The study of episodic and autobiographical memory has been researched for decades. Technology advancement has enabled scientists to understand brain activity and processes clearer than in previous centuries. Autobiographical memory is the ability to remember one’s history, including the place of birth and childhood. It can be episodic (recollection of events), semanticize (involving numbers, concepts, and other knowledge), or spatial (remembering location details) (Wang et al., 2017). Episodic memory is a subset of autobiographical memory and is one’s ability to remember past events and experiences. As age advances, the brain tends to forget past or historical experiences and events. However, this forgetfulness is not universal across various groups and might differ based on sex, race, education, and other distinguishing features.
This research paper focuses on the methods of study, results, conclusions, and limitations of six articles on memory decline in old age. Understanding the brain activity and changes associated with memory decline in old age is an important topic, as it can help in the development of interventions to improve memory. Scientists can invent methods of delaying or reducing the rate of memory decline to prevent or control disorders such as dementia. In addition, early interventions will lead to a better quality of life for the elderly and their families while creating healthier communities.
Depending on many factors, such as the intended outcome and resource availability, scientists utilize differing research techniques. The most commonly used research methods include neuroimaging, MRI scans, autobiographical interviews, episodic memory tasks, region segmentation, encoding, and relevant questionnaires. The results of each study are distinct and unique because the hypotheses under investigation are different. However, most scientific researches published, including those reviewed in this paper, will reach a closely related conclusion.
All but one article employed some form of brain imaging as a research method. These include anatomical and functional scans (Spreng et al., 2018), structural and diffusion imaging (Merenstein et al., 2021), and blood-oxygen level-dependent (BOLD) fMRI scans (Subramaniapillai et al., 2019). The other two articles are longitudinal studies of existing literature and have discussed various neuroimaging techniques. The scientists used either the 3T GE Discovery MR750 or the 3T Siemens Magnetom Trio scanner for the images. Anatomical or structural imaging was done with participants in a resting state and before any cognitive tests or tasks were accomplished. The resulting images are skull stripped to enable researchers to focus only on the brain.
Scanner settings differed from one experiment to another in terms of echo, repetition, and inversion times, acceleration, flip angle, matrix size, the field of view, bandwidth, slices, and isotropic voxels. These configurations also changed from one type of imaging to another during the experiment. Merenstein et al. (2021) included preprocessing data for diffusion purposes to ensure that only brain tissue is recorded and correct any distortion from head movement. Nyberg (2017) introduces the Positron Emission Tomography (PET) used for scanning intracellular tau and amyloid-B in dementia studies. A combination of PET and MRI would yield better results in brain learning.
Autobiographical Interview (AI)
This method involves asking participants to describe three to five significant past events in as much detail as possible. The scoring of AI involves word count and detailing of the events (Spreng et al., 2018). AI was introduced in 2002 as a new measure for reporting recalled events during memory studies. Spreng et al. (2018) used AI to collect descriptions of individually significant events from participants at various stages in life, such as early childhood, teenage, and adulthood. The researchers used three levels in probing recall of the events, including free recall, general, and specific probe. The interviews were then recorded, transcribed, checked for accuracy, and blindly scored by well-trained scorers.
Episodic Memory Task
Memory tasks involve reading out words to participants or providing them with written lists. After some break, usually 30 minutes, the participants’’ ability to remember is tested. Later, a list containing all the words from two lists is presented, and participants must choose which were on the first list, leading to hits and misses. Merenstein et al. (2021) used the Rey Auditory Verbal Learning Task (RAVLT) to assess episodic memory. It comprised two lists, A and B, of 15 words each used to gather data on delayed recall and recognition performance. RAVLT delayed recall was measured as the words from list A that the participant would remember after 30 minutes (Merenstein et al., 2021). RAVLT recognition was assessed by subtracting the words correctly identified as being in list A from those wrongly identified.
Other Neuropsychological and Cognitive Tests
There is a myriad of brain and memory performance tests that researchers can use. NIH Toolbox of Cognition was used to calculate composite scores for fluid intelligence (Spreng et al., 2018). The Montreal Cognitive Assessment (MoCA) and Mini Mental State Exam (MMSE) were applied in the measurement of general cognitive status, which could be impaired or normal (Merenstein et al., 2021). Subramaniapillai et al. (2019) used MMSE, the Beck Depression Inventory (BDI), and the National Adult Reading Test (NART) with 50 English or 40 French words, depending on the participant’s preference. Fan et al. (2017) used the Cognitive Failures Questionnaire (CFQ) and the Survey of Autobiographical Memory (SAM) to measure the usual cognitive functioning and autobiographical abilities, respectively. Although SAM and CFQ were almost similar to the episodic memory task, the exercises were conducted online.
Some widely used statistical methods involve software such as SPSS, MATLAB, R, and Prism, among others. Statistical data analysis is most applicable in a quantitative research design, where researchers must deal with numbers or percentages. Spreng et al. (2018) used a MATLAB extension called Group ICA fMRI Toolbox (GIFT) for statistical analysis of resting-state data to obtain the default network. The tool follows three steps: data compression, ICA group level computation, and ICA maps reconstruction for individual subjects (Spreng et al., 2018). Merenstein et al. (2021) conducted complex statistical analyses that combined three software, including SPSS, R Studio, and Prism. The techniques and computations used are linear regression, coefficients of determination, likelihood ratio tests, SPSS’s PROCESS macro, confidence interval, and Akaike Information Criterion (AICc). Subramaniapillai et al. (2019) conducted a linear mixed-effects regression using R software to measure the interaction between test difficulty, age, and sex. They also used multivariate B-PLS to analyze complex relationships between the variables. Fan et al. (2020) also used R for statistical analyses, utilizing the multilevel linear model and Bayesian analysis. Statistical methods result in data tables, graphs, or charts that are easy to comprehend.
All the research articles reported a relationship between memory decline and age, whereby episodic and autobiographical memory deteriorate with old age. Spreng et al. (2018) found that older adults had significantly lower fluid intelligence scores than younger ones, but had higher crystallized intelligence levels. In addition, older people’s memories had more external than internal details compared to young adults. When comparing detail density of events’ descriptions, older adults scored higher in semantic but lower in episodic density than their younger counterparts.
Chronological age is known to affect white matter during one’s lifespan. Merenstein et al. (2021) established that fractional anisotropy (FA) increased while radial and axial diffusivity decreased with age. Advanced age was, therefore, associated with magnified “age-related differences in white matter microstructure” (Merenstein et al., 2021, p. 286). The authors linked older age to poorer memory performance, worse recognition, and recall rates. When the researchers considered the hyperintensity volume of the white matter, older age was linked to higher volumes than younger age (Merenstein et al., 2021). Two older age participants had hyperintense volumes that deviated from the mean by over four points of standard deviation measure.
When performance on easy and hard spatial memory was measured against age, sex, and education, useful correlations were revealed. Subramaniapillai et al. (2019) reported significant differences by age in retrieval accuracy in LMER testing for interaction between task difficulty, sex, and age. Age alone and task difficulty had a significant interaction as participants performed worse on hard spatial tasks than on easy ones. As age increased, the retrieval period (RT) also became longer, indicating a significant effect of age (Subramaniapillai et al., 2019). Better retrieval accuracy was achieved from higher activity in the negative salience brain regions, showing that brain scores affected memory performance.
Brain changes during baseline tasks and episodic memory encoding activities can reveal differences of age in the memory performance. Nyberg (2017) established that several cortical regions of the brain, including the Hippocampus–prefrontal cortex (PFC), right, and left Hippocampus, have heightened interaction during episodic memory encoding. However, their connectivity decreased with an increase in the participants’ age, lowering memory performance (Nyberg, 2017). Connectedness between PFC and the media temporal lobe (MTL) was found to be instrumental in episodic memory’s strength.
Both the binding and resource deficit hypotheses have been used to explain memory performance changes by age. Wang et al. (2017) found that self-initiating episodic tasks processing is related to resource deficit, while relational memory or recollection of item by event and item-by-item associations is linked to the binding deficit. In both cases, however, episodic memory declines as age advances. MTL is required for binding to support episodic, working, and perception memories (Wang et al., 2017). Brain atrophy that occurs with age was found to affect both MTL and PFC, which are associated with binding and resource deficits. Diffusion tensor imaging (DTI) has revealed that white matter in the parietal and frontal tracts reduces with age.
Episodic memory tasks and exams similar to autobiographical interviews can be done online. Through online tasks, Fan et al. (2020) established that age was not related to semantic, episodic, future, or spatial domains of SAM. However, a multilevel linear model showed a negative relationship between age and memory scores and Cambridge Brain Science (CBS) tasks, including grammatical reasoning, paired associates, odd one out, and rotations (Fan et al., 2020). In addition, no correlations were reported between aging and CFQ scores. A negative relationship was also established between SAM episodic scores and those of CFQ.
Discussion and Conclusion
All the studies indicate that aging affects brain activities and physiology, which contributes to a decline in functioning and memory. Adults possess a larger amount of past events than their younger counterparts, but accessing it and providing quality recollection is affected by aging (Spreng et al., 2018). Although the knowledge of oneself and access to common facts is not eroded with age, the ability to re-experience certain events or recollect specific details declines with age. Nevertheless, older adults use adaptive crystallized intelligence to make more complex decisions than do younger adults using fluid intelligence.
The decline in the white and gray matter of the brain is a leading cause of memory decline among older adults. While there is a linear white matter decline among youngest old adults beginning from around 55 years, the oldest old individuals of 90 years and above experience rapid nonlinear deterioration (Merenstein et al., 2021). The scientists found an indirect relationship between aging and memory performance arising from the medial temporal fiber classes’ changes.
Sex is a mediating factor in the effects of age on memory performance. Subramaniapillai et al. (2019) concluded that although age changes brain composition affecting task performance, certain differences exist among males and females. For example, the left parahippocampal gyrus (PHG) and lateral frontal-parietal increased for females during encoding, while temporal, occipital, and ventrolateral PFC (VLPFC) decreased for males. In essence, neural networks for spatial context memory deteriorate with age but differently for females and males. Additionally, the brain areas that facilitate memory functioning in old age are different between men and women. While neuroimaging has placed the hippocampus at the center of episodic memory performance, its significance is most understood in the context of a large brain network (Nyberg, 2017). Identifying a specific area that leads to a decline in memory is crucial in developing effective interventions for memory diseases. Wang et al. (2017) presented fMRI, anatomical, and behavioral evidence to support brain changes in old age. A decline in PFC and MTL with age advancement is responsible for deficiency in the binding process and executive functioning, which impairs memory performance.
Research has sought to establish why differences exist between individuals in-memory performance decline. Fan et al. (2020) concluded that differences in memory decline affect day-to-day functioning, problem-solving, and decision making. While each article has its specific findings, they all agree that episodic and autobiographical memory declines with age. Of course, there are several factors involved in the transition and brain atrophy to ensure reliable conclusions are drawn.
Future directions for the study of memory decline with age are multifaceted. Researchers can study wide lifespans, beginning from childhood to adulthood, as these articles only studied older adults. They might also focus on the correlations between cognitive control and default coupling across such a lifespan. Since memory, especially white matter, is known to grow from childhood to adulthood, such studies might uncover the initial stages of memory decline.
Another direction that future research can take is considering other factors, apart from brain changes, that influence differences in memory decline. Individual aspects, such as sex, education, stress management, and genetics, could be responsible for certain differences in memory performance among older adults. For example, Wang et al. (2017) suggest that there is a possibility of some people coping with the reductions by shifting to other MTL and PFC resources. Such a shift would compensate for binding processes and resource deficits related to aging.
The most useful future direction involves using the knowledge gained from memory decline studies to develop interventions that would alter or improve the MTL and PFC performance. If scientists can devise treatment or support techniques to improve memory performance among older adults, the world would benefit from these studies. Nyberg (2017) recommends the exploration of physical and cognitive methods to foster better memory function. Future research can explore how better stress management, improved diet, and balanced sleep cycles can affect the hippocampus and memory.
Each study experienced its limitations depending on the method, duration, and sampling techniques used, among other dynamics. Nyberg (2017) and Wang et al. (2017) involved the study of existing literature, which is limited by virtue of using old data and information. In addition, any drawbacks or limitations experienced in the past literature are carried into the new articles. Future research can overcome this challenge by conducting fresh experiments to ascertain previous findings. Nonetheless, such studies are cheaper to complete and useful in identifying research gaps for further probing.
One research faced a limitation related to the research method employed. Fan et al. (2020) hypothesized that lower trait episodic memory protects against mnemonic changes linked to aging. Such a hypothesis requires longitudinal methods that would factor changes over time. However, the researchers applied a cross-sectional study design to obtain their data. Future researchers can use a longitudinal technique to collect data over time, which would establish a difference in individual aging patterns.
Another limitation involved the age of participants recruited for experiments. Spreng et al. (2018) and Merenstein et al. (2021) included younger, older, and oldest adults in their cohorts. Therefore, their findings might be inapplicable to children, teenagers, and young adults. Spreng et al. (2018) recommend sampling of all decades of life to study the changes in memory performance. Merenstein et al.’s (2021) study participants were tested at varying locations and could be confounded by age effects of interest as a limitation. When investigating how sex affects memory decline in aging, Subramaniapillai et al. (2019) did not factor in other gender-specific factors that would influence memory performance, such as stress management and hormonal differences. Future researchers should expand the factors of study to include these sex-specific differences between males and females.
The study of how age affects memory is a wide one, and researchers can only narrow it down to a specific aspect of the topic. In this paper, research articles related to episodic and autobiographical memory decline during old age have been analyzed. All the studies have concluded that aging leads to memory decline, but each article studies a unique subtopic. Although researchers used different research techniques, neuroimaging was almost universal as it allows real-time investigation of the human brain. A 3T MRI scanner was common with brain imaging in the experiments. Other important methods include Levine’s Autobiographical Interview, Episodic memory task, Mini Mental State Exam, encoding, and others.
Future research on the topic is critical in advancing the usability of these studies. Researchers must investigate memory changes since childhood and in every decade of life to identify critical points of change and the accompanying brain processes. Further understanding will lead to the development of an intervention that can delay or eliminate the diseases related to memory impairment. For example, scientists must find out how physical exercises, a positive outlook, and cognitive activities can improve existing memory decline disorders.
Fan, C. L., Romero, K., & Levine, B. (2020). Older adults with lower autobiographical memory abilities report less age-related decline in everyday cognitive function. BMC Geriatrics, 20(1), 1-12. Web.
Merenstein, J. L., Corrada, M. M., Kawas, C. H., & Bennett, I. J. (2021). Age affects white matter microstructure and episodic memory across the older adult lifespan. Neurobiology of Aging, 106, 282-291. Web.
Nyberg, L. (2017). Functional brain imaging of episodic memory decline in ageing. Journal of Internal Medicine, 281(1), 65-74. Web.
Spreng, R. N., Lockrow, A. W., DuPre, E., Setton, R., Spreng, K. A., & Turner, G. R. (2018). Semanticized autobiographical memory and the default–executive coupling hypothesis of aging. Neuropsychologia, 110, 37-43. Web.
Subramaniapillai, S., Rajagopal, S., Elshiekh, A., Pasvanis, S., Ankudowich, E., & Rajah, M. N. (2019). Sex differences in the neural correlates of spatial context memory decline in healthy aging. Journal of Cognitive Neuroscience, 31(12), 1895-1916. Web.
Wang, W. C., Daselaar, S. M., & Cabeza, R. (2017). Episodic memory decline and healthy aging. Learning and Memory: A Comprehensive Reference, 3(2), 475-497. Web.