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Article

Resilience of Neural Cellularity to the Influence of Low Educational Level

by
Viviane A. Carvalho de Morais
1,
Ana V. de Oliveira-Pinto
1,
Arthur F. Mello Neto
1,
Jaqueline S. Freitas
1,
Magnólia M. da Silva
2,
Claudia Kimie Suemoto
2,
Renata P. Leite
2,
Lea T. Grinberg
3,
Wilson Jacob-Filho
2,4,
Carlos Pasqualucci
2,
Ricardo Nitrini
2,
Paulo Caramelli
5 and
Roberto Lent
1,6,*
1
Neuroplasticity Laboratory, Instituto de Ciências Biomédicas, Universidade Federal do Rio de Janeiro, Rio de Janeiro 21941-902, RJ, Brazil
2
Biobank for Aging Studies, Faculdade de Medicina, Universidade de São Paulo, São Paulo 01246-903, SP, Brazil
3
Memory and Aging Center, University of California San Francisco, San Francisco, CA 94158, USA
4
Laboratory of Medical Research in Aging (LIM-66), Faculdade de Medicina, Universidade de São Paulo, São Paulo 01246-903, SP, Brazil
5
Behavioral and Cognitive Neurology Research Group, Faculdade de Medicina, Universidade Federal de Minas Gerais, Belo Horizonte 30130-100, MG, Brazil
6
D’Or Institute of Research and Education, Rio de Janeiro 22281-100, RJ, Brazil
*
Author to whom correspondence should be addressed.
Brain Sci. 2023, 13(1), 104; https://doi.org/10.3390/brainsci13010104
Submission received: 21 October 2022 / Revised: 28 December 2022 / Accepted: 30 December 2022 / Published: 5 January 2023
(This article belongs to the Special Issue Environmental Exposures, Neurodevelopment, and Mental Health)
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Abstract

:
Background: Education is believed to contribute positively to brain structure and function, as well as to cognitive reserve. One of the brain regions most impacted by education is the medial temporal lobe (MTL), a region that houses the hippocampus, which has an important role in learning processes and in consolidation of memories, and is also known to undergo neurogenesis in adulthood. We aimed to investigate the influence of education on the absolute cell numbers of the MTL (comprised by the hippocampal formation, amygdala, and parahippocampal gyrus) of men without cognitive impairment. Methods: The Isotropic Fractionator technique was used to allow the anisotropic brain tissue to be transformed into an isotropic suspension of nuclei, and therefore assess the absolute cell composition of the MTL. We dissected twenty-six brains from men aged 47 to 64 years, with either low or high education. Results: A significant difference between groups was observed in brain mass, but not in MTL mass. No significant difference was found between groups in the number of total cells, number of neurons, and number of non-neuronal cells. Regression analysis showed that the total number of cells, number of neurons, and number of non-neuronal cells in MTL were not affected by education. Conclusions: The results indicate a resilience of the absolute cellular composition of the MTL of typical men to low schooling, suggesting that the cellularity of brain regions is not affected by formal education.

Graphical Abstract

1. Introduction

Cognitive Reserve (CR) can be defined as the brain’s ability to withstand damage from a pathological condition, or simply as a result of the aging process, thus delaying the clinical manifestations of a pathology [1]. Each individual has his/her specific CR; therefore, differences can be observed between individuals regarding neural networks or cognitive processes [2], which in turn can explain why each person responds differently in the presence of a disease [1,3].
The concept of CR was proposed to elucidate the lack of a systematic relationship between the amount of brain damage and the intensity of symptoms [4]. When comparing people with the same brain alterations caused by a neuropsychiatric condition such as dementia, those with higher CR may have less severe symptoms than those with low reserve [3]. Several factors can directly impact CR, including educational level, leisure activities, and occupation, and thus can contribute to a greater tolerance to pathological events [5,6,7,8,9].
Some studies have shown an association between education and features of several cortical regions, bilaterally: premotor area, cingulate cortex, perisylvian area, and insular cortex [10]. These findings are in line with other studies that revealed a greater volume of gray matter in the temporoparietal and frontal lobes bilaterally [11], transverse temporal cortex, insula, cingulate cortex [12], right superior temporal cortex, left insula and bilateral anterior cingulate [13], left anterior cingulate cortex [14] and right middle frontal gyrus, right middle and posterior cingulate gyrus, and right inferior parietal lobe [4]. Our work focused on the medial temporal lobe (MTL).
The relevance of MTL [15] in the context of education can be emphasized by the number of recent reviews relating some of its component regions to memory [16], and therefore learning [17]. Indeed, the hippocampus and parahippocampal gyrus, in particular, have been focused on by many researchers at different levels of inquiry, from behavioral, physiological, and morphological angles, employing microscale in vitro investigations [18], in vivo approaches in animals [19,20], structural imaging [21], and postmortem morphological [22] approaches, and functional imaging [23], behavioral [24], and clinical analyses [25].
At the cellular level, few studies have been conducted to clarify the mechanisms involved in literacy and its neurobiological consequences. Jacobs et al. [26] demonstrated that, in cognitively healthy individuals, education exerted an important influence on the dendritic arborization of pyramidal neurons in the regions classically known as Wernicke’s area. It is also known that the greater the number of school years, the greater the performance on verbal memory tests in older adults. Furthermore, education can affect morphometric measures of hippocampal subregions, namely CA1 and the subiculum [27]. However, nothing is known about the influence of education on the number of total cells, neurons, and non-neurons in the MTL, particularly with regard to the hippocampus.
At the macroscopic level, Brayne et al. [28] found that higher education was associated with a greater brain mass and lower risk of dementia. Along the same lines, Ferretti-Rebustini et al. [29] also demonstrated that higher education was associated with greater brain mass. More specifically, in individuals with a higher education, the decline in hippocampal volume is less pronounced with age than in individuals with low education [30]. An older adult cohort study evaluated how higher education can maintain cognitive functions over a 10-year period. Preservation of cognitive function was shown to be associated with a greater volume of MTL at baseline when compared to older people with cognitive decline [31].
Education can also exert direct and indirect influences on CR, particularly in regions that are more vulnerable to dementia and Alzheimer’s disease [10]. It is believed that education can protect the brain or provide resilience against dementia [7,28]. In older people with a higher educational level with Alzheimer’s dementia, better cognitive performance was observed when compared to those with Alzheimer’s dementia and less education [32,33], suggesting an important role of education in maintaining brain function, even when there is a pathological process installed [34].
Formal education consists of the systematic transmission and acquisition of new information and skills for better social and intellectual performance, as well as professional training. It encompasses the continuous improvement of various cognitive functions such as memory, language, reasoning, and socioemotional skills, and usually takes place throughout childhood and early adulthood, when the brain is more plastic [35]. Therefore, it is conceivable that this intensive acquisition of new functions and skills leads to changes and adaptations in several brain areas [36].
The level of education can affect an individual’s general health, as more educated people can generally have higher life expectancy compared to less educated subjects [37,38,39]. Recent discoveries using neuroimaging techniques and neuropsychological assessments shed light on the ability of the human brain to acquire new cultural skills, such as reading and arithmetic [40]. Consistent evidence indicates that reading acquisition is a milestone in an individual’s development and is accompanied by structural changes in the brain both of individuals who learned to read during childhood [41] and in those who learned to read later, during adulthood [42].
Not only high educational levels promote better performance of executive functions; even a few years of schooling are enough to promote changes in brain structure and function [43,44,45]. Individuals with an average of four years of formal education, compared to illiterate individuals, had higher fractional anisotropy values in the upper right longitudinal fasciculus, suggesting that even a few years of formal education can contribute not only to CR, but also to greater integrity of the white matter microstructure in regions that connect the visual cortex with language areas [46].
As mentioned before, we tackled this issue by focusing on MTL, due to its role in memory, and therefore learning and education. To the best of our knowledge, no study has compared the MTL mass between individuals with high and low education. In addition, no study has evaluated whether education affects the absolute cellular composition of the brain, especially the MTL.
The donation and storage of post-mortem brain tissue in brain banks have driven research towards many discoveries, improving our understanding of the functioning of the nervous system [47,48,49]. Post-mortem brain studies can be performed employing stereological analyses on histologically processed tissues [50,51,52,53] or by using the Isotropic Fractionator (IF) method [54,55,56]. IF is a technique created by Herculano-Houzel and Lent [57], which consists of transforming anisotropic brain tissue into an isotropic suspension of nuclei, labeled with specific and universal molecular markers. Investigation in post-mortem human brains using IF has made important discoveries and has broken some dogmas [58]. Building on these previous works, the objective of this study was to investigate the influence of education on the absolute quantitative cellular composition of the MTL (comprising the hippocampus, amygdala, and parahippocampal gyrus) of men without cognitive impairment. The work addresses the possible influence of cultural variables on the cellular underpinnings of CR.

2. Materials and Methods

2.1. Subjects

This is a cross-sectional study approved by the Research Ethics Committees of the D’Or Institute and of the University of São Paulo Medical School. A total of 26 individuals were studied, with brains provided by the Biobank for Aging Studies of the University of São Paulo, Brazil. Once the primary relatives or caregivers agreed to donate the brains, an informed consent form was signed.

2.2. Inclusion and Exclusion Criteria

The subjects included in the study were middle-aged men between 47 and 64 years, with low formal education (0–4 school years) or high formal education (≥8 school years). Cases with intermediate periods of schooling (5–7 years) were not included. Choice of these ages was made to guarantee the interruption of formal education in youth, and to avoid the incidence of cognitive decline in the elderly. The informant needed to be capable of adequately responding to the clinical interview. Cases with congenital malformations, disseminated cancer, stroke, drug abuse, alcohol-use disorder, mental illness, and dementia or mild cognitive impairment were excluded. A detailed histopathological examination was used to exclude brains with signs of lesions, neurodegenerative diseases, tumors, hippocampal sclerosis, and other pathologies.
Clinical interviews were carried out with the subjects’ relatives to obtain sociodemographic data, clinical background, and lifestyle habits. Retrospective cognitive and functional assessments were obtained through the Clinical Dementia Rating (CDR) [59] and the Informant Questionnaire on Cognitive Decline in the Elderly (IQCODE) [60]. Only cognitively normal subjects (CDR = 0 and IQCODE ≤ 3.4) were included. Men only, not women, were chosen to avoid sex dimorphisms, and age range was defined to avoid brain changes with aging, as previously shown [61].

2.3. Dissection of the MTL

After brain removal, each left hemisphere was fixed in 2% paraformaldehyde (PFA) for 36–40 h. Then, the material was moved to phosphate-buffered saline (PBS) and maintained in the refrigerator at 4 °C until processing. First, meninges and blood vessels were removed. Subsequently, the medial temporal lobe was dissected employing a standardized anatomical protocol as used by Oliveira-Pinto et al. [61].

2.4. Chemomecanical Dissociation

After dissecting the MTL, the tissue was weighted and sliced into smaller fragments. Then, a chemomechanical dissociation was performed, which consisted of tissue maceration in a dissociation solution composed of 40 mM sodium citrate and 1% Triton X-100. The tissue fragments were placed in potters coupled with a semiautomatic machine with moving pistons controlled for translation and rotation with the help of a computer [62]. This procedure made it possible to rupture the plasma membrane of the cells, leaving the nuclear membrane intact, thus allowing the subsequent counting of the cell nuclei. In the nervous tissue, since every cell contains only one nucleus, and the nuclear suspension is isotropic, the number found is an accurate estimate of the total number of MTL cells [57,63].

2.5. Immunocytochemistry

To quantify the total number of cells, all nuclei were labeled with 2% DAPI (4′-6-diamino-2-phenylindol dihydrochloride), a DNA-specific marker. Aliquots of 10 µL from the isotropic suspension were collected and placed in a hemocytometer (Neubauer chamber) for counting using a fluorescence microscope (Zeiss AxioImage). When the counts varied by more than 15%, the suspension was not considered isotropic and further fractionation was performed before resuming counting. Only intact, DAPI-stained nuclei, or those slightly damaged by fractionation, were counted.
Once the number of total cells was obtained, a 1000 µL aliquot of the nuclei suspension was employed for immunocytochemical labeling of the NeuN protein. Samples were subsequently centrifuged for five minutes, washed three times with 1000 µL PBS, and incubated overnight in primary anti-NeuN IgG antibody produced in mice (1:200, 200 µL of PBS). The next day, the samples were centrifuged for five minutes and washed three times with PBS, incubated for two hours in Alexa Fluor 546 secondary antibody (1:300, 260 µL of PBS, 30 µL of normal goat serum, and 10 µL of DAPI), centrifuged for five minutes, washed three times with PBS, and resuspended in PBS. Then, 10 µL aliquots were removed from the samples and placed in the Neubauer chamber for counting under the fluorescence microscope (Zeiss AxioImage).
Of a total of 500 DAPI-stained cell nuclei from each sample, those colocalized with NeuN were counted. Therefore, it was possible to obtain the number of total cells (density of DAPI+ nuclei in the suspension multiplied by the total volume of the suspension), the number of neurons (total number of DAPI+ nuclei multiplied by the percentage of those doubly labeled with DAPI and NeuN), and that of non-neuronal cells (estimated by subtraction of the number of neuronal nuclei from the total number of nuclei in the suspension). The entire experimental procedure was blindly conducted regarding the subjects’ data. The MTL mass and number of neurons, number of non-neurons, and total number of cells found in the left hemisphere were multiplied by 2 to represent both hemispheres.

2.6. Statistical Analysis

Statistical analysis was conducted using STATA software (version 13). A significance level of 5% (α = 0.05) was considered. Data for sociodemographic variables were analyzed and presented as mean and standard deviation. For the comparison of brain mass, MTL mass, and number of cells between the high and low education groups, Student’s Independent Test was used. The mean and standard deviation graphs with the number of neurons, non-neuronal cells, and total cells were generated in GraphPad Prism, version 5.0.
Unadjusted linear regression tests were used to investigate the association between education and the absolute cellular composition of MTL. Subsequently, we adjusted these models for age, height, and Braak stage for neurofibrillary distribution to account for possible confounding factors. We also performed unadjusted linear regression tests to investigate the association of education as a continuous measure, and the absolute cellular composition of MTL.

3. Results

3.1. Clinical and Demographic Data of Participants with Collected Brain Samples

The sociodemographic data of the subjects are shown in Table 1. The mean age of the sample was 56.7 (±5.6) years, ranging from 47 to 64 years. The mean educational level was 7.5 (±4.5) years, ranging from 0 to 16 years. All 26 subjects scored 0 on the CDR and 3 on the IQCODE.

3.2. Brain Mass and MTL Mass Related to High and Low Educational Level

The average brain mass in the high education group was 1386 g (±168.1), compared to 1267 g (±91.5) in the low education group. Student’s Independent Test showed that, on average, the groups with high and low education present a significant difference in brain mass (p = 0.044; Figure 1A).
The average MTL mass in the high education group was 31.35 g (±3.9), similar to the low education subjects with 32.10 g (±5.5). Student’s Independent Test showed that, on average, the groups with high and low education did not show a significant difference in MTL mass (p = 0.81; Figure 1B).

3.3. Cell Numbers (Neurons, Non-Neurons, and All Cells) Related to High and Low Educational Level

The average total number of MTL cells in the high education group was 1.93 (±0.28) billion, compared with 1.95 (±0.33) billion among the low education subjects. Student’s Independent Test showed that the two groups did not present a significant difference in the total number of cells (p = 0.93; Figure 2).
The average number of neurons in the high education group was 0.30 (±0.17) billion, against 0.39 (±0.23) billion neurons in the low education group. Student’s Independent Test showed that the groups with high and low schooling did not show a significant difference in number of neurons (p = 0.69; Figure 2).
The average number of non-neurons among high education subjects was 1.63 (±0.37) billion cells, compared with an average of 1.56 (±0.33) billion cells in the low education group. Student’s Independent Test showed that there was no significant difference in number of non-neurons cells (p = 0.31; Figure 2).
Unadjusted linear regression tests showed that education was not associated with the total number of cells, the number of neurons, nor the number of non-neurons, indicating that the cellularity of MTL was not affected by educational level (Table 2). Additionally, unadjusted linear regression tests taking education as a continuous measure showed it was not associated with the same variables, confirming that cellularity of MTL was not affected by schooling (Table 3).

4. Discussion

This work has investigated the possible impact of education on the microscale structure of the MTL in humans. Despite reduced brain mass in subjects with low education, no difference was found in neuronal and non-neuronal absolute numbers within the MTL in relation to education.
Several studies have addressed the importance of education for cognitive function [11,31] and its ability to promote changes in brain structure [10,37,42,64,65,66]. However, the mechanisms involved in these changes and their potential benefits still need to be better clarified. It is conceivable, therefore, that education may be an environmental factor that contributes to cognitive and brain reserve, explainable by either the size of the brain, the number of neurons, or the number of synapses [2]. Our work confirms the findings of two previous studies that observed a greater brain mass in individuals with high education than in those with low education [28,29]. Mass and volume measured for the whole brain, however, may conceal localized changes, and do not reveal which microscale elements are responsible for the identified alterations: Neurons? Glial cells? Synapses? Where exactly in the whole brain? Furthermore, if higher educational levels do improve or protect the brain, would the opposite contribute to cognitive decline? How resilient are brain units—neurons, especially—in relation to low education?
The results of the present study suggest that the level of education is not capable of causing changes in the absolute cellularity of the MTL, indicating that cells in this brain region are resilient to this environmental influence. Using the isotropic fractionator, we verified that education did not affect the number of total cells, neurons, and non-neuronal cells in the MTL of cognitively healthy men. Thus, the absolute cellular composition of MTL may not be involved with the structural changes observed in magnetic resonance imaging studies [10,65,67]. Furthermore, the number of cells in the MTL also does not explain the better cognitive performance observed in individuals with high education [11,31,68].
The absence of a significant association between education and the absolute cellular composition of the MTL can also be explained by the operational definition of high and low education used in this study, since the low level (i.e., four years of schooling or less) could already be enough to explain the resilience of cellularity to change as a result of this variable [43,44,45]. Another possible explanation for the absence of associations between cell counts and education is the small sample size, mainly for associations between education and the total cells and neurons. The post hoc power results for these cells were 43% and 29%, raising the possibility of a type 2 error. On the other hand, the estimated power was 78% for non-neurons, indicating that the absence of association between these cells and education is quite acceptable.
The study by Resende et al. [44] in a sample of older adults with four years of formal education demonstrated a strong association between left hippocampal volume and episodic memory scores. Therefore, even very few years of study might be enough to modify the brain’s macrostructure and thus build CR. However, as shown by the present data, this increase in volume would not be explained by an increase in cellularity.
Consequently, other mechanisms may underpin the benefits caused by education, namely angiogenesis, synaptogenesis, or myelination [2], and not necessarily neurogenesis or programmed cell death, phenomena that impact cellularity. Recently, there has been much debate about the existence of neurogenesis in adulthood, and there is still controversy as to whether the production of new neurons indeed takes place in the adult human brain [69] or not [70]. Neuronal recycling, a phenomenon that has been proposed for promoting changes in brain functionality, may explain why education did not affect cell composition in the MTL. This term can be defined as neuronal connections that compose existing networks, recycled to be used in a different function. It is a form of neuroplasticity that occurs when the individual acquires some cultural or cognitive skills; for example, reading [64].
Before reading acquisition, this neural network responds to images of faces and objects, but with literacy, it starts to respond to images of letters and words [64,65], while the homologous region located in the right hemisphere continues to respond to faces [42]. Conceivably, the neuronal recycling taking place in the visual word form area (VWFA) could also occur in other brain areas, thus reorganizing preexisting circuits, which will be led to play new roles.
As well as reading, mathematics is also an important culturally acquired cognitive skill. Functional magnetic resonance imaging studies have found that some regions of the MTL participate in numerical learning [71,72,73]. Other studies have demonstrated the electrophysiological activity of neurons in the MTL during the performance of a calculation task through microelectrodes implanted in the nervous tissue [74,75]. Therefore, it is conceivable that neuronal recycling also takes place in the MTL, more precisely in the hippocampus, just as it does in the VWFA.
In addition to the neuronal recycling discussed above, white matter properties are also associated with reading ability [76]. Synaptogenesis and myelination are two mechanisms involved in plasticity and learning processes [77]. Studies have revealed changes in the white matter of the human brain after learning [45,76,77]. Indeed, differences were observed in the fractional anisotropy of the left inferior and arcuate longitudinal fascicles of children with above-average reading ability compared to children with below-average reading ability [76]. In addition to children, older individuals with an average of four years of formal education, when compared to illiterate individuals, had higher fractional anisotropy values in the right upper longitudinal fasciculus [45]. Several histological features can affect white matter anisotropy, including the number and thickness of axons [78]. In addition, it could be explained by the myelination of nerve bundles [79]. As a result, the difference observed in cognitive performance between more educated and less educated individuals [68], and between illiterate individuals and those with low education [45], may be due to a difference in the conduction speed of neural circuits, as this property optimizes the transmission time of information conveyed by the neural circuits [77].
Our study has some limitations. Among them, we can mention the inclusion and exclusion criteria, which may restrict the interpretation of our results to a limited group of individuals (men without cognitive impairment, age range between 47 and 64 years). Furthermore, as this is a cross-sectional study, we cannot establish a causal relationship between education and cell numbers. The participant’s nutritional status and the practice of leisure and physical activities are other factors that can interfere with cognitive function and brain structure. However, the difficulty obtaining detailed information about these variables in our cohort did not allow the acquisition of these data. The absolute cell composition was evaluated in the MTL as a whole, a region that houses the amygdala, the parahippocampal gyrus, and the hippocampus, but it is known that one of the structures that can benefit from education is the hippocampus [30], and within it, the dentate gyrus, which presents neurogenesis well demonstrated in adulthood. However, the difficulty of performing standardized dissection protocols of these subregions made it impossible to analyze them specifically with our technique. Therefore, it could be the case that although there is a difference in the number of cells in the hippocampus, other structures—for example, the amygdala or the cortex of the parahippocampal gyrus—may have masked the effect. Another limitation is the small sample size, as mentioned above. Post hoc power calculations ranged from 29% to 78%, with estimated sample sizes from 28 to 128 individuals. However, it is important to note that these numbers are very difficult to reach for human brains in optimal conditions to undergo counts with the isotropic fractionator.
Despite the above limitations, this is the first study that evaluated the influence of education on the number of total cells, neurons, and non-neuronal cells in the MTL of individuals without cognitive impairment. These factors are already well defined for promoting benefits to brain health, albeit with poorly understood mechanisms.

5. Conclusions

In conclusion, the results of this study suggest that the number of total cells, the number of neurons, and the number of non-neuronal cells in the MTL of men without cognitive impairment are not affected by educational level. The absence of association in our study may indicate that brain cellularity is resilient to these environmental factors. This finding may have an appreciable evolutionary significance, hindering the erosion of neural hardware, which is, moreover, irreplaceable during life due to the lack of appreciable cell proliferation in most neural regions. Therefore, we conclude that the differences observed in the brains of illiterate and literate individuals, as revealed by the various studies mentioned above, cannot be explained by alterations in the cellularity of the MTL, which proved resilient to this type of environmental influence.

Author Contributions

Experimental design: V.A.C.d.M., R.L. and R.N.; Data acquisition: V.A.C.d.M., A.V.d.O.-P., A.F.M.N., J.S.F., M.M.d.S., C.K.S. and R.P.L.; Data analysis: V.A.C.d.M., R.L. and C.K.S.; Supervision and lab facilities: R.L., C.K.S., P.C., R.P.L., L.T.G., W.J.-F. and C.P.; Manuscript writing: V.A.C.d.M., C.K.S. and R.L. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by the Brazilian National Council for Scientific and Technological Development (CNPq Grants to “National Institutes of Science and Technology”, Proc. 465346/2014-6; Science Productivity Fellowship, Proc. 303883-2021-9), and Rio de Janeiro Foundation for the Support of Science (FAPERJ APq1 E-26/010.000080/2018).

Institutional Review Board Statement

The study was conducted in accordance with the Declaration of Helsinki and approved by the Ethics Committee of INSTITUTO D’OR DE PESQUISA E ENSINO (protocol code 2.150.107 (IDOR), 30/06/2017 (IDOR)) and the Ethics Committee of FACULDADE DE MEDICINA, UNIVERSIDADE DE SÃO PAULO (protocol code 2.186.086 (FMUSP), 25/07/2017 (FMUSP)).

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

Data are available from the first author upon justified request.

Acknowledgments

We thank Emily Castro-Fonseca for her intellectual and experimental insights on this work. We also thank Camila Lopes, Carolina Batista, and Jessica Cavalheiro for their technical support.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Stern, Y. What is cognitive reserve? Theory and research application of the reserve concept. J. Int. Neuropsychol. Soc. 2002, 8, 448–460. [Google Scholar] [CrossRef] [PubMed]
  2. Stern, Y. Cognitive reserve. Neuropsychologia 2009, 47, 2015–2028. [Google Scholar] [CrossRef] [PubMed]
  3. Stern, Y. Cognitive reserve: Implications for assessment and intervention. Folia Phoniat. Logopaed. 2013, 65, 49–54. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  4. Boller, B.; Mellah, S.; Ducharme-Laliberté, 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, 304–317. [Google Scholar] [CrossRef] [PubMed]
  5. Stern, Y. The concept of cognitive reserve: A catalyst for research. J. Clin. Exp. Neuropsychol. 2003, 25, 589–593. [Google Scholar] [CrossRef]
  6. Stern, Y. Cognitive reserve and Alzheimer disease. Alzheimer Dis. Assoc. Disord. 2006, 20, S69–S74. [Google Scholar] [CrossRef]
  7. Stern, Y. Cognitive reserve in ageing and Alzheimer’s disease. Lancet Neurol. 2012, 11, 1006–1012. [Google Scholar] [CrossRef] [Green Version]
  8. Stern, Y.; Arenaza-Urquijo, E.M.; Bartrés-Faz, D.; Belleville, S.; Cantilon, M.; Chetelat, G.; Ewers, M.; Franzmeier, N.; Kempermann, G.; Kremen, W.S.; et al. Whitepaper: Defining and investigating cognitive reserve, brain reserve, and brain maintenance. Alzheimer’s Dement. 2018, 16, 1305–1311. [Google Scholar] [CrossRef]
  9. Suemoto, C.K.; Bertola, L.; Grinberg, L.T.; Leite, R.E.; Rodriguez, R.D.; Santana, P.H.; Pasqualucci, C.A.; Jacob-Filho, W.; Nitrini, R. Education, but not occupation, is associated with cognitive impairment: The role of cognitive reserve in a sample from a low-to-middle-income study. Alzheimer’s Dement. 2022, 18, 2079–2087. [Google Scholar] [CrossRef]
  10. Kim, J.P.; Seo, S.W.; Shin, H.Y.; Ye, B.S.; Yang, J.J.; Kim, C.; Kang, M.; Jeon, S.; Kim, H.J.; Lee, J.M.; et al. Effects of education on aging-related cortical thinning among cognitively normal individuals. Neurology 2015, 85, 806–812. [Google Scholar] [CrossRef]
  11. Foubert-Samier, A.; Catheline, G.; Amieva, H.; Dilharreguy, B.; Helmer, C.; Allard, M.; Dartigues, J.F. Education, occupation, leisure activities, and brain reserve: A population-based study. Neurobiol. Aging 2012, 33, 423.e15–423.e25. [Google Scholar] [CrossRef] [PubMed]
  12. Liu, Y.; Julkunen, V.; Paajanen, T.; Westman, E.; Wahlund, L.O.; Aitken, A.; Sobow, T.; Mecocci, P.; Tsolaki, M.; Vellas, B.; et al. Education increases reserve against Alzheimer’s disease—Evidence from structural MRI analysis. Neuroradiology 2012, 54, 929–938. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  13. Arenaza-Urquijo, E.M.; Landeau, B.; La Joie, R.; Mevel, K.; Mézenge, F.; Perrotin, A.; Desgranges, B.; Bartrés-Faz, D.; Eustache, F.; Chételat, G. Relationships between years of education and gray matter volume, metabolism and functional connectivity in healthy elders. Neuroimage 2013, 83, 450–457. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  14. Rzezak, P.; Squarzoni, P.; Duran, F.L.; de Toledo Ferraz Alves, T.; Tamashiro-Duran, J.; Bottino, C.M.; Ribeiz, S.; Lotufo, P.A.; Menezes, P.R.; Scazufca, M.; et al. Relationship between brain age-related reduction in gray matter and educational attainment. PLoS ONE 2015, 10, e0140945. [Google Scholar] [CrossRef]
  15. Squire, L.R.; Stark, C.E.; Clark, R.E. The medial temporal lobe. Ann. Rev. Neurosci. 2004, 27, 279–306. [Google Scholar] [CrossRef] [Green Version]
  16. Carlesimo, G.A. The temporal lobes and memory. Handb. Clin. Neurol. 2022, 187, 319–337. [Google Scholar]
  17. Zeithamova, D.; Mack, M.L.; Braunlich, K.; Davis, T.; Seger, C.A.; van Kersteren, M.T.R.; Wutz, A. Brain mechanisms of concept learning. J. Neurosci. 2019, 39, 8259–8266. [Google Scholar] [CrossRef] [Green Version]
  18. Stepan, J.; Dine, J.; Eder, M. Functional optical probing of the hippocampal trisynaptic circuit in vitro: Network dynamics, filter properties, and polysynaptic induction of CA1 LTP. Front. Neurosci. 2015, 9, 160. [Google Scholar] [CrossRef] [Green Version]
  19. Cheyne, J.E.; Montgomery, J.M. The cellular and molecular basis of in vivo synaptic plasticity in rodents. Am. J. Physiol. Cell Physiol. 2020, 318, C1264–C1283. [Google Scholar] [CrossRef]
  20. Ko, S.Y.; Frankland, P.W. Neurogenesis-dependent transformation of hippocampal engrams. Neurosci. Lett. 2021, 762, 136176. [Google Scholar] [CrossRef]
  21. Raine, P.J.; Rao, H. Volume, density, and thickness brain abnormalities in mild cognitive impairment: And ALE meta-analysis controlling for age and education. Brain Imaging Behav. 2022, 16, 2335–2352. [Google Scholar] [CrossRef] [PubMed]
  22. Kok, F.K.; van Leerdam, S.L.; de Lange, E.C.M. Potential mechanisms underlying resistance to dementia in non-demented individuals with Alzheimer’s disease neuropathology. J. Alzheimer’s Dis. 2022, 87, 51–81. [Google Scholar] [CrossRef] [PubMed]
  23. McDonough, I.M.; Festini, S.B.; Wood, M.M. Risk for Alzheimer’s disease: A review of long-term episodic memory encoding and retrieval fMRI studies. Ageing Res. Rev. 2020, 62, 101133. [Google Scholar] [CrossRef] [PubMed]
  24. Pollmann, S.; Schneider, W.X. Working memory and active sampling of the environment: Medial temporal contributions. Handb. Clin. Neurol. 2022, 187, 339–357. [Google Scholar] [PubMed]
  25. Hengstschläger, A.; Sommerland, A.; Huntley, J. What are the neural correlates of impaired awareness of social cognition and function in dementia? A systematic review. Brain Sci. 2022, 12, 1136. [Google Scholar] [CrossRef]
  26. Jacobs, B.; Schall, M.; Scheibel, A.B. A quantitative dendritic analysis of Wernicke’s area in humans. II. Gender, hemispheric, and environmental factors. J. Comp. Neurol. 1993, 327, 97–111. [Google Scholar] [CrossRef]
  27. Tang, X.; Varma, V.R.; Miller, M.I.; Carlson, M.C. Education is associated with sub-regions of the hippocampus and the amygdala vulnerable to neuropathologies of Alzheimer’s disease. Brain Struct. Funct. 2017, 222, 1469–1479. [Google Scholar] [CrossRef] [Green Version]
  28. Brayne, C.; Ince, P.G.; Keage, H.A.; McKeith, I.G.; Matthews, F.E.; Polvikoski, T.; Sulkava, R. Education, the brain and dementia: Neuroprotection or compensation? EClipSE Collaborative Members. Brain 2010, 133, 2210–2216. [Google Scholar] [CrossRef] [Green Version]
  29. Ferretti-Rebustini, R.E.D.L.; Jacob-Filho, W.; Suemoto, C.K.; Farfel, J.M.; Leite, R.E.P.; Grinberg, L.T.; Pasqualucci, C.A.; Nitrini, R. Factors associated with morphometric brain changes in cognitively normal aging. Dement. Neuropsychol. 2015, 9, 103–109. [Google Scholar] [CrossRef] [Green Version]
  30. Noble, K.G.; Grieve, S.M.; Korgaonkar, M.S.; Engelhardt, L.E.; Griffith, E.Y.; Williams, L.M.; Brickman, A.M. Hippocampal volume varies with educational attainment across the lifespan. Front. Hum. Neurosci. 2012, 6, 307. [Google Scholar] [CrossRef] [Green Version]
  31. Rosano, C.; Aizenstein, H.J.; Newman, A.B.; Venkatraman, V.; Harris, T.; Ding, J.; Satterfield, S.; Yaffe, K. Neuroimaging differences between older adults with maintained versus declining cognition over a 10-year period. Neuroimage 2012, 62, 307–313. [Google Scholar] [CrossRef] [PubMed]
  32. Perneczky, R.; Wagenpfeil, S.; Lunetta, K.L.; Cupples, L.A.; Green, R.C.; DeCarli, C.; Farrer, L.A.; Kurz, A. Education attenuates the effect of medial temporal lobe atrophy on cognitive function in Alzheimer’s disease: The MIRAGE study. J. Alzheimer’s Dis. 2009, 17, 855–862. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  33. Nitrini, R.; Bottino, C.M.; Albala, C.; Capuñay, N.S.C.; Ketzoian, C.; Rodriguez, J.J.L.; Maestre, G.E.; Ramos-Cerqueira, A.T.A.; Caramelli, P. Prevalence of dementia in Latin America: A collaborative study of population-based cohorts. Int. Psychogeriatr. 2009, 21, 622–630. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  34. Caramelli, P.; Poissant, A.; Gauthier, S.; Bellavance, A.; Gauvreau, D.; Lecours, A.R.; Joanette, Y. Educational level and neuropsychological heterogeneity in dementia of the Alzheimer type. Alzheimer Dis. Assoc. Disord. 1997, 11, 9–15. [Google Scholar] [CrossRef] [PubMed]
  35. Kim, J.; Chey, J.; Kim, S.E.; Kim, H. The effect of education on regional brain metabolism and its functional connectivity in an aged population utilizing positron emission tomography. Neurosci. Res. 2015, 94, 50–61. [Google Scholar] [CrossRef] [PubMed]
  36. López-Barroso, D.; de Schotten, M.T.; Morais, J.; Kolinsky, R.; Braga, L.W.; Guerreiro-Tauil, A.; Dehaene, S.; Cohen, L. Impact of literacy on the functional connectivity of vision and language related networks. NeuroImage 2020, 213, 116722. [Google Scholar] [CrossRef]
  37. Lager, A.C.J.; Torssander, J. Causal effect of education on mortality in a quasi-experiment on 1.2 million Swedes. Proc. Natl. Acad. Sci. USA 2012, 109, 8461–8466. [Google Scholar] [CrossRef] [Green Version]
  38. Clark, D.; Royer, H. The effect of education on adult mortality and health: Evidence from Britain. Am. Econ. Rev. 2013, 103, 2087–2120. [Google Scholar] [CrossRef] [Green Version]
  39. Davies, N.M.; Dickson, M.; Smith, G.D.; Van Den Berg, G.J.; Windmeijer, F. The causal effects of education on health outcomes in the UK Biobank. Nat. Hum. Behav. 2018, 2, 117–125. [Google Scholar] [CrossRef]
  40. Dehaene, S. Evolution of human cortical circuits for reading and arithmetic: The “neuronal recycling” hypothesis. In From Monkey Brain to Human Nrain: A Fyssen Foundation Symposium; MIT Press: Cambridge, MA, USA, 2005; pp. 133–157. [Google Scholar]
  41. Dehaene-Lambertz, G.; Monzalvo, K.; Dehaene, S. The emergence of the visual word form: Longitudinal evolution of category-specific ventral visual areas during reading acquisition. PLoS Biol. 2018, 16, e2004103. [Google Scholar] [CrossRef] [Green Version]
  42. Dehaene, S.; Cohen, L.; Morais, J.; Kolinsky, R. Illiterate to literate: Behavioural and cerebral changes induced by reading acquisition. Nat. Rev. Neurosci. 2015, 16, 234–244. [Google Scholar] [CrossRef] [PubMed]
  43. Farfel, J.M.; Nitrini, R.; Suemoto, C.K.; Grinberg, L.T.; Ferretti, R.E.L.; Leite, R.E.P.; Tampellini, E.; Lima, L.; Farias, D.S.; Neves, R.C.; et al. Very low levels of education and cognitive reserve: A clincopathological study. Neurology 2013, 81, 650–657. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  44. Resende, E.D.P.F.; Rosen, H.J.; Chiang, K.; Staffaroni, A.M.; Allen, I.; Grinberg, L.T.; Carmona, K.C.; Guimarães, H.C.; Carvalho, V.A.; Barbosa, M.T.; et al. Primary school education may be sufficient to moderate a memory-hippocampal relationship. Front. Aging Neurosci. 2018, 10, 381. [Google Scholar] [CrossRef] [PubMed]
  45. Resende, E.D.P.F.; Tovar-Moll, F.F.; Ferreira, F.M.; Bramati, I.; Souza, L.C.D.; Carmona, K.C.; Guimarães, H.C.; Carvalho, V.A.; Barbosa, M.T.; Caramelli, P. White matter microstructure in illiterate and low-literate elderly Brazilians: Preliminary findings. Cognit. Behav. Neurol. 2018, 31, 193–200. [Google Scholar] [CrossRef] [PubMed]
  46. Resende, E.D.P.F.; Tovar-Moll, F.F.; Ferreira, F.M.; Bramati, I.; Souza, L.C.D.; Carmona, K.C.; Guimarães, H.C.; Carvalho, V.A.; Barbosa, M.T.; Caramelli, P. Integrity of white matter structure is related to episodic memory performance in the low-educated elderly. Arq. Neuro-Psiquiatr. 2017, 75, 778–784. [Google Scholar] [CrossRef] [Green Version]
  47. Shankar, S.K.; Mahadevan, A. Brain banking in India: Relevance in current day practice. Indian J. Pathol. Microbiol. 2022, 65, 218–225. [Google Scholar]
  48. Nicoll, J.A.R.; Bloom, T.; Clarke, A.; Boche, D.; Hilton, D. BRAIN UK: Accessing NHS tissue archives for neuroscience research. Neuropathol. Appl. Neurobiol. 2022, 48, e12766. [Google Scholar] [CrossRef]
  49. Anestakis, D.; Petanidis, S.; Salifoglou, A.; Tsolaki, M. Clinicopathological guidelines for the management of brain and tissue banking in Greece. Res. Neurol. 2014, 2014, 121634. [Google Scholar]
  50. Pakkenberg, B.; Pelvig, D.; Marner, L.; Bundgaard, M.J.; Gundersen, H.J.G.; Nyengaard, J.R.; Regeur, L. Aging and the human neocortex. Exp. Gerontol. 2003, 38, 95–99. [Google Scholar] [CrossRef]
  51. Joelving, F.C.; Billeskov, R.; Christensen, J.R.; West, M.; Pakkenberg, B. Hippocampal neuron and glial cell numbers in Parkinson’s disease—A stereological study. Hippocampus 2006, 16, 826–833. [Google Scholar] [CrossRef]
  52. Kjær, M.; Fabricius, K.; Sigaard, R.K.; Pakkenberg, B. Neocortical development in brain of young children—A stereological study. Cereb. Cortex 2017, 27, 5477–5484. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  53. Perry, J.C.; Pakkenberg, B.; Vann, S.D. Striking reduction in neurons and glial cells in anterior thalamic nuclei of older patients with Down syndrome. Neurobiol. Aging 2019, 75, 54–61. [Google Scholar] [CrossRef] [PubMed]
  54. Azevedo, F.A.; Carvalho, L.R.; Grinberg, L.T.; Farfel, J.M.; Ferretti, R.E.; Leite, R.E.; Jacob Filho, W.; Lent, R.; Herculano-Houzel, S. Equal numbers of neuronal and nonneuronal cells make the human brain an isometrically scaled-up primate brain. J. Comp. Neurol. 2009, 513, 532–541. [Google Scholar] [CrossRef] [PubMed]
  55. Andrade-Moraes, C.H.; Oliveira-Pinto, A.V.; Castro-Fonseca, E.; da Silva, C.G.; Guimaraes, D.M.; Szczupak, D.; Parente-Bruno, D.R.; Carvalho, L.R.; Polichiso, L.; Gomes, B.V.; et al. Cell number changes in Alzheimer’s disease relate to dementia, not to plaques and tangles. Brain 2013, 136, 3738–3752. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  56. Oliveira-Pinto, A.V.; Santos, R.M.; Coutinho, R.A.; Oliveira, L.M.; Santos, G.B.; Alho, A.T.; Leite, R.E.; Farfel, J.M.; Suemoto, C.K.; Grinberg, L.T.; et al. Sexual dimorphism in the human olfactory bulb: Females have more neurons and glial cells than males. PLoS ONE 2014, 9, e111733. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  57. Herculano-Houzel, S.; Lent, R. Isotropic fractionator: A simple, rapid method for the quantification of total cell and neuron numbers in the brain. J. Neurosci. 2005, 25, 2518–2521. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  58. Lent, R.; Azevedo, F.A.; Andrade-Moraes, C.H.; Pinto, A.V. How many neurons do you have? Some dogmas of quantitative neuroscience under revision. Eur. J. Neurosci. 2012, 35, 1–9. [Google Scholar] [CrossRef]
  59. Morris, J.C. Current vision and scoring rules the clinical dementia rating (CDR). Neurology 1993, 43, 2412–2414. [Google Scholar] [CrossRef]
  60. Jorm, A.F.; Jacomb, P.A. The Informant Questionnaire on Cognitive Decline in the Elderly (IQCODE): Socio-demographic correlates, reliability, validity and some norms. Psychol. Med. 1989, 19, 1015–1022. [Google Scholar] [CrossRef]
  61. Oliveira-Pinto, A.V.; Andrade-Moraes, C.H.; Oliveira, L.M.; Parente-Bruno, D.R.; Santos, R.M.; Coutinho, R.A.; Alho, A.T.; Leite, R.E.; Suemoto, C.K.; Grinberg, L.T.; et al. Do age and sex impact on the absolute cell numbers of human brain regions? Brain Struct. Func. 2016, 221, 3547–3559. [Google Scholar] [CrossRef]
  62. Azevedo, F.A.; Andrade-Moraes, C.H.; Curado, M.R.; Oliveira-Pinto, A.V.; Guimarães, D.M.; Szczupak, D.; Gomes, B.V.; Alho, A.T.L.; Polichiso, L.; Tampellini, E.; et al. Automatic isotropic fractionation for large-scale quantitative cell analysis of nervous tissue. J. Neurosci. Meth. 2013, 212, 72–78. [Google Scholar] [CrossRef] [PubMed]
  63. Neves, K.; Guimarães, D.M.; Rayêe, D.; Valerio-Gomes, B.; Iack, P.M.; Lent, R.; Mota, B. The reliability of the isotropic fractionator method for counting total cells and neurons. J. Neurosci. Meth. 2019, 326, 108392. [Google Scholar] [CrossRef] [PubMed]
  64. Dehaene, S.; Cohen, L. Cultural recycling of cortical maps. Neuron 2007, 56, 384–398. [Google Scholar] [CrossRef] [PubMed]
  65. Dehaene, S.; Pegado, F.; Braga, L.W.; Ventura, P.; Filho, G.N.; Jobert, A.; Dehaene-Lambertz, G.; Kolinsky, R.; Morais, J.; Cohen, L. How learning to read changes the cortical networks for vision and language. Science 2010, 330, 1359–1364. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  66. Cachia, A.; Roell, M.; Mangin, J.F.; Sun, Z.Y.; Jobert, A.; Braga, L.; Houde, O.; Dehaene, S.; Borst, G. How interindividual differences in brain anatomy shape reading accuracy. Brain Struct. Funct. 2018, 223, 701–712. [Google Scholar] [CrossRef] [PubMed]
  67. Dehaene, S.; Dehaene-Lambertz, G. Is the brain prewired for letters? Nat. Neurosci. 2016, 19, 1192–1193. [Google Scholar] [CrossRef]
  68. Bento-Torres, N.V.; Bento-Torres, J.; Tomás, A.M.; Costa, V.O.; Corrêa, P.G.; Costa, C.N.; Jardim, N.Y.; Picanço-Diniz, C.W. Influence of education and age on cognitive performance in healthy older adults. Braz. J. Med. Biol. Res. 2017, 50, e5892. [Google Scholar] [CrossRef] [Green Version]
  69. Boldrini, M.; Fulmore, C.A.; Tartt, A.N.; Simeon, L.R.; Pavlova, I.; Poposka, V.; Rosoklija, G.B.; Stankov, A.; Arango, V.; Dwork, A.J.; et al. Human hippocampal neurogenesis persists throughout aging. Cell Stem Cell 2018, 22, 589–599. [Google Scholar] [CrossRef] [Green Version]
  70. Sorrells, S.F.; Paredes, M.F.; Cebrian-Silla, A.; Sandoval, K.; Qi, D.; Kelley, K.W.; James, D.; Mayer, S.; Chang, J.; Auguste, K.I.; et al. Human hippocampal neurogenesis drops sharply in children to undetectable levels in adults. Nature 2018, 555, 377–381. [Google Scholar] [CrossRef]
  71. Supekar, K.; Swigart, A.G.; Tenison, C.; Jolles, D.D.; Rosenberg-Lee, M.; Fuchs, L.; Menon, V. Neural predictors of individual differences in response to math tutoring in primary-grade school children. Proc. Natl. Acad. Sci. USA 2013, 110, 8230–8235. [Google Scholar] [CrossRef] [Green Version]
  72. Qin, S.; Cho, S.; Chen, T.; Rosenberg-Lee, M.; Geary, D.C.; Menon, V. Hippocampal-neocortical functional reorganization underlies children’s cognitive development. Nat. Neurosci. 2014, 17, 1263–1269. [Google Scholar] [CrossRef] [PubMed]
  73. Menon, V. Memory and cognitive control circuits in mathematical cognition and learning. Prog. Brain Res. 2016, 227, 159–186. [Google Scholar] [PubMed] [Green Version]
  74. Kreiman, G.; Koch, C.; Fried, I. Category-specific visual responses of single neurons in the human medial temporal lobe. Nat. Neurosci. 2000, 3, 946–953. [Google Scholar] [CrossRef] [PubMed]
  75. Kutter, E.F.; Bostroem, J.; Elger, C.E.; Mormann, F.; Nieder, A. Single neurons in the human brain encode numbers. Neuron 2018, 100, 753–761.e4. [Google Scholar] [CrossRef] [Green Version]
  76. Yeatman, J.D.; Dougherty, R.F.; Ben-Shachar, M.; Wandell, B.A. Development of white matter and reading skills. Proc. Natl. Acad. Sci. USA 2012, 109, E3045–E3053. [Google Scholar] [CrossRef] [Green Version]
  77. Fields, R.D. A new mechanism of nervous system plasticity: Activity-dependent myelination. Nat. Rev. Neurosci. 2015, 16, 756–767. [Google Scholar] [CrossRef]
  78. Tallal, P. The science of literacy: From the laboratory to the classroom. Proc. Natl. Acad. Sci. USA 2000, 97, 2402–2404. [Google Scholar] [CrossRef] [Green Version]
  79. Carreiras, M.; Seghier, M.L.; Baquero, S.; Estévez, A.; Lozano, A.; Devlin, J.T.; Price, C.J. An anatomical signature for literacy. Nature 2009, 461, 983–986. [Google Scholar] [CrossRef]
Brainsci 13 00104 g001 550
Figure 1. Brain mass and medial temporal lobe mass of subjects with high and low education. (A). Average brain mass for both groups, showing high education subjects with larger mass than those of low education. Asterisk denotes significant statistical differences between high and low education (p < 0.05). (B). MTL mass of subjects with high and low education. Differences were non-significant. (A,B). Means and standard deviations are indicated by black bars.
Figure 1. Brain mass and medial temporal lobe mass of subjects with high and low education. (A). Average brain mass for both groups, showing high education subjects with larger mass than those of low education. Asterisk denotes significant statistical differences between high and low education (p < 0.05). (B). MTL mass of subjects with high and low education. Differences were non-significant. (A,B). Means and standard deviations are indicated by black bars.
Brainsci 13 00104 g001
Brainsci 13 00104 g002 550
Figure 2. Number of cells of subjects with high and low education in the medial temporal lobe. Differences were non-significant for the number of neurons, number of non-neurons, and total number of cells between high and low education groups. Means and standard deviations are indicated by black bars.
Figure 2. Number of cells of subjects with high and low education in the medial temporal lobe. Differences were non-significant for the number of neurons, number of non-neurons, and total number of cells between high and low education groups. Means and standard deviations are indicated by black bars.
Brainsci 13 00104 g002
Table
Table 1. Demographic and autopsy-related data.
Table 1. Demographic and autopsy-related data.
CaseAge at DeathSchoolingCause of DeathTime from Death to Fixation Cerebral Weight (g)MTL Weight(g)BraakCERADAP Diagnostic
1628Acute myocardial infarction11 h 30 min122031.120Normal
2478Pulmonary edema18 h158239.900Normal
3498Pulmonary Alveolar Hemorrhage16 h 38 min121035.7600Normal
46311Retroperitonial Hemorrhage12 h 10 min117627.610Normal
55612Acute Pulmonary edema11 h 20 min129835.5410Normal
66411Acute myocardial infarction18 h 42 min145332.5410Normal
74716Broncho-pneumonia13 h 30 min177530.8410Normal
85415Pulmonary thromboembolism8 h 01 min131030.6800Normal
94811Acute myocardial infarction16 h 07 min136226.1420Normal
104915Pulmonary edema14 h 59 min149233.100Normal
11618Acute Pulmonary edema9 h 25 min154429.9810Normal
125512Pulmonary thromboembolism18 h 55 min124928.4800Normal
135811Pulmonary infarction12 h 45 min138225.7610Normal
145811Pulmonary thromboembolism14 h 52 min135131.4820Normal
15594Acute renal failure13 h 35 min127143.3620Normal
16584Sepsis15 h 25 min121024.9420Normal
17594Bilateral caseous bronchopneumonia10 h 18 min136033.710Normal
18532Pulmonary tuberculosis13 h 24 min128231.4430Normal
19494Chronic Pneumopathy11 h122131.4610Normal
20554Pulmonary edema12 h 19 min115037.5811Normal
21620Septic shock17 h 57 min120927.2810Normal
22601Acute myocardial infarction9 h 40 min148435.661ANormal
23624Pulmonary edema13 h 38 min123824.620Normal
24624Hemiperitoneum12 h 1 min123228.0800Normal
25644Broncho-pneumonia16 h 25 min120030.6200Normal
26624Acute Pulmonary edema19 h 26 min135236.4430Normal
Table
Table 2. Association between education and absolute cell composition of the medial temporal lobe (n = 26).
Table 2. Association between education and absolute cell composition of the medial temporal lobe (n = 26).
Unadjusted ModelModel 1 * Model 2 **
β (95% CI)pβ (95% CI)pβ (95% CI)p
Neurons0.95 (−0.07; 0.26)0.250.12 (−0.08; 0.32)0.220.12 (−0.09; 0.34)0.23
Non-neurons−0.08 (−0.36; 0.21)0.59−0.17 (−0.51; 0.16)0.30−0.20 (−0.55; 0.15)0.26
Total cells0.20 (−0.23; 0.27)0.87−0.05 (−0.35; 0.24)0.71−0.07 (−0.38; 0.24)0.63
* Model 1: Linear regression models adjusted for age and height. ** Model 2: Linear regression models adjusted for age, height, and Braak stage for neurofibrillary tangles.
Table
Table 3. Association between education as a continuous measure and absolute cell composition of the medial temporal lobe (n = 26).
Table 3. Association between education as a continuous measure and absolute cell composition of the medial temporal lobe (n = 26).
Unadjusted ModelModel 1 * Model 2 **
β (95% CI)pβ (95% CI)pβ (95% CI)p
Neurons−0.003 (−0.022; 0.015)0.70−0.005 (−0.030; 0.020)0.70−0.005 (−0.030; 0.021)0.71
Non-neurons0.005 (−0.027; 0.037)0.740.020 (−0.021; 0.060)0.330.022 (−0.020; 0.0640.29
Total cells0.002 (−0.026; 0.029)0.900.015 (−0.020; 0.050)0.390.018 (−0.019; 0.054)0.33
* Model 1: Linear regression models adjusted for age and height. ** Model 2: Linear regression models adjusted for age, height, and Braak stage for neurofibrillary tangles.
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de Morais, V.A.C.; de Oliveira-Pinto, A.V.; Mello Neto, A.F.; Freitas, J.S.; da Silva, M.M.; Suemoto, C.K.; Leite, R.P.; Grinberg, L.T.; Jacob-Filho, W.; Pasqualucci, C.; et al. Resilience of Neural Cellularity to the Influence of Low Educational Level. Brain Sci. 2023, 13, 104. https://doi.org/10.3390/brainsci13010104

AMA Style

de Morais VAC, de Oliveira-Pinto AV, Mello Neto AF, Freitas JS, da Silva MM, Suemoto CK, Leite RP, Grinberg LT, Jacob-Filho W, Pasqualucci C, et al. Resilience of Neural Cellularity to the Influence of Low Educational Level. Brain Sciences. 2023; 13(1):104. https://doi.org/10.3390/brainsci13010104

Chicago/Turabian Style

de Morais, Viviane A. Carvalho, Ana V. de Oliveira-Pinto, Arthur F. Mello Neto, Jaqueline S. Freitas, Magnólia M. da Silva, Claudia Kimie Suemoto, Renata P. Leite, Lea T. Grinberg, Wilson Jacob-Filho, Carlos Pasqualucci, and et al. 2023. "Resilience of Neural Cellularity to the Influence of Low Educational Level" Brain Sciences 13, no. 1: 104. https://doi.org/10.3390/brainsci13010104

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