Disrupted morphological brain network organization in subjective cognitive decline and mild cognitive impairment.

We aim to investigate the alterations in gray matter for subjective cognitive decline (SCD) and mild cognitive impairment (MCI) from the perspective of the human connectome. High-resolution T1-weighted images were acquired from 54 patients with SCD, 95 patients with MCI, and 65 healthy controls (HC). Morphological brain networks (MBN) were constructed using similarities in the distribution of gray matter volumes between regions. The strength of morphological connections and topographic metrics derived from the graph-theoretical analysis were compared. Furthermore, we assessed the relationship between the observed morphological abnormalities and disease severity. According to the results, we found a significantly decreased morphological connection between the somatomotor network and ventral attention network in SCD compared to HC and MCI compared to SCD. The graph-theoretic analysis illustrated disruptions in the whole network organization, where the normalized shortest path increased and the global efficiency (Eg) decreased in MCI compared to SCD. In addition, Montreal Cognitive Assessment scores of SCD patients had a significantly negative correlation with Eg. The primary limitations of the present study include the cross-sectional design, no enrolled AD patients, no assessment of amyloidosis, and the need for more comprehensive neuropsychological tests. Our findings indicate the abnormalities of morphological networks at early stages in the AD continuum, which could be interpreted as compensatory changes to retain a normal level of cognitive function. The present study could provide new insight into the mechanism of AD.

Characteristics of imaging in hepatic inflammatory pseudotumors: a comparison between IgG4-related and IgG4-unrelated cases.

OBJECTIVES: The objective of this study was to examine the imaging features of hepatic inflammatory pseudotumors (IPTs) associated with IgG4-related and IgG4-unrelated conditions and to enhance the approach toward distinguishing between these two types of IPTs. METHODS: A retrospective study was conducted, involving 20 patients diagnosed with hepatic IPTs. Imaging procedures were conducted within a timeframe of 4 weeks prior to hepatectomy or biopsy. The imaging features were then analyzed and compared using chi-squared analysis. RESULTS: Seventeen (81.0%) IPTs were located in the hepatic subcapsular area; six (66.7%) IgG4-related IPTs were distributed around the hepatic hilum; and eleven (91.7%) IgG4-unrelated and three (33.3%) IgG4-related IPTs had unclear boundaries. All lesions exhibited similar characteristics in CT scans, T1-weighted imaging (T1WI), T2-weighted imaging (T2WI), and diffusion-weighted imaging (DWI), with the apparent diffusion coefficient (ADC) values slightly higher than the surrounding liver tissue. Delayed hypoenhancement, observed in five cases (55.6%), was exclusively present in IgG4-related IPTs. The remaining IPT lesions displayed progressive enhancement, septal and marginal enhancement, and persistent enhancement. Central enhancement was absent in three IgG4-related IPTs (33.3%) and ten IgG4-unrelated IPTs (83.3%). The duct-penetrating sign was identified in two IgG4-unrelated IPTs (16.7%) and seven IgG4-related IPTs (77.8%). Furthermore, seven patients with IgG4-related IPTs had additional lesions outside the liver. CONCLUSIONS: IgG4-related lesions are frequently found in the vicinity of the hepatic hilum; they display the duct-penetrating sign and affect other organs as well. Both groups exhibited progressive or persistent contrast enhancement in typical IPT lesions, but delayed hypoenhancement was only observed in the IgG4-related IPT group. IgG4-unrelated IPT lesions often exhibited indistinct boundaries lacking central enhancement. CRITICAL RELEVANCE STATEMENT: Differences in imaging features differentiate IgG4-related and -unrelated inflammatory pseudotumors (IPT). IgG4-related lesions are frequently near the hepatic hilum, display duct-penetrating sign, and affect other organs. Only the IgG4-related group demonstrated delayed hypoenhancement. IgG4-unrelated IPT lesions often exhibited indistinct boundaries lacking central enhancement. KEY POINTS: Compared with IgG 4-unrelated IPTs, IgG4-related IPTs show delayed hypoenhancement and affect other organs. IgG4-unrelated IPTs have unclear boundaries and lack central enhancement. Improved IPT diagnostic capabilities can help minimize additional, potentially unnecessary, interventions.

Deep learning for differentiation of osteolytic osteosarcoma and giant cell tumor around the knee joint on radiographs: a multicenter study.

OBJECTIVES: To develop a deep learning (DL) model for differentiating between osteolytic osteosarcoma (OS) and giant cell tumor (GCT) on radiographs. METHODS: Patients with osteolytic OS and GCT proven by postoperative pathology were retrospectively recruited from four centers (center A, training and internal testing; centers B, C, and D, external testing). Sixteen radiologists with different experiences in musculoskeletal imaging diagnosis were divided into three groups and participated with or without the DL model's assistance. DL model was generated using EfficientNet-B6 architecture, and the clinical model was trained using clinical variables. The performance of various models was compared using McNemar's test. RESULTS: Three hundred thirty-three patients were included (mean age, 27 years ± 12 [SD]; 186 men). Compared to the clinical model, the DL model achieved a higher area under the curve (AUC) in both the internal (0.97 vs. 0.77, p = 0.008) and external test set (0.97 vs. 0.64, p < 0.001). In the total test set (including the internal and external test sets), the DL model achieved higher accuracy than the junior expert committee (93.1% vs. 72.4%; p < 0.001) and was comparable to the intermediate and senior expert committee (93.1% vs. 88.8%, p = 0.25; 87.1%, p = 0.35). With DL model assistance, the accuracy of the junior expert committee was improved from 72.4% to 91.4% (p = 0.051). CONCLUSION: The DL model accurately distinguished osteolytic OS and GCT with better performance than the junior radiologists, whose own diagnostic performances were significantly improved with the aid of the model, indicating the potential for the differential diagnosis of the two bone tumors on radiographs. CRITICAL RELEVANCE STATEMENT: The deep learning model can accurately distinguish osteolytic osteosarcoma and giant cell tumor on radiographs, which may help radiologists improve the diagnostic accuracy of two types of tumors. KEY POINTS: • The DL model shows robust performance in distinguishing osteolytic osteosarcoma and giant cell tumor. • The diagnosis performance of the DL model is better than junior radiologists'. • The DL model shows potential for differentiating osteolytic osteosarcoma and giant cell tumor.

Persistent alterations in gray matter in COVID-19 patients experiencing sleep disturbances: a 3-month longitudinal study.

ABSTRACT: Sleep disturbances are among the most prevalent neuropsychiatric symptoms in individuals who have recovered from severe acute respiratory syndrome coronavirus 2 infections. Previous studies have demonstrated abnormal brain structures in patients with sleep disturbances who have recovered from coronavirus disease 2019 (COVID-19). However, neuroimaging studies on sleep disturbances caused by COVID-19 are scarce, and existing studies have primarily focused on the long-term effects of the virus, with minimal acute phase data. As a result, little is known about the pathophysiology of sleep disturbances in the acute phase of COVID-19. To address this issue, we designed a longitudinal study to investigate whether alterations in brain structure occur during the acute phase of infection, and verified the results using 3-month follow-up data. A total of 26 COVID-19 patients with sleep disturbances (aged 51.5 ± 13.57 years, 8 women and 18 men), 27 COVID-19 patients without sleep disturbances (aged 47.33 ± 15.98 years, 9 women and 18 men), and 31 age-and gender-matched healthy controls (aged 49.19 ± 17.51 years, 9 women and 22 men) were included in this study. Eleven COVID-19 patients with sleep disturbances were included in a longitudinal analysis. We found that COVID-19 patients with sleep disturbances exhibited brain structural changes in almost all brain lobes. The cortical thicknesses of the left pars opercularis and left precuneus were significantly negatively correlated with Pittsburgh Sleep Quality Index scores. Additionally, we observed changes in the volume of the hippocampus and its subfield regions in COVID-19 patients compared with the healthy controls. The 3-month follow-up data revealed indices of altered cerebral structure (cortical thickness, cortical grey matter volume, and cortical surface area) in the frontal-parietal cortex compared with the baseline in COVID-19 patients with sleep disturbances.Our findings indicate that the sleep disturbances patients had altered morphology in the cortical and hippocampal structures during the acute phase of infection and persistent changes in cortical regions at 3 months post-infection. These data improve our understanding of the pathophysiology of sleep disturbances caused by COVID-19.

Alteration of white matter microstructure in patients with sleep disorders after COVID-19 infection.

BACKGROUND: The pathophysiology of coronasomnia remains unclear. This study aimed to investigate changes in white matter (WM) microstructure and inflammatory factors in patients with sleep disorders (SD) characterized by poor sleep quantity, quality, or timing following coronavirus disease 2019 (COVID-19) infection in the acute phase (within one month) and whether these changes could be recovered at 3-month follow-up. METHODS: 29 acute COVID-19 patients with SD (COVID_SD) and 27 acute COVID-19 patients without SD (COVID_NonSD) underwent diffusion tensor imaging (DTI), tested peripheral blood inflammatory cytokines level, and measured Pittsburgh Sleep Quality Index (PSQI), and matched 30 uninfected healthy controls. Analyzed WM abnormalities between groups in acute phase and explored its changes in COVID_SD at 3-month follow-up by using tract-based spatial statistics (TBSS). Correlations between DTI and clinical data were examined using Spearman partial correlation analysis. RESULTS: Both COVID_SD and COVID_NonSD exhibited widespread WM microstructure abnormalities. The COVID_SD group showed specific WM microstructure changes in right inferior fronto-occipital fasciculus (IFOF) (lower fractional anisotropy [FA]/axial diffusivity [AD] and higher radial diffusivity [RD]) and left corticospinal tract (CST) (higher FA and lower RD) and higher interleukin-1ß (IL-1ß) compared with COVID_NonSD group. These WM abnormalities and IL-1ß levels were correlated PSQI score. After 3 months, the IFOF integrity and IL-1ß levels tended to return to normal accompanied by symptom improvement in the COVID_SD relative to baseline. CONCLUSION: Abnormalities in right IFOF and left CST and elevated IL-1ß levels were important neurophenotypes correlated with COVID_SD, which might provide new insights into the pathogenesis of neuroinflammation in SD patients induced by COVID-19.

Altered cortical and subcortical morphometric features and asymmetries in the subjective cognitive decline and mild cognitive impairment.

Introduction: This study aimed to evaluate morphological changes in cortical and subcortical regions and their asymmetrical differences in individuals with subjective cognitive decline (SCD) and mild cognitive impairment (MCI). These morphological changes may provide valuable insights into the early diagnosis and treatment of Alzheimer's disease (AD). Methods: We conducted structural MRI scans on a cohort comprising 62 SCD patients, 97 MCI patients, and 70 age-, sex-, and years of education-matched healthy controls (HC). Using Freesurfer, we quantified surface area, thickness, the local gyrification index (LGI) of cortical regions, and the volume of subcortical nuclei. Asymmetry measures were also calculated. Additionally, we explored the correlation between morphological changes and clinical variables related to cognitive decline. Results: Compared to HC, patients with MCI exhibited predominantly left-sided surface morphological changes in various brain regions, including the transverse temporal gyrus, superior temporal gyrus, insula, and pars opercularis. SCD patients showed relatively minor surface morphological changes, primarily in the insula and pars triangularis. Furthermore, MCI patients demonstrated reduced volumes in the anterior-superior region of the right hypothalamus, the fimbria of the bilateral hippocampus, and the anterior region of the left thalamus. These observed morphological changes were significantly associated with clinical ratings of cognitive decline. Conclusion: The findings of this study suggest that cortical and subcortical morphometric changes may contribute to cognitive impairment in MCI, while compensatory mechanisms may be at play in SCD to preserve cognitive function. These insights have the potential to aid in the early diagnosis and treatment of AD.