We aimed to investigate the role of the APOE genotype in cognitive and motor trajectories in Parkinson's disease (PD). Using PD registry data, we retrospectively investigated a total of 253 patients ...with PD who underwent the Mini-Mental State Exam (MMSE) two or more times at least 5 years apart, were aged over 40 years, and free of dementia at the time of enrollment. We performed group-based trajectory modeling to identify patterns of cognitive change using the MMSE. Kaplan-Meier survival analysis was used to investigate the role of the APOE genotype in cognitive and motor progression. Trajectory analysis divided patients into four groups: early fast decline, fast decline, gradual decline, and stable groups with annual MMSE scores decline of - 2.8, - 1.8, - 0.6, and - 0.1 points per year, respectively. The frequency of APOE ε4 was higher in patients in the early fast decline and fast decline groups (50.0%) than those in the stable group (20.1%) (p = 0.007). APOE ε4, in addition to older age at onset, depressive mood, and higher H&Y stage, was associated with the cognitive decline rate, but no APOE genotype was associated with motor progression. APOE genotype could be used to predict the cognitive trajectory in PD.
Abstract
Microglial activation is a central player in the pathophysiology of Alzheimer’s disease (AD). The soluble fragment of triggering receptor expressed on myeloid cells 2 (sTREM2) can serve as a ...marker for microglial activation and has been shown to be overexpressed in AD. However, the relationship of sTREM2 with other AD biomarkers has not been extensively studied. We investigated the relationship between cerebrospinal fluid (CSF) sTREM2 and other AD biomarkers and examined the correlation of plasma sTREM2 with CSF sTREM2 in a cohort of individuals with AD and without AD. Participants were consecutively recruited from Asan Medical Center from 2018 to 2020. Subjects were stratified by their amyloid positivity and clinical status. Along with other AD biomarkers, sTREM2 level was measured in the plasma as well as CSF. In 101 patients with either amyloid-positive or negative status, CSF sTREM2 was closely associated with CSF T-tau and P-tau and not with Abeta42. CSF sTREM2 levels were found to be strongly correlated with CSF neurofilament light chain. The comparison of CSF and plasma sTREM2 levels tended to have an inverse correlation. Plasma sTREM2 and P-tau levels were oppositely influenced by age. Our results suggest that neuroinflammation may be closely associated with tau-induced neurodegeneration.
We developed and investigated the feasibility of a machine learning-based automated rating for the 2 cardinal symptoms of Parkinson disease (PD): resting tremor and bradykinesia.
Using OpenPose, a ...deep learning-based human pose estimation program, we analyzed video clips for resting tremor and finger tapping of the bilateral upper limbs of 55 patients with PD (110 arms). Key motion parameters, including resting tremor amplitude and finger tapping speed, amplitude, and fatigue, were extracted to develop a machine learning-based automatic Unified Parkinson's Disease Rating Scale (UPDRS) rating using support vector machine (SVM) method. To evaluate the performance of this model, we calculated weighted κ and intraclass correlation coefficients (ICCs) between the model and the gold standard rating by a movement disorder specialist who is trained and certified by the Movement Disorder Society for UPDRS rating. These values were compared to weighted κ and ICC between a nontrained human rater and the gold standard rating.
For resting tremors, the SVM model showed a very good to excellent reliability range with the gold standard rating (κ 0.791; ICC 0.927), with both values higher than that of nontrained human rater (κ 0.662; ICC 0.861). For finger tapping, the SVM model showed a very good reliability range with the gold standard rating (κ 0.700 and ICC 0.793), which was comparable to that for nontrained human raters (κ 0.627; ICC 0.797).
Machine learning-based algorithms that automatically rate PD cardinal symptoms are feasible, with more accurate results than nontrained human ratings.
This study provides Class II evidence that machine learning-based automated rating of resting tremor and bradykinesia in people with PD has very good reliability compared to a rating by a movement disorder specialist.
This study aimed to identify predictors for the recurrence of spontaneous intracranial hypotension (SIH) after epidural blood patch (EBP).
Epidural blood patch is the main treatment option for SIH; ...however, the characteristics of patients who experience relapse after successful EBP treatment for SIH remain understudied.
In this exploratory, retrospective, case-control study, we included 19 patients with SIH recurrence after EBP and 36 age- and sex-matched patients without recurrence from a single tertiary medical institution. We analyzed clinical characteristics, neuroimaging findings, and volume changes in intracranial structures after EBP treatment. Machine learning methods were utilized to predict the recurrence of SIH after EBP treatment.
There were no significant differences in clinical features between the recurrence and no-recurrence groups. Among brain magnetic resonance imaging signs, diffuse pachymeningeal enhancement and cerebral venous dilatation were more prominent in the recurrence group than no-recurrence group after EBP (14/19 73% vs. eight of 36 22% patients, p = 0.001; 11/19 57% vs. seven of 36 19% patients, p = 0.010, respectively). The midbrain-pons angle decreased in the recurrence group compared to the no-recurrence group after EBP, at a mean (standard deviation SD) of -12.0 16.7 vs. +1.818.3° (p = 0.048). In volumetric analysis, volume changes after EBP were smaller in the recurrence group than in the no-recurrence group in intracranial cerebrospinal fluid (mean SD -11.6 15.3 vs. +4.8 17.1 mL, p = 0.001) and ventricles (mean SD +1.0 2.0 vs. +2.0 2.5 mL, p = 0.003). Notably, the random forest classifier indicated that the model constructed with brain volumetry was more accurate in discriminating SIH recurrence (area under the curve = 0.80 vs. 0.52).
Our study suggests that volumetric analysis of intracranial structures may aid in predicting recurrence after EBP treatment in patients with SIH.
Normal pressure hydrocephalus (NPH) patients had altered white matter tract integrities on diffusion tensor imaging (DTI). Previous studies suggested disproportionately enlarged subarachnoid space ...hydrocephalus (DESH) as a prognostic sign of NPH. We examined DTI indices in NPH subgroups by DESH severity and clinical symptoms. This retrospective case-control study included 33 NPH patients and 33 age-, sex-, and education-matched controls. The NPH grading scales (0-12) were used to rate neurological symptoms. Patients with NPH were categorized into two subgroups, high-DESH and low-DESH groups, by the average value of the DESH scale. DTI indices, including fractional anisotropy, were compared across 14 regions of interest (ROIs). The high-DESH group had increased axial diffusivity in the lateral side of corona radiata (1.43 ± 0.25 vs. 1.72 ± 0.25, p = 0.04), and showed decreased fractional anisotropy and increased mean, and radial diffusivity in the anterior and lateral sides of corona radiata and the periventricular white matter surrounding the anterior horn of lateral ventricle. In patients with a high NPH grading scale, fractional anisotropy in the white matter surrounding the anterior horn of the lateral ventricle was significantly reduced (0.36 ± 0.08 vs. 0.26 ± 0.06, p = 0.03). These data show that DESH may be a biomarker for DTI-detected microstructural alterations and clinical symptom severity.
Abstract
We aimed to investigate the predictive value of preoperative clinical factors and dopamine transporter imaging for outcomes after globus pallidus interna (GPi) deep brain stimulation (DBS) ...in patients with advanced Parkinson’s disease (PD). Thirty-one patients with PD who received bilateral GPi DBS were included. The patients underwent preoperative
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F FP-CIT positron emission tomography before DBS surgery. The Unified Parkinson’s Disease Rating Scale (UPDRS) were used to assess outcomes 12 months after DBS. Univariate and multivariate linear regression analysis were performed to investigate the association between clinical variables including sex, age at onset of PD, disease duration, cognitive status, preoperative motor severity, levodopa responsiveness, daily dose of dopaminergic medication, and dopamine transporter availability in the striatum and outcomes after GPi DBS. Younger age at onset of PD was associated with greater DBS motor responsiveness and lower postoperative UPDRS III score. Greater levodopa responsiveness, lower preoperative UPDRS III score and lower striatal dopamine transporter availability were associated with lower postoperative UPDRS III score. Younger age at onset was also associated with greater decrease in UPDRS IV score and dyskinesia score after GPi DBS. Our results provide useful information to select DBS candidates and predict therapeutic outcomes after GPi DBS in advanced PD.
Single-nucleotide variants (SNVs) associated with Parkinson's disease (PD) have been investigated mainly through genome-wide association studies. However, other genomic alterations, including copy ...number variations, remain less explored. In this study, we conducted whole-genome sequencing of primary (310 PD patients and 100 healthy individuals) and independent (100 PD patients and 100 healthy individuals) cohorts from the Korean population to identify high-resolution small genomic deletions, gains, and SNVs. Global small genomic deletions and gains were found to be associated with an increased and decreased risk of PD development, respectively. Thirty significant locus deletions were identified in PD, with most being associated with an increased PD risk in both cohorts. Small genomic deletions in clustered loci located in the GPR27 region had high enhancer signals and showed the closest association with PD. GPR27 was found to be expressed specifically in brain tissue, and GPR27 copy number loss was associated with upregulated SNCA expression and downregulated dopamine neurotransmitter pathways. Clustering of small genomic deletions on chr20 in exon 1 of the GNAS isoform was detected. In addition, we found several PD-associated SNVs, including one in the enhancer region of the TCF7L2 intron, which exhibited a cis-acting regulatory mode and an association with the beta-catenin signaling pathway. These findings provide a global, whole-genome view of PD and suggest that small genomic deletions in regulatory domains contribute to the risk of PD development.
The aim of the present study was to predict amyloid-beta positivity using a conventional T1-weighted image, radiomics, and a diffusion-tensor image obtained by magnetic resonance imaging (MRI). We ...included 186 patients with mild cognitive impairment (MCI) who underwent Florbetaben positron emission tomography (PET), MRI (three-dimensional T1-weighted and diffusion-tensor images), and neuropsychological tests at the Asan Medical Center. We developed a stepwise machine learning algorithm using demographics, T1 MRI features (volume, cortical thickness and radiomics), and diffusion-tensor image to distinguish amyloid-beta positivity on Florbetaben PET. We compared the performance of each algorithm based on the MRI features used. The study population included 72 patients with MCI in the amyloid-beta-negative group and 114 patients with MCI in the amyloid-beta-positive group. The machine learning algorithm using T1 volume performed better than that using only clinical information (mean area under the curve AUC: 0.73 vs. 0.69, p < 0.001). The machine learning algorithm using T1 volume showed better performance than that using cortical thickness (mean AUC: 0.73 vs. 0.68, p < 0.001) or texture (mean AUC: 0.73 vs. 0.71, p = 0.002). The performance of the machine learning algorithm using fractional anisotropy in addition to T1 volume was not better than that using T1 volume alone (mean AUC: 0.73 vs. 0.73, p = 0.60). Among MRI features, T1 volume was the best predictor of amyloid PET positivity. Radiomics or diffusion-tensor images did not provide additional benefits.
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‐18F18‐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.
The change of cerebellar gray matter volume could serve as an early marker of neurodegeneration in amnestic mild cognitive impairment (MCI) with no amyloid deposition. A significant cerebellar gray matter volume reduction was observed in the bilateral crus I/II in those who converted to dementia from amyloid‐negative amnestic MCI. This finding may be a novel imaging biomarker for predicting disease progression in amyloid‐negative amnestic MCI patients.
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