In this study, FI was constructed using 34 items available in the PLAD dataset, according to a standard procedure, which was similar to previous study reports (Searle et al., 2008; Hao et al., 2016). All selected variables meet the following criteria: associated with health status; increased with age (generally); not saturate too early; cover a range of important systems (Searle et al., 2008). The 34 variables in the construction of FI were Instrumental Activities Daily Living (IADL) and Activities Daily Living (ADL) disability items (n = 14), disease (n = 9), psychological problems (n = 1), symptoms (n = 5), and abnormality in the physical examination (n = 6). Items used to assess the cognitive function such as all items in MMSE were excluded. A binary variable was coded as present = 1 or absent = 0. For variables with 3–4 scale levels, the intermediate response was coded between 0 and 1. For each oldage person, the FI was calculated as the sum of all deficits present divided by the total number of whole considered variables (here it is 34), which made the FI a continuous variable, theoretically ranging between 0 and 1. We set FI = 0.21 as a cut-off point for diagnosis of frailty, in accordance with Hoover et al. (2013) study.

#### Evaluation of Cognitive Function

In this study, the 30-item Mini-Mental State Examination (MMSE) scale was used to evaluate cognitive function, as it is a reliable and widely used method of assessment of the condition, and it includes the measurements of the following parameters: attention and calculation, orientation, recall, language, and ability to follow simple commands (Tuijl et al., 2012). Visual and auditory abilities were basic requirements for most items of MMSE (Holtsberg et al., 1995). Our study excluded 100 participants (28 men and 72 women) who were unable to complete the MMSE test due to hearing or visual problems, in order to be able to address the influence of hearing and visual impairment on cognitive function. In Asian people, the cut-off point of MMSE is highly variated, ranging from 17 to 29 (Rosli et al., 2016). The educational level of most subjects in this study was low (illiterate or primary school; 97.4%), and cognitive impairment was defined as an MMSE score of 0–18. An MMSE score of 19–30 was defined as "without cognitive impairment," according to previous reports, and this cutoff point has been shown to be 80 to 100% specific and 80 to 90% sensitive for diagnosis of cognitive impairment (Katzman et al., 1988; Tombaugh and McIntyre, 1992; Zhu et al., 2006; Cui et al., 2011; Matusik et al., 2012). Furthermore, we performed several methods to promote the assessment quality and methodological reliability, which includes the following: (1) MMSE assessors were trained by experienced geriatricians in comprehensive geriatric assessment, provided research manually, and video for all researchers; (2) observed MMSE administrators performing the MMSE on standardized patients; (3) quality control researchers received and responded to feedback or questions, while conducting the MMSE on the participants.

# Mortality Data and Other Co-variables

The mortality data, the status of survival (died or survived), and the time of death, were collected for all participants from local government records, relatives, or neighbors in 2009. There were about 48 (5.5%) participants lost to follow-up. Age, gender, educational levels (illiteracy, primary school, or secondary school and advanced), weight, height, waist circumference, systolic blood pressure, diastolic blood pressure, smoking, alcohol drinking, exercise, and comorbidity were collected as co-variables. Comorbidity was defined as two or more chronic illnesses occurring in the same participant. All chronic diseases were diagnosed by certified physicians in the local hospital.

# Statistical Analysis

In this study, baseline characteristics of the participants were shown according to the status of frailty and cognitive impairment of the data types. Continuous variables were presented as means and standard deviations. Categorical variables were presented as numerals and percentages. The differences between groups were tested by Analysis of Variance (ANOVA) or unpaired Student's t-test for continuous variables or Chi-square test for categorical variables. Cox proportional hazard regression models were employed to estimate the hazard ratio (HR) and its 95% confidence interval (CI) of the status of frailty and cognitive impairment as a function of increased mortality. Age, gender, and educational levels were regarded as general covariates in adjusted Cox regression model 1. Lifestyle factors (smoking, alcohol consumption, and exercise) and chronic diseases were added in Cox regression model 2. Several other co-variables (P < 0.1, when compared among different groups for baseline variables), regarded as potential confounders, were adjusted further in model 3. Statistical Product and Service Solutions (SPSS) software package for Windows, version 17.0 (SPSS Inc., Chicago, IL, United States), was used in all statistical analyses. Two-tailed P-values of <0.05 were set as statistically significant.

# RESULTS

#### Baseline Characteristics, Frailty, and Cognitive Impairment

Overall, we included 705 participants in this study. The percentage of females was 67.4%, and the mean age of the subjects was 93.6 ± 3.3 years, ranging from 90 to 108. The maximum, mean, and median FI scores of the participants were 0.62, 0.26, and 0.25, respectively. The standard deviation of FI is 0.10. The 99th percentile obtained for the FI was 0.53. The maximum, mean, and median MMSE scores of the participants were 28, 14.82, and 15, respectively. The standard deviation of MMSE was 5.68.

Women had significantly higher FI scores and lower MMSE scores than men (0.26 ± 0.11 vs. 0.24 ± 0.10; t = −2.53, P = 0.012; 13.70 ± 5.28 vs. 17.14 ± 5.79; t = 7.86, P < 0.001) and more females presented in the frailty and cognitive group than males (66.1 vs. 58.7%, X 2 = 3.68, P = 0.055; 83.6 vs. 54.8%, X 2 = 67.01, P < 0.001). The overall prevalence of frailty and cognitive impairment among the whole population were 63.7% (95% confidence interval (CI) = 60.1–67.2%) and 74.2% (95% CI = 70.8–77.3%), respectively.

The combined prevalence of frailty and cognitive impairment, frailty alone, cognitive impairment alone, and no frailty nor cognitive impairment (control group) were 50.1% (95% CI = 46.4–53.8%), 13.6% (95% CI = 11.3–16.4%), 24.1% (95% CI = 21.1–27.4%), and 12.2% (95% CI = 10.0–14.8%), respectively. Subjects with combined frailty and cognitive impairment were older with significantly higher percentage of female, illiteracy, comorbidity, and death, but significantly lower weight, height, and systolic blood pressure (SBP), compared with the control group. **Table 1** shows the characteristics of the study participants, according to their frailty and cognitive impairment status.

## Baseline Characteristics and All-Cause Mortality

The 4-year death rate was 53.8% in these old-aged individuals. Those who died were slightly older than the survival group, but there was no statistical significance (93.8 ± 3.3 vs. 93.4 ± 3.4, t = −1.86, P = 0.063). Mortality was significantly enhanced in participants with higher FI but lower MMSE scores than the survival group (0.27 ± 0.11 vs. 0.24 ± 0.10, t = −4.32, P < 0.001; 13.86 ± 5.83 vs. 15.94 ± 5.31, t = 4.93, P < 0.001). The proportion of frailty and cognitive impairment was also higher in the death group than in the survival group (68.3 vs. 58.3%, X 2 = 7.66, P = 0.006; 78.6 vs. 69.0%, X 2 = 8.45, P = 0.004). Lifestyle habit, regular exercise, was also less common in the death group than in the survival group (33.0 vs. 47.2%, X 2 = 14.63, P < 0.001). There was no statistically significant difference between the death and the survival groups for comorbidity (57.3 vs. 56.4%, X 2 = 0.047, P = 0.828) and other co-variables (see **Table 2** for more details).

### The Relationship Between Frailty, Cognitive Impairment, and All-Cause Mortality

**Table 3** shows the results from unadjusted and adjusted Cox proportional hazard regression models for the frailty and cognitive impairment status, as a function of increased risk of death. Compared to the control group, subjects with combined frailty and cognitive impairment had a significantly higher risk of mortality [HR: 1.82, 95% CI (1.27, 2.61), P = 0.001] than those with the individual syndrome. Frailty only could not predict the risk of death in the study population [HR: 1.29, 95% CI (0.83, 2.00), P = 0.256] when compared with the control group. A similar result was yielded when participants with cognitive impairment alone were compared with the control group [HR: 1.31, 95% CI (0.88, 1.94), P = 0.184]. This model was stable after

TABLE 1 | Characteristics of the study population according to frailty and cognitive impairment status.

Data are the mean ± SD unless otherwise indicated. Comorbidity was defined as the presence of two or more chronic diseases (hypertension, cardiovascular disease, cerebrovascular disease, diabetes, respiratory disease, digestive disease, chronic renal disease, and osteoarthritis).

Frontiers in Aging Neuroscience | www.frontiersin.org

Data are the mean ± SD unless otherwise indicated. Comorbidity was defined as the presence of two or more chronic diseases (hypertension, cardiovascular disease, cerebrovascular disease, diabetes, respiratory disease, digestive disease, chronic renal disease, and osteoarthritis).  Subjects with joint frailty and cognitive impairment had a significantly higher risk of mortality, compared to the control group after adjustment of these potential confounding factors (HR: 2.13, 95% CI (1.39, 3.24), P < 0.001). Neither frailty alone nor cognitive impairment alone was able to predict the risk of mortality, as compared to the control group. **Figure 1** shows the survival curves of the study population according to their frailty and cognitive impairment status at baseline.

#### DISCUSSION

In this report, we studied the relationship between frailty, cognitive impairment, and mortality in community-dwelling oldest-old people (90–108 years) in Dujiangyan, Chengdu, and Sichuan province in China. Our present study is the first to investigate the combined role of frailty and cognitive impairment in predicting mortality among old people with advanced age. We have shown that the combined syndromes, and not frailty alone or cognitive impairment alone, is a significant risk factor for death among the oldest-old Chinese people. This study indicates that it is critical to assess a combination of frailty and cognitive function than as separate entities to predict the risk of mortality among old-age people, and also to define the existence of "cognitive frailty," coined by the ICG in 2013.

The IANA and the IAGG organized the ICG in 2013 who first proposed the operational definition of cognitive frailty, described as the simultaneous presence of both physical frailty (Fried frailty phenotype) and cognitive impairment (clinical dementia rating [CDR] = 0.5) (Kelaiditi et al., 2013). The prevalence of cognitive frailty was estimated to be 1.0–1.8% among the community setting of old-age people without dementia or other neurodegenerative conditions, which suggested a limited clinical utility of cognitive frailty in the elderly (Sugimoto et al., 2018). Shimada et al. (2013) included 5104 older adults (mean age 71 years) in Japanese community studies and found that the prevalence of combined frailty and cognitive impairment was only 2.7%. However, our present study, which included Chinese non-agenarians and centenarians, revealed that the prevalence of combined frailty and cognitive impairment was 50.1% (95% CI = 46.4–53.8%), which indicated that cognitive frailty is more common in the very old population and supports the idea that the prevalence of the dual syndrome increases with age (Feng et al., 2017a). In other words, the simultaneous presence of both physical frailty and cognitive impairment is common among the oldest-old population.

The 34-item FI was employed to assess the presence of frailty in this study, while the frailty phenotype proposed by Fried et al. (2001) was most commonly used in previous studies (Zaslavsky et al., 2013). Additionally, the frailty phenotype was recommended to define cognitive frailty by the international consensus (Kelaiditi et al., 2013). To date, although multiple operational frailty assessment methods have been validated, frailty phenotype and FI are two most common measures of frailty (Cesari et al., 2014; Blodgett et al., 2015). Since we did not have data on grip strength and walking speed in our study, we could not use frailty phenotype to define frailty and to compare FI with frailty phenotype in predicting mortality. However, the operational definition of frailty in cognitive frailty also needed to be discussed. Although these two commonly used measures of frailty are different, both are associated with mortality and cognitive impairment (Cesari et al., 2014; Sugimoto et al., 2018). Furthermore, FI could be used to classify more people as frail, as it is based on a more comprehensive geriatric assessment, such as physical examinations, multi-functional measures, and diagnostic data, and hence, more capable of predicting mortality than frailty phenotype measurements (Theou et al., 2013; Cesari et al., 2014). Thus, it is rational to use FI to define frailty in the operation of cognitive frailty.

Although CDR = 0.5 was recommended to assess cognition in cognitive frailty by the international consensus (Kelaiditi et al., 2013), the majority of studies identified cognitive impairment according to global cognitive assessment scales, including MMSE and Montreal Cognitive Assessment (MoCA) (Sugimoto et al., 2018). Among these studies, MMSE was used to define cognitive impairment, focusing on the association of physical frailty with cognitive impairment, and the cut-off point varied from 18/30 to 26/30 (Matusik et al., 2012; Sugimoto et al., 2018). In contrast, our study, which focused specifically on very old cohort as most

TABLE 3 | Estimate of the effect of frailty and cognitive impairment mortality modeled with Cox regression model.

aAdjusted for age, gender, and educational levels. bAdjusted for factors in adjusted model 1 plus smoking, alcohol drinking, exercise habit, and comorbidity. cAdjusted for factors in adjusted model 2 plus BMI, body mass index, height, weight, and systolic blood pressure (SBP). ∗∗P < 0.01.

of the participants (97.4%) with low educational level (illiterate or primary school), yielded mean and median scores of MMSE equal to 14.82 ± 5.68 and 15, respectively. We set 18 as the cut-off point for cognitive impairment, according to previous studies (Matusik et al., 2012). In the Chinese population, this value has been shown to effect acceptable sensitivity (80–90%) and specificity (80–100%) for diagnosis of cognitive impairment (Katzman et al., 1988; Tombaugh and McIntyre, 1992; Zhu et al., 2006; Cui et al., 2011). Had we used 26 as the cutoff point for the MMSE score in our present study, the cognitive impairment would have been 97.9%. Therefore, we considered the MMSE = 18 as an acceptable cut-off point for the determination of diagnosis of cognitive impairment among the very old people. However, only using MMSE to assess cognitive function would be considered as one of the limitations in the present study.

It is a well-known fact that the risk of death increases exponentially with age during the human lifespan (Searle and Rockwood, 2015) and mortality is high among very old people. We obtained a 4-year death rate of 53.8% with our study participants, and it is the first study to analyze the combined effect of frailty and cognitive impairment in the oldest-old people. Our results are consistent with those of the previous studies conducted among old people in nursing homes by Matusik et al. (2012). Their study included 86 old people, living in two nursing homes, with ages ranging from 66 to 101 years (mean age: 83.8 ± 8.3 years). They predicted mortality (50.0%) of the combined frailty and cognitive function in a 1-year follow up, but did not find statistical significance between mortality with the separated syndromes among the disabled geriatric patients (Matusik et al., 2012). Our study extends the funding to community-dwelling of very old Chinese people, which might be related to the inconsistencies in reports from other groups (Jacobs et al., 2011; Forti et al., 2014). Forti et al. (2014) found that a clock drawing test other than frailty phenotype might predict the 7-year risk of all-cause mortality, but combining these two syndromes (frailty and cognitive impairment) did not improve the prognostic abilities among 766 dementia-free Italian community dwellers (mean age: 73.6 ± 5.9 years). On the other hand, Jacobs et al. (2011) found frailty phenotype other than cognitive impairment (assessed by MMSE) to be significantly predictive of 5-year mortality among 840 community-dwelling

people with ages ranging from 85 to 90 years (Jacobs et al., 2011). In the present study we did not find blood pressure, smoking, or obesity to influence mortality, which did not support the evidence generated from other populations (Park et al., 2013; Pan et al., 2015). The differences of these observations might be explained by the differences in age groups, races, and followup periods. The potential mechanism of combining frailty and cognitive impairment in predicting mortality should be further investigated, using a large sample size, validated assessment methods, and reasonable follow-up period.

The results of our present study should be interpreted with caution for the following limitations. First, we included 705 participants (males: 230, females: 475) in this study, and the number of subjects is low in the control group (no frailty and no cognitive impairment group, n = 86). This might reduce the efficiency of statistical analysis and could limit the detection of the association of frailty or cognitive impairment and mortality. The small number of male participants also limited us from conducting subgroup analysis according to gender. However, the prevalence of frailty, cognitive impairment, and death ranked high in this specific cohort, which gave us the opportunity to examine these associations in a stable elderly population. Second, we only included Chinese Han oldest-old people, which is a good model to avoid major fatal diseases. However, this migh t have caused survival bias in our study, which could not be avoided. Moreover, we cannot extend the conclusion of our study to other races and it only could be extended to elderly people around the same age. Third, although we have adjusted age, gender, education level, and other potential confounding factors, all of the other potential confounders such as bilingualism, work life, neuropsychiatric or emotion issues, and family or social support may also play a role affecting both frailty and cognitive functioning with age. Fourth, most subjects (90%) in PLAD from a rural community were farmers who, now in their old age, would have usually had regular physical activities in their work age, limiting the extension of our study conclusion to the urban population. Fifth, we did not have data for the reason of death of our participants, so we cannot attribute only frailty and cognitive impairment to all-cause mortality in this study.

## REFERENCES

# CONCLUSION

Both frailty and cognitive impairment are very common among non-agenarians and centenarians. The combined syndrome, and not frailty or cognitive impairment alone, is a significant risk factor for death among the oldest-old Chinese people. This indicates that frailty and cognitive function should be assessed jointly other than separately in predicting mortality among the elderly population and defining cognitive frailty as essential for the prediction of mortality and assisted caregiving decisions for the elderly. Additionally, prospective studies with large sample size starting in middle age, and following up the participants in early old age (60–65 years) and then every 4–5 years, are warranted to inform about the mechanistic relationship between frailty and cognitive functions.

## AUTHOR CONTRIBUTIONS

QH conducted the data analysis and drafted the initial manuscript. MY and Biao D helped with results interpretation and gave critical comments for the manuscript. Birong D and YW secured funding for data collection and verified the analysis outcomes. All authors read and approved the final manuscript.

## FUNDING

This work was supported by the National Natural Science Foundation of China (No. 81601220), the Project of Science and Technology Bureau of China and Sichuan Province (2017RZ0040, 2017YFC0840101, and 2006Z09-006-4), and the Science Foundation for Young Researchers of Sichuan University (2017SCU11044).

## ACKNOWLEDGMENTS

The authors appreciate the staff of the Department of Geriatrics, Dujiangyan Hospital, and all participants and their legal proxies for their assistance in data collection.

longitudinal ageing studies (SLAS). J. Am. Med. Dir. Assoc. 18, 252–258. doi: 10.1016/j.jamda.2016.09.015

**Conflict of Interest Statement:** The authors declare that the sponsors of this research studies did not partake in the design, methods, data collection, analysis, or in the preparation of this manuscript.

Copyright © 2018 Hao, Dong, Yang, Dong and Wei. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

fnagi-10-00295 October 16, 2018 Time: 14:56 # 8

# Blood-Based Kinase Assessments in Alzheimer's Disease

Jacques Hugon1,2\*, François Mouton-Liger 1,2 , Emmanuel Cognat 1,2 , Julien Dumurgier 1,2 and Claire Paquet 1,2

1Center of Cognitive Neurology, Lariboisiere Fernand-Widal Hospital, APHP, University Paris Diderot, Paris, France, 2 INSERM U 942, Paris, France

Alzheimer's disease (AD) is marked by memory disturbances followed by aphasia, apraxia and agnosia. Brain lesions include the accumulation of the amyloid peptide in extracellular plaques, neurofibrillary tangles with abnormally phosphorylated tau protein and synaptic and neuronal loss. New findings have suggested that brain lesions could occur one or two decades before the first clinical signs. This asymptomatic preclinical phase could be an opportunity to put in place a secondary prevention but the detection of these brain lesions can only be achieved so far by cerebrospinal fluid (CSF) evaluation or molecular amyloid and tau PET imaging. There is an urgent need to find out simple and easily accessible new biomarkers to set up an efficient screening in adult and aging population. Neuropathological and biochemical studies have revealed that abnormal accumulations of potentially toxic kinases are present in the brains of AD patients. Kinase activation leads to abnormal tau phosphorylation, amyloid production, apoptosis and neuroinflammation. Increased levels of these kinases are present in the CSF of mild cognitive impairment (MCI) and AD patients. Over the last years the search for abnormal kinase levels was performed in the blood of patients. Glycogen synthase kinase 3 (GSK 3), protein kinase R (PKR), mamalian target of rapamycin (mTOR), dual specificity tyrosine-phosphorylation-regulated kinase 1A (DIRK1A), c-Jun N-terminal kinase (JNK), protein 70 kD ribosomal protein S6 kinase (P70S6K), ERK2 and other kinase concentrations were evaluated and abnormal levels were found in many studies. For example, GSK3 levels are increased in MCI and AD patients. PKR levels are also augmented in peripheral blood mononuclear cells (PBMC) of AD patients. In the future, the assessment of several blood kinase levels in large cohorts of patients will be needed to confirm the usefulness of this test at an early phase of the disease.

Keywords: Alzheimer, kinase, blood, biomarkers, diagnosis

#### INTRODUCTION

Neuropathological lesions in Alzheimer's disease (AD) include amyloid plaques made of Aβ peptides, neurofibrillary tangles composed of hyperphosphorylated tau proteins, amyloid angiopathy and synaptic and neuronal loss (Duyckaerts et al., 2009). Recent findings concerning the pathological evolution of AD have converged and proposed that abnormal biochemical modifications of the brain, including Aβ accumulation, could occur one or two decades before the

#### Edited by:

Gabriel Gold, Geneva University Hospitals (HUG), Switzerland

#### Reviewed by:

Carlo Cervellati, University of Ferrara, Italy Charlotte Elisabeth Teunissen, VU University Amsterdam, Netherlands

> \*Correspondence: Jacques Hugon

<EMAIL_ADDRESS>
Received: 26 June 2018 Accepted: 05 October 2018 Published: 14 November 2018

#### Citation:

Hugon J, Mouton-Liger F, Cognat E, Dumurgier J and Paquet C (2018) Blood-Based Kinase Assessments in Alzheimer's Disease. Front. Aging Neurosci. 10:338. doi: 10.3389/fnagi.2018.00338 first clinical sign, reflecting the presence of a long clinically silent period of the disease (Jack et al., 2018). The amyloid cascade hypothesis proposes that Aβ oligomers could be toxic for neurons and could induce progressive neuroinflammation and neurodegeneration (Selkoe and Hardy, 2016). In this model, the activation of kinases by Aβ in neurons and other brain cell types could induce tau phosphorylation and the triggering of detrimental cellular pathways leading to neuronal demise and microglial activation (Iqbal et al., 2009). The current diagnosis of AD is now clearly facilitated by molecular imaging biomarkers or cerebrospinal fluid (CSF) biomarkers (Lista et al., 2015). Previous works have revealed that many kinase levels are modified in the brain and CSF of AD patients including glycogen synthase kinase 3 (GSK3; Pei et al., 1997), cyclin dependent kinase 5 (CDK 5; Baumann et al., 1993), c-Jun N-terminal kinase (JNK; Gourmaud et al., 2015), mamalian target of rapamycin (mTOR; Lafay-Chebassier et al., 2005), proapoptotic kinase R (PKR) (Chang et al., 2002b), protein kinase C (PKC; Masliah et al., 1990). Many of these kinases can phosphorylate tau but they are also pro-apoptotic and can lead to neurodegeneration (Fielder et al., 2017). The rational for assessing kinase levels or activities in blood is linked to the recent findings revealing that plasma Aβ levels could be associated with CSF Aβ concentrations in AD patients (Janelidze et al., 2016; Hanon et al., 2018). The links between kinase levels in the brain and in the blood or in blood cells are not fully understood but since kinase concentrations are enhanced or decreased in AD brains and CSF, it was proposed by several teams that evaluating blood kinase levels in AD patients and controls could be worth trying as a new search for easily accessible biomarkers. During the last years several studies have been performed in AD patients, mild cognitive impairment (MCI) and controls, comparing blood concentrations or activities of specific kinases involved in brain lesions. The major goal of these studies was to determine if the results of blood kinase levels could be utilized in the future as possible biomarkers in AD. It seems clear that evaluating on a large scale, individuals with or without cognitive symptoms using molecular imaging or CSF biomarkers is not realistic due to the cost or the invasive nature of the approaches.  The justification of this preventive attitude will be fully justified when an appropriate and efficient disease modifying treatment for AD will be found. Large-scaled proteomics of the human kinome have been already developed but so far never extensively applied to possible AD patients (Oppermann et al., 2009). The use of kinase as biomarkers has already largely been assessed in oncology and has served as a validation for target engagements in drug discovery (Yu et al., 2007). The purpose of this brief review is to describe the major current results performed in AD or MCI due to AD patients using blood-based kinase evaluations. All studies published so far have not included the results of sensitivity, specificity and individual predictive values. In addition many studies have used semi-quantitative methods to assess kinase levels. The findings cannot be referred as real biomarkers but could only reflect abnormal blood kinase metabolisms occurring in AD patients.

#### GLYCOGEN SYNTHASE KINASE 3 (GSK 3)

Glycogen synthase kinase 3 (GSK 3) is widely expressed in many human tissues and is present in peripheral blood mononuclear cells (PBMC). GSK3 is expressed with two isoforms α and β. GSK3 is implicated in tau phosphorylation and in Aβ precursor protein (APP) processing linking the two pathological processes in AD (Hooper et al., 2008). In an initial clinical study, the levels of GSK3 α and β and their respective regulating phosphorylated epitopes (serine 9 and serine 21) have been assessed using western blots in PBMC from AD patients (N = 60), in MCI (N = 33) and from healthy aging individuals (N = 20; Hye et al., 2005). The findings revealed that total levels of GSK 3 α and β were increased in AD and MCI patients as compared to controls but the seine 9 phosphorylated epitope was not significantly different in the three groups. The authors concluded that measuring GSK3 PBMC levels could be useful as diagnostic biomarker in AD. Another report has mentioned different results in a cohort of patients suffering from AD, MCI, depression and in normal individuals (Marksteiner and Humpel, 2009). Using an ELISA method in PBMC, the authors revealed that GSK3-β levels were significantly decreased only in MCI patients and no differences were observed in other groups. It was proposed that both GSK3-β and PI3K, which regulates GSK3-β, could be similarly modulated in PBMC. In 2011, a new research also using western blots in PBMC has shown in a cohort of 20 AD, 25 Parkinson's disease patients and 30 healthy controls that the concentrations of total GSK3 α and β and their respective phosphorylated epitopes were significantly increased in AD as compared to controls, confirming the first results (Armentero et al., 2011). Further studies are needed in exploratory and confirmatory cohorts to determine the usefulness of GSK3 as a possible blood biomarker in MCI and AD patients.

# CYCLIN DEPENDENT KINASE 5 (CDK5)

Cyclin dependent kinase 5 (CDK5) is a key enzyme controlling tau phosphorylation and has been implicated in the pathogenesis of AD (Cruz and Tsai, 2004). To the best of our knowledge, a recent survey of published data has not found any evaluation of cdk5 in PBMC or serum in MCI or AD patients. In contrast, the cdk5 gene polymorphism seems to be a risk factor for AD. A first report in 2009 has shown in a cohort of 549 AD patients and 5728 controls that the haplotype GG of the SNP rs2069442 of cdk5 gene increased the risk for AD in ApoE4 negative patients. In incident non-ApoE4 AD cases, the risk was augmented by 1.9-fold (Arias-Vásquez et al., 2008). This type of association was not observed in a Polish population of 257 AD patients and 80 controls concerning cdk5 polymorphisms and AD risk factor nor with several other blood biochemical parameters (Czapski et al., 2012). The evaluation of cdk5 levels and activities in PBMC of AD and MCI patients could be worth trying to decipher putative abnormal cdk5 levels.

 JNK1 and JNK2 isoforms are widely found in tissues, whereas JNK3 is mainly located in the brain (Davis, 2000). Activated JNK (pJNK) accumulations have been detected in AD brains and we have demonstrated that JNK3 levels were enhanced in AD CSF, and were linked to cognitive decline in patients (Gourmaud et al., 2015). JNK can induce enhanced Aβ production, through the phosphorylation of APP on threonine 668 (Thr668), leading to an exacerbated amyloïdogenic processing (Standen et al., 2009). P38 is also a MAPK kinase that has been implicated in neuroinflammation and tau phosphorylation and is the focus of new targeting therapies in this disease (Munoz and Ammit, 2010). A recent study has revealed that assessing total and activated JNK and p38 PBMC levels can lead to interesting results in AD (Wang et al., 2014). The authors found in a small cohort of 20 AD patients and 20 controls that PBMC levels of activated JNK and p38 were significantly increased in AD and correlated with the duration of evolution and MMSE scores. Patients with low MMSE scores had increased PBMC levels of both MAPK kinases. Another recent data has demonstrated that the EDTA plasma levels of two other MAPK kinases were associated with AD. The levels of MAPKAPK5 were positively associated with the cognitive tests assessed over a period of 10 years and the levels of MAP2K4 were negatively associated with the volume of the left entorhinal cortex (Kiddle et al., 2015). Further studies will be needed to confirm these results in large validated cohorts.

#### MAMALIAN TARGET OF RAPAMYCIN (mTOR)

The kinase mTOR has also been implicated in the pathogenesis of AD (Pei and Hugon, 2008). mTOR is a serine/threonine kinase originating from two genes TOR1 and TOR2. Only mTORC1 interferes with rapamycin. mTOR can control cell growth, cell proliferation, protein synthesis and autophagy. Among the mTOR downstream targets controlling translation, the protein 70 kD ribosomal protein S6 kinase (p70S6K) has also been studied in AD. Previous studies of mTOR and p70S6K levels in AD brains have found contradictory results with either decreased concentrations (Lafay-Chebassier et al., 2005) or increased levels (Caccamo et al., 2010). We have carried out, using western blots, a research in a cohort of 32 AD patients and 33 controls assessing the levels of mTOR and p70S6k in PBMC (Paccalin et al., 2005, 2006a,b). mTOR levels were significantly reduced in AD patients as compared to controls and these levels correlated with cognitive scores of the free and cued recall tests (FCRT), the MMSE scores and the reverse digit span tests. Similar findings were detected for p70S6k levels which correlated with FCRT, reverse digit span test an oral denomination test. AD patients at an early stage of the disease had increased levels of these kinases and rather preserved neuropsychological scores. In addition, p70S6k levels also correlated with the results of emotional memory tests. Although it is difficult to extrapolate these results to neurons and brain, it is known that stressed neurons can perhaps modulate protein translation and increase the expression of the APP (Lesort et al., 1997). The links between kinase levels in brain and PBMC need to be compared in future researches. Nevertheless assessing the PBMC levels of these kinases might be a useful way to determine new screening biomarkers in patients with memory complaints.

# EUKARIOTIC INITIATION FACTOR KINASE 2 (PKR2)

PKR is a proapoptotic serine/threonine kinase activated by virus, inflammatory and toxic cellular signals and is responsible for translation initiation blockade via the phosphorylation of its downstream target, the eukaryotic initiation factor two α (eIf2α). In addition to triggering apoptosis, PKR is involved in innate immunity and the process of inflammation that are typical features observed in AD brains (Dabo and Meurs, 2012). Using immunohistochemistry, we have demonstrated in 2002 that the levels of PKR and eIf2α were increased in AD brains as compared to control individuals. PKR labeling was often but not always associated with neurofibrillary tangles and hyperphophorylated tau protein (Chang et al., 2002a,b). Later we have observed increased levels of PKR and phosphorylated PKR (pPKR) in the CSF of AD and MCI patients as compared to neurological controls (Mouton-Liger et al., 2012). CSF activated PKR levels were predictive of cognitive decline of the patients; high CSF concentrations of PKR were observed in rapidly declining patients over a 2-year period (Dumurgier et al., 2013). We have also assessed the levels of total PKR and activated PKR and eIf2α in PBMC from AD patients and control individuals. The results demonstrated significant increased levels of these two cellular signals in AD patients. These concentrations were correlated with MMSE scores, FCRT, and reverse digit span cognitive tests (Paccalin et al., 2006b). High levels of PKR and eIf2α were detected in the more severely affected AD patients. These findings associated with the previous ones suggest that mTOR levels in PBMC are higher at the onset on the clinical disease whereas PKR levels are higher over the course of the disease. How the levels of these biomarkers are during the silent and preclinical periods of AD remains to be explored in order to determine their usefulness as markers of the biological phase of the disease (Jack et al., 2018). Although the links between cerebral and PBMC levels of these kinases are not very well known, it is worthy to notice that the kinase mTOR favors cell growth whereas the kinase PKR is part of the cellular apoptotic process.

#### PROTEIN KINASE C (PKC)

The PKC family includes transmembrane serine/threonine kinases regulating signal transduction and encompasses in mammals 12 isoenzymes including PKC-α, PKC-δ, and PKC-ζ. PKC deficit has been implicated in the pathogenesis of AD (Malik et al., 2015). It has been postulated that a PKC deficiency could lead to memory loss, increased Aβ levels, enhanced phosphorylated tau and augmented neuroinflammation. There are two studies reporting results of blood PKC levels in AD patients. In 2006, a manuscript showed abnormal PKC conformation in red blood cells of affected patients (Janoshazi et al., 2006). Using a new and specific fluorescent probe called Fim-1, the authors demonstrated that PKC conformation is modified in AD patients (N = 33) and not in Parkinson's patients (N = 15) as compared with healthy individuals (N = 25). This alteration was not linked to age or to duration of disease and the authors proposed that this test could be a screening method in patients with cognitive problems. In 2008 another report using flow cytometry analyzed three PKC isoforms PKC-α, PKC-δ, and PKC-ζ in T-lymphocytes extracted from PBMC and exposed to Aβ. AD patients (N = 40) and healthy controls (N = 40) were explored. The findings have shown that Aβ activated T-lymphocytes from AD patients revealed highly expressed cell subpopulation with phosphorylated PKC-δ and PKC- ζ. This result was not observed in PBMC from controls as well as in freshly purified non Aβ-exposed T-lymphocytes from both groups (Ciccocioppo et al., 2008). The assessment of PKC in PBMC or red blood cells could be performed in further research to validate this approach as a screening tool in patients with cognitive complaints.

#### OTHER KINASES OR PHOSPHATASES

Dual specificity tyrosine-phosphorylation-regulated kinase 1A (DIRK1A) is able to phosphorylate many substrates and is implicated in tau phosphorylation via an interaction with GSK3β. A recent study has demonstrated that serum levels of DIRK1A were modified in AD. Using slot-blotting method in human serum to detect kinase relative expression, the results in 26 AD patients and 25 controls revealed a significant reduction of DIRK1A levels in AD patients as compared to controls. Serum levels correlated with CSF tau and phosphorylated tau concentrations (Janel et al., 2014).

Phosphatases have also been explored as putative AD biomarkers. For example, thiamine diphosphatase and thiamine monophosphatase are implicated in thiamine metabolism and their heparin plasma activities were recently measured in 45 AD patients and 38 control individuals. Significantly increased activities of both phosphatases were detected in AD patients as compared to controls suggesting that a disturbed thiamine diphosphate metabolism was present in AD patients (Pan et al., 2017).

G-protein-coupled receptor kinase-2 (GRK2) is largely involved in the regulation of the G-protein-coupled receptors at the cell membrane. GRK2 is increased in the brain of AD patients (Obrenovich et al., 2006). Using qPCR and western blots, GRK2 mRNA and protein levels were explored in blood lymphocytes of mild (MMSE: 18–24) and moderate to severe (MMSE < 18) AD patients. The findings revealed that GRK2 mRNA and protein levels were significantly increased in both groups of AD patients with a major augmentation in severely affected patients (Leosco et al., 2007).

#### MULTI-TARGET KINASE THERAPIES AND BIOMARKERS

There is so far no treatment able to block or attenuate AD brain lesions in humans and new disease modifying drugs are currently evaluated in many clinical trials. Kinase inhibitors have been already tested in AD without current success. For example, the GSK3 inhibitor tideglusib was assessed but no significant clinical outcomes were obtained (Lovestone et al., 2015). We have seen in this review that many kinases present in human brains are involved in signaling pathways implicated in AD brain lesions such as Aβ metabolism, tau phosphorylation, neuronal apoptosis and neuroinflammation. A new therapeutic approach targeting several kinases was recently highlighted (Tell et al., 2016). A pharmacological intervention in AD using this method will need blood companion biomarkers readily available to evaluate drug targeting of these new kinase inhibitors. In the future, bloodbased kinase biomarkers could be included in diagnostic and therapeutic evaluations in preclinical patients or MCI due to AD patients.

# CONCLUSION

We have seen that many results concerning blood-based kinase biomarkers in AD need new findings in lager confirmatory cohorts and in longitudinal follow-ups of patients. There are several limitations in the studies reported so far in AD. (1) Very few publications have reported the results in confirmatory cohorts with a large numbers of patients. (2) Most of the studies have focused the research on one or a few kinase levels or activities but large-scaled proteomics focalized on this type of proteins should be worth assessing in MCI, AD patients and controls. Phospho-peptides could also be assessed on a large scale. (3) The real signification of blood kinase changes described in this review is not so far perfectly elucidated. Further studies will be needed to assess if these modifications originate from a general disturbed kinase metabolism or from signals coming from the AD brain.

It is plausible to envisage in the future that several kinase levels could be simultaneously assessed in the blood (PBMC, RBC Proceed, plasma) of patients. The use of an algorithm incorporating this kinome analysis might be useful to screen MCI or pre-symptomatic patients and eventually to compare these results with those obtained in the CSF of patients as seen for PKR and JNK3. Finally specific blood-based kinase biomarkers could be incorporated as surrogate markers of target engagement in new clinical trial assessing kinase inhibitors.

#### AUTHOR CONTRIBUTIONS

JH, EC and CP made the review of the literature and wrote the manuscript. FM-L and JD performed some experiments. All authors read and corrected the manuscript.

## REFERENCES

activated protein kinase 1b (Jun N-terminal kinase-3). J. Neurochem. 76, 316–320. doi: 10.1046/j.1471-4159.2001.00102.x

**Conflict of Interest Statement**: 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.

Copyright © 2018 Hugon, Mouton-Liger, Cognat, Dumurgier and Paquet. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

# Regional Cerebral Perfusion and Cerebrovascular Reactivity in Elderly Controls With Subtle Cognitive Deficits

Merel van der Thiel1,2 \*, Cristelle Rodriguez3 , Dimitri Van De Ville1,2 , Panteleimon Giannakopoulos3 and Sven Haller2,4,5

1 Institute of Bioengineering, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland, 2 Faculté de Médecine, Université de Genève, Geneva, Switzerland, 3 Division of Institutional Measures, Geneva University Hospitals, Geneva, Switzerland, 4 CIRD – Centre d'Imagerie Rive Droite, Geneva, Switzerland, 5 Department of Surgical Sciences, Division of Radiology, Uppsala University, Uppsala, Sweden

Background: Recent studies suggested that arterial spin labeling (ASL)-based measures of cerebral blood flow (CBF) as well as cerebral vasoreactivity to CO2 (CVR CO2) show significant alterations mainly in posterior neocortical areas both in mild cognitive impairment (MCI) and Alzheimer disease. It remains, however, unknown whether similar changes occur in at risk healthy elders without clinically overt symptoms. This longitudinal study investigated patterns of ASL perfusion and CVR CO2 as a function of the cognitive trajectories in asymptomatic elderly individuals.

#### Edited by:

Donna M. Wilcock, University of Kentucky, United States

Reviewed by: Lakshmi Rajagopal, Northwestern University, United States Ai-Ling Lin, University of Kentucky, United States

> \*Correspondence: Merel van der Thiel<EMAIL_ADDRESS>
Received: 30 May 2018 Accepted: 21 January 2019 Published: 19 February 2019

#### Citation:

van der Thiel M, Rodriguez C, Van De Ville D, Giannakopoulos P and Haller S (2019) Regional Cerebral Perfusion and Cerebrovascular Reactivity in Elderly Controls With Subtle Cognitive Deficits. Front. Aging Neurosci. 11:19. doi: 10.3389/fnagi.2019.00019 Methods: Seventy-nine community-dwelling subjects (mean age: 78.7 years, 34 male) underwent three neuropsychological assessments during a subsequent 3-year period. Individuals were classified as stable-stable (SS), variable (V), or progressive-progressive (PP). Between-group comparisons were conducted for ASL CBF and transit-time delay maps and β-maps of CO2 response. Spearman's rho maps assessed the correlation between ASL (respectively, CVR CO2 measures) and Shapes test for working memory, as well as Verbal fluency test for executive functions. Three group-with-continuouscovariate-interaction designs were implemented to investigate group-based differences on the association between neuropsychological scores and ASL or CO2 measures.

Results: Comparison of CBF maps demonstrates significantly lower perfusion in the V-group as to PP-cases predominantly in parietal regions, including the precuneus and, to a lesser degree, in temporal and frontal cortex. A stronger CVR CO2 response was found in the PP-group in left parietal areas compared to the V-group. V-cases showed a stronger ASL-Shape value relationship than V-group in right temporoparietal junction and superior parietal lobule. CO2-Shape value correlation was significantly higher in both SS and PP-groups compared to the V-group in right insular and superior perisylvian regions.

Conclusion: Our data indicate the presence of decreased ASL and CVR CO2 values mainly in parietal and fronto-temporal areas in cases with the first signs of cognitive

instability (V-group). Importantly, the PP-group, at high risk for MCI transition, displays an increase of both parameters in the same areas. Clinicoradiologic correlations also indicate a clear distinction between the V-group and both PP and SS-cases. These data imply the presence of an inverted U-shape pattern of regional blood flow and CVR in old age that might predict subsequent cognitive fate.

Keywords: arterial spin labeling, asymptomatic controls, cerebrovascular reactivity, brain perfusion, clinicoradiologic correlations, CO2

#### INTRODUCTION

Age-related changes in cerebral blood flow (CBF) have been related to increased risk for cognitive decline and Alzheimer disease (AD), implying that cerebrovascular mechanisms play a pivotal role in brain health and sustenance of cognition (Wierenga et al., 2014; Montagne et al., 2015; Popa-Wagner et al., 2015; Sierra-Marcos, 2017). For the past three decades, the nuclear medicine techniques Single Photon Emission Computed Tomography (SPECT) and Positron Emission Tomography (PET) have served as gold standards for perfusion and metabolism studies in brain aging and AD. These techniques, however, require use of radioactive tracers and are more expensive that the more recently developed perfusion-weighted MRI techniques. Among these latter, arterial spin labeling (ASL) uses magnetically labeled arterial blood water as a diffusible endogenous tracer and displays similar diagnostic ability to detect AD as fluoro-2-deoxy-D-glucose (FDG)-PET (Tosun et al., 2016). This technique revealed brain hypoperfusion mainly in bilateral parietal areas, precuneus, angular and posterior cingulate cortex in MCI and early AD, which overlaps with the patterns of hypometabolism on FDG-PET observed later in disease progression, indicating the potential to use ASL for early detection of cognitive decline (for review see Alsop et al., 2008; Chen et al., 2011; Haller et al., 2016; Fällmar et al., 2017; Riederer et al., 2018). The rare ASL studies showed a more diffuse hypoperfusion in posterior inferior and frontal aspects of the brain in at risk healthy controls (Tosun et al., 2016; de Vis et al., 2018). Higher ASL measured-CBF in medial frontal, lateral temporal, parietal cortex, insula, and basal ganglia was reported in APOE ε4 carriers with the worst cognitive performances (Zlatar et al., 2016).

The capacity of brain vasculature to enhance blood flow in response to challenging conditions is found to be a key parameter in very early stages of neurodegeneration, even prior to the development of clinically overt cognitive deficits. In fact, higher ASL values in these cases may be the consequence of the brain vasculature's efforts of adapting to a threatening cellular environment. Blood oxygenation level dependent (BOLD) functional MRI allows for assessment of this reactivity. Recent data has indicated that the cerebral vasoreactivity CO2 (CVR CO2) could detect significant dysfunctions both in MCI and AD cases. In contrast, whether or not similar changes occur in at risk healthy elders is still unknown (Cantin et al., 2011; Yezhuvath et al., 2012; Richiardi et al., 2015).

Within this study, both ASL perfusion and CVR CO2 patterns were explored in 79 community-dwelling elderly individuals who were cognitively preserved at inclusion and had undergone two neuropsychological assessments during a subsequent 3-year period. The data revealed distinct patterns of brain perfusion and cerebrovascular reactivity as a function of the cognitive trajectories of elderly controls.

#### MATERIALS AND METHODS

#### Participants

The series used in our analysis is part of a population-based longitudinal study on healthy aging funded by the Swiss National Foundation of Research in Geneva. The research protocol was approved by the Ethics Committee of the University Hospitals of Geneva. All experimental procedures were carried out in accordance with the approved guidelines and with the principles of the Declaration of Helsinki. All participants were given written informed consent prior to inclusion. Participants were contacted via advertisements in local media to guarantee a community-based sample. Exclusion criteria included psychiatric or neurologic disorders, sustained head injury, history of major medical disorders (neoplasm or cardiac illness), alcohol or drug abuse, regular use of neuroleptics, antidepressants or psychostimulants and contraindications to MR imaging. To eliminate possible confounding effects of cardiovascular disease, individuals with subtle cardiovascular symptoms and a history of stroke and transient ischemic episodes were also excluded from the present study.

The final sample included 79 participants, classified as cognitively healthy controls (mean age 78.7 ± 3.5 years; 44 women) who underwent three neuropsychological evaluations (baseline 18 months and 36 months follow-up) and a MRI-T1 examination (only baseline). Clinical assessment included the Mini-Mental State Examination (MMSE, Folstein et al., 1975), the Lawton Instrumental Activities of Daily Living (IADL, Barberger-Gateau et al., 1992) and the Hospital Anxiety and Depression Scale (HAD, Zigmond and Snaith, 1983), the Consortium to Establish a Registry for Alzheimer's Disease (CERAD) neuropsychological battery, Digit Span (Wechsler, 1955) and Corsi block (Milner, 1971) for verbal and visual working memory respectively, the Trail Making Test A and B (Reitan, 1958) for executive functioning, Digit-Symbol-Coding (Wechsler, 1977) for attention, Boston Naming (Kaplan et al., 1983) for language, Ghent Overlapping Figures (Schnider et al., 1997) for visual gnosis and RI-48 Cued Recall Test (RI-48) for episodic memory (Haller et al., 2017). Individuals meeting the DSM-IV criteria of dementia or for MCI on the basis of

clinical and neuropsychological assessments, were excluded from the study (Haller et al., 2017). In order to explore correlations between the neuropsychological performances and imaging variables, and given the limited sample size, we selected two main tests that encompass visual working memory (Shapes test, Baddley et al., 1994) and executive functions (Phonemic verbal fluency Bruyer and Tuyumbu, 1980). Descriptive statistics and statistical differences between the SS, V, and PP group on age, gender and the neuropsychological scores were calculated with separate one-way ANOVA's using IBM SPSS Statistics version 25.

#### Neuropsychological Follow-Up

For follow-up measurements, which took place 18 months after inclusion, the cognitively healthy individuals underwent full neuropsychological assessment once again. Individuals who obtained stable cognitive scores over the baseline and followup evaluation were classified as stable controls. The progressive control group obtained a follow-up evaluation of at least 0.5 standard deviations (SD) lower than measured at baseline, on a minimum of two cognitive tests. Two neuropsychologists clinically assessed all individuals independently. The final classification was determined by a trained neuropsychologist taking into account both the results of the neuropsychological tests and overall clinical assessment (Xekardaki et al., 2015). All of the cases were assessed once again 18 months later with the same neuropsychological battery. The participants were subsequently grouped as described above (−0.5 SD in at least two cognitive tests), with comparison of the scores of the latest assessment. Stable individuals showing no changes in the second assessment were classified in the stable-stable (SS) group and progressive individuals demonstrating a further decline as progressive-progressive (PP). The variable group (V) refers to participants demonstrating a fluctuating scoring pattern, incorporating stable-progressive, progressive-stable or progressive-improved individuals. The final sample included 24 SS, 33 V, and 22 PP cases.

#### ASL

#### MR Imaging

As described previously in more detail (van der Thiel et al., 2018), ASL imaging was performed on a 3T GE MR750w using a 32-channel head array coil. Perfusion images were acquired with a 3D stack-of-spiral fast spin echo sequence preceded by a Hadamard encoded Pseudo-Continuous ASL (PCASL) module with background suppression. A total label duration of 4 s. was encoded into seven sub-blocks. The label durations were 0.22, 0.26, 0.30, 0.37, 0.48, 0.68, and 1.18 s, post label delays were chosen to be 1.00, 1.22, 1.48, 1.78, 2.15, 2.62, and 3.32 s. The total scan time lasted for 4.02 min.

Images were created at all delay times. The combined delay map consists of the sum of the delay times per subject. Two CBF maps were used for subsequent analysis, the raw uncorrected flow maps and the transit time corrected flow maps adjusted for arterial transit time.

Key imaging parameters were: field of view (FOV) = 22.0 cm, slice thickness 4.0 mm, 32 slices, bandwidth ± 62.5 kHz, 4 arms with 640 points each. The PCASL images had a matrix size of 128 × 128 and a voxel size of 1.88 × 1.88 × 4.0. Images were acquired with an echo time of 10.5 ms and a repetition time of 5936 ms. Acquisition included a T1/PD weighted reference image with matched parameters for quantification. The T1/PD combination image was acquired with the same TR as the perfusion images (5936 ms) and was formed by saturation recovery sequence with a 2.0 s saturation time. This saturation time is the same as that of the ASL sequence. The T1 value for blood assumed in the CBF quantification was 1.60 s. The Image reconstruction was performed using IDL based recon code and reconstructed images were stored as DICOM images into the scanner's database.

#### Data Preprocessing ASL

The ASL data was processed using the fMRI utility of the Brain Software Library (FSL, Version 5.0.91 ). The combined delay image was obtained per participant and non-brain tissue was removed using the Brain Extraction Tool (BET2 , part of FSL). The brain extracted combined delay maps were normalized to Montreal Neurological Institute (MNI) standard space using an Echo Planar Image (EPI) template from the Statistical Parametric Mapping (SPM8) toolbox, standard space using linear registration (FMRIB Linear Image Registration Tool3 , part of FSL). The concatenated transformation matrix of the transit delay maps was then applied to the uncorrected flow maps, the transit time corrected flow maps and the transit delay maps to spatially normalize the data to the EPI template.

 Due to the low intrinsic signal to noise level of ASL, we have decided to apply spatial smoothing in order to improve detection of group differences since spatial normalization intrinsically includes a certain degree of smoothing. Furthermore, some statistical procedures require smoothed data.

#### ASL Group Comparisons

All of the following analysis were executed for the corrected, uncorrected and delay maps separately. The cognitive groups were compared using voxel-wise permutation-based testing (Randomize5 , part of FSL), with threshold-free clusterenhancement correction for multiple comparisons applied (Smith and Nichols, 2009) and p < 0.05 considered significant. Five thousand permutations were executed per contrast. The MNI 2 mm brain extracted mask was used during randomization for masking of non-brain voxels. A triple T-test design was administered with age and gender as non-explanatory coregressors (Liu et al., 2012). Age was defined as the difference in days between the date of birth and the day of scanning.

1http://fsl.fmrib.ox.ac.uk/fsl

2http://fsl.fmrib.ox.ac.uk/fsl/fslwiki/BET

3http://www.fmrib.ox.ac.uk/fslwiki/FLIRT

4http://www.fmrib.ox.ac.uk/fslwiki/Fslutils

5http://fsl.fmrib.ox.ac.uk/fsl/fslwiki/Randomize

#### ASL-Based Clinicoradiologic Correlations

For each group, Spearman rho correlations were calculated between the Shapes test and the Verbal fluency test and the corrected, uncorrected and delay maps separately using MATLAB version R2017b, to assess clinicoradiologic correlations in each group.

Group differences in correlation between the neuropsychological scores on the Shapes test, and the Verbal fluency test and the ASL response were investigated with a continuous-covariate-interaction-design6 .

To see whether the relationship between the ASL response and the neuropsychological test differed between groups, a triple T-test with 6 contrasts was carried out per neuropsychological test, representing all possible differences in scores between groups. The cognitive groups were once again compared using voxel-wise permutation-based testing with 5000 permutations per contrast (Randomize; see text footnote5 , part of FSL), threshold-free cluster-enhancement correction for multiple comparisons applied (Smith and Nichols, 2009) masked with the 2 mm brain extracted mask and p < 0.05 considered significant.

# CVR CO2

#### CO2 Admission

The CO2 administration protocol is described in more details elsewhere (Richiardi et al., 2015). In short, the CO2 challenge consisted of 9 min of CO2 admission via a nasal cannula. A concentration of 7% CO2 mixed with synthetic air was given with the sequence 1 min OFF, 2 min ON, 2 min OFF, 2 min ON, 2 min OFF. Subjects were asked to breathe normally through the nose. During admission, EPI covering the entire brain was acquired using a 32 multi-channel coil with the following parameters: FOV = 28.5 cm, 96 × 96 matrix, voxel size of 2.9688 × 2.9688 × 3 mm3 , echo times of 30 ms, repetition time of 3000 ms, 45 repetitions.

#### Preprocessing MRI

The CO2 data was processed using FSL Version 5.0.9 (Jenkinson et al., 2012). The functional sequences were realigned to the mean per sequence to correct for motion effects (Jenkinson et al., 2002) and non-brain tissue was removed using the Brain Extraction Tool (Smith, 2002). Individual structural images were skull-stripped and co-registered to a standard 2 mm MNI brain extracted by employing standard FLIRT procedure (Jenkinson et al., 2002).

The transformation matrices from the functional to the subject space were calculated using the mean functional images per subject with the epi\_reg script provided by FSL (Jenkinson et al., 2002).

White matter (WM) and Cerebral Spinal Fluid (CSF) masks were calculated as follows; WM and CSF were segmented from the individual high-resolution 3D images with FMRIB's automated segmentation tool (Zhang et al., 2001). The resulting masks were compared with a priori tissue mask containing an average of MRI images of 152 subjects, as provided by the MNI and hereafter binarized with a threshold of 0.6 in subject space. For both WM and CSF masks, a mean time series of the functional data within the tissue specific mask was calculated and used to filter out the WM and CSF effects, and additionally the motion correction parameters with the FSL command-line tool. Smoothing was applied to the denoised functional data with a 5 mm FWHM Gaussian kernel using the brain-extracted mask of the mean functional image. As for the ASL data, we applied spatial smoothing to reduce the effects of noise on the group analysis. In addition, to ensure an analysis pipeline as similar as possible for both the CO2 and ASL data, the application of spatial smoothing was appropriate. In this manner, comparability of the two techniques was optimized.

The denoised, smoothed functional CO2 data was normalized to the standard 2 mm MNI brain extracted template by usage of the FMRI Expert Analysis Tool v6.00, with the same normalization method employed as described earlier. In addition, a high pass filter of 270 s was applied to the data and pre-whitening was performed with FMRIB's Improved Linear Model.

#### First-Level Analysis CVR CO2

The pre-processed functional CO2 sequences were used to carry out the first-level analysis. A standard first-level FSL pipeline was employed to fit the CO2 response to the subjects' response. Convolution of the ON/OFF response of the CO2 admission was done using a simple square wave form, correcting for wash-in and wash-out effects in a straightforward and effective way (Mutch et al., 2012; Richiardi et al., 2015). The square wave form has shown to approximately model the increase and decrease of CO2 levels during administration (Richiardi et al., 2015).

#### CVR CO2 Response Group Comparisons

To investigate whether there was a difference in CVR CO2 response between the SS, V and PP group, an F-test design was applied. To further specify which groups differentiated from one another on the CO2 response, a triple T-test design was executed with a standard higher-level FSL pipeline.

#### CVR CO2 Response-Based Clinicoradiologic Correlations

Spearman rho correlation was computed between the Shapes test and Verbal fluency test and CO2 maps for each group, using MATLAB version R2017b. The relationship between the neuropsychological scores (the Shapes and the Verbal fluency tests) and the CVR CO2 response was investigated by a three group with continuous-covariate-interaction-design see text footnote6 . To see whether the relationship between the CVR CO2 response and the neuropsychological test differed between groups, triple T-tests with 6 contrasts were carried out per neuropsychological test, covering all potential group-differences.

6https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/GLM#Two\_Groups\_with\_continuous\_ covariate\_interaction

#### RESULTS

#### Demographic Data and Neuropsychological Test Scores

No statistically significant group differences were found in demographic variables (age and gender). Neuropsychological scores at baseline did not differ between the three groups (see **Table 1**).

#### ASL Group Comparisons

fnagi-11-00019 February 19, 2019 Time: 12:40 # 5

The quantitative CBF and CVR values of the included study groups are shown in **Table 2**. Comparison of the perfusion maps between the three groups demonstrated a significantly higher perfusion value in both the corrected and uncorrected maps of the PP group as compared to the V group. As can be seen in **Figure 1**, these regions largely overlap in both perfusion maps. Differences within the corrected flow maps between the groups can be clearly seen within the parietal regions, including the precuneus but also in temporal and frontal regions. Example corrected perfusion maps from the different groups emphasize the hypoperfusion within the precuneus of the V group once more, while in the example maps of both the SS and PP group perfusion in these regions remains preserved (**Figure 2**).

Group comparison of the uncorrected maps demonstrates a significantly higher perfusion in PP compared to the V group that was diffusely present in several neocortical areas. No other significant group differences were found in respect to ASL delay maps.

## ASL-Based Clinicoradiologic Correlations

The Spearman correlation maps per group reveal distinct patterns as a function of the group of reference only for the Shapes test. In SS group, negative associations between ASL values and Shapes test performances were found in most areas with the exception of the frontal and cerebellar regions (**Figure 3**). A similar predominance of negative correlations was observed in PP group. Interestingly, the Spearman rho maps of

TABLE 1 | Demographical and neuropsychological data of the included study groups of the SS, V, and PP participants.

No significant group-differences were found on age, gender or any of the neuropsychological tests.

TABLE 2 | Quantitative CBF and CVR values of the included study groups of the SS, V, and PP participants.

regions including the precuneus. The PP group similarly shows significantly higher perfusion than the V group within the parietal lobe, but also temporal and frontal regions.

the V group show prominent positive correlations in areas that show negative rho values in the SS group. Group comparisons showed significant differences in ASL-Shapes test correlation between the V and SS group in right temporo-parietal junction and superior parietal lobule (**Figure 4A**).

# CVR CO2 Response Group Comparisons

The F-test demonstrated that there were significant differences between groups on the CO2 response (data not shown). The T-tests demonstrated a significantly stronger CO2 response of the PP group in left parietal areas as compared to the V group (**Figure 5**). No regional differences in CO2 response were found between the other groups.

# CVR CO2 Response-Based Clinicoradiologic Correlations

No group differences were identified in respect to the association between Verbal Fluency test performances and CVR CO2 maps. **Figure 6** shows the Spearman's rho maps referring to the relationship between the CVR CO2 maps and Shapes test. The SS and PP groups display a fairly similar pattern of correlation, showing a positive relationship between the CVR CO2 response and Shapes test performance in medial brain regions with negative rho values in frontal cortex. In contrast, the V group demonstrates an opposite pattern with negative medial correlations, along with positive associations represented in parietal areas. CVR CO2-Shapes test correlation was significantly higher in both the SS and PP groups compared to the V group in the right insular region and superior perisylvian areas (**Figure 4B**).

# DISCUSSION

To our knowledge, this is the first study combining ASL perfusion and CVR CO2 measures with a longitudinal followup of cognitive abilities in elderly individuals with preserved neuropsychological performances at baseline. Our data indicate that both imaging measures show a subtle decrease in cases with the first signs of cognitive instability (V group) suggesting the presence of cerebral hypoperfusion and decreased cerebrovascular reactivity mainly in parietal and fronto-temporal association areas in this particular group. Importantly, cases with

FIGURE 2 | Example ASL perfusion maps of stable-stable (SS), V, and PP group separately. The examples of the V group demonstrate a lower perfusion within the precuneus, while in the example maps of both the SS and PP group perfusion in these regions remains relatively preserved.

continuous cognitive decline (PP group) at high risk for MCI transition display an increase of both parameters in the same areas. The distinct profile of cognitively unstable cases compared to the two other groups of healthy controls is also documented by our clinicoradiologic correlations.

Early ASL-MRI contributions showed both hypoperfusion and hyperperfusion areas in MCI and AD cases stressing the brain efforts to compensate lesion invasion and cognitive loss. Despite controversial observations, two main patterns have been identified. A marked hypoperfusion in posterior cingulate cortex, precuneus and parietal cortex is already present in MCI cases (Johnson et al., 2005; Dai et al., 2009). In AD, a global decrease in blood flow is observed as compared to healthy controls, but region specific decrease in perfusion have also been detected (Austin et al., 2011). The hypoperfusion areas become more diffuse in clinically overt AD as to MCI including temporooccipital and parieto-occipital cortices as well as orbitofrontal cortex (Alsop et al., 2000; Asllani et al., 2008; Fleisher et al., 2009; Austin et al., 2011; Bron et al., 2014; Haller et al., 2016). A hyperperfusion in hippocampus and basal ganglia was reported both in MCI and AD cases but also in nonsymptomatic high risk APOE ε4 carriers (Alsop et al., 2008; Dai et al., 2009; Fleisher et al., 2009; Ding et al., 2014). ASL data on cognitively preserved elderly persons are very scarce. They showed both hypoperfusion in frontal, parietal and cingulate areas but also a strong negative association between diffuse hyperperfusion in neocortical association areas and cognitive performances in APOE ε4 carriers pointing to the presence of compensatory mechanisms explained by a pathological elevation of neural activity, inflammation or increased blood supply through vascular dilation or increased vascular density. In a longitudinal study, we first reported that decreased ASL values in posterior cingulate cortex were associated with subtle cognitive changes in cognitively intact elderly subjects (Xekardaki et al., 2015). More recently, ASL perfusion rates in medial frontal and anterior cingulate cortex predicted cognitive performances in a 4-year follow up of healthy elders (de Vis et al., 2018). Our longitudinal findings in carefully selected healthy controls shed some light into subtle changes of brain perfusion in the very initial stages of cognitive instability. In agreement with the observations made in MCI and AD cohorts, we found a subtle decrease of ASL values mainly in parietal cortex and precuneus (and to a lesser degree in temporal and frontal cortex) that is already present at inclusion in cases with cognitive fluctuations over a subsequent 3-year follow-up. PP cases showed increased ASL values in the same areas at inclusion (even higher than those in the SS group yet nonsignificant), but they deteriorate continuously suggesting that

group, with positive correlations residing in the frontal and cerebellar regions. The PP group also displays a negative association in most of the brain areas especially in regions surrounding the ventricles. Interestingly, the Spearman rho maps of the V group show global prominent positive correlations in areas that show negative rho values in the SS group, residing largely in the medial part of the brain.

hyperperfusion in these areas is not an efficient defense against the neurodegenerative process. This idea is further supported by the CVR CO2 measures showing a slightly decreased brain cerebrovascular reactivity in V cases with a steady increase in PP cases mainly in parietal areas. As for ASL, early studies using BOLD functional MRI documented CVR CO2 decrease mainly in temporal, parietal and posterior cingulate areas in AD cases but also in hippocampus in MCI cases (Cantin et al., 2011; Yezhuvath et al., 2012; Richiardi et al., 2015). Taken together, these data imply the presence of an inverted U-shape pattern of regional blood flow and cerebral vasoreactivity in parietal cortex in old age that might predict subsequent cognitive fate. In fact, the cerebral vasoreactivity to CO2 slightly decreased in control cases with fluctuant cognitive performances, shows a compensatory increase in these with continuous decline prior to the MCI status before a marked decrease in MCI and AD cases. The clinico-radiologic correlations between Shapes test measures, an indicator of visual working memory, and both ASL and CVR CO2 data point further to the different behavior of V cases compared to both PP and SS cases. Similar differences were not observed in respect to Verbal fluency possibly because of the absence of significant associations

FIGURE 4 | Group differences in the relation between neuroimaging parameters (ASL, CVR-CO2) and Shapes test performance. (A) Stronger positive relationship in the tempo-parietal junction and superior parietal lobe in V group as compared to the SS group. (B) Stronger positive correlation in both the PP and SS group as compared to the V group in right peri-sylvian and superior areas.

between cognitive performance and imaging parameters for

demonstrates negative correlations in medial brain regions, along with positive ones in parietal areas.

this test. The biological significance of these observations remains matter of debate.  However, increased glucose metabolism was also reported in amyloid-negative amnestic MCI cases and is thought to reflect compensatory mechanism to the neuronal damage occurring early in the disease process (for review see Ashraf et al., 2015). Whether the hypoperfusion observed mainly in parietal and posterior cingulate areas is causally related to AD or only its epiphenomenon remained unclear. Without bringing a definite answer, our data suggest that parietal and posterior cingulate cortex hypoperfusion is an early event in totally asymptomatic cases with fluctuations in cognitive performances within the normal range. It has been long thought that the hyperperfusion patterns in clinically overt AD and MCI may reflect increased neural activity as part of compensatory mechanisms aiming to counterbalance the cognitive decline. Alternatively, they may reflect alteration in brain vasculature due to increased angiogenesis or increased cerebrovascular reactivity possibly reflecting dysregulation of the neurovascular unit without any significant gain in terms of cognitive performance (for review

see Zlatar et al., 2016; Sierra-Marcos, 2017). The present observations combining ASL and CVR CO2 measures clearly support the second hypothesis. In fact, our PP cases displayed higher ASL and CVR CO2 values in parietal areas prior to their cognitive decline. These results parallel the findings of Zlatar et al. (2016) in APOE ε4 healthy carriers who reported a negative association between verbal memory function and ASL values in medial fronto-temporal and parietal cortex.

Strengths of the present study include the 3-year neuropsychological follow-up, careful exclusion of MCI and incipient AD cases, and combined use of ASL and CVR-CO2 techniques. Several limitations should, however, be considered when interpreting these data. In the absence of longer followup, the cognitive fate of PP and V cases remains uncertain. No CSF measures of tau and Aβ protein were available in this work so that the real extent of AD pathology remains unknown. Most importantly, the small sample size may mask subtle imaging differences between SS and PP as well as V cases. Future studies including PET amyloid and tau assessment

#### REFERENCES

of AD pathology as well as longer follow-up are warranted to define better the role of cerebrovascular mechanisms in the prediction of cognitive deterioration in asymptomatic elderly individuals.

#### AUTHOR CONTRIBUTIONS

All authors contributed to the concept and preparation of the manuscript. MvdT, SH, and PG were responsible for data interpretation. CR, SH, and PG collected the data.

#### FUNDING

This work was supported by Swiss National Science Foundation grants SNF 3200B0-1161193 and SPUM 33CM30-124111 and an unrestricted grant from the Association Suisse pour la Recherche sur l'Alzheimer.

MRI and FDG-PET in Alzheimer disease. Neurology 77, 1977–1985. doi: 10. 1212/WNL.0b013e31823a0ef7

**Conflict of Interest Statement:** 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.

Copyright © 2019 van der Thiel, Rodriguez, Van De Ville, Giannakopoulos and Haller. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

# Neurocompensatory Effects of the Default Network in Older Adults

Bryant M. Duda1 \*, Max M. Owens1 , Emily S. Hallowell1 and Lawrence H. Sweet1,2

1 Department of Psychology, University of Georgia, Athens, GA, United States, 2 Department of Psychiatry & Human Behavior, The Warren Alpert Medical School of Brown University, Providence, RI, United States

The hemispheric asymmetry reduction in older adults (HAROLD) is a neurocompensatory process that has been observed across several cognitive functions but has not yet been examined in relation to task-induced relative deactivations of the default mode network. The present study investigated the presence of HAROLD effects specific to neural activations and deactivations using a functional magnetic resonance imaging (fMRI) n-back paradigm. It was hypothesized that HAROLD effects would be identified in relative activations and deactivations during the paradigm, and that they would be associated with better 2-back performance. Forty-five older adults (M age = 63.8; range = 53–83) were administered a verbal n-back paradigm during fMRI. For each participant, the volume of brain response was summarized by left and right frontal regions of interest, and laterality indices (LI; i.e., left/right) were calculated to assess HAROLD effects. Group level results indicated that age was significantly and negatively correlated with LI (i.e., reduced left lateralization) for deactivations, but positively correlated with LI (i.e., increased left lateralization) for activations. The relationship between age and LI for deactivation was significantly moderated by performance level, revealing a stronger relationship between age and LI at higher levels of 2-back performance. Findings suggest that older adults may employ neurocompensatory processes specific to deactivations, and task-independent processes may be particularly sensitive to age-related neurocompensation.

#### Keywords: older adults, neurocompensation, fMRI, default mode network, HAROLD

#### INTRODUCTION

The number of individuals aged 65 or older is projected to exceed 1.5 billion in 2050 (National Institute on Aging and World Health Organization, 2011) and will comprise approximately 30% of the population by 2060 (Parker et al., 2012). While this expansion is due in part to advancements in healthcare, the aging population will present new challenges for older adults (OAs) and their caretakers. In addition to OAs affected by neurodegenerative conditions, such as Alzheimer's disease (Alzheimer's Disease International, 2010), OAs without such conditions also exhibit wellstudied patterns of neurocognitive decline. These include declines in processing speed (Salthouse, 1996; Charness, 2008), selective attention (Barr and Giambra, 1990; Madden, 1990), working memory (WM) (Balota et al., 2000; Zacks et al., 2000), and task-switching (Kray and Lindenberger, 2000; Kray et al., 2002). Underscoring their functional significance, these decrements have been linked to declines in independent living among healthy OAs (Suchy et al., 2011; Duda et al., 2014).

#### Edited by:

Panteleimon Giannakopoulos, Université de Genève, Switzerland

#### Reviewed by:

Manuela Berlingeri, University of Urbino Carlo Bo, Italy Nichole Lighthall, University of Central Florida, United States Lindsay Conner, University of Central Florida, United States, in collaboration with reviewer NL

#### \*Correspondence:

Bryant M. Duda<EMAIL_ADDRESS>
Received: 22 April 2018 Accepted: 29 April 2019 Published: 04 June 2019

#### Citation:

Duda BM, Owens MM, Hallowell ES and Sweet LH (2019) Neurocompensatory Effects of the Default Network in Older Adults. Front. Aging Neurosci. 11:111. doi: 10.3389/fnagi.2019.00111

Consistent with cognitive findings, neuroimaging studies have identified patterns of neural changes in non-demented OAs. Structural neuroimaging studies have revealed age-related reductions in prefrontal and parietal white matter integrity (Persson et al., 2005; Grady, 2012) and hippocampal and prefrontal gray matter volumes (Haug and Eggers, 1991; Raz et al., 2004). Functional neuroimaging studies, using positron emission tomography (PET) and functional magnetic resonance (fMRI), have revealed age-related increases and decreases in task-related brain activity. These changes were initially thought to reflect poorer brain function underlying cognitive decline; decreased activation was thought to reflect a reduced allocation of neural resources, whereas increased activation was considered a marker of reduced efficiency and selectivity of neural responses, referred to as dedifferentiation (Li and Lindenberger, 1999; Li and Sikström, 2002; Grady, 2008). While dedifferentiation is still a viable explanation for some age-related neural changes, some patterns of alterations have been positively associated with cognitive function (Berlingeri et al., 2010) and have contributed to a general theory of neural compensation (Grady, 2012).

The theory of neurocompensation was initially invoked to explain observations of increased neural activity while OAs performed cognitive tasks as well as younger adults (YAs; Cabeza, 2002), or when increased activity positively correlated with performance only in OAs (McIntosh et al., 1999; Reuter-Lorenz et al., 2000). This view suggests that activation of alternate brain regions may serve to counteract cognitive decline (Cabeza, 2001, 2002; Cabeza et al., 2004).  These effects are frequently reported in the prefrontal cortex (PFC; Goh, 2011), which is consistent with prior literature of neurocompensatory processes in OAs (Cabeza et al., 1997; Madden et al., 1999a,b). These findings collectively support the age-related compensatory hypothesis, although several questions remain unanswered. For example, it is unclear which cognitive processes are supported by neurocompensatory mechanisms (Rajah and D'Esposito, 2005; Greenwood, 2007; Zarahn et al., 2007).

One prominent model of age-related neurocompensation is the hemispheric asymmetry reduction in OAs (HAROLD), which postulates that under similar circumstances, PFC activation during cognitive processes tends to be less lateralized in OAs than YAs (Cabeza, 2002). HAROLD and other agerelated neurocompensatory theories offer different predictions of age-related neurocompensatory processes. For example, the compensation-related utilization of neural circuits hypothesis (CRUNCH) theory posits that the magnitude of network activation increases with task demand (Reuter-Lorenz and Cappell, 2008), while the scaffolding theory of aging and cognition (STAC) suggests a recruitment of additional networks when the primary network becomes inefficient (Park and Reuter-Lorenz, 2009). The HAROLD model is particularly attractive for studying age-related neurocompensation due to its focus on the PFC, which is consistent with the majority of agerelated neurocompensatory findings (see Goh, 2011 for review). Moreover, patterns of activation consistent with HAROLD have been well-validated using a range of neuroimaging paradigms, from simple motor tasks (Mattay et al., 2002) to more complex tasks, such as verbal working memory (VWM) and episodic retrieval (Bäckman et al., 1997; Cabeza et al., 1997; Reuter-Lorenz et al., 2000; Morcom and Friston, 2012). HAROLD thus offers a useful framework for investigating age-related patterns of neurocompensation.

While the HAROLD model has been well-documented, several aspects of this model are not fully delineated. For example, some evidence suggests that HAROLD patterns observed in prefrontal regions may be specific to higher levels of task demand (Berlingeri et al., 2013). In addition, studies assessing the HAROLD activation pattern have not consistently evaluated or found support for the relation between neurocompensatory activity and behavioral performance. For example, using PET to investigate age-related neural changes in verbal and spatial WM, OAs showed an increased bilateral response in the PFC that was associated with slower reaction time but equivalent WM accuracy. There has also been some evidence to suggest the presence of HAROLD relative to functional connectivity (FC), though with limited evidence of cognitive change (Li et al., 2009).

Another aspect of the HAROLD model that has not been clarified is the distinction between relative activation and deactivation. Despite substantial evidence for HAROLD effects in task-related activation patterns, neurocompensation relative to task-independent neural processes have been understudied. The default mode network (DMN) has been defined by coactivation within a distributed network of cortical regions, which characterizes the resting state of the human brain (Greicius et al., 2004). Evidence suggests that several properties of the DMN are sensitive to the aging process (Spreng et al., 2016; Klaassens et al., 2017). First, reduced DMN coherence has been associated with declines in processing speed (Ng et al., 2016), WM (Hampson et al., 2006), and cognitive control (Turner and Spreng, 2015). Second, suppression of DMN regions (relative deactivation), which occurs during task engagement (Anticevic et al., 2012; Binder, 2012) is a marker of efficient executive functioning (EF) that becomes compromised in OAs (Duverne et al., 2008; Petrella et al., 2011; Spreng et al., 2016; Avelar-Pereira et al., 2017).

Evidence suggests that DMN deactivations may be particularly susceptible to aging and a potential contributor to neurocompensatory processes. For example, using fMRI and spatial judgment tasks, Park et al. (2010) reported significantly reduced deactivations in OAs, and importantly, faster reaction times were demonstrated by OAs who did effectively deactivate DMN regions. In addition, Miller et al. (2008) reported enhanced DMN deactivation in YAs that correlated with successful memory encoding; however, OAs did not show this pattern, and reduced deactivation was most evident among poorer performers. Using a verb generation task, Persson et al. (2007) also reported an age-related reduction in DMN deactivations that was associated with slower reaction time performance. Perhaps the strongest evidence of DMN-related neurocompensation is described by

Davis et al. (2008), who developed the posterior-anterior shift in aging (PASA) hypothesis to explain the observation that OAs showed a pattern of reduced deactivations of posterior midline cortex, with increased deactivations of medial frontal cortex during a verb generation task. When matching groups on performance, OAs showed greater DMN deactivation of the left anterior cingulate and right anterior insula, as well as increased activations in the left middle frontal gyrus and right supramarginal gyrus. Both deactivation and activation effects were seen bilaterally, which, in the context of the relation between the DMN and task-positive network functions, suggests that the deactivations may also support cross-hemispheric neurocompensatory processes. Importantly, given the inverse correlation between DMN and task-positive networks that supports cognitive functioning (Fox et al., 2005), these bilateral effects suggest that an age-related neurocompensatory process (i.e., HAROLD) of relative deactivations may support successful task-positive functioning.

Further investigations of task-independent processes are needed to determine how the DMN and task-independent networks may interact with other models of neurocompensation, such as the HAROLD pattern in OAs. Since HAROLD and PASA models do not have direct support from studies of relative deactivation and given a lack of clarity in prior literature whether effects included summation of activation and relative deactivation or only activation effects, we considered examination of activation and relative deactivation separately to have high theoretical and methodological importance. The present study provides the first examination of HAROLD lateralization effects in both brain activations and relative deactivations in healthy OAs. Using fMRI during a WM paradigm, we evaluated the presence of the HAROLD pattern in the task-dependent brain response because neural activation associated with VWM has previously demonstrated sensitivity to age-related neurocompensatory processes, including the HAROLD effects. Second, using a resting fixation baseline to isolate suppression of baseline DMN activity, we evaluated the presence of HAROLD among task-induced deactivations. Moderation analyses were conducted to address whether observed HAROLD effects were associated with cognitive performance (i.e., n-back accuracy). Two homologous frontal lobe regions of interest (ROI) were used to calculate the laterality indices (LI) and examine the expected HAROLD effects. It was hypothesized that (1) OAs would demonstrate HAROLD patterns of task-elicited relative brain activation and deactivations, and (2) effects would be significantly moderated by 2-back performance, as a reflection of successful compensation.

#### MATERIALS AND METHODS

#### Participants

Participants included 45 healthy, right handed English-speaking men and women over the age of 50 (25 women, age range 53–83, M age = 63.78 years, SD = 7.99) who comprised a healthy control group in a larger study of neurocognitive function in cardiovascular disease (Haley et al., 2009). These participants were recruited from the community via advertisements in the Providence, RI area. All participants underwent standard informed and written consent procedures. Assessments were conducted over three visits that spanned approximately 6 weeks and included a neuropsychological assessment, an echocardiogram, and an MRI scanning session. Exclusion criteria included left-hand dominance, corrected visual acuity poorer than 20:40, below 60% performance accuracy on the 2-back VWM task (i.e., < 1 SD from 50%), low global cognitive function (>1.5 SDs below the sample population on the Mini Mental Status Examination), or any MRI contraindications (e.g., metal implants). Significant medical (e.g., surgery, heart infarct), neurological (e.g., multiple sclerosis, traumatic brain injury with loss of consciousness), and psychiatric problems (e.g., substance abuse with hospitalization, diagnosis of any current psychiatric illness) were exclusion criteria that were assessed by interview, physical examination, review of medical records and self-report questionnaires. Participants were compensated for their participation. The study was approved and monitored by the university and hospital institutional review boards (IRB) where the research took place and conformed to the Helsinki Declaration on human subjects' protection.

Demographic characteristics, estimated intellectual functioning, and 2-back performance are displayed in **Table 1**. The study sample comprised OAs with right-handed dominance (M = 86.7, SD = 14.40; range = 55–100; on a scale of 0-100, with increasing values representative of increasing right-dominance) and above average intellectual functioning (WTAR range = 96– 119, mean = 111.53, SD = 6.40) and years of educational attainment (range = 12–21 years; mean = 16.36; SD = 1.96). WM performance (i.e., 2-back accuracy) was consistent with prior 2-back literature (Braver et al., 1997; Smith and Jonides, 1999; Sweet et al., 2008).

#### Behavioral Measures

**Verbal working memory paradigm.** The n-back paradigm was employed to challenge VWM systems during two imaging runs. The n-back has been widely used in functional neuroimaging research for more than 20 years and has the advantage of a welldescribed fMRI neural response (e.g., Braver et al., 1997; Smith and Jonides, 1999; Owen et al., 2005; Sweet et al., 2008). During

MMSE, Mini mental status exam; Handedness was measured on a scale of 1-100, with increasing values representative of right-handed dominance, and all subjects reported right-handedness; Education, years of formal education attained; FSIQ, Full-scale intelligence quotient from the Wechsler Test of Adult Reading; 2-back accuracy includes correct hits and rejections proportional to the number of trials.

the 2-back, six series of 15 consonants were presented visually for 500 ms each, with an interstimulus interval (ISI) of 2500 ms. Participants were asked to make a yes or no button-press response following each consonant to report whether or not it was the same as the consonant (irrespective of capitalization) presented two earlier in the series (e.g., underlined letters in the following sequence would be answered "yes": w, N, r, N, R, Q, r, q, N, W, n . . .). Six 0-Back control blocks of nine consonants each were presented with the same duration of letter presentation and ISI. 0- Back blocks preceded each 2-Back block during the first imaging run and followed the 2-Back during the second run. Participants responded yes when a predetermined target consonant ("H" or "h") appeared and no for other consonants. Consonant blocks of both conditions contained 33 percent "yes" targets in random locations within each series. Capitalization was randomized throughout to encourage verbal encoding. Two 27-s (27000 ms) blocks of resting fixation blocks were presented between the 0-Back/2-Back cycles. 2-back performance was calculated for each participant using the following formula: (number of correct positives + correct negatives)/ total number of letters presented. Two subjects were excluded from behavioral analyses due to nearchance 2-back performance (<60% accuracy). A diagram of the n-back task is presented in **Supplementary Materials**.

#### Neuroimaging Measures

**MRI Acquisition.** Whole-brain echo-planar fMRI was conducted using a Siemens TIM Trio 3 tesla scanner (TR = 2500 ms, TE = 28 ms, FOV = 1922 , matrix size = 642 , in 42 3-mm-thick axial slices). This procedure yielded 116 whole-brain volumes for each of the two 288s imaging runs, yielding a spatial resolution of 3 mm3 per voxel. Whole-brain high-resolution T1 images were also acquired in the sagittal plane for anatomical reference (TR = 1900 ms, TE = 2.98 ms, FOV = 2562 mm, matrix size 2562 ). Two conditions of the n-back paradigm (i.e., 2-back and 0-back) and a resting state "+" were presented using E-prime (Psychology Software Tools, Sharpsburg, MD, United States) and back-projected onto a screen visible to the participant via a mirror mounted to the head coil.

**MRI analysis.** MRI dataset processing and statistical analyses were performed with Analysis of Functional NeuroImages version 18.0.05 software (AFNI; Cox, 1996). Preprocessing of the functional runs included slice-time correction and registration of each volume to the third volume of the first imaging run to correct for head movement. Data from participants with head movement of > 3.0 mm in any direction (i.e., x, y, z, yaw, pitch, and roll) or movement greater than 0.3 mm on more than 25% of repetitions were omitted from analyses. The functional volumes were aligned to the anatomical volume in Talairach space. A 5 mm full-width half maximum Gaussian filter was applied and the raw time-series was scaled to a mean of 100. For each subject, the general linear model (GLM) was used to quantify 2 back activity relative to a resting state (i.e., fixation across) after controlling for 0-back active control task and other covariates (i.e., movement parameters) in order to facilitate examination of task-independent deactivations.

**Individual activity maps.** The resulting individual activity maps were thresholded (two-tailed α = 0.01) and corrected for multiple comparisons using AFNI's false discovery rate (FDR; q = 0.05) procedure in order to provide a measure of volumetric activity, measured by significantly activated voxels beyond the threshold per ROI, for each participant.

**Group level processing.** For group level-analyses, two frontal ROIs were defined based on left or right hemisphere (i.e., relative to Talairach coordinate x-plane = 0) which extended to y-plane = 0. LIs, defined as left relative to right (left/right) frontal ROI response to the 2-back were calculated using each individual's volume of significant response in each ROI (i.e., voxels of significant activity beyond threshold). Consistent with prior literature (Deblaere et al., 2004; Baciu et al., 2005; Yuan et al., 2006), an LI was calculated by subtracting intensity effects within the right frontal ROI from intensity effects within the left frontal ROI, then dividing this value by the average of mean activity across the two frontal ROIs (i.e., (left frontal − right frontal) / (left + right frontal). Thus, the scaling of LI was such that positive values indicate left-lateralized function and negative values indicate right-lateralized function. This process was conducted separately for relative activation and deactivation and repeated for volumes of significant activation and deactivation.

**Group level analyses.** Qualitative procedures were first conducted to examine the validity of brain activation patterns exhibited by the sample. In order to generate relative group level activation and deactivation maps for comparison to prior literature, the whole-brain unthresholded voxel-wise effects were tested against a hypothetical mean of zero (i.e., no 2-back effect) using one-sample t-tests. These whole-brain analyses were conducted using an FDR-corrected threshold of p = 10−9 (voxels greater than t = 8.42) with and a minimum of 10 contiguous voxels. Clusters of significant n-back response exhibited by our sample was compared to prior n-back literature (e.g., Smith and Jonides, 1999; Owen et al., 2005); in addition, the Neurosynth Image Decoder was used to quantify the concordance between the whole-brain results and neuroimaging studies included in the publicly available database. Next, hemispheric lateralization was examined. Consistent with prior literature, hemispheric dominance was determined by the size of the LI (Deblaere et al., 2004; Baciu et al., 2005; Yuan et al., 2006), following the criteria adopted by Yuan et al., 2006, in which an LI threshold of 0.10 (representative of 10 percent greater left relative to right hemispheric activity) was considered evidence of lateralization. Lastly, support for HAROLD effects were assessed by performing bivariate correlation analyses between age and each LI.

**Behavioral analyses.** Hierarchical multiple regression and moderation analyses were conducted using the Statistical Package for Social Sciences IBM SPSS Statistics 21.0 to test the influence of 2-back accuracy on HAROLD effects. Before conducting moderation analyses, assumptions of multiple linear regressions were examined, including homoscedasticity, independence of residuals and normality of residuals (Cohen et al., 2003). Multicollinearity between the independent variable and moderator were reduced through the use of centering (Aiken and West, 1991). For moderation analyses, the PROCESS SPSS macro plug-in (Hayes, 2012) was applied to examine the moderating influence of n-back performance on the relation

("+") from GLM analysis. Slices are provided in Talairach space and shown in RAI orientation (right = right). Yellow, activations; Blue, deactivations. Cluster centers of

mass are reported in Table 2. Slice number (Z) is located above each slice. Color bars indicate magnitude effects for activations and deactivations.

between age and LI. For the present study, the interactions were visually probed in order to examine the nature of any moderation effects by examining conditional effects (i.e. simple slopes) at low (−1 SD below the mean) and high (+1 SD above the mean) levels of 2-back performance.

#### RESULTS

#### Whole Brain Voxelwise Analyses

The 2-back versus baseline contrast revealed widespread activation patterns commonly associated with n-back performance (Smith and Jonides, 1999; Owen et al., 2005), including the bilateral middle frontal gyrus, medial frontal gyrus, inferior parietal cortices, insula, and cerebellum. Relative deactivations were also consistent with prior literature (Anticevic et al., 2012; Binder, 2012) and overlapped substantially with regions associated with the DMN (Buckner et al., 2008), including the medial frontal gyrus/anterior cingulate, posterior cingulate, and superior frontal gyrus. Results from the Neurosynth Decoder, which was used to quantify the concordance of the present results to those in the publicly available database, indicated a value of 0.32 that was most consistent with taskspecific measures of WM. See **Figure 1** and **Table 2** for neural patterns and related activity maps.

#### Laterality and HAROLD Effects

Volumetric effects of the 2-back were summarized by the two frontal ROIs for both activations and deactivations. LIs were then calculated from these values and averaged at the group level. The activation LI yielded left-lateralization of this VWM task TABLE 2 | Clusters of significant neural response to 2-back versus resting state.

L, Left; R, Right; B, Bilateral. Coordinates reported in center of mass Talairach space, RAI orientation. These clusters are shown in Figure 1.

(M = 0.15, SD = 0.19) as expected (Jonides et al., 1997). Similarly, the relative deactivation LI was left-lateralized (M = 0.17, SD = 0.19). Descriptive statistics and the computed LI values are presented in **Table 3**.

The presence of HAROLD was then examined via bivariate Pearson correlations between age and each LI. The LI of neural activations was significantly and positively associated with age for volumetric effects r(42) = 0.34, p = 0.02 (see **Table 4**). Consistent with the HAROLD model predictions, the LI of neural

ROIs, Regions of interest relative to task-induced activations and deactivations. Mean, Mean number of significantly active voxels within each frontal ROI; SD, Standard deviation of significantly active voxels within each frontal ROI. ∗Denotes left-lateralization of LI (Yuan et al., 2006).

TABLE 4 | Correlations among demographics, cognition, and LI.

∗p < 0.05. A significant relation between age and the laterality index of neural deactivations was interpreted as consistent with the HAROLD effects.

deactivations were significantly and negatively associated with age r(42) = −0.31, p = 0.04 (see **Table 4**). Scatter plots of the relation between age and LIs for relative activations and deactivations are presented in **Figures 2**, **3**, respectively, with residuals shown in **Supplementary Materials**.

#### Moderating Effect of 2-Back Accuracy

Hierarchical multiple regression and moderation analyses were conducted to examine the influence of 2-back performance on the relation between age and LIs. Two models were used to explore the four statistically significant relations between age and LIs of relative activations and deactivations.

For neural activations, age significantly predicted LI (b = 0.01, p = 0.03). However, 2-back accuracy (b = 0.00, p = 0.98), and the age × 2-back accuracy interaction term (b = 0.00, p = 0.96) were not significantly predictive of LI (total R 2 = 0.13, p = 0.96). Moderation analysis specific to neural deactivations, however, yielded significant effects. Over and above age (b = −0.01; p = 0.02), 2-back accuracy (b = 0.01; p = 0.05) was a significant predictor of unique variance in LI (total R 2 = 0.28; F(3, 40) = 5.09, p < 0.001). In addition, the interaction term of 2-back accuracy x age was a significant predictor of unique variance in LI over and above age and 2-back accuracy (1R 2 = 0.08; 1F(3,40) = 4.33; b = 0.00; p = 0.04). Results of this moderation effect are presented in **Table 5**.

Given evidence of an interaction effect between age and 2-back accuracy on the relative deactivation LI, simple slopes analyses were conducted by estimating the conditional effect of age at specific values of 2-back performance (in this case, ± 1 SD from the sample mean, or −11.50 and 11.50, respectively) and tested whether the slopes were statistically significant from zero by a null hypothesis test (see Hayes, 2013). Results of these analyses revealed an association between age and LI, with age significantly related to LI for higher levels of 2-back performance (b = −0.01, p = 0.01, 95% CIs = −0.05, −0.00) but not significantly related to LI for lower levels (b = −0.00, p = 0.82, 95% CIs = −0.01, 0.01). In other words, high 2-back performers exhibited a stronger negative relationship between age and deactivation LI, or HAROLD effects. A plot of the simple slopes analyses is presented in **Figure 4**.

#### DISCUSSION

Hemispheric asymmetry reduction in older adults is a well-established neurocompensatory process that has been consistently demonstrated to support healthy OAs maintenance of cognitive function (Reuter-Lorenz et al., 2000; Cabeza, 2002; Morcom and Friston, 2012). However, to date, no studies have

∗p < 0.05.

investigated HAROLD effects on relative deactivations of the DMN. The present study sought to determine if (a) HAROLD would generalize to task-induced activations and deactivations using a verbal n-back paradigm, and (b) if HAROLD effects would be significantly and negatively associated with 2-back performance. Results of whole-brain voxelwise analyses revealed expected patterns of neural response associated with the 2 back vs. a resting state baseline (Owen et al., 2005), including deactivations in DMN regions (Buckner et al., 2008). LIs calculated from 2-back-associated neural activation indicated left-lateralization that was consistent with previous findings (Smith and Jonides, 1999; Owen et al., 2005). Extending this finding, the deactivation LI also indicated left-lateralization. Regarding neurocompensatory processes, OAs demonstrated an unexpected age-related increase in left-lateralized activity that was not overtly associated with 2-back performance. In contrast, and consistent with our second hypothesis, an age-related change in the deactivation LI reflected a HAROLD pattern that was influenced by 2-back performance, such that high- but not low-performers demonstrated this shift.

The significant positive correlation between age and activation LI suggests that an age-related increase in left hemispheric brain response may have supported OAs' maintenance of cognitive functioning. While this finding appears to contradict the HAROLD model, it is consistent with evidence supporting the presence of other neurocognitive compensatory processes in OAs. For example, differences in capacity (i.e., the degree to which a brain network is maximally recruited to perform a task) has been associated with cognitive maintenance (Bosch et al., 2010). At the network level, the compensation-related utilization of neural circuits hypothesis (CRUNCH) theory predicts that increased cognitive demands will result in a concomitant increase in network function (Reuter-Lorenz and Cappell, 2008). In the same vein, evidence has supported the STAC, which posits that recruitment of additional neural circuits or even networks occur when the primary networks have become inefficient or damaged due to pathology (Park and Reuter-Lorenz, 2009). Thus, one or more broader theories of compensation may complement the HAROLD model as it relates to successful maintenance of VWM. The younger age range of the present sample, relative to the age of participants in prior HAROLD studies (Cabeza, 2002) is not inconsistent with more traditional compensatory models.

Uncertainty regarding the function of overactivation, however, has led to increased interest in cognitive performance

performance moderates the relation between age and laterality index. Plot represents laterality index (greater number depicts greater left lateralization) as predicted by age at fixed values of the moderator, 2-back: +1 standard deviation (11.5) and –1 standard deviation (–11.5) from the sample mean.

correlates. For example, overactivation among OAs has been linked to both reduced (Logan et al., 2002; Milham et al., 2002; Johnson et al., 2004; Park et al., 2004; Thomsen et al., 2004) and maintained cognitive performance (Grady et al., 2003; Persson et al., 2004; Rossi et al., 2004; Gutchess et al., 2005) which have been interpreted to reflect dedifferentiation and compensation, respectively. In the present study, 2-back accuracy did not moderate the relation between age and LI for activations, suggesting that increased left-lateralized activity did not function to maintain performance. However, lack of an observable relation between overactivation and performance may not necessarily mean that these alterations are not compensatory in nature; for example, compensatory increases in bilateral recruitment may not be extensive enough to offset opposite-hemisphere reduction in efficiency (i.e., resulting in performance declines), or individual differences may not be related to performance on a specific task (see Grady, 2012 for review). Importantly, while higher performing OAs in the current study did not evidence a greater degree of overactivation, lower performing OAs also did not. It is therefore plausible that the lower performing OAs maintained previous levels of cognitive function prior to age-related decline via increased left-lateralized activation.

Findings of the present study support the notion that relative deactivations are sensitive to the aging process, lateralized similarly to task-induced activations, and associated with

successful task performance on a measure of VMW. A significant negative correlation between age and the deactivation LI, coupled with maintained 2-back performance (i.e., > 60% for each participant) suggests that reduced asymmetry of deactivations supported the OAs' maintenance of cognitive functioning. Therefore, the current results provide evidence of a novel HAROLD finding, extending previous findings to deactivations, and more specifically, via a within-group fMRI study of community-dwelling OAs. This finding is further strengthened by an observed moderation effect of 2-back accuracy, suggesting that reduced asymmetry of deactivations may support maintenance of baseline cognitive abilities. Results of simple slopes analyses, conducted to elucidate the nature of this effect, revealed that higher performing OAs demonstrated an increased reduction of asymmetry specific to deactivations, while lower performing OAs did not (see **Figure 2**). This is consistent with the growing expectation that neurocompensatory processes relate to an observable maintenance in cognition.

Due in part to observations of their importance in neurocognitive function, task-induced deactivations of the DMN have become the target of increased investigation (Anticevic et al., 2012; Binder, 2012; Gilbert et al., 2012; Spreng, 2012; Hansen et al., 2014; Spreng et al., 2016). Evidence suggests that deactivations are reduced in the aging process (Lustig et al., 2003; Rombouts et al., 2005; Grady et al., 2006) and associated with poorer task performance (Persson et al., 2007). The present findings suggest that DMN deactivations may also help to explain neurocompensatory processes. This notion is further substantiated by the relation between DMN and taskpositive network functioning (Anticevic et al., 2012; Spreng et al., 2016); for example, cross-hemispheric neurocompensatory activations (e.g., HAROLD) may correspond with crosshemispheric alterations in DMN deactivation patterns that are required for effective reallocation of neural resources.

Consistent with this notion, the PASA model has been used to explain an age-related posterior-anterior shift of task-related DMN deactivations that appear to support cognitive function, such as semantic fluency (Davis et al., 2008). Others have since identified general age-related reductions in DMN deactivation that were associated with slower reaction time on measures of spatial skills and higher scores on tests of EF (Persson et al., 2007; Park et al., 2010). Results of the present study provide further evidence that DMN deactivations may be particularly important for the maintenance of cognitive function in the face of age-related decline and warrant future investigations of neurocompensatory processes.

#### Limitations

Few studies have investigated HAROLD effects specific to WM among healthy OAs, and most were older PET studies. Prior literature has also focused almost exclusively on group differences (i.e., YA vs. OA) in HAROLD effects specific to task-dependent activations associated with WM. Interpretation of our findings by comparison to prior studies is therefore limited. A YA comparison group would have aided the interpretation of results. In addition, the sample of the present study was relatively young with respect to the aging literature (range = 53 to 83 years; mean = 63.78, SD = 7.99), as researchers often sample OA populations from 65 years and beyond. Interpretation of study findings are also limited by a relatively small sample size (i.e., not adequately powered to detect small effects). The sample was above average in intelligence, well-educated, and predominantly of Caucasian ethnicity (96%), which poses potential problems with generalizability.

#### Future Directions

As the first investigation of age-related task-independent neurocompensatory processes utilizing a within-group experimental design, findings of the current study warrant replication. Consistent with the CRUNCH model and a prior study of HAROLD (Berlingeri et al., 2013), results of the present study suggest that multiple compensatory processes may support cognitive function in old age. Given our identification of dual neurocompensatory processes (i.e., bilateralization of task-independent deactivations and increasing left-lateralization of task-related activations) during a VWM paradigm, future investigations may also benefit from consideration of both taskdependent and task-independent brain responses in the context of newer models of neurocompensation such as CRUNCH and STAC. Evidence of HAROLD effects among relative to deactivations suggest that baseline DMN processing may be altered in OAs. Therefore, decline in cognitive performance and associated task-related brain response may be explained, in part, by changes in baseline processing.

A better understanding of factors that influence neurocompensatory processes may aide healthy OAs in maintaining cognitive function and aging gracefully. For example, several sociological and cultural factors have been linked to maintenance of cognitive function in aging, such as education, occupational complexity, social activity, and physical exercise (Katzman, 1993; Valenzuela et al., 2008; Stern, 2012), and brain changes associated with these factors is a topic of growing interest (Barulli and Stern, 2013; Stern, 2016). Future research of age-related neurocompensatory processes may also aide researchers and clinicians in identifying problem areas in OAs (e.g., less efficient processing, overactivations and performance declines), and those at-risk of developing neurodegenerative conditions. Early identification of at-risk OAs may in turn inform developing cognitive training programs, which refer to a range of structured programs (e.g., computerized tasks engaging EF systems) intended to maintain cognition or ameliorate cognitive deficits (Bahar-Fuchs et al., 2013). A growing literature suggests that cognitive (Karbach and Verhaeghen, 2014) and physical exercise interventions (Bherer, 2015) induce neural changes associated with improved cognitive performance (for review, see Bamidis et al., 2014) and appear to be a promising future complement to pharmacological interventions for OAs and warrants continued research.

#### ETHICS STATEMENT

The study was approved and monitored by the university and hospital institutional review boards (IRB) where the research

took place and conformed to the Helsinki Declaration on human subjects' protection. All participants underwent standard informed and written consent procedures.

#### AUTHOR CONTRIBUTIONS

BD, MO, EH, and LS contributed to the design and implementation of the research, analysis of the results, and writing of the manuscript.

#### REFERENCES

#### ACKNOWLEDGMENTS

This work was supported by R01HL084178-01.

#### SUPPLEMENTARY MATERIAL

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fnagi. 2019.00111/full#supplementary-material

human working memory. Neuroimage 5, 49–62. doi: 10.1006/nimg.1996. 0247

**Conflict of Interest Statement:** 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.

Copyright © 2019 Duda, Owens, Hallowell and Sweet. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

# Early Secure Attachment as a Protective Factor Against Later Cognitive Decline and Dementia

Emilie Walsh1†, Yvonne Blake2†, Alessia Donati 1†, Ron Stoop2 and Armin von Gunten1 \*

1 Service of Old Age Psychiatry, Department of Psychiatry, Lausanne University Hospital (CHUV), Lausanne, Switzerland, 2 Center for Psychiatric Neurosciences, Department of Psychiatry, Lausanne University Hospital (CHUV), Lausanne, Switzerland

The etiology of neurodegenerative disorders such as dementia is complex and incompletely understood. Interest in a developmental perspective to these pathologies is gaining momentum. An early supportive social environment seems to have important implications for social, affective and cognitive abilities across the lifespan. Attachment theory may help to explain the link between these early experiences and later outcomes. This theory considers early interactions between an infant and its caregiver to be crucial to shaping social behavior and emotion regulation strategies throughout adult life. Furthermore, research has demonstrated that such early attachment experiences can, potentially through epigenetic mechanisms, have profound neurobiological and cognitive consequences. Here we discuss how early attachment might influence the development of affective, cognitive, and neurobiological resources that could protect against cognitive decline and dementia. We argue that social relations, both early and late in life, are vital to ensuring cognitive and neurobiological health. The concepts of brain and cognitive reserve are crucial to understanding how environmental factors may impact cognitive decline. We examine the role that attachment might play in fostering brain and cognitive reserve in old age. Finally, we put forward the concept of affective reserve, to more directly frame the socio-affective consequences of early attachment as protectors against cognitive decline. We thereby aim to highlight that, in the study of aging, cognitive decline and dementia, it is crucial to consider the role of affective and social factors such as attachment.

Keywords: attachment, protective factor, aging, cognitive decline, dementia

# INTRODUCTION

As the prevalence of dementia continues to increase, the fear of cognitive decline is becoming a central preoccupation in the elderly population. Multiple genetic and environmental factors play a role in the development of dementia, and a great deal of scientific interest is currently focused on identifying relevant risk and protective factors. Various types of dementia exist, including Alzheimer's Disease, Vascular Dementia, Lewy Body Dementia, and Frontotemporal Dementia. Additionally, Mild Cognitive Impairment is used to describe an intermediate state between healthy

#### Edited by:

Robert Perneczky, Ludwig Maximilian University of Munich, Germany

#### Reviewed by:

Douglas F. Watt, Lesley University, United States Christian Griñán-Ferré, Fundacio Bosch i Gimpera, Spain

> \*Correspondence: Armin von Gunten<EMAIL_ADDRESS>
†These authors have contributed equally to this work

> Received: 22 July 2018 Accepted: 12 June 2019 Published: 04 July 2019

#### Citation:

Walsh E, Blake Y, Donati A, Stoop R and von Gunten A (2019) Early Secure Attachment as a Protective Factor Against Later Cognitive Decline and Dementia. Front. Aging Neurosci. 11:161. doi: 10.3389/fnagi.2019.00161 aging and dementia. Mild Cognitive Impairment is characterized by cognitive deficits and related dysfunction not severe enough to be diagnosed as dementia, though the presentation can vary considerably among individuals (Winblad et al., 2004). Even in healthy aging, it is normal to observe some decline in several but not all cognitive domains. Whereas working memory, episodic memory, processing speed, and some aspects of short-term memory are typically impacted, other abilities such as general language or basic conceptual functions tend to be spared.

In the search for protective factors against cognitive decline and dementia, the potential role of early attachment has been largely overlooked. Many studies have demonstrated that a supportive social environment in old age can protect against the progress of cognitive decline (Bennett et al., 2006; Gow et al., 2007). However, early social support may also have long lasting psychosocial, cognitive and neurobiological consequences. Furthermore, a safe early social environment could offer protection against cognitive decline through its effects on the establishment of particular emotions regulation strategies.

Early attachment refers to the quality of the interaction between the child and the primary caregiver. By repetition, the child progressively integrates these interactions into mental representations, which allow for the establishment of longterm attachment patterns. These patterns will then determine social behavior and emotional regulation strategies throughout adult life, and will be particularly useful in times of stress as they may help the individual to regain a feeling of comfort and well-being (Bretherton and Munholland, 1999; Cassidy, 2000; Waters and Waters, 2006). Prompted by multiple experiences of loss, separation, and vulnerability associated with aging, these established attachment patterns may pervade a person's perceptions, feelings and attitudes, both in healthy aging and in the presence of cognitive disorders (Bradley and Cafferty, 2001; Perren et al., 2007). Therefore, we believe that it is highly important to study attachment patterns in this particular population.

We will begin this review by introducing the concept of attachment and it's impact on an individual's social relations. We will then discuss how quality social relations may protect against cognitive decline. Next, we will examine how attachment might affect cognitive and neurobiological development of various brain structures and systems and the consequences this may have for later cognitive decline, as a vast literature has shown that some early environmental factors can significantly modulate and drive the interplay of numerous processes involved in brain maturation (Gluckman et al., 2008; Brummelte, 2017). Finally, we will discuss how early attachment might contribute to brain and cognitive reserve, which play a role in protecting against cognitive decline. We will consider evidence from different developmental fields of study including epigenetics, neurobiology and psychology.

# ATTACHMENT, SOCIAL FACTORS, COGNITIVE DECLINE, AND DEMENTIA

"Man is by nature a social animal" Aristotle, Ethique à Nicomaque.