Spinal Alterations of Reil Insula in Alzheimer's Disease

The possible connection between neutrophil-to-high-density lipoprotein ratio and cerebral perfusion in clinically established corticobasal syndrome: a pilot study

Introduction

Progressive supranuclear palsy (PSP) and corticobasal syndrome (CBS) are tauopathic atypical parkinsonisms. Given their overlap in terms of clinical manifestation, there is growing interest in the mechanisms leading to these entities.

Materials and methods

In total, 71 patients were included in the study, 19 of whom were clinically diagnosed with CBS, 37 with PSP, and 15 with Parkinson's disease (PD). The mean ages of the participants were 72.8, 72.9, and 64.0 years, respectively, and the disease duration varied from 3 to 6 years. Each individual underwent blood collection. Morphological and biochemical evaluation of blood samples was performed to analyze the neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), and neutrophil-to-high-density lipoprotein ratio (NHR). A single-photon emission computed tomography (SPECT) with technetium-99m hexamethylpropyleneamine oxime (99Tc-HMPAO) tracer was used to assess perfusion in two regions of interest (ROI): the thalamus and insula. Using Pearson correlation to assess the linear relationship between NHR and perfusion in the insula and thalamus for CBS, PSP, and PD patients, the authors intended to verify possible correlations between NLR, PLR, and NHR and perfusion in the indicated ROIs.

Results

The study revealed a negative linear correlation between NHR and perfusion of both the left (Insula L; R = -0.59) and right (Insula R; R = -0.58) insula regions. Similar to the insula, a linear correlation between NHR and activity in both the left (Thalamus L) and right (Thalamus R) thalamus regions in CBS subjects with a relatively stronger correlation in the right thalamus (R = -0.64 vs. R = -0.58) was found. These observations were not confirmed in PSP and PD patients.

Conclusion

Simultaneously using non-specific parameters for peripheral inflammation (NLR, PLR, and NHR) and perfusion, SPECT may be an interesting beginning point for further analysis of inflammatory disease mechanisms. To the best of our knowledge, this is the first study to address the potential correlation between the peripheral neuroinflammatory markers NLR, PLR, and NHR and perfusion disturbances in particular ROIs.

Local Brain Network Alterations and Olfactory Impairment in Alzheimer's Disease: An fMRI and Graph-Based Study

Alzheimer's disease (AD) is associated with the abnormal connection of functional networks. Olfactory impairment occurs in early AD; therefore, exploring alterations in olfactory-related regions is useful for early AD diagnosis. We combined the graph theory of local brain network topology with olfactory performance to analyze the differences in AD brain network characteristics. A total of 23 patients with AD and 18 normal controls were recruited for resting-state functional magnetic resonance imaging (fMRI), clinical neuropsychological examinations and the University of Pennsylvania Smell Identification Test (UPSIT). Between-group differences in the topological properties of the local network were compared. Pearson correlations were explored based on differential brain regions and olfactory performance. Statistical analysis revealed a correlation of the degree of cognitive impairment with olfactory recognition function. Local node topological properties were significantly altered in many local brain regions in the AD group. The nodal clustering coefficients of the bilateral temporal pole: middle temporal gyrus (TPOmid), degree centrality of the left insula (INS.L), degree centrality of the right middle temporal gyrus (MTG.R), and betweenness centrality of the left middle temporal gyrus (MTG.L) were related to olfactory performance. Alterations in local topological properties combined with the olfactory impairment can allow early identification of abnormal olfactory-related regions, facilitating early AD screening.

Effective Diagnosis of Alzheimer's Disease via Multimodal Fusion Analysis Framework

Alzheimer’s disease (AD) is a complex neurodegenerative disease involving a variety of pathogenic factors, and the etiology detection of this disease has been a major concern of researchers. Neuroimaging is a basic and important means to explore the problem. It is the main current scientific research direction for combining neuroimaging with other modal data to dig deep into the potential information of AD through the complementarities among multiple data points. Machine learning methods possess great potentiality and have reached some achievements in this research area. A few studies have proposed some solutions to the effects of multimodal data fusion, however, the overall analytical framework for data fusion and fusion result analysis has thus far been ignored. In this paper, we first put forward a novel multimodal data fusion method, and further present a new machine learning framework of data fusion, classification, feature selection, and disease-causing factor extraction. The real dataset of 37 AD patients and 35 normal controls (NC) with functional magnetic resonance imaging (fMRI) and genetic data was used to verify the effectiveness of the framework, which was more accurate in classification and optimal feature extraction than other methods. Furthermore, we revealed disease-causing brain regions and genes, such as the olfactory cortex, insula, posterior cingulate gyrus, lingual gyrus, CNTNAP2, LRP1B, FRMD4A, and DAB1. The results show that the machine learning framework could effectively perform multimodal data fusion analysis, providing new insights and perspectives for the diagnosis of Alzheimer’s disease.