ** Highly significant P<0.01 # trend wise significant. R2: R squared; (b): standardized regression coefficient; P: two-tailed significance level of t (t of B).


Table 7. Multiple regression analysis of clinical characteristics as predictors of average cerebral perfusion in ROI  in relation to cerebellum.

 

Outcome

Variable

Predictor

Variable

(b)

P-value

R2

Left frontal

Age

-0.205

0.092

0.206**

Gender

0.283

0.007**

Education

-0.028

0.777

Risk factors

-0.251

0.055#

Left temporal

Age

-0.083

0.518

0.105*

Gender

0.135

0.219

Education

-0.051

0.631

Risk factors

-0.282

0.042*

Left parietal

Age

-0.356

0.002**

0.336**

Gender

0.367

0.000**

Education

-0.056

0.543

Risk factors

-0.175

0.142

Left occipital

Age

-0.358

0.003**

0.270**

Gender

0.322

0.002**

Education

-0.085

0.377

Risk factors

-0.084

0.497

Left thalamus

Age

-0.068

0.588

0.146**

Gender

0.269

0.013*

Education

-0.205

0.051#

Risk factors

-0.243

0.073#

Left basal

Age

0.017

0.895

0.137**

Gender

0.286

0.009**

Education

0.006

0.955

Risk factors

-0.347

0.011*

* significant P<0.05   ** Highly significant P<0.01 # trend wise significant. R2: R squared; (b): standardized regression coefficient; P: two-tailed significance level of t (t of B).

 

 

 

 

Fig. (2) : Scatterplot denoting  age as significant predictor of perfusion in left parietal lobe (b) = -0.356

Table 8. Multiple regression analysis of average cerebral perfusion in ROI in relation to cerebellum as predictors of performance on tests for fluid analytic abilities and executive functions.

 

Outcome

Variable

Predictor

Variable

(b)

P-value

R2

Fluid analytic abilities

Right frontal

1.233

0.000**

0.565**

Right temporal

-0.324

0.010**

Right basal

-0.479

0.004**

Left parietal

-0.494

0.103

Left occipital

-0.586

0.105

TMT A

Left frontal

-0.629

0.03*

0.377**

Left temporal

0.142

0.277

Left occipital

0.655

0.026*

TMT B

Left frontal

-0.643

0.045*

0.432**

Left parietal

0.572

0.094

Left thalamus

0.165

0.279

Visual search

Left frontal

-0.531

0.08#

0.230**

Left occipital

0.528

0.087#

* significant P<0.05   ** Highly significant P<0.01 # trend wise significant. R2: R squared; (b): standardized regression coefficient; P: two-tailed significance level of t (t of B).

 

 

Table 9. Multiple regression analysis of average cerebral perfusion in ROI in relation to cerebellum as predictors of performance on tests for crystallized abilities.

 

Outcome

Variable

Predictor

Variable

(b)

P-value

R2

Intentional memory

Right occipital

-0.639

0.026*

0.282**

Left parietal

0.706

0.024*

Left thalamus

0.364

0.047*

Incidental memory

Right parietal

0.648

0.022*

0.307**

Right occipital

-0.881

0.005**

Right thalamus

0.633

0.002**

Left temporal

-0.278

0.044*

Vocabulary

Left frontal

0.828

0.031*

0.101#

Left parietal

-0.808

0.035*

Similarities

Right temporal

0.242

0.071#

0.293**

Left frontal

1.132

0.002**

Left parietal

-1.023

0.008**

* significant P<0.05   ** Highly significant P<0.01 # trend wise significant. R2: R squared; (b): standardized regression coefficient; P: two-tailed significance level of t (t of B).

 

Table 10. Multiple regression analysis of average cerebral perfusion in ROI in relation to cerebellum as predictors of performance on tests for visual perception and constructional abilities.

 

Outcome

Variable

Predictor

Variable

(b)

P-value

R2

Picture Completion

Left thalamus

-0.070

0.630

0.105

Block design

Right frontal

0.997

0.003**

0.336**

Right basal

-0.318

0.098

Left parietal

-0.830

0.008**

* significant P<0.05   ** Highly significant P<0.01 # trend wise significant. R2: R squared; (b): standardized regression coefficient; P: two-tailed significance level of t (t of B).

 

 

 


DISCUSSION

 

Normal brain aging affects several domains of neuropsychological function, including fluid intellectual abilities, complex attentional processes, some aspects of memory, psychomotor speed, accessing word knowledge, visuospatial skills, some forms of abstract reasoning and complex problem-solving26. One of the methods that directly assess the functional integrity of specific brain regions in neuropsychological research and clinical practice  is single photon emission tomography (SPECT). It has also been used trying to understand what brain regions are responsible for which cognitive abilities and behaviors1.

Neuropsychological performance and regional cerebral perfusion with a baseline [99Tc(m)]-d,l-hexamethyl- propylene amine oxime (HMPAO) SPECT study, at rest, were studied in 48 normal subjects (aged 21 to 84 years), with IQ more than 70 and MMSE score higher than 23 points. The effect  of age, gender, education and cerebrovascular risk factors as well as regional cerebral perfusion on neuropsychological performance were investigated.

According to Van Gorp et al.27, over learned well practiced familiar abilities and knowledge are crystallized and remain essentially unchanged whilst fluid activities, involving reasoning, problem solving and the efficient processing of rapid information, decline with increasing age. They suggested that psychomotor slowing can account for most if not all of the measured changes in performance that deteriorate with age. This was verified in our studied group where advancing age, after adjusting for gender, vascular risk factors and education, was a strong predictor of decline in performance on tasks mostly considered as frontal lobe functions including  fluid  analytic abilities (abstract reasoning and visuospatial processing) and  executive functions especially: decision making, problem solving, flexibility of thinking, visual-motor speed, set-shifting, inhibition, speed of information processing and perceptual mental strategies9. Age-related decline in executive functions is one reasonable explanation for the decline in  the ability to care for one's self, that accompanies old age10.

Other tasks that were influenced by aging were picture completion for assessment of visual perception and recognition, and block design testing visuospatial and constructional abilities. On the other hand, crystallized abilities i.e. intentional memory, vocabulary and similarities were not affected by advancing age, they were rather predicted for by years of education. Such findings have been reported beforehand28-29. Cognitive speed, consisting of attention span and processing speed, is the most sensitive measure as they are first manifestations of age-related cognitive decline. In old persons memory remains relatively intact until late stages of cognitive decline, whereas cognitive speed declines more rapidly30. This was confirmed in our study where tests for speed of perceptual and information processing declined with advancing age whereas memory tests were not affected. Perceptual speed accounts for >80% of age-related variance in performance on memory tasks31. It has been postulated that normal cognitive aging, mainly age-related decline of fluid intelligence is mediated by perceptual speed and age-related atrophic changes in frontal brain structures responsible for weakening of executive abilities32.

A solid body of research, carried out primarily in North America and Western Europe, has established that men excel on spatial tasks while women excel on tasks of verbal and item memory33-34. This was confirmed in the present work where there was a significant association between female gender and better performance on incidental memory (P=0.004). The gender difference on memory is in line with many published studies35-36. In previous findings women had better cognitive function than men, despite their lower level of formal education which was attributed to biological differences such as atherosclerosis being more in men30,37.

However, our findings were in accordance with Ferguson et al.38 who concluded that, gender effects are of modest magnitude compared with the influence of age and education on neuropsychological test performance.

In this study, education was a significant predictor  of  better  performance  on  tests of fluid analytic abilities, crystallized abilities, executive functions, visuospatial and constructional abilities. Previous studies have described associations between limited formal education, poor cognitive function, and susceptibility  to  develop  dementia30, 39-40.

Little is known about the association of cognitive performance and risk factors such as hypertension, where results have been inconsistent41. In this study, although vascular risk factors were present in 45.8% of group under study and was significantly associated with advancing age (P<0.001) yet they were only significant predictors of impaired fluid analytic abilities and executive functions controlled by frontal lobe, in accordance with previous reports42.

Despite its widespread use, there is little data on patterns of regional cerebral perfusion with (HMPAO) SPECT in normal elderly subjects, although other methods of measurement suggest a fall in cerebral blood flow with age43. Accordingly, knowledge of the regional cerebral blood flow (rCBF) distribution in normal aging is a factor of the utmost importance44.

We investigated whether it is only age that affects regional cerebral perfusion or whether other factors could explain this relationship. Using multiple linear regression analysis, increasing age was significantly associated with perfusion decrease in right frontal, bilateral parietal and occipital regions, a pattern that has been previously reported34,45-47. Others reported age-related decrease mainly in frontal cortex10 as well as in parietal, tempro-parietal, and temporal cortex44,48.

Risk factors for cerebrovascular disease, including hypertension, history of cardiac disease, cholesterol, smoking, and diabetes mellitus accounted for a significant proportion of variance in average cerebral perfusion of left frontal, left temporal, right thalamus and bilateral basal ganglia regions. As these regions were not influenced by advancing age, our findings confirmed those reported previously48. Moreover, significant relative sex-based differences were described, where men had higher perfusion in the left anterior temporal cortex, orbito-frontal and cerebellar cortices while women had higher perfusion in the right inferior parietal cortex; with no significant differences between the functional and morphometric data34. On the contrary, this study indicated higher perfusion in females in both hemispheres except in temporal region. These results were similar to previous studies49-50. As a hypothesis, it is suggested that the higher flow level in women may be a systemic phenomenon e.g. a higher cardiac index in females. The sex differences in regional flow pattern might be due to differences in the functional organization of the cortex and/or to differences in the mental processes of the "resting" state51. On the other hand other studies could not reveal any gender differences52-53.

The temporal lobe was not influenced by advancing age or gender in this study suggesting that average cerebral perfusion changes were not secondary to underlying structural pathology. Neuroimaging revealed that the combination of mesial temporal lobe atrophy and parietotemporal hypoperfusion is common in Alzheimer's disease, much less common in other dementias, and rare in normal controls43.

According to Van Laere et al.34 functional differences are based on structural properties as  contrary to widely held belief, neuronal concentration remains essentially constant during normal aging, but neuron size decreases presumably as a result of reduced dendritic arborization, dendritic spine concentration and number of glial cells. These phenomena occur with substantial variability according to genetic factors, education, profession, lifestyle, intellectual and physical activity, and general physical condition. Accordingly it was hypothesized that the majority of observed age-related regional perfusion changes can be attributed to underlying changes i.e. increase in atrophy.

It has been reported that decrease in cerebral perfusion suggests a regionally specific loss of cerebral function with age. The affected areas were mostly association cortices. Therefore, these decreases may constitute the cerebral substrate of the cognitive changes that occur during normal aging54-55.

As advancing age, in this study, was associated with decreased perfusion of right frontal, bilateral parietal and occipital regions, where perfusion of right frontal region was related to performance on tests for fluid analytic abilities and block design; while perfusion of right parietal and right occipital regions were associated with incidental and intentional memory respectively, we concluded that age-related reductions in these regions suggested altered abstract reasoning, sensory and verbal short-term memory, speed of information processing, perceptual mental strategies, verbal abilities, as well as visuospatial and constructional abilities in older adults.

Increased relative perfusion in these regions, in connection with better neuropsychological performance, is compensated for by decreased perfusion of right temporal and basal ganglia in association with fluid analytic abilities; of left occipital with executive functions; of left thalamus and left parietal with intentional memory; of right occipital and left temporal with incidental memory; and of left parietal with visuospatial, constructional and crystallized abilities56. Another possibility for the decreased perfusion in these regions, other than compensation, could be that the recruitment of these regions reflect a lack of inhibition. There is evidence that inhibitory processes influence task performance57 and that inhibition deficits play a role in cognition56,58. A third explanation could be task-induced deactivation which refers to a regional decrease in blood flow during an active task relative to a "resting" or "passive" baseline. This is suggested to represent reallocation of processing resources from areas in which deactivation occurs to areas involved in task performance59. Young adults typically deactivate specific brain regions during active task performance. Deactivated regions overlap with those that show reduced resting metabolic activity in aging and dementia, raising the possibility of a relation60.

In this study increased perfusion of left frontal lobe was a significant predictor of better performance on tasks of  executive functions confirming previous reports about aging being associated with disproportionate frontal atrophy, frontal hypometabolism by SPECT and a dysexecutive pattern of cognitive test performance10, 61.

Block design which is a measure of visuospatial functions and constructional abilities, regardless cultural or academic experiences, is   known to be most strongly affected by non-dominant right hemisphere lesions, mainly parietal lobe. The scores of this test tend to be very low in cases with lesions in the prefrontal cortex and right parietal lobe62-63. This was confirmed in present study where decreased perfusion of right frontal was a strong predictor of lower scores on block design. This could be interpreted by the sensitivity of the superior parietal cortex to stimulus location, whereas the frontal activation being more bound to overt motor responses64.

The human frontal cortex helps mediate working memory, a system that is used for temporary storage and manipulation of information and that is involved in many higher cognitive functions. Working memory includes two components: short-term storage (on the order of seconds) i.e. incidental memory and executive processes that operate on the contents of storage (65). The findings from a number of functional neuroimaging studies (PET) converge to suggest that memory retrieval is associated with activation of a distributed network of brain regions66. In this study, increased perfusion of right parietal and right thalamus and decreased perfusion of right occipital and left temporal regions were significant predictors of better performance on incidental (short-term) memory testing. This has been proven previously, where one area of activation in PET studies that is seen repeatedly, is parietal cortex, in the regions labeled by Brodmann as 7 and 40. This area is characteristic of studies of verbal working memory67. The pattern of activations that have been found for parietal cortex, implicates it in mechanisms involved in the storage of verbal material68.

In addition, a number of PET and fMRI studies have found that intentional as well as incidental encoding processes are associated with prefrontal brain regions. Specifically, brain-imaging findings suggest that input from frontal regions to medial temporal regions affects the binding of sensory information into memory trace66.

Furthermore, regions with increased activity during intentional learning conditions were bilateral motor/premotor cortex, bilateral inferior parietal regions, and posterior cingulate, occipital and mid-dorsolateral prefrontal cortex in the right hemisphere69. Increased perfusion of right occipital region and decreased perfusion of left parietal and thalamic regions were associated with better performance on intentional memory test in present work. As this sensory area is an unlikely candidate locus for intentional memory, we hypothesize that this increase is attributable to use of visual mental imagery during the task70.

Although previous studies reported that right lateralized activations likely reflected the nonverbal nature of the stimuli66, in the present study increased perfusion on right side was associated with stimuli that are verbal in nature. This could be explained by attentional mechanisms involved in intentional memory (also known as sensory memory) where the first stage corresponds approximately to the initial moment that an item is perceived. Some of this information in the sensory area proceeds to the sensory store, which is referred to as short-term memory. The sensory memories act as buffers for stimuli received through the senses. Information is passed from sensory memory into short-term memory by attention, thereby filtering the stimuli to only those which are of interest at a given time24-25. In most situations, attention and intention are inextricably linked, since attention is usually directed to objects on which one acts71.

Several lines of evidence suggest that the neurotransmitter dopamine plays an important role in human cognition. Computational modeling studies indicate that dysfunction in dopamine systems accounts for abnormal cognitive control in the prefrontal cortex whereas pharmacological enhancement of dopaminergic activity can produce improvements in specific cognitive domains dependent on the integrity of the prefrontal cortex. The major mechanism by which the synaptic activity of dopamine is terminated is reuptake, followed by metabolic degradation. Catechol O-methyltransferase (COMT) is the major mammalian enzyme involved in the metabolic degradation of released dopamine and accounts for more than 60% of the metabolic degradation of dopamine in the frontal cortex72.

It has been suggested that a regional activation observed in functional imaging tells us little about the necessity of that region for task performance73. A more interesting possibility, however, is that the functional imaging data contain important additional information about the way healthy subjects perform the task74.

 

Conclusion:

Performance on psychometric tests is associated with activation of a distributed network of brain regions which varies with advancing age. The recognition of functional imaging correlates to changes in cognitive function is important in enabling the distinction to be made between age- and dementia-related abnormalities, especially that functional abnormalities may be detected earlier than counterpart anatomical changes on morphometric data.

 

Recommendation:

It is recommended that future studies, in particular those combining psychometric tasks and neuroimaging to be done while a subject is performing a concentration task.

 

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