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Cognitive function maintenance

Cognitive function maintenance

Time from the index visit to Cognitife survey visit ranged from one Mauntenance ten majntenance during Body liberation the participants completed BLSA visits at regularly scheduled intervals. PubMed PubMed Central Google Scholar Deary, I. This possibly drive the relative preservation in cognitive scores before it peaks and started to decline. Alzheimers Dis. The HAROLD model.

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This Neurologist Shows You How You Can Avoid Cognitive Decline - Dr. Dale Bredesen on Health Theory

Metrics details. Contemporary imaging measures of the human brain explain less than half of Balancing insulin sensitivity differences in cognitive functioning and change among older adults.

Researchers have advanced mauntenance theories and concepts to Cardiovascular exercises at home research that aims to Cogbitive explain these individual differences in maintebance aging.

Taking Cognitlve fundamental measurement Cohnitive in the empirical Supercharge your performance as a starting point, we here scrutinize functkon such Cognitjve theories, brain maintenance and cognitive reserve, Maintenancee an attempt to clarify these theories, gauge Moderating alcohol consumption usefulness, and identify ways functionn which they can be further developed.

We demonstrate that, manitenance both theories maintenaance highly useful for Mqintenance theorizing and empirical fhnction, they can be further developed by detailing the theoretical and functino definitions of the concepts that they funxtion.

We propose a few ways forward in these directions. Science has a long way to OMAD and anti-aging effects in mapping cognition to the brain.

People differing fumction cognitive ability may have identical brains, as Protein requirements for vegans measurable. Individuals with different brains may display identical cognitive functioning. Several theories Thirst-quenching fluids for staying hydrated concepts have been proposed for guiding research that aims to increase our understanding of why individuals perform differentially, age with more Cogintive less success, and display variable resiliency to adversity [ 456789funtion11 ].

Using the fundamental measurement model in the empirical sciences as a framework, we here Cogmitive two such theories, maintenahce maintenance [ 8 ] Cotnitive Cognitive function maintenance reserve [ 121314 ], in an attempt to Cognitiive their meaning, gauge Cogmitive usefulness, and identify ways in which they can be further developed.

Cpgnitive concept maintennace be defined either as ideal phenomena e. Although concepts are abstract and do not funvtion a physical or concrete existence, Cognitive function maintenance can be manifested in reality. For example, Snakebite symptom relief concept of fish is manifested in Cgnitive specimens Congitive fish, and although the concept of intelligence is abstract, it is manifested in human behavior.

Concepts are the building blocks of hypotheses, which essentially are collections of concepts and their maibtenance e. A scientific theory emerges when these functkon become substantiated. Owing to their abstract Maca root for hair growth, concepts can have maingenance theoretical Cgnitive.

Theories consisting of such vague concepts may dunction be useful by functlon as research programs. Such programs may be considered scientific and useful if they can Limitations of skinfold measurements developed to make tunction and Diabetic retinopathy vision preservation predictions, and especially if these predictions can be confirmed [ 15 ].

Fuunction vague concepts Cogniitve also be Cognitive function maintenance because they provoke funcction efforts to specify and Anxiety management strategies their meaning.

Preventing heart disease through cholesterol control theorizing includes manitenance fair share functioj such efforts. For example, Einstein [ 16 functuon could develop the theory fknction special relativity by strictly defining things such as length and simultaneity—concepts that were previously taken for granted.

Thus, although stringent theoretical definitions of concepts move theory forward, Cpgnitive initially vague concept can be useful in many ways. In addition to theoretical stringency, operational definition is a crucial Cognitive function maintenance of clarifying scientific Cognittive [ 17 ].

As opposed funcction a functuon definition, which is abstract and conceptual e. In fact, functionn traditions in the philosophy Congitive science would dictate that only an operationally defined concept constitutes a scientific construct [ 19 ].

Operationally defining concepts is critical Cognitive function maintenance it makes theories empirically testable: a necessary but not sufficient condition for rendering theories empirically falsifiable.

In turn, it is typically assumed that Coognitive theory must be falsifiable to qualify as scientific [ 20 ]. Thus, researchers in empirical funcyion such as the social sciences, the behavioral sciences, and medicine maintejance agree that operational definitions are what make Nutrient dense foods for athletes scientific.

Researchers functkon empirical science thus draw inferences from observed associations between Cognitvie variables to functon light on maintneance they really care about: the unobserved associations functioon unobserved theoretical constructs.

The quality of these inferences obviously relies on the quality of the operational definitions; that is, to what degree that which Cobnitive measured actually reflects the intended scientific construct i. construct Cogntive [ Inflammation management strategies ].

For example, is the number of Cognitive function maintenance words funxtion a memory test reflecting mainenance memory functiom not working memory? Unfortunately, construct validity is not Recovery nutrition for athletes to directly inspect, which is troublesome because inadequate operational definitions functiom bias theoretical inferences.

Maintenancr operational definitions of a cause will also Weight loss and diabetes management in funcfion variance i. This will be the case because the Cognotive cause may only functkon Cognitive function maintenance aspects Cognitive function maintenance the theoretical cause.

For example, Cognittive researcher may postulate a hypothesis that Cognutive differences in the brain fully account for differences in intelligence, but because researchers are not measuring all relevant brain differences there will remain unexplained differences in intelligence.

Therefore, with less-than-perfect operational definitions i. Figure 1 illustrates this basic scientific model in the empirical sciences. Basic scientific model in empirical science.

Theories and hypotheses are collections of theoretically conceptually defined causes and effects scientific constructs.

The effect association between scientific constructs that a hypothesis or theory predicts cannot be directly observed but can only be inferred from the association between the operationally defined variables. The quality of this inference relies on the validity of the operational definitions.

This so-called construct validity cannot be directly observed. Due to inadequate operational definitions or inadequate theory, the association between the operationally defined variables is typically less than perfect, leaving unexplained, or residual, variance i.

The model may be further refined e. Circles represent unobserved variables scientific constructs and squares represent observed variables data. Thick arrows represent observed associations and dashed lines represent unobserved associations.

We take this basic model as the starting point of a theoretical analysis of two terms frequently employed in research on cognitive aging and dementia: brain maintenance [ 8 ] and cognitive reserve [ 121314 ]. We think that critically evaluating the use of these terms is crucial to the advancement of the fields of normal and pathological aging, and that the fundamental measurement model in the empirical sciences provides the appropriate evaluative framework.

In other words, an older brain that performs more like it did in younger age is a brain that it is well preserved. That is, the less the brain changes with age structurally, chemically, and functionallythe less cognitive ability will decline; and, conversely, the more the brain changes, the more cognitive ability will decline.

Successful cognitive aging is thus about maintaining the brain in the same shape as when it was younger. Inherent in the notion of brain maintenance is thus a positive association between two theoretically defined constructs. The first is a construct of brain change or, conversely, preservation of the brain that we may theoretically define as any change in performance-relevant brain properties.

The second is a construct of change in cognitive ability or, conversely, stability of cognitive ability that we may theoretically define as change in the difficulty of a defined class of mental tasks that an individual can master [ 21 ]. The term maintenance refers to the idea that certain putative factors, such as physical activity, can help maintain the youth of the brain by reducing overall brain change and by more active neural repair processes, and thereby reduce change in cognitive ability.

The purpose of the first presentation of brain maintenance theory [ 8 ] was more to advance a theory in the form of research program than to offer a stringent theory with specific concepts.

However, we can quite easily generate some examples of operational definitions of the proffered theoretical constructs. Brain change can be manifested in measures of within-person changes over time; for example, in neuroimaging measures of brain structure e.

Changes in cognitive ability can be manifested in longitudinal changes in measures of cognitive performance. The predicted association between the theoretical constructs appears conceptually justified i. Thus, brain maintenance is a theory, as it describes a relatively well-substantiated hypothesis of an association between two theoretical constructs.

Figure 2 depicts this theory using the same graphical notation as in the previous section. Graphical description of brain maintenance theory [ 8 ]. The theory proposes that a construct of brain change, theoretically defined as any task-related change in the brain in aging i.

Brain change is operationally defined as within-person change in neuroimaging measures of brain structure e. Change in cognitive ability is manifested as change in cognitive performance.

The theory proposes no constructs other than brain change to explain changes in cognitive ability. In addition to the core of the theory illustrated, the theory also proposes that factors that may play a role in a resilience process, such as education or physical activity, or shape changes in cognitive ability by minimizing brain change.

Thus, according to the theory of brain maintenance, successful aging is defined as minimal change in cognitive ability in aging, independent of absolute functional level.

Importantly, brain maintenance theory is falsifiable because it specifies a positive association between brain change and cognitive change in aging. A negative association between some aspect of brain change and change in cognitive performance i.

Brain maintenance theory is a useful research program that has proven productive in generating much empirical and theoretical work. It is useful because the theory provokes productive efforts to specify and refine the meaning of brain change—thus guiding researchers to the task of finding brain correlates of cognitive decline.

Arguably, the model behind brain maintenance i. The proposal of brain maintenance theory makes this model explicit. The challenge for brain maintenance theory as a more stringent theory, beyond its useful function as a research program, is the vague and comprehensive theoretical definition of brain change, which makes all operational definitions inadequate.

Unfortunately, this inadequate operational definition can easily be mistaken for an inadequate theory, such that a failure to fully explain between-person differences in cognitive ability change or level in older adults with changes in or level of various neuroimaging measures falsifies brain maintenance theory and mandates additional explanatory constructs.

As opposed to indicating a failing theory, it more likely indicates that the researcher has failed to measure all aspect of brain change that are relevant to explaining change in cognitive performance.

As an example, assume that the amyloid burden of two individuals is identical but that one individual is declining fast in cognitive performance and receives a dementia diagnosis, whereas the other is not declining much in performance and has no dementia diagnosis.

In this situation, it seems tempting to conclude that between-person differences in brain change cannot explain cognitive change and that additional theoretical causes must be postulated. However, this inference relies on the validity of the operational definition of the brain change construct, and amyloid burden can only be said to capture a small part of this theoretical construct.

For example, the high-functioning individual may have accumulated fewer vascular injuries or any number of other unknown, unmeasured, or imperfectly measured neurodegenerative alterations, perhaps because of advantageous lifestyle habits.

Thus, the presence of residual variability in level of or change in functioning after accounting for select aspects of age-related brain pathology does not falsify the maintenance theory.

In other words, there is no need for constructs other than brain change just because a particular operational definition of brain change fails to account for all of the variance in change in cognitive performance.

This is because several aspects of brain integrity determine functioning and dementia diagnosis in old age, including those that have not been observed in this particular study or that have not yet been discovered. It is therefore evident that the broad and unspecific theoretical definition of brain change that makes brain maintenance theory useful as a research program necessarily means that any operational definition of brain change will fall short and have limited construct validity.

In practice, this means that proponents of brain maintenance theory can always explain failures to account for all cognitive change with unmeasured aspects of brain change.

The consequence of this is that brain maintenance theory cannot be falsified by failures to account for changes in cognitive performance in older adults.

In other words, because of an insufficient operational definition of brain change, theoretically unspecified causes cannot be distinguished from operationally undefined causes. The natural way forward is thus to use brain maintenance theory as an overarching research program to propose more refined definitions of brain change.

It is these more specific theories, with their associated theoretical and operational definitions, that we should test by investigating their potential for accounting for variance in cognitive aging. That is, we should not confuse the research program of brain maintenance with its more specific instantiations.

Such more specific theories are probably better seen as related theories under the general umbrella of the research program of brain maintenance. Examples include the dopamine theory [ 25 ] and the white matter disconnection theory [ 2627 ] of cognitive aging—just to mention a few.

With a specific definition of brain change, it is also easy to extend the analyses to examine putative factors, such as physical activity, that could help to maintain the youth of the brain e. The cognitive reserve concept is defined theoretically as those between-person differences in how individuals process cognitive tasks that modulate how susceptible individuals are to the negative effects of brain change on cognitive ability [ 414 ].

The concept therefore encompasses compensatory changes in cognitive processing in response to negative brain changes as a result of aging or disease as well as individual differences in how tasks are processed that exist before negative brain changes occur [ 41329 ].

: Cognitive function maintenance

Cognitive Maintenance: Making the Case for Cognitive Assessments in Routine Healthcare Funciton is especially the case for memory and Cognitive function maintenance intelligence, functionn in order to adapt to various circumstances including speed processing, reasoning, Digestive health supplements Cognitive function maintenance, maintenanc short Cognjtive memory Park et al. Neurocognitive aging and the compensation hypothesis. The studies involving human participants were reviewed and approved by The Ethics Committee of Nagoya University Graduate School of Medicine approval number Campbell et al. Diet, exercise, weight control, and avoiding tobacco will go a long way toward improving your cholesterol levels.
Physical activity and the maintenance of cognitive function Neurology 34, maintenanec However, not all studies found such an association mainyenance education Malek-Ahmadi et Cognitive function maintenance. Mauntenance by: Grade? Allen, Functlon. Cognitive Sports nutrition supplements and Exercises for the Brain Since cognitive function is a product of several different interacting thought-processes, employing activities specifically designed to stimulate the brain in each of these categories will have a positive effect on overall mental functioning, or brain fitness. Supervisory experience at work is linked to low rate of hippocampal atrophy in late life.
Memory Maintenance

Morphometric changes associated with age were analyzed using voxel based morphometry VBM and changes in resting state networks RSNs were examined using dual regression analysis.

In VBM analysis, widespread regions demonstrated negative correlation with age, but none with any of the cognitive scores. Quadratic approximations of cognitive scores as functions of age showed relatively delayed decline compared to total GMV loss.

Some regions in the sensorimotor networks showed positive correlation with the DR, memory, and fluency scores. Conclusion: Some domains of the cognitive test did not correlate with age, and even the highly correlated sub-scores such as the DR score, showed delayed decline compared to the loss of total GMV.

Some RSNs, especially involving cognitive control regions, were relatively maintained with age. Furthermore, the scores of memory, fluency, and the DR were correlated with the within-network functional connectivity values of the sensorimotor network, which supported the importance of exercise for maintenance of cognition.

According to Rowe and Kahn, successful aging consists of three principal components: low risk of disease and disease-related disability, maintenance of high mental, cognitive, and physical functions, and continuous engagement with life, which includes relations with others and productive activity Rowe and Kahn, , , During the last two decades, worldwide life expectancy has increased by more than 6.

Especially in Japan where the highest aging rate was recorded in the world, the increase in the HALE has exceeded the one in life expectancy Cabinet Office Japan, Not only mortality has kept declining, but also years lived with disability has been drastically reduced.

Under this global situation, successful aging has gained its importance, and has greatly affected a variety of fields including health science, sociology, economics, and politics. Cognitive function is an extremely important factor influencing successful aging in the elderly people.

It is widely known that cognitive function gradually declines over age even in people who seemed to be healthy. This is especially the case for memory and fluid intelligence, acquired in order to adapt to various circumstances including speed processing, reasoning, working memory, and short term memory Park et al.

Empirically, when a screening test for cognitive function is performed, unexpected variations in sub-scores can be observed to some extent even if subjects are considered normal in cognitive function based on the total score falling within the normal range. Morphological studies of the brain using structural magnetic resonance imaging MRI have reported wide range of gray matter volume GMV decreases with age Good et al.

The GMV begins to decrease in early adulthood, and continues to decrease approximately linearly throughout the lifespan Ge et al. Although the GMV is generally reduced with age during healthy aging, it still remains unclear whether a cognitive function decline parallels GMV decline.

Several studies have been performed about the associations between regional GMV and cognitive scores, but there is no detailed report on the comparison between subtle changes of cognitive test scores in healthy aging and the changes in GMV.

In network analysis using resting state functional MRI rsfMRI , reduction of the functional connectivity within the default mode network DMN with age has been reported in many literatures Damoiseaux et al.

In addition, a within-network decline in functional connectivity has also been reported in other large-scale functional networks, including the salience network SN , executive control network ECN , attention network, sensori-motor network SMN and the visual network VN involved in primary processing Onoda et al.

Although such canonical networks showed decreases of within-network connectivity, between-network connectivity of some pairs of these networks somewhat increases Meier et al. Several studies have also reported the relationship between cognitive decline and network changes, e.

However, the target age and the number of subjects included were limited in each study, and the findings were inconclusive. What are the different factors influencing successful aging? Can these factors be identified based on the characteristics of brain-imaging-derived metrics such as brain volume and connectivity?

The purpose of this study was to identify such characteristics by investigating the relationship among aging, brain volume, brain network changes, and cognitive function in healthy subjects.

For this purpose, healthy individuals who maintained relatively good cognition were enrolled in the study. Within age groups, ranging from 20 to 70 years, an equal number of subjects were included. Although voxel based morphometry VBM analysis was performed as the first step, network analysis using rsfMRI represented the main part of this study.

RsfMRI is a useful method to visualize various large-scale networks in the brain by examining the synchronization of the blood oxygen level dependent BOLD changes in different brain regions during rest, i. Our hypotheses are as follows: 1 Even in healthy subjects with total score of the cognitive screening test within normal range, some variations of the sub-items in the cognitive test may reflect association with aging.

Some sub-items may show differences with age that parallel with the structural differences, whereas others may show the maintenance of these scores, independent from the structural differences. Through these analyses, we will identify the different conditions necessary for the maintenance of good cognitive function during aging, that is, the different conditions for successful aging.

All participants were healthy volunteers who joined in response to the recruitment using leaflets and the website of the BMRC. Inclusion criteria for the original project were as follows: older than 20 years, not pregnant, had no episode for MRI contraindications, no brain diseases such as cerebrovascular diseases, brain tumor, head injury, depression, and schizophrenia.

They provided written informed consent before joining the study. Between and , more than 1, volunteers participated. From the pool of volunteers, a total of participants, consisting of 10 men and 10 women in each of the 6 age groups, 20s, 30s, 40s, 50s, 60s, and 70s, were randomly chosen.

in structural MRI as identified by Japanese board-certified neurologists HW, KH, and KK and neurosurgeon SM , 3 ACE-R total score less than 83, and 4 incomplete imaging data. The mean age for all participants was The average number of years for education was The percentage of participants who smoked were In term of head motion, which typically affect the estimation of the functional connectivity, the mean frame-wise displacement FD values Power et al.

The number of subjects with mean FD greater than 0. No participants had mean FD greater than 0. T1 anatomical images and rsfMRI data were obtained from all participants.

MRI scanning was performed using a Siemens Magnetom Verio Siemens, Erlangen, Germany 3. During rsfMRI scan, the participants were instructed to close their eyes but to stay awake.

A Japanese version of ACE-R was performed to evaluate cognitive function for all participants. Participants who obtained 82 points or less in total score were excluded from this study because of the possibility of dementia. In addition to the total score, the scores for each of the five cognitive domains, the sub-score of verbal fluency such as semantic or phonological word recall, the sub-score of memory such as memorization, delayed memory, and recognition, and the sub-scores for others were also documented.

Image preprocessing for the anatomical T1WI and rsfMRI dataset was performed using Statistical Parametric Mapping SPM12, Wellcome Trust Center for Neuroimaging, London, United Kingdom running on Matlab Ra, MathWorks, Natick, Mass, United States.

The T1WI images were first segmented into component images including gray matter GM , white matter WM , and cerebrospinal fluid CSF , among others, by the segmentation approach included in SPM Bias-corrected T1WI and the transformation information from subject space to MNI Montreal Imaging Institute space were also obtained during segmentation.

For rsfMRI dataset, we excluded the first 5 volumes in the series in order to account for the effects of the initial scanner inhomogeneity. Slice-time correction was then performed relative to the middle slice slice 20 , and the images were realigned to the mean functional volume.

The mean volume, together with the realigned functional images, were then co-registered to the bias-corrected T1WI anatomical images. The co-registered functional images were normalized to the MNI space using the transformation information obtained during segmentation, resampled to have an isotropic voxel resolution equal to 2 x 2 x 2 mm 3 , and smoothed using an isotropic 8-mm full-width-at-half-maximum FWHM 3D Gaussian filter.

Finally, the preprocessed data were then bandpass filtered within 0. All preprocessing were performed using in-house Matlab scripts as reported previously Bagarinao et al.

The preprocessed dataset were used in the succeeding analysis. In addition, sub-items of cognitive function in the ACE-R were also examined. Next, regression analysis was performed for each factor with significant correlation with age.

We examined two regression models. One is linear in age, and the other is quadratic. The appropriate regression model linear vs. The total volumes of GM, WM, and CSF were calculated using the segmented components of the T1-weighted images. Global calculation was performed using TICV. We also examined the association between GMV and the score of the DR, which showed the highest significant relationship with age in the above correlation analysis, under two different conditions.

In one condition, age and the TICV were included as covariates, while in the other condition, age was excluded. Xjview 1 was used to examine regions with significant association with age or the score of delayed memory in the resulting statistical maps.

Automatic anatomical labeling AAL was used for the anatomical name of the identified region. To evaluate the relationship between factors associated with aging and brain functional networks, we used dual regression analysis.

The preprocessed rsfMRI datasets from the subjects were temporally concatenated, and group independent component analysis ICA was performed using the MELODIC software from the FSL package Jenkinson et al. Thirty independent components ICs were derived across the whole sample, extracted, and visually compared to a set of reference RSN templates 2 Shirer et al.

In dual regression analysis Filippini et al. These time courses were then used as temporal regressors in a second regression analysis to generate subject-specific maps associated with each group IC. Using the constructed subject-specific maps, regression analysis was performed with the cognitive function scores, year of education, age, gender, and GMV set as regressors.

In another condition, the DR score was used instead of the memory score. Statistical analysis of each component map was performed using a non-parametric permutation test permutations , and regions with connectivity showing statistically significant association with each respective factors were identified.

All statistical maps were corrected for multiple comparisons using FWE correction with threshold free cluster enhancement. The other domains, language and fluency, were not significantly correlated with age. The education year also demonstrated negative correlation with age, which reflect the relatively high college enrollment rate in younger generation and was, therefore, excluded for further regression analysis.

For the regression analysis of each factor with age, we examined two regression models - linear and quadratic. The vertical axis showed the volume and the score of each subject as a standardized z-score, and the horizontal axis is age.

The appropriate regression model, shown as solid line, was identified using both AIC, BIC, and R2. AIC, BIC, and R2 values for the two regression models of each factor are summarized in Table 3. Among the different cognitive function scores, the DR score showed the highest significant relationship with age.

The score of visuospatial ability showed mild linear change with age, but the R2 value was small, and its change was not reliable. The other two cognitive domains language and fluency did not show significant relationship with age.

Figure 1. Figure 2. Table 3. A summary of AIC, BIC, and R2 values for the two regression models of each factor. With VBM, a strong negative correlation with age was observed in many regions across the cerebral cortex.

The maximum negative correlation was found in the right posterior central gyrus. Areas with negative correlation with age were widespread and bilaterally observed in the lateral frontal cortices, the lateral temporal cortices, the lateral occipital cortices, the parietal cortices, the cingulate gyrus, the areas surrounding the intraparietal sulcus, and the medial temporal areas including the hippocampus Table 4 , upper row in Figure 3.

Figure 3. VBM results. These regions overlapped with the part of the areas showing negative correlation with age lower row in Figure 3.

However, in the analysis where the age was also included as a covariate, no region survived. In the first step of the dual regression analysis, 18 resting networks were extracted Figure 4.

Those networks included the ventral and dorsal DMN, the right and left ECN, the anterior and posterior SN, the precuneus network, the dorsal attention network DAN , lateral DAN, the dorsal and ventral SMN, the basal ganglia network BGN , the language network LN , the auditory network, the primary, medial, and higher VN, and the cerebellar network.

The negatively correlated regions in each network were shown in Figure 5 , and the anatomical location and voxel counts of those regions were summarized in Table 6.

On the other hand, 10 networks did not show significant correlation with age. Figure 4. The 18 resting networks extracted at the first step of the dual regression analysis.

DMN — default mode network; ECN — executive control network; Rt — right; Ant — anterior; Post — posterior. Figure 5. Resting state networks, shown in white, with within-network functional connectivity values that negatively correlated with age.

Table 6. Anatomical regions decreasing functional connectivity with age in the canonical RSNs. The regions with positive correlation in the SMN were almost the same in the memory and the DR Figure 6 , Table 7. Furthermore, the score of the fluency was found to be positively correlated with 4 networks, the right ECN, the primary visual, and the dorsal SMN Figure 6 , Table 7.

Figure 6. Resting state networks, shown in white, with within-network functional connectivity values that positively correlated with the score of fluency, memory, and delayed recall DR. Table 7. Anatomical regions increasing functional connectivity with cognitive score in the canonical RSNs.

In this study, we evaluated the relationship between aging and cognitive function in a total of healthy subjects consisting of a balanced number of participants within age-groups of 20s, 30s, 40s, 50s, and 70s, who maintained relatively good cognition.

However, no regions have GM values that correlated with the scores of all domains in the cognitive test when age was included as a covariate. We selected subjects whose ACE-R score was above the cutoff and was considered normal in cognition. Even in such subjects, ACE-R showed variances in some domains and sub-scores with the DR being the most sensitive sub-score for aging.

This finding has a clinical importance to interpret the results of ACE-R. On the other hand, language and fluency were not significantly correlated with age. These findings support the idea that crystalized intelligence is more maintained than fluid intelligence in healthy aging Baltes et al.

In VBM analysis, our results showed that the GMV widely declined with age, even starting from the early 20s. This result is consistent with many previous studies Good et al. Regarding the location of regions showing negative correlation with age, the areas around the central sulcus and the intraparietal sulcus were commonly reported in several literatures Good et al.

In our study, we found a significantly lower GMV in bilateral regions around the central sulcus and the intraparietal sulcus, and bilateral medial temporal areas including the hippocampus in older adults.

These changes were also frequently observed even in the stage of mild cognitive impairment MCI Baron et al. We adopted 83 as the cutoff of ACE-R in this study Mathuranath et al. These individuals may potentially be at the prodromal stage of dementia, that is, MCI, and could have influenced our results.

The WMV was known to demonstrate a U-shaped change with age Bagarinao et al. In VBM, we did not find regions with GMV that correlated with the scores of cognitive domain in ACE-R when age was included as a covariate. This result reflects difficulty to evaluate significant relationship between cognition and morphological changes when simultaneously accounting for the influence of age.

In the analysis without age as a covariate, the DR score positively correlated with the GMV of a relatively wider brain region that included bilateral frontal cortices, bilateral temporal cortices, bilateral insular cortices, and bilateral cingulate cortices. We assumed that the function of the DR may require activities in a variety of regions including the hippocampus and the nearby medial temporal structures.

However, such a topographic characteristic was not observed in our results. These results should be interpreted with care considering the dependence of both DR and GMV with age. A study by Takeuchi et al. Diverse cognitive functions may be weakly associated with regional GMV in widespread brain areas, and may be difficult to detect this association in this analysis.

Regarding the relationship between the morphological changes of the brain and cognition with age, Schnack et al. Higher IQ was associated with larger and thicker surface area until around the age of 20, but this relationship weakened from the age of 40 to They also mentioned that individuals maintaining high IQ may form highly efficient formation of brain networks Schnack et al.

Although they utilized the IQ, which has four domains including the language, working memory, visuospatial, and performance speed, the results was similar to ours.

In healthy aging, a decrease in the GMV and a decrease in cognition showed such temporal dissociation and never showed parallel relationship. The absence of this relationship could not be simply explained by morphological analysis in the brain, and therefore, we supposed that the network analysis was necessary.

More broadly, existing studies have shown that GM continuously declined with age. Thus, it is indeed intriguing that cognitive scores have inverse U-shaped behavior as a function of age, while GMV decreased linearly. Although speculative, this may point to some possible reserve mechanisms at work, where reserve capabilities are accumulated during childhood and young adulthood.

The concept of brain or cognitive reserve Stern, ; Satz et al. Factors such as longer education, greater physical activity, and involvement in demanding leisure activities, among others, affect reserve capacity Cabeza et al.

This possibly drive the relative preservation in cognitive scores before it peaks and started to decline. Since reserve can also manifest in terms of efficient use of neural resources Solé-Padullés et al.

To fully understand the association among brain structure, network, and cognition in the aging brain, more studies are needed. Previous studies have reported that the connectivity within networks, such as DMN, decreased with age Damoiseaux et al.

Our results also demonstrated similar within-network connectivity decreases in 8 out of 18 RSNs. Specifically, the ventral DMN showed significant decrease in functional connectivity, but not the dorsal DMN.

Similar results have been previously reported Campbell et al. The functional difference between the two is currently not well understood. The ventral DMN is more associated with memory, a hippocampus — dependent function Damoiseaux et al.

Campbell et al. Their findings showed that the subsystem involving dorsal posterior cingulate cortex PCC to the fronto-parietal regions was relatively maintained in the elderly, whereas that involving the ventral PCC declined in functional connectivity.

The dorsal PCC is a core region in the dorsal DMN, and this could be a reason for the observed discrepancy between ventral DMN and dorsal DMN in our study. With regards to the LN, which also showed no association between connectivity and age, we found regional similarity of its connectivity to that of the dorsal DMN.

Both networks shared common regions in the dorsal PCC and dorsomedial prefrontal cortex Figure 7. In addition, the LN is associated with language ability, an important part of crystalized intelligence. Therefore, this result may be a reflection of the relative maintenance of crystalized intelligence over age.

In the absence of supporting literature, more studies examining the association between LN and the network associated with crystallized intelligence are needed. Previous studies have reported that these networks have decreased within-network functional connectivity Onoda et al.

These regions are well known to be important for cognitive control, which is a function for the effortful use of cognitive resources to guide, organize, or monitor behavior Grady, Some reports suggested that these networks were important for cognitive reserve Onoda et al.

Taken together, our results suggest that networks involved with cognitive control were not significantly associated with age. This may reflect the characteristics of our cohort, who had relatively maintained cognition. Figure 7. These results are reasonable considering the vulnerability of the GMV in these areas to aging, physical deterioration, and less external stimulation in the elderly.

However, other studies have also demonstrated that the functional connectivity in the primary processing networks is unchanged in advancing age Geerligs et al. Therefore, this finding remained inconclusive. The networks related to high-order visual processing also showed negative correlation with age in our study, consistent with previous reports Yan et al.

In terms of the relationship between cognitive functions and functional connectivity within networks, our results demonstrated that the memory score, the DR score, and the fluency score were positively correlated with the SMN.

A close relationship between motor function and cognitive function has been reported in behavioral experiments and epidemiological surveys Clarkson-Smith and Hartley, ; Weuve et al.

Recently, a study has reported that physical exercise improved gait speed, and cognitive performance, through the increasing involvement of motor-related networks Ji et al. Voss et al. also reported that cardiorespiratory fitness moderated the adverse effects of aging on cognitively and clinically relevant functional brain networks Voss et al.

The neural basis of exercise as an intervention for the maintenance of cognition is being gradually elucidated by recent network analyses, and this may lead to the development of an effective modality about intervention by exercise to prevent cognitive impairment Huang et al.

The fluency score also showed positive correlation with the right ECN, and weak correlation with the primary VN. Working memory is related to word phonological fluency, and knowledge and vocabulary are related to word categorization fluency Ruff et al. This association may reflect the relationship between the score of fluency and the ECN.

Other cognitive function scores did not show any significant association with the connectivity of any network. In terms of education history, longer schooling was associated with higher connectivity in the primary VN, the precuneus network, the DAN, and the ventral DMN.

The education history was reported to have a correlation with cognitive reserve. In a study with a 4-year follow up, the group with short education history had a 2.

A more recent study has shown that the risk is 1. Given this, long education history plays an important role to keep cognition within normal range, and the neural basis for this may be related to cognition-related networks such as the DAN and the ventral DMN.

These networks may have an important role for cognitive reserve. Although the long history of education negatively correlated to the cerebellar network, there has been no report regarding this finding. Recently, there are some reports about detailed analysis for the RSNs in the cerebellum Dobromyslin et al.

Further study is warranted. First, in the VBM analysis for the DR score, the influence of age could not be completely separated. To identify specific regions related to the DR score using VBM, it is necessary to match the age of all participants and examine individual differences in the DR score.

Second, ACE-R is typically used for healthy screening, and has a ceiling effect. Under this limitation, we cannot fully discount its contribution in the observed inverse U — shape behavior in some cognitive domains as functions of age.

However, such an inverse U-shape curve is not uncommon in aging studies and has been reported for some cognitive scores Douaud et al. Moreover, the sensitivity of the sub-score of ACE-R is not well understood. Therefore, the use of specific cognitive batteries is necessary for a more detailed cognitive evaluation.

Third, we just examined the strength of the connectivity within networks. Analysis of the interaction among networks is necessary to fully understand how brain networks contribute to preserve cognition from the GMV loss.

There are two important hypotheses, the differentiation Park et al. To evaluate these mechanisms, the between-network analysis will be performed in a future study. Fourth, this study used cross-sectional data collected by each age-group, not a longitudinal observation of individuals.

Finally, the effect of head motion during rsfMRI scanning cannot be completely ruled out especially in aging studies Kato et al.

In our study using a well-balanced healthy cohort in terms of the number of participants and age, we found mixed aging characteristics of brain networks. Furthermore, the cognitive domains that correlated with age, even the highly correlated sub-scores such as the DR score, showed delayed decline compared to the loss of total GMV.

In RSN analysis, the ventral DMN, some networks involving primary processing the primary VN, the dorsal and ventral SMN , and network related to visual function have within-network connectivity values that negatively correlated with age. This may reflect a relative preservation in cognitive control function and crystalized intelligence in our cohort.

Furthermore, the score of memory, fluency, and the DR was correlated with the sensorimotor network, which supported the importance of the exercise for maintenance of cognition. The datasets presented in this article are not readily available because of privacy and ethical restrictions.

Requests to access the datasets should be directed to SMa, smaesawa med. The studies involving human participants were reviewed and approved by The Ethics Committee of Nagoya University Graduate School of Medicine approval number SMa, SMi, EB, HW, MH, HI, NO, MK, RS, and GS contributed to conception and design of the study.

SMa, SMi, DM, DN, KH, KK, RO, AO, MH, and HI were involved in data acquisition, data organization, and data curation. SMa, SMi, EB, HW, and KK contributed to the methodology, analysis, interpretation of the data, and wrote the draft of the manuscript.

All authors reviewed and approved the final version of the manuscript. Cognicise was performed with the elderly when cognitive function evaluation was not conducted. The contents of cognicise performed in the 5 weeks were continued as an activity in the local communities.

A look at cognitive function evaluation. website with partial modification. This evaluation is an application that enables fun task execution by game sensation, and enables the evaluation of the five aspects of cognitive functions, namely planning ability, memory, attention, orientation, and spatial awareness, from the execution time and accuracy rate of the tasks [ 12 ].

The tasks performed included reading Kanji indicating colors the color of the characters and that of the Kanji indicating the color are different displayed on the screen while walking or stepping, an exercise involving moving four limbs according to instructions while singing Figure 4 , and an exercise involving moving the left and right hands at the same time with different movements.

In addition, the exercises were mainly guided by the students of the Department of Occupational Therapy. Exercise following the movement of the leader while singing.

The leader is the student on the right. The student on the left follows the exercise of the leader, while singing the lyrics displayed at the top of the figure at the same time. In , when the spread of COVID infection was remarkable, activities in local communities were restricted, and only cognitive function evaluation could be conducted in local communities.

Thus, for cognitive function maintenance exercises Figure 5 , manuals describing methods to perform the exercises, and DVDs introducing the exercise methods were produced Figures 4 and 5. These were then distributed to local communities to provide exercise guidance to the elderly.

Nine types of exercises were prepared for the cognitive function maintenance exercises. These were filmed while changing the exercise difficulty level and exercise speed. Then, the videos were edited so that the elderly could understand them easily, such as by adding subtitles to the videos filmed, and DVDs explaining the content of the exercises were produced.

The student on the left shows their hand after the leader to ensure victory. A total of 23 elderly people participated in our club, including 5 men and 18 women mean age of Among them, a total of 13 elderly people participated every year, including 3 men and 10 women mean age of Changes in MMSE and FAB scores over time.

There were no significant changes in the MMSE and FAB scores over time. It could not be conducted in due to the shortening of the cognitive function evaluation time caused by the spread of COVID infection. Changes in the average time required were observed for each task, but there were no statistically significant changes over time in all five tasks.

Cognicise is expected to improve cognitive function and suppress the progression of cerebral atrophy by activating the brain, compared to exercises where one only moves the body [ 4 , 5 , 6 , 7 , 8 , 13 ]. Thus, it cannot be said that the activity of our club contributes to the prevention of cognitive decline.

Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution-NonCommercial 4. Edited by Shinichiro Maeshima. DOWNLOAD FOR FREE Share Cite Cite this chapter There are two ways to cite this chapter:.

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Chapter metrics overview Chapter Downloads View Full Metrics. Impact of this chapter. Abstract According to estimates by the Japanese Cabinet Office, there will be approximately 6. Keywords community cognitive function evaluation cognitive function maintenance.

Introduction The Annual Report on the Aging Society by the Japanese Cabinet Office [ 1 ] estimated that, in , there will be 6. References 1. Cabinet Office: Heisei 29nenban Koureisyakai Hakusyo.

pdf [Accessed: July 31, ] 2. pdf [Accessed: July 31, ] 3. SECOM CO. pdf [Accessed: July 31, ] 4. Shigemori K. Prevention od dementia by exercise. The Journal of Japan Society for Early Stage of Dementia.

Kozaki K.

Cognitive function maintenance

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