Effects of strategic white matter hyperintensities of cholinergic pathways on basal forebrain volume in patients with amyloid-negative neurocognitive disorders.

BACKGROUND: The cholinergic neurotransmitter system is crucial to cognitive function, with the basal forebrain (BF) being particularly susceptible to Alzheimer's disease (AD) pathology. However, the interaction of white matter hyperintensities (WMH) in cholinergic pathways and BF atrophy without amyloid pathology remains poorly understood. METHODS: We enrolled patients who underwent neuropsychological tests, magnetic resonance imaging, and 18F-florbetaben positron emission tomography due to cognitive impairment at the teaching university hospital from 2015 to 2022. Among these, we selected patients with negative amyloid scans and additionally excluded those with Parkinson's dementia that may be accompanied by BF atrophy. The WMH burden of cholinergic pathways was quantified by the Cholinergic Pathways Hyperintensities Scale (CHIPS) score, and categorized into tertile groups because the CHIPS score did not meet normal distribution. Segmentation of the BF on volumetric T1-weighted MRI was performed using FreeSurfer, then was normalized for total intracranial volume. Multivariable regression analysis was performed to investigate the association between BF volumes and CHIPS scores. RESULTS: A total of 187 patients were enrolled. The median CHIPS score was 12 [IQR 5.0; 24.0]. The BF volume of the highest CHIPS tertile group (mean ± SD, 3.51 ± 0.49, CHIPSt3) was significantly decreased than those of the lower CHIPS tertile groups (3.75 ± 0.53, CHIPSt2; 3.83 ± 0.53, CHIPSt1; P = 0.02). In the univariable regression analysis, factors showing significant associations with the BF volume were the CHIPSt3 group, age, female, education, diabetes mellitus, smoking, previous stroke history, periventricular WMH, and cerebral microbleeds. In multivariable regression analysis, the CHIPSt3 group (standardized beta [ßstd] = -0.25, P = 0.01), female (ßstd = 0.20, P = 0.04), and diabetes mellitus (ßstd = -0.22, P < 0.01) showed a significant association with the BF volume. Sensitivity analyses showed a negative correlation between CHIPS score and normalized BF volume, regardless of WMH severity. CONCLUSIONS: We identified a significant correlation between strategic WMH burden in the cholinergic pathway and BF atrophy independently of amyloid positivity and WMH severity. These results suggest a mechanism of cholinergic neuronal loss through the dying-back phenomenon and provide a rationale that strategic WMH assessment may help identify target groups that may benefit from acetylcholinesterase inhibitor treatment.

Achieving imaging and computational reproducibility on multiparametric MRI radiomics features in brain tumor diagnosis: phantom and clinical validation.

OBJECTIVES: The Image Biomarker Standardization Initiative has helped improve the computational reproducibility of MRI radiomics features. Nonetheless, the MRI sequences and features with high imaging reproducibility are yet to be established. To determine reproducible multiparametric MRI radiomics features across test-retest, multi-scanner, and computational reproducibility comparisons, and to evaluate their clinical value in brain tumor diagnosis. METHODS: To assess reproducibility, T1-weighted imaging (T1WI), T2-weighted imaging (T2WI), and diffusion-weighted imaging (DWI) were acquired from three 3-T MRI scanners using standardized phantom, and radiomics features were extracted using two computational algorithms. Reproducible radiomics features were selected when the concordance correlation coefficient value above 0.9 across multiple sessions, scanners, and computational algorithms. Random forest classifiers were trained with reproducible features (n = 117) and validated in a clinical cohort (n = 50) to evaluate whether features with high reproducibility improved the differentiation of glioblastoma from primary central nervous system lymphomas (PCNSLs). RESULTS: Radiomics features from T2WI demonstrated higher repeatability (65-94%) than those from DWI (38-48%) or T1WI (2-92%). Across test-retest, multi-scanner, and computational comparisons, T2WI provided 41 reproducible features, DWI provided six, and T1WI provided two. The performance of the classification model with reproducible features was higher than that using non-reproducible features in both training set (AUC, 0.916 vs. 0.877) and validation set (AUC, 0.957 vs. 0.869). CONCLUSION: Radiomics features with high reproducibility across multiple sessions, scanners, and computational algorithms were identified, and they showed higher diagnostic performance than non-reproducible radiomics features in the differentiation of glioblastoma from PCNSL. CLINICAL RELEVANCE STATEMENT: By identifying the radiomics features showing higher multi-machine reproducibility, our results also demonstrated higher radiomics diagnostic performance in the differentiation of glioblastoma from PCNSL, paving the way for further research designs and clinical application in neuro-oncology. KEY POINTS: • Highly reproducible radiomics features across multiple sessions, scanners, and computational algorithms were identified using phantom and applied to clinical diagnosis. • Radiomics features from T2-weighted imaging were more reproducible than those from T1-weighted and diffusion-weighted imaging. • Radiomics features with good reproducibility had better diagnostic performance for brain tumors than features with poor reproducibility.

Clinical Features and Brain MRI Findings in Korean Patients with AGel Amyloidosis.

PURPOSE: AGel amyloidosis is systemic amyloidosis caused by pathogenic variants in the GSN gene. In this study, we sought to characterize the clinical and brain magnetic resonance image (MRI) features of Korean patients with AGel amyloidosis. MATERIALS AND METHODS: We examined 13 patients with AGel amyloidosis from three unrelated families. Brain MRIs were performed in eight patients and eight age- and sex-matched healthy controls. Therein, we analyzed gray and white matter content using voxel-based morphometry (VBM), tract-based spatial statistics (TBSS), and FreeSurfer. RESULTS: The median age at examination was 73 (interquartile range: 64-76) years. The median age at onset of cutis laxa was 20 (interquartile range: 15-30) years. All patients over that age of 60 years had dysarthria, cutis laxa, dysphagia, and facial palsy. Two patients in their 30s had only mild cutis laxa. The median age at dysarthria onset was 66 (interquartile range: 63.5-70) years. Ophthalmoparesis was observed in three patients. No patient presented with muscle weakness of the limbs. Axial fluid-attenuated inversion recovery images of the brain showed no significant differences between the patient and control groups. Also, analysis of VBM, TBSS, and FreeSurfer revealed no significant differences in cortical thickness between patients and healthy controls at the corrected significance level. CONCLUSION: Our study outlines the clinical manifestations of prominent bulbar palsy and early-onset cutis laxa in 13 Korean patients with AGel amyloidosis and confirms that AGel amyloidosis mainly affects the peripheral nervous system rather than the central nervous system.

Utility of 7 Tesla Magnetic Resonance Imaging in Patients With Epilepsy: A Systematic Review and Meta-Analysis.

Objective: 7 Tesla magnetic resonance imaging (MRI) enables high resolution imaging and potentially improves the detection of morphologic abnormalities in patients with epilepsy. However, its added value compared with conventional 1.5T and 3.0T MRI is unclear. We reviewed the evidence for the use of 7 Tesla MRI in patients with epilepsy and compared the detection rate of focal lesions with clinical MRI. Methods: Clinical retrospective case studies were identified using the indexed text terms "epilepsy" AND "magnetic resonance imaging" OR "MR imaging" AND "7T" OR "7 Tesla" OR "7T" in Medline (2002-September 1, 2020) and Embase (1999-September 1, 2020). The study setting, MRI protocols, qualitative, and quantitative assessment were systematically reviewed. The detection rate of morphologic abnormalities on MRI was reported in each study in which surgery was used as the reference standard. Meta-analyses were performed using a univariate random-effects model in diagnostic performance studies with patients that underwent both 7T MRI and conventional MRI. Results: Twenty-five articles were included (467 patients and 167 healthy controls) consisting of 10 case studies, 10 case-control studies, 4 case series, and 1 cohort study. All studies included focal epilepsy; 12 studies (12/25, 48%) specified the disease etiology and 4 studies reported focal but non-lesional (MRI-negative on 1.5/3.0T) epilepsy. 7T MRI showed superior detection and delineation of morphologic abnormalities in all studies. In nine comparative studies, 7T MRI had a superior detection rate of 65% compared with the 22% detection rate of 1.5T or 3.0T. Significance: 7T MRI is useful for delineating morphologic abnormalities with a higher detection rate compared with conventional clinical MRI. Most studies were conducted using a case series or case study; therefore, a cohort study design with clinical outcomes is necessary. Classification of Evidence: Class IV Criteria for Rating Diagnostic Accuracy Studies.

The cerebellum could serve as a potential imaging biomarker of dementia conversion in patients with amyloid-negative amnestic mild cognitive impairment.

BACKGROUND AND PURPOSE: As part of network-specific neurodegeneration, changes in cerebellar gray matter (GM) volume and impaired cerebello-cerebral functional networks have been reported in Alzheimer disease (AD). Compared with healthy controls, a volume loss in the cerebellum has been observed in patients with continuum of AD. However, little is known about the anatomical or functional changes in patients with clinical AD but no brain amyloidosis. We aimed to identify the relationship between cerebellar volume and dementia conversion of amyloid-negative mild cognitive impairment (MCI). METHODS: This study was a retrospective cohort study of patients over the age 50 years with amyloid-negative amnestic MCI who visited the memory clinic of Asan Medical Center with no less than a 36-month follow-up period. All subjects underwent detailed neuropsychological tests, 3 T brain magnetic resonance imaging scans including three-dimensional T1 imaging, and fluorine-18[F18 ]-florbetaben amyloid positron emission tomography scans. A spatially unbiased atlas template of the cerebellum and brainstem was used for analyzing cerebellar GM volume. RESULTS: During the 36 months of follow-up, 39 of 107 (36.4%) patients converted to dementia from amnestic MCI. The converter group had more severe impairments in all visual memory tasks. In terms of volumetric analysis, reduced crus I/II volume adjusted with total intracranial volume, and age was observed in the converter group. CONCLUSIONS: Significant cerebellar GM atrophy involving the bilateral crus I/II may be a novel imaging biomarker for predicting dementia progression in amyloid-negative amnestic MCI patients.

Deep-learned time-signal intensity pattern analysis using an autoencoder captures magnetic resonance perfusion heterogeneity for brain tumor differentiation.

Current image processing methods for dynamic susceptibility contrast (DSC) magnetic resonance imaging (MRI) do not capture complex dynamic information of time-signal intensity curves. We investigated whether an autoencoder-based pattern analysis of DSC MRI captured representative temporal features that improves tissue characterization and tumor diagnosis in a multicenter setting. The autoencoder was applied to the time-signal intensity curves to obtain representative temporal patterns, which were subsequently learned by a convolutional neural network. This network was trained with 216 preoperative DSC MRI acquisitions and validated using external data (n = 43) collected with different DSC acquisition protocols. The autoencoder applied to time-signal intensity curves and clustering obtained nine representative clusters of temporal patterns, which accurately identified tumor and non-tumoral tissues. The dominant clusters of temporal patterns distinguished primary central nervous system lymphoma (PCNSL) from glioblastoma (AUC 0.89) and metastasis from glioblastoma (AUC 0.95). The autoencoder captured DSC time-signal intensity patterns that improved identification of tumoral tissues and differentiation of tumor type and was generalizable across centers.