A Systematic Review of MRI Neuroimaging for Education Research

Alzheimer's disease: a comprehensive review of epidemiology, risk factors, symptoms diagnosis, management, caregiving, advanced treatments and associated challenges

Background

Alzheimer's disease (AD) is a chronic, progressive neurodegenerative disorder characterized by cognitive decline, memory loss, and impaired reasoning. It is the leading cause of dementia in older adults, marked by the pathological accumulation of amyloid-beta plaques and neurofibrillary tangles. These pathological changes lead to widespread neuronal damage, significantly impacting daily functioning and quality of life.

Objective

This comprehensive review aims to explore various aspects of Alzheimer's disease, including its epidemiology, risk factors, clinical presentation, diagnostic advancements, management strategies, caregiving challenges, and emerging therapeutic interventions.

Methods

A systematic literature review was conducted across multiple electronic databases, including PubMed, MEDLINE, Cochrane Library, and Scopus, from their inception to May 2024. The search strategy incorporated a combination of keywords and Medical Subject Headings (MeSH) terms such as "Alzheimer's disease," "epidemiology," "risk factors," "symptoms," "diagnosis," "management," "caregiving," "treatment," and "novel therapies." Boolean operators (AND, OR) were used to refine the search, ensuring a comprehensive analysis of the existing literature on Alzheimer's disease.

Results

AD is significantly influenced by genetic predispositions, such as the apolipoprotein E (APOE) ε4 allele, along with modifiable environmental factors like diet, physical activity, and cognitive engagement. Diagnostic approaches have evolved with advances in neuroimaging techniques (MRI, PET), and biomarker analysis, allowing for earlier detection and intervention. The National Institute on Aging and the Alzheimer's Association have updated diagnostic criteria to include biomarker data, enhancing early diagnosis.

Conclusion

The management of AD includes pharmacological treatments, such as cholinesterase inhibitors and NMDA receptor antagonists, which provide symptomatic relief but do not slow disease progression. Emerging therapies, including amyloid-beta and tau-targeting treatments, gene therapy, and immunotherapy, offer potential for disease modification. The critical role of caregivers is underscored, as they face considerable emotional, physical, and financial burdens. Support programs, communication strategies, and educational interventions are essential for improving caregiving outcomes. While significant advancements have been made in understanding and managing AD, ongoing research is necessary to identify new therapeutic targets and enhance diagnostic and treatment strategies. A holistic approach, integrating clinical, genetic, and environmental factors, is essential for addressing the multifaceted challenges of Alzheimer's disease and improving outcomes for both patients and caregivers.

Between neurons and networks: investigating mesoscale brain connectivity in neurological and psychiatric disorders

The study of brain connectivity has been a cornerstone in understanding the complexities of neurological and psychiatric disorders. It has provided invaluable insights into the functional architecture of the brain and how it is perturbed in disorders. However, a persistent challenge has been achieving the proper spatial resolution, and developing computational algorithms to address biological questions at the multi-cellular level, a scale often referred to as the mesoscale. Historically, neuroimaging studies of brain connectivity have predominantly focused on the macroscale, providing insights into inter-regional brain connections but often falling short of resolving the intricacies of neural circuitry at the cellular or mesoscale level. This limitation has hindered our ability to fully comprehend the underlying mechanisms of neurological and psychiatric disorders and to develop targeted interventions. In light of this issue, our review manuscript seeks to bridge this critical gap by delving into the domain of mesoscale neuroimaging. We aim to provide a comprehensive overview of conditions affected by aberrant neural connections, image acquisition techniques, feature extraction, and data analysis methods that are specifically tailored to the mesoscale. We further delineate the potential of brain connectivity research to elucidate complex biological questions, with a particular focus on schizophrenia and epilepsy. This review encompasses topics such as dendritic spine quantification, single neuron morphology, and brain region connectivity. We aim to showcase the applicability and significance of mesoscale neuroimaging techniques in the field of neuroscience, highlighting their potential for gaining insights into the complexities of neurological and psychiatric disorders.

The Brain Economy: Advancing Brain Science to Better Understand the Modern Economy

The coming years are likely to be turbulent due to a myriad of factors or polycrisis, including an escalation in climate extremes, emerging public health threats, weak productivity, increases in global economic instability and further weakening in the integrity of global democracy. These formidable challenges are not exogenous to the economy but are in some cases generated by the system itself. They can be overcome, but only with far-reaching changes to global economics. Our current socio-economic paradigm is insufficient for addressing these complex challenges, let alone sustaining human development, well-being and happiness. To support the flourishing of the global population in the age of polycrisis, we need a novel, person-centred and collective paradigm. The brain economy leverages insights from neuroscience to provide a novel way of centralising the human contribution to the economy, how the economy in turn shapes our lives and positive feedbacks between the two. The brain economy is primarily based on Brain Capital, an economic asset integrating brain health and brain skills, the social, emotional, and the diversity of cognitive brain resources of individuals and communities. People with healthy brains are essential to navigate increasingly complex systems. Policies and investments that improve brain health and hence citizens' cognitive functions and boost brain performance can increase productivity, stimulate greater creativity and economic dynamism, utilise often underdeveloped intellectual resources, afford social cohesion, and create a more resilient, adaptable and sustainability-engaged population.

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.

Improved Alzheimer's Disease Detection by MRI Using Multimodal Machine Learning Algorithms

Adult-onset dementia disorders represent a challenge for modern medicine. Alzheimer's disease (AD) represents the most diffused form of adult-onset dementias. For half a century, the diagnosis of AD was based on clinical and exclusion criteria, with an accuracy of 85%, which did not allow for a definitive diagnosis, which could only be confirmed by post-mortem evaluation. Machine learning research applied to Magnetic Resonance Imaging (MRI) techniques can contribute to a faster diagnosis of AD and may contribute to predicting the evolution of the disease. It was also possible to predict individual dementia of older adults with AD screening data and ML classifiers. To predict the AD subject status, the MRI demographic information and pre-existing conditions of the patient can help to enhance the classifier performance. In this work, we proposed a framework based on supervised learning classifiers in the dementia subject categorization as either AD or non-AD based on longitudinal brain MRI features. Six different supervised classifiers are incorporated for the classification of AD subjects and results mentioned that the gradient boosting algorithm outperforms other models with 97.58% of accuracy.