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Summary: Background: Although dysfunction of large-scale brain networks has been frequently demonstrated in patients with α-Synucleinopathy (α-Syn, i.e., Parkinson's disease, dementia with Lewy bodies, and multiple system atrophy), a consistent pattern of dysfunction remains unclear. We aim to investigate network dysfunction in patients with α-Syn through a meta-analysis. Methods: Whole-brain seed-based resting-state functional connectivity studies (published before September 1st, 2020 in English) comparing α-Syn patients with healthy controls (HC) were retrieved from electronic databases (PubMed, Web of Science, and EMBASE). Seeds from each study were categorized into networks by their location within a priori functional networks. Seed-based effect size mapping with Permutation of Subject Images analysis of between-group effects identified the network systems in which α-Syn was associated with hyperconnectivity (increased connectivity in α-Syn vs. HC) or hypoconnectivity (decreased connectivity in α-Syn vs. HC) within and between each seed-network. This study was registered on PROSPERO (CRD42020210133). Findings: In total, 136 seed-based voxel-wise resting-state functional connectivity datasets from 72 publications (3093 α-Syn patients and 3331 HC) were included in the meta-analysis. We found that α-Syn patients demonstrated imbalanced connectivity among subcortical network, cerebellum, and frontal parietal networks that involved in motor functioning and executive control. The patient group was associated with hypoconnectivity in default mode network and ventral attention network that involved in cognition and attention. Additionally, the patient group exhibited hyperconnectivity between neural systems involved in top-down emotion regulation and hypoconnectivity between networks involved in bottom-up emotion processing. Interpretation: These findings supported neurocognitive models in which network dysfunction is tightly linked to motor, cognitive and psychiatric symptoms observed in α-Syn patients.

Radiomics-based researches have shown predictive abilities with machine-learning approaches. However, it is still unknown whether different radiomics strategies affect the prediction performance. The aim of this study was to compare the prediction performance of frequently utilized radiomics feature selection and classification methods in glioma grading. Quantitative radiomics features were extracted from tumor regions in 210 Glioblastoma (GBM) and 75 low-grade glioma (LGG) MRI subjects. Then, the diagnostic performance of sixteen feature selection and fifteen classification methods were evaluated by using two different test modes: ten-fold cross-validation and percentage split. Balanced accuracy and area under the curve (AUC) of the receiver operating characteristic were used to evaluate prediction performance. In addition, the roles of the number of selected features, feature type, MRI modality, and tumor sub-region were compared to optimize the radiomics-based prediction. The results indicated that the combination of feature selection method L1-based linear support vector machine (L1-SVM) and classifier multi-layer perceptron (MLPC) achieved the best performance in the differentiation of GBM and LGG in both ten-fold cross validation (balanced accuracy:0.944, AUC:0.986) and percentage split (balanced accuracy:0.953, AUC:0.981). For radiomics feature extraction, the enhancing tumor region (ET) combined with necrotic and non-enhancing tumor (NCR/NET) regions in T1 post-contrast (T1-Gd) modality provided more considerable tumor-related phenotypes than other combinations of tumor region and MRI modality. Our comparative investigation indicated that both feature selection methods and machine learning classifiers affected the predictive performance in glioma grading. Also, the cross-combination strategy for comparison of radiomics feature selection and classification methods provided a way of searching optimal machine learning model for future radiomics-based prediction.

BackgroundThe polygenic nature of Alzheimer's disease (AD) suggests that multiple variants jointly contribute to disease susceptibility. As an individual's genetic variants are constant throughout life, evaluating the combined effects of multiple disease-associated genetic risks enables reliable AD risk prediction. Because of the complexity of genomic data, current statistical analyses cannot comprehensively capture the polygenic risk of AD, resulting in unsatisfactory disease risk prediction. However, deep learning methods, which capture nonlinearity within high-dimensional genomic data, may enable more accurate disease risk prediction and improve our understanding of AD etiology. Accordingly, we developed deep learning neural network models for modeling AD polygenic risk.MethodsWe constructed neural network models to model AD polygenic risk and compared them with the widely used weighted polygenic risk score and lasso models. We conducted robust linear regression analysis to investigate the relationship between the AD polygenic risk derived from deep learning methods and AD endophenotypes (i.e., plasma biomarkers and individual cognitive performance). We stratified individuals by applying unsupervised clustering to the outputs from the hidden layers of the neural network model.ResultsThe deep learning models outperform other statistical models for modeling AD risk. Moreover, the polygenic risk derived from the deep learning models enables the identification of disease-associated biological pathways and the stratification of individuals according to distinct pathological mechanisms.ConclusionOur results suggest that deep learning methods are effective for modeling the genetic risks of AD and other diseases, classifying disease risks, and uncovering disease mechanisms.

Changes in the levels of circulating proteins are associated with Alzheimer's disease (AD), whereas their pathogenic roles in AD are unclear. Here, we identified soluble ST2 (sST2), a decoy receptor of interleukin-33-ST2 signaling, as a new disease-causing factor in AD. Increased circulating sST2 level is associated with more severe pathological changes in female individuals with AD. Genome-wide association analysis and CRISPR-Cas9 genome editing identified rs1921622 , a genetic variant in an enhancer element of IL1RL1, which downregulates gene and protein levels of sST2. Mendelian randomization analysis using genetic variants, including rs1921622 , demonstrated that decreased sST2 levels lower AD risk and related endophenotypes in females carrying the Apolipoprotein E (APOE)-ε4 genotype; the association is stronger in Chinese than in European-descent populations. Human and mouse transcriptome and immunohistochemical studies showed that rs1921622 /sST2 regulates amyloid-beta (Aβ) pathology through the modulation of microglial activation and Aβ clearance. These findings demonstrate how sST2 level is modulated by a genetic variation and plays a disease-causing role in females with AD.

BackgroundThe differential diagnosis of frontotemporal dementia (FTD) and Alzheimer's disease (AD) is difficult due to the overlaps of clinical symptoms. Structural magnetic resonance imaging (sMRI) presents distinct brain atrophy and potentially helps in their differentiation. In this study, we aim at deriving a novel integrated index by leveraging the volumetric measures in brain regions with significant difference between AD and FTD and developing an MRI-based strategy for the differentiation of FTD and AD.MethodsIn this study, the data were acquired from three different databases, including 47 subjects with FTD, 47 subjects with AD, and 47 normal controls in the NACC database; 50 subjects with AD in the ADNI database; and 50 subjects with FTD in the FTLDNI database. The MR images of all subjects were automatically segmented, and the brain atrophy, including the AD resemblance atrophy index (AD-RAI), was quantified using AccuBrain®. A novel MRI index, named the frontotemporal dementia index (FTDI), was derived as the ratio between the weighted sum of the volumetric indexes in "FTD dominant" structures over that obtained from "AD dominant" structures. The weights and the identification of "FTD/AD dominant" structures were acquired from the statistical analysis of NACC data. The differentiation performance of FTDI was validated using independent data from ADNI and FTLDNI databases.ResultsAD-RAI is a proven imaging biomarker to identify AD and FTD from NC with significantly higher values (p

The COVID-19 pandemic has caused tremendous suffering for patients with dementia and their caregivers. We conducted a survey to study the impact of the pandemic on patients with mild frontotemporal dementia (FTD). Our preliminary findings demonstrate that patients with FTD have significant worsening in behavior and social cognition, as well as suffer greater negative consequences from disruption to health-care services compared to patients with AD. The reduced ability to cope with sudden changes to social environments places patients with FTD at increased vulnerability to COVID-19 infection as well as to poorer clinical and social outcomes. Caregivers of FTD patients also demonstrate high burden during crisis situations. A proportion of patients with FTD benefitted from use of web-based interactive platforms. In this article, we outline the priority areas for research as well as a roadmap for future collaborative research to ensure greatest benefit for patients with FTD and their caregivers.

IntroductionMany consequences of cerebrovascular disease are identifiable by magnetic resonance imaging (MRI), but variation in methods limits multicenter studies and pooling of data. The European Union Joint Program on Neurodegenerative Diseases (EU JPND) funded the HARmoNizing Brain Imaging MEthodS for VaScular Contributions to Neurodegeneration (HARNESS) initiative, with a focus on cerebral small vessel disease.MethodsSurveys, teleconferences, and an in-person workshop were used to identify gaps in knowledge and to develop tools for harmonizing imaging and analysis.ResultsA framework for neuroimaging biomarker development was developed based on validating repeatability and reproducibility, biological principles, and feasibility of implementation. The status of current MRI biomarkers was reviewed. A website was created at www.harness-neuroimaging.org with acquisition protocols, a software database, rating scales and case report forms, and a deidentified MRI repository.ConclusionsThe HARNESS initiative provides resources to reduce variability in measurement in MRI studies of cerebral small vessel disease.

IntroductionThe Meta VCI Map consortium performs meta-analyses on strategic lesion locations for vascular cognitive impairment using lesion-symptom mapping. Integration of data from different cohorts will increase sample sizes, to improve brain lesion coverage and support comprehensive lesion-symptom mapping studies.MethodsCohorts with available imaging on white matter hyperintensities or infarcts and cognitive testing were invited. We performed a pilot study to test the feasibility of multicenter data processing and analysis and determine the benefits to lesion coverage.ResultsForty-seven groups have joined Meta VCI Map (stroke n = 7800 patients; memory clinic n = 4900; population-based n = 14,400). The pilot study (six ischemic stroke cohorts, n = 878) demonstrated feasibility of multicenter data integration (computed tomography/magnetic resonance imaging) and achieved marked improvement of lesion coverage.DiscussionMeta VCI Map will provide new insights into the relevance of vascular lesion location for cognitive dysfunction. After the successful pilot study, further projects are being prepared. Other investigators are welcome to join.

Lipoprotein-associated phospholipase A2 (Lp-PLA2) and superoxide dismutase (SOD) are linked to regulating vascular/neuro-inflammation and stroke. Using a retrospective design, we investigated whether circulating Lp-PLA2 and SOD in cerebral small vessel disease (CSVD) patients were associated with cognitive impairment. Eighty-seven CSVD patients were recruited. Plasma Lp-PLA2 and SOD were determined, and cognitive status was measured by the Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA). The severity of white matter hypoerintensities (WMHs) in CSVD patients was rated according to Fazekas scales, and Lp-PLA2/SOD levels and MMSE/MoCA were compared. Multiple linear regressions were used to evaluate the relationship between Lp-PLA2 and SOD and the cognitive impairment. Ordinal logistic regression and generalized linear models (OLRGLMs) were applied to confirm whether Lp-PLA2 and SOD are independent risk factors for cognitive impairment in CVSD. Lp-PLA2 and SOD with mild or severe cognitive impairment were lower than those with normal congnition. Lp-PLA2 and SOD in CSVD patients with severe WMHs were significantly lower than those with mild or moderate WMH lesions. We noted positive linear associations of Lp-PLA and SOD with cognitive impairment in CSVD, independent of LDL-C. OLRGLMs confirmed that Lp-PLA2 and SOD were independent risk factors of cognitive impairment in CSVD. Lp-PLA2 and SOD are independently associated with cognitive impairment and WMH lesion, and may be useful for the rapid evaluation of cognitive impairment in CSVD. Lp-PLA2/SOD are modifiable factors that may be considered as therapeutic targets for preventing cognitive impairment in CSVD.

Alzheimer's disease (AD) is a leading cause of mortality in the elderly. While the coding change of APOE-ε4 is a key risk factor for late-onset AD and has been believed to be the only risk factor in the APOE locus, it does not fully explain the risk effect conferred by the locus. Here, we report the identification of AD causal variants in PVRL2 and APOC1 regions in proximity to APOE and define common risk haplotypes independent of APOE-ε4 coding change. These risk haplotypes are associated with changes of AD-related endophenotypes including cognitive performance, and altered expression of APOE and its nearby genes in the human brain and blood. High-throughput genome-wide chromosome conformation capture analysis further supports the roles of these risk haplotypes in modulating chromatin states and gene expression in the brain. Our findings provide compelling evidence for additional risk factors in the APOE locus that contribute to AD pathogenesis.

Objective: To determine multidimensional impulsivity levels across different early stages of α‐synucleinopathy. Methods: This cross‐sectional study investigated motor and decisional impulsivity levels using a panel of computerized tasks among drug‐naïve parkinsonism patients, isolated/idiopathic rapid eye movement sleep behavior disorder (iRBD) patients and their first‐degree relatives (iRBD‐FDRs), and control participants. Trait impulsivity and impulse control behaviors were assessed by self‐reported questionnaires. Results: A total of 27 drug‐naïve parkinsonism patients, 157 iRBD patients, 66 iRBD‐FDRs, and 82 control participants were recruited. Parkinsonism and iRBD patients had fewer numbers of extracted beads in beads task 1 and 2 (both p < 0.001), and a higher rate of irrational choice in task 1 (p = 0.046) before making decisions, and fewer numbers of pumps of unexploded blue balloons in the balloon analog risk task (p = 0.004) than control participants, indicating a higher level of reflection impulsivity and a lower level of risk taking, respectively. iRBD patients had more no‐go errors in the go/no‐go task than control participants (padjusted = 0.036), suggesting a higher level of motor impulsivity. iRBD‐FDRs with dream‐enactment behaviors had fewer numbers of extracted beads (p = 0.047) in beads task 2 than FDRs without dream‐enactment behaviors, suggesting a possible higher level of reflection impulsivity. Interpretation: A complex construct of altered impulsivity with decreased risk taking, but increased reflection and motor impulsivity, has already occurred at the prodromal and early stages of α‐synucleinopathy, which have implications for underlying pathophysiology and clinical management of α‐synucleinopathy, especially for impulse control behaviors upon dopaminergic drug treatment. ANN NEUROL 2024;95:544–557 [ABSTRACT FROM AUTHOR]

Background: Stroke not only substantially increases the risk of incident dementia early after stroke but also the risk remains elevated years after. Aim: We aimed to determine the risk factors of dementia onset more than three to six months after stroke or transient ischemic attack. Methods: This is a single-center prospective cohort study. We recruited consecutive subjects with stroke/transient ischemic attack without early-onset dementia. We conducted an annual neuropsychological assessment for five years. We investigated the association between baseline demographic, clinical, genetic (APOEɛ4 allele), and radiological factors as well as incident recurrent stroke with delayed-onset dementia using Cox proportional hazards models. Results: In total, 1007 patients were recruited, of which 88 with early-onset dementia and 162 who lost to follow-ups were excluded. Forty-nine (6.5%) out of 757 patients have incident delayed-onset dementia. The presence of ≥3 lacunes, history of ischemic heart disease, history of ischemic stroke, and a lower baseline Hong Kong version of the Montreal Cognitive Assessment (MoCA) score were significantly associated with delayed-onset dementia. APOEɛ4 allele, medial temporal lobe atrophy, and recurrent stroke were not predictive. Conclusion: The presence of ≥3 lacunes, history of ischemic heart disease, history of ischemic stroke, and a lower baseline MoCA score are associated with delayed-onset dementia after stroke/transient ischemic attack. [ABSTRACT FROM AUTHOR]

Background: Intracranial arterial stenosis (ICAS) is an important cause of stroke worldwide. Separate reports in Caucasians and Asians with stroke/transient ischaemic attack (TIA) have suggested lower ICAS prevalence in Caucasians, but there has been no direct comparisons of the two ethnic groups with the same criteria to define ICAS.Methods: Acute minor stroke or TIA patients in two cohorts respectively recruiting patients in Oxford (2011-2018, predominantly Caucasians) and Hong Kong (2011-2015, predominantly Chinese) were compared. ICAS was defined as ≥50% stenosis/occlusion in any major intracranial artery in MR/CT angiography. Prevalence, distribution and risk factors of ICAS were compared between the two cohorts. We also systematically reviewed literature on ICAS prevalence in stroke/TIA patients in different populations.Results: Among 1287 patients from Oxford and 691 from Hong Kong (mean age 69 vs 66), ICAS prevalence was higher in Chinese than in Caucasians (43.0% vs 20.0%; OR 3.02; 95% CI 2.47 to 3.70; p<0.001), independent of age (age-adjusted OR 3.73; 95% CI 3.00 to 4.63; p<0.001) and vascular risk factors (multivariable-adjusted OR 3.21; 95% CI 2.56 to 4.02; p<0.001). This ethnic difference was greater (p interaction=0.005) at age <70 years (OR 5.33; 95% CI 3.79 to 7.50; p<0.001) than at ≥70 years (OR 2.81; 95% CI 2.11 to 3.74; p<0.001). ICAS prevalence increased with age and with vascular risk factors in both cohorts, with equivalent prevalence in Chinese aged <60 years and Caucasians aged ≥80, and in Chinese with no vascular risk factor and Caucasians with two vascular risk factors. ICAS locations also differed between Chinese and Caucasian patients.Conclusions: Chinese are more susceptible to ICAS than Caucasians, with an earlier onset age and a higher prevalence, independent of vascular risk factors. [ABSTRACT FROM AUTHOR]