Subjective cognitive decline (SCD) is a high-risk population in the preclinical stage of Alzheimer's disease (AD), and olfactory dysfunction is a risk factor for dementia progression. The present study aimed to explore the patterns of functional connectivity (FC) changes in the olfactory neural circuits during olfactory stimulation in SCD subjects. A total of 56 SCD subjects and 56 normal controls (NCs) were included. All subjects were assessed with a cognitive scale, an olfactory behavior test, and olfactory task-based functional magnetic resonance imaging scanning. The FC differences in olfactory neural circuits between the two groups were analyzed by the generalized psychophysiological interaction. Additionally, we calculated and compared the activation of brain regions within the olfactory neural circuits during odor stimulation, the volumetric differences in brain regions showing FC differences between groups, and the correlations between neuroimaging indicators and olfactory behavioral and cognitive scale scores. During odor stimulation, the FC between the bilateral primary olfactory cortex (bPOC) and the right hippocampus in the SCD group was significantly reduced; while the FC between the right hippocampus and the right frontal cortex was significantly increased in the SCD group. The bPOC of all subjects showed significant activation, but no significant difference in activation between groups was found. No significant differences were observed in the volume of the brain regions within the olfactory neural circuits or in olfactory behavior between groups. The volume of the bPOC and right frontal cortex was significantly positively correlated with olfactory identification, and the volume of the right frontal cortex and right hippocampus was significantly correlated with cognitive functions. Furthermore, a significant correlation between the activation of bPOC and the olfactory threshold was found in the whole cohort. These results suggested that while the structure of the olfactory neural circuits and olfactory behavior in SCD subjects remained stable, there were significant changes observed in the FC of the olfactory neural circuits (specifically, the POC-hippocampus-frontal cortex neural circuits) during odor stimulation. These findings highlight the potential of FC alterations as sensitive imaging markers for identifying high-risk individuals in the early stage of AD.

Rationale and Objectives: Homozygous deletion (HD) of CDKN2A/B holds important prognostic value in gliomas. This study aimed to explore the predictive potential of conventional MRI characteristics combined with dynamic contrast-enhanced MRI parameters in predicting CDKN2A/B HD status in gliomas. Materials and Methods: Preoperative MRI data of 105 patients (69 without CDKN2A/B HD, and 36 with CDKN2A/B homozygous deletion) with gliomas were retrospectively collected. Conventional MRI features and dynamic contrast-enhanced-MRI qualitative parameter time-intensity curve type, quantitative parameters Ktrans, Kep, Ve, Vp, and iAUC were obtained. Logistic regression models for prediction of CDKN2A/B HD status were constructed in all types of gliomas and both subtypes of IDH-mutant and IDH-wild gliomas. Results: Multivariate analysis for all patients demonstrated that age (OR = 1.103, p = 0.002) and Ktrans (OR = 1.051, p < 0.001) independently predicted CDKN2A/B HD. In IDH-mutant subgroup, multivariate analysis results indicated that Ktrans (OR = 1.098, p = 0.031) emerged as autonomous predictors of CDKN2A/B HD. In IDH-wild subgroup, age (OR = 1.111, p = 0.002) and Ktrans (OR = 1.032, p = 0.001) were independent predictors of CDKN2A/B HD according to the multivariate analysis. The areas under the receiver operating characteristic curve of the corresponding models were 0.90, 0.95 and 0.84, respectively. Conclusion: Ktrans can serve as valuable predictive parameters for identifying CDKN2A/B HD status in all types of gliomas and both subtypes of IDH-mutant and IDH-wild gliomas. These findings provide a foundation for precise preoperative non-invasive diagnosis and personalized treatment approaches for glioma patients.

The Delta variant of COVID-19 has emerged and spread globally since May 2021 and has been reported in more than 70 countries. The status of the vaccination, symptom onset time, and CT imaging signatures in the infected population have not been fully investigated and clarified. This study included 131 patients who were infected with the Delta variant of COVID-19. After screening, 106 patients with 458 follow-up CT scans were retrospectively selected and divided into complete and incomplete vaccination groups (66 and 40 patients, respectively). Imaging features were automatically extracted, and infection distribution in lung fields and progression pattern tendency were investigated. Furthermore, we extracted the most related clinical and imaging features to establish a vaccination status classification model. An independent testing dataset with 55 patients from another inpatient ward was enrolled to evaluate the generalizability of the model. The severity of infection in the lung and lung fields of the complete vaccination group was overall lower than those of the incomplete vaccination group. A relatively earlier peak CT abnormality was found on days 8–11 in the complete vaccination group than in the incomplete vaccination group on days 12–15 after the first positive PCR time. The vaccination status classification model achieved the highest performance with an AUC of 0.929 and accuracy of 0.864 in the testing set and an AUC of 0.858 and accuracy of 0.727 in the independent testing set. In summary, compared to the incomplete vaccination group, the fully vaccinated patients had milder CT abnormalities and earlier peak time for chest impairment. Therefore, the vaccination status is determinable through dynamic imaging and clinical features.

Cerebral small vessel disease (CSVD) is a common disease that seriously endangers people’s health, and is easily overlooked by both patients and clinicians due to its near-silent onset. Dynamic functional connectivity (DFC) is a new concept focusing on the dynamic features and patterns of brain networks that represents a powerful tool for gaining novel insight into neurological diseases. To assess alterations in DFC in CSVD patients, and the correlation of DFC with cognitive function. We enrolled 35 CSVD patients and 31 normal control subjects (NC). Resting-state functional MRI (rs-fMRI) with a sliding-window approach and k -means clustering based on independent component analysis (ICA) was used to evaluate DFC. The temporal properties of fractional windows and the mean dwell time in each state, as well as the number of transitions between each pair of DFC states, were calculated. Additionally, we assessed the functional connectivity (FC) strength of the dynamic states and the associations of altered neuroimaging measures with cognitive performance. A dynamic analysis of all included subjects suggested four distinct functional connectivity states. Compared with the NC group, the CSVD group had more fractional windows and longer mean dwell times in state 4 characterized by sparse FC both inter-network and intra-networks. Additionally, the CSVD group had a reduced number of windows and shorter mean dwell times compared to the NC group in state 3 characterized by highly positive FC between the somatomotor and visual networks, and negative FC in the basal ganglia and somatomotor and visual networks. The number of transitions between state 2 and state 3 and between state 3 and state 4 was significantly reduced in the CSVD group compared to the NC group. Moreover, there was a significant difference in the FC strength between the two groups, and the altered temporal properties of DFC were significantly related to cognitive performance. Our study indicated that CSVD is characterized by altered temporal properties in DFC that may be sensitive neuroimaging biomarkers for early disease identification. Further study of DFC alterations could help us to better understand the progressive dysfunction of networks in CSVD patients.

Brain dynamics and the associations with spatial navigation in individuals with subjective cognitive decline (SCD) remain unknown. In this study, a hidden Markov model (HMM) was inferred from resting-state functional magnetic resonance imaging data in a cohort of 80 SCD and 77 normal control (NC) participants. By HMM, 12 states with distinct brain activity were identified. The SCD group showed increased fractional occupancy in the states with less activated ventral default mode, posterior salience, and visuospatial networks, while decreased fractional occupancy in the state with general network activation. The SCD group also showed decreased probabilities of transition into and out of the state with general network activation, suggesting an inability to dynamically upregulate and downregulate brain network activity. Significant correlations between brain dynamics and spatial navigation were observed. The combined features of spatial navigation and brain dynamics showed an area under the curve of 0.854 in distinguishing between SCD and NC. The findings may provide exploratory evidence of the reconfiguration of brain network dynamics underlying spatial deficits in SCD.

Background Subjective cognitive decline (SCD) may serve as a symptomatic indicator for preclinical Alzheimer’s disease; however, SCD is a heterogeneous entity regarding clinical progression. We aimed to investigate whether spatial navigation could reveal subcortical structural alterations and the risk of progression to objective cognitive impairment in SCD individuals. Methods One hundred and eighty participants were enrolled: those with SCD ( n = 80), normal controls (NCs, n = 77), and mild cognitive impairment (MCI, n = 23). SCD participants were further divided into the SCD-good (G-SCD, n = 40) group and the SCD-bad (B-SCD, n = 40) group according to their spatial navigation performance. Volumes of subcortical structures were calculated and compared among the four groups, including basal forebrain, thalamus, caudate, putamen, pallidum, hippocampus, amygdala, and accumbens. Topological properties of the subcortical structural covariance network were also calculated. With an interval of 1.5 years ± 12 months of follow-up, the progression rate to MCI was compared between the G-SCD and B-SCD groups. Results Volumes of the basal forebrain, the right hippocampus, and their respective subfields differed significantly among the four groups ( p < 0.05, false discovery rate corrected). The B-SCD group showed lower volumes in the basal forebrain than the G-SCD group, especially in the Ch4p and Ch4a-i subfields. Furthermore, the structural covariance network of the basal forebrain and right hippocampal subfields showed that the B-SCD group had a larger Lambda than the G-SCD group, which suggested weakened network integration in the B-SCD group. At follow-up, the B-SCD group had a significantly higher conversion rate to MCI than the G-SCD group. Conclusion Compared to SCD participants with good spatial navigation performance, SCD participants with bad performance showed lower volumes in the basal forebrain, a reorganized structural covariance network of subcortical nuclei, and an increased risk of progression to MCI. Our findings indicated that spatial navigation may have great potential to identify SCD subjects at higher risk of clinical progression, which may contribute to making more precise clinical decisions for SCD individuals who seek medical help.

OBJECTIVE: As hospital admission rate is high during the COVID-19 pandemic, hospital length of stay (LOS) is a key indicator of medical resource allocation. This study aimed to elucidate specific dynamic longitudinal computed tomography (CT) imaging changes for patients with COVID-19 over in-hospital and predict individual LOS of COVID-19 patients with Delta variant of SARS-CoV-2 using the machine learning method.MATERIALS AND METHODS: This retrospective study recruited 448 COVID-19 patients with a total of 1761 CT scans from July 14, 2021 to August 20, 2021 with an averaged hospital LOS of 22.5 ± 7.0 days. Imaging features were extracted from each CT scan, including CT morphological characteristics and artificial intelligence (AI) extracted features. Clinical features were obtained from each patient's initial admission. The infection distribution in lung fields and progression pattern tendency was analyzed. Then, to construct a model to predict patient LOS, each CT scan was considered as an independent sample to predict the LOS from the current CT scan time point to hospital discharge combining with the patients' corresponding clinical features. The 1761 follow-up CT data were randomly split into training set and testing set with a ratio of 7:3 at patient-level. A total of 85 most related clinical and imaging features selected by Least Absolute Shrinkage and Selection Operator were used to construct LOS prediction model.RESULTS: Infection-related features were obtained, such as the percentage of the infected region of lung, ground-glass opacity (GGO), consolidation and crazy-paving pattern, and air bronchograms. Their longitudinal changes show that the progression changes significantly in the earlier stages (0–3 days to 4–6 days), and then, changes tend to be statistically subtle, except for the intensity range between (−470 and −70) HU which exhibits a significant increase followed by a continuous significant decrease. Furthermore, the bilateral lower lobes, especially the right lower lobe, present more severe. Compared with other models, combining the clinical, imaging reading, and AI features to build the LOS prediction model achieved the highest R2 of 0.854 and 0.463, Pearson correlation coefficient of 0.939 and 0.696, and lowest mean absolute error of 2.405 and 4.426, and mean squared error of 9.176 and 34.728 on the training and testing set.CONCLUSION: The most obvious progression changes were significantly in the earlier stages (0–3 days to 4–6 days) and the bilateral lower lobes, especially the right lower lobe. GGO, consolidation, and crazy-paving pattern and air bronchograms are the most main CT findings according to the longitudinal changes of infection-related features with LOS (day). The LOS prediction model of combining clinical, imaging reading, and AI features achieved optimum performance.

Background:The coronavirus disease (COVID-19) pandemic, which has been ongoing for more than 2 years, has become one of the largest public health issues. Vaccination against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection is one of the most important interventions to mitigate the COVID-19 pandemic. Our objective is to investigate the relationship between vaccination status and time to seroconversion.Methods:We conducted a cross-sectional observational study during the SARS-CoV-2 B.1.617.2 outbreak in Jiangsu, China. Participants who infected with the B.1.617.2 variant were enrolled. Cognitive performance, quality of life, emotional state, chest computed tomography (CT) score and seroconversion time were evaluated for each participant. Statistical analyses were performed using one-way ANOVA, univariate and multivariate regression analyses, Pearson correlation, and mediation analysis.Results:A total of 91 patients were included in the analysis, of whom 37.3, 25.3, and 37.3% were unvaccinated, partially vaccinated, and fully vaccinated, respectively. Quality of life was impaired in 30.7% of patients, especially for mental component summary (MCS) score. Vaccination status, subjective cognitive decline, and depression were risk factors for quality-of-life impairment. The chest CT score mediated the relationship of vaccination status with the MCS score, and the MCS score mediated the relationship of the chest CT score with time to seroconversion.Conclusion:Full immunization course with an inactivated vaccine effectively lowered the chest CT score and improved quality of life in hospitalized patients. Vaccination status could influence time to seroconversion by affecting CT score and MCS score indirectly. Our study emphasizes the importance of continuous efforts in encouraging a full vaccination course.

Identifying individuals with incipient Alzheimer’s disease (AD) are critical for early and targeted intervention before the dementia develops as AD progresses. Recently, emerging data have suggested that spatial navigation and neuroimaging could be utilized to identify individuals with prodromal AD. Compared to episodic memory, spatial navigation has fewer cultural and educational discrepancies and could serve as a promising marker for diagnosis and outcome measures in multicenter longitudinal studies with large cohorts. Furthermore, neuroimaging studies have contributed to our understanding of the structural and functional neural basis underlying spatial navigation and provided sensitive and non-invasive neuroimaging markers. The current review summarizes neuroimaging associations with spatial navigation impairment in the AD continuum, their potential pathophysiological mechanisms, and nonpharmacological interventions for spatial navigation impairments. We highlight the promising role of spatial navigation in the early identification of the preclinical and prodromal patients with potential risk of developing AD dementia. Multicenter large-scale longitudinal studies on patients across the AD continuum coupled with a standardized routine assessment of spatial navigation abilities in clinical settings are needed. This review may have implications for clinical practice and future research directions.