Cerebral neurovascular alterations in stable chronic obstructive pulmonary disease: a preliminary fMRI study

Abnormal Brain Functional Connectivity in Patients with Chronic Obstructive Pulmonary Disease and Correlations with Clinical and Cognitive Parameters

Background

Cognitive impairment is a major comorbidity of chronic obstructive pulmonary disease (COPD), but the underlying mechanisms are not fully understood. In this study, we used resting-state functional magnetic resonance imaging to investigate brain functional connectivity (FC) abnormalities in patients with COPD and explored the correlation between abnormal FC and COPD-related clinical parameters.

Methods

Forty-one patients with COPD, without a definite diagnosis of cognitive impairment or depression, and 30 age- and sex-matched controls were recruited. A total of 184 resting-state functional connectivity (RSFC) maps were generated for all seed points. Welch's t-test was used to assess differences in RSFC between the COPD and control groups, and the correlation coefficients between RSFC and clinical parameters were calculated.

Results

Patients with COPD had lower scores on the Mini-Mental State Exam (MMSE) and Korean version of the Montreal Cognitive Assessment and higher scores on the Beck Depression Inventory than the control group. Additionally, patients with COPD showed decreased RSFC in the left middle-posterior cingulate cortex, left posterior-dorsal cingulate cortex, and right superior occipital gyrus and increased RSFC in the left superior temporal sulcus, left posterior transverse collateral sulcus, right occipital pole, and right precentral gyrus. The regions showing differences in FC correlated with MMSE score, COPD symptom assessment scales, such as the COPD Assessment Test and modified Medical Research Council Dyspnea Scale, and pulmonary function parameters, including forced expiratory volume in one second and forced vital capacity.

Conclusion

Patients with COPD showed significant differences in FC within specific brain regions that correlated with symptoms, cognition, and lung function.

COPD, PRISm and lung function reduction affect the brain cortical structure: a Mendelian randomization study

Chronic obstructive pulmonary disease (COPD) has been associated with alterations in the brain cortical structure. Nonetheless, the causality between COPD and brain cortical structure has not been determined. In the present study, we used Mendelian randomization (MR) analysis to explore the causal effects of genetic predicated COPD on brain cortical structure, namely cortical surface area (SA) and cortical thickness (TH). Genetic association summary data for COPD were obtained from the FinnGen consortium (N = 358,369; Ncase = 20,066). PRISm summary genetic data were retrieved from a case-control GWAS conducted in the UK Biobank (N = 296,282). Lung function indices, including forced expiratory volume in one second (FEV1), forced vital capacity (FVC), and FEV1/FVC, were extracted from a meta-analysis of the UK Biobank and SpiroMeta consortium (N = 400,102). Brain cortical structure data were obtained from the ENIGMA consortium (N = 51,665). Inverse-variance weighted (IVW) method was used as the primary analysis, and a series of sensitivity tests were exploited to evaluate the heterogeneity and pleiotropy of our results. The results identified potential causal effects of COPD on several brain cortical specifications, including pars orbitalis, cuneus and inferior parietal gyrus. Furthermore, genetic predicated lung function index (FEV1, FVC and FEV1/FVC), as well as PRISm, also has causal effects on brain cortical structure. According to our results, a total of 15 functional specifications were influenced by lung function index and PRISm. These findings contribute to understanding the causal effects of COPD and lung function to brain cortical structure.

Utilizing Siamese 4D-AlzNet and Transfer Learning to Identify Stages of Alzheimer's Disease

Alzheimer's disease (AD) is the general form of dementia, leading to a progressive neurological disorder characterized by memory loss due to brain cell damage. Artificial Intelligence (AI) assists in the early identification and prediction of AD patients, determining future risks and benefits for radiologists and doctors to save time and cost. Since deep learning (DL) approaches work well with massive datasets and have recently become helpful for AD detection, there remains an area for improvement in automating detection performance. Present approaches somehow addressed the challenges of limited annotated data samples for binary classification. This contrasts with prior state-of-the-art techniques, which were constrained by their incapacity to capture abstract-level information. In this paper, we proposed a Siamese 4D-AlzNet model comprised of four parallel convolutional neural network (CNN) streams (Five CNN layer blocks) and customized transfer learning models (Frozen VGG-19, Frozen VGG-16, and customized AlexNet). Siamese 4D-AlzNet was vertically and horizontally stored, and the spatial features were passed to the final layer for classification. For experiments, T1-weighted MRI images comprised of four distinct subject classes, normal control (NC), mild cognitive impairment (MCI), late mild cognitive impairment (LMCI), and AD, have been employed. Our proposed models achieved outstanding accuracy, with a remarkable 95.05% accuracy distinguishing between normal and AD subjects. The performance across remaining binary class pairs consistently exceeded 90%. We thoroughly compared our model with the latest methods using the same dataset as our reference. Our proposed model improved NC-AD and MCI-AD classification accuracy by 2% 7%.