Background: Argyrophilic grain disease (AGD) is a common tauopathy in the elderly, but its neuroimaging features remain less well characterized compared with Alzheimer's disease (AD). Notably, structural covariance network (SCN) analysis has not previously been applied to AGD. Objective: This study aimed to investigate SCN alterations in pathologically confirmed AGD and AD and to characterize disease-specific patterns of network disruption. Methods: We examined 12 AGD, 13 AD, and 18 healthy controls (HC). Individualized structural covariance matrices were constructed from regional gray matter volumes, and global and nodal graph-theoretical metrics were computed for each participant. Group differences were assessed using analysis of covariance adjusting for age and sex, and partial correlations were performed to examine associations between global metrics and Mini-Mental State Examination (MMSE) scores. Results: Global SCN metrics showed a graded pattern, with strength, clustering coefficient, and efficiency lowest in AD, highest in HC, and intermediate in AGD. All global metrics except modularity were significantly correlated with MMSE. Nodal analyses revealed widespread reductions in closeness centrality in AD, with more limited decreases in AGD. Betweenness centrality showed an AD > AGD > HC pattern, whereas closeness centrality showed the opposite trend. Eigenvector centrality also suggested a graded trend (AD < AGD < HC), despite regional variability. Conclusions: SCN-derived metrics were consistent with disease-related volume patterns and revealed that AGD exhibits an intermediate network profile between AD and healthy aging. These findings suggest that SCN-based measures offer complementary insights into disease-related patterns of network disruption.
BackgroundAlthough neuroimaging differences from Alzheimer's disease (AD) in argyrophilic grain disease (AGD), a limbic-predominant 4-repeat tauopathy, have been evaluated, edge-wise alterations in structural similarity remain unexamined.ObjectiveAs an exploratory pilot study, to apply network-based statistics (NBS) to individualized structural covariance (ISC) matrices to identify morphological-similarity alterations in AGD and AD.MethodsWe analyzed 3D T1-weighted MRI from 13 pathologically confirmed AGD, 17 AD, and 18 healthy controls (HC). Gray-matter volumes from a 170-region atlas were used to compute ISC matrices. Between-group differences were assessed using NBS (threshold t = 3.5; 5000 permutations). Component strength was the sum of ISC edge weights within the significant component. Stability was assessed via leave-one-out analyses and bootstrap confidence intervals. Significant edges were mapped to Yeo 7 resting-state networks.ResultsAll three comparisons yielded a single significant component (HC versus AD: 340 edges; HC versus AGD: 80 edges; AGD versus AD: 39 edges). Component strength was higher in the group with less widespread morphological disruption (HC > AD; HC > AGD; AGD > AD), with non-overlapping bootstrap confidence intervals. Mapping to resting-state networks revealed widespread posterior cortico-limbic involvement in AD and a limbic-predominant pattern in AGD. In the AGD versus AD comparison, AD showed greater disruption within frontoparietal, somatomotor, and ventral attention systems. Component strength correlated positively with Mini-Mental State Examination scores (r = 0.48, p = 0.01).ConclusionsNBS applied to ISC matrices provides preliminary evidence of altered morphological similarity in AGD and AD, with AGD showing a limbic-predominant subnetwork and AD demonstrating broader posterior cortico-limbic involvement.
Colon perforation with fecal peritonitis is usually fatal without prompt surgical intervention. We report the case of an 89-year-old woman with colon perforation and extraluminal feces who survived for 174 days without surgery in the presence of extensive postoperative peritoneal adhesions. The patient had previously undergone two abdominal surgeries for colorectal cancer, resulting in severe intra-abdominal adhesions. Despite radiological evidence of intraperitoneal free air and extraluminal feces, she was managed conservatively with antibiotics, morphine hydrochloride, supportive care, and close monitoring. This case suggests that, in carefully selected patients with marked peritoneal adhesions, non-operative management may warrant reconsideration as a potential therapeutic option for colon perforation before deeming that the condition is inoperable.
BackgroundArgyrophilic grain disease (AGD) is a common yet underrecognized tauopathy that often mimics Alzheimer's disease (AD) in clinical and imaging presentations. While regional atrophy in AGD has been reported on magnetic resonance imaging (MRI), network-level structural changes remain poorly understood.ObjectiveWe aimed to explore a gray matter volume network related to AGD.MethodsStructural MRI data were collected from 12 patients with pathologically confirmed AGD (age at MRI, 87.7 ± 5.5 years; male, 4), 12 patients with pathologically confirmed AD (83.4 ± 10.0 years; male, 4), and 9 healthy controls (HCs; 82.4 ± 1.9 years; male, 2) at Fukushimura Hospital in Japan. Scaled Subprofile Model with principal component analysis was applied to preprocessed gray matter volume data of AGD and HCs to identify an AGD-related network.ResultsAn AGD-related network involving relative reduction in the ambient gyrus, entorhinal cortex, hippocampus, amygdala, and thalamus was identified. Represented by principal components 1, 2, and 3, this network showed significantly higher expression in patients with AGD than HCs (p < 0.0001, permutation test). The expression of the network was also higher in patients with AD than HCs (p < 0.0001, t-test).ConclusionsThis exploratory study identified a gray matter volume network related to AGD, providing a basis for future research of network-based imaging approaches.
