Age associated variance, gross brain changes and system performance characteristics Patrick Rabbitt, University of Oxford University of Western Australia

Global brain atrophy predicts previous rate of age-related change Rabbitt, Lunn & Ibrahim Neuropsychology 2007

Mary Lunn and Said Ibrahim used data gathered during longitudinal testing of all scanned participants to predict rates of change over the previous 17 years from current levels of atrophy. (Rabbitt, Lunn & Ibrahim Neuropsychology 2006) Atrophy, head size adjusted estimated loss, measured In 2002/3 Individuals’ rates of change in Performance 1985/2002 High Low

Age related variance between individuals That part of the variance between individuals that is associated with differences in their ages.

Some correlations between Age and scores on behavioural cognitive tests. Scores on tests of speed and intelligence are the best predictors of scores on others, but the simple correlations are not remarkably strong. The strongest correlations are only r = 0.45 (for Age and Intelligence test scores).

Comparing Age sensitivity of different cognitive tests.

TaskCorrelation between Task Score and Calendar Age between 42 and 96 years Percentage of total variance between individuals accounted for by differences in their Ages. AH4-1 intelligence test-.366**13.4% AH4-2 intelligence test-.453**20.5% Cattell Culture Fair Intelligence Test -.436**19.0% Mill Hill A Vocabulary Test % Mill Hill B Vocabulary Test-.048*.02% WAIS Vocabulary Test-.169**2.6% Free Recall of 30 Words-.323**16.4% Free Recall of 10 Words-.297**8.8% Recognition Memory for 40 Pictures -.247**6.1% Digit Span-.194**3.8% Letter/Letter Coding-.431**18.5% Visual Search Speed-.347**12.0% Recall of Information about Imaginary people -.293**8.6% Recall of Names of 12 Objects -.377**14.2% **Significant at p<.01; * Significant at p<.05

“Speed” explains nearly all age-related variance in all laboratory tasks, including working memory tasks By age-related variance: we mean that part of the variance between individuals that is accounted for by differences in their ages Variance in individuals’ ages All variance in scores between individuals in a particular task Age related Variance in task scores

We now consider how much of the very small proportions of age related variance in test scores Can be accounted for by scores on intelligence tests and tests of speed.

100% of variance associated with differences in ages 100% of variance associated with differences in ages 100 % of variance in intelligence test scores due to all differences between people including gtheir ages For the best case about 16% Of total variance is associated with Differences in age between 41 and 92 years “Age Associated Variance”

TasksAge- related Var. by Cattell Intell. test Scores Age-related variance by Let/Let Coding Speed Age-related variance by Visual Search Speed. AH4-1 intelligence test100% 93% AH4-2 intelligence test98%96%88% Cattell intelligence test %81% Free-Recall of 30 words91%93%80% Free-recall of 10 words86%88%73% Recognition memory for 40 pictures 95%96%83% Cumulative Learning of 15 nouns 92%93%81% Digit Span94%98%100% Letter/Letter Coding Speed99%----92% Visual Search Speed93%99%----- Recall of 3 items of Information about each of 5 people 88%85%65% Recall of Names of 12 common objects 86%88%76%

CVD approx 64% CVD + Diabetes approx 68% Cancers approx 30% Similarly, Using this method to examine effects of pathologies we get the following figures for amounts of age – related variance associated with individual pathologies

We should pause to consider what measures of cognitive performance such as “intelligence” or “information processing speed” can actually mean. 1.Task performance indices 2.System performance characteristics 3.Neurological performance characteristics 4.Psychometric statistical constructs

Are we sure that we understand what we mean by “speed” ? Or what we mean by “age” ? For example there are at least 4 very different levels of description of “speed” within which we may operate: Speed as a task performance index: a speed task is one in which performance is measured in units of speed. A performance characteristic in a hypothetical functional model of a system: e.g. input processing speed in Broadbent’s dichotic listening model. A neurophysiological performance parameter: e.g. synaptic transmission rate; level of random noise in a system; a global parameter, such as “system temperature” that determines level of function. A statistically derived psychometric construct: e.g. a “speed” factor derived from scores on a set of different tests in contrast to other, similar, “factors” such as “gf” or “memory”.

There are also at least three quite distinct levels of description of “Age” within which we can operate. “Age” as chronological age; years from birth “Age” as the accumulation of events of various kinds – e.g. burden of pathologies or accidents or “negative life events” (Jolles et al) Age as “biological age” or “neurological age” indicated by direct measures of brain integrity.

“Neurological Age” Three gross indices were obtained: Head size adjusted brain volume, Basilar and Carotid artery blood flow Estimates of total White matter lesions

Test GroupTestBeta for age given atrophy Beta for atrophy given age Beta for age given CBF Beta for CBF given age CF-0.424* *0.049 gfAH * *0.207 AH * *0.149 SpeedL/D Code * ** VS * * MemoryPaP-0.303* *0.154 OL-0.456* *0.095 Rey-0.436* *0.153 ExecutiveBrixton0.281* *-0.269* C. Fluency *-0.309*0.201 The relative independent contributions of Age, atrophy and CF after variance associated with sex had been accounted for.

Some recent findings: Individual differences in age-related loss of age related loss of gross brain volume, in Cerebral Blood Flow and in White Matter Lesions account for ALL age related variance between individuals in scores on tests of Speed…… But for NO age related variance in tests of Intelligence And only for SOME but not for ALL age related variance in working memory tasks. (Rabbitt, Scott, Lowe et al Neuropsychology 2006; 2007)

2. Biological age; “biomarkers” The strong relationships between hearing, vision and balance and cognitive function pioneered by Anstey and emphasised by Lindenberger and Baltes (1996; 1997). Gross neurophysiological changes that entirely account for these relationships (e.g. Rabbitt et al Neuropsychologia 2006).

The association between biomarkers (in this case balance) and gross brain changes can resolve the tricky issue of Misleading “correlations between functionally different “bioclocks” All changes in the world occur in linear time, and so rates of all changes, though functionally independent, are correlated,

A good estimate can be found using Lindenberger and Potter’s (1996) adaptation of Multiple Regression models: Variance in Age Variance in task scores Balance (Tinetti battery)

When scores on the Tinetti Balance test were entered: They accounted for all of the age- related variance in tests of speed.

Using SEMs to examine relationships between Age, Speed Memory, Intelligence and Vocabulary Dan Lunn, Oemetse Mogape et al (Rabbitt, Mogape, Lunn et al Neuropsychology in press 2007)

Re-examine Age and Speed relationships when gross brain changes are also considered.

The message: If individual differences in gross age-related losses of brain volume and in cerebral blood flow are taken into consideration Calendar age: i.e. a direct index of differences in life durations, is a crude index of differences in things that have happened to people in their lives, no longer predicts any significant proportion of individual differences in speed

What about Memory ? We have seen that nearly all of that proportion of variance between individuals in behavioural tests of memory, that is associated with differences in their calendar ages, is accounted for by behavioural measures of speed.

So what about Age, Neuro and Memory ? How much of the differences in memory ability between individuals is explained by differences in their Calendar Ages and by differences in their Ages and in their gross neurophysiological states ?

Age and Neuro as predictors of variance on Frontal Tasks

Age and Neuro as Predictors of Intelligence Test Scores

Age and Neuro as Predictors of Vocabulary Test Scores

Cognitive Performance Construct Direct Effect of AgeDirect Effect of Neuro SpeedNONESignificant Substantial IntelligenceSignificant Modest FrontalSignificant Modest MemorySignificant Modest VocabularyNONESignificant but very Little

These analyses emphasise the limitations of behavioural data. Behavioural data suggest that declines in scores on all kinds of cognitive tests are mainly driven by slowing of information processing speed. It would be reasonable to conclude that all individuals show the same pattern of speed-driven age cognitive change. But we see that speed, alone, does not completely determine amounts of change in different abilities. Other changes occur that are not marked by speed but are marked by gross brain changes.

There are four different points made by these analyses

The first point : Rabbitt, Lunn & Ibrahim Neuropsychology (2006) Gross brain measures at a particular time point, T, predict rates of change in speed scores over the previous T 1 to T 17 years

The second point: Variance in speed scores associated with balance are accounted for by changes in gross brain measures. (Rabbitt et al Neuropsychologia 2005)

The third point: Indices of gross age-related brain changes account for all of age-related change in “speed” but for none of the age-related change in “intelligence” (see Rabbitt et al Neuropsychology 2005 and 2006)

The fourth point The effects of our portmanteau variable derived from three measures of neurophysiological integrity are mainly on performance on “speed” tests. Effects on Intelligence tests, tests of frontal and executive function and memory tests are, entirely or mainly modulated through effects of gross changes on “speed”

Thanks for your patience.