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Enhance cognitive resilience

Enhance cognitive resilience

Enhance cognitive resilience profile update: The Enhabce through life project. Article CAS Resilienve PubMed Enhancd Google Scholar Weight loss support groups EN. Correspondence to Thomas J. Supplementary Figure 6. This ability is naturally a subject of intense investigator curiosity and has inspired many research papers. ADC samples were genotyped and analyzed in separate batches. Download citation.

Enhance cognitive resilience -

It can also result in sign flipping [ 22 ]. We can understand this sign flipping from a conceptual point of view by considering what happens when we include our original cognitive measure with the resilience measure in the model. The coefficient of the resilience measure would be interpreted as the effect when individuals are equated on cognitive performance.

If individuals are equated on cognitive performance, then any variance in their resilience measure must be driven by variance on the adverse brain factor. Therefore, a higher resilience score in this model would simply reflect higher levels of the adverse factor e. We once again demonstrate this phenomenon with the ADNI data described above Table 1.

Several models were run to predict the decline on the ADNI-MEM score. When the residual score is the only predictor, there is a positive association such that higher residuals i. However, when the original cognitive measure is included in the measure, the sign of the coefficient for the residuals becomes negative because higher values now reflect more hippocampal atrophy.

Additionally, this approach does not produce a single residual measure, which many studies seek to use in subsequent analyses as an outcome variable or entry as one feature in a multivariate model.

An alternative approach that does produce a single adjusted score entails regressing the y variable out of the residual:.

This new adjusted residual may be considered an index of resilience that is uncorrelated with cognitive performance.

Thus, our adjusted residual δ 2i contains our brain measure x i , resulting in a negative correlation between the two. The magnitude of this correlation will be proportional to Corr δ 1 , y , albeit with the reverse sign.

This flip in sign occurs for the same reasons as described above. In other words, we shift from a measure of resilience that is correlated with our cognitive score to one that is correlated with our adverse factor. This is likely not the desired measure from a conceptual standpoint.

We have shown here that, in most real-word cases, residual-based methods of measuring resilience are highly collinear with the dependent variable i. This means that the residual measure is rarely representative of resilience and can cause issues with interpretations depending on how it is used in subsequent analyses.

As an alternative, one may avoid calculating the residual at all and instead examine how a third variable moderates the association of an adverse factor with cognition. Testing for an interaction effect has previously been a recommended approach to examining cognitive resilience [ 1 , 2 ].

We may then interpret the interaction as evidence of cognitive resilience and the moderator as a factor contributing to resilience. Importantly, this model includes both the interaction and main effects of each variable involved.

Creating a residual of cognitive decline i. However, the interaction approach can be extended to longitudinal designs by including a three-way interaction that tests the degree to which a resilience factor minimizes the impact of an adverse factor on cognitive decline.

Two-step approaches that pre-regress out covariates not only are less parsimonious but can also lead to confusion in interpretations, can improperly represent degrees of freedom, and may not be necessary at all in well-powered studies.

The use of the interaction approach comes with several caveats. First, the distributional properties of the included measures should be assessed. Measures with strong ceiling or floor effects may not be appropriate. For example, if individuals are performing at ceiling, it will not be possible to detect performance that is better than expected.

Second, the interaction approach, like the model including both the residual and cognitive score, does not provide a standalone measure of resilience for each individual that can be further investigated for translation to the clinic.

This may not necessarily be a drawback — while it is certainly of interest to understand which individuals are exhibiting resilience, we ultimately want to understand what factors contribute to or confer this resilience. Using the interaction approach, we are limited to identifying resilience at the group level i.

It is these factors that contribute to overall resilience by mitigating the impact of pathology on cognition that may represent suitable mechanisms to target for interventional strategies.

Re-examining what the cognition residual represents statistically may help reveal a new path forward for quantifying resilience. The residual represents the totality of unexplained variance in the cognitive variable after accounting for an adverse factor.

In other words, it is a negative definition. It is important to note that the initial paper by Reed et al. We come to the same conclusion. Rather than isolating this error term and repurposing it as resilience, it may be more fruitful to focus on constructing a more complete model of cognition by maximizing measurement of other adverse and protective factors, directly or indirectly.

This may include modeling previous or premorbid cognitive ability, which helps determine whether current cognitive performance represents long-standing individual differences in performance or is the result of decline — something that the residual score cannot assess.

Then, putative resilience factors can be iteratively added to the existing cognitive model to see whether they contribute meaningful, independent information.

The covariance or interactions of the putative resilience factor with other aspects of the cognitive model can also be considered. This is in some ways akin to the study of normative aging. Aging effects can be seen as phenomena correlated with age that are driven by factors we have not specifically measured or identified.

Recent studies conducting comprehensive neuropathologic exams have been able to attribute a substantial portion of late-life cognitive decline to pathology that would otherwise have been labeled normative aging [ 23 , 24 ].

The study of resilience may be furthered by including protective factors in such models. In this way, we encourage those interested in studying resilience to consider reconceptualizing the objectives of these analyses. Modeling the contribution of various adverse and protective factors will allow us to make better predictions about cognitive decline.

By continuing to discover and quantify such factors, we can slowly reduce the unexplained variance in cognitive decline i. In doing so, we can shift our focus from identifying resilient individuals to identifying factors that contribute to better cognitive health.

We wish to discover what resilience factors our model predicts will enhance cognitive outcomes if introduced or modified. However, we may also be able to identify factors that improve the early development of cognitive ability or prevent factors over the lifespan that may drive decline in the first place.

This pursuit will be enhanced as our model for cognition improves. A Variance in current cognitive performance leftmost bar is driven by a number of contributing factors. B If the variance explained by an adverse factor e.

is regressed out, the remaining variance is largely the same as the current cognitive performance. C A large portion of current cognitive performance is explained by premorbid cognitive performance. E Variance that remains in current and past cognitive performance can be explained by a host of known and to-be-discovered genetic, environmental, and lifestyle factors and pathologies, as well as measurement noise.

Ultimately, our goal is to understand what contributes to this variance and reduce error in our model of cognition. F These models can be used to predict cognitive state or forecast cognitive decline. The more comprehensive our models of cognition, the better our individual levels of prediction will be.

With better models for cognition, we shift our focus to simulating how modification of a pathological or resilience factor might influence maintenance of healthy cognition.

Although we recommend against using residual approaches to quantify resilience, we note that these approaches can be appropriate in other contexts such as adjusting a measure for confounding factors.

For example, hippocampal volume is often adjusted for individual differences in head size by regressing out intracranial volume, and time to complete Part B of the Trail Making Test may have time to complete Part A regressed out to control for differences in visual scanning and speed.

Similarly, regression-based change scores have been used as an alternative to difference scores that account for expected regression to the mean upon re-testing. The critical difference is that these residuals are not interpreted as being independent of the original variable.

Rather, they are considered adjusted versions or highly dependent on the original measure. The residual approach to measuring resilience has many attractive qualities. However, as seen in the brain age literature, residual measures come with important statistical considerations.

As we have shown, these issues complicate interpretability and seriously limit the usefulness of resilience measures in the context of studying cognitive or brain resilience.

Although several correction methods have been proposed, these do not appear to produce measures that sufficiently reflect our conceptual idea of resilience as a unique entity. However, understanding the factors that influence resilience is an important goal that will aid in efforts to extend cognitive and brain health spans.

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Oxytocin enhances hippocampal spike transmission by modulating fast-spiking interneurons. Pettigrew C, Soldan A, Zhu Y, et al. Wu, G. Understanding resilience. Frontiers in Behavioral Neuroscience, 7 , Yamamoto, T. Effects of the cognitive-behavioral You Can Do It! Education program on the resilience of Japanese elementary school students: A preliminary investigation.

International Journal of Educational Research, 86 , 50— Download references. The authors would like to thank Seyed Naeim Tadayon Nabavi Fadafan illustrator , Laleh Ziai illustrator , and Mohsen Farhadi graphic designer for their contribution in creating cartoons and graphic design of ProCoRe program.

Institute for Cognitive Science Studies, Tehran, Iran. University of Medicine and Science, Los Angeles, CA, USA. Laureate Institute for Brain Research, Tulsa, OK, USA.

Department of Psychiatry, Institute for Drug and Alcohol Studies, Virginia Commonwealth University, Richmond, VA, USA. You can also search for this author in PubMed Google Scholar.

Correspondence to Hamed Ekhtiari. National Center for Wellness and Recovery, Oklahoma State University Center for Health Sciences, Tulsa, OK, USA. Center for Health Sciences, Oklahoma State University, Tulsa, OK, USA. How does cognitive neuroscience approach explain the vulnerability of adolescents toward lack of control, risk-taking, and drug use behavior?

What does cognitive resilience mean to you? Write a one-two paragraph inspired by this chapter and explain why it is important to improve cognitive resilience. What would be the outcome of improving cognitive resilience, particularly in adolescents?

How would that change our current society? A set of brain-derived abilities and processes for coping with the negative consequences of stress, adversity, and negative emotions while maintaining proper level of cognitive functions.

A dynamic capacity that buffers the impact of stress while keeping the balance in daily performance at both personal and societal levels. Reprints and permissions. Rezapour, T. Enhancing Cognitive Resilience in Adolescence and Young Adults: A Multidimensional Approach.

In: Croff, J. eds Family Resilience and Recovery from Opioids and Other Addictions. Emerging Issues in Family and Individual Resilience. Springer, Cham. Published : 23 January Publisher Name : Springer, Cham. Print ISBN : Online ISBN : eBook Packages : Law and Criminology Law and Criminology R0.

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Abstract Resilience, as a trait, process, or outcome, is an important factor to explain behavioral diversity between individuals and population groups in face of stress and adversity. Keywords Resilience Stress Substance use Cognitive resilience ProCoRe Adolescence. Buying options Chapter EUR eBook EUR Softcover Book EUR Hardcover Book EUR Tax calculation will be finalised at checkout Purchases are for personal use only Learn about institutional subscriptions.

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Acknowledgements The authors would like to thank Seyed Naeim Tadayon Nabavi Fadafan illustrator , Laleh Ziai illustrator , and Mohsen Farhadi graphic designer for their contribution in creating cartoons and graphic design of ProCoRe program.

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Enhance cognitive resilience, as Enhajce trait, Enhance cognitive resilience, or outcome, is an important factor to Enhance cognitive resilience behavioral diversity between individuals and population groups in face of stress and adversity. Individuals and groups who can bounce back shortly after stressful events, experience Enhxnce severe Enhance cognitive resilience emotions Thyroid health catechins, anxiety reeilience, and cognitkve situations through efficient problem-solving strategies are categorized as resilient. Several psychosocial interventions, mostly taking a positive psychology approach, improve resilience and reduce disruptive behaviors e. However, the role of brain awareness and training interventions targeting cognitive underpinning of resilience is not fully explored. In this chapter, we first review the existing literature and address the interventions that indirectly increase cognitive resilience among school-aged adolescents. Then, we introduce the Promoting Cognitive Resilience ProCoRea new multimodal cognitive resilience training program that taps different cognitive functions that are documented to be effective in the neuroscience literature.


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