People around the world may be smart to change their facial hairstyle, according to new recommendations released by the Centers for Disease Control and Prevention (CDC).
Since the outbreak of the novel coronavirus, also known as COVID-19, face masks and special respirators have become common accessories, protecting against contracting the disease that has infected around 81,000 people worldwide.
But facial hair has now been said to cause a problem in creating the perfect barrier. Health officials have said that hair on the face can keep the exhalation valve on face masks and special respirators from working properly, according to KDVR-TV.
The CDC has released a colourful visual aid to help people who sport beards alter the style to allow their face masks to sit more effectively.
Their infographic displays dozens of styles compatible with masks, including side whiskers, soul patch, handlebar and, of course, clean shaven.
Simply put, hair shouldn’t cross the respirator-sealing surface, or the individual wearing the mask won’t be properly protected from outside germs.
Masks are more commonly worn than respirators, which cover the nose and mouth and should only be worn within workplaces, the CDC warns.
Respiratory viruses, the organization adds, typically happen among close contacts, within six feet.
Coronavirus has continued to spread globally. For the first time on Tuesday, the number of cases counted outside of China was higher than inside the country where it originated.
Worries over the ever-expanding economic fallout of the COVID-19 crisis multiplied, with factories idled, trade routes frozen and tourism crippled, while a growing list of countries braced for the illness to breach their borders.
Even the Tokyo 2020 Olympics, five months away, wasn’t far enough off to keep people from wondering if it would go on as planned.
“We don’t expect a miracle in the short term,” said Kianoush Jahanpour of the Health Ministry in Iran, where an official tally of infections of 139 was doubted by some who thought the problem was far bigger.
More coronavirus cases reported outside China than inside: WHO
© 2020 Global News, a division of Corus Entertainment Inc.
The challenges integrating U=U into HIV care around the world – aidsmap
Advocates from around the world came together at the U=U Global Summit at the 24th International AIDS Conference (AIDS 2022) in Montreal last month to share successes and challenges that continue to hamper full-scale integration of the ‘Undetectable = Untransmittable’ (U=U) message in diverse global contexts.
A central theme was that structural barriers – especially poverty, limited access to treatment and viral load testing, stigma, and widespread inequalities – continue to shape health outcomes. HIV criminalisation is also a formidable barrier in many contexts, and advocates discussed the possible role of U=U in challenging HIV criminal laws.
Judy-Ann Nugent, from the Jamaican Network of Seropositives (JN+), spoke about challenges in the Caribbean, where there has been limited U=U buy-in from healthcare providers and people living with HIV. She emphasised the role of stigma, poverty, weak health systems and low levels of literacy in limiting treatment uptake and adherence.
“Simply put, if people are not fed, paid – have enough money or food – if their basic needs are not met, taking HIV medication will not be a priority for them,” she said.
However, there has been progress, with 70% of all people living with HIV in the region accessing treatment in 2021 and incidence continuing to drop. According to the latest UNAIDS data, 84% of people living with HIV in the Caribbean know their status, 83% are on treatment and 87% are virally suppressed.
To promote more widespread awareness of U=U, Nugent recommended that U=U messaging is embedded in funding agreements with PEPFAR and the Global Fund so that countries are required to take proactive steps to integrate U=U into national programmes in order to receive funding. PEPFAR’s updated country guidance for 2022 does just this, making extensive mention of the need for countries to integrate U=U messaging along the HIV care continuum.
Dr Franco Bova, from the Argentinian organisation Asociación Ciclo Positivo, shared that only 60% of those on treatment are virally suppressed in Latin America, falling far short of the previous 90 and the current 95 targets for viral suppression. It is also one of the regions where HIV incidence has increased since 2020. Bova said poverty and inequality perpetuate new infections and are barriers preventing people living with HIV from achieving viral suppression.
Various approaches have been successful at creating awareness of U=U in the region. In Argentina, activists have worked with community-based organisations, NGOs, universities, and local government to spread the U=U message at large public events, such as Pride, and through social media. Bova spoke about some successful strategies used in other Latin American countries, such as storytelling in Mexico, music videos and concerts in Venezuela and official government campaigns in Brazil. He also highlighted important gaps that make it challenging to speak about U=U at all. For instance, in Peru, the Ministry of Health does not collect any data on viral suppression.
Bova’s organisation is promoting a virtual platform, Indetectable LAC, to bring stakeholders in Latin America and the Caribbean together to share information and to enable better networking in the regions.
The Middle East and North Africa
HIV infections increased by 33% in this region from 2020 to 2021. It is one of only three global regions, along with Latin America, and eastern Europe and central Asia, where HIV is still on the rise. In 2021, only 67% of people living with HIV knew their status, 50% were on treatment and 44% were virally suppressed.
“The Middle East and North Africa is the region where the international HIV community has failed,” stated Arda Karapinar, founder of Red Ribbon Istanbul, Turkey’s leading HIV civil society organisation. He emphasised the distinct contextual challenges in the region. HIV-related stigma, combined with conservative religious attitudes towards sex and limited human rights, present formidable challenges in getting the U=U message out.
However, he also spoke of how passionate local activism can result in change and create awareness. “I know from my own experience in Turkey how sometimes, just one activist from a country or a region, dedicated to creating a change in society for the benefit of all, may be highly sufficient. There are great activists in the region who are defending U=U. They continue to work despite countless risks.”
Karapinar argued that Turkey is uniquely positioned between Europe and the Middle East, and can act as a meeting point and a safe harbour for those hoping to improve HIV outcomes and U=U awareness in the Middle East and North Africa region.
Activist Fungai Murau spoke about the gaps that still exist in U=U awareness, even in the UK. She shared the story of a young woman who had acquired HIV vertically and had never heard about U=U. “Children who acquired HIV vertically in the UK are being transferred from adolescent clinics to adult clinics without being told about U=U,” she said. “Because we are assuming that paediatric doctors should not be talking to young girls about sex. This is not correct. We need to change that. We need to ensure that by the time they transfer to adult clinics, we have closed that gap.”
She advocated for integration across different healthcare services in the UK. “My HIV clinic is my champion, but my GP or my dentist may not know about U=U.”
Criminalisation in the United States
The US is one of the leading countries criminalising people with HIV under laws ranging from non-disclosure to alleged transmission. Convictions under these laws can result in lengthy prison terms and registration as a sex offender.
While some activists have argued that U=U should be used as a basis for decriminalisation, Catherine Hanssens, founder of the Center for HIV Law and Policy, spoke about the potential pitfalls of being overly reliant on U=U when advocating for HIV decriminalisation, particularly because of the structural barriers to achieving viral suppression in the US.
Hanssens emphasised that advocacy on behalf of an individual is very different from advocacy for equitable policy and law reform. While it may certainly be beneficial to show proof of undetectability (and subsequent lack of ability to transmit HIV) in individual cases, there might be unintended negative consequences if advocates call for undetectable status to be codified into laws – especially for the groups most likely to be targeted by HIV criminalisation.
If viral load is a factor in determining whether a person is guilty, it can lead to using a person’s failure to stay in health care or to achieve viral suppression as evidence of guilt. It can also lead policymakers and prosecutors to believe, and argue, that people living with HIV who are not undetectable pose a significant risk of transmission to sexual partners. “Current science makes it clear that HIV is not easy to transmit,” Hanssens said. “And even when transmitted, it is easily survivable with appropriate treatment.”
She argued that efforts to reform HIV criminal laws should be based on whether intent to harm was present or not, and the fact that HIV is a manageable chronic illness with appropriate treatment, not a death sentence.
newsGP – COVID has had 'profound' mental health impact on mothers – RACGP
Many women experiencing mental health issues during lockdowns did not access support from GPs or psychologists, new study findings show.
It is no revelation that the mental health and wellbeing of many people has been impacted by the COVID-19 pandemic.
However, new findings indicate that for women, particularly mothers, that impact has been ‘profound’.
‘The pandemic has highlighted gaps in the current service delivery frameworks, especially for women with limited financial resources,’ social epidemiologist Professor Stephanie Brown said.
‘These gaps have resulted in many women in need of mental health support being unable to access mental health services.’
Professor Brown is Head of Intergenerational Health at the Murdoch Children’s Research Institute (MCRI) and led the Mother’s and Young People’s Study used to inform a policy brief on the pandemic’s impact on maternal mental health and wellbeing.
The prospective cohort study was originally investigating women’s health after childbirth, but expanded to include children and young people’s health and wellbeing, and how it links with their mother’s.
It identified that gaps in current health service delivery had widened during the pandemic, resulting in many women being unable to access appropriate services for mental health support.
In response, the researchers are calling for further policy action, including an extension of mental health strategies across the whole family.
‘It is important to provide multi-service frameworks that enable mothers, fathers, children and young people under 18 to receive appropriately tailored support,’ Professor Brown said.
According to an online survey of 418 women conducted as part of the study during Victoria’s second lockdown, almost one in three women reported ‘clinically significant’ mental health issues.
Notably, less than half (45%) of these women received support from health professionals, with just one in four talking to a GP or a psychologist.
More than half (55%) did not receive any mental health support from primary care or mental health services, and only 4% of women experiencing ‘clinically significant’ depression or anxiety had called a telephone support line.
The reported reasons for not receiving support from health professionals included prioritising support for their children’s mental health over their own, psychologists closing their books to new clients/long waiting periods, and a lack of confidence using telehealth.
Additionally, women experiencing mental health issues were almost four times more likely to delay their own medical care due to the cost of services.
Chair of RACGP Specific Interests Psychological Medicine Dr Cathy Andronis is not surprised by the findings.
‘The sense of isolation and disconnection from normal life as a result of the pandemic leaves many vulnerable people feeling abandoned by others and exacerbates underlying negative emotions
and thoughts, leading frequently to helplessness and hopelessness,’ Dr Andronis told newsGP.
‘People give up on asking for help, particularly when there are urgent tasks at hand such as caring for a new baby that is needy night and day, and more helpless than their mother.’
The RACGP has recently raised a number of concerns around current Medicare structures for providing mental health care, leading to fragmentation and poor patient outcomes.
Acknowledging GPs’ essential place in providing mental health care, the college is lobbying for this space to be properly funded, including by implementing higher Medicare rebates for longer
And although telehealth has helped expand access to care, Dr Andronis believes it is not always appropriate when it comes to mental health care.
‘The lack of human physical connection of telehealth services exacerbates this isolation [experienced by women in the study],’ she said.
‘We need real human, face-to-face connection when we are most distressed. Empathy and compassion online or over the phone is usually not as effective or responsive as live consultations.
‘Fear by mothers of bringing COVID into their household was one more major stress that needed to be avoided when they were just coping with the necessary adjustments of the postnatal period.’
The Mother’s and Young People’s Study survey cohort also revealed that many women experienced:
- fatigue (53%)
- anxiety (41%)
- irritability (33%)
- sadness (27%)
- loneliness (21).
In January and April 2021, when many restrictions were lifted, 391 women took part in a subsequent survey which revealed that despite reports of these issues being reduced, they remained ‘well above’ pre-pandemic levels.
Professor Brown said the findings are expected given the many family disruptions caused by the pandemic.
‘Much of the responsibility for remote schooling was shouldered by women,’ she said.
‘For some women, this meant giving up their paid job, taking leave without pay or reducing their hours of work significantly.
‘The challenges of remote learning were particularly acute for mothers of children experiencing neurodevelopmental conditions such as ADHD or autism, and for women whose children started at a new school just prior to the pandemic.’
While underlying mental health issues were exacerbated by the pandemic, one in five women with no prior history of depression also reported ‘clinically significant’ depressive symptoms during the pandemic.
One third of women from the study continue to experience significant mental health problems including ongoing fatigue and parenting stress.
Professor Brown says these ongoing impacts present further cause for policy action.
‘[The] continuing day-to-day effects of the pandemic are likely to have both short- and longer-term impacts on women’s workforce participation, their own mental health and wellbeing, and the mental health and wellbeing of other family members,’ she said.
The MCRI policy brief states that the ‘process of healing and recovery from the pandemic will take time’, suggesting GPs will continue to play a major role supporting mothers well beyond the perinatal period for years to come.
‘GPs have been the most accessible and available healthcare providers during this pandemic and are likely to continue to be so,’ Dr Andronis said.
‘We are able to meet these women in our clinics and offer timely support. We are highly appreciated by vulnerable people when we offer our support and hold hope for them.
‘Managing these life events and transitions, collaboratively with patients is something we do well.’
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Brain aging differs with cognitive ability regardless of education | Scientific Reports – Nature.com
Higher general cognitive ability (GCA) is associated with lower risk of neurodegenerative disorders, but neural mechanisms are unknown. GCA could be associated with more cortical tissue, from young age, i.e. brain reserve, or less cortical atrophy in adulthood, i.e. brain maintenance. Controlling for education, we investigated the relative association of GCA with reserve and maintenance of cortical volume, -area and -thickness through the adult lifespan, using multiple longitudinal cognitively healthy brain imaging cohorts (n = 3327, 7002 MRI scans, baseline age 20–88 years, followed-up for up to 11 years). There were widespread positive relationships between GCA and cortical characteristics (level-level associations). In select regions, higher baseline GCA was associated with less atrophy over time (level-change associations). Relationships remained when controlling for polygenic scores for both GCA and education. Our findings suggest that higher GCA is associated with cortical volumes by both brain reserve and -maintenance mechanisms through the adult lifespan.
Does higher intelligence protect against brain atrophy in aging? Numerous findings motivate this question: General cognitive ability (GCA) is positively associated with brain volume and cortical characteristics at various life stages, including young adulthood and older age1,2,3,4,5. GCA is consistently associated with all-cause mortality and health, with higher GCA related to lower risk of diseases and lifestyle factors known to negatively affect brain health4. In part, associations are still found after controlling for factors such as educational attainment, suggesting that contemporary GCA in itself is of importance4. While higher education has been posited as a protective factor against neurodegenerative changes6,7, we recently documented in a large-scale study of multiple cohorts that education is not associated with rates of brain atrophy in aging8. A more promising candidate influence on brain aging may thus be GCA independently of education. Whether GCA level is predictive of longitudinal cortical change has primarily been investigated in older cohorts, and with mixed results9,10,11. The relationship of GCA level and cortical changes through the adult lifespan has to our knowledge hitherto not been investigated.
In this context, the lifespan perspective is critical and has implications for understanding functional loss in older age. Several studies indicate that people with higher GCA in young adulthood may be at lower risk of being diagnosed with neurodegenerative disorders in older age4,12,13. Recent findings from large datasets point to a relationship between family history of Alzheimer´s Disease (AD) and cognitive performance level four decades before the typical age of onset of AD14. However, GCA-AD risk associations have not been consistently observed, and mechanistic factors are poorly understood15. Possible explanations include both a brain reserve, i.e. “threshold model”16, as well as a brain maintenance17 account. The brain reserve model would entail that higher GCA as a trait is related to greater neuroanatomical volumes early in life, young adulthood inclusive, thus delaying the time when people fall below a functional threshold of neural resources in the face of neurodegenerative changes with age. This would happen even if such changes in absolute terms are of similar magnitude across different ability levels, i.e. slopes are parallel, indicating “preserved differentiation”18, where initial differences in young are upheld with age16,19. The brain maintenance17, or “differential preservation”18 account would on the other hand predict less brain change in adulthood for people of higher ability, and therefore a smaller risk of cognitive decline and dementia19. The brain reserve and maintenance accounts of the relationships between GCA, brain characteristics and clinical risk are not mutually exclusive, but their relative impact through the adult lifespan is unknown. Collectively, the current findings indicate a need to understand whether there is a relationship between GCA as a trait and brain changes, independently of education, over the adult lifespan.
We tested whether GCA predicted brain aging as indexed by cortical volume, area and thickness change measured longitudinally in 7002 MRI scans from several European cohorts covering the adult lifespan in the Lifebrain consortium20 and the UK Biobank (UKB)21,22 (n = 3327, age range 20–88 years at baseline, maximum scan interval of 11 years, see Online Methods for details). To disentangle possible environmental and genetic influences on the relationship between GCA and brain aging, we controlled for educational attainment in the main analyses, and in a second step for polygenic scores (PGSs) for education and GCA23,24. Established PGSs are only moderately predictive of GCA23, but in view of evidence that the polygenic signal clusters in genes involved in nervous system development23, we did expect such scores to explain part of the intercept effect, with no or weaker effects on brain aging. We expected any effects of GCA on cortical changes to apply to all ages, but in view of recent findings of greater relationships between brain and cognitive function in older than younger individuals3, we also tested the age interaction. Based on previous findings, including from broader cross-sectional Lifebrain cohorts25, and mixed results from smaller longitudinal older cohorts9,10,11, we hypothesized that GCA would be positively related to anatomically widely distributed cortical characteristics through the adult lifespan (intercept effect), but that associations with differences in cortical aging trajectories (slope effects) may be observed to a lesser extent. We expected effects of GCA to be at least partially independent of education8, both for intercept and slope associations.
The main models of associations of GCA with cortical characteristics, and their change, were run separately for samples within the Lifebrain consortium (n = 1129, 2606 scans)20 and the UK Biobank (UKB, n = 2198, 4396 scans)21,22, and then meta-analyses were run on the results, using the metafor package26. Using the estimate and standard error at each vertex, random effects meta-analyses were conducted at each vertex separately. In all main models, sex, baseline age, scanner, time (interval from baseline) and education were entered as covariates. In modeling the effects of GCA on cortical characteristics (level-level analyses), GCA was entered as the predictor (explanatory variable), whereas in modeling the effects of GCA on brain aging (level-change analyses), the interaction term of GCA × time was entered as the predictor, and education × time was entered as an additional covariate along with GCA and education. Since brain aging (i.e. change) was of chief interest, we did not include intracranial volume (ICV), which is stable, in the main analyses. For direct comparison, we also then present level-level analyses without controlling for ICV. This was also chosen given the paucity of evidence for region-specific associations, and previous studies indicating that neuroanatomical volume in and of itself, when controlling for sex, may be associated with GCA9,25. Results from models including ICV, as well as models without education, as covariates, can be found in the Supplementary Information (SI). Additional analyses included the interaction term baseline age × time as a covariate, and in one set of analyses we entered the interaction term baseline age × time × GCA as predictor (with relevant two-way interaction terms as covariates), to test if effects differ reliably across the lifespan.
GCA level: brain level analyses
Cluster p-value maps across Lifebrain and UKB for the relationship of GCA and cortical characteristics controlled for education, are shown in Fig. 1. For cortical volume and area, there were widespread positive associations of GCA bilaterally across the cortical mantle seen in all lobes. For area, significant effects were seen across 47.6% and 44.2% of the left and right hemisphere surface, respectively. For volume, similar numbers were 37.4% and 19.5% for left and right, respectively.
For cortical thickness, only minor positive effects were seen, in proximity of the left central sulcus, covering only 1.1% of the surface.
Results of analyses per sample, controlling and not controlling for education are shown in Supplementary Figs. 1 and 2. Effects were largely similar, though slightly more restricted spatially, when controlling, than when not controlling for education. When adding ICV as a covariate, the intercept effects for cortical volume and area in the meta-analysis shown in Fig. 1 became non-significant, with only a very small effect on cortical thickness in the left hemisphere remaining (see Supplementary Fig. 3), pointing to these being broad effects grounded in greater neuroanatomical structures in general, rather than being region-specific.
To show effect sizes, we calculated the effect of 1 SD increase in GCA on cortical volume. Across Lifebrain and UKB, 1 SD higher GCA was associated with 1.0% larger cortical volume. Effect size maps for level-level analyses showing the regional variation in effect sizes for each sample separately are shown in Supplementary Fig. 4. Effect sizes were numerically smaller in Lifebrain (0.6%) than in UKB (1.3%). Restricting the analyses to regions where significant effects were seen, 1 SD increase in GCA was associated with 2.0% larger cortical volume in Lifebrain and 1.6% larger volume in UKB, but please note that these latter effect sizes are inflated by being within significant regions. Similar analyses for cortical area showed that 0.8% larger area was associated with 1 SD higher GCA across the cortex, with effects being 0.6% in Lifebrain and 0.9% in UKB. Restricting the analyses to regions where significant effects were seen, 1 SD increase in GCA was associated with 1.6% larger cortical volume in Lifebrain and 1.2% larger volume in UKB, with the same caveat as above. For thickness, effects were minute: 0.06% across studies (Lifebrain 0.04%; UKB 0.08%). Within significant clusters (UKB only), the effects of 1 SD higher GCA was 0.8%.
GCA level: brain change analyses
Having confirmed the expected positive relationships between GCA and cortical volume and area controlled for education in terms of an intercept effect, we investigated the question of slope effects. Associations of GCA level at baseline and change in cortical characteristics, controlled for education, are shown in Fig. 2. As expected, effects were more spatially limited than those seen for intercept models, with only restricted regions showing significant relationships: Higher baseline GCA was associated with less regional cortical volume reduction in the left middle cingulate gyrus, a medial area around the central sulcus and a part of the lingual gyrus. The most extensive effects were seen for thickness change, where higher baseline GCA was associated with less thinning in regions corresponding to the volume effects, in addition to parts of the right anterior and lateral temporal cortex and an area in the most medial part of the intersection between the central sulcus and the superior frontal cortex. No associations with area change were observed Taken together, this means that the observed positive associations of GCA with volume change primarily reflect less cortical thinning with higher GCA. (See Supplementary Fig. 5 for result for each subsample separately).
Associations of GCA with cortical change were essentially unaffected by adding ICV as a covariate (Supplementary Fig. 6).
In order to illustrate the GCA-cortical change relationships, and to characterize consistency of effects across samples (UKB and Lifebrain), we plotted the generalized additive mixed model (GAMM) for the different GCA quintiles, from lowest to highest (Fig. 3), depicting change trajectories for average cortical volume and thickness within the regions showing significant GCA x time associations. Across samples, subgroups with higher GCA started with higher volume and had less volume loss over time. For instance, on average, people with maximum cognitive score in UKB are expected start out with a regional average cortical volume of 1.72 mm3 that would be maintained for the next three years, whereas those with the lowest GCA would on average start out with 1.64 mm3 and decrease to 1.61 mm3 over the next three years. Thus, the greatest GCA-associated differences in cortical volume are found in the intercepts (level), whereas differences in slope (change) are smaller in the follow-up period. For cortical thickness, the change trajectories were also very consistently ordered, but those with higher GCA did not uniformly have thicker cortex at first timepoint in these areas. Rather, differential rates of cortical thinning over time were critical in creating cortical thickness differences in these regions in aging. This was evident in both samples, but especially pronounced in UKB.
To further assess effect sizes, we created histograms of the vertex-wise distribution of effects of one SD higher GCA for each metric for absolute volume, area and thickness as well as their change, shown in Fig. 4. (For cortical distributions of such effect sizes per sample, see Supplementary Fig. 7). As can be seen, almost all vertices show positive level–level relationships between GCA and volume and area. For thickness, the distribution is only slightly shifted to the right of zero, confirming the weak GCA-thickness relationships. As for GCA level-brain change, the histograms showed that for area, effects were distributed almost perfectly around zero. For volume, there was a clear shift rightwards, meaning that higher GCA tended to be related to less volume reductions, but substantially less than for the offset effects. Cortical thickness showed the most rightward skewness of the distribution, much larger than for the offset results. Inspecting all histograms, it is clear that higher GCA is related to larger cortical volume and area, and less thickness change.
Influence of polygenic scores (PGSs) for GCA and education on the level-level and level-change associations
Next, we investigated whether effects were maintained when covarying for established PGSs for GCA and education in the UKB23,24. Fifty-two participants in the main models were excluded due to missing genetic data. In these analyses, we regressed out the first ten genetic ancestry factors (GAFs) from the GCA variable prior to analysis. The intercept associations of GCA and cortical characteristics that were observed in the main model (Fig. 1) largely remained when controlling for the PGSs, but the extent of the significant regions were somewhat reduced for cortical volume and area (Supplementary Fig. 8). The associations of GCA and cortical change largely remained and were only slightly attenuated when controlling for PGSs for GCA and education (Fig. 5; compare to UKB results in Supplementary Fig. 5).
Influence of age on the level-change associations
We next tested the three-way interaction baseline GCA × baseline age × time, to see whether the level-change associations differed reliably across the lifespan. Significant interaction effects were seen for change in small regions of the left hemisphere, mostly laterally for volume, and for slightly more extended regions, for cortical thickness (see Fig. 6).
The positive three-way interactions of baseline GCA × baseline age × time indicates that higher level of GCA is associated with less atrophy at distinct ages. To visualize these interaction effects, we divided the cohorts according to whether participants were above or below age 60 years. This division point was chosen in view of it being an approximate age at which select cognitive and regional cortical volume and thickness changes have been reported to accelerate in longitudinal studies27,28. In order to explore how GCA level related to cortical thickness change over time across the two age groups, we plotted the expected cortical change trajectories, within the significant regions shown in Fig. 6, as a function of GCA, with each sample divided into quintiles, from lowest to highest GCA. The plots are shown in Fig. 7. While GCA level was weakly, and in Lifebrain even inversely related to atrophy in these regions in the younger group, the expected trajectories for the older group were relatively consistently ordered so that persons with a higher GCA level had less decline, especially of cortical thickness. The GCA quintile differences are more pronounced in the older group, suggesting the latter half of the lifespan is driving the interaction. As one outlier in the older group in Lifebrain was noted as having a high cortical thickness value for age at the first timepoint in the region of interest, we carefully checked this segmentation, but found no sign of flawed segmentation, and thus decided to keep this person in analyses.
The current study provides novel findings on GCA not only as a marker of brain characteristics, but also of brain changes in healthy aging. The finding that higher GCA level is associated with larger neuroanatomical structures to begin with, i.e. greater brain reserve, confirms findings in previous studies of various age groups1,2,3,4,25,29. While level of GCA has been associated with cortical change in some older groups10,11, but not others9, the current demonstration of an association of GCA levels, controlled for education, on cortical volume and -thickness declines through the lifespan in multiple cohorts across a relatively long follow-up time, constitutes a novel finding. Also, the finding of an age-interaction with pronounced effects of GCA on cortical thinning and volume changes only in older ages in select regions, is novel.
The association of GCA level and cortical change appears relatively moderate. This may explain why such associations have not previously been consistently found. The “effect” of GCA on cortical change must be viewed in relation to the intercept effects, which, as shown here, constitute a major source of GCA-related cortical volume variation through the lifespan: Those with higher GCA have greater cortical area to begin with, yielding higher cortical volumes in young adulthood. We have previously found that cortical area seems in part determined neuro-developmentally early on, is associated with GCA, and shows parallel trajectories for higher and lower GCA groups1. As there is a relatively minor age change in area, compared to thickness28, slope effects on cortical volume are chiefly caused by moderately different rates of cortical thinning for people of differential cognitive ability. Differences in cortical thinning are thus key to the maintenance effects of GCA, whereas early differences in cortical area drive the intercept effect. Through the adult lifespan, both will affect cortical volume.
It is of interest that these GCA-brain change associations were found when education was controlled for, suggesting that the contemporary GCA level may not only be related to brain reserve16 to begin with, and preserved differentiation18, but also brain maintenance17, and differential preservation18. This is evident from the—across samples—consistently steeper slopes of regional cortical decline with lower GCA (as illustrated in Fig. 3). With our recent findings on the variable nature of education-brain-cognition relationships, as well as education not being associated with atrophy rates in aging25,30, this points to the component of GCA not being associated with education variance as a more promising candidate for predictive or potentially protective effects on brain aging. There is evidence that education may serve to increase GCA31,32. However, while GCA level may be impacted, slope, i.e. cognitive decline, is likely not32,33. There is also evidence to suggest that education, without mediation through adult socioeconomic position, cannot be considered a modifiable risk factor for dementia34.
While one would then think the underlying mechanism in the observed GCA-brain change relationships may be genetic, known genetic factors only partially explained relationships, as effects remained after controlling for PGSs for general cognitive ability and education. However, the PGSs are known to be only moderately predictive of GCA23, and genetic pleiotropic effects on GCA and cortical characteristics and their change may still likely be part of the underlying mechanism. While it has been suggested that GCA may associate with differences in epigenetic age acceleration, it was recently reported that such epigenetic markers did not show associations with longitudinal phenotypic health change35. While it is possible that individual differences in epigenetic age acceleration in older age could be caused by e.g. behaviors associated with intelligence differences over the life course, differences in epigenetic markers and GCA could also both be the result of a shared genetic architecture or some early, including in-utero, environmental event35,36.
A significant three-way interaction of baseline GCA by baseline age by time on regional cortical thickness changes was observed by meta-analysis across cohorts. These effects indicated that higher level of GCA is more associated with less atrophy at older ages. However, as these regional interaction effects were highly restricted, and also seemed to rest in part on unexpected, albeit weakly, inverse direction of smaller effects in younger28 age in Lifebrain, we consider them tentative until replicated. The higher baseline age of the UKB sample, also may make it less suitable to study adult lifespan interactions. Moreover, greater power would be desired to study three-way interactions of possibly smaller effect size.
Some further limitations to the present study should also be noted: The samples included are heterogeneous and may have varying degrees of representativeness of the populations of origin, indeed, lack of population representativity is known37. Data from relatively short time periods were used. Changing exposure trends over time, in health, education, and welfare, may thus relate to age at baseline, and could have effects that could not readily be studied in isolation here. Furthermore, as cognitively healthy participants were recruited, sample representativity may vary with age. Since persons with known neurodegenerative disorders were excluded, results cannot readily be generalized to persons suffering from various types of dementia. To shed light on the potential genetic contribution to the observed GCA-cortical change relationships, we controlled for PGSs for GCA and educational attainment. While these results indicated negligible genetic contributions, direct investigation of the genetic relations using standard methods, e.g. linkage disequilibrium score regression38, may be better suited to investigate this, when large-scale GWAS for longitudinal cortical changes in adulthood becomes available. Finally, change-change relationships between GCA and cortical characteristics could not readily be addressed in the present samples with similar models, due to variability in availability of comparable test data across timepoints. In a lifespan perspective, we know that such relationships do exist, in that both brain and cognition increase in development and decline in aging17,27,28,39. However, to what extent individual differences in GCA change are related to individual differences in cortical trajectories in the present samples, is beyond the scope of this study.
In conclusion, the present study shows that with higher GCA, primarily brain reserve, but also brain maintenance yield higher cortical volumes through the adult lifespan. These effects were seen when controlling for effects of education. As there is otherwise scarce evidence so far that human behavioral traits are associated with differential brain aging trajectories, this is of great interest to investigate further. While controlling for known polygenetic markers for GCA and education did not substantially diminish the effects, the underlying mechanisms may still be related to genetic pleiotropy. However, this leaves open the possibility that factors associated with increased GCA other than education, and possibly genes, could serve to diminish cortical atrophy in aging. Such factors affecting normal individual differences in GCA are not known with certainty, but as childhood GCA is highly predictive of GCA in aging40, they likely work at developmental, rather than adult and senescent stages.
Materials and methods
The UK Biobank (UKB)22 and the Lifebrain samples are described in Table 1. The samples from the European Lifebrain (LB) project (http://www.lifebrain.uio.no/)20 included participants from major European brain studies: the Berlin Study of Aging II (BASE II)41, the BETULA project27, the Cambridge Centre for Ageing and Neuroscience study (Cam-CAN)42, Center for Lifebrain Changes in Brain and Cognition longitudinal studies (LCBC)1, and the University of Barcelona brain studies (UB)43,44,45.
GCA was measured by partially different tests in the different cohorts. National versions of a series of batteries and tests were used, see SM for details. These included the UKB Fluid Intelligence test46, tests from the Wechsler batteries47,48,49 combined with the National Adult Reading Test (NART)50, the Cattell Culture Fair Test51 combined with the Spot The Word task52, as well as local batteries, for which procedures are described in SM and elsewhere53,54. It is clearly a limitation that content and reliability of the GCA measures may vary, but there is reason to assume that the measures index partally similar abilities. For instance, the UKB fluid intelligence measure has been shown to have moderate to high reliability, and correlated > 0.50 with a measure of GCA created using 11 reference tests, including NART and Wechsler measures55. See SM for further details.
MRIs were processed using FreeSurfer, version 7.1 for Lifebrain, and version 6.0 for UKB (https://surfer.nmr.mgh.harvard.edu/https://surfer.nmr.mgh.harvard.edu)56,57,58,59. We ran vertex-wise analyses to assess regional variation in the relationships between cortical structure and the measures of interest, i.e. GCA and the interaction of GCA × time. Cortical surfaces were reconstructed from the same T1-weighted anatomical MRIs, yielding maps of cortical area, thickness and volume. Surfaces were smoothed with a Gaussian kernel of 15 mm full-width at half-maximum. Spatiotemporal linear mixed models60,61 were performed running on MATLAB R2017a (using FreeSurfers ST-LME package https://surfer.nmr.mgh.harvard.edu/fswiki/LinearMixedEffectsModels), for each of the samples separately, with GCA, and then additionally with the interaction term of GCA and time in turn as predictors, and sex, baseline age, scanner, time (interval since baseline scan) and education were entered as covariates unless otherwise noted. These models also account for the spatial correlation between residuals at neighboring vertices and the temporal correlation of residuals within repeated measurements of single participants. Surface results were tested against an empirical null distribution of maximum cluster size across 10 000 iterations using Z Monte Carlo simulations, yielding results corrected for multiple comparisons across space (p < 0.01 corrected)62.
All studies were conducted, and all methods performed, in accordance with relevant guidelines and regulations as set forth by the relevant authorities, including the Declaration of Helsinki, all participants gave informed consent, and subprojects were approved by the relevant ethical review boards. UK Biobank has approval from the North West Multi-centre Research Ethics Committee as a Research Tissue Bank approval. The Lifebrain project was approved by Regional Committees for Medical Research Ethics–South East Norway. For additional details, see SM. Screening criteria were not identical across studies, but participants were recruited to be cognitively healthy and did not suffer from neurological conditions known to affect brain function, such as dementia. All samples consisted of community-dwelling participants, some were convenience samples, whereas others were contacted on the basis of population registry information. Further details on samples, GCA measures, MRI acquisition and processing and statistical analyses, are presented in SM. The Lifebrain data supporting the results of the current study are available from the PI of each sub-study on request (see SM), given approvals. UK Biobank data requests can be submitted to http://www.ukbiobank.ac.uk. Computer code used for the analyses is available on github: https://github.com/Lifebrain/p032-gca-brain-change.
Walhovd, K. B. et al. Neurodevelopmental origins of lifespan changes in brain and cognition. Proc. Natl. Acad. Sci. USA. 113, 9357–9362. https://doi.org/10.1073/pnas.1524259113 (2016).
Fjell, A. M. et al. High-expanding cortical regions in human development and evolution are related to higher intellectual abilities. Cereb. Cortex 25, 26–34. https://doi.org/10.1093/cercor/bht201 (2015).
Cox, S. R., Ritchie, S. J., Fawns-Ritchie, C., Tucker-Drob, E. M. & Deary, I. J. Structural brain imaging correlates of general intelligence in UK Biobank. Intelligence 76, 101376. https://doi.org/10.1016/j.intell.2019.101376 (2019).
Deary, I. J., Hill, W. D. & Gale, C. R. Intelligence, health and death. Nat. Hum. Behav. 5, 416–430. https://doi.org/10.1038/s41562-021-01078-9 (2021).
Deary, I. J., Cox, S. R. & Hill, W. D. Genetic variation, brain, and intelligence differences. Mol. Psychiatry https://doi.org/10.1038/s41380-021-01027-y (2021).
Livingston, G. et al. Dementia prevention, intervention, and care: 2020 report of the Lancet Commission. Lancet 396, 413–446. https://doi.org/10.1016/S0140-6736(20)30367-6 (2020).
Livingston, G. et al. Dementia prevention, intervention, and care. Lancet 390, 2673–2734. https://doi.org/10.1016/S0140-6736(17)31363-6 (2017).
Nyberg, L. et al. Educational attainment does not influence brain aging. Proc. Natl. Acad. Sci. USA 118, 18. https://doi.org/10.1073/pnas.2101644118 (2021).
Jancke, L., Sele, S., Liem, F., Oschwald, J. & Merillat, S. Brain aging and psychometric intelligence: A longitudinal study. Brain Struct. Funct. 225, 519–536. https://doi.org/10.1007/s00429-019-02005-5 (2020).
Ritchie, S. J. et al. Brain volumetric changes and cognitive ageing during the eighth decade of life. Hum. Brain Mapp. 36, 4910–4925. https://doi.org/10.1002/hbm.22959 (2015).
Raz, N. et al. Neuroanatomical correlates of fluid intelligence in healthy adults and persons with vascular risk factors. Cereb. Cortex 18, 718–726. https://doi.org/10.1093/cercor/bhm108 (2008).
Yeo, R. A., Arden, R. & Jung, R. E. Alzheimer’s disease and intelligence. Curr. Alzheimer Res. 8, 345–353. https://doi.org/10.2174/156720511795745276 (2011).
Nyberg, J. et al. Cardiovascular and cognitive fitness at age 18 and risk of early-onset dementia. Brain 137, 1514–1523. https://doi.org/10.1093/brain/awu041 (2014).
Talboom, J. S. et al. Family history of Alzheimer’s disease alters cognition and is modified by medical and genetic factors. Elife 8, 4619. https://doi.org/10.7554/eLife.46179 (2019).
Rodriguez, F. S. & Lachmann, T. Systematic review on the impact of intelligence on cognitive decline and dementia risk. Front. Psychiatry 11, 658. https://doi.org/10.3389/fpsyt.2020.00658 (2020).
Katzman, R. et al. Clinical, pathological, and neurochemical changes in dementia: A subgroup with preserved mental status and numerous neocortical plaques. Ann. Neurol. 23, 138–144. https://doi.org/10.1002/ana.410230206 (1988).
Nyberg, L., Lovden, M., Riklund, K., Lindenberger, U. & Backman, L. Memory aging and brain maintenance. Trends Cogn. Sci. 16, 292–305. https://doi.org/10.1016/j.tics.2012.04.005 (2012).
Salthouse, T. A., Babcock, R. L., Skovronek, E., Mitchell, D. R. D. & Palmon, R. Age and experience effects in spatial visualization. Dev. Psychol. 26, 128–136. https://doi.org/10.1037/0012-16220.127.116.11 (1990).
Tucker-Drob, E. M. Cognitive aging and dementia: A life span perspective. Annu. Rev. Dev. Psychol. 1, 177–196. https://doi.org/10.1146/annurev-devpsych-121318-085204 (2019).
Walhovd, K. B. et al. Healthy minds 0–100 years: Optimising the use of European brain imaging cohorts (“Lifebrain”). Eur. Psychiatry 50, 47–56. https://doi.org/10.1016/j.eurpsy.2017.12.006 (2018).
Alfaro-Almagro, F. et al. Image processing and quality control for the first 10,000 brain imaging datasets from UK Biobank. Neuroimage 166, 400–424. https://doi.org/10.1016/j.neuroimage.2017.10.034 (2018).
Nobis, L. et al. Hippocampal volume across age: Nomograms derived from over 19,700 people in UK Biobank. Neuroimage Clin. 23, 101904. https://doi.org/10.1016/j.nicl.2019.101904 (2019).
Hill, W. D. et al. A combined analysis of genetically correlated traits identifies 187 loci and a role for neurogenesis and myelination in intelligence. Mol. Psychiatry 24, 169–181. https://doi.org/10.1038/s41380-017-0001-5 (2019).
Lee, J. J. et al. Gene discovery and polygenic prediction from a genome-wide association study of educational attainment in 1.1 million individuals. Nat. Genet. 50, 1112–1121. https://doi.org/10.1038/s41588-018-0147-3 (2018).
Walhovd, K. B. et al. Education and income show heterogeneous relationships to lifespan brain and cognitive differences across European and US cohorts. Cereb. Cortex https://doi.org/10.1093/cercor/bhab248 (2021).
Viechtbauer, W. Conducting meta-analyses in R with the metafor package. J. Stat. Softw. 36, 1–48. https://doi.org/10.18637/jss.v036.i03 (2010).
Nyberg, L. et al. Biological and environmental predictors of heterogeneity in neurocognitive ageing: Evidence from Betula and other longitudinal studies. Ageing Res. Rev. 64, 101184. https://doi.org/10.1016/j.arr.2020.101184 (2020).
Storsve, A. B. et al. Differential longitudinal changes in cortical thickness, surface area and volume across the adult life span: Regions of accelerating and decelerating change. J. Neurosci. 34, 8488–8498. https://doi.org/10.1523/JNEUROSCI.0391-14.2014 (2014).
Walhovd, K. B. et al. Cortical volume and speed-of-processing are complementary in prediction of performance intelligence. Neuropsychologia 43, 704–713. https://doi.org/10.1016/j.neuropsychologia.2004.08.006 (2005).
Raz, N., Rodrigue, K. M., Kennedy, K. M. & Acker, J. D. Vascular health and longitudinal changes in brain and cognition in middle-aged and older adults. Neuropsychology 21, 149–157. https://doi.org/10.1037/0894-418.104.22.168 (2007).
Lager, A., Seblova, D., Falkstedt, D. & Lovden, M. Cognitive and emotional outcomes after prolonged education: A quasi-experiment on 320 182 Swedish boys. Int. J. Epidemiol. 46, 303–311. https://doi.org/10.1093/ije/dyw093 (2017).
Lovden, M., Fratiglioni, L., Glymour, M. M., Lindenberger, U. & Tucker-Drob, E. M. Education and cognitive functioning across the life span. Psychol. Sci. Public Interest 21, 6–41. https://doi.org/10.1177/1529100620920576 (2020).
Seblova, D., Berggren, R. & Lovden, M. Education and age-related decline in cognitive performance: Systematic review and meta-analysis of longitudinal cohort studies. Ageing Res. Rev. 58, 101005. https://doi.org/10.1016/j.arr.2019.101005 (2020).
Seblova, D. et al. Does prolonged education causally affect dementia risk when adult socioeconomic status is not altered? A Swedish natural experiment in 1.3 million individuals. Am. J. Epidemiol. 190, 817–826. https://doi.org/10.1093/aje/kwaa255 (2021).
Stevenson, A. J. et al. Childhood intelligence attenuates the association between biological ageing and health outcomes in later life. Transl. Psychiatry 9, 323. https://doi.org/10.1038/s41398-019-0657-5 (2019).
Li, S. et al. Genome-wide average DNA methylation is determined in utero. Int. J. Epidemiol. 47, 908–916. https://doi.org/10.1093/ije/dyy028 (2018).
Stamatakis, E. et al. Is cohort representativeness passe? Poststratified Associations of lifestyle risk factors with mortality in the UK biobank. Epidemiology 32, 179–188. https://doi.org/10.1097/EDE.0000000000001316 (2021).
Bulik-Sullivan, B. K. et al. LD Score regression distinguishes confounding from polygenicity in genome-wide association studies. Nat. Genet. 47, 291–295. https://doi.org/10.1038/ng.3211 (2015).
Walhovd, K. B. et al. Long-term influence of normal variation in neonatal characteristics on human brain development. Proc. Natl. Acad. Sci. U.S.A. 109, 20089–20094. https://doi.org/10.1073/pnas.1208180109 (2012).
Deary, I. J., Whalley, L. J., Lemmon, H., Crawford, J. R. & Starr, J. M. The stability of individual differences in mental ability from childhood to old age: Follow-up of the 1932 Scottish Mental Survey. Intelligence 28, 49–55. https://doi.org/10.1016/S0160-2896%2899%2900031-8 (2000).
Bertram, L. et al. Cohort profile: The Berlin aging study II (BASE-II). Int. J. Epidemiol. 43, 703–712. https://doi.org/10.1093/ije/dyt018 (2014).
Shafto, M. A. et al. The Cambridge Centre for Ageing and Neuroscience (Cam-CAN) study protocol: A cross-sectional, lifespan, multidisciplinary examination of healthy cognitive ageing. BMC Neurol. 14, 204. https://doi.org/10.1186/s12883-014-0204-1 (2014).
Rajaram, S. et al. The Walnuts and Healthy Aging Study (WAHA): Protocol for a nutritional intervention trial with walnuts on brain aging. Front. Aging Neurosci. 8, 333. https://doi.org/10.3389/fnagi.2016.00333 (2016).
Uribe, C. et al. Patterns of cortical thinning in nondemented Parkinson’s disease patients. Movement Disord. 31, 699–708. https://doi.org/10.1002/mds.26590 (2016).
Vidal-Pineiro, D. et al. Task-dependent activity and connectivity predict episodic memory network-based responses to brain stimulation in healthy aging. Brain Stimul. 7, 287–296. https://doi.org/10.1016/j.brs.2013.12.016 (2014).
Sudlow, C. et al. UK biobank: An open access resource for identifying the causes of a wide range of complex diseases of middle and old age. PLoS Med. 12, e1001779. https://doi.org/10.1371/journal.pmed.1001779 (2015).
Wechsler, D. Wchsler Abbreviated Scale of Intelligence (The Psychological Corporation, 1999).
Wechsler, D. Wechsler Adult Intelligence Scale (WAIS-III): Administration and Scoring Manual 3rd edn. (The Psychological Corporation, 1997).
Wechsler, D. Wechsler Adult Intelligence Scale: Fourth Edition (WAIS-IV) (Pearson Education Inc., 2008).
Nelson, H. & Willison, J. The National Adult Reading Test (NART) (Windsor NFER-Nelson, 1991).
Cattell, R. B. & Cattell, H. E. P. Measuring Intelligence with the Culture Fair Tests (The Institute for Personality and Ability Testing, 1973).
Baddeley, A., Emslie, H. & Nimmosmith, I. The spot-the-word test: A robust estimate of verbal intelligence based on lexical decision. Br. J. Clin. Psychol. 32, 55–65. https://doi.org/10.1111/j.2044-8260.1993.tb01027.x (1993).
Duzel, S. et al. The subjective health horizon questionnaire (SHH-Q): Assessing future time perspectives for facets of an active lifestyle. Gerontology 62, 345–353. https://doi.org/10.1159/000441493 (2016).
Nilsson, L. G. et al. The Betula prospective cohort study: Memory, health, and aging. Aging Neuropsychol. Cogn. 4, 1–32. https://doi.org/10.1080/13825589708256633 (1997).
Fawns-Ritchie, C. & Deary, I. J. Reliability and validity of the UK Biobank cognitive tests. PLoS ONE 15, e0231627. https://doi.org/10.1371/journal.pone.0231627 (2020).
Dale, A. M., Fischl, B. & Sereno, M. I. Cortical surface-based analysis I. Segmentation and surface reconstruction. Neuroimage 9, 179–194. https://doi.org/10.1006/nimg.1998.0395 (1999).
Fischl, B. et al. Whole brain segmentation: Automated labeling of neuroanatomical structures in the human brain. Neuron 33, 341–355 (2002).
Reuter, M., Schmansky, N. J., Rosas, H. D. & Fischl, B. Within-subject template estimation for unbiased longitudinal image analysis. Neuroimage 61, 1402–1418. https://doi.org/10.1016/j.neuroimage.2012.02.084 (2012).
Jovicich, J. et al. Brain morphometry reproducibility in multi-center 3T MRI studies: A comparison of cross-sectional and longitudinal segmentations. Neuroimage 83, 472–484. https://doi.org/10.1016/j.neuroimage.2013.05.007 (2013).
Bernal-Rusiel, J. L. et al. Statistical analysis of longitudinal neuroimage data with linear mixed effects models. Neuroimage 66, 249–260. https://doi.org/10.1016/j.neuroimage.2012.10.065 (2013).
Bernal-Rusiel, J. L. et al. Spatiotemporal linear mixed effects modeling for the mass-univariate analysis of longitudinal neuroimage data. Neuroimage 81, 358–370. https://doi.org/10.1016/j.neuroimage.2013.05.049 (2013).
Hagler, D. J. Jr., Saygin, A. P. & Sereno, M. I. Smoothing and cluster thresholding for cortical surface-based group analysis of fMRI data. Neuroimage 33, 1093–1103. https://doi.org/10.1016/j.neuroimage.2006.07.036 (2006).
The Lifebrain project is funded by the EU Horizon 2020 Grant Agreement Number 732592 (Lifebrain). In addition, the different sub-studies are supported by different sources: LCBC: The European Research Council under grant agreements 283634, 725025 (to A.M.F.) and 313440 (to K.B.W.), as well as the Norwegian Research Council (to A.M.F., K.B.W.), The National Association for Public Health’s dementia research program, Norway (to A.M.F). Betula: a scholar grant from the Knut and Alice Wallenberg (KAW) foundation to L.N. Barcelona: Partially supported by an ICREA Academia 2019 grant award; by the California Walnut Commission, Sacramento, California. BASE-II has been supported by the German Federal Ministry of Education and Research under Grant Numbers 16SV5537/ 16SV5837/ 16SV5538/ 16SV5536K /01UW0808/ 01UW0706/ 01GL1716A/ 01GL1716B, the European Research Council under grant agreement 677804 (to S.K.). The Cambridge Centre for Ageing and Neuroscience (Cam-CAN) was supported by a programme grant from the UK Biotechnology and Biological Sciences Research Council (Grant Number BB/H008217/1) and by continued intramural funding from the UK Medical Research Council to the Cognition & Brain Sciences Unit in Cambridge. Part of the research was conducted using the UK Biobank resource under Application Number 32048.
The authors declare no competing interests.
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Walhovd, K.B., Nyberg, L., Lindenberger, U. et al. Brain aging differs with cognitive ability regardless of education.
Sci Rep 12, 13886 (2022). https://doi.org/10.1038/s41598-022-17727-6
Received: 04 March 2022
Accepted: 29 July 2022
Published: 16 August 2022
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