What type of memory decreases with age

Be particularly vigilant if you take diuretics or laxatives or suffer from diabetes, high blood sugar, or diarrhea. Side effects of medication. Many prescribed and over-the-counter drugs or combinations of drugs can cause cognitive problems and memory loss as a side effect. This is especially common in older adults because they break down and absorb medication more slowly.

Common medications that affect memory and brain function include sleeping pills, antihistamines, blood pressure and arthritis medication, muscle relaxants, anticholinergic drugs for urinary incontinence and gastrointestinal discomfort, antidepressants, anti-anxiety meds, and painkillers.

As well as certain individual medications, taking too many medications can also create cognitive problems. A recent study found that the more medications you take, the higher your risk for brain atrophy. Researchers found that the loss of gray matter was most acute in people who took three or more different medications. The same practices that contribute to healthy aging and physical vitality also contribute to a healthy memory. Stay social. Quality face-to-face social interaction can greatly reduce stress and is powerful medicine for the brain, so schedule time with friends, join a book club, or visit the local senior center.

Stop smoking. Smoking heightens the risk of vascular disorders that can cause stroke and constrict arteries that deliver oxygen to the brain. When you quit smoking , the brain quickly benefits from improved circulation. Manage stress. Cortisol, the stress hormone, damages the brain over time and can lead to memory problems. But even before that happens, stress or anxiety can cause memory difficulties in the moment.

But simple stress management techniques can minimize these harmful effects. Get enough sleep. Sleep deprivation reduces the growth of new neurons in the hippocampus and causes problems with memory, concentration, and decision-making. It can even lead to depression—another memory killer.

Watch what you eat. Eating too many calories, though, can increase your risk of developing memory loss or cognitive impairment.

Exercise regularly. Starting a regular exercise routine , including cardio and strength training, may reduce your risk of developing dementia by up to 50 percent. New research indicates that walking six to nine miles every week can prevent brain shrinkage and memory loss. Just as physical exercise can make and keep your body stronger, mental exercise can make your brain work better and lower your risk of mental decline. Try to find brain exercises that you find enjoyable.

The more pleasurable an activity is to you, the more powerful its effect will be on your brain. Authors: Melinda Smith, M. American Psychiatric Association. Neurocognitive Disorders. National Institute on Aging. Understanding Memory Loss. Clinical investigators, emboldened by successes in diagnosing neurological diseases with high morbidity, have been inclined to tackle subtle entities such as mild memory deficits.

Beyond expanding our knowledge base, the accumulation of new findings has served to identify important questions critical in understanding age-related changes in higher cortical function. These questions, and attempts at providing answers, are reviewed herein. Do age-related changes occur equally across all cognitive domains, or is memory function uniquely sensitive to the effects of aging?

Age-related processes, some which underlie cognitive decline, do not target cortical regions equally. Insofar as different cognitive domains involve independent cortical topographies, a starting assumption is that the effect of aging will not be cognitively diffuse. Neuropsychologic studies 1 - 6 have attempted to address this question using cross-sectional or longitudinal designs. Both approaches, however, have inherent limitations.

Cross-sectional findings are limited by the sensitivity of cognitive tests to demographic differences. This cohort effect is most effectively addressed by following up a group of subjects prospectively and observing how performance changes with time.

Administering a cognitive test repeatedly, however, results in improving performance, 8 and this learning effect can obscure an underlying cognitive decline. Furthermore, longitudinal effects often necessitate long follow-up, which results in greater subject attrition, and this could minimize the change over time because attrition might occur differentially among subjects with greater cognitive decline.

Thus, cross-sectional studies may overestimate cognitive decline because of the cohort effect, whereas longitudinal studies may underestimate decline because of the learning effect—or if the learning effect is controlled, then because of selective subject attrition.

Recent studies 9 , 10 have addressed these limitations by using a mixed experimental design. Results of these studies show that across cognitive domains, memory performance undergoes conspicuous decline with increasing age. Future studies using complicated designs are needed to further establish the precise profile of age-related cognitive decline and to determine which aspects of cognition are preserved throughout the life span.

One of the fundamental findings to emerge from cognitive science in the past half century is that memory is a fractionated process and that memory subtypes localize to different anatomical sites. Although the neuroanatomic mapping of any complex cognitive function oversimplifies, the basic scheme of declarative memory is illustrated in Figure 1.

The long-term storage of memory resides in the same associational neocortical sites accessed during perception and activated during memory acquisition. This consolidation phase likely lasts weeks to months, and maybe longer. Where does memory decline localize to within this functional circuit? Neuropsychologic, physiological, 15 and brain imaging 16 - 18 studies suggest that the prefrontal cortex and the medial temporal lobes are most sensitive to age-related changes.

Nevertheless, it remains undetermined whether all causes of memory decline spare the posterior associational neocortices. Since first addressed by Kral 19 in the late s, numerous studies 2 - 4 , 10 , 20 - 22 have documented poorer memory performance in older compared with younger age groups.

In other words, does it reflect an abnormal state? Statistical definitions of normality can be used to address this issue. If memory decline is a normal occurrence with age, distribution curves of memory scores in old and young age groups should have similar variance, with a leftward shift in the mean for the older age group. However, if memory decline is not normal, the distribution curve of the old age group should have increased variance, and might show bimodality, compared with the young age group.

Studies 24 with humans and animals have shown that the variance of memory performance in aging samples increases with age, and some studies have found clear bimodal distributions. These findings suggest that memory decline is not inevitable with increasing age and therefore should be considered a clinical entity. A more ecological approach to defining abnormal memory decline has less to do with population statistics and more to do with whether the decline has a negative impact on functional ability.

The number and proportion of aging individuals in the population is increasing. These aging individuals expect to lead intellectually challenging lives in an environment rich with information and reliant on rapidly changing technologies. The ability to negotiate this environment depends on cognitive skills that include the specific types of memory systems most vulnerable to age-associated changes.

Memory decline interferes with an aging individual's activities of daily living, without necessarily progressing to amnesia or extending into dementia. Nondegenerative disorders causing dementia—metabolic, toxic, infectious, and structural—can present with isolated memory deficits, 28 - 30 but such causes account for only a small percentage of elderly people with isolated cognitive decline.

Because Alzheimer disease AD is relatively common in individuals older than 65 years, and because AD pathological processes target the hippocampal formation early in its course, 31 early AD is a major contributor to memory decline in otherwise healthy and nondemented older people. These studies 34 , 35 have shown that among brains free of AD pathology, cell loss occurs in select subfields of the hippocampal formation in an age-dependent fashion. Indirect support of non-AD causes of memory decline is provided by animal studies 24 , 36 : all nonhuman mammalian species demonstrate some form of hippocampal-based memory decline with age.

None develop the pathognomic features associated with AD, and the memory decline therefore is caused by non-AD processes. It is unlikely that a non-AD process pervasive across all mammalian species would spare our own. The exact cause of non-AD memory decline is still a matter of debate and is the focus of ongoing investigations. As shown in Figure 2 , likely causes include age-related changes in adrenal and gestational hormonal levels, 37 , 45 - 48 changes in cerebrovascular supply, 49 and age-related accrual of oxidative stress.

There is indirect evidence suggesting that age-related memory decline might have a genetic component. Twin studies 54 show an association between genes and cognition—including language, visuospatial ability, and memory function.

Memory function is unique, however, because its genetic association seems to increase in an age-related fashion. Although all individuals in a population might be equally exposed to pathological processes that target the hippocampal formation, individuals expressing the "vulnerability" gene would be at greater risk to sustain hippocampal lesions as they age. With time, therefore, this subpopulation would be more likely to undergo memory decline, and in late life those with and without the gene would segregate along memory performance scores.

There are, in fact, genes that might act in this manner. The APOE gene is one candidate because it is expressed with relative selectivity in hippocampal neurons, 56 its products are involved in mechanisms of neuronal repair, 57 and its expression is up-regulated in the setting of hippocampal injury. What variable should be measured in assessing cortical abnormalities associated with memory decline? An important observation to emerge from recent studies 63 is that age-related memory decline need not be associated with clear structural lesions.

This corresponds to the fact that many age-related processes result in physiological dysfunction 64 and not neuronal loss. Some processes that do ultimately manifest in tissue damage, such as AD pathology, often have a prodromal stage during which neuronal dysfunction occurs in the absence of cell death.

Thus, techniques that assess neuronal physiological dysfunction independent of structure are best suited to detect and localize brain regions associated with age-related memory decline. Neuropsychologic testing, electrical recording electroencephalogram and evoked potentials , and functional imaging single photon emission computed tomography, positron emission tomography, functional magnetic resonance imaging, and magnetoencephalography can accomplish this goal at a gross anatomical level.

What is the optimal spatial resolution for evaluating physiological dysfunction? Recognition was significantly reduced for items studied 60 min prior to testing, relative to those studied immediately before. Priming was calculated for each individual as the difference between the median identification RT for old immediate and delayed and new items, in proportion to their baseline new item RT [i. Priming was numerically reduced in older compared to young individuals, but there was no significant main effect of age or delay though as noted above, when Ward et al.

Figure 1. Recognition performance for immediate and delayed items in Ward et al. Right panel: proportion of hit and false-alarm responses. Bars indicate experimental data error bars indicate SE of the mean , and symbols indicate the mean expected result from each model when fit to each individual's data.

Figure 2. Continuous identification CID task performance in Ward et al. The proportional priming effect was calculated as the difference in the median identification RT to new and old items divided by the median identification RT for new items. Bars indicate the mean of the median priming across participants error bars indicate SE of the mean , and symbols indicate the mean expected proportional priming relative to the expected identification RT for new items, see Table 3 from each model when fit to each individual's data.

Right panel: mean identification RTs ms of immediate, delayed, and new items. The models were fit to the data using maximum likelihood estimation. An outline is given here; full details of the general fitting procedure can be found in Berry et al. An automated procedure was used to find the values of the parameters that maximized the summed log likelihood across trials, for each participant. Certain parameter values were fixed as in Berry et al. The likelihood of each identification RT RT and recognition judgment Z on a given trial is given as follows:.

Given the best fitting parameter values for a model, the expected model results can be calculated analytically see Table 1. The maximum likelihood estimates of the parameters are given in Table 2 , and the goodness of fit of the models is given in Table 3. Unsurprisingly, the greater flexibility of the MS2 model allows it to provide the best fit to the data, as indicated by the smallest ln L values.

Figure 3 shows the proportion of participants that were best fit by each of the models. None were best fit by the MS2 model. Table 2. Mean and standard deviation in parentheses of the parameter estimates of the models derived by maximum likelihood estimation.

Figure 3. Model selection results. The expected model results are presented in Figures 1 , 2 , 4. All models reproduced the trend for recognition memory to be greater for immediate than for delayed items.

The models all also predicted that priming in the young adult group would be greater than that in the older adult group Figures 1 , 2. With regards to the expected proportional priming effects Figure 2 ; calculated as specified in Table 1 , the SS model results mirror those of the recognition data: that is, priming in the older group is lower than that of the young group, and priming for immediate items is greater than that of delayed items.

Under the MS1 and MS2 models, priming was lower in the older group than the young group for both immediate and delayed items. Furthermore, in the older group, the expected priming effect for delayed items was less than for immediate items; however, in the young group, the expected priming for delayed items was greater than for immediate items.

Figure 4. Prediction 1 concerns whether fluency effects occur within new and old items i. Prediction 2 concerns whether the magnitude of the priming effect overall across all items is greater than the priming effect for items judged new.

The results concerning the specific predictions of the models are shown in Figure 4. In both groups, in line with the advance predictions of the SS model, there were trends for items judged old to have shorter identification RTs than items judged new i. This was the case within new items [i. Similarly, in both groups, and for both immediate and delayed items, there was a trend for the priming effect to be greater than the priming effect for items judged new Prediction 2; but all differences were non-significant.

In sum, the patterns of recognition and priming for young and older adults reported in Ward et al. Two plausible multiple-system models fared considerably worse when using advanced model selection techniques.

From a multiple systems perspective, this could suggest that aging affects both the explicit and implicit memory systems, yet the poor performance of the MS1 model is evidence against the idea that the memory signals driving recognition and priming are completely independent. The qualitative results of the more flexible MS2 model were similar to the SS model. According to this model, there is a substantial degree of correlation between the explicit and implicit memory strengths of an individual item.

The evidence presented in this article strongly draws into question the claim that implicit memory is preserved in normal aging. When appropriate methods and controls are used, priming is reduced in older individuals compared to young, although the effect is smaller than the reduction in explicit memory. Indeed, age differences in priming rarely reach significance, but this is likely to be due to a combination of low statistical power and low measure reliability.

The profile of memory decline in aging suggests that there is a general impairment to a unitary memory system. Consistent with this, we provide new evidence that the patterns are more compatible with a formal single-system model than feasible multiple-systems alternatives.

It is important to note that even though age effects on priming may be small and hard to detect in low-powered studies, the single-system model emphatically predicts that such effects exist.

This can be seen in the left panel of Figure 2 , where the predictions of the model are shown for the Ward et al. In one sense, therefore, it is important that studies are conducted which have adequate power to detect an effect of age on priming: the SS model could be falsified by a high-powered study which failed to detect such an effect.

But in another sense it is less important whether age effects on priming are observed or not. All the models can predict such an effect see Figure 2 , and a far more discriminating test is to determine via formal model-fitting techniques computing AIC and BIC, for instance which model provides the best fit to the data. Instead, more will be learned from taking the underlying quantitative data and asking which theoretical model provides the best fit.

The results are consistent with previous applications of the single-system model to data from individuals with amnesia. For example, Conroy et al. This type of dissociation has been taken as strong evidence for multiple-systems views.

The SS model was able to closely fit this pattern Berry et al. Although controversial, this is consistent with proposals that, when carefully examined, priming is not completely preserved in amnesia e. Another commonly cited strand of evidence for multiple systems is that manipulations tend to produce different effects on tests of recognition and priming in healthy young individuals. For example, Butler and Klein showed that manipulating attention at study resulted in chance-level recognition for ignored items, whereas priming for such items was robust see also Vuilleumier et al.

The study by Berry et al. Moreover, the data patterns were again consistent with the predictions of the single-system model. Lastly, the multiple-systems account argues that an implicit memory system is resistant to age-related decline, but in order to make this claim in the future one must provide evidence of completely equivalent priming in young and older individuals, or demonstrate conditions in which priming is greater in older relative to young adults, while at the same time explicit memory is weaker.

This kind of double dissociation would present a challenge to the single system model, as the pattern would not occur if performance on explicit and implicit tasks is driven by a single memory signal. We hope that this discussion will lead to further useful developments in this longstanding debate.

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Abbenhuis, M.

Episodic memory in dementia of the Alzheimer type and in normal ageing: similar impairment in automatic processing. A 42, — Akaike, H. Petrov and F.

Caski Budapest: Academiai Kiado , — Berry, C. On the relationship between repetition priming and recognition memory: insights from a computational model. CrossRef Full Text. A single-system account of the relationship between priming, recognition, and fluency.

Trends Cogn. A test of a single-system model of recognition and repetition priming. Models of recognition, repetition priming, and fluency: exploring a new framework. Buchner, A. On the reliability of implicit and explicit memory measures. Burnham, K. Model Selection and Inference. New York, NY: Springer. Butler, B. Inattentional blindness for ignored words: comparison of explicit and implicit tasks.

Chiarello, C. Adult age differences in implicit and explicit memory: time course and encoding effects. Ageing 3, — Christensen, H. Does ageing inevitably lead to declines in cognitive performance? A longitudinal study of elite academics. Conroy, M. On the contribution of perceptual fluency and priming to recognition memory.

Davis, H. Lexical priming deficits as a function of age. A ten-year longitudinal examination of repetition priming, incidental recall, free recall, and recognition in young and elderly. Brain Cogn. Dunn, J. The elusive dissociation. Cortex 39, — The effect of feedback delay and feedback type on perceptual category learning: the limits of multiple systems.

Fleischman, D. Repetition priming in ageing and Alzheimer's disease: an integrative review and future directions. Cortex 43, — Repetition priming in normal ageing and Alzheimer's disease: a review of findings and theories. Ageing 13, 88— A longitudinal study of implicit and explicit memory in old persons. Ageing 19, — Gabrieli, J. Cognitive neuroscience of human memory. Foster and M.