Mechanisms of cognitive impairment in epilepsy

Cognitive impairment and dementia are increasingly reported as people with epilepsy grow older, with major impact on quality of life. The underlying mechanisms of cognitive dysfunction, however, and the magnitude of associated dementia risk in epilepsy remains unclear. This thesis will explore how cardiovascular risk factors and hippocampal dysfunction are two important mechanistic links between epilepsy and dementia. Using data from a large population-based cohort, the studies in this thesis demonstrate that cardiovascular risk factors are closely related to impairment of executive function. One finding highlighted a continuous dose-response relation of cognition with blood pressure, even in non-hypertensive individuals. The relationship between cardiovascular risk and cognition was mediated through changes in both frontoparietal white and grey matter structural networks. In the same cohort, people with epilepsy and a high cardiovascular risk were over 13 times more likely to develop dementia compared to healthy controls with low cardiovascular risk. People with epilepsy had a greater risk of developing dementia even compared with individuals with a history of stroke, with associated changes to hippocampal volume. To examine specific hippocampal-related cognitive mechanisms that may underlie memory difficulties in epilepsy, I examined two processes believed to be central to encoding and retrieval in the hippocampus: pattern separation and pattern completion. I devised a novel computer-based behavioural paradigm called the Memory Pinhole Task to distinguish them. Impairment of these two cognitive operations have been identified in ageing and other conditions such as Alzheimer’s disease. Compared to healthy controls, pattern separation deficits were observed in people with epilepsy while reduced pattern completion was seen in healthy older individuals. I then mapped these findings from the Memory Pinhole task onto potential brain mechanisms through computational modelling using a neural network. The work in this thesis describes how modifiable cardiovascular risk factors significantly contribute to cognitive ageing and dementia risk in healthy individuals and people with epilepsy. This has important implications for personal health practices and clinical guidelines, especially in people with epilepsy as there is no current guidance to mitigate dementia risk. Further, I describe a framework for testing encoding and retrieval of information into memory which may allow us to better understand difficulties seen in both health and disease

Solving a class of multi-scale elliptic PDEs by Fourier-based mixed physics informed neural networks

Deep neural networks have garnered widespread attention due to their simplicity and flexibility in the fields of engineering and scientific calculation. In this study, we probe into solving a class of elliptic partial differential equations (PDEs) with multiple scales by utilizing Fourier-based mixed physics informed neural networks (dubbed FMPINN), its solver is configured as a multi-scale deep neural network. In contrast to the classical PINN method, a dual (flux) variable about the rough coefficient of PDEs is introduced to avoid the ill-condition of neural tangent kernel matrix caused by the oscillating coefficient of multi-scale PDEs. Therefore, apart from the physical conservation laws, the discrepancy between the auxiliary variables and the gradients of multi-scale coefficients is incorporated into the cost function, obtaining a satisfactory solution of PDEs by minimizing the defined loss through some optimization methods. Additionally, a trigonometric activation function is introduced for FMPINN, which is suited for representing the derivatives of complex target functions. Handling the input data by Fourier feature mapping will effectively improve the capacity of deep neural networks to solve high-frequency problems. Finally, to validate the efficiency and robustness of the proposed FMPINN algorithm, we present several numerical examples of multi-scale problems in various dimensional Euclidean spaces. These examples cover low-frequency and high-frequency oscillation cases, demonstrating the effectiveness of our approach. All code and data accompanying this manuscript will be publicly available at https://github.com/Blue-Giant/FMPINN

Extreme Learning Machine-Assisted Solution of Biharmonic Equations via Its Coupled Schemes

Obtaining the solutions of partial differential equations based on various machine learning methods has drawn more and more attention in the fields of scientific computation and engineering applications. In this work, we first propose a coupled Extreme Learning Machine (called CELM) method incorporated with the physical laws to solve a class of fourth-order biharmonic equations by reformulating it into two well-posed Poisson problems. In addition, some activation functions including tangent, gauss, sine, and trigonometric (sin+cos) functions are introduced to assess our CELM method. Notably, the sine and trigonometric functions demonstrate a remarkable ability to effectively minimize the approximation error of the CELM model. In the end, several numerical experiments are performed to study the initializing approaches for both the weights and biases of the hidden units in our CELM model and explore the required number of hidden units. Numerical results show the proposed CELM algorithm is high-precision and efficient to address the biharmonic equation in both regular and irregular domains

Association of dementia risk with focal epilepsy and modifiable cardiovascular risk factors

Importance: Epilepsy has been associated with cognitive impairment and potentially dementia in older individuals. However, the extent to which epilepsy may increase dementia risk, how this compares with other neurological conditions, and how modifiable cardiovascular risk factors may affect this risk remain unclear. Objective: To compare the differential risks of subsequent dementia for focal epilepsy compared with stroke and migraine as well as healthy controls, stratified by cardiovascular risk. Design, Setting, and Participants: This cross-sectional study is based on data from the UK Biobank, a population-based cohort of more than 500 000 participants aged 38 to 72 years who underwent physiological measurements and cognitive testing and provided biological samples at 1 of 22 centers across the United Kingdom. Participants were eligible for this study if they were without dementia at baseline and had clinical data pertaining to a history of focal epilepsy, stroke, or migraine. The baseline assessment was performed from 2006 to 2010, and participants were followed up until 2021. Exposures: Mutually exclusive groups of participants with epilepsy, stroke, and migraine at baseline assessment and controls (who had none of these conditions). Individuals were divided into low, moderate, or high cardiovascular risk groups based on factors that included waist to hip ratio, history of hypertension, hypercholesterolemia, diabetes, and smoking pack-years. Main Outcomes and Measures: Incident all-cause dementia; measures of executive function; and brain total hippocampal, gray matter, and white matter hyperintensity volumes. Results: Of 495 149 participants (225 481 [45.5%] men; mean [SD] age, 57.5 [8.1] years), 3864 had a diagnosis of focal epilepsy only, 6397 had a history of stroke only, and 14 518 had migraine only. Executive function was comparable between participants with epilepsy and stroke and worse than the control and migraine group. Focal epilepsy was associated with a higher risk of developing dementia (hazard ratio [HR], 4.02; 95% CI, 3.45 to 4.68; P < .001), compared with stroke (HR, 2.56; 95% CI, 2.28 to 2.87; P < .001), or migraine (HR, 1.02; 95% CI, 0.85 to 1.21; P = .94). Participants with focal epilepsy and high cardiovascular risk were more than 13 times more likely to develop dementia (HR, 13.66; 95% CI, 10.61 to 17.60; P < .001) compared with controls with low cardiovascular risk. The imaging subsample included 42 353 participants. Focal epilepsy was associated with lower hippocampal volume (mean difference, −0.17; 95% CI, −0.02 to −0.32; t = −2.18; P = .03) and lower total gray matter volume (mean difference, −0.33; 95% CI, −0.18 to −0.48; t = −4.29; P < .001) compared with controls. There was no significant difference in white matter hyperintensity volume (mean difference, 0.10; 95% CI, −0.07 to 0.26; t = 1.14; P = .26). Conclusions and Relevance: In this study, focal epilepsy was associated with a significant risk of developing dementia, to a greater extent than stroke, which was magnified substantially in individuals with high cardiovascular risk. Further findings suggest that targeting modifiable cardiovascular risk factors may be an effective intervention to reduce dementia risk in individuals with epilepsy

Artificial intelligence for dementia prevention

INTRODUCTION: A wide range of modifiable risk factors for dementia have been identified. Considerable debate remains about these risk factors, possible interactions between them or with genetic risk, and causality, and how they can help in clinical trial recruitment and drug development. Artificial intelligence (AI) and machine learning (ML) may refine understanding.// METHODS: ML approaches are being developed in dementia prevention. We discuss exemplar uses and evaluate the current applications and limitations in the dementia prevention field.// RESULTS: Risk-profiling tools may help identify high-risk populations for clinical trials; however, their performance needs improvement. New risk-profiling and trial-recruitment tools underpinned by ML models may be effective in reducing costs and improving future trials. ML can inform drug-repurposing efforts and prioritization of disease-modifying therapeutics.// DISCUSSION: ML is not yet widely used but has considerable potential to enhance precision in dementia prevention

Identification of Genes with Allelic Imbalance on 6p Associated with Nasopharyngeal Carcinoma in Southern Chinese

Nasopharyngeal carcinoma (NPC) is a malignancy of epithelial origin. The etiology of NPC is complex and includes multiple genetic and environmental factors. We employed case-control analysis to study the association of chromosome 6p regions with NPC. In total, 360 subjects and 360 healthy controls were included, and 233 single nucleotide polymorphisms (SNPs) on 6p were examined. Significant single-marker associations were found for SNPs rs2267633 (p = 4.49×10−5), rs2076483 (most significant, p = 3.36×10−5), and rs29230 (p = 1.43×10−4). The highly associated genes were the gamma-amino butyric acid B receptor 1 (GABBR1), human leukocyte antigen (HLA-A), and HLA complex group 9 (HCG9). Haplotypic associations were found for haplotypes AAA (located within GABBR1, p-value  = 6.46×10−5) and TT (located within HLA-A, p = 0.0014). Further investigation of the homozygous genotype frequencies between cases and controls suggested that micro-deletion regions occur in GABBR1 and neural precursor cell expressed developmentally down-regulated 9 (NEDD9). Quantitative real-time polymerase chain reaction (qPCR) using 11 pairs of NPC biopsy samples confirmed the significant decline in GABBR1 and NEDD9 mRNA expression in the cancer tissues compared to the adjacent non-tumor tissue (p<0.05). Our study demonstrates that multiple chromosome 6p susceptibility loci contribute to the risk of NPC, possibly though GABBR1 and NEDD9 loss of function