The impact of a lack of mathematical education on brain development and future attainment

High School Curriculum and Cognitive Function in the Eighth Decade of Life

INTRODUCTION

Formal educational attainment, or years of schooling, has a well-established positive effect on cognitive health across the life course. We hypothesized that the content and difficulty of the curriculum influence this relationship, such that more challenging curricula in high school lead to higher levels of socioeconomic attainment in adulthood and, in turn, to better cognitive outcomes in older adulthood.

METHODS

We estimated multilevel structural equation models (MSEMs) in data from 2,405 individuals who attended one of 1,312 US high schools in 1960 and participated in the Project Talent Aging Study in 2018.

RESULTS

A college preparatory curriculum and a greater number of semesters of math and science in high school were positively related to word recall and verbal fluency at an average age of 75. Effects were robust to controlling for adolescent cognitive ability, academic performance, socioeconomic background, and school characteristics.

DISCUSSION

We discuss the implications of these findings for educational policy.

Differences in educational opportunity predict white matter development

Coarse measures of socioeconomic status, such as parental income or parental education, have been linked to differences in white matter development. However, these measures do not provide insight into specific aspects of an individual's environment and how they relate to brain development. On the other hand, educational intervention studies have shown that changes in an individual's educational context can drive measurable changes in their white matter. These studies, however, rarely consider socioeconomic factors in their results. In the present study, we examined the unique relationship between educational opportunity and white matter development, when controlling other known socioeconomic factors. To explore this question, we leveraged the rich demographic and neuroimaging data available in the ABCD study, as well the unique data-crosswalk between ABCD and the Stanford Education Data Archive (SEDA). We find that educational opportunity is related to accelerated white matter development, even when accounting for other socioeconomic factors, and that this relationship is most pronounced in white matter tracts associated with academic skills. These results suggest that the school a child attends has a measurable relationship with brain development for years to come.

Digital twins for understanding mechanisms of learning disabilities: Personalized deep neural networks reveal impact of neuronal hyperexcitability

Learning disabilities affect a significant proportion of children worldwide, with far-reaching consequences for their academic, professional, and personal lives. Here we develop digital twins - biologically plausible personalized Deep Neural Networks (pDNNs) - to investigate the neurophysiological mechanisms underlying learning disabilities in children. Our pDNN reproduces behavioral and neural activity patterns observed in affected children, including lower performance accuracy, slower learning rates, neural hyper-excitability, and reduced neural differentiation of numerical problems. Crucially, pDNN models reveal aberrancies in the geometry of manifold structure, providing a comprehensive view of how neural excitability influences both learning performance and the internal structure of neural representations. Our findings not only advance knowledge of the neurophysiological underpinnings of learning differences but also open avenues for targeted, personalized strategies designed to bridge cognitive gaps in affected children. This work reveals the power of digital twins integrating AI and neuroscience to uncover mechanisms underlying neurodevelopmental disorders.

Excitatory and inhibitory neurochemical markers of anxiety in young females

Between the ages of 10-25 years the maturing brain is sensitive to a multitude of changes, including neurochemical variations in metabolites. Of the different metabolites, gamma-aminobutyric acid (GABA) has long been linked neurobiologically to anxiety symptomology, which begins to manifest in adolescence. To prevent persistent anxiety difficulties into adulthood, we need to understand the maturational trajectories of neurochemicals and how these relate to anxiety levels during this sensitive period. We used magnetic resonance spectroscopy in a sample of younger (aged 10-11) and older (aged 18-25) females to estimate GABA and glutamate levels in brain regions linked to emotion regulation processing, as well as a conceptually distinct control region. Within the Bayesian framework, we found that GABA increased and glutamate decreased with age, negative associations between anxiety and glutamate and GABA ratios in the dorsolateral prefrontal cortex, and a positive relationship of GABA with anxiety levels. The results support the neural over-inhibition hypothesis of anxiety based on GABAergic activity.

Neurobiology of numerical learning

Numerical abilities are complex cognitive skills essential for dealing with requirements of the modern world. Although the brain structures and functions underlying numerical cognition in different species have long been appreciated, genetic and molecular techniques have more recently expanded the knowledge about the mechanisms underlying numerical learning. In this review, we discuss the status of the research related to the neurobiological bases of numerical abilities. We consider how genetic factors have been associated with mathematical capacities and how these link to the current knowledge of brain regions underlying these capacities in human and non-human animals. We further discuss the extent to which significant variations in the levels of specific neurotransmitters may be used as potential markers of individual performance and learning difficulties and take into consideration the therapeutic potential of brain stimulation methods to modulate learning and improve interventional outcomes. The implications of this research for formulating a more comprehensive view of the neural basis of mathematical learning are discussed.

The Relationship Between Level of Education, Cognitive Function and Medication Adherence in Patients with Schizophrenia

Purpose

Several studies have explored the relationship between level of education and medication adherence, as well as the relationship between level of education and cognitive function. However, there have been few studies on the relationships between level of education, cognitive function, and medication adherence. This study aimed to explore whether cognitive function has a mediating effect between level of education and medication adherence in patients with schizophrenia.

Patients and Methods

A total of 329 participants were included in this study. Cognitive function was assessed using the Brief Assessment of Cognition in Schizophrenia, and medication adherence using the Medication Adherence Questionnaire. The relationships between the clinical factors and cognitive function that contributed to medication adherence were tested through multivariable linear regression analysis. The mediating effect of medication adherence was tested using the bootstrapping approach with the PROCESS macro.

Results

Family history, insight and executive function were associated with medication adherence in individuals with schizophrenia, and executive function had a mediating effect between level of education and medication adherence.

Conclusion

Adopting specific education programs that promote cognitive development as well as actively intervening in executive function might be conducive to improve medication adherence in patients with schizophrenia.

Higher education level as a protective factor against executive dysfunction in patients with epilepsy in Mataram, Indonesia

OBJECTIVE

Since the concept of cognitive reserve is applicable in epilepsy-associated cognitive impairment, the role of cognitive reserve components as a protective factor against epilepsy-associated executive dysfunction needs further investigation. This study aimed at investigating the association between cognitive reserve components and the frequency of epilepsy-associated executive dysfunction in Mataram, Indonesia.

METHODS

This case-control study involved both epilepsy outpatient and healthy participants recruited consecutively in 5 hospitals in Mataram, between October 2021 and September 2022. Data on sociodemographic, cognitive reserve components, and executive function status were collected from both groups, while data on seizure were collected only from epilepsy participants. The association between cognitive reserve components and the frequency of epilepsy-associated executive dysfunction was tested using logistic regression.

RESULTS

A total of 119 epilepsy patients and 93 healthy participants were recruited. The frequency of epilepsy-associated executive dysfunction was 50.4%. Multivariate logistic regression analysis showed that higher education level was the only cognitive reserve component protective against epilepsy-associated executive dysfunction (odds ratio [OR]: 3.36; 95% confidence interval [CI]: 1.33 - 8.50).

CONCLUSION

A high frequency of epilepsy-associated executive dysfunction was observed in Mataram. Higher education level was a cognitive reserve component protective against executive dysfunction in these patients.

Modifiable risk factors of dementia linked to excitation-inhibition imbalance

Recent evidence identifies 12 potentially modifiable risk factors for dementia to which 40% of dementia cases are attributed. While the recognition of these risk factors has paved the way for the development of new prevention measures, the link between these risk factors and the underlying pathophysiology of dementia is yet not well understood. A growing number of recent clinical and preclinical studies support a role of Excitation-Inhibition (E-I) imbalance in the pathophysiology of dementia. In this review, we aim to propose a conceptual model on the links between the modifiable risk factors and the E-I imbalance in dementia. This model, which aims to address the current gap in the literature, is based on 12 mediating common mechanisms: the hypothalamic-pituitary-adrenal (HPA) axis dysfunction, neuroinflammation, oxidative stress, mitochondrial dysfunction, cerebral hypo-perfusion, blood-brain barrier (BBB) dysfunction, beta-amyloid deposition, elevated homocysteine level, impaired neurogenesis, tau tangles, GABAergic dysfunction, and glutamatergic dysfunction. We believe this model serves as a framework for future studies in this field and facilitates future research on dementia prevention, discovery of new biomarkers, and developing new interventions.

NIfTI-MRS: A standard data format for magnetic resonance spectroscopy

PURPOSE

Multiple data formats in the MRS community currently hinder data sharing and integration. NIfTI-MRS is proposed as a standard spectroscopy data format, implemented as an extension to the Neuroimaging informatics technology initiative (NIfTI) format. This standardized format can facilitate data sharing and algorithm development as well as ease integration of MRS analysis alongside other imaging modalities.

METHODS

A file format using the NIfTI header extension framework incorporates essential spectroscopic metadata and additional encoding dimensions. A detailed description of the specification is provided. An open-source command-line conversion program is implemented to convert single-voxel and spectroscopic imaging data to NIfTI-MRS. Visualization of data in NIfTI-MRS is provided by development of a dedicated plugin for FSLeyes, the FMRIB Software Library (FSL) image viewer.

RESULTS

Online documentation and 10 example datasets in the proposed format are provided. Code examples of NIfTI-MRS readers are implemented in common programming languages. Conversion software, spec2nii, currently converts 14 formats where data is stored in image-space to NIfTI-MRS, including Digital Imaging and Communications in Medicine (DICOM) and vendor proprietary formats.

CONCLUSION

NIfTI-MRS aims to solve issues arising from multiple data formats being used in the MRS community. Through a single conversion point, processing and analysis of MRS data are simplified, thereby lowering the barrier to use of MRS. Furthermore, it can serve as the basis for open data sharing, collaboration, and interoperability of analysis programs. Greater standardization and harmonization become possible. By aligning with the dominant format in neuroimaging, NIfTI-MRS enables the use of mature tools present in the imaging community, demonstrated in this work by using a dedicated imaging tool, FSLeyes, for visualization.

The effect of parietal glutamate/GABA balance on test anxiety levels in early childhood in a cross-sectional and longitudinal study

The increased prevalence of test anxiety in our competitive society makes it a health issue of public concern. However, its neurobiological basis, especially during the years of formal education, is currently scant. Previous research has highlighted the association between neural excitation/inhibition balance and psychopathology and disease. We examined whether the glutamate/GABA profile tracks test anxiety levels in development, using a cross-sectional and longitudinal design in a cohort spanning from early childhood to early adulthood (N = 289), reassessed approximately 21 months later (N = 194). We used magnetic resonance spectroscopy to noninvasively quantify glutamate and gamma-Aminobutyric acid (GABA) levels in the intraparietal sulcus (IPS) and the middle frontal gyrus. We show that the glutamate/GABA balance within the IPS relates to current individual variation in test anxiety levels and predict future test anxiety approximately 21 months later. Critically, this relationship was observed during early childhood but not during the later developmental stages. Our results extend the use of the excitation/inhibition balance framework to characterize the psychopathology mechanisms of test anxiety, an underexplored yet widespread and debilitating condition that can impact early child development. Our findings provide a better understanding of the neurotransmitter basis underlying the emergence of anxiety disorders during development.

Identifying Non-Math Students from Brain MRIs with an Ensemble Classifier Based on Subspace-Enhanced Contrastive Learning

In current research processes, mathematical learning has significantly impacted the brain’s plasticity and cognitive functions. While biochemical changes in brain have been investigated by magnetic resonance spectroscopy, our study attempts to identify non-math students by using magnetic resonance imaging scans (MRIs). The proposed method crops the left middle front gyrus (MFG) region from the MRI, resulting in a multi-instance classification problem. Then, subspace enhanced contrastive learning is employed on all instances to learn robust deep features, followed by an ensemble classifier based on multiple-layer-perceptron models for student identification. The experiments were conducted on 123 MRIs taken from 72 math students and 51 non-math students. The proposed method arrived at an accuracy of 73.7% for image classification and 91.8% for student classification. Results show the proposed workflow successfully identifies the students who lack mathematical education by using MRI data. This study provides insights into the impact of mathematical education on brain development from structural imaging.

Toward Gender Equality in Education—Teachers’ Beliefs about Gender and Math

Math has a strong gender-related image, even among teachers. As teachers hold beliefs about their work, their role, their subject, and their students, they shape girls’ and boys’ mathematical beliefs and attitudes. Research during the past 20 years has shown that teachers’ gender beliefs about mathematics significantly favor boys, thereby reinforcing girls’ low math ability self-concept. Still, there is a lack of studies that examine teachers’ gender-related beliefs based on their underlying assumptions. Our study provides the first empirical evidence of the relationship between general gender stereotypes and math stereotypes. To this end, we used partial correlation and MANCOVA to analyze data from an online survey in 2019/2020 conducted in Switzerland (195 women, 80 men) as part of a cross-cultural comparison study. We therefore created a differentiated profile of prospective teachers by examining their beliefs about their self-image, their image of men and women in society, their essentialist and gender role ideology beliefs, and their math stereotypes. Then, we linked prospective teachers’ beliefs about gender (based on 48 characteristics) to their beliefs about mathematics and about girls’ and boys’ competencies in math. The extensive analysis provides knowledge about prospective teachers and is particularly important for teacher education.

Psychological Resilience and Neurodegenerative Risk: A Connectomics-Transcriptomics Investigation in Healthy Adolescent and Middle-Aged Females

Adverse life events can inflict substantial long-term damage, which, paradoxically, has been posited to stem from initially adaptative responses to the challenges encountered in one's environment. Thus, identification of the mechanisms linking resilience against recent stressors to longer-term psychological vulnerability is key to understanding optimal functioning across multiple timescales. To address this issue, our study tested the relevance of neuro-reproductive maturation and senescence, respectively, to both resilience and longer-term risk for pathologies characterised by accelerated brain aging, specifically, Alzheimer's Disease (AD). Graph theoretical and partial least squares analyses were conducted on multimodal imaging, reported biological aging and recent adverse experience data from the Lifespan Human Connectome Project (HCP). Availability of reproductive maturation/senescence measures restricted our investigation to adolescent (N =178) and middle-aged (N=146) females. Psychological resilience was linked to age-specific brain senescence patterns suggestive of precocious functional development of somatomotor and control-relevant networks (adolescence) and earlier aging of default mode and salience/ventral attention systems (middle adulthood). Biological aging showed complementary associations with the neural patterns relevant to resilience in adolescence (positive relationship) versus middle-age (negative relationship). Transcriptomic and expression quantitative trait locus data analyses linked the neural aging patterns correlated with psychological resilience in middle adulthood to gene expression patterns suggestive of increased AD risk. Our results imply a partially antagonistic relationship between resilience against proximal stressors and longer-term psychological adjustment in later life. They thus underscore the importance of fine-tuning extant views on successful coping by considering the multiple timescales across which age-specific processes may unfold.

An MRI Study on Effects of Math Education on Brain Development Using Multi-Instance Contrastive Learning

This paper explores whether mathematical education has effects on brain development from the perspective of brain MRIs. While biochemical changes in the left middle front gyrus region of the brain have been investigated, we proposed to classify students by using MRIs from the intraparietal sulcus (IPS) region that was left untouched in the previous study. On the cropped IPS regions, the proposed model developed popular contrastive learning (CL) to solve the problem of multi-instance representation learning. The resulted data representations were then fed into a linear neural network to identify whether students were in the math group or the non-math group. Experiments were conducted on 123 adolescent students, including 72 math students and 51 non-math students. The proposed model achieved an accuracy of 90.24 % for student classification, gaining more than 5% improvements compared to the classical CL frame. Our study provides not only a multi-instance extension to CL and but also an MRI insight into the impact of mathematical studying on brain development.

Predicting learning and achievement using GABA and glutamate concentrations in human development

Previous research has highlighted the role of glutamate and gamma-aminobutyric acid (GABA) in learning and plasticity. What is currently unknown is how this knowledge translates to real-life complex cognitive abilities that emerge slowly and how the link between these neurotransmitters and human learning and plasticity is shaped by development. While some have suggested a generic role of glutamate and GABA in learning and plasticity, others have hypothesized that their involvement shapes sensitive periods during development. Here we used a cross-sectional longitudinal design with 255 individuals (spanning primary school to university) to show that glutamate and GABA in the intraparietal sulcus explain unique variance both in current and future mathematical achievement (approximately 1.5 years). Furthermore, our findings reveal a dynamic and dissociable role of GABA and glutamate in predicting learning, which is reversed during development, and therefore provide novel implications for models of learning and plasticity during childhood and adulthood.