Role of Carotid Ultrasonography Combined with Monocyte/HDL Ratio in Internal Carotid Artery Stenosis.

BACKGROUND: Carotid duplex ultrasonography (DUS) is the primary screening tool for carotid artery stenosis, but has low reliability. MHR, which is the ratio of monocytes to high-density lipoprotein cholesterol (HDL-C), can be a marker for the degree and distribution of extracranial and intracranial atherosclerotic stenosis. OBJECTIVE: We determined the diagnostic value of DUS+MHR for internal carotid artery (ICA) stenosis. METHODS: We divided 273 hospitalized patients into non-stenosis (<50%) and ICA stenosis (≥50%) groups based on Digital Subtraction Angiography (DSA). We determined the peak systolic velocity (PSV) in the ICA on DUS, calculated the MHR, and investigated their relationship with ICA stenosis. RESULTS: On DSA, 34.1% (93/273) patients had moderate-to-severe ICA stenosis. DUS and DSA showed low concordance for detecting ICA stenosis (kappa = 0.390). With increasing age, the incidence of moderate-to-severe ICA stenosis increased. PSV, monocyte count, and MHR were significantly greater in the stenosis group than in the non-stenosis group (P < 0.001), while the HDL-C level was significantly lower (P = 0.001). PSV (OR: 1.020, 95% CI: 1.011-1.029, P < 0.001) and MHR (OR: 5.662, 95% CI: 1.945-16.482, P = 0.002) were independent risk factors for ICA stenosis. The area under the receiver operating characteristic curve of PSV+MHR (0.819) was significantly higher than that of PSV or MHR alone (77.42% sensitivity, P = 0.0207; 73.89% specificity, P = 0.0032). CONCLUSION: The combination of ICA PSV on DUS and MHR is better than PSV alone at identifying ICA stenosis and is well-suited to screen high-risk patients.

Multi-networks connectivity at baseline predicts the clinical efficacy of left angular gyrus-navigated rTMS in the spectrum of Alzheimer's disease: A sham-controlled study.

INTRODUCTION: Neuro-navigated repetitive transcranial magnetic stimulation (rTMS) is effective in alleviating cognitive deficits in Alzheimer's disease (AD). However, the strategy for target determination and the mechanisms for cognitive improvement remain unclear. METHODS: One hundred and thirteen elderly subjects were recruited in this study, including both cross-sectional (n = 79) and longitudinal experiments (the rTMS group: n = 24; the sham group: n = 10). The cross-sectional experiment explored the precise intervention target based on the cortical-hippocampal network. The longitudinal experiment investigated the clinical efficacy of neuro-navigated rTMS treatment over a four-week period and explored its underlying neural mechanism using seed-based and network-based analysis. Finally, we applied connectome-based predictive modeling to predict the rTMS response using these functional features at baseline. RESULTS: RTMS at a targeted site of the left angular gyrus (MNI: -45, -67, 38) significantly induced cognitive improvement in memory and language function (p < 0.001). The improved cognition correlated with the default mode network (DMN) subsystems. Furthermore, the connectivity patterns of DMN subsystems (r = 0.52, p = 0.01) or large-scale networks (r = 0.85, p = 0.001) at baseline significantly predicted the Δ language cognition after the rTMS treatment. The connectivity patterns of DMN subsystems (r = 0.47, p = 0.019) or large-scale networks (r = 0.80, p = 0.001) at baseline could predict the Δ memory cognition after the rTMS treatment. CONCLUSION: These findings suggest that neuro-navigated rTMS targeting the left angular gyrus could improve cognitive function in AD patients. Importantly, dynamic regulation of the intra- and inter-DMN at baseline may represent a potential predictor for favorable rTMS treatment response in patients with cognitive impairment.

Retinal Alterations as Potential Biomarkers of Structural Brain Changes in Alzheimer's Disease Spectrum Patients.

Retinal imaging being a potential biomarker for Alzheimer's disease is gradually attracting the attention of researchers. However, the association between retinal parameters and AD neuroimaging biomarkers, particularly structural changes, is still unclear. In this cross-sectional study, we recruited 25 cognitively impaired (CI) and 21 cognitively normal (CN) individuals. All subjects underwent retinal layer thickness and microvascular measurements with optical coherence tomography angiography (OCTA). Gray matter and white matter (WM) data such as T1-weighted magnetic resonance imaging and diffusion tensor imaging, respectively, were also collected. In addition, hippocampal subfield volumes and WM tract microstructural alterations were investigated as classical AD neuroimaging biomarkers. The microvascular and retinal features and their correlation with brain structural imaging markers were further analyzed. We observed a reduction in vessel density (VD) at the inferior outer (IO) sector (p = 0.049), atrophy in hippocampal subfield volumes, such as the subiculum (p = 0.012), presubiculum (p = 0.015), molecular_layer_HP (p = 0.033), GC-ML-DG (p = 0.043) and whole hippocampus (p = 0.033) in CI patients. Altered microstructural integrity of WM tracts in CI patients was also discovered in the cingulum hippocampal part (CgH). Importantly, we detected significant associations between retinal VD and gray matter volumes of the hippocampal subfield in CI patients. These findings suggested that the retinal microvascular measures acquired by OCTA may be markers for the early prediction of AD-related structural brain changes.

Applying Retinal Vascular Structures Characteristics Coupling with Cortical Visual System in Alzheimer's Disease Spectrum Patients.

Cortical visual system dysfunction is closely related to the progression of Alzheimer's Disease (AD), while retinal vascular structures play an important role in the integrity of the function of the visual network and are a potential biomarker of AD. This study explored the association between the cortical visual system and retinal vascular structures in AD-spectrum patients, and it established a screening tool to detect preclinical AD based on these parameters identified in a retinal examination. A total of 42 subjects were enrolled and were distributed into two groups: 22 patients with cognitive impairment and 20 healthy controls. All participants underwent neuropsychological tests, optical coherence tomography angiography and resting-state fMRI imaging. Seed-based functional connectivity analysis was used to construct the cortical visual network. The association of functional connectivity of the cortical visual system and retinal vascular structures was further explored in these subjects. This study found that the cognitive impairment group displayed prominently decreased functional connectivity of the cortical visual system mainly involving the right inferior temporal gyrus, left supramarginal gyrus and right postcentral gyrus. Meanwhile, we observed that retinal vascular structure characteristics deteriorated with the decline in functional connectivity in the cortical visual system. Our study provided novel insights into the aberrant cortical visual system in patients with cognitive impairment that strongly emphasized the critical role of retinal vascular structure characteristics, which could be used as potential biomarkers for diagnosing and monitoring the progression of AD.

Altered local gyrification and functional connectivity in type 2 diabetes mellitus patients with mild cognitive impairment: A pilot cross-sectional small-scale single center study.

Aims: This research aimed to explore alterations in the local gyrification index (GI) and resting-state functional connectivity (RSFC) in type 2 diabetes mellitus (T2DM) patients with mild cognitive impairment (MCI). Methods: In this study, 126 T2DM patients with MCI (T2DM-MCI), 154 T2DM patients with normal cognition (T2DM-NC), and 167 healthy controls (HC) were recruited. All subjects underwent a battery of neuropsychological tests. A multimodal approach combining surface-based morphometry (SBM) and seed-based RSFC was used to determine the structural and functional alterations in patients with T2DM-MCI. The relationships among the GI, RSFC, cognitive ability, and clinical variables were characterized. Results: Compared with the T2DM-NC group and HC group, T2DM-MCI patients showed significantly reduced GI in the bilateral insular cortex. Decreased RSFC was found between the left insula and right precuneus, and the right superior frontal gyrus (SFG). The altered GI was correlated with T2DM duration, global cognition, and episodic memory. The mediation effects of RSFC on the association between GI and cognition were not statistically significant. Conclusion: Our results suggest that GI may serve as a novel neuroimaging biomarker to predict T2DM-related MCI and help us to improve the understanding of the neuropathological effects of T2DM-related MCI.

Lobar Cerebral Microbleeds Are Associated With Cognitive Decline in Patients With Type 2 Diabetes Mellitus.

Purpose: Combined the number, volume, and location of cerebral microbleeds (CMBs), this study aimed to explore the different features of CMBs and their correlation with cognitive ability in patients with type 2 diabetes mellitus (T2DM). Methods: This study recruited 95 patients with T2DM and 80 healthy control (HC) individuals. AccuBrain®, an automated tool, was used to obtain the number and volume of CMBs. The scores on global cognition and five cognitive domains were derived from a battery of cognitive tests. The logistic regression and multivariate linear regression were conducted to determine the relationship between the CMBs (number, volume, and location) and cognitive ability in patients with T2DM. Results: After adjusting for several variables, the total volume of CMBs (OR = 0.332, 95%CI: 0.133-0.825, and p = 0.018) was independent risk factor for cognitive impairment, whereas the total number of CMBs was not (OR = 0933, 95%CI: 0.794-1.097, and p = 0.400). Furthermore, the volume of CMBs in lobar regions was independently associated with working memory (ß = -0.239, 95%CI: -0.565 to -0.035, and p = 0.027). However, no significant correlation between the number of CMBs (both lobar and deep/infratentorium) and any cognitive domains was observed. Conclusions: Lobar CMBs was related with cognitive impairment in patients with T2DM and might be a potential early warning signal. Compared with the counting analysis, the quantitative method offered a more sensitive and objective measurement for studying imaging features of CMBs.

Cognitive Improvement via Left Angular Gyrus-Navigated Repetitive Transcranial Magnetic Stimulation Inducing the Neuroplasticity of Thalamic System in Amnesic Mild Cognitive Impairment Patients.

BACKGROUND: Stimulating superficial brain regions highly associated with the hippocampus by repetitive transcranial magnetic stimulation (rTMS) may improve memory of Alzheimer's disease (AD) spectrum patients. OBJECTIVE: We recruited 16 amnesic mild cognitive impairment (aMCI) and 6 AD patients in the study. All the patients were stimulated to the left angular gyrus, which was confirmed a strong link to the hippocampus through neuroimaging studies, by the neuro-navigated rTMS for four weeks. METHODS: Automated fiber quantification using diffusion tensor imaging metrics and graph theory analysis on functional network were employed to detect the neuroplasticity of brain networks. RESULTS: After neuro-navigated rTMS intervention, the episodic memory of aMCI patients and Montreal Cognitive Assessment score of two groups were significantly improved. Increased FA values of right anterior thalamic radiation among aMCI patients, while decreased functional network properties of thalamus subregions were observed, whereas similar changes not found in AD patients. It is worth noting that the improvement of cognition was associated with the neuroplasticity of thalamic system. CONCLUSION: We speculated that the rTMS intervention targeting left angular gyrus may be served as a strategy to improve cognitive impairment at the early stage of AD patients, supporting by the neuroplasticity of thalamic system.

Machine learning based on the multimodal connectome can predict the preclinical stage of Alzheimer's disease: a preliminary study.

OBJECTIVES: Subjective cognitive decline (SCD) may be a preclinical stage of Alzheimer's disease (AD). Neuroimaging studies suggest that abnormal brain connectivity plays an important role in the pathophysiology of SCD. However, most previous studies focused on single modalities only. Multimodal combinations can more effectively utilize various information and little is known about their diagnostic value in SCD. METHODS: One hundred ten SCD individuals and well-matched healthy controls (HCs) were recruited in this study (the primary sample: 35 SCD and 36 HC; the validation sample: 21 SCD and 18 HC). Multimodal imaging data were used to construct functional, anatomical, and morphological networks, respectively. These networks were used in combination with a multiple kernel learning-support vector machine to predict SCD individuals. We validated our model on another independent sample. Multiple linear regression (MLR) analyses were conducted to investigate the relationships among network metrics, cognition, and pathological biomarkers. RESULTS: We found that the characteristics identified from the multimodal network were primarily located in the default mode network (DMN) and salience network (SN), achieving an accuracy of 88.73% (an accuracy of 79.49% for an independent sample) based on the integration of the three modalities. MLR analyses showed that increased AV45 SUVRs were significantly associated with impaired memory function, the enhanced functional connectivity, and the decreased morphological connectivity. CONCLUSION: This study suggests that abnormal multimodal connections within DMN and SN can be used as effective biomarkers to identify SCD and provide insight into understanding the pathophysiological mechanisms underlying SCD. KEY POINTS: • Multimodal brain networks improve the detection accuracy of SCD. • Abnormal connections within DMN and SN can be used as effective biomarkers for the identification of SCD.

Core-Centered Connection Abnormalities Associated with Pathological Features Mediate the Progress of Cognitive Impairments in Alzheimer's Disease Spectrum Patients.

BACKGROUND: Abnormal default mode network (DMN) was associated with the progress of Alzheimer's disease (AD). Rather than treat the DMN as a unitary network, it can be further divided into three subsystems with different functions. OBJECTIVE: It remains unclear the interactions of DMN subsystems associated with the progress of cognitive impairments and AD pathological features. METHODS: This study has recruited 187 participants, including test data and verification data. Firstly, an imaging analysis approach was utilized to investigate disease-related differences in the interactions of DMN subsystems in test data (n = 149), including 42 cognitively normal subjects, 43 early mild cognitive impairment (EMCI), 32 late mild cognitive impairment (LMCI), and 32 AD patients. Brain-behavior-pathological relationships regarding to the interactions among DMN subsystems were then further examined. Secondly, DMN subsystems abnormalities for classifying AD spectrum population in the independent verification data (n = 38). RESULTS: This study found that the impaired cognition relates to disturbances in the interactions between DMN subsystems but preferentially in core subsystem, and the abnormal regulatory processes of core subsystem were significantly associated with the levels of cerebrospinal fluid Aß and tau in AD-spectrum patients. Meantime, the nonlinear relationship between dysfunctional core subsystem and impaired cognition was observed as one progresses through the stages of MCI to AD. Importantly, this classification presented a higher sensitivity and specificity dependent on the core-centered connection abnormalities. CONCLUSION: The abnormal interaction patterns of DMN subsystems at an early stage of AD appeared and presented as core-centered connection abnormalities, which were the potential neuroimaging features for monitoring the development of AD.

Brain Structural Network Compensation Is Associated With Cognitive Impairment and Alzheimer's Disease Pathology.

BACKGROUND: Structural network alterations in Alzheimer's disease (AD) are related to worse cognitive impairment. The aim of this study was to quantify the alterations in gray matter associated with impaired cognition and their pathological biomarkers in AD-spectrum patients. METHODS: We extracted gray matter networks from 3D-T1 magnetic resonance imaging scans, and a graph theory analysis was used to explore alterations in the network metrics in 34 healthy controls, 70 mild cognitive impairment (MCI) patients, and 40 AD patients. Spearman correlation analysis was computed to investigate the relationships among network properties, neuropsychological performance, and cerebrospinal fluid pathological biomarkers (i.e., Aß, t-tau, and p-tau) in these subjects. RESULTS: AD-spectrum individuals demonstrated higher nodal properties and edge properties associated with impaired memory function, and lower amyloid-ß or higher tau levels than the controls. Furthermore, these compensations at the brain regional level in AD-spectrum patients were mainly in the medial temporal lobe; however, the compensation at the whole-brain network level gradually extended from the frontal lobe to become widely distributed throughout the cortex with the progression of AD. CONCLUSION: The findings provide insight into the alterations in the gray matter network related to impaired cognition and pathological biomarkers in the progression of AD. The possibility of compensation was detected in the structural networks in AD-spectrum patients; the compensatory patterns at regional and whole-brain levels were different and the clinical significance was highlighted.

Hyperconnectivity of Self-Referential Network as a Predictive Biomarker of the Progression of Alzheimer's Disease.

BACKGROUND: Self-referential processing is associated with the progression of Alzheimer's disease (AD), and cerebrospinal fluid (CSF) proteins have become accepted biomarkers of AD. OBJECTIVE: Our objective in this study was to focus on the relationships between the self-referential network (SRN) and CSF pathology in AD-spectrum patients. METHODS: A total of 80 participants, including 20 cognitively normal, 20 early mild cognitive impairment (EMCI), 20 late MCI (LMCI), and 20 AD, were recruited for this study. Independent component analysis was used to explore the topological SRN patterns, and the abnormalities of this network were identified at different stages of AD. Finally, CSF pathological characteristics (i.e., CSF Aß, t-tau, and p-tau) that affected the abnormalities of the SRN were further determined during the progression of AD. RESULTS: Compared to cognitively normal subjects, AD-spectrum patients (i.e., EMCI, LMCI, and AD) showed a reversing trend toward an association between CSF pathological markers and the abnormal SRN occurring during the progression of AD. However, a certain disease state (i.e., the present LMCI) with a low concentration of CSF tau could evoke more hyperconnectivity of the SRN than other patients with progressively increasing concentrations of CSF tau (i.e., EMCI and AD), and this fluctuation of CSF tau was more sensitive to the hyperconnectivity of the SRN than the dynamic changes of CSF Aß. CONCLUSION: The integrity of the SRN was closely associated with CSF pathological characteristics, and these findings support the view that the hyperconnectivity of the SRN will play an important role in monitoring the progression of the pre-dementia state to AD.

Aberrant White Matter Microstructure as a Potential Diagnostic Marker in Alzheimer's Disease by Automated Fiber Quantification.

Neuroimaging evidence has suggested white matter microstructure are heavily affected in Alzheimer's disease (AD). However, whether white matter dysfunction is localized at the specific regions of fiber tracts and whether they would be a potential biomarker for AD remain unclear. By automated fiber quantification (AFQ), we applied diffusion tensor images from 25 healthy controls (HC), 24 amnestic mild cognitive impairment (aMCI) patients and 18 AD patients to create tract profiles along 16 major white matter fibers. We compared diffusion metrics [Fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (DA), and radial diffusivity (DR)] between groups. To assess the diagnostic value, we applied a random forest (RF) classifier, a type of machine learning method. In the global tract level, we found that aMCI and AD patients showed higher MD, DA, and DR values in some fiber tracts mostly in the left hemisphere compared to HC. In the point-wise level, widespread disruption were distributed on specific locations of different tracts. The point-wise MD measurements presented the best classification performance with respect to differentiating AD from HC. The two most important variables were localized in the prefrontal potion of left uncinate fasciculus and anterior thalamic radiation. In addition, the point-wise DA in the posterior component of the left cingulum cingulate displayed the most robust discriminative ability to identify AD from aMCI. Our findings provide evidence that white matter abnormalities based on the AFQ method could be as a diagnostic biomarker in AD.

The compensatory phenomenon of the functional connectome related to pathological biomarkers in individuals with subjective cognitive decline.

BACKGROUND: Subjective cognitive decline (SCD) is a preclinical stage along the Alzheimer's disease (AD) continuum. However, little is known about the aberrant patterns of connectivity and topological alterations of the brain functional connectome and their diagnostic value in SCD. METHODS: Resting-state functional magnetic resonance imaging and graph theory analyses were used to investigate the alterations of the functional connectome in 66 SCD individuals and 64 healthy controls (HC). Pearson correlation analysis was computed to assess the relationships among network metrics, neuropsychological performance and pathological biomarkers. Finally, we used the multiple kernel learning-support vector machine (MKL-SVM) to differentiate the SCD and HC individuals. RESULTS: SCD individuals showed higher nodal topological properties (including nodal strength, nodal global efficiency and nodal local efficiency) associated with amyloid-ß levels and memory function than the HC, and these regions were mainly located in the default mode network (DMN). Moreover, increased local and medium-range connectivity mainly between the bilateral parahippocampal gyrus (PHG) and other DMN-related regions was found in SCD individuals compared with HC individuals. These aberrant functional network measures exhibited good classification performance in the differentiation of SCD individuals from HC individuals at an accuracy up to 79.23%. CONCLUSION: The findings of this study provide insight into the compensatory mechanism of the functional connectome underlying SCD. The proposed classification method highlights the potential of connectome-based metrics for the identification of the preclinical stage of AD.

Synthesis of Sulfur-Containing Olefinic Peptide Mimetic Farnesyl Transferase Inhibitors Using the Nozaki-Hiyama-Kishi Reaction and Cuprate S(N)2' Displacements.

Syntheses of the potent sulfur-containing tetrapeptide mimetic farnesyl transferase inhibitors B956 (22) and B957 (23) are described. The two double bonds in 22 and 23 were constructed by application of iterative NHK and cuprate S(N)2' reactions. Normal syn NHK reaction and substrate-dependent syn and anti-S(N)2' diastereoselectivities accompanied by exclusive E-olefin selectivity were observed for the first NHK iteration (1 --> 4). In the second iteration, unexpected epimerization and a strong preference for syn diastereoselectivity was observed for the NHK reaction (5b --> 7a + 9a) while an unusual Z-olefin was observed for the S(N)2' reaction (7b --> 11). Deprotection conditions were optimized to ensure high purity and yield of the final aminothiol compounds.