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Frontotemporal structure preservation underlies the protective effect of lifetime intellectual cognitive reserve on cognition in the elderly

Alzheimer's Research & Therapy volume 16, Article number: 255 (2024) Cite this article

Abstract

Background

Cognitive decline with age has heterogeneous, which might be related to the accumulation of protective factors called cognitive reserve, especially intellectual engagement factors over the life course. However, how lifetime intellectual cognitive reserve (LICR) protects cognitive function in the elderly remains unclear. We aimed to examine the relationship between LICR and cognition and the mild cognitive impairment (MCI) risk, as well as the neural mechanism of LICR on cognition.

Methods

A total of 5126 participants completed extensive neuropsychological tests, with LICR indicator encompassing early education, midlife occupational complexity, and mental leisure activities after retirement. Confirmatory factor analysis was performed to derive LICR score and cognitive function scores, then the hierarchical regression analysis was used to explore the relationship between LICR and cognitive functions and the risk of MCI. We further explored the macro- and micro-structural preservation underly LICR in 1117 participants. Multiple regressions and tract-based spatial statistics were used to explore the relationship between LICR and gray matter volume and white matter microstructure (FA value). Finally, using the mediation model to explore the relationship of “LICR-brain-cognition”.

Result

The new LICR index, which was more protective than its single indexes, could protect widespread cognitive functions and was associated with a reduction in MCI risk (Odds Ratio, 0.52; 95% CI, 0.47–0.57). For the structure basis of LICR, the higher LICR score was associated with the greater gray matter volume in right fusiform gyrus (t = 4.62, FDR corrected, p < 0.05) and left orbital superior frontal gyrus (t = 4.56, FDR corrected, p < 0.05), and the higher FA values in the frontotemporal related white matter fiber tracts. Furthermore, the right fusiform gyrus partially mediated the relationship between LICR and executive processing ability (β = 0.01, p = 0.02) and general cognitive ability (β = 0.01, p = 0.03).

Conclusions

The new comprehensive cognitive reserve index could promote the temporal macro-structural preservation and thus contribute to maintain better cognitive function. These findings highlight the importance of intellectual CR accumulation over the life course in successful cognitive aging and MCI prevention, thereby contributing to improve the quality of life in the elderly.

Background

Cognitive reserve (CR) theory proposed that the experiences individuals accumulated through the life-course could increase resilience against the cognitive impairment even Alzheimer Dementia (AD), helping to maintain cognitive function [1, 2]. Among them, the proxy indices that showed more protection included education level, occupational status and leisure activities, especially mental leisure activities [3,4,5]. Actually, the above proxy indices belong to different periods of life, confirming that leading an intellectually challenging life throughout lifetime means increasing cognitive stability and may serve as a hedge against mild cognitive impairment (MCI) or AD [6]. Therefore, dynamically investigating the influence of intellectual cognitive reserve (ICR) factors on cognitive function in later life from the life-course perspective, called lifetime intellectual cognitive reserve (LICR), may help to identify interventions to delay cognitive decline and prevent MCI.

Few studies have conducted comprehensive investigations on the abovementioned factors. Previous studies had shown that occupational psychological demands and educational level had cumulative protective effects on language and executive function [7], older adults engaging in more cognitively leisure activities could compensate for the cognitive aging associated with less cognitively challenging occupations [8], and high levels of education and occupational complexity were associated with a lower risk of cognitive impairment [9]. Overall, existing studies could not determine whether there is a cumulative advantage in the protective impact of LICR on cognitive function, and in reducing the risk of MCI in later life.

The cognitive reserve provided by abundant mental stimulation may be realized its protection through brain structure [10, 11]. Researchers have focused more on the brain structure associated with a single ICR factor and found that higher level of single ICR was associated with the greater macro gray matter volume in the whole brain [12] and frontotemporal regions [13,14,15,16,17], the higher level of micro white matter integrity in the frontotemporal fiber tracts [18, 19]. As for the neural basis underly LICR, researchers usually included partial ICR factors [20] or only found frontal macro-structural preservation [21]. There is a lack of systematic research on the basis of the brain structure underly the cumulative effect of the ICR factor throughout life, and it is not clear whether this is explained by macro- or/and micro-brain structures.

More importantly, whether this protective effect on brain structures are an important neural mechanism for maintaining better cognitive function. The scaffolding theory pointed out that compensatory scaffolds throughout life and protective factors contribute to brain health, thus further promoting successful cognitive aging and reducing the risk of MCI [22]. This may provide new insights into how the accumulation of ICR factors in different periods of life improves cognition. Existing studies had focused mainly on single indices, such as, right medial frontal gyrus gray matter volume could mediate the influence of education on processing speed [23]; the white matter integrity in the superior longitudinal fasciculus could mediate the influence of the interaction between age and education on executive function [24]; and average FA could partially mediate the relationship between cognitive activity in later life and general cognition [18]. Therefore, whether LICR could improve cognitive function through greater brain structural preservation is unknown.

In conclusion, the present study, which relied on a large-scale community sample in China, first constructed the operational definition of LICR, which consists of early life education level, midlife occupational complexity and later life mental cognitive activities, then aimed to investigate: (1) whether LICR could protect cognitive functions and reduce MCI risk in the elderly; (2) the macro- and micro-brain structure characteristics of the LICR; and (3) the mediating role of brain structural characteristics between LICR and cognitive function. The present study could help to understand the important value of LICR in maintaining better cognitive function in the elderly and its neural mechanism, and may provide a reference for the development of early cognitive intervention.

Methods

Participants

A total of 5126 participants (1052 participants were mild cognitive impairment and the rest were normal cognition) aged 55 and older were from the Beijing Aging Brain Rejuvenation Initiative (BABRI) database, which is an ongoing community-based prospective cohort study in China [25]. All participants in the present study met the following criteria: no dementia; were native Chinese speakers; were retired (female for 55 years old, male for 60 years old); and had no history of neurologic, psychiatric, or systemic illnesses known to influence cerebral function. They completed structural MRI scans (1117 participants, right-handed) and an extensive cognitive battery within a month (Fig. 1). All participants provided written informed consent for our protocol, which was approved by the ethics committee of the State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University.

Fig. 1
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Participant flow chart. BABRI: Beijing Aging Brain Rejuvenation Initiative

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The diagnostic criteria for mild cognitive impairment (MCI) based on Petersen’s criteria included (1) the presence of subjective cognitive complaints (self-reported and/or by informants); (2) normal general cognitive ability (a score higher than 23 on the Mini-Mental State Examination [MMSE], which is not the main criterion to distinguish between normal vs abnormal cognition); (3) normal daily living ability (a score of 0 in the Instrumental Activity of Daily Living [IADL] and ADL); and (4) objective cognitive impairment in at least one domain (one of the neuropsychological test scores corresponding to different cognitive domains were less than 1.5 standard deviations below the age- and education-adjusted mean of the Chinese elderly population, see Additional file 1) [26, 27]. The normal cognition participants were non-MCI and the neuropsychological test scores within 1.5 standard deviations.

The measure of lifetime intellectual cognitive reserve

An indicator of lifetime intellectual cognitive reserve (LICR) combined three measures over the life-course: early life education, midlife occupational complexity, and mental leisure activities after retirement. Education level was assessed by recording the number of years of schooling. The matrix derived for U.S. occupations using the 1970 U.S. census was used to measure the complexity of occupation [6, 14]. Each occupation was assigned a score reflecting the level of complexity at which a typical occupation, with Chinese occupation codes matched to the best-fitting category from the U.S. occupational complexity was assessed along the dimensions of data, people, and things with continuous scores ranging from simple (value 0) to complex tasks (values 6, 7, and 8, respectively). To comprehensively examine occupational complexity, the scores of the three dimensions were added together, and then the score was reversed; that is, the higher the total score is, the greater the occupational complexity (0–21) [28]. Mental leisure activities performed for enjoyment were measured using a personal information questionnaire with 23 questions that asked participants to recall their leisure activity in the past year, including reading; writing; participating in a senior citizens’ university; playing chess, poker, or mahjong; and handcrafts. The frequency of each activity was defined as frequent if they participated several times per week and rare if they participated less than once per week, and the total score was based on a weighted score of these 23 questions [24].

Neuropsychological test and personal information questionnaire

All participants underwent a battery of neuropsychological tests which were then used to assess several cognitive domains: 1) the Auditory Verbal Learning Test (AVLT, including N1N3, N4, N5 and N1N5); 2) the Rey-Osterrieth Complex Figure test (ROCF, including copy and delay recall); 3) the Digit Span Test (DST, including forward and backward); 4) the Trail Making Test (TMT, including A time, B time and B-A time); 5) the Symbol Digit Modalities Test (SDMT); 6) the Stroop Color and Word Test (SCWT, including B time, C time and C-B time); 7) the Clock Drawing Test (CDT); 8) the Category Verbal Fluency Test (CVFT, including animal, vegetable and fruit); and 9) the Boston Naming Test (BNT) [24].

The personal information questionnaire included demographic information and medical history. Demographic information included age and sex. Medical history included questions on a series of chronic diseases, including hypertension, hyperlipidemia, diabetes, coronary heart disease and cerebral small vessel disease. The number of these chronic diseases that the elderly suffered from, called disease number, as well as age and sex, were all used as control variables in subsequent statistical analyses.

MRI data acquisition

MRI data were acquired using a Siemens TRIO 3T scanner at the Imaging Center for Brain Research at Beijing Normal University and Beijing Tiantan Hospital, Capital Medical University (Beijing, China). Participants laid supine with their head fixed snugly by straps and foam pads to minimize head movement. T1-weighted, sagittal 3D magnetization prepared rapid gradient echo (MP-RAGE) sequences were acquired and used to cover the entire brain, and the scan parameters were as follows: for Beijing Normal University, 176 sagittal slices, repetition time (TR) = 1900 ms, echo time (TE) = 3.44 ms, slice thickness = 1 mm, flip angle = 9°, field of view (FOV) = 256 × 256 mm2, acquisition matrix = 256 × 256; for Beijing Tiantan Hospital, 192 sagittal slices, repetition time (TR) = 2300 ms, echo time (TE) = 2.32 ms, slice thickness = 1 mm, flip angle = 8°, field of view (FOV) = 256 × 256 mm2, acquisition matrix = 256 × 256.

DTI scans were obtained in the axial plane with a single-shot, spin-echo, echo planar sequences in the axial plane, and the scan parameters were as follows: for Beijing Normal University, a total of 70 slices covered the entire hemisphere and brainstem without a gap. TR = 9500 ms, TE = 92 ms, FOV = 256 × 256 mm2, matrix = 128 × 128, slice thickness = 2 mm; for Beijing Tiantan Hospital, a total of 75 slices covered the entire hemisphere and brainstem without a gap. TR = 8000 ms, TE = 60 ms, FOV = 256 × 256 mm2, matrix = 128 × 128, slice thickness = 2 mm. To increase the signal-to-noise ratio, two or three repetitions were performed. Diffusion sensitization gradients of each scan were applied in 30 non-collinear directions with a b value of 1000 s/mm2 and a non-diffusion weighted direction with a b value of 0 s/mm2.

Macro- and micro-structural MRI data processing

T1-weighted images were processed using the Computational Anatomy Toolbox (CAT12, http://dbm.neuro.uni-jena.de/cat12/) of Statistical Parametric Mapping software (SPM12, http://www.fil.ion.ucl.ac.uk/spm) implemented in MATLAB (R2014a). First, the NIfTI files converted from the raw DICOM T1-weighted images were segmented and spatially normalized into gray matter (GM), white matter (WM) and cerebrospinal fluid in standard MNI space using optimized shooting algorithm [29]. After segment, the total intracranial volume (TIV) and the volume of gray matter (GMV)/white matter (WMV) were estimated from xml files for each subject which contain these raw values using the “Estimate Total Intracranial Volume” module. Finally, Gaussian kernel of 8 mm full-width-half-maximum (FWHM) was used to smooth the image of gray matter tissue components.

DTI images were processed using the Pipeline for Analyzing braiN Diffusion imAges (PANDA, http://www.nitrc.org/projects/panda/). First, the DICOM files of all subjects were also converted into NIfTI files. The preprocessing included the following steps: (1) Brain extraction, in which the parameter for extracting brain tissue was 0.25 and the cropping gap was 3 mm; and (2) Eddy current and head motion correction, which was corrected by applying an affine alignment of each diffusion-weighted image to the b = 0 image. Accordingly, the b-matrix was reoriented based on the transformation matrix. (3) The average of the three scans was taken. (4) The diffusion tensor was estimated, and calculated the fractional anisotropy (FA), which is the most sensitive index to reflect the integrity of white matter. Six elements of the 3 × 3 diffusion tensor were determined via multivariate least-squares fitting of diffusion-weighted images. The tensor was diagonalized to obtain three eigenvalues (λ1−3) and three eigenvectors (ν1−3). (5) Tract-based spatial statistics: The FA images of each subject were nonlinearly registered to the FMRIB58_FA standard space, the average FA images of all subjects were calculated, and the average FA skeleton was generated at the same time. Finally, the FA images of all subjects were projected onto the average FA skeleton for subsequent analysis. (6) Smooth: the above images were smoothed using 6 mm FWHM.

Statistical analysis

Behavior data statistical analysis

First, to acquire more representative cognitive abilities, exploratory factor analysis (EFA) and confirmatory factor analysis (CFA, Mplus8.3) were performed via the cross-validation method to derive cognitive domain scores and general ability according to neuropsychological tests. Model fit effect was assessed using chi square (χ2) goodness of fit, the Comparative Fit Index (CFI), the Tucker-Lewis Index (TLI), the Root Mean Square Error of Approximation (RMSEA), and the Standardized Root Mean Square Residual (SRMR). The predicted values of the latent variables were derived via the maximum likelihood method and determined via Savedata statements based on the weights of their respective indicators.

The same confirmatory factor analysis was used to determine the lifetime intellectual cognitive reserve (LICR) score, which included the total number of years of education in the early life, the total occupational complexity score in middle life and the total frequency of mental leisure activities after retirement. Later, reliability and validity tests were carried out. Each numerical variable in the questionnaire was included in the reliability analysis; that is, the Cronbach-α coefficient was calculated. Given that we have not been able to exhaust all the proxy measures of cognitive reserve, we have not been able to compare these measures with the LICR. Previous researchers have often regarded a single indicator such as education level as a proxy index of cognitive reserve, so we compared the regression coefficients of LICR and its single cognitive reserve index on cognitive function using permutation test to test the validity of LICR. Finally, another hierarchical regression analysis was used to explore whether LICR was a significant positive predictor of each cognitive ability, and the difference in regression coefficients was tested to compare whether the protective effect was cognitive domain specific or domain general. Furthermore, logistic regression was used to explore the relationship between LICR and the risk of MCI. All of the above analyses were controlled for age, sex and disease number.

Macro-structural MRI data statistical analysis

For T1 data analysis, SPM12 and CAT12 in MATLAB were used to perform voxel-based morphometry (VBM) analysis. A general linear model was used, with LICR as the independent predictor, sex, age, disease number, MRI site (Beijing Normal University or Beijing Tiantan Hospital), and total intracranial volume (TIV) only for VBM analysis as covariates. The significance level was set to p < 0.05 after false discovery rate (FDR) correction, and the cluster size was set to > 50. In addition, to verify the relationships between whole-brain volume and cognitive reserve found in previous studies, partial correlation was used to explore the relationships between LICR and these whole-brain volume indicators, including TIV, GMV, WMV, relative GMV and WMV (GMV and WMV divided by TIV). Finally, we extracted regional gray matter volume which were significantly correlated with LICR in the above regression analysis, and partial correlation was used to explore the relationship between them and cognitive function.

Micro-structural MRI data statistical analysis

For DTI data analysis, the derived FA data were analyzed using tract-based spatial statistics (TBSS, FA threshold = 0.2) analysis and region of interest (ROI) analysis. For TBSS analysis, all the FA images of the elderly were projected on the average FA skeleton, and then the “randomize” tool in the FSL toolkit and the GLM were used for statistical analysis. The GLM statistical model used the average FA data as the statistical template, cognitive reserve as the independent variable, sex, age, disease number and MRI site as covariables to construct a design matrix in which continuous variables were centralized, and the statistical results were obtained by 5000 permutation tests. The significance level was set to p < 0.05 after threshold-free cluster enhancement (TFCE) transformation and family wise error (FWE) correction. For the ROI analysis, the JHU-ICBM-DTI-81 atlas, which included 50 fiber fasciculi, was used for the anatomic labels. According to previous studies, associative fibers were more closely related to cognitive ability and aging [30]. In the present study, 19 associative fibers of interest were selected from the above atlas, including the fornix (column and body of fornix) (FX), sagittal stratum (including the inferior longitudinal fasciculus and inferior fronto-occipital fasciculus) (SS), external capsule (EC), cingulate gyrus (CGC), cingulum (hippocampus) (CGH), fornix (cres)/stria terminalis (FX/ST), superior longitudinal fasciculus (SLF), superior fronto-occipital fasciculus (SFOF), inferior fronto-occipital fasciculus (IFOF) and uncinate fasciculus (UNC). Then, partial correlation was used to explore the relationships between the FA values of the above fibers and cognitive function, with the above factors used as control variables. The significance level was set to p < 0.05 after family wise error (FWE) correction (p = 0.0026).

To further explore whether the protection of LICR on cognitive functions occurred through a larger gray matter volume or more complete white matter microstructure, we further used structural equation model to construct a mediation model of “LICR-brain structural characteristics-cognitive function”, with the above factors used as control variables.

Results

Construction of the latent variable model of LICR

The demographics, intellectual cognitive reserve and cognitive performance of the elderly in the behavioral sample were shown in Table 1.

Table 1 Demographic and cognitive characteristics of the elderly
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First, CFA was performed on 5126 participants, and the best-fitting model generated 3 factors, called episodic memory, language ability and executive processing ability, then the general cognitive ability score was predicted by the three cognition scores. The model fit indices were as follows: χ2 = 370.02, df = 32, p < 0.001; RMSEA = 0.045; CFI = 0.982, TLI = 0.975; SRMR = 0.028. The cross-validation factor results are presented in Additional file 1.

Second, the LICR score was further extracted. In the first instance, there was a close relationship between early years of education, middle occupational complexity and mental leisure activities in later years (reducation and occupation complexity = 0.47, p < 0.001; reducation and mental leisureactivities = 0.33, p < 0.001; roccupation complexity and mental leisure activities = 0.22, p < 0.001). CFA showed that the three observed variables in the saturated model were all significant at the level of p < 0.001, then the composite score was determined based on the weights of these factors, as shown in Fig. 2. Subsequent reliability analysis revealed that the Cronbach-α coefficient was 0.74, indicating that the reliability was within the acceptable range. Hierarchical regression analysis revealed that LICR and its sub-indexes could not only significantly predict various cognitive functions; more importantly, the difference tests showed that the regression coefficients of LICR in predicting cognitive abilities were significantly greater than those of its sub-indexes (Table 2).

Fig. 2
figure 2

Standardized estimates from the CFA with three observable indicators of a latent reserve construct. The values indicate the loadings of the three factors to lifetime intellectual cognitive reserve. e1, e2, and e3 indicate the measurement error for each cognitive reserve factor

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Table 2 Hierarchical regression analysis of Lifetime Intellectual Cognitive Reserve (LICR) and its sub-indexes and cognitive functions
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The influence of LICR on cognitive function and the risk of MCI

Hierarchical regression analysis revealed that the LICR score significantly positively predicted episodic memory, language ability, executive processing ability and general cognitive ability. The permutation tests of the regression coefficient further showed that the influence of LICR on executive processing ability was significantly greater than that on other cognitive abilities and general cognitive ability, its influence on language ability and general cognitive ability was significantly greater than that on episodic memory, and its influence on general cognitive ability was significantly greater than that on language (Table 2 and Figure S2-A in Additional file 1). Moreover, better executive processing ability completely mediated the positive effects of LICR on episodic memory (β = 0.24, p < 0.001), language ability (β = 0.23, p < 0.001) and general cognitive ability (β = 0.20, p < 0.001) (Figure S2-B in Additional file 1). The effects of LICR on cognitive function of the elderly with different cognitive states were shown in Additional file 1. In addition, logistic regression showed that LICR was associated with a reduction in MCI risk (odds ratio, 0.52; 95% CI, 0.47–0.57, p < 0.001).

The brain structure characteristics of LICR

The demographics, lifetime intellectual cognitive reserve and cognitive performance of the participants in the MRI sample are shown in Table 1. Partial correlation analysis revealed significant positive correlations between LICR and TIV (1389.27 ± 138.59, r = 0.12, p < 0.001), GMV (598.35 ± 50.18, r = 0.12, p < 0.001) and WMV (474.40 ± 52.55, r = 0.10, p < 0.001) (Fig. 3-A). However, there was no significant correlation between LICR and the relative GMV (0.43 ± 0.02, r = -0.02, p = 0.47) and WMV (0.34 ± 0.02, r = 0.02, p = 0.55). The VBM results showed that individuals with a higher LICR had significantly greater gray matter volume in the left orbital part of the inferior frontal gyrus (F = 4.56, FDR corrected, p < 0.05, voxel number = 31) and right fusiform gyrus (F = 4.62, FDR corrected, p < 0.05, voxel number = 25) (Fig. 3-B and Table 3). The TBSS results showed that a higher LICR was related to greater FA in a number of white matter regions, including the corticospinal tract, sagittal stratum, external capsule, fornix (cres)/stria terminalis, superior longitudinal fasciculus, and inferior fronto-occipital fasciculus et al. (Fig. 3-C). The ratios of the number of significant voxels to the total number of voxels for all the FA values are presented in Table S1 in Additional file 1.

Fig. 3
figure 3

Relationships between brain image characteristics and LICR. A The relationship between the LICR and total volume indicators. TIV: total intracranial volume, GMV: gray matter volume, WMV: white matter volume. B Regression analysis between gray matter volume and LICR score (FDR corrected, p < 0.05). C White matter regions in which a higher LICR was related to a higher FA are shown in red-yellow (thickened for better visibility, FWE corrected, p < 0.05), and the corresponding white matter skeleton is shown in green

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Table 3 Partial correlation between brain structure characteristics and cognitive functions
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Finally, controlling for the covariates, the relationships between gray matter volume (left orbital part of the inferior frontal gyrus and right fusiform gyrus), FA values and various cognitive abilities were explored. The partial correlation results showed that the right fusiform gyrus was significantly positively correlated with executive processing ability, language ability and general cognitive ability. As shown in Table S1 in Additional file 1, the ratios of the number of significant voxels to the total number of voxels in the FX and SFOF were too small, so the subsequent partial correlation was calculated only for the remaining 16 fibers. Episodic memory was significantly positively correlated with bilateral FX/ST, left CGC, bilateral SLF and bilateral UNC; executive processing ability and general cognitive ability were significantly positively correlated with the FA values of the left SS, left CGH, bilateral EC, bilateral FX/ST, bilateral CGC, bilateral SLF, bilateral UNC and bilateral IFOF; and language ability was significantly positively correlated with the FA values of the bilateral FX/ST, bilateral EC, left IFOF, bilateral SLF, bilateral UNC and bilateral CGC (Table 3).

The underlying neural mechanism by which LICR protects cognitive function

To further explore whether the protective effect of LICR on cognitive function in the elderly occurs through changes in gray matter volume or white matter fibers, a structural equation model was constructed. The results showed that only the right fusiform gyrus played a partial mediating role between LICR and executive processing ability and general cognitive ability, as shown in Table 4 and Fig. 4.

Table 4 The mediation model of LICR- gray matter volume-cognition
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Fig. 4
figure 4

The mediation model of “LICR-gray matter volume-cognition”. Significance: ***, p < 0.001; **, p < 0.01; *, p < 0.05

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Discussion

Based on the BABRI database, the present study aimed to explore the effects of LICR on cognitive function and its neural mechanism in the elderly in China. The main findings include the following: (1) from the life-course perspective, the present study proposed a new cognitive reserve index, called lifetime intellectual cognitive reserve (LICR), which included early life education level, midlife occupational complexity and mental leisure activity after retirement, and confirmed that the index was a comprehensive protection factor with more protection advantages than its single proxy indices; (2) LICR could protect extensive cognitive functions, especially executive processing ability, and reduce the risk of MCI; (3) for the brain structure basis of LICR, a higher LICR was related to the greater gray matter volume in frontotemporal cortex, including the left orbital part of the inferior frontal gyrus and the right fusiform gyrus, and the greater the integrity of the frontotemporal-related white matter fiber bundle; and (4) the right fusiform gyrus played a partial mediating role between LICR and cognitive functions. These results could enrich the measurement index of cognitive reserve theory, provide new evidence for understanding how lifetime intellectual involvement improving cognitive function of the elderly, enhance their quality of life, promote their successful cognitive aging, and provide important reference for early cognitive intervention.

Compared with the single proxy index, LICR has a cumulative advantage for cognitive function in the elderly. This finding is consistent with previous study results showing that early life combined with middle life [9] and middle life combined with late life intellectual proxy indices [8] all had cumulative protective effects on cognitive abilities. Lifespan cognitive reserve could also help to reduce risk of MCI and dementia [31], and delay MCI progression to dementia [32]. These results all suggested the importance of integrating intellectual cognitive reserve through the life course to explore its ability to protect cognitive function in the elderly.

LICR could protect a wide range of cognitive functions, especially executive processing ability. Previous studies had confirmed that the three single cognitive reserve proxy indices have positive effects on executive function, memory and language ability [19, 33, 34]. Cognitive reserve is dynamic [1, 2], and combining multiple experiences is more conducive to a comprehensive understanding of its positive impact on cognition in later life. Few studies have comprehensively explored the lifetime intellectual factors involved in the present study. Although researchers have focused on the effects of lifetime intellectual enrichment on cognitive performance in non-demented adults, they separated the variables into 2 nonoverlapping principal components: education/occupation score and mid/late-life cognitive activity, and found both components contributed to the maintenance of good cognitive performance, but only mid/late-life cognitive activities helped to delay longitudinal cognitive decline [35]. Nonetheless, previous studies had found that the pairwise combination of the three proxy indices has cumulative protective advantages for executive function and general cognitive ability [7, 35], which validated the broader advantages of the accumulation of lifetime intellectual protection factors. Several psychological theories could help to explain why LICR protects cognitive functions in later life. The scaffolding theory of cognitive aging [22], the life-span perspective and the life-course perspective [36] indicate that multiple factors in the life course jointly determined the final cognitive ability of the elderly. To some extent, our results provide data supporting for the above psychological theories related to cognitive enhancement and are also an active exploration of how to achieve successful cognitive aging.

The present study not only verified the relationship between total volume indicators and LICR [12, 37], but also revealed the preservation of the frontotemporal structure underlying LICR, including macro gray matter volume and white matter microstructure. These results have improved our understanding of the brain structural mechanism of cognitive reserve, especially the whole life cycle of intellectual cognitive reserve.

The macro- and micro-structural preservation regions associated with LICR overlapped with the protective regions of single intellectual cognitive reserves [12, 13, 15, 18, 19], and other comprehensive cognitive reserves [20, 21], all of which were concentrated in frontal regions, and extra temporal regions preservation may be related to the life-course comprehensive indices we examined. Frontotemporal regions also overlapped with age-susceptible brain regions. Previous studies had shown that there was age vulnerability in cortical volume during brain aging, among which the prefrontal and temporal cortices were strongly correlated with age [38]. It was also found that frontotemporal connecting fibers, such as the uncinate fasciculus, superior longitudinal fasciculi, and inferior fronto-occipital fasciculus, undergone significant age-related changes, and the integrity of white matter decreased significantly in later years [39, 40] and even in MCI patients [41]. Active participation in intellectual events during the life course may enhance the positive plasticity of macro- and micro-frontotemporal structures, especially micro-structural preservation. On the one hand, this was consistent with the results of previous studies focusing on single indices, education [42] and occupational cognitive complexity [19], which were more related to white matter integrity. On the other hand, the results suggested that lifetime cognitive complexity may preserve axon tract structure more than age-related deterioration [43]. Alternatively, lifetime cognitive complexity may help promote neural plasticity or myelination to strengthen or increase white matter connections [44].

The scaffolding theory suggested that neural enrichment induced by protective factors at different periods could further promote successful cognitive aging directly through relatively complete brain structure [22]. The study revealed that LICR could protect cognitive ability in the elderly by the greater gray matter volume in right fusiform gyrus; that is, LICR maybe ameliorate the age vulnerability of the frontotemporal cortex, especially the temporal lobe, and in turn contribute to maintaining better cognitive function. This study empirically tested the scaffolding theory and expanded its practical application value.

The previous intellectual cognitive reserves had indeed confirmed that frontotemporal brain structure indicators can mediate the positive effects of education or cognitive activities in later life on cognitive function [18, 23, 24]. Researchers found that a higher comprehensive cognitive reserve score buffered the impact of temporal cortical thinning on attention, cognitive flexibility, and executive control [45]. The present study enriches the understanding of the brain imaging mechanism of the influence of life-course cognitive reserve on cognitive function in later life. The right fusiform gyrus, a region strongly associated with memory [46, 47], facial recognition [48] and social cognitive ability [49, 50], also had important diagnostic value in the progression of AD [51], which highlighted its important role in maintaining cognitive function in later life. The possible neural mechanism is that continuous cognitive stimulation throughout the lifetime makes one stay mentally active, helps to maintain the integrity of the fusiform gyrus gray matter structure and enables the individual to maintain better insight of their own social cognitive ability, thus helps to maintain better cognitive function in later life and even slow the progression of cognitive impairment. Of course, this needs to be tested using the measure of self-awareness such as self-efficacy, as well as longitudinal studies.

The strengths of this study include the large sample size based on Chinese community elderly and encompass intellectual cognitive reserve indices from the life-course perspective. Nonetheless, the current study has several limitations. First, the present study only used the cognitive function as a representative index of their quality of life in old age; in fact, quality of life is also reflected in other broad aspects, such as emotion and life satisfaction. Second, we paid more attention to the relatively mature brain structure indicators used by previous researchers. The small clusters of gray matter volume associated with LICR after FDR correction may be related to the conventional, rather than more detailed macrostructural indicators chosen in the study. Besides, we did not find mediating role of microstructural characteristics in LICR protection of cognitive functions, which may be related to the lack of detailed microstructure information in imaging protocol and statistical analysis methods, because the TBSS analysis based on the data obtained from single shell, which may underestimate diffusion restriction in voxels within crossing fibers [52, 53]. Therefore, using other advanced techniques, such as multi-shell imaging which could model more detailed features of the cellular environment from the differential tissue responses elicited by multiple b-values [54], advanced statistical methods and more rigorous correction methods, such as FWE, to explore brain structure characteristics of LICR. Third, we recommend using longitudinal data to explore whether lifetime intellectual cognitive reserve contributes to delay cognitive decline and promote positive plasticity of brain structure. As for the future prospects and directions, in view of the importance of genetic factors and early pathological features in cognitive development and cognitive impairment, exploring the mediating or chain mediating role of risk genes and early Aβ deposition and other factors in the environmental variables concerned in this study on the brain and cognitive function will help to answer the joint effects of genetics and environment on the cognitive development of individuals in later life.

Conclusions

The present study proposed the LICR based on the life-course perspective and integrated it with the concept of intellectual engagement. The results revealed that the LICR score was associated with widespread cognitive function and a reduction in MCI risk. We further explored the neural basis of LICR and showed that the higher the LICR score was related to the greater macro- and micro-structural preservation in frontotemporal regions. Further mediation analysis showed that only the right fusiform gyrus partially mediated the relationship between LICR and cognitive functions. To a certain extent, the present study enriches the concept of cognitive reserve and its measurement index, helps to understand the important role of intellectual engagement throughout one’s lifetime in maintaining better cognitive function in the elderly and its neural mechanism, and may provide a reference for the development of early cognitive intervention.

Data availability

The datasets used and analysed during the current study are available from the corresponding author on reasonable request.

References

  1. Stern Y. What is cognitive reserve? Theory and research application of the reserve concept. J Int Neuropsychol Soc. 2002;8(3):448–60.

    Article  PubMed  Google Scholar 

  2. Stern Y, Arenaza-Urquijo EM, Bartrés-Faz D, Belleville S, Cantilon M, Chetelat G, et al. Whitepaper: defining and investigating cognitive reserve, brain reserve, and brain maintenance. Alzheimers Dement. 2020;16(9):1305–11.

    Article  PubMed  Google Scholar 

  3. Hertzog C, Kramer AF, Wilson RS, Lindenberger U. Enrichment effects on adult cognitive development: can the functional capacity of older adults be preserved and enhanced? Psychol Sci Public Interest. 2010;9(1):1–65.

    Article  Google Scholar 

  4. Petkus AJ, Gomez ME. The importance of social support, engagement in leisure activities, and cognitive reserve in older adulthood. Int Psychogeriatr. 2021;33(5):433–5.

    Article  PubMed  Google Scholar 

  5. Yu JT, Xu W, Tan CC, Andrieu S, Vellas B. Evidence-based prevention of Alzheimer’s disease: systematic review and meta-analysis of 243 observational prospective studies and 153 randomised controlled trials. J Neurol Neurosurg Psychiatry. 2020;91(11):1201–9.

    Article  PubMed  Google Scholar 

  6. Kuh D, Karunananthan S, Bergman H, Cooper R. A life-course approach to healthy ageing: maintaining physical capability. Proc Nutr Society. 2014;73:237–48.

    Article  Google Scholar 

  7. Then FS, Luck T, Luppa M, Arélin K, Schroeter ML, Engel C, et al. Association between mental demands at work and cognitive functioning in the general population - results of the health study of the Leipzig research center for civilization diseases (LIFE). Journal of occupational medicine and toxicology. 2014;9: 23.

    Article  PubMed  PubMed Central  Google Scholar 

  8. Andel R, Finkel D, Pedersen NL. Effects of preretirement work complexity and postretirement leisure activity on cognitive aging. J Gerontol B Psychol Sci Soc Sci. 2016;71(5):849–56.

    Article  PubMed  Google Scholar 

  9. Cations M, Draper B, Low LF, Radford K, Trollor J, Brodaty H, et al. Non-genetic risk factors for degenerative and vascular young onset dementia: results from the INSPIRED and KGOW Studies. J Alzheimers Dis. 2018;62(4):1747–58.

    Article  PubMed  CAS  Google Scholar 

  10. Kramer AF, Louis B, Colcombe SJ, Willie D, Greenough WT. Environmental influences on cognitive and brain plasticity during aging. Journals of Gerontology. 2004;59A(9):940–57.

    Google Scholar 

  11. Lindenberger U, Lvdén M. Brain plasticity in human lifespan development: the exploration–selection–refinement model. Annual Review of Developmental Psychology. 2019;1(1):197–222.

    Article  Google Scholar 

  12. Foubert-Samier A, Catheline G, Amieva H, Dilharreguy B, Helmer C, Allard M, et al. Education, occupation, leisure activities, and brain reserve: a population-based study. Neurobiol Aging. 2012;33(2):423.e415–423.e425.

    Article  Google Scholar 

  13. Boller B, Mellah S, Ducharme-Laliberte G, Belleville S. Relationships between years of education, regional grey matter volumes, and working memory-related brain activity in healthy older adults. Brain Imaging Behav. 2017;11(2):304–17.

    Article  PubMed  Google Scholar 

  14. Dekhtyar S, Marseglia A, Xu W, Darin-Mattsson A, Wang HX, Fratiglioni L. Genetic risk of dementia mitigated by cognitive reserve: a cohort study. Ann Neurol. 2019;86(1):68–78.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  15. Maguire EA, Gadiant DG, Johnsrude IS, Good CD, Ashburner J, Frackowiak RSJ, et al. Navigation-related structural change in the hippocampi of taxi drivers. Proc Natl Acad Sci. 2000;97(8):4398–403.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  16. Schultz SA, Larson J, Oh J, Koscik R, Dowling MN, Gallagher CL, et al. Participation in cognitively-stimulating activities is associated with brain structure and cognitive function in preclinical Alzheimer’s disease. Brain Imaging Behav. 2015;9(4):729–36.

    Article  PubMed  PubMed Central  Google Scholar 

  17. Arenaza-Urquijo EM, de Flores R, Gonneaud J, Wirth M, Ourry V, Callewaert W, et al. Distinct effects of late adulthood cognitive and physical activities on gray matter volume. Brain Imaging Behav. 2017;11(2):346–56.

    Article  PubMed  Google Scholar 

  18. Arfanakis K, Wilson RS, Barth CM, Capuano AW, Vasireddi A, Zhang S, et al. Cognitive activity, cognitive function, and brain diffusion characteristics in old age. Brain Imaging Behav. 2016;10(2):455–63.

    Article  PubMed  PubMed Central  Google Scholar 

  19. Kaup AR, Xia F, Launer LJ, Sidney S, Nasrallah I, Erus G, et al. Occupational cognitive complexity in earlier adulthood is associated with brain structure and cognitive health in midlife: the CARDIA study. Neuropsychology. 2018;32(8):895–905.

    Article  PubMed  PubMed Central  Google Scholar 

  20. Bartrés-Faz D, Solé-Padullés C, Junqué C, Rami L, Bosch B, Bargalló N, et al. Interactions of cognitive reserve with regional brain anatomy and brain function during a working memory task in healthy elders. Biol Psychol. 2009;80(2):256–9.

    Article  PubMed  Google Scholar 

  21. Conti L, Riccitelli GC, Preziosa P, Vizzino C, Marchesi O, Rocca MA, et al. Effect of cognitive reserve on structural and functional MRI measures in healthy subjects: a multiparametric assessment. J Neurol. 2021;268(5):1780–91.

    Article  PubMed  Google Scholar 

  22. Reuter-Lorenz PA, Park DC. Cognitive aging and the life course: a new look at the Scaffolding theory. Curr Opin Psychol. 2024;56: 101781.

    Article  PubMed  Google Scholar 

  23. Mortby ME, Burns R, Janke AL, Sachdev PS, Cherbuin N. Relating education, brain structure, and cognition: the role of cardiovascular disease risk factors. Biomed Res Int. 2014;2014:1–13.

    Article  Google Scholar 

  24. Chen YJ, Lv CL, Li X, Zhang JY, W CK, Liu ZW, et al. The positive impacts of early-life education on cognition, leisure activity, and brain structure in healthy aging. Aging. 2019;11(14):1–20.

    Article  Google Scholar 

  25. Yang C, Li X, Zhang J, Chen Y, Li H, Wei D, et al. Early prevention of cognitive impairment in the community population: The Beijing Aging Brain Rejuvenation Initiative. Alzheimers Dement. 2021;17(10):1610–8.

    Article  PubMed  Google Scholar 

  26. Petersen RC, Morris JC. Mild cognitive impairment as a clinical entity and treatment target. Arch Neurol. 2005;62(7):1160–3.

    Article  PubMed  Google Scholar 

  27. Yang Y, Chen Y, Sang F, Zhao S, Wang J, Li X, et al. Successful or pathological cognitive aging? Converging into a “frontal preservation, temporal impairment (FPTI)” hypothesis. Science Bulletin. 2022;67(22):2285–90.

    Article  PubMed  CAS  Google Scholar 

  28. Boyle R, Knight SP, De Looze C, Carey D, Scarlett S, Stern Y, et al. Verbal intelligence is a more robust cross-sectional measure of cognitive reserve than level of education in healthy older adults. Alzheimer’s research & therapy. 2021;13(1):128.

    Article  CAS  Google Scholar 

  29. Ashburner J, Friston KJ. Diffeomorphic registration using geodesic shooting and Gauss-Newton optimisation. Neuroimage. 2011;55(3):954–67.

    Article  PubMed  Google Scholar 

  30. Mito R, Raffelt D, Dhollander T, Vaughan DN, Tournier JD, Salvado O, et al. Fibre-specific white matter reductions in Alzheimer’s disease and mild cognitive impairment. Brain. 2018;141(2):888–902.

    Article  PubMed  Google Scholar 

  31. Xu H, Yang R, Qi X, Dintica C, Xu W. Association of lifespan cognitive reserve indicator with dementia risk in the presence of brain pathologies. JAMA Neurol. 2019;76(10):1184–91.

    Article  PubMed  PubMed Central  Google Scholar 

  32. Xu H, Yang R, Dintica C, Qi X, Song R, Bennett DA, et al. Association of lifespan cognitive reserve indicator with the risk of mild cognitive impairment and its progression to dementia. Alzheimers Dement. 2020;16(6):873–82.

    Article  PubMed  Google Scholar 

  33. Ferreira N, Owen A, Mohan A, Corbett A, Ballard C. Associations between cognitively stimulating leisure activities, cognitive function and age-related cognitive decline. Int J Geriatr Psychiatry. 2015;30(4):422–30.

    Article  PubMed  Google Scholar 

  34. Opdebeeck C, Martyr A, Clare L. Cognitive reserve and cognitive function in healthy older people: a meta-analysis. Aging Neuropsychol Cogn. 2016;23(1):1–21.

    Article  Google Scholar 

  35. Vemuri P, Lesnick TG, Przybelski SA, Machulda M, Knopman DS, Mielke MM, et al. Association of lifetime intellectual enrichment with cognitive decline in the older population. JAMA Neurol. 2014;71(8):1017–24.

    Article  PubMed  PubMed Central  Google Scholar 

  36. Franz CE, Hatton SN, Elnian JA, Warren T, Kremen WS. Lifestyle and the aging brain: interactive effects of modifiable lifestyle behaviors and cognitive ability in men from midlife to old age. Neurobiol Aging. 2021;108(1):80–9.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  37. Walhovd KB, Fjell AM, Wang Y, Amlien IK, Mowinckel AM, Ulman L, et al. Education and income show heterogeneous relationships to lifespan brain and cognitive differences across European and US cohorts. Cereb Cortex. 2021;32(4):839–54.

    Article  PubMed Central  Google Scholar 

  38. Fjell AM, Mcevoy L, Holland D, Dale AM, Walhovd KB. What is normal in normal aging? Effects of aging, amyloid and Alzheimer’s disease on the cerebral cortex and the hippocampus. Prog Neurobiol. 2014;117:20–40.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  39. Slater DA, Melie-Garcia L, Preisig M, Kherif F, Lutti A, Draganski B. Evolution of white matter tract microstructure across the life span. Hum Brain Mapp. 2019;40(7):2252–68.

    Article  PubMed  PubMed Central  Google Scholar 

  40. Storsve AB, Fjell AM, Yendiki A, Walhovd KB. Longitudinal changes in white matter tract integrity across the adult lifespan and its relation to cortical thinning. PLoS One. 2016;11(6):1–21.

    Article  Google Scholar 

  41. Hyun C, Won YD, Min SY, Saeng KB, In KY, Bin CY, et al. Abnormal integrity of corticocortical tracts in mild cognitive impairment: a diffusion tensor imaging study. J Korean Med Sci. 2008;23(3):477–83.

    Article  Google Scholar 

  42. Piras F, Cherubini A, Caltagirone C, Spalletta G. Education mediates microstructural changes in bilateral hippocampus. Hum Brain Mapp. 2011;32(2):282–9.

    Article  PubMed  Google Scholar 

  43. Nyberg L, Lvdén M, Riklund K, Lindenberger U, Bckman L. Memory aging and brain maintenance. Trends Cogn Sci. 2012;16(5):292–305.

    Article  PubMed  Google Scholar 

  44. Chanraud S, Zahr N, Sullivan EV, Pfefferbaum A. MR diffusion tensor imaging: a window into white matter integrity of the working brain. Neuropsychol Rev. 2010;20(2):209–25.

    Article  PubMed  PubMed Central  Google Scholar 

  45. Ferreira D, Bartres-Faz D, Nygren L, Rundkvist LJ, Molina Y, Machado A, et al. Different reserve proxies confer overlapping and unique endurance to cortical thinning in healthy middle-aged adults. Behav Brain Res. 2016;311:375–83.

    Article  PubMed  Google Scholar 

  46. Raz N, Rodrigue KM, Kennedy KM, Acker JD. Vascular health and longitudinal changes in brain and cognition in middle-aged and older adults. Neuropsychology. 2007;21(2):149–57.

    Article  PubMed  Google Scholar 

  47. Zhu B, Chen C, Loftus EF, He Q, Lei X, Dong Q, et al. Hippocampal size is related to short-term true and false memory, and right fusiform size is related to long-term true and false memory. Brain Struct Funct. 2016;221(8):4045–57.

    Article  PubMed  Google Scholar 

  48. Brunyé TT, Moran JM, Holmes A, Mahoney CR, Taylor HA. Non-invasive brain stimulation targeting the right fusiform gyrus selectively increases working memory for faces. Brain Cogn. 2017;113:32–9.

    Article  PubMed  Google Scholar 

  49. Muñoz-Neira C, Tedde A, Coulthard E, Thai NJ, Pennington C. Neural correlates of altered insight in frontotemporal dementia: a systematic review. NeuroImage clinical. 2019;24: 102066.

    Article  PubMed  PubMed Central  Google Scholar 

  50. Valera-Bermejo JM, De Marco M, Mitolo M, McGeown WJ, Venneri A. Neuroanatomical and cognitive correlates of domain-specific anosognosia in early Alzheimer’s disease. Cortex. 2020;129:236–46.

    Article  PubMed  Google Scholar 

  51. Convit A, De Leon MJ, Tarshish C, De Santi S, Tsui W, Rusinek H, et al. Specific hippocampal volume reductions in individuals at risk for Alzheimer’s disease. Neurobiol Aging. 1997;18(2):131–8.

    Article  PubMed  CAS  Google Scholar 

  52. Lebel C, Deoni S. The development of brain white matter microstructure. Neuroimage. 2018;182:207–18.

    Article  PubMed  Google Scholar 

  53. Lebel C, Treit S, Beaulieu C. A review of diffusion MRI of typical white matter development from early childhood to young adulthood. NMR Biomed. 2019;32(4): e3778.

    Article  PubMed  Google Scholar 

  54. Pines AR, Cieslak M, Larsen B, Baum GL, Cook PA, Adebimpe A, et al. Leveraging multi-shell diffusion for studies of brain development in youth and young adulthood. Dev Cogn Neurosci. 2020;43: 100788.

    Article  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

The authors would like to express their gratitude to the participants and staff involved in data collection and management in the Beijing Aging Brain Rejuvenation Initiative.

Funding

This work was supported by STI2030-Major Projects (grant number 2022ZD0211600), the Natural Science Foundation of China (grant number 32171085), National Key Research and Development Program of China (grant number 2023YFC3605400), State Key Program of National Natural Science of China (grant number 82130118), Tang Scholar and Open Research Fund of the State Key Laboratory of Cognitive Neuroscience and Learning.

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Author notes

  1. Dandan Wang and Xin Li contributed equally to this work.

Authors and Affiliations

  1. State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, 100875, China

    Dandan Wang, Xin Li, Mingxi Dang, Shaokun Zhao, Feng Sang & Zhanjun Zhang

  2. Faculty of Education, Beijing Normal University, Beijing, 100875, China

    Dandan Wang

  3. Beijing Aging Brain Rejuvenation Initiative (BABRI) Center, Beijing Normal University, Beijing, 100875, China

    Dandan Wang, Xin Li, Mingxi Dang, Shaokun Zhao, Feng Sang & Zhanjun Zhang

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Contributions

DDW, XL, and ZJZ contributed to the conception and design of the study; DDW, and MXD contributed to the acquisition and analysis of data; DDW, SKZ, and FS contributed to draft the original text and preparing the figures; DDW, XL, MXD, and ZJZ contributed to review and editing the draft. All the authors approved the final draft.

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Correspondence to Zhanjun Zhang.

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The study was conducted in accordance with the institutional review board (IRB) at the State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University (protocol code was ICBIR_A_0041_002_02). All participants provided written informed consent for our protocol.

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Wang, D., Li, X., Dang, M. et al. Frontotemporal structure preservation underlies the protective effect of lifetime intellectual cognitive reserve on cognition in the elderly. Alz Res Therapy 16, 255 (2024). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s13195-024-01613-6

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Alzheimer's Research & Therapy

ISSN: 1758-9193