Case Report: Taxifolin for neurosurgery-associated early-onset cerebral amyloid angiopathy.

Cases of iatrogenic cerebral amyloid angiopathy (CAA) have been increasingly reported recently, particularly those associated with neurosurgery. Preclinical studies have shown taxifolin to be promising for treating CAA. We describe a young 42-year-old man with a history of childhood traumatic brain injury that required a craniotomy for hematoma evacuation. He later presented with recurrent lobar intracerebral hemorrhage (ICH) decades later, which was histologically confirmed to be CAA. Serial 11C-Pittsburgh compound B positron emission tomography (11C-PiB-PET) imaging showed a 24% decrease in global standardized uptake value ratio (SUVR) at 10 months after taxifolin use. During this period, the patient experienced clinical improvement with improved consciousness and reduced recurrent ICH frequency, which may be partly attributable to the potential amyloid-ß (Aß) clearing the effect of taxifolin. However, this effect seemed to have diminished at 15 months, CAA should be considered in young patients presenting with recurrent lobar ICH with a history of childhood neurosurgery, and serial 11C-PiB-PET scans warrant further validation as a strategy for monitoring treatment response in CAA for candidate Aß-clearing therapeutic agents such as taxifolin.

Using blood transcriptome analysis for Alzheimer's disease diagnosis and patient stratification.

INTRODUCTION: Blood protein biomarkers demonstrate potential for Alzheimer's disease (AD) diagnosis. Limited studies examine the molecular changes in AD blood cells. METHODS: Bulk RNA-sequencing of blood cells was performed on AD patients of Chinese descent (n = 214 and 26 in the discovery and validation cohorts, respectively) with normal controls (n = 208 and 38 in the discovery and validation cohorts, respectively). Weighted gene co-expression network analysis (WGCNA) and deconvolution analysis identified AD-associated gene modules and blood cell types. Regression and unsupervised clustering analysis identified AD-associated genes, gene modules, cell types, and established AD classification models. RESULTS: WGCNA on differentially expressed genes revealed 15 gene modules, with 6 accurately classifying AD (areas under the receiver operating characteristics curve [auROCs] > 0.90). These modules stratified AD patients into subgroups with distinct disease states. Cell-type deconvolution analysis identified specific blood cell types potentially associated with AD pathogenesis. DISCUSSION: This study highlights the potential of blood transcriptome for AD diagnosis, patient stratification, and mechanistic studies. HIGHLIGHTS: We comprehensively analyze the blood transcriptomes of a well-characterized Alzheimer's disease cohort to identify genes, gene modules, pathways, and specific blood cells associated with the disease. Blood transcriptome analysis accurately classifies and stratifies patients with Alzheimer's disease, with some gene modules achieving classification accuracy comparable to that of the plasma ATN biomarkers. Immune-associated pathways and immune cells, such as neutrophils, have potential roles in the pathogenesis and progression of Alzheimer's disease.

A blood-based multi-pathway biomarker assay for early detection and staging of Alzheimer's disease across ethnic groups.

INTRODUCTION: Existing blood-based biomarkers for Alzheimer's disease (AD) mainly focus on its pathological features. However, studies on blood-based biomarkers associated with other biological processes for a comprehensive evaluation of AD status are limited. METHODS: We developed a blood-based, multiplex biomarker assay for AD that measures the levels of 21 proteins involved in multiple biological pathways. We evaluated the assay's performance for classifying AD and indicating AD-related endophenotypes in three independent cohorts from Chinese or European-descent populations. RESULTS: The 21-protein assay accurately classified AD (area under the receiver operating characteristic curve [AUC] = 0.9407 to 0.9867) and mild cognitive impairment (MCI; AUC = 0.8434 to 0.8945) while also indicating brain amyloid pathology. Moreover, the assay simultaneously evaluated the changes of five biological processes in individuals and revealed the ethnic-specific dysregulations of biological processes upon AD progression. DISCUSSION: This study demonstrated the utility of a blood-based, multi-pathway biomarker assay for early screening and staging of AD, providing insights for patient stratification and precision medicine. HIGHLIGHTS: The authors developed a blood-based biomarker assay for Alzheimer's disease. The 21-protein assay classifies AD/MCI and indicates brain amyloid pathology. The 21-protein assay can simultaneously assess activities of five biological processes. Ethnic-specific dysregulations of biological processes in AD were revealed.

The role of visual rating and automated brain volumetry in early detection and differential diagnosis of Alzheimer's disease.

BACKGROUND: Medial temporal lobe atrophy (MTA) is a diagnostic marker for mild cognitive impairment (MCI) and Alzheimer's disease (AD), but the accuracy of quantitative MTA (QMTA) in diagnosing early AD is unclear. This study aimed to investigate the accuracy of QMTA and its related components (inferior lateral ventricle [ILV] and hippocampus) with MTA in the early diagnosis of MCI and AD. METHODS: This study included four groups: normal (NC), MCI stable (MCIs), MCI converted to AD (MCIs), and mild AD (M-AD) groups. Magnetic resonance image analysis software was used to quantify the hippocampus, ILV, and QMTA. MTA was rated by two experienced neurologists. Receiver operating characteristic area under the curve (AUC) analysis was performed to compare their capability in differentiating AD from NC and MCI, and optimal thresholds were determined using the Youden index. RESULTS: QMTA distinguished M-AD from NC and MCI with higher diagnostic accuracy than MTA, hippocampus, and ILV (AUCNC = 0.976, AUCMCI = 0.836, AUCMCIs = 0.894, AUCMCIc = 0.730). The diagnostic accuracy of QMTA was superior to that of MTA, the hippocampus, and ILV in differentiating MCI from AD. The diagnostic accuracy of QMTA was found to remain the best across age, sex, and pathological subgroups analyzed. The sensitivity (92.45%) and specificity (90.64%) were higher in this study when a cutoff value of 0.635 was chosen for QMTA. CONCLUSIONS: QMTA may be a better choice than the MTA scale or the associated quantitative components alone in identifying AD patients and MCI individuals with higher progression risk.

High-resolution omics of vascular ageing and inflammatory pathways in neurodegeneration.

High-resolution omics, particularly single-cell and spatial transcriptomic profiling, are rapidly enhancing our comprehension of the normal molecular diversity of gliovascular cells, as well as their age-related changes that contribute to neurodegeneration. With more omic profiling studies being conducted, it is becoming increasingly essential to synthesise valuable information from the rapidly accumulating findings. In this review, we present an overview of the molecular features of neurovascular and glial cells that have been recently discovered through omic profiling, with a focus on those that have potentially significant functional implications and/or show cross-species differences between human and mouse, and that are linked to vascular deficits and inflammatory pathways in ageing and neurodegenerative disorders. Additionally, we highlight the translational applications of omic profiling, and discuss omic-based strategies to accelerate biomarker discovery and facilitate disease course-modifying therapeutics development for neurodegenerative conditions.

White Matter Hyperintensity Volume and Poststroke Cognition: An Individual Patient Data Pooled Analysis of 9 Ischemic Stroke Cohort Studies.

BACKGROUND: White matter hyperintensities (WMH) are associated with cognitive dysfunction after ischemic stroke. Yet, uncertainty remains about affected domains, the role of other preexisting brain injury, and infarct types in the relation between WMH burden and poststroke cognition. We aimed to disentangle these factors in a large sample of patients with ischemic stroke from different cohorts. METHODS: We pooled and harmonized individual patient data (n=1568) from 9 cohorts, through the Meta VCI Map consortium (www.metavcimap.org). Included cohorts comprised patients with available magnetic resonance imaging and multidomain cognitive assessment <15 months poststroke. In this individual patient data meta-analysis, linear mixed models were used to determine the association between WMH volume and domain-specific cognitive functioning (Z scores; attention and executive functioning, processing speed, language and verbal memory) for the total sample and stratified by infarct type. Preexisting brain injury was accounted for in the multivariable models and all analyses were corrected for the study site as a random effect. RESULTS: In the total sample (67 years [SD, 11.5], 40% female), we found a dose-dependent inverse relationship between WMH volume and poststroke cognitive functioning across all 4 cognitive domains (coefficients ranging from -0.09 [SE, 0.04, P=0.01] for verbal memory to -0.19 [SE, 0.03, P<0.001] for attention and executive functioning). This relation was independent of acute infarct volume and the presence of lacunes and old infarcts. In stratified analyses, the relation between WMH volume and domain-specific functioning was also largely independent of infarct type. CONCLUSIONS: In patients with ischemic stroke, increasing WMH volume is independently associated with worse cognitive functioning across all major domains, regardless of old ischemic lesions and infarct type.

Sex Differences in Poststroke Cognitive Impairment: A Multicenter Study in 2343 Patients With Acute Ischemic Stroke.

BACKGROUND: Poststroke cognitive impairment (PSCI) occurs in about half of stroke survivors. Cumulative evidence indicates that functional outcomes of stroke are worse in women than men. Yet it is unknown whether the occurrence and characteristics of PSCI differ between men and women. METHODS: Individual patient data from 9 cohorts of patients with ischemic stroke were harmonized and pooled through the Meta-VCI-Map consortium (n=2343, 38% women). We included patients with visible symptomatic infarcts on computed tomography/magnetic resonance imaging and cognitive assessment within 15 months after stroke. PSCI was defined as impairment in ≥1 cognitive domains on neuropsychological assessment. Logistic regression analyses were performed to compare men to women, adjusted for study cohort, to obtain odds ratios for PSCI and individual cognitive domains. We also explored sensitivity and specificity of cognitive screening tools for detecting PSCI, according to sex (Mini-Mental State Examination, 4 cohorts, n=1814; Montreal Cognitive Assessment, 3 cohorts, n=278). RESULTS: PSCI was found in 51% of both women and men. Men had a lower risk of impairment of attention and executive functioning (men: odds ratio, 0.76 [95% CI, 0.61-0.96]), and language (men: odds ratio, 0.67 [95% CI, 0.45-0.85]), but a higher risk of verbal memory impairment (men: odds ratio, 1.43 [95% CI, 1.17-1.75]). The sensitivity of Mini-Mental State Examination (<25) for PSCI was higher for women (0.53) than for men (0.27; P=0.02), with a lower specificity for women (0.80) than men (0.96; P=0.01). Sensitivity and specificity of Montreal Cognitive Assessment (<26.) for PSCI was comparable between women and men (0.91 versus 0.86; P=0.62 and 0.29 versus 0.28; P=0.86, respectively). CONCLUSIONS: Sex was not associated with PSCI occurrence but affected domains differed between men and women. The latter may explain why sensitivity of the Mini-Mental State Examination for detecting PSCI was higher in women with a lower specificity compared with men. These sex differences need to be considered when screening for and diagnosing PSCI in clinical practice.

Familial α-synucleinopathy spectrum features in patients with psychiatric REM sleep behaviour disorder.

BACKGROUND: Rapid eye movement (REM) sleep behaviour disorder (RBD) is one of the earliest and most specific prodromes of the α-synucleinopathies including Parkinson's disease (PD). It remains uncertain whether RBD occurring in the context of psychiatric disorders (psy-RBD), although very common, is merely a benign epiphenomenon of antidepressant treatment, or whether it harbours an underlying α-synucleinopathy. We hypothesised that patients with psy-RBD demonstrate a familial predisposition to an α-synucleinopathy. METHODS: In this case-control-family study, a combination of family history and family study method was used to measure the α-synucleinopathy spectrum features, which included RBD, neurodegenerative prodromal markers and clinical diagnoses of neurodegenerative disorders. We compared the risk of α-synucleinopathy spectrum features in the first-degree relatives (FDRs) of patients with psy-RBD, psychiatric controls and healthy controls. RESULTS: There was an increase of α-synucleinopathy spectrum features in the psy-RBD-FDRs, including possible and provisional RBD (adjusted HR (aHR)=2.02 and 6.05, respectively), definite RBD (adjusted OR=11.53) and REM-related phasic electromyographic activities, prodromal markers including depression (aHR=4.74) and probable subtle parkinsonism, risk of prodromal PD and clinical diagnosis of PD/dementia (aHR=5.50), as compared with healthy-control-FDRs. When compared with psychiatric-control-FDRs, psy-RBD-FDRs consistently presented with a higher risk for the diagnosis and electromyographic features of RBD, diagnosis of PD/dementia (aHR=3.91) and risk of prodromal PD. In contrast, psychiatric controls only presented with a familial aggregation of depression. CONCLUSION: Patients with psy-RBD are familially predisposed to α-synucleinopathy. The occurrence of RBD with major depression may signify a subtype of major depressive disorders with underlying α-synucleinopathy neurodegeneration. TRIAL REGISTRATION NUMBER: NCT03595475.

Neuroimaging standards for research into small vessel disease-advances since 2013.

Cerebral small vessel disease (SVD) is common during ageing and can present as stroke, cognitive decline, neurobehavioural symptoms, or functional impairment. SVD frequently coexists with neurodegenerative disease, and can exacerbate cognitive and other symptoms and affect activities of daily living. Standards for Reporting Vascular Changes on Neuroimaging 1 (STRIVE-1) categorised and standardised the diverse features of SVD that are visible on structural MRI. Since then, new information on these established SVD markers and novel MRI sequences and imaging features have emerged. As the effect of combined SVD imaging features becomes clearer, a key role for quantitative imaging biomarkers to determine sub-visible tissue damage, subtle abnormalities visible at high-field strength MRI, and lesion-symptom patterns, is also apparent. Together with rapidly emerging machine learning methods, these metrics can more comprehensively capture the effect of SVD on the brain than the structural MRI features alone and serve as intermediary outcomes in clinical trials and future routine practice. Using a similar approach to that adopted in STRIVE-1, we updated the guidance on neuroimaging of vascular changes in studies of ageing and neurodegeneration to create STRIVE-2.

Gut microbiome dysbiosis across early Parkinson's disease, REM sleep behavior disorder and their first-degree relatives.

The microbiota-gut-brain axis has been suggested to play an important role in Parkinson's disease (PD). Here we performed a cross-sectional study to profile gut microbiota across early PD, REM sleep behavior disorder (RBD), first-degree relatives of RBD (RBD-FDR), and healthy controls, which could reflect the gut-brain staging model of PD. We show gut microbiota compositions are significantly altered in early PD and RBD compared with control and RBD-FDR. Depletion of butyrate-producing bacteria and enrichment of pro-inflammatory Collinsella have already emerged in RBD and RBD-FDR after controlling potential confounders including antidepressants, osmotic laxatives, and bowel movement frequency. Random forest modelling identifies 12 microbial markers that are effective to distinguish RBD from control. These findings suggest that PD-like gut dysbiosis occurs at the prodromal stages of PD when RBD develops and starts to emerge in the younger RBD-FDR subjects. The study will have etiological and diagnostic implications.

Deep learning-based polygenic risk analysis for Alzheimer's disease prediction.

BACKGROUND: The 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. METHODS: We 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. RESULTS: The 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. CONCLUSION: Our 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.

Polygenic diseases, such as Alzheimer's disease (AD), are those caused by the interplay between multiple genetic risk factors. Statistical models can be used to predict disease risk based on a person's genetic profile. However, there are limitations to existing methods, while emerging methods such as deep learning may improve risk prediction. Deep learning involves computer-based software learning from patterns in data to perform a certain task, e.g. predict disease risk. Here, we test whether deep learning models can help to predict AD risk. Our models not only outperformed existing methods in modeling AD risk, they also allow us to estimate an individual's risk of AD and determine the biological processes that may be involved in AD. With further testing and optimization, deep learning may be a useful tool to help accurately predict risk of AD and other diseases.

Cerebral Hemodynamics Underlying Artery-to-Artery Embolism in Symptomatic Intracranial Atherosclerotic Disease.

Artery-to-artery embolism (AAE) is a common stroke mechanism in intracranial atherosclerotic disease (ICAD), associated with a considerable risk of recurrent stroke. We aimed to investigate cerebral hemodynamic features associated with AAE in symptomatic ICAD. Patients with anterior-circulation, symptomatic ICAD confirmed in CT angiography (CTA) were recruited. We classified probable stroke mechanisms as isolated parent artery atherosclerosis occluding penetrating artery, AAE, hypoperfusion, and mixed mechanisms, largely based on infarct topography. CTA-based computational fluid dynamics (CFD) models were built to simulate blood flow across culprit ICAD lesions. Translesional pressure ratio (PR = Pressurepost-stenotic/Pressurepre-stenotic) and wall shear stress ratio (WSSR = WSSstenotic-throat/WSSpre-stenotic) were calculated, to reflect the relative, translesional changes of the two hemodynamic metrics. Low PR (PR ≤ median) and high WSSR (WSSR ≥ 4th quartile) respectively indicated large translesional pressure and elevated WSS upon the lesion. Among 99 symptomatic ICAD patients, 44 had AAE as a probable stroke mechanism, 13 with AAE alone and 31 with coexisting hypoperfusion. High WSSR was independently associated with AAE (adjusted OR = 3.90; P = 0.022) in multivariate logistic regression. There was significant WSSR-PR interaction on the presence of AAE (P for interaction = 0.013): high WSSR was more likely to associate with AAE in those with low PR (P = 0.075), but not in those with normal PR (P = 0.959). Excessively elevated WSS in ICAD might increase the risk of AAE. Such association was more prominent in those with large translesional pressure gradient. Hypoperfusion, commonly coexisting with AAE, might be a therapeutic indicator for secondary stroke prevention in symptomatic ICAD with AAE.

Impact of Cerebral Microbleeds in Stroke Patients with Atrial Fibrillation.

OBJECTIVES: Cerebral microbleeds are associated with the risks of ischemic stroke and intracranial hemorrhage, causing clinical dilemmas for antithrombotic treatment decisions. We aimed to evaluate the risks of intracranial hemorrhage and ischemic stroke associated with microbleeds in patients with atrial fibrillation treated with vitamin K antagonists, direct oral anticoagulants, antiplatelets, and combination therapy (i.e. concurrent oral anticoagulant and antiplatelet). METHODS: We included patients with documented atrial fibrillation from the pooled individual patient data analysis by the Microbleeds International Collaborative Network. Risks of subsequent intracranial hemorrhage and ischemic stroke were compared between patients with and without microbleeds, stratified by antithrombotic use. RESULTS: A total of 7,839 patients were included. The presence of microbleeds was associated with an increased relative risk of intracranial hemorrhage (adjusted hazard ratio [aHR] = 2.74, 95% confidence interval = 1.76-4.26) and ischemic stroke (aHR = 1.29, 95% confidence interval = 1.04-1.59). For the entire cohort, the absolute incidence of ischemic stroke was higher than intracranial hemorrhage regardless of microbleed burden. However, for the subgroup of patients taking combination of anticoagulant and antiplatelet therapy, the absolute risk of intracranial hemorrhage exceeded that of ischemic stroke in those with 2 to 4 microbleeds (25 vs 12 per 1,000 patient-years) and ≥ 11 microbleeds (94 vs 48 per 1,000 patient-years). INTERPRETATION: Patients with atrial fibrillation and high burden of microbleeds receiving combination therapy have a tendency of higher rate of intracranial hemorrhage than ischemic stroke, with potential for net harm. Further studies are needed to help optimize stroke preventive strategies in this high-risk group. ANN NEUROL 2023;94:61-74.

An optofluidic platform for interrogating chemosensory behavior and brainwide neural representation in larval zebrafish.

Studying chemosensory processing desires precise chemical cue presentation, behavioral response monitoring, and large-scale neuronal activity recording. Here we present Fish-on-Chips, a set of optofluidic tools for highly-controlled chemical delivery while simultaneously imaging behavioral outputs and whole-brain neuronal activities at cellular resolution in larval zebrafish. These include a fluidics-based swimming arena and an integrated microfluidics-light sheet fluorescence microscopy (µfluidics-LSFM) system, both of which utilize laminar fluid flows to achieve spatiotemporally precise chemical cue presentation. To demonstrate the strengths of the platform, we used the navigation arena to reveal binasal input-dependent behavioral strategies that larval zebrafish adopt to evade cadaverine, a death-associated odor. The µfluidics-LSFM system enables sequential presentation of odor stimuli to individual or both nasal cavities separated by only ~100 µm. This allowed us to uncover brainwide neural representations of cadaverine sensing and binasal input summation in the vertebrate model. Fish-on-Chips is readily generalizable and will empower the investigation of neural coding in the chemical senses.

Efficacy and safety of cilostazol in decreasing progression of cerebral white matter hyperintensities-A randomized controlled trial.

Introduction: Cerebral small vessel disease (SVD) is an important cause of dementia that lacks effective treatment. We evaluated the efficacy and safety of cilostazol, an antiplatelet agent with potential neurovascular protective effects, in slowing the progression of white matter hyperintensities (WMHs) in stroke- and dementia-free subjects harboring confluent WMH on magnetic resonance imaging (MRI). Methods: In this single-center, randomized, double-blind, placebo-controlled study, we randomized stroke- and dementia-free subjects with confluent WMHs to receive cilostazol or placebo for 2 years in a 1:1 ratio. The primary outcome was change in WMH volume over 2 years. Secondary outcomes were changes in brain volumes, lacunes, cerebral microbleeds, perivascular space, and alterations in white matter microstructural integrity, cognition, motor function, and mood. Results: We recruited 120 subjects from October 27, 2014, to January 21, 2019. A total of 55 subjects in the cilostazol group and 54 subjects in the control group were included for intention-to-treat analysis. At 2-year follow-up, the changes in WMH volume were not statistically different between cilostazol treatment and placebo (0.3±1.0 mL vs -0.1±0.8 mL, p = 0.167). Secondary outcomes, bleeding and vascular events, were also not statistically different between the two groups. Discussion: In this trial with stroke- and dementia-free subjects with confluent WMHs, cilostazol did not impact WMH progression but demonstrated an acceptable safety profile. Future studies should address the treatment effects of cilostazol on subjects at different clinical stages of SVD.

A deep learning model for detection of Alzheimer's disease based on retinal photographs: a retrospective, multicentre case-control study.

BACKGROUND: There is no simple model to screen for Alzheimer's disease, partly because the diagnosis of Alzheimer's disease itself is complex-typically involving expensive and sometimes invasive tests not commonly available outside highly specialised clinical settings. We aimed to develop a deep learning algorithm that could use retinal photographs alone, which is the most common method of non-invasive imaging the retina to detect Alzheimer's disease-dementia. METHODS: In this retrospective, multicentre case-control study, we trained, validated, and tested a deep learning algorithm to detect Alzheimer's disease-dementia from retinal photographs using retrospectively collected data from 11 studies that recruited patients with Alzheimer's disease-dementia and people without disease from different countries. Our main aim was to develop a bilateral model to detect Alzheimer's disease-dementia from retinal photographs alone. We designed and internally validated the bilateral deep learning model using retinal photographs from six studies. We used the EfficientNet-b2 network as the backbone of the model to extract features from the images. Integrated features from four retinal photographs (optic nerve head-centred and macula-centred fields from both eyes) for each individual were used to develop supervised deep learning models and equip the network with unsupervised domain adaptation technique, to address dataset discrepancy between the different studies. We tested the trained model using five other studies, three of which used PET as a biomarker of significant amyloid ß burden (testing the deep learning model between amyloid ß positive vs amyloid ß negative). FINDINGS: 12 949 retinal photographs from 648 patients with Alzheimer's disease and 3240 people without the disease were used to train, validate, and test the deep learning model. In the internal validation dataset, the deep learning model had 83·6% (SD 2·5) accuracy, 93·2% (SD 2·2) sensitivity, 82·0% (SD 3·1) specificity, and an area under the receiver operating characteristic curve (AUROC) of 0·93 (0·01) for detecting Alzheimer's disease-dementia. In the testing datasets, the bilateral deep learning model had accuracies ranging from 79·6% (SD 15·5) to 92·1% (11·4) and AUROCs ranging from 0·73 (SD 0·24) to 0·91 (0·10). In the datasets with data on PET, the model was able to differentiate between participants who were amyloid ß positive and those who were amyloid ß negative: accuracies ranged from 80·6 (SD 13·4%) to 89·3 (13·7%) and AUROC ranged from 0·68 (SD 0·24) to 0·86 (0·16). In subgroup analyses, the discriminative performance of the model was improved in patients with eye disease (accuracy 89·6% [SD 12·5%]) versus those without eye disease (71·7% [11·6%]) and patients with diabetes (81·9% [SD 20·3%]) versus those without the disease (72·4% [11·7%]). INTERPRETATION: A retinal photograph-based deep learning algorithm can detect Alzheimer's disease with good accuracy, showing its potential for screening Alzheimer's disease in a community setting. FUNDING: BrightFocus Foundation.

The Hong Kong Grocery Shopping Dialog Task (HK-GSDT): A Quick Screening Test for Neurocognitive Disorders.

The Hong Kong Grocery Shopping Dialog Task (HK-GSDT) is a short and easy-to-administer cognitive test developed for quickly screening neurocognitive disorders (NCDs). In the test, participants are instructed to do a hypothetical instrumental activity of daily living task of purchasing ingredients for a dish from a grocery store and verbally describe the specific shopping procedures. The current study aimed to validate the test with a sample of 545 Hong Kong older adults (58.8% female; aged 73.4 ± 8.37 years), including 464 adults with normal cognitive function, 39 with mild NCD, and 42 with major NCD. Demographic characteristics (i.e., sex, age, education) and clinical diagnosis of cognitive states (i.e., major NCD, mild NCD, and normal aging) were collected. Cognitive functioning was measured using the HK-GSDT and several standardized NCD-screening tests. The results showed good reliability (i.e., internal consistency) and structural validity in the HK-GSDT. It discriminated among different cognitive conditions, particularly between major NCDs and the other conditions, as effectively as did the existing standardized neurocognitive tests (e.g., Montreal Cognitive Assessment, Hong Kong List Learning Test). Moreover, the HK-GSDT explained additional variance of cognitive condition on top of those standardized neurocognitive tests. These results indicate that the HK-GSDT can be used alone, or in combination with other tests, to screen for NCDs.

Antibody stabilization for thermally accelerated deep immunostaining.

Antibodies have diverse applications due to their high reaction specificities but are sensitive to denaturation when a higher working temperature is required. We have developed a simple, highly scalable and generalizable chemical approach for stabilizing off-the-shelf antibodies against thermal and chemical denaturation. We demonstrate that the stabilized antibodies (termed SPEARs) can withstand up to 4 weeks of continuous heating at 55 °C and harsh denaturants, and apply our method to 33 tested antibodies. SPEARs enable flexible applications of thermocycling and denaturants to dynamically modulate their binding kinetics, reaction equilibrium, macromolecular diffusivity and aggregation propensity. In particular, we show that SPEARs permit the use of a thermally facilitated three-dimensional immunolabeling strategy (termed ThICK staining), achieving whole mouse brain immunolabeling within 72 h, as well as nearly fourfold deeper penetration with threefold less antibodies in human brain tissue. With faster deep-tissue immunolabeling and broad compatibility with tissue processing and clearing methods without the need for any specialized equipment, we anticipate the wide applicability of ThICK staining with SPEARs for deep immunostaining.

Validation of the Alzheimer's disease-resemblance atrophy index in classifying and predicting progression in Alzheimer's disease.

Background: Automated tools for characterising dementia risk have the potential to aid in the diagnosis, prognosis, and treatment of Alzheimer's disease (AD). Here, we examined a novel machine learning-based brain atrophy marker, the AD-resemblance atrophy index (AD-RAI), to assess its test-retest reliability and further validate its use in disease classification and prediction. Methods: Age- and sex-matched 44 probable AD (Age: 69.13 ± 7.13; MMSE: 27-30) and 22 non-demented control (Age: 69.38 ± 7.21; MMSE: 27-30) participants were obtained from the Minimal Interval Resonance Imaging in Alzheimer's Disease (MIRIAD) dataset. Serial T1-weighted images (n = 678) from up to nine time points over a 2-year period, including 179 pairs of back-to-back scans acquired on same participants on the same day and 40 pairs of scans acquired at 2-week intervals were included. All images were automatically processed with AccuBrain® to calculate the AD-RAI. Its same-day repeatability and 2-week reproducibility were first assessed. The discriminative performance of AD-RAI was evaluated using the receiver operating characteristic curve, where DeLong's test was used to evaluate its performance against quantitative medial temporal lobe atrophy (QMTA) and hippocampal volume adjusted by intracranial volume (ICV)-proportions and ICV-residuals methods, respectively (HVR and HRV). Linear mixed-effects modelling was used to investigate longitudinal trajectories of AD-RAI and baseline AD-RAI prediction of cognitive decline. Finally, the longitudinal associations between AD-RAI and MMSE scores were assessed. Results: AD-RAI had excellent same-day repeatability and excellent 2-week reproducibility. AD-RAI's AUC (99.8%; 95%CI = [99.3%, 100%]) was equivalent to that of QMTA (96.8%; 95%CI = [92.9%, 100%]), and better than that of HVR (86.8%; 95%CI = [78.2%, 95.4%]) or HRV (90.3%; 95%CI = [83.0%, 97.6%]). While baseline AD-RAI was significantly higher in the AD group, it did not show detectable changes over 2 years. Baseline AD-RAI was negatively associated with MMSE scores and the rate of the change in MMSE scores over time. A negative longitudinal association was also found between AD-RAI values and the MMSE scores among AD patients. Conclusions: The AD-RAI represents a potential biomarker that may support AD diagnosis and be used to predict the rate of future cognitive decline in AD patients.